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Industrial ecology (IE) was defined as a dynamic evolution process characterized by the coupling coordination relationship between ecological efficiency and industrial efficiency. This paper used data envelopment analysis and technique for order preference by similarity to ideal solution method to explore the spatiotemporal evolution of ecological efficiency and industrial efficiency in China’s loess plateau region (LPR) from 2006 to 2017. Based on the definition, LPR’s IE development and spatial correlations were studied separately using coupling evaluation model and social network analysis. Findings include: Although eco-efficiency and industrial efficiency were mutually promoted, and both have improved during the study period, LPR’s human-earth relation was still facing uncertainties. There were significant disparities in industrial efficiency, ecological efficiency, and IE levels among cities. The evolutionary patterns of cities were classified as coordinated development mode and professional development mode. The IE interactive network was characterized by “scale-free” and “small world,” where provincial capitals and big cities functioned as growth poles and intermediaries. The correlations of IE brought challenges as well as favorable conditions for setting up a cross-regional coordination mechanism. This paper provided a new perspective to understand IE and a “quantity and structure” driven development idea for policymakers.

This paper utilizes the slacks-based measure (SBM) undesirable model to analyze the environmental regulation efficiency (ERE) from 2003 to 2013, and uses the Data Envelopment Analysis (DEA) Malmquist productivity index to divide the environmental regulation total factor productivity (ERTFP) into a technical progress index, pure technical efficiency index and a scale efficiency index from the perspective of scale change. The results show that the ERE presents “polarized” effect and the ERTFP has an obvious “agglomeration” effect in China. There is also a very significant “match-up” effect between the ERE and economic development level. In addition, the paper also finds that the technical progress index has the largest contribution to ERTFP, followed by pure technical efficiency index and then the scale efficiency index. The preceding results can provide scientific suggestions for regional environmental departments. [ABSTRACT FROM AUTHOR]

Abstract: Agriculture in Scotland is facing immense challenges for the future. Scottish policy now promotes sustainable economic growth, which to many implies both a growth in resource use and the requirement to meet targets for reducing greenhouse gas emissions. Attempts to increase economic gains can be in conflict with environmental aims and the question arises how both objectives can be met simultaneously. This paper examines the relationship between the technical and environmental efficiency of dairy farms in Scotland with respect to greenhouse gas emissions. It estimates the technical efficiency of a sample of dairy farms based on survey data, applying a number of approaches based on the non-parametric Data Envelopment Analysis (DEA) method. The farms’ greenhouse gas emissions are calculated using a variety of sources and were used to estimate environmental efficiency. This paper finds that, within the study sample, farms which are more technically efficient, are bigger or have higher yields are also more efficient in their emissions of GHG emissions. These results suggest that the potential exists for, Scottish dairying to improve its competitiveness and lower greenhouse gas emissions by increasing efficiency, though the extent of the environmental benefits achieved largely depends on how efficiency is increased. [Copyright &y& Elsevier]

The high-frequency on-line measurements coming from sensors installed in wastewater treatment plants (WWTPs) can be used for alert systems and decision support tools. Given the complexity of WWTPs, many authors developed efficient techniques for plant assessment. By contrast, few contributions are focussed on the identification of regime shifts in WWTPs on short-term, even if the early detection of occurring inefficiencies is a relevant information for plant managers. In order to fill this gap, the present paper uses a daily LCA + DEA analysis in order to monitor the potential deterioration of the eco-efficiency. This approach consists of an original combination of environmental life cycle assessment (LCA), data envelopment analysis (DEA), time series analysis and statistical tests. The main innovation in DEA algorithm consists of the set of decision-making units (DMUs), here composed of the 1-day operation datasets of a single WWTP. The results show a good performance in the regime shifts classification, for which the receiving operating characteristic (ROC) analysis returned a score of 0.8 (in a range 0–1). [ABSTRACT FROM AUTHOR]

[Display omitted] • Define IE as an evolutionary process characterized by the dimensions of industry and ecology. • Industrial efficiency and eco-efficiency were analyzed from spatial-temporal perspectives. • Cities were classified as coordinated development mode and professional development mode. • Social network analysis was used to examine the spatial correlations of IE. • The IE interaction network was characterized by "scale-free" and "small world." Industrial ecology (IE) was defined as a dynamic evolution process characterized by the coupling coordination relationship between ecological efficiency and industrial efficiency. This paper used data envelopment analysis and technique for order preference by similarity to ideal solution method to explore the spatiotemporal evolution of ecological efficiency and industrial efficiency in China's loess plateau region (LPR) from 2006 to 2017. Based on the definition, LPR's IE development and spatial correlations were studied separately using coupling evaluation model and social network analysis. Findings include: Although eco-efficiency and industrial efficiency were mutually promoted, and both have improved during the study period, LPR's human-earth relation was still facing uncertainties. There were significant disparities in industrial efficiency, ecological efficiency, and IE levels among cities. The evolutionary patterns of cities were classified as coordinated development mode and professional development mode. The IE interactive network was characterized by "scale-free" and "small world," where provincial capitals and big cities functioned as growth poles and intermediaries. The correlations of IE brought challenges as well as favorable conditions for setting up a cross-regional coordination mechanism. This paper provided a new perspective to understand IE and a "quantity and structure" driven development idea for policymakers. [ABSTRACT FROM AUTHOR]

Eco-efficiency is of great significance in facilitating a region’s sustainable development. However, there are some inconsistencies among the concepts and also relationships of eco-efficiency and its relevant efficiencies in recent studies. This paper integrates eco-efficiency and three related-efficiencies (economic efficiency, energy efficiency, and environmental efficiencies) into a total-factor analysis framework, regarding them as composite eco-efficiency indicators. Employing a new data envelopment analysis approach, named Meta-US-SBM, which considers meta-frontier, undesirable outputs, super efficiency, and slacks simultaneously, the measure system of these four efficiencies is established. An empirical study is conducted using Chinese provincial data from 2001 to 2014. Results indicate that, overall, the regional economic efficiency is better than other related efficiency indicators. Furthermore, different provinces are adopting different development modes, as denoted by significant heterogeneity among eco-efficiency indicators. [ABSTRACT FROM AUTHOR]

Data Envelopment Analysis (DEA) is a production-theoretical benchmarking process that allows us to compare the relative efficiency of so-called “decision-making units” (DMUs). Although the method was originally applied to the comparison of production lines or companies, a previously published paper (Deilmann et al., 2016) has shown its suitability for the investigation of cities. In this approach, cities (specifically local administrations and municipal councils) are understood to be DMUs. The DMUs are Germany’s kreisfreie Städte , which are towns and cities with special status as independent administrations. The primary interest was to investigate efficiency in land use. In this previous study an economic and an ecological model were used to apply the DEA to cities. The efficiency values of both models are presented in a nine-field matrix. The current study goes beyond these principal findings by introducing two further interpretative approaches. First: In addition to the input and output indicators of the DEA, other illustrative indicators are adopted as a second stage of investigation in order to understand which specific structural, economic, ecological and social feature values can be attributed to highly efficient cities as opposed to less efficient cities. Under this interpretative approach, DEA helps shed light on the concept of efficiency. Findings show that medium-sized cities make the most efficient use of land (developed as settlement and traffic area), that settlement area is not the only factor determining high efficiency, that high density alone is no guarantee of efficiency, that some large cities show high land productivity yet low economic efficiency (in Germany there is a clear division between old industrialised cities and cities benefitting from the new economy). Second: By combining variable and constant returns to scale, it is possible to calculate scale efficiency scores for cities. Under this second interpretative approach, development pathways can be discerned for cities to increase efficiency. Some cities seem to have the potential to improve their economic and ecological efficiency without changing the amount of land developed for settlements and traffic ( e.g . by unsealing and restoring brownfields). Furthermore, cities operating in the range of increasing returns of scale should be able to increase their settlement area without jeopardizing efficiency. Although the focus of the presented model is on urban quality connected to the efficient usage of settlement and traffic area, it may also be applied to the investigation of other relations depending on the respective research interest. Such observations can support urban developers in estimating the effects of land-use change, brownfields restoration and redevelopment, as well as pinpointing factors to improve economic and ecological performance. [ABSTRACT FROM AUTHOR]

Based on energy and CO 2 emission efficiencies, this paper proposes a definition of the elasticity of the potential of emission reduction to energy saving ( E peres ), and measures the elasticity in China’s 30 provincial regions. Although E peres is a relative definition, it can be used (1) to measure the amount of reduced CO 2 emissions per unit fossil energy saving, (2) to reflect the effectiveness of fossil energy saving for CO 2 emission reduction in different regions, and (3) to provide decision-making criteria for selecting pathways for emission reductions in different regions. The results show that compared with energy saving, emission reduction is a more serious issue in China. This indicates that energy saving policies have been highly effective since their implementation during the 11th “Five-Year Plan”. With respect to provincial disparities, the emission reductions caused by fossil energy saving are not significant in Beijing, Shanghai, and Guangdong. Fujian province has significant E peres , indicating that emission reduction causing by fossil energy saving is effective. E peres has been increasing over time in Hunan and Hubei. Hainan’s E peres has remained less than 1, indicating that its emission-reduction effect of fossil energy saving is worse than in other provinces. Moreover, the elasticity of Eastern China is greater than that of Central China and Western China. This finding sheds light on pathway selection for energy saving and emission reduction in China: it would be more appropriate to encourage fossil energy saving in Eastern China, and to promote clean energy use (e.g., water electricity and solar energy) in Central China and Western China. [ABSTRACT FROM AUTHOR]

The purpose of this paper is to explore CO 2 emission efficiency in china’s agricultural sector during the period 2001–2012. We also analyze the CO 2 emission reduction potential as well as its two main contributors (i.e. technology gap inefficiency and managerial failure). The meta-frontier framework is adopted to reflect technology heterogeneities in China’s agricultural sector among the eastern, central and western regions. Based on the DEA (data envelopment analysis) method, the models are estimated by means of linear programming. Empirical results indicate that CO 2 emission efficiency of the agricultural sector in western China is the lowest compared with eastern and central China; stemming from the fact that most provinces in western China generally fall behind in terms of technology. The total potential agricultural CO 2 emissions reduction is estimated at 1161.33 million tons, which can be attributed to managerial failure in the case of eastern and central China. For western China, it can be attributed to both technological gap inefficiency and managerial failure. [ABSTRACT FROM AUTHOR]

Environmental management, being an important component in strategies for achieving sustainable development of processes and products, has emerged as a proactive approach in majority of the manufacturing organizations. From the strategic perspective environmentally conscious manufacturing (ECM) programs lead to better environmental management practice. The objective of the current paper is to present an integrated and holistic framework to evaluate ECM programs. This framework combines three multi-criteria decision analysis (MCDA) methods to consider eight major environmentally conscious manufacturing indicators (ECMI) in order to identify the efficiency of each ECM program. First the interdependence relationship among the ECMIs is established using decision-making trail and evaluation laboratory (DEMATEL). Then a range of weightage (i.e. upper and lower bounds) is created for each ECMI using analytic network process (ANP) to include managerial preferences. Finally, this range of weightage for each indicator is applied to perform restricted multiplier data envelopment analysis (RMDEA). Results show that the technical efficiency of the inefficient ECM programs for integrated RMDEA, on average, is calculated as 53.2% whereas traditional input oriented DEA provides the same score as 72.3%. This clearly indicates that integrated RMDEA is better than the input oriented DEA because same level of output could be produced with lesser resources if the ECM programs perform on the frontier. Hence, the advantage of this methodology is that the managerial preferences are successfully implemented through this newly developed hybrid methodology that will help to reduce less resource consumption and lead to better environmental policy. [ABSTRACT FROM AUTHOR]

This paper estimates water use efficiency of 31 provinces in China during 2004–2013 using slack based measure-data envelopment analysis (SBM-DEA) model which takes consideration of sewage. By using panel data model, factors that influence water use efficiency are explored. The results show that: (1) water use efficiency is higher in economically developed provinces such as Beijing, Shanghai and Tianjin. (2) In general, inefficiencies of labor input and water input are larger than that of capital input. (3) After taking into account sewage as unexpected output, inefficiency of gross domestic product (GDP) for each province is quite low, but most provinces have inefficient sewage emission. (4) It is found in the analysis of influencing factors that the ratio of added value of agricultural sector, water usage per capita and sewage per unit of output have negative impact on water resources use efficiency, while the import dependence and export dependence have positive impact. The results are robust. [ABSTRACT FROM AUTHOR]

• The eco-efficiency is an agricultural sustainability index for policy makers. • Italian regions reach different levels in European Common Agricultural Policy (CAP) • Northern Italian Regions show a strong productivity orientation of their farms. • Southern Italian farms are more oriented towards sustainability and eco-efficiency. • The Italian agricultural sector seems getting the CAP's sustainability goals. Agriculture plays a key role in providing a wide range of ecosystem services, such as food, feed, fiber and biofuel, thus taking part in the economic development of countries. On the other hand, this sector also gives rise to negative externalities. The eco-efficiency has been considered as a meaningful index for assessing how efficient economic activities are in terms of resource-use and environmental pressures: measuring eco-efficiency provides policy makers with important information for developing policies focused on sustainable management and efficient use of natural resources in the agricultural sector. In this context, sustainable development is now one of the most important objectives of the European Union Common Agricultural Policy (CAP) that has a key role in facing the challenges of the new paradigm of sustainability of agriculture. In this direction, the aim of this paper is to evaluate the eco-efficiency of the Italian agricultural sector, as an index useful for emphasizing the differences among some national geographical areas. This paper tries to fill the lack of scientific studies on agricultural eco-efficiency in Italy, despite the strategic role played by Italy in Europe. For this purpose, the Data Envelopment Analysis (DEA) methodology was used, focusing on the integration between agricultural productivity and resource conservation, in order to develop a support tool for policy makers and managers. The analysis had shown a better orientation in saving resources for the Southern Regions and a greater orientation in productivity for the Northern Regions. Overall, Italy seems to have a good capacity for sustainable management of agricultural resources although there is still space for improvement. In this regard, the measurement of eco-efficiency provides a useful index for policy makers to achieve better performances in terms of agricultural sustainability. This means that CAP subsidies should be granted in exchange for specific environmental externalities provided by farmers as a result of more ecologically friendly management with a land use planning avoiding the depleting of Ecosystem Services rich areas, allowing for the achievement of a balance between economic growth and ecosystem protection. Although the paper has expanded the literature on agricultural eco-efficiency, this work has some limitations that could serve as a reference for future studies that can include other ecological variables such as the provision of some ecosystem services that can be enhanced or impacted by agricultural development. Finally, the challenge to realize sustainable agriculture can represent a long-term guarantee of food security as well as societal well-being. [ABSTRACT FROM AUTHOR]

• An existing method for gauging the performance of MWMSs is extended to the treatment of spatial effects. • The radius within which proximity effects are enclosed is computed. • A final efficiency indicator is estimated to benchmark each MWMS against its local peers. • Spatial conditioned efficiency indicators are used to evaluate scale effects in a case study. • Differences between spatial conditioned and unconditioned efficiency scores are shown. In an article recently published in this journal, Sarra et al. (2017) presented an approach to measure the efficiency of municipal waste management systems (MWMSs) that allows a joint consideration of their performance in both environmental and economic terms. Such an approach does not consider the existence of the effects of spatial proximity among municipalities. This could result in biased efficiency scores that may lead to inappropriate policy decisions. To overcome this weakness and to provide policy makers with a more reliable tool for the joint evaluation of the environmental and cost performance of MWMSs, this paper proposes to integrate a multistep procedure that would control for spatial effects with the approach of Sarra et al. (2017). This paper also presents an application of the proposed approach to the same data used by Sarra et al. (2017) to allow a comparison between the results obtained. The findings of this study suggest that jointly assessing the environmental and economic efficiency of MWMSs through data envelopment analysis-based methods can lead to distorted judgments if spatial effects are not properly taken into consideration. [ABSTRACT FROM AUTHOR]

REDD+ reference levels directly impact the benefits which a country may receive. However, the existing “Compensation Reduction” (CR) and “Compensated Successful Efforts” (CSE) are only considered from a unilateral perspective of outputs or inputs. The combination of these two approaches is considered to estimate the REDD+ reference levels through the Zero-Sum-Gains Data Envelopment Analysis in this paper. The agricultural labor force and agricultural land area are used as input variables, and the gross agricultural production and carbon emissions from deforestation are considered as output variables. The REDD+ reference levels of 89 countries are calculated and classified through the Zero-Sum-Gains DEA model. The results demonstrate that the REDD+ reference levels are estimated efficiently through the Zero-Sum-Gains DEA model, and all countries with deforestation are in the Zero-Sum-Gains DEA frontier, indicating the overall Pareto optimality has been achieved. The empirical results also indicate that the use of Zero-Sum-Gains DEA model is more beneficial for Latin American and the Caribbean, while the countries that may see a revenue drop in REDD+ are in Africa, Asia and Oceania. Consequently, the final REDD+ reference levels should take into account both efficiency and fairness by selecting the appropriate fairness-efficiency weighting factor. [ABSTRACT FROM AUTHOR]

• An LCA procedure at corporate level which incorporated the absorption of CO 2 by trees. • A joint LCA and time-series DEA methodology to calculate eco-efficiency score. • An input–output indicator system constructed to assess the eco-efficiency. • Environmental impact, wood fiber and energy are key factors to enhance eco-efficiency. Complex forestry enterprises have the potential to lead the current industrial upgrade under the strategy of carbon neutrality and carbon peaking. This paper aims to investigate the inputs and outputs influencing the eco-efficiency of complex forestry enterprises through a case study. A hybrid ensemble approach of life cycle assessment (LCA) and time-series data envelopment analysis (time-series DEA) was proposed to evaluate the case enterprise's eco-efficiency. It is found that, from the perspective of the average intensity of key input and output factors, environmental impact in efficient years is 45.25% of that in inefficient ones, and when it comes to the fiber consumption and the energy consumption, this percentage turns into 65.53% and 77.66%. With the findings, we provide guidelines for complex forestry enterprises to enhance the eco-efficiency in improving environmental impact and in giving full play to the advantages of forestry industrial chain resources. Our findings also contribute to the existent literature by providing a systematic study based on a corporate time-series dataset. [ABSTRACT FROM AUTHOR]

As a main method for achieving environmental protection, improvement of environmental efficiency is vitally important for reducing environmental risk and level of ecological scarcity. Thus, quantitative analysis of environmental efficiency is not only an important premise for understanding the situation of regional environmental protection, but also a prerequisite for designing and adjusting relevant policies. In this paper, we treat the undesirable output in an economic activity by a linear monotonic decreasing transformation approach. We employ data envelopment analysis (DEA) technique to evaluate environmental efficiency in 30 provinces of China during the period 2001–2010, and conduct hypothesis tests on these environmental efficiencies. Furthermore, we investigate the horizontal differences of environmental efficiency in six regions of China and the vertical differences among different years. The results show that our empirical tests have a high statistical reliability that provides support for prompting environmental policies in different parts of China. [ABSTRACT FROM AUTHOR]

This paper adopts data envelopment analysis method to calculate the regional energy efficiency from the perspective of total-factor energy efficiency. The Markov chain and spatial Markov chain are the common methods to test the club convergence of regional energy efficiency in China. Results indicate that the regional energy efficiency in China has been globally characterized by “club convergence” since 1999, and the energy efficiency transitions in China are closely connected with regional characteristics. A high level of energy efficiency has a positive influence on a region, whereas a low level of energy efficiency has a negative influence. This empirical analysis provides a spatial explanation to the “convergence clubs” of regional energy efficiency in China. [ABSTRACT FROM AUTHOR]

As environmental problems become more and more serious all over the world, environmental efficiency evaluation has drawn increasing interests of many scholars and governments’ decision makers. Data envelopment analysis (DEA) as a non-parametric approach for evaluating the relative efficiency of a group of decision making units (DMUs) has been widely extended and applied in many areas. Among various DEA models, enhanced Russell measure (ERM) model can measure the inefficiencies from input orientation and output orientation simultaneously. In this paper, we proposed a new non-orientation DEA approach based on enhanced Russell measure for measuring the environmental efficiency of a DMU and meanwhile, provided the closest target for the evaluated DMU to efficient with less effort. At last, our approach was applied to a practical example about thermal power enterprises. The results show that our model can provide a much easier way for the inefficient enterprises to improve their efficiencies than the enhanced Russell measure method. Moreover, the provided benchmarks for enterprises through our model can be used to further rank the efficient ones. [ABSTRACT FROM AUTHOR]

Abstract: With the implementation of the strategic policy and the construction of Wanjiang demonstration area recently, the government cares more about the natural environment. To protect the environment of this demonstration area, a quantitative analysis of environmental efficiency and its influencing factors is needed. In this paper, we measure the environmental efficiency of the demonstration area, and then analyze the total factor productivity of the area by Malmquist productivity index through data envelopment analysis (DEA) approach. Through the index and its decomposition, we are able to reveal the changing trend of the environmental efficiency. Moreover, we apply Bayesian estimation approach to analyze the influencing factors of the efficiency and discuss the relationship between these factors and the environmental efficiency. Results show that the efficiency of Wanjiang demonstration area bares a significant difference among constituent cities. Each area should devise proper environmental policy according to its particular circumstance. [Copyright &y& Elsevier]

Abstract: This paper presents a methodology for building a composite indicator to evaluate the sustainability of nature-based tourism destinations. It combines Principal Component Analysis (PCA), the distance to a reference point, and Data Envelopment Analysis (DEA) with the aim of addressing some of the objections related to the aggregation procedure. The synthetic index obtained is based on a representative series of sub-indicators of the concept of sustainable tourist development as outlined by the World Tourism Organization (WTO). We apply it to evaluate Cuban nature-based tourism destinations. The results identify the strengths and weaknesses of destinations according to sustainability, and serve as a guideline for tourism planning in the future. [Copyright &y& Elsevier]

Abstract: The non-parametric Data Envelopment Analysis approach is increasingly used to construct composite indicators for country performance monitoring, benchmarking, and policy evaluation in a large variety of fields. The flexibility in the definition of aggregation weights is praised as the method's most important advantage: DEA allows each evaluated country to look for its own optimal weights that maximize the composite indicator relative to the other countries. However, this flexibility also carries a potential disadvantage as it may allow countries to appear as a brilliant performer in a manner that is hard to justify: by ignoring or overemphasizing one or multiple of the judiciously selected performance indicators. To illustrate this issue of undesirable specialization in DEA-based evaluations, this paper compares the Environmental Performance Index (EPI) as computed by the optimistic and pessimistic version of the DEA-model as proposed by Zhou et al. (2007). Based on both computed composites, undesirable specialization in performance is identified. [Copyright &y& Elsevier]

• The proposition of transition cycles for a low-carbon economy based on human development and climate action. • A climate justice index in line with the United Nations' concepts of sustainable development. • A climate justice index that benchmarks nations with the same level of economic development. • The proposed index can help policy-makers and national government to identify synergy between climate actions and human development policies. Since the climate change context is increasingly the focus of decision-makers, indices capable of helping them in the transition to a low-carbon economy are considered necessary. Therefore, the objective of this work is to propose an index of climate justice that includes factors of human development and climate action. The index was developed using the Data Envelopment Analysis (DEA) technique and, in this study, assessed the climate justice of 198 nations. After the calculation, statistical analysis was used to compare the climate justice of the aforementioned countries, grouped by level of development and geographical location. The results obtained were: a) there is currently no nation that fully guarantees climate justice to its population; b) there is a difference in the performance of climate justice between the Annex I, Non-Annex I and Least Developed Countries (LDCs) groups; c) nations located in Africa, Latin America and the Caribbean, Asia and Eastern Europe are similar in terms of climate justice index performance. The main contribution of this paper is in the fact that the proposed index can help policy-makers and national governments to define climate and development policies, creating a synergy between such policies. [ABSTRACT FROM AUTHOR]

• Sensitivity of soil carbon sequestration efficiency index (SCI) was analyzed. • SCI was computed using metafrontier data envelopment analysis. • SCI scores are varied by types of returns-to-scales/orientations/frontiers. • Output-oriented free-disposable hull SCI is useful to identify inefficient sites. • The observed "unexpected samples" need further investigation. The data envelopment analysis (DEA) is useful for the measuring of efficiencies of functions in various research fields. In previous studies the soil carbon sequestration (SCseq) efficiency index (SCI) was assessed at regional scale; however, the effects of various calculation factors—including returns-to-scales (RTSs), selection of orientation (input or output), and global (or meta-)/local (or group) frontiers—on the SCI scores are still unclear. Thus, in this paper, the sensitivity of DEA frontier models with various types of RTSs and orientations was investigated. The models examined the SCseq rates as target outputs in three land use/land cover (LULC) types (pinelands, improved pastures, and wetlands). The rates were calculated based on lab-measured soil organic carbon concentration collected from the topsoil (~20 cm depth) between 1965 and 2009 in Florida, United States. Pedogenic, hydrologic, and environmental variables relevant to the carbon sequestration function were chosen as inputs, including soil available water capacity, sand concentration, pH, soil total nitrogen and phosphorus, Enhanced Vegetation Index, and Normalized Difference Vegetation Index. The input and output variables for the three LULC types informed multivariate normal distributions that served to simulate the local frontiers. Results show that the SCseq rates in wetland soils were statistically lowest (p < 0.05), while the SCI scores were highest. This suggests that the efficiency of the soil carbon sequestration function was distinctively different from the SCseq rates. The SCI scores also demonstrated to identify internal causes (i.e., local management practices) for inefficiency in the SCseq in pasture soils and suitability of the function among the LULC types. A higher ability in output orientation to discern efficient from inefficient sites for the SCseq function was also observed than the input orientation. The preferred choice of RTSs in either conservative or progressive approaches to SCseq efficiency management was also identified in the study. Sensitive scores reflected regions that are in need for efficient soil management (such as no-tillage) to improve the performance of individual soils in terms of SCseq efficiency. SCseq efficiency scores enable land resource managers to refine their approaches to optimize soil carbon and possibly other soil and ecological functions. Further research is invited to identify potential cause(s) that determine(s) the local/global frontiers. [ABSTRACT FROM AUTHOR]

• We contribute an indicator of environmental performance in municipal solid waste treatment. • The environmental performance of European Union member states is empirically assessed. • Major differences in performance are found, with the best performers being Nordic and Central European countries. This paper proposes a measure of environmental performance in the treatment of municipal solid waste, which is defined as a ratio between a composite indicator of waste treated through environmentally desirable operations –recycling and recovery in our case study– and a composite indicator of waste treated through undesirable operations –landfill and incineration. Moreover, it contributes both overall and treatment-specific indicators of performance. Data Envelopment Analysis (DEA) techniques are used to compute the environmental performance indicators and they are illustrated with an empirical assessment of the environmental performance of the European Union-28 (EU-28) members in their treatment of municipal waste, with data for the year 2017. Our results point to a worryingly low average level of performance, with the best performers being mainly Nordic and Central European countries such as Sweden, Germany, Belgium, Finland and Denmark; at the opposite end of the spectrum, environmental performance in the treatment of waste is particularly low in most of the Eastern European countries that joined the EU-28 from the 2000s, and some Southern member states. The determinants of performance are also investigated, the main finding being a positive and statistically significant association between environmental performance in municipal waste treatment and the level of economic development. [ABSTRACT FROM AUTHOR]

The data envelopment analysis (DEA) is useful for the measuring of efficiencies of functions in various research fields. In previous studies the soil carbon sequestration (SCseq) efficiency index (SCI) was assessed at regional scale; however, the effects of various calculation factors—including returns-to-scales (RTSs), selection of orientation (input or output), and global (or meta-)/local (or group) frontiers—on the SCI scores are still unclear. Thus, in this paper, the sensitivity of DEA frontier models with various types of RTSs and orientations was investigated. The models examined the SCseq rates as target outputs in three land use/land cover (LULC) types (pinelands, improved pastures, and wetlands). The rates were calculated based on lab-measured soil organic carbon concentration collected from the topsoil (~20 cm depth) between 1965 and 2009 in Florida, United States. Pedogenic, hydrologic, and environmental variables relevant to the carbon sequestration function were chosen as inputs, including soil available water capacity, sand concentration, pH, soil total nitrogen and phosphorus, Enhanced Vegetation Index, and Normalized Difference Vegetation Index. The input and output variables for the three LULC types informed multivariate normal distributions that served to simulate the local frontiers. Results show that the SCseq rates in wetland soils were statistically lowest (p

Since the climate change context is increasingly the focus of decision-makers, indices capable of helping them in the transition to a low-carbon economy are considered necessary. Therefore, the objective of this work is to propose an index of climate justice that includes factors of human development and climate action. The index was developed using the Data Envelopment Analysis (DEA) technique and, in this study, assessed the climate justice of 198 nations. After the calculation, statistical analysis was used to compare the climate justice of the aforementioned countries, grouped by level of development and geographical location. The results obtained were: a) there is currently no nation that fully guarantees climate justice to its population; b) there is a difference in the performance of climate justice between the Annex I, Non-Annex I and Least Developed Countries (LDCs) groups; c) nations located in Africa, Latin America and the Caribbean, Asia and Eastern Europe are similar in terms of climate justice index performance. The main contribution of this paper is in the fact that the proposed index can help policy-makers and national governments to define climate and development policies, creating a synergy between such policies.

• We measure the green growth degree of China's industrial sectors by considering the constraint of total energy consumption. • We proposed a new common equilibrium efficient frontier, which considers each DMU's own adjustment strategy on fixed-sum energy input. • The proposed approaches are applied to analyze the green growth and energy and environmental efficiency of 36 of China's industrial sectors. Tight energy constraints and severe environmental pollution are two main problems affecting economic growth and sustainable development. As an important economic growth mode, green growth seeks to achieve both economic growth and environmental sustainability. Previous studies on the efficiency evaluation of green growth ignored fixed-sum energy consumption; this is a significant omission because this constraint is general in China: the government has set targets to reduce energy consumption by 20% and 16% per unit of GDP during its the eleventh (2006-2010) and twelfth (2011-2015) five-year plans. To fill this gap, we propose a new data envelopment analysis (DEA) model to analyze the green growth degree of China's industrial sectors. The proposed DEA model considers the constraint of total energy consumption in order to conform to the policy of energy and emission reduction. Previous theoretical studies on fixed-sum output construct the common equilibrium efficient frontier (CEEF) only by considering the minimal reduction strategy for the fixed-sum output of all decision making units (DMUs), while ignoring each DMU's adjustment strategy for fixed-sum input/output. In this paper, we construct a new CEEF by considering each industrial sector's adjustment of energy consumption. The new CEEF not only guides the industrial sectors to alter their energy consumption structure to achieve green growth but also allows a more accurate evaluation of the energy and environmental efficiency (EEE) of industrial sectors. Last, our proposed model is applied to analyze the green growth and EEE of 36 of China's industrial sectors. [ABSTRACT FROM AUTHOR]

• A new Water Quality Index is devised through a DEA-based methodology. • New input variables are defined directly from the observed data. • Water quality can be improved with reference to local water source benchmarks. • Water treatment process can be prioritized via slack analysis. • New methodology is applied on 47 dams located in the Tellian region, Algeria. The present paper aims to develop a new Water Quality Index (WQI) based on Data Envelopment Analysis (DEA). Rather than using subjective weights of a judgmental process as inputs of the DEA model, we propose more objective variables, identified as "optimistic closeness values", appropriately derived from the observed values of the hydrochemical parameters. The proposed approach was employed to assess the water quality of 47 dams in Algeria, defined with a dataset of 10 hydrochemical parameters. The results of the DEA-based WQI application revealed that (i) 21.27%, 27.66%, 25.53%, 4.25% and 21.27% of the total dams are categorized as "Poor", "Marginal", "Medium", "Good" and "Excellent" water quality, respectively; (ii) the best water quality is found in "Kissir" and the worst one in "Bougara"; (iii) a priority scale on the hydrochemical parameters can be set for the treatment of water using the notion of slack value. Collectively, the new methodology has proven its effectiveness not only for categorizing or ranking sites based on water quality but also as an alternative tool to be used to assist decision-makers in allocating funds and managing water resources. [ABSTRACT FROM AUTHOR]

Tight energy constraints and severe environmental pollution are two main problems affecting economic growth and sustainable development. As an important economic growth mode, green growth seeks to achieve both economic growth and environmental sustainability. Previous studies on the efficiency evaluation of green growth ignored fixed-sum energy consumption; this is a significant omission because this constraint is general in China: the government has set targets to reduce energy consumption by 20% and 16% per unit of GDP during its the eleventh (2006-2010) and twelfth (2011-2015) five-year plans. To fill this gap, we propose a new data envelopment analysis (DEA) model to analyze the green growth degree of China’s industrial sectors. The proposed DEA model considers the constraint of total energy consumption in order to conform to the policy of energy and emission reduction. Previous theoretical studies on fixed-sum output construct the common equilibrium efficient frontier (CEEF) only by considering the minimal reduction strategy for the fixed-sum output of all decision making units (DMUs), while ignoring each DMU’s adjustment strategy for fixed-sum input/output. In this paper, we construct a new CEEF by considering each industrial sector’s adjustment of energy consumption. The new CEEF not only guides the industrial sectors to alter their energy consumption structure to achieve green growth but also allows a more accurate evaluation of the energy and environmental efficiency (EEE) of industrial sectors. Last, our proposed model is applied to analyze the green growth and EEE of 36 of China’s industrial sectors.

The present paper aims to develop a new Water Quality Index (WQI) based on Data Envelopment Analysis (DEA). Rather than using subjective weights of a judgmental process as inputs of the DEA model, we propose more objective variables, identified as “optimistic closeness values”, appropriately derived from the observed values of the hydrochemical parameters.The proposed approach was employed to assess the water quality of 47 dams in Algeria, defined with a dataset of 10 hydrochemical parameters. The results of the DEA-based WQI application revealed that (i) 21.27%, 27.66%, 25.53%, 4.25% and 21.27% of the total dams are categorized as “Poor”, “Marginal”, “Medium”, “Good” and “Excellent” water quality, respectively; (ii) the best water quality is found in “Kissir” and the worst one in “Bougara”; (iii) a priority scale on the hydrochemical parameters can be set for the treatment of water using the notion of slack value.Collectively, the new methodology has proven its effectiveness not only for categorizing or ranking sites based on water quality but also as an alternative tool to be used to assist decision-makers in allocating funds and managing water resources.