Search Results (592)

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. Full article

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. Full article

As the global urban economy accelerates its transition from an “industrial economy” to a “service economy”, consumption has replaced investment as the core engine driving economic development. Commercial land serves as the physical foundation for consumer activities and plays a vital role in boosting urban economic vitality, enhancing residents’ quality of life, and promoting regional sustainable development when appropriately allocated. This study constructs a technical framework for analyzing the mismatch between commercial land supply and residential consumption demand, along with its impact mechanism, based on the integrated application of the multidisciplinary quantitative models such as the Boston Consulting Group Matrix (BCGM), Exploratory Spatial Data Analysis (ESDA), Decoupling Model (DM), and Explainable Machine Learning (EML). It conducts empirical research across 87 county-level cities in Gansu Province. The findings reveal that commercial land supply and consumption demand exhibit dynamic diversification, with prominent regional disparities and spatial autocorrelation characteristics. Commercial land in Gansu faces a severe mismatch, with demand exceeding supply and supply exceeding demand occurring simultaneously, and the former holding absolute dominance. The formation of mismatched relationships is influenced by many factors, exhibiting significant path nonlinearity, spatial non-stationarity, and relational interactivity. It is suggested that strategies of planning zoning and regional coordination be developed for mismatch governance, and differentiated management measures be implemented based on local conditions. This will provide a scientific basis for commercial territorial space planning and consumption policy design. Full article

The conflict between the economy and the ecological environment is prominent in China’s coastal cities, and these cities contend with heightened uncertainty. Therefore, this study uses the econometric model to analyze the spatial–temporal pattern characteristics and affecting factors of the coupling coordination level between urban economic resilience (ER) and urban ecological resilience (EcR) in China’s coastal cities based on improvement of the evaluation index system, thus advancing policy suggestions. The main conclusions are as follows: (1) The coupling coordination degree (CCD) between ER and EcR across different types of coastal cities strongly correlates with their spatial distribution patterns of economic development. From the East China Sea to the South China Sea and Yellow and Bohai Sea Coast cities and from central cities to industrial cities, other types of cities, and resource-based cities, CCD exhibits an overall declining trajectory. (2) The gap in CCD in China’s coastal cities generally shows an expanding trend. (3) The spatial distribution pattern of the centrality of CCD in China’s coastal cities has a relatively high consistency. Urban spillover roles are highly consistent with levels of economic development. (4) The number and diversity of dominant influencing factors have steadily increased. Full article

The purpose of this study is to investigate the impact of new-type urbanization on land use efficiency and its spatial spillover effects, aiming to provide theoretical support and practical references for improving land resource allocation and optimizing regional development strategies. Using panel data from 61 counties in Zhejiang Province between 2010 and 2022, this research applies a two-way fixed effects model, supplemented by mediation effect analysis and spatial econometric models, to empirically examine these relationships. The results indicate that: (1) both the level of new-type urbanization and land use efficiency show an overall upward trend, exhibiting a spatial pattern characterized by “coastal regions outperforming inland areas, and northern Zhejiang surpassing the south”; (2) new-type urbanization exerts a significantly positive impact on land use efficiency, with industrial structure upgrading serving as a partial mediator in this relationship; (3) significant spatial spillover effects are observed—new-type urbanization not only enhances local land use efficiency but also generates positive spillovers to neighboring regions through spatial diffusion mechanisms; (4) the influence of new-type urbanization on land use efficiency displays regional heterogeneity, with stronger promoting effects observed in coastal and low-efficiency areas, whereas marginal effects diminish in non-coastal and high-efficiency regions. In conclusion, strategic priorities should be established to enhance the quality of new-type urbanization, foster green and intensive development, optimize the industrial structure, and strengthen land conservation practices. Furthermore, region-specific policies are essential to improve land use efficiency across diverse areas, which will ultimately contribute to coordinated regional development. Full article

Financial systems are quintessential complex adaptive systems, where stability emerges from the dynamic interactions among multiple subsystems and regulatory components. Grounded in systems theory, this study re-frames the establishment of China’s fintech regulatory sandbox as a systemic intervention within the broader financial governance framework. Utilizing this policy as a quasi-natural experiment, we employ a difference-in-differences (DID) model integrated with spatial econometric modeling to evaluate its impact on regional financial system risk—an emergent property of the system. The benchmark regression results indicate that this systemic policy innovation significantly enhances regional financial resilience, with effects that are both continuous and robust. Mechanism tests, analyzed through the lens of subsystem coordination, demonstrate that the policy curbs systemic risk by improving the synergy within economic inner cycles, outer cycles, and their dual-cycle integration, thereby optimizing the system’s internal structure and feedback loops. Further analysis reveals a significant negative spatial spillover effect, evidencing the policy’s role in reshaping inter-regional systemic linkages: it reduces financial risk in both implementing and neighboring regions, with the effect’s intensity following an inverted U-shaped pattern relative to distance. Heterogeneity analysis shows that the policy’s inhibitory effect varies significantly across different systemic configurations, including risk circulation patterns, macro–micro risk perspectives, financial inclusion coverage, government–market relationships, and the north–south regional divide. These findings provide critical insights for developing synergistic macro-prudential and micro-behavioral regulatory mechanisms, contributing to a more robust and adaptive financial security framework from a systems governance perspective. Full article

Digital technologies such as big data are reshaping resource allocation, raising interest in whether and how heterogeneous science and technology innovation (STI) policies can help unlock urban carbon lock-in. Using panel data for 286 prefecture-level cities in China from 2009 to 2023, this paper examines the relationship between heterogeneous STI policy intensity—classified as supply-side, demand-side, complementary-factor, and institutional-reform policies—and urban carbon unlocking efficiency. We develop a mechanism-based framework and empirically assess (i) the moderating roles of digital infrastructure, science and technology finance, and government green attention, and (ii) spatial spillover effects using spatial econometric models. The results show that all four policy types show a significant positive association with local carbon unlocking efficiency, with institutional-reform policies exhibiting the strongest association. When the four types are included jointly, only supply-side and demand-side policies retain statistically significant direct associations. Heterogeneity analyses indicate that demand-side, complementary-factor, and institutional-reform policies are more strongly associated with efficiency gains in low-pollution cities, whereas supply-side and demand-side policies have a stronger association in high energy-consuming cities. Mechanism analysis reveals that regional digital infrastructure exerts a selective moderating effect on the relationship between heterogeneous sci-tech innovation policies and urban carbon emission reduction efficiency. It positively reinforces the effectiveness of supply-side, demand-side, and institutional reform-oriented policies, while its interaction with complementary policies is statistically insignificant. Technology finance and government green policies function as a “resource catalyst” and an “institutional guarantee” respectively, significantly enhancing the correlation between heterogeneous sci-tech innovation policies and urban carbon emission reduction efficiency. Finally, carbon unlocking efficiency displays significant spatial dependence: the intensity of supply-side and institutional-reform policies is positively associated with carbon unlocking efficiency in neighboring cities, while complementary-factor policies exhibit a negative spatial association. Overall, the findings provide empirical evidence to inform the design and coordination of heterogeneous STI policy portfolios aimed at improving urban carbon unlocking efficiency. Full article

Conventional infrastructure appraisal in Africa prioritizes short-term economic performance while insufficiently accounting for the environmental conditions that govern long-term sustainability, spatial equity, and development resilience. To address this gap, this study develops an explicitly SDG-oriented spatial–ecological framework to examine how environmental quality conditions the economic returns of large-scale infrastructure investments under corridor-based development. The primary objective is to quantify infrastructure–environment complementarity and identify ecological thresholds regulating spatial spillovers and investment effectiveness along Tanzania’s Belt and Road Initiative (BRI) corridors. High-resolution remote sensing and spatially explicit socioeconomic data for 2012–2023 are integrated within a spatial econometric design. A Spatial Durbin Model (SDM) incorporating the Normalized Difference Vegetation Index (NDVI) is estimated to capture non-linear interaction effects, with economic activity proxied by Night-Time Light (NTL) intensity across 2680 corridor grid cells. The results identify a statistically robust ecological threshold at NDVI = −0.8σ, beyond which infrastructure investments shift from low to high economic effectiveness. A strong positive infrastructure–environment interaction (β = 6.44, p < 0.001) indicates that environmental quality functions as a productive modulating factor rather than a passive constraint. Spatial classification shows that 63% of corridor areas are investment-ready, while 15% require ecological restoration prior to effective infrastructure deployment. Although institutional quality and long-term post-construction dynamics are not explicitly modeled, the framework provides a replicable and policy-relevant decision-support tool, offering actionable guidance for aligning corridor development with SDGs 9, 11, and 13 and advancing sustainable infrastructure planning in the Global South. Full article

Spatial disparities in housing values during the Great Depression reflect not only regional housing market conditions but also deeper inequalities in economic opportunity, social infrastructure, and environmental resilience that are central to place-based sustainability. Despite extensive research on housing inequality during this period, spatial disparities in housing values—particularly in relation to race beyond the neighborhood level—remain underexplored. This study examines county-level spatial disparities in housing values in the United States between 1930 and 1940, framing housing values as an indicator of place-based sustainability. Using spatial visualization, global and local spatial econometric models, and Multi-Scale Geographically Weighted Regression (MGWR), we analyze how economic shocks, environmental stressors, and socioeconomic and demographic factors jointly shaped uneven housing outcomes across space. Our findings reveal distinct regional trends: higher housing values were concentrated in the Northeast, Midwest, and West Coast, while lower values prevailed in the Mountain and Southern regions. Housing values declined from 1930 to 1940, with the Dust Bowl intensifying losses in affected areas. Socioeconomic factors, such as higher illiteracy and unemployment rates, were associated with lower housing values, whereas higher retail sales per capita, a proxy for income, were linked to higher values. Housing values also varied significantly by racial and nativity composition, with persistent disparities disadvantaging Black and other minority populations relative to native White populations within the same regions. By quantifying spatial inequality and identifying uneven regional vulnerability and resilience during a major historical crisis, this study contributes a place-based sustainability perspective on long-term housing inequality and its structural roots. Full article

Africa is a continent experiencing the highest yearly rate of deforestation. As a result, there is debate about the causes and consequences of this phenomenon, as well as on the effectiveness of actions undertaken to address this problem. This study offers insights into economic aspects of deforestation in Africa with regard to the use of econometric and spatial data analysis and the inclusion of determinants not considered by previous research. Special attention is paid to the participation of African countries in UN-REDD (United Nations Collaborative Program on Reducing Emissions from Deforestation and Forest Degradation in Developing Countries) and grouping countries according to the level of their forest cover. We demonstrate a negative relationship between economic activity and forest cover using both econometric modeling and spatial data analysis, and present some moderate arguments in favor of the UN-REDD program and its effectiveness in mitigating deforestation in Africa. Importantly, there are no universal patterns across countries characterized by different levels of forest cover. Therefore, we conclude that advancement of this research area requires new methodological approaches based on big data, machine learning, and artificial intelligence to supplement existing approaches and enhance our understanding of the interplay between forest loss and economic growth. Full article

The coordinated development of the rural economy and the ecological environment remains a central challenge in China’s rural revitalization agenda. Against this backdrop, the rapid expansion of the digital economy (DE) has the potential to reshape traditional development pathways and ease the longstanding tension between economic growth and environmental sustainability. However, existing studies have predominantly examined the economic or environmental effects of digitalization in isolation, leaving its role in fostering their coordinated development largely unexplored. Using balanced panel data for 30 Chinese provinces from 2011 to 2021, this paper constructs an index of the coupling coordinated development of the rural economy–environment (CREE) and employs a two-way fixed-effects framework, complemented by mediation analysis, panel threshold regression, and a spatial Durbin model, to examine the impact of the DE on CREE and its transmission mechanisms. The results show that the DE significantly enhances CREE on average. This positive effect, however, is non-linear and conditional: it emerges only after rural educational attainment exceeds a critical threshold, and its marginal contribution diminishes as the level of digital development increases. Mechanism analyses indicate that the DE promotes CREE primarily by stimulating technological innovation and advancing urbanization, while improvements in the structure of human capital further strengthen this relationship. Spatial econometric evidence reveals pronounced spillover effects of the DE on CREE across regions, with spillovers based on economic distance outweighing those associated with geographic proximity. By adopting a coupling perspective that integrates economic and environmental dimensions, this paper clarifies the non-linear dynamics, transmission channels, and spatial diffusion processes through which the DE contributes to rural green development. The findings underscore the importance of strengthening rural education foundations, deepening the application of digital technologies, and enhancing regional coordination to fully harness the DE’s role in promoting coordinated economy–environment development. Full article

This study investigates the spatial effects of employment density on the economic, technological, and carbon efficiency of China’s manufacturing sector, using panel data from 30 provinces from 2008 to 2022. A multidimensional performance framework and spatial econometric models are employed to identify both direct impacts and spatial spillovers. The results show that employment density significantly enhances local economic performance while imposing negative spillover effects on neighboring regions. Technological performance exhibits uneven spatial returns, indicating a “technology siphoning” effect in more agglomerated provinces. Carbon efficiency presents a divergent pattern of “local improvement but neighboring deterioration,” highlighting cross-regional ecological externalities. In addition, human capital, capital investment, and regional policy intensity are found to regulate the strength and direction of spatial spillovers across the three performance dimensions. Based on these findings, this study recommends optimizing the spatial layout of manufacturing and population, strengthening interregional innovation collaboration, promoting green transformation, and improving the quality of human capital. These policy implications provide empirical support for advancing sustainable manufacturing development and enhancing regional governance capacity. Full article

Drawing on evidence from China’s land market, this study systematically investigates the impact of land misallocation on economic resilience and reveals the underlying mechanism that operates by suppressing technological advancement. A theoretical model of economic resilience is developed, incorporating technology and factor allocation. Empirical analysis is conducted using a panel dataset of 95 Chinese cities (2012–2024) through spatial econometric and mediation models. The findings indicate that land misallocation significantly reduces local economic resilience and exhibits negative spatial spillover effects. The core mechanism is identified as follows: subsidies via low-priced industrial land delay the market exit of low-efficiency firms, hindering the reallocation of production factors to more productive sectors. This suppression of technological progress ultimately weakens a region’s capacity to withstand external shocks. Based on the findings, policy implications include optimizing land supply structure, accelerating fiscal system reform, and strengthening policy coordination. Full article

Urban vitality is a crucial indicator of a city’s sustainable development and the quality of life of its residents. Investigating the spatiotemporal patterns and influencing mechanisms of urban vitality is essential for optimizing the built-environment and improving governance. Using the central urban area of Guiyang, China, as a case study, this research integrates multi-source urban sensing data to investigate the spatiotemporal patterns of urban vitality and their driving factors. Geographically weighted regression (GWR) and machine learning combined with SHapley Additive exPlanations (SHAP) are applied to capture spatial heterogeneity, nonlinear relationships, and threshold effects among influencing variables. Results show that urban vitality exhibits a Y-shaped, single-core, multi-center, and clustered spatial configuration, with slightly higher intensity on weekdays and similar diurnal rhythms across weekdays and weekends. The effects of influencing factors display strong spatial non-stationarity, characterized by a concentric gradient radiating outward from the historic Laocheng core. Building density (BD), residential point density (RED), normalized difference vegetation index (NDVI), and road density (RD) emerge as the dominant contributors to urban vitality, while topographic conditions play a relatively minor role. The relationships between key landscape and built-environment variables and urban vitality are highly nonlinear, with distinct threshold effects. By integrating spatial econometric modeling and explainable machine learning, this study advances methodological approaches for urban vitality research and provides practical insights for landscape-oriented urban planning and human-centered spatial design. Full article

This study examines the relationship between income and municipal waste generation within the Waste Kuznets Curve (WKC) framework, with a focus on selected disaggregated household waste fractions (paper and cardboard, glass, bulky waste, and biowaste). The aim is to assess whether increases in earnings per capita are associated with non-linear waste dynamics once spatial interactions and local socio-demographic characteristics are taken into account. The study employs a spatial panel dataset for 378 Polish counties over the period 2017–2024. Fixed-effects panel models, supplemented with random-effects panel models with Mundlak’s approach, are estimated alongside spatial panel specifications. Control variables include population ageing, urbanisation, and tourism, while spatial effects are decomposed into direct and indirect impacts. The results indicate that, in non-spatial models, an inverted U-shaped relationship between earnings and waste generation is observed for most waste fractions. However, once spatial dependence is explicitly incorporated, income effects weaken. In contrast, demographic structure—the share of retirement-age population—emerges as a robust and spatially persistent determinant of waste generation. Urbanisation and tourism exert only a limited influence across waste fractions. The paper advances WKC research by using spatial econometric methods and disaggregated waste fractions at the county level. The evidence suggests that conclusions about income-driven waste decoupling are sensitive to spatial dependence, emphasising the need for locally tailored waste management strategies. Full article