Spatial Durbin Model Research Articles

Research on the Spatio-Temporal Evolution and Impact of China’s Digital Economy and Green Innovation

It is of great significance to study the impact of China’s digital economy on green innovation under present conditions. In this work, panel data were used, and research tools such as the entropy method, the Markov chain with a spatial Markov probability transition matrix, and a spatial Durbin model were applied to analyze the temporal and spatial evolution of the digital economy and green innovation in 287 Chinese cities from 2011 to 2021, exploring the influence of the digital economy on green innovation. The results show that the digital economy and green innovation in Chinese cities exhibited an upward trend. There was a basic spatial pattern consisting of “high levels in the east and low levels in the west” regarding the digital economy and green innovation, with the aggregation types primarily being “HH” and “LH”. Moreover, the types of digital economy and green innovation in Chinese cities are relatively stable, with neighboring areas influencing local changes. The digital economy has a significant promotional effect on green innovation, as well as spatial spillover effects; the differing influences over time can be used to categorize the cities into four groups, with most falling within the first two categories. Based on these findings, relevant countermeasures are proposed, seeking to further enhance the role of the digital economy in promoting green innovation. This work provides a research basis and policy suggestions to contribute to continuous improvements in China’s digital economy and green innovation, leveraging the promotional effects of the former on the latter.

Spatiotemporal evolution and driving factors of agricultural non-point source pollution in the context of economic green development.

Spatiotemporal evolution and driving factors of agricultural non-point source pollution in the context of economic green development.

Influence of R&D investment and employee education level on carbon emissions in manufacturing enterprises

ABSTRACT From the perspective of micro-subject listed manufacturing companies, this article used two models (i.e. the dynamic spatial Durbin model and the dynamic threshold model) to explore corporate technology and educational factors on its carbon emissions. The results show that: ① Overall, the increase in R&D investment can play a role in carbon emission reduction, while the effect of employees’ education level on carbon emission ranges from non-significant to significant negative effect. ② There is significant regional heterogeneity. The R&D investment and the education level of employees in the eastern and central regions of China have significant carbon emission reduction effects. ③ There is a significant threshold effect. When R&D investment reaches a certain threshold (0.019), the improvement of employee education level can reduce carbon emissions; when the intensity of environmental regulation reaches a certain threshold (0.005), R&D investment and employee education can play the role of carbon emission reduction. Therefore, listed companies should pay attention to technology investment and education investment. According to its own development characteristics, corresponding measures are taken to promote the interaction between environmental regulation and enterprise R&D technology, in order to help achieve the goal of ‘double carbon’.

Interaction Effect of Economic Globalization and Income per Capita on Unemployment

A Kuznets-like curve for unemployment suggests that unemployment initially rises with early economic development due to sectoral shifts but decreases as economies mature, diversify, and adapt. This explains how gross national income (GNI) per capita influences or mediates the effect of globalization on unemployment. This study investigates the impact of the interaction between GNI per capita and economic globalization on unemployment rates in 158 countries from 1991 to 2019 using the spatial Durbin model (SDM) and a weight matrix based on cultural, political, social, language, and historical backgrounds and trade pacts (CPSLHT) for better estimates. The results indicate that the direct and indirect effects of gross domestic product (GDP) on unemployment are negative and significant in the short and long term. Population growth positively and significantly impacts unemployment, while female labor force participation shows negative and significant indirect effects. The net migration effect is negative but insignificant in the overall model and becomes significant in the decomposed globalization models. The direct and indirect effects of inflation were consistently negative and significant. Trade openness exerts a significant indirect negative effect on unemployment, while the interaction between GNI per capita and economic globalization, especially through trade and foreign direct investment, also reduces unemployment. Persistent unemployment and spatial spillover effects highlight the importance of regional cooperation. Controlling population growth, enhancing GDP growth, permitting mild inflation, promoting female workforce participation, and adopting effective migration policies can reduce global unemployment.

Research on the Impact of Atmospheric Environment Self-Purification Capacity on Fog-Haze Pollution

Why is fog-haze pollution very serious in Hebei province, where there are many pollution-intensive industries, and in Guangdong province, where it is not so serious? This paper uses the spatial Durbin model, the threshold effect model, and relevant local city data, etc., to explore the effect of the atmospheric environment’s self-purification capacity on haze pollution from the perspective of green technology innovation. We found that the great haze outbreak in China is due to the large amount of ultrafine-particle low-cost emissions caused by the haze detection by weight method implemented in 2011 and 2012. This study also found that haze pollution in China has a significant impact on the atmospheric environment’s self-purification capacity. The atmospheric environment’s self-purification capacity has an inhibitory effect on haze pollution. When green technology innovation reaches the first threshold, the atmospheric self-purification capacity can significantly reduce the impact of haze pollution. When green technology innovation reaches the second threshold, the atmospheric self-purification capacity to reduce haze pollution is significantly enhanced. China’s local haze pollution is serious due to the industrial layout being unreasonable, caused by high-pollution industries emitting particles beyond the limits of atmospheric environment self-purification capacity. Industries in Hebei Province and Guangdong Province are more pollution-intensive, and haze pollution in Hebei Province is serious due to the weak self-purification capacity of the atmospheric environment. Guangdong Province’s atmospheric environment self-purification capacity is strong, and its haze pollution is not serious. Given the scientific use of atmospheric environment self-purification capacity and regional differences in green technology innovation, the development of targeted green input and atmospheric self-purification capacity enhancement policies in areas with serious air pollution, along with green technology innovations based on a region with less pollution, would be beneficial. To increase the amount of green technology innovation investment in regions where the atmospheric environment is not seriously polluted and green technology innovation is based on a bad region, more green funds should be invested in the atmospheric environment’s self-purification capacity. In regions where the atmospheric environment is not seriously polluted and the foundation of green technology innovation needs improvement, more green funds should be invested into atmospheric environment self-purification capacity to fully harness its inhibition of haze pollution. This should be accompanied by scientific planning and adjustments to the high-pollution industrial layout, etc., to effectively enhance the self-purification capacity of the regional atmospheric environment. In addition, the gradient transfer of high-pollution industries should be implemented based on atmospheric environment self-purification capacity to effectively reduce the impact of haze pollution.

Does Financialization Alleviate the Funding Dilemma of Green Innovation in Heavily Polluting Firms?: Evidence from China’s A-Share Listed Companies

Green innovation in heavily polluting firms is crucial for sustainable development, yet financial constraints remain a major barrier. This study employs a Spatial Durbin Model to analyze how financialization influences green innovation in China’s A-share listed firms. The results indicate that financialization intensifies financing constraints, leading to a suppression of green innovation. This effect is primarily driven by the “crowding-out effect”, which outweighs the “reservoir effect” that financialization may provide. Additionally, industry-wide peer effects further spread the negative impact, while agency conflicts and managerial incentives exacerbate the problem. Regional disparities are also observed, with stronger negative effects in eastern and central regions and among firms with high managerial compensation. To address these issues, the study recommends strengthening policy guidance, expanding green finance mechanisms, promoting industry collaboration, and improving corporate governance. These findings enhance our understanding of the dual impact of financialization on green innovation and provide actionable policy recommendations for achieving sustainable development in high-pollution industries.

Spatial Effects and Driving Factors of Consumption Upgrades on Municipal Solid Waste Eco-Efficiency, Considering Emission Outputs

To achieve the goal of building zero-waste cities, managing greenhouse gas (GHG) emissions generated from municipal solid waste (MSW) treatment is a critical step toward carbon neutrality. Waste produced by consumption activities constitutes an essential component of MSW management. Using the Super Slacks-Based Measure Data Envelopment Analysis (SSBM-DEA) model and the Spatial Durbin Model (SDM), this study investigates the spatial impacts of consumption upgrading (CU) on municipal waste management across 30 provinces in China, with a particular focus on GHGs as undesirable outputs. In this study, we construct a framework from the dimensions of consumption level, consumption structure, and green consumption. Additionally, other socioeconomic factors influencing waste management are explored. The results indicate a convergence trend in the uneven distribution of consumption upgrading, with the gaps between regions gradually narrowing. Consumption upgrading significantly enhances the eco-efficiency of local waste management and exhibits notable spatial spillover effects, positively influencing the eco-efficiency of neighboring regions. Furthermore, the promotion effect of consumption upgrading on the central and western regions, compared with the eastern region, is more pronounced. This indicates that the technological catch-up resulting from consumption upgrading, supported by policies, can further enhance the eco-efficiency of MSW. This study also provides insights for other regions transitioning from scale expansion to high-quality development in waste management.

Impact of Food Imports on Water Pressure in Food Production in China: An Analysis from the Perspective of Virtual Water Trade

Motivation: Breaking through the constraints of water scarcity is a crucial factor for the efficient and sustainable production of food in China. Objective: To explore a new strategy to alleviate the water resource pressure in food production in China, based on the theory of resource flow, this study empirically explores the relationship between food imports and the water pressure in food production in China from the perspective of virtual water trade. Data and Method: This study collects panel data from 30 provincial-level administrative regions in China from 2003 to 2020 and employs methods such as the two-way fixed effects model, instrumental variable approach, and spatial Durbin model for empirical analysis. Results: (1) China’s net food imports surged from −0.000397 billion tons (Bt) in 2003 to 0.118325 Bt in 2020, with a rapid annual growth rate of about 9.37%. Changes in net imports are accompanied by virtual water flows. Between 2003 and 2020, the virtual water content of China’s net food imports increased from 31.7086 Bt to 187.7511 Bt, a yearly increase of 10.39%. (2) Virtual water for food imports has a mitigating effect on the water pressure in food production. Every 0.100 Bt of virtual water imported will reduce the water pressure in food production index by 0.026. The impact has a spatial spillover effect. Moreover, as there is high pressure on water resources in food production in northern regions and major grain-producing areas, the mitigating effect of food imports on the pressure of water resources in food production is also enhanced. The quantile regression found that as the water pressure in food production increases, the mitigating effect of virtual water for food imports on the water pressure in food production gradually increases. Implications: This study examines the relevance of resource flow theory within the context of food trade, thereby broadening the scope of research on virtual water trade in food. Additionally, this study offers valuable insights for the development of strategies aimed at mitigating the pressure on water resources associated with food production in China.

A Study on the Mechanisms of New Quality Productive Forces Enabling the Upgrading of the Modern Tourism System: Evidence from China

Entering the new development stage, empowering the modern tourism system by upgrading it with new quality productive forces (NQPF) is of great significance in promoting the high-quality development of China’s tourism industry. Based on the panel data of 30 provinces in China between 2018 and 2022, the two-way fixed effects model, the mediated-effects model, and the spatial Durbin model SDM were constructed using STATA 16 for empirical analysis. Results indicated that NQPF have a significant facilitating effect on upgrading the modern tourism system, which is reflected in four aspects: industrial efficiency upgrading, industrial technology upgrading, industrial structure upgrading, and open sharing upgrading. The results of the mechanism test show that the dynamic capacity of the industry plays an important intermediary role in the process of NQPF promoting the upgrading of the modern tourism system. In addition, NQPF has a spatial spillover effect on upgrading the modern tourism system. Based on the above conclusions, strengthening the cultivation and development of NQPF, optimizing the industry dynamic capacity, promoting coordinated regional development, and optimizing the policy environment are proposed in order to further enhance the overall level of the modern tourism system and to realize the high-quality development of tourism.

Drivers of civil aviation emissions in China: Considering spatial heterogeneity and interdependence.

Drivers of civil aviation emissions in China: Considering spatial heterogeneity and interdependence.

Evaluating the Impact of Energy Efficiency on Green Growth in Chinese Cities: A Spatial Durbin Model Approach

Evaluating the Impact of Energy Efficiency on Green Growth in Chinese Cities: A Spatial Durbin Model Approach

Spatial Effects of New Quality Productivity on the Low-Carbon Transformation of Energy Consumption Structure—Evidence from Provincial Data in China

The low-carbon transformation of an energy consumption structure (ECS) is related to the sustainable development of society. New quality productivity (NQP) is an important means to promote the green transformation of an ECS. This paper examines the spatiotemporal evolution of NQP development and low-carbon transformation of an ECS, utilizing panel data from 30 Chinese provinces (2012–2020) through kernel density estimation and spatial analysis methods. The impact of NQP development on the ECS and its spatial spillover effects are empirically analyzed using a two-way fixed-effects model and a spatial Durbin model. The study reveals that NQP development in China is generally positively correlated with ECS, although significant regional disparities exist. A significant positive spatial correlation exists between NQP and ECS. NQP development significantly promotes the transformation of local ECS but exerts a negative spatial spillover effect on neighboring regions. Further analysis reveals that NQP exhibits an “inverted U-shaped” impact on the spatial spillover effects of local ECS. Spatial attenuation analysis indicates that the negative spillover effects of NQP on ECS diminish with geographical distance, with a maximum attenuation boundary of 600 km. The study found that China is currently facing significant regional development imbalances. Promoting the balanced development of NQP is crucial for achieving high-quality and sustainable development.

How the digital economy promotes urban–rural integration through optimizing factor allocation: theoretical mechanisms and evidence from China

The digital economy plays an increasingly crucial role in bridging the gap between urban and rural areas. This study investigates how the development of the digital economy can foster the integrated development of urban and rural areas by optimizing factor allocation, with an emphasis on its potential to narrow the urban-rural divide. The study aims to examine the impact of the digital economy on urban-rural integration, focusing particularly on the mediating role of optimized factor allocation. Using panel data from 30 Chinese provinces between 2011 and 2022, we construct indicators for digital economy development and urban-rural integration. The analysis employs a two-way fixed-effects model, a mediating effect model, and a spatial Durbin model to explore the spatial evolution and impact of the digital economy on urban-rural integration. Findings suggest that the digital economy enhances urban-rural integration both directly and indirectly. It directly contributes to integration and indirectly optimizes the allocation of labor, capital, land, technology, and information, further promoting urban-rural convergence. The effects of these mechanisms exhibit significant threshold effects and spatial heterogeneity. These results underline the importance of accelerating the digital economy and optimizing factor mobility as key strategies for urban-rural integration. Policy implications focus on enhancing the efficiency of resource allocation across urban and rural areas to accelerate balanced development.

Green R&D Efficiency and China's Industrial Carbon Intensity: New Evidence From a Spatial Perspective

ABSTRACTOptimising the allocation of limited research and development (R&D) resources significantly enhances the environmental benefits. Based on the innovation value chain theory, this study defines green R&D efficiency and investigates its impact on industrial carbon intensity from a spatial perspective. Using the spatial Durbin model, the results indicate that green R&D efficiency reduces industrial carbon intensity both locally and in neighbouring provinces. Endogenous bias and robustness check further confirm these findings. The key mechanisms driving this effect include green output efficiency and green industrial transformation. Additional analysis manifests that the reduction effect is more pronounced during the development stage of green R&D activities than during the research stage. However, both stages work synergistically to reduce industrial carbon intensity. Environmental regulation and a free market environment positively influence the environmental performance of green R&D efficiency. Finally, policy recommendations are provided.

The Role of Government Spending, FDI, and Infrastructure Energy on Poverty between Regions in Riau Province; Spatial Approach

This study attempts to investigate poverty that occurs between regions in Riau Province in the form of trends that occur and the role of government spending, FDI, and energy infrastructure on poverty levels, as well as spatial dependence from neighboring areas. This study uses panel data from 12 districts/municipalities for the period 2011–2023 with a spatial econometric approach - spatial durbin model (SDM). The results found that the overall poverty trend has decreased, with the highest figure in Meranti district and the municipal area at 3%. The results of the spatial regression found that government spending, last year’s FDI, and economic growth significantly increased poverty. In addition, the results of this study provide evidence that the influence of neighboring regional characteristics significantly affects the decline in poverty levels in key areas such as government spending on social aid, economic growth, education, and infrastructure energy.

Digital economy and environmental quality of African countries: evidence from spatial Durbin model

Abstract This study examined the spatial impact of the digital economy on the environmental quality of 36 African countries between 1995 and 2020. Methodologically, the study used the fixed effect instrumental variable regression model, spatial Durbin model and two alternative proximity matrices—contiguity and inverse distance. The empirical outcomes have produced three important findings: (1) there is evidence of spatial spillover on the impact of digital economy on environmental quality in Africa, i.e. third-country’s digital economy plays a significant role in determining the environmental quality of host country; (2) spatial spillover of environmental quality in the region, which is due to interconnected ecosystems, shared natural resources, and transboundary environmental factors such as air and water pollution; (3) African countries are not independent when it comes to environmental issue, i.e., environmental quality of a specific country is being influenced by its drivers from neighbouring countries. The estimates are also robust to alternative measures of the digital economy and a different proximity matrix—inverse distance. The study suggests that African policymakers should undertake a joint task effort to enforce the regulatory framework on recycling electronic waste. Also, promoting the use of renewable energy sources, such as wind and solar, is essential for meeting the energy needs of digital infrastructure.

Studying the Impact of Socioeconomic and Environmental Factors on Nitrogen Oxide Emissions: Spatial Econometric Modeling

This study aims to identify the socioeconomic drivers of nitrogen oxide (NOx) emissions and their spatial relationships in 31 Chinese regions in 2022 using spatial econometric models. Our research incorporates a comprehensive set of variables, including electricity consumption, per capita household consumption expenditure (PCEXP), Expenditure on research and development (R&D), numbers of vehicle in operation, population density, green- covered areas, land use patterns, and cultivated land area. Following a comparative analysis, we selected the spatial Durbin model (SDM) as the most appropriate statistical model for analyzing provincial NOx emissions. Results indicate that electricity consumption and land use patterns are significant contributors to NOx emissions. Specifically, we found that a one billion kilowatt-hour (kWh) increase in electricity consumption corresponds to an increase of approximately 367.3 tons of NOx emissions within the same region. Similarly; a one-million-hectare expansion in land used for urban, rural, industrial, and mining activities is associated with an increase of about 161.9 thousand tons of NOx emissions in the same region. Importantly, our analysis revealed positive spillover effects for PCEXP and cultivated land area, suggesting that changes in these factors in one region can influence NOx emissions in neighboring regions. Contrary to some previous findings and prevailing assumptions, urban population density and green-covered areas did not show significant direct or indirect impacts on NOx emissions. This unexpected result challenges existing notions about urbanization and green space effects on air quality, warranting further investigation. Based on these findings, the study proposes recommendations for mitigating NOx emissions and improving air quality in Chinese regions.

The influence of administrative border on the spatial correlation of house prices: evidence from China

This paper empirically examines the spatial correlation differences and mechanisms of housing prices in prefecture-level cities, considering the influence of administrative boundaries. Using an extended spatial Durbin model and panel data from 292 prefecture-level cities in China between 2000 and 2013, the study identifies the attributes and stage characteristics of real estate. The results indicate a significant spatial correlation in housing prices between neighboring prefecture-level cities, with inter-provincial administrative boundaries reducing the spatial correlation between these cities by 62.11%. Further research reveals significant changes in the spatial correlation of housing prices following the 2007 subprime mortgage crisis, with the transformation of real estate’s dominant attributes being the primary reason for these changes.

A simple estimation of the dynamic panel spatial Durbin model with interactive fixed effects

ABSTRACT This article offers a new estimation method for a dynamic panel spatial Durbin model with interactive fixed effects. Based on the correlated random effects approach, we propose a simple spatial two-stage least squares estimator. Simulation results show that the proposed estimator has good finite-sample properties.

Can E‐commerce development improve farmers' incomes? Evidence from prefecture‐level data and the spatial difference‐in‐difference approach

AbstractThe penetration of E‐commerce into rural areas has created unprecedented opportunities for rural revitalisation and increased farmers' incomes. While the impact of E‐commerce development on income growth has been widely discussed, its spatial spillover effects and subsequent mechanisms remain insufficiently explored. This study utilises panel data of 278 prefecture‐level cities in China and employs a spatial difference‐in‐difference model incorporating the spatial Durbin model to explore the contribution of E‐commerce development to farmers' incomes, focussing on the direct mechanisms and spatial spillover effects associated with the characteristics of E‐commerce. Results indicate that E‐commerce development promotes local farmers' income growth and exerts a positive spillover effect on neighbouring cities. The significant positive direct contribution effect is achieved through mediating effects of technological innovation and capital investment, while the mediating effect of industrial structure upgrading is found to be significantly negative, indicating farmers remain incapable of obtaining digital dividends from emerging economic opportunities. E‐commerce development is also found to better increase farmers' incomes in less developed regions and regions with lower digitalisation levels, indicating a stronger impetus in the early stages of development.