Search Results (336)

Network development level and inequality level within urban agglomerations profoundly shape regional sustainability and global competitiveness. Existing studies predominantly focus on economic scale, geographic proximity, and static cross-sectional data analysis, often lacking systematic consideration of people’s wellbeing, network connectivity, and dynamic evolutionary processes. Furthermore, insufficient attention has been paid to the intrinsic connection between network development and inequality levels. Addressing this gap, this study systematically investigates the network development level, inequality level, and the correlation mechanisms between them, focusing on six national-level urban agglomerations located in China’s Yangtze River and Yellow River basins. Key findings include the following: (1) The overall connectivity efficiency of development networks within urban agglomerations continues to improve, yet the disparity in network status among node cities has gradually widened. The network centrality of core cities initially strengthens and then weakens. (2) Urban agglomerations with relatively lagging economic development often exhibit greater internal disparities in development momentum. Wealth distribution inequality within agglomerations has gradually eased, while the urban–rural development gap shows a trend of first increasing and then decreasing. Conversely, social welfare distribution inequality has intensified overall. (3) Network indicators centering on centrality significantly contribute to the imbalance in regional wealth distribution. As social welfare distribution inequality increases, the influence of network centrality characteristics on it strengthens. (4) The impact of regional network development level on inequality level is hierarchical and directional. Increasing network density, average distance, and degree of association helps mitigate wealth distribution imbalance, whereas increasing network density and centrality may exacerbate regional development inequality. Overall, this study provides empirical evidence and theoretical support for policy-making of sustainable development in urban agglomerations, and enriches the theoretical connotation of regional balanced development. Full article

Against the backdrop of the accelerated advancement of regional economic integration, industrial synergy in urban agglomerations has become a core pathway to break homogeneous competition, enhance urban productivity and achieve sustainable economic growth, yet existing studies have mostly focused on the local effects of industrial agglomeration, and complementary linkages from the perspective of industrial chain supply and demand, as well as their cross-city spatial spillover mechanisms, remain insufficiently explored. Taking the nine cities in the Pearl River Delta (PRD) as the research object, this paper constructs an Industrial Complementarity index (ICI) based on urban panel data from 2012 to 2017 and multi-regional input–output tables. The findings reveal the following: (1) Industrial complementarity in the PRD exhibits significant uneven distribution characteristics, with the network structure gradually evolving from a single-core concentrated pattern centered on Shenzhen in 2012 to a multi-polar dispersed pattern centered on Zhaoqing, Zhongshan, and Dongguan in 2017. Resource-based cities play a key fundamental connecting role in the intermediate input supply network. (2) Industrial complementarity significantly promotes urban productivity growth, and its impact is mainly realized through spatial spillover channels. Moreover, productivity spillovers show an obvious distance decay characteristic, and marginal cities obtain significantly higher marginal benefits from spillovers than core cities. (3) Mechanism tests indicate that financial deepening and human capital accumulation are important channels through which industrial complementarity affects urban productivity. Full article

There is a typical coopetition relationship between the urban rail transit (URT) network scale and the urban development (UD) level. A reasonable URT network scale is essential for promoting sustainable UD. Currently, the determination of the URT network scale for planning primarily relies on qualitative approaches such as static estimation and analogical methods, which fail to dynamically reflect the coopetition relationship between URT and UD. An improved time-varying parameter Lotka–Volterra (LV) model derived from ecological theory is employed to describe the coopetition relationship between the URT network scale and the UD level. The sliding-window least squares method is applied to estimate parameters of the model. Based on the improved LV model, the lower bound of the URT network scale is obtained by solving for the minimum network scale required to promote sustainable UD under a cooperative relationship; the upper bound of the URT network scale is obtained by solving for the maximum network scale that urban resources can support under competitive conditions. The proposed model is validated using eight Chinese cities with different UD levels. The study offers quantitative theoretical insights for determining the reasonable URT network scale for planning. Full article

The transition to electric vehicles (EVs) in urban logistics presents complex operational challenges, driven primarily by limited battery capacities, charging station scheduling, and dynamic traffic congestion. This paper introduces a framework to solve the Capacitated Multi-Depot Electric Vehicle Routing Problem (MD-EVRP). We propose a novel Multi-Depot Rotational Sweep Cluster K-means (MD-RSCK) algorithm to partition large-scale spatial data while strictly adhering to vehicle capacity constraints. To optimize intra-cluster routing, we develop an Ant Colony Optimization (ACO) engine augmented with a Time-Dependent Congestion Model. Furthermore, the framework integrates an Energy-Aware Route Refiner (EARR). This architecture utilizes recursive backtracking to ensure battery-feasible routes, adapting to both symmetric Euclidean approximations and real-world asymmetric traffic networks. The framework is evaluated against standard IEEE EVRP benchmarks and a multi-depot urban case study based on the road network of Shanghai, China. Experimental results demonstrate that this integrated architecture achieves competitive distance and cost metrics within a 2.44% optimality gap of state-of-the-art algorithms while ensuring strictly feasible battery states and preventing cyclic entrapment, providing a scalable operational tool for modern sustainable logistics. Full article

The Sustainable Development Goals (SDGs) Prosperity Cluster (SDGs 7–11) represents a multidimensional framework linking economic growth, social inclusion, environmental sustainability, and resilient development. This review critically examines the interconnections among Affordable and Clean Energy (SDG 7), Decent Work and Economic Growth (SDG 8), Industry, Innovation and Infrastructure (SDG 9), Reduced Inequalities (SDG 10), and Sustainable Cities and Communities (SDG 11), with the aim of exploring how these goals collectively contribute to sustainable prosperity. Adopting a structured literature review methodology informed by PRISMA principles, the study synthesizes peer-reviewed and gray literature collected from major academic databases and institutional sources. The findings indicate that progress toward the prosperity-oriented SDGs remains uneven across regions due to disparities in governance quality, technological capacity, infrastructure development, and social inclusion. Renewable energy transitions, digital innovation, circular economy initiatives, green infrastructure, and sustainable urban planning emerge as critical drivers of long-term prosperity, while inequality, weak institutional coordination, inadequate human-capital investment, and uneven access to technology remain major barriers. The review further demonstrates that progress in one SDG strongly influences outcomes in others, emphasizing the importance of integrated and policy-coherent approaches rather than isolated sectoral actions. Conceptually, the paper advances the understanding of the “Prosperity Cluster” by positioning dynamism, equity, and environmental stewardship as mutually reinforcing dimensions of sustainable development. The study concludes that achieving sustainable prosperity requires governance systems capable of balancing economic competitiveness with environmental responsibility and social justice. Greater international cooperation, inclusive policymaking, and investment in resilient infrastructure and human capital are essential to ensure that prosperity benefits present and future generations without leaving vulnerable populations behind. Full article

Land use/land cover (LULC) is one of the core driving factors affecting terrestrial ecosystem carbon storage and exacerbating global warming. As an area with the most intense land–sea interactions, China’s coastal zone has experienced drastic LULC transition and carbon storage fluctuations during the rapid urbanization process. Based on the InVEST model, this study analyzes the spatiotemporal dynamics of LULC and carbon storage (CS) in China’s coastal regions from 2000 to 2024, and simulated multi-scenario carbon storage trajectories for 2050 integrating the SSP-RCP scenarios of the Coupled Model Intercomparison Project Phase 6 (CMIP6). Furthermore, the XGBoost-SHAP and generalized additive models (GAMs) were introduced to deeply analyze the nonlinear characteristics and temporal heterogeneity of the driving mechanisms of CS evolution. The results show the following: (1) During the study period, the LULC structure of the coastal region was dominated by cropland and forestland consistently accounting for over 85%, but exhibited a competitive pattern characterized by the continuous expansion of built-up land severely squeezing ecological spaces. (2) The total regional CS showed an overall phased downward trend, accompanied by increasing fragmentation of high carbon sink areas. Notably, as the core carbon pool, the reduction in forest area was the dominant factor causing regional net carbon losses. (3) CS remained relatively stable under SSP1-2.6, representing a sustainable development pathway with low greenhouse gas emissions. In contrast, SSP2-4.5, SSP3-7.0, and SSP5-8.5 exhibited more pronounced declines in carbon storage by 2050, indicating that SSP1-2.6 is the most favorable pathway for maintaining long-term carbon storage stability in China’s coastal regions. (4) The driving mechanism of CS has undergone a profound shift from being dominated by natural ecological baselines to human activities. Land use intensity (LUI) has emerged as the strongest predictor in the model, and the nonlinear impacts of human activities have grown increasingly complex over time. This study highlights the complex impacts of high-intensity human disturbances on the coastal carbon cycle, providing a scientific basis for formulating differentiated carbon management strategies and adaptive spatial land-use planning oriented toward the “Dual Carbon” goals. Full article

Early-stage residential building design in dense urban environments involves complex interactions among zoning regulations, geometric configuration, environmental performance, and economic feasibility. Conventional CAD–spreadsheet workflows and parametric BIM-based approaches remain limited in systematically resolving these interdependent trade-offs and typically rely on heuristic iteration and post hoc regulatory verification. To address this limitation, this study proposes REGEN, a regulation-aware BIM-enabled multi-objective optimization framework for sustainable residential building design. The framework formalizes planning and building-control regulations as explicit algebraic constraints embedded within a parametric BIM environment and integrates them with the Non-dominated Sorting Genetic Algorithm II (NSGA-II) to generate regulation-compliant design alternatives with respect to the encoded planning and building-control regulations. REGEN simultaneously optimizes five competing objectives: maximizing project profit, green-area provision, and building efficiency while minimizing geometric shape factor and building footprint area. A real condominium feasibility case in Bangkok, Thailand, is used to benchmark the proposed framework against conventional practice and parametric BIM-based design under identical site and regulatory conditions. The results reveal a non-convex Pareto front that exposes complex trade-offs among environmental, geometric, and economic objectives. The selected closest-to-utopia solution achieves 65.50% building efficiency, 606 m² of green area, a shape factor of 0.399, and a building footprint area of 1078 m² while maintaining a competitive project profit of 104.55 million THB without maximizing FAR utilization. The findings suggest that regulation-aware generative optimization has the potential to serve as an explainable and decision-oriented approach for sustainable construction and early-stage residential development planning. Full article

Urban vitality is regarded as a cornerstone of sustainable urban development. While land use mix (LUM) is widely acknowledged for fostering vitality, most empirical evidence relies on mean-effect models, neglecting the heterogeneous impacts across different vitality levels. This overlooks the complex, context-dependent nature of LUM and risks perpetuating one-size-fits-all planning. Based on a theoretical framework that links LUM analysis with contemporary urban revitalization, public governance, and smart city development discussions, this study leverages a Spatial Durbin Quantile Regression (SDQR) framework with multi-source geospatial data from 511 blocks in Ningbo, China, to systematically investigate the distributional heterogeneity of LUM’s effects on urban vitality. We decompose LUM into “diversity”, “proximity”, and “coordination” dimensions, revealing three distinct mechanisms across the vitality spectrum. Results show “coordination” acts as a fundamental “ignite” mechanism, consistently driving vitality across all quantiles, especially in new towns and low-vitality areas. “Diversity” primarily serves as a “boost” mechanism, enhancing vitality in medium-to-high vitality areas, demonstrating a non-linear, conditional effect. Crucially, “proximity” exhibits a novel “cap & siphon” mechanism: its direct effect is often insignificant or negative in low-vitality areas (suggesting structural mismatch), while its significant negative spatial spillover effect (siphon effect) across all quantiles, particularly in low-vitality zones, highlights intense inter-area competition. Furthermore, LUM’s direct effects tend to diminish in high-vitality areas, indicating a saturation or “cap” effect. By revealing these heterogeneous impacts and spatial spillover dynamics, this research refines the boundary conditions of classic mixed-use propositions and provides a differentiated planning paradigm, moving from universal zoning to context-specific, stage-calibrated interventions that address areas based on their current vitality levels, spatial interactions and governance contexts. Full article

This study addresses the fragmented integration of smart technologies into sustainable tourism management, where digital tools are often implemented without sufficient coordination, interoperability, or clear links to sustainability objectives. Such a situation limits the potential of smart solutions to improve destination governance, visitor experience, and the long-term competitiveness of tourism destinations. The aim of the study is to evaluate how the Smart Region concept can be operationalized at the urban level by analysing the city of České Budějovice (Czech Republic)—the primary regional tourism and administrative hub—as a critical case study. The research first analysed relevant municipal and regional strategic documents, then examined secondary data and publicly available digital services and technological solutions, and finally conducted a structured observation of selected tools relevant to tourism management. The findings show that the city has already introduced several elements of smart tourism management, especially in digital information services, transport management, and sustainable mobility. However, the analysis also reveals important shortcomings in data sharing, cross-sector coordination, and the integration of tourism-oriented digital tools. The study concludes that deeper institutional cooperation and more coherent smart governance are necessary to strengthen sustainability, improve efficiency, and support the long-term competitiveness of the destination. Full article

This article examines the medium to long-term enrollment patterns of an AI-based platform designed to support children in learning robotics and participating in a national robotics competition in Romania. Drawing on registration and participation data covering students and teachers across urban and rural schools between 2020 and 2025, the study documents a consistent pattern: an initial period of high enrollment and rapid adoption followed by a steady decline over time. A key feature of the initiative is that hardware, platform access, and learning resources were provided entirely free of charge, allowing cost-related explanations for the decline to be set aside and structural and human factors to be examined directly. The paper makes two primary contributions. First, it proposes a System Dynamics framework grounded in innovation diffusion theory as a first-generation calibration model for understanding AI-enabled educational robotics adoption in a resource-constrained national context. The model is designed to be progressively tested and refined as anonymized aggregate data accumulates, and it relies exclusively on anonymized aggregated public data in accordance with GDPR requirements. Second, it advances the hypothesis that an AI-based educational platform, even one from which all financial barriers have been removed, will experience sustained enrollment decline in the absence of adequate human teacher involvement. The empirical trajectory and model outputs are consistent with this hypothesis and motivate further investigation. This represents a hypothesis-generating and framework-building paper. The framework reveals pronounced urban-rural disparities and differential outcomes by age of entry. All findings are presented as model-generated hypotheses rather than empirically demonstrated conclusions. The paper invites researchers gathering comparable data from similar initiatives in other countries to collaborate in testing and refining the model. The central conclusion is cautiously optimistic: AI may support robotics education adoption, but it is not a substitute for dedicated teachers, and without sustained investment in human capital, even a financially accessible platform is insufficient to maintain long-term enrollments. Full article

Despite geographical proximity and broadly similar agro -climatic conditions, China and Vietnam show sharply divergent coffee export performance, with Vietnam ranking as the world’s second largest exporter, while China’s exports remain modest. This study compares the determinants of their bilateral coffee exports over 2001 to 2022, using a gravity model estimated by Poisson pseudo maximum likelihood with partner and year fixed effects, a specification that retains zero trade flows and absorbs global price and demand shocks. Once these common shocks and fixed bilateral factors are controlled, trading-partner demand characteristics such as GDP, population, and urbanization are not robust determinants of exports for either country. The most consistent determinant is domestic production, which is positively associated with exports for both nations and helps explain their divergent export scale. Domestic consumption cannot be separated cleanly from production, so it is not interpreted as crowding out exports. On the policy dimension, Vietnam’s WTO accession shows a positive association with exports while China’s Belt and Road participation shows none, but these are institutionally different forms of integration and are read as associations, rather than causal effects. The findings carry implications for sustainable development, linking producer competitiveness to livelihoods under Goal 1, growth and decent work under Goal 8, and the balance between domestic and export use of production under Goal 12. Full article

This article examines how smart cities can act as catalysts for enterprise development by integrating technological, infrastructural, governance and human capital dimensions into a coherent urban innovation ecosystem. Drawing on an extensive literature review, the study first conceptualizes smart cities as adaptive systems that combine physical infrastructure, digital data layers, and institutional frameworks, creating conditions for knowledge spillovers, entrepreneurial opportunities, and business model innovation. Empirically, the research is based on an expert survey conducted among 54 specialists from academia, business, and public administration, who assessed the importance of technological, infrastructural, governance, innovation ecosystem, and human capital factors for enterprise development in the context of smart cities. The results suggest that advanced digital technologies, smart infrastructure, open data, R&D support, startup programs and talent development are perceived by experts as key, mutually complementary drivers of firms’ innovation, efficiency, sustainable growth, and competitiveness, with notable differences between expert groups. On this basis, the study proposes a synthetic model of relationships and impact pathways linking smart city components with enterprise outcomes. The paper concludes with a discussion of the study’s limitations, related to the expert-based, country-specific, and perceptional character of the data, and outlines directions for further quantitative and qualitative research on the firm-level effects of smart city development. Full article

The Pearl River Delta region suffers from congestion in the urban road network, noise, air pollution, and other “urban diseases”. Vigorously developing inland water transportation can greatly alleviate these “urban diseases”. However, it is difficult to take advantage of the inland waterway transportation cost advantages due to the Pearl River Delta’s short haul distance characteristics. In recent business practice, a novel, environment-friendly, and competitiveness-enhanced inland waterway transportation mode has emerged in the area, called the leftover-cargo mode in this paper. This mode is composed of first-tier (big companies) and second-tier (small companies) inland barge companies, which establish a cooperative relationship and jointly meet the needs of shippers and can lead to a modal shift from inland truck to inland waterway transportation. In real practice, the pricing methods of this novel mode still rely on experience. We propose four pricing game theory models based on channel leadership in order to investigate how decision-making impacts the pricing and income of the two-tier companies. We find that, if the market price ceiling is low, second-tier inland barge companies always benefit more than first-tier companies, which is very interesting and counter to the existing literature. These findings offer pricing insights into economically viable leftover-cargo cooperation and its role in supporting sustainable road-to-waterway freight modal shift in the Pearl River Delta. Full article

Rapid urbanization and stringent ecological protection policies in China have reshaped spatial competition among urban, agricultural, and ecological spaces. However, existing studies often overlook how this competition evolves across different slope structures. To address this, this study establishes a fine-scale analytical framework using H3 hexagonal grids and slope spectrum analysis to investigate slope structure evolution and spatial competition patterns from 1990 to 2023. The results reveal a distinct topographic stratification: urban space dominates low-slope regions (<6°) but exhibits a pervasive “upslope expansion” trend, with its average slope increasing from $1.81°$ to $2.07°$, equivalent to an annualized increase of approximately $0.008°{\mathrm{yr}}^{-1}$; agricultural space characterizes the transition zones (6–$15°$), showing an “upslope migration” in the Southeastern Hills associated with urban expansion pressure in low-slope areas; and ecological space functions as a stable barrier in steep terrains (>15°) but faces encroachment in transition zones. Furthermore, cluster analysis identifies significant regional heterogeneity aligned with China’s macro-topography, including “low-slope agglomeration” in the Eastern Plains, “interwoven upslope” patterns in the Southern Hilly Regions, and ecological dominance in the Western Highlands. Association analysis using GeoDetector and Multiscale Geographically Weighted Regression (MGWR) indicates that competition intensity is most strongly associated with human activity factors, especially human footprint and nighttime lights ($q>0.29$), which show the highest explanatory power among the examined factor groups. The interaction between human activity and elevation further shows relatively high explanatory power ($q=0.41$), suggesting that spatial competition is more pronounced where intensive human activities overlap with topographic constraints. Crucially, this study challenges the traditional flat-projection planning model. We propose a transition to “three-dimensional topographic regulation,” advocating differentiated management strategies—such as strict “slope redlines” for urban-agricultural transition zones—to mitigate intensifying spatial conflicts in complex terrains and safeguard agricultural sustainability. Full article

Understanding how sustainable urban competitiveness evolves within megaregions has become a central concern in urban and regional studies, particularly under the pressures of carbon neutrality, spatial inequality, and network-driven urbanization. This study develops a multidimensional framework to assess the sustainable competitiveness of cities in the Beijing–Tianjin–Hebei “2 + 36” urban agglomeration and examines its spatio-temporal evolution and relational structure. Using a 30-indicator system grounded in factor foundations, economic performance, innovation capacity, openness, and environmental livability, we construct a composite competitiveness index through principal component analysis (PCA). Kernel density estimation reveals a pattern of overall improvement accompanied by widening disparities, characterized by selective agglomeration and the emergence of a pronounced high-value tail. Spatial autocorrelation consistently indicates significant spatial dependence, while LISA analysis identifies persistent low–low clusters and limited spillover absorption around core cities. A modified gravity model further uncovers a transition from a linear, corridor-based linkage structure to a more polycentric and networked competitiveness system, albeit with enduring peripheral weak nodes. The study contributes theoretically by conceptualizing sustainable urban competitiveness as a multidimensional nexus shaped jointly by territorial attributes and relational network structures. It demonstrates that competitiveness dynamics in megaregions emerge from the interplay of hierarchical consolidation, spatial divergence, and network reconfiguration—challenging the traditional assumption of simple core-to-periphery diffusion. The findings offer broader global implications, showing that the Beijing–Tianjin–Hebei case mirrors worldwide megaregional patterns, where proximity alone is insufficient to ensure functional integration, and where coordinated governance, network embeddedness and sustainability transitions increasingly determine regional competitiveness. This research provides a comprehensive analytical foundation for understanding and governing megaregional competitiveness in the era of sustainable development. Full article