Low-carbon technology innovation responding to climate change from the perspective of spatial spillover effects

China's technological efforts to tackle climate change have lasted for many years. It is necessary to test the effect of these efforts with quantitative method. To be exact, whether and how China's low-carbon technology innovation responds to climate change should be tested. Based on the2004-2015 panel data of 30 provinces in China, we use the method of ESDA analyzing the spatial correlation of China's low-carbon innovation technology. Furthermore, we use the spatial Durbin model empirically analyzing the spatial spillover effects. The results obtained are as follows: first,supply and demand of Chinese low-carbon innovation has some deviation in the spatial distribution. The low-carbon technology innovation as the supply factor shows the characteristics of expanding from the east to the west. Innovation in eastern China has always been the most active, but innovative activities in the middle and western China are gradually decreased.However, carbon emissions have the characteristics of moving westward, implying the change of technology demand different from technology supply. Second, China's low-carbon innovation actively responds to the trend of climate change, indicating China's technological efforts have paid off. However, the spatial spillover effects are not significant, showing that the efforts in each region of China still work for himself. Third, environmental regulation and market pull are important factors for low-carbon technology innovation. Among them, both supporting policy and inhibitory policy have significant impact on the local low-carbon technology innovation, but no significant spatial spillover effects. It shows that environmental policies in different regions are competitive and lack of demonstration effects. Economic growth and export as market pull have higher level of effect on low-carbon technology innovation for both local and adjacent areas.Some policy implications are proposed based on these results finally.

Spatial spillover effects of transport infrastructure on regional economic growth

Spatial spillover effects,either positive or negative,of transport infrastructure,highways/expressways,etc.,on regional economic growth are proposed.Using the panel data for 11 cities of Zhejiang province from 1994 to 2003,a spatial production function is applied to examine the spatial spillovers which can be generated as a positive output spillover from the transport infrastructure between neighboring cities.Some spatial weighted matrices are adopted to define different neighboring cities to measure how easily factors or economic activities can migrate between regions.The estimation results show that the output elasticity of the highway infrastructure in 11 cities are all insignificant at a 5% significance level;hence,highway infrastructure in a region cannot explain the same region's economic growth.On the other hand,the highway infrastructure of other contiguous regions has positive spillover effects on a same region's economic growth.

Energy consumption,air quality,and air pollution spatial spillover effects:evidence from the Yangtze River Delta of China

Based on analysis of the theoretical impact of energy consumption on air quality,taking 20 heavily polluted cities in the Yangtze River Delta of China as the object,we construct a Spatial Dubin Model,and estimate the effect of energy consumption on air quality and the spatial spillover effects of air pollution.We come to the following conclusions:First,the regional air quality has significant spatial dependence and spatial heterogeneity.Second,under three kinds of spatial associated mode,energy consumption has a negative impact on air quality,and the air pollution arising from energy consumption has a negative intra-regional spillover effect.The effect is strongest under the spatial distance weight matrix,followed by the economic distance,and the adjacent spatial weight matrix,which are−0.7926,−0.4547,and−0.4539,respectively.Third,in addition,under the adjacent space and economic distance space matrix,energy consumption has a significant negative effect on air quality,and the inter-regional spillover effects are−0.1513 and−2.5736,respectively.Meanwhile,considering spatial distance and economic development,the inter-regional spillover effect is much larger than is the intra-regional spillover effect.In general,the total spillover effect is at−0.6053 and−3.0284.

Impact of digital economy development on carbon productivity:An empirical analysis based on threshold effect and spatial spillover effect

Utilizing provincial panel data from 2014 to 2020,this study employs a fixed effect model,a threshold effect model,and a spatial lag model to empirically examine the correlation between digital economic development and carbon productivity.The findings indicate that digital economic development significantly contributes to the enhancement of carbon productivity in the long term.Furthermore,through instrumental variable method,replacement of explanatory variables and other methods to test its endogeneity and stability,the results remain robust.In terms of regional heterogeneity,the impact of digital economic development on carbon productivity is less pronounced in the central and western regions compared to the eastern region.Additionally,further investigation reveals that industrial structure upgrading and science and technology investment level exhibit different threshold effects on the influence of digital economy development level on carbon productivity.Moreover,there is a significant spatial spillover effect of digital economy development on carbon productivity with H-H and L-L agglomeration spatial correlation.

Spatial Spillover Effects of the Impact of Agricultural Mechanization on Carbon Emission Intensity in Agriculture:An Empirical Study Based on the Panel Data of 282 Cities

The extreme weather caused by the global warming effect has triggered huge losses to agricultural production.A hot issue for governments and scholars is how to effectively reduce carbon emission intensity in agriculture.The agricultural farming practices that are high pollution and high energy cosuming have exacerbated the vulnerability of regional agroecosystems.The sustainable development of agriculture is faced with the two dilemmas of a low utilization rate of green resources and the serious pollution of farmland.Further,environmental and ecological carrying capacities have reached theirlimits,seriouslyhinderingtthe high-quality development of low-carbon agriculture in China.Thus,based on the panel data of 282 cities,the Spatial Dubin Model(SDM)is employed to examine the impact of agricultural mechanization on carbon emission intensity in agriculture.It is found that from 1999 to 2019 carbon emission intensity in agriculture showed an overall downward trend;as of 2019,the agricultural field had completed the target of carbon emission reduction,,oneyear aheadof schedule.From a local perspective,approximately 14.89%6of fagricultural industries in prefecture-level city have still not achieved carbon emission reduction targets,and agricultural carbon emission reduction tasks were better completed in major grain-producing areas than in nonmajor grain-producing areas.Agricultural mechanization has significantly reduced carbonemission intensityyinlocal agriculture production.The impact of agricultural mechanizationoncarbon emission intensity in agriculture has not only a significant negative spatial spillover effect but also a significant effect on spatial carbon emission reduction.Compared with non-major ggrain-producingareas,agricultural mechanization plays a greater role in reducing spatial carbon emissions in major grain-producing areas.Further studies find that agricultural mechanization is conducive to overcome difficulties,such as instability of property rights and land fragmentation,and to achieve large-scale agricultural production,thereby reducing agricultural carbon emissions in nearby regions.However,the transfer of rurallabor,adjustments to the structure of agricultural cultivation,and the centralized use of rural land restrict the development of the crossregional service market for agricultural machinery,which in turn weaken its contribution to spatial carbon emission reduction.At the end of this paper,it is suggested that Chinese governments at all levels should introduce subsidy policies for the cross-regional operation of agricultural machinery to solve the problem of their service market failure.Efforts should be made to stimulate the market to develop more energy-efficient and environmentally friendly agricultural machinery products while strictly controlling changes in the use of arableland in non-grain-producing areas,which aims to serve further agricultural mechanization and boost the high-quality development of low-carbon agriculture.

Has Transport Infrastructure Promoted Regional Economic Growth?——With an Analysis of the Spatial Spillover Effects of Transport Infrastructure

Taking full account of the synergistic effects of multidimensional factors on regional economic growth in China, this paper constructs a model of the spatial spillover effects of transport infrastructure on regional economic growth. Using provincial panel data from 1993 to 2009 and employing spatial econometric techniques, our empirical analysis comes to the following conclusions. （1） The total output elasticity of transport infrastructure for regional economic growth varies between 0.05 and 0.07, indicating its important role in such growth. （2） Transport infrastructure has very clear spatial spillover effects on regional economic growth; its role in regional economic growth will be overestimated if these are neglected. （3） For a specific region, transport infrastructure in other regions has mainly positive spillover effects on economic growth, but there is also evidence of negative spillover effects. （4） Among multidimensional factors contributing to regional economic growth, labor pluscapital stock from other parts of the public sector make the greatest contribution to regional economic growth in China, followed by the new economic growth factors and new economic geography.

Regional inequality, spatial spillover effects, and the factors influencing city-level energy-related carbon emissions in China

Data show that carbon emissions are increasing due to human energy consumption associated with economic development. As a result, a great deal of attention has been focused on efforts to reduce this growth in carbon emissions as well as to formulate policies to address and mitigate climate change. Although the majority of previous studies have explored the driving forces underlying Chinese carbon emissions, few have been carried out at the city-level because of the limited availability of relevant energy consumption statistics. Here, we utilize spatial autocorrelation, Markov-chain transitional matrices, a dynamic panel model, and system generalized distance estimation(Sys-GMM) to empirically evaluate the key determinants of carbon emissions at the city-level based on Chinese remote sensing data collected between 1992 and 2013. We also use these data to discuss observed spatial spillover effects taking into account spatiotemporal lag and a range of different geographical and economic weighting matrices. The results of this study suggest that regional discrepancies in city-level carbon emissions have decreased over time, which are consistent with a marked spatial spillover effect, and a ‘club' agglomeration of high-emissions. The evolution of these patterns also shows obvious path dependence, while the results of panel data analysis reveal the presence of a significant U-shaped relationship between carbon emissions and per capita GDP. Data also show that per capita carbon emissions have increased in concert with economic growth in most cities, and that a high-proportion of secondary industry and extensive investment growth have also exerted significant positive effects on city-level carbon emissions across China. In contrast, rapid population agglomeration, improvements in technology, increasing trade openness, and the accessibility and density of roads have all played a role in inhibiting carbon emissions. Thus, in order to reduce emissions, the Chinese government should legislate to inhibit the effects of factors that promote the release of carbon while at the same time acting to encourage those that mitigate this process. On the basis of the analysis presented in this study, we argue that optimizing industrial structures, streamlining extensive investment, increasing the level of technology, and improving road accessibility are all effective approaches to increase energy savings and reduce carbon emissions across China.

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.

Evolution and Determinants of Population Agglomeration in Less Developed Metropolitan Areas:A Case Study of the Taiyuan Metropolitan Area,China

It is of importance to enhance the urban areas'capacity for population aggregation in underdeveloped regions,aiming to rectify the imbalanced and insufficient pattern of economic development in China.Taking the Taiyuan Metropolitan Area(TMA)in central China as a case study,this paper examines the evolutionary process and characteristics of population agglomeration from 2000 to 2020,and identifies factors associated with agglomeration and their spatial effects.The findings indicated that:1)against the background of sustained population shrinkage in the provincial area,the TMA showed a demographic trend of steady increase,albeit with a decelerated growth rate.In the metropolitan area,urban population size continued to grow rapidly,whereas the rural areas endured sustained losses.Disparities in city size continued to widen,and the polarization of concentrated population in the core cities kept increasing.2)Agglomerations in both secondary and service industries had significant positive effects on local population agglomeration,with the former effect being stronger.Regional economic development,government fiscal expenditure,and financial advancement all contributed to facilitating local population clustering.From a spatial spillover perspective,service agglomeration and financial development promoted population agglomeration in surrounding areas.Conversely,fiscal expenditure inhibited such agglomeration.As for industrial agglomeration and regional economic development,their spatial spillover effects were non-significant.The results obtained reveal several policy implications aimed at enhancing the population agglomeration capacity of the metropolitan area in underdeveloped regions during the new era.

A Spatial Econometric Approach to Studying Regional Air Pollution in China

Using spatial econometric method,this paper investigates the mutual influence of air pollution among 31 Chinese provincial regions,together with the effects of energy mix and economic variations.Global spatial autocorrelation analysis reveals that significant positive spatial correlation exists for air pollution;Local spatial autocorrelation analysis indicates that pollution aggregation hot spots are concentrated in Beijing-Tianjin-Hebei region,the Yangtze River Delta and part of the central region between these two economic growth poles.This paper believes that industrial relocation is a major reason behind such distribution of air pollution in China as it has deepened the spatial correlation between interregional economy and pollution,which will further give rise to the spatial spillover effect of pollution.With the creation of the regression model of spatial and Environmental Kuznets Curve,the authors discovered that the level of pollution is closely related to energy mix and industrial structure.In addition,the inverted U-shape relationship between air pollution and economic development as demonstrated by previous studies does not exist or is yet to appear in China,where continuous growth of per capita GDP is accompanied by an increasing level of pollution.According to empirical analysis,the improvement of environmental quality at the expense of industrial relocation to neighboring regions is temporary.Due to the existence of spillover effect of pollution,regions that have enforced tighter environmental regulation such as Beijing and Tianjin are unable to acquire all benefits from such regulation.Treatment of air pollution necessitates interregional joint prevention and control.In the long run,adjusting energy mix and optimizing industrial structure are the key to fighting air pollution.But in the short run,reducing the consumption of inferior coal is the most effective option for China,which has seen multiple-fold increase in imports of inferior coal each year.

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.

Is the spatial impact of digital financial inclusion on CO_(2) emissions real?A spatial fluctuation spillover perspective

Digital financial inclusion(DFI)has the advantage of promoting information sharing,reducing transaction costs,and providing microloan platforms for small and medium-sized enterprises.It has also made outstanding contributions to decreasing CO_(2) emissions.However,the volatility correlation between DFI and CO_(2) emissions is still relatively unexplored.This research uses the spatial autoregressive process with conditional heteroscedastic errors(SARspARCH)model to evaluate the spatial fluctuation spillover impacts of DFI on CO_(2) emissions in 284 Chinese cities covering the period 2011-2016 following the IPAT model.The results indicate that CO_(2) emissions have significant spatial spillover and volatility effects.The fitted value of SARspARCH estimation results is more realistic than the SAR and spARCH model.DFI alleviates average CO_(2) emissions in Chinese cities.Moreover,spatial volatility weakens the negative influence of DFI on average carbon emissions.This study provides insights from which governments can strengthen inter-regional communication and synergistic emission-reduction capabilities,and promote the digitization of the financial sector to achieve carbon neutrality goals.

Poverty reduction effects of integrating high-quality development and cultural tourism in China

The integration of the cultural tourism industry with high-quality development is believed to be an important method of alleviating poverty.Most research in this area has focused on single towns,cities,or regions without considering the spillover effects of neighboring areas.To fill this gap,this study applies a spatial panel econometric model to empirically test the spatial spillover effects of integrating the cultural tourism industry with high-quality developments and their mechanisms of poverty alleviation based on provincial panel data of the Chinese Mainland from 2010 to 2020.Four key results are presented.First,there is an obvious spatial dependence on the high-quality development scale,specialization level,and poverty level of cultural tourism integration.The common panel model is found to overestimate the impact of this integration on poverty alleviation because it ignores the spatial spillover-related explanatory variables.Second,the scale of development quality is found to have no significant impact on poverty alleviation when integrating cultural tourism;however,the level of development specialization has both a direct impact on poverty alleviation and the spatial spillover effect.Third,the integration of the cultural tourism industry in the Central and Western regions is shown to have a strong direct effect on poverty reduction through high-quality development.However,the spillover effect on poverty reduction in the Eastern region is greater than that in the Central and Western regions.Fourth,the integration of high-quality development and cultural tourism is found to have a direct impact on poverty alleviation overall by promoting tourism consumption,material capital accumulation,and structural transformation.

The impact of carbon emissions trading policy on carbon emission efficiency in Chinese cities:Evidence from a quasi-natural experiment

Based on a panel dataset spanning from 2003 to 2019 and encompassing 284 prefecture-level cities in China,this study treats the implementation of the carbon emissions trading policy(CETP)as a quasi-natural experiment.In addition,it employs a spatial difference-in-differences(DID)framework to quantify both the direct and spatially mediated impacts of CETP on urban carbon emission efficiency(CEE).The investigation further delves into the underlying channels of influence and variations within this context.The findings demonstrate that CETP effectively enhances CEE within the cities chosen for piloting;however,it concurrently dampens CEE in nonpiloting neighboring cities.These conclusions remain robust across diverse sensitivity tests.The analysis of mechanisms reveals that CETP’s influence on urban CEE primarily operates through the avenues of technological innovation and optimization of energy structure.Moreover,the study of variances discloses that CETP’s direct effect significantly advances CEE in eastern,old industrial base,and central cities.In terms of indirect effects,a pronounced adverse spatial spillover effect is observed in eastern and old industrial base cities,while noteworthy positive spatial spillover effects emerge in central cities.Notably,the spatial extent of CETP’s influence on urban CEE declines after reaching a distance of 900 km.These insights furnish valuable guidance for China in refining its nationwide carbon market and expediting the shift toward a low-carbon economy.

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.

Regional Correlation and Spatial Spillovers in China's Regional Economic Growth

Using an exploratory spatial data analytical tool, this paper examines the spatial distribution patterns and features of GDP per capita in each of China＇s provinces. The results indicate that a positive global spatial autocorrelation exists and increases over time. At the same time, local correlation shows that China＇s local spatial agglomeration is becoming ever more marked. Based on a new economic geography model signaling the effects of market potential on regional economic development, this paper further investigates spatial spillover effects on Chinese regional economic growth through econometric analysis. Our empirical analysis shows that spatial spillovers play an important role in China＇s regional economic development. Every 1 percent increase in market potential leads to an increase of 0.47 percent in regional GDP per capita, outperforming increases in regional fixed assets investment in terms of elasticity. Of course, our analysis also shows that spatial spillover effects decrease as inter-regional distance increases.

Spatial impact of digital finance on carbon productivity

Low carbon productivity has been identified as a key direction for China’s future development.As an important driving force for economic growth,the question of whether digital finance that is reliant on digital technology can support the development of a low-carbon urban economy remains unresolved.Based on the carbon productivity measured by panel data from 201 cities for the period 2011-2020,this study applies the spatial Dubin model and threshold regression model to explore the impact of digital finance on carbon productivity,yielding the following key conclusions.First,the spatial distribution heterogeneity of carbon productivity in China’s eastern region is higher than that in the western region,and both productivity and digital finance are characterized by high(low)-high(low)dotted spatial agglomeration.Second,digital finance can significantly improve carbon productivity via two transmission channels:the human capital and marketization effects.At the same time,digital finance exerts a spatial spillover effect on carbon productivity,and rising local digital finance levels will increase carbon productivity in neighboring areas.Heterogeneity analysis indicates that the spillover effect of digital finance in urban agglomerations and eastern regions is more significant.Third,fixed-asset investment has a positive nonlinear moderating effect on digital finance,thus improving carbon productivity.When the per capita investment in fixed assets does not exceed 682.73 yuan,digital finance exerts only a limit pulling effect on carbon productivity;when it is higher than this value,the pulling effect is intensified.

Impact of environmental regulations on the efficient control of industrial pollution in China

The continuous progress of industrialization is a fundamental cause of China’s increasingly severe environmental pollution problem.Improving the efficiency of industrial pollution control is an inevitable choice to effectively decrease pollution emissions,thus winning the battle of pollution prevention and control.In this paper,we used the stochastic frontier analysis(SFA)model to measure the provincial efficiency of industrial pollution control based on the input and output data of industrial pollution control of 29 administrative provinces in China from 2000 to 2017.On this basis,a spatial econometric model was used to explore the influence of environmental regulation intensity on the efficiency of industrial pollution control.In addition,the spatial spillover effect of pollution reduction was thoroughly examined.The results show that:(1)The efficiency of industrial pollution control in China has improved year by year,but the overall efficiency is still low,with the average value increasing from 0.165 in 2000 to 0.309 in 2017.Furthermore,there is significant regional heterogeneity with the highest efficiency level in the east and lowest efficiency level in the west.(2)By increasing the financial and material input,the efficiency of industrial pollution control has increased.However,the increase of human input has not been so helpful.(3)The global Moran’s I index is significantly greater than zero,indicating a strong spatial correlation and agglomeration in the efficiency of industrial pollution control,which is reflected in high-high agglomeration in the eastern region and low-low agglomeration in the western region.(4)Stringent environmental regulation has a positive effect on improving the efficiency of industrial pollution control.It also imposes a positive spatial spillover effect,indicating a strategic interaction and coordination of regional pollution control.In line with this,related proposals have been made to optimize the investment structure for environmental pollution control,establish a flow mechanism for the factor market,and strengthen the environmental responsibility awareness of state-owned enterprises.On this basis,we expect to provide a policy for improving the efficiency of industrial pollution control and promoting regional joint pollution control in China.

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.

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.