Under the background of green development,the function direction of technological innovation to green development efficiency,which includes economy,resources and environment,needs to be observed by demonstration.In th...Under the background of green development,the function direction of technological innovation to green development efficiency,which includes economy,resources and environment,needs to be observed by demonstration.In this paper,the green development efficiency of 30 provinces(cities and districts)in China from 2004 to 2017 is measured and its intertemporal changes,regional differences of green development efficiency are analyzed by using the super efficiency SBM model,further through theoretical analysis and empirical study,the influence of technological innovation on regional green development efficiency and its impact mechanism are investigated.The influence mechanisms of the technological innovation on green development efficiency are clarified and empirically tested by spatial econometric models from the perspectives of the growth sources and quantitative analysis.The results show that during the observation period,the green development efficiency in China exhibits a U-shaped variation,but there are huge regional differences with the obvious polarization in Eastern and Midwestern regions,and that technological innovation has some effect in promoting the regional green development efficiency,but not significant enough,which are heterogeneous according to the time periods and regions.展开更多
Cities are important carriers of green innovation.The foundation for accelerating China's ecological civilization construction and fostering regionally coordinated and sustainable development is quantitative analy...Cities are important carriers of green innovation.The foundation for accelerating China's ecological civilization construction and fostering regionally coordinated and sustainable development is quantitative analysis of the spatial evolution pattern and influencing factors of urban green innovation,as well as revealing the development differences between regions.This study's research object includes 284 Chinese cities that are at the prefecture level or above,excluding Xizang,Hong Kong,Macao,and Taiwan of China due to incomplete data.The spatial evolution characteristics of urban green innovation in China between 2005 and 2021 are comprehensively described using the gravity center model and boxplot analysis.The factors that affect urban green innovation are examined using the spatial Durbin model(SDM).The findings indicate that:1)over the period of the study,the gravity center of urban green innovation in China has always been distributed in the Henan-Anhui border region,showing a migration characteristic of‘initially shifting northeast,subsequently southeast',and the migration speed has gradually increased.2)Although there are also noticeable disparities in east-west,the north-south gap is the main cause of the shift in China's urban green innovation gravity center.The primary areas of urban green innovation in China are the cities with green innovation levels higher than the median.3)The main influencing factor of urban green innovation is the industrial structure level.The effect of the financial development level,the government intervention level,and the openness to the outside world degree on urban green innovation is weakened in turn.The environmental regulation degree is not truly influencing urban green innovation.The impact of various factors on green innovation across cities of different sizes,exhibiting heterogeneity.This study is conducive to broadening the academic community's comprehension of the spatial evolution characteristics of urban green innovation and offering a theoretical framework for developing policies for the all-encompassing green transformation of social and economic growth.展开更多
Smart cities,a new kind of urbanization,offer a means of achieving the condition in which environmental conservation and economic growth are mutually beneficial.As a result,it is important to think about whether and h...Smart cities,a new kind of urbanization,offer a means of achieving the condition in which environmental conservation and economic growth are mutually beneficial.As a result,it is important to think about whether and how the development of smart cities might support the high-quality growth of urban economies.Based on the panel data of 163 prefecture-level cities in China from 2009–2018,the green total factor productivity(GTFP)of each prefecture-level city is measured using the SBM-GML model,and the appropriate spatial econometric model is screened by various types of tests.The spatial effect of smart city construction on GFTP is studied,and it is concluded that the pilot cities have a significant positive spatial spillover effect.The decomposition econometric model also shows that the pilot cities have a significant positive spatial spillover effect,and it also indicating that the smart city construction can also drive the surrounding cities to jointly improve the quality of economic development.Finally,the robustness of the spatial effect of smart city policy is also verified by changing the spatial measurement model and the type of spatial weight matrix,which also shows that the results of the spatial spillover effect of smart city construction are reliable.展开更多
Great Lakes Regions(GLRs)in China often confront landscape fragmentation,wetland degradation,and ecological resilience(ER)losses owing to extensive and intensive urbanization.In GLRs,however,the ER responses to urbani...Great Lakes Regions(GLRs)in China often confront landscape fragmentation,wetland degradation,and ecological resilience(ER)losses owing to extensive and intensive urbanization.In GLRs,however,the ER responses to urbanization remain unclear.This study explored the spatiotemporal evolution of ER and urbanization in five GLRs in China to analyze the ER dynamic patterns along center−lakeside−periphery gradient.The Spatial Durbin Model(SDM)and Panel Threshold Model(PTM)were combined to reveal the spillover and threshold effects of urbanization in five GLRs.The results indicate that the ER in five GLRs declined with a rate of 21%from 2000 to 2020.There was a clear“center-periphery”contraction trend with low ER areas primarily spreading to human activity-concentrated regions such as lakesides,riversides,and road networks.Driven by economic and land urbanization,the average urbanization level increased from 0.06 to 0.13,where lakesides,riversides,and road networks were key areas undergoing expansion.The urbanization showed a noticeable negative spatial spillover effect on ER.Away from central lakes,the negative impacts on ER exhibited a two-phase decrease with the threshold of 81 km.This study contributes to the understanding of human-environment interactions by examining the ecological resilience response process of GLRs under the impact of urbanization.Based on a multidimensional“center−lakeside−periphery”analytical model,this study provides a strategic framework for ecological construction in GLRs in China,promoting sustainable development and adaptive capacity in vulnerable areas.展开更多
This study analyzes the evolution of China's green technology innovation cooperation network from 2011 to 2020,utilizing green patent application data.Employing a Spatial Durbin Model(SDM),we scrutinized the netwo...This study analyzes the evolution of China's green technology innovation cooperation network from 2011 to 2020,utilizing green patent application data.Employing a Spatial Durbin Model(SDM),we scrutinized the network's influence on urban carbon emissions,utilizing panel data encompassing 323 city nodes.Results show network expansion and a shift in central nodes from eastern coastal areas to interior cities,with Beijing,Shenzhen,Nanjing,and Shanghai consistently acting as key innovation hubs.A core-periphery structure emerged,clustering cities into high-and low-cooperation clusters.Core cities,particularly Beijing,which gain informational advantages by bridging non-overlapping nodes and exhibit distinct characteristics in terms of the structural hole indexes,reflecting their multifaceted roles within the network.SDM analysis indicates that the green technology innovation cooperation network has a significant positive impact on urban carbon reduction efforts.Specifically,degree centrality,closeness centrality,effective size,efficiency,and hierarchy of node cities exhibit a negative correlation with carbon emissions,suggesting that higher centrality and efficiency within the network correlate with lower emissions.Conversely,betweenness centrality and constraint have a positive impact on emissions,indicating that cities that act as bridges in the network may paradoxically contribute to higher emissions.Moreover,the network's influence on carbon emissions is nuanced across different green technology sectors.Cooperation in areas such as waste management,alternative energy production,energy conservation,agriculture and forestry,and transportation is found to have a more substantial impact on carbon reduction than cooperation in nuclear power,and administrative,regulatory,and design fields.展开更多
基金This research is supported by Shanxi Province Philosophy and Social Science Project(Grant No.W20191012)Shanxi province Soft Science Project(Grant No.2019041015-1).
文摘Under the background of green development,the function direction of technological innovation to green development efficiency,which includes economy,resources and environment,needs to be observed by demonstration.In this paper,the green development efficiency of 30 provinces(cities and districts)in China from 2004 to 2017 is measured and its intertemporal changes,regional differences of green development efficiency are analyzed by using the super efficiency SBM model,further through theoretical analysis and empirical study,the influence of technological innovation on regional green development efficiency and its impact mechanism are investigated.The influence mechanisms of the technological innovation on green development efficiency are clarified and empirically tested by spatial econometric models from the perspectives of the growth sources and quantitative analysis.The results show that during the observation period,the green development efficiency in China exhibits a U-shaped variation,but there are huge regional differences with the obvious polarization in Eastern and Midwestern regions,and that technological innovation has some effect in promoting the regional green development efficiency,but not significant enough,which are heterogeneous according to the time periods and regions.
基金Under the auspices of National Natural Science Foundation of China(No.42371192)Natural Science Foundation of Hunan Province(No.2023JJ30100)Social Science Foundation of Hunan Province(No.23ZDAJ023,23YBA133)。
文摘Cities are important carriers of green innovation.The foundation for accelerating China's ecological civilization construction and fostering regionally coordinated and sustainable development is quantitative analysis of the spatial evolution pattern and influencing factors of urban green innovation,as well as revealing the development differences between regions.This study's research object includes 284 Chinese cities that are at the prefecture level or above,excluding Xizang,Hong Kong,Macao,and Taiwan of China due to incomplete data.The spatial evolution characteristics of urban green innovation in China between 2005 and 2021 are comprehensively described using the gravity center model and boxplot analysis.The factors that affect urban green innovation are examined using the spatial Durbin model(SDM).The findings indicate that:1)over the period of the study,the gravity center of urban green innovation in China has always been distributed in the Henan-Anhui border region,showing a migration characteristic of‘initially shifting northeast,subsequently southeast',and the migration speed has gradually increased.2)Although there are also noticeable disparities in east-west,the north-south gap is the main cause of the shift in China's urban green innovation gravity center.The primary areas of urban green innovation in China are the cities with green innovation levels higher than the median.3)The main influencing factor of urban green innovation is the industrial structure level.The effect of the financial development level,the government intervention level,and the openness to the outside world degree on urban green innovation is weakened in turn.The environmental regulation degree is not truly influencing urban green innovation.The impact of various factors on green innovation across cities of different sizes,exhibiting heterogeneity.This study is conducive to broadening the academic community's comprehension of the spatial evolution characteristics of urban green innovation and offering a theoretical framework for developing policies for the all-encompassing green transformation of social and economic growth.
基金Jilin Province Social Science Project:Path Analysis and Empirical Research on Empowering Rural Industry Integration with Digital Economy in Jilin Province 2023B40Key Project of Education Science Planning in Jilin Province:Exploration of Talent Training Model for Economic Statistics Majors in Universities Based on OBE Theory-Taking Jilin Jianzhu University as an Example.ZD22028.
文摘Smart cities,a new kind of urbanization,offer a means of achieving the condition in which environmental conservation and economic growth are mutually beneficial.As a result,it is important to think about whether and how the development of smart cities might support the high-quality growth of urban economies.Based on the panel data of 163 prefecture-level cities in China from 2009–2018,the green total factor productivity(GTFP)of each prefecture-level city is measured using the SBM-GML model,and the appropriate spatial econometric model is screened by various types of tests.The spatial effect of smart city construction on GFTP is studied,and it is concluded that the pilot cities have a significant positive spatial spillover effect.The decomposition econometric model also shows that the pilot cities have a significant positive spatial spillover effect,and it also indicating that the smart city construction can also drive the surrounding cities to jointly improve the quality of economic development.Finally,the robustness of the spatial effect of smart city policy is also verified by changing the spatial measurement model and the type of spatial weight matrix,which also shows that the results of the spatial spillover effect of smart city construction are reliable.
基金supported by the National Natural Science Foundation of China(Grants No.42301226,42271209 and 42471199)the Fundamental Research Funds for the Central Universities(Grant No.2024CDJXY014)+2 种基金the Natural Science Foundation of Jiangxi Province(Grant No.20242BAB25170)Special Funds for Water Resources in Jiangxi Province(Science and Technology Projects)(Grant No.202425YBKT16)the Young Talent Cultivation and Innovation Fund Project of Nanchang University(Grant No.XX202506030028).
文摘Great Lakes Regions(GLRs)in China often confront landscape fragmentation,wetland degradation,and ecological resilience(ER)losses owing to extensive and intensive urbanization.In GLRs,however,the ER responses to urbanization remain unclear.This study explored the spatiotemporal evolution of ER and urbanization in five GLRs in China to analyze the ER dynamic patterns along center−lakeside−periphery gradient.The Spatial Durbin Model(SDM)and Panel Threshold Model(PTM)were combined to reveal the spillover and threshold effects of urbanization in five GLRs.The results indicate that the ER in five GLRs declined with a rate of 21%from 2000 to 2020.There was a clear“center-periphery”contraction trend with low ER areas primarily spreading to human activity-concentrated regions such as lakesides,riversides,and road networks.Driven by economic and land urbanization,the average urbanization level increased from 0.06 to 0.13,where lakesides,riversides,and road networks were key areas undergoing expansion.The urbanization showed a noticeable negative spatial spillover effect on ER.Away from central lakes,the negative impacts on ER exhibited a two-phase decrease with the threshold of 81 km.This study contributes to the understanding of human-environment interactions by examining the ecological resilience response process of GLRs under the impact of urbanization.Based on a multidimensional“center−lakeside−periphery”analytical model,this study provides a strategic framework for ecological construction in GLRs in China,promoting sustainable development and adaptive capacity in vulnerable areas.
基金supported by the National Natural Science Foundation of China(72573020,72103022).
文摘This study analyzes the evolution of China's green technology innovation cooperation network from 2011 to 2020,utilizing green patent application data.Employing a Spatial Durbin Model(SDM),we scrutinized the network's influence on urban carbon emissions,utilizing panel data encompassing 323 city nodes.Results show network expansion and a shift in central nodes from eastern coastal areas to interior cities,with Beijing,Shenzhen,Nanjing,and Shanghai consistently acting as key innovation hubs.A core-periphery structure emerged,clustering cities into high-and low-cooperation clusters.Core cities,particularly Beijing,which gain informational advantages by bridging non-overlapping nodes and exhibit distinct characteristics in terms of the structural hole indexes,reflecting their multifaceted roles within the network.SDM analysis indicates that the green technology innovation cooperation network has a significant positive impact on urban carbon reduction efforts.Specifically,degree centrality,closeness centrality,effective size,efficiency,and hierarchy of node cities exhibit a negative correlation with carbon emissions,suggesting that higher centrality and efficiency within the network correlate with lower emissions.Conversely,betweenness centrality and constraint have a positive impact on emissions,indicating that cities that act as bridges in the network may paradoxically contribute to higher emissions.Moreover,the network's influence on carbon emissions is nuanced across different green technology sectors.Cooperation in areas such as waste management,alternative energy production,energy conservation,agriculture and forestry,and transportation is found to have a more substantial impact on carbon reduction than cooperation in nuclear power,and administrative,regulatory,and design fields.
