Search Results (12,635)

As one of the top contributors to global environmental impact, the building and construction sector has significant potential to mitigate resource consumption both during and after construction. The Leadership in Energy and Environmental Design (LEED) certification has formalized this mitigation process, but it remains unclear whether the operational performance of LEED-certified buildings matches their theoretical design in reducing environmental impacts and advancing sustainable development in the built environment. This study contributes to the growing body of knowledge on real-world building performance by evaluating the operational energy use of LEED-certified buildings in Philadelphia relative to their immediate urban neighbors. The methodology includes identifying buildings from the Philadelphia Large Building Energy Benchmarking dataset, along with U.S. Green Building Council (USGBC) certification records, and analyzing LEED-certified buildings in comparison with their functionally similar non-LEED buildings in proximity. The research employs a multi-dimensional analytical framework grounded in the Energy and Atmosphere (EA) credit structure of LEED. In raw city-wide terms, certified buildings used far more energy per floor area than non-certified buildings (79.4 vs. 22.7 kWh/sq ft), but this gap largely reflects differences in building function, size, and location. After structural clustering and geographically constrained matching, certified buildings still showed a higher mean energy use intensity, by roughly 56 to 59 kWh/sq ft across all neighborhood sizes (k = 3, 5, 10). However, none of these differences was statistically significant at the 95% level. This apparent gap was not uniform: it was concentrated in large, service-intensive types such as healthcare and public/cultural facilities, rather than observed across all building categories. The results therefore provide no evidence that certified buildings outperform comparable non-certified peers in operational energy use, rather than positive evidence that they underperform. By utilizing large-scale benchmarking data and comparative analytical methods, this work enhances understanding of the effectiveness of LEED-related energy interventions and supports evidence-based decision-making for policymakers, designers, contractors, and building owners seeking to improve energy performance in existing buildings. Full article

The Cau river basin in northern Vietnam is experiencing increasing pressures on water resources due to rapid urbanization, industrial development, agricultural expansion, and inadequate wastewater management. Understanding the interactions between surface water, groundwater, and water quality is essential for developing effective and sustainable water management strategies. This study developed and applied a coupled MIKE SHE–MIKE 11 framework to simulate surface–groundwater connectivity and its influence on water quality dynamics in the Cau river basin. Hydrometeorological and water quality datasets collected during 2023–2024 were used to calibrate and test the integrated model at key monitoring locations, including Cha, Phuc Loc Phuong, and Dap Cau stations. The hydrological component demonstrated satisfactory performance, with Nash–Sutcliffe Efficiency (NSE) values ranging from 0.55 to 0.79 for water level simulations, indicating a reliable representation of surface and subsurface flow processes. Simulated river–aquifer exchange fluxes revealed pronounced spatial variability across the basin. Upstream reaches predominantly functioned as groundwater recharge zones, whereas the middle and downstream sections exhibited dynamic bidirectional exchanges governed by river stage fluctuations, hydraulic gradients, and local hydrogeological conditions. Water quality simulations for BOD₅, COD, NH₄⁺, total nitrogen (TN), and total phosphorus (TP) showed good agreement with observations, with calibration and testing errors generally remaining below 25%. Incorporating surface–groundwater interactions improved the representation of pollutant transport, residence time, and nutrient accumulation processes compared with conventional river-only simulations. The results demonstrate that river–aquifer connectivity plays a critical role in regulating both hydrological processes and water quality conditions in the basin. The coupled modeling framework provides a robust scientific basis for identifying critical interaction zones, assessing pollution risks, optimizing monitoring programs, and supporting integrated water resource planning. By explicitly linking hydrological connectivity with water quality dynamics, the proposed framework serves as a practical decision-support tool for sustainable water resource management in the Cau river basin and other river–aquifer systems facing increasing environmental pressures and progressive water quality degradation. Full article

This study investigates the key drivers and barriers influencing the adoption of electric vehicles (EVs) in urban freight logistics, using Lithuania as a case study. An integrated methodological framework combining Delphi, Fuzzy logic, DEMATEL, and System Dynamics is applied to identify critical factors and analyse their interdependencies. Four main drivers are identified: infrastructure, acquisition costs, technological development, and policy measures. Expert evaluations are transformed into fuzzy values to quantify factor importance, which are then incorporated into a dynamic simulation model to assess EV adoption and CO₂ emission trends. In addition to baseline scenarios, extreme scenario analysis is conducted to evaluate system sensitivity to economic, technological, and policy changes. The results reveal strong nonlinear relationships between factors and highlight the importance of their balanced development. The findings suggest that rapid EV adoption in urban logistics requires a coordinated approach integrating infrastructure expansion, financial incentives, technological progress, and policy support. The study provides practical insights for policymakers and logistics companies aiming to accelerate sustainable urban transport transitions. Full article

The study investigates whether cultivated land abandonment (CLA) reflects structural transformation or an intensifying crisis. CLA is defined as land that has remained uncultivated for a minimum of two consecutive years, with the exclusion of land that is subject to deliberate programs such as the “Grain-for-Green” initiative. Utilizing the China Land Cover Dataset and a moving-window approach, we conducted a comprehensive analysis of spatiotemporal patterns across 2847 Chinese counties from 1992 to 2022. The research employed OLS, Tobit, high-dimensional fixed effects and instrumental variable regressions. The findings of the present study indicate an annual average abandonment rate of 2.3995%, with 12.3649% of cropland abandoned at least once and 9.2028% reclaimed, suggesting a fragile equilibrium. The Huang-Huai-Hai region and Northeast China’s plains emerged as low-abandonment clusters. Cropland fragmentation was found to trigger abandonment, while a higher ecological land ratio significantly exacerbates CLA. Rural labor migration and urbanization drive cumulative abandonment, worsened by the COVID-19 pandemic. Effective governance requires context-specific interventions that address key constraints and integrate land reuse into sustainable rural development frameworks. The research methods and theoretical mechanisms presented offer a reference for balancing food security, rural revitalization, and ecological sustainability worldwide. Full article

The transition from traditional siloed to intelligent cities allows for the deployment and management of information and communication technologies in the urban context to be driven by holistic sustainability requirements rather than technical ones such as feasibility and fragmented, siloed operational patterns. This work proposes a multi-dimensional decision-making framework to manage a smart city as an urban cognitive Cyber–Physical System (CPS) across environmental, economic, and social sustainability pillars, metrics and their trade-offs. A methodology based on Deep Reinforcement Learning (DRL), specifically adopting Deep Q-Networks (DQNs), is proposed to represent and assess sustainability pillar dependencies and their interplay. A case study on Low-Power Wide-Area Network planning, deployment and management in a Sicilian municipality has been developed to demonstrate the effectiveness of the proposed approach in dealing with the dynamics and non-linear dependencies of the sustainability pillars. Full article

Due to the impacts of climate change and human activities, the regional climate of the Three Gorges Reservoir area in China has undergone significant changes, bearing important implications for regional sustainable development. Based on annual average temperature data from 15 cities in the reservoir area from 1980 to 2025, this study employs linear regression and anomaly analysis to systematically analyze the regional characteristics of urban temperature changes and their associations with the construction phases of the Three Gorges Project. The results show that: (1) The annual average temperature in the reservoir area exhibits a significant warming trend, with an overall increase of 1.66 °C (0.36 °C/decade) and a multi-year average temperature of 15.91 °C. (2) All 15 cities in the reservoir area show warming trends, with the downstream section of the reservoir (near the dam) experiencing higher warming rates than the midstream and upstream sections. (3) Divided into three construction phases, the annual average temperature in the reservoir area shows a warming trend in each phase, with warming rates of 0.028 °C/yr, 0.031 °C/yr, and 0.065 °C/yr, respectively, indicating an accelerating trend. Both the warming magnitude and the annual average temperature of the cities in Phase III are generally higher than those in the previous two phases. The findings of this study can provide scientific support for urban climate management and sustainable development in the reservoir area. Full article

Innovation districts have emerged as a prominent urban strategy to catalyse knowledge-based development and economic growth. Yet their contribution to sustainability and an understanding of what they entail beyond economic growth remains underexplored. This paper proposes a conceptual framework rooted in urban monitoring to identify and synthesise the contributions of innovation districts (or similar development strategies) into the city’s sustainable development. Specifically, it targets the initial processes of intention and objective setting, where different and conflicting policies overlap to define definite city-level objectives. Using Oslo as a single embedded case study, the paper further proposes a methodology for identifying and localising urban sustainability indicators (USIs) based on the city’s three innovation districts: Oslo Science City, Hovinbyen Circular Oslo, and Punkt Oslo. This methodology integrates global frameworks, such as the UN Sustainable Development Goals and the Global Urban Monitoring Framework, with local priorities and stakeholder perspectives through a combined top-down and bottom-up approach. The process resulted in a set of 40 indicators across five sustainability domains—social, environmental, economic, cultural, and governance—revealing governance as a critical enabler for sustainable implementation. While most indicators stem from theoretical conceptions of innovation districts, governance indicators rather reflect local experiences and institutional dynamics. Full article

Rural spatial restructuring in rapidly urbanizing regions is jointly shaped by road expansion and ecological constraints, yet existing studies often examine transportation and ecological systems in isolation. This study develops a coupled “road–ecology” network framework to investigate the evolution of county spatial structure in Huangchuan County, China, from 2013 to 2023. Using administrative village, transportation, and land-use data, we construct and analyze road and ecological networks with complex network metrics, multilayer network analysis, and Exponential Random Graph Models (ERGM). The results show that the road network evolved from a single-center hierarchical structure toward a balanced multi-center configuration, while the ecological network maintained structural stability through strengthened regional ecological clustering. The coupled network underwent a transition from spatial exclusion to limited integration, and its persistently negative interlayer assortativity values are consistent with ecological land configuration acting as a spatial constraint on road expansion. Multilayer network metrics further indicate a trend toward increased local coordination in the road-ecology coupled system, indicating a gradual evolution toward a more spatially coordinated configuration. This study advances the application of multilayer network approaches in rural spatial research and provides a new perspective for understanding sustainable county spatial restructuring under driving-constraint balance. Full article

Old residential community renovation projects face intense socio-technical risks driven by complex construction environments, diverse stakeholders, and management frictions. To optimize risk governance, this study develops a systematic risk measurement framework by integrating correlation coefficient analysis and structural equation modeling (SEM). Based on multi-source empirical data, five critical risk dimensions are identified: personnel, materials and equipment, technology, management, and environment. Following indicator screening via correlation metrics, an SEM path analysis is performed using risk probability, consequence severity, control difficulty, and exposure degree as endogenous variables. The results demonstrate that the model achieves robust statistical goodness-of-fit, effectively uncovering a tripartite driving mechanism encompassing risk triggering, amplification, and constraint across varying dimensions. This study delivers a replicable quantitative tool and an actionable decision-making basis for the refined safety management and sustainable implementation of complex urban renewal projects. Full article

The mapping and monitoring of urban green space (UGS) are of great significance for ecological assessment and sustainable development in urban settlements. However, for fine-grained classification tasks in high-resolution remote sensing imagery, existing methods suffer from significant challenges due to the high spectral similarity between low-growing and dense vegetation, as well as the complexity of spatial structures. To overcome these challenges, this paper proposes a Dual-Encoder Multi-Source Feature Fusion Network (DMSF-Net) for fine-grained urban green space segmentation. The proposed method constructs a parallel encoding structure for RGB and auxiliary features (NDVI and LBP), introduces an Adaptive Feature Fusion Module (AFFM) during the encoding phase to achieve dynamic weighted fusion of cross-source features, and designs a Boundary-Aware Up-Sampling Module (BAM) during the decoding phase to strengthen the representation of complex boundary regions through joint modeling of regional semantics and boundary information. Experimental results on a self-constructed UrbanGreen dataset and the publicly available Vaihingen dataset demonstrate the superior performance of DMSF-Net over existing mainstream methods across several evaluation metrics, achieving mIoU values of 82.27% and 74.73%, with improvements of 1.07% and 0.57% over the best baselines, respectively. The model demonstrates particularly strong discrimination capability for the fine-grained category of low vegetation. Ablation experiments further validate the usefulness of each structural module, with AFFM playing a key role in overall performance improvement, while the BAM improves boundary delineation as observed in visual comparisons. Through the synergistic integration of multi-source feature information and structural optimization, DMSF-Net effectively enhances fine-grained UGS segmentation in complex urban scenes, thereby providing an effective approach for high-resolution remote sensing-based urban ecological monitoring. Full article

Nitrogen (N) and Phosphorus (P) are essential macronutrients whose unsustainable extraction and use pose growing environmental and geopolitical challenges. In the European Union, tightening regulatory frameworks, including the Urban Waste Water Treatment Directive (EU 2024/3019) and the Farm to Fork Strategy, have positioned nutrient recovery as a fundamental pillar of the circular economy. Despite the availability of mature technologies, comprehensive techno-economic assessments applied comparatively across multiple industrial sectors remain scarce. This study addresses that gap by evaluating the economic feasibility of five nutrient recovery systems across European waste valorization case studies: ammonia stripping from digestate (Spain), sewage sludge composting (Latvia/Lithuania), whey valorization via ultrafiltration and reverse osmosis (Hungary), algae-based dairy wastewater treatment (Slovakia), and pyrolysis of sewage sludge (Denmark). A structured data collection methodology was applied to assess capital expenditures (CAPEX), operational expenditures (OPEX), mass and energy flows, and nutrient recovery yields. Results demonstrate that all five systems show technical operability and economically relevant cost structures, with unit treatment costs ranging from €0.005/kg to €1.60/kg of waste treated, supporting their further development and scale-up as viable nutrient recovery pathways. N recovery was prioritized in most configurations, while P was predominantly co-recovered in solid residues. The findings provide a cross-sectoral comparative framework to support decision-making in the transition towards sustainable nutrient management and circular economy models. Full article

Urban expansion is known to drive biodiversity loss in China, but the future impacts of rural built-up land change remain a critical blind spot. Unlike concentrated urban growth, rural development is dispersed and closely tied to livelihood transitions, yet no studies have systematically projected its biodiversity consequences under alternative socioeconomic pathways. To understand the magnitude and distribution of such impacts, we explore spatially explicit projections of China’s urban-rural settlement dynamics from 2020 to 2070, and assess the impacts on biodiversity. By 2070, urban areas are projected to expand to 1.29–1.74 times their 2020 levels, with the most significant growth occurring in eastern China. Rural built-up land increases under scenarios SSP1, SSP2, SSP3, and SSP4, except for SSP5, with rural shrinkage mainly occurring in eastern and southwestern China. Habitat loss caused by the expansion of rural built-up areas is expected to surpass that caused by urban expansion. Furthermore, habitat loss resulting from cropland displacement will exceed the loss from built-up area expansion. Birds and reptiles are identified as the most vulnerable groups to the expansion of rural-urban built-up areas. The findings contribute to more coordinated biodiversity conservation efforts and provide scientific support for achieving sustainable development goals. Full article

As one of the key vehicles to integrating culture and tourism in urban and rural development, cultural tourism-oriented characteristic towns are increasingly facing natural and social disaster risks caused by global climate variability, large-scale expansion of town areas, and intensified human engineering activities. In particular, characteristic towns that have rapidly developed through tourism based on historical and cultural heritage face challenges such as compact layouts of ancient architectural complexes, extensive outward expansion of newly developed areas, and inadequately planned emergency evacuation systems—making them ill-equipped to cope with increasingly uncertain disaster risks. In response to these issues, this study takes Zhangguying Town in Yueyang County, Hunan Province, as a case study. Through field investigations, interviews, and GIS-based hydrological simulations, the research systematically identifies the characteristics and influencing factors of disaster risks in the town. It also reveals the core dilemmas confronting current disaster prevention planning and proposes strategies such as enhancing chain disaster prevention measures, promoting micro-scale, site-specific disaster prevention retrofitting, and establishing a multi-scale disaster prevention system through “point-line” linkages. By reducing disaster risks, preserving cultural heritage, and optimizing emergency response capacities, this research effectively supports the sustainable development of cultural tourism-oriented characteristic towns from a disaster prevention perspective, enabling these towns to withstand natural hazards while sustaining their historical, cultural, and socio-economic functions. The findings provide a theoretical basis and methodological reference for comprehensive disaster prevention planning in similar cultural tourism-oriented characteristic towns. Full article

Urban traffic congestion is a primary driver of greenhouse gas emissions, wasted fuel, and degraded air quality, presenting a significant barrier to achieving sustainable cities (SDG 11) and climate action (SDG 13). Standard Adaptive Traffic Signal Control (ATSC) systems are either financially prohibitive for developing countries or lack certified safety mechanisms for physical deployment on live roads. This paper proposes and validates SYTRAC (System for Adaptive Traffic Control), a low-cost, safety-critical Adaptive Traffic Signal Control system designed for resource-constrained urban environments. SYTRAC implements an asynchronous co-design that combines real-time visual vehicle detection on an NVIDIA Jetson Nano GPU with deterministic safety execution on a Siemens S7-1200 Programmable Logic Controller (PLC). The core of the system is the Density-Weighted Adaptive Green Extension (DWAGE) algorithm. DWAGE provides a stable, interpretable, and computationally lightweight alternative to complex optimization methods such as genetic algorithms, particle swarm optimization, or Deep Reinforcement Learning. We establish a formal mathematical queue-stability guarantee using a closed-form Foster–Lyapunov drift argument. A three-mode fault-tolerant state machine with a 2 s watchdog automatically transitions to fixed-time fallback in the event of hardware or camera stream failures, protecting physical intersection safety. The system was validated through hardware-in-the-loop field deployments at a live intersection in Ouargla, Algeria. SYTRAC achieved a statistically significant 22.1% reduction in average vehicle delay ($p<0.001$), while microscopic simulations confirmed up to 28.0% delay suppression during lane-blockage incidents. Critically, this delay reduction translates to an environmental saving of 53.5–72 kg of CO₂ avoided per day, alongside annual fuel savings of 8430 L. Assembled within a $1257 hardware budget, SYTRAC delivers a cost-effective, open-source, and reproducible platform that bridges the gap between adaptive intelligence and industrial safety, providing a scalable blueprint for sustainable urban traffic management in emerging economies. Full article

As passenger travel and freight transport demand continue to rise, transportation remains a major source of global carbon emissions. Predicting transportation carbon emissions helps support sustainable transportation planning and the development of scientifically sound emission reduction policies. However, there are currently no unified standards for accounting for transportation carbon emissions, and different approaches yield widely varying predictions, creating challenges for formulating carbon emission policies. In this study, a top–down approach based on China’s energy statistical data is adopted to calculate regional transportation carbon emissions (TCE). Twenty factors influencing carbon emissions are selected, and Spearman correlation analysis is used to examine the correlations among these factors. Lasso regression and the STIRPAT model are employed to quantitatively analyze the influence of each factor. Subsequently, an improved hybrid deep learning model, GA–CNN–LSTM–Attention, is proposed to predict carbon emissions under baseline, restriction, and control scenarios. Finally, an empirical analysis is grounded in a case study of Fujian Province, China. The quantitative analysis results show that, of all the factors, the urbanization rate has the greatest impact on transportation carbon emissions in the region, and the GA–CNN–LSTM–Attention prediction model achieves higher forecasting accuracy. Under the first two scenarios, carbon emissions in the region show a year-by-year increasing trend from 2022 to 2035, with no emissions peak observed. Under the controlled scenario, carbon emissions are projected to peak in 2030, reaching approximately 70.28 million tons of CO₂. The prediction results provide a scientific basis for sustainable transportation development and government policymaking on carbon reduction. Full article