This paper adopts the non-expected output-super-efficiency SBM(Slacks-Based Model)model and principal component analysis to calculate the green economy efficiency and the digital economy level of 27 prefecture-level c...This paper adopts the non-expected output-super-efficiency SBM(Slacks-Based Model)model and principal component analysis to calculate the green economy efficiency and the digital economy level of 27 prefecture-level cities in China’s Yangtze River Delta urban agglomeration between 2011 and 2019,respectively,and examines the impact of the digital economy on the green economy efficiency by using benchmark regression and mechanism analysis.The findings show that,first,the digital economy has a significant contribution to the green economic efficiency of cities,and this conclusion still holds after robustness tests such as replacing explanatory and interpreted variables and introducing province-fixed effects.Second,through the mechanism test,it is found that the digital economy can indirectly promote urban green economic efficiency through the positive mechanism effect of promoting industrial structure upgrading.展开更多
Cities are the main material processors asso- ciated with industrialization. The development of urban production based on fossil fuels is the major contributor to the rise of greenhouse gas density, and to global warm...Cities are the main material processors asso- ciated with industrialization. The development of urban production based on fossil fuels is the major contributor to the rise of greenhouse gas density, and to global warming. The concept of urban industrial structure optimization is considered to be a solution to urban sustainable develop- ment and global climate issues. Enforcing energy con- servation and reducing carbon emissions are playing key roles in addressing these issues. As such, quantitative accounting and the evaluation of energy consumption and corresponding carbon emissions, which are by-products of urban production, are critical, in order to discover potential opportunities to save energy and to reduce emissions. Conventional evaluation indicators, such as "energy consumption per unit output value" and "emissions per unit output value", are concerned with immediate consumptions and emissions; while the indirect consump- tions and emissions that occur throughout the supply chain are ignored. This does not support the optimization of the overall urban industrial system. To present a systematic evaluation framework for cities, this study constructs new evaluation indicators, based on the concepts of "embodied energy" and "embodied carbon emissions", which take both the immediate and indirect effects of energy consumption and emissions into account. Taking Beijing as a case, conventional evaluation indicators are compared with the newly constructed ones. Results show that the energy consumption and emissions of urban industries are represented better by the new indicators than by conventional indicators, and provide useful information for urban industrial structure optimization.展开更多
基金Jiangxi Provincial Social Science Foundation Project“Research on the Impact of Digital Economy Development on Employment Structure and Quality in Jiangxi Province and Countermeasures”(Grant No.23YJ55D)Jiangxi Province University Humanities and Social Sciences Research Project“Research on the Dynamic Mechanism and Countermeasures of Industrial Digitalization to Promote the High-Quality Development of Jiangxi’s Manufacturing Industry”(Grant No.JJ22218).
文摘This paper adopts the non-expected output-super-efficiency SBM(Slacks-Based Model)model and principal component analysis to calculate the green economy efficiency and the digital economy level of 27 prefecture-level cities in China’s Yangtze River Delta urban agglomeration between 2011 and 2019,respectively,and examines the impact of the digital economy on the green economy efficiency by using benchmark regression and mechanism analysis.The findings show that,first,the digital economy has a significant contribution to the green economic efficiency of cities,and this conclusion still holds after robustness tests such as replacing explanatory and interpreted variables and introducing province-fixed effects.Second,through the mechanism test,it is found that the digital economy can indirectly promote urban green economic efficiency through the positive mechanism effect of promoting industrial structure upgrading.
文摘Cities are the main material processors asso- ciated with industrialization. The development of urban production based on fossil fuels is the major contributor to the rise of greenhouse gas density, and to global warming. The concept of urban industrial structure optimization is considered to be a solution to urban sustainable develop- ment and global climate issues. Enforcing energy con- servation and reducing carbon emissions are playing key roles in addressing these issues. As such, quantitative accounting and the evaluation of energy consumption and corresponding carbon emissions, which are by-products of urban production, are critical, in order to discover potential opportunities to save energy and to reduce emissions. Conventional evaluation indicators, such as "energy consumption per unit output value" and "emissions per unit output value", are concerned with immediate consumptions and emissions; while the indirect consump- tions and emissions that occur throughout the supply chain are ignored. This does not support the optimization of the overall urban industrial system. To present a systematic evaluation framework for cities, this study constructs new evaluation indicators, based on the concepts of "embodied energy" and "embodied carbon emissions", which take both the immediate and indirect effects of energy consumption and emissions into account. Taking Beijing as a case, conventional evaluation indicators are compared with the newly constructed ones. Results show that the energy consumption and emissions of urban industries are represented better by the new indicators than by conventional indicators, and provide useful information for urban industrial structure optimization.