Search Results (424)

This study is situated at the critical stage of comprehensive implementation of China’s territorial spatial planning system, addressing the strategic need for planning evaluation and optimization. We innovatively construct a Computable General Equilibrium Model for China’s Territorial Spatial Planning (CTSPM-CHN) that integrates dual factors of construction land costs and energy consumption costs. Through designing two policy scenarios of rigid constraints and structural optimization, we systematically simulate and evaluate the dynamic impacts of different territorial spatial governance strategies on macroeconomic indicators, residents’ welfare, and carbon emissions, revealing the multidimensional effects and operational mechanisms of territorial spatial planning policies. The findings demonstrate the following: First, strict implementation of land use scale control from the National Territorial Planning Outline (2016–2030) could reduce carbon emission growth rate by 12.3% but would decrease annual GDP growth rate by 0.8%, reflecting the trade-off between environmental benefits and economic growth. Second, industrial land structure optimization generates significant synergistic effects, with simulation results showing that by 2035, total GDP under this scenario would increase by 4.8% compared to the rigid constraint scenario, while carbon emission intensity per unit GDP would decrease by 18.6%, confirming the crucial role of structural optimization in promoting high-quality development. Third, manufacturing land adjustment exhibits policy thresholds: moderate reduction could lower carbon emission peak by 9.5% without affecting economic stability, but excessive cuts would lead to a 2.3 percentage point decline in industrial added value. Based on systematic multi-scenario analysis, this study proposes optimized pathways for territorial spatial governance: the planning system should transition from scale control to a structural optimization paradigm, establishing a flexible governance mechanism incorporating anticipatory constraint indicators; simultaneously advance efficiency improvement in key sector land allocation and energy structure decarbonization, constructing a coordinated “space–energy” governance framework. These findings provide quantitative decision-making support for improving territorial spatial governance systems and advancing ecological civilization construction. Full article

In this study, the technical, economic, and environmental performances of a Linear Fresnel Reflector (LFR) integrated with an Organic Rankine Cycle (ORC), designed with a non-storage approach, and a monocrystalline photovoltaic (PV) system were comparatively evaluated in meeting a building’s 10 kW electricity demand. Solar-based electricity generation systems play a critical role in reducing carbon emissions and increasing energy self-sufficiency in buildings, yet small-scale, storage-free LFR-ORC applications remain relatively underexplored compared to PV systems. The optimal areas for both systems were determined using the P₁–P₂ methodology. The electricity generation of the LFR-ORC system was calculated based on experimentally measured thermal power output and ORC efficiency, while the production of the PV system was determined using panel area, efficiency, and measured solar irradiation data. System performance was assessed through self-consumption and self-sufficiency ratios, and the economic analysis included life cycle savings (LCS), payback period, and levelized cost of electricity (LCOE). The results indicate that the PV system is more advantageous economically, with an optimal payback of 4.93 years and lower LCOE of 0.053 €/kWh when the economically optimal panel area is considered. On the other hand, the LFR-ORC system exhibits up to 35% lower life-cycle CO₂ emissions compared to grid electricity under grid-connected operation (15.86 tons CO₂-eq for the standalone LFR-ORC system versus 50.57 tons CO₂-eq for PV over 25-year lifetime), thus providing superiority in terms of environmental sustainability. In this context, the study presents an engineering-based approach for the technical, economic, and environmental assessment of small-scale, non-storage solar energy systems in line with the United Nations Sustainable Development Goals (SDG 7: Affordable and Clean Energy and SDG 13: Climate Action) and contributes to the existing literature. Full article

To address the conflicts among objectives and the decision-making challenges in the multi-condition adaptive design of battery boxes for new energy vehicles, this study proposes a multi-objective collaborative optimization method based on an improved relaxation factor, aiming to achieve a comprehensive enhancement in both structural lightweighting and mechanical performance. A finite element model of the CTB high-strength steel roll-formed battery box was established and validated through modal testing. According to the Chinese National Standard GB 38031-2025, the mechanical responses of the battery box under random vibration, extreme operating conditions, and impact loads were analyzed to identify performance weaknesses. Sensitivity analysis was conducted to screen the design variables, and an improved relaxation factor strategy based on weight distribution difference information was introduced to construct a multi-objective collaborative optimization model. Furthermore, the entropy-weighted TOPSIS method was employed to enable intelligent decision-making on the Pareto solution set. The results demonstrate that the proposed method outperforms conventional approaches in both convergence speed and solution distribution uniformity. After optimization, the mass of the battery box was reduced by 12.38%, while multiple mechanical performance indicators were simultaneously improved, providing valuable theoretical and engineering guidance for the structural design of power battery systems. Full article

Pragmatic sustainability emphasizes that policies must adapt to the reality of multi-level governance to balance targets and feasibility. To explore how this concept is embodied in China’s carbon peaking policies, this study adopted natural language processing (NLP) and machine learning methods to conduct a systematic quantitative analysis of 316 carbon peaking policy documents spanning from the national to county levels in China. The findings reveal that the policy system presented a distinct logic of pragmatic coordination. The application of legal instruments decreased with descending administrative levels, whereas that of supervision instruments showed the opposite trend; central-level targets were more flexible, while local governments demonstrated higher policy intensity in specific targets and livelihood-related sectors. The regional differences in policy intensity were closely associated with local economic development and energy structure, indicating that future policy optimization should more thoroughly implement the principle of common but differentiated responsibilities in target decomposition and dynamic adjustment. This study not only provides a novel quantitative perspective for investigating pragmatic sustainability in carbon peaking policy texts but also offers critical empirical evidence for synergistically advancing SDG 13 (climate action) with other SDGs. Full article

In the era of digital transformation, transaction-intensive e-business applications—such as high-frequency trading (HFT), e-monetary services and decentralized marketplaces—require infrastructures that are not only fast and secure but also sustainable. Current solutions often prioritize short-term performance over long-term resilience, leading to inefficiencies in energy use and system reliability. This paper introduces a conceptual framework for sustainable transaction processing, leveraging energy-efficient hardware accelerators, real-time communication protocols inspired by industrial automation and lightweight authentication mechanisms. By integrating associative memory-based matching engines and optimized network architectures, the proposed approach ensures predictable latency, robust security and scalability without compromising sustainability. The framework aligns with the United Nations Sustainable Development Goal 9 (Industry, Innovation, and Infrastructure) by reducing resource consumption, enhancing operational resilience and supporting future-ready digital ecosystems. Full article

This study aims to develop a methodology for implementing solar photovoltaic systems (SSFV) in Caribbean hotels. It begins with an analysis of building characteristics to design and size the SSFV, considering panel support structures, system layout, and grid integration. The methodology also evaluates economic and environmental impacts at both company and national levels. Machine learning analysis identified the variables (Degree Days (DG) and Hotel Days Occupied (HDO)) $HDO\times DG$ as key determinants of energy consumption, with a high coefficient of determination ($R^2$ = 0.97). Implementing a target energy-saving line achieved a 5.3% reduction (1028 kWh) relative to the baseline. Using a genetic algorithm to optimize the SSFV azimuth angle increased photovoltaic energy production by 14.75%, enhancing efficiency and installation area use. Economic assessments showed a challenging scenario for hotels, with a negative internal rate of return of −10%, a 17 year payback period, and a net present value of USD 20,000. However, on a national scale, significant annual savings of USD 225,990.8 from reduced fuel imports were projected. Additionally, carbon emissions reductions of 18,751.4 tons ($tC{O}_2$) were estimated. The findings highlight the feasibility and benefits of SSFV implementation, emphasizing its potential to improve energy efficiency, reduce costs, and enhance sustainability in the Caribbean hotel sector. Full article

Cheesemaking is an energy-intensive process that relies heavily on heating and cooling operations traditionally powered by fossil fuels and electricity from the national grid. Reducing this dependence and integrating renewable energy sources are essential to align the sector with European decarbonization targets. This study presents the development of a simulation tool for optimizing the energy management of a cheese production facility by integrating solar, wind, and biomass systems. The model evaluates techno-economic and environmental performance under different climatic conditions and operational scenarios. Experimental validation was carried out using a prototype installed at the Polytechnic Institute of Beja (Portugal), achieving a deviation of only 2.3% in renewable energy contribution between simulated and measured data. Results demonstrate that renewable integration can reduce non-renewable energy consumption, achieving weekly profits up to 0.019 €/kg of cheese and carbon emissions as low as 0.0109 kg CO₂e/kg. The proposed approach provides a reliable decision-support tool for small- and medium-scale cheese producers, promoting both environmental sustainability and economic competitiveness in rural regions. Full article

The accelerating pace of urbanization and the pressing need for sustainability have compelled cities worldwide to integrate renewable energy into their infrastructure. While solar, wind, and hydro sources offer cleaner alternatives to fossil fuels, their inherent variability creates challenges in maintaining balance between supply and demand in urban energy systems. Traditional statistical forecasting methods are often inadequate for capturing the nonlinear, weather-driven dynamics of renewables, highlighting the need for advanced artificial intelligence (AI) approaches that deliver both accuracy and interpretability. This paper proposes a spatio-temporal framework for smart city energy management that combines a Convolutional Neural Network with Long Short-Term Memory (CNN-LSTM) for renewable energy generation forecasting, a Gradient Boosting Machine (GBM) for urban demand prediction, and Particle Swarm Optimization (PSO) for cost-efficient energy allocation. The framework was first validated using Spain’s national hourly energy dataset (2015–2018). To rigorously test its generalizability, the methodology was further validated on a separate dataset for the German energy market (2019–2022), proving its robustness across different geographical and meteorological contexts. Results indicate strong predictive performance, with solar generation achieving a 99.03% R² score, wind 96.46%, hydro 93.02%, and demand forecasting 91.56%. PSO further minimized system costs, reduced reliance on fossil-fuel generation by 18.2%, and improved overall grid efficiency by 12%. These findings underscore the potential of AI frameworks to enhance reliability and reduce operational costs. Full article

This study presents the design, real-time implementation, and full-scale experimental validation of a rule-based Energy Management Strategy (EMS) for a dual-stack Fuel Cell Hybrid Electric Vehicle (FCHEV) developed on a Jeep Wrangler platform. Unlike previous studies, predominantly focused on simulation-based analysis or single-stack architectures, this work provides comprehensive vehicle-level experimental validation of a deterministic real-time EMS applied to a dual fuel cell system in an SUV-class vehicle. The control algorithm, deployed on a National Instruments CompactRIO embedded controller, ensures deterministic real-time energy distribution and stable hybrid operation under dynamic load conditions. Simulation analysis conducted over eight consecutive WLTC cycles shows that both fuel cell stacks operate predominantly within their optimal efficiency range (25–35 kW), achieving an average DC efficiency of 68% and a hydrogen consumption of 1.35 kg/100 km under idealized conditions. Experimental validation on the Wrangler FCHEV demonstrator yields a hydrogen consumption of 1.67 kg/100 km, corresponding to 1.03 kg/100 km·m² after aerodynamic normalization (Cd·A = 1.624 m²), reflecting real-world operating constraints. The proposed EMS promotes fuel-cell durability by reducing current cycling amplitude and maintaining operation within high-efficiency regions for the majority of the driving cycle. By combining deterministic real-time embedded control with vehicle-level experimental validation, this work strengthens the link between EMS design and practical deployment and provides a scalable reference framework for future hydrogen powertrain control systems. Full article

Under the national carbon peaking and carbon neutrality goals, electrical equipment plays a crucial role in energy production, transmission, and end-use systems, making the research on its lifecycle carbon emissions and mitigation strategies particularly significant. Based on the Life Cycle Assessment (LCA) framework, this review systematically examines carbon emission characteristics across raw material acquisition, manufacturing, transportation, usage, and end-of-life recycling stages of electrical equipment. The analysis indicates that the manufacturing and usage stages are generally the main contributors to total lifecycle emissions. Moreover, challenges such as incomplete carbon data, inconsistent boundary definitions, and insufficient recycling systems are highlighted. Correspondingly, this review summarizes key mitigation pathways, including low-carbon design and material optimization, low-carbon manufacturing processes, energy-efficient operation supported by intelligent monitoring, and enhanced recycling and remanufacturing practices. Finally, future perspectives are proposed, emphasizing the need to establish unified LCA databases, develop intelligent and data-driven carbon monitoring systems, and strengthen cross-sector collaboration to support the green and low-carbon transformation of electrical equipment industries. Full article

The environmental crisis precipitated by climate change has accelerated the urgency of urban green and low-carbon transformation. In 2024, China’s Action Plan for the National Standardization Development Outline (2024–2025) stipulated requirements for continuously improving the standard system for carbon peaking and carbon neutrality in public institutions. As key venues for knowledge innovation and energy consumption, the low-carbon transformation of higher education institutions holds significant importance for China’s achievement of its dual carbon goals. However, China lacks a systematic evaluation framework specifically designed for university campus carbon emissions. Existing green campus assessment standards often suffer from inadequate indicator adaptability, a lack of update mechanisms, and limited coverage. The STARS sustainability assessment system, widely adopted in North America, offers valuable reference points for establishing campus carbon emissions evaluation frameworks due to its features of indicator adaptability, dynamic update mechanisms, and comprehensive evaluation dimensions. This paper conducts an exploratory comparative case study of Princeton University (USA) and Tianjin University (China)—two leading research-intensive institutions—within the STARS 2.2 framework. It systematically analyses their divergent approaches to carbon management and evaluation, not as representatives of their respective continents, but as exemplars of how advanced universities operationalize low-carbon transitions. Based on this analysis and a review of domestic Chinese standards, it proposes a development pathway for China’s university campus carbon emissions evaluation system: (1) Establish a differentiated indicator system combining ‘universal fundamentals with discipline-specific types’ to enhance adaptability to campus characteristics; (2) Establish a mechanism for periodic version updates to the evaluation standard itself, ensuring alignment with evolving national carbon goals and technological advancements; (3) Develop a comprehensive and transparent carbon accounting framework that integrates direct emissions, purchased energy, and indirect sources. This research provides theoretical foundations and methodological support for institutional development and practical optimization in carbon emissions evaluation within Chinese higher education institutions. Full article

The transition to net-zero energy solutions in university campuses is essential for advancing sustainability and enhancing energy efficiency. This paper presents a mathematical optimization model for implementing net-zero energy strategies in Saudi Arabian universities, focusing on Imam Mohammad Ibn Saud Islamic University (IMSIU) as a case study. An administration building within IMSIU campus, using real operational data with daily peak loads of 900 kW, are analyzed to determine optimal configurations of renewable and storage systems. Simulation results show that optimally deploying a 3,500 kW photovoltaic array integrated with a 560 kW/2,800 kWh battery energy storage system can effectively meet building-level energy demands and achieve seasonal net-zero balance during both winter and summer periods. The model demonstrates a substantial reduction in grid dependency while promoting the integration of renewable energy resources, showing strong alignment with the targets of the Saudi Green Initiative and national pathways for accelerating renewable energy deployment and energy sustainability. The findings provide a scalable and replicable framework for universities seeking to transition toward net-zero readiness, promoting sustainability in higher education and supporting the broader national goal of carbon-neutral development. Full article

South Africa’s persistent electricity shortages and recurrent load shedding remain among the most pressing challenges to national economic growth and social stability. This paper presents a techno-economic framework to assess how optimized deployment of photovoltaic (PV) and battery energy storage systems (BESSs) can mitigate these disruptions under realistic grid and regulatory constraints. Despite recent operational improvements at Eskom—including a 10-month period without load shedding in 2024—energy insecurity persists due to aging coal assets, limited transmission capacity, and slow renewable integration. Using hourly demand and solar-resource data for 2023, combined with Eskom’s load-reduction records, a Particle Swarm Optimization (PSO) model identifies cost-optimal hybrid system configurations that minimize the Levelized Cost of Electricity (LCOE) while maximizing coverage of unserved energy. Three deployment scenarios are analyzed: (i) constrained regional grid capacity, (ii) flexible redistribution of capacity across six provinces, and (iii) unconstrained national deployment. Results indicate that constrained deployment covers about 86% of curtailed load at 1.88 USD kWh⁻¹, whereas flexible and unconstrained scenarios achieve over 99% coverage at ≈0.58 USD kWh⁻¹. The findings demonstrate that targeted PV–BESS expansion, coupled with selective grid reinforcement, can effectively eliminate load shedding and accelerate South Africa’s transition toward a resilient, low-carbon electricity system. Full article

As critical national energy arteries, long-distance large-scale cross-regional gas transmission networks are characterized by high operating pressures, extensive spatial coverage, and complex topological structures. Thus, the multi-hazard profiles threatening its safety and reliability operation differ significantly from those of local urban gas distribution networks. This research develops a resilience assessment framework capable of quantifying resistance, adaptation, and recovery capacities of such energy systems. The framework establishes performance indicator systems based on design parameters, installation environments, and construction methods for long-distance trunk pipelines and key facilities such as storage facilities. Furthermore, based on complex network theory, the size of the largest connected component and global efficiency of the transmission network are selected as core topological metrics to characterize functional scale retention and transmission efficiency under disturbances, respectively, with corresponding quantification methods proposed. A cross-regional pipeline transmission network within a representative municipal-level administrative region in China is used as a case for empirical analysis. The quantitative assessment results of pipeline and network resilience are analyzed. The research indicates that trunk pipeline resilience is significantly affected by characteristic parameters, the laying environment, and installation methods. It is notably observed that installation methods like jacking and directional drilling, used for road or river crossings, offer greater resistance than direct burial but considerably lower restoration capacity due to the complexity of both the environment and the repair processes, which increases time and cost. Moreover, simulation-based comparison of recovery strategies demonstrates that, in this case, a repair-time-prioritized strategy more effectively enhances overall adaptive capacity and restoration efficiency than a node-degree-prioritized strategy. The findings provide quantitative analytical tools and decision-support references for resilience assessment and optimization of cross-regional energy transmission networks. Full article

Resource-based regions play an indispensable role as strategic bases for national energy and raw material supply in the global industrialization and urbanization process. However, intensive and large-scale natural resource exploitation—particularly mineral extraction—often triggers dramatic land use/cover changes, leading to a series of problems including cultivated land degradation, ecological function deterioration, and human settlement environment degradation. However, a systematic understanding of the functional transitions within the land use system and their drivers in such regions remains limited. This study takes Shenmu City, a typical resource-based city in the ecologically vulnerable Loess Plateau, as a case study to systematically analyze the transition characteristics and driving mechanisms of land use functions from 2000 to 2020. By constructing an integrated “element–structure–function” analytical framework and employing a suite of methods, including land use transfer matrix, Spearman correlation analysis, and random forest with SHAP interpretation, we reveal the complex spatiotemporal evolution patterns of production–living–ecological functions and their interactions. The results demonstrate that Shenmu City has undergone rapid land use transformation, with the total transition area increasing from 27,394.11 ha during 2000–2010 to 43,890.21 ha during 2010–2020. Grassland served as the primary transition source, accounting for 66.5% of the total transition area, while artificial surfaces became the main transition destination, receiving 38.6% of the transferred area. The human footprint index (SHAP importance: 4.011) and precipitation (2.025) emerged as the dominant factors driving land use functional transitions. Functional interactions exhibited dynamic changes, with synergistic relationships predominating but showing signs of weakening in later periods. The findings provide scientific evidence and a transferable analytical framework for territorial space optimization and ecological restoration management not only in Shenmu but also in analogous resource-based regions facing similar development–environment conflicts. Full article