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China's paper industry accounts for a larger proportion of the global paper industry and consumes large amounts of energy. Energy conservation and pollution reduction of the industry have become the focus of China's national development strategy. Energy efficiency is an important index which directly determines the amounts of energy consumption and pollution emissions. To understand the energy efficiency evolution of China's paper industry, this paper establishes an input-output data of the paper industry for 29 provinces in China and applies DEA method and econometric models to estimate and comprehensively discuss the energy efficiency issues based on the framework of total factor efficiency. Our results indicate that China's paper industry has a large energy saving potential even though its energy efficiency has improved during the period 1990–2013. The saving potential is about 65% under the meta-frontier and 44% under the group-frontier. Moreover, there are significant differences between different regions in production technology and energy efficiency. The performance of eastern China is better than other regions. But the technology gaps between the different regions are shrinking and converging over time. Finally, some policy recommendations are proposed to improve the energy efficiency of the industry. [ABSTRACT FROM AUTHOR]

The mitigation of climate change requires that agricultural development would proceed in accordance with green and sustainable practices. This implies that economic performance should be improved while minimizing the impact on the environment. One way to assess the underlying costs of sustainable agricultural development, and to model the relationship between the environment and the economy in general, is to use the carbon shadow price (CSP). In this paper, we utilize a by-production model within a non-parametric framework to estimate the agricultural CSP from 1997 to 2020 in 31 provinces of China. The patterns of the agricultural CSP are unveiled through kernel density analysis and convergence tests. Additionally, this paper constructs a comprehensive agricultural informatization indicator using the entropy method. We then empirically investigate its influence on agricultural CSP. The results reveal that China's agricultural CSP has demonstrated consistent growth over the past two decades which suggests serious improvement in environmental performance. Furthermore, the CSP show cross-province variation, albeit with conditional β convergence. Our empirical findings indicate that agricultural information technology contributes to an increase in CSP, thereby indicating its positive role on the green growth of agriculture in China. [ABSTRACT FROM AUTHOR]

The paper introduces a robust methodology for evaluating complex electricity system efficiency, making several contributions. It systematically decomposes the electric power system into various processes, including power generation, transmission, distribution, and power consumption, with parallel sub-processes for production and domestic power consumption. Employing data envelopment analysis (DEA), the paper develops a network directional slacks-based (DSB) model to evaluate the whole system and the internal processes. The model is theoretically proven to be feasible, providing efficiency scores and identifying inefficiencies in the system and its processes. A noteworthy addition is the introduction of the DSB green Luenberger productivity index and its decomposition, offering an analysis of productivity changes in electricity systems. Empirical analysis covering 30 Chinese provinces from 2013 to 2019 uncovers the performance variations among provinces and processes, pointing out inefficiencies and changes in technology and efficiency over time. The empirical findings aim to support efforts in enhancing the overall efficiency of China's interprovincial power system. • We decompose the complex electric power system into four processes with dependence. • We build a network directional slacks-based (DSB) model for the complex power system. • We give a DSB green Luenberger productivity index (DPI) for our network model. • The efficiency score and DPI of the system are decomposed into those of processes. • We apply the model and indexes to electric power systems in Chinese provinces. [ABSTRACT FROM AUTHOR]

This paper presents an analysis of the water-energy-food nexus (WEFN) system, focusing on its interlinked structure and dynamic characteristics. This paper proposes a dynamic series-loop data envelopment analysis (DEA) model with a directional distance function for measuring WEFN efficiency in China. A spatial Durbin model is constructed to investigate the impact of extreme temperatures on WEFN efficiency. The results are as follows. (1) Compared with the black-box DEA model and series network DEA model, a series-loop network DEA model can reflect the "networking effect" and "looping effect" in the WEFN system. (2) The WEFN system performs well with an average series-loop efficiency of 0.875, but the efficiency of the water subsystem scores low and needs improvement. (3) At the national level, extremely high and low temperatures have no significant effect on the WEFN efficiency in local or adjacent provinces, whereas average temperature promotes the WEFN efficiency in local provinces but hinders it in neighboring provinces. In the regions with a low temperature difference, average temperature has no impact on the WEFN efficiency of both the province itself and its neighboring provinces. Overall, this study provides valuable insights into the complex interlinkages within the WEFN system and highlights the importance of considering these relationships when improving the system efficiency. [ABSTRACT FROM AUTHOR]

Over recent decades, China's economy has continued to develop steadily and rapidly, contributing to a large environmental cost. Therefore, many researchers propose various methods to evaluate environmental efficiency to deal with these serious issues accurately. This paper is the first to evaluate environmental efficiency using a model featuring not only a parallel structure with three component processes (primary, secondary, and tertiary industries) in the first stage but also a sewage treatment process in the second stage. Simultaneously, we propose radial but also nonradial versions of the Environmental Multiple Variable Return to Scale (EMVRS) model. These two models both consider shared inputs as well as shared outputs, and the proportion of the shared inputs and shared outputs utilized in each component process is assumed to be unknown. Additionally, we further analyze the factors influencing environmental efficiency by utilizing the Tobit model. The results when EMVRS is applied to Chinese provinces can be divided into 3 parts: (1) Most provinces do not purify wastewater appropriately contributing to the bad environmental efficiency. (2) The efficiency values of the three industry types are similar or identical to each other in most provinces. (3) The population of people employed negatively impacts environmental efficiency. [ABSTRACT FROM AUTHOR]

As an effective approach in ranking decision making units (DMUs), cross-efficiency evaluation is only extended to two basic network systems: series and parallel, which is not applicable to the common complex network system in reality. By using the directional distance function (DDF), this paper extends the cross-efficiency evaluation to a complex system called the parallel two-stage system, which consists of several independent parallel processes and each of them is a sub-system with a general two-stage series structure. The proposed method is able to decompose the cross-efficiency measure of the system into the weighted average of those of processes, stages and overall stages, respectively, and as a result, successfully achieves the layer-by-layer decomposition of cross-efficiency measures under complex nested networks. An empirical study on 40 open-ended equity funds in China from 2018 to 2020 verifies that the newly proposed method not only has a stronger performance discrimination power than self-evaluation, but also can identify the relationships among efficiencies of the system, processes and stages, and meanwhile it can reflect the effects of processes and stages on the system. More importantly, our method has a better practical value for actual fund investment selection. [ABSTRACT FROM AUTHOR]

An increasing amount of resources is being utilized to combat the pollution problem in China. Measuring and improving the efficiency of this resource allocation is an effective way to promote pollution treatment at the present technical level. This paper utilizes a two-stage data envelopment analysis (DEA) model to attain allocation results based on the zero-sum gain and fixed-sum gain assumptions. We divide all the inputs into discretionary and non-discretionary inputs and construct two models for allocating resources based on the environmental efficiency scores. The new models are applied to calculate the performance and resource allocation scheme of 30 Chinese provinces from 2011 to 2015. Most provinces are inefficient at controlling pollution emission or treating pollution while using the given level of input resources. Based on the production possibility set of each province in 2015, the processing capacity of wastewater and gas could be increased by 1% and 3%, respectively, by reallocating resources for pollution treatment among different provinces. Environmental efficiency scores and resource allocation plans based on various assumptions are obtained in this paper to enlighten the corresponding decision-makers. Image 1 • The paper constructs two resource allocation models based on the DEA approach. • This research considers the allocation of public pollution treatment resources between different sectors. • Two strategies for resource allocation are composed to deal with different conditions relating to resources. • We apply the new models to measure the performance of 30 regions in China. [ABSTRACT FROM AUTHOR]

The metal industry (MI) in China is energy and emissions intensive. This paper tries to provide guidance for the green development transformation of China's MI, by investigating the green total-factor productivity (GTFP) and its influencing factors using a global data envelopment analysis and a spatial Tobit regression model. The estimation results show that: (1) from 2000 to 2015, GTFP of China's MI enjoyed a considerable improvement. However, the current value of GTFP of China's MI is still less than 0.5, indicating that GTFP of China's MI is far from efficient; (2) the spatial correlation test shows that GTFPs of China's provincial MIs have positive spatial correlations. Most of the provinces scattered in "high-high" and "low-low" agglomerations; (3) GTFP of China's MI is significantly positive related to the degree of marketization, fiscal decentralization, environmental regulation, industrial scale, is significantly negative related to resources endowment and product structure, and has "U" shaped relationship with the level of economic development. The above results suggest that China already has made remarkable achievements in promoting green development of its MI. However, there is much room for further improvement. Policy options for the green development transformation of China's MI include but are not limited to promoting market-oriented and financial system reforms, strengthening environmental supervision, and optimizing industrial scale and product structure. Finally, recommendations for the effective implementation of these policies are provided at the end of the study. • This paper analyzed the historical performance and influencing factors of GTFP of China's MI. • Results show that GTFP of China's MI achieved considerable improvement in the past few years. • However, the current level of GTFP of China's MI is still very low. • There are many effective policy options for further promoting GTFP growth of China's MI. • Some implications for the implementation of these policies are provided in this paper. [ABSTRACT FROM AUTHOR]

Wastewater treatment plants (WWTPs) play a pivotal role in natural water recycling and safeguarding the water security of approximately 42% of the world's population, yet they confront challenges posed by escalating energy consumption and regional energy constraints. In this study, we developed an evaluation framework that integrated regional energy scarcity with the operation conditions of WWTPs to quantify their energy efficiencies by applying multiregional input–output (MRIO) analysis and data envelopment analysis (DEA). A case study using this framework was conducted on 3776 WWTPs in 30 provinces in China. The results showed that the average scarcity-based energy efficiency level of WWTPs in China was low, of which those in northern provinces were higher compared to southeastern provinces. Furthermore, only 24 WWTPs in China attained the highest efficiency levels, while the rest had different degrees of input redundancy, indicating an energy-saving potential of 9.05 × 109 kWh. This paper enables the development of optimal efficiency strategies for WWTPs in different provinces considering the potential energy scarcity risks caused by trading activities. • The DEA and MRIO analyses were combined for 3776 WWTPs in China. • Efficiency evaluation index was modified considering energy scarcity levels. • Scarcity-based energy efficiency assessment framework for WWTPs was developed. [ABSTRACT FROM AUTHOR]

The five major power generation corporations dominate the power industry in China, and play vital roles in China's carbon trading scheme. Under this circumstance, this paper studies the allocation of carbon emission quotas to China's five major power generation corporations based on the fairness and efficiency principles, which proves the primary prerequisite for setting up the national carbon market. Specifically, a bi-level programming model is developed to optimally allocate carbon emission quotas to the corporations, and then a zero sum gains data envelopment analysis (ZSG-DEA) model is adopted to evaluate the efficiency of the allocation so as to adjust towards the optimal solution. The results indicate that power generation has significantly positive influence on the allocation of carbon emission quotas. Moreover, the difference between the sum of optimal carbon emissions and the sum of carbon quotas allocated is 1.431 billion tons, which implies that the emission reduction potential of the five major power generation corporations is tremendous. Finally, the bi-level model in this paper can well capture the context of China's power industry and provide effective results for the allocation of carbon quotas. [ABSTRACT FROM AUTHOR]

Data Envelopment Analysis (DEA) is used for not only assessing the performance of a set of homogeneous decision-making units (DMUs), but finding the target operating points to improve inefficient DMUs. When inefficient DMUs are far from their target, it will be impossible to reach those in a single move, therefore, a strategy of gradual improvements with intermediate targets has been proposed. In this study, we propose a method to determine benchmarking paths, which consider the improvement steps as the learning of the production functions, not the learning of inputs and outputs. Under the constant returns-to-scale (CRS) assumption, the paper, for each inefficient DMU, sets the ultimate target as the closest target and determines the shortest improvement path with existing DMUs by Dijkstra's algorithm, and this is illustrated with a numerical experiment. As a real-world example, the method is introduced to the irons and steel enterprises in China, so that the specific steps to make efficient ones has been pointed out. The proposed method is the first study to discuss stepwise benchmarking path from the perspective of the production function. • The benchmarking paths are composed of existing DMUs. • The ultimate target is set as the closest efficient DMU. • The inefficient DMU learns the production functions of DMUs. • The distance between two production functions is calculated. • Dijkstra's algorithm is used to find the optimal benchmarking path. [ABSTRACT FROM AUTHOR]

Considering both regional heterogeneity and industrial heterogeneity, this paper proposes a three-hierarchy meta-frontier data envelopment analysis (DEA) to decompose energy efficiency and energy-saving potential into three components, i.e., industrial structure, regional balance development, and management. Based on this approach, the paper then analyses the historical evolution and current status of energy efficiency in China. The results show the following. (1) From 2001 to 2015, Mainland China's energy efficiency increased by a factor of 2.48, primarily because of technological progress and the optimization of its industrial structure. (2) Nonetheless, the current energy efficiency in Mainland China remains very low and energy inefficiency is as high as 0.5922, indicating that 59.22% of total energy consumption is invalidly used. Severe industrial structure inefficiency and regional balance development inefficiency are the two main factors responsible for the very low energy efficiency. (3) By promoting industrial restructuring, regional balance development, and management improvement, Mainland China is expected to save, respectively, 13.67%, 24.54%, and 61.73% of its current total invalid energy consumption. Based on the estimation results, this paper formulates strategies to improve energy efficiency and save energy for China's provinces based on their specific situations, considering not only the direction of industrial restructuring in both the short and long terms but also the path to improved energy efficiency. In addition, a brief discussion about the applicability and the preconditions for an application of the multi-hierarchy meta-frontier approach is given at the end of the paper. [ABSTRACT FROM AUTHOR]

Obtaining the most economic benefits while using the least resources and causing the least environmental damage is an urgent and key problem in achieving sustainable development. The concept of eco-efficiency provides a comprehensive perspective for observing and solving this problem. This paper proposes an extended data envelopment analysis model, named the WIN-US-SBM, as a comprehensive eco-efficiency evaluation method that can accommodate undesirable outputs, all the identification problems of decision-making units (DMUs), possible slacks of variables, and the problems of an insufficient number of available DMUs and intertemporal comparability. China's Western Taiwan Straits Economic Zone (WTS Economic Zone) is an important regional economic growth center. By applying WIN-US-SBM and ArcGIS software, we investigated data on the temporal and spatial evolution characteristics of eco-efficiency in the WTS Economic Zone for the years 2009–2016. Later, the global Malmquist–Luenberger index was used to decompose the sources of growth in eco-efficiency. Finally, the main driving factors of eco-efficiency were explored through a Tobit regression analysis. The empirical results show that: (1) The overall eco-efficiency of the WTS Economic Zone had greatly improved, increasing by 11.46% annually, and the cumulative growth rate of eco-efficiency was 110.84% from 2009 to 2016. (2) The eastern area had the highest average eco-efficiency, followed by the western area and the middle area in that order. Also, the growth rate of the middle area ranked first (16.68%), whereas the eastern area had the slowest growth in eco-efficiency (7.22%). (3) On the whole, the growth of eco-efficiency was driven mainly by technological progress and was restricted by the decrease in management level, which implies that policy makers should focus on improving management and technical levels. (4) High levels of economic development, population density, market openness, and industrial structure had positive effects on the eco-efficiency of the WTS Economic Zone, whereas energy intensity and expenditure on science and technology had negative impacts. • An extended DEA model is proposed to measure eco-efficiency more comprehensively. • The proposed model can be widely applied to input-output efficiency evaluations. • The eco-efficiency of the WTS Economic Zone was evaluated based on a novel model. • The growth of eco-efficiency was driven mainly by technological progress. • The eco-efficiency level is negatively related to energy intensity. [ABSTRACT FROM AUTHOR]

In recent years, the profit growth of wind power enterprises in China is generally weak, and the phenomenon of "wind curtailment" in some areas is relatively common. The comprehensive evaluation of wind power performance in various regions of China and the study of the driving forces of wind power performance differences in different regions are of great significance for scientific planning and layout of wind power investment. Based on the panel data, this paper combines Data Envelopment Analysis (DEA) and Technique for Order Preference by Similarity to an Ideal Solution (TOPSIS) to establish an evaluation model to evaluate the wind power performance of China's 29 provinces and cities from 2011 to 2018. Then uses the evaluation results as input data of Adaptive Neuro-Fuzzy Inference System (ANFIS) prediction model to predict the wind power performance of 2019 in various regions of China under the current policy and environment. Finally, the main factors affecting the performance of China's wind power generation are explored by establishing a regression model. The research results show that at present, the main factors affecting the performance of China's wind power generation are local power consumption capacity, economic development degree and the rate of wind abandonment. On the premise of solving the problem of wind curtailment, appropriately promoting power consumption and renewable energy policy reform, from Feed-in-Tariff (FiT) to a Renewable Portfolio Standard (RPS), are effective means to promote the development of wind power generation in China. Image 1067 • In wind-intensive areas, wind power performance levels are relatively low. • Power consumption capacity and wind curtailment rate are the main influencing factors. • Current policy cannot effectively solve the problem of wind curtailment. • Renewable energy policies with regional differences should be adopted. [ABSTRACT FROM AUTHOR]

This paper investigates the effect of the implementation of smart city policy (SCP) on energy efficiency (EE) in China. For this purpose, we have developed a non-convex metafrontier data envelopment analysis (DEA) approach to measure EE, and empirically examine the impacts of SCP on China's EE using Difference-in-Differences (DID) analysis, using a dataset of 251 cities between 2003 and 2016. Results show that there exists significant regional heterogeneity across cities, with the SCP exerting a significant positive effect on EE, indicating that the SCP can effectively improve EE during the study period. These findings are robust to different sub-sample, energy efficiency alternative measures, estimation strategies, and Baidu Search Index. Based on empirical results, the implications of SCP are outlined. • Energy efficiency measures of 251 Chinese prefectural-level cities are estimated. • Eastern region performs the best, followed by the central and western regions. • The effects of smart city policy on energy efficiency is investigated using DID method. • The smart city policy can effectively improve energy efficiency during the study period. • Empirical results are robust to different sub-sample, energy efficiency alternative measures, etc. [ABSTRACT FROM AUTHOR]

China has developed a remarkable economic growth in the past 30 years by accompanying with undesirable outputs. The assumptions of weak and strong of undesirable outputs have become the focus discussion in data envelopment analysis (DEA) models. To have a comprehensive environmental efficiency measurement, this paper proposes three DEA models considering weak disposability, strong disposability and distinguishing weak and strong disposability of undesirable outputs, respectively. We further combine with the Malmquist-Luenberger productivity index to assess the environmental efficiency and the dynamic change trend of 30 provinces in China from 2005 to 2014. The results show that distinguishing technical features of undesirable outputs makes a significant difference to final environmental efficiency score at different regions. The environmental efficiency of the whole nation, however, does not show any increasing trend during the ten years. Enhancing environmental efficiency of central and west area is an urgent task for Chinese government. The change trend of Malmquist-Luenberger productivity index and technological change shows a similar shape. Technical progress is the main driving force to enhance the environmental efficiency. Relevant suggestions are presented for improving the regional environmental efficiency in China in the future. [ABSTRACT FROM AUTHOR]

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]

In the face of environmental degradation, sustainable development has become a common goal across the globe. Making a scientifically based investment scheme is of great significance to promote the sustainable development of China's economy. However, there is scarce research related to such an investment scheme of sustainable development. This paper proposes a new inverse data envelopment analysis method with undesirable outputs to make several scientifically based investment schemes from different perspectives, namely, the natural, regulation, and optimal perspectives. By this method, decision makers can scientifically forecast the specific amount of investment based on their actual sustainable development objectives, which is conducive for reducing the blindness of investment in the future. In addition, a new ideal perspective is defined to guide a definite direction for improving the level of sustainable development. Combined with the gray forecasting model GM(1,1), the methods proposed by this paper were then applied to analyze the investment problem for China's sustainable development during the 2015–2024 period. The results show that: the unbalanced distribution of labor investment and the excessive investment in capital and energy are serious barriers to China's sustainable development in the short term; and in the long term, the demand for investment in labor and capital will continue to increase along with a lower demand for energy investment, and that appropriately strengthening environmental regulations will not affect the overall demand for investment. Meanwhile, improvement directions for improving China's sustainable development are discussed, and the results show that most of developing and undeveloped regions in China have great potential for improvement. Finally, some suggestions are proposed in order to create better conditions for China's sustainable development. [ABSTRACT FROM AUTHOR]

The development of China's forestry resources has never been more challenging due to serious problems such as shortage, inferiority and uneven distribution of forestry resources. Therefore, the study and evaluation of China's forestry resources has a great significance to improve efficiency and ensure the sustainable development of the forestry resource. Meanwhile, the vast territory, huge population and widespread forest landscape of China have led to the numerous indexes and the huge data. To perform the research and evaluation accurately, this paper utilized the big data theory to analyze the relevant data of China's forestry resources. This study collected the data from 31 inland provinces and municipalities of China from 2005 to 2013, after which we carefully examined economic, social and ecological factors to choose assessment indexes and processed data accordingly. Firstly, we performed a cross-sectional dataset analysis using the method of data envelopment analysis to investigate the forestry resources efficiency in 31 inland provinces and municipalities of China in years 2008, 2012 and 2013. Secondly, we analyzed time series data of the 31 inland provinces and municipalities from 2005 to 2013 using the Malmquist total factor productivity index method. Our results showed the dominant factor that restraining forestry resources efficiency for the 31 inland provinces and municipalities is the implemented technology. So we suggest increasing the investment in science and technology to improve the overall efficiency of forestry resources, along with improvement of operation and management by relevant administrative departments to improve technology utilization. The innovation of this paper lies in the dynamic process of analysis. [ABSTRACT FROM AUTHOR]

Schematic diagram of the research framework: A new network DEA method. [Display omitted] • A new network DEA allocation model considering cross-border transmission of SO 2. • The WRF-CMAQ is combined with the DEA allocation model for the first time. • The principles of optimal efficiency, GDP protection and fairness is considered. • The per capita relative deprivation coefficient was used to evaluate the fairness. • The new network DEA model is a powerful tool for SO 2 emission rights allocation. Currently SO 2 emission rights are allocated without considering the cross-border transmission characteristics of pollutants, and the allocation scheme cannot reflect the fact that air pollutant overflows in space. In this paper, the WRF (Weather Research and Forecasting Model)-CMAQ (Community Multiscale Air Quality) is combined with the data envelopment analysis (DEA) allocation model for the first time, and a two-stage (Stage-1: SO 2 formation, Stage-2: SO 2 treatment) network DEA allocation model is proposed with the SO 2 spatial transmission taken as the intermediate output or input index between decision-making units. Based on the principles of efficiency, economic protection and fairness, the model is applied to the study of SO 2 emission rights allocation in the Yangtze River Delta city agglomeration with high homogeneity in China. The results show that: (1) When the weight coefficient (α 1) in stage-1 is 0.7, 0.8 or 0.9, the per capita relative deprivation coefficient (PRDC) of each decision-making unit (DMU) is less than 0.5 in the allocation scheme, indicating that our scheme can be used as a reference to allocate emission rights in the Yangtze River Delta city agglomeration. (2) With great robustness and universality, our allocation model allows decision-makers to adjust the GDP constraint coefficient and the weight coefficients in the two stages according to their preferences, so as to obtain the optimal allocation results. The model proposed in this paper can help decision-makers to formulate effective policies to reduce pollutant emission, and promote energy conservation & emission reduction and coordinate environmental governance in regions of city agglomerations. [ABSTRACT FROM AUTHOR]

• Game method is used in interval cross evaluation. • Build a variety of evaluation strategies to linearize the model. • Using entropy theory to aggregate the results of different evaluation strategies. Cross-efficiency evaluation is an effective method for the ranking of decision-making units (DMUs) in data envelopment analysis, which is the performed method for peer-evaluation and self-evaluation. In most cross-efficiency evaluation methods, the peer evaluation result is completely constrained by the evaluator's optimal self-evaluation weights. DMUs are connected by a competitive relationship; therefore, they can be regarded as players in the game and the cross efficiency value can be regarded as payment because of this competition. DMUs need to consider the impact of their own weights on other DMUs to identify a set of weights for evaluating themselves. Based on the idea of non-cooperative games in the cross evaluation of DMUs, this paper considers the interval cross game efficiency of DMUs with the interval data. The interval data theory is combined, the best production state and the worst production state are taken as reference states, and four evaluation strategy models are proposed according to the opposition and coordination relationship. Then, the bijective and injective interval game cross models, as well as the algorithm corresponding to the four evaluation strategies are constructed. Moreover, the result of the self-evaluation is used as the initial parameter to prove the existence of the solution and the convergence of the algorithm. Based on the priority relationship of the evaluation results under different strategies, the interval entropy method is used to aggregate the results and obtain the final cross efficiency of the interval game. Furthermore, both the applicability and superiority of this model are exemplified by an example. Finally, based on the method of this paper, the efficiency of forest carbon sequestration in China are studied, seven natural regions in China are ranked by forest carbon sequestration efficiency values reasonably, and the current regions development of forest carbon sequestration in China are evaluated. [ABSTRACT FROM AUTHOR]

It is important to investigate the sustainability nexus using composite ecological efficiency (eco-efficiency) indicators from quantitative and computational modeling perspectives. To provide a more comprehensive measure of eco-efficiency, this paper develops three data envelopment analysis (DEA) models incorporating metafrontier and undesirable outputs into a slacks-based measure (SBM), an epsilon-based measure (EBM), and minimum distance to weak efficient frontier (MinDW), respectively, named Meta-U-SBM, Meta-U-EBM, and Meta-U-MinDW. We show theoretically: if a decision-making unit (DMU) is being Meta-U-SBM-efficient, it is also being Meta-U-EBM-efficient and Meta-U-MinDW-efficient, and vice versa. For each DMU, its score measured by Meta-U-MinDW is the highest, while its score measured by Meta-U-SBM is the lowest. Using the proposed models, we make a first empirical attempt at measuring the eco-efficiency of the Key Environmental Protection prefecture-level cities in China from 2003 to 2015. The results show that: (1) the eco-efficiency of eastern cities performs the best, followed by central cities and western cities. Cities not listed as resource-based (RB) outperform RB cities; (2) eastern cities are closer to metafrontier than central and western cities; and (3) efficiency change and technological progress dominate the basic trend of the eco-efficiency growth. Results derived from the proposed methodologies can help managers and policymakers in the city to assess sustainability development quantitatively and comprehensively, they can also provide implications for the environmental management of sustainability development at city level. Policy implementations are presented based on the empirical results. [ABSTRACT FROM AUTHOR]

• A high spatial resolution CO 2 emissions inventory of 280 Chinese cities was compiled. • Three methods were compared to evaluate the carbon efficiency of Chinese cities. • The best and worst-performing cities were consistent regardless of different methods. • Top-ranking cities were located in the coastal regions of China. • Lagging-behind cities were located in the central and western regions of China. Comprehensive and accurate city-level carbon dioxide emissions inventory is key to success in China's pursuit of low-carbon development. Based on a high-resolution gridded data, this paper evaluates and benchmarks the carbon emissions efficiency of 280 Chinese cities using three different methods, i.e., composite index, data envelopment analysis and stochastic frontier analysis. The comparative analysis reveals that the average carbon emissions efficiency of Chinese cities is about 70–90%, with only 9 efficient cities (3%). Although the ranking of intermediate cities varies greatly according to the method employed, the most and least carbon-efficient cities remain consistent. While the best-performing cities are found in the coastal regions, the worst-performing cities are located in the central and western regions, which suggests an imbalanced low-carbon development in China. Guided by the results, local governments are encouraged to compete and learn from similar ranking benchmarks in a new regime of "Top Runner City". Finally, the scores of carbon emissions efficiency can provide a scientific basis for the bottom-up allocation of carbon emissions rights to boost the development of the nationwide carbon market in China. [ABSTRACT FROM AUTHOR]

Abstract China is the world's largest producer and consumer of non-ferrous metals, and the non-ferrous metals industry is energy- and emission-intensive. The CO 2 emissions of the industry have increased nearly four times from the level in 2000 to reach 310 million tons in 2014. To cope with the issues of CO 2 emission and other environmental problems under the background of low-carbon development, it is necessary to analyze the CO 2 emission performance of China's non-ferrous metals industry. Based on group heterogeneity, this paper uses data envelopment analysis to calculate the Shephard distance function and then uses the Shephard distance function and non-parametric meta-frontier methods to construct the meta-frontier Malmquist CO 2 emission performance index (MMCPI) of China's non-ferrous metals industry during the period 2006–2014. The index is further decomposed into three parts: technical efficiency change, technological progress change and technological catch-up effect. The results showed that the MMCPI of China's non-ferrous metals industry exhibits an upward trend during the review period. The improvement in CO 2 emission performance is most significant in the central region, followed by the eastern and western regions. Based on the decomposition of MMCPI, the effect of technological progress are significantly different for the different regions, indicating that more targeted policies are need for different regions. [ABSTRACT FROM AUTHOR]

Abstract In recent years, urban energy efficiency has received much attention from researchers. However, ignorance of the competitive relationships among regions results in inaccurate energy efficiency. Game cross-efficiency data envelopment analysis (DEA), which can account for the competitive relationships, is a reliable method to measure energy efficiency. First, this paper applies game cross-efficiency DEA to analyze the urban total factor energy efficiency (UTFEE) of 26 Chinese prefectural-level cities from 2005 to 2015 under environmental constraints. Then, a comparative analysis and a concrete analysis are conducted based on the urban energy efficiency. Finally, a Tobit model is used to examine the effects of 10 potential influence factors. The measurement results show that the urban energy efficiency considering the competitive relationship is lower than the traditionally calculated efficiency. Additionally, the urban energy efficiency did not improve during the research period. Furthermore, regression analysis suggests that economic development and city scale can promote urban energy efficiency, while government expenditure, industrial structures, energy prices, foreign investments, research investments and production endowment have negative impacts on urban energy efficiency. According to our results, some implications of theory and practice are discussed for researchers and policy makers. [ABSTRACT FROM AUTHOR]

Abstract Improving CO 2 emissions efficiency has become a crucial task faced by China to develop a low-carbon economy and choose a CO 2 emissions mitigation path. Existing researches have focused on estimating national CO 2 emissions efficiency, but have ignored the industrial heterogeneity in the region or explanation for inefficiency. To fill the gaps, this paper applies a metafrontier-Malmquist index analysis method combined with a three-level common frontier model to comprehensively estimate and decompose the carbon emissions efficiency and dynamic changes of China's industries and regions during 2010–2015. First, we use data envelopment analysis with directional distance function by considering three-level frontiers to calculate CO 2 emissions efficiency. Results show that China's overall CO 2 emissions efficiency is at the middle level with an average value of 0.511. The efficiency of CO 2 emissions in the eastern region is the highest, followed by the central and western regions, and the CO 2 emissions efficiency in the tertiary industries is greater than that in the primary and secondary industries. Second, we decompose CO 2 emissions efficiency into technological, industrial and management efficiency, and found that management efficiency is the main reason for low CO 2 emissions efficiency in developed regions and industries. Third, the Malmquist-Luenberger index is utilized to decompose CO 2 emissions efficiency into 9 items, and we obtained that continuous technological progress is the leading cause driving different degrees of improvement in CO 2 emissions efficiency. Graphical abstract Image Highlights • Apply non-radial model to estimate CO 2 emissions efficiency of China. • Use industrial, technical and managerial efficiency to explain inefficiency. • Build three-level metafrontier-Malmquist-Luenberger index to discuss dynamic changes. • Eastern region and tertiary industry have the highest CO 2 emissions efficiency. • Continuous technological progress is the main reason for gaps of efficiency. [ABSTRACT FROM AUTHOR]

Abstract Carbon emissions trading is one of the important economic means to solve the environmental externality. Based on the data of CO 2 emissions from the thermal power plants over the period from 2005 to 2010 in China, this paper uses no-parametric optimization models to estimate the potential economic gains and potential carbon emissions reduction under three allocation strategies of carbon emissions trading allowance. Results show that the maximum potential gains can be obtained by the spatial allocation scenario for the combination of CO 2 -SO 2 emissions trading, and the output growth ratio among power plants varies huge. The establishment of jointed CO 2 -SO 2 emissions trading is usually highly beneficial to achieve the three targets, including the optimal allocation of resources, the increase of potential gains and the reduction of pollutant emissions. [ABSTRACT FROM AUTHOR]

Abstract The green efficiency of water resources is important for industrial cleaner production and sustainable development. Existing studies pay much attention to the economic efficiency of water resource utilisation, ignoring its green efficiency. In order to solve this problem, an index of green total factor water efficiency (GTFWE) that can reflect the economic efficiency and green efficiency of water resources is constructed based on the DEA-SBM model with undesirable output. In addition, GTFWE is comprised of green total factor water management efficiency (GTFWME) and green total factor water environmental efficiency (GTFWEE) using the four-stage DEA model. The empirical results show that GTFWE in China's industry is at a low level, the effect of GTFWEE on GTFWE is generally increasing, and GTFWME is decreasing. However, there are large differences in these trends between regions and provinces in China. The GTFWME of Beijing, Tianjin, and seven other provinces needs improvement, while the GTFWME and GTFWEE of Hebei, Inner Mongolia, and other 15 provinces is low and also need improving. In contrast, both GTFWME and GTFWEE are high in the Shandong and Guangdong provinces. This paper provides a method for assessing GTFWE in China's industry and proposes some important policy implications for promoting the efficiency of China's industrial green total factor water resources. [ABSTRACT FROM AUTHOR]

Abstract Technology advances are considered to be effective in reducing carbon emissions. However, studies show that the effect of such advances on reducing carbon emissions is not as effective as expected, owing to the existence of the rebound effect (RE). This paper proposes a new method integrating the Data Envelopment Analysis (DEA) production model and the sequential Malmquist-Luenberger (SML) index to calculate China's macro-carbon RE. An extended DEA production model is used to eliminate the impact of input scale, and the SML index is employed to avoid the phenomenon of technology regress. Results show that the carbon RE can be calculated more precisely. The new method is then applied to calculate carbon RE values from 30 Chinese provinces from 1996 to 2015, and results indicate that the carbon RE does indeed exist in China and that national average carbon RE values vary from 7.4% to 43.8%. Furthermore, both the backfire effect and the super conservation effect are evident in some provinces. Based upon the obtained results, certain policy implications are suggested to facilitate China in effectively implementing carbon emission reduction actions. [ABSTRACT FROM AUTHOR]

The rapid development of urbanization has had a dramatic impact on the ecological environment in China. In this paper, we first measure the urban eco-efficiency of 281 prefecture-level cities in China during 2006–2013 using the super-efficiency data envelopment analysis (SEDEA) model. We then propose a new comprehensive index system to assess urbanization. Finally, we quantitatively explore the relationship between urbanization and urban eco-efficiency based on a spatial econometric analysis. The results showed that China's average urban eco-efficiency and comprehensive level of urbanization of 281 prefecture-level cities had shown increase during 2006–2013. However, distinct spatial disparities exist among different cities. China's urbanization has significant characteristics of spatial agglomeration and spatial convergence during 2006–2013, with a high-to-low spatial distribution from eastern China to western China. Spatial effects play an important role in the changes of urban eco-efficiency in China through spatial spillover effects and regional replication and competition. Evidence from spatial econometric analysis reveals that an N-shaped relationship exists between urbanization and urban eco-efficiency, indicating that with the improvement of urbanization, urban eco-efficiency first increases, then declines and then increases again. The improvement of the level of technology and the formulation of effective environmental policies will promote the increase in urban eco-efficiency. The results provide significant references for urban ecological management and sustainable urban transformation in China. [ABSTRACT FROM AUTHOR]

this paper applies an epsilon-based measure DEA approach to evaluate the regional ecological energy efficiency for 30 regions in China and its three major areas for the period 2007–2015. Based on this, the temporal and spatial disparities of China's regional ecological energy efficiency are investigated. To explore the sources of China's regional ecological energy inefficiency, the overall ecological energy inefficiency is decomposed into several input-specific inefficiencies. And then, the energy saving and pollution abatement potentials of each Chinese region is calculated using the differences between the target values and the actual values of energy consumption and pollution emissions. The empirical results show that China's ecological energy efficiency is relatively low and regional differences are significant for the period 2007–2015. It can be demonstrated that the inefficiency to abate pollution is the main contributor to China's regional ecological energy inefficiency. By calculating the potentials of energy saving and pollution abatement, it can be found that the east area has the lowest potential, followed by the central area, with the west area having the highest potential. [ABSTRACT FROM AUTHOR]

Cities have been one of the most important areas of CO 2 emissions. It is increasingly important to research the effect of urbanization on CO 2 emissions, especially in large emerging and developing economies, due to the indispensable need for understanding the effect of urbanization on CO 2 emissions, evaluating carbon reduction tasks and providing the scientific basis for low-carbon urbanization. Utilizing a balanced panel dataset in the Yangtze River Delta (YRD), China, during the period of 2000–2010, this paper employed data envelopment analysis (DEA) window analysis and a spatial lag panel Tobit model to investigate the effect of urbanization on CO 2 emissions efficiency (the ratio of the target CO 2 emissions to the actual CO 2 emissions). The results show that the average CO 2 emissions efficiency was 0.959 in 2010, and CO 2 emissions efficiency ranged from 0.816 to 1 and exhibited spatial clustering in the region. The larger potential of CO 2 emissions reduction appeared in Zhenjiang and Yangzhou, indicating that more CO 2 emissions reduction tasks should be allocated to these two cities. Urbanization has negative effects on improving CO 2 emissions efficiency, and there is a U-curve relation between CO 2 emissions efficiency and urbanization, indicating that CO 2 emissions efficiency decreases at the early stage of urbanization, then increases when urbanization reach a high level. There is spatial spillover effect among the prefecture-level cities, suggesting that different prefecture-level governments should coordinate with each other to improve CO 2 emissions efficiency in the whole area. Gross domestic product (GDP) per capita also plays a markedly positive role in improving CO 2 emissions efficiency. This research highlights the effect of urbanization on CO 2 emissions efficiency and the importance of improving CO 2 emissions efficiency in developing countries. [ABSTRACT FROM AUTHOR]

Environmental efficiency improvement has played a crucial role in the theory and practice of stimulating clean production. This paper analyzes the interaction between environmental efficiency and output efficiency, particularly whether they reinforce each other or compete with each other, on the basis of a data set of 137 firms in the textile industry in China's Jiangsu Province. In the first stage, generalized data envelopment analysis is applied to calculate efficiency measures of energy, waste water, waste gas, soot, and output efficiency taking capital, labor, water, and energy as inputs, industrial output value as desirable output, and waste water discharges, waste gas and soot emissions as undesirable outputs. In the second stage analysis, a structural equation model with latent variables is applied to analyze the interaction between the latent variable environmental efficiency, measured by the four observed environmental indicators, and output efficiency, taking also into account the endogenous variable profit. The main outcomes of the structural equation model are the following. Firstly, environmental efficiency negatively impacts on profit while profit positively impacts on environmental efficiency. In a similar vein, output efficiency is found to depress profit while profit increases output efficiency. Thirdly, environmental efficiency has a positive impact on output efficiency while there is no effect of output efficiency on environmental efficiency. Fourthly, taxes impair a firm's output efficiency. From the findings it follows that a swap of general taxes for an energy tax is likely to improve both output efficiency and energy efficiency. The latter outcome implies a win–win situation which will facilitate the further implementation and adoption of environmental policy. Finally, the paper illustrates the applicability of structural equation modeling in efficiency analysis. [ABSTRACT FROM AUTHOR]

Sustainability has been a global consensus for the governments worldwide to address serious environmental issues. However, there is a lack of data envelopment analysis (DEA) studies evaluating the performance of government in promoting various sustainability sub-systems. To do this evaluation work, our study proposes new parallel-series network DEA models with alternative network structures of incorporating intermediate products. Particularly, this study develops a new methodological framework to evaluate various inefficiency sources. The framework contains an extended method of network meta/group frontier approach and a new concept of efficiency Foster-Greer-Thorbecke indicator. Empirically, this study considers the case of Chinese government-sustainability system from 2010 to 2017. Such work cannot be found in the existing studies. The main findings are summarized as follows. First, China has succeeded in realizing an overall improved efficiency during the examined periods. The importance of various divisions changes over time. Second, the occurrence of method heterogeneity and significant linkage inefficiency implies that the modelers should incorporate intermediate products in a well-suited way. Finally, inland provinces show higher technology gap inefficiency as for economy division. To improve the performance, inland provinces should promote their human capital and adopt advanced technology. This paper proposes new parallel-series network DEA models with alternative network structures of incorporating intermediate products. Particularly, this study proposes a unified framework to analyze the relationship between network structures and inefficiency sources through the use of meta/group-frontier approach. To identify the main technology diffusion that maybe further cause the inefficiency value, this study applies the FGT indicators to the productivity and efficiency field for the first time. The FGT indicators can be used to explore the structure of technology inequality and identify barriers to technology diffusion. [Display omitted] • We propose a new approach for modelling the parallel-series network structure. • This study applies new frontier framework to identify sources of inefficiency. • There has been growing trends in Chinese government-sustainability system. • Managerial inefficiency had a negative annual growth rate of −6.01%. • Inland provinces showed higher technology gap inefficiency for economy division. [ABSTRACT FROM AUTHOR]

The process of a regional industrial system in China can be separated into two internal stages: production stage and pollutant abatement stage. This paper aims to examine the efficiency of Chinese regional industrial systems by taking the operational performance of its two internal stages into consideration. Unlike traditional data envelopment analysis models that treat decision making units as “black box” when measuring their efficiencies with undesirable factors, this paper proposes a two-stage model based on slacks-based measure to evaluate the efficiency of Chinese regional industrial systems. By decomposing the efficiency into production efficiency and abatement efficiency, the proposed model can estimate the efficiencies of the whole regional industrial system, its production stage and abatement stage simultaneously. The model provides a way of improving performance of a regional industrial system through identifying its inefficient internal stages. This can help to find out the main sources of the inefficiency that arise from the internal stages within the system, which cannot be done by using conventional environmental data envelopment analysis models. Findings resulting from the model application show that there are great disparities in regional industrial systems' efficiencies, and the inefficiency of Chinese industrial system is largely driven by the abatement stage. [ABSTRACT FROM AUTHOR]

Abstract: The rapid economic development of China has intensified the country's environmental problems such as pollution and waste. As industry is the main contributor to environmental deterioration, the development mode of industry has attracted attention from the government and the public. In this paper, we have assumed that the industry is permitted a fixed total amount of pollution in order to avoid excessive pollution. If one province shows increased pollution, other provinces are required to reduce pollution by the same amount to maintain the total level of pollution. However, so far, few studies have considered such competition over outputs. This paper presents a new data envelopment analysis approach, which considers both the fixed and the variant sum desirable outputs in the performance improvement of a decision making unit, to evaluate the environmental efficiencies of China's industry from 2007 to 2011. The analysis results demonstrate that some economically developed provinces have better performance than less developed provinces; in particular, all efficient provinces are the developed ones. The Chinese government should focus on the increasing differences in environmental efficiencies among regions and take measures to address them. [Copyright &y& Elsevier]

On the basis of the fact that China is setting up world's largest electricity network with the extra high voltage (EHV) and ultra high voltage (UHV) as their backbones, creating the foundation for large-scale accession of clean power which may deeply affect regional energy efficiency across the country, this paper attempts to empirically study the joint effects of clean power accessing to power grids on regional total-factor energy efficiency. To complete this job, we first measured the total-factor energy efficiencies of 29 provinces in China over the period 1991–2013, based on one of the data envelopment analysis methods, then, the static panel model considering panel fixed effect with instrument variable (FE-IV), dynamic panel model of system generalized method of moments (SYS-GMM) and panel smooth transition regression (PSTR) were employed, respectively, to investigate separate and joint effect of clean power and its grid-connection with EHV and UHV electricity network. The results show that the joint effect of clean power accessing to EHV and UHV power grid is either not significant or even negative although clean power itself separately promote regional energy efficiency. Thus, while the investment in grid infrastructure continues to increase, the grid-connection mechanism of clean power needs to be optimized therewith. [ABSTRACT FROM AUTHOR]

Rapid economic growth and urbanization in China have resulted in the dual threats of shortage of water and pollution of water. It is increasingly acknowledged that the efficiency of water resource system is influenced by not only the Water Use (WU) process, but also the process of Wastewater Treatment (WT). This paper considers both WU and WT as an integrated two-stage WUWT system to provide more comprehensive efficiency evaluation. In the first stage (WU subsystem), there are two parallel units, economic and social WU sub-minors, which consume water and capital to produce economic and social benefits. Meanwhile, the undesirable wastewater inevitably arises, which is treated in the second stage (WT subsystem). In order to assess the performance of the integrated WUWT system, an extended mixed network structure two-stage Slacks-Based Measure (SBM) Data Envelopment Analysis (DEA) model is developed to measure the detailed efficiencies and spot the weak link of the system. For case study, the provincial panel data during 2006–2015 of China are selected to verify the applicability of the proposed approach. Discussion is made from both time- and region-perspectives: over the ten years, the WU efficiencies are often higher than the WT efficiencies, so more actions should be carried out to improve the process of WT; from 2006 to 2011, social WU efficiencies are often lower than economic WU efficiencies, but the social WU efficiency has been improved after 2012; in terms of the overall efficiencies, the economically and socially developed provinces also have the higher efficiencies. Furthermore, the results demonstrate that the integrated WUWT system is efficient if and only if both WU and WT subsystem are both efficient, and the WU subsystem is efficient if and only if both economic and social WU sub-minors are efficient. [ABSTRACT FROM AUTHOR]

With the rapid economic development of China, the increasing emission of industrial waste gas as well as its poor control efficiency have caused serious air pollution issues and put threats to human health and sustainable development. Under the current development model, China's industrial sectors still remain the dominant economic component. Based on existing academic research in China and abroad, as well as with the help of the Data Envelopment Analysis (DEA) model, this paper adopted 39 industrial sectors of China as Decision Making Units (DMU) and their input-output data from 2003 to 2014 to calculate and evaluate the TFE of waste gas control in different industries of China. By designing an original MATLAB algorithm for optimization solutions of multi-variable constrained nonlinear functions, we found that there were 24 industries (61.5% of total industries) whose waste gas control efficiency at period end was lower than that at period beginning. Although the tobacco industry kept the optimal level, the major sectors with the biggest industrial waste gas emission all had poor waste gas control TFE. Based on the results, we further explained possible reasons for the difference in TFE across different industries from 2003 to 2014, and provided policy recommendations accordingly. [ABSTRACT FROM AUTHOR]

Currently, the iron and steel industry (ISI) accounts for more than 16% of China's total energy consumption. Improving energy efficiency (EE) and promoting energy savings in this industry have vital significance for China's further sustainable development. Using provincial panel data from 2000 to 2014 and meta-frontier data envelopment analysis (DEA), this paper investigates EE and the energy-saving potential (EP) of China's ISI by considering regional technology heterogeneities. The results show that (1) from 2001 to 2014, EE in China's ISI achieved significant improvement. Technological progress is the most powerful driving factor, while the declines in scale efficiency and pure technical efficiency are two obstructive factors; (2) the sources of energy inefficiency (EI) show distinct spatial difference characteristics. EI in the eastern region mainly stems from scale inefficiency and pure technical inefficiency, while that in the central and western regions stems from three aspects of scale inefficiency, pure technical inefficiency, and technology gap; and (3) there is considerable EP in China's ISI, which represents nearly 80% of ISI's current total energy consumption. Because the sources of EP vary across regions, the focus of future work for China's regional ISI should also vary. [ABSTRACT FROM AUTHOR]

The banking industry plays an essential role in China's economic growth, and the assessment of bank efficiency has attracted increasing interest in the past two decades. Given that the data envelopment analysis cross-efficiency method can provide a more reliable reference set, a large number of studies targeting performance evaluation and its applications adopt this method. Few studies, however, address the issue of the non-uniqueness of weights in cross-efficiency evaluation. Whereas, this paper proposes a cross-efficiency interval considering all weighting schemes along with a VIKOR aggregate model to gauge the universal productive efficiency of 16 major Chinese commercial banks of China and rank them for the period of 2007–14. Specifically, we use the cross-efficiency interval to generate the global performance of each bank based on internal and external assessment while using VIKOR to compare the solution with the attitude index which ranks all of the banks. The results indicate that the cross-efficiency interval can provide more information than the traditional DEA model, that the banking system efficiency of China has been improved during the study period, especially for SOBs; that JSBs are more efficient compared with SOBs; and the efficiency gap narrowed during the study period. [ABSTRACT FROM AUTHOR]

The increasingly high frequency of heavy air pollution in most regions of China signals the urgent need for the transition to an environmentally friendly production performance by socioeconomic sectors for the sake of people's health and sustainable development. Focusing on CO 2 and major air pollutants, this paper presents a comprehensive environmental efficiency index based on evaluating the environmental efficiency of major socioeconomic sectors, including agriculture, power, industry, residential and transportation, at the province level in China in 2010 based on a slack-based measure DEA model with non-separable bad output and weights determined by the coefficient of variation method. In terms of the environment, 5, 16, 6, 7 and 4 provinces operated along the production frontier for the agricultural, power, industrial, residential and transportation sectors, respectively, in China in 2010, whereas Shanxi, Heilongjiang, Ningxia, Hubei and Yunnan showed lowest efficiency correspondingly. The comprehensive environmental efficiency index varied from 0.3863 to 0.9261 for 30 provinces in China, with a nationwide average of 0.6383 in 2010; Shanghai ranked at the top, and Shanxi was last. Regional disparities in environmental efficiency were identified. A more detailed inefficiency decomposition and benchmarking analysis provided insight for understanding the source of comprehensive environmental inefficiency and, more specifically, the reduction potential for CO 2 and air pollutants. Some specific research and policy implications were uncovered from this work. [ABSTRACT FROM AUTHOR]

Enterprises, which are the entities that must implement emission reduction policies, as well as other regional management bodies exhibit significant differences with respect to certain indicators. Previous studies mainly focus on one-indicator or one-sector evaluation methods, which are overly simple and inadequate for enterprise analysis. To solve this problem, this paper selects three key indicators for enterprise evaluation and proposes an integrated classification approach grouping enterprises into sets. The proposed approach comprises one main model and three sub-models. The sub-models (Emission Model, Data Envelopment Analysis Model and Geo-spatial Model) pre-compute the indexation of emissions, overall efficiency and geo-location conditions as input data for the main model. The main model synthesizes these three sub-models’ inputs and classifies enterprises into different classification sets. For verification, we conduct a case study of 8106 manufacturing enterprises in Shanghai. In the case study, the proportions of enterprises in top set, middle-top set, middle-low set and low set were 19.5%, 47.5%, 29.4% and 3.6%, respectively. The classification approach provides adequate quantitative technical support for flexible policy design and has the potential to promote efficient regional emission reduction, optimize regional industrial structures and distribute enterprises rationally. [ABSTRACT FROM AUTHOR]

Manufacturing industry is the pillar industry in China, making China become the world's manufacturing center. While, the rapid development of manufacturing industry in China not only promotes the economic growth, but also consumes lots of resources and generates too much carbon dioxide. To make a sustainable development, this paper adopts a non-radial DEA model with natural and managerial disposability to examine the energy-environmental performance of China's manufacturing industry during the 2006–2014 period. The energy-environmental performance is evaluated by the unified efficiency under natural and managerial disposability (UENM). Additionally, the investment strategy is confirmed by industrial types of damages to return (DTR). The empirical results indicate that the unified efficiencies of most manufacturing industries present a rising trend in the sample interval. Moreover, the unified efficiency of most manufacturing industries in light industry have a little higher value than the efficiency of those in heavy industry. Besides, the light industry presents more investment effect than the heavy industry, and many industries in heavy industry have little potential for investment in terms of developing the green growth. According to these results, China should increase investment and enhancing the R&D input to improve the unified efficiency of light industry. Policy support need focus on boosting innovation in strategic emerging industries instead of traditional manufacturing industries. Part of industries with excess capacity should be transferred to other countries through outbound investment. [ABSTRACT FROM AUTHOR]

The unbundling policy reform of power industry has become a worldwide trend since 1980s. There are quite many studies focusing on the efficiency changes of transmission and distribution sectors in European and Latin American countries while few reports on China's issues have been found. This paper employs a nonparametric frontier estimation combined with meta-frontier and bootstrapping method to measure the productive efficiency and changes in the decomposition index for China's 31 grid companies from 2004 to 2013. In addition, it further employs Tobit regression model to analyze the influences of relevant policies and heterogeneity factors, such as natural conditions and economic development. The results indicate that the grouping approach extends the application of non-parametric data envelopment analysis, which may decrease the assumption of homogeneity for the decision making units and provide better efficiency estimates by increasing the credibility of the evaluation results. Overall productive efficiency increased during the whole study period, with technology progress the main contributor to this improvement. Efficiency trends fluctuated with changes in economic development and power supply situation, and the economics and customer density had positive effects on productive efficiency while the influence of clean power proportion is negative. [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]

This paper presents a DEA approach with multiple abatement factors to evaluate CO 2 emission performance and allocate CO 2 emission quotas in Chinese cities. We first consider the difference of marginal abatement costs among cities, and construct the non-radial directional distance function with multiple abatement factors. The total-factor CO 2 emission performance index and its dynamic change index are then proposed to measure CO 2 emission performance. Considering equity and efficiency, we develop a composite index by the hybrid method to allocate emissions quota, which considers CO 2 emissions as well as CO 2 emission performance. Then we conduct an empirical study using inputs and outputs dataset of 71 Chinese cities in 2005–2012. Chinese cities have poor energy efficiency and still have high CO 2 emissions. The eastern region outperforms the central region and the western region performs worst, whereas the dynamic CO 2 emission performance of the central region has the largest increase. The change of CO 2 emission performance is driven mainly by technological advances. As for the CO 2 emission allocation, the composite index method encourages cities to reduce emissions and enhance emission performance through carbon trading market. It also motivates cities with high historical emissions to reduce their emissions by improving technology when they have poor CO 2 emission performance. [ABSTRACT FROM AUTHOR]

Improvement of coal consumption efficiency is very important both for China in solving its problems of energy security and environmental pollution and for the world in addressing the issue of greenhouse gas emissions. Using a total-factor framework, this paper employs a data envelopment analysis (DEA) approach to evaluate the coal consumption efficiency of six energy intensive sub-industries in China in 2015. Coal consumption efficiency is factorized into economic efficiency and environmental efficiency, with sulfur dioxide, nitrogen oxide, and industrial smoke, dust and soot emissions treated as undesirable outputs of energy consumptions. The results show the following. (1) Of the six energy intensive sub-industries studied, two exhibit both DEA-effective coal economic efficiency and coal environmental efficiency. Coal economic efficiency is found to be greater than coal environmental efficiency, indicating that the benefits of economic efficiency have masked the negative effects of the environmental impact. China should therefore pay more attention to the clean utilization of coal. (2) Low environmental efficiency mainly resulted from the joint effects of pure technical efficiency and scale efficiency; therefore, enterprises in these industries must engage in management efforts to improve their ability to manage large enterprises. (3) The energy intensive industries characterized by coal environmental efficiency that failed to achieve pure technological effectiveness all exhibited input redundancy or output inadequacy of varying degrees while showing a potential for energy conservation and emissions reductions. [ABSTRACT FROM AUTHOR]

Recently, performance evaluation of coal-fired power generation with high pollution based on data envelopment analysis (DEA) method has become an advanced research hotspot in China. Instead of a single process in previous studies, we divide the production system of coal-fired power plants into the production process and pollutants abatement process, with pollutant intermediates as the link to connect the two processes in the current paper. Then we present a new two-stage network DEA model to evaluate the performance of China's coal-fired power generation by improving the link constraint that different with traditional network DEA approaches. Meanwhile, we conduct an empirical study to verify the applicability of the proposed approach and find that the proposed model is more effective than the previous models in evaluating the inefficiencies of coal-fired power generation. The empirical results also show that: the inefficiencies of the pollutants abatement process bring poor performance of the regional coal-fired power plants; and the efficiency results vary greatly among different areas of China, efficiency is best in the west area but the worst in the northeast area. Finally, some appropriate propositions are present in order to create better conditions for China's sustainable development. [ABSTRACT FROM AUTHOR]