Search Results (35)

This study investigates the spatial patterns and drivers of carbon emissions across China’s three major urban agglomerations—Beijing–Tianjin–Hebei (BTH), the Yangtze River Delta (YRD), and the Pearl River Delta (PRD)—from 2011 to 2020. A sequential analytical framework was employed to examine emission inequality, spatial dependence, dynamic transitions, and multi-scale drivers. Specifically, the Gini and Theil indices were used to quantify and decompose regional disparities. Spatial clustering patterns and heterogeneity were then identified through global and local Moran’s I analysis. Following this, spatial Markov chains modeled state transitions and neighborhood spillover effects. Finally, the Spatial Durbin Model (SDM) was applied to distinguish between the direct and indirect effects of key socioeconomic drivers. The findings reveal that disparities in emissions are largely driven by factors within each region. In BTH, heavy industrial lock-in accounts for 47.1% of the within-group inequality. By contrast, the YRD and PRD show noticeable convergence, achieved through industrial synergy and technological restructuring, respectively. The mechanisms of spatial spillover also differ across regions. In the YRD, emissions exhibit strong clustering tied to geographic proximity, with Moran’s I consistently above 0.6. In BTH, policy linkages play a more central role in shaping emission patterns. Meanwhile, in the PRD, widespread technological diffusion weakens the conventional distance-decay effect. The influence of key drivers varies notably among the urban agglomerations. Economic growth has the strongest scale effect in the PRD, reflected by a coefficient of 0.556. Industrial transformation significantly lowers emissions in the YRD, with a coefficient of −0.115. Technology investment reduces emissions in BTH (−0.124) and the PRD (−0.076), but is associated with a slight rebound in the YRD (0.037). Overall, these results highlight the persistent path dependence and distinct spatial interdependencies of carbon emissions in each region. This underscores the need for tailored mitigation strategies that are coordinated across administrative boundaries. Full article

This study proposes a novel framework for allocating CO₂ emission rights within the Yangtze River Delta (YRD) urban agglomeration, tackling the inter-city CO₂ transmission dynamics frequently neglected in conventional allocation models. Current emission allocation methods fail to capture the spatial spillover effects of CO₂ emissions driven by atmospheric transport, resulting in potential inequities. Leveraging the WRF model to simulate carbon emissions across 27 cities, we develop a two-stage network Data Envelopment Analysis (DEA) model that integrates both emission generation and governance capacities. Our findings highlight significant inter-city CO₂ transmission, with the wind direction and speed playing a pivotal role in emissions spread. In contrast to traditional models, our approach considers the regional interdependence of emissions, enhancing both fairness and efficiency in the allocation process. The results indicate that cities with stronger governance systems, including green technology investments and effective air quality management, are rewarded with higher carbon allowances. Moreover, our model demonstrates that policies prioritizing environmental governance over raw emission levels can foster long-term sustainability. This work provides a comprehensive methodology for achieving a balanced allocation of emission rights that integrates economic growth, environmental management, and equity considerations within complex urban agglomerations. Full article

This study investigates how housing prices influence urban–rural income inequality (URG) in mainland China by explicitly incorporating spatial interdependence and nonlinear adjustment mechanisms, features often neglected in previous research. Using a balanced panel of 31 provinces from 2005 to 2023, we develop a dynamic spatial panel threshold model that jointly accounts for temporal persistence, spatial spillovers, and regime-dependent estimation. This framework enables a full decomposition of housing price effects into direct, indirect (spillover), and total impacts across distinct market regimes. The results reveal three major insights. First, URG in mainland China displays strong temporal persistence, suggesting that income disparities evolve gradually over time. Second, rising housing prices significantly widen the urban–rural income gap, both within provinces and through interprovincial transmission, underscoring the amplifying role of spatial spillovers. Third, threshold estimation identifies a critical housing price level of ln(HP) = 8.4843 (approximately 4838.21 RMB/m²), revealing that the inequality-enhancing effect of housing prices is stronger in low-price regions but diminishes as markets mature and affordability constraints intensify. These findings provide new empirical evidence that the housing market functions as a nonlinear and asymmetric driver of regional inequality in mainland China, with implications for housing policy and inclusive growth. Full article

This paper utilizes unique high-frequency, daily electricity consumption data across economic sectors to examine the impact of monetary policy shocks on economic output, with a particular focus on the network spillover effects and sectoral heterogeneity introduced by inter-sector linkages. The study finds that quantity-based monetary policy (e.g., M2) generates significant positive and cascading spillover effects within the production network. However, the total effects of monetary policy shocks are broadly similar across upstream, midstream, and downstream sectors, exhibiting only minor differences. Notably, the proportion of network (indirect) effects increases systematically from upstream to downstream sectors and displays marked sectoral heterogeneity. In contrast, interest-rate-based monetary policy displays insufficient spatial spillover through production networks. These findings offer important insights for policymakers to optimize structural policy design and promote coordinated sectoral chain development, which can guide the pursuit of sustainable economic strategies that balance growth, resource utilization and sectoral interdependencies. Full article

Sustainable tourism development has emerged as a strategic priority across ASEAN countries, yet the role of green innovation and environmentally responsible investment in shaping tourism outcomes remains underexplored. Existing studies often overlook the spatial interdependencies that characterize regional integration and cross-border environmental dynamics. This study investigates how green patents and green foreign direct investment (FDI) influence sustainable tourism development, both within and across ASEAN nations. Drawing on endogenous growth theory, ecological modernization, and FDI spillover frameworks, the analysis employs a Spatial Durbin Model (SDM) using panel data from 2000 to 2023. The findings reveal that green innovation and green FDI significantly enhance tourism development, with notable spatial spillover effects that benefit neighboring countries. These effects are most pronounced in leading ASEAN economies, where institutional capacity and absorptive readiness amplify the impact of green practices. The relationship is further shaped by economic growth, human capital, and political stability, while environmental degradation and inflation pose constraints. The study underscores the nonlinear and regionally heterogeneous nature of green tourism development, offering policy insights for fostering inclusive, resilient, and environmentally sustainable tourism strategies across ASEAN. Full article

In confronting escalating economic uncertainty, achieving a win–win situation for low-carbon transition and improved employment structure will contribute to economic recovery and sustainable growth but also contribute to building a community with a shared future for mankind. A critical issue for China’s economy and societal welfare, as well as a core component of sustainable development, concerns whether low-carbon economic transition influences employment skill structure. This study utilizes data from 30 provinces (municipalities and autonomous regions) in China from 2006 to 2021. Employing the entropy method, a low-carbon economic development level indicator system was constructed from four aspects: low-carbon output, low-carbon consumption, low-carbon resources, and low-carbon environment to measure the low-carbon economy and explore its direct and indirect effects on employment skill structure and spatial effects. The research findings indicate that low-carbon economies not only directly and significantly promote employment skill structure optimization but also indirectly generate promotional effects through pathways such as industrial structure adjustment, green innovation’s innovative effects, and factor substitution effects of increased pollution control investment. Among these, the indirect impact of industrial structure adjustment contributes most substantially. Low-carbon economies’ influence on employment skill structures exhibits spatial spillover effects, with neighboring regions’ low-carbon economies exerting positive spillover effects on local skill structures. Additionally, significant negative interdependence exists among regional employment skill structures. Based on the aforementioned research conclusions, the following recommendations are proposed: accelerate low-carbon economy development and employment skill structure enhancement in central and western regions to diminish regional disparities; encourage green innovation and promote traditional industry upgrading and transformation; formulate regional coordinated development plans, thereby strengthening the low-carbon economy’s optimizing role upon employment skills structure; and increase educational investment and strengthen labor skill training. Full article

New infrastructure construction (NIC) is pivotal for advancing China’s sustainable development, yet the spatial interdependencies between NIC and inclusive green growth (IGG) remain critically underexplored. This study quantifies provincial-level NIC–IGG coordination dynamics across China (2011–2023) using a novel coupling coordination model. We further dissect regional disparities through Dagum Gini decomposition and identify causal drivers via QAP regression analysis. Key findings reveal: (1) Despite a gradual upward trend, overall NIC–IGG coordination remains suboptimal, hindering sustainable transition; (2) Regional disparities follow a “U-shaped” trajectory, primarily driven by inter-regional imbalances; (3) Uneven marketization is the dominant factor fragmenting spatial coordination. Our results expose systemic barriers to regionally integrated sustainable development and provide actionable pathways for place-based policies that synchronize NIC investment with IGG objectives. Full article

This study investigated the spatiotemporal dynamics, regional disparities, and spatial spillover effects of carbon emissions in China’s livestock sector from 2003 to 2022. By integrating carbon accounting, decoupling elasticity analysis, kernel density estimation, Theil index decomposition, and the Spatial Durbin Model, the research revealed a 6.5% reduction in national livestock carbon emissions alongside intensified spatial polarization. The decoupling relationship evolved dynamically, with strong decoupling dominating but regional fluctuations persisting, particularly in resource-dependent areas. The distribution of emission intensity shifted from unimodal right-skewness to bimodal concentration, indicating technological diffusion barriers and structural divergence across regions. Spatial econometric analysis confirmed significant emission interdependence (ρ = 0.214, p < 0.01), where neighboring economic growth increased local emission intensity. These findings highlighted the limitations of uniform policy approaches and emphasized the need for region-specific governance, market-based incentives, and localized technological innovation. The study provided empirical evidence and a policy framework to address cross-regional coordination and sustainable low-carbon transitions in agriculture. Full article

Amid global economic transformation, a persistent productivity gap exists between developed and developing nations in agriculture sector, shaped by technological advancements and shifting resource allocation patterns. Agricultural socialized services (ASS), defined as organized systems providing technical support, mechanization assistance, information services, market linkages, and resource optimization to farmers, have emerged as critical mechanisms for agricultural development. In developing economies, these services catalyze gains in agricultural labor productivity through the integration of advanced technologies and the mechanization of farming practices. Using panel data from 30 Chinese provinces during 2011 to 2022, this study investigates the relationship between ASS and ALP, focusing on regional heterogeneity, threshold effects, and spatial spillovers. The combination of spatial econometric methods and threshold analysis was selected for its unique capacity to capture both the geographic interdependencies and nonlinear relationships that characterize agricultural development processes. These thresholds at 5.254 and 8.478 represent critical points where the impact of ASS on ALP significantly changes in magnitude, revealing a nonlinear relationship that evolves across different stages of agricultural development. The study highlights notable regional disparities in the impact of ASS. Specifically, ASS is more effective on ALP in eastern, central and key food-producing regions, while its impact is relatively weak in western and non-food-producing regions. Spatial spillover analysis indicates that advancements in ASS create positive externalities, extending beyond their immediate implementation zones and facilitating inter-provincial agricultural cooperation and development. These findings provide crucial guidance for policymakers and agricultural service providers to optimize resource allocation and service delivery strategies. By identifying critical development thresholds and regional variations, this research offers evidence-based support for government officials designing targeted agricultural policies and enterprises developing region-specific service models to foster sustainable agricultural growth across diverse regional landscapes. Full article

The Zaatari Camp in Jordan exemplifies how Syrian refugees transform a planned grid settlement into an organic urban environment through socio-spatial adaptation, reflecting their cultural identity and territorial practices. This study investigates the camp’s morphological evolution, analyzing how refugees reconfigure public and private spaces to prioritize privacy, security, and community cohesion. Using qualitative methods—including archival maps, photographs, and field observations—the research reveals how formal public areas are repurposed into private shelter extensions, creating zones of influence that mirror traditional Arab-Islamic urban patterns. Key elements such as mosques, markets, and hierarchical street networks emerge as cultural anchors, shaped by refugees’ prior urban experiences. However, this organic growth introduces challenges, such as blocked streets and undefined spaces, which hinder safety and service delivery, underscoring tensions between informal urbanization and structured planning. The findings advocate urban resilience and participatory planning frameworks that integrate socio-cultural values, emphasizing defensible boundaries, interdependence, and adaptable design. Refugees’ territorial behaviors—such as creating diagonal streets and expanding shelters—highlight their agency in reshaping urban systems, challenging conventional top-down approaches. This research focuses on land-use dynamics, sustainable cities, and adaptive urban systems in crisis contexts. By bridging gaps between displacement studies and urban theory, the study offers insights into fostering social inclusion and equitable infrastructure in transient settlements. Future research directions, including comparative analyses of refugee camps and cognitive mapping, aim to deepen understanding of socio-spatial resilience. Ultimately, this work contributes to global dialogues on informal urbanization and culturally responsive design, advocating for policies that align with the Sustainable Development Goals to rebuild cohesive, resilient urban environments in displacement settings. Full article

Infrastructure projects that transform ecosystems and landscapes, such as hydropower developments, are essential for economic growth but pose significant socio-environmental challenges. Addressing these complexities requires advanced, dynamic management strategies. This study presents the Bayesian integrated risk mitigation model (BIRMM), a novel probabilistic framework designed to augment traditional environmental impact assessments. BIRMM enables comprehensive risk evaluation, scenario-based analysis, and mitigation planning, empowering stakeholders to make informed decisions throughout project lifecycles. BIRMM integrates socio-environmental and economic risks using a three-dimensional risk assessment approach grounded in a Bayesian belief network influence diagram. It provides a holistic view of risk interactions by capturing interdependencies across spatial, temporal, and magnitude dimensions. Through simulation of risk dynamics and adaptive evaluation of mitigation strategies, BIRMM offers actionable insights for resource allocation, enhancing project resilience, and minimizing socio-environmental disruptions. The framework was validated using the Balakot Hydropower Project in Pakistan. BIRMM successfully simulated proposed risks and assessed mitigation strategies under varying scenarios, demonstrating its reliability in navigating complex socio-environmental challenges. The case study highlighted its potential to support adaptive decision-making across all project phases. With its versatility and practical ease, BIRMM is particularly suited for large-scale energy, transportation, and urban development projects. By bridging gaps in traditional methodologies, BIRMM advances sustainable development practices, promotes equitable stakeholder outcomes, and establishes itself as an indispensable decision-support tool for modern infrastructure projects. Full article

Understanding the coupling mechanisms and coordinated development dynamics of the water–energy–food (WEF) system is crucial for sustainable river basin development. This study focuses on the Yellow River Basin, conducting a comprehensive analysis of the system’s coupling mechanisms and influencing factors. A structured evaluation framework is established, integrating the entropy weight–TOPSIS method, the coupling coordination degree model, and spatial correlation analysis. Empirical analysis is conducted using data from nine provinces (regions) along the Yellow River from 2003 to 2022 to assess the spatiotemporal evolution of the coupling coordination level. The Tobit regression model is employed to quantify the impact of various factors on the system’s coupling coordination degree. Results indicate that the comprehensive evaluation index of the WEF system in the Yellow River Basin exhibits an overall upward trend, with the system coupling degree remaining at a high level for an extended period, up from 0.231 to 0.375. The interdependence among the three major systems is strong (0.881–0.939), and while the coupling coordination degree has increased over time despite fluctuations, a qualitative leap has not yet been achieved. The evaluation index follows a spatial distribution pattern of midstream > downstream > upstream, characterized by a predominantly high coupling degree. However, the coordination degree frequently remains at a forced coordination level or below, with a general trend of midstream > downstream > upstream. From 2003 to 2008, a positive spatial autocorrelation was observed in the coupling and coordinated development of the WEF system across provinces, indicating a strong spatial agglomeration effect. By 2022, most provinces were clustered in “high-high” and “low-low” areas, reflecting a positive spatial correlation with minimal regional differences. Key factors positively influencing coordination include economic development levels, industrial structure upgrading, urbanization, and transportation networks, while technological innovation negatively affects the system’s coordination. Based on these findings, it is recommended to strengthen balanced economic development, optimize the layout of industrial structures, improve the inter-regional resource circulation mechanism, and promote the deep integration of technological innovation and production practices to address the bottlenecks hindering the coordinated development of the water–energy–food system. Policy recommendations are proposed to provide strategic references for the sustainable socioeconomic development of the Yellow River Basin, thereby achieving the high-quality coordinated growth of the water–energy–food system in the region. Full article

A pragmatic approach for Local Area Energy Planning to capture Whole System interactions and meet the dual goals of informing regulated infrastructure requirements while informing businesses and local authorities on building their business plans, is presented. Unlike existing approaches, the method presented in this paper aids market change by considering policy requirements and prioritisation, commercial relationships, place-based resources, processes and interfaces, people (skills and vulnerabilities), and energy vector interdependencies, and focuses on spatially distributed economic segments (e.g., agriculture, food logistics, etc.). The methodology promotes co-location opportunities for symbiotic clusters to avoid growth in resource-constrained regions (e.g., grid capacity), and presents a temporal visualisation method that connects policy, regulation, infrastructure, technology, place, and people. To provide a case study to design, evolve, and test the methodology, the Greater Lincolnshire Region’s Economic Zone in the UK is selected; specifically, the logistics segment. Adopting this type of Whole System approach provides business planning clarity and stakeholder confidence to drive the adoption of new technologies. It also identifies where inward investment for strategic locations is needed and develops an evidence base for policy lobbying and influencing. Full article

The expansion of construction land is a key feature of urbanization. Understanding its spatiotemporal evolution in Yunnan’s plateau basins is crucial for minimizing resource waste and promoting coordinated regional development. This study employs land use and nighttime light data to analyze the spatiotemporal dynamics of construction land expansion and its decoupling from economic growth, using various indices and the Tapio decoupling model. The results reveal a steady rise in urban construction land from 1990 to 2020, characterized by significant spatial variations in expansion speed and intensity. Edge expansion predominated throughout all periods, accounting for over 50% in most regions. After 2010, expansion spread into smaller basins, markedly increasing the number of areas experiencing new expansion. The decoupling between construction land expansion and economic growth in these basins remains primarily weak and unstable, indicating a strong reliance on land for economic development. Factors such as socioeconomic conditions, geography, ecology, and policy influence both land expansion and economic growth, highlighting the interdependence between the two. These findings provide a foundation for sustainable basin development and offer valuable insights for planning and policy-making. Full article

As a pivotal region for safeguarding China’s food security, Northeast China requires a quantitative evaluation of crop yield dynamics, planting structure shifts, and their interdependent mechanisms. Leveraging MODIS NPP data and remote sensing-derived crop classification data from 2001 to 2021, this study established a crop yield estimation model. By integrating the Theil–Sen median slope estimator and Mann–Kendall trend analysis, we systematically investigated the spatiotemporal characteristics of maize, rice, and soybean yields. Phased attribution analysis was further employed to quantify the effects of crop type conversions on total regional yield. The results revealed: (1) strong consistency between estimated yields and statistical yearbook data, with validation R² values of 0.76 (maize), 0.69 (rice), and 0.81 (soybean), confirming high model accuracy; (2) significant yield growth areas that spatially coincided with the core black soil zone, underscoring the productivity-enhancing role of conservation tillage practices; (3) all three crops exhibited upward yield trends, with annual growth rates of 1.33% (maize), 1.20% (rice), and 1.68% (soybean). Spatially, high-yield maize areas were concentrated in the southeast, rice productivity peaked along river basins, and soybean yields displayed a distinct north-high-south-low gradient; (4) crop type transitions contributed to a net yield increase of 35.9177 million tons, dominated by soybean-to-maize conversions (50.41% contribution), while maize-to-soybean shifts led to a 2.61% yield reduction. This study offers actionable insights for optimizing planting structures and tailoring grain production strategies in Northeast China, while providing a methodological framework for crop yield estimation in analogous regions. Full article