Expansion of environmental impact assessment for eco-efficiency evaluation of China's economic sectors: An economic input-output based frontier approach

Measuring performance of supply chains based on data envelopment analysis and multi-regional input-output analysis: An application to 18 manufacturing sectors in 43 countries

This study developed a novel framework that combined data envelopment analysis and multi-regional input-output database to investigate the economic and environmental productivity change in the global supply chains associated with 18 manufacturing sectors in 43 countries from 2000 to 2014. Two models are developed; manufacturer model is used to evaluate performance of direct production activity of a sector in countries and supplier model is used to evaluate performance of indirect production activity of upstream suppliers of the sector. The proposed framework enables us to separately analyze the performance of supply chains into direct production activity and indirect production activity of suppliers. The empirical results show that the environmental productivity of direct production activity of 18 manufacturing sectors was improved by 12.9 percent, while the environmental productivity of the upstream suppliers was improved by only 4.7 percent during 2000-2014 on average. Different patterns of economic and environmental productivity growth were observed between the direct production activity and upstream suppliers in all sectors. The finding suggests that the performance of an entire supply chain should be separately analyzed to consider industry-specific policies. The proposed framework is used to identify countries that succeed/fail to improve economic and environmental performance. Based on the results, this study discusses policies regarding production and supply chain management toward CO2 mitigation.

Ecological performance assessment of ecologically fragile areas: a perspective of spatiotemporal analysis

Sustainable development in ecologically fragile areas (EFAs) has faced significant challenges in recent years, but the traditional analytical approaches fail to provide an ideal assessment for ecological performance due to spatiotemporal variability in EFAs. This paper evaluates the ecological performance of EFAs based on a modified ecological footprint model, and ecological footprint intensity (EFI) is considered an essential indicator to measure ecological performance, especially for EFAs. Empirically, taking the Π-shaped Curve Area in the Yellow River basin of China as the study area, the spatiotemporal heterogeneity of EFI of 17 cities in the area is analyzed. Then, the extended STIRPAT and geographically and temporally weighted regression (GTWR) models are employed to explore the spatiotemporal heterogeneity of the factors driving EFI. The results show that from 2006 to 2019, the overall level of EFI in the area has decreased; EFI of the area offers a significant spatial agglomeration effect; results of the GTWR model suggest that factors driving EFI have spatiotemporal heterogeneity; the impact of population size, openness, marketization, technology, industrial structure rationalization, and information communication level on EFI was two-sided, while that of affluence, government scale, environmental regulation, and industrial structure advancement show inhibitory impact with the intensity of inhibition varying across periods and cities.

Data-Driven Eco-Efficiency Analysis and Improvement in the Logistics Industry in Anhui

The ecological efficiency (eco-efficiency) of a regional logistics industry (RLI) is widely regarded as a key factor affecting sustainability of economic development, environmental protection, and resources utilization. This study applied a data-driven method to evaluate and increase the eco-efficiency of an RLI. Based on RLI-related data, which were converted into proper dimensionless indices, data envelopment analysis (DEA), which assumes that the decision-making units (DMUs) are in the situation of variable returns to scale, the Banker, Charnes, and Cooper (BCC) model, and Malmquist index model were used to assess the eco-efficiency of the RLI from both static and dynamic viewpoints. Then, a Tobit regression model was built to explore the factors that influence eco-efficiency. The effectiveness of this approach was verified by its application to an example from Anhui Province. This study has theoretical and practical value for the assessment and promotion of the ecological eco-efficiency of the RLI. We believe that our approach offers a powerful tool to assist logistics enterprises and local governments in coordinating the relationship between the RLI economy and the ecological environment, facilitating the drive to carbon neutrality.

A Effectiveness-and Efficiency-Based Improved Approach for Measuring Ecological Well-Being Performance in China

Finding solutions to the challenges posed by China's urbanization is an urgent, pressing global concern. An effective approach for evaluating the ecological well-being performance (EWP) is a guideline for improvement. Most previous studies have focused on the evaluation of EWP efficiency without considering the effectiveness of the EWP, which may mislead the practice of improving the EWP. This paper proposed a bi-dimensional effectiveness and efficiency perspective evaluation of the EWP for pursuing sustainable development goals. The Ecological Consumption Index and the Human Development Index are selected to evaluate indicators for the EWP. The entropy method, line-weighted method, and four-quadrant evaluation framework are used to disclose EWP effectiveness. A Super SBM model and the DEA moving split-windows analysis method are applied to calculate the EWP efficiency. Data from 30 provinces in China for the period of 1997 to 2019 have been collected for empirical study to demonstrate the effectiveness of the proposed method. The main findings of the case study are: (1) The ECI and HDI increased during the study period, while the annual average value of the EWP efficiency among 30 provinces in China has decreased with fluctuation; (2) provinces in southern China and Chongqing have a low level of ECI and demonstrate good performance in the HDI; and (3) most developed regions, such as Beijing, Shanghai, and Guangdong, have not presented the best EWPs. The results of this study can provide a basis for understanding the EWP in China so as to formulate targeted sustainable-development strategies.

A systematic literature review of data envelopment analysis implementation in agriculture under the prism of sustainability

Safeguarding natural resources and energy is essential to ensure food security for future generations. Given the increase of published papers in the agricultural field applying Data Envelopment Analysis (DEA), this review seeks to address the special requirements of this methodology when implemented in the agricultural sector as well as to classify papers under sustainability aspects (economic, environmental, social). More specifically, 120 papers from Scopus and Web of Science databases were included in this review by using PRISMA methodology, and they were tested in the following groups (i) General information, (ii) DEA implementation, (iii) DEA extensions, (iv) Data type, (v) Data collection and processing, and (vi) Sustainability dimensions. Results indicate that there is a great need for weights use when performing DEA in the agricultural sector, to acquire results with greater explanatory power. Moreover, systematic data collection of multiple factors could lead to the implementation of complex methodologies, providing feasible solutions to the involved stakeholders. Lastly, the social aspect is the least represented dimension out of the three aspects of sustainability, indicating the need for the integration of social factors in such analyses, especially when DEA is used to create a policy framework in a specific area.

China's carbon allowance allocation strategy under the EU carbon border adjustment mechanism: An integrated non-parametric cost frontier approach

The implementation of the European Union (EU) Carbon Border Adjustment Mechanism (CBAM) will affect the cost efficiency of Chinese exporters, and few studies have considered how to deal with its impact from the carbon market perspective. Based on this, this paper constructs two price-variable resource allocation (VPRA) models to investigate China's allowance allocation strategies to mitigate the impact of the EU CBAM from a carbon market perspective. This paper takes the steel and cement industries in each province of China for example for cost efficiency assessment and carbon allowance allocation, and finds that: (1) The EU CBAM has a negative impact, and it is necessary to incorporate the CBAM into the top-level system design of allowance allocation to obtain an allowance allocation scheme that maintains the cost efficiency level of each industry. (2) A higher and more stable carbon price in China can effectively counteract the impact of the EU CBAM and ensure that the cost efficiency of each industry does not fluctuate significantly. (3) When the Chinese carbon price exceeds 60 RMB/ton, the impact of the EU CBAM on the cost efficiency of each industry will become significantly smaller. (4) Measures to reduce the number of products exported to the EU are not effective in mitigating the impact of the EU CBAM when the number of products exported to the EU exceeds a certain rate. The findings of this paper provide useful policy insights for China and other developing countries to actively address the challenges of the EU CBAM.

Spatiotemporal characteristics and influencing factors of agricultural eco-efficiency in Jilin agricultural production zone from a low carbon perspective

Agricultural eco-efficiency is a meaningful index that assesses agricultural sustainable development. Based on the super SBM-DEA approach incorporating agricultural carbon emissions and panel data regression, this study evaluates agricultural eco-efficiency and investigates the influencing factors in the agricultural production zone of Jilin Province. The empirical results show the following. (1) During the observation period, the average agricultural eco-efficiency exhibits a flat "M-shaped" fluctuating trend, a trend of fluctuant growth with phase characteristics, and the agricultural eco-efficiency of each county still has much room for improvement. (2) Significant spatial differences exist in agricultural eco-efficiency across counties. All of the studied counties, except for Nong'an, Huadian, Lishu, Yitong, Gongzhuling, and Qianguo, need to change their input and output structure to optimize agricultural eco-efficiency. (3) The panel data regression estimation results indicate that the agricultural technology extension level, multiple-crop index, agricultural economic development level, agricultural technology extension level, and urbanization level have close correlations with agricultural eco-efficiency. (4) The research findings have important implications for policy makers formulating agricultural environmental policies in accordance with the local conditions of various counties.

Evaluation and Dynamic Evolution of the Total Factor Environmental Efficiency in China’s Mining Industry

The mining industry plays an extremely important strategic role in China’s economic and social development. In the new era of pursuing circular/green/efficient development, the evaluation of the total factor environmental efficiency (TFEE) of China’s mining industry is essential for alleviating resource waste and environmental pollution. The Epsilon-Based Measure (EBM) model effectively solves the shortcomings of radial and non-radial DEA models. In addition, the Malmquist–Luenberger (ML) index can measure the dynamic change of efficiency value. Combining the EBM model and the ML productivity index, this paper evaluates the TFEE from the static and dynamic perspective in China’s 31 provincial mining industries over the period 2007–2016. The Theil index is employed to reveal the root of the overall provincial TFEE gap (OGTFEE) in China’s mining industry. The results show that the average total factor static environmental efficiency (TFSEE) of China’s provincial mining industry exhibits a low score of 0.6589 and with significant spatio-temporal differences. The provincial TFEE gap within four major areas (WGTFEE), especially that in east and west areas, is the main cause of the OGTFEE in China’s mining industry. Technical change contributes more to the TFEE decline in China’s mining industry. There are differences in improving the TFEE among China’s 31 provincial mining industries, and corresponding countermeasures can be formulated accordingly. This study provides theoretical and practical basis for the clean and green development of China’s mining industry.

Economic and Environmental Aspects of Agriculture in the EU Countries

The analysis of the economic efficiency of agriculture has been the subject of numerous studies. An economically efficient agricultural sector is not always environmentally efficient. Agriculture is a large emitter of greenhouse gases. The Intergovernmental Panel on Climate Change states that food production and agriculture are responsible for 21–37% of total global CO2 emissions. Due to the comprehensive assessment of the agricultural efficiency, it is worthwhile to apply to its measurement an integrated approach based on economic, energy and environmental aspects. These aspects were the main reasons for undertaking this research. The purpose of the study was to determine the economic, energy and environmental efficiency of agriculture in the EU Member States in 2019. The environmental analyses relate to the period 1990–2019. A total of 26 member states of the European Union (excluding Malta and Luxembourg) were selected for research. The sources of materials were Eurostat and the European Environmental Agency. This study was based on the Data Envelopment Analysis method, and used the DEA model focused on minimizing inputs. The research also adopts energy productivity and greenhouse gas emission efficiency indicators. The DEA model features the following variables: one effect (value of agricultural production) and four inputs (land, labour, use of fertilizers and use of energy). It was found that seven out of the 26 studied EU countries have efficient agriculture. The efficient agriculture group included The Netherlands, Denmark, Greece, Cyprus, the United Kingdom, Italy and Ireland. Based on the DEA method, benchmarks have been defined for countries with inefficient agriculture. On the basis of these benchmarks for inefficient agricultural sectors, it was possible to determine how they could improve efficiency to achieve the same results with fewer inputs. This issue is particularly important in the context of sustainable agricultural development. In the next stage of the research, the analysis of economic and energy efficiency was combined with the analysis of GHG emission efficiency in agriculture. Four groups of countries have been distinguished: eco-efficiency leaders, eco-efficiency followers, environmental slackers, eco-efficiency laggards. The leaders of the classification were The Netherlands, Italy, Greece, Cyprus and Portugal.

Eco-efficiency evaluation model: a case study of the Yangtze River Economic Belt

After the concept of ecological efficiency (eco-efficiency) was put forward and constantly supplemented, it generally refers to the maximization of economic benefits with minimum energy consumption and environmental damage. In a new eco-efficiency model proposed by this paper, the input indexes take into account the consumption of capital, human, resources and energy, and the environmental load caused by them. The output indexes take into account GDP, income, and tax revenue. An optimal weighted cross-evaluation efficiency (OWCE) model based on data standardization is proposed, by improving the traditional data envelopment models of CCR and BCC. The OWCE model not only objectively weights but also unifies the comparison scale, and facilitates the establishment of the super-efficiency decomposition model, which is conducive to further exploring the reasons for the difference of eco-efficiency in various regions. Empirically, the eco-efficiencies of 11 provinces (municipalities) along the Yangtze River Economic Belt (YREB) were analyzed based on the data from 2008 to 2019. The results show that there has been a serious imbalance in the 11 provinces, showing a trend of high in the east and low in the west, although the eco-efficiency has been improving continuously in the past 10 years. Shanghai, Zhejiang, and Jiangsu, which are located in the traditional Yangtze River lower delta region, are the top in terms of eco-efficiency, among which Shanghai ranks the first place with absolute advantage, and also is far ahead in sub-efficiencies of basic input, energy consumption, capital and human input, and environmental cost. Geographical location, especially whether it is close to the ocean, and the length of river flow have a certain positive impact on eco-efficiency. Through in-depth analysis, high-energy consumption, high pollution, and low economic output are the main reasons for low eco-efficiency in the middle and upper reaches of the Yangtze River.

China's Provincial Eco-Efficiency and Its Driving Factors-Based on Network DEA and PLS-SEM Method

This study aims to estimate the eco-efficiencies of China at provincial levels. The eco-efficiencies of production and treatment stages are disentangled by the network data envelopment analysis (DEA) method. The key driving factors are identified by the integrative use of driving force-pressure-state-impact-response frame model (DPSIR) model and partial least squares structural equation modeling (PLS-SEM) method. This study provides several important findings. In general, the eco-efficiencies of most regions in China are inefficient and show significant regional differences. All DPSIR factors have significant and strong impacts on the eco-efficiency of the treatment stage. The eco-efficiency of the production stage evidently outweighs the eco-efficiency in economically well-developed regions. The originality of this study lies in three aspects. First, using two-stage network DEA, this study dissects the overall eco-efficiency into production efficiency and treatment efficiency. Empirical results provide insights into the root cause of the low efficiency of each province (municipality). Second, on the basis of the DPSIR model, an expanded pool of driving factors is investigated. Third, using the PLS-SEM method to analyze eco-efficiency is more reliable and effective than applying other traditional regression models.

Green economic efficiency and its influencing factors in China from 2008 to 2017: Based on the super-SBM model with undesirable outputs and spatial Dubin model

Due to the pressure of global ecological degradation, the coordination of economic increase and ecological protection has drawn attention from policymakers and practitioners. Green economic efficiency (GEE) is a comprehensive index to measure economic, social, and environmental development. As China is the second-biggest economy in the world with high-energy consumption, it is necessary to investigate its green economy efficiency. In this paper, we innovatively adopt a super-SBM (slacks-based measure) model with undesirable outputs to calculate the GEE in 30 provinces of China between 2008 and 2017, and then comprehensively apply a spatial Dubin model (SDM) to investigated its influencing factors. The results showed that the overall GEE in China during the study period was at a low level with significant regional differences. The inter-regional GEE generally showed a gradient decreasing pattern of "East-Middle-West", which demonstrates a gradual decline from the East to the West in China. The trend of the national GEE initially dropped and then gradually stabilized over the study period. Foreign trade dependence and direct investment had significant positive effects on the GEE, while the secondary industry and urbanization level had a significant negative effect.

An Evaluation of the Impact of Ecological Compensation on the Cross-Section Efficiency Using SFA and DEA: A Case Study of Xin’an River Basin

This paper aims to address the issue of evaluating watershed environmental efficiency with ecological compensation. One of the main objectives is the dynamic assessment of cross-section efficiency. The first transboundary watershed ecological compensation pilot of Xin’an river basin serves as the case study, and stochastic frontier analysis (SFA) is adopted as the main evaluation method. Furthermore, the efficiency is estimated by the data envelopment analysis (DEA) approach. The results validate that SFA can be used as an effective method on such issues by comparing it with the benchmark and the result of DEA, which proves the feasibility of our research methodology. The other purpose is to investigate the factors affecting environmental efficiency. This paper explores the relationship between environmental efficiency with 21 pollutant emission factors as well as the correlation between environmental efficiency and five macro factors of economic development, industrial structure, population density, degree of environmental protection, and natural environment. Finally, suggestions are provided for future improvement. This paper therefore presents a comprehensive reference analysis that contributes to facilitating balanced regional development and environmental conservation in the future.

Identification of leading hazardous waste generating industries with high improvement potential in Spain

This paper employs Economic Input-Output Life Cycle Assessment and Data Envelopment Analysis to attribute total (direct and indirect) hazardous waste generation to the different industries and to connect total hazardous waste generation to the value added generated by each industry in Spain. In difference with previous studies we include all the industries of the economy and we focus on one specific type of environmental burden: the generation of hazardous waste. The results show that there is a very high concentration in total hazardous waste generation: only three industries accounted for almost 80% of total hazardous waste generation. This concentration also affects the type of hazardous waste generated. Thus, we find two main types of waste that account for 93% of total hazardous waste. Overall, the greatest hazardous waste generating industries are also the least efficient ones and exhibit a high improvement potential. Prevention measures directed to these industries should be implemented.

Measuring performance of road transportation industry in China in terms of integrated environmental efficiency in view of Streaming Data

Road transportation industry not only promotes economic development, but also brings many environmental issues in China. It is necessary to evaluate the integrated environmental efficiency of road transportation industry, including carbon dioxide emissions, traffic accident and traffic noise which is a Steaming Data. In this study, a parallel DEA model with sub-system preference is proposed to measuring the integrated environmental efficiency of road transportation industry in 2013-2017 considering various undesirable outputs. Then, the convergence analysis approach is employed to investigate the regional differences in terms of efficiency of road transportation. The empirical results show that the overall integrated environmental efficiency underperformed nationwide, and the passenger transportation sub-system outperformed freight transportation when the latter is restricted in some hours. From a regional perspective, the efficiency performances show downward trends in areas eastern, central and western. The passenger transportation sub-system outperforms in eastern area and the freight transportation outperforms in central area. Furthermore, the convergence analysis results indicate that the integrated environmental efficiency in eastern and western areas not only converged to their own stable levels but also that the differences in the integrated environmental efficiency between eastern and western areas are condensing. The contributions of this study lie in not only proposing a parallel DEA model with sub-system preference and considering the streaming data, but also providing suggestions to improving the road transportation industry in terms of integrated environmental efficiency. Finally, the research limitations and further research directions were conducted.

Efficiency assessment of industrial solid waste generation and treatment processes with carry-over in China

Industrial solid waste (ISW) harms the eco-environment as well as human health; thus, both the generation and treatment processes of ISW are important aspects of a reduction of discharged ISW. Currently, only part of the generated ISW is treated, while the remainder is stored for later treatment, which is referred to as carry-over between two adjacent periods. According to such a two-stage process (with carry-over), a network slacks-based measure model is proposed to measure the overall and divisional efficiencies of 30 Chinese regions during 2011-2015. The main findings are summarized in the following: firstly, failing to consider carry-over underestimates both overall and divisional efficiencies. Secondly, the average annual generation efficiency exceeded the average annual treatment efficiency during the study period; however, this gap increasingly narrowed. Thirdly, the overall efficiency is more closely related to the treatment efficiency than to the generation efficiency. Moreover, synergies were identified between generation efficiency and treatment efficiency for each region. Fourthly, significant regional differences affect both overall and divisional efficiencies, and significant stage efficiency differences also affect both coastal and inland areas. To increase the overall efficiencies of different regions of China, different policies are suggested.

The potential for energy saving and carbon emission reduction in China's regional industrial sectors

Rapid economic growth of China's industry has brought many problems. Among them, the problems of energy shortage and environmental pollution have become increasingly serious. The quick development of the big data has brought new challenges and opportunities for environmental management. In this paper, we propose a new data envelopment analysis (DEA) model to analyze the energy and environmental efficiency of industrial sectors from China's 30 provincial-level regions in order to determine the potential and route for energy saving (ES) and carbon emission reduction (CER). The new DEA model not only considers the dynamic data, but also involves the technology heterogeneity and closest targets, which could achieve the potential or provide the route for ES and CER step by step with least effort. The new approach is illustrated by using the regional industrial dataset of China and some implications for ES and CER are proposed.

Analysis of Scale Factors on China’s Sustainable Development Efficiency Based on Three-Stage DEA and a Double Threshold Test

Based on the Driver-Pressure-State-Impact-Response (DPSIR) framework, this paper constructs the input, expected output, and unexpected output of China’s sustainable development. This paper calculates the sustainable development efficiency of 31 provinces and cities in China using a super-slack-based measure (SBM) model that eliminates the influence of scale factors through a three-stage data envelope analysis (DEA) approach. Taking the level of science and technology as the control variable, and the relative scale efficiency as the threshold variable, this paper calculates the effects of the absolute scale of labor force, education, economy, enterprise, and transportation on sustainable development efficiency. The results show that: (1) there is an upward trajectory for sustainable development efficiency of China’s provinces and municipalities overall from 0.65 in 2004 to 0.68 in 2017, with significant regional differences in which the ecological efficiency in the Eastern region is 0.26 higher than that of the Central region; (2) it enhances the absolute scale of the labor force, education, and transportation, in order to reduce the inhibition on sustainable development efficiency; and (3) shifts our attention to the promotion of absolute economic scale to the promotion of green economic development, and increases control of polluting enterprises.

Benchmarking energy efficiency of water treatment plants: Effects of data variability

This study evaluates, for the first time, the energy efficiency of a sample of drinking water treatment plants (DWTPs) using the data envelopment analysis (DEA) tolerance method, which is based on the simulation of scenarios to integrate data variations. The integration of data uncertainty in energy efficiency estimation changes drastically results for approximately one-third of the DWTPs evaluated. The results showed that, even in the best-case scenario, most of the DWTPs evaluated are inefficient and may therefore, be able to reduce the energy used to treat raw water. From a policy perspective, the findings of this study reveal that omitting data variability in benchmarking would involve critical repercussions when efficiency scores are used by regulators to set water tariffs. Omitting the degree of data uncertainty is likely to result in biased conclusions; in the scenarios evaluated, the inclusion of this information altered the rankings of some energy-efficient DWTPs.

Environmental efficiency and equality embodied in China's inter-regional trade

Embodied emissions in trade have been widely studied; however, there is still a lack of studies that explore whether a country is benefitting from its inter-regional trade in terms of pollutant emissions. This study took sulfur dioxide (SO₂) emissions as an example and employed modified input-output (MIO) model and traditional input-output (IO) model to quantify emissions under no-trade and trade conditions, and further investigated environmental efficiency and equality of inter-regional trade in China in 2010. The results show that inter-regional trade had increased emissions by 28% compared to no-trade emissions, which confirms the environmental inefficiency of inter-regional trade in China. This was largely because regions with better technology and low emission intensities tended to outsource the production of pollution-intensive but low value-added goods to regions with high emission intensities through inter-regional trade. The exchanges of pollution-intensive products in inter-regional trade have led to notable environmental inequities. Eastern regions usually gained the greatest environmental benefits from trade, while central regions (especially Shanxi, Henan, and Hebei) suffered the largest environmental loss induced by trade. Specifically, Guangdong plundered other regions the most (796 G gram (Gg)), while Shanxi was plundered the most by other regions (790 Gg). Polices to differentiate reduction criteria for emission intensity in different regions and adjust trade patterns within China could be recommended in order to achieve trade-related environmental efficiency as well as environmental equality.

Dynamic eco-efficiency evaluation of the semiconductor industry: a sustainable development perspective

Serious environmental problems have accompanied remarkable global economic growth for decades. To assist managers in the semiconductor industry with economic and environmental management, this study executes DuPont analysis to examine economic impacts from the effective implementation of sustainability initiatives. We propose a two-stage process including economic development efficiency and environmental protection efficiency through the dynamic data envelopment analysis (DDEA) to reflect the characteristics of eco-efficiency. Through DuPont analysis, the main finding shows the potential improvement in firms' return on equity (ROE) by efficiently utilizing assets to generate sales quickly.Relative to economic development efficiency, the firms show lower scores and higher standard deviations in the environmental protection ability, thus denoting a large gap in the level of environmental protection production technology. The findings in this study reveal that the financial foundations and sustainable development of industries should be improved simultaneously even though specific levels of semiconductor industrial eco-efficiency improvement vary among companies in Taiwan.

Evaluation of eco-efficiency in China from 1978 to 2016: Based on a modified ecological footprint model

Eco-efficiency has become an important indicator for measuring sustainable development that emphasizes multiple dividends including basic needs, economic growth, resource conservation, and environmental protection. In this paper, we firstly modify the traditional ecological footprint (EF) model through amending the equivalence factors and enriching the account types to estimate the ecological pressure in China. Then, we evaluate the annual eco-efficiency of China based on the modified EF model from the perspectives of biological needs, resource consumption, and environmental pollution from the reform and opening-up until recently. Finally, the decoupling status between resource consumption, pollution emissions and economic growth is explored. The results indicated that: (1) The per capita EF of China increased from 1.83hectares (ha) in 1978 to 6.98ha in 2016, in which EF of energy experienced a significant increase and accounted for the largest proportion of total per capita EF in 2016, followed by air pollution and freshwater; (2) the total eco-efficiency in China increased from 210yuan/ha in 1978 to 1235yuan/ha in 2016, in which biological efficiency performed the best starting from 264yuan/ha in 1978 with an annual growth rate of 7.9% to 2016, resource efficiency roughly presented an upward tendency with a decline during 2002-2005, and environmental efficiency pictured a U-shaped curve; (3) "weak decoupling" between resource consumption, pollution emissions and economic growth is the main characteristic at present after undergoing through large fluctuation. Furthermore, implications towards an eco-friendly society are provided.

Dynamic Assessment of Environmental Efficiency in Chinese Industry: A Multiple DEA Model with a Gini Criterion Approach

While China’s rapid industrialization has brought great pressure on environmental pollution, great variations appear in terms of environmental pollution levels among industries. The effective assessment of the environmental performance of different industries is not only conducive to identifying the major sources of pollution in China but also of great significance to the Chinese government in formulating differentiated industry environmental control policies in a targeted manner. Using data of 36 Chinese industries from 2006 to 2015 and a multiple data envelopment analysis (DEA) with a Gini criterion as well as a systematic clustering approach, this study first calculates the environmental efficiency score of Chinese industries and then identifies those pollution sources based on a ranking and clustering analysis. The main result indicates that the ranking of environmental efficiency of various industries overall varies greatly by time. In addition, using a clustering analysis, this study finds that 13 labor-intensive light industries and heavy chemical industries with high energy use and high emissions are medium- and high-pollution industries. Important policy implications are drawn to achieve green industrial development.

The Effects of Ecological Policy of Kyrgyzstan Based on Data Envelope Analysis

Kyrgyzstan is located in the runoff formation area of Aral Sea basin. The ecological protection of this country has a great effect on the downstream and Aral Sea conservation. There were a few studies about this region carried out from ecological policy aspects. The aim of this study was two-fold. First, we characterized the eco-policy system structure of Kyrgyzstan in different stages. It means the transition from “command and control” to “market-based” ecological policy. The priority of ecological policy was changed from “sustain economic growth and poverty reduction” to “sustainable development and green economy”. We then used a data envelope analysis method to evaluate the effects of eco-policy and government governance through eco-efficiency. The results are that (1) the eco-efficiency achieved the relative optimal state and the ecological policy obtained a relatively positive effect as a whole; (2) economic development promotes the protection of the ecological environment. It is the first time that quantitative method has been used to analyze the effects of ecological policy of Kyrgyzstan. The model results can reflect comprehensive effects of ecological policy in social, economic, and ecological aspects. This study provides a reference for those who want to shape the next generation of eco-environmental policies both in Kyrgyzstan and other Central Asian countries.

An empirical study on productivity analysis of Indian leather industry

Purpose

It is essential to track the development of resource and pollution intensive industries such as textile, leather, pharmaceutical, etc., under burgeoning pressure of environmental compliance. Therefore, the purpose of this paper is to analyze the progress of Indian leather industry in terms of individual factors and total factor productivity.

Design/methodology/approach

This study applies and examines the various concepts of productivity such as labor productivity, capital productivity, material productivity and energy productivity. Further, it assesses and compares the performance of Indian leather industry in Tamil Nadu (TN), West Bengal (WB) and Uttar Pradesh (UP) based on productivity analysis, spatial variations determinants in productivity and technology closeness ratio.

Findings

The findings suggest that as per the productivity analysis, WB leather clusters have performed remarkably better in terms of partial factor productivity and technical efficiency (TE), followed by TN and UP. This can be attributed to shifting of leather cluster of WB to a state-of art leather complex with many avenues for resource conservation. Further, the findings reveal that the firm size and partial factor productivities have significant positive correlation with TE which supports technological theory of the firm.

Practical implications

The results of this study can be useful for the policy makers associated with the Indian leather industry especially to design interventions to support capacity building at individual firm level as well as cluster level to enhance the efficiency and productivity of overall industry.

Social implications

The findings also support the resource dependence theory of firm according to which the larger size firms should reflect on resource conservation practices, for instance the concept of prevention is better than cure based upon 3R (reduce, recycle and reuse) principles.

Originality/value

The paper gives an explanation of the productivity in the leather industry in terms of its factor productivity and TE.

Re-Examining Regional Total-Factor Water Efficiency and Its Determinants in China: A Parametric Distance Function Approach

It is accepted that improving water efficiency is a key task for China in achieving water sustainability, as the knowledge of water efficiency and its determinants can provide critical information for water policy formulation. To this end, this paper presents a parametric frontier approach to analyze water efficiency performance and its influencing factors in one step. The proposed approach first introduces the Shephard water distance function to construct total-factor water efficiency (TFWE) index and then adopts the stochastic frontier analysis (SFA) technique to compute the index and its determinants. A case study of regions in China from 2000 to 2015 is presented. The main findings are summarized as follows: (1) Both the overall China and most of the regions still have room for improvement in water efficiency. SFA and data envelopment analysis (DEA) might lead to different results in benchmarking water efficiency. Moreover, SFA has higher discriminating power than DEA in this regard. (2) There exists significant disparity of water efficiency among the regions of China, and the difference in TFWE takes on a U-shaped evolution trend, which first decreases in a fluctuation way and then increases monotonically. (3) Factors like industrial structure, import and export trade, environmental regulation and urbanization level have a positive impact on water efficiency, while resource endowment and economic level exhibit negative and nonlinear effects, respectively. Finally, several policy recommendations are made to improve water efficiency levels and promote water sustainability.