Journal articles on the topic 'Spatio-temporal Durbin model'
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1
Croonenbroeck, Carsten, and Marcel Palm. "A spatio-temporal Durbin fixed effects IV-Model for ENTSO-E electricity flows analysis." Renewable Energy 148 (April 2020): 205–13. http://dx.doi.org/10.1016/j.renene.2019.11.133.
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Hawkins, Jason, and Khandker Nurul Habib. "Spatio-Temporal Hedonic Price Model to Investigate the Dynamics of Housing Prices in Contexts of Urban Form and Transportation Services in Toronto." Transportation Research Record: Journal of the Transportation Research Board 2672, no. 6 (June 8, 2018): 21–30. http://dx.doi.org/10.1177/0361198118774153.
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A spatio-temporal hedonic price model is developed for the Greater Toronto area to examine the effects of urban configurations and proximity to transit services on housing price. A spatial Durbin panel model is utilized to account for both spatial and temporal autocorrelation. This model is shown to have advantages through its ability to reduce the number of explanatory variables required to obtain a strong fit with empirical data. Analysis is completed for the period of 1996 to 2017 and distinctions are made in housing stock between single-family houses, townhouses, and condominiums. It is shown that heterogeneities exist between the hedonic representations of each dwelling type and that separate models should be employed for each. In all cases, the average income of the community, its distance to the central business district (CBD), and population and employment density are found to be significant factors in the determination of price.
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Xu, Zhen, Xiang Zhu, Guoen Wei, and Xiao Ouyang. "Spatio-Temporal Evolution and Mechanism Analysis of China’s Regional Innovation Efficiency." Sustainability 13, no. 19 (October 7, 2021): 11089. http://dx.doi.org/10.3390/su131911089.
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Improving regional innovation efficiency is the key to developing an innovative country. Exploring the spatio-temporal evolution characteristics of regional innovation efficiency is crucial in the formulation of regional policies and the choice of innovation models. This study used the superdata envelopment analysis method with undesirable outputs in evaluating the innovation efficiency of Chinese provinces. To assess the spatial spillover effects of innovation factors, the spatial autocorrelation and spatial Durbin model were adopted to characterize the spatio-temporal evolution, spatial correlation, and mechanisms of innovation efficiency. The highlights of the results are as follows: (1) The time-series changes in innovation efficiency showed a general trend from declining to increasing. (2) There were pronounced regional differences in innovation efficiency. The innovation efficiencies at the provincial level evolved from being decentralized to concentrated. The innovation efficiency was relatively stable in the eastern region and increased significantly in the central and western regions. The east–center–west evolution pattern gradually weakened. (3) The innovative efficiency exhibited spatial dependence, and the spatial agglomeration continued to increase. The extent of hot spots expanded, while cold spots shrunk slightly. (4) The scientific research environment, entrepreneurial environment, labor quality, and market environment were the essential elements that improved innovation efficiency. The impact of the different factors on innovation efficiency at different periods exhibited significant spatial heterogeneity.
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Su, Hao, and Shuo Yang. "Spatio-Temporal Urban Land Green Use Efficiency under Carbon Emission Constraints in the Yellow River Basin, China." International Journal of Environmental Research and Public Health 19, no. 19 (October 4, 2022): 12700. http://dx.doi.org/10.3390/ijerph191912700.
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In the context of rapid urbanization and limited land amount, it is essential to scientifically evaluate the urban land green use efficiency (ULGUE) to promote regional sustainable development. Current studies are of great value for enriching the theoretical system and application research of ULGUE. Still, most of them only consider industrial pollution but ignore carbon emission as an essential environmental influencing indicator. This paper introduced carbon emissions into the input-output indicator system, measured ULGUE of 57 cities in the Yellow River Basin (YRB) over the 2004–2017 periods using the super-efficiency slacked-based measure (Super-SBM) model, analyzed its spatio-temporal patterns with the kernel density estimation (KDE) model and spatial autocorrelation model, and then identified the influencing factors with the Spatial Durbin model (SDM). As shown by the results, firstly, the ULGUE in the YRB over the 2004–2017 periods showed a trend of first decreasing and then increasing. Secondly, the ULGUE exhibited spatio-temporal imbalance characteristics across the YRB. Thirdly, ULGUE was the interaction of multiple indicators, and its influencing factors had spatial spillover effects. All in all, this paper is fundamental to the high-quality development of cities in the background of the Chinese policy of “carbon peak, carbon neutralization”.
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Ma, Debin, Jie Zhang, Ziyi Wang, and Dongqi Sun. "Spatio-Temporal Evolution and Influencing Factors of Open Economy Development in the Yangtze River Delta Area." Land 11, no. 10 (October 16, 2022): 1813. http://dx.doi.org/10.3390/land11101813.
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Since economic globalization is unstable, it is difficult for the traditional open economic development model to meet the requirements of China’s development, and there is an urgent need for new ideas and models to be reoriented. Based on the analysis of the development mechanism of China’s open economy at this stage, we used the entropy method, Theil coefficient, Gini coefficient, and spatial Durbin model (SDM) to analyze the spatio-temporal pattern and influencing factors of the high-quality development level of the open economy in the Yangtze River Delta Area (YRDA). The results indicated that during the study period, the development level and development difference of open economy were on the rise, and the spatial difference in different regions was significant. The development of open economy was affected by many factors, and there was a spatial spillover effect. Based on the existing problems, at the stage of high-quality economic development, the YDRA should seize the opportunities brought by the new development pattern, improve government services, optimize innovation drive, and promote the development of open economy to a higher level. We believe that the results of this study can also provide relevant experience for the development of open economy in other regions of China.
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Zhuang, Rulong, Kena Mi, and Zhangwei Feng. "Industrial Co-Agglomeration and Air Pollution Reduction: An Empirical Evidence Based on Provincial Panel Data." International Journal of Environmental Research and Public Health 18, no. 22 (November 18, 2021): 12097. http://dx.doi.org/10.3390/ijerph182212097.
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Industrial co-agglomeration plays a significant role in the moving up of the manufacturing industry in the value chain and in transforming China from a manufacturing giant into a world manufacturing power. This study establishes a co-aggregation index to explore spatio-temporal changes of the co-agglomeration between manufacturing and producer services in 30 provinces of China from 2004 to 2019. Furthermore, we use spatial Durbin model to analyze the impact of industrial co-agglomeration on air pollution reduction. We find that (1) the co-agglomeration index varies remarkably at spatio-temporal scale; (2) high co-agglomeration index is mainly distributed in eastern and central China, while low co-agglomeration index is mainly located in the western region; (3) the co-agglomeration index presents a cluster pattern among provinces, with the cluster of high value in eastern China and the cluster of low value in western China; and (4) the co-agglomeration between manufacturing and producer services is proven effetely to reduce air pollution, which is accompanied with spatial spillover effect. We also provided policy implications in line with diverse industries, multi hierarchies, and different regions, promoting the coordination of manufacturing and producer services and improving air quality.
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Vallés-Giménez, Jaime, and Anabel Zárate-Marco. "A Dynamic Spatial Panel of Subnational GHG Emissions: Environmental Effectiveness of Emissions Taxes in Spanish Regions." Sustainability 12, no. 7 (April 3, 2020): 2872. http://dx.doi.org/10.3390/su12072872.
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In this paper we study the effectiveness of environmental taxes and policies of the regional level of government to reduce GHG emissions. We use panel data for the 17 Spanish regions in the period 1999–2017, controlling the spatial nexus between regions and using a dynamic Durbin model. The results show there is spatial dependence and spatio-temporal persistence of GHG emissions at the regional level in Spain, and that in this context, the taxes and policies intended to manage emissions introduce a slight disincentive to generating them. This fact, together with some relative decoupling which seems to exist between growth and emissions at the regional level, would suggest the need for tougher measures to combat environmental pollution in order to meet EU commitments.
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Hu, Shuyun, Dazhuan Ge, Guojian Hu, Jingwen Sun, Yingyi Ma, Mengqiu Lu, and Yuqi Lu. "The Patterns and Mechanisms of Land Price Divergence in Multiple Industries from the Perspective of Element Flows: The Case of the Yangtze River Delta, China." Land 11, no. 2 (January 25, 2022): 188. http://dx.doi.org/10.3390/land11020188.
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An in-depth exploration of the inner mechanisms of the spatio-temporal evolution of land prices in multiple industries (residential, commercial service and industrial) is of great significance for improving the overall economic development efficiency and resource allocation capacity of the region. Based on urban land price transaction data provided by the China Land Market Network, 307 county-level units in the Yangtze River Delta were used as the study area and spatial auto-correlation and spatio-temporal hotspot analysis were used to explore the spatial variation and temporal changes of land prices in multiple industries in the Yangtze River Delta from 2008 to 2018. The three-dimensional driving theory of land “demand + supply + market” was used as the basis to construct the index system of influential factors and the Spatial Durbin Model was used to explore the mechanism of the spatio-temporal variation of land prices in multiple industries. The results show that the land prices of multiple industries in the Yangtze River Delta are generally high in the east and low in the west and high in the south and low in the north, which is spatially consistent with the level of regional economic development. Due to the disparity in economic development between the regions, factors such as population, capital, technology and information are redistributed and fed into each other’s cycles between cities. The resulting spatial differences in land market supply and demand are intrinsic to the spatial differentiation of urban land prices. It is further proposed that land prices are a monetized expression of the abundance of resources in a city and that land prices are determined by the combined ability of regional resource factors to be allocated. Thus, land price differentiation reflects differences in the level of comprehensive regional development. Finally, the dynamic interaction of various factors on land values is used to promote the division of urban functions and regional economic development, which is an effective way to promote high-quality integrated regional development.
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Zhang, Shumin, Yongze Lv, and Baolei Zhang. "Spatio-Temporal Evolution and Influencing Factors of Green Development in the Yellow River Basin of China." Sustainability 14, no. 19 (September 29, 2022): 12407. http://dx.doi.org/10.3390/su141912407.
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Globally, nations and regions have pushed for “green development (GD)”, a sustainable development strategy that considers the integrated growth of “economy–environment–society”. As it is an area of China that provides an ecological function and is an important energy base, it is necessary to explore the current situation and factors influencing GD in the Yellow River Basin (YRB). Therefore, first, this paper constructs a GD indicator system from a multi-dimensional perspective, measures the GD of 79 prefecture-level cities in the YRB from 2006 to 2019 by using the entropy method, and analyzes the evolution of time series according to the results. We found that the YRB’s GD showed an overall increase during the study period, rising from 0.1261 to 0.2195, but the level was low. Second, we analyzed the spatial characteristics of the YRB’s GD using a spatial analysis method and concluded that GD varied significantly across cities in the YRB. The YRB presented spatial distribution characteristics with obvious “quad-core pieces”, and there was a high intensity of spatial correlation and agglomeration. The spatial center of gravity of GD moved toward the southeast year by year. Third, we examined the influencing factors of the GD of the YRB through the spatial Durbin model. The study found that the spatial spillover effect on GD in the YRB was obvious, and the reasons affecting the GD of the YRB were heterogeneous. Finally, according to the conclusions of this research, we propose differentiated policies that are suitable for GD in the YRB.
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Jankiewicz, Mateusz, and Elżbieta Szulc. "Analysis of Spatial Effects in the Relationship between CO2 Emissions and Renewable Energy Consumption in the Context of Economic Growth." Energies 14, no. 18 (September 15, 2021): 5829. http://dx.doi.org/10.3390/en14185829.
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The paper presents a spatial approach to the analysis of the relationship between air pollution, economic growth, and renewable energy consumption. The economic growth of every country is based on the energy consumption that leads to an increase in national productivity. Using renewable energy is very important for the environmental protection and security of the earth’s resources. Promoting environmentally friendly operations increases awareness of sustainable development, which is currently a major concern of state governments. In this study, we explored the influence of economic growth and the share of renewable energy out of total energy consumption on CO2 emissions. The study was based on the classical environmental Kuznets curve (EKC) and enriched with the spatial dependencies. In particular, we determined the spatial spillovers in the form of the indirect effects of changes in renewable energy consumption of a specific country on the CO2 emissions of neighboring countries. A neighborhood in this study was defined by ecological development similarity. The neighborhood matrix was constructed based on the values of the ecological footprint measure. We used the spatio-temporal Durbin model, with which the indirect effects were determined in relation to the spatially lagged renewable energy consumption. The results of our study also show the strength of the effects caused by imitating actions from the states with high levels of environmental protection. The study was conducted using data for 75 selected countries from the period of 2013–2019. Cumulative spatial and spatio-temporal effects allowed us to determine (1) the countries with the greatest impact on others and (2) the countries that follow the leading ones.
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Lu, Fei, Huaiguo Ren, and Xinglong Zhai. "Spatio-temporal evolution and influencing factors of culture and tourism integration efficiency in Shandong Province, China under high-quality development." PLOS ONE 17, no. 12 (December 30, 2022): e0277063. http://dx.doi.org/10.1371/journal.pone.0277063.
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Improving culture and tourism integration efficiency is an important way to promote the high-quality development of cultural tourism. According to the inherent requirements of high-quality development, this paper constructed an evaluation indicator system for culture and tourism integration efficiency. Then, the culture and tourism integration efficiency of 16 cities in Shandong Province, China during the period from 2010 to 2019 was measured with the benevolent DEA cross-efficiency model. On the basis of exploratory spatial data analysis and dynamic spatial Durbin model, we explored the spatio-temporal evolution characteristics and influencing factors of culture and tourism integration efficiency in Shandong Province. The results show that from 2010 to 2019, the culture and tourism integration efficiency in Shandong Province has experienced three stages of "rapid growth-rapid decline-stable rise period". The spatial pattern has changed from "high in the east and low in the west" to "high in the central and low in the north and south", and regions with high integration efficiency are mainly concentrated in Jiaodong Peninsula. The level of economic development significantly promotes the culture and tourism integration efficiency in local and neighboring cities in the short and long term, while policy environment has a significant negative impact. Traffic conditions and human capital only promote the culture and tourism integration efficiency in local cities. The level of information development and openness degree only have a long-term effect on the culture and tourism integration efficiency, without short-term effect. The research results are of great significance to improve the growth quality and sustainable development of cultural tourism in Shandong Province. Our work could provide a scientific basis for maximizing the allocation benefits of cultural and tourism resources in similar regions in the world.
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Wu, Yue, Zexu Han, Auwalu Faisal Koko, Siyuan Zhang, Nan Ding, and Jiayang Luo. "Analyzing the Spatio-Temporal Dynamics of Urban Land Use Expansion and Its Influencing Factors in Zhejiang Province, China." International Journal of Environmental Research and Public Health 19, no. 24 (December 9, 2022): 16580. http://dx.doi.org/10.3390/ijerph192416580.
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The 21st century expansion of built-up areas due to rapid urbanization has recently been at the forefront of global land use/land cover research. Knowledge of the changing dynamics of urban land use is crucial for the monitoring of urbanization and the promotion of sustainable urban development. In this paper, Zhejiang Province was selected as the study area. It is a region with rapid urban growth located along the southeastern coast of China, with a highly developed economy but with a shortage of land resources. We employed remotely sensed and socio-economic panel data for the period between 1990 and 2020 to monitor urban land use changes and utilized the spatial Durbin model (SDM) to examine the urbanization process and the various driving factors of rapid urban expansion in Zhejiang Province, China, from 1990 to 2020. The study’s results revealed substantial urban growth of about 6899.59 km2, i.e., 6.6%, whereas agricultural land decreased by 4320.68 km2, i.e., 4.19%. The rapid urban development was primarily attributed to the transformation of farmlands, forestlands, and water bodies into built-up areas by nearly 86.9%, 6.94%, and 6.06%, respectively. The built-up areas revealed features of spatial clustering. The study showed that the expansion hotspots were mainly distributed within the urban fabric of cities such as Hangzhou, Ningbo, Jinhua–Yiwu, and Wenzhou–Taizhou. The results further revealed the substantial influence of urban growth on the local areas of the province. As the core explanatory variables, population and economic development significantly promoted local urban expansion. The study’s findings indicated a positive spatial spillover effect as regards the influence of economic development on the study area’s urban growth, whereas the spatial spillover effect of the population was negative. Therefore, economic development was a major driving factor contributing immensely to the expansion of urban areas in Zhejiang Province, especially in the 26 mountainous counties of the province. The study enriches our understanding of the transformation of LULC and the changing dynamics of urban areas in China and provides the necessary research data that are vital for urban land-use planners and decision-makers to overcome the negative consequences of the expansion of urban areas due to the continuous economic growth of China.
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Wang, Zhen, Xupeng Zhang, Chaozheng Zhang, and Qing Yang. "How Regional Integration Affects Urban Green Development Efficiency: Evidence from Urban Agglomeration in the Middle Reaches of the Yangtze River." International Journal of Environmental Research and Public Health 19, no. 13 (June 28, 2022): 7937. http://dx.doi.org/10.3390/ijerph19137937.
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Unlocking the relationship between regional integration and urban green development efficiency (UGDE) is of great importance for boosting regional high-quality development and promoting sustainable urban development patterns. Although studies have analyzed the spatio-temporal evolution and influencing factors of regional integration and UGDE, the impact of regional integration on UGDE remains untested. In this paper, we construct a conceptual framework to analyze how regional integration can influence UGDE through promoting the factors mobility and optimizing the industrial layout. In addition, we further choose the urban agglomeration in the middle reaches of the Yangtze River (UAMRYR), a rapidly growing urban agglomeration in central China, as a case to investigate the spatial spillover effect of regional integration on UGDE from 2003 to 2017. We quantify the UGDE with a random forest model, then estimate the underlying determinants of the UGDE with a spatial Durbin model. Results indicated that (1) the regional integration level and the UGDE index of the UAMRYR and its three sub-urban agglomerations show an increasing trend; (2) for every 1% increase in the level of regional integration, the level of UGDE will increase by 0.8307%; (3) the impact of regional integration on UGDE has obvious regional heterogeneity; while playing a promoting effect in the Wuhan urban agglomeration and the Changsha-Zhuzhou-Xiangtan urban agglomeration, it shows an inhibitory effect in the Poyang Lake urban agglomeration. We conclude that regional integration in agglomeration areas can accelerate the factors flow and optimize the industrial layout for improving UGDE.
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Li, Haishi, Xiangyi Xu, and Shuaishuai Li. "Does Entrepreneurship Contribute to Innovation Performance When Considering Spatial Spillover Effects? Evidence From the Automobile Industrial Cluster in China." SAGE Open 10, no. 4 (October 2020): 215824402098299. http://dx.doi.org/10.1177/2158244020982994.
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Entrepreneurship, as one of the important factors to promote industrial innovation, is closely related to the development of the regional economy. Based on the methods of Kernel density and standard deviation ellipse, this article presents the spatio-temporal patterns of entrepreneurship and innovation performance. The article also examines the spatial spillover mechanism of entrepreneurship on innovation performance by establishing spatial Durbin models. The heterogeneous results of the spatial regression models in six clusters are also discussed. The final results show that the spatio-temporal patterns of entrepreneurship are gradually presenting three major hot spots and two secondary hot spots while the spatio-temporal patterns of innovation performance are presenting four major hot spots and a secondary hot spot; the spatial distribution of both entrepreneurship and innovation performance are changing regularly; the spillover effects of entrepreneurship and innovation performance are both significant; the spatial spillover mechanisms in six automobile industrial clusters are different. The results can provide empirical support for decision-making in the automobile industry in China in the future.
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Lee, Dae-Jin, María Durbán, Diego Ayma, and Jan Van de Kassteele. "Modeling latent spatio-temporal disease incidence using penalized composite link models." PLOS ONE 17, no. 3 (March 10, 2022): e0263711. http://dx.doi.org/10.1371/journal.pone.0263711.
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Epidemiological data are frequently recorded at coarse spatio-temporal resolutions to protect confidential information or to summarize it in a compact manner. However, the detailed patterns followed by the source data, which may be of interest to researchers and public health officials, are overlooked. We propose to use the penalized composite link model (Eilers PCH (2007)), combined with spatio-temporal P-splines methodology (Lee D.-J., Durban M (2011)) to estimate the underlying trend within data that have been aggregated not only in space, but also in time. Model estimation is carried out within a generalized linear mixed model framework, and sophisticated algorithms are used to speed up computations that otherwise would be unfeasible. The model is then used to analyze data obtained during the largest outbreak of Q-fever in the Netherlands.
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Song, Xueqian, Yongping Wei, Wei Deng, Shaoyao Zhang, Peng Zhou, Ying Liu, and Jiangjun Wan. "Spatio-Temporal Distribution, Spillover Effects and Influences of China’s Two Levels of Public Healthcare Resources." International Journal of Environmental Research and Public Health 16, no. 4 (February 17, 2019): 582. http://dx.doi.org/10.3390/ijerph16040582.
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In China, upper-level healthcare (ULHC) and lower-level healthcare (LLHC) provide different public medical and health services. Only when these two levels of healthcare resources are distributed equally and synergistically can the public’s demands for healthcare be met fairly. Despite a number of previous studies having analysed the spatial distribution of healthcare and its determinants, few have evaluated the differences in spatial equity between ULHC and LLHC and investigated their institutional, geographical and socioeconomic influences and spillover effects. This study aims to bridge this gap by analysing panel data on the two levels of healthcare resources in 31 Chinese provinces covering the period 2003–2015 using Moran’s I models and dynamic spatial Durbin panel models (DSDMs). The results indicate that, over the study period, although both levels of healthcare resources improved considerably in all regions, spatial disparities were large. The spatio-temporal characteristics of ULHC and LLHC differed, although both levels were relatively low to the north-west of the Hu Huanyong Line. DSDM analysis revealed direct and indirect effects at both short-and long-term scales for both levels of healthcare resources. Meanwhile, the influencing factors had different impacts on the different levels of healthcare resources. In general, long-term effects were greater for ULHC and short-term effects were greater for LLHC. The spillover effects of ULHC were more significant than those of LLHC. More specifically, industrial structure, traffic accessibility, government expenditure and family healthcare expenditure were the main determinants of ULHC, while industrial structure, urbanisation, topography, traffic accessibility, government expenditure and family healthcare expenditure were the main determinants of LLHC. These findings have important implications for policymakers seeking to optimize the availability of the two levels of healthcare resources.
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Chang, Jie, Pingjun Sun, and Guoen Wei. "Spatial Driven Effects of Multi-Dimensional Urbanization on Carbon Emissions: A Case Study in Chengdu-Chongqing Urban Agglomeration." Land 11, no. 10 (October 20, 2022): 1858. http://dx.doi.org/10.3390/land11101858.
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Previous studies lacked attention to the spatial heterogeneity of the impact of urbanization on carbon emissions. To fill this knowledge gap, this study analyzed the spatio-temporal variations of carbon emissions (TCE), the per capita carbon intensity (PCI), and the economic carbon intensity (ECI) in the Chengdu-Chongqing urban agglomeration (CUA) based on the Open-Data Inventory for Anthropogenic Carbon dioxide (ODIAC) from 2000–2018. Bivariate spatial autocorrelation, and spatial Durbin models were combined to quantify the spatial correlation and driving mechanisms between carbon emission intensity and multi-dimensional urbanization (population, economic, and land urbanization). The following are the main results: (1) The TCE in CUA increased by 3.918 million tons at an average annual growth of 6.86%; CUA ranked last among China’s national strategic urban agglomerations in terms of TCE, PCI, and ECI. (2) High carbon emission values were concentrated in the Chengdu and Chongqing metropolitan areas, presenting a spatial feature of “Core-Periphery” gradient decay. (3) Nearly 30% of the agglomeration had carbon emission growth at low rates, with the growth cores concentrated in the main urban areas of Chengdu and Chongqing. (4) The “Low-Low” positive correlation was the main correlation type between multi-dimensional urbanization and carbon emissions and was distributed mainly in mountainous areas (e.g., Leshan and Ya’an). (5) Among the urbanization dimensions, the impacts on carbon emissions in local and adjacent areas exhibited varying levels of spatial heterogeneity. Economic urbanization was found to have the strongest positive direct and spillover effects; land urbanization inhibited the growth of carbon emissions in local and adjacent areas; population urbanization promoted carbon emission reduction in adjacent areas. Our findings provide support for CUA to carry out cross-city joint governance strategies of carbon emissions, also proving that regional carbon emission reduction should be an integration of various efforts including low-carbon living of residents, green transformation of economy and optimal land management.
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Wang, Qian, and Junfei Chen. "Spatio-temporal evaluation of the emergency capacity of the cross-regional rain-flood disaster in the Yangtze River Economic Belt in China." Scientific Reports 11, no. 1 (January 28, 2021). http://dx.doi.org/10.1038/s41598-021-82347-5.
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AbstractThis paper assesses the emergency capacity of rain-flood disaster in provinces along the Yangtze River Economic Belt (YREB), China, from 2013 to 2017. In this study, the evaluation index system of emergency capacity on rain-flood disaster was built from the economic-social and environmental-natural aspects, and spatial auto-correlation analysis was used to analyze spatial differentiation characteristics of the emergency capacity. Then, the Spatial Durbin Model (SDM) was used to analyze the influence mechanism of the development level of economic-social factors (ESF) subsystem, environmental-natural factors (ENF) subsystem and the coupling level of these two subsystems on the emergency capacity of rain-flood disaster in provinces. The findings show that the emergency capacity distribution of rain-flood disaster in the YREB presented a “decreasing” spatial pattern of eastern, central and western regions. The development of two subsystems has produced spatial spillover effect and diffusion effect on the neighboring areas. There was a high coupling degree between these two subsystems in the YREB. Although spillover effect existed in space, the spillover did not depend on economic distance.
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Zhang, Lijuan, Yuzhu Yu, and Yongheng Chen. "The Spatial-Temporal Evolution Characteristics and Driving Factors of the Green Utilization Efficiency of Urban Land in China." Frontiers in Environmental Science 10 (July 7, 2022). http://dx.doi.org/10.3389/fenvs.2022.955982.
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With the acceleration of urbanization, the urban land area of different spatial scales in China continues to increase, the urban land use efficiency is relatively low, and there are great regional differences. Land is an important material carrier for human production, life, and socio-economic activities. Reasonable measurement of the green utilization efficiency of urban land (Glande) has important theoretical and practical significance for the realization of urban land green use and the formulation of related policies. In this context, based on the publicly available data of 282 China’s cities at prefecture-level and above from 2009 to 2019, this study measures Glande with the Super-SBM data envelopment analysis model. After analyzing the spatio-temporal evolution characteristics of Glande, we employ the spatial dynamic Durbin model to analyze the spatial spillover effects. The results showed that: 1) Glande in China shows a fluctuating and rising trend, but it has not reached the effective state; 2) the agglomeration characteristics of urban land green use efficiency are significant, and from a regional point of view, it shows the pattern of western > eastern > central; 3) Glande is the result of the interaction of many factors. In the future, measures should be taken according to urban policies and local conditions, and differential measures should be taken to improve Glande.
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Cui, Zhe, Fang-rong Ren, Qizheng Wei, and Zhengfeng Xi. "What drives the spatio-temporal distribution and spillover of air quality in China’s three urban agglomerations? Evidence from a two-stage approach." Frontiers in Ecology and Evolution 10 (September 14, 2022). http://dx.doi.org/10.3389/fevo.2022.977598.
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Beijing-Tianjin-Hebei urban agglomeration (BTH), Yangtze River Delta (YRD), and Pearl River Delta (PRD) are the most important economic hinterlands in China, offering high levels of economic development. In 2020, their proportion of China’s total GDP reached 39.28%. Over the 5 years of 2014–2018, the annual maximum air quality index (AQI) of the three major urban agglomerations was greater than 100, thus maintaining a grade III light pollution (100 < AQI < 200) in Chinese air standards. This research thus uses a two-stage empirical analysis method to explore the spatial-temporal dispersal physiognomies and spillover effects of air quality in these three major urban agglomerations. In the first stage, the Kriging interpolation method regionally estimates and displays the air quality monitoring sampling data. The results show that the air quality of these three major urban agglomerations is generally good from 2014 to 2018, the area of good air is gradually expanding, the AQI value is constantly decreasing, the air pollution of YRD is shifting from southeast to northwest, and the air pollution of PRD is increasing. The dyeing industry shows a trend of concentration from northwest to south-central. In the second stage, Moran’s I and Spatial Durbin Model (SDM) explore the spatial autocorrelation and spillover effects of air quality related variables. The results show that Moran’s I values in the spatial autocorrelation analysis all pass the significance test. Moreover, public transport, per capita GDP, science and technology expenditure, and the vegetation index all have a significant influence on the spatial dispersal of air quality in the three urban agglomerations, among which the direct effect of public transport and the indirect effect and total effect of the vegetation index are the most significant. Therefore, the China’s three major urban agglomerations (TMUA) ought to adjust the industrial structure, regional coordinated development, and clean technology innovation.
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Ding, Xuemeng, Lifei Gao, Guojun Wang, and Ying Nie. "Can the development of digital financial inclusion curb carbon emissions? Empirical test from spatial perspective." Frontiers in Environmental Science 10 (October 24, 2022). http://dx.doi.org/10.3389/fenvs.2022.1045878.
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As digital finance ushers into a new era, carbon emissions in China have been peaking, highlighting the necessity of carbon neutrality. This work uses a dynamic spatial Durbin model, combined with a mediating effect model of the data from 30 provinces from 2011 to 2019, to explore the impact, transmission paths, and spatio-temporal heterogeneity of digital finance (Df) on carbon emission intensity (Cg). Meanwhile, the validation explores the mediating role of technological innovation (Rd), industrial restructuring (Is), and entrepreneurial effects (Es) in the process of digital finance influencing green low-carbon development. The empirical results show that: first, digital finance (Df) has a promoting effect on regional CO2 reduction capacity (Cg), and this conclusion still holds under multiple robustness tests; second, digital finance (Df) can promote the regional CO2 reduction capacity (Cg) through two paths, namely, promoting technological progress (Rd) and optimizing industrial structure (Is); third, the impact of inclusive digital finance on CO2 emission intensity is heterogeneous. By analyzing regions with different economic development levels, we found that digital inclusive finance in the eastern region can enhance CO2 reduction capacity, while in the central and western regions, the impact is not significant. Given this situation, China, to achieve carbon neutrality, should boost financial development’s ability to reduce carbon emission, promote technological progress, and optimize the industrial structure, thus forming a green and low-carbon economic cycle. This paper fills the research gap on how digital finance can effectively promote green development while exerting economic effects, and at the same time, enriches the literature on factors influencing green and low-carbon development.
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Xie, Yifan, Liye Wang, Rui An, Xuan Luo, Yanchi Lu, Yaolin Liu, Shunbo Yao, and Yanfang Liu. "The Effect of Sloping Land Conversion Program on Soil Erosion in Shaanxi Province, China: A Spatial Panel Approach." Frontiers in Environmental Science 10 (May 30, 2022). http://dx.doi.org/10.3389/fenvs.2022.913712.
Full textAbstract:
Soil erosion is a land degradation process that may threaten the sustainability of ecosystem as well as cause severe social and economic problems. The studies on the effect of ecological restoration policies on soil erosion have been well documented, while the spatial relationships and spillover effects of the policies on soil erosion have been rarely scrutinized, though it is of great policy implications in soil erosion control. Based on the analysis of the spatio-temporal evolution of soil erosion in 107 counties of Shaanxi Province with Revised Universal Soil Loss Equation, this study employs a panel spatial Durbin model (SDM) with multi-source data for 107 counties from 2000 to 2015 to investigate the spatial effects of the Sloping Land Conversion Program (SLCP) on soil erosion control with investment data of SLCP. The results show that during 2000–2015, the average amount of soil erosion exhibited a significant downtrend contributed by the continuous investment of SLCP. The spatial economic results demonstrate that the own effect, as well as the spillover effect of investment on soil erosion control, is significant and positive. A 0.1 billion CNY increase in investment in a county will result in a reduction of the total soil erosion by 288.69 thousand t/a, of which 63% is contributed to the own direct effect on local soil erosion control and the 37% of it is contributed to the spillover effect on the neighbors. Moreover, rainfall has a significant and positive moderating effect on the mitigation effect of SLCP on soil erosion at county scale; the marginal contribution of investment of SLCP on soil erosion control in the county with less rainfall is greater than that with more rainfall. These findings contribute to further our understanding of the long-term effects of ecological restoration policies and transboundary ecological governance.