Urban Sustainability: A Game-Based Approach (Springer Texts in Business and Economics) 3030670155, 9783030670153

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Springer Texts in Business and Economics

Jason Papathanasiou Georgios Tsaples Anastasia Blouchoutzi Editors

Urban Sustainability A Game-Based Approach

Springer Texts in Business and Economics

Springer Texts in Business and Economics (STBE) delivers high-quality instructional content for undergraduates and graduates in all areas of Business/Management Science and Economics. The series is comprised of self-contained books with a broad and comprehensive coverage that are suitable for class as well as for individual self-study. All texts are authored by established experts in their fields and offer a solid methodological background, often accompanied by problems and exercises.

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Jason Papathanasiou • Georgios Tsaples Anastasia Blouchoutzi Editors

Urban Sustainability A Game-Based Approach

Editors Jason Papathanasiou Department of Business Administration University of Macedonia Thessaloniki, Greece

Georgios Tsaples Department of Business Administration University of Macedonia Thessaloniki, Greece

Anastasia Blouchoutzi Department of International and European Studies University of Macedonia Thessaloniki, Greece

ISSN 2192-4333 ISSN 2192-4341 (electronic) ISBN 978-3-030-67015-3 ISBN 978-3-030-67016-0 (eBook) https://doi.org/10.1007/978-3-030-67016-0 # The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2021 This work is subject to copyright. All rights are solely and exclusively licensed by the Publisher, whether the whole or part of the material is concerned, specifically the rights of translation, reprinting, reuse of illustrations, recitation, broadcasting, reproduction on microfilms or in any other physical way, and transmission or information storage and retrieval, electronic adaptation, computer software, or by similar or dissimilar methodology now known or hereafter developed. The use of general descriptive names, registered names, trademarks, service marks, etc. in this publication does not imply, even in the absence of a specific statement, that such names are exempt from the relevant protective laws and regulations and therefore free for general use. The publisher, the authors, and the editors are safe to assume that the advice and information in this book are believed to be true and accurate at the date of publication. Neither the publisher nor the authors or the editors give a warranty, expressed or implied, with respect to the material contained herein or for any errors or omissions that may have been made. The publisher remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. This Springer imprint is published by the registered company Springer Nature Switzerland AG The registered company address is: Gewerbestrasse 11, 6330 Cham, Switzerland

Introduction

Sustainable development is among the grand challenges of the twenty-first century. Towards achieving such a state, the European Union (EU) is committed to mainstream the Sustainable Development Goals (SDGs) in the European policy framework and Commission priorities. Special attention is paid to the means for the achievement of SDGs, their universal application to all countries and the simultaneous address of their economic, environmental and societal dimensions. To achieve such daunting objectives, there is the need of not only raising awareness, but acquiring sustainability literacy, in the sense of a functional education that will provide the necessary skills and motives to cope with the challenges of and contribute to sustainable development. The difficulty with sustainability arises from its abstract nature and the fact that related problems have long-term horizons. However, the seeds and efforts for sustainable development already lie in the lives of millions of people. Two characteristic examples are: – Urban mobility. Countries, municipalities and ordinary citizens attempt to take measures and find solutions that will transform mobility to sustainable mobility and stop the uncontrolled growth of vehicles and the downgrading of the quality of life. – Societal metabolism. All those interactions that people do with natural systems every day and facilitate the flows of material and energy. Already in the aforementioned examples lie the implications for the need to fully comprehend the long-term horizon of sustainability problems and their backward relation with the present (for example understanding how the choice of a transportation mean affects the future status of an urban system), as well as the need to translate complex notions into simple ones (for example what is the impact of consumption habits on the local environment). As a result, it is essential to provide an innovative pedagogy that will allow the exploration of what sustainable development is, how it can be manifested and how it can be achieved. Moreover, such a pedagogy should provide the necessary means that would allow students (or adults, policymakers, children, etc.) to experience processes of complex decision making, sharpen their clarity of thought, enhance v

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their communication abilities and help them develop critical thinking as well as key competences to address the complexities of sustainable development. This type of education needs to become a process that moves towards being transformed into a more experiential and student-centred way of learning, hence allowing students to constantly assess their surrounding environment, operate and adapt to it through processes of revision from their frames of reference and provide the appropriate materials to comprehend systemic complexity. The SUSTAIN project1 was funded under the ERASMUS+ KA2 Strategic Partnerships for Higher Education Programme (Agreement n 2017–1-EL01KA203–036303) with the objective of commencing sustainability literacy among students of higher education. To achieve the objective, the partners designed, developed and implemented a course—hybrid in nature—that combined gamebased learning with an analytical style of education. This type of hybrid and experiential education has been increasingly growing in importance in all levels of education (Hauge et al., 2014) and in particular with regard to game-based learning that has become an important issue for economy, society and research (Wang & Tseng, 2014). Such games that combine entertainment with an educational dimension are called Serious Games (SGs) (Laamarti, Eid, & Saddik, 2014; Michael & Chen, 2005). The potential of SGs to provide educational enhancement (Bellotti, Berta, & DeGloria, 2010) is already acknowledged in the research community along with the ability to: • Allow learners to experience situations that are impossible in the real world for reasons of safety, cost, time, etc. (Squire, 2002) • Engage the user in a pedagogical journey and have a positive impact in the development of a number of skills (Mitchell & Savill-Smith, 2004) • Enable improved self-monitoring, problem recognition and solving, decision making, etc. (Katsaliaki & Mustafee, 2012) • Create a context of communication, collaboration and sense of belonging (Klopfer, Osterweil, & Salen, 2009) All these elements are essential for sustainability literacy, showing also the increased capacity of SGs as an innovative approach to twenty-first century education and the SUSTAIN project is a demonstration of its capabilities. In more detail, the SUSTAIN course was offered as an elective to the students of the department of Business Administration of the University of Macedonia and the course dealt with: 1. Transportation sustainability, societal metabolism and decision making under those contexts. The purpose was to teach students what the definitions of the notions are, how they are translated in everyday life, what insights can they get

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from studying the respective models and finally formalize the, sometimes complex, mathematic notions that are necessary to make robust decisions. Furthermore, the developed material illustrated best and failed practices that have been applied to countries around the world and provided insights into how sustainable transportation and a balanced societal metabolism can affect an urban environment. 2. Studying the official terminology of sustainable transportation and societal metabolism, successfully comprehending the mathematics that are involved in decision making and adopting best practices from other countries do not guarantee the success of an education. Issues such as sustainability are inherently complex because human behaviour determines their functions. However, human behaviour is in itself inherently complex and uncertain as well; thus students need to learn that any policy towards the goal of sustainable development must be dynamic and constantly evaluated, since the environment constantly changes, either as a result of the policy, or independently or by the actions of other people (Quadrat-Ullah & Karakul, 2007). As a result, simulation is a natural candidate to address issues of sustainability because: (a) it allows the representation and analysis of large-scale systems, (b) it can illustrate abstract notions in concrete ones, (c) it allows the analysis of systems that are not easily quantifiable, especially those that are driven by human behaviour and (d) it is easy to use and communicate (Armenia, Tsaples, & Carlini, 2018; Zhang & Peeta, 2011). Hence, the project team developed simulation models that allowed experimentation in a consequence-free environment. The simulation models can be used to identify scenario exemplars on how we can achieve sustainable urban transportation and a balanced societal metabolism, while taking into account formal decision-making process. Thus, greater insights were provided to the policymakers of the future regarding the complexities of decisions in uncertain issues in which many stakeholders are involved. 3. The approach of the SUSTAIN project is hybrid and as such the material developed, was translated, in elements and mechanics of a Serious Game. The purpose was to create a board game that allowed students to learn about transportation sustainability and societal metabolism through playing. The current volume contains the effort and the material developed during the SUSTAIN project and it is our aspiration that it will be either used by universities (and people) around the world or serve as an inspiration for more efforts to promote sustainability literacy. More analytically, Ciobanu and Onofrei provide Chap. 1 on societal metabolism, its definition, and how the notions of energy and materials flows can be translated into elements of everyday life. Myrovali and Morfoulaki in Chap. 2 introduce and familiarize the reader with basic notions and principles of urban mobility, show them the challenges and educate them on sustainable urban mobility planning while finally offering motivation on how each and every one of us can become an active agent of change. In Chap. 3 Tsaples, Papathanasiou and Digkoglou focus on the foundations of decision aid, providing formal definitions and descriptions of

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decision-making processes and where in that process mathematical models can assist decision-makers. In Chap. 4 Armenia et al. introduce the reader to the Systems Thinking approach and foster the understanding of urban dynamics and develop a simulation model using System Dynamics to offer the possibility of identifying and testing scenarios about strategies and policies for pursuing sustainable, urban development. In Chap. 5, Kułakowska and Solińska-Nowak illustrate how the theoretical and mathematical notions of the previous chapters can be translated into game mechanism. Finally, the board game itself along with instructions on how to play and experiment with it is offered in the final chapter of the volume by Luigi Ferrini and the team of Ergo Ludo Editions. The editors of this volume would like to thank all the authors for their support, patience and confidence during this process and for their valuable contributions not only with the material of the volume but also for their efforts to make SUSTAIN a successful project. Finally, we would like to express our appreciation to the publisher and the team that worked closely with us to deliver our vision to a wide audience.

References Armenia, S., Tsaples, G., & Carlini, C. (2018). Critical events and critical infrastructures: A system dynamics approach. International Conference on Decision Support System Technology (pp. 55– 66). Heraclion, Greece: Springer, Cham. Bellotti, F., Berta, R., & DeGloria, A. (2010). Designing effective serious games: Opportunities and challenges for research. iJET, 5(SI3), 22–35. Hauge, J., Kalvenrkamp, M., Forcolin, M., Westerheim, H., Franke, M., & Thoben, K. (2014). Collaborative Serious Games for awareness on shared resources in supply chain management. IFIP International Conference on Advances in Production Management Systems (pp. 491–499). Berlin, Heidelberg: Springer. Katsaliaki, K., & Mustafee, N. (2012). A survey of serious games on sustainable development. Proceedings of the 2012 Winter Simulation Conference IEEE. Klopfer, E., Osterweil, S., & Salen, K. (2009). Moving learning games forward. Cambridge, MA: The Education Arcade. Laamarti, F., Eid, M., & Saddik, A. (2014). An overview of serious games. International Journal of Computer Games Technology, 11. Michael, D. R., & Chen, S. L. (2005). Serious games: Games that educate, train and inform. Muska & Lipman/Premier-Trade. Mitchell, A., & Savill-Smith, C. (2004). The use of computer and video games for learning: A review of the literature. Learning and Skills Development Agency. Quadrat-Ullah, H., & Karakul, M. (2007). Decision making in interactive learning environments: towards an integrated model. Journal of Decision Systems, 16(1), 79–99. Squire, K. (2002). Cultural framing of computer/video games. Game Studies, 2(1), 1–13. Wang, T.-L., & Tseng, Y.-F. (2014). An empirical study: Develop and evaluation a mobile serious game on environmental education. In ninth International Conference on Computer Science and Education (ICCSE). Zhang, P., & Peeta, S. (2011). A generalized modeling framework to analyze interdependencies among infrastructure systems. Transportation Research Part B: Methodological, 45(3), 553–579.

Contents

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Societal Metabolism: A Brief Introduction . . . . . . . . . . . . . . . . . . . . Natalia Ciobanu and Camelia Onofrei

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Sustainable Urban Mobility . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Glykeria Myrovali and Maria Morfoulaki

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Decision Making in the Context of Sustainability . . . . . . . . . . . . . . . Georgios Tsaples, Jason Papathanasiou, and Panagiota Digkoglou

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System Dynamics Modelling for Urban Sustainability . . . . . . . . . . . . 131 Stefano Armenia, Federico Barnabè, Alessandro Pompei, and Rocco Scolozzi

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Translating Models into a Game Design . . . . . . . . . . . . . . . . . . . . . . 175 Michalina Kułakowska and Aleksandra Solińska-Nowak

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The Board Game . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 205 Luigi Ferrini

Appendix . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 215 Index . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 261

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Stefano Armenia Department of Research, Link Campus University, Via del Casale di San Pio V, Rome, Italy Federico Barnabè Department of Business and Law, University of Siena, Siena, Italy Natalia Ciobanu Societatea pentru Consum Responsabil, Gura Humorului, Romania Panagiota Digkoglou Department of Business Administration, University of Macedonia, Thessaloniki, Greece Luigi Ferrini Ergo Ludo Editions, Rome, Italy Michalina Kułakowska Stowarzyszenie Centrum Rozwiązań Systemowych, Wrocław, Poland Maria Morfoulaki Centre for Research and Technology Hellas/Hellenic Institute of Transport (CERTH/HIT), Thessaloniki, Greece Glykeria Myrovali Centre for Research and Technology Hellas/Hellenic Institute of Transport (CERTH/HIT), Thessaloniki, Greece Camelia Onofrei Societatea pentru Consum Responsabil, Gura Humorului, Romania Jason Papathanasiou Department of Business Administration, University of Macedonia, Thessaloniki, Greece Alessandro Pompei Department of Mechanical and Aerospace Engineering, Sapienza University of Rome, Rome, Italy Rocco Scolozzi Department of Sociology and Social Research, University of Trento, Trento, Italy Aleksandra Solińska-Nowak Stowarzyszenie Centrum Rozwiązań Systemowych, Wrocław, Poland Georgios Tsaples Department of Business Administration, University of Macedonia, Thessaloniki, Greece

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Societal Metabolism: A Brief Introduction Natalia Ciobanu and Camelia Onofrei

1.1

Introduction

Societal metabolism is equally an approach and a framework—a way of looking at how society interacts with the environment by analysing the interflow or exchange of materials between society and the environment (Fisher-Kowalski & Hütter, 1999) in an effort to increase the sustainability of the way society provides for its basic needs. Figure 1.1 below illustrates a very simple conceptual model of the Societal Metabolism framework. Human-controlled material and energy flows between nature and societies are a basic feature of all societies. Nevertheless, their magnitude and diversity differ from culture to culture. The set of such flows that occur between nature and society, between different societies, and within societies are defined as societal metabolism. The term is also referred to as social metabolism or socioeconomic metabolism. Over the last decade, the concept of Social Metabolism has gained reputation as a theoretical instrument for the required analysis at different scales in spaceand time. By looking at the sets of flows of materials and energy occuring between nature and society, between different societies, and within societies, this instrument is useful in addressing multiple sustainability challenges posed by the increase in human population, economic activities, environmental impacts, and urbanization.

1.1.1

Background and Need of Societal Metabolism as an Integrative Approach Towards Development

Population in the world is currently approaching eight billion, growing at a rate of around 1.09% per year (World Population Clock, 2018). While in 1804 there were N. Ciobanu (*) · C. Onofrei Societatea pentru Consum Responsabil, Gura Humorului, Romania # The Author(s), under exclusive license to Springer Nature Switzerland AG 2021 J. Papathanasiou et al. (eds.), Urban Sustainability, Springer Texts in Business and Economics, https://doi.org/10.1007/978-3-030-67016-0_1

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Fig. 1.1 The social metabolism describes the exchange of energy and materials across social and environmental systems. (Image source: Potsdam Institute for Climate Impact Research)

Fig. 1.2 Change in world population throughout history (United Nations, 2015)

just around one billion people on the planet, estimates show that it is soon expected to reach the ten billion milestone. Figure 1.2 illustrates how world population has changed since 1800 until today, and its projected increase until 2100. Every person in every society needs water, food, shelter, green space, and mobility, while to have access to that, they simultaneously produce waste and consume resources. In fact, the levels of consumption of natural resources have been increasing on average both at the per capita and global levels (Fig. 1.2). Figure 1.3, for example, shows how the estimated global water use has changed from 1900 to 2016. In Fig. 1.4, The increase in production/consumption of food globally is expressed as the increase in food supply (kcal) per person per day from 1961 to 2012. When the per capita increase is combined with the growing population, the picture becomes even more revealing. Besides that, all household,

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Fig. 1.3 Estimated global water use from 1900 to 2016. (Image source: UN FAO)

Fig. 1.4 Food supply in the world from 1961 to 2012, expressed in kcal produced per capita per day. (Image source: FAOSTAT)

industrial, agricultural and other activities carried out to meet the demands of a growing population requires energy resources. Figure 1.5 shows the change in global energy consumption trend from 1990 to 2016. Meanwhile, although all people have the need and right to live in clean, healthy and safe environments, the levels of pollution, and connected environmental degradation in many areas on the globe has been increasing. Since the start of the industrial revolution in the nineteenth century, environmental pollution has grown into a global, transboundary problem that affects air, water, soil and ecosystems. These, in turn, are linked directly to human health and well-being. Pollution is linked to three main human activities: fossil-fuel combustion, primarily by industry and transport; the application of synthetic fertilisers and pesticides in agriculture; and the growing use and complexity of chemicals. Figure 1.6 depicts the change in global CO2 emissions from 1751 (before the Industrial Revolution) to 2015. CO2 is one of the main outputs from fossil fuel consumption. It is equally one of the main Greenhouse Gases (GHG), which contribute to global climate change.

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Fig. 1.5 Global energy consumption trend from 1990 to 2016. (Image source: Enerdata)

Fig. 1.6 Global CO2 emissions by world region from 1751 to 2015. (Image source: CDIAC)

Alongside growing emissions, the area of forests has been steadily decreasing. Figure 1.7 Shows how the forest area as percentage of total land area has been decreasing in the world between 1990 and 2015. Forests play a key role is CO2 absorption, in this way reducing the CO2 concentration in the atmosphere. The bigger the CO2 concentration in the atmosphere, the bigger the effect on, and impacts of climate change on our society and the environment. Figure 1.8 illustrates how climate change directly and indirectly impacts human health and causing various diseases, while Fig. 1.9 depicts the exponentially increasing numbers of vertebrate species that have been going extinct between 1500 and 2014, as recorded by International Union for Conservation of Nature in 2012.

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Fig. 1.7 Forest area as % of land area in the World was reported at 30.83% in 2015. The graph illustrates the decrease in forested area in the world between 1990 and 2015. (Image source: World Bank)

Fig. 1.8 Direct and indirect impacts of climate change on health. (Image source: Lance Commission on Health and Climate Change)

1.2

Understanding Key Terminology

• Societal Metabolism • Urban Metabolism

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Fig. 1.9 Cumulative vertebrate species recorded as extinct by IUCN (2012)

• • • • • • • •

Stocks Flows of materials Flows of Energy Environmental Impact Sustainability Systems Thinking Circular Economy Sustainable Urban Mobility

As in any other discussion, whenever a topic is being introduced, one needs to make sure that the terms being used are well understood. This is especially important in case of certain core principles and concepts. In this section we introduce the main definitions used in the societal metabolism analysis. When correctly understood by a group, it helps in assessing situations better, have more meaningful conversations and make better decisions. To help the reader grasp the meaning of the core concepts more easily, this section will feature graphics, examples, and also encourage the reader to check some additional resources.

1.2.1

Societal Metabolism

Societal metabolism, sometimes also referred to as social metabolism or socioeconomic metabolism is a framework, a way of looking at how society interacts

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with environment by analysing the flow or exchange of materials between society and the environment (Fisher-Kowalski & Hütter, 1999). The most common use of the word “metabolism” is usually linked to the metabolism of a body, or of a cell. When talking about metabolism, one likely thinks of: • Amounts and types of food a human consumes; • The speed of its digestion, and what are the nutritious elements in which the food is being broken down by the body and turned into something useful for a person’s health; • Amounts of water a human body consumes and how quickly it is being released from the body. For readers fond of cell biology, the image that comes to mind might be similar to the one of the cell, as illustrated in Fig. 1.10. Whether one thinks of a cell or a body, metabolism means that an entity takes something in (material or energy), transforms it and releases it in a changed form. Therefore, when we talk about societal metabolism, one can easily thing along these lines: a society is bigger entity that needs food and energy to survive, grow and develop. The difference would be that instead of talking about a single cell or body, we talk about a community, a village, a city, a country, etc. That is to say, regarded as

Fig. 1.10 A simplified representation of a metabolic process in a cell. (Image source: Community College of Rhode Island)

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a process, societal metabolism is when a group of people takes from nature materials and energy, use it and transform it to secure their existence and development, and release back into nature energy and waste in various forms. Regarded as a framework for understanding society-environment interaction, societal metabolism supports the analysis of: • Types and quantities of material and energy a society takes from the environment; • Processes that this material and energy goes through within the society; • Types and quantities of waste and energy is being released back to the environment; • Areas for improvement of the above processes so as to minimize society’s negative impact on the environment. For example, if we were to look at the societal metabolism of water in a village, we would look into such things as: • • • • •

What is that village’s water source? How much water does the population of that village consume? What is the water used for within the village? Possible ways to minimize the use of water in the village; Whether or not and how well the wastewater is treated before being released back into the environment; • Possible ways to treat or improve the treatment technology of wastewater before discharging it; • Alternative ways to use wastewater within the village instead of directly discharging it, etc. Additional Resources To find more about the origins and evolution of societal metabolism framework, consider checking: 1. “The Weight of Nations. Material Outflows from Industrial Economies” by World Resources Institute: http://pdf.wri.org/weight_of_nations.pdf 2. “Social Metabolism” page on the website of the Institute of Social Ecology at Klagenfurt University: https://www.aau.at/en/social-ecology/research/ social-metabolism/

1.2.2

Urban Metabolism

Urban metabolism is a model of analyzing interactions between natural and human systems in specific regions. This model/way of looking at the human-environment interaction describes and analyses the exchange of materials and energy within

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Fig. 1.11 A simplified representation of urban metabolism model as an application of societal metabolism framework at an urban level. (Image source: Cooperative Research Centre for Water Sensitive Cities)

cities, such as undertaken in a material flow analysis of a city (Pincetl, Bunje, & Holmes, 2012). To put it in simpler terms, urban metabolism is societal metabolism applied to an urban community (Fig. 1.11). It has become an increasingly popular approach in the recent years, due to increased urbanization. According to the UN Environment-led Global Initiative for Resource Efficient Cities, since 2007, for the first time in history more people live in cities than in rural areas. Cities consume 75% of Earth’s resources, and account for 60–80% of global greenhouse gas emissions, and these numbers are growing. In other words, urban metabolism looks at what happens to resources in a city between their points of entry and their exit from the city as wastes. By viewing the city as an organism that consumes resources and produces wastes, we can find ways to improve resource use, and reduce environmental impact. Additional Resources For a better insight into the concept, the following are some of the many simple, clear and accurate resources: 1. “What is Urban Metabolism?”—short video from the UN Environment: https://www.youtube.com/watch?v¼uu-a1hFEV7Q 2. Visualizations and data on urban metabolism of Paris, France: http:// metabolisme.paris.fr/#t/paris/matter/1

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Stocks

A stock is, simply put, an accumulation of something. For example, a bathtub is a stock of water that accumulates the water flowing from the tap, as shown in Fig. 1.12. Likewise, if we think about the atmosphere accumulating CO2 in a way similar to that in which a bathtub accumulates water (Fig. 1.13), then we can refer to the atmosphere as a stock for CO2 (or other greenhouse gasses, for that matter). A battery (Fig. 1.14) is another and perhaps one of the most straightforward examples of stocks, because that is what it used for: stocking electrical energy. The stock of electrical energy increases as we charge or recharge it, and decreases as it is being consumed by an electronic device, such as a phone, a player, etc. In the context of urban metabolism, one example of a stock can be the waste disposal facility where all the waste collected from the inhabitants of a town/city is deposited (Fig. 1.15). As more waste is brought in, the total volume of waste piles up, thus increasing the stock. In some unsustainable cases, the waste is deposited on landfills, leading to multiple sustainability challenges (bigger stock of waste requires bigger landfills and ever larger space for landfills).

Fig. 1.12 A bathtub with water is an example of a stock/ an accumulation of water. (Image source: PBS Learning Media)

Fig. 1.13 The atmosphere is a stock of CO2 in the same way in which a bathtub is a stock of water

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Fig. 1.14 Batteries are stocks of energy that increases as they are being charged, and decreases as the electricity from them is being consumed by electronic devices

Fig. 1.15 In more sustainable waste management practices, the depositing facilities are temporary. After the waste is stocked at such a facility, it is then sorted by types of waste (plastic, metal, glass, paper, etc.) and sent to corresponding recycling facilities. As the recyclable waste is taken away, the stock of waste at the facility decreases. (Image source: Urban One)

1.2.4

Flow of Materials (Material Flow)

In the context of Urban Metabolism, flow of materials is the description of the transportation of raw materials, objects, and products as a flow of entities within an urban area (city), and between a city and the surrounding environment. One way of thinking about it is going back to the example of the bathtub in Fig. 1.12. While the bathtub itself accumulates water, or acts as a stock of water, the

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Holding tube

Raw milk in

Raw milk storage

Heat treatment

Hot water preparation

Steam

Intermediate storage

Pasteurised milk out

Ice water

Clarification

Fig. 1.16 Generalized block chart of the milk pasteurization process. (Image source: Dairy Processing Handbook)

Fig. 1.17 Flows of material (and energy) through an urban settlement. (Image source: Biopolus)

water coming from the tap is an incoming flow of the stocked material, i.e. water. Similarly, the water going out from the bathtub through the sink is the outflow of the same material. Thus, the water is transported from the tap and drained through the sink as a flow. An alternative, simpler way of understanding the flow of materials in an urban metabolism context is, for example, to look at a milk pasteurization process (Fig. 1.16). The “raw milk in” is the inflow of material. After it goes through the heat treatment process, it results in an outflow of pasteurized milk. Likewise, one can think of a flow of materials through an urban settlement, as a throughput of various types of inflowing and outflowing materials (Figs. 1.17 and 1.18), which flow into the city with a certain consistency and physical, chemical and biological quality, and flow out with different consistencies and qualities.

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URBAN SETTLEMENT Energy (Electrical, kCal/year)

Energy (Termal, kCal/year)

URBAN SETTLEMENT Carbon inflow (as part of fossil fuel, tones/year)

Carbon emissions (as part of CO2 emissions, tones/year)

URBAN SETTLEMENT Food coming to the city (tones/year)

Generating organic waste (tones/year)

URBAN SETTLEMENT Goods being acquired by the city inhabitants (tones/year)

Generating inorganic waste (e.g. by throughing away pachages, tones/year)

Fig. 1.18 Examples of flows of material and energy depicted as inflows and outflows using specialised system dynamics software Vensim

It is important to keep in mind that when talking about flows, an inflow of materials can also be an outflow of materials, and an outflow can also be an inflow in a continuous process. Figure 1.19 illustrates one such example embodied by the carbon cycle, i.e. flow of carbon naturally throughout across the globe in various forms.

1.2.5

Flow of Energy

In the context of Urban Metabolism, the flow of energy is the description of the transportation of energy in its various forms (depending on what and why we’re analyzing this flow) within an urban area (city), and between a city and its surrounding environment. While the flows of materials are rather simple to understand, flows of energy could pose some difficulties because there is no material to visualize energy. Below are some illustrations meant to help the reader in developing a better understanding of what this concept means.

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Fig. 1.19 Carbon cycle is an example of continuous and complex flows of carbon in various forms throughout across the globe. (Image source: Thinglink) Fig. 1.20 Heat transfer is an example of flow of thermal energy. (Image source: Climate Science Investigations South Florida)

Taking heat transfer as a first example (Fig. 1.20), the heat that is being transmitted from the warmer object to the cooler object can be referred to as a flow of thermal energy from the stock that has more of it (warmer object) to the stock that has less thermal energy/heat (cooler object). Most people are familiar with another type of energy: electricity, or electrical energy. This is the type of energy that we, as society, mostly use to light up our homes, offices and streets, to make electrical equipment function, etc. Figure 1.21a illustrates the flow of electrical energy as another type of energy that flows from the battery (or a generator, or another energy source/stock) to the light bulb.

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Fig. 1.21 (a) Electric current flow is another example of flow of energy. (b) The light bulb transforms (metabolises) electric energy flow into light energy, which flows out to light up the surroundings. (Image source: Physics and Radio-Electronics)

Fig. 1.22 Electric current flow is another example of flow of energy (Barragán-Escandón, Terrados-Cepeda, & Zalamea-León, 2017)

However, it is important to keep in mind that inflows and outflows of energy are not always of the same type. In the example of electrical energy illustrated above, the light bulb not only acts as a stock of electric energy; it also metabolizes (transforms) it into light energy (Fig. 1.21b). The energy outflow from the light bulb then lights up the room. Other examples of energy flows within the city and between the city and its surrounding are illustrated in Fig. 1.22 below.

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Additional Resources To find more about the flows of energy and materials in cities, the reader may consider checking the following resource: 1. “Urban metabolism: flows of energy and materials in cities” by the Mediterranean Center for Climate Change (https://www.cmcc.it/article/urbanmetabolism-and-flows-of-energy-and-materials-in-cities#)

1.2.6

Environmental Impact

When we talk about environmental impact, the meaning is what we commonly refer to as the impact on the environment created by humans either as a community, or through an industry, service, plan, or project. The impact can be negative or positive—depending on the consequences for the environment. However, the term environmental impact is generally associated with a negative consequence on the environment. In more specialized terms, human impact on the environment is also known as anthropogenic impact, and refers to the alteration of the natural environment by human activity. In very general terms, environmental impact can be summed up into a few categories, including: pollution, deforestation, loss of biodiversity, overproduction of waste, overuse of natural resources, accelerated climate change, and others. Indeed, the diversity and complexity of such impacts is too wide to be covered in this chapter alone. The examples illustrated below are meant to give the reader a better insight into what could constitute environmental impacts in an urban metabolism context.

1.2.6.1 Example: Plastic Pollution Think of a person going to shop for household products and food in a supermarket. As it usually happens, most of these goods are covered in plastic or mixed packaging, which makes plastic and packaging one of the greatest pollutants on earth (Fig. 1.23a, b). When disposed of improperly, plastic packaging is blown by the wind and ends up in river valleys. The same happens when waste is deposited on the side of riverbanks. It is then carried to the seas and ocean all over the planet (Fig. 1.24). Once it reaches habitats of various animal species, plastic poses multiple threats to health and livelihoods of those species. 1.2.6.2 Example: Impact of Car Use Cars have made our lives much easier in many regards. Yet, besides running on a finite resource, which is the fossil fuel, our automobiles cause all sorts of pollution. Car pollutants cause immediate and long term effects on the environment, be it air,

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Fig. 1.23 (a) Global plastic production by industrial sector (2015). (b) Global primary plastics waste generation, 1950–2015 (UN Environmental Programme)

water, noise or even land. Car exhausts emit a wide range of gases and solid matter, causing global warming, acid rain, and harming the environment and human health. Engine noise and fuel spills also cause pollution. Additional Resources Congestion, air and noise pollution, and road safety are examples of commonly shared problems in European cities. Read more on the impact of car use (continued)

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Fig. 1.24 Plastic pollution is one example of environmental impact. Mismanaged plastic waste ends up in rivers, seas and oceans. (Image source: Helmholtz Centre for Environmental Research)

in our cities in the Report on European Urban Mobility (European Commission, 2017) https://ec.europa.eu/transport/sites/transport/files/2017-sustainable-urbanmobility-policy-context.pdf

1.2.7

Sustainability

Sustainability is the ability of something to be maintained at a steady level without exhausting natural resources or causing severe ecological damage. It also means that something can keep going, can continue into the future and go on forever. From a human perspective, sustainability for our planet means that it can continue to provide fresh air, clean water, produce food, and allow us all to have a high quality of life indefinitely. Additional Resources This 2 min video, called “Sustainability explained through animation” simply and briefly explains the concept of sustainability: https://www.youtube.com/ watch?v¼B5NiTN0chj0

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Fig. 1.25 The quest to finding the right amount of water in a bathtub as a parallel to the quest for sustainability

Going back to the example of a bathtub, it might help to think of sustainability as something that depends on a balance, or on “just about the right balance” between inflow and outflow (Fig. 1.25). Suppose a person wants to take a bath in the bathtub that has a permanent inflow and outflow of water. If the inflow rate would be higher that the outflow rate, then the water would flow over the edges. This is not something anyone would be happy about, as it would flood the bathroom. On the other hand, if the drainage/outflow rate of water is higher than the inflow, then there would not be enough water in the bathtub, and it would eventually run empty. Similarly, one can think about finding the right balance between emissions and reduction of greenhouse gases from the atmosphere as finding the right balance between adding and draining water in a bathtub. Hence, in climate sustainability, the challenge is to maintain just the right amount of greenhouse gasses in the atmosphere that can be balanced enough to not cause further global warming. Sustainable development is a perspective on how our society should develop. It is based on the concept of sustainability, and it has been defined in 1987 by the General Assembly of the United Nations as the “development that meets the needs of the

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Fig. 1.26 Three pillars of sustainable development: a safe and healthy environment supports the development of human society. A healthy society is the basis of a healthy economy

present without compromising the ability of future generations to meet their own needs” (United Nations, 2019). To put it simpler, it has three dimensions: Environmental dimension, Social dimension and Economic dimension (Fig. 1.26). The Environmental dimension deals with the way society uses natural resources and ensures environmental protection. The Social dimension covers issues related to civil rights, social inclusion and cultural identity. The Economic dimension looks at wealth creation, property and employment. All in all, this perspective seeks to ensure that we don’t drain the stock of resources and safety on our planet while increasing the stock of welfare for the society. To make the overarching goal of sustainability in our development more reachable, in 2015 countries adopted a set of goals to end poverty, protect the planet and ensure prosperity for all (Fig. 1.27). Each goal has specific targets to be achieved over the next 15 years.

1.2.8

Systems Thinking

Systems thinking can mean different things to different people. A system is any kind of entity that is made up of parts that interact. Together these parts and interactions create a whole, which in turn produces some kind of result. Using a systems perspective is important, because it helps us to better understand what helps or hinders the success of an intervention into a particular system, such as public transportation system (Fig. 1.28). In the context of this course, we shall refer to systems thinking as a discipline (or a way of looking at and perceiving things around us) that concerns the understanding of a system by examining the linkages and

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Fig. 1.27 Sustainable Development Goals (SDGs) are part of the new sustainable development agenda adopted by countries on September 25th, 2015 (United Nations Development Programme, 2019)

Fig. 1.28 Advanced public transportation system architecture for Wollongong, Australia. (Image source: Vu The Tran, P.V. Eklund and C. Cook, 2013)

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interactions between the components that comprise the entirety of that specific system. An example of looking at public transport intervention from a systems thinking perspective is Wollongong, Australia. As such, the transport system is not regarded only as the totality of infrastructure items—busses, computers, passengers, etc. It also takes into consideration how the components are connected, how they interrelate and influence each other. Standing in contrast to traditional, reductionist thinking (Fig. 1.29), systems thinking sets out to view systems in a holistic manner. Systems thinking is a new paradigm that encourages and enables us to understand complex systems. It shows us how all the various components within systems interact with and depend on one another. By using systems thinking, we are better able to understand our world and develop meaningful, strategic, and lasting solutions. To understand what differentiates a systems thinking perspective from a traditional thinking perspective, Figs. 1.30 and 1.31 illustrate different approaches towards solving a traffic congestion problem. In the first case (Fig. 1.30), a linear, traditional way of, thinking perspective would reason that more parking maneuver time in city centers leads to increased congestion, and therefore more parking spaces inside city centers would potentially lead to less traffic. From a systems thinking perspective, that can be thought of as a sustainable modern way of approaching an issue. However, this might not be the optimal solution, because there are many other factors that are directly or indirectly linked to traffic congestion (Fig. 1.31). An example of such a factor is people’s willingness Fig. 1.29 Traditional, linear thinking in comparison with systems thinking

Fig. 1.30 An (unsustainable) example of solving a traffic congestion problem from a linear thinking perspective

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Fig. 1.31 Looking at the causes of traffic congestions from a systems thinking perspective allows decision makers to identify better interventions to address the problem

to use public transport instead of cars (adopting a car free sustainable lifestyle). This should decrease traffic congestion, especially when sustainability awareness is high and other accompanying traffic calming measures apply, e.g. when parking spaces inside the city center decrease (therefore, car restrictions apply), or when investments on public transport are promoted. In short, more parking spaces inside city centers would lead to reduced maneuver times and eventually less (perceived) traffic, but it also leads to people being more willing/likely to drive, so there would still be increasingly more cars in (real) traffic. Hence, different solutions or combination of measures (equilibrium) need to be sought in the system. Additional Resources To find out more about the systems thinking, the following resources are useful: 1. Learning for sustainability website: http://learningforsustainability.net/ systems-thinking/ 2. System Dynamics in Action website: http://sdaction.kytt.org

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Circular Economy

The examples above show how sometimes some components in systems are connected through causal loops. That is, what is considered to be a cause can also be seen as an effect of change in that component, which is influenced by the initial cause. Such was the example of traffic congestion: an increase in traffic congestion causes less people to want to drive. Then, as the people chose not to drive, the congestion decreases. Therefore, the congestion, the number of people driving and their willingness to drive are all equally causes and effects. They are connected through a causal loop. Circular economy is a regenerative system in which resource inputs and waste, emissions, and energy leakages are minimized by slowing, closing, and narrowing energy and material loops. In a circular economy, when a product reaches the end of its life, it is used again to create further value. This can bring major economic benefits, thus contributing to innovation, growth, job creation, and to a cleaner environment (Fig. 1.32).

Fig. 1.32 The circular economy is an approach to maximize value and eliminate waste by improving (and in some cases transforming) how goods and services are designed, manufactured and used (Image source: Circular Economy Lab)

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Additional Resources For a better insight into the concept, the following are some of the short and useful resources: 1. “Circular Economy”—short video from the European Environment Agency: https://www.youtube.com/watch?v¼_9mHi93n2AI 2. “Re-thinking Progress: The Circular Economy”—short video from Ellen MacArthur Foundation: https://www.youtube.com/watch? v¼zCRKvDyyHmI 3. “Don’t waste your waste”—video from County Administrative Board of Östergötland, Sweden: https://www.youtube.com/watch?v¼Ptp6JGAF3o0 4. Website with informative visualizations and data on urban metabolism of Paris, France: http://metabolisme.paris.fr/#t/paris/matter/1

1.2.9.1 Example: Circular Economy Principles in Action Around 88 million tons of food are wasted annually in the European Union (Stenmark, Jensen, Quested, & Moates, 2016). Wasting food is not only an ethical and economic issue but it also depletes the environment of limited natural resources. Cities are one of the biggest consumers and generators of food waste. Usually, food is discarded as waste into the waste bins, and taken by waste management actors to landfills or incinerators outside the cities. In line with circular economy principles, one way to deal with this issue would be to use the food scraps for composting/ creating fertile soil for agricultural use, or to use fried oil for biodiesel production. Whatever the choice and technical possibilities, the main idea is to maintain it within the economic circuit instead of discarding it as waste. Figure 1.33 illustrates other ways to deal with food waste based on circular economy principles. Additional Resources More on circular economy is available in these interesting and useful resources: 1. Good practice examples of various circular economy projects on European Circular Economy Stakeholder Platform: https://circulareconomy.europa. eu/platform/en/good-practices?key_area¼All§or¼All&country¼PL& title¼ 2. Ellen MacArthur Foundation web page on circular economy: https://www. ellenmacarthurfoundation.org/circular-economy 3. The Waste and Resources Action Programme web page on circular economy: http://www.wrap.org.uk/about-us/about/wrap-and-circular-economy

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Fig. 1.33 Enhancing a resource-efficient, circular economy in the food and drink industry. (Image source: Food Drink Europe)

1.3

Connecting the Societal Metabolism Approach to Real Life Examples

In this section, the terminology defined in the previous sections is illustrated by using examples from real life.

1.3.1

Example of Unsustainable Practices: Fossil Fuel Transport

Why is the use of fossil fuel an unsustainable practice? Because all modes and means of transportation that consume petroleum based fuels contribute to the pollution of air and water, and the capabilities to neutralize this pollution are limited. This happens both through burning the fuel, and through the process of refueling. Table 1.1 showcases the use of the presented concepts in the specific case of discussing about fossil fuel transport. Additional Resources A scan through this text will give the reader an idea on the complexity of the problem: https://www.aps.org/policy/reports/popa-reports/energy/transportation.cfm

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Table 1.1 Applying key terminology in practice when analyzing the case of fossil fuel transport Terminology Urban metabolism

Stock Flow of materials (material flow)

Flow of energy

Environmental impact

Sustainability

Systems thinking

Circular economy

Sustainable urban mobility

How to apply the key concepts when analyzing the practice? Fossil fuel is metabolized into air pollutants, including greenhouse gases (GHG) (carbon monoxide, nitrogen oxides, volatile organic compounds including hydrocarbons). To identify where we can inter look at what the fossil fuel is used for, when it is being used, what is it decomposed into (e.g. cars burn the fuel and generate emissions), etc. Fossil fuel accumulates in the city as substance (petrol, gas for cars) and/or energy (kCal, KJ, etc.) To understand what are the sources of pollution, it is needed to investigate how fossil fuel is brought into the city, how much of it is brought in per month/year. Then, one looks at what goes out from the city as a result of its urban metabolism (e.g. tones/year of emissions) It is needed to take a look at how much energy the amount of fossil fuel brought into the city can generate, and then look at how that energy goes out from the city as a result of its urban metabolism (e.g. tones/year of thermal energy/heat that warms up the environment) This is a qualitative metric, defined by looking at what are the negative and positive impacts of the use of fossil-fuel based transport. E.g. CO2 emissions contribute to climate change. Thermal energy (heat) can create “heat islands” in the cities. Particulate matters (e.g. PM5, PM10) contributes to air pollution This characteristic of the practice of using fossil fuel in transportation is defined by analyzing if the use of fossil fuel based transport can be sustained into the future. Can it be carried on indefinitely? Or is there a limitation for its use (e.g. time, resources)? When analyzing urban metabolism and sustainability of fossil-fuel based transport, a systems way of thinking looks at the means of transport that use fossil fuel, people that use personal cars or public transport, etc. as components of a system. At the same time, this requires looking at the linkages and interactions between the components. Some examples include: Decisions that people take to use cars or transport; what increases the attractivity of fossil-fuel based cars compared to electric cars or public transport, how the price of fuel and the regulations of city councils influences the amount of fossil fuel consumed by city inhabitants, etc. In contrast to the common, linear economy (i.e. materials in—waste out), by adopting this perspective, one looks into what can be done to reduce the amount of fossil fuel consumed and the waste generated, as well as how to reintroduce the waste into the economy of the city as a reusable material/resource. For example, one way to reduce resource consumption and waste would be to share rides. However, the waste generated by burning fossil fuel (PMs and CO2) cannot be reintroduced in the economy. This is one of the reasons why fossil fuel based transport is considered unsustainable From this standpoint, one needs to look at what are the more sustainable alternatives to fossil-fuel based transport. Some examples may include: Electric public transport (tramways, trolley buses, trains, etc), bicycles, or walking

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1.3.2

Practices That Contribute to Sustainable Urban Metabolism

1.3.2.1 Example: Ecopixel—Recycled and Recyclable Plastic Organization or Company: ECOPIXEL® Country: Italy Description: ECOPIXEL is a recycled, recyclable, circular, sustainable plastic material that melts at minimum temperatures so to have the minimum ecological impact also during its transformation (Table 1.2). Additional Resources Scan through the details of this practice here: https://circulareconomy.europa. eu/platform/en/good-practices/ecopixel-recycled-and-recyclable-plasticand here: http://www.ecopixel.eu/index.html

1.3.2.2 Example: Every Can Counts Organization or Company: AluPro Country: 12 European countries (UK, France, Austria, Hungary, Romania, Ireland, Greece, Spain, Montenegro, Serbia, Poland, Belgium). Description: The “Every Can Counts” programme works to improve recycling by enabling and encouraging people to recycle beverage cans used outside the home (e.g. workplaces, festivals, tourist locations, etc.) (Table 1.3). 1.3.2.3 No Food Waste Aiud Organization or Company: Society for Responsible Consumption Country: Romania Description: A group of young volunteers from the small town of Aiud in Romania decided to tackle the issue of increasing food waste through grass-root level actions in their local market. In September 2016 they started a food waste prevention project, and just in the first year of activity they collected over 1300 kg of “ugly” but quality vegetables. These were later donated to more than 80 people in the local community. Table 1.4 showcases examples of using the terminology in the specific discussion of this case study. Additional Resources Find out more about this project here https://www.youtube.com/watch? v¼Zj7qt_9ZaVM and here: https://www.romania-insider.com/pumping-lifesmall-romanian-town-reducing-food-waste/

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Table 1.2 Applying key terminology in practice when analyzing the case of ECOPIXEL Terminology Urban metabolism

Stock

Flow of materials (material flow)

Flow of energy

Environmental impact

Sustainability

Systems thinking

How to apply the key concepts when analyzing the practice? Human society consumes/metabolizes the low-density polyethylene (LDPE) waste-material in order to obtain a new recycled and recyclable material that can be used for different products. The raw material used for ECOPIXEL comes from industrial waste or from any other field including household-waste. The waste material is shredded, melted at a low temperature and re-transformed into products used by the urban community The accumulation (stock) of plastic waste in a city is increased through generation of waste by urban population, and is decreased by disposal, incineration or recycling of plastic waste. In this example, the stock of plastic waste is significantly reduced due to the fact that the waste material is recycled, and the generated product is still recyclable at the end of its life When looking at the dynamic of plastic waste in a city, one can identify the input of goods (plastic goods, packaged non-plastic goods) into the shops and markets of a town as inflow of plastic material. Once consumed, the generated material can be considered as an outflow of waste from households Within the manufacturing process of ECOPIXEL production, the flow of material can be visualised as follows: Inflow: ‘Raw’ waste material Outflow: Products manufactured from recycled plastic One way of looking at flows of energy in this example is by considering that to recycle the plastic material product, manufacturers consume energy. For example, there is an inflow of electric energy to the recycling and manufacturing machines in order to keep them functioning. There is also an inflow of heat to melt the plastic. Depending on the production process, the outflow can be either as heat, as well, or/and as chemical energy binded into the recycled plastic product Some non-exhaustive examples of analyzing the environmental impact of ECOPIXEL during its lifecycle include: • Using material composed of 100% recycled low-density polyethylene (LDPE) reduces the need to generate new raw material from petrol ! minimizes impact on finite natural resources; • Recyclable material that can be remelted multiple number of times without altering its properties ! minimizes generated waste; • Using waste as raw material for production of consumer goods provides an alternative for plastic incineration or storage ! minimizes pollution of the environment with plastic materials Plastic waste recycling into new plastic objects answers the needs of consumer society without needing new, finite raw material resources to be extracted. It also provides a solution for waste management that can be repeated multiple times into the future. In addition, the resulted products can be further recycled at the end of their lifetime, which makes this practice a sustainable one Systems thinking means that one looks at the connections and the implications of plastic waste materials and the changes that ECOPIXEL is bringing. What causes and consequences are there for (continued)

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Table 1.2 (continued) Terminology

Circular economy

How to apply the key concepts when analyzing the practice? generation of plastic waste? What are the stocks and flows of materials in this process? What are the factors that influence the generation and recycling rates of plastic waste? Are there “side-effects” of plastic generation and recycling process that have an unexpected effect on the system?—these are some of the questions a systems thinker would look at ECOPIXEL collects, separates, chips into pieces and re-transforms into products what others throw away. It is made from ‘raw’ waste material that can be reintroduced into the economy nearly infinite number of times, thus creating a continuous cycle in material-use

Additional Resources More on urban metabolism and resource efficient cities is available here: https://resourceefficientcities.org/wp-content/uploads/2017/09/Urban-Metabo lism-for-Resource-Efficient-Cities.pdf

Exercise To practice the use of the concepts presented in this chapter, we encourage the reader to do the following exercise: Identify an example from your local community or country that exemplifies a sustainable/ less sustainable case of urban metabolism. Explain to a friend, colleague or family member why it is a good/bad practice. In your arguments make use of: • The definitions used in the course; • Data from official sources (e.g. UNEP, website of environmental agency, reports from WHO, country statistics, and other reliable sources of information).

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Table 1.3 Applying key terminology in practice when analyzing the case of “Every Can Counts” Terminology Urban metabolism

Stock

Flow of materials (material flow)

Flow of energy

Environmental impact

Sustainability

Systems thinking

How to apply the key concepts when analyzing the practice? The beverage cans are introduced in urban societies as packages for various sorts of drinks and foods. As the packed goods are consumed, the cans are discarded as waste. AluPro is an initiative to transform this waste into new recycled cans There are a number of accumulations in this process, for example: Stock of canned beverage, stock of cans discarded as waste, stock of recycled cans, and others. The stock of aluminium waste is increased by the consumption of aluminium-packed beverage, and decreased by incineration or recycling Inflowing can-packed beverages determine how much potential aluminium waste will be generated after product consumption. Similarly, once generated, the discarded cans enter the recycling process. At this stage, discarded cans are both outflow from the consumers, as well as inflow for the recycling process. It takes only 60 days for a single aluminium can to be produced, filled, distributed, consumed and recycled into a new can Recycling aluminium uses only 5% of the energy needed to produce it from virgin materials, reducing greenhouse gas emissions by 95%. By recycling the aluminium, we reduce the need for additional energy to produce new cans. Thus, the required inflow of energy with in process is reduced Aluminium is valuable at every stage of a product’s life cycle—from production to end of use. It can be repeatedly recycled while keeping the properties of the recycled material. Using recycled aluminium reduces the need for primary aluminium and therefore minimizes need for mining for aluminium ores and avoids finite resource depletion, minimizes waste, and lowers greenhouse gas emissions across a product’s life cycle This program is trying to provide a more environmentally and economically-friendly alternative to the depletion of natural resources and mining that has often caused ecological damage by exploiting raw aluminium ores If pre-treated and/or sorted, aluminium products can be recycled for use in almost all aluminium applications since the metal’s atomic structure is not altered during melting. Aluminium recycling benefits present and future generations by conserving energy and other natural resources A systems thinker would look not only at what generates the waste and what is the output of waste generation directly and try to tackle the problem of excessive waste from this perspective solely. Instead, looking at aluminium waste from beverage products in a systemic way, one would seek to understand what is the pathway of aluminium throughout the urban system. For example (but not exhaustively): Who generates it and how much of it is generated? What is the capacity to generate aluminium from raw materials/mining? What could be alternative uses? How much of it can be potential input for a recycling process? What would be a reasonable recycling rate given the waste generation rate? How would aluminium be best reintroduced in the economy? (continued)

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Table 1.3 (continued) Terminology Circular economy

How to apply the key concepts when analyzing the practice? Aluminium is a material that can be melted and remolded (i.e. recycled) multiple times. In this way, its inherent properties do not change during use and following repeated recycling into new products. Encouraging recycling of aluminium cans is thus one way to achieve a circular economy, whereby waste is regenerated as new products

Appendix Image Sources

Fig. 1. Potsdam Institute for Climate Impact Research. https://www.pik-potsdam. de/research/transdisciplinary-concepts-and-methods/research/researchareas/metab (accessed February 2019) Fig. 2. Worldometers, based on UN data. https://www.worldometers.info/worldpopulation/#pastfuture (accessed February 2019) Fig. 3. UN Food and Agriculture Organisation. http://www.fao.org/land-water/ outreach/graphs-and-maps/en/ (accessed February 2019) Fig. 4. FAOSTAT. http://www.fao.org/faostat/en/#data/FBS/visualize (accessed February 2019) Fig. 5. Enerdata Global Energy Statistical Yearbook. https://yearbook.enerdata. net/total-energy/world-consumption-statistics.html (accessed February 2019) Fig. 6. Our World in Data. https://ourworldindata.org/uploads/2017/04/GlobalCO2-emissions-by-region-since-1751.png(accessed February 2019) Fig. 7. Trading Economics. https://tradingeconomics.com/world/forest-area-per cent-of-land-area-wb-data.html (accessed February 2019) Fig. 8. Lance Commission on Health and Climate Change. https://www.thelancet. com/action/showFullTextImages?pii¼S0140-6736%2815%2960854-6 (accessed February 2019) Fig. 9. Science Advances. https://advances.sciencemag.org/content/1/5/e1400253/ tab-figures-data (accessed February 2019) Fig. 10. Community College of Rhode Island. http://faculty.ccri.edu/lmfrolich/ Microbiology/MetabolismOverview.htm (accessed February 2019) Fig. 11. Cooperative Research Centre for Water Sensitive Cities. https:// watersensitivecities.org.au/content/mimicking-nature-urban-metabolismframeworks-guide-decision-making-maximise-water-efficiency/ (accessed February 2019)

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Table 1.4 Applying key terminology in practice when analyzing the case of “No Food Waste Aiud” Terminology Urban metabolism

Stock

Flow of materials (material flow)

Environmental impact

Sustainability

Systems thinking

Circular economy

How to apply the key concepts when analyzing the practice? Food is perhaps one of the products that is the easiest to visualize as something that is metabolised by a society. That is because any human and any society needs food for survival. It comes into the urban communities through imports from other countries, or nearby farms and factories. It exits the urban community as waste (wastewater, solid waste) after being consumed, or compostable material: Food remains that were not edible, or unused and discarded fresh food. About a third of the food produced globally is lost or wasted. This initiative seeks to reduce the wasted food by reintroducing “unattractive” and thus potentially wasted food into the food consumption circuit of the urban community The stock of fresh food waste increases with food being thrown away, and decreases as it is either discarded for compost, or deposited on landfills, or by recovering the good fruits and vegetables and reintroducing them into the metabolic circuit Food comes into the town of Aiud as an inflow of raw or processed products from other countries, or nearby farms and factories. The outflows can be multiple: Sewerage, biodegradable/compostable solid waste. To reduce the solid waste, this initiative recovers the food and reintroduces it as inflow into the the food supply of the town Producing food requires the use of arable land, energy, water, chemicals and other valuable materials, as well as often— contamination of environment with pesticides, artificial fertilisers, and greenhouse gasses. The food from the local market that is considered “ugly” or not fresh enough to be sold to customers is recovered by the volunteers and reused as good food to help the people in need. This is translated in a reduced waste of resources, reduced landfill stock and reduced greenhouse gases Discarding food that could have been eaten by humans, except for the case when it is spoiled, is a total lost for the environment, society and economics The “no waste Aiud” program is a sustainable practice because it contributes to reducing the waste of resources (food and resources used to produce food). This results in environmental and economic benefits. Simultaneously, by providing people in need with food, it also has a social benefit. This initiative can be carried out into the future as a means to address the above-mentioned environmental, economic and social issues Thinking in terms of multiple casualties and benefits of a single activity is a feature of a systems thinking. Almost a third of food products in Romania are wasted annually. This reality collides with the fact that poverty and social exclusion in the country is among the highest in the European Union: 40.2%. Reducing food waste is among one of the most at-hand measures to tackle multiple, yet connected environmental and social problems

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Fig. 12. PBS Learning Media. https://www.pbslearningmedia.org/resource/ syslit14-sci-sys-stockflow/stocks-and-flows/#.Wy-3oi17Gu4 (accessed February 2019) Fig. 13. Climate Interactive. https://www.climateinteractive.org/tools/climate-bath tub-simulation/ (accessed February 2019) Fig. 14. Colourbox. https://www.colourbox.com/vector/colorful-batteries-iconsand-symbols-of-battery-level-vector-5501950 (accessed February 2019) Fig. 15. Urban One. http://www.urbanone.com/resources/articles/leed-construc tion-waste-management-green-recycling-methods-for-reducing-carbonfootprint-by-jr-riddle (accessed February 2019) Fig. 16. Dairy Processing Handbook. https://dairyprocessinghandbook.com/chap ter/designing-process-line (accessed February 2019) Fig. 17. Biopolus. http://www.biopolus.net/wp-content/uploads/2013/03/chal lenge_slider_new-011.png (accessed February 2019) Fig. 18. Original artwork. Fig. 19. Thinglink. https://www.thinglink.com/scene/832296461617070081 (accessed February 2019) Fig. 20. Climate Science Investigations South Florida. http://www.ces.fau.edu/ nasa/images/Energy/HeatTransfer.jpg (accessed February 2019) Fig. 21. Physics and Radio-Electronics. http://www.physics-and-radio-electronics. com/blog/wp-content/uploads/2016/10/closedswicthbulbon.png (accessed February 2019) Fig. 22. Barragán-Escandón, A., Terrados-Cepeda, J., & Zalamea-León, E. (2017). The Role of Renewable Energy in the Promotion of Circular Urban Metabolism. Sustainability, 9(12). Fig. 23. A) Geyer, R., J. Jambeck, R., Law, K. L. (2017) Production, use, and fate of all plastics ever made. Science Advances 3(7):e1700782. B) UNEP data on Global Alliance for Incinerator Data. https://www.noburn.org/wp-content/uploads/article-pratibha-im2.png (accessed February 2019) Fig. 24. Helmholtz Centre for Environmental Research. https://www.ufz.de/index. php?en¼36336&webc_pm¼34/2017 (accessed February 2019) Fig. 25. Original artwork. Fig. 26. Original artwork. Fig. 27. UN Sustainable Development Goals. https://www.un.org/ sustainabledevelopment/sustainable-development-goals/ (accessed February 2019) Fig. 28. Vu The Tran, Eklund, P.W., Cook, C. (2013) Evolutionary simulation for a public transit digital ecosystem: A case study. MEDES, 29-31October 2013, Neumünster Abbey, Luxembourg. https://www.researchgate.net/ publication/287177501_Evolutionary_simulation_for_a_public_transit_ digital_ecosystem_A_case_study (accessed February 2019) Fig. 29. Kindling. https://kindling.xyz/next-systems/systems-thinking-complexworld/ (accessed February 2019) Fig. 30. Original artwork.

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Fig. 31. SD Action. http://sdaction.kytt.org (accessed February 2019) Fig. 32. Circular Economy Lab. http://circulareconomylab.com/circular-economyframework/ (accessed February 2019) Fig. 33. Food Drink Europe. https://circulareconomy.fooddrinkeurope.eu (accessed February 2019)

List of Links 1. https://www.pik-potsdam.de/research/transdisciplinary-concepts-and-methods/ research/research-areas/metab 2. https://www.worldometers.info/world-population/#growthrate 3. http://www.worldometers.info/world-population/#pastfuture 4. http://www.fao.org/land-water/outreach/graphs-and-maps/en/ 5. http://www.fao.org/faostat/en/#data/FBS/visualize 6. https://yearbook.enerdata.net/total-energy/world-consumption-statistics.html 7. https://ourworldindata.org/wp-content/uploads/2017/04/Global-CO2emissions-by-region-since-1751.png 8. http://cdiac.ess-dive.lbl.gov/ 9. https://tradingeconomics.com/world/forest-area-percent-of-land-area-wb-data. html 10. https://www.thelancet.com/action/showFullTextImages?pii¼S0140-6736% 2815%2960854-6 11. http://advances.sciencemag.org/content/1/5/e1400253/tab-figures-data 12. http://faculty.ccri.edu/lmfrolich/Microbiology/MetabolismOverview.htm 13. http://pdf.wri.org/weight_of_nations.pdf 14. https://www.aau.at/en/social-ecology/research/social-metabolism/ 15. https://www.youtube.com/watch?v¼uu-a1hFEV7Q 16. http://metabolisme.paris.fr/#t/paris/matter/1 17. https://watersensitivecities.org.au/content/mimicking-nature-urban-metabo lism-frameworks-guide-decision-making-maximise-water-efficiency/ 18. https://www.pbslearningmedia.org/resource/syslit14-sci-sys-stockflow/stocksand-flows/#.Wy-3oi17Gu4 19. http://www.worldbank.org/en/news/feature/2016/10/03/ukraine-reaffirms-cli mate-commitments-to-tackle-ghg-emissions-from-industry 20. https://www.climateinteractive.org/tools/climate-bathtub-simulation/ 21. https://www.colourbox.com/vector/colorful-batteries-icons-and-symbols-of-bat tery-level-vector-5501950 22. http://www.urbanone.com/resources/articles/leed-construction-waste-manage ment-green-recycling-methods-for-reducing-carbon-footprint-by-jr-riddle 23. http://www.jbsdowntown.com/ 24. http://www.sobebodymind.com/body-mind-fundamentals/metabolism/ 25. http://www.biopolus.org/wp-content/uploads/2013/03/challenge_slider_new011.png 26. https://www.thinglink.com/scene/832296461617070081

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27. http://www.ces.fau.edu/nasa/images/Energy/HeatTransfer.jpg 28. http://www.physics-and-radio-electronics.com/blog/wp-content/uploads/2016/ 10/closedswicthbulbon.png 29. http://www.mdpi.com/2071-1050/9/12/2341/htm 30. https://www.cmcc.it/article/urban-metabolism-and-flows-of-energy-andmaterials-in-cities 31. https://wedocs.unep.org/bitstream/handle/20.500.11822/25496/ singleUsePlastic_sustainability.pdf?isAllowed¼y&sequence¼1 32. https://www.youtube.com/watch?v¼0uU1ZyQ1OwA 33. https://www.youtube.com/watch?v¼mYsJESXhnu0&t¼17s 34. https://www.sciencedaily.com/releases/2017/10/171017110028.htm 35. https://ec.europa.eu/transport/sites/transport/files/2017-sustainable-urban-mobil ity-policy-context.pdf 36. https://www.youtube.com/watch?v¼B5NiTN0chj0 37. https://www.un.org/sustainabledevelopment/sustainable-development-goals/ 38. https://www.researchgate.net/figure/Advanced-Public-Transportation-SystemArchitecture-for-Wollongong-Australia_fig1_287177501 39. http://kindling.xyz/next-systems/systems-thinking-complex-world/ 40. http://learningforsustainability.net/systems-thinking/ 41. http://sdaction.kytt.org 42. https://www.youtube.com/watch?v¼_9mHi93n2AI 43. https://www.youtube.com/watch?v¼zCRKvDyyHmI 44. https://www.youtube.com/watch?v¼Ptp6JGAF3o0 45. http://metabolisme.paris.fr/#t/paris/matter/1 46. http://circulareconomylab.com/circular-economy-framework/ 47. http://www.eu-fusions.org/phocadownload/Publications/Estimates%20of%20 European%20food%20waste%20levels.pdf 48. https://circulareconomy.europa.eu/platform/en/good-practices?key_area¼All& sector¼All&country¼PL&title¼ 49. https://www.ellenmacarthurfoundation.org/circular-economy 50. http://www.wrap.org.uk/about-us/about/wrap-and-circular-economy 51. https://circulareconomy.europa.eu/platform/en/good-practices/stories-circulareconomy-italian-atlas-and-competition 52. https://circulareconomy.fooddrinkeurope.eu/ 53. https://www.aps.org/policy/reports/popa-reports/energy/transportation.cfm 54. https://circulareconomy.europa.eu/platform/en/good-practices/ecopixelrecycled-and-recyclable-plastic 55. http://www.ecopixel.eu/index.html 56. https://circulareconomy.europa.eu/platform/en/good-practices/when-it-comesrecycling-aluminium-every-can-counts 57. https://www.everycancounts.eu/ 58. https://www.youtube.com/watch?v¼Zj7qt_9ZaVM 59. https://www.romania-insider.com/pumping-life-small-romanian-town-reduc ing-food-waste/

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60. https://resourceefficientcities.org/wp-content/uploads/2017/09/Urban-Metabo lism-for-Resource-Efficient-Cities.pdf

References Barragán-Escandón, A., Terrados-Cepeda, J., & Zalamea-León, E. (2017). The role of renewable energy in the promotion of circular urban metabolism. Sustainability, 9(12). European Commission. (2017). European urban mobility. Policy context. Brussels: DirectorateGeneral for Mobility and Transport, Directorate Investment, Innovative & Sustainable Transport, Unit B4—Sustainable & Intelligent Transport. Fisher-Kowalski, M., & Hütter, W. (1999). Society’s metabolism: The intellectual history of materials flow analysis, part II, 1970–1998. Journal of Industrial Ecology, 2(4). Pincetl, S., Bunje, P., & Holmes, T. (2012). An expanded urban metabolism method: Toward a systems approach forassessing urban energy processes and causes. Landscape and Urban Planning, 107, 193–202. Stenmark, A., Jensen, C., Quested, T., & Moates, G. (2016). Estimates of European food waste levels. Stockholm: FUSIONS Project. United Nations. (2015). World urbanization prospects: The 2014 revision. New York: Department of Economic and Social Affairs, Population Division. United Nations. (2019, April 30). Our common future: Report of the world commission on environment and development. Retrieved from http://www.un-documents.net/ocf-02.htm#I United Nations Development Programme. (2019). Sustainable development goals. Retrieved from https://www.undp.org/content/undp/en/home/sustainable-development-goals/goal-11-sustain able-cities-and-communities.html World Population Clock. (2018). Retrieved December 2018, from current world population. Growth rate: https://www.worldometers.info/world-population/#growthrate

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Sustainable Urban Mobility Glykeria Myrovali and Maria Morfoulaki

2.1

Urbanization and Mobility

From the beginning of the twentieth century and mainly after the industrial revolution, urban areas have been constantly growing as a result of a continuous flow of people from rural areas to cities seeking jobs and therefore better living conditions (Fig. 2.1). Urbanization is linked to the need of reducing time and expenses in commuting and transportation while increasing access to jobs, education, health and rest public services accessibility and leisure. According to the United Nations today, 55% of the world’s population lives in urban areas (United Nations, 2018). The global urban land coverage vary considerably, ranging from less than 1% of global land area (Loveland et al., 2000; Schneider, Friedl, Mclver, & Woodcock, 2003, Schneider, Friedl, & Potere, 2009; Angel, Sheppard, & Civco, 2005; Bartholome & Belward, 2005) to 3% of global land surface (Gamba & Herold, 2009; Grimm et al., 2008; Global Rural-Urban Mapping Project). Following a similar trend, in Europe, an increasing share of the European Union (EU’s) population lives and works in cities, a pattern that seems to continue (Eurostat, 2017). Background Information According to the United Nations, in July 2007 the world’s urban population overtook the rural population for the first time. While urbanisation has the potential to raise wealth, it often does so accompanied by negative impacts. Leaving behind the opportunities offered in an urban G. Myrovali (*) · M. Morfoulaki Centre for Research and Technology Hellas / Hellenic Institute of Transport (CERTH/HIT), Thessaloniki, Greece e-mail: [email protected]; [email protected] # The Author(s), under exclusive license to Springer Nature Switzerland AG 2021 J. Papathanasiou et al. (eds.), Urban Sustainability, Springer Texts in Business and Economics, https://doi.org/10.1007/978-3-030-67016-0_2

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Fig. 2.1 Global population distribution (and trend) among urban and rural areas (UN World Urbanization Prospects, 2018)

environment, the opposite side of urbanization has to do with the negative impacts on peoples’ life, on health, on environment (pollution), on social relationships (alienation, isolation) and on personal welfare (increased cost of life). Background Information Brainstorming: Let’s think of the opportunities and threats arising from urbanization. Coming to transportation, the high level of urbanization in EU cities results in increased congestion phenomena affecting cities’ daily operation and citizens’ quality of life; extreme delays, pollution, noise, accidents and many billion Euros lost every year are only some of the negative effects of urban transportation. Background Information Brainstorming: Can you think of private car use negative effects on area’s sustainability (environment, society and economy)? Term Mobility Accessibility

Definition The efficient movement of people and goods/the ability to move The ability to reach opportunities/the ability/easiness to reach the destination of a trip

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Table 2.1 Transport externalities Economic impacts Congestion Low accessibility Accidents Increased costs Bottlenecks in logistic chain

Environmental impacts Pollution Noise Climate change

Social impacts Social inequity Health problems Lower quality of life Aesthetic damage

According to official EU data, urban mobility accounts for 40% of CO2 emissions of road transport and up to 70% of other pollutants from transport causing subsequent health and well-being problems. Furthermore, congestion phenomena, arising from irrational addiction to and use of cars, are common in EU cities which are in turn associated with huge time and money losses—costing nearly 100 billion Euros, or 1% of the EU’s GDP, annually (EC COM (2007) 551 final). These numbers reveal the threats of uncontrolled private-car use and show that much progress is still needed in order to increase urban mobility system efficiency and sustainability. Main externalities connected with low efficient and sustainable urban mobility systems are summarized in Table 2.1. What is necessary is to maintain and enhance cities attractiveness while supporting them as drivers of growth. Part of the wider challenge of greening and smoothing urban areas operation is linked to urban mobility upgrade. Sustainable urban mobility is a common goal for all cities and Europe has started putting forward proposals and articulating them for reaching the sustainable development goals. Background Information ‘The European Commission has long recognized the important role that local authorities play in improving the environment, and their high level of commitment to genuine progress. The European Green Capital Award was conceived as an initiative to promote and reward these efforts, to spur cities to commit to further action, and to showcase and encourage exchange of best practice among European cities.’ European Green Capital. https://ec.europa.eu/environment/europeangreencapital/winning-cities/ Acknowledging that life quality should be kept in the center of sustainable policies while recognizing at the same time that urban transportation is an important facilitator of growth, employment and territorial development, the EU has started from the early 2000, working towards building effective and sustainable urban mobility plans. The concept of Sustainable Urban Mobility Plans has been officially set out by the European Commission in 2013 with the Urban Mobility Package, ‘Together towards competitive and resource-efficient urban mobility’ (EC COM (2013) 913 final) while different aspects of urban mobility management policies and plans development have been much examined in a series of European projects (e.g. ELTISplus, CIVITAS MODERN, CIVITAS ELAN, CIVITAS MIMOSA, Poly-SUMP, CH4LLENGE, ADVANCE). In 2014, the Commission presented

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guidelines introducing the concept and the benefits of Sustainable Urban Mobility Plans as a paradigm to be followed by cities, urban transport and mobility practitioners and stakeholders. In contrast to traditional approaches on transportation planning, the most recent guidelines focus on citizens’ needs for enhanced accessibility, on social equity and improved environmental performance. Urban sustainable mobility refers to a large pool of measures and solutions able to tackle urban mobility challenges linked to the uncontrolled private car ownership and use. Indicative wide clusters of measures promoting sustainable mobility are: • • • •

Public Transport (PuT) modes promotion. Alternative/active transport modes (walking, cycling) promotion. Alternative fuel driven vehicles (e.g. electric). Multimodality enhancement (e.g. development of fully interconnected and accessible terminals). • Intelligent Transport Systems development (exploiting the advantages of Information and communications technology—ICT). • Services integration (integrated information to passengers for all modes, integrated ticketing and harmonized timetables among PuT modes). Sustainable mobility measures aims to help at the transition to a low carbon era and to zero emissions vision while the adoption and transferability of already proven successful measures to other cities should include the consideration of local context and area’s specific characteristics. Transforming today’s cities into sustainable urban environments that meet Europe’s 2020 goals for tackling congestion, improving air quality, enhancing accessibility and sustainability, apart from requiring a comprehensive integrated policymaking and decision-making approach, presupposes citizens’ collective endeavour and participation in the mobility planning procedure. Citizens’ engagement however is a very complex issue since it depends on successful citizens’ training and the respective daunting task of (travel) behavioural change, thus the development of a culture that favours responsible choices. Background Information Relative links: • United Nations, 2018 Revision of World Urbanization Prospects, https:// www.un.org/development/desa/publications/2018-revision-of-world-urban ization-prospects.html • Eurostat regional yearbook 2019, https://ec.europa.eu/eurostat/web/ products-statistical-books/-/KS-HA-19-001 • The International Transport Forum at the OECD, https://www.itf-oecd.org/

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The History and Future of Mobility in EU Cities

The European Commission (EC) has emphasized the inherent and increasing value of mobility to boost European cities economic growth and development and very early recognized its substantial role on territorial cohesion. The EC Green Paper on Urban Environment (COM_1990_0218_FIN) was one of the first devoted attempts on enhancing urban environments in EU; it concludes that integrated approach from all policy levels is required and that the urban environments are both the source of environmental problems and the framework of solutions identification (Marvin, 1992). Following, and in line with the Treaty of Amsterdam (which started the dialogue on sustainable development urgent need) and the Gothenburg European Council, the EU published the White paper 2001— ‘European transport policy for 2010: time to decide’ (COM (2001) 370 final). In this paper, the EC proposes some 60 measures aimed at developing a European transport system capable of modal shift towards friendlier modes of transport than private car, revitalising railways, promoting sea and inland waterways based transport and controlling air transport growth. The starting point of the 2001 White Paper on Transport Policy is that a modern transport system must be sustainable from an economic and social as well as from an environmental viewpoint. Background Information Definitions: • Green Papers are official documents published by the European Commission to stimulate discussion on specific topics, are acting as a trigger for launching consultation and debate for the proposed actions. https://eur-lex. europa.eu/summary/glossary/green_paper.html • European Commission White Papers are documents containing proposals for European Union (EU) action in a specific area. In some cases, they follow on from a Green Paper published to launch a consultation process at EU level. https://eur-lex.europa.eu/summary/glossary/white_paper.html In 2002, the European Commission launched the CIVITAS Initiative, a network of cities for cities dedicated to cleaner, better transport in Europe and beyond. Since 2002, CIVITAS has tested and implemented over 800 measures and urban transport solutions as part of demonstration projects in more than 80 Living Lab cities Europewide. In 2006, the mid-term review of the White Paper introduced two important shifts compared to previous Commission position: • The position that mobility must be disconnected from its negative side-effects rather than from economic activity;

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• The introduction of the concept of co-modality (‘use of different modes on their own and in combination’ in the aim to obtain ‘an optimal and sustainable utilisation of resources’). The mid-term review of the Transport White Paper 2001 took place in 2006; the European Commission announced in that occasion its intention to go further ahead by presenting an Urban Transport Green Paper. The Green Paper ‘Towards a new culture for urban mobility’ published in 2007 (COM (2007) 551 final) agreed on the necessity to join actions and forces towards achieving the goal of free-flowing and greener cities. With the Green Paper, the Commission set a new European agenda for urban mobility, respecting local, regional and national authorities’ responsibilities and trying to reinforce citizens and stakeholders engagement in the common target of successful urban mobility management. The launching of the new era of accessible, safe and secure urban transport was planned in parallel with the identification of the obstacles hindering successful urban mobility and of ways to overcome problems. Issues as the improved quality of collective transport, clean and energy efficient technologies, walking and cycling promotion and respect of passengers’ rights on public transport were among the core discussion subjects of the Green Paper. 10 years once the ‘European transport policy for 2010: time to decide’ White Paper, the Commission adopted a new Transport White Paper, that defines its transport policy agenda for consequent decade. Within the ‘Roadmap to one European Transport space—Towards a competitive and resource economical transport system’, White Paper 2011 (COM/2011/0144 final), the EC adopted a roadmap of 40 concrete initiatives for the consequent decade to make a competitive transport system able to increase sustainable mobility, take away major barriers in key areas and support growth and employment. At the same time, the proposals can dramatically scale back Europe’s fossil oil dependence on oil and reduce carbon emissions in transport sector by 60% by 2050. As major emission contributor, urban transport greening holds a central position within the goals list. In December 2013, via the Urban Mobility Package (COM (2013) 913 final), the Commission reinforces its supporting measures in the area of urban transport by: • Sharing experiences, show-casing best practices, and fostering cooperation • Consolidation and dissemination of experiences and best practices (studies, web portals): Urban Mobility Portal (Eltis); Platform on Sustainable Urban Mobility Plans; Member States Expert Group • Providing targeted financial support, i.e. Structural funds, ESI-Funds, TEN-T • Focusing research and innovation on delivering solutions for urban mobility challenges i.e. • CIVITAS Initiative (2002) launching, Smart Cities and Communities, Clean Vehicles Initiative • Involving the Member States and enhance international cooperation.

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Fig. 2.2 ‘European Commission 2013—Urban Mobility Package’ objectives (Leiner, 2014)

The central element of the Urban Mobility Package is the communication ‘Together towards competitive and resource efficient urban mobility’ that aims at providing the basis for a continuous debate on urban mobility at EU and member states levels (Fig. 2.2). All these years, EC Multiannual Financial Frameworks have supported projects and ideas implementation at EU cities for sustainable urban mobility. Transnational European Cooperation Programmes (e.g. Interreg MED, Europe, SEE, ADRION) and Framework Programmes for research and technological development have given the opportunity to partners from different EU countries to come close, discuss, conduct research and implement policies in transport field. Coming in today, smart, green and integrated transport has been identified as one of the major societal challenges and simultaneously of EU2020 goals (‘A European strategy for smart, sustainable and inclusive growth’ COM (2010)). EU flagship initiative ‘Resource efficient Europe’ concentrates on helping decouple economic growth from the use of resources, by decarbonizing economy, increasing the use of renewable sources, modernizing the transport sector and promoting energy efficiency (Fig. 2.3).

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Fig. 2.3 Cross-sectorial approach for mobility planning [Urban Agenda for the EU (2018)]

Background Information Key EU targets for 2020 • 20% cut in greenhouse gas emissions compared with 1990 • 20% of total energy consumption from renewable energy • 20% increase in energy efficiency Key EU targets for 2030 • At least 40% cut in greenhouse gas emissions compared with 1990 • At least 27% of total energy consumption from renewable energy • At least 27% increase in energy efficiency [EU flagship initiative ‘Resource efficient Europe’]. At global level, on the first of January 2016, the 17 Sustainable Development Goals (SDGs—Fig. 2.4) (UN, 2015) of the 2030 Agenda for Sustainable Development—adopted by world leaders in September 2015 at an historic UN Summit— officially came into force.

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Fig. 2.4 SDG goals, United Nations

Although not legally binding the SDGs are expected to be taken into account by countries and take integrated measures for ending all forms of poverty, fight inequalities and tackle climate change, while ensuring development for all. Table 2.2 presents in brief the (desired) connection to UN’s Sustainable Development Goals and EU2020 goals. Background Information Relative links: • UN Sustainable Development Goals https://sustainabledevelopment.un. org/?menu¼1300 • EU2020 ‘A European strategy for smart, sustainable and inclusive growth’, https://ec.europa.eu/eu2020/pdf/COMPLET%20EN%20BARROSO% 20%20%20007%20-%20Europe%202020%20-%20EN%20version.pdf • White Papers on transport (2001, 2011) – https://ec.europa.eu/transport/sites/transport/files/themes/strategies/doc/ 2001_white_paper/lb_com_2001_0370_en.pdf – https://eur-lex.europa.eu/legal-content/EN/ALL/? uri¼CELEX:52011DC0144 • Urban Agenda EU, https://ec.europa.eu/futurium/en/node/1829 • Green Paper ‘Towards a new culture for urban mobility’, COM (2007) 551 – http://eur-lex.europa.eu/legal-content/EN/ALL/? uri¼CELEX:52007DC0551 • CIVITAS initiative, https://civitas.eu/ • Interreg Transnational Cooperation Programmes, https://ec.europa.eu/ regional_policy/sources/cooperate/10_things_transnat_en.pdf

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Table 2.2 Sustainable mobility interventions and their link to UN’s Sustainable Development Goals and EU2020 goals

Sustainable mobility interventions NEW MOBILITY SCHEMES & ENHANCED REGIONAL CON NECTIVITY and PERFORMANCE New forms of mobility (soft mobility schemes, drive-sharing, ride-sharing, crowd shipping, crowd delivery, connected and automated vehicles, innovative flying vehicles, Mobility as a Service) Balanced development between urban and rural areas (increased intermodality and higher resilience of the transport system between the metropolitan region and the neighbouring cities and rural areas) Meeting the challenge of reducing the environmental impact of commuting and inter-urban transport Overall regional development (including connectivity to TEN-T corridors) Daily performance upgrade (competitiveness, sustainability, social cohesion, equity, and citizen well-being)

INNOVATION Introduction of innovative transport technologies Information systems exploitation Market up-take of innovations (also supporting company logistics)

HAND BY HAND SPATIAL & MOBILITY GROWTH Increased coordination between multimodal infrastructure mobility and spatial-economic development, including reduction of inequalities Reduced congestion, energy, emissions of air pollutants, carbon

Relation with UN’s sustainable development goals (in some cases indirectly related to the transport sector) Goal 11: Sustainable Cities and Communities ‘Make cities and human settlements inclusive, safe, resilient and sustainable.’ Goal 8: Decent Work and Economic Growth ‘Promote sustained, inclusive and sustainable economic growth, full and productive employment and decent work for all.’ Goal 3: Good Health and WellBeing for people ‘Ensure healthy lives and promote well-being for all at all ages.’ Goal 7: Affordable and Clean Energy ‘Ensure access to affordable, reliable, sustainable and modern energy for all.’ Goal 10: Reduced Inequalities ‘Reduce income inequality within and among countries.’ Goal 5: Gender Equality ‘Achieve gender equality and empower all women and girls.’ Goal 9: Industry, Innovation and Infrastructures ‘Build resilient infrastructure, promote inclusive and sustainable industrialization and foster innovation.’

Goal 7: Affordable and Clean Energy ‘Ensure access to affordable, reliable, sustainable and modern energy for all.’ Goal 13: Climate Change ‘Take urgent action to combat climate change and its impacts by regulating emissions and

First level connection to EU2020 goals (and to flagship initiatives) SUSTAINABLE GROWTH • Resource efficient Europe • An industrial policy for the globalisation era

SMART GROWTH • Innovation Union • Youth on the move (ended in December 2014) • A digital agenda for Europe SUSTAINABLE GROWTH (as above)

(continued)

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Table 2.2 (continued)

Sustainable mobility interventions

Relation with UN’s sustainable development goals (in some cases indirectly related to the transport sector)

footprint, noise and land-use within the identified metropolitan regions CITIZENS ACTIVE ENGAGE MENT Behavioural change towards sustainable mobility

promoting developments in renewable energy.’ Goal 12: Responsible Consumption and Production ‘Ensure sustainable consumption and production patterns.’

EFFECTIVE COOPERATION SCHEMES Operational and business models Aid to decision makers for efficient planning Cooperation schemes

Goal 17: Partnerships for the Goals ‘Strengthen the means of implementation and revitalize the global partnership for sustainable development.’

2.3

First level connection to EU2020 goals (and to flagship initiatives)

INCLUSIVE GROWTH • An agenda for new skills and jobs • European platform against poverty and social exclusion SMART-SUS TAINABLEINCLUSIVE GROWTH (all the above)

Sustainable Urban Mobility Planning

Based on the Commission’s Urban Mobility Package (COM (2013) 913 final), Sustainable Urban Mobility Planning is one of the most crucial aspects to be handled by competent authorities for improving urban areas’ accessibility, connectivity and sustainability. In order to support sustainable mobility promotion and in order to facilitate local authorities in developing effective Sustainable Urban Mobility Plans (SUMPs), the European Commission developed Guidelines ( GUIDELINES — Developing and implementing a Sustainable Urban Mobility Plan—first edition of the European SUMP Guidelines) that provide local authorities with a clear framework for SUMPs development and implementation. What is then needed at national and regional/local level is the mainstreaming with national policies and the active involvement of different stakeholders categories that can guarantee the right development of the plans. The ‘Guidelines for developing and implementing a Sustainable Urban Mobility Plan’ have been revised in 2019 (second edition of the European SUMP Guidelines). According to the Guidelines, ‘A Sustainable Urban Mobility Plan is a Strategic Plan’ designed to satisfy the mobility needs of people and businesses in cities and their surroundings for a better quality of life. It builds on existing planning practices and takes due consideration of integration, participation, and evaluation principles. The main innovation of SUMPs is that in general they are presented as a ‘new way of planning urban mobility’ that include as primary objectives the accessibility and quality of life, as well as sustainability, economic viability, social equity, health and

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environment quality. The SUMP is built on an 8-principles basis (second edition of the European SUMP Guidelines); • • • • • • • •

Plan for sustainable mobility in the entire functional city Cooperate across institutional boundaries Involve citizens and stakeholders Access current and future performance Define a long term vision and a clear implementation plan Develop all transport modes in an integrated manner Arrange for monitoring and evaluation Assure quality. Background Information Equity in transportation is the fairness with which mobility related impacts (benefits and costs) are distributed to citizens (Victoria Transport Policy Institute, https://www.vtpi.org/).

The following subchapters present the ‘power’ of this modern approach on mobility planning (differences with traditional transport planning approached and benefits), provide an overview of the SUMP cycle—steps for SUMP development and close with the presentation of a SUMP success story. Now, probably more than ever before, the COVID-19 pandemic and the need to identify alternatives in almost all aspects of everyday life, sustainable mobility and active transport modes use seem to be the answer regarding transportation (COVID19 SUMP Practitioner Briefing). Urban transportation has entered in a transit era and authorities (cities, regions and national authorities) have understood well that mobility should be strongly aligned not only with sustainability vision but also with resilience and the latest safety protocols. Among the most important actions in order to enter to an exit from this crisis as highlighted by key European stakeholders and networks in the sector of transport are (Letter: Exit strategy must include an integrated and sustainable approach to urban mobility): • • • •

need to go fast towards sustainable, safe and integrated urban mobility focusing on green technology for transport support future-proof urban freight logistics getting close to decarbonisation goals exploiting European Green Deal (COM (2019) 640 final) that is a tool going further beyond strict environmental purposes by generating jobs • actively support local and regional authorities, public transport stakeholders, cycling businesses and walking and cycling associations to tackle with the new circumstances

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Digitalization seems also to be part of the transition era; a good example is the ‘Re-open EU’ (https://reopen.europa.eu/en), the new web platform to help travellers and tourists launched by the European Commission on the middle of June, 2020. COVID-19 and social distancing conditions have brought big changes also huge changes in public engagement procedures in mobility planning; during the last years, planners and marketing experts have mobilized online communication tools and channels i.e. virtual meetings, online surveys, virtual visits however the face-to-face engagement was the most applied method. Today redefinition of public engagement has unexpectedly and forcibly arrived to our lives; physical events have been replaced with virtual meetings and online tools are used to reach both stakeholders and citizens with the latter to be even more complex.

2.3.1

Differences of SUMP Approach Compared to the Traditional Way of Planning

The new mobility planning approach places the citizen and the traveller into the centre of the planning procedure which means that it asks for active engagement of the public into the development of the mobility plans. Given the limited available public budget, especially during the last years (since the end of 2009) that European economy is in the midst of the deepest recession since the 1930s, the new mobility planning era focuses more on softer interventions (integrated services, harmonized Public Transport timetables, ICT exploitation etc) than at investments requiring large capitals (hard measures, infrastructure based). Furthermore, regular assessment and continuous monitoring of plans’ effectiveness in the framework of clearly defined targets (indicators and target values) play crucial role in the SUMP since they facilitate risk management and unlock potentials for constant feedback and upgrade of the plans according to the latest data and needs. As revealed by Table 2.3, accessibility is a SUMP core objective so as to ensure that all citizens are treated equally and are offered transport options and services that provide access enabling their trips (reaching their final destination). Equity in transportation and in accessibility refers to the coverage of mobility needs of all social groups, including groups such as children, older people and disabled people. Ways to enhance equity in mobility and increase accessibility are related to: • re-planning of urban space to increase connectivity (i.e. high density development and mixed-use development); • providing mobility/transport options for all (first edition of the European SUMP Guidelines) • increasing Public Transport interconnectivity • re-define the fare policy

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Table 2.3 Differentiation among traditional and modern sustainable way of mobility planning, European SUMP Guidelines Traditional transport planning Focus on traffic Traffic flow capacity and speed increase as primary objectives Modal-focussed

Infrastructure focus Sectorial planning document

Short- and medium-term delivery plan Related to an administrative area Domain of traffic engineers Planning by experts Limited impact assessment

Sustainable urban mobility planning Focus on people Accessibility and quality of life, as well as sustainability, economic viability, social equity, health and environmental quality as primary objectives Balanced development of all relevant transport modes and shift towards cleaner and more sustainable transport modes Integrated set of actions to achieve cost-effective solutions Sectorial planning document that is consistent and complementary to related policy areas (such as land use and spatial planning; social services; health; enforcement and policing; etc.) Short- and medium-term delivery plan embedded in a long-term vision and strategy Related to a functioning area based on travel-to-work patterns Interdisciplinary planning teams Planning with the involvement of stakeholders using a transparent and participatory approach Regular monitoring and evaluation of impacts to inform a structured learning and improvement process

Background Information Self—assessment exercise: Which are the main innovations in the modern planning approach?

2.3.2

Benefits of the Modern Mobility Planning Approach

Sustainable Mobility Planning, thus planning mobility services and options with as an ultimate aim the facilitation of people’s mobility and the enhancement of accessibility, provides with a large set of benefits both for the traveller as well as for the environment and the society as a whole (economy, welfare, health etc.). The Table 2.4 concentrates the main benefits as indicated in the SUMP Guidelines and as already proved by the experience. Having realized the above benefits and being supported by the Commission’s guidelines, many European cities have shown much interest in this modern way of mobility planning e.g. Copenhagen, Helsinki, Budapest, Bristol, Malmo and

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Table 2.4 SUMP approach benefits Benefit Improving life quality Creating economic benefits Supporting health while protecting the environment Increasing accessibility and facilitating trips Adding on the resource efficiency Engaging public and get support from people Listening to the needs and improving policies

Transforming plans to efficient schemes

Supporting cooperation Becoming agents of change—get close to each other’s needs

Added value of intensive strategic planning within urban areas/cities/towns Reducing private cars Reaching improved air quality and less noise Identify effective and cost-efficient measures to overcome challenges Travelling more actively (by walking and cycling more often) Analyse and assess local transport problems and challenges Balancing economic development, social equity and environmental quality Choose and agree an appropriate feasible set of measures Understand interests and expectations of transport system users Develop a common vision on urban transport development Prioritise and schedule measures: • According to most urgent problems • Easy-to-achieve ‘quick wins’ • In line with available budget and implementation capacities Develop a common vision on urban transport development Agreeing on a common vision that goes beyond electoral cycles and that can include less attractive elements when they provide long-term benefits

Manchester are just a few good examples of cities having developed SUMPs with a clear vision straightforwardly linked to their wider strategy (Table 2.5). Background Information City specific question: Which is the vision for your city regarding the future of the transport sector?

2.3.3

The 12 Steps of Sustainable Urban Mobility Planning

The current section aims at better introducing the SUMP cycle, thus the 4 phases (and the 12 steps) of the methodology described in the Commission’s Guidelines— updated SUMP cycle (second edition) (Fig. 2.5).

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Table 2.5 Good Examples of SUMPs and the vision of the cities CITY Copenhagen Helsinki Budapest

Bristol Malmo

Manchester Thessaloniki

VISION of the future city ‘to make mobility in Copenhagen more efficient and green in order to stimulate growth, contribute to aCO2-neutral city and to the good life for Copenhageners’ ‘high-quality and eco-efficient means of mobility and transport promote development and wellbeing of the Helsinki region’ ‘Budapest is a liveable, attractive capital city with a unique character and a respected member of the European Network of cities as the innovative economic and cultural centre of the country and the city region’ ‘to enable movement to and through the BTQEZ’ (Bristol Temple Quarter Enterprise Zone—at the heart of the city) ‘Walking, cycling and public transport are the first choice for all who work, live or visit in Malmö. These travel choices, together with efficient and environmentally friendly freight and car traffic’ ‘World class connections that support long-term, sustainable economic growth and access to opportunity for all’ ‘sustainable mobility for Thessaloniki to further support its role as a vivid, resilient and fully operational cell where entrepreneurship and cultural heritage are highlighted’

Fig. 2.5 The SUMP cycle, second edition of the European SUMP Guidelines (Rupprecht, 2019)

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Summarizing the cycle’s main tasks, the approach presented includes: 1. Preparation phase (common understanding and setting structures) & analysis of the current situation regarding mobility 2. Well-defining of the strategy—vision co-development phase 3. Mobility interventions planning phase 4. Real implementation & continuous monitoring and updating phase More analytically; (a) Phase 1: Preparation and Analysis The first phase of the SUMP cycle starts having as milestone the ‘Decision for preparing the plan’ and its three steps refer to (1)setting up of the working structures, (2) determining planning framework and (3) analysing mobility situation. This phase ends with ‘Concluded analysis of Problems and opportunities’ milestone. The first phase of a SUMP development consists of the necessary preparatory steps for understanding the current situation in the city or region and whether sustainability principles at mobility planning already apply or not. The selfassessment under a wider perspective (e.g. considering of wider national strategies and targets to be reached) is followed by: • Resource availability identification; establishing a team able to prepare the SUMP (management, technical, operational), verification of the existence/ identification of financial resources for running the planning process and for implementing measures. • Timeline definition; embedment of SUMP into existing policies, appropriate timeframe for building a strategic and operational framework for the planning process, timeframe for the sustainable urban mobility planning process, consideration of any decision-making windows (e.g. elections). • Stakeholder groups identification as well as their objectives, their power, their capacity and their planning resources; variety of groups; residents/business groups; NGO’s; transport user groups (Table 2.6). • Definition of the development process and the scope of the plan; integrating local and intra-local mobility needs; Information gathering; Household Travel Diary Surveys; Journey Time Surveys; Roadside Interview Surveys; Car Parking surveys; Bus Passenger Surveys; Classified Traffic Counts; Data analysis; Traffic models development/exploitation. • Mathematical representation of the real world and of the real transport network and services are used to predict how people will behave and how the transport network will respond under different situations (e.g. applied measures) now and in the future. • Mobility situation analysis and scenarios of possible future mobility situations development.

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Table 2.6 Proposed Stakeholders Categories for SUMP consultation, European SUMP Guidelines Authorities Local, regional authorities Nearby cities Traffic police Transport authorities Politicians Decision makers Emergency services European Union Relevant ministries

Business/operators Transport operators

Communities NGOs

Transport engineers, consultants Car sharing companies Bike sharing companies

Citizens

Other transport providers Regional & local industry Private financiers

Tourists Forums

Others Research & academia Training institutions Foundations

Trade unions Media Disabled people Motorists associations Cycling/pedestrian associations

Background Information Question: Why do we need to know as deeper as possible the current situation in order to plan the future?

Background Information Question: Can you think of other types of stakeholders that are closely related to the transport sector? Is there multi-sectorial stakeholders’ engagement necessary for effective mobility planning? (b) Phase 2: Strategy Development The three steps of the second phase of the SUMP cycle refer to (1) building and co-assessing future scenarios, (2) vision and strategy development jointly with the stakeholders and (3)setting of the targets and monitoring indicators—concrete objectives indicating the type of change desired and measurable smart targets (indicators) accompanying the vision (Fig. 2.6). This phase ends with ‘Agreed vision, objectives and targets’ milestone. (c) Phase 3: Measure Planning The three steps of the third phase of the SUMP cycle refer to: • Identifying Measures—best practices exchange, best value for money seeking (Table 2.7) and measures packaging—developing packages of measures serving a common goal (Fig. 2.7). • Assignment of Responsibilities & Resources

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Fig. 2.6 Strategic themes for a SUMP [REFORM Interreg Europe (2014–2020), ‘SUMP Training Programme’] Table 2.7 Examples of sustainable mobility measures Examples of sustainable mobility interventions & measures 1. Introduction of alternative forms of public transport operation—e.g. Public Transport on demand 2. Active transportation support—cycling & walking interventions 3. Demand management strategies (access restrictions, environmental zones, congestion charging) 4. Specialist mobility services (e.g. public buses, car sharing, carpooling, etc.) including private initiatives 5. Clean vehicles 6. Mobility management (mobility agencies, ecopoints system rewarding the use of public transport and other sustainable mobility options instead of the private car) 7. Collective passenger transport (new forms of public transport services, access for elderly and disabled passengers, integration of modes 8. Transport telematics (e-ticketing, traffic management and control, travel and passenger information) 9. Less car dependent mobility options (car sharing, carpooling) 10. Sustainable urban logistics concepts (transportation methods, the handling and storage of goods, the management of inventory, waste and returns, as well as home delivery services)

• Secure efficient and effective allocation of resources (human, knowledge, funds), detailed technical and budgetary plan development This phase ends with ‘Adopted Sustainable Urban Mobility Plan’ milestone.

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Fig. 2.7 Packaging of measures in SUMP concept

Background Information Question: What comes to your mind when you hear the phrase ‘sustainable mobility measures’? (d) Phase 4: Implementation & Monitoring The three steps of the fourth phase of the SUMP cycle refer to: • Ensuring proper management and communication with the public and the rest stakeholders • Continuous Monitoring of plan implementation, systematic collection of date on specified indicators, information provision for potential adjustment & re-planning

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• Regular Evaluation & feedback loop, systematic and objective assessment of ongoing/completed plan, understanding effectiveness, realizing the future and getting prepared for the next generation of SUMPs The last phase of SUMP cycle ends with ‘Evaluated measures implementation’ milestone. Background Information Question: Which is the role of the continuous monitoring of a plan? Summing up, the SUMP guidelines and the whole background of the modern participatory approach on sustainable mobility planning provide a solid ground for achieving wider sustainability, safety and equity goals and can support cities being aligned with global sustainability goals.

2.3.4

European Mobility Week and SUMP Awards; Recognizing Excellence and Good Practices in Sustainable Mobility

Since 2002, on the initiative of European Commission, an awareness raising campaign for sustainable mobility entitled ‘European Mobility Week’ was launched. Every year from 16 to 22 September, towns and cities participate in the campaign by organizing engagement activities, implementing and testing permanent mobility measures and holding a car-free day. Getting feedback from the public, therefore identifying potentials and acceptability of measures, is a unique opportunity for cities that have realized the need for shifting towards environmental friendlier ways of transport. The European Mobility Week ‘is also an excellent opportunity for local stakeholders to get together and discuss the different aspects of mobility and air quality, find innovative solutions to reduce car-use and transport emissions, and test new technologies and planning measures’ (https://mobilityweek.eu/the-campaign/). Every year, two types of complementary awards are offered; The European Mobility Week Award is given to municipalities for their outstanding activities during European Mobility Week (two categories—one for municipalities larger than 50,000 inhabitants, and one for smaller municipalities under this threshold), whereas the SUMP Award recognizes local and regional authorities for excellence in sustainable urban mobility planning, focusing on a specific topic. The cities having won a European Mobility Week Award (EMW) are presented in Table 2.8 while more information are available in campaign’s website. Regarding the other category, as mentioned above, winner of the seventh SUMP Award was Greater Manchester (previous municipalities awarded are: sixth SUMP Award Winner: Turda, Romania, fifth SUMP Award winner: Brussels, Belgium, fourth SUMP Award winner: Malmö, Sweden, third SUMP Award winner: Bremen, Germany, second SUMP Award winner: Rivas Vaciamadrid (Spain), first SUMP Award winner: Aberdeen, UK).

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Table 2.8 EMW winners European Mobility Week Award Winners 2018 Lisbon, Portugal Lindau, Germany (for larger municipalities) (for smaller municipalities) 2017 Vienna, Austria Igoumenitsa, Greece (for larger municipalities) (for smaller municipalities) 2016 Malmö, Sweden 2015 Murcia, Spain 2014 Östersund, Sweden 2013 Ljubljana, Slovenia 2012 Zagreb, Croatia 2011 Bologna, Italy 2010 Almada, Portugal 2009 Gävle, Sweden 2008 Budapest, Hungary 2007 Koprivnica, Croatia 2006 León, Spain 2005 Copenhagen, Denmark 2004 Nantes, France 2003 Ljubljana, Slovenia 2002 Ferrara (Italy), Geneva (Switzerland), Lund (Sweden) and Krakow (Poland)

Background Information Relative links: • ELTIS platform “The urban mobility observatory”, https://www.eltis.org/ mobility-plans/european-platform • European Commission, 2009: Action Plan on Urban Mobility COM (2009) 490/5. Source: http://ec.europa.eu/transport/themes/urban/urban_mobility/ action_plan_en.htm • Guidelines for developing and implementing a Sustainable Urban Mobility Plan (second edition), https://www.eltis.org/mobility-plans/sumpguidelines • European Commission, Transport and Mobility, https://ec.europa.eu/ transport/home_en • European Mobility Week; https://mobilityweek.eu/the-campaign/

2.4

Building Capacity and Training the Authorities for the New Planning Concept

According to the authors, institutional capacity building or authorities’ capacity building refers to advanced skills development through the gaining of deep understanding on the specific issue with which one is dealing that arises from a global

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insight on its dynamics (complex interrelations with other parameters/issues) and takes into account sustainability goals. Background Information Definition of CAPACITY BUILDING according to the United Nations Development Programme (UNDP): ‘In the global context, capacity refers to the ability of individuals and institutions to make and implement decisions and perform functions in an effective, efficient and sustainable manner. At the individual level, capacity building refers to the process of changing attitudes and behaviours-imparting knowledge and developing skills while maximizing the benefits of participation, knowledge exchange and ownership. At the institutional level it focuses on the overall organizational performance and functioning capabilities, as well as the ability of an organization to adapt to change. It aims to develop the institution as a total system, including individuals groups and the organization itself. Traditionally, interventions at the systemic level were simply termed institutional strengthening. This reflected a concern with human resource development as well as assisting in the emergence and improvement of organizations. However, capacity development further emphasizes the overall policy framework in which individuals and organizations operate and interact with the external environment, as well as the formal and informal relationships of institutions. Capacity is not the mere existence of potential but rather existing potential must be harnessed and utilized to identify and solve problems in order to be considered as capacity.’ Capacity building is considered as a crucial success factor in developing policies that are long-lasting and effective. The notion integrates also aspects of experiential learning (Kolb, Boyatzis, & Mainemelis, 2001) and sustainability learning (PahlWostl and Tàbara, 2007); the role that experience plays in learning process and knowledge creation and the need for global trends adaptation and investment on resilience. Recognizing the criticality of capacity building, EU is making a series of capacity-development efforts among which are the development of European Commission’s Toolkit for Capacity Development (2010) and April’s 2015 joint communication on Capacity building in support of security and development— Enabling partners to prevent and manage crises (EPRS, Briefing 2017). Coming to urban transportation, capacity building in mobility planning is associated with the active role of competent authorities’ staff and relevant stakeholders in mobility plans development; deep understanding of mobility and sustainability needs, ownership feeling for city’s vision creation, involvement in a fruitful and constant dialogue at a cross-sectorial level and ability to ‘listen’ to real needs and propose tailored-made effective mobility interventions.

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Fig. 2.8 The (online) sustainable mobility Competence Center of the Region of Central Macedonia (GR) (https://www.keyp-svak-rcm.imet.gr/)

Capacity building and training of the staff of competent authorities in the necessity to follow the modern approach of sustainable mobility planning seems to be an imperative need for contemporary cities. A good example of an effort to inspire the staff of competent authorities to develop acceptable and long-lasting sustainable mobility plans can be found in Thessaloniki, the second largest city in Greece with a population of around 800,000 inhabitants at the metropolitan area (census 2011). The city, which is now considered as a smart transport laboratory/ecosystem due to the stable cooperation among core stakeholders (Quadruple Helix—competent authorities, research/facilitators: Hellenic Institute of Transport (CERTH/HIT), transport providers, industry (i.e taxi association—ICT enablers—private companies, citizens)), has many efforts to showcase in sustainable mobility among which the capacity building effort of ‘the sustainable mobility Competence Center of the Region of Central Macedonia’ (Fig. 2.8) developed in the framework of REFORM Interreg Europe Project (2014–2020).

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The online front-end web SUMP Competence Center (CC) of Region Central Macedonia (RCM) provides the interested staff and stakeholders with: • Information to get acquainted with SUMPs, the European actions and the Greek legislation • Up-to-date technical guidance, delivering the SUMP cycle in an interactive—user friendly mode • Library of useful documents (including examples of public procurement documents for SUMP development) • Frequently Asked Questions (FAQs) for each sub step of the SUMP cycle • Recent developments in SUMP in RCM and beyond • Online forum for staff member of municipalities • Online communication form with the sup-port team • Training material The navigation to and the frequent use of the functionalities of the CC as proven (structured survey to staff members of relevant city’s departments) have substantially contributed in the familiarization of the staff with the new approach in urban mobility, the increase of knowledge on SUMPs, the acceleration of administrative procedures and in the enhancement of synergies between cities/staff of different cities that added value of an integrated effective planning (Chatziathanasiou, Morfoulaki, Mpessa, & Tsoli, 2020). In the framework of the same EU project, REFORM Interreg Europe (2014–2020), a well structured training material on SUMP cycle was developed with an aim to achieve capacity building. The training material provides answers to the following, crucial for the deep understanding of the modern planning approach, questions: • What makes a successful SUMP—good practice in SUMP development? • How should stakeholder engagement be approached to support SUMP development? • Which is the role of Strategic Environmental Assessment? • What are the challenges in obtaining and analysing data to effectively support a SUMP? • How to establish a balanced SUMP strategy—including suitable ‘carrots and sticks’? • How to identify the best solutions and measures to deliver a SUMP Vision? • What are the challenges in SUMP implementation and how to overcome these? • What makes a good monitoring and evaluation framework for a SUMP? 2-days training seminars took place in REFORM participating regions (Region of Central Macedonia, Region Emilia-Romagna, Parkstad Limburg and Greater Manchester) structured in a modular approach (Fig. 2.9) while a targeted exercise named ‘Anyregion case study’ was used throughout the course on Sustainable Urban Mobility Planning. The Anyregion represented a medium sized coastal region of a European country which was about to start the development of its first Sustainable Urban Mobility Plan

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Fig. 2.9 The modular approach of REFORM training material [REFORM Interreg Europe (2014– 2020), Sustainable Urban Mobility Planning—Enhancing Planning Capacities Trough Training & Learning]

Fig. 2.10 The case training exercise of Anyregion and Anycity [REFOMR project, Sustainable Urban Mobility Planning—Enhancing Planning Capacities Trough Training & Learning]

(SUMP) and trainees were asked to take the role of a different city department that is engaged in a SUMP development and take the most effective decisions (Fig. 2.10). This interactive learning experience, as assessed from the participants, assisted them much to gear up for success by posing the challenge of reflection, interaction and

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practical application of their knowledge (REFORM Interreg Europe (2014–2020), Sustainable Urban Mobility Planning—Enhancing Planning Capacities Trough Training & Learning).

2.5

Building the Participatory Approach of Urban Mobility Planning

Putting citizens in the position of city planners while giving them a sense of purpose and plans’ ownership is a difficult task since the win-win effect of participating in mobility planning is, in the majority of cases, not properly communicated. Background Information Question: Are you aware of consultation procedures in mobility planning? How do you think the view of a real user of the transport system can assist the development of effective mobility plans? The thorough study of the widely known ‘Planning cycle for a sustainable urban mobility plan’ presented above, concluded in the identification of potential ambiguities that hinder the implementation of the plans. Seeking within reasons potentially blocking the adoption of acceptable and thus effective plans, public rejection and public indifference cannot be overlooked (Myrovali et al., 2018). According to Dobos and Jenei (2013), the actual meaning of engagement corresponds not only to information provision for awareness raising but also—and strongly—to reinforcing the sense of community and building up citizenship. Previous research has indicated major barriers in engaging citizens in transport planning (Bickerstaff & Walker, 2001) which extend from limited awareness and knowledge on the role of citizens and their power in planning to lack of authorities capacity in providing opportunities for participation. From the other side, Booth and Richardson (2001) have highlighted the benefits from citizens’ involvement in enhancing planning quality that include large brainstorming, evaluating and testing evidence and addressing uncertainty. Gaventa and Barrett (2012) refer to the engagement as a way of strengthening a sense of citizenship and building responsive states. Among the lessons learnt during the first period of SUMPs development is that stakeholders and citizens should be part of the planning procedure from the very first steps of planning and that their interest should be kept alive until the whole process. Timing, techniques and tools for citizens and stakeholders’ engagement, awareness raising and feedback collection should be well defined and implemented. Informing, consulting, involving and collaborating have been proven to be necessary parallel and continuous actions to accompany the rest planning actions (Fig. 2.11). As examined in depth in e-smartec project (e-smartec Interreg Europe (2014– 2020)), the process of developing and implementing a SUMP is complex and it requires specific and strategic actions that aims at co-planning and behavioural change. An overview of the experience of the six participating regions among

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Fig. 2.11 Engagement needs in SUMP cycle [e-smartec Handbook for success tips on marketing techniques]

which experience exchange on marketing techniques for achieving engagement took place (Region of Central Macedonia, Lazio Region, West Midlands, Bratislava SelfGoverning Region, North—Limburg Region, State of Hessen) is depicted in Fig. 2.12. It illustrates methods that have been used and proven effective given the goal of the engagement process, either increasing awareness or engaging in co-planning. For more information, the interested reader is redirected to e-smartec project deliverables that examines marketing techniques for citizens’ and stakeholders participation in SUMP procedure in a great detail (https://www. interregeurope.eu/e-smartec/). Among interesting practices for enhancing the participatory approach of mobility planning are: (a) The MOTIVATE platform and apps; born exactly from the need to enhance the participatory approach of mobility planning, the MOTIVATE platform idea was generated and the funding for its development came from Interreg MED programme 2014–2020 (MOTIVATE Project (2014–2020)). The platform is an ICT based tool (internet platform http://motivate.imet.gr/ and iOS and Android Apps ‘Motivate Project’) that tries to strengthen citizens’ involvement in the mobility planning process. The term ICT based tool for encouraging citizens’ involvement in the sustainable planning procedure refers to, free of time and location constraints, technology-mediated forms of citizens’ participation; no need for physical attendance at conventional stakeholders events where citizens are usually represented by just few members taking the role of simple

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Fig. 2.12 Methods for delivering engagement goals in SUMP cycle [e-smartec Handbook for success tips on marketing techniques]

observers (Misra, Gooze, Watkins, Asad, & Le Dantec, 2013; Slotterback, 2010; Green paper on the urban environment, COM (2001) 370 final). Focusing on citizens’ involvement in the development and implementation of SUMPs, the MOTIVATE app tries to capture citizens’ and visitors’ mobility habits & needs developing in this way a good (database) starting point for the authorities to plan interventions and improve services. In order to ‘motivate’ the travellers to daily use the specific platform providing their personal data, an awareness raising game was developed (Fig. 2.13)—providing personal data (daily trips, existing mobility measures evaluation, future mobility interventions assessment) allows for points collection that can be redeemed at the game.

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Fig. 2.13 MOTIVATE game interface (MOTIVATE project, Design of the Logical Architecture of MOTIVATE platform)

Through the game, the user understands that individual and fragmented actions do not have a significant result towards a more sustainable transportation system and that what is needed is a deep and long-lasting travel behaviour change. Users are experiencing a web-based attractive training activity to adopt a bottom up approach and view the transportation system as a sum of interrelated parts that affect not only themselves and each other but also the environment, the economy and the viability of the future. Finally, the game offers insights to its users, as it puts them in the position of a decision-maker, and demonstrates how difficult it is to achieve sustainability and how individual choices and trade-offs can affect balances of the entire system (COM (2001) 370 final). MOTIVATE app has been accepted as best practice of low carbon tools in Interreg Europe Programme Good Practices Database recognizing its potential contribution in enhancing mobility planning procedure. Furthermore, it is a COVID-19 tagged practice, meaning that it is an engagement and co-planning tool that can be used with success also during this crisis conditions (https:// www.interregeurope.eu/policylearning/good-practices/item/3611/motivate-appa-crowdsourcing-and-interactive-learning-environment/).

Background Information Question: Do you know any other crowd-sourcing applications used for collecting travellers’ opinions? What type of contribution from travellers do you think that would be helpful for the authorities?

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(b) The awareness raising effort launched by the Transport for Greater Manchester and Manchester (TfGM) exploiting the benefits of a well-designed gamification approach. The title of the promotion is ‘Metrolink Monsters’, being a campaign launched in connection with TfGM’s Metrolink services (travel via tram) which is a competition broken down into two parts as follows: • a platform game whereby the entrant navigates through a tram whilst avoiding the ‘Metrolink Monsters’ and collecting coins and ‘Travelcards’ for extra points (‘the Game’) and • a multiple choice quiz whereby the entrant can find out which ‘Metrolink Monster’ they are most similar to (‘the Quiz’). Prizes were given to the winners (direct benefit) while the quiz and the game acted as channels for passing travel behaviour change messages. (c) Trendsportal card game—a game for engaging stakeholders in SUMP development in Venlo, North—Limburg, The Netherlands A good example in engagement of stakeholders and citizens during the vision co-development step in North—Limburg is the exploitation of a card game; Trendsportal card game consists of cards each of which are representing the goals of the Sustainable Urban Mobility Plan (SUMP) of the Municipality of Venlo. Players (staff of the Municipality, entrepreneurs, citizens, teachers/ pupils/students, cyclists, people with disabilities, town council members) were asked to select specific cards (linked to goals) and state their thoughts and proposals for achieving the goals. In this way the vision for the city was co-agreed and co-designed offering the sense of overall ownership that is associated with respect of proposed mobility measures and packages respect (e-smartec Interreg Europe (2014–2020)). Background Information Relative links: • MOTIVATE Interreg MED project—Promoting citizens’ active involvement in the development of Sustainable Travel Plans in Med Cities with Seasonal Demand; https://motivate.interreg-med.eu/ – MOTIVATE platform; http://motivate.imet.gr/ – MOTIVATE app (android); https://play.google.com/store/apps/details? id¼hit.certh.motivateapp • Transport for Greater Manchester and Manchester; https://tfgm.com/ e-smartec—enhanced sustainable mobility with marketing techniques Interreg Europe Project; https://www.interregeurope.eu/e-smartec/

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2.6

Leading by Example: Cities and Efforts Offering Inspiration for Reaching Sustainable Mobility Vision

Background Information Question: Which European city do you think that is a good practice in mobility planning from your experience?

2.6.1

Successful Sustainable Mobility Practices at the City of Thessaloniki, Greece

The current section refers to best practices from the Greek experience regarding sustainable mobility interventions.

2.6.1.1 Thessaloniki’s Intelligent Urban Mobility Management System Thessaloniki’s Intelligent Urban Mobility Management System is a unified effort of the key players of the city dealing with urban mobility, transport and environment. The system aims, through the services provided, to help citizens move around the city easily avoiding the traffic congested areas and also to raise the environmental public consciousness and to promote public transportation and alternative ways of transport (walking, cycling).At the same time, through intelligent traffic management and control in the central area of Thessaloniki, the system aims to reduce the negative influence of the gaseous pollutants. The direct involvement of citizens in planning their trips will give them the right and the opportunity to actively contribute in the improvement of the environmental quality of the city. Finally, through special urban mobility training programs that the system provides, a new culture for urban mobility is formed in the city. The project has been executed as a partnership of public bodies (Regional Authority of Central Macedonia, Municipality of Thessaloniki), research centres (Centre for Research and Technology Hellas—Hellenic Institute of Transport, National Observatory of Athens, Norwegian Institute of Transport Economics— TOI), public transport authorities (Thessaloniki’s Integrated Public Transport Authority). Financing of services & infrastructures was at 50% by the European Economic Area (EEA) Grants and at 50% by the Greek Ministry of Finance. The system is divided into two separate although complimentary services: • The ‘Center for Urban Mobility’ • The ‘Traffic Control Center’ The services of Thessaloniki’s Intelligent Urban Mobility Management System are provided to the citizens through project’s electronic platform and include: • routing services with all modes (public transport, private vehicle, pedestrian and combined routing) taking into account several user objectives, such as the shortest

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route, the fastest route, the most economic route or the environmental friendlier route • mobility information provision, such as traffic information, environmental information, timetables’ information and points of interest information • sustainable urban mobility awareness tool, for increasing the knowledge of the citizens towards sustainable urban mobility Background Information Question: Do you know any applications supporting car-free lifestyles? In addition, the central area of the city has been equipped with an adaptive traffic management and control system, aiming to provide higher levels of services for the travellers along the city’s main arterial. All services rely on real-time mobility data, collected by newly installed field equipment, which is aggregated and processed by the electronic platform, developed by the Center for Research and Technology Hellas/Hellenic Institute of Transport (CERTH/HIT).

2.6.1.2 THESi: The Parking App for Thessaloniki City Centre THESi is the Smart Controlled Parking System of Thessaloniki Municipality. It is one of the most advanced and integrated technological systems in Europe, with online functions—service interface—control—public information. THESi aims to facilitate search and finding of a parking place in the city, to prevent illegal parking and all the problems this causes in Thessaloniki as well as to relieve to traffic congestion. THESi ensures: • • • • • • • • • • • •

Free parking spaces (blue line) for residents in their area. Visitor parking spaces (white line) with a price corresponding to the parking time. Caring for disabled fellow citizens-visitors. Paperless policy (scratch cards, phone cards, etc.). Easy parking process through phone calls, SMS, App, internet and smart devices. Easy access –with license plate number, used as ‘key’ for all system operations (vehicle registration, parking, legality check). Easy purchase of parking time at one of the 400 points of sale (kiosks, mini market, OPAP agencies) with special marking. Possibility of proportional billing (per minute) and not pre-purchased parking time for registered users. Fast and effective control by the competent authority. Foreign tourist friendly, since it operates in two languages. Limitation of traffic problems and the resulting pollution. Upgrading of quality of life.

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2.6.1.3 OASTH E-Services OASTH, Organization of Urban Transportation of Thessaloniki, in an effort to modernize its profile and to attract passengers from private cars to PuT provides a series of tools and information services that facilitate bus based trip planning. All necessary information regarding buses operation (timetables, routes, stops, duration, frequency, Points of Interest) are provided via a user friendly interface along with more advanced tools which helps passengers make a customized trip organization (trip planner, real time info regarding bus positions and bus arrivals per stop). OASTH offers through its webpage a series of information to passengers; – Optimal routing (specifying starting and destination points) with buses of OASTH – Information on bus routes, schedules, the service area, the area served and the stops – Information about the arrival of a bus lines – Presentation of the current location of buses (real time) Through the OASTH webpage passengers can have access to all information regarding the Organization and its services and also to more advanced tools (trip planner by origin and destination—address or point of interest, real time monitoring of a bus, arrivals of bus route or buses per stop).

2.6.1.4 Thessaloniki’s Bike Sharing System THESSBIKE is one of the biggest providers of bike and mobility vehicle rentals in Greece. THESSBIKE owns and manages Thessaloniki’s bike sharing system, which has 8 stations and 350 bicycles. The fleet of the company apart from common 2600 and 2800 bicycles consists also of children bicycles, electric bicycles, family bicycles with two and four seats and electric mobility scooters. Along with the renting activity, the company organizes bike tours in the city of Thessaloniki with the cooperation of certified tour guides. THESSBIKE’s main goal is to motivate people to use bicycles as a main means of transport and therefore add on city’s sustainability. For its initiatives concerning sustainable mobility, the company was honoured in May 2014 at the Greek Transport and Logistics Awards 2014.

2.6.2

A SUMP Success Story: Manchester, England

For SUMP of Region of Greater Manchester (named Local Transport Plan—LTP in UK) has been recognized as a good example regarding the approach followed for its development. Briefly, among the main ‘powers’ of Greater Manchester (GM) LTP/ SUMP are (REFORM Interreg Europe Project, EU good practices on sustainable mobility planning and SUMP):

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• its integrated vision for sustainable mobility and spatial growth—‘By developing the SUMP with a spatial approach it has enabled the city region to maintain regional, strategic and multi-modal oversight in transport planning and service delivery. It also enables cross-sector integration, particularly with land-use planning.’ • the organizational structure of the SUMP—it was created through a strong cooperation of local authorities, one umbrella authority (Greater Manchester Combined Authority—GMCA) and the public body responsible for co-ordinating transport services throughout Greater Manchester in North West England (Transport for Greater Manchester—TfGM), therefore it involved almost all necessary bodies. Furthermore, TfGM, as an organization that can support transport delivery across the region, guarantees direct and quick interventions’ implementation. ‘GM LTP/SUMP demonstrates an effective structure by which a SUMP can be prepared while ensuring shared organisational and political ownership across districts.’ • It builds on an exhaustive mobility and spatial planning dataset and tools, therefore traffic modelling processes are strictly followed which guarantees traffic demand forecasting reliability—‘GM SUMP Evidence Base and Information Gathering (traffic and mobility related data)’ • Consultation processes and a strong communication plan is followed in order to guarantee SUMP efficiency and mobility interventions acceptability • Innovation in local transport planning within a UK context is taken seriously into account—‘Greater Manchester demonstrates the benefits of a continual process of LTP preparation and the need to understand that the SUMP document is not the end’ (REFORM Interreg Europe (2014–2020), Department for Transport (2009), Transport for Greater Manchester (2016a), Transport for Greater Manchester (2016b), Greater Manchester Combined Authority (2016), Greater Manchester Combined Authority (2018), Greater Manchester Low Carbon Hub (2016–2020), Parliament of the United Kingdom (2002), Parliament of the United Kingdom (2009)). Summing up, GM SUMP represents a successful plan aiming at an integrated mobility and spatial growth for an area covering 10 districts: Bolton, Bury, Oldham, Rochdale, Salford, Stockport, Tameside, Trafford and Wigan. The plan took a holistic view of the necessary investments and interventions (connectivity improvement; trip durations decrease; linkage between jobs and housing, ‘first and last mile’ connections, sustainable mobility options) while set out objectives and the respective detailed implementation programme for achieving the desired goals and identified funding opportunities. At the current stage, GM SUMP has entered the implementation phase aiming to deliver the new era of mobility. For the record, Greater Manchester SUMP is the winner of the seventh SUMP Award (SUMP Award recognizes excellence in sustainable urban mobility planning) focusing on multimodality (which refers to the use of the most effective combination of different modes for conducting a trip).

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2.6.3

A Targeted Approach to Cycling Promotion; Lisbon, Portugal

Lisbon, the capital of Portugal and a city of more than half million of inhabitants, is making integrated efforts on promoting sustainable mobility and shifting travellers’ behaviour from car-dependent lifestyles to eco-friendly options. Bike promotion seems to be on the crux of city’s agenda; Lisbon’s residents are offered multimodal opportunities to carry on train their bikes for free, they have an extended bike network giving a high level of accessibility to bicyclists, around the city one can find many bike-sharing stations as well as parking spaces, the city organizes bike trips, free bicycle repair workshops and a special prize for people and organisations that actively supports and adopts bike in everyday life (European Mobility Week Winner for larger municipalities, 2018). In the middle of the COVID-19 crisis, the city ranks on the top EU cities along with Milan, London, Berlin and Paris that took the hidden opportunity to reshape streets by removing cars and giving space to bikes among other actions. Moreover, plans have been made for an extra 7750 secure bikes parking places and there is a ‘mobility fund’ to subsidize bicycle purchases (Forbes,com, 2020). Background Information Relative links: • Thessaloniki’s Intelligent Urban Mobility Management System; https:// www.mobithess.gr/ • OASTH—Organisation of Urban Transportation of Thessaloniki webpage; www.oasth.gr • THESi—The parking app for Thessaloniki city centre; https://www.thesi. gr/ • THESSBIKE—private provider of shared and rental bicycles in Greece; https://www.thessbike.gr/en/ • REFORM Interreg Europe 2014–2020 project; https://www. interregeurope.eu/reform • POLIS Network, Post-Lockdown Mobility Webinar report: Public transport and COVID-19: Getting the offer right. . . and safe!, https://www. polisnetwork.eu/news/post-lockdown-mobility-webinar-report-publictransport-and-covid-19-getting-the-offer-right-and-safe/

2.7

Self-Assessment: Exercises

2.7.1

Describe Main Mobility Problems in Your City and Propose Solutions

Please describe a mobility problem you can identify in your city and select a measure for alleviating the situation:

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Which is the current situation? Which are the mobility needs that should be covered? Which is (/are) the measure(s) you propose for enhancing mobility and accessibility? Argue on the solution(s). Is it cost-efficient? Where would you seek for money in order to implement the measure(s)? Who should cooperate in order to implement and monitor the measure?

Think that you are mayor/decision maker! Does the city you live in face urban mobility challenges? Or has it (recently) solve the major mobility problems? Please answer the above questions, add a link to a picture or video and then read other entries to learn what others think of.

2.7.2

TEST Your Knowledge of Sustainable Mobility

Is it right or wrong? RIGHT

WRONG

Urban transportation is not a major emissions’ generator Urban mobility decisions should be taken regardless citizens’ view The use of private cars should be encouraged through sustainable mobility planning Congestion causes social and environmental negative effects Awareness raising plays an important role in attracting travellers at Public Transport Modern approach on mobility planning is linked with road network enlargement Scenarios in SUMP cycle refer to different future situations as regards transport system operation Capacity building refers just to knowledge increase in staff members of relevant authorities and do not involve wider audience Engagement can be achieved via different marketing methods (or combination of methods)

2.7.3

Select the Right Answer

1. According to the United Nations during which decade did the world’s urban population overtake the rural population for the first time? • 1970–1980 • 1980–1990 • 1990–2000 • 2000–2010 2. How much of all CO2 emissions of road transport does urban mobility account for (approximately for 2010–—2020 decade)?

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• 20% • 30% • 40% • 50% How much does congestion cost in the EU (approximately for 2010–—2020 decade): • 100 million Euros • 1 billion Euros • 10 billion Euros • 100 billion Euros What is the role of gamification in mobility planning? • to collect mobility data • to incentivize people to use eco-friendly modes of transport • to promote a mobility intervention • all the above The concept of Sustainable Urban Mobility Plans has been officially set out by the European Commission: • in the Transport White Paper, ‘European transport policy for 2010: time to decide’ 2001 • via the European Commission launched the CIVITAS Initiative, 2002 • in the Urban Mobility Package, ‘Together towards competitive and resourceefficient urban mobility’, 2013 • in the ‘Roadmap to a Single European Transport Area—Towards a competitive and resource efficient transport system’, White Paper 2011 Car sharing • is not considered as a sustainable mobility measure • improves environmental performance • replaces 10 private cars on average • is cost ineffective for travellers Urban tolls • alleviate city centres from congestion • is a highly acceptable measure by the public • should apply to all vehicles; e.g. ambulances • should apply to all types of cities e.g. even to those not facing congestion related problems Accessibility • refer to specific groups of citizens • should be for all • of disabled people is not part of the modern approach on mobility • is not the primary objective of SUMPs Connectivity refers to: • bringing people closer, a notion encompassing both hard (infrastructural) and soft (i.e. ICT) measures • increasing the frequency of public transport

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• reducing the use of private cars • the movement of freight and people SUMP cycle supports planning under a cooperation scheme that do not involve: • citizens • competent authorities • private bodies/companies • none of the above Engagement procedure in mobility planning refers to the involvement of which of the following in the SUMP procedure: • public transport operators • local and regional authorities • all relevant stakeholders and citizens • citizens Which is the last step in mobility planning—SUMP • learn from the lessons and fine-tune/update • monitor and impact assessment • analyse the current situation • building future scenarios Which of the following is not a crucial principle of a successful SUMP • cooperation at a cross-sectorial level • citizens’ involvement • infrastructure focus • quality assurance Which of the following aspects is not directly linked to ‘smart mobility’? • connected transport systems • data collection / crowdsourcing • increased public transport capacity • data analytics and big data tools SUMP Awards (Awards for Sustainable Urban Mobility Planning) recognise: • local and regional authorities for excellence in sustainable urban mobility planning • national authorities for excellence in sustainable urban mobility planning • citizens’ involvement in sustainable urban mobility planning • international cooperation schemes development for improving mobility in cities

2.7.4

Sustainable Mobility Planning Refers to. . . (Multiple Choice) Citizens’ centred approach Private car use increase Priority to pedestrians and cyclists Private car speed increase (continued)

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Accessibility for specific groups of people Vulnerable groups mobility Seamless trips via Public Transport Information provision and services integration Dialogue just among authorities and top-down decisions

References Angel, S., Sheppard, S. C., & Civco, D. L. (2005). The dynamics of global urban expansion. Washington, DC: The World Bank. Bartholome, E., & Belward, A. S. (2005). GLC2000: a new approach to global land cover mapping from Earth observation data. International Journal of Remote Sensing, 26(9), 1959–1977. Bickerstaff, K., & Walker, G. (2001). Participatory local governance and transport planning. Environment and Planning, 33, 31–451. Booth, C., & Richardson, T. (2001). Placing the public in integrated transport planning. Transport Policy., 8, 141–149. Chatziathanasiou, M., Morfoulaki, M., Mpessa, K., Tsoli, L. (2020). A Regional Competence Centre for SUMPs in Central Macedonia, responding to the identified local needs. In 5th Conference on Sustainable Urban Mobility—CSUM2020 (virtual). CIVITAS. (2002). Cleaner and better transport in cities. Retrieved from https://civitas.eu/ Department for Transport. (2009). Guidance on local transport plans, Great Britain. Dobos, A., & Jenei, A. (2013). Citizen engagement as a learning experience. Procedia—Social and Behavioral Sciences, 93, 085–1089. e-smartec Interreg Europe 2014-2020 Project, https://www.interregeurope.eu/e-smartec/library/ European Commission, COM. (1990). 1990_0218_FIN, Green paper on the urban environment: communication from the commission to the council and parliament. Retrieved from https:// publications.europa.eu/en/publication-detail/-/publication/0e4b169c-91b8-4de0-9fedead286a4efb7/language-en European Commission, COM. (2001). 370 final, WHITE PAPER European transport policy for 2010: time to decide. Retrieved from https://ec.europa.eu/transport/sites/transport/files/themes/ strategies/doc/2001_white_paper/lb_com_2001_0370_en.pdf European Commission, COM. (2007). 551 final, GREEN PAPER Towards a new culture for urban mobility. Retrieved from https://eur-lex.europa.eu/legal-content/DE/TXT/? uri¼CELEX:52007SC1209. European Commission, COM. (2010). 2020, Communication from the Commission Europe 2020 A strategy for smart, sustainable and inclusive growth. Retrieved from http://ec.europa.eu/ eu2020/pdf/COMPLET%20EN%20BARROSO%20%20%20007%20-%20Europe%202020% 20-%20EN%20version.pdf European Commission, COM. (2011). 2011/0144 final, White Paper on transport, Roadmap to a Single European Transport Area—Towards a competitive and resource—efficient transport system. Retrieved from https://eur-lex.europa.eu/legal-content/EN/ALL/?uri¼celex% 3A52011DC0144 European Commission, COM. (2013). 913 final, COMMUNICATION from the Commission to the European Parliament, the Council, the European Economic and Social Committee and the Committee of the Regions; Together towards competitive and resource-efficient urban mobility. Retrieved from https://ec.europa.eu/transport/sites/transport/files/themes/urban/doc/ump/com (2013)913_en.pdf European Commission, COM. (2019). 640 final, COMMUNICATION from the Commission to the European Parliament, the Council, the European Economic and Social Committee and the

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Committee of the Regions; The European Green Deal. Retrieved from https://ec.europa.eu/info/ sites/info/files/european-green-deal-communication_en.pdf European Mobility Week. (2018). Retrieved from http://www.mobilityweek.eu/the-campaign/ European Parliamentary Research Service (EPRS). (2017). Understanding capacity-building/ capacity development, Briefing. Retrieved from https://www.europarl.europa.eu/RegData/ etudes/BRIE/2017/599411/EPRS_BRI(2017)599411_EN.pdf Eurostat Regional Yearbook. (2017). ISBN: 978–92–79-71617-1, ISSN: 1830–9674, https://doi. org/10.2785/568258, Eurostat cat.: KS-HA-17-001-EN-C). Forbes.com. (2020). Forbes article ‘Lisbon Latest City To Rein Back Car Use With 34 Miles Of Pop-Up Cycleways Installed By September’, Carlton Reid. Retrieved from https://www.forbes. com/sites/carltonreid/2020/06/04/lisbon-latest-city-to-rein-back-car-use-with-34-miles-of-popup-cycleways-installed-by-september/#53e8a92cef4f Gamba, P., & Herold, M. (2009). Global mapping of human settlement: Experiences, datasets and prospects. Boca Raton, FL: CRC. Gaventa, J., & Barrett, G. (2012). Mapping the outcomes of citizen engagement. World Development, 40(12), 399–2410. Greater Manchester Combined Authority. (2016). Transport for Greater Manchester. Greater Manchester Air Quality Action Plan 2016–2021. Greater Manchester Combined Authority. (2018). Greater Manchester Transport Strategy 2040— Vision. Retrieved from https://assets.ctfassets.net/nv7y93idf4jq/Ykfjd8IKMCe4cYuyy2i6y/ 89f015d16abcfb2595630ceb1be9d99e/14-1882_GM_Transport_Vision_2040.pdf Greater Manchester Low Carbon Hub. (2016–2020). Climate Change and Low Emission Strategies’ Whole Place Implementation Plan for Greater Manchester. Retrieved from https:// www.greatermanchester-ca.gov.uk/media/1273/climate-change-and-low-emisson-implementa tion-plan.pdf Grimm, N. B., Faeth, S. H., Golubiewski, N. E., Redman, C. L., Wu, J., Bai, X., & Briggs, J. M. (2008). Global change and the ecology of cities. Science, 319(5864), 756–760. Kolb, D. A., Boyatzis, R. E., & Mainemelis, C. (2001). Experiential learning theory: Previous research and new directions. Perspectives on thinking, learning, and cognitive styles, 1(8), 227–247. Leiner, V.. (2014, March). European Commission 2013—Urban Mobility Package. Study Tour Graz. Retrieved from http://civitas.eu/sites/default/files/urban_mobility_package_ec.pdf Loveland, T. R., Reed, B. C., Brown, J. F., Ohlen, D. O., Zhu, Z., Yang, L., & Merchant, J. W. (2000). Development of a global land cover characteristics database and IGBP DISCover from 1 km AVHRR data. International Journal of Remote Sensing, 21(6–7), 1303–1330. Marvin, J. (1992). Towards sustainable urban environments: the potential for least-cost planning approaches. Journal of Environmental Planning and Management, 35(2), 193–200. https://doi. org/10.1080/09640569208711920. Misra A., Gooze A., Watkins K., Asad M., & Le Dantec C. A. (2013). Crowdsourcing and its application to transportation data collection and management. In TRB 2014 Annual Compendium of Papers. Georgia Institute of Technology. MOTIVATE Interreg MED 2014–2020, https://motivate.interreg-med.eu/. Myrovali G., Tsaples G., Morfoulaki M., Aifadopoulou G., & Papathanasiou J. (2018). An interactive learning environment based on system dynamics methodology for sustainable mobility challenges communication & citizens’ engagement. In Paper submitted in ICDSST 2018. Pahl-Wostl, C., & Tàbara, J. D. (2007). Sustainability Learning in Natural Resource Use and Management. Ecology and Society, 12. https://doi.org/10.5751/ES-02063-120203 Parliament of the United Kingdom. (2002). The Transport Act 2000 (Commencement No. 9 and Transitional Provisions) Order. Parliament of the United Kingdom. (2009). The Local Transport Act 2008 (Commencement No. 1 and Transitional Provisions) Order. REFORM Interreg Europe (2014–2020), https://www.interregeurope.eu/reform/library

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Rupprecht Consult (editor). (2019). Guidelines for developing and implementing a sustainable urban mobility plan (2nd ed.) Retrieved from https://www.eltis.org/sites/default/files/sump_ guidelines_2019_interactive_document_1.pdf. Schneider, A., Friedl, M. A., Mclver, D. K., & Woodcock, C. E. (2003). Mapping urban areas by fusing multiple sources of coarse resolution remotely sensed data. Photogramm Eng Remote Sens, 69(12), 1377–1386. Schneider, A., Friedl, M. A., & Potere, D. (2009). A new map of global urban extent from MODIS satellite data. Environmental Research Letters, 4, 044003. Slotterback, C. S. (2010). Public involvement in transportation project planning and design. Journal of Architectural and Planning Research, 27, 144. Transport for Greater Manchester. (2016a). Greater manchester transport strategy 2040. Retrieved from https://downloads.ctfassets.net/nv7y93idf4jq/7FiejTsJ68eaa8wQw8MiWw/ bc4f3a45f6685148eba2acb618c2424f/03._GM_2040_TS_Full.pdf Transport for Greater Manchester. (2016b). Greater manchester transport strategy 2040 Delivery Plan 1: 2016/17–2021/22. Retrieved from https://assets.ctfassets.net/nv7y93idf4jq/ 1KAoqZcSdqcma8c0OumkmA/881659659ac10db8f0a7a6ea4359dc1a/05._GM_2040_TS_ Delivery_Plan.pdf United Nations. (2015). Sustainable development goals. Retrieved from https://www.un.org/ sustainabledevelopment/development-agenda/ United Nations, Department of Economic and Social Affairs. (2018). Revision of world urbanization prospects. Retrieved from https://population.un.org/wup/Publications/Files/WUP2018Report.pdf Urban Agenda for the EU. (2018). Partnership on innovative and responsible public procurement final draft action plan. Retrieved from http://www.astrid-online.it/static/upload/2187/ 218785fbfc2c037492650a69a62068b1.pdf

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Decision Making in the Context of Sustainability Georgios Tsaples, Jason Papathanasiou, and Panagiota Digkoglou

3.1

Problem Solving and Decision Making

Problem solving can be defined as a process of recognising a gap between the state of a system as it is and as it is desired to be and taking the necessary measures to make the gap smaller. Any problem-solving process involves the following steps: • • • • • • •

Identify and define the problem Determine the set of alternative actions Determine the criteria that will be used for the evaluation of the alternative actions Perform the evaluation of the alternatives Make the choice Implement the chosen alternative Evaluate the impact of the chosen alternative to the perceived gap (Anderson, Sweeney, Williams, Camm, & Cochran, 2018).

Decision making is usually associated with the first step of the problem-solving process. Furthermore, problems can be quite complex, thus a decision-maker can require the deployment of a quantitative method to reach a decision. The reasons for using a quantitative method are that quantitative analysis can assist in defining the boundaries of the problem, provide a clear picture of what the purpose is, then provide a clear structure of how it can be solved (and use the same structure to solve similar problems in the future) and finally offer the decision-maker an alternative/ solution that can be easily justified. To achieve this objective, mathematical models are used. Models are abstract representations of reality (or a situation, an object etc). The value of a mathematical G. Tsaples (*) · J. Papathanasiou · P. Digkoglou Department of Business Administration, University of Macedonia, Thessaloniki, Greece e-mail: [email protected] # The Author(s), under exclusive license to Springer Nature Switzerland AG 2021 J. Papathanasiou et al. (eds.), Urban Sustainability, Springer Texts in Business and Economics, https://doi.org/10.1007/978-3-030-67016-0_3

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model is that it can provide insights into the real world and assist in solving the problem. The present output will focus on decision aid models and more specifically: • Data Envelopment Analysis (DEA): A method that allows a decision-maker to evaluate the performance of units (factories, vehicles, bureaus etc.) • Multiple Criteria Decision Aid (MCDA): A class of methods that help a decisionmaker to rank a set of alternative actions based on several criteria.

3.2

Data Envelopment Analysis

Data Envelopment Analysis (DEA) is a non-parametric,1 mathematical programming technique that is used for the evaluation of the technical efficiency of Decision Making Units (DMUs)2 relative to one another. DEA’s foundations lie in the works of Debreu (1951), Diewert (1973) and Farrell (1957) and originated in the seminal papers of Charnes, Cooper, and Rhodes (1978) and Banker, Charnes, and Cooper (1984). Its original inception was to be used as a general-purpose evaluation method for a firm’s efficiency, when multiple criteria are involved. The definition of a DMU’s efficiency originates in the engineering disciplines and is defined as the ratio of the sum of its weighted outputs divided by the sum of its weighted inputs (Ishizaka & Nemery, 2013). P woutput  y technical efficiency ¼ P , woutput  x ¼ output level

where

x ¼ input level and y ð3:1Þ

In other words, technical efficiency is a measure of how well a DMU can transform its inputs into outputs. For example, in the context of urban metabolism, technical efficiency can be considered how well a city (as a DMU) can transform material and energy (inputs that are necessary for everything to function) to quality services to its citizens (outputs). In the context of transportation, the technical efficiency of a public transportation organization can be considered how well the vehicles and drivers it employs (inputs) are translated into passengers serviced by the particular public transportation mode. The definition of technical efficiency allows the comparative assessment of DMUs in situations where price information is not available; where for example we could compare how much it costs to manufacture something versus how much 1 By non-parametric it is meant that there are no assumptions as to the forms of the parameters, the form of the frequency distributions etc. 2 DMUs are entities such as people, firms, organizations (public and private) that convert multiple inputs to multiple outputs. In DEA the DMUs are considered homogeneous in terms of identical inputs and outputs.

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are the revenues from its sale. As a result, DEA is considered a suitable method for aggregating economic, social and environmental factors with different units with the purpose of constructing a multi-dimensional indicator of efficiency. This indicator can help a decision-maker to comprehend better which DMUs under his control are efficient, where there is room for improvement and how this improvement can be achieved (Ishizaka & Nemery, 2013). Furthermore, DEA has several advantages over other parametric methods of performance assessment: • It does not require the identification of the relationship between inputs and outputs; we are not concerned what is the process that transforms inputs to outputs. In the above example, we are only studying how well material and energy are transformed into services, but we do not examine what is relationship between those two (how energy is transformed to fuel for a car, that transports people, to go to their job, to gain a salary and thus make a living and have a good quality of life). • It requires less information than traditional methods • It can indicate the reasons for which a unit is inefficient and propose plans to improve it (He, Wan, Feng, Ai, & Wang, 2016; Shi, Bi, & Wang, 2010).

3.2.1

Formulation of a DEA Problem

A DEA problem is defined as follows: Suppose there are N homogeneous DMUs, where each DMU uses m inputs to produce s outputs. This is the typical formulation and what the DEA methods tries to achieve is identify the most efficient DMU by maximizing each one’s individual efficiency. To do so, each DMU’s efficiency is calculated relative to an efficient frontier. DMUs located at the efficiency frontier has an efficiency score of 1 (or 100%), while those under the frontier have an efficiency of less than 1 (or 100%), hence they have the capacity to improve (Ishizaka & Nemery, 2013).

3.2.2

Basic DEA Models

There are two basic models of DEA. The first one was proposed by Charnes et al. (1978) and it assumes that we have Constant Returns to Scale (CRS). This model is appropriate when all DMUs are operating at an optimal size, which further assumes that they are operating in a perfectly competitive environment. These assumptions are considered rather ambitious and for that reason Banker et al. (1984) extended the original model to one which assumes Variable Returns to Scale (VRS). This model is appropriate when all DMUs are not operating at an optimal size, usually because of imperfect competition, government regulations etc.

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Returns to scale are terms that describe what happens in the production when all inputs change by a factor of a, where a > 0. There are three interrelated and sequential laws of return to scale: • Constant Returns to Scale: When output increases by the same proportional change as all inputs change. • Increasing returns to scale: In this situation, the average inputs consumption decreases while output rises. For example, a change in the output of 10% results in change in inputs of less than 10%. For a DMU, operating in increasing returns to scale and wishing to improve, it means an expansion of output is necessary. • Decreasing returns to scale: In this situation, the average inputs consumption increases while output rises. For example, a change in output of 10% results in change in inputs of more than 10%. When a DMU is operating under decreasing returns to scale and wishing to improve, it means a reduction of output. For a more mathematical definition of returns to scale in DEA, let’s assume that a DMU is using x inputs to produce y outputs. If the inputs are scaled by a: a*x, a > 0, then the outputs could vary by b: b*y. Furthermore, let’s assume:  p ¼ lim

a!1

b1 a1

 ð3:2Þ

Then: • If p > 1 the DMU is operating under increasing returns to scale • If p ¼ 1 the DMU is operating under constant returns to scale • If p < 1 the DMU is operating under decreasing returns to scale (Thanassoulis, 2001).

3.2.3

Input or Output Orientation

A DEA model can be input or output oriented: • Under input orientation, DEA minimizes input for a given output; in other words, it indicates how much a DMU should reduce the level of its inputs for the given level of outputs • Under output orientation, DEA maximizes output for a given input; in other words, it indicates how much a DMU should increase its level of outputs for the given level of inputs. Which orientation should be chosen depends on the variables that the decisionmaker has control over. For example, in transportation systems, a central authority (ministry, mayor etc.) has control over how many vehicles of public transportation will be available in a region, how many drivers, how much the individual ticket will

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Fig. 3.1 CRS frontier for the evaluation of DMUS (Ishizaka & Nemery, 2013)

cost etc. In other words, the decision-maker has control over the inputs and not over the outputs (how many passengers will use the specific mean of public transportation). In such a case, the DEA problem should be solved under input orientation. In the model of Charnes et al. (1978) it was assumed that we operate under Constant Returns to Scale (CRS). An example of a CRS frontier is given in the Fig. 3.1 below. In the model by Banker et al. (1984) it was assumed that we operate under Variable Returns to Scale (VRS). An example of a VRS frontier is illustrated in Fig. 3.2 below.

3.2.3.1 Example Background Information Worker placement board games are a category of games, where each player is required to select individual actions from a set of actions available to all players. These actions are represented by figures, tokens, etc. There is usually a limit in the number of tokens (workers) that a player can place in a single turn in specific places (https://boardgamegeek.com/boardgamemechanic/2082/ worker-placement). Famous worker placement games are: • Agricola (https://boardgamegeek.com/boardgame/31260/agricola) • The pillars of the Earth (https://boardgamegeek.com/boardgame/24480/ pillars-earth) (continued)

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Fig. 3.2 VRS frontier for the evaluation of DMUs (Ishizaka & Nemery, 2013)

• Lords of Waterdeep (https://boardgamegeek.com/boardgame/110327/ lords-waterdeep) For a detailed introduction and history of worker placement games please check the link below: https://www.youtube.com/watch?v¼4E6al0Lx80M Let us assume that in a potential board game there are 5 mines in which each player must place some workers. Looking at the Fig. 3.3 above (which corresponds to one player), the mines are represented by the nodes labeled A to E. The x-axis represents the number of workers (input) and the y-axis the kilograms (kg) of precious metal that they excavated in each mine (output). For example, in mine A, 2 workers have been placed and the excavated 1 kg of precious metals, in mine B 3 workers have been placed that excavated 4 kg of precious metals etc. To evaluate the efficiency of each mine all that is necessary is to check its position with regards to the frontiers and contemplate on the nature of scales. To identify the nature of scale, focus on the slope of the VRS efficient frontiers (or the projection of VRS inefficient points). In Fig. 3.3 below, the projections of mines D and C in the VRS frontier are the points Dvrs-I and Cvrs-i. To calculate the slopes, the following equations are necessary: change in y yi  yo ¼ change in x xi  xo For example, the slope of Dvrs-I is:

ð3:3Þ

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Fig. 3.3 Slopes, nature of returns to scale and projections (Ishizaka & Nemery, 2013)

yi  yo 30 ¼ 1:1 ¼ 2:67  0 xi  xo

ð3:4Þ

While the slope of A on the VRS frontier is: yi  yo 10 ¼ 0:5 ¼ 20 xi  xo

ð3:5Þ

The two slopes indicate that the productivity of A is inferior to Dvrs-I. The ratio of productivity is increasing with scale (size). Therefore, mines A and D are facing decreasing returns to scale. On the other hand, the slopes of Cvrs-I and E are: slope C ¼

5 ¼ 1:25 4

ð3:6Þ

slope E ¼

7 ¼ 1:17 6

ð3:7Þ

The slopes indicate that the ratio of productivity is decreasing with scale (size). Therefore, mines C and E are facing decreasing returns to scale. A few more comments on the example: • Mine A is located on the VRS frontier, but not on the CRS frontier. This indicates that the efficiency of mine A is because of inappropriate scale. A is facing increasing returns to scale, thus it must increase its output.

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• Mine B is located both on the VRS and CRS frontiers, indicating that it is efficient. • Mine C is located neither on the VRS nor on the CRS frontier, indicating that its inefficiency occurs due to inappropriate scale and poor management. Mine C is facing decreasing returns to scale. • Mine D is located neither on the VRS nor on the CRS frontier, indicating that its inefficiency occurs due to inappropriate scale and poor management. Mine D is facing decreasing returns to scale. • Mine E is located on the VRS frontier but not on the CRS frontier, indicating that the inefficiency of mine E occurs because of inappropriate scale. Mine E is facing decreasing returns to scale, thus it must decrease its output.

3.2.4

Mathematical Modeling

So far, the basics notions of DEA have been described along with an approach to calculating efficiency by graphical means. However, this qualitative approach is limiting; if the number of DMUs increases or in a case of multi-input and multioutput the graphs cannot help to identify efficiency. For that reason, there is the need to define the DEA method mathematically. Data Envelopment Analysis is approached as a Linear Programming problem (Cooper, Seiford, & Zhu, 2004; Khezrimotlagh & Chen, 2018; Thanassoulis, 2001; Tone, 2017; Zhu, 2014). Let’s assume that there are N DMUs using m inputs to produce s outputs under Constant Returns to Scale (CRS). We denote xij(i ¼ 1. . .m, j ¼ 1. . .N ) the level/ value of the ith input of DMU j, and yrj (r ¼ 1. . .s, j ¼ 1. . .N ) the level/value of DMU. Then the calculation for the technical input efficiency can be found by solving: min Θ0  e

m X

S i

s X

þ

i¼1

! Sþ r

ð3:8Þ

i ¼ 1...m

ð3:9Þ

r¼1

subject to constraints: N X

λ j  xij ¼ Θ0  xijo  S i ,

j¼1 N X

λ j  yrj ¼ yrjo þ Sþ r ,

r ¼ 1...s

ð3:10Þ

 Sþ r , Si  0

ð3:11Þ

j¼1

λ j  0,

j ¼ 1 . . . N,

The technical input efficiency of DMUj0 is Θ0. The character e is a very small value. In practice the model is solved in two stages. In the first stage, Θ0 is minimized

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ignoring the slack3 variables Si and Sr+. Thus, we get the technical input efficiency for DMUj0, denoted as Θ0*. We substitute the value to the above model and re-solve it but this time with the objective of maximizing: m X i¼1

S i þ

s X

Sþ r

ð3:12Þ

r¼1

The second stage calculates an optimal set of slacks, while ensuring that we have the optimal technical input efficiency in the calculations. The results from solving the LP problem can be: • DMUj0 is Pareto-efficient4 if and only if Θ0* ¼ 1 and Si and Sr+ ¼ 0 for all inputs and outputs. • If one of the slack values is positive at the optimal solution of the model, it means that the corresponding input (or output) of DMUj0 can be further improved. • If none of the above applies, then DMUj0 has technical input efficiency equal to Θ0*.

3.2.4.1 Example of Assessing Metro Lines A company operates 4 metro lines transporting people. Table 3.1 summarizes the data for one quarter. The inputs are driver hours and operational costs while the output is the passengers serviced. Hence, if we wish to form the DEA model to measure technical input efficiency for Metro Line 1:    þ min Θ1  e S þ S þ S ð3:13Þ drivers Cost Passengers subject to constraints: Table 3.1 Metro line costs Metro line 1 2 3 4

3

Driver hours (100 h) 4.1 3.8 4.4 3.4

Operational costs (10,000 €) 2.3 2.4 2.0 3.4

Passengers (per 10,000) 90 100 95 120

In Linear Optimization, a slack variable is one that is added to an inequality constraint to transform it into an equality. In the solution of the problem, if a slack variable is zero then the constraint is binding, if a slack variable is positive the constraint is non-binding and if a slack variable is negative then the constraint is violated, and the solution is not feasible. 4 If a solution is Pareto efficient, no changes can be made in one criterion without worsening any other.

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4:1  Θ1  S drivers ¼ 4:1  λ1 þ 3:8  λ2 þ 4:4  λ3 þ 3:4  λ4

ð3:14Þ

2:3  Θ1  S Costs ¼ 2:3  λ1 þ 2:4  λ2 þ 2:0  λ3 þ 3:4  λ4

ð3:15Þ

90 þ Sþ Passengers ¼ 90  λ1 þ 100  λ2 þ 95  λ3 þ 120  λ4 λ j  0,

j ¼ 1, 2, 3, 4

ð3:16Þ ð3:17Þ

The mathematical model and the example are focused on assessing the technical input efficiency of a DMU under Constant Returns to Scale. For the technical output efficiency of a DMU under Constant Returns to Scale the following LP model needs to be solved [P.2]: max h0 þ e

m X

s X

S i þ

i¼1

! Sþ r

ð3:18Þ

r¼1

subject to constraints: N X

λ j  xij ¼ xijo  S i ,

i ¼ 1...m

ð3:19Þ

j¼1 N X

λ j  yrj ¼ h0  yrjo þ Sþ r ,

r ¼ 1...s

ð3:20Þ

j¼1

λ j  0,

j ¼ 1 . . . N,

 Sþ r , Si  0

ð3:21Þ

The technical output efficiency of DMUjo is: 1 h0

ð3:22Þ

The slack variables of Si and Sr+ represent any additional increase and/or input reductions that are feasible. The results from solving the LP problem can be: • DMUjo is Pareto-efficient if ho* ¼ 1 and Si and Sr+ ¼ 0 • DMUjo has technical output efficiency of 1/ho* Finally, it should be noted that technical input efficiency and technical output efficiency of a DMU under Constant Returns to Scale are equal: Θ0 ¼

1 h0

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3.2.4.2 Example of Assessing Metro Lines (Continued) From the previous example, if we wish to formulate the LP model to assessing the technical output efficiency of Line 1:    þ max h1 þ e S drivers þ SCost þ SPassengers

ð3:23Þ

4:1  S drivers ¼ 4:1  λ1 þ 3:8  λ2 þ 4:4  λ3 þ 3:4  λ4

ð3:24Þ

2:3  S Costs ¼ 2:3  λ1 þ 2:4  λ2 þ 2:0  λ3 þ 3:4  λ4

ð3:25Þ

90  h1 þ Sþ Passengers ¼ 90  λ1 þ 100  λ2 þ 95  λ3 þ 120  λ4

ð3:26Þ

subject to constraints:

λ j  0,

3.2.5

j ¼ 1, 2, 3, 4

ð3:27Þ

DEA Under Variable Returns to Scale

The evaluations that were performed thus far, assumed that the DMUs operated under Constant Returns to Scale. However, as it was seen before, this may be an optimistic assumption. As a result, we need to calculate the efficiency under Variable Returns to Scale. The VRS DEA model does not differ much from their CRS counterparts. For assessing the technical input efficiency of a DMU under Variable Returns to Scale (VRS), the following LP problem needs to be solved [P.3]: min Θ0  e

m X

S i

þ

i¼1

s X

! Sþ r

ð3:28Þ

i ¼ 1...m

ð3:29Þ

r¼1

subject to constraints: N X

λ j  xij ¼ Θ0  xijo  S i ,

j¼1 N X

λ j  yrj ¼ yrjo þ Sþ r ,

r ¼ 1...s

ð3:30Þ

j¼1 N X j¼1

λj ¼ 1

ð3:31Þ

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Fig. 3.4 Data to formulate the DEA model

Fig. 3.5 Definition of objective function and DMU under evaluation

λ j  0,

j ¼ 1 . . . N,

 Sþ r , Si  0

ð3:32Þ

The only difference is that another constraint was added, the one which the sum of the λ parameters equals to 1. This means that the efficiency frontier does not take the form of Fig. 3.3, but that of the piecewise linear form of Figs. 3.4 and 3.5. The results of the P.3 model can yield: • DMU is Pareto-efficient if Θ0* is equal to 1 and the slack variables Si and Sr+ are equal to zero. The technical efficiency of a DMU under Variable Returns to Scale is called pure technical efficiency, to separate it from its CRS counterpart. • DMU has pure technical efficiency of Θ0*. • The pure technical efficiency of a DMU cannot be less than its technical input efficiency (Thanassoulis, 2001).

3.2.5.1 Example of Assessing Metro Lines A company operates 4 metro lines transporting people. Table 3.2 summarizes the data for one quarter. 1. Define the inputs and the outputs of the DEA model 2. Write the LP model to calculate the pure technical input efficiency of Metro Line 3

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Table 3.2 Metro line data Metro line 1 2 3 4

Driver hours (100 h) 4.1 3.8 4.4 3.4

Operational costs (10,000 €) 2.3 2.4 2.0 3.4

Passengers (per 10,000) 90 100 95 120

Similarly, to the CRS DEA models, apart from the pure technical input efficiency, the technical output efficiency can be calculated by solving the following LP model [P.4]: max h0 þ e

m X

s X

S i þ

i¼1

! Sþ r

ð3:33Þ

r¼1

subject to constraints: N X

λ j  xij ¼ xijo  S i ,

i ¼ 1...m

ð3:34Þ

j¼1 N X

λ j  yrj ¼ h0  yrjo þ Sþ r ,

r ¼ 1...s

ð3:35Þ

j¼1 N X

λj ¼ 1

ð3:36Þ

j¼1

λ j  0,

j ¼ 1 . . . N,

 Sþ r , Si  0

ð3:37Þ

The technical output efficiency of DMUjo is 1 h0

ð3:38Þ

The P.4 model can yield the following results: • DMU is Pareto-efficient if h0* ¼ 1 and the slack variables Si and Sr+ are equal to zero. • DMU has pure technical output efficiency of 1 h0

• The pure technical output efficiency cannot be less than the technical output efficiency of model P.2 (Thanassoulis, 2001).

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3.2.6

How to Solve a DEA Model with Excel

Thus far the theory, notations and how to formulate a DEA model under Constant and Variables Returns to Scale have been explained. In this section, there will be a presentation on how to solve the particular models using Excel. As an example, the example of the Metro Lines will be used. The data for the 4 metro lines are as in Table 3.3. The first thing is to formulate the LP model in Excel; thus we transfer the above table to an Excel Spreadsheet like the picture (Fig. 3.4) below. Following, we define a column for the λ values (blue circle) for which we need to set the initial values. Furthermore, we define the objective function (red circle) and the DMU under evaluation (green circle, Fig. 3.5). In the next step we formulate the constraints according to Fig. 3.6 below. In the respective cells we set: • • • •

Cell B9: ¼SUMPRODUCT(B2:B5,$E$2:$E$5) Cell B10: ¼SUMPRODUCT(C2:C5,$E$2:$E$5) Cell B11: ¼SUMPRODUCT(D2:D5,$E$2:$E$5) Cell B12: ¼SUM(E2:E5) Then, we define the right-hand sides of the constraints as following:

• Cell D9: ¼$H$8*INDEX(B2:B5,G7,1) • Cell D10:¼$H$8*INDEX(C2:C5,G7,1) Table 3.3 Problem data Metro line 1 2 3 4

Driver hours (100 h) 4.1 3.8 4.4 3.4

Fig. 3.6 Definition of constraints

Operational costs (10,000 €) 2.3 2.4 2.0 3.4

Passengers (per 10,000) 90 100 95 120

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• Cell D11:¼INDEX(D2:D5,G7,1) • Cell D12: ¼1 Finally, to solve the model we need to use Excel’s solver add-in. To Do so: 1. 2. 3. 4. 5.

Click the File tab and then click Options Click Add-ins and the in the Manage box select the Excel Add-ins option Click Go In the Add-ins available, select the Solver Add-in and then OK. Go to Data tab and select the Solver option.

The following window will pop-up (Fig. 3.7). After you have copied the values above, press Solve. The solution to the model is displayed in cell H8.

Fig. 3.7 Solver parameters setting

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Multiple Criteria Decision Aid

Multiple Criteria Decision Aid (MCDA) is an evolving area of operations research that aims to provide the decision-maker with the tools necessary in solving complex decision problems, where often contradictory and competitive aspects should be considered. The main objective of MCDA and a common element of all methodological approaches in this domain is the development and use of synthesis patterns of all the basic parameters of a problem so as to provide support in making rational decisions on the basis of its system of values and preferences (Zopounidis & Pardalos, 2010). Multiple criteria aid approaches differ in the way they combine the elements of the problem in hand. Formal multiple criteria aid techniques usually provide a specific system of relative gravity for different criteria. The key role of these techniques is to address the difficulties that decision-makers seem to have to handle consistently and logically large numbers of complex information. They can be used to determine the preferred option, to rank options, to list a limited number of options, to follow a detailed evaluation, or simply to separate acceptable and unacceptable possibilities. Most decisions that decision-makers need to take are characterized by conflicting criteria and more than one alternative, thus it is necessary to provide a clear structure of the problem before arriving to its solution. MCDA methods use a series of steps that clarify the elements and boundaries of the problem (Belton & Stewart, 2002) and consist of a mathematical model that ranks the alternatives. MCDA methods are assumed to provide clear, rational and easily justifiable explanations, can deal with quantitative and qualitative data and can involve the preferences of more than one decision-maker (Pohekar & Ramachandran, 2004). There is a large variety of MCDA methods and in the following sections two of the most popular methods will be described in detail.

3.3.1

The PROMETHEE Method

3.3.1.1 Introduction PROMETHEE is an acronym that stands for Preference Ranking Organization METHod for Enrichment of Evaluations. PROMETHEE expresses the method of organizing the preference ranking for an enhanced assessment of a problem. Thus, the PROMETHEE method provides a decision, either with a single choice or with alternatives, based on preference grades among the available options (Ishizaka & Nemery, 2013). The method was developed by Brans et al. (Brans, 1982; Brans & Mareschal, 1994; Brans, Vincke, & Mareschal, 1986) and it belongs to the outranking family of MCDA methods. The method is considered popular and is used in a variety of research areas (Vicke & Brans, 1985; Brans & Mareschal 1992). A report from June of 2018 states the total number of references mentioning any of the variations of PROMETHEE to 1827 papers. In the following two tables (Tables 3.4 and 3.5), the scientific areas where PROMETHEE is more popular, and also how its popularity has grown the last years is presented in Table 3.4 (Mareschal, 2018).

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Table 3.4 Delivery of papers on PROMETHEE by application areas (Mareschal, 2018)

Area Environment management Services and/or public applications Industrial applications Energy management Water management Finance Transportaion Procurement Other topics

97

N 366 343 278 165 121 107 81 61 78

% 20 18.80 15.20 9 6.60 5.90 4.40 3.30 4.20

Table 3.5 Delivery of papers on PROMETHEE by countries (Mareschal, 2018) Date Papers #Countries %Europe Authors

23/11/2015 1236 63 48.10% 2138

12/03/2016 1317 65 48.10% 2272

03/12/2016 1469 68 46.60% 2564

19/03/2017 1526 70 46.40% 2681

26/06/2018 1827 75 44.90% 3256

3.3.1.2 Methodology The method is characterized by the following steps: 1. Construct the decision matrix with all the alternatives, criteria, preference parameters and weights. 2. Calculate the differences between the evaluations of the actions for each criterion. 3. Construct the pairwise comparison matrix for each criterion. 4. Calculate the unicriterion net flows. 5. Calculate the weighted unicriterion flows. 6. Calculate the global preference net flows. 7. Rank the actions according to PROMETHEE I or II (Papathanasiou & Ploskas, 2018a). It is quite important to state that the decision-maker can use both qualitative and quantitative criteria. The final ranking of the alternatives is the result of the global flows. The PROMETHEE method is known as one of the simplest multicriteria methods. There are some visual tools, which make every problem more understandable to the decision-makers. In addition, there are sensitivity analysis tools to analyze the robustness of the final ranking of the alternatives. Furthermore, some software tools are available for free, contributing to the extended use of the method (Ishizaka & Nemery, 2013). In the following sections, several important aspects of the method will be clarified, while a numerical, step-by-step example will be presented at the end of the section.

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Unicriterion Preference Degrees The PROMETHEE method is based on the calculation of preference degrees. Preference degrees range between 0 and 1, expressing whether one option is more preferred than another according to a decision-maker’s attitudes. Preference degree 1 means a strong or total preference for an action with respect to the specific criterion. If there is no preference, then 0 is the value chosen. For an intermediate state, i.e. when there is a preference but not strong enough, then a value between 0 and 1 is selected. We use the term pairwise preference degree, since the preference of action A over action B cannot be derived from the preference of action B on action A (and vice versa). The PROMETHEE method will help the decision-maker evaluate these unicriterion pairs of preference degrees. For each criterion, the degree of unicriterion preference is calculated by adjusting or enriching the assessments of the actions through additional preference information. In multiple criteria decision making an important point is how the decision-maker takes into consideration the differences between ratings of the alternatives (or actions) for every specific criterion. This also plays an important role in PROM ETHEE. These comparison pairs are based on the difference between the evaluations of the two actions (i.e. the price difference between the actions). Therefore, the decision-maker can choose between six types of preference functions: Usual, U-shape, V-shape, Level, V-shape with indifference (also known as linear function) and Gaussian function. These preference functions are shown in Fig. 3.8 with their definitions and parameters. It is very important for every decision-maker to understand how these functions interact with the data, in order to choose the most appropriate for every problem. For instance, if the decision-maker opts for a linear function (type 5), preferences will gradually increase as a function of the difference between the ratings on a particular criterion. When using the Gaussian preference function (type 6), the increase follows an exponential function (Ishizaka & Nemery, 2013). For setting each preference mode, one or two parameters are required. The linear preference function requires two parameters: an indifference threshold q and a preference threshold p. On the other hand, the Gaussian function requires only one parameter: the inflexion point s. If the difference between evaluations of a criterion is less than the threshold of indifference q, then no discrepancy between these two actions can be perceived by the decision-maker (i.e. the degree of preference is 0). If the difference is higher than the preference threshold p, then the preference is strong (i.e., the degree of preference is 1). The preference function gives the value of the preference for the differences between the indifference threshold q and the preference threshold p. It is worth mentioning, that the PROMETHEE method can use not only numerical values, but can easily handle scales (e.g. good, excellent, etc.) and verbal values that can be translated to numerical ones.

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Fig. 3.8 Types of generalized criteria (Brans & Mareschal, 2005)

Unicriterion Positive, Negative and Net Flows The decision-maker needs specific information in order to draw conclusions for the problem under consideration and it is not an easy procedure to understand the ranking of the alternatives through a table with preference degrees or from the graphical representation of the table, especially when the number of actions is large. Therefore, the so-called positive flows, negative flows and net flows are calculated. They measure the way in which any action is preferred over all other actions or the way all the other actions are preferred to it.

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Positive flows: The unicriterion positive flow has values between 0 and 1. These values indicate how an action is preferred over all other actions on that criterion. The higher the positive flow, the more preferred the action to all the other actions. Negative flows: On the other hand, negative flows represent how other actions are preferred against the chosen action. The values also vary between 0 and 1. The lower the negative flow, the more preferred the action over all the other actions is. Net flows: In the final stage both positive and negative flows are taken into consideration. So, the net flows are calculated by subtracting the negative flows from the positive flows. The values for the net flows range between 1 and 1. The net flows need to be maximized and the reason why is that they express the balance between the global strength and the global weakness of every action (Brans et al., 1986; Brans & Mareschal, 2005). Global flows: In the above section, only one criterion at a time was examined. However, PROMETHEE needs to take into consideration all the criteria together. For that, the relative importance of each criterion needs to be provided by the decision-maker. For example, a car’s fuel consumption may be twice more important than its price. On the other hand, price might be more important than the power. In other words, there is a need for the decision-maker to determine a weight for each criterion that allows the aggregation (through a weighted sum) of all positive, negative and net flows of unicriterion to global positive flows, global negative flows and global net flows, taking into account all the criteria at once. The overall positive score indicates how an action is preferred globally to all other actions when considering all the different criteria. Since weights have been normalized, the global positive score is always between 0 and 1. The higher the value, the most preferred the action is. Accordingly, global negative scores indicate how an action is not preferred when compared with all the other actions. The negative score is always between 0 and 1 the lower the value the more the alternative is preferred over the others. The net global flows are calculated by subtracting the global negative flows from the global positive flows.

3.3.1.3 The PROMETHEE I Partial Ranking There are different variations of the PROMETHEE method. PROMETHEE I offers a partial ranking of the alternatives based on the positive and negative flows. When the flows of two actions need analysis, four different scenarios can be formed: • An action is better ranked by the other if the overall positive and negative flows are at the same time better (i.e. if the overall positive score is higher and the global negative flow is lower). • An action has a worse score than another, when positive and negative scores are worse at a global level. • Two actions are said to be incomparable if an action has a better global positive flow but a worse global negative flow (or vice versa). • Two actions are called indifferent if they have identical (equal) positive and negative flows.

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3.3.1.4 The PROMETHEE II Complete Ranking PROMETHEE II’s complete ranking is based only on net flows and leads to a full ranking of all the actions (i.e. the incomparable situation does not exist). The actions are ranked from best to worst. 3.3.1.5 Gaia Plane The Gaia plane presents a decision problem in a two-dimensional figure, and it attempts to include all aspects of the decision problem: the actions, criteria and information about the decision-maker’s preferences (preference parameters and weights). The actions are represented by bullets and the criteria with arrows, and the position of the actions gives the decision-maker a first picture of the existing similarities, for example the closer the actions, the more similar they are. Similarity and non-similarity are determined by the limit of indifference and preference. This means that the Gaia plane depends on the preference information provided by the decision-maker. Accordingly, the relative position of the criteria indicates the correlation and anti-correlation (or conflict) of criteria. The closer the arrows are, the more important are the criteria for the problem. The greater the angle between the criteria, the greater the conflict between them. The Gaia plane allows the illustration of contradictory views. In addition, the length of the arrow, which represents a criterion, measures its ‘discriminating’ or ‘differentiating’ power as a function of the data. The more different the actions in a criterion, the greater the arrow and, therefore, the most distinctive criterion is. The discretionary power of a criterion depends on the selected limits and the corresponding weight. Finally, the arrow represented by the letter D, known as the decision stick, illustrates the compromise chosen by the decision-maker as it corresponds to the weight adjustment. The visibility of the actions on this line represents their priorities. The greater the visibility of an action on the stick, the better the action ranks. However, since it is only a two-dimensional representation, it can lead to a loss of information. The amount of information held, so-called delta or D, depends on the data and the number of criteria. As a consequence of the loss of information, the classification resulting from the projection of the decision stick does not necessarily have the exactly same results with PROMETHEE II (Ishizaka & Nemery, 2013). 3.3.1.6 Example: Case Study A big company of the clothing and accessories industry wants to build a new factory in order to strengthen its production activity. After excluding many options, the company has ended up with six final options, taking into account four important criteria: 1. 2. 3. 4.

the capital requirements (for the new investment), the available staff (for the new factory), the delivery distance (from the new factory to the customers) and the environmental impact.

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Table 3.6 Data for the numerical example

Weight Option 1 Option 2 Option 3 Option 4 Option 5 Option 6

Capital requirements (million €) 0.4 7 8 9 5 11 9

Available staff (hundred employees) 0.3 8 6 10 4 10 8

Delivery distance (hundred kilometers) 0.1 2 5 5 1 3 6

Environmental impact (1 to 7) 0.2 5 4 10 2 8 7

Table 3.7 Preference parameters of all the criteria Criterion Capital requirements (million €) Available staff (100 employees) Delivery distance (100 km) Environmental impact (1 to 7)

Function Usual Usual Usual Usual

wi 0.4 0.3 0.1 0.2

qi 0 0 0 0

pi 0 0 0 0

Table 3.8 Step 1: Differences between the evaluations of the Options on the criterion ‘Capital Requirements’ Difference Option 1 Option 2 Option 3 Option 4 Option 5 Option 6

Option 1 0 1 2 2 4 2

Option 2 1 0 1 3 3 1

Option 3 2 1 0 4 2 0

Option 4 2 3 4 0 6 4

Option 5 4 3 2 6 0 2

Option 6 2 1 0 4 2 0

The first three criteria are quantitative and the fourth is qualitative with values ranging from 1 (worst) to 7 (best). All the criteria need to be maximized. In Tables 3.6 and 3.7, we have all the data for the example. The weights are given hypothetically, and they are not derived with a specific procedure, but they represent the preferences of the hypothetical decision-maker.

Computation of the Unicriterion Net Flows and Next Steps In the following Tables 3.8, 3.9, 3.10, 3.11 and 3.12, the reader can study the steps of the methodology in detail. Using the headings from each table, every step of the procedure is depicted.

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Table 3.9 Step 1: Differences between the evaluations of the options on the criterion ‘Available Staff’ Difference Option 1 Option 2 Option 3 Option 4 Option 5 Option 6

Option 1 0 2 2 4 2 0

Option 2 2 0 4 2 4 2

Option 3 2 4 0 6 0 2

Option 4 4 2 6 0 6 4

Option 5 2 4 0 6 0 2

Option 6 0 2 2 4 2 0

Table 3.10 Step 1: Differences between the evaluations of the options on the criterion ‘Delivery Distance’ Difference Option 1 Option 2 Option 3 Option 4 Option 5 Option 6

Option 1 0 3 3 1 1 4

Option 2 3 0 0 4 2 1

Option 3 3 0 0 4 2 1

Option 4 1 4 4 0 2 5

Option 5 1 2 2 2 0 3

Option 6 4 1 1 5 3 0

Table 3.11 Step 1: Differences between the evaluations of the options on the criterion ‘Environmental Impact’ Difference Option 1 Option 2 Option 3 Option 4 Option 5 Option 6

Option 1 0 1 5 3 3 2

Option 2 1 0 6 2 4 3

Option 3 5 6 0 8 2 3

Option 4 3 2 8 0 6 5

Option 5 3 4 2 6 0 1

Option 6 2 3 3 5 1 0

Table 3.12 Step 2: Pairwise comparison matrix for the criterion ‘Capital Requirements’ Option 1 Option 2 Option 3 Option 4 Option 5 Option 6

Option 1 0 1 1 0 1 1

Option 2 0 0 1 0 1 1

Option 3 0 0 0 0 1 0

Option 4 1 1 1 0 1 1

Option 5 0 0 0 0 0 0

Option 6 0 0 0 0 1 0

Table 3.8 illustrates the differences on the various options on the criterion ‘Capital Requirements’. For example, the first row is: Option 1—Option 1 equals 0 since option 1 cannot be compared with itself. Option 1—Option 2 ¼ 7–8 ¼ 1;

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Fig. 3.9 PROMETHEE I Partial Ranking

Option 1—Option 3 ¼ 7–9 ¼ 2 etc. The other cells of the matrix are calculated similarly. Table 3.9 illustrates the differences of the options on the criterion Available staff. For example, the first row is: Option 1—Option 1 ¼ 8–8 ¼ 0, Option 1—Option 2 ¼ 8–6 ¼ 2, Option 1—Option 3 ¼ 8–10 ¼ 2 etc. The other cells in the matrix are calculated similarly. Tables 3.10 and 3.11 show the differences of the evaluations of the various options for the remaining two criteria. The next step in the method is to calculate the pairwise comparison matrix of the options for all the criteria. Table 3.12 shows the pairwise comparison matrix for the criterion ‘Capital Requirements’. From Table 3.7 it is known that the preference function for the criterion is the ‘Usual’. From Fig. 3.9, it can be observed that the ‘Usual’ function does not require the preference parameters p and q and relies only on the difference in the evaluations. Thus, the preference can be calculated by the function:

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Table 3.13 Step 2: Pairwise Comparison matrix for the criterion Available Staff Option 1 Option 2 Option 3 Option 4 Option 5 Option 6

Option 1 0 0 1 0 1 0

Option 2 1 0 1 0 1 1

Option 3 0 0 0 0 0 0

Option 4 1 1 1 0 1 1

Option 5 0 0 0 0 0 0

Option 6 0 0 1 0 1 0

Table 3.14 Step 2: Pairwise Comparison matrix for the criterion Delivery Distance Option 1 Option 2 Option 3 Option 4 Option 5 Option 6

Option 1 0 1 1 0 1 1

Option 2 0 0 0 0 0 1

 PðdÞ ¼

0, d  0 , 1, d > 0

Option 3 0 0 0 0 0 1

Option 4 1 1 1 0 1 1

Option 5 0 1 1 0 0 1

d is the difference in the evaluations

Option 6 0 0 0 0 0 0

ð3:39Þ

For example, for the first row in Table 3.8 the set of values is {0, 1, 2, 2, 4, 2}. Hence the pairwise preference comparison of Option 1 with all the other options (first row of Table 3.12) is {0,0,0,1,0,0}. Similar calculations are performed for the other options (rest of the rows in Table 3.12). In case that the preference function of the criterion was not the ‘Usual’, but the ‘V-shape’ (Fig. 3.9), then the preference pairwise comparison matrix would take its values from the function: 8 > >
> : 1, d > p

ð3:40Þ

For example, for option 1- compared with the other options (first row of Table 3.8), with hypothetical q ¼ 1, p ¼ 3, the values would become: (0, 0, 0.5, 0, 0.5). Tables 3.13, 3.14 and 3.15 depict the pairwise comparison matrices for the criteria ‘Available Staff’, ‘Delivery Distance’ and ‘Environmental Impact’ respectively. The next step consists of calculating the unicriterion net flows. For option 1 for example, the positive net outranking flow is calculated by summing row 1 of Table 3.12 and divide the result by the number of options minus 1, since option 1 cannot be compared with itself. As a result, the positive outranking flow for option 1 for the criterion ‘Capital Requirements’ is:

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Table 3.15 Step 2: Pairwise Comparison matrix for the criterion Environmental Impact Option 1 Option 2 Option 3 Option 4 Option 5 Option 6

Option 1 0 0 1 0 1 1

Option 2 1 0 1 0 1 1

Option 3 0 0 0 0 0 0

Option 4 1 1 1 0 1 1

Option 5 0 0 1 0 0 0

Option 6 0 0 1 0 1 0

Table 3.16 Computation of the Uniriterion Net Flows

Options Option 1 Option 2 Option 3 Option 4 Option 5 Option 6

Capital requirements Net Flows 0.6 0.2 0.4 1 1 0.4

Available staff Net Flows 0 0.6 0.8 1 0.8 0

Delivery distance Net Flows 0.6 0.4 0.4 1 0.2 1

0þ0þ0þ1þ0þ0 ¼ 0:2 61

Environment impact Net Flows 0.2 0.6 1 1 0.6 0.2

ð3:41Þ

The negative outranking flow for option 1 (for the criterion ‘Capital requirements’) is calculated by summing the elements of the first column of Table 3.12 and divide the result by the number of options minus 1. As a result, the negative outranking flow for option 1 for the criterion ‘Capital Requirements’ is: 0þ1þ1þ0þ1þ1 ¼ 0:8 61

ð3:42Þ

The net flow is calculated by simply subtracting the negative outranking from the positive one: 0.2–0.8 ¼ 0.6. The calculations for the other options are performed in a similar manner. Table 3.16 below illustrates the net flows for all the options for all the criteria. The next step consists of multiplying the unicriterion net flows with the respective weights for each criterion (Table 3.8). Finally, the total net flows are calculated by summing the elements of each row of Table 3.17. Table 3.18 provides a total ranking, thus PROMETHEE II is used. For a PRO METHEE I ranking we would have stopped at Table 3.6 because the positive and negative outranking flows are necessary. Table 3.18 informs that Option 5 ranks the best, while Option 4 appears to be the worst of the set.

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Table 3.17 Computation of the weighted unicriterion flows

Options Option 1 Option 2 Option 3 Option 4 Option 5 Option 6

Capital requirements Net Flows 0.24 0.08 0.16 0.4 0.4 0.16

Table 3.18 Computation of the total net flows

Available staff Net Flows 0 0.18 0.24 0.3 0.24 0

Options Option 1 Option 2 Option 3 Option 4 Option 5 Option 6

Delivery distance Net Flows 0.06 0.04 0.04 0.1 0.02 0.1

Environment impact Net Flows 0.04 0.12 0.2 0.2 0.12 0.04

Total Net Flows 0.34 0.34 0.64 1 0.74 0.3

3.3.1.7 Visual PROMETHEE Visual PROMETHEE5 can assist in graphically depicting the positive and negative outranking flows for all the options (PROMETHEE I). Figure 3.9 illustrates the flows for the six options of the case study. As it can be observed, Option 5 has the best positive outranking flow (Phi+ bar)— max value and the best negative outranking flow (Phi- bar)- min value. Therefore, Option 5 is the best option in the ranking and Option 3 closely follows, with Option 4 having the worst performance. Figure 3.10 illustrates the PROMETHEE II complete ranking from the Visual P ROMETHEE software and Fig. 3.11 summarizes the results. 3.3.1.8 GAIA Analysis As it was mentioned in a previous section, the GAIA plane can be also used to provide further insights on the behavior of the various options under all the criteria. Figure 3.12 illustrates an instance of the GAIA plane for the case study. We can see the options and our selected criteria, which are represented by bullets and arrows, respectively. The available options are not quite similar, due to the fact that they are not close the one to another. On the other hand, the criteria ‘Capital Requirements’, ‘Environmental Impact’ and ‘Available Staff’ are the most important criteria for our problem, as the closer the arrows are, the more important are the criteria for the 5

http://www.promethee-gaia.net/software.html

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Fig. 3.10 PROMETHEE II Complete Ranking

Fig. 3.11 PROMETHEE Flow Table. (figure from Visual PROMETHEE)

problem. We also see that the decision stick, which illustrates the compromise solution, is more close to Option 3, despite the fact that Option 3 is second on the ranking of the Fig. 3.11. Similarly, Option 5 is a little more distant from the decision

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Fig. 3.12 GAIA Visual Analysis (figure from Visual PROMETHEE)

stick, despite the fact that it is first on the ranking of the Fig. 3.11. This happens due to the loss of information of the Gaia plane, as it is already mentioned on the corresponding theoretical section.

3.3.2

Analytic Hierarchy Process

3.3.2.1 Introduction The Analytic Hierarchy Process (AHP) is a technique developed by Saaty (1977, 1980). The development of the method was a reflection of the lack of a practical and easily applicable method for setting priorities, and decision-making. It is a technique for dealing with complex decision problems, based on mathematics and human psychology, and is designed to combine logic, feelings and intuition into an effective framework for solving multi-criteria problems. The AHP methodology is based on a group of axioms that clearly define the scope of a problem, represent its structure, quantify its information, relate the individual elements of the problem to later objectives, and evaluate alternatives. AHP does not require judgments to be consistent or transient, the degree of consistency is communicated to the decision-maker, and it is he who will decide whether the rating of alternatives is credible. The method addresses the problem of weights in a set of activities according to their degree of importance. For this purpose, binary comparisons are made, and a preference scale is developed between

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Table 3.19 Delivery of papers on AHP by application areas (Vaidya & Kumar, 2006)

Area Engineering Personal Social Manufacturing Industry Government Education Political Other topics

N 26 26 23 18 15 13 11 6 12

% 17.30 17.30 15.30 12 10 8.70 7.30 4 8

the activities based on the estimates of the decision-makers. This process results in the creation of a weight table and an estimate table for each criterion. The initial problem is broken down into individual segments or variables, the variables are hierarchically classified by giving numerical values to the relative significance estimates, and finally, the composition of the estimates is made to determine which variable has the highest priority—influence on the result (Papathanasiou & Ploskas, 2018b). Although AHP has been criticized from a methodological point of view, it has been established today as one of the most applied decision-making techniques, and its diffusion is due to its simplicity, clarity and ease of realization. Hierarchical Analysis of Decisions is ultimately a decision-making method for complicated multiple criteria problems that can be used without requiring special knowledge while allowing decision-makers to combine their experience, knowledge, and intuition. The best way to describe the method is by describing its four key functions: (a) (b) (c) (d)

the hierarchical analysis of the decision problem in decision elements, the collection of preferences by the decision-maker on decision elements, the calculation of individual priorities for the decision elements; the composition of the individual priorities in the general priorities of the alternatives (Ishizaka & Nemery, 2013).

There are plenty of AHP scholar works and applications in the literature. The method due to its good results and reliability has received many modifications, for example the Fuzzy AHP (Buckley, 1985; Chang, 1996; Van Laarhoven & Pedrycz, 1983) and group decision making (Chen, 2000; Dong, Zhang, Hong, & Xu, 2010; Saaty, 1989). In Table 3.19 the scientific areas where AHP is most popular is depicted, where there are selected 150 papers, and are analyzed for their application area, as a glimpse of the most popular publications of AHP (Vaidya & Kumar, 2006).

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3.3.2.2 Methodology Problem Structuring AHP is known through a phrase: divide and conquer. Every problem usually is complex, and in MCDA, it is simpler to divide the problem in smaller pieces and solve one sub-problem at each step. AHP has the characteristic of the division during: 1. the structuring of the problem and 2. the elicitation of the priorities through pairwise comparisons (Ishizaka & Nemery, 2013). In AHP, there are no numerical judgments, but the decision-maker creates a table with the comparative value of one alternative over another, making the comparisons for all the alternatives (Ishizaka & Lusti, 2004). The following equation will help the reader to understand more easily the way that the pairwise comparisons are structured (Saaty, 1989). 2

1 6 1 6 6 x12 6... 6 6 X¼6 6... 6 6 6... 4 1 x1n

x12

...

... ...

1

...

... ...

...

xij

... ...

...

... 1 xij ...

...

...

... ...

...

...

...

1

x1n

3

7 ...7 7 ...7 7 7 7 ...7 7 7 ...7 5 1

ð3:43Þ

where xij is the comparison between element (alternatives or criteria) i and j: xij ¼

1 xji

and

xji ¼

1 xij

ð3:44Þ

It can be observed that for the diagonal of the matrix xii ¼ 1 as each alternative (or criterion) is equally important to itself. In addition, when the matrix X is perfectly consistent, we have the transitivity rule for every comparison (Saaty, 1989): xij ¼ xik xkj

AHP Steps The following paragraphs present a step-by-step explanation of AHP.

ð3:45Þ

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Step 1. Pairwise COMPARISON Matrix of the Criteria

The first step for AHP is the definition of how two criteria can be compared to each other. The decision-maker expresses his/her preferences. The function n2  n 2

ð3:46Þ

expresses how many comparisons are needed for the pairwise comparison matrix (where n is the number of criteria) and it leads to a nxn table. A 1 to 9 scale has been proposed by Saaty (1977) and it is used in most of the publications. A smaller scale may lead the decision-makers to a confusion with regard to the psychological part of the problem, as the differences would not be so clear. However, the decision-maker can use other scales in AHP, too (Harker & Vargas, 1987; Ishizaka, Balkenborg, & Kaplan, 2011; Lootsma, 1989) (Table 3.20).

Step 2. Consistency Check

The maximum eigenvalue of the pairwise comparison of the criteria, λmax, is equal to n if and only the matrix is consistent, in other words λmax is greater than n if the matrix is not consistent. The function that describes the above is proposed by Saaty (1990) and it proposes the Consistency Index (CI) as CIðX Þ ¼

λmax  1 n1

ð3:47Þ

However, the Consistency Index is not fully fair in comparing matrices with different orders and a rescale needs to be done, because the expected value of the Consistency Index for a random matrix with size n + 1 is on average greater than the expected value of the Consistency Index for a random matrix with size n. So, the rescaled version of the Consistency Index, the Consistency Ratio (CR), is defined and is calculated by:

Table 3.20 The 1 to 9 scale by Saaty (1977) Intensity of importance 1 2 3 4 5 6 7 8 9

Definition Equal importance Weak Moderate importance Moderate plus Strong importance Strong plus Very strong or demonstrated importance Very, very strong Extreme importance

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CRðX Þ ¼

113

CIðX Þ RIn

ð3:48Þ

RIn expresses the average Consistency Index which is obtained from a large data set of matrices with size n and they are randomly generated. Matrices with CR < 0.10 are acceptable, but matrices with CR > 0.10 are inconsistent (Saaty, 1977).

Step 3. Priority Vector of Criteria

For the priority vector of criteria w ¼ w1, w2,. . ., wn there are several methods for its calculation. Here, three such methods will be explained and used in the case study. Eigenvector Method

Saaty (1980) has proposed the most well-known method for the calculation of the priority vector, which is the principal eigenvector of X. In general, we have the formulation of the type Xw ¼ nw, which implies that n and w are an eigenvalue and an eigenvector of X, respectively. 2 w1 6 w1 6 w2 6 Xw ¼ 6 6 w1 6⋮ 4 wn w1

w1 3 2 3 2 3 wn 7 w1 nw1 w2 7 7 6 7 76 ... 6 w2 7 6 nw2 7 wn 7 ¼ ¼ nw 6 7 6 74 ⋮ 5 4 ⋮ 7 5 ... ⋮7 5 wn wn nwn ... wn

w1 w2 w2 w2 ... wn w2

...

ð3:49Þ

Vector w can be calculated for a pairwise comparison matrix X: 

Xw ¼ λmax wT ½1, 1, . . . , 1T ¼ 1

ð3:50Þ

The Normalized Column Sum Method

The priority vector is expressed as the sum of all the elements in a row, which is further divided by the sum of the elements of the matrix X (Saaty, 1977). Pn j¼1 xij P wi ¼ n Pn n¼1

j¼1 xij

ð3:51Þ

Geometric Mean Method

Crawford and Williams (1985) have proposed the geometric mean method, which calculates the priority vector through the division of the elements on a row by the normalization term, thus the sum of w is equal to 1.

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Q n wi ¼

j¼1 xij

Pn Qn i¼1

1n

j¼1 xij

ð3:52Þ

1n

Step 4. Pairwise Comparison Matrices of the Alternatives

The fourth step is similar to the first step, as a pairwise comparison matrix of the alternatives is created through the preferences of the decision-maker. The matrix is of size mxm, where m is the number of alternatives. Step 5. Consistency Check on the Pairwise Comparison Matrices of the Alternatives

The fifth step is similar to the second step, as a consistency check takes place on all pairwise comparison matrices of the alternatives, but here the number of criteria is replaced by the number of the alternatives. Step 6. Compute the Local Priority Vectors

The sixth step is similar to the third step, as the local priority vectors of all the alternatives are calculated and again the number of criteria is replaced by the number of the alternatives. Step 7. Aggregate the Local Priorities: Rank the Alternatives

In the last step, the global alternative priorities are calculated through the combination of the priority criteria and the local alternative priorities (Saaty, 1977).

3.3.2.3 Example: Case Study A similar example that was used in the PROMETHEE method section will be used to further illustrate the steps of AHP. However, there are some important differences. The decision-maker does not use numerical evaluations, like those in the PROME THEE method. Eventually, he/she will form the pairwise comparison matrices of the criteria and alternatives through his/her personal (and thus subjective) opinion about the preferences among the criteria. Therefore, there are again four criteria (‘Capital Requirements’, ‘Available Staff’, ‘Delivery Distance’ and ‘Environments impact’) and three alternatives (Option 1, Option 2 and Option 3). Hypothetically, the following comparisons apply (Table 3.21): Table 3.21 Compare pairwise the importance of the criteria as regard to the goal

Capital requirements Available staff Delivery distance Environmental impact

Capital requirements 1

Available staff 1/7

Delivery distance 2

Environmental impact 2

7 1/2 1/2

1 6 2

1/6 1 1

1/2 1 1

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• Capital Requirements are very strong more important than the Available Staff (x12 ¼ 7, x21 ¼ 1/7). • Capital Requirements are weakly important than the Environmental Impact (x14 ¼ 2, x41 ¼ 1/2). • Delivery Distance is strong plus more important than the Available Staff (x32 ¼ 6, x23 ¼ 1/6). • Environmental Impact is equally important as the Delivery Distance (x43 ¼ x34 ¼ 1). • Environmental Impact is weakly important than the Available Staff (x42 ¼ 2, x24 ¼ 1/2). • Capital Requirements is weakly important than the Delivery Distance (x13 ¼ 2, x31 ¼ 1/2). Eigenvector Method In Table 3.22, we have the values for every criterion, using the eigenvector method. In detail, we have for the ‘Capital Requirements’ criterion: 1 1 1 1  1 þ  7 þ 2  þ 2  ¼ 4, 7 2 2 1 1 1 1 7  1 þ 1  7 þ  þ  ¼ 14:333 6 2 2 2 and so on. Then we have the sum of each row and the eigenvector: 32:381=181:190 ¼ 0:179, 48:333=181:190 ¼ 0:267 and so on. Hypothetically again, in the following Tables 3.23, 3.24, 3.25 and 3.26 we have the comparison matrices for our three alternatives to every of the four criteria. In Tables 3.27, 3.28, 3.29 and 3.30 we have the local priorities respectively for every alternative to every criterion. For instance, in Table 3.27 we have for cell (Option 1—Option 1): 1 1 1 11þ 6þ2 þ 2¼3 6 2 2 and so on. Then we have the sum of the rows and the local priorities 30,000/ 45000 ¼ 0.667 and so on. The final stage is represented in Table 3.31, where we synthesize all the above and we calculate the global priorities of the alternative. As we can see, the Option 1 is first, the Option 2 is second and the Option 3 is the last one. As a result, the Option 1 is the best solution. We have the local priorities for each criterion, the eigenvector as the criteria priorities and finally the global priority:

Capital requirements Available staff Delivery distance Environmental impact

Capital requirements 4.000 14.333 43.500 15.500

Available staff 16.286 4.000 14.071 10.071

Table 3.22 Square the comparison matrix, sum the rows, and normalize Delivery distance 6.024 14.833 4.000 3.333

Environmental impact 6.071 15.167 6.000 4.000 SUM

Sum of rows 32.381 48.333 67.571 32.905 181.190

Eigenvector 0.179 0.267 0.373 0.182 1.000

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Table 3.23 Compare pairwise the alternatives (‘Capital Requirements’)

Table 3.24 Compare pairwise the alternatives (‘Investment costs’)

Table 3.25 Compare pairwise the alternatives (‘Delivery Distance’)

Table 3.26 Compare pairwise the alternatives (‘Environmental Impact’)

117

Option 1 Option 2 Option 3

Option 1 1 1/6 1/3

Option 2 6 1 2

Option 3 3 1/2 1

Option 1 Option 2 Option 3

Option 1 1 1/4 1/2

Option 2 4 1 1/2

Option 3 2 2 1

Option 1 Option 2 Option 3

Option 1 1 1/5 2

Option 2 5 1 3

Option 3 1/2 1/3 1

Option 1 Option 2 Option 3

Option 1 1 2 2

Option 2 1/2 1 1/4

Option 3 1/2 4 1

Table 3.27 Calculate the local priorities with the eigenvector method (‘Capital Requirements’) Option 1 Option 2 Option 3

Option 1 3 1/2 1

Option 2 18 3 6

Option 3 9 1 1/5 3 SUM

Sum of rows 30.000 5.000 10.000 45.000

Local priorities 0.667 0.111 0.222 1.000

Table 3.28 Calculate the local priorities with the eigenvector method (‘Investment costs’) Option 1 Option 2 Option 3

Option 1 3 1 1/2 1 1/8

Option 2 9 3 3

Option 3 12 4 1/2 3 SUM

Sum of the rows 24.000 9.000 7.125 40.125

Local priorities 0.598 0.224 0.178 1.000

Table 3.29 Calculate the local priorities with the eigenvector method (‘Delivery Distance’) Option 1 Option 2 Option 3

Option 1 3 1 4 3/5

Option 2 11 1/2 3 16

Option 3 2 2/3 3/4 3 SUM

Sum of rows 17.167 4.833 23.600 45.600

Local priorities 0.376 0.106 0.518 1.000

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Table 3.30 Calculate the local priorities with the eigenvector method (‘Environmental Impact’) Option 1 3 12 4 1/2

Option 1 Option 2 Option 3

Option 2 1 1/8 3 1 1/2

Option 3 3 9 3 SUM

Sum of rows 7.125 24.000 9.000 40.125

Local priorities 0.178 0.598 0.224 1.000

Table 3.31 Global priority of the alternatives (Eigenvector)

Option 1 Option 2 Option 3

Capital requirements 0.667

Available staff 0.598

Delivery distance 0.376

Environmental impact 0.178

Criteria priorities 0.179

Global priority 0.451

0.111

0.224

0.106

0.598

0.267

0.228

0.222

0.178

0.518

0.224

0.373

0.321

0.182

1.000

0:667  0:179 þ 0:598  0:267 þ 0:376  0:373 þ 0:178  0:182 ¼ 0:451 and so on. The Normalized Column Sum Method Let us use the normalized column sum method and analytically to see if there are differences at every step of the methodology, and at the final results. Here, we are going to use the sum of every row, dividing it by the total sum, in order to find every criterion priority (Table 3.32). Thus, we have the sum of the rows and the criteria priorities: 5.143/26.81 ¼ 0.192 and so on. In Tables 3.33, 3.34, 3.35 and 3.36 we have the comparison matrices for all the alternatives as regards to every criterion. For the global priority, we have: 0:667  0:192 þ 0:323  0:238 þ 0:463  0:317 þ 0:160  0:168 ¼ 0:487 and so on (Table 3.37). There are some minor differences to the global priorities considering the eigenvector method and the normalized column sum method. However, the order of the alternatives has not changed. Geometric Mean Method The final version is the geometric mean method for our example. Following the same procedure as before, we have the following tables. As the reader can see, here we use the geometric mean, which we calculate for each row by multiplying the values of each row and raising the result to 0.25 (Table 3.38). Thus, for the geometric mean, we have: (1*(1/7)*2*2)0.25 ¼ 0.87 (Geometric mean for Capital Requirements) and so on. For the criteria priorities, we have: 0.87/

Capital requirements Available staff Delivery distance Environmental impact

Capital requirements 1 7 1/2 1/2

Available staff 1/7 1 6 2

Delivery distance 2 1/6 1 1

Table 3.32 Square the comparison matrix, sum the rows, and normalize Environmental impact 2 1/2 1 1 SUM

Sum of the rows 5.143 8.667 8.500 4.500 26.810

Criteria priorities 0.192 0.323 0.317 0.168 1.000

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Table 3.33 Local priority of the alternatives (‘Capital Requirements’) Option 1 Option 2 Option 3

Option 1 1 1/6 1/3

Option 2 6 1 2

Option 3 3 1/2 1 SUM

Sum of the rows 10.000 1.667 3.333 15.000

Local priorities 0.667 0.111 0.222 1.000

Table 3.34 Local priority of the alternatives ‘(Investment costs’) Option 1 Option 2 Option 3

Option 1 1 1/4 1/2

Option 2 4 1 1/2

Option 3 2 2 1 SUM

Sum of the rows 7.000 3.250 2.000 12.250

Local priorities 0.571 0.265 0.163 1.000

Table 3.35 Local priority of the alternatives (‘Delivery Distance’) Option 1 Option 2 Option 3

Option 1 1 1/5 2

Option 2 5 1 3

Option 3 1/2 1/3 1 SUM

Sum of the rows 6.500 1.533 6.000 14.033

Local priorities 0.463 0.109 0.428 1.000

Table 3.36 Local priority of the alternatives (‘Environmental Impact’) Option 1 Option 2 Option 3

Option 1 1 2 2

Option 2 1/2 1 1/4

Option 3 1/2 4 1 SUM

Sum of the rows 2.000 7.000 3.250 12.250

Local priorities 0.163 0.571 0.265 1.000

Table 3.37 Global priority of the alternatives (normalized column sum)

Option 1 Option 2 Option 3

Capital requirements 0.667

Available staff 0.571

Delivery distance 0.463

Environmental impact 0.163

Criteria priorities 0.192

Global priority 0.487

0.111

0.265

0.109

0.571

0.323

0.238

0.222

0.163

0.428

0.265

0.317

0.275

0.168

1.000

4.06 ¼ 0.214 (Criteria priorities for Capital Requirements) and so on (Tables 3.39, 3.40, 3.41, 3.42 and 3.43). Finally, here we have:

Capital requirements Available staff Delivery distance Environmental impact

Capital requirements 1 7 1/2 1/2

Available staff 1/7 1 6 2

Delivery distance 2 1/6 1 1

Table 3.38 Square the comparison matrix, sum the rows, and normalize Environmental impact 2 1/2 1 1 SUM

Geometric mean 0.87 0.87 1.32 1.00 4.06

Criteria priorities 0.214 0.215 0.324 0.246 1.000

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Table 3.39 Local priority of the alternatives (‘Capital Requirements’) Option 1 Option 2 Option 3

Option 1 1 1/6 1/3

Option 2 6 1 2

Option 3 3 1/2 1 SUM

Geometric mean 2.060 0.537 0.904 3.501

Local priorities 0.588 0.153 0.258 1.000

Table 3.40 Local priority of the alternatives (‘Investment costs’) Option 1 Option 2 Option 3

Option 1 1 1/4 1/2

Option 2 4 1 1/2

Option 3 2 2 1 SUM

Geometric mean 1.682 0.841 0.707 3.230

Local priorities 0.521 0.260 0.219 1.000

Table 3.41 Local priority of the alternatives (‘Delivery Distance’) Option 1 Option 2 Option 3

Option 1 1 1/5 2

Option 2 5 1 3

Option 3 1/2 1/3 1 SUM

Geometric mean 1.257 0.508 1.565 3.331

Local priorities 0.378 0.153 0.470 1.000

Table 3.42 Local priority of the alternatives (‘Environmental Impact’) Option 1 Option 2 Option 3

Option 1 1 2 2

Option 2 1/2 1 1/4

Option 3 1/2 4 1 SUM

Geometric mean 0.707 1.682 0.841 3.230

Local priorities 0.219 0.521 0.260 1.000

Table 3.43 Global priority of the alternatives (‘geometric mean’)

Option 1 Option 2 Option 3

Capital requirements 0.588 0.153 0.258

Available staff 0.521 0.260 0.219

Delivery distance 0.378 0.153 0.470

Environmental impact 0.219 0.521 0.260

Criteria priorities 0.214 0.215 0.324 0.246

Global priority 0.414 0.267 0.319 1.000

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0:588  0:214 þ 0:521  0:215 þ 0:378  0:324 þ 0:219  0:246 ¼ 0:414 and so on. Comparing the final ranking with the results of the other two methods, there are again some differences to the numbers of global priorities, but the order of the alternatives still remains the same. As the reader can observe, there are some differences to the two methods described above, but despite their differences the order of the alternatives in the example has not changed. Perhaps, in another example, with more criteria and more alternatives, the use of each of the three methods could lead to quite different results, changing the order of the final ranking. However, here no big differences were observed. As a result, through the different variations the reader can easily understand why AHP is considered one of the most famous and versatile methods.

Appendix List of Links 1. History of Data Envelopment Analysis https://www.youtube.com/watch? v¼fxAASxxCeFc 2. Introduction to basics of DEAhttps://youtu.be/qE2mzqKTI58 3. Economies of Scalehttps://youtu.be/JdCgu1sOPDo 4. History of worker placementhttps://www.youtube.com/watch?v¼4E6al0Lx80M 5. PROMETHEE https://www.youtube.com/watch?v¼qQJO2UYJRc8 6. AHP https://www.youtube.com/watch?v¼J4T70o8gjlk 7. AHP https://www.youtube.com/watch?v¼3yTBcDP_JN8 8. AHP https://www.youtube.com/watch?v¼zsld4TQacBU

Multiple Choice Questions 1. Which of the following steps is part of the Problem-solving process (a) Identify the problem (b) Identify the available alternatives (c) Evaluate the alternatives (d) All of the above 2. What is the main purpose of Data Envelopment Analysis? (a) It measures the technical efficiency of Decision Making Units. (b) It measures the productivity of Decision Making Units. (c) It measures the profitability of Decision Making Units. 3. Technical Efficiency in Data Envelopment Analysis is: (a) A representation of the linear relations between inputs and outputs (b) A measure of the importance of outputs given specific inputs (c) A measure of how well inputs are transformed into outputs (d) None of the above

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4. The measure of technical efficiency in Data Envelopment Analysis is: (a) An Objective measure of how well a Decision Making Unit is performing (b) Comparative measure since the Decision Making Units are compared with each other (c) None of the above 5. How is efficiency measured? (a) By the ratio of input over output (b) By the ratio of output over input (c) By the ratio of weighted input over weighted output (d) By the ratio of weighted output over weighted input 6. The need for Decision Aid methods has risen because: (a) Most problems that decision makers face are complex in nature, monocriterion and require mathematical knowledge to make a good decision (b) Most problems that decision makers face are complex in nature, polycriterion and require a mathematical process to structure them and rank the alternatives (c) Most problems that decision makers face involve a quantity that needs either maximization (d) None of the above 7. PROMETHEE I results in a: (a) Partial ranking of the alternatives (b) Complete ranking of the alternatives (c) Complete ranking of the alternatives with a consistency measure (d) None of the above 8. Which of the following notions is not part of PROMETHEE: (a) Unicriterion flows (b) Global flows (c) The Saaty scale to pairwise compare criteria (d) Preference function 9. The GAIA plane is: (a) A software that is used to calculate partial rankings in PROMETHEE I (b) software that is used to rank alternatives with AHP (c) A tool that helps decision makers to visualize the information from the P ROMETHEE method (d) None of the above 10. Which of the following Multi-criteria Decision Aid methods uses a subjective 1–9 scale to compare the criteria: (a) PROMETHEE (b) AHP (c) None of the above 11. It is important for AHP that the judgements of the decision maker are consistent: (a) Yes it is important (b) No it is not important 12. Which of the following steps is not part of the AHP methodology:

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(a) Calculation of the consistency check (b) Pairwise comparison of the alternatives (c) Calculation of the local priority vectors (d) Calculation of the global flows 13. AHP and PROMETHEE can deal: (a) Only with quantitative criteria (b) Only qualitative criteria (c) There are no criteria involved in the ranking of the alternatives (d) None of the above 14. AHP and PROMETHEE are methods that: (a) Are formal and do not have variations to deal with different types of decision problems (b) Have many variations that take into account different types of decision problems (c) Are not used extensively because they involve difficult mathematical operations

Exercises 1. A taxi owner wishes to evaluate the performance of five of the drivers that he employs in his taxi and has gathered the following data. Table 3.44 Problem data Driver A B C D

Hours worked (100 h) 4.2 4.0 4.3 4.7

Passengers serviced during these hours 500 350 800 530

Revenues gathered (100 €) 17.5 17.0 24.2 18.9

(a) What can be considered the inputs and outputs of the evaluation (b) By employing the DEA method, you must choose if you wish to evaluate the input or output efficiency. Which one would you use in the particular case? (c) Formulate the LP model to calculate the performance of: • Driver A • Driver D 2. A taxi owner wishes to evaluate the performance of five of the drivers that he employs in his taxi and has gathered the following data.

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Table 3.45 Taxi problem data Driver A B C D

Hours worked (100 h) 4.2 4.0 4.3 4.7

Passengers serviced during his hours 500 350 800 530

Revenues gathered (100 €) 17.5 17.0 24.2 18.9

Formulate the LP model to calculate the pure technical input and output efficiencies of: (a) Driver A (b) Driver D 3. A transport company wants to locate its new terminal bus station in a city. There are some criteria that should be taken into consideration, in order to find the best location among the available options. The available options are the locations in the city center, west of the city and east of the city. The criteria that the transport company has set are the following: (a) infrastructure costs (hundreds €) (b) distance from the city center (km) (c) distance from petrol stations (km) (d) traffic level (1 to 7) (e) social impact (1 to 7) All the criteria need to be maximized. Table 3.46 Data for the exercise

Weight City Center West East

Infrastructure costs (hundreds €) 0.4 8

Distance of the City Center (km) 0.2 3

Distance of petrol stations (km) 0.2 6

Traffic level (1 to 7) 0.1 7

Social impact (1 to 7) 0.1 5

6 7

15 26

2 5

3 4

2 2

Table 3.47 Preference Parameters of all the criteria (Usual) Criterion Infrastructure costs (hundreds €) Distance of the City Center (kms) Distance of petrol stations (kms) Traffic level (1 to 7) Social impact (1 to 7)

Function Usual Usual Usual Usual Usual

wi 0.4 0.2 0.2 0.1 0.1

qi 0 0 0 0 0

pi 0 0 0 0 0

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(a) The Tables provide the available data and information. Find out what is the best location for the company in order to place its terminal station. (b) The preference functions of the various criteria followed the ‘Usual’ function, so no preference parameter was necessary. Solve the same problem with different preference functions, as provided in the Table 3.48 below. Does the ranking change? Table 3.48 Preference Parameters of all the criteria (U-shape & Linear) Criterion Infrastructure costs (hundreds €) Distance of the City Center (km) Distance of petrol stations (km) Traffic level (1 to 7) Social impact (1 to 7)

Function U-shape U-shape U-shape Linear Linear

wi 0.4 0.2 0.2 0.1 0.1

qi 2 11 4 2 3

pi – – – 3 3

4. The local authorities of a region want to choose the best form of transportation in order to make a new investment to the city. The available options are Metro, Trolley, and Tram. The local authorities are taking into account the following criteria: • investment cost, • transit time, • accessibility, • power requirements, • social impact and • environmental impact For the above criteria, we have the following expressions: • The Investment Cost is extremely more important than the Accessibility (x13 ¼ 9, x31 ¼ 1/9). • The Power Requirements are strongly more important than the Transit Time (x42 ¼ 7, x24 ¼ 1/7). • The Accessibility is equally important as the Social Impact (x35 ¼ x53 ¼ 1). • The Environmental Impact is strongly more important than the Social Impact (x65 ¼ 5, x56 ¼ 1/5). • The Transit Time is weakly more important than the Environmental Impact (x26 ¼ 2, x62 ¼ 1/2). • The Investment Cost is strongly more important than the Transit Time (x12 ¼ 5, x21 ¼ 1/5). • The Power Requirements are moderately more important than the Investment Cost (x41 ¼ 3, x14 ¼ 1/3). • The Environmental Impact is strongly more important than the Power Requirements (x64 ¼ 5, x46 ¼ 1/5).

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• The Environmental Impact is strongly more important than the Investment Cost (x61 ¼ 5, x16 ¼ 1/5). • The Accessibility is equally important as the Transit Time (x32 ¼ x23 ¼ 1). • The Transit Time is moderately more important than the Social Impact (x25 ¼ 3, x52 ¼ 1/3). • The Power Requirements are strongky more important than the Accessibility (x43 ¼ 5, x34 ¼ 1/5). • The Investment Cost is strongly more important than the Social Impact (x15 ¼ 5, x51 ¼ 1/5). • The Environmental Impact is extremely more important than the Accessibility (x63 ¼ 9, x36 ¼ 1/9). • The Power Requirements are moderately more important than the Social Impact (x45 ¼ 3, x54 ¼ 1/3). We encourage the reader to solve this problem with the three methods that we have explained on the above sections (the eigenvector method, the normalized column sum method, and the geometric mean method) in order to understand completely the procedure of every method. Table 3.49 Compare pairwise the importance of the criteria as regard to the goal

Investment cost Transit time Accessibility Power requirements Social impact Environmental impact

Investment cost 1

Transit time 5

Accessibility 9

Power requirements 1/3

Social impact 5

Environmental impact 1/5

1/5 1/9 3

1 1 7

1 1 5

1/7 1/5 1

3 1 3

2 1/9 1/5

1/5 5

1/3 1/2

1 9

1/3 5

1 5

1/5 1

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System Dynamics Modelling for Urban Sustainability Stefano Armenia, Federico Barnabè, Alessandro Pompei, and Rocco Scolozzi

4.1

What Is System Dynamics

System Dynamics (Forrester, 1961, 1968; Richardson & Pugh, 1981; Sterman, 2000) is a computer-aided modelling and simulation approach to policy analysis and design. It’s a methodology that applies to dynamic problems arising in complex social, managerial, economic, or ecological systems—literally to any dynamic systems characterized by interdependence, mutual interaction, information feedback, and circular causality (Sterman, 2000). It uses modelling and computer simulation to improve the understanding of complex systems, often associated with complex problems. The main advantage in using this type of methodology is that it provides with a vision that considers many aspects inside a system as interconnected with each other, contrary to those (for the most now past) approaches where problems were analysed individually and on a sectoral basis. In fact, the SD approach faces the theme of variables interconnection through the creation of diagrams and models where the effects of such interdependencies are evaluated (and possibly corrected) through simulation and observation of results.

S. Armenia (*) Department of Research, Link Campus University, Via del Casale di San Pio V, Rome, Italy e-mail: [email protected] F. Barnabè Department of Business and Law, University of Siena, Siena, Italy e-mail: [email protected] A. Pompei Department of Mechanical and Aerospace Engineering, Sapienza University of Rome, Rome, Italy e-mail: [email protected] R. Scolozzi Department of Sociology and Social Research, University of Trento, Trento, Italy e-mail: [email protected] # The Author(s), under exclusive license to Springer Nature Switzerland AG 2021 J. Papathanasiou et al. (eds.), Urban Sustainability, Springer Texts in Business and Economics, https://doi.org/10.1007/978-3-030-67016-0_4

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The system dynamics approach involves: • Defining problems dynamically, in terms of graphs over time. • Striving for an endogenous, behavioural view of the significant dynamics of a system, a focus inward on the characteristics of a system that themselves generate or exacerbate the perceived problem. • Thinking of all concepts in the real system as continuous quantities interconnected in loops of information feedback and circular causality. • Identifying independent stocks or accumulations (levels) in the system and their inflows and outflows (rates). • Formulating a behavioural model capable of reproducing, by itself, the dynamic problem of concern. The model is usually a computer simulation model expressed by means of nonlinear equations, but it is occasionally left unquantified as a diagram capturing the stock-and-flow/causal feedback structure of the system. • Deriving understandings and applicable policy insights from the resulting model simulation. • Implementing changes resulting from model-based understandings and insights. A systems perspective implies the existence of interconnected elements to fulfil a function or a purpose over time. Those elements can be of physical or information composition. While observing food systems, for example, we find interrelations among different elements carrying out the food production, supply, processing, distribution and consumption activities. Thus, food production requires elements such as land, seeds, water, capital, workers and their labour. Some elements constitute stocks which accumulate or decrease over time (e.g., land for agriculture, water or capital), due to information or material flows. Information flows mainly drive decisions: in this case, an investment rate is made after gathering certain information on the system performance or conditions. Material flows constitute physical elements altering the state of stocks. Those accumulation processes determine the change of critical resources or drivers for food production or distribution and are fundamental to assess their sustainability (Armendáriz, Armenia, & Atzori, 2016). Modelling in this way the system under study allows policy-makers to make decisions based on scientific analysis of future scenarios and provide them with a supporting tool that could be used in synergy while planning and defining policies to get economic and socio-environmental benefits (Armenia, Bellomo, Medaglia, Nonino, & Pompei, 2019).

4.1.1

System Dynamics Tools

The System Dynamics approach employs various tools for extrapolating information about complex systems and discovering hidden and/or counter-intuitive behaviours. In this sense, the CLD approach, typical of the Systems Thinking approach, is heavily qualitative but is the starting point for the production of a quantitative model. Notwithstanding its qualitative value, the analysis of CLDs can introduce

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Fig. 4.1 Starting from the left: Positive loop, negative loop, positive loop due to even number of negative links and delayed loop

several important results. The main advantage in using this type of analysis is that it provides with a vision that considers many aspects of a system as interconnected with each other. The outcome of a CLD is a combination of causal links between variables. Links can be of two types: • Positive (S): when the independent variable (arrow tail) changes, then the dependent variable (arrow head) changes in the same direction. • Negative (O): when the independent variable (arrow tail) changes, then the dependent variable (arrow head) changes in the opposite direction. There are two types of feedback loops: reinforcing feedback loop and balancing feedback loop (indicated by + and
inside the loop). Also, it is possible to indicate a time delay between the two variables (Fig. 4.1). Positive, negative, and delayed loops can give birth to a variety of systemic structures, named systems archetypes, which can assist in taking a closer look at the problem displayed by a certain system and diagnosing the optimal solution (Mirchi, Madani, Watkins, & Ahmad, 2012). Systems archetypes are modular causal structures that highlight a particular behavioural pattern. They can be used, individually or together with others, to infer a set of behaviours that can be found in the evolving observable variables of a system. Senge (1990) states in his seminal book The Fifth Discipline that “if reinforcing and balancing feedbacks and delays are like the nouns and verbs of systems thinking, then the systems archetypes are analogous to basic sentences or simple stories that get retold again and again”. Therefore, by recognizing these causal structures inside a system, it is possible to give a deeper explanation about its dynamics and performance. Thanks to this, the following actions of problem fixing will be more accurate and thorough. Another important step in the System Dynamics methodology is represented by the translation of a CLD into a Stocks and Flows Diagram (SFD). The SFD is a model that represents the system under study from a quantitative point of view and that can be simulated, hence allowing users for policy experimentation in freeconsequence environments. Furthermore, using computer simulation models to know if the targeted objectives could be achieved when a certain policy is

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Fig. 4.2 S&F conceptualization

implemented is much cheaper, in terms of costs and time, than experimenting with the actual real system (Timms, Guerin, Arnold, & Vaudreuil, 2011). The SFD symbolism consists of (Fig. 4.2): • stocks (represent things in the model that can accumulate, the stock will rise and drop depending on its flows and will remain constant while in equilibrium), • flows (are the rate of change connected to their stocks. Inflows add to a stock, outflows take away from the stock. Equilibrium occurs when inflows to all stocks are equal to the outflows), • and Information Links (blue arrows generally represent the direct influence of the current value on another, red arrows represent and opposite influence). In the end, the SD modelling and simulation approach is an iterative process that helps getting a better system understanding and designing and evaluating better policies. As shown in Fig. 4.3, the modelling process demands the identification and definition of a problem or a goal. The overall system conceptualization results in a qualitative or quantitative model formalization, which often improves our initial system’s understanding. The simulation model allows testing the model validity with empirical data, experimenting with policy alternatives and eliciting insights which increase the likelihood of performing a good policy analysis.

4.2

State of The Art on (Urban) Sustainability Models

As D. Meadows (1989, p. 635) stated, “operational games based on System Dynamics models have been used in System Dynamics teaching since the first days of the field”. Nowadays, SD games are used to analyse complex issues in a variety of domains and sectors, thus boosting learning and decision-making. A comprehensive review of the state of art would be nearly impossible; on the contrary, it is possible to search and dig the wide body of literature as well as the available online sources in order to get a glimpse of the extent to which SD-based games are used in education (managerial education as well as school education). Subsequently, this literature review has as its primary sources the following ones:

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Fig. 4.3 Model creation process

• • • •

Google Scholar the official website of the System Dynamics Society (SDS), the SD bibliography published on the website of the SDS, and the list of cases retrieved from the website of the SDS.

Notably, the SDS website already provides a repository of case studies dealing with a variety of systems and complex issues. The repository can be accessed at the following URL: https://www.systemdynamics.org/list-of-all-cases (see Appendix A). Other repositories provide a variety of case studies and SD-games. Examples can be found while surfing the website of the MIT Sloan School of Management and the websites of the main software houses/SD experts. The former one has a specific page dedicated to various educational simulators (https://mitsloan.mit.edu/LearningEdge/simulations/Pages/System-Dynamics.aspx) that provides a number of SD-based games (full description in Appendix B), mainly dealing with climate and environmental-related issues. The latter ones refer instead to a number of software houses producing and promoting their own SD software, or to webpages managed by SD experts who developed SD software. These websites provide literally hundreds of SD simulations and models in a variety of sectors, ready to use. With reference to the SD-games panorama investigation goal of this section, it is worth mentioning the following ones:

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Strategy Dynamics, at https://www.strategydynamics.com/microworlds/ iSee Systems, at https://exchange.iseesystems.com/ Forio, at https://forio.com/solutions/business-strategy-simulation-games/ Powersim, at https://www.powersim.com/ Vensim, at https://vensim.com/

Worth mentioning, also with regards to the specific aims of the SUSTAIN project, our review of the literature, as well as of the online sources, was particularly limited to a set of complex issues (and therefore variables) considered relevant for the development of an educational boardgame in a later stage of the project. Specifically, the review focused on the key domains of urban sustainability and related keywords or sub-systems, as follows: • Environment – Green areas – Purifiers – Waste management policies (processes efficiency improvement) – Waste management infrastructures and assets (landfills, incinerators) • Energy – Consumption effect toward market – Market energy prices – Nearly Zero Energy Building • Transport – Public transport – Urban zoning – Goods/food supply chain • Urban planning – Road planning – Space allocation (housing, parks, industrial, services) • Services – Healthcare – Education & Culture – Entertainment & Sport – Administration The review subsequently allowed us identifying a number of SD-based games/ simulations, whose key features are summarized in the following sub-sections.

4.2.1

Environment (Table 4.1)

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Table 4.1 List of simulation models on environment Main sector Renewable resources

Name Fishbanks: A renewable resource management simulation By: Dennis Meadows, John Sterman and Andrew King

Waste management

Sustainable zero waste Tyre life cycle (by Aldrich)

Waste management

Kapmeier and Gonçalves (2018). Policies for Small Island States to manage tourism-driven growth while controlling waste generation the case of the Maldives

Main issue and key concept Fishbanks is a multiplayer web-based simulation in which participants play the role of fishers and seek to maximize their net worth as they compete against other players and deal with variations in fish stocks and their catch. Participants buy, sell, and build ships; decide where to fish; and negotiate with one another. Policy options available to instructors include auctions of new boats, permits, and quotas. To provide the opportunity for students to learn about the challenges of managing resources sustainably in a common pool resource setting, with realistic resource dynamics https://mitsloan.mit.edu/ LearningEdge/simulations/ fishbanks/Pages/fish-banks. aspx Describes the supply chain of tyres from creation to disposal, recycle and renew. Modelling zero waste supply chains, understanding processes, reorganizing phases, dealing with several dynamic issues https://exchange.iseesystems. com/?query¼waste& name¼on&description¼on& keywords¼on&sims¼on& models¼on&diagrams¼on& limit¼100&fromAuthor¼ The tension between tourismdriven economic growth and environmental degradation from a limits-to-growth perspective. Findings are counterintuitive; policies focused on better waste management alone are selfdefeating, because they increase tourism, growth and waste generation, undermining attractiveness and growth later.

Key stocks • Fish • Ships • Money

• Tyres • Landfill • Tyres recycled

• Tourists • Waste • Resorts • Tourist awareness of pollution

(continued)

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Table 4.1 (continued) Main sector

Name

Waste management

Sudhir, Srinivasan, and Muraleedharan (1997). Planning for sustainable solid waste management in urban India

Urban metabolism (water)

Wei, Lou, Yang, and Li (2016). A system dynamics urban water management model for Macau, China

4.2.2

Energy (Table 4.2)

4.2.3

Transport (Table 4.3)

4.2.4

Urban Planning (Table 4.4)

Main issue and key concept Policies that limit tourism demand improve economic and environmental health https://onlinelibrary.wiley. com/doi/full/10.1002/sdr.1607 This article presents a system dynamics model which captures the dynamic nature of interactions among the various components of the USWM system in a typical metropolitan city in India (causal loop of sociodemographic and socioeconomic structure, household structure, consumption and mobility behaviour of population) https://bit.ly/2ALJugv A system dynamics urban water management model was proposed to simulate the dynamic interactions between urban water demands and society, economy, climate, and water conservation https://doi.org/10.1016/j.jes. 2016.06.034

Key stocks

(only CLD) • External costs • Total transport volume • GHG emissions

• Water demand • GDP • Population

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Table 4.2 List of simulation models on energy field Main sector Energy (water)

Name De Stercke, Mijic, Buytaert, and Chaturvedi (2018). Modelling the dynamic interactions between London’s water and energy systems from an end-use perspective

Energypollution

Wu and Ning (2018). Dynamic assessment of urban economyenvironment-energy system using system dynamics model: A case study in Beijing

Urban metabolism (carbon)

Elliot, Rugani, Almenar, and Niza (2018). A Proposal to Integrate System Dynamics and Carbon Metabolism for Urban Planning

Main issue and key concept Cities are concentrations of demand to water and energy systems that rely on resources under increasing pressure from scarcity and climate change mitigation targets. In this work, a novel system dynamics model is developed with an explicit representation of the waterenergy interactions at the residential end use and their influence on the demand for resources. The modelling tool provides a base for this that can be adapted to the context of any industrialised country https://bit.ly/2TFe6qX The study combines system dynamics model and geographic information system to analyze the energyenvironment-economy (3E) system both temporally and spatially, which explicitly explore the interaction of economics, energy, and environment and effects of the key influencing factors https://bit.ly/2CZIhUh This study introduced a system dynamics model to study the stocks, flows, activities, and drivers within an urban system, and developed a double-layer model to implement metabolic modeling at two spatial scales. The system dynamics model is built from the perspective of demanddriven to simulate the input of different resources and materials in the urban metabolism https://bit.ly/2RGbTOk

Key stocks (Several sub-models) • Demand service per capita • Demand consumption per capita • Efficiency addition • Population • Water distribution water supply • Water supply capacity per Electricity

• SO2 emission • COD emission • Nonrenewable stock • Solid waste emission • Capital input • Technology in production

• Population • Income • Industrial commodities • Agricultural stock • Total wealth product

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Table 4.3 List of simulation models on transport field Main sector Transport

Name Shepherd (2014). A review of system dynamics models applied in transportation

Transport— air pollution

Stave (2002). Using system dynamics to improve public participation in environmental decisions

Urban transport

Haghshenas, Vaziri, and Gholamialam (2015). Evaluation of sustainable policy in urban transportation using system dynamics and world cities data: A case study in Isfahan

Main issue and key concept A review over 50 journal papers which have applied system dynamics to a transportation problem, which demonstrates the application of CLD or stock flow models which provide something different to the more traditional transport modelling approach either in terms of insight or coverage of the problem https://bit.ly/2FiOn4m Computer simulation models and systems thinking could be powerful tools for democracy, this article describes a case study using group model building to support a stakeholder advisory group examining transportation and related air quality problems. One of the most valuable effects of the approach was the information feedback it added to the advisory process https://bit.ly/2M5eoox Urban transportation causal loops were conceptualized and the dynamic relations among urban transportation variables were created to develop the pertinent urban dynamics model. Trip generation, modal share, transportation supply and equilibrium between supply and demand were the key modules of the developed model https://doi.org/10.1016/j. cities.2014.11.003

Key stocks (Several models) • Congestion • Vehicles • CO2 emission

(17 stocks, e.g.) • Population • Lane miles • Buses • CO • Bicycle routes

(only CLD, main variables) • Transportation pollution • Transportation energy consumption • Transportation land consumption • Transportation cost for government • Direct trip cost for user • Indirect transportation cost for user (continued)

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Table 4.3 (continued) Main sector Transport

Name Li, Zhou, and Yang (2013). A System Dynamics Approach for Evaluating Policies on Prioritizing Public Transportation

Transport

Armah, Yawson, and Pappoe (2010). A systems dynamics approach to explore traffic congestion and air pollution link in the city of Accra, Ghana

Transport

Chao and Zishan (2013) System dynamics model of Shanghai passenger transportation structure evolution

Main issue and key concept The paper provides a modeling framework based on the system dynamics approach by which policy makers can understand the dynamic and complex nature of the policies on prioritizing public transportation within a transportation socioeconomic system By using this framework, researchers and practitioners can make modifications on the system structure to evaluate the impacts of policies on prioritizing public transportation such as: License control policy, policy of low fares, traffic control policy The paper aims to provide a system dynamics perspective of the problems. Most of the drivers and cause-effect relationships of traffic congestion and its attendant air pollution are investigated and analyzed using causal loop diagrams Through causal loop diagrams, alternatives were proposed to limit the effectiveness of negative loops. The goal is to stimulate public transport use while decreasing car use in the near future. Three main measures that policy makers could consider are development of a public transport system, road network expanding and enhancing, and travel demand management alternatives Based on the data from a comprehensive transportation survey of Shanghai in 2004 and 2009, this paper analyzed the

Key stocks • GDP • Population • NOx • Number of private vehicles

CLD only

• Population • Car • Coach • Motorcycle • Taxi (continued)

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Table 4.3 (continued) Main sector

Name

Transport

Yang and Wu (2011). Factors analysis of urban transport system in Beijing: Based on system dynamics

Transport

Fontoura, Chaves, and Ribeiro (2019). The Brazilian urban mobility

Main issue and key concept

Key stocks

evolution of urban passenger transportation structure using the system dynamics approach Using statistical methods to calibrate the parameters and validate the model. This model is practical in actual quantitative analysis of the evolution of Shanghai passenger transportation structure. The next step is to apply this model to simulate the dynamic development trend of Shanghai urban passenger transportation structure for long-term dynamic strategic quantitative analysis This paper first establishes a system dynamics model of Beijing urban transport system. Then, based on data simulation, it tries to find out the main factors of urban traffic and its impact on urban transport According to the simulation results, we propose the following traffic control Measures: (l) compared with the vehicle purchase tax, the policy effect of fuel surcharges is more obvious, it can be taken as the focus regulatory methods; (2) in a certain stage, GRPC is obvious positive correlate with RPPD, therefore, Beijing should accelerate the development of subway System; (3) increase in the number of public buses, with optimized circuit design and improved bus service, private car growth rate can be adjusted This paper aims to analyze the influence of Brazilian policies in the urban

• Bus • Rail • Truck

• Total number of family • Parking spaces • Car price • Population • Disposable income per capita • Number of private cars • Bus • Taxi fuel cost • Rail passengers

• Vehicle population • Population (continued)

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Table 4.3 (continued) Main sector

Transport

Name

Main issue and key concept

Key stocks

policy: The impact in São Paulo transport system using system dynamics

transport system focusing on environmental, economic and traffic variables. For this, it conducted a case study in the metropolitan region of São Paulo (MRSP). The results point to the importance of the policy implementation to reduce the negative externalities of urban transport system A strong policy implementation reduces the emission of pollutants, especially if the learning in its implementation is fast. The desirable impact of mobility management strategies implementation in the traffic congestion was achieved when this implementation is rapid and effective In the proposed model, the BUMP encourages only the replacement of private transport by the public transport. The model did not check the policy effect for replacing the motorized modes by nonmotorized ones https://www.sciencedirect. com/science/article/pii/ S0967070X17302354 The paper aims to provide system dynamic approach applied to study the existing urban transportation situation of Hanoi. In the paper, all causes and effects of traffic congestion was investigated and analyzed by the effected method. Long-term policies and measures are proposed and evaluated based on it In order to avoid the positive loop (3), government policy and planning would be considered as a main factor to affect transport situation of

• GDP • Number of public bus • Road network • Stock of CO2

Anh (2003). System dynamic applied to study the urban traffic congestion of Hanoi

CLD only

(continued)

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Table 4.3 (continued) Main sector

Transport

Name

Main issue and key concept

Key stocks

Jifeng, Huapu, and Hu (2008). System dynamics model of urban transportation system and its application

Hanoi. Development of public transportation system is the first priority, with busses. On the supply side: Capacity of transportation system in general and road network in specific is needed to expand. On the demand side: Travel demand management is needed for reducing the potential increasing number of motorcycles and cars. Many alternatives to control the number of private vehicles were proposed, for example, import tax, motorcycle and car registration fee, vehicle owner fee, different prices of petrol for public and private vehicle This paper presents a system dynamics approach based on the cause-and-effect analysis and feedback loop structures. The proposed SD model comprises 7 submodels: Population, economic development, number of vehicles, environmental influence, travel demand, transport supply, and traffic congestion The suggestions to the policy on vehicles include three aspects: (1) restrict the ownership and use of vehicles in an acceptable way; (2) restrict private vehicles and simultaneously improve the service level of public transport; (3) implement the restriction policy and simultaneously put emphasis on the research and development of emission-reducing technologies

• GDP • Population • Stocks of NO2 • Total-lane kilometers

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Table 4.4 List of simulation models on urban planning Main sector Urban planning

Name Ghaffarzadegan, Lyneis, and Richardson (2011). How small system dynamics models can help the public policy process

Urban planning

Tan, Jiao, Shuai, and Shen (2018). A system dynamics model for simulating urban sustainability performance: A China case study

Urban agriculture

Rich, Rich, and Dizyee (2018). Participatory systems approaches for urban and periurban agriculture planning: The role of system dynamics and spatial group model building

4.2.5

Services (Table 4.5)

Main issue and key concept The major strength of the URBAN1 model is its ability to illustrate in a concise manner how the feedback structure of an URBAN system can endogenously generate stagnation and then decay. (. . .) Growth does not slow fast enough, though, to prevent overshoot in the population, stock of housing, and stock of business structures. (. . .) Eventually, an equilibrium is reached in which “the standard of living declines far enough to stop further inflow” (. . .) https://www.sciencedirect. com/science/article/pii/ S0895717711003803 The sustainability performance of Beijing from 2005 to 2030 is simulated based on three scenarios https://doi.org/10.1016/j. jclepro.2018.07.154 System dynamics and participatory planning can be used in urban agriculture policy. https://doi.org/10.1016/j.agsy. 2016.09.022

Key stocks • Business structures • Population • Housing

• Total population • Urbanization rate • GDP • Marketed products • Consumer awareness • Income • Population • Participant UA

EBank simulator By: Kim Warren

Beefeater restaurants By: Kim Warren

Services

Services

Healthcare

People express simulator By: Sterman

Name The University Game By: Barlas and Diker

Services

Main sector Education

Table 4.5 List of simulation models on services Main issue and key concept The model focuses on the academic aspects of university management systems. It specifically deals with resource allocation and provides a broad set of performance indicators to monitor the results of these allocations Understanding complexity in an academic setting. Learning to balance a set of policies https://www.researchgate.net/publication/221000175_ An_interactive_dynamic_simulation_model_of_a_university_management_system Managing a company, in order to promote growth and a quality service Key decisions: Aircraft purchases, fares charged, marketing as a fraction of revenue, hiring and target service scope The purpose of the simulator is to give the player insight into the issues raised by the case; to illustrate the difficulties of co-ordinating operations and strategy in a growth market; and to understand the dynamic interconnections among a firm, its market, and its competitors https://www.strategydynamics.com/microworlds/people-express/features.aspx eBank presents the difficult challenge of matching staffing levels with rapidly varying workloads, when there are unavoidable delays in getting the staff required. Customer acquisition and retention is dictated by interest rates being set Balancing of key resources is vital for successful business performance, and difficult when things are changing fast https://www.strategydynamics.com/microworlds/ebank/ Users act as the executive management team of a business starting with 10 restaurants and work to grow the business and its profitability over 10 years. Decisions control customer experience, profitability, and business growth Business performance over time (growth in sales and profits) depends on building the organization’s resources, which requires investor support https://www.strategydynamics.com/microworlds/beefeater/ • Customers • Restaurants • Cash

• Savers • Trainees • Experienced staff

• Aircrafts • Customers • Cash

Key stocks • Graduate faculty members • Under-graduate faculty members • Vacant positions • No. of students

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Microworld By: Hirsch G.B., Immediato J.M. and Kemeny M.

The model stimulates to understand competitive market dynamics in a highly complex system. Also, it aims at teaching how to manage resources, balance short and long term effects, and allocate resources Management simulators can be effective tools to improve strategic thinking in an organization and help its management and other staff respond to change https://www.systemdynamics.org/assets/conferences/1998/PROCEED/00018.PDF

• Population • Health status • Cash

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4.3

SUSTAIN Model

4.3.1

The Causal Loop Diagram

The Causal Loop Diagram (CLD) developed for the SUSTAIN model is composed by different variables, that represent areas of interest in a general modern urban system at the same time giving emphasis to management issues related to more specific areas/sectors of an urban environment. Stated differently, the model includes both sectors devoted to specific areas of a modern urban environment (e.g., environment, transport, urban planning, waste and water management) and a core sector where “common” variables (e.g., GDP and population) are included and are affected by the decisions taken in other areas of the model. As the CLD (and the subsequently developed Stock and Flow Diagram) was the basis for the development of the rules and elements of the boardgame (which is one of the main outcomes of the SUSTAIN project), the core of the model revolves around one of the most important parameters for deciding who will win the game, i.e. the Attractiveness of the city. This variable is the synthesis of multiple variables that belong to many aspects of the urban system, defining the “wellbeing” of the population who lives in it. The most important effect due to variations in the Attractiveness of the city is a variation of the number of people who live there; this generates many impacts on different urban levels, triggering in turn a certain number of feedback loops. In fact, most of the feedback loops we identified “pass” through the Population variable. It is kind of natural that this happens as, in the end, urban systems exist because of its inhabitants, indeed. Analysing the CLD (see Fig. 4.4), the most important feedback loops were identified and then divided into three main groups. The first group is composed of loops belonging to the “core” of the model, that is constituted by the relation between population, GDP and Industries and Services (Fig. 4.5). The first two reinforcing feedback loops (R1 and R2) trigger when a variation in the Attractiveness of the city causes an increase in Population, which generally has a positive effect on the GDP: the more the GDP, the more the development of industries and services. This generates a twofold positive effect on attractiveness: on one hand, there is the availability of more services and developed industries; on the other hand, more services and industries lead to more jobs for inhabitants. The former phenomenon is limited by a balancing feedback loop (B1), which depicts the saturation of jobs in the city. Finally, GDP and Industries and Services are tied together by a simple reinforcing feedback loop (R3). The second group is composed by loops which belong to the “environmental” part of the model. Water, waste and transport have direct impacts on the total pollution and, in turn, on the Attractiveness of the city. As opposed to the reinforcing loops previously described, there are two balancing loops (B2 and B3) that tend to stabilize the Attractiveness of the city through the

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Fig. 4.4 Overall CLD model

Fig. 4.5 Causal loops details—part 1

possible increase in population, which in turn causes an increase in waste generation and water consumption, with consequences on pollution and water shortage. Another reinforcing feedback (R4) describes how traffic congestion influences the usage of public transport and, in turn, how it impacts pollution. This loop is balanced by two loops (B4 and B5): on one hand, the usage of public transport naturally reduces the problem of traffic congestion; on the other hand, external policies could increase the roads’ capacity and length addressing the same problem (Fig. 4.6).

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Fig. 4.6 Causal loops details—part 2

Fig. 4.7 Causal loops details—part 3

The third and last group of main feedback loops concerns the topic of “land availability”. Cities cannot indeed grow indefinitely; above all, it is important to dedicate some space for green areas and parking lots, which complete the viability of the city. The loops belonging to the third group are balancing loops which limit (Fig. 4.7):

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• Roads extension due to congestion (B6) • Households construction due to population (B7) • Industries and Services development due to GDP (B8).

4.3.2

The Stocks and Flows Model

This section describes the Stocks & Flows Diagram (SFD) of the SUSTAIN model from which the SUSTAIN boardgame was subsequently designed. The online illustrative simulation model allows for experimentation in a consequence-free environment. The simulation model can be used to support decision-making in identifying possible scenarios related to how we can achieve a sustainable and balanced societal metabolism. The whole model is available through the project’s website (and also at the following link: https://exchange.iseesystems.com/public/ale25/sustain) in the form of an Interactive Learning Environment (ILE) that allows students (along with policy-makers and non-experts) to experiment freely before and after playing the boardgame. It is noteworthy to underline that it was not possible to include in the boardgame all of the aspects originally included in the SUSTAIN model and in the ILE; therefore, we have developed the simulation model by focusing on the key elements that were already considered in the preliminary design as well as those provided by relevant literature. The model is divided into several sections: • • • • • • •

Investment-general variables Transport Waste management Water management Environment Energy Urban planning

Even though each of the sectors abovementioned has its own variables and internal dynamics, from a systemic point of view they must be seen as a whole big system which represents the concept of a “city”. Specifically, there are multiple links and interconnections between the variables belonging to different sectors thereby generating the dynamics for the whole city. For example, building a new school has an impact not only at the urban planning level (because it occupies a portion of available land), but also on the waste management, water management, and energy sectors (e.g., in terms of new activities, additional consumption of resources, additional waste, etc.). To provide a realistic representation of a medium-sized city, and use real data, we selected the city of Bologna (Italy) as the underlying reference point. Therefore, all the parameters used to initialize the model (such as the population—i.e., 388.000

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Population City’s budget Urban planning Total area Business units Parks Houses (nearZero houses) Hospitals Areas for education (schools) Leisure & sport areas Total urban roads Transport Private vehicles (electric) Public vehicles (electric) % of citizens using public transport Emissions NOx PM10 Waste and water Waste in landfill Water (reservoir)

Initial used value 388,000 20 M € 7.972 ha 800 72 147 k (23 k) 3 100 250 ha 1600 km 106 k (4.3 k) 300 (70) 25% 2,25B g 200 M g 5 M tons 110B L

citizens -, land covers, etc.) are derived or approximate those of the data taken from the Municipality of Bologna website (see Table 4.6 for more details). Additionally, we set an initial budget at the players’ disposal equal to € 20 M.

4.3.2.1 The Objective The basic objective of planning and managing a city, both in the ILE and in the boardgame, can be summarized as trying to simultaneously manage three specific aspects: the population (social aspect), the city’s budget (economic aspect) and the environmental well-being (environmental aspect). The success (or failure) will be measured through the following key-performance indicators (KPIs): 1. 2. 3. 4.

Population number (maximizing) City’s budget (maximizing) NOx and PM10 (minimizing) Water availability (maximizing).

The optimization goal can be reached by investing in specific sectors, but it will also be necessary to pay attention to the side effects of each investment. Only by “thinking in systems” such planning and management of a city can be sustainable.

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4.3.2.2 Investment-General Variables The fundamental variables of the model as a tool to connect to the board game are the Investments; these represent the decisions of the players in terms of type of investment and amount of money invested. Notably, there are two main kinds of investments. The first group includes investments that will directly affect specific stocks (i.e., resources) and, indirectly, the Attractiveness of the city, these are (also see Table 4.7): • • • • • • • • • • •

Investment on Hospitals Investment on Schools Investment on Business Units Investment on Houses Investment on nearZero energy Houses Investment on Houses conversion to nearZero energy Investment on Leisure and Sport areas Investment on Parks Investment on New Roads Investment for new traditional public vehicles Investment for new electric public vehicles

The second group of investments is devoted to maintaining the efficiency of existing public equipment and infrastructures at the standard level (i.e., if the players do not invest money in these maintenance activities the efficiency of equipment will be lower than usual). These investments include the following ones (also see Table 4.7): • • • •

Investment (i.e., incentives) to encourage electric vehicles adoption Investment on recycling processes Investment for water purification Investment for wastewater infrastructure

Beyond these, the most important variables are those forming the core of the model: Population, Attractiveness of the city, Pollution, City Budget. A city exists, indeed, when some persons live in it. Every aspect of the city is affected by the number of persons that live in the city. The more the persons, the more the resources consumed, activities carried out, waste generated, etc. Population varies over time depending not only on its net birth rate (births—deaths) but also on immigration and emigration. The Attractiveness of the city represents the “wellness” and the satisfaction of the citizens and is influenced by several variables (e.g. number of schools and hospitals, level of pollution in the city, etc.). Notably, all these variables, directly through the Attractiveness of the city, or indirectly in the form of a constraint, influence migration rates in and out of the city, thereby defining the population level. The level of Pollution is a significant factor that can definitely impact on the life of citizens and can also determine their decision of leaving the City. In this model,

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Table 4.7 Available investment decisions for policymakers Investment decision Investment on hospitals

Investment on schools

Investment on business units Investment on houses Investment on nearZero energy houses

Investment on houses conversion

Investment on leisure and sport areas Investment on parks

Investment on new roads

Investment for new traditional public vehicles

Investment for new electric public vehicles Investment to encourage electric vehicles adoption

Investment on recycling

Description All big cities need hospitals. Their number has to be adequate to the size of population. In this model it is possible to create new hospitals but this requires time (4 year), so it is impossible to fill the gap instantly, but it is necessary to plan accurately All cities need schools. Their number has to be adequate to the size of population. In this model it is possible to create new hospitals but this requires time (2 year), so it is impossible to fill the gap instantly, but it is necessary to plan accurately To develop the economy of city, it is necessary to create new business areas to attract new investors. This will create many new jobs for new potential citizens The citizens need houses to live. If there are no empty houses, there cannot be new citizens Near-zero houses have the same function of traditional houses with a higher cost, but the impact of these houses on city’s energy consumption is 0 (with benefits for the energy supply system) It is possible to make a conversion of some actual traditional houses into near-zero layout. This is useful when city’s energy consumption decrease is needed quickly This variable includes all the leisure services, entertainment, recreations and sports that make the city a pleasant place to live City parks and green areas serve an important purpose: In the midst of the population centers, they provide a sense of peace and relaxation. Their extents must be adequate to the city’s area When population grows, the number of vehicles grows as well. Increasing the capacity of the vehicle network is one of the potential solutions in the short-term Local public transport of city has its own fleet that could be enforced by increasing the number of buses. This choice gives benefit to the vehicle network because less private vehicles will circulate on roads The purchased buses could be also electric. Their cost is higher, but they guarantee no emissions compared to the “traditional” ones To tackle the emissions from vehicle traffic, it is possible to push the citizens to purchased new electric cars, for example by deleting parking fees for electric cars or making available more charging centers around the city

Unit cost 200 M

3M

2,25 M

103 k 140 k

32 k

21 M

8M

20 k per km 200 k

400 k

Up to 20 M

Up to 5M (continued)

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Table 4.7 (continued) Investment decision

Description The waste generated by city activities need to be processed and managed. One of the solutions is to recycle part of the total waste

Investment for water purification

The water is a scarce resource that is fundamental for the city life. A percentage of wastewater could be reused as long as sewage treatment plants work well thanks to investments Investments in wastewater infrastructure guarantee that there will be no water losses throughout the network

Investment for wastewater infrastructure

Unit cost

Up to 5M

Up to 4M

pollution is related to the presence of NOx (oxides of nitrogen) and PM10 (particulate matter) emissions. The City Budget represents the liquid assets at the city administration’s disposal. Taxes from people and business activities, as well as revenues from public transport and value from recycled materials, increase the City Budget, which is subsequently (based on actual availability) reinvested in different city sectors. The section of the SUSTAIN Model that depicts the variables aforementioned is shown below (Fig. 4.8).

4.3.2.3 Transport The transport sector in the model reflects a high-level description of transport dynamics inside a city (see Fig. 4.9). There are three transport choices for citizens: private vehicles, electric private vehicles or public vehicles. The latter could also be traditional or electric, but this depends on the policy-maker’s decision. Based on the usage of one or the other mode there is a variation in the number of private vehicles (i.e. new cars purchase), and therefore the car fleet in the city. There are also other variables outside the transport section which affect this variation (i.e. standard purchase rate or adequacy of charging infrastructure for electric cars). The variation of private vehicles which circulate inside the city produces important feedback at section level as well as at overall model level. The modal choice is the core aspect in this section, for the reasons explained before, and is defined by two essential factors: Cost and Time. The cost factor is represented by the cost ratio between the annual cost for private transport and the public one. Each citizen has an annual average kilometre per capita. Private transportation cost results from private car fixed costs (insurance, maintenance, car purchase) and variable cost (fuel) times annual kilometres. While public transportation cost results easily from price per kilometre and annual kilometres. The time factor is represented by the ratio of time needed to arrive at the destination with both modes, considering the different mean velocity.

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Fig. 4.8 Investment-general variables

Public transport fleet could be developed by investing both on traditional (e.g. petrol busses) and electric vehicles (e.g. trams). Each of them has different costs of purchase and maintenance, also they have a different impact in terms of GHG/CO2 emissions. Such sector is controlled by Urban planning sector through the planning of new lane-kilometres. At the same time, investments on roads have an impact also at the urban planning level, because they consume the city’s land availability for other buildings and activities. Another important factor is represented by traffic congestion, which is due to the number of private vehicles, public vehicles and network capacity. The congestion negatively affects the attractiveness of the city indirectly by the related traffic emissions which influence the sector Environment (that in turn has impact on the attractiveness of the city).

4.3.2.4 Waste Management The Waste Sector is modelled in order to represent the whole cycle of waste from its generation to its disposal, elimination and/or transformation into recycled raw

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Fig. 4.9 Transport section

Fig. 4.10 Waste management section

materials (see Fig. 4.10). In detail, the supply chain makes clear that waste is first generated (it depends on the number of the various activities in the city and the average waste generated by each class of them) and needs to be managed and processed subsequently.

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Three main outputs can be obtained managing the waste generated in the city. First, part of the waste is recycled; second and subsequently, part of the waste is burnt using the incinerator, or sent to the Landfill. These rates depend on the levels of investments carried out in this sector by the players. Specifically, the players have to decide the levels of investments devoted to recycling activities, which are needed to avoid sending a great amount of waste to the incinerator or the Landfill. Notably, this area impacts on other sectors, since the Landfill generates pollution, the incinerator provides energy but also generates emissions, and recycled materials are worthy.

4.3.2.5 Water Management The Water sector of the model is based on the main idea that any activity in the City consumes water and generates wastewater (see Fig. 4.11). Water consumption is modelled multiplying each category of stock for a constant, representing the average consumption of water for that specific class: for example, the total number of hospitals is multiplied by the “Average water consumption per hospital” to define the Water Consumption for this specific class. Part of the water that is used creates wastewater that needs to be purified and is subsequently treated in an Advanced purification plant). Wastewater going through advanced purification plants is therefore purified and represents an inflow to (pure) Water in the City’s Reservoir. The Reservoir is also increased by Rainwater naturally entering in it.

Fig. 4.11 Water management section

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Investments in this area are required to keep the purification plants in order and efficient, otherwise they will underperform, and less water will be purified. Overall, if the Reservoir will not be high enough in comparison with the City’s needs, there will be a water shortage. A specific variable named “Water availability” is calculated in order to inform the players’ decision about such a possibility. This variable feedbacks within the system influencing the Attractiveness of the City.

4.3.2.6 Environment All the activities carried out within the cities produce pollution (see Fig. 4.12). In the project we refer to two of the main pollutants in urban areas: NOx and PM10; these are commonly monitored variables and the relative data sets are easy to find. NOx and PM10 are side effects of different types of activities such as industry, transport and waste management, which is why they form a balancing loop (negative feedback) on the attractiveness of the city with respect to the desirable growth of economic activities.

Fig. 4.12 Emissions section

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Fig. 4.13 Energy section

4.3.2.7 Energy Cities consume energy based on the number of schools, hospitals, leisure structures, industries and households (see Fig. 4.13). The total energy consumption of the city is an important factor to consider, because it defines the capability of the city to meet the need for energy of its citizens and business activities. In fact, an inappropriate level of energy capacity could cause some local blackouts, compromising the liveability and so the attractiveness of the city. This kind of problem could be softened by the use of incinerators during the waste management process. In fact, incinerators provide city with additional energy, lowering the total level of consumption (anyway, incinerators have their own emission to be considered). 4.3.2.8 Urban Planning The urban planning sector (see Fig. 4.14) concerns the consumption of available land for functional areas, dedicated to specific purposes (land uses or land covers). The functional areas, for housing (NearZero houses and Classic houses), health services (Hospitals), leisure and recreation (Leisures and Parks), parking (Parking space) and education (Schools), and production or job creation (Industries) increase the Attractiveness of the city, but at the same time they exhaust the available land. Each function, once established, also implies negative consequences on the quality of the city itself, in terms of waste, emissions, energy and water consumption, as well as influencing the transport sector. These negative consequences are modelled by other sectors such as Water, Waste and Environment. The distinction between NearZero houses and Classic houses allows to choose different type of buildings with different costs and associated energy consumptions

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Fig. 4.14 Urban planning section

(e.g. for cooling/heating). In the board game, such difference could be played using different actions such as investment in a standard “classic house” or in “near-zeroemission house” (or in investing conversion of standard into near-zero emission house).

4.3.3

SUSTAIN Scenario Analysis and Simulations Results

The SUSTAIN model offers the possibility of identifying and testing hypothetical scenarios related to strategies for pursuing sustainable urban development, to be balanced between different needs of transport, construction, services and environmental quality. The “scenario analysis” is a well-known methodology that, building on the use of different hypotheses or decision-making options, allows for the inspection of the outputs and outcomes thereby facilitating a deeper understanding of the consequences stemming from our decisions and actions.1 Faced with specific conditions (related to the scenario to be selected and played), players are challenged to make efficient decisions, having the opportunity to analyse and understand the consequences generated by their decisions and actions. Stated differently, through simulations players can better understand the system being analysed, thus developing their decision-making skills and encouraging learning. The learning situation is playing the role of a city council (or other decision maker) willing to invest to develop the city and increase the number of “happy citizens”, associated with the variable city Attractiveness. As above described, in this 1

For more details on the use and potentials of scenario analysis see Schoemaker (1993, 1995).

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Fig. 4.15 Landing page of the ILE (https://exchange.iseesystems.com/public/ale25/sustain)

challenge there are two main reinforcing feedback loops that support Attractiveness, through the variables GDP and Population, involving Industries and services and Available jobs; these favourable loops are counteracted by balancing feedback loops involving processes such as pollution, resource consumption, traffic congestion, space limitation and service saturation, that representing the “wellness” and the satisfaction of the citizens and is influenced by several variables (e.g. number of schools and hospitals, level of pollution in the city, etc.). Scenarios can be easily created and tested by modifying the inputs to the model, that is setting the slider bars in the web-based simulator (the mentioned ILE,2 Figs. 4.15 and 4.16) corresponding to the investment decisions in areas such as Public buildings, Public Housing, Transport, Water Management, Waste & Environment. Several graphs show the effects generated by the decision (i.e. variable settings). The simulation runs respond to the chosen settings by providing plausible outputs, making the effects of the aforementioned feedback loops evident; obviously, the results we propose in this chapter should be intended as didactic examples, which are thus not calibrated on real data. Inspired by the scenario planning approach,3 in order to explore the space of possibilities, we considered two key variables, GDP and Population, as the two most uncertain and relevant for the sustainability of the city (being dependent on

2

https://exchange.iseesystems.com/public/ale25/sustain Schwartz (2012). The Art of the Long View: Planning for the Future in an Uncertain World. Crown Publishing Group. 3

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Fig. 4.16 Decision board with slide bars associated to the model variable settings

Fig. 4.17 Quadrant of strategic scenarios: the arrows indicate some of the possible developments of the model city between different scenarios

many processes beyond a city government), and we used them to structure a “quadrant” of strategic scenarios. All possible combinations of conditions for the simulated city can be considered being included in one scenario belonging to one of the four quadrants. The “position” of the simulated city in the quadrant of scenarios (Fig. 4.17) has to be considered as dynamically changing: this means that if scenario 1 is preferable, this cannot be taken for granted once it has been reached, it will always be at risk of

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Table 4.8 Extreme scenarios and related possible variable settings Extreme scenarios 1. Attractiveness of dreams 2. Rich but few 3. Few and unhappy 4. Austerity and green generation

Variable/slide bar settings (other variables being equal) Max value for Conversion of houses into NZ Houses, New Electric public vehicles, Investment in wastewater infrastructure, Investments in water purification, Investments on recycling processes, New Parks Max value for New Business units Max value for New Business units, New Houses Max value for New Schools, New hectares for leisure and sport areas, Conversion of houses into NZ Houses, Investment in wastewater infrastructure, Investments in water purification, New Parks

Fig. 4.18 Extreme scenarios with dynamics in the key variables (left: Population; right: City budget) resulted from variable setting shown in Table 4.8

falling into other situations (scenarios 2, 3 or 4). The arrows indicate a possible worsening of the city model starting from the desired state, as well as the expected direction of change as a consequence of investments in different sectors, starting from problematic situations. This provides a framework for organizing reasoning from two perspectives in terms of possible scenarios that may follow possible investments or future scenarios that are to be achieved or avoided through the investment strategy. Table 4.8 shows the variable settings corresponding to the four extreme scenarios depicted in Fig. 4.17, that results in the dynamics illustrated in Fig. 4.18. This framework can be used as a reference for those common situations in which a city finds itself, as well as a reference, in the ILE, for the motivation of players to challenge each other in improving the city behaviour. In fact, the general objective of

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the players (in the role of municipal administrators) is to increase the number of happy citizens, “satisfied” with the space available for life and leisure, accessible services (such as education and school, health and hospitals), and a job. Any investment in one of these sectors will have consequences on other sectors while it could in turn increase pollution, waste production, energy consumption, water consumption, and reduce available land. Successful (or winning) policies will consist of balanced investments in various sectors, which can be achieved by trial, error, and learning. Among the numerous combinations of states of the considered variables, Table 4.9 shows a few examples of typical issues of urban planning and management and possible coping strategies, here in terms of leverages settings for each input variable, which ultimately lead to a change of situation (towards a desired scenario). Recognizing the fact that a city is a complex system, and that every action on a system always has more than one consequence, it is not guaranteed that the strategies that we expect to lead to a desirable scenario will be effective in the medium and Table 4.9 Common situations, possible associated policies, and references to the strategic scenarios Situation (title) A. Black-out again!?

B. One bus every half hour. . . C. The air is unbreathable!

D. Water shortage

E. Garbage everywhere! F. Each citizen requires a job first a

Problem The city consumes too much energy, due to the increasing number of public structures, roads (street lighting) and houses. This increases the probability of blackouts The number of bus is too low to cover the city area. Citizens have to wait too much time for the buses Increasing level of PM10, due to vehicles, make the air unhealthy The city growth entails a huge increase water consumption, water shortages is becoming likely The growing business areas and facilities generate a huge amount of waste Every citizen requires a job first, the ruling party intends to invest enormously and only in new jobs

Possible policies (variable settingsa) Invest on new NZ houses and on conversion of traditional house into NZ ones

Scenario reference At best, it approaches scenario 1, but at risk of budget fall

Increase the number of buses (traditional or electric ones)

At best scenario 1 but at risk of becoming 4

Incentives to buy electric cars and invest in new electric public vehicles

At best, scenario 1, but at risk of economic stagnation At best, scenario 1, but at risk of slight stagnation

Invest on wastewater infrastructure and to keep the water purification plants efficient Invest in recycling activities Invest in functional areas (business units)

The mentioned variables are set to the maximum of their value

At best, scenario 1, but at risk of slight stagnation At best, scenario 1, but at risk of natural resources scarcity (water)

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Table 4.10 Scores of the simulated policies

Key scores Base case A. Black-out again!? B. One bus every half hour. . . C. The air is unbreathable! D. Water shortage E. Garbage everywhere! F. Each citizen requires a job first.

(Maximize) population 321 k 314 k 377 k

(Maximize) City budget 2.52B
956 M 2.1B

(Minimize) Pollution (NOx / PM10) 2.68 k/300 2.71/299 3 k/156

403 k

1.94B

1.38/89.8

73.3

343 k 329 k 322 k

2.41B 2.44B 1.63B

2.71 k/307 2.31 k/303 2.68 k/300

232 80 57.5

(Maximize) Water reservoir 73.3 35.3 73.3

long term, or that the ideal situation achieved is stable; Table 4.9 also shows possible risks deriving from the dynamic evolution of a city over time. For example, a fast-growing city will inevitably lead to an increased generation of waste that will need to be collected and subsequently treated; if players neglect to invest enough on recycling processes (see the associated slide bar in Fig. 4.16), the City will find itself full of garbage (see situation E in Table 4.9). In other words, while playing and experimenting with expected scenarios or current situations, players have the opportunity to test their ideas and policies, at the same time understanding the wide range of consequences that will stem from their decisions across the various city model sectors. In order to compare the performance of different policies, the ILE includes a “key score” module showing the key criteria of evaluation: population, city budget, pollution and water reservoir (Table 4.10).

4.3.4

SUSTAIN Strategies and Key Learning Points

At the end of this chapter, we would like to emphasize some specific learning points that emerge from the Project and are related to the methodological principles and the simulations tools here employed and suggested. The starting point is to recognize that urban sustainability is a complex concept which entails the simultaneous management of several variables and policy levers. Stated differently, urban sustainability is a concept that needs to be addressed in— and studied with—a systemic perspective. More in detail, a systems perspective implies the existence of interconnected elements to fulfil a function or a purpose over

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time. Indeed, a systemic and comprehensive approach to “urban sustainability management” is the focal point of this project. To reach this aim, we adopted the System Dynamics paradigm, that provides researchers and learners with a comprehensive set of principles and tools, both qualitative and quantitative. Among the qualitative tools, we have shown that causal loop diagrams (CLDs) may help to analyse a complex system by representing the complex causal interconnections among its variables. More interesting, CLDs allow identifying feedback loops (reinforcing or balancing) the basic structures creating the non-linear dynamics in systems. Coming to the quantitative tools, System Dynamics is centred on the use of simulation models and interactive learning environments, which add a graphical interface atop of the simulation model. These tools were used to visual the structures of a city system and to simulate the results of the interventions on the city system model. Thus, players by interacting with the ILE can explore their ideas, develop and test policies and subsequently observe and understand the impacts generated by the decisions being carried out. To support the discussion and reasoning about sustainability strategies we proposed the strategic scenario approach, helpful to disentangle the uncertainties and to explore the space of possibility. The described scenario quadrant reports four extreme possible scenarios, useful in terms of common references in group discussion. Different situations (in the present) or different scenarios (in the future) to achieve or to avoid correspond to different positions in the scenario quadrant, while a variety of variable settings can represent strategies expected to lead to a change of situation. We can summarize some specific “learning points” from SUSTAIN model and crucial for urban sustainability: 1. An urban environment is a complex system where many variables interact at the same time. This entails identifying and focusing on the relationships among the various sectors of that system rather than focusing on single (or small) parts of it (e.g., investing in electric vehicles is a policy that will impact also on the environment and not just on the urban transport system). 2. In a such system, the many variables will co-exist and must be managed simultaneously. This implies not only identifying such variables but also understanding that they will be characterized by multiple trade-offs (e.g., to build new business areas we need to invest money, in this way depleting the City Budget; at the same time, building new business areas will generate more waste and pollution; etc.). 3. Time is a key feature of our systems. It takes time (i.e., there are time delays) to build new hospitals or schools. Stated differently, we need to plan for the future taking into account the time-delays involved by the actions we will be carrying out. 4. Explore your ideas and test your policies. A simulation model, such as an ILE, allows experimenting policies within a safe environment. Simulations can be repeated, different policies can be developed and tested, and fine-tuning is possible through multiple simulation runs.

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5. Do not rush your decisions. Instead, consider carefully which policies could be developed and which investment decisions will be actually applied. 6. Scenario analysis can help challenging your beliefs. The opportunity to perform scenario analysis within an ILE is a fundamental learning point: challenged with problematic situations, the players will speed up their understanding and learning process in order to come up with feasible solutions. 7. Learning comes first. The Sustain Model is not to be used to search for the “best” policy, rather explore the dynamics existing within a complex system and the effect generated by our own policies. Analysing the results, understanding the impacts of investment decisions, spotting the relationships among variables should be our primary targets. Overall, we believe that the SUSTAIN project and, in more detail, the SUSTAIN SD model may provide students and practitioners with an innovative training and learning tool through which they will be able to find new challenges on their road to the analysis, understanding and efficient and sustainable management of a modern (and future) urban environment.

Appendix A: Generic Case Studies Using System Dynamics Available Online Title HIV/AIDS Response Programs

Wind Tunneling Business Strategy Dealer Hoarding, Sales Push and Seed Returns Attrition of Staff Hughes Aircraft Northrop Grumman Recycling Rate Litton

Client Ministry of Health and Public Hygiene, Republic of Côte d’Ivoire President’s Emergency Plan for AIDS Relief (PEPFAR), US Embassy in Côte d’Ivoire Vertex, Inc.

First Author Paulo Gonçalves, Simplice Takoubo Kamdem

Sector Health Care

Kevin Boettcher

Hybrid seed supplier (e.g., Monsanto, Pioneer, Syngenta) United Nations Mission in Kosovo (UNMIK) Hughes Aircraft Company Northrop Grumman Waste Agency of Oslo, Norway Litton Industries

Paulo Gonçalves

Computer, Information Human Resource Management, Sales and Marketing Human Resource Management Aerospace

Paulo Gonçalves Ken Cooper Ken Cooper John Egil Nilssen Ken Cooper

Aerospace Waste management Manufacturing (continued)

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Firm-Level Capability Development Tradeoffs MasterCard

N/A

Hazhir Rahmandad

Financial Services

MasterCard

Kenneth Cooper

Anemia Control

N/A

Oral Health

State Secretary of Health of Paraná Centers for Disease Control and Prevention (CDC) and National Heart, Lung, and Blood Institute (NHLBI) You can find a list of funders here, and clients, and users here. Coca-Cola Inc.

James T. McCarthy Mitsue Fujimaki

Research and Development Health Care Health Care

Jack Homer

Health Care

John Sterman

Environment

Foresight Associates

Food

Kaveh Dianati

Telecommunications, Information Logistics & Transportation

PRISM

C-ROADS

Pushing the boundaries of marketing ROI at Coca-Cola Data Center Capacity Planning Keep on rolling— managing a large rail improvement project Are You vMad To Go For Surgery? Risk Assessment for Transmission of vCJD via Surgical Instruments A case study in strategic human resource management System Dynamics & Agent-Based approaches to face HR constraints Children’s Oral Health

The Organizational Responsibility Model for Public Companies Social Determinants of Health in a Diverse Urban Population

Telecomputing, Norway London Underground

Steve Curram

UK Department of Health

Curram S

Health Care

German service provider in the logistics industry Public ICT company localized in Minas Gerais, Brazil; Primary Hospital Localized in Foz do Iguaçu, Brazil Colorado Department of Public Health and Environment, New York State Department of Health Brazilian state of Minas Gerais

Andreas Größler

Logistics & Transportation

Passos GF

Multisector planning

Gary Hirsch

Health Care

Passos GF

Other

Mahamoud A

Health Care

Wellesley Institute (Toronto)

(continued)

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ReThink Health Dynamics Haaglanden Managing the Inventory of Test Items used in ComputerBased Educational Testing Projecting Motorcycle Parts and Accessories Sales Analyzing Price Cycles in Commodity Chemicals Strategies to Improve Freight Railroad Performance Hardware Maintenance Field Service Dynamics Cocaine Use Prevalence Estimation and Policy Analysis Marketing Strategy for a New CholesterolLowering Drug Antibiotic Resistance Dynamics Hospital Surge Capacity Planning

Local Strategy for Chronic Disease Management and Prevention Obesity Population Dynamics HealthBound

Medicare Payment Rate

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Fannie E. Rippel Foundation (New Jersey) Housing market Educational Testing Service (ETS), the world’s leading developer and provider of standardized educational tests A large US manufacturer of motorcycles A large global chemical company (Dow Chemical) A major freight railway company (CSX Transportation) Major producer of diagnostic equipment used in semiconductor wafer fabrication National Institute of Justice, US Department of Justice A large global pharmaceutical company (Sandoz, now Novartis) Texas Department of Health Health Resources and Services Administration (HRSA), US Department of Health and Human Services A large hospital in Washington State

Centers for Disease Control and Prevention (CDC) Centers for Disease Control and Prevention (CDC) Government

Jack Homer

Health Care

Eskinasi M Homer J

Urban Planning Education

Homer J

Manufacturing

Homer J

Chemicals

Homer JB

Logistics & Transportation

Homer J

Manufacturing

Homer J

Criminal Justice

Homer J

Health Care

Homer J

Health Care

Manley W

Health Care

Homer J

Health Care

Homer J

Health Care

Bobby Milstein

Health Care

Jack Homer, Gary Hirsch

Health Care (continued)

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Cement industry price dynamics in Iran National airspace system Logistics support system for the US coast guard Monetary policy Spatial planning in Indonesia

Health and social care policy in the UK Energy policy analysis in Mauritius Climate change and energy

Urban dynamics

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Cement Investment and Development Company US Federal Aviation Administration US coast guard

Ghaffarzadegan N Ventana Systems Ellis RE

Chemicals

Central Bank of Colombia Ministry of Human Settlements and Regional Infrastructures Development, Indonesia UK Government

Arenas F

Financial Services

Radianti J

Urban Planning

Wolstenholme E

Health Care

Bassi AM

Energy

Bassi AM

Energy

Swanson J (Sdgworld; Steer Davies Gleave) Warren K (Strategy Dynamics)

Urban Planning

Health Care

Water

Chemicals

Ministry of Renewable Energy and Public Utilities, Mauritius National Commission on Energy Policy, Environmental Defense Fund, WAI UK local authorities

International Council on Systems Engineering (INCOSE) Supply chain options in pharmaceuticals

INCOSE

State planning in Sarawak Medical Device Company

Sarawak State Planning Unit ALK-Abello, Denmark

Pharmaceutical product life-cycles

Anonymous Pharma Co

Sustainable water management in Laikipia District (Kenya) Maintenance improvement at ONEgas

CETRAD

Jones L (Ventana Systems UK) Brian Dangerfield Kim Warren (Strategy Dynamics) Jones L (Ventana Systems UK) Gallati J

ONEgas

Venderbosch T

Anonymous pharma co

Logistics & Transportation Defense

Engineering

Multisector Planning Health Care

Health Care

(continued)

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Criminal justice Project management at Fluor Process innovation at Du Pont Diabetes Polio eradication Pharmaceutical Product Branding Strategies General Motors OnStar

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Ministry of Justice, the Netherlands Fluor

Rouwette EAJA

Criminal Justice

Kenneth Cooper

Du Pont

Repenning NP

Engineering, Construction Chemicals

CDC World Health Organization (WHO) Numerous Pharmaceutical Companies General Motors

Jones AP Thompson KM

Health Care Health Care

Mark Paich

Health Care

Vince Barabba

Logistics & Transportation, Information

References Anh, T. T. (2003). System dynamic applied to study the urban traffic congestion of Hanoi. In Proceedings of the Eastern Asia Society for Transportation Studies (Vol. 4, pp. 1693–1697). Madison, WI: University of Wisconsin. Armah, F., Yawson, D., & Pappoe, A. A. (2010). A systems dynamics approach to explore traffic congestion and air pollution link in the city of Accra, Ghana. Sustainability, 2(1), 252–265. Armendáriz, V., Armenia, S., & Atzori, A. (2016). Systemic analysis of food supply and distribution systems in city-region systems—An examination of FAO’s policy guidelines towards sustainable Agri-food systems. Agriculture, 6(4), 65. Armenia, S., Bellomo, D., Medaglia, C. M., Nonino, F., & Pompei, A. (2019). Water resource management through systemic approach: The case of Lake Bracciano. Journal of Simulation, 1–17. https://doi.org/10.1080/17477778.2019.1664266. Chao, Y., & Zishan, M. (2013). System dynamics model of Shanghai passenger transportation structure evolution. Procedia-Social and Behavioral Sciences, 96, 1110–1118. De Stercke, S., Mijic, A., Buytaert, W., & Chaturvedi, V. (2018). Modelling the dynamic interactions between London’s water and energy systems from an end-use perspective. Applied Energy, 230, 615–626. Elliot, T., Rugani, B., Almenar, J. B., & Niza, S. (2018). A proposal to integrate system dynamics and carbon metabolism for urban planning. Procedia CIRP, 69, 78–82. Fontoura, W. B., Chaves, G. D. L. D., & Ribeiro, G. M. (2019). The Brazilian urban mobility policy: The impact in São Paulo transport system using system dynamics. Transport Policy, 73, 51–61. Forrester, J. W. (1961). Industrial dynamics. Cambridge, MA: The MIT Press. Forrester, J. W. (1968). Principles of systems. Cambridge, MA: The MIT Press. Ghaffarzadegan, N., Lyneis, J., & Richardson, G. P. (2011). How small system dynamics models can help the public policy process. System Dynamics Review, 27(1), 22–44. Haghshenas, H., Vaziri, M., & Gholamialam, A. (2015). Evaluation of sustainable policy in urban transportation using system dynamics and world cities data: A case study in Isfahan. Cities, 45, 104–115. Jifeng, W. A. N. G., Huapu, L. U., & Hu, P. E. N. G. (2008). System dynamics model of urban transportation system and its application. Journal of Transportation Systems Engineering and Information Technology, 8(3), 83–89.

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Kapmeier, F., & Gonçalves, P. (2018). Wasted paradise? Policies for Small Island states to manage tourism-driven growth while controlling waste generation: The case of the Maldives. System Dynamics Review, 34(1–2), 172–221. Li, K. D., Zhou, S. E., & Yang, X. M. (2013). A system dynamics approach for evaluating policies on prioritizing public transportation. Applied Mechanics and Materials, 391, 628–632. Meadows, D. (1989). Gaming to implement system dynamics models. In P. M. Milling & E. O. K. Zahn (Eds.), Computer based Management of Complex Systems (pp. 635–640). Berlin: Springer. Mirchi, A., Madani, K., Watkins, D., & Ahmad, S. (2012). Synthesis of system dynamics tools for holistic conceptualization of water resources problems. Water Resources Management, 26(9), 2421–2442. Rich, K. M., Rich, M., & Dizyee, K. (2018). Participatory systems approaches for urban and periurban agriculture planning: The role of system dynamics and spatial group model building. Agricultural Systems, 160, 110–123. Richardson, G. P., & Pugh, A. (1981). Introduction to system dynamics modeling with dynamo. Waltham: Pegasus Communications. Schoemaker, P. J. (1993). Multiple scenario development: Its conceptual and behavioral foundation. Strategic Management Journal, 14(3), 193–213. Schoemaker, P. J. (1995). Scenario planning: A tool for strategic thinking. Sloan Management Review, 36(2), 25–40. Schwartz, P. (2012). The art of the long view: Planning for the future in an uncertain world. Crown Publishing Group. Senge, P. M. (1990). The Fifth Discipline. New York: DoubleDay. Shepherd, S. P. (2014). A review of system dynamics models applied in transportation. Transportmetrica B: Transport Dynamics (Pembroke, Ont), 2(2), 83–105. Stave, K. A. (2002). Using system dynamics to improve public participation in environmental decisions. System dynamics review: The journal of the system dynamics. Society, 18(2), 139–167. Sterman, J. D. (2000). Business dynamics: Systems thinking and modeling for a complex world. New York: Irwin Professional McGraw–Hill. Sudhir, V., Srinivasan, G., & Muraleedharan, V. R. (1997). Planning for sustainable solid waste management in urban India. System Dynamics Review: The Journal of the System Dynamics Society, 13(3), 223–246. Tan, Y., Jiao, L., Shuai, C., & Shen, L. (2018). A system dynamics model for simulating urban sustainability performance: A China case study. Journal of Cleaner Production, 199, 1107–1115. Timms, B. S., Guerin, D. R., Arnold, M. R., & Vaudreuil, M. P. (2011). System dynamics computer simulation Modelling to forecast the energy demands for the Montachusett region under a variety of simulations and scenarios. Montachusett: Worcester Polytech Institute Digital WPI. Wei, T., Lou, I., Yang, Z., & Li, Y. (2016). A system dynamics urban water management model for Macau, China. Journal of Environmental Sciences, 50, 117–126. Wu, D., & Ning, S. (2018). Dynamic assessment of urban economy-environment-energy system using system dynamics model: A case study in Beijing. Environmental Research, 164, 70–84. Yang, T. J., & Wu, L. (2011). Factors analysis of urban transport system in Beijing: Based on system dynamics. In Proceedings of 2011 IEEE international conference on service operations, logistics and informatics, Beijing, China, 10–12 July 2011 (pp. 168–171).

5

Translating Models into a Game Design Michalina Kułakowska and Aleksandra Solińska-Nowak

5.1

Link with SUSTAIN Project

Urban sustainability is a complex concept that is composed of many elements, which are difficult to comprehend and understand without a wider perspective on the whole urban system. A serious game can give the participants of the SUSTAIN course a chance to experience the components of the urban sustainability described in the previous chapters. It can also provide them with the safe environment for facing the basic challenges and trade-offs existing in every city. Authors of this chapter aim at translating the topics from the previous chapters into potential game elements and mechanics to prepare a foundation for further development of the game by the Ergo Ludo Editions. This translation will be supplemented with propositions of the accompanying materials and a list of resources for further reading to supplement the knowledge of both lecturers, who intend to use the SUSTAIN course.

5.1.1

What Is a Serious Game?

A serious game is an activity that combines game elements with a serious goal and as such may be used to, e.g., guide skill or knowledge development (Djaouti, Alvarez, & Jessel, 2011). In its contemporary use, the term is commonly attributed to Abt who used it in his book “Serious games” to refer to activities that “are not intended to be played primarily for amusement”. He also provided several examples of such games, which included both digital and “pen-and-paper” games, such as simple activities that enhance mathematical skills development (Abt, 1970). A special type of serious game is a social simulation. Unlike other serious games that may be played alone (e.g. single-player computer game or quiz), social M. Kułakowska (*) · A. Solińska-Nowak Stowarzyszenie Centrum Rozwiązań Systemowych, Wrocław, Poland e-mail: [email protected] # The Author(s), under exclusive license to Springer Nature Switzerland AG 2021 J. Papathanasiou et al. (eds.), Urban Sustainability, Springer Texts in Business and Economics, https://doi.org/10.1007/978-3-030-67016-0_5

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simulations always entail lively interactions with other players. In this type of activity, participants gather in one room and enter a special place in time and space, a magic circle (Huizinga, 1955) that is governed by simulation-specific rules and players’ individual or collective goals. Immersed in the simulation, participants become meaningfully engaged in something that could be described as creative group scenario building, storytelling or role-playing (Geurts, Duke, & Vermeulen, 2007). In this different reality, players discard their regular social roles and assume new identities. Liberated from their daily obligations, they may freely exploit their creativity and come up with completely new solutions to the emerging dilemmas (Geurts et al., 2007). Importantly enough, unlike regular purely entertaining games, social simulations are predominantly focused on cooperative or collaborative interactions rather than rivalry. Through sharing new problem perspectives and different kinds of knowledge and expertise, diverse people can observe, understood or even inspire each other to develop new mindsets and modify their behaviors through a collective process, referred to as social learning (Bouwen & Taillieu, 2004).

5.1.2

Learning Through Serious Games

How can this learning occur? The time and space in social simulations are compressed—an hour may correspond to a year or decade, a small board or map may represent the whole region, city or country. Yet, the basic mechanisms and challenges presented and played out by participants correspond to real processes and situations. The immersion in a simplified yet realistic simulation world may help players obtain the “gestalt,” “the big picture” (Duke, 1974) of a specific problem, discover the interrelationship between their decisions, the decisions of other players and their emerging consequences. This capacity of social simulations (and other serious games) to reveal “how things work” is referred to as procedural rhetoric— “the practice of using processes persuasively” or “authoring arguments through processes” (Bogost, 2008). In procedural rhetoric, arguments about “how things work” are made implicitly rather than explicitly, not through actual warnings, advising or recommendations, but through being actively engaged in a dynamic model and exploring its boundaries to figure it out. For example, a flight simulator models how the mechanical and professional rules of aviation work (Bogost, 2008). Social simulations, on the other hand, may model complex challenges, such as energy transition, social conflicts or policy-making. Learning through social simulation and serious games differs thus significantly from traditional knowledge acquisition, like that based on textbooks and lectures. Learners (or players, to be more precise) in social simulations are not passively exposed to lectures, diagrams or figures but are engaged in collective problemsolving, directly testing and verifying different strategies. In other words, games and social simulations naturally follow Experiential Learning Cycle, based on a “trial and error” process by which experience is turned into knowledge (Kolb, 1984).

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Fig. 5.1 Experiential learning

According to experiential learning theory, knowledge is acquired in four sequential stages: (1) a concrete experience (what we see, feel or touch), (2) reflective observation (what we think about it and how we relate it to what we already know), (3) abstract conceptualization (making sense of the information available and drawing conclusions or developing theories.), and (4) active experimentation via applying new or modified ideas into the world to check what happens (Fig. 5.1) (Kolb, 1984). When faced with a dilemma in a simulation (concrete experience), players use critical thinking and their ability to exploit their existing knowledge to deal with a new situation. Almost immediately after their decision, a result phase reveals its consequences. It may bring about, for example, a financial gain/loss or an unexpected natural disaster. It may also unlock new possibilities or, on the contrary, leave a player penniless and dependent on others. When faced by frustrating or unexpected outcomes, players may easily modify their assumptions and base their subsequent decisions (in the following rounds) on a more sound recognition of a problem. Experiencing this decision-feedback cycle several times during one game session, helps with verification of ineffective mental models and consolidation of new, more adequate strategies towards optimal solutions. As a result, players may develop self-reflection and undertake corrective action, fostering what is referred to as double loop learning (Argyris, 2002). Such active experimentation with solutions is very natural, rooted in intrinsic human curiosity and our willingness to try things out. For this reason, serious games and simulations are gaining recognition as a tool for addressing problems that require strategic thinking and collective decision-making. They are also often used in education, healthcare, defense, advertising, environmental awareness and sustainability, communication, politics, etc. (Djaouti et al., 2011).

5.2

Serious Games: Good Practices

With the contemporary socio-economic issues and climate crisis (IPCC, 2019) becoming more visible for average people, a need has appeared for new tools to talk about and engage the public in solving the biggest challenges of our times.

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A number of board games have been developed in recent years to address sustainability issues, including The World’s Future (Solinska-Nowak & Anthony, The World’s Future is in our hands, 2019), Ruritania Game (Anthony, 2019), Nexus Game (Mochizuki, Magnuszewski, & Linnerooth-Bayer, 2018), World Climate (Sterman et al., 2014), Flood Resilience Game (Magnuszewski et al., 2019). Those games, which include elements of or introduce the sustainability-related topics and themes, will be hereinafter referred to as the sustainability games. Many of those games try to translate urban environment components into elements of playable game that would be both entertaining and educational, with varied levels of success. In this chapter we present the examples of several sustainability games, together with a summary of observed good practices, which could be adopted in the design of the Sustain board game.

5.2.1

Serious Games for Sustainability

With the resurgence of topics related to the sustainability and adoption of the Sustainable Development Goals, also known as the Global Goals, many organizations and companies started to use and develop games, ranging from workshop-based role-plays to commercially published board games that could be used at home with friends and families. Most of the examined games are multiplayer games, designed for 2–30 participants, which adds to a lively atmosphere and spurs creative exchange of thoughts, especially in larger groups where several perspectives may be represented. The majority of games require forethought and strategic thinking, as participants have to carefully weigh their actions against the decisions of other players and the resulting consequences. In The World’s Future (Table 5.1), players have to literally think about the world’s future where their country’s supply is constantly challenged by the demands of the growing population. In a different game, Suburbia (Table 5.2), each player is developing their own neighborhood from available building tiles. Each tile is different, and while proper placement can generate significant profits, unfortunate one may radically reduce the tile’s value (Smith, 2015). Strategic planning is also at the core of the Let’s Make a Bus Route game (Table 5.3), as players (acting as bus drivers) cannot drop their passengers off randomly but rather carefully think about the order they let them out (for example, if a player stops by the shrine and allows a tourist to get off the bus too soon, he or she won’t score many points in the end). Route planning gets even more complicated in the course of playing because players are not allowed to cross back over an intersection they have been to previously. As a result, “[y]ou need to think ahead as you plan out your map to make sure you get the stuff you need, while also making sure don’t accidentally trap yourself on one side of the board” (SGL, 2018). An additional advantage of the games described is the fact that they encourage creativity and self-expression. In Let’s Make a Bus Route, players are given markers and dry erase boards to plan their routes, predict hot spots and avoid potential traffic

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Table 5.1 Game of Urban Renewal and its characteristics Description In the Game of Urban Renewal, players take one of the following roles: City Councilor, Developer, Community Activist, City Planning Employee, Man-OnThe-Street, Academic Urban Theorist, Resident of Existing Development to be Demolished, Mayor, Random Federal Politician, Skyscraper Enthusiast, Garbage Man. From this perspective, they jointly try to manage the city. Each of them takes turn in spinning the Decision Engine Wheel to guide their action: build something (condominium, commercial building, public house, school or park) on a selected space on the board, demolish a building using (Trevisan, 2018)

Advantages No losers, no winners: The game lasts until all players have left the game in pursuit of other interests Creativity: Players can add variety and enliven gameplay by introducing new buildings from bottle caps, pebbles, dried pasta, crystals, small candies

Disadvantages Lack of sustainability aspect: The game spurs creativity and interest in architecture, landuse and urban planning but does not really highlight the sustainability aspect

Table 5.2 Suburbia City Building Board Game and its characteristics Description The Suburbia is a tile-laying game that allows players to build and expand their neighborhoods. Participants are encouraged to invest in infrastructure that in the course of time will encourage population growth. As the town grows, players may increase their income and their reputation (Smith, 2015)

Advantages Engaging: The element of rivalry is very engaging for the players. Realistic: a lot of different types of buildings and localizations, including drivethroughs, casinos, lakes and everything in between

Disadvantages Rivalry: Everyone is developing their own suburb, often players weight up how much they are willing to buy a tile vs. the risk of another player buying it

Table 5.3 Let’s Make a Bus Route and its characteristics Description In the Let’s Make a Bus Route, players become the employees of a bus company in Kyoto. Their task is to create new bus routes that will respond to the needs of locals and tourists (SGL, 2018)

Advantages Transportation: The game highlights the importance of efficient traffic management Creativity: Players can draw their own roads

Disadvantages Complexity: Many elements on the boards, long instructions

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Table 5.4 Solar City and its characteristics Description The Solar City is a board game that applies build-and-block mechanics. Players imagine that it is 2035 and the world has been destroyed by the biggest corporations Their task is to restore the ecological balance and turn cities into “liveable places overflowing with lush greenery and powered by the energy of the sun.” (Ropka & Kijowska, 2019)

Advantages Look into a future: Players travel to the future to make decision in a new setting

Disadvantages Rivalry: The game triggers competition rather than cooperation Extrinsic reward: The game is based on the extrinsic motivation to gain a reward (to win the game)

jams. Creative expanding of their neighbors or all cities is included in Suburbia (where players can choose from among a variety of buildings, including drivethroughs and casinos, homeowners associations and lakes and everything in between) and Solar City, in which players become the inhabitants of the future and try to rebuild their world after the eco-apocalypse (Ropka & Kijowska, 2019). In the Game of Urban Renewal players can even enliven gameplay by introducing new buildings from bottle caps, pebbles, dried pasta, crystals, small candies, etc. (Table 5.4). Although creative and enjoyable, not many of the existing board games meet all the criteria of comprehensive simulations. First of all, only some of them apply any realistic systemic model. There are, of course, board games that are based on existing case studies, such as social simulations Nexus Game (Mochizuki et al., 2018), The World’s Future, and some computer-based games (e.g. MIT LAB tools). Those which very closely relate to the research data, like Sustainable Urban Heating Simulation (Solinska-Nowak et al., 2019, tend to be very complex and targeted at professionals and stakeholders who use them as a sandbox to test ideas and experiment with new solutions (Tables 5.5, 5.6, and 5.7). Most of the analyzed board games (especially those linked directly with the urban sustainability) are either set in a completely fictitious setting or only loosely inspired by real places. For example, in Solar City, a science-fiction genre is exploited to set the context of the game. Players imagine that it is 2035 and huge corporations destroyed natural environment, leading to depletion of resources and millions of starving people (Ropka & Kijowska, 2019). Trying to reverse these impacts, participants strive to repopulate the world and redesign the deteriorated city infrastructure. Even in the Let’s Make a Bus Route, which, in theory, takes place in Kyoto, the reference to the real city is in fact purely symbolic. Players move around a board to deliver tourists, commuters, students, and the elderly to selected destinations. While doing that, they have to be careful and avoid traffic jams. The city-specific transportation challenges are, however, not significant because the main goal for a player is to score as many points as possible (each passenger type is scored differently, for example tourists are most profitable as they may generate

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Table 5.5 Nexus Game and its characteristics Description The Nexus Game offers an opportunity to explore the challenges of water management for energy and food production. Participants are put into the roles of key ministries of two countries sharing the same river. From this perspective, they may brainstorm and collaborate to creatively form and verify a number of policies. As they try to meet the growing water demands, they still have to consider ecological and economical balance within and across the borders (Centre for Systems Solutions, 2018b)

Advantages Procedural rhetoric: The simulation uses procedural rhetoric to relay the processes Cause-effect: Players can observe a cause-effect in a very linear way Cooperation: The simulation motivates cooperation through roleplaying elements

Disadvantages Complexity: The simulation is quite complex and may be very long, even up to 4 h

Table 5.6 The World’s Future and its characteristics Description The World’s Future is a social simulation that enables players to steer the future of the fictional countries that they become the leaders of. As the simulation progresses, they experience the pressure of making tradeoffs and the thrill of finding synergies involved in pursuing sustainable development (Centre for Systems Solutions, 2019)

Advantages Realistic: The simulation is based on a systems model: Intrinsic motivation: The simulation doesn’t have any winning goals for players, players are motivated by their own in-game experiences. Sandbox for testing ideas: Players have a vast range of possible decisions to test and create their own future scenarios

Disadvantages Complexity: The simulation is very broad subject, long (around 5–6 h) Preparations: The simulation requires a lot of preparations (room, internet connection) and at least two facilitators to lead a workshop

42 points vs. 24 from students). Additionally, placing checks for passengers and areas (sightseeing spots, stations, universities) before other players can earn you extra bonus points, and so can elderly passengers or specific destinations (such as shrines or pagodas) (SGL, 2018). Such immediate gratification in the form of scores or bonuses is a common strategy used in these games. For example, in the Solar City, introducing infrastructure activates a part of the city on a player’s board, generating profits to the player who performed the action. More importantly, the action blocks the possibility of activating this part of the city for other players. Similarly, in the Suburbia, correctly planned placement of a building may significantly raise a player’s reputation and in consequence increase population (and the winner at the end of the game is the player with the largest population).

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Table 5.7 Sustainable Urban Heating Simulation and its characteristics Description The Sustainable Urban Heating Simulation enables participants to experience and identify specific opportunities, challenges and risks of transition towards low or zero emission heating options. The simulation enables participants to practice decision-making, negotiation and consensus building under uncertainty, triggering creative thinking and inspiring them to seek more realistic heating solutions for their homes and cities (Solinska-Nowak et al., 2019)

Advantages Realistic: The simulation includes real data and type of buildings, investments Varied decisions: The simulation lets players make both individual decisions (which impact only them), and decisions that directly impact the city’s policies as a whole Sandbox for testing ideas: Players have a vast range of possible decisions to test and create their own future scenarios

Disadvantages Complexity: The simulation requires mathematical skills to play, because of the data included. The simulation can be also quite long, around 3 h. Preparations: The simulation requires computer and spreadsheet for the calculations, which is quite limiting. The simulation also requires at least 2 moderators to lead a workshop

On one hand, this reward mechanism may force players to strategically plan each of their decisions. On the other, however, it may hamper their motivation and negatively affect their problem-solving skills. For example, studies on 51 children interested in drawing proved that the expected reward had decreased the amount of spontaneous interest the children took in what was perceived as their hobby before the study (Lepper, Greene, & Nisbett, 1973). Similar results were obtained in adults. Reviewing 128 studies on the effects of rewards Deci et al. (1999, p. 658) concluded that: Tangible rewards tend to have a substantially negative effect on intrinsic motivation (. . .) Even when tangible rewards are offered as indicators of good performance, they typically decrease intrinsic motivation for interesting activities.

Rewards can even make people less creative and thus less willing to look for effective solutions to problems. Why? The key to understanding this phenomenon lies in the difference between intrinsic and extrinsic motivation. When people do something because they like it or truly believe this is important, they are intrinsically motivated. They will willingly invest time and money to, for example, act towards sustainability; segregate rubbish, choose public transport rather than commuting by car or support energy transition. However, when they do something because they expect a reward for it (e.g. money, reputation or winning the game), they are driven by extrinsic motivation. Extrinsic motivation may be misleading, as it does not stem from one’s true propensity or desire but is conditioned by a reward. Such external motivation is fragile and may be easily undermined. It does not provide real impetus or inspiration to act and as such does not render into any significant long-term engagement (Ryan & Deci, 2000).

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Furthermore, rewarding spurs rivalry rather than collaboration. For example, in Suburbia or Solar City players may intentionally act against other players because they are afraid that their “opponents” will block their moves or gain more points. Also, although the majority of the games are multiplayer, the extent to which joint decision making can occur is limited. In most of the games, social interactions are not really encouraged and players are foremost pursuing their individual goals rather than working together for the common good. In most cases, players are either preoccupied with developing their own neighborhood (Suburbia) or blocking investments possibilities available to other players (Solar City). Such decision making is not only inefficient (it does not lead to increased sustainability) but also rather simplified, as real decision-making in urban contexts has to be based on the collective wisdom of many actors (including public authorities, engineers, architects, planners, transport managers, activists or citizens), rooted in thorough analysis of potential trade-offs and synergies, and jointly negotiated. What is more, players’ actions are often guided by luck rather than a conscious decision-making process. For example, in The Game of Urban Renewal, players spin a Decision Engine Wheel to check what move can be made (building, demolishing or drawing a card from the Planning Directive Cards). Also in Let’s Make a Bus Route, participants draw cards to see where they can move their bus. Though it is intended to simulate uncertainty, it often deviates too much from the real-life situation, where uncertainty comes from the places where people naturally cannot make any decisions (disaster risk, weather) (Climate & Development Knowledge Network, 2014). It limits players from making informed decisions and can lead to the frustration and negative emotions that may be directed towards the game itself. The analyzed games have been created for specific groups, including sustainability professionals (The World’s Future) and children and youth (New Shores; Go Goals). They are more often focused on a specific element of the system e.g., water-food-energy nexus (Nexus Game), disaster risk management (Flood Resilience Game, Hazagora) (Tables 5.8, 5.9, 5.10, and 5.11). Some of the games specifically target urban sustainability (The Game of Urban Renewal, Suburbia, Let’s Make a Bus Route or Solar City). To address the gaps and combine them with positive aspects of existing board games, Sustain board game was designed.

5.3

Designing SUSTAIN the Board Game

Designing the game-based learning tool requires translating the concrete system into the game mechanics and game elements. For this reason, the approach is similar, with exceptions, to the design thinking theory.

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Table 5.8 New Shores and its characteristics Description The New Shores—a Game for Democracy is a multiplayer online game in which players are striving to develop a thriving society on a small fictional island. To do so, they may use the island’s natural resources, earn money and invest it in new facilities, both private and public. At the same time, they have to be careful not to upset the ecological balance of the island and avoid potential disasters (Centre for Systems Solutions, 2017)

Advantages Simplicity: The simulation teaches about complex system in an easy way, can be seen as a metaphor for a larger system Adaptability: The simulation was prepared to be used during classes, can be used within limited time Support: The simulation is supplemented by the free teaching materials

Disadvantages Skills required: Players are expected to have at least basic digital skills and use mobile devices. It may be thus less accessible to the elderly or to players from poorer countries

Table 5.9 Go Goals and its characteristics Description The Go Goals is a game targeted at children aged 8 to 10. Players move their tokens along the board and answer questions. The right answer is rewarded by the additional roll of a dice (United Nations, 2018)

5.3.1

Advantages Simplicity: The game is very short and easy for all groups of players Adaptability: The simulation was prepared to be used during classes, can be used within limited time Availability: The game is also prepared for the self-print, which makes the game easily available through the internet

Disadvantages Simplicity: The game is too simple to be engaging for older audiences Lack of procedural representation: The game to represent processes and interconnections Lack of social interaction: The game has a form of a quiz which doesn’t require any form of social interactions Rivalry: The game may trigger competition rather than cooperation

Design Thinking, Definition and Characteristics

Design thinking is commonly defined as “an analytic and creative process that engages a person in opportunities to experiment, create and prototype models, gather feedback, and redesign”. The final result of design comprises design concepts (proposals for new products, buildings, machines or programs). Design thinking has become an integral part of design, engineering, business or education. It usually starts with dissatisfaction with some existing solutions and determination to take action to solve the problem (Razzouk & Shute, 2012). At its core, design thinking refers to how designers see and how they consequently think (Liu, 1996). It is an iterative and interactive process where a designer

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Table 5.10 Flood Resilience Game and its characteristics Description The Flood Resilience Game allows players to experience and learn about the flood risk and resilience of communities in river valleys. Players become members of a community living in a floodexposed area and are expected to come up with and implement effective flood-resilience strategies and policies (Centre for Systems Solutions, 2018a)

Advantages Support: The simulation is supplemented by the free teaching materials Simulation: No winning goals Debriefing: Strong focus on a reflection after the game session

Disadvantages Complexity: Can be long and requires a moderator Preparation: Very focused on the resilience, does not allow creation of transformative strategies

Table 5.11 Hazagora and its characteristics Description In the Hazagora, players take on the roles of inhabitants of a volcanic island. Their main task is to develop and sustain their communities. Players have to prepare themselves to face several hazards that can hit the island, i.e., earthquakes, lava flows, tephra fall, and tsunamis (Mossoux et al., 2015)

Advantages Simulation: No winning goals Debriefing: Strong focus on a reflection after the game session. Additional moderated discussions during the game

Disadvantages Very focused on the resilience, does not allow creation of transformative strategies

or (preferably) a team of designers analyzes some representation of problem-solving concepts/ideas, finds correlations between them to solve the problem, and observes the result to inform further design efforts (Do & Gross, 2001; Lloyd & Scott, 1995). According to Braha and Reich (2003), the design process is iterative, exploratory, and sometimes chaotic. It often starts with a diagrammatic representation that is transformed to more complex visual depiction by adding detail. The objective of using the visual “prompts” is to spur reflection, discussion, and self-critique and therefore test the idea. The ongoing process of modification of the idea/concept is supposed to remove “discrepancies and establish a fit between the problem space [. . .] and the proposed design solution” (Razzouk & Shute, 2012).

5.3.1.1 Forms of Thinking in Design Thinking Several forms of thinking can be observed in designing (Dörner, 1999): • Design begins as a vague idea about what the design/product should look like and how it should work. • In the course of time, sketches and models help transform the vague idea into a clearer and more concrete form. They also clarify the characteristics of the

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product and help to form a specific line of thought that facilitates the development process. • The third form of design thinking consists of “picture-word cycle,” that is, putting ideas into words that help the designer specify and work on details of the idea.

5.3.1.2 Design-Thinker Characteristics Design thinkers should not only display creativity but a range of other characteristics (Owens, 2007): • Human- and environment-centered concern: Designers must keep in mind how the final product will address human needs. They should also consider environmental interests. • Ability to visualize: Designers should be able to work visually (i.e., apply depiction of ideas). • Predisposition toward multifunctionality: Designers should look at a range of possible solutions to a problem and keep “the big picture” of the problem in mind. • Systemic vision: Designers should consider a problem from a systemic approach and apply systemic solutions (involving different procedures and concepts to create a holistic solution). • Ability to use language as a tool: Designers should be able to explain their process not only visually but also verbally. • Affinity for teamwork: Designers should develop interpersonal skills to be able to communicate with a wide range of across disciplines and work with other people. • Avoiding the necessity of choice: Designers should look for competing alternatives before moving to choice making or decision making. They try to find a solution that avoids decision and combines the best possible choices.

5.3.1.3 Processes in Design Thinking During the design process, designers are engaged in a number of cognitive processes (Koloder & Willis, 1996) • Preparation: In this process, designers have to decide what to focus on. Therefore, this phase includes specifications and constraints of the problem, reinterpretation of ideas, visualization, problem reformulation, situation assessment and elaboration. • Assimilation: In this process, designers make sense of the proposed solution, data, and observations coming from the design environment, such as feedback from experiments with prototypes. • Strategic control: In this phase, designers must make many decisions over the course of a design (e.g., which idea to elaborate or adapt next, which constraints to relax, how to set priorities). They also move among various tasks, subproblems, and design processes in a flexible and highly opportunistic manner.

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The basic elements of design thinking include generation, exploration, comparison, and selection. The generation and exploration widen a problem space, while the comparison and selection narrow a problem space: When widening a problem, solutions are generated and then examined in relation to the goal. Then, in an iterative process, solutions may be modified or new solutions may be developed until an optimal solution is found. Narrowing a problem entails comparing two or more ideas and then selecting the solutions based on specific and relevant goal criteria (Razzouk & Shute, 2012).

5.4

Designing Sustain Board Game

The concept of a tool should be based on the problem, target groups’ needs, and if possible, research in existing games focused on similar themes. In the first phase of game design, it is good to learn from the mistakes and successes of others rather than our own. When designing the initial concept for the game, authors took extra care to base the assumption and the prototype in the system model. In this section of the book, authors explain the steps taken to create the concept and the first prototypes of the SUSTAIN board game.

5.4.1

What Is the Problem, Objective and Target Groups?

The main problem, or in this case, the main assumption is that a group of people wants to use the potential of game-based learning as a support tool for the course that includes elements of urban metabolism, sustainable transportation, decision-making and the basic systems thinking. Some of the objectives of the game will thus coincide with the objectives of the course, namely: • • • • •

Introducing the topics of sustainable transportation and urban metabolism. Supporting the understanding of a complex system Providing a safe (and playful) environment for learning. Promoting a sustainable way of living. Learning about and experiencing sustainability related issues.

Other objectives, on the other hand, will focus on improving interpersonal skills through the play. Communication Good communication consists in collecting insights and perspectives from many different people and plays a key role in any decision-making process. A playful environment of games is perfect for (continued)

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triggering the exchange of opinions and experiences and joint decisionmaking in safe and respectful way (Geurts et al., 2007). Creativity In the game, players assume fake roles and are thus encouraged to move away from their habitual thinking. As a result, they may be more creative and find the courage to think outside the box, explore and test innovative solutions to the emerging problems (Geurts et al., 2007). Consensus Unless they realize that some of the goals they pursue are shared with others, people tend to be focused predominantly on their own interests. Games make use of this tension between individual and collective priorities, triggering rivalry but at the same time leaving enough room for collaboration. Sometimes, a decision to cooperate calls for personal sacrifice and thus offers a valuable lesson for students to overcome their “greedy” instincts and practice mutual understanding (Geurts et al., 2007). After the problem and objectives have been set, we can think of target groups. In the case of the SUSTAIN board game, we can identify 2 target groups: (1) students and (2) university, and high school teachers. The game is expected to serve as support material supplementing the final output of the Erasmus + SUSTAIN course. Background Information Main target groups: university students, university lecturers. Problem/main objective: Experience, learn and understand the key concepts of urban sustainability, such as societal metabolism and urban transportation. Other objectives • Players practice decision-making, in particular decision-making under stress and uncertainty. • Players learn how to communicate and collaborate more effectively. • Players develop negotiation skills. • Players face the complexity of the real world and explore solutions of the real-life problems in a safe environment. • Players diagnose organizational challenges of city management. • Players experience challenges connected with transitions in complex systems where multiple stakeholders’ interests collide. • Players learn how they can affect the urban system in the real world. • Players develop critical thinking. After the target groups are established, some assumptions about them can be made (preferably based on interviews or other interactions with the target groups’ representatives, Table 5.12). In this case, the expertise was supplemented by the expert partners of the Sustain project.

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Table 5.12 Target group characteristics What we know about target group and their environment?

University students University lecturers May be familiar with May not be familiar with commercial board games game-based learning May have varied speed of Have to be able to facilitate learning the game by themselves The number of students in class can change The class is confined to the room The class is limited by the time

Table 5.13 General game assumptions What do we know about target groups and their environment? May not be familiar with gamebased learning Has to be able to facilitate the game by herself/himself May be familiar with commercial board games May have varied speed of learning The number of students in class can change The class is confined to the room The class is limited by the time

Assumptions Easy to moderate, if possible no moderation 1 moderator The game could be self-facilitated, the students may understand the mechanics quicker than the moderator Everyone should be engaged in the learning of the mechanics process, peer-based support is recommended The game should be scalable to accommodate more or less players depending on the game session The game should not include too many elements. Moderator should be able to set it up in the classroom The game should be introduced, played and debriefed in the span of 1–3 h

Based on our knowledge of the target group, it is possible to make general assumptions about the game (Table 5.13). Additional game assumptions can be inferred from the project-specific limitations, such as budget constraints and possibility to offer downloadable and self-printable materials. The SUSTAIN project assumes that the game will be available for self-print. As a result, the format of materials should ensure easy printing and cutting. Moreover, the project accounts for participants with disabilities, thus the game materials should be accessible or adaptable in terms of text size, materials, and other accommodations that would enable it to be used in an inclusive environment. Knowing the general game assumptions is a first step in the game creation process. The assumptions can change during the ideation, iteration and prototyping. However, they can provide a framework and let designers limit their ideas and find specific solutions that address the target groups’ needs.

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Real-Life Analysis and Game Components

To achieve the tool’s objectives and successfully support learning through the game, game designers should identify important and relevant real-life elements (infrastructure); trade-offs, threats, shocks and time scales and incorporate them into the game. In the case of the SUSTAIN board game, the real-life analysis and the identification of key elements are introduced in the previous sections of the book (societal metabolism, transportation sustainability). Additional links between said elements are introduced through section on simulation models. Section on the decision making in the context of sustainability focuses on useful tools for both game design, and for feedback and deepening the reflection.

5.4.2.1 System The model created by the Italian Chapter of the System Dynamics Society, one of the partners in the SUSTAIN project, became the basis of the simplified model that could be used in the game (Fig. 5.2). When creating a simplified model, authors took into consideration the elements underlined by the Intellectual Output 1 and 2 of the Erasmus + SUSTAIN project, and those elements that could affect the said elements directly. Besides the elements of the system, it was important to understand what threats and shocks can be observed within the system represented in the SUSTAIN board game. This data was also provided by the model prepared by Erasmus + SUSTAIN project partner System Dynamics Italian Chapter. Thus, the simplified model focuses on Pollution flows, understood as air pollution, waste, and wastewater, as well as CO2 emissions. All greatly affect the urban environment and can be used as an example of the urban metabolism in the game. Another very important element from the standpoint of the course is highlighting the topics and concepts related to sustainable transport.

Fig. 5.2 Simplified model used in the prototype of Sustain board game

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Attractiveness of the city defined as a final and most important indicator. It is directly affected by the Population level, Standard of Living and GDP per capita, and indirectly by Pollution, Sustainable transport and land availability (and also by the existing infrastructure). Standard of living defined as a collection of indicators related to the wellbeing of the citizens. This indicator is directly related to the Attractiveness of the city. GDP per capita defined as a monetary measure of economic wellbeing of the citizens. It will directly affect the Attractiveness of the city (through an average salary) and all the departments’ budgets (through taxes). Population level is defined as the number of people living in the city. This indicator is linked with the Attractiveness of the city (through migration—a direct link). It will also affect the Standard of living (through the housing availability and the Labor force to jobs ratio—through events) and Pollution and CO2 emissions. Sustainable transport defined as % of private vehicles and % of sustainable modes of transportation. Players will be able to influence this indicator by investing in Sustainable means of transportation (e.g. in bicycle paths, new bus stops, various campaigns) or in programmes and policies that would discourage the use of private vehicles (high cost of parking, zones limited only to public transport) or encourage the use of public transport (various campaigns, lower prices for the public transit). Pollution (air pollution, waste, wastewater) accumulation defined as an accumulation of air pollution, waste and wastewater produced each round. The tangible infrastructure in the city (residential areas, hospitals, schools, industry and more) will have a specific set pollution production. Using public infrastructure such as the Wastewater Treatment plant, players will have to deal with the existing pollution. Otherwise, the Pollution accumulation level will increase. Players can lower the accumulation level by investing in pollution treatment facilities and programmes or by working on lowering the pollution emissions (energy efficiency technologies, pro-renewables campaigns and subsidies, water efficiency, promotion of bio products and more). Population level might additionally affect the pollution produced. CO2 emissions defined as the city’s emissions of CO2 (from population, industry, business, etc.). Land availability defined as the number of plots in the city available for a new use, development or investment. The indicators above were defined according to the priorities of the project, learning goals of the game, and systems model designed by SYDIC and simplified by the authors of this chapter for the social simulation.

5.4.2.2 Stakeholders: What Roles to Put into the Game? A big challenge for the game design was to identify the stakeholders whose voice should be heard and who should be represented by the in-game roles. Systemic models more than often do not include the human element and focus solely on stocks and flows (Sterman, 2000). The decision making process is a key element of the whole SUSTAIN project and as such, it had to be taken into consideration in the design process. Therefore, it was

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decided that the players should learn about the system from the point of view of the city-level decision-makers, namely, the local administration representatives. This choice has many advantages. First of all, the players can make city-wide decisions and experience their results, not only related to their immediate environment but also to the whole urban system. When playing the game with many rounds, players have a chance to repeat the experiential learning circle (Fig. 5.1) until they find solutions that fit their purposes. Another advantage is to give players who normally do not have such competencies in real life an opportunity to engage in large-scale decision making (Kolb, 1984). It can potentially not only let them learn about the relations and trade-offs that affect the system on the biggest scale but also show the everyday struggles and goals of various interest groups that try to lobby the local governments. But to achieve this, the specifics of the real-life actors have to be determined. The challenge in the design of this element lies in diversified administration structures and priorities that can be found throughout European cities. After analyzing the city departments of several cities, including Wroclaw, Warsaw, Athens, and London, a few interesting things came to the game designers’ attention. The city departments from different cities, even within the borders of the same country, bear various names and have often overlapping responsibilities. Furthermore, there are many discrepancies between their responsibilities and actions. It would be impossible to place all of the actors in the game, which is why the roles were generalized and their responsibilities were limited to the indicators, so it would be instinctual for players during the game (Table 5.14). Example Department of Infrastructure Congratulations! You have just been nominated as the head of the infrastructure department. You are partially responsible for keeping the GDP per capita at a high level. You should also keep an eye on the location of new buildings in the city. The most important buildings shouldn’t be too far from residential areas, otherwise it could negatively affect the standard of living. Another important thing that you should monitor is the attractiveness of the city! It’s the ultimate indicator of your city’s awesomeness. The residents will judge your every move and express their confidence in your actions. Department of Environment & Sustainability Congratulations! You have just been nominated as the head of the Environment & Sustainability Department. Your task will be to promote sustainable solutions for waste and pollution management. You are responsible for lowering the CO2 emissions and keeping the city’s pollution levels at a minimum. Another important thing that you should monitor is the attractiveness of the city! It’s the ultimate indicator of your city’s awesomeness. The residents will judge your every move and express their confidence in your actions. Department of Transportation Congratulations! You have just been nominated as the head of the Department of Transport. As such you will be responsible for efficient transport within the city. Try to address any problems

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Table 5.14 A proposition of description of the roles which could be used as an introduction for the players Role All Department of Infrastructure

Department of Environment & Sustainability

Department of Transportation

Department of Welfare

Department of Law & Public Services Department of Culture, Sport & Education

Tasks and responsibilities Meta: Communication between different departments Public infrastructure Energy distribution Revitalization & development of the new infrastructure Level of pollution Level of emissions Promoting sustainable solutions Waste management Efficient transportation within the city Public transportation Streets and sanitation Welfare of the community Health Contacts with NGOs Awareness campaigns Law enforcement Public safety Disaster preparedness & response Cultural projects & festivals Sport facilities Educational projects Awareness campaign

Indicators Attractiveness of the City GDP per capita Standard of living Attractiveness of the City CO2 emissions Pollution Accumulation and Production Attractiveness of the City Sustainable transport Attractiveness of the City

Standard of living Attractiveness of the City

Standard of living Attractiveness of the City Standard of living Attractiveness of the City

connected sustainable transport. The most important buildings should be well connected to residential areas. Another important thing that you should monitor is the attractiveness of the city! It’s the ultimate indicator of your city’s awesomeness. The residents will judge your every move and express their confidence in your actions. Department of Welfare Congratulations! You have just been nominated as the head of the welfare department. Therefore, the standard of living (health, safety, education, culture and more) of the city residents is in your hands. Ensure your people have access to educational facilities and jobs. Another important thing that you should monitor is the attractiveness of the city! It’s the ultimate indicator of your city’s awesomeness. The residents will judge your every move and express their confidence in your actions. Department of Law & Public Services Congratulations! You have just been nominated as the head of the public services department. As such you are responsible for the standard of living of the residents. Make sure that the city is well protected against crime and unexpected events.

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Another important thing that you should monitor is the attractiveness of the city! It’s the ultimate indicator of your city’s awesomeness. The residents will judge your every move and express their confidence in your actions. Department of Culture, Sport & Education Congratulations! You have just been nominated as the head of the culture, sport & education department. Your task is to ensure the residents equal and open access to cultural, educational and leisure activities, so the standard of living of the city’s residents. Make sure that the offer is affordable and matches the needs of all age and social groups. Another important thing that you should monitor is the attractiveness of the city! It’s the ultimate indicator of your city’s awesomeness. The residents will judge your each move and express their confidence in your actions.

5.4.3

Elements and Their Representations: Prototyping the Game

Another challenge of designing the SUSTAIN board game was identifying which of the elements of the system could be translated into concrete objects in the game and which should be represented by the rules or mechanics. The representation of the system element is never random. Game designer has to examine the role of the element in the system and its importance in achieving the game’s objectives. In other words, each physical element of the game must have a specific function connected to the real-life system elements, trade-offs or shocks. The city’s current status in the game is symbolically represented by a number of indicators. Their changing values are reflected by progress tracks. Additionally, the city’s plan and infrastructure, which also play an important role in the game, are represented on the board by a limited number of tiles. The game materials include also different types of cards. Firstly, Role cards represent the roles in the game. They provide players with basic information about their tasks and responsibilities (indicators/KPIs), and the allocated budget and current reputation. The number of Role cards corresponds to the number of players. The next type of cards—Event cards, sets up the events for the round. Cards that belong to this category have a big impact on the whole city, e.g., a new nationwide policy that city has adapted to. Those cards must be sorted before each session according to the specific scenario. Additional scenarios would add a replay ability factor to this version of the game. More cards can be added to highlight the uncertainty (for example, natural disasters, change of political parties and more). Event cards can also be used to facilitate the game—Event cards with instructions can replace the facilitator during the game but at the same time ensure the just-in-time information and the understanding of all rules by all of the participants. Each Role starts with a separate deck of possible investments (Solutions). Each solution has a specific name (i.e., Build a new school); description, which doesn’t

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have to be included directly on the card, but can be added as a supportive material; effect on one or more indicators, and level of public confidence in department; requirements to implement the action on a card and cost. The description can be omitted on the card but should be added to the game/course materials. Additional materials can still add to the learning objectives without being disruptive for more advanced students. Each department has to consider the trade-offs, as not all of the solutions have only positive impacts on the city. Solutions can be infrastructurebased (i.e., facilities, residential buildings, bicycle paths, parks, business and entertainment facilities) or policy-based (i.e. m subsidies, campaigns and more). The Solutions could be permanent (e.g., new schools/hospitals but also lasting policies) or last only for one round. Only a few Solutions are available for each department at the beginning of the simulation. The rest of the cards will be drawn in the next rounds by departments, according to the scenario and in relation to citizen requests. Additional Solutions could also be attained in relation to the events. Another deck of cards—Request cards—furthers the narration for the gameplay. Request cards represent both requests and complaints from various interest groups in the city and can touch on different topics. For example, the request can involve the food/water/ energy nexus, issues connected to public transport or urban greenery, facilities within the city and many more. The Request cards may ask players for investments that are not quite necessary or welcomed in the city but represent the need to “respond to the citizens’ requests.” It may be that investing in some solutions may bring very positive individual outcomes for the player, i.e., doubling the public confidence in the player. Requests that have not been addressed will bring negative effects, e.g., change in the governance or the lowering of some city indicators. Requests are common for the whole city and can have a negative effect on public confidence in all departments if not solved. The faster they are answered, the bigger increase in the public confidence in the department that invests in the requested Solution. Requests and Solutions should be, at least partially, based on the good and bad practices described in the other outputs and observed in European cities. The state of the city and players’ actions (some of the solutions) may affect the outside world by increasing the concentration of CO2. It is possible to add some linkages to the relevant SDGs both through cards or even on a more practical level. For example, inequalities can be represented by different departments having different budgets—thanks to the immersion; the players will consider it on a more personal level. The game ends after a specific number of rounds or after specific indicators reach certain levels, either desired (e.g., the attractiveness of the city) or undesired (e.g. pollution accumulation and production or CO2 emissions). The results are summarized by comparing different indicators to represent the effectiveness of each department and the city as a whole. Despite these indicators, there is no clear winner or loser of the game and players will be free to interpret the results, though some explanations and examples from the real world should be prepared to reflect on the linkages to the current world problems. The identified indicators could be represented by the paper tracks or cards with points. The sections on societal metabolism and transportation sustainability

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identified some infrastructure and projects that could be represented in the game as specific game elements. For example, facilities connected to the waste flows were taken directly from the model. So was transport infrastructure—both sustainable and unsustainable, i.e., roads or bicycle paths. The players will receive feedback on a few levels. First, when investing in Solutions, departments will receive reputation points (individual feedback) and check the effects their actions have had on the city’s indicators (state of the city). Secondly, some of the indicators, such as pollution/waste, wellbeing and more, will affect each other through synergies and event cards. Both the players’ actions and the levels of indicators will affect the outside world, which will be represented by the CO2 emissions indicator. After making assumptions about the physical representation of the systems elements, it’s time to prepare a prototype and test it.

5.4.4

Further Development and Iterations

The prototyping is very important in the process of iteration. The prototype should be easy to modify and flexible. The first prototype hardly ever becomes the final version of the game. It is especially true in the case of serious games, where the user’s context may be dramatically different from the context in which the tool has been developed (Mehm, Dörner, & Masuch, 2016). Before reaching its final form, the game has to be tested many times, ideally with varied groups or, if there is any— a specific target group. Each iteration of the prototype removes elements of systems that are unneeded or too complex and adds elements that were missing. It might happen that the physical representation of the elements could completely change to accommodate problems observed during the test. Each text, instruction or icon should be user tested in terms of inclusivity. To this end, tests should gather as many people with limitations and special needs as possible. Their inclusion often leads to change in the color palette, type and size of the font. The SUSTAIN board game was tested with representatives of various groups with different backgrounds, such as high school students, PhD students, policy experts and game designers, aged 17 to 40+ (Fig. 5.3). The tests were facilitated simultaneously in Poland by the Centre for Systems Solutions, and in Italy by Ergo Ludo. The version of the game used in the lead course at the Macedonia University will be much different than the initial prototype prepared for the first tests (Fig. 5.4).

5.5

Moderating and a Deeper Reflection

The presence of a moderator during the workshops with simulation is necessary. There are three main reasons for moderator presence during the social simulation workshop: (1) explanation of the rules and workshop process; (2) players experiencing strong emotions linked to the outcomes of the game; (3) guidance

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Fig. 5.3 One of the tests conducted in Wroclaw, Poland

Fig. 5.4 Photo from the test led by Ergo Ludo during the transnational meeting in Rome, Italy

through the debriefing sessions. Those points are described in more detail in the following sections. The Moderator’s role during the game should be minimal. The Moderator should lead the intro to the game and facilitate the debriefing. During the game, the rules and gameflow will be explained through game elements. The Moderator keeps an eye on the players and reacts when ground rules are broken.

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Explanation the Rules and the Workshop Process

At least a short explanation of social simulation & serious games is necessary for players to understand what will happen during the class/workshop. At least short introduction of the basic rules is necessary in the form of a short presentation. The moderator doesn’t necessary need any supportive materials for that. The short introduction can be supported by the game elements which, at this point should be already set on the table(s). Example What should be explained? • General introduction to the city • General introduction to the roles—at this point, moderator can divide players into roles. • Different types of cards • Number of rounds & time • No winners, no losers • Your own goal—moderator should ask players to set their own goals (and write them down), and reiterate them when they get to know the game better. Moderator should follow up on this point in the debriefing. Players will receive a more detailed introduction to the rules of the game through the in-game instructions.

5.5.2

Magic Circle and Ground Rules

Players may experience strong emotions when playing the game. Some conflicts between players may arise. Moderators need to create a safe space for all participants to freely express themselves (within the reasonable limitations). To make sure that players leave the negative emotions behind them after the game end, moderators should introduce them to the concept of magic circle. Example Example of an Introduction of the Idea of Magic Circle In the simulation we are within the magic circle. Upon entering it, we start to identify with the adopted roles. Remember that these are only roles and they should be separated from the actual people playing them. What happens in the circle—all your actions and interactions—should stay in the circle. If you experience any discomfort during the simulation, please let us know, we can stop it at any moment.

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To make sure that all needs of the participants are taken into account, moderator can also propose some ground rules for communication between players. Example Examples of ground rules: • One person speaks at a time; • Questions may be asked to clarify ideas; • Criticizing others must always occur in a careful, respectful and constructive manner; • Feelings may be expressed; they are not to be suppressed or denied; • If anyone feels uncomfortable, you can stop the game.

5.5.3

Debriefing

A summary discussion is a key element of a game-based workshop. It is usually referred to as “debriefing” and defined as “the occasion and activity for the reflection on and the sharing of the game experience to turn it into learning” (Crookall, 2010). During the debriefing, participants are encouraged to air out their emotions, analyze and reflect on their moves and draw lessons from the shared experience. A proper debriefing session enables to clarify any debatable facts or situations encountered in the game and helps participants address any stressful aspects of the workshop, transforming the play into a learning opportunity. • Results overview: facts about the game situation at the end – Individual reflection for all roles – Goals – Challenges • Relationships with other roles – Interlinkages in the system • Roles summary • Discussion + facilitator’s feedback • Bridging with the real world • Individual reflection: what have I learned? • Optional: survey

5.5.3.1 Examples of Debriefing Questions: Individual Each player will receive individual feedback in the form of department’s reputation, which will not always be positive. The Individual results could additionally be measured through the final level of indicators. The game’s rules do not define the winner.

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Example Examples of questions about individual results: • • • • • • • •

What are your individual results? Do you feel satisfied? What was more important to you? Reputation or the condition of the city? What were your goals in the game? Did you achieve them? What helped and what blocked you on your way to achieve your goals? What was your role in the group? Who answered to at least one Request? Why? What was the request? Was it difficult to make decisions under uncertainty?

5.5.3.2 Examples of Debriefing Questions: City The players should receive feedback on the condition of Population and the city (pollution and Attractiveness of the city). The final results should be correlated with descriptions/explanations of what the level could mean in the real world city, i.e. via a table with levels/ranges of results. The players should also receive feedback on the environmental effects of their actions, i.e. CO2 production, pollution and in an in-classroom game with more than one city—CO2 accumulation across games. Example Examples of questions about final results for the City: • • • • • •

5.6

What is the result of the game? What are the results for the City? Who has implemented the most Solutions? What does it mean to win in this game? Did you apply any strategy that governed your decisions in the game. How did your city affect the world outside of it? Where would it lead?

Summary

Our objective is to further develop the game, even after the project’s completion. To this end, we will gather the feedback from all players, and use it later to improve the tool. Taking into account the notion of generational learning, we aim to make use of solutions and projects conceived by the students during the course and either add them to the game or develop them into alternative scenarios.

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Appendix Further Reading

1. Bogost, I. (2008). The Rhetoric of Video Games. http://www.arts.rpi.edu/ public_html/ruiz/EGDFall10/readings/RhetoricVideoGames_Bogost.pdf 2. Kolb, et al. (1999). Experiential Learning Theory: Previous Research and New Directions. http://www.d.umn.edu/~kgilbert/educ5165-731/Readings/experien tial-learning-theory.pdf 3. Dewey, J. (1916) (2007 edition). Democracy and Education. Teddington- Echo library. 4. Candy, S. https://futuryst.blogspot.com/2018/10/experiential-futures-brief-out line.html 5. Piaget, J. (2013). The construction of reality in the child (Vol. 82). Routledge. 6. Sawyer, & Rejeski, D. (2002). Serious Games: Improving Public Policy through Game-Based Learning and Simulation. Washington, DC: Woodrow Wilson International Center for Scholars. 7. Ritterfeld, U., Cody, M., & Vorderer, P. Eds. (2009). Serious Games: Mechanisms and Effects, Routledge. 8. Critelli, M., Schwartz, D. I., & Gold, S. (2012). Serious social games: designing a business simulation game. In Proceedings of the 4th IEEE 2012 International Games Innovation Conference (IGiC ’12), September 2012. 9. Magnuszewski, P., Królikowska, K., Koch, A., Pająk, M., Allen, C., Chraibi, V., Giri, A., Haak. D., et al. (2018). Exploring the Role of Relational Practices in Water Governance Using a Game-Based Approach. Water 10(3): 346. 10. Kiili, K., de Freitas, S., Arnab, S., & Lainema, T. (2012). The Design Principles for Flow Experience in Educational Games. Procedia Computer Science 15 (2012): 78–91. 11. Geurts, J. L. A., Duke, R. D., & Vermeulen, P. A. M. (2007). Policy Gaming for Strategy and Change. Long Range Planning 40(2007): 535–558. 12. Fabricatore, C., & López, X. (2014). A Model to Identify Affordances for Game-Based Sustainability Learning. Busch, C. (ed.) Proceedings of the 8th European Conference on Game Based Learning, pp. 99–109, Reading UK: Academic Conferences and Publishing International Limited. 13. Pajak, M., & Daszynska-Zygadlo, K. (2016). Educating About Complexity and Sustainability Through Serious Games. Perspectives on Computer Gaming in Higher Education, Bogucki Wydawnictwo Naukowe, pp. 21–34. 14. Wouters, P., van Nimwegen, C., van Oostendorp, H., & van der Spek, E. D. (2013, February 4). A Meta-Analysis of the Cognitive and Motivational Effects of Serious Games. Journal of Educational Psychology. Advance online publication. 10.1037/a0031311 15. Vygotsky, L. S. (1980). Mind in society: The development of higher psychological processes. Harvard University Press.

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More Games

1. 2. 3. 4.

Games4Sustainability: https://games4sustainability.org Games for Sustainability: https://gamesforsustainability.org/resources/ Games for change: http://www.gamesforchange.org/games/ Games for Cities http://www.gamesforcities.com/database/

Audiovisual Materials 1. We cannot predict our future but we can design it, part 1. by Centre for Systems Solutions Description: a short overview of what systems solutions are, and on how to solve problems in systemic way. https://bit.ly/2lqTFTe 2. We cannot predict our future but we can design it, part 2. by Centre for Systems Solutions Description: a short overview of what systems solutions are, and on how to solve problems in systemic way. https://bit.ly/2lDT94f 3. How did we create the RURITAGE game? Description: an example of game design process. Here the Ruritania game was created as a part of the Horizon 2020 project Ruritage. https://bit.ly/2n86Dpi

References Abt, C. C. (1970). Serious games. New York: Viking Press. Anthony, A. (2019). Social simulation: (just) a game to explore possible futures? Retrieved from RURITAGE: Heritage for Rural Regeneration: https://www.ruritage.eu/news-events/news/ living-conservation-rural-heritage-full-of-new-life-2-2-2/ Argyris, C. (2002). Teaching smart people. Reflections: The Sol Journal, 4, 4–15. Bogost, I. (2008). The Rhetoric of Video Games. In K. K. Salen (Ed.), The ecology of games: Connecting youth, games, and learning, The John D. and Catherine T. MacArthur Foundation Series on Digital Media and Learning (pp. 117–140). Cambridge, MA: The MIT Press. Bouwen, R., & Taillieu, T. (2004). Multi-party collaboration as social learning for interdependence: Developing relational knowing for sustainable natural resource management. Journal of Community & Applied Social Psychology, 14, 137–153. Braha, D., & Reich, Y. (2003). Topological structures for modeling engineering design processes. Research in Engineering Design, 14, 185–199. Centre for Systems Solutions. (2017). New Shores. Retrieved from Social Simulations: https:// newshores.socialsimulations.org/ Centre for Systems Solutions. (2018a). Flood Resilience Game. Retrieved from Social Simulations: https://floodresilience.socialsimulations.org/

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Centre for Systems Solutions. (2018b). Nexus Game. Retrieved from Social Simulations: https:// nexus.socialsimulations.org/ Centre for Systems Solutions . (2019). The World’s future. Retrieved from Social Simulations: https://worldsfuture.socialsimulations.org/ Climate & Development Knowledge Network. (2014). Using games to experience. Retrieved from Red Cross/Red Crescent Climate Centre: http://www.climatecentre.org/downloads/files/Games/ CDKNGamesReport.pdf Crookall, D. (2010). Serious games, debriefing, and simulation/gaming as a discipline. Simulation & Gaming, 41, 898–920. Deci, E. L., Koester, R., & Ryan, R. M. (1999). A meta-analytic review of experiments examining the effects of extrinsic rewards on intrinsic motivation. Psychological Bulletin, 125(6), 627–668. Djaouti, D., Alvarez, J., & Jessel, J.-P. (2011). Origins of serious games. In M. Ma & A. Oikonomou (Eds.), Serious games and edutainment applications (pp. 25–43). London: Springer. Do, E. Y.-L., & Gross, M. D. (2001). Thinking with diagrams in architectural design. Artificial Intelligence Review, 15, 135–149. Dörner, D. (1999). Approaching design thinking research. Design Studies, 20(5), 407–415. Duke, R. D. (1974). Gaming: The future’s language. New York: Halstead Press. Geurts, J. L., Duke, R. D., & Vermeulen, P. A. (2007). Policy gaming for strategy and change. Long Range Planning, 40, 535–558. Huizinga, J. (1955). Homo Ludens: A study of the play-element in culture. Boston: The Beacon Press. IPCC. (2019). An IPCC special report on climate change, desertification, land degradation, sustainable land management, food security, and greenhouse gas fluxes in terrestrial ecosystems. Geneva: IPCC. Kolb, D. A. (1984). Experiential learning: Experience as the source of learning and development. Englewood Cliffs, NJ: Prentice-Hall. Koloder, J. L., & Willis, L. M. (1996). Powers of observation in creative design. Design Studies, 17 (4), 385–416. Lepper, M. R., Greene, D., & Nisbett, R. E. (1973). Undermining children’s intrinsic interest with extrinsic reward: A test of the “overjustification” hypothesis. Journal of Personality and Social Psychology, 28(1), 129–137. Liu, Y.-T. (1996). Is designing one search or two? A model of design thinking involving symbolism and connectionism. Design Studies, 17(4), 435–449. Lloyd, P., & Scott, P. (1995). Difference in similarity: Interpreting the architectural design. Planning and Design, 22, 383–406. Magnuszewski, P., Jarzabek, L., Keating, A., Mechler, R., French, A., Laurien, F., et al. (2019). The flood resilience systems framework: From concept to application. Journal of Integrated Disaster Risk Management, 9, 56–82. Mehm, F., Dörner, R., & Masuch, M. (2016). Authoring processes and tools. In R. Dörner, S. Göbel, W. Effelsberg, & J. Wiemeyer (Eds.), Serious games: Foundations, concepts, practice (pp. 83–106). Cham: Springer. Mochizuki, J., Magnuszewski, P., & Linnerooth-Bayer, J. (2018). Games for aiding stakeholder deliberation on Nexus policy issues. In S. Hülsmann & R. Ardakanian (Eds.), Managing water, soil and waste resources to achieve sustainable development goals (pp. 93–124). Cham: Springer. Mossoux, S., Delcamp, A., Poppe, S., Michellier, C., Canters, F., & Kervyn, M. (2015). HAZAGORA: will you survive the next disaster? – a serious game to raise awareness about geohazards and disaster risk reduction. https://doi.org/10.5194/nhessd-3-5209-2015 Owens, C. (2007). Design thinking: Notes on its nature and use. Design Research Quarterly, 2(1), 16–27.

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Razzouk, R., & Shute, V. (2012). What is design thinking and why is it important? Review of Educational Research, 82, 330–348. Ropka, M., & Kijowska, V. (2019). Solar City. Retrieved from Zagram w to https://zagramw.to/ solar-city Ryan, R. M., & Deci, E. L. (2000). Intrinsic and extrinsic motivations: Classic definitions and new directions. Contemporary Educational Psychology, 25, 54–67. SGL. (2018). Games from Japan: Let’s make a bus route review. Retrieved from SGL: http://sgl.la/ blog/letsmakeabusroute-review Smith, Q. (2015). Suburbia review: Ballardian town planning on your dinner table. Retrieved from The Guardian: https://www.theguardian.com/technology/2015/sep/05/suburbia-reviewballardian-town-planning-on-your-dinner-table Solinska-Nowak, A., & Anthony, A. (2019). The World’s future is in our hands. Retrieved from Social Simulations: https://socialsimulations.org/the-worlds-future-is-in-our-hands/ Solinska-Nowak, A., Kulakowska, M., Jarzabek, L., Magnuszewski, P., Pajak, M., Hamm, J. A., et al. (2019). Social simulations for behavioral change in urban sustainability contexts. Retrieved from Systems Solutions: https://systemssolutions.org/wp-content/uploads/2019/04/ Social-simulations-for-behavioral-change-in-urban-sustainability-contexts_correct-references. pdf Sterman, J. (2000). Business dynamics. Systems thinking and modeling for a complex world. Boston: Irwin McGraw-Hill. Sterman, J., Franck, T., Fiddman, T., Jones, A., McCauley, S., Rice, P., et al. (2014). WORLD CLIMATE: A role-play simulation of climate negotiations. Simulation & Gaming, 46, 348–382. Trevisan, F. (2018). The game of urban renewal (Special Regent Park Edition). Flavio Trevisan. Retrieved from http://flaviotrevisan.com/2011/the-game-of-urban-renewal/ United Nations. (2018). Go Goals. Retrieved from Go Goals: https://go-goals.org/

6

The Board Game Luigi Ferrini

6.1

From the First Draft to the Final Version

Following theoretical modelling, the O5 output may be considered as a first draft of the game from a “serious game” paradigm. The game already in this form would have been suitable for a didactic use, according to the model described above, but in the project one more step was required, that is to transform the product into a real board game that could be played even without the presence of a facilitator. Therefore, starting from the many good ideas present in Chap. 5, especially with regard to the thematic contents (the Departments impersonated by the players, the events, the requests from the citizens...) the game flow has been rethought (and, in many ways, distorted) according to the requirements that have been identified together with all the partners. The first knot to untie, and which paradoxically turned out to be the most difficult one, was the number of players: a commercial game is usually designed to seat 2 to 6 players around the table and this seemed too much of a limitation for the game to be enjoyed in a class (which by its nature has very variable and potentially very high numbers). The choice was therefore to propose a modular solution in two different ways: the game therefore accommodates from 2 to 6 players (and certainly makes the best with the maximum number of 6), but also allows you to play with larger groups. You can choose to set up several tables and then, during the debriefing phase, compare the outcomes of the different games to assess how the different behaviours have led to different conclusions; alternatively or even in addition, each department within a single game can be played by a small group of players who will decide together the moves to be performed. Although the final version of the game already provides for a high level of interaction in terms of debate, adding a level of discussion can certainly have a greater educational function, although

L. Ferrini (*) Ergo Ludo Editions, Rome, Italy # The Author(s), under exclusive license to Springer Nature Switzerland AG 2021 J. Papathanasiou et al. (eds.), Urban Sustainability, Springer Texts in Business and Economics, https://doi.org/10.1007/978-3-030-67016-0_6

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accommodating a very large number of people around the same table can be problematic and should therefore be assessed on a case-by-case basis. For this reason, a path of flexibility in terms of game materials has also been chosen. In addition to the “DeLuxe version” which features professional materials and graphics (made by Back2Brain studio), the game also relies heavily on the downloadable “print & play” version: this version is accessible to anyone expressing a manifestation of interest in the project and allows both to make more copies at low cost (to have more tables) and to make a single copy with greatly enlarged materials (to allow greater usability to large groups). In both versions, instead of providing numerous tokens and markers to identify the buildings and structures built in the city, it was decided to take advantage of a mechanics that is currently very fashionable in commercial products, namely that of writing on game materials. Whether you use markers on erasable blackboards or pens and pencils on freshly printed sheets, the effect is the same: the city takes shape thanks to the graphic contribution of all players and in the end, it will look absolutely unique.

6.2

Winning Alone, Losing Together

Another aspect that required a close discussion with all the partners was the conditions for victory. Often in serious games the conditions of victory are almost superfluous: what matters is not winning, because the game emphasizes the cooperative spirit, expressly asking players to play the role of someone who has an interest in making the best choices for the common good. In a board game this can’t happen: you need conditions of victory and precise and effective scoring systems, because these are the engines that create the dynamics of interaction between players. That the game should be cooperative was an assumption shared by all from the beginning: the construction of the “public thing” is common, and failure has common causes and consequences. But in a board game cooperation only works well if the game “fights” against the players, and in the case - like this one - where all the information is public and communication is free, the lack of discordant goals among the players produces an extremely negative dynamic, so the game is not fun and is prone to a defect coded in literature as “alpha player”, when a player, who has better understanding of how the game works than the others, or simply has more strength in expressing their ideas by virtue of a natural leadership role in the group, plays for everyone by suggesting the best moves. After all, even the proposal to have a completely competitive game led to the wrong consequences: one lost the perception of how everyone’s actions could compromise victory for everyone. The game would then be reduced to a mere calculation of how to score more points for your side. This time too, the solution went through the most difficult way, the semi-cooperative game! Each player follows a personal agenda and will score points differently from the others, but acting solely for their own benefit will make everyone lose. There are in fact three ways to achieve a collective defeat: accumulate too much

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CO2, produce discontent in the population and erect buildings without criteria, occupying all the urban space. If you can avoid these three situations, you will get to count the points of each one to determine a single winner.

6.3

Requests and Solutions

Another mechanism present in Chap. 5, that of the “Requests” of the citizens, has been integrated in a preponderant way in the final version: in particular a very brilliant idea has been kept almost intact. It is a “tutorial” that explains the game through the events that are drawn during the game itself. In this way you can tackle the first scenario of the game without reading the rules at all, but making the game itself explain how it works. A complete reading of the rules will then be possible before starting the second scenario, to refresh and possibly clarify doubts about the game modes. Let’s close with a final note on the mechanisms, which allows us to understand how we have managed to remove the need for an “omniscient facilitator” from the dynamics of the game. In fact, in the face of precise requests provided by the game and a number of possible solutions, the first version provided for a human intervention that explained the precise effect of each solution adopted in relation to the present requests. Wanting to free the facilitator from this demanding role, but also wanting to avoid the very presence of the facilitator if the game takes place outside a classroom, it was decided to exploit the two sides of the “solution” cards in order to make them become simultaneously the engine for discussion and the one for the resolution of the effects. On one side of the cards, therefore, the proposed solution is presented in terms of financial commitment and with a purely qualitative description: both these factors are a stimulus to the discussion among the players as to whether or not to pursue the solution. Only once the solution has been adopted can the card be turned around, thus revealing the effects on the game indicators and possible side effects.

6.4

Future Developments

In addition to the tutorial scenario, which is a full-fledged scenario in which some waste management issues are addressed, four other scenarios have been designed to confront the players with many challenges: building communication infrastructure, water rationing, industrial investment, the effects of corruption, energy production and consumption, etc. All scenarios were used in the Management course at the Business Faculty of the University of Macedonia, Thessaloniki (Greece), partner and coordinator of the project, with positive results. It is also considered that the product can be used with similar success in higher education courses on sustainable development and Agenda 2030 (https://unric.org/it/agenda-2030/).

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Finally, it is not excluded that new developments and new ideas may be introduced in the near future, e.g. to create a replayable scenario with unpreordained events and outcomes; or to develop a slightly simplified version for a younger student (high school) audience. In addition, the use of Internet-based board game platforms such as Tabletopia.com, already successfully tested with the first scenario during the Covid-19 lockdown, could become an established practice to propose the game in its entirety even during online courses and lessons. At the present time the first scenario is publicly available for free playing on Tabletopia.com and the others will be made available soon.

6.5

Example of Play

Around the table are sit Irene (Department of Infrastructure), Emil (Department of Environment), Thomas (Department of Transport), Sarah (Department of Public Services) and William (Department of Welfare). The Department of Culture is unused and is then available to all players. This is the first scenario, so all the players will learn the game during the play. They read the cards from 1 to 8, that explain all the main rules of the game. They draw some buildings on the board, move some indicators and add population to the City. The result is something like that in Fig. 6.1. Now the players receive their budget: each coin represents one million bucks. The total budget is 24, so each player receives 4 coins, and the spare 4 coins are set aside and will be added to the total budget of the next year! The players are now ready to afford the first “Event phase”! The Event card number 9 is drawn: Sarah reads it loudly. This is a “Request” card, that will remain in play until resolved (Fig. 6.2).

Fig. 6.1 Example of the board

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Fig. 6.2 Example of an event card

Three solutions are drawn from the Solution deck and placed on the table. In this turn, other than the building facilities that each player has on his Department sheet, the players may enact three different policies: the construction of a new Landfill Area, the launch of an Awareness campaign on how to live healthy and the designation of a special bus lane in order to reduce traffic congestion (Fig. 6.3). Now it’s time to discuss! Emil thinks that the Request card may be tricky: it seems that the simple solution can be inefficient or simply wrong. Irene disagrees: the Landfill Area is mandatory in order to develop new houses. She would build a big Condo in order to allocate space for two new citizens, but she has only 4 coins out of the five needed. She asks if somebody may give to her part of his budget in order to accomplish this task. Sarah has no idea of how to invest her coins, so she chooses to give one of her coins to Irene. William suggests that each player invest 1 or 2 coins in the Landfill Area: cooperation should be the key of the success! Thomas and Emil agree with him, but other players seem doubtful. William is in charge for the clock. Five minutes are passed, so he announces the end of the discussion. Starting from the first player (in this turn, it’s Emil), each player invests some of their money in order to accomplish solution cards or erect buildings from his personal Department sheet.

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Fig. 6.3 Example of solution cards Fig. 6.4 Budget allocation/ policy and effect on indicators

Emil would contribute with the construction of the Landfill Area, but he wants also maximize his score. So, he decides to invest first 3 of his 4 coins in a Park from his Department Sheet. He pays 3 coins and draws a Park on the Map: Pollution decreases by 1 and Attractiveness of the City increases by 1 (Fig. 6.4). At his turn, Thomas decides to start investing in a Bus Station: he pays 2 coins from his budget and places them on the “Bus Station” square on his Department sheet. The required amount is 7, so the Bus Station is still under construction: the coins will remain on the square until the amount will be fulfilled (Fig. 6.5). Sarah invest 1 of her 3 coins on the “Build a New Landfill Area” solution cards, as sign of good will. Again, the coin stays there because the requested amount of 5 is not fulfilled yet. William, that is in charge for Department of Welfare, pays 2 coins and completes the “Awareness campaign on how to live healthy”: he flips the card in order to reveal the effects of the fulfilment of the solution. The effect is that Standard of living indicator increases by 1 (Fig. 6.6).

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Fig. 6.5 Second example of budget allocation/policy and effect on indicators

Fig. 6.6 Third example of budget allocation/policy and effect on indicators

Irene thinks that the population will grow very soon, so she follows her purpose to build a Residential Condo. She chooses to invest all her 5 coins (she received 1 coin from Sarah during the discussion phase) on her Department sheet and build a Residential Condo that she draws on the map. Unfortunately, building a new Condo implies that Pollution increases by 2, Attractiveness of the City decreases by 1 and Sustainable transport decreases by 20%. A Disaster! (Fig. 6.7). It’s Emil turn again. He has only 1 remaining coin and decides to invest it in the Landfill area construction, that reaches an amount of 2, still far from the end. Thomas invests as well 1 of his 2 coins in the Landfill area. It’s to Sarah, that decides to invest 1 of her 2 remaining coins on building a School. The School is on the sheet of Department of Culture, but this Department is

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Fig. 6.7 Fourth example of budget allocation/policy and effect on indicators

not assigned to any player, so everyone may invest on its projects. The School is not completed yet, because the requested amount is 4. William chooses to invest his 2 remaining coins to complete the building of the Landfill Area: he discards the 5 coins stacked on the card, draws the Landfill area on the map and then flip the card to discover the impact on indicators. The effects are very bad: Pollution increases by 2 and Attractiveness of the City decreases by 1, but William hopes that this investment will satisfy the Request card in play. Irene and Emil must pass because they ran out of coins. Thomas has still 1 coin but he chooses to keep it for the next turn and passes as well. Sarah places her remaining coin on the Fire Station in her Department sheet. William is out of coins too, so each player passes and the turn is over. A new turn begins! Emil passes the first player token to Thomas, the indicators are updated based on their values, new citizens come to City and are drawn on the map. Then the players share the budget: the total amount is still 24 coins, but the spare 4 coins from previous year are added to a total of 28: each player receives 5 coins and 3 coins are set aside for the next year. Then, a new event card is drawn and resolved (Fig. 6.8). The Landfill Area solution card has been completed, so we can read the first paragraph of the card: the Request card “Garbage everywhere!” has been fulfilled, but if the Landfill Ares has been placed near the Houses, someone should be upset. In this game indeed is very important to draw your building with accuracy: the growth of a City is not an easy task, and the adjacency of certain buildings may grant a large amount of points to some players rather than others. So be careful! The Event card also says to draw another Event card. . . And the new card is a new Request! No time to take a break for our city! (Fig. 6.9). Three new Solution cards are placed on the board, and a new discussion phase begins. . . But we stop here because we don’t want to spoil the entire scenario! Enjoy a complete play with your friends. . . the City counts on you!

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Fig. 6.8 Event card

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214 Fig. 6.9 Second event card

L. Ferrini

Appendix

# The Author(s), under exclusive license to Springer Nature Switzerland AG 2021 J. Papathanasiou et al. (eds.), Urban Sustainability, Springer Texts in Business and Economics, https://doi.org/10.1007/978-3-030-67016-0

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1. SUSTAIN, THE BOARD GAME 1.1 A new project The SUSTAIN project, Game-based Learning on Urban Sustainability, is a new project in many ways. It is new in the first place because it develops a board game taking as a model some fundamental aspects of System Dynamics (www.systemdynamics.it), a modeling approach for the simulaon of complex systems based on a holisc view of the various problems under analysis and taking into account the interdependencies between the various components of the system. Secondly, because the system itself is the complex and arculated system of a modern city: the economic, producve, social and environmental components are strongly connected and in very complex ways. As modelling shows us, the system can react to managerial acons even in highly counter-intuive ways. The game has among its objecves to demonstrate this aspect as well: the dynamics recreated in the game is in fact the one that leads the players to clash on the qualitave aspects of the decisions and then see their effects in quantave terms. Compared to the proposed model, the selecon of indicators to be controlled during the game has been reduced to a limited subset of aspects but, although it is to all intents and purposes an abstracon of the model, it has the merit of illustrang with very few parameters a complex situaon made up by the percepon of the well-being of the city, the percentage of sustainable mobility and the producon of polluon in all its forms. The same methodology has also been used to develop the online simulator, accessible from the project’s website, which allows further in-depth analysis of urban management issues. The board game, however, has a different objecve from the themac depth of t he simulator, and this is a further new element: it is in fact a game not didacc but designed for teaching. Typically, managerial didaccs make use of “playful” but not strictly “playing” tools: gamificaon soluons are not infrequent in didaccs, but they are precisely didacc tools, which exhaust their funcon in conveying informaon contents within controlled and extremely piloted environments, oen coordinated by a facilitator to whom all forms of mediaon and interacon between players are delegated. Alternavely, board games or role-playing games are used in didaccs, or playful environments (such as “adventure parks”) to reinforce transversal skills such as leadership, teamwork or highlight negave dynamics within working groups. In these cases, the skills conveyed are almost never vercal, strongly linked to the contents of the courses, but generic as they are based on commercial games created for purposes other than training. SUSTAIN’s challenge, instead, was to create a real board game, playable in itself even outside of training environments, but which would sll serve as a support tool for educaonal acvies by conveying the vercal contents in an experienal way. This challenge, we feel we can say, has been won; and this thanks to the collaboraon of an extremely varied and competent team that has seen in Ergo Ludo Edions only the last link in a very strong chain.

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1. 2 From the first dra to the final version Following theorecal modelling, one of the partners (Centre for System Soluon) has in fact developed a first dra of the game from a “serious game” paradigm. The game already in this form would have been suitable for a didacc use, according to the model described above, but in the project one more step was required, that is to transform the product into a real board game that could be played even without the presence of a facilitator. Therefore, starng from the many good ideas present in the first dra, especially with regard to the themac contents (the Departments impersonated by the players, the events, the requests from the cizens...) the game flow has been rethought (and, in many ways, distorted) according to the requirements that have been idenfied together with all the partners. The first knot to une, and which paradoxically turned out to be the most difficult one, was the number of players: a commercial game is usually designed to seat 2 to 6 players around the table and this seemed too much of a limitaon for the game to be enjoyed in a class (which by its nature has very variable and potenally very high numbers). The choice was therefore to propose a modular soluon in two different ways: the game therefore accommodates from 2 to 6 players (and certainly makes the best with the maximum number of 6), but also allows you to play with larger groups. You can choose to set up several tables and then, during the debriefing phase, compare the outcomes of the different games to assess how the different behaviours have led to different conclusions; alternavely or even in addion, each department within a single game can be played by a small group of players who will decide together the moves to be performed. Although the final version of the game already provides for a high level of interacon in terms of debate, adding a level of discussion can certainly have a greater educaonal funcon, although accommodang a very large number of people around the same table can be problemac and should therefore be assessed on a case-by-case basis. For this reason, a path of flexibility in terms of game materials has also been chosen. In addion to the “De-Luxe version” which features professional materials and graphics (made by Back2Brain studio), the game also relies heavily on the downloadable “print&play” version: this version is accessible to anyone expressing a manifestaon of interest in the project and allows both to make more copies at low cost (to have more tables) and to make a single copy with greatly enlarged materials (to allow greater usability to large groups). In both versions, instead of providing numerous tokens and markers to idenfy the buildings and structures built in the city, it was decided to take advantage of a mechanics that is currently very fashionable in commercial products, namely that of wring on game materials. Whether you use markers on erasable blackboards or pens and pencils on freshly printed sheets, the effect is the same: the city takes shape thanks to the graphic contribuon of all players and in the end, it will look absolutely unique.

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1.3 Winning alone, losing together Another aspect that required a close discussion with all the partners was the condions for victory. Oen in serious games the condions of victory are almost superfluous: what maers is not winning, because the game emphasizes the cooperave spirit, expressly asking players to play the role of someone who has an interest in making the best choices for the common good. In a board game this can’t happen: you need condions of victory and precise and effecve scoring systems, because these are the engines that create the dynamics of interacon between players. That the game should be cooperave was an assumpon shared by all from the beginning: the construcon of the “public thing” is common, and failure has common causes and consequences. But in a board game cooperaon only works well if the game “fights” against the players, and in the case - like this one where all the informaon is public and communicaon is free, the lack of discordant goals among the players produces an extremely negave dynamic, so the game is not fun and is prone to a defect coded in literature as “alpha player”, when a player, who has beer understanding of how the game works than the others, or simply has more strength in expressing their ideas by virtue of a natural leadership role in the group, plays for everyone by suggesng the best moves.

Aer all, even the proposal to have a completely compeve game led to the wrong consequences: one lost the percepon of how everyone’s acons could compromise victory for everyone. The game would then be reduced to a mere calculaon of how to score more points for your side. This me too, the soluon went through the most difficult way, the semi -cooperave game! Each player follows a personal agenda and will score points differently from the others, but acng solely for their own benefit will make everyone lose. There are in fact three ways to achieve a collecve defeat: accumulate too much CO2, produce discontent in the populaon and erect buildings without criteria, occupying all the urban space. If you can avoid these three situaons, you will get to count the points of each one to determine a single winner.

1.4 Requests and soluons Another mechanics present in the first game model, that of the “Requests” of the cizens, has been integrated in a preponderant way in the final version: in parcular a very brilliant idea proposed by CSS has been kept almost intact. It is a “tutorial” that explains the game through the events that are drawnduring the game itself. In this way you can tackle the first scenario of the game without reading the rules at all, but making the game itself explain how it works. A complete reading of the rules will then be possible before starng the second scenario, to refresh and possibly clarify doubts about the game modes. Let’s close with a final note on the mechanics, which allows us to understand how we have managed to remove the need for an “omniscient facilitator” from the dynamics of the game. In fact, in the face of precise requests provided by the game and a number of possible soluons, the first version provided for a human intervenon that explained the precise effect of each soluon adopted in relaon to the present requests. Wanng to free the facilitator from this demandi ng role, but also wanng to avoid the very presence of the facilitator if the game takes place outside a classroom, it was decided to exploit the two sides of the “soluon” cards in order to make them become simultaneously the engine for discussion and the one for the resoluon of the effects. On one side of the cards, therefore, the proposed soluon is presented in terms of financial commitment and with a purely qualitave descripon: both these factors are a smulus to the 6

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discussion among the players as to whether or not to pursue the solution. Only once the solution has been adopted can the card be turned around, thus revealing the effects on the game indicators and possible side effects.

1. 5 Conclusions In addition to the tutorial scenario, which is a full-fledged scenario in which some waste management issues are addressed, four other scenarios have been designed to confront the players with many challenges: building communication infrastructure, water rationing, industrial investment, the effects of corruption, energy production and consumption, etc. All scenarios were used in the Management course at the Business Faculty of the University of Macedonia, Thessaloniki (Greece), partner and coordinator of the project, with positive results. It is also considered that the product can be used with similar success in higher education courses on sustainable development and Agenda 2030 (https://unric.org/it/agenda-2030/). Finally, it is not excluded that new developments and new ideas may be introduced in the near future, e.g. to create a replayable scenario with unpreordained events and outcomes; or to develop a slightly simplified version for a younger student (high school) audience. Finally, the use of Internet based board game platforms such as Tabletopia.com, already successfully tested with the first scenario during the Covid-19 lockdown, could become an established practice to propose the game in its entirety even during online courses and lessons.

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2. Print and play section The reader may freely print and play the following material!

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City electric vehicle sharing programme

will surely be increased!

IV - 15

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Establish a municipal bicycle sharing system

will surely be increased!

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Electric car charging columns

Electric car owners will appreciate this solution very much.

IV - 14

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Index

A Accessibility, 51, 52 Analytic hierarchy process (AHP) application area, 110 CI, 112, 113 decision-maker, 109 decision-making techniques, 110 eigenvector method, 113, 115, 117, 118 functions, 110 fuzzy AHP, 110 geometric mean method, 113, 118, 122 local priority vectors capital requirements, 120, 122 delivery distance, 120, 122 environmental impact, 120, 122 investment costs, 120, 122 normalized column sum, 120 methodology, 109 normalized column sum method, 113, 118 pairwise comparison matrices criteria, 112 capital requirements, 117 delivery distance, 117 environmental impact, 117 goal, 114 investment costs, 117 priority vector of criteria, 113 problem structure, 111 scale, 112 Attractiveness, 162 Attractiveness of the city, 191

B Behavioural model, 132 Biodiesel production, 25 Board games, 85, 86 allocation/policy and effect, 210

budget allocation/policy and effect, 211, 212 cards, 207 coins, 212 commercial game, 205 DeLuxe version, 206 event card, 209, 213, 214 full-fledged scenario, 207 game materials, 206 internet-based, 208 omniscient facilitator, 207 players, 208 print and play version, 206 public thing, 206 request card, 207, 209, 212 semi-cooperative game, 206 serious game paradigm, 205 solution cards, 207, 210, 212 victory, 206

C Capacity building, 60–65 Carbon cycle, 14 Causal links, 133 Causal loop diagram (CLD) approach, 132 boardgame rules and elements, 148 feedback loops, 148, 150 reinforcing loops, 148, 149 variables, 148 variations, 148 Cell biology, 7 Circular economy economic benefits, 24 principles, 25 regenerative system, 24

# The Author(s), under exclusive license to Springer Nature Switzerland AG 2021 J. Papathanasiou et al. (eds.), Urban Sustainability, Springer Texts in Business and Economics, https://doi.org/10.1007/978-3-030-67016-0

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262 Classic houses, 160 Climate sustainability, 19 CO2 concentration, 4 CO2 emissions, 191 CO2 emissions indicator, 196 Communication, 187 Consensus, 188 Consistency Index (CI), 112, 113 Constant Returns to Scale (CRS), 83–85, 87, 88, 90–93 Constraints, 94 COVID-19, 50, 51, 68, 74, 208 Creativity, 188

D Data Envelopment Analysis (DEA) board games, 85, 86 constraints, 94 CRS, 83, 84 data, 92 DMUs, 82 excel, 94 formulation, 83 input/output orientation, 84, 85 mathematical model, 88–91 mathematical programming technique, 82 nature of scale, 86 non-parametric, 82 performance assessment, 83 problem data, 94 returns to scale, 84, 87, 88 social and environmental factors, 83 solver parameters setting, 95 technical efficiency, 82 VRS, 83, 87, 91–93 Decision-feedback cycle, 177 Decision making DEA (see Data Envelopment Analysis (DEA)) mathematical models, 81 MCDA (see Multiple criteria decision aid (MCDA)) problem-solving process, 81 Decision-making process, 187, 191 Decision Making Units (DMUs) CRS, 83, 85, 88, 91 definition, 82 efficiency, 83 objective function, 92 Pareto-efficient, 92, 93 pure technical efficiency, 92, 93 technical efficiency, 82

Index technical input efficiency, 88–91 technical output efficiency, 93 VRS, 83, 86, 91 Design thinking definition, 184 design thinkers characteristics, 186 diagrammatic representation, 185 integral part, 184 iterative and interactive process, 184 processes, 186, 187 thinking forms, 185, 186 Designing sustain board game assumptions, 188, 189 components stakeholders, 191, 192 system, 190, 191 objectives, 188 problem/main objective, 187, 188 real-life analysis, 190 target groups, 188, 189 Digitalization, 51 Double loop learning, 177

E EC Green Paper on Urban Environment, 43 EC Multiannual Financial Frameworks, 45 Economic dimension, 20 ECOPIXEL-recycled/recyclable plastic, 28 Eigenvector method, 113, 115, 117, 118 Energy flows, 13–15 Environmental degradation, 3 Environmental dimension, 20 Environmental impact anthropogenic, 16 car use, 16 categories, 16 definition, 16 plastic pollution, 16 Environmental pollution, 3 Erasmus + SUSTAIN project, 190 Ergo Ludo Editions, 175 E-smartec project, 65 EU cities, urban mobility CIVITAS, 43 cross-sectorial approach, 46 EC Green Paper, 43 EC Multiannual Financial Frameworks, 45 European Commission, 43 European transport system, 43 Green Paper, 44 SDG goals, United Nations, 47, 48 Transport White Paper, 44

Index urban transport, 44 White Paper, 43 European Commission (EC), 41, 43, 59, 60 European economy, 51 European Union (EU), 43 Event cards, 194, 209, 213, 214 “Every Can Counts” programme, 28, 31 Experiential Learning Cycle, 176 Experiential learning theory, 177

F Feedback loops, 133 Flood Resilience Game, 183, 185 Food supply, 3 Forests, 4, 5 Formal multiple criteria aid techniques, 96 Fossil-fuel combustion, 3 Fossil fuel transport, 26, 27 Frustrating/unexpected outcomes, 177

G GAIA analysis, 107, 109 Gaia plane, 101 Game-based learning, 187 Game of Urban Renewal, 179, 180, 183 Gaseous pollutants, 70 GDP per capita, 191 Generational learning, 200 Geometric mean method, 113, 118 Global CO2 emissions, 4 Global energy consumption, 3, 4 Global plastic production, 17 Global water use, 3 Go Goals game, 184 Greenhouse gases (GHG), 3

H Human-controlled material and energy, 1 Human-environment interaction, 8

I Indicators, 195 Inequalities, 195 Interactive Learning Environment (ILE) energy, 160 environment, 159 experiment, 151 investment-general variables, 153–155 planning and managing objectives, 152

263 realistic representation, 151 scenario analysis, 168 simulation model, 167 transport, 155–157 urban planning, 160, 161 variables and internal dynamics, 151 waste management, 156, 157 water management, 158, 159 International Union for Conservation of Nature, 4 Internet-based board game platforms, 208 Investments, 194

K Key-performance indicators (KPIs), 152

L Land availability, 191 Let’s Make a Bus Route game, 178–180 Linear Programming problem, 88

M Magic circle, 176, 198 Material flow, 11–13, 132 Mathematical modeling, 88–91 Metabolic process, 7 Metabolism, 7 Metro line costs, 89 Metro line data, 93 ‘Metrolink Monster’, 69 Milk pasteurization process, 12 Mismanaged plastic waste, 18 Moderator, social simulation workshop debriefing, 199 city, 200 definition, 199 individual feedback, 199, 200 learning opportunity, 199 facilitation, 197 magic circle, 198, 199 social simulation, 198 workshop process, 198 MOTIVATE app, 66–68 Multiple criteria decision aid (MCDA) AHP (see Analytic hierarchy process (AHP)) formal, 96 mathematical model, 96 operations research, 96

264 Multiple criteria decision aid (MCDA) (cont.) PROMETHEE (see Preference Ranking Organization METHod for Enrichment of Evaluations (PROMETHEE)

N NearZero houses, 160 New Shores game, 184 Nexus Game, 180, 181, 183 “No Food Waste Aiud”, 28, 33 Normalized column sum method, 113, 118

O OASTH e-services, 72 Online front-end web SUMP Competence Center, 63 Online sustainable mobility Competence Center, 62

P Pesticides in agriculture, 3 Plastic packaging, 16 Plastic pollution, 16, 18 Pollution, 3 Pollution accumulation, 191 Population level, 191 Preference Ranking Organization METHod for Enrichment of Evaluations (PROMETHEE) application areas, 97 available staff, 103 capital requirements, 102, 103 clothing and accessories industry, 101 countries, 97 data, 102 decision, 96 delivery distance, 103 environmental impact, 103 GAIA analysis, 107, 109 Gaia plane, 101 generalized criteria, 99 global flows, 100 linear preference function, 98 MCDA, 97, 98 methods, 97 negative flow, 100 net flows, 100 pairwise comparison matrix available staff, 105

Index capital requirements, 103, 104 delivery distance, 105 environmental impact, 106 positive flow, 100 preference degrees, 98 preference parameters, 102 PROMETHEE I partial ranking, 100 PROMETHEE II complete ranking, 101 research areas, 96 total net flows, 106, 107 uniriterion net flows, 105–107 visual, 107 Problem solving, 81 Procedural rhetoric, 176 PROMETHEE Flow Table, 108 PROMETHEE I partial ranking, 100, 104 PROMETHEE II complete ranking, 101, 108 Public transportation system, 20, 21

Q Quadrant of strategic scenarios, 163

R Real-life system elements, 194 Recycling processes, 166 REFORM Interreg Europe Project, 62–64 Request cards, 195, 212 Requests, 195 Returns to scale, 84 Role cards, 194

S S&F conceptualization, 134 Scenario analysis dynamics, 164 ILE, 164, 166 input modifications, 162 learning situation, 161, 162 methodology, 161 players, 161 quadrant of strategic scenarios, 163 scenario planning approach, 162 simulation, 162 variables, 164, 165 Scenario planning approach, 162 SD-based games complex issues analysis, 134 education, 134 educational simulators, 135 energy, 136, 139

Index environment, 136, 137 literature review, 134 panorama investigation goal, 135 planning, 136 services, 136, 146 transport, 136, 140 urban planning, 145 SDS website, 135 Self-assessment, 55 Serious games definition, 175 learning, 176, 177 mathematical skills development, 175 practices, 177, 178 social simulation, 175 Social dimension, 20 Social learning, 176 Social simulations, 176 Societal metabolism analysis, 6, 8 conceptual model, 1, 2 fossil fuel transport, 26, 27 nature materials and energy, 8 theoretical instrument, 1 waste and consume resources, 2 world population, 2 Society-environment interaction, 8 Solar City, 180, 181, 183 Solution cards, 210, 212 Solutions, 195 Standard of living, 191 Stock and Flow Diagram (SFD) online illustrative simulation model, 151 symbolism, 134 system under study, 133 Stocked material, 12 Stocks, 10, 132, 134 Strategic scenarios, 163, 165 Suburbia City Building Board Game, 179 Sustain board game design thinking, 183–187 development and iterations, 196 elements and representation, 194–196 Game of Urban Renewal, 179, 180, 183 Let’s Make a Bus Route game, 178–180 multiplayer games, 178 Nexus Game, 180, 181, 183 positive aspects, 183 prototype, 190 Solar City, 180 Suburbia, 178, 179 The World’s Future, 178 SUSTAIN model

265 CLD, 148–151 innovative training and learning, 168 scenario analysis, 161–166 SFD (see Interactive Learning Environment (ILE)) strategies, 166–168 SUSTAIN strategies CLDs, 167 methodological principles and simulations, 166 strategic scenario approach, 167 urban sustainability, 166, 167 Sustainability, 18–20 Sustainable development, 19 Sustainable Development Goals (SDGs), 20, 21, 41, 46, 178 Sustainable mobility measures, 57 Sustainable mobility practices, Thessaloniki (Greece) bike sharing system, 72 Intelligent Urban Mobility Management System, 70, 71 OASTH e-services, 72 THESi, 71 Sustainable transport, 191 Sustainable Urban Heating Simulation, 182 Sustainable urban metabolism ECOPIXEL-recycled/recyclable plastic, 28, 29 “Every Can Counts” programme, 28 “No Food Waste Aiud”, 28 Sustainable urban mobility air quality, 42 EU cities (see EU cities, urban mobility) EU data, 41 global population distribution, 40 industrial revolution, 39 life quality, 41 measures, 42 rural areas, 40 self-assessment, 74 SUMPs (see Sustainable Urban Mobility Plans (SUMPs)) transport externalities, 41 urban areas, 40, 41 urbanisation, 39, 40 urban transportation, 41 Sustainable Urban Mobility Plans (SUMPs) accessibility, 51 benefits, 52, 53 capacity building, 60–65 citizens, 65 COVID-19, 50, 51

266 Sustainable Urban Mobility Plans (SUMPs) (cont.) cycle, 53, 54 digitalization, 51 EMW, 59, 60 engagement methods, 67 engagement needs, 66 e-smartec project, 65 European economy, 51 European stakeholders, 50 implementation and monitoring, 58, 59 Lisbon, Portugal, 74 Manchester, England, 72, 73 measures, 56–58 MOTIVATE app, 66–68 planning cycle, 65 planning procedure, 51 preparation and analysis, 55 principles, 50 quality of life, 49 stakeholders, 56, 65 Strategic Plan, 49 strategy development, 56 strategy themes, 57 sustainable mobility promotion, 49 TfGM, 69 traditional and modern sustainable, 52 urban transportation, 50 vision of the cities, 54 Sustainability games, 178 Synthetic fertilisers, 3 System Dynamics (SD) approaches, 132 computer-aided modelling, 131 computer simulation, 131 diagrams and models, 131 material flows, 132 methodology, 133 online case studies, 168–172 paradigm, 167 simulation approach, 134 SUSTAIN (see SUSTAIN model) systems perspective, 132 tools, 132–134 System Dynamics Society (SDS), 135 Systems archetypes, 133 Systems thinking complex systems, 22 definition, 20

Index perspectives, 22 public transport intervention, 22 quantitative model, 132 traffic congestion problem, 23

T Tasks and responsibilities (indicators/KPIs), 194 Technical efficiency, 82 Technical input efficiency, 88 THESi (parking app for Thessaloniki city centre), 71 THESSBIKE (providers of bike and mobility vehicle rentals), 72 Traditional knowledge acquisition, 176 Traffic congestion problem, 24 Transnational European Cooperation Programmes, 45 Transport for Greater Manchester and Manchester (TfGM), 69 Transport infrastructure, 196 Transportation systems, 84 Trial and error process, 176

U UN Environment-led Global Initiative for Resource Efficient Cities, 9 Urban environment, 167 Urban metabolism, 8, 9, 30 Urbanization, 39–42 Urban mobility management policies, 41 Urban Mobility Package, 45 Urban sustainability, 166 Urban Transport Green Paper, 44 Urban transportation, 41, 50, 61

V Variable Returns to Scale (VRS), 83, 86, 87, 91 Visual PROMETHEE, 107

W White Paper on Transport Policy, 43 The World’s Future, 181 Worker placement games, 85