Abstract This study investigates the influence of urban form compactness on industrial carbon emissions using microlevel tax survey data and nighttime light imagery from Chinese cities between 2008 and 2016. We construct a novel measure of urban compactness based on spatial geometry and examine its causal effect on firm-level CO 2 emissions. Our main findings reveal that higher urban compactness significantly reduces industrial carbon emissions, supporting the hypothesis that densely developed cities facilitate more efficient production and lower environmental impacts. We identify three key mechanisms through which compactness operates: enhanced industrial agglomeration promotes resource sharing and scale economies, improved green technology diffusion accelerates the adoption of low-carbon innovations, and optimized resource allocation directs factors toward more efficient firms. Furthermore, we show that digital infrastructure development, high-tech industrial parks, and smart city initiatives amplify the emission reduction effects of compact urban forms. These results underscore the importance of integrated urban planning and spatial policy in achieving China’s Dual Carbon Goals and promoting sustainable industrial development.
Abstract Cultural sustainability assessment is essential for the renewal of historic districts. However, there is a significant deficit of quantitative research focusing on cultural sustainability in Chinese historic districts. The selection of relevant indicators tends to be limited, and existing international evaluation systems do not fully align with local historical cultures and conservation concepts. This study proposes a novel methodology for evaluating the cultural sustainability of historic districts in China. Based on the three modes through which cultural sustainability contributes to sustainable development (in, for, and as), five evaluation dimensions of cultural sustainability are established: cultural heritage, cultural vitality, society, economy, and environment. Drawing upon extensive literature and optimizing through the 14th Five-Year Plan for Cultural Development, the 2030 Sustainable Development Goals, and the Culture 2030 indicators, a 31-indicator system and corresponding scoring system are developed. The Bayesian Best–Worst Method is employed to assign weights to these indicators. Additionally, given the specificity of the culture, multisource data are obtained through expert questionnaires and spatial analysis using a geographic information system to calculate scores for each indicator. The Cloud-Matter Element Model is then used for a comprehensive evaluation of cultural sustainability. Utilizing the Shaocheng Historic and Cultural District in Chengdu as a case study, this research validates the applicability of the methodology and assesses its cultural sustainability, thereby facilitating the identification of further improvement strategies. Finally, an analysis of the sensitivity of the evaluation results to indicator weights demonstrates the reliability of the model. The results show that commercial and cultural integration, historic building preservation benefits and authenticity, and intensity of specialized funding are the most critical indicators determining the cultural sustainability of historic districts. Moreover, the findings reveal issues within Shaocheng district, including insufficient preservation of traditional features and excessive building density. The results of this study offer implications for policy regarding the advancement of the cultural sustainability of historic districts in China.
Abstract Enhancing urban economic resilience is essential for cities exposed to external economic shocks. This study examines whether the opening of urban rail transit (URT) improves urban economic resilience. We use panel data for 262 Chinese prefecture-level cities from 2001 to 2023. We treat the first URT opening as a quasinatural experiment and estimate a multiperiod difference-in-differences (DID) model. We conduct robustness checks using multiple approaches. The results indicate that URT openings have a statistically significant positive effect on urban economic resilience. On average, a city’s economic resilience index increases by approximately 0.054 points after URT opens. A mechanism analysis suggests that URT improves economic resilience mainly through industrial agglomeration, labor mobility, and consumer market activity. These channels account for approximately 3.156%, 2.639%, and 1.522% of the total effect, respectively. The effects are larger in medium-sized cities with populations of 1–5 million and in the central and western regions. Artificial intelligence positively moderates the relationship between URT and urban economic resilience. Further analysis shows that cities with larger URT networks and longer URT operating histories experience greater improvements in economic resilience. The spatial DID estimates suggest that the main results are robust to spatial dependence. Urban economic resilience exhibits spatial correlation, and the spillover effect is primarily concentrated among geographically neighboring cities.
Abstract Rapid urbanization in heritage-rich cities like Varanasi has led to significant land use changes, necessitating high-resolution monitoring of built-up dynamics. This study addresses the research gap in quantifying fine-scale spatiotemporal urban transformations by employing the Normalized Difference Built-up Index (NDBI) from Sentinel-2 imagery over the period of 2019–2025. Statistical analysis reveals the lowest mean NDBI in 2021 −0.0568), indicating a temporary reduction in built-up intensity, followed by a steady increase to −0.0223 in 2025. Spatial autocorrelation assessed via Local Moran’s I shows strong clustering in 2019 (mean I = 0.2755; skewness = 3.53) and persistent hotspots through 2025, reflecting structured urban growth. k -means clustering identified the top five high-density built-up zones per year, with mean intercluster distance peaking in 2021 (0.0867°) and decreasing convex hull area to 0.0020 deg 2 in 2025, suggesting increased decentralization yet confined urban expansion. A novel quantile-mapped gradient analysis detected an average NDBI shift magnitude of 5.76°, highlighting both directional growth and localized development shocks. The correlation analysis shows that absolute NDBI shifts are strongly influenced by both X - and Y -directional movements, with high Pearson correlations ( r > 0.76). In contrast, the weaker correlation between X and Y shifts ( r = 0.27) suggests spatially varied directional growth patterns. These results fill a methodological void by integrating spatial autocorrelation, clustering, and gradient-based shift analysis. The spatial data can guide equitable infrastructure planning and heritage conservation amid urban growth. Future scope includes integrating socioeconomic and policy variables with remote sensing indicators to develop predictive urban growth models supporting sustainable urban planning initiatives.
Abstract This article explores the foundations, mechanisms, and evaluation methods of territorial resilience in the context of disaster management. It highlights the importance of a systemic and multidimensional approach, integrating social, economic, environmental, and political factors. Key resilience-building processes include risk identification, preparedness, response, and postdisaster recovery. To assess resilience, both quantitative indicators and participatory methods are considered essential, offering complementary insights. This study underscores the need for integrated strategies and robust measurement tools to enhance territorial capacity to anticipate, absorb, adapt to, and recover from disasters, while guiding informed and sustainable decision-making.
