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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]

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]

Abstract: In recent years, two primary methods, the data envelopment analysis (DEA) and stochastic frontier analysis (SFA) have widely been applied to measure energy utilization efficiency as they both can estimate the energy efficiency appropriately. However, both of the methods have merits as well as drawbacks. In this paper we use the super efficiency of DEA (SEDEA) model and SFA to estimate the energy productive efficiency of 30 administrative regions of mainland China during the period 1998–2009. The comparative study of the two methods shows that despite the estimated values being quite different, the ranks of these two results are very similar. In addition, the results of the two methods suggest that energy efficiency in the eastern region of China possesses an advantage over the western regions. Further analysis indicates that SEDEA not only has the advantages of DEA and SFA, but also has the advantage of evaluation energy efficiency effectively. In addition, this paper decomposes the Malmquist index so as to explore energy efficiency comprehensively and dynamically. [Copyright &y& Elsevier]

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

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

As the second largest economic entity in the world, China plays an important role in controlling global carbon dioxide (CO 2 ) emissions. The land transportation sector (including railway transportation and road transportation) has been the most important source of emissions in China and the average CO 2 emissions of land transportation were 529.31 million tons during the period 2009–2012. In this study, a parallel Slack-Based Measure Data Envelopment Analysis model is proposed, which is used for evaluating the overall efficiency of the land transportation sector and individual efficiencies of the railway transportation and highway transportation subsectors at the same time, considering CO 2 emissions. The empirical results lead to three conclusions: (a) only Anhui province is efficient each year during 2009–2012. (b) The environmental efficiency of the Eastern area in China is the best, followed by the Central area, with the Western area being the worst. (c) The performance of railway transportation is better than that of highway transportation. These three conclusions lead to policy suggestions to promote highway transportation technological innovation and narrow the regional imbalances in land transportation. This paper makes two main contributions: the model advances improvements to methods used in the Data Envelopment Analysis technique, and also provides governments with a practical and yet easy-to-adopt perspective to implement land transportation performance measurement that aids in decision making. [ABSTRACT FROM AUTHOR]

China's economic has got remarkable achievements during the past 30 years, which has brought tremendous pressure on natural resources and serious environmental pollution at the same time. In this paper, we aim to make environmental efficiency analysis of China's regional industry. Data envelopment analysis (DEA) method is incorporated and the slacks-based measure (SBM) model considering undesirable outputs is introduced to measure the environmental efficiency of different regions. Big data theory is introduced in the collection and selection of the input and output data of the regions. Further, we evaluate the environmental efficiencies of China's industry using data from 2008 to 2012. The results show that apart from several developed provinces, the environmental efficiencies of China's industry are generally low. And the environmental efficiencies of the regions did not show any increasing trend through the past 5 years. Additionally, lager differences exist in environmental efficiencies between the regions in China. Finally, we suggest the Chinese government to focus on the low environmental efficiencies and the unbalanced development of its regional industry. [ABSTRACT FROM AUTHOR]

China's economic reform and opening policy has resulted in China's rapid growth, especially in terms of economy. The growth, however, is mainly based on huge consumption of natural resources, and that consumption should be reduced by significant amounts to protect global ecosystems. Emerged in 1980s, “big data” has developed very quickly in recent years, bringing new perspectives and opportunities for all kinds of academic fields, especially in DEA since it is a research highly based on data. In this paper, we propose a SBM-DEA model based on natural resource input orientation to evaluate the efficiency of natural resource utilization for 26 provincial regions in mainland China from 2005 to 2012. A DEA-based approach is developed to allocate a reduced total of natural resources among the 26 provincial regions. Drawing support from the developments of “big data”, we use each provincial region's previously observed production data to characterize its own production technology, which allows a better analysis of how the production will change after allocations of natural resources are reduced. Our empirical study shows that most provincial regions perform well in terms of efficiency in utilizing natural resources, but that is based on huge consumption of natural resources. From an area perspective, the eastern area of China achieved the highest average efficiency value, followed by the central and western areas. Our study also indicates that the most effective allocation of the natural resources requires about half of the 26 provincial regions to reduce their natural resource consumption while the remaining ones can maintain their original consumption. [ABSTRACT FROM AUTHOR]

State-ownership has often been discussed as one of the major causes of poor industrial energy efficiency performance. This paper utilizes long-run historical data on the energy and material use in one specific industrial sector – the iron and steel production – in countries with both central-planning and market-based system, with a particular focus on former Czechoslovakia paralleled with the developments in China. Czechoslovak productive efficiency of the iron and steel sector fluctuated below the energy efficiency frontier. Until the early 1970s, the country’s iron sector was one of the least efficient ones in our sample. It was, however, during the decades of 1970s and 1980s that efficiency measures were adopted and the energy efficiency of the Czechoslovak iron and steel sector increased significantly to, despite of a priori expectations, reach the energy efficiency frontier. Empirical results for other planned economies show similar development of catching-up to the market economies, particularly in the iron production sector during 1980s. A pattern of efficiency convergence was identified. In China, despite its move toward more market oriented economy, the productive efficiency lagged behind as recently as in 2000 (20–35 percent below the efficiency frontier). The relatively late adoption of energy conservation programs and the persistent government control of the sector in certain provinces slowed down the efficiency improvements. In the socialist economies of Eastern Europe, though, central-planners were able to achieve satisfactory productivity increases, primarily driven by efficiency and saving policies and adjustments in existing technology. It is likely, that as was the case of Eastern Europe, the adoption of vigorous energy policies with clearly defined targets accompanied by monitoring and supervision, will have a tremendous impact on the energy intensity as well as the absolute energy use of the sector in China. [ABSTRACT FROM AUTHOR]

Improving energy efficiency has been recognized as the most effective way to reduce the greenhouse effect and achieve sustainable development. From the perspective of air emissions, this paper adopts data envelopment analysis approach to evaluate the energy efficiency in China’s coastal areas over the period of 2000–2012. Carbon dioxide, sulfur dioxide and nitrogen oxide are treated as undesirable outputs of energy consumptions. The proposed global Epsilon-based measure is used to estimate the static energy efficiency with an annual cross-section of data. The weights of the three undesirable outputs are determined according to their treatment costs. A global Malmquist-Luenberger productivity index based on directional distance function is employed to dynamically evaluate the energy efficiency. The results indicate the following in China’s coastal areas: (1) the level of economic development is positively related to energy efficiency scores; (2) energy efficiency scores decrease when considering undesirable outputs except Beijing and Hainan; (3) the Circum-Bohai Sea Economic Region greatly improves energy efficiency and has great potential of air emission; (4) the annual growth rate of Malmquist-Luenberger productivity index change is overestimated; (5) energy efficiency improvement is mainly driven by technological improvement, and scale efficiency and management level are the main obstacles. [ABSTRACT FROM AUTHOR]

The use of data envelopment analysis (DEA) in China’s regional energy efficiency and carbon emission efficiency (EE&CE) assessment has received increasing attention in recent years. This paper conducted a comprehensive survey of empirical studies published in 2006–2015 on China’s regional EE&CE assessment using DEA-type models. The main features used in previous studies were identified, and then the methodological framework for deriving the EE&CE indicators as well as six widely used DEA models were introduced. These DEA models were compared and applied to measure China’s regional EE&CE in 30 provinces/regions between 1995 and 2012. The empirical study indicates that China’s regional EE&CE remained stable in the 9th Five Year Plan (1996–2000), then decreased in the 10th Five Year Plan (2000–2005), and increased a bit in the 11th Five Year Plan (2006–2010). The east region of China had the highest EE&CE while the central area had the lowest. By way of conclusion, some useful points relating to model selection are summarized from both methodological and empirical aspects. [ABSTRACT FROM AUTHOR]

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

Data envelopment analysis (DEA) is a powerful nonparametric approach for assessing the performance of decision making units (DMUs). Traditional DEA models usually utilize a self-evaluation mode to evaluate DMUs, which have the following shortcomings such as efficiency overestimation and incomplete ranking. As the extension of traditional DEA, cross-efficiency (CE) evaluation remedies these limitations. It connects self-evaluation with peer-evaluation to appraise DMUs and rank them. However, most existing studies on CE evaluation utilize the arithmetic average method to aggregate self-evaluation efficiency and peer-evaluation efficiency, while ignoring the significance of self-efficiency and the irrational psychology of decision-makers (DMs). Observed by the aforementioned phenomena, this paper employs regret theory to portray the regret aversion behavior of DMs and further proposes a novel regret cross-efficiency aggregation (RCEA) method. Considering aggressive and benevolent cross-efficiency models, we apply the RCEA method to calculate the regret-based aggressive cross-efficiency and the regret-based benevolent cross-efficiency, and introduce a parameter reflecting the psychological preference of DMs to aggregate them. To attain consensus between DMUs' expectations for efficiency and the ultimate integration results, we design a regret cross-efficiency adjustment algorithm. This algorithm provides a more acceptable evaluation result and derives a full ranking for all DMUs. Finally, the validity and feasibility of the proposed model are illustrated by evaluating the efficiency of scientific research activities in 13 universities in China. • We develop a novel regret cross-efficiency aggregation method. • Aggregate the regret cross-efficiency in line with DMs' psychological preferences. • A novel regret cross-efficiency ranking model considering consensus is proposed. • The validity of our model is verified by the evaluation of 13 Chinese universities. [ABSTRACT FROM AUTHOR]

Improving eco-efficiency is an important way to promote environmental decoupling. Based on the input-output model and data envelopment analysis (DEA), this paper evaluates the eco-efficiency of 44 sectors in China with a new DEA model considering inter-sectoral linkages, then explores how the industrial network would be optimized as inefficient sectors become efficient under constant resources supply and constant final demand. The empirical results indicate that 22 sectors are inefficient and deserve further improvement. Inefficient sectors becoming efficient will drive the industrial network to evolve in a more intensive direction, and allow the economy to achieve greater economic benefits with less energy consumption and pollution. Accounting under constant resources supply suggests that increasing external demand should be a key to promoting environmental decoupling in the economy. Accounting under constant final demand indicates more stringent environmental regulations should be used to drive technology upgrades. • A new eco-efficiency measurement combining the input-output model and DEA is built. • The eco-efficiency and potential deficiencies of 44 sectors in China are evaluated. • The impact of inefficient sectors becoming efficient on the economy is measured. • The industrial network presents significant potential for environmental decoupling. • Key constraints to environmental decoupling in the industrial network are identified. [ABSTRACT FROM AUTHOR]

Improving the efficiency and productivity of the water supply sector is related to the mitigation of a country's water resources crisis and the governance of the water ecological environment. This paper employs a sequential data envelopment analysis (DEA) approach to examine the comparative efficiency of China's urban water industry by using the country-level data from 1998 to 2017, and then attempts to make a cross-country comparison of the technology gap in the urban water industry between China and major OECD countries. The results show that China's urban water sector performance is improving despite falling short of urban water development in the major OECD. The gap between the performance of China's urban water sector and that of major OECD countries is narrowing. Efficiency improvement has become the main driver of productivity growth of urban water sector in both China and the major OECD countries. Besides, the reduction of capital deepening in the water industry and the easing of water stress could improve the water industry's performance in efficiency. While increased foreign direct investment instead reduces the water industry's efficiency, increased R&D expenditure can improve efficiency, but both are not statistically significant. These findings indicate that compared to major OECD countries, China's urban water industry should strengthen the reform change as soon as possible from asset-heavy to asset-light operation and service, and then improve the water conservation system. [ABSTRACT FROM AUTHOR]

In this paper, a model for the slacks-based measure of super-efficiency in data envelopment analysis (SBM-DEA) was used to measure the green innovation efficiency (GIE) of 79 cities in the Yellow River Basin (YRB) from 2003 to 2018. In addition, the panel threshold model was employed to test the nonlinear relationship between environmental regulation intensity (ERI) and GIE. According to the results, the average GIE in the YRB was 0.68. However, the GIE exhibited a rising and fluctuating temporal trend and the following spatial trend: "lower reaches > upper reaches > middle reaches." Moreover, ERI had a double threshold effect on GIE in the YRB. That is, ERI had a significant positive impact on GIE only when ERI ranged between 0.595 and 0.899. Furthermore, the upper, middle, and lower reaches of the YRB exhibited different nonlinear correlations between ERI and GIE. Therefore, it is essential to implement environmental regulations that are appropriate for the actual conditions of each region. In addition, the per capita income, teacher resources, industrial structure, and actual utilization of foreign capital are significant factors affecting GIE in the YRB. These findings can help governments formulate suitable environmental policies and promote green innovation in the YRB, thereby contributing toward the goal of global sustainable development. • The efficiency of green innovation in the Yellow River Basin increases. • The efficiency of green innovation shows a spatial pattern of "lower reaches > upper reaches > middle reaches". • Regulations have a double threshold effect on the efficiency of green innovation. • The efficiency of green innovation must be improved based on the actual development of each region. [ABSTRACT FROM AUTHOR]

Abstract: The objective of this paper is to deal with the performance evaluation problem in the presence of large dimensionality of data set, in which there exist multiple correlations among the original variables. Firstly, this paper analyzes the weakness of principal component analysis (PCA), and introduces a maximum variance method to reduce data dimension via Gram–Schmidt process, through which the corresponding transformed variables (Schmidt variables) can be obtained. And at the same time, a measure of investigating the cumulative variation information for selected Schmidt variables is described. Then based upon the chosen Schmidt variables, a new modified DEA approach with a natural assurance region (AR) is presented. In the developed DEA approach, the selected Schmidt variables are treated as outputs. The proposed approach will be then contrasted with the traditional CCR DEA model and the DEA model using principal components obtained from PCA with simulated data, and it is also applied to real world data set that characterizes economic performance of 31 provinces or cities in China. [Copyright &y& Elsevier]

Abstract: The suitability of the project delivery system (PDS) selected for a project greatly influences the efficiency to conduct a project. It is not an easy task to select an appropriate PDS as a large amount of ambiguous information exists. The paper aims to develop a PDS selection model to help owners to make a decision. The similar projects are identified through the similarity metrics between the target project to be predicted and those in the database. In addition, some of the indicator values are examined and modified through DEA-BND model, and then they are trained by ANN model to predict an appropriate PDS for the target project. A survey was conducted by postal questionnaire to empirically validate the reliability of the model in China. The indicator system for the selection of an appropriate PDS is established. Through the comparison of results predicted by different models, it is found that the PDS selection model developed in this paper can predict PDS precisely, and shows higher reliability than the ANN model. A PDS selection model is developed by inputting project-specific data, which proves to be more accurate and less dependent on experts’ judgment. Its practical application will benefit the owner’s decision making in the selection of the PDS. [ABSTRACT FROM AUTHOR]

Abstract: This paper proposes two quadratic-constrained DEA models for evaluation of mutual funds performance, from a perspective of evaluation based on endogenous benchmarks. In comparison to previous studies, this paper decomposes two vital factors for mutual funds performance, i.e. risk and return, in order to define mutual funds’ endogenous benchmarks and give insights and suggestions for managements. Of the two quadratic-constrained DEA models, one is a partly controllable quadratic-constrained programming. The approach is illustrated by a sample of twenty-five actual mutual funds operating in the Chinese market. It identifies the root reasons of inefficiency and ways for improving performance. The results show that although the market environment in year 2006 was much better than that in 2005, average efficiency score declines in year 2006 due to relaxing of system risk control. The majority of mutual funds do not show persistence in efficiency ranking. The most important conclusion is that the ranking of mutual funds in China depends mostly on system risk control. [ABSTRACT FROM AUTHOR]

China is a large country of egg consumption, the study on China's laying hens breeding green total factor productivity (LHBG) is conducive to reducing pollution emissions while improving the productivity of laying hens to promote the cleaner production. Obviously, existing studies on laying hens breeding neglect regional and scale differences as well as serious pollution issues. Therefore, based on the directional distance function (DDF) - Data Envelopment Analysis (DEA) model, this paper constructs a three-level Metafrontier-Malmquist-Luenberger (MML) index, considering negative output, to evaluate LHBG from 2004 to 2018. Firstly, in terms of scale, the results show that LHBG is the largest in middle scale, followed by small scale and large scale. Secondly, from a regional point of view, the results indicate that China's LHBG presents a reverse stair-step distribution, that is, LHBG is the lowest in the eastern region, followed by the central region and the highest in the western region. The differences between regions and provinces are affected by the traditional farming habits, non-agricultural employment opportunities, the level of urbanization and resource conditions. Thirdly, this paper obtained that the three decomposition efficiencies of LHBG are manifested as the decrease in management efficiency, and the increase in scale structure efficiency and technical efficiency. Fourthly, climate factors have an impact on the laying hens breeding. According to the empirical results, this paper finally puts forward policy suggestions for improving LHBG and environmental protection policy recommendations for layer breeding. • Research on the efficiency of China's laying hens, which subject is innovative. • Environmental factors are introduced into the evaluation system. • The scale heterogeneity and regional heterogeneity of laying hens are considered. • Construct three-levels DDF-MML model and decompose into three efficiencies. [ABSTRACT FROM AUTHOR]

The construction of ecological civilization has become necessary to ensure that emerging industrialized countries can achieve healthy and sustainable development under the background of tightening resource constraints, serious environmental pollution, and the deterioration of the ecological environment. In the case of China, one representative emerging industrialized economy, this paper builds an ecological civilization performance evaluation system to measure the efficiency of ecological civilization in 30 provinces and municipalities in China by using the data envelopment analysis (DEA) game cross-efficiency model. From the perspective of the "five-in-one" approach, and by taking full account of the three main bodies of government, enterprises and residents, this paper selects nine explanatory variables, namely industrial structure and degree of marketization (Economic perspective), government environmental risk awareness and government green actions (Political perspective), residents' happiness and corporate environmental responsibility (Social perspective), culture education investment (Cultural perspective), and green travel and ecological construction (Ecological perspective). Geographically Temporally Weighted Regression (GTWR) is used to analyze determinants of the Chinese ecological civilization performance. The results show that China's ecological civilization performance is low, and the efficiency ranking of most provinces fluctuates greatly. The regional development of China's ecological civilization performance presents a spatial distribution pattern that was high in the east and low in the west. The impact of various influencing factors on the China's ecological civilization performance is largely positive, and indicates regional characteristics. These results not only contribute to advancing the existing literature on ecological civilization performance evaluation, but also merit particular attention from policy makers in emerging industrialized countries. • A new indicator is built to assess ecological civilization performance. • Vertical and horizontal scatter degree, DEA game cross-efficiency, GTWR are employed. • ECP measured by CCR and Game Cross have significant differences. • ECP in China is low, fluctuating greatly in ranking and obvious east-west differentiation. • The same influencing factors exert different impacts in different regions. [ABSTRACT FROM AUTHOR]

Under the premise that the total emissions of PM (particulate matter) of air pollution is established, the allocation of PM emission rights in various provinces of China is the feasible way to governance the ash haze. In this paper, a data envelopment analysis (DEA) model that takes into account the objectives of efficiency and equity is constructed. On the basis of evaluating the PM emission performance of various provinces in China, the corresponding allocation steps are put forward. Then, the results of the DEA model and information entropy are compared. The research shows that: (1) under the same constraint conditions, the emission right of PM is allocated with DEA model, which leads to lower cost, less fluctuation of distribution amount and higher overall national performance. (2) the population Gini coefficient method is used to evaluate and it is found that the Gini coefficient value is smaller according to the distribution results of DEA model, indicating that the PM emission right amount is more fairly distributed among provinces. The functions of this paper are as follows: (1) a new double-objective DEA model is constructed, which is suitable for multi-agent performance evaluation with multiple objectives; (2) a scheme to allocate PM among provinces is proposed, which can provide reference for the practice of haze reduction in China [ABSTRACT FROM AUTHOR]

High energy consumption by the industry of developing countries has led to the problems of increasing emission of greenhouse gases (GHG) (primarily CO 2 ) and worsening energy shortages. To address these problems, many mitigation measures have been utilized. One major measure is to mandate fixed reductions of GHG emission and energy consumption. Therefore, it is important for each developing country to disaggregate their national reduction targets into targets for various geographical parts of the country. In this paper, we propose a DEA-based approach to allocate China’s national CO 2 emissions and energy intensity reduction targets over Chinese provincial industrial sectors. We firstly evaluate the energy and environmental efficiency of Chinese industry considering energy consumption and GHG emissions. Then, considering the necessity of mitigating GHG emission and energy consumption, we develop a context-dependent DEA technique which can better characterize the changeable production with reductions of CO 2 emission and energy intensity, to help allocate the national reduction targets over provincial industrial sectors. Our empirical study of 30 Chinese regions for the period 2005–2010 shows that the industry of China had poor energy and environmental efficiency. Considering three major geographical areas, eastern China’s industrial sector had the highest efficiency scores while in this aspect central and western China were similar to each other at a lower level. Our study shows that the most effective allocation of the national reduction target requires most of the 30 regional industrial to reduce CO 2 emission and energy intensity, while a few can increase or maintain their 2010 levels. [ABSTRACT FROM AUTHOR]

Data envelopment analysis has recently gained popularity and attention in operational and environmental assessment. A common feature of previous studies is decreasing an input vector to decrease the vector of undesirable outputs which is referred to as natural disposability. However, little research has considered how to increase the amount of capital input for clean production technology to decrease the amount of undesirable outputs in a data envelopment analysis framework, which is referred to as managerial disposability. Thus, this paper proposes an improved approach that considers measuring unified (operational and environmental) efficiency for managerial disposability, and compares this approach with the one for natural disposability. The proposed approach provides a positive strategy to reduce undesirable outputs and improve unified efficiency through increasing capital investment in clean production technology. A case study of regional industries in China in 2006–2010 utilizing this approach is also presented. The empirical study shows that the unified efficiencies of regional industries in China have relatively high scores for managerial disposability, and increasing capital investment is vital to achieve sustainable development in clean production technology. [ABSTRACT FROM AUTHOR]

The environmental impact of materials and energy is an important concern when cities and regions produce goods and services. To examine this requires an evaluation tool which can deal with the complex system of resources, energy inputs, products and wastes involved. This paper evaluates the urban material and energy flow efficiencies of 31 cities in China using a combination of the Data Envelopment Analysis model and Principal Component Analysis method. The results show that the urban flow system is composed of several input and output principal components, including energy, materials, urban capital investment, overall output, wastes, and others. In 2010, 16 cities were efficient as judged by the material and energy inputs and outputs. The remaining 15 cities were not efficient, primarily due to product shortfalls and waste excesses. Since 2000, half of the analyzed cities experienced a decline in the urban flow efficiencies, while only six cities showed a positive trend. The reasons include excess inputs of coal, coke, electricity, cement, investment, as well as output shortfalls in terms of overall output, wastes, services, agriculture and transport. We conclude that key priorities for improving urban systems with material and energy flow inefficiency are the use of urban renewable supplies, focus on sustainable endogenous development model, and improvement management of waste output, in order to increase efficiency and sustain economic growth. [ABSTRACT FROM AUTHOR]

The highly fragmented coal industry in China has experienced a consolidation process since late 2008. Major coal-rich provinces have adopted nationalization and market-oriented approaches to reduce the number of small coal mines, leading to a marked increase in the proportion of the total output produced by large and medium-sized coal mines. Relying on a Range-Adjusted Measure model of the Data Envelopment Analysis, this article constructs three indicators, namely operational efficiency, environmental efficiency, and unified efficiency, to reflect sustainability impact of this consolidation policy. By comparing two representative provinces – Shanxi and Inner Mongolia over the period 2005–2012, this paper assesses the policy effectiveness on the transition of coal mining industry to sustainable development. The results suggest that the consolidation reform in China has a negative impact on coal productivity, but significantly it improves the environmental performance of the coal industry. However, the nationalization approach does not prove to outweigh market-oriented approach, because the inefficiency in the capital factor offsets efficiency-improving outcome. The market-driven regulation can reduce the negative impact of the market restructuring on the operational efficiency and thus achieve a higher unified efficiency from both operational and environmental perspectives. [ABSTRACT FROM AUTHOR]

In recent years, the economic performance of Chinese industry has improved significantly; however, the congestion caused by excessive investment has plagued its sustainable development. In order to make full use of resources, it is necessary for the Chinese government to measure industrial congestion from different perspectives. However, most studies for measuring congestion only consider desirable outputs, even if, in practice, these are always accompanied by undesirable outputs. In this paper, three policy objectives are proposed for industrial development in China: the economic priority, the environmental priority, and the economic and environmental win-win. Three new data envelopment analysis (DEA) models with undesirable outputs were built for measuring undesirable congestion, desirable congestion, and double-congestion, respectively, for these policy objectives. These approaches can, not only identify congestion situation, but also calculate the total amount of congestion, and were applied to an analysis of the congestion of regional industries in China. Results show that most regions have some form of longstanding input congestion under different policy objectives, with this resulting in serious waste of resources. Based on the results obtained, some suggestions for sustainable industrial development in China are proposed. [ABSTRACT FROM AUTHOR]

The energy conservation has become a considerable issue in construction industry for achieving sustainable development. Therefore it is crucial for policy-makers to look for optimized and flexible tools to measure the total energy consumption efficiency in construction industry. Data envelopment analysis (DEA) can be applied for multiple input and output decision-making units (DMUs) productivity measurement. DEA-based Malmquist productivity index (MPI) can be used as a tool for measuring the productivity change longitudinally. In this paper, we use input-oriented model to measure the energy consumption productivity change of the construction industry from 2004 to 2009 with data in 26 provinces of China. It is found that only Guangdong demonstrated effective energy improving in the whole reference period. It also identified the regional groups always lie on the efficiency frontier of energy consumption as benchmarks. Regional analysis shows that energy conservation gaps exist between northeastern, western region and central, eastern region. DEA-based Malmquist productivity index provides a good way to measure the energy consumption efficiency and can help policy-makers to improve strategies of sustainable development in China. [ABSTRACT FROM AUTHOR]

Thermal power has accounted for the major part of the electricity generation over recent several decades in China. It plays an important role in supporting China's economic development while it also brings great pressure to the environment protection because of a large amount of pollution generated during its production. In order to solve or alleviate the environmental problem caused by thermal power firms, efficiency evaluation is the first important step. As thermal power firm is a complex system with multiple inputs and multiple outputs, usually including fuzzy data and undesirable outputs in the productions, in this paper, we build an integrated Enhanced Russell measure model based on data envelopment analysis for evaluating the performance of decision making units in the presence of the undesirable outputs in fuzzy circumstance. Then, this new model is applied to analyze the environmental performance and provide the benchmarks for the thermal power firms in China which can guide the decision makers to make suitable future production plans for improving their performance. [ABSTRACT FROM AUTHOR]

At present, PM 2.5 is still one of the most serious air pollution issues in China. By empirically testing the actual inhibitory effect of China's total factor energy efficiency progress on PM 2.5 , this paper can provide a new evaluation index for energy conservation in China and help Chinese cities understand the beneficial environmental effect of total factor productivity (TFP) progress under the constraint of energy consumption to point out an effective path of energy conservation and emission reduction that is highly in line with China's current high-quality development strategy. Based on the panel data of 105 key environmental protection cities in China, this paper evaluates total factor energy efficiency with range directional model (RDM) and global Malmquist (GM) productivity index based on data envelopment analysis (DEA); it then uses spatial econometric models to test the impact of energy efficiency progress on PM 2.5 concentration. The efficiency evaluation results show that the total factor energy efficiency of 105 key environmental protection cities has increased by an average of 35.9% from 2004 to 2015, with an average annual progress rate of 2.2%. Compared with Eastern and Central China, the progress of energy efficiency in the western region during 2004–2015 is relatively lagged. The average energy efficiency progress rate of higher administrative hierarchy cities and super cities with more than 10 million permanent residents is much higher than that of other cities. The results of GM index decomposition show that technological progress is the main driving force of energy efficiency progress. The spatial econometric analysis results show that there is a significant positive spatial autocorrelation in the concentration of PM 2.5 in sample cities, and the energy efficiency progress can significantly inhibit the PM 2.5 pollution in local and adjacent cities. The influence of each explanatory variable on PM 2.5 concentration is mainly composed of the spatial spillover effect. Image 1 • DEA-based RDM and Global Malmquist index are used to evaluate energy efficiency. • The energy efficiency of 105 cities has increased by an average of 35.9% in 12 years. • Technological progress is the main driving force of energy efficiency progress. • Energy efficiency progress can significantly reduce PM2.5 concentration. • The impact of each factor on PM2.5 is mainly composed of spatial spillover effects. [ABSTRACT FROM AUTHOR]

The impact of technical change on economic growth is closely related to the structure of input factors, and guiding technical change towards energy-saving can achieve the dual goals of economic growth and energy conservation. Based on the provincial panel data of 2006–2016, this paper first uses the slacks-based measure model and the bootstrap data envelopment analysis (bootstrap-DEA) to calculate the biased technical change (BTC) index and its factor bias of China's iron and steel industry (ISI). Next, the influencing factors of BTC are examined using panel regression analysis. The results corrected by bootstrap-DEA show that the level of ISI's BTC in China is 1.0110, with a contribution rate of 20.75% to green total factor productivity. Moreover, China's ISI experienced a labor-saving, energy-saving, and capital-using technical change during 2006–2016. In two-factor comparisons, technical change biased towards saving labor in labor-capital and labor-energy, and towards saving energy in energy-capital. The results of regression analysis show that improving energy efficiency, promoting urbanization, expanding production scale and optimizing industrial structure effectively promote BTC and improving energy efficiency is the primary pathway; however, excessive capital deepening has a significant negative correlation with BTC. Based on these results, this paper proposes policy recommendations for ISI's green development. • Biased technical change of iron and steel industry is increasingly significant. • Technical change biases towards labor-saving, energy-saving and capital-using. • An energy-saving technical change exists in the iron and steel industry of most provinces. • Energy efficiency can significantly promote the biased technical change. [ABSTRACT FROM AUTHOR]

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

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

The construction sector is an important industry in China's economic development and energy consumption, and research on its energy efficiency has attracted increasing attention from researchers. Currently, the traditional data envelopment analysis (DEA) model ignores the competition among regions, resulting in inaccurate estimation of efficiency. This paper employed traditional DEA, cross-efficiency DEA and game cross-efficiency DEA approaches to measure the energy efficiency of the construction industry in 30 of China's provinces from 2005 to 2016. The temporal trend and spatial distribution characteristics of energy efficiency determined through the game cross-efficiency method were then depicted by clustering analysis. Finally, the spatial heterogeneity of energy efficiency was analysed in detail. The results indicated that the efficiency calculated using the game cross-efficiency DEA model considering the competitive relationship was lower than the traditionally calculated efficiency and higher than the efficiency calculated using cross-efficiency DEA. Moreover, during the period of study, the energy efficiency showed a holistic growth trend with volatility. Clustering analysis demonstrated that the energy efficiency of eastern China was significantly higher than that of central and western China. Additionally, the spatial gaps in China's construction industry narrowed, while the spatial agglomerations strengthened. This paper also found a significant positive agglomeration in the energy efficiency of the construction sector in neighbouring provinces of China in the space dimension. In conclusion, province-specific policies based upon temporal and spatial heterogeneity were proposed to improve the energy efficiency of the construction industry in China. • Game cross-efficiency is first used in construction sector. • Provincial energy efficiencies of construction sector are examined in 2005–2016. • Spatial distribution characteristics of energy efficiency are depicted. • Energy efficiency is assessed in four agglomeration areas. • Positive spatial agglomeration exists in energy efficiency of construction sector. [ABSTRACT FROM AUTHOR]

China's economy is currently moving from a stage of high-speed growth to a stage of high-quality development. Agricultural green total factor productivity is of great significance for promoting the high-quality development of the economy. Based on the panel data of China's provincial agriculture, this paper uses the Super-SBM model to calculate China's agricultural green total factor productivity based on carbon emissions. On this basis, this paper uses the nuclear density estimation method to investigate its dynamic evolution trend and the panel data model to empirically study the influencing factors of China's agricultural green total factor productivity. The results show that China's agricultural carbon emissions show an inverted-"U" trend, but the overall growth rate shows a gradual declining trend. The main concentration areas are transferred from the eastern region to the central region, and agricultural fertilizer is the main source of such emissions. China's agricultural green total factor productivity overall shows a fluctuating growth trend, and the differences between provinces show an increasing trend. Agricultural factor endowments and regional characteristics affect China's agricultural green total factor productivity, and there are regional differences in these effects. An important way to improve China's agricultural green total factor productivity and promote the coordinated development of agricultural regions and the high-quality development of the economy is by promoting clean agricultural production, strengthening the research and development of agricultural science and technology, expanding the agricultural opening-up level, and promoting the deep integration of industrialization and agricultural modernization. Image 1 [ABSTRACT FROM AUTHOR]

In this paper, we comprehensively consider the environment and economic performance by employing non-radial distance function to study the regional eco-efficiency of China's Non-ferrous Metals Industry (NMI) during 2000–2016, from the perspective of total-factor through Data Envelopment Analysis (DEA)-Malmquist analysis framework. The change of regional technology gap ratio and the source of regional eco-inefficiency is also explored. Moreover, this paper analyzes the evolution of regional eco-efficiency and the main factors that contribute to changes in eco-efficiency. The results indicate that: First, the eco-efficiency of China's NMI has improved and has shown a certain degree of regional heterogeneity. Second, the main source of eco-inefficiency is management inefficiency in eastern China. The central, western and northeastern China all showed an increasing trend of technology gap inefficiency and a declining trend of management inefficiency during the sample period. Third, eastern China is regarded as a technology leader. The improvement of eco-efficiency in the central and northeastern China is mainly due to the promotion of technological progress, while the western region relies on the improvement of technical efficiency. The study suggests that strengthening scientific and technological research, promoting inter-regional technical exchanges are particularly important for eco-efficiency improvement and regional coordinated development in China's NMI. [ABSTRACT FROM AUTHOR]

Industrial convergence is one of the important strategies to promote China's sustainable development, and its development is phased. In order to better explore the heterogeneous effect of industrial convergence on regional metabolism in different stages, we takes the convergence of manufacturing and producer services in 26 provinces of China as the research object, and for the first time attempt to empirically test the impact of industrial convergence at different stages on metabolic efficiency. In this paper, we measure metabolic efficiency using emergy analysis and index data envelopment analysis (Index-DEA) and measure the convergence degree of the two industries using the coupling evaluation model. Then, we analyze the effects of industrial convergence on metabolic efficiency using the Tobit model. The results show that industrial convergence can significantly promote the metabolic efficiency of China. Specifically, in the emerging industry convergence zone, industry convergence has a significant promoting effect on the advancement of metabolic efficiency, while in the mature industry convergence zone, industry convergence has no effect on metabolic efficiency. Moreover, this paper considers six other factors that affect regional metabolism. In addition to government interaction, the other five factors have an impact on metabolism efficiency. Finally, special issues are briefly discussed. The results provide important insights for policy makers to better promote industrial convergence and improve regional metabolism. • Consider the two stages of industrial convergence. • Analyze six factors that affect regional metabolism. • Found the heterogeneous effect of industrial convergence on regional metabolism. • Provide an excellent comprehensive evaluation tool for the government's management. [ABSTRACT FROM AUTHOR]

• Dynamic network data envelopment analysis and Tobit regression are used for study. • The efficiency of production process is higher than the waste treatment process. • Significant regional differences exist in the industrial eco-efficiency in China. • Industrial agglomeration impedes the eco-efficiency of industrial sector. • Industrial structure rationalization promotes the industrial eco-efficiency. The industrial sector is the largest energy consumption sector and major source of environmental pollution in China. Improving the industrial eco-efficiency is significant for China to realize high-quality development. Based on the panel data of industrial sector in China's 30 provinces from 2006 to 2015, this paper first adopts a dynamic network data envelopment analysis to evaluate the eco-efficiency of industrial sector, and then uses panel Tobit regression model to investigate the impacts of industrial policies on the eco-efficiency of industrial sector. The results reveal that, (1) the overall eco-efficiency of China's provincial industrial sector is 0.7581 during 2006–2015, and the average efficiency of the industrial production process is higher than that of the waste treatment process. (2) The eco-efficiency of the industrial sector differs greatly by region, the eastern region is the highest, followed by the western and central regions, and the northeast is the lowest. (3) Industrial agglomeration impedes the improvement of industrial eco-efficiency, while industrial structure rationalization can promote industrial eco-efficiency. (4) The influence of industrial agglomeration and industrial structure rationalization on the efficiency of industrial waste treatment process is larger than that of the industrial production process. The findings of this paper reveal the importance of improving the efficiency of industrial sub-processes, and can also offer lessons for other industrialized nations to improve the industrial eco-efficiency. [ABSTRACT FROM AUTHOR]

Given the current circumstances of increasingly serious resource consumption and environmental pollution, the development of China's green economy will profoundly impact the nation's future economic prosperity and even global economic development. Based on panel data from 2008 to 2017, this paper measures the green economic efficiency (GEE) of Chinese regions and analyzes its dynamic evolution using time series, based on a novel data envelopment analysis (DEA) model. Using environmental DEA techniques, this paper introduces the Super-PEBM (EBM based on pearson correlation coefficient) model, which is developed from the Epsilon based measure (EBM) model, and combines Super-PEBM with the window analysis method. Firstly, environmental DEA technology is used to analyze GEE at the regional level in China. Secondly, based on panel data, the window analysis method is used to analyze the regional differences of China's regional GEE. Finally, the development trend of China's regional GEE in different time windows is obtained by combining the Super-PEBM model and window analysis. The empirical analysis results using panel data for regions in China from 2008 to 2017 show that: (1) the overall GEE of China is slowly increasing, and the regional differences are still significant, and (2) the improvement of GEE can help to reduce regional differences. [ABSTRACT FROM AUTHOR]

• The new power supply structure resulting from the new policy has been considered. • The new models project the inefficient points onto a more realistic efficient frontier. • The new models have the power to allocate the shared resources. • Results show that the power production stage is allocated less shared resources. • The power sale stage's efficiency is lower than the one of power production stage. To relieve the problems of energy shortage and environmental pollution, many countries have implemented a variety of policies to improve the energy efficiency of the power industry. To understand the impacts of such policies on energy efficiency, this paper constructs novel network data envelopment analysis models to recognize the policy-induced structural changes in the power system. Compared with previous studies, the new models project the inefficient points onto a more realistic efficient production frontier, considering how the distribution proportion of shared resources will change when the power system, modeled as a power generation stage and a power sale stage, is led by the latter stage. Our empirical study on China's power system shows that the province of Guangdong and the eastern region of China perform best, and all power production stages perform better than the corresponding power sale stages. The development of the two power subsystems in the four regions is unbalanced. More than half of the power supply systems choose to allocate more proportion of the shared resources to the sale stage rather than use a more balanced distribution between the two stages. [ABSTRACT FROM AUTHOR]

Emissions trading system (ETS) is a cost-effective way towards achieving the carbon neutrality goal. Based on the gaps in the research on carbon allowance allocation vis-à-vis the carbon neutrality target, this study examines how to allocate carbon allowances equitably and effectively among Chinese provinces. Different from the previous research that mainly considers responsibility, ability, and potential of equity dimensions, this paper proposes a composite indicator allocation approach based on the equity of history, demand, contribution, and guarantee, and it also employs the entropy method and expert survey method to obtain the indicator weight values in different scenarios. Results show that the carbon allowances of some provinces, such as the forest-rich province of Yunnan, vary significantly in five scenarios, but little differences are observed for some provinces, such as the high historical emissions province of Shanxi. More provinces achieve data envelopment analysis efficiency in the preferred scenario. Compared with the objective scenario, the required emission reduction in the preferred gross domestic product (GDP) scenario is 231 million tonnes less, and the emission reduction cost is 190 billion yuan less. It is suggested that the Chinese government should adopt the allocation scheme in preference for the GDP scenario. [ABSTRACT FROM AUTHOR]

Improving the express industry's environmental efficiency is of practical significance to green and sustainable economic development. The development of internet may help achieve this goal. However, previous studies have not provided a consistent result about the relationship between internet development and the express industry's environmental efficiency. This paper provides a deeper understanding of this relationship by analyzing the threshold effects from the perspectives of road network density, openness to international trade and competition, and e-commerce level. Based on Chinese provincial data from 2012 to 2017, we first use a range directional data envelopment analysis model to evaluate the express industry environmental efficiency. Then, a threshold model is constructed to analyze the threshold effects in the relationship between internet development and environmental efficiency. The results show that: (1) The impact of internet development on the express industry's environmental efficiency shows a single threshold effect as road network density increases, which indicates that internet development significantly improves that environmental efficiency when the road network density is above a threshold value. (2) The impact of internet development on express industry environmental performance shows a dual-threshold effect as the degree of openness increases. (3) There is also a dual-threshold effect in this relationship as the proportion of e-commerce business transactions increases, but this effect is not significant. Based on these results, suggestions are given for the express industry to improve its environmental efficiency in China. [ABSTRACT FROM AUTHOR]

The coal industry is an essential energy industry in China. From 2008 to 2020, the global economic crisis, the policy of de-capacity, and the decline of the coal industry all profoundly affected the development of coal mining areas. This paper combined emergy synthesis and super-efficiency Slacks-Based Measure-Data Envelopment Analysis (SBM-DEA) considering undesirable output to evaluate the eco-efficiency in the Longkou coal mining area's circular economy industrial chain from 2008 to 2020. This research finds that the eco-efficiency in the coal mining area was 0.73 in 2008 and then showed an upward trend from 2008 to 2020. The eco-efficiency was relatively effective in 2011, 2013–2016, 2018–2020. Due to the progress in mining technology and the decline in waste emission, the eco-efficiency showed an upward trend, and the peak eco-efficiency value was 1.26 in 2019. The circular industrial chain contributed a lot to the high eco-efficiency in general. The eco-efficiency in 2012, 2017 was low owing to the recession of the coal industry and the overcapacity of coal. According to the analysis results, the coal mining areas should develop circular economy, cleaner production, and extend the circular economy industrial chain. In addition, the coal mining areas ought to expand enterprise-scale, develop new energy industries (e.g., biomass energy, wind energy, photovoltaic power generation). • Evaluated eco-efficiency in the coal mining area's circular economy industrial chain. • Emergy synthesis combines undesirable super-efficiency SBM-DEA is verified. • Eco-efficiency showed an upward trend from 2008 to 2020. • The recession of coal industry and overcapacity of coal affected eco-efficiency. • Coal mining area should develop circular economy, cleaner production. [ABSTRACT FROM AUTHOR]

This study investigates the impact of the state-owned sector on industrial transformation and upgrading as well as its mechanisms. Using provincial panel data for China from 2001 to 2019, we found that SOEs have a positive influence on value-enhancing upgrading, while the effects on resource-saving and environment-friendly upgrading are inverted U-shaped. These results indicate that innovation partially mediates the relationship between SOEs and the three types of industrial upgrading. Employing Data Envelopment Analysis (DEA), we also measured the Malmquist index of SOEs and non-SOEs in China and empirically tested the spillover effects of SOEs. The empirical results reveal a significant innovation spillover effect of SOEs. SOEs are able to promote industrial transformation and upgrading with strong and far-reaching spillovers. • This paper defines three types of industrial upgrading. • State-owned enterprises (SOEs) have a positive impact on value-enhancing upgrading. • Innovation partially mediates the relationship between SOEs and industrial upgrading. • Our results reveal a significant innovation spillover effect of SOEs. [ABSTRACT FROM AUTHOR]

Abstract: Data envelopment analysis (DEA) has recently become a popular approach in measuring the energy and environmental performance at the macro-economy level. A common limitation of several previous studies is that they ignored the undesirable outputs and did not consider the separation of inputs into energy resources and non-energy resources under the DEA framework. Thus, within a joint production framework of considering both desirable and undesirable outputs, as well as energy and non-energy inputs, this study analyzes China’s regional total-factor energy and environmental efficiency. This paper utilizes improved DEA models to measure the energy and environmental efficiency of 29 administrative regions of China during the period of 2000–2008. In addition, the DEA window analysis technique is applied to measure the efficiency in cross-sectional and time-varying data. The empirical results show that the east area of China has the highest energy and environmental efficiency, while the efficiency of the west area is worst. All three areas of China have similar trends in the variation of efficiency and in general the energy and environmental efficiency of China slightly increased from 2000 to 2008. The regions of the east area have a more balanced development than the regions of the central area and west area according to energy and environmental efficiency. [Copyright &y& Elsevier]

Abstract: Recently, environmental pollution has attracted a lot of attention domestically and internationally. It is known that energy utilization is a major source of China’s environmental pollution. Measuring environmental efficiency of energy utilization in Chinese provinces first, the paper then offers decision supports for relevant departments in order to achieve sustainable development of energy and environment in China. This research chooses CO2 emission as a substitute variable for environmental effects of energy utilization and puts it into the study framework based on total-factor production theory to measure the environmental efficiency of energy utilization of each province and three major regions. On the basis of the above results this research further measures the cost of environmental regulation for each province. Provincial panel data during the period of 2000–2007 are used to carry out empirical study. The analysis indicates that the environmental efficiency of energy utilization in Chinese provinces varies greatly from each other, meaning that both economic output and CO2 emissions need considerable improvement. In addition, the results of empirical study also show that the environmental efficiency of energy utilization in China is quite low. Therefore, to ensure sustainable development of China’s energy, economy and environment, we should resort to advanced technology as the support to promote the efficient and clean utilization of energy resources. Besides, most Chinese provinces have spent quite a lot on environmental regulation, implying that in these provinces development of the economy and the environment are not in harmony. The contributions of this research are as follows: it constructs the models for measuring environmental efficiency of energy utilization in Chinese provinces; it introduces the idea of “environmental regulation” into research system and measures the environmental regulation cost of each Chinese province. [Copyright &y& Elsevier]

Abstract: This paper proposes a non-radial DEA approach consisting of both a static and a dynamic environmental performance index (EPI) for measuring environmental performance. The static EPI is defined as the ratio of a non-radial efficiency measure for reducing undesirable outputs to that for increasing desirable outputs. The proposed non-radial DEA approach has been applied to model the environmental performance of industrial sectors in different provinces of China from 1998 to 2009. It has been found that the static non-radial EPI has a higher discriminating power than the EPI derived from the non-radial undesirable outputs orientation DEA models. Our empirical results also show that the environmental performance of industrial sectors in China improves by 58% in 1998–2009 that is mainly driven by the technological change. [Copyright &y& Elsevier]

Abstract: In this paper, the environmental efficiencies of 39 industrial sectors are measured using a non-radical, non-oriented slacks-based measure (SBM) model that considers the environmental impact of each industry. The economic efficiency of each industrial sector is measured using traditional data envelope analysis (DEA) methods and the differences between environmental efficiency and economic efficiency are analyzed using the transverse analysis method. A total of 39 industrial sectors are examined within three main industrial sectors (mining, manufacturing, and supply). Using the longitudinal analysis method, the environmental efficiencies of the three main industrial sectors from 2002 to 2009 are analyzed using the average value of their sub-industries’ environmental efficiencies. The time regression equation of the three industrial fields’ environmental efficiencies is examined using a regression analysis method. Based on these results, tactics for enhancing the environmental efficiency of Chinese industries are suggested in accordance with the characteristics of each industrial sector’s environmental efficiency. [Copyright &y& Elsevier]

Abstract: During the fast developing process of the Chinese economy, China consumes lots of energy and environmental pollution caused by energy consumption currently urgently needs solving. So, paying more attention to energy saving and emission reduction is very important. Therefore, to reasonably allocate resources and reduce emissions in China’s different regions has practical significance. So this paper firstly considers the model construction method for resource allocation considering undesirable outputs between different decision making units based on the DEA framework under the CRS and VRS assumption. We have proposed some resource allocation models as a multiple objective linear problem which considers the input reduction, desirable output reduction and undesirable output reduction. Then, to demonstrate the practical application of the resource allocation model, numerical examples are used to illustrate the approach. In addition, we explore the resource allocation problem of China’s regions in 2006–2010. The empirical results show that the resource allocation models dealing with undesirable outputs have effective configuration results, that is, the reduction proportion for desirable output is smaller than the reduction proportion for undesirable output. From the resource allocation results in China’s regions, we find that the eastern area has more emission reduction than the central and western areas corresponding to the same energy consumption reduction, and the energy and environmental policy should focus more on the central and western areas. All of these conclusions will give some policy recommendations on saving energy and reducing pollution emissions. [Copyright &y& Elsevier]

Excessive CO 2 emissions from manufacturing has become a major factor that constrains China's sustainable economic development. Therefore, considering the regional and industrial heterogeneities, this paper introduces a global multi-hierarchy meta-frontier Data Envelopment Analysis approach to analyze the dynamic performance of CO 2 emissions and to estimate the current CO 2 emissions inefficiency and reduction potential in the manufacturing industry of China's 30 provinces, which are separated into three components, namely, structural characteristics, technology gaps, and resource allocation levels. The results show that: (1) From 2003 to 2015, the CO 2 emissions efficiency of China's manufacturing industry increased by a factor of 4.7, of which technological progress and optimization of industrial structure were the main driving forces. (2) At present, the CO 2 emissions inefficiency of the manufacturing industry in mainland China is 0.7804, reflecting that 78.04% of its total CO 2 emissions is excessive emissions, which is mainly caused by the low allocation efficiency. (3) The heavy sub-industry and the east region have the greatest potential for CO 2 emissions reduction in the manufacturing and in the three regions of the country, respectively. In addition, by promoting the industrial restructuring, regional coordinated development, and strengthening the environmental regulations and promoting market-oriented reforms, China's manufacturing industry could reduce 29.65%, 11.55% and 36.84%, respectively, of its current CO 2 emissions. Based on the estimation results, this paper also points out the short-term and long-term directions for industrial structure adjustment and develops the specific paths for China's 30 provinces to achieve CO 2 emissions reduction in manufacturing industry. • CO 2 emissions efficiency of China's manufacturing industry increased by 4.7 times. • Technological progress are the primary driving force for efficiency improvement. • CO 2 inefficiency and reduction potential show a spatial difference characteristic. • 78.04% of the total CO 2 emissions can be reduced by corresponding measures currently. • The CO 2 emissions reduction strategies are formulated for China's 30 provinces. [ABSTRACT FROM AUTHOR]