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The high-quality development of national high-tech zones is a matter of great concern for both the government and scholars as it serves as an important base for realizing scientific and technological innovation and industrialization of achievements. This paper constructs a conceptual model of the national high-tech zone innovation system by breaking it down into two subsystems: the innovation R&D subsystem and the innovation incubation subsystem. Through the use of a two-phase Data Envelopment Analysis (DEA) model, the innovation efficiency of each subsystem is measured for the 42 national high-tech zones in Beijing-Tianjin-Hebei, Yangtze River Delta, and Pearl River Delta urban agglomerations. The research conclusion indicates that the majority of the high-tech zones in China are high in both research and development (R&D) efficiency and achievement conversion rate, indicating generally high overall innovation efficiency. However, there is a gap between the three urban agglomerations in terms of innovation resources and transformation environment, with Beijing-Tianjin-Hebei High-tech Zone having low transformation efficiency, and some areas of Yangtze River Delta and Pearl River Delta having low efficiency in technology R&D. Furthermore, the study finds that the innovation modes and paths of high-tech zones within urban agglomerations are quite different in the process of coordinated development of innovation. Therefore, the paper proposes two innovative development paths: the endogenous growth path and the exogenous driving path. The research results provide a tool for more objective evaluation of the innovation efficiency of national high-tech zones in different urban agglomerations in China, and provide a decision-making basis for different regions to implement corresponding innovative development paths. [ABSTRACT FROM AUTHOR]

[Display omitted] • We adopts a perspective of equity and efficiency and constructing a framework to assess the improvement of ESV in China. • We reveals the regional unfairness between China's ESV and economic growth. • We evaluates the ESV improvement in China, considering both static and dynamic efficiency. • We provides policy suggestions for ecological environmental governance. Ecosystems provide the material and energy resources necessary for human development, improving people's quality of life and well-being. Thus, enhancing the effectiveness of ecosystem services valuation has become a focal point of concern. Here, we adopt a perspective of equality and efficiency and construct a framework based on the ecological Gini coefficient (E-Gini coefficient) and the three-stage Data Envelopment Analysis-Malmquist (DEA-Malmquist) model, by evaluating the input of financial investments and human resources and the output of ecosystem service value (ESV) production, to assess the improvement of ESV in China from 2008 to 2020. The research findings reveal that the E-Gini during the study period remained consistently above 0.54, which is categorized as "high inequality". ESV equality across different regions follows the order of northwest > south > north concerning economic and social development. Furthermore, through efficiency analysis, we find that there is room to improve the improvement efficiency of China's ESV. Static efficiency analysis indicates that the average improvement efficiency of China's ESV is low (0.331). This indicates that the input resources did not achieve the optimal ESV production. Dynamic efficiency analysis reveals that there is a 3.2 % decline in ESV improvement efficiency over the entire study period. Encouragingly, ESV improvement efficiency has been increasing annually since 2017. Moreover, there exists spatial heterogeneity in ESV improvement efficiency, with the northwest region showing significantly higher than the north and south. The research results can provide policy recommendations for ecological environmental governance, production efficiency, and ecological compensation. [ABSTRACT FROM AUTHOR]

The concept of green economic efficiency has attracted attention over the last decade. Several measurements of green economic efficiency and their various aspects have been proposed, and these have been the main focus in the literature. However, calculating the influence of each province on the country's green economic efficiency is essential to assist in targeted management and institutional design at the provincial level by the government. To perform such analysis, this study extended the data envelopment analysis model. Data from 30 provinces in China from 2010 to 2018 are used in the study. The results show that the provinces in the eastern and southern coastal regions have the greatest influence on the country's green economic efficiency, but the majority of the provinces in the Middle Yellow River region have the least influence on the entire green economy. Interestingly, high-efficiency provinces do not always exert the greatest influence, suggesting that even low-efficiency provinces can influence significantly on the economy. These findings can serve as a baseline for improving provincial influence and further promoting China's green economic efficiency. [Display omitted] [ABSTRACT FROM AUTHOR]

The efficient management of water resources in Chinese agriculture is crucial for ensuring food security and mitigating environmental consequences such as water scarcity and pollution. Agricultural technological innovation is crucial in optimizing agricultural practices and making them more sustainable. To this end, this study investigates the dynamic relationship between agricultural technological innovation and total-factor agricultural water usage efficiency (TFAWUE) in Chinese provinces from 2000 to 2020. The study utilizes the Data Envelopment Analysis (DEA) Malmquist productivity index approach to measure the overall efficiency of water usage in agriculture, known as total-factor agricultural water usage efficiency (TFAWUE). The findings suggest that the mean TFAWUE score of Chinese provinces is 1.1356, surpassing a value of 1. It illustrates that Chinese provinces witnessed a growth of 13.56 in TFAWUE over the study period. Technological change is the primary determinant of growth in the TFAWUE, as technology change (TC) is higher than efficiency change (EC). Subsequently, by employing a rigorous econometrics series, this study provides valuable insights into the intricate dynamics of agricultural technological innovation and its impact on total-factor agricultural water usage efficiency. The study constructs a composite multidimensional index of agricultural technological development, encompassing various technologies pivotal to the agriculture sector. Analysis shows that agricultural technologies enable farmers to implement water conservation practices effectively to enhance agricultural water usage efficiency. However, the farm scale reduces the efficiency of agricultural water usage. Additionally, sprinkler technology positively enhances water usage efficiency in agriculture. These findings provide valuable insights for policymakers in the agricultural sector, offering guidance on sustainable practices and policies for managing water resources in conjunction with improvements in agricultural technologies. • The average total factor agricultural water usage efficiency of China is 1.1356. • Technological change is the growth determinant of the total factor agricultural water usage efficiency. • Agri-technology enables farmers to implement water-saving practices effectively. • Farm scale reduces the level of agricultural water usage efficiency. • Sprinkler technology significantly improves total factor agricultural water usage efficiency. [ABSTRACT FROM AUTHOR]

• An evaluation model is proposed by considering risk attitudes and aggregation method. • Our distance entropy function reduces the inconsistent degree of evaluation results. • Empirical example is performed to validate the applicability of the proposed model. • Sensitivity analysis is conducted to test the effect of parameter changes on results. Cross-efficiency evaluation with the data envelopment analysis (DEA) model is an effective way to assess performance and provide a complete ranking of decision-making units. However, it is generally assumed that decision makers are perfectly rational in the cross-efficiency model, which fails to consider the subjective preferences of decision-makers. Moreover, the arithmetic average method is usually adopted to aggregate efficiency scores in traditional cross-efficiency methods, which underestimates the importance of self-evaluation. To address these issues, we extend cross-efficiency with the DEA model by incorporating prospect theory and the distance entropy function. First, we calculate the prospect values of decision-making units to describe the non-rational subjective preferences under the risk of decision-makers. Second, based on prospect cross-efficiency, a new distance entropy function is developed to aggregate the ultimate prospect cross-efficiency values. More specifically, some traditional cross-efficiency evaluation models can be considered special cases of prospect cross-efficiency models with appropriate adjustments to the parameters. Finally, an empirical example is used to evaluate the prospect cross-efficiency results with the high-tech industries of 29 provinces in China to illustrate the applicability and effectiveness of the proposed model in ranking observations. [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]

To assess the efficiency of high-standard farmland construction (HSFC), this study utilized data spanning from 1998 to 2020 obtained from 30 provinces, autonomous regions, and municipalities in China. This assessment was conducted using a three-stage super-efficient slack-based measure data envelopment analysis(SBM-DEA)model. Furthermore, we investigated the spatial and temporal variances and convergence of HSFC efficiency using the Dagum Gini coefficient and convergence analysis. The results firstly show that from 2007 to 2020, China's HSFC efficiency displayed fluctuations and declines, significantly influenced by environmental and stochastic factors. Secondly, it showed that the gap in China's HSFC efficiency has widened, with super-variable density being demonstrated as the primary source of spatial imbalance in HSFC efficiency. Thirdly, it showed that the deviation in the efficiency of HSFC in the central region of China from the average level has shown a gradually declining trend, while the deviation in the efficiency of HSFC in the national, western, eastern, and northeastern regions has not presented a downward trend. Fourthly, it demonstrated that over time, the efficiency of building high-standard agriculture throughout the nation and its four main areas will converge to the same level. • The three-stage super efficiency model is used to measure the efficiency. • The efficiency of high-standard farmland construction is affected by external environmental factors. • The gap of construction efficiency in various parts of our country expands unceasingly. • Hypervariable density is the main source of spatial imbalance in the efficiency. • There was β absolute convergence and β conditional convergence across the country. [ABSTRACT FROM AUTHOR]

The CO 2 emission control targets (CET) allocation faces the unavoidable issues of "energy equity" and "carbon sink equity", which are complicated by changing regional characteristics. By combining regional characteristics assessment, energy consumption status in the "1 + N" policy, and Data Envelopment Analysis (DEA) modeling, two schemes for China's net CET allocation in 2020–2030 are implied to reveal the impacts of regional carbon sinks and renewable energy endowments differences on the allocation. The results indicate that: firstly, significant regional differences are inevitably intrinsic within China's renewable energy endowments and carbon sinks to substantially affect the equity of carbon emission allocation in 2020–2030; secondly Provinces with high fossil energy reliance, such as Hebei, Shandong, Shanxi, and Inner Mongolia, are generally facing continuous pressure to reduce CO 2 emissions during the 14th FYP and 15th FYP; thirdly, as a regional ecological advantage, carbon sinks are an essential source of additional carbon budgets for provinces, as in Sichuan and Yunnan. As a dominating impact factor of carbon sinks on CET allocation, terrestrial carbon sinks are much higher than ocean carbon sinks with clear provincial attribution; fourthly, in our allocation scheme, the exploitation of renewable energy endowments directly affects the provincial carbon budget and can promote low carbon energy supply; finally, the foundation to build the CET allocation mechanism, China's carbon market needs to be improved. This study helps policymakers understand and balance the equity issues in CET allocation due to regional differences in renewable energy endowments and carbon sinks. [Display omitted] • We assess carbon sinks and renewable energy potential as allocation indicators. • We construct a DEA model applicable to provinces with net carbon sinks. • Carbon sinks are essential sources of additional carbon budgets for provinces. • Rich but underdeveloped renewable energy endowments limit carbon budgets. [ABSTRACT FROM AUTHOR]

In recent years, the rapid development of transportation industry brings economic benefits, at the same time, the problem of environmental pollution is becoming more and more serious. The emission of CO 2 intensifies the trend of global warming. How to measure the development of environment and economy is a huge challenge for us. This paper proposes a data envelopment analysis (DEA) method based on slacks-based measure (SBM) to evaluate the environmental and economic performance of the transportation industry from 2015 to 2017. According to the economic region, 30 provinces in China are divided into northeast, east, central and western regions for efficiency analysis. The model can determine the low efficiency decision units and finally put forward improvement suggestions for the low efficiency areas. [ABSTRACT FROM AUTHOR]

The imbalance of regional development is an important problem in China's economic development. This paper measures the allocation efficiency of financial resources of 31 provinces in China from 2008 to 2018. This paper makes up for the defciency of the traditional DEA model in variable selection and effciency identification. Firstly, we use AIC, BIC, and HQC information criteria to select variables, and construct a more scientific and reasonable financial effciency evaluation index system. On this basis, the Shannon entropy index is used to improve the discrimination ability. The results show that: (1) The combination of Shannon entropy index makes the result more accurate and improves the discrimination ability. (2) The Eastern region has the highest CES score, followed by the Midland and Western region, and the Northeast region has the lowest CES score. (3) Overall, from 2008 to 2018, the effciency of financial resource allocation in China showed an upward trend. [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]

Promoting the synergies between economic growth and carbon reduction is crucial for achieving carbon neutrality goals. Understanding the determinants of carbon emission performance (COP) is important for improvement. However, empirical examinations of the linkages between green finance agglomeration (GFA) and COP remain limited. Utilizing a panel data of 30 Chinese provinces from 2007 to 2019, we measure COP via meta-frontier data envelopment analysis. We employ a spatial Durbin model (SDM) to investigate the spatial spillover effects of GFA on COP. Our findings reveal COP has a substantial positive spatial correlation between provinces. We further observe that GFA has a direct and spatially dependent impact on regional COP. The effects vary based on different green finance instruments and regions. GFA enhances COP through capital formation, energy transition, and technological advancement, although asymmetry effects exist between local and neighboring regions. Promoting regional GFA formation through interprovincial cooperation and coordination for optimizing green finance allocation is vital. Moreover, increased financial support towards green, low-carbon projects and enterprises can raise overall regional COP. • Carbon emission performance (COP) was estimated with meta-frontier DEA approaches. • The spatial effect of green finance agglomeration (GFA) on COP was estimated. • Agg has a significant contribution and spatial spillover effect on regional COP. • The impact mechanisms of GFA on COP and its heterogeneity were investigated. • Establish GFA centers to provide better financial services to enhance regional COP. [ABSTRACT FROM AUTHOR]

Increased urban land use efficiency (ULUE) is a pivotal symbol of urbanization's evolution from rapid expansion to a focus on high-quality development. However, the extent to which the spatial-temporal trends in China's future urban sprawl efficiency could hinder the attainment of Sustainable Development Goals (SDGs) 11: "Building Sustainable Cities and Communities" remains uncertain. This study endeavors to shed light on this issue, offering insights that could facilitate the dynamic adjustment of land resource use efficiency assessment strategies to align with the SDG11 urbanization process. We leverage socio-economic shared pathways (SSPs) thematic data through the application of the slack-variable-based data envelopment analysis (SBM-DEA) methodology to project ULUE for China's 368 prefectural-level administrative divisions from 2020 to 2050. In addition, we utilize the coupled coordination degree (CCD) model to anticipate the future harmonization trend of the ULUE with the UN's endorsed SDG indicator, SDG11.3.1: "Land Consumption Rate to Population Growth Rate (LCRPGR)". We harness tools such as Kernel Density Analysis, Moran's Index, and Multi-scale Geographically Weighted Regression (MGWR) to unveil the principal factors influencing the CCD changes and spatial clustering trends. We find that: the higher ULUE areas in China gradually shift from coastal cities and provincial capitals to the western regions, and the value of China's LCRPGR gradually decreases. By 2050, China's ULUE and LCRPGR will reach a high degree of coupling under the SSP2, SSP4, and SSP5 routes. The spatial heterogeneity of the CCD is increasing, with urban land areas having the most substantial adverse effect on CCD, and Gross Domestic Product (GDP), population, and metropolitan green space areas having sequentially decreasing positive effects on CCD. This study assesses the future SDG 11.3.1 process, which helps to adjust the assessment strategy of SDG11 in due time and promote sustainable urban development. [Display omitted] • To predict the ULUE and LCRPGR of prefecture-level divisions in China 2020–2050. • Revealing the coupled coordination between ULUE and LCRPGR in China 2020–2050. • ULUE and LCRPGR reach excellent coordination under the SSP2, SSP4, SSP5 in 2050. • CCD has the strongest negative and positive correlation with land area and GDP. • SDG11.3.1 needs to include economic development and ecological service indicators. [ABSTRACT FROM AUTHOR]

With the increasingly severe global climate change situation, the construction of low-carbon and resilient cities is expected to be an effective means of accelerating the achievement of carbon peak and carbon neutrality goals. As a major global economic and carbon-emitting power, it is necessary to advocate for China to accelerate the construction of low-carbon and resilient cities. We have formulated a comprehensive methodological framework to appraise the effects of urban resilience (UR) on carbon dioxide (CO 2) emissions. This study first constructs a "social-economic-institutional-ecological-engineering" UR assessment framework. Based on this framework, we establish an evaluation system for the UR of the urban agglomeration to explore the dynamic evolution of its spatial and temporal patterns. And then this study uses the SBM-DEA method that includes undesirable outputs to measure the carbon emission efficiency of the urban agglomeration. Finally, the spatial Durbin model is used to analyze the spatial spillover effects of UR on CO 2 emissions, and the mechanism of CO 2 emission spatial effects is theoretically explained by introducing geographical laws. The results show that the overall UR level in the region increased from 0.107 in 2000 to 0.318 in 2020, with an average carbon emission efficiency of 0.82, but still at a relatively low level. Moreover, there are significant differences in the regional level, with Guangzhou and Shenzhen and their surrounding cities showing significantly higher levels compared to other cities. Regarding the spillover effects analysis, CO 2 emissions exhibit significant spatial spillover effects between cities. Improving UR can reduce urban CO 2 emissions, but it may also have negative spillover effects on surrounding cities. This research can provide a new perspective for improving the study of regional CO 2 emissions reduction and its influencing mechanisms while also offering important references for accelerating the construction of UR and achieving regional carbon peak targets. • We have developed a methodological framework to assess the influence of urban resilience on carbon dioxide emissions. • A "social-economic-institutional-ecological-engineering" urban resilience assessment framework is designed. • The carbon emission efficiency of the cities explored by the SBM-DEA method with undesirable outputs. • Improving urban resilience can directly promote the reduction of carbon emissions in cities. [ABSTRACT FROM AUTHOR]

Poverty eradication and ecological protection play a fundamental role in achieving the Sustainable Development Goals. The two issues are paid great attention to in China and are generally intertwined in impoverished rural areas where ecosystems are heavily relied on. The current measures to end poverty depend on the local willingness and lack of adequate consideration of ecological performance, resulting in significant tradeoffs. In this context, this study proposes a framework integrating static and dynamic eco-efficiency evaluation based DEA-Malmquist-Tobit model by taking on 25 key assisted counties in southwest China as cases. Besides, it offers a value-based ecological development index (EDI), aiming to provide a more scientific basis for exploring optimizing pathways for achieving the co-benefits of poverty alleviation and ecological protection. The results reveal that the average annual static eco-efficiency of 8 counties and dynamic eco-efficiency of 17 counties is > 1 during 2000–2020, while there is a noticeable decrease in eco-efficiency in most studied counties during 2005–2010. Improving infrastructure and industrial structures can increase static eco-efficiency. Promoting technological progress in economic development increases dynamic eco-efficiency. The range of the EDI has increased more than 10 times during the studied periods, indicating the increase in the economy of the studied counties is over that of the cost of pollution treatment. Improving market transactions is identified as the optimal pathway to achieve the co-benefit. Finally, the policy implications are proposed, including promoting investment in social capital, optimizing resource allocation, and establishing a multi-government department cooperative development system. [Display omitted] • Eco-efficiency of the 25 key assisted counties in China is evaluated. • The balance between economic outputs and ecological impacts is quantitatively evaluated. • Advancements in technology are the key factor contributing to eco-efficiency growth. • Market transaction is the optimal pathway for achieving the co-benefits. [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 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]

[Display omitted] • A base point network DEA model is proposed to evaluate ISW prevention and treatment efficiencies. • The CCD model is constructed to investigate the CCD of ISW prevention and treatment efficiencies. • ISW prevention efficiency is higher than treatment efficiency. • The CCD in three regions (Jiangxi, Guangxi, Shaanxi) is imbalanced. • The improvement of CCD mainly depends on industrial servitization and industrial structure. Both prevention and treatment are crucial for reducing industrial solid waste (ISW). The primary objective of this study is to explore the coupling coordination degree (CCD) of China's provincial ISW prevention and treatment from the perspective of efficiency analysis during 2011–2020 and then investigate its driving factors in three steps. The first step is to develop a base point network slacks-based measure data envelopment analysis model to address the issue where the indicators (ISW discharged, ISW stored) of some regions are zero during the evaluation of the ISW prevention and treatment efficiencies. Secondly, the CCD model is constructed to investigate the CCD of ISW prevention and treatment efficiencies. Finally, the driving factors of the CCD are explored using the spatial Tobit model. The results show that: (1) In 76.67% of regions, the prevention efficiency is higher than treatment efficiency, and in 23.33% of regions, the treatment efficiency is higher than prevention efficiency. The prevention efficiency of the entire country exhibits a steady downward trend, while the treatment efficiency, on the whole, is increasing. (2) The regions with high coordination are mainly situated in the east, those with moderate coordination are primarily located in the central region, and those with basic coordination are mostly located in the west. Only three regions (Jiangxi, Guangxi, Shaanxi) exhibit moderate imbalance. (3) The improvement of CCD mainly depends on industrial servitization and industrial structure, while property structure, ISW treatment investment intensity, and traffic infrastructure construction significantly impede the increase of the CCD. [ABSTRACT FROM AUTHOR]

As a strategic lever, internal carbon pricing allows enterprises to cope with potential climate-related risks and motivate their low-carbon transformation. But internal carbon pricing, still in its early exploratory stage, lacks standard criteria for optimization. The traditional goal of cost minimization or profit maximization is not directly applicable to internal carbon pricing because internal payment for carbon emissions does not affect enterprises' aggregated costs or profits. How to optimize internal carbon prices to reduce carbon emissions more efficiently becomes an important issue. This study proposed a grouped internal carbon pricing method considering both internal carbon pricing and comprehensive efficiency. We found that there exists an optimal partition of internal units with corresponding differentiated carbon prices. Compared with other internal carbon pricing methods, the average efficiency of differentiated carbon pricing was significantly higher than the average efficiency of uniform carbon pricing. We then applied the method to a large coal producer with 16 subdivisions in China. The results showed that if the enterprise adopted the grouped internal carbon pricing under its current technology level, the carbon emissions would reach an optimal level, and its comprehensive efficiency would be significantly improved. • A corporate grouped internal carbon pricing (ICP) framework was proposed. • The ICP level of a large coal enterprise in China was measured. • The performance differences among different ICP types were studied. • Differentiated carbon pricing types lead to higher enterprise efficiency. [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]

Pollutant removal is a critical procedure for wastewater treatment because it directly determines the effectiveness of wastewater treatment and water conservation. Therefore, assessing pollutant discharge efficiency in wastewater treatment is imperative. In this study, an environmental production technology with a non-radial data envelopment analysis model is developed to examine the discharge efficiency and reduction potential of pollutants in wastewater treatment in 68 key cities by considering the disparity of city scales. By comparing discharge efficiency and reduction targets released in the 12th Five-Year Plan, this study identified the key areas that need to be addressed for wastewater treatment with regard to different city types. Results showed that chemical oxygen demand (COD) and ammonia nitrogen (AN) have similar discharge efficiency values, mainly ranging from 0.5 to 0.7. More specifically, the discharge efficiency of COD is comparatively lower than that of AN. City size generated a positive impact on efficiency improvement. Centralized treatment system is beneficial for achieving the scale effect in wastewater treatment. An examination of the progress of reduction target systems indicated that AN is comparatively inferior in reduction completion. Apart from the technical barriers, this fact is highly related to the willingness and power of the local government. Cities should identify the key areas that need to be addressed and adopt the corresponding reduction strategies by referring to their scales. The findings of this study can facilitate the inclusion of pollution mitigation in the urban agenda, thereby fostering an effective wastewater treatment process and creating a benchmarking framework for governing wastewater pollution. • City-level pollutant discharge efficiency of wastewater treatment is measured. • Inter-city efficiency difference and the scale effect are discussed. • Cities' role in achieving national reduction targets in COD and AN is explored. • Cities are divided to four types to take stratified reduction strategies. [ABSTRACT FROM AUTHOR]

Establishing a scientific allocation scheme for transportation carbon allowance (TCA) is an inevitable requirement of China's sustainable development. Previous studies on the allocation problems of TCA considered the single goal, but this study constructs an allocation model of TCA under the framework of zero-sum gains data envelopment analysis and a spatial simultaneous panel. Furthermore, a profit maximization function model is used to measure the allocative efficiency and the differences in regional abatement costs of three different transport allocation mechanisms in China's top 10 economic regions from 1998 to 2019. Empirical results revealed four key results. (1) The efficient scheme is beneficial to regions with a high level of economic development and high-carbon emission efficiency, but it is easy to cause widening regional wealth disparities. (2) The equitable scheme is beneficial to regions with low transportation carbon emission efficiency, immature transportation structures, and green transportation technology, but it is easy to cause an unsuitable punishment effect. (3) Compared with the above two schemes, the comprehensive scheme is beneficial to more regions because it can both minimize the average marginal abatement costs and mitigate regional development inequalities. (4) With the gradual improvement of the national transportation carbon trading market, the comprehensive scheme is more sustainable and should be used more in the future. This study contributes to the formulation and implementation of energy allocation policies in the transportation sector. [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]

The importance of good performance in the logistics industry contributing towards a low-carbon economy is widely recognised. However, there are few studies on carbon emissions performance for the logistics industry, especially at the city level. Therefore, this study attempts to analyse the carbon emissions performance of the logistics industry at the city level by examining sixteen cities within Yunnan Province. In particular, the Data envelopment analysis (DEA) model and Malmquist index from both the static and dynamic perspective were explored. To further capture the driving factors of the carbon emissions performance in logistics, the Tobit model was applied to perform regression analysis. The results indicate that (1) with regards to static performance, two northwest cities, Nujiang and Diqing, have reached the technological frontier; (2) regarding dynamic performance, although six cities have improved, the average dynamic logistics carbon emissions performance of all the cities decreases by approximately 5.9% from 2011 to 2015; (3) in terms of driving factors, the economic development factor is found to be positively and significantly related to static carbon emissions performance, whereas others indicate negative and significant relationships. • We investigate carbon emissions performance in logistics at the city level. • An empirical study on sixteen cities in China is conducted. • Logistics-related carbon emissions of sixteen cities have increased from 2011 to 2015. • Carbon emissions performance in logistics vary across cities. • Carbon emissions performance in logistics attributes to different driving factors. [ABSTRACT FROM AUTHOR]

Eco-efficiency is of great significance in the attainment of a sustainable society, and is the most severe issue facing industrial sectors in China. In this work, a hybrid super-efficiency data envelopment analysis (DEA) was conducted to measure eco-efficiency and analyze the problem of inefficiency facing industrial sectors in China. DEA has been widely used in eco-efficiency analysis; however, existing DEA models focus on the evolution and comparison of eco-efficiency values, and do not consider the evolution characteristics of the input nor undesirable output inefficiency and neglect the radial and non-radial classifications of input and output indices simultaneously. Thus, this work proposes a hybrid super-efficiency DEA model which combines hybrid DEA with super-efficiency DEA, separating the input and undesirable output variables into radial and non-radial parts using variable correlations. For the panel data of the industrial sectors, the Malmquist index is introduced in this approach. Subsequently, this approach is illustrated using a real data set from 22 industrial sectors in China corresponding to the period of 2006–2015. Results show that eco-efficiencies of the 22 industrial sectors increased consistently from 2006 to 2015. Due to a lack of momentum in sustained growth, technological progress regarding the improvement of eco-efficiency requires acceleration. More and more sectors have achieved rapid enhancements in input and undesirable output inefficiencies, but 10 traditional sectors with high resource consumption and heavy emissions of pollutants remain inefficient. • A hybrid super-efficiency DEA model is proposed to measure eco-efficiency. • The Malmquist index is introduced in the DEA to deal with panel data. • The problem of inefficiency is studied by the definition of three efficiency states. • The approach is applied to measure eco-efficiencies of 22 industrial sectors in China. [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]

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]

Data envelopment analysis (DEA) and the Biennial Malmquist–Luenberger (BML) productivity index are applied to measure and decompose traditional energy efficiency under the carbon emission constraints of China's textile industry for the period 2000–2012. It is found that: (1) overall, the average growth rates of technical improvement, labor, capital for energy substitution, and output structural change are positive, indicating that these are the main factors that improve energy productivity, with output structural change making the largest contribution; (2) the average growth rates of the energy structure effect and technical efficiency are negative, which indicates that the energy structure effect is the main factor hindering energy productivity growth and that the deterioration of the technical efficiency change can inhibit this growth; (3) energy productivity is generally higher in eastern China than in central and western China; and, (4) in the decomposition of energy efficiency, the difference in energy efficiency between eastern and western China is mainly due to structural effects. • Output structure making the largest contribution to improving energy productivity. • Energy structure effect is the main factor hindering energy productivity growth. • Energy productivity is higher in eastern China than in central and western China. • Future policy for energy efficiency in China's textile industry is suggested. [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]

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]

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]

Abstract This study utilizes the data envelopment analysis technique with materials balance condition to evaluate the inherent trade-offs between environmental and cost outcomes among different types of energy consumptions in China's construction industry. Environmental and cost efficiency that is decomposed into technical efficiency and allocative efficiency are estimated, and the possible environmental impact and economic cost of reallocating energy inputs for improving efficiency are obtained. The estimation results show that: i) China's construction industry has the ability to produce its current level of industrial added value with fewer CO 2 emissions and fewer energy input cost through removing technical inefficiency and adjusting energy consumption structure. ii) There are 31.9% and 6.1% reduction potentials on CO 2 emissions if this industry attained the most environmentally efficient and the most costly efficient situation, respectively. iii) The average shadow cost of CO 2 emissions reduction in this industry is very low, suggesting that it should control CO 2 emissions through optimizing energy consumption structure and improving energy efficiency, instead of relying on end-of-pipe emission abatement technologies or emission trading systems. [ABSTRACT FROM AUTHOR]

Abstract China's most important three urban agglomerations, the Beijing-Tianjin-Hebei (BTH), Yangtze River Delta (YRD) and Pearl River Delta (PRD), are expected to play a leading role in energy saving and emissions reduction in China. Based on the biennial Malmquist productivity index and the non-radial direct distance function, this study improved the data envelopment analysis (DEA) model to evaluate the energy-saving and emissions-reduction performance (EEP) of these three urban agglomerations. The empirical results indicated that BTH did not show improvement in performance, whereas YRD and PRD showed a significant improvement. The annual growth rate of performance was higher in the PRD than in the other two urban agglomerations. The "catch-up effect" of the three urban agglomerations vanished after 2010, and the possible reasons for this change are the large-scale economic stimulus plan adopted by the government to address the global crisis and the recent decline of economic growth. The technical progress level increased at first, then decline, and increased again. This study also found that β convergence of performance existed in BTH and YRD. However, there was no β convergence of performance in PRD. Highlights • The non-radial directional distance function is adopted to evaluate the EEP. • The "catch-up effect" and technical progress do not always exist in 2005–2016. • Pearl River Delta makes a significant improvement in EEP. • There exists β convergence of performance in BTH and YRD, while not in PRD. [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]

Highlights • Driving factors of carbon emissions of Belt and Road countries are explored. • A production-theoretical decomposition analysis is applied. • GDP and the potential carbon emissions factor are main promoting factors. • Abatement technology and energy intensity have the greatest inhibiting effect. • China plays the leading role in terms of three factors of all 'B&R' countries. Abstract Little attention has been given to the carbon emissions issues of 'Belt and Road Initiative' countries although most countries have set explicit targets for Intended Nationally Determined Contributions. Taking advantage of the production theory, distance function, and data envelopment analysis, we employed a production-theoretical decomposition analysis to decompose the total carbon dioxide emission changes of the "Belt and Road Initiatives" countries from 2000 to 2014 into the contribution of seven driving factors and focused particularly on a production technology perspective. The main findings are as follows: (1) the economic development as proxied by the gross domestic product and the potential carbon emissions related to energy consumption were the two most important factors affecting carbon emission growth. The driving factor values for these two factors were 1.989 and 1.37. The average CO 2 emissions of the "Belt and Road Initiatives" countries in 2014 were 1.425 times larger than those in 2000. (2) The carbon abatement technology changes and potential energy intensity changes were the two main inhibiting factors return with factor values of 0.775 and 0.793. Moreover, we divided all countries into four groups, namely China and three other groups based on the income level. The results showed that the three groups had similar characteristics in terms of the carbon abatement technology effect and the carbon dioxide abatement technological efficiency, whereas China exhibited larger fluctuations. The promoting effect of economic development is significantly higher in China than in the other three groups. In addition, this promoting effect exhibited a decrease over time, indicating a decoupling between economic development and CO 2 emissions. [ABSTRACT FROM AUTHOR]

Abstract The allocation of carbon emission quotas is crucial to the establishment of carbon emission trading scheme (ETS) in response to China's 2030 emission reduction target. Thus, based on equality and efficiency principles, we apply a methodology combining DEA models with entropy method to analyze China's carbon quota allocation from both provincial and regional perspectives. We integrate three indicators (total population, accumulated historical carbon emissions and emission efficiency) to construct a comprehensive indicator and use the integrated weighting approach to simulate the allocation of carbon emission quotas among China's 30 provinces. Different from previous studies, we use the non-radial directional distance function based on Kuosmanen technology to calculate emission efficiency. Also, we introduce an environmental Gini coefficient to evaluate the equality level of allocation results. Our main findings are as follows: (1) In 2030, provinces with large shares of carbon emission quotas are mainly located in China's southern region (e.g. Guangdong), while most northern provinces have small shares of carbon emission quotas (e.g. Xinjiang and Inner Mongolia). (2) Due to low emission efficiency, the emission quota shares of Hebei, Xinjiang, Shandong and Inner Mongolia decrease significantly during 2015–2030, indicating that these provinces will face greater pressure to reduce carbon emissions in the future. (3) The environmental Gini coefficient is 0.3177, which proves the equality of allocation results. (4) From the regional perspective, due to the low emission efficiency and large historical emissions, the carbon quota shares of the Middle Yellow River and Northern coastal regions decrease significantly during 2015–2030. This indicates that the two regions will need to purchase emission quotas from other regions in the future. Based on the results above, provinces and regions which undertake large emission reduction obligations need to further improve emission efficiency such that the 2030 emission reduction target can be realized. [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]

Graphical abstract Highlights • Provincial agricultural WUE was assessed based on data envelopment analysis in China. • We compared the results between the blue-only and the green-blue scenarios. • The lower potential can be improved under the green-blue scenario while higher potential for blue-only scenario. • The pure technical efficiency was higher under the green-blue scenario in Northern provinces. • Improving blue water use in NC and green water use in SC is a priority to enhance national WUE. Abstract Uneven water resources and growing food demand due to an increasing population bring challenges to China. One important mechanism to address these challenges is to enhance water use efficiency (WUE). This requires information on current efficiencies in water use for agricultural production. In this study, we provide a benchmarking tool to assess relative agricultural WUE in 31 provinces in China during 2003-2013. Data Envelopment Analysis (DEA) was used with both green-blue water and blue-only scenarios. Results show that China's agricultural WUE has improved evidently after 2008. Overall technical efficiency (TE) and the pure technical efficiency (PTE) in China based on the green-blue scenario are relatively high, with the average potential increase less than 10% (8% and 4%, respectively). However, there is a larger potential for blue water use efficiency (14% and 7% respectively). The PTE in Northern China (NC) is higher than that in Southern China (SC) while the TE in NC is lower under green-blue scenario. Moreover, the TE and PTE in NC are lower than that in SC under blue-only scenario. These results indicate that green water management techniques in NC are superior to SC but the scale efficiency (SE) in NC is lower. There are four provinces where the efficiency values are on the frontier in four cases, i.e. two scenarios (green-blue and blue-only) and two assumptions in DEA, but fourteen provinces where the efficiency values are not on the frontier in any case and most of them were located in SC. Our results also suggest that improving SE can substantially contribute to national WUE, but exploring the solutions to enhance blue water use efficiency in China is also a key task in the future works. The research results have important implications for China and different provinces to improve agricultural WUE by water policies and management. [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 China's recent rapid socioeconomic development has caused its land use patterns to evolve rapidly. Land use changes are significantly influencing carbon emissions in China. Thus, assessing carbon emissions from land use change patterns is significant for sustainable socioeconomic and ecological development. This study applied a geographic information system and data envelopment analysis (DEA) to estimate provincial-level carbon emission efficiency in China during 1999–2014. The results indicate that China's total carbon emissions and carbon sequestration from land use showed annual rising trends but with an increasing difference in growth. At provincial level, the highest provincial emissions were 3–4 times greater than the lowest. In addition, carbon sources increased 20 times faster than carbon sinks. The contribution of carbon emissions from construction land to total carbon emissions was greater than 90%, also, the contribution of carbon sinks from forest land to total carbon sinks was greater than 90%; Carbon emissions were most intensive in Northeast China, and they gradually decreased in a radiating pattern to the north, west, and south. Among 31 provinces, only Qinghai, Inner Mongolia, Yunnan, Guangxi, and Heilongjiang showed effective reductions in carbon emissions. The spatial disparities in carbon emissions were likely due to differences in technological efficiency and scale of operation. [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]

There is increasing recognition of the importance of energy productivity in China due to the considerable growth in industrial energy consumption in the past several decades. Energy productivity improvements should be approached through changes in factors such as efficiency, technology use and factor substitution. Thus, the aim of this study is to decompose the energy productivity rate of change in a non-parametric framework. We combine data envelopment analysis (DEA) and the Malmquist index, using a data set of 35 two-digit industries in China from 1998 to 2011. The conclusions highlight three key points. First, the dynamic DEA reveals that technical progress is the primary source of energy productivity improvement. Technical efficiency plays a promoting role. The contribution from factor substitution is the least. Second, regarding change, from 1998 to 2003, the positive effect of technology progress was very prominent; however, technological efficiency played a small role and may have even impeded progress. From 2007 to 2010, technology progress had insufficient power; however, the role of efficiency improvement was obvious. The dynamic changes between technology progress and efficiency improvement result in a wide range of technological innovations and efficiency improvements in the entire industry. Third, factor substitution has a prominent positive contribution in light industrial sectors but hinders the improvement of energy productivity in heavy industrial sectors. The most efficient industry is telecommunications equipment, computer and other electronic equipment. This study concludes that China should raise the relative cost of energy consumption and optimize energy allocations and other production factors. [ABSTRACT FROM AUTHOR]

To guide the policy adjustment in the new normal economic mode, this study uses a range-adjusted measure and data envelopment analysis model to evaluate the provincial comprehensive efficiencies and explore the factors causing poor performance in China during 2001–2014. Empirical analysis draws the following conclusions. 1) The comprehensive efficiencies of most provinces present upward trends, which imply the positively contribution of previous efficiency-related policies. 2) Contrary to economic levels, the comprehensive efficiency of the central region is lower than that in the east but higher than that in the west. This result confirms the existence of extensive growth and implies that developed regions should be largely responsible for the low comprehensive efficiency of the country. 3) Except for a few outliers, the comprehensive efficiency level of a province is highly correlated to its climatic characteristics. This phenomenon offers new ideas for the central government to create differential efficiency-related policies. 4) For east provinces, labor input, energy input, gross domestic product (GDP) output, and CO 2 emissions are all important in improving the comprehensive efficiency. Efficiency-related policies should focus on technical progress. For west and central provinces, labor input and GDP output are the main contributors to inefficiency. The key policy direction of these provinces should be the improvement of human resource efficiency. [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]