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

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]

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

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

Within the urban water cycle, energy issues have been mostly studied for wastewater treatment plants, ignoring that drinking water treatment plants (DWTPs) also consume a significant quantity of energy. Knowing the real energy consumption of DWTPs is the starting point for any energy-saving initiative. This paper benchmarks the energy efficiency measures of a sample of real DWTPs using the data envelopment analysis methodology. Subsequently, whether these facilities are affected by economies of scale is investigated. This issue is essential for planning new DWTPs that minimize energy consumption. In the second stage of analysis, some structural and managerial variables affecting energy efficiency are explored. The results showed that most of the DWTPs analysed have a suitable size but can greatly improve in terms of saving energy. It was found that the age of the plants and the water company operating them significantly affects its energy efficiency. The approach applied in this paper is of great interest for water regulators and company managers since it enables them to learn from the best practices for reducing energy consumption in DWTPs and contributes to improving the sustainability and efficiency of the urban water cycle. [ABSTRACT FROM AUTHOR]

With the advent of the era of big data, large-dimensional spatial panel data is gradually used to do empirical research in the macroeconomic field. This paper adopts a Data Envelopment Analysis(DEA) approach to calculate regional energy efficiency based on the perspective of total-factor energy efficiency using statistical data of 30 administrative regions in China. On basis of spatial effects, the paper assesses the convergence of regional energy efficiency in China using large-dimensional spatial panel data model. Our results indicate that (1) There is significant spatial autocorrelation and clear spatial effects in China's regional energy efficiency. Thus, spatial effects should not be ignored when assessing the convergence of regional energy efficiency in China; (2) During the period from 2000 to 2014 China's regional energy efficiency not only exhibits absolute β -convergence but also exhibits conditional β -convergence, the convergence rate is higher than the rate of absolute convergence after controlling for the initial conditions of the level of economic development, foreign direct investment and government influence; (3) The convergence rate of China's regional energy efficiency from 2004 to 2014 is higher than the convergence rate for the period of 2000–2004, which indicates that industry transfer has contributed to improvement in the convergence of regional energy efficiency in China since 2004. [ABSTRACT FROM AUTHOR]

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

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

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

Alleviating energy poverty is a significant issue in many countries, for which effective energy poverty measurement is the prerequisite. This paper seeks to make a comprehensive and systematic assessment of the energy poverty performance of 30 provinces in China for 11-year period (2007–2017) by emphasizing their spatial-temporal changes. The paper firstly constructed energy poverty measurement conceptual framework based on overviewing previous studies. Secondly, a technique based on the integrated fuzzy DEMATEL (Decision-Making Trial and Evaluation Laboratory) and Data Envelopment Analysis - Assurance Region (DEA-AR) for performance assessment was proposed. Then, the energy poverty performance was assessed, and clustering analysis was conducted to investigate the spatial-temporal changes of energy poverty in China. Finally, sensitivity analysis was applied for verifying the robustness of the proposed method. It is found that the energy poverty condition in China tends to be similar among most regions, but the gap between the best and worst performers gets wider. In addition, provinces with better economic conditions, have higher performance and are classified into the progressive group for alleviating energy poverty. On the contrary, provinces, whose economy lags, have regressed the most and are also the worst performers in reducing energy poverty. Thus, policies to improve household energy cleanability in high-income provinces and increase energy affordability in low-income regions would help reduce energy poverty in China. • A comprehensive energy poverty assessment framework was proposed. • An integrated Fuzzy DEMATEL/DEA-AR was proposed to evaluate the performance. • The comparison between the proposed Fuzzy DEMATEL/DEA-AR model and conventional DEA was conducted. • Spatial and temporal analysis of energy poverty performance in China was conducted. [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]

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

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

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]

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

There is a belief that sustainability-certified coffee production helps increase economic benefits to farmers and reduces negative environmental impacts. However, the international empirical evidence is not conclusive. Also, there is a lack of empirical evidence for Vietnam - the world's second-largest coffee producing country. This paper provides the first empirical examination of the differences in eco-efficiency between conventional and sustainability-certified coffee-growing farms in Vietnam. Data of 726 farms in Vietnam over three crop years from 2012/13 to 2014/15 are analysed. Environmental pressures measured by the level of consumption of nitrogen, phosphorus, irrigation water, pesticides, herbicides, fungicides, and land are investigated in relation to the value-added of coffee production. Empirical results show that in each crop year, coffee farms could reduce environmental pressures by more than 50% while holding the value-added of outputs constant. On average, sustainability-certified farms are found to be more eco-efficient than conventional farms, but efficiency differences appear to converge over time. This convergence may be due to positive externalities of certification, less compliance to certification standards or the combination of these effects. Higher eco-efficiency levels are also correlated with farms located in higher elevation locations, having wind-break trees, and using drip or spray irrigation systems. These should be taken into account as policy options to sustain and improve the positive effects of certification in regard to both economic and environmental aspects, rather than rapid expansion of certified production. Further, one could incorporate ecological and environmental dimensions and welfare into eco-efficiency models and a stochastic production environment may be a useful modeling approach. [ABSTRACT FROM AUTHOR]

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

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

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

The article reassesses a recent case study on six bioethanol production processes in China, to develop a methodology that discriminates the most energy-efficient process. The original research explored the joint method of Life Cycle Assessment and Data Envelopment Analysis (LCA + DEA). However, DEA is sensitive to problems with a disproportionate number of variables to alternatives and LCA usually requires analysis with several inputs and outputs. The original research indicated two sources as being tied for energy efficiency, which is not relevant to decision-makers aiming to invest in the most efficient process. The method proposed here uses a combinatorial and probabilistic approach to improve discrimination and create scenarios for the reduction of inputs and the increase of outputs, even for efficient processes. Results show that the proposed method has better discriminatory power than the referenced paper. [ABSTRACT FROM AUTHOR]

This paper aims to extract determinants of efficiency in an industrial-scale biogas project treating food waste in a major Asian megacity. The research involved a 4-step methodology combining statistical and operations research tools. The findings were as follows: (1) the project suffered from high variability and low performance across several important process parameters; (2) Principal component analysis (PCA) showed that approximately 40.51% of variability in the data could be explained by two principal components; (3) Data envelopment analysis (DEA) revealed that 47% of the decision making units (DMUs) were inefficient, and 73% of DMUs exhibited increasing returns to scale; (4) regression results showed that adjusted R 2 values ranged between 0.913 and 0.996 for the models of DEA efficiency, and significant explanatory variables were extracted based on type III sum of squares. This research is significant in the biowaste-to-energy literature in that it provides a robust method for identifying process bottlenecks in industrial-scale anaerobic digestion of food waste. [ABSTRACT FROM AUTHOR]

Because industrial sectors in China are energy-intensive, improving energy efficiency and promoting energy savings in these sectors are of great importance for the further sustainable development of China’s economy. Using a proposed meta-frontier data envelopment analysis (DEA), this paper analyzes the total-factor energy efficiency and energy savings potential in China’s provincial industrial sectors for the years 2000–2014 from three perspectives, namely, the technology gap, management, and scale. The results indicate that (1) driven by technological progress, energy efficiency during the sample period was greatly improved, though the expansion of technology gaps between regions and the decline in scale efficiency represented two significant handicaps; (2) the current energy efficiency in China’s industrial sectors remains extremely low, indicating that there is considerable potential for energy efficiency improvement and energy savings; and (3) the sources of energy inefficiency and energy savings potential in China’s industrial sectors exhibit spatially distinct characteristics. Accordingly, it is determined that most of China’s eastern provinces should focus on improving scale and management efficiency, whereas most of its central and western provinces should concentrate on three factors: narrowing the regional technology gap, improving management efficiency, and optimizing the industrial scale. [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]

This paper examines the potential of Mergers & Acquisitions (M&As) as a novel approach to energy use optimization. The investigations are carried out through inverse data envelopment analysis (DEA). Contrary to traditional DEA approaches that restrict the energy savings to individual production units, the proposed methodology looks at the issue from the perspective of possible mergers among these units. The new methodology, which deploys over two stages, is applied to pairwise consolidations among 51 tomato greenhouse (GH) farms from Biskra, Algeria. An inverse DEA model is implemented in the first stage to discern all possibly productive post-merger GH farms, i.e., those mergers that are likely to generate energy gains. In the second stage, a new procedure is devised to find the best matchings among partners of potential mergers and derive the best merger plan out of the whole sample of GH farms. The results of the inverse DEA application revealed that potential gains per energy input can be substantial, reaching proportions as high as 80.78% and above. The derived optimal merger plan exhibited a post-merger energy saving index of 70.23%, that is, 33 times the index of the traditional DEA approach. Practically, these findings leave no doubt that mergers can contribute significantly to energy savings, enough to support new policies for promoting mergers as strategic options towards optimal energy consumption. The application scope of the proposed methodology can be duly extended to other sectors where energy optimization might be a critical issue. • First study on the contribution of Merger & Acquisition to optimal energy consumption. • Application of Inverse DEA to quantify such contribution in agricultural production. • New DEA approach to select best merging partners based on post-merger's outcomes. • A sample of 51 tomato GH farms from Biskra, Algeria, is adopted as a case study. • Post-merger energy saving index is 70.23%, i.e., 33 times the pre-merger index. [ABSTRACT FROM AUTHOR]

In the fight against global climate change, improving energy efficiency and reducing related emissions are critical to mitigating sustainability issues such as global warming. In this regard, technological progress is known to improve energy efficiency and reduce related emissions. However, previous sustainability studies on Africa have paid less attention to energy and emission efficiency convergence among countries, along with the contribution of technological progress to the continent's sustainability. Accordingly, this paper develops a novel framework that assesses energy and emission efficiency convergence, gauges technology inequality, identifies the barriers to technology diffusion, and explores the factors influencing energy and emission efficiency convergence in Africa. The proposed framework incorporates Data Envelopment Analysis (DEA), several economic convergence concepts, a K-means clustering model, the concept of efficiency Gini coefficient, and a group-based inequality decomposition method. We empirically covered 39 African countries from 2006 to 2018. The results show that African countries are, on average, 71.1% energy efficient and 65.3% CO 2 emission efficient, leaving more room for energy saving and emissions reduction. Second, there is strong evidence of convergence in energy and emission efficiencies in Africa. Third, despite the observed convergence process, there is significant heterogeneity between two research groups (i.e., low-and high-efficiency groups of countries) in terms of energy and emission efficiencies, with persistent technology inequality suggesting a lower contribution of environmental technological advancements to the continent's sustainability. Furthermore, cross-group inequality is the primary barrier to technology diffusion. Fourth, regional cooperation accelerates the convergence process in the two efficiency indicators. While economic development, industrial structure, and renewable energy consumption (REC) tend to favor energy and emission efficiency growth, rising foreign direct investment (FDI) and energy prices have a negative impact. Meanwhile, the effects of energy prices, FDI, and REC on emission efficiency are insignificant. Based on our findings, we discussed policy implications to promote energy and emission efficiency convergence and narrow environmental technology gaps. • Convergence in Total-factor energy and emission efficiency in Africa is explored. • Technology inequality across African countries is measured. • There is strong evidence of convergence in TFEE and TFCEE in Africa. • A persistent technology inequality is found among African countries. • Cross-group efficiency inequality is the main barrier to technology diffusion. [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]

There is great interest in measuring energy efficiency of China's industry using data envelopment analysis (DEA) approaches. This paper proposes a new exponential transformation of undesirable outputs into desirable outputs as part of a DEA approach for calculating environmental efficiency by using all kinds of classic models. Different from the existing transformations, the exponential model should provide a more flexible way to deal with undesirable outputs, allowing the user to adjust the slope and distribution of the transformation function by setting a parameter. A strategy for maximizing the standard variance among the undesirable outputs after transformation is employed in this paper to determine the optimal parameter value for the exponential model. This paper empirically examines the energy performance of sectors in Chinese industry from 2010 to 2014 using the proposed DEA model and the results show that most sectors in Chinese industry have not performed well, especially the sectors concerned with energy extraction. Based on the results of the efficiency evaluation, this paper calculates how can each sector achieve efficiency improvement through energy conservation and emission reduction, and propose specific improvement target values for each sector accordingly. Image 106227 • Alternative approach is constructed to deal with undesirable output in DEA models. • A new transformation is provided to reflect the exponential relationship between desirable and undesirable outputs. • The slope and distribution of data under the new transformation function are controllable. • The new presented approach is applied to measure the performance of 38 industries in China. [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]

Environmental protection is one of the main concerns of the international community. Countries show meticulous attention to environmental issues by considering eco-efficiency. The eco-efficiency evaluation of countries is a complex and multidisciplinary topic. Data envelopment analysis has widely been applied to measure the eco-efficiency of organization for economic co-operation and development countries. In previous studies, energy inputs have not been contemplated. The subject becomes more important when there are non-discretionary factors. In this paper, the eco-efficiency of countries in the organization for economic co-operation and development is assessed given energy inputs, undesirable outputs, and non-discretionary factors. To this end, slack-based and range-adjusted measures are extended. Also, new environmental indices are introduced. The relationship between consumption of energy inputs and production of undesirable outputs can be addressed by two new environmental indices. Spearman correlation test indicates a direct and positive relationship between potential of energy saving and potential of undesirable output abatement. France, Germany, Luxemburg, Norway, Sweden, and the United Kingdom are recognized as eco-efficient countries. South Korea and Italy have the highest and lowest potential for energy saving, respectively. Poland and Iceland are countries with the highest and lowest potential for undesirable output abatement, respectively. It can be concluded that countries producing high undesirable outputs may not operate eco-efficiently and thus have an extreme potential to save the optimum energy. Moreover, countries consuming low energy may operate eco-efficiently and have a low potential to reduce undesirable outputs. [ABSTRACT FROM AUTHOR]

Global energy interconnection (GEI) is vital for countries to achieve clean energy production and carbon neutrality. To improve the construction efficiency of GEI projects, capital allocation efficiency and its driving factors must be analyzed. Therefore, this study uses a three-stage data envelopment analysis (DEA) model to evaluate the capital allocation efficiency of 161 countries. The results demonstrate that there is spatial heterogeneity in global capital allocation efficiency and that most countries have low levels of efficiency. The average pure efficiency is 0.364 and the average scale efficiency is only 0.176. To determine the fastest possible method of improving efficiency in different countries, this paper combined an adjusted three-stage DEA model with a geographically weighted regression (GWR) model to quantify the driving factors of capital allocation efficiency. The level of economic development is the main driving factor, whereas government support has the least influence on capital allocation efficiency. Furthermore, the coefficients of the influence of each driving factor vary significantly across different regions. In conclusion, this study quantitatively estimates the efficiency of capital allocation for GEI projects and explores the key driving factors of efficiency from the perspective of heterogeneity, and makes targeted recommendations for countries to improve efficiency. • Environmental variables generally masks the inefficiency of GEI capital allocation. • The average pure efficiency is 0.364 and the average scale efficiency is only 0.176. • Capital allocation efficiency are spatially correlated globally. • The economic development level is the main driving factor of capital allocation. [ABSTRACT FROM AUTHOR]

Livestock production systems, such as dairy farming, are one of the most important contributors to resource use and if not managed well, it can be environmentally detrimental. Iranian livestock sector faces a variety of the challenges such as high costs of energy and environmental legislations as well as an increasing demand for dairy products to respond the growing population. This paper aims to contribute to the discussion on technical efficiency as a key indicator of energy use within dairy farming systems. A Window Data Envelopment Analysis (W-DEA) with energy use as inputs and milk production as output was modelled with data from 25 provinces during the last 22 years (1994–2016) in Iran. In addition, the Slack-Based Model (SBM) was used to compare the radial DEA model with non-radial SBM, both in a dynamic environment (window analysis). The average efficiency score of Iranian dairy farming production system was estimated at approximately 0.85. Through the years, three provinces including Zanjan, Ardabil and Hormozgan had the highest technical efficiencies. Window analysis represented that provinces are distinctive in terms of their technical efficiencies and energy consumption over the years. Applying the SBM model improved the accuracy of the estimated efficiency scores compared to the radial (DEA) model. Further analysis represented a significant difference between the technical efficiency of different milk production levels. Provinces that produced higher volumes of milk had lower technical efficiencies. Based on the results it can be concluded that there is a substantial space for upgrading the technical efficiency of dairy farming in Iran by improving resource use efficiency which leads to an optimized energy consumption. It is recommended to reform Iranian livestock farming policies by applying mechanized systems, optimal strategies for water, electricity and fossil fuel consumption, use of renewable energy and better feed management while enhancing milk productivity and technical efficiency. In this respect, it is suggested that policy makers consider different indicators such as energy use efficiency and environmental impacts when allocating subsidies and resources to different provinces and farms. • Iranian dairy farming was assessed for the technical efficiency over 22 years. • Efficiency of 25 provinces were identified using window data envelopment analysis. • The average efficiency score of dairy farming was approximately 0.85. • We propose improvement of resource management in order to minimize energy use. • Milk production was found as an effective factor on efficiency of provinces. [ABSTRACT FROM AUTHOR]

Measures of societal welfare should account for the multiple goals that governments face in improving environmental goods, economic goods and social goods such as those tracked by the Organization of Economic Cooperation and Development. To account for these multiple goals this paper constructs a multi-product performance measure using Data Envelopment Analysis. The multi-product technology studied employs a network structure where inputs produce intermediate products and final outputs that consist of desirable goods and undesirable by-products such as greenhouse gas emissions that occur when fossil fuels are used as the energy source. The network technology incorporates a capacity utilization constraint and directly links energy and polluting emissions via a costly disposability assumption. A directional distance function is used to represent the technology and measures a producer's inefficiency. An empirical example finds that producers could reduce energy use and greenhouse gas emissions by 15% via increased technical efficiency and by operating at capacity. • A capacity utilization constraint is nested within a two-stage network model. • Joint by-production of undesirable outputs from energy use is accounted for. • Aggregate well-being is derived using the Benefit of the Doubt method. • Inefficiency is measured relative to a best-practice network frontier. • An example of the model uses data on 47 Japanese prefectures during 2001–2014. [ABSTRACT FROM AUTHOR]

China's transportation industry, with its typical characteristics of "high energy consumption and high emissions", is a key point for China's CO 2 emission reduction. An investigation of the driving factors of China's transport energy-related CO 2 emissions has great importance for clarifying the "gains" and "losses" as well as the future focuses of CO 2 emissions emission reduction in this sector. Existing studies on the driving factors of transport CO 2 emissions in China fail to incorporate effects of technology and efficiency into decomposition analysis framework. Based on the provincial panel data of China's transportation industry for the period of 2004–2016, this paper uses a comprehensive decomposition framework, which combines the logarithmic mean Divisia index method (LMDI) and production-theoretical decomposition analysis (PDA), to decompose China's transport CO 2 emissions into nine components: emission factor effect, energy structure effect, scale effect, regional structure effect, energy-saving technology effect, production technology effect, energy efficiency effect, production efficiency effect, and potential energy intensity effect. The main results indicate that: (1) during the sample period, China's transport industry witnessed 498.998 million tons (Mt) CO 2 emissions growth. Scale effect was the largest contributor, followed by production technology, energy-saving technology, and energy structure; (2) the improvement of energy efficiency and production efficiency, the decline of electricity's emission factor, and regional structure adjustment played active roles in the CO 2 emission reduction of transportation industry; (3) the performance of various driving factors varies greatly in different provinces. Local governments should establish and implement policies tailored to their characteristics. • A production-theoretical approach was used for China's transport CO 2 emissions. • It can decompose CO 2 emissions from technology and efficiency perspectives. • Results show that during 2004–2016, China's transport CO 2 emissions increased by 498.998 Mt. • Output effect was the largest contributor to this remarkable increase of transport CO 2 emissions. • Technology and efficiency factors cumulatively reduced 242.1 Mt transport CO 2 emissions. [ABSTRACT FROM AUTHOR]

In order to keep pace with the global trend of controlled and proper energy usage, the Korean government has enforced its energy-related regulations. However, the firms under these regulations are of the view that this approach may threaten their financial performance. In response to the growing interest of industry in Korea, this paper investigates whether energy efficiency has a positive relationship with firms' financial performance. For the purpose of accommodating various factors for measuring energy efficiency, we extended an existing two-stage network DEA (Data Envelopment Analysis) model that distinguished pure-energy efficiency and economy efficiency by developing a multi-period model with the aim to identify any change in efficiency during each period. An empirical analysis on Korean firms reveals interesting findings; First, while it is true that energy efficiency has changed over time, there is a difference in the magnitude of changes by industry. Firms in more energy-intensive industry have experienced more changes in their energy efficiency performance. Second, it is impossible to improve both categories of efficiency (i.e., pure-energy and economic efficiencies) at the same time. Thus, a firm should understand its current position in order to determine the extent and direction of efficiency improvements. Third, the energy efficiency has been found to have a significant relationship with financial performance. However, firms whose pure-energy efficiency was found to be relatively high did not always achieve better financial performance. [ABSTRACT FROM AUTHOR]

Energy efficiency analysis and production efficiency improvement are effective ways to promote the sustainable and stable development of ethylene industries. Therefore, this paper proposes novel data envelopment analysis cross-model integrated interpretative structural model and analytic hierarchy process for energy efficiency evaluation and optimization modeling. The interpretative structural model integrating analytic hierarchy process is used to fuse the multidimensional production data to reduce the dimension and obtain comprehensive evaluation indicators, thus reducing the multidimensional indicators influence on the data envelopment analysis model. Then these several fusion results are used as the input indicators of the improved data envelopment analysis cross-model. And the ethylene yield is used as the output index to build the energy efficiency evaluation and optimization model of ethylene production plants. Finally, according to cross-efficiency values and slack variables, the energy efficiency levels of ethylene plants in each month and year are analyzed. The experimental results show that the distinction of the efficiency values is more obvious and accurate. Moreover, the proposed method can optimize production efficiency and improve the proportion of the efficiency value greater than 0.8 increased by 66.6% of ineffective production plants. Image 10547 • A novel DEACM integrated ISM-AHP method is proposed. • Energy efficiency evaluation and optimization model is obtained. • The distinction of the efficiency values is more obvious and accurate. • The proportion of ineffective production plants is increased by 66.6%. [ABSTRACT FROM AUTHOR]

Carbon emissions and energy consumption have serious impacts on humans and ecosystems. This paper investigates the effects of industrial reconstructuring on energy conservation and emission reduction. A multi-objective optimization model was established. The model classifies industrial sectors into four groups according to their carbon emission levels and contributions to economic growth, then non dominated sorting genetic algorithm is applied to solve the model and the Pareto frontier is obtained. The best solution is selected from the Pareto frontier by using a super data envelopment analysis model which measures the degree of coordination between economy and environment. The model is used to analyze the effects of industrial reconstructuring in Beijing during 2018–2020. The results show that industrial reconstructuring enabled economic growth to reach the government's planned rate while the carbon intensity and energy intensity surpassed the goal of the 13th Five-Year Plan. This case can provide decision-making basis for the sustainable development of ecology and economy in other regions. Fig. 6. Pareto front in Beijing region in 2020 The optimal Pareto solution for carbon emissions, energy consumptions and GDP targets after industrial structure reconstructuring. Image 1 • Classifying industrial sectors into four groups according to carbon emission levels and economic growth contributions. • A multi-objective optimization model was established and solved by the genetic algorithm. • A super DEA model evaluates the final plan which reflects the coordination of the economy and environment. • The analysis of each group can help achieving maximum benefits of economic and environment. [ABSTRACT FROM AUTHOR]

To evaluate the energy-saving and emission reduction potential of China's tobacco industry, this paper introduces RAM-DEA models to evaluate the energy and CO 2 emission efficiencies of 18 cigarette companies in China, compared with four of the leading multinational cigarette companies from 2009 to 2016. The results show the following: (1) The average energy and environmental efficiency of Chinese cigarette enterprises is only 0.373, the gap of the comprehensive efficiency between Chinese cigarette manufactures and the technological frontier of the world is big, but it is gradually shrinking. (2) The energy-saving and CO 2 emission reduction potential of Chinese cigarette companies are 57.2% and 68.1%, respectively, and the annual average comprehensive energy-saving and emission reduction quantities are 3.466 million MWH and 1.744 million tons. The energy-saving and emission reduction potential based on a scale efficiency improvement is relatively large. (3) The CO 2 emission efficiency gap of Chinese cigarette companies at domestic and foreign production levels is 25.5%, and it is larger than the energy efficiency gap, 14.1%, due to the high proportion of electric energy consumption. (4) Scale efficiency improvements and energy structure optimization are the main ways to save energy and reduce CO 2 emissions in the future. • New RAM-DEA models for evaluating the energy and emission efficiency are proposed. • Performance between tobacco companies at home and abroad are compared. • Regional differences in the performance of tobacco companies are analyzed. • The influence of production scale on efficiency performance are discussed. • Performance improving policies and strategies for different regions are proposed. [ABSTRACT FROM AUTHOR]

Environmental regulation is of great concern to international academics and policy makers. Previous studies often regarded environmental regulation as an influencing factor when calculating eco-efficiency or energy efficiency. More attention should be given to the efficiency of environmental regulation, especially in industrial field. According to exiting studies, industrial environmental regulation efficiency (IERE) may be influenced by external environmental factors, to explore the influence and obtain an actual efficiency, this paper calculated IERE in China by using the panel data of 30 regions from 2011 to 2015 (the 12th Five-Year Plan period) through the three-stage data envelopment analysis approach. Firstly, the Undesirable-slacks-based model method was used to calculate the original IERE. Secondly, the economic base, industrial structure, urbanization level, and education level were taken as external environmental variables, and the effect of external environmental factors and stochastic disturbance were excluded. Thirdly, the actual IERE can be calculated after adjusting input values. The results showed that China's IERE was underestimated due to the impact from external environmental factors. Moreover, the IERE was more influenced by pure technical efficiency than scale efficiency. The IERE of each region varied, thus, targeted policies should be formulated according to local conditions. This research provided a new analytical method for calculating environmental regulation efficiency. The method can be applied either in other countries or in other sectors. • The environmental regulation efficiency in the industrial field of China was evaluated. • The three-stage DEA method was utilized, the role of environmental variables could be considered. • Industrial environmental regulation efficiency was more influenced by pure technical efficiency. • Three kinds of efficiencies were all underestimated due to the influence of external environmental factors. [ABSTRACT FROM AUTHOR]

Most European countries have committed to ambitious emissions reduction goals. Energy generation in particular is responsible for more than 30% of global emissions, where significant focus has been placed on renewable energy generation, including biomass. On the one hand, there are countries, like the UK, where the biomass stock is insufficient to meet the demand; on the other hand, there are countries, like Brazil, where the stock significantly exceeds the demand. To promote a natural symbiosis, it is necessary to take on the challenge of transporting biomass through long distances in an environmentally and economically efficient manner. This paper aims to assess the efficiency of alternative pathways of international biomass supply-chains. The alternatives involve different biomass origin regions, transportation modes, export ports and processing technologies, including torrefaction. Data Envelopment Analysis (DEA) has been used for the first time to assess the efficiency of the alternative biomass supply chain pathways in a Latin American context, considering multiple-criteria relating to economic and environmental performance simultaneously, such as the biomass delivered cost, the environmental impact and the fossil energy consumption. Additionally, a sensitivity analysis was performed to analyse the robustness of the results under uncertainty in parameter values. The DEA approach presented can assist the process of planning biomass sourcing and improve decision-making under multiple decision criteria. The results can support medium- and long-term strategic decisions for decision- and policy-makers. • Efficiency assessment of biomass supply chain pathways between Brazil and the UK. • 56 alternative pathways considered with different supply chain structure. • Data Envelopment Analysis used for multi-criteria efficiency assessment. • Results can support decision and policy making. • Trade-offs between different criteria identified. [ABSTRACT FROM AUTHOR]