Climate change caused by carbon emissions is a hot topic of concern.Enhancing carbon emission performance(CEP)emerges as a pivotal strategy to curtail carbon emissions,with the digital economy recognized as a crucial ...Climate change caused by carbon emissions is a hot topic of concern.Enhancing carbon emission performance(CEP)emerges as a pivotal strategy to curtail carbon emissions,with the digital economy recognized as a crucial instrument for bolstering CEP.Grounded in theoretical analysis,this article takes the Yangtze River Delta region(YRD)as the research object and conducts empirical analysis for the period from 2010 to 2021.The Super Epsilon-Based Measure(EBM)model was employed to assess CEP,while the entropy method was used to quantify the level of the digital economy.Baseline regression models and mediation effect models were constructed to test the research hypotheses.Additionally,the Spatial Durbin Model(SDM)was utilized to analyze the spatial spillover effects of the digital economy.Some conclusions were drawn as follows.Firstly,both the digital economy and CEP exhibit growing trends and demonstrate significant spatial distribution characteristics.Cities with high CEP are increasingly concentrated along the Yangtze River and coastal areas.Meanwhile,the digital economy generally demonstrates a spatial distribution pattern of being higher in the southeast and lower in the northwest.Secondly,the digital economy exerts a notable and consistent positive influence on CEP,but this impact is not primarily achieved through promoting green technology innovation.Instead,the digital economy exhibits a stronger intermediary effect on CEP by facilitating industrial structure upgrading and rationalization.Thirdly,the digital economy significantly enhancing local CEP but having an insignificant impact on neighboring cities'CEP.To address these findings,cities ought to invest in digital infrastructure,incentivize digital innovation through policy and financial backing,and harness advanced technologies like 5G and blockchain to promote low-carbon,intelligent production and lifestyles,while enhancing industrial structure and regional cooperation to foster a low-carbon digital economy network.展开更多
This study analyzed the spatial-temporal heterogeneity of green development efficiency and its influencing factors in the growing Xuzhou Metropolitan Area for the period 2000–2015.The slacks-based measure(SBM)model,s...This study analyzed the spatial-temporal heterogeneity of green development efficiency and its influencing factors in the growing Xuzhou Metropolitan Area for the period 2000–2015.The slacks-based measure(SBM)model,spatial autocorrelation,and the geographically weighted regression(GWR)model were used to conduct the analysis.The conclusions were as follows:first,the overall efficiency of green development of the Xuzhou Metropolitan Area decreased,the regional differences and spatial agglomeration shrunk and differences within the region were the main contributors to the regional differences of green development efficiency.Second,the counties with high-efficiency green development were distributed along the coast,and along the routes of the Beijing-Shanghai and the Eastern Longhai railways.A developing axis of the high-efficiency counties was the main feature of the spatial pattern for green development efficiency.Third,regarding spatial correlation and green development efficiency,the High-High type counties in the Xuzhou Metropolitan Area formed a centralized distribution corridor along the inter-provincial border areas of Henan and Jiangsu,whereas the Low-Low type counties were concentrated in the external,marginal parts of the metropolitan area.Fourth,the major factors(ranked in decreasing order of impact)influencing green development efficiency were innovation,government regulations,the economic development level,energy consumption,and industrial structure.These factors exerted their influence to varying extents;the influence of the same factor had different effects in different regions and obvious spatial differences were observed for the different regions.展开更多
Guangdong Province,as one of China’s fast-developing regions,an important manufacturing base,and one of the national first round low-carbon pilots,still faces many challenges in controlling its total energy consumpti...Guangdong Province,as one of China’s fast-developing regions,an important manufacturing base,and one of the national first round low-carbon pilots,still faces many challenges in controlling its total energy consumption.Coal dominates Guangdong’s energy consumption and remains the major source of CO2.Previous research on factors influencing energy consumption has lacked a systematic analysis both from supply side(factors related to scale,structure,and technologies)and demand side(investment,consumption,and trade).This paper develops the logarithmic mean Divisia index(LMDI)method that focuses on the supply side and the structural decomposition analysis(SDA)method that focuses on the demand side to systematically identify the key factors driving coal consumption in Guangdong.Results are as follows:(1)Supply side analysis indicates that economic growth has always been the most important factor driving coal consumption growth,while energy intensity is the most important constraining factor.Industrial structure and energy structure have different impacts on coal consumption control during different development phases.(2)Demand side analysis indicates that coal is consumed mainly for international exports,inter-provincial exports,fixed capital formation,and urban household.(3)Industries with the fastest coal consumption growth driven by final demand have experienced significant shifts.Increments in industrial sectors were mainly driven by inter-provincial exports and urban household consumption in recent years.(4)Research on energy consumption in subnational regions under China’s new development pattern of“dual circulation”should not only focus on exports in the context of economic globalization but also pay more attention to inter-provincial exports on the background of strengthened interregional connections.展开更多
Examining the spatiotemporal dynamics and determinants of land urbanization is critical for promoting healthy urban development and the rational use of land resources.Based on the dataset consisting of land use change...Examining the spatiotemporal dynamics and determinants of land urbanization is critical for promoting healthy urban development and the rational use of land resources.Based on the dataset consisting of land use change data and selected factors in 2010 and2020,this study used visual analysis to reveal the spatiotemporal dynamics of land urbanization across prefecture-level cities in China.Meanwhile,the driving forces underlying land urbanization were examined by using geographical detector technique.Following are the findings:1)we find that there exist notable spatial variances in land urbanization across prefecture-level cities.Currently,the differentiation in land urbanization between the northern and southern cities is more pronounced than that between the coastal and inland cities,or between the eastern and western cities.Prefecture-level cities located in central and western China have experienced the most rapid growth in land urbanization.Conversely,the growth rate in northeastern China is the lowest,while the velocity in eastern China remains relatively stable.By using spatial autocorrelation analysis,this study reveals that the land urbanization level in prefecture-level cities has significant spatial agglomeration.2)We further find that land urbanization in China is influenced by factors related to urban land supply and demand,and urban population growth,economic growth,land financial and political incentive have greater impact on land urbanization than other factors.3)We also find that the impacts of determinants on China’s land urbanization vary over time,the explanatory power of economic development increased,while the explanatory power of state forces declined.We argue that integrating the supply and demand factors of land urbanization can provide a more comprehensive understanding of the driving mechanisms underlying land urbanization in China and other transitional countries,and help decision-makers in these countries formulate more detailed and specific land urbanization policies.展开更多
Strong-motion site effect was checked for four sites at comparable epicentral distances from the March 11, 2011 Mw9.0 Tohoku earthquake in Japan. The result showed that site condition had a significant impact on peak ...Strong-motion site effect was checked for four sites at comparable epicentral distances from the March 11, 2011 Mw9.0 Tohoku earthquake in Japan. The result showed that site condition had a significant impact on peak acceleration, and on maximal amplitude and characteristic period of its response spectrum. Shorter-period seismic waves were amplified mainly in shallower soils, while longer-period waves in deeper soft rocks and soils, where the characteristic periods of response spectra were longer than those on ground surface.展开更多
基金Under the auspices of the National Natural Science Foundation of China(No.42101164)Major Project of Philosophy and Social Sciences in Colleges and Universities by Jiangsu Province(No.2024SJZD025)。
摘要Climate change caused by carbon emissions is a hot topic of concern.Enhancing carbon emission performance(CEP)emerges as a pivotal strategy to curtail carbon emissions,with the digital economy recognized as a crucial instrument for bolstering CEP.Grounded in theoretical analysis,this article takes the Yangtze River Delta region(YRD)as the research object and conducts empirical analysis for the period from 2010 to 2021.The Super Epsilon-Based Measure(EBM)model was employed to assess CEP,while the entropy method was used to quantify the level of the digital economy.Baseline regression models and mediation effect models were constructed to test the research hypotheses.Additionally,the Spatial Durbin Model(SDM)was utilized to analyze the spatial spillover effects of the digital economy.Some conclusions were drawn as follows.Firstly,both the digital economy and CEP exhibit growing trends and demonstrate significant spatial distribution characteristics.Cities with high CEP are increasingly concentrated along the Yangtze River and coastal areas.Meanwhile,the digital economy generally demonstrates a spatial distribution pattern of being higher in the southeast and lower in the northwest.Secondly,the digital economy exerts a notable and consistent positive influence on CEP,but this impact is not primarily achieved through promoting green technology innovation.Instead,the digital economy exhibits a stronger intermediary effect on CEP by facilitating industrial structure upgrading and rationalization.Thirdly,the digital economy significantly enhancing local CEP but having an insignificant impact on neighboring cities'CEP.To address these findings,cities ought to invest in digital infrastructure,incentivize digital innovation through policy and financial backing,and harness advanced technologies like 5G and blockchain to promote low-carbon,intelligent production and lifestyles,while enhancing industrial structure and regional cooperation to foster a low-carbon digital economy network.
基金Under the auspices of the National Natural Science Foundation of China(No.41671123,41971158,41671122)Major Project of Philosophy and Social Science Research of Jiangsu Universities(No.2018SJZDA010).
摘要This study analyzed the spatial-temporal heterogeneity of green development efficiency and its influencing factors in the growing Xuzhou Metropolitan Area for the period 2000–2015.The slacks-based measure(SBM)model,spatial autocorrelation,and the geographically weighted regression(GWR)model were used to conduct the analysis.The conclusions were as follows:first,the overall efficiency of green development of the Xuzhou Metropolitan Area decreased,the regional differences and spatial agglomeration shrunk and differences within the region were the main contributors to the regional differences of green development efficiency.Second,the counties with high-efficiency green development were distributed along the coast,and along the routes of the Beijing-Shanghai and the Eastern Longhai railways.A developing axis of the high-efficiency counties was the main feature of the spatial pattern for green development efficiency.Third,regarding spatial correlation and green development efficiency,the High-High type counties in the Xuzhou Metropolitan Area formed a centralized distribution corridor along the inter-provincial border areas of Henan and Jiangsu,whereas the Low-Low type counties were concentrated in the external,marginal parts of the metropolitan area.Fourth,the major factors(ranked in decreasing order of impact)influencing green development efficiency were innovation,government regulations,the economic development level,energy consumption,and industrial structure.These factors exerted their influence to varying extents;the influence of the same factor had different effects in different regions and obvious spatial differences were observed for the different regions.
基金National Key Research and Development Program(2019YFB2103101)Southern Marine Science and Engineering Guangdong Laboratory(Guangzhou)(GML2019ZD0301)+2 种基金GDAS Special Project of Science and Technology Development(2020GDASYL-20200102002)GDAS Special Project of Science and Technology Development(2020GDASYL-20200301003)National Natural Science Foundation of China(41501144)。
摘要Guangdong Province,as one of China’s fast-developing regions,an important manufacturing base,and one of the national first round low-carbon pilots,still faces many challenges in controlling its total energy consumption.Coal dominates Guangdong’s energy consumption and remains the major source of CO2.Previous research on factors influencing energy consumption has lacked a systematic analysis both from supply side(factors related to scale,structure,and technologies)and demand side(investment,consumption,and trade).This paper develops the logarithmic mean Divisia index(LMDI)method that focuses on the supply side and the structural decomposition analysis(SDA)method that focuses on the demand side to systematically identify the key factors driving coal consumption in Guangdong.Results are as follows:(1)Supply side analysis indicates that economic growth has always been the most important factor driving coal consumption growth,while energy intensity is the most important constraining factor.Industrial structure and energy structure have different impacts on coal consumption control during different development phases.(2)Demand side analysis indicates that coal is consumed mainly for international exports,inter-provincial exports,fixed capital formation,and urban household.(3)Industries with the fastest coal consumption growth driven by final demand have experienced significant shifts.Increments in industrial sectors were mainly driven by inter-provincial exports and urban household consumption in recent years.(4)Research on energy consumption in subnational regions under China’s new development pattern of“dual circulation”should not only focus on exports in the context of economic globalization but also pay more attention to inter-provincial exports on the background of strengthened interregional connections.
基金Under the auspices of National Natural Science Foundation of China(No.42201202,42271177)General Project of Philosophy and Social Science Research in Jiangsu Universities(No.2022SJYB1161)Priority Academic Program Development of Jiangsu Higher Education Institutions(PAPD)。
摘要Examining the spatiotemporal dynamics and determinants of land urbanization is critical for promoting healthy urban development and the rational use of land resources.Based on the dataset consisting of land use change data and selected factors in 2010 and2020,this study used visual analysis to reveal the spatiotemporal dynamics of land urbanization across prefecture-level cities in China.Meanwhile,the driving forces underlying land urbanization were examined by using geographical detector technique.Following are the findings:1)we find that there exist notable spatial variances in land urbanization across prefecture-level cities.Currently,the differentiation in land urbanization between the northern and southern cities is more pronounced than that between the coastal and inland cities,or between the eastern and western cities.Prefecture-level cities located in central and western China have experienced the most rapid growth in land urbanization.Conversely,the growth rate in northeastern China is the lowest,while the velocity in eastern China remains relatively stable.By using spatial autocorrelation analysis,this study reveals that the land urbanization level in prefecture-level cities has significant spatial agglomeration.2)We further find that land urbanization in China is influenced by factors related to urban land supply and demand,and urban population growth,economic growth,land financial and political incentive have greater impact on land urbanization than other factors.3)We also find that the impacts of determinants on China’s land urbanization vary over time,the explanatory power of economic development increased,while the explanatory power of state forces declined.We argue that integrating the supply and demand factors of land urbanization can provide a more comprehensive understanding of the driving mechanisms underlying land urbanization in China and other transitional countries,and help decision-makers in these countries formulate more detailed and specific land urbanization policies.
基金supported by the National Natural Science Foundation of China(41004020)the Director Foundation of Institute of Seismology,China Earthquake Administration(IS200926044)
摘要Strong-motion site effect was checked for four sites at comparable epicentral distances from the March 11, 2011 Mw9.0 Tohoku earthquake in Japan. The result showed that site condition had a significant impact on peak acceleration, and on maximal amplitude and characteristic period of its response spectrum. Shorter-period seismic waves were amplified mainly in shallower soils, while longer-period waves in deeper soft rocks and soils, where the characteristic periods of response spectra were longer than those on ground surface.