This paper proposes an energy efficiency index based on the data envelopment analysis approach. The index is used to examine energy efficiency in China. Using 1997-2006 panel data for 29 provinces, we find that energy efficiency is negatively associated with the secondary industry share in GDP, the state-owned economic share in GDP and the government expenditure share in GDP, and is positively associated with the technical level and non-coal share in energy consumption. In addition, we find that there exists a large gap in energy efficiency among eastern, central and western regions. The eastern region has a significantly higher energy efficiency level than the central and western regions. We conclude that the different levels of industry structure, government intervention, energy structure, and the technology content in the three regions lead to the differences in energy efficiency. Copyright (c) 2009 The Authors Journal compilation (c) 2009 Institute of World Economics and Politics, Chinese Academy of Social Sciences.

Urban agglomerations are not only the core areas leading economic growth but also the fronts facing severe resource and environmental challenges. This paper aimed to increase our understanding of urban eco-efficiency and its influencing factors and thus provide the scientific basis for green development. We developed a model that incorporates super-efficiency, slacks-based-measure, and global-frontier technology to calculate the total-factor eco-efficiency (TFEE) and used a spatial panel Tobit model to take into account spatial spillover effects. An empirical study was conducted utilizing a prefecture-level dataset in the Yangtze River Delta Urban Agglomeration (YRDUA) from 2003 to 2016. The main findings reveal that significant spatial differences exist in TFEE in the YRDUA: high-TFEE cities were majorly located in the coastal areas, while low-TFEE cities were mostly situated inland. Overall, TFEE shows a trend of “decline first and then rise with fluctuation”; the disparity between inland and coastal regions has expanded. Further regression analysis suggests that industrial structure, environmental regulation, and innovation were positively related to TFEE, while foreign direct investment was not conducive to the growth in TFEE. The relationship between population intensity and urban eco-efficiency is an inverted U-shaped curve. Finally, several specific policy implications were raised based on the results.

Abstract:The rapid development of China's textile industry has led to consumption and pollution of large volumes of water. Therefore, the textile industry has been the focus of water conservation and waste reduction in China's 13th Five-Year Plan (2016-2020. The premise of sustainable development is to achieve decoupling of economic growth from water consumption and wastewater discharge. In this work, changes in the blue water footprint, grey water footprint, and the total water footprint of the textile industry from 2001 to 2014 were calculated. The relationship between water footprint and economic growth was then examined using the Tapio decoupling model. Furthermore, factors influencing water footprint were determined through logarithmic mean Divisia index (LMDI) method. Results show that the water footprint of China's textile industry has strongly decoupled for five years (2003, 2006, 2008, 2011, and 2013) and weakly decoupled for four years (2005, 2007, 2009, and 2010). A decoupling trend occurred during 2001-2014, but a steady stage of decoupling had not been achieved yet. Based on the decomposition analysis, the total water footprint mainly increased along with the production scale. On the contrary, technical level is the most important factor in inhibiting the water footprint. In addition, the effect of industrial structure adjustment is relatively weak.

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