This paper created an evaluation index system for agricultural green resilience,consisting of five dimensions:resistibility,recoverability,adaptability,innovatability,and reconstructability.We used the entropy method ...This paper created an evaluation index system for agricultural green resilience,consisting of five dimensions:resistibility,recoverability,adaptability,innovatability,and reconstructability.We used the entropy method to measure the agricultural green resilience levels of 30 provinces(municipalities/autonomous regions)in China from 2007 to 2021 and employed spatial Markov chains and geographic detectors to reveal the dynamics and evolution of the patterns and influencing factors of the agricultural green resilience.The study shows that the level of agricultural green resilience in China displayed a slight upward trend from 2007 to 2021,but the overall level remained low.Spatially,a distribution pattern of“eastern China>central China>northeastern China>western China”was observed.The transfer process for agricultural green resilience exhibited a“path dependence”characteristic that maintained its initial state,while it also showed a“trickle-down effect.”This means that the regions adjacent to provinces(municipalities/autonomous regions)with higher levels of agricultural green resilience tend to have an increased probability of an upward movement in their ranking.However,such movements are not leapfrogging and only occur at the adjacent levels.The spatial differentiations in the agricultural green resilience levels are primarily driven by the technological innovation capacity and market maturity,with interactions between these factors exhibiting both dual-factor enhancements and nonlinear enhancements.Accordingly,efforts should be made to strengthen support for the less-developed regions,increase research and development investment in the agricultural sector,and improve the market systems for agricultural products to enhance agricultural green resilience.展开更多
The dynamics of regional convergence include spatial and temporal dimensions. Spatial Markov chain can be used to explore how regions evolve by considering both individual regions and their geographic neighbors. Based...The dynamics of regional convergence include spatial and temporal dimensions. Spatial Markov chain can be used to explore how regions evolve by considering both individual regions and their geographic neighbors. Based on per capita GDP data set of 77 counties from 1978 to 2000, this paper attempts to investigate the spatial-temporal dynamics of regional convergence in Jiangsu. First, traditional Markov matrix for five per capita GDP classes is constructed for later comparison. Moreover, each region’s spatial lag is derived by averaging all its neighbors’ per capita GDP data. Conditioning on per capita GDP class of its spatial lag at the beginning of each year, spatial Markov transition probabilities of each region are calculated accordingly. Quantitatively, for a poor region, the probability of moving upward is 3.3% if it is surrounded by its poor neighbors, and even increases to 18.4% if it is surrounded by its rich neighbors, but it goes down to 6.2% on average if ignoring regional context. For a rich region, the probability of moving down ward is 1.2% if it is surrounded by its rich neighbors, but increases to 3.0% if it is surrounded by its poor neighbors, and averages 1.5% irrespective of regional context. Spatial analysis of regional GDP class transitions indicates those 10 upward moves of both regions and their neighbors are unexceptionally located in the southern Jiangsu, while downward moves of regions or their neighbors are almost in the northern Jiangsu. These empirical results provide a spatial explanation to the "convergence clubs" detected by traditional Markov chain.展开更多
Climate change resulting from CO_2 emissions has become an important global environmental issue in recent years.Improving carbon emission performance is one way to reduce carbon emissions.Although carbon emission perf...Climate change resulting from CO_2 emissions has become an important global environmental issue in recent years.Improving carbon emission performance is one way to reduce carbon emissions.Although carbon emission performance has been discussed at the national and industrial levels,city-level studies are lacking due to the limited availability of statistics on energy consumption.In this study,based on city-level remote sensing data on carbon emissions in China from 1992–2013,we used the slacks-based measure of super-efficiency to evaluate urban carbon emission performance.The traditional Markov probability transfer matrix and spatial Markov probability transfer matrix were constructed to explore the spatiotemporal evolution of urban carbon emission performance in China for the first time and predict long-term trends in carbon emission performance.The results show that urban carbon emission performance in China steadily increased during the study period with some fluctuations.However,the overall level of carbon emission performance remains low,indicating great potential for improvements in energy conservation and emission reduction.The spatial pattern of urban carbon emission performance in China can be described as"high in the south and low in the north,"and significant differences in carbon emission performance were found between cities.The spatial Markov probabilistic transfer matrix results indicate that the transfer of carbon emission performance in Chinese cities is stable,resulting in a"club convergence"phenomenon.Furthermore,neighborhood backgrounds play an important role in the transfer between carbon emission performance types.Based on the prediction of long-term trends in carbon emission performance,carbon emission performance is expected to improve gradually over time.Therefore,China should continue to strengthen research and development aimed at improving urban carbon emission performance and achieving the national energy conservation and emission reduction goals.Meanwhile,neighboring cities with different neighborhood backgrounds should pursue cooperative economic strategies that balance economic growth,energy conservation,and emission reductions to realize low-carbon construction and sustainable development.展开更多
Balanced regional development is essential to China's economic stability and efficiency and achievement of the goal to build a moderately prosperous society in all respects. Based on the DMSP/OLS nighttime lights ...Balanced regional development is essential to China's economic stability and efficiency and achievement of the goal to build a moderately prosperous society in all respects. Based on the DMSP/OLS nighttime lights data of 291 cities at or above prefecture level during 1992-2013, this paper examines the regional disparities and trends of Chinese mainland's economic development. The findings are as follows:(1) During sample observation period, China's overall regional disparities generally declined despite some volatility; China's intra-regional disparities have been curbed yet a consistent framework for inter-regional economic coordination is lacking.(2) Southern coastal region contributes a significant share to China's overall regional disparities as the developed cities of Guangdong Province did not create a significant spatial spillover effect on neighboring regions.(3) According to the result of spatial Markov transition probability estimation, spatial factor has played a remarkable role in the evolution of China's regional economy and proximity to high-level regions will accelerate a region's transition toward higher levels.展开更多
基金supported by the Research on the Mechanisms and Policies for the Collaborative Promotion of High-Quality Development in the Private Enterprises of Fujian Province Through Digital Finance and Green Finance(FJ2024MGCA021)a key project of basic theory research in philosophy and social sciences guided by Marxism in universities in Fujian Province and Research on the Mechanism and Pathways for Empowering Green Innovation in Fujian Province Through the Deep Integration of Digital Economy and Real Economy(FJKX-2023XKB003)a Science and Technology Innovation Think Tank Research Project of Fujian Association for Science and Technology.
文摘This paper created an evaluation index system for agricultural green resilience,consisting of five dimensions:resistibility,recoverability,adaptability,innovatability,and reconstructability.We used the entropy method to measure the agricultural green resilience levels of 30 provinces(municipalities/autonomous regions)in China from 2007 to 2021 and employed spatial Markov chains and geographic detectors to reveal the dynamics and evolution of the patterns and influencing factors of the agricultural green resilience.The study shows that the level of agricultural green resilience in China displayed a slight upward trend from 2007 to 2021,but the overall level remained low.Spatially,a distribution pattern of“eastern China>central China>northeastern China>western China”was observed.The transfer process for agricultural green resilience exhibited a“path dependence”characteristic that maintained its initial state,while it also showed a“trickle-down effect.”This means that the regions adjacent to provinces(municipalities/autonomous regions)with higher levels of agricultural green resilience tend to have an increased probability of an upward movement in their ranking.However,such movements are not leapfrogging and only occur at the adjacent levels.The spatial differentiations in the agricultural green resilience levels are primarily driven by the technological innovation capacity and market maturity,with interactions between these factors exhibiting both dual-factor enhancements and nonlinear enhancements.Accordingly,efforts should be made to strengthen support for the less-developed regions,increase research and development investment in the agricultural sector,and improve the market systems for agricultural products to enhance agricultural green resilience.
基金Under the auspices ofthe National Natural Science Foundation of China (No .40301038)
文摘The dynamics of regional convergence include spatial and temporal dimensions. Spatial Markov chain can be used to explore how regions evolve by considering both individual regions and their geographic neighbors. Based on per capita GDP data set of 77 counties from 1978 to 2000, this paper attempts to investigate the spatial-temporal dynamics of regional convergence in Jiangsu. First, traditional Markov matrix for five per capita GDP classes is constructed for later comparison. Moreover, each region’s spatial lag is derived by averaging all its neighbors’ per capita GDP data. Conditioning on per capita GDP class of its spatial lag at the beginning of each year, spatial Markov transition probabilities of each region are calculated accordingly. Quantitatively, for a poor region, the probability of moving upward is 3.3% if it is surrounded by its poor neighbors, and even increases to 18.4% if it is surrounded by its rich neighbors, but it goes down to 6.2% on average if ignoring regional context. For a rich region, the probability of moving down ward is 1.2% if it is surrounded by its rich neighbors, but increases to 3.0% if it is surrounded by its poor neighbors, and averages 1.5% irrespective of regional context. Spatial analysis of regional GDP class transitions indicates those 10 upward moves of both regions and their neighbors are unexceptionally located in the southern Jiangsu, while downward moves of regions or their neighbors are almost in the northern Jiangsu. These empirical results provide a spatial explanation to the "convergence clubs" detected by traditional Markov chain.
基金Fundamental Research Funds for the Central UniversitiesNo.19lgzd09+2 种基金Guangdong Special Support ProgramPearl River S&T Nova Program of GuangzhouNo.201806010187
文摘Climate change resulting from CO_2 emissions has become an important global environmental issue in recent years.Improving carbon emission performance is one way to reduce carbon emissions.Although carbon emission performance has been discussed at the national and industrial levels,city-level studies are lacking due to the limited availability of statistics on energy consumption.In this study,based on city-level remote sensing data on carbon emissions in China from 1992–2013,we used the slacks-based measure of super-efficiency to evaluate urban carbon emission performance.The traditional Markov probability transfer matrix and spatial Markov probability transfer matrix were constructed to explore the spatiotemporal evolution of urban carbon emission performance in China for the first time and predict long-term trends in carbon emission performance.The results show that urban carbon emission performance in China steadily increased during the study period with some fluctuations.However,the overall level of carbon emission performance remains low,indicating great potential for improvements in energy conservation and emission reduction.The spatial pattern of urban carbon emission performance in China can be described as"high in the south and low in the north,"and significant differences in carbon emission performance were found between cities.The spatial Markov probabilistic transfer matrix results indicate that the transfer of carbon emission performance in Chinese cities is stable,resulting in a"club convergence"phenomenon.Furthermore,neighborhood backgrounds play an important role in the transfer between carbon emission performance types.Based on the prediction of long-term trends in carbon emission performance,carbon emission performance is expected to improve gradually over time.Therefore,China should continue to strengthen research and development aimed at improving urban carbon emission performance and achieving the national energy conservation and emission reduction goals.Meanwhile,neighboring cities with different neighborhood backgrounds should pursue cooperative economic strategies that balance economic growth,energy conservation,and emission reductions to realize low-carbon construction and sustainable development.
文摘Balanced regional development is essential to China's economic stability and efficiency and achievement of the goal to build a moderately prosperous society in all respects. Based on the DMSP/OLS nighttime lights data of 291 cities at or above prefecture level during 1992-2013, this paper examines the regional disparities and trends of Chinese mainland's economic development. The findings are as follows:(1) During sample observation period, China's overall regional disparities generally declined despite some volatility; China's intra-regional disparities have been curbed yet a consistent framework for inter-regional economic coordination is lacking.(2) Southern coastal region contributes a significant share to China's overall regional disparities as the developed cities of Guangdong Province did not create a significant spatial spillover effect on neighboring regions.(3) According to the result of spatial Markov transition probability estimation, spatial factor has played a remarkable role in the evolution of China's regional economy and proximity to high-level regions will accelerate a region's transition toward higher levels.
