Search Results (567)

Global climate change results in increasing challenges to the structural security of China’s food system, while pronounced spatial heterogeneities in provincial production and consumption intensify the risk of supply-demand imbalance. This study examines the resilience of China’s inter-provincial staple food flow network from a systemic perspective and identifies its key drivers. Inter-provincial food flows are first inferred using a cost-minimization optimization model. Network resilience is then evaluated by integrating complex network analysis with ecological network resilience theory. Finally, econometric analysis is applied to quantify the relative contributions of multiple structural factors to resilience dynamics. The results reveal an overall decline in the resilience of aggregated staple food, alongside persistently low resilience in soybeans network, indicating heightened structural vulnerability. Substantial heterogeneity is observed across staples in both resilience levels and underlying mechanisms. In general, greater connectivity and diversity of flow paths enhance system resilience, although this effect is markedly weaker for soybeans due to concentrated and import-dependent supply structures. By explicitly linking flow, network structure, and resilience, this study provides system-level insights into the functioning of inter-provincial food flow networks. The proposed analytical framework offers a transferable tool for assessing interregional food flow resilience and supports evidence-based strategies for validating system robustness under uncertainties. Full article

Industrial development is a fundamental driver of socio-economic progress, and industrial structure upgrading plays a vital role in advancing rural revitalization. Based on county-level panel data from Fujian Province from 2017 to 2022, this study employs Ordinary Least Squares (OLS) and spatial econometric models—including the Spatial Lag Model (SLM) and Spatial Error Model (SEM)—to empirically assess the impact of county-level industrial structure upgrading on rural revitalization, as well as its spatial transmission mechanisms. The findings reveal that: (1) an increase in the proportion of secondary and tertiary industries significantly enhances the rural revitalization development index at the 1% level of significance; (2) rural revitalization development exhibits strong spatial dependence and positive spatial spillover effects, indicating a “local club convergence” pattern among neighboring counties; and (3) the SEM outperforms OLS and SLM, suggesting that inter-county disparities in rural revitalization primarily result from spatial heterogeneities such as infrastructure and public service quality. Additionally, factors such as transportation accessibility, social public services, and per capita GDP have significant positive effects, while the impact of fiscal agricultural investment appears limited. This study provides empirical evidence to support coordinated development between industrial upgrading and rural revitalization strategies and offers policy insights for constructing an integrated and regionally synergistic framework for rural development in China. Full article

The Songliao River Basin, as a core area of multicultural integration in Northeast China, still lacks systematic research on the spatial distribution of religious sites and their influencing factors. This study integrates spatial pattern analysis methods (kernel density, standard deviation ellipse, imbalance index) and spatial econometric models (spatial error model, geographically weighted regression model) to explore the spatial distribution characteristics of 1288 religious sites in the basin and the influencing mechanisms of natural, socio-economic, and cultural factors. Results: (1) Religious sites in the basin show a clustered distribution of “higher density in the south than the north, one main cluster and two sub-cores”, with a northeast–southwest trend and poor balance at the prefectural-city scale. (2) Cultural factors are the core driver; cultural memory and social capital in traditional villages promote the agglomeration of religious sites and shape the “one village, multiple temples” pattern. Intangible Cultural Heritage, Major Historical and Cultural Sites Protected at the National Level, and religious sites form a tripartite symbiotic spatial relationship of “cultural practice—spatial carrier—institutional identity”; natural factors lay the basic pattern of spatial distribution. (3) Policy factors have a significant impact: A-rated Tourist Attractions and Performing Arts Venues show a positive effect, while museums exhibit spatial inhibition due to functional competition. (4) Economic, Population, and Transportation factors had no statistically significant effects, indicating that their spatial distribution is driven primarily by endogenous cultural mechanisms rather than external economic drivers. This study fills the gap in research on the spatial distribution of religious sites in Northeast China. By integrating multiple methods, a quantitative demonstration of the coupling mechanism of multiple factors was conducted, providing scientific support for religious cultural heritage protection policies and sustainable development strategies amid rapid urbanization. Full article

This study examines the impact of Environmental, Social, and Governance (ESG) strategies on reducing air pollution in the West Kazakhstan region, a major hub for Kazakhstan’s oil and gas industry. A spatial analysis of atmospheric emissions reveals an uneven distribution of emission sources, predominantly concentrated in the northern industrialized part of the region, where the Karachaganak oil and gas condensate field is located. The ESG model of Karachaganak Petroleum Operating b.v. (KPO), implemented as an integrated management system based on Global Reporting Initiative (GRI) standards, is compared with the ESG strategies of leading oil and gas companies in Kazakhstan and globally, aligning with current international research trends. The analysis underscores the interdependence of technological and social aspects in the transition to a low-carbon economy, confirming the importance of integrating the environmental, social, and governance components of ESG into a unified strategic planning framework for sustainable development. Using econometric modeling, the study establishes a relationship between ESG indicators and the reduction in atmospheric pollution and provides a forecast for emission reductions by 2030. The key measures proposed to improve regional air quality are linked to long-term decarbonization strategies within the context of the sustainable development of the entire region. The proposed algorithm for implementing ESG principles helps to identify the concentration of functions and associated risks at different management levels within Highly Polluting Enterprises (HPEs) and optimizes business processes by focusing efforts on air pollution mitigation. The findings are applicable to other countries, as oil and gas producers worldwide face a number of common air pollution challenges. Full article

Amid intensifying challenges of global climate change, China—as the world’s largest carbon emitter and a major manufacturing hub—occupies a pivotal position in the global industrial green transformation. Drawing on environmental federalism theory and China’s decentralized governance model, this study develops a framework of “green finance–local government competition–industrial green transformation.” Using panel data from 283 cities in China, we employ spatial econometrics and mediation effect models to test the dual mechanisms by which green finance promotes industrial green transformation. The findings indicate that (1) green finance promotes industrial green transformation; (2) green finance advances industrial green transformation by dismantling China’s traditional local government competition–based development model and removing the institutional suppression arising from “race-to-the-bottom competition”; (3) the effect of green finance exhibits long-run characteristics and a “benchmark–imitation” pattern; (4) baseline environmental conditions strengthen the influence of green finance on industrial green transformation; (5) incorporating ecological civilization development into officials’ performance evaluations can effectively reshape policy incentives and amplify the positive role of green finance. Thus, we propose differentiated green finance policies, the construction of a governance mechanism that integrates fiscal–financial–ecological compensation, and the optimization of ecological civilization assessment indicators to curb campaign-style governance. Full article

Comprehending the spatial–temporal transformation of urban resilience (UR) is fundamental for promoting sustainable urban growth in the Chinese context. In this study, a multi-dimensional index framework is developed to cover economic, social, ecological, and infrastructural aspects of resilience, assessing urban resilience across 282 prefecture-level cities between 2005 and 2022. By integrating the Time-Varying Entropy Method (TEM) with the Two-Stage Nested Theil Index (TNTI), we quantify the intensity and origins of spatial disparities in UR. Furthermore, spatial econometric models are employed to examine β convergence across regional and temporal dimensions. Additionally, the research adopts an Optimal Parameter-based Geographical Detector (OPGD) approach to explore and quantify the major determinants affecting urban resilience. The results reveal that (1) UR has significantly improved nationwide, with higher levels concentrated in eastern and southern China; (2) intra-provincial disparities are the dominant source of spatial differences, and continue to expand; (3) UR shows robust β-convergence nationally and regionally, although σ-convergence is limited to specific periods; (4) savings deposits per capita, ratio of employees, per capita fiscal expenditure and market size are identified as the core factors driving UR. The findings offer new insights into urban spatial governance under multi-dimensional constraints and challenges and serve as empirical guidance for narrowing resilience gaps and promoting balanced regional development. Full article

Urban river restoration provides significant ecological and social benefits, yet its market valuation remains underexamined in rapidly urbanizing inland cities. This study estimates the economic value of integrated blue–green spaces generated by the Bai River Ecological Restoration Project in Nanyang, China, using a spatially explicit hedonic pricing framework that links geocoded resale transactions with NDVI-based vegetation measures. Properties located within blue–green zones—areas jointly characterized by restored waterways and enhanced riparian greening—command an average price premium of 17.9% (CNY 1509/m²). Visual accessibility further increases housing values, although interaction effects indicate diminishing marginal premiums where multiple amenities co-occur. Quantile regressions show stronger capitalization effects in lower- and middle-priced segments, suggesting that ecological improvements may yield broad-based rather than elite-focused benefits. Spatial dependence diagnostics confirm significant autocorrelation, and Spatial Error Model estimates remain consistent with the baseline results. Overall, the findings provide robust evidence of supra-additive blue–green synergies and demonstrate the utility of combining NDVI with spatial econometric hedonic modeling. The study offers a transferable framework for supporting nature-based urban planning and informing cost–benefit evaluations of integrated ecological restoration initiatives. Full article

We compare three modern Bayesian approaches, Hamiltonian Monte Carlo (HMC), Variational Bayes (VB), and Integrated Nested Laplace Approximation (INLA), for two classic spatial econometric specifications: the spatial lag model and spatial error model. Our Monte Carlo experiments span a range of sample sizes and spatial neighborhood structures to assess accuracy and computational efficiency. Overall, posterior means exhibit minimal bias for most parameters, with precision improving as sample size grows. VB and INLA deliver substantial computational gains over HMC, with VB typically fastest at small and moderate samples and INLA showing excellent scalability at larger samples. However, INLA can be sensitive to dense spatial weight matrices, showing elevated bias and error dispersion for variance and some regression parameters. Two empirical illustrations underscore these findings: a municipal expenditure reaction function for Île-de-France and a hedonic price for housing in Ames, Iowa. Our results yield actionable guidance. HMC remains a gold standard for accuracy when computation permits; VB is a strong, scalable default; and INLA is attractive for large samples provided the weight matrix is not overly dense. These insights help practitioners select Bayesian tools aligned with data size, spatial neighborhood structure, and time constraints. Full article

This study extends the Environmental Kuznets Curve (EKC) framework to analyze the growth–emissions nexus in twelve post-socialist European countries by integrating agricultural development, regulatory quality, renewable energy, and transport dynamics. Employing advanced panel econometric techniques—FMOLS, DOLS, and PARDL—and treating regulatory quality (REGURAQUAL) as an exogenous determinant, the analysis identifies the structural and institutional factors shaping carbon intensity (CI). The results indicate that regulatory quality, transport efficiency, and long-run emissions trajectories significantly reduce carbon intensity, while the independent contribution of renewable energy is comparatively weaker. Agricultural productivity exhibits a nonlinear relationship with emissions, validating the EKC hypothesis: emissions increase during early growth but decline beyond a threshold as modernization and climate-smart practices enhance efficiency. The study’s scientific value lies in its integrated approach, combining economic, institutional, and sectoral dimensions to explain long-run decarbonization in transitional economies. By focusing on post-socialist Europe, it advances EKC research beyond income-based models and underscores the importance of governance and structural transformation. Limitations include data coverage and cross-country heterogeneity, suggesting future work should adopt spatial and nonlinear frameworks and include adaptation and resilience metrics. Overall, robust governance and technological innovation can guide post-socialist economies toward sustainable, low-carbon growth. Full article

Aquavoltaics policy has been introduced in Taiwan to promote the integration of solar photovoltaic facilities on aquafarms. To explore the effects of the aquavoltaics policy on aquafarm price and small-scale aquaculture, we collected data on aquaculture and renewable energy materials. Subsequently, three groups of factors that influence the use of aquafarms (land, aquaculture, and renewable energy attributes) were analyzed using the hedonic price model to examine the effects of the aquavoltaics policy on aquafarm prices. We employed spatial econometrics models to estimate each variable’s influence and analyze the factors that affect aquafarm prices, as well as the possible effects of implementing an aquavoltaics policy. The empirical results indicate that the implementation of the Two-Year Solar Promotion Plan has led to an approximately 10% increase in aquafarm prices, reflecting the policy’s influence on land valuation and market expectations. Variables such as distance to urban areas, proximity to feeder lines, shellfish farming and empty ponds were found to significantly affect aquafarm prices. These findings suggest that when aquavoltaics policies are implemented in regions dominated by small-scale aquaculture, a systematic approach to aquafarm use and pricing is required. Moreover, developing integrated energy blueprints and aquavoltaic plans that balance economic, environmental, and fishery objectives is essential for achieving synergy between the fishery and renewable energy sectors. Full article

Being a crucial barometer of labour market stability, employment successfully predicts changes in business cycles, becoming a relevant indicator to policymakers and economists worldwide. The scope of this paper is to investigate the impact of socioeconomic and demographic factors on the employment rate in the European Union through a spatial approach, as well as to compare pre- and post-pandemic characteristics of European labour markets. A persistent North–South divide in employment was observed among the main findings, with Southern regions having lower employment rates and being more vulnerable to the pandemic shocks than Northern regions. Furthermore, the comparison between the spatial econometric models estimated for 2019 and 2022 showed a significant change in the influences of regional employment performance. These discoveries could be of interest to both governments and corporate decision-makers in order to elaborate knowledgeable policies and strategies regarding the labour force. Full article

In the context of China’s “dual carbon” strategy, carbon productivity serves as a central in dicator for coordinating economic development with carbon emissions. While the digital economy reshapes spatial economic configurations and affects regional carbon productivity, its spatial interdependence and spillover effects remain insufficiently explored. Our study constructs composite indicators to measure both digital economy development and carbon productivity, examining 30 Chinese provinces from 2011 to 2022 using the super-efficiency SBM model and exploratory spatial data analysis. Spatial regression is applied to assess the spatial influences of the digital economy and the moderating role of industrial structure transforming. Results reveal that: (1) China’s carbon productivity has improved overall but with notable regional disparities; (2) a U-shaped linkage between digital development and carbon productivity is confirmed, with early-stage suppression and later environmental benefits; (3) industrial rationalization and upgrading significantly enhance this relationship, though structural frictions remain obstacles. Full article

Against the backdrop of the global imperative for carbon neutrality, in this study, we systematically assessed the roles of spatial spillover and underlying mechanisms along with threshold characteristics of the digital economy on agricultural carbon emissions as related to green innovation. Using provincial panel data from China, as obtained over the period from 2013 to 2022, we determined agricultural carbon emissions as measured using the emission coefficient method and constructed a comprehensive digital economy index via the entropy weight method. An array of econometric models, including linear regression, the Spatial Durbin Model (SDM), mediation effect models, and panel threshold models were employed to examine both direct and indirect pathways, spatial interactions, and nonlinear moderating effects of digital economy. The results indicate that the following findings: (1) The digital economy significantly reduces agricultural carbon emissions, with a coefficient of approximately –2.051 in the baseline model. (2) Green innovation serves as a key mediator. The mediation effect analysis revealed that green innovation has a mediation effect value of 1.896 in the digital economy’s carbon reduction effect. (3) Significant negative spatial spillovers were observed upon reducing neighboring regions’ digital development of local emissions, with indirect effects ranging from –1.434 to –2.708 under different spatial matrices. (4) Urbanization rates exhibit a dual-threshold effect (73.38% and 74.79%), with the carbon reduction effect of the digital economy showing a notable strengthening when these rates extend beyond these thresholds. Heterogeneity analysis reveals a stronger effect in western China (coefficient: –6.079), attributable to higher marginal returns from digitalization as compared with that observed in less developed regions. Limitations associated with this study include the use of provincial-level data which may mask sub-regional heterogeneity, reliance on green patent counts as a proxy for green innovation output, and omissions of effects of exogenous policy programs such as the “Dual Carbon” policy. Future research would markedly benefit from micro-level data and more dynamic tests of the mechanisms involved. Full article

This study examines the role of private investment in promoting multidimensional poverty reduction in a sustainable manner in Vietnam by analyzing both spatial and temporal spillover effects. Provincial panel data for 2010–2024 are employed. To assess the spatial spillover effects, three econometric models are applied: SAR, SEM, and SDM. Diagnostic tests suggest that the SDM model is the most appropriate for the research data. Results based on the contiguity and inverse distance weight matrices show that private investment not only reduces poverty in recipient provinces but also generates benefits for neighboring areas, highlighting the need for coordinated planning of industrial zones and regional economic hubs. To analyze this relationship over both the short-term and long-term horizons, the study employs PMG and CCEP estimators, while the DCCEP model verifies robustness in a dynamic framework. The findings consistently confirm that private investment contributes to multidimensional poverty reduction. An additional result from the DCCEP model indicates that literacy and urbanization rate have significant positive effects on poverty reduction, while these relationships are not detected in other models. This finding carries important implications for building an enabling investment environment to attract and effectively utilize private capital to implement multidimensional poverty reduction strategies towards sustainability and aligned with sustainable development objectives. Full article

In response to global sustainability challenges, this study investigates how Digital New-Quality Productive Forces (DNQPF), which integrate digitalization with green innovation, contribute to Sustainable Industrial Structural Upgrading (SISU) in China. Using panel data from 30 provinces spanning 2011–2023, a multidimensional DNQPF index was constructed, and a comprehensive econometric framework was applied, including two-way fixed effects, mediation and moderation analyses, Hansen threshold models, and Spatial Durbin models. The results indicate that DNQPF significantly enhances SISU (β = 0.291, p < 0.01), with household consumption upgrading serving as the key mediating channel. Regional heterogeneity is evident: Eastern provinces show strong effects (β = 0.295, p < 0.01) and central provinces exhibit catch-up potential (β = 0.467, p < 0.10), while the Western and Northeastern regions display insignificant effects due to digital infrastructure disparities. The threshold effects reveal diminishing returns beyond a DNQPF level of 0.239 (coefficient decline from 0.518 to 0.323, p < 0.01), a marketization level of 6.181, and an innovation level of 9.520. Spatial analysis further confirms positive spillovers (direct effects = 0.282–0.320; indirect effects = 0.260–1.317; p < 0.05). These findings enrich endogenous growth theory by integrating digital and green development into emerging economies and underscore DNQPF’s role in advancing SDG 9 (Industry, Innovation, and Infrastructure) and SDG 12 (Responsible Consumption and Production). Coordinated digital–green strategies, institutional reforms, and inclusive infrastructure are therefore critical for achieving sustainable industrial transformation in China and beyond. Full article