Search Results (4,309)

Building climate-resilient cities strengthens urban livability and sustainable development levels. This paper constructs a difference-in-differences model to examine the impact of the pilot policy for climate-resilient city construction (CRCC—CRCC is used uniformly in the following text to represent the policy) on green sustainable innovation, using panel data of 260 prefecture-level Chinese cities from 2009 to 2023. The results reveal that CRCC can significantly promote green sustainable innovation in Chinese cities. Additionally, CRCC promotes green sustainable innovation by increasing the level of informatization, improving green total-factor energy efficiency, boosting corporate ESG performance, and alleviating corporate financing constraints. Therefore, it is necessary to further strengthen the implementation and promotion of China’s climate pilot policy. Attention should be paid to optimizing the pathways through which the pilot policy affects green sustainable innovation. Differentiated regional policies should be implemented based on local conditions. A tripartite linkage mechanism involving the government, enterprises, and the public should be established to increase societal awareness and support for climate-resilient city construction. Full article

Landslides have always been one of the most harmful geological hazards in terms of human lives and economic loss worldwide. Landslides can develop in both continental and underwater environments characterized by the presence of slopes with devastating consequences on structures, infrastructure, economic activities, and communities. They can also occur in flat or sub-flat areas, such as sinkholes, a particularly insidious phenomenon in densely urbanized areas. For this reason, landslide research is a priority and requires a wide range of knowledge and expertise to describe them, characterize them, and address the related risk prevention and mitigation aspects due to the wide variety of phenomena in terms of typology and triggering conditions. The purpose of this Special Issue is to collect and group together quality scientific papers on these research fields and derive practical/operational applications. The topics related to this Special Issue therefore concern the innovative methodologies to achieve landslide identification, classification, characterization, and hazard/risk evaluation through geomorphological field surveys and studies; remote sensing applications; Geographic Information System (GIS) data mapping, processing and representation; the analysis of predisposing and triggering factors; landslide early warning system implementation; and the impact of current and future climate change scenarios on the evolution of these phenomena. Full article

Against the background of green transformation and sustainable urban development, improving urban eco-environmental resilience (UER) is essential for enhancing ecological security and long-term urban sustainability. Using panel data from 260 Chinese cities from 2006 to 2023, this study constructs a new quality productive forces (NQPF) index based on new-quality laborers, new-quality means of labor, and new-quality labor objects, and measures UER from the dimensions of resistance, recovery, and adaptation. The results show that: (1) NQPF has a significant U-shaped effect on UER, indicating that it may inhibit UER in the early stage due to transformation costs and insufficient institutional adaptation but promotes UER after crossing a certain development level; (2) NQPF improves both green innovation level (GIL) and green innovation efficiency (GIE), while GIL faces short-term transformation constraints and GIE more directly enhances UER; (3) threshold, heterogeneity, and spatial analyses show that the positive effect of NQPF is stronger in cities with higher economic development levels and in the eastern region, and both NQPF and UER exhibit spatial clustering. This study provides empirical evidence for promoting productivity upgrading, ecological resilience, and sustainable urban transition. Full article

In developing countries, promoting sustainable income growth for farmers is a major priority. This study constructs an evaluation index system for the whole agricultural industry chain from the perspective of synergy among the innovation chain, supply chain, value chain, and capital chain. It also empirically tests the enabling mechanisms and spatial effects of the whole agricultural industry chain on farmers’ income. The entropy value method was used to measure the development level of the whole agricultural industry chain. Two-way fixed effects, mediation effects, and spatial Durbin models were applied to investigate the impacts, mechanisms, and spatial characteristics of the whole agricultural industry chain on farmers’ income. The whole agricultural industry chain significantly promotes farmers’ income growth, with the expansion of the non-agricultural employment scale and the improvement of urbanization levels serving as the main pathways through which the whole agricultural industry chain drives increases in farmers’ income. The heterogeneity analysis reveals that the innovation chain and capital chain contribute the most prominent marginal effects; the effect intensity of the whole agricultural industry chain on farmers’ income presents a spatial gradient pattern of “Central > Western > Eastern”; and its income-increasing effect is more noticeable for middle- and low-income farmers, demonstrating significant pro-poor characteristics. Further analysis indicates that the whole agricultural industry chain exerts a significant positive spatial spillover effect on farmers’ income. Therefore, it is essential to optimize the layout of the whole agricultural industry chain, smooth the transmission channels of non-agricultural employment and urbanization, and enhance the benefit linkage mechanism targeting middle- and low-income farmers. Full article

As a form of living heritage, Historic Urban Landscapes (HULs) have long been limited by the static perspectives and reductionist tendencies of conventional conservation and research approaches. Although the geological and archaeological concept of “stratification” offers a methodological basis for understanding the diachronic evolution of heritage, its unidimensional temporal lens fails to capture the inherent complexity and systemic nature of historic urban landscapes. To address this gap, this study proposes a “multidimensional stratification” theoretical framework through theoretical critique and paradigm reconstruction. The framework introduces innovations at the ontological, epistemological, and methodological levels, positing that the evolution of historic urban landscapes emerges from the nonlinear interaction and dynamic interweaving of four core dimensions: time, space, society, and value. It further systematizes five intrinsic attributes of such landscapes: authenticity, integrity, continuity, adaptability, and dynamism. Building on this foundation, the paper constructs a systematic analytical pathway—elements–processes–patterns–modes–drivers–characteristics—that enables dynamic analysis from micro-level identification to macro-level generalization, offering a scalable tool for HUL conservation and regeneration. To demonstrate the framework’s applicability, the historic urban area of Shenyang—a nationally designated historical and cultural city—is selected as a case study. Its urban landscape comprises four core districts: the Shengjing City District, the South Manchuria Railway Concession District, the Commercial Port District, and the Tiexi Industrial District, representing historical strata from the Qing dynasty capital, modern colonial planning, commercial opening, to industrial heritage. Using the multidimensional stratification approach, this study elucidates the spatial complexity, temporal nonlinearity, social dynamism, and value pluralism embedded in Shenyang’s historic urban area. Corresponding conservation strategies grounded in holism, dynamism, and differentiation are proposed. The research not only advances the theoretical understanding of HUL but also provides a novel paradigm—integrating holistic, dynamic, and operational perspectives—for the conservation, renewal, and regenerative practice of historic urban landscapes worldwide. Full article

In the context of accelerating technological and industrial transformation, innovation-driven productivity has garnered significant attention. Based on multi-source data, this study employs the entropy method and spatial pattern analysis to delve into the spatial pattern of innovation-driven productivity. The results are as follows: (1) there is a huge difference in innovation-driven productivity at the street and township level in Shanghai; (2) innovation-driven productivity exhibits global spatial autocorrelation in Shanghai; (3) innovation-driven productivity shows a circle structure with high–high agglomeration at the center and low–low agglomeration at the periphery; (4) innovation-driven productivity hot spots are concentrated in the central region, while cold spots are distributed in a southeast–northwest trend around them. This study is of great significance for Shanghai to achieve an accurate allocation of resources, a coordinated development of industries, and an improvement of urban functions. Full article

In the context of increasing water scarcity, the new paradigm in efficient water management relies on the digitalisation of water infrastructure to optimise resource use. One of the key factors in addressing the new challenges facing urban water cycle companies is the shortage of qualified technical staff. This context highlights the new training needs of technical personnel required by companies in the urban water cycle sector due to the increasing digitalisation of tools and the new technological requirements of jobs which are not yet sufficiently reflected in the existing training offer. Companies express their dissatisfaction with how poorly existing training programs meet their current needs. Vocational training has a fundamental role to play in providing high-quality, technically up-to-date training that is aligned with the needs of water management companies. This mission involves the adoption of innovative teaching strategies and methods and the development of innovative teaching resources. This paper presents the design of a bench-scale plant specifically designed as a teaching resource at a Spanish vocational training centre that offers intermediate-level training in water networks and treatment plants and advanced-level training in water management. The plant, occupying a footprint of 4 × 5 m, simulates a drinking water distribution network, from the intake to the distribution network via a pumping station with two pumps (1 + 1) of 0.75 kW each that provide a flow range of 4–12 m³/h with a range of 22–10 m water column and a regulating reservoir of 1 m³ located above the water network. The plant is equipped with sensors that allow operational data to be monitored: pressures, flow rates, consumption and levels, enabling multiple operational scenarios to be simulated: leaks, sectorisation, pressure and flow management, etc. Its design has focused on facilitating the acquisition by students of the skills and learning outcomes required in the curricula of the different professional modules that make up the aforementioned studies, through learning based on multidisciplinary collaborative projects. Full article

Driven by advances in artificial intelligence, immersive technologies, and related digital innovations, public cultural spaces are undergoing significant mediatization. Although architectural research has addressed this transformation through media architecture, responsive architecture, digital architecture, and intelligent architecture, it has largely focused on urban-scale interfaces or treated media as an external addition to space. To address this gap, this study examines mediated space at the interior scale of public cultural spaces and proposes a holistic analytical framework that understands media as an intrinsic condition of spatial operation. Through literature review, case analysis, and theoretical modeling, mediated space is classified into four types: physical, digital, interactive, and intelligent. The concept of the Spatial Organism is then introduced and structured into three interrelated entities: the media functional entity, the information exchange entity, and the symbolic representation entity. Building on this framework, the study develops a space–user perceptual interaction model and identifies four interaction stages and three interaction modes. The study argues that media intervention is reshaping spatial organization, meaning production, and evaluation, with interactivity, adaptability, and operational capacity as key evaluative dimensions. It thus offers a mechanism-oriented framework for understanding the mediatization of contemporary public cultural spaces. Full article

The rapid expansion of e-commerce has transformed market access in developing economies, yet its impact on the spatial structure of market participation remains insufficiently understood. While existing studies primarily examine welfare outcomes such as income growth and consumption smoothing, few investigate how digital platforms reshape the balance of market entry between urban and rural areas. Drawing on New Economic Geography and platform economics theory, this study proposes that e-commerce development rebalances urban–rural market vitality through three associative pathways: alleviating rural capital constraints, improving rural innovation environments, and promoting agricultural-industry agglomeration. Using county-level panel data covering 2725 Chinese counties from 2011 to 2022, we employ a Double Machine Learning (DML) framework to examine the association between designation as an “E-commerce into Rural Comprehensive Demonstration County” and changes in the urban–rural market vitality balance (URMAR). The results indicate that demonstration county designation is associated with a statistically significant reduction in urban–rural market disparity, as measured by both the Theil index and the absolute difference in new enterprise registrations. The directional URMAR indicator further reveals that this convergence is driven primarily by accelerated rural enterprise formation. Subsample analysis confirms that the rebalancing interpretation holds across counties with different baseline market structures. Mechanism analysis provides suggestive evidence consistent with all three proposed associative pathways. Heterogeneity analysis further reveals that these effects are stronger in economically developed eastern regions, in counties linked to higher-tier cities, and in secondary and tertiary industries. These findings advance a market-structure perspective on digital development that complements existing welfare-based approaches and offer policy insights for fostering balanced regional development through targeted digital and complementary investments. Full article

How AI can contribute to green sustainable development (GSD) in China is a critical yet underexplored question. Leveraging the staggered implementation of the National New Generation Artificial Intelligence Innovation and Development Pilot Zone (AIPZ) as a quasi-natural experiment, this study employs a difference-in-differences approach with panel data from 285 prefecture-level cities (2017–2022). The main findings are threefold. First, AI directly promotes GSD and, more importantly, indirectly enhances GSD by upgrading new-quality productivity (NQP)—a novel mechanism that distinguishes this study from conventional environmental policy evaluations. Second, the facilitating effect is not uniform: significant positive effects are detected in the western, eastern, and central regions, but not in the northeastern region; among major urban agglomerations, the Pearl River Delta, Chengdu-Chongqing, and Yangtze River Deltaexhibit significant effects, whereas the Middle Reaches of the Yangtze River and Beijing-Tianjin-Hebei region does not. Third, spatial spillover analysis reveals that AI’s favorable effect on GSD spreads primarily through intercity similarity in economic development level. These findings provide actionable insights for policymakers aiming to harness AI for sustainable development, highlighting the importance of fostering NQP and designing regionally differentiated strategies. Full article

The intersection of financial innovation and environmental sustainability offers important opportunities for low- and middle-income (LMI) countries. This study examines the association between FinTech investment, geopolitical-economic uncertainty, urbanization, economic development, and carbon dioxide (CO₂) emissions in LMI countries. CO₂ emissions per capita are used as an environmental outcome indicator rather than as a direct measure of green finance. Using a panel dataset covering 2010–2021, the study applies fixed-effects panel regressions as the main empirical approach and reports one-step difference the Generalized Method of Moments (GMM) estimates as exploratory dynamic evidence. The fixed-effects results indicate that GDP per capita is positively and significantly associated with CO₂ emissions, while FinTech investment and urbanization do not show consistent significant associations. Geopolitical risk is positively associated with CO₂ emissions in some static specifications, but this association becomes insignificant once gross domestic product (GDP) per capita is included. The exploratory GMM results, estimated with collapsed instruments and restricted lag depth, do not provide statistically significant evidence that FinTech investment is associated with lower CO₂ emissions. Overall, the findings suggest that FinTech investment may be relevant for environmental outcomes in LMI countries, but its role is neither automatic nor uniform and remains sensitive to model specification. Policy implications emphasize the need to strengthen digital financial infrastructure, regulatory transparency, institutional stability, urban planning, and climate-oriented investment channels to support FinTech-driven environmental performance. Full article

This review paper examines innovative urban design strategies for sustainable noise management through a structured analysis framed by ten guiding questions. It begins with an overview of conventional noise assessment technologies and progresses to advanced mitigation approaches. Core principles of sustainable urban design are explored, alongside evaluations of urban and transportation planning, traffic-reduction measures, green infrastructure, and resilient architectural strategies. Material innovations and modern noise-control technologies are presented as complementary solutions. Community-based methods, including citizen science and participatory planning, are highlighted for fostering inclusive governance. The discussion concludes by addressing key challenges and future directions, underscoring interdisciplinary collaboration to transform urban noise pollution into opportunities for healthier, more livable cities. Full article

As a key indicator of low-carbon economic transformation, the influencing factors of carbon productivity (CP) have attracted considerable academic attention. However, the study of the role of artificial intelligence (AI) technology innovation is comparatively confined. Using China’s prefecture-level-and-above cities as the sample, this study measures regional AI technology innovation based on AI patent stocks and empirically examines its impact on carbon productivity. The principal findings of this paper are as follows: (1) AI technology innovation boosts urban carbon productivity through three channels: enhancing green innovation, reducing transaction costs, and increasing AI attention. (2) The regional heterogeneity analysis shows that this positive impact of AI technology innovation on carbon productivity exerts a stronger facilitating effect on eastern regions, resource-dependent cities, and central cities. The heterogeneity analysis at the technological level further provides evidence of the effect of AI technology innovation on carbon productivity varying along different tiers of technological development, innovation mode, and innovation role. (3) The analysis identifies the energy structure as a pivotal threshold variable governing the efficacy of AI innovation in bolstering carbon productivity. Notably, crossing the threshold of clean energy penetration triggers an escalating positive feedback loop between AI innovation and carbon productivity. (4) Estimation of temporal effect dynamics via non-parametric panel model shows that the impact of AI technology innovation on CP exhibits phased characteristics. The coefficient became significantly positive in 2010 and peaked in 2015, after which its effect gradually weakened. This study provides comprehensive empirical evidence for understanding the relationship between AI technology innovation and CP and provides policy references for the use of AI technology to promote the coordinated achievement of economic growth and carbon reduction. 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

The deep integration of the digital economy and green finance is a key strategic arrangement for promoting high-quality industrial development in the new stage of development. This paper uses China’s dual pilot program—comprising both Big Data Comprehensive Pilot Zones and Green Finance Reform and Innovation Pilot Zones—as a quasi-natural experiment. Based on panel data from 285 prefecture-level cities spanning 2010–2023, and employing a dual machine learning approach, the study investigates how the digital economy and green finance synergistically enhance urban carbon total factor productivity. The study finds that, compared to cities with single-policy pilot programs, the synergy of digital and green policies can promote an increase in urban carbon total factor productivity. This conclusion remains valid after a series of robustness tests, including changing the sample period, adjusting machine learning model settings, and introducing instrumental variables. Mechanism tests indicate that the synergy of digital and green policies can enhance urban carbon total factor productivity through three pathways: increasing government focus on green development, raising the level of urban green technological innovation, and expanding the scale of green investment. Heterogeneity analysis reveals that the synergistic effects vary across cities with different resource endowments and geographical locations. This study uncovers the underlying logic of how the digital economy and green finance synergistically drive urban development and transformation, providing empirical evidence from China for the formulation of sustainable development policies tailored to local conditions. Full article