Abstract Collaboration among nearby local governments has emerged as an efficient strategy for reducing carbon emissions globally. However, the collaboration among local governments remains unsatisfying. Based on 793 questionnaires from civil servants in the Yangtze River Delta, this study employs the nonlinear PLS-SEM method and the Pressure–State–Response theory to identify drivers of collaboration effectiveness, and then conduct a heterogeneity analysis. The findings reveal that transaction and governance costs negatively impact collaboration effectiveness, while other factors exert positive influences, and supervisory pressure exhibits a U-shaped direct effect. Additionally, governments in larger cities are more responsive to governance costs and environmental attention, while those in smaller cities are more influenced by supervisory pressure, transaction cost, and collaborative benefit. This study enhances our understanding of local governments’ preference for independent versus collaborative carbon reduction strategies by identifying key influencing factors and broadening perspectives on governmental decision-making. The heterogeneity effect of city size offers practical insights for designing region-specific collaboration policies. These insights enrich the field of collaborative governance by providing nuanced insights into the motivations behind intergovernmental collaboration and offering practical recommendations for enhancing collaboration in other regions.
Abstract Against the backdrop of green economic strategy and economic transformation, enhancing the human capital benefits of higher education and promoting the green transition of energy consumption are key drivers for regional high-quality development (HQD) and overcoming resource-environmental constraints. This paper employs a spatial Durbin model to explore the impact of higher education development and the decarbonization of energy consumption structures on HQD in the context of carbon neutrality, using panel data from Chinese provinces during 2013–2022. The following conclusions are drawn: (i) Higher education significantly enhances regional development quality and generates positive spatial spillover effects through knowledge diffusion and innovation networks. (ii) Development imbalances exist in some regions due to resource “siphoning effects,” while the low carbonization of energy consumption structures exhibits pronounced spatial nonuniformity and path dependence. (iii) Eastern regions demonstrate strong synergy between green transformation and economic growth, while western regions still face “transition pains” and negative spatial spillovers due to industrial lock-in and insufficient innovation capacity. These findings provide crucial theoretical references and policy implications for optimizing higher education resource allocation, implementing differentiated energy transition strategies across regions, and elevating the overall level of HQD.
Abstract Despite the clear advantages of green infrastructure, quantitative frameworks evaluating living wall (LW) implementation in arid regions remain scarce. This study explored the multidimensional benefits of LW adoption within the Saudi Arabian construction sector to bridge the gap between theoretical potential and practical industry application. Using data collected from 90 industry professionals via a questionnaire, LW benefits were categorized into five major constructs by employing exploratory factor analysis; subsequently, a predictive model was developed using structural equation modelling-partial least squares, revealing ‘Urban Vitality and Sustainability’ as the most influential construct (path coefficient β = 0.404), followed by ‘Sustainable Urban Ecosystems’ (0.241), ‘Environmental Sustainability Benefits’ (0.215), ‘Green Building Enhancements’ (0.210), and ‘Energy Conservation’ (0.152), each exerting significant positive impacts on LW benefits. Furthermore, the findings suggest that stakeholders should prioritize urban vitality, ecological sustainability, and energy conservation to optimize LW performance, underscoring the interconnectedness of environmental resilience and green construction. Ultimately, integrating LWs into Saudi Arabian housing construction provides a robust pathway towards energy efficiency, climate change mitigation, and improved quality of life for inhabitants, thereby supporting the broader goals of sustainable development and establishing LWs as a cornerstone of sustainable urban design.
Abstract Rural residents are the main actors in the source classification of rural household waste. Guiding rural residents in developing the habit of household waste classification is important for promoting the construction of a beautiful countryside. On the basis of survey data from rural residents in the National Ecological Civilization Pilot Area (Jiangxi), this paper employs fuzzy set qualitative comparative analysis to explore the multiple concurrent factors and complex causal mechanisms underlying the formation of rural residents’ household waste classification habits. The research findings indicate that a single factor is insufficient to drive rural residents to develop household waste classification habits; instead, the formation of such habits results from the combined effects of multiple factors. There are five equivalent pathways driving the development of rural residents’ household waste classification habits, among which subjective norms and waste classification facilities play relatively universal facilitating roles in cultivating these habits. Conversely, seven equivalent pathways lead to nonclassification habits among rural residents, and the mechanisms underlying the formation of classification and nonclassification habits exhibit an asymmetric relationship.
Abstract Designed as a crucial policy instrument for downstream emission reduction, the Carbon Generalized System of Preferences (CGSP) nonetheless suffers from a problem of “strong willingness but weak practice” among residents, thereby undermining its long-term effectiveness. In this study, fuzzy-set Qualitative Comparative Analysis was employed to investigate the antecedent configurational pathways to residents’ participation willingness–behavior gap from a three-dimensional perspective of “psychology–policy–society.” The findings indicate that the willingness–behavior gap in urban residents’ CGSP participation stands at 55.07%. This gap is most pronounced among female residents with lower educational attainment and those in older age groups. Furthermore, the participation willingness–behavior gap can be synergistically influenced by multiple factors, with no single necessary factor capable of independently explaining this gap. Three configurational pathways were identified: first, the value consensus deficit pathway, wherein residents fail to internalize the core value of CGSP; second, the policy transmission obstruction pathway, hindered by a “last-mile” bottleneck between policy design and implementation; third, the incentive-response disconnect pathway, driven by a misalignment between provided incentives and residents’ perceived value. This study demonstrates the multiple configurations behind the willingness–behavior gap in CGSP participation, providing critical theoretical and practical insights for bridging this gap, thereby contributing to the long-term success of CGSP and the reduction of downstream carbon emissions.
Abstract Due to the lack of a comprehensive evaluation of all stages in the full life cycle of vehicles, including material acquisition, material processing, and operational use, there is insufficient data assessment at various stages. This study proposes two methods, combining life cycle climate performance (LCCP) and hybrid life cycle assessment for electric vehicles, and using life cycle assessment for gasoline vehicles. The former utilizes LCCP to measure the impact of climate on the carbon emissions of electric vehicles, while the latter employs variational mode decomposition for quantitative analysis of data. Experimental results show that when the improved method was applied to carbon emission testing of Wuling Hongguang, the carbon emission was 1045.23 kgCO2e, whereas the Greenhouse Gases, Emissions, and Energy Use in Transportation model evaluated it at 1156.45 kgCO2e, which was higher than the evaluation method proposed in this study. The optimized method evaluates an energy deduction of 15 325 MJ, which is more conservative than the traditional method’s 16 450 MJ. The results above indicate that analyzing electric and gasoline vehicles using different full life cycle methods can further promote the green production capacity of automobile manufacturing. This research contributes to the accurate assessment of vehicles during production and use, enabling the measurement of carbon emission efficiency for both electric and gasoline vehicles.
Abstract Anomaly detection in building energy systems faces significant challenges in extreme climates, where conventional models often fail to distinguish genuine faults from weather-driven fluctuations. This study proposes Multimodal Transformer (MMT)-Adapt, a self-supervised multimodal transformer framework featuring a novel context-aware adaptive thresholding mechanism. Unlike static methods, this mechanism dynamically adjusts sensitivity based on real-time environmental and occupancy cycles. Validated on real datasets from Saudi Arabia, MMT-Adapt achieves a state-of-the-art F1-Score of 0.90 and reduces the false alarm rate to 0.054 (a 56% improvement over long short-term memory baselines). Beyond technical accuracy, the framework’s enhanced resilience reduces undetected energy-wasting faults, offering an estimated 12%–15% saving in annual cooling energy. MMT-Adapt provides a low-carbon solution for smart building management in heat-stressed regions. By reducing unnecessary maintenance visits and optimizing HVAC efficiency, the framework contributes directly to global emission reduction.
Abstract To tackle the challenges of express parcel classification and detection in complex scenarios, this paper proposes a lightweight and low-carbon improved YOLO algorithm (GD-YOLO) based on YOLOv5. Aiming to balance model performance, lightweight deployment, and low-carbon computing requirements, this work integrates energy efficiency optimization strategies into the model design, targeting reduced computational resource usage, minimized carbon footprint, and enhanced detection/classification performance for express parcels. First, the proposed GhostCTRBottleneck module integrates feature fusion mechanisms to comprehensively capture cross-dimensional feature dependencies; this not only simplifies model structure and reduces parameter complexity but also directly cuts energy consumption and carbon emissions during training and inference, laying a foundation for lightweight and low-carbon operation. Second, the Bottleneck_DCN module embeds deformable convolution into the feature fusion pipeline, enhancing the model’s ability to adapt to occluded, noisy, or irregularly shaped express parcels in complex environments while maintaining efficient feature propagation. Quantitative analysis shows that the proposed GD-YOLO reduces training and inference energy consumption and CO2 emissions by approximately 7% compared with YOLOv5s, validating its superior low-carbon and energy-efficient properties. These advantages enable seamless deployment on edge devices with limited energy supply, expanding the application of low-carbon detection technology in logistics scenarios and reducing energy waste from server-side inference. Further experiments on the PASCAL VOC 2012 dataset confirm the method’s generalizability and robustness. Overall, this work realizes a synergistic optimization of lightweight design, low-carbon computing, and feature fusion for express parcel classification and detection, providing an efficient and environmentally friendly technical solution for intelligent logistics.
Abstract In order to study the propagation characteristics of the secondary explosion of coal dust in the pipe network, the diagonal network model was established by using computational fluid dynamics (CFD) software, the process of sedimentary coal dust flying up and subsequent secondary explosion was numerically simulated, which were caused by coal dust explosion at the entrance of the pipeline network. The results showed that after the coal dust was ignited, as the combustion reaction progressed, the explosion shock wave propagated outward and the explosion pressure continued to increase. At each moment, the maximum airflow velocity was always located before the maximum pressure of the shock wave. As the shock wave pressure increased, the airflow velocity in the front flow field increased sharply, and the deposited coal dust was lifted up. The position of the maximum dust concentration was always consistent with the position of the maximum airflow velocity; At a certain moment, the concentration of deposited coal dust increases and reaches the lower limit of the theoretical explosion reaction. When it encounters a flame that propagates to this point and is higher than the ignition temperature of the coal dust, the deposited coal dust begins to participate in the secondary explosion combustion reaction, forming a new secondary explosion source. After the second explosion, due to the influence of combustion reaction and the gravity of coal dust particles themselves, the concentration of coal dust in each section continuously changes, resulting in changes in the combustion reaction rate and flame temperature. At a certain moment, the explosion reaction reaches its strongest state.
Abstract Recent studies on building envelopes in hot and semi-arid climates have mainly focused on static shading or photovoltaic (PV) combination individually, while the combined impact of real-time shading control and building-integrated PV on net energy performance and peak demand remains inadequately quantified. This research investigated a contemporary single-family dwelling under two warm climates, hot-arid (Najaf) and semi-arid/continental (Tashkent) climates, using EnergyPlus simulations. Five scenarios were analyzed to isolate the effects of static shading, closed-loop internet of things-based control, PV utilization, and their combination. Results showed that closed-loop shading reduces cooling demand by about 16% in Najaf. When combined with PV, net site energy (NSE) decreases by approximately 44% and peak grid import drops from 7.5 to 4.8 kW. In Tashkent, the combined strategy reduces NSE by about 20% and peak demand from 6.0 to 4.9 kW while preserving winter solar gains.
Medium-term wind energy production forecasting, spanning time scales from days to several months, is essential for optimizing wind farm integration into electricity generation systems. This optimization leverages renewable energy resources advantages, including a reduced environmental footprint and greater energy independence, while tackling energy price volatility and reducing exposure to potential supply crises. Accurate forecasts are fundamental for modern power systems, enabling effective power generation coordination, maintenance planning, and resource management. As systems increasingly incorporate higher shares of renewable energy sources, challenges such as reduced system flexibility, constrained storage capacity, and escalating operational complexity become more pronounced. This growing complexity amplifies the need for reliable medium-term forecasts, which enable informed planning and decision-making to manage variability and uncertainty and support more flexible and resilient system operation. Wind energy production is inherently variable and highly dependent on local weather conditions, underscoring the necessity for advanced forecasting methods. This study presents a novel methodology that integrates seasonal climate forecast ensembles with wind turbine characteristics and historical wind speed data to generate probabilistic energy forecasts, thereby quantifying and constraining uncertainty. To assess its real-world performance, the methodology was applied to a substation in South Euboea, Greece, which aggregates production from four wind parks. Over a 42-month testing period with seven distinct seasonal forecast datasets, the approach demonstrated a mean absolute percentage error under 17% and produced actionable operational signals, such as a guaranteed minimum production level. These findings underscore the value of seasonal forecasts for developing effective medium-term prediction tools to aid operational decisions.
This study tackles nonlinear programming challenges caused by the dual constraints of sparse, discrete rural e-commerce logistics networks and low-carbon time requirements. The study develops a path optimization model that combines a carbon tax mechanism with dynamic scheduling strategies. Traditional swarm intelligence algorithms often fall into local optima and lose population diversity in high-dimensional discrete spaces. To address these issues, this study proposes an improved Chicken Swarm Optimization (CCSO). It uses a genetic algorithm crossover operator to restructure population information exchange, breaking the unidirectional hierarchical dependence of the original Chicken Swarm Optimization (CSO). This design enhances global search coverage while maintaining convergence accuracy. Benchmark function tests and simulations on the A-n37-k6 case demonstrate that CCSO is robust in solving complex multiobjective constrained problems. Optimized paths can significantly reduce total system operation and carbon emission costs. The approach offers a computational paradigm with both theoretical depth and practical value for precise low-carbon scheduling in rural logistics.
Anhui Province is a major mineral resource area of China, which has facilitated economic growth. With the promotion of nationwide green mine construction, mining has become more scientific and environmentally friendly. According to Geographic Information System (GIS) and remote sensing (RS) technologies with high-resolution RS data, environmental restoration of Anhui Province mines in 2017 was investigated through RS interpretation, interactive verification, and on-site investigations. The study examines restoration characteristics, models, and challenges, providing recommendations to enhance mine management. Conclusions provide critical data and technical references to aid national mine restoration and sustainable use of mineral resources. In addition, this study highlighted the potential contribution of mine restoration (e.g. afforestation and wetland creation) to carbon sequestration, low-carbon development, and renewable energy production, thereby supporting low-carbon development approaches.
Accurate state-of-health (SOH) prediction of lithium-ion batteries is challenging due to nonstationary degradation behavior and multistage aging characteristics. In this study, a hybrid CEEMDAN-temporal convolutional network (TCN)-bidirectional long short-term memory (BiLSTM) framework is adopted for battery SOH estimation. CEEMDAN is used to decompose capacity degradation data into multiscale components, alleviating nonstationarity, while a TCN and a BiLSTM network are employed to capture local temporal features and long-term degradation dependencies, respectively. The framework is evaluated using a publicly available battery aging dataset under multiple operating scenarios, including full charge-discharge cycles, partial charge-discharge conditions, noise-contaminated data, and different discharge current and cutoff voltage settings. Comparative results with benchmark data-driven models show improved prediction accuracy and stability across different scenarios, indicating that the adopted framework is effective for modeling complex battery degradation behavior.
Reliable fixed-speed diesel generator sets remain essential for distributed power, yet exhaust emissions must be mitigated while fuel conversion is preserved. Common rail direct injection (CRDI) split injections control atomization and combustion phasing. A duty-cycle-anchored calibration that reconciles brake thermal efficiency (BTE) with NOx under fixed-speed genset duty remains insufficiently quantified. This study aims to identify a pilot-main split window that raises BTE while constraining NOx, smoke, and hydrocarbons during the ISO 8178 D2 duty cycle. A single-cylinder CRDI engine was operated at 1500 rpm across the ISO 8178 D2 modes, and a fixed pilot share was selected by screening. A central composite design within response surface methodology varied the injection pressure from 600 to 1000 bar and pilot timing from 20 to 30 degrees CA bTDC, and ANOVA ranked the factor effects. The response surfaces indicate that BTE ranged from 28.2% to 39.59% across the design space, while NOx ranged from 876 to 1494 ppm. The desirability solution identifies 1000 bar and 20 degrees CA bTDC, at which BTE was 36.868% and NOx was 1153.21 ppm. These results support implementable calibration guidance for stationary service and enable an INR-based fuel-cost index per kilowatt-hour. Future innovation integrates closed-loop injection control and mass-based emission conversion to grams per kilowatt-hour for compliance reporting, thereby increasing energy efficiency.
Abstract Accurately predicting the coefficient of performance (COP) in vapor compression refrigeration systems demands accounting for numerous thermodynamic and operational variables, frequently resulting in intricate and protracted analytical expressions. To mitigate this complexity, this research explores the utility of Artificial Neural Network (ANN) and Adaptive Neuro-Fuzzy Inference System (ANFIS) models for predicting the performance of an R600a-based domestic refrigerator employing Al₂O₃/SiO₂ hybrid nanolubricants. The experimental findings reveal that the baseline system, running without nanolubricants, demonstrates a COP of 2.5; this value rises to 2.79 upon the incorporation of 0.4 g/l of Al₂O₃/SiO₂ hybrid nanolubricants. Under identical operating conditions and refrigerant charge, the ANN model predicts an improved COP of 3.4 accompanied by mitigated compressor power consumption. In comparison, the ANFIS model further enhances the COP to 3.5 while simultaneously achieving a lower compressor work of ~100 W as well as a refrigeration influence of 215 W at the optimal nanolubricant concentration of 0.4 g/l and a refrigerant charge of 70 g. Additionally, ANFIS predictions suggest a decrease in compressor work to roughly 100 W. The maximum predicted COP of 3.5 is attained through the ANFIS methodology, exceeding both ANN predictions and experimental findings. In addition, the ANFIS model presents a considerably lower training error of 0.29901, hence validating its superior accuracy and durability in predicting cooling system performance.
Abstract Municipal wastewater discharge threatens aquatic environments, requiring sustainable, low-cost remediation. The efficacy of floating wetland (FW) treatment systems employing Hibiscus rosa-sinensis and Chrysopogon zizanioides (CZ) for municipal wastewater remediation was examined. Each system received 120 l of raw municipal wastewater for 30 days of hydraulic retention. Monitoring water quality parameters, such as pH, dissolved oxygen, total suspended solids, total dissolved solids, biochemical oxygen demand, chemical oxygen demand (COD), nutrients, and microbial indicators, was conducted at regular intervals and two sampling depths. Vegetated systems removed pollutants far better than the control system. CZ removed COD, total nitrogen, and total phosphorus over 70%. The results show that FW systems may treat municipal wastewater efficiently and sustainably.
Abstract This study investigates the impact of exterior door operations on energy consumption in kindergarten buildings, with a focus on how door-opening behavior affects indoor–outdoor environmental interactions and the resulting energy loss. Through computational fluid dynamics simulations and field data collection, the research reveals the significant role of wind pressure, airflow rates, and door configurations in determining the energy efficiency of the building. The findings indicate that simultaneous opening of multiple exterior doors leads to substantial energy loss, while configurations with fewer open doors (e.g. combination C6) significantly reduce energy loss. Wind direction and the relative positioning of doors are shown to strongly influence airflow patterns and indoor air quality, highlighting the importance of considering these factors when optimizing door usage strategies. Additionally, the study emphasizes the interaction between building orientation, prevailing wind direction, and wind pressure distribution on exterior doors, which collectively govern airflow dynamics and energy loss. The results suggest that intelligent management of door openings, tailored to real-time wind conditions, can effectively reduce the demand for mechanical heating, thereby improving overall building energy efficiency. However, this study has limitations, such as the exclusion of vestibules or buffer spaces, which are known to reduce air infiltration. Furthermore, the simulation did not account for interactions between internal spaces, such as corridors, or the impact of interior door openings on energy loss. These factors represent areas for further investigation to enhance the accuracy and comprehensiveness of future studies on energy efficiency in educational buildings. This research provides valuable insights into how exterior door operations can influence energy performance in kindergartens, particularly in cold climates, and offers theoretical support for optimizing energy-saving strategies in similar buildings.
Dual-channel fusion module leverages a channel attention mechanism to achieve cross-channel fusion and enhancement between depth and intensity features, effectively improving the expression of critical information. Experimental results on a representative power equipment image dataset demonstrate that the proposed method outperforms existing approaches such as Reciprocal Rank Fusion (RRF), joint bilateral upsampling, and Depth-Net in terms of peak signal-to-noise ratio (PSNR) and root mean square error (RMSE). In particular, for 4 & times; super-resolution reconstruction tasks, the method achieves an average PSNR improvement of 6.79 dB and an average RMSE reduction of 0.94, validating the effectiveness and superiority of the proposed approach in power equipment image data extraction and reconstruction.
Evaluating urban low-carbon development is essential for advancing China's dual-carbon strategy, yet existing approaches face challenges in handling ambiguity, indicator correlation, and decision bias. This study develops an integrated multicriteria evaluation framework under Fermatean fuzzy (FF) environments. The framework incorporates an improved FF bidirectional projection method, a modified criteria importance through intercriteria correlation weight allocation mechanism, and regret theory to mitigate similarity misjudgment, information distortion, and bounded rationality. An empirical study of five Chinese cities verifies the effectiveness of the proposed model. Moreover, the sensitivity analysis confirms the robustness of the method under different parameter settings, and the comparative analysis further demonstrates the superiority of the proposed approach. The findings indicate that the framework yields stable and discriminative evaluation results, offering methodological advancements and practical insights for assessing urban low-carbon transitions.
