查看更多>>摘要：Against the backdrop of increasing consumption-based carbon emissions, mitigating household carbon emissions is crucial for achieving China's carbon reduction goals. This study investigates the impact of the household singlehood rate on per capita carbon emissions from a consumption-based perspective. Utilizing panel data from the China Family Panel Studies (CFPS) encompassing 63,412 households from 2010 to 2022, our analysis demonstrates that a rising singlehood rate significantly increases per capita carbon emissions, with a baseline impact coefficient of 0.244. The impact is channeled through three mechanisms: diminished economies of scale in household public goods, insufficient sharing of domestic energy, and increased per capita consumption driven by convenience-seeking behaviors. Heterogeneity analyses uncover systematic disparities: the effect is strongest for males, rural dwellers, the youth, high-income earners and high-education singlehood households. Based on these insights, we propose targeted policy recommendations focused on leveraging learning curves, intervening at sensitive points, and implementing personal carbon budgets to systematically guide and incentivize lowcarbon transitions among the singlehood population.
查看更多>>摘要：This study developed a gasification model for hybrid filtration combustion reactor using CFD approach. The effects of the H_2O/O_2 ratio (0.5–4), wall temperature (adiabatic and 1050–650 K), and varying thermal conductivities of inert porous media (1, 5, 20, 60, and 200 W/(m⋅K)) on two-phase reaction characteristics were investigated. The results show that steam enhances the reaction rates of the Steam-carbon and Water-gas shift reactions, promoting H2 production while suppressing CO formation, ultimately achieving targeted CO_2 enrichment through combustion. Changes in the H_2O/O_2 ratio effectively regulate the H2/CO ratio, with the syngas ratio increasing from 0.31 to 55.96 as the steam content in the feed increases, corresponding to a 17,841.83 % increase. The effect of wall temperature on syngas ratio regulation is less significant compared to the H_2O/O_2 ratio, with CO gradually increasing along the bed height in lower wall temperature conditions, making it difficult to maintain. However, an adiabatic wall significantly extends the Boudouard reaction zone. Variations in the thermal conductivity of inert porous media have minimal impact on the flue gas composition. Radiation enhances the sensitivity of the syngas ratio to changes in the H_2O/O_2 ratio, with the syngas ratio increasing from 0.20 to 0.91 when radiation is neglected, showing a significant decrease by 80.10 % compared to the scenario when radiation is considered. Furthermore, radiation heat transfer greatly suppresses CO formation, with the CO content in the flue gas increasing by 43.70 % to 6001.25 % as the H_2O/O_2 ratio rises.
查看更多>>摘要：As the world's largest developing nation, China faces significant challenges in balancing economic growth and environmental sustainability. While existing studies explored various strategies for reducing carbon emissions, the impact of artificial intelligence on marginal emission reduction costs across diverse regional contexts remains to be clarified furthermore. Using provincial panel data from 2014 to 2024, this study investigates how artificial intelligence adoption influences emission reduction costs in China's industrial sectors. Applying the generalized method of moments, it finds that the marginal cost of emissions reduction decreases by approximately 13.7%- 16.3% when the artificial intelligence investment one unit. But it varies significantly across regions. For example, artificial intelligence investment improves efficiency by 18.2% in the more technologically advanced eastern provinces. In contrast, central regions present a modest 4.7% cost reduction due to lower artificial intelligence integration. However, the cost of western provinces increase can by 6.3% because of high transitional costs, misaligned industrial structures, and inadequate digital infrastructure that limit artificial intelligence's effectiveness. Robustness checks confirm the validity of the results across alternative specifications, including variable substitution using artificial intelligence patent counts, sample restriction to the post-2018 rapid artificial intelligence expansion period, and exclusion of special municipalities, demonstrating consistent and statistically significant findings. Our findings underscore the need for region-specific policies to maximize artificial intelligence's role in sustainable environmental progress, offering key insights for policymakers seeking to integrate technological innovation with emission reduction efforts.
查看更多>>摘要：Remanufacturing, owing to its advantages in resource recycling and carbon emission reduction, has evolved into a critical strategy for accelerating the achievement of net-zero emission targets. With market opportunities driven by trade-in programs that encourage consumer participation in recycling and boost the intent to buy remanufactured products, it is imperative to determine which remanufacturing models can effectively balance emission reduction costs and economic benefits under carbon cap-and-trade regulation (CATR). To tackle this issue, considering a closed-loop supply chain (CLSC) under trade-in scenarios within CATR framework, this study tries to explore the pricing strategies for three remanufacturing modes (autonomous, authorization, and outsourcing remanufacturing mode) by formulating three Stackelberg game models, facilitating to discover the best selection strategy based on the comparison of optimal decisions and profits under these three modes. Research findings show that under autonomous and authorization remanufacturing mode, excessively high collection quality may lead to a decline in remanufactured product sales. However, within the outsourcing remanufacturing mode, improvements in collection quality can continuously increase the sales volume of remanufactured products. Notably, regardless of fluctuations in collection quality, carbon trading prices, and consumer acceptance of remanufactured products (CARP), the autonomous and outsourcing remanufacturing modes have consistently demonstrated superior performance compared to authorization remanufacturing mode for original equipment manufacturers (OEMs) and retailers. Furthermore, an increase in carbon trading price encourages OEMs to transition from outsourcing remanufacturing mode to autonomous remanufacturing mode.
查看更多>>摘要：Studying and optimizing water resources carrying capacity(WRCC) is an inevitable requirement for regional water security guarantee and sustainable development. The existing evaluation methods ignore the temporal dynamics of WRCC, and the research on the driving mechanism of WRCC is not yet comprehensive, which limits the reference value of the research results.This study constructed a new time-weighted-varying VIKOR method to assess WRCC dynamically, and based on obstacle diagnosis model and optimal parameter-based geographical detector, revealed the obstacle factor and driving factor of WRCC. A case study was carried out in the Beijing- Tianjin-Hebei urban agglomeration(BTH) to verify the rationality and reliability of the method. The results show that: (1) The entropy-CRITIC method can allocate the weight more reasonably and avoid overly average or extreme weight distribution (2) Time-weighted-varying VIKOR accurately capture the dynamic change direction and rate of WRCC in each city of BTH, where Baoding has the fastest improvement rate, and Tianjin has the greatest multi-year change range. (3) The binary coupling effect of “driving-obstacle” were comprehensively deconstruct. The most significant indicator to restrict WRCC and cause spatial differentiation of driving forces of BTH is the ecological water consumption, followed by urbanization rate, per capita GDP, proportion of secondary production. These research 1results can help decision-makers understand the long-term trends of WRCC changes in the BTH, and provide a reference for optimizing regional water resource allocation and water usage structure, improving the ecological environment, adjusting the industrial structure, and optimizing the spatial layout of urban population.
查看更多>>摘要：This study introduces a novel multi-generation system integrating a combined cycle power plant with a trireforming methanol production unit, uniquely utilizing on-site freshwater and hydrogen to enhance resource efficiency. A comprehensive Artificial Intelligence (AI) framework compared Genetic Programming (GP), Deep Neural Networks (DNN), and XGBoost for process modeling, with XGBoost demonstrating superior predictive accuracy (R~2 > 0.975 for critical outputs). Furthermore, Multi-objective Salp Swarm Algorithm (MSSA) and Nondominated Sorting Genetic Algorithm Ⅲ (NSGA-Ⅲ) were employed to address conflicting thermodynamic, economic, and environmental objectives. The optimized configuration yielded substantial improvements over the base case, achieving up to a 54.50% increase in methanol production, a 32.60% reduction in payback period, and a 38.90% decrease in overall environmental impact. This work bridges the gap between power generation, carbon capture, and chemical production, offering a robust, AI-driven framework for sustainable industrial processes.
查看更多>>摘要：The energy-intensive nature of gas thermal desorption strategies, along with their associated environmental impacts, particularly greenhouse gas emissions, has raised widespread concern. Using the life cycle assessment framework, this study conducted the first comparative analysis of in situ and on-site ex situ twin strategies, employing the same energy source and remediation principles from a unit process perspective. The results reveal that in situ gas thermal desorption exhibits significantly higher energy consumption (1.9 × ), normalized lifecycle environmental impacts (nearly 4 × ), and greenhouse gas emissions (more than 2 × ) than on-site ex situ strategy for the remediation of 1 m~3 of contaminated soil. Both technologies rely on fossil fuels for over 97% of their energy mix, with natural gas, electricity, and concrete being the primary contributors to greenhouse gas emissions. Unit process analysis further identifies that heating in the in situ strategy is the main driver of its environmental impacts, as the high temperature requirement significantly increases energy consumption. In contrast, off-gas treatment in on-site ex situ strategy is the main source of life cycle greenhouse gas emissions. Reduction potential analysis reveals that optimizing heating in in situ gas thermal desorption can reduce greenhouse gas emissions by 68.24%, while optimizing off-gas treatment in on-site ex situ strategy can reduce emissions by 51.81%. This study highlights the significant environmental performance differences between the two technologies and provides a scientific foundation for developing targeted emission reduction strategies at unit process level.
查看更多>>摘要：Rapid urbanization and rising waste generation in Indian cities necessitate scientifically robust approaches for identifying suitable landfill sites. This study develops a spatial multi-criteria decision-making framework integrating Geographic Information Systems (GIS), the Analytic Hierarchy Process (AHP), and the Combinative Distance-Based Assessment (CODAS) method to evaluate landfill site suitability in Vadodara, India. Fourteen environmental, socio-economic, and infrastructural criteria were incorporated to generate thematic layers, and their relative importance was assessed using AHP. Eight candidate sites were subsequently evaluated using the CODAS method, enabling identification of the most suitable location for landfill development. The results indicate that land-use characteristics, economic considerations, and terrain conditions exert a dominant influence on site suitability. Sensitivity analysis conducted across multiple weighting scenarios confirmed the robustness of the results, with less than 5% variation in suitability scores and the overall site hierarchy remaining stable. The proposed framework demonstrates the effectiveness of combining AHP, CODAS, and GIS-based spatial analysis to support systematic, transparent, and evidence-based landfill site selection, providing a practical decision-support tool for sustainable waste management planning in rapidly urbanizing regions.
查看更多>>摘要：As economic globalization and complex supply chains continue to expand, understanding the temporal dynamics and transfer patterns of CO_2 emissions is crucial for effective climate governance. This study employs the environmentally extended multi-region input-output (EE-MRIO) model and structural path analysis (SPA) methods to examine global carbon emissions from both production- and consumption-based perspectives during 1990-2019 and trace embodied carbon flows across sectors and nations. Our analysis reveals that global cumulative CO_2 emissions reached 860.7 Gt over the past three decades, with China (22.7%) and the U.S. (19.1%) leading production- and consumption-based emissions, respectively. Sectoral analysis shows energy-intensive industries such as electricity, gas and water (EGW) dominate production-based emissions (36.6%) and consistently served as net emission outflow sectors throughout the study period. Conversely, construction (CON) and education, health & other services (EHO) emerged as leading consumption-based emitters due to their reliance on embodied carbon, with CON transitioning from a net-zero sector to a net emission inflow sector. Nationally, 9.7% (83.2 Gt) of global CO_2 emissions have been embodied in international trade driven by final consumption, revealing distinct patterns: developing nations (e.g., China, India) consistently were net carbon exporters, while developed nations (e.g., U.S., Japan) were net importers. The global carbon flow network has become more complex, with traditional economic powers (U.S., Japan, China) retaining dominance, while India rises as a key emitter. These findings highlight the need for fair climate strategies that consider evolving supply chains and foster international cooperation.
查看更多>>摘要：Conventional textile dyeing remains one of the most resource-intensive stages of garment production, characterised by high water and energy use and the generation of chemically contaminated effluent. This study explores an alternative approach to conventional dyeing through the development and evaluation of a laser dyeing process termed peri-dyeing, a digitally driven, non-contact colouration technique in which dye fixation was initiated by targeted laser irradiation directly at the fibre surface. Optimisation of laser parameters and dye application methods enabled controlled surface colouration of wool fabrics. Colour measurements, SEM imaging, and tensile strength analysis confirmed that high dye fixation efficiencies (82–96%) were achieved without compromising fibre integrity. Standardised testing demonstrated strong wash and rub colour fastness, indicating technical performance compatible with commercial textile applications. Design sampling validated the technique's ability to produce fine linear detail, smooth tonal gradients, and multicolour imagery on both flat and textured substrates. The peri-dyeing process demonstrates the technical feasibility of a digitally controlled approach to textile colouration that avoids immersion dye baths and enables targeted dye application. The results indicate potential for reduced resource use and increased production flexibility. The paper highlights opportunities for integration into direct-to-garment and on-demand manufacturing contexts, supporting the development of more efficient and adaptable textile colouration workflows.
NETL
NSTL
Elsevier