Spatial Durbin Model Research Articles

Has the geography of collaborative spaces been affected by the pandemic? France as a case study

ABSTRACT This study investigates the potential devolution of collaborative spaces (CSs) to rural areas during the COVID-19 pandemic and explores the factors driving this shift. Using a geolocalised database of French CSs and employing a spatial Durbin model, we found that the pandemic may have driven CS distribution towards less densely populated regions. Our study highlights the impact of the pandemic on economic landscapes and spatial patterns of shared workspaces, with implications for place-based policies and regional development. The results suggest that supporting CSs in remote areas with longer commuting times and distances can address the urban–rural divide, fostering territorial resilience in the post-pandemic era.

Innovation and wage inequalities: Evidence from Brazilian regions

AbstractInnovation can lead to wage inequalities through different channels. This paper explores this relationship, focusing on the direct and spillover (indirect) effects of these channels on personal wage inequalities in Brazilian regions. Product and process innovation are measured by patents and the acquisition of foreign technology through imports of capital goods respectively. Based on data that covers 482 regions from 2003 to 2014, we use the Spatial Durbin Model with spatial fixed effects to control spatial dependence and heterogeneity across regions. The results point to a positive association between our measures of innovation and wage inequalities. However, as we found a non‐linear relationship between imports of capital goods and wage inequality, an increase in imports of capital goods may reduce wage inequality. Other variables may also cause inequality to fall, such as the share of the population with a higher education degree and social skills. Drivers behind an increase in inequality are female participation in the workforce and the level of GDP per capita.

Research on the Impact of Digital Green Finance on Agricultural Green Total Factor Productivity: Evidence from China

Green development has become one of the important concepts leading China’s economic developments, and it is extremely meaningful to boost the continuous growth of agricultural green total factor productivity (AGTFP) to achieve the construction of a powerful agricultural country. Using China’s provincial data from 2011 to 2020, this manuscript calculates AGTFP through the SBM–GML model, and the digital green finance (DGF) through a comprehensive indicator system. The double fixed-effect model, quantile model and spatial Durbin model are used for in-depth study of the benchmark influence, the nonlinear effect and spatial spillover effect of DGF on AGTFP. The main research conclusions of the article are as follows: (1) DGF is significantly conducive to the improvement of AGTFP. Along with the continuous growth of AGTFP, the promoting effect of DGF has gradually increased. (2) In terms of impact path, green finance can properly promote the growth of AGTFP, while the role of the degree of digitization is not very significant. Meanwhile, the main channel for DGF to promote AGTFP is through green technology efficiency. (3) The impact of DGF on AGTFP varies spatially, while the role is more effective in regions with a higher degree of economic development and well-developed modernization. (4) There is a spatial spillover effect of DGF’s impact on AGTFP, which means that DGF can simultaneously promote the growth of AGTFP in local regions and neighboring regions.

Spatial analysis of the digital economy’s influence on urban sustainable development: A decade-long study of Chinese prefecture-level cities

Urban Sustainable Development (USD) is crucial for social advancement, and the rise of the Digital Economy (DE) offers potential pathways to achieve it. This study has employed the DPSIR framework and an entropy-based approach to evaluate the DE and USD across 284 Chinese prefecture-level cities from 2011 to 2020. Using the Spatial Durbin Model, the results have revealed that DE significantly enhances USD and has a spatial spillover effect of up to 300 km. The eastern region, as well as the Pearl River Delta and Yangtze River Delta regions, show stronger spillover effects than the central and western regions and the Beijing-Tianjin-Hebei region. These findings have provided insights for policymakers to leverage digital advancements to promote urban sustainability.

Exploring the Influence of the Digital Economy on Marine Pollution Mitigation: A Spatial Econometric Study of Coastal China

This work employs the spatial econometric model to explore the potential relation between the condition of marine ecosystems and the rapid development of the digital economy (DE), focusing on the coastal area of China. While the environmental benefits of the DE are well studied regarding the land and atmospheric pollution, its influence on marine pollution (MP) remains underexplored, and this work aims to fill in this gap. By analyzing panel data from 42 coastal cities in China using a spatial Durbin model to examine both the direct and indirect impacts of the DE on MP, the results highlight the positive role that the DE plays in reducing MP, benefitting not only the local marine environments but also those of neighboring areas through non-negligible spatial spillover effects. In addition, we find a non-linear, inverted U-shaped relationship between the DE and MP. These results are further confirmed through extensive robustness tests. This work enriches the field of environmental economics by reporting the first empirical study on the marine benefits of the DE and offers policy recommendations to optimize digital technologies for marine environmental preservation.

A pathway to sustainable development in China: The impact of local higher education expenditure on green total factor productivity

With the global warming crisis looming, the question of how to advance green total factor productivity (GTFP) has become an important concern confronting many developing countries. Although existing studies have demonstrated that total human capital can improve GTFP, the research has neglected to consider the influence of local higher education expenditure (LHEE), and no research has examined how LHEE spatially affect GTFP. Therefore, based on spatial economics theory, this study explores the spatial autocorrelation of LHEE and GTFP in China's 30 provinces from 2004 to 2021, employing a spatial Durbin model to analyze the spillover effect and influence mechanism of LHEE on GTFP. The results reveal that LHEE and GTFP exhibit positive global spatial autocorrelation. LHEE primarily improves GTFP and its subcomponents through spillover effects. The positive spillover effects in the three regions of China are significantly higher than the direct effects, whereas the direct effects in the eastern and central regions are positive but insignificant. Furthermore, LHEE promotes GTFP by advancing green technological innovation. The findings provide valuable insights to help policymakers address sustainable development goal 4 and develop synergistic regional GTFP growth policies to establish sustainable societies worldwide.

Spatial spillover effect of the enterprise sector on local residents’ income in Vietnam

AbstractThis study uses a panel data set from all 63 provinces in Vietnam and spatial regression models to investigate the spatial spillover effect of the enterprise sector on local residents’ income. The results show that expanding the enterprise sector can directly and indirectly increase local income. Although this spillover effect is positive and significant, it is smaller than the direct effect and gradually diminishes when the distance between provinces increases. For example, the estimation results applying the spatial Durbin model holding other variables constant show that a 10% increase in the enterprise capital volume in Ho Chi Minh City can raise local total income in this city, and first- and second-order neighbours by 0.53%, 0.22%, and 0.17%, respectively. This study provides novel evidence on the spatial spillover effect of the enterprise sector on local incomes using spatial econometric models. The findings offer valuable information for policymakers in planning enterprise sector development to improve local residents’ income in Vietnam.

Can Rural Digitization and the Efficiency of Agricultural Carbon Emissions Be Coupled and Harmonized under the “Dual-Carbon” Goal?

A major driving force behind China’s low-carbon, environmentally friendly development of agriculture and the accomplishment of the “dual-carbon” goal is the digital transformation of rural areas. In this study, on the basis of clarifying the degree of rural digitization and agricultural carbon emissions efficiency in China from 2010 to 2021, the degree of coupled coordination and the spatiotemporal pattern characteristics between the two are examined using the coupled coordination model. Then, the influencing factors are analyzed in depth using the spatial Durbin model. Our findings reveal that, first, in terms of the degree of rural digitization, the index increases overall and the spatial imbalance is obvious, with a spatial distribution pattern of “high in the east and low in the west”. Regarding the efficiency of agricultural carbon emissions, there is an overall “N-shaped” change, which is mainly influenced by technological progress in agricultural production, and the regional annual averages are, in descending order, the Western, Eastern, Northeastern, and Central regions, with obvious regional differences. Second, the coupling coordination index shows a fluctuating upward trend, from “extreme disorder” to “high-level coordination”. Furthermore, there are obvious regional characteristics. The regional growth rates are, in descending order, the Western, Eastern, Central, and Northeastern regions. Third, coupling coordination is jointly influenced by a variety of factors, including government input, educational level, industrial structure, energy use, urbanization rate, living standards, driving temporal patterns, and regional differences. This study not only helps to clarify the relationship between the two, offering a reference for the realization of the “dual-carbon” goal, but also broadens the concepts of the low-carbon and environmentally friendly development of agriculture.

Does carbon reduction and sequestration conflict with food security in rural China?—What, why and how?

Based on panel data of 31 provinces in rural China from 1997 to 2020, this manuscript first applies a carbon reduction and sequestration (CRS) model from the perspective of agricultural carbon emissions and agricultural carbon sinks. We then construct a food security evaluation system to examine the four dimensions of quantity, quality, ecological and economic security. Finally, the study uses a spatial Durbin model to empirically analyze the impact of CRS on food security and the moderating effect of fiscal decentralization. The relevant results: First, from 1997 to 2020, carbon emissions rose from 221.9794 million tons (1997) to 251.1368 million tons (2020), representing an increase of 13.14 %. The total amount of carbon sinks increased from 518.259 million tons (1997) to 758.887 million tons (2020); an increase of 46.43 %. CRS exhibited a fluctuating downward trend, falling from 0.98 (1997) to 0.90 (2020). However, food security showed an increasing trend, rising 0.12 (1997) to 0.32 (2020), with an average annual growth rate of 6.94 %. Second, in the short term, national CRS has had a significantly negative impact on food security, whereas the long term the result is exactly the opposite. In terms of control variables, planting structure, openness to the world, and economic development have significantly positive impact on food security, and urbanization, technological progress, and environmental regulation have significantly negative impact on food security. Regional heterogeneity is evident in the three functional attribute areas. Third, fiscal decentralization can enhance the negative impact of CRS on food security in the short term and weaken the positive impact of CRS on food security in the long term. Similarly, some regional heterogeneity is found among different regions.

Haze Management Competition and Industrial Green Transformation

To clarify the relationship between haze management competition and industrial green transformation, this paper is based on the theoretical perspectives of local government competition and environmental federalism, the specific form of haze governance competition is analyzed by applying the two-region spatial Durbin model, and then the impact of haze governance competition on industrial green transformation and its transmission mechanism is explored by constructing a spatial panel linkage model and adopting the generalized spatial three-stage least squares method. The empirical results show that, with the profound changes in the performance appraisal system since the 18th National Congress, the interaction of environmental governance strategies between geographically adjacent provinces is imitative. In contrast, that which is between economically adjacent provinces is a “race to the top”, and haze governance is conducive to promoting the development of industrial green transformation. Further research found that the competition of haze governance is heterogeneous, and smog control in the eastern region contributes to the green transformation of local industries, while smog control in the central and western regions inhibits their industrial green transformation, and haze governance mainly enhances industrial green transformation through the innovation compensation effect, the human capital effect, and the industrial structure optimization effect. Therefore, the central government should carry out overall planning for the industry, and local governments should strengthen the joint prevention and control of haze and promote technological innovation, to improve environmental quality and realize high-quality economic development.

Collaborative management of environmental pollution and carbon emissions drives local green growth: An analysis based on spatial effects

Collaborative management of environmental pollution and carbon emissions (CMPC) has been a major policy instrument to promote Sustainable Development Goals (SDG) in recent years. However, the relationship between the benefits and drawbacks of this environmental management practice for green growth in and around a local area remains to be clarified. Using 30 provinces in China during 2001–2019 as the object of analysis, we assessed the efficiency of local CMPC practices using the nonradial directional distance function (NDDF) model, predicted local green growth using the frontier green complexity index (GCI), and empirically examined the spatial effects, locational heterogeneity, and threshold characteristics of the relationship using the spatial Durbin model and the panel threshold model. Our study finds that although efficient CMPC does drive local green growth, the promotion effect is nonlinear with decreasing marginal effect. This effect is particularly obvious in economically developed regions with higher CMPCs, which will absorb resources from neighboring regions and create a "siphoning" effect. It was found that local financial support and foreign direct investment (FDI) can radiate green growth to neighboring regions; therefore, CMPC practice needs to pay more attention to the effect of joint governance, supplemented by financial and foreign investment policy tools, to better promote the green transformation of local economy.

Will structural adjustment and financial support affect low-carbon agricultural production in the Yellow River Basin?

Based on the panel data of 75 cities in the Yellow River Basin from 2000 to 2020, this manuscript measures the agricultural low-carbon production efficiency scientifically through the Super-SBM model. In addition, the deviation degree of agricultural industry is used as the index of structural adjustment. Finally, the spatial Durbin model is used to analyze the effect direction and degree of structural adjustment, financial support, and their synergistic effect on agricultural low-carbon production efficiency. The results show that ① the agricultural low-carbon production efficiency in the Yellow River Basin shows a trend of fluctuating downward and a spatial distribution pattern of "high in the east and low in the west". ② Structural adjustment in local region and adjacent areas has a significantly negative impact on agricultural low-carbon production, and the inhibitory effect in adjacent areas is more obvious, and the negative spatial spillover effect is strong. Financial support has a significantly positive impact on agricultural low-carbon production, but the spatial spillover effect of adjacent areas is not obvious. ③ By region, structural adjustment has a significantly negative impact on low-carbon agricultural production in the midstream and downstream regions, while financial support has a significantly positive impact on low-carbon agricultural production in the upstream region. The impact of control variables on agricultural low-carbon production varies from region to region. ④ The synergistic effect of structural adjustment and financial support in the whole and midstream region shows a significantly positive impact on agricultural low-carbon production, indicating that financial support has a certain correction effect on structural adjustment. The coefficient between the upstream and downstream regions is positive but not significant. The conclusions have important reference significance for promoting the ecological protection and high-quality development and agricultural low-carbon development in the Yellow River Basin.

The impact of digital economy on China’s green-oriented transition of energy: multiple mediation effects from a spatial perspective

With the continuous advancement of the new generation of information technology revolution, digital economy is gradually becoming an important driving force for energy transition while restructuring the new form of the economy. This paper selects panel data of 30 provinces in China from 2011 to 2021, empirically verifies the special impacts of digital economy on green-oriented transition of energy by using the spatial Durbin model, and further constructs a multi-intermediary model to verify its transition pathways. The main findings are as follows: First, the overall level of green-oriented transition of energy in China’s eastern coastal areas is relatively high, and the western regions have been more capable of catching up in recent years. Second, digital economy has played a significant role in promoting green-oriented transition of energy, and there is a positive spatial spillover effect. Third, digital economy could indirectly promote the green-oriented transition of energy by promoting the upgrading of industrial structure, improving the level of technological innovation and strengthening environmental regulations. This study provides empirical insights and reference for policymakers in accelerating the comprehensive green-oriented transition of energy.

The impacts of technological progress and industrial structure distortion on water use intensity in China

Based on the employment and output data of China's three industries, this paper measures the industrial structure distortion index of each province in China from 2000 to 2020, and uses a spatial panel model to examine the impact of industrial structure distortion on water intensity. The results show that China's industrial structure distortion index decreases from 0.4046 in 2000 to 0.2042 in 2020, and the industrial structure distortion index is 0.1247 in the east, 0.2139 in the center, and 0.2767 in the west. The regression of the Spatial Durbin Model shows that the indirect effect and total effect of industrial structure distortion both significantly increase the water use intensity, and the influence coefficients are 0.1712 and 0.1822, respectively. For other variables, water resource endowment significantly increases water intensity in the region, with an effect coefficient of 0.0100, and its indirect and total effects are both significantly negative, at −0.0465 and −0.0366, respectively. Foreign trade significantly inhibits water intensity in the region, with a degree of inhibition of −0.0164, and its indirect and total effects are not significant; and increasing Foreign Direct Investment increases water intensity in other regions and in general. The coefficients are 1.3170 and 1.2477, respectively; research and development input has no significant effect on water intensity. Therefore, China should eliminate distortions in its industrial structure, break the urban-rural dichotomy in labor mobility, improve the efficiency of the application of innovative technologies, and enhance the awareness of water crisis and water conservation and protection of the whole society.

Driving Factors of NOx Emissions in China: Insights from Spatial Regression Analysis

China’s rapid industrialization and urbanization have led to significant nitrogen oxide (NOx) emissions, contributing to severe atmospheric pollution. Understanding the driving factors behind these emissions is crucial for effective pollution control and environmental management. Therefore, this study is an attempt to provide insights into the influence of socioeconomic factors and explore spatial dependencies of NOx emissions in China in 2022 employing spatial regression models (SRMs). Among the SRMs considered, the spatial Durbin model (SDM) is identified as the most suitable for analyzing regional NOx emissions. The study highlights the importance of controlling electricity consumption and vehicle emissions for addressing air pollution in Chinese regions. Specifically, a one billion kilowatt-hour increase in electricity consumption leads to approximately 549.6 tons of NOx emissions, and an increase of 1000 vehicles in a region results in an average increase of 7113.4 tons of NOx emissions in the same region. Furthermore, per capita consumption expenditure (PCEXP) and research and development (R&D) expenditure exhibit negative direct and spillover impacts. Contrary to previous studies, this research finds that changes in urban population density do not have a significant direct or indirect effect on NOx emissions within the studied areas. Moreover, we conducted additional investigations to assess the effectiveness of government action plans in reducing NOx emissions. Specifically, we evaluated the impact of Phases 1 and 2 of the Clean Air Action Plan, launched in 2013 and 2018, respectively, on the socioeconomic drivers of NOx emissions. Therefore, the data were modeled for the years 2013 and 2017 and compared to the results obtained for 2022. The findings indicate that over the entire period (2013–2022), the emission controls mandated by the action plan resulted in significant reductions in the impact of many of the studied NOx drivers. In conclusion, based on the results, this study presents recommendations to mitigate NOx emissions.

The Impact of Demonstration Cooperatives on the “Empty Shell” of Farmer Professional Cooperatives in China

With the advancement of farmer professional cooperatives in China, the prevalence of the “empty shell” phenomenon has posed a significant challenge to the health and sustainability of these cooperatives. Utilizing data from 1570 such cooperatives in Guizhou Province, China, this study uses the multi-time differential model and the spatial Durbin model to study the impact of demonstration cooperatives on the “empty shell” of cooperatives and the spatial spillover effect of demonstration cooperatives. The study results show the following: (1) Agricultural professional cooperatives are rated as national or provincial demonstration cooperatives, which are conducive to their development, and the policy evaluation of demonstration cooperatives can effectively inhibit the “empty shell” phenomenon of cooperatives. (2) National and provincial demonstration cooperatives can effectively inhibit the “empty shell” of cooperatives in the region. (3) When there are provincial demonstration cooperatives in the surrounding areas, there will be a spatial spillover effect and the empty shell of regional cooperatives will be inhibited. However, to restrain the “empty shell” of cooperatives, it is essential to capitalize on the spatial policy influence of “demonstration cooperatives” while being vigilant against potential spatial siphoning effects.

Asymmetric spatial competition and dynamics in digitalization: Will public R&D support and marketization exacerbate spatial polarization?

This paper investigates the asymmetric spatial spillover effects of digital development across different types of cities. The results of the two-regime spatial Durbin models, using the panel data of 279 cities in China from 2011 to 2021, reveal the spatial polarization phenomenon in digitalization, where stronger cities such as smart cities, provincial capitals, and port cities become more dominant while weaker cities struggle to catch up. Increasing public R&D support leads to positive spatial spillover effects but exacerbates the spatial “Matthew effect” of digitalization. Additionally, marketization plays a moderating role by enhancing resource allocation efficiency and suppressing negative spatial spillover effects. Higher levels of marketization result in smaller digital gaps between cities. The findings provide valuable insights for decision-makers and stakeholders seeking to foster balanced and inclusive urban development in the digital age.

Pemodelan Regresi Global (Glm) dan Regresi Spasial (Sar Dan Sdm) Pada Kasus Indeks Pembangunan Manusia Di Provinsi Kalimantan Selatan

The Human Development Index (HDI) is a parameter that functions to assess the success of the quality of human life. The factors used in the research are the severity of poverty, population density and net participation rate. The research carried out aims to see what factors influence the HDI in South Kalimantan Province in 2022, the HDI value for South Kalimantan Province is below the Indonesian HDI value and quite a few regencies/cities in South Kalimantan Province have HDI values below the HDI value. Indonesia. The statistical analysis used is a spatial approach, where the SAR and HR spatial regression models will be searched. The Global Regression Model (GLM) obtained in this study is , while the Spatial Autoregressive (SAR) model is and Spatial Durbin Model is The best model that can be obtained is the Spatial Durbin Model (SDM) with an AIC value of 52,82654 and an value of 95,86%.

How can the digital economy reduce carbon emissions? Empirical evidence from China.

China is transitioning into the digital economy era. The advancement of the digital economy could offer a fresh mechanism to attain carbon peak and carbon neutrality objectives. Applications of the digital economy, such as smart energy management, intelligent transport systems, and digital agricultural technologies, have significantly reduced carbon emissions by optimizing resource use, reducing energy waste, and improving production efficiency. This research does so by devising a theoretical model that looks into the multi-faceted power of the digital economy under a two-sector paradigm. Utilising a panel model, a mediation effect model and a spatial Durbin model to assess the digital economy's power on carbon emissions. This research has determined that the digital economy can significantly diminish carbon emissions, with green tech innovations and industrial transformation being key contributors. The spatial spillover effect was used for the digital economy to aid in lowering carbon emissions in adjacent districts and upgrading better environmental stewardship. The influence of the digital economy has better performance in lowering carbon emissions in mid-western China than in the eastern area. This paper deepens understanding of the drivers of low-carbon growth and the significance, mechanism and regional disparities of the digital economy's effect on reducing carbon emissions. It offers valuable policy insights and guidance for globally achieving digital economy growth, reducing carbon emissions and reaching carbon peak and neutrality goals.

SHAP-powered insights into spatiotemporal effects: Unlocking explainable Bayesian-neural-network urban flood forecasting

Given the increased incidence of pluvial floods due to climate change and urbanization, the demand for highly efficient and accurate modeling within urban drainage systems has intensified, making machine learning and deep learning techniques increasingly popular. Nonetheless, these data-driven approaches face challenges in adequately capturing and interpreting dynamic process-evolving features, especially the spatiotemporal effects emanating from manholes during urban waterlogging events. To address these issues, this study proposes a general framework that extracts the spatiotemporal effects using the dynamic spatial Durbin model, integrates such effects with four machine learning models (i.e., artificial neural network, Bayesian neural network (BNN), light gradient boosting machine, and long short-term memory network), and clarifies decision-making processes of the best model by employing the Shapley Additive Explanations (SHAP) method. The results indicate that (1) the BNN with spatiotemporal effects (BNNST) not only outperforms other benchmark models but also provides accurate forecasts with quantifiable uncertainties; (2) compared to the original model, spatiotemporal effects enhance the models’ understanding of urban flooding dynamics, thereby improving predictive precision; (3) spatiotemporal effects comprise roughly 14 % of the contributions to the BNNST’s output, as interpreted by SHAP-based explanations; (4) incorporating interpretability into the BNN technique underscores the trustworthiness of the explanations at varying confidence levels, thereby deepening the understanding of decision-making processes.