Search Results (657)

Against the backdrop of China’s “dual carbon” goals and urban green transition, improving urban land use efficiency is essential for shifting land development from extensive expansion to intensive and low-carbon use. Using the Low-Carbon City Pilot Program as a quasi-natural experiment, this study examines panel data from 285 prefecture-level cities in China from 2007 to 2023. We apply a multi-period difference-in-differences model, a threshold regression model, and a spatial Durbin model to assess the program’s impact on urban land use efficiency. The results show that the pilot program significantly improves urban land use efficiency, and the effect persists over time. This finding remains robust across a series of robustness checks. Heterogeneity analysis shows that the efficiency gains are stronger in cities with lower air pollution control pressure, higher industrial pollution control pressure, and lower fiscal pressure. Further threshold analysis shows that digital connectivity is a key condition for strengthening the policy effect. The spatial analysis suggests that the policy effect shows some spatial association. However, the decomposed indirect and total effects are not robust, so the spatial results should be interpreted with caution. This study provides empirical evidence on how low-carbon city pilots affect urban land governance and land use efficiency. Its conclusions, however, remain subject to limitations related to efficiency measurement, policy identification, and the availability of city-level data. Full article

This study investigates the spatial effects of artificial intelligence (AI) innovation on carbon intensity using provincial panel data from 30 Chinese provinces over 2010–2023. Employing the Spatial Durbin Model (SDM), we find that a 1% increase in AI patent count reduces local carbon intensity by 0.034% (direct effect, p < 0.01) but increases carbon intensity in neighboring regions by 0.069% (indirect effect, p < 0.05). Heterogeneity analysis shows that AI innovation reduces local carbon intensity by 0.069% in non-western regions (p < 0.01) but has no significant effect in the western region. In regions with above-median R&D intensity, both direct and indirect effects become negative (−0.094% and −0.069%, respectively), indicating that AI innovation reduces carbon intensity locally and in neighboring areas. Mechanism tests confirm that industrial structure upgrading mediates this relationship, with AI innovation increasing the industrial structure hierarchy coefficient by 0.004 (p < 0.05). These findings provide quantitative evidence that AI innovation has opposing local and spillover effects on carbon intensity, and that high R&D intensity can reverse negative spillovers into positive ones. The results offer empirically grounded policy recommendations for China’s dual-carbon targets and sustainable development. Full article

The relationship between forest ecosystem services and human health has emerged as a key topic in forest economics and health policy research. This study develops a spatial modelling framework to quantify the health benefits of forest ecosystem services and proposes policy mechanisms to incorporate these benefits into governmental health strategies. Using county-level panel data from 66 administrative units in Zhejiang Province, China, covering the period 2013–2023, we analyse the relationship between forest-mediated air purification services and two population health outcomes: the incidence of respiratory diseases and cardiovascular disease mortality. We employ a Spatial Durbin Model (SDM) to estimate both direct and spatial spillover effects across county boundaries. The findings indicate that forest ecosystem services exert significant negative effects on adverse health outcomes, with spillover effects extending beyond administrative boundaries. The monetised health benefit of forests is estimated at approximately RMB 1108.6 per hectare per year, substantially exceeding current ecological compensation standards and suggesting systematic undervaluation of forest health services. Heterogeneity analysis reveals that health benefits are greater in urbanised regions and among vulnerable population groups, including the elderly. These findings provide an empirical basis for reforming health-oriented ecological compensation mechanisms and offer implications for sustainable land use governance aligned with SDG 3 (Good Health and Well-being) and SDG 15 (Life on Land). Full article

In developing countries, promoting sustainable income growth for farmers is a major priority. This study constructs an evaluation index system for the whole agricultural industry chain from the perspective of synergy among the innovation chain, supply chain, value chain, and capital chain. It also empirically tests the enabling mechanisms and spatial effects of the whole agricultural industry chain on farmers’ income. The entropy value method was used to measure the development level of the whole agricultural industry chain. Two-way fixed effects, mediation effects, and spatial Durbin models were applied to investigate the impacts, mechanisms, and spatial characteristics of the whole agricultural industry chain on farmers’ income. The whole agricultural industry chain significantly promotes farmers’ income growth, with the expansion of the non-agricultural employment scale and the improvement of urbanization levels serving as the main pathways through which the whole agricultural industry chain drives increases in farmers’ income. The heterogeneity analysis reveals that the innovation chain and capital chain contribute the most prominent marginal effects; the effect intensity of the whole agricultural industry chain on farmers’ income presents a spatial gradient pattern of “Central > Western > Eastern”; and its income-increasing effect is more noticeable for middle- and low-income farmers, demonstrating significant pro-poor characteristics. Further analysis indicates that the whole agricultural industry chain exerts a significant positive spatial spillover effect on farmers’ income. Therefore, it is essential to optimize the layout of the whole agricultural industry chain, smooth the transmission channels of non-agricultural employment and urbanization, and enhance the benefit linkage mechanism targeting middle- and low-income farmers. Full article

Reducing agricultural carbon emissions is an important component of China’s efforts to achieve its carbon peaking and carbon neutrality goals. As an important policy oriented financial instrument, green credit can facilitate lower agricultural carbon intensity by directing resources more efficiently across regions and encouraging low carbon transformation in agriculture. Using panel data for 30 Chinese provinces from 2005 to 2022, this study measures agricultural carbon emission intensity (ACEI) from six sources. It then examines the spatial spillover effects, transmission channels, and nonlinear characteristics associated with green credit by using a spatial Durbin framework, mediation analysis, and panel threshold model. The results indicate that: (1) green credit development is significantly associated with lower ACEI; (2) green credit exhibits significant spatial spillover effect, being associated with lower ACEI both within a province and in neighboring provinces; (3) green credit exhibits marked regional heterogeneity in its impact on ACEI: it shows both direct and spillover effects in the eastern region, only spillover effects in the central region, and only direct effects without effective diffusion in the western region; (4) green credit is associated with lower ACEI through industrial structure upgrading and lowering agricultural energy consumption intensity; (5) green credit has a single threshold effect on ACEI based on its own development level. After crossing the threshold, the emission intensity reduction effect weakens but remains significant. These results offer empirical evidence for refining green credit arrangements and advancing coordinated agricultural emission reduction across regions. Full article

Against the backdrop of digital transformation, industrial integration between modern services and advanced manufacturing has become an important driver of sustainable industrial development. Nevertheless, existing studies have mainly examined its direct effects, while paying insufficient attention to temporal path dependence, spatial spillovers, and the underlying transmission mechanisms. Using panel data for 29 Chinese provinces from 2005 to 2024, this study investigates how industrial integration affects manufacturing upgrading in China within a dynamic spatial econometric framework. To this end, a dynamic Spatial Durbin Model, spatial mediation analysis, and instrumental-variable estimation are employed. The empirical results indicate that industrial integration significantly promotes manufacturing upgrading. In the benchmark model, a 1% increase in the coupling-coordination index between modern services and advanced manufacturing is associated with an approximately 0.121% increase in the manufacturing upgrading index. Manufacturing upgrading also shows strong temporal persistence, as reflected by a lagged dependent variable coefficient of 0.878. The decomposition of spatial effects further reveals that industrial integration produces both local promotion effects and cross-regional spillovers, with a direct effect of 0.135 and an indirect effect of 0.156. In addition, mechanism analysis shows that innovation efficiency serves as an important transmission channel linking industrial integration to manufacturing upgrading. These findings imply that industrial integration can support sustainable development by improving resource allocation efficiency, strengthening innovation capacity, and promoting more coordinated regional industrial development. This study enriches the literature on industrial integration and manufacturing upgrading from a dynamic spatial perspective and provides policy-relevant evidence for the design of differentiated and sustainability-oriented industrial integration strategies. Full article

Digitalization is the driver of regional competitiveness and sustainable development, but its geographical impacts differ significantly across Europe. This study was conducted to determine if digital transformation results in regional sustainability or if it increases spatial inequalities, concentrating on European NUTS-1 regions for the period 2021–2025. A composite Regional Digitalization Index was developed by means of Principal Component Analysis (PCA) based on indicators measuring internet access, internet usage, and the availability of digital public services. Dynamic spatial panel econometric models were used for empirical investigation, including a Spatial Autoregressive (SAR) model and a Spatial Durbin model (SDM), which facilitated the exploration of both temporal dependence and spatial spillover. Three main conclusions can be derived from the results, as follows: The level of digitalization in a region is highly stable over time, whereby the development depends most on previous paths. Subsequently, human capital is highly significant for digital development, and its effects are not only local but also spill over to neighboring regions. Lastly, spatial interactions consist of two opposite forces—the positive diffusion from digitally advanced neighboring regions and the competitive effects related to the economic strength of neighboring regions—that further intensify the core–periphery divide. Full article

Against the backdrop of China’s “dual carbon” goals and mounting fiscal pressures at the local level, local governments face a dilemma between offering tax incentives and reducing industrial carbon emissions. This study uses data from 30 Chinese provinces between 2000 and 2022. It employs the Dagum Gini coefficient to characterize regional disparities and spatial heterogeneity in industrial carbon emissions, utilizes the Super-Slack-Based Measure (SBM) model and kernel density estimation to estimate the spatiotemporal evolution of tax competition, constructs a Spatial Durbin Model (SDM) to examine its direct effects and spatial spillover effects, and conducts robustness tests using four different methods. The study finds that: (1) tax competition has a significant positive direct effect on local industrial carbon emissions and generates positive spatial spillovers; bottom-up tax competition exacerbates overall regional carbon emissions; (2) control variables such as economic development level and energy intensity all exhibit significant spatial spillover characteristics; and (3) the carbon emission effects of tax competition exhibit regional heterogeneity, with positive spatial spillovers in the eastern region and predominantly negative spillovers in the central and western regions. From a spatial competition perspective, this paper reveals the underlying mechanisms and regional differences between these two factors. The findings provide empirical insights and policy references to optimize the competitive landscape among local governments, improve the regional collaborative green tax system, promote low-carbon industrial transformation, and achieve the “dual carbon” goals. Full article

Cultural-tourism integration (CTI) is increasingly regarded as a strategic pathway toward common prosperity (CP). Drawing on balanced panel data from 11 prefecture-level cities in the Pearl River–Xijiang Economic Belt (2013–2022), this study employs a Spatial Durbin Model (SDM) to estimate CTI’s effects on CP. A pronounced ‘east–strong, west–weak’ spatial pattern generates positive spatial spillovers to neighboring regions. At the same time, the significantly negative spatial autoregressive coefficient points to potential spatial polarization. Gains in core areas may be accompanied by relative stagnation in peripheries. CTI’s impact is markedly stronger on economic affluence than on equitable sharing, and control variables such as industrial structure and infrastructure even exhibit negative spillovers in some cases. Overall, while CTI can act as a regional connectivity mechanism, its prosperity dividends are not automatically inclusive. Strengthening cross-jurisdictional coordination and prioritizing equitable distribution of public services could help mitigate polarization risks and translate economic integration into broadly shared well-being. Full article

Environmental economics and policy research has paid limited attention to interfirm spillover effects on firm-level performance. This study addresses this gap by distinguishing between the direct and spillover effects of environmental regulation and firm-specific resources on firm performance. Using panel data for Korean manufacturing firms, we estimate a dynamic spatial Durbin model (SDM) that accounts for both temporal persistence and spatial dependence. The empirical results provide clear evidence. First, environmental regulation and firm-specific factors—including intellectual capital, physical capital, and organizational slack—exert statistically significant positive direct effects on firms’ sustainable growth rate (SGR). Second, interaction effects are crucial: environmental regulation significantly enhances SGR when combined with organizational slack, highlighting the importance of internal resource conditions. Third, spatial spillover effects are identified only under specific configurations. Environmental regulation generates positive spillover effects when interacting jointly with intellectual capital, physical capital, and organizational slack, rather than as an independent driver. Similarly, physical capital produces spillover effects through its interactions with other firm resources. Importantly, these effects vary across firms. Spillover effects are more pronounced in firms with high absorptive capacity, whereas they are weaker or insignificant in firms with low absorptive capacity. Overall, the findings indicate that environmental regulation affects firm performance primarily through resource complementarities and conditional spatial interactions, offering policy implications for more targeted regulatory design Full article

Housing markets display geographical linkages that contravene conventional regression assumptions; yet, Central and Eastern European towns are markedly underrepresented in spatial econometric research. This study provides a systematic spatial econometric analysis of Zagreb’s housing market. It looks at both asking sale and rental prices throughout the city’s 17 administrative districts. There are five model specifications used in the analysis: Ordinary Least Squares (OLS), Spatial Lag of X (SLX), Spatial Autoregressive Model (SAR), Spatial Error Model (SEM), and Spatial Durbin Model (SDM). The findings demonstrate significant positive spatial autocorrelation in both markets: Global Moran’s I = 0.29 (p = 0.007) for sales and 0.42 (p < 0.001) for rents. LISA analysis finds important groups of high-priced homes in the center districts and lower-priced homes on the edges. Spatial models significantly surpass OLS: SLX exhibits AIC enhancements of 9.90 (sales) and 20.20 (rentals), but SAR and SEM yield no enhancements, suggesting that local spillover effects from adjacent characteristics prevail over global spatial diffusion or correlated shocks. The higher Moran’s I and AIC gains in rental markets show that there are different spatial processes for different types of tenure. These results address a significant empirical deficiency in post-socialist housing research, illustrate that neglecting spatial dependencies may lead to biased estimates and reduced model performance, and furnish methodologically sound evidence that spatial econometric techniques are essential for accurate modeling for precise urban housing analysis in intermediate-sample scenarios. Policy implications stress the need to use spatial approaches in choices about property value, forecasting, and urban planning. Full article

This study systematically examines the impacts of extreme temperatures on the digital economy development index and the underlying mechanisms based on panel data from 281 prefecture-level cities in China from 2012 to 2023. This study explicitly distinguishes the distinctive adaptive capacity of the digital economy in responding to climate risks. Through global and local spatial autocorrelation analysis, the study finds that both extreme temperatures and the digital economy exhibit significant spatial clustering. This study employs the spatial Durbin model (SDM) and effect decomposition and further incorporates the GS2SLS estimator alongside dual instrumental variables constructed from historical geographic characteristics to address endogeneity, thereby identifying the asymmetrical impacts of extreme heat and extreme cold on the digital economy with great rigor. Specifically, extreme heat fosters short-term local digital demand that is subsequently translated into long-term growth in IT human capital and infrastructure, thereby increasing the DEDI. However, its net spatial effect is inhibitory due to energy crowding out. Extreme cold, by contrast, primarily disrupts supply chains and intensifies energy consumption, with its impact largely confined to the local scope. Green technological innovation mitigates the impact of extreme heat on the digital economy through demand substitution, while, under extreme cold, it manifests as the physical protection of infrastructure. Meanwhile, an optimized industrial structure substantially reduces the economy’s dependence on supply chains, amplifying the promotional effect of extreme temperatures on the digital economy and reflecting the transformation capacity of regions under complex environmental conditions. Heterogeneity analysis demonstrates that the effects of extreme temperatures vary significantly across different urban agglomerations, economic zones, geographic regions and city types. This study not only extends the theoretical framework for the economic assessment of climate risks and spatial econometric analysis to the climate sensitivity of the digital economy but also provides empirical evidence for understanding the complex relationship between climate change and digital economy development and offers references for differentiated policies in a coordinated regional digital economy. Full article

Enhancing carbon productivity is fundamental to achieving carbon neutrality while sustaining economic growth. Utilizing a comprehensive dataset of Chinese cities from 2010, 2015, and 2020, this study investigates the spatiotemporal patterns and underlying drivers of urban carbon productivity (UCP). Methods including kernel density estimation, spatial autocorrelation analysis, and the spatial Durbin model (SDM) are employed. The results reveal that China’s UCP has improved significantly overall, yet with increasing internal disparities among cities. The SDM decomposition indicates a fundamental shift in driving mechanisms. Green technological innovation has supplanted generalized R&D expenditure as the most dependable core driving force for improving local carbon productivity. Moreover, the economic development level also exerts positive spatial spillover effects in the later stage, which jointly contribute to the formation of a multi-centered pattern. Urban form metrics exert dual influences: urban compactness (ENN_MN) shows a stable positive local effect, whereas urban fragmentation (PD) and urban sprawl (CONTAG) exhibit a paradoxical “local inhibition–neighborhood promotion” effect, highlighting intricate inter-city spatial interactions. The findings underscore the necessity for differentiated local practices, namely, policy must target differentiated city roles and manage spatial spillovers for synergistic regional green and sustainable transition. Full article

Digital economy and agricultural technological innovation are key drivers of agricultural economic resilience and sustainable development. However, existing research has yet to clarify how they jointly affect agricultural economic resilience, particularly through potential nonlinear patterns and spatial spillover effects. Using panel data from 30 Chinese provinces, this study measures digital economy development and agricultural economic resilience via the entropy weight method. It systematically examines the direct impact, transmission mechanisms, threshold effects, and spatial spillover effects using two-way fixed effects, mediation, threshold regression, and spatial Durbin models. The findings are as follows. First, the digital economy significantly improves agricultural economic resilience, a result robust to various tests and endogeneity treatments. Second, agricultural technological innovation plays a partial mediating role, accounting for 19.37% of the total effect. Third, the resilience-enhancing effect of agricultural technological innovation exhibits a double-threshold pattern: its positive impact gradually strengthens as the digital economy develops to a higher level. Fourth, the digital economy generates a positive spatial spillover effect on agricultural economic resilience. Fifth, although the digital economy and agricultural technological innovation show synergistic development, their coupling coordination degree remains relatively low, indicating substantial untapped potential for synergy. From a sustainable development perspective, this study reveals the mechanisms through which the digital economy and agricultural technological innovation enhance agricultural economic resilience, providing empirical evidence and policy insights for strengthening agricultural risk resistance and achieving agricultural sustainability via digital transformation and technological progress. Full article

As a vital engine of economic growth, the digital economy can boost agricultural productivity while curbing carbon emissions from grain production, thereby facilitating the green transformation of traditional agriculture and the sustainable development of grain production systems. It serves as a pivotal anchor for achieving China’s dual-carbon strategic goals in the agricultural sector and supporting the long-term sustainability of national grain security. This paper conducts an in-depth analysis of the carbon emission mitigation mechanisms of the digital economy for sustainable agricultural production. Using panel data covering 30 provincial-level regions in China from 2012 to 2021, this study employs and integrates panel regression estimation, mediating effect analysis, and the Spatial Durbin Model (SDM) framework to identify the underlying pathways through which the digital economy affects carbon emissions from grain production and drives low-carbon sustainable transformation of agriculture. The findings reveal the following: (1) The digital economy exerts a significant negative effect on carbon emission intensity in grain production, laying an empirical foundation for digital-enabled sustainable grain production; (2) It indirectly reduces carbon emission intensity by promoting the development of green finance as a mediating channel, unlocking the sustainable empowerment mechanism of green finance for agricultural low-carbon transition; (3) The development of the digital economy presents pronounced spatial spillover effects: improved digital development in one region also lowers grain production carbon emission intensity in neighboring areas, supporting cross-regional coordinated sustainable development of grain production; (4) The carbon-reduction effects of the digital economy exhibit regional heterogeneity, with more significant emission-reduction outcomes observed in eastern and central regions, while such effects are less prominent in western regions, providing a basis for formulating differentiated regional agricultural sustainable development policies. Based on these findings, this paper puts forward a series of targeted policy recommendations, offering theoretical and practical references for the high-quality development of green and low-carbon agriculture and the overall advancement of sustainable agricultural and rural modernization. Full article