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Research opinion on informatization is divided between two opposite poles—that it promotes or inhibits the spillover of regional economies. These conflicting viewpoints are called “the paradoxical geographies of the digital economy”. Information-based investment and diffusion of informatization contribute to breaking the economic space constraints caused by distance, leading to interregional spillover effects, according to the results of the Durbin model of spatial lag applied to Japanese regional data. Clearly, the local direct effects and the perimeter region’s indirect effects of informatization are both positive. This proves the existence of network externality, which causes increasing returns to scale. Extensive diffusion of information technology plays a significant role in the process, in addition to rapid accumulation and infiltration of information resources, which strengthens the information-based investment spillover effect. In this empirical analysis, evidence seems to support the view that informatization promotes economic development in Japan.

Correct understanding of the positive role and mechanism of green finance in promoting ecological environment is an important premise and guarantee for promoting green finance to better serve the improvement of ecological environment. Based on the panel data of 31 provinces (municipalities and autonomous regions) in China from 2009 to 2017, this paper constructs a spatial Dubin model based on the distance weight matrix and empirically analyzes the impact of green finance on the ecological environment and its spatial spillover effects. The empirical results show that (1) the development of green finance promotes the improvement of the ecological environment in this region and (2) the influence of green finance on the ecological environment has a significant positive spatial spillover effect, that is, the development of green finance in this region will promote the improvement of the ecological environment in the surrounding areas.

The Strategy of Spatial Development of the Russian Federation until 2025 aims at the economic growth acceleration and reduction of the intra-regional socio-economic differences. Therefore, the factors affecting the economic growth of regions, convergence of regions, spillover effects from the neighbouring regions are of importance. Russian regions are very different and do not converge to a unique equilibrium path. 80 Russian regions were divided into the groups of poor, middle and rich regions. Three main hypotheses were considered, based on the differences in the 1) convergence speed, 2) influence of the same factors, 3) different mutual influence of regions. They were tested using a modified spatially autoregressive model for the three groups using the Russian regional data for 2000–2017. Beta-convergence was found only for the middle and rich regions, the rate of convergence was higher in the rich regions. The poor regions did not grow faster than the other regions, confirming the relevance of the Strategy of Spatial Development. The similarities and differences were identified in the factors ensuring the economic growth of regions belonging to the three groups. The growth in all regions is stimulated by the regional economy openness. The growth of rich regions can be achieved by increasing the investment and reducing the investment risk. However, the investments in the poor and middle regions are not effective. The poor and middle regions receive positive spillovers from the growth of the neighbouring regions. It is possible to expect reduced differences in the living standards between the poor and rich regions.

The topic of air pollution and its effect on public health has become a hot policy issue that has attracted worldwide attention, but this attention has seldom been extended to the causal relationship between atmospheric environmental policy (AEP), air pollution, and public health. This paper uses panel data from 30 provinces in China to construct spatial econometric models that analyze the impact of AEP on air pollution, the impact of air pollution on public health, and the mediation effect that air pollution may have between AEP and public health. The results demonstrate that there is a significant positive spatial spillover effect of soot and dust (SD) emission intensity and the overall air pollution level as measured by the Air Pollution Index (API). The AEP has significant inhibitory effects on the intensity of sulfur dioxide and SD emissions, as well as on overall air pollution. An increase in the overall air pollution level has a significant detrimental effect on public health as measured by average life expectancy. Air pollution as measured by API is a mediating factor in the relationship between AEP and public health. The study results could help to effectively control air pollution and promote public health by leading to improvements in regional pollution prevention and control mechanisms and strengthening of the central government’s policy formulation and local governments’ policy implementation process.

Environmental pollution has become a common topic in the world. The existing literature rarely studies the relationship between information technology and environmental pollution from a spatial perspective. This paper uses spatial econometric models and the panel data of 285 cities in China from 2003 to 2016 to study the effect of information technology on environmental pollution. The results of spatial auto-correlation analysis show that there is a significant positive spatial auto-correlation between information technology and environmental pollution, and the spatial path dependence characteristics are obvious. The estimation results of the spatial econometric models show that information technology has significantly aggravated environmental pollution, and the rebound effect of information technology on environmental pollution has played a leading role, indicating that the policy effect of China using information technology to improve the environment quality is not obvious.

Green technology innovation is one of the driving forces of industrial structure upgrading. This innovation is thought to be related to environmental regulation. The study uses panel data for 30 Chinese provinces and cities from 2009 to 2020 and presents a comprehensive research-based explanation of how environmental regulations impact green innovation. This study employs the spatial Durbin model to analyze the spillover effect of the region. The results show that the total impact of environmental regulations is 0.223%, of which the direct effect is 0.099%. This impact includes the effects of both formal and informal environmental regulation. It indicates that ecological regulations significantly enhance green technology innovation. Furthermore, the spatial spillover effect is significantly positive at the 1% level with a coefficient of 0.124. Such spillover effects represent a learning effect of regional environmental regulation. Based on the results, the study suggests a few policy measures based on the detailed outcomes.

Improving agricultural green total factor productivity is crucial to promoting high-quality agricultural development. This paper selects the panel data of 30 provinces in China from 2009 to 2020 and uses the super-efficiency SBM model with undesirable outputs to measure the agricultural green total factor productivity of all regions in China. On this basis, this paper uses the panel data fixed-effect model and spatial Durbin model to empirically discuss the impact of agricultural credit input on agricultural green total factor productivity and its spatial spillover effect. The main conclusions are as follows: First, from 2009 to 2020, the average values of agricultural green total factor productivity in national, eastern, central, and western regions are 0.8909, 0.9977, 0.9231, and 0.8068, respectively, and the agricultural green total factor productivity needs to be further improved. Second, the agricultural green total factor productivity presents a significant and positive spatial correlation, and the spatial distribution of agricultural green total factor productivity is not random and irregular. Third, agricultural credit input can significantly promote agricultural green total factor productivity in the local region, but it hinders the improvement of agricultural green total factor productivity in the adjacent regions. Fourth, the impact of agricultural credit input on the agricultural green total factor productivity and its spillover effect has a significant regional heterogeneity. This paper believes that paying attention to the spatial spillover effect of agricultural total factor productivity, optimizing the structure and scale of agricultural credit input, and formulating reasonable agricultural credit policies can improve agricultural green total factor productivity.

To enhance the dynamic perception and accuracy of tourism demand forecasting in smart tourism scenarios, this paper proposes a forecasting framework integrating a spatial econometric model and deep learning. This framework aims to address the limitations of traditional methods, namely insufficient spatial correlation modeling and weak interpretability of deep learning models. The model leverages spatial lag factors, spatial agglomeration indicators, and regional interaction behavior features. It constructs a geographical dependency structure based on spatial econometric methods, which is then embedded into a long short-term memory (LSTM) network for joint forecasting. This design achieves a balance between time series modeling and spatial structure identification. In this study, three types of datasets are selected: tourist flow data of scenic spots in Beijing, online tourism behavior data from Ctrip, and GeoLife Global Positioning System Trajectory (GeoLife) data. A multi-dimensional experimental system covering 12 performance indicators is established. The results show that the optimized model achieves the following performance on the CityBrain Beijing Tourism Flow (CB-BJTF) dataset: mean absolute error (MAE) of 9.653, root mean square error (RMSE) of 12.118, mean absolute percentage error (MAPE) of 14.538%, and R 2 of 0.924, significantly outperforming comparative models such as informer and ST-GCN. In terms of the spatial dimension, the residual Moran’s I is 0.094, and the spatial R 2 reaches 0.868. Spatial sensitivity analysis indicates that after excluding the tourist flow of neighboring areas, the model’s MAE increases to 12.284, and the spatial fitting degree decreases significantly. This verifies the key role of spatial information in forecasting. Therefore, this paper provides theoretical support and empirical evidence for spatial perception modeling and deep fusion forecasting in the field of smart tourism, and holds certain value for application promotion and academic innovation.

Facing the growing problem of carbon emission pollution, the scientific and reasonable division of environmental management power between governments is the premise and institutional foundation for realizing China’s carbon emission reduction target in 2030. In this article, we directly assess the degree of environmental decentralization according to the allocation of environmental managers among different levels of government. By incorporating fiscal decentralization indicators, the provincial panel data and dynamic spatial econometric model are used to empirically test the impact of environmental decentralization on carbon emissions from a spatial perspective. The results show that (1) China’s provincial carbon emissions have significant inertia dependence and spatial path dependence. The increase (decrease) of provincial carbon emissions will lead to the increase (decrease) of carbon emissions in neighboring regions. (2) At the national level, environmental decentralization, environmental administrative decentralization, and environmental monitoring decentralization significantly reduce China’s carbon emissions, while environmental supervision decentralization and fiscal decentralization significantly increase carbon emissions. Similarly, the interaction of environmental decentralization and its decomposition indicators and fiscal decentralization also significantly promotes carbon emissions, and the impact is related to the types of environmental management decentralization. (3) The carbon emission effects of environmental decentralization in different regions are heterogeneous. The inhibition effect of environmental decentralization, environmental administrative decentralization, and environmental monitoring decentralization on carbon emissions in the western region is significantly greater than that in the eastern and central regions, but the inhibitory effect of the interaction of environmental decentralization and its decomposition index and fiscal decentralization on carbon emissions in the eastern region was significantly stronger than that in the central and western regions. The above results provide theoretical support for China to construct a differentiated carbon emission environmental management system from two aspects of regional differences and environmental management power categories.

We employ a spatial econometric approach to investigate the factors influencing residential property prices in La Spezia province (Italy). Unlike traditional hedonic models, which often overlook spatial dependencies, our methodology explicitly accounts for spatial autocorrelation, thereby yielding more robust and accurate estimates. Diagnostic spatial tests reveal significant spatial dependence in both property prices and context variables. To address this, we adopt the Spatial Durbin Error Model (SDEM), using a first-order Queen contiguity weight matrix. This model not only enhances explanatory power but also improves predictive accuracy. By incorporating spatial effects, the SDEM enables the disentanglement of direct and spillover influences, offering a more comprehensive understanding of the determinants of property prices. The findings demonstrate the importance of spatially-aware models not only in the formulation of effective housing policies and urban development strategies but also in appraisal practices, where they improve the accuracy of real estate valuation.