Digital financial inclusion and common prosperity in China: Spatial spillovers and regional divergence

Affluence, Spatial Spillovers, and Inequality in Household Energy Transitions: Exploring the Determinants of Sustainable Technology Adoption in Ireland

The spatial spillover effect and nonlinear influence mechanism of digital economy on urban shrinkage

Accurately identifying the spatiotemporal evolution and spatial differentiation of carbon emission intensity in the transport sector is essential for formulating region-specific carbon reduction policies. This study develops an analytical framework that integrates both static and dynamic perspectives to examine spatial disparities in transport sector carbon emission intensity. From a static perspective, the Dagum Gini coefficient is employed to quantify spatial differences and their sources of transport carbon emission intensity. From a dynamic perspective, kernel density estimation is applied to depict the evolution trajectories of transport carbon emission intensity. Furthermore, the traditional Markov chain model is refined to construct a spatial Markov chain model that accounts for spatial adjacency, enabling identification of persistence and spatial spillover effects. The empirical results indicate that (1) The carbon emission intensity of the transport sector in China presents an overall declining trend with significant spatial heterogeneity among provinces. Regional disparities have expanded, with the largest gap between the eastern and western regions, where inter-regional differences contribute an average of 47.374% to total disparity, representing the main source of variation. (2) The carbon emission intensity in the national, eastern, and central regions tends to converge gradually, while the western region shows a pattern of initial convergence followed by renewed divergence. Within each region, several provinces maintain carbon emission intensity levels significantly higher than the average, forming a clear spatial gradient structure. (3) The traditional Markov chain analysis reveals evident persistence and club convergence in transport carbon emission intensity. The spatial Markov chain analysis further shows that neighboring regions strongly influence local transition probabilities, demonstrating spatial spillover and path dependence effects. Hypothesis testing confirms the necessity of incorporating spatial dependence into the analysis. Based on these findings, this study proposes that carbon reduction strategies in the transport sector should be tailored to regional disparities and spatial interdependencies, aiming to enhance overall mitigation efficiency and foster coordinated governance.

ABSTRACT We examine whether FinTech raises green total factor productivity (GTFP) and how any gains propagate across space. Using an annual panel of 270 Chinese cities from 2007 to 2021, we construct a city-level FinTech index based on news-search intensity and compute GTFP with an EBM–GML index. Employing a spatial Durbin model, we decompose the impact into local treatment effects and spatial spillovers. FinTech significantly increases local GTFP and generates sizable positive spillovers that typically exceed the local effect. Spillovers exhibit a non-monotonic decay pattern, emerging beyond 100 km and persisting up to 1000 km, consistent with digital diffusion overcoming physical distance. Heterogeneity analyses demonstrate that urban absorptive capacity amplifies FinTech’s returns. Notably, resource-based cities exhibit negligible local gains due to carbon lock-in but act as upstream nodes that generate strong spillovers, whereas key environmental-protection cities and administrative hubs show intensified spillover effects. Mechanism tests indicate that green innovation and industrial upgrading serve as channels, primarily mediating the local impact. Results are robust to alternative weights and instrumental-variable strategies. The findings suggest that regional coordination and investments in absorptive capacity are crucial for maximizing FinTech’s environmental payoffs.

In the comprehensive advancement of rural revitalization, strengthening the development of regional public brands for agricultural and livestock products plays a critical role in increasing the income of farmers and herders. This study uses Inner Mongolia as a case and utilizes county-level panel data from 2012 to 2023. Employing empirical methods such as the fixed effects model, threshold effects model, and spatial econometric model, it examines the impact of regional public brand development on the income growth of farmers and herders. The results demonstrate that, in Inner Mongolia, the development of these regional public brands significantly boosts the income of farmers and herders by driving the growth of the agricultural industry. In terms of heterogeneity, the income-enhancing effect of regional public brands exhibits clear regional variation, and the impact on income growth markedly differs across different types of brands. Further analysis reveals that as the level of county urbanization increases, the income-enhancing effect of regional public brand development shows a continually strengthening trend. Moreover, this brand development exhibits a distinct spatial spillover effect; it not only promotes income growth within the brand's own region but also radiates to neighboring areas, driving up incomes in adjacent regions. Based on this analysis, this paper argues for persistently and steadily advancing the development of regional public brands for agricultural and livestock products. It is essential to actively promote the processing industry for these products, strengthen industrial chain integration, improve benefit-linking mechanisms and urban-rural coordination systems. By tailoring strategies to local conditions and sustaining long-term efforts, the brand effect can be substantially enhanced to better empower farmers and herders in increasing their incomes.

Enhancing the carrying capacity of the ecological environment serves as a pivotal pathway to achieving sustainable development and also constitutes a concrete response to the UN SDGs. Based on a provincial panel dataset covering 30 Chinese provinces spanning 2011–2023, the present work examines how digital finance shapes EECC and explores the corresponding transmission mechanisms. Findings from the empirical analysis confirm that digital finance exerts a significant positive effect in boosting ecological environmental carrying capacity. Heterogeneity tests further show that this catalytic influence is most salient in eastern China, while it lacks statistical significance or even turns negative in the central and western areas. Meanwhile, the catalytic function of digital finance becomes more distinct in highly urbanized areas. Mechanism analysis verifies that digital finance assumes a partial mediating function by cutting down energy consumption intensity and boosting human capital accumulation. Further analysis reveals that as digital finance matures, the above impact exhibits increasing marginal returns. Our spatial spillover assessment further indicates that digital finance contributes to stronger EECC within host provinces, while also facilitating coordinated improvements in this key indicator across neighboring jurisdictions. Accordingly, we propose that economies speed up the building of digital-related infrastructure, expand the outreach of digital finance, and properly steer the orderly movement of population, thus facilitating the eco-friendly sustainable advancement of the natural environment.

ABSTRACT To achieve carbon reduction goals, shifting industry toward low-carbon sectors is crucial. In China, local governments play a major role in shaping economic development through their control over land allocation. One practical way they can encourage this shift is by supplying more land to high-tech industries (HTI), which are typically low-carbon. This makes the amount of land allocation to such industries a useful and observable measure of industrial transformation. This study examines whether the carbon emissions trading system (ETS) facilitates this process by incentivizing local governments to reallocate land towards HTI. Using Chinese land transaction and urban panel data (2010-2019) and employing DID and SDID methods, this study finds that the ETS significantly encourages local governments to increase land supply for HTI. This policy impact exhibits regional heterogeneity, being stronger in eastern cities and weaker in central/western cities, and more pronounced in ordinary cities than in sub-provincial cities. Moreover, the ETS induces a significant spatial spillover effect. Further analysis reveals that the policy effect of ETS is influenced by local conditions, including industrial structure, land finance dependence, and secretary tenure. These findings highlight the ETS’s role in aligning local incentives with green industrial policy. Accordingly, further nationwide expansion of the ETS is recommended, with policy design that accounts for regional heterogeneity and promotes inter-regional collaboration to maximize its overall effectiveness.

Climate change, spatial spillovers, and sustainable productivity of Teff, Maize, and wheat across Ethiopian districts using integrated climate and agricultural data from 2000 to 2024

The rapid expansion of the low-altitude economy has created new opportunities for digital transformation in agriculture; however, empirical evidence on its role in advancing smart agriculture remains limited. Using panel data from 30 Chinese provinces (2013–2023), this study constructs multidimensional indices to examine how the low-altitude economy shapes smart agriculture development. It contributes to the literature by systematically introducing the low-altitude economy into the analytical framework of digital agriculture and empirically uncovering its transmission mechanisms, spatial spillovers, and regional heterogeneity. The results indicate that: (1) the development of the low-altitude economy significantly promotes smart agriculture, primarily through improvements in transportation efficiency and the agricultural ecological environment; (2) heterogeneity analysis reveals that this promotional effect is more pronounced in regions with stronger agricultural foundations and higher levels of factor agglomeration; and (3) spatial effect analysis shows that while the low-altitude economy significantly enhances local smart agriculture development, it exerts a certain inhibitory effect on neighboring regions, reflecting differentiated spatial transmission patterns under interregional factor competition.

Enhancing agricultural carbon productivity under the dual-carbon goal is important for mitigating global climate change, and the development of digital technology provides a new impetus for the low-carbon transformation of agriculture and the green development of the economy. Using the panel data of 30 provinces in China from 2013 to 2023, the projection pursuit model based on the accelerated genetic algorithm and the emission coefficient method are used to measure the development levels of rural digital technology and agricultural carbon productivity, respectively, and the panel fixed-effect model and spatial Durbin model are used to study the impact of digital technology development on agricultural carbon productivity. The results showed that: ① The development of digital technology in China had a spatial distribution pattern of decreasing gradient from east to west, and the agricultural carbon productivity in the southwest region had a high-high clustering pattern, with both showing an increasing trend year by year. ② The development of rural digital technology had a significant effect on the improvement of agricultural carbon productivity. ③ The heterogeneity test showed that the effect of digital technology on the improvement of agricultural carbon productivity was "western > central > eastern" and "balanced grain production and marketing area > main grain production area > main grain marketing area." ④The development of digital technology had a positive spatial spillover effect on agricultural carbon productivity in neighboring regions, with intensive spillovers within 300 km and detectable effects extending to 700 km of the spillover effect.

This study explores the impact of industrial intelligent transformation on industrial carbon emission efficiency and its spatial spillover effect, which is closely related to industrial sustainability. Based on panel data of 284 cities in China from 2011 to 2023, we find that intelligent transformation significantly improves urban industrial carbon emission efficiency, and reducing energy consumption intensity and promoting green technological innovation are two critical mediating channels. Moreover, both marketization level and environmental regulation stringency strengthen the promoting role of intelligent transformation on industrial carbon emission efficiency. Heterogeneity analysis demonstrates that the promotional effect of intelligent transformation on industrial carbon emission efficiency is strongest in Eastern China, followed by Central China, and weakest in Western China. In addition, this effect is significant in non-resource-based cities but insignificant in resource-based cities. Furthermore, intelligent transformation exerts a negative “competitive spillover effect” on industrial carbon emission efficiency of geographically adjacent cities, while generating a positive “demonstration spillover effect” on cities with similar economic development levels.

The Dual Role of Economic Migration in Pakistan: Direct and Spatial Spillover Effects on Food Insecurity

Breaking through the constraints of traditional agricultural resources and expanding food supply channels have become essential for safeguarding food security. The non-timber forest-based economy (NTFE), which integrates multiple understory production activities including planting, breeding, and foraging, expands the variety of food sources and provides a new pathway for enhancing regional diversified food supply capacity (DFSC). Based on this perspective, this study constructs evaluation indicator systems for both DFSC and NTFE development. The entropy-weighted TOPSIS method is employed to measure the levels of DFSC and NTFE development across 31 Chinese provinces from 2011 to 2022. A two-way fixed effects model and a spatial Durbin model are applied to empirically investigate the mechanisms through which the NTFE enhances DFSC. The results show the following: (1) Between 2011 and 2022, both the DFSC and the level of NTFE development in China exhibited a sustained upward trend. Specifically, the level of NTFE development grew rapidly before 2019, with a slowdown in growth in the later years, while DFSC maintained a steady increase throughout the study period. (2) NTFE development significantly promotes DFSC. (3) The NTFE enhances DFSC by facilitating the upgrading of the forestry industrial structure and improving forestland productivity. (4) The NTFE generates positive spatial spillover effects on DFSC, and these spillover effects are stronger than direct local effects.

Climate-induced homicides in Brazil: Evidence of spatial spillovers

In the context of China’s pursuit of high-quality economic development, enhancing agricultural productivity is crucial for ensuring food security and promoting common prosperity. This paper constructs a systematic IV-LP-ACF-SAR econometric framework to analyze agricultural Total Factor Productivity (TFP) growth using panel data from 31 Chinese provinces spanning 2014 to 2023 (n = 341 observations). The framework employs the instrumental variable (IV)-based Levinsohn–Petrin (LP) proxy variable method under the Ackerberg–Caves–Frazer (ACF) system to estimate a Translog production function while addressing endogeneity using multiple spatial weight matrices. TFP growth is decomposed into technical change (TC), technical efficiency (EC), and scale efficiency (SC). A Spatial Autoregressive (SAR) model with Dynamic Common Correlated Effects (DCCE) explores spatial spillover effects and regional heterogeneity. Results show that China’s agricultural TFP remained largely stagnant from 2014 to 2023 with an average annual growth rate of −0.18%, where technical efficiency decline (−0.33% annually) was the main constraint. Technical change remained neutral, while scale efficiency contributed positively (+0.15% annually). Mechanization showed the highest output elasticity (0.99), while fertilizers, pesticides, and labor exhibited negative marginal returns. Spatial analysis revealed significant negative scale efficiency spillovers with regional patterns of “scale synergy in the Northeast/Northwest” and “efficiency synergy in East/North China.” These findings suggest that productivity policy should shift toward a dual-driver model combining efficiency enhancement and optimal scaling, with differentiated regional policies and inter-provincial coordination mechanisms necessary to mitigate negative spillovers and enhance sustainable agricultural growth quality.

Assessing urban resilience under compound shocks requires observable and comparable process evidence that can inform resilient land governance and cross-jurisdiction planning. Using China’s Pilot Free Trade Zones (PFTZs) as a staged institutional setting, this research examines whether institutional exposure is associated with deviation–recovery trajectories of urban activity during the 2020 COVID-19 shock and whether these associations propagate through spatial spillovers with an identifiable scale profile. Institutional exposure is operationalized by the prefecture-level cities actually covered by PFTZ functional areas. With harmonized administrative boundaries, we construct an annual city-level VIIRS nighttime light (NTL) series for 2013–2024 and treat NTL as an activity-change signal rather than a direct proxy for output. We trace shock deviation in 2020 and subsequent recovery via staged differencing. Spatial interaction frictions are represented by least-cost path distance (LCPD) derived from a multi-source cost surface, which is used to build a gravity-based spatial weight matrix. Estimation relies on the Spatial Durbin Model (SDM), with LeSage–Pace impact decomposition to distinguish direct and spillover effects, complemented by distance-threshold diagnostics to map attenuation patterns. Results indicate persistent clustering within the PFTZ-related urban system. The shock year is characterized by compressed connectivity and fragmented brightening, whereas recovery proceeds in a layered manner with earlier core repair, partial corridor reconnection, and weaker adjustment at the periphery. Spatial dependence in activity change is statistically significant. Associations linked to institutional exposure are realized primarily locally, while structural and scale conditions more readily operate through spatial externalities. Spillovers are most detectable at meso-scales and attenuate gradually across distance thresholds. Overall, the integrated earth-observation and spatial-econometric framework provides replicable geospatial evidence to support resilient land governance and regional coordination under common shocks.

This paper investigates the resilience of local economic systems (LLMAs) in Italy over 2008 to 2022 distinguishing between two resistance periods (2008–13 and 2019–20) and two recoveries (2013–19 and 2020–22). The main focus is on the role of diversification patterns, to check whether –and to what extent- related/unrelated variety is important in determining local performance. We estimate a spatial model and include a number of control variables. Results show that relatedness is important both for resistance and for recovery; unrelated variety is less important. We also find significant spatial spillovers only for recovery, with ambiguous effects over time. Results confirm the country’s general de-industrialization trend, coupled with growing importance for services, in particular for the less knowledge-intensive ones. While the South recovers better than the rest of the country, we tentatively explain this fact in terms of the role played by traditional services.

ABSTRACT Industrial robot adoption’s environmental impact is time-dependent and spatially heterogeneous. Using data from 281 Chinese cities between 2008 and 2023 with STIRPAT models, we find robots significantly reduce total carbon emissions short-to-medium term but become insignificant long-term, while carbon intensity consistently decreases across all horizons. This divergence stems from a rebound effect: efficiency gains are offset by enterprise scale expansion. Mechanism analysis reveals robots promote green innovation and energy structure upgrading. Spatial Durbin models uncover asymmetries: robots reduce total emissions more in neighboring regions than locally due to a siphoning effect whereby advanced automation areas attract high-carbon production from surroundings. In contrast, carbon intensity reductions are stronger locally. Cities in China’s new energy demonstration program maintain long-term emission reductions, suggesting clean energy infrastructure mitigates rebound effects. Findings inform policies accounting for temporal dynamics and spatial spillovers.

ABSTRACT New quality productivity not only helps to optimize the allocation of resources among regions, but also promotes economic development. Therefore, studying the influence of new quality productivity on the spatial spillover effect of resource mismatch is of great significance for formulating scientific and reasonable regional economic policies and promoting high-quality economic development. This paper analyses regional differences between new quality productive forces and resource misallocation using the Moran Index and Trend Surface Analysis. A spatial Durbin model is constructed to further explore the spatial effects of new quality productive forces on resource allocation. The conclusions are as follows: (1) From 2012 to 2022, the new quality productive forces show an obvious growth trend, while the resource misallocation exhibits an overall decreasing trend. (2) Spatially, new quality productive forces are highest in the Central region, followed by the South. Resource misallocation is highest in the West, followed by the East, and higher in the Central and North than the South, with a pronounced gap between the East and West. (3) There is a significant negative correlation between new quality productive forces and resource misallocation. Additionally, there is a significant spatial spillover effect where new quality unproductive forces inhibit resource misallocation.