Sustainability : Multi-Disciplinary Perspectives [1 ed.] 9781608051038, 9781608054299

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Sustainability: Multi-Disciplinary Perspectives Editors

Heriberto Cabezas U.S. Environmental Protection Agency Office of Research and Development Cincinnati, Ohio USA &

Urmila Diwekar Vishwamitra Research Institute Clarendon Hills, Illinois USA

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DEDICATIONS To, My dear wife, Isaura Vázquez Pantoja, and my two amazing children, Amanda Isabel Cabezas and Víctor Manuel Cabezas, for their love and encouragement. To, Two important women in my life, my mother Leela Murlidhar Diwekar and my mother in law Leela Vasudeo Joag.

CONTENTS Foreword

i

Preface

iii

List of Contributors

iv

CHAPTERS 1.

Introduction

3

H. Cabezas 2.

Principles of Sustainability From Ecology

9

Audrey L. Mayer 3.

The Economics of Sustainability

40

Joshua C. Farley 4.

Actualizing Sustainability: Environmental Policy for Resilience in Ecological Systems

65

Ahjond S. Garmestani, Matthew E. Hopton and Matthew T. Heberling 5.

Human Interactions and Sustainability

88

Michael E. Gorman, Lekelia D. Jenkins and Raina K. Plowright 6.

On the Matter of Sustainable Water Resources Management

112

W.D. Shuster 7.

Sustainable Infrastructure and Alternatives for Urban Growth

141

Arka Pandit, Hyunju Jeong, John C. Crittenden, Steven P. French, Ming Xu and Ke Li 8.

Engineering Urban Sustainability Ke Li, John Crittenden, Subhrajit Guhathakurta and Harindra Joseph Fernando

173

9.

Sustainability Indicators and Metrics

197

H. Cabezas 10. Implications of Thermodynamics for Sustainability

222

Bhavik R. Bakshi and Geoffrey F. Grubb 11. Industrial Ecology and Sustainable Development: Dynamics, Future Uncertainty and Distributed Decision Making. 243 Jim Petrie, Ruud Kempener and Jessica Beck 12. Green Engineering and Sustainability

273

Urmila Diwekar 13. The Case and Practice for Sustainability in Business

310

Beth Beloff 14. Summary

340

Urmila Diwekar Index

347

i

FOREWORD In 1987, the United Nations World Commission on Environment and Development (the Brundtland Commission in deference to its chair, Dr. Gro Harlem Brundtland) released its report “Our Common Future” in which it expressed the need for countries to adopt an approach to human interactions with the environment that was referred to as “sustainable development”, ”… development that meets the needs of the present without compromising the ability of future generations to meet their own needs”. The Brundtland Report, and this book, make it clear that while sustainable development is enabled by technological advances and economic viability, it is first and foremost a social construct that seeks to improve the quality of life for the world’s peoples--physically through the equitable supply of human and ecological goods and services, aspirationally through making available the widespread means for advancement through access to education, systems of justice, and healthcare, and strategically through safeguarding the interests of generations to come. In this sense sustainability sits among a series of human social movements that have occurred throughout history: human rights, racial equality, gender equity, labor relations, and environmental conservation, to name a few. Although the Brundtland Report did not, technically, invent the term “sustainability”, it was the first credible and widely disseminated study that probed its meaning in the context of the global impacts of humans on the environment, emphasizing the connections among social equity, economic productivity, and environmental quality. In the intervening period of time this rather idealistic and broadly defined concept has become the driving force for a new meta-discipline, one that has had to establish its own conceptual theories, develop characteristic investigative metrics and methodologies, collect appropriately defined data, forge new methods of data interpretation and analysis, and generate a body of knowledge that is the foundation for continued advances—in short a new “science of sustainability”. And as with any knowledge-based enterprise, a number of text books, reference sources, and treatises on the topic have been and will continue to be released. This is a book about sustainability. It is written from a distinctly multidisciplinary perspective, as any serious book on the topic must. But it is worth reflecting just

ii

what, in this case, that means. Merging existing disciplines to create first a metadiscipline and eventually a distinctly new discipline is not new. For example public health emerged from a combination of microbiology, epidemiology, and medicine in the 1890s, biochemistry from concepts of cell biology and chemistry in the 1940s and 50s, and environmental engineering science from microbiology, chemistry, and sanitary science in the 1960s. Butwhat is extraordinary about the meta-discipline surrounding sustainability is both the sheer number and breadth of disciplines that are called upon to contribute to sustainability science. It is not easy to put together a book on sustainability. “Sustainability: Multidisciplinary Perspectives” is a collection of fourteen papers, written by 23 authors drawn from fifteen distinct disciplinary backgrounds ranging from engineering to public policy, from ecology to thermodynamics, from organizational behavior to social psychology, and from industrial ecology to economics. It has something to offer for everyone interested in sustainability, and a great deal to offer for a smaller number who are in serious pursuit of understanding the nature of research and pedagogy in support of sustainability science. The editors, Heriberto Cabezas and Urmila Diwekar (whom I have known and with whom I have interacted for several years), intend this book to be a reference for those interested in sustainability, but it provides much more: a useful introduction to the topic, a thorough summary of the current state of knowledge, an intricate examination of its foundational principles, a thoughtful exposition of the implications of sustainability for society, a practical basis for framing problems that the sustainability paradigm seeks to solve, and ways to measure the outcomes of our solutions and policies. The information contained in this book is timely and important, but just as important is the bold manner in which it presents views on sustainability through so many disciplinary lenses. This may be its ultimate contribution—to stimulate others, perhaps in yet additional domains, to undertake the task of incorporating sustainability thinking into their own disciplines, thereby extending and deepening the sustainability knowledge base.

Thomas L. Theis Institute for Environmental Science and Policy University of Illinois at Chicago USA

iii

PREFACE The concept of sustainability is inherently multi-disciplinary because it concerns the management of a complex system having economic, technological, ecological, political, and other perspectives. Consequently, any effort in the area of sustainability involves concepts, principles, and methods from engineering, the social sciences including economics and social psychology, the biological sciences including ecology, and the physical sciences. The purpose of this book is, therefore, to discuss in a coherent and comprehensive manner the salient concepts, principles, and methods relevant to sustainability from the perspective of different disciplines. Although there are number of books that have been recently published on the topic of sustainability, most of them do not cross the disciplinary boundaries, or at least address them as thoroughly as necessary. This book is different in that respect. It provides perspectives from ecology, environment, economics, social-sciences, policy, infrastructure, industrial ecology, engineering, and business written by experts trained and experienced in the respective disciplines The book is intended for a diverse audience ranging from undergraduate and graduate students from various disciplines, to researchers, academics, policy makers and practitioners. It is the objective of the current endeavor to provide to provide a timely source and reference material on sustainability. This book is primarily designed to serve as a reference book. However, it has also been used in a multi-disciplinary undergraduate course on sustainability in Michigan Technological University with excellent feedback from the students, it may properly in any such course of instruction. To readers in academia, the industries, and government organization alike, we hope that the ideas and pages that follow will give as much enjoyment and stimulation of creative interest as they have for us, the writers and editors, right from the day when we started working in this area.

Urmila Diwekar Vishwamitra Research Institute Clarendon Hills, Illinois USA

Heriberto Cabezas U.S. Environmental Protection Agency Office of Research and Development Cincinnati, Ohio USA

iv

List of Contributors Jessica Beck Büro für Energiewirtschaft und technische Planung GmbH Aachen Germany E-mail: [email protected]

Beth Beloff Beth Beloff & Associates; President Bridges to Sustainability Institute 3501Nottingham St. Houston, TX 77005 USA E-mail: [email protected] Bhavik Bakshi Department of Chemical and Biomolecular Engineering Ohio State University 335 A KOFFOLT 140 W Ninteenth Avenue Columbus, OH 43210 USA E-mail: [email protected] Heriberto Cabezas Office of Research and Development U.S. Environmental Protection Agency Office of Research and Development 26 West Martin Luther King Drive Cincinnati, OH 45268 USA E-mail: [email protected] John Crittenden School of Civil and Environmental Engineering, Georgia Institute of Technology Atlanta, Georgia USA E-mail: [email protected]

v

Urmila Diwekar Vishwamitra Research Institute Center for Uncertain Systems: Tools for Optimization and Management CUSTOM 368 56-th Street, Clarendon Hills, IL 60514 USA Clarendon Hills, Illinois, USA E-mail: [email protected] Joshua Farley Community Development and Applied Economics Main St. Rm 617 University of Vermont Burlington, VT 05405 USA E-mail: [email protected] Harindra Joseph Fernando Department of Civil Engineering and Geological Sciences University of Notre Dame Notre Dame, Indiana USA E-mail: [email protected] Steven P. French Center for Geographic Information Systems Georgia Institute of Technology Atlanta, Georgia USA E-mail: [email protected] Ahjond Garmestrani U.S. Environmental Protection Agency Office of Research and Development 26 West Martin Luther King Drive Cincinnati, OH 45268 USA E-mail: [email protected]

vi

Michael E. Gorman Department of Science, Technology & Society University of Virginia PO BOX 400744, 351 McCormick Rd. Thornton Hall, Rm. A217 E-mail: [email protected]

Subhrajit Guhathakurta School of Geographical Sciences & Urban Planning Arizona State University Tempe, AZ USA E-mail: [email protected] Matthew T. Heberling U.S. Environmental Protection Agency Office of Research and Development 26 West Martin Luther King Drive Cincinnati, OH 45268 USA E-mail: [email protected] Matthew E. Hopton U.S. Environmental Protection Agency Office of Research and Development 26 West Martin Luther King Drive Cincinnati, OH 45268 USA E-mail: [email protected] Hyunju Jeong Department of Civil and Environmental Engineering Brook Byers Institute for Sustainable Systems Georgia Institute of Technology Atlanta, Georgia USA E-mail: [email protected]

vii

Lekelia D. Jenkins David H. Smith Conservation Research Fellows, Research Associate University of Washington School of Marine Affairs 3707 Brooklyn Avenue NE Seattle, WA 98105 USA E-mail: [email protected] Ruud Kempener 3Kennedy School of Government Harvard University Cambridge MA USA E-mail: [email protected] Ke Li Faculty of Engineering University of Georgia Athens, Georgia USA E-mail: [email protected] Audrey Meyer School of Forest Resource & Environmental Science 209 Academic Office Bldg. Michigan Technological University 1400 Townsend Drive Houghton, Michigan 49931 USA E-mail: [email protected] Arka Pandit Department of Civil and Environmental Engineering Brook Byers Institute for Sustainable Systems Georgia Institute of Technology Atlanta, Georgia USA E-mail: [email protected]

viii

Jim School of Chemical and Bio-molecular Engineering, University of Sydney NSW 2006 Australia and University of Cape Town Rondebsoch, 7700, South Africa E-mail: [email protected] Raina K. Plowright David H. Smith Conservation Research Fellow Center for Infectious Disease Dynamics The Pennsylvania State University University Park, PA 16802 USA E-mail: [email protected] Ming Xu School of Natural Resources and Environment, University of Michigan Ann Arbor, Michigan USA E-mail: [email protected]

Sustainability: Multi-Disciplinary Perspectives, 2012, 3-8

3

CHAPTER 1 Introduction H. Cabezas* Sustainable Technology Division, National Risk Management Research Laboratory, Office of Research and Development, U.S. Environmental Protection Agency, Cincinnati, Ohio, USA WHAT DOES SUSTAINABILITY MEAN? The word sustainability has over the past two or three decades become widely used in both professional and public discourse. But what does it actually mean? As again mentioned in Chapter 9, according to one standard reference of the English language, Webster’s New World Dictionary states that the word sustainability originates from a fusion of two Latin words: sus which means up and tenere which means to hold [1]. Hence, the roots of the word sustainability would indicate that it means to hold up. But the question remains as to what it is that is being held up. To further investigate this question, we next note that the first written use of the term sustainability in the modern context appears in the January 1972 issue of the The Ecologist (Goldsmith et al., 1972) [2]. The article by Goldsmith et al., places sustainability in the context of ecosystems which are required to support human existence. Lastly, we revisit the more recent and most widely accepted definition of sustainability or sustainable development which is attributed to the World Commission on Environment and Development (WCED, 1987) [3] or Brundtland Commission – the commission chair was Gro Harlem Brundtland. The Brundland Commission stated in the final report that “Sustainable development is development that meets the needs of the present without compromising the ability of future generations to meet their own needs.” The Bruntland Commission places sustainability in the social, economic, and political context of insuring that both present and future generations of humans can prosper. If we combine the linguistic definition of sustainability together with *Address correspondence to H. Cabezas: Sustainable Technology Division, National Risk Management Research Laboratory, Office of Research and Development, U.S. Environmental Protection Agency, Cincinnati, Ohio, USA; Tel: 513-569-7350; Fax: 513-487-7787; BB 513-633-8447; E-mail: [email protected] Heriberto Cabezas and Urmila Diwekar (Eds) All rights reserved-© 2012 Bentham Science Publishers

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the ecological allusions of the article in The Ecologist and the results from the deliberations of the Bruntland Commission, the conclusion is that sustainability is at least in the everyday context about “holding up” human existence. In fact, sustainability is truly about insuring that people, generation after generation, can continue to exist, meet their needs, and prosper on planet Earth. This is about people. Sustainability is in summary a concept developed by people to help promote the welfare of people. WHY IS SUSTAINABILITY IMPORTANT? There are possibly a great many arguments that one could make for the importance of sustainability to the present human condition. But I would beg the reader’s indulgence to consider the following three facts: 1.

According to the United States Bureau of the Census, the human population of the Earth increased from approximately to 2.5 to 6.9 billion in the period between 1950 to 2011 A.D. [4]. Projections indicate that the human population will likely increase up to about 9.2 billion by 2050 A.D. [5]. For reference, consider that in historical times, the estimated human population was between 0.17 and 0.40 billion [6].

2.

According to the United Nations Development Program [7], over the period from 1970 to 1995 A.D. human expenditures for consumption increased from US$8.3 millions to US$16.5 millions in the industrial nations, and from US$1.9 millions to US$5.2 millions in the developing nations. This is approximately a doubling of consumption in industrial nations and a tripling of consumption in developing nations.

3.

Humans constitute approximately 0.5% of the biomass on planet Earth [8], but they consume approximately 20% of the World primary production, i.e., the energy from the sun that plants are able to capture and convert to biologically available energy in the form of food [9].

This situation seems quite unprecedented in the history of the Earth. For instance, to the best of the author’s knowledge, there has never been a population of six

Introduction

Sustainability: Multi-Disciplinary Perspectives 5

billion plus members for a species of large animals with large per capita resource consumption budgets. It would seem, therefore, that we truly exist in extraordinary times, where humanity is taxing the Earth in a manner never seen before. Such a situation, while not necessarily and irrevocably leading to a catastrophe, does raise enough alarm to call for fresh and new approaches to managing the environment so that it can continue to meet human needs and support human existence well into the future. WHY ARE MANY DISCIPLINES NECESSARY? Each academic discipline or school of study is typically devoted to understanding one or at most a few closely related aspects of observable reality. For example, engineering [10] primarily focuses on understanding and applying physical, chemical, and biological principles to the creation of technologies that serve a human need. Questions of society and economics, with the exception of engineering economics, are secondary. On the other hand, ecology [11] primarily seeks to study and understand the behavior of living systems, but technology and economics are again secondary topics not the central focus. It is, perhaps, economics [12] that most aptly characterizes this situation by treating as externalities effects external to human choice making such as environment and technology. In summary, each discipline as represented by a community of human practitioners focuses on the study of a limited aspect of reality, perhaps to use time effectively within the finite human life-span. One of the fundamental realities in sustainability science is that the natural boundary for the system is the entire planet Earth. That includes the Earth and everything contained within the Earth, including the physical planet, biological system, human society, economy, etc. The reason for this scale is that it is only at the scale of the Earth that we find a system that is physically, biologically, socially, and economically reasonably self-contained, at least on the time scale of human history. All other systems within the Earth, including continents, regions, and even the remotest islands are not self-contained. Rather they interact and exchange material, energy, biological species, and even ideas with each other. Therefore, any adequately complete study of sustainability must coherently address as many of the relevant aspects as possible. This immediately brings us to

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the need to coherently bring together the knowledge and unique insights from as many disciplines as possible. One reason for addressing the different aspects of sustainability simultaneously and coherently is that a study that neglects that runs the risk of at best being ineffective and at worst simply shifting problems from one area to another. For example, imagine an effort to develop a highly fuel efficient automobile, which can then decrease the social and regulatory pressure to drive less. Over time, this could reverse any initial gains in environmental improvement from the operation of more efficient automobiles. Also imagine that a regulatory effort to manage water scarcity can result in serious economic consequences and, possible, political instability unless done considering stakeholder interests. WHY WAS THIS BOOK WRITTEN? This book was created as a resource for students and researchers bringing together as many of the different aspects of sustainability as possible. While the sustainability literature is abundant, and books on sustainability are not in short supply, there is still a need for a book that assembles into a coherent whole the unique and distinct insights on sustainability from a group of experts, each a well distinguished practitioner of a particular discipline. Hence, we offer chapters dealing with energy and thermodynamics, industry and business, sustainability metrics, cities, engineering, economics, law and policy, human-technology interactions, ecology, and water and hydrology. As can be surmised, these draw on a wide range of expertise. The list of topics is by no means exhaustive, but it is a reasonable start for a comprehensive look at sustainability. This list of chapters should further provide a primer on the basics of sustainability science and engineering. The section on energy and thermodynamics deals with issues of energy generation, efficiency, and related areas such as exergy all of which relate humanity’s increasing need for energy. The part on industry and business addresses the very real and practical issues of applying sustainability thinking in an environment where profit and simply staying in business is critical. Sustainability metrics addresses the very important need to quantitatively, or at least semi-quantitatively, measure progress towards sustainability in a

Introduction

Sustainability: Multi-Disciplinary Perspectives 7

scientifically defensible manner. Cities have been and increasingly are the principal place of human habitation, and any discussion of sustainability has to include its relationship to the city. Engineering is the means by which science is translated into technology and practices that have an impact on human existence, and sustainability has to be incorporated into engineering in this age of technology. Economics is the science of human choice making under scarcity, and its inclusion here aims to insert sustainability thinking into choices. Law and policy are the formal institutions that regulate the behavior humans individually and human societies in general, and it is, therefore, necessary for law and policy to be consistent with sustainability principles and concepts. Human–technology interactions are the core of modern civilization as the present age is one dominated by science and its application in the form of technology, and this touches nearly every aspect of existence having impacts on human society, ecosystems, and the physical environment. Ecosystems are the underlying support systems for humanity, and they are important for sustainability because human existence would not be possible without the services that ecosystems provide raging from oxygen and food production to waste processing. Lastly, water and the science of water flow, hydrology, are critical to sustainability because human existence is again impossible without access to potable water, and access to water may be an emerging sustainability issue. In summary, the hope is that this book will stimulate present and future generations of students and researchers into addressing sustainability for what it actually is: a very complex but still tractable multidimensional problem of the highest urgency. As such, this book could serve as the reference basis for a graduate or undergraduate course in any number of disciplines in environmental science and engineering. The different chapters are reasonable self-contained and can be chosen and used as needed depending on the needs and orientation of the reader. CONFLICT OF INTEREST The author(s) confirm that this chapter content has no conflicts of interest. ACKNOWLEDGEMENT Declared none.

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REFERENCES [1] [2] [3] [4] [5] [6] [7] [8] [9] [10] [11] [12]

Guralnik, D.B. (Ed). Webster’s New World Dictionary of the American Language, 2nd Coll. Ed.; The World Publishing Company, : New York, 1972. Goldsmith, E.;Allen, R.; Allaby, M.; Avoll, J. The Ecologist, 1972, 2 (1). World Commission on Environment and Development. Our Common Future; Oxford University Press: Oxford, 1987. U.S. Census Bureau, www.census.gov/ipc/www/idb/worldpopinfo.php U.S. Census Bureau, www.census.gov/ipc/www/idb/worldpoptotal.php U.S. Census Bureau, www.census.gov/ipc/www/worldhis.html U.N. Development Program, Human Development Report 1998, Chap. 3; Oxford Univ. Press: New York, 1998. (http://hdr.undp.org/en/media/hdr_1998_en_chap3.pdf) O'Neill, R. V.; Kahn, J. R., Bioscience, 2000, 50, 333. Imhoff, M.L.; Bounoua, L.; Ricketts, T.; Loucks, C.; Harriss, R.; Lawrence, W.T., Nature, 2004, 429, 870. Ertas, A.; Jones, J. The Engineering Design Process, 2nd ed.; John Wiley & Sons: New York, 1996. Odum, E.P. Fundamentals of Ecology; W.B. Saunders Co.: Philadelphia, 1971. Daly, H.E.; Farley, J.C. Ecological Economics: Principles and Applications, 2nd ed.; Island Press: Washington, D.C.:2004.

Sustainability: Multi-Disciplinary Perspectives, 2012, 9-39

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CHAPTER 2 Principles of Sustainability From Ecology Audrey L. Mayer* School of Forest Resources and Environmental Science and Department of Social Sciences, Michigan Technological University, Houghton, Michigan, USA Abstract: Most sustainability principles can be broadly described under four themes: resilience, desirability, intergenerational (temporal) equity, and intragenerational (spatial) equity. While the field of ecology does contribute much of what we generally know about the environmental dimension of sustainability, many subfields, hypotheses, and theoretical frameworks have influenced sustainability science, policy and assessment. In particular, ecology has emphasized: the critical need for understanding how dynamic complex systems evolve resilience to, and are governed by, disturbances; the spatial and temporal scales at which we discuss and seek to achieve sustainability, and the effect of systematic connectivity at multiple scales on our ability to reach sustainability goals. The importance of diverse, functioning ecosystems to many vital processes, including nutrient cycling, water purification, flood regulation, biomass production, and many others, influences the goods and services that societies require; these are the resources that will most likely be needed for many future generations.

Keywords: Resilience, Desirability, Intergenerational equity, Temporal equity, Intragenerational equity, Spatial equity, Ecology, Complex systems, Ecosystem goods and services, Self-organization, Diversity, Biotic homogenization INTRODUCTION Although there is no universally accepted list of sustainability principles, most lists include principles clustered around four general themes. First, systems and organizations must be resilient; they must not be so damaged as to have lost their ability to resist and recover from disturbances. Second, systems must have processes and features that are desirable, as not all resilient systems are desirable (such as planets that cannot support life). Desirable ecosystems are those that provide a predictable flow of necessary goods and services from ecosystems to *Address correspondence to Audrey L. Mayer: School of Forest Resources and Environmental Science and Department of Social Sciences, Michigan Technological University, Houghton, Michigan, USA; Tel: 906-487-3448; E-mail: [email protected] Heriberto Cabezas and Urmila Diwekar (Eds) All rights reserved-© 2012 Bentham Science Publishers

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linked human societies. Third, management and policy decisions must be made with respect to temporal fairness and equity (intergenerational equity), so that critical environmental resources are available to future generations. Finally, these decisions must also be fair and equitable spatially (intragenerational equity); some communities should not persist at the expense of others. Progress towards these four principles (resilience, desirability, temporal and spatial equity) has been significantly advanced by ecology. Ecology as a discipline began over one hundred years ago, and originally focused on purely natural phenomena such as the cyclical behavior of animal populations, patterns of species richness and abundance, and long-term evolutionary changes in species driven by shorter-term competitive interactions between them [1]. Most ecologists studied systems that they considered “pristine” or absent of human intervention or influence, as it was thought that this approach would be necessary to identify and understand core ecological laws. Furthermore, by understanding how populations, communities, and ecosystems operate without human presence, it was thought that we could then learn how to manage the environment (and hopefully ourselves) so that no changes due to human activities would occur. As evidence of long-term human influence and impact in even the most remote corners of the planet began to surface [2, 3], truly pristine places became quite difficult to find. Earlier work conducted in what was thought to be pristine areas was later found to be profoundly influenced by the historic activities of indigenous people. Whether through the use of fire and flooding, alterations to soil and hydrology for agriculture, or overharvest of native species, humans have significantly influenced most ecosystems for tens of thousands of years [e.g., 4-9]. With the rapid increase in human affluence, technology, and population size [10], and the increased scale and speed at which humans, goods, and species are travelling across the planet [11], anthropogenic disturbances and stresses now overshadow many of the natural processes that ecologists have historically studied [12-14] (Fig. 1). Ecologists now undertake research with an understanding that human societies exist as an important subcomponent of local, regional, and increasingly global ecosystems (Fig. 2). Indeed, human societies evolve and develop within their local

Principles of Sustainability From Ecology

Sustainability: Multi-Disciplinary Perspectives 11

and regional ecosystems, and therefore we can expect that humans and their societies are influenced by and subject to the same ecological laws and physical limits as other systems. However, humans are different than other species in several important ways. We can transmit information over many generations and now to many communities through writing, electronic communication, and other means, and we have many tools, from scanning microscopes to satellite imagery, to help us conceptualize when we are violating ecological and other limits at scales far beyond those that are immediately obvious to us. While we are not the first species to alter environmental conditions at a global scale, we are the first to realize that we are doing so. As our understanding of our planet and its ecosystems progresses, we are in a better position to understand exactly how we are placing these systems and our societies at risk, and therefore better able to mitigate these risks or at least adapt to them.

Figure 1: The Florida Everglades. Historially, water flowed in an uninterrupted shallow sheet from Lake Okeechobee (center top) south through the Everglades into Florida Bay (center bottom). Water now travels in canals past the agricultural area on Lake Okeechobee’s southern shore to several Water Conservation Areas, where it is held until floodgates are opened to direct the water into the Everglades National Park [15]. Red-green-blue false-color composite image courtesy of NASA [16].

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Figure 2: The relative importance of the environment to socio-economic systems. From the ecologists’ viewpoint, natural resources and environmental conditions serve as the foundation of all human societies. In this framework, resource use by social and economic sectors must remain within the biophysical limits set by the ecosystem upon which it depends [17].

First, some terms. Ecologists typically think in hierarchies. The biosphere is the living layer of the planet and interacts with other layers, such as the earth’s crust, or lithosphere, and the atmosphere, through abiotic and biotic processes [18]. The biosphere is made up of various biomes, which have characteristics dictated by topography, daily solar radiation, elevation, and prevailing winds1. These biomes host ecosystems 2, which are composed of a patchwork of different communities of many species that are locally adapted to the abiotic conditions. Populations affect each other through predation, competition, and symbiotic interactions. Finally, populations are comprised of individuals that (with the exception of clones) differ from each other genetically. While the origin and role of diversity at each of these levels is hotly contested, ecologists understand that diversity at these levels provide ecological systems with the variety needed to adapt to new conditions, resist recurring disturbances, and evolve over time into novel systems with potentially new functions. The accelerating loss of unique populations and species, at a pace that outstrips our hurried efforts to identify and catalogue them, represents a clear and present threat to the ability of ecologists to understand what human societies need from their ecological systems before it is too late to save 1

Latitude is a strong predictor of biome locations; many of the world’s deserts are located at 30° latitude. Deserts also occur on the leeward side of mountains in the “rain shadow” zone [19]. 2 The general term “ecological systems” refers to any living system composed of two or more individuals, groups, or species, while “ecosystem ” refers more specifically to the hierarchical scale represented by two or more communities.

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them [20]. We do not fully understand these losses, nor do we understand the degree to which these losses will cascade into losses in – and eventual failures of – other ecological, economic, and social systems [11]. RESILIENCE OF SOCIO-ECOLOGICAL SYSTEMS Ecologists have long understood that ecological systems are dynamic, and that connections and feedbacks at many scales can either stabilize or change the system over time. Systems change as species evolve and go extinct, as disturbances such as fires and floods open up new areas to colonize, and as climate change imposes cycles of droughts or favorable conditions to which species and communities adapt. To minimize the impacts of these disturbances and changes, living systems at any scale have self-organized internal feedbacks to try to remain stable, or if pushed away from a stable state, mechanisms to return to a stable state (either to the same state prior to the disturbance or to a new state) [21, 22]. Much of the recent gains in understanding the resilience of ecosystems and linked human-ecological systems come from complex systems theory, as have some specific hypothetical constructs such as the Gaia hypothesis and the Panarchy model. Complex Systems Theory and Applications in Ecology Ecologists have recently adopted a theoretical framework for complex systems as a way to understand the behavior of ecological systems, and to develop tools for predicting catastrophic changes in them [23]. Early work in this area began in the field of population dynamics, developing mathematical models that had few parameters but displayed complex behaviors; these models described the temporal patterns observed among small groups of species linked by competition or predation [24]. Competition and predation, coupled with environmental limits on resources, often results in predictable, stable fluctuations in populations of individuals, whether they are in a controlled laboratory experiment or in the field. These population -level efforts were scaled up to communities and ecosystems [24], and advances in complex systems theory made in other fields were applied to ecological systems, resulting in a confluence of bottom-up and top-down modeling and empirical research. Ecological systems were often found to spend

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disproportionate time in a stable state with low temporal variability, and then quickly shift to a different stable state. Multiple stable states3 have now been observed in a growing number of ecosystems, from terrestrial to marine to aquatic [25-30]. Shifts between these states are often nonlinear and usually perceived as rapid, and states often differ in their resilience to the same disturbances. Resilience is an endogenous, self-organized condition, determined by the strength of the feedbacks formed within the ecosystem itself [21, 22]. Freshwater lakes have proven to be a useful model ecosystem for this dynamic systems approach [31, 32]. Several dynamic mathematical models have been developed to predict the rapid shift in lake conditions from oligotrophic (low nutrient, high dissolved oxygen) to eutrophic (high nutrient, low dissolved oxygen) at different nitrogen and phosphorus loads in runoff from the surrounding landscape [e.g., 33]. These models have illustrated not only the rapid shift from oligotrophic to eutrophic conditions, but also the extreme efforts required to restore a lake back to an oligotrophic state [34]. In many cases, this shift takes the form of a hysteresis4, in which the nutrient load must be reduced well below the one at which the lake originally shifted to eutrophic conditions. This resistance to restoration is the emergent resilience that the lake develops in response to its new conditions; feedbacks between the lake’s sediments, vascular plants and algae communities form quickly to build resilience in the system against future regime shifts in either direction. Concurrently with methods to estimate the resilience of states, graphical and statistical tools have been developed to determine whether and when a system is in transition between states. Temporal and spatial patterns observed at the larger scale are often driven by processes at smaller scales [36]. A change or interruption in these patterns can offer a visual cue of the breakdown of key processes, and an impending collapse and reorganization of the system [37-39]. For example, the self-organized spatial maze patterns observed in semi-arid grassland vegetation change to a spotted pattern just before a complete collapse in vegetation cover, as a response to a weakened feedback between plant-enhanced water availability, grazing intensity, and rainfall [37, 40]. 3 4

These are also called “alternative stable states” or “dynamic regimes” [23]. This is equivalent to Thom’s “cusp catastrophe ” [35].

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These observed pattern changes can be temporal as well, including an increase in the variability observed in many of the system’s components5, such as nutrient flow rates or population size fluctuations, and increased statistical autocorrelation among these components [37, 42, 45]. These shifts are hypothesized to be the result of the weakening of key self-organized processes that are present in ecosystems and linked human-environmental (or “socioecological”) [46, 47] systems alike. If changes in spatial or temporal patterns can be linked to increased vulnerability to catastrophic shifts, then they may serve as an important warning signal for managers and government agencies [48, 49]. Increased instability in social systems (e.g., riots or political coups), and in linked socio-ecological systems (e.g., productivity losses due to climatic instability and invasive species) could be telling indicators of an impending catastrophic shift to a new state [14, 50-52]. The concept of resilience is now being used to find ways to better manage the impact of human societies on ecosystems, particularly in the presence of large scale processes such as climate change [12, 22, 23, 32, 48, 54]. Adaptive ecosystem management uses policy changes and management actions as experiments, comparing ecological data on either side of the change to determine whether the action is increasing the system’s resilience or further destabilizing it [48, 53]. However, adaptive management can be quite challenging for socioecological systems, as the number of potential feedbacks and thresholds, at multiple scales, suggests that “surprise” catastrophic shifts or even cascades of shifts [55, 56] may be almost inevitable [54]. The Gaia Hypothesis While the idea that the Earth is a self-regulating system may not seem to be controversial, the implications of Lovelock and Margulis’ [57] “Gaia Hypothesis” for humanity were and remain extremely controversial. The hypothesis represents one of the earliest in ecology which used a dynamic systems approach to understanding the Earth’s climate. The Gaia hypothesis describes Earth as a “superorganism” with some of the same self-regulating mechanisms, such as negative feedbacks, that keep all living 5

Several groups have observed an increase in “red noise” or entropy [41-44].

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organisms within particular limits [36, 57]. Much as endothermic species (including humans) have evolved physiological processes, such as sweating and shivering, to maintain a relatively constant temperature regardless of external temperatures, the hypothesis argues that Earth has “evolved” similar processes to maintain its climatic conditions to be supportive of life in general. While the hypothesis remains an important philosophical contribution to sustainability research and policy, it has been refuted on both theoretical and empirical grounds. Theoretically, the contention that an evolutionary process occurs at the scale of planets is untestable, given that there is only one sample (Earth), and this in itself has cast negative light on the entire concept [36, 58, 59]. Evolution can act on systems larger than an individual (such as a tribe or group of individuals), however there is no evidence to suggest that evolution directly drives the homeostatic properties of the Earth or its climate system [36]. Individuals are selected to alter their environments to favor their growth and reproduction6. At larger scales, due to the collective adaptive traits and behaviors of individuals, the environment can look as if it is perfectly suited to a particular species. This version of the original hypothesis is often referred to as the “Coevolutionary Gaia” hypothesis, and is more widely accepted due to the growing evidence for niche construction at the species level, and evidence for biotic influences on global and regional climate historically and at present [59]. More concretely, while the existence of global -scale stabilizing feedbacks is strongly supported [61], the presence of many destabilizing feedbacks at several scales in the climate system refute the hypothesis’ central assumption of the system existing to maintain conditions suitable for life [58, 62-64]. These feedbacks are often called “runaway greenhouse ” effects, and include the positive feedback dynamic in the Arctic between ice cover/open water and heat absorbed by the ocean [65], as well as the failure of the planet to maintain more constant atmospheric CO2 levels despite recent anthropogenic emissions [62, 66]. While the Gaia hypothesis may not be acceptable to many in its entirety, the hypothesis has influenced many ecologists, climatologists, geophysicists, and 6

This is a process called “niche construction” by ecologists [60].

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others to use this large-scale perspective to understand how complex systems such as the global climate are governed by thresholds and negative and positive feedbacks [36, 59, 62]. Indeed, many areas of inquiry in ecology are closely related to or have contributed to the Gaia hypothesis and its spin-off model “Daisyworld”: niche construction (a.k.a. ecosystem engineering); social evolution (shared self-interest); the influence of diversity on ecosystem stability (e.g., the insurance hypothesis); and ecosystem goal functions (e.g., maximizing energy capture and throughput, nutrient cycling) [59]. The Panarchy Model A main thread through the history of the discipline of ecology involves the extent to which determinism controls ecosystems. After disturbance, does a system return to the same type of system dominated by the same vegetative community? If not, what influences the direction and endpoint of successional processes? These questions speak to what we can, and want to, sustain in ecological and socioecological systems. Clements [67] argued that all communities develop towards one specific “climax” or mature community defined by a characteristic set of dominant plant species, such as a “spruce-fir” forest or mangrove forest. Mature communities remain in this climax state until a disturbance removes most or all of the individuals, providing a clean slate for new individuals (from the same set of dominant species) to recolonize the area and start the development process anew. Given that native species7 are highly adapted to local conditions, development after each disturbance is likely to be extremely similar to previous succession events, and therefore the same climax community should return repeatedly. And since this set of species is adapted to each other’s existence, the climax community is reinforced by feedbacks 8 between species and individuals to maintain the system in the climax state until the next disturbance. Clements’ hypothesis was supported especially at the global scale, as the ecosystems that are likely to develop and 7

Those species that have evolved in-place. Non-native species are those that have recently colonized an area; “recently” usually means after humans have substantially altered the ecosystems and communities. 8 Reinforcing feedbacks between two species would mostly commonly be symbiosis or coevolution.

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persist in a biome are highly predictable. The concept of a “climax” state has been used in the field of Industrial Ecology to determine sustainable materials and energy flows; a climax state is presumed to have the tightest connectivity between components and therefore the most efficient flow of biomass and energy through the system [68]. Gleason [69] supported an opposing view: he believed that a “climax” community or state does not exist. Similar to Clements’ hypothesis, after each disturbance a community will develop dominant plant communities that are most adapted to the current abiotic conditions. However, without a predetermined climax community, Gleason believed that the assembly of the community is greatly affected by chance competitive outcomes between species. Due to broad climatic changes and random occurrences, communities can develop quite differently after each disturbance. With each regenerative cycle, as a community matures it looks different than the previous cycle because the external conditions are likely to be different (even slightly), along with the set of potential species. More importantly, Gleason [69] argued that, due to turnover and differing dispersal abilities, “climax” communities will not exist. Even though changes in species composition may slow as a community matures, because of death and dispersal the turnover in individuals and species never stops. Even without a disturbance, a community will continue to change in perpetuity. This philosophical debate was the precursor of the recent theoretical and applied research in the Panarchy model of resilience. While the resilience literature has remained focused on the ability of a system to resist a disturbance, and the speed and form of shifts from one state to another, the Panarchy literature has gone beyond this to examine how a dynamic system adapts to these seemingly inevidable systemic shifts and the implication of this adaptation for future system behavior. Building off of the Gleasonian view of succession in ecosystems, the Panarchy model assumes that all dynamic systems go through repeated cycles of growth and development, maturity, decay or destruction, and renewal, at multiple spatial and temporal scales [70-73] (Fig. 3). In the case of ecosystems, as species adapt to new local climates and the presence of other species in the community, the system

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gradually builds resilience to disturbances through negative, stabilizing feedbacks. However, as these feedbacks become entrenched, and some species become dominant at the expense of others, the “mature” system can lose the redundancy that can lend stability and becomes less resilient to new disturbances or changes in the intensity of familiar disturbances. Ultimately the system is degraded and pushed into a new state, and the process begins again. However, the system retains knowledge through the existing suite of species and their adaptations (or the system’s “wealth”) [71] from the previous cycle, and this knowledge remains available to help the system quickly adapt to new conditions; this is the “adaptive cycle” of the Panarchy model.

Figure 3: The general pathway of ecosystems through the Panarchy model. Mature ecosystems tend to have high accumulated biomass and connectedness among components, however may be less resilient to disturbances that cause a destruction and reorganization of the system, not always to the previous regime [70].

Not only do systems visit the development stages of growth, maturity, disturbance and reorganization repeatedly, but they may proceed from one stage to another at different speeds and at different scales [71, 73]. As species develop stabilizing feedbacks, changes within the system slow down, until a disturbance quickly moves that system from maturity into a stage of reorganization. In addition, the connectedness within the system also changes throughout this process; connectedness is highest in mature systems with many stabilizing feedbacks [71]. The Panarchy model has been applied to many different socioecological systems to explain how the current resilience of ecosystems are influenced by historic land use, and why current states may or may not be resilient (such as the repeated collapse of the Romanian agro-ecological system) [74]. These insights can lead to

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well-designed adaptive management experiments to guide systems towards more resilient states [70, 75, 76]. Panarchy may also help us more philosophically, to think about and understand what it means to live in a society that is undergoing collapse and reorganization [70, 77]. DESIRABILITY: ECOSYSTEM GOODS AND SERVICES AND THEIR LIMITS When we consider the sustainability of a set of states toward which we might guide our socioecological systems, we not only want states that are resilient so that internal feedbacks do some of the work to bring stability to the system, but also states that are desirable [78]. Not only do we desire stability in our ecosystems9, but we also want functional ecosystems that provide the large variety of goods and services that are critical to functional societies and their economic systems. However, physical constraints present upper limits on how much of (and how quickly) these goods and services can be provided by ecosystems, and the degradation of ecosystems by overuse or disruption of ecological processes can reduce these limits considerably. Ecosystem Goods and Services As ecologists have begun to apply ecological theories to real world systems, the impact of degraded ecosystems on human societies is now readily apparent. Given that the flow of biomass, energy, water and nutrients between ecosystems and social systems is governed primarily by economic behaviors, ecologists are working with economists to adapt ecological concepts and terminology into language that could be more easily grasped by non-ecologists. Ecosystem goods are those things that humans can harvest directly, such as food, fuel, and fiber, while services are processes such as air and water purification, climate regulation, flood protection, pollination, pest control and nutrient recycling [13, 79, 80]. In theory, if the value of ecological goods and services could be made apparent to economies, then they would not be considered free or external to the market and would be protected as a valuable resource. Each ecosystem provides a wide variety of goods and services, some of which (such as carbon sequestration) are provided by most ecosystems [79, 81]. For 9

Here one may also infer “predictable”.

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example, forests not only provide timber and other products, but also regulate hydrologic cycles by holding soil in place and allowing water to filter through it (instead of run directly into water bodies), help regulate local and regional climate by providing wind breaks and shade, and can regulate global climate through carbon sequestration [82, 83]. However, research on important variables such as how much of a good or service an ecosystem provides, under what kinds of conditions, is nascent and incomplete [84]. Many valuation studies rely on proxies such as how much a society would pay to protect an environmental service, or how much it would cost to build and maintain an alternative service. These proxies depend solely upon human valuation of the service itself, and neglect the many connections to other valued services that would be lost. However preliminary, rough estimates of the economic value residing in ecosystems are usually vastly larger than produced by the economic system itself [81]. The positive correlation between biodiversity and many ecosystem functions suggests that the rapid loss of biodiversity will directly affect key ecosystem services that our societies rely upon, in particular the productivity of our landscapes with respect to the production of food, fuel, and fiber [85]. Communities of multiple species produce more biomass than one comprised of a single species [86-92], which suggests that our modern monocultural agricultural systems may be inefficient with respect to land and resources (such as fertilizers). And even in those cases in which one species or taxonomic group may be more productive and efficient in the provision of a service than a more diverse community, greater biodiversity maximizes the number and kinds of functions and services that are provided by an ecosystem [80, 85, 93]. The economic valuation of biodiversity itself, however, can be problematic [94]. The scale at which biodiversity in general, and particular species, influence ecosystem function and services is unknown for most ecosystems. Given the multiple scales at which humans use and affect ecosystems, this information is critical for informed natural resource and land management policies [80]. The provisioning of ecosystem goods and services is beginning to inform environmental protection policy. For example, instead of building a new water purification plant for New York City, the city instead used the money to purchase the development rights for most of the Catskill Mountain watershed, which is the

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main source of the city’s water [84, 95]. This prevented deforestation in the watershed, and the preserved forests now prevent soil erosion (reducing siltation of water supply reservoirs) and increase the retention of water in the higher reaches of the watershed, so that water can be supplied more reliably throughout the year. Ecosystem valuation is an active and quickly growing area of research, although there are still many questions to be answered before the methods and information obtained from them can be used to make reliable policy decisions [93, 96, 97]. The theoretical concept of ecosystem valuation has been applied to real socioecological systems through Payment for Ecosystem (or Environmental) Services (PES) programs [98]. There has been great interest in PES programs as a way to simultaneously reduce environmental degradation and alleviate poverty, although these programs have met with mixed success in both environmental and social dimensions [99]. Ecological Limits Even if we preserve a completely intact ecosystem, it cannot provide an unlimited flow of goods and services. Limitations exist for all biological processes at all scales; these limits are most often a function of thermodynamic laws and the rate at which energy can be converted to mass. However, processes such as competition can also place limits on energy and natural resources, so that the sustainable amount of goods and services that can be harvested from ecosystems is a fraction of what can theoretically be produced; the majority of the resources are needed by the organisms in that ecosystem to maintain critical structures and functions. In ecology, these limitations are described in terms of niches and carrying capacities. At the scale of the individual, resource limits are experienced as an envelope of biophysical conditions that an organism best occupies, called a “niche ” by Grinnell [100]. He observed that, in general, species with a very large distribution range (that is, they can be found in many different regions) often tolerate a large range in environmental conditions10. Later work differentiated between an organism’s “optimal niche”, delineated by biophysical constraints, and the smaller “realized 10

e.g., means and extremes in temperature, precipitation, habitat type, etc.

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niche”, the limits of which are set by competition for resources with other species [101]. Two corollaries of the niche then develop the concept of a food web. First, the wastes of organisms can be a niche for other organisms (such as fungi), which maintain the constant flow of nutrients through other organisms and the environment. Second, there are no empty niches; an accumulation of nutrient or energy resources will quickly be colonized by a new species, or appropriated by an existing species through an expanded or modified niche, most often through a new behavior. Of course, while nutrients can cycle endlessly through niches and webs, energy only flows one way, from lower to higher entropy. Scaling up to populations and species, a human demographer began a historic contemplation of the limits to population growth that all species face. Malthus’ [102] prediction that the human population would outgrow its agricultural support was a foundational inspiration for Charles Darwin’s evolutionary theories, drew intense criticism from his peers, and spurred centuries of effort to increase agricultural yields. Later, in their population growth models, Pearl and Reed [103] and Gause [104] found that biophysical limits, and competition with other species, resulted in populations that increased and then leveled off, best fit by a logistic growth model [105]. The population size at which this leveling off occurred would later be called the population’s “carrying capacity”. Subsequent ecologists have sounded alarms regarding human population growth and the pressures it places on ecosystems [106], based on the concept of an environmental carrying capacity. Every population of all species has a carrying capacity, dictated by physiological11 and behavioral12 constraints; this was Gause’s definition. However, given that environmental conditions are dynamic, the availability of resources can be highly variable, and therefore the number of individuals that an environment can support is not fixed (as Gause found in his laboratory experiments) but variable. Human populations are no exception to these theories involving population limits set by niche and environmental carrying capacity. However, Ehrlich and others later revised their concerns, placing more emphasis on per capita consumption of 11

This includes the amount of energy, water, and other basic requirements for growth and subsistence. 12 Examples of behavioral constraints include the size of the territory that can be defended, or competition with other individuals and species.

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resources rather than only on population size13 [10, 107-109]. This is not to downplay the dangers of overpopulation, but to emphasize the ways in which human populations and societies can run into resource limits and risk undesirable population-reducing events (e.g., violent conflict; [52]). The concept of an “ecological footprint ” for a society, and the increasingly popular Ecological Footprint sustainability index, are intellectual descendants of the carrying capacity theory [110-114]. An Ecological Footprint of a society is often calculated in tandem with its Biocapacity, which measures the amount of resources that are available in the area over which that society presides (e.g., within national political boundaries). Those societies that live in an area with a small biocapacity must make choices as to whether and how they will meet their needs sustainably: by matching per capita consumption levels with resource availability (either by reducing consumption, population, or both); or by importing resources from other areas. The importation of resources (i.e., trade) may increase the economic sustainability of a society, but may reduce the environmental sustainability of both that society and its trading partner if resource depletion signals cannot be observed by the consuming society14 [115-119]. If these two trading partners are also highly connected ecologically, damage caused by unsustainable resource extraction can ultimately impact the importing society’s ecosystems as well [120, 121]. TEMPORAL AND SPATIAL DYNAMICS OF SUSTAINABLE SYSTEMS The temporal and spatial dynamic behaviors of ecosystems and socioecological systems are increasingly critical to understand as globalization moves people, products, and pollutants over longer distances than ever before [11]. Ecologists have developed several research subfields regarding the spatial and temporal patterns we observe, and the processes that govern why species, communities, and ecosystems are found in particular places. In particular, the field of landscape ecology explores changes in patterns and processes in both temporal and spatial 13

Ehrlich and Holdren [107] introduced the I=PAT equation, where environmental impact is determined by a combination of human population size, affluence of that population, and technology advancements. 14 This effect has been called “leakage” or “seepage” in academic circles, indicating that the effects of national or regional policies often leak or seep across borders to impact other societies.

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dimensions simultaneously, and therefore has contributed substantial knowledge to the sustainability of humans and their environments with respect to inter- and intra-generational equity. Landscape Ecology, Land Use Change, and Sustainability Science The field of landscape ecology seeks to understand spatially and temporally explicit patterns and the processes that create and maintain them [122]. Scale is comprised of two components, grain (or resolution) and extent (or time period), and is relative to the scale at which heterogeneity in pattern or process is maximized. Therefore, the scale at which humans perceive landscapes is much larger than the landscape scale for a mouse, for example. Some landscape scale processes are scale invariant, and can produce similar patterns at different scales. In these cases, models developed to test a generic system could be applied at several scales; the shading and evapotranspiration processes of a single tree can create a microclimate of cooler, wetter conditions to allow moss to thrive at its base, and these same processes of a forest can create a cooler, moister landscape than the same area devoid of trees. Top-down vs. bottom-up system influences is the primary dichotomy for the investigation of ecological processes in this field. Landscape ecologists began with an emphasis on theoretical constructs: observing patterns, identifying potential links between particular patterns and processes, and developing quantitative methodologies and metrics to test these links [122, 123]. Ecological processes such as the spread of disturbances (fires, invasive species), nutrient cycling, and climatic constraints were hypothesized to govern the observed patterns in habitat and species distributions over space and time. Although these processes influenced and were influenced by human societies and their land use activities, the human element in landscapes (both past and present) was ignored until relatively recently. Indeed, many processes such as species invasions and nutrient cycling have been sped up and globalized by humans, and at the same time have often negatively impacted how these processes affect human societies [124-126]. Understanding the importance of scale to the distribution and connectivity of individuals, species, and communities across landscapes of varying size can provide an important scientific foundation to sustainability science [127]; indeed,

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a conservation legacy has always been at the heart of applied landscape ecology [128]. The dynamics that we see occurring at the global and regional scales can be the result of the decisions, processes, and relationships at smaller, local scales [129]. This phenomenon suggests that sustainability policies must explicitly account for these scale effects, and carefully consider whether top-down or bottom-up approaches are more likely to achieve sustainability goals. Human-driven changes in land use and land cover have altered patterns and processes at increasingly large scales, and have highlighted positive and negative feedbacks between human and ecological processes [127, 130] (Fig. 4). Deforestation, desertification, and urbanization have altered landscapes and regions in ways that may be irreversible; desirable ecosystem services such as soil fertility and flood protection may not be easily restored to such landscapes. On the other hand, over long periods of time many countries have gone through a “forest transition” [130, 131], where forest cover eventually recovers to a pre-deforestation state, although particular characteristics of forests (such as species composition and age structure) may not recover [132]. Quantitative metrics developed for landscapes (such as patch connectivity, edge density and fractal dimension) can help determine where reforestation, ecosystem restoration, and greater protection would have the largest impact on species and community recovery, and thereby increase the sustainability of a degraded region [133; but see 134].

Figure 4: Due to decades of deforestation in Haiti (left), its border with the Dominican Republic (right) can be clearly seen from satellite imagery. Deforestation has resulted in considerable soil loss and disruptions to water supplies; when seasonal rains arrive, the water quickly runs off the deforested land and carries the soil out to sea [135, 136]. Image courtesy of the NASA/Goddard Space Flight Center, Scientific Visualization Studio.

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There have been increasing calls for a shift in landscape ecology from a primary focus on theoretical advancements to a practical focus on landscape sustainability, for which the field can offer practical information on thresholds, feedbacks, and processes at large spatial and temporal scales [127, 137, 138]. To do so, the field will not only have to transcend scales, but disciplines as well, and explicitly incorporate social, economic, and historical dynamics into models and observations of both the causes and the effects of land use change on landscape sustainability [127, 130]. This new field of “holistic landscape ecology” or “land change science” will identify management strategies for land use that will increase sustainability through stabilizing ecological processes, and advise against activities that increase the vulnerability of the human-ecological system to catastrophic events [130, 139]. This new direction may also focus on managing landscapes with respect to sustainable flows of ecosystem goods and services [125]. Biogeography and Biodiversity Biogeography is the study of the interaction between geological and ecological processes, and their influence on broad patterns in biodiversity [140, 141]. The similarity in the flora and fauna of North America and Eurasia, and the stark differences between these species and those of Australia, for example, is directly related to the length of time over which the continents upon which these species evolved have been separated. Likewise, ecological communities on islands close to a continent or mainland are more similar than to the communities on very distant, isolated islands, such as those on the Hawaiian archipelago [142]. The amount of contact that a community has with another not only influences how similar those communities are likely to be, but also influences the success of an invasive species and the magnitude of damage that species does to the new community; this is an active area of research in ecology. Studies in biogeography have also investigated the predictability of diversity and its patterns, specifically how many species can be supported by a particular area: the species-area curve [140, 141]. This curve predicts that, as we leave less habitat for other species and simplify landscapes (e.g., monocrop agriculture and urbanization), there will be fewer species supported and therefore less stability (and perhaps productivity) in the landscape. With additional pressures of exotic

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species introductions, global climate change, and profound alterations to nitrogen and phosphorus cycles, the impact on global and regional biodiversity is likely to be profound [143]. While dispersal and extinction are two core processes of community assembly, the rate at which, and distance over which, these processes are now occurring is unprecedented. Humans have been unmatched by any other species in our ability to cause massive losses of biodiversity as we spread onto new continents, and now anthropogenically-driven climate change threatens habitat disturbance and destruction at a global scale [5, 13]. Humans also have what seems to be a unique talent for intentionally (in terms of domesticated species) and unintentionally introducing novel species into each ecosystem they enter. The end result of this propensity to spread novel species is a global “biotic homogenization ”, creating a world in which a small subset of invasive species, well-adapted to human disturbance, lead to the loss of biodiversity and ecosystem functioning on every continent [144-146]. The combination of an invasion rate many times higher than the background rate, with an extinction rate perhaps 1000 times higher than the background rate, suggests a massive conversion is underway of a regionally diverse, species-rich world into one with many fewer species, and little difference between regions [147, 148]. The implications this conversion has for the stability, productivity, and resilience of ecosystems and the human societies dependent upon them may be profound [149]. We are gaining more insight into the advantages that diversity confers upon systems [90, 91]. Diverse systems may be more stable than less diverse ones, if diverse systems are composed of a number of “redundant” species; should one species disappear from the system or go extinct, another species with a similar niche and function can replace it with little disruption to the overall ecosystem. Furthermore, diverse systems may be more productive, converting more solar energy into biomass than species-poor systems [86, 87, 91, 92]. As humans have industrialized their agricultural systems, they have increased crop productivity through the massive inputs of fossil fuel-based fertilizers and pesticides, risking global climate change and long-term damage to ecosystem functionality by such practices [150, 151].

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Evolution and Adaptive Landscapes The theory of evolution forms the foundation of all biological sciences, including ecology. The differential survival of genes over time through processes such as natural and sexual selection, and the creation of new genes through mutation, give rise to the diversity that we observe at many scales, from individual to ecosystem [152]. The evolution of organisms is guided by competition for resources, mates, and the selection mechanism of environmental conditions. Traits that seem perfectly adapted to current conditions can quickly become maladaptive when new environmental conditions arise, when new competitors appear, or when coadapted species (such as pollinators) disappear [153, 154]. For this reason, may populations and species depend on genetic and behavioral diversity to allow for persistence of the species through a constantly changing mixture of biotic and abiotic conditions and interactions. Evolutionary ecologists study changes in populations and species over time as they adapt to a constantly changing “complex adaptive landscape ” [154]. In times when communities and environments are in flux, these landscapes change quickly as well. Populations and species that have retained a high diversity of traits and behaviors are often the most likely to survive (and even thrive) under these conditions. Additionally, higher diversity allows species to colonize many different kinds of environments; indeed, it is most likely that our diversity and adaptability allowed humans to colonize every continent on the planet [155-157]. Placing sustainability problems in the context of complex socioecological systems evolving through constantly-changing conditions can provide tremendous insight for identifying potential solutions. Human societies adapt to changing conditions as all complex systems do, and theoretical constructs such as complex adaptive landscapes have been applied to social and economic systems with interesting results [e.g., 6, 158, 159]. We have learned that diversity has great value for providing adaptive traits and behaviors in a variety of conditions, and for surviving rapidly changing conditions. Therefore, truly sustainable solutions are most likely those that are locally or regionally adaptive and retain systemic diversity.

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CONCLUSIONS Ecology is responsible for a substantial contribution to sustainability science alone, and as an interdisciplinary effort. Ecological theories and hypotheses have inspired new and creative ways to create more sustainable systems. For example, a hypothesized positive effect of higher diversity on the productivity and stability of a system has been applied to alternative agricultural systems; polyculture systems mimicking the structures and functions of tallgrass prairies have been found to be as productive as monocultures of grains and legumes, while preserving the regenerative functions of the soil community and surrounding ecosystems [160]. In particular, the complex systems approach to understanding ecosystems has readily incorporated linked human systems into these models, and allowed for a greater understanding of the impacts of specific human activities on the entire socioecological system [30, 161, 162]. As anthropogenic forces at many scales exert pressure on the ecosystems on which we depend for key goods and services [151], ecology remains at the forefront for sustainably managing complex, coupled human-environmental systems [54]. Globalization has increased the rate and distance that species, diseases, and cultural impacts to the landscape can spread [11, 126]. We are entering an era of ecological patterns and processes that may not just be quantitatively different but instead are qualitatively different. Increased connectivity may induce ecological patterns and processes that are fundamentally new, and drive ecosystem changes with which ecologists are not familiar. Therefore, management efforts to preserve ecosystem services and desirable ecosystem regimes will be all the more difficult and complicated, yet important [163, 164]. Ecologists have began to work more closely with anthropologists and economists, even to the extent of forming new disciplines such as “human ecology ” and “ecological economics ” that study humans and their environments as one highly connected system. An increased focus on how humans interact and influence ecosystem patterns and processes, and are affected by changes in them, will require many ecological fields to adapt integrated, interdisciplinary approaches at multiple

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scales. Through these approaches, ecology will better provide the necessary foundation for the emerging meta-field of sustainability science [127, 165]. CONFLICT OF INTEREST The author(s) confirm that this chapter content has no conflicts of interest. ACKNOWLEDGEMENT Declared none. REFERENCES [1] [2] [3] [4] [5] [6] [7] [8] [9] [10] [11] [12] [13] [14]

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[135] Steyaert LT. Inadvertent climatic change – Investigating the coincidence of deforestation, inadequate soil conservation practices, soil erosion, and disasterous food shortages in northwest Haiti. B Am Meteorol Soc 1979; 60(5): 549. [136] Dolisca F, McDaniel JM, Teeter LD, Jolly CM. 2007. Land tenure, population pressure, and deforestation in Haiti: The case of Forêt des Pins Reserve. J Forest Econ 2007; 13(4): 277-289. [137] Naveh Z. Landscape ecology and sustainability. Landscape Ecol 2007; 22(10): 1437-1440. [138] Musacchio LR. The scientific basis for the design of landscape sustainability: A conceptual framework for translational landscape research and practice of designed landscapes and the six Es of landscape sustainability. Landscape Ecol 2009; 24(8): 993-1013. [139] Li B-L. Why is the holistic approach becoming so important in landscape ecology ? Landscape Urban Plan 2000; 50(1-3): 27-41. [140] Rosenzweig M. Species diversity in space and time. Cambridge, UK: Cambridge University Press 1995. [141] Whittaker RJ. Island biogeography: Ecology, evolution, and conservation. New York: Oxford University Press 1998. [142] MacArthur RH, Wilson EO. The theory of island biogeography. Monographs in Population Biology 1. Princeton, New Jersey; Princeton University Press 1967. [143] Sala OE, Chapin III FS, E Huber-Sannwald E. Potential biodiversity change: Global patterns and biome comparisons. In Chapin III FS, Sala OE, Huber-Sannwald E, Eds. Global biodiversity in a changing environment: Scenarios for the 21st Century. Ecological Studies 152. New York: Springer-Verlag 2001; pp.351-360 [144] McKinney ML, Lockwood JL. Biotic Homogenization. New York: Kluwer Academic Press 2001. [145] McKinney ML. Urbanization as a major cause of biotic homogenization. Biol Conserv 2006; 127(3): 247-260. [146] Pauchard, A, Aguayo M, Peña E, Urrutia R. Multiple effects of urbanization on the biodiversity of developing countries: The case of a fast-growing metropolitan area (Concepción, Chile). Biol Conserv 2006; 127(3): 272-281. [147] Myers N, Knoll AH. 2001. The biotic crisis and the future of evolution. P Natl Acad Sci USA 2001; 98(10): 5389-5392. [148] Olden JD, Poff NL, Douglas MR, Douglas ME, Fausch KD. Ecological and evolutionary consequences of biotic homogenization. Trends Ecol Evol 2004; 19(1): 18-24. [149] Loreau M, Naeem S, Inchausti P, Bengtsson J, Grime JP, Hector A, Hooper DU, Huston MA, Raffaelli D, Schmid B, Tilman D, Wardle DA. Biodiversity and ecosystem functioning: current knowledge and future challenges. Science 2001; 294(5543): 804-808. [150] Piorr H-P. Environmental policy, agri-environmental indicators and landscape indicators. Agr Ecosyst Environ 2003; 98(1-3): 17-33. [151] Hooper DU, Chapin III FS, Ewel JJ, Hector A, Inchausti P, Lavorel S, Lawton JH, Lodge DM, Laoreau M, Naeem S, Schmid B, Setälä H, Symstad AJ, Vandermeer J, Wardle DA. Effects of biodiversity on ecosystem functioning: A consensus of current knowledge. Ecol Monogr 2005; 75(1): 3-35. [152] Gould SJ. The structure of evolutionary theory. Cambridge, MA: Belnap Press of Harvard University Press 2002. [153] Thompson JN, Nuismer SL, Gomulkiewicz R. Coevolution and maladaptation. Integr Comp Biol 2002; 42(2): 381-387.

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[154] McGhee GR Jr. The geometry of evolution: Adaptive landscapes and theoretical morphospaces. Cambridge, UK: Cambridge University Press 2006. [155] Mesoudi A. An experimental simulation of the “copy-successful-individuals” cultural learning strategy: adaptive landscapes, producer-scrounger dynamics, and informational access costs. Evol Hum Behav 2008; 29(5): 350-363. [156] Hamilton MJ, Burger O, DeLong JP, Walker RS, Moses ME, Brown JH. Population stability, cooperation, and the invisibility of the human species. P Natl Acad Sci USA 2009; 106(30): 12255-12260. [157] McKellar AE, Hendry AP. How humans differ from other animals in their levels of morphological variation. PLOS ONE 2009; 4(9): e6876. [158] Douthewaite B, de Hann NC, Manyong V, Keatinge D. Blending “hard” and “soft” science: the “follow-the-technology” approach to catalyzing and evaluating technology change. Conserv Ecol 2002; 5(2): 13. URL: http://www.consecol.org/vol5/iss2/art13. [159] Martin R, Sunley P. Complexity thinking and evolutionary economic geography. J Econ Geogr 2007; 7(5): 573-601. [160] Jackson W. 2002. Natural systems agriculture: a truly radical alternative. Agr Ecosyst Environ 2002; 88(2): 111-117. [161] Carpenter S, Brock W, Hanson P. Ecological and social dynamics in simple models of ecosystem management. Conserv Ecol 1999; 3(2): 4. URL: http://www.consecol.org/vol2/iss2/art4/. [162] Güneralp B, Barlas Y. Dynamic modeling of a shallow freshwater lake for ecological and economic sustainability. Ecol Model 2003; 167(1-2): 115-138. [163] Rees WE. Globalization, trade and migration: Undermining sustainability. Ecol Econ 2006; 59(2): 220-225. [164] Perrings C, Dehnen-Schmutz K, Touza J, Williamson M. How to management biological invasions under globalization. Trends Ecol Evol 2005; 20(5): 212-215. [165] Clark WC, Dickson NM. Sustainability science: The emerging research program. P Natl Acad Sci USA 2003; 100(14): 8059-8061.

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CHAPTER 3 The Economics of Sustainability Joshua C. Farley* Department of Community Development and Applied Economics, University of Vermont, Burlington, Vermont, USA Abstract: Human society currently faces a number of unprecedented challenges, ranging from global climate change and biodiversity loss to peak oil and natural resource depletion. Unfortunately, competitive market economies systematically favor the conversion of ecosystem structure into economic products over its conservation in order to provide vital ecosystem services. In order to design a sustainable economic system, we must assess the desirable ends of economic activity as well as the scarce resources required to attain them. The most critical desired ends require cooperation, not competitive markets. Fortunately, humans have evolved as cooperative, social animals, and proper economic institutions can elicit cooperative behavior in order to attain these ends. Building a sustainable economy requires a scientific approach in which institutions for allocation are determined by the ends we hope to attain and the physical characteristics of the resources at our disposal, not by ideological commitments to a market economy.

Keywords: Biodiversity loss, climate change, peak oil, human needs, sustainable, scale, just distribution, scarce resources, rivalry, excludability, stock-flow, fundservice, cooperative behavior, collective action, public goods, open access resources, price rationing. INTRODUCTION Economic systems, other institutions and resource availability co-evolve over time [1, 2]. The industrial revolution, the use of fossil fuels and large scale social reorganization interacted with profound economic changes in the late 18th and 19th centuries. Microeconomic theory was developed to explain and channel these forces. The theory’s central focus was on the role of prices in balancing supply and demand while allocating the factors of production towards the highest value economic products, and those products towards the consumers who valued them *Address correspondence to Joshua C. Farley: Department of Community Development and Applied Economics, University of Vermont, Burlington, Vermont, USA; Tel: 802.656.2989; E-mail: [email protected] Heriberto Cabezas and Urmila Diwekar (Eds) All rights reserved-© 2012 Bentham Science Publishers

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the most. A serious crisis, the Great Depression, illuminated a major flaw in the theory, which failed to predict that a drop in demand could lead to reduced production, unemployment and further decreases in demand in a vicious circle. Macroeconomic theory was developed to explain this inherent flaw, and propose solutions in the form of government intervention through expansionary monetary and fiscal policies. Rather than simply balancing supply with demand, macroeconomic theory called for endless increases in both. The economy was like a bicycle that must move forward or fall down. Both microeconomics and macroeconomics were examples of true paradigm shifts, fundamental changes in our understanding of the nature of economic systems. Society currently faces imminent changes as profound as the industrial/fossil-fuel revolution, and emerging threats far more serious than the great depression. The great depression was the result of a lack of demand, but the current ecologicaleconomic crises we face are the result of resource constraints, a lack of supply. Natural resource depletion threatens not only to deprive the economy of the raw materials needed for production, but also complex ecosystems and the biodiversity they sustain. No economy can function without energy, and today’s industrial economies depend on oil. Global oil discoveries peaked in the midsixties [3] and oil production reached a plateau in 2005; even 200% price increases in 2008 scarcely affected output [4]. Waste absorption capacity for carbon dioxide and other pollutants, sink constraints, may be even more limiting than source constraints [5, 6]. Overuse of both sources and sinks is threatening the planet’s biodiversity and the ability of ecosystems to generate life sustaining ecosystem services [7]. Sustainability in a complex, evolving world requires adaptation. Addressing the new crises humanity now confronts demands a fundamental rethinking of our entire economic system. A major new paradigm shift is required to develop a biophysically sustainable earth economics 1.

1

David Batker, Executive Director of Earth Economics, coined this phrase. I have also borrowed from him the analogies with micro and macroeconomics. There is no difference between earth economics and ecological economics, except the former phrase accentuates the role of increasing scale and the progression from micro to macro to earth.

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WHAT IS ECONOMICS? In moving forward to develop a new theory of economics, it helps to first return to the basics. What does an economic system do? A standard definition of economics is the study of the allocation of scarce resources among alternative competing ends. An economic system strives to balance what we have with what we want, biophysical possibility with social, psychological and moral desirability. It follows from this definition that we must understand the desirable ends and the nature of the scarce resources before we can decide how to allocate [8]. THE DESIRABLE ENDS Conventional micro and macroeconomics both come from a utilitarian tradition, which defines the desirable ends of economic activity as the maximization of utility. Utility cannot be readily measured, but conventional economists assume that rational people will strive to maximize utility, and therefore their preferences for activities that increase utility are revealed by their actions, or specifically, by their purchasing habits [9]. Utility is considered equal to consumption of market goods, and more is always better. Work is assumed to generate disutility. The utility of society is typically taken as the weighted sum of individual utilities. While utility is inherently subjective, economists argue that revealed preferences are objective. Economists recognize the concept of diminishing marginal utility, which means that each additional unit of something desirable confers less utility than the previous unit. One implication of this assumption is that the wealthy receive less utility from the marginal dollar than the poor, and a more equal distribution of wealth would increase total utility. However, conventional economists generally assume that we cannot compare utility across individuals. We should therefore strive for a system that achieves all exchanges that make at least one person better off without making anyone else worse off. A situation in which no further such exchanges exist is said to be Pareto efficient. Under a number of very strict assumptions, competitive free markets reach Pareto efficient outcomes, and thus maximize utility for society. All of this is covered in virtually any introductory text in economics.

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Unfortunately, there are several serious problems with this supposedly objective approach to defining the desirable ends and establishing that free markets maximize utility. First, revealed preferences weight actual preferences by purchasing power. The preferences of the poor therefore receive negligible weight. Second, revealed preferences for market commodities tell us nothing about the role of non-market commodities in generating well-being. Third, the assumption that more is always better may not be justified. There is evidence that beyond a certain level well below that of the wealthier nations, increasing wealth does not correlate with an increase in happiness or satisfaction with life as a whole [10-12]. In fact, those who continually strive for higher income and more consumption may be less happy and more stressed than those who do not [13]. Once basic needs are met, we may derive satisfaction from our wealth relative to others, in which case increases in general wealth may have little effect on individual well-being [14, 15]. Fourth, if utility results from our ability to satisfy our wants and needs, and the economic system continually creates new wants for new products as rapidly as it increases our ability to satisfy them, growth does not increase utility. Finally, as explained in detail below, continuous increases in consumption are biophysically impossible on a finite planet. A sustainable economic system does not need to abandon the utilitarian approach, but must recognize that any definition of desirable ends beyond simple survival is normative, not positive. Perhaps most people would agree that an appropriate intermediate end for economic activity is a high quality of life for this and future generations. However, quality of life consists of far more than consumption of market goods. At a minimum, a high quality of life must satisfy basic human needs. Max-Neef [16] identifies these needs as subsistence, protection, affection, understanding, participation, leisure, creation, identity and freedom. These needs are satiable, and only appear insatiable when we pursue the wrong means for satisfying them. Advertisements tell us that material consumption will satisfy these needs, and when it fails to do so, many believe that they simply are not consuming enough. How we satisfy these needs differs across cultures, but there are three essential instrumental ends necessary to attain a high quality of life for this and future generations. The first is ecologically sustainable scale, where scale is defined as

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the size of the economy in relation to the ecosystems that sustain and contain it. We cannot exceed the carrying capacity of the earth. Given the complexities of the global ecological-economic system and the potential for surprises, we cannot pinpoint a single specific carrying capacity for human populations and artifacts, but increasing evidence suggests we have already exceeded acceptable safety margins [17]. We must focus on restoring and sustaining ecological resilience. The second is a just distribution of resources. Sustainability focuses on the just distribution of resources across generations, and the same ethical standard requires a just distribution within each generation. We cannot ask the poor to sacrifice consumption to meet the needs of the yet unborn. Furthermore, people inherently care about fairness [18, 19], and an unjust distribution of resources is incompatible with a high quality of life. Finally, given a finite planet with many degraded ecosystems and unmet human needs, a sustainable society requires an efficient allocation of resources, where efficiency is defined as the ratio of welfare gained from economic services to welfare lost in ecosystem services [20]. THE SCARCE RESOURCES What resources do we have available to achieve a high quality of life? We know from the first law of thermodynamics that matter-energy cannot be created or destroyed. This means that all economic production requires the transformation of raw materials provided by nature. We know from the second law of thermodynamics that energy is required to do work, and that work increases entropy, or disorder. In economic terms, low entropy resources are useful, and high entropy resources are not. The economic system transforms low entropy resources from nature into economic products, increasing entropy in the process. The ultimate resource at our disposal is the planet’s finite supply of low entropy matter-energy, and the finite flow of solar energy [21]. Ultimately, a sustainable economy cannot increase entropy faster than solar energy can replenish it. The laws of thermodynamics limit the physical size of our economy. Physical growth of the economy must eventually cease. Fossil fuels are a particularly important source of low entropy energy. The market economy and the fossil fuel economy emerged hand in hand during the 18th century, spurred by the development of the steam engine, which was used to

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pump water from coalmines. One barrel of oil contains energy equivalent to 25,000 of human labor [22]. Economic growth and increasing fossil fuel use have gone hand in hand. It is not at all clear in fact whether the explosion in economic production initiated by the industrial revolution is the result of the magic of the market or the magic of fossil fuels. Fossil fuels are a finite commodity. Oil discoveries peaked in the early 60s, and have declined steadily since. Consumption has exceeded new discoveries for decades [3]. Oil production may have reached a plateau in 2005, as a subsequent tripling of prices failed to increase output by more than 4% [4]. Raw materials from nature such as food, fibers, fuels, minerals and water are not only essential inputs into economic production, but also serve an alternative role as the structural building blocks of ecosystems. When we remove ecosystem structure and return waste to the environment, we lose or degrade ecosystem functions, including functions essential for the survival of humans and all other species. Ecosystem functions of value to humans are known as ecosystem services, and include regulation of water, atmospheric gasses, and climate, provision of food, fiber and fuel, habitat, and cultural benefits, among many others. These services are also essential inputs into economic production. For example, agriculture, our most important economic activity, may be catastrophically affected by the loss of climate stability. While economists refer to the unintended, uncompensated loss of ecosystem services resulting from economic activity as an externality, the laws of physics and ecology ensure that such losses are an inherent part of the economic process. We must reduce the rate at which we extract raw materials from nature and spew back waste. Finally, even the most primitive hunting and gathering requires knowledge of what foods are edible and where they are located. All economic activity requires information and this is truer now than ever. We have truly entered the era of the information economy CHARACTERISTICS OF THE SCARCE RESOURCES Before we can address the problem of allocation, we must understand the basic characteristics of the scarce resources. One important distinction is between stock-flow

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and fund-service resources. Another is between rival and non-rival resources, and a third between excludable and non-excludable resources. Together, these characteristics interact to determine what types of institutions are appropriate for allocation. STOCK-FLOW AND FUND SERVICE RESOURCES The raw materials provided by nature and physically transformed into economic products are stock-flow resources. Such resources can be used at any rate we choose. We can clear cut a forest in a year or harvest slowly over decades, pump the world’s oil reserves over decades or centuries, as we choose. Stock flow resources are physically transformed into what they produce, which means they are used up in the act of production. A tree is transformed into a house, and fossil energy is transformed into alternative forms of energy or waste. Stock flow resources can be stockpiled. Most stock-flows generated by ecosystems can be bought and sold in markets and rapidly converted into financial wealth. Fund-service resources, including ecosystem services, have entirely different characteristics. Fund services result from a specific configuration of stock-flow resources. For example, a car is a specific configuration of metal, rubber, glass and plastic, and generates the service of transportation. A forest ecosystem is a particular configuration of plants, animals, soils, water, nutrients and so on, and generates a wide variety of ecosystem services. When the forest is clear-cut (but before the trees are removed) it consists of precisely the same structural components, but no longer generates the same services. Funds generate services at a given rate over time. A forest can regulate and purify a certain amount of water per day, or regenerate itself at a given rate over time. A car can transport a certain number of people a certain distance in a given day. Fund-services cannot be stockpiled. If one refrains from driving a car for six days, it is not capable of generating more transportation services on the seventh day. Fund services are not physically transformed into what they produce, though production can change them qualitatively. A car is worn out a bit more each time it is driven. Ecosystems undergo qualitative changes when they generate services, but they are continually maintained by solar energy flows. For practical purposes, solar energy and information act like fund-services [23, 24]. Most fund services provided by nature cannot be bought or sold in markets.

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Because stock flow resources generated by ecosystems can be bought and sold in markets, and most fund-services cannot be, markets will favor the conversion of stock flow resources into market products over their conservation in specific configurations that generate critical ecosystem services. Furthermore, even if fund-services were valued and traded on the market, they provide flows of value over time, whereas stock-flows can be liquidated at the rate we choose. Markets are impatient, valuing money now over money in the future, and again favor conversion over conservation. RIVALRY Another critical resource characteristic is rivalry. A rival resource is one for which use by one person leaves less in quality or quantity for another person. Rival resources are therefore also known as subtractive resources. When rival resources are scarce, there is competition for their use. They must therefore be rationed to prevent depletion or degradation and to ensure they are allocated efficiently towards those uses that contribute most to a sustainable and high quality of life. The value of a rival resource is determined by the benefits received by the single user who consumes it. Market prices are one possible rationing mechanism, ensuring that the resource will go to whoever is willing to pay the most for it. Examples of rival resources include food, fiber, fossil fuels, land, minerals, drinking or irrigation water, waste absorption capacity and so on. All stock-flow resources and most market commodities are rival. In contrast, use of a non-rival resource by one person does not leave less for others to use. Such resources are not scarce in the conventional sense, as there is no competition for use and no need for rationing. If we price non-rival resources, use is reduced, rationed to those able and willing to pay the price, even though additional use imposes no additional societal costs. Rationing needlessly lowers sustainable quality of life. Non-rival resources are most efficiently allocated when they are open access, free for all to use. The value of a non-rival resource is determined by the sum of benefits received by all users. However, there is typically a real cost to providing or protecting nonrival resources. Since competitive markets will not bear these costs as long as

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consumption is open access, cooperative or public institutions should provide them2. All non-rival resources are fund-service in nature, though not all fund-services are non-rival. Examples of non-rival resources include climate stability, the ozone layer, disturbance protection, streetlights, lighthouses, and scenic beauty, among many others—no matter how much one person uses, there is just as much left for others. Information is not only non-rival, but actually improves through use. Technology builds on older technologies in a steady process of improvement. Language, literature, music and art behave in a similar fashion. Some types of information, such as vaccines for contagious diseases or environmental friendly technologies become more valuable the more they are used. Rather than subtractive in nature, information is additive, and using the price mechanism to ration it may create a “tragedy of the non-commons”[25]. As an example, the ozone hole reached a record size in 2006 [26], largely because China and India continue to rapidly increase use of ozone depleting HCFCs [27]. Non-ozone depleting substitutes are available, but they are also patented and charge royalties, raising their price and decreasing their use. As another example, countries conventionally provide new strains of viruses to the World Health Organization (WHO), which provides unlimited access for anyone seeking a cure. Typically those seeking cures are corporations, and for a potentially serious virus, thirty separate teams of scientists may be seeking a cure. As the teams are competing to be the first to develop and patent a vaccine, they are unlikely to collaborate, which is likely to slow research and make it more expensive. Publicly financed research with shared knowledge is likely to be much cheaper. When one team finally develops and patents a cure, it will be sold at the profit-maximizing price, rationing use to a limited number of potential victims, and increasing the likelihood of a pandemic. As a result, when a new strain of 2

Private provision would be more cost effective only if it is so much cheaper than public sector provision that it compensates for the reduction in use caused by pricing, which may be possible for very dysfunctional public sectors. However, pricing is likely to systematically exclude the poor, and distribution issues may trump cost effectiveness as a desirable end.

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avian flu virus was discovered in Indonesia, Indonesia initially refused to turn it over to the WHO, and planned instead to sell it to the highest bidder, with the reasoning that Indonesians would not otherwise be able to afford the patented vaccine [28]. Indonesia’s market approach would have meant only one team of scientists seeking a cure, rather than thirty, with presumably lower chances of developing one. It’s critical to recognize that rivalry is a physical characteristic of a resource in a particular use, and not a policy variable. There are many references in the economics literature to supposedly non-rival but congestible resources, such as roads, golf courses, and beaches. At low levels of use, one person’s use does not leave less for others, while at high levels of use, it does. This gives the impression that rivalry is at least somewhat a policy variable as it is affected by the number of users, and that access to non-rival resources should be rationed. However, the physical space a car, golfer or bather occupies is in fact rival. When there are few users, the resource is abundant, which means there is no competition among users. With more and more users, the resource becomes scarce. We must not confuse abundance with non-rivalry. EXCLUDABILITY For rationing of a resource to take place, it must be made excludable. A resource is excludable when one person or group can use a resource while preventing others from doing so; in simple terms, property rights (state, common or private)3,

3

Daniel Bromley [29] offers the following definitions: “State Property: The political community is the recognized owner of the asset. Individuals in the political community may benefit from the asset but must observe rules of the government agency responsible to the political community. Examples: national forests and parks, military bases, government office buildings. Private Property: Individual members of the political community have a recognized right to benefit from the asset, subject to legislative mediation and judicial review. Non-owners have a duty to allow owners to behave as above. Examples: Fee-simple land and buildings, automobiles, personal objects. Common property: A group of owners holds rights in common, including the right to exclude nonowners. Individual owners have specific rights and duties with respect to their ability to benefit from the asset subject to legislative mediation and judicial review within the larger political community. Non-owners have a legal duty to respect boundaries of the regime.”

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exist and are enforced. Excludability is a prerequisite for markets to exist for obvious reasons. Most stock flow resources can be made excludable, as can information 4. Excludability is the result of institutions and a policy variable. A resource is non-excludable when no property rights exist. Such resources are open access by definition. Some non-excludable resources such as waste absorption capacity or oceanic fisheries can be made excludable by appropriate institutions. Many resources however are inherently non-excludable, including many ecosystem services. As long as the service exists in a given location, there is no practical way that we can let some people use a stable climate, the ozone layer or disturbance protection while preventing others from doing so. As pointed out above, competitive markets will not provide adequate levels of open access resources, so it falls on cooperative, public institutions to do so. HOW DO WE ALLOCATE? By assessing the desirable ends of economic activity and the scarce resources required to achieve them, we have laid the groundwork for determining what allocative mechanisms and institutions are appropriate. However, it is first worth returning briefly to the desired ends now that we have a more complete understanding of the scarce resources. Our goal remains to achieve the highest possible quality of life, but we must first understand what are the most binding constraints on achieving this. Historically, both raw materials from nature and ecosystem services were abundant, while human made capital, labor and economic products, all rival and generally excludable, were relatively scarce. The challenge lay in how to allocate capital and labor towards the transformation of raw materials into the most desirable economic products. Over the past two centuries however, the situation has reversed. Human populations are now Hereafter, this chapter will refer to state and common property rights together as public property rights. 4 Though entirely different concepts, many people confuse excludability and rivalry. The case of information helps to clarify the difference. Information is inherently non-rival, and nothing can be done to change this. However, patents can make information excludable, legally rationing use to those who pay royalties.

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vast, and per capita consumption of human made products is at record levels. Natural resources and biodiversity have been grossly depleted, while waste emissions have soared, resulting in serious threats to ecosystem services. The scarcity of resources provided by nature, both raw materials and ecosystem services, likely present the greatest threats to our quality of life, and without these economic production is also impossible. Most ecosystem services are non-rival and non-excludable. Our challenge now is to protect and restore life sustaining ecosystem services, which requires limits on the quantity of ecosystem structure that can be converted into economic products and waste. Specifically, we must address the problems of climate change, natural resource depletion, biodiversity loss, and peak oil. We must ensure that global ecosystems are capable of sustaining the global economy and resilient enough to recover from unpredictable shocks. Competitive free markets are currently the dominant mechanism for allocating resources and it’s worth understanding briefly how they work. Market allocation is based on the price mechanism. Natural resources are allocated towards whichever firm can generate the most profit from its use, equivalent to adding the most monetary value, and hence pay the highest price. This is known as the allocative function of price. Economic products in turn will go to whichever consumer is willing to pay the highest price for them. This is known as the rationing function of price. In both production and consumption, the price mechanism maximizes monetary value, and does so based on individual choices made by producers and consumers with no centralized information required. As mentioned earlier however, markets equate utility with monetary value, so markets are only an appropriate allocation mechanism to the extent that maximizing monetary value is an appropriate desirable end. In addition, monetary value is determined by preferences weighted by purchasing power. Market allocation is plutocratic, based on one dollar, one vote. The preferences of the poor and future generations, and hence just distribution and sustainability, are systematically ignored. A more democratic system for decisions concerning ecosystem services and the shared inheritance of natural resources might be far more just. Even if we do accept the maximization of monetary value as a desirable end, markets fail to achieve this for non-rival resources. As explained above, the value of non-rival resources is paradoxically maximized at a price of zero, at

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which price markets fail to supply them. If society makes non-rival resources excludable so that markets will provide them, markets will not only fail to maximize their monetary value, but governments also must bear the costs of enforcing excludability. In fact, markets always rely on governments or other cooperative institutions to enforce property rights, and the conclusion that markets maximize monetary value does not account for the costs of making resources excludable. Markets fail to supply non-excludable resources. Finally, all market production degrades non-marketed ecosystem services, and in the absence of government regulations, market forces ignore these costs [30]. In other words, though markets have impressive positive attributes, they also have serious shortcomings, particularly when allocating non-rival or non-excludable resources. Unfortunately, most of the major problems human society currently faces involve such resources. Climate stability, protection from UV radiation, and many of the benefits from biodiversity, including ecosystem resilience, are inherently non-rival and non-excludable, and markets fail to protect them. Cooperative international efforts have already banned many ozone-depleting compounds, though concrete evidence that the ozone layer is actually recovering remains elusive [31]. Utilizing the fact that carbon absorption capacity is rival, global society is currently striving to forge cooperative institutions to make it excludable and allow no more emissions than ecosystems can absorb. Society can then directly charge for use via carbon taxes (actually user fees for waste absorption capacity), in which cases prices will determine the level of emissions, or issue tradable permits that allows for market allocation of socially determined supply5, in which case ecological limits determine price. Taxes or auctioned quotas are equivalent to public property rights, in which society owns waste absorption capacity, a critical means of production. Negotiations on limits unfortunately are hampered by short sighted notions of national self-interest. However, limits on emissions alone are probably inadequate. Confronting both climate change and peak oil will undoubtedly require the rapid development and dissemination of new low carbon energy technologies. Unfortunately, economists 5

Offsets in the form of increased sequestration capacity (e.g., reforestation projects) can actually increase the supply of waste absorption capacity, but are currently negligible.

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tend to focus on the failure of markets to produce open access resources, and seek to solve the problem by creating and protecting intellectual property rights. However, this simply creates a new ‘market failure’ in the form of price rationing a non-scarce resource. A cooperative global effort to develop these technologies would instead ensure that adequate resources are available to produce essential new technologies, which would remain open access. Shared information would likely speed up the discovery process. Privately produced and owned information would be price rationed, reducing use of new technologies in favor of coal and other carbon based fuels [32]. Protecting oceanic fisheries, endangered species and endangered habitats will also require cooperative institutions. As should be obvious from these examples, the use of cooperative economic institutions is an important solution to the shortcomings of competitive market allocation in both theory and practice. Markets already rely on such institutions to defend property rights, and could not exist without them. Nobel Laureate Elinor Ostrom and her colleagues have studied real life experiences with the management of resources which are rival but difficult to make excludable, known as common pool resources. While in many cases such resources are overexploited, in numerous other circumstances a variety of institutions emerge that lead to sustainable, just and efficient management. One key to making such institutions work is that community members own the resources in common, while non-community members are not allowed to use them—they are common goods when viewed from within the community, but private goods from the perspective of other communities. It also helps when community members have broad input into management strategies, can effectively monitor resource use, sanction those who fail to respect community rules, and have access to mechanisms for cheaply and easily mediating any conflicts [33, 34]. To reiterate a central point, whether competitive or cooperative institutions are more appropriate mechanisms for allocation depends on the physical characteristics of the resources involved, as well as the desirable ends. Table 1 provides a summary. IS COOPERATION POSSIBLE? For generations, mainstream economists have assumed that economic man (Homo economicus) is purely self-interested and rational. Institutions requiring altruism

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or cooperative behavior do not conform to human behavior. The best possible outcome was the invisible hand of market institutions that channeled our egoism into the greatest good for the greatest number. For many decades, evolutionary biologists supported these assumptions, arguing that altruistic tendencies would be selected out of a population by genetic forces [35, 36]. In recent years however, both behavioral economists and evolutionary biologists have come to accept that cooperative altruistic behavior is both possible and desirable. THE EVOLUTION OF COOPERATIVE BEHAVIOR The best way to illustrate the evolution of cooperative behavior is with a concrete example. Throw a bunch of Pseudomonas flourescens bacteria in a beaker with food, and they will rapidly reproduce until they become starved for oxygen. At this point, the survival advantage shifts to a mutant type known as the ‘wrinkly spreader’, which can create a film that binds them together into a floating colony with access to oxygen from above and nutrients from below. Cooperation allows the group to thrive. However, within this cooperative colony there may be some defectors—they produce none of the sustaining film, but instead free-ride on that produced by others. With the energy they save by not producing the film, they are able to have more offspring than the cooperative Pseudomonas. Competitive individuals (i.e., defectors) within the group out-compete cooperative ones. However, if there are too many defectors, the colony can no longer stay afloat, and plunges to the depths of the beaker, losing its relative fitness. Those colonies with fewer defectors will continue to thrive and leave more descendants than others [37]. What we see in fact is two distinct types of evolutionary pressure; competitive pressure at the individual level, and cooperative pressure at the group level. The basic rule is that “Selfishness beats altruism within single groups. Altruistic groups beat selfish groups” [38]. Humans evolved as small bands competing with other bands (and of course with other species) for resources. Those bands that engaged in cooperative behavior outcompeted those that did not, which selected for increasingly pro-social behavior. Humans are social animals who almost certainly owe their evolutionary success to their ability to cooperate. Unlike simple bacteria however, human culture can evolve as well, leading to the development of institutions that promote (or hinder) pro-social behavior [39].

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Examples of the evolution of cooperative behavior are widespread in biology, but one other example bears mentioning for the analogy it provides to human society. Myxococcus xanthus is a bacterial predator that hunts cooperatively under conditions of food scarcity, traveling as a swarm to capture and digest prey. In starvation conditions, the bacteria cooperates to an even greater degree, forming a spore mass in which the vast majority sacrifice themselves to allow a few to sporulate and await better conditions. However, when these bacteria are placed into conditions of abundance, cooperative behavior is no longer necessary. If left in such conditions for long enough, they lose the ability to hunt as packs or sporulate in conditions of scarcity. However, if placed with cooperative populations, they become ten times more likely to form a spore than cooperators [40]. It would appear that when placed in conditions of resource abundance, these bacteria evolve to approximate the rational, self-interested behavior of H. economicus. One must wonder whether the advent of the fossil fuel economy, an era of abundance, has favored the evolution of economic systems based on selfish, competitive behavior that could not thrive in conditions of scarcity. If so, we can only hope that humans do not also lose the ability to cooperate, or that those that do lose the ability prove unable to increase their fitness by free-riding on the cooperation of others. COOPERATIVE BEHAVIOR IN SOCIETY, AND INSTITUTIONS FOR PROMOTING IT Fortunately, humans are not entirely constrained by our genetics to be selfish or cooperative, and abundant evidence exists that we are still a highly cooperative species. One simple experiment is called the dictator game. One participant is offered a sum of money, and told to divide it with another anonymous participant in whatever proportion she likes. While a selfish person would obviously keep all the money, few participants actually do so. American college students on average give away 20% of the money [41]. Two experimental games closely approximate the cooperation problems posed by common pool (non-excludable but rival) and public good (non-excludable and non-rival) resources. In the common pool game, participants can withdraw any amount up to some fixed limit from a common pot. What remains in the pot then

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‘grows’ by some pre-specified proportion, say 50%, and is redistributed equally to all, regardless of how much each person withdrew. In the public good game, participants start with a fixed sum, and are allowed to donate as much as they want to a fixed pool. This money is then doubled (or increased by some other prespecified amount) and redistributed equally to all, regardless of how much each person contributed. If people act in their rational self interest, then they will withdraw as much as possible and contribute as little as possible in the two games, even though minimum withdrawal and maximum contribution generate the greatest wealth for the group as a whole. Once again, experimental evidence fails to support conventional economists’ assumption that people act only out of pure self-interest. Most people in the voluntary contribution game contribute something to the common pool. University students tend to contribute 40-60% of the total amount they are given, on average, with one mode at zero contribution and a typically smaller one at full contribution. However, in repeated games either among the same group or with different group members (i.e., each person plays the game multiple times, but with different people) contribution rates fall. It appears that those who initially cooperate engage in a tit for tat strategy6: the most generous individuals ratchet down their contributions to the mean contribution, which further drives down the mean. Is there a way to avoid this sub-optimal outcome? In one variation of the game, participants learn after each round who contributed and how much, and are allowed to punish those who did not contribute. Punishment is costly: for example the punisher may have to give up 1/3 unit of reward to punish defectors by 1 unit. Yet, when punishment is allowed, the rates of cooperation go up with repeated rounds, not down. This is an example of what is known as altruistic punishment: individuals sacrifice their own welfare to make defection a losing strategy, encouraging cooperation even from people who are purely selfish, and even when they make up a significant percentage of the group. In other words, altruistic punishment can make cooperation the dominant strategy in prisoner’s dilemma type situations even for selfish individuals [42-45]. 6

‘Tit for tat’ simply means acting towards your partners as they acted towards you in the previous round. In a famous experiment, ‘tit for tat’ was found to the most successful overall strategy in repeated prisoner dilemma games [49].

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Altruistic punishment is not the only way to achieve cooperation, however. If participants in experimental games are allowed to talk about their strategies ahead of time, they are much more likely to cooperate. This is true even for “cheap talk”, which means that the decisions participants ultimately make are not revealed to others, and there is no way to create binding contracts [46]. Studies from behavioral economics suggest that some people are purely selfish by nature, like H. economicus, most are conditional cooperators, and some are very pro-social [47, 48]. The choice of institutions helps determine what behavior predominates, and the nature of the scarce resources and our choice of desirable ends determines what behaviors are appropriate. SUMMARY AND CONCLUSIONS As human institutions and the nature of resource scarcity have evolved over time, so too have economic systems. Human society currently faces a number of serious challenges, ranging from climate change and biodiversity loss to natural resource depletion, including growing scarcity of fossil fuels, and the economic system must again adapt. In order to intelligently guide a successful adaptation process, we must clearly understand the goals of the economic system, the resources available to achieve these goals, and human nature. Perhaps the least controversial economic goal is survival. An economic system must use available energy to transform the raw materials provided by nature into basic necessities such as food, potable water, shelter, and energy. For the past two hundred years, we have relied on competitive markets and fossil fuels to meet all of these needs. Food, water, shelter and fossil fuels are rival (e.g., if I eat a sandwich or burn a barrel of oil, there is less available for you) and scarce (there is not enough for every desired use), so competition for them is inevitable. Because they can also be made excludable, market competition is possible. Using free choice and decentralized knowledge, markets and fossil fuels have helped us transform increasing amounts of ecosystem structure into highly valuable human artifacts. This transformation of course left less ecosystem structure available to generate ecosystem services, but such services were relatively abundant and there was little need to worry over them.

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Table 1: Suitable Institutions for Allocation, as Determined by the Physical Characteristics of the Resources Involved Excludable (property rights exist, state, common or private)

Non-excludable (open access)

Rival and scarce competition exists

Market Goods Provision: Potential private sector Consumption: Rationing required, price rationing may be appropriate Examples: Stock-flow: Oil, food, fiber, water (for drinking, irrigation, industrial uses) Fund-service: land, labor, human made capital

Common pool resources, open access regimes Provision: Direct provision by public sector, or else public sector regulation or creation of property rights, which either allow market allocation of socially determined supply, or can create incentives for private sector provision if offsets are permitted. Consumption: Rationing appropriate, price rationing suitable, but only possible if institutions make resource excludable. Examples: Stock-flow: Stocking of fisheries (direct provision); treaties that provide state property rights to fisheries such as the exclusive economic zone; individual fishing quotas that assign privileges to a share of state owned fisheries (creation and regulation of property rights). Fund-service: Public sector clean up of pollution (direct provision); cap and trade for carbon (allows market allocation of socially determined supply).

Rival, borderline scarce Congestible; potential for competition

Club or toll goods Provision: Potential private sector Consumption: Rationing required when scarce, price rationing suitable Examples: Fund Service: Recreational uses of land, roads, national parks, game reserves, etc.

Provision and consumption: same as above when scarce or threatened by scarcity, no institutions needed when abundant. Examples: Stock-flow: Oxygen (still abundant, inherently non-excludable) Fund-Service: toll roads; golf courses; fees at public parks

Non-rival no competition; all fundservice

Inefficient market goods (“tragedy of the non-commons”) Provision: Potential private sector with government enforced monopolies on intellectual property rights. Consumption: rationing inefficient, price rationing decreases economic surplus. Should be open access. Examples: Patents on technologies that protect and restore ecosystem services, Property rights to genetic information and chemical components of biodiversity; Intellectual property rights in general.

Public Goods Production: Public sector financing required and desirable. Consumption: open access exists and is desirable. Examples: Protection and restoration of ecosystems to provide many regulatory services (flood regulation, disturbance regulation, erosion control, climate regulation, etc.)., supporting services (habitat for biodiversity) and cultural services (scenic beauty, spiritual values, etc.).; publicly funded, open access technologies, especially those that protect and restore ecosystem services.

However, the very success of the fossil fuel market economy has dramatically changed the nature of scarcity. The scarcest and most important resources are

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increasingly non-rival, in which case price rationing is inefficient; nonexcludable, in which case market allocation is impossible; or both. To reiterate points made previously, the issue of climate change effectively illustrates the problem. Fossil fuel emissions are the major source of greenhouse gases triggering climate change. Though fossil fuels are a rival, excludable market good, their combustion causes a negative externality, disrupting the ecosystem service of climate regulation. The disruption occurs because greenhouse gas emissions exceed planetary waste absorption capacity. This absorption capacity is rival, in that its use by one country leaves less available for other countries to use, but is also non-excludable at the global level: There are no laws in most countries that prevent people from spewing as much CO2 into the atmosphere as they choose. To solve this problem, collective action institutions must limit waste emissions to no more than absorption capacity, then distribute (ideally in a just fashion) the right to use it. As explained above, tradable permits and taxes both use price rationing. Markets cannot determine sustainable limits or just distribution. The same argument holds true for all waste sinks. A stable climate itself is a pure public good, both non-rival and non-excludable. Because my use of a stable climate to grow crops leaves no less for you, it would be inefficient to ration access. It is also impossible to do so. The only solution is collective provision. Given the high stakes, urgency and uncertainty involved, simply limiting GHG emissions is probably inadequate, we should undertake other cooperative activities to sequester carbon, such as reforestation. The same arguments hold true for all public goods, including the ecological resilience provided by biodiversity. We must also develop new, low carbon energy technologies such as wind and solar. As pointed out above, the market will only produce these technologies if they can be patented (i.e., made excludable) and sold at a price. On the production side, when the private sector competes to obtain patents, they do not share information, which can slow down the advance of science. On the consumption side, patents create a monopoly with monopoly pricing, rationing use to the wealthy, even though ubiquitous use would maximize human welfare. The optimal approach is publicly financed cooperative production with the results freely available to all. Open access to non-rival resources maximizes their value.

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In short, cooperation is essential to solving the problems of ecological sustainability and just distribution, which are currently the most pressing problems facing human society. The market can only be trusted once these two problems have been solved. It would of course be futile to design an economic system based on cooperation if people were inherently incapable of cooperative behavior. Fortunately, in spite of assertions by conventional economists that humans are inherently self-interested, evolutionary biologists have convincingly established that group selection favors cooperative behavior. Behavioral economics in turn has fortuitously identified mechanisms that promote cooperative behavior. One such type of institution is societal rules that allow the punishment of greedy, self-interested behavior. Unfortunately, capitalism tends to glorify greed, and actually claims that it is in the public interest. While market economies may be very efficient for allocating certain types of resources, they are very ineffective at allocating others, and have played a central role in creating the most serious problems we now face. Many economists seek to solve these problems by placing monetary values on all nonmarketed ecosystem services so that they can be included in the market mechanism. This is an ideological approach however that decides on the allocative mechanism before examining the desired ends or the nature of the scarce resources. Economics is too important to be left to ideology. Rather than trying to force all resources into the market model, we must adapt economic institutions to the physical characteristics of the resources in question. A paradigm shift in economics is again necessary. Close examination of the desirable ends and physical characteristics of resources tells us that some resources can be effectively allocated through competitive markets, while others require institutions based on cooperation. Evolutionary biologists, behavioral economists and political economists have shown the mechanisms through which cooperative behavior can evolve and increase fitness. Following the great depression, the world’s leading economic powers met together in Bretton Woods, New Hampshire, to craft a new set of institutions, including the World Bank, the International Monetary Fund, and the predecessor

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of the World Trade Organization. We need a new set of cooperative global economic institutions to produce and protect non-rival and non-excludable resources [50]. Rival resources such as atmospheric waste absorption capacity and oceanic fisheries should be rationed, which first requires the creation of global common property rights [51, 52]. After ensuring adequate access to these resources to meet basic human needs, the privilege to use remaining ecological capacity can then be auctioned off. The revenue should then be invested in inherently non-excludable or non-rival global public goods, such as the technologies required to protect and provide other global public goods [53]. Wealthy nations that refuse to participate should be sanctioned. In conclusion, a more scientific approach to economics must first assess desirable ends and next the nature of the resources required to attain them before deciding which allocative mechanisms are appropriate. Institutions can promote competitive or cooperative economic mechanisms as appropriate. Economics can no longer be left to ideologues who favor a single allocative mechanism for all economic problems. CONFLICT OF INTEREST The author(s) confirm that this chapter content has no conflicts of interest. ACKNOWLEDGEMENT I would like to acknowledge the pioneering work of Herman Daly in the field of Ecological Economics. His ideas and insights are evident throughout this chapter. REFERENCES [1] [2] [3] [4] [5]

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CHAPTER 4 Actualizing Sustainability: Environmental Policy for Resilience in Ecological Systems Ahjond S. Garmestani*, Matthew E. Hopton and Matthew T. Heberling U.S. Environmental Protection Agency, Office of Research and Development, National Risk Management Research Laboratory, 26 W. Martin Luther King Dr., Cincinnati, OH 45268, USA Abstract: The loss of biodiversity has the capacity to decrease the resilience of ecological systems, which can result in the loss of ecosystem services essential for human survival. Thus, the protection of biodiversity is a critical component to actualizing sustainability. In this paper, we suggest several possible policy instruments for dealing with the apparent intractability of economics, ecology, and the law. In order to actualize sustainability for future generations, we must develop new, or adapt established, policy to protect biological diversity and ecosystem services.

Keywords: Resilience, sustainability, panarchy, policy, economics, law, environmental management, trusts, biodiversity. INTRODUCTION Society benefits from ecological systems in many ways. These benefits are often referred to as ecosystem services (MA 2005) [1]. Because these services are fundamental to humans, they are critical to sustainability. Sustainability has many definitions, but for this chapter, we link our definition to the conditions necessary for the resilience of ecological systems because it implies the persistent flow of ecosystem services. Loss of biological diversity (e.g., biodiversity) has the capacity to erode the resilience of conditions favorable to humanity, as it can lead to the degradation or loss of ecosystem services critical to human survival (Duffy 2009) [2]. Fortunately, ecosystems have inherent resilience and can persist under some human-induced impacts, but this resilience will eventually degrade to such an extent that we will begin to lose ecosystem functions and the services they *Address correspondence to Ahjond S. Garmestani: U.S. Environmental Protection Agency, Office of Research and Development, National Risk Management Research Laboratory, 26 W. Martin Luther King Dr., Cincinnati, OH 45268 USA; Tel: 513-569-7856; E-mail: [email protected] Heriberto Cabezas and Urmila Diwekar (Eds) All rights reserved-© 2012 Bentham Science Publishers

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provide. To be sustainable, and perhaps continue to exist, it is in humanity’s interest to protect these ecosystem services. Adequately protecting these systems calls for the implementation of policy and associated management schemes. Of course, sound policy requires sound scientific research. As Reid (2006) [3] states, “[W]e cannot manage these systems effectively if we do not actively seek to measure the flows of these services, examine who is benefiting from them, and consider a range of policies, incentives, technologies, and regulations that could encourage better management and sharing of the benefits.” In this chapter, we describe aspects of complex systems that contribute to system resilience. We use research on coral reefs as an example to demonstrate many of these principles. Next, we provide an overview of policy instruments that could protect ecosystems and the services they provide. Although there has been a significant amount of research conducted upon resilience, this paper considers the conditions necessary for preserving resilience, maintaining ecosystem services, and actualizing sustainability. Ecosystems have Inherent Properties that Lead to Their Persistence Ecosystems have evolved interrelated subsystems that provide the capacity to resist perturbations that could radically change the system (Gunderson and Holling 2002) [4]. This interrelatedness is a property of redundancy in the system and can result in resilience of the system. Resilience is an emergent property of ecosystems, and from a human perspective, it is crucial because it implies a steady delivery of expected ecosystem services and is therefore vital to sustainability. Unfortunately, it may not be possible to measure the resilience of an ecological system (Holling 1973) [5]. Thus, developing proxies for resilience in said systems is critical to characterize sustainability (Carpenter et al., 2001) [6]. Within the context of ecological resilience, ecosystems are more stable when the thresholds between dynamic regimes are higher (Ives and Carpenter 2007) [7]. An ecological threshold is the point at which an ecosystem undergoes a major change, in quality, system properties, or phenomena, from a seemingly small event or environmental driver (Groffman et al., 2006) [8], and a regime is the current state of the system (Garmestani et al., 2009a) [9]. Sustainability then is the ability to maintain or

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create the conditions necessary for the resilience of a favorable regime by either increasing thresholds or moving the system further away from thresholds (Garmestani et al., 2009b) [10]. A number of ideas have been proposed to describe or explain how resilience emerges in complex systems, and we treat two of the most important of these ideas in this manuscript: the cross-scale resilience model and panarchy. The cross-scale resilience model was developed to explain how ecosystem resilience results from diverse, overlapping functions within a temporal or spatial scale and by seemingly redundant species that operate at different temporal or spatial scales (Peterson et al., 1998) [11]. The result of this overlap and redundancy is a reinforcement of function across scales and results from species diversity (Peterson et al., 1998) [11]. Biodiversity itself is scale-dependent and refers to all of the hereditarily based variation from genes in a single population to the communities of ecosystems (Wilson 1997) [12]. Numerous models have been proposed to explain biodiversity, but often they fail to account for scale. One ostensibly successful attempt to account for such cross-scale interactions was explained by Simon (1973) [13]. Simon proposed that complex systems are composed of levels in a hierarchy that correspond to different functions. The functions of the lower levels are inherited by the upper levels and at the same time, the different levels in the hierarchy are separated from the levels above and below them. The self-organization of systems into hierarchies functions as a mechanism for systems to be resilient to perturbations (May et al., 2008) [14]. Strong interaction of components in complex systems (e.g., forest fires, epidemics) can lead to loss of resilience (May et al., 2008) [14], although most systems benefit from, and are adapted to some level of perturbation as is suggested by the intermediate disturbance hypothesis (Grimes 1973) [15]. Such perturbations can lead to increased species diversity, which then can increase resilience (Roxburgh et al., 2004) [16]. Moreover, such perturbations may increase the rate at which hierarchies develop. Thus, the evolution of modularity or compartmentalization (e.g., hierarchy) should be expected as cross-scale interactions increase the resilience of the system (Simon 1973) [13]. Panarchy explains such cross-scale dynamics. A panarchy is a nested set of adaptive cycles operating at discrete scales (Gunderson and Holling 2002) [4].

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Discontinuities are the thresholds between dynamic levels (i.e., adaptive cycles) of a panarchy (Holling and Gunderson 2002) [4]. Discontinuities are manifest in key cross-scale variables. They can appear as gaps in rank-size distributions of variables in complex systems, such as animal body masses in ecosystems or city size over a historical period. These size classes, or in panarchy parlance, basins of attraction, reflect the scales of opportunity available in a given system (Garmestani et al., 2007) [17]. In a simplified explanation, individuals going about their daily business of survival are utilizing resources that are available at a specific scale. Selection can then act on those individuals and the population may evolve to take advantage of available resources. Darwin beautifully explained how this works with his theory of natural selection. Simply stated, populations exhibit four basic properties that drive them to evolve: there is variation in a population; individuals inherit traits from their parents; most individuals have more offspring than an ecosystem can support; and some individuals are better than others are at obtaining resources and surviving (Darwin 1859) [18]. Those individuals that are better suited to a particular scale will pass those traits on to their offspring and produce more offspring (e.g., increased fitness). All biota, including humans, interact with the environment at distinct scales and create selfreinforcing patterns resistant to change (Peterson, 2002) [19]. Natural selection can shape populations, which can lead to separation that is more distinct between scales. The result is that multiple but distinct scales of self-organization, and the ensuing distribution of function within and across temporal and spatial scales, create resilient systems (Peterson et al., 1998) [11]. Thus, a system’s resilience is dependent upon the interactions between the system’s structure and dynamics at multiple scales. Ecosystem Functions and our Lack of Understanding of Ecosystem Dynamics: Coral Reef Systems as an Example Functional groups typically perform a similar function in an ecosystem (Bellwood et al., 2004) [20] and have similar effects on specific ecosystem-level biogeochemical processes (Vitousek and Hooper 1994) [21]. However, these groups of species, depending on the researchers’ focus, could share similar morphologies, physiologies, behaviors, or trophic positions. Within this context, resilience is not only dependent upon the functional diversity of a system (i.e., the

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number of functions within an ecosystem), but also dependent upon the range of responses (i.e., response diversity) within functional groups (Bellwood et al., 2004) [20], and the range of responses by individual species within these functional groups (Solbrig 1994) [22]. Such diversity results in species that may fill a function both within and across ecosystems (Solbrig 1994) [22]. One such functional group, for example, contains denitrifying microorganisms that provide a critical ecosystem function of cycling nitrogen. Denitrifying microorganisms comprise a number of bacteria from different taxonomic groups, many of which use different forms of nitrogen, occupy different habitats, and utilize different substrates to reduce ionic nitrogen oxides (Meyer 1994) [23]. Functional diversity is an important aspect of resilience, but it must be coupled with functional redundancy to generate cross-scale resilience (Peterson et al., 1998) [11]. The difference between functional redundancy and response diversity is that functional redundancy will be ineffective if every species of the same functional group interacts in the same manner (Bellwood et al., 2004; see example above) [20]. Response diversity is a direct result of species diversity. Species diversity increases resilience by increasing the redundancy of species (Lawton and Brown 1993) [24] and increasing the redundancy in functional traits (Chapin III et al., 1997, Folke et al., 2004) [25, 26]. A system with limited response diversity would be susceptible to frequent regime shifts or crashes because the loss of a single species or functional group would result, in an extreme example, in the loss of cycling of nutrients or flow of energy. Thus, the value of high functional richness and redundancy is lost if redundant species do not “respond” differently to different stimuli (Bellwood et al., 2004) [20]. Further, response diversity is critical, as the interaction between species and stimuli is scale-dependent (Nystrom 2006) [27]. Coral reef systems provide a good example for our purposes, as there is a large body of research on biodiversity and aspects of resilience in these systems (Moberg and Folke 1999) [28]. Coral reef systems are amongst the most productive and biologically diverse systems on the planet and provide critical ecosystem services (e.g., recreation, tourism and capture fisheries; buffer zones in coastal regions, which provide natural hazard regulation; and nursery habitat for marine species; MA 2005) [1]. Coral reef systems appear to be resilient to natural

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disturbances (e.g., tropical storms), but lose resilience in the face of escalating human impacts (e.g., over-fishing, pollution; Moberg and Folke 1999) [28]. Spatial resilience occurs when coral reefs withstand perturbations at a regional scale, as opposed to the resilience of an individual reef (Nystrom and Folke 2001) [29]. Ecological memory is the structure and dynamics of species and the ecological systems in which they exist, including the history of a system’s responses to environmental change (Nystrom and Folke 2001) [29]. Spatial heterogeneity may increase the resilience of ecological systems and lessen the chance for large-scale regime shifts (van Nes and Scheffer 2005) [30], as numerous small-scale impacts may “scale up” and lead to a regime shift, whereas a spatially heterogeneous system can resist such impacts. Heterogeneous systems are resilient because they can maintain higher levels of functionally similar taxa (e.g., Hold 1984, Cantrell and Cosner 1998, Chesson 2000) [31-33]. For coral reefs, the resilience of these systems depends on their genetic variability, the functional richness within a scale, the functional redundancy across scales, and the variability of adjacent habitats (Nystrom and Folke 2001) [29], in other words, biodiversity. Indo-Pacific reefs have been more resilient to change than Caribbean reefs, perhaps because while the two regions share similar functional groups of species, Caribbean reefs have fewer species, and therefore less redundancy, within functional groups (Nystrom and Folke 2001) [29]. For example, Nystrom (2006) [27] found sea urchins were the principle herbivore on Caribbean reefs, but they suffered massive casualties due to a disease outbreak. As a result, these reefs became more vulnerable to perturbations because of a lack of functional redundancy and response diversity (Nystrom 2006) [27]. Many of the world’s coral reefs are degraded to such an extent they are no longer coral-dominated systems and have undergone a regime shift (Bellwood et al., 2006) [34]. Research by Bellwood et al., (2004, 2006) [20, 34] shows regime shifts on many Caribbean coral reefs were preceded by loss of macroinvertebrates, declines in fish stocks, an increase in echinoid (e.g., sea urchins; class Echinoidea) herbivory, reduced coral recruitment, elevated destructive grazing by sea urchins, as well as elevated nutrient and sediment influx from the terrestrial system. Each of these phenomena was well known, but the effect they

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individually had, or in combination, on reef resilience was not foreseen. These authors point out that the loss of herbivores often leads to regime shifts, which then causes algal domination of reef systems. As Nystrom and Folke (2001) [29] noted, grazing is a critical function for the resilience of coral reef systems. If the number and types of grazers (e.g., herbivorous fish and sea urchins) declines significantly, the growth of algae may increase on a reef system, reducing its resilience (Nystrom and Folke 2001) [29]. Bellwood et al., (2006) [34] conducted a large-scale induced regime shift experiment and found that a rapid shift from a macro-algal dominated to a coral- and epilithic algal-dominated regime was not driven by the herbivores (i.e., parrotfishes or surgeonfishes, Family Labridae and Acanthuridae, respectively) expected to drive the shift. Their results reveal the shift was driven by one species of batfish (Platax pinnatus), which had not been considered herbivorous. The batfish are considered a “sleeping” functional group because they are capable of performing a vital function in the reef system, but only under extreme conditions such as a regime shift (Bellwood et al., 2006) [34]. This study demonstrates the importance of sleeping functional groups. In this case, this species of batfish was not a known herbivore of the macro-algae that took over the coral reef systems and it was not known that batfish could drive a reef system to a “healthier” state (Bellwood et al., 2006) [34]. Therefore, sleeping functional groups likely play a role, albeit unknown, in Caribbean reefs, which have shown reduced cross-scale resilience due to a myriad of impacts (e.g., overfishing, pollution; Bellwood et al., 2006) [34], further complicating conservation efforts. It is conventional wisdom in ecology that systems with high species richness and functional diversity are more resilient to perturbations than systems with low species richness and functional diversity (Petchey and Gaston 2002, Bellwood et al., 2003) [35, 36]. In contrast, Bellwood et al., (2003) [36] found evidence of the loss of ecosystem resilience in a system (i.e., coral reefs) with high biodiversity and functional complexity. Overfishing of a single species drove the loss of resilience in this seemingly “healthy” system: the giant humphead parrotfish (Bolbometopon muricatum; Bellwood et al., 2003) [36]. Thus, we must look past mere species richness and identify and protect species that perform key functions (i.e., keystone species; Paine 1969) [37] in ecological systems (Bellwood et al.,

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2003) [36]. Bellwood and Hughes (2001) [38] studied coral and reef fish species assemblages from 113 locations in the Indian and Pacific Oceans. They found that species composition was constrained within a narrow range of configurations and the taxonomic composition of coral and reef fishes was conservative. These results indicate that coral reef assemblages are “islands” in the vast matrix of the world’s oceans with predictable proportions of species of corals and reef fish; thus, it is critical to maintain large areas of suitable habitat for maintaining coral reef resilience (Bellwood and Hughes 2001) [38]. Due to the patchy nature of reefs, it may be possible to save them because existing healthy reefs may act as refugia or reservoirs for the degraded reef systems (e.g., Dalton 2010) [39]. This idea has been much explored in the literature on terrestrial systems and is a major focus of landscape ecology (e.g., Forman 1996) [40]. The seascape consists of mangroves, seagrass and coral reefs, and these systems cannot be managed in isolation if we are to protect coral reef systems (Moberg and Folke 1999) [28]. Metapopulations of reef species must be considered and can only be protected if source reefs are protected within the context of a seascape (Moberg and Folke 1999) [28]. This is not a simple task because terrestrial impacts such as agriculture, deforestation, and pollution have direct effects on coral reefs, as do other direct insults such as overfishing and change in water temperature and pH. Thus, policy makers must protect coral reef systems as a broader sea/landscape if we are to build resilience and maintain ecosystem services from these systems (Moberg and Folke 1999) [28]. Environmental Policy and Ecosystem Services In the preceding sections, we described the complex nature of ecosystems and the interactions and linkages responsible for producing resilience in the systems. We used coral reef systems as an example to highlight aspects of resilience in complex systems, as well as illuminate some of the difficulties associated with actualizing sustainability, but such aspects are common to most ecosystems. As the coral reef examples demonstrate, environmental policy should focus on ensuring the resilience of ecological systems, as resilience is necessary for sustainability. Next, we discuss alternatives for actualizing sustainability via several policy instruments in the following section.

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Because there are a number of studies that analyze policy instruments (e.g., Salzman (2005) [41]; Salamon (2002) [42]; Jack et al., (2008) [44]; Baumol and Oates (1993) [46]), we do not provide an exhaustive review. Rather, we briefly discuss the broad categories of legal and economic policy instruments or tools that we view as promising. Legal Instruments for Sustainability Environmental law plays a key role in shaping policy for sustainability, as the types of legal instruments, institutions, and the response of law to the inherent variability in social-ecological systems is critical (Richardson and Wood 2006) [47]. Scale is a critical variable that is often overlooked in policymaking and related management decisions. For instance, local or small-scale perturbations such as land clearing and burning of fossil fuels can “scale up” to manifest into the global problems of biodiversity loss and climate change (Satake et al., 2008) [48]. Historically, the common law has encompassed an anti-wilderness, and therefore an anti-ecosystem services bias (Ruhl 2007) [49]. In fact, American courts actively interpreted the common law in a manner that favored the conversion of wilderness to “productive uses” (e.g., agriculture, urban development; Ruhl 2007) [49]. This expansionist policy seemingly served the U.S. well in its infancy as a country, but as we now see, the policy has long since outlived its usefulness. It is clear that the policy was environmentally destructive and what were once considered “positive” outcomes, threatens biodiversity and ecosystem services. Further, there are no mechanisms in American law that have emerged to require landowners to protect ecosystem structures and processes that generate critical ecosystem services (Lant et al., 2008) [50]. Ruhl (2007) [49] contends that the Takings Doctrine established in Lucas v. South Carolina Coastal Council may have opened the door for the progression of the common law to account for the “value” of ecosystem services. This result was not what Justice Scalia intended, but his majority opinion allowed for new knowledge and changed circumstances to affect the “relevant background principles” (Ruhl 2007) [49]. In turn, our understanding of the importance of ecosystem services and the manner in which biodiversity is critical to maintaining those services, has improved with time, but it is still woefully incomplete. From a human perspective,

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sustainability is dependent upon ecosystem services that are part of ecological systems, and as it is currently interpreted, the common law is in conflict with this reality (Ruhl 2007) [49]. Ruhl (2007) [49] asserts “landowners have almost total discretion over natural capital on land they own, with strong incentives to destroy it, and they have no inherent rights in the continued provision of ecosystem services from land by others. There is no gap in private property rights to be filled, in other words, but rather a well-constructed wall to be taken down.” For instance, modern societies no longer tolerate nuisances such as open sewers along city streets, nor allow freely despoiling rivers and streams due to mining activities on private property. As research on ecosystem services has progressed, it has been driven by a desire to link the environment to utilitarian costs and benefits. Thus, (Ruhl 2007) [49] contends this new understanding of ecosystem services should trigger a shift in the anti-ecosystem baseline in the common law. In particular, Ruhl (2007) [49] sees two areas of the law that have begun to adapt to emerging knowledge associated with ecosystem services: public nuisance and the public trust doctrine. Ruhl (2007) [49] contends that the law of public nuisance is well suited for dealing with “ecosystem service nuisances.” Ruhl reviewed a recent Rhode Island trial court case that resolved outstanding issues from a well-known U.S. Supreme Court case (Palazzolo v. Rhode Island). In its opinion, the trial court considered a regulatory takings claim filed by a landowner, but held the doctrine of public nuisance is a preclusive defense to takings claims (Ruhl 2007) [49]. The author contends that this case inserts ecosystem services into the public nuisance doctrine. Quite simply, the court found that the public was entitled to the ecosystem services (i.e., filtration and cleaning of water) provided by the marsh owned by a private landowner (Ruhl 2007) [49]. Thus, in this case, the public benefit from those ecosystem services outweighed the landowner’s private property rights, and the landowner had no right to fill the marsh that provided said services (Ruhl 2007) [49]. One possible solution to the conflict between the perceived need for economic growth and conservation is treating our natural resources much like a trust. Ruhl (2007) [49] contends that ecosystem services should stand on equal footing with economically valuable uses under the public trust doctrine. The public trust doctrine flows from the common law and places a duty on the states to preserve

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natural resources for the benefit of U.S. citizens (Kanner 2005) [51]. A state, as a trustee, has an affirmative duty to protect natural resources, and may not “alienate or extinguish the trust” (Kanner 2005) [51]. Several states have recognized that change is a constant in ecological systems, and thus the public trust doctrine should evolve to deal with new environmental threats and advances in science (Kanner 2005) [51]. Turnipseed et al., (2009) [52] advocate that a federal public trust doctrine could operate as a mechanism for protecting marine resources (i.e., coral reefs), and entrust U.S. federal agencies with managing these “trusts ” for the benefits of all U.S. citizens. This would require a shift in legal interpretation, as the public trust doctrine extends from the common law and has been established only at the state level (Turnipseed et al., 2009) [52]. In addition, there are few examples (e.g., California) even at the state level where the public trust doctrine has been extended to the level of broad-scale ecosystem protection (e.g., marine systems; Craig 2009) [53]. Thus, there are challenges to the use of the public trust doctrine as a policy instrument for ecosystem management, but it should be further explored as a policy option. Even if the public trust doctrine cannot be modified in a manner that captures ecosystem services within the scope of the doctrine, it is possible that ecosystem properties can be recouched within the context of the doctrine (Ruhl and Salzman 2006) [54]. Ruhl and Salzman (2006) [54] contend that protected public trust resources typically provide ecosystem services to the public, which therefore should extend protections under the doctrine to the ecosystem services on trust lands that are enjoyed by the public. Under this interpretation, which is limited to navigable and tidally influenced waters, coral reefs would be afforded protection under the doctrine (Ruhl and Salzman 2006) [54]. Trust law evolved from the common law, and defers to legal precedent, but is also responsive to current norms (Scott 1999) [55]. Most importantly, the standards of care for trustees first developed over 100 years ago, and thus are characterized by a reasonable degree of acceptance of those standards across jurisdictions (Scott 1999) [55]. In particular, trust law requires the trustee to avoid risks one might take with one’s own assets (Scott 1999) [55]. For the trustee, there are assets (e.g.,

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natural resources) that must be managed under varying degrees of risk and uncertainty, and thus, caution must prevail in the management of those assets (Scott 1999) [55]. Land trusts are another policy mechanism to supplement government acquisition of ecological systems (Merenlender et al., 2004) [56]. Scott (1999) [55] claims that trust law is defined by responsible decision-making, with respect to trust assets, in the face of risk and uncertainty. Land trusts hold promise for natural resources management, because they are not dependent upon profit maximization on the part of the trustees (Scott 1999) [55]. In fact, trustees must preserve trust principal, which would preclude them from maximizing expected utility (Scott 1999) [55]. This is particularly relevant to social-ecological systems, as these systems are characterized by an inherent degree of unpredictability. The downside to land trusts are the upfront costs associated with land acquisition, and the management of this land thereafter (Fishburn et al., 2009) [57]. Salzman (2005) [41] also notes that there is the possibility of individual landowners holding out or free riding. Although there are many land trusts operating in the U.S., their efforts could be improved by tailoring purchases around habitats identified as most important for the generation of ecosystem services (e.g., “buffers” for coral reef resilience). Funding has primarily come from private interests historically, but at least one land trust (i.e., Sonoma County Agricultural Preservation and Open Space District) is funded by a 0.25% county sales tax, which provides the trust with around $12 million annually for land purchases (Merenlender et al., 2004) [56]. A recent U.S. Supreme Court case provides another relevant example with ramifications for freshwater resources, and indirectly, the protection of coral reefs. In Rapanos v. United States, Justice Kennedy’s significant nexus test suggests that ecosystem functions and their services should be part of the argument for including certain “waters” within the Clean Water Act (Craig 2008) [58]. Craig (2008) [58] concludes that: “Justice Kennedy’s concurrence should prompt the EPA, the Army Corps and the federal courts to more clearly and more forcefully articulate the functional interconnectedness of the nation’s aquatic ecosystems and the services that those ecosystems provide to the American public.” As Craig (2008) [58]

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mentions, there are a number of connections with the Clean Water Act and ecosystem services. For example, the designated uses, or water quality goals, identify the potential ecosystem services the regulators deem important to that particular stream segment. If the pollutants prevent the attainment of those uses, then individuals will not benefit as much from the ecological system. Craig (2008) [58] finds a direct connection or mapping between designated uses and certain ecosystem services. However, the uses can also have an indirect connection to ecosystem services where there is more than a one-to-one mapping. For example, attaining the criteria established for fish consumption may enhance aesthetics and recreational services, such as with the protection of coral reefs (US EPA 2008a) [59]. Further, Bradley et al., (2009) [60] point out that the Clean Water Act, which has several options for controlling water pollution, can indirectly protect coral reefs. Economic Tools for Sustainability In order to see why economic tools are needed for sustainability, we must first explain why there are environmental problems from an economic perspective. Natural capital (as opposed to manufactured capital) can produce goods and services that are marketable and/or ecosystem services that are difficult to trade in a market (Kemkes et al., 2009) [43]. The incentives public and private landowners face determines how they will use the natural capital (i.e., preserve, enhance or destroy; Dasgupta et al., 2000) [61]. When too little of an ecosystem service that benefits society is being provided, it tends to be caused by market failures (e.g., see Dasgupta et al., 2000; Tietenberg 2000 for other reasons why markets fail) [61, 62]. For example, biodiversity is essential to maintain many ecosystem services and the resilience of ecological systems as described above (Dasgupta et al., 2000) [61], but is an aspect that landowners typically do not have the incentives to preserve. Because the benefits of biodiversity or the costs due to the loss accrue to individuals other than the private landowner, markets will fail (i.e., poorly defined property rights). Economists have identified different tools that create incentives for private landowners to make social, rather than private choices. Economic incentives, rather than command-and-control approaches, create flexibility in how landowners behave (Jack et al., 2008) [44]. For example, payments based on the quantity and quality of non-market goods and services alter the incentives private

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landowners face by encouraging them to consider the ecosystem services or land uses that provide those services (Kemkes et al., 2009) [43]. A land retirement program, like the Conservation Reserve Program (USDA 2009) [63], is an example of a payment approach (Jack et al., 2008) [44]. Taxes, on the other hand, make landowners or firms face the costs they are creating through their activities (e.g., water pollution; Freeman 2006) [45]. If policymakers consider these economic approaches for resilience or preventing the loss of biodiversity, incorporating important ecological principles may be necessary for success (e.g., Tietenberg 2000; Jack et al., 2008) [44, 62]. For example, Smith and Shogren (2001) [64] create an agglomeration bonus for landowners who protect habitat that connects to other protected habitat. The approach works within land retirement programs and it produces contiguous habitat that matches ecological needs (Parkhurst et al., 2002) [65], and could serve as a mechanism to build resilience. Another approach is developing markets for ecosystem services where ecosystem services like clean water are placed on a similar level as say producing an agricultural good or building a house. Markets are frameworks that guide individual behavior toward “environmentally beneficial outcomes” (OECD 2004) [66]. Water quality trading is a market-based approach where facilities with high pollution control costs can meet effluent reduction requirements by purchasing lower cost pollution reduction (or allowances) from another source (US EPA 2004) [67]. Reverse auctions are another mechanism that have potential for protecting ecosystem services (Goldman et al., 2007) [68]. In a reverse auction, landowners indicate the level of payment they require in order to engage in the protection of ecosystem services on their property (Goldman et al., 2007) [68]. The U.S. Environmental Protection Agency has used a reverse auction to distribute best management practices to control stormwater flows at the watershed level (Thurston et al., 2008) [69]. Reverse auctions allocate the resources to landowners willing to accept the lowest price for the action, with prices determined by the market (US EPA 2008b) [70]. Another mechanism that could preserve resilience is biodiversity markets. Since biodiversity is a beneficial outcome, and important for resilience, markets can

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make habitat valuable by internalizing the costs of its destruction and the benefits of its preservation (Merrifield 1996) [71]. For biodiversity, the markets can take different forms (e.g., markets in land, uses of land, flows of biodiversity, etc.; OECD 2004) [66]. However, utilizing the market is not always appropriate; it can sometimes have its own set of potential problems (e.g., Fullerton and Stavins 2000) [72]. Many requirements, such as a large number of buyers and sellers, are necessary for markets to run smoothly. Without these conditions, markets would fail, a potential reality for many ecosystem goods, services, or pollution control practices (Fullerton and Stavins 2000) [72]. Dasgupta et al., (2000) [61] discuss the fact that poor societies may react differently to market mechanisms than affluent societies. This could influence the effectiveness of market mechanisms. Further, markets must operate within an appropriate ecological context if they are to build resilience in ecological systems. As Jack et al., (2008) [44] point out, markets that are more complex are needed when the marginal benefits of ecosystem services are not constant. Threshold effects can cause problems for market approaches or, at least, increase the costs of designing the market (Jack et al., 2008) [44]. Imperfect information about ecological systems will also hamper how the market functions (Shogren et al., 2001) [73]. Accounting for resilience and dealing with complex ecological systems will most likely require complex market designs. Interconnection between Legal and Economic Tools It is difficult to talk about the economic approaches in isolation of legal tools because a number of market mechanisms and incentives require some regulations to function properly (e.g., Salzman 2005) [41]. For example, property rights play an important role for markets because they can determine whether a market will fail. When discussing markets, scarcity created through legal means is important. As Tietenberg (2002) [74] suggests, some target or limit is a necessary step for using markets and can be compatible with sustainability. For example, Total Maximum Daily Loads (TMDLs), which are the pollutant budgets, limit the number of allowances available (US EPA 2004) [67]. Allowances provide sources the right to discharge one unit of pollution. If properly designed, market forces will allocate the limited allowances cost-effectively (Tietenberg 2000) [62]. Another example is the individual transferable quota for protecting commercial

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fisheries. The government sets the quota or total allowable catch that creates the scarcity (Young 1999) [75]. The individual quota shares that individuals receive are transferable and provide the right to harvest a percentage of the allowable catch (Anderson 1995) [76]. These market programs, in order to account for resilience, must be consistent with an adaptive management framework (Tietenberg 2002) [74]. Some examples of this flexibility do exist, such as changing the total allowable catch in fisheries based on new conditions (Tietenberg 2002) [74]. From this perspective, we believe a multidisciplinary approach is necessary to focus policy on resilience and sustain ecosystem services. For example, TMDLs require an understanding of what water quality criteria are necessary to meet water quality goals, individual transferable quotas require an understanding of biological concerns such as changing fish stocks (Anderson 1995) [76], and biodiversity credits require an understanding of habitat; all important considerations to maintain resilience in a managed ecological system such as the abovementioned coral reef example. Collaboration is necessary in order to set sustainable limits and implement economic incentives for meeting those targets. CONCLUSION This chapter is an attempt to bridge the gap between biodiversity science, economics, law and policy. Policymakers want information from scientists that allows them to make black and white decisions, but as many scientists know, such guidance is unlikely due to inherent uncertainties in ecological systems (McNeely 1999) [77]. Scientists know that predicting the dynamics of complex systems is rife with uncertainty, and uncertainty is at odds with the law, which seeks certainty for public policy purposes (Bradshaw and Borchers 2000) [78]. Uncertainty is part of science, a fact, which for society raises doubts about the validity of some science (Bradshaw and Borchers 2000) [78]. Scientists that work for government agencies are in a difficult position, as admitting there are known uncertainties, let alone the unknown uncertainties, makes them look less credible in the eyes of politicians, policymakers, and others that do not understand science. Responsible politicians and policymakers rely upon science for guidance on policy, but the fundamental uncertainty in much of science creates credibility gaps for those in the political arena.

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Bradshaw and Borchers (2000) [78] make the following important points: (1) for most of the public, science lies outside their realm of perception; and (2) the relatively short temporal cycles for American political regimes limit long-term environmental management programs. These points cannot be understated, and create tremendous difficulties for environmental management. Policymakers have shown the ability to look past short-term gains or incentives and demonstrate long-term vision as is evident in the formation of the U.S. Environmental Protection Agency and resulting regulation. Many negative impacts, short-term, to businesses and industry are outweighed by the long-term benefit to Americans, the U.S., and all of humanity. Such long-term thinking and associated policies are necessary to protect the environment and the ecosystem services they provide the public. Ideally, such policy will only have short-term negative impacts and the benefits will quickly become evident. However, there is urgent need for sound science to fill in our lack of understanding and help guide policy. In this paper, we have suggested several possible policy instruments for dealing with the apparent intractability of economics, ecology, and the law. Research is necessary to understand how systems operate, the functions they provide, and the organisms responsible for said functions. For instance, Rey Benayas et al., (2009) [79] conducted a meta-analysis of ecosystem restoration projects and found that these efforts can increase biodiversity and ecosystem services. They also found that biodiversity and ecosystem service measures were positively correlated, indicating the strong link between structure and processes in ecological systems (Rey Benayas et al., 2009) [79]. On the down side, “restored” systems registered lower biodiversity and ecosystem service measures than “intact” ecological systems, highlighting the importance of avoiding regime shifts via conservation (Rey Benayas et al., 2009) [79]. This result demonstrates the importance of accounting for resilience in ecological systems, as once a system has experienced a regime shift it is unlikely the system will supply the same “quality ” of ecosystem services when restored. Metrics must be developed that can capture and quantify functional groups, functional redundancy, and response diversity (Bellwood et al., 2004) [20] to enable us to identify our impact on the system and how it affects ecosystem services, and therefore sustainability. Recent research shows there are system-

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specific conditions that indicate that a system is losing resilience and may be approaching a regime shift (Brock et al., 2008) [80]. For example, in shallow lakes, a rapid shift from an oligotrophic to a eutrophic regime can be preceded by an increase in periphyton and a reduction in the proportion of piscivorous fish to other fish (Brock et al., 2008) [80]. It would be beneficial to identify such an approaching regime shift while there is time to address or correct the situation. Cabezas et al., (2005) [81] suggest that Fisher Information (FI) may act as a method to quantify system resilience. Moreover, they suggest FI may identify impending regime shifts, based on loss of information as the system degrades. Research on methods for identifying impending regime shifts should be high priority, as it is critical to gaining better understanding of the dynamics of complex systems. The single greatest failure of American environmental law is its inability to protect ecosystems and the services from those systems that humans depend upon for existence (Salzman et al., 2001) [82]. Salzman et al., (2001) [82] suggest four basic considerations that must be addressed with respect to ecosystem services, including: (1) identify the ecosystem service, as well as its importance, range and status; (2) economic valuation of the ecosystem service; (3) explore the obstacles to developing a market for the ecosystem service; and (4) developing policy to protect the ecosystem service. In light of these considerations, it is apparent that the current model of relying upon government to set aside parks and preserves to protect biodiversity is simply not adequate to insure system resilience and therefore expected ecosystem services (Myers 1999) [83]. For example, it is estimated that 95% of habitat for threatened and endangered species in the U.S. is on private land (Merenlender et al., 2004) [56]. In order to actualize sustainability, we must develop new, or adapt established, policy to protect biological diversity. Such protection may guard against the loss of ecological resilience, thereby protecting ecosystem services, which are vital to sustainability. The purpose of this manuscript was to characterize our understanding of resilience in ecological systems using coral reef systems as an example. From there we discussed several legal and economic policy instruments we view as promising for protecting biodiversity and ecosystem services in ecological systems. We hope we have sparked interest in further exploration of

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economic and legal policy mechanisms accounting for resilience in ecological systems, and therefore actualizing sustainability. CONFLICT OF INTEREST The author(s) confirm that this chapter content has no conflicts of interest. ACKNOWLEDGEMENT Declared none. REFERENCES [1] [2] [3] [4] [5] [6] [7] [8]

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CHAPTER 5 Human Interactions and Sustainability Michael E. Gorman1,*, Lekelia D. Jenkins2 and Raina K. Plowright3 1

Department of Science, Technology & Society, University of Virginia, USA; School of Marine and Environmental Affairs, University of Washington, 3707 Brooklyn Avenue Northeast, Seattle, Washington 98105 USA and 3David H. Smith Conservation Research Fellow, Center for Infectious Disease Dynamics, The Pennsylvania State University, University Park, PA 16802 USA 2

Abstract: This chapter describes a framework for understanding and managing complex systems that couple human beings, nature and technology. The framework includes five major components; the first three are necessary capabilities for accomplishing the last two.  Superordinate goals: Human beings have to see the urgent necessity of working together to solve problems like climate change and depletion of natural resources.  Moral imagination: Differences in values can prevent adoption of a superordinate goal. Moral imagination is the equivalent of interactional expertise concerning values; it involves being able to ‘step into the shoes’ of another stakeholder and see the problem from her or his perspective.  Trading zones: Linking multiple stakeholders will require setting up a series of trading zones for exchanging ideas, resources, and solutions across different communities and interests. Developing the three capabilities above will permit:  Adaptive management: This strategy involves treating management interventions like hypotheses, subjecting them to empirical tests, and revising the strategy based on the results. Adaptive management is difficult in tightly coupled humantechnological-natural systems, where hypotheses should be constructed not only about environmental impacts, but also about effects on stakeholders.  Anticipatory governance: Global problems and opportunities will require adding more anticipatory, adaptive capability to governance mechanisms, linking decision makers with other stakeholders. These exchanges will have to be motivated by a superordinate goal so urgent that governance structures can be transformed, if necessary. *Address correspondence to Michael E. Gorman: Department of Science, Technology & Society, University of Virginia, USA; Tel: (434) 924-3439; E-mail: [email protected] Heriberto Cabezas and Urmila Diwekar (Eds) All rights reserved-© 2012 Bentham Science Publishers

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This framework will be applied to two detailed case studies, one concerned with developing better management practices for reducing bycatch in fisheries, the other with ecosystem disruptions like the 2001 outbreak of foot and mouth disease in the UK. Limitations of the framework will be discussed in the light of these case studies, along with suggestions for how it can be improved.

Keywords: Antropocene, Trading zones, Interactional expertise, Mental models, Moral imagination, Superordinate goals, Adaptive management, Anticipatory governance, Conservation, Bycatch reduction, Turtle Excuder Device, Foot and mouth disease INTRODUCTION One of Jared Diamond’s students, after reading the chapter on Easter Island in Collapse, asked how anyone could have cut down the last tree on Easter Island. Easter Island should have been a simple sustainability case, because the island is so small that the disappearance of trees would have been obvious to anyone. Easter Island had giant palms when humans first arrived, so the full-grown trees were a significant resource. Once they were gone, there would have been no new canoes, or fire. Surely it was obvious to everyone that they ought to preserve the trees? Easter Island was divided into several tribal zones. If each tribe were trying to exploit dwindling resources for its own advantage, there might have been a rush to seize and cut down the final trees. Diamond cites evidence that Easter island society collapsed catastrophically, with violence and even cannibalism. Diamond cites the case of another island, Tikopia, whose inhabitants were able to sustain its population on their small island by adapting their socio-technical system to their natural environment. The term socio-technical system refers to the fact that society and technology are so intertwined that they become hard to distinguish: technologies embody ways of life. The early Tikopians followed the Easter Island pattern: they cut and burned most of their trees, and also ate most of the fish and killed the local seabirds. But in response, Tikopians adopted pigs as a source of protein and learned to store breadfruit to guard against famine. In 1600, all the pigs on the island were killed because this system was no longer sustainable and the natives returned to fish, shellfish and turtles. They also preserved small sections of rainforest and grew additional trees for nuts. It helped that the Tikopeians were a single society throughout—but the fact that they did not

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splinter into sub-groups and fight over resources shows that conflict over scarcities is not inevitable. The point of these two cases is that sustainability depends on social and environmental and technological factors—and that human beings can destroy their local ecosystem or work together to sustain them. On a larger scale, the Earth itself can be viewed as an island in space. The Earth is a much more complex system and contains many more potential resources than an island like Easter. It also contains multiple societies representing different experiments in living that can exchange knowledge and practices—or try to hoard resources and dominate each other. The Holocene has yielded to the Anthropocene.1 Human beings are now the keys to sustaining the current species diversity on the planet—“we have met the enemy and he is us.”2 A FRAMEWORK FOR MANAGING THE ANTHROPOCENE This chapter describes a framework for understanding and managing complex systems that couple human beings, nature and technology [1]. The framework includes five major components; the first three are necessary capabilities for accomplishing the last two. Trading zones: Linking multiple stakeholders will require setting up a series of trading zones for exchanging ideas, resources, and solutions across different communities and interests. Peter Galison studied the development of radar, which was motivated by an urgent goal: the survival of Great Britain at the beginning of World War II. He noticed that theoretical physicists, experimental physicists, instrument makers and engineers formed trading zones to work together to achieve this goal [2]. Participants from different expertise communities did not have to understand each others’ paradigms in order to cooperate; they simply had to agree on mechanisms and terms of exchange. In order to trade, participants in a zone have to develop one or more of the following:

1 2

Nobel laureate Paul Crutzen first coined this term in 2000, Porkypine, in Walt Kelly’s Pogo.

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

A reduced common language, which begins with participants in a zone agreeing on shared meanings for certain terms, then progresses to a kind of pidgin and eventually to a creole, which is a new language born out of old ones. At the creole stage, the trading zone may morph into a new expert community. Galison includes the possibility that visual and mathematical ‘creoles’ may also be formed in trading zones3. Consider the use of indicators of systems change [3] which can serve a quantitative or, if translated into graphic representations, a visual role similar to a creole.



A boundary object or system can give participants in the zone a common reference point. The Everglades, for example, serves as a boundary system that is represented differently by multiple stakeholders in a trading zone and leads to boundary objects like documented plans that form the basis for negotiations.4



Interactional expertise on the part of one or more participants who will serve a role similar to trade agents, facilitating exchanges of ideas and resources. Interactional expertise is the ability to carry out a sophisticated conversation with members of an expert community that shows real understanding of how they view and solve problems [4]. The interactional expert cannot do the research, but she or he can understand it.

Goals: Trading zones vary in the extent to which participants agree on goals and the means to achieve them. In the case of radar, all participants had to share in a goal, but different specialties could employ different means to achieve partial solutions. In the development of technologies, smaller trading zones can be nested within larger ones, e.g., a team of diverse specialists can work on a major component of a system like radar or a Mars Rover or a new jet airplane, and another higher-level trading zone can negotiate what components are needed and how they fit into the overall system. 3

See Peter Galison, Trading with the enemy, in M.E. Gorman (Editor) Trading zones and interactional expertise: Creating new kinds of collaboration (MIT Press, 2010). 4 Boyd Fuller, Trading Zones: cooperating and still disagreeing on what really matters. Under revision for probable publication in Journal of Planning Education and Research.

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Figure 1: Compatibility of Goals Assessment

The compatibility of goals can be assessed using two dimensions: the extent to which members of a trading zone agree on outcomes, and the extent to which they agree on the means used to achieve the outcome, as illustrated in Fig. 1. Each trading zone could be placed on this graph based on the extent to which members agreed on both outcomes and means—and as the trading zone shifted, one could track its trajectory on the graph. Superordinate Goals: The social psychologists Muzafer and Carolyn Sherif created this term to describe goals that can unite groups whose members had previously been in competition [5]. The Sherifs set up a summer camp and deliberately encouraged rivalry between two groups of boys; their goal was to figure out the most effective means of ending the intergroup hostility. One uniting strategy is the need to defeat a common enemy, which motivated the formation of the trading zone around radar —if scientists, engineers and military officers did not work together, Britain would be devastated by the Luftwaffe. The problem with the common enemy goal is that there always has to be an enemy; therefore, the Sherif’s tried what they termed a superordinate goal, or a

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problem that affected the basic needs of both rival groups. When there was a problem with the camp’s water supply (simulated by the staff in a believable way) the boys were sent out to solve it, and could only do it by cooperating. Later, the boys had to help jump start the truck that was going to get their lunches by pulling on ropes; the two groups first lined up on separate ropes, but then they mingled. When the camp ended, the rivalries had dissolved. In the Anthropocene, human activity affects every aspect of the planetary ecosystem. Management of this activity therefore creates a set of superordinate goals on which our survival depends. Creating a shared sense of urgency around superordinate goals across a diverse population of distincty ethnic groups, economic strata and languages is orders of magnitude harder than convincing a homogeneous group of boys at a summer camp to work together. But global systems management would be much simpler if everyone saw the urgent need to work together—the same kind of urgency that exists during war or a natural disaster, but now focused on longer-term management to prevent crises—and the disparities in resources that are one factor in fueling (pun intended) wars. Achieving a common sense of urgency depends on the next capability. Moral imagination is the equivalent of interactional expertise concerning values; it involves being able to see a situation from the perspective of another stakeholder. Moral imagination involves a reflexive component: one must be aware of one’s own perspective before being able to inhabit another. If each group in a trading zone sees its values as reality, then there will be no possibility of adopting a superordinate goal. Say, for example, my group thinks that our God is on schedule to end the world anyway. Then there is no reason to work with others to stave off the inevitable—indeed, it might even be sacrilegious. To trade with such a group, one has to be able to see the world as they see it. The end result is not relativism; seeing another’s view is not the same as agreeing with it. Moral imagination does enhance negotiations by allowing each party in a trading zone to understand where another party is coming from. It also opens up the possibility of evolving new shared values that transcend existing differences. For example, those who currently have more resources also have more resilience when it

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comes to dealing with climate change and other potential system disruptions—at least in the short term. Therefore, they do not see preventing global warming as superordinate goal. Moral imagination is required to make that leap. Developing the Above Capabilities above is Essential For: Adaptive Management: This strategy involves treating management interventions like hypotheses, subjecting them to empirical tests, and revising the strategy based on the results. The empirical tests can be based on historical interventions and data on their impacts, which can lead in turn to new hypotheses which can be implemented and tested going forward [6]. Adaptive management is difficult in tightly coupled human-technological-natural systems, where hypotheses should be constructed not only about environmental impacts, but also about effects on stakeholders. Complex systems are not amenable to classic single or multivariable hypothetico-deductive techniques, because the results of small perturbations are not always predictable—it is hard to say when a small change will tip the system into a new state. New measures of systems change [7] could be coupled with other metrics to make adaptive management possible in these sorts of complex, coupled systems. Anticipatory Governance 5: Current governance structures around the world are best at responding to well-characterized problems like the hole in the ozone layer. Response to this problem was rapid and effective. The ozone example illustrates the power of a superordinate goal. It would, however, have been even better to have the problem not occur at all. Therefore, current regulatory and legal systems need to be complemented by a capacity for anticipatory governance: oversight mechanisms that provide the equivalent of early warning signals and systems-level management structures that can respond to these signals. Anticipatory governance requires at least three actions: the anticipation and assessment of an emerging situation; the engagement of stakeholders that are mostly still latent; and the integration of broader considerations into contexts that have been largely self-governing [8]. 5

Anticipatory governance is a new concept that forms the focus of Arizona State University’s Center for Nanotechnology in Society (http://cns.asu.edu/).

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The accelerating pace of technological change compounds the problem. As Allenby notes, technological change is autocatalytic, meaning each change catalyzes other changes at an increasing rate that varies from situation to situation. The interactions among all of these technologies can lead to emergent, unanticipated consequences. Current political institutions are inadequate to perform this kind of anticipation for complex socio-ecological-technical systems [8]. When the complexity of socioecological-technical systems does not permit an accurate prediction of consequences, adaptive management is critically important. Adaptive management can be done by multiple stakeholders forming trading zones that cut across traditional governance structures [9]. However, governments have to be able to respond as well. Tools like scenario development can be used to anticipate possible futures, including metrics to see which possible future is emerging, but will only be an effective part of governance if management structures are in place to respond effectively—including the ability to reverse an intervention if it is not having the desired effect. These anticipatory structures need not be governmental—they could come from NGOs, industries and sciences. But all of these efforts would need coordination or at the very least blessing from government. Consider, for example, the Dutch response to the possibility of global climate change. Instead of waiting until the changes are obvious, the Dutch are anticipating them by building floating cities. If it turns out that global climate change is averted, the Dutch will have sunk millions into systems that do not reduce their quality of life and will still provide additional insurance against huge storms. What the Dutch have done locally needs to be done globally—sophisticated dams and floating cities in the Netherlands will not help Bangladesh if climate change occurs. It is far better to deal with the CO2 levels than hope for a rapid response if the variance in climate increases around a slowly rising temperature. Technologies like carbon sequestration will certainly be part of the solution, as will changes in behavior. In a coupled socio-technical-natural system, any response will have to be behavioral, environmental and technological.

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THE FRAMEWORK IN ACTION This framework has been applied to a variety of situations6, including 

the development of a new science of services (Gorman)



bridging the gap between biologists and programmers who need to work together (Shrager)



creating appropriate metrics for a large, public, urban school district (Mehalik)



seamlessly linking humanities and engineering, using a laboratory as a focus (Fisher & Mahajan)



helping businesses anticipate disruptive technologies and practices (Von Oettinger)



selling the female condom to women in the developing world who needed it as AIDs protection, but could not afford it (Leeper, Powell & Werhane)

In this chapter, we will use one of the most theoretically sophisticated of the cases reported in the volume [10] to further develop the framework. Bycatch Reduction Bycatch (i.e., non-target organisms that are unintentionally caught or harm by fishing gear) is a major environmental concern that makes many fisheries unsustainable. In order to reduce bycatch, policy -makers and managers are encouraging the use of conservation technologies (i.e., a technology that is primarily used to protect organisms and habitat). In the United States two of the most famous examples of this are a suite of dolphin conservation technologies used to prevent dolphin entanglement and death in tuna nets and the turtle 6

All of the examples in the bulleted list are described in a volume edited by Gorman, Trading zones and interactional expertise: Creating new kinds of collaboration, MIT Press, 2010. Each of the cases in the list is a separate chapter in the volume, and the author(s) names are listed at the end of each bullet.

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excluder device, a type of escape hatch used to prevent sea turtles drowning in shrimp nets. Using these two examples, we will illustrate boundary organization and interactional expertise trading zones in practice, describe the impact of goal compatibility, and discuss opportunities for the use of moral imagination that might have changed the course of history in these examples. We will also identify an instance of anticipatory governance at work. Dolphin Conservation Technologies The use of dolphin conservation technologies offers examples of moral imagination at work and the impact of incompatible means on the success of environmental initiatives. The governance mechanism was the Marine Mammal Protection Act of 1972, which required the executive branch of the federal government to reduce dolphin bycatch. The passage of the Act created a legislative mandate for the government and an economic incentive for the tuna fishing industry, driving both to solve the dolphin bycatch problem. The tuna fishing industry feared the complete closure of the of fishery if dolphin mortality was not reduced [11-13]. To achieve their common goals government and industry needed to work together. The government required the fishing knowledge and experience of the industry and industry required the financial and scientific support of the government. Unfortunately, several points of contention made it difficult for them to coordinate their actions. Concerned about negative media coverage and potential lawsuits from environmentalists, the federal government was secretive about its research. This lack of transparency led many fishers to question whether or not the federal government—which had always been an advocate of fishery development—was truly committed to the continuance of the tuna fishery. Many government personnel, on the other hand, believed that the majority of tuna fishers were not committed to fully addressing the dolphin bycatch problem. Distrust of each other's motives created an unstable foundation on which to build collaborative projects. This tenuous but necessary sharing resulted in a boundary object trading zone [14]. An excellent example of this boundary object trading zone is the interaction between the fisherman who invented the Medina Panel (an area of safety netting that helps prevent dolphin entanglement), and the government scientist who tried to improve it.

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Their goals were similar but not the same. Both men wanted to reduce dolphin mortality while minimizing the impact on traditional fishing procedures and tuna catch. They differed in that the fisherman placed more value on minimizing the impact on the amount to tuna catch and the scientist placed more value on reducing dolphin bycatch. This is evidenced by the fisherman’s insistence that constructing the Medina Panel with a mesh-size smaller than two inches would create an unacceptable amount of drag when the net is pulled through the water, making fishing inefficient. The scientist was more concerned about minimizing dolphin entanglement and so experimented with mesh sizes as small as one inch [15]. Though they shared a common goal, the difference in priorities meant that they did not have a shared superordinate goal. As a result, they each worked around the same boundary object, the Medina Panel, but produced different outcomes. The fisherman produced simple, commercially practical designs, while the scientist produced complex, multi-functional designs that were troublesome to use under commercial fishing conditions. The boundary object trading zone was held together by mutual interests in each other's secondary priorities and by the insight that the other's work could shed on improving their designs [10, 14, 16, 17]. The testing of these various Medina Panel designs illustrates an instance of compatible goals with incompatible means. Both the industry and the government had the goal of scientifically evaluating the effectiveness of the Medina Panel designs, during the course of normal commercial fishing. Although the experiment involving a comparative analysis across twenty vessels was poorly controlled, lacked statistical power, and the results were compromised by a court ordered halt of the experiment, industry still favored this as a means of evaluating new technologies. They believed that testing technologies simultaneously on numerous commercial vessels would allow more technologies to be more thoroughly evaluated in less time. They also believed it would also expose more fishermen to the conservation technologies, thus increasing adoption. The federal government, however, thought that this approach was be too costly and could not yield more statistically useful results than the status quo of using a single vessel as a means to evaluate prototype dolphin conservation technologies [14]. Once it was established that the Medina Panel in conjunction with a number of other dolphin conservation technologies could substantially reduce dolphin

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bycatch, the federal government had a new goal of convincing the tuna fishermen to adopt these technologies. After a few years, technology adoption rates and reductions in dolphin bycatch reached a plateau. So in 1977, the federal government passed regulations requiring the formation of an Expert Skippers’ Panel in an attempt to identify and provide more personalized training to skippers of tuna boats that had high levels of dolphin bycatch. In 1975, industry proposed and created a plan for a Senior Captains’ Advisory Panel, but it was not established. However, the goals of this proposed panel matched many of those of the Expert Skippers’ Panel [14]. The federal government gave the tuna fishing industry the authority to create organizational rules and procedures for the Panel as well as the authority to take corrective action. Furthermore, the government solicited and with few exceptions heeded the advice of the Panel about the prosecution of skippers who violated regulations about dolphin bycatch reduction. The general procedure was that the government supplied the Panel with records of skippers' performance in reducing bycatch. The Panel analyzed this information and in problem cases the Panel either made recommendations to the government on how to address the problem or took action itself. When the Panel decided to take action it would call a meeting with the poorly performing skipper, who was usually a younger individual new to the fishery. During the meeting, the problem skipper would describe his fishing process and the panel members would give him advice on how to improve it. None of the problem skippers who attended one of these meetings ever needed to appear again before the Panel. The effectiveness of the Expert Skippers’ Panel is credited with improving skippers’ skill levels industry-wide, resulting in a decrease in dolphin mortality [14]. The creation of the Expert Skippers' Panel was an act indicative of moral imagination. The federal government realized that its previous promotional efforts were insufficient and that the fishery industry itself was best equipped to identify and remove the obstacles to further technology adoption and dolphin bycatch reduction. This example also illustrates that the result of an exercise in moral imagination does not necessarily default into a compromise position. The federal government adopted the industry's concept of a Senior Captains’ Advisory Panel almost entirely as the industry had envisioned it. The government ceded authority

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to analyze and correct industry performance to the industry itself. Even with the authority the government retained, the ability to prosecute offenders, it heeded the advice of the Panel about which cases to pursue and in which cases to show leniency. This reveals the power of moral imagination to yield solutions that are not just tolerable to all parties, but rather solutions that are best for addressing the problem, even at the cost of ceding power and authority. The case of dolphin conservation technologies also offers an example of the impact of compatible means and incompatible outcomes on the fulfillment of conservation goals. In order to pressure tuna fishers to reduce dolphin bycatch, a number of environmental groups baned together and waged a campaign of boycotts and lawsuits. The result of this campaign was the dolphin-safe tuna label. This label, which appears on nearly every can of tuna sold in the United States, signifies that the tuna fishers were using a fishing method that did not involve dolphins in any way. The campaign in combination with the use of dolphin conservation technologies, successfully reduced the number of dolphins killed each year in this tuna fishery from several hundred thousand to less than two thousand [12]. Further reduction in the level of dolphin bycatch would likely require a fishing method that while better for dolphins would result in the bycatch of nearly 15, 000 other animals for every dolphin saved. These include species of sharks, rays, marlins and sea turtles, a number of which are threatened or endangered with extinction [18]. Faced with this trade-off, environmental organizations with a general focus on marine conservation, including Greenpeace, World Wildlife Fund, and the Ocean Conservancy, halted their aggressive campaigns and supported a change to more flexible regulations that would likely maintain the current level of dolphin bycatch and not increase bycatch of other species. In contrast, Earth Island Institute, an organization that was founded on the cause of reducing dolphin bycatch, persisted in a lawsuit that upheld strict regulations. Initially all the environmental groups shared the same means--lawsuits and boycottt-but their ultimate outcomes were related but critically different. All the groups wanted to reduce dolphin bycatch, but only some of the groups wanted to reduce dolphin bycatch in harmony with improving the general health of the ocean. The organizations could persist in a cooperative relationship, during the initial effort to

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reduce dolphin bycatch from its massive level. The cooperative relationship crumbled, however, when one organization wanted to continue reduction in dolphin bycatch at the cost of increased bycatch of endangered species. Without both shared means and shared outcomes there is no superordinate goal. This case study has illustrated elements of the framework for managing the anthropocene, namely a boundary object trading zone, shared goals with incompatible means and compatible outcomes, and moral imagination. The following case study offers additional examples of these elements and describes how together they can support anticipatory governance. Turtle Excluder Devices (TEDs) A TED is a type of escape hatch in a shrimp net that allows sea turtles to exit the net while retaining the shrimp catch. The original TED arose from a melding of ideas from a fisherman and a government scientist. Despite this, much of the shrimp fishing industry viewed the TED with suspicion, because scientists developed it within a government controlled invention system. The shrimp fishing industry felt that government personnel had developed the TED without industry input and were forcing it on them as the only acceptable solution to the turtle bycatch problem. The industry believed that they had viable alternative ideas to solving the turtle bycatch problem that the federal government ignored. Thus, few shrimp fishers used the early versions of the TED [14, 19]. Because of the controversial nature of this situation, Sea Grant had kept its distance. Sea Grant is an outreach agency charged with engaging the fishing community on numerous issues, such as transferring new fishing technologies and educating the industry about new regulations. Most often Sea Grant serves to transfer information from government to the fishing industry, to do so Sea Grant agents must speak the 'language' of government scientists and managers as well as fishers. This in essence makes them professional interactional experts; this interactional expertise could have served to broker more productive trades between NMFS and the shrimping industry. Given the range of its responsibilities, however, Sea Grant was wary of spending precious social capital on the controversial sea turtle issue, especially in light of federal government's

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controlling approach to TED invention and previous resistance to fisher and Sea Grant ideas [10] In 1982, in order to overcome Sea Grant's reticence, a NMFS manager staged an exchange to secure Sea Grant’s active support in TED development and promotion. The NMFS manager asked industry representatives to publicly request the help of Sea Grant with technology transfer during a workshop. The industry representatives assured Sea Grant that they wanted their help with the TED issue, and helped ease Sea Grant concerns that the controversial nature of TEDs might tarnish its relationship with industry. In this way the interactional expertise of Sea Grant was brought to bear, creating an interactional expertise trading zone. Interactional expertise involves the time-consuming development of a new linguistic ability. Even in the case of Sea Grant agents, who already had this ability, their use of it was dependent on establishing relationships between NMFS and shrimpers through which to exercise this ability. In the TED case, Sea Grant agents developed these relationships over the course of four years. They engaged with shrimpers, who had begun to develop new TEDs on their own, external to the government TED invention system. They also interacted with government personnel about the continued refinement of the original government-invented TED [10]. The TED case yields one solid example of anticipatory governance that clearly illustrates the fulfillment of the three required actions: the anticipation and assessment of an emerging situation; the engagement of stakeholders that are mostly still latent; and the integration of broader considerations into contexts that have been largely self-governing [8]. Sea Grant agents anticipated that the federal government might pass regulations requiring mandatory use of TEDs. The Sea Grant agents further assessed that for shrimp fishers to accept TEDs, the devices would need to be more practical for commercial use and that shrimp fishers would need more options in types of TEDs they could use. In addition, Sea Grant agents believed the best way to achieve this would be to bring shrimpers’ TED ideas to the attention of government scientists. So in 1986, Sea Grant sponsored a demonstration event comparing the government's TED with three fisher-invented TEDs. Drawing on their interactional expertise and cultural understanding of the two groups, Sea Grant convinced shrimpers and government scientists to participate. As a result of this event and further testing, the federal government

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certified all three shrimper-invented TEDs for commercial use. Also, in keeping with Sea Grant's anticipatory assumptions, shrimp fishers began to use more of the fisher-invented TEDs than of the government TED. This event solidified the interactional expertise trading zone, which rapidly engaged more fishers who had not previously been involved in trying to reduce sea turtle bycatch. Innovative shrimpers continue to bring their ideas for new TEDs to Sea Grant agents, who then communicate these ideas to the federal government. In this way, Sea Grant broadened the considerations about TEDs to include whether they were commercially practical and opened a door to shrimp fishers to take part in oversight of the TED invention-system, which had previously been governed exclusively by the federal government. This case has provided examples of boundary object and interactional expertise trading zones and their significance for one important aspect of anticipatory governance: the development of new technologies and practices with input from multiple stakeholders. But conclusions from this case study might not generalize to other situations, therefore we need to test the usefulness of this framework in other situations, as a step towards developing more rigorous methods for determining which kinds of trading zones work best in what situations, and when trading zones are not useful. Let us briefly consider a domain we are currently researching: management of epidemics affecting livestock. Our hypothesis is that, as in the case of the fishers and the marine scientists, the best results will be obtained if scientists and regulators form trading zones with farmers and veterinarians who have local knowledge of practices. Foot and Mouth Disease In 2001 the UK experienced a devastating outbreak of foot and mouth disease (FMD), a virulent and highly infectious disease of domestic ungulates. The epidemic and its control resulted in the death of approximately 10 million animals [20], destruction of livestock on over 10, 000 premises [21], an economic cost of around US $12 billion [20] and an unquantifiable social cost. Throughout the epidemic the public watched mounds of dead animals being burnt on funeral pyres, and carcasses being thrown into mass burial pits. Farmers lost their valued

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animals, there were suicides among those involved in the slaughter, and a general public disgust and anger at the ruthlessness of the slaughter [20]. The loss of British FMD-free status meant automatic bans on agricultural exports, with major economic consequences to a farming industry still ailing from the impacts of “mad cow disease” (Bovine Spongiform Encephalopathy). The Blair Government was under intense pressure from the agricultural lobby to secure UK livestock markets as soon as possible and wanted to be seen as enacting a swift resolution before the 2001 general election [22]. The conditions were hence established for agreement on a common goal to eradicate FMD as quickly as possible. The controversy arose over the means to achieve this goal. In this section we examine how the failure to develop trading zones and a lack of moral imagination between the two major players in disease control, veterinarians and mathematical modelers, may have led to the unnecessary slaughter of millions of animals. We examine how anticipatory governance might have prevented the mistakes made during the FMD outbreak by laying the groundwork for effective scientific exchange. Most controversial during the outbreak was the policy of pre-emptive slaughter, a control strategy based on mathematical models. Traditionally FMD has been controlled by bans on movement of livestock, and rapid detection and slaughter of infected and in-contact animals [20, 23]. During the 2001 FMD epidemic, mathematical models indicated that traditional approaches would not control the epidemic and additional “firebreak culling” [23] was required. “Firebreak culling” involved automatic pre-emptive slaughter of all susceptible animals on properties within 3 kilometers of infected premises, whether virus was suspected to be present or not, and resulted in the slaughter of unprecedented numbers of animals with severe economic and social costs. Veterinarians argue that the preemptive slaughter was unnecessary [20, 23], while the modelers argue that if “firebreak culling” had not been carried out, the impact and duration of the epidemic would have been much higher than that which actually occurred during 2001 [24] some argue that British countryside farming could have been wiped out altogether. The breakdown in communication between veterinarians and modelers can be traced back to a number of historical factors. First, senior government advisors had personal

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connections with a team of modelers at Imperial College. Early conversations with this group led to the ad hoc formulation of a Science Advisory Group heavily weighted by modelers, contrary to pre-arranged contingency plans. The recent mad cow disease epidemic had created a loss of political confidence in veterinary science, and hence veterinarians were initially marginalized from the process. Furthermore, the mad cow disease inquiry had concluded that politicians failed to pay sufficient attention to scientists [22], laying out the conditions for modeling to play a disproportionate role with its “air of intellectual superiority pretence of precision, knowledge and control” [20]. By the time veterinarians had increased input, the contentious decisions had already been made [22]. Veterinarians felt disempowered and a conflict ensued over what constituted good science. Farmers and veterinarians have contextual knowledge of local environmental conditions, individual herds and animals (they were carrying out the slaughter and understood the practical reality of implementing the model derived policy). The modelers knowledge is decontextualised, they apply statistical criteria to data to make judgments about disease transmission risk and are somewhat removed from the events occurring on the ground [22]. However, modelers understand disease dynamics at large scales—the population scale, or in this case the scale of the UK—a contrast to veterinarians who are generally limited to the individual or herd scale. When veterinarians criticized the contiguous culling policy, modelers responded with statements that denigrated veterinary knowledge to ‘experience and intuition’ in contrast to the ‘complex and seemingly abstract’ models of the epidemiologists [22]. For example, in one media interview Anderson (modeler from Imperial College), stated that veterinarians were “basing their stance on personal opinion rather than hard scientific assessment” [22], he went on to recommend that their role be in implementation of policy, not deciding policy, and suggested that their close personal connection to the issues impaired their judgment, while modelers had greater objectivity [22]. The veterinarians were equally mistrustful of the epidemiologists, epitomized by the statement of one veterinarian calling the culling “carnage by computer”, characterizing the modelers as isolated remote and inhumane, while suggesting that personal involvement was needed to understand the impacts of the culling [20].

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The fractious atmosphere that developed inhibited the formation of trading zones and subsequently the free flow of data and ideas between experts. The modelers argue that veterinarians would not provide data and the quality of the data, when it was forthcoming, was poor (for example farms were located in the ocean, census data on stock levels were out of date etc.). Veterinarians argued that models were not appropriate predictive tools in this case and the models didn’t reflect biological reality. For example, dairy farms faced five times greater risk of infection yet sheep were the species most intensively slaughtered on the basis of the models [25]. Veterinarians advocated a more local approach, where local experts are consulted and decisions made on a property by property basis (Michel 2001). The breakdown in communication between modelers and vets led to fundamental assumptions being based on flawed data and probably led to unnecessary culling.7 For example, veterinarians investigating the source of infection for an infected premises often assumed that infection spread from the nearest infected premises [25] while in fact the source was usually a distant property, infection being spread by vehicles and people’s clothing etc. If veterinarians had communicated their assumptions to the modelers, fundamental mistakes in the modeling may have been avoided. These mistakes led to overestimating the importance of local spread (e.g., by wind), and therefore overestimating the efficiency of a “firebreak” pre-emptive cull policy [20, 23]. ANTICIPATORY GOVERNANCE, TRADING ZONES, MORAL IMAGINATION AND AN ADAPTIVE MANAGEMENT APPROACH TO DISASTER MANAGEMENT The UK authorities were poorly prepared for the unprecedented scale or characteristics of the 2001 FMD outbreak. Contingency planning was based on 7

Brian Wynn describes a similar case, where scientists did not consult Cumbrian sheep farmers about radiation levels after Chernobyl. The scientific models predicted a negligible effect from fallout, but considered only the amount of rainfall, not where it would collect at different locations in the fells. Initially, the scientists said there would be no problem, but after six weeks, they recommended a ban on sale of the sheep, which was imposed. The farmers felt that the government ban and remediation plan ignored their knowledge and included silly recommendations like having the sheep eat straw until the grass was no longer radioactive [26]. A trading zone between farmers and scientists, perhaps mediated by someone like a veterinarian who had interactional expertise, would have produced a better plan for dealing with the radioactive fallout.

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the likelihood of there being a maximum of ten FMD cases at any one time, while the 2001 outbreak involved over fifty cases in the opening days of the epidemic [27]. Furthermore there are 7 strains of FMD, and control policies aimed at one particular strain may not be applicable to any other strain. Anticipatory governance with an adaptive, elastic response framework and trading zones between vets and modelers prior to the outbreak would have led to more effective outbreak control. A lack of interactional expertise and moral imagination led to the failure to develop trading zones between vets and modelers. Cross training of vets in ecology, epidemiology and modeling, and modelers in the context of disease control at the farm to farm level, would have ensured there were experts who could understand the languages and practices of both communities. Moral imagination is important to ensure that each community would be willing to make the effort to see the problem the way the other community did. In the concluding remarks of their retrospective analysis of the crisis, Haydon et al., (2004) [23] state: “What is now required is a marriage of the value of the expert advice so staunchly defended by the veterinary practice, with the benefits of modern surveillance, diagnostic and data management technologies and the analytical capabilities of theoretical modelling at the strategic level.” If the modelers from Imperial College had been put in a trading zone with veterinary specialists and farmers, then the models could have been used adaptively. For example, the initial assumption about airborne spread could have been quickly compared with data from the field about the actual pattern of spread. Did the data support the model? If not, modify the model and see how the pattern of spread changes. Adaptive management provides a framework for continuous monitoring and evaluation to determine the best strategy to control disease outbreaks. In adaptive management, triangulation between multiple methodologies, notably field, laboratory and computer-based approaches should be taken [28]. Models are more appropriate as tools to model hypothetical scenarios that can be tested in the field and used in conjunction with veterinary expertise to provide guidance in decision-making [20, 25, 29]. Because of the rarity of FMD epidemics and the multiple strains of virus that present a risk, the

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model could not have been tested before the outbreak, and should have been subjected to ongoing collection and analysis of data during the epidemic. Anticipatory governance would involve building the capacity to handle epidemics by having interactional experts already engaged in trading zones across disciplinary and expertise cultures. Here moral imagination comes into play. Those making the policy prescriptions have to see the impact as if they were a farmer who has breed lines preserved for generations that will be destroyed. Seeing the other’s perspective does not prevent decisive action, but it does mean the action is more likely to be carried out with mutual understanding. Moral imagination is hard in the middle of a crisis: that is why the capacity must be built in advance. In this case, that would have meant contacts with farmers who could quickly help test assumptions from the models as they worked to prevent contagion. The farmers themselves would ideally be interactional experts, at least in the methods used by scientists. CONCLUSION In this chapter, we have improved on an existing framework for managing sociotechnical-natural systems, using two case studies: 1.

Preservation of biodiversity by reducing bycatch.

2.

Learning lessons from the way in which the UK handled foot and mouth disease that will lead to more effective response to future epidemics.

These two cases center on an issue that is critical to the survival of any civilization: maintenance of a long-term, reliable food supply without reducing the resilience of the environmental system that sustains life. In the case of bycatch, the answer is to manage fishing so only the species used for food are caught, preserving biodiversity. A corollary is that one must not overharvest the desired species, and bycatch reduction may help with this by developing management techniques that can be used to selectively harvest some species while leaving others to grow back. Such management will depend on the ability to mobilize stakeholders capable of understanding each other and willing to work

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together in trading zones that share not only a common goal, but can develop shared means to attaining the goal. The case of the UK response to foot and mouth disease illustrates what happens when trading zones are not mobilized and expertise is not shared. One community dominated the response—in this case, quantitative modelers—and their model did not fit the reality as the veterinarians and farmers saw it. Here trading zones facilitated by interactional experts might have helped—but how could they be mobilized in time? The answer is to put in place anticipatory governance capabilities. The stakeholder communities need to be given incentives and opportunities to maintain trading zones in the absence of a crisis, preserving the kind of common language and shared trust that makes them able to mobilize rapidly in a crisis. Moral imagination is a critical element: the different stakeholders need to be able to ‘walk in each others shoes’ regularly. When a civilization fragments into groups that do not care about and cannot communicate with one another, it collapses. This is just as true for our emerging global civilization and shared sustainability crisis as for civilizations of the past. CONFLICT OF INTEREST The author(s) confirm that this chapter content has no conflicts of interest. ACKNOWLEDGEMENT Declared none. REFERENCES [1]

[2]

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Cabezas, H., Pawlowski, C. W., Mayer, A. L., & Hoagland, N. T. Sustainability: Ecological, social, economic and systems perspectives. Clean Technology and Environmental Policy, 167-180, 2003. Collins, H., Evans, R., & Gorman, M. Trading zones and interactional expertise. Studies in History and Philosophy of Science, 39(1), 657-666, 2007. Sherif, C. W., Orientation in social psychology. New York: Harper & Row, 1976. Gregory, R., Ohlson, D., & Arvai, J. Deconstructing adaptive management: criteria for applications to environmental management. Ecological Applications, 16(6), 2411-2425, 2006. Mayer, A. L., Pawlowski, C. W., & Cabezas, H, Estimating sustainability of a simple human society and its associated ecosystem using resilience and fisher information. Proceedings of the International Congress on Modelling and Simulation, MODSIM 2003 Conference, 2003. Barben, D., Fisher, E., Selin, C., & Guston, D. H., Anticipatory governance of nanotechnology: Foresight, engagement, and integration. In Edward J. Hackett, O.A., Michael Lynch and Judy Wajcman (Ed)., The handbook of science and technology studies (pp. 979-1000). Cambridge, MA: MIT Press, 2007. Folke, C., Hahn, T., Olsson, P., & Norberg, J., Adaptive governance of social-ecological systems. Annual Review of Environmental Resources, 30, 441-473, 2005. Jenkins, L. D., Profile and influence of the successful fisher-inventor of marine conservation technology, Conservation & Society. 8, 44-54, 2010. Joseph, J. and J. W. Greenough, International Management of Tuna, Porpoise, and Billfish: Biological, Legal, and Political Aspects. Seattle, University of Washington Press, 1979. NRC, Dolphins and the Tuna Industry. Washington DC, National Academy Press, 1992. Joseph, J., "The tuna-dolphin controversy in the eastern pacific ocean: Biological, economic, and political impacts." Ocean Development and International Law 25(1): 1-30, 1994. Jenkins, L. D., The invention and adoption of conservation technology to successfully reduce bycatch of protected marine species. Nicholas School of the Environment and Earth Sciences. Durham, Duke University. PhD: 652, 2006. Coe, J. M., Holts, D. B., Butler, R. W., The tuna-porpoise problem: NMFS dolphin mortality reduction research, 1970-1981. Marine Fisheries Review 46: 18-33, 1984. Jenkins, L. D., Bycatch: interactional expertise, dolphins and the US tuna fishery. Stud. Hist. Phil. Sci. 38: 698-712, 2007. Hall, M. A., .An ecological view of the tuna-dolphin problem: impacts and tradeoffs, Reviews in Fish Biology and Fisheries 8: 1-34, 1998. Margavio, A. V. and C. J. Forsyth. Caught in the Net: The Conflict between Shrimpers and Conservationists. College Station, Texas A&M University Press, 1996. Kitching, R P, M V Thrusfield, and N M Taylor, Use and abuse of mathematical models: an illustration from the 2001 foot and mouth. Rev. sci. tech. Off. int. Epiz. 25, no. 1: 293-311, 2006. Shirley, M, and S Rushton, Where diseases and networks collide: lessons to be learnt from a study of the 2001 foot-and-mouth disease epidemic. Epidemiology and Infection: 10231032. doi:10.1017/S095026880500453X, 2005. Bickerstaff, K., and P. Simmons,. The right tool for the job? Modeling, spatial relationships, and styles of scientific practice in the UK foot and mouth crisis. Environment and Planning 22: 393-412. doi:10.1068/d344t, 2004.

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Haydon, D.T, R. R Kao, and R. P. Kitching, The UK foot-and-mouth disease outbreak — the aftermath. Nature Reviews: Microbiology 2: 675-680, 2004. Tildesley, M. J, P. R. Bessell, M. J. Keeling, and Mark E J Woolhouse. The role of preemptive culling in the control of foot-and-mouth disease. Proc. R. Soc. B, no. July: 32393248. doi:10.1098/rspb.2009.0427, 2009. Crispin, S. M. Foot-and-mouth disease: the vital need for collaboration as an aid to disease elimination. Veterinary Journal, Mar; 169(2):162-4, 2005. Wynne B., in Irwin, A., & Wynne, B. (Eds) Misunderstanding science?: The public reconstruction of science and technology. Cambridge; New York: Cambridge University Press, 19-46, 1996. Ward, N., and A. Donaldson, Policy framing and learning the lessons from the UK's foot and mouth disease crisis. Environment and Planning 22: 291-306. doi:10.1068/c0209s, 2004. Plowright, R. K., Sokolow, S. H., Gorman, M. E., Daszak, P., & Foley, J. E., Causal inference in disease ecology: Investigating ecological drivers of disease emergence. Frontiers in Ecology and the Environment, 6, 420-429, 2008. Taylor, N. Review of the use of models in informing disease control policy development and adjustment. Reading, UK: University of Reading; 2003.

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CHAPTER 6 On the Matter of Sustainable Water Resources Management W.D. Shuster* Sustainable Environments Branch, ML498; National Risk Management Research Laboratory, Office of Research and Development; United States Environmental Protection Agency, USA Abstract: This chapter attempts to develop the concept of sustainability and make it operational in the realm of water resources management. Water is unique in its primacy among natural resources as an essential component of life itself. Due to its equally unique chemical and physical properties, water carries with it a history of where it has been and its courses in the hydrologic cycle are readily measured or observed. However, the transience of some fluxes of water in the hydrologic cycle is more difficult to predict or manage. From a management perspective, this is problematic as it is often impossible to accumulate all of the necessary freshwater resources in one place by rainfall and runoff alone. Therefore we must manage other parts of the water cycle to account for anthropogenic objectives, and this would include mining groundwater, controlling evaporation in irrigated systems among a host of other tradeoffs that currently depend in great part upon economic factors. We will explore through case studies in Germany and Cincinnati OH how social-equity and environmental objectives must also be considered with the same weight as economic factors. The influence of an expanded and more integrated view of the hydrologic cycle is illustrated through examples and case studies, and provides an introduction to and ideas for the notion that water resources can be more sustainably managed through the recognition of exchangeable social, cultural, natural resource, and technological capitals.

Keywords: Weak-strong sustainability, social capital, storm water management, incentives, urban water cycle, agricultural hydrology, environmental management, wastewater management in Germany, integrated water management. WATER AND SUSTAINABILITY Water is unique in its primacy among natural resources as an essential component of life itself and its underlying processes. In terms of its physical and chemical properties, water is an excellent solvent, which means that water can carry with it a history of its transformations as it moves through ecosystems and its constituent *Address correspondence to W.D. Shuster: 26 W. Martin Luther King Drive; Cincinnati, OH, USA; Tel: 513-569-7244; Fax: 513-569-7677; E-mail: [email protected] Heriberto Cabezas and Urmila Diwekar (Ed) All rights reserved-© 2012 Bentham Science Publishers

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abiotic and biotic processes. The weight and density of water requires relatively high gradients or larger inputs of energy to move this resource around the hydrologic cycle. Fortunately, the large majority of this energy is provided as ecosystem services through climate inputs as precipitation and climate demand for moisture, the plant physiological phenomena of transpiration; energy inputs that drive evaporative processes; transport of water through porous media via capillarity; and gravitational gradients that direct runoff into streamflow and drains free water from soils, stores the remaining water as soil moisture, or percolates downwards as recharge to aquifers. While water will cycle relatively freely in a natural setting, substantial anthropogenic disturbance has led to unique – and not particularly welcome – impacts on the distribution of water at local, regional, and global scales. The reasons for this are multifold and include the laying down of impervious surface as a by-product of urbanization, and anthropogenic activities that contribute to climate change and the consequent redistribution of latent heat and precipitation. These physical factors are joined by policy and governance impacts on water use and distribution that further confound the holistic and effective management of water resources. The matter of how to integrate principles of sustainability into water resources management may be stymied by the lack of practical definitions of sustainability. For the purposes of this work, we cite Bruntland (1987) [1] to provide a working definition of sustainability, which is: “sustainable development is development that meets the needs of the present without compromising the needs of future generations to meet their own needs”. Importantly, this definition incorporates a temporal or intergenerational imperative; brings forward the concept of “need” in that water availability is a requirement for civilization and is a human right; and advances the idea of limitations imposed by presently available technologies, social organization, and capacity of the current resource base to meet present and future needs. This definition also suggests that the social-equity, economic, and environmental pillars of sustainability call for an interdisciplinary approach to research the formidable challenges of defining and expediting just and equitable water resources management over intergenerational time scales (i.e., 50 to 100 years). It is also important to note that the definition is explicit about the objectivity of needs vs. the subjective possibility of “wants”.

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Another challenge to understanding the role of sustainability is to move beyond a definition and illustrate sustainability in a more operative sense. A move toward sustainability in the management of water resources calls for understanding that there are multiple types of capital. We utilize the weak-strong sustainability nomenclature (Ayres et al., 1998) [2] as an organizational frame to illustrate practical attributes of sustainability. This approach has been used as a theoretical framework to illustrate the possible roles of environmental management in effecting a more sustainable use of resources or types of capitals. Among the limited set of social, cultural, technological, and natural resource capitals, we presently see a greater dependence upon technological and natural resource capitals. Yet, due to dependence upon technological and natural resources to maintain ecosystem functions, we may then fail to appreciate the potential for cultural and social capitals and their development as viable substitutes for technical solutions and an unmitigated drawing down limited natural resources. In short, this model of sustainability uses differences in substitutability amongst different capitals as a qualifier for weaker or stronger forms of sustainability. The sole or strict dependence upon any one type or source of capital would qualify as a weaker form of sustainability. Theoretically, weak sustainability is due to the use of or reliance upon one or two capitals with an underutilization of other available or untapped capitals. For example, mining finite groundwater resources is not particularly sustainable. In the absence of freshwater runoff resources or other water sources, the temporal scale of groundwater recharge is inconsistent with the level of frequent and heavy anthropogenic demand in settled areas. The dependence upon a natural resource that occurs in limited quantity (groundwater, fossil fuels) and technological (extraction wells, pumps) capitals to provide drinking water would constitute a system with weaker prospects for sustaining a resource over the long-term. On the other hand, if the less-tangible and non-structural social (awareness and interest in the commonwealth) and cultural (developed through education, a sense of appreciation, or aesthetic perspective) capitals are employed, then there is potential to modulate natural resource use patterns, reduce the emphasis on technologicallyintensive capitals, possibly leading to (at least in theory) substitution of cultural and social capitals for technological and natural resources capitals to yield a stronger

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form of sustainability. The conservation of groundwater and the energy otherwise expended in its extraction and distribution may be made operative by satisfying water demand with other sources (detention of rainfall, conservation through reduction of water quantity consumption, grey water recycling, etc.). All of these approaches can be induced via shifts in individual or collective awareness, perception, and awareness of the citizen’s role in taking on some level of environmental management, and providing benefits to a commonwealth. From this arrangement of substitutability among multiple capitals, there are expanded opportunities for water resources management. In particular, leveraging social and cultural capitals also presents the opportunity to measure or estimate capacity for change in citizen consciousness and perception. Under the current operative paradigm of weak sustainability, there is a near-complete dependence upon technology to suitably transform raw natural resources. Under strong sustainability, there is potential for a shift towards the recognition that different sources of capital may substitute for each other. Likewise, the dependence upon any one water resource may also be considered unsustainable. The reduction of, reuse, and recycling of water resources may be fostered through proactive and opportunistic approaches that may break with traditional notions, and thus the need for testing and changing perceptions is highlighted. However little work has been conducted to determine the feasibility and nature of these substitutions, as there may be a linear or highly non-linear relationship between sources of capital management interventions. This uncertainty must be classified at some level so as not to discourage the creative and innovative use of capital substitutions. One major source of uncertainty in water resources management is due to climate change. There is a great deal of misplaced reliance upon the long-standing assumption of stationarity in the spatial and temporal distributions of rainfall and runoff patterns for planning purposes (Milly et al., 2008; IPCC 2007) [3]. Many years of climate and flood data have been synthesized to quantify the probabilities of storm events of a particular intensity, duration, and frequency; that would lead to a certain flooding event. The statistical basis for these probabilistic assessments assumed constancy in climate conditions. Yet, as the global distribution of thermal energy and rainfall patterns shift due to climate change, the basis for the predictions is altered, and the accuracy of any

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prognosis is thereby altered against a changing reference condition. The adoption or use of expectation values drawn from arguably outdated or incomplete storm analysis for water resources management and associated infrastructure design will ultimately lead to poorly-conceived infrastructure. In order to meet this challenge, the water resources management community must consider the value of long-term or permanent climate and hydrometric monitoring networks to quantify nonstationarity in climate and hydrologic factors and lend support to modeling the stochastic nature of hydrometric variables. The additional purpose of this monitoring network would be to define areas where hydrologic fluxes are in the midst of relatively high spatial and temporal variation. Overall, some attempt must be made to focus on uncertainty in hydrologic fluxes, rather than taking a rote approach with synthetic storms as a tool for prediction and engineering of infrastructure with a service life that will be predictably cut short as the operational life of the infrastructure extends through a changing climate regime. In conversations about the sustainability of water resources, we find that questions of water quantity and water quality drive the dialogue. Water quantity and quality are intertwined as quantity can provide dilution of a water quality issue, but this is not always true, and water quantity is often the “master variable’ in driving ecosystem processes (Konrad and Booth 2005) [4]. Furthermore, the overall management objective is the provision of water in sufficient quantity and of acceptable quality in order to meet the requirements of social, economic, and environmental needs. This requires that water resources are managed so that generations of the future have resources and opportunities - that may differ from what we enjoy today - though fully-supporting. This is not arguable in light of the fact that water is a necessity. A full treatment and assessment of research and concepts on the broad issue of sustainable water resources management would fill several volumes, and so instead the presentation is constrained to a survey of the major water resource impacts of the Anthropocene (Harrington 2009; Crutzen and Stoermer 2000) [5, 6], which is the present age in geologic time for which humans have had an overwhelming influence on earth (and hydrologic) processes (Chesworth 2002) [7]. One example of a newly identified hydrologic flux is that of virtual water, which is the volume of water that is moved around the world as exports of fruits, vegetables, bottled water, among other products. These products

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are then imported by others countries that may or may not need to offset their own water deficits or water stress. Without the current, vast network of global commerce and the resources that it consumes, the geographic scale of virtual water would be more localized, thereby increasing the potential that local water resources would stay local. SITUATIONAL OR GEOGRAPHIC ASPECTS OF SUSTAINABILITY IN WATER RESOURCES MANAGEMENT A primary problem in sustainable water resources management is brought about by the spatial and temporal distribution of rainfall and runoff. Global citizens increasingly find that there is not enough runoff or freshwater in the right place, at the right time, and for long enough to satisfy human needs for drinking water, food production and preparation, fiber production and processing, and shelter. As it is, the evaluation of a water scarcity index suggests that nearly one-third of the world’s population resides in areas that are stricken with water-stress (Oki and Kanae 2006) [8]. Beyond these basic needs for water resources, we recognize several relatively recent entries into the portfolio of demand upon extant water resources. There is the concept of environmental flow standards for rivers and streams, which will require that fewer withdrawals are made from surface waters. The maintenance of ecological flows support sensitive aquatic ecosystems and place less demand on groundwater resources that often provide for base flow in surface water systems. In-stream (or ecological) flow requirements specify flows that are of a magnitude and duration so that habitat and geomorphic processes are maintained and sustained in river and stream ecosystems. Therefore, land use affects water management and the need for transport of resources and products. A watershed basis for water resources planning and management is arguably a prerequisite for sustainability for any type of supply to meet a realistic demand. The formal definition of a watershed (or, catchment, drainage area, etc.). is that a unit volume of rain falling on a specific land area that is delineated by topography will drain as runoff or shallow subsurface flow to a certain, predictable point in the landscape, which is the outlet. Since groundwater resources can underlay and extend across a large geographic area, special consideration must be given to this resource which is practically always a shared commodity. A watershed accounting

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or inventory of available water resources will give a realistic impression of carrying capacity for that particular watershed. Currently, geopolitical delineations are presently used to plan and prioritize management of water resources. These imperatives are generally set by political or social agendas, which have little to do with the actual water resources setting. This also imposes an ongoing, reactionary approach to water resources management wherein populations use water resources through inter-basin transfers, using water from aquifers that underlay many municipalities or cities, among other water resource uses that consume (or waste) more water than is locally available. Land within a watershed can be intentionally managed in such a way to optimize rainfall routing, climate demand for moisture, streamflow, and groundwater recharge. A shift in land use or intensity of management will typically limit the continuity of the hydrologic cycle at local and regional scales, depending on the extent and intensity of disturbance. Yet, no where is this more evident that in the case of urban development, as cities support large human populations, and stand to increase due to movement of population from rural areas into cities (Bettencourt et al., 2007) [9]. This can lead to an undesired positive feedback by the continual expansion of urban centers, which increases pressure on water resources management and distribution (Kaufman and Marsh 1997; Scalenghe and Marsan 2009) [10, 11]. The overall negative impacts of urbanization processes on the hydrologic cycle are multi-fold. Due to the replacement of vegetation with impervious surface, urban hydrologic cycles are characterized by few losses (e.g., interception, infiltration) and proportionally greater contribution of direct runoff production from hard surfaces such as roads, rooftops, sidewalks, and other impermeable surfaces. By limiting infiltration opportunities, urban areas often have low potential for recharge of shallow water tables or replenishment of groundwater resources. These impacts are multiplied and further extended across landscapes by the indirect impacts of construction and development. These impacts include the phenomena of compaction and soil sealing in formerly pervious areas. Through a increase in the bulk density of compressible soils, compaction leads to less potential storage capacity for soil moisture and can otherwise limit the range of uses that soil might be capable of. Soil sealing occurs during excavation in wet soil conditions where the surface is smeared, destroying pore continuity; or where soil surfaces are

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not protected and splash erosion distributes silt and clay material to fill in surfaceconnected pores that would otherwise transmit and distribute water. In each instance of soil sealing, soil horizons and land mass are hydraulically-disconnected from each other, limiting the reach of redistribution and recharge processes, which are foundational to the hydrologic cycle. As Gandy (2004) [12] points out, the urban landscape has become more fragmented and diffuse (making effective management more difficult), and decreased investment in public needs and aging urban infrastructure; and in particular, leading to the marketing and provision of vital resources like water through private rather than municipal authorities. In urban areas, centralized wastewater and stormwater collection systems bypass the majority of the full hydrologic cycle. Wastewaters are conducted through extensive pipe networks from the source (i.e., residences) to waste water treatment plants. Even modest rainfall depths over large developed areas can create rapid and voluminous runoff of excess rainfall into sewer systems. These wastewater systems tend to become quickly overburdened as development sprawls and the resulting volumes of runoff are far beyond the original design capacity specifications. The inability of wastewater systems to completely prevent the intermixing of septic and storm flows, and the introduction of pathogen-rich wastewaters to bathing, recreational, or drinking water inlets, increases risks and costs of treatment (Gaffield et al., 2003) [13]. Save for the emergent issue of ageing infrastructure and unintended leaks into or out of wastewater and drinking water pipe systems, there are few losses, natural or otherwise, in the urban hydrologic cycle that would otherwise service requirements of the hydrologic cycle. Urbanization is a major factor in hydrology, and it is driven most often by economic priorities. Although many governments are committed to reductions in land development through preservation or restoration, their efforts do not address the key issue of how development occurs in the first place. Sprawl has its roots in the relatively high degree of local power to determine local development patterns (due to tax base and other fiduciary factors). Localities compete for workers and commerce by offering residential, commercial, and industrial infrastructure; and new development is still seen as a panacea for sluggish economic conditions (even though this typically and automatically leads to more surplus). Technological

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capital and brute-force infusions of financial capital have stayed society’s perception of environmental damages due to sprawl development. Due to the accumulative impacts of sprawl, there is no longer such an easy exchange among financial capitals and the consumption of irreplaceable natural resource capitals for building. There is presently better articulation of environmental impacts that are a direct consequence of sprawl (American Rivers, Natural Resources Defense Council, Smart Growth America; 2002) [29]. What is required is a new interplay between the environmental (in this case hydrologic or water balance) impacts of sprawl and linking these to social consciousness and governance so that it can eventually permeate all levels of the social and economic hierarchy that governs and otherwise makes value judgments about development (Haase and Nuissl 2007) [30]. In operative terms, this would require that local planning, wastewater, building and commerce, parks, and engineering departments would be conversant in the common goal of fully-utilizing present built space; seriously debating the pros and cons of new development (especially that laying outside of the more dense urban core areas); and all towards transforming a runoff-based urban hydrologic cycle to one that is more infiltration and evapotranspiration-based. The prospects of land conservation and management in the form of green infrastructure (i.e., contiguous land mass used for intentional provision and enhancement of ecosystem services), may affect water balance and should be considered as an avenue for sustainable water resources management. The work of Haase and Nuissl (2007) [30] is particularly important in this respect, as it points out the phenomena of urban sprawl (using the city of Leipzig as an example) has led to the present problem of development that was made in the name of progress, yet due to numerous social (reduced birth rates and increased interest in urban living) and economic (decline in industry, migration of worker populations) the landscape that is the product of this development is degraded and underutilized. The increased impervious surface from development produces that much more runoff volume, limits infiltration and evapotranspiration processes, all the while increasing flood risk and damage to terrestrial and aquatic ecosystems. A possible response is development banking would require analysis of present development to possibly preclude new and unnecessary development. As shown by many workers (in a review by Shuster et al., 2005; Hasse and Nuissl 2007) [31, 30], the hydrologic impacts of impervious surface

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begin around 10 percent fractional impervious area. As proportional impervious areas reaches 20 percent, any pervious areas (that might infiltrate some runoff) are overwhelmed by direct runoff produced by impervious surface. The only benefit of increased development on poorly-infiltrative soils or land cover is that pre- and postdevelopment hydrology are similar, and while not as much is lost, not much is gained either. Planners should focus on the development of sites that exhibit high-runoff and yet have good soil mechanical qualities rather than sites with soils that are arable, useful for detention of water, among other attributes. The re-connection of runoff and infiltration components of the hydrologic cycle can help to break traditional patterns of wastewater management and changing its definition through the use of stormwater runoff as an managed input to freshwater cycles. Not all problems with water resources management are a consequence of urbanization, as water resource management issues arise from any situation that involves the large-scale and intensive use of land, such as agriculture. Agriculture is a major user of groundwater resources for irrigation, and a major producer of wastewater. In terms of wastewater production, some of this is unintentional or due to poor management. A small amount of animal waste, pesticide, or fertilizer can foul surface or groundwater resources, and thereby limit its use to a narrower band of options. Since agriculture manages land and water for maximum productivity, the dense plantings employed tend to maximize transpiration. Due to uniform patterns of soil topography that result from tillage, runoff production is also uniform, and runoff management is important to control the detachment, entrainment, and transport of soil particles through various types of erosion processes. Where the area of land available for crop production has soils that retain more water, drainage is often enhanced with subsurface tile drain systems. These systems offer the benefit of draining excess water from surface soils, which permits a workable soil over the greater part of the growing season. The tiles act as large-scale macropores that shunt excess water to outlets that typically pour into gullies or ditches. Unfortunately, not all of this water is of high quality, as agrochemicals and nutrients comprise some proportion of the solutes in drainage water. Therefore, tile drainage presents not only a challenge to quantity, but also quality management. Agricultural management may also be improved towards the maintenance of environmental quality through a PPP that serves to facilitate a (compensated)

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regulatory lever on non-point source polluters in a drinking water source area. For example, some German water authorities will compensate farmers for loss in productivity due to protection of source waters. Contracts between the farmer and water authority are binding, and this is because funds are exchanged between the water authority (usually the private side) and the farmer. Since these contracts are typically handled by the private arm of the water authority, the records are not kept at the state level, and it is therefore difficult to perform summary statistics on the breadth or extent of the program. Some farmers moved into low-input approaches that can reduce the need for nutrients and pesticides, while others practice organic or biodynamic farming in an attempt to retain nutrients for primary production than leaching or other losses. The role of monitoring for effectiveness of nutrient management and pesticide application is key. An informal “windshield survey” by the author indicate that farm holdings on the west side of the country are generally smaller in size, and perhaps more amenable to the more management-intensive (e.g., human inputs of knowledge and time) organic matter and pest management options offered by biodynamic practices, which may limit the availability of nutrients to water resources. At the largest geographic scales, there is an implied and natural attribute of sustainability in water resources due to a favorable geographic location within a particularly rich water resources setting. For example, the unique water resources setting in Bavaria (Germany) has ensured the long-term provision and availability of water resources through proximity to the Alps mountain range. Precipitation as snow melt and rainfall in the northern range of Alps is run off or filtered through the geologic formations. A considerable amount of energy may be saved due to natural gravitational gradients that are large enough in magnitude to move water volume to the north, where it is then collected and processed to service consumptive needs of large population centers such as Munich. As wastewater is produced, it is treated and released into the Isar River and networks of wetlands at the outskirts of the city. Therefore, relatively little additional energy is required to move water to where it is needed. The treated waste water that is introduced as streamflow may also supply sufficient subsurface flow such that hydrologic connectivity amongst river, stream, and wetland networks is maintained. In the face of climate change, these favorable patterns in the interaction between climate

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and demand for water resources may also change. In the present climate pattern, Bavaria has more rainfall in the south than the north, so that gravity gradients move in the right direction as regarding the movement of water resources to drier areas. Yet, it is predicted that climate change will lead to more frequent and prolonged dry periods in the north. In spite of these forecasted shifts in precipitation, the trend in the Bavarian state still favors the distribution of a stable water supply. Alternately, there are numerous instances where spatial or geographic factors are not amenable to sustaining water resource availability. The starkest examples are found in the Southwestern US, locations in the Middle East, among other semi-arid or arid regions that are vulnerable to extended periods of water stress. Presently, there are some technologies (e.g., desalinization, unsustainable groundwater overdrafts, harvest of scant rainfall), or other approaches to collecting water for further management in arid areas that are prone to drought. Local knowledge, expectations, and cultural norms are important to consider in any situation where natural resources are to be managed, and critically important in locales where resources are not easily accessible or in short supply. Local interests eventually take primacy and priority over outside interests and so water resource managers are well-advised to account for local practices to form a practical basis for local water resources management. There are several examples of how local cultural norms and expectations can color the perception of resource availability. A relatively recent example involves the work of Zaretsky et al., (see: http://www.villagelifeoutreach.org/sitepages/PROJECTS.html#roche_health_center) who have designed a health clinic to be located in the Tarime area of Tanzania. The total rainfall volume is moderate at just over one meter per year, yet monsoons deliver this annual volume in only two periods. This set of circumstances leads to the need for storage, conservation, and minimizing draw from groundwater drawn from a low-yielding well, which not incidentally itself requires some amount of energy to operate. In order to create a water resources system that minimizes consumption and “closes the loop” on as many water cycles as possible, Zaretsky et al., have taken a careful look at water use, and how this might be minimized overall. The use of flush toilets that use more than one litre of water per flush and do not separate waste type can rapidly draw down available water reserves, and create volumes of wastewater

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that are difficult to process under the constraints of limited energy and other inputs needed for wastewater treatment. In particular, flush-toilets would be a major draw on limited available or mined water volume, and so their use (and level of cultural acceptance) will be compared to separating composting toilets. It is apparent that flush toilets will likely be installed to satisfy the cultural and aesthetic requirements of professional staff such as doctors and nurses, who are used to amenities such as flush toilets. However, a well-maintained demonstration of a composting toilet system would also be installed with sufficient capacity to be cleaned out only twice per year; this work would be done by a contractor several hours away. The reason that a remotely-located third-party (and thereby expensive) would need to execute cleanout work is that the vast majority of people in this area are not amenable to this work, even though a properly-maintained composting system yields only an odorless, stabilized product, rich in nutrients and organic matter. Furthermore, wastewater lagoons are a body of water in an area with few surface water resources and thereby considered to be a source of water for human consumption and bathing, so it is also a cultural issue that confounds the safe storage of wastewater. This dilemma shows how strongly that cultural and otherwise aesthetic perceptions and values can systemically affect water use patterns in the most waterstressed areas of the world. It is also an illustration of an opportunity wherein social and cultural capitals may be developed and otherwise induced to change perceptions and behaviors to adapt to appropriate technologies for systems with high risk of water-stress. The objective is to induce a change in perception such that habits and behaviors are accepting of the composting toilet approach, which would yield great savings in terms of water use, energy use, and logistical issues with wastewater treatment. The substitution of financial capitals for water use is unique in this situation, as the amount of water volume needed to operate flush toilets is not sustainable compared to raising funds to finance periodic (i.e., six-month intervals) clean-outs of composting toilets. It is expected that some time may be necessary for the populations to become acclimated to a different approach to human waste management, though acceptance may spread through society, on the basis of a shift in culture. Therefore, the staggered deployment of several types of toilet types would be a critical experiment that would potentially illuminate how to mediate the relationship between water consumption and waste management.

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The use of groundwater for agricultural irrigation is a prime example of water resources management that is not sustainable. The Ogallala aquifer runs northward from Texas to South Dakota and stands as an illustration to the impacts of mining water for the irrigation of water-intensive crops. It is significant that nearly thirty-percent of the irrigated land in the United States overlies the Ogallala aquifer system, providing the same percentage of ground water used for irrigation, and an even higher percentage (~82) depend upon this aquifer for drinking water. Yet, recharge of the aquifer with fresh water occurs at a slow rate and falling water table levels indicate that groundwater removal is far in excess of replacement of the paleowater that currently fills the aquifer. There is an interactive water resource, land management impact on the rate at which recharge water enters the aquifer. Since US High Plains region is semi-arid, evapotranspiration constitutes a significant loss. In many areas, soil horizons above the aquifer are underlain by a hard pan layer (i.e., caliche) that effectively prevents percolation or deep recharge. An alternate avenue for recharge to the aquifer is the playa lakes, which collect and permit subsurface exchange with the aquifer. Much like the draining and destruction of wetlands in the prairie (and beyond) areas of the US, the reclamation of playa lakes for agriculture decreases the available recharge area, and thus also reduces potential for recharge. Points where aquifers are contaminated show that intensive agriculture not only pumps irrigation water from aquifers, but these managed agroecosystems also promote circumstances that allow the percolation of agricultural chemicals downwards where the quality and utility of the groundwater resource is compromised. Agriculture is acknowledged as a large part of the non-point source pollution problem, which includes sedimentation, infiltration and percolation of unwanted chemicals into groundwater reserves, nutrient or sediment mobilization to sensitive ecosystems, among other impacts on the environment. Throughout the USA and European Union, source water protection zones have been implemented to restrict land use, land management, and emergency response protocols (e.g., a chemical spill in the source water drainage area) so as to acknowledge the negative impacts that agriculture or other industrial activity can have on the quantities and quality of waters in local and regional hydrologic cycles. In these limited use source water protection areas, farming is allowed in

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only specific parts of these zones, usually far enough away so as not to interact with the reach of confined or unconfined aquifers. Within public-private partnerships (PPP) that operate some of the German water authorities, there is a sort of agricultural extension that work with farmers to adapt or learn new management practices or put land into set-aside, and all for the benefit of maintaining source water quality. Set-aside of land mass for natural hydrologic processes can provide many ecosystem services such as water filtration, which may yield other positive impacts on water quality. OPTIONS FOR SUSTAINABLE WATER RESOURCES MANAGEMENT Following a brief survey of some considerable water resource management challenges, we are left with the task of exploring innovative methods for the management of water resources for sustainability. Since it is unlikely that new water supplies will be discovered, one approach to dealing with shortfalls in runoff volume for consumptive use is to maximize the productivity of each unit volume of water consumed. Indeed, the continued, uncontrolled and open-ended use of any water resource will lead to the exhaustion and irreversible loss of a critical ecosystem component. As pointed out by Jury and Vaux (2005) [14], a smaller-scale, decentralized water resources management system would be community-based and draw upon a blend of traditional and non-traditional technologies to adapt local water resources to the particular needs of the community. These systems would be designed on the basis of reduction, reuse, and recycling of water, but also account for the availability and reliability of local energy sources (Ashbolt and Goodrich 2009) [15]. For example, Jury and Vaux (2005) [14] illustrate the importance of manual treadle pumps, economical diesel power combined with innovative sanitation technologies to create closed water cycles. Work done by Otterpohl et al., (1999) [16] has shown the possibilities for the utility of not only grey water (water volume that comes from showers, sinks, washing machines, among other domestic uses), but also processing and purification of brown (i.e., a solution of faecal matter) or yellow (i.e., a urine solution) waters in linking wastewater with the larger hydrologic cycle. Blackwater is a combined solution of faeces and urine which need to be separated into distinct brown and yellow waste streams for proper, economical, and effective treatment and recycling of organic matter.

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Wastewater treatment can be separated into different streams for targeted treatment that yields products with qualities consistent with end-use requirements. For example, drinking-water quality products could be used solely for human and livestock consumption or for industrial processes that require potable water. Based on risk assessments, water with other chemical and biological qualities could be used to fulfill volume requirements for fire fighting, and serve other retail, commercial, and industrial requirements. These are not new ideas and are just waiting for shifts in social and cultural perspectives, and for increased deployment of retrofit technologies. Much as in service areas like Berlin Germany, there is a slightly-different mindset required to accept the process of bank filtration as the basis for the production of drinking water. The Berlin drinking water system relies upon natural services and processes to achieve filtration, yielding a putatively pure product that is consumed by the populace. In the United States, where drinking water quality volume is used for domestic use as well as fire fighting and a myriad of other uses, the scale of disinfection and distribution treatment is comparatively massive. However, many German cities occasionally use chloramine (a common disinfectant used in the production of drinking-quality water) to disinfect and otherwise finish treatment of drinking water as needed; this risk-based approach is employed most notably in the population centers of North Rhine Westphalia and the Bavarian city of Munich. The level of investment in public wastewater treatment (since effluent quantity and quality impacts all other future uses) and frequency of disinfection (which may indicate wastewater system integrity and a population’s tolerance for risk) are both potential metrics for determining the degree of engagement that a society has with the environmental aspects of wastewater management. In order to manage the urban hydrologic cycle, planners and managers working in urban areas (Villareal et al., 2004; Semadeni-Davies 2008) [17, 18] require an innovative approach to compensate for the lack of pervious, infiltrative areas in urban centers. Although stormwater management has traditionally stayed in the purview of municipal engineers, Keeley (2007) [19] showed that there is ample opportunity for municipal planners to foster a closed hydrologic cycle in the city. The detention of relatively-clean stormwater volume at the parcel level is based on a decentralized approach to relieving the municipal burden to manage large volumes

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of storm flow and instead externalizing management of tractable volumes of water by individual parcels. Keeley (2007) [19] goes on to illustrate how parcel-scale assessments of runoff potential can support public policy with incentives; inform larger-scale watershed planning with parcels and building blocks; and start to actively engage the public regarding connections between their perceptions and land use choices, and the nature of local-to-regional environmental problems. Gandy (2004) [12] sees that the imperative for change in water management will re-link the above- and below-ground aspects of the urban experience. For too long, the network of below-ground pipes and infrastructure has delivered energy and other services to support the human experience and buttress public health. Though with the breakdown of these civic infrastructure due to age and neglect, there is little left be done than expose these for what they are, and start to manage these by restoring more natural water cycles. This “daylighting” or bringing to the surface of water resources management thereby requires broader thinking and perception of the potential for adding detention capacity at the surface and in the vadose zone to take pressure off of ageing infrastructure. This may include the use of vacant lots and managed or restored urban soils to catch and infiltrate precipitaiton and provide entry points into a new and potentially more natural urban hydrologic cycle. Other productive shifts in land uses may include the conversion of previously urban land into contiguous green infrastructures based on agriculture or silvicultural land uses (Lovell and Johnston 2009) [20]. Concepts in low-impact development and green infrastructure are articulated in practical terms as: green roofs, biodetention, bioswales, pervious pavement among other technologies that contribute intentional hydrologic losses (e.g., interception, abstraction, infiltration, evapotranspiration) and strike connections amongst pools in the hydrologic cycle. The implementation of these “green” practices - that create hydrologic losses where there were none - can be facilitated by the use of economic incentives (Parikh et al., 2005) [21], that also may foster coordination around participatory stormwater management. It has been shown that citizens can play a meaningful role in environmental management and that this participation can be stimulated through the inducement of social and cultural capitals (Shuster et al., 2009) [22]. In experimental work centered on a small urbanized watershed (Shepherd Creek, Cincinnati OH, USA) we addressed the questions of whether

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economic incentives could effectively spur public acceptance of on-lot, retrofit stormwater detention practices (rain gardens, rain barrels), and whether this approach would lead to a decrease in stormwater runoff quantity and subsequent improvement in other metrics of environmental quality. Voluntary reverse auctions were held in 2007 and 2008. For this type of auction, homeowners who chose to participate in the program bid the amount that they would require to have a rain garden or rain barrels placed on their property. The reverse auction was the clear choice for working within existing property rights issues. This approach ensured that all residents had a voluntary opportunity to participate in this environmental management program (Parikh et al., 2005) [21]. Two types of retrofit management practices were offered in the auction, which were: up to four 284 L (75 gallon) rain barrels, a single 16 m2 rain garden. A landowner could freely bid on either practice or both. A landscape-level metric for projected effectiveness accounted for both cost and environmental effectiveness in an effort to rank bids, so as to minimize costs and maximize effectiveness. The overall approach to this research was multidisciplinary and inclusive. It involved governance at the neighborhood, city, county, state, and federal levels to ensure awareness and coordination amongst each level of environmental management authority. Our process engaged engineers, lawyers, and public administration officials, but ultimately partnered with individual citizen landowners on the matter of stormwater management. In an example of diverse and decentralized stormwater management that substitutes for conveyance or storage in-pipe, we relied upon volume detention in rain barrels; or storage in rain gardens (Konrad and Booth; 2005) [4]. The managed plant-soil ecosystem of the rain garden offers storage in pore-spaces, deep drainage, and evapotranspiration to create further and desirable hydrologic losses. Since rain gardens are a living system with some inherent capacity to adapt to changing soil-climate conditions, the soil ecosystem in the rain garden may exhibit the ecosystem attribute of resilience. Over the course of successive auctions in 2007 and 2008, 83 rain gardens and 176 rain barrels were installed on more than one-fifth of the 350 residential properties in the watershed. The auctions resulted in an estimated 360 m3 increase in detention capacity for excess stormwater runoff over pre-implementation conditions. This was achieved through the disconnection of impervious surface from storm outlets and the

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reconnection of pervious patches across the watershed. Interestingly, about 50% of auction participants entered zero dollar bids (for both rain barrels and rain gardens), which indicated that the subsidized SWMPs (and their maintenance for three years) were a sufficient incentive. Given the positive response that was received, this type of program may lower the cost of this type of stormwater management implementation program, since cash payouts were not required for the sub-group of participants with zero-dollar bids. The mean non-zero bids that resulted in cash payout were relatively small as mean bids for rain gardens and rain barrels were (US)$ 70.12 and 36.44, respectively (Thurston et al., 2008) [23]. According to our records and observations, not one rain barrel or rain garden had malfunctioned as of January 2010. Although stormwater management practices were distributed relatively evenly throughout the watershed, one micro-catchment area (29 houses; Fig. 1) exhibited an unusually high landowner participation rate of ~50%. In this neighborhood, the choices that the landowners made were 4, 3, 8, and 14 participants adopting rain barrels only, rain garden only, both rain barrels and a rain garden, and nonparticipation, respectively. Given the positive impact of rain barrels and rain gardens on neighborhood-level stormwater volume detention capacity, modeling exercises suggested that detention is implemented densely enough in this area to effect decreased stormwater quantity (relative to pre-management conditions) at the neighborhood stormwater outfall (Shuster et al., 2012) [26]. One interpretation of this outcome is that intentional engagement of citizens can develop and provide a source of social and cultural capital towards environmental management of stormwater. This is a practical example of managing towards strong sustainability (from a less substitutable, weaker sustainability setting) such that social and cultural capital may effectively substitute (see Ayres et al., 1998) [2] for the presently dominant technological capital of “grey” infrastructure, which is traditional, underground infrastructure that emphasizes pipes and other capital infrastructure for stormwater management (shunting storm flow to streams). Yet, it is important to explicitly define the roles of different capitals in this process, and so in this context, social capital is defined as an action that promotes social cohesion and personal investment in the community. In the setting of the neighborhood, we see (Fig. 1) that the southwestern area of the neighborhood has houses next to each

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other that all participated in the auction. Although no follow-up surveys were conducted, it is possible that collusion amongst homeowners represented social capital towards reclaiming some investment in local environmental management. Accordingly, the neighborhood is small enough that informal contacts amongst honeowners may have also played a

Figure 1: This microcatchment drainage lies tributary to the Shepherd Creek near Cincinnati OH (USA) and this map illustrates the location of stormwater management practices (SWMPs) where the neighborhood highlighted with a dashed ellipse has an unusually high density of SWMPs. The adoption of stormwater management practices was unusually high (~50%) compared to other neighborhoods that were offered the same incentive (from Shuster et al., 2010, submitted to Novatech 2010) [26].

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role in increasing the number of stormwater management practices in this outfall drainage area. On the other hand, forms of cultural capital may have been realized through the auction and for those who adopted a practice, the process and formality of licensure (i.e., a license was issued for each stormwater practice installed). The auction process employed educational materials to introduce potential citizenmanagers to the problems and opportunities presented by stormwater runoff. In this project, stormwater management practices transferred a tangible economic capital to the landowner through a formal recognition of ownership. Some additional disconnected impervious area has been gained through homeowners routing rain barrel overflow to their proximate rain garden or lawn. This may be an example of social (detention of stormwater to lift a community burden) and cultural (application of education and knowledge) capitals working in tandem to mitigate against excess, uncontrolled stormwater runoff. Our approach to engagement with citizens may illustrate the value of auctions for placement of stormwater management practices onto private property. Furthermore, the use of economic incentives may offer a coordinating mechanism for participatory water resources management. The Shepherd Creek experience may stand as an example of how institutional and objectified forms of cultural capital can be induced in a Midwestern US neighborhood setting. The preceding example of local, decentralized management of storm flows shows that there is substantial room for creating or reinstating linkages among facets of the hydrologic cycle. This requires a great deal of intention and willingness to deploy non-traditional methods (e.g., soil pore space instead of pipes to infiltrate storm flows that can cause combined sewer overflows) at larger scales of implementation. Depending upon the extant water resources network, soils, and proximity of available land for adjunctive drainage, some urban centers can better utilize or reintegrate the hydrologic cycle than others. In addition, this multidisciplinary approach to watershed management offers a specific example of stormwater management that should be readily transferable to other residential watersheds. Yet, the effectiveness of implementation is reliant upon maximizing citizen participation to get a high level of detention capacity, and consistent monitoring to better understand the ramifications of relying on green infrastructure (in whole or in part) for stormwater management. These basic studies of implementation strategies for

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green infrastructure could be built upon, and eventually inform the adaptation of incentives to appeal to municipal governments. At larger scales of management, there is also a need for managing another outcome of storm flows, which is urban flooding. As Aronica and Lanza (2005) [24] showed with a modeling approach, urban catchments have a great deal of efficient (impervious) drainage surfaces that are arranged in a highly connected and heterogeneous micro-topography. Simulations confirmed that the combination of these landscape-level attributes can lead to significant local flooding due to high flow velocities, short travel times, leading to highly concentrated flow in relatively small areas. One vision is for governance to be incentivized towards the restoration of impervious into infiltrative surface at progressively larger physical scales. The larger scales of management would involve treatment of transportation surfaces and mass transformation of city vacant land into contiguous green infrastructures. Regional flood risk reduction may be fostered and likewise multiplied by creating losses in the urban hydrologic cycle through detention and redistributing water resources across a more connected urban hydrologic cycle. A decentralized, scalable approach to manage flood risk in urban residential in the port city of Hamburg Germany was addressed by Pasche et al., (2008) [25], who present a system that uses cascading flood compartments that scale flood protection to the severity of the flooding event that is encountered. The system is therefore an adaptive response where the homeowners are given site specific tools to respond to floods, which may effectively change their perception of risk and vulnerability to flood damage. The tools range from friction-fit water barriers that seal off doorways and windows, to large portable dikes to close off larger extents of neighborhood-scale flood-prone areas. ECONOMIC ASPECTS OF WATER RESOURCES MANAGEMENT IN THE CONTEXT OF SUSTAINABILITY Much like the arid southwest US where rainfall is rarer than in other more humid areas of the world, Spaniards often can be heard to say that northern Europeans get their water for free due to a generally wetter climate in the north. Furthermore, climate change has led to shifting patterns in precipitation and runoff, adding a new dynamic to the economy of freshwater resources. As part of the triple bottom line of

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sustainability, economic tools can interlock with environmental and social-equity objectives to play a role in the conservation and management of water resources. The work of Arrow et al., (1995) [27] illustrates that the utility and importance of sending economic signals can show in no uncertain terms that water resources are scarce. Under the constraint of an exhaustible water resource, Krause et al., (2003) [28] used survey results and econometric methods to show that consumer response to scarcity was characterized by heterogeneous demand among different consumer groups (students, working adults, and retirees). The specific demand profiles from each consumer were detailed through survey and econometric work. This information was used to develop a system of pricing that supports incentives that send appropriate signals for conservation of water, but are also an optimal approach to meeting both consumer expectations and cost constraints (Krause et al., 2003) [28]. The internalization of costs related to water services is another approach to changing patterns of water use. Under EU law (Water Directive Framework; WDF), all water services must cover costs of production, treatment, and other functions. In the German experience, the combined impact of full cost coverage and climate change raises the hope that German citizens will tend towards conservation of water. Although drinking water prices in Germany are set at the state-level, the local water authorities can formulate their own pricing structure. Differences in water pricing along political and geographic boundaries may be due to variability in how states and local authorities incorporate depreciation on capital projects; differences in interest rates (private concerns cannot obtain lower government interest rates); sole usage-distribution rights (as a result of a noncompete clause); the relative ease or difficulty of extracting and transporting water resources; and costs of paying farmers to farm a certain way to promote protection of source water from agricultural pollution. Water costs in Germany typically range between 0.6 – 5 Euros per cubic meter; and average annual cost of water services is about 340 Euros per year. In the more industrialized NRW, the total (drinking water, storm water, waste water) water costs are higher than the national average of 2-13 euros per cubic meter of water, as most of this tariff goes towards the higher costs for wastewater treatment. Berlin water is relatively expensive, and consumption for the average Berliner is around 100 l per day, which is about one-third that of North American consumptive

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use. One outcome of the expense for water is that there is some motivation for detaining and storing rainfall. Therefore, Berliners tend to collect as much rain water in rain barrels as they can, and subtract this volume from municipal water billing. Stormflow detention can reduce the amount of expensive municipal water that is required for domestic use, and a credit is issued for this detention capacity to count against stormwater tariffs. Yet, there are several reasons why infiltration or evapotranspiration methods for stormwater abatement are not employed. Parcel size is one factor. Parcels are not particularly large in dense urban areas like Berlin, and therefore rain gardens are not practical. A resident would lose value overall if stormwater were simply collected, temporarily stored, and then transpired from, for example, a roof garden. This interplay between pricing and water use limits the amount of water that be routed back into the local hydrologic cycle. Instead, detained water is used either for watering plants, to supply toilets or domestic laundry needs among other practical uses. However, the total amount of water that is stored by Berliners is indeed somewhat nominal, though apparently valuable enough for Berliners to pursue with intent. Low rates of consumption also means that plenty of water volume sits in the Berlin drinking water system, and this is one reason why some water delivery and treatment systems are moving to a demand-side approach, rather than a supplyside approach where potential demand is met at any cost. At the same time, there is pressure on municipalities to minimize infrastructure and define a thrifty and practical supply volume. In keeping with a shift to demand-side provision of water to Berliners, the overall size and capacity of the Berlin water system is shrinking. Pipe length is decreased opportunistically due to pipe route consolidation during routine operation and maintenance activities. In another related approach, system capacity is optimized and decreased by decommissioning infrastructure where population is less dense and consumption is thereby less. Programs in the UK designate that for areas with less than 8m piping per capita, the water authority would provide no further repair or maintenance, which would involve use of on-site wastewater treatment systems. One challenge to demand-side management is that the provision of the larger volumes of water that are traditionally needed in the morning and evening would require some degree of additional storage for holding to buffer supplies during periods of high demand. The Berlin area also uses its existing wastewater

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infrastructure (surplus sewer system capacity) on an episodic or periodic basis to detain stormwater runoff. Shifting to a demand-side water supply system may also increase capacity for limited stormwater storage volume, further linking parts of the urban hydrologic cycle. The economic and social-equity relationships between governance and private commerce can also affect the manner in which water is cycled and managed in the environment. In order to gather capital for infrastructure programs and foster administrative efficiencies typically associated with (though not unique to) privatelyrun business concerns, public-private partnerships (PPPs) have gained in popularity. This is likely in part due to the fact that water services (drinking, wastewater, purification, treatment, etc.). fulfill a basic human demand for reliable and quality water services. The proliferation of PPPs may also be partly due to the need to help the water provision and treatment industry to become reliable and financially stable, and remedy poorly-maintained water service infrastructure. The use of PPPs has been popular in the post-reunification rehabilitation of highly degraded wastewater and drinking water processing facilities within former German Democratic Republic conurbations. As the wastewater services became more stable, the financial and investment attributes of this public service attracted interest from the private sector, and likely due to the prospects for profit or at least break-even in investments. Interestingly, Berlin is a privately-held water management system, restricting financing arrangements to commercial interest rates that are typically higher than that available to public or governments. The resultant increased costs were therefore borne by the user community. However, some cost efficiencies may be observed from the Berlin water resources setting. The River Spree winds through Berlin, and extraction wells draw water in through the banks from the river, and filtered through the sandy riverbank soils. It is estimated that Berlin drinking water supplies are comprised from 30% groundwater and 70% bank-filtered streamflow (personal comm., 2009, Joerg Simon; CEO Berlinerwasserbetriebe). The majority of water quality improvement due to bank filtration has been said to take place within the first 10m of the riparian zone. This pumped water is next turned into drinking water with little treatment. The groundwater has low nutrient status, and reportedly little carbon. The lack of nutrients or substrate for water pathogens, and this may be one reason why there is no need for chlorination in the treatment process. Wastewater is treated at a more-or-less traditional wastewater treatment plant (WWTP). The effluent from the WWTP is then

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deposited to upstream areas of the Spree R. where it contributes to streamflow and is thereby recycled as an input to drinking water wells once again. Table 1: This table is adapted from Ashbolt and Goodrich (2008) [32] wherein key dimensions of water resources management are presented as traditional perspectives, then countered by propositions that may contribute to a more sustainable arrangement of resources and methods to obtain, treat, and distribute water resources to users – all on the basis of different and realistic standards for quality and quantity. Dimension of management

Traditional approach or perspective

A proposition for sustainability

Demand and supply

Supply-side grows an infrastructure that fully services any demand

Demand-side approach consistent with resource limitations

Quality

Treat all wastewater inflows to a drinking water standard

Treat water volume to standards that fit specific purposes

Scale

Centralized, dependent upon economies of scale

Decentralized to watershed level, diseconomies of scale

Cycling of water resources

Single pass through a treatment system

Opportunity for reuse, reclamation, recycling across entire enterprise

Dominant infrastructure

Grey (concrete, metal construction, piped bypass, centralized drainage)

Grey/Green: mimic ecosystem processes to treat water, mix grey and green infrastructures in a complementary fashion

Diversity in methods

Standardized, limits on mixture of capitals and system complexity

Allow diverse technologies and capitals to respond to local and watershed -level circumstances

Integration of infrastructure

Wastewater is separated into different streams for treatment with potential for sewer overflows built-in to the system

Reuse wastewater (especially grey water) to greatest extent possible to reduce or eliminate discharge or need for treatment

Integration of institutions

Responsibilities and budgets for water resources management are split up and kept separate among many different departments or agencies that do not typically interact

All phases or water resources management are coordinated within a centralized authority with interdisciplinary capacities

Public participation

Outcome of decisions made in isolation are presented to the public after-the-fact

Engagement of public starts early at the concept stage, maintained as a collaboration with the objective of mutualism throughout the life of the project

Stormwater management

Nuisance flows are controlled to reduce flood risk, sewer overflows tolerated

Resource as input to hydrologic cycle or detained for later use

Human faecal/urine wastes

Nuisance, public health risk

Resource - recycle water, extract nutrients and energy from biomass

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CONCLUSIONS This book chapter has employed several different interpretations of sustainability and attempted to push beyond interpretation into possibilities of practice. The Brundtland definition and its emphasis on intergenerational needs is general, but makes an essential point – that without equity of resource distribution across generations, there is little to plan for. The triple-bottom line approach provides potential to see beyond market-driven culture that precariously balances on the leg of economics only. Addition of environmental and social-equity as equally important factors adds two additional legs to sustainability, and to provide a more solid grounding. The weak-strong theory of sustainability with its illumination of resource limitations and the potential exchangeability of capitals thereby provide a framework for thinking about sustainability in an actionable context. As the water resources management community move towards sustainability there are some avenues for moving towards a more cyclic approach that embraces reduction, reuse, and recycling. As in Table 1, aspects of water resources management are broken down into a scale hierarchy. The power of tradition and history can influence how we perceive the present water resources setting compared to a different time in the past where abundance of resources and expectations were different. The world needs a wider range of water products that are “fit-for-purpose”, which requires a more flexible sense of which applications require, for example potable water. The author hopes that this chapter provides some introduction and ideas for the notion that water resources can be more sustainably managed through the recognition of exchangeable capitals and their relationships to different fluxes in the hydrologic cycle. CONFLICT OF INTEREST The author(s) confirm that this chapter content has no conflicts of interest. ACKNOWLEDGEMENTS A great deal of content and inspiration for this work was gathered by the author over the course of time spent in Germany as a 2009 McCloy Environmental Affairs Fellow, sponsored by the American Council on Germany (www.acgusa.org).

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CHAPTER 7 Sustainable Infrastructure and Alternatives for Urban Growth Arka Pandit1,2, Hyunju Jeong1,2, John C. Crittenden1,2,*, Steven P. French3 and Ming Xu4, Ke Li5 1

Department of Civil and Environmental Engineering, Georgia Institute of Technology, Atlanta, Georgia, USA; 2Brook Byers Institute for Sustainable Systems, Georgia Institute of Technology, Atlanta, Georgia, USA; 3Center for Geographic Information Systems, Georgia Institute of Technology, Atlanta, Georgia, USA; 4School of Natural Resources and Environment, University of Michigan, Ann Arbor, Michigan, USA and 5Department of Biological and Agricultural Engineering, University of Georgia, Athens, Georgia, USA Abstract: With the global urban population becoming 7.0 billion by 2050, there will be a huge demand of the provision of basic infrastructure to this population. With the growing concerns over climate change and energy /resource scarcity, there is a need of paradigm shift from the ‘Romanesque’ idea of infrastructure provision to a sustainable and resilient urban infrastructure which should be designed, constructed and operated within the means of nature. The goal of a sustainable and resilient urban infrastructure is not only to provide the infrastructure amenities but also to develop the socioeconomic attributes of the urban system. In order to attain this goal, the interconnection between the individual infrastructure components and their inter-relation with the socioeconomic attributes needs to be understood. Based on this understanding, many Low Impact Development alternatives for urban infrastructure, including but not limited to stormwater management, can be assessed designed and applied to attain this goal.

Keywords: Best Management Practice (BMP), ‘Big-pipe concept’, Bluebelt, Combined Heat and Power (CHP), Compact Communities, Daylighting, Decentralized Infrastructure, District heating and cooling, Energy infrastructure, Freshwater stress, Global population, Green Landscaping, Greenhouse Gas (GHG) emission, Greywater heat recovery, Indigenous Landscape, Infrastructure, Interdependence, Land-use, Light-Emitting Diode (LED) lighting, Low-flow fixtures, Low-Impact Development (LID), On-site wastewater treatment, Pervious Pavements, Plug-in Hybrid Electric Vehicles (PHEVs), Rainwater harvesting, *Address correspondence to John C. Crittenden: Department of Civil and Environmental Engineering, Georgia Institute of Technology, Atlanta, Georgia, USA; TN: (404) 894-5676; E-mail: [email protected] Heriberto Cabezas and Urmila Diwekar (Eds) All rights reserved-© 2012 Bentham Science Publishers

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Resilient/Resiliency, ‘Romanesque’ idea, Socio-economic environment, Stormwater Management, Sustainable/Sustainability, Transportation infrastructure, Urban infrastructure, UrbanSim, Urine separation, Wastewater infrastructure, WaterEnergy Nexus, Water infrastructure, What-If, Window Design. BACKGROUND Historically, majority of the infrastructure was built based on cheap and nonrenewable fossil fuels. However, with an ever increasing global population coupled with increasing emission of green house gases (GHG), provision of sustainable infrastructure is of utmost importance. To cater to the service requirements of nearly 7.0 billion urban population by 2050, the urban infrastructure can be expected to grow two-fold over the next 3 to 4 decades [1], [2]. Providing the global population with access to safe drinking water can be regarded as a critical challenge. According to UN Water Statistics, about 894 million people worldwide (or more than 1 in six people) do not have access to the basic freshwater requirement of 20-50 liters per day for drinking, cooking and cleaning [3]. In addition, it has been predicted that by 2025, close to 2 billion people will be living in areas of absolute water scarcity and about two-thirds of the world population would be under water stress, attributed to population growth and climate change Fig. 1 [3].

Figure 1: Worldwide Freshwater Stress [4].

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In addition to the scarcity of freshwater resources and rising demand of community infrastructure, the global GHG emissions have also been projected to increase mostly attributed to the dependence on fossil fuels (Fig. 2). The increased GHG emissions already have pronounced effects on global warming and climate change. To avoid the most catastrophic disasters the dependence of fossil fuels must be radically reduced and more sustainable alternatives for infrastructure development must be pursued. To achieve the goal of sustainable development the interconnections among infrastructure components and the interrelation between the natural and socio-economic environments need to be understood. In this context, these interconnections are described and sustainable alternatives and relevant analytical tools are discussed in this chapter.

Figure 2: Global Carbon dioxide emissions [5].

COMPREHENSIVE SYSTEMS

NETWORK

OF

URBAN

INFRASTRUCTURE

The goal of sustainable and resilient infrastructure development is to provide integrity of natural environment, a healthy population and a robust economy. The linked environmental and socio-economic aspects should be understood in order to design more sustainable and economically viable infrastructure (Fig. 3). Supplying utility and mobility to the urban area improves the socio-economic environment including tax revenue, quality of life, job creation, population and so on [4]. Essentially, the development of water and energy infrastructure increases

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the efficacy of water and energy use in an urban area while development of transportation infrastructure provides mobility to residential and commercial areas. These amenities attract more businesses to the area creating more jobs and enhancing the overall quality of life. This in turn increases the tax base of the area form the creation of jobs and alleviation of the quality of life attracts more people in the area increasing the property value. Hence the tax-base of the area increases, a part of which goes back to infrastructure development and the cycle continues. However, current infrastructure development has not paid heed to these interconnections. The historic developments considered individual infrastructure components to be independent of one another and were built on the ‘Romanesque’ i.e., the ‘big-pipe concept’, causing irrecoverable damage to the natural environment. A paradigm shift is necessary in conceptualization of urban infrastructure to develop a sustainable infrastructure which is in harmony with natural and socio-economic environments.

Figure 3: Comprehensive Interconnection of the Urban System

More sustainable and resilient development can result when the interdependencies among infrastructures are considered along with the interconnection between infrastructure and natural and socio-economic environments. As shown in Fig. 4, one of the major interdependencies is the energy -water nexus. It requires water to produce energy which may be as a direct input as in hydropower or indirectly for

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cooling purposes as in thermoelectric power. On the other hand, energy is required for treatment and conveyance of water. The U.S. statistics show that on an average 2000 kWh/MGal energy is consumed for the treatment and supply of water. In addition, it should be noted that conveyance of water requires 80% of that energy and 20% is required for treatment [5]. On the other hand, in 2005, 3.7 billion gallon water per day was consumed and 146.6 billion gallon was withdrawn per day for thermoelectric energy generation in the U.S. [6]. The weighted average evaporative consumption for power generation over all energy sectors is around 2.0 Gal/kWh for the U.S.[6] The water demand for energy production is anticipated to increase most significantly among all water consumption sectors [7]. Hence, uncertain water resources are more likely to be detrimental to energy production in future.

Figure 4: Interdependence of different Infrastructure Components

For example, in the summer of 2003, the temperature in France soared resulting in very low water levels in its rivers and there was not enough to provide cooling water for its nuclear reactors. Thus, the total electricity generation decreased while the demand rose due to increased use of air conditioning necessitating France to cut its power exports in half to cover for the difference [8].

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Another example of such interdependency can be drawn between land use and infrastructure development. Land use is a primary factor to decide the infrastructure location and scale, the network for water and energy distribution, and transportation routes. Infrastructure development also determines regional development and land use. For example, a commercial area is likely to grow around a train station [9]. The development of dense urban areas require a complex distribution system involving water pipelines and electricity cables, as well as significant amount of water and energy to account for the loss in supply; but at the same time dense urban areas are too compact to provide enough space for building a large scale infrastructure for water and energy production. Thus, the decentralized or distributed energy and water production may be more beneficial as the corresponding alternatives are scattered and small-scaled infrastructures which no longer depend on complex distribution systems. Especially, when we consider the fact that 80% of energy consumption in providing water to the consumers is for its distribution from a centralized treatment plant. Accordingly, decentralized water resource development such as rainwater harvesting, may provide significant energy savings. This can be very important even in arid places. For example, it has been estimated that around 75% of the water demand of residential homes and businesses for Tucson, AZ could be met using rainwater harvesting, provided all the rainfall could be harvested [10]. Though it is improbable to harvest 100% of the rainwater, it definitely demonstrates the capacity of rainwater harvesting to meet water demand. The interrelation between stormwater management and creation of green space is an important connection and its connection to the socio-economic environment is illustrated. Low Impact Development (LID) strategies for stormwater management include bioretention facilities, rain gardens, vegetated rooftops, rain barrels, and permeable pavements. These strategies control and reduce the run-off volume thus reducing the volume of wastewater in the central treatment facility where combined sewer and storm water collection systems are used. As a consequence, the possibility of combined sewer overflows would significantly reduce thus reducing its impacts on receiving water bodies. It would also result in reduced energy demand, lower GHG emissions and lower cost due to the diminished footprint of the treatment facility.

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Furthermore, LID techniques would reduce the hydrologic damage of stormwater by preventing channel degradation from erosion and sedimentation and LID can recharge groundwater enriching the local water resource. The reduced downstream flooding and property damage saves on the costs of cleanups and stream bank restoration and protects floodplains by creating park space or wildlife habitat. This can be critical for places where frequent flooding is a major concern. In addition to the stormwater management, green space exerts cooling effect mitigating urban heat island phenomena and saves the energy for cooling in summer. Also, people are willing to pay more for the homes based on LID approaches than conventionally designed subdivisions because they can be near aesthetic amenities such as green spaces. This can bring more revenue for the city from real estate and property taxes and there can be cost savings for the dwellers from cooling effect of green space and reduced stormwater impacts [11]. Creating green space can also attract city dwellers to live in more compact space which save energy and materials. In addition, decentralized energy production is regarded as another attractive alternative for compact living space in the sense that the waste heat could be used for hot water, heating, and air conditioning. Accordingly, the compact living scenario functions as a complimentary aid to decentralized or distributed systems for water and energy when the spent energy, water or material are recovered and reused on site. Some alternatives enabling the compact living infrastructure to work for the energy and water infrastructure include reuse of heat from greywater, small scale wastewater treatment systems for non-potable purposes or generation of methane gas from digestion of organic solid waste. In other words, the compact building design combined with green landscaping can enhance the reliability and resilience of energy and water system compared to the traditional subdivisions depending on centralized drinking water/wastewater system or conventional electric grids. Therefore, exerting the positive effects in the comprehensive interconnection between infrastructures and environmental and socio-economic aspects; the modified land use pattern increases the property value and peoples’ attractiveness. Similar interrelation has also been observed between transportation and energy. It is estimated that on a national basis, the current U.S. electricity infrastructure is

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able to convert 73% of the vehicles to plug-in hybrids [12]. According to the possibility, green house gas emissions and the U.S. oil consumption are reduced by 26% and 50%, respectively. However, for this transition to be viable associated factors like sufficient numbers of recharging stations, etc. need to be considered. Switching from gasoline to electricity as the transportation energy source enhances the energy security for a robust economy because this amount of oil that would be saved equals to the amount of oil imported by U.S. If a distributed energy generation network is developed, not only would it be more beneficial for electrification of transportation by providing a better network of charging stations for plug-in hybrids, it would also have some other significant benefits. It would reduce the energy lost during transmission, currently estimated at 7.2% for U.S., and would provide a greater opportunity of harvesting the excess heat produced during power generation for home heating and/or cooling purposes. In addition, it would also be easier to connect small-scale renewable, especially photovoltaic energy producers to the grid, where they can trade their excess generation, which in turn provides a more sustainable and resilient power distribution network. MORE SUSTAINABLE ALTERNATIVES Several alternatives are suggested to aid in sustainable urban development. Major concepts and actual cases are described based on the network of urban components, i.e., infrastructures and natural and socio-economic environments. Sustainable Water, Wastewater and Stormwater Management Alternatives Green Landscaping Storm water is one of the most mismanaged resources in the world. While the precipitating rain is one of the most pure forms of natural water which could be harvested for potable purposes with minimal treatment, in most cases it gets contaminated by non-point sources like agricultural and urban runoffs and end up combined with municipal wastewater if a combined sewer system is used. If this storm water is collected separately, and treated above ground with one of the numerous LID technologies, instead of being collected it and treated at a centralized facility, there could be multiple benefits. Firstly, it would reduce the volume of wastewater in the central treatment facility thus reducing the footprint of the treatment scheme as well as reduced energy demand. Secondly, it would

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significantly reduce, if not eliminate, the occurrence of combined sewer overflows thus negating the possibility of impairment of surface water bodies due to discharge of raw sewage. The implementation of these LID techniques for stormwater management is also efficient in controlling the runoff volume, which would greatly reduce the downstream flood risk. Last, but not the least, the LID techniques in general, create green space for storm water treatment, which have positive influence on the aesthetics of the area and as a result increase the value of the property resulting in subsequent revenue growth from real estate and property taxes. As an example, for the City of Vancouver, the cost of separating the storm water collection system from the wastewater collection system and its discharge was estimated to be $4 billion. However, when they opted for a decentralized storm water treatment system through LID techniques, they estimated a profit of $ 400 million from the associated increase of real estate values and other related factors. Moreover, this treated water could safely be discharged into the salmon bearing rivers that run through the City [13]. The landscaping project undertaken increases the land value as it addresses multiple aspects of the infrastructure and socioeconomic environment by functioning as roads, mitigating stormwater impact, restoring fish habitat, and enhancing the aesthetics of the area. The watershed management for urban water supply in New York City is another example of green landscaping application. Under the condition of mandating filtration plants to comply with the federal Safe Drinking Water requirements, the City tried to explore more affordable options instead of building the plants with a cost of $8 billion. The City proposed the first upstream/downstream collaboration work to link water quality protection with an economic objective, i.e., preservation of the watershed’s farming economy [14]. The comprehensive program started with acquiring undeveloped land near reservoirs, wetlands, and water courses, or land that is otherwise water-quality sensitive. The areas were opened for hiking, cross-country skiing, and snowshoeing, which enhances the recreational value of the area. Through the partnership with watershed agriculture and forestry programs, Whole Farm Plan (WFP) and Best Management Practices (BMPs) were defined to manage environmental issues without compromising the farm business and reducing pollutants leaving the farm. Sewage systems and

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septic systems were constructed or rehabilitated for wastewater management and LID techniques like bioretention basins were implemented to prevent stormwater from flowing directly into the water body. Instead the flow was rerouted through the restored creeks. Interdependence of long-term watershed protection and enhanced profitability of privately owned agricultural and forestry lands were maintained and enhanced [15]. The protection of water quality and the promotion of land conservation in the watershed region have supported the economic viability of agriculture and forestry. With the numerous advantages of the LID techniques there has also been some concern about creating contaminated sites, which are formed at the bioretention sites due to deposition and entrapment of heavy metals when the runoff passes through the site. These sites may become a source of pollution [16]. However, the contamination is contained in the top soil layer and this can dealt with scheduled maintenance [17]. Creation of Bluebelt for Stormwater Management Bluebelts are large-scale stormwater management systems which incorporate numerous BMPs and preserve the natural drainage corridors, like streams and other wetlands to control the quantity and quality of the runoff. In addition to the efficient management of stormwater, bluebelts provide open spaces for the community and save on the infrastructure capital cost. One classic example of a successful bluebelt implementation is the Staten Island Bluebelt in the City of New York which utilizes around 90 BMPs to manage 16 watersheds over 10, 000 acres. Besides giving a stellar performance in reducing the runoff volume and the pollutant loading, it saved the City over $80 million in capital cost [18]. Areas which were flooded even by ‘minor precipitations’ handled 6.4 inches (16cm) of rain over a period of 24 hours during the combined effect of tropical storm Tammy and subtropical storm 22 on October 8-9, 2005 without any issues [18]. Rainwater Harvesting Rainwater harvesting is another easy-to-implement high-beneficial LID technique for stormwater management and sustainable water management. It is extensively

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practiced in some pockets over the world, notably in New Zealand 11% of the houses has rainwater as their sole source of water supply [19]. Studies suggest that in the U.S. harvesting 70% of the roof runoff of a 2000ft2 roof surface would be able to provide 30-40% of the residences’ water demand, equal to the typical outdoor demand, depending on the climate of the region [20, 21]. Pervious Pavements for Stormwater Management The installation of pervious pavements reduces the volume of stormwater runoff by increasing the net pervious surface area of the location. The stormwater either infiltrates to the ground or is conveyed by gravity-drains to other BMPs depending on site suitability. The pervious pavements are ideally suitable for being used in parking lots, as they have huge paved areas with low-traffic load. Additionally they can be used for side-walks, driveways, and other low-flow streets. Though the main purpose of pervious pavement installation is to control the runoff volume, they can significantly reduce the nutrient (by ~80%) and metal (by ~90%) load owing to the filtration and adsorptive removal by the soil [22]. Pocket Parks Pocket parks are distributed small scale parks used for various purposes in dense urban area. Vacant parking lots or unused spaces can be utilized for the creation of these parks which reduces the construction cost. The parks function as small event spaces, play areas for children, spaces for relaxing or meeting friends or taking lunch breaks, etc. In addition to its direct functions, scattered existence of the parks connects greenways or bike paths facilitating the construction of walkable and bikeable sidewalks. The green patches also provide shelters to wild life in urban areas. Regularly scheduled maintenance should be considered to maximize the functions and lifespan of these parks. One of the most successful implementation cases is “Paley Park”, a midtown pocket park of Manhattan, NY [23]. It provides a resting area with a view of the bustle of Manhattan and its connectivity with street promotes a walkable and bikeable street. While attempting to function just for relaxing with a small area, the neighborhood became busy and popular due to the high density of workers, shoppers and tourists.

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Walkable and Bikeable Street Landscaping Compact and walkable communities can reduce traffic congestion, related air pollution and improve public health by allowing residents to walk to services, shopping, and schools. For this purpose, streets need to be designed to satisfy all users including pedestrians, bicyclists, transit users, and motorists as well as other community requirements like provision for fire engine maneuvering. Above all, efficient routes or networks, and complete and safe trail systems are fundamental to traffic calming, signal and crossing improvements, and bicycling. In addition to the physical improvement, district policies can promote walkable and bikeable conditions. In the case of Roosevelt Middle School and surrounding community of Eugene, Oregon, the regulation for preferred parking lots reduces the vehicular traffic around the school premises making the surrounding streets safer for the children [24].

Figure 5: Hercule's Waterfront District including 26ft residential street with one-side parking and 12ft edge drive with at-grade sidewalk [25]

Large subdivisions with hierarchical networks of wide streets, long blocks and disconnected, dead-end cul de sacs have been transformed to narrower, traditional-style streets for livable, sustainable, and smart growing neighborhoods. Accordingly, street width was diminished due to sidewalk, bicycle lane and onstreet parking conflicts with emergency response. Fire trucks and waste collecting trucks require wide 36 - 40 ft streets to ensure good access while the wide lane increases traffic and threatens residential environment and pedestrian and bicyclist

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safety due to increased vehicular speed. Thus, smart strategies are required to solve the conflict. In case of Waterfront district of Hercules, CA, adjacent walkway was lowered to street level and colored permeable pavers were used to distinguish the normal vehicle surface from walkway in order to provide the required street width for emergency vehicles and providing on-site stormwater drainage without including drainage ponds (Fig. 5) [25]. The practice to remove curbs is not usual for smart growth street design. Indigenous Landscapes for Efficient Agriculture Use of native plants for landscaping have more advantages in addition to the benefits of LID techniques for stormwater management. Native plants require less water, fertilizer and pesticides because the plants are resistant to local conditions. Therefore, green landscaping for stormwater management as discussed previously is possible with less maintenance. Furthermore, the reduction in fertilizer and pesticides use improves the stormwater quality securing indirect water resources. In case of the green roof top of Minneapolis Public Library, Minnesota’s indigenous plants were used to retain stormwater for later irrigation [26]. The alternative increases the longevity of roof and enhances the water quality for irrigation with lesser effort. One can also chose the plants that sequester more carbon dioxide. On-Site Wastewater Treatment and Non-Potable use A conventional wastewater collection system transports wastewater to a centralized treatment facility and a treated effluent is discharged to surface water. Again, potable water processed from surface or ground water in a drinking water plant has been used for both potable and non-potable purposes including irrigation, toilet flushing, cooling tower and fire flow in addition to drinking. However, on-site wastewater treatment coupled with non-potable use system treats wastewater produced in a building onsite and recycle the treated water for non-potable purposes including irrigation, toilet flushing, cooling system, fire flow, building cleaning, etc. Accordingly, the water demand and wastewater generation decrease reducing the load on water and wastewater transport network. The Visionaire tower located in Battery Park City, Manhattan, NY is an example of a high-rise residential condominium (Fig. 6). The residential tower has an in-

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building advanced wastewater treatment system, which has a capacity to recycle 25, 000 gallons per day for toilet flushing, irrigation and air-conditioning cooling system. In addition, a rain harvesting system collects up to 12, 000 gallons per year to irrigate the rooftop garden which occupies 70% of the roof surface. Consequently, the Visionaire consumes 55% less potable water than a residential community of similar size [27].

Figure 6: Visionarie, Battery Park City, Manhattan, NY [28].

An analogous project has been proposed in Malibu, CA. The proposed reclamation plant is designed to handle 15, 000 Gal/d wastewater and the site is expected to produce another 5000 Gal/d of segregated greywater. The project proposes to use subsurface-drip irrigation to irrigate the landscaping of the 2.4 Ha site with the reclaimed water saving about 9000 Gal/d of potable water supply. During the months of low irrigation-water demand, the reclaimed water would be allowed to percolate directly to the groundwater [29]. Low Flow Fixtures for Reduction in Water Demand The use of low flow fixtures is intended to maximize water efficiency within buildings to reduce the burden on municipal water supply and wastewater systems. These fixtures include water closets, urinals, lavatory faucets, showers, and kitchen or break room sinks. According to the City of Calgary’s (Alberta,

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Canada) statistics for water savings, dual flush toilet shows relatively higher saving rate (75%) than clothes washer (50%), low-flow showerhead (35%), faucet aerator (25%), and dishwasher (45%) [30]. .

The frequency of toilet flushes per toilet is often greater in offices than in homes, although the frequency varies widely from facility to facility. When the dual flush toilet is furnished in commercial or office building, the water saving is maximized. As shown in the cases of Molasky Corporate Center (Las Vegas, NV), Empire State Building 32nd Floor (Manhattan, NY), etc., more than 30% reduction of water consumption was achieved mainly through the dual toilets or waterless urinals [31, 32]. If the urine separation alternative is applied to the toilet type, the nutrient recovery is an added benefit. Also, if the greywater is reused for the flushing, the water efficiency will be much higher. However, comprehensive strategies need to be implemented for reduction in water use of residential buildings as the water use is diverse in residences. Considering a significant amount of water is consumed in showers, dishwashers, and washing machines, the use of water efficient appliances can be very beneficial. Hence, the use of energy star appliances will be helpful for water savings. Urine Separation Urine separation is a novel concept in terms of wastewater pollution control and resource conservation. Typically, the urine contributes to 80% of nitrogen, 50% of phosphorus and 90% of potassium of wastewater while it accounts for less than 1% of the total volume of wastewater [33]. About 60~70% of the pharmaceuticals and hormones excreted by human also end up in urine [34]. To treat urine separately is one of the decentralized wastewater treatment alternatives. The process starts with the separation of urine in a urine separation toilet, its storage and transport, followed by subsequent treatment and fertilizer production processes as shown in Fig. 7. The toilet equipped in buildings or households collects the urine at the front of bowl and drains into a separate tank. The urine is collected with its piping system or the storage tank and transportation system for consequent treatment to remove micropollutants like hormones and pharmaceuticals which might affect human health. The ensuing urine is then processed to convert it to a liquid or solid fertilizer (struvite, MgNH4PO4), which can be applied to agricultural land.

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Most wastewater treatment facilities employ tertiary treatment methods to control nutrients, such as nitrification-denitrification and biological phosphorus removal. With the growing concern over micropollutants, more advanced treatment technologies would be needed in the future. In addition, issues such as the high energy requirement for ammonia production from atmospheric nitrogen and the exhaustion of the world reserve of phosphate rocks within the next 50 - 100 years require managing nutrients appropriately. The urine separation alternative is able to reduce the scale of wastewater treatment facility, enhance the quality of the water produced from the facility for reuse, conserve the nutrients and recycle the nutrients for agricultural use [35]. Economic benefits are also attained in replacing the centralized treatment with on-site treatment because of nutrients and micropollutants removal and savings in the water bills [33].

Figure 7: Urine Separation Process

Economic benefits are also attained in the on-site treatment replacing the centralized treatment processes dealing with nutrients and micropollutants removal and a reduction in the water bill results from saving the flush water [33]. In terms of social aspect, the acceptances for toilet (design, hygiene, smell and seating comfort), urine-fertilizers and food cultivated with the fertilizer were reported between 70% and 85% of 2700 respondents of 38 Urine Separation projects in 7 northern and central European countries [36, 37]. Even though 60% of users answered that there needs to be more work to prevent urine pipe blockage, urine separation is expected to contribute to saving energy with emitting

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less pollutant to wastewater, consuming less water, and recovering nutrients for fertilizer as one of the decentralized wastewater alternatives [37, 38]. In addition, preventing urine from flowing into the line is one of methods to reduce sewer pipeline corrosion. The City of Phoenix has investigated that the condition of large diameter through sewer line with flat slopes and high temperatures favoring the conversion of hydrogen sulfide into corrosive sulfuric acid which accelerates corrosion problem [39]. So, the urine separation as one of sulfate source control technologies could be beneficial to enhance sewer pipeline reliability [40]. In the case of Basel-landschaft cantonal library in Liestal, Switzerland full implementation of the separation technology was piloted. Urine from 200, 000 visitors per year is stored in a tank and transported by tanker to a treatment plant [35]. The urine is processed in the treatment plant to concentrate and eliminate hormones and pharmaceuticals and stored in a stable form as a nutrient solution. In addition to the existing treatment processes, further processes including phosphate recovery through the formation of struvite is being studied on a laboratory scale. Sustainable Energy Alternatives Plug-in Hybrid Electric Vehicles (Phevs) The transportation sector is responsible for 33% of the total U.S. CO2 emissions. Gasoline use contributes to 60% of transportation emissions [41]. Alternatives for improving the fuel efficiency and replacing gasoline as the primary fuel in transportation infrastructure were earmarked to mitigate the global GHG emission. In the case of PHEVs, both electric motor and gasoline engine are in use reducing the PHEV’s operating cost per mile and dependence on gasoline. Such vehicles are classified with the number of miles they could theoretically operate in an all-electric mode. The improved engine efficiency of PHEVs and lesser gasoline use reduce the GHG emission to some extent. However, mitigation of carbon emission is not dramatic because currently U.S. produces majority of its electricity from fossil fuels [42]. In addition to fuel economy of transportation infrastructure, PHEVs also play a consequential role in the choice of energy infrastructure planning as described previously. The charging station in the parking lots is better managed with a distributed energy infrastructure. In particular, with on-site electricity generation from renewable sources like solar or wind power, charging station will be a self-

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sustained renewable and distributed power infrastructure requiring no electricity distribution network. They improve infrastructure resiliency, as they are selfsustained, distributed and small-scale in nature. Furthermore, dependency on renewable energy sources increases the energy security and reduces GHG emissions from transportation sector significantly and reduces water use for energy production.

Figure 8: Typical CHP System Configuration at Wastewater Treatment Facilities

Small-Scale Energy Generation with Combined Heat and Power (Chp) Renewable energy resources and decentralized energy infrastructure is the key to unravel the GHG emissions and climate change crisis because these concepts call for alternatives replacing the large-scale, centralized and fossil fuel based energy infrastructure. Wind, solar, hydrogen fuel, biofuel, etc. have been studied and applied to provide electricity and fuel as carbon neutral and renewable resources. The alternative energy resources not only contribute to reduce carbon emission but also secure the future energy demand through multiple renewable energy resources. PHEVs, fuel cells and microturbines have been studied as small-scale energy generation options for the decentralized infrastructure which is applicable to densely populated urban areas. The distributed placements contribute to being resilient against stressors such as transmission system interruptions and reduce expensive overhead transmission lines.

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The environmental impact of the small-scale energy generation options varies with the energy sources selected for operation. In addition to the selection of energy sources, cogeneration is another representative alternative diminishing environmental impacts by enhancing the efficiency of practices. The U.S. Environmental Protection Agency defined cogeneration, i.e., combined heat and power (CHP), as “an efficient, clean, and reliable approach to generate electricity and heat energy from a single fuel source”. When electricity is generated in the system, excess heat is recycled to produce processed heat and additional power. When it is assumed that the electricity generation efficiency is about 33% with the wasted energy being around 66%; the heat recovery alternative may boost energy efficiency up to 66%. In the case of fuel cells, the CHP technology is applicable when the fuel cells supply electricity to buildings through stationary installations. The efficiency test for the fuel cell /CHP systems was performed on two different systems. One system included a small polymer electrolyte membrane (PEM) fuel cell operated on natural gas and the other was a larger phosphoric acid fuel cell (PAFC technology) operated on biogas from landfills and a wastewater treatment plant. The test reports show that the efficiency is 23.8 - 38% for electricity generation alone; which increases up to 93.8% if the heat were recovered [43]. Also, the performance tests on several commercial microturbine/CHP systems reported that the electrical efficiency ranges between 20% and 26% and the combined efficiency increases up to 33-72% when the generated heat is utilized [43]. In the case study of Columbia Boulevard Wastewater Treatment Plant (Portland, OR), the plant is equipped with a 200 kW CHP system (combined with both fuel cell and microturbine) which operates on the anaerobic digester gas to produce electricity and thermal energy for the facility (Fig. 8). The electricity and heat generated from the biogas can be used for the operation of pumps and blowers in the treatment processes, maintenance of optimal digester temperatures, space heating and drying of biosolid [44]. Consequently, the increased energy efficiency and power generation from renewable resources reduced the GHG emission and saved $14, 000 per year in net operating cost for the facility treating an average of 80 to 90 mgd [44].

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Similar approach towards energy and heat recovery has been implemented in King County, WA. The methane (CH4) from the digester gas is converted to hydrogen gas by blowing air and subsequently fed to the molten carbonate fuel cell for power generation with a target capacity of 1MW. The power plant system was supplemented with natural gas as required to achieve the target capacity [45]. Also, the grease from restaurants and other similar establishments can be collected and added to the anaerobic digester after conditioning with digester solids. As demonstrated by the wastewater treatment plant in Millbrae, CA, this process not only increases the digester gas production significantly but also reduces the biosolids production by about 33% [46]. Recovery of Drain Water Heat Water heating is one of the major energy uses of a regular home, accounting for 12.5% of residential energy consumption [47]. Drain-water heat recovery technology can decrease the energy bill by recovering heat from the hot water used in showers, bathtubs, sinks, dishwashers, and clothes washers utilizing heat exchanger equipment. As warm water flows down the drain, incoming cold water gets heat from the exchanger and goes to water heater preheated to some extent. In addition to the energy bill reduction, the technology is beneficial by avoiding the problems caused by the higher temperature of discharged wastewater. Prices for greywater heat recovery systems range from $300 to $500 and the installation is usually not very expensive in new home construction [48]. Paybacks range from 2.5 to 7 years depending on the use of hot water which again is dependent on the local climate [48]. In the case of Folsom Dore apartment project launched by Citizens Housing Corporation (CHC), based in San Francisco, CA, to provide affordable housing; the goal was to provide affordable housing combined with sustainability [49]. The building project used environmental-friendly building materials and incorporated energy saving alternatives including greywater heat recovery. Wastewater heat recovery devices were installed on tub waste drain to capture the heat, which preheats cold water going to the water heater to save on the net energy requirement for the water heater. The highly dense development was developed on a brown field which was for a warehouse and a surface parking lot [50]. As a result, the land value of the area enhanced providing affordable housing and an environmentally and economically sustainable utility to low income Californians.

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The energy required for water heating in the City of Calgary is up to 30% of typical household energy use and corresponds to an annual GHG emission between 1,500 and 2,500 kg [51]. Generally, the heat recovery equipment is installed under tubs to recover heat from shower drain water as 37% of the typical hot water uses (shower, bath dishwater, laundry and faucet) is for shower and 85 90% of the heat remains in the greywater [48]. Cold water coming in at 11oC is preheated up to 25oC by the drain water at 37oC. Considering the shower water to be at 50oC, about 35% energy for water heating is saved and the amount of GHG emission avoided can be measured accordingly. District Heating and Cooling with Energy from Sewage Water Recovering the heat of wastewater produced in a community, Neighborhood Energy Utility (NEU) provides space heating and domestic hot water to the buildings in the community. Considering the system of Southeast False Creek NEU, Vancouver, Canada, the circulating water in the distribution pipe transfers the heat captured from the sewers to the heat transfer station of each building as shown in Fig. 9. In the heat pump, the heat recovered from the wastewater is transferred to the cooler water, which gets warmed up. The energy transfer stations deliver space heat and hot water to occupants through a variety of hydronic heat distribution systems including radiant floor/ceiling systems, radiant baseboards and fan coil forced-air systems. The consumed energy is measured in the stations, which also work for sharing the solar thermal energy generated by the customer buildings. The network between energy infrastructure, water infrastructure and land use is shown in the NEU system. The system is a flexible energy infrastructure which does not only increase the energy security by using renewable energy options but also is beneficial for water infrastructure by eliminating the excess heat from the wastewater. The community energy center was built at a brown field site and the center was designed as an interpretive facility to showcase the innovative use of sustainable technology targeting LEED Gold certification. The system also encourages compact development of the community for the system efficiency and, accordingly, space for green landscaping is created. In addition to the values created by such land use, the radiant hot water heating systems in buildings

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provide customers with a higher level of comfort at a lower energy use as compared to conventional space heating options. The system supplies 70% of the annual energy demand eliminating over 50% of the carbon emissions associated with the heating of buildings excluding the associated social benefits, [52]. The self-funded NEU, will provide return on investment to the City’s tax payers and enable the developers to build the energy efficient building economically [53]. Window Design Fenestration performance is one of the key to energy efficiency and occupant satisfaction. Proper design and placement of windows and skylights can drastically reduce the energy consumption of a building through daylight harvesting and ‘glasshouse’ effect. Proper placement of windows and interior space allocation for occupants aided with light sensors alone can save up to 20% of the lighting energy required for the building, which is the largest energy consuming sector in commercial buildings by and far [54, 55]. In addition, appropriate design and placement of windows considering the solar orientation of the region can trap the solar radiant heat inside the residence in winter and keep out the heat in summer to decrease the energy requirement for space heating and cooling and the associated costs as well. Use of double-glazed, low emissivity, high solar co-efficient glass for the southern windows would be beneficial for the northern climate to provide solar heating. Passive solar heating, overhangs on south windows (to cut off the solar radiation in summer), deciduous trees on west and south (to provide shade in summer and not blocking the sunlight in winter) are a few of the applicable strategies. Incorporation of efficient fenestration design is one of the primary steps towards net-zero buildings. In a recent study done by Lawrence Berkeley National laboratory for the new headquarters of the New York Times, the daily lighting energy savings on an average was estimated to be 75% at 10 ft from the glass to 37% at 25ft, during the test period of February to May [56]. Also, passive-solar-heating, as mentioned earlier can contribute significantly to energy savings. For a residence in Xanthi, Greece equipped with an attached sunspace and thermal storage floor, more than 80% of the auxiliary electrical energy requirement was met [57]. However, it must

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be noted that due to the variation in solar orientation over different places there is no single solution.

Figure 9: False Creek Neighborhood Energy Utility System

Street and Building Orientations and Wind Tunnel Effects Buildings that are oriented with the wide axis across the prevailing wind direction will have a greater impact on ground-level winds than a building oriented with the long axis along the prevailing wind direction. Wind conditions can affect pedestrian safety on sidewalks and in other public areas. Specifically, winds between 8 and 13 mph disturb hair cause clothing to flap, and extend a light flag mounted on a pole while winds between 4 and 8 mph are felt on face. In San Francisco, CA, wind speeds of the street that run east/west are greater on average because the buildings funnel winds. Especially, wide streets bordered by tall buildings are vulnerable to wind funneling. The tunnel effects can be reduced by the presence of tall, bushy trees along streets to force the wind to stay above street level. Streets running north-south tend to have lighter winds because buildings on the west side of the street provide the shelter from prevailing winds. Winding streets that do not follow a grid pattern also tend to have lighter winds at

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pedestrian level, as the building orientations generally keep high winds above the buildings [58]. For the Cardinal Place development in London, which included construction of a 100m tall Portland House adjacent to two new buildings and a covered plaza; the windiness of pedestrian level for 44 locations in the area was assessed through an appropriate wind tunnel test and full-scale meteorological data. Based on the data obtained from the wind tunnel testing, an additional suspended canopy was attached to the building as well as the wind-mitigation screens were optimized for their size and orientation to ensure safe wind speed in the pedestrian level [59, 60]. Led (Light-Emitting Diode) Lighting LEDs have been in use as indicator lights in consumer products since the 1960s. However, more recently LEDs have become for practical for general lighting purposes. There are several advantages of using LEDs. The primary advantage comes as energy savings and related reduction in CO2 emissions. It is estimated that the energy savings is in the range of 50-80% with the use of LEDs [61]. Another significant advantage associated with LEDs is the ability to produce directional lighting, which offers more control on what needs to be lighted (e.g., street) and what needs to be avoided (e.g., night sky), thus reducing the consumption of energy and light pollution. The replacement of 140, 000 city street light fixtures with LED fixtures and remote monitoring system in the City of Los Angeles has a projected post-retrofit annual energy savings of 68.64 GWh and CO2 emission reduction of 40.5 kt CO2/yr [62]. One of the concerns with LEDs has been their upfront cost, but estimates show that there is a significant savings beyond the payback period which is between 4-7 years [61, 62]. Also, LEDs do not have the concern associated with their disposal as is the case with the compact fluorescent lights owing to the mercury vapor contained in the later. DECISION SUPPORT FOR SUSTAINABILITY There are obviously a large number of actions that could be undertaken to make cities more sustainable. To select those options that will produce the greatest impact and to better understand the interactions and potential synergies among individual actions, the city needs to develop a stronger decision support system. Such a system

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would be comprised of a set of interoperable modeling tools that allow decision makers to test and evaluate the sustainability of alternative urban growth scenarios. The system should provide decision supports at a variety of scales, including evaluating metropolitan-scale urban form, testing alternative infrastructure technologies and analyzing project- or neighborhood-level social and environmental impacts. To forecast water demand, water quality, habitat preservation, traffic congestion, air quality and energy consumption for an urban area it is first necessary to forecast the future amount, type and location of population, housing and firms for different time periods. The forecast information is critical for estimating infrastructure and resource needs required for water and sewer systems as well as building and energy systems. Existing population forecasts with better temporal and spatial resolutions are needed to produce reliable estimates of the sustainability effects of alternative actions. A detailed geographic information system (GIS) database is needed that describes existing conditions including address, road type, commercial/residential, sale price, tax value, year built, number of stories, bedrooms, acreages, for all buildings and land parcels in the area. There are a number of land use forecasting models that could be used in this decision support system, including UrbanSim, PECAS, or What If? TM. We have developed several future growth scenarios for the Atlanta metropolitan area using What if?TM, a GIS-based planning support system. Fig. 10 shows the development suitability factors used to model urban development. Alternative scenarios were developed by varying the weighting of the suitability layers including floodplain, highway, sewer service, proximity to employment centers, lake, public lands and parks, and proximity to freeway ramps, Future land uses like residential, employment, open water, undeveloped areas, wetlands, and undevelopable area for 2030 in the case of business as usual and compact growth scenario are shown in Fig. 11. In contrast to What if?TM, the advanced simulation models like UrbanSim and PECAS predict population and employment levels and simulate land market dynamics and behavior of developers, firms and households through manipulating policy, land use, transportation and resource conservation variables. Combining models of specific infrastructure systems (water, wastewater, stormwater, electric power and transportation) will provide a framework that allows decision makers to understand the impacts of alternative land use patterns, of investments in

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different types of infrastructure systems and the impacts of large development projects. Such a holistic modeling framework is useful to visualize alternative land use choices and associated infrastructure solutions. A decision support system that integrated land use with the full set of infrastructure systems can help decision makers evaluate the wide variety of possible actions, approaches and policies to guide toward a more sustainable future.

Figure 10: Sustainability Index for What IfTM ?

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Figure 11: Land Use Prospect of Atlanta in 2030 for Business as Usual and Compact Growth Scenario

SUMMARY The interconnection between infrastructures (land use, water, energy, and transportation) and environmental (resource, air quality, biodiversity, etc.). and socio-economic environments (population, jobs, property value, human health, tax revenue, etc.). should be comprehensively understood for the sustainable infrastructure development.

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The interdependencies among infrastructure themselves are critical concepts to create sustainable alternatives (e.g., “energy water nexus” or “small-scale decentralized or distributed infrastructure design”). Green landscaping and PHEVs may have the most potential to be sustainable alternatives in all aspects like the interdependency of infrastructures and the interconnection between infrastructures and environmental and socio-economic environments. On-site wastewater treatment and reuse system and low flow fixtures can reduce water demand and wastewater generation. Dual flush toilets save a significant amount of water and nutrient recovery is also possible when urine separation is practiced. Water efficient home appliances are also helpful to reduce the water consumption. The concept of heat recovery is applied widely to energy efficient alternatives. In most of the decentralized power infrastructure, the combined heat and power system is being used in pilot scale to enhance the energy production efficiency. On a smaller scale, the heat recovered from grey water can be used to preheat the cold water as it enters the water heater. Also, on a community scale, the captured heat from sewage can be distributed to the buildings for space heating and hot water. Various urban simulation models can predict future land use, socio-economic and conditions and can be used to compare different scenarios in order to select the best case scenario for a sustainable future. CONFLICT OF INTEREST The author(s) confirm that this chapter content has no conflicts of interest. ACKNOWLEDGEMENTS The authors would like to thank the National Science Foundation Grant # 0836046, the Brook Byers Institute for Sustainable Systems at Georgia Institute of Technology and the Hightower Chair and Georgia Research Alliance for funding this research.

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The opinions expressed or statements made herein are solely those of the authors, and do not necessarily reflect the views of funding agencies mentioned above. REFERENCES  [1] [2] [3] [4] [5] [6] [7] [8] [9] [10] [11] [12] [13] [14]

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CHAPTER 8 Engineering Urban Sustainability Ke Li1,*, John Crittenden2, Subhrajit Guhathakurta3 and Harindra Joseph Fernando4 1

Faculty of Engineering, University of Georgia, Athens, Georgia, USA; 2School of Civil and Environmental Engineering, Georgia Institute of Technology, Atlanta, Georgia, USA; 3School of Geographical Sciences & Urban Planning, Arizona State University, Tempe, AZ, USA and 4Department of Civil Engineering and Geological Sciences, University of Notre Dame, Notre Dame, Indiana, USA “With a high degree of confidence we can say that intuitive solutions to the problems of complex social systems will be wrong most of the time. Herein lies most of the explanation for… troubles of urban areas” Jay Forrester, 1969. Abstract: Population growth and increased concentration of economic activities in urban areas pose significant challenges for sustainable development of urban regions. Our ability to make correct choices about future development will depend upon our understanding of the impact of these choices on the future quality of life of the urban inhabitants. Land use, construction material use and construction practices are among the most critical drivers of sustainability, given that where and how we choose to build will affect a range of environmental attributes including energy use, water use, air quality, waste handling, and public health, among others. The challenge is the quantification and visualization of the interdependent future consequences in the regional/urban landscape by independent decisions at the urban area. This requires a meta-model framework that integrates the outcomes resulting from growth and spatial distribution of economic and social activities. A framework that integrates land use, construction practices, transportation and air quality were given as an example.

Keywords: Brown revolution, urbanization, urban systems, sustainability science and engineering, complex adaptive system, complexity, metamodels, system dynamics, sustainability metrics, ecological footprint index, environmental sustainability index, urbansim, material demands, life-cycle assessment, life cycle inventory, life cycle impact assessment (LICA), ground level ozone, ozone precursors, onroad *Address correspondence to Ke Li: Faculty of Engineering, University of Georgia, Athens, Georgia, USA; Tel: 706-542-2201; Fax: 706-542-8806; E-mail: [email protected] Heriberto Cabezas and Urmila Diwekar (Eds) All rights reserved-© 2012 Bentham Science Publishers

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mobile sources, nonroad mobile sources, point source, area sources, biogenic sources. INTRODUCTION The global human population has rapidly increased from 2.5 billion in the early 1950s to 6.2 billion by the end of the century. Associated with this growth was the so-called brown revolution [1], a large population influx into urban centers. Currently, ~49% of the world’s population and ~81% of the US population live in urban areas. This figure is expected to grow to ~61% and ~87%, respectively, by 2030 [2]. Urban areas occupy only 2.8% of the Earth ’s surface, but as centers of concentrated population, they dominate resource consumption and waste generation. Not only the aggregation of people but also aspects of their behavior—consumption of subsistence and luxury goods, travel patterns, and economic activities, which vary according to culture, wealth, and socioeconomic setting—greatly impact the environment. Resource consumption increases not only as population grows but also as per capita demand rises. To orient urban areas toward more sustainable development, tradeoffs between economic prosperity and overall quality of life are critical. As Klaus Toepfer, the U.N. Environment Program chief, stated in 2005, "Cities pull in huge amounts of resources, including water, food, timber, metals and people. They export large amounts of wastes, including household and industrial wastes, wastewater and the gases linked with global warming. So, the battle for sustainable development, for delivering a more environmentally stable, just and healthier world, is going to be largely won and lost in our cities" [3]. The resource consumption associated with urbanization increases not only as population grows but also as per capita demand rises. As urban areas grow and develop, they rely on resources from far beyond their immediate environments. Increased resource consumption yields increased waste production and disposal. In general, waste disposal sites are being pushed further from urban areas, resulting in additional transportation costs, infrastructure wear, and air pollution. Pollution from urban runoff, irrigation leaching and industrial discharges has

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adversely affected groundwater resources and many freshwater bodies, leaving scores of cities and surrounding areas without safe water supplies. Large concentrations of cars and industries cause serious air pollution that has impacts far beyond the urban area. A classic example is acid rain in the Northeast U.S. Unsustainable urban development, consumption patterns and inadequate waste management are the major causes of elevated resource consumption and environmental impacts associated with urban areas.

 

Indirect Linkage

Figure 1: The interaction between decision making on land use, air quality, energy and water use

However, not all aspects of urbanization hinder sustainability. Rather, by hosting half of the population into less than 3% of the land, cities made it possible to keep most of the land for primary production. Studies also show that urban ecosystems are more diverse than rural ecosystems, and can provide important eco-services if properly managed. The aggregation of human activities enables more efficient resources utilization through scaling effects and optimizing resource flow. For example, urban dwellers use fewer resources and generate less waste per capita as compared to those living in rural areas [4]. The versatility of urban activities also increases the feasibility of recycling, reusing, and implementation of industrial ecology. In addition, the large scale, versatile and delicately divided economic structure in urban areas is much more elastic and efficient than the mono-industry economy in rural area. However, past experiences with urbanization have almost always resulted in a cluster of problems. The key for successful urban sustainability engineering is a transformative and sound ontology on the self-

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reflective and adaptive nature of the urban systems as an integrated organism, and, based on this ontology, new engineering methodologies. URBAN SYSTEM AS COMPLEX ADAPTIVE SYSTEM Urban systems function through a complex web of interactive subsystems with disparate space-time scales, including: physical (geological, hydrological, atmospheric), biological (microbial, botanical, zoological), social (economic, demographic, cultural, political, institutional), engineered (water, energy, sewer, telecom, agriculture, transportation) and built (construction, architecture). Many times, it is hard to define the relevant network for analysis because it is a complex interplay of the system and observer. One of the examples of these interactions and cascade of outcomes is shown in Fig. 1. Land use planning drives the development pattern of an urban area, which dictates the location of the built environment and associated infrastructure. Among other factors, the characteristics of the built environment and type of transportation systems, determine urban-rural temperature differences (e.g., urban heat island), which in turn, affects water use, energy demand, micro-climate, air quality (ozone and carbon dioxide concentrations), and the quality of life of urban habitants. The linkage between different networks may vary not only in the ways they affect each other but also in the significance of their interdependence. For example, there is a significant link between transportation and air quality, less so between choices of beverages and air quality (bottled water requires more traffic for delivery, which impacts the air quality). Therefore, the study of urban systems is therefore highly multidisciplinary (even metadisciplinary), requiring a detailed quantitative and qualitative understanding of each subsystem and the nonlinear interactions among them. Clearly, a broad metadiscipline such as Sustainability Science and Engineering is needed to develop more sustainable regional choices [5, 6]. One way to conceptualize urban systems is as a complex adaptive system (CAS) with subsystems constantly evolving in relation to themselves and each other. According to the definition of John Holland [7] “A Complex Adaptive System (CAS) is a dynamic network of many agents (which may represent cells, species, individuals, firms, nations) acting in parallel, constantly acting and reacting to what the other agents are doing.” In the urban system, the agents are stakeholders at all

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levels, the market, as well as the biotic and abiotic environment. The inclusion of human agents differentiates urban systems from other CASs due to the fact that human agents generally plan their behavior according to available information and knowledge about the other subsystem while the agents of a CAS apparently interact in random ways. However, due to the absence of a prior system level understanding, these planning efforts are generally motivated by crisis and are often succeeded by displaced environmental burdens. Therefore, these planning efforts are an adaptive behavior of human beings to the emergent properties of the urban system, rather than a preemptive practice that guides the patterns of the system. CHALLENGES OF SUSTAINABILITY

ENGINEERING

URBAN

SYSTEM

TOWARD

The challenge of studying urban areas for sustainable engineering is complicated by the complexity and unpredictability of the human systems and institutions, especially their self-reflexive character, as well as the objectives of sustainability. Urban systems have the common properties of emergence, co-evolution, and sub optimal. The pattern of an urban system is a result of the random interactions of agents at all sub-systems and is constantly changing. All subsystems exist within the web of other subsystems. They change to adapt to their environment of which they are one part. At any time, the system does not have to be perfect in order for it to thrive within its environment. This raises a conceptual conflict with engineering that is aimed at convergence, optimization and stable functionality. The classical engineering methodology is “divide and conquer” whereas the total of a CAS is more than the sum of the parts. Although debates are ongoing regarding the definition, the concept of CAS also conflicts with sustainability, which is about overall optimization and equity within environmental, economical, and societal conditions over an extended time span. Therefore, as Ottino [8] pointed out, it may be impossible to develop governing laws from which consequences can be derived for the engineering of complex systems. Rather, a framework that describes relationships with caveats could be developed to guide the engineering practices. Modeling of Urban Systems Computer-based urban simulation models date to the 1950s. Traditionally, urban location models have dealt with the exchange of goods, populations, jobs, and

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traffic as aggregate flows between coarsely-represented spatial units for snapshots in time and separated by large gaps. However, that tradition is changing due to the emergence of a new generation of models that treat cities as complex adaptive systems, composed of massive amounts of agent-actors, each represented at their own atomic scale, moving to their own clocks and agendas, and connected and interacting dynamically in economic, environmental, political, and social spaces [9]. This is part of a broader paradigm shift in social and life sciences, facilitated by advances in computing, dataware, policy -oriented science, and systemsintegrative theory.

 

Figure 2: Key engineering systems if integrated urban simulation framework

Limitation of Current Models and Model Integration Measuring and analyzing the dynamic interactions among physical and engineered systems and environmental sustainability are critical to the management and improvement of complex urban systems. However, most urban models have technical barriers that limit their ability to address the environmental problems. Metamodels are needed that not only simulate the aggregate and global level (top-down) dynamics of the urban system in the conventional manner, but also integrate microscale (bottom-up) dynamics. The models should be able to address ways to combine economic, ecological, and urban systems governed by different spatial and temporal processes therefore require a broad disciplinary

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breadth that has been limited into separate institutions. Even with existing models, paradigms for effectively sharing information between models (including metadata standards) have not been established. More important, the mechanisms for controlling behavior between agents in different models, often referred to as metaagents, needed to be fully investigated. For the purpose of engineering urban sustainability, the metamodel need to include at least the key engineered systems showing in the Fig. 2 and their natural and socio-economic environment, which could be treated as exogenous factors. One of the techniques of modeling the dynamic interactions is drawn from the field of system dynamics, which, by definition, is a methodology for studying and managing complex systems and describing its changes over time. The first application of using system dynamics to study the urban system was Forrester’s [10] Urban Dynamics. Three agents: industry, housing and people were simulated to provide guidance for revitalizing aged cities. The results of his model showed that demolition rather than constructing more premium houses is the solution to the stagnation of aged cities. This contradicted to the intuition and general practices of most planners and was questioned widely at the time. It is, as suggested by Forrester, a proof of the fact that our normal way of thinking about complex system is often limited and misguided. Many other modeling techniques have been applied to different types of complex systems and a good review of those can be found online at(http://www.complexsystems.net.au/ wiki/ Comparison_of_Complex _Systems_Approaches_and_Applications). Nonetheless, models that can create first principle while be applied for engineering urban systems are still missing. Data Availability and Quality To ensure the development and validation of the metamodels, high-quality and dynamic data at different spatial and temporal scales is essential to support this metadisciplinary study. Monitoring of urban systems requires the observation of dynamic processes at temporal rates and resolutions that cannot be satisfied with existing sensor networks and remote sensor technologies. The recent development of cyberinfrastructure and sensor technologies enabled system level research on the urban/regional environmental system management. However, the ability to

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develop and integrate a wealth of data with different spatial-temporal scale, from various engineering domains including land use/transportation, air quality/heat islands, material use/life cycle, water resources and quality, and energy planning are still missing. These data are critical for model design and development, parameterization of integrated model execution scenarios, real-time monitoring and validation of coupled modelling scenarios, and support for research, outreach, and decision making. The functional requirements for the data development include the ability to access data from a diversity of static and dynamic sources, manage large amounts of data in a querable form, execute complex workflows coupling diverse and distributed components, and support a scalable range of visualization and presentation applications. Sustainability Metrics In addition to the complexity of urban system, the ambiguity of the sustainability definitions and objectives further complicates urban system engineering. Sustainability metrics (or indicators) are generally used to assess current conditions, clarify objectives, monitor progress, and educate citizens and stakeholders. Many environmental metrics have been proposed for sustainability evaluation at the national and regional levels. However, there are too many metrics which differ in scope, comprehensiveness, source of generation, and aspects of sustainability they address. There is no objective way of selecting/measuring among the metrics, as most of them are normative in application and are not maintained. Moreover, they all have their limitations because of our incomplete knowledge of environmental sustainability and the uncertainty inherent in incomplete data and natural, human and environmental variability. Actually, the results obtained from different metrics may deliver opposite information on the sustainability of an area. For example, a large ecological footprint index, implying reduced sustainability, may correspond to a higher environmental sustainability index, which means more sustainable [11]. This makes the task of selecting and evaluating a set of good metrics important yet challenging. A set of metrics can be assessed by its capability for capturing the significant ecological imbalances of an urban area, its global impact, its effectiveness of creating public awareness of sustainability, and its ability to influence public policy. In general, the public favors metrics created in a public process, but these

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metrics may not measure environmental sustainability with precision because of uneven expertise in the appropriate fields. On the other hand, metrics developed by academics are effective from ecological and policy standpoints but need improvement in terms of public awareness. An integrated framework could be used to test the effectiveness of metrics by projecting far into the future. CASE STUDIES OF INTEGRATED URBAN SYSTEM MODELING A framework was built to study a subset of the engineered systems, shown in Fig. 3, to demonstrate the complexity that needs to be dealt with in an integrated urban systems study. Among all the engineered systems, construction is the largest economic sector in terms of material use. As a start point, the framework investigated the interactions between land use and built environment, construction material demand and consumption, as well as the synergistic local, regional and global environmental impacts.

Material Demands Quantification

Wood material Limestone Sand Clay and shale Gypsum Iron and steel

Traffic Pattern

Cement Aluminium

Construction

Transportation

Life-Cycle Analysis

NOX , CO, VOC, CH4 , NMH Emissions

Operation & Maintenance

Manufacturing

Extraction

Ground-level Ozone Formation

Environmental Impact Quantification

Urban Growth Simulation

Fine aggregate Coarse aggregate

Demolition

Disposal

Figure 3: Coupling of urban development, material flow and environmental Quality (Reproduced with permission from ES&T 2007. Copyright 2007 American Chemical Society).

Approach Future land use development and layout of households and jobs were projected by UrbanSim [12]. The projected household growth enables the quantification of construction material demand. The material demand for residential building

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construction was quantified for ten materials that are commonly used in Phoenix area. A life-cycle impact assessment was performed to quantify energy use and environmental impacts on a local, regional, global scale. Because the material demands are decided by design and construction methods, this framework allows us to compare various construction scenarios. In this case study, the material demands for two designs (a single-story and a two-story residential building) were analyzed. From the future spatial urban growth pattern and the life cycle assessment, a variety of subsequent environmental metrics can be quantified spatially and temporally. For example, the emission inventory of ozone precursors, i.e., NOx, CO, VOC, and SOx, can be calculated and used to project the formation and spatial distribution of ground-level ozone by atmospheric models. Urban Growth Model The urban growth model employed in this study is UrbanSim, a land-use model that has been well-tested operationally across the United States and has become the de facto toolkit in the urban studies community as well as enjoying popular use in several metropolitan planning agencies. UrbanSim is not a single model, but a micro-simulation system consisting of a family of models reflecting key choices of households, business, developer and policy -maker and capturing their interactions in the land development process. Households is modeled as proactive agents seeking to settle or relocate in a citywide property market and opportunity space and their actions are driven by preferences for housing, location, and accessibility to key services and resources. Businesses seek out locations subject to the availability of developed land and structures catering to their needs and the accessibility concerns of their employees. Policy -makers represent agency of government and regulatory bodies and urban planning and management entities, with behaviors that reflect the use of planning and policy tools such as zoning, density restrictions, land use controls, and growth boundaries. The UrbanSim model functions as a massively interactive complex adaptive system. Households, businesses, and developers interact dynamically through supply-demand relationships, mediated by the influence of policy -makers and framed within the backdrop of existing land-use and geographic conditions in the

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city (A more detailed description http://www.urbansim.org/index.shtml).

of

UrbanSim

is

available

at

Most data for running UrbanSim were from Maricopa Association of Governments (MAG), a regional agency that studies land use, transportation, and air-quality monitoring for the 24 municipalities comprising the Phoenix metropolitan area. The preparation of UrbanSim input database includes a collection of procedures which was introduced in our previous study [13]. Residential Construction Material demand An analysis of energy and material flow will provide important information on energy efficiency, material cycling, waste management, and infrastructure in an urban system [14, 15]. In order to quantify the material demand for new house construction, data on construction designs (including housing type, supply of key materials, and construction methods) were acquired with the assistance of local stakeholders. A total of 16 typical development types were identified and the following are materials commonly used in current construction practices: fine aggregate, coarse aggregate, wood material, limestone, sand, clay and shale, gypsum, iron and steel, cement, and aluminum. The actual amount of materials for a single house depends on the detailed design. For the sake of discussion, two designs were chosen to perform the analysis hereafter. Both designs have the median square footage of Phoenix houses, stucco exteriors with concrete tile roofs, and post tension foundations. The two designs use the same standard materials with the same R-values for exterior walls and roof. More design details are provided in Table 1. Table 1: Detail of the two house designs Bedrooms

Baths

Number of Garages

Livable area 1st floor

Livable area 2nd floor

Total Livable areas

Garage

SF

SF

SF

SF

1 story

4

2

2

2142

0

2142

442

2 story

4

2 1/2

2

941

1279

2220

410

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Concrete, metal, wood, insulation, gypsum, windows, paint and roof tile were quantified for each house. Concrete and lumber are transported to Phoenix, Arizona for construction use. Cement for concrete comes primarily from Hermosillo, Mexico which is 303 miles from Phoenix, and there is a secondary source from Victorville, California which is 624 miles from Phoenix. Ready mix concrete is delivered to the construction site within the Phoenix area by truck. Lumber is transported 1, 645 miles, by train, from Vancouver, British Columbia to Phoenix. Lumber is then packaged for delivery to the construction site by truck. The materials used for each design were estimated by identifying all structures and their geometries and quantifying the material in each structure. In summary, the material demands are listed in Table 2 below. Table 2: List of material demand for a single house based on two sample household designs Single Story Two Story

Single Story

Two Story

Concrete 20 MPa: yd3

45.0

25.2

Joint Compound: Tons

0.75

0.83

Nails: Tons

0.37

0.38

Paper Tape: Tons

0.0086

0.0095

Welded Wire Mesh / Ladder Wire: Tons

0.26

0.13

Water Based Latex 88.6 Paint: gallons

149

Rebar, Rod, Light Sections: Tons

0.44

0.32

Stucco over metal mesh: ft2

1520

2620

Galvanized Sheet: Tons

0.49

0.60

Aluminium: Tons

0.61

0.63

9.81

11.0

Vinyl: ft

2

14500

15000

Oriented Strand Board: msf (3/8inch basis) 6.97

7.97

#15 Organic Felt: 100 ft2

17.1

29.3

Batt. Fiberglass: ft2 (1")

55000

39100

#30 Organic Felt: 100 ft2

131

70

6 mil Polyethylene: ft2

7750

5870

EPDM membrane: 581 pounds

599

1/2" Regular Gypsum Board: ft2

5800

5470

Concrete Tile: ft2

6860

3710

2

1520

2620

Low E Tin Argon Filled Glazing: ft2

2080

1970

3

Softwood Lumber, kiln-dried: m

5/8" Regular Gypsum Board: ft

Total material demand for constructing new houses is obtained by simply multiply the number of projected new houses by the demand per house.

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Life Cycle Assessment By their very nature, urban areas can be sustainable only when considered in connection with the region, nation, and world of which they are a part. The everincreasing degree of outsourcing of raw materials and the manufacture of some products on a regional and global level make the LCA necessary for evaluating the impact of the material and energy on urban sustainability. Life Cycle Assessment (LCA) captures the environmental effects of a product through the various stages of its life: production, transport, use, reuse (where applicable), recycling and final disposal [16, 17]. The flow of an LCA proceeds in two steps. First the life cycle inventory (LCI) is estimated, which enumerates masses of material use and emissions in the supply chain delivering the target good or service. The LCI is then input into Life Cycle Impact Assessment (LCIA), which maps physical masses of material use and emissions to their impacts on environmental issues of concern, such as climate change, acidification and human toxicity. Several databases and tools are available to provide life-cycle inventory information of construction materials: the Athena Institute LCI (Life Cycle Impact) database [18], the ecoinvent database [19], U.S. Life-Cycle Inventory Database (http://www.nrel.gov/lci/ database/default.asp), LCA software GaBi (http://www.gabi-software.com), and Pavement Life-Cycle Assessment Tool for Environment and Economic Effects (PaLATE) (http://www.ce.berkeley.edu/ ~horvath/palate.html) etc. Many models have been developed for LCA of construction materials. ATHENA™ Environmental Impact Estimator version 3.0.1 was used to determine the environmental impact summaries for the one story and two story house constructions. ATHENA’s™ focus is on generating environmental impacts summaries for resource extraction, manufacturing, on-site construction, occupancy, maintenance, demolition, recycling, reuse, and disposal. Resource extraction includes harvesting, mining, and quarrying; building access roads; reforestation and reclamation; transportation of raw materials; and the effects on biodiversity, soil stability, water quality, and ecological carrying capacity. Manufacturing includes raw material, energy use; emissions to air, water and land; and is consistent with

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ISO14000 series standards for Life Cycle Assessment. On-site construction determines the amount of waste generated; energy used including transportation and on-site construction; and emissions to air, water, and land. Legend:

a) Life cycle energy consumption by category

b) Life cycle air emissions by category

c) Life cycle emissions to water by category

d) Life cycle resources consumption

Figure 4: Comparison of the overall life cycle impacts of the two designs without considering operating energy. (One story design was used as baseline. The Y-axis shows the percentage compared to the baseline).

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The occupancy and maintenance analysis includes energy use of heating, cooling, lighting; water use; and emissions to air, water, and land. Demolition includes the energy used in demolition and the energy used in transportation due to demolition. Recycling, reuse and disposal include the environmental implications of landfilling or incineration and energy used in recycling and transportation of materials. All these factors can be evaluated as quantified metrics. If the impact from the operational energy consumption is not considered, the life cycle impacts of the two-story house are less than those of one-story house in most categories, as shown in Fig. 4. Without considering the operational energy, the embodied energy of the two-story house (889 GJ) is 6% less than that of the one-story house (946 GJ), and the two-story house generates 10.2% less solid waste, 5.5% less air pollution, and 6.1% less green house gas emissions. The lower environmental impacts of the two-story house are due to its lower construction material demands for the smaller footprint. Life cycle assessment considers different stages of the life cycle of materials. Many of the impacts are external to the urban area, especially in the raw material extraction, manufacturing and transportation stage. Therefore, the above metrics can be viewed as regional or global metrics. However, local metrics can be derived based on the emission from the construction and operating stage. This chapter presents the quantification of ground level ozone concentration, which is one of the most important metrics (another is water availability), in the Phoenix area. It is not the purpose of this chapter to discuss the detail of the atmospheric modeling, but the quantification process is illustrated with references to detailed technological publications. O3 Precursor Emission Inventory As shown in Fig. 3, once the environmental impacts of material flow and the growth pattern are defined, it is possible to quantify many secondary metrics, such as ozone precursor inventory, which can be used for the calculation of ground level ozone concentration. Ozone is linked to at least 15 chemical precursors with the most significant contributors being oxides of nitrogen (NOx), volatile organic compounds (VOCs), carbon monoxide (CO), and oxides of sulfur (SOx), among which VOCs and NOx are the two most important precursors. In the urban

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environment, these precursors are generated from a wide variety of sources, which are often grouped into five categories: on-road mobile sources, non-road mobile sources, point sources, area sources, and biogenic sources. To comply with the EPA regulations, the Maricopa County Environmental Services Department (MCESD) has created a Periodic Ozone Emissions Inventory every three years since 1990. The reported inventories indicated that all five-category sources are important and need to be considered when preparing the precursor inventory. In this study, in addition to these five major sources, the construction of residential buildings was considered as the sixth ozone precursor source. However, the operation and maintenance (O&M) of residential building was not counted as a source, although generating the energy for the O&M emits ozone precursors. The reason is that the electricity generation facilities are point sources and their emissions are counted in the inventory. To avoid double-counting, O&M of residential buildings should not be counted. In addition, the power plants are not located in the City of Phoenix and thus the emissions from the plants are subtracted from the inventory. Onroad Mobile Sources Onroad mobile sources accounted for 26.84% of the total VOC emissions, 50.27% of the total NOx emissions, and 50.53% of the total CO emissions in Maricopa County in 1999 [19]. The Maricopa Association of Governments (MAG) developed these estimates after producing two important sets of data: vehicular emission factors and vehicle miles traveled. The vehicular emission factors were generated by the Environmental Protection Agency’s MOBILE5a model [20], and the vehicle miles traveled estimate was taken from the 1999 Highway Performance Monitoring System (HPMS). The UrbanSim onroad mobile source emissions estimate was produced in a similar manner using a combination of the MOBILE6 modeling program and a population -based projection of vehicle miles traveled. Nonroad Mobile Sources According to the 1999 Periodic Ozone Emissions Inventory, nonroad mobile sources include “aircraft, locomotives, diesel equipment, 4-stroke gasoline equipment, and 2-stroke gasoline equipment”. Aircraft and locomotives may not be relevant with respect to the UrbanSim program so the focus of this category

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would be on diesel equipment that may be used for construction or agricultural purposes and smaller gasoline equipment that may be used for recreational, construction, industrial, commercial, and agricultural activities. The 1999 Inventory contains a summary table that documents the emissions distribution between diesel, 4-stroke gasoline, and 2-stroke gasoline equipment. This data could be decomposed into a per capita or per household number using demographic data provided in the report, which could then be used in conjunction with the UrbanSim program. Point Sources The 1999 Periodic Ozone Emissions Inventory defines a stationary point source as one that “emits ten tons or more per year of VOC, as well as one that emits 100 tons or more per year of VOC, CO, or NOx and is located within 25 miles of the nonattainment area”. The point sources defined above, in addition to previously documented sources emitting more than 5 tons, amounted to 188 total point sources, which include power plants and large manufacturing facilities. Due to the large number and wide variety of point sources, developing emission factors that can be incorporated into the UrbanSim program is somewhat difficult. Two possible methods may be employed: 

Develop emission factors on a per employee basis for the major point source categories using 1999 U.S. Census Bureau employment data.



Predict future emissions by combining the 1999 emissions with growth factors provided by the EPA’s EGAS4.0 model.

The Economic Growth Analysis System (EGAS) was developed by the EPA to facilitate emissions forecasting at the nation, state, and county levels. EGAS4.0 provides county-specific economic growth factors for the different tier codes listed in the point source summary table provided in the 1999 Inventory. Area Sources The 1999 Periodic Ozone Emissions Inventory defines area sources as stationary sources in the nonattainment area that are too small to be considered point sources

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but are too many to be discounted. Area sources include petroleum storage and transport, combustion sources, industrial processes, solvent utilization, and waste disposal sources. Area sources, similar to point sources, pose a significant problem with respect to modeling because they are so varied and numerous. In order to account for this type of emission, it may be beneficial to use a methodology similar to that of the point sources: 

Use a per capita, per household, or per employee basis to develop an emission factor for each area source category.



Use economic growth factors from the EGAS4.0 model to forecast future emissions.

The procedure and justification for the area source estimate are exactly the same as that of the point source estimate. Biogenic Sources Ozone precursors are also emitted from indigenous vegetation, crops, and landscape vegetation, which are known collectively as biogenic sources. The 1999 Periodic Ozone Emissions Inventory provides a summary table for biogenic emissions, which was developed using a computer model known as MAG-BEIS2. In case that land-use data was not yet available from the UrbanSim program, a constant value for the biogenic emissions was imposed to the future year. Biogenic emissions data was not provided for the 1990, 1993, and 1996 inventories so only the estimates from the 1999 and 2002 inventories were considered. The constant 1991-2000 emissions estimates are based on the 1999 Periodic Ozone Emissions Inventory, and the constant 2001-2010 emissions estimates are based on the 2002 Periodic Ozone Emissions Inventory. Table 3 lists the inventory of the 4 precursors and the contribution from each source in the base year (1999) and a future year (2015). It is worth mention that, although the population increased approximately 50%, the onroad emissions of the major precursors are much less than the base year. This is mainly due to the decrease of the emission factors assumed by EPA models. The technical advance and increasingly strict regulations are expected to result in a significant decrease in the emission factors of 2015 in comparison with

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those of 1999. The emission factors from MOBILE6 are 2.55g NOx/mile, 1.46g VOC/mile, 16.2g CO/mile, and 0.113g SOx/mile for the year of 1999, and 0.59g NOx/mile, 0.42g VOC/mile, 5.20g CO/mile, and 0.046g SOx/mile for the year of 2015. It is an evidence of the impact of technology on the urban sustainability. The actual situation will, of course, depending on the adoption of the technology and the enforcement of the regulations. Table 3: Ozone precursor inventories in 1999 and 2015 (Unit: ton) 1999 NOx

VOC

2015

CO

SOX

NOx

VOC

CO

SOX

Onroad Mobile

60.0

34.4

380.9

2.7

20.4

14.5

178.1

1.6

Nonroad Mobile

34.1

28.7

195.8

0.6

14.3

13.8

201.8

0.4

Point Sources

5.5

6.0

1.8

0.2

6.6

11.0

2.4

0.2

Area Sources

7.8

31.9

5.9

0.3

10.1

48.0

7.9

0.3

Biogenic Sources

3.7

17.8

0.0

0.0

8.3

24.2

0.0

0.0

A complex suite of atmospheric models be incorporated to accurately quantify this metric. The Mesoscale Model 5 (MM-5) meteorological modeling system was used for predicting air-flow patterns and the EPA’s recently released Models3/CMAQ was adopted to simulate pollutant concentrations.

a) Snap shot of the animation of NOx spatial distribution with air flow in base year.

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b) Snap shot of the animation of O3 spatial distribution with air flow in base year.

c) Animations of the temporal, spatial O3 distribution in a typical summer time in the base year (left) and a future year (right).

d) Difference of Maximum Ozone Concentration in Entire Domain (left) Figure 5: Spatial-temporal distributions of O3 precursors and O3 in the base and future year. All the time is the UTC time. (Reproduced with permission from ES&T 2007, . Copyright 2007 American Chemical Society).

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Meteorological information obtained from MM5 together with hourly gridded emission data from the preprocessor SMOKE (Sparse Matrix Operator Kernel Emissions module), including ozone precursors and particulate matter, will provide the required input for the Models-3/CMAQ. SMOKE uses information from the projected emission inventory that reflects a change of pollutant emissions. The “baseline” emission inventory without the effect of projected changes is already available in model-ready form as a part of our ongoing studies. Models-3/CMAQ will be simulated with the baseline and with the projected emission changes so that the effects of urban growth can be delineated. Using EPA 1999 emission inventory for a base-year simulation, and the projected 2015 emissions inventory and land-use patterns from UrbanSim simulation, CMAQ models were used to calculate the ground level ozone concentration. Because of the decrease in the emission of ozone precursors, the ozone concentrations during daytimes are slightly decreased in 2015. In particular, hot spots with high ozone concentration are decreased. The temporal and spatial distribution of ozone in the base and future year are illustrated as animations in the supplemental materials. Fig. 5d shows the fluctuation of the maximum concentration in the entire domain during simulation periods. The National Ambient Air Quality Standards 1990 (NAAQS) requires the annual fourth-highest daily maximum 8-hour average ozone concentration should be less than or equal to 80 ppb [21]. The 2015 projection shows that the duration for ozone levels exceeding the 80ppb standard will be approximately 3 hours shorter and the maximum ozone concentration will be about 5 ppb lower than that in the base year of 1999. This result is statistically significant at a significant level of 0.05. Fig. 5 a-c are screen snapshots for the 2-D animation of the spatial and temporal distribution of NOx (one of the major ozone precursors), and O3 over the Greater Phoenix area, which is delineated in the black line. Because of the complexity nature of the problem and the many scientific concepts behind the models, it is critical to deliver the results in an easy-to-comprehend way. This needs to be done by intensive communication with the stakeholders. Models generating output over time were viewed dynamically on maps in 2D or 3-D graphical information systems. Under the best scenario, knowledge about

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“how sustainable the future is” would be effectively diffused throughout society, and a feedback mechanism would stimulate the evolution of sustainable decisionmaking within a region. FINAL COMMENTS The conclusions we can draw from the case study are: 1) Integrated modeling framework can be developed to examine the cascading impact of subsystems of urban system. Externalized impacts can be quantified using life cycle analysis tools. 2) Integrated modeling of urban systems is heavily data and calculation demanding and careful handling of models with temporal and spatial scale is required. The case study did not discuss the sensitivity and uncertainty of the coupled model framework yet it is an important topic of research. This research could lead to the answer of questions such as how much complexity should be included in an integrated framework. A framework is meant to be shorthand for key components and interactions that represent the system as a whole. By including too much complexity, we will likely encounter data-availability problems and the truly valuable interdependences may get lost in competition with other interactions. On the other hand, an oversimplified framework demands high accuracy of each system data and miss important interactions. 3) Metrics or environmental policy, such as the EPA’s emission factors standard in the case study, can be examined using integrated modeling framework to assess its validity in terms of evolving growth of urban system. 4) The case study is only a very preliminary work toward the development of new oncology. System dynamics tools might be useful to explicitly build the feedback loop between urban engineering and environmental consequences therefore lead to better understanding of the cascading effect of an alternation in one of the subsystems. This case study is by no means a complete study on urban sustainability yet is a good template for investigators to appreciate the needs for integrated framework and the efforts and information needed for implementing such framework. To tackle the sustainability of urban systems, one must understand that urban systems are complex adaptive systems. The challenge that we face is to engineer the emergent patterns and understand their resilience, and sustainability. This is a huge challenge because it is far removed from the traditional focus of engineers

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who focus on designing technology around a purpose. Integrated system studies are required to develop transformative ontology and methodology to guide the engineering practices towards sustainability. Urban systems have to make the most of information, energy and materials resources in order to become more sustainable. Besides this complexity in urban system, much of the impacts of the urban development were externalized. Therefore, any study that aims at sustainability of urban systems needs to holistically consider the social, economic, and environmental impacts at the local, regional and global scale. On the other hand, it has to be focused enough to inform and guide current practices. CONFLICT OF INTEREST The author(s) confirm that this chapter content has no conflicts of interest. ACKNOWLEDGEMENTS The authors would like to thank the National Science Foundation Grant # 0836046, High Tower Chair and Georgia Research Alliance at Georgia Tech Brook Byers Institute for Sustainable Systems (BBISS). The opinions expressed or statements made herein are solely those of the authors, and do not necessarily reflect the views of funding agencies mentioned above. REFERENCES [1] [2] [3] [4] [5] [6] [7]

Economist, The Brown Revolution, The Economist, 73-75, 2002, http://www.economist.com/science/displayStory.cfm?story_id=1120305. UNEP. Population Division of the Department of Economic and Social Affairs of the United Nations Secretariat: World population prospects, the 2004 revision and world urbanization prospects. 2005. http://esa.un.org/unpp. ABC News (Australian Broadcasting Corporation). UN Urges Green Planning for Sprawling Cities. June 5, 2005. http://www.abc.net/au/news (accessed June 5, 2008). Marilyn Brown, Frank Southworth, and Andrea Sarzynski, “Shrinking the Carbon Footprint of Metropolitan America” (Washington: Brookings Institution, 2008). NAE. (2000). "Earth Systems Engineering." Washington DC. Mihelcic, J. R., Crittenden, J. C., Small, M. J., Shonnard, D. R., Hokanson, D. R., Zhang, Q., Chen, H., Sorby, S. A., James, V. U., Sutherland, J. W., and Schnoor, J. L. (2003). "Sustainability Science: The Case for a New Metadiscipline." Environ. Sci. Technol.(37), 5314. M. Mitchell Waldrop. (1994). Complexity: the emerging science at the edge of order and chaos. Harmondsworth [Eng.]: Penguin

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Ottino, J. M. Engineering complex systems. Nature 2004, 427(6973), 399. Benenson, I., Torrens, P.M. (Eds). (2004) “Geosimulation: Object-Based Modelling of Urban Phenomena”. Special Issue, Computers, Environment and Urban Systems, 28 (1/2) Forrester, J., Urban Dynamics 1968, Productivity Press, Portland. Esty, D. C., M. A. Levy, T. Srebotnjak, and A. deSherbinin. 2005. 2005 Environmental Sustainability Index: benchmarking national environmental stewardship. Yale Center for Environmental Law and Policy, New Haven, Connecticut, USA. Waddell, P. (2002). UrbanSim: Modeling Urban Development for Land Use, Transportation and Environmental Planning. Journal of the American Planning Association, Vol. 68, No. 3, pp. 297-314.2. Joshi, H.; Guhathakurta, S., Konjevod, G., Crittenden, J., Li, K., (2006) Simulating Impact of Light Rail on Urban Growth in Phoenix: An Application of the Urbansim Modeling Environment, Journal of Urban Technology, 13(2). Collins, J. P., Kinzig, A., Grimm, N., Fagan, W., Hope, D., Wu, J., Borer, E. (2000) A New Urban Ecology, American Scientist, 88: 416-425. Grimm, N. B., Baker, L. A., Hope, D. (2002) In Understanding Urban Ecosystems: A New Frontier for Science and Education; A.R. Berkowitz, C.H. Nilon, Hollweg, K. S., Eds.; Springer-Verlag: New York. Curran, M.A. (1996) Environmental Life Cycle Assessment. New York, USA: McGraw Hill. Hendrickson, C., L. Lave, H. Scott Matthews, A. Horvath, S. Joshi, F. McMichael, H. MacLean, G. Cicas, d. Matthews, and J. Bergerson, (2006) Environmental Life Cycle Assessment of Goods and Services: An Input-Output Approach, RFF Press: Washington D.C. Trusty, W.B. (2004) Life Cycle Assessment, Databases and Sustainable Building, Presentation at The Latin-American Conference on Sustainable Building, Sao Paolo, July. Frischnecht, R., N. Jungbluth, H.J. Althaus, G. Doka, R. Dones, T. Heck, S. Hellweg, R. Hischier, T. Nemecek, G. Rebitzer, M. Spielmann, (2004) Overview and Methodology, Ecoevent report No. 1. Swiss Centre for Life Cycle Inventories, Dubendorf. Maricopa County Environmental Services Department (MCESD) (2002) 1999 Periodic Ozone Emissions Inventory, MCESD: Phoenix, AZ. U.S. Environmental Protection Agency. (2003) User’s Guide to MOBILE6.1 and MOBILE6.2. Mobile Source Emission Factor Model. EPA420-R-03-010. August http://www.epa.gov/otaq/models/mobile6/420r03010.pdf (Last accessed May 2007) U.S. Environmental Protection Agency, (1990) Clean Air Act Amendments, Washington, D.C.: U.S. Environmental Protection Agency.

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CHAPTER 9 Sustainability Indicators and Metrics H. Cabezas* Sustainable Technology Division, National Risk Management Research Laboratory, Office of Research and Development, U.S. Environmental Protection Agency, Cincinnati, Ohio, USA Abstract: Sustainability is about preserving human existence. Indicators and metrics are absolutely necessary to provide at least a semi-quantitative assessment of progress towards or away from sustainability. Otherwise, it becomes impossible to objectively assess whether progress is being made. The subject, however, is by its nature complex and multidisciplinary. Indicators and metrics must, therefore, encompass a wide of issues relevant to human existence, and they must be useful in “steering” the system towards a sustainable trajectory. They must, however, be well grounded in science and allow for comparisons across different systems. As a minimum, metrics must represent economic strength, human environmental burden, energy use, and system order and stability. A comprehensive suite of metrics is essential to the kind of adaptive management that is necessary for practical implementation of sustainability.

Keywords: Adaptive management, biocapacity, complex system, economics, ecological foot print, ecosystem, emergent properties, emergy, exergy, fisher information, green accounting, gross domestic product, indicators, metrics, biocapacity, ecological footprint, emergy, Exergy, green net domestic or regional product, power, sustainability definitions, system scale, system trajectory, uncertainty sources, temporal scale. INTRODUCTION According Webster’s New World Dictionary, the word sustainability derives from a combination of two Latin words: sus meaning up and tenere meaning to hold [1]. However, the first use in the modern context appears in the January 1972 issue of the The Ecologist (Goldsmith et al., 1972) [2]. Although the most widely *Address correspondence to H. Cabezas: Sustainable Technology Division, National Risk Management Research Laboratory, Office of Research and Development, U.S. Environmental Protection Agency, Cincinnati, Ohio, USA; Tel: 513-569-7350; Fax 513-487-7787; BB 513-633-8447; E-mail: [email protected]

Heriberto Cabezas and Urmila Diwekar (Eds) All rights reserved-© 2012 Bentham Science Publishers

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accepted definition of sustainability or sustainable development is attributed to the World Commission on Environment and Development (WCED, 1987) [3]. The commission is informally known as the Brundtland Commission in reference to the commission chair, Gro Harlem Brundtland. The Commission stated in its final report that “Sustainable development is development that meets the needs of the present without compromising the ability of future generations to meet their own needs”. By alluding to present and future generations the statement places sustainability squarely as an issue of human survivable on planed Earth. By addressing itself to the issue of meeting human needs, the statement links sustainability to the broad range of needs on which human existence depends, and this reaches out to every aspect of human existence. There are, for example, the needs for: a biological system that supports people, the use energy, access to clean air and water, a functioning social system and economy, and so on. The study of sustainability, therefore, requires engagement across a broad range of different scientific areas of study. While the aforementioned conceptual picture is important, a necessary element to operationalizing and turning into practice the statement from the Brundtland Commission is that of assessing progress towards or away from sustainability, at least in a semi-quantitative manner. This brings us to the need for indicators and metrics of sustainability which is the focus of this chapter. These indicators and metrics must cover biological, economic, social, technological, and other aspects of the system as has been argued already. As one might expect for such a broad and complex topic, the number of sustainability indicators and metrics in the scientific literature is substantial, and a few are cited here for completeness without attempting to treat the topic comprehensively. Some sources address sustainability indicators (Bell and Morse 1999) [4], (Spangenberg 2002) [5], (Evan et al., 2006) [6], while others focus on sustainability metrics (Shane and Graedel 200) [7], (Sikdar 2003) [8], (Martins et al., 2007) [9]. The difference between sustainability indicators and metrics is not always clear in the scientific literature, and we often find significant overlap between the two in practice. However, in this chapter, the convention (Meyer 2008) [10] followed is that of using the word indicator for a variable that represents a single very specific aspect of the system, e.g., the electrical energy input in the case of an industrial system. An indicator is usually a directly observable or measurable variable. But a metric

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is an aggregate quantity made up of many indicators that represents a broader aspect of the system, e.g., the energy efficiency of an industrial process including all types of energy such as electrical, mechanical, chemical, etc. Metrics are often conceptual, and they are usually not directly observable or measurable quantities. However, this use of the word metrics differs from the previously cited source (Mayer 2008) [10] which uses the word index or indexes for the quantity(s) that here is referred to as a metric (s). One should note that the present chapter capitalizes on and summarizes the work of many of my colleagues at the U.S. EPA and at other institutions (Fath et al., 2003) [11], (Cabezas et al., 2003) [12], Mayer et al., 2004) [13], (Mayer et al., 2007) [14], (Cabezas et al., 2007) [15], (Karunanithi et al., 2008) [16], (Shastri et al., 2008) [17], (Hopton et el. 2010) [18]. The chapter is organized in the following manner: the working concept of sustainability is discussed, sustainability indicators and metrics are addressed in general, metrics suitable for local, regional, and global sustainability are expanded on, the use of sustainability metrics is discussed, and finally a summary brings all of these topics together. SUSTAINABILITY As has already been mentioned, sustainability is about human survivable. The fossil record (Knoll 2003) [19], (Raup and Sepkoski 1982) [20] seems to indicate that Nature has over time evolved and extinguished a great many species as the physical and biological environment has changed. But humans, possibly the first fully sentient species on planet Earth, are aware of the possibility of their own extinction and would like to avoid it, and it is, perhaps, with this thought in mind that they created the concept of sustainability as part of a survivable effort. To survive in an evolving environment, however, requires a dynamic strategy the goal of which is to maintain physical and biological conditions that favor human existence over the long term. One way of illustrating this concept is to imagine a linear space (Fig. 1) where the dimensions are time along with indicators or metrics for economy, energy, ecology, society, etc.

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Energy Catastrophic Event

Not Sustainable

System Trajectory Ecology

Sustainable Society

Time

Figure 1: Idealized trajectory over time for a complex system with integrated economic, energetic, ecological, and social components.

Then the system as illustrated in Fig. 1 is sustainable only while its trajectory lies within the “tunnel” that abstractly represents conditions favorable to human existence. A sustainable management strategy is then one of “steering” the system so as to keep it within desirable boundaries, and, most certainly, to avoid a catastrophic event where the system progressively “runs away” over time from desirable conditions. This is at least conceptually the crux of sustainable environmental management, and this is the concept through which the otherwise abstract subject of sustainability becomes practical and actionable. The most relevant and challenging issue, however, is to develop and apply scientifically sound indicators or metrics of sustainability, i.e., how does one draw the trajectory and the boundaries depicted in Fig. 1 for real systems? The question of scientific soundness when it comes to indicators and metrics is critical because without that, these may or may not adequately represent system behavior. SUSTAINABILITY: THE SCALE ISSUE One of the fundamental difficulties in sustainability science is that the natural boundary for the system is the entire planet Earth. The reason is that it is only at the scale of the Earth that we find a system that is biologically self-contained, at least on the time scale of human history. All other systems within the Earth including continents, regions, and even the remotest islands are not biologically self-contained as evidenced by the fact that human beings and many other biological species live, have lived, or at least have visited them. From other

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perspectives, the Earth is open to energy which enters mostly from the Sun – neglecting tidal forces from other heavenly bodies – and eventually radiates into space as heat. If the input of mass from meteorites, comets, and space dust and the loss of atmospheric gases are neglected, then the Earth can be considered as closed system, again at least on the time scale of human history. This is all important because if sustainability is about sustaining human existence on Earth, then the preservation of that existence has to be considered on a planet-wide scale because it is at that scale that biology, humans included, operates. The difficulty is that at the planetary scale when ecosystems, societies, and economies are considered, the system becomes very large and very complex. Hence, the resources necessary to conduct sustainability studies at that scale are unavailable at the present time. Even if resources were available to conduct such scientific studies, the global legal and political institutions that would implement the results into practical decision making are still under development. It is, therefore, necessary as a practical matter to conduct sustainability studies at spatial local and regional scales where most environmental decision making occurs. This subject will be further discussed later under sustainability metrics for local, regional and global decision making. INDICATORS As already discussed, indicators are observable or directly measurable quantities that characterize the behavior of the system. While these often can provide a sense of immediacy to the phenomena, they do not generally lend themselves to generalization. As an illustration of the concept of indicators, consider a “simple“ physical system such as a pure liquid, say water, flowing down a circular pipe at steady state (Fig. 2). The list of indicators here would include temperature T(K), density ρ(kg/m3), volumetric flow rate through the pipe Q(m3/s), pressure at the entrance of pipe Ps(Pa), pressure at the exit from the pipe Pe(Pa), difference in height between the entrance and the exit from the pipe Δh(m) = he - hs, and the fluid viscosity μ(kg/m·s) which may not be directly measurable. There may be other relevant indicators depending on the phenomena being studied, but a complete list of indicators can always be found for a simple system such as this one.

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Fluid In T (K) Ps (Pa) ρ (kg/m3) Q (m3/s) µ (kg/ms)

hs

he

T (K) Pe (Pa) ρ (kg/m3) Q (m3/s) µ (kg/ms)

Figure 2: Illustration of an incompressible fluid flowing through a simple and smooth round pipe along with the observable variables and/or indicators that characterize the dynamic state of the system.

For the case of the very complex and integrated real systems relevant to sustainability, listing the indicators is not as straight forward as for the simple system previously discussed. However, there is sufficient understanding of many of the systems and subsystems, and of the underlying processes such that a scientifically defensible, although not rigorous, listing of indicators can be formulated. Hence, adopting the frame work illustrated in Fig. 1, we will focus primarily on the ecological, energy, and economic aspects. Not that the social aspects of sustainability are unimportant, but rather that for the U.S. EPA the primary focus is ecological with energy and economy as ancillary factors, leaving the social aspects of sustainability in the realm of politics, the public, and democratic institutions. Turning first to ecosystems, to develop a listing of indicators consider that there are certain conditions and processes that are necessary for the existence and maintenance of ecosystems. For example, most ecosystems, at least on Earth, must have relatively mild temperatures, a source of nutrients (water, nitrogen, phosphorus, carbon, oxygen, sulphur, etc.)., a source of energy, and the ability to cycle. As energy is extracted from the inflow of nutrients, there has to be an outflow, often cyclic, of mass in the form of biological wastes. Again, there must be a flow of energy through the ecosystem to support its function and organization, and the ecosystem must be able to conserve and manage its energy resources. Lastly, the ecosystem must be able to use the flow of energy and nutrients to maintain a regime of ordered self-organization. Likewise, for the economy there are a set of observable indicators which could include personal

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income, employment rates, prices of goods and services, personal savings rates, corporate profits, etc. Lastly, the indicators for energy could include the amounts and types of energy used by people, amount solar energy input, fossil energy use, the amounts and types energy stored in the system, etc. While there have been a number of excellent studies on real systems where a reasonably comprehensive and scientifically sound set of indicators have been assembled and used (Bastianoni and Tiezzi 2008) [24], (Haberland and Wackernagel 2004) [25], (Hanley et al., 1999) [26] in sustainability assessment, here I focus on the San Luis Basin Sustainability Metrics Project (Hopton et al., 2010) [18], (Heberling and Hopton, Eds. 2010) [23] as an illustration mainly because of greater familiarity with the details. For the case of the San Luis Basin Sustainability Metrics Project, a comprehensive listing of indicators was compiled from mostly public sources and some private ones. These included population and personal income from the Bureau of Economic Statistics; land area, solar and wind data, and food production from the National Agricultural Statistics Service; carbon dioxide emissions and energy consumption from the Energy Information Administration; food consumption from the U.S. Department of Agriculture-Agricultural Research Service; forest harvest from the U.S. Department of Agriculture-Forest Service; water budget from the Colorado Decision Support System; precipitation from the PRISM (Parameter-elevation Regression on Independent Slopes Model) Climate Group; imports and exports of commodities and energy from Global Insight, and wind erosion from various sources. Note that the list of indicators is broad and covers the biological, energy, climate, economic, and social subsystems. Although no one would argue that any listing of indicators for any such complex system would be ever truly comprehensive. METRICS As already mentioned, metrics are more conceptual quantities which are often not directly measurable or observable, but which lend themselves to generalization and to the formulation of general principles. For the aforementioned simple case of a pure fluid flowing down a circular pipe, the relevant metrics would be

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quantities that characterize the dynamic state of the system in a general manner that would allow comparison with other similar systems. The metrics here would include the Reynolds number (Re), the Froude number (Fr), and possibly the power required (Pw) to keep the fluid flowing (Bird et al., 1960) [21] as illustrated next (Fig. 3). Re, Fr

Fluid Out

Fluid In

Pw

Figure 3: Illustration of an incompressible fluid flowing through a simple smooth and round pipe along with the relevant metrics characterizing the dynamic regime of the system. Note that the number of metrics is less than the number of observable variables and indicators.

The Reynolds number is defined by Re≡ρvD/μ where v(m/s)=Q/πD2 is the mean velocity of the fluid through the pipe, and D(m) is the hydraulic diameter of the pipe, approximately equal to its inside diameter, and where the other symbols have been defined already. The Reynolds number is the dimensionless ratio of inertial forces to viscous forces in the fluid. It naturally arises from the theory of fluid flow when the equation of motion is written in dimensionless form (Bird et al., 1960) [21]. It is important because it defines the boundaries between the laminar and the turbulent flow regimes in fluids. This boundary is in the neighborhood of Re≈3, 000, 000 according to engineering practice (Faust et al., 1960) [22]. At high Reynolds numbers (Re>3, 000, 000) inertial forces exceed viscous forces which indicates a fast flowing fluid, and a low Reynolds (Ret0. Note that the sphere at t0 is smaller than that at t1.

Figuer 7: Conceptual illustration of the increase in uncertainty here represented by spheres as measurement of the state of the system at some initial time t0 is used to develop a prediction of the state of the system at some future time t1.

As a result, all models for these systems are “lumped” parameter models that contain a number of approximations, and these often lead to inaccuracy when the model is taken to extremes such projecting behavior well into the future.

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Because of these limitations, system management for sustainability over time, i.e., keeping the system within its sustainable boundaries as shown in Fig. 1, has to be done on an adaptive basis. This means that: (1) data and models are used to make short term predictions into the future, (2) management action is taken based on an assessment of the system at present and at a short term into the future, and (3) once the time point of the projection is reached by the actual system, new data is collected on which a new short term projection is made and action taken. This adaptive management scheme is, in fact, an iterative process. USES OF SUSTAINABILITY METRICS One could imagine many uses for sustainability metrics such as informing the public about sustainability, and developing better understanding of complex systems with respect to sustainability. But here we are going to focus on their use in the management of systems for sustainability. The process of managing systems for sustainability needs to happen through an adaptive management process as already discussed. This requires a series of iterative steps as illustrated in Fig. 8. One sequence of steps is to: 1.

Compute a suite of sustainability metrics over a period of time, say thirty years, to establish the system trajectory and its trends, and to assess whether the system is moving towards or away from sustainability.

2.

Based on the sustainability assessment of Step #1, identify problems areas and develop plans for management action with the goal of moving the system trajectory towards sustainability over time.

3.

Implement the management actions designed to correct the problems identified in Step #2. Note while sustainability metrics need to be general and aggregated as has been discussed already, management actions need to focus on more specific system indicators or even variables.

4.

Return to Step #1 and recompute the sustainability metrics after the management actions have been implemented to assess whether they

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have succeeded sustainability.

in

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moving

the

Assessment: Sustainability Metrics

system

trajectory

towards

Identify Problem and Plan Actions

Adaptive Sustainable Environmental Management

10

Management Action

Figure 8: Graphical depiction of a general adaptive management process for sustainability. Note that while the process can in principle be initiated at any step, it is probably best to start with an assessment.

One should note it is possible to start the adaptive management process at any step. However, it would probably be more effective to start with a sustainability assessment based on sustainability metrics to identify which issues that affect sustainability need to be addressed before planning or attempting any management action. The reason again is that the objective of these actions is to move the system trajectory towards sustainability. One should note that as has already been discussed, the kinds of metrics that are necessary to develop scientifically valid sustainable management strategies can be relatively abstract, e.g., exergy, emergy, ecological footprint, green net domestic product, and Fisher information. Hence, for public use, the results obtained from the sustainability metrics analysis needs to be expressed in terms of simple indicators. For example: 1.

Exergy results would be expressed in terms of energy savings.

2.

Emergy results could be expressed in terms the energetic value of a product or a process.

3.

Ecological foot print results may be given in terms of land area used.

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4.

Green Net Domestic Product results could be given as a measure of money or value conservation.

5.

Fisher information results may be expressed as a measure of stability.

However, there remains a good deal of work that needs to be done in relation scientifically-based sustainability metrics to policy and public use indicators. One last but very important principle in adaptive management for sustainability is the hypothesis that a system is moving away from sustainability when any of the metrics that define the sustainable trajectory so indicate. That is, if even one of the sustainability metrics violates the criteria that define the system’s sustainable trajectory, then the entire system can be considered to be moving away from sustainability. The reason is that each of the metrics is grounded in a particular discipline or cluster of related disciplines representing a school of thought in the study of nature, e.g., green net regional products was developed from macroeconomics, emergy was developed from energy systems theory, ecological footprint and biocapacity originate systems ecology, and Fisher information was developed from information theory. Hence, each metric is going to best detect adverse trends that are closely related to its disciplinary origin, i.e., issues driven by economics are likely to be seen first as changes in the green net regional product, problems related to energy use and flow will likely be first noticed from emergy analysis, difficulties arising from a large human burden on the environment are most easily detectable by analyzing the ecological footprint or the biocapacity, and issues of system regime change or instability will most likely first manifest themselves as changes in Fisher information. One should also keep in mind that the appropriate system for sustainability is an integrated system that has economic, ecological, energetic, and other aspects, and that these interact with each other. Hence, there is no reason to think that an effect centered in one of the subsystems will have no effect on the others. This is, perhaps, similar to the situation in an integrated and complex biological system such as human body, where the effect of one malfunctioning subsystem, say the endocrine system, affects all other subsystems and the entire system to varying degrees, some effects more easily observable than others. This will admittedly

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introduce some redundancy in the metrics, but it will likely make problem detection and management action more sensitive and effective. SUMMARY In the span of this brief chapter, I have attempted to discuss sustainability and sustainability indicators and metrics from a practical and scientific perspective. Sustainability is essentially about preserving an environment that is supportive of human existence. To turn this vision into an actionable plan, one needs indicators and metrics of sustainability that can guide an adaptive management effort for keeping the system on a sustainable path. The indicators and metrics must be well grounded in science as has been argued using a simple illustration drawn from fluid mechanics. Metrics and indicators are related by implicit or explicit relationships that are based in science as well. Because sustainability is focused on long term environmental management, the issue of uncertainty, particularly in long term prediction of the state of the system is paramount. This is a well known feature of complex dynamic systems which makes long term prediction very difficult, if not impossible. One practical approach is to conduct environmental management for sustainability through an iterative adaptive management process. Lastly, when a subsystem within a larger dynamic system is on a trajectory away from sustainability, it is likely that the entire system, in fact, is on a trajectory away from sustainability. However, much work remains to be done in relating scientifically based sustainability metrics and indicators to policy actions, and this is the focus of on-going research. CONFLICT OF INTEREST The author(s) confirm that this chapter content has no conflicts of interest. ACKNOWLEDGEMENTS The author acknowledges mane useful discussions with co-workers over the course of many years including Drs. Brian Fath, Christopher Pawlwosky, Audrey Meyer, Matthew Hopton, Matthew Heberling, Tarsha Eason, and Ahjond Garmestani. The author is deeply indebted to their many contributions.

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CHAPTER 10 Implications of Thermodynamics for Sustainability Bhavik R. Bakshi* and Geoffrey F. Grubb William G. Lowrie Department of Chemical and Biomolecular Engineering, The Ohio State University, Columbus, Ohio 43210, USA Abstract: This chapter describes and demonstrates the role that thermodynamics can play in assessing the sustainability of technological activities and in improving their design. Since thermodynamics governs the behavior of all systems, it can play a crucial role in understanding fundamental physical limits of technologies and for quantifying the contribution of resources. The concept of exergy captures the first and second laws of thermodynamics. Since exergy is the common currency that flows and gets transformed in industrial and ecological systems, it allows the joint analysis of industrial and ecological systems. This insight permits accounting of the role of ecosystem goods and services in supporting human activities. Since ecosystems are critical to sustainability, accounting for their role must be a part of all methods aimed toward the analysis and design of sustainable systems. Thermodynamics provides a scientifically rigorous approach for meeting this challenge. In addition, exergy analysis of industrial processes and life cycles helps in identifying areas of maximum resource inefficiency and opportunities for improvement. This approach complements the insight obtained from assessing the impact of emissions. Case studies based on the life cycle of biofuels and nanomanufacturing are used to demonstrate the important role that thermodynamics can play in sustainability engineering.

Keywords: Thermodynamics, first law, second law, Gibbs free energy, Exergy, Energy analysis, Energy return on investment, Energy quality, Cumulative exergy consumption, Transformity, Emergy, Biofuels, Nanotechnology. MOTIVATION The sustainability of human activities requires resources for supporting industrial, economic, recreational, cultural and other activities that contribute to human wellbeing. All economic goods and services are produced by transforming natural *Address correspondence to Bhavik R. Bakshi: William G. Lowrie Department of Chemical and Biomolecular Engineering, The Ohio State University, Columbus, Ohio 43210, USA; Tel:(614) 292-4904; E-mail: [email protected] Heriberto Cabezas and Urmila Diwekar (Eds) All rights reserved-© 2012 Bentham Science Publishers

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resources or ecological products, which rely on the availability of ecosystem services such as genetic resources, food, fuel, biomass, climate regulation, pest regulation, pollination, cultural basis, etc. Ecosystem services rely on global energy flows: mainly solar, tidal and crustal. Thus, at a fundamental level, sustainability of human activities relies only on the “single bottom line” of ecosystem goods and services. Thermodynamics, being the study of energy and resource transformation, governs the transformation of global energy sources to ecosystem services, and their further conversion to economic products. Thus, thermodynamics is essential for understanding economic and ecological systems, and their interaction in terms of sustainability. Over the decades, thermodynamics has been used extensively in diverse fields, including ecology and engineering, and provides the common language needed to bridge engineering and ecological systems. Thermodynamics can contribute to understanding and evaluating the sustainability of human activities due to its ability to, 

Quantify the role of resources in technological activities. Sustainability requires consideration of ecological resources in the form of various goods and services, and thermodynamics provides a unique and rigorous approach for such accounting. The thermodynamic concept of exergy provides a common currency for industrial and ecological systems.



Allow estimation of the resource input or degradation due to technological activities. The first and second laws can be used to estimate the “best case scenarios” for new technologies, which can provide a feel for resource intensity.



Help in identifying opportunities for improving systems at multiple spatial scales and for designing such systems. This includes individual equipment, processes, or life cycles.

This chapter introduces some basic principles of thermodynamics and describes some ways by which it can be used to understand and improve technological life cycles.

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BACKGROUND The first law of thermodynamics states that energy can neither be created nor destroyed. This means that energy is always conserved and any loss of energy from the system must be balanced by an equal gain elsewhere outside the system. The primary ways in which energy is moved or converted are through heat and work. Heat exists where energy flows from areas of higher temperature to areas of lower temperature. In physics work is defined as force applied over a distance. In thermodynamic terms, work is a means of transferring and storing energy. This law is of limited value for understanding industrial systems and their sustainability since it does not account for the fact that a positive gradient is essential for any energy transformation. The second law of thermodynamics has been defined in many ways over the years. Simply stated, no process can convert heat completely to work. Another way of explaining it is that decreasing entropy (increasing order) in a system must result in equal or greater increase in the entropy (disorder) of the surroundings. The heart of the matter is the inequality of heat and work. A strictly first law energy analysis does not capture this difference and considers work and heat as equally substitutable forms of energy. The concept of exergy combines the first two laws of thermodynamics and is defined as the maximum amount of work that can be extracted from a stream of mass or energy when it is brought into equilibrium with its reference state. This state is commonly taken to be the standard temperature and pressure and the composition of sea water and the earth’s crust [1]. Ultimately this means that work can be done whenever there is a gradient between a stream and its surroundings. Temperature and pressure gradients contribute to the physical exergy of a stream, while the difference in chemical concentrations between a stream and the defined reference state determine the chemical exergy of the stream. The average composition of the surrounding reference state has been carefully studied and quantified for the atmosphere, lithosphere, and hydrosphere [2]. The exergy of a stream is calculated in reference to the sphere to which it is most relevant. Other forms of exergy such as potential, kinetic, and nuclear are typically considered negligible for chemical process applications.

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Physical exergy can be calculated as,  

=

(1)

The standard chemical exergy of a compound can be calculated as,  ∆

 ∑

(2)

where, ∆Gf is the Gibbs standard free energy of formation for the compound, nel is the number of moles of each element per unit of compound, and b0chel is the standard chemical exergy of the elements found in the compound. Values of ∆Gf can be found in the literature and in thermodynamic handbooks for most compounds [3, 4]. Standard values of b0chel have been calculated for each element according to its standard reaction of formation and can be found in [2, 5]. The chemical exergy of fuels is closely related to heating value. The relation is close to unity and can be found in [6] for some common fuels. Exergy is attractive for resource accounting and sustainability engineering, and has been described as the “ultimate limiting resource” [7]. It has been shown to be valuable for identifying the areas in the process with the greatest lost work [8]. Unlike traditional first law energy analysis, lost work contains information about the quality of losses. A large amount of warm water being rejected from a chemical process has a large energy (enthalpy) value, but its ability to do work is very limited due to a small gradient with the surroundings, so its exergy content is much smaller. Exergy provides a tool for identifying losses of quality resources in a process, and improvement analysis can use this information to look for alternatives that reduce losses, or to make decisions about the recycle of high quality waste streams. In a first law energy analysis, at steady state, the balance over a given control volume is the difference between all flows in and out. The sum of all flows in an energy balance at steady state must be zero as shown in the following equation [9].  ∑

∑

   ∑

 ∑

 ∑

 ∑ (3)

where, is the flowrate, H is the enthalpy, is the rate of work, is the rate of heat flow, and subscripts in and out represent streams flowing in and out of the system. While energy is conserved, some flows are usually considered to be

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losses. Most often these losses are in the form of waste streams or unusable heat. When the second law of thermodynamics is included, irreversibilities introduce a different kind of loss. Due to entropic losses, exergy is not conserved. Any operation which is not reversible will have some exergy loss, commonly called lost work. In a realistic system, there will always be some lost work, as shown in the following exergy balance equation for a steady state system [9].  ∑

∑

   ∑

 ∑

 ∑

 ∑

(4)

where, LW is the lost work, B is the exergy, T0 is the reference temperature, and T is the system temperature. Comparing these two equations also makes clear some of the differences between energy and exergy analyses. The most obvious differences are the Carnot efficiency factor applied to heat flows in the lost work calculation and the use of exergy instead of enthalpy. THERMODYNAMIC METHODS FOR RESOURCE ACCOUNTING Thermodynamics has been the source of various resource accounting and aggregation methods that are popular for life cycle evaluation of technologies. This section introduces these methods and their relationships. The ability of each approach to provide insight about resource use is discussed and illustrated via an application to transportation fuels. In general, methods for resource aggregation combine physical properties of each individual resource as a weighted sum [10]:  ∑

(5)

where, λi is a weight applied to the physical property xi of the i-th resource and is the resulting aggregate quantity. Calorific value (often referred to as energy) and exergy are commonly used physical properties. It is important to realize, but often forgotten that aggregation by this equation assumes substitutability between resources since it implies that the quantity λixi of the i-th resource may be replaced by λi+1xi+1 of the (i + 1)-th resource. Thus, λi may be thought of as a quality correction factor that justifies the assumption of substitutability.

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ENERGY ANALYSIS This approach is referred to as Energy Analysis, Net Energy Analysis or Full Fuel Cycle Analysis. It has been popular for a few decades [11, 12], and focuses on the life cycle consumption of fossil fuels. Here energy usually refers to the calorific value of fossil resources: mainly coal, crude oil and natural gas. In terms of Equation 5, energy analysis considers λi = 1 for the resources considered. Thus, all fossil resources are considered to be substitutable. This need not be true in practice since, for example, converting natural gas into electricity is more efficient than converting coal into electricity [13]. Despite this approximation, metrics such as Energy Return on Investment and Net Energy have been widely used. Energy ROI is defined as,  

(6)

with Enp representing the calorific value of the product, and Enproc is the fuel value required for converting the feedstock into the product, as depicted in Fig. 1. Note that the feedstock energy is not included in the denominator, making this a different metric than efficiency.

Figure 1: Types of inputs and outputs in typical processes and life cycles.

Energy analysis has been applied to determine the “energy cost” of many products, including transportation fuels and electricity. Application to transportation fuels has been the topic of much recent discussion and debate, particularly for the feasibility of corn ethanol. Most studies have calculated an Energy ROI of corn ethanol to be only slightly larger than unity indicating its limited appeal. However, the assumption of substitutability between fossil resources has caused some researchers to proclaim energy ROI results as being irrelevant for guiding decisions [14].

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Cleveland et al., [15] review approaches for capturing the quality of resources, and suggest that monetary weighting may be most appropriate. They recommend calculating λi as the ratio of the price of the i-th fuel and that of another fuel chosen as the basis. This is based on the expectation that monetary value captures factors such as resource quality, impact of its use, and human preferences. However, as shown in the biofuels case study section, this approach can provide counter-intuitive results, particularly for new products and technologies. INDUSTRIAL CUMULATIVE EXERGY CONSUMPTION Exergy is a better indicator of resource quality than fuel value since it only considers that part of a resource that can be converted into work. It can capture differences in resource quality due to temperature, pressure and concentration. A quality indicator based on exergy may be defined as the ratio of the resource exergy, Bi, to its enthalpy, xi as, λi = Bi/xi [16]. Since both fuels and nonfuel resources have exergy, this quantity can be used to compare and aggregate many different types of resources: a distinct advantage over energy analysis.

Figure 2: ICEC only accounts for exergy consumed in economic processes, while ECEC also accounts for exergy used in ecosystems.

Aggregating the exergy of resources over a life cycle or supply chain has been popular [17], and aggregate metrics such as the cumulative degree of perfection [17], exergetic breeding factor [18] and renewability index [19] have been defined. These methods only consider the exergy of industrial processes, hence the cumulative quantity is called Industrial Cumulative Exergy Consumption (ICEC), and is depicted in Fig. 2. ICEC implies substitutability between the exergy of different resources, which unfortunately is not always true. For example, a joule of solar exergy cannot be substituted for a joule of coal exergy. Consequently, as shown in the biofuels case study section, methods based on ICEC can also provide misleading results,

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particularly for systems where renewable and nonrenewable resources are combined or compared. The cumulative exergy extracted from the natural environment (CEENE) approach [20] tries to overcome this shortcoming of ICEC by only considering the exergy that is extracted from nature and directly enters the industrial network. Thus, for biomass, CEENE does not account for all the solar exergy incident on the relevant land area but only about 2% of it, since that is the fraction extracted by biomass. Similarly, for minerals, CEENE excludes the exergy of tailings. This approach does better at accounting for quality differences than ICEC, but as illustrated in the biofuels case study section, it can provide counter intuitive results. ECOLOGICAL CUMULATIVE EXERGY CONSUMPTION ECEC expands ICEC by including the exergy consumption in ecosystems for making natural resources, as shown in Fig. 2. This approach is closely related to the concept of emergy, developed in systems ecology [21, 22]. It aims to represent all resources in terms of a common unit of solar equivalent joules (sej). As shown in Fig. 3, for a simple chain that converts 106 J of sunlight into 103 J of biomass, which is converted into 102 J of fuel, the ECEC for all products is 106 solar equivalent joules (sej). For this supply chain, 10 J of fuel may be substituted by 1000 J of biomass or 106 J of sunlight. The quality correction factor, λi may be defined as the ratio of ECEC to exergy. Considering sunlight to have a λ of unity, for this illustrative example, the quality indicator for biomass is 106/103 = 103 and for fuel is 104. Emergy analysts refer to this quality correction factor as transformity. This is an appealing way of accounting for quality differences between resources, and transformities for a large variety of products and processes have been calculated in the emergy analysis literature. This approach is shown to provide intuitive results in the biofuels case study. A shortcoming of this approach is that it requires knowledge about ecological networks and energy transformation in them, which is not always available, and introduces uncertainty in the results. Since ECEC includes exergy consumption in ecological systems, it also has the added benefit of accounting for those ecosystem services that can be represented

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thermodynamically. This includes services such as geological processes that support mineral concentration for mining, the hydrological cycle, ocean and atmospheric processes [21]. Among the metrics based on ECEC/emergy analysis the following are most popular. The emergy yield ratio (EYR) is defined as the ratio of the product emergy to the emergy in economic goods and services bought to support the activity. In terms of Fig. 1,  

(7)

where, Emp is the product emergy while Emproc is the processing emergy, which represents goods and services that are bought from the economy. Recently, this metric has been modified to permit meaningful results for products that have the same use, but different emergy. For example, corn ethanol and gasoline may have the same use as a transportation fuel, but different emergy value. For such products, EYR may provide misleading results, and an emergy ROI is suggested instead [23]. The difference between EYR and emergy ROI is only in the numerator. For the emergy ROI, the numerator for products with the same usefulness is identical, while for EYR it may vary according to the efficiency of the life cycle. As in ICEC analysis, ECEC also permits a similar definition of a renewability index. Other metrics that have been defined based on emergy include the environmental loading ratio (ELR) and sustainability index. The former is the ratio of the nonrenewable and economic inputs in solar equivalents to the ECEC of the product. The sustainability index is defined as the ratio of the EYR to ELR. More details about their use are in [24, 25].

Figure 3: Flow of exergy in a typical food/industrial chain.

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CASE STUDY – THERMODYNAMIC LIFE CYCLE EVALUATION OF BIOFUELS The methods described in this section and its variations may be used easily via the Eco-LCA on-line software [26]. This software represents an integrated economicecological model of the U.S. economy based on combining the economic inputoutput model with data about ecosystem goods and services and their transformation to thermodynamic quantities [27]. The result is an aggregate model at the scale of economic sectors. In addition to providing insight into life cycle resource use and emissions of about 500 sectors, this model can also be combined with more detailed process level data to result in a more accurate “hybrid” model. The results presented in this section are from such a hybrid model described in [10]. Fig. 4 compares the contribution of various resources to the life cycles of corn ethanol, gasoline, soybean biodiesel and diesel, in terms of ICEC, ICEC with metabolized sunlight, and ECEC. All the resources that can be represented in thermodynamic units are included in these plots. In Fig. 4a, sunlight is the dominant energy source, even for fossil fuels. In fact, these fuels seem to be 70% renewable. This high contribution of sunlight is entirely due to indirect consumption via other sectors that contribute to the fossil fuel life cycle such as agriculture and forestry. Such a counter intuitive result is due to differences in the quality of resources, which is not considered in ICEC. As discussed in the section on industrial cumulative exergy consumption, a joule of solar exergy is much more dilute and lower quality than a joule of crude oil exergy. Fig. 4b considers only the fraction of sunlight that is metabolized. This approach is similar to that of Dewulf and coworkers [20] which only considers the exergy that is actually extracted from the natural environment. Only about 2% of sunlight is metabolized by plants, hence 98% is excluded from this approach. As shown in the figure, the contribution of sunlight goes down drastically, but is still higher than what would be intuitive. Gasoline and diesel are still found to be 5% renewable. Fig. 4c is based on ECEC. Due to the quality correction via representing all resources in solar equivalents, sunlight is nearly invisible in this figure. Now both fossil based fuels are almost completely nonrenewable, while corn ethanol and

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Figure 4: Comparing the results of LCA with different thermodynamic quantities. (a) ICEC, (b) ICEC with metabolized exergy (similar to CEENE [12]), (c) ECEC. [10]

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biodiesel are 25% and 45% renewable. These results are more intuitive and closer to those from other studies. In addition, the importance of resources such as soil, water and stone for the selected biofuels is much more apparent. The approach described in this section has also been applied to the life cycle of ethanol from a variety of cellulosic and waste sources [28]. The results reveal a dilemma facing the quest for alternative transportation fuels: in general, biofuels have a smaller return on investment than fossil fuels, but are more renewable. Furthermore, those biofuels that have the most attractive return on investment are made from waste materials, but these materials are limited in their availability. Such results confirm the importance and high quality of fossil fuels. These attractive features are due to the fact that significant ecological effort has gone into the transformation of biomass to fossil fuels. Using today’s biomass for fuels requires such effort to be put forth via human activities, which tend to make such fuels economically and environmentally less attractive. Agriculture is among the most resource intensive steps in the biofuel life cycle. Consequently, biomass that is less dependent on intensive agriculture is likely to be more attractive. THERMODYNAMICS FOR IMPROVING THE LIFE CYCLE Improvement analysis is a step in the ISO standardized LCA method [29], but has received limited attention. In general, steps that have the largest life cycle impact due to its emissions are the focus of improvement efforts. However, such information is usually not available at the scale of specific equipment, since life cycle inventory databases provide emissions information for the overall process. Thermodynamic methods have been popular for identifying improvement opportunities in manufacturing processes, by focusing on those steps that have the largest lost work. For a given process or unit operation, Equations (3) and (4) can be applied to find the energy losses and exergy losses or lost work. For improvement purposes, these metrics can be very enlightening. By definition, lost work represents losses that can be avoided, since under reversible conditions, the lost work is zero. Therefore, the unit operations with the greatest lost work also should have the greatest potential for improvement. Design alternatives which move toward the reversible limit will have smaller and smaller lost work values. This section demonstrates how such an approach can be extended to improvement of steps in a product life cycle.

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Use of a thermodynamic approach for identifying improvement opportunities has been demonstrated recently via application to the process and life cycle scales for titanium dioxide nanoparticles [30]. Fig. 5 shows the processes included in the life cycle and the dashed box shows the block considered in detail for the process level analysis. The flow and degradation of exergy at the process scale is shown in Fig. 6. This figure shows that the largest losses are in the spray hydrolysis step, followed by distillation and pyrohydrolysis. This figure is based on assuming the processes to be operating under ideal thermodynamic conditions. Under similar conditions, an energy flow diagram would not provide much insight into the most important candidate processes for improvement. As shown in [30], the largest energy losses in this process are for the distillation column due to the enthalpy content of the condensed water. However, this stream has little exergy content, as shown in Fig. 6. In addition, energy analysis relies only on the first law, so there will not be any losses in the processes under ideal conditions since energy is always conserved. Thus, the insight for improving this process from energy analysis will be of limited use. Exergy analysis provides much more insight and helps focus the improvement effort on the least efficient processes. The impact of emissions at the process scale is included in [30]. It shows global warming potential (GWP) and human toxicity potential (HTP) as the most significant impact categories due to direct emissions from the process. Fugitive emissions of hydrochloric acid contribute to the HTP in the early stages of the process. Release of carbon dioxide from the combustion of methane as well as fugitive emissions of methane make up the GWP in the hydrolysis units. Improvement analysis based on these observations would suggest prevention, collection, or sequestration of the carbon dioxide produced, as well as careful scrutiny of fugitive emissions of harmful chemicals such as hydrochloric acid. Comparing the improvement insight from exergy analysis with that from impact assessment of emissions described in the previous two paragraphs, both approaches focus on spray hydrolysis as the step with most harmful emissions and highest exergy loss. However, subsequent insight from both methods diverges, as impact assessment focuses on emissions of hydrochloric acid, while exergy analysis directs

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attention toward losses in the distillation column due to the dissipative use of steam. This example indicates that impact assessment and exergy analysis can be complementary by providing unique insight into improvement opportunities. Exergy analysis may also be done at the life cycle scale by calculating various types of cumulative exergy consumption discussed in the thermodynamic methods section. Regardless of the approach, results for the TiO2 process show that unlike the process scale where exergy losses are mainly due to the use of energetic resources, the loss at the life cycle is mainly due to the use of material resources [30]. Specifically, the exergy lost in processes that extract resources from the lithosphere is extremely high. This is due to the low concentration in ores and the large amount of mining waste. ICEC identifies iron ore and water to have the highest contribution to mining processes, while ECEC decreases the importance of water as compared to iron ore due to the former’s smaller transformity. Thus, exergy analysis at the life cycle scale indicates the importance of improving mining processes or relying on recycled materials instead of virgin materials. Similar insight based on analyzing a broader array of manufacturing processes is also provided in [31]. This work also shows the trend toward highly energy and materials intensive industries that can manipulate materials at increasingly small scales. These materials such as computer chips and nanoparticles provide significant benefits in the form of electronic and nano products. However, any claims of their sustainability will also require addressing the high energy and exergy use. An energy analysis or traditional life cycle impact analysis would not identify such improvement opportunities since materials are typically excluded from energy analysis and impact assessment. At the life cycle scale, energetic and exergetic efficiencies are found to be nearly identical for the processes required to produce the main raw materials. Such results where energy and exergy efficiencies are very similar are not uncommon [32], and is one reason why exergy analysis has not received much attention in life cycle research [33]. However, a closer look at the exergy loss in each process shows that material losses in the life cycle are just as large as energy losses due to the highly materials intensive upstream processes such as mining. An energy analysis is unable to consider material losses, indicating a significant benefit of using exergy analysis even at the life cycle scale.

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Figure 5: Life cycle for producing nano titania [30]. The manufacturing process is in the dotted line, while important processes in the life cycle are shown in the dashed line.

Exergoenvironmental Analysis is a recent approach proposed by Tsatsaronis and coworkers [34], as an extension of exergoeconomic analysis [35]. This approach is useful when information about environmental impact is available for the entire life cycle such as that from life cycle inventory databases, but not for individual processes or equipment that constitute the life cycle network. It uses exergy loss in a process and equipment to allocate the impacts from its LCA. Impact assessment is done to obtain highly aggregated results such as those by ecoindicator 99 [36]. This indicator is partitioned among specific pieces of equipment in proportion to their exergy losses. This approach allows assignment of impact assessment scores that are based on emissions data at a coarser scale to individual equipment even when emissions data from the equipment are not available. Such an approach for allocating costs is the basis of exergoeconomic analysis, and has been used for improving manufacturing processes [1, 37].

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Figure 6: Grassmann diagram for nano titania [30].

THERMODYNAMICS, COMPLEXITY, AND SUSTAINABILITY Most current efforts in Sustainable Engineering such as those described so far in this chapter are oriented toward expanding the system boundary to include the life cycle and then focusing on ways of minimizing life cycle impact or resource use. While such approaches may encourage environmentally friendlier industrial activities and innovation, it is not at all clear whether enhancing resource efficiency and minimizing impact by considering industrial systems in the life cycle will lead to sustainability. A thermodynamic view conveys that most industrial activities aim to decrease entropy by making the desired products [38]. As per the second law, this must result in an equal or larger rate of entropy increase in the surroundings. Often, this entropy increase appears as harmful environmental impact, such as that due to

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pollutants produced while burning coal to generate electricity. The entropy increase in the environment due to the impact of these emissions may be decreased by finding uses for the emissions. This can be achieved by establishing byproduct synergy or industrial symbiosis networks such as those where the ash from coal combustion is used as an additive to concrete and the sulfur dioxide is used to make gypsum [39, 40]. However, such networks are still not likely to lead to sustainability, since the role of ecosystem services that support the industrial systems must also be considered. This may be accomplished via the establishment of networks of industrial and ecological systems such that the ecosystems provide the services necessary for the functioning of industrial systems [41]. Such networks will be highly complex, nonlinear and adaptive. Unfortunately current understanding of such complex systems indicates that developing such networks of technological-ecological systems to maximize resource efficiency may make them brittle and susceptible to collapse. Efforts to understand the behavior of such complex networks indicate that various thermodynamic principles and interpretations may be relevant to their design and management and emerging areas of research. In general complex adaptive networks are far from equilibrium but can sustain themselves with an input of energy. Schr¨odinger suggested that life aims to “suck orderliness from the environment” [42]. Prigogine proposed that nonequilibrium and complex systems dissipate energy or produce entropy for maintaining their organization [43]. The Maximum Entropy Production (MEP) principle [44] states that complex systems far from thermodynamic equilibrium are characterized by a non-equilibrium state where the rate of entropy production is maximized. It provides a foundation to understand a wide range of Earth systems including hydrology, climate, and terrestrial ecosystems [45]. Ecologists have proposed goal functions that influence the behavior of ecosystems. These include include maximum power, maximum empower (emergy per time), maximum dissipation, maximum storage, maximum cycling. Fath et al., have shown that these independently suggested goal functions are actually complementary to each other [46]. It has been suggested that such goal functions are also appropriate for understanding sustainability of human activities [47], but much more research is needed.

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CONCLUSIONS Thermodynamics has played a central role in understanding and assessing both industrial and ecological systems since both systems involve transformation of resources and are governed by thermodynamic laws. Since developing sustainable technological systems requires consideration of industrial networks and their supporting ecosystem services, thermodynamics is uniquely positioned to play an important role in the analysis and design of sustainable systems. The concept of exergy is very useful because it captures the first and second laws of thermodynamics and represents a common currency that governs the behavior of industrial and ecological systems and can provide insight into their reliance on ecosystem goods and services. A variety of exergy based methods have been developed by engineers and ecologists, and using them for sustainability assessment requires understanding of the underlying assumptions when aggregating resources in terms of their exergy or cumulative exergy. The approach of ecological cumulative exergy consumption or emergy seems to be best suited for sustainability assessment since it accounts for the role of industrial and ecological systems. Furthermore, exergy analysis also helps in identifying opportunities for improving the life cycle and can complement the insight from life cycle impact assessment. Improving the resource efficiency of industrial systems is not likely to be adequate for ensuring sustainability, since the relevant networks are complex adaptive systems. Thermodynamic insight can play a crucial role in understanding the behavior of complex systems, and represents an emerging area of research. CONFLICT OF INTEREST The author(s) confirm that this chapter content has no conflicts of interest. ACKNOWLEDGEMENT Funding for this work was provided by the National Science Foundation and the Environmental Protection Agency. REFERENCES [1]

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A. Baral and B. R. Bakshi. Emergy analysis via economic input-output models with application to transportation fuels. Ecological Modelling, 221(15):1807–1818, 2010. M. T. Brown and S. Ulgiati. Emergy -based indices and ratios to evaluate sustainability: monitoring economies and technology toward environmentally sound innovation. Ecological Engi-neering, 9(1-2):51–69, 1997. B. R. Bakshi. A thermodynamic framework for ecologically conscious process systems engineering. Computers & Chemical Engineering, 24(2-7):1767–1773, 2000. PSE Group and Center for Resilience, The Ohio State University. Ecologically based life cycle assessment, 1997 US benchmark model, beta version. http://resilience. Accessed May 31, 2010. Y. Zhang, A. Baral, and B. R. Bakshi. Accounting for ecosystem services in life cycle assessment, part II: Toward an ecologically based LCA. Environmental Science & Technology, 44(7):2624–2631, 2010. A. Baral and B. R. Bakshi. Hybrid Eco-LCA of some fossil and biomass fuels: Understanding the dilemma of renewable fuels. Technical report, The Ohio State University, 2010. International Standard Organization. Environmental management - life cycle assessment, 1998. G. F. Grubb and B. R. Bakshi. Appreciating the role of thermodynamics in LCA improvement analysis via an application to titanium dioxide nanoparticles. Technical report, Department of Chemical and Biomolecular Engineering, The Ohio State University, 2010. T. G. Gutowski, M. S. Branham, J. B. Dahmus, A. J. Jones, A. Thiriez, and D. P. Sekulic. Thermodynamicanalysis of resources used in manufacturing processes. Environmental Science and Technology, 43(5):1584–1590, 2009. Marc A. Rosen and I. Dincer. Thermoeconomic analysis of power plants: An application to a coal fired electrical generating station.Energy Conversion and Management, 44:2743– 2761, 2003. Ruedi Mueller-Wenk. Depletion of abiotic resources weighted on base of ”virtual” impacts of lower grade deposits used in future. IWO Diskussionsbeitrag, (57), 1998. Lutz Meyer, George Tsatsaronis, Jens Buchgeister, and Liselotte Schebek. Exergoenvironmental analysis for evaluation of the environmental impact of energy conversion systems. Energy, 34:75–89, 2009. George Tsatsaronis. Thermoeconomic analysis and optimization of energy systems. Progress in Energy and Combustion Science, 19(3):227–257, 1993. M. Goedkoop and R. Spriensma. The ecoindicator 99, a damage oriented method for life cycle assessment: Methodology report. Technical report, www.pre.nl, 1999. George Tsatsaronis and Javier Pisa. Exergoeconomic evaluation and optimization of energy systems - application to the CGAM problem.Energy, 19(3):287–321, 1994. T. G. Gutowski, D. P. Sekulic, and B. R. Bakshi. Preliminary thoughts on the application of thermodynamics to the development of sustainability criteria. In Proc. IEEE-ISSST, pages 121–126, May 2009. M. R. Chertow. ”uncovering” industrial symbiosis. J. Industrial Ecology, 11(1):11–30, 2007. United states business council for sustainable development. www.usbcsd.org. Accessed March 1, 2008.

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CHAPTER 11 Industrial Ecology and Sustainable Development: Dynamics, Future Uncertainty and Distributed Decision Making Jim Petrie1,2,*, Ruud Kempener3 and Jessica Beck4 School of Chemical and Bio-molecular Engineering, University of Sydney, NSW 2006, Australia; 2University of Cape Town, Rondebsoch, 7700, South Africa; 3IRENA Innovation and Technology Center, Robert-Schuman-Platz 3, 53175 Bonn, Germany and 4Büro für Energiewirtschaft und technische Planung GmbH, Aachen, Germany

1

Abstract: This chapter describes ways to enhance the operational potential of Industrial Ecology. Attention is paid to specific challenges for sustainable development beyond the broad aims of achieving economic competitiveness, ensuring environmental stewardship, and promoting both intra- and inter-generational equity. The focus is on the distributed decision making practices of individual agents within networks of industry, business and government, which, in various combinations, provide the underlying structure of any industrial ecology. Here, we consider the need for design and analysis tools to engage with the dynamics and uncertainty which characterize complex hierarchical socio-technical systems, including the ability to observe and interrogate system behaviour over multiple spatial and temporal scales, and to embrace the vitality which comes from human judgment in decision making within these systems. This capability should support the transition of such networks to ones which are both resilient and adaptive whilst pursuing.sustainability goals. We explore the role of both simulation and optimization toolkits in this regard, and conclude that there is value in a dual approach. Agent-based models of industrial ecosystems can be coupled with scenario analysis techniques to engage with future uncertainties. The way in which individual agents internalize such “world view” scenarios in their own distributed decision making practices, is highlighted. The effectiveness of agent interventions to support successful transitions to more sustainable practices can be measured against goal programming objectives, which in turn are defined by exploring the dynamic multiple objective optimisation decision space.

Keywords: Industrial Ecology, Sustainable Development, Decision Making, Simulation, Agent-based modelling, Uncertainty industrial eco-systems, industrial networks, value chains, resource efficiency, robustness, resilience, adaptiveness, sustainability assessment, decision support frameworks, objectives hierarchies, *Address correspondence to Jim Petrie: School of Chemical and Bio-molecular Engineering, University of Sydney, NSW 2006, Australia; Tel: +61-402-003039; E-mail: [email protected] Heriberto Cabezas and Urmila Diwekar (Eds) All rights reserved-© 2012 Bentham Science Publishers

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decision trees, network structure, network characteristics, network performance, embeddedness, norms, routines, scenario analysis, cognition, mental models, bounded rationality, strategic choice. INTRODUCTION There are significant challenges in translating the guiding principles of the “Bruntland definition” of sustainable development [1] into practical tools for industry, business and government. Perhaps, most importantly, there is an urgent need to move beyond the simplistic notion that “triple bottom line” reporting is an adequate response to the imperative to promote more sustainable living. Such an approach has become increasingly reductionist, with little real attention being given to how implicit trade-offs between different objectives are explored, how considerations of system dynamics and uncertainty are accommodated, and how distributed decision making across multiple value chains and business networks can be managed to achieve high level sustainability goals. This position is encumbered further by lack of appreciation of the (often) different philosophical world views and the practical cognitive skills of decision makers faced with these tradeoffs, [2]. More recent definitions of sustainability, e.g., that of Ehrenfeld [3] which states that “…. sustainability is the possibility that human and other life will flourish on Earth forever”, embrace the complexity which comes from the interdependence between evolving natural systems and the socio-technical systems which underpin (and to some extent) define both the structure and performance of the global economy. Central to this view is the recognition that this complexity emanates from the dynamic, uncertain and unpredictable nature of such interactions. Following from this, Ehrenfeld and others [4, 5] have recognized the need for design and analysis tools which engage with the role of human value judgments in decision making, and how these are informed by the world views of decision makers. The distinction between “sustainable development” and “sustainability” has often relied on simple notions that the former is about processes and pathways which contribute to the latter goal. Whilst this distinction is naïve, it does offer an important point of entry for this current chapter. Consideration of complex sociotechnical systems around sustainability objectives needs to engage with system

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dynamics, interventions and transitions, and the underlying decision making processes and policy positions which give rise to these. This need has been reinforced by other authors too. Kay [4] stated that“…. Expectations that decision makers can carefully control or manage changes in societal or ecological systems have also to be challenged. Adaptive learning and adjustment, guided by a much wider range of human experience and understanding than disciplinary science are also necessary”. In a similar manner, Ruth et al. [5] assert that “there is widespread and growing consensus that models of industrial material use, energy use and emissions cannot be prescriptive, because such models involve a multitude of psychological, social, technological, economic and political factors that can never be known with sufficient certainty to render such an analysis useful for forecasting”. Both these statements reaffirm the need to look more critically at systems approaches to the design and analysis of industry networks Industrial Ecology attempts to identify opportunities to promote sustainable development by viewing industrial systems holistically, i.e., through their interaction with other technological and economic systems, but also in terms of the interface they form between nature and society [6]. Industrial Ecology draws on a variety of frameworks and tools. These range from concepts such as dematerialisation and cleaner production; to procedural tools such as eco-design; to analytical tools, such as material flow analysis and life cycle assessment [7]. Existing analysis tools are classified according to the system description (e.g., symbiosis/geographical system or metabolism/product-based chain [8, 9]; the strategy they recommend (e.g., dematerialisation); or at which level of detail the system is analysed (e.g., intra-firm, inter-firm or the regional/global perspective [10]. Together, this suite of tools represents a fairly comprehensive attempt to capture the relationship between industry activity and the sustainability of the external operating environment. However, as will be argued in this paper, none of these approaches gives any insight into the structure of industry networks, nor how this influences performance - an omission which we address in this chapter. Neither are they particularly helpful in addressing, for example, the dynamics of technology change and lock-in effects, nor the time lags between investment decisions and technology roll-out. All of these features influence the evolution of

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any industry network over time. An excellent review of these underlying challenges is given by Ruth and Davidsdottir [11]. Due to the structure of industrial networks, it is a considerable challenge to identify those interventions and transitions which could assist them to evolve along a pathway that is sustainable. We define “intervention” here to mean those intentional actions of one or more organizations inside or outside the industrial network directed to stimulate the network’s contribution to sustainable development. There are two broad categories of interventions that should be considered: policy interventions emphasizing the evolution of the network; and changes in strategic behavior by organizations directly engaged in transforming and exchanging resources. Industrial networks comprise any number of organizations that are linked to each other through the exchange of resources. Whilst some such structures are quite simple, others comprise complex interacting structures spanning multiple value chains. Organisations within such systems are assisted or impeded in the pursuit of their own goals by input from a wide range of interested and affected parties (IAPs) (e.g., government, NGOs and CBOs, universities etc.). All these IAPs contribute to the functioning of the industrial ecosystem or network by, for example, providing information, setting rules, introducing innovations, setting standards, or simply by setting the demand for outputs from the network. Although all may profess to want to work together with respect to the functioning of the industrial network as a whole, the reality is different, as each has its own individual agenda. For example, industrial organisations are interested in maximising profit; governmental organisations are interested, amongst others, in increasing the contribution of the industrial network to national welfare; and environmental organisations want to reduce the negative environmental impact of the industrial network. The IAP grouping contributes to defining the external environment in which a given network will operate. The interplay between the individual decision making processes of network players, and those of the IAPs, and how this is shaped by the transactional exchanges between network players, results in industrial networks which are dynamic and evolve continuously [12]. Any transition towards a more sustainable system is unlikely to be a gradual linear process. Instead, it is more likely to consist of alternating stages of rapid change

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and stability - a dynamic equilibrium. No single organization within the network, or aligned with it, is able to coordinate an effective transition on its own. Secondly, it is unclear how the actions of individual organizations contribute to the performance and evolution of industrial networks as a whole. The final challenge in coordinating a transition is uncertainty about the end goal. This brings us back to the starting point of this analysis, and the distinction between pathways and goals. The sustainability of any industry network depends on the social / environmental context in which it operates, and how resilient and adaptive it is to future shocks and shifts. Since the future, including possible shocks and shifts, is inherently unknown, it is a challenging task to decide now what network functionalities are required at any given time, nor how these are best endowed to the network through intervention. And the question remains as to how one is able to understand the consequences of any such intervention, either for individual organizations or the network as a whole, especially considering the underlying complexity of the network. A systems approach is a necessary prerequisite to understanding interaction between organizations, and its consequences for the socio-economic and biophysical system in which organizations function. We need to understand the interaction between all organizations that in one way or another contribute to the transformation of resources entering the network and goods and services leaving the network. This then is the challenge. How can we build on the suite of sustainability tools and analytical approaches which exist, including Industrial Ecology, by making explicit the means by which dynamic behaviour and future uncertainty are considered, both by individual organizations, and by the industry network as a whole? HOW INDUSTRY NETWORKS MANAGE UNCERTAINTY Industrial networks are characterized by both their function and structure. Understanding how both these properties evolve dynamically is critical to any rigorous sustainability assessment. Function can be focused internally, i.e., sustaining an organization’s position within the network, with continued access to resources, or more widely amongst the institutions it embraces; or externally, in the form of providing goods and services. Structure is defined by the flow of

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resources1 between organizations [13]. Both function and structure are influenced by competition and cooperation between organizations trying to maintain or improve their position within the network [14]. It is this dynamic behaviour which makes industrial networks inherently uncertain. Although different operational paradigms exist to address market uncertainty through either vertical integration, outsourcing or different licensing-subcontracting models, uncertainty is a fundamental part of industrial networks and needs to be understood holistically to analyze the function, structure and dynamics of industrial networks through time [15, 16]. The first type of uncertainty relates to the potential consequences of a particular strategic decision by any organisation. The second type of uncertainty is about other organizations and the resources they provide or demand [13]. Fundamentally, it is the uncertainty which exists within industry networks which affects the way in which organizations make decisions [17]. The effect of uncertainty in decision making has been articulated most clearly around the question of whether organizations behave as rational optimizers, or whether more (seemingly) irrational behaviour exists [18]. However, it has become clear that rational behavior, conceptualized as a ‘powerful analytical and data-processing apparatus’, does not really exist, because information about consequences is unknown, computational capabilities are bound and preferences are not stable [19, 20]. Furthermore, it has become necessary to adopt simplifying strategies to guide decision making [21, 22]. Use of routines [23], heuristics [24, 25], social relations [26-28], and social norms [29] all play a role in strategic decision making. This collective understanding from the social sciences provides a helpful adjunct to the understanding of uncertainty we gain from the management sciences – which we will draw on for this chapter. Future uncertainty also has consequences for the way in which we evaluate interventions to induce transitions. If perfect foresight was possible, interventions could be ranked according to their effectiveness in achieving a particular goal. However it is not possible to measure the concept of “effectiveness” objectively – any such exercise is influenced by value judgments. Similarly, since industrial 1

Resources are defined as “anything which could be thought of as a strength or weakness of a given organization” Wernerfelt, B. ("A resource-based view of the firm." Strategic Management Journal; 1984: 5(2): 171-181.

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networks evolve continuously, there is a need to consider the effectiveness of the intervention at any point in time, not simply at predetermined intervals, or at some end point [30]. In this chapter, we introduce an approach to sustainability assessment for industry networks which addresses the challenges identified above. The approach combines a systems’ view of sustainable development with agent-based modeling and quantitative scenario analysis - to explore how perceptions of future uncertainty might affect the behavior of organizations. It is specifically aimed at evaluating the robustness of interventions along the pathway rather than a specific end point. Scenario analysis is a strategic planning tool that explicitly considers the plurality of different world views instead of one single truth [31]. Traditionally, it has been used to explore different system level contexts in which the analyst (a government, industrial organization or group of stakeholders) might find themselves and how information about different futures can inform actions today [32-34]. Although scenario analysis focuses on how different futures might affect current actions, it does not include an analysis of how current views about the future might affect the future itself. This so-called “second-order reflexivity” has been the topic of discussion around the challenge of governance in sustainable development [35], but has not been connected to quantitative models of interventions and transitions. We address this deficiency in this chapter. Agent-based modeling (ABM) is a simulation approach that has gained considerable attention in recent years for the modeling of industrial networks [36, 37]. Such models depict the evolution of systems through a set of rules which describe the behavior and interactions of heterogeneous agents operating within a particular system. Any ABM is a multi-scale model with agent behavior “playing out” at one scale, and consequential system evolution at a higher scale (both in space and time). The rules by which individual agents decide to act may vary, and range from very simple to complex. The advantage of agent-based models over those characterized as general equilibrium, optimization or econometric models, is that they are able to reproduce and explain nonlinear aggregate patterns without the need for regressions or simplifying assumptions. Furthermore, agent-based

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modeling is particularly useful for analyzing systems in transition [38]. More than any other modeling tools, ABM allows for the spatial and natural setting of organizations; for participatory approaches whereby different stakeholders can explore different sets of assumptions; and for explicit representation of relationships on different scales of analysis [39]. ABMs have a role, both as generative processes (where the focus is on emergent behaviour and system evolution); and as tools to facilitate participative discourse amongst stakeholders as to the problem context which merits consideration in the first place. It is this continuum – from problem conception to resolution – which can be facilitated by ABMs. We will find this useful when we look to deliberative decision support frameworks. This combined learning experience i.e., the improved understanding of problem complexity across all problem dimensions (including temporal), is an important message. We need to focus on the way in which simulation tools aid comprehension and understanding within the realm of complex adaptive systems, and how such understanding, ultimately, reinforces the “reality” of the ABMs. However, in their routine application ABMs face similar problems to other modeling approaches when it comes to predicting the consequences of interventions, including their ability to capture the richness of agent behaviour. Many ABMs limit their description of agent behaviour to simple economic rules [40, 41] Other models do engage with wider considerations, but consider only simplistic social interactions, which lack a rigorous foundation [42-44]. Finally, there are several ABM that are empirically based and replace conventional income maximization functions with alternative socio-psychological theories of decision rules [45] or with descriptive rules [46]. Most importantly, the value of any ABM is based on the comprehensiveness of its system description. Whilst it is possible to define a set of realistic rules for individual agents at any point in time, the uncertainty of future events and external influences makes this a challenge dynamically. Intuitively, the use of scenario analysis techniques coupled to ABM should provide a more rigorous and defensible approach to sustainability assessment. However, as routinely practised, this combination does not explicitly engage with future uncertainty, and excludes

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the role of other organizations and their responses to potential policy interventions. We address this limitation in this chapter, by addressing the cognitive processes and behavioural assumptions employed by individual organizations to map out their decision space within the specific context of sustainable development. AGENT BASED SCENARIO ANALYSIS The central premise of agent-based scenario analysis (ABSA) is that the most important driver for industrial network evolutions is the behaviour of organizations, which, in turn, are responsive to dynamics in the network environment. Their actions determine what resources are used, how they are used, where they are used, and how they are transformed into goods and services that fulfil societal needs. In such a dynamic, complex and uncertain environment, organizations have to find a way to understand and comprehend the consequences of their actions. It is argued here that it is the organizational perception of, and response to, future uncertainty, rather than future uncertainty itself, that are the most important determinants of industrial network evolutions. In other words, although there will be major unforeseen disruptions in the future that change the way we live, the evolutionary pathway of industrial networks is shaped by the perception that organizations have about the future, rather than the future itself. By systematically exploring different perceptions of the future, it is possible to explore a large range of plausible network evolutions. Subsequently, these plausible network evolutions can be used as scenarios within which to develop robust interventions, including policy. Traditional scenario analysis only considers the different ‘world views’ of the analyst, in order to explore different system level contexts. It does not consider the subjects of analysis, i.e., the organizations within the industrial network facing this uncertainty, nor their way of dealing with this uncertainty. In contrast, ABSA explicitly considers the possible responses of the organizations within the network. The way in which these possible responses are explored is consistent with methods in scenario analysis. Different ‘mental models ’ are constructed, whereby each model represents an internally consistent and coherent way in which organizations might deal with future uncertainty. Each ‘mental model’ is

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represented by a set of assumptions along two dimensions. The first dimension represents assumptions about what kind of information the organization uses in its decision making process. The second dimension consists of assumptions about the way in which organizations transform that information into action. It is argued here that, by systematically exploring how uncertainty is dealt with in mental models of organizations, it is possible to explore how organizational behavior affects industrial network evolutions and the potential consequences of interventions to stimulate sustainable development. The term ‘agent scenario’ is used to refer to a consistent set of assumptions representing a particular ‘mental model’. The remaining part of this section will discuss in more detail how ‘agent scenarios’ are constructed and how ‘agent scenarios’ are combined with ‘context scenarios’ to be used to explore the robustness of interventions. Fig. 1 shows the difference between traditional scenario analysis and ABSA.

Figure 1: The Agent-Based Scenario Analysis Approach

In ABSA, this dual representation of uncertainty – both through different “world views”, and through the different cognitive processes (“mental models”) employed by organisations in their decision making, is important for several reasons. Firstly, it disconnects the presumptions of the analyst from the exploration of the system. In complex adaptive systems this is an important advantage, because it allows for an analysis of the system as it is, rather than as it is viewed by the analyst [47]. Secondly, this approach reduces the reliance on accurate data to represent the current state of the system. In traditional scenario

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modelling exercises, the modelling results are highly dependent on the accuracy of initial input variables, and changes in the initial conditions can have important effects on the modelling outcome. The use of various statistical sampling techniques to explore the effects of uncertainty in initial conditions can help to some extent [48]. However, it is impossible to explore all the different initial conditions in large scale systems like industrial networks, because of the interdependencies between the variables, and because of the large variety of external environmental conditions possible. ABSA is less sensitive to initial data, because the different scenarios are based on the different ways in which organizations can use this data (the ‘mental models’) rather than the data itself. Finally, ABSA reduces the need to assess a large range of different ‘context scenarios’, and increases the robustness of the analysis [49]. In traditional scenario analysis, the analyst has certain model biases about the context of the industrial network, so it is important to reflect a large range of fundamentally different futures. However, by using ABSA, the precise form of these different context scenarios is less important, because it is the processes that organizations use to deal with the future which shapes the evolution of the system, rather than the pre-conceived perception of the analyst2. The application of ABSA to the design and analysis of industrial networks for sustainable development requires four steps. Firstly, different mental models employed by individual organizations to describe uncertainty in their external environment (and informed by different “context scenarios”) have to be identified. Secondly, these mental models have to be developed further into a set of explicit cognitive processes to support the decision making of individual organisations. Thirdly, these cognitive processes need to be implemented within a defensible decision support framework which brings together the full spectrum of learning processes and dynamic behaviours which can take place within an industrial network – from simple adjustment of business practices for any individual organisation, to the development and growth of institutional behaviour across the 2

For example, consider the choice of either traditional scenario analysis or ABSA to explore an energy network [77, 78]. For scenario analysis, exploring particular demand growth rates is important. For ABSA, the modeling results depend more on the organizations’ mental model, rather than the actual growth rate number. As a result, it is possible to explore a much wider range of possible futures using ABSA, by capturing the diversity of mental models.

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network as a whole. Finally, the worth of specific interventions needs to be assessed within a multi-criteria analysis. In this last stage, objective goals have been pre-defined through a global dynamic optimisation of the system operating as a coherent whole, with one “top down” decision maker. The remainder of this chapter outlines these stages in more detail. STEP 1: STRATEGIC DECISION MAKING UNDER UNCERTAINTY Strategic decisions are a ‘set of consistent behaviours’ directed towards a match between the internal capabilities of an organization and its external environment [50-53]. Strategic decisions can range from investment strategies; to the formation of strategic partnerships across a value chain; to involvement in community projects to promote corporate social responsibility – all valid activities under a commitment to sustainable development [54-56]. There is a wide variety of organizations involved in industrial networks (governments, advocacy groups, manufacturers, wholesalers and retailers) whose primary motivation and commitment to sustainable development is different. Hence, the development of a uniform set of ‘mental models ’, which reflects the different ways in which organizations can make strategic decisions, is a challenging task. However, one characteristic that organizations in industrial networks do share is that they all have limited information about what the future holds, and limited control over the consequences of their actions. According to Bernstein [17], the development of industrial networks is intrinsically linked to the management of uncertainty. Only if one acts upon uncertainty can one create knowledge to proactively shape the direction of the future. Bernstein argues that the way in which organizations deal with uncertainty is the most important driver for industrial network evolutions. There are several schools of thought on how to deal with uncertainty in strategic decision making. Theories range from descriptive to normative. Mintzberg and Lampel [57] provide an overview of the different schools of thought based on a two dimensional representation (see Fig. 2). The first dimension is related to how an organization interprets uncertainty in its environment (referred to as the ‘representation process’), and the second dimension is related to how it deals with

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this uncertainty in its decision making process (referred to as the ‘choice process’). The continuum along each axis is informed by the range of views about the different processes that can be used to interpret the external world (on one axis), and of the different cognitive processes used to make decisions on the other. The analysis process itself, which flows from this representation, can take a range of possibilities. On the one hand, the process can become reductionist and entirely resource-focused, involving network attributes like input and output prices, technology specifications, regulation and infrastructure [50]. On the other hand, the interpretation process can be viewed as ‘institutional’, affected by individual norms and values, symbolic meanings, relational characteristics like trust and loyalty and legislative, normative and cognitive rules [27, 29, 58]. The ‘choice’ process reflects different assumptions about how an organization acts in the face of such information. On the one extreme, in the so-called “rational” approach, an organization chooses to maximize its subjective expected utility (SEU); while the so-called “natural” approach assumes that choices are made ‘on the fly’, in the form of heuristics, routines and/or imitation [24, 59, 60].

Figure 2: Mapping the Decision Making Process under Uncertainty

The different ways of dealing with uncertainty about consequences are not only reflected in the decision making process, but also in the learning processes to which organizations contribute [61]. The learning process is an essential part of

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the ‘mental models’ of organizations, and needs to be explored in conjunction with the processes that inform strategic decision making. March argues that, at one end of the spectrum, organizations assume that consequences are a true reflection of their actions, and therefore can exploit this to their competitive advantage. At the other extreme, organizations view the consequences of their actions as inherently uncertain, and therefore adopt explorative methods of learning. This dichotomy is consistent with the categorization of Mintzberg and Lampel [57], who assert that some organizations accept the view that the world is comprehensive and controllable, and that strategic choice involves fitting internal strengths to external opportunities; while other organizations view the world as unpredictable and uncontrollable STEP 2: DEFINING STRUCTURAL RELATIONSHIPS AND DECISION RULES Our understanding of business decision making is supported by a rich academic literature, spanning cognitive sciences, behavioural psychology, and management practice. As we seek to “operationalise” the discipline of industrial ecology, there is merit in exploring how we can harness this rich understanding to real benefit in the context of sustainable development. There are a number of descriptive theories of decision making practice which resonate with this intent, most notably: 1) bounded rationality, 2) use of politics and power, and 3) the “garbage can” model. Bounded rationality proposes that organizations are constrained only by limitations in the information available and their calculations, that their perceptions of the environment are biased, and that organizational decisions are affected by internal conflicts [62]. The “politics and conflicts” approach expands on these ideas and describes the process of decision making as one which results from the imbalance between different interest groups, personal preferences and political games [63]. The “garbage can” model, the least restrictive of all, describes decisions as an accidental meeting of choices, problems and participants [64] . The conceptualization of organizational decision making by Cyert and March [62] is the simplest, because it considers the organization as a single unit, while the

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others see organizational decisions as emergent properties of a sequence of decisions between different people within the organization. Whilst all have value, in this chapter we choose not to drill down to a level of detail beyond the “single decision maker” model. Although such a perspective has received considerable critique, there are several practical arguments in favor of adopting a simplified perspective of organizational decision making in the context of ABSA. First, the view that an organization acts as a single decision maker can represent the decision making process in a management board room. There is particular information and alternative courses of action presented at the beginning of the meeting and depending on the decision rules applied, a strategic action is proposed at the end. This applies to both purely economic decisions as well as decisions that involve social or environmental consequences. Furthermore, individuals within organizations develop shared norms, values and assumptions that govern how organizations function [65]. Thirdly, the regulatory, normative and cognitive institutions affecting decision making in organizations apply to the organization as a whole as well as to the individuals within the organization [66]. If one considers an organization as a single decision unit, the Mintzberg and Lampel [57] typology of Fig. 2 can be viewed as different ‘mental models ’ for strategic decision making [22]. Such a view coincides with Simon’s theory on human decision making and the role of mental models. According to Simon, this dual side of uncertainty, a cognitive side and an ecological side [24], is reflected in the mental models that people and organizations use: “Human rational behavior is shaped by a scissors, whose two blades are the structure of task environments and the computational capabilities of the actor” [67]. These two components of mental models are independent. An organization can operate in a fairly open environment and represent this environment accordingly, using socially-determined characteristics, such as social norms and values. Contrarily, an organization can simultaneously be in a situation where it views the consequences of its choices as certain. It is argued here that, by systematically exploring these two different extremes of mental models and how they deal with uncertainty, it is possible to develop a set of scenarios that can create a richer understanding of industrial network evolutions.

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Instead of assuming that the decision situation ‘triggers’ the mental model, ABSA assumes that an organization can apply any cognitive process regardless of the characteristics of its decision situation (which in the case of long-term strategic decisions is often one of limited control and uncertain future). The choice to use a particular cognitive theory does not depend on the actual situation in which an organization makes a decision, but rather on the perception of the organization about that decision situation. As such, a whole range of cognitive theories is plausible and any ‘mental model’ can be applied at any time throughout the industrial network evolution. As a consequence, all ‘mental models ’ need to be considered if one wants to explore the range of possible directions over which industrial networks might evolve. The ‘mental models ’ of organizations can also be used as a basis for scenario analysis, within which different processes to deal with uncertainty can be explored [68]. At this point, there is merit in juxtaposing our understanding of cognitive mental models with structured models of decision making processes. Typically, organizations make decisions based on some form of ‘choice’ between alternative courses of action. The first stage of this process deals with recognition of a decision situation, followed by some form of design stage whereby alternatives or options are identified, and a final stage which involves the decision itself. In any structured process of this nature, it is important to capture explicitly the unfolding relationship between these strategic actions of organizations and their consequences. Mintzberg, Raisinghani et al., [69] developed one of the formative, and most elaborate, models of strategic decision making. This built on the work of Cyert, Simon et al., [70], which observed that strategic decision making in organisations does not follow a rational choice process. Rather, the sequence of decision making processes is not clear, alternatives are not given apriori but need to be sought or developed, and the consequences of decisions are almost always unknown or information about them is incomplete. In addition, the decision making process is not linear, but involves many feedback loops and non-linearities.

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One of the important observations of Mintzberg et al. [69] was that the moment of action was often not the appearance of a defined problem, opportunity or crisis, but that the determining factor could be viewed as the relationship between cumulative amplitude of stimuli and some action threshold. For example, the need for an industrial organization to improve the working conditions of its employees might only become apparent after a number of accidents have taken place. Furthermore, the magnitude of each stimulus is a variable too, depending on the decision maker’s interests, the perceived payoff of taking action, the uncertainty associated with the stimuli as well as the perceived success of the decision. For example, the awareness of environmental and social consequences of strategic actions has become more apparent to organizations now that shareholders have become interested in these subjects. The decision process model of Mintzberg et al. [69] is based on four distinct stages: identification, development, evaluation and selection. This allows for a wide range of decision possibilities – where the decision only involves a single proposed action, without any development activities, and where the outcome is a single go/no-go decision; to dynamic design processes involving relatively large investments, complex design activities and the likelihood of new options interrupting the process, over extended time periods. Fig. 3 shows this structure.

Figure 3: The process of decision making

This figure makes explicit how different choice processes can lead to different decision outcomes, and therefore different industrial network evolutions. The

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dotted line illustrates a mental model where an organization perceives the consequences of its actions as certain. In such a situation, the organization will evaluate the consequences of all alternatives and select the alternative that maximizes its utility. It will attempt to learn from its consequences by adjusting the alternatives chosen. At the other extreme, an organization that is uncertain about the consequences of any alternatives, will imitate actions of others in the network (dotted line). In between these two extremes, there are ‘choice processes’ where decisions are made on the basis of routines which are updated over time. In our previous work, we reviewed different organizational and psychological theories of decision making in an effort to better understand the characteristics of organizational and network structure which influence the way in which the external environment is represented to the decision maker [12]. The four different characteristics of industrial networks that inform strategic decision making are: 

Functional characteristics



Implicit behavioral characteristics of organisations



Implicit relational characteristics between organisations



Implicit network characteristics.

Functional characteristics are those characteristics that are recognized formally by all organizations within an industrial network, and which affect their position within the network. Examples of functional characteristics are the price and quantity of resources, the relative of organizations within a network, infrastructure availability, and regulatory environment. Implicit characteristics, on the other hand, are those characteristics that impact the decision making process of organizations, but which are not formally part of the network. From an organizational perspective, implicit characteristics are the norms and values that an organization uses to determine its objectives, position and potential action. Implicit relational characteristics, most importantly trust and loyalty, are used to determine what potential partners or competitors are important to consider; and implicit network characteristics are those social norms and values that emerge through social embeddedness.

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These four levels can be used to capture different levels of uncertainty from the perspective of an organization [71]. If an organization perceives the world as certain, it will rely entirely on functional characteristics to inform its decision making. If it perceives the world as less certain, functional characteristics are augmented with implicit behavioral characteristics to represent (and make sense of) the world. Finally, in a highly uncertain environment, an organization will rely on implicit relational and network characteristics to represent its world. Fig. 4 overlays the structure of the decision making process with the different ways in which an organization can extract information from its environment in order to inform its decision making processes. The four layers of this picture represent the different characteristics –from functional (at the bottom), to network-level / institutional behaviours (at the top).

Figure 4: The role of functional and implicit characteristics on decision making

An organization that uses only functional characteristics to interpret its environment perceives the world as certain. For example, in choosing a supplier for resources, this organization will base its decision purely on the price per unit of resource. On the other hand, an organization may base its decision for a supplier purely on the basis of the supplier’s social status. This is tantamount to perceiving the world as uncertain. Here an organization relies on implicit network characteristics to inform its decisions.

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STEP 3: IMPLEMENTING AGENT SCENARIOS We are now in a position to develop a set of “agent scenarios” which capture the behaviour of individual organizations within a given decision making situation, taking into account the underlying cognitive elements of decision making, as well as the way in which organizations engage with dynamics and uncertainty. This synthesis is the penultimate stage of the ABSA methodology. As in conventional scenario analysis, the challenge is to develop a range of scenarios which span the range of possible eventualities. Given that an organization can employ any number of different mental models (covering both cognitive processes and approach to uncertainty management), this could lead potentially to a very large number of scenarios – which is not that helpful. However, after closer analysis, we have found that a very wide range of decision behaviours can be captured in a simple 3 x 3 matrix (see Fig. 5).

Figure 5: Mapping the decision space

Three cognitive processes in mental models are distinguished: 1) rational behavior, 2) behavior on the basis of heuristics and 3) imitation. These are juxtaposed against the organisation’s perception of uncertainty thus: 1) the use of purely factual characteristics to inform decision making, 2) the use of norms and values to inform decision making, and the use of implicit relational characteristics to choose potential partners and 3) the use of social constructs to inform decisions.

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Each of these nine decision scenarios is defined by a particular set of rules describing how information is extracted from the environment, and by a different set of rules describing how this information is converted into action. This means that, although the position, actions, norms and values and objectives of organizations in the network are different (the parameters for each organization are uniquely defined at any point in time), they all use the same rules to represent the world and convert that information into action. Whilst, in reality, any decision situation might involve all, or a combination of, these mental models, their delineation in this matrix format is a helpful device to capture the full range of possibilities. These nine scenarios can be augmented by others which reflect the decision analyst’s own world views about the evolving context within which the industrial network operates. But the lesson here is that, whilst taking a broad view on the underlying decision support processes employed by any discrete organization in an industry network, it is possible to capture the full richness of decision environments within an analysis platform based on a combination of agent-based models and scenario analysis. Judgment and decision rules can be encoded within the agent-based model using different rules to represent an organization’s perception of uncertainty and its associated activities to deal with this uncertainty. STEP 4: ASSESSING THE WORTH OF INTERVENTIONS At this point within ABSA, it is now possible to explore different interventions according to the decision support framework just described, and to assess the degree to which any of these might be preferred against specific sustainability goals. In other words, this assessment should be helpful for decision makers in deciding which evolutionary pathways are more robust when set against sustainability objectives. We make use of the general toolbox of Multi-Criteria Decision Analysis [72-74], supported by the generic description of a decision cycle [75]. The first part of this process is the so-called “problem structuring” stage of a decision, the aim of which is to identify the stakeholders and obtain agreement about the exact decision at hand, the objectives that need to be satisfied by the decision outcome,

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the alternatives available, how to assess these (i.e., to what extent they meet decision objectives), and to elicit the preferences of stakeholders for particular decision outcomes. The various approaches to problem structuring are helpful in defining the context scenarios which are used to represent the external environment within ABSA. This process (or set of processes) is followed by “problem analysis”, which involves the evaluation of the alternatives under consideration, to determine to what extent these satisfy the decision objectives. This is followed by the selection of a preferred alternative, and sensitivity analyses to ensure that the conclusion is robust. All of these processes are shaped by the way in which decision makers engage with underlying uncertainty, as elaborated in the first 3 steps of ABSA. We started this chapter by suggesting different types of intervention which could potentially influence the way in which a given industry network evolves – policy interventions which form part of the external environment; and strategic decisions taken by individual organizations within the network which bear on the way resources are exchanged, and network relationships are shaped. We recognize too that any evaluation of different interventions needs to be dynamic and cumulative i.e., to map the evolving pathway / transition that results from the intervention, over a reasonable time period. Because any number of network evolutions is possible, depending on the way in which individual organizations make decisions, a rigorous method of comparative evaluation is required. This is where Multi Criteria Decision Analysis (MCDA) is appropriate – in particular, its extension to the integrated goal programming / scenario planning approach advocated by Durbach and Stewart [30]. To apply this technique within ABSA requires the selection of a set of sustainability performance indicators which can be applied within an MCDA framework. Broadly speaking, we wish to critique the various network evolution possibilities against economic, social, and environmental criteria. The identification of these criteria evolves from meaningful stakeholder engagement. Of course, each organization within the network may choose to focus on different goals, depending on their own strategic needs. How then, is it possible to reconcile these potential differences within a unified framework which enables the performance of the network as a whole to be determined dynamically? In our own work exploring the potential of new energy networks to stimulate regional development in countries with emerging economies [76, 77], we have

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chosen to focus on high level economic, social and environmental criteria which are largely uncontestable. For example, we use simple financial metrics like NPV or rate of return to assess the economic viability of specific policy interventions; CO2 footprints to assess climate change impacts; and new household energy supply to assess the social development angle. In all cases, these indicators can be quantified for the network as a whole, at any point in time. At the same time, they can be employed by individual organizations in the network to assess their own contributions. However, none of these metrics says anything about the dynamic character of the network itself. For example, they do not tell us how many organizations are part of network at any given time, how many physical relationships they have with other players in the network, nor how robust or adaptable these linkages are. In our own work [78] we advocate use of the following additional performance indicators for the network as a whole: Efficiency - a measure of quantitative contribution to economic / social /environmental objectives as a function of resource input Effectiveness – a measure of qualitative contribution as a function of the total perceived value Resilience – a function of structural diversity within the network (linkage redundancy etc.) Adaptability – a function of qualitative diversity within the network (variety, balance etc.) There are many ways to quantify these indicators. We do not give details here, but rather refer readers to Kempener and Petrie [79], which demonstrates this approach for a bio-energy network in an emerging economy, where acute developmental pressures sit alongside economic imperatives and environmental challenges associated with climate change, land use planning etc. Suffice it to say that, even for relatively complex industry networks (both in terms of scale and application), it is possible to define compound indicators based on the above 4 attributes, and to assess their cumulative impact over a meaningful time period

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which maps the evolution of the network in question. In this way, it is possible to describe the evolution of the network in terms of its functional, operational and relational characteristics. Fig. 6 shows a simplified objectives ’ hierarchy for 2 competing network pathways, where each is assessed according to the scheme just described.

Figure 6: Objectives hierarchy for competing evolutionary pathways

Underscoring this figure are two important elements of this analysis. Firstly, any MCDA requires that performance scores in any criterion be converted to some measure of value – i.e., how significant the contribution is, and how importantly it is viewed by stakeholders and decision makers. The easiest way to do this requires normalization against “best” and “worst” performance scores in any criterion. Through this exercise, it is possible to (at least) understand how preferences are elicited within any specific criterion. In the case of evolving industry networks, however, neither of these extremes is known apriori. Given the range of possible network evolutions, how does one proceed? To answer this question, we have chosen to reflect again on the need to introduce agent-based models in the first place – the desire to capture the richness of behaviours which result from

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distributed decision making by individual organizations within an industry network. This is what happens in reality. That said, it could be argued that the “best” outcome of any network’s endeavours would result from a situation where all discrete organizations work together in harmony around a commonly-held set of objectives, and the means by which to attain these. Likewise, the “worst” outcome for the network would derive from a situation in which all the organizations in a network choose to limit a network’s outputs constructively. Both these situations equate to an environment in which there is a single decision maker with absolute control over network actions and evolution. This more prescriptive situation can be analysed within a multi-objective dynamic optimization (MODO) modeling context. We have used this capability to good effect in previous work [76, 77]. By referencing the genuine performance of industry networks against what is potentially achievable, it is possible, not only to set stretch targets for policy setting and strategic planning, but also to challenge the constraints to more collaborative action within a network in order to achieve sustainability goals. The second dimension of Fig. 6 relates to how the complete range of possible network evolutions can be assessed according to the resulting robustness conveyed upon the network. This requires an understanding of the relative weights which are assigned to each of the assessment criteria, something which needs to be assessed deliberatively with active stakeholder engagement. For any given intervention, and considering the range of all possible network evolutions based on this intervention, it is possible to determine what type of intervention is likely to lead to network evolutions which are more desired in terms of sustainability objectives. Details of this approach are given elsewhere [79]. CONCLUSIONS This chapter started from the premise that any consideration of the role which industrial ecology can play to promote sustainable development needs to engage with dynamics and uncertainty; in particular how these system properties influence the distributed decision making practices of individual organisations which make up any industry network. With a focus on decision making, we proposed the notion that the way individual organisations behave is directly linked

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to their own perceptions of future uncertainty, and how this influences the allocation of resources across the network of which they are part. We have drawn from the literature of the behavioural and cognitive sciences to explore how such perceptions shape actual decision making. Beyond this, we recognise that the way in which networks evolve is dependent on complex inter-relationships between organisations within the network, and the growth of institutional norms which support the many feedback loops which influence decision making. We have proposed the need to characterise a network’s performance not only by its functional properties – how resource allocation influences high level sustainability assessment criteria – but also by its evolving structural properties, as it is these which impart to the network a level of robustness. This is the link to sustainability, and the underlying intent of industrial ecology to build on the metaphor of ecosystem resilience. Our particular contribution to this discussion is the development of a modelling platform which we have termed Agent-Based Scenario Analysis (ABSA). This draws on agent based modelling and scenario analysis techniques to provide a unified framework within which the behaviour of individual organisations can be explored in a way which highlights the ramifications of their actions for the network as a whole. This approach is helpful in understanding the evolution of any industry network over time, and can be used to assess the network’s explicit contribution to sustainable development by focusing on high level economic, environmental and social performance criteria. In addition, ABSA is helpful in understanding how different strategic interventions in the network (through planning and policy) help shape the structural evolution of the network. In this way, it is possible to decide which network transitions or evolutionary pathways are more effective in promoting sustainable development. This analysis is made possible by focusing on structural characteristics of the network, and how these impart resilience and adaptability – both of which are critical for sustainable development. In this way, we argue that it is possible to stimulate industrial ecologies in an active manner, rather than simply allowing them to grow organically, and to work with such networks in pursuit of sustainability goals.

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CONFLICT OF INTEREST The author(s) confirm that this chapter content has no conflicts of interest. ACKNOWLEDGEMENT Declared none. REFERENCES [1] [2] [3] [4] [5] [6] [7] [8] [9] [10] [11] [12] [13] [14] [15] [16]

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CHAPTER 12 Green Engineering and Sustainability: A Systems Analysis Perspective Urmila Diwekar* Center for Uncertain Systems: Tools for Optimization & Management, Vishwamitra Research Institute, Clarendon Hills, IL 60514, USA Abstract: This chapter presents a systems analysis perspective that extends the traditional plant design framework to green engineering, green energy and industrial ecology leading to sustainability. For green engineering this involves starting the design decisions as early as chemical and material selection stage on one end, and managing and planning decisions at the other end. However, uncertainties and multiple and conflicting objectives are inherent in such a design process. Green engineering principles are illustrated here using a green energy sector case study. Uncertainties increase further in industrial ecology. The concept of overall sustainability goes beyond industrial ecology and brings in time dependent nature of the ecosystem and multi-disciplinary decision making. Optimal control methods and theories from financial literature can be useful in handling the time dependent uncertainties in this problem. Decision making at various stages starting from green plant design, green energy, to industrial ecology, and sustainability is illustrated for the mercury cycling. Power plant sector is a major source of mercury pollution. In order to circumvent the persistent, bioaccumulative effect of mercury, one has to take decisions at various levels of the cycle starting with greener power systems, industrial symbiosis through trading, and controlling the toxic methyl mercury formation in water bodies and accumulation in aquatic biota.

Keywords: Green engineering principles, uncertainties, decision making, optimization, optimal control, stochastic optimization, multi-objective optimization, stochastic optimal control, mercury cycle, methyl mercury, pH control, nutrient control, trading, Savanna river water shed, fuel cell power plants time dependent uncertainty, Ito processes, financial engineering INTRODUCTION Sustainability is a multi-disciplinary concept as is presented in this book. This chapter is particularly devoted to engineering approach to sustainability in terms *Address correspondence to Urmila Diwekar: Center for Uncertain Systems: Tools for Optimization & Management, Vishwamitra Research Institute, Clarendon Hills, IL 60514, USA; Tel: (630)886-3047; E-mail: [email protected] Heriberto Cabezas and Urmila Diwekar (Eds) All rights reserved-© 2012 Bentham Science Publishers

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of systems analysis. In particular, engineering sustainability into design problems means greener designs that involve looking at the design in terms of not only profitability which is the traditional approach to design, but include considerations like environmental impact, safety, and reliability. Uncertainties are inherent in considerations of these objectives as many of them are not well defined. Greener designs need to look at the system in totality rather than at individual units in the plant. This is illustrated in this study by greener energy production systems. Green engineering means green processes, green products, green energy, and eco-friendly management as shown in Fig. 1. In industrial ecology, this decision making changes from the small scale of a single unit operation or industrial production plant to the larger scales of an integrated industrial park, community, firm or sector. Then the available management options expand from simple changes in process operation and inputs to more complex resource management strategies, including integrated waste recycling and reuse options. The concept of overall sustainability goes beyond industrial ecology and brings in the time dependent nature of the system. Decisions regarding regulations and human interactions with the system come into picture. It involves dealing with various time scales and time dependent uncertainties which require appropriately modeling these. The systems analysis approach to sustainability is to find efficient methods for solving these decision making problems at various spatial and temporal scales in the face of uncertainties. This chapter describes these methods and presents a case study of sustainable management of mercury cycling starting from green process designs at the plant levels to industrial sector level management to ecosystem level management.

Figure 1: Green Engineering to Industrial Ecology to Sustainability [1].

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GREEN ENGINEERING Green engineering involves green processes, clean products, green energy, and eco-friendly management. In 2003, Ananstas and Zimmerman [2] presented 12 principles (shown in Table 1) of green engineering to achieve sustainability through science and technology. Fig. 2 shows the integrated framework developed by Diwekar [3] to include these green engineering principles at all stages of plant design. In the traditional design where engineers are looking primarily for lowcost options, environmental considerations include various objectives such as long-term and short-term environmental and other impacts. This new framework includes decisions at all levels starting from the chemical or material selection level. This is an important level in green engineering as it has been shown that it is important to start as early as possible in the design process in order to enhance the impact of waste minimization as shown in Fig. 3 [4].

Figure 2: Integrated Framework for Green Engineering [3].

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Figure 3: Opportunities for Waste Minimization in Green Engineering [4] (the bars represent costs) Table 1: 12 Principles of Green Engineering & Systems Analysis Terminology [2] No. 1 2 3 4 5 6 7 8 9 10 11 12

Green Engineering Designers need to ensure that all material and energy inputs and outputs are inherently as nonhazardous as possible It is better to prevent waste than treat or clean up waste after it is formed Separation and purification operations should be designed to minimize energy consumption and materials use Products, processes, and systems should be designed to maximize mass, energy, space, and time efficiency through process intensification Products, processes, and systems should be ”output pulled” rather than ”input pushed” through the use of energy and materials Embedded entropy and complexity must be viewed as an investment when making design choices on recycle, reuse, or beneficial disposition Targeted durability, not immortality, should be a design goal Design for unnecessary capacity or capability (e.g., ”one size fits all”) solutions should be considered a design flaw Material diversity in multi-component products should be minimized to promote disassembly and value retention Design of products, processes, and systems must include integration and interconnectivity with available energy and material flows Products, processes, and systems should be designed for performance in a commercial ”after life” Material and energy inputs should be renewable rather than depleting

Systems Analysis Terms Qualitative objective Qualitative objective Multiple objectives Multiple objectives Design directions Design directions Design directions Design directions Objective Design directions Qualitative objectives Qualitative objectives

Definition of various objectives is a key component in the design and implementation of clean technologies, and it has been identified as the most

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difficult task. The goals in terms of profitability are relatively easy to define, and researchers in academia and industry have used simulators and modeling tools to achieve profitability where environmental considerations are considered as definable constraints. However, for “complete ecological considerations” to be included as environmental impact objectives is a formidable task. Thus, multi-objective optimization methods are necessary to handle the conflicting and different objectives involved in the problem of greener by design. A multi-objective optimization approach is particularly valuable in the context of pollution prevention [5, 6], waste management [7, 8], life cycle analysis (LCA [9]), and sustainability as there are a large number of desirable and important objectives that are not easily translated into dollars. Extending the envelope from simulation to material selection/ chemical synthesis on one end and management and planning on the other end, and broadening the scope to include multiple objectives other than profitability increase uncertainties. Furthermore, the decision-making then involves discrete decisions related to selection of alternatives as well as continuous decisions that define the operations and design of a plant or management decisions. Thus, at the crux of the framework based on this “water fall” kind of model (Fig. 2) are efficient algorithms, methods, and tools for multi-objective optimization and uncertainty analysis. This algorithmic framework is described in section 2. There are objectives like minimizing life cycle impacts, maximize renewability, diversity, commercial "after-life" considerations that involve knowledge in various forms like qualitative, and order of magnitude. Hence these objectives are shown to be outside the bracket of the framework. ALGORITHMIC FRAMEWORK The algorithms behind the integrated framework consists of five calculation levels as shown in the Fig. 4. The basis of this framework is numerical optimization algorithms for selecting discrete and continuous decisions in the face of multiple objectives, and probabilistic uncertainty analysis to account for uncertainties and variabilities in objectives, constraints, and parameters. Level 1, Model loop: is the inner most level and corresponds to models for simulation. These models link decision variables and uncertain variables with objective function and constraints for plants, products, processes and management.

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Figure 4: Algorithmic Framework.

Level 2, Sampling loop: The diverse nature of uncertainty, such as estimation errors and process variations, can be specified in terms of probability distributions. In general, uncertainties can be characterized and quantified in terms of probabilistic distributions. Some of the representative distributions are shown in Fig. 5. The type of distribution chosen for an uncertain variable reflects the amount of information that is available. For example, the uniform and loguniform distributions represent an equal likelihood of a value lying anywhere within a specified range, on either a linear or logarithmic scale, respectively. Further, a normal (Gaussian) distribution reflects a symmetric but varying probability of a parameter value being above or below the mean value. In contrast, lognormal and some triangular distributions are skewed such that there is a higher probability of values lying on one side of the median than the other. A beta distribution provides a wide range of shapes and is a very flexible means of representing variability over a fixed range. Modified forms of these distributions, uniform* and log-uniform*, allow several intervals of the range to be distinguished. Finally, in some special cases, user-specified distributions can be used to represent any arbitrary characterization of uncertainty, including chance distribution (i.e., fixed probabilities of discrete values).

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Figure 5: Examples of Various Distribution Functions for Uncertainty Analysis

Once probability distributions are assigned to the uncertain parameters, the next step is to perform a sampling operation from the multi-variable uncertain parameter domain. One of the most widely used techniques for sampling from a probability distribution is the Monte Carlo sampling (MCS) technique, which is based on a pseudo-random generator to approximate a uniform distribution (i.e., having equal probability in the range from 0 to 1). The specific values for each input variable are selected by inverse transformation over the cumulative probability distribution. A Monte Carlo sampling technique also has the important property that the successive points in the sample are independent. Nevertheless, in most applications, the actual relationship between successive points in a sample has no physical significance; hence, the randomness/independence for approximating a uniform distribution is not critical. In such cases, uniformity

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properties play a more critical role in sampling, as a result, constrained or stratified sampling techniques are more appealing. Latin hypercube sampling (LHS) is one form of stratified sampling that can yield more precise estimates of the distribution function. The main drawback of LHS stratification scheme is that, it is uniform in one dimension and does not provide uniformity properties in kdimensions. Sampling based on cubature techniques or collocation techniques face similar drawback. These sampling techniques perform better for lower dimensional uncertainties. Therefore, many of these sampling techniques use correlations to transform the integral into one or two dimensions. An efficient sampling technique (Hammersley sequence sampling, HSS) based on Hammersley points was developed by my group [10, 11]. HSS uses an optimal design scheme for placing the n points on a k-dimensional hypercube. This scheme ensures that the sample set is more representative of the population, showing uniformity properties of random variables in multi-dimensions, unlike Monte Carlo, LHS, and its variant, the Median Latin hypercube sampling technique. Fig. 6 shows samples generated for two uniform uncertain (random variables) using MCS and HSS. It has been found that the HSS technique is at least 3 to 100 times faster than LHS and Monte Carlo techniques and hence is a preferred technique for uncertainty analysis, as well as optimization under uncertainty, and it is used in this framework. For higher dimensions (higher than 20 variables), we have a variant of the HSS technique that is based on leaping the prime numbers used in the HSS technique [12]. (a) Monte Carlo

(b) Hammersley Sequence

1.0

1.0

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0.0

0.0 0.0

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0.6

Figure 6: Comparison of 100 Samples Generated by Monte Carlo and HSS.

0.8

1.0

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Level 3, Continuous optimizer: This step involves continuous decisions like design and operating conditions for a plant. Derivative based quasi-Newton methods are commonly used for this purpose [13, 14]. If the model is linear then linear programming methods are used. Level 4, Discrete optimizer: This involves dealing with discrete decisions such as different alternatives for process configurations, or different management options. This is the most difficult optimization step. The commonly used methods for this level are mixed integer linear and nonlinear programming methods or probabilistic methods like simulated annealing and genetic algorithms [13, 14]. Level 5, Multi-Objective Programming-(MOP): This represents the outermost loop in Fig. 4. A generalized Multi-objective optimization (or Multi-objective Programming) problem can be formulated as follows: Min.Z  Z i ...i  1,.. p; p  2 h( x, y )  0

g ( x, y )  0

where x and y are continuous and discrete decision variables, and p is the number of objective functions. The functions h(x, y) and g(x, y) represent equality and inequality constraints, respectively. There is large array of analytical techniques to solve this MOP problem; however, the MOP methods are generally divided into two basic types: preference-based methods and generating methods. Preference-based methods like goal programming attempt to quantify the decision-maker's preference, and with this information, the solution that best satisfies the decision-maker's preference is then identified [13, 14]. As is well known, mathematics cannot isolate a unique optimum when there are multiple competing objectives. Mathematics can at most aid designers to eliminate design alternatives dominated by others, leaving a number of alternatives in what is called the Pareto set. Generating methods, such as the weighting method and the constraint method, have been developed to find the exact Pareto set or an approximation of it. For each of these designs, it is

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impossible to improve one objective without sacrificing the value of another relative to some other design alternative in the set. From among the dominating solutions, it is then a value judgment by the customer to select which design is the most appropriate. At issue is an effective means to find the members of the Pareto set for a design problem, especially when there are more than two or three objectives; the analysis per design requires significant computations to complete, and there are an almost uncountable number of design alternatives. A pure algorithmic approach (based on the constraint method) to solving this is to select one to minimize while the remaining objectives are turned into an inequality constraint with a parametric right-hand-side, Lk. The problem takes on the following form: Min.Z  Z i ...i  1,.. p; p  2 h( x, y )  0

g ( x, y )  0 Z k  L k k  1,... j  1, j  1,... p

where Zj is the chosen j-th objective that is to be optimized. Solving repeatedly for different values of Lk chosen between the upper and lower bounds of Zj leads to the trade-off surface or Pareto set. The multi-objective optimization algorithm used in this work uses the Hammersly sequence sampling to generate combinations of the right-hand-side. The aim is to MInimize Number of Single Objective Optimization Problems (MINSOOP) by exploiting the n-dimensional uniformity of the HSS technique. Fig. 7 shows how this MINSOOP [15] algorithm improves efficiency for a simple, nonlinear, convex optimization problem, as the number of objectives increases. GREEN ENERGY CASE STUDY Fuel cell is more or less a "zero" emission technology as the emissions are much lower than other competitive technologies in the market for energy generation. Selecting a fuel cell power plant for generation of electricity follows the green engineering principle, Principle 2. However, the extremely low emissions of this

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technology require higher costs, and there are a number of challenges and issues that need to be addressed to make the use of this technology widespread and economically viable.

Figure 7: Comparison of MINSOOP vs. Conventional Constraint Method.

Distributed power generation is one of the most attractive applications of the fuel cell technology. A major bottleneck in the design of fuel cell power plants for this application is to package them in a system balance of plant (BOP) that allows them to function effectively. Here the principles of process intensification (Principle 4) and process integration (Principle 8) can be utilized. This is explained in following paragraphs. Process Intensification: All fuel cells, especially those operating at high temperatures like Molten Carbonate Fuel Cell (MCFC) and Solid Oxide Fuel Cell (SOFC) need spent fuel utilization or waste heat recovery subsystems to increase process efficiency. Low temperature fuel cells (e.g., Polymer Electrolyte Membrane, PEM) also require fuel-reforming subsystems. A common approach for providing the BOP has been to integrate the fuel cell with a heat engine which gives rise to a fuel cell/heat engine hybrid system. This is also a very effective means to utilize the waste heat of the fuel cell but compromises on several features of the fuel cell and distributed power generation. The plant is no longer a

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compact module and it is now difficult to scale it up. The addition of the heat engine bottoming cycle with moving parts leads to increase in noise and calls for more maintenance. Moreover, the dynamics and turndown characteristics of the fuel cell plant no longer remain simple. These considerations have fueled the development of fuel-cell/fuel-cell hybrid power plants as a very effective way to achieve the BOP. Generally, a high temperature internally reforming fuel cell (SOFC) needs to be combined with a low temperature fuel cell (PEM) having a complementary electrolyte (anion-conducting vs. cation-conducting electrolytes) to get such a synergistic effect. Fig. 8 shows the schematic of such a hybrid power plant. This SOFC-PEM hybrid plant [16] uses the following process integration steps to enhance the efficiency further.

Figure 8: Schematic of a SOFC -PEM Hybrid Plant [16].

Process Integration: The plant contains two fuel cells (one each of SOFC and PEM) combined with a heat recovery steam generation cycle. The use of two fuel cells makes this cycle up to 37.8% more efficient than the case where only a SOFC is used (maximum efficiency of 52.4%). Natural gas is used as the fuel. It is processed in a fuel pre reformer to scrub H2S and other sulphur compounds and fed to the SOFC. The solid oxide fuel cell acts both as an electricity producer as well as a fuel reformer for PEM. The exhaust fuel from the SOFC is cooled and shifted in a low temperature shifter that also functions as a low-pressure steam boiler. Shifted fuel gas is then treated with pure oxygen in a selective catalytic oxidizer to reduce CO from several hundred parts per million (ppm) to below 10 ppm. The utilization of this reformed fuel is completed in the PEM. The exhaust

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from the PEM cell goes to waste hydrogen burner and heat recovery steam generator to utilize the waste heat of the exhaust stream to make steam from water. This steam produced in the low temperature shifter and the heat recovery steam generator is used in both the SOFC and PEM. In the SOFC, steam is used as a reactant for the reforming and downstream shift reactions and to control against the carbon. In the PEM, steam is used to humidify fuel and air streams to maintain water balance in the electrolyte and electrodes. Using green engineering Principle 5, the fuel utilization is chosen as the decision variable to manipulate the output. The fuel utilization in SOFC can be limited to a range necessary, only to reform the natural gas and make the exhaust gas suitable for use by PEM after some treatment, but not completely oxidize it. This way, the reformed fuel (SOFC exhaust) can be completely oxidized in the PEM where more favorable thermodynamics apply rather than forcing a higher fuel utilization in the SOFC which can cause a drop in current density and cell voltage. Another important SOFC design aspect is internal recuperation, whereby efficient heat management is used to maximize system compactness and power density by minimizing the required air-fuel equivalence ratio. One of the important points that should be noted in applying Principle 5, the optimal decisions will depend on what objective (output) you are trying to manipulate in this multi-objective situation. Multi-objective Optimization: The following is the formulation of the problem of design of SOFC -PEM Hybrid power plant as a multi-objective optimization problem: Max. Overall efficiency(ACEFE) Min. Capital Cost (ACEFECAP) Min. Cost of Electricity(COE) Max. SOFC Current Density (CDSOFC) Max. PEM Current Density (CDPEM) Subject to: Power rating (PWRTG) = 1472 Mass and Energy Balances

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The decision variables used are the fuel utilization (UTIL) and equivalence ratio (ERAT) in the SOFC (derived from the green engineering Principle 5) and the pressure of the PEM (PPEM) and fuel and air flows. The algorithmic framework shown in Fig. 4 is used to solve this problem. Since, the discrete decisions related to process synthesis are decided using the process intensification and process integration heuristics obtained from expert knowledge. At this stage, we included uncertainties related to SOFC and PEM models, this case study uses loop 2, 3, and 5 in Fig. 4 to obtain the Pareto or the trade-off surface for various objectives. Payoff Table: As a first step towards obtaining the whole Pareto set, the bounds for all the objective functions are generated, this is the payoff table. A payoff table is widely used in decision analysis, where it specifies the alternatives, acts, or events. Especially in MOP, a payoff table shows a potential range of values of each objective. In more detail, a payoff table contains individual objective values (Zk*) for single optimization problems (k), and also provides potential ranges of the objectives on the Pareto surface (i.e., ZL to ZU). In this way, an approximated range of the righthand-side Lk in the Pareto surface is determined. These are presented in Table 2. It has been found that the Capital cost (CAP) and Cost of electricity (COE) follows the same trend so these objectives can be combined in one. Again, the SOFC current density and CO2 emissions are seen to have the same trend and can be combined as one while the PEM current density objective can be dropped because there is not much change. The reduced objective set is then used to obtain the complete Pareto Set of a power plant of fixed capacity, i.e., 1472 kW. Table 2: Pay-off Tables with Multiple Objectives[16]. MAX

MIN

MIN

MIN

MAX

MIN

MAX

MIN

MIN

OBJECTIVES

ACEFF

ACEFF

CAP

COE

CO2EM

CO2EM

CDPEM

CDPEM

CDSOFC

PWRTG

1475.1856

ACEFF

0.7232446

0.58273

0.5426174

CAP

1456.4933 1599.80938 994.783579 993.38655

COE CO2EM CDSOFC CDPEM

5.67E-02

1465.51 1568.32587 1469.0955 1634.293405 0.52 5.63E-02

0.2728659 0.37885684 101.86

672.009

290.3345

304.847

0.6008321

1470.4051 1500.24142

1496.3793

0.7066749 0.57530437

0.7077066 0.726258777

1471.963

1444.397

1281.92286

739.9451

1289.6887

4.28E-02

5.05E-02

4.15E-02

3.68E-02

5.15E-02

6.28E-02

0.3284591 0.3386584

0.36369784

0.2793638

0.343033 0.27885679

0.2717334

4.16E-02 531.131

616.0684

308.62476 307.62895

737.209689 157.6117844 294.63637

678.179

294.38629

319.5917

149.0192

1664.6514

76.34382

287.22287 293.602876

UTIL

0.69993

0.55147

0.43617

0.40266

0.41114

0.7

0.4

0.7

0.7

PPEM

23.3115

36.8556

39.7391

36.189

20.1208

26.3631

75

20

25.3265

ERAT

1.37434

5.69168

1.25

1.25

3.01218

1.79611

1.88651

1.70141

1.25

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Table 2: cont…. FUEL

20.1683

27.8188

25.8102

24.9279

29.7814

20.5743

25.7853

20.9073

20.0408

AIR

189.276

851.883

137.29

122.408

359.824

252.367

189.831

242.929

171.08

Pareto Set or Trade-off Surface: The algorithmic framework shown in Fig. 4 is used to obtain the complete Pareto set for deterministic and stochastic cases using only 150 single optimization problem instead of 10, 000 optimization problems required by other algorithms. The deterministic results are plotted in Figs. 9 and 10. Different colors of the plot represent different capital cost contours. These plots provide great insights into the design especially in the face of such diverse objectives. The high efficiency and low emissions regions involve high capital costs. There are some low cost regions at high SOFC current density but involve low efficiency and high emissions. Another major low cost region is with SOFC current density between 350-500 mA/cm2, overall efficiency between 60-65%, and CO2 emissions are between 0.30 and 0.32 kg/kWh. Although CO2 emissions for these designs are significantly less than CO2 emissions from currently operating power plants (0.920 kg/kWh) based on coal, these emissions need to be reduced to achieve sustainability. Fig. 11 shows the stochastic Pareto surface for overall efficiency CO2 emissions and capital cost. Compared to the deterministic trade-off surface shown in Fig. 10, this surface contains better designs. This shows that the deterministic analysis underestimated the performance of this plant.

Figure 9: Pareto Surface for Efficiency, Capital Cost, and SOFC Current Density [16].

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Figure 10: Pareto Surface for Efficiency, Capital Cost, and CO2 Emissions [16]. SOFC-PEM hybrid plant (1472 kW) Stochastic Design 00 11

00

00 10

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0.62

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10 00

00 11

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Overall Efficiency

10 100 0

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0.64

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CO 2 emissions (kg/kWh) Figure 11: Stochastic Pareto Surface for Efficiency, Capital Cost, and CO2 Emissions [16].

INDUSTRIAL ECOLOGY & SUSTAINABILITY Industrial ecology is the study of the flows of materials and energy in industrial and consumer activities, of the effects of these flows on the environment, and of the influences of economic, political, regulatory, and social factors on the use,

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transformation, and disposition of resources [17]. Industrial ecology applies the principles of material and energy balance, traditionally used by scientists and engineers to analyze well-defined ecological systems or industrial unit operations, to more complex systems involving natural and human interaction. These systems can involve activities and resource utilization over scales ranging from single industrial plants to entire sectors, regions or economies. In so doing, the laws of conservation must consider a wide range of interacting economic, social, and environmental indicators. Fig. 12 presents a conceptual framework for industrial ecology applied at different scales of spatial and economic organization, evaluating alternative management options using different types of information, tools for analysis, and criteria for performance evaluation [18]. As one moves from the small scale of a single unit operation or industrial production plant to the larger scales of an integrated industrial park, community, firm or sector, the available management options expand from simple changes in process operation and inputs to more complex resource management strategies, including integrated waste recycling and reuse options. Special focus has been placed on implementing the latter via industrial symbiosis, for example, through the pioneering work of integrating several industrial and municipal facilities in Kalundborg, Denmark [19]. To evaluate the full range of options illustrated in the framework shown in Fig. 12 apart from a multi-objective analysis [20], uncertainty analysis is needed. In next section, mercury cycle illustrates the sector level mercury abatement. The topic of sustainability goes beyond industrial ecology and is, perhaps, operationally and conceptually one of the most complex that modern science has faced as it involves socio-economic interactions and their effect on the overall ecosystem. Sustainability involves forecasting and long term decision making. Time dependent uncertainties are inherent part of decision making where time dependent uncertainties are well studied in financial literature. In engineering optimal control methods are used for forecasting. We combined these two approaches to deal with long term decisions in sustainability. The algorithmic framework is changed from Fig. 4 to Fig. 13. In Fig. 13, the modeling step is combined with stochastic optimal control methods to handle time dependent uncertainties. These uncertainties are modeled using Ito processes as building blocks [13, 14]. Ito processes are part of a class of stochastic processes called Weiner processes.

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A Wiener process [13, 14] can be used as a building block to model an extremely broad range of variables that vary continuously and stochastically through time. A Wiener process has three important properties: 1.

It satisfies the Markov property. The probability distribution for all future values of the process depends only on its current value.

2.

It has independent increments. The probability distribution for the change in the process over any time interval is independent of any other time interval (non-overlapping).

3.

Changes in the process over any finite interval of time are normally distributed, with a variance that increases linearly with the time interval.

Figure 12: Conceptual Framework for Industrial Ecology [18].

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Figure 13: Algorithmic Framework for Sustainability.

Stochastic processes like Weiner processes do not have time derivatives in the conventional sense and, as a result, they cannot be manipulated using the ordinary rules of calculus as needed to solve the stochastic optimal control problems. Ito provided a way around this by defining a particular kind of uncertainty representation based on the Wiener process. An Ito process is a stochastic process x(t) on which its increment dx is represented by the equation: dx  a ( x, t ) dt  b( x, t ) dz

where dz is the increment of a Wiener process ( dz   dt ), and a(x, t) and b(x, t) are known functions.  is a unit normal distribution. In general mathematical methods to solve optimal control problems involve calculus of variations, the maximum principle and the dynamic programming technique. Nonlinear Programming (NLP) techniques can also be used to solve this problem provided all the system of differential equations is converted to nonlinear algebraic equations. For details of these methods, please see [14]. In the

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maximum principle, the objective function is reformulated as a linear function in terms of final values of state variables and the values of a vector of constants resulting in ordinary differential algebraic equation that are easier to solve as compared to calculus of variations or dynamic programming. However, this maximum principle formulation needs to include additional variables and additional equations. We use the maximum principle and the stochastic maximum principle formulation (with sampling) [21] along with NLP optimization technique to obtain the control profiles. MERCURY CYCLE Mercury has been recognized as a global threat to our ecosystem, and it is fast becoming a major concern to environmentalist and policy makers. Mercury is a major pollutant from power plants. The task of mercury pollution management is arduous due to the complex environmental cycling of mercury compounds. Successful handling of the issues calls for a sustainability based approach. Mercury can cycle in the environment in all media as part of both natural and anthropogenic activities [22]. Fig. 14 shows schematic of mercury cycle from air to humans. Majority of mercury is emitted in air in elemental or inorganic form, mainly by coal fired power plants, waste incinerators, industrial and domestic utility boilers, and chloro-alkali plants. However, most of the mercury in air is deposited into various water bodies such as lakes, rivers and oceans through processes of dry and wet deposition. In addition, the water bodies are enriched in mercury due to direct industrial waste water discharge, storm water runoffs, and agricultural runoffs. Once present in water, mercury is highly dangerous not only to the aquatic communities but also to humans through direct and indirect effects. Methylation of inorganic mercury leads to the formation of methyl mercury which accumulates up the aquatic food chains. The consumption of these aquatic animals by humans and wild animals further aids bioaccumulation along the food chain. The consumption of these aquatic animals by humans and wild animals further aids bioaccumulation along the food chain. As a result, contaminated fish consumption is the most predominant path of human exposure to mercury. This has resulted in fish consumption advisories at various water bodies throughout the US. Given this complex cycling, management options at multiple stages must be

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considered to effectively mitigate the impact. The work proposes sustainable management strategies at various levels of mercury cycle. Industry level environmental control technologies selection and design. 

Industrial sector (inter-industry) level symbiosis through trading. Combined with industry level management resulting in mixed integer nonlinear programming (MINLP) and stochastic mixed integer nonlinear programming (SMINLP) problems.



Ecosystem level management: Effective control strategies of mercury bioaccumulation in water bodies. These strategies are given below. o

Lake pH control to manage methyl mercury formation.

o

Manipulation of the regimes of species population by controlling Fisher information variation.

The algorithmic framework described earlier in Fig. 13 is used for case study of sustainable water system management for mercury contamination. 

Figure 14: The Mercury Cycle

Savannah River Watershed Case Study We have selected this region (Fig. 15) for our case study as Georgia issued 178 fish consumption advisories relating to 40 different rivers and 34 lakes and ponds

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in the Savannah river basin in 2004. Total Maximum Daily Load (TMDL) of 32.8 kilograms/year, which represents the maximum permitted cumulative loading for the watershed, has been established by the USEPA for five contiguous segments of the Savannah River in the state of Georgia, leading to the applicable water quality standard (WQS) of 2.8 ng/l (parts per trillion) in the watershed [23]. In all, there are 29 significant point sources (PS) discharging mercury in the watershed, including 13 major municipal polluters, 12 major industrial polluters, 2 minor municipal polluters and 2 minor industrial polluters. The TMDL is implemented by applying the common WQS of 2.8 ng/l to all point source (PS) discharges across the watershed. The sum of the individual wasteload allocations is 0.001 kg/year, which is significantly less than 0.33 kg/year, the cumulative wasteload allocation provided to all PS [23]. This difference appears because there are 50 more point sources in the watershed that were ignored, either because the discharges were very small or not measurable with certainty. The overall reduction needed to achieve the TMDL is about 44% [23]. Since the current discharge concentrations for the 29 point sources are not reported in the literature, the individual discharge values are computed by taking 29 random samples so that the mean required reduction for the watershed based on the WQS is about 44%.

Figure 15: Savannah River Watershed in Georgia

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Industrial Level and Industrial Sector Level Mercury Management In the wake of increasingly stringent discharge regulations on mercury, efficient management at the individual level is not sufficient. Innovative methods are required that will analyze the problem from the industrial sector level achieving simultaneous economic and ecological sustainability. Some states are considering trading as one of the strategies. Industrial Level Management: Environmental Control Technologies Three environmental control technologies are considered for this problem, and they are available to all industries for implementation. These include: coagulation and filtration, activated carbon adsorption and ion exchange process (Table 3). The capital requirement and reduction capability of any process is expected to be nonlinearly related to the capacity of the treatment plant and the form and concentration of the waste to be treated, amongst many other factors. The total plant cost is reported as a function of the waste volume [24]. Since waste volumes encountered in this case study are mostly greater that 1 Million Gallons per Day (MGD), asymptotic values reported in [24] are used. The treatment efficiencies depend on the waste composition and concentration. In general though, a more efficient treatment is likely to be more expensive. This criterion, along with data [25], is used to decide the treatment efficiencies. Table 3 gives the technology data. The nonlinear cost functions are reported [26]. The models are not reproduced here for the sake of brevity and interested readers are referred to the mentioned reference. Table 3: Data for the Various Environmental Control Technologies. Process

Mercury Reduction Capability (ng/lit)

Capital Requirement gallons)

Activated carbon adsorption (A)

3.0

1.5

Coagulation and Filtration (B)

2.0

1.0

Ion exchange (C)

1.0

0.6

($/1000

Industrial Sector Level Management: Pollutant Trading Pollutant trading is a market based strategy to economically achieve environmental resource management. The goal is to attain the same or better

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environmental performance with respect to pollution management at a lower overall cost for the industrial sector. The concept is attributed to Crocker [27], Dales [28], and Mongomery [29]. Various aspects of watershed based trading have been extensively discussed [30, 31] and hence not reproduced here. To summarize the aspects relevant for this work, the state or federal authority proposes a regulation such as Total Maximum Daily Load (TMDL) which establishes the loading capacity of a defined watershed, identifies reductions or other remedial activities needed to achieve water quality standards, identifies sources, and recommends waste load allocation for point (and nonpoint) sources. To comply with the regulation, a point source (industry) in the watershed may need to reduce its discharge level. It has two options to accomplish this: (1) the point source can implement an environmental control technology; (2) the point source can trade a particular amount of pollutant to another point source in the watershed that is able to reduce its discharge more than that specified by the regulation. Trading optimization problem formulation: The formulation considers that a TMDL (Total Maximum Daily Load) regulation has already been developed by the state in consultation with USEPA. This translates into a specific load allocation for each point source. Consider a set of point sources (PSi), i=1., N, disposing mercury containing waste water to a common water body or a watershed. Let j=1, ., M be the set of waste reduction technologies available to the point sources. Let Di be the discharge quantity of polluted water from [volume/year], redi be the desired pollutant quantity reduction in discharge of PSi [mass/year], Pi be the treatment cost incurred by PSi in absence of trading, and fj(j, Di) be the linear or nonlinear cost function for technology j at PSi [$]. Here, j is the set of design variables for technology j. Qj is the pollution reduction possible from technology j implementation [mass/volume]. r is the trading ratio, defined as the number of units of reduction that must be purchased to trade one unit of the pollutant and which is typically higher than 1 to account for reduction uncertainties, and F is the transaction cost, which is the amount paid by the point source for trading one unit of pollutant [$/mass]. The trading optimization problem is then formulated as:

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Minimize

M

 f

j ( j , Di ).bij

i 1 j 1

t ii  0

i  1,..., N

M

red i 

 j1

M

Pi 

 j1

N

q j .Di .bij 



N

t ik - r

k 1

b ij . f j ( j , Di )  F (

t

ki

k 1

N

 k 1

N

t ik 

t

ki )

k 1

where, bij are the binary variables representing point source technology correlation. The variable 1 when i installs technology j. tik [mass/year] is the amount of pollutant traded by PSi with PSk. All parameters are on annual basis. The objective function gives the sum of the technology implementation costs for all point sources. Although each PS will also spend or gain from practicing trading, expense for one PS in a watershed is earning for one or more PS in the same watershed. As a result, for the complete watershed, trading does not contribute to the cost objective. The first set of constraints eliminates trading within the same PS. The second set of constraints ensures that all the regulations are satisfied, with or without trading. The trading ratio r is usually set higher than 1 to account for data uncertainty and provide a buffer [30]. Consequently, the PS accepting additional discharge reduction responsibility has to reduce the pollutant by an amount equal to the actual quantity traded (tik) times the trading ratio. The last constraint ensures that the expenses incurred by each PS with trading are not more that those without trading. Since participation in trading is voluntary, a polluter will participate in trading only if there is a financial incentive, which is modeled by this constraint. Moreover, since transaction costs are paid on the basis of the actual amount traded, the trading ratio does is not included in the final constraint. It is considered that trading is possible between all point sources. For simplicity, a single trading policy exists between all possible pairs of point sources, and a single trading ratio r and transaction fee F is applicable to all the trades. The proposed model is applied to the Savannah River watershed. There are 29 major polluters in this watershed including a power plant.

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Uncertainty Consideration: The optimization model presented above assumes that all data is deterministically known. However, there are various possible sources of uncertainty in this framework. For example, the Mercury Study Report to Congress [26] states that uncertainty in point estimates of anthropogenic mercury emissions ranges from medium (25%) to high (50%). A stochastic optimization (stochastic programming) problem must be formulated to account for these uncertainties. In this work, the deterministic formulation is extended to include uncertainty in the cost functions of the various control technologies. The resulting stochastic programming problem is computationally difficult to solve. However, the framework described earlier is used to solve the problem efficiently. Health care cost: The bioaccumulative nature of mercury and its slow dynamics make the long term effects of mercury exposure important. Hence, it is essential to account for such effects while quantifying health care costs. The majority of mercury accumulates in the food chain as methyl mercury. Therefore, quantification of health care costs based on methyl mercury concentration is most appropriate. Health care cost is assumed to be a function of fish consumption, safe concentration in fish, and LC50 value for mercury. Addition of health care cost in the formulation results in a multi-objective optimization problem, where compliance cost and health care cost are the two objectives. 160

145

140 120 100 80

59

60 40 20

40 25

15

11

25 12

6

0 Linear Deterministic

Nonlinear Deterministic

Granular Activated Carbon

Nonlinear Stochastic

Coagulation and Filtration

Ion Exchange

Figure 16: Optimal Technology Selection with Different Models (y axis is number of technology implementation)

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Results and discussions: Fig. 16 shows the implications of nonlinearity and uncertainty inclusion on technology selection for trading option. The figure shows the number of times each technology is implemented over the complete TMDL range. It can be seen that there are definite implications on technology selection. With linear technology models, various small industries implement technologies along with large industries. However, for nonlinear model, large industries implement most of the technologies and smaller industries satisfy the regulations by trading with these large industries. Multi-objective Problem: The multi-objective optimization problem in the presence of uncertainty was solved using the weighting method. Here, each objective in the cumulative objective function is multiplied by a coefficient, which is called as the weight on that objective. Higher value of the weight for a particular objective represents greater importance of that objective in the cumulative objective function. In this analysis, the weight on the cost based objective is maintained at 1 while the weight on the health care cost is progressively increased from 50 to 500, indicating greater importance to health care cost. Fig. 17 shows the cumulative distribution of technology selection by different point sources for various TMDL values for different weights. It illustrates a preference towards the selection of a more efficient treatment technology. It must be noted that the granular activated carbon adsorption was the most efficient and most expensive technology while ion exchange was the least efficient and least expensive technology. The percentage of activated carbon adsorption increased from 49.28% for the weight of 50 to 57.88% for the weight of 500. On the contrary, the percentage of coagulation and filtration decreased and that of ion exchange remained almost the same. Moreover, the total number of times technologies are implemented also increased from 69 for the weight of 50 to 311 for the weight of 500. Ecological Level Management Mercury and its compounds exist in different segments of the water body such as water column, sediment (active and passive), and biota (fish). Mercury can undergo various transformations in a water body such as oxidation, reduction, volatilization, methylation and demethylation. All these transformations are simultaneously observed in a given water body. The relative concentration of each

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chemical form depends on the extent of various reactions, which can differ for different water bodies. Of the various chemical forms of mercury, methyl mercury (MeHg) is considered to be the most dangerous due to its bioaccumulative potential. As a result, the concentration of methyl mercury in large aquatic animals (such as predatory fishes) is many times more than the water column or sediment concentration. This work explores two strategies for ecosystem mercury management: (1) the time dependent liming strategy of lakes and rivers to control water pH and (2) controlling nutrient flow to manipulate eating habits of organisms.

Figure 17: Optimal Technology Selection for Different Weights for Health Care Cost in the Objective Function. Models (y axis shows % of times technology implemented)

Liming and pH Control Methylation of mercury to MeHg is a key step in the bioaccumulation of mercury in aquatic food chains [32]. The exact mechanism of the methylation reaction is however not well understood. Studies have also been carried out to understand the effect of physical and chemical conditions such as pH, dissolve oxygen, dissolved organic carbon (DOC), temperature, salinity etc., on methylation [33, 34]. These studies have shown a strong correlation between acidic conditions (low pH values)

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and high mercury bioaccumulation in fish. Therefore, lake liming, which means addition of lime to a given water body, has been proposed as a management tool. This controls the pH, therefore, should lead to less MeHg formation and consequently less bioaccumulation. It is a valuable short-term management option that can be implemented on a case-by-case basis till the original problems of mercury pollution and acid runoffs are satisfactorily addressed. Although lake liming for pH control has been relatively successful in Scandinavian countries, there are various issues related to liming that need further in-depth research. These are: (1) Liming accuracy: Currently, most of the liming decisions (liming dosage) are based on rule of thumb. The amount of lime to be added is decided using parameters such a lake volume, current lake pH, targeted pH, water salinity etc. [35]. These are mostly static decisions and do not take into account the dynamic nature of the natural system. It is obvious that such heuristics based decisions do not lead to accurate liming results. (2) Cost of liming: Liming entails considerable costs. Hence, it is essential that the liming operation is optimized to reduce expenses. Even though the liming technique is the major factor deciding the expenses, efficient implementation of the selected technique can reduce expenses. Previous work in this area includes [36, 37]. (3) Presence of uncertainty: Liming operation has to deal with presence of various kinds of uncertainties, such as lack of information on the exact pH of the lake, seasonal variations in lake pH, and topological effects of liming. Moreover, the spatial and temporal effects of liming on lake biota are subjective. In order to make liming implementable, one needs to incorporate these uncertainties in the analysis. Due to these issues, lake liming has not been a widespread practice in North America. To make liming more accurate, an effective approach is to use time dependent liming where liming decisions (amount of lime to be added) change with time based on the current lake conditions. The reliability of liming can be further

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improved if these dynamic liming decisions are based on a systematic approach rather than heuristics. Basic liming model: The basic lake liming model is presented in [38] and further discussed in [35, 36]. It is a mixed model consisting of both statistical regression and dynamic interactions. An empirical model is used to predict the initial pH (mean annual pH). The model also includes a regression that predicts natural pH. In addition to these empirical sub-models, the lake liming model consists of dynamic (time dependent) interactions. It is a compartmental model with three different compartments, namely, water, active sediment and passive sediment. Accordingly, the three model variables are: lime in water, lime in active sediment and lime in passive sediment. Four continuous flows of lime connect the three compartments: sedimentation to active sediments, internal loading from active sediments to water, outflow from the lake water and transport from active to passive sediments. In addition, two flows give the inflow of lime from the liming, one to the lake water and one directly to the active sediments. Natural pH of a lake varies seasonally and hence constitutes an uncertain parameter. In this work, mean revering Ito process is used to model fractional variation in pH owing to its success in modeling various time dependent stochastic parameters [14, 39, 40]. Optimal control problems require establishing an index of performance for the system and designing the course of action so as to optimize the performance index. The goal in lake liming operation is to maintain the pH value at some desired level or within a desired range. Since cost of liming is also a concern, this converts the problem into multi-objective optimal control problem, where minimization of pH variation and minimization of liming cost are the two objectives. Results and discussions: Fig. 18 presents the result of deterministic and stochastic optimal control problems indicating that the targeted pH is effectively achieved. The plots also show that the stochastic optimal control leads to better pH control. The steady drift observed while using deterministic control is not a generic result but rather depends on the specific case study and the particular realization of the uncertain variable. The nature of these fluctuations, therefore, may be different for

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different case studies. However, solutions of different problems showed that the deterministic control is always inferior to stochastic control. The results of the multi-objective optimal control problem, not reported here, show the trade-off between cost and liming accuracy. Such results are very useful to decision makers to finalize the liming policy as a function of the available resources. For details of this liming case study, please refer to [41].

Figure 18: Lake Liming Deterministic and Stochastic Optimal Control.

Manipulation of Regime Fig. 19 shows the bioaccumulation of Mercury in various species. It has been illustrated that a major portion of mercury found in the tissues of various aquatic organisms enters through food (ingestion). As a consequence, the eating habits of these organisms are expected to have a significant impact on the mercury intake by these organisms. The eating habits depend to quite an extent on the various

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species populations and their pattern of fluctuations at a given time in the water body. In ecological literature, these different patterns are referred to as regimes. A regime, therefore, if maintained for sufficient duration, is expected to affect the steady state mercury bioaccumulation levels in different species. As a result, manipulation of the regimes of these species populations presents a tool to control mercury bioaccumulation levels [42, 43]. This work presents an optimal control analysis to achieve regime shifts for mercury bioaccumulation reduction. The population dynamics in the water body are modeled by a three species predatorprey model (Canale’s model), where the three species are called as prey, predator and super-predator. The bioaccumulation level in the super-predator is of concern for humans since humans often eat those species. Mercury bioaccumulation is modeled using the bioenergetics approach. The predator-prey model and the bioaccumulation model are inter-related by correlating the food intake of any particular species with the mercury intake for the bioaccumulation model. Thus, changes in the dynamics of the Canale’s model change the instantaneous food intake for the predators and super-predators (due to changing predation rates). This affects the total mercury that is taken by these species through food. Hence, any regime shift in the predator-prey model, which affects the predation rates, affects the mercury intake by the species. If the particular regime is maintained for a sufficient duration, the steady state mercury concentration in these species can be altered. This is the basic foundation for the proposed work. It must also be ensured that the new regime is stable and does not compromise the other functions of the system such as primary productivity. Hence, it is very critical to select the right approach to achieve the regime shift. It is also important to note that the total Hg or MeHg levels in the water are not altered. Rather, the objective is to alter the dynamics to ensure lower bioaccumulation levels in the super-predator. Regime change and optimal control: Optimal control theory presents an option to derive time dependent management strategies that can effectively achieve regime shifts in food chain models. Past work by the authors has illustrated the success of this approach [39, 40]. That work used a Fisher information based sustainability hypothesis, proposed by Cabezas and Fath [44], to formulate time dependent objective functions for the control problem. That work proved that Fisher information and its variation can successfully be correlated to a specific regime. A

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similar approach, therefore, has been used in this work. Once the targeted regime with lower mercury bioaccumulation is known, the regime shift is to be achieved by minimizing the variation of the time averaged Fisher information of the system around the constant Fisher information of the targeted regime. Canale’s model exhibits various regimes such as cyclic low frequency, cyclic high frequency, stationary, and chaotic [45]. The idea proposed in this work is to move from a high mercury bioaccumulation regime to a low mercury bioaccumulation regime, both in terms of the super-predator. The control variables to achieve the regime shift are: nutrient inflow rate and nutrient input concentration.

Figure 19: Bioaccumulation of Mercury.

Results and discussion: Fig. 20 shows the regime shift achieved by control of nutrient input concentration for the integrated model (Canale’s model and the bioaccumulation model). The results illustrate that there is a strong correlation between the regime and steady state mercury bioaccumulation in predator and super-predators. Hence, the objective of causing a regime change in justified. In addition to this, the Fisher information based objective and optimal control theory is successful in driving the system to the desired regime. Moreover, since the new regime is stable, the bioaccumulation level is expected to stay at the new level until the whole system is perturbed beyond the resilience limits. The control profile has not been reported in the interest of space. However, it is observed that

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the nutrient inflow concentration increases on an average basis for the simulation horizon. Once the new regime is achieved, the control variable can be maintained at a constant value to ensure that the system remains in the same regime.

MeHg in Super-predator

290 270 250 230 210 190 170 150 1

2001

4001

6001

8001

10001

12001

Time (units) Uncontrolled

Controlled

Figure 20: Reducing Bioaccumulation by Nutrient Control.

CONCLUSIONS This chapter presents the systems analysis (engineering) approach to sustainability. This approach starts at the plant level where green engineering principles are applied to plant design, operations, and management. In order to implement these principles, systems analysis tools of multi-objective optimization approach. Uncertainties in various objectives as well as models and new technologies need to be considered at this stage. This approach advocates thinking of green engineering principles at very early stages of design. Industrial ecology deals with industrial sector and higher level of management. Multi-objective optimization under uncertainty methods are useful at this stage also. Sustainability requires thinking about future and forecasting methods are necessary. The decision making at this stage includes time dependent decisions and time dependent uncertainties. I argue that financial literature presents theories for

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handling time dependent uncertainties coupled with engineering optimal control methods provides systems analysis tools for sustainability. As a case study mercury cycle is presented. DISCLOSURE Note from the author: Part of information included in this chapter/article has been previously published in Computers & Chemical Engineering Volume 34, Issue 9, 7 September 2010, Pages 1348–1355. CONFLICT OF INTEREST The author(s) confirm that this chapter content has no conflicts of interest. ACKNOWLEDGEMENT Declared none. REFERENCES [1] [2] [3] [4] [5] [6] [7] [8] [9] [10] [11]

Diwekar U., From green process design to industrial ecology to sustainability, Resources, Conservation and Recycling, 2005: 44, 215-235. Anastas, P.; Zimmerman, J. B., Design through the twelve principles of green engineering, Environ. Sci. Technol. 2003, 37 (5), 95A-101A. Diwekar U., Greener by design, Environ. Sci. Technol. 2003, 37, 5432-5444. Y.Q. Yang and L. Shi. Integrating environmental impact minimization into conceptual chemical process design a process systems engineering review. Comput. Chem. Eng, 2000, 24, 1409-1419. Cohon, J.; Rothley, K. In Design and Operations of Civil and Environmental Engineering Systems; Revelle, C., Mcrarity, A., Eds.; John Wiley: New York, 1997. Fu Y. and U. Diwekar, Cost effective environmental management for utilities, Advances in Environmental Research, 2003, 8, 173-196. NWTRB. Disposal and storage of spent nuclear fuel: Finding the right balance. Technical Report; Nuclear Waste Technical Review Board: Arlington, VA, 1996. Johnson, T.; Diwekar, U., Hanford waste blending and the value of research: stochastic optimization as a policy tool J. Multi-Criteria Decision Anal. 2001, 10, 87-99. Azapagic, A., Life cycle assessment and its application to process selection, design and optimisation, Chem. Eng. J. 1999, 73, 1-21. Kalagnanam, J. R.; Diwekar, U. M., An efficient sampling technique for off-line quality control, Technometrics 1997, 39, 308-319. Diwekar U. and S. Ulas, Sampling Techniques, Kirk-Othmer Encyclopedia of Chemical Technology, Online Edition, 2007, 26, 998-1052.

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Subramanyan K., Y. Wu, U. Diwekar and M. Q. Wang, Uncertainties in LCA: new stochastic simulation capability applied to the GREET model, International Journal of LCA, 2008, 13, 278-285 Diwekar U. M., Introduction to Applied Optimization, Kluwer Academic Publishers, Netherlands, 2003. Diwekar U. M; Introduction to Applied Optimization, Second Edition, Springer-Verlag, Cambridge, MA, 2008. Fu Y. and U. Diwekar, An efficient sampling approach to multi-objective optimization, Annals of Operations Research, 2004, 132, 109-134. Subramanyan K., U. Diwekar, and A. Goyal, Multi-objective optimization for hybrid fuel cells power system under uncertainty, Journal of Power Sources, 2004, 132, 99–112 White A. Preface. In: The greening of industrial ecosystems. Washington, DC: National Academy of Engineers, National Academy Press; 1994. Diwekar U, Small M. Process analysis approach to industrial ecology. In: Ayres, Ayres, editors. A handbook of industrial ecology. UK: Edward Elgar; 2002., 115–37. Ehrenfeld J, Gertler. Industrial ecology in practice: the evolution of interdependence at kalundborg. J Ind Ecol, 1997, 1, 1-67. Chad D, Allen D., Minimizing chlorine use: assessing the trade-off. between cost and chlorine use in chemical manufacturing. J Ind Ecol 1997, 1(2), 111–134. V. Rico-Ramirez and U. Diwekar, Stochastic maximum principle for optimal control under uncertainty, Computers & Chemical Engineering, 2004, 28, 2845-2849. USEPA, Mercury research strategy. Technical report: EPA/600/R-00/073, United States Environmental Protection Agency, Office of Research and Development, Washington DC 20460, 2000. USEPA, Total Maximum Daily Load (TMDL) for total mercury in fish tissue residue in the middle and lower Savannah river watershed. Report, United States Environmental Protection Agency, Region 4, 2001. USDOI, Total plant costs: For contaminant fact sheets Technical report, U.S. Department of Interior, Bureau of Reclamation, Water treatment engineering and research group, Denver CO 80225.41, 2001 USEPA, Capsule report: Aqueous mercury treatment. Technical report: EPA/625/R-97/004, United States Environmental Protection Agency, Office of Research and Development, Washington DC 20460, 1997. USEPA, Mercury study report to congress. Report to congress: EPA-452/R-97-003, United States Environmental Protection Agency, 1997. Crocker, T., The economics of air pollution, chap. The structuring of air pollution control systems. New York: W.W. Norton, 1966. Dales, J., . Pollution, property and prices. Toronto: University of Toronto Press, 1968. Montgomery D., Markets in licences and efficient pollution control programs, J. Economic Theory, 1972, 5, 395-418. USEPA, Draft Framework for Watershed-Based Trading.Tech. rep., EPA 800-R-96-001. Washington, DC: United States Environmental Protection Agency, Office of Water, 1996. USEPA, Water Quality Trading Policy. Tech. rep., Office of Water, Washington, DC: United States Environmental Protection Agency, 2003 Sorensen, J., Glass, G., Schmidt, K., Huber, J., Rapp, G., Airborne mercury deposition and watershed characteristics in relation to mercury concentrations in water, sediments,

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plankton and fish of eighty northern minnesota lakes. Environmental Science and Technology, 1990, 24, 1716–1727. [33]Winfrey M. R., Rudd J. W. M., . Environmental factors affecting the formation of methylmercury in low pH lakes. Environmental Toxicology and Chemistry 1990, 9, 173174. [34] Driscoll, C., Blette, V., Yan, C., Schofield, C., Munson, R., Holsapple, J., The role of dissolved organic carbon in the chemistry and bioavailability of mercury in remote Adirondack lakes. Water, Air and Soild Pollution, Hakanson, L. (2003). A general management model to optimize lake liming operations. Lakes & Reservoirs:Research and Management, 1995, 8, 105–140. [35] Hakanson, L., Boulion, V., The lake foodweb. Backhuys Publishers, Leiden, 2002. [36] Hakanson, L., A general management model to optimize lake liming operations. Lakes & Reservoirs:Research and Management, 2003, 8, 105–140. [37] Riely, P., Rockland, D., Evaluation of liming operations though benefit-cost analysis. Water, Air and Soil Pollution, 1988, 41, 293–228. [38] Ottosson, F., Hakanson, L., Presentation and analysis of a model simulating the pH response of lake liming. Ecological Modelling, 1997, 105, 89–111. [39] Shastri, Y., Diwekar, U. Sustainable ecosystem management using optimal control theory: Part 1 (Deterministic systems). Journal of Theoretical Biology, 2006, 241, 506–521. [40] Shastri, Y., Diwekar, U., Sustainable ecosystem management using optimal control theory: Part 2 (Stochastic systems). Journal of Theoretical Biology, 2006, 241, 522–532. [41] Shastri Y. and U. Diwekar. Optimal control of lake pH for mercury bioaccumulation control, Ecological Modelling, 2008, 216, 1-17. [42] Wang, W-X., Stupakoff I., Gagnon C. and Fisher N.S., Bioavailability of Inorganic and Methylmercury to a Marine Deposit-Feeding Polychaete, Environmental Science and Technology, 1998, 32, 2564-2571. [43] Monson B.A. and Brezonik P.L., Seasonal patterns of mercury species in water and plankton from softwater lakes in Northeastern Minnesota, Biogeochemistry, 40, 147-162. [44] Fath B. and H. Cabezas, Towards a theory of sustainable systems, Fluid Phase Equilibria, 2002, 2, 194-7. [45] Gragnani, A., De Feo, O. and Rinaldi, S., Food chains in the chemostat: Relationships between mean yield and complex dynamics. Bulletin of Mathematical Biology, 1998, 60 (4), 703–719.

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CHAPTER 13 The Case and Practice for Sustainability in Business Beth Beloff1,* and Arnaud Chevallier2 1

Principal, Beth Beloff & Associates, President, BRIDGES to Sustainability Institute, Houston, TX, USA and 2Director of Operations, Beth Beloff & Associates, Houston, TX, USA Abstract: Over the past fifty years, the concept of corporate sustainability has been evolving. To date, there is not a single commonly accepted definition for it, although managing the triple bottom line—economic, environmental and social aspects—is gaining traction. Companies see different types of value in embracing sustainability, from reducing costs to enhancing their brand reputation and fostering product innovation. This chapter describes the multiple meanings of corporate sustainability, introduces the drivers of sustainability, explains how key historical events have shaped it and reviews the typical path that companies have taken to incorporate sustainability into their business agenda. It also provides a description of the value proposition that motivates companies to embrace it and challenges in achieving that value.

Keywords: Altamira hydroelectric dam, apartheid, bhopal, brand management, brent spar, brundtland commission, copenhagen climate conference, corporate citizenship, corporate social responsibility, corporate sustainability, corporate sustainability learning curve, deepwater horizon BP oil spill, eco-efficiency, ESG (environment social governance), environmental, social, economic dimensions, exxon-valdez, GEMI global environmental management initiative, greenhouse gas measurement protocols, greenwashing, innovation, IPCC, ISO 14001, Kayapo Indians, lifecycle assessment, our common future, precautionary principle, Rio conference, silent spring, sustainability, sustainable development, supply chain, triple-bottom-line, value chain. INTRODUCTION This chapter explores the evolving practice of sustainability in business. The first section provides a brief definition of sustainability in the business environment and a framework for its consideration. The next section considers some of the *Address correspondence to Beth Beloff: Principal, Beth Beloff & Associates; President, BRIDGES to Sustainability Institute, Houston, TX, USA; E-mail: [email protected] Heriberto Cabezas and Urmila Diwekar (Eds) All rights reserved-© 2012 Bentham Science Publishers

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drivers and historical events that, over the past fifty years, have defined it and shaped the corporate response to it. Next is a review of the typical path that companies take to incorporate sustainability into their business agenda—from only complying with regulatory directives all the way through to placing it at the center of the business's strategy and using it as an engine to drive innovation. The last section presents the value proposition, why companies are embracing it, from the point of view of business executives. The chapter concludes with discussing important challenges in achieving this value proposition. THE MULTIPLE MEANINGS OF CORPORATE SUSTAINABILITY While there is no consensus on the definition of sustainability, the weaving of environmental, social and economic considerations into a system of thought is common to most. The Brundtland Commission definition of sustainable development [1] remains standard: “development that meets the needs of the present without compromising the ability of future generations to meet their own needs.” For its part, the World Business Council for Sustainable Development conceptualized sustainable development as living off the interest the earth generates: living within natural system limits while not drawing down that principal [2]. While both are solid foundations on which to build, they aren’t operational definitions for corporations. In the business environment, sustainability is often referred to as having a triple bottom line (a term coined by John Elkington in 1994 [3]); that is, extending beyond the financial bottom line to the environmental and social ones. There is a recognition that all three are inextricably linked; economic reality includes costs associated with environmental and social impacts created in the course of doing business, as well as the value realized by creating positive effects on the environment and the community. It is not about altruism; it is primarily about making business sense. Sustainability programs in businesses are called many things, including corporate social responsibility and environmental, social governance (ESG). Many companies focus these programs solely on environmental aspects, while others also include social programs, economic implications, and governance factors.

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Some corporate definitions of sustainability refer to improving quality of life while protecting ecosystems, and some bring in the concept of long-term thinking, preserving quality of life opportunities for future generations. There’s one certainty: sustainability means different things to different organizations. To help conceptualize the extent of sustainability in the corporate context, the following framework was developed. It captures the three dimensions of sustainability, the various stages along the value chain, the contexts through which companies evaluate what is most important to them, and the regulatory basis on which the entire ensemble lies.

Figure 1: A Sustainability Framework, developed by Beloff and Beaver [4], presents a holistic consideration of sustainability in business. It includes the three dimensions—environmental, economic and social—and encompasses the entire value chain to consider what issues are most important at which stage. This matrix sits on a platform of corporate governance, structure and culture. The context summarizes some of the key filters or lenses through which sustainability strategies and actions are considered.

The dimensions of the triple bottom line are interdependent: The environmental dimension includes natural resource utilization—water, energy, materials, land—as well as the waste and pollutant streams resulting from their use and ecosystem and human health effects.

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The economic dimension includes internal costs, revenue opportunities and shareholder value, as well as costs and benefits of externalities resulting from corporate activities. Financial flows to the community from taxes, philanthropic dollars, community investment and job creation are considered, as are intellectual capital and other capital assets. Eco-efficiency is a concept that resides in the interface between the environmental and the economic dimensions; it focuses on reducing environmental impacts per unit of economic output in order to make the system more efficient. The social dimension incorporates the human factor, including workplace conditions and labor practices; employee health, safety and well being; and security, ethics and human capital development. Additional social factors include social impacts from operations, contribution to quality of life in the community, fulfillment of basic human needs, social investment, promotion of human rights, understanding of stakeholder and community engagement, and equity. These dimensions apply to the company as well as its supply chain, as sustainability considerations are increasingly transcending any given organization to incorporate a life cycle perspective, from the extraction of natural resources to the end of life or rebirth of products. The framework shows how the three dimensions, considered throughout the company’s value chain, sit within the context of the governance structure and corporate culture. The sustainability issues on which a company chooses to focus are significantly influenced by its corporate values, policies and practices: What is its commitment to the triple bottom line? Is it engaged in product stewardship and supply chain leadership? What is the degree of accountability for decisions that influence sustainability and what is the degree of transparency around sustainability performance? To what degree is sustainability integrated into the organization and across functions? and so on. Lastly, an organization’s sustainability context further depends on a number of variables, including: 

Time – Is the time horizon for sustainability-related decision making short and/or long term? How are costs and benefits distributed over time? Is the company seeking incremental or revolutionary change over time?

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Place – Where in the world is the company and at what geo-political scale is it considering its effects (local, regional, national, global)? What is the socio-political landscape of the place where it is operating? What are the factors that determine the quality of life for its residents? What makes this place special to those who inhabit it?

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Values – What are the values and attitudes of the local people? What is important to members of the local community? How do they value that which is important? What are their sustainability-related concerns and issues? What are their priorities? What are their attitudes?

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Resources – What are the resource constraints of the place? Which natural and human resources that the company needs are abundant? Which are scarce? Where are the potential future discontinuities? How might those natural resources change over time as a result of population growth, economic activity, climate change ? How are organizations improving their eco-efficiencies around resource use? Is there equitable distribution and consumption of resources, given the limits within a given area?

TRENDS AND DRIVERS OF SUSTAINABILITY There are a number of recent surveys of corporate executives that address the question of what is driving companies to move toward greater sustainability. According to a study of 1, 500 business leaders by the Boston Consulting Group and MIT Sloan Management Review [5], the three issues that have the most significant impacts on their organization are government legislation, concern by consumers, and interest among employees. These were followed in descending order by pollution, depletion of non-renewable resources, social pressures, water supply and access issues, global political security, population growth, and climate change. Accounting for two of the three most impactful factors, the pressure that consumers and other stakeholders are placing for greater transparency regarding what companies do is rapidly becoming a primary driver for more sustainability. As a result, there has been a significant increase in sustainability-related reports and websites: nearly 40% of US firms on the S&P 500 Index filed non-financial

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reports in 2009, a one third increase over 2008 [6]. Similarly, carbon reporting in 2009, according to the Carbon Disclosure Project, has seen a 16 percent increase over the previous year [6] from US companies on the Global 500 index. As consumer concern over health and sustainability of products has grown, so have green marketing claims. According to a 2009 survey by TerraChoice [7], an environmental marketing firm, 98% of 2, 200 products reviewed exaggerated their environmental claims. This level of greenwashing has led to the emergence of companies and nonprofits to check the health, environmental and social impacts of consumer products and related this information directly to the consumer. Among these new groups are GoodGuide.com and HealthyStuff.org. With the push for more transparency of information from companies, there is also a proliferation of measurement protocols characterizing various aspects of “greenness.” A World Business Council for Sustainable Development (WBCSD) e-newsletter headline in 2010 indicated that there were 30+ greenhouse gas (GHG) measurement protocols, causing much confusion among companies as to what to use [12]. As well, there have been numerous footprint measures developed for water and other aspects of sustainability. To help companies address “metrics mania” and navigate through not only the numerous stakeholder requests for metrics but also the confusing array of calculation methods, GEMI (Global Environmental Management Initiative) developed a workbook, the Metrics Navigator™ [13]. This was designed to help companies identify the “critical few” most material issues, objectives to address those issues, and then metrics to be used to guide strategic sustainability decision making. Globalization is another prominent driver of sustainability, as determined by a survey of major chemical companies in 2004. Companies with global operations are increasingly under pressure by not only customers but also investors and NGOs to follow sustainability best practices and be responsible for their operations and for those of their suppliers [8]. Wal-Mart has emerged as a driving force in sustainable supply chain management by demanding more information on their suppliers’ environmental and social impacts. As the global retail leader, Wal-Mart’s commitment to reducing impacts from products in its stores is having ripple effects throughout all of the suppliers’

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industries as well as among other retailers. Further, Wal-Mart has provided seed funding to create the Sustainability Consortium to develop the metrics and subsequent standards by which products will be evaluated along their lifecycles. This push for more sustainable supply chains and for driving responsibility up the chain has resulted in the formation of numerous industry consortia designed to develop common means to qualify supplier practices and product characteristics. These industry groups include fast-moving packaged goods, electronic, toy and automotive manufacturers, an alliance of U.S. electric utilities, and pharmaceutical companies. Their goal is often to improve their suppliers’ practices rather than to punish them for unsustainable practices. Furthermore global regulatory actions and voluntary markets around carbon management have helped drive significant movement toward cleantech and alternative energy technologies. Overall it is becoming more apparent that businesses will be increasingly required by governmental agencies, their customers and other stakeholders to take responsibility for the environmental and social impacts that occur in their supply chains. This is all the more interesting considering that accounting for sustainability in the business world is a relatively recent trend. A CORPORATE SUSTAINABILITY TIMELINE Numerous seminal historical events over the last fifty years have shaped the concept of sustainability and caused industry to improve its environmental, social and economic performance. It is therefore worth reflecting on some of these events as they, collectively, along with numerous other major ones not listed here, have brought into focus many of the sustainability issues and drivers that are evident today. By all accounts, sustainability has become a part of mainstream thinking within the last few years, and it has also become a legitimate business issue. According to Steve Fludder, vice president of GE ’s Ecomagination, “… the world has reached a tipping point now. We’re beyond the debates over whether [addressing sustainability] is something that needs to be done or not—it’s now mostly about

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how we do it. And from the perspective of ecomagination, it’s not about altruism, it’s about creating value” [5]. This section highlights events that influenced the definition of and a response to sustainability over time. This truncated walk through history should provide a few key takeaways: 

Sustainability started with environmental and health and safety concerns and the recognition that these are global as well as local.

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Pollution prevention pays; environmental externalities represent future risks and costs to companies.

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Corporate citizenship and social responsibility require ethical behavior and the adoption of meaningful codes of conduct.

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A growing set of investors and financial institutions consider risks in environmental and social impacts in making decisions.

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Stakeholder engagement—bringing key stakeholders into the product and technology developmental process early, before decisions are made—leads to a better understanding of concerns in the communityat-large and helps produce better solutions that address those concerns while meeting business objectives.

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Stakeholders are increasingly demanding transparency about sustainability regarding practices, products and technologies, and companies are increasingly reporting their performance.

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Companies are not only accountable for their direct social and environmental impacts but also increasingly responsible for the practices of their suppliers.

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A business’ reputation is its greatest intangible asset; it can be lost by one bad action or decision, and it may never gain back the trust lost.

The following is a selection of events that reflect the way in which sustainability thinking progressed and impacted business:

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1962 - The publication of Rachel Carson ’s bestseller, “Silent Spring, ” is widely credited with launching the modern environmental movement, focusing attention on pesticides and pollution. It led to the ban of DDT.

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1982 - Johnson & Johnson provided a school case example of high corporate responsibility when they recalled 31 million bottles of Tylenol following the death of seven people in the Chicago area [15]. Although the company was hit by the crisis, their exemplary handling of the situation led them to regain most of the lost ground in just a few months [14]. Contrasting with the typical response of, say, a petrochemical company after a spill—which usually consists of deflecting the blame and results in individuals boycotting that company for several years—Johnson & Johnson's response has been cited as an example in business schools the world over for almost thirty years. The company's action was all the more commendable in that, before 1982, recalls were virtually unheard of [15]. A case could be made that the company eventually benefited economically from the crisis, an outcome that would have been inconceivable had they reacted in any other way.

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1982 - 1993 - The Texaco’s exploration and development operations in the Amazon region of Ecuador, from the early 80s until the early 90s, produced massive pollution and a huge international response from international environmental NGOs, who helped bring together the largest alliance of indigenous peoples ever to protest environmental damage. This was the first major international environmental protest fueled by Internet messaging. Texaco left Ecuador in 1992, amidst lawsuits charging massive pollution from former its operation. The most recent Ecuadorean judgment ruled against Chevron, a successor to the Texaco operation, supporting the indigenous peoples’ claims against the oil company.

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1984 - The explosion in Bhopal, India at the Union Carbide plant was cited as the world’s worst industrial disaster. It led to a renewed focus

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on occupational health and safety, as well as a call for greater corporate accountability and responsibility. 

1986 - The South Africa anti-apartheid and divestment campaign and boycott had its roots in the late 70s, when the Reverend Leon Sullivan developed a corporate code of conduct promoting social responsibility. The Sullivan Principles were originally designed to apply economic pressure on South Africa in response to its antiapartheid practices. The divestiture movement accelerated in mid-80s, culminating with US legislation, the Comprehensive Anti-Apartheid Act, which is widely credited as the beginning of the socially responsible investment movement, and the Principles eventually gained wide acceptance by US corporations.

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1987 – Our Common Future Brundtland Report came from the World Commission on Environment and Development, headed by Norwegian Prime Minister Gro Harlem Brundtland, to bring together issues that were environmental, social / cultural, and economic and to offer solutions. The Commission first introduced the term “sustainable development” and defined it as follows: “sustainable development seeks to meet the needs and aspirations of the present without compromising the ability to meet those of the future.”

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1987 - 1989 The Montreal Protocol was the first step in phasing out CFCs and other substances depleting the ozone layer. Although it would eventually take 20 years to show its full benefits, The Protocol was widely seen as an example of successful international cooperation around global environmental issues [16].

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1988 - The Intergovernmental Panel on Climate Change was established to assess scientific data available in the field [17].

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1989 - Deforestation in the Amazon emerged as a central environmental issue in the late 80’s, with the Kayapo Indians at the center of a major protest of a World Bank plan to build a hydroelectric dam at Altamira, Brazil. Amid the vocal protestors were international

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celebrities who brought much media attention to the plight of the indigenous peoples of the Amazon. The World Bank ended up canceling the project. 

1989 - The Exxon-Valdez oil spill was symbolically considered one of the most devastating human-caused environmental disasters, though by far not the largest. It was considered so damaging because it occurred in a pristine area in Alaska. This led to the demonization of Exxon by environmentalists and reinvigorated environmental activism, backed by strong legislative intervention.

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1992 - The Rio Conference / Earth Summit was a UN conference held in Rio de Janeiro in 1992. It was attended by thousands of NGOs and over 170 countries that addressed a broad range of environmental and development issues. There were agreements reached on Agenda 21, the Convention on Biological Diversity, and, among other things, the first Framework Convention on Climate Change, which would lead to the Kyoto Protocol. This conference was widely credited with legitimizing worldwide NGO environmental activism as well as thoughtful corporate response. The WBCSD was formed as a legitimate high-level business forum for addressing the challenges of sustainable development [18]. Agenda 21, a UN program, resulted from the conference as a comprehensive blueprint of actions to be taken by all levels of governments and major groups to protect the environment and ensure sustainable development [19].

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1992 - The Precautionary Principle was enshrined at the Earth Summit: “Where there are threats of serious or irreversible damage, lack of full scientific certainty shall not be used as a reason for postponing cost-effective measures to prevent environmental degradation.” Within just a few years, the EU became the first international body to invoke the Precautionary Principle in a case involving a ban on imports of hormone-fed beef [20].

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1995 - In the Shell/Brent Spar case, Shell's inability to appreciate the growing power of environmental activists in shaping the environmental agenda led to the now infamous fiasco in the North Sea

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when its attempted to transform the obsolete drilling platform into an 'eco-friendly' artificial reef, Although most independent scientists believed that that was the most environmentally-sensitive way to handle its defunct facility, the proposal met vehement and dramatic opposition from Greenpeace and eventually a full-scale German boycott of Shell gas stations. Shell capitulated to the Greenpeace position and dismantled the platform. This led to Shell engaging with Greenpeace and other environmental stakeholders before taking controversial actions, and was the first wave of real stakeholder engagement in a basic materials and natural resource company. 

1995 - The Fourth World Conference on Women was held in Beijing and was instrumental in advancing greater equality and opportunities for women with respect to human rights, poverty, and violence, among others [21].

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1995 - ISO 14001 became the international voluntary standard for corporate environmental management systems.

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1997 - The Kyoto Protocol, an international agreement linked to the UN Framework Convention on Climate Change, was adopted. It set binding targets for most of the industrialized world (37 countries and the EU community) for reducing their collective greenhouse gas emissions by around five percent from 1990 levels over the five-year period of 2008-2012. This protocol was a significant step in acknowledging the importance of stabilizing the climate by reducing the effects of human-derived greenhouse gases, and it laid the foundation for establishing a price for carbon and creating resulting market-based solutions [22].

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1998 - Nike Labor Protests resulted from child labor practices in Nike’s contract workforce in Asia. As a result, Nike introduced a series of changes affecting its contract workforce, and pledged to allow independent inspections of factories. This created compelling recognition that a company is responsible for the practices of its

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suppliers everywhere across the globe, and it was a boon to human rights advocates and to the accountability/transparency movement. 

1999 - The use of Genetically Modified Organisms (GMOs) by Monsanto led to mass protests across Europe and destruction of field trials of genetically modified trees and corn crop by anti-GMO protestors. Anti-GMO protests became mainstream over concerns about the risks associated with introducing GMOs into the environment. Again, a major company had been blind-sided by vehement concerns over the risks posed by its actions, and the stakeholders had not been sufficiently engaged to provide Monsanto with an understanding of their concerns and an ability to systematically address those concerns early in the product development process. This further encouraged the stakeholder engagement movement.

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2000 - At the United Nations Millennium Summit, the gathering of the largest group ever assembled of world leaders, adopted the UN Millennium Declaration from which 8 international development goals—the Millennium Development Goals (MDGs)—were developed to assist the world’s poorest countries improve their social and economic conditions. According to the UN Millennium Development Goals website, all 192 UN member states and at least 23 international organizations agreed to achieve these goals by 2015. They include reducing extreme poverty, reducing child mortality rates, fighting epidemics, and developing a global partnership for development. These issues and causes began showing up on corporate social responsibility agendas as well, stimulating not only philanthropic interest but also new technology, product, and service innovations.

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2002 -. The collapse of Enron, WorldCom and others spurred scrutiny of corporate governance, transparency, accountability, and passage of the Sarbanes-Oxley Act, which provided new regulatory control in the US of corporate accounting and disclosure of risks. This resulted in a

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dramatic increase in filing of Corporate Social Responsibility or Corporate Responsibility reports. 

2003 - The Equator Principles were developed by the financial community as voluntary standards for determining, assessing and managing social and environmental risk in project financing. Financial institutions that have signed onto the Equator Principles commit to not providing project financing where the borrower will not comply with the policies and procedures underlying the Principles, thus ensuring that projects financed are socially responsible and reflect good environmental management practices. This has reinforced the consideration of sustainability issues in determining financial risks [23].

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2004 - GE initiated its Ecomagination Program in response to the need to produce innovative products and services that address to climate change issues.

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2005 – Wal-Mart initiated its “green” initiative, which led to an effort to reduce its own environmental footprint and those of its suppliers.

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2006 - Al Gore ’ documentary about climate change, An Inconvenient Truth, followed by his winning the Nobel Prize in 2007, along with the members of the IPCC who produced a consensus document from scientists around the world about climate change. These events were seen as an international tipping point, dramatically escalating public awareness of Climate Change issues. At the same time, there was growing visible evidence of climate change effects that catapulted the issues into public awareness and concern. The case was made that climate change should be taken seriously.

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2008 - The Financial Tsunami created renewed focus on transparency, disclosure and accountability, key tenets underlying the sustainability movement.

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2009 - Obama’s Green Initiatives - for the first time, a U.S. Administration embraced a “green agenda, ” including billions of dollars of funding and tax breaks linked to the environment, the development of a green economy and green jobs.

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2009 - The Copenhagen Climate Conference failed to meet the expectations of most, such as generating a legally binding treaty to curb greenhouse gas emissions, but there were some positive developments. In particular, there was a common target: limiting temperature increase to two degrees centigrade. Also industrialized countries are more committed to helping developing countries meet reduction goals, committing $100bn to support their actions.

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2010 - The Deepwater Horizon BP oil spill in the Gulf of Mexico brought again sustainability and environmental concerns to the front page of international medias during several weeks and caused a sixmonth offshore drilling moratorium.

These events and countless others contributed to place sustainability in the corporate agenda of many organizations. But–just as sustainability has different meanings for different people—the response of the business world wasn't unified; rather, different organizations fell in different places along the sustainability learning curve. INDUSTRY SUSTAINABILITY LEARNING CURVE Many companies have moved along a common path in deepening their understanding and implementation of sustainability. They have leveraged their large cache of information about their performance and connected the dots differently to create new knowledge, business models, practices and partnerships to support their sustainability efforts. Above all, many companies have found that sustainability is at the heart of innovation in many organizations, both in terms of business and technology innovation and contributing to both bottom and top line value. For business, sustainability is about creating value that strengthens and broadens their business strategies and objectives. Sustainability thinking encourages, even requires, taking a systemic approach and shifting perspectives, as depicted in the Sustainability Learning Curve model (Fig. 2). The table below summarizes the shift in perspective that accompanies a more mature understanding of sustainability.

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Figure 2: The Sustainability Learning Curve, depicting the path companies travel to become more sustainable [8]. “Traditional” Approach Short term financial perspective

Sustainability Approach Longer term perspective that incorporates intangibles and externalities and manages uncertainty

Focus on impacts from company operations within the Extends consideration of impacts to its supply chain, company’s boundaries, and protective of company its value chain and the broader social system, and is information transparent about its progress Internal personnel address sustainability-related Outside stakeholders are actively engaged in issues; communication with outside stakeholders is a addressing issues and forging new solutions and traditional public relations activity alliances Environmental aspects and impacts dominate sustainability perspective; regulations drive the response

Sustainability includes a holistic view of environmental, social, and building the economic business case for voluntary action

Natural resources not limited

Natural resources seen as finite, now or in the future, and human resources are viewed as an unlimited source of creativity and compassion if properly developed and incentivized

Environment, Health, Safety function drives the sustainability effort

Responsibility for sustainability practices is spread across many key functional areas within the company. Cross-functional teams solve problems and there is a

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Sustainability is a defensive risk management

Sustainability embraced as an opportunity to drive innovation

Sustainability is seen as an add-on to normal business Sustainability supports business strategy, is integrated activities; there is no clear business case into the organization as a must-have, and there is a clear business case

A description of the differences in the practices of companies that embrace sustainability vs. “traditional” approaches. Depending on its commitment to sustainability, a company can be characterized as falling within one of the following five categories: Stage 1. Meeting minimum standards, primarily with respect to environmental impacts and legal compliance, is typically the first step for a company in moving into the sustainability space. It represents the floor in terms of managing risks and preserving a license to operate in the community. It is often reactive and fragmented where sustainability is an add-on approach typical of companies that are just becoming aware of it. Smart companies have proactively looked at the array of regulations across the geo-political landscape in which they operate and chosen to comply with the most stringent of those standards as these often represent how companies will be required to behave in the future. By moving toward uniform corporate compliance with the most stringent standards, rather that pursuing a piecemeal array of varying regulations, the company can get in front of future regulations in some areas and gain first-mover advantages that can drive innovation in responding early to future requirements. Examples abound in the automotive industry, in which technologies such as hybrid engines were developed in anticipation of fuel consumption and emission standards. In the electronics industry, Hewlett-Packard experimented with the development of lead-free solders in advance of European regulations restricting the use of lead in electronics products. Stage 2. Improving the floor. The second step is about moving beyond regulatory compliance to meeting voluntary codes and standards developed by industry and nongovernmental organizations to reduce environmental and social impacts. Often, these represent best practices.

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This step also involves moving toward greater efficiencies—efficiencies in operations by using environmental management systems; eco-efficiency in product development and process selection, through understanding lifecycle impacts; reducing waste in operations through reduction of resource use, reuse and recycle; improving energy efficiencies and reducing carbon intensity; and promoting improvements in the supply chain. These efficiency gains can lead to improvements in productivity and lower costs. In particular, innovations are derived from finding ways to reduce environmental impacts: carbon management; improving water, energy, and material efficiencies; and identifying more sustainable sources of raw materials. Deriving value from efficiency gains leads companies to more formally measuring and tracking progress. Often, companies at this stage are primarily focusing on environmental factors and becoming more environmentally friendly, while more standard corporate functions of human resources management, philanthropy, communications and outreach, for example, represent social aspects of their sustainability programs. The approach to sustainability in this stage is often fragmented within the company, having many unconnected initiatives in various areas of the company and the environmental group is more likely to be in charge of sustainability efforts. Stage 3. Expanding the vision is about making the business more environmentally effective and socially engaged. It is more about moving from embracing these issues as a form of risk management to one that captures a host of opportunities for the business. In this stage, the social dimension is more fundamentally incorporated; companies engage their stakeholders, looking more deeply at what they care about and what consumers really are asking for. With a better understanding of stakeholder concerns, there are better efforts in social investment and building better communities, as well as developing better products and services. The company seeks partnerships with nongovernmental organizations, community groups, government agencies, and other companies. The environmental and economic gains from ecoefficiency have produced incentives to look more deeply at the value of designing more sustainable products and services, as well as greening the organization.

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Companies in this category are encouraging considerations of green building, using more energy efficient fleets, telecommuting, and traveling in sustainable ways. Sustainability-related volunteerism, and creating sustainability challenges and awards are all part of this. Sustainability ideas are pushed into more areas of the organization, with sustainability accountability and management at executive and even board levels. Sustainability vision and strategies are developed, and performance tracking for management decision-making is underway. Companies move sustainability concepts into the value chain, and they are making efforts to address both supply chain issues and product stewardship, sustainable packaging and end-of-life considerations. Companies think about integrative sustainability programs; they may have a sustainability website and they issue sustainability reports. They may use lifecycle assessments to determine where the greatest environmental impacts are along their product’s life so that improvements can be made; they may have institutionalized the use of tools like lifecycle assessment and total cost assessment in screening and developing new products. The companies develop their business case for doing more and have recognized the opportunities that can be derived from sustainability thinking. Stage 4. Raising the ceiling is about developing new business models that are informed by deeply considering environmental and social issues and constraints. It is also about looking for new business models that involve partnering with others to provide more sustainable products and services. Examples of creative partnerships include FedEx partnering with its print shop, Kinko’s, to reduce the miles traveled by documents by transferring them electronically, having them printed at local Kinko’s outlets, and sending them only the last mile through document delivery. This has saved time and money, as well as provided environmentally friendly solutions. In this stage, sustainability challenges and issues have become powerful drivers of innovation, and companies seek opportunities to redefine themselves and their services in terms of sustainability solutions. GE provides an example of how a company captured the demand for environmental solutions to drive its line of products and services through Ecomagination. Further, companies are more

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fundamentally screening their new products, processes and services so that they eliminate up front those which have unintended and unsustainable results in their lifecycles. They practice lifecycle management across all or many of their products, technologies and services. In this stage, companies are proactive and more forward looking, integrating issues like carbon management and energy efficiency into their strategies, as well as considering security and business continuity issues, including supply chain continuity, and how the big issues of our time relate to that. They are capturing new opportunities derived from looking holistically at sustainability and they might be involved in cleantech developments, socially responsible investments, and/or being considered as investment opportunities for socially responsible investment (SRI) funds. These companies incentivize employees to take responsibility for their sustainability actions within the company, and they provide training, tools and other resources to help make this possible. They follow a code of conduct that helps to ensure ethical behavior. Many functions are represented by the sustainability effort, such as R&D, EHS, manufacturing operations, PR and communications, and HR. Furthermore, crossfunctional teams address corporate sustainability. These companies are part of an industry or cross industry group that develops and/or share best practices in sustainability. They are measuring sustainability performance to not only report to stakeholders but also to support management decision making, and benchmark performance relative to other industry players. They issues reports about their progress, possibly following the GRI format or as a part of the Carbon Disclosure Project or other such credible group promoting transparency of results. Companies in this group have established long-term goals for performance and seek external ratings of performance from well-recognized external groups like DJSI, FTSE4good, and Pacific Institute. Stage 5. Leading the pack is about changing the game and reinventing the company with a holistic approach to sustainability. The business case is clear and there is a corporate commitment to sustainability, which drives change more rapidly. Sustainability performance is explicit and is tracked; employees are rewarded for progress toward corporate goals.

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The goal for these companies is to integrate sustainability across the whole enterprise. There are awards/recognition programs around innovations in sustainability, from environmental to social. Corporate branding incorporates sustainability. Employees are encouraged to take concepts home and to improve their own actions and those of others in their communities. Others in the industry watch them and follow their lead. They lead by example, “walking the talk, ” and implement programs to improve everything from their own physical plant (green buildings, energy efficiency, greener fleets) and daily operations (sustainable travel, telecommuting, etc.) to building sustainability-oriented qualifications into decisions about suppliers and perhaps even customers. These comanies are on the cutting edge in developing new products, processes, technologies and services which not only have reduced environmental and social impacts along their lifecycles, but also may be developed to solve sustainability problems. They use tools like lifecycle assessments and some form of total cost assessment to address these challenges. They look to mentor those in their value chain who may not be as knowledgeable. They attempt to be integrative, holistic, inclusive and passionate. They are attracting and retaining the best talent because of their commitment to sustainability, and this in turn helps them innovate. These companies are thought leaders in organizations like the WBCSD, and they are valued players sitting at the table with government and NGOs in building new ideas and partnerships. They have used sustainability strategies to drive the development of next-practice platforms, which challenge existing paradigms and produce innovations that lead to new business platforms [9]. These innovations can be influenced by the desire to significantly reduce resource use and capture information in ways that enhance service value, not resource use. An example is the development of the smart grid to combine the best of digital technology to manage the flow of energy and consumer demand, improving energy efficiency, lowering the cost of energy, and making energy use more efficient. These ideas bring together players across industries to partner in new ways that drive more sustainable business practices. DUPONT: AN EXAMPLE OF SUSTAINBILITY INNOVATION DuPont is considered a leader in sustainability by its peers. Since 1990 it has rewarded employees for producing innovations that parallel its sustainability

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drive. The following is a description of the evolution of the sustainability awards program that DuPont has implemented. It is an excellent example of how a leading company has evolved its focus and grown its sustainability practice, as reflected in its awards for sustainability related innovations. DuPont Sustainable Growth Excellence Awards Contributed by Dawn Rittenhouse, Sustainable Development Director for the DuPont Company The Sustainable Growth Excellence Awards seek to honor those teams and individuals who have made significant contributions toward DuPont meeting its sustainable growth mission and vision. DuPont started the corporate awards program in 1990 following the announcement of their first set of footprint reduction goals. The idea for the program came from an operator at a manufacturing site; he encouraged Ed Woolard, the CEO at the time, to develop a process for recognizing those teams that were making significant progress on accomplishment of the goals. The program started as the Environmental Respect awards and recognized 12 accomplishments the first year. As the company expanded their focus, the program was expanded to encompass the broader agenda. In 1995 the program was changed to the Safety, Health and Environmental (SHE) Excellence Awards to link to the adoption of the SHE Commitment and the Goal of Zero for all injuries, illnesses, incidents, waste and emissions. In 1999 the program was again expanded to become the Sustainable Growth (SG) awards as the company looked to drive innovation and new business from products and services that reduced the environmental footprint of the entire supply chain. One of the most significant values of the program has been the make up of the selection panel, which includes both DuPont people and external people from around the world. Over the years the panel has included external representatives from environmental groups, academia, students, government, residents from site communities, and the media. Selection panel representatives have come from China, India, Taiwan, Australia, Brazil, Columbia, Mexico, Europe, Canada and the US. The engagement process through the discussion of the accomplishments has been significant in helping the company recognize the key areas that need to be focused on. Each nomination asks for the description of the accomplishment and information on the impact from an economic, SHE, and societal perspective. The selection panel uses seven dimensions to rate the nominations: • Reduced footprint: Injuries reduced, illnesses reduced, incidents reduced, pounds of emissions or waste reduced, amount of natural resources and energy conserved. • Business impact: Fostering better customer relationships, cost reductions, revenue enhancements, and/or reduced capital expenditures. • Community/Society impact: Value to the local community and/or society as a whole. • Initiated proactively: Did outside forces create the requirement for program or did the team see a need and pursue a solution before it was a requirement? • Creativity/Innovation: Were talents engaged in a purposeful and creative manner? Did individuals and teams experiment with new solutions to solve problems or create new opportunities? • Vision/Persistence: Were the contributions beyond expectations in context of position, scope, or experience of the nominees; was there demonstrated tenacity to overcome resistance? • Example for others: Did the activities that led to achievements demonstrated trust, speed, flexibility, responsiveness, and leadership skills consistent with the five corporate leadership principles? For the 2009 program, the recipients accomplished significant energy savings and reduction of greenhouse gas emissions, reaching out and using business management processes to drive real change in a poor community in Mexico, driving waste to landfill to zero, engaging with various stakeholder organizations early in the development process to assure product acceptability, and developing new

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products based on renewable resources. The cost savings and new revenues are worth millions of dollars to DuPont. The accomplishments are shared broadly both internally and externally to try to drive sharing of best practices across the company and with others in our supply chain. The winning descriptions are posted on the DuPont website at: http://www2.dupont.com/Sustainability/en_US/sustain_action/examples/excellence_awards.html Since the program inception, over 225 teams have been honored. In additional to the trophy and trip to the awards ceremony at headquarters in Wilmington, DE, each team receives a $5, 000 grant to donate to the safety, health, environmental, or educational initiative of their choice. Over $1.1 million has now been donated in association with this awards program.

THE VALUE PROPOSITION: WHY DO COMPANIES MOVE UP THE LEARNING CURVE? Different companies see different value in embracing sustainability. As reported by The Boston Consulting Group [5], “While sustainability’s novice practitioners thought of the topic mostly in environmental and regulatory terms, with any benefits stemming chiefly from brand or image enhancement, practitioners with more knowledge about sustainability expanded the definition for sustainability well outside the “green” silo. They tended to consider the economic, social, and even political impacts of sustainability-related changes in the business landscape. Simply put, they saw sustainability as an integral part of value creation.” Companies moving up the Sustainability Learning Curve have created value as a result, from the bottom line improvements that come from greater operational efficiencies and productivity in resource use to the employment of a more skilled and satisfied workforce. These result in real bottom line cost savings. There is also greater access to capital from socially responsible investors as well as financial institutions that consider the risk of environmental and social impacts. The risk management approach lowers the risk of non-compliance with regulations and raises the company’s ability to enter new markets and attract new customers, as well as avoidance of negative publicity that creates reputational risks. The flip side is that companies that are seen as more “green” also experience positive gains with respect to their brand image. To the extent that companies extend their risk management practices to their supply chain, they also lower the risk of supply discontinuity.

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Figure 3: Value Creation: from bottom to top line, showing the type of value that businesses can derive from embracing sustainability.

Sustainability provides a powerful catalyst for growth once companies move into the opportunity space, driving the development of new products, technologies and services that fulfill unmet social and/or environmental needs, and lead to the development of new intellectual property. These also lead to the development of new customers, markets and greater market share in existing markets. The benefits include reputation enhancement: not only are companies avoiding negative publicity, but they are achieving greater brand image, as well as customer and employee loyalty. They are more able to attract and retain the best talent. Another important top line value that results from sustainability practices and real engagement with stakeholders is that new partnership and alliance opportunities present themselves, with stakeholder assistance in innovating better solutions. The company is also more readily invited to sit at the table with governmental and non-governmental organizations that are looking to develop best practices. All of this rolls up to real competitive advantage.

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McKinsey recently proposed a classification of the benefits of sustainability along four dimensions: growth, returns on capital, risk management and management quality [10]. The table below builds on this proposition to classify the value of sustainability. Classification of the value of sustainability Dimension

Components

Growth

1. 2. 3. 4. 5. 6. 7.

Improve brand image / reputation Access new markets through exposure of sustainability program Develop new products / increase product differentiation Increase market share Foster innovation Attract new, “responsible” investors Generate revenue from new sources

Return on capital

1. 2. 3.

Improve operational efficiency (energy, input and waste disposal costs) Improve employee efficiency through higher satisfaction Justify price premium

Risk management

1. 2. 3. 4.

Reduce regulatory risk Improve relationship with NGOs / address NGO demands Avoid negative publicity Secure a stronger supply chain

Quality management

1. 2. 3. 4.

Facilitate adaptation to changing environments Facilitate long-term thinking Foster business model and process innovation Improve consistency of production through lower employee turnover

 A classification of the value of sustainability, showing how companies can benefit from sustainability programs (adapted from Bonini et al., [10]).

The evidence suggests real pockets of value creation, but what do company executives think about the value proposition? A primary finding of a 2008 Economist Intelligence Unit survey [11] of over 1, 200 business executives was that sustainability is very much compatible with strong financial results. In fact, a majority of executives think that it pays for itself: “the benefits of pursuing sustainability practices outweigh the costs… Specifically, sustainable practices can help reduce costs, open up new markets and improve the company’s reputation.” Executives see the primary value of sustainability as improving the image of the company. This is a recurrent finding in numerous surveys of business executives.

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The 2009 Boston Consulting Group /MIT survey [5] of 1, 500 corporate executives also found that the primary benefit of embracing sustainability was in improved company or brand image. In fact, the value associated with improved image was recognized by three times as many respondents as the value of any other benefits of sustainability. Then came cost savings; competitive advantage; employee satisfaction, morale or retention; competitive advantage; product, service or market innovation; new sources of revenue or cash flow; effective risk management; enhanced stakeholder relations; and others. This echoes the results of McKinsey [10] where 169 CFOs, investment professionals, and corporate social responsibility professionals defined the ways in which environmental, social, and/or governance programs improve companies’ financial performance. Again, brand management came first, with 78% of the respondents indicating that maintaining a good corporate reputation and brand equity was the highest value resulting from these programs. A majority of respondents also indicated that value was derived from attracting, motivating and retaining talented employees. These were followed in descending order of response by meeting society ’s expectations for good corporate behavior, improving operational efficiency and/or decreasing costs, opening new growth opportunities, improving risk management, strengthening competitive position, and improving access to capital. While most business executives agree that sustainability impacts their business success, there is also agreement that it will become increasingly important to their business in the future. Furthermore, they feel that their companies are not acting decisively on developing these practices, in part because they have not yet established the business case for sustainability [11, 12]. The companies most often cited by the BCG survey [5] respondents as those “first-class companies in sustainability” are General Electric, IBM, Royal Dutch Shell, Nike and Wal-Mart; Toyota was also cited as a leader in 2009, but that may have changed as a result of its ongoing product safety issues and multiple recalls. DuPont shows up in many forums as a sustainability leader within the chemical industry. These are the companies that are perceived as having highly integrated sustainability planning efforts and enjoying full benefits as a result.

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What all of the surveys suggest is that corporate sustainability is here to stay as an important business concept that companies feel they need to pursue; however, most companies feel that they are not doing a good job at it. The business case has yet to be established. As companies move up the learning curve, they are deriving more significant value from their sustainability efforts and are therefore more able to make the business case. In spite of the mainstreaming of sustainability in the business lexicon, many companies are struggling with its implementation and feel that there is a material gap between talk and action. They appear to lack a holistic view and an overall plan and therefore have not been able to cross- leverage the ideas throughout the organization. The result is that their programs consist of a variety of disconnected initiatives associated with everything from products, processes, facilities, fleets, employees, commuting and travel, and the community [5]. Furthermore, their progress is incremental; they haven’t found the language, narrative or framework that can link the various initiatives and drive sustainability deeply into their culture. While the initiatives themselves may demonstrate value, they are missing the opportunity to change the game and derive significant value by incorporating sustainability into both business strategy and operations, and to measure success in terms of both business and societal value. Measurement is indeed a fundamental piece of the sustainability implementation puzzle, as an organization must evaluate how well it integrates sustainability into its daily operations in a measurable and meaningful way to determine its progresses [8]. The greatest challenge is in selecting the right indicators and metrics that reflect that which is most material to the business and its key stakeholders, and to do so with measures that are integrative, reflect relationship and resource dynamics, and take into account systems thinking and health. CONCLUSIONS Over the past fifty years, the concepts of sustainability have gradually penetrated the business world, with an acceleration of business attention in the last decades. As stakeholders continue to demand greater sustainability practices, it appears that

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these trends are long term and not just a fad of the day. As such, companies now have to actively think about becoming more sustainable—willfully or not—and find themselves moving along the corporate sustainability learning curve. On that curve, the first stages correspond to complying with regulations and the most advanced categories indicate that sustainability is at the heart of the organization's operations and is a significant driver of innovation. While the traditional approach to sustainability is for companies to react to stakeholder pressure and to reduce business risks, more companies are finding that it is a good lens for identifying and pursuing opportunities to innovate and build value; as such they are migrating to more advanced stages along the learning curve and significantly modifying their value offerings. To really build value from sustainability and utilize it as a way to support business strategies, companies are elevating the concepts to a strategic place at the center of the company. Doing so entails putting sustainability into the language of business and utilizing it as an integrative set of concepts, cutting across functions and disciplines. Several surveys indicate a growing consensus amongst business executives that efforts to make a company more sustainable at least offset their costs. While this is promising in terms of seeing more companies embrace sustainable practices, the next challenge is to develop the right measures of performance that link sustainability decisions and actions to business performance This requires developing the right insights and information that underlie the metrics. As an organization improves its ability to deeply assess its sustainability performance, the business case becomes more apparent and sustainability becomes a central strategic pillar of the business proposition. CONFLICT OF INTEREST The author(s) confirm that this chapter content has no conflicts of interest. ACKNOWLEDGEMENT Declared none.

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[20] [21] [22] [23]

United Nations, Report of the United Nations Conference on Environment and Development. Accessed at http://www.un.org/documents/ga/conf151/aconf151261annex1.htm on March 11, 2011. United Nations, Beijing and its Follow-Up. Accessed at http://www.un.org/womenwatch/daw/beijing/ on March 19, 2010. United Nations, Kyoto Protocol. Accessed at http://unfccc.int/kyoto_protocol/items/2830.php on March 20, 2010. The Equator Principles. Accessed at http://www.equator-principles.com/principles.shtml on February 12, 2010.

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CHAPTER 14 Summary Urmila Diwekar* Center for Uncertain Systems: Tools for Optimization and Management (CUSTOM), Vishwamitra Research Institute, 368 56-th Street, Clarendon Hills, IL 60514, USA Sustainability is a multi-disciplinary field. Therefore, in this book we have perspectives from various fields, including ecology, economics, social science, policy, industrial ecology, engineering, and business. Although, each discipline provided a different point of view about sustainability, there are common themes like resiliency, adaptability, and systems approach for sustainability as you have seen. In this chapter I am trying to summarize the common themes and diversities involved in various perspectives. Ecosystem sustainability is critical to the sustainability of our planet. In her chapter on Principles of Sustainability from Ecology, Mayer presented perspective on how historically the science of ecosystem sustainability has helped in understanding and managing ecological systems. She argued that resilience, desirability, and biodiversity are important to sustainability of ecosystems. Further, the field of complex systems with adaptive management can provide the basis for understanding and managing ecosystem sustainability. Her chapter provided a complete overview of various hypotheses based on which various systems models are developed to study the planet. She also proposed that ecologist should work with anthropologists and economists forming new disciplines like human ecology and ecological economics to study sustainability. In line with Mayer’s emphasis on coupling economics and ecology, Farley presented the economics of sustainability in Chapter 3. According to him, historically, industrial revolution and use of fossil fuels with large scale *Address correspondence to Urmila Diwekar: Center for Uncertain Systems: Tools for Optimization and Management (CUSTOM), Vishwamitra Research Institute, 368 56-th Street, Clarendon Hills, IL 60514, USA; Tel: (630) 886-3047; E-mail: [email protected] Heriberto Cabezas and Urmila Diwekar (Eds) All rights reserved-© 2012 Bentham Science Publishers

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organization structures resulted in development of microeconomic theory. This theory focused on prices in balancing supply and demand. The great depression led to macroeconomic theory calling for government’s intervention through expansion of monetary and fiscal policies. However, sustainability requires a new paradigm shift where a sustainable economics does not need to abandon the utilitarian approach of free markets but recognizes the limits of planets and importance of quality of life. A sustainable society requires an efficient allocation of resources where efficiency is a combination of economics and ecology. He embraces the concept of entropy and limits put by the laws of thermodynamics (A way of thinking sustainability in terms of thermodynamics laws is addressed in detail in Chapter 10 by Bakshi and Grub). He argued that efficient allocation of resources required both private as well as cooperative institution. Economists view that individuals act only in selfish manner but this is fortunately not supported by evolutionary biologist. Behavioral economics have identified mechanisms to promote cooperative behavior necessary for the sustainability of planet and this provides hope for our planet. These mechanisms are also discussed in various chapters like human interactions and sustainability, and industrial ecology and sustainability. As indicated in the first chapters, biodiversity and resiliency are important for sustainability and economic tools can provide incentives for sustainability. In the fourth chapter Germstani et al., presented tools needed for sustainability implementation based on ecological science, economics and law. These tools include treating natural resources such as trusts, environmental laws like clean water act, land retirement programs like the conservation reserve program, developing market mechanisms based on complex market design with some target limits for markets like pollutant budget, and multi-disciplinary approach to policy based on adaptive management and sustainable metrics. Use of these tools is also illustrated in chapters on sustainable management of water, infrastructure sustainability, and green engineering and sustainability. We looked at the science of ecology, economics, and policy perspectives on sustainability. However, human interactions and their effect on sustainability cannot be neglected. In the chapter by Gorman et al., socio-technical interactions of humans are presented using real world case studies involving large human

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footprint. In order to reduce the human footprint on sustainability a framework of five different components are presented. These five components include: (1) establishing trading zones for diverse expert communities where participants cooperate to agree on mechanisms of exchange, (2) goals agreed by various participants are discussed, (3) moral imagination for understanding each others’ views, (4) adaptive management which is a key issue mentioned in earlier as well later chapters of this book, and (5) anticipatory governance. Water, energy, and infrastructure are necessities of today’s civilization. In that water is a nature resource and is unique as it is an essential component of life and the processes that sustain life. Therefore, a chapter on sustainable water resource management is presented separately in this book. You see water in forefront in the infrastructure and urban sustainability chapters as well as in the case study of green engineering chapter. In his chapter on the matter of sustainable water resource management, Shuster talked about weak and strong sustainability. He argued that depending upon just technological solution to water management represents weak sustainability. Under strong sustainability, there is a potential for shift towards recognition that different sources of capital may substitute for each other, e.g., ground water demand can be substituted with retention of rainfall which demands shifts in awareness. He advocates systems analysis approach for management considering linear, nonlinear interactions, and also transient and stochastic nature of climate change (also referred to as time dependent uncertainty in chapter on green engineering). A watershed basis for water resources planning and management is arguably a prerequisite for sustainability. This is also highlighted in the green engineering chapter case study where watershed level management is taken into consideration. Again urban and infrastructure sustainability (like low impact development) considerations are important for water sustainability. This is also emphasized in the next two chapters on infrastructure and urban sustainability. However, it should be remembered that not all problems with water resources management are a consequence of urbanization, land use policy also affects water management. It is important to have community based management system which uses traditional and non-traditional technologies. It is important to have the triple-bottom line approach defined in the business sustainability chapter for water sustainability.

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Chapter 7 is related to infrastructure sustainability. Water, energy, land use, and transportation are the important components of infrastructure sustainability. It is important to consider infrastructure sustainability because of increase in world population and per capita consumption is going to put lot of stress on our planet. Several alternatives are suggested for low impact and sustainable urban development. As stated in the earlier chapter, water and energy are the most important part of sustainability. Several alternatives are suggested for water and energy infrastructure in this chapter. It is suggested that decentralized or distributed energy and water production will help in making it more sustainable. A paradigm shift is necessary for urban sustainability development with that come the necessity of decision support tools. Again forecasting is an important part of these tools. This is also stressed in the green engineering chapter. In the next chapter engineering urban sustainability, Li et al., present challenges of engineering urban sustainability. The focus is to study detailed qualitative and quantitative understanding of all subsystems of urban infrastructure and make decisions about planning. They first present the hypothesis urban system can be studied as complex adaptive system (CAS). Inclusion of human agent complicates this hypothesis. Further, there is a conceptual conflict between CAS and engineering systems and also with sustainability. However, CAS can provide a framework for engineering. The models of this framework should be able to address the ways to combine economic, ecological, and urban systems governed by different spatial and temporal processes. Both for models and data a system level approach is needed (see similar thinking in chapter on green engineering). Uncertainties are part of sustainability. Addressing uncertainties is these models and data is the subject of the green engineering chapter. One of the problems of engineering these systems is defining sustainability metrics. This problem is common in all chapters. Therefore, we have devoted a complete chapter on sustainability metrics. A case study of urban sustainability is presented in this chapter by considering various sustainability metrics. Life cycle analysis is important in engineering sustainability whether it is related to infrastructure or manufacturing. LCA case study is presented in this chapter for infrastructure sustainability while the chapter by Bakshi and Grub presnts LCA case studies for sustainability of manufacturing processes. In the chapter on sustainability indicators and metrics, Cabezas presented sustainability as an issue of human survival on planet Earth. He argued that the

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sustainability indicators must therefore, encompass a wide issues relevant to human existence, and they must be useful in “steering” the system towards sustainable trajectory. He defined indicator as a directly observable or measurable variable and a metric is an aggregate quantity made of many indicators that represents broader aspect of the system. List of indicators is broad and covered biological, energy, climate, economic and social subsystems. Metrics represented economic strength, human environmental burden, energy use, and system order and stability. For ecosystems, the appropriate metrics are not well defined, but they could include ecological footprint and biocapacity, biodiversity, ecosystem primary information, ecosystem primary productivity, and emergy and emergy based metrics. For economics, the metrics and indicators like GDP, NIPA and seven summary accounts such as Domestic Income and Product Account, Private Enterprise Income and Outlay Account, Government Receipts and Expenditure Accounts, Foreign Transaction Current Account, Domestic Capital Account, and Foreign Transaction Capital Account are defined. In environmental economics these metrics are modified by various methods of green accounting like gNPD. For self organization, order, and stability of system Fisher information is relevant. It should be remembered that for sustainability, the scale for which these metrics are evaluated ranges from local, regional, and global. This results in a complex system. Further, since sustainability is focused on long term, the issue of uncertainty, particularly in long term prediction of the state of the system is paramount. While Cabezas presented general indicators and metrics for sustainability, Bakshi and Grubb concentrated on indicators and metrics based on thermodynamics. Thermodynamics is useful in quantifying role of resources in technological activities and ecological resources. Thermodynamics has been the source of various resource accounting and aggregation methods that are popular for life cycle evaluation of technologies. They argued that the thermodynamic concept of exergy provides a common currency for industrial and ecological system. Exergy has been described as the “ultimate limiting resource”. Aggregate metrics such as cumulative degree of perfection, exergetic breeding factor and renewability index for considering the exergy of industrial processes. A cumulative quantity called Industrial Cumulative Exergy Consumption (ICEC) is defined. Ecological Cumulative Exergy Consumption (ECEC) expands ICEC by including the exergy consumption in ecosystems for making natural resources. This approach is closely related to the concept of emergy described by Cabezas in

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the earlier chapter. The emergy based metrics include emergy yield ratio, environmental loading ratio, and sustainability index. Life cycle analyses of two case studies are presented illustrating importance of various metrics. Industrial ecology attempts to identify opportunities to promote sustainable development by viewing industrial systems holistically, i.e., through their interaction with other technological and economic systems, but also in terms of the interface they form between nature and society. In the chapter on industrial ecology and sustainable development, Petri et al., present a systems analysis approach for industrial networks consisting of number of organizations like business and government are linked to each other through the exchange of resources. They use agent based modeling coupled with scenario analysis techniques and addressing the role of uncertainty for assessment of sustainability. Similar to the chapter on green engineering and sustainability described below, dynamics and uncertainty are important part of this decision making framework. Further, multi-criteria decision analysis is also part of evaluation. The difference between the approach presented in this chapter and the next chapter is the models and methods used in this chapter is more qualitative than quantitative. In the chapter on green engineering and sustainability, again a systems analysis approach is presented in terms of a computer aided framework for decision making. This framework extends traditional industrial manufacturing plant design to green engineering, green energy, industrial ecology, to sustainability. For green engineering, it involves starting decisions as early as chemical and material selection stage on one end, and managing and planning decisions at the other end. However, uncertainties, and multiple and conflicting objectives are inherent in such a process. Efficient multi-objective optimization and uncertainty analysis algorithms are useful in this context. Uncertainties increase further in industrial ecology where industrial sector level decisions are concerned. The concept of overall sustainability goes beyond industrial sector and brings in time dependent nature of ecosystem and multi-disciplinary decision making. Forecasting is essential in this case as outlined in earlier chapters. Optimal control methods and financial theories can be useful in handling time dependent uncertainties, forecasting, and dynamic decision making. This framework is illustrated using a watershed and decision making for intervening in mercury cycle.

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The last chapter of this book deals with an important aspect of sustainability that is related to business practices. Beloff presents the business sustainability as is based on the concept of triple bottom line—the management of economic, environmental, and social aspects. She describes multiple meaning of corporate sustainability and explains how historical events helped define sustainability and reviews the typical path that companies have taken to incorporate sustainability in their business agenda. She argues that there is economic value to the companies to embrace sustainability. I hope you have enjoyed this book. Sustainability science is evolving and this book provides a current perspective. In coming years, we will see more developments in all these areas which we hope to bring to you in the future.

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Index Activated carbon adsorption, 295, 299 Adaptive.15, 16, 19, 20, 29, 37, 38, 67, 68, 80, 83, 84, 88, 89, 94, 95, 106, 107, 109, 110, 133, 140, 173, 176, 177, 178, 182, 194, 197, 216, 217, 218, 219, 238, 239, 243, 245, 247, 250, 252, 340, 341, 342, 343. Adaptive landscape, 29, 38. Adaptive management, 15, 20, 80, 83, 88, 89, 94, 95, 106, 107, 109, 197, 216, 217, 218, 219, 340, 341, 342, Al Gore, 323, Anthropogenic, 90, 93, 101, 116, 139, 31, Anticipatory governance, 88, 89, 94, 97, 101, 102, 103, 104, 106, 107, 108, 109, 342, Apartheid, 310, 319, Area sources, 174, 188, 189, 190, 191, Best Management Practices, 139, 149, 78, Bhopal, 310, 318, Big-pipe concept, 141, 144, Bioaccumulation, 292, 293, 300, 301, 303, 304, 305, 306, 309, Bioaccumulative, 298, 300, 273, Biocapacity, 344, 24, 36, 197, 205. 206, 218, 344, Biodiversity, 21, 27, 28, 31, 32, 33, 35, 38, 40, 41, 51, 52, 57, 58, 59, 61, 65, 67, 69, 70, 71, 73, 77, 78, 79, 80, 81, 82, 83, 84, 86, 87, 108, 167, 185, 340, 341, 344, Biogenic sources, 174, 188, 190, 191, Biogeography, 27, 38, Biomass, 205, 223, 229, 233, 241, 4, 9, 18, 19, 20, 21, 28, 35, 137, Biotic homogenization, 9, 28 , 38, Bluebelt, 141, 150, 169, Boston Consulting Group, 314, 332, 335, 338, Bounded rationality, 244, 256, 270, Brand management, 335, 310, Brown revolution, 173, 174, 195, Brundland Commission, 3, Brundtland Commission, I, 3, 98, 310, 311, Bruntland, 3 , 4 , 113, 138, 244, commission, , I, 3 , 4 , 7, 135, 138, 198, 220, 269, 310, 311, 319, 338, Bruntland Commission, 3, 4, Heriberto Cabezas and Urmila Diwekar (Eds) All rights reserved-© 2012 Bentham Science Publishers

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Bycatch, 89, 96, 97, 98, 99, 100, 101, 103, 108, 110, Catastrophe, 5, 14, 34, climate change, 13, 15, 28, 32, 33, 34 , 40, 51, 52, 57, 59, 61, 73, 88, 94, 95, 113, 122, 123, 133, 134, 139, 141, 142, 143, 158, 170, 171, 172, 185, 265, 314, 319, 320, 321, 323, 338, 342, CO2 emissions, 157, 164, 286, 287, 288, coagulation and filtration, 295, 298, 299, collective action, 40, 59, 63, combined heat and power, 141, 158, 159, 168, complex systems, 13, 17, 29, 30, 66, 67, 68, 72, 80, 82, 83, 88, 90, 94, 177, 179, 195, 216, 238, 239, 271, 272, 289, 340, 9, conservation, 26, 31, 35, 37, 38, 40, 47, 62, 71, 74, 78, 81, 85, 86, 87, 88, 89, 96, 97, 98, 100, 110, 115, 120, 123, 134, 150, 155, 165, 218, 240, 289, 307, 341, 348, 11, cooperative behavior, 341, 348, 40, 54, 55, 60, Copenhagen Climate Conference, 310, 324, corporate social responsibility, 335, 348, 354, 271, 310, 311, 322, 323, corporate sustainability, 336, 337, 346, 348, 310, 311, 316, 329, daylighting, 128, 141, decentralized, 343, 348, 57, 126, 127, 129, 132, decision support, 164, 165, 166, 203, 243, 250, 253, 263, 343, 310, 324, desirability, 340, 9, 10, 20, 42, Dominican Republic, 26, DuPont Sustainable Growth Excellence Awards, 331, Dynamic, 9, 13 , 14, 15, 16, 18, 22, 23, 24, 26, 27, 31, 32, 33, 34, 37, 38, 39, 44, 66, 68, 70, 80, 82, 83, 88, 105, 133165, 169, 173, 176, 178, 179, 180, 182, 193, 194, 195, 199, 202, 204, 205, 208, 209, 210, 212, 219, 221, 222, 243, 244, 245, 246, 247, 248, 250, 251, 253, 254, 259, 262, 264, 265, 267, 269, 270, 271, 284, 285, 291, 292, 298, 301, 302, 304, 309, 336, 341, 345, Earth, 31, 33, 41, 44, 63, 90, 100, 109, 110, 116, 174, 195, 198, 199, 200, 201, 202, 206, 207, 209, 220, 224, 238, 242, 244, 311, 320, 343, 4, 5, 12, 15, 16, ecological footprint, 36, 173, 180, 197, 207, 206, 217, 218, 221, 344, 24, ecological limits, 52, 22, ecology, 33, 34, 35, 36, 37, 38, 39, 45, 63, 65, 71, 72, 81, 83, 84, 85, 87, 107, 111, 139, 175, 196, 199, 200, 218, 221, 223, 229, 241, 243, 245, 247, 249, 251, 253, 255, 256, 257, 259, 261, 263, 265, 267, 268, 269, 270, 271, 272, 273, 274, 288, 289, 290, 306, 307, 308, 340, 341, 345, 5, 6, 8, 9, 10, 11, 13, 15, 17, 18, 19, 21, 22, 24, 25, 26, 27, 29, 30, 31, 32, economic dimension, 310, 313,

Index

Sustainability: Multi-Disciplinary Perspectives 349

economics, 40, 41, 42, 43, 45, 47, 49, 51, 53, 55, 56, 57, 59, 60, 61, 62, 63, 64, 65, 80, 81, 84, 85, 86, 87, 138, 140, 169, 197, 206, 218, 221, 240, 269, 270, 271, 308, 340, 341, 344, 5, 6, 7, 8, 30, ecosystem, 41, 44, 45, 46, 47, 50, 51, 52, 57, 58, 59, 60, 61, 62, 65, 66, 67, 68, 69, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 89, 90, 93, 109, 112, 113, 114, 116, 117, 120, 125, 126, 129, 130, 137, 139, 171, 175, 196, 197, 201, 202, 205, 208, 215, 222, 223, 228, 229, 231, 238, 239, 241, 242, 243, 246, 268, 269, 270, 273, 274, 289, 292, 293, 300, 308, 309, 312, 340, 344, 345, 3, 7, 9, 10, 11 , 12 , 13 , 14, 15, 17 , 18, 19, 20, 21, 22, 23, 24, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 37, 38, 39, 40. ecosystems, , 66, 67, 68, 69, 72, 76, 82, 83, 84, 85, 112, 3, 7, 9, 10, 11, 12, 13, 14, 15, 17, 18, 20, 21, 66, 125, 175, 201, 202, 205, 208, 312, 344, 7, 11, 14, 15, 18, 20, 21, Ecuador, 318, Embeddedness, 244, 260, 270, emergent properties, 117, 197, 211, 212, 214, 257, emergy, 197, 206, 207, 211, 217, 218, 221, 222, 229, 230, 238, 239, 240, 244, 345, energy, 197, 198, 199, 201, 202, 203, 205, 207, 211, 214, 217, 218, 221, 222, 224, 225, 226, 227, 228, 229, 231, 233, 234, 235, 238, 240, 241, 245, 253, 264, 265, 269, 272, 273, 274, 275, 276, 282, 285, 288, 289, 312, 316, 327, 328, 329, 330, 331, 332, 334, 342, 343, 344, 345, 4 , 5, 6, 17, 18, 20, 22, 23, 28, 41, 44, 45, 46, 52, 54, 57, 59, 61, 62, 69, 113, 115, 122, 123, 124, 124, 126, 128, 137, 140, 141, 142, 143, 144, 145, 146, 147, 148, 149, 155, 156, 157, 158, 159, 160, 161, 162, 163, 164, 165, 167, 168, 169, 170, 171, 172, 173, 175, 176, 180, 182, 183, 185, 186, 187, 188, 195, engineering, 204, 222, 223, 225, 237, 240, 241, 243, 273, 274, 275, 276, 277, 279, 281, 282, 283, 285, 286, 287, 289, 291, 293, 295, 297, 299, 301, 303, 305, 306, 307, 308, 309, 340, 341, 342, 343, 345, ii , iii, v, vi, vii, 5, 6, 7, 8, 17, 96, 109, Enron, 322, environmental dimension, 312, 9, Equator Principles, 323, 339, Equity, I, 9, 10, 25, 112, 113, 134, 136, 138, 177, 243, 313, 335, ESG, environmental social governance, 310, 311, Evolution, 10, 16, 17, 23, 29, 34, 35, 38, 40, 41, 45, 54, 55, 60, 61, 62 63, 67, 84, 173, 174, 177, 1294, 195, 245, 246, 247, 249, 250, 251, 252, 253, 254, 257, 258, 259, 263, 264, 266, 267, 268, 270, 271, 272, 308, 313, 331, 340, 341, Excludability, 40, 49, 50, 52, Exergy, 6, 197, 211, 217, 221, 222, 223, 224, 225, 226, 228, 229, 230, 231, 232, 233, 234, 235, 236, 239, 240, 344,

350 Sustainability: Multi-Disciplinary Perspectives

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Exxon-Valdez, 310, 320, Feedback, iii, 13, 14, 15, 16, 17, 19, 20, 26, 27, 34, 118, 194, 258, 268, first law, 44, 222, 224, 225, 234, fisher information, 82, 109, 197, 205, 206, 208, 209, 210, 212, 213, 214, 217, 218, 220, 221, 293, 304, 305, 344, 33, Florida Everglades, 11, flow regimes, 204, 205, 209, fluid flow, 202, 203, 204, 205, 214, food web, 23, 32, foot and mouth disease, 89, 103, 108, 109, 110, Fourth World Conference on Women, 321, Framework, 321, 336, 342, 343, 345, 9, 12, 13, 31, 33, 34, 36, 37, 62, 78, 80, 85, 88, 89, 90, 96, 101, 103, 107, 108, 114, 134, 138, 165, 166, 173, 177, 178, 181, 182, 194, 241, 243, 245, 250, 253, 263, 264, 268, 269, 273, 275, 277, 278, 280, 286, 287, 289, 290, 291, 293, 298, 308, 310, 312, 313, 320, 321, 336, 342, 343, 345, 9, 12, 113, 31, 33, 34, 36, 37, 62, 78, 80, 85, 88, 89, 90, 96, 101, 103, 108, 114, 134, 138, 165, 166, 173, 177, 178, 181, 182, 194, 241, 243, 245, 250, 253, 263, 264, 268, 269, 273, 275, 277, 278, 280, 286, 287, 289, 290, 291, 293, 298, 308, 310, 312, 313, 320, 321, 336, 342, 343, 345, Freshwater, 39, 76, 112, 114, 117, 121, 133, 141, 142, 143, 169, 171, 175, 14, Froude number, 204, 205, fuel cell, 273, 282, 283, 284, 307, 158, 159, 160, 170, fund-service, 46, 47, 48, 58, Gaia hypothesis, 13, 15, 16, 17, 33, 34, GEMI, Global Environmental Management Initiative, 310, 315, 338, Global, , 195, 199, 201, 203, 210, 214, 215, 223, 234, 242, 244, 245, 254, 292, 310, 314, 315, 316, 317, 319, 322, 338, 344, I, 10, 11, 16, 17, 21, 24, 25, 26, 28, 30, 31, 33, 34, 36, 37, 38, 39, 40, 41, 44, 51, 52, 53, 59, 61, 64, 73, 85, 86, 88, 93, 94, 95, 109, 113, 115, 117, 139, 140, 141, 142, 143, 157, 168, 172, 174, 178, 180, 181, 182, 185, 187, GoodGuide.com, 315, green accounting, 344, 197, 206, 211, green engineering principles, 273, 275, 306, green net domestic product, 206, 217, 218, greenhouse gas emissions, 321, 324, 332, 59, greenhouse gases, 321, 59, 170, greenwashing, 310, 315, 338, greywater 147, 141, 147, 154, 155, 160,

Index

Sustainability: Multi-Disciplinary Perspectives 351

gross domestic product, 197, 205, 208, ground level ozone, 173, 181, 182, 187, 193, Haiti, 26, 37, Hammersley sequence sampling, 280, HealthyStuff.org, 315, heat recovery, 161, 168, 171, 283, 284, 285, 141, 159, 160, hierarchies, 243, 12, 67, humans, 199, 209, 292, 304, 341, I, 3, 4, 7, 10, 11, 16, 17, 20, 21, 25, 28, 29, 30, 31, 38, 40, 45, 54, 55, 60, 65, 68, 82, 83, 89, 116, hydroelectric dam, 310, hydrology, 238, 6, 7, 10, 112, 119, 121, 139, 140, incentives, 328, 341, 58, 66, 74, 77, 79, 80, 81, 85, 86, 109, 112, 128, 129, 132, 133, 134, 139, indigenous landscapes, 153, industrial ecology, 341, 345, ii, iii, 18, 175, 241, 243, 245, 247, 249, 251, 253, 255, 256, 257, 259, 261, 263, 265, 267, 268, 269, 271, 272, 273, 274, 288, 289, 290, 306, 307, 308, 340, industrial networks, 345, 239, 243, 246, 247, 248, 249, 251, 253, 254, 258, 260, 269, industrial park, 274, 289, industrial symbiosis, 289, 238, 241, 269, 273, industry, 293, 296, 316, 324, 326, 327, 329, 330, 335, 338, 6, 81, 97, 98, 99, 100, 101, 102, 104, 110, 120, 136, 175, 179, 243, 244, 245, 246, 247, 248, 249, 263, 264, 265, 266, 267, 268, 272, 277. Infrastructure, 341, 342, 343, iii, 116, 119, 120, 128, 130, 132, 133, 135, 136, 137, 139, 141, 142, 143, 144, 145, 146, 147, 148, 149, 150, 151, 153, 155, 157, 158, 159, 161, 163, 165, 166, 167, 168, 169, 171, 174, 176, 179, 183, 208, 255, 260. interactional expertise, 88, 89, 91, 93, 96, 97, 101, 102, 103, 106, 107, 109, 110, interdependence, 141, 145, 150, 176, 194, 244, 308, intergenerational, 9, 10, 113, 138, equity, 10, 25, 112, 113, 134, 136, 138, 177, 243, 313, 335, I, 9, Intergovernmental Panel on Climate Change, 34, 319, 338, ion exchange, 295, 298, 299, ISO 14001, 310, 321, John Elkington, 311, Johnson & Johnson, 318, 338, just distribution, 40, 44, 51, 59, 60, Kayapo Indians, 310, 319, landscape ecology, 24, 25, 26, 27, 37, 72, 139,

352 Sustainability: Multi-Disciplinary Perspectives

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landscaping, 141, 148, 149, 152, 153, 154, 161, 168, Latin hypercube sampling, 280, Law, 341, 6, 7, 8, 10, 11, 22, 38, 41, 44, 45, 59, 62, 65, 69, 73, 74, 75, 76, 80, 81, 82, 83, 84, 85, 87, 97, 100, 106, 109, 110, 129, 132, 134, 162, 177, 196, 222, 223, 224, 225, 226, 234, 237, 239, 242, 276, 289, LEDs, 164, life cycle assessment, 173, 182, 185, 186, 187, 196, 239, 241, 245, 307, 310, life cycle impact assessment, 173, 182, 185, 239, 240, life cycle inventory, 173, 185, 233, 236, liming, 300, 301, 302, 303, 308, 309, low flow fixtures, 141, 154, 168, low-impact development, 128, 141, Markov property, 290, Marx, 208, 221, maximum principle, 291, 292, 308, MCFC, 283, McKinsey, 334, 335, 338, mental models, 89, 244, 251, 252, 253, 254, 256, 257, 258, 262, 263, 272, mercury, 292, 293, 294, 295, 296, 298, 299, 300, 301, 303, 304, 305, 307, 308, 309, 345, 164, 273, 274, 289, 292, 293, 294, metamodels, 173, 178, 179, 299, 300, metrics, 307, 315, 316, 336, 337, 338, 341, 343, 344, 345, I, 6, 25, 26, 37, 81, 94, 95, 96, 127, 129, 173, 180, 181., 182, 187, 194, 197, 198, 199, 200, 201, 203, 204, 205, 206, 207, 209, 210, 211, 213, 214, 215, 216, 217, 218, 219, 220, 221, 227, 228, 230, 233, 240, 365, MINSOOP, 282, 283, mobile sources, 174, 188, Molten Carbonate Fuel Cell, 283, 160, Monte Carlo sampling, 279, Montreal Protocol, 319, 338, MOP, 281, 286, moral imagination, 342, 88, 89, 93, 94, 97, 99, 100, 101, 104, 106, 107, 108, 109, multi-objective optimization, 273, 277, 281, 282, 285, 298, 299, 306, 307, 345, nanotechnology, 222, 94, 109, network characteristics, 244, 260, 261, network structure, 244, 260, niche, 16, 17, 22, 23, 28, 34, 36,

Index

Sustainability: Multi-Disciplinary Perspectives 353

norms, 244, 248, 257, 260, 262, 263, 268, , 75, 123, nutrient, 273, 300, 305, 306, 9, 14, 15, 17, 20, 23, 25, 46, 54, 69, 70, 121, 122, 124, 125, 136, 137, 151, 155, 156, 157, 168, 202, 205, Obama’s Green Initiatives, 323, Objectives, 274, 275, 276, 277, 281, 282, 286, 287, 298, 302, 306, 315, 317, 324, 345, 122, 134, 177, 180, 243, 244, 260, 263, 264, 265, 266, 267, 272, 273, . optimal control, 289, 291, 302, 303, 304, 305, 307, 308, 309, 345, 273, ozone precursors, 173, 182, 188, 190, 193, Panarchy model, 13, 17, 18. 19, Pareto set, 281, 282, 286, 287, payoff table, 286, peak oil, 40, 51, 52, PEM, 159, 283, 284, 285, 286, 288, plug-in hybrids, 148, point source, 122, 125, 148, 174, 188, 189, 190, 191, 294, 296, 297, 299, policy, 72, 73, 75, 76, 78, 80, 81, 82, 83, 84, 85, 87, 96, 104, 105, 106, 108, 109, 110, 111, 113, 128, 139, 140, 165, 168, 178, 180, 181, 182, 194, 196, 218, 219, 221, 240, 245, 246, 251, 264, 265, 267, 268, 269, 270, 271, 272, 292, 297, 303, 307, 308, 340, 341, 342, ii, iii, 6, 7, 9, 10, 15, 16, 21, 22, 31, 36, 37, 38, 49, 50, 61, 62, 65, 66, 72, 73, 75, 76, 78, 80, 81, 82, 83, 84, 85, 87, 96, 104, 105, 106, 108, 109, 110, 111, 113, 128, 139, 140, 165, 169, 178, 180, 181, 182, 194, 196, 218, 219, 221, 240, 245, 246, 251, 264, 265, 267, 268, 269, 270, 271, 272, 292, 297, 303, 307, 308, 340, 341, 342, population, 4, 10, 12, 13, 15, 23, 24, 29, 31, 33, 36, 38, 44, 50, 54, 55, 63, 67, 68, 72, 89, 93, 105, 117, 118, 120, 122, 124, 127, 135, 141, 142, 143, 165, 167, 168, 172, 173, 174, 175, 177, 188, 190, 195, 203, 206, 280, 293, 304, 314, 343, power, 43, 51, 60, 94, 98, 100, 105, 119, 126, 138, 141, 144, 145, 148, 157, 158, 159, 160, 168, 169, 188, 189, 197, 204, 205, 238, 241, 248, 256, 273, 282, 283, 284, 285, 286, 287, 292, 297, 307, 310, 328, 333, Precautionary Principle, 310, 320 predator-prey model, 304, price, 40, 41, 45, 47, 48, 51, 52, 53, 58, 59, 62, 78, 134, 160, 165, 203, 228, 255, 260, 261, 308, 321, 334, 341, probabilistic distributions, 278, probability distributions, 278, 279, public goods, 40, 58, 59, 61, 63,

354 Sustainability: Multi-Disciplinary Perspectives

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quality, 43, 44, 47, 50, 51, 62, 66, 77, 78, 80, 81, 85, 86, 95, 106, 116, 121, 125, 126, 127, 129, 136, 137, 143, 144, 149, 150, 153, 156, 165, 167, 173, 174, 175, 176, 179, 180, 181. 183, 185, 193, 208, 222, 224, 225, 226, 228, 229, 231, 233, 281, 282, 294, 296, 307, 308, 312, 313, 314, 321, 334, 341, I, Rachel Carson, 318, rainwater harvesting, 141, 146, 150, 169, Raising the ceiling, 328, Rationing, 40, 47, 48, 49, 51, 53, 58, 59, Resilience, 147, 194, 241, 265, 268, 305, 340, 9, 10, 13, 14, 15, 18, 19, 28, 31, 32, 33, 34, 44, 52, 59, 65, 66, 67, 68, 69, 70, 71, 72, 76, 77, 78, 79, 80, 81, 82, 83, 84, 93, 108, 109, 129, resource efficiency, 237, 238, 239, 243, resources, 148, 153, 158, 159, 169, 171, 172, 174, 175, 180, 182, 186, 195, 201, 202, 207, 222, 223, 225, 226, 227, 228, 229, 231, 233235, 239, 240, 241, 246, 247, 248, 251, 260, 261, 264, 268, 271, 289, 303, 307, 313, 314, 325, 327, 329, 331, 332, 341, 342, 344, 345, ix, 9, 10, 12, 13, 21, 22, 23, 24, 29, 36, 40, 42, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 57, 58, 59, 60, 61, 68, 74, 75, 76, 78, 83, 85, 88, 89, 90, 91, 93, 110, 112, 113, 114, 115, 116, 117, 118, 119, 120, 121, 122, 123, 124, 125, 126, 127, 128, 129, 131, 132, 133, 134, 135, 136, 137, 138, 139, 140, 141, 143, 145, return on investment, 172, 222, 227, 233, Reynolds number, 204, 205, 208, 209, Rivalry, 40, 47, 49, 50, 92, Robustness, 243, 249, 253, 267, 268, Routines, 244, 248, 255, 260, runaway greenhouse, 16, sampling, 253, 278, 279, 280, 282, 292, 307, Savannah River Watershed, 293, 294, 297, 308, Scale, 243, 249, 250, 253, 265, 269, 274, 278, 284, 289, 314, 321, 340, 344, 5, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 21, 22, 25, 26, 27, 28, 29, 30, 31, 35, 40, 41, 43, 62, 67, 68, 69, 70, 71, 73, 75, 83, 84, 85, 90, 105, 106, 109, 113, 114, 117, 118, 121, 122, 126, 127, 128, 132, 133, 137, 138, 139, 146, 147, 148, 150, 151, 156, 157, 158, 159, 164, 165, 167, 168, 175, 176, 178, 179, 180, . 182, 191, 194, 165, 167, 200, 201, 207, 310, 211, 214, 215, 223, 231, 233, 234, 235, 236, Scenario, 38, 95, 107, 147, 165, 167, 168, 180, 182, 193, 223, 243, 244, 249, 250, 251, 252, 253, 257, 258, 262, 263, 264, 268, 270, 272, 345, second law, 44, 222, 223, 224, 226, 237, 239, 242, self-organization, 67, 68, 202, 205, 208, 9, 32,

Index

Sustainability: Multi-Disciplinary Perspectives 355

Shell/Brent Spar case, 320, Silent Spring, 310, 318, social capital, 101, 112, 113, 114, 130, 131, societal, 31, 34, 35, 47, 60, 177, 245, 251, 269, 331, 336, society, ii, vii, 5, 7, 20, 21, 24, 33, 34, 35, 37, 40, 41, 42, 44, 52, 55, 57, 60, 61, 65, 77, 80, 83, 88., 89, 94, 109, 110, 120, 124, 127, 139, 170, 181, 192, 194, 199, 200, 208, 221, 242, 245, 270, 271, 331, 335, 338, 341, 345, SOFC, 283, 284, 285, 286, 287, 288, Solid Oxide Fuel Cell, 283, 284, Sparse Matrix Operator Kernel Emissions, 193, Spatial, 289, stable state, 32, 13, 14, stochastic optimal control, m 273, 289, 291, 302 303, stochastic programming, 298, stock-flow, 40, 45, 46, 47, 58, stormwater management, 128, 129, 130, 131, 132, 137, 139, 140, 141, 142, 146, 147, 140, 149, 150, 151, 153, 169, strategic choice, 244, 256, superordinate goals, 88, 89, 92, 93, system trajectory, 197, 200, 213, 216, 217, systems analysis, 269, 273, 274, 276, 306, 307, 342, 345, temporal, 9, 10, 13, 14, 15, 18, 24, 25, 27, 67, 68, 81, 113, 114, 115, 116, 117, 165, 178, 179, 180, 182, 192, 193, 194, 197, 210, 215, 243, 250, 274, 301, 343, thermodynamics, 44, 222, 223, 224, 225, 226, 227, 229, 231, 233, 235, 237, 239, 240, 241, 242, 285, 341, 344, 353, ii, 6, threshold, 62, 66, 67, 68, 79, 83, 259, 15, 17, 27, 32, 33, thresholds, 67 68, 83, 15, 17, 27, 33, time dependent uncertainties, 273, 274, 289, 306, 307, 345, TMDL, 79, 80, 294, 296, 299, 308, Total Maximum Daily Load, 79, 294, 296, 308, trading zones, 88, 89, 90, 91, 95, 96, 97, 103, 104, 105, 106, 107, 108, 109, 342, transportation, 46, 133, 142, 144, 146, 147, 148, 155, 157, 158, 165, 167, 173, 174, 176, 180, 181, 183, 185, 186, 187, 196, 206, 226, 227, 230, 233, 240, 343, trusts, 341, 65, 75, 76, 85 turtle excluder device, 101, uncertainties, 80, 243, 273, 274, 277, 278, 280, 286, 289, 296, 298, 301, 306, 307, 343, 345,

356 Sustainability: Multi-Disciplinary Perspectives

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uncertainty, 76, 80, 87, 115, 116, 180, 194, 197, 215, 219, 229, 243, 244, 247, 248, 249, 250, 251, 252, 253, 254, 255, 257, 258, 259, 261, 262, 263, 264, 267, 268, 270, 271, 272, 273, 277, 278, 279, 280, 289, 291, 297, 298, 299, 301, 306, 307, 308, 325, 342, 344, 345, 59, urban infrastructure, 119, 141, 142, 143, 144, 169, 343, urban systems, 173, 176, 177, 178, 179, 181, 194, 195, 343, urbanization, 113, 118, 119, 121, 139, 168, 173, 174, 175, 195, 142, 26, 27, 38, UrbanSim, 142, 165, 173, 181, 182, 183, 188, 189, 190, 193, 196, urine separation, 142, 155, 156, 157, 168, value chains, 243, 244, 246, wastewater, 112, 119, 120, 121, 122, 124, 126, 127, 134, 135, 136, 137, 141, 142, 146, 147, 148, 149, 150, 153, 154, 155, 156, 157, 158, 159, 160, 161, 165, 168, 170, 174, water, 122, 123, 124, 125, 126, 127, 128, 129, 130, 131, 132, 133, 134, 135, 136, 137, 138, 139, 140, 141, 142, 143, 144, 145, 146, . 147, 148, 149, 150, 151, 152, 153, 154, 155, 156, 157, 158, 159, 160, 161, 162, 163, 164, 165, 167, 168, 169, 170, 171, 172, 173, 174, 175, 176, 180, 184, 185, 186, 187, 198, 201, 202, 203, 206, 224, 225, 223, 234, 235, 273, 277, 285, 292, 293, 294, 296, 297, 299, 300, 301, 302, 304, 308, 309, 310, 312, 314, 315, 324, .327, 341, 342, 343, 6 , 7, 9, 11, 14, 16, 20, 21, 22, 23, 26, 39, 45, 46, 47, 57, 58, 72, 74, 75, 76, 77, 78, 80, 85, 86, 93, 98, watershed, 117, 118, 128, 129, 130, 132, 137, 139, 140, 149, 150, 169, 293, 294, 296, 297, 308, 342, 345, 21, 22, 78, 86, weak-strong sustainability, 112, 114, Wiener process, 290, 291,