Spatial-temporal Divergence Characteristics and Driving Factors of Green Economic Efficiency in the Yangtze River Economic Belt of China

The spatial and temporal variation of green economic efficiency and its driving factors are of great significance for the con-struction of high-efficiency and low-consumption green development model and sustainable socio-economic development.The research focused on the Yangtze River Economic Belt(YREB)and employed the miniumum distance to strong efficient frontier DEA(MinDs)model to measure the green economic efficiency of the municipalities in the region between 2008 and 2020.Then,the spatial autocorrel-ation model was used to analyze the evolution characteristics of its spatial pattern.Finally,Geodetector was applied to reveal the drivers and their interactions on green economic efficiency.It is found that:1)the overall green economic efficiency of the YREB from 2008 to 2020 shows a W-shaped fluctuating upward trend,green economic efficiency is greater in the downstream and smallest in the upstream;2)the spatial distribution of green economic efficiency shows clustering characteristics,with multi-core clustering based on‘city clusters-central cities'becoming more obvious over time;the High-High agglomeration type is mainly clustered in Jiangsu and Zheji-ang,while the Low-Low agglomeration type is clustered in the western Sichuan Plateau area and southwestern Yunnan;3)from input-output factors,whether it is the YREB as a whole or the upper,middle and lower reaches regions,the economic development level,labor input,and capital investment are the leading factors in the spatial-temporal evolution of green economic efficiency,with the com-prehensive influence of economic development level and pollution index being the most important interactive driving factor;4)from so-cio-economic factors,information technology drivers such as government intervention,transportation accessibility,information infra-structure,and Internet penetration are always high impact influencers and dominant interaction factors for green economic efficiency in the YREB and the three major regions in the upper,middle and lower reaches.Accordingly,the article puts forward relevant policy re-commendations in terms of formulating differentiated green transformation strategies,strengthening network leadership and informa-tion technology construction and coordinating multi-factor integrated development,which could provide useful reference for promoting synergistic green economic efficiency in the YREB.

Green economic efficiency in the Yangtze River Delta:spatiotemporal evolution and influencing factors

Based on economic-social-resource-environment perspective,which people’s welfare was considered compared with the traditional perspective,using SSU and PP model,spatial analysis method,spatial econometric model to study green economy efficiency(GRE)of 26 Cities in the Yangtze River Delta from 2005 to 2015.The results show the following:Corrected GRE is markedly lower than conventional efficiency;Stage characteristics are obvious of GRE.An overall spatial pattern has emerged of lower efficiency in the east and higher efficiency in the west,and exist clear signs of spatial agglomeration.The spatial Dubin model has abetter fitting effect.The biggest direct effect on local green economic efficiency and spatial spillover effects on nearby areas is proportion of tertiary industry.

Industrial Green Spatial Pattern Evolution of Yangtze River Economic Belt in China

We use the directional slacks-based measure of efficiency and inverse distance weighting method to analyze the spatial pattern evolution of the industrial green total factor productivity of 108 cities in the Yangtze River Economic Belt in 2003–2013.Results show that both the subprime mortgage crisis and ‘the new normal' had significant negative effects on productivity growth,leading to the different spatial patterns between 2003–2008 and 2009–2013.Before 2008,green poles had gathered around some capital cities and formed a tripartite pattern,which was a typical core-periphery pattern.Due to a combination of the polarization and the diffusion effects,capital cities became the growth poles and ‘core' regions,while surrounding areas became the ‘periphery'.This was mainly caused by the innate advantage of capital cities and ‘the rise of central China' strategy.After 2008,the tripartite pattern changed to a multi-poles pattern where green poles continuously and densely spread in the midstream and downstream areas.This is due to the regional difference in the leading effect of green poles.The leading effect of green poles in midstream and downstream areas has changed from polarization to diffusion,while the polarization effect still leads in the upstream area.

Are the Economic Consequences of Climate Change Really “Pro-Poor”?

In order to comprehensively study the influence of climate change on economic growth and energy conservation&emission reduction,this paper fi rst uses the non-radial directional distance function(NDDF)to calculate the city-level green economic efficiency in China during 2003-2016.The causal effect of daily temperature changes on green economic efficiency is then identifi ed to evaluate the economic consequences of climate change.It fi nds that r elative to the 6~12℃temperature benchmark,any decrease or increase in temperature will pose negative influence on green economic efficiency;moreover,such effects are only observed in developed cities,but not signifi cant in less-developed ones.This refl ects that the economic consequences of climate change are“robbing the rich”to some extents,which differs widely from the“pro-poor”conclusion in the majority of literature previously.Subject to the robustness test and with possible competitive explanations excluded,this finding still stands.The mechanism test reveals that temperature rise brings about economic consequences that“rob the rich”by affecting labor productivity,efficiency of energy conservation&emission reduction and execution of environmental regulations by local government.This study brings a different perspective for understanding the economic consequences of climate change and offers empirical basis for identifying responsibilities of local government in climate governance.