Spatiotemporal Dynamics of Green Total-factor Water-use Efficiency and Its Influencing Factors in China

In this study,we developed an evaluation index system for green total-factor water-use efficiency(GTFWUE)which reflected both economic and green efficiencies of water resource utilization.Then we measured the GTFWUE of 30 provinces/municipalities/autonomous regions(hereafter provinces)in China(not including Tibet,Hong Kong,Macao,Taiwan as no data)from 2000 to 2018 using a minimum distance to the strong frontier model that contained an undesirable output.We further analyzed the regional differences and spatial correlations of GTFWUE using these values based on Global and Local Moran’s I statistics,and empirically determined the factors affecting GTFWUE using a spatial econometric model.The evaluation results revealed that the GTFWUE differed substantially between the regions.The provinces with high and low GTFWUE values were located in the coastal and inland areas of China,respectively.The eastern region had a significantly higher GTFWUE than the central and western regions.The GTFWUEs for all three regions(eastern,central,and western regions)decreased slowly from 2000 to 2011(except 2005),remained stable from 2012 to 2016,and rapidly increased in 2017 before decreasing again in 2018.We found significant spatial correlations between the provincial GTFWUEs.The GTFWUE for most provinces belonged to the high-high or low-low cluster region,revealing a significant spatial clustering effect of provincial GTFWUEs.We also found that China’s GTFWUE was highly promoted by economic growth,population size,opening-up level,and urbanization level,and was evidently hindered by water endowment,technological progress,and government influence.However,the water-use structure had little impact on GTFWUE.This study fully demonstrated that the water use mode would be improved,and water resources needed to be used more efficiently and green in China.Moreover,based on the findings of this study,several policy recommendations were proposed from the aspects of cross-regional cooperation,economy,society,and institution.

Total-factor energy efficiency of ten major global energy-consuming countries

Improving energy efficiency is regarded as a key path to tackling global warming and achieving the Sustainable Development Goals(SDGs).In 2020,the energy consumption of the world's ten major energy-consuming countries accounted for 66.8%of the global total.This paper applied data envelopment analysis(DEA)to calculate these ten major energyconsuming countries'total-factor energy efficiency(TFEE)at national and sectoral levels from 2001-2020,and explored the infuencing factors of total-factor energy efficiency with the Tobit regression model.The results showed significant difference in the ten countries'energy efficiency.The United States and Germany topped the list for total-factor energy efficiency,while China and India were at the bottom.Meanwhile,the energy efficiency of the industrial subsector has increased significantly over the past two decades,while that of the other subsectors has been relatively fat.The industrial structure upgrading,per capita GDP,energy consumption structure,and foreign direct investment had significant impacts on energy efficiency with national heterogeneity.Energy consumption structure and GDP per capita were determinative factors of energy efficiency.

Urban Energy Efficiency from Cities in China and Policy Implications

Using 1995-2006 panel data for 210 cities,this article firstly measures total factor energy efficiency for Chinese cities with constant return to scale data envelopment analysis model,and then describes the features of regional differences.The results show that the changing trend displays four "reversed U" shapes.A turning point of widening gap occurred among cities in 2000 and there was a certain convergence among four regions.Urban energy efficiency level is unstable.Cities with low efficiency and high energy input will be the focus of energy-saving and reducing emissions.According to Tobit model,resource endowment,temperature,industrial structure,technology level and variety of policy factors should be treated differently in different situations.

Study on the Refineries＇ Energy Efficiency Based on SFA- Model

The study on the enterprise＇s energy efficiency is one of the most important fields of energy efficiency research. Most studies used DEA and aggregate data to estimate the energy efficiency of enterprises. In this study, based on Cobb-Douglas production function, we make a SFA model which takes the energy input and CO2 emission into account. By using the SFA model, we calculate the refineries＇ total-factor energy efficiency with Sinopec refineries＇ micro-data from 2004 to 2009. Meanwhile, we do empirical study on the factors which influence the energy efficiency. In the last, we put forward some advices so as to improve energy efficiency.

基于生态足迹的湖南省洞庭湖生态经济区全要素生态效率研究

以2005—2015年湖南省洞庭湖生态经济区3市25个县(市、区)为研究对象,运用"省公顷"模型计算生态足迹,作为生态投入要素,与资本、劳动力共同纳入全要素生态效率框架,运用包含非期望产出的超效率SBM模型,测算区域全要素生态效率;结合Malmquist指数及投入产出松弛率,探析效率变化成因及改进方向。研究结果表明:①区域全要素生态效率为弱有效水平,且空间差异显著;经历先恶化后改善、最后趋稳的过程,市级特征趋同,县级差异较大,仅8个保持稳定或较高水平。②2012—2015年全区的效率提升速度加快,技术效率变化、技术进步变化交错推动效率提升,2014—2015年实现同步推动作用。③劳动力投入和生态投入冗余明显,资本投入冗余不显著,期望产出不足和非期望产出过量的情况不显著。因此,政策上建议加强区域资源整合和环境规制,调整产业及能源结构。

Regional characteristics of industrial energy efficiency in China： application of stochastic frontier analysis method

This paper analyzed regional industrial energy efficiency in China with Total-Factor Energy Efficiency （TFEE）. The East region has the best energy efficiency and the Central and the West regions stand as the second and the third respectively. However, it is found that industrial energy efficiency of all regions increased from 1998 to 2006. This result is consistent with level of economic development of every region. The industries of all provinces in China are not yet at the frontier efficiency position, therefore, to the frontier as target, their technol- ogy levels and production processes should be adjusted accordingly. Compared with the conventional energy efficiency, the inverse of energy intensity, which is defined as the ratio of actual output to energy input, is regarded as Single-Factor Energy Efficiency （SFEE） index. Although TFEE ranks are not changed for each region, they are different for each province. The comparative result also shows that the substitution among inputs （labor, capital stock, and energy） to produce the output is significant. The SFEE scores could be over-estimated if energy is taken as the single input in the production. Finally, we identified determining factors affecting industrial energy efficiency using Tobit model. The results indicate that an increase of per capita Gross Domestic Product （GDP）, the percentage of output value of industry invested by Hong Kong, Macao, Taiwan and abroad, energy price and investment of scientific and technological activities for industry could be possible contributors and drivers to the industrial energy efficiency. However, increasing of heavy industry will lead to worse industrial energy efficiency.