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Water and land resources are related to the security and stability of agricultural production, and the degree of matching in time and space directly affects regional agricultural production capacity and sustainable agricultural development. This paper intends to use the panel data of nine provinces in the Yellow River Basin from 2000 to 2019 and incorporate the static and dynamic spatial Durbin models with spatial effects under the geographical adjacency matrix and the comprehensive weight matrix of economic geography, so as to explore the direct effects and indirect effects, short-term effects and long-term effects of the matching coefficient of agricultural water and land resources on the agricultural economic growth in the Yellow River Basin. The results show the following: (1) The matching situation of agricultural water and land resources in different provinces along the Yellow River Basin are different; some are relatively short of water resources, some are relatively balanced in water and land resources, and some are relatively short of land resources. (2) The static spatial Durbin model shows that the direct effect of the matching coefficient of agricultural water and land resources on the agricultural economic growth of the province is not significant; the indirect effect and the total effect of the spatial spillover is significantly positive. (3) The dynamic spatial Durbin model under the two matrix forms shows that the short-term total effect of the matching coefficient of agricultural water and land resources on agricultural economic growth is significantly positive, while the long-term total effect is significantly negative, and the direction and degree of the short-term and long-term effects are inconsistent. This study provides a comprehensive analysis framework from the perspective of local and neighborhood effect, and short-term and long-term effect, which can provide a reference to reasonably adjust the matching of agricultural water and land resources to promote agricultural sustainable economic growth, especially for developing countries.

Regional scientists frequently work with regression relationships involving sample data that is spatial in nature. For example, hedonic house-price regressions relate selling prices of houses located at points in space to characteristics of the homes as well as neighborhood characteristics. Migration, commodity, and transportation flow models relate the size of flows between origin and destination regions to the distance between origin and destination as well as characteristics of both origin and destination regions. Regional growth regressions relate growth rates of a region to past period own- and nearby-region resource inputs used in production. Spatial data typically violates the assumption that each observation is independent of other observations made by ordinary regression methods. This has econometric implications for the quality of estimates and inferences drawn from nonspatial regression models. Alternative methods for producing point estimates and drawing inferences for relationships involving spatial data samples comprise the broad topic covered by spatial econometrics. Like any subdiscipline, spatial econometrics has its quirks, many of which reflect influential past literature that has gained attention in both theoretical and applied work. This article asks the question: “What should regional scientists who wish to use regression relationships involving spatial data in an effort to shed light on questions of interest in regional science know about spatial econometric methods?”

The increasing concern about the environmental issue and its serious adverse effects on human health has made China’s industrial green transformation being a matter of public concern. In this study, a network slack-based measure (NSBM) was applied to explore China’s industrial green development level from the perspective of environmental welfare efficiency (EWE), considering not only the impact of industrial development on environment and economy, but also the impact on human well-being. Based on the data of 30 provincial administrative regions in China from 2004 to 2017, the comprehensive efficiency (CE) of China’s industrial sector was measured and decomposed. The results show that the industrial production efficiency (IPE) is much higher than the EWE, and the improvement of the EWE will be the key to realize the green transformation of China’s industry. On this basis, considering the effects of spatial interaction, the spatial Durbin model was established to analyze the driving factors of EWE. Finally, this research puts forward promotion path of industrial green development.

Increased connectivity between regions in Indonesia is believed to impact the productivity capacity of each region, as well as its economic growth. Moreover, the influence of connectivity on the surrounding area is commonly known as the indirect effect (spillover effect). This effect can increase the number of products, goods and services used as production factors. The study aims to examine the effect of transportation infrastructure on economic growth. We used spatial modelling to estimate the impact of transportation infrastructure on the economy of 34 provinces in Indonesia in 2017. We applied the spatial lag of X model (SLX), spatial autoregressive model (SAR), spatial error model (SEM), spatial autoregressive combined model (SAC), spatial Durbin model (SDM), spatial Durbin error model (SDEM), and spatial autoregressive combined mixed model (SAC mixed). According to the estimation results, the SAC mixed model is the best spatial model, as it has the smallest value of the Akaike information criterion (AIC) and significant coefficients of ρ (rho) and λ (lambda) parameters. The results show that the indicators “bus stations”, “domestic investment” and “foreign investment” have a direct effect on the economic growth in 34 Indonesian provinces. In addition, we revealed the presence of indirect effects (spillovers) between provinces in Indonesia for the same variables.

Although the digital economy has become a new driving force for development worldwide, it is still unclear how digital economy development affects green total factor energy efficiency (GTFEE). Using panel data from 281 prefecture-level cities in China from 2003 to 2018, this study empirically analyzes the effect of digital economy development on GTFEE by adopting a dynamic panel model, a mediation effect model, a dynamic threshold panel model, and a spatial Durbin model. The empirical results show that digital economy development has a significantly negative direct effect on GTFEE. The digital economy can impact GTFEE by the mechanisms of electrification, hollowing out of industrial scale, and hollowing out of industrial efficiency. Neither innovation nor environmental regulations significantly change this negative impact. The dynamic threshold panel model shows a nonlinear relationship between digital economy development and GTFEE, which indicates that the effect of digital economy development on GTFEE significantly inverts from negative to positive as the digital economy develops. In addition, GTFEE has a significantly positive spatial correlation, and the digital economy has a positive spatial spillover effect on GTFEE.

China is currently undergoing an important stage wherein it is adjusting its development mode and upgrading its industrial structure. Green investment has become a major driving force through which China can achieve green and sustainable development. Based on the panel data of 30 Chinese provinces for the 2006–2017 period, this paper uses a spatial Durbin model and a dynamic threshold model to empirically analyze the impact of green investment and institutional quality on environmental pollution. The research results show that China’s environmental pollution is significantly characterized by spatial dependence. Local environmental pollution is negatively impacted by green investment, but it is not affected by green investment in neighboring areas; this conclusion remains valid after a series of robustness tests. Green investment can reduce environmental pollution by improving efficiency of energy conservation and emission reduction, expanding technological innovation capabilities and upgrading the industrial structure. The regression results of the dynamic threshold model show that green investment has a nonlinear impact on environmental pollution that is dependent on institutional quality. A higher degree of regional corruption can lead to a gradual decrease in the role of green investment in reducing environmental pollution. However, improvements in marketization and intellectual property protection can increase the positive influence of green investment in reducing environmental pollution. Significant regional heterogeneity is also found in the impact of green investment on environmental pollution, and this impact gradually decreases from the eastern coast to the western region.

China is the largest carbon emitter in the world, with agricultural carbon emissions accounting for 17% of China’s total carbon emissions. Agricultural carbon emission reduction has become the key to achieving the “Double Carbon” goal. At the same time, the role of the digital economy in achieving the “dual carbon” goal cannot be ignored as an important engine to boost the high-quality development of China’s economy. Therefore, this paper uses the panel data of 30 provinces in mainland China from 2011 to 2019 to construct a spatial Durbin model and a mediation effect model to explore the impact of the digital economy on agricultural carbon intensity and the mediating role of agricultural technological progress. The research results show that: (1) China’s agricultural carbon intensity fluctuated and declined during the study period, but the current agricultural carbon intensity is still at a high level; (2) The inhibitory effect of the digital economy on agricultural carbon intensity is achieved by promoting agricultural technological progress, and the intermediary role of agricultural technological progress has been verified; (3) The digital economy can significantly reduce the carbon intensity of agriculture, and this inhibition has a positive spatial spillover effect. According to the research conclusions, the government should speed up the development of internet technology and digital inclusive finance, support agricultural technology research and improve farmers’ human capital, and strengthen regional cooperation to release the contribution of digital economy space.

With the continuous advancement of the technological revolution and industrial transformation, environmental governance supported by digital finance has become an important engine for achieving carbon neutrality. Based on panel data from 30 provinces in China, this study discusses the spatial spillover effect and transmission mechanism between digital finance and environmental pollution. Our research results confirm that the inhibitory effect of digital finance on local environmental pollution gradually increases with the improvement of digital finance. Interestingly, digital finance has a significant positive spatial spillover effect on environmental pollution in surrounding areas. The mediating effect shows that digital finance can alleviate environmental pollution by improving technological innovation, industrial upgrading and industrial structure rationalization. A higher degree of marketization and governmental support can increase the positive influences of digital finance on pollution reduction. This research proves the effectiveness of digital finance in improving environmental governance, and it encourages policy-makers around the world to rely on digital finance to promote ecological governance and achieve high-quality economic development.

Environmental regulation and green innovation are two main fulcrums in the realization of green transition of industrial growth. However, few studies have done an empirical analysis of the impact of environmental regulation and green innovation on green development. Based on the theory of systematic interduality, regional industrial green development is regarded as a dynamic system composed of two subsystems: the state and the process subsystem. Using provincial industrial panel data from 2007–2015 and the spatial Durbin model under the unified analysis framework, this paper examines the role and mechanism of environmental regulation (divided into administrative environmental regulation, market-based environmental regulation, and public participation environmental regulation) in the impact of green innovation (divided into green product innovation and green craft innovation) on industrial green development. The results indicate a sharp fluctuating trend in China’s overall industrial green development performance, and that China’s 30 provinces can be divided into four categories, based on the development levels of two subsystems of industrial green development. There is a clear positive spatial correlation between the industrial green development performance in different provinces. Considering the impact of environmental regulation on industrial green development performance, different types of environmental regulation have different regional influences. Considering the impact of green innovation on industrial green development performance, in the absence of environmental regulation constraints, green product innovation shows a certain promotional role, and green craft innovation has a significant inhibitory effect. However, under environmental regulation constraints, market-based environmental regulation through the encouragement of green craft innovation rather than green product innovation achieves a positive impact on industrial green development.