Search Results (1,687)

This study presents a compact controlled urban geophysics test site developed at the University of Malta to evaluate the response of complementary near-surface sensing methods under known shallow subsurface conditions. The experimental setup is designed to investigate buried target detection and the response to a simulated shallow leak, used here as a controlled water-release experiment in a shallow carbonate setting characterized by thin, laterally variable soil cover and anthropogenic disturbance. A preliminary passive seismic survey based on the horizontal-to-vertical spectral ratio (HVSR) method was used to compare candidate sectors and select the most suitable area for installation. The test site includes a buried iron plate and a perforated PVC pipe, the latter used to release water under controlled shallow conditions. Ground-penetrating radar (GPR), smartphone magnetometry, electrical resistivity tomography (ERT), and UAV-based thermal imaging were applied to assess target detectability and leak-related surface–subsurface responses. Results show that GPR provides the clearest response for static target detection, while smartphone magnetometry identifies the buried ferrous target under favourable conditions. For the simulated leak experiment, ERT provides the most robust subsurface evidence of moisture redistribution after water injection. UAV thermal imaging captures a complementary surface thermal response influenced by both moisture dynamics and local surface disturbance. The results show that a compact controlled test site can support the comparison of professional and low-cost sensing methods for shallow target detection and simulated leak assessment. In this configuration, the controlled water-release experiment provides a practical basis for evaluating leak-related surface–subsurface responses under known shallow conditions. The proposed setup has implications for methodological assessment, training, and near-surface environmental monitoring in heterogeneous urban settings. Full article

Against the backdrop of the deep advancement of the carbon peak and carbon neutrality goals and the superposition of the transportation power strategy, leveraging the spatial restructuring of highway networks to optimize the low-carbon layout of county-level industries has become a crucial lever for balancing economic quality improvement with carbon intensity control. This study selects panel data from 129 counties in Yunnan Province spanning 2015–2024, constructing a comprehensive highway network development index from four dimensions: highway density, road network connectivity, weighted hierarchical structure, and county accessibility. Using a two-way fixed effects benchmark model, a stepwise mediation effect testing framework, and a regional heterogeneity identification strategy, the paper systematically examines the marginal effects, transmission pathways, and spatially differentiated characteristics of highway network development on county-level industrial carbon emission intensity. Key findings are as follows: Enhanced highway network development significantly suppresses the increase in county-level industrial carbon emission intensity, and a well-developed road network can provide long-term empowerment for the low-carbon transformation of county-level industries. Mechanism analysis confirms that highway network development reduces emissions through two core pathways: first, a direct emission reduction effect achieved by optimizing the county-wide freight organization system, reducing inefficient transport energy consumption, and improving overall transport efficiency; second, an indirect low-carbon enabling effect realized by breaking down administrative barriers in county markets, lowering cross-regional business transaction costs, deepening industrial division of labor and collaboration, and forcing resource allocation improvements. Heterogeneity analysis reveals that the low-carbon dividends of highway network development exhibit significant gradient differentiation: the emission reduction enabling effect is strongest in counties within the Central Yunnan urban agglomeration, followed by cultural tourism counties in western Yunnan and border counties in southern Yunnan, with the weakest marginal enabling effect observed in traditional agricultural counties in northeastern Yunnan. Full article

Amid China’s transition from rapid urbanization to high-quality development, quantifying urban building metabolism is crucial for building resilient resource management systems. However, current research predominantly focuses on eastern cities, largely overlooking non-residential buildings. Here, we apply dynamic material flow analysis (dMFA) to quantify the material stocks of residential and non-residential buildings in two major economic hubs in western China, Xi’an and Chengdu. The stock patterns from 1950 to 2050 and the underlying drivers are further clarified. Model projections suggest that material stocks in both cities will peak around 2040, reaching 2.2 billion tons in Chengdu and 1.08 billion tons in Xi’an, under the intensive scenario. Chengdu reaches stock saturation 2 to 3 years earlier than Xi’an, and the total stocks are approximately twice those of Xi’an. Reinforced concrete and steel structures dominate future building development and increase the accumulation of cement and steel. Sand and gravel still account for the majority of building materials. Demand for new construction materials shows a pronounced double-peak pattern, occurring in 2016 and 2026. Construction waste is projected to rise sharply by mid-century; scenario analysis indicates that an 80% material recovery rate has the potential to largely offset new material demand. Sensitivity analysis identifies building lifetime extension and construction technology improvement as the strategies with the greatest potential for mitigating future waste generation. This study expands the scope of urban building material metabolism research and provides a scientific basis for low-carbon urban planning and construction waste management in China. Full article

In the context of global climate change and China’s dual-carbon strategy, this analysis examines how land-use transition is associated with land-use carbon effects in China’s resource-based cities. From the perspective of urban development stages, an analytical framework is built by linking development stage, land-use structure, and carbon source–sink structure. Using 262 resource-based cities from 2011 to 2023, we estimate land-use-related carbon emissions, carbon sequestration, and net land-use carbon effects with the carbon emission coefficient method and analyze their spatiotemporal patterns and driving factors using GeoDetector. The results show clear differences among city types. Mature cities form the largest group. Growth cities show the fastest expansion of impervious surfaces, while regenerative cities present signs of ecological recovery. This suggests that land-use transition is not simply the expansion of impervious surfaces, but a stage-dependent process of structural change. Land-use carbon effects also differ across stages. Mature cities maintain high and stable carbon-source effects. Growth cities exhibit increasing carbon-source effects, declining cities show reduced emissions but limited improvement in the carbon source–sink structure, and regenerative cities show improved carbon-sink capacity under ecological restoration. Overall, net land-use carbon effects follow a rise–decline–rebound pattern and show clear spatial heterogeneity and visually apparent clustering patterns. Population size has strong explanatory power, while interactions between socioeconomic and land-use factors further shape spatial differences. These results support stage-specific low-carbon transition strategies. Full article

Rapid urbanization and climate change are reshaping land-use systems, intensifying conflicts among urban growth, cultivated land conservation, and ecosystem protection. Understanding how land-use change affects carbon balance is important for designing sustainable land management and climate-resilient spatial planning. Taking Nanjing, China, as a case study, this study investigates how land-use change shaped carbon emissions, carbon sequestration, and net carbon emissions from 2000 to 2020 and further evaluates their future changes in 2030 under SSP–RCP scenarios. By integrating land-use simulation, carbon accounting, and contribution–sensitivity analysis, this study distinguishes land-use conversion effects from intra-type intensity change effects associated with changes in carbon emission or sequestration intensity within unchanged land categories. From 2000 to 2020, Nanjing experienced a substantial increase in net carbon emissions, with construction land expansion and higher emission intensity of construction land serving as the primary drivers. Although the carbon sink function was still mainly supported by cultivated land and forest land, land conversion and changes in sequestration intensity weakened the regional carbon balance. Under all SSP–RCP scenarios, simulated net carbon emissions for 2030 exceed the 2020 level, even though lower carbon intensity under SSP1–2.6 can partially mitigate emission growth. Conversion to construction land shows the highest carbon cost, especially when cultivated or ecological land is occupied. These findings highlight the need to coordinate urban expansion control, farmland protection, ecological restoration, and low-carbon industrial transformation. The study offers empirical support for improving sustainable land management and guiding spatial planning toward low-carbon development. Full article

This study examines the long- and short-run effects of sustainable development, economic growth, energy consumption, urbanization, investment and trade openness on Carbon Dioxide Emissions (CO₂) in the GCC countries utilizing the PMG-ARDL approach by including the data spanning from 2000 to 2022. In the short -run, the sustainable development index demonstrates a positive and substantial impact while it exhibits adverse long-run impact on CO₂ emission. The study also indicates a U-shaped correlation between economic growth and emissions, contrasting with the conventional Environmental Kuznets Curve (EKC) where economic growth at lower income levels often leads to a reduction in emissions; however, income increases beyond around USD 29,942 per capita correlate with higher emissions. Besides, energy use is identified as the primary factor influencing emissions, reflecting global patterns that indicate greater energy usage, particularly from fossil fuels directly boosts emissions. Moreover, the urbanization intensifies this problem, resulting in higher energy demand and greater emissions. Additionally, the study finds that gross capital formation and investments in infrastructure contribute to emissions in the short run, though these effects diminish over time. Our results are robust as it similar to the outcomes obtained from dynamic panel-data System GMM. The GCC policymakers must utilize the sustainable development framework to legally mandate national planning towards low-carbon paths while balancing for short-term transition costs with significant long-run emission reductions. This necessitates the implementation of market-oriented carbon pricing to address the post-threshold U-shaped emissions rebound, the systematic elimination of fossil fuel subsidies to promote renewable energy adoption, and the enforcement of sustainable development regulations to mitigate urbanization pressures. Full article

Clarifying the mutual feedback relationship and coordinated evolution characteristics between low-carbon transportation (LCT) and high-quality economic development (HQED) is of great significance for the green transformation of resource-based and ecologically fragile urban agglomerations. Taking 18 cities in the Yellow River Jiziwan Metropolitan Area as the research objects, this paper constructs an evaluation indicator system for LCT and HQED based on panel data from 2013 to 2022, and comprehensively applies the ISM-MICMAC model, a modified coupling coordination degree model, a gravity model, an obstacle degree model, and a combined GM-ARIMA forecasting model to analyze the interaction relationships, spatiotemporal evolution, spatial correlations, and scenario differences between the two systems. The results indicate that: (1) A hierarchical mutual feedback relationship exists between LCT and HQED, in which the relevant factors exhibit a hierarchical association within the system structure, extending from basic input, transportation supply, and economic operation to green and low-carbon outcomes. (2) During the study period, the comprehensive development levels of the two systems generally improved, with the mean coupling coordination degree rising from 0.4374 in 2013 to 0.4702 in 2022, remaining overall at a borderline coordination stage, while inter-city divergence was relatively pronounced. (3) The spatial connection network gradually exhibited multi-node linkage characteristics, yet strong connections remained concentrated in a few core cities. (4) Scenario predictions reveal that the synergistic development scenario is most conducive to enhancing the coupling coordination level, and the differences among scenarios gradually widen after 2026. Simultaneously advancing LCT and HQED is an important pathway to enhance the regional synergy level of the Yellow River Jiziwan Metropolitan Area. Full article

Buildings account for nearly 30% of global energy-related carbon emissions. In rapidly developing economies, the operational phase of buildings represents a major and growing source of emissions. However, emission pathways in hot-summer-cold-winter (HSCW) regions remain understudied. This study analyzes carbon emission peaks and influencing factors in the operational phase of existing civilian buildings in Anhui Province. It integrates energy balance tables, the LEAP model, carbon emission factors, and the STIRPAT model. The energy balance table method disaggregates building energy consumption into urban, rural residential and public sectors. It adjusts for transportation energy by deducting specific proportions of gasoline and diesel from industrial, commercial, and residential sectors. Heating energy calculations are simplified because the region has a HSCW climate with limited centralized heating. The LEAP model projects emissions under four scenarios from 2020 to 2060. The STIRPAT model with ridge regression reveals that the permanent population and energy structure negatively influence residential emissions with elasticities of −2.646 and −1.465, respectively. This finding is consistent with the province’s energy transition, where coal use dropped from 28.48% in 2005 to 0.45% in 2020 and electricity use rose from 39.86% to 59.01%. In contrast, per capita GDP, building area, and energy intensity show positive effects. For public buildings, tertiary industry added value and energy structure are key determinants. Scenario analysis identifies the blueprint scenario as optimal, with residential emissions peaking at 34.29 million tons in 2025 and declining to 9.19 million tons by 2060 through measures such as 10% building retrofits by 2025, 75% energy-saving standards for new constructions, 50% retrofits by 2060, and renewable energy integration with building electrification, outperforming the baseline scenario that peaks in 2036 at 49.46 million tons and other intermediate scenarios. The study underscores that energy structure optimization significantly decouples energy consumption from emissions, offering actionable pathways for dual carbon goals through policy synergies in building efficiency, population management, and clean energy adoption to foster sustainable development and the construction industry’s low-carbon transition. Full article

This study investigates the psychological and contextual mechanisms through which consumers’ awareness of agriculture’s contribution to climate change translates into a willingness to pay (WTP) for low-carbon agricultural products. Drawing on data from Eurobarometer 93.2 (ZA7739; N = 24,193), the research applies a moderated mediation model (Hayes’ PROCESS Model 14) to examine the mediating role of support for mandatory environmental labelling and the moderating effect of residential context. The results indicate that climate change awareness is significantly and positively associated with WTP. Moreover, support for mandatory labelling partially mediates this relationship, suggesting that institutionalized transparency may serve as a key mechanism through which environmental concern becomes economically actionable. The findings further reveal that this indirect effect is moderated by the level of urbanization, being stronger in urban areas than in rural settings. This highlights the importance of socio-spatial context in shaping consumer responses to sustainability information. Overall, the study contributes to the literature on sustainable consumption by demonstrating that willingness to financially support low-carbon agriculture depends not only on environmental awareness but also on trust-enhancing policy instruments and contextual factors. The findings offer important implications for policymakers aiming to promote sustainable food systems through information-based regulation. Full article

This study proposes a multi-criteria decision-making framework for determining optimal locations for shared micromobility stations in Kars, Türkiye. The approach integrates spatial data with structured expert evaluation and applies the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) to rank candidate locations. Eight representative locations were evaluated based on five criteria: points of interest (POId), public transport distance, activity level, accessibility, and installation suitability. Spatial indicators were obtained through map-based measurements, while qualitative criteria were assessed using expert-based scoring by 11 experts. The results indicate that locations with high activity density, strong accessibility, and a high concentration of POIs achieve the highest suitability scores. The city center (L2) and Kafkas University (L1) were identified as the most suitable locations, with closeness coefficients of 0.862 and 0.783, respectively. In contrast, the train station (L5) showed the lowest suitability, with a closeness coefficient of 0.326. A sensitivity analysis confirmed that the ranking structure remained unchanged under moderate variations in criteria weights, indicating the robustness of the proposed model. The findings suggest that micromobility systems are primarily driven by intra-urban mobility demand rather than by long-distance transportation nodes. From a sustainability perspective, the proposed framework supports evidence-based planning of shared micromobility infrastructure, which can contribute to reducing dependence on private automobiles, improving urban accessibility, and promoting low-carbon transportation. The findings provide practical guidance for municipalities seeking to develop environmentally sustainable, socially accessible, and resource-efficient urban mobility systems in medium-sized cities. The framework can also support broader sustainable urban development strategies and contribute to the achievement of sustainable mobility objectives. Full article

Residential energy consumption exhibits substantial behavioral uncertainty and temporal heterogeneity, which pose challenges for demand-side management and residential load profiling. However, existing studies often focus on isolated temporal or spatial scales and predominantly employ hard clustering methods based on geometric distance metrics. To address these limitations, this study proposes a multi-scale residential load profiling framework utilizing the Gaussian Mixture Model (GMM) and nearly three years of hourly electricity consumption data from 13 residential buildings in Vancouver. First, schedule-driven and seasonal variations in residential energy consumption were examined through multi-temporal comparative analyses and paired-sample t-tests. The results indicate statistically significant differences between working-time and non-working-time energy consumption patterns in most buildings (p < 0.001). Second, individual-building clustering was performed to identify long-term intra-building daily load evolution characteristics, revealing 2–5 typical daily profiles across different households. Finally, inter-building clustering identified three representative residential groups characterized by low-energy stable patterns, high-energy intensive patterns, and intermediate commuting-oriented patterns. The average daily energy consumption levels of the three clusters were 13.11 kWh, 36.74 kWh, and 21.61 kWh, respectively. The proposed framework provides a data-driven approach for understanding residential energy-use heterogeneity across multiple scales and offers potential guidance for residential demand-side management and urban low-carbon energy planning. Full article

In the new development stage of China’s green and low-carbon transition, agricultural industry agglomeration serves as a key catalyst for sustainable agricultural practices. Its effects on agricultural carbon reduction and energy conservation urgently need investigation. This research uses panel data from 31 Chinese provinces spanning 2005 to 2021 to investigate the nonlinear effects of agricultural industry agglomeration on agricultural carbon emissions and energy consumption, employing econometric models such as the two-way fixed effects model, mediation model, and moderation model. The findings indicate that (1) there’s a clear inverted U-shaped pattern linking agricultural industry agglomeration to both carbon emissions and energy consumption in agriculture; (2) agricultural scale effects and socialized services are key mechanisms; (3) marketization and environmental regulation positively moderate this relationship; and (4) the carbon reduction and energy-saving effects are more pronounced in regions with higher agricultural modernization levels, higher urbanization rates, and plain areas. This finding contributes to optimizing the path of agricultural industry agglomeration and facilitates the synergy of carbon reduction and energy conservation in such agglomeration. Full article

Extreme climate events have emerged as a critical threat to the economic resilience and environmental sustainability of urban systems. As a central pillar of the low-carbon transition, improvements in carbon emission efficiency (CEE) are increasingly recognized as a potential pathway to mitigate the occurrence and intensity of such events. Drawing on a balanced panel dataset of 163 cities from 2006 to 2022, this study integrates an Extreme Gradient Boosting (XGBoost) model augmented with SHAP (Shapley Additive Explanations) analysis and a Geographically and Temporally Weighted Regression (GTWR) framework to examine the nonlinear and spatially heterogeneous effects of CEE on the Climate Physical Risk Index (CPRI). The results reveal a distinct two-stage dynamic pattern, in which CEE initially exacerbates and subsequently mitigates climate risk, indicating a nonlinear transition from short-term intensification to long-term alleviation. This relationship shows clear differences across city levels and climate types. The strongest effects appear in peripheral cities and in areas with extreme rainfall dominance (ERD). Spatial analysis based on GTWR also shows a clear north–south pattern. The effect of CEE in reducing risk becomes stronger from the south to the north. Based on these results, the study suggests different land-use policy strategies for different city types and climate conditions. The results give actionable insights for designing targeted carbon governance policies. These policies aim to deal with the growing challenges caused by extreme climate events under ongoing climate change. Full article

Urban canopy height is an essential indicator for characterizing vegetation structure and carbon sequestration, yet satellite LiDAR often lacks sufficient spatial resolution, airborne LiDAR is costly, and SAR has limited sensitivity to vegetation structure. This study proposes a canopy height inversion framework using high-resolution stereo pairs from the Gaofen-7 (GF-7) satellite. A 0.65 m Digital Surface Model (DSM) was generated from GF-7 data, and a relative surface height was derived by differencing the GF-7 DSM from a coarse 30 m DSM reference. Key features were selected via Boruta and Random Forest Recursive Feature Elimination (RF-RFE), and six models—linear, polynomial, support vector machine, backpropagation neural network, XGBoost, and RF—were compared. The results showed that the Boruta feature set improved average R² by 8.2%. Among all models, RF performed best (test set R² = 0.71, RMSE = 1.70 m) and exhibited the strongest resistance to overfitting. Canopy heights within Beijing’s Fifth Ring Road showed an “outer-high, inner-low” pattern: large parks exceeded 30 m, while the Central Business District remained below 3 m. GF-7 stereo pairs enable efficient and cost-effective retrieval of canopy height in fragmented urban green spaces, supporting ecological parameter quantification and urban green-space management. Full article

Achieving carbon-neutral cities requires innovative strategies that integrate technological carbon capture, sustainable urban infrastructure, and proactive public engagement. While microalgae-based systems have shown promise for CO₂ sequestration and resource recovery, their scalability remains constrained by high costs and energy-intensive photobioreactor (PBR) designs. Here, we propose the retrofit of existing urban fountains into high-efficiency microalgae cultivation systems—microalgae-enhanced fountain landscapes—as an integrated solution that bridges ecological function and social outreach. This approach capitalizes on ubiquitous fountain infrastructure to minimize deployment costs, employs advanced fountain-style cultivation technology to enhance biomass productivity, and leverages strategic locations in high-footfall urban zones to actively elevate public carbon literacy and motivate low-carbon behavioral shifts through immersive engagement—a vital step toward city-wide participatory climate action. We critically analyze the feasibility of this system, highlighting its potential for multi-stakeholder value creation across developers, municipalities, and citizens. Furthermore, we synthesize recent advances in suspended microalgae cultivation, building-integrated PBRs, and microalgae-informed landscape design to contextualize the development pathway of fountain-based systems. By uniting technical efficiency with civic education, this work establishes a replicable framework for scalable urban deployment—simultaneously advancing carbon mitigation, public awareness, and circular resource flows in the transition toward climate-resilient cities. Full article