Search Results (3,879)

In response to global climate change, low-carbon transition in the industrial sector has become essential for emission reduction. Industrial parks, as concentrated centers of production, are major sources of urban energy use and carbon emissions. Whether park-based policy interventions can generate broader decarbonization effects remains unclear. This study conceptualizes China’s National Low-Carbon Industrial Park Pilot Policy (NLCIPP) as a meso-level systemic intervention and examines its impact on urban carbon intensity (UCI). Using panel data for 282 Chinese cities from 2006 to 2020, causal effects are identified through a multi-period DID framework combined with a synthetic DID approach. The results show that the NLCIPP significantly reduces UCI, indicating that energy-oriented interventions at the industrial park level can induce broader decarbonization outcomes. The policy effect mainly works via reduced energy consumption and enhanced green technological capability, while the contribution of industrial structural upgrading is relatively limited. Stronger impacts appear in central regions, cities with stricter environmental regulation, and non-resource-based cities, highlighting the context-dependent effectiveness of energy transition policies. These findings provide empirical evidence for designing effective industrial energy policies to promote low-carbon transition. Full article

Accurate freeway Improvements in traffic state prediction accuracy and enhanced stability enable more proactive traffic control and demand management strategies, thereby reducing congestion spillover effects, unnecessary acceleration–deceleration cycles, and the resulting fuel consumption and emissions. Yet, this remains challenging due to the interplay between deterministic traffic flow mechanisms and stochastic disturbances. Purely data-driven models suffer from error accumulation under out-of-distribution conditions, while physics-based models lack flexibility in capturing nonlinear deviations. This paper proposes MDURP, a physics-constrained residual learning framework that reformulates prediction as a residual-space learning problem. A calibrated Cell Transmission Model generates a physically admissible baseline; deep learning models are then restricted to learning the residuals. Wavelet decomposition and GARCH volatility modeling address the multi-scale and heteroskedastic characteristics of these residuals. Experimental results demonstrate that MDURP consistently outperforms baseline models, reducing MAE by an average of 6.8%, RMSE by an average of 4%. The framework also suppresses long-term error accumulation, with MAPE escalation slowing from 0.79% to 0.58% per step. These gains confirm that anchoring deep learning within a physics-defined residual space enhances both accuracy and stability. Full article

This research addresses the urgent need to reduce carbon emissions in the footwear manufacturing industry by utilizing digital twin technology with project management frameworks. It focuses on identifying critical emission sources across the entire life cycle of shoe production from (i) material sourcing, (ii) manufacturing, and (iii) transportation, to (iv) end-of-life disposal. By data collection, infusing project management, and integrating digital twin approaches, the study offers a dynamic, data-driven method to simulate, monitor, and optimize carbon reduction strategies in real time. An extensive literature review and industry data analysis informs the assessment of carbon emissions and energy consumption patterns. Based on these insights, a tailored project management approach is followed to analyze the feasibility of the footwear sector to adopt sustainable practices such as renewable energy adoption, eco-friendly material sourcing, and closed-loop production systems. Validation was conducted using plant simulation software to model emissions scenarios and evaluate the effectiveness of proposed interventions. Case studies from leading brands, including Nike, Adidas, and Puma, were examined for Scope 1, 2 and 3, to extract the best practices and strategic insights. The research underscores the importance of combining digital tools with sustainability goals to create an environmentally conscious manufacturing ecosystem, highlights the role of policymakers in incentivizing green practices, and emphasizes collaborative industry efforts to accelerate change. The paper concludes by highlighting that digital twin systems provide effective, scalable solutions for reducing carbon emissions in footwear manufacturing. Full article

The operation of the gas–steam–electricity multi-energy coupling system in iron and steel enterprises faces critical challenges: conflicts between energy efficiency and economic objectives, insufficient scheduling accuracy, and low energy utilization caused by source–load fluctuations. To address these issues, this paper proposes a hybrid scheduling model based on condition awareness and multi-objective optimization. The model integrates three key components. First, an energy fluctuation prediction technology based on working condition changes is developed. By acquiring real-time production signals and gas flow data, combined with a condition definition management module, it enables automatic identification and tracking of equipment operation status. A working condition sample curve superposition method is used to calculate energy medium imbalances, generating visual prediction curves for key parameters such as blast furnace, coke oven, and converter gas holder levels, achieving an average prediction accuracy of ≥95%. Second, a peak-shifting and valley-filling scheduling model for gas holders is designed, leveraging time-of-use electricity prices. During valley price periods, power purchases are increased and surplus gas is stored; during peak price periods, gas power generation is increased to reduce purchased electricity. A nonlinear model capturing the load–efficiency relationship of boilers and generators is established to dynamically optimize scheduling strategies. This reduces the proportion of peak hour power purchases by 10.3%, energy costs by 3.12%, and system energy consumption by 2.16%. Third, a multi-period and multi-medium energy optimization scheduling model is formulated as a mixed-integer nonlinear programming (MINLP) problem, with dual objectives of minimizing operating cost and energy consumption. Constraints include energy supply–demand balance, equipment operating limits, gas holder capacity, and generator ramp rates. The Pareto optimal solution set is obtained using the AUGMECON2 method and efficiently computed with the IPOPT solver. Application results demonstrate that the model achieves zero gas emissions, a dispatching instruction accuracy of 95%, and a 0.8% increase in the proportion of peak–valley-level self-generated power, outperforming comparable technologies. It provides technical support for the safe, efficient, and economic operation of multi-energy systems in iron and steel enterprises. Full article

Supersulfated cement (SSC) is an environmentally friendly cementitious material with a low clinker content, in which industrial byproduct gypsum serves as the sulfate source, thereby enabling the valorization of solid waste. The hydration process, pore structure, microstructure, and hydration products were investigated using paste samples by means of isothermal calorimetry, X-ray diffraction (XRD), thermogravimetric analysis (TG–DTG), Fourier transform–infrared spectroscopy (FT-IR), mercury intrusion porosimetry (MIP), and scanning electron microscopy (SEM), while compressive strength was evaluated using mortar specimens. Compared with ordinary Portland cement (OPC), SSC offers clear advantages in reducing energy consumption and greenhouse gas emissions. In this study, the effects of titanium gypsum (TG) and flue gas desulfurization gypsum (FGD) on the hydration behavior, fluidity, mechanical properties, and microstructural evolution of an anhydrite (AH)–phosphogypsum (PG)-based SSC were systematically investigated. The results indicate that the incorporation of 11% TG and FGD mitigates the strong sulfate environment caused by the rapid dissolution of soluble AH, thereby regulating the hydration process. As the proportion of TG and FGD increased, the cumulative heat release within 72 h gradually decreased. When AH was completely replaced, the cumulative heat release of TG4 and FG4 decreased by approximately 19.7% and 28.6%, respectively. TG and FGD exhibited opposite effects on the fluidity of SSC while both promoting strength development. Among all mixtures, TG2 and FG2 showed the best performance, with the highest 28-day compressive strengths of 50.15 MPa and 51.95 MPa, respectively. Microstructural analysis reveals that differences in particle size distribution and dissolution kinetics among gypsums governed the sulfate release characteristics and slag activation mechanisms, thus leading to distinct hydration pathways, pore structure evolution, and microstructural densification. This study provides a theoretical basis for the efficient utilization of various industrial byproduct gypsums and offers important guidance for the controllable design of SSC performance. Full article

Energy Management Strategies (EMSs) are crucial for enhancing fuel economy and reducing emissions in light commercial vehicles (LCVs). This paper presented three EMS approaches for LCVs with hybrid powertrains: Rule-Based Control (RBC) and two optimization-based strategies, the Equivalent Consumption Minimization Strategy (ECMS) and Model Predictive Control (MPC). To enhance robustness under varying operating conditions, optimization algorithms were designed and tuned using the WLTC City driving cycle, and adaptive components were included. For a fair assessment of overall efficiency, all strategies were compared under identical constraints on hydrogen and electrical energy consumption. The results showed that, under these constraints, MPC achieved the longest driving distance, highlighting its superior energy utilization capability. In a broader comparative analysis, both the ECMS and MPC outperformed the benchmark RBC, with MPC demonstrating the most consistent performance, enhanced stability, and strong adaptability in dynamic scenarios. The findings indicate that MPC offers notable advantages for LCV energy management, combining efficiency, robustness, and interpretability, positioning it as a promising candidate for practical implementation in future hybrid powertrain systems. Full article

Soil respiration (Rs) plays a pivotal role in carbon cycling within semi-arid ecosystems. In our millet field experiment, we measured Rs, autotrophic respiration (Ra), heterotrophic respiration (Rh), water consumption (ET), yield (Y), water use efficiency (WUE), and key soil environmental properties to examine the effects of supplemental irrigation and selenium application on Rs dynamics and to clarify the controlling factors. The experiment was conducted from 2023 to 2024 with four treatments and three replicates per treatment each year. These treatments comprised conventional rainfed (CK), supplemental irrigation (SI, 50 mm), rainfed with Se addition (CS, 67.84 g·hm⁻²), and supplemental irrigation with Se addition (SIS). SI increased CO₂ emissions in the millet field, whereas selenium application (CS) suppressed them. Ra was the dominant component of Rs and was 1.03–4.01 times greater than Rh. SI and CS significantly affected cumulative CO₂ emissions through Ra (p < 0.05), whereas their effects on Rh were minor. The CS treatment resulted in the lowest cumulative CO₂ emissions at 4233 and 4009 g·m⁻² in 2023 and 2024, respectively. Diurnal variation patterns of Rs, Ra, and Rh differed across millet growth stages. Both supplemental irrigation and selenium application improved soil water retention, soil enzyme activity, and soil organic matter (SOM), and moderated soil temperature. Classification and Regression Tree (CART) algorithm analysis revealed that Ra was primarily driven by soil temperature, with a feature weight of 86.95% determined by CART based on machine learning, whereas Rh was mainly influenced by soil enzyme activity, with a feature weight of 76.11%. The CS treatment enhanced production while promoting emission mitigation. The combined SIS treatment achieved the highest WUE and maintained a lower Rs than SI. These findings suggest an environmentally sustainable management strategy for millet production in semi-arid regions. However, due to the limited number of parcels in this study, further field-scale validation and additional experimental research involving multiple levels of supplemental irrigation and Se addition are necessary. Full article

Climate change poses a growing threat to global health, yet healthcare systems contribute substantially to environmental harm through energy use, waste, and greenhouse gas (GHG) emissions. Among hospital departments, Intensive Care Units (ICUs) are among the most resource- and energy-intensive, generating disproportionately high greenhouse gas (GHG) emissions. The aim of this systematic review is to synthesize the literature on the environmental footprint of ICUs and to develop evidence-based strategies for creating sustainable ‘Green ICUs’ in accordance with the PRISMA 2020 guidelines. Peer-reviewed studies published between 2012 and October 2025 were identified through searches of major biomedical databases. Eligible studies examined the impacts of climate change on human health and infectious diseases, the ecological footprint of medical imaging and personal protective equipment, and sustainability interventions relevant to adult intensive care units. The environmental footprint of ICUs ranges from 88 to 178 kg CO₂-equivalents per patient per day. High electricity consumption, especially from heating, ventilation, and air-conditioning (HVAC) systems, along with single-use medical supplies and diagnostic imaging, drives this impact. Life-cycle assessments consistently demonstrate that reusable textiles, optimized energy systems, and rationalized diagnostic practices significantly reduce emissions and waste. Educational and behavioral interventions were effective in reducing unnecessary consumable use while maintaining patient safety. A “Green ICU” model integrating energy efficiency, sustainable procurement, waste reduction, and staff education can substantially reduce environmental harm without compromising quality of care. Full article

Since 2021, the design and construction of nearly zero-energy buildings (nZEBs) have been mandatory for European Union Member States. Subsequent requirements for the building sector, characterized by high energy demand and significant environmental impact, include the minimization of carbon footprint and the introduction of climate-neutral building standards. The carbon footprint comprises both embodied emissions related to materials and construction processes and operational emissions resulting from building use. This paper analyzes both types of carbon footprint using a residential building that is part of an experimental housing estate consisting of 44 semi-detached buildings as a case study. Analyses of energy consumption optimization and carbon footprint reduction were conducted at both the individual building scale and the scale of the entire housing complex. The estate was developed in two stages. In the first stage (completion of construction in 2024), the primary criterion for technology selection was investment cost while maintaining compliance with applicable technical and building regulations. Prior to the implementation of the second stage, the investor conducted a social participation process in the form of a survey among future users. The survey addressed environmental aspects of the newly designed buildings and enabled the selection of materials, technologies, and energy sources aligned with user preferences. The results indicate that environmental aspects are important to future users; however, investment decisions are strongly balanced against economic factors. At the same time, the energy analyses demonstrate that a substantial reduction in the operational carbon footprint can be achieved, enabling a significant progression toward climate neutrality, both at the level of individual buildings and across the entire housing estate. Social participation, therefore, becomes an important element in the pursuit of climate neutrality in buildings. However, it must be taken into account already at the design stage. The results of the analyses carried out in the article showed that, taking into account public participation in the design process and user recommendations, the selected optimal variant (W5) allows for a reduction in the EP index by over 90% compared to the variant based on standard low-cost solutions (W0) (EP (W0) = 243.64 kWh/(m² year); EP (W5) = 18.42 kWh/(m² year). In terms of the embodied carbon footprint, the optimal option W5 allows for a reduction of over 30% in the embodied carbon footprint of the building structure (W0—51,585.32 [kgCO₂e]; W5—35,537.87 [kgCO₂e]). The optimal variant indicated by users (W5) allows for a reduction in the operational carbon footprint by approximately 80% compared to the basic variant (W0): W0—604,189.50 [kgCO₂e/kWh]; W5—247,402.0 [kgCO₂e/kWh]. The results obtained indicate that public participation is not only a complementary element of the design process, but it can also be a key component of the decarbonisation strategy in residential construction. Involving future users in the decision-making process increases the likelihood of achieving long-term greenhouse gas emission reductions and supports the implementation of long-term climate policy goals. Full article

The recent expansion of mass public transit in Bangkok has increased demand for public motorcycle taxis as a first- and last-mile solution for sustainable urban mobility. This study presents the results of a real-world demonstration project that transitioned 50 conventional public motorcycle taxis into electric motorcycles supported by a battery-swapping system. The project evaluated vehicle performance, operational patterns, electricity consumption, and greenhouse gas (GHG) emissions under actual traffic conditions. Electric motorcycles deployed in taxi services must accommodate additional passenger load, provide sufficient acceleration for dense urban traffic, and sustain high daily travel distances. The findings show that participating riders accumulated a total driving distance of 759,354 km during the project period, demonstrating the technical and operational feasibility of electrification in high utilization fleets. Based on measured electricity consumption and Thailand’s grid emission factor, the transition resulted in an estimated reduction of approximately 1708.4 metric tons of CO₂ equivalent emissions, excluding additional benefits associated with modal shifts to mass public transit. The results further indicate that battery-swapping infrastructure is a critical operational enabler, as daily travel distances frequently exceed the single-charge range of typical electric motorcycles. Scenario projections aligned with Thailand’s 30-by-30 electric vehicle policy target suggest that large-scale electrification of motorcycle fleets could contribute substantially to national mitigation efforts, supporting the country’s accelerated goal of net-zero emissions target by 2050. Full article

In developing countries, sustainable development strategies are increasingly shifting toward a green economy that integrates economic, social, and environmental dimensions. Despite the growing importance of green economic growth, comparative empirical studies examining its determinants in Turkey and Romania remain limited. This study investigates the dynamic relationships between environmentally sustainable growth, carbon emissions, life expectancy, renewable energy consumption, education, and technological innovation in Turkey and Romania over the period 1980–2023. Using annual time series data, the analysis applies the Augmented Dickey–Fuller and Zivot–Andrews unit root tests to examine stationarity and potential structural breaks. The empirical framework is based on the Autoregressive Distributed Lag (ARDL) bounds testing approach, which allows the estimation of both long-run equilibrium relationships and short-run dynamics. The results provide partial evidence of long-run relationships among the variables. Although the ARDL bounds test results fall within the inconclusive region, the negative and statistically significant error correction terms indicate that deviations from long-run equilibrium are corrected over time. The findings also reveal heterogeneous short-run causal interactions across the two countries, suggesting that the drivers of environmentally sustainable growth differ between Turkey and Romania. Overall, the results highlight the importance of country-specific policy frameworks, institutional structures, and energy transition pathways in promoting green economic growth. Full article

Research on Consumption-Based Carbon Footprints has recognised that lifestyles change significantly along the urban–rural continuum, with urban typically manifesting as an increase in the footprint of consumption while rural areas have a higher footprint for vehicle usage. However, there is limited research on the extent to which land-use patterns defined by urban plans influence these outcomes. To fill this lack, we controlled for household income and housing type and measured Spearman and Pearson partial correlations between the coverage of different zoning land-use types in the neighbourhoods and the footprints of different subdomains: Goods and services, Leisure travel, Vehicle, and Total footprint. These domains are central to both modern lifestyles and urban planning with related objectives. We found out that high Goods and Services and Leisure travel footprints do align with the urban land-use types, while Vehicle footprints show inverted results. However, the mirrored impact for higher Vehicle and Total footprint is not recognised in exurban areas, while the impact on Goods and services and Leisure travel is inverted. These findings diverge from the common per capita analysis of supply-side emissions used to analyse zoning impacts and call for more detailed research on the net climate impacts of the built environment designated with land-use plans. Full article

Model Predictive Control (MPC) has emerged as a promising approach for energy management in hybrid electric vehicles, enabling predictive optimization of powertrain operation. The energy management problem in parallel hybrid powertrains constitutes a Mixed-Integer Nonlinear Programming (MINLP) problem, combining continuous decision variables such as torque distribution with discrete decisions including engine on/off states and clutch engagement. This problem structure presents distinct challenges for different optimization approaches. Gradient-based methods such as Sequential Quadratic Programming (SQP) solve continuous, differentiable optimization problems and require auxiliary methods to handle integer variables, while metaheuristic approaches such as Genetic Algorithms (GA) can handle the mixed-integer structure directly at the cost of increased computational effort. This study presents a systematic comparison between GA and SQP as optimization solvers within an MPC framework for a P1P3 parallel hybrid powertrain. A multi-objective cost function is formulated to simultaneously optimize system efficiency, battery state of charge management, and noise emissions. Both approaches are evaluated across the WLTC as well as a real-world RDE scenario. On the WLTC, both MPC approaches reduce fuel consumption by 0.5–1.0% and improve system efficiency by 3.7–4.6% compared to a state-of-the-art deterministic reference strategy optimized for fuel consumption. At the same time, both approaches additionally achieve substantial reductions in noise emissions compared to the deterministic reference, which was not optimized for acoustic behavior. On both cycles, the GA-based MPC achieves favorable performance compared to SQP, with the performance gap widening from the WLTC to the RDE cycle. Both methods achieve real-time capability, yet SQP reduces computational time by a factor of four compared to GA. As long as computational resources in automotive ECUs remain constrained, this efficiency advantage positions gradient-based optimization for series production applications, whereas metaheuristic methods offer greater flexibility for concept development stages with relaxed real-time requirements. The findings contribute to the understanding of optimization algorithm selection for MINLP energy management problems in hybrid electric vehicles. Full article

To fill the research gap of green building assessment theory being underutilized in sports architecture and advance the use of life-cycle assessment (LCA) for complex public building types, this study develops a comprehensive life-cycle green evaluation indicator system specifically for public sports venues. First, the factors influencing green performance were systematically identified across four life-cycle stages—planning and design, construction, operation and maintenance, and end-of-life—leading to the establishment of an initial indicator pool. This pool was subsequently refined through a two-round Delphi expert consultation. The weights of the indicators were then determined using a combined Analytic Hierarchy Process (AHP) and Entropy Weight Method (EWM) approach to quantify the relative importance of each indicator. The resulting framework comprises a comprehensive green evaluation indicator system for the whole life cycle of public sports venues, consisting of 4 first-level, 12 second-level, and 28 third-level indicators. The results reveal a pronounced front-loaded influence in the life-cycle weight distribution, indicating that decisions made during the planning and design stage are most critical for the green performance of sports venues. Based on the weight distribution characteristics, this study further delineates a phase-specific governance logic for green development: the planning and design stage should prioritize design optimization to maximize life-cycle green performance potential; the construction stage should focus on controlling resource input and process carbon emissions; the operation and maintenance stage should emphasize energy consumption optimization and resource recycling; and the end-of-life stage should address resource regeneration. This study not only extends green building assessment and life-cycle assessment theories to sports architecture—a complex and under-researched building typology—but also provides stakeholders with a robust decision-support tool to advance the sustainable development of public sports venues. Full article

This study developed a calibrated, data-supported energy simulation model for the Arrivals Terminal of Cluj-Napoca International Airport (Romania), addressing challenges in modelling complex building typologies. The objective is to improve the accuracy of predicting energy savings and CO₂ emission reductions, supporting renovation and decarbonization strategies aligned with the 2050 targets. The hourly multizone simulations over one year integrated measured operational data, building documentation, and two types of climate datasets (AMY and TMY). The calibration methodology introduces a “Miscellaneous equipment” variable, representing unmonitored indoor electricity consumption, which is incorporated as an internal heat gain in the thermal balance. Validation against real energy measurements showed high agreement (AMY-based RMSE: 3.13 kWh/m²·yr for thermal energy and 1.57 kWh/m²·yr for electricity; relative errors: 2.3% and 0.5%, respectively). The results demonstrate that calibrated modelling reduces the performance gap and provides a robust alternative to standard design-condition energy assessments, which are inadequate for airport terminals but mandatory for several countries, including Romania. The developed model enhances predictive reliability and can guide energy efficiency measures and investment decisions for similar complex buildings. Full article