Advances of Footprint Family for Sustainable Energy and Industrial Systems 3030764400, 9783030764401

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Green Energy and Technology

Jingzheng Ren   Editor

Advances of Footprint Family for Sustainable Energy and Industrial Systems

Green Energy and Technology

Climate change, environmental impact and the limited natural resources urge scientific research and novel technical solutions. The monograph series Green Energy and Technology serves as a publishing platform for scientific and technological approaches to “green”—i.e. environmentally friendly and sustainable—technologies. While a focus lies on energy and power supply, it also covers “green” solutions in industrial engineering and engineering design. Green Energy and Technology addresses researchers, advanced students, technical consultants as well as decision makers in industries and politics. Hence, the level of presentation spans from instructional to highly technical. **Indexed in Scopus**. **Indexed in Ei Compendex**.

More information about this series at http://www.springer.com/series/8059

Jingzheng Ren Editor

Advances of Footprint Family for Sustainable Energy and Industrial Systems

Editor Jingzheng Ren Department of Industrial and Systems Engineering The Hong Kong Polytechnic University Hong Kong SAR, China

ISSN 1865-3529 ISSN 1865-3537 (electronic) Green Energy and Technology ISBN 978-3-030-76440-1 ISBN 978-3-030-76441-8 (eBook) https://doi.org/10.1007/978-3-030-76441-8 © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2022 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

Preface

In order to achieve sustainable development, the concepts and tools of “footprint family” such as ecological footprint, carbon footprint, water footprint, nitrogen footprint and life cycle assessment have been widely used for measuring environmental impacts from different perspectives. However, it lacks such a book which can systematically and comprehensively introduce the concepts and tools of “footprint family” and illustrate the methods for determining the ecological footprint, carbon footprint, water footprint, nitrogen footprint, etc. This book aims to present various methods for determining the ecological footprint, carbon footprint, water footprint and nitrogen footprint and to illustrate these methodologies through various applications. More specifically, (1) (2)

The methods for determining the ecological footprint, carbon footprint, water footprint, nitrogen footprint, etc. The applications of “footprint family” in economic system, ecological system, beef production system, cropping system, building, food chain, sugarcane bioproducts and the Belt and Road Initiative.

The unique feature of this book is that it can systematically and comprehensively introduce the tools/methods for calculating the ecological, carbon, water and nitrogen footprints and illustrate the tools/methods through different case studies. The readers of this book can obtain a comprehensive understanding of ecological footprint, carbon footprint, water footprint, nitrogen footprint, etc. It can be used as an effective handbook for determining various environmental footprints. There are a total of ten chapters in this book, and they are: 1. 2. 3. 4. 5.

How Does Ecological Footprint React to Economic Growth Dynamics? Evidence from Emerging Economies Life Cycle Assessment and Carbon Footprint Analysis of Recycled Aggregates in the Construction of Earth-Retaining Walls During Reconstruction The Input–Output Method for Calculating the Carbon Footprint of Tourism: An Application to the Spanish Tourism Industry Environmental Impact of Beef Production Systems Carbon Footprint Management for a Sustainable Oil Palm Crop v

vi

6. 7. 8. 9. 10.

Preface

Understanding of Regional Trade and Virtual Water Flows: The Case Study of Arid Inland River Basin in Northwestern China Water Footprint of the Life Cycle of Buildings: Case Study in Andalusia, Spain Nitrogen Footprint of a Food Chain Footprint Analysis of Sugarcane Bioproducts Overview of Footprint Family for Environmental Management in the Belt and Road Initiative.

This book is of interest to energy/environmental researchers, Ph.D. students, M.Sc. students, undergraduate students, engineers, decision-makers, etc. As the editor of this book, I appreciate the nice work of the authors of each chapter as well as the help and assistance of Anthony Doyle, Chandra Sekaran Arjunan and Werne Hermens from Springer Nature in the whole journey. Hong Kong SAR, China April 2021

Jingzheng Ren

Contents

How Does Ecological Footprint React to Economic Growth Dynamics? Evidence from Emerging Economies . . . . . . . . . . . . . . . . . . . . . . Zubeyde Senturk Ulucak, Salih Cagri Ilkay, Ahmet Koseoglu, and Savas Savas Life Cycle Assessment and Carbon Footprint Analysis of Recycled Aggregates in the Construction of Earth-Retaining Walls During Reconstruction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Jason Maximino C. Ongpeng and Clarence P. Ginga The Input–Output Method for Calculating the Carbon Footprint of Tourism: An Application to the Spanish Tourism Industry . . . . . . . . . . María-Ángeles Cadarso, María-Ángeles Tobarra, Ángela García-Alaminos, Mateo Ortiz, Nuria Gómez, and Jorge Zafrilla

1

15

35

Environmental Impact of Beef Production Systems . . . . . . . . . . . . . . . . . . . C. Buratti, E. Belloni, and F. Fantozzi

59

Carbon Footprint Management for a Sustainable Oil Palm Crop . . . . . . . David Arturo Munar, Nidia Ramírez-Contreras, Yurany Rivera-Méndez, Jesús Alberto Garcia-Nuñez, and Hernán Mauricio Romero

93

Understanding of Regional Trade and Virtual Water Flows: The Case Study of Arid Inland River Basin in Northwestern China . . . . . . . . . 111 Aihua Long, Xiaoya Deng, and Jiawen Yu Water Footprint of the Life Cycle of Buildings: Case Study in Andalusia, Spain . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 135 Cristina Rivero-Camacho and Madelyn Marrero Nitrogen Footprint of a Food Chain . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 167 Kaisa Grönman, Laura Lakanen, and Heli Kasurinen Footprint Analysis of Sugarcane Bioproducts . . . . . . . . . . . . . . . . . . . . . . . . 183 Noé Aguilar-Rivera vii

viii

Contents

Overview of Footprint Family for Environmental Management in the Belt and Road Initiative . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 215 Kai Fang, Siqi Wang, Jianjian He, Junnian Song, Chuanglin Fang, and Xiaoping Jia

How Does Ecological Footprint React to Economic Growth Dynamics? Evidence from Emerging Economies Zubeyde Senturk Ulucak, Salih Cagri Ilkay, Ahmet Koseoglu, and Savas Savas

Abstract There have been many attempts investigating how environmental conditions are affected by economic growth in the literature by mainly following the environmental Kuznets curve approach that is figured out an inverted U-shaped relationship between economic growth and environmental degradation. However, the literature has ignored the role of growth dynamics in this relationship by using economic growth instead of employing essential factors of growth equations. Contrary to prevailing literature, this study employs labour, capital and human capital factors as main drivers of economic growth. The study also observes environmental deterioration by using the ecological footprint that is widely accepted as a strong environmental sustainability indicator recently. Empirical results produced by advanced panel data methodologies taking cross-section dependence into account for emerging economies confirm that human capital accumulation that is the unique driver of economic growth is useful to shrink ecological footprint. Keywords Ecological footprint · Environmental degradation · Human capital · Economic growth · Panel data

Z. S. Ulucak (B) Faculty of Economics and Administrative Sciences, Department of Public Finance, Erciyes University, Kayseri, Turkey e-mail: [email protected] S. C. Ilkay · A. Koseoglu · S. Savas Faculty of Economics and Administrative Sciences, Department of Economics, Erciyes University, Kayseri, Turkey e-mail: [email protected] A. Koseoglu e-mail: [email protected] S. Savas e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Switzerland AG 2022 J. Ren (ed.), Advances of Footprint Family for Sustainable Energy and Industrial Systems, Green Energy and Technology, https://doi.org/10.1007/978-3-030-76441-8_1

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1 Introduction Economic growth that enables people to get more purchasing power has been the main problem of humanity since back. However, in the academic setting, the first systematic attempt to explain growth dynamics is Robert Solow’s seminal paper titled “A Contribution to the Theory of Economic Growth” published in 1956. Then, Cobb–Douglas production function has been the basic equation to solve growth dynamics by mainly including labour and capital factors [42]. According to the Solow model, capital stock and output growth rate is equal to the sum of population growth (representing labour force) and technological progress rate. One of Solow [50] model’s critical result is that capital accumulation is not solely sufficient for driving economic growth. The long-run equilibrium growth rate can be sustained by only two exogenous factors, population growth and technological progress. However, technology is included as an exogenous variable in neoclassical growth models. These models reach that technological change is the main driver of long-run economic growth but do not explain determinants of technological progress endogenously. This shortcoming in the literature has been overcome by Romer [43] modelling long-run economic growth under increasing returns. Based on Arrow’s [6] idea of learning by doing, Romer [43] expanded the process of capital accumulation to include knowledge and physical capital goods. In the model, the main sources of the increasing returns caused by the increase in productivity are the spillovers of knowledge across producers and external benefits from human capital. Another study that has played a significant role in the new growth literature is Lucas [32], which constructs an endogenous growth model with human capital by following Arrow [6], Uzawa [52]1 and Romer [43]. Lucas [32]2 model’s significant contribution is that it emphasizes the importance of human capital accumulation and especially of human capital externalities in the economic development process. Endogenous economic growth literature that truly reflects the R&D and innovation process began with the pioneering paper of Romer [44]. Then, Grossman and Helpman [26] and Aghion and Howitt [3] made significant contributions to this field. Consequently, as in the Romer [44] model, the major determinants of economic growth in the Aghion and Howitt [3] model are the technological innovations that arise from profit-maximizing agents’ like intentional R&D activity and human capital stock. On the other hand, the environmental consequences of economic growth have been remained as a controversial subject. To fulfil the basic needs of humans and increase their welfare requires economic activities to expand. Therefore, rapid production and income growth become the primary goal of economic development. In the first place, governments only focused to promote economic development without considering its environmental outcomes. As a result, primary energy consumption increased around 3% with almost doubling its 10-year average annual increase rate in 2018. On the 1 Uzawa [52] model, considered one of the pioneers of endogenous growth literature, explains long-run economic growth as a result of human capital accumulation. 2 Lucas [32] has included human capital accumulation in his model by addressing it through schooling and learning by doing.

How Does Ecological Footprint React to Economic Growth …

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other hand, the recent growth of global energy demand led a significant increase on greenhouse gas (GHG) emissions [22]. Carbon dioxide (CO2 ) emissions increased by 2.1% in 2018 and became almost 34,000 million tons with representing the fastest growth in the last decade [12]. However, as primary energy demand slowed and renewable energy sources and natural gas displaced other fossil energy sources from the energy mix, CO2 emissions decelerated in 2019 and became 0.5% after the dramatic increase observed in 2018 [13]. While some authorities observe the emergence of pollution problems, the lack of motivation and success in dealing with climate change and global warming increased the volume of concerns from all walks of life in quest to provide environmental sustainability. Consequently, many international organizations have tended to establish a consortium to respond on international public’s concerns about environmental degradation. For example, at the October 2007 meeting of the United Nations (UN), the Executive Heads of UN agencies, funds and programmes agreed on moving their respective organizations towards climate neutrality.3 Hence, the UN authorities firstly agreed to estimate the greenhouse emissions of UN system organizations in line with accepted international standards. Secondly, the authorities also agreed to undertake efforts to provide greenhouse gas emission reduction. Finally, they decided to examine the cost implications and detect budgetary modalities of purchasing carbon offsets to accomplish climate neutrality. When we come back to 2020, the UN is on course to accomplish climate neutrality. The organization continues to lead and support several efforts in all fields of environmental sustainability. The concept of the ecological footprint, which has been widely used as an indicator of environmental conditions of countries, clearly expresses the human impact on the environment. According to the originators, there is an interesting fact that, if all individuals adapt to North American standard of living, this global transformation will require three earths [53]. The simple conclusion from this statement is that we are living beyond our biophysical possibilities [34]. Therefore, it is clear that people must live within their means in terms of ecological limits and practicable solutions must be required to transform economic activities into environmentally friendly processes. In this path, human capital is a unique dynamic of long-run economic growth, and it is also one of the most important sources of environmental awareness, which makes it precious as a part of green growth pursuits. From this point, this study investigates how ecological footprint of emerging economies is affected by main growth dynamics. To this end, using yearly data covering 1970–2017 with 48 observations for each country, second-generation panel data methodologies are conducted. Unlike the prevailing literature which employs economic growth to investigate its environmental consequences through the environmental Kuznets curve concept, this study observes the impact of increases in labour, capital and human capital factors. So, it is expected to contribute to the literature through the experience of emerging economies.

3 Climate neutrality means increasing the reduction of greenhouse gas emissions as much as possible.

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2 Literature Review In recent years, the studies which analyse the nexus between economic growth and environmental quality reveal various results according to their model, variable, sample, period and methodology selection. Hence, this research subject has a dynamic structure which leads a hot debate among environmental economists and policy makers. With a pioneering study, Grossman and Krueger [27] investigated the nexus between economic growth and environmental pollution for the first time, and after a short period, the environmental Kuznets curve (EKC) was firstly introduced by Shafik and Bandyopadhyay [48] and Panayotou [36]. Therefore, over the last three decades, the economic growth and the environment quality nexus has been investigated through the EKC model [16, 25, 51]. Accordingly, the relevant literature was developed by the studies which tried to analyse the drivers that shape the relationship between economic development and environmental degradation [41]. However, the literature on economic growth and the environment is largely inconclusive about the environmental consequences of economic growth dynamics. At that point, there were several attempts to establish a model which will involve economic growth dynamics such as labour, capital and human capital. In addition to the early literature, Brock and Taylor [15] established the EKC hypothesis and Solow model nexus. In their study, the authors took the underlying assumptions of the neoclassical growth model which was developed by Solow [50] and extended the basic Cobb–Douglas production function by adding environmental pollution to the equation. As a result, they established a new model named the Green Solow model which explains the bell-shaped pattern between environmental degradation and economic growth with the same components that assure the task of economic growth [15]. From the existing applied studies, one can find out that Granger causality test has been generally carried out to investigate the direction of causality between economic growth and environmental degradation [24]. Although it is obvious that the related studies revealed inconclusive results, there is no consensus on the existence and the direction of causality [24, 35]. The main reason for this ambiguity is that the Granger causality test in a bivariate structure is likely to be biased due to the omission of relevant determinant affecting economic growth and environmental degradation nexus [35]. This drawback has led some Granger causality-based studies to incorporate labour and capital in the multivariate models [9, 31, 46]. In this context, Apergis and Payne [5] used the variables of labour force and real gross fixed capital formation as labour and capital proxies and found out that neither economic growth, labour force nor the capital formation has statistically significant impact on energy consumption for nine South American Countries. Bartleet and Gounder [9] used the same variables for labour and capital to analyse the causality structure between labour, capital and energy consumption in the case of New Zealand. The Granger causality results showed that labour force and capital formation have statistically significant impact on energy consumption. Although the research topics differ in several studies [31, 35,

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46] labour force and reel gross fixed capital formation variables have been selected to represent the dynamics of economic growth. Last but not least, human capital is widely considered as a production factor coordinated with physical capital, and its growth is both a condition and result of economic growth [33]. Moreover, human capital accumulation has a crucial role in determining environmental quality [4, 8, 11]. Researchers generally pick human capital index which is based on the average years of schooling and the rate of return to education as a human capital proxy and execute different econometric methods such as Bayer–Hanck cointegration, linear/nonlinear/threshold autoregressive distributed lag (ARDL) models and first- & second-generation panel data analysis [21, 47, 55]. The results conclude that, human capital accumulation increases economies readiness to adopt environmentally friendly technologies and improves energy efficiency by rising individual’s productivity. Hence, there is a consensus on human capital’s positive contribution on environmental quality.

3 Model Data and Methodology There are two alternative approaches widely used in the literature to capture environmental impacts of economic activities, which one of them is the environmental Kuznets curve concept and the other is IPAT model. The environmental Kuznets curve approach is based on a quadratic function in which per capita income and its square are the regressors. However, the IPAT signifies environmental impacts (I) of population (P), affluence (A) and technology (T ). The IPAT procedure is relatively consistent in a relationship between growth dynamics and environmental degradation. Because population represents the labour force in growth models and capital stock may be an alternative indicator of affluence. Moreover, human capital is the unique source of technology of modern economic growth theories [32, 43, 44]. Therefore, following model in Eq. (1) is established to see how growth dynamics affect the ecological footprint. EFPCit = L it + K it + HCit + eit

(1)

EFPC is per capita ecological footprint and is obtained from the database of Global Footprint Network, L represents the ratio of employment to population, K denotes per capita capital stock, and HC stands for human capital, which are derived from Penn World Tables. The panel sample of the analysis that is cross sections showed by i in the Eq. (1) consists of fifteen emerging economies classified by S&P Down Jones indices (Turkey, China, Indonesia, South Africa, Brazil, Chile, Egypt, Thailand, Colombia, Mexico, Philippines, Peru, Malaysia, Poland and India). The time dimension of the equation indicated by t covers the period 1970–2017 with annual data. Descriptive statistics of data for each variable used in the study are reported in Table 1. Accordingly, the highest mean value was calculated for the capital variable,

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Table 1 Descriptive statistics for data Mean

EFPC

L

K

HC

2.366032

0.385147

3429.447

2.130893

Median

2.183359

0.382173

54.54343

2.118094

Maximum

6.238714

0.597423

68637.73

3.404202

Minimum

0.644238

0.216487

9.563865

1.173621

Std. dev.

1.145795

0.087769

12487.13

0.476026

Skewness

0.854807

0.410697

3.800359

0.150243

Kurtosis

3.546421

2.582338

16.15491

2.392252

Jarque–Bera

96.64065

25.47385

6924.680

13.78949

Probability

0.000000

0.000003

0.000000

0.001013

Observations

720

720

720

720

while the lowest inevitably belongs to labour variable that is a share of employment to population. The similar case was verified for the volatility so that the capital variable is the most volatile with the highest standard deviation value. Moving on the per capita ecological footprint data, it has 2.36 mean value, while the maximum value is 6.23, and minimum value is 0.644 for 15 emerging economies within 720 observations during the period of 1970–2017. On the other hand, Jarque– Bera statistics show that the null hypothesis of normal distribution should be rejected for all variables. The use of logarithmic transformation of data is helpful to eliminate non-normal tendencies of data and enable us to comment coefficients as elasticity. So, we transformed all data into logarithmic terms before proceeding to apply each step of the estimation. Prior to the estimation of the Eq. (1), some initial conditions or preliminary checks should be followed in panel data analyses. All variables must be stationary and crosssectionally independent to achieve an efficient and unbiased outcome [49]. Therefore, first, it is essential to check whether cross-sectional dependence (CSD) exists or not since panel data models are usually assumed to be cross-sectionally independent. Breusch and Pagan [14] developed a new test named Lagrange multiplier (LM) to examine cross-sectional dependence. It is a test which is based on the median of the squared pair-wise correlation of the residuals. Nevertheless, this method is not suitable to apply when the cross section dimension is large and alternative two tests are suggested by Pesaran [37], which are based upon the average of pair-wise correlation coefficients of the ordinary least squares (OLS) residuals from the individual regressions in the panel. Further, a bias-adjusted procedure of Breusch and Pagan [14] is proposed by Pesaran et al. [39]. For each test, the null hypothesis bases on that there is not cross-sectional dependence. If the null hypothesis is rejected, then one should check stationarity by using a panel unit root test which accounts for the CSD. In this context, Smith et al. [49] propose a more powerful unit root strategy that calculates three statistics and simulates their critical values through bootstrapping to consider the CSD. In case non-stationarity is verified for study variables, the further step is to

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confirm the existence of a cointegration relationship between study variables in order to proceed to the estimation without observation loss. A cointegration relationship refers to an equilibrium relationship indicating a co-movement of variables in the long run. However, both the cointegrations check for study model, and the estimation process should consider the CSD if exists. In order to check the cointegration under the CSD issue, Westerlund [54] proposes two tests using common factors. These two cointegration tests are called as Durbin Hausman group and Durbin Hausman panel since these statistics calculated by following the well-known Durbin Hausman procedure. The final step of the estimation is to use an appropriate estimator which should meet the requirements of the previous steps mentioned above. In this path, Bai et al. [7] developed two alternative estimation methods for non-stationary but cointegrated panels, which is robust against serial correlation, heteroskedasticity and CSD issues, by extending the fully modified ordinary least squares estimator (FMOLS) with common factors to deal the CSD issue. The procedure for the estimation can be shown as following. Bit = αi + β  Ait + μit

(2)

Ait = Ai,t−1 + εit , μit = λi f t + n it

(3)

where

where λi is factor loadings as well as f t is unobserved I(0) factors. A signifies the explanatory variable of the estimation. Phillips and Hansen [40] FMOLS estimator is adjusted to examine the existence of factors as it stands in the Eq. (4). ⎛ βˆ F M = ⎝

T N    i=1 t=1

Ait − A¯ i



⎞⎞ ⎞−1 ⎛ ⎛ N  T

    + + ˆ ⎠⎠ ˆ ¯ ¯ ⎝ ⎠ ⎝ ˆ ˆ Ait − Ai Ait − Ai Bit − T εμ + ε f λi A i=1 t=1

(4) β coefficients which are computed by the FMOLS are replicated by obtaining residuals from the FMOLS in each early stage till the convergence, which called continuously updated FMOLS, CUP_FMOLS hereafter [20]. Bai et al. [7] also correct biases in the estimation, and this procedure is named as bias corrected FMOLS (BC_FMOLS hereafter). CUP_FMOLS and BC_FMOLS estimators are consistent with the presence of exogenous regressors. Additionally, these estimators are robust to mixed I(1)/I(0) factors, and regressors. Each estimator provides consistent and robust outputs even though if the endogeneity problem exists as they are based on FMOLS [7].

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4 Results and Discussions The study performed several initial checks before proceeding to estimate long-run coefficients which enlighten how growth dynamics affect ecological footprint of emerging economies. Basically, the first step is to determine whether panel variables with a long time series dimension are stationary or not to avoid spurious regression problem [30]. However, there are two alternative approaches to check stationarity according to the cross-correlation among panel sections. One should decide to select the appropriate approach by checking cross-sectional dependence (CSD) and should proceed to apply each required step of the long-run parameter estimation by secondgeneration panel methodologies taking cross-sectional dependence into account if it is verified. For this, several CSD tests are widely used in the literature, proposed by Breusch and Pagan [14], Pesaran [38], Pesaran et al. [39]. Therefore, we firstly checked the CSD issue for each variable included into the estimation model. Table 2 documents CSD test results under the null hypothesis of no cross-sectional dependence. According to the results, there is no inconsistency among the four tests, and all of them strongly reject the null hypothesis at 1% significance level, meaning that each variable has a strong dependency among its cross sections. The strong crosscorrelation among panel units for each variable requires to apply a new panel unit root test considering the dependence. Smith et al. [49] propose a more powerful unit root strategy that calculates three statistics and simulates their critical values through bootstrapping. These three statistics (Max, Ws and MinLM) of the proposed panel unit root strategy test the null hypothesis “series has a unit root”. The panel unit root test results based on the two options, with constant, or constant & trend, are presented in Table 3. Each option mostly provided consistent results except MinLM statistics for the KPC variable although Max and WS statistics point out that the capital variable (K) has unit root at the level. Results for other variables, EFPC, L and HC, strongly suggest the rejection of the null hypothesis of unit root. According to results, all the series has unit root, so they are not stationary at the level and do not meet the stationarity condition for the estimation of model coefficients that can be estimated by panel ordinary least squares method. On the other, one should be sure whether series turns to be stationary when their first differences are taken, which signifies integration levels of variables and a probable cointegration relationship between variables. Therefore, we checked the Table 2 CSD test results for variables Breusch–Pagan LM

EFPC

L

K

HC

2021.575a

2326.493a

4769.491a

4771.612a

Pesaran-scaled LM

132.2563a

153.2976a

321.8804a

322.0268a

Bias-corrected scaled LM

132.0967a

153.1380a

321.7209a

321.8673a

Pesaran CD a represents

14.74564a

significance level at 1%

23.01361a

68.97302a

69.03667a

How Does Ecological Footprint React to Economic Growth …

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Table 3 Panel unit root test results under the CSD at level Constant High income

Constant and trend

Statistic

Bootstrap p-value

Statistic

Bootstrap p-value

Max_statistic

−0.597

0.969

−1.965

0.270

WS_statistic

−0.676

0.972

−2.139

0.322

1.465

0.745

4.443

0.229

Max_statistic

−0.183

0.999

−1.330

0.964

WS_statistic

−0.139

1.000

−1.664

0.945

1.886

0.412

2.693

0.939

0.053

0.512

−1.380

0.196

−0.215

0.932

−1.963

EFPC

MinLM_statistic L

MinLM_statistic K Max_statistic WS_statistic MinLM_statistic

4.431

0.001*

5.024

0.129 0.033**

HC Max_statistic WS_statistic MinLM_statistic

0.378

0.759

−1.481

0.141

−0.265

0.876

−2.001

0.161

0.711

0.972

3.768

0.101

* and **represent significance level at 1% and 5%

first differenced data for all variable to see whether they become stationary. Results with bootstrap p values are provided in Table 4. Results reported in Table 4 show that all the variables, EFPC, K, L and HC, become stationary in their first differences. This information is critical to proceed with the estimation of the model equation without observation loss if we can verify a cointegration relationship that refers to a long long-run relationship between study variables. Otherwise, we need to run panel OLS estimators by using differenced data, which leads to observation loss. In order to determine the cointegration relationship, one should recheck the CSD issue, but it should be checked for the model equation in this time. By doing so, we tested the CSD for the estimation model and confirmed the strong dependence by outputs indicated in the upper part of Table 5. We therefore followed Durbin Hausman procedure that accounts for the CSD in cointegration examination proposed by Westerlund [54]. According to Durbin Hausman group and panel statistics with the null hypothesis of no cointegration, whose results are shown in the lower part of Table 5, the null hypothesis can be rejected, meaning that a cointegration relationship exists for the estimation model, thus model variables are cointegrated, and they move together within a long-run equilibrium relationship. Having confirmed a cointegration relationship, one may continue to estimate longrun cointegration parameters to reveal how much a change in any regressor would lead to an increase or decrease in the ecological footprint by using cointegration

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Table 4 Panel unit root test results under the CSD at level Constant High income

Constant and trend

Statistic

Bootstrap p-value

Statistic

Bootstrap p-value

Max_statistic

−7.062

0.000

−7.180

0.000

WS_statistic

−7.252

0.000

−7.449

0.000

MinLM_statistic

24.006

0.000

24.685

0.000

Max_statistic

−5.258

0.000

−5.601

0.000

WS_statistic

−5.455

0.000

−5.911

0.000

MinLM_statistic

17.958

0.000

19.495

0.000

Max_statistic

−0.192

0.886

−2.010

0.063

WS_statistic

−0.415

0.961

−2.310

0.077

0.694

0.989

6.351

0.007

Max_statistic

−1.278

0.157

−8.120

0.000

WS_statistic

−1.531

0.082

−8.579

0.000

2.017

0.223

21.124

0.000

EFPC

L

K

MinLM_statistic HC

MinLM_statistic

Table 5 CSD and cointegration test results

Test statistic

Prob-value

398.416

0.000

20.248

0.000

CSD tests Breusch–Pagan LM Pesaran LM Pesaran CD

5.176

0.000

41.651

0.000

Durbin Hausman group

−3.016

0.001

Durbin Hausman panel

−1.985

0.024

Bias-corrected LM Cointegration tests

estimators. To this end, we employed robust cointegration estimators proposed by Bai et al. [7] and presented the results in Table 6. According to estimation outputs, which were obtained by the using logarithmic transformation enabling us to comment on each coefficient as elasticity, labour factor has not statistically significant impact on the ecological footprint. However, it has positive coefficient as is theoretically expected. Considering the BC_FMOLS result, the t-statistic is near to traditional significance level at 10%, which may imply that the ecological footprint increases as the employment of labour factor rise. Even so, this output clearly points out that the increase in labour factor in our panel sample

How Does Ecological Footprint React to Economic Growth …

11

Table 6 Long-run cointegration parameters CUP_FMOLS L K HC

0.01925

t-statistic 0.4901

BC_FMOLS 0.0583

t-statistic 1.4721

0.2938

4.5298*

0.2762

4.3562*

−0.3899

−6.5676*

−0.3491

−6.0045*

* denotes statistically significance level at 1%

consisting of emerging fifteen economies has not influenced the ecological footprint. Apart from labour factor, capital and human capital have statistically significant impact on the ecological footprint. Of them, capital factor has positive impact on the ecological footprint, meaning that the ecological footprint of the sample countries expands as the use of capital factor rise. More specifically, a one per cent increase in the use of capital would increase the ecological footprint by nearly 0.29% in these countries. This mechanism is consistent with the theoretical underpinning such that countries increase physical capital to produce more and obtain higher gross domestic product. But this process causes both more resources/ecological assets to be used and more pollution to be created, which in turn deepens the deterioration in the environment. Hence, many studies investigating the role of the gross domestic product on the ecological footprint approved the validity of this mechanism which is also called the scale effect of the environmental Kuznets curve concept [10, 23, 29, 45]. Moving on the role of human capital factor, results produced by CUP_FMOLS and BC_FMOLS are statistically significant and show that a 1% increase in human capital accumulation would result in 0.38 decrease in the ecological footprint. This finding supports theoretical background on how economic growth can be beneficial to the environment by leading to arise the technique effect that is the most critical expectation of the environmental Kuznets curve hypothesis. Because the technique effect refers to an increase in technological progress and productivity rise, less material and resources would be used and less pollution would arise in production and consumption activities [28]. This factor also is expected to increase environmental awareness that is one of most crucial components of green development policies [17, 19]. This output for human capital is consistent with those found by studies using human capital to reveal its effect on the ecological footprint [4, 18, 47, 51]. However, some other studies found no significant impact of human capital on the ecological footprint [21, 29].

5 Conclusions The increasing pressure on the environment has been gradually expanding, which makes the economic growth a worrisome phenomenon since it basically refers to produce more. However, this process may be an environmentally friendly and sustainable if it is well managed. Indeed, new growth studies introduce a sustainable growth

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by improving the quality of the environment through directed technological change [1, 2]. Based on this, this study is an attempt to reveal how growth dynamics affect ecological footprint in emerging economies which tend to grow faster than others. To this end, it employs labour, capital and human capital factors that are the main inputs of production process. Conducting second-generation non-stationary panel time series methodologies, the study reaches statistically significant outputs for physical capital and human capital factors. Accordingly, results showed that physical capital increases the ecological footprint. Also, results confirmed that human capital accumulation that is the unique driver of economic growth in the long run helps to shrink ecological footprint in the context of emerging economies. Empirical results suggest policy makers who are willing to protect the environment and to stay within ecological meets that they should spend more effort to increase human capital accumulation. These efforts are also expected to increase economic growth through increased productivity as is proved by endogenous growth models. Human capital accumulation is mostly supported by education and the increased qualification of labour force. Therefore, the most solid implication to increase human capital accumulation is to improve the quality of educational attainments and is to support training activities at work.

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Life Cycle Assessment and Carbon Footprint Analysis of Recycled Aggregates in the Construction of Earth-Retaining Walls During Reconstruction Jason Maximino C. Ongpeng and Clarence P. Ginga Abstract In a nation, reconstruction is needed to provide resiliency and maintain economic growth. To remedy the damage done on roads and highways after an event, the reconstruction of earth-retaining walls (ERWs) before road/highway rehabilitation is of great importance. This would provide land transportation routes from airports or emergency personnel services to save lives and transport supplies/materials to disaster-stricken areas. It is one of the most common structures in civil engineering designed to retain earth pressure on roads and highways. It is constructed using concrete, a widely used construction material with high material consumption and carbon footprint. Aside from these, construction and demolition wastes (CDW) arise from the damaged ERWs and any concrete materials contributing to adverse impacts on the environment. These alarming facts are some of the many reasons for evaluating construction materials, such as using life cycle assessment (LCA) on CDWs. This paper investigates the use of ERWs using concrete from cradle-to-gate with natural aggregates (NAs) and recycled aggregates (RAs) from CDW. It considers three ERW types, such as gravity wall, cantilever wall, and mechanically stabilized earth (MSE) wall. It was found that the construction of MSE walls, among other types of earth retaining structures, is found to be 50–70% of less impact than other types of ERWs in this study. The utilization of RA in the production of concrete is up to 15% less impactful than NA, even with the additional 10% increase in cement content to compensate for the strength loss from the use of RA to NA. In ideal condition, the transport distances of NA and RA should be around 15–20 km from extraction of raw materials and processing, to concrete pouring. A limit of 100 km transport distance for RA must also be considered so that the environmental benefits from the use of RA would not be outweighed. Further studies on the economic aspect and the sustainability of its supply chain during the reconstruction are recommended. Keywords Life cycle assessment · Recycled aggregate · Retaining wall · Concrete · Construction and demolition waste J. M. C. Ongpeng (B) · C. P. Ginga Department of Civil Engineering, De La Salle University, 2401 Taft Avenue, 0922 Manila, Philippines e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Switzerland AG 2022 J. Ren (ed.), Advances of Footprint Family for Sustainable Energy and Industrial Systems, Green Energy and Technology, https://doi.org/10.1007/978-3-030-76441-8_2

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1 Introduction The need for reconstruction is evident with the number of property damages in millions of USD and casualties across the globe. These stem from natural disasters that significantly affect the community, such as earthquakes, floods, typhoons, and others that can heavily damage the built environment. Figure 1 shows the number of earthquakes with a magnitude of more than 7.1 from 2005 to 2019, including the one in Yogyakarta in Indonesia with a magnitude of 6.4. The damage reached 300 billion USD in Tohoku, Japan, last March 2011 and 300,000 casualties from Haiti last January 2010. High casualties may arise from post-disaster events wherein access of rescue workers and supplies of essential materials experience challenges and obstructions from land transportation routes. Figure 2 shows the number of floods and landslides recorded from 2005 to 2019, recording more than 5,000 casualties in India last June 2013 and property damages of 5 billion USD in China last May 2010. The graph shown in Fig. 3 exhibits that the cumulative property damages and loss of lives are proportional to each other. An interesting note can be observed from the figure with the given scale and units before and after 2015. Before 2015, the cumulative loss of lives is higher than the cumulative property damages, while it is the opposite after 2015. This may be due to the United Nations Office for Disaster Risk Reduction (UNDRR), especially on implementing the Sendai Framework for Disaster Risk Reduction 2015–2030. In this framework, member countries implement, monitor, and share their risk reduction and prevention experience. This brings awareness and lessons learned from what risks may arise and how communities can improve their resilience during a time of disaster.

Fig. 1 Number of earthquakes with magnitude greater than 7.1 and with substantial casualties from 2005 to 2019

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Fig. 2 Number of floods and landslides with substantial casualties from 2005 to 2019 8,00,000.00

7,00,000

7,00,000.00

6,00,000

6,00,000.00

5,00,000

5,00,000.00

4,00,000

4,00,000.00 3,00,000

3,00,000.00

2,00,000

2,00,000.00

1,00,000

1,00,000.00 0.00 2004

2006

2008

2010

2012

Cumulative Damages (Millions USD)

2014

2016

2018

0 2020

Cumulative loss of lives

Fig. 3 Cumulative property damages and loss of lives from 2004 to 2020

As one of the countries with a high count of earthquakes, floods, and landslides (Figs. 1 and 2), the study focuses on the Philippines. The Philippines is a country in Southeast Asia situated in the Pacific Ring of Fire, a region home to some of the world’s most active volcanoes. Strong earthquakes often cause a large number of landslides, including large-scale landslides in mountainous areas and volcanic activities that could trigger giant landslides on mountain slopes [25]. Areas, such as the Cordillera Range, are home to some of the most complex geology, active plate margin tectonism, heaviest rainfall, and steepest terrain in the world [18]. Aside

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from earthquakes and volcanic activities, the Philippines, on average, experiences 20 typhoons a year, five of which are destructive [3]. These natural disasters resulted in more than PHP 15 billion in damages and nearly 2,000 deaths from 1990 to 1994 [6]. A single earthquake, such as the Mt. Pinatubo eruption in June 1990, costs almost PHP 8 million in infrastructure damages, while Typhoon Glenda in 2014 costs roughly PHP 4.5 million in infrastructure damages [4]. Due to the need to remedy and avoid accidents, injuries, fatalities, and property damage, the construction of earthretaining walls (ERWs) is prioritized to give emergency personnel access to routes so that they would be able to save more lives and bring supplies to disaster-stricken areas. Through the build back better (BBB) strategy, disaster recovery stakeholders collaborate to make a disaster recovery program that establishes better pre-and postdisaster planning and operations [7]. More solutions include the use of waste materials in producing mycelium bricks [30] and the use of recycled polyethylene terephthalate (PET) bottles in strengthening concrete columns [31]. In addition to this, resource allocation models can assign contractors to do different work in reconstruction projects [27]. It was found out that carbon footprint can be managed and reduced by using a mixed-integer linear programming model [29]. However, the application of sustainable construction materials and resource allocation during reconstruction can only happen if access routes, like roads and highways supported by ERWs, are prioritized. This study highlights the need for proper analysis of ERWs and the sustainable materials that can be used during reconstruction. ERWs are the most common structures in civil engineering used to retain earth pressure, prevent collapse, and protect slopes from landslides. In geotechnical practice, retaining walls are specifically used for shielding permanent and temporary excavations for slope stabilization [17]. The most common earth-retaining walls are buttresses, mechanically stabilized earth (MSE) walls, cantilever walls, gravity walls, masonry walls, and gabion walls. In a study by Pons et al. [32], the life cycle assessment (LCA) of four ERWs was investigated. It included the use of concrete for cantilever and gravity walls and stones as masonry walls and gabion walls. It was discovered that stones as ERWs produce the lowest global impact [32]. In this study, LCA on reinforced concrete walls is considered using natural aggregates (NAs) and recycled aggregates (RAs) on three types of walls: gravity wall, cantilever wall, and MSE. The RA is used to consume the CDW from the damage brought by the disaster instead of disposing it in landfill. Concrete is one of the most widely used building materials, with a global usage of approximately 25 Gt and a contribution of 5% of the world’s annual carbon emission [19]. With this, waste from construction and demolition is an inevitable outcome. The CDWs are the by-products of construction sites and the total or partial demolition of buildings and structures [16]. CDWs can also be generated following a natural disaster [1]. The use of LCA must be done to assess the effectiveness of recycled CDWs on new construction applications. Debris from disaster-stricken areas that count as CDWs can be processed and used as RA for concrete in the construction of ERWs for new roads. Aside from debris from the said areas, surplus or waste materials from nearby construction and demolition sites can also be utilized as RA.

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This method was done in Taiwan after a severe earthquake in 1999, wherein 30 million tons of CDW were used to rehabilitate damaged structures [33]. RAs are materials extracted through the processing of debris generated from the demolition of concrete structures and other construction debris, such as waste concrete, rejected precast concrete members, and leftover concrete from ready mix concrete plant, broken masonry, and concrete roadbeds [5]. RAs are used in various new construction applications around the world. Examples include the production of nonstructural dry-mixed concrete hollow blocks in Spain [24], utilization in the construction of rammed earth structures in Italy [2], and production of selfcompacting concrete [22]. The use of RAs in new construction applications varies from different experiments and studies done. In a summary done by researchers, replacement on NAs using 40% or lower amounts of RAs come to par with natural aggregate concrete (NAC) or concrete that is typically used in construction operations. In cases where 100% replacement were use, additional materials, such as cement, are used to compensate for the strength loss due to RAs having lower mechanical properties than their natural counterpart [16]. LCA is recognized as an excellent technique to determine the environmental impact component of sustainability assessments of structures in civil engineering projects at the time of design [8]. It can guide decision-makers during the design stage of retrofitting works in buildings [28]. LCA is also utilized to overcome the significant amounts of energy use, resource depletion, and waste generation associated with the use of construction materials, which can cause negative impacts on the environment [11]. This paper compares the environmental impacts of using 100% RAs derived from CDWs as a substitute in the production of ERWs in a reconstruction project following a natural disaster. An environmental impact assessment from cradle-to-gate is used from the concrete elements of ERWs made from NA and RA.

2 Materials and Methods LCA is a method used to investigate and evaluate the potential environmental impacts caused by a product throughout its entire life cycle, from raw material extraction to the final disposal or recycling [32]. The LCA of ERWs is done according to ISO 14040, which covers the principles and framework for LCA, including the definition of the goal and scope of LCA, the life cycle inventory (LCI) analysis phase, the life cycle impact assessment (LCIA) phase, and the life cycle interpretation phase.

2.1 Goal and Scope Definition This study aims to evaluate, analyze, and compare the environmental impacts caused by the construction of ERWs, specifically on the most common types, such as gravity walls, cantilever walls, and MSE walls. These would then be compared with their

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counterparts using RAs from CDWs. This study limits LCA with the concrete element used in constructing the ERW structure and assumes that reinforcing steel, reinforcing strips, and backfill material’s environmental impact are consistent with the various ERWs since the only difference is the utilization of RAs in concrete mixes that are considered in this paper. This paper also assumes the use of steel forms, which also aid in the sustainability of the construction of ERWs compared to phenolic wood forms that are disposable depending on the number of usage. It creates a guideline for designers to select an adequate type of ERW and the most sustainable material to be used in the construction of ERWs for embankment roads in reconstruction.

2.2 Types of Wall As mentioned in the previous section, gravity walls, cantilever walls, and MSE walls are considered in this study mainly because these are the most common and versatile types of walls in retaining earth in road construction. Figure 4 shows the specific details of the ERWs mentioned.

2.3 Functional Unit To properly define the functional unit of the ERWs, the wall’s height is considered since the amount of raw and recycled materials depends on it. In concept and actual applications, an increase in wall height proportionally increases the wall’s base and thickness to resist higher soil pressures. For cantilever walls, the ratio of steel reinforcement bars per volume of concrete increases as the wall’s height increases [32]. Aside from the wall’s height, a 1 m length of the wall’s face is a sufficient basis. Therefore, considering both the height and 1 m wall length, a functional unit of the linear meter of wall per-determined height is used in compliance with ISO 14040, which requires a functional unit to arrive at a uniform comparison between different LCA products.

2.4 Life Cycle Inventory (LCI) The LCI acts as data for the environmental inputs and outputs during the product’s life cycle, which comprises the modeling system for the process, collecting data, and verifying the data for inputs and outputs of the product system [26]. Figure 5 shows the typical life cycle of conventional ERW from production to end-of-life. The inclusions and exclusions to the LCA are seen in Fig. 5. The exclusions are the form works, reinforcing bars, equipment such as crane, and other assemblies not made out

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Fig. 4 Typical section elevations of ERWs a Gravity wall, b Cantilever wall, and c Mechanically stabilized earth wall (MSE)

of concrete. This is to compare the concrete with NA and RA given the three types of wall. Comparing the life cycle of ERWs made of RAs, most of the processes from the production up to use are identical, except that the production and transport of RAs from processing plants are added in the process. In recycling CDW to become recycled aggregate concrete (RAC), distances from processing plants to construction sites have a vital role in recycling’s environmental impact. Recycled materials

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Fig. 5 Life cycle of conventional ERW

generate lower environmental impacts than conventional materials, and their environmental profile is strongly influenced by transport distances [15]. In most analyses found in the works of literature studied for this paper, RAs are found to be a more suitable option than NA in all impact categories, specifically when the additional distance from the production to processing plants is up to 15 km [20] or 20 km [34]. To put it simply, RAs are better options than NAs, as long as transport distances of RAs are conservatively limited to 15–20 km more than that of NAs. However, recycling plants that utilize CDWs into RAs made into RACs are not yet implemented in the Philippines. As of now, there are no known CDW processing plants in the country, unlike in Singapore, Taiwan, the European Union, the United States of America, and other developed countries that utilize CDW in new construction applications. Distances of 100 km or more would outweigh the environmental benefits of RAs [10]. In place of recycling or processing plants, mobile recycling or processing plants can be implemented in the Philippines to reduce hauling distances [23].

2.5 Data Collection for LCI, Analysis and Interpretation Once the LCI is established, quantities can be derived from the construction drawings. A bill of materials is produced and converted to its equivalent functional unit

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23

described in this paper. The library ecoinvent 3 and the end point method of ReCiPe 2016 Endpoint H/World 2010 H/A were used to ensure data’s uniformity. This step is the final phase in the LCA of ERWs. This stage assesses every scenario with the utilization of SimaPro software.

3 ERW Design and Bill of Materials ERWs are designed to resist active earth pressures typically based on Rankine or Coulomb theories [14].). In this paper, the ERWs studied are the most common types of ERWs used in the construction of embankment and high-embankment roads in the Philippines. The use of gravity walls is limited to 3 m, cantilever walls are used in 3 and 4.5 m, while MSE walls can be used flexibly. Table 1 shows the parameters used for the design of walls. Figures 6, 7, and 8 show these three walls. The amount of concrete per linear meter of wall for every type is shown in Table 2. For concrete, 30 MPa is considered with the design parameters shown in Table 1 with the following dosage per cubic meter derived from ACI 211 for a 0.5115 water– cement ratio: 405 kg of cement, 207 kg of water, 736 kg of 10 mm coarse aggregate, and 932 kg of natural sand. Using a higher strength concrete would result in slightly different results depending on the cement dosage and other additives per cubic meter of concrete. However, for comparison purposes, the 30 MPa concrete is considered because it has the lowest possible ERW strength, resulting in the lowest environmental impact [32]. Cantilever walls and MSE walls require a lean concrete foundation with Table 1 Design parameters of ERWs

Design parameters (All ERWs) Design live load

20 kPa

Concrete compressive strength 30 MPa Steel reinforcement (for all ERWs)

Grade 60 (fy = 414 MPa)

Steel reinforcing strips (for MSE wall)

60 mm × 4 mm; Grade 450 (fy = 450 MPa)

Structural backfill Angle of internal friction

30°

Cohesion

N/A

Unit weight

18 KN/m3

Percent passing 4

100

No. 40

0–60

No. 200

0–15

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Fig. 6 Gravity wall with maximum height of 3 m

grade 20 concrete; deriving the batch weights based on ACI 211 for 0.68 water– cement ratio arrives at a dosage of 305 kg of cement, 207 kg of water, 736 kg of 10 mm coarse aggregate, and 1,020 kg of natural sand. Since the use of RA is explored in this paper, it is worth noting that RAC yields a different mechanical property compared to NAC. From previous study, compressive strength varied from the control by approximately 5, 12, 10, and 11.5% on RA replacements of 30, 50, 70, and 100%, respectively [36]. In other studies, concrete made with 100% RAs has 20–25% less compressive strength than conventional concrete with the same water–cement ratio and cement quantity. However, medium compressive strength concrete (30–45 MPa) made with 25% RAs achieved the same mechanical properties as that of conventional concrete, while 50–100% RA replacement needs 4–10% lower water–cement ratio and 5–10% more cement to achieve the same compressive strength [12]. Currently, around 40% or lower amounts of RAs are feasible without additional additives, and 41% or more would require additional additives to ensure that the mechanical properties of RAC are at par with that of NAC [16]. However, in some studies where commercially produced RACs were studied, mixtures with similar volumetric mixture proportions and workability exhibited no significant difference at the 5% confidence level in the 28-day compressive strengths of concrete made with commercial RA [35]. When commercially produced RAs are used for concrete, the compressive strength of RAC with 75% replacement is comparable to NAC with a water–cement ratio of 0.6 or better, while 100% replacement is comparable to NAC with a water–cement ratio of 0.55 or better [33]. Finding mixed conclusions of whether or not the use of 100% replacement of RAs result in RAC is comparable to NAC, the utilization of 100% replacement of RAs with 10% lower water–cement ratio and 10% more cement is used for a conservative approach,

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Fig. 7 Reinforced cantilever wall with heights 3 and 4.5 m

and to ensure that, strength loss due to use of RAs is compensated. In actual conditions, proper batching and testing with good quality assurance and control should be considered. Transport distances from the source to processing for both NAs and RAs are assumed to be 20 km, and the distance from batching plants to the actual site is assumed to be a maximum of 5 km. This is based on the prevailing practice in actual construction in the Philippines since concrete must also be used for up to 90 min from the time raw materials are mixed in a rotating drum mixer [9].

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Fig. 8 Typical details of MSE wall foundation Table 2 Concrete used per linear meter per height classification of wall Type of ERW

2 m height (m3 )

3 m height (m3 )

4.5 m height (m3 )

Gravity wall

1.64

2.14

N/A

Cantilever wall

N/A

2.1(structure); 0.1 lean concrete

3.5 m (structure); 0.21 lean concrete

MSE wall

0.36

0.54

0.81

*Lean concrete for MSE wall is constant at 0.0525

m3

per linear meter

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4 LCA Interpretation 4.1 Concrete Composition As shown in Figs. 9 and 10, using SimaPro to analyze the process network in the production of concrete for a particular type of ERW, cement contributes the highest impact with 70–90% in both types of concrete, whether it is NAC or RAC. Regarding the other material’s impact, transportation of raw materials from source to production and production to batching plants comes in second. Other material production

Fig. 9 Process network of 1 m3 of NAC for ERWs

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Fig. 10 Process network of 1 m3 of recycled aggregate from CDW concrete for ERWs

impacts, such as processing of coarse and fine aggregates and water use, are almost negligible compared to the impacts of cement production.

4.2 Comparison of ERWs As shown in Figs. 11, 12 and 13, SimaPro is used to compare the damage assessment on human health, ecosystem, and resources of the three types of ERW studied in this paper with various heights of 2, 3, and 4.5 m, respectively. The construction of gravity walls using NA has the highest damage assessment, while cantilever walls come in second, and MSE walls came in last, showing a significant difference in

Fig. 11 Impact assessment comparison using NA and RA concrete of 2 m gravity wall (green hue) and MSE wall (orange and yellow hue)

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Fig. 12 Impact assessment comparison using NA and RA concrete of 3 m gravity wall (green hue), cantilever wall (orange and yellow hue), and MSE wall (blue hue)

Fig. 13 Impact assessment comparison using NA and RA concrete of 4.5 m cantilever wall (green hue) and MSE wall (orange and yellow hue)

damage assessment compared to the first two. This came in no surprise since the amount of concrete needed to construct a 1 m span of a gravity wall or cantilever wall is up to four times more than the construction of a 1 m span of MSE wall. As can be observed in the height limitations of gravity and cantilever walls, the use of the former is limited to heights of 3 m and below since it is unreinforced and requires considerable amount of concrete, while the latter cannot be used in minimal heights since the dimensions cannot carry the lateral loads distributed from the road and the weight of the backfill. The construction and installation of MSE walls are advantageous since they are versatile in any height requirement and require less raw materials than their counterparts. This fortifies the advantages of MSE walls in the construction of ERWs for roads and highways.

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4.3 Lean Concrete Lean concrete is used as a concrete foundation or base to level the ERWs. Considering that both cantilever and MSE walls require additional concrete in the form of 20 MPa lean concrete for leveling purposes, as well as protection against the earth, lean concrete of cantilever walls indicates almost twice the damage assessment as compared to the lean concrete required in the construction of MSE walls as shown in Figs. 14 and 15. In terms of constructing a lean concrete foundation before the installation or construction of ERW, the MSE wall has more than half the impact of the cantilever wall. From the two figures shown, as the wall’s height increases, the corresponding impact gap between cantilever walls and MSE walls also widens from half in the 3 m wall category to a fifth in the 4.5 m height category. This is a clear sign that the use of MSE walls, especially in high-embankment roads, is of less impact and promotes more sustainability than its counterparts. In using NA and RA, it can be discerned that even with the 10% increase in cement and 10% less water–cement ratio to compensate for the strength loss due to

Fig. 14 Impact assessment comparison using NA and RA lean concrete of 3 m cantilever wall (green hue) and MSE wall (orange and yellow hue)

Fig. 15 Impact assessment comparison using NA and RA lean concrete of 4.5 m cantilever wall (green hue) and MSE wall (orange and yellow hue)

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Fig. 16 Impact characterization of concrete for ERWs using NA and RA

the use of RAs, which equates to more cement, in all impact categories and damage assessment characterization, the use of RAs is about 5–15% less impactful than their natural counterparts as shown in Fig. 16. Highlighting the damage assessment of resources in previous figures, RAs equate to almost half of the impact than their natural counterparts. With this, we can surmise that the use of RAs is beneficial in the conservation of natural resources and the construction industry’s sustainability. CDWs being dumped into landfills and incinerators are also reduced since CDWs are reused in new construction applications. There is a significant difference in impact, specifically in the terrestrial ecotoxicity. This stems from environmental pollutants to land organisms and their environment [13], stratospheric ozone depletion which is the destruction of the ozone layer caused by chlorofluorocarbons which are potent sources of free radicals [21], and water consumption.

5 Conclusions and Recommendations Reconstruction is essential after a disaster event happened. From damaged roads, highways, and structures, construction debris and waste (CDW) can be reused as recycled aggregates (RAs) in the production of concrete. In this study, MSE walls have at least 50–70% less impact than the other types of ERWs. The adoption of MSE walls would considerably lessen the environmental impact of ERW construction as it consumes up to 80% less concrete than cantilever and gravity walls. Using RA from CDWs shows up to a 50% decrease in impact, particularly in the damage assessment of resources where the use of RAs can equate to nearly half the impact of aggregate resource production due to the significant decrease in the use of NA. This significant decrease can contribute to the construction industry’s sustainability and lessen its overall environmental impact. Distances between the source and processing plants, especially if the difference between NA and RA is more than 20 km, outweigh the benefits of using RA from CDW.

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It is recommended to have studies that assess the environmental impact of all the processes like steel production and backfilling in the reconstruction of ERWs and roads. Incorporation of substitute materials for cement production like supplementary cementitious materials can be considered. A significant reduction in the environmental impacts of cement can significantly benefit the sustainability of the construction industry. Acknowledgements The researchers would like to thank Engr. Buenaventura S. Solo of J.F. Cancio & Associates, a design consultancy firm, for providing the necessary design drawings and specifications that were used in the calculation of material quantities of ERWs.

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The Input–Output Method for Calculating the Carbon Footprint of Tourism: An Application to the Spanish Tourism Industry María-Ángeles Cadarso, María-Ángeles Tobarra, Ángela García-Alaminos, Mateo Ortiz, Nuria Gómez, and Jorge Zafrilla Abstract The tourism sector is one of the most affected by COVID-19 pandemic. The global shutting down of non-essential sectors and the maintained global mobility restrictions have led to the industry’s partial closure worldwide. Tourism could play a leading role as the driver for achieving the sustainable development goals (SDG) and as an engine of wealth generation and cultural preservation. However, the negative impacts on the environment have to be considered when shaping the forthcoming and refurbished post-pandemic tourism industry of the future. In this chapter, we propose an environmentally extended input–output model to estimate the tourism carbon footprint to assess the sustainability of the tourism industry and applied it to tourism in Spain. This modelling allows for identifying direct and indirect emissions hot spots along the complex and intricate global value chains. The main results show how while Spain’s tourism contribution to GDP accounts for 12.3%, its carbon footprint accounts for 15% of the Spanish total emissions, which is above the global average (8%). In global terms, 29% of the total carbon footprint is imported, so it is, directly or indirectly, embodied in the global production chains. It is concentrated in some close European Union countries, China, BRIIAT, and the United States. Sectorally, the Spanish tourism carbon footprint is concentrated in some sectors where emissions are mostly domestic (air transport, land transport, or retail trade). Keywords Tourism carbon footprint · Multiregional input–output model · Carbon accounting · Tourism sustainability

1 Introduction COVID-19 pandemic has had a severe impact worldwide, provoking an economic slowdown that shows more profound decreases than the 2008 crisis. In the fight against the disease, the imposed confinement measures, the shutting of non-essential activities, the closure of borders, and the breakdown of global value chains affected M.-Á. Cadarso (B) · M.-Á. Tobarra · Á. García-Alaminos · M. Ortiz · N. Gómez · J. Zafrilla Global Energy and Environmental Economics Analysis Research Group (GEAR), University of Castilla-La Mancha, Plaza de la Universidad, 1, 02071 Albacete, Spain e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Switzerland AG 2022 J. Ren (ed.), Advances of Footprint Family for Sustainable Energy and Industrial Systems, Green Energy and Technology, https://doi.org/10.1007/978-3-030-76441-8_3

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the tourism sector with extraordinary virulence [19]. The pandemic consequences will result in a different tourism sector from the one we know, and the tourism carbon footprint calculations we propose here can help guide this transformation. One of the lessons we should learn from this crisis period is how much we must accomplish in such a short time to achieve the Paris Agreement goals. The magnitude of the challenge and deepness of the change have been exposed since the strict confinement measures implied such a decrease in emissions that put us (temporarily) on the right path to those goals [51]. The 2020 crisis represents a risk but also an opportunity to increase ambition and speed up the decarbonization process of the energy sector and the economy in general, as well as tourism, improving their sustainability and resilience. Statistics show that tourism is one of the activities most affected by the crisis triggered by the pandemic and the measures taken to fight against it. According to the World Tourism Organization (UNWTO), international arrivals fell by 72% over the first tenth months of 2020, meaning 900 million fewer tourists and a loss of 935 billion US dollars. To put these figures in perspective, these tourism losses are more than ten times those experienced in the 2008 crisis and bring tourism back to 1990 levels [66]. The forecasts before the rise of COVID-19 by the UNWTO showed an expected growth in tourism activities of around 3–4%, while the last estimations render a decline around 3–4%. In Spain, one of the countries with the highest number of visitors per year in the world, the tourist entrance fell by 84% in October 2020, and a 76% of the cumulative drop since January, with tourists’ spending showing even higher reductions (90% in October 2020 and 77% of cumulative decline since January) [25, 26]. Some voices had been demanding a more sustainable tourism sector for a long time. For instance, the Agenda 21 for the Tourism and Travel Industry promulgated by the World Travel and Tourism Council, the WTO, and the Earth Council already addressed energy consumption as a key focus area in the 90s (World Travel and Tourism Council [72], World Tourism Organization (WTO), Earth Council 1997; [64]. More voices emerge now asking for a profound transformation in tourism [36], not only for a responsible and sustainable recovery [67], but proposing new strategies such as managing relationships with governments or tour-operators [16], or taking advantage of the opportunities of proximity tourism [49, 52] or e-tourism [17]. Clearly, the recovery process should be seen as an opportunity to increase tourism resilience [28], avoiding a rebound in emissions. Tourism is crucial for jobs and wealth creation and cultural preservation, as well as in the fight against poverty and taking advantage of natural and cultural assets. However, its adverse effects are far less known, mainly because of a lack of measurement, in part due to its multi-sectoral character. The existing trend that prioritizes tourism’s economic (positive) impacts over its environmental (negative) consequences leads to many pro-tourism policies across countries that ignore the harmful effects. Balancing this asymmetry would allow enjoying the benefits from tourism while harnessing its potential for sustainable development. IPCC greatly emphasizes the importance of having a consistent and valid national emission inventory as this is vital to carbon management and the design of mitigation strategies to

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achieve Paris Agreement goals. In the same vein, national/regional tourism inventories and accounting can be linked with carbon management practices to overcome the current information asymmetry problem. This is even more crucial in this case, because tourism (and its positive economic and social effects) is very vulnerable to climate change [54]. It is, therefore, necessary to know how tourism contributes to it and, at the same time, how tourism can be involved in the fight against it. Tourism can then be seen both as a driver of climate change and as a part of the solution. First, we need a standardized measurement of carbon emissions of tourism, where input– output modelization can play a significant role. Second, we must increase awareness among stakeholders. The input–output analysis can also be relevant because the footprint measure has engaged society. This chapter proposes calculating the carbon footprint of tourism using a multiregional input–output model (MRIO). The MRIO model is extended to include the impact of production on carbon emissions, and combined with tourism consumption information allows linking the supply of tourism services in one country to the CO2 emissions to the atmosphere due to the direct production of tourism services and all the inputs this production requires, both directly and indirectly, all over the world. Furthermore, it allows tracking those emissions through the complex network of global value chains, identifying domestic and international hot spots, countries, and sectors crucial to the tourism carbon footprint. The environmentally extended MRIO model has been widely used to account for the environmental impact due to production, including international trade and global value chains [43, 69], although this is not so common in the tourism carbon accounting [58]. When used to estimate the global footprint of tourism, it has found that the tourism industry is not a lowcarbon one [33]. The proposed method is applied to calculating the carbon footprint of tourism in Spain for the year 2018. Previous calculations of this carbon footprint have involved a single region model (not multiregional) and the restrictive domestic technology assumption for the production of imports for the period 1995–2007 [5] while adding the impact of the tourism investments [6]. Using a similar input–output methodology, Cazcarro et al. [8] estimate the water footprint of tourism in Spain in 2004. Regarding the sustainable development goals (SDG), tourism can be seen as a driver and an accelerator for achieving them due to its crosscutting and multiplying effect on other sectors and industries. Fostering and exploiting tourism potential to advance SDGs would require integrated policies (UNWTO and UNEP 2017) that include mitigation goals. According to the UNWTO, around 40% of the Nationally Determined Contributions (NDCs) mention tourism in some way, as a country priority, as part of their mitigation or adaptation strategies, or as a sector vulnerable to climate change. However, the lack of a sectoral framework (both for companies and countries) to capture and report on the full economic, social, and environmental impacts of tourism hinders the creation of links to account for the contribution of tourism industries to the NDCs and the efforts undertaken by tourism stakeholders at international level. As a global activity, tourism provides opportunities for increased efficiency and acceleration of climate action [65]. The systemic character of input– output analysis assesses tourism’s carbon footprint as part of the national and global

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economy, making possible international and sectoral comparisons and benchmarking against other economic activities. The remaining of the chapter is structured as follows. Section two presents a depiction of the most common methods for tourism carbon accounting. Section three is dedicated to a detailed presentation of the methodology and the databases used in the tourism footprint calculation. Section four presents and evaluates the main results regarding estimating the Spanish tourism footprint for the year 2018. And finally, section five concludes including a discussion about the effects of the COVID-19 pandemic and the opportunities presented.

2 Standard Methods for Tourism Carbon Accounting There are two types of standard methods to calculate tourism carbon footprints, each of them of a different nature. One is bottom-up, and the other top-down, with particular advantages and limitations, which can be complementary and useful depending on tourism carbon management [14, 57]. Becken and Patterson [2] estimate the carbon emissions associated with tourism in New Zealand using both types of methods and also reflect on their respective properties. Gössling [18] reviews the literature regarding the role of tourism in national emissions using both kinds of models. Recently, Sun et al. [58] critically review the literature that uses the input– output method to account for tourism carbon emissions. Here, we briefly discuss the main characteristics, drawbacks, and advantages of each type of method. The bottom-up method is widely applied in the study of the carbon footprint of tourism. It estimates carbon emissions from a micro-level perspective by identifying emissions factors for each travel type of consumption, which is then combined with visitor consumption. Emission factors, usually in physical units, are compiled through industry surveys at the firm level [2, 12]. This calculation approach provides a precise measurement of services consumed by tourists and their associated energy and emissions and facilitates specificity and accuracy in emissions analysis. This is one of its key advantages because it allows establishing a direct and detailed linkage between travel and tourist consumption patterns and related energy consumption and carbon emissions. Bottom-up methods also show the ability to focus on particular types of tourism or specific activities or itineraries [12, 21, 46]. Besides, at the business firm level, the bottom-up approach can identify hot spots (energy- or emission-intensive components) and areas for improvement to assist the management of energy use, looking for the most efficient and the lowest carbon energy sources or equipment [29]. These analyses are especially prevailing for the accommodation sector [9, 50, 53, 62] and recreational sites [68]. The bottom-up approach also shows some limitations. The major criticism is its incompleteness, which renders only a partial picture of the full impact tourism activities have on the environment [3, 33]. This disadvantage has two aspects. On the one side, emissions associated with two relevant tourism consumption components, such as shopping and food & beverage, are often ignored. The main reason is the

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difficulty of data compilation and the definition of emission factors, caused by their considerable variation and possibilities. On the other side, most bottom-up studies do not capture the indirect emissions associated with the intermediate inputs’ production through the supply chains. Usually, bottom-up methods focus on the emissions onsite (the so-called scope 1) or, at the most, they include the first level of suppliers and inputs or extend the analysis to include emissions related to the generation of the electricity supplied to the hotel [20]. In doing this, all the energy and emissions linked to numerous suppliers that are producing all intermediate inputs are generally not included. An additional limitation of the bottom-up approach is the amount of data required because it relies extensively on the primary-data collection on trip behaviour. The specificity of the bottom-up method, mentioned before as an advantage, is also a constraint because it hinders the comparison between studies. The top-down methods follow a macro-level perspective, looking to the industry level energy use and emissions in fulfilling tourism consumption needs. One of the most common top-down approaches relies on the environmentally extended input– output analysis (EEIO), which merges data from input to output national or multiregional tables with environmental impacts or pressures of tourism data (usually from tourism satellite accounts). One of the strengths of EEIO is its systemic vision and comprehensiveness [71]. On the one hand, this implies that the tourism carbon footprint is calculated and assessed not separately but as part of the entire economy [38]. On the other side, EEIO can track the impacts of exogenous demand and production all along the global supply chains covering all indirect effects of upstream production [13]. The consequence is that it provides a complete assessment of all the scopes (including scope 3) without truncation errors [32]. The potential of EEIO is fully deployed in the multiregional models (MRIO) that are increasingly developed along with the recent development of multiregional databases [45, 63]. MRIO provides a full representation of trade flows that allows mapping the complex network of interrelationships between sectors and countries all over the world through the global supply chains [69], avoiding assumptions like the use of the domestic technology for the production of imported products and services [6]. As a result, when applied to tourist consumption, it allows tracking the way tourists’ consumption drives environmental impacts elsewhere in the world. Despite this, tourism carbon accounting using MRIO models is not very common yet [58]. Given that several databases provide the most relevant data, the EEIO and MRIO framework’s weaknesses are related mainly to its complexity and the substantial effort required for setting up and updating MRIO tables and related extensions, including efforts to improve their accuracy and confidence [71]. The complexity and volume of information required in an MRIO also cause a high level of sector and spatial aggregation. The aggregation implies that the sector’s technology and emissions’ intensity are the average of all the products or services produced within the same sector classification. Sometimes, even not all the products within a sector are related to tourism. The problem of spatial aggregation is similar to that of sectoral aggregation. It arises because tourism, both in terms of available touristic products and services and tourist behaviour and consumption patterns, can be very different within a nation or even within a region (coastal tourism versus rural tourism or

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city tourism, for instance). The EEIO would take the average of these different elements. Moreover, tourism analysis at subnational, regional, and local levels is often confronted by the lack of specific input–output data, so more information about reliability and uncertainty and regular inter-comparison studies will be welcomed. In the same line of reasoning, temporal aggregation of input–output tables (usually, a year) makes it challenging to study other tourism behaviour strongly linked to specific seasons, for instance. Because it is a relatively recent procedure together with the different level of data available, there is no commonly accepted standard of calculation of input–output tourism accounting, with studies using other system boundaries that render confusing results [58, 59]. Other weaknesses are those related to the basic assumptions of input–output analysis. These are the specification of a production function that assumes constant returns to scale with fixed technical coefficients, where structural, technological, and price changes or substitution of inputs are ignored and result in linear and homogeneous production functions (so a constant and linear relationship between inputs and outputs is assumed) [4, 32, 39]. However, this does not pose a significant problem in static analyses referring to one base year or short- to medium-term analysis.

3 Methods and Materials 3.1 The Environmentally Extended Multiregional Input–Output Model Input–output analysis (IOA) describes the structure of the economy as an interlinked network where industries provide intermediate products (inputs) to other industries and final products to final demand (that includes households and public sector consumption, investment, and exports) and generate value-added (as payments for using labour and capital factors). The final demand for goods or services from one industry is the trigger that leads to impacts in other economic sectors that can be measured by the so-called Leontief inverse. Since the first developments of the input–output model by Leontief [34], it has been widely used to assess environmental issues, as Leontief himself started in the seventies [35]. Environmentally extended input–output models (EEIO) have become a valuable approach for analysing the environmental impacts of economic activities and supporting related economic and environmental policies, being one of the fastest-growing areas in IOA, mainly since 1999 [23]. The EEIO use coefficients of environmental impacts or pressures (GHG or pollutants) by sector to modify the Leontief inverse and provide the total, direct and indirect, impact associated with the production of one unit of the final demand. Briefly, EEIO distributes the total impact generated by a production–consumption system over different final demand categories. In this way, the components of final demand are the production final drivers in the sector directly demanded and also in those sectors providing the first with inputs directly and indirectly. As a result, final

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demand is the driver of the environmental impact that results from these processes also. IOA expresses the industry output as the sum of the interindustry flows and sales to final demand. The first ones can be described by technical coefficients that show the direct input requirements by a unit of output for each sector (under the assumption of linear technology) recorded in a technical coefficients matrix, A. The total output of an economy, x, then, can be expressed as the sum of intermediate consumption Ax and final demand, y, as follows in expression (1): x = Ax + y

(1)

When expression (1) is solved for total output considering final demand exogenous, we have the fundamental equation of IOA (expression 2): x = (I − A)−1 y

(2)

where (I − A)−1 is the Leontief inverse that provides the total, direct and indirect, requirements of an additional unit of final demand product. The environmental extension provides total emissions (or other impacts) by using a vector of carbon emissions coefficients, e, that shows the direct emissions by a unit of output (e = E xˆ −1 ), where E is the vector of total carbon emissions, as is shown in expression (3) that can be used in single region models or in multiregional models: E = e(I − A)−1 y

(3)

The multiregional input–output model (MRIO) appears back in the 50s, but its use has been recently fuelled by the recent developments of several multiregional input–output databases [27, 48]. The standard MRIO model [39] includes regions and countries with their own technology, and trade is split into intermediate trade with specific industry and country destination and final trade. The environmental extension of the MRIO model [11, 47] provides the environmental impact of production processes in each country and is the prevailing method to analyse the direct and indirect environmental impacts of economic activities throughout global value chains. Following Miller and Blair [40], the environmental extension of the MRIO model with n countries and m industries is defined by the expression (4): F = eˆ (I − A)−1 y

(4)

where A is a matrix of technical coefficients or direct requirements of inputs by every industry and country of nm × nm dimensions, which distinguishes the industries and countries of origin (in rows) and destination (in columns). More in detail, matrix A is integrated by App in the main diagonal, which is the domestic matrix of coefficients of production (intraregional matrix), and Apq in the off-diagonal positions, which indicates the trade between industries from region p to region q (intermediate exports

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of region p or intermediate imports of region q). I is the identity matrix, and (I − A)−1 , known as the Leontief inverse, is the matrix of direct and indirect requirements per unit of finished products intended for final demand. y is a matrix of final demand diagonalized by blocks, where each block contains a m-element diagonalized vector yˆ pq that represents the production of every industry in country p that is consumed in country q final demand (that is, the trade between industries in region p to final agents in region q or final exports of region p to final imports of region q). In the MRIO model, y represents the final demand forces that trigger multiple stages of production in numerous industries and countries around the global economy. eˆ is a diagonal matrix with the coefficients of environmental impacts per unit of production. eˆ may include different types of environmental impacts, e.g. emissions of polluting gases, energy use, water use, materials, etc. [1, 15, 22, 41, 44, 56, 70]. In this study, eˆ contains the CO2 emissions released per US million $ of output for every industry and country. Therefore, the resulting matrix F, of dimensions nm × nm, gathers the direct and indirect CO2 emissions released worldwide in all the production stages required to meet the final demand covered by y, distinguishing the industries and countries that directly release the emissions (in rows) and the countries whose consumption of final demand products has those emissions contained (in columns). pq Elements in F consist of fij which represents the CO2 emissions directly released by sector i in country p to produce intermediate and final products required to meet the final demand of country q for industry j’s products (where p, q = 1, . . . , n and i, j = 1, . . . , m). Adding elements along the rows of emissions released  by country p results in the production-based accounting (PBA) of that country ( nq=1 Fpq ), whereas summing up the F-elements along with the columns of country  q results in the consumption-based accounting (CBA) or carbon footprint of q ( np=1 Fpq ). CBA provides total emissions generated in every sector in every country, including all the global value chains, for attending each product final demand in a country/region. In this way, CBA shows total emissions required to supply each country’s population needs and lifestyle. Expression (4) in matrix form for n countries and m sectors, and considering emissions coefficients and final demand as diagonal vectors (denoted by ˆ) would be in a MRIO context as indicated in expression (5): ⎡

FSPA t

F 11 ⎢ F 21 ⎢ =⎢ . ⎣ ..

F 12 F 22 .. .

... ... .. .

⎤ F 1n F 2n ⎥ ⎥ .. ⎥ . ⎦

F n1 F n2 . . . F nn ⎡ 1 ⎤⎡ 11 eˆ 0 . . . 0 L ⎢ 0 eˆ 2 . . . 0 ⎥⎢ L21 ⎢ ⎥⎢ =⎢ . . . . ⎥⎢ . ⎣ .. .. .. .. ⎦⎣ .. 0 0 . . . eˆ n

L12 L22 .. .

... ... .. .

⎤⎡ 11 yˆ L1n 2n ⎥⎢ 21 L ⎥⎢ yˆ .. ⎥⎢ .. . ⎦⎣ .

Ln1 Ln2 . . . Lnn

yˆ 12 yˆ 22 .. .

... ... .. .

⎤. yˆ 1n yˆ 2n ⎥ ⎥ .. ⎥ . ⎦

yˆ n1 yˆ n2 . . . yˆ nn

(5)

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For this study’s purposes, to calculate the carbon footprint of tourism in one country (Spain), the final demand matrix (y) only contains the final consumption of tourists within the Spanish territory, distinguishing the origin entry and industry that provide the finished products. Thus, for q = {Spain}, the diagonalized vectors yˆ pq include data of finished products from country p consumed by tourists in Spain. Otherwise, for q = {Spain}, all the elements in yˆ pq will be zero. Assuming that Spain is located in position 1 on the input–output tables, for the shake of simplicity, the equation used for our tourism carbon footprint in Spain estimations is represented by expression [6]: ⎡

FSPA t

F 11 ⎢ F 21 ⎢ =⎢ . ⎣ ..

0 0 .. .

... ... .. .

⎤ ⎡ 1 eˆ 0 ⎢0 0⎥ ⎥ ⎢ .. ⎥ = ⎢ .. .⎦ ⎣ .

F n1 0 . . . 0

0 eˆ 2 .. .

... ... .. .

0 0 .. .

0 0 . . . eˆ n

⎤⎡

L11 ⎥⎢ L21 ⎥⎢ ⎥⎢ . ⎦⎣ ..

L12 L22 .. .

... ... .. .

⎤⎡ 11 yˆ L1n ⎢ yˆ 21 L2n ⎥ ⎥⎢ .. ⎥⎢ .. . ⎦⎣ .

Ln1 Ln2 . . . Lnn

0 0 .. .

... ... .. .

⎤ 0 0⎥ ⎥ .. ⎥. .⎦

yˆ n1 0 . . . 0

(6) where L = (I − A)−1 is the Leontief inverse, and the resulting matrix FSPA covers t the direct and indirect emissions released all over the world to produce the goods and services consumed by tourists in Spain, distinguishing the country and industry where those emissions are directly released, the source or emitting industry (PBA emissions, by rows) and the type of finished products that embodied those emissions and are ultimately consumed by tourists in Spain (CBA emissions, by columns).

3.2 Materials: MRIO Database and Tourism Satellite Accounts The MRIO model employed relies on the “World Input-Output Database” (WIOD) [60] in its 2016 release [61]. This database contains annual time-series of input– output tables (IOTs) and factor requirements for 43 countries and “rest of the world” region, providing detail for 56 sectors per country for 2000–2014. In this work, we combine the last WIOD IOT available (2014) with the environmental satellite accounts for the same year by Corsatea et al. [10], which provide CO2 emissions compatible with the WIOD structure. To calculate the carbon footprint for Spanish tourism, the final demand matrix introduced in the MRIO model only reflects tourists’ final consumption—both national and international—within the Spanish territory. This final demand matrix has been built from the latest National Tourism Satellite Account (TSA) [24], more explicitly retrieving internal tourist consumption data for the last year available (2018). Internal tourist consumption is defined in the TSA as “the addition of domestic tourist expenditure and other components of tourist consumption including

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services linked to own-account holiday accommodation, in-kind tourist social transfers and other imputed consumption” [24]. TSA provides this indicator disaggregated into 11 tourism characteristic products plus a general category of other non-characteristic products. Before generating the final demand matrix, we need to implement some transformations on the internal tourist consumption indicator to make both datasets (WIOD and TSA) compatible. First, TSA provides internal tourism consumption data in purchasing prices, which need to be converted into basic prices since IO databases follow National Accounts methodology. The TSA provides a supply table for tourism products with a conversion from purchasing to basic prices for 2016, with data about taxes, trade, and transport margins. We combine this information with data from the National Accounts (NA) to distribute the tourism consumption of non-characteristic products into the different industries according to the share of households’ consumption. After that, we detract taxes and reallocate margins to the retail trade and transport sectors, obtaining a vector of tourism consumption at basic prices for all 110 NA products. Second, we need to transform this vector with detail for 110 NA products into demand for 81 industries. We use the so-called model D [37], which implies keeping technology constant by product. For the supply table in NA, we divide each element by the row sum, and we multiply the transpose of the resulting matrix by our vector of tourism consumption. In this fashion, we reallocate consumption for each product to the corresponding industries where they are produced, as some of those industries are the source of more than one type of good or service. The next step is to deflate TSA data. This dataset is expressed in million euros of 2018, while units in WIOD are million dollars for 2016, which makes it necessary to deflate our data using NA GDP chain volume indices to obtain the appropriate deflators by industry and then convert them from euros into US dollars with the 2016 annual average of the daily BCE official exchange rate. Finally, the obtained vector reflects the expenditure of tourists within Spain in the 56 WIOD sectors for 2018. However, this vector lacks detail on the geographical origin of the goods consumed inside each category. For this reason, we have split the expenditure on each sector into the 44 regions in WIOD following the same import pattern as final consumption expenditure by households does, according to WIOD data. In this way, we obtain a vector of 2464 elements (n × m) suitable to be diagonalized by blocks in order to be introduced in expression (6). Despite implementing the model with the 44 regions and 56 industries of the WIOD structure, the Spanish tourism carbon footprint results are displayed according to aggregation into ten regions (Spain, Germany, France, United Kingdom, Italy, Rest of Europe, China, United States, BRIITA—which contains Brazil, Russia, India, Indonesia, Turkey, and Australia—and rest of the world) and 14 sectors (agriculture and mining, manufactures, electricity, water, gas, and waste, construction, wholesale trade, retail trade, land transport, air transport, other transports, accommodation and food services activities, professional services, real state and rental activities, other services, and leisure). The vector of tourism consumption (aggregated to 14 sectors) with no distinction between the regions of origin of the goods or services is shown in

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Table 1 Tourism consumption in Spain, 2018 (2016 million dollars) Tourism consumption

Share (%)

Agriculture and mining

1416

Manufactures

6399

4

194

0

1379

1

1092

1

Electricity, water, gas, and waste Construction Wholesale trade Retail trade Land transport Air transport Other transports Accommodation and food services activities Professional services Real estate and rental activities Other services Leisure Total

1

10,626

7

7156

5

13,617

9

1214

1

58,717

38

1486

1

33,915

22

3645

2

14,673

9

155,529

100

Source own elaboration from TSA [24]

Table 1. The distribution of expenditures of tourism consumption shows a clear cut between accommodation and food services activities (38%) and real estate (22%), and then at a distance, leisure (9%), air transport (9%), and retail trade (7%), that altogether account for 85% of total tourism consumption in Spain. It is important to note that we are including imputed rents of owner-occupied dwellings in real estate, which explains that high share.

4 Results and Discussion Our calculations result in a total carbon footprint of 35,240 Kt CO2 for Spanish tourism, due to the domestic and international emissions generated by the production and distribution of all tourism consumption that takes place in Spain. This figure amounts to 15% of the Spanish carbon footprint in 2018, a very high apportionment of responsibility compared to global figures, which are estimated at 8% of global greenhouse gas emissions [33]. On the one side, it is justified by the important weight of tourism on the Spanish economy and the general tourists’ consumption patterns where highly polluting goods and services are central. On the other side, according to Lenzen et al. [33], the majority of the tourism footprint is carried out by and in highincome countries. However, that share is even higher than the percentage of Spanish tourism on employment or gross domestic product (12.7 and 12.3%, respectively), pointing to an unbalance of positive and negative tourism spillovers that require

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additional efforts in the decarbonization of tourism activities. The figures also show a growing trend in time, from 10.6% in 2007 [6], and a high impact when compared to other countries, like Germany (7.6% projected for 2020), Sweden (16% projected for 2020), Australia (5% for 2004), according to the literature review by Gössling [18], China [2.4% for 2010, [38]], Japan (10% for 2017, [30]), or the previously mentioned, 8% worldwide [33]. This is also 135.21 kg CO2 per overnight visitor, 27.85 per night, and 0.228 kg CO2 per euro of tourist expenditure. These ratios are in the range of magnitude for results by Cai [7], but lower (around 28%) as a result of the reduced carbon intensity for the Spanish economy, and similar to those for the Netherlands in 2008 [18]. Comparing the carbon emissions per euro spent in Spain with the global results obtained by Lenzen et al. [33], the tourism in Spain is, on average, much more carbon-efficient than the global average, that is 0.975 (Table 2), derived from lower total emissions per dollar in every sector where the comparison is possible. The higher carbon efficiency of tourism in Spain entails that tourism-related expenditure emissions are below other Spanish economy sectors (like agriculture or manufacturing). Hence, its growth is not an accelerator of emissions, contrary to what happens globally. However, we Table 2 Comparison of carbon emissions total intensities (carbon multiplier), kg CO2 by dollar (total carbon emissions/tourism consumption) Spanish total intensities

Global total intensities in 2013 [33]

Agriculture and mining

0.399

2.155a

Manufactures

0.390

–

Electricity, water, gas, and waste

0.877

–

Construction

0.219

0.683

Wholesale trade

0.141

–

Retail trade

0.094

–

Land transport

0.599

2.011a

Air transport

1.192

1.360

Other transports

0.415

3.210

Accommodation and food services activities

0.104

0.467a

Professional services

0.056

–

Real estate and rental activities

0.041

–

Other services

0.090

0.429

Leisure

0.110

–

Tourism average

0.227

0.975

Source own elaboration and Lenzen et al. [33], table SI 6c Note a indicates that the figure is a non-weighted average of the sectors shown in the original table

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should take into account that the estimations use different databases (WIOD in the present work and EORA in the one by Lenzen and colleagues) and are referred to other years (2018 and 2013, respectively), and there is a decreasing trend in total intensities, as shown by Lenzen et al. [33] by around 13% (between 2009 and 2013). The identification of the main polluting sectors is made from two different perspectives, and both bring out the usual suspects among the tourism’ characteristic expenditure sectors found in the previous literature. From the CBA perspective, that is, for the purchased commodities sector, which is the final sector that satisfies consumers’ demand, Fig. 1 shows those sectors with a responsibility higher than 1%. Three industries concentrate over three-quarters of all emissions, with air transport (46%) being the main culprit, followed at a distance by the accommodation and food services activities (17%) and land transport (12%). High transport emissions figures are also found by Neger et al. [42], Sharp et al. [55] or Lenzen et al. [33] for high and middle-income countries, or Kitamura et al. [30]. Even considering that all transport activities represent 60% of all emissions, these results underestimate their responsibility since figures do not account for emissions generated in combustion while travelling in own or rented (non-electrical) vehicles. When comparing these sectors with the distribution of expenditure for tourist air and land transport, these add up to only 14% of total tourist expenditure. This decoupling between expenditure and emissions points to these highly polluting sectors as priorities for emissions curving policies that should incentivize technological improvements. Also, manufactures’ emissions double expenditure, mainly due to high energy and material requirements,

Fig. 1 Spanish tourism carbon footprint by purchase commodities sectors

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while accommodation share of emissions is half of its expenditure share, due to its labour intensity. It is also interesting to note that, in terms of where emissions for those products are generated, 29% of emissions overall are embodied in imports. This is similar to the shares for Australia, 17% [13] and Taiwan, 25% [57], although much lower than the one found in Cadarso et al. [6]. The differences can be related to the increase and change in pattern for tourism consumption, but also because we use a better MRIO methodology compared to previous studies based on the domestic technology assumption for imports (that applies the same Spanish emission coefficients to imported inputs). Nevertheless, we find a wide range in this percentage of imported emissions for different products, much lower for energy and transport where emissions are mostly domestic (land transport, 13.7%, air transport, 18.4%, electricity, 19.2%), but almost exclusively imported for most manufactures (particularly for textiles, pharmaceuticals, computers, and machinery) and relatively high for crops and animal production (51.4%), mining (69.9%), and leisure (51%). This indicates that, even for a service-centred activity as tourism, global value chains providing inputs worldwide explain why almost 30% of emissions, for which Spanish tourism consumption is responsible, are generated outside Spanish borders. From a PBA perspective, for emitting or source industries, Fig. 2, which again shows those sectors with a responsibility higher than 1%, portrays a slightly different pattern. Air transport (38%) continues at the top, but now electricity, water, gas, and waste (20%) come in second place, manufactures (15%) is third, land transport (14.5%) is still in the top four, and agriculture and mining (7%) enter the ranking.

Fig. 2 Spanish tourism carbon footprint by source or emitting industries

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Fig. 3 Spanish tourism carbon footprint by emitting countries

Together these five industries represent over 85% of total emissions. The importance of transport, mainly air transport, is still the most characteristic feature, but we now get a better view of where emissions occur. This indicates how high energy and material demanding sectors (electricity, manufactures, agriculture) supply inputs to tourism characteristic products (accommodation and food services, real estate, leisure, travel agents) become the ultimate origin for those emissions. The emitting industries approach is less common in previous literature other than Lenzen et al. [33], whose results resemble our findings considering changes in classification. In terms of the country of origin for emissions of the Spanish tourism carbon footprint, we would expect most of them to occur in Spain. The results corroborate this, as shown in Fig. 3, which indicates that 71% of all emissions linked to Spanish tourism consumption are generated domestically. However, that leaves, as said before, 29% of emissions linked to imported inputs from all over the world, especially from the rest of the EU (Germany, 1.1%, France, 1.1%, Italy 0.7%) and other European countries (the UK, 0.8%), China (3.9%), BRIIAT (3.9%), and the USA (1.3%). We also need to point out that 11.4% of emissions fall within the rest of the world category. These are mostly linked to imported fossil fuels from the Middle East (most of it directed to air transport), as well as agriculture and primary products (for the food, hospitality, and leisure industries) from Latin America and Africa. Proper accounting of the indirect emissions that take place along global value chains requires models such as MRIO. As Cai [7] points out, these models adequately differentiate between the diverse intensities of pollution by country and region from which intermediate and final goods are imported. Emissions embodied in imports are relevant in the tourism carbon footprint, and strategies addressing tourism sustainability should not ignore them. Figure 4 shows

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Fig. 4 Percentage of emissions embodied in imports over total emissions per purchase commodities sector (by size of the sector’s total emissions in the Spanish tourism footprint)

the percentage of imported over total emissions for each purchase commodities industry, where the size of each bubble represents the participation of the sector’s total emissions in the Spanish tourism footprint. When we analyse the differences by sectors, we realize the average share of imported emissions over total emissions per sector (29%) results from significant variance, as represented in Fig. 4. This shows the percentage of imported over total emissions for each industry according to the level of emissions. It is possible to see that most industries have a share of imported emissions above 30%, as illustrated by the textiles, food manufactures, crop and animal production, or creative, sports, and recreation activities. Accommodation and food services and water transport are closer to the global average (37.2 and 31.9%, respectively). However, the average is driven down by a few prominent industries, namely air transport (18.4%), land transport (13.7%), and retail trade (25%). Therefore, we can conclude that, even though the general percentage of imported over total emissions is relatively high for the Spanish economy as a whole, the concentration of the tourism footprint in some sectors where emissions are mostly domestic is the reason for that low share. Which sectors in Spain are responsible for emissions embodied in imports in the Spanish tourism carbon footprint? This is answered in Fig. 5, where the imported part of the Spanish tourism footprint’s global value chains is represented. It shows the Spanish sectors that require inputs for tourism consumption (on the right side of the Figure) and the countries or regions of origin of those imports and, as a result, where the emissions are taking place (on the left side). We can consider these sectors as the drivers of these emissions because it is their demand for inputs that leads to emissions. Accommodation and air transport are the main drivers of emissions embodied in imports of the tourism footprint, mainly coming from the RoW region and explained by the emissions embodied in imported fuels, as indicated previously.

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Fig. 5 Country of origin and sector of destination of imported Spanish tourism carbon footprint (carbon emissions embodied in imports). Note On the right side, the circos graphic [31] shows the Spanish sectors that require for attending tourism consumption in Spain imports from the countries and regions on the left side of the figure. Only sectors with a share above 1% are represented. S1: Crop and animal production, hunting and related service activities; S11: Manufacture of chemicals and chemical products; S22: Other manufacturing; S27: Construction; S30: Retail trade.

Those two sectors are also the main drivers for emissions coming from BRIAT, China, and the Rest of Europe (ROE), although, in the last region, land transport is also an important driver. In the case of China, textiles also appears as a driver of emissions. This last sector, but, mostly, recreation and rental and leasing, requires imports from RoW that generates more than half of imported emissions in these sectors.

5 Conclusions The economic crisis caused by the COVID-19 pandemic has undoubtedly hit the tourism industry globally. The limitations resulting from the shutting down of nonessential sectors due to the severe containment measures that have taken place since

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February have led to the industry’s closure in hundreds of countries for many months. The easing of the most stringent measures has not been enough to correct this negative shock to the industry, as mobility restrictions, both nationally and internationally, have been some of the longest-lasting global measures and remain as the main barrier weighing on the recovery of international and national tourism. In Spain’s case, we find one of the countries suffering the hardest from the economic crisis, since it is one of the most vulnerable as the tourism industry represents a significant volume of both national GDP and domestic employment. Throughout 2020, Spain has faced the closure of many hospitality facilities, travel agencies, and many other related activities that have not endured the fall in demand that has taken place in these months. As one of the countries with the highest ratio of international tourist arrivals, Spain has not been able to cope with the halt in international passenger transport by compensating with the tourism of national origin or through short-term government aid programs. As other studies have shown, the current economic crisis has forced an artificial (not based on structural or permanent changes) reduction in global emissions volume. The downturn in industries such as tourism, which demands greenhouse gas-intensive goods and services, has caused many countries to reduce their emissions inventories. However, we know that these numbers do not imply structural changes that will remain over time, so we have a unique opportunity to redirect the patterns and paths through which the tourism industry must travel in the medium and long term to meet the objectives of the Paris Agreement and the sustainable development goals. To know where Spain stands, in this chapter, we have estimated the Spanish tourism sector total emissions responsibility (direct and indirect, domestic and imported). The results show that the percentage of the footprint of tourism in Spain over the country’s total footprint (15%) is higher than the sector’s weight on the whole economy (12.7 and 12.3% in terms of employment and GDP, respectively). The use of methods such as the EEIO provides a framework for countries and companies to measure their international and sector performances in carbon footprints. These tools allow countries to evaluate the benchmark level, set up mitigation plans, and incrementally improve their performance. For the case of Spain, this study has provided some useful insights. There is a decoupling between tourism expenditures and the carbon footprint concentration by sectors. While accommodation and food services activities account for more than 38% of total expenditures, this sector represents 17% in terms of carbon footprint. Simultaneously, while the air transport industry accounts for 9% of total spending, it is responsible for the highest carbon footprint (46%) and is the highest emitting industry (38%). The EEIO method allows us to identify both the target industries to focus on future climate policy actions, which could not be related to those with the highest expenditures levels. Another feature of this method is the possibility to evaluate the domestic-imported weight of environmental responsibility. In the Spanish case, 29% of total (direct and indirect) emissions occur beyond the Spanish borders along the global value chains. This top-down approach copes with the necessity to account for the global impacts of demand-driven decisions derived from the growing importance of globalization,

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mainly during and after an economic crisis that has jeopardized the supposed stability and maturity of the international trade structures. The reinforcement of the European Union domestic linkages, both in terms of economic and environmental consequences, and the degree of non-European Union global dependence must be on the political agenda given the crisis’s social, economic, and environmental consequences. In this sense, the extensions (environmental and social) of input–output analysis give powerful insights into sustainability’s three pillars to uncover possible tradeoffs and synergies. An overall assessment of tourism sustainability must include social impacts, employment and quality of employment, income, and other relevant environmental impacts such as local pollution, material, land, or water footprints, following the SDG’s guidelines and with the goal of achieving resilient societies. Acknowledgements All the authors gratefully acknowledge the funding of the University of Castilla-La Mancha and the European Fund for Regional Development (FEDER) (Ref. 020-GRIN29137). M. Ortiz thanks the Spanish Ministry of Economics and Competitiveness (MEC) and the European Social Fund (ESF) for the grant BES-2017-079618. Á. García-Alaminos acknowledges financial support from the Spanish Ministry of Universities through the National FPU Program (Grant ref. FPU18/00738).

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Environmental Impact of Beef Production Systems C. Buratti, E. Belloni, and F. Fantozzi

Abstract Livestock production as a contributing factor of global warming has become a critical aspect of policy development among scientists, institutions, governments, and societies. Improving the animal farms performance in the several countries is a key strategy to meet the demand for animal protein, reducing greenhouse gas emissions, and improving resource use efficiency. In this context, this chapter presents a global overview of beef production systems, their diversity, the way they can contribute to major global environmental issues and the evaluation of specific points for intervention. The characteristics of the beef production systems all over the world are analyzed, together with the goal and scope, the types of analysis (methods for the evaluation of the carbon footprint), the functional units generally implemented for the analyses, the allocation methods, and the uncertainties of the studies. This detailed overview allows a critical analysis of the selected studies, which are discussed in the last section of the chapter. It was found that it is important to improve the understanding of biological processes involved in the emissions of methane and nitrous oxide processes, in order to obtain more valid and reliable carbon footprint results. Keywords Carbon footprint · Beef · Milk · Environmental impact · GHG emissions

List of symbols AG biomass

Above-Ground biomass

C. Buratti (B) · E. Belloni · F. Fantozzi Department of Engineering, University of Perugia, via G. Duranti 63, 06125 Perugia, Italy e-mail: [email protected] E. Belloni e-mail: [email protected]; [email protected] F. Fantozzi e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Switzerland AG 2022 J. Ren (ed.), Advances of Footprint Family for Sustainable Energy and Industrial Systems, Green Energy and Technology, https://doi.org/10.1007/978-3-030-76441-8_4

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BG biomass CF CP DCCEE DM ECM EPD FU GE GHG GWP IDF IPCC LCA LCI LWG MC PEF PCR TDN

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Below-Ground biomass Carbon Footprint Crude Protein Department of Climate Change and Energy Efficiency Dry Matter Energy-Corrected Milk Environmental Product Declaration Functional Unit Gross Energy Green House Gasses Global Warming Potential International Dairy Federation Intergovernmental Panel on Climate Change Life Cycle Assessment Life Cycle Inventory Live Weight Gain Monte Carlo Product Environmental Footprint Product Category Rules Total Digestible Nutrients

1 General Overview The agriculture sector was estimated to contribute up to 30% of the greenhouse gas (GHG) emissions worldwide, including crop and livestock production, forestry, and associated land use changes [1]. Nevertheless, the environmental impact of the agricultural production significantly differs from one of other sectors, such as energy and transport, where the dominant emission is the fossil carbon dioxide (CO2 ); in the agricultural sector, the main emissions are non-CO2 GHGs, such as nitrous oxide (N2 O) and methane (CH4 ) [2–4]. It especially occurs in the livestock production systems [5], where the non-CO2 GHGs originate from complex biological processes in soil, livestock, and manure. The inherent variability of the biological systems makes the estimation of the GHGs emissions much more difficult than fossil carbon dioxide, increasing the uncertainties in the environmental impact assessments [6]. According to [6], uncertainty in the total greenhouse gas inventories ranges from 95 to 20% in five industrialized countries. The differences in uncertainty are especially due to different subjective assessment of nitrous oxide emissions from agricultural soils, whereas the fraction of CO2 has a little effect on the uncertainty. According to FAOSTAT (2019) [7], there are about 1.6 billion cattle in the world; the vast majority is eventually culled and served as meat, but cattle are also kept and raised for the wide range of products and functions they deliver, taking multiple forms and involving a wide range of supply chains.

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Fig. 1 Global emissions from the main agriculture categories (1961–2010) and related percentage contribution [8]

The largest source of GHG emissions in agriculture, at global level, is enteric fermentation, which in 2012 accounted for 38.6% of the total sector (Fig. 1). The livestock sector is responsible for about 7.1 Gt CO2eq per year (corresponding to 14.5% of all anthropogenic emissions), with the cattle sector accounting for about 4.3 million tons of CO2eq [8]. In 2010 the ruminant sector accounted for about 29% of global meat production (79% from cattle sector, 21% from buffalo and small ruminants) and 83% of milk, playing an important role in providing high quality protein essential for human diets, but also in GHG emissions. The demand is forecasted to grow at a rate of 1.2, 1.5, and 1.1% for bovine meat, mutton, and milk respectively in the period 2006–2050 [9]. The global emissions by animal type are reported in Fig. 2a [8], in which it is possible to observe that dairy and non-dairy cattle account for 75% of the total emissions from the animals. According to FAOSTAT [7], the emissions related to the dairy and non-dairy cattle were about 1.6 Gt of CO2eq in 2018. Most of the animals are located in Asia and Central and South America (31 and 28%, respectively), followed by Africa (24%) (see Fig. 2b). The relevance of the beef production systems is highlighted when considering the livestock sector, in which beef commodity plays an important role in terms of environmental impacts, giving a significant aggregated contribution to global environmental issues, such as climate change and land use (Fig. 3) [10–14].

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Fig. 2 Global emissions by animal type (average data 2000–2010) (a) [8] and distributions of dairy and non-dairy cattle among the continents (b)

Fig. 3 A typical pasturing mountain system in Italy (picture taken by the authors)

South America contributes significantly to the global supply of beef (24% in the world, according to FAOSTAT 2019 [7]), with some countries acting as important players in the international market. Brazil is the world’s second largest producer after the USA (90% of the Northern America production, [7]) and the world-leading beef exporter, whereas Argentina, Colombia, Uruguay, and Paraguay are also established producers at an international scale (FAO, 2019) [15]. Brazil accounts for 17% of the enteric fermentation emissions in the world, followed by India (9%) and the USA (8%). Beef production is one of the drivers of land degradation and deforestation [16], but data show a large variation in the literature [17]; land use varies in the 27 - 49 m2 per kg of edible beef, whereas emissions of GHG in the 14–32 kg of CO2eq range. These variations are due both to differences in methodological choices and among beef production systems (origin of the calves, type of feed used during fattening, and so on) [5]. Beef supply chains are estimated to globally emit about 2.8–2.9 Gt of CO2eq , which represent about 40% of all the livestock emissions when using a life cycle approach [9, 18].

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When considering organic beef systems, an increase in environmental impact is in general observed [1]; in Europe, the organic cattle livestock for meat production is still not widespread (3% of the whole bovine sector in EU-15 (EC, 2013)), despite the number of certified organic cattle heads registered a significant growth from 2005 to 2012 (+118% in the EU-27, 2014). In the evaluation of the beef systems environmental analysis it is important to highlight several issues, which will be considered in the following sections: • the link between the mean product of the system (meat and/or milk) and the coproducts; it is an important issue to be investigated, with an accurate approach in terms of allocation and/or system boundary definition; • the competition for land, possibly leading to land use change with greenhouse gas emissions and loss of biodiversity as important implications [19]; • the characteristics of the beef production systems, which could strongly influence the CF outputs, depending on the geographical location and on the management methodology; • different methodological approaches, which could lead to contradictory results in studies on environmental impact of beef production systems.

2 Impact Assessment Methodologies In LCA analysis, the use of resources and the emissions from all production stages are quantified; they are assigned to environmental impact categories and they are related to the main output of the system, considering several functional units. LCA method was widely used to evaluate the environmental performance of beef production systems, comparing different origin of calves (dairy or suckler calves), production method (organic or conventional), and different types of diet (concentrateor roughage-based systems) [1, 5]. Environmental impacts generally considered in LCAs of animal source of food are use of fossil energy, land, water, global warming potential (GWP) or Carbon Footprint (CF), acidification potential, and eutrophication potential. Considering the cattle farming, in general the life cycle involves the raw material stage (including grain agriculture), the production stage (industrial processing), the transports, the usage phase (including the expulsion of beef feces and urine), and the final disposals [20–23]. The Life Cycle Assessment methodology is performed according to the recommendations of the standards ISO 14040, 14044 [24, 25], and ISO 14046; in particular, ISO 14064-1 and 14067 [26–28] are considered for the water and the carbon footprint evaluation of products. The carbon footprint considers all the emissions related to climate change potential (i.e., greenhouse gases—GHG emissions). Furthermore, it presents most of the current attention among different environmental impact categories in LCA [2, 29, 30] due to many factors, including agricultural intensification.

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Several methodologies are available for the calculation of the CF (PAS2050, the GHG protocol, IPCC, FAO, and IDF [3, 4, 17, 31]): FAO and IDF are the most suitable for dairy products. Many different standards and documents are also available for the methods, such as the International Dairy Federation (IDF) carbon footprint guidelines: “A common carbon footprint approach for dairy” [5, 31, 32]. The above-mentioned methodologies are based on the ISO 14040 series [24, 25], in compliance with IDF [31]. Differences among these methods are related to the allocation approach and to the possibility of considering or not biogenic carbon emissions [29]. In the most Life Cycle Analyses, the global warming potential (GWP) is calculated for a 100-year time horizon, in terms of kg carbon dioxide equivalents (CO2e ): 1 for CO2 , 25 or 28 for CH4 , and 265 or 298 for N2 O are the scale factors generally used [33]. Primary and secondary data are taken from the databases available in the literature, in order to standardize the LCA methodology. One of the most commonly used LCA software tools is SimaPro, especially for computing the produced CF. Cattle husbandry in general produces both meat and milk, therefore functional units should be carefully chosen and the environmental burden must be assigned between those outputs based on an allocation method. In many systems, production of milk and meat is interrelated: (dairy) cows produce milk and meat, and surplus calves are fattened for meat production. Specialized beef production systems, however, produce only meat from beef cows and their calves [5]. These and other aspects, such as the boundary conditions, the allocation approach, the life cycle inventory features, and the uncertainties of the analysis will be examined in the following sections. Several environmental studies of milk and meat production were performed using the life cycle assessment (LCA) approach, in order to estimate the contribution of milk or meat to climate change and other environmental impacts, e.g., [17, 34, 35]. Even though these studies are all based on the LCA methodology, there are still differences in how the GHG emissions are calculated and the data included in the analyses and the comparison of the results from different studies are not always significant [36]. Methodological aspects include all the choices that have to be made when performing a CF study, e.g., LCA approach [37], co-product handling [38] or system boundary setting [39]. Therefore, at present, it might not be valid to compare existing published CF values for milk and meat with each other and state that one production system actually has a different CF than the other, since a higher or lower CF result can only be the result of methodological assumptions and/or data choices.

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3 Beef Production Systems 3.1 Classification The recognition of the typologies of beef production systems is not so easy, due to the high diversity in the goods and services they deliver as well as in the environmental interactions [40]. In general, the perception is of specialized factory farming, whereas these are a limited part of a sector still dominated by family farms operating on mixed systems [41]. Cattle production occurs within different agro-ecological conditions, diverse breeds, and with several goods and services as output [14]; the main factors in determining this difference are the type and source of feed given to the animals (especially the share of grazing in the feeding system), the herd management, the level of market integration, and so on. A classification of the beef production systems is proposed in [14]: (a)

(b)

(c)

grazing systems: more than 90% of dry matter fed to animals comes from rangelands, pastures, and annual forages, and less than 10% of the total value of production comes from non-livestock farming activities [40]; mixed systems: more than 10% of the dry matter fed to animals comes from crop by-products or stubble, or more than 10% of the total value of production comes from non-livestock farming activities [42]. According to [18], mixed systems could be divided in dairy herd (producing both milk and meat) and beef herd (only producing meat); feedlots: almost exclusively dedicated to food production; feedlot feed is purchased off-farm and beef cattle are mostly fed on purchased grain, sometimes up to 95% in dry matter (70 to 90% in the USA). These systems present high energy rations and high daily weight gains.

In Fig. 4 [9], it is possible to see the distribution of these typologies of farms all over the world. Another classification can be found in [5]. Beef can be produced in systems where: • the production of milk and meat is interrelated: (dairy) cows produce milk and meat, and surplus calves are fattened for meat production; • only meat is produced from beef cows and their calves (specialized systems). In [5] beef production systems are classified according to three main characteristics of management practices: (a)

(b) (c)

origin of the calves: dairy-based system (calves bred by dairy cows) and suckler-based system (calves bred by suckler cows); when a system includes calves from both origins, it can be classified according to the origin of the majority of calves; production method: certified organic and non-organic beef production systems; type of diet fed to calves: concentrate-based (calves fattened on a diet of at least 50% concentrates on a dry matter basis in the weaning to slaughter period)

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Fig. 4 Distribution of grazing, mixed, and feedlots systems all over the world [14]

and roughage-based (calves fattened on a diet with an average proportion of less than 50% concentrates on a dry matter basis in the weaning to slaughter period). Regardless of the classification, beef production includes several processes that generate GHG emissions [43]: • beef methane from enteric fermentation (part of the digestive process of ruminant animals, in which carbohydrates are broken down by microbial activity, generally the largest emissions source); • animal excreta (additional CH4 and nitrogen lost as nitrous oxide N2 O); • inputs to agricultural soils (fertilizers, urea, and lime application, which results in further N2 O and CO2 emissions); • farm energy use, either in the form of electricity or fuel, which results in further CO2 emissions. Land use and land use change can also result from beef production, with CO2 either emitted to or sequestered from the atmosphere, depending upon changes in plant biomass and soil organic content [43].

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3.2 Characteristics The characteristics of the beef production systems, such as the type of farming, the year, the type of management practices, the geographic location, strongly influence the environmental impact results. The geographical location is in particular investigated in [44], in which the carbon footprint of beef cattle is calculated for Canada, USA, European Union, Australia, and Brazil. The values ranged between 8 and 22 kg CO2eq per kg of live weight, with significant reductions for most of the countries in the last thirty years. As an example, in Canada, the mean carbon footprint of beef cattle at the exit gate decreased by about 50% from 1981 to 2006, thanks to improved genetics, better diets, and more sustainable land management practices. Further improvements are due to co-products other than meat (such as hides, offal, and fat). The differences in CF outputs are mostly related to the geographic areas in which the beef production systems are located, which define their main characteristics. In the following several examples all over the world are described. In Brazil, two typical systems are present [45] (see features in Table 1): • extensive systems, which represent the traditional South Brazilian pastoral system, mainly characterized by the use of large tracts of land with little or no subdivision, where the animals can continuously graze on the natural pasture throughout the year, with little or no supplementation; • improved systems, which have lower impacts of seasonal grassland production of native pastures, increased forage production and feed quality, due to the introduction of winter forage species and weekly rotational grazing. This management has a relatively low cost and provides increases in production rates. In the USA, beef calves are commonly finished on feedlots, and this fattening phase is based mainly on concentrates [46, 47]. The environmental impact is strongly influenced when considering systems where calves were weaned: • directly to feedlots; • weaned to out-of-state wheat pastures and finished in feedlots; • finished wholly on managed pasture and hay. When considering equilibrium conditions for soil organic carbon, the highest impacts per live weight kg of beef were found for pasture-finished beef, for all impact categories, and lowest ones for feedlot-finished beef. On the other hand, substantial reductions in net greenhouse gas emissions for pasture systems were found when considering positive soil organic carbon sequestration potential [47]. Moreover, significant differences can be found when considering organic and nonorganic systems: organic systems show in general a higher overall environmental impact, despite this trend is not observed in all the process steps [1] (Fig. 5). In New Zealand [2] cows graze all year on perennial grass/clover pasture, with only a small amount of brought-in feed (maize silage and grass silage), used to sustain milk production when pasture growth is low. Grass silage is usually produced on the

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Table 1 Main features of the extensive and improved systems [45] Herds Descriptions

Systems Extensive

Improved

Herd structure Weaned calves weight (kg)

170

210

Weaned heifers weight (kg)

150

190

Replacement rate (%/y)

20

12.5

Weaning rate (%)

55

78

Mortality rate (%)

4

1

Male-female ratio

1:01

1:01

Weight gain first year (kg/d)

0.1

0.2

Weight gain (kg/d)

0.23

0.6

First calving (months)

48

30

Milk production (l/head/d)

1.1

2.2

Slaughter weight-males (kg)

440

500

Slaughter weight-females (kg)

420

480

Body weight, bulls (kg)

600

700

Body weight, cows (kg)

380

400

Forage intake (kg DM/head/d)

8.1

9.99

Water consumption (l/head/d)

50

50

Common salt consumption (g/head/d)

50

–

Mineral salt consumption (g/head/d)

–

150

Manure handling

On pasture

On pasture

Grasslands Descriptions

Systems Extensive

Improved

Pasture type

Native Pasture

Native pasture with winter grasses and leguminous

Winter species

–

Ryegrass, oat, clover and birds foot trefoi

Implementation Phosphorus fertilization (kg P2 O5 /ha/2y)

–

100

Potassium fertilization (kg K2 O/ha/2y)

–

130

Liming (ton/ha/6y)

–

2

Mowing/y

–

1

Overseed/2y

–

1

Composition

(continued)

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Table 1 (continued) Seed grass (kg/2y)

–

40

Seed leguminous (kg/2y)

–

10

Production (kg DM/ha/y)

3000

11500

Digestibility energy (% DM)

47

55

Crude Protein (% DM)

12

15

Ym factor (% GE intake)

7.2

6.5

Forage utilization efficiency (%) 50

70

Parameters quality

DM Dry Matter; GE Gross Energy; y year

Fig. 5 Carbon Footprint values obtained from the LCA analysis for three steps of the process in organic and conventional systems (from the graphical abstract in [1])

farm itself or a nearby area, whereas maize silage is in general bought. Almost all cows calve during late winter, so that animal feed requirements match the pattern of pasture production throughout the year. In Sweden [2], dairy livestock are kept indoors and the average outdoor grazing period is approximately 2.5 months for cows and 5.5 months for heifers, resulting in a feed intake from grazing of less than 10% of total intake. Organic farms (only

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5% of total milk deliveries in Sweden in 2005) and some conventional farms use a longer grazing season, whereas conventional farms only graze for 6–8 h per day in summer [48]. Among Flemish beef farmers (Belgium) [49] it is possible to identify different farming models, depending on the sets of practices and on the diverse pursuits of agro-ecological principles: • conventional farmers, with a bare minimum of practices contributing to agroecology; • farmers which integrate significant elements of agro-ecology, which correspond to low-inputs, low-capital, but knowledge-intensive model, embedded within alternative commercial and social networks and actively seek to become independent from regime institutions; • conventional beef farmers, which try to find advantages within the mainstream market environment. In [15], a typical farm in Paraguay was investigated, with a high total agricultural area (more than 3000 hectares of arable land and pastures). For arable crops, production is always organized in a double-cropping system per year and, for grazing animals, forage is available all year round.

4 Goal and Scope, Functional Unit, and Allocation Goal and scope definition is the first step of a LCA study, aiming at defining the reasons of the study, the information that is expected to obtain, how it is going to be used, the intended audience of the report, and the limits of the system (ISO series [24–26]). In the study of beef production systems, the environmental impact is often expressed in terms of carbon footprint CF, such as in [1]; in this case, the goal was the evaluation and the comparison of the CF of two Chianina beef production systems in Central Italy: a conventional and an organic one. Cow-calf farms, characterized by self-produced cattle, were considered, the most typical in this region. The goal and scope of an environmental impact evaluation is also to identify the steps of a process in which the environmental impact is more significant, in order to propose in future scenarios focused mitigation strategies and improvement options in comparison with the actual situation [50]. In this context, a recent LCA study aimed at quantifying the environmental impact related to a semi-intensive cattle rearing system for beef production in Paraguay [15], with a focus on the processes most responsible for the impact. Results were compared with previous studies both in South America and in other areas with more intensive rearing systems (e.g., USA, Europe). Results showed intensive greenhouse gas emissions for systems predominantly on pasture, especially for the breeding step; the feedlot stage, despite its limited duration with respect to the overall rearing cycle, also weighs significantly in some categories (non-methane volatile organic compounds emissions, toxicity, land occupation and

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fuel consumption, especially because of feed production). As possible mitigation solutions, improved efficiency in resource use and herd management were proposed, with positive environmental (reduction in all the impact category), economic, and food security effects. In [51] the environmental impacts and energy balance of the manufacturing process of mature cheese derived from cow milk in a Portuguese dairy factory were assessed. Improvement alternatives, such as a reduction of energy consumption, the use of a more environmental friendly fuel in the boilers and of new trucks with lower exhaust emissions were proposed; results showed that environmental impact could be slightly reduced (especially in categories such as acidification, eutrophication, global warming, and photo-oxidants formation) with modest economic investments. A particularly important issue in environmental impact comparisons is the functional unit FU or comparison basis; it is a measure of the performance of a product system to which all inputs and outputs are related. In many cases, one cannot simply compare product A and B, as they may have different performance characteristics. Defining a functional unit can be quite difficult, as the performance of products is not always easy to describe. When considering beef production systems, it is necessary to establish if the aim of the process is to produce meat, milk, or both and depending on this the functional unit could be, as an example, 1 kg of meat, 1 l of milk, and so on (see, as an example, Fig. 6 [19]). In the cited beef production system in Paraguay [15], being its function to deliver a certain amount of live beef cattle for slaughter, 1 kg of live weight of animals leaving the farm was chosen as the FU, as suggested in LEAP guidelines [52]. Similarly, in [1] the production of 1 kg of live weight meat from bullocks and heifers was chosen as functional unit. In this case, being low the quality of the meat produced from cows and bulls (culled when they are too old to continue production or when they have

Fig. 6 Product flows and functional units of organic (O) and high yielding conventional (C) dairy systems [tonnes of energy-corrected milk (ECM) and of meat (carcass weight CW)] [19]

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other problems), GHG emissions were split into two products. For beef production and slaughter cattle farms, live weight gain (LWG) is quantified as weight (kg) of animals produced from the farm, assuming no change in size of stock on the farm and no animals bought into the farm. Gross energy concentration is calculated from daily gross energy (GE) intake estimated for each animal category based on diet digestibility and daily net energy requirements for maintenance, activity, growth, lactation, and pregnancy [33]. As regards milk, in [2] 1 kg of energy-corrected milk (calculated according to [53] to correct for fat and protein) provided at the farm gate was considered as FU. Beef production systems in some cases produce also cheese; in these cases, different FUs are considered; as an example in [51] a typical multioutput system is considered: 1 kg of cheese was chosen as FU and whey powder as co-product. These are indeed the typical situations in which not only cheese is produced, but also other co-products that could or not be computed in the analysis. A further important issue is the role of these co-products, which are usually considered in the allocation process, consisting in partitioning the input and/or output flows of a process to the product system under study, according to ISO 14040 series. In general, ISO 14040 recommends using system expansion, whenever possible, instead of allocation and often it is not possible to identify the specific consumptions of mass and energy flows associated with each product and co-product, due to lack of information; in these cases the system is considered as a black box with some main co-products. Economic allocation is commonly used in food product-related studies [54, 55], but it has the disadvantage to be vulnerable to fluctuating prices and demand [55]. For this reason, other approaches of allocation should be proposed: a mass allocation approach, no allocation approach, fat content of the products (not significant and close to the no allocation approach when the fat content is low with respect to co-products). When a dairy farm produces not only milk but also meat, an economic allocation approach is in general applied, in order to partition the environmental loads between both products, in accordance with Castanheira et al. [56]. On the other hand, the expansion of the system boundaries suggested by the ISO Standard requires the evaluation of a different way to obtain the same product, but it is not always possible to identify an alternative product system for the co-product [1]. For this reason, the economic allocation is in general preferred to other allocation criteria indicated by ISO prescriptions (e.g., physical allocations) [51] and it is also in compliance with the guidelines of the Product Category Rules (PCR) form eat of mammals of the International EPD System. The allocation factors can be drawn from Environdec [57, 58]. In [2], where the main product from the dairy system is milk but there are also co-products such as surplus calves, meat from culled dairy cows, and manure, it was assumed that all manure is used in fodder production at the farms, hence no allocation is needed. Furthermore, no allocation is conducted for milk, surplus calves, and culled dairy cows, whereas for feedstuffs (e.g., soy meal, rapeseed meal, wheat bran, palm kernel expeller), economic allocation was applied, in order to divide GHG emissions among the feed and its co-products.

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A way to reduce GHG emissions from milk production is often the increase in milk yield per cow but, when accounting for other affected systems (e.g., beef production), it is not certain that it leads to a reduction in total GHG emissions per kg of milk. Investigating 23 dairy farms (both organic and conventional) in Sweden [19], it was shown that the use of a fixed allocation factor of 90% (based on economic value) indicates a reduction in CF with increased milk yield, whereas no correlation can be noticed when system expansion is applied (Fig. 6). The CF is somewhat lower for the organic farms (with a lower milk yield per cow, but more meat per kg milk), but when a 90% allocation factor is used, the CF is somewhat higher for the organic farms compared to the high yielding conventional farms. Thus milk and meat production scenarios should be analyzed in an integrated approach, in order to reduce the environmental impact. Finally, in [47] the purpose of the analysis was to describe the biophysical environmental dimensions of a food production system; the allocation was based on the gross chemical energy content of co-product streams, the caloric energy food production chemical energy in raw materials is divided between processed quantifiable outputs.

5 System Boundaries In the beef production, the system boundaries are in general chosen considering a cradle-to-farm gate approach. The system boundaries are fixed starting from the emissions within farm gate, but to give a complete account of the emissions generated as a result of the whole beef production system, the boundaries should be expanded. They have to include the impacts incurred in the production of farm inputs (‘pre-farm gate’), such as agricultural and land use emissions from the production of feedstuffs grown, the energy used to manufacture fertilizers, and so on. Including these emissions, it is possible to cover the production process from initial inputs (‘cradle’) to the point at which finished animals leave the farm, often referred to as “cradle-to-gate”. System boundaries can be further expanded to downstream (post-farm gate) emissions resulting from transport of animals, abattoir energy and resource use, refrigeration, and cooking (for a complete “cradle-to-fork” approach) [43]. In [1], a cradle-to-farm gate approach was considered, taking into account for all the processes upstream of farm production up to the product leave the farm gate: direct impacts due to on-farm production processes, but also indirect ones (manufacture and transport of synthetic fertilizers, diesel, pesticides, seeds, off-farm feeds production, and plastic) were taken into account. On the other hand, emissions deriving from the construction of equipment and farm facilities were not included, as often occurs. Furthermore, in the specific case, the following emissions were neglected: • emissions from direct land use change, because the surface invested for animal feed production was already a cropland;

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• CO2 from livestock respiration, because it is not considered a net source according to the Kyoto Protocol; • soil carbon changes, because of a lack of site-specific data, but a sensitivity analysis to the carbon sequestration by soils was carried out. Also in [2] the system boundaries were limited to dairy farm gate. All inputs related to production and raw material extraction were considered and the main GHG emissions (CH4 , fossil CO2 , and N2 O) associated with inputs or outputs were calculated. Some minor emissions were omitted, such as production of pesticides, detergents, medicines, whereas capital goods were included for energy and transport, but not for other activities. Also in this case the emissions from land use and land use change were not considered, since there is no consensus on methodology for incorporating these effects and since the existing data were sparse and uncertain. In [15] a cradle-to-farm gate perspective was also adopted, so impacts resulting from post-production transport, processing, distribution, consumption, and all related waste disposal were excluded from the assessment. The boundaries included: • manufacture (with the extraction of raw materials), supply, and use in the crops production step (fuels, fertilizers, pesticides, seeds, lime, etc.); • indirect environmental burdens of tractors and other machinery (including maintenance and final disposal), due to the high level of agricultural mechanization; • whole cattle rearing cycle, considering the consumption of inputs (self-produced maize silage and oat hay, purchased mineral supplements and feeds), mechanized operations (pasture renewal, ration distribution during finishing phase); • animal-related emissions (enteric fermentations, manure-related emissions). The following impacts were instead neglected: • indirect impact of the farm’s capital goods (buildings, warehouses, fences, etc.); • impacts associated with production and usage of veterinary medicines and semen for artificial insemination, due to lack of information. A recent study [59] focused on the multiple environmental burdens of dairy production, by expanding the boundaries of the system in order to account for coupled dairy-beef production systems and consequences for pure-beef farms. A large number of farms in Costa Rica (552) was analyzed, by applying an expanded boundary approach, in order to evaluate the impact of milk and beef production from the whole cattle system, and considering the following five impact categories: GWP, eutrophication potential, acidification potential, resource depletion potential, and land occupation. The analysis, carried out with a cradle-to-farm gate approach, highlighted that the largest GWP and land occupation footprint were found for milk obtained in dual-purpose farms, whereas the specialist farms showed the smallest impacts. An example of representation of the system boundaries of a CF analysis is shown in Fig. 7 [50]. It is evident that the choice of the system boundaries is a key issue in environmental impact evaluation and results are strongly dependent on the system boundary

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Fig. 7 A schematic representation of a beef production system: dotted line represents the system boundary, blue box represents off-farm emission sources, and green box represents on-farm sources [50]

definition. It is recommended to make comparisons between systems with the same boundaries and to expand them only when reliable data are available.

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6 Life Cycle Inventory Data collection for the evaluation of the carbon footprint is mostly based on the onsite investigations carried out in the farms during the four seasons. The investigated period of observation can be considered sufficiently representative when the manager of each farm confirms little variability of the management practices. The lacking data are integrated with literature studies and LCA software databases. Among the used guidelines, Intergovernmental Panel on Climate Change directive [3] gives a description of the calculation of the total amount of GHG emissions and it does not include the allocation of GHG emissions to a particular product. In 2012, specific Product Category Rules (PCR) were developed according to the international Environmental Product Declaration (EPD) system [58] for mammal meat, including beef, in which slaughter activities, packaging processes, and storage are the core processes [60]. The Product Environmental Footprint (PEF) Guide provides detailed and comprehensive technical guidance on how to conduct a PEF study, that may be used for a variety of purposes. This document should be considered as guidelines for a correct life cycle assessment analysis approach. Input data are different for different kind of farms (dairy systems, couple dairyfattening system, farms with only meat production). Particular attention should be paid on the evaluation of emissions, which can be categorized in emissions from feed production, from enteric fermentation, and from manure storage. Feed production, whether pasture or crops, is the main activity through which cattle use land and water resources [61]. Over 60% of the dry matter global cattle feed ration is made of grass and tree leaves. Considering the feed production, it is important to evaluate if the fodder is produced inside the farm system or if it is purchased from outside. In the first case, it is possible to have primary data derived from the farm information and field survey can be used with the help of structured questionnaires; in the second scenario data for the external feeds production can be collected from databases, such as GaBi LCA or Ecoinvent. In this case, particular attention should be paid to the emissions related to the transports of the food and feed: the transport distance from the manufacturer to the farm and the kind of vehicles should be also included. It is always important to check if the yield values and the N-fertilizer input data are in line with the integrated production regional guidelines and with recent statistical data of the country. In particular, in [1] the IPCC (2006)-Tier 2 approach was used to calculate both direct and indirect emissions when nitrogen-based synthetic or organic fertilizers are applied to the soil. The volatile fraction of fertilizers (ammonia and nitrogen oxides) was assumed to be 10% for synthetic fertilizers and 20% for organic fertilizers, as suggested in IPCC, 2013 [62]. Enteric fermentation is the digestive process during which carbohydrates are broken down by anaerobic micro-organisms in the rumen of the animals. Methane is created as a by-product and mostly emitted through the mouth of the cow. Also methane emissions arising from enteric fermentation are in general estimated

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according to IPCC Tier 2 approach in [1], based on gross energy requirements and digestible energy of feed. The gross energy (GE) intake could be calculated using feed composition tables of all ingredients [63] and CH4 emissions could be determined as a percentage of the GE intake, by considering different conversion factors for each diet. The IPCC approach is one of the most used for these evaluations; another relevant method is [64, 65] Tier 2 approach, used in [32]. The IPCC Tier 2 approach was also employed for the evaluation of CH4 emissions from manure management, based on the volatile solid production by the cows, the CH4 conversion factor for the manure management practice, and the maximum possible CH4 production rate from the volatile solids in the manure [1]. For direct N2 O emissions from manure management, IPCC Tier 2 algorithm was used based on the amount of nitrogen excreted as feces and urine, and the manure management system. Indirect N2 O emissions related to volatilization and leaching processes of manure N management were also found on the basis of IPCC Tier 2 approach, considering nitrogen excreted and manure management system. For Life Cycle Inventory step, Gollnow et al. [32] used a web-based data collection software to enable secure collection and management of confidential data, validate data entries, minimize errors due to data transfer, and create a system that can be used for future updates; the carbon footprints of mineral fertilizers were taken from the GaBi database [66]. The datasets used for the study distinguish between the type of fertilizer (for nitrogen: ammonium nitrate, calcium ammonium nitrate, and urea; for phosphate: superphosphate; for potassium: potassium chloride; and for magnesium, magnesium sulfate). As usually, organic fertilizers were considered waste products and therefore greenhouse gas emissions for production were not allocated to the dairy farms. Other important input data are related to water, natural gas, and electricity supplied to the farms. National averages are considered for the supply and combustion of diesel, gasoline, and LPG, as published by the Department of Climate Change and Energy Efficiency [65]. Diesel consumed for activities conducted by contractors should be also estimated. Greenhouse gas emissions associated with the onsite pumping of water can be finally captured in the on-farm energy use (electricity and diesel) [32].

7 Sensitivity Analysis The uncertainties of the carbon footprint analysis of beef productions depend on several aspects: Life Cycle Inventory inputs, data sources and their quality, boundary conditions, choice of the functional unit. According to [36], it is essential to correctly identify the key parameters which play an important role in the uncertainty evaluation. The uncertainty evaluation can be carried out by means of Monte Carlo (MC) method, the most common technique used for uncertainty quantification, due to its simplicity and good statistical results and thanks to its easily parallelizable algorithm.

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This method was used in [29]: a sensitivity analysis of the dry matter consumptions in each beef production system and an uncertainty evaluation were carried out. The effect of feed diet modifications on the Carbon Footprint of the Brazilian dairy systems was performed by means of a MC analysis. It was evaluated the CF per 1 kg of energy-corrected milk at the farm gate for different dairy production systems, when considering good technologies. The sensitivity analysis was carried out on total digestible nutrients (TDN) and crude protein (CP), which were collected from the literature [67] and accepted by Brazilian experts in nutrition in dairy production [68, 69]. TDN and CP were both considered in the analysis, due to their significant effect on CH4 and N2 O emissions from enteric fermentation and manure, respectively. Results showed low uncertainty when varying TDN and CP of the studied dairy farms: the coefficient of variation was 1.1, 0.7, and 1.0% for the confined feedlot, semi-confined feedlot, and pasture systems, respectively. In [29] a sensitivity analysis was carried out by varying the dry matter intake in animal diet in confined feedlot, semi-confined feedlot, and pasture systems. The influence on enteric CH4 and manure N2 O emissions resulted in the highest coefficient of variations for N2 O, especially in semi-confined feedlots (5.5%, see Table 2); the average values of the coefficient of variation of the three types of beef production systems were 1.67 and 4.00% for CH4 and N2 O emissions, respectively. Although the N2 O coefficient of variation was higher in the semi-confined feedlot system (Table 2), the N2 O from manure was higher in the confined feedlot case; it explains the greater coefficient of variation. The carbon sequestration by pastures and crops was not considered, due to the lack of specific data related to the geographical area. According to [70], the carbon sequestration by grassland has strong potential to partially mitigate the GHG balance of ruminant production systems, but a correct evaluation of the emissions mitigations and of the adaptation of livestock production Table 2 CF of Brazilian dairy farms in terms of enteric CH4 and manure N2 O emissions and Monte Carlo analysis results (data elaborated from [29]) Type of system

Emissions (kg per cow per Standard year) mean value deviation

Confined feedlot CH4 enteric N2 O manure Semi-confined feedlot

CH4 enteric

Pasture

CH4 enteric

N2 O manure N2 O manure

Average values

CH4 enteric N2 O manure

94.52 0.7523 92.09 0.3961 83.61 0.0534 90.07 0.4006

Coefficient of variation (%)

Emissions variation with respect to confined feedlot (%)

1.76

1.9

–

0.0271

3.6

–

1.44

1.6

−3

0.0219

5.5

−19

1.28

1.5

−12

0.0016

2.9

−93

1.49

1.67

n.a

0.0169

4.00

n.a

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systems to climate change will require a major international collaborative effort and long-term experiments by researchers. The adding of carbon sequestration by soils in the feed production is a highly debated topic [71] in agricultural practices, such as organic farming. Therefore, the estimation of this kind of contribution to the carbon footprint of beef production is very unreliable, due to the small amount of some data [1, 29]. In the Literature, the inclusion of soil carbon changes in the Life Cycle Assessment of agricultural products was taken into accounts by means of several methods. Petersen et al. [72] and Mogensen et al. [71] followed an approach able to evaluate differences in carbon sequestration between crops, assuming that 10% of the carbon input into soils is sequestered in a 100-year perspective [72]. Carbon input was considered as the sum of above-ground (AG) biomass, below-ground (BG) crop residues and manure. This method was applied in [1], in which the carbon content of AG and BG was assumed as 45% of the dry matter, whereas the carbon input of the manure was determined assuming a C:N ratio of 21:1 [71]. A land use factor equal to 0.8 for all crops [62] was considered, taking into account the root turnover and the duration of the crops. Both for conventional and organic agricultural systems, carbon sequestration allowed to reduce the carbon footprint of beef, mainly due to the employment of high amounts of manure produced as deep bedding. However, its contribution on the CF of beef is limited for both production systems, allowing to decrease the GHG emissions of only about 1 kg CO2e /kg live weight in both the cases. In [2] the carbon footprint (CF) of milk production at the farm gate was analyzed for an outdoor pasture grazing system in New Zealand and a mainly indoor housing system in Sweden, as assessed above. National average data were used to model the dairy system and the uncertainty in CF estimates due to variation in emission factors for methane from enteric fermentation and nitrous oxide emissions were analyzed through MC simulation. Although the two milk systems are very different, there were great similarities regarding the parameters that have much influence on the CF. When MC simulation was performed simultaneously and the same emission factors were used for the two countries at each run, the CF for New Zealand milk production was lower than that for Sweden in 89% of the outcomes. Nevertheless, when the Monte Carlo simulation was run independently for the two countries, it gave a mean value of 1.00 kg CO2e /kg for New Zealand and 1.16 kg CO2e /kg for Sweden, with a standard deviation of 0.26 and 0.19, respectively (Fig. 8). It is possible to observe that, when the difference in CF values is relatively small (15–20%) and the uncertainties in their estimates are relatively high, it is difficult to find with certainty which milk production system allows less GHG emissions. Therefore, an extensive sensitivity analysis of the emission factors used in estimates of biogenic GHGs is very important to give a meaningful picture of the CF of products from agricultural sector. Sykes et al. [73] demonstrated that uncertainty in modeling coefficients relating to (a) N2 O emissions from manure and fertilizer, (b) enteric emissions, (c) embedded emissions from feed production, and (d) nutritional quality of the ration (especially digestibility) are highly influential in the derivation of uncertainty for a modeled beef production system. They employ Monte Carlo simulation to assess the sensitivity of the modeled GHG footprint to evaluate the uncertainty of the model.

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Fig. 8 Probability distribution of GHG emissions of 1 kg of ECM in New Zealand (top) and Sweden (bottom), based on the Monte Carlo simulation in SimaPro (vertical continuous line: mean values; vertical dotted lines: lower and upper limits of the predicted interval) [2]

Model Risk (Vose Software) was incorporated into the AgRE Calc model to provide MC functionality. The model was calculated for one annual time-step and it was run both deterministically, using best estimate values for the coefficients, and a MC of 10,000 repeats was conducted, which formed the basis for the uncertainty assessment. Following a cradle-to-gate approach, an emissions intensity of 19.20 ± 2.49 kg CO2e /kg live weight was estimated for the stochastically modeled system. For the deterministically one, the emissions intensity of the system as a whole was estimated at 17.7 kg CO2e /kg live weight. The emissions intensity of production for the system was dominated by CH4 emissions from enteric fermentation, which accounted for about 46%. The three largest categories (enteric fermentation, feed production, and manure deposition) accounted for 84% of the total footprint (Fig. 9), but their contribution to overall

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Fig. 9 Stochastically calculation of the breakdown of the total emissions intensity. Error bars are calculated via Monte Carlo simulation. Total % breakdown by gas is given inside brackets in the legend [73]

uncertainty highly depended on the emissions type (Fig. 10). Nitrous oxide emissions were most variable, despite being lower in magnitude than CH4, whereas embedded emissions showed uncertainty similar to CH4 . Both N2 O and embedded emissions showed a strong positive skew, whereas methane emissions were relatively unskewed. The obtained results can be in general applied to northern hemisphere beef production: the variation is especially due to uncertainties in N2 O emission factors. Their improvement would considerably reduce this uncertainty, despite variability in production practices and yields also represents an important contributor to the uncertainty in emissions for off-farm feed production, and this aspect is difficult to mitigate.

Fig. 10 Histograms showing uncertainty and distribution for different emissions types [73]

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8 Selection of LCA Studies and Impact Assessment Analyses Several studies were performed in the literature in order to evaluate the environmental impacts of beef production systems. In the following a selection of analyses is presented and the most significant issues are highlighted. M. de Vries et al. [5] compared cradle-to-farm gate environmental impacts of beef produced in contrasting systems, by means of life cycle assessment (LCA) in terms of Global Warming Potential (GWP). The studied systems were classified according to the description reported in Sect. 3.1 and were located in Europe [74– 79], North America [47, 80–83] and Oceania [84]. For each study, LCA results were expressed on a relative basis: the classified systems were compared only if the classification characteristics did not differ each other. Considering several aspects, no large differences in GWP were found between organic and non-organic systems: reduced carbon dioxide and nitrous oxide emissions in organic systems were observed, due to a lower use of (synthetic) fertilizers, but they were compensated by increased methane emissions caused by a higher amount of roughage in organic diets. On average, GWP was 28% (range 4–48%) lower in concentrate-based systems when compared with roughage-based ones, partly due to a higher growth rate of calves in concentrate-based systems. For different origins of the calves, it was observed that environmental impacts per unit of product were consistently lower for dairy-based systems when compared to suckler-based ones (on average 41% lower GWP (range 13–76%) than the dairy-based systems). From this review study, it was concluded that dairy-based beef production showed a largest potential to mitigate environmental impacts of beef. An area where beef and dairy farms are widespread is South America, where recent studies focused on the comparison of the environmental impact of beef production sector. A few studies compared the environmental intensity impact of only milk production and combined beef and milk production in this region. Recently, life cycle assessment of interconnected cattle systems was investigated in an interesting study by comparing beef and milk production [59]. In general, LCA studies dealing with milk production are performed on a “cradle-to-farm gate” basis [85] and have shown that higher milk yields per cow lead to smaller environmental footprints, especially in terms of Global Warming Potential [86], but they do not consider implications for coupled dairy-beef production and pure-beef production systems. This is very important because beef has a higher environmental intensity than milk production. In these South America regions, Mazzetto et al. selected more than 500 farms (203 beef and 349 dairy farms in Costa Rica) by applying an expanded boundary approach (see Fig. 11), in order to investigate the impact of milk and beef production from the whole cattle system. The dual-purpose farms were subdivided based on their product allocation, whether producing more beef (Dual Purpose Toward Beef—DPTB), more milk (Dual Purpose Toward Dairy—DPTD), or having an equivalent production of both milk and beef (Dual Purpose—DP).

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Fig. 11 Database profile in terms of farm typologies. SD Specialized Dairy; E Extensive; I Intensive; L Lowlands; U Uplands; DP Dual Purpose. “Fattening dairy males” represent farms fattening male animals from dairy farms; “Fattening dairy females” represent farms fattening female animals from dairy farms; “Suckler beef” represents a normal beef farm. [59]

Five different impact categories were evaluated, with particular attention to GWP, in addition to eutrophication potential, acidification potential, resource depletion potential, and land occupation. Figure 12 shows the main results for the selected farms in terms of GWP, by considering the average milk and beef footprints across the dairy farm typologies. The enteric fermentation (in brown in Fig. 12) was the main source of GHG for all typologies, in accordance with previous milk and beef footprint studies. GHG emissions from manure management were extremely low, thanks to year-round grazing systems which are very diffused in the region of Costa Rica. Dual Purpose and Dual Purpose Toward Dairy farms had the highest GWP footprints for milk and dairybeef, whereas the smallest footprints were found for the Specialized Dairy Intensive in the Uplands (SD_I_U) typology (Fig. 12). Environmental footprints were higher for beef from the beef-only farms than from dairy farms: this is mainly due to the necessity of dedicated suckler cows to rear beef calves on pure-beef farms. Emissions arising from suckler cows are not allocated across milk and meat, as they are in dairy systems. This comparative study on cattle production systems emphasizes the necessity of considering both milk and meat production as well as multiple environmental pressures across interconnected milk and beef production systems, and the importance of pursuing intensification strategies for the impacts mitigation. Emissions can also be expressed on a per protein basis and in this case beef presents the highest emission intensity (around 300 kg CO2eq per kg of protein), followed by meat and milk from small ruminants, with averages of 165 and 112 kg CO2eq per kg of protein, respectively (Fig. 13) [14].

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Fig. 12 Average milk and beef footprints across the dairy farm typologies. SD Specialized Dairy; E Extensive; I Intensive; L Lowlands; U Uplands; DP Dual Purpose. The error bars represent the 95% confidence interval [59]

Fig. 13 Global GHG emission intensities by commodity [14]

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Emissions from dairy herds are attributed to milk and meat, whereas emissions from beef herds are allocated only to meat (in both cases, a limited fraction is allocated to other goods and services, such as draft power and manure used as fuel). Due to the allocation of the GHG emissions, there is a distinct difference between beef produced from dairy herds and from specialized beef herds: the emission intensity of beef from specialized beef herds is almost fourfold than that produced from dairy herds (68 versus 18 kg CO2eq per kg of carcass weight). Furthermore, when only fattening animals are considered, specialized beef and surplus dairy calves have similar emission intensity per kg of carcass weight [18]. Finally, the effect of the dry matter intake on the CH4 and N2 O emissions from manure management in confined feedlot, semi-confined feedlot, and pasture systems [29] showed an average value of about 90 and 0.4 kg per cow per year of enteric CH4 and manure N2 O emissions, respectively. The highest emissions were found for the confined feedlot system (94.5 kg CH4 per cow per year and 0.752 kg N2 O per cow per year). The pasture system resulted in the lowest values, with a reduction of about 12% for CH4 and more than 90% for N2 O with respect to confined feedlot, whereas semi-confined feedlot is in an intermediate position (the same reductions are 3 and 19%, see Table 2 in paragraph 7).

9 Conclusion and Outlook Most of the research studies in environmental impact of beef systems show the significant contribution that the beef sector makes to environmental issues and its critical role in the development of sustainable food systems. The sector needs to respond to the growing demand for livestock products, in order to be sustainable, and it should use natural resources efficiently, enhance its contribution to nutritional security, secure livelihoods, human, animal, and environmental health and welfare. In the chapter, a review on the environmental impact of beef production systems is provided, analyzing more than 80 papers found in the literature in the last 20 years. Crucial issues are addressed, starting from the description of the methodologies used in the evaluation of the environmental impact of beef production systems, which mainly consist in Life Cycle Assessment (LCA) analysis, global warming potential (GWP), and/or Carbon Footprint (CF) calculation. The characteristics of the beef production systems have a great influence on the results of the evaluations; a wide variety of systems is widespread all over the world, depending on: • the geographic location and the availability/not availability of wide areas for animals pasture; • the animals rearing methods; • the animals diet; • the products and co-products considered as outputs of the system.

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All these parameters strongly influence the processes generating GHG emissions, which are methane from enteric fermentation (in general the largest emissions source), animal excreta, inputs to agricultural soils, farm energy use. When approaching an environmental study, the first step is goal and scope definition, which in beef systems is in general the comparison of their main environmental indexes and the identification of the most significant steps of a process, in order to propose mitigation strategies and improvement options. The choice of the functional unit is crucial in the environmental analysis; in beef systems, it is in general 1 kg of live weight of animals leaving the farm, 1 kg of milk, 1 kg of cheese, and so on, depending on the aim of the system. Several co-products are also considered as outputs of the system; they are taken into account in the allocation process, which can follow different approaches: in beef production systems, such as in most of the food product-related studies, the economic allocation is commonly used. The system boundaries are in general chosen considering a cradle-to-farm gate approach. They can be further expanded considering post-farm gate emissions (transport of animals, abattoir energy and resource use, refrigeration, and cooking) for a complete cradle-to-fork approach. A very important step in the environmental evaluation process is the Inventory analysis, in which data are gathered; data have to be related to a period of observation representative of the process, i.e., when the manager of each farm confirms little variability on the management practices. As in other environmental studies, it could be possible to have primary data derived from the farm information and field survey, also with the help of structured questionnaires, i.e., if the fodder is produced inside the farm system; data for the external feeds production can be collected from databases, such as GaBi LCA or Ecoinvent, or from the scientific literature. Data about enteric fermentation have to be carefully chosen, due to their high influence on the final results; they are available on the same databases GaBi, Ecoinvent, IPCC, and so on. Other important input data are water, natural gas, and electricity supplied to the farms, for which the national average values are in general considered. Data in the literature show that the environmental impact of the beef production systems is characterized by a strong variability, depending on the variety of the characteristics and of the approaches. It results in a significant uncertainty of the analysis outputs, which is in general performed throughout the Monte Carlo method. In the last part of the chapter, a selection of studies considered significant in terms of characteristics of the systems and of the results are discussed.

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Carbon Footprint Management for a Sustainable Oil Palm Crop David Arturo Munar, Nidia Ramírez-Contreras, Yurany Rivera-Méndez, Jesús Alberto Garcia-Nuñez, and Hernán Mauricio Romero

Abstract Oil palm yields five to ten more oil per hectare per year than other oil crops. Less than 10% of the land planted with oil crops produces more than 35% of the oil consumed worldwide. Oil palm needs less land, pesticides, fertilizers, and energy; thus, it generates a lower impact on the environment. Oil palm has been criticized for its impact on GHG emissions and loss of carbon stocks in peat soils, especially in Malaysia and Indonesia. In Colombia, the crop’s expansion has occurred mainly in deforested lands, degraded soils, or land devoted to cattle. To better monitor, this crop’s environmental impacts, carbon footprint, and life cycle analyses have been conducted in several countries. Here, we summarize the results of those studies with particular reference to the Colombian case. Also, we present the comparison between different carbon footprint calculators used to measure oil palm GHG emissions. Finally, we discuss the use of carbon footprint estimations and their role in improving the crop’s sustainability. Keywords Greenhouse gas emissions · Carbon footprint calculator life cycle analysis

D. A. Munar · N. Ramírez-Contreras · J. A. Garcia-Nuñez Processing Research Program, Colombian Oil Palm Research Center—Cenipalma, Calle 98 # 70A-91, Bogotá, Colombia Y. Rivera-Méndez · H. M. Romero (B) Oil Palm Biology and Breeding Research Program, Colombian Oil Palm Research Center—Cenipalma, Calle 98 # 70A-91, Bogotá, Colombia e-mail: [email protected] H. M. Romero Department of Biology, Universidad Nacional de Colombia, Carrera 30 Calle 45, Bogotá, Colombia © The Author(s), under exclusive license to Springer Nature Switzerland AG 2022 J. Ren (ed.), Advances of Footprint Family for Sustainable Energy and Industrial Systems, Green Energy and Technology, https://doi.org/10.1007/978-3-030-76441-8_5

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1 Introduction 1.1 Oil Palm and Environment Oil palm is an important crop worldwide for oil production for human consumption and industrial use. Palm oil is the most used vegetable oil (76.0 million tons), with 32.9% of the world’s fats and oil production and a potential to reach 46% by 2050 [8]. Oil palm is characterized by large productivity (ten times more per unit of land than the next oil crop, soybean) and a long life span (≥25 years), for that is a commodity among oleaginous crops [24]. Compared to other oil crops (such as rape or soy), and despite environmental differences in production, oil palm is highly efficient in yield and agronomic behavior. Indeed, oil palm has the highest yields and is not as landintensive as other vegetable oils, growing only 10% of the allocated and suitable land around the world. It needs less land, pesticides, fertilizers, and energy to produce oil (Table 1). Thus oil palm generates a lower impact on the environment and contributes to the conservation and maintenance of biodiversity [18]. The world’s largest oil palm producers are Indonesia (58%), Malaysia (26%), Thailand (4%), and Colombia (2%). Thus, Colombia is currently the world’s fourthlargest producer of oil palm and the first largest producer in South and Central America. Oil palm represents for Colombia about 11% of the Gross Domestic Product. In 2019, 559,582 ha were planted with oil palm in Colombia in four zones (41% in the Eastern zone, 31% in the Central zone, 24% in the Northern zone, and 4% in the Southwestern zone). 73,577 hectares were under development from the total planted area, and 486,006 were in the productive stage; that is, 13% of the planted area was in unproductive age and 87% in the production phase [8]. Oil palm cultivation has been at the center of environmental controversy. In Malaysia and Indonesia, oil palm has generated a lousy image related to GHG emissions, loss of biodiversity, loss of carbon stocks in peat soils, and conflicts around the land property [26]. Contrary to Southeast Asia, the expansion of the crop in Colombia has occurred in lands that had another form of production system previously, and therefore, their environmental consequences are low [24]. Indeed, the expansion of cultivation in Colombia has occurred mainly on previously deforested lands, lands used for other crops, or lands devoted to cattle raising [28]. Since 1990, only 9% of oil palm expansion replaced woody vegetation (forest fragments and Table 1 Productive and input yields of oil palm compared to other oil crops Oil palm

Soy

Sunflower

Rape

Yield (t oil/ha)

3.82

0.30

0.48

0.79

Fertilizers (kg/t oil)

47

315

202

99

Pesticides (kg/t oil)

2

29

23

11

Energy (GJ/t oil)

0.3–0.5

2.9–8.3

6.3

0.7–3.2

Adapted from [6, 24]

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regenerating forests) instead of undisturbed primary forest at the country-scale [5]. Thus, 91% of oil palm expansion in Colombia has occurred on previously intervened lands (e.g., pastures, croplands, bananas), mainly herbaceous vegetation or primarily cattle pastures (not flooded savannas) for livestock (59%), short cycle crops (30%), and bananas (2%) [9]. The net effect of a process on climate change is determined by the balance between carbon uptake and sequestration from the atmosphere by photosynthesis and emission to the atmosphere of greenhouse gases, GHGs [23]. The carbon footprint (CF) constitutes one way to evaluate the capacity of a system to reduce (GHG) emissions [18]. The CF is based on the Life Cycle Analysis (LCA), which measures the environmental impact of a product, process, or system throughout its entire life cycle. CF allows identifying the stage where the impact is significant so that a strategy to improve environmental performance could be defined and implemented [3]. Oil palm cultivation has excellent potential to reduce GHG. In addition to CO2 , the main GHGs associated with oil palm cultivation and processing are methane (CH4 ) and nitrous oxide (N2 O) [16]. These gases have global warming potentials (GWPs) of 25 and 298, respectively, rendering them more effective per unit mass than CO2 (based on an assumed residence time of 100 years) in increasing mean surface air temperature [3]. Forest reserves and oil palm plantations are key sectors that contribute to the carbon balance, being, at least for tropical areas, the leading net carbon sinks, by absorbing carbon dioxide and fixing it in the form of biomass [14]. It is more relevant for the Colombian case, where about 52% of the territory (approximately 60,000,000 ha) are protected forests. The trend is towards the growth of oil palm planted areas; whose cultivation can eliminate CO2 from the atmosphere as a forest. Indeed, the oil palm is like a forest because of its perennial nature and its ecophysiological response at the ecosystem scale. Thus, under certain circumstances, the oil palm crop can exceed the levels of photosynthesis, oxygen production, biomass, and carbon accumulation of a rain forest [24]. The average annual GHG balance for oil palm cultivation in Colombia over the 50 or so years of its history has been positive, e.g., sequestration has exceeded emission. This positive balance contrasts with findings for Indonesia, which show large negative balances. However, there are similarities in terms of the relative impacts of individual components on the budgets. Thus, the top carbon sink (sequestration) was the oil palm itself (58% of total sequestration in Malaysia, 64% in Colombia), and the principal emission source was a land-use change (LUC) (62% in Malaysia, 41% in Colombia) [15]. The lower LUC in Colombia was the principal reason for the small positive GHG balance versus the large negative balances in Southeast Asia that arise from converting land with high carbon stocks [9, 28]. Greenhouse gas emissions from oil palm biodiesel produced in Colombia are reduced by 83% compared to its fossil equivalent and comply with the parameters established by the European Community and the Environmental Protection Agency of the USA [30]. The carbon footprint to produce oil palm fresh fruit bunches is favorable, and 606 kg of CO2 are fixed for each ton produced. This amount of fixed CO2 could be increased if the use of organic fertilizers is increased, renovations or

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new plantings occur on degraded or marginal lands (land with low carbon stock), biodiesel is used as a substitute for fossil fuels. The best agricultural practices that lead to higher yields are adopted [23].

1.2 Carbon Footprint in the Colombian Oil Palm Sector The carbon footprint technically is the algebraic sum of the GHG sequestration and emissions caused directly or indirectly by an individual, organization, or product for a defined period (all or part of the life cycle) or concerning a specified unit of product. In the case of oil palm, the unit can be a ton of fresh fruit bunch (FFB) or a ton of crude palm oil (CPO) [23]. Because the demand for food and bioenergy puts pressure on agricultural production, crops such as oil palm require sustainable production with low GHG emissions to supply food and energy ([12]; [22]. In Colombia, oil palm emissions have been evaluated by several studies. They have identified emissions related to LUC, use of fertilizers, diesel consumption, and methane emissions from the palm oil mill effluent (POME) as the highest contributions to total emissions of the production chain [11, 14, 22, 23, 29, 30]. • LUC related GHG emissions: Emissions associated with LUC could positively or negatively impact the oil palm CF of CPO. GHG emissions’ most significant impact is generated when a tropical forest becomes an oil palm plantation (1.1 to 5.3 kg CO2 eq /kg CPO). On the contrary, the lowest emissions are reported when the palm is sown in croplands, savannas, or shrublands (−0.3 to 0.5 kg CO2 eq /kg CPO) [4]. Compared to other oil palm producing countries, Colombia’s situation differs mainly in terms of LUC’s impact, as it was previously mentioned [9, 28]. In addition to the above, carbon stocks could occur in the oil palm crop cultivation [14] because carbon reserves have been reported in the range of −894 to −5,373 kg CO2 eq /t CPO according to data in Table 2. Despite the considerations taken for the LUC analysis, in a country like Colombia, greater precision is required in the use of detailed information on the conversion area because the landscape can change enormously over short distances (e.g., mountains, valleys, plains, wetlands, etc.) [14]. For this reason, the use of the soil suitability map (scale 1: 100,000) for oil palm cultivation would help to better estimate the impact related to LUC, mainly in the new plantations around the country. • Use of fertilizers: Chemical fertilization has been one of the main contributors to GHG emissions in oil palm plantations [22, 23]. Within the overall carbon balance, N2 O is the most relevant GHG during the crop stage. One of the activities that contributed most to GHG emissions is the use of chemical fertilizers. When N2 O is released directly into the atmosphere, it significantly impacts global GHG emissions due to its warming potential ([12]). Considering the range of fertilization emissions shown in Table 2, it is estimated that oil palm crops’ chemical fertilization should be improved to reduce its impact on total GHG emissions in the short-term.

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Table 2 The carbon footprint of the Colombian oil palm sector Conditions of the study area

Five biodiesel (BD) plants

11 scenarios

Hypothetical POM

A specific plantation

70% of the total palm oil production

Study sources

1

2

3

4

5

Unit

kg CO2 eq/t BD

kg CO2 eq/t BD

kg CO2 eq/t CPO

kg CO2 eq/t CPO

kg CO2 eq/t CPO

Carbon stock

−6,081.0

−894

−5,373.0

−3,388.0

−3,014.0

LUC emissions

34.4

343

49.1

74.9

537.6

Fertilization

450.5

61

224.2

351

860.5

Agrochemicals

5.3

–

6.6

–

6.3

POME (CH4 )

945.6

179

1,689.5

778.7

778.7

Compost production

–

–

–

–

–

Steam produced

332.4

–

879.8

–

–

Diesel consumption

468.6

255

79.6

79.6

114.7

Electricity used

56.6

–

60.8

–

14.7

Cogeneration

–

–

355.7

–

9.1

Refining-BDb

40.3

–

–

–

–

Remnants process

374.2

–

0.2

46.8

2.6

Total emissions

2,707.9

838

3,345.5

1,331.0

2,324.3

CF

−3,372.9

−56

−2,027.1

−2,057.3

−689.8

Adapted from [22] a Study sources: 1. [30], 2. [14], 3. [11], 4. [23], 5. [22] b Refining-BD process includes the refining-transesterification process inputs (methanol, sodium methylate, citric acid, hydrochloric acid, and spent bleaching earth) c Remnant processes contribute less than 1% to total GHG emissions

Furthermore, organic fertilization (e.g., compost or biomass) could reduce the consumption of chemical fertilizers as proposed in the study by [22], where each kilogram of compost replaced 0.1 kg of chemical fertilizer. Thus, to reduce the impact caused by chemical fertilization, the recycling of nutrients from oil palm biomass can be optimized without radically replacing chemical fertilization because oil palm requires high levels of available nutrients for its development [10]. It is also emphasized that the recycling of nutrients is one of the criteria to be met in the certification systems as a strategy to maintain the structure, organic matter content, and soil microbiological health in plantations. • Diesel consumption: Throughout the CPO production chain, the highest consumption of diesel fuel occurs in the oil palm cultivation due to the transport used in the different stages of the process (e.g., application of inputs, harvest, and transportation of FFB) [23]. In the palm oil mill (POM), the use of diesel

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occurs in the power generation, mainly in the mills that are not connected to the national grid, approximately 13% nationwide (although there is a recent increase in the use of energy from cogeneration and biogas) [22]. • Methane emissions from the POME: At the mill, a significant contribution to GHG emission has been identified. About 28 m3 of biogas /t FFB are produced in the biodigester (i.e., POME treatment system), and approximately 64% of the biogas composition is methane [11, 25]. When CH4 is released directly into the atmosphere, it significantly impacts global GHG emissions due to its warming potential ([12]). According to [22], about 70% of the Colombian POMs do not capture biogas. Thus, methane emissions are released directly into the atmosphere. While the remaining 30% of POMs do not report methane emissions from CPO production when POM’s biogas is captured and burned to generate electricity. In the current conditions of CPO production in Colombia, biogas capture is key to the potential emission reduction in the production chain, considering that about 35% of total emissions come from the POME treatment system [11, 25]. The CF can include several assumptions resulting in a diversity of emissions according to the study’s objective, as shown in Table 2. The CF of crude palm oil varies from −2,057.3 to −56 kg CO2 eq /t CPO, depending on the study area conditions evaluated. Even though many studies are based on assumptions, it is highlighted that the most recent review of GHG emissions of the Colombian oil palm industry involved primary data taken from 70% of oil palm fruit processed nationwide [22]. Considering that average CF, −689.8 kg CO2 eq /t CPO (Table 2) and the Colombian CPO production in 2019, 1,528,739 t [8], the carbon storage of the oil palm crops in Colombia was 1,054,524 t CO2 eq /year. On the other hand, a potential reduction of CO2 eq /t CPO could be obtained by improving the palm oil production chain processes. The improvements include reducing LUC related to the use of available land with low carbon stock; better agricultural practices focused on zero deforestation, agricultural landscape management, and increased crop yield. The reduction in chemical fertilization and diesel consumption and biogas capture (zero CH4 emissions) can contribute to the sustainable production of oil palm [6, 22, 23]. Regarding the production of Colombian biodiesel, palm oil biodiesel has 83% GHG reduction potential with respect to diesel fuel [30]. This biodiesel production performance is mainly associated with the efficient use of resources in the cultivation stage, low impact of LUC, and modern processing technology ([12]). On the other hand, GHG emissions in the mill can be reduced by using solid biomass to produce pellets, compost, electricity, bio-oils, and biochar [11]. For instance, compost production showed a reduction between 20 and 30% of the total emissions of CPO production when using fresh POME (before lagoons) and solid biomass, in addition to biogas capture [25].

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1.3 Carbon Footprint Calculators Carbon footprint calculators (CFC) are tools that allow the measurement of GHG emissions from production processes by quantifying the consumption and production of goods and services. A CFC facilitates identifying the supply chain that contributes to GHG emissions [1]. In this way, it contributes to identifying emission reduction opportunities and the mitigation of global warming to achieve low-carbon economies [19]. Thus, the use of CFC has been implemented to compare the balance of GHG emissions and removals from energy crops and other renewable energy sources with the equivalent fossil energy option [20]. Many countries’ goal is to increase the renewable energy sources within their energy matrixes, increasing the production of sustainable biomass to meet the needs of products such as electricity, heat, and fuels [7]. In this regard, the use of CFC contributes to achieving sustainability and finding cleaner energy sources. Thus, several certification systems applicable to the production and use of biomass have promoted the use of CFC for the balance estimation of GHG emissions and removals of both raw material and biobased products [21, 26]. Therefore, it is necessary to have a harmonized and representative CFC methodology that allows their use and application. In addition to facilitating the comparison of emissions regardless of the production chain being assessed [21]. In the oil palm sector, the use of certification systems for sustainable production is currently applied, namely Roundtable on Sustainable Palm Oil (RSPO) or International Sustainability and Carbon Certification (ISCC). Within these certification systems, GHG emissions have an essential role in communicating the sustainability of bio-based products, so RSPO and ISCC have developed their CFC. The RSPO has a CFC called PalmGHG®, while the ISCC created a CFC called BioGrace® [21]. Despite having these two CFCs, the Colombian oil palm sector has developed a CFC (Cenipalma CFC) for its oil palm production chain. Cenipalma CFC reports specific conditions in terms of land use and management practices of the crop and has been developed under ISO 14067, the IPCC guidelines, and national and international scientific literature.

2 Methods The comparison of the balances of GHG emissions and removals of the PalmGHG®, Biograce®, and the Cenipalma CFC was made. For this, an LCA inventory was carried out for a crop with an average FFB yield of 19 t FFB /ha/year and an annual fruit production of 120,000 t FFB. For the mill, a processing capacity of 70 t FFB/h was considered, with energy generation (electric and caloric), the use of biodigesters with methane burning, and the production of compost (Table 3). The changes in soil coverage made for establishing the crop (LUC) are found in Table 4.

100 Table 3 LCA inventory, critical parameters for FFB and CPO production

D. A. Munar et al. Parameter

Fact

Unit

Plantation size

6,315

ha

Yield

19

t FFB/ha/year

Fruit production

120,000

t FFB/year

8.61

kg/t FFB

P2 O5

11.20

kg/t FFB

K2 O

11.31

kg/t FFB

Compost

169.36

kg/t FFB

MgO

1.17

kg/t FFB

CaO

1.35

kg/t FFB

K2 O

4.31

kg/t FFB

P2 O5

1.30

kg/t FFB

N

1.84

kg/t FFB

Herbicide

0.10

kg/t FFB

Diesel

8.76

kg/t FFB

Processed fruit

4,523

kg/t CPO

Steam

2,668

kg/t CPO

Process water

3.03

m3 /t CPO

Oil mill capacity

70

t FFB/h

Biodigester

yes

n.a.

Methane use

Combustion only

n.a.

Biomass cogeneration

yes

n.a.

Compost production

yes

n.a.

Crude palm oil

1,000

kg/t CPO

Palm kernel oil

82.65

kg/t CPO

Synthetic fertilization N

Organic fertilization

Agrochemicals

Oil palm mill

Products

Palm kernel cake

128.92

kg/t CPO

Empty fruit bunches

995.01

kg/t CPO

Fiber

750.78

kg/t CPO

Oil palm shell

167.33

kg/t CPO

POME

4,568

kg/t CPO

In chemical oxygen demand

274.08

kg/t CPO

Out chemical oxygen demand

13.70

kg/t CPO (continued)

Carbon Footprint Management for a Sustainable Oil Palm Crop Table 3 (continued)

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Parameter

Fact

Unit

Power

137.70

kWh/t CPO

Power biomass cogeneration

99.03

kWh/t CPO

Power National grid

38.67

kWh/t CPO

Diesel

0.48

gal/t CPO

n.a. Not apply

Table 4 LUC for the establishment of oil palm

Previous coverage

% Conversion to oil palm

Grasslands

68.73

Scrubland

1.10

Primary forest

5.69

Transient crops

16.98

Oil palm renovation

4.30

Other areas without vegetation

3.00

3 Results The GHG emissions balance for the crop stage is shown in Fig. 1. The most important emission sources were fertilization and emissions from LUC. Concerning fertilization, nitrogen was a primordial element for developing the oil palm and the production of its fruits. When applied to the soil, one part is absorbed by the plant, and another 1000

500 kg CO2eq / t FFB

Crop sequestration Land use change

0

-58 -326

Diesel consumption -351

-500

N O emissions Chemical fertilisers Agrochemicals Carbon footprint

-1000

-1500 Cenipalma®

PalmGHG®

Biograce®

Fig. 1 Comparison of GHG balance results for the crop stage by each CFC

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part decomposes in the field, generating direct and indirect N2 O emissions [4]. GHG emissions from the use of fossil fuels were not significant because the LCA calculation was from the cradle to the oil palm mill’s door. For the FFB processing stage, the GHG generating activities were compost production, electricity consumption, fugitive methane emissions in the biodigesters, emissions from fossil fuels, and CH4 and N2 O from the burning of biomass for cogeneration. Compost production generates CH4 and N2 O emissions due to the degradable carbon and nitrogen content of the inputs [2, 17]. The generation of these gases depends on the aerobic conditions of the composting process (Fig. 2). GHG emissions can reach zero ensuring a total (a)

1000 726 458

500

kg CO2eq / t FFB

98 2

1

170 70

117 75 92

186 1

8 10

0 -58 -326

-500 Cenipalma®

-351

-496

PalmGHG® Biograce®

-1000

-1002 -1250

-1500 Agrochemicals

Chemical fertilisers

N O emissions

Diesel consumption

Land use change

Crop sequestration

Carbon footprint

(b)

kg CO2eq / t CPO

200 Fugitive methane emissions POME

150

Diesel Electricity-National grid

195

100

Compost production

50

8

Combined Heat and POWER (CHP)

40

0

15

8

8 15

Cenipalma®

PalmGHG®

Biograce®

*Treated POME irrigation was null

Fig. 2 Comparison of GHG emissions by activity and CFC: a crop stage; b processing stage

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aerobic condition; in the worst-case scenario (10% aerobic), emissions can reach 250 kg CO2eq/ t FFB [27]. The main differences between carbon footprints for the FFB by CFC (Cenipalma®, PalmGHG®, and Biograce®) were in fertilization, N2 O emissions, and LUC, and carbon storage (Fig. 2). Emissions from fertilization are different due to the emission factors used by each calculator. N2 O emissions were different due to the methodology used by each calculator. While Biograce® and Cenipalma® used the IPCC methodology, PalmGHG® used emission factors to calculate direct and indirect N2 O emissions. The differences in LUC emissions and carbon storage were given by the carbon stocks of the CFC and the growing periods. The carbon stocks for Biograce® and Cenipalma® were based on the European Renewable Energy Directive (RED). However, Cenipalma CFC could use other carbon stock values. For example, using the country’s coverage data given by the Colombian Institute of Hydrology, Meteorology and Environmental Studies (IDEAM). PalmGHG®’s stocks were lower than those used by Biograce® and Cenipalma® (Fig. 3). However, this calculator also allowed the entry of carbon stock. Regarding the cultivation period, this was 25 years for PalmGHG® and Cenipalma®, and 20 years for Biograce®. Cenipalma® and Biograce®’s CFC did not consider the fugitive emissions of methane generated in the biodigesters in the processing stage at the mill. NonCO2 emissions from burning biomass were not included by PalmGHG®, and GHG emissions from the composting process were only included by Cenipalma®’s CFC. Additionally, there were also differences in the CFC due to the carbon credits and bonuses, Cenipalma®’s calculator did not have any carbon credits or bonuses, while PalmGHG® and Biograce®’s CFCs did. PalmGHG® allowed for the inclusion of carbon credits associated with conservation areas within crops in the balance of 983

1000 PalmGHG®

900

Cenipalma® 774

Crabon stock (t CO2 / ha)

800 700 600

539 473

500 414

400 300

234

183

200

275

169

121

100 18

31

0 Grassland

Food crops/ Annual crops

Shrubland

Oil Palm

Tree crops

Fig. 3 Comparison of carbon stocks for PalmGHG and Cenipalma calculators

Undisterbd forest

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Table 5 Credits and carbon bonus for each CFC Crop

Cenipalma® PalmGHG®

Biograce®

None

Conservation Areas

• Establishment of cultivation in degraded soils • Emission saving from soil carbon accumulation via improved agriculture management • Emission saving from carbon capture and geological storage • Emission saving from carbon capture and replacement

• Sale of surplus electrical energy • Sale of surplus biomass

• Emission saving from excess electricity from cogeneration

Processing plant None

GHG emissions and removals. In the processing stage, it allowed for the inclusion of carbon credits from the sale of surplus electricity and biomass. Biograce® included carbon credits for the use of degraded soils for crop establishment, emission saving from soil carbon accumulation via improved agriculture management, emission saving from carbon capture and geological storage, and emission saving from carbon capture and replacement (Table 5). The emissions allocation methodology gave another significant difference, Cenipalma® used economic allocation, Biograce® energy allocation, and PalmGHG® allocated all emissions to the main product. It is necessary to have tools that harmonize the different methodologies for calculating the product’s CF. Ideally, these calculators should produce the same results when the same inventory data is used. Moreover, the GHG emissions calculations in the calculators are based on values from the IPCC Guidelines; these values are designed to estimate GHG emissions at the national level, not at the project or local level [13]. The estimates of GHG emissions under these calculations are far from reality and generate considerable uncertainty in the results. Ideally, methodologies should be available to measure the values in the field and then add them to the calculators with a harmonized methodology.

4 Discussion 4.1 Carbon Footprints for What? The CF of a product or service under the life cycle assessment allows identifying the critical points with the most significant environmental impact and establishing

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environmental, technological, economic, and social actions related to the monitoring and mitigation of GHGs [23]. The CF is an indicator of sustainability for the oil palm sector that identifies GHG emissions for the entire production chain and planning reduction strategies through best agro-industrial practices. As a result, the Colombian oil palm sector defined and prioritized the best agro-industrial practices that, in the short, medium, or long term, allow to effectively reduce GHG emissions in the oil palm industry. Implementing these practices guides and facilitates decision-making throughout the stages of sustainable CPO production, from planning, through design, establishment, operation, and maintenance of the crop, RFF processing, renewal, and replanting [16]. These most representative practices for the oil palm sector in Colombia with low carbon content are the followings: reduction of the impact of change in land use, use of permanent soil cover (legumes and nectarifers), efficient use of fertilizers and agrochemicals, conservation of soil quality, treatment of effluents from the mill and biogas capture, use of biomass and minimization of fossil fuel consumption [6]. • Reduction of the impact of change in land use – Avoid developing oil palm plantations in areas of environmental protection (natural parks and forests). – Identify and protect/conserve wetland systems, flooded savannas, gallery forests, areas defined for river rounds, and other environmental importance areas. – Identify suitable areas for the planting and development of the crop. – Plant oil palm in areas that meet the edaphoclimatic requirements and show the least environmental risk for a crop. – Quantify the water supply of the area where the oil palm will be planted. – Generate detailed information about the protection and conservation areas at the farm level. – Conserve or increase carbon stocks, e.g., forest and natural vegetation, by incorporating landscape management tools. • Use of permanent soil cover – Increase natural vegetation by planting nectarifers, quick-hedge, soil covers, forest fragments, etc. – Plant native species in the protection and conservation areas. – Plant and maintain an organic cover (legumes, nectarifers) permanently or semi-permanently to protect the soil. – Keep vegetable coverings and accompanying plants. • Efficient use of fertilizers and agrochemicals – Implement foliar and soil analyses to specify the nutritional requirements of the oil palms. – Formulate and implement a nutrition plan to optimize the use of fertilizers. – Use fertilizers that report a low CF from production to application.

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– Carry out integrated management of pests and diseases, trying to use biological control. – Apply fertilizers in the rhizosphere, preferably organic fertilizers (compost, biochar from oil palm biomass, pruning residues, inflorescences and cobs, ashes from the boilers). – Avoid applying chemical fertilizers or agrochemicals in river rounds. – Use and exploit soil microorganisms to improve the absorption and assimilation of nutrients. • Conservation of soil quality – Carry out a detailed study of the soils to plant following the technical specifications. – Formulate and implement a nutrition plan to optimize the use of fertilizers. – Plant and maintain an organic cover (legumes, nectarifers) permanently or semi-permanently to protect the soil. – Design and maintain irrigation and drainage canals. – Implement low-impact technologies for the construction of roads, infrastructure, irrigation, and drains canals. – Restrict traditional tillage practice. • Treatment of effluents from the mill and biogas capture – Design and implement the most effective treatment for POME. – Make compost from the biomass of the processing plant with the addition of effluents. – Capture biogas to reduce methane emissions and, if possible, use it to generate electricity. – Search alternatives to produce zero dumping. – Comply with environmental legislation for the disposal of discharges. – Implement a risk management plan for the management of discharges. • Use of biomass – Prepare and carry out an annual plan for the disposal of biomass in the plantation. – Make compost with POME and incorporate it into the crop, considering the chemical characterization and nutrition plans. – Avoid applying by-products generated in the mill on areas of High Conservation Value. – Avoid open burning of biomass. – Use kernel shell for maintenance of internal roads in plantations. – Use the ash from the boiler as a replacement for cement, concrete, and additives. – Produce and use biochar from oil palm biomass for crop application. – Evaluate the use of empty fruit bunches, fiber, kernel shell, and POME to obtain alternative products with high added value. – Optimize the amount of biomass used in the boilers to produce steam and electrical energy and minimize heat losses.

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– Minimize the use of fossil fuels. – Sell surplus electricity from cogeneration and the use of biogas. – Operate steam generation systems to reduce atmospheric emissions. • Minimization of fossil fuel consumption – Design and implement the harvesting, lift, and transport methods of FFB according to the terrain and logistics conditions. – Carry out mass and energy balances to reduce consumption and increase resources’ efficiency (energy and fuels). – Minimize the use of fossil fuels. – Use adapted vehicles to FFB transporting to the collection points and the mill. – Carry out maintenance of internal roads and the fleet. – Take advantage of excess water/steam, wind, electricity. – Set energy and environmental performance indicators. – Implement energy management and renewable energy generation systems. – Identify the stages of the process with inefficiencies in the use of industrial services.

5 The Colombian Case The present and future developments of oil palm in Colombia are unique and differentiated. They have been managed to reduce the negative impacts on various environmental components, especially on climate change. Further, the Colombian oil palm sector has devoted a great deal of its work towards the consolidation of a sustainable agribusiness, recognizing sustainability as a priority for ensuring this industry’s competitiveness and viability over time. The oil palm sector has deployed projects and activities that promote good environmental practices for the development of oil palm cultivation in harmonious coexistence with natural environments, without causing deforestation, using natural resources efficiently, and preventing and mitigating environmental impacts. Consequently, oil palm in Colombia is a sustainable crop with strategies to manage carbon footprints such as the increase of planted area and the production certified by international standards such as Rainforest Alliance Certified (RAC), Roundtable on Sustainable Palm Oil (RSPO), or the International Sustainability and Carbon Certification (ISCC); adherence to the Zero-Deforestation Agreement for the palm oil value chain and the production of CPO; adoption of technologies for reducing GHG emissions and generating renewable energy from by-products of palm oil extraction; and the integral use of solid biomass by the composting technique. By the end of 2019, there were 26 sustainable certified companies in Colombia, equivalent to 342,002 tons of sustainable certified CPO. That is, 22.4% of national production, or 115,409 sustainable-certified hectares, which represent 20.6% of the national total. Certifications promote the production of sustainable oil palm under certain principles and criteria, leading to improving the environmental performance

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of products, i.e., oil palm has lower GHG emissions than the non-certified in Malaysia Indonesia [26]. The prevention and control of oil palm-related deforestation is a priority for the Colombian oil palm sector. Since the end of 2017, The National Federation of Colombian Oil Palm Growers (FEDEPALMA) and a growing number of companies in the oil palm agroindustry have joined the zero-deforestation Agreement for the Colombian palm oil value chain. It is a public-private project that covers all the links in this value chain and seeks to eradicate the deforestation footprint in the Colombian oil palm agroindustry, making visible the palm oil production that is free of deforestation in this country. Around 36% of CPO domestic production is represented in the Agreement [8]. The Colombian palm oil agroindustry has enormous potential for generating renewable energy from the use of the by-products of palm oil extraction and significantly reducing their GHG emissions. It involves covering anaerobic lagoons from biodigester (i.e., POME treatment system) to capture methane and prevent this gas from reaching the atmosphere and the subsequent generation of electric power and GHG certified emission reductions sales in the carbon market. Using methane for power generation, palm oil mills may supply 100% of their power demand, thus selling energy surpluses to the national electrical interconnection network or nearby populations, making the palm oil sector a great contributor to supply the demand for electricity in non-interconnected zones. At present, there are seven palm oil mills with covered lagoons for methane capture (10% of total mills at the national level). Five of them meet their demand for energy from biogas, and two more also sell electricity to the external network [8]. Composting is a good alternative for using by-products -also known as solid biomass- (empty FFB, fiber, shell) resulting from palm oil extraction, creating added value for these inputs, and closing matter-energy cycles in the oil palm sector. By the end of 2019, 23 of the 69 palm oil mills (33% of total mills at the national level) had established composting plants [8]. These commitments, coupled with the definition of best agricultural and agroindustrial practices and development of its carbon footprint calculator, support the sustainability of the Colombian oil palm sector, especially that related to a favorable carbon footprint throughout the CPO production chain.

6 Conclusion Sustainable palm oil production is a commitment of the industry worldwide. Certification schemes such as the RSPO give the framework for the production, commercialization of oil consumption that is friendly to the people and the planet. Life cycle assessments and water and carbon footprint estimation are tools to monitor and improve the sustainability of the crop. Worldwide the palm oil industry is improving to better meet the consumer’s demands in terms of quality, price, and sustainability. The Colombian oil palm industry is part of that commitment, with the advantage

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of low production of greenhouse gas emissions given by the plantings with zero deforestation, on marginal lands, and with low ILUC. Thus, the oil palm is called to supply the world’s oil needs, and it is doing it with sustainable criteria.

References 1. Adewale C, Reganold J, Higgins S, Evans R, Carpenter-Boggs L (2018) Improving carbon footprinting of agricultural systems: boundaries, tiers, and organic farming. Environ Impact Assess Rev 71:41–48. https://doi.org/10.1016/j.eiar.2018.04.004 2. Brown S, Kruger C, Subler S (2008) Greenhouse gas balance for composting operations. J Environ Qual 37(4):1396–1410. https://doi.org/10.2134/jeq2007.0453 3. Calvo E, Tanabe K, Kranjc A, Baasansuren J, Fukuda M et al (2019) Refinement to the 2006 IPCC Guidelines for national greenhouse gas inventories, vol 2. IPCC. https://doi.org/10.9784-88788-232-4 4. Castanheira E, Acevedo H, Freire F (2013) Greenhouse gas intensity of palm oil from Colombia: implications of land use change and nitrogen fertilization. Appl Energy 114:1 5. Castiblanco C, Etter A, Aide TM (2013) Oil palm plantations in Colombia: a model of future expansion. Environ Sci Policy 27:172–183. https://doi.org/10.1016/j.envsci.2013.01.003 6. Chaparro-Triana D, Ramírez-Contreras N, Munar-Florez D, García-Nunez J, Cammaert C, Rincón-Bermúdez S (2020) Guía de mejores prácticas bajas en carbono asociadas a la producción de aceite de palma sostenible en Colombia 7. Dunn J, Han J, Seabra J, Wang M (2017) In: Khanna D, Zilberman M (ed) Biofuel life-cycle analysis, vol II. Springer International Publishing. https://doi.org/10.1007/978-1-4939-6906-7 8. Fedepalma (2020) Anuario estadístico 2020: Principales cifras de la agroindustria de la agroindustria de la palma de aceite en Colombia y en el mundo 2015–2019 9. Furumo P, Mitchell T (2017) Characterizing commercial oil palm expansion in Latin America: Land use change and trade. Environ Res Lett 12(2): https://doi.org/10.1088/1748-9326/aa5892 10. Galindo T, Romero HM (2012) Compostaje de subproductos de la agroindustria de palma de aceite en Colombia: estado del arte y perspectivas de investigación (Boletín Té) 11. García-Nunez JA, Rodríguez D, Fontanilla C, Ramírez N, Silva E, Frear C et al (2016) Evaluation of alternatives for the evolution of palm oil mills into biorefineries. Biomass Bioenerg 95:310–329. https://doi.org/10.1016/j.biombioe.2016.05.020 12. Gauch M (EMPA) (2013). Life cycle analysis of colombian biodiesel. Revista Palmas 34(II):331–344 13. Hennecke A, Faist M, Reinhardt J, Junquera V, Neeft J, Fehrenbach H (2013) Biofuel greenhouse gas calculations under the European renewable energy directive—a comparison of the BioGrace tool vs. the tool of the roundtable on sustainable biofuels. Appl Energy 102:55–62. https://doi.org/10.1016/j.apenergy.2012.04.020 14. Henson IE, Ruiz R, Romero HM (2012a) The greenhouse gas balance of the oil palm industry in Colombia: a preliminary analysis. I. Carbon sequestration and carbon offsets. Agronomía Colombiana 30(3):359–369 15. Henson IE, Ruiz R, Romero HM (2012b) The greenhouse gas balance of the oil palm industry in Colombia: a preliminary analysis. II. Greenhouse gas emissions and the carbon budget. Agronomía Colombiana 30(3): 370–378 16. Ivanova Y, Tristán M, Romero M, Charry A, Lema S, Choy J et al (2020) Hacia una cadena de palma aceitera, que contribuya a la conservación de bosques y reducción de gases de efecto invernadero en la región Ucayali [Centro Internacional de Agricultura Tropical (CIAT) (ed.)] 17. Levis J, Morton B (2013) Composting process model documentation. North Carolina State University (ed). http://www4.ncsu.edu/~jwlevis/AD.pdf 18. Meijaar E, Brooks T, Carlson K, Slade E, Garcia-Ulloa J et al (2020) The environmental impacts of palm oil in context. Nature Plants 6:1418–1426. https://doi.org/10.31223/OSF.IO/E69BZ

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19. Mulrow J, Machaj K, Deanes J, Derrible S (2019) The state of carbon footprint calculators: an evaluation of calculator design and user interaction features. Sustain Prod Consump 18:33–40. https://doi.org/10.1016/j.spc.2018.12.001 20. Peter C, Helming K, Nendel C (2017) Do greenhouse gas emission calculations from energy crop cultivation reflect actual agricultural management practices? A review of carbon footprint calculators. Renew Sustain Energy Rev 67:461–476. https://doi.org/10.1016/j.rser.2016.09.059 21. Ramírez-Contreras N, Faaij A (2018) A review of key international biomass and bioenergy sustainability frameworks and certification systems and their application and implications in Colombia. Renew Sustain Energy Rev 96(March):460–478. https://doi.org/10.1016/j.rser. 2018.08.001 22. Ramírez-Contreras N, Munar D, García-Nunez J, Mosquera-Montoya M, Faaij A (2020) The GHG emissions and economic performance of the Colombian palm oil sector; current status and long-term perspectives. J Clean Prod 258:1–19. https://doi.org/10.1016/j.jclepro.2020.120757 23. Rivera-Méndez Y, Rodríguez T, Romero HM (2017) Carbon footprint of the production of oil palm (Elaeis guineensis) fresh fruit bunches in Colombia. J Clean Prod 149:743–750. https:// doi.org/10.1016/j.jclepro.2017.02.149 24. Rivera-Mendez YD, Romero HM (2018) Los mitos ambientales de la palma de aceite. Revista Palmas 39(4):58–68 25. Rodríguez D, Ramírez N, García J (2015) Evaluación de la incidencia de la producción de compost, usando biomasa de la planta de beneficio, en la huella de carbono del aceite de palma. Estudio de caso. Revista Palmas 36(1):27–39 26. Schmidt J, De Rosa M (2020) Certified palm oil reduces greenhouse gas emissions compared to non-certified. J Clean Prod 277: https://doi.org/10.1016/j.jclepro.2020.124045 27. Stichnothe H, Schuchardt F (2011) Life cycle assessment of two palm oil production systems. Biomass Bioenerg 35(9):3976–3984. https://doi.org/10.1016/j.biombioe.2011.06.001 28. Vijay V, Pimm S, Jenkins C, Smith S (2016) The impacts of oil palm on recent deforestation and biodiversity loss. PLoS One 11(7):11–19. https://doi.org/10.5061/dryad.2v77j 29. Yañez E, Silva E, da Costa R, Torres E (2009) The energy balance in the Palm OilDerived Methyl Ester (PME) life cycle for the cases in Brazil and Colombia. Renew Energy 34(12):2905–2913. https://doi.org/10.1016/j.renene.2009.05.007 30. Yañez E, Martínez L, Gualdrón M (2011) Estimación de las emisiones de gases de efecto invernadero en la producción de biodiesel a partir de aceite de palma utilizando como herramienta el análisis de ciclo de vida (ACV). In: Informe

Understanding of Regional Trade and Virtual Water Flows: The Case Study of Arid Inland River Basin in Northwestern China Aihua Long, Xiaoya Deng, and Jiawen Yu

Abstract Living with the increasingly severe water stress has currently become a crucial concern in the arid inland river basin in northwestern China. Despite water scarcity, water consumption in the basin has been on the rise, due to improvement in the standards of living and a rapid growth of the basin population over the past few decades. We present the first analysis of virtual water flows across all economic sectors within arid inland river basin in northwestern China, the area with the geopolitical importance of China’s Belt and Road Initiative, and with domestic importance as a major agricultural producer and trade power. Results show that the arid inland river basin in northwestern is an absolute net exporter (gross exports greater than gross imports). Approximately, 72.3% of water consumption in the basin is for exported commodities, with the biggest export flows of virtual water being associated with agricultural production. The traded volumes of virtual water have been increasing progressively over the years. It is important to note that the basin produces and exports water-intensive products but imports water non-intensive commodities as the basin in northwestern China where the water scarcity is a problem and the environment is negatively affected. This opens the domain question of whether environmental damage in the arid basin caused by water consumption is worth the socioeconomic benefits. We highlight the major role of economic scale in increasing virtual water changes in the basin over the time period of around 10 years. Demand for water use in agriculture will continue to increase as a result of growing population and economic growth. Environmental demands for water will also vie for scarce water supplies in A. Long · X. Deng (B) · J. Yu State Key Laboratory of Simulation and Regulation of Water Cycle in River Basin, Department of Water Resources, China Institute of Water Resources and Hydropower Research (IWHR), Beijing, People’s Republic of China e-mail: [email protected] A. Long e-mail: [email protected] J. Yu e-mail: [email protected] J. Yu College of Water and Architectural Engineering, Shihezi Univeristy, Shihezi, Xinjiang, People’s Republic of China © The Author(s), under exclusive license to Springer Nature Switzerland AG 2022 J. Ren (ed.), Advances of Footprint Family for Sustainable Energy and Industrial Systems, Green Energy and Technology, https://doi.org/10.1007/978-3-030-76441-8_6

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the future. A better detailed understanding of regional trade and virtual water flows within arid inland river basin in northwestern China can in turn help decision-making processes when trying to promote appropriate policy measures, reflecting local water scarcities, water prices and ecological health concerns. Keywords Regional virtual water · Water transfer · Networks · Sustainability · Arid inland river basin of northwestern China

1 Introduction Within the arid inland river basin of northwestern China, freshwater resources have become scarcer with increasing anthropogenic pressure and climate change [53]. Over-withdrawal of surface water and groundwater in the basin has led to depletion of water resources and serious ecosystem degradation. The severe related environmental crises in the basin include the complete dry-up of downstream Heihe River Basin West and East Juyanhai lakes in 1960s [19] and the cut off by zero-flow events in the lower reaches of the Tarim River in the 1970s [14]. Previous studies [10, 24, 57] have shown that the arid inland river basin in northwestern China is facing chronic water scarcity due to its extreme environmental conditions. Competing water demands from urban areas, industry, ecosystems, agriculture, and other sectors in the basin have created a need for critical information related to sustainable water use and management. Water scarcity in arid regions can be addressed by using the concept of “virtual water” in advancing our understanding of real water resources management [50]. In the last two decades, however, the virtual water trade has got quite some attention. It may provide a solution to the spatial and temporal mismatches between water supply and demand, especially for water-deficient countries and regions, e.g., Israel, India, South Africa, Spain, and the Nile Basin [1, 16, 39, 54]. Knowing the actual national virtual water balance is essential for developing a rational national policy with respect to virtual water trade. This has been recently recognized by more countries [21, 27, 33, 38]. In 2017, China has first time incorporated virtual water strategy into the nation water strategy in its “13th Five-Year Special Plan on Scientific and Technological Innovation in Resources Areas”, which have suggested transferring virtual water to alleviate the regional water stresses. While, in some scientific literatures, it showed that international or domestic virtual water trade does not follow the spatial pattern of freshwater resources availability [17, 18, 49, 60]. In terms of China, in many already water-scarce regions or provinces, use their already scarce water to produce and export water-intensive products for other regions or provinces where are rich in water resources. However, this phenomenon would undoubtedly have exacerbating scarcity in water-stressed regions in China which are already suffering from water and environmental problems.

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The basin is one of the major irrigated agricultural regions in China. Rainfall is critical for agriculture; it supplies approximately 80% of the water used for agricultural production worldwide [35]. Agriculture in northwest China relies on annual precipitation of 50–500 mm, 70% of which occurs from July to September, and annual evaporation from 1500 to 2600 mm [7]. Hence, the uneven spatiotemporal distribution of rainfall in the basin, however, is scarce. Most of basin’s water for agricultural production comes from irrigation (blue water). Approximately, 94.3% of the water withdrawn from the environment in the basin is used in agriculture, 5.1% by secondary industry, and 0.6% by tertiary industry (National Bureau of Statistics of China [34]). The measurement of virtual water flow and water footprints in the basin has been regarded as a tool to aid water policy development combining with both economic and environmental policy. However, few studies have analyzed internal virtual water flows dynamics between northwestern China arid regions and provinces. Such knowledge is relevant for specific subregions in northwestern China, such as Heihe River Basin [59], Tarim River basin [31], northern and southern Xinjiang, [28, 40] and Zhangye City [47]. We have only a fragmented understanding of virtual water quantification of the arid inland river basin in northwestern China. Knowledge on virtual water flow and trade and the development of appropriate agricultural policies incorporating the economic and environmental policies are essential for addressing the arid regions water crisis in China. The case study in this chapter covers the entire arid inland river basin in northwestern China, the present-day (2002–2012), the latest years for which multi-regional economic IO tables could be obtained for the regions. The point of departure of the case study in this chapter is to assess the bilateral water trade and show the value of analyzing water flows between the basin and the other Chinese provinces using the multi-regional input–output techniques. In seeking to understand the virtual water flows and driving forces of the historical changes in the arid inland river basin in northwestern China. The following sections analyzed the virtual water trade of the basin in depth. Sections 2 and 3 describe the geographic, hydro-climatic, and water resources endowments within the arid inland river basin in northwestern China. Section 4 introduces water IO and IO-SDA models. Section 5 presents the findings of the case study, with subsequent analysis, and our main conclusion remarks are addressed in Sect. 6.

2 Geographic and Hydro-Climatic Located at the Eurasian hinterland, the territory of the arid inland river basin in northwestern China draining areas of over 2,160,000 km2 corresponds to 22.5% of the total area of China (Fig. 1). In its entirety, the basin comprises 22 districts, in which their territories partially belong to Xinjiang, Gansu, Inner Mongolia, and Qinghai. The location of the basin is the key for the geopolitical importance of China’s Belt and Road Initiative (BRI).

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Fig. 1 Geographic location of the arid inland river basin in northwestern China

The geophysical diversity of the arid inland river basin in northwestern China encompasses great mountain ranges, but also steppes and arid landscapes, which can be divide the basin into three geographic regions: the mountainous interior, including Tianshan, Altyn, Altay, Kunlun, and Qilian Mountains; the lowland areas, including Turpan Basin, Tarim River Basin, and Qaidam Basin; and Hexi Corridor, whose inland delta reaches into Inner Mongolia. The contiguous arid inland river basin in China has been broadly divided into three climatic zones. The mountain area in the arid inland river basin has semi-humid climate with a mean annual rainfall (MAR) range of 400–800 mm. Inner Mongolia and Hexi Corridor have semi-arid climate, with a MAR range of 200–400 mm. For the lowland areas, Turpan, Tarim River Basin and Qaidam Basin, the MAR is below 200 mm, where is consistent with arid climate (Fig. 2).

3 Socioeconomic Characteristics and Water Resources Endowments The basin in 2018, with an estimated gross domestic product (GDP) of 1419 billion CNY, was the 1.5% of the total economy in China. The economic activity of the basin is very diversified. According to the composition of GDP, the whole economy is divided into three industries: the primary, secondary, and tertiary industries [30].

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Fig. 2 Spatial distribution of precipitation

The primary industry includes one sector—“Agriculture, Aquaculture, Livestock.” Secondary industry is disaggregated into four sectors—“Mining”, “Manufacturing”, “Electric Power, Gas and Water” and “Construction.” The second level of disaggregation within the secondary industry sectors is further divided into 23 sub-sectors. Tertiary industry includes four sectors—“Post and Telecommunication Services,” “Catering Services,” “Wholesale and Retail Trade,” and “Public and other services.” The economy in the basin is dominated by tertiary industry with a 44.5% share of GDP in 2018, against 40.6% for secondary industry, and 14.9% for primary industry. While Xinjiang has made remarkable progress human development in recent times, its GDP in 2018 amounted to an estimated 1098 billion CNY, with the largest economy within the arid inland river basin in northwestern China. Economic growth rate of eight districts from northern Tianshan in Xinjiang is the highest among those districts in the other provinces (Fig. 3). Apart from its significant geopolitical location for BRI, the arid inland river basin sustains the northwestern China’s four major river basins—Northern Tianshan Mountain-Junggar Basin, Tarim River Basin, Hexi Corridor Inland River Basin, and Qinghai Lake-Qaidam basin (Fig. 4). Three rivers as international rivers shared between China and Kazakhstan—Irtysh River, IIi River, and Emin River—are not involved in this case study. The major river basins have been subdivided into 24 subregions, delineating in Table 1, located on four provinces in China ranging from semi-humid to arid. The mean annual rainfall throughout the basin is estimated at 286.1 billion m3 . In seasonal and annual scales, highest amount of precipitation occurs in summer, normally in the mountain areas [56]. About 91.5% of the water resource is from surface water, while only 8.5% is from groundwater [2]. The arid inland river basin

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Fig. 3 GDP of the arid inland river basin in northwestern China. Source China Statistical Yearbook, 2002–2018

Fig. 4 Watersheds of the arid inland river basin in northwestern China

in northwestern China, so far, supports the importance of animal husbandry and irrigated crop agriculture in China [22]. Table 2 gives the current level of water resource utilization in arid inland river basin in northwestern China. Agriculture in all four major river basins demands vast amounts of water (Fig. 5). Irrigated agriculture in all sub-basins demands vast amount of water and accounts for 94.3% of all water withdrawals, compared to 5.1% for secondary industry and 0.6% for tertiary industry (China Water Resources Bulletin [11]). The Tarim River Basin ranks the first in

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Table 1 Subregions in the arid inland river basin of northwestern China Name Northern Tianshan Mountain–Junggar Basin

Tarim River Basin

Hexi Corridor Inland River Basin

Qinghai Lake-Qaidam Basin

No.

Subregions

Province

1

Bayi Basin

2

Hami Basin

Xinjiang (Northern Tianshan)

3

Turpan Basin

4

Gurbantunggut Desert

5

The East of Northern Tianshan Mountain

6

The Middle of Northern Tianshan Mountain

7

Ebinur Lake

8

Khotan River Basin

9

Yarkant River Basin

10

Kashgar River Basin

11

Aksu River Basin

12

Weigan River Basin

13

Kaidu-Kongque River Basin

14

Keriya River Basin

15

Qarqan River Basin

16

Tarim River

17

Taklimakan Desert

18

Kumuta Desert

19

Shiyang River Basin

20

Shule River Basin

21

Heihe River Basin

22

Qinghai Lake

23

Eastern Qaidam Basin

24

Western Qaidam Basin

Xinjiang (Southern Tianshan)

Gansu, Inner Mongolia

Qinghai

terms of total water consumption in the basin (refer to Table 2). It is followed by the other three basins in the arid inland river basin of Northwestern China–Northern Tianshan Mountain–Junggar Basin, Hexi Corridor Inland River Basin, and Qinghai Lake–Qaidam Basin.

4 Water IO and IO-SDA Analysis Over the past few years, input–output analysis (IO) has been applied to analyze regional and national water consumption and pollution issues [8, 18, 23, 61]. The

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Table 2 Water resource utilization (108 m3 ) in the arid inland river basin of northwestern China Year

Sectors

Northern Tianshan Mountain-Junggar Basin

Tarim River Basin

Hexi Corridor Inland River Basin

Qinghai Lake-Qaidam Basin

Total

2007

Primary

106.85

288.31

71.93

10.17

477.26

6.03

3.20

4.28

3.21

16.71

Secondary

2012

Tertiary

3.78

2.91

2.40

0.47

9.56

Subtotal

116.66

294.42

78.61

13.84

503.53

Primary

180.18

274.95

69.80

13.87

538.81

11.72

7.88

8.02

1.51

29.14

Tertiary

0.75

0.56

1.70

0.30

3.32

Subtotal

192.65

283.39

79.53

15.69

571.26

Secondary

Fig. 5 Sector and basin shares of water consumption (in percent) in the arid inland river basin in China. Source China Statistical Yearbook, 2012

appeal of the input–output (IO) methodology is that it directly addresses the interaction between water usage and economic activity, allowing us to trace the flow of “virtual water” through entire supply chains [38]. The extended water IO table in this chapter to show the volume of water consumed in production processes within the basin is taken from Deng et al. [13]. The IO tables used for the case study identify 28 sectors (Table 3), more aggregated than the 42 sectors used for the official IO tables produced by the National Bureau of Statistics of China [34]. This choice of sectoral classification is based on alignment with the resolution of the water data. The water data of the agricultural and industrial sectors for the basin are taken from China Water Resources Bulletin (Ministry of Water Resources of P. R. China, 2002–2012).

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Table 3 IO aggregation scheme for arid inland river basin in China Economic sector

Economic sector

S1

Irrigated agriculture, aquaculture, livestock

S15

Metal products

S2

Coal mining and processing

S16

Machinery and equipment

S3

Petroleum and natural gas

S17

Transport equipment

S4

Metal on mining

S18

Electric equipment

S5

Non-ferrous mineral mining

S19

Telecommunication equipment

S6

Food and tobacco processing

S20

Instrument manufacturing

S7

Textile goods

S21

Other manufacturing

S8

Wearing

S22

Electricity production and supply

S9

Sawmills and furniture

S23

Gas and water production and supply

S10

Paper and products

S24

Construction industry

S11

Petroleum processing

S25

Post and telecommunication services

S12

Chemicals

S26

Catering services

S13

Non-metal mineral products

S27

Wholesale and retail trade

S14

Metal smelting and products

S28

Public and other services

The structural decomposition analysis (SDA) is based on input-output coefficients and final demands from input-output tables. It is capable of more refined decompositions of changes linking sectoral economic activity, explaining the observed variation, e.g., indirect demand, production structure, and final demand [20, 51]. Since the 1990s, SDA initially focused on energy studies [9], but has been extended to explain the reasons of the water use changes in the region and nations [29, 42, 43, 55]. Here, SDA is applied to quantify the driving forces of changes in regional virtual water flow within the arid inland river basin in northwestern China.

5 Virtual Water Flows and Trade in Arid Inland River Basins of Northwestern China The arid inland river basin in northwestern China is an increasingly water challenged area and is a major agricultural trader in China. Virtual water trade or trade in waterembedded agricultural commodities has been promoted as a useful tool to alleviate national and regional water paucity [12]. Policy-makers and water managers cannot identify the best choices and smart strategies focusing just on its water abundances or shortages [48]. Understanding the direction and quantum of virtual water trade within the basin would has instrumental value for water policy and strategic trade decisions and the achievement of good water status.

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5.1 Direct and Indirect Water Use Intensity Sectoral direct water use coefficient is an indicator that reveals sectoral water use intensity. Economic sectors with low direct water use coefficient are generally a high level of water use efficiency and vice versa [5]. The primary industry (S1) in the basin is the largest input of direct water per unit output, followed by the tertiary industry (S25–S28) and secondary industry (S2–S24). At the sectoral level in 2002 (Fig. 6), S1 (irrigated agriculture, aquaculture, livestock) is the high direct water-intensive sector, followed by S21 (other manufacturing), S2 (coal mining and processing), S28 (public and other services), and S20 (instrument manufacturing). There is a different trend in the ranking of similar sectors of the direct water use coefficient in 2012, in the following five economic sectors: S1 (irrigated agriculture, aquaculture, livestock), S23 (gas and water production and supply), S19 (telecommunication equipment), S20 (instrument manufacturing), and S2 (coal mining and processing). This change in sectors ranking can be explained by the sectoral variation in water use efficiency. While, even S1 (irrigated agriculture, aquaculture, livestock) still exhibits high direct water use coefficient in 2012. Its total water use coefficient, however, showed a trend of continuous decline, related to the decline in both direct and indirect water use coefficients. From Table 4, we observed an increasing trend of total water use coefficient in S23 (gas and water production and supply) in 2002–2012. It can be influenced significantly by direct water use and the use of water-abundant products of the primary industry [44]. By the fact that over 96% of the economic sectors in the basin, these sectors’ production activities use water indirectly when compared to the corresponding direct water coefficients. Only S1 is associated with a lot of water-intensive direct water uses. Since we have defined the direct and the total water use coefficient, we can easily find an indirect water use. Indeed, the proportion of indirect water intensity to

Fig. 6 Direct water use coefficient for the arid inland river basin in northwestern China in 2002/2012

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Table 4 Direct, total water use coefficient (m3 per CNY 10,000) for the arid inland river basin in northwestern China in 2002 and 2012 Economic sector

2002 Direct (D)

Total (T)

(T-D)/T (%)

Direct (D)

2012 Total (T)

(T-D)/T (%)

S1

5850.43

9343.42

37.4

2389.54

3275.33

27.0

S2

98.63

359.85

72.6

20.30

129.36

84.3

S3

83.23

219.92

62.2

17.05

72.78

76.6

S4

73.93

479.29

84.6

15.60

161.82

90.4

S5

75.61

420.73

82.0

6.52

146.58

95.6

S6

24.13

4607.44

99.5

12.31

1238.55

99.0

S7

33.45

2429.82

98.6

7.44

1252.49

99.4

S8

28.25

3307.78

99.1

13.88

970.91

98.6

S9

42.27

2083.59

98.0

10.79

607.57

98.2

S10

63.76

1427.94

95.5

13.72

262.07

94.8

S11

24.97

326.03

92.3

5.56

118.25

95.3

S12

42.25

811.31

94.8

11.26

429.27

97.4

S13

54.35

505.98

89.3

10.38

171.41

93.9

S14

56.35

427.40

86.8

11.50

172.53

93.3

S15

41.71

471.45

91.2

8.62

189.11

95.4

S16

57.47

496.79

88.4

12.71

183.52

93.1

S17

38.54

712.25

94.6

8.25

143.94

94.3

S18

42.21

441.31

90.4

6.55

156.37

95.8

S19

37.07

502.63

92.6

45.57

157.01

71.0

S20

91.42

731.71

87.5

25.12

163.84

84.7

S21

99.22

1388.87

92.9

13.74

427.37

96.8

S22

80.02

321.76

75.1

24.70

146.53

83.1

S23

61.38

343.61

82.1

1755.53

2407.73

27.1

S24

46.57

579.95

92.0

10.76

172.56

93.8

S25

75.84

616.37

87.7

4.82

328.33

98.5

S26

74.94

1974.08

96.2

5.49

626.72

99.1

S27

68.07

423.28

83.9

7.18

93.81

92.3

S28

93.60

352.92

73.5

6.35

118.10

94.6

total water intensity ((T-D)/T) in the basin is 37.4% in S1, but it is more than 80% in the most of the other 27 sectors. Typical examples of sectors with high indirect water use are S7 (textile goods), S26 (catering services), and S4 (metal on mining). Overall, it is worth noting that the total water use coefficient in the most economic sectors of the basin exhibited a decreasing trend, while indirect water intensity is slightly increased. The water use efficiency of the basin agriculture and business has improved in response to the regional water scarcity [45].

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5.2 Domestic Import and Export Trade Overall, the basin was an absolute net exporter (gross exports greater than gross imports). A large amount of virtual water transfers was a result of domestic trade (with the remaining provinces in China), that is also an indicator of the important contribution made by water resources from the basin to the rest of the Chinese economy. In 2012, the basin exported 91% of its total 41,392 Mm3 virtual water net export volume to other provinces in China, and 9% to the rest of the world through staple commodities. Large proportions of virtual water exports from the arid inland river basin were dominated by blue water, which was exported to eastern water-abundant areas in China. Table 5 presents the ranking of the domestic trade between the basin and the rest of provinces that import and export by water source. The total net export virtual water of the basin to the rest of China was 37,726 Mm3 , 20 times as much as the total net import virtual water of the basin from the rest of China (1846 Mm3 ). Guangdong imports the most virtual water from the basin (i.e., 4183 Mm3 ), driving its jurisdiction to be ranked first in terms of virtual water trad volume. The two big eastern economies provinces in China, Shandong and Shanghai, rely mostly (~19.4%) on virtual water within their jurisdictions from the arid inland river basin (Fig. 7). It is evident that those jurisdictions, Beijing, Shanghai and Tianjin, have a more limited range of agricultural output, hence are more reliant on the production of food from the other provinces [18, 46]. The virtual water transfers among different regions can help alleviate the stress the regions face from scarce local water resources [4]. In the past few years, it has been argued that international trade of agro-production from wet countries to arid and semiarid countries, which is a promising strategy for relieving local water stress [39]. The findings of the trade pattern in the arid inland river basin in northwestern China are apparently inconsistent with the original hypothesis. It produces and exports water-intensive products but import water nonintensive commodities as the basin in northwestern China where the water scarcity is a problem and the environment is negatively affected. If production decisions were only based on water endowment situation in the basin, it would be preferable for the basin to import good from the other water-abundant regions, particularly in the water-intensive sectors. It is worth noting that Xinjiang becoming the net exporters of virtual water is predominately driven by the market, not totally determined by water resources. The production factors like labor, capital, and land may also influence the virtual water trade direction [37]. This finding calls for attention on the impact interregional virtual water transfer on local water stress which requires a synergy of decision-making for both economic and environmental policies [58].

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Table 5 Ranking of the arid inland river basin in northwestern China that exchange the most virtual water in 2012 Rank

Province

Destinations

Province

Origin

1

Guangdong

4183

Heilongjiang

288

2

Shandong

3120

Guangdong

209

3

Shanxi

2402

Liaoning

147

4

Shanghai

2185

Guangxi

127

5

Guangxi

2115

Hunan

96

6

Heilongjiang

2092

Jiangxi

88

7

Henan

1984

Henan

87

8

Hubei

1655

Anhui

87

9

Beijing

1643

Shanxi

72

10

Yunnan

1641

Jiangsu

65

11

Sichuan

1584

Yunnan

63

12

Jiangsu

1548

Sichuan

61

13

Liaoning

1509

Shannxi

57

14

Hebei

1459

Hebei

48

15

Chongqing

1399

Fujian

42

16

Jiangxi

1263

Guizhou

36

17

Tianjin

1011

Chongqing

36

18

Fujian

845

Hainan

35

19

Guizhou

820

Ningxia

33

20

Shannxi

753

Hubei

31

21

Jilin

571

Zhejiang

25

22

Hunan

540

Shanghai

22

23

Anhui

529

Shandong

19

24

Hainan

392

Tibet

25

Zhejiang

221

Jilin

26

Ningxia

197

Tianjin

19

27

Tibet

65

Beijing

46

5 1

Note that volume data are provided in Mm3 . Source Authors’ calculations

5.3 Sectoral Depencies Virtual water flow between different economic sectors is triggered by trade between various regions in China and abroad, including both direct and indirect water consumption for producing the exports. Virtual water transfers in 2012 from water embedded in goods and services traded between the arid inland river basin in northwestern China and the remaining provinces in China or with the international community in each economic sector for the basin are clearly shown by assessment results

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Fig. 7 Virtual water flow between the arid inland river basin in northwestern China and the other Chinese provinces in 2012

in Table 6. Through the transfer of water embedded in traded goods and services, the basin was a net virtual water exporter for domestic trade in most of economic sectors. Only the following six economic sub-sectors were net virtual water importer: S4 (Metal on mining), S16 (Machinery and equipment), S18 (Electric equipment), S19 (Telecommunication equipment), S24 (Construction industry), and S28 (Public and other services). Economic sectors S1 (irrigated agriculture, aquaculture, livestock) and S23 (gas and water production and supply) are exporting large volumes of virtual water to other provinces in China and to the rest of the world, especially in economic sector S1. The volume of water embodied in S1 trade is approximately 83.2% that of domestic virtual water trade (=31,410/37,730). The trade of virtual water that is embodied in S1 could estimate to go to a higher level depending on the assumptions of water requirements, driven by domestic and international demand for agricultural products. The virtual water flows in S1 trade can be explained by the variables considered by economic agents at the time trade decisions are made, linking directly to price and product characteristics rather than on water used for production. The magnitude and direction of the virtual water trade embodied in agricultural products could also influenced by the complex interactions between technology, policy, investments, environment, and human behavior [36]. We graph the virtual water flows between different economic sectors within the arid inland river basin in northwestern China in Fig. 8. This illustrates that the agriculture sector (S1) is the biggest direct water consumer and also is the major contributor to the embodied water of basin exports. Half of that agricultural water use being for products that subsequently undergo some further processing by several sub-sectors in secondary industry and tertiary industry, e.g., food and tobacco processing (S6)-22.3%, wearing (S8)-10.1%, chemicals (S12)- 9.9%, construction industry (S24)-9.5%, textile goods (S7)-7.9%, public and other services (S28)-5.9%, etc.

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Fig. 8 Embodied water flows among sectors through the basin economy for the year 2012. Source Authors’ calculations

5.4 Driving Forces of Changes in Virtual Water Among Sectors We decompose changes in virtual water in the basin between 2002 and 2012 into “technological,” “effectiveness,” “scale,” and “structural” effects (Fig. 9). The change in economic scale serves to increase virtual water over this period and accounts for

Fig. 9 Relative contribution of each factor to the total change in virtual water within the arid inland river basin in northwestern China, 2002–2012. Source Authors’ calculations

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58.67% of the total absolute effects of the four determinants, increasing the gross virtual water in the basin by 18,067 Mm3 in 2012. In contrast, the technological effect has a positive forcing on decreasing virtual water, with the 25.46% of the total absolute effects of all the determinants. However, it is not sufficiently, to the offset, the increase from the economic scale effect over the period of our analysis. We see that the economic structural and effectiveness effects served to increase the gross virtual water only over the time series 2007–2012. Between 2007 and 2012, these two effects actually served to increase gross virtual water in the basin northwestern China. A graphical representation of the all factors by 28 economic sectors within the basin is included in Fig. 10 over the time series, 2002–2012. In comparison with other factors, we see the significant contribution of the economic scale effect over this period in all sectors, showed over 55% of the total absolute effects to increase gross virtual water specifically in nine sectors, e.g., S11 (petroleum processing), S5 (Non-ferrous mineral mining), S24 (construction industry), S3 (petroleum and natural gas), etc. These nine sub-sectors, however, both belong to the secondary industry. That would have been consistent with strategy of industrial restructuring in northwestern China that helps promote the local economy. However, we can be expected that the economic scale will still be the primary contributor to drive the virtual water up with the urbanization, giving more pressures on domestic water resources. Given the importance of the technological effect to offset the increase from the economic scale effect, recent years are a testimony to astonishing improvement of the technology, an increasingly important factor in decreasing actual water use and virtual water [3]. All sectors had improved (reduced) its sectoral intensity of water use sufficiently through technological improvement, particular within some sub-sectors in secondary industry, S3 (petroleum and natural gas), S11 (petroleum processing), S5 (non-ferrous mineral mining), S15 (metal products) and S13 (non-metal mineral products), etc. The basin is the net water export region, technological upgrades mainly

Fig. 10 Relative contribution of each factor to the total change in virtual water within the arid inland river basin in northwestern China by economic sectors over the time series, 2002–2012 Source Authors’ calculations

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improved the efficiency and productivity of blue water utilization in the agricultural sector [52]. Improving water use efficiency in the agricultural sector is key to mitigating water scarcity [32]. The Chinese government strongly supports the irrigation technology progress and has already implemented many water-saving irrigation measures nationwide. Water-saving practices are progressively implemented within arid regions in northwestern China since 2010. Moreover, new technology of industry water use will be good for reducing water use and energy consumption. In terms of the economic effectiveness and structure effects, which in earlier years, it had a negative forcing on decreasing gross virtual water in the basin, and in later years had more positive forcing. Because these changes are strongly correlated with the changes in lifestyles and marketing systems. With the rises of living standards over time, residents’ demand pattern changes which is characterized by altering food demand patterns and secondary products. The basin sectoral economic structure has been adjusted toward to the tertiary industry.

5.5 Changes Over Time Water is a fundamental pillar of the basin’s economy. Blue water inputs for irrigation in the basin are responsible for large actual water use. Cotton has the lion’s share of agricultural production within arid inland river basin in northwestern China, as its high-quality cotton is in great demand in China [15], especially in Xinjiang. The large quantity of water used to irrigate cotton fields certainly plays a big part in increasing the per capita water consumption within the basin. It is clear that virtual water export volume from the basin in 2012 is very substantial, equivalent to approximate 72.3% of total actual water use (Tables 6 and 7). In Xinjiang, the majority (95%) virtual water exports to the other regions are from final products forming by the Agriculture and Food/Tobacco Processing sectors [25]. Thus, through virtual water transfers, the arid inland river basin is using its local water resources to support production of goods and services that are consumed in other provinces or regions in China and the rest of the world. The existing pattern of virtual water trade in the basin is exacerbating the acute physical and economic water scarce. Clearly, better water endowments do not lead to higher virtual water exports [41]. The investigation in 146 countries across the globe for virtual water trade pattern demonstrated that no relation exists between water availability and the volume of virtual water trade [26]. Case study in China recorded that several water scarcity provinces are the high net virtual water exports. Xinjiang and Heilongjiang are the two major virtual water exporters in China [6]. The virtual water trade of commodities between the basin and the other provinces depends on a lot more factors related to economic development in various sectors, with the nationwide economic development strategies governed by the Chinese government such as the “Western Development” (2001), “Revitalization of the Northeast China” (2004), and “Rise of Central China” (2003). These national strategies had no doubt influenced the virtual water flows as well as regional water use.

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Table 6 Virtual water transfer at the sectors for arid inland river basin in northwestern China in 2012 Economic Sector

Foreign trade

Domestic trade

Imports

Exports

Imports

Exports

Virtual water net exports

S1

359

5705

1295

31,410

35,461

S2

16

5

2

27

15

S3

76

8

4

225

152

S4

47

4

0

27

−15

S5

8

1

1

10

1

S6

8

9

42

45

4

S7

3

19

1

5

19

S8

2

62

16

9

52

S9

3

6

2

2

3

S10

6

9

2

8

9

S11

12

5

3

44

34

S12

22

19

9

76

65

S13

9

8

1

11

9

S14

27

25

3

122

117

S15

5

5

2

6

4

S16

15

9

54

32

−28

S17

7

10

42

57

19

S18

6

3

27

26

−4

S19

7

7

35

42

−13

S20

3

2

2

4

1

S21

22

5

1

24

5

S22

17

17

0

63

63

S23

3

184

41

5346

5385

S24

17

10

74

32

−50

S25

12

5

2

16

7

S26

2

1

1

4

1

S27

17

10

7

22

8

S28

49

12

17

50

−4

782

6164

1717

37,726

41,392

Total all sectors

Note that volume data are provided in Mm3 . Source Authors’ calculations

The water consumption associated with the provision of goods and services has decreased by 7% for imports. In contrast, the increment in water consumption by exports (73%) has been much greater and decreased by 4% for local expenditures and capital formation (Table 8). Also interestingly, changes in local expenditures and capital formation water use have been relatively stable, while total water consumption

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Table 7 Sector actual water use for the arid inland river basin in northwestern China in 2002/2012 Economic sector

2002

2012

Economic sector

2002

2012

S1

36,571

53,881

S15

14

12

S2

45

134

S16

22

22

S3

227

315

S17

9

5

S4

14

57

S18

13

23

S5

7

18

S19

2

10

S6

64

126

S20

3

1

S7

21

14

S21

13

8 261

S8

2

5

S22

138

S9

06

4

S23

22

996

S10

16

13

S24

307

326

S11

66

112

S25

216

53

S12

75

144

S26

71

15

S13

56

61

S27

194

57

S14

85

248

S28

780

206

Note that volume data are provided in Mm3 . Source Authors’ calculations

Table 8 Changing virtual water for arid inland river basin in northwestern China, between 2002 and 2012, expressed as a fraction of the equivalent 2002 values 2002

2007

2012

Imports

1.00

0.39

0.93

Exports

1.00

1.42

1.73

Direct water use

1.00

0.94

0.96

Total water use

1.00

1.29

1.46

and virtual water exports increased across the time slices. Keeping the above in mind, it is also important to shed light on adverse impact of increasing virtual water exports. Virtual water exports can bring social and economical benefits, but also impair aquatic ecosystems. Our current water policy is unstable and unsustainable when facing water crisis in the arid regions. Based on this, the future virtual water trade in the basin needs to formulate appropriate water and trade strategies according to its water resource availability that are in line with the goal of its sustainable water management.

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6 Concluding Remarks The arid inland river basin in northwestern China plays an important role in meeting the need of goods and services in China, an arid region with chronic water scarcity due to extreme environmental conditions. To our knowledge, this is the first research from a qualitative point of view to get more accurate estimates of the virtual water “flows” within the arid inland river basin in northwestern China, as the basin is a major agricultural producer, consumer, and economic power in China. The overall water use growth in the basin is dominated by the increase in export virtual water in waterintensive goods and services. While the existing pattern of virtual water trade are not dictated by water resources endowments of the basin, it is predominately driven by the market. For the years studied, the basin, being a net exporter of agricultural products, exports pressure on its water resources to supply the exports to the other Chinese provinces and the rest of the world. The basin used up more than 72.3% of its actual water resources for producing exports to other regions, associated with the trade of water intensive commodities such as agricultural crops, processed food, textiles, and chemical products. The progressively increasing virtual water exports can bring social and economic benefits for the basin, but also endanger the local ecological balance. While in headline, there is a significant reduction in measure of water use intensity over the time period within the arid inland river basin in northwestern China, and we see that almost all sectors see an improvement in water use intensity. Additionally, we seek to understand the changes in the factors that influence virtual water flow over a 10-year time frame within the basin. The importance of the economic scale, economic structural, economic system effectiveness, and technological effects was confirmed. Changes in economic scale strongly increased the basin’s virtual water. By contrast, technological improvement, economic system effectiveness, and economic structural have a positive forcing on decreasing virtual water. However, it is not sufficiently, to the offset, the increase from the economic scale effect. The trade of virtual water that is embodied in agricultural products could estimate to go to a higher level depending on the assumptions of water requirements for domestic and international demand of agricultural products. This would deplete freshwater stocks and environmental flows and consequently endanger aquatic ecosystem. Competition between economic sectors requires appropriate strategies to manage water resources in a sustainable way. The future virtual water trade in the basin should integrate water security, agricultural policy, economic policy, and environmental policy into water policy and trade strategies. It will be critical in pursuing the sustainable development of the arid inland river basin in northwestern China. Acknowledgements This chapter is partially based on the results of case study completed under the support by Ministry of Science and Technology of the People’s Republic of China through the core funds of 2016YFA0601602 and 2017YFC0404300. Many thanks go to Professor Zhong MA from Northwest Normal University for in-depth discussions, and Shoujuan SU, Yang HAI, Jing LIU, Lili ZHANG for data collection and technical support. The authors would like to express their

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deep and sincere thanks to all those, who helped to undertake and review the research reported in this chapter.

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45. Wang YY, Long AH, Xiang LY et al (2020) The verification of Jevon’ sparadox of agricultural water conservation in Tianshan district of China based on water footprint. Agr Water Manage 139: 46. Wang ZY, Huang K, Yang SS et al (2013) An input-output approach to evaluate the water footprint and virtual water trade of Beijing, China. J Clean Prod 42:172–179 47. Wang Y, Xiao HL, Lu MF (2009) Analysis of water consumption using a regional input– output model: model development and application to Zhangye City. Northwestern China. J Arid Environ 73(10):894–900 48. Wichelns D (2015) Virtual water and water footprints do not provide helpful insight regarding international trade or water scarcity. Ecol Indic 52:277–283 49. Yang H, Zehnder A (2001) China’s regional water scarcity and implications for grain supply and trade. Environ Plann A 33(1):79–95 50. Yang H, Zehnder A (2007) “Virtual water”: An unfolding concept in integrated water resources management. Water Resour Res 43:W12301. https://doi.org/10.1029/2007WR006048 51. Yang ZW, Liu HL, Xu XY et al (2016) Applying the water footprint and dynamic structural decomposition analysis on the growing water use in China during 1997–2007. Ecol Indic 60:634–643 52. Ye Q, Li Y, Zhang W et al (2019) Influential factors on water footprint: A focus on wheat production and consumption in virtual water import and export regions. Ecol Indic 102:309–315 53. Zang CF, Liu J, Velde M et al (2012) Assessment of spatial and temporal patterns of green and blue water flows under natural conditions in inland river basins in Northwest China. Hydrol Earth Syst Sci 16:2859–2870 54. Zeitoun M, Allan JT, Mohieldeen Y (2010) Virtual water ‘flows’ of the Nile Basin, 1998–2004: A first approximation and implications for water security. Glob Environ Change 20(2):229–242 55. Zhang C, Anadon LD (2014) A multi-regional input–output analysis of domestic virtual water trade and provincial water footprint in China. Ecol Indic 100:159–172 56. Zhang J, Zhou JL, Zhang GQ et al (2020) Climate-and human-driven variations in lake area and number in North Xinjiang. China. Int J Remote Sens 42(2):469–485 57. Zhang Q, Xu H, Li Y et al (2012) Oasis evolution and water resource utilization of a typical area in the inland river basin of an arid area: a case study of the Manas River valley. Environ Earth Sci 66(2):683–692 58. Zhang SD, Taiebat M, Liu Y et al (2019) Regional water footprints and interregional virtual water transfers in China. J Clean Prod 228:1401–1412 59. Zhang Y, Zhou Q, Wu F (2017) Virtual Water Flows at the County Level in the Heihe River Basin, China. Water 9(9):687 60. Zhao X, Liu J, Liu Q et al (2015) Physical and virtual water transfers for regional water stress alleviation in China. P Natl Acad Sci 112(4):1031–1035 61. Zhao X, Yang H, Yang ZF et al (2010) Applying the input-output method to account for water footprint and virtual water trade in the Haihe River basin in China. Environ Sci Technol 44:9150–9156

Water Footprint of the Life Cycle of Buildings: Case Study in Andalusia, Spain Cristina Rivero-Camacho and Madelyn Marrero

Abstract The building sector is one of the major consumers of water resources, according to the United Nations Environmental Program, buildings and their associated industry consume 30% of the fresh water available worldwide. Optimizing this resource usage is a key factor and makes it necessary to analyze it with environmental and economic indicators, so that the magnitude of the impact can be qualified and quantified, and covering all the building life cycle. The analysis includes the first stage, the project conception, follows with the assessment of raw materials and its manufacture, continues with the use and maintenance, and finalizes with the demolition of the building. The water consumed in all those processes or Virtual Water (VW) can be the key to the reduction of the built environment impact. Because the total water consumption of a building includes not only the water that has been required off-site to manufacture the materials used, as well as the water embodied in the production of energy, also the direct water used in the building needs to be studied. This together can be considered the building water footprint (WF). A methodology based on the quantity surveying of the building project which includes materials and machinery is used for the inventory. The WF quantification is treated similarly to a project budget. A case study of a residential building in Huelva, Spain is evaluated. The most impacting stage is the use followed by the construction, being other stages less significant. Keywords Water footprint · Building budget · Construction · Building life cycle · Resources consumption

1 Introduction Building, and other factors directly or indirectly involved in the construction of the city, have significant environmental impacts in terms of consumption of natural resources and energy or greenhouse gas emissions, hence the need to consider the C. Rivero-Camacho · M. Marrero (B) Department of Architectural Constructions II. School of Building Engineering, University of Seville, Avenida Reina Mercedes no 4, 41012 Seville, Spain e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Switzerland AG 2022 J. Ren (ed.), Advances of Footprint Family for Sustainable Energy and Industrial Systems, Green Energy and Technology, https://doi.org/10.1007/978-3-030-76441-8_7

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environmental dimension as key in a sustainable construction approach. More specifically, the building sector is one of the great consumers of available water resources. According to the United Nations Environmental Programme [83] buildings and their associated industry consume 30% of the world’s available fresh water. Water management has become a global priority in recent years due to its scarcity and water imbalances in some countries. The European Sustainable Development Strategy [19] states that water management is one of the main short- and mediumterm challenges. In this situation, the most advanced technologies for reducing water consumption, treatment, and recycling will become more important. Ensuring the supply of drinking water and sewerage for all by 2030 is one of the six targets directly related to water in the SDGs. For their part, in December 2015, at the UN Climate Change Conference in Paris, leaders around the world recognized the fundamental role that water will play in a world under global warming. Water security has been included in most countries’ climate change response plans and has been part of numerous multilateral discussions and agreements between participating countries [25]. A large part of the effort in buildings to reduce water consumption and be more efficient focuses only on direct water consumption through more efficient systems, devices, and appliances, and on better wastewater treatment and recycling. But direct consumption accounts for only 12% of total water demand [26]. Another large part of the consumption is done indirectly in the production processes of materials, products, and equipment, which is often referred to as indirect water consumption or virtual water (AV). The concept of Virtual Water (VW) was formulated by Allan [4] as the indicator of fresh water that is consumed directly and indirectly to produce goods and services in cubic meters per year, (m3 /year) [86]. Although the detractors that this concept has [84, 6], it has been developed since its original definition and has shown useful for better water management associated with buildings. However, few building studies use this indicator. Among them stand out some Australian studies in the service sector that highlight the consumption of VW during the construction stage compared to the rest of the Building Life Cycle(BLC) [52]. Bardham [5] analyses water consumption in the construction of homes in India, identifying its importance. Crawfrod and Pullen [13] also analyzed water in the life cycle of residential buildings over a 50year period and concluded that VW in building materials is greater than direct home consumption, so water policies should also include virtual consumption reduction. Férriz Papí [21], conducted a study on the water used by building materials throughout their life cycle. This author obtained statistical results during a 3-year period of 200 projects in Catalonia. The environmental impact caused by water consumption of the manufacturing process of building materials is relevant but often forgotten. Building materials consume water both, in their extraction and manufacturing process, as well as in its utilization processes and in its waste treatment. Also, water is part of a natural cycle, and it is also important the quality it is returned to nature. In order to evaluate water consumption from a more complete perspective. The term water footprint (WF) was defined by Hoekstra and Hung [31], which is the total volume of fresh water

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used to produce the goods and services consumed by an individual or community [32]. Although most research to date, focus on the quantification of VW, The International Organization for Standardization (ISO [39] has approved the first international standard on the Water Footprint, ISO 14046, which establishes the principles, requirements, and guidelines for the evaluation of the WF of products, processes, and organizations, from the life cycle analysis, LCA, point of view. To do this, it establishes three types of WF, namely blue, green, and gray. See Fig. 1. Blue HH is the volume of fresh water consumed in a productive process from the planet’s water resources, i.e. surface and groundwater. That water can be incorporated into a product or it can be evaporated in the process itself. In a simple way, it is the consumption of water provided by supplying companies. Green water is the volume of water from precipitation which is stored in the soil in form of moisture, and does not become runoff, and is incorporated into a production process. That water can be incorporated into a product or it can be evaporated in the process itself. It is all water that is related to the natural water cycle. The grey water footprint is the volume of contaminated water that is associated with the production of goods and services. It can be quantified as the volume of water required to dilute contaminants to the point where water quality is above acceptable standards, taking as reference environmental quality standards, associating established limits with good quality for the environment and humans. The WF of buildings can be analyzed from a global perspective [11] through an input-output analysis of total consumption in the country or models that analyze components in construction projects [53]. It is also worth noting the research of Fig. 1 WF footprint types Source Own elaboration

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the ARDITEC group, where they incorporate the WF indicator [66, 68, 69] in the environmental assessment of construction projects. They show that it is an interesting indicator in the sector thanks to the simplicity of its message and easy inclusion in the economic control of the projects. With all the above and to improve the environmental performance of buildings, it is necessary to analyze them so that the magnitude of the impacts can be qualified and quantified throughout the life cycle of the building (BLC), from the extraction of raw materials, for the manufacture of materials, to the demolition of the building, see Fig. 2. However, the huge possible combinations in the building designs, together with the great casuistic within the duration of the BLC, make it difficult to analyze it [80]. To measure the building’s interaction with the environment and identify load balancing between all stages of a building’s service period, life cycle analyses (LCA) are recommended [20, 81, 82]. LCA is regulated by ISO 14040 (International Standards Organisation) international standards. Environmental Management [38] and ISO 14044 (International Standards Organisation. Environmental Management, [38] taking into account all flows exchanged between the product/system analyzed and the environment. LCA provides an overview of the environmental performance of the object studied and helps to support circularity between different product systems. It has been widely applied in the construction sector and is increasingly used as an advocate of decision-making at all levels of the built environment: material [41, 87], systems [29, 42], entire buildings [8, 85] and neighborhoods [70, 76]. The application of LCA methodologies to the construction sector is complicated and varies because existing standards have not been able to establish a clear methodology and, therefore, researchers use their own interpretations of these standards [9]. This work is part of the research carried out in the ARDITEC group, which are aimed at calculating, the total impact of projects on the BLC. It will take the model previously developed by the authors among others, in Spain (Solis-Guzman, Marrero, and Ramirez-De-Arellano [73]. The calculation models for the different stages of the BLC have been defined: urbanization [47], construction [27, 28, 24], use and maintenance [50, 67], and rehabilitation or demolition [3]. The methodology is capable of determining the ecological or carbon footprint of the elements that are part of the traditional construction cost bases, focusing it from a new perspective of “environmental budget”, using the tools in place for cost control of the budgets of building projects.

Fig. 2 Building Life Cycles (BLC) general diagram for WF calculation. Adapted from Rivero et al. [63]

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This research responds to the full evaluation of the BLC through the adaptation of the model for the application of the WF environmental indicator. It is presented with a methodology based on the building project quantities, to which environmental coefficients will be applied [62]. With this idea of environmental budget (where materials and machinery are contemplated) in the design phase of the building project, “cheaper” environmental solutions can be identified and included and, consequently, reduce the environmental impact on the BLC. Once the limits of the system and the duration of the stages are established, it is applied to an actual project of a residential building in Andalusia. The analysis stands on the budgetary structure of the construction project in such a way as to enable a double evaluation, economic and environmental. This facilitates its understanding and application by the professionals of the sector. Being a structure used in the economic control of projects, that also allows their transfer to other agents involved in the construction projects.

2 Objective and Methodology The main objective of this research is to define a simple and replicable procedure to assess the water consumed during the stages of the BLC. The life cycle starts with the transformation from rustic to urbanized land, continuing with the construction of buildings and its use, and the final stage, its demolition. The application of the WF indicator to all building processes will provide interesting criteria for the analysis of the sustainability of projects. These objectives will be carried out according to the methodological diagram in Fig. 3. The materials are formed by the definition

Fig. 3 Methodological diagram for BLC evaluation. Adapted from Rivero et al. [63]

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of the elements that form each stage of the life cycle, based on the norms for the construction sector, the construction cost data bases, and LCA data available. All those elements are then combined and put together in the creation of an economic and environmental database. Finally, the model is applied to an actual case study located in Huelva, Spain.

2.1 Materials The methodological map starts with the data collection. To this end, the following subdivisions shall be considered: (a)

Duration of the BLC stages

This section defines the time limits of the system by determining when the BLC begins and ends and the boundaries between the phases so that there are no duplications in the quantification of different impact sources. In this sense, in the case of two multi-family buildings, the cycle begins with the transformation of the rural land for its construction and ends with the completion of the demolition works. The start time of the use and maintenance stage is established after the completion of the construction of the building and the delivery to the occupants. This, being in availability to be occupied, begins the longest-running stage of its life cycle, which is usually estimated between 40 and 100 years. Defining when its lifespan ends is not simple. For example [2] divides the BLC into three phases, subdivided in turn into manufacture of the materials, their transport to the construction site, the construction of the building, the occupation of the building with an intermediate period dedicated to the renovation, to end the demolition, and waste or recycling. This staged division is quite widespread, with slight variations from one study to another, and limits the phase of use and maintenance to energy consumption and renovation or renovation works. CEN/TC 350 “Sustainability of construction work”, UNE-EN 15804 [10], recommends the consideration of not three, but four stages in the life cycle of buildings, separating the construction from the manufacture of products [8], so that a first phase of production would be held, which includes: extraction of raw materials, transport by factory, and manufacturing; a second phase that defines construction: transport and construction or installation processes onsite; the third that focuses on the use including, maintenance, repair and replacement of products, reforms, operational energy (heating, air conditioning, ventilation, hot water, and lighting), and operational water consumption; and the fourth and final, the end-of-life phase which includes deconstruction, transport, recycling or reuse, and disposal. These models have in common that the end of life of buildings consists in their demolition, but what if instead of being demolished, it is rebuild? If the route of the renovation is chosen, the situation appears as to whether the post-renovated building could be considered the same building that has been extended its useful life or, on the contrary, it can be considered a new building which its life cycle begins.

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In response to the questions raised, it is assumed for the present work that the end of life of the building occurs when it ceases to exist, that is, after its demolition. From the point of view of the renovations proposed, they are considered part of the maintenance, prolonging its service life in a moderate way. With this in mind, every time the building is renovated, its energy efficiency will be improved. This will be motivated by the changes, both in their constructive solutions and in their installations, with the consequent change in consumption patterns. UNE-15459-1- Energy efficiency of buildings. Economic evaluation procedure of the energy systems of buildings. BLC evaluation method. Inputs and outputs for the calculation of the energy efficiency of the building [81]; and the UNE-156861-Material life [37] is employed to establish the time when building renovations will take place. This is combined with the Technical Building Code [65], which establishes on the basis of Eurocode EN-1990, that residential buildings must be designed for a duration of 50 years or if for public use the life expectancy is risen to 100 years. The last is employed in the study, considering an increase in the service life of the building due to a correct maintenance and periodic renovations. It is estimated that the renovations will take place in the years 20, 40, and 70, and adapting to those the duration of the different installations and components of the building. Since the installations are the most frequently renovated elements, they will be intervened in each renovation. At 40 years old, the replacement of the doors and windows and relevant repairs to the enclosure takes place. And again at the 70 years old, where together with the above elements, structural repairs will be included and replacing the sewerage system. With all the above in this section, and the durability considerations of the materials presented in Larralde’s research [43]. In summary, the BLC begins with the urbanization and construction works of the building, which would cover all the environmental impacts associated with the extraction, manufacture, transport, and commissioning of the materials. Once the building is occupied, the 100-year-long stage of use begins. This stage will be subdivided into four periods of use due to the different renovation works proposed at 20, 40, and 70 years old. Finally, demolition takes place when the building is 100 years old when it is estimated that the building will no longer be inhabited. (b)

System limits

Once the time limits are established, the cross-cutting limits are defined, which will form the separation between our system and other sectors of production, such as the furniture industry, household appliances, waste treatment plants, etc. This will be critical to avoid double quantifications and overlaps between cycles. For the definition of limits, the ISO 14040 standard [38] on LCA and the UNE-EN 15978 [78] (Sustainability of construction work, Assessment of the environmental performance of buildings, Calculation method) shall be considered. The impacts produced during the manufacture of furniture, appliances, decoration, and other objects belonging to the occupants of the building are assigned to the corresponding industrial sector, not the building. Similarly, the impacts generated by the occupants, such as food consumption, mobility, and municipal solid waste,

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are their personal impact [51]. Regarding wastewater and waste generated by other consumables (coal, biomass, etc.), it is considered part of the impact caused by the occupants, not the building itself. In short, the limits of the system are the resources consumed by the building, namely: the building materials and their transport, the machinery used, in addition to the power and water consumed during the use stage. (c)

Systematic classification of construction works

The selection of the correct systematic classification of the building works will be essential to establish a robust and reliable structure for the incorporation of environmental data and to enable the unification of criteria for all life cycle stages. The model is based on the work classification system (WCS) employed in quantity surveying, stand out among others: MasterFormat [14], Uniformat (UniFormatTM), The Construction Specifications Institute [82], Standard Method of Measurement of Civil Engineering [75], CI/SfB [40] and the Uniclass [59]. Then each stage of the life cycle is defined using the same structure, and data and process automation can be used as management tools [23]. All these work classifications are proposed as good tools for the economic assessment or budgeting and also as an integrative element since their system of decomposition and hierarchy makes it possible to introduce a standardized process. The classifications divide a complex problem into simpler parts that can then be added, without overlaps or repetitions, to define the complete development of projects. In Spain the construction cost databases have their own classification systems and their scope of implementation is regional, i.e., Catalonia [34, 58], Madrid [54], Basque Country [16], Valencia [56], and Andalusia [1, 45]. The model used in this work is the Andalusia Construction Cost Database (ACCD) [45], of widespread use for the estimation of costs in construction and developed by the Andalusian public administration. Based on a hierarchical and arborescent structure, where each group is divided into subgroups of homogeneous characteristics. The divisions are called chapters, and each represents a construction process, such as demolition, earth movements, foundations, sewerage system, structure, partition, ceiling, installation, insulation, finishing, carpentry, glass and polyester, cladding, decoration, urbanization, safety and waste management [45]. The divisions and examples are listed in Fig. 4. The classification system materializes the coding of each concept, which means that each code has a unique concept, allowing accurate identification. Among other advantages, it also facilitates IT management and solves the location of concepts in the budget structure. This organization of work and its components offer a very robust and stable system when dividing a complex system such as the work budget into simpler elements, i.e., in materials, machinery, and labor [24]. The baseline cost base is the most used in our environment as it has the “Andalusia Construction Cost Base” [1], widely used in Andalusia, which has been published continuously since 1986. Its structure is created with clearly defined levels, in which

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Fig. 4 Systematic classification structure with examples. Adapted from Rivero et al. [63]

from the apex of the hierarchy it is descended to the lower levels, fractionating each group into subgroups of homogeneous characteristics (see Fig. 5). In this way, the base of the pyramid consists of supply prices (SUP), which connect directly to the system with the factor markets: labor, materials, machinery, subcontractors, etc. At the bottom of the pyramid are the work basic units or basic costs (BC), which connect the information with the market. The structure is completed by interspersing between the extremes, depending on the degree of detail sought, intermediate levels [45], auxiliary costs (AC) formed by the union of BC; unit cost (UC) formed by the union of BC exclusively or in combination with AC. This price encoding hierarchy is represented in detail in Fig. 5. At the apex of the pyramid are exogenous costs such REAL ESTATE MARKET Project total cost Taxes and overhead costs

Chapters Unit costs Basic costs Supplies prices SUPPLIER´S MARKET

Fig. 5 ACCD structure quantifies the construction materials, machinery, and labor necessary in the building construction

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as industrial profit (IP), taxes and overhead or general expenses (GE) of the construction company. All these characteristics facilitate the incorporation of environmental impact assessment by employing the WCS.

2.2 Methods (a)

Stages of the life cycle

Together with data obtained in the previous section, it is necessary to identify the characteristics and activities or actions scheduled in each stage of the BLC. Those can be defined using the information of the building project design. This implies that the design stage establishes the future maintenance and renovation needs during the life cycle. For each of the stages, information is defined using the project budget of each renovation or maintenance project to take place. The construction works carried out at each stage of the life cycle are, • • • •

Urbanization: road works, sewerage, and facilities, utilities, etc. Construction: The construction of the building. Renovation 20: Renovation of ACS air conditioning and generation facilities. Renovation 40: Energy retrofitting of the roof and facades (including windows), including their insulation. Renovation of the air conditioning facilities, ACS. Wet cores, floors, doors. Renovation of elevators. • Renovation 70: Structural repairs, cracks, and cracks. Replacement of all facilities: electricity, water, and sanitation. • Demolition: Complete demolition of the building. (b)

Application of WF Indicator

This section defines the materials and methods used for the calculations, the necessary formulation, the required auxiliary data, and coefficients and transformation factors. International LCA databases of construction products have been used [49] and EPDs available on ECO-Platform [18www.eco-platform.org/], an European platform in the construction sector [77]. Among the different LCA databases, the Ecoinvent database [22], implemented in Simapro software and developed by the Swiss Center for Life Cycle Inventories, is chosen due to its transparency (reports, flowcharts, methodology…), consistency, references, and it fuses data from various databases of the construction industry [49]. Environmental families have been defined from this database, which groups all basic cost in the ACCD. The WF calculation is shown in Fig. 6, where the different concepts evaluated, classified at various levels (impact sources and footprints) are included. It is based on an adaptation of the ecological and carbon footprint quantification model [46, 73, 63] developed by the research group ARDITEC, for the evaluation of building projects.

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Fig. 6 WF Methodology flowchart

The model has a budget structure, where each resource is assigned coefficients for calculating its impacts through footprint indicators. In this work, coefficients are added for the first time that contemplates the water incorporated in the manufacture of the materials, VW, as well as the water involved in the commissioning and the direct water consumption of the building use and the embodied water of electric power production. This methodology applies to indirect resources used in traditional construction budgets (energy, water, building materials, etc.) and to waste generated in the construction of residential buildings, generating what has been called the “Resource Inventory”, this being the starting point for environmental assessment. According to the limits set out in UNE-EN 15978, labor resources are not included within the quantification of the environmental impact of the construction project [78]. The quantification includes the rest of the building entries extracted from the budget, which makes it possible for the model to be applied at all stages of the BLC, since the established cost classification, structured according to the ACCD, allows to quantify the resources used in the actions to be carried out during the building life cycle. Conversion factors are categorized in the schematic as intermediate elements, and their mission is to transform consumed resources into VW volume to allow to calculate the different impacts that are part of the total WF. Next, we proceed to explain each of the elements and the approximations adopted for the calculation, following the scheme shown in Fig. 6. The summary of all the equations used are summarized in Table 1.

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Table 1 WF calculation equations

Equations Construction materials WFma: Partial water footprint of consumption of materials (m3)  WFma = 1 (Cmai · VWmai ) C mai : Consumption of material i (kg) VWmai : Virtual water of material i (m3 /kg) WFtr : Partial Water Footprint of the transport of materials (m3 )    W mai 2 WFtr = T cap · D ma · T con · VW f W mai : Weight consumption of material i (t) T cap : Truck capacity (t) Dma : Average distance (km) T con : Truck consumption (l/100 km) VWf : Virtual water factor of fuel (m3 /l) Machinery. WF mc : Partial Water Footprint of machinery (m3 )   WFmc = H mci · C f i · V W f i 3 H mci : Hours of use of machinery i (h) C fi : Consumption factor of machinery i (l/h or kW) E fi : Virtual water factor of fuel used by machinery i (m3 /l or m3 /kWh) Adapted from Marrero et al. [48]

(1)

Materials

Materials shall be classified by nature or components of similar physical properties. These will be analyzed in the Simapro computer tool [72], using Ecoinvent’s LCA database [17], to obtain the volume of VW used to produce each kg of material. The built-in water consumption of each resource, AVi, can be calculated by multiplying its Pi weight, by its built-in water volume, where Ci is the water consumption of resource i. See Eq. 1 in Table 1 for detailed calculation. The calculation of the virtual water of the building is solved with the sum of the virtual water incorporated in each element or resource. Up to this point, the environmental impact of materials during the life cycle from cradle to door is collected. To evaluate aspect A4 of UNE-EN 15978 [77], an analysis of the transport of the material is also carried out by establishing distance approximations traveled by means of transport, see Table 2 [24]. As an example, you can see in Fig. 7, the most representative material families in building projects along with their WF per reference unit. As a unique element, the input of tap water stands out. This is the water that is used in the commissioning. The VW of the tap water inlet is not only the amount of water use directly at the construction site, but we must also count the energy expenditure

Water Footprint of the Life Cycle of Buildings … Table 2 Data for calculating the impact of transport

147 Concrete

Other materials

Truck load capacity (kg)

24.000

2.000

Distance to factory (km)

20

250

Average diesel consumption (liter/100 km)

26

26

Diesel emissions (tCO2/liter)

2.62E-03

2.62E-03

Source Freire et al. [24]

Fig. 7 WF of the main families of materials of the project studied (m3 /t)

for transport from origin to point of consumption, as well as leaks and breakdown losses. (2)

Machinery

The VW of the machinery is calculated by using its engine power and performance, and its working hours. Electrical machines are differentiated from fossil fuels (diesel or gasoline) [71]. A coefficient is applied to the power of each engine to obtain the liters of fuel consumed per hour. The coefficient defines the amount of embodied water in one liter of fuel, i.e., the VW per liter of fuel from the Ecoinvent database. For the consumption of the electrical machinery used in construction, a similar path is followed, analyzing the engine power and hours of use, obtaining the total kWh consumed (Freire-Guerrero and Marrero [23]. This data is applied the coefficient indicating the embodied water of the Spanish energy mix [60]. (See Eq. 3 in Table 1). (3)

Construction and demolition waste

Construction and demolition waste (CDW) are the remains, waste, or demolition materials. Therefore, they will not have embodied water because it is included when the material is new. The WF is obtained from the working hours of the machinery necessary for the collection and transport to the landfill, when necessary. Eq. 3 of

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Table 1 is applicable. These CDW management activities are included in the budget, separately in their corresponding chapter, as required by Royal Decree 105/2008 (Ministry of Presidency, [55] and Marrero and Ramírez-De-Arellano [45]. (4)

Direct consumption during use stage

In general, empirical data is obtained from supplying companies, other researchers [15], Naredo [57], the Technical Building Code (CTE) [65] and organizations such as the Spanish National Institute of Statistics [35], Institute for Diversification and Energy Savings [33], the World Health Organization (WHO), etc. The parameters used for the estimates for each of the direct consumptions are defined in detail below. (4.1)

Water consumption

For the estimation of water consumption, data is provided by the Spanish National Statistical Institute of the average water consumption per person per year, from 1996 to 2015. In addition, the optimal consumption value provided by the World Health Organization (WHO) is 50 l/capita/day, consumption of human water in the home (drinking, cooking, personal hygiene, household cleaning). After analyzing the evolution of water consumption in recent years, a trend of savings has been contemplated and thus a reduction in water consumption. In 2000, the average water consumption in Spanish households was above 150 litres per person per day, compared to 132 litres per person per day established for 2018 according to the latest data collected by the National Statistical Institute [35]. Based on this, a polynomial trend line has been created, see Fig. 8, which extends over the years of the BLC, to estimate the consumption of the buildings under study. Once the trend line reaches the 50 l/capita/day recommended by WHO, the value remains constant. According to the data obtained, this figure is expected to be achieved around 2038. Finally, the number of residents per dwelling is obtained from CTE values [65]. It is possible to obtain a forecast of the water consumption per household.

Fig. 8 Trend human consumption of water per capita. Adapted from Rivero et al. [63], OMS and INE [35]

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For the environmental impact assessment associated with tap water, it should be remembered that it is not only the amount of water consumed that must be considered, but that energy expenditure for transport from origin to point of consumption, as well as leak and breakdown losses, must also be accounted for. In Spain, 15.9% losses of public urban supply networks are currently estimated, according to a report by the National Statistical Institute [35]. In addition, the energy associated with urban water collection, supply and distribution treatments is estimated at a pumping power consumption of 8,345 kW/m3 according to the Institute for Energy Diversification and Savings [33]. With all the above, a VW of 2,420 m3 /m3 per water consumed [63]. (4.2)

Electricity consumption

The energy sources or energy mixes are those corresponding to the year of construction, those that generate the electric power supplied. The energy mix is combined with the data obtained from water consumption by energy source [30, 64], and its VW is been calculated. See detailed calculations for the case study in Table 3. The power consumption during the use stage of the BLC changes in time with modification to the building envelope and its installations. To this end, case studies have undergone a simplified analysis of energy consumption with energy simulation software, CE3x_viviendas (CE3X v2.3.) Initial configurations and new improved transmittances in successive renovation projects have been modeled, obtaining consumption and emissions data at each stage. An important thing to consider given the duration of the BLC are the possible changes in the environmental impact of the energy consumed. That is why the model includes the evolution of the energy mix over time. With the combination of data collected from energy mix predictions [60] and data obtained from LCA databases [64], a possible future scenario has been generated, where the different (polynomial) trends of the energy sources of the Spanish mix were analyzed. To conclude and facilitate environmental updates considered for the WF indicator, Table 3 lists, as a summary, the impacts of electricity adapted to the expected future scenario, for the different consumption periods established in the BLC. (c)

Economic and environmental database for the BLC

It is essential to create an auxiliary database and systems that allow the management of the large amount of data from the building project, whether at the construction level, use, and maintenance, or demolition of the building. It is considered necessary to develop a project measurement management system, which organizes and classifies Table 3 Environmental impact on WF of electricity consumption for periods

Electric power

WF (m3 /kWh)

Period 0–20

0.001

Period 21–40

0.009

Period 41–70

0.018

Period 71–100

0.025

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the necessary data during each stage of the BLC. To do this, we will follow the ISO/TR standard 14177:1994 [36]. The data obtained from the project quantities are structured according to the systematic classification of the ACCD and are expressed in units of measurement per unit of floor area (u/m2 ) for each stage of the BLC. The data obtained is then merged, resulting in an economical and environmental BLC database. The unit cost (UC) considered for each phase, together with building use simulations, will allow us to estimate the economic and environmental costs for the 100 years duration of the BLC. Figure 9 shows an example of a unit cost containing the necessary information in terms of resource quantities for the calculation of the WF. The ACCD pyramid base, together with the WF methodology, has enabled the creation of a database combining economic and environmental information (Fig. 10). The first step to be able to obtain the WF of each element (or BC) is to perform a conversion of the source unit of measure of each basic price (m3 , m2 , m, t, thousands of units, etc.) to m3 , so as to allow the density established in the support documents used, Catalogue of Constructive Solutions of the Technical Building Code [34] and the Basic Structural Safety Document of the Technical Building Code. Actions in the DB-SE AE Building [65], and thus obtain the weight of each element. Once WF is obtained for each of the materials listed in the ACCD BC list, these are complemented by their “environmental costs” per reference unit, which will mark the basic environmental impacts. Figure 10 shows an example of an UC, where the WFs (BWF) of a basic cost (or element) have been incorporated next to its basic cost. With all these basic impacts created, and following the initial pyramidal structure, unit impacts (UWF) are obtained in the same way that UCs are obtained in the

Fig. 9 UC example of the economic and environmental database created for the BLC. Adapted from Freire et al. [24]

Water Footprint of the Life Cycle of Buildings …

(a)

151

(b)

Fig. 10 Case study residential complex. a Building view; b localization, Palma del Condado, Andalusia, Spain. Pictures from Google Maps

ACCD. Finally, if this new created economic and environmental cost database, the economic costs, and environmental costs (WF) that any building project will entail are calculated simultaneously.

3 Case Study The WF is determined of a project of two residential buildings representative of the most built typologies in Spain between 2006 and 2010 [27]. In total 107 homes and 9,524.17 m2 built. See image of the case study project in Fig. 10. The selected project began its urbanization and construction in 2008–2009 and starting the stage of use in 2010. The number of people living per dwelling is, based on averages in the Royal Degree 314/2006, 3 people for two bedrooms. The results of the quantity surveying are summarized in Table 4.

3.1 Definition of Urbanization and Construction Stages The following items are included in the urbanization project: – Cleaning and clearing of the ground. – Road works, which included the movements of land of grading, compaction, and paving of roads; construction and taping of concrete slabs base and construction of the pipelines to be built on the subsoil of the roads or slabs. – Sewerage networks include general and partial collectors, connections to urban networks, and pits. – Works for the installation and operation of public water, electricity, and street lighting, telephony, and telecommunications services.

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Table 4 Quantity surveying summary of all chapters in the budget of the construction project referred to ACCD classification codes Code

Concept

Quantity

Unit

Code

Concept

Quantity

Unit

02E

Excavations

1714,351

m3

08FC

ACS conduits

1523,867

m

02R

Fillings

1238,142

m3

08FD

Drainage conduits

190,483

u

02T

Earth transport

2095,317

m3

08FF

AFS conduits

2952,493

m

03A

Reinforcement

85241,322

kg

08FG

Taps

190,483

u

03E

Formwork

285,725

m2

08FS

Bathroom appliances

190,483

u

03HA

Concrete foundations

1333,384

m3

08FT

Thermal/heating units

95,242

u

03HM

Mass concrete

95,242

m3

08NA

Hot water deposits

95,242

u

04A

Pits

95,242

u

08NE

Load-bearing structures

95,242

u

04C

Collectors

476,209

m

08NO

Solar collectors

95,242

u

04B

Drainpipes and cups

1047,659

m

08NP

Primary circuit

857,175

m

05F

Forging

9428,928

m2

09T

Insulation

6095,469

m2

05AA

Reinforced

85241,322

kg

10AA

Tiling

3333,460

m2

10AC

Coating

9047,962

m2

05HE

Formwork

7714,578

m2

05HA

Reinforced concrete

952,417

m3

10CE

Rendering

12857,630

m2

06DC

Partition walls (chambers)

6095,469

m2

10CG

Finishes

21524,624

m2

06DT

Partition walls

6285,952

m2

10S

Flooring

8381,270

m2

06LE

Brick walls exterior

9047,962

m2

10SS

Concrete floors

285,725

m2

06LI

Brick walls interior

2857,251

m2

10T

Roofing

666,692

m2

07H

Horizontal roof

2762,009

m2

10R

Finishing

761,934

m

08CA

Air conditioning

95,242

u

11CA

Steel frames

1047,659

m2

08CC

A/C Conducts

95,242

m

11CL

Aluminum frames

1047,659

m2

08CR

Radiators

95,242

m2

11 M

Wood frames

190,483

m2

08EC

Electric circuits

5047,810

m

11MP

Doors

1047,659

m2

08ED

General lines

1047,659

m

11P

Blinds

571,450

m2

08EL

Light points

952,417

u

11R

Enclosure

380,967

m2

08ET

Electrical socket

1809,592

u

12A

Windows

952,417

m2

08EP

Electrical ground

857,175

m

13PE

Exterior paints

11333,762

m2

(continued)

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Table 4 (continued) Code

Concept

08MA

Lifts

Quantity 8

Unit

Code

Concept

Quantity

u

13PI

Interior paints

23905,667

Unit m2

For the construction of the houses has been designed a building with surface foundation with reinforced concrete slab and reinforced concrete structures, as well as metal formwork systems. For horizontal structures, ceramic big format bricks are used. For the enclosure brick walls of 24 cm thick with an air chamber, a 3 cm polystyrene insulation, and an exterior finishing with artificial stone. The roofs are horizontal, walkable, and ventilated. The interior divisions are executed with 9 cm brick partitions. The interior finishes are plasters, terrazzo flooring, and plaster ceilings with metallic fixation. Windows are, lacquered aluminum frames with RPT, 6 + 12 + 6 thermo-acoustic glass. Other elements are, wooden doors covered with melamine. Air conditioning system is a heat pump console terminal. For the hot water system, solar energy panels with electric deposit for support. The water supply pipe lines are made of copper and drain pipes are of reinforced PVC. For accessibility, elevator installation is included.

3.2 Definition of the Stages of Use and Renovations For this research, electric power consumption is established constant throughout the life span, without considering variations related to climate changes. However, if the variations caused by the renovations in the building, improve the energy efficiency, those will be taken into account. For this purpose, different periods of consumption are established, starting and ending with the different renovation works. The first one goes from the construction of the building to the 20th year, the second from the year 21 to 40, the third from 41 to 70 and the last from year 71 to 100, 100 is the end of the useful life and the demolition takes place. On the one hand, the thermal transmittances (U) of the enclosure, both roof and facade, and windows have been improved. These improvements will take place in scheduled renewals at 40. At the age of 70 they will be repaired while maintaining the same U values. On the other hand, in each of the interventions, action will be taken in domestic hot water (DHW) production and air conditioning systems. For the characteristics of the air conditioning installation, the values for the nominal yields of the initial heat pumps installed in the building are 220% for heating and 200% for cooling. 20 years later the equipment is replaced, it is estimated that yields will be improved by industrial advances, using values for simulations of 350% for heating and 330% for cooling. With regard to the renovation of the DHW installation, the characteristics of the elements that make up the solar panel system are maintained. However, the electrical deposits supporting the system, and covering 30% of the demand, will be

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improved by installing a thermo-accumulator with a capacity of 100 l and thermal insulation. In terms of performance, it is estimated that the DWH installation will remain in its initial state. In terms of water consumption, an average consumer is estimated, with responsible consumption habits. In order to project these consumption habits, the trend towards reducing drinking water consumption has been taken into account based on the studies presented in the theoretical model, see Fig. 8. The results obtained passed on by constructed area for water consumption in BLC are 68.50 m3 /m2 of floor area.

3.3 Definition of the Demolition Stage At the end of the year 100 of the life of the building and considering that the building does not meet the conditions of habitability, the demolition project of the building takes place. Demolition will be carried out massively by mechanical means. In addition, the management and transport to the treatment plant of all the CDW generated is included. In Fig. 11 all the works taking place in the BLC are represented using the ACCD classification.

4 Results The results are exposed by differentiating the resources incorporated in the consumption of the works (indirect consumption), from the consumptions during the use of the building, i.e. water and electricity utilities (direct consumption). All this is evaluated economically and environmentally, and normalized by its floor area (m2 ). Next, to facilitate the comparison of results with other similar studies, the total impacts are presented, adding the values per year of duration of the BLC, which, in the case of this study, is 100. The data for utilities consumption is obtained based on predictions of future scenarios calculated in previous sections.

4.1 Indirect Consumption Table 5 is presented with a double division of the results. The basic resources for the execution of each stage are broken down, and the working hours of machinery. The total W F data is expressed in volume of built-in water per unit of floor area (m3 /m2 ). As expected, the focus of consumption takes place at the construction stage. There is also an important the total working hours of the machinery required in the renovation in the 70th year. This is related to the increase in CDW generated, at this age, the works include the repair of the structure. In addition, these results can be related to those presented in the investigations of Alba et al. [3], in which the

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155

Fig. 11 Activities in each phase of the building life cycle per ACCD classification. Adapted from Marrero et al. [48] Table 5 Resources, CDW, and environmental impacts in WF by phases of the BLC Resources per floor area (unit/m2 )

WF (m3 /m2 )

41.54

1.73

Land transformation

Materials (kg) Machinery (h)

0.16

0.06

Construction

Materials (kg)

2,159.25

9.062

Machinery (h)

0.41

0.106

Materials (kg)

7.22

0.47

Machinery (h)

0

0.0001

Renovation 40

Materials (kg)

659.98

6.727

Machinery (h)

0.26

0.040

Renovation 70

Materials (kg)

1,102.82

8.429

Machinery (h)

0.41

0.070

Materials (kg)

0

0

Machinery (h)

0.04

0.298

Renovation 20

Demolition

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economic and environmental cost of this type of intervention is evaluated. If these results are analyzed in detail, the first thing that is observed is that the consumption of resources at the construction stage is twice as high as required at renovation stage 70. However, by focusing attention on CDW, it is found that CDW is increased 10 times more in renovation than those generated in construction. These results significantly increase its impact because the machinery employed in CDW management. In the case of the demolition stage, the low impact it entails is more evident, since no material resources are consumed, with the machinery being the only impact associated with this stage of the building, both for the execution of the demolition and for the subsequent management of the generated CDWs. It should be remembered that CDWs have no material environmental impact because it was counted when it was incorporated into the building as new material. When materials are removed from the building, already considered CDW, it is only associated with impacts by the machinery necessary for its management and in this way, avoid duplication in the quantification of impacts. Finally, the renovation stages at the age of 20, stands out as the lowest impact stage, hovering around 1%. In the case of the renovation at the age of 20, these small impacts are due to the replacement of air conditioning and DHW equipment. The impacts associated with materials and CDWs are discussed in more detail below. For this purpose, Fig. 12 represents the main material resources consumed and the CDW generated total in the BLC. Quantification has been in weight of material per floor area (kg/m2 ). For comparison the data on the EF from previous work [63] has also been included. It can be appreciated that the concrete is an important construction material in all aspects analyzed, WF, EF, and weight. In the case of ceramic bricks have high EF but not as important WF, making more interesting reducing its carbon

Fig. 12 Analysis of consumed resources, its WF and EF, and generated CDW (The EF is obtained from Rivero et al. [63]

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157

footprint. For the contrary, WF is more important in plastic material in the project that its EF. The materials that have the greatest presence in the BLC after concrete are aggregates and stones, followed by ceramic bricks. On the contrary, the least representative materials are glass, wood, and plastic, in order from lowest to highest consumption in the BLC. The 10 main families of materials identified in the results, coincide with those identified by Solís-Guzmán et al. [74] in Spain, in Italy [7, 12], Brazil [44], and Chile [61]. Looking at the materials and their associated environmental impacts, it can be seen how the greatest impacts are not generated by the most consumed materials, as is the case with plasters and metals. Finally, another option is provided by the results presented in Fig. 12 is to analyze the feasibility of revaluation of CDWs, to convert them back into building materials. This would respond to the demand for resources while reducing the deposit of CDWs in landfills.

4.2 Results Associated with Direct Consumption Focusing on the results in Table 6, the first thing that is observed is the impact of indirect consumption on the BLC, which accounts for 60% of the total impact. This places the focus of action on the works, i.e. on controlling building projects. Therefore, if it is intended to reduce the damage caused by the building sector to the environment, it will be achieved in a much more efficient way if material resources are controlled, building machinery rather than just focusing the effort on reducing direct consumption during the stages of use. To all this, it should be added that, by progressively improving the efficiency of the new buildings, the weight of the construction stage is accentuated, making more evident the intervention in the building projects to minimize the impacts of the sector.

4.3 Comparison of Economic Versus Environmental Impact Below is a summary of the implementation budgets of the different phases of the BLC, as well as their impacts on WF for the interventions required at each stage of the BLC. As can be seen in the methodology described and in the results set out in the comparative table (see Table 7) the obtaining of budgets is obtained in a manner complementary to environmental impacts. This is because they are the resources that are extracted directly from the quantification of the through the structure of the ACCD, obtaining at the same time the two budgets, the economic and the environmental of the projects evaluated. Annual consumption starting in 2010 results in a total cost of approximately 1,963.93 e/m2 . It should be clarified that, although there is the possibility of economic

Total impact

6.08

628.524 kWh/m 2 269,329 kWh/m 2

70–100

Indirect consumption

Electricity

Water

26.99

220.54

17.93

551.99 kWh/m

15.64

15.10

2

165.78

1,263.53 kWh/m 2

68.50

40–70

0–20

Electricity 20–40

0–100

Water

WF (m3 /m2 )

m3 /m2

Consumption

Indirect consumption: Materials, machinery and labor in BLC (Total impact)

Direct consumption

Period (years)

Table 6 Analysis of the total direct and indirect consumption of the BLC

158 C. Rivero-Camacho and M. Marrero

Water Footprint of the Life Cycle of Buildings … Table 7 Costs of direct and indirect consumption in stages and projects in the BLC

BLC stage Urbanization Construction

159 COSTS (e/m2 ) WF (m3 /m2 ) 21.715

1.790

635.29

9.168

Renovation 20

95.93

0.470

Renovation 40

323.12

6.767

Renovation 70

371.31

8.499

Demolition Total works costs Power

37.36

0.298

1,484.725

26.99

325.062

54.76

Water

154.13

165.78

Total consumption of utilities

479.20

220.54

Total in BLC

1,963.93

247.532

updates with rates such as those of the inflation in the analyses presented, it has been chosen not to apply any monetary index. Figure 13 compares the different projects that have taken place in the BLC (economic (a); water (b)) for each phase, together with the assessment of consumptions in the different periods of use. It is appreciated that the economic impact is focused on the construction stage and the renovation at 70 years. Analyzing the WF produced by the project in the BLC, see Fig. 13b, indirect consumption is around 11% of the total impact. The remaining impacts (electricity and water consumption) together account for the remaining 89%, with 67% of the total WF, mainly due to water supply.

5 Conclusions Water consumption and building conservation draw global attention for their importance in water management. Although many studies provide a broad approach to quantify water consumption during the operation and maintenance stage of the building, there appears to be a lack of research on virtual water in the processes and consumptions of buildings. In addition, most existing virtual water research is based solely on either process analysis or input-product analysis, which inevitably introduces intrinsic errors from virtual water accounting systems. After the application of the case study, the analysis obtained has allowed to identify the materials or work units more impacting. In addition, the elements that have the greatest impact on environmental impact have been obtained; that, in the case of materials, the higher WF corresponds to concrete, metals, and plastics, main material resources to control and incentivize their reduction and consequently minimize the impacts of the BLC; and in terms of waste treatment, the machinery that manages it is its main impact.

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1.90%

a)

7.85%

18.91%

3.37% 16.45%

3.83%

16.55%

1.64%

7.71%

4.88% 32.35%

1.11%

b) 66.99%

7.25% 6.32%

22.13%

2.46% 0.71%

2.73% 0.11% 3.43% Land transformation Renewall 20 Renewall 70 Electricity 0-20 Electricity 41-70 Water consumption

6.10%

3.70% 0.19%

Building Renewall 40 Demolition Electricity 21-40 Electricity 70-100

Fig. 13 Comparison of a economic impacts; and b WF

The total virtual water of the BLC building studied is estimated to be 26.99 m3 /m 3 2 of floor area, while direct water consumption is 220.54 m /m2 of floor area, i.e., the environmental impact of the BLC when applying the WF indicator focuses on the stage of use of the building, unlike other indicators that focus the greatest impacts on the construction stages of the building, with almost 67% of the total impact being the impact of the tap water direct consumption. With the focus on the use stage, the results also show the importance of evaluating the entire BLC as can be seen from the results, not only should our efforts focus on

Water Footprint of the Life Cycle of Buildings …

161

energy efficiency and consumption savings at the stage of use, but the manufacturing and use of the resources required in the construction units must be optimized so that our objectives of reducing the impacts of the construction sector are met. But the stages of construction and renovation at the age of 70 of the building generate the greatest economic impacts. This is due to the high consumption of resources for its execution. Also, the WF is high, this is due to the reinforced concrete structure, and approximately 65% of the total water consumption in the materials is incorporated into the concrete and steel elements. Therefore, choosing materials with low water intensities in their manufacture is another way to decrease WF consumption. This study provides guidelines for designers and project builders in balancing a building’s total water budget and can therefore find wide application in other virtual water studies in the future. After observing the results obtained, it has been shown that is possible to include the assessment of the sustainability of a building project. This proposal is part of simple and accessible data. Simplicity is offered by the cost base used, the ACCD and project budgets, which has served as a vector for introducing sustainability. This has generated a robust and replicable model that delivers results, with a double economic and environmental aspect. In this way, it is possible to make decisions from the project phase that will result in the improvement of the “environmental rating” of the entire life of the building. Acknowledgements The University of Seville is grateful for funding the research work presented, through a pre-doctoral contract, for the development of the R&D&I program.

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Nitrogen Footprint of a Food Chain Kaisa Grönman, Laura Lakanen, and Heli Kasurinen

Abstract Nutrients such as nitrogen are required to secure food production. However, nitrogen cycles have been disturbed by excess nitrogen intake and low nitrogen use efficiency (NUE), which have several environmental impacts. In order to address nitrogen-related issues, the magnitude of the problem and hotspots in the value chain must first be identified. Various methods to quantify nitrogen use, NUE, and nitrogen-related environmental impact potential have been proposed to tackle this challenge. The approaches, methods, and indicators that can be used in assessing particular food systems are presented in this chapter. The methods serve different purposes and present certain differences in terms of scoping and system boundaries. The aim of this chapter is to present currently relevant methods to analyze the nitrogen footprint of a food chain in order to help those tasked with carrying out assessments to choose the method which best meets their needs. Keywords Nutrient · Nitrogen · Nitrogen footprint · Food chain · Environmental impacts

1 Introduction Nitrogen and other nutrients such as phosphorus and potassium are essential in all forms of food production. Due to human interaction, however, nutrient cycles have been disturbed. Population growth, efficiency efforts in agriculture, and increased energy use have led to a multiplying of nutrient intake. The intake of N2 from the atmosphere and its conversion to reactive nitrogen has already exceeded the safe operating space of planetary boundaries [19, 21]. Production chains might not be utilizing nutrients efficiently but, rather, have nutrient leakages. It has been estimated that over 80% of the nitrogen taken into use is lost into the environment [22]. These emissions have various environmental impacts affecting water bodies, soil, and air. In addition, the production of nitrogen fertilizers with the traditional K. Grönman (B) · L. Lakanen · H. Kasurinen Lappeenranta-Lahti University of Technology LUT, Lappeenranta, Finland e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Switzerland AG 2022 J. Ren (ed.), Advances of Footprint Family for Sustainable Energy and Industrial Systems, Green Energy and Technology, https://doi.org/10.1007/978-3-030-76441-8_8

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Haber-Bosch process, in which nitrogen in the atmosphere and methane from natural gas are converted into ammonia, is very energy-intensive and highly polluting. The Haber-Bosch process produces 450 million tons of carbon dioxide annually, which corresponds to approximately 1% of all human CO2 emissions [20]. The aforementioned challenges related to nutrients have led to the development of various methods to quantify their usage and impacts on the environment. As the major nutrient flows are interconnected with food chains, mainly in fertilizer and food production as well as food consumption [1], it is natural for this chapter to concentrate on those methods developed for food chains. In this chapter, the focus is on nitrogen alone. Phosphorus and potassium are also essential in the food chain; however, their cycles and related challenges are different. For example, in fertilizer manufacturing, 92.5% of energy used is for nitrogen, whereas potassium consumes 4.5% and phosphorus 3.0% [8]. Nitrogen reserves are abundant in the atmosphere, while phosphorus, in particular, is a limited resource mined from the Earth’s crust. Due to the different nature of the nutrients, some indicators have been developed solely for nitrogen. Such nitrogen-related methods are presented in this chapter, along with other methods, indicators, or footprints that are suitable for examining nitrogen-related challenges in food chains.

2 Indicators for Nitrogen This chapter presents several methods that have been proposed to consider the use of nitrogen and/or its environmental impacts on the food chain. The studied indicators are: N-print tools including the N-calculator and other relevant approaches [15]; the full chain nutrient use efficiency (NUE) by Sutton et al. [22]; the whole food chain nitrogen use efficiency (NUEFC) by Erisman et al. [6]; the nutrient footprint by Grönman et al. [10]; the life cycle nitrogen use efficiency by Uwizeye et al. [24]; the N food-print by Chatzimpiros and Barles [4]; and, finally, the life cycle assessment (LCA), as methods which assess the environmental impacts related to nitrogen. All these methods provide information for N users or policy makers about the use of N resources and their impacts on the environment. All approaches are complemented with a figure, presenting the characteristics of each approach. Firstly, the figures indicate the aim or purpose of the approach, whether it is meant for understanding nitrogen flows at a local scale and related to food consumption or is it more on studying and improving the N balance of a specific food production system. In addition, the suggested system boundaries, differentiating the presented methods are shown in Figs. 1, 2, 3, 4, 5, 6, and 7. The included life cycle phases, as well as included nitrogen categorizations are colored with a gray pattern for each of the presented N indicators.

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2.1 N-Print by Leach et al. Leach et al. [15] were among the first to tackle the challenge of disrupted N cycles by a collection of tools into a system they named N-print. Their aim was to create a set of tools for consumers, producers, and policy makers to make informed decisions to reduce N-related challenges without endangering food production. Leach et al. [15] presented the N-calculator to quantify consumer footprint, plans for the N-producer to quantify producer footprint, and the N-policy to calculate the effect of policies on the N cycle. In addition, N-print forms the base for the N footprint label [14], institutional N-print [16] N neutrality approach [25], and N-loss indicator [2, 3, 9].

2.1.1

N-Calculator

The N-calculator is designed to estimate the nitrogen footprint of a consumer in a certain country. It utilizes food consumption data from the UN Food and Agriculture Organization (FAO), virtual N factors (VNFs) of units of food consumed, and fossil fuel consumption needed in housing, transportation, goods, and services. VNFs are created for different foods and describe the share of reactive nitrogen released to the environment in relation to unit of Nr consumed [15]. The N-calculator was first developed for consumers in the USA and the Netherlands but has since been applied in several European and Asian countries as well as Australia and Tanzania [9, 14]. The N-calculator characteristics are presented in Fig. 1. To further elaborate on the N-calculator tool by Leach et al. [15], it quantifies the annual amount of Nr released into the environment as part of food consumption and production in a country [kgN capita−1 year−1 ]. For example, the WHO has estimated

Fig. 1 N-calculator by Leach et al. [15]

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the required protein intake in a healthy diet, based on which healthy N consumption can be determined. Comparing the footprint value to the healthy value shows whether a country is experiencing overconsumption of N, as is the case in most developed countries. Options to decrease this N footprint include reducing protein intake to a healthy level, using less animal protein, and reducing Nr losses to the environment by developing wastewater treatment through increasing denitrification and improving sludge recycling for agricultural production [9]. The N-calculator has been expanded to meet different purposes. It bases the N footprint of a consumed food on its protein content; hence, the N footprint of protein-free foods is treated as zero. Hayashi et al. [11] developed an N-calculator application to assess the N footprint of protein-free food, such as oils and sugar. In the model of Hayashi et al., the VNF is replaced by the virtual nitrogen factor for protein-free foods (VNFree), which can be defined as the potential N load per unit weight of consumed food. Consequently, more realistic nitrogen footprints for protein-free foods can be derived.

2.1.2

N Footprint Label

The purpose of a product’s N footprint label is to inform consumers about its N footprint, which is expressed in the unit [% of daily N footprint of a healthy diet]. A definition of a healthy diet can be obtained from the guidelines given by health authorities [9]. The N footprint label can say, for example, that one serving of a certain product represents 2% of your daily N footprint. The N footprint is based on Nr released to the environment by food production. Energy production from fossil fuels can be included or excluded [14].

2.1.3

Institutional N Footprint

The institutional N footprint is developed from the N-print indicator by extending the N-print to institutions. It depicts all Nr that enters an institution and which is generated by, or due to, the institution’s activities [kg N/year] (e.g., food, energy, transportation) [9, 16]. Leach et al. [16] applied the institutional N footprint to a university, and it may be further applied to a range of organizations and even cities. Based on the information provided by the indicator, organizations can formulate strategies to reduce their institutional N footprint [9].

2.1.4

N Neutrality

The N-calculator has been used as a basis for calculating the amount of Nr in order to reduce that to zero. N neutrality requires actions to () reduce the N footprint by directly reducing Nr released to the environment (e.g., changing diet); and (2) compensate the N footprint that which cannot be reduced through mitigation actions under 1) (e.g., reducing the N footprint elsewhere or increasing sustainable land management in food production) [9, 25]. The compensation measures suggested by

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the N neutrality approach can be applied to products, individuals, organizations, or regions [9].

2.1.5

N-Loss Indicator

The N-loss indicator is another application of the N-print indicator. In contrast to the N-print, which shows the loss of N due to consumption by individuals, the N-loss indicator depicts reactive nitrogen losses to the environment at a country or regional level due to production and consumption of food, and energy use [Nr loss capita−1 year−1 ] [3, 9]. The indicator does not distinguish between losses to air, soil, or water [3]. It allows easy comparison of countries or regions, such as continents, in terms of Nr losses. The food production and consumption components are especially highlighted in regions with extensive livestock production and meat consumption, while the energy consumption component in similarly highlighted in industrialized countries. The indicator is used in the context of the Convention on Biological Diversity [2, 3, 9].

2.2 Full Chain Nutrient Use Efficiency by Sutton et al. One of the first studies to expand the concept of nutrient use efficiency to cover the whole food system was presented in the Our Nutrient World report published in 2013. Sutton et al. [22] proposed full chain NUE, which, in the case of nitrogen, can be defined as the ratio of nitrogen in final products to new nitrogen inputs. Full chain NUE, N =

N in food and durable products Industrial N production + BNF + combustion source NOx

N inputs include, for example, virgin N inputs through Haber-Bosch, biological N fixation, and NOx formation. Sutton et al. explicitly excluded secondary nutrients, such as manure and animal feed, from the inputs as they regarded these as not directly representing the goal of feeding people. However, they state that use of these secondary nutrients is advisable, and it shows in the reduced need for primary nutrients. The characteristics of the full chain NUE by Sutton et al. are presented in Fig. 2.

2.3 Whole Food Chain Nitrogen Use Efficiency by Erisman et al. Different approaches to the whole food system NUE have been introduced in addition to that by Sutton et al. [22]. Erisman et al. [6] proposed whole food chain nitrogen

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Fig. 2 Full chain NUE by Sutton et al. [22]

use efficiency (NUEFC), which is an indicator suitable for broader application at national scale. In this study, NUEFC is defined as the ratio of the N protein available for human consumption to the newly fixed and imported nitrogen input to the food system. In other words, NUEFC describes what percentage of input N to the food system is converted to food protein N available for consumption. NUEFC helps to identify strategies for more efficient nitrogen use in food production, minimize N losses in the food system, and, additionally, recognize which phases in the food chain have the lowest N efficiency. Therefore, NUEFC could be used in policy making to promote efficiency and steer consumers to use products with efficient N use. The characteristics of the whole food chain nitrogen use efficiency are presented in Fig. 3. The NUEFC can be calculated by using the budgeting approach. As the food chain consists of a chain of different sectors and activities, the NUE for each step within it must be determined. This requires the amount of used N in each sector to be calculated, after which the NUE for each step in the food chain can be calculated as consumed N (the outputs) divided by the new N (the inputs). Hereafter, NUEFC can be defined as follows: NUEFC =

N food availability fertilizer + BNF + atm.dep. + (import − export) + changes in stock

(1)

In Eq. 1, N food availability refers to consumption or, in other words, N supplied to households. N food availability is then divided by the inputs, like fertilizer N, biological nitrogen fixation (BNF), atmospheric deposition of N, difference of imports and exports, and changes in N stock. The N stock refers to the annual net balance of a country’s N imports and exports, including storage of products. Although NUEFC is best suited to examining the whole food chain, it can also be applied across different sectors, such as the agricultural system or consumer sector [6].

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Fig. 3 NUEFC by Erisman et al. [6]

2.4 Nutrient Footprint by Grönman et al. The nutrient footprint proposed by Grönman et al. [10] is a combined indicator for nutrient intake and NUE. It is suitable for the assessment of the nitrogen balances of food chains and other bio-based production chains. In addition to nitrogen, the nutrient footprint is recommended for use in assessing phosphorous. The method is designed to assess and improve specific food chains. The characteristics of the nutrient footprint are presented in Fig. 4.

Fig. 4 Nutrient footprint for nitrogen by Grönman et al. [10]

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The nutrient footprint takes into account the whole life cycle of the food chain, starting from raw material extraction and ending in the end-of-life phase with waste disposal or recycling. Throughout the life cycle of the studied food chain, the entering nitrogen inputs and exiting nitrogen outputs are identified. Nitrogen inputs are separated into virgin and recycled nitrogen. Virgin nitrogen refers to nutrients captured from the atmosphere and converted into reactive form to be utilized in this particular chain. In addition to industrial fertilizers, other primary material and fuels consisting of nitrogen, such as nitrogen entering the system through BNF, are considered virgin nitrogen. Recycled nitrogen, on the other hand, relates to nitrogen that has already been taken into use and is thus present in the nutrient cycle. For this particular process, recycled nitrogen can, for example, comprise process side-flows, manure, and sewage sludge which is continuing its life cycle in the studied food chain. The total amount of nutrient intake in the studied food system [kg N/functional unit] forms the first basis of the Grönman et al. [10] nutrient footprint indicator. The authors emphasize the importance of identifying virgin and recycled nutrients separately, so that although NUE is improved, the share of virgin nutrients is the primary target for reduction. Secondly, the nitrogen outputs leaving the system are quantified. A distinction is made between nitrogen that is released to the environment as emissions or waste and whose nutrient content is thus no longer utilized, and the nitrogen which continues to serve as a recycled nutrient. N2 released to the environment is also considered to be wasted, as it requires a great deal of nutrient inputs in terms of energy to return it to the nutrient cycle [10]. When the amounts of nitrogen entering and exiting the food chain have been quantified, one can calculate the NUE of the food item: NUE, Nfood =

Nitrogen content of the food × 100 (2) Total amount of nitrogen captured by the food chain

The higher the percentage of Eq. 1, the smaller the quantity of nitrogen taken into use throughout the food chain, and the more the captured nutrients remain in the food item. In Eq. 2, other utilization possibilities along the food chain are also noted: NUE, Ntotal =

Nitrogen content of the food + Utilized secondary nitrogen × 100 Total amount of nitrogen captured by the food chain

The total nitrogen use efficiency also taking into account the exiting nutrient flows which are captured and whose life cycle is continued, is more useful if one desires to develop the whole food chain and, for instance, find use purposes for side-flows. The nutrient footprint method proposed by Grönman et al. [10] is different to methods such as life cycle inventory in life cycle assessment, material or substance flow analysis, the N-print [15], or the NUE of Sutton et al. [22], as it categorizes the input nutrients into virgin recycled nitrogen and output nutrients into utilized secondary nutrients and losses throughout the life cycle of the food product. In

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addition, including the utilization of secondary nutrient flows allows more detailed improvement potential for nitrogen efficiency to be found. The proposed nutrient footprint approach has been utilized to assess vegetable food products (oat flakes and porridge) [10] and animal food products (beef) [13]. For nitrogen use efficiency, the results are reported as follows: 1000 kg of Finnish oat flakes and porridge consumed requires 42 kg of nitrogen, of which 88% is considered virgin nitrogen. Nitrogen use efficiency in the oats chain is 55%, and the nitrogen use efficiency including secondary products is 71% [10]. 1000 kg of Finnish beef consumed requires 1700 kg of N of which 50% is virgin nitrogen. Nitrogen use efficiency in the beef chain is only 1%, but if secondary products are taken into account, NUE, Ntotal increases to 47% [13].

2.5 Life Cycle Nitrogen Use Efficiency by Uwizeye et al. Improving the N use efficiency along the supply chain is essential when aiming to increase the sustainability of nutrient use. The study by Uwizeye et al. [24] introduced an LCA-based framework to assess life cycle nitrogen use efficiency from a regional or global perspective in the livestock supply chain. The framework can be utilized in the assessments of Nr flows in crop production, animal production, and processing, and it includes internal processes, loops, and recycling of Nr. The characteristics of the life cycle nitrogen use efficiency are presented in Fig. 5. Uwizeye et al. proposed that three indicators are needed to entirely describe the nitrogen dynamics in the livestock supply chain: life cycle NUE, life cycle net nutrient balance (NNB), and nutrient hotspot index (NHI). They concluded that the combination of these three indicators gives relevant and complementary information to

Fig. 5 Life cycle nitrogen use efficiency by Uwizeye et al. [24]

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monitor nutrient management performance. Moreover, it helps to understand efficiency of nutrient use, as well as nutrient balance per hectare, and distribution of nutrient pressures along the chain. These indicators are introduced in the following subchapters.

2.5.1

Life Cycle Nitrogen Use Efficiency

Life cycle NUE defines how efficiently nutrient inputs are recovered in final products. Additionally, it considers nutrient mobilization, use, change in nutrient stocks, and recycling. It can be calculated as one unit of nutrient in final products divided by the amount of “new” nutrient mobilized in the supply chain to produce it [24].

2.5.2

Life Cycle Net Nutrient Balance

Life cycle NNB is expressed as Nr losses (kg) per area of land used (ha). In other words, it indicates the amount of nutrients that are used for neither end-products nor the build-up of soil fertility, wherever they occur in the chain [24].

2.5.3

Nutrient Hotspot Index

The NHI is calculated as the standard deviation of NNB divided by the average of NNB of all stages of the supply chain. High NHI indicates that there is one or a few significant nutrient hotspots in the supply chain. Conversely, a low NHI indicates an evenly distributed nutrient balance along the supply chain [24].

2.6 N Food-Print by Chatzimpiros and Bales The N food-print is a consumption-based indicator of Nr inputs and losses from spatially scattered livestock systems [4]. As N use efficiency is examined from a system perspective, the N food-print can be used, for example, to track where the most significant N emissions occur along the production chain and, subsequently, inform measures to reduce N losses [4]. The characteristics of the N food-print are presented in Fig. 6.

2.7 LCA and Environmental Impact Assessment LCA addresses potential environmental impacts throughout a product’s life cycle, from raw material extraction to end-of-life treatment. Once the goal and scope of a

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Fig. 6 N food-print by Chatzimpiros and Barles [4]

study are defined, LCA includes inventory analysis and impact assessment phases [12]. The relation between footprints and LCA is ambiguous. Some footprints can be addressed according to LCA principles; however, many footprint indicators exist independently of LCA [7]. Ridoutt et al. [18] suggested a new paradigm, areas of concern, that ties footprints and LCA together without requiring a comprehensive environmental evaluation from footprints and supports developing footprints with a narrower scope within the LCA community. Many of the reviewed nitrogen footprint methods are at the inventory analysis level. That is, nitrogen flows (inputs and outputs) at different stages of a life cycle are identified and quantified without any evaluation of their further impacts on the environment. For example, the nutrient footprint method by Grönman et al. [10] considers virgin and recycled nutrient inputs, outputs lost from or continuing in the nutrient cycle, and nutrient use efficiency of a (food) system. Methods based on the N-calculator by Leach et al. [15] focus on emissions of Nr into the environment. The LCA community has criticized the N footprints for not including impact assessment according to the LCA principles [5]. LCA includes further assessment of potential environmental impacts of input and output flows. In life cycle impact assessment (LCIA), input and output flows are classified into impact categories. As concerns the environmental impacts of nitrogen, the focus is on nitrogen emissions (outputs from a system). As nitrogen resources are abundant in the atmosphere, input impact categories, such as resource scarcity, are irrelevant, even though atmospheric N2 conversion into reactive form causes environmental impacts, particularly due to high energy consumption. Impacts of nitrogen emissions include damage to the natural and human-made environment and to human health through various impact pathways. Typical impact categories of nitrogen emissions that contribute to such damage include aquatic and terrestrial

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Fig. 7 Life cycle assessment

eutrophication potential, acidification potential, global warming potential, depletion of stratospheric ozone, formulation of tropospheric ozone, ecotoxicity, and particulate matter (PM)/respiratory inorganics [1, 22]. The characteristics of LCA are presented in Fig. 7. The form of nitrogen release determines the possible impact categories. For example, mineral (ammonium and nitrate) nitrogen releases, which are available for further use as a nutrient, may cause terrestrial eutrophication; N2 O contributes to global warming and the formulation of tropospheric ozone; and NOx from combustion processes causes acidification and eutrophication, and contributes to PM and respiratory inorganics [1, 22]

3 Summary and Conclusions As presented above, several indicators can be used to assess the use, use efficiency, and nature of used nitrogen, and the environmental impacts of nitrogen. They usually aim either at improving a specific food production system or at understanding and minimizing nitrogen flows at national or regional scale and in relation to food consumption. Some methods, such as LCA, go as far as quantifying the environmental impact potential of nitrogen, but most indicators, as presented in this chapter, communicate and categorize nitrogen resource use and release in a food chain. Different approaches address different life cycle phases in the study, based on their aim and scope. NUE approaches always compare the amount of nutrient in the food product against the nutrients needed to produce it. However, there are differences in where the system boundaries in terms of life cycle phases are laid, and which nutrients are included in the study.

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Tables 1 and 2 summarize the presented methods. Different approaches to studying nitrogen in the food life cycle are presented for various needs to improve nitrogen cycles. Moreover, planetary boundaries could be included in the assessment. Li et al. [17] and Uusitalo et al. [23] have suggested Table 1 Summary of the presented indicators assessing NUE in the food chain Indicator

Author and year

Short description

Scope

Indicator result example or unit

N-calculator Leach et al. [15]

An N footprint tool which Local calculates annual per scale capita N losses to the (national) environment caused by food consumption. For each food category a VNF is defined which equals total N loss in the production chain divided by the N that remains in the consumed product

“The nitrogen footprint of the Netherlands is 24 kg N/capita/yr”

Full chain nutrient use efficiency

Full chain NUE, defined as the ratio of nutrients in final products to new nutrient inputs

Local scale (national)

“Nutrients in food available for human consumption in a country as a % of the total nutrient inputs to that country”

Nitrogen Erisman use et al. [6] efficiency of a food chain

Ratio of the protein (expressed as nitrogen) available for human consumption to the (newly fixed and imported) nitrogen input to the food system

Local scale (national)

“The NUEFC in the Netherlands for 2005 was estimated at 18%”

Nutrient Grönman footprint for et al. [10] nitrogen

The amount of captured virgin and recycled nutrients (kg) for use in the production chain and the share of nutrients utilized [%] either in the food itself or in the entire production chain, accounting also for side-products

Specific food production system

“1000 kg of Finnish oat flakes and porridge consumed requires 42 kg of nitrogen, of which 88% is considered virgin nitrogen. NUE in the oats chain is 55% and the NUE including secondary products is 71%”

Life cycle Uwizeye nitrogen use et al. [24] efficiency

Includes three indicators: life cycle NUE, life cycle NNB, and NHI

Local scale (national)

“For France, the life cycle NUE-N was estimated at 44%, life cycle NNB-N at 105 kg N/ha, and NHI-N at 123%”

Sutton et al. [22]

(continued)

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Table 1 (continued) Indicator

Author and year

Short description

Scope

N food-print Chatzimpiros Consumption-based Local & Bales [4] indicator of Nr inputs and scale losses from spatially (national) scattered livestock systems. N food-print of a product is the N loss associated with its agricultural production

Indicator result example or unit “Beef farming to feed an individual in France uses 11.1 kg N/capita/yr, of which 3.8 kg N/capita/yr (or 35%) is the N food-print, 7% is recovered in retail products, and 3% is slaughter waste”

Table 2 Summary of indicators assessing nitrogen-related environmental impacts through LCA Indicator

Short description

Scope

Indicator result example or unit

Eutrophication potential

Impacts on terrestrial or aquatic ecosystems due to emissions of nutrients, which causes, e.g., acceleration of algae growth and oxygen depletion

Depends on the aim and scope of the study, but most often used to study a specific food production system

mol N eq./functional unit (terrestrial), kg P equivalent/functional unit (freshwater), kg N equivalent/functional unit (marine)

Acidification potential

Impacts due to emissions of acidifying substances

mol H + eq./functional unit

Global warming potential

The capacity of a greenhouse gas to affect radiative forcing in a specified time horizon

kg CO2-eq./functional unit

Depletion of stratospheric ozone

Degradation of stratospheric ozone due to emissions of ozone-depleting substances

kg CFC-11 eq./functional unit

Formulation of tropospheric ozone

Formation of ozone at the ground level of the troposphere caused by photochemical oxidation of VOCs and CO in the presence of NOx and sunlight. Damages vegetation, the human respiratory system, and human-made materials

kg NMVOC eq./functional unit

(continued)

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Table 2 (continued) Indicator

Short description

Scope

Indicator result example or unit

Ecotoxicity

Toxic impacts on an ecosystem (damage to species and changes in the structure and functioning of the ecosystem) due to emissions of ecotoxic substances

CTUe (Comparative Toxic Unit for ecosystems)/functional unit

PM/Respiratory inorganics

Adverse impacts on human health caused by PM and its precursors, such as NOx and NH3

kg PM2.5 eq./functional unit

that footprint indicators become more meaningful when compared to biophysical limits (planetary boundaries). Consequently, Li et al. [17] introduced a phosphorus exceedance footprint that shows excessive phosphorus use in relation to the sustainable use defined by planetary boundaries caused by a country, mainly due to food consumption and production. The approach could be further applicable, for example, to excessive Nr releases, although this has not yet been demonstrated.

References 1. Antikainen R (2007) Substance flow analysis in Finland—four case studies on N and P flows. Monographs of the Boreal Environment Research. Finnish Environment Institute, Finland. https://helda.helsinki.fi/bitstream/handle/10138/39343/BERMon_27.pdf?sequence= 1&isAllowed=y 2. Biodiversity Indicators Partnership (2016) Trends in loss of reactive nitrogen to the environment. https://www.bipindicators.net/indicators/trends-in-loss-of-reactive-nitrogen-to-the-env ironment. Accessed 3 Dec 2020 3. Bleeker A, Sutton M, Winiwarter W et al (2013) Economy-wide nitrogen balances and indicators: Concept and methodology. https://one.oecd.org/document/ENV/EPOC/WPEI(2012)4/ REV1/en/pdf. Accessed 8 December 2020 4. Chatzimpiros P, Barles S (2013) Nitrogen food-print: N use related to meat and dairy consumption in France. Biogeosciences 10:471–481. https://doi.org/10.5194/bg-10-471-2013 5. Einarsson R, Cederberg C (2019) Is the nitrogen footprint fit for purpose? An assessment of models and proposed uses. J Environ Manag 240:198–208. https://doi.org/10.1016/j.jenvman. 2019.03.083 6. Erisman JW, Leach A, Bleeker A, Atwell B, Cattaneo L, Galloway J (2018) An integrated approach to a nitrogen use efficiency (NUE) indicator for the food production-consumption chain. Sustainability 10:925. https://doi.org/10.3390/su10040925 7. Fang K, Heijungs R (2015) Rethinking the relationship between footprints and LCA. Environ Sci Technol 49:10–11. https://doi.org/10.1021/es5057775

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8. Galloway JN, Townsend AR, Erisman JW et al (2008) Transformation of the nitrogen cycle: recent trends, questions, and potential solutions. Science 320:889–892. https://doi.org/10.1126/ science.1136674 9. Galloway JN, Winiwarter W, Leip A et al (2014) Nitrogen footprints: past, present and future. Environ Res Lett 9:115003. https://doi.org/10.1088/1748-9326/9/11/115003 10. Grönman K, Ypyä J, Virtanen Y et al (2016) Nutrient footprint as a tool to evaluate the nutrient balance of a food. J Clean Prod 112:2429–2440. https://doi.org/10.1016/j.jclepro.2015.09.129 11. Hayashi K, Oita A, Nishina K (2020) Concealed nitrogen footprint in protein-free foods: an empirical example using oil palm products. Environ Res Lett 15: https://doi.org/10.1088/17489326/ab68ea 12. ISO 14040 (2006) Environmental management. Life cycle assessment. Principles and framework 13. Joensuu K, Pulkkinen H, Kurppa S et al (2019) Applying the nutrient footprint method to the beef production and consumption chain. Int J Life Cycle Assess 24:26–36. https://doi.org/10. 1007/s11367-018-1511-3 14. Leach AM, Emery KA, Gephart J et al (2016) Environmental impact food labels combining carbon, nitrogen, and water footprints. Food Policy 61:213–223. https://doi.org/10.1016/j.foo dpol.2016.03.006 15. Leach AM, Galloway JN, Bleeker A et al (2012) A nitrogen footprint model to help consumers understand their role in nitrogen losses to the environment. Environ Dev 1(1):40–66. https:// doi.org/10.1016/j.envdev.2011.12.005 16. Leach AM, Majidi AN, Galloway JN et al (2013) Toward institutional sustainability: A nitrogen footprint model for a university. Sustainability 6(4):211–219. https://doi.org/10.1089/sus.2013. 9852 17. Li M, Wiedmann T, Hadjikakou M (2019) Towards meaningful consumption-based planetary boundary indicators: The phosphorus exceedance footprint. Glob Environ Change 54:227–238. https://doi.org/10.1016/j.gloenvcha.2018.12.005 18. Ridoutt BG, Pfister S, Manzardo A et al (2016) Area of concern: a new paradigm in life cycle assessment for the development of footprint metrics. Int J Life Cycle Assess 21:276–280. https://doi.org/10.1007/s11367-015-1011-7 19. Rockström J, Steffen W, Noone K et al (2019) A safe operating space for humanity. Nature 461:472–475. https://doi.org/10.1038/461472a 20. Service, RF (2019) New reactor could halve carbon dioxide emissions from ammonia production. Science. 6 November. Available at: https://www.sciencemag.org/news/2019/11/new-rea ctor-could-halve-carbon-dioxide-emissions-ammonia-production. Cited at 23 September 2020 21. Steffen W, Richardson K, Rockström J et al (2015) Planetary boundaries: Guiding human development on a changing planet. Science 347(6223):1259855. https://doi.org/10.1126/sci ence.1259855 22. Sutton, MA, Bleeker A, Howard CM et al (2013). Our nutrient world: the challenge to produce more food and energy with less pollution. Global overview on nutrient management. Centre for Ecology and Hydrology, Edinburgh on behalf of the Global Partnership on Nutrient Management and the International Nitrogen Initiative. Available at: https://library.wur.nl/WebQuery/ wurpubs/reports/434951. Cited at 20 November 2020 23. Uusitalo V, Kuokkanen A, Grönman K et al (2019) Environmental sustainability assessment from planetary boundaries perspective—A case study of an organic sheep farm in Finland. Sci Total Environ 687:168–176. https://doi.org/10.1016/j.scitotenv.2019.06.120 24. Uwizeye A, Gerber PJ, Schulte RPO, de Boer IJM (2016) A comprehensive framework to assess the sustainability of nutrient use in global livestock supply chains. J Clean Prod 129:647–658. https://doi.org/10.1016/j.jclepro.2016.03.108 25. Leip A, Leach A, Musinguzi P, et al (2014) Nitrogen-neutrality: a step towards sustainability. Environ. Res. Lett. 9:115001. https://doi.org/10.1088/1748%2D9326%2F9%2F11%2F115001

Footprint Analysis of Sugarcane Bioproducts Noé Aguilar-Rivera

Abstract Global warming and the generation of greenhouse gases are strongly pushing anthropocentric activities to develop methodological frameworks for measurement such as the ecological, carbon and water footprint and to apply or develop novel technologies to determine key points to drastically reduce their impacts. Agroindustries such as sugarcane and the industrialization of their waste and by-products have been evaluated in a multidisciplinary way in various environmental, technological and management contexts, mainly in the largest producing countries, where the components of the ecological footprint have been calculated. However, these values cannot be generalized to all producing countries. Therefore, local actions such as technological management are necessary to minimize the generation of environmental impacts and move toward sustainability. This work carried out the study of the impact of the sugarcane harvest with the burning system in Mexico, demonstrated as a highly emitter of greenhouse gases and significantly impacting the agroindustrial quality of stalks and the ecosystem. Besides, sustainable alternatives for the use of trash in the Cordoba-Golfo sugarcane region were evaluated. Likewise, in the La Huasteca Region, precision agriculture techniques were applied to determine the regionalization of areas highly susceptible to drought and higher requirements for inputs and water. The results showed that it is feasible to potentially reduce the ecological footprint of sugarcane cultivation through a scientific approach based on improving cane stalks production conditions and conversion of trash into bioproducts. Keywords Ecological footprint · Sugarcane crop · Productive diversification · Precision agriculture · Trash

N. Aguilar-Rivera (B) Facultad de Ciencias Biológicas y Agropecuarias, Universidad Veracruzana, Km. 1 Carretera Peñuela Amatlán de Los Reyes S/N. C.P., 94945 Córdoba, Veracruz, Mexico e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Switzerland AG 2022 J. Ren (ed.), Advances of Footprint Family for Sustainable Energy and Industrial Systems, Green Energy and Technology, https://doi.org/10.1007/978-3-030-76441-8_9

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1 Introduction One of the greatest challenges for the relation nature-society in the twenty-first century is to meet biomass sources to solve the growing demand for energy for transportation, heating and industrial processes, and to provide raw material for the conventional chemical process and biorefineries in a sustainable way in concordance with goals of 2030 agenda of sustainable development and The Paris Agreement to avoid climate change by limiting global warming to well below 2 °C. However, the expansion of bioenergy and biocommodities production is expected to drive land use change, increase the use of water and inputs (agrochemical, fuels and chemicals) in order to provide biomass feedstocks to extracting more energy from the same unit of land as one of the challenges of the bioenergy and biofuels future. Therefore, sustainability requires the redesigning of production, consumption, and waste management. This goal is leading to a wide range of possible ecosystem responses and environmental consequences such as the ecological footprint (EF). Balancing human demands for food, feed, fiber, raw materials, energy and ecosystem requirements for clean water and biological diversity is a critical concern in planning for the sustainable development of a bioeconomy where CCU technologies (Carbon capture and utilization) and multidisciplinary approaches are required. Furthermore, Leal Filho et al. [36] outlines the need for a greater emphasis on a critical analysis and empirical assessments of the degree to which the 17 SDGs are being implemented. In relation to the above, a “footprint” is a quantitative measurement describing the appropriation of natural resources by humans [31]. This ecological indicator describes how human activities can impose different types of burdens and impacts ˇ cek on global sustainability. Moreover, exist according to Vanham et al. [64] and Cuˇ et al. [17] a family of footprints that can be used for the assessment of environmental sustainability and the progress toward sustainable development goals (SDGs) in each anthropogenic activity. The term Carbon footprints (CF) originated as a subset of the “Ecological Footprint” (EF) which is referred to the biologically productive land and sea area required to sustain a given human population, expressed as global hectares. According to this concept, CF is the land area that will assimilate the CO2 produced during the lifetime of a person or total global population and the water footprint (WF) represents the total volume of direct and indirect freshwater used, consumed, and/or polluted. A WF consists of blue, green, and gray water footprints, which represent the consumption of surface and groundwater, the consumption of rainfall, and the volume required to dilute pollutants to water quality standards, respectively [30]. Currently, bioenergy, bioeconomy, biorefineries, green growth has been globally promoted as the option to reduce the environmental and economic impact of use of fossil fuels and products to mitigate the emissions of greenhouse gases (GHGs). Nevertheless, various questions have been raised about the potential environmental impacts or ecological footprint of bioproducts production, including those on water quality and availability (scarcity). Therefore, freshwater scarcity and pollution will be aggravated in the future due to a significant increase in demand for water and

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a decrease in availability and quality mainly by factors such as population growth, economic development, changes in production models and trade patterns, increasing competition over water because of increased demands for domestic, industrial and agricultural uses and how different sectors of society will respond to constraints [22, 28]. In relation to this complex and global problem, Zhou et al. [70] proposed as framework the novel 5R approach for managing urban water resources: Recover (stormwater), Reduce (toilet flushing water), Recycle (gray water), Resource (black water), and Reuse (advanced-treated wastewater). The 5R generation incorporates the latest ideas for harvesting stormwater, gray water, and black water in its several forms. The worldwide methodological approach most widely used by stakeholders to assess environmental impacts of products is usually defined through a Life Cycle Assessment (LCA). LCA is conventionally characterized as a “cradle-to-grave” approach, as an open loop [51].

2 Literature Reviews 2.1 Ecological Footprint of Agriculture Agriculture, agroindustries and agroecosystems are essential for the food and social well-being of a growing world population. Typically, the main environmental concerns about agroindustrial operations and rural production are related to crops or commodities, biomass and water consumption, wastewater production, waste generation, energy consumption and, above all, anthropogenic emissions of greenhouse gases as CO2 emission [50]. Besides, it generally causes emissions of large amounts of GHG of the so-called “non-CO2 gases” including N2 O and CH4 with a heating power of 265 and 28 times, respectively, greater compared to CO2 . However, agroecosystems also have great GHG mitigation potential when crop residues are conserved or industrialized at the farm level or in biorefineries, tillage is reduced and cover crops, intercropped or in rotation are introduced alongside the agroindustrial crop such as sugarcane [23, 58]. Therefore, the transition to sustainable management practices, inputs and incorporating novel technologies as precision agriculture (PA) is essential for competitiveness. Climate change, as a consequence of progress, exposes people, societies, economic sectors as agriculture and ecosystems to risk as sugarcane crop and agroindustries related. The review of Linnenluecke et al. [37] discuss how researchers and agroindustry stakeholders have developed a body of knowledge of how changes in climate lead to changes and impacts in the primary production of sugarcane (yield losses or gains), modeling, crop harvestability, plant diseases, water availability, the risks to sugarcane workers of heat exposure, the negative environmental consequences of expanding sugarcane landholdings and across the value chain to better

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understand how projected changes in variables such as increase in air temperature, reduction of periods of rainfall, and increase in atmospheric carbon dioxide (CO2 ) concentration mainly due to the harvest with burning of cane fields influence sugarcane production and concluded It is imperative that research findings are translated to stakeholders in the industry so that they have a deep understanding about future climate change, as well as information on risks, opportunities, response strategies and adaptation options and technologies. The number of carbon footprinting research of agricultural systems is increasing, but due to widespread differences in region and countries and agroecological zones, their comparison remains difficult. Nevertheless, such studies represent the contribution of cultivation practices in a better way than merely focusing on soilborne GHG emissions, carbon sequestration, or energy intensity individually. Therefore, it is necessary to evaluate proposals to reduce or mitigate the ecological footprint (EF) of carbon (CF) and water (WF) of agricultural systems such as sugarcane [48]. However, the conditions of crops produced at any location are highly variable so it will be impossible to measure the components of the ecological footprint to establish a universal strategy of reduction of impact in the value chain. Therefore, traditional disciplinary approaches are unable to provide integrated management solutions for reducing EF, and an approach based on whole systems analysis is essential to bring about beneficial change to transition of sustainability of sugar industry. In this regard, carbon footprinting (CF) has potential as a tool for assessing and comparing GHG performances of different agricultural products and crop management practices along with identification of points to improve environmental efficiencies. Pandey and Agrawal [48] review the available scientific literature on the concept and calculations of carbon footprint, and its application to the agriculture sector. In this way, the burning of biomass before harvest is a very widespread practice in the tropics, where it is used as an agricultural management tool for the elimination of biomass, control of pests and weeds, removal of dry material and reduce the costs of harvesting by removing the foliage; and subsequent re-burning to remove excess residues prior to soil preparation and replanting. Reid et al. [56] and Ramos et al. [54] reviewed the scientific literature to evaluate studies of exposure to smoke from burning crops in mortality and in respiratory, cardiovascular, mental, and perinatal health. Within these reviewed works, associations between exposure to biomass burning smoke and general effects on respiratory health are documented, specifically exacerbations of asthma, chronic obstructive pulmonary disease (COPD), associations with an increased risk of respiratory infections, allergies and conjunctivitis with Growing evidence supporting an association with all-cause mortality.

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2.2 Impact of the Sugarcane Harvesting System with Burning on the Carbon Footprint (CF) Currently, the environmental sustainability of worldwide sugar industry is measured by parameters included the general situation of each industry, the production of cane (area cultivated, yield, productivity, cane quality, harvest and control, performance of small producers, price of cane and research, development and extension), milling of cane (number of factories, sugar production, milling efficiency, price of sugar locally and internationally) and diversification (biofuel, electricity cogeneration and others) without considering the ecological footprint or alternatives to reduction [8]. Sugarcane (Saccharum officinarum L.) is an agroindustrial crop that has a wide resilience and capacity for adaptation when is subjected to unfavorable climate in tropical and subtropical regions, management practices and soil conditions because as plant has advantages such as its adaptation to a wide range of agroecological conditions, low sensitivity to poor soil fertility conditions and prolonged warmhumid regimes and pests, weeds and diseases [55]. However, these adverse conditions require a greater number of inputs as agrochemicals to compensate limiting factors but generate a greater amount of GHG and the resulting product (stalks) has low productivity and quality with high production costs (Fig. 1). Notwithstanding sugarcane is one of the plants with the highest photosynthetic efficiency, in the use of solar energy. The total plant material is composed of 1/3 the leaves and tops, and 2/3 the stem, of which 1/3 would be fiber and 1/3 the juice from which various types of sucrose are manufactured, industrial ethanol anhydrous and hydrated and biofuel and electrical energy from cogeneration mainly but it has been investigated that by-products (trash, bagasse, filter mud, ashes, vinasses) are potentially raw material for more than 100 derivatives and bioproducts but present unexploited potentials for further product portfolio diversification according to the conclusions of Formann et al. [24] and Meghana and Shastri [42] (Fig. 2). Nevertheless, the sugar industry has been traditionally subjected to frequent and strong volatility in sugar prices, driven by many causes, but mostly by surpluses in global production. Worldwide sugar-producing countries are looking seriously into diversification under different approaches and priorities since the 1970s, aiming to reduce the risk of relying strongly on one global commodity—sucrose or table sugar.

Fig. 1 Agricultural soils in adverse conditions and low-quality cane stalks

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Fig. 2 Potential of sugarcane for bioproducts

It is widely recognized that today’s sugar industry needs to diversify to a biorefinery with a lot of bioproducts minimizing GHG. Before making bioproduct decisions, an extensive study of the alternatives is needed because there are different bioproducts, scale production, price and technological sophistication. The starting feedstocks are many, such as sucrose, juice, syrups and molasses (sucrochemicals), bagasse, trash (solid biofuels by cogeneration), vinasses or stillage and filter mud (biofertilizers), and these can be used in several routes to produce several bioproducts: alcohols and ethanolchemistry, amino acids, organic acids, polyols, polyphenols, nutraceuticals,

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animal feed, sweeteners, specialty sugars and many more as lignochemicals. Some of these alternatives are being used commercially and others are still under development but there is high interest and potential [35, 60, 65, 66]. Yang et al. [69] developed the process of manufacturing value-added products included extracted bioactive organic extracts from the edible and medicinal plant homologs and co-crystallized with either white sugar or brown sugar (sugar-based nutraceutical industry). These sugar and brown sugar value-added products have a wider market potential due to changes in taste, and supportive health developed to improve the nutritive value after co-crystallization have wider societal acceptability. Sugarcane bagasse is the best known and most researched by-product of the sugar industry, as lignocellulosic biomass presents great potential for the production of derived biofuels using gasification, rapid pyrolysis, or hydrolysis followed by fermentation and other novel technologies. For further development of these technologies, detailed knowledge of the physical and chemical characteristics of sugarcane bagasse is necessary. Besides, bagasse has a set of constraints as low bulk density and high moisture content, and the biological degradation process [47]. Carvalho et al. [15] concluded that the processing of bagasse for cogeneration generation (as one of the main current uses of cane biomass) the majority of emissions were related to the production of sugarcane itself in crop fields equivalent to 85.4% of overall emissions. The main contributors to the carbon footprint associated with the production of sugarcane were nitrogen fertilization and irrigation. Hiloidhari et al. [29] reviewing the potential of sugarcane as biomass. They mentioned that the environmental performance of sugarcane-derived bioenergy and bioproducts vary with sugarcane cultivation practices and energy conversion technologies. The conventional uses of sugarcane by-products (trash, bagasse, filter mud, stillage) contribute to negative emission and lower the overall global warming potential (GWP) by 13–15%. The sugar mill producing only sucrose has a much higher environmental impact than producing both sucrose and surplus electricity for the grid. Brazilian sugarcane biorefinery is the most developed and integrated biofuel, bioenergy and bioproducts agribusiness in the world adding value to the available carbon from sugarcane by-products maximizing their utilization as feedstock. They have evolved from typical single product plants (sugar-producing plants) to polygeneration plants that produce sugar, ethanol and electricity. Moreover, this polygeneration plant can produce an even higher variety of products if lignocellulosic residues (trash and bagasse) are used as feedstock to produce heat, electricity, biofuels and other biochemicals and potentially decrease the environmental impacts [33, 35]. Accordingly, bioenergy is key to supporting the UN Sustainable Development Goals (SDGs) where the sugarcane is the highly recommended option for developing countries producers because is the crop-based fuel that presents the highest positive energy balance (the relation renewable energy/fossil energy used is between 8:1 and 10:1), high efforts have been made by researches, government and industry stakeholders in order to maximize efficiency and reduce GHG emissions in the crop fields and the production stage [11].

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Consequently, sugar industry needs to transit to “Industrial Symbiosis” in which related industries exchange raw materials, energy, water and/or by-products, which plays an important role in the transition toward sustainable development reducing the ecological footprint. Specifically, Industrial Symbiosis addresses issues related to resource depletion, waste management and pollution by using waste streams to generate value more efficiently across networks of industrial stakeholders [10]. Sugarcane is grown in more than 130 countries worldwide, mainly in developing countries on small farms without sustainable management practices. The crop area is 26,269,819 ha worldwide distributed in 1,547,616 ha in Africa, 13,919,856 ha in America, 10,279,738 ha in Asia, 37,739 ha in Europe and 484,869 ha in Oceania with a world production of cane stalks of 1,907,024,730 t distributed in 94,925,364 t in Africa, 10,22,785,798 t in America, 7,51,902,468 t in Asia, 2,280,152 t in Europe and 35,130,948 t in Oceania (FAOSTAT, 2020) (Fig. 3).

2.3 Sugarcane Crop in Mexico Mexico is the seventh largest sugarcane producer in the world with yields per hectare below the world average. It is harvested in 15 states of the Republic (Campeche, Chiapas, Colima, Guerrero, Jalisco, Michoacán, Morelos, Nayarit, Oaxaca, Puebla, Quintana Roo, San Luís Potosí, Tabasco, Tamaulipas and Veracruz) which in turn make up 6 sugarcane regions administrative and cane stalks are processed in 50 sugar mills (Fig. 4) within which Puebla, Morelos and Chiapas stand out for their high average annual field yield; in addition to Veracruz, Jalisco and San Luis Potosí for their high arable area.

Fig. 3 Sugarcane producing countries (Data of FAOSTAT, 2020, http://www.fao.org/faostat/es/# data/QC)

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Fig. 4 Sugarcane producing regions in Mexico

During the 2019/2020 harvest season, 49,274,468 tons of cane were harvested in 783,486 ha with a productivity of 62,891 tons ha−1 , which generated a sugar production of 5,278,320 tons and 12,111,980 L of ethanol. However, this harvest season was highly vulnerable to the effects of climate change, droughts and floods, reflecting an increase in the area harvested due to deforestation in recent years. On the other hand, sugarcane is harvested with the cane-burning system (pre-burning, burning and re-burning) in 90.66% and mechanized harvesting in 17,606% of the national total (Fig. 5), directly influencing a stagnation of productivity of the yield and quality of sugarcane and sucrose by increasing the amount of non-crystallizable sugars due to the effects of the harvest with burning [59]. That is, the burning of sugarcane and post-harvest residues not only pollutes the environment and aggravates the extreme heat situation in the harvest season, but also affects the fertility of the soils by reducing the content of organic matter and nitrogen in the soil generating a huge ecological footprint, accelerates the degradation of cane stalks and their quality. Besides is the main cause of wild forest fires at the national level and contributes to the phenomenon of global warming of the atmosphere as has been analyzed by Carvalho et al. [15]; Luca et al. [39] and Mugica-Álvarez et al. [45]. Post-harvest and during the growing season trash represent a substantial input of C and N to the soil. This waste if properly processed and densified, can be used as an alternative source of carbon-neutral renewable energy in farms and biorefineries. Thus, trash burning, or removal reduces the soil C/N ratio and increases N2 O emissions of N fertilizer modifying the potential mitigation of GHG emissions offered by sugarcane as a bioenergy crop. This approach could reduce C losses during the

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Fig. 5 Harvest type (Data from CONADESUCA, 2020) https://www.gob.mx/conadesuca/es/articu los/6-informe-estadistico-del-sector-agroindustrial-de-la-cana-de-azucar-en-mexico?idiom=es

agricultural phase of the bioproducts production by restoring or avoiding the gradual loss of soil C [67]. In this sense, technological actions and approaches are required to restrict trash (tops, dry and green leaves) burning in sugarcane fields targeting at diminishing black carbon and other GHG emissions from the harvesting operation to reduce the carbon footprint generated by the production of sugarcane mainly during harvest, establishing incentives and alternative uses of trash to generate income derived from its conversion into bioproducts within the farm, sugar mill or biorefinery. Therefore, bagasse and trash have the greatest potential for use in the short and medium as waste mainly with electricity generation of low environmental impacts to the grid, but currently, the whole bagasse is burnt in mill boilers to produce steam and electricity solely to operate the sugar mill.

2.4 Application of Precision Agriculture to Reduce Carbon (CF) and Water (WF) Footprint of Sugarcane Crop Sugarcane is a highly efficient perennial crop. However, to manifest its maximum productive potential, it requires a precise study of environmental, meteorological and

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soil conditions. Coelho et al. [16] established that the potential production of biomass and sucrose is achieved when sufficient humidity is available in the form of rainfall, irrigation or both, but also depends on the photoperiod, altitude, the structure and architecture of stalks and leaves (canopy), population and distribution. Bocca et al., [13] concluded that potential production depends on the action of limiting factors such as the availability of water and nutrients, such as (1) quantity, frequency and intensity of rainfall, efficient technologies and irrigation performance and water quality (2) The physical and chemical characteristics of the soil: texture, organic matter, structure, depth, pH, salinity and sodicity. (3) The presence of high groundwater levels that are harmful to the crop, which is related to the existence, depth, separation and effectiveness of a drainage system. (4) Factors derived from the cane genotype, such as early flowering and the degree of stem erection. (5) An ideal climate for sugarcane cultivation is one that presents two different seasons: a hot and humid one, to provide germination, tillering and vegetative development, followed by a cold and dry one, to achieve maturity and the consequent accumulation of sucrose on the stems. (6) Agroecological management of plant health, external pollutants, natural phenomena (floods, frosts, hurricanes and accidental burns). (7) Technology for the administration of crop management as precision agriculture (PA). (8) efficient management of environmental, political, social and economic factors. In this context, remote sensing (RS), geographic information systems (GIS) geotechnologies and precision agriculture (PA) can play a unique role due to their ability to provide in real time, quickly and relatively accessible agrometeorological, environmental and management data in large sugarcane areas, that is, remote sensing (RS) as an agricultural model, allows carrying out radiometric measurements (≈400– 2500 nm) on a large scale integrating the biochemical and biophysical characteristics of the canopy, where different data o Information must be integrated at various scales by combining it from various sources, such as mathematical models and observations in space and time of the variables of interest to obtain reliable results [68]. PA is an innovative, integrated and internationally standardized approach aimed at increasing the efficiency of resource use and reducing the uncertainty of decisions required to manage variability on farms while minimizing potential environmental pollution [4]. In sugarcane crop can serve several purposes: [1] classification and mapping, [2] identification of phenological stages and degree-days of growth, [4] discriminate varieties, [3] monitoring of irrigation and nutritional stress, [5] detection of damage by insects, pests, weeds and diseases, [6] prediction of yields, [7] management of crop residues in order to increase productivity (yields and quality of the crop) with the reduction of production costs. The advantage of this system (GIS-GPS) is that the records and data are only measured once, since they change very little over time and useful long-term maps can be generated, for example, in the monitoring of effects of climate change, direction of fieldwork to collect data on culture and consequently cost reduction through digital processing and the generation of maps showing the spatial variability of crops,

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development of universal models applicable to prediction applicable under variable agroclimatic and phenological conditions such as droughts and define precisely agroecological zoning of lands with aptitude to the cultivation of sugarcane [5, 7, 61].

3 Materials and Methods Therefore, in this work the study carried out at first in the producing region “CórdobaGolfo” to characterize the trash, determine the agroindustrial quality of cane stalks by green and burned harvest and evaluate alternative uses of trash to reduce its burning and therefore reduce the carbon footprint to avoid affecting socioeconomic stability of sugarcane region and cane cutters. In this region, works have been carried out related to the burning of sugarcane fields [46], showing great environmental impacts such as an increase in the carbon footprint, generation of particles with an effect on health, and loss of N and organic matter. The harvesting practice is generally carried out between the end of October and until July with a total average duration of 150–180 days, with important variations each year depending on parameters such as climatological variables, maximum and minimum temperatures, thermal oscillation, rainfall, relative humidity, evaporation and drought, harvesting systems (green or burning, manual or mechanized), available transportation and conditions of roads, and highways. In relation to the harvest, when the sugarcane is previously burned in this region, 85% of the leaves produced by the plant are consumed by fire, while the remaining 15% remains in the field, incorporating -in the best of cases- to the soil as burnt leaves or ash. However, burning harvest prevails for an eminently economic purpose without considering mechanizable and semi-mechanized harvesting as an alternative due to its low cost, low soil compaction, low damage to roots undercuts and mainly to the culture and tradition of the growers. This practice is widespread in the elimination of leaves of cane and weed control that facilitate the preparation and replanting of soils with reduction of harvest costs, facilitating transport and protecting field-workers from poisonous animals; while reducing the water in stalks, at the same time they cause the affectation of the environment by GHG and particules, the destruction of organic matter and the loss of the soil structure with greater desiccation and erosion. These effects, together with the compaction of the soils due to the passage of machinery, constitute the main causes of the decrease in yields in sugarcane cultivation [21, 14]. The paper of García et al. [25] concluded that the agricultural stage of cane stalks in Mexico contributes the majority of carbon emissions (59–74%) from fertilizer production, nitrous oxide (N2 O) emissions and biomass burning. Pryor et al. [52] determined that green cane harvesting reduces energy inputs and greenhouse gas emissions by 4% and 16%, respectively. Meza-Palacios et al. [43] using The LCA showed that sugarcane growing and harvesting stage provides the most harmful environmental impacts (52%) in region Córdoba-Golfo Mexico.

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3.1 Characterization of the Residue The analysis of chemical composition of the trash included the determination of polysaccharides (holocellulose), lignin, ash and extractives according to the TAPPI techniques (American Association of Pulp and Paper Industries Technicians) [62] and the AOAC (Official Association for Analytical Chemistry) methods for bromatological analysis [6].

3.2 Quality of Cane Stalks The goal was to determine the post-harvest deterioration of the cane stalks of several cultivars by effect of the type of harvest (green or burning) in the variables, moisture percentage, fiber, juice Pol, reducing sugar, Brix and pH according to Santana et al. [57].

3.3 Production of Compost The determination of LCA of Kaab et al. [34] recommended the use of reduced tillage and no tillage cultivation operations and organic fertilizers such as compost in cane farms, for that reason the production of biofertilizers or organic manures from by-products of the sugar industry can be an alternative to reduce the use of NPK inorganic fertilizer and GHG emissions, increase the organic matter in soils and as source of income for cane farmers [20]. Biofertilizer in the form of compost was produced over 3 months from by-products such as filter mud, ash from sugar mill and trash from crop fields and evaluated for their nutritional value according to Mexican NOM-021-RECNAT-2000 [19].

3.4 Production of Edible Mushrooms Edible mushrooms (Pleurotus ostreatus) were grown as individual substrates (leaves, tops, and flowers) and mixed (trash) from crop fields with control bagasse from sugar mill. These lignocellulosic wastes (500 g of dry substrate) were first washed with filtered tap water and hydrated for 12 h to a moisture content of 65–75% with a 2% solution of Ca(OH)2 (alkaline immersion method). Then the substrates were placed in metallic container that can be reused to avoid generating waste and GHG by consuming plastic bags and fossil fuel, firewood or charcoal at the conventional process of production of edible mushroom with hot water or steam sterilization. At

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the substrates was added 5% of mushroom spawn before being incubated at 25 ± 1 °C for 21 days [40, 12]. The alkaline method is a simple procedure that is mainly used in rural communities, where small-scale production will suffice. Compared to other strategies, this method presents many advantages, such as low cost, competitive biological efficiency, no fungal contamination, a shorter colonization time, and absence of need for fossil energy and zero GHG generation [32]. The biological efficiency (BE %), productivity (%) and yield (%) of mushrooms produced from sugarcane trash was determined according to Raman et al. [53]. Trash and single component are competitive substrate for the production of edible mushrooms with the added benefit of being a low-cost substrate (Tables 4 and 5 and Fig. 13) using this waste for the production of food, in turn employing agricultural workers in a new productive activity and avoiding the emission of greenhouse gases due to the harvest with burning.

3.5 Precision Agriculture to Minimizing CF and WF in Sugarcane Crop This case study was carried out in the sugarcane producing region “La Huasteca” to apply precision agriculture tools for the analysis of the limiting factors of the cane cultivation, mainly the access to water through the use of the Normalized Difference Vegetation Index (NDVI) as a tool to determine scarce or excess humidity and therefore direct management practices to reduce the carbon and water footprint without reducing yields (Fig. 6).

3.6 Identification of the Area Cultivated with Sugarcane and Water Footprint (WF) by NDVI La Huasteca is located at the national level among the first three producers of this crop and sucrose and within the producing regions with the lowest field yield (Fig. 7) mainly affected by droughts. In this sense, according to Morote Seguido et al. [44] drought has been considered an exceptional situation and traditionally the main instruments used to solve have been extraordinary reactive and emergency measures, that is, infrastructures to increase the supply of water resources and economic compensation by governments for the damage and losses caused. According to the above, comprehensive diagnostics and methodological frameworks are required to increase profitability with the least environmental impacts. Therefore, for the analysis of the sugarcane crop in “La Huasteca” the approach of spatial analysis and visual interpretation of maps derived from Landsat 7 ETM +

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t/ha

Fig. 6 Framework for sugarcane crop sustainability analysis 80 75 68 70 60 60 65 60 57 57 59 56 60 54 55 55 55 56 50 51 48 49 45 40 35 30 1970 1980

75 67

65 57

58

55

53

68 59

58

55

52

51

53

65

61

5860 61

57 58

51

45

46

2000

54

45 39

1990

57

55

54

53 46

69

69 65

2010

38

2020

Harvest season (zafra)

Fig. 7 Cane productivity (tha−1 ) at La Huasteca (1970–2020) (Data from CONADESUCA, 2020)

(Enhanced Thematic Mapper) 2645, 2745 and 2744 (orbit/point) acquired at the end of the Huasteca Potosina harvest, with spatial resolution of 30 m x 30 m, which were reprojected using the North American Datum of 1927 and the Transversal Mercator projection (UTM zone 14 North). These images were used for visual interpretation (photointerpretation) to establish the limits for sugarcane (digital polygons) of other land uses and their spectral response related to the productivity/humidity relationship (NDVI bands 3 and 4). The RGB 432 composition was used in the sugarcane region of “La Huasteca” because it is the one with the highest contrast for identification. For the digital processing of the images and the calculation of the NDVI, the ILWIS 3.3 software (Integrated Land and Water Information System, ITC, ILWIS System)

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was used, where the area corresponding to sugarcane was extracted; and then, in the form of a mask of the respective NDVI, for the sugarcane for each group of pixels; and were stored in a spreadsheet for analysis. The Normalized difference vegetation index (NDVI) derived by dividing the difference between infrared and red reflectance measurements, by their sum, provides effective measure of photosynthetically active sugarcane biomass and hydric stress [63] studied different linear combinations of RED, GREEN, and NIR bands for monitoring vegetation properties such as biomass, vegetative vigor of plants, leaf water content, and chlorophyll content. He found that the ratio and related NIR and RED linear combinations were superior to the GREEN and RED linear combinations for monitoring vegetation. The value of this index ranges from −1 to 1. The common range for sugarcane green vegetation is 0.2–0.8. In the first instance, they were differentiated into four productivity levels, usually associated with healthy vegetation or with high moisture content (High, Medium, Low and Very Low), to coincide with the classification adopted for the sugarcane-sugar areas.

Agroecological zoning of sugarcane cultivation in La Huasteca The relationship to determine productivity and environmental impacts of sugarcane crops as carbon and water footprint is strongly related to constraints as climate, rainfall, temperatures, soil type and drought and how the cultural and management practices are selected and apply. The FAO’s agroecological zoning project was one of the first planning tools in an inter-disciplinary approach, based on inputs from crop-ecologists, pedologists, agronomists, climatologists and economists, to quantify systematically the extent of potentially cultivable land in developing countries, and estimate productivity that can be expected for different crops under varying levels of inputs, and as policy instrument through which the governments intervenes in the economic domain to organize the relationship between space and production imposing conditions on the conduct of stakeholders and guiding their activities according to the limits of nature. It might also be advantageous to consider it as a model or combined geomatic framework which integrates high spatial resolution imagery, high temporal resolution with agroecological constraints for crops [49]. In the first place, the construction of thematic maps was carried out taking into account the climatic and edaphic requirements of the crop (Temperatures, Rain Regime, Drought Severity Index, Climates, Soils, Altitude and Slope) for the sugarcane region of study in ArcGis 10.3 software and the framework of Aguilar et al. [4].

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For the modeling of the agroecological zoning, the MaxEnt software was used based on maximum entropy theory for the agroecological development of the crop according to the framework of Gbemavo et al. [26].

4 Results and Discussion 4.1 Trash Management in Sugarcane Regions The trash has a composition which makes it a potential biomaterial for various applications such as the production of solid, liquid or gaseous biofuels, fibers, food, biofertilizers, livestock feed and lignochemistry. Sugarcane trash represents around one-third of the plant’s total primary energy content, but its use for different application, mostly power generation and animal feed are still incipient (Tables 1 and 2). The results of the quality of cane obtained in green and burnt harvest are presented in Figs. 8, 9, 10, 11, 12, and 13. Table 1 Chemical composition of sugarcane trash

Chemical component

%

Extractives ETOH/toluene

4.55

Extractives ETOH

7.46

Extractives Toluene

5.46

Extractives Benzene

2.89

Extractives water

6.56

Ashes

Table 2 Bromatological composition of sugarcane trash

5.66

Lignine

20.29

Holocellulose

74.67

α Cellulose

52.3

Pentosanes

22.4

Chemical component Crude Protein (N × 6.25)

% 7.43

N

1.64

Crude Fat

0.98

Total reducing sugars

0.042

Crude Fiber

71.47

N-free extract

33.40

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72 71

71

70

70 69

70.8

69

69.432

69.250

69.1 68.020

68 67

66.4 66.475

66 65 64

CP-72-2101

MEX79-431

MEX69-290

Moisture (Green) %

Mixture of varieties

Average

Moisture (Burning) %

Fig. 8 Moisture percentage in cane stalks (green harvest and burning) 15.000 14.500

14.350 14.037

14.000 13.500 13.000

13.290 12.935

12.500

13.400

13.317 13.195

12.864 12.230

12.146

12.000 11.500 11.000

CP-72-2101

MEX79-431 Fiber (Green) %

MEX69-290

Mixture of varieties

Fiber (Burning) %

Fig. 9 Fiber percentage in cane stalks (green harvest and burning)

Average

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20.00 18.00 16.00

18.099

17.323 15.78

15.20

15.795

18.68119.027

18.324 16.288

15.49

14.00 12.00 10.00 8.00 6.00 4.00 2.00 0.00

CP-72-2101

MEX79-431 Brix (%) (Green)

MEX69-290

Mixture of varieties Brix (%) (Burning)

Average

Fig. 10 Brix (%) in cane stalks (green harvest and burning) 98.00

96.00

94.00

96.98 95.78

93.58

93.194

94.307

93.767 93.155

92.00

94.111

91.124

90.53 90.00

88.00

86.00

CP-72-2101

MEX79-431 Pol (%) (Green)

MEX69-290

Mixture of varieties

Pol (%) (Burning)

Fig. 11 Juice Pol (%) in cane stalks (green harvest and burning)

Average

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0.350

0.320

0.308 0.300 0.250

0.215

0.200 0.150

0.174 0.140

0.138

0.136 0.114

0.106 0.100

0.074

0.050 0.000

CP-72-2101

MEX79-431

MEX69-290

Reducing sugars (%) (Green)

Mixture of varieties

Average

Reducing sugars (%) (Burning)

Fig. 12 Reducing sugar (%) in cane stalks (green harvest and burning) 5.650 5.600

5.600 5.550

5.530

5.550 5.500

5.535 5.465

5.460

5.450 5.400 5.350

5.344 5.318

5.317

MEX79-431

MEX69-290

5.339

5.300 5.250 5.200 5.150

CP-72-2101

Ph (Green)

Ph (Burning)

Mixture of varieties

Average

Fig. 13 pH in cane stalks (green harvest and burning)

The data analyzed establish that the loss in the agroindustrial quality of the varieties due to the effect of the burned harvest in the Córdoba-Gulf Region are related to the decrease in Brix of 11.11% and Pol of 3.27%, increase in reducing sugar of 64.4% and moisture loss of 1.58%, Ph 3.67% and fiber 0.93%, which establishes the need to rethink the harvesting practice with burning cane fields toward the transition

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to other systems and to use trash in the production of various bioproducts. This result is consistent with that carried out by Aguilar et al. [2] who determined that along with the reduction in the agroindustrial quality of cane stalks the N losses from cane burning at harvest were estimated by subtracting the N in the ash after burning from the N in the trash before burning 89.94 kg N/ha is lost by burning cane at harvest, representing about 60% of the total amount of nitrogen required by the crop. Likewise, in another sugarcane region of Mexico called “La Huasteca” Hermida et al. [27] determined in a mixture of plant cycle varieties that before and after burning crop fields there is a reduction of 26.315% in yield of sugarcane (tha−1 ), 42% Organic matter, 25% of N (%), 70, 43% in Ca (me/100 g), 23.76% of Mg (me/100 g) and an increase of 10.45% P (ppm), 7.92% in K (ppm) and 2.4% pH in soils planted with sugarcane. Besides in the agroindustrial quality of stalks due to the effect of the burning harvest were registered a reduction of 23.34% in Moisture (%) and 21.3% in Brix (%) as well as an increase of 18.10% in Fiber (%) and 32.14% in Reducing sugars (%). Therefore, in the following agricultural season, these deficiencies must be replaced with a high use of chemical fertilizers that will not necessarily generate high yields because of their application due to the continuous monoculture, degradation of soils and environmental effects. Actions such as intercropping or rotating crops, use of organic manures and biofertilizers, agroecological management of plant health, use of trash in livestock feeding avoiding burning, among others, will contribute to reducing the carbon footprint of the crop (Fig. 14). de Aquino et al. [18] concluded eliminate burned harvest for soil management with trash promotes the productivity of ratoons. Keeping 50% of trash as mulch is enough to improve the growth and yield of sugarcane with drought occurrences. Therefore, the composting of sugarcane residues and the production of edible mushroom are options for the management of a high percentage of trash collected (50%) as a consequence of the green harvest. The results are shown in Tables 3, 4, and 5 and Fig. 15.

Fig. 14 Intercropping crops, application of compost, trash and cane as feed

204 Table 3 Chemical composition of compost

Table 4 Indicators of production of edible mushroom Pleurotus ostreatus in cane trash

Table 5 Morphological parameters of edible mushroom Pleurotus ostreatus growed in cane trash

N. Aguilar-Rivera Parameter

Value

Moisture

%

46.248

pH

–

6.046

Electric conductivity

Sdm−1

2.458

Ashes

%

43.466

Organic matter

%

51.868

Total carbon

%

32.612

Total nitrogen

%

1.795

C/N ratio

–

18.168

Calcium (CaO)

%

5.867

Magnesium (MgO)

%

0.733

Sodium (Na2 O)

%

0.150

Potassium (K2 O)

%

0.518

Phosphorus (P2 O5 )

%

3.724

Iron (Fe)

%

0.756

Copper (Cu)

%

0017

Zinc (Zn)

%

0.041

Manganese (Mn)

%

0.278

Substrate

Biological efficiency (BE %)

Productivity (%)

Yield (%)

Trash

72.5

2.503

2.503

Leaves

52.5

1.891

11.296

Tops

60

1.154

16.080

Flowers

63.33

2.72

10.45

Bagasse

40

1.818

5.882

Substrate

Cap diameter (cm)

Height (cm)

Trash

8.25–4.7

7.05–4.5

Leaves

6.03–3.68

5.75–4.63

Tops

8.31–3.9

6.88–3.82

Flowers

6.83–3.24

6.32–4.2

Bagasse

5.8–3.4

4.7–.4

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Fig. 15 Production of edible mushroom from trash in metallic container

Fig. 16 Image from Satellite LANDSAT 7 ETM + 432, digital processing and sugarcane cultivation polygon of “La Huasteca”

4.2 Precision Agriculture in Sugarcane Regions Figure 16 shows the processing of satellite images in the sugarcane region of La Huasteca to obtain the polygon of the crop. Lofton et al. [1, 38] discussed the suitability of temporal NDVI profiles for studying vegetation phenologies, especially those of crops such as sugarcane to identify (i) areas having different vegetation cover types and (ii) agricultural areas following different calendars and water stress of crops through multi-temporal NDVI data obtained through AVHRR/NOAA images, SPOT and others at various scales and temporalities (Fig. 17). According to the spatial analysis, it was determined that the sugarcane crop in “La Huasteca” is 75,328.83 ha. However, of the total sugarcane area, the low potential productivity of cane fields due to water stress, according to the NDVI vegetation index, was the one that presented the highest proportion (44.16%) and the average productivity (30.32%), in contrast to cane fields with very low productivity (17.54%) and high levels of productivity (7.98%) (Figs. 18 and 19). However, to accurately determine the effect of these large-scale variables on water stress and the impact of the crop on the water footprint (WF) in sugarcane areas, it is necessary to establish besides conventional meteorological parameters as water availability, rainfall, evapotranspiration (ETo), crop coefficient (Kc), water

-1 -0.22 -0.19 -0.17 -0.16 -0.15 -0.14 -0.13 -0.12 -0.11 -0.1 -0.09 -0.08 -0.07 -0.06 -0.05 -0.04 -0.03 -0.02 -0.01 0 0.01 0.02 0.03 0.04 0.05 0.06 0.07 0.08 0.09 0.1 0.11 0.12 0.13 0.14 0.15 0.16 0.17 0.18 0.19 0.2 0.21 0.22 0.23 0.24 0.25 0.26 0.27 0.28 0.29 0.3 0.31 0.32 0.33 0.34 0.35 1 1752.42 1413.73 960.97 631.84 337.9 127.38 46.02 11.99 1.98 0.43 0.03 0.01 1.48

2471.86

4825.01

4541.55 3951.79 4147.49 3520.12 3317.55

0.47 0.01 0.01 0.02 0.05 0.1 0.11 0.11 0.07 0.64 3.7 8.72 13.33 34.14 60.74 123.86 192.26 256.97 312.06 404.46 579.83 636.02 884.21 1131.84 1490.41 1922.76 2417.71 2830.54 3129.81 3228.55 3692.91 4222.9 5719.37 5987.64 6297.13 6094.65 5404.42

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Fig. 17 NDVI analysis scales at La Huasteca

NORMALIZED DIFFERENCE VEGETATION INDEX (NDVI)

Fig. 18 Profile of the NDVI in the sugarcane region of La Huasteca

Fig. 19 Spatial classification by plant vigor or level of water stress in sugarcane cultivation

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requirement satisfaction index (WRSI), etc. the time series during the phenological stages of the crop from satellite images from SPOT (Satellite pour l’Observation de la Terre) or NOAA-AVHRR (National Oceanic and Atmosphere Administration Advanced Very High Resolution Radiometer) sensors or other multispectral images that require monitoring over extended periods of time of the relevant variables related to the functioning of the sugarcane ecosystem [3] (Fig. 20). In the particular case of access to water, a total rainfall between 1500 and 1800 mm is adequate in the months of vegetative growth, provided that the light distribution

Fig. 20 Temporal analysis of water availability in rainfed, NDVI and development of the phenological cycle of sugarcane cultivation in La Huasteca during a growing season

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Fig. 21 Environmental characteristics of the sugarcane region of La Huasteca

is appropriate and abundant. Then there should be a dry period for ripening. During the period of active growth, the rainfall stimulates the rapid growth of the cane, elongation and the formation of internodes. However, the occurrence of intense rains during the ripening period is not recommended, because it produces a poor quality of juice and favors vegetative growth; in addition, it makes harvesting and transportation difficult [41]. The results showed that, based on the NDVI vegetation index, as an indicator of plant vigor, it was possible to zoning the phenological cycle of the sugarcane by approximating the location and quantification of humidity, which determines the water stress during the dry season in the lower parts of the sugarcane fields and due to waterlogging during the rainy season; Therefore, the NDVI is useful as a diagnostic tool at a given time to direct the total or survival irrigation, giving a general idea of the potential water footprint (WF) of the crop in a spatial way, and in time series, to characterize the sugarcane crop yield and productivity, but it must be accompanied by climatic and edaphological data on maps with defined climatic and edaphic characteristics that allows decision makers to visualize scenarios in the sugarcane region (Fig. 21). Figures 22 and 23, which shows the agroecological zoning of cane in La Huasteca and Cordoba-Golfo were obtained because of this process. These maps represent the areas with high agroecological development potential for cultivation in both regions and carry out a differentiation of areas that require a greater or lesser number of inputs such as agrochemicals, fossil fuels, water, labor force and machinery and therefore differentiate the potential ecological footprint of carbon and water. The analysis of this comparison of sugarcane regions shows that for La Huasteca the variables: precipitation (rainfall), climate and drought index are those that are considered most relevant for the development of sugarcane production. Of the total suitability, 75.7% is explained by climatic variables: precipitation (rainfall), which in turn is the variable with the greatest weight in productivity (37.9%), climate elements (20.5%), index of drought (17.3%), temperature (8.2%) followed by edaphological variables: slope, soil type and altitude (16.2%) for rainfed conditions. In the CórdobaGulf region, the precipitation variable (rainfall) is the most relevant with 72%. At these regions, the impact of irrigation on the WF of sugarcane should be estimated considering three water regimes: rainfed (RF), salvage irrigation (SI) and full irrigation (FI), besides the agroecological zoning, NDVI profile, equations and methodologies for the calculation of hydraulic needs. In all stages the blue WF

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Fig. 22 Agroecological zoning of the Huasteca by level of potential productivity

component increases with the use of irrigation mainly by gravity system. However, the total water footprint could be reduced in about 1% and 7%, from the rainfed to salvage and full irrigation regimes, respectively, considering agroecological zoning, precision agriculture tools and novel technologies as irrigation fueled by solar energy for water management for its reduction and impacts [28]. To promote the agroecological transition, stakeholders (researcher, technical institutes, cooperatives, millers, farmers and unions) need to create a partnership and capitalize on the knowledge gained from successful experiences and engage in learning

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Fig. 23 Agroecological zoning of the Cordoba-Golfo region by level of potential productivity

processes. Modeling is an essential tool for understanding and sharing knowledge on innovative sustainable cropping systems and can be used to design new cropping systems or new agroecological practices to reduce ecological footprint [9].

5 Conclusions The methodological framework used allowed determining the impact of the burning of cane fields on the agroindustrial characteristics of the cane stalks, demonstrating their deterioration. Likewise, productive alternatives such as the production of edible mushrooms and compost from trash will allow the establishment of food production and the recovery of organic matter from the soil as a socioeconomic option to reduce the carbon footprint by gradually reducing this common and widespread practice in the production of cane worldwide. On the other hand, the application of precision agriculture techniques was spatially able to determine the areas with the highest risk of drought through the NDVI algorithm and agroecological zoning those that are potentially suitable and not suitable for the production of sugarcane stalks. Monitoring these areas will allow the sustainable use of inputs mainly water such as rainfall

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or irrigation decreasing the water and carbon footprint while increasing productivity with differentiated cultural and management practices from planting to harvesting according to the environmental conditions of each agricultural season.

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Overview of Footprint Family for Environmental Management in the Belt and Road Initiative Kai Fang, Siqi Wang, Jianjian He, Junnian Song, Chuanglin Fang, and Xiaoping Jia

Abstract Over the past few years, the Belt and Road Initiative (BRI) proposed by China has made a notable contribution to the rapid growth of cross-border trade. This however has been accompanied by unexpected burden shifting of resource extractions and environmental emissions to less developed countries. Given that little attention has been paid to the trade-embodied resources and emissions throughout the BRI, this paper, for the first time, accounts for the water, land, carbon, nitrogen, and phosphorus footprints of 65 BRI nations and traces the flows embodied in international trade between the BRI and remaining 124 economies by employing a global multi-regional input–output model. Overall, distribution of the BRI’s environmental footprints shows strong spatial heterogeneity, amongst China, India, and Russia have the highest total environmental footprints. Furthermore, reverse patterns of spatial distribution can be observed between the total and per capita footprints of BRI nations. When it comes to the global scale, the BRI as a whole is found to be a net exporter of trade-embodied flows except for virtual water. Remarkably, 29% of the BRI nations experience a role transition in supply chains across scales, either from net exporters on the BRI level to net importers on the global level, or in reverse. Our findings provide a holistic picture of environmental footprints at scales ranging from single nations, regions, BRI, and even globe, highlighting the significance of a global K. Fang · S. Wang · J. He School of Public Affairs, Zhejiang University, Hangzhou 310058, China K. Fang (B) Center of Social Welfare and Governance, Zhejiang University, Hangzhou 310058, China e-mail: [email protected] J. Song College of New Energy and Environment, Jilin University, Changchun 130012, China C. Fang Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China X. Jia (B) School of Environment and Safety Engineering, Qingdao University of Science and Technology, Qingdao 266042, China e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Switzerland AG 2022 J. Ren (ed.), Advances of Footprint Family for Sustainable Energy and Industrial Systems, Green Energy and Technology, https://doi.org/10.1007/978-3-030-76441-8_10

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view in finding ways to tackle environmental challenges and fulfill the Sustainable Development Goals throughout the BRI countries by 2030. Keywords Environmental footprint · Multi-regional input–output (MRIO) · Trade-embodied flows · Spatial distribution · Supply chain · Belt and Road Initiative (BRI)

1 Introduction Globalization has considerably promoted international trade and geospatial separation of production and consumption, resulting in substantial cross-border flows of resources and emissions embodied in global supply chains of commodities [57]. As part of globalization processes, the Belt and Road Initiative (BRI) launched by China in 2013 has been recognized as a means to improve economic and geopolitical relations between Asia, Europe, and Africa [25, 38]. By bringing together a large number of developed and developing economies into an open and inclusive cooperation network, the BRI covers over 60% of the global population and 30% of the global GDP [21]. In 2017, the BRI countries’ total amount of foreign trade reached 9.3 trillion US dollars, accounting for over a quarter of global trade [44]. In the Anthropocene, we are increasingly confronting dysfunctional Earth systems and unprecedented environmental risks—water shortage, land degradation, climate change, eutrophication, etc. [23]. The BRI is by no means an exception [3, 47]. For instance, the imbalance of water resources is exacerbated among the BRI countries [37, 46]. Unexpected displacement of land through international trade is argued to be another challenge facing some of the BRI nations [54]. Many research studies have provided tangible evidence of carbon, nitrogen and phosphorus leakage in developing countries associated with the ever-expanding global supply chains [17, 36, 57]. Moreover, as most of the state members are emerging and developing countries, the Environmental Kuznets Curve (EKC) hypothesis implies that the BRI’s environmental footprints will continue to increase in paralleling with economic growth [1, 38]. All these clearly reflect the growing impact of international trade on the BRI’s environmental sustainability. Environmental footprints that have reached worldwide popularity in the past decades are universally recognized as a useful tool for measuring such trade-related impact [20]. Since the first appearance of the term “footprint” by Rees [39] who come up with the ecological footprint, a suite of footprints have been introduced and ultimately constituted a footprint family [11]. Since studies that are narrowed down to individual environmental footprints are incapable of capturing the full complexity of trade-related impact, we consider the concept of footprint family an important step forward for the simultaneous measurement of various human pressure and environmental impact to avoid blind spots and burden shifting [13].

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Normally two approaches for environmental footprints accounting can be identified: the bottom-up approach and the top-down approach [16]. Both of them have pros and cons with different scopes of application [11, 35]. The input–output analysis (IOA) is a typical top-down approach that is particularly appropriate for use in quantifying the footprint family of a wide range of entities (e.g., cities, regions, nations) in today’s globalizing world [55, 57]. Recent progress in global multiregional input–output (MRIO) models represents a sophisticated way to create a globally consistent accounting of environmental footprints at macro- and mesoscales from a consumption-based perspective [49], as proved by the success of the IOA-based footprint community [26]. Such a consumption perspective can even be extended to socioeconomic dimensions to uncover trade-related inequality and social responsibility [57]. While numerous existing studies on environmental footprints have managed to create a holistic view of the displacement of virtual resources and leakage of embodied emissions due to international trade, the BRI has often been a neglected field of analysis. To our knowledge, only few researchers have applied MRIO models to trace the trade flows of embodied carbon inside and outside the BRI [7, 19, 52, 58]. A focus on other environmental footprints (e.g., land, nitrogen, phosphorus) of BRI is lacking in literature, let alone an integrated analysis of the footprint family. To close these gaps, this paper attempts to account for the water, land, carbon, nitrogen and phosphorus footprints of the 65 nations in BRI, traces the flows embodied in international trade between the BRI and other 124 economies throughout the world by employing a global MRIO model. By doing so, an overall picture of the spatial distribution of the BRI’s environmental footprints and their international trade embodiments can be provided in a systematic way. Finally, we provide an in-depth discussion of the scientific contribution and policy implications of this research, conclude the major findings, and end with up recommendations for further improvement.

2 Methods and Data 2.1 Environmental Footprints Accounting As mentioned, the water, land, carbon, nitrogen and phosphorus footprints are selected as a footprint family that measures critical human impact on the BRI’s environment. These five environmental footprints are also correlated with global freshwater use, landuse change, climate change and biogeochemical cycles discussed in the planetary boundaries framework [40, 45]. One should be noted that an environmental footprint accounting comprises both direct and indirect components, in which the latter that refers to virtual environmental flows is not a physical part of traded commodities, but rather generated in the production of these commodities for

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Table 1 Descriptions of the five environmental footprints Environmental footprint

Description

Water footprint

Total green and blue water volume of all commodities

Land footprint

Total cropland area and pasture area of all commodities

Carbon footprint

Total carbon emissions of all commodities

Nitrogen footprint

Total nitrogen emissions from fertilizer and manure of all commodities

Phosphorus footprint

Total phosphorus emissions from fertilizer and manure of all commodities

which the final consumers should be responsible from a consumption perspective. A brief description of each environmental footprint is provided in Table 1.

2.2 Multi-Regional Input–Output Analysis Assuming that the MRIO model contains n regions, and that each region has t sectors, the total output matrix can be expressed as: ⎡

⎤ x1 ⎢ x2 ⎥ ⎢ ⎥ X = ⎢ . ⎥ ⎣ .. ⎦

(1)

xn where x n is the total output vector of country n. The technical coefficient matrix A can be expressed as: ⎡

A11 ⎢ A21 ⎢ A = ⎢ . ⎣ ..

A12 A22 .. .

··· ··· .. .

⎤ A1n A2n ⎥ ⎥ .. ⎥ . ⎦

(2)

An1 An2 · · · Ann

The diagonal elements in the matrix A are the intraregional trade of intermediate products, while the non-diagonal elements are the trade of intermediate products xinm j between different countries. The element ainm j = x mj in the matrix is the input from sector i in country n to produce per unit output of sector j in country m. The final demand matrix Y can be expressed as equation [3], which can be divided into two components: domestic final demand (Y nn ) and international exports (Y nm , n = m).

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⎡

Y 11 +

219



Y 1m

⎤

m=1 ⎥ ⎢ ⎢ Y 22 +  Y 2m ⎥ ⎥ ⎢ ⎥ ⎢ m=2 Y = ⎢ ⎥ .. ⎥ ⎢ ⎥ ⎢ . ⎣ nn  nm ⎦ Y Y +

(3)

m=n

As per the trade balance relationship between total output and total input, the basic input–output relationship can be expressed as: AX + Y = X

(4)

Then the total output matrix X can be expressed as: X = (I − A)−1 Y = LY

(5)

where I is the identity matrix, and L = (I − A)−1 is the Leontief inverse matrix, in which the element linm j is both direct and indirect output of sector i in country n to meet per unit final demand of sector j in country m. Then the consumption-based environmental footprint can be calculated by the following formula: E f = E LY

(6)

E f is the resource consumption or environmental emissions caused by final en∗

demand, E is the intensity matrix, in which the element enj = xjn is direct resource j consumption or environmental emissions per unit output of sector j in country n. The multiplier EL is direct and indirect resource consumption or environmental emissions caused by one unit of the final demand. Thus, the consumption-based environmental footprint can be calculated by summing each column of the matrix E f , in which the diagonal and non-diagonal elements are the direct and indirect components of environmental footprint, respectively.

2.3 Data At present, global MRIO databases, such as Eora, WIOD, EXIOBASE, and GTAP, have been broadly adopted as a basis for environmental footprints accounting. As exemplified by the carbon footprint, the disagreement across these four MRIO databases for most major nations is