A process parameters optimization method of multi-pass dry milling for high efficiency, low energy and low carbon emissions

The Energy-conservation and Emission-reduction Paths of Industrial sectors: Evidence from Chinas 35 industrial sectors

High-occupancy vehicle (HOV) lanes have been promoted to encourage carpools, reduce traffic congestion, and improve air quality. At the partial equilibrium level, commuting with three workers per automobile clearly reduces highway congestion, lowers carbon emissions, and saves energy compared to three single drivers. This paper develops a numerical urban simulation model to generate the general equilibrium effects of HOV lanes on urban spatial structure, energy use, and greenhouse gas emissions. The major findings are that while HOV lanes reduce traffic congestion and improve welfare, the fall in transportation cost leads to urban sprawl, which results in higher dwelling energy use and a larger carbon footprint. Overall, the HOV lane policy has little effect on total energy consumption and carbon emissions. This is another classic case of general equilibrium effects reversing the partial equilibrium effects of an urban policy. In contrast, a gasoline tax policy leads less urban sprawl but is less effective at lowering energy consumption and carbon emissions. Imposing congestion tolls is a more effective tool at reducing traffic congestion, saving energy, and lowering carbon emissions.

High‐occupancy vehicle (HOV) lanes have been promoted to encourage carpools, reduce traffic congestion, and improve air quality. At the partial equilibrium level, commuting with three workers per automobile clearly reduces highway congestion, lowers carbon emissions, and saves energy compared with three single drivers. This paper develops a numerical urban simulation model to generate the general equilibrium effects of HOV lanes on urban spatial structure, energy use, and greenhouse gas emissions. The major findings are that while HOV lanes reduce traffic congestion and improve welfare, the fall in transportation cost leads to urban sprawl, which results in higher dwelling energy use and a larger carbon footprint. Overall, the HOV lane policy has little effect on total energy consumption and carbon emissions. This is another classic case of general equilibrium effects reversing the partial equilibrium effects of an urban policy. In contrast, a gasoline tax policy leads to less urban sprawl but is less effective at lowering energy consumption and carbon emissions. Imposing congestion tolls is a more effective tool at reducing traffic congestion, saving energy, and lowering carbon emissions.

Traffic volume is increasing dramatically due to the quick development of technologies like online gaming, on‐demand video streaming, and the Internet of Things (IoT). The telecommunications industry's large‐scale expansion is increasing its energy usage and carbon footprint. Given the desire to minimize energy consumption and carbon emissions, one of the most essential concerns of future communication networks is ensuring rigorous performance restrictions of IoT services while improving energy efficiency. In this regard, a convolutional neural network‐based hierarchical reinforcement learning approach is provided to lower total energy consumption and carbon emissions in the dynamic service function chaining situations. This method can more effectively lower energy consumption and carbon emissions when compared to other hierarchical algorithms based on conventional deep neural networks and non‐hierarchical algorithms. The suggested method is tested in three typical complicated networks with different network parameters to show its suitability in different network scenarios.

China has committed to peak its carbon emissions by 2030, which puts forward a new issue for underground metal mines—selecting a cleaner mining method which requires less energy and generates less carbon emissions. This paper proposes an enterprise-level model to estimate life-cycle energy consumption and carbon emissions, which takes more carbon sources (e.g., cement and carbon sink loss) into consideration to provide more comprehensive insights. Moreover, this model is integrated with the energy-conservation supply curve and the carbon abatement cost curve to involve production capacity utilization in the prediction of future performance. These two approaches are applied to 30 underground iron mines. The results show that (1) caving-based cases have lower energy consumption and carbon emissions, i.e., 673.64 GJ/kt ore, 52.21 GJ/kt ore (only considering electricity and fossil fuel), and 12.11 CO2 eq/kt ore, as compared the backfilling-based cases, i.e., 710.08 GJ/kt ore, 63.70 GJ/kt ore, and 40.50 t CO2 eq/kt ore; (2) caving-based cases present higher carbon-abatement potential (more than 12.95%) than the backfilling-based vases (less than 9.68%); (3) improving capacity utilization facilitates unit cost reduction to mitigate energy consumption and carbon emissions, and the energy-conservation and carbon-abatement potentials will be developed accordingly.

Climate change and environmental issues have been increasingly in the forefront of the media and government agendas. However, despite much discussion and fanfare, little has been done in the way of serious commitment and clear course of actions since the adoption of the UN Framework Convention on Climate Change in 1992 to bring carbon emissions to sustainable levels. To tackle the immensity of the climate change challenge, a paradigm shift in understanding is necessary to balance the course of global human development with energy demand and consumption patterns. Accounting for over 40% of global energy demand and more than 30% of greenhouse gas emissions, the building sector offers the greatest mitigation potential for reducing carbon emissions in both the short and long term, with positive implications for a range of associated sectors and industries. Promoting behavioral change among end-users for reduced energy consumption as well as encouraging the building industry to embrace sustainable design, low-carbon construction practices and materials, and renewable technologies, is fundamental to mitigating the impact of the built environment on planetary biospheres and preserving quality of life for generations to come. This paper starts by drawing attention to the building sector and related EU policy, outlining the challenges and opportunities for reducing energy consumption and carbon emission levels. Such policy provides the essential framework to engage stakeholders and allow supporting factors to foster progress in the sector. The paper contends that information on climate change has not led to significant improvements in meeting global targets and that what is needed is behavioral change among individuals and society as a whole. On the basis of research project experiences and literature review, it puts forth and explores five key elements contributing to behavioral change for reduced energy consumption and lower carbon emissions in the building sector, focused on: information and education; financial incentives and energy services; modern technologies and sustainable design; social and community norms; and biophilia (contact with the natural environment). The paper suggests opportunities for further research and concludes with recommendations for policy-makers and related stakeholders.

Life cycle assessment of energy consumption and carbon emissions of a green maintenance material for asphalt pavement: Warm mix OUFC-5

Due to continuous worsening of the environment and depletion of natural resources, concept of sustainability in manufacturing sector is gaining an increasing attention. New processes and strategies characterized by efficient use of energy and materials, low processing time and zero or nearly zero green-house emissions are being explored. Incremental sheet forming (ISF) is a comparatively new sheet forming process with high economic payoff for small production runs and customized orders. The current study presents a comprehensive review on the sustainability aspects of the process. The process performance in terms of power demand, energy consumption, cost, CO2 emissions, processing time, and material wastage/usage is analyzed on the basis of published literature, and important conclusions and recommendations are drawn to utilize the process for cleaner, economical and time efficient production. The review establishes that ISF is more sustainable process than conventional forming methods for small production runs. Further, its sustainability performance can be further enhanced by choosing optimum process conditions especially lower spindle rotation, larger feed rate and step size. Shorter tool paths and advent of energy efficient machines is likely to further elevate the process performance. The review also identifies future work and presents fundamental guidelines and recommendations for establishing the sustainable ISF process.

Solar thermal catalysis for sustainable and efficient polyester upcycling

Assessment of energy use and carbon footprint for low-rank coal-based oxygen-thermal and electro-thermal calcium carbide manufacturing processes

Global building life cycle assessment: Comparative study of steel and concrete frames across European Union, USA, Canada, and Australia building codes

Does new energy consumption conducive to controlling fossil energy consumption and carbon emissions?-Evidence from China

In this study, carbon emissions and energy consumption were evaluated to establish a design strategy which has low energy consumption and carbon emission production, by using life cycle energy (LCE) and life cycle CO 2 (LCCO 2 ) calculation methods in life cycle management(LCM) tools. After improvement design projects, the energy consumption and CO 2 emission were calculated and compared in three sewage treatment plants (STPs), which are A STP, B STP, and C STP. The reduction of carbon emissions was 28,020.1 ton CO 2e/yr, 37,399.6 ton CO 2e/yr and 8,788.3 ton CO 2e/yr, respectively. Production of energy was 792 TOE/yr, 1,235 TOE/yr and 1,023 TOE/yr, respectively. As a result, the estimation of energy and energy self-sufficiency was 5.1 %, 14.5 % and 23.5 %, respectively. The result of this study shows the LCM can be contributed to establish strategy for energy and carbon emission reduction in sewage treatment plants.

This study proposes a Transformer–NSGA-III multi-objective optimization framework for high-rise residential buildings in Haikou, a coastal city characterized by a hot summer and warm winter climate. The framework addresses four conflicting objectives: Annual Energy Demand (AED), Predicted Percentage of Dissatisfied (PPD), Global Cost (GC), and Life Cycle Carbon (LCC) emissions. A localized database of 11 design variables was constructed by incorporating envelope parameters and climate data from 79 surveyed buildings. A total of 5000 training samples were generated through EnergyPlus simulations, employing jEPlus and Latin Hypercube Sampling (LHS). A Transformer model was employed as a surrogate predictor, leveraging its self-attention mechanism to capture complex, long-range dependencies and achieving superior predictive accuracy (R2 ≥ 0.998, MAPE ≤ 0.26%) over the benchmark CNN and MLP models. The NSGA-III algorithm subsequently conducted a global optimization of the four-objective space, with the Pareto-optimal solution identified using the TOPSIS multi-criteria decision-making method. The optimization resulted in significant reductions of 28.5% in the AED, 24.1% in the PPD, 20.6% in the GC, and 18.0% in the LCC compared to the base case. The synergistic control of the window solar heat gain coefficient and external sunshade length was identified as the central strategy for simultaneously reducing energy consumption, thermal discomfort, cost, and carbon emissions in this hot and humid climate. The TOPSIS-optimal solution (C = 0.647) effectively balanced low energy use, high thermal comfort, low cost, and low carbon emissions. By integrating the Energy Performance of Buildings Directive (EPBD) Global Cost methodology with Life Cycle Carbon accounting, this study provides a robust framework for dynamic economic–environmental trade-off analyses of ultra-low-energy buildings in humid regions. The work advances the synergy between the NSGA-III and Transformer models for high-dimensional building performance optimization.

Study on the model of high efficiency and low carbon for grinding parameters optimization and its application