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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]

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]

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]

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]

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]

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

The 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]

Abstract: The vegetable oil industry is considered as one of the major industries contributing to energy consumption in Iran. Despite the increasing need to improve energy efficiency in this industry, previous studies show that energy-efficient technologies have not been applied in most plants and other energy efficiency measures have not yet been adopted. Accordingly, this paper addresses the implementation of energy efficiency measures in the vegetable oil industry. Using a data envelopment analysis (DEA), the relative efficiency of plants in the vegetable oil industry is compared at different stages of the implementation of energy efficiency measures. The most effective indices for improving energy efficiency were identified and a model was designed to implement energy efficiency measures in industrial plants. In the model, the implementation process consisted of 5 stages, each of which had its own inputs and outputs. A multi-stage DEA model was employed to evaluate the performance of vegetable oil plants at each individual stage as well as the aggregate efficiency of each plant. After estimating the total efficiency of all plants and evaluating performance at the individual stages for each plant, the plants with ideal conditions for implementing energy efficiency measures were identified. These plants can serve as a benchmark for other plants. Estimation of the average efficiency across plants at each stage indicated that the plants suffered from poor performance in the first and fourth stages. Therefore, the results of this study indicate a need to develop new strategies for improving energy efficiency at these two stages. [Copyright &y& Elsevier]

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]