Influence of technological innovation on regional green development efficiency in China(2004-2017):An empirical analysis based on the spatial Durbin model

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.

Synergistic dance of digital economy and green finance on carbon emissions:Insights from China

China has recently implemented a dual-carbon strategy to combat climate change and other environmental issues and is committed to modernizing it sustainably.This paper supports these goals and explores how the digital economy and green finance intersect and impact carbon emissions.Using panel data from 30 Chinese provinces over the period 2011-2021,this paper finds that the digital economy and green finance can together reduce carbon emissions,and conducts several robustness tests supporting this conclusion.A heterogeneity analysis shows that these synergistic effects are more important in regions with low levels of social consumption Meanwhile,in the spatial dimension,the synergistic effect of the local digital economy and green finance adversely impacts the level of carbon emissions in surrounding areas.The findings of this paper provide insights for policymakers in guiding capital flow and implementing carbon-reduction policies while fostering the growth of China’s digital economy and environmental sustainability.

Does Foreign Direct Investment Affect S0_(2) Emissions in the Yangtze River Delta?A Spatial Econometric Analysis

As the major source of air pollution,sulfur dioxide(S0_(2))emissions have become the focus of global attention.However,existing studies rarely consider spatial effects when discussing the relationship between foreign direct investment(FDI)and S0_(2) emissions.This study took the Yangtze River Delta as the research area and used the spatial panel data of 26 cities in this region for 2004-2017.The study investigated the spatial agglomeration effects and dynamics at work in FDI and S0_(2) emissions by using global and local measures of spatial autocorrelation.Then,based on regression analysis using a results of traditional ordinary least squares(OLS)model and a spatial econometric model,the spatial Durbin model(SDM)with spatial-time effects was adopted to quantify the impact of FDI on S0_(2) emissions,so as to avoid the regression results bias caused by ignoring the spatial effects.The results revealed a significant spatial autocorrelation between FDI and S0_(2) emissions,both of which displayed obvious path dependence characteristics in their geographical distribution.A series of agglomeration regions were observed on the spatial scale.The estimation results of the SDM showed that FDI inflow promoted S0_(2) emissions,which supports the pollution haven hypothesis.The findings of this study are significant in the prevention and control of air pollution in the Yangtze River Delta.

The environmental effect of technical change:A spatial model with city-level data in China

This paper studies the environmental effects of technical change using a spatial model with panel data from 284 prefecture-cities over 2004-2015 in China.We find that the effects of technical change vary across different dimensions of technical change and different pollution indicators.Furthermore,we also provide robust evidence for the existence of the spatial effects of technical change on environmental pollution across cities.First,indigenous technical change displays three patterns of effects on the four pollutants:a positive effect on wastewater,a negative effect on PM_(2.5)concentrations,and an inverted U-shaped relationship with SO_(2)and soot emissions.The spatial effect of indigenous technical change promotes cleaner industrial productions(fewer emissions of SO_(2),soot and wastewater)but higher PM_(2.5)concentrations.Second,technology transfers from foreign direct investment are associated with less pollution except for wastewater,and their spatial effects are unanimously negative on all pollutants.Finally,absorptive capacity can also promote cleaner industrial production,but its spatial effects can do otherwise.Accordingly,the government should take the spatial spillover effects of technical change into account when implementing specific policies,pin down specific pollutants to make full use of the pollution-reducing effects of technical change,and improve the absorptive capacity of domestic firms.

The Spatial Spillover Effect of Input and Output of Scientific Progress on Regional Economic Growth: The Case of Guangdong Province

This paper makes an empirical analysis of the spatial spillover effect of regional economic growth by using Moran’s I and Spatial Durbin Model to study the input and output of technological progress, with the panel data of 21 prefecture-level cities in Guangdong Province from 2008 to 2017. The empirical results show that the spatial autocorrelation exists in the economic development of Guangdong Province, and both the input and output of scientific research innovation have a significant positive effect on the regional economic growth. Under the spatial contiguity weights matrix, the output of scientific research and innovation has a more obvious spillover effect on the economic growth of neighboring cities than the input of scientific research and innovation.

Development?A Study based on Spatial Effects and Threshold Characteristics

As a pivotal element within the modern service industry,tourism possesses the capacity to reconfigure regional economic dynamics,alter resource flow patterns,and notably influence urban green development.By establishing an evaluation index system for urban green development across 60 prefecture-level cities within the Yellow River Basin from 2006 to 2021,this study employs the spatial Durbin model to delve into the nonlinear relationship between the tourism economy and urban green development.Furthermore,it investigates the heterogeneous impact of the tourism economy on green development across varying levels of urbanization.This study reveals several key findings:(1)Both tourism economy and urban green development exhibit significant spatial clustering,with tourism economy showing“midstream>downstream>upstream”and urban green development showing“downstream>midstream>upstream”.(2)The tourism economy exerts a non-linear positive influence on the green development of cities,characterized by a non-linear inverted“S”shape in its direct impact and a nonlinear“S”shape in its indirect impact.(3)As urbanization rate level escalate,the positive influence of the tourism economy on urban green development follows a non-linear trajectory,initially declining before ascending.Specifically,when the urbanization rate level is below the first threshold value,the tourism economy notably promotes urban green development.However,between the first and second threshold values,this positive impact diminishes,only to rebound beyond the second threshold value.

Digital economy,industrial structure,and carbon emissions:An empirical study based on a provincial panel data set from China

This paper uses the mediation effect and a spatial panel model using panel data from 30 provinces in China from 2011 to 2019 to study the relationship between the digital economy,industrial structure,and carbon emission.The research results show that the development of digital economy can effectively promote the reduction of carbon emissions.The development of the digital economy has a significant role in promoting the rationalization of the industrial structure.The digital economy not only directly suppresses carbon emissions,but also indirectly has a significant inhibitory effect on carbon emissions by promoting the rationalization and improvement of the industrial structure.The development of the digital economy suppresses the optimization of the industrial structure.The improvement of industrialization has hindered the industrialization process.It is necessary to strengthen research and development into digital technology and enhance the capacity of the digital economy to promote carbon emissions reduction.

How does ecological civilization construction affect carbon emission intensity?Evidence from Chinese provinces’panel data

Ecological civilization construction is a new concept and trend in the era of China's high-quality development.It requires the collaborative propulsion of an ecological economic civilization,ecological social civilization,and ecological environment civilization.Reducing carbon emission intensity is an important issue facing the Chinese government in the backdrop of global warming.Thus,studying the influence of ecological civilization construction on carbon emission intensity from different perspectives has important theoretical and practical significance.In this study,the influences of the three subsystems of an ecological civilization on carbon emission intensity are empirically analyzed using Chinese provincial panel data from 2004 to 2016 and a spatial Durbin model based on the STIRPAT model.First,the Moran's I of carbon emission intensity in Chinese provinces was between 0.425 and 0.473.This indicates positive spatial correlation and illustrates that the carbon emission intensity of China's provinces can influence each other.The reasons behind this correlation include close ties between neighboring provinces and similarities in natural,economic,and social characteristics.Second,the correlation coefficients of ecological economic civilization,ecological social civilization,and ecological environment civilization to carbon emission intensity are−4.743139,2.865884,and−0.3246447,respectively.This illustrates that an ecological economic civilization and ecological environment civilization can reduce carbon emission intensity,while an ecological social civilization can increase it.To reduce total carbon emission intensity,the three subsystems of ecological civilization should have a negative relationship with carbon emission intensity,so the effect of ecological social civilization on carbon emission intensity should be changed.Third,the spatial spillover effect of ecological social civilization did not pass the significance test.The correlation coefficients of spatial spillover effect to ecological economic civilization and ecological environment civilization are 2.046531 and−3.238323,respectively.Improving the ecological economic civilization can increase the carbon emission intensity of periphery provinces,while improving the ecological environment civilization can reduce it.Thus,it is necessary to enhance cooperation between periphery provinces and establish a trans-provincial cooperation mechanism for reducing carbon emissions.

Drivers of Regional Environmental Pollution Load and Zoning Control:A Case Study of the Yangtze River Economic Belt,China

The high environmental pollution load caused by the massive pollutant emissions and the accumulation of endogenous and cross-regional pollution has become an important obstacle to the current ecological civilization construction in the Yangtze River Economic Belt(YREB)in China.Taking the YREB as an example,by using four environmental pollutant emission indicators,including chemical oxygen demand(COD),ammonia nitrogen(NH_(3)-N),sulfur dioxide(SO_(2)),and nitrogen oxides(NO_(x)),this paper established an environmental pollution load index(EPLI)based on the entropy-based measurement.Moreover,the Spatial Durbin Model was used to quantitatively analyze the drivers and spatial effects of environmental pollution load.Finally,specific scientific references were provided for formulating environmental regulations of pollution source control in the YREB.The results showed that:1)During2011-2015,the EPLI in the YREB was reduced significantly and the environmental pollution load increased from upstream to downstream.Among them,the pollution load levels in the Upper Mainstream subbasin,Taihu Lake subbasin,and Lower Mainstream subbasin were the most prominent.2)The environmental pollution load situation in the YREB was generally stable and partially improved.High load level areas were mainly concentrated in the Yangtze River Delta Region and the provincial borders in upstream,midstream,and downstream areas.The high load level areas already formed in Chengdu and Chongqing were also the key regulatory points in the future.3)The degree of local environmental pollution load was apparently affected by the adjacent cities.The population size,industrialization level,and the fiscal decentralization not only drove the increase of the local environmental pollution load level,but also affected the adjacent areas through the spatial spillover effects.The land development intensity mainly drove the increase in the local EPLI in the YREB.While factors such as economic development level and agricultural economic share could only act on the environmental pollution load process in adjacent cities.4)According to the differentiation characteristics of drivers of each city,the YREB was divided into seven zones based on k-medoids cluster method,and targeted zoning control policy recommendations for alleviating environmental pollution load in the YREB were proposed.

Assessment of Electricity Productivity in China:Regional Differences and Convergence

Electricity productivity is regarded as a major assessment indicator in the design of energy saving policies,given that China has entered a“New Normal”of economic development.In fact,enhancing electricity productivity in an all-round way,as is one of the binding indicators for energy and environmental issues,means that non-growth target of total electric energy consumption in the economic development is feasible.The Gini coefficient,Theil index,and Mean log deviation are utilized to measure regional differences in China’s electricity productivity from 1997 to 2016 in five regions,and conditionalβconvergence is empirically analyzed with the spatial Durbin model.The results show that:(1)China’s electricity productivity is improving,while the overall feature is that the eastern area has a higher efficiency than the western area.(2)The difference in electricity productivity is the smallest in the northeast and the largest in the northwest.Interregional difference plays an important role and is the main cause for the differences.(3)The electricity productivity in China exhibitsβconvergence,except for the northwest.The positive driving factor is urbanization level(0.0485%),and the negative driving factor is FDI(–0.0104%).Moreover,the urbanization rate(0.0669%),foreign direct investment(0.0960%),and the industrial structure(–0.0769%)have a spatial spillover effect on improving regional electricity productivity.Based on this conclusion,the study provides some recommendations for saving energy policy design in China’s power industry.

Industrial carbon emissions and influencing factors in the Yangtze River Delta urban agglomeration

This paper calculates the industrial carbon emissions of the Yangtze River Delta urban agglomeration over the period 2006-2013. An empirical analysis is conducted to find out the influencing factors of industrial carbon emissions of the Yangtze River Delta urban agglomeration, using a spatial Durbin panel model. The results show that cities with larger industrial carbon emissions often enjoy low annual growth rates, while the cities with smaller ones enjoy higher annual growth rate; There exists a comparatively strong positive correlation in space in per capita carbon emission; urbanization, and total population. GDP per capita and international trade are the main influencing factors of industrial carbon emissions; There are spatial spillover effects on international trade and urbanization of neighboring cities, which have a significant impact on local industrial carbon emissions.

Identifying Neighborhood Effects on Geohazard Adaptation in Mountainous Rural Areas of China:A Spatial Econometric Model

In mountainous rural settlements facing the threat of geohazards,local adaptation is a self-organizing process influenced by individual and group behaviors.Encouraging a wide range of local populations to embrace geohazard adaptation strategies emerges as a potent means of mitigating disaster risks.The purpose of this study was to investigate whether neighbors influence individuals'adaptation decisions,as well as to unravel the mechanisms through which neighborhood effects exert their influence.We employed a spatial Durbin model and a series of robustness checks to confirm the results.The dataset used in this research came from a face-to-face survey involving 516 respondents residing in 32 rural settlements highly susceptible to geohazards.Our empirical results reveal that neighborhood effects are an important determinant of adaptation to geohazards.That is,a farmer's adaptation decision is influenced by the adaptation choices of his/her neighbors.Furthermore,when neighbors adopt adaptation strategies,the focal individuals may also want to adapt,both because they learn from their neighbors'choices(social learning)and because they tend to abide by the majority's choice(social norms).Incorporating neighborhood effects into geohazard adaptation studies offers a new perspective for promoting disaster risk reduction decision making.

Investigating spatio-temporal characteristics and influencing factors for green energy consumption in China

The green transformation of energy consumption is beneficial for promoting green development in China.This study constructed a green energy consumption evaluation index system and measured the green energy consumption levels in 30 provinces of China from 2000 to 2019 using the fuzzy comprehensive evaluation method.This study further employed the spatial Durbin model to examine influencing factors and spillover effects of green energy consumption.The results showed that,temporally,China’s green energy consumption levels had a fluctuating upward trend.While,spatially,the overall levels of green energy consumption in China showed apparent characteristics of“high in the west and low in the east”.In terms of influencing factors,environmental regulations played an important role in promoting green energy consumption in the region,while economic development,opening up,and industrial structure had considerably inhibiting effects.Additionally,economic development,opening up,and industrial structure of neighboring regions showed marked positive spillover effects,while urbanization level and technological innovation showed substantial negative spillover effects.The regional heterogeneity test results showed that environmental regulation and industrial structure rationalization were the important factors for promoting green energy consumption in the eastern region,environmental regulation played an important driving role in the central region,and opening to the outside world and technological innovation helped improve the level of green energy consumption in the western region.

Spatial econometric analysis on influencing factors of water consumption efficiency in urbanizing China

Due to the limitation of total amount of water resources, it is necessary to enhance water consumption efficiency to meet the increasing water demand of urbanizing China. Based on the panel data of 31 provinces in China in 1997-2013, we analyze the influencing factors of water consumption efficiency by spatial econometric models. Results show that, 1） Due to the notable spatial autocorrelation characteristics of water consumption efficiency among different provinces in China, general panel data regression model which previous studies often used may be improper to reveal its influencing factors. However, spatial Durbin model may best estimate their relationship. 2） Water consumption efficiency of a certain province may be influenced not only by its socio-economic and eco-environmental indicators, but also by water consumption efficiency in its neighboring provinces. Moreover, it may be influenced by the neighboring provinces＇ socio-economic and eco-environmental indicators. 3） For the macro average case of the 31 provinces in China, if water consumption efficiency in neighboring provinces increased 1%, water consumption efficiency of the local province would increase 0.34%. 4） Among the ten specific indicators we selected, per capita GDP and urbanization level of itself and its neighboring provinces have the most prominent positive effects on water consumption efficiency, and the indirect effects of neighboring provinces are much larger. Therefore, the spatial spillover effects of the economic development level and urbanization level are the primary influencing factors for improving China＇s water consump- tion efficiency. 5） Policy implications indicate that, to improve water consumption efficiency, each province should properly consider potential influences caused by its neighboring prov- inces, especially needs to enhance the economic cooperation and urbanization interaction with neighboring provinces.

Air pollution effects of industrial transformation in the Yangtze River Delta from the perspective of spatial spillover

The Yangtze River Delta(YRD) is a region in China with a serious contradiction between economic growth and environmental pollution. Exploring the spatiotemporal effects and influencing factors of air pollution in the region is highly important for formulating policies to promote the high-quality development of urban industries. This study uses the spatial Durbin model(SDM) to analyze the local direct and spatial spillover effects of industrial transformation on air pollution and quantifies the contribution of each factor. From 2008 to 2018, there was a significant spatial agglomeration of industrial sulfur dioxide emissions(ISDE) in the YRD, and every 1% increase in ISDE led to a synchronous increase of 0.603% in the ISDE in adjacent cities. The industrial scale index(ISCI) and industrial structure index(ISTI), as the core factors of industrial transformation, significantly affect the emissions of sulfur dioxide in the YRD, and the elastic coefficients are 0.677 and-0.368, respectively. The order of the direct effect of the explanatory variables on local ISDE is ISCI>ISTI>foreign direct investment(FDI)>enterprise technological innovation(ETI)>environmental regulation(ER)> per capita GDP(PGDP). Similarly, the order of the spatial spillover effect of all variables on ISDE in adjacent cities is ISCI>PGDP>FDI>ETI>ISTI>ER, and the coefficients of the ISCI and ISTI are 1.531 and 0.113, respectively. This study contributes to the existing research that verifies the environmental Kuznets curve in the YRD, denies the pollution heaven hypothesis, indicates the Porter hypothesis, and provides empirical evidence for the formation mechanism of regional environmental pollution from a spatial spillover perspective.

生产性服务业集聚对PM2.5的影响——以黄河流域为例

Regional and persistent PM_(2.5) pollution seriously undermines the development of urban ecological civilizations and the advancement of high-quality economies.The producer service sector,an example of a typical knowledge-intensive service industry,plays an important role in advancing the manufacturing industry and fostering economic growth while concurrently improving urban environmental conditions.Based on panel data of prefecture-level cities in the Yellow River Basin from 2006 to 2019,this study constructed a Spatial Durbin Model and a mediation effect model to comprehensively explore the impact of producer services agglomeration on PM_(2.5) pollution.The main conclusions are as follows:(1)From 2006 to 2019,PM_(2.5) pollution in the study area exhibited an initial rise followed by a subsequent decline,with notable spatial heterogeneity.PM_(2.5) pollution in the lower reaches of the Yellow River was significantly higher than in the middle and upper reaches.In addition,the spatial pattern of producer services agglomeration showed distinct “core-edge” characteristics.(2) The agglomeration of producer services had a significant negative impact on local and adjacent PM_(2.5) pollution,and there was a more pronounced haze reduction effect in the case of specialized agglomerations of producer services and low-end producer services.(3) The agglomeration of producer services indirectly improved PM_(2.5) pollution by promoting technological innovation and optimizing industrial structure,with the latter playing a greater mediating effect.This study not only helps expand the theoretical and empirical research on producer services agglomeration but also offers valuable insights for pursuing a green transformation of the Yellow River Basin by optimizing industrial patterns through the producer services sector.This approach represents a reference for curbing PM_(2.5) pollution and guiding the region toward a greener future.

Quantitative spatiotemporal impact of dynamic population density changes on the COVID-19 pandemic in China’s mainland

The coronavirus disease 2019(COVID-19)and its mutant viruses are still wreaking global havoc over the last two years,but the impact of human activity on the transmission of the pandemic is difficult to ascertain.Estimating human dynamic spatiotemporal distribution can help in our understanding of how to mitigate COVID-19 spread,which can help in maintaining urban health within a county and between counties within a country.This distribution can be computed using the Volunteered Geographic Information(VGI)of the citizens in conjunction with other variables,such as climatic conditions,and used to analyze how human’s daily density distribution quantitatively affects COVID-19 transmission.Based on the estimated population density,when the population density increases daily by 1 person/km^(2) in a county or prefectural-level administrative unit with an average size of 26,000 km^(2),the county would have an additional 3.6 confirmed cases and 0.054 death cases after 5 days,which is the illness onset time for a new COVID-19 case.After 14 days,which is the maximum incubation period of the COVID-19 virus,there would be 5 new confirmed cases and 0.092 death cases.However,in neighboring regions,there can be 0.96 fewer people infected with COVID-19 on average per day as a result of strong intervention of local and neighboring authorities.The primary innovation and contribution are that this is the first quantitative assessment of the impacts of dynamic population density on the COVID-19 pandemic.Additionally,the direct and indirect effects of the impact are estimated using spatial panel models.The models that control the unobserved factors improve the reliability of the estimation,as validated by random experiments and the use of the Baidu migration dataset.

Spatiotemporal evolution of PM_(2.5)concentrations in urban agglomerations of China

As the main form of new urbanization in China,urban agglomerations are an important platform to support national economic growth,promote coordinated regional development,and participate in international competition and cooperation.However,they have become core areas for air pollution.This study used PM_(2.5)data from NASA atmospheric remote sensing image inversion from 2000 to 2015 and spatial analysis including a spatial Durbin model to reveal the spatio-temporal evolution characteristics and main factors controlling PM_(2.5)in China's urban agglomerations.The main conclusions are as follows:(1)From 2000 to 2015,the PM_(2.5)concentrations of China's urban agglomerations showed a growing trend with some volatility.In 2007,there was an inflection point.The number of low-concentration cities decreased,while the number of high-concentration cities increased.(2)The concentrations of PM_(2.5)in urban agglomerations were high in the west and low in the east,with the"Hu Line"as the boundary.The spatial differences were significant and increasing.The concentration of PM_(2.5)grew faster in urban agglomerations in the eastern and northeastern regions.(3)The urban agglomeration of PM_(2.5)had significant spatial concentrations.The hot spots were concentrated to the east of the Hu Line,and the number of hot-spot cities continued to rise.The cold spots were concentrated to the west of the Hu Line,and the number of cold-spot cities continued to decline.(4)There was a significant spatial spillover effect of PM_(2.5)pollution among cities within urban agglomerations.The main factors controlling PM_(2.5)pollution in different urban agglomerations had significant differences.Industrialization and energy consumption had a significant positive impact on PM_(2.5)pollution.Foreign direct investment had a significant negative impact on PM_(2.5)pollution in the southeast coastal and border urban agglomerations.Population density had a significant positive impact on PM_(2.5)pollution in a particular region,but this had the opposite effect in neighboring areas.Urbanization rate had a negative impact on PM_(2.5)pollution in national-level urban agglomerations,but this had the opposite effect in regional and local urban agglomerations.A high degree of industrial structure had a significant negative impact on PM_(2.5)pollution in a region,but this had an opposite effect in neighboring regions.Technical support level had a significant impact on PM_(2.5)pollution,but there were lag effects and rebound effects.

Understanding inter-term fossil energy consumption pathways in China based on sustainable development goals

Sustainable development goals(SDGs)and fossil energy are the core elements of almost all major challenges and opportunities for achieving social development.Particularly,energy sustainability has become one of the pivotal drivers of China’s economy.This study constructed a comprehensive evaluation index system for the provincial-level sustainable development of fossil energy in China covering three major dimensions(socio-economic,resource,and environmental).Moreover,a set of criteria for measuring the SDGs of fossil energy at the national level in China was developed.Based on the provincial panel data collected from 30 provinces from 2010 through 2019,a spatial econometric model was applied to empirically evaluate the effects of SDGs on fossil energy consumption.The results showed that the SDGs not only promote the reduction of fossil energy consumption with substantial negative spatial spillover effects,but also revealed differences between northern and southern China.To promote the early achievement of sustainable fossil energy development in China,the transformation and upgradation of fossil energy systems should be conducted early and inter-regional cooperation should be strengthened according to local conditions to jointly achieve the SDGs.