Search Results (19,247)

Land-use change affects regional carbon accounting through ecological conversion and the concentration of energy-intensive economic activity. We combined 30 m China Land Cover Dataset maps for 2010, 2015, 2020, and 2025 with provincial socioeconomic and energy statistics to quantify land-use transitions and carbon emissions across nine Yellow River Basin provinces. Construction-land-associated emissions were decomposed using the logarithmic mean Divisia index, factors associated with land expansion were examined using random-forest models, and three 2030 scenarios were evaluated. Construction land expanded by 38.87% from 2010 to 2025, with 71.33% of new construction land converted from cropland and 17.75% from grassland. Net land-use carbon emissions increased by 69.82%, from 1139.06 to 1934.33 million t C. Economic-output density contributed 1144.84 million t C to the increase in construction-land-associated emissions, compared with 576.11 million t C from land expansion, whereas declining energy intensity offset 922.72 million t C. Projected 2030 emissions ranged from 2124.72 million t C under ecological protection to 2866.55 million t C under urban expansion. Construction-land expansion was substantial, but economic-output density made the larger positive contribution to historical emission growth. The projected 2030 estimates depended on the combined trajectories of construction-land demand, economic growth, and energy intensity. These findings highlight the importance of coordinating land-use planning, economic development, and energy-efficiency improvement for sustainable low-carbon transitions. Full article

Landslide and slope-instability hazards increasingly threaten residential areas in rapidly urbanizing Andean cities, where informal construction is common and formal geotechnical assessment is limited. Protection Motivation Theory and the Theory of Planned Behavior have been widely applied to explain protective intention toward natural hazards, yet few studies have jointly examined perceived geotechnical risk, trust in technical assistance, and willingness to pay for mitigation within a single model. This study assessed these constructs, together with mitigation adoption intention, among 420 residents and construction-related professionals in Cuenca, Ecuador, using a cross-sectional, descriptive–correlational design. Data were collected through a structured online questionnaire with seven Likert-scale constructs and a contingent-valuation item, analyzed using non-parametric correlation and group-comparison tests, followed by confirmatory factor analysis and structural equation modeling. Perceived risk, trust, and mitigation intention were all high and positively associated, while economic constraint correlated positively, rather than negatively, with intention, an effect that a structural model showed to be fully mediated through intention instead of acting directly on willingness to pay, which points to a recognized structural barrier and not an individually suppressive one; attitudinal willingness to pay did not consistently predict the declared monetary amount, and civil engineering professionals reported higher willingness to pay than other groups. These findings indicate that respondents in this sample reported high risk awareness and trust in technical assistance, while perceived economic constraint was the dimension most consistently associated with lower stated readiness for geotechnical mitigation in this Andean urban context. These findings contribute empirical evidence on the perceptual, trust-related, and economic drivers of household engagement with geotechnical mitigation, informing sustainable, resilience-oriented approaches to reducing disaster risk in informally built urban settlements across the Andean region. Full article

This study examines how Saudi Vision 2030 is reshaping architectural design and practice in Riyadh amid the challenge of balancing rapid urban development with local architectural identity. The study is framed by the concept of transformative resilience, defined here as the capacity of an urban system not merely to absorb disturbance but to deliberately reconfigure its structure, function and identity while sustaining cultural continuity. The research investigates Vision 2030’s impact across four analytical dimensions; design, implementation, functional performance, and socio-cultural context; and explores whether evaluative perceptions differ according to sociodemographic characteristics, including gender, age, nationality, educational level, professional background, and length of residency. A descriptive analytical methodology was adopted, employing a structured questionnaire. Instrument reliability was established with a Cronbach’s alpha of 0.70. Data were collected from a stratified random sample of 384 residents and professionals in Riyadh (response rate 93.7%). Because the instrument records perceived rather than objectively measured performance, the two evidence streams are reported separately: the survey data establish social legitimacy and acceptance, while the case studies supply the physical, technical and documentary evidence base. To contextualise the survey findings, three projects were purposively selected and examined as representative case studies, supported by field photographic documentation. Quantitative results demonstrate a strong consensus regarding the positive influence of Vision 2030 on architectural development, with mean scores ranging from 4.07 (design) to 4.13 (functional and socio-cultural dimensions). The highest-rated item (mean = 4.23) related to strengthening sense of place. Inferential analysis, now reported with effect sizes, revealed statistically significant differences across all sociodemographic variables, with age being the most influential factor, although all effects were small in magnitude. Qualitative findings identify preserving architectural identity and heritage as the main challenge, while diversifying contemporary design approaches is the most frequently proposed future direction. The study concludes that achieving Vision 2030’s architectural objectives requires regulatory frameworks that embed local identity, supported by community capacity building and sustained institutional commitment to culturally responsive and environmentally sustainable design. The principal contribution is an operationalised assessment framework that translates transformative resilience from a descriptive narrative into a set of architectural indicators applicable to other vision-led urban transformations. Full article

An in-depth understanding of the factors that enhance carbon footprints is a plausible pathway to enthrone environmental sustainability. Currently, the implications of energy and mineral depletion for carbon footprints in South Africa and Nigeria have received minimal empirical attention. The few available studies are non-exhaustive, often limiting broad-based policy refinements. With datasets spanning more than five decades (1971–2022), the novel Fourier Bootstrap Autoregressive Distributed Lag estimator was selected to account for structural breaks and nonlinearities. The empirical findings reveal divergent environmental pathways: South Africa’s carbon footprint (0.666% increase) is more related to energy depletion, reflecting coal-dominated electricity generation. Mineral depletion emerges as the primary culprit in Nigeria (10.856% increase), attributable to unregulated mining activities, including gas flaring and deforestation. Both countries face common challenges from urbanization (0.094% and 0.087% increases) and economic growth (0.107% and 0.046% increases). Trade openness shows insignificant effects. Short-run carbon footprint reductions from resource depletion improvements do not persist, underscoring the need for policy consistency. Policy effectiveness analysis identifies coal phase-out (0.9) and carbon pricing (0.8) as South Africa’s highest-return interventions. Mining regulation (0.9), green mining (0.9), and artisanal formalization (0.9) emerge as Nigeria’s priorities. The integration frameworks outperform siloed approaches. This implies South Africa may capture higher returns from policy coherence, while Nigeria may depend more on enforcement capacity. These findings provide evidence-based guidance for context-specific environmental policy design in resource-dependent economies. Full article

Nighttime light (NTL) data is widely used to monitor urban development, economic activity and population distribution, supporting socio-economic analysis and sustainable development planning. SDGSAT-1 NTL data, with its multiple bands and high resolution, has become an important source for studying human activity patterns. However, directly summing radiance values on geographic grids assumes equal contributions from all grid cells, regardless of the differences in the ground area represented by each cell. As a result, regional nighttime light totals may be systematically distorted, particularly in cross-latitude analyses where pixel areas differ substantially. To address this aggregation issue, this study proposes a physically explicit area-weighting framework that converts SDGSAT-1 radiance to radiant flux by incorporating the actual surface area represented by each grid cell. The framework is validated using 25 cities spanning different latitudes and development levels in the Northern Hemisphere. Results show that the radiant flux model, which captures total emitted power, demonstrates improved performance over radiance-based aggregation (a density-based measure) in population estimation (R² = 0.85 vs. 0.82). By shifting from light intensity to integrated total energy, this approach improves cross-latitude comparability and enhances the methodological robustness of NTL-based analyses. Full article

New Light Rail Transit (LRT) systems are frequently promoted as a means of reducing car dependence and supporting urban sustainability; however, their effectiveness depends on how different traveller segments respond to LRT relative to competing modes. This study examines whether car owners and non-car owners exhibit distinct preference structures and policy sensitivities towards a proposed LRT system in Kerman, Iran. A stated-preference mode-choice survey conducted in November 2023 generated 1916 valid choice observations, including 955 observations from car owners and 961 from non-car owners, based on 736 completed questionnaires. Multinomial Logit (MNL), Nested Logit (NL), and Mixed Logit (MXL) models were estimated separately for each segment. Policy implications were assessed using elasticities, marginal effects, and scenario-based simulations. The results show that, for car owners, the MXL specification provides a slightly better statistical fit, indicating modest preference heterogeneity, while the main behavioural conclusions remain consistent across model structures. For non-car owners, the NL model performs best, revealing clearer substitution between public-transport modes (bus/LRT) and on-demand services (taxi/ride-hailing). Although LRT shows a negative baseline preference, particularly among car owners, its uptake increases substantially with improved LRT travel-time performance and higher private-car operating costs. Overall, improving LRT travel-time competitiveness is critical for both groups, while attracting car owners additionally requires coordinated measures that raise the generalised cost of private-car use and strengthen the relative advantage of LRT. These findings highlight the importance of segment-sensitive policy design for maximising the contribution of LRT to sustainable urban transport and long-term urban sustainability. Full article

Recent research has advanced fine-scale mapping and driver analysis of cropland fragmentation, but composite indices may mask structurally different fragmentation configurations, and evidence on how terrain and urban-system location jointly relate to fragmentation remains limited in rapidly urbanizing delta regions. This study quantified cropland fragmentation in the Yangtze River Delta (YRD), China, between 2000 and 2020. A Cropland Fragmentation Index (CFI) integrating edge density (ED), patch density (PD), and mean patch area (MPA) was calculated at a 1 km grid scale, and K-means clustering was used to identify fragmentation configurations. Pearson correlation and random-forest regression were used to examine spatial associations with selected 2020 natural and socioeconomic variables. Cropland area declined by 7.97%, while the regional-mean CFI increased from 0.29 to 0.32. Four configurations were identified, with the largest type (38.45% of grids) characterized by small patches and complex boundaries. Elevation and slope showed the strongest bivariate correlations with CFI, whereas distance to urban areas had the highest random-forest importance. These results reveal distinct fragmentation pathways and support differentiated cropland management in rapidly urbanizing regions. Full article

Many current studies have purely regarded green finance as green credit, ignoring the background of carbon neutrality and energy transition. This paper investigates the relationships among green finance, green technological innovation, and urban ecological welfare performance. Using 279 Chinese cities as examples, this paper reveals the following: there is (1) a direct positive effect, where a 0.1-unit absolute increase in the green finance index (GFI) is associated with an average 0.0605-unit rise in ecological welfare performance (EWP); (2) a partial mediation mechanism through green technological innovation, establishing a “finance → technology → ecology” pathway; (3) temporal persistence of positive effects across pre-2012 and post-2012 policy periods; and (4) regional disparities with significant impacts in Eastern/Western/Northeastern China but insignificant effects in Central China due to lower green credit allocation and weaker R&D intensity. Robustness checks, including Winsorization and subgroup analyses, validate these results. This study advances the integration of environmental finance with sustainable development theory, offering actionable insights for achieving ecological welfare goals. Full article

Carbon sequestration and emission reduction via biochar represent a promising approach for carbon storage. Continuously collected roadway fallen leaves serve as an attractive precursor for biochar-based carbon sequestration. However, the realistic carbon sequestration potential (derived from biochar production) of these urban waste resources remains poorly quantified and deserves further investigation. Against the backdrop of global sustainability goals, this study estimated the potential of using roadway fallen leaves for carbon sequestration at the urban scale of Hangzhou, based on government reports and pyrolysis experiments. Results indicated that there were a total of 691,472 roadway trees in Hangzhou. If the fallen leaves of the roadway trees in Hangzhou were subjected to oxygen-limited pyrolysis at a temperature of 300 °C for carbon sequestration purposes, the potential total carbon sequestration capacity was estimated to be 2273 ± 55 tons of carbon, equivalent to 8334 ± 200 tons of carbon dioxide. Leaf-derived biochar carbon sequestration index (LBCS) was established for potential use in the future Certified Emission Reduction (CCER). Different leaf types and pyrolysis conditions correspond to different LBCS values. LBCS for Camphor tree (300 °C) was 3.64 $\mathrm{kg} \mathrm{C}·{\mathrm{tree}}^{-1}{·\mathrm{year}}^{-1}$. Thermal maps of carbon sequestration potential of roadside leaf resources in 13 counties and districts of Hangzhou made by this study provided a reference for potential future practices. It was further estimated that with one-third of the tree leaves used for leaf-derived biochar (300 °C) carbon sequestration between 2027 and 2060, the cumulative carbon sequestration potential could reach up to 18.96 billion tons CO₂-eq by 2060, contributing 29–68% of the global GHGs emission reduction goal for bioenergy with carbon capture and storage (BECCS). We noted that this long-term cumulative estimate relied on linear extrapolation assumptions and contained uncertainties. This research offered new insights and quantitative baseline data for advancing negative-carbon technology using urban leaf waste, supporting local waste recycling practices and the urban implementation of the United Nations 2030 Agenda toward carbon-neutrality objectives. Full article

The operation and management of urban dynamic systems often rely on information, including holiday and event calendars, to anticipate deviations from routine demand. However, such calendars cannot capture irregularly announced holidays or undocumented physical disruptions and require continuous maintenance. This study proposes Structural Equilibrium, a reference state for interpreting urban dynamic systems without external calendars. Temporal variation is represented as Concentration–Dispersion–Relative Balance (CDR) structural states under two representative Structural Equilibria: Information-oriented and Stability-oriented Equilibria. Applying the two Equilibria to Jeju Airport electricity consumption and Gangnam Station subway passenger-flow data showed that structural representations diverged at specific times, producing structural Gaps. Moving-block bootstrap analysis showed that these divergences exceeded the range typically expected under the dataset’s own temporal dependence structure. Without calendar information, the divergences corresponded to the 2024 Chuseok holiday period in the subway data and a government-designated temporary public holiday and major snowstorm in the airport data. One high-Gap observation at Jeju Airport had no external cause despite an unremarkable raw magnitude, showing that structural analysis can reveal unusual conditions overlooked by magnitude-based monitoring. These findings indicate that Structural Equilibrium supports calendar-independent interpretation and adaptive monitoring under changing conditions, contributing to resilient and sustainable urban infrastructure operation. Full article

Smart cities and smart real estate are increasingly used to enhance urban efficiency, sustainability, and quality of life. Nevertheless, their implementation is hindered by interconnected challenges across concepts, technology, governance, economics, society, ethics, and the environment. Thus, this paper examines how and why interconnected limitations constrain the smartness of smart city and smart real estate initiatives and identifies the governance and implementation responses revealed by cross-case comparison. The analysis of secondary academic sources, policy papers, standards, and industry reports compares Toronto, Songdo, NEOM, and Barcelona. The results identify six categories of limitations: conceptual and strategic; technological and infrastructural; governance and institutional; economic and financial; social and ethical; and environmental. The analysis shows that the challenges facing these initiatives go beyond technology alone and include ambiguous definitions of smartness, poor governance, limited public involvement, privacy and surveillance risks, high implementation and maintenance costs, interoperability issues, and unverified assumptions that technological advancement will deliver sustainability. The comparison of cases highlights that the success of these initiatives depends on effective governance, responsible data management, public trust, financial feasibility, institutional coordination, and a life cycle approach. The study concludes that smartness should be understood as an integrated institutional, economic, social, technological, and environmental capacity rather than as the mere adoption of digital technologies. Full article

Ports are progressively evolving from traditional logistics nodes into integrated energy ecosystems, characterised by increasing electrification of maritime and land-based operations, the deployment of renewable energy sources, and the emergence of new and highly variable energy demand profiles. In this context, the main challenge is not only the availability of renewable energy but also the capacity of port energy systems to provide sufficient electrical power, flexibility, and resilience under increasing operational constraints. These issues are particularly relevant in Mediterranean ports, where limited grid capacity, infrastructure constraints, load variability, and interactions with surrounding urban areas strongly influence energy planning strategies. This paper proposes an integrated framework for the development of sustainable port energy hubs based on renewable generation, energy storage, green hydrogen systems, port microgrids, and intelligent energy management strategies (EMS). The main novelty lies in the integration of these energy vectors within a unified framework that explicitly accounts for the specific operational and infrastructure constraints of Mediterranean ports. The proposed approach aims to optimise the interaction between energy production, distribution, storage, and consumption, with particular attention to the role of hydrogen as a long-duration energy storage vector and as an energy carrier for selected port logistics applications. Through a data-driven Port Energy Baseline Assessment (PEBA), port operational characteristics are translated into quantified energy demand and power requirements, providing the basis for power adequacy assessment and the evaluation of alternative transition pathways. An illustrative application to a representative Mediterranean port, characterized by a peak electricity demand of 42 MW, illustrates how the framework quantifies power requirements, assesses power adequacy under infrastructure constraints, and compares alternative transition pathways based on renewable generation, battery storage, and hydrogen. Full article

Urban pluvial flooding shows clear temporal accumulation, delayed response, and spatial heterogeneity. Better flood-depth prediction from a spatiotemporal coupling perspective can support urban flood risk identification and refined management. This study develops a spatiotemporal prediction model that integrates a Transformer and graph neural network (GNN). The Transformer module captures temporal dependencies in rainfall processes and flood-depth evolution. The graph attention network (GAT) represents spatial associations constrained by terrain, drainage networks, and neighboring spatial relationships. A fusion attention mechanism then adaptively couples temporal and spatial features. This study uses multi-source data, including hourly meteorological observations, terrain, land cover, drainage networks, and water-system data. It selects the heavy rainfall event caused by Typhoon In-Fa in Huai’an City in July 2021 as a typical case. The study analyzes the temporal evolution of regional average flood depth and the spatial differentiation of inundated grid cells at the municipal scale. The results show three main findings. First, during the typical heavy rainfall event, regional average flood depth follows a continuous process of low-level stability, sustained rise, rapid increase, delayed peak, slow recession at a high level, and rapid recession. The flood peak lags behind the rainfall peak by about 3 h. This result indicates clear accumulation and delayed response in urban pluvial flooding. Second, at the municipal scale, inundated grid cells show a pattern of concentrated distribution in urban built-up areas, secondary distribution in county-level built-up areas, and scattered distribution in non-construction land. Different depth grades also show clear hierarchical differentiation. Mild and moderate inundation covers a wider area. Medium-high inundation concentrates locally. High-grade inundation appears as a small number of nested high-value cells. Third, the spatial differentiation of medium- and high-grade inundated grid cells does not result from low-lying terrain or construction land alone. It forms under the combined effects of low-lying terrain, local relative depressions, and impervious surfaces in construction land. This pattern shows clear built-up-area clustering, grade differentiation, and land-cover correspondence. The results provide methodological support and decision references for urban flood risk identification, grid-based risk management, and emergency dispatch during extreme rainfall. Full article

Addressing the lack of quantitative basis for dynamic response characterization and emergency shutdown decision-making in urban high-pressure gas pipeline leaks, this study takes a JS pipeline as the engineering case and establishes a dynamic simulation model based on OLGA incorporating leak and shutoff valve modules. The effects of aperture size, leak location, inlet flow rate, and valve operation on release intensity and economic losses are systematically analyzed. Results reveal that without intervention, leak-point pressure follows a four-stage evolution—steady operation, sharp drop, gradual decline to equilibrium, and post-plugging recovery—while leakage rate exhibits positively coupled synchronous behavior. Aperture size acts as an exponential-level hazard control factor; apertures ≥150 mm and rupture cases mandate immediate valve closure. Upstream leaks primarily threaten supply continuity, whereas downstream leaks exhibit sustained high-rate venting with greater release intensity, which may lead to more severe accident consequences depending on local atmospheric conditions and dispersion patterns. Inlet flow mainly modulates pressure equilibrium with limited influence on release rate. Emergency shutoff valves achieve loss reductions of 46.9–67.6% for apertures ≥150 mm, corresponding to savings of 0.47–3.48 million CNY within 4 h. These findings provide dynamic quantitative support for leak classification, coordinated valve control strategies, and emergency repair decision-making. Full article

Mobility-as-a-Service (MaaS) has emerged as a promising approach to urban transportation by integrating multiple mobility services into a single digital platform for trip planning, booking, and payment. More recently, Artificial Intelligence (AI) has expanded the capabilities of MaaS, enabling more efficient data processing, predictive analytics, personalized services, and intelligent decision support. This narrative review examines the current state of research on AI-enabled MaaS from both technological and socioeconomic perspectives. The analysis covers five major research areas: data integration and interoperability, predictive systems and demand forecasting, AI-enabled decision support for policy and planning, fairness and ethical AI, and cybersecurity and privacy protection. The findings show that successful implementation depends not only on advances in AI algorithms but also on high-quality interoperable data, effective governance, regulatory support, public trust, and collaboration among stakeholders. The review concludes that the main challenges facing AI-enabled MaaS are no longer primarily technical but organizational, institutional, and social. Future research should focus on trustworthy and explainable AI, privacy-preserving learning, standardized evaluation methods, fairness-aware optimization, resilient cybersecurity, and long-term assessments of MaaS impacts on sustainable urban mobility. Full article