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Abstract With fast development and urbanization, Chinese cities are facing several challenges including issues of environmental pollution, climate change, resource depletion and landfills shortage. Waste recycling is one effective way to respond these challenges. Under such a situation, this paper aims to evaluate energy saving and carbon reduction potential of recycling wastes by taking Shanghai, the biggest city in China, as a case study. Results show that 8.7 Mt standard coal (tce) and 16.81 Mt CO 2 could be reduced under current recycling system in Shanghai. And recycling of waste steel and nonferrous metals were the two dominant contributors, accounting for about 44% and 42% of energy saving, and 60% and 33% of CO 2 reduction, respectively. If the recycling rates of main recyclable wastes were improved to 100%, additional 6.44 Mt energy and 10.02 Mt CO 2 could be further saved, mainly from recycling waste glass, waste plastic and waste metals. Adoption of RPF (Refuse Plastic & paper Fuel) could further contribute to 1.24 Mt energy saving and 2.67 Mt CO 2 reduction. Finally, policy implications on establishing a mature recycling system and promoting RPF technologies are discussed, in which the potential on reducing total energy consumption and CO 2 emission of Shanghai can reach 6.6% and 4.9%, respectively. [ABSTRACT FROM AUTHOR]

Abstract Pulp and paper manufacturing sector constitutes one of the largest industry segments in the world in terms of water and energy usage as well as of significant use and release of chemicals and combustion products. Since its chief feedstock –wood fiber– is renewable, this industry can play an important role in sustainable development, becoming an example of how a resource can be managed to provide a sustained supply to meet society's current and future needs. This calls for a thorough assessment of environmental costs and impacts associated to pulp and paper operations, including both direct and indirect inputs supporting the whole papermaking process as well as the main outputs, co-products and by-products. By means of Life Cycle Assessment (LCA) methodology, this paper aims at assessing the environmental sustainability of the pulp and paper production so as to identify those phases across the whole supply chain that entail the highest environmental loads, thus requiring improvements. To determine the environmental impacts as accurately as possible, the manufacturing stages performed in the pulp and paper mill complex of Stora Enso Oyj Veitsiluoto Mills at Kemi, Northern Finland, were taken as a model and assessed by means of the SimaPro 8 LCA software, utilizing ReCiPe Midpoint (H) method for the impact assessment. As expected, most of the resulting impacts are caused by the industrial production phase. The production processes of pulp and paper jointly affect all the investigated impact categories with the highest shares, ranging from 50% of generated impacts on water depletion up to 88% on freshwater eutrophication. Generally, the main contributions to environmental loads come from the electricity and heat requirements and, only at a minor extent, from the use of chemicals such as the sodium hydroxide and sodium chlorate. In particular, pulp production process generates the main loads on global warming (46% of the total impacts), ozone depletion (39%), freshwater eutrophication (55%), human toxicity (46%), metal depletion (42%) and fossil depletion (46%). In the remaining investigated impact categories, namely terrestrial acidification, photochemical oxidant formation and terrestrial ecotoxicity, most of impacts derive from the use of optical brighteners and fillers in the final steps of paper production and from the intensive consumption of water in the recycling step of end-of-life affecting water depletion. Moreover, the implementation of measures for material and energy efficiency in the assessed system, such as the use of renewable energy generated in situ from black liquor and residual biomass to support the requirements of the integrated pulp and paper mills and the waste paper recycling, resulted to be crucial in lowering the environmental burdens. In particular, the partial fulfillment of electricity and heat requirements by means of a circular use of residues within the system leads to a noteworthy reduction of impacts in all the investigated impact categories, up to more than 70% in global warming and fossil depletion potentials, thus contributing to higher process sustainability compared with other averaged European systems for paper production. The obtained research results are a valuable source of management information for the decision makers, at both company and national levels, with the aim to improve the environmental performance of pulp and paper industry. Highlights • A Life Cycle Assessment (LCA) of papermaking in Finland was performed. • The main environmental hotspots in each process step were identified and discussed. • Energy and material efficiency solutions leads to a noteworthy reduction of impacts. • Pulp and paper industry is a benchmark model to transition towards a circular economy. [ABSTRACT FROM AUTHOR]

Globally, small and medium-sized enterprises (SMEs) constitute a significant industrial segment. However, their energy-efficiency potential has remained largely untapped due to several barriers. The prevailing approaches on energy optimization in the energy-intensive SMEs are focused on the technological improvements at the operational level and lack a strategic approach. This study developed an energy management system (EnMS) for SMEs by integrating technical and managerial energy saving activities (ESAs), suggested in energy management literature. The proposed EnMS adopted a PDCA (Plan-Do-Check-Act) process approach for consistent energy efficiency and cleaner production. For the validation of the proposed EnMS, a case of small-sized Indian paper mill was selected. The case exhibited the ramp of continuous improvement in the energy efficiency at the mill by illustrating two PDCA cycles. Step I (Plan) involved creating energy management roles with management support, energy auditing, identifying energy-saving opportunities and developing action plan; step II (Do) was about creating communication, awareness and motivation before the implementation of the action plan; step III (Check) involved periodic monitoring, analysis and reporting of the energy key performance indicators (KPIs), and benchmarking enterprise energy performance. The case analysis showed a 35% reduction in specific energy consumption (SEC) of one of the Paper Machine's (PM) vacuum system by the implementation of ESAs for vacuum optimization, generating an annual energy-saving of 0.3 GJ/t. In step IV, the subsequent PDCA cycle was triggered by the biannual management review for exploring the new energy saving opportunities at the enterprise. The next improvement cycle targeted at steam and condensate system optimization in another PM resulted in an additional energy cost saving of USD 26,900. [ABSTRACT FROM AUTHOR]

This research addresses the critical necessity for energy-efficient solutions in port operations. The primary objective of this paper is to introduce and assess the viability of an innovative infrastructure termed Underground Reefer Container Storage (URCS) devised to mitigate the significant and increasing energy demand posed by reefer containers in ports. This study employs a discrete-event simulation (DES) model to evaluate the feasibility, environmental implications, and cost-effectiveness of three distinctive URCS infrastructure designs. These designs are compared against conventional storage methods to ascertain their capacity for minimizing energy usage and greenhouse gas emissions. The analysis reveals that the best scenario for a specific case study involves a multi-block layout comprising two underground yards, capable of accommodating 39% of the overall refrigerated container capacity at a port. This layout cuts CO 2 emissions by 61% from the 2.704 GWh/year energy used by refrigerated containers, yielding around 1.4 million USD/year cost savings. Further analyses using weighted searches to prioritize URCS container slotting led to shorter container dwell times at ports, rendering energy savings 23% higher than in similar unplanned settings. Moreover, this study presents URCS as an eco-friendly alternative for port-based reefer container storage, offering practical alignment with sustainability goals and regulations. The innovative design and its successful application provide holistic insights, paving the way for enhanced port energy management and environmental care. In addition, the implementation of URCS has the potential to reshape port procedures, yielding economic and ecological benefits. This initiative contributes to diminishing the carbon footprint of ports, aligning with global endeavors towards greener and more responsible practices. [Display omitted] • Conventional stacking at ports consumes high energy for reefer containers, risking the cold chain. • Underground reefer container storage (URCS) reduces heat exchange by storing reefers in a colder underground deposit. • Multi-level block storage is preferred in URCS, according to a multi-criteria assessment. • Simulation recommends energy savings with proper stacking in multi-level block storage. • URCS enables environmentally responsible port operations, improving standards feasibly. [ABSTRACT FROM AUTHOR]

The application of a photovoltaic (PV) system for zero energy buildings is crucial to balance energy consumption. This paper investigated the potential of PV systems for buildings in different climate zones of China. The tilt angle, orientation, plot ratio, PV conversion efficiency and location were selected to study their influences on electricity production. The performance of the PV system was analyzed based on a residential building, and employed RETScreen in combination with TRNSYS. The results indicate that southwest China is the best place to develop zero energy buildings. Low-rise residential buildings can realize zero energy in China when the PV conversion efficiency is higher than 20%. However, an energy balance only occurs in the southwest when the PV conversion efficiency is 10%. For different building heights, with a high conversion efficiency (20%), the energy consumptions rates are less than 15 kW h/m2a all over China. In the southeast, the effect of the building shape on energy generation can be ignored due to the high solar elevation angle in the summer. Although the optimal tilt angle varies for different cities, the energy deviations are less than 10% when tilt angle is within 10° of the optimal angle. This study proposed a formula to evaluate the potential of a building PV system. The maximum difference between the simulated and calculated results is approximately 10%, which is reasonable and acceptable. This paper provides some references for the application of PV systems in zero energy buildings. [ABSTRACT FROM AUTHOR]

In this paper, we estimate the energy efficiency of educational buildings with the case study of buildings in City University of Hong Kong by constructing an energy demand stochastic frontier model. The model is estimated by using the university statistical data from 2011 to 2015. For the consistent frequency of data among the variables, we have adopted the quadratic-match sum method to convert annual university report data into a monthly dataset. Our result shows the average energy efficiency is 0.87, implying that 13% of total energy consumption can be saved. We then calculate how much of the energy saving potential has been achieved by constructing the performance score, which increases from 0.08 to 0.17. It implies that the campus performs more efficiently in saving energy over time. We further develop econometric decomposition analysis based on the energy demand frontier model to identify the factors affecting energy consumption. It suggests that research activities account for a large share of overall energy consumption. Analysis on energy end-use shows university should improve efficiency in lab instrument as it is least efficient among the four usages. We expect this paper can provide the fundamental and methodological guideline for university-scale energy efficiency estimation. • Estimating energy efficiency of educational buildings needs to consider the composition of research related factors. • Energy efficiency of campus building presents increasing tendency with seasonal cycle over time. • Research related variables affect significantly on energy efficiency of educational buildings. • Energy use in lab instrument needs to be effectively managed to save energy. [ABSTRACT FROM AUTHOR]

This paper focuses on the properties and mechanism of dissolved gases permeate membrane and energy recovery efficiency in the anaerobic forward osmosis membrane bioreactor under different membranes types (CTA-ES and CTA-NW), membrane orientations, temperatures (25 °C and 35 °C) and influent COD concentrations (3000 mg/L,6000 mg/L and 9000 mg/L). CTA membrane allowed the permeation of dissolved H 2 , and the concentration of dissolved H 2 in the draw side (between 2.4 mg/L and 4.2 mg/L) was higher than the feed side (between 1 mg/L and 3.4 mg/L). This was beneficial to the anaerobic digestion because the partial pressure of H 2 in the reactor was reduced. A small amount of dissolved CO 2 (around 7 mg/L in draw side while between 56 mg/L and 256 mg/L in feed side) was allowed to pass through the CTA membrane. CTA membrane nearly 100% blocks the permeation of dissolved CH 4 at different operation conditions in this paper. The highest methane yield was 0.256 L CH 4 /g COD at 35 °C with the influent COD was 6000 mg/L. This showed that AnOMBR could solve the problem of effluent containing dissolved methane in conventional anaerobic treatment, and had great potential in energy recovery. Image 1 [ABSTRACT FROM AUTHOR]

We present a mechanics based model for predicting the power consumption of drilling operations. Different from existing power models in machining that ignore the tool wear, our model takes into full consideration the tool wear which is particularly pronounced in drilling and causes extra power consumption. For any given spindle speed n and feed rate f , our model establishes the relationship between the length of drill and the total power consumption as well as the amount of tool wear. With this prediction model established, we can then optimize the drilling parameters (n , f) towards different objectives, such as the two applications reported in this paper – to minimize the average power consumption per unit length of drill and to maximize the tool usage before its replacement. Physical drilling experiments of the proposed power prediction model and its two optimization applications are also reported in this paper which have validated the accuracy of the model and convincingly demonstrated its efficacy in deciding optimal drilling parameters (n , f) for energy minimization and other objectives. • An energy consumption model (ECM) for drilling process considering the tool wear is proposed. • The proposed ECM is more accurate and suitable for actual processing and production. • The proposed ECM can conveniently and effectively predict tool life and monitor tool wear. • Optimization of cutting parameters to achieve maximum tool life and minimum energy consumption. [ABSTRACT FROM AUTHOR]

Based on the panel data of 30 provinces in China from 2006 to 2016, the Gaussian kernel density estimation was initially used to describe the characteristics of the inter-provincial differences in household energy consumption (HEC), a dynamic panel data model was established secondly to study the main factors affecting HEC, and finally the counterfactual decomposition method was used to calculate the contribution of each factor to the inter-provincial differences in HEC. The research results are shown as follows: First, the inter-provincial HEC in China shows a phenomenon of agglomeration and growth, and the HEC level of different regions presents difference and imbalance. Second, the logarithms of consumption habits, total population and residents' income have significant effects on the logarithm of HEC, the influence coefficients are 0.191, 0.073 and 0.745, respectively; HEC not only has significant inertia characteristics, but also has certain population size effect and wealth effect. Third, the logarithms of technological progress, urbanization and household energy price have significant effects on the logarithm of HEC, the influence coefficients are 0.864, 0.195 and −0.346, respectively; the HEC saved by technological transformation is not sufficient to offset the increase in HEC demand, urbanization can improve HEC significantly, but the household energy price significantly inhibits HEC. Finally, from the perspective of contribution, the total population, residents' income and urbanization contribute a lot to the inter-provincial differences, totaling 73.02%. The possible innovations of this paper are as follows: First, the contribution of the influencing factors to the difference of inter-provincial HEC is studied, which is rarely seen in the previous literature. Second, compared with traditional decomposition method, counterfactual decomposition method is more convenient for economic interpretation. Third, the divergent conclusions of scholars are pointed out in this paper, and the actual impact of influencing factors on HEC are clarified. Image 10086681 • The level of inter-provincial HEC shows difference and imbalance. • HEC has significant inertia characteristics, population size effect and wealth effect. • HEC saved by technology is not sufficient to offset the increase in HEC demand. • Total population, residents' income and urbanization contribute 73.02%. • The statistical analysis tool used in this paper is Stata15. [ABSTRACT FROM AUTHOR]

In this paper, a 1 kW-class Solar-Ocean Thermal Energy Conversion integrated Air-Conditioning (S-OTEC/AC) experimental system, which can provide both electrical energy and cooling energy without any pollution, is constructed for the first time. Based on the experimental data, the system's overall performance was comprehensively evaluated in terms of energy, exergy, economy, and environment (4 E). Furthermore, the performance difference between the system using solar energy (i.e., S-OTEC/AC) and the system without solar energy (i.e., OTEC/AC) was compared. The experimental results revealed that the maximum thermal efficiency of the S-OTEC/AC system could reach 2.10%, which was 1.91 times higher than the OTEC/AC system. For the OTEC/AC and S-OTEC/AC systems, the maximum cold energy utilization efficiency was 4.12% and 10.09%, and the maximum isentropic efficiency was 18.81% and 76.84%, respectively. The minimum payback period for the S-OTEC/AC system was 13.90 years, which was much lower than the 18.40 years of the OTEC/AC system. Additionally, the maximum primary energy saving achieved by the OTEC/AC system was 0.53 kW, while the S-OTEC/AC system reached a higher value of 1.56 kW. From the results, the superiority of the S-OTEC/AC system over the OTEC/AC system was confirmed. This work not only provides data references for the actual operating characteristics of the S-OTEC integrated system, but also provides a new technical way for comprehensive utilization of ocean temperature difference energy. • 1 kW class S-OTEC/AC experimental system was built for the first time. • The first 4 E analysis was used to characterize S-OTEC/AC with experimental data. • The S-OTEC/AC system has a thermal efficiency of up to 2.10%. • The S-OTEC/AC system achieves a cooling energy utilization of 10.09%. • The S-OTEC/AC system is superior to the OTEC/AC system in all aspects. [ABSTRACT FROM AUTHOR]

The train generates regenerative braking energy (RBE) during braking. When the RBE generated by the braking train is too large to be absorbed and utilized, the OCS voltage will rise rapidly. It may even exceed the rated voltage threshold, resulting in hidden dangers for driving safety. How to use RBE efficiently and reasonably has become a hot issue in recent years. This paper presents a metro timetable optimization method for multi-train operation to improve the RBE utilization efficiency of trains. Firstly, an energy-saving multi-objective model for multi-train operation is established, which takes the total energy consumption and travel time as the objective functions and the dwell time and departure interval as the constraints. Then, the genetic algorithm (GA) is used to generate the composition sequence of dwell time and departure interval, and the multi-node nonlinear model is used to calculate the total energy consumption of the metro line. Finally, Guangzhou metro line 7 is selected as an example to illustrate the effectiveness of the developed optimization model. The results show that the energy consumption of the whole line can be effectively reduced by adjusting the dwell time and departure interval. Meanwhile, the timetable optimization model with GA has better energy-saving effects by comparative analysis. • A metro timetable optimization method based on improved genetic algorithm is developed. • The multi-node nonlinear model is developed to calculate the total energy consumption. • The developed optimization model has better energy-saving effects than previous methods. [ABSTRACT FROM AUTHOR]

Two economic activities have been integrated in southern Brazil: olive cultivation and sheep farming. The favorable edaphoclimatic conditions in the southern region for cultivating olive trees and cultural preferences for the consumption of sheep meat make this integration a valuable strategy. Considering that every activity causes environmental impact and the exploitation of natural resources, new systems that promote sustainable development and the efficient use of these resources become necessary. This paper presents the first study of the carbon footprint of the life cycle of integrating of sheep and olive farming, with the objective to build an inventory based on primary data collected on olive and sheep farms in Brazil, determine the carbon footprint of the conventional and integrated system and estimate the potential for mitigating GHG emissions. The evaluation was performed using the life cycle assessment (LCA) methodology. The study focuses on the detailed analysis of the processes and activities that are responsible for GHG emissions, including enteric methane emissions, methane emissions from manure management, emissions from nitrogen fertilizer use and limestone application and energy consumption. For this, two major scenarios were considered, the first centered around on the traditional olive cultivation and sheep farming systems individually, and the second involved their synergistic integration. The findings indicate that the conventional system is associated with a global warming impact of 6.8 kg CO 2 eq per liter olive oil and kg live weight. In contrast, the integrated system demonstrates a significant mitigation potential of 27.7%, resulting in a reduced global warming impact of 4.92 kg CO 2 eq. This advantage is a consequence of the synergy between the systems, as they now occupy the same area, which allows the sharing of agricultural activities and agrochemical inputs, in addition to optimizing nutrient recycling and complementing each other. Additionally, the results of a sensitivity analysis showed that enteric methane from sheep and the application of limestone to the soil are the most influential parameters on the carbon footprint, indicating that they are key parameters for the development of GHG mitigation strategies, some of which were discussed in the article. • First life cycle assessment of integration between olive and sheep farming in Brazil. • The inventory was constructed with primary data collected in situ. • The study assesses the carbon footprint of the olive and sheep farming system. • The results show environmental advantages of the integration of olive and sheep farming systems. [ABSTRACT FROM AUTHOR]

With the rapid expansion of computational infrastructure, the electricity consumption of geographically dispersed Internet Data Centers (IDCs) has imposed a substantial influence on the power grid. Geographically distributed IDCs, which present a versatile electricity demand with significant operational flexibility, hold potential for mitigating cost issues and balancing regional supply and load demand. To exploit this flexibility, this paper has developed a dual-drive coordinated operational strategy, which integrates locational electricity prices and unit carbon emission costs based on the dynamic carbon emission intensity (DCEI) of various regions as interaction signals to improve coordination effectiveness. The upper-level determines the consumption behaviour of IDCs, the lower-level provides feedback on dual-drive signals factoring in IDCs' involvement in grid operation, whilst an iterative algorithm is suggested to solve the bi-level model. Case studies based on real-world datasets demonstrate that the proposed operation strategy can achieve arbitrage through multi-regional carbon-electricity markets, significantly reduce operating costs, and support the rapid development of IDCs. Additionally, this strategy facilitates optimal matching of power load in IDCs with renewable energy sources, thereby mitigating imbalances in regional renewable energy supply and demand. • Analysing the necessity of integrating IDCs into the carbon market. • Proposing a dual-drive coordination to activate spatiotemporal flexibility of IDCs. • An iterative algorithm for solving the bi-level model is designed. • Comparing impacts of carbon policies in IDC costs and renewable energy accommodation. [ABSTRACT FROM AUTHOR]

- The integration of multi-energy sources in microgrids offers a promising approach to address the challenges of energy efficiency and bolster environmental stewardship. This paper presents a novel optimization scheduling model for multi-energy microgrids (MEMG) with carbon capture and storage (CCS) technology in various renewable energy scenarios. The model effectively coordinates integrated demand response (IDR) and the flexible operation of waste heat utilization, enhancing energy utilization efficiency while ensuring user satisfaction. Furthermore, introducing flexible operation combined with ladder-type carbon trading minimizes operating costs and carbon emissions. The improved particle swarm optimization (PSO) algorithm is employed to solve the eco-economic economic scheduling model. Simulation analysis shows that integrating CCS into MEMG can reduce carbon emissions by 74.4%, while lowering the overall cost by 37.9%. These findings not only improve MEMG's low-carbon and economic efficiency but also pave the way for environmentally responsible energy management practices. [ABSTRACT FROM AUTHOR]

Based on 196 prefecture-level cities in China from 2006 to 2021, this paper constructs a weight matrix of urban economic geography and uses the spatial differential model to verify the impact of smart city policies on urban green energy efficiency. The results show that the efficiency value of green energy fluctuates in Chinese cities. Different test results show that smart city policies can effectively improve urban green energy efficiency, but mainly from the time effect rather than the individual effect. It means the improvement in urban green energy efficiency comes more from the fast-growing of the Chinese economy and urban construction in this period than from the country's smart city pilot policy. In addition, from the spatial spillover effect result, China needs to design a better urban spatial development plan to improve the capacity of smart city energy services and vigorously develop intelligent technology represented by information and communication science. [ABSTRACT FROM AUTHOR]

Traditional methods of polymers joining for industrial applications are limited to lasers, induction, or conduction heating. It is difficult to achieve precision and efficiency in large-scale applications simultaneously. In this paper, we report a precise microwave welding method induced by localized surface plasmons of metallic macro-gaps under microwave excitation. The proposed method uses energy from well-developed commercial microwaves and benefits from the precision of localized surface plasmons, an oscillating electron cloud excited by electromagnetic waves, which is currently under investigation only at the micro or nanoscales. This method is employed in plastic microwave welding, and the numerical analyses and experimental results reveal that, in comparison to traditional microwave heating methods, this process effectively focuses microwave energy to achieve precision area heating of a diameter of 4 mm or less; this results in a temperature increase at a rate of 333.8 °C/s, and the maximum tensile strength of the welded samples reaches as high as 668 N, which is comparable to that of the base material. The results demonstrate that the processing method is an environmentally friendly approach characterized by high quality, efficiency, and low energy consumption. [Display omitted] • Develop a novel plasmon local field enhancement assisted microwave welding method. • Achieve focused microwave energy and precise polymer heating. • Welded samples exhibit excellent mechanical properties. • Method offers high quality, efficiency, cleanliness, and low consumption advantages. [ABSTRACT FROM AUTHOR]

With the increasing number of integrated energy system (IES) construction, a multi-agent system group composed of multiple IESs would be formed in a certain region. Moreover, complex uncertainties gradually existed in the IES which affect the system optimal situation and optimization results. In order to take full use of each agent's advantages and improve the performance of the whole region under uncertainty, this paper focuses on the multiple energy types' interaction of multi-agent IESs within the region to realize the stable operation, economic and environmental improvement of each agent and the whole region. To this aim, a multi-objective multiple energy types' multi-agent interactive optimization model considering energy demand and renewable energy output uncertainties was built. The model was based on Nash bargaining cooperative game theory and took economy, flexibility and carbon emission as objectives. Moreover, energy interaction scheduling and its corresponding price can be optimized through the model. The results of non-cooperative operation mode without interaction and cooperative operation mode with interaction were compared to analyze the superiority of interaction from the cost, carbon emission and flexibility aspects. The overall regional carbon emission and the economic cost has been reduced by 13.84% and 9.94%, for each IES, the carbon emission and economic cost could reduce by up to 41.5% and 44.5%. Moreover, the flexibility of the IES was correspondingly increased by nearly 47%. These results denoted that after multi-agent interaction, not only the overall region's energy, economic and environmental performance are improved, but also that of each agent are significantly improved. In addition, different energy types among multiple agents could be effectively used and regional joint energy storage is realized, improving the region flexibility level and resilience. • A multi-objective integrated energy systems interaction model is established. • The interaction model considers multiple energy types and uncertainties. • The model can improve economy and flexibility of each system agent and the region. [ABSTRACT FROM AUTHOR]

At present, the energy and environmental crisis is increasingly prominent, and how to solve the global energy and environmental problems is one of the challenges facing the world today. China is a major producer and consumer of organosilicon, so it is necessary to explore the stage of high energy consumption and high pollution in the organosilicon industrial chain. In 2022, the proportion of submerged arc furnaces of 25000 kVA and higher in China reached 40%. Further expansion of these furnaces in the future is inevitable. However, research on the life cycle assessment of organosilicon manufactured using large-capacity submerged arc furnaces is still blank.Therefore,in this paper, the life cycle assessment method is used to evaluate the synthesis process of chemical grade silicon-organosilicon monomer prepared by large capacity (33000 kVA) submerged arc furnace, and the dominant process in various environmental impacts is defined.At the same time, the measures to improve the production process of organosilicon monomer were put forward. In the production process of 1000 kg organosilicon monomer, global warming potential (GWP) contributed the most (73.43%) to the comprehensive environmental impact, followed by chronic public health impact (HTI, 10.55%). Energy consumption (EN) accounted for 8.50%, while resource consumption (RU) accounted for 7.43%. The contributions of other environmental factors were relatively small (0.09%).The energy consumption and environmental pollution in the production of organosilicon monomer can be reduced by strictly controlling the quality of raw materials into the furnace, using heat pump technology in the distillation unit, and comprehensively utilizing the heat of reaction by-products and steam condensate. [Display omitted] • This study is the first one in China to conduct LCA for the preparation of chemical grade silicon-organosilicon monomer synthesis process in 33000 kVA submerged arc furnace. It provides the best improvement suggestions for reducing the environmental load of the product life cycle process and achieving cleaner production. • In response to the national development policy of large-scale submerged arc furnace, the dominant process of preparing organosilicon monomer based on 33000 kva submerged arc furnace in various environmental impacts was clarified. • The main technical improvement directions are: Strictly control the quality of raw materials for submerged arc furnace; Strictly control the sulfur content and nitrogen content of carbonaceous reductant, and adopt the most suitable flue gas treatment technology route; Smelt with large submerged arc furnace; Use heat pump technology for distillation unit; Optimize the monomer synthesis device. [ABSTRACT FROM AUTHOR]

As the path of industrial eco-efficiency (IEE) needs to be re-examined from a multi-dimensional perspective, this research opens the internal "black box" of China's industrial eco-economic system driven by sustainable development goals (SDGs) and decomposes IEE into industrial production and sustainable development stages. It further divides the industrial production stage into two parallel subsystems of water use efficiency (WUE) and energy efficiency (EE) and derives SDG6 efficiency (SDG 6 E) and SDG7 efficiency (SDG 7 E) in the sustainable development stage respectively. The paper proposes a meta two-stage parallel entropy dynamic DDF-DEA model to re-measure IEE of 30 provinces in China from 2015 to 2021, discusses the relationship between overall efficiency and stage efficiency, and by using the Tobit model explores various types of influencing factors by subregion. The results show that the modified DDF-DEA model can explore China's IEE more accurately under SDGs. China's IEE is more stable from 2015 to 2021, with the efficiency value always fluctuating between 0.59 and 0.64, the overall level is low, and there is more room for improvement. There are significant inter-provincial differences in IEE and efficiency inertia. In more than 75% of the provinces SDG 7 E constrains IEE improvement, while the dynamic evolution of IEE and its subsystem efficiency varies across regions. Environmental regulation is a common factor affecting IEE across the country and in the east, central, and west regions, whereas the level of economic development, openness to the outside world, and urbanization rate are differential factors affecting IEE across the country and in the three major regions. This study offers new perspectives for exploring the path of IEE enhancement and providing empirical references for the collaborative construction of industrial ecological civilization. [Display omitted] [ABSTRACT FROM AUTHOR]

The operation of solar photovoltaic installations for many years may lead to problems such as reduced energy efficiency and aging of components. Under this framework, replacing certain components in the facility to extend the service life of the power plant is viewed, in some cases, as a feasible strategy. However, despite its enormous strategic interest for the solar PV industry, we find that revamping PV installations has been the focus of very few scientific publications, and even less so when it comes to analyzing real facilities. Thus, this paper performs a techno-economic analysis of three revamping strategies at a real PV power plant located in the south-east of Spain. The strategies consist of substituting, in each case, the PV modules, the inverter, and both components. The simulated results show that the highest production value by installed power is obtained when both the modules and the inverter are replaced. In the same vein, the economic analysis reveals this scenario as the most favorable in terms of profitability. Based on the results obtained, an interesting future line of research would be focused on the extension of the analysis at country level to study the joint impact of numerous revamping actions. • Techno-economic analysis of revamping projects of a real solar PV power plant. • Replacing modules and inverter emerges as the most feasible strategy. • Study's applicability extends to other revamping initiatives and geographical regions. • Economic outcomes allow the most cost-effective alternatives to be determined. • This research can contribute to efficient and sustainable energy planning. [ABSTRACT FROM AUTHOR]

With the comprehensive progression of China's electric power reform, the participation of hydropower in the spot market has become inevitable. For hydropower-dominated power grids, the tight hydraulic and electric coupling relationship between cascade hydropower plants and the policy requirement of clean energy consumption greatly increase the challenge of optimal scheduling. Based on the market characteristics and actual demand of the hydropower-dominated power grids, this paper discusses the day-ahead spot market trading mechanism to promote the guaranteed consumption of renewable energy. On this basis, a day-ahead optimal scheduling model is established which aims to minimize the comprehensive power purchase cost of the power system. The nonlinear factors in the scheduling model, such as the relationship between the water level and storage capacity of hydropower plants, the relationship between the water level and water consumption rate, and the constraints on the start-up and shutdown durations of thermal power units, are linearized through multiple linearization techniques. The original nonlinear model is transformed into a mixed integer linear programming (MILP) model and is then solved using a CPLEX solver. The developed model is verified by taking 10 thermal power plants and 22 hydropower plants in Yunnan Power Grid as a case study. The calculation time is 183.6s, the comprehensive power purchase cost of the system is 155.35·million CNY, and no abandoned water is generated in the three basins. The simulation results show that the proposed model has high solution accuracy and efficiency, and maximizes the hydropower consumption under the premise of meeting the minimum comprehensive power purchase cost. The findings of this study can provide a scientific basis for the development of the day-ahead spot market of hydropower-dominated power grids. [Display omitted] • A day-ahead spot market trading mechanism to promote consumption of renewable energy. • Day-ahead optimal scheduling of hydropower-dominated power grids under a spot market environment. • A novel MILP approach is employed to solve the model. • Proposed model has high solution accuracy and efficiency. [ABSTRACT FROM AUTHOR]

Energy quota trading is critical to both energy conservation and the achievement of dual-carbon goals. However, little remains to be done on total factor productivity and economic potential for Chinese industry. The purpose of this paper is to counterfactually model and explore the effects of energy quota trading on total factor productivity and economic potential in industrial sector. We propose a command-controlled DEA model and an energy quota trading DEA model through non-parametric linear programming based on two policy strategies of energy consumption control. We then assess total factor productivity and its growth sources for 35 two-digit industrial sector in China, as well as examine economic potential and optimal path forecasts for carbon peaking. The results show that total factor productivity and economic potential for energy quota trading are higher than those for command-controlled energy policy. Energy-saving effect can be achieved for energy quota trading because the total energy input is lower. Additionally, the sources of productivity growth differ in timing. The average economic potential and total factor productivity of different industries differ due to the heterogeneity of the production conditions and scale of development of individual market players. Moreover, the catch-up emission reduction path with an increase of 8% in output and 4.76% in energy consumption is the optimal carbon peaking path under energy quota trading. Our findings shed valuable light on China's efforts to expand energy quota trading for high-quality development in the context of carbon peaking. • Propose a command-controlled DEA model and an energy quota trading DEA model. • Assess TFP and its growth sources for industrial sector in China. • Compare the impact of different energy policies on economic potential and TFP. • Estimate the energy saving and emission reduction potential of energy quota trading. • Examine the optimal energy-saving and emission-reduction path forecasts for carbon peaking.. [ABSTRACT FROM AUTHOR]

The efficient removal of organic impurities in waste salts is significant for their recycling and environmental protection. The pyrolysis process of organic impurities in waste salt was investigated by thermogravimetric experiment to analyze the reaction kinetics characteristics. The results indicate that the pyrolysis reaction temperature range for the present waste salt is between 641.15 K and 726.22 K. The activation energy is 177.59 kJ mol−1, and the pre-exponential factor is 2.98 × 1011 s−1. Based on this, a numerical model was established to investigate the pyrolysis performance of organic impurities in practical tubular reactor. The effects of configuration and operating parameters on reaction performance and energy consumption were revealed. It was discovered that increasing the heating rate and wall temperature can accelerate pyrolysis rate but results in higher energy consumption. To balance reaction time and energy consumption, the heating rate of 15 K/min and heating temperature of 773.15 K are recommended. The content of organic impurities and bed porosity have minimal impact on the reaction rate and energy consumption, indicating that the tubular reactor has wide applicability in various working conditions. Additionally, reducing bed porosity can improve the processing rate and daily processing capacity. This paper provides a method to analyze the pyrolysis performance of organic impurities in waste salt, which is significant for designing and selecting working conditions of practical reactor for waste salt treatment. [Display omitted] • Pyrolysis kinetics characteristics of organic impurities were investigated. • Reaction temperature is between 641.15 K and 726.22 K. • Activation energy and pre-exponential factor are 177.59 kJ/mol and 2.98 × 1011 s−1. • Numerical model for pyrolysis process of organic impurities was established. • Effects of parameters on reaction rate and energy consumption were investigated. [ABSTRACT FROM AUTHOR]

The present study explores net carbon-neutral road transport options in the EU27 in 2050 from a well-to-wheel (WtW) perspective. To this aim, three scenarios have been developed regarding the evolution of the road vehicle fleet composition in terms of the degree of electrification of powertrains, and technical measures such as vehicle efficiency improvements, transport flow, and transport volumes have been considered. The fleet scenarios are further combined with four scenarios deploying different mixes of alternative fuels in 2050, including electricity, e-fuels, liquid/gaseous advanced biofuels and remaining fossil fuel components. Two different electricity production pathways are considered, as well, one of them assuming fully renewable electricity production, and an alternative one including residual shares of nuclear and natural gas power plants plus carbon capture and storage. The Joint Research Centre's DIONE model was used for the scenario calculations. From a tank-to-wheel (TtW) perspective, each scenario reduces energy consumption, mainly due to powertrain electrification, with EU27 TtW road vehicle energy consumption in 2050 ranging from roughly 650 to 1650 TWh, which is 20%–53% of that of 2019. For the most strongly electrified fleet scenario with optimistic assumptions on measures to improve vehicle efficiency, transport flow and transport volumes and renewable electricity production, well-to-tank (WtT) energy consumption is around 110–160 TWh, depending on the fuel scenario. If a moderately electrified fleet is propelled with e-fuels, even when assuming fully renewable electricity and optimistic measures, the WtT energy consumption increases ten times or more, to 1300 TWh (2150 TWh with pessimistic measures). The results show that fleet electrification is the strongest lever for WtW transport energy consumption reduction among the options considered. Other efficiency measures contribute significantly to energy savings, but their benefit decreases with increasing fleet electrification. The production pathways of fuels make a substantial difference for WtW fleet energy requirement if the fleet is moderately electrified, in which case e-fuels require significant amounts of additional renewable electricity. Nevertheless, fuel production pathways become irrelevant from an energetic point of view under very ambitious electrification scenarios since fuel volumes become marginal. The economic feasibility and the societal acceptability of different pathways towards WtW carbon-neutrality of road transport is out of scope of this paper and requires further investigation. • The study examines well-to-wheel scenarios for 2050 carbon neutral road transport. • Electrification is the strongest driver in transport energy consumption reduction. • Vehicle efficiency and activity impacts decrease with fleet electrification. • With high fleet electrification fuel production barely impacts energy consumption. • E-fuels require particularly large amounts of additional renewable electricity. [ABSTRACT FROM AUTHOR]

Battery electric industrial vehicles (BEIVs) offer significant environmental protection and decarbonization potential for rapidly growing logistics and transportation sectors in China. However, the limited driving range remains a major obstacle to the widespread adoption of BEIVs. Eco-driving is an effective solution that can improve energy efficiency and extend the driving range of vehicles by controlling driving behavior and speed. Therefore, this paper introduces an energy efficient speed planning method for BEIVs, in which data-driven models are developed to estimate energy consumption. Real-world driving data collected from a BEIV are used to model the energy consumption by curve fitting, random forest, and LSTM. The speed planning process is considered a Markov Decision Process, which is solved using Dynamic Programming and Q-learning, respectively. The case study of a battery electric forklift shows that speed planning can reduce energy consumption by 13%–27% compared to time-optimal speed, while only increasing the driving time by 10%–20%. • Energy efficient speed planning methods for electric industrial vehicles are proposed. • The speed planning method uses data-driven vehicle energy consumption models. • The energy consumption models are developed using random forest and LSTM. • The speed planning problem is formulated as a Markov decision process. • The speed planning problem is solved by Dynamic Programming and Q-learning. [ABSTRACT FROM AUTHOR]

The energy crisis caused by climate change is the most significant challenge of the 21st century. Single-objective and multi-objective optimization studies for buildings often only consider the building itself, without considering the impacts of climate change. However, in the future, energy-efficient design and appropriate operating parameters will be crucial considerations in the face of global climate change. This paper aims to discuss the impact of climate change in the humid-hot region over the past 60 years on the energy consumption of office buildings and propose countermeasures. Firstly, the meteorological data of Guangzhou from 1960 to 2020 is statistically analyzed, and four typical meteorological years and corresponding air conditioning design days are established. Secondly, Energyplus is used to simulate the building's energy consumption, and the energy consumption factors are analyzed for sensitivity using jEplus. Finally, a multi-objective optimization is performed using jEPlus + EA with the Non-dominated Sorting Genetic Algorithm (NSGA-II). The results found that climate change led to a significant increase in the duration of high outdoor temperatures during summer. Specifically, the daily dry-bulb temperature for air conditioning design increased by 0.4–0.7 °C, and due to climate change, the energy consumption of the office building increased by about 4.6 %. The main factor affecting the cooling energy consumption in summer was the indoor cooling set point (8.29%–30%), infiltration air volume (1.0%–8.6%), fresh air volume (3.2%–16.06%), the glazing solar transmission rate (1.25%–9.0%), building orientation (0%–1.43%), and relatively small influence of envelope insulation performance. The operation scheme determined by multi-objective optimization can reduce the cooling energy consumption of the building by 10.6%–16.8%, which can mitigate the rise in energy consumption resulting from climate change to a greater extent. [Display omitted] • Discuss the impact of climate change over the past 60 years on the energy consumption of office buildings. • There was a significant increase in the duration of high outdoor temperatures during the summer. • Multi-objective optimization is performed using jEPlus + EA. • Climate change caused energy consumption to increase by about 4.6 %. • Optimal solutions can reduce the cooling energy consumption of the building by 10.6%–16.8%. [ABSTRACT FROM AUTHOR]

The flexibility of power systems is critical to cope with the variability and uncertainty caused by the integration of renewable energy resources. Incorporating district cooling systems (DCSs) into power systems is an emerging approach to provide flexibility for renewable energy utilization. However, the existing models of DCS components, such as ice-storage cooling sources, cooling pipelines, and cold warehouses, are simplified, failing to fully utilize that flexibility, which brings difficulties for renewable energy utilization. Therefore, this paper proposed a coordinated operational strategy for an integrated power and cooling system combining detailed operating characteristics of DCSs to exploit the potential flexibility of DCSs for better integration of renewable energy. The primary cooling source components, including the chiller, ice storage tank, pump, and cooling tower, are established to model a detailed process of cooling generation and storage. The thermal inertial of district cooling networks (DCNs) is formulated as a quasi-dynamic model by considering dynamical temperature characteristics and transmission delay to exploit the virtual energy storage capacity of the cooling water pipeline. Moreover, the thermal inertia of the demand side, such as buildings and cold warehouses, is considered to utilize thermal storage capacity. The objective function of the proposed optimization model is to minimize the overall operating cost of the integrated power and cooling system. Numerical simulations show that the proposed method could reduce the operating cost by 15.4% and the wind curtailment rate by 86.2%, indicating better performance of the proposed method in improving economic benefits and reducing renewable energy curtailment. • We proposed a coordinated operational strategy combining flexible resources of DCSs. • We established a detailed model of cooling source including chiller, ice storage tank, pump, and cooling tower. • The thermal inertial of district cooling networks is formulated as a quasi-dynamic model. • The combined cooling and power system can reduce the costs and wind curtailment. [ABSTRACT FROM AUTHOR]

Acid gas enrichment (AGE) is a new method to increase the H 2 S conversion in conventional Clause process. In this paper effect of methyl diethanolamine (MDEA) flow rate, MDEA temperature, acid gas flow rate, and AGE tower pressure on the outlet H 2 S and CO 2 concentration and energy consumption have been studied. A novel procedure based on enthalpy of combustion in the reaction furnace and incinerator is conducted for energy consumption calculations. By changing MDEA and inlet acid gas flow rates, a maximum H 2 S concentration in the enriched stream could be attainable. Increasing MDEA temperature will decrease maximum H 2 S concentration and enriched acid flow rate; also, CO 2 concentration in off-gas will be decreased. By increasing AGE pressure, maximum H 2 S concentration in the enriched stream will be diminished. At higher MDEA flow rates, lower AGE pressures will bring higher H 2 S concentration in enriched acid gas stream. According to flame temperature simulation in the reaction furnace, energy consumption is directly related to H 2 S concentration in the sulfur recovery unit (SRU) feed stream. Results show that maximum H 2 S concentration in the enriched acid gas stream has the best conditions from energy point of view. At optimum conditions, H 2 S concentration in enriched acid gas is 62.45 mol%, which has been increased by 24.8%, compared with existing plant conditions, and energy consumption has been decreased by 25.05 GJ/h, and CO 2 concentration in off-gas is 94.94 mol%. • Study the effect of process variables on the AGE streams by ProTreat software. • Determine the relation between reaction furnace temperature and H 2 S concentration. • Generate an EES procedure for heat of combustion in reaction furnace. • Evaluate the fuel gas consumption and therefore energy consumption. • Multi-objective optimization based on the TOPSIS method. [ABSTRACT FROM AUTHOR]

Despite the growing interest, a thorough explanation of how geopolitical and energy security risks relate to carbon emissions is still missing. To fill this gap, this paper investigates the impact of these factors, both individually and jointly, on carbon dioxide emissions in European Union countries from 1990 to 2021, also accounting for the effects of per capita final energy consumption and real income. Empirical findings from the augmented mean group and novel dynamic common correlated effects mean group estimator reveal that the strongest long-term escalation in carbon dioxide emissions is attributable to final energy consumption, followed by energy security risk. Geopolitical risk, however, has a mild short-term mitigating effect on carbon emissions. In the long run, this effect is statistically insignificant. The joint effect of energy security and geopolitical risks indicates that they may reinforce each other and lead to even higher carbon dioxide emissions in the long run. Therefore, policies aimed at reducing carbon emissions need to address both risks simultaneously, while also considering the causal relationships observed between the variables under study. • Examined the individual, joint, and causal geopolitical and energy security risks effects on CO 2 emissions. • Energy consumption and energy security risks were identified as the main drivers of CO 2 emissions. • Energy security risk's effect on CO 2 emissions was escalating, gradual, and long-lasting. • Geopolitical risk's effect on CO 2 emissions was mild, short-term, and mitigating. • Found significant long-term joint effect of geopolitical and energy security risks. [ABSTRACT FROM AUTHOR]

The operation of tidal pumping station has an energetic impact on energy course of massive water pumping for a long time. Thus, a multi-objective optimal operation model was developed to optimize the operation scheme and reduce the energy consumption of the tidal pumping station. In the model, a day is divided into several sub-periods according to the regulation of water level in tidal reach. The partition of sub-periods, the number and blade angle of operating units in each sub-period are adjusted to minimize daily operation energy consumption. And the less switching times, the better the reliability and durability of the unit, the lower the failure rate. Therefore, the reduction of wearing parts' service life is quantified for each switching time in the form of cost. A case study is performed with the tidal pumping station of the South-to-North Water Transfer Project in China. In this paper, energy consumption and switching times are quantified as costs respectively, and the result shows that the daily energy consumption could be saved about 2.28%–6.56%, and the switching times reduced at least 60%. Therefore, this paper could provide an effective optimal operation scheme for the tidal pumping station whose operation is affected by tides. [ABSTRACT FROM AUTHOR]

Addressing energy poverty and promoting energy equity are crucial for China to eradicate poverty comprehensively and achieve shared prosperity. Through a combined dynamic evaluation approach to accurately measure the extent of energy poverty, this paper analyzes the staged time series, spatial distribution characteristics of energy poverty applies the Dagum Gini coefficient and variance decomposition to observe the imbalance characteristics of energy poverty and identify the sources of variation. The objective of this paper is to mitigate the influence of spatial disparities on coordinating efforts to eradicate energy poverty across different regions. Consequently, provides valuable insights for decision-making to effectively eradicate energy poverty and expedite the achievement of energy equity. The results indicate: The reduction process of energy poverty in China exhibit phased characteristics from 2000 to 2020, with significant variations in reduction levels among provinces. Different sources of energy poverty exhibits imbalanced spatial distribution and evolution. The overall disparity in energy poverty widens as the phases progress, with characteristic nodes experiencing a temporal delay of 2–3 years compared to the policy nodes. Inter-regional disparities are the primary spatial source of energy poverty differences, while energy use capacity is the main factor contributing to variation. [ABSTRACT FROM AUTHOR]

The coordinated decision-making approach for considering sequential activities of the supply chain results in additional benefits by optimizing production, inventory, and distribution operations. Accordingly, this paper proposes a mixed integer linear mathematical model to optimize a multi-period production routing problem utilizing electric vehicles. The proposed model optimizes the total cost associated with fixed and variable production, holding inventory, and routing, including the fixed cost of utilizing electric vehicles and travel time. However, mileage limitation is one of the main restrictions of utilizing electric vehicles in performing deliveries which is strongly affected by the consumed energy. Although optimization of the routes of vehicles can facilitate using them, considering the variation of travel speed network links during different times of the day because of traffic conditions can obviously affect the required energy to perform the assigned deliveries. To the best of our knowledge, this paper is the first to study simultaneous multi-period dynamic production routing problems using a set of heterogenous electric vehicles whose travel time of links can vary by dividing each production period into several hourly time intervals to capture different traffic conditions. Finally, a series of capable and hybrid metaheuristic algorithms are designed and implemented to solve this problem in a real-case dimension, and all proposed algorithms are compared. • Proposing a multi-period dynamic electric vehicles production routing problem. • Developing a coordinated decision support system to optimize the supply chain. • Considering the variation of travel speed network that affect energy consumption. • Designing a mix integer linear mathematical model to address proposed problem. • Developing a set of capable metaheuristic algorithms to solve proposed problem. [ABSTRACT FROM AUTHOR]

Pressure servo control plays a crucial role in the majority of industrial applications that use compressed air as their power source. How to reduce the energy consumption of the pneumatic system while ensuring the high accuracy of pressure control remains a key problem to be solved. This paper proposes a Gaussian Process (GP) 1 model predictive controller (MPC) applied to a pressure servo control system based on high-speed on-off valves. The smallest quantity of collected data is used to create the GP model. The entire model, which includes some predictive data, is produced once the generated model has been optimized. The GP model is combined with the MPC to perform comparative experiments using an industrial controller with a Field Programmable Gate Array (FPGA). The system responds quickly and with little tracking error in steady-state response studies as well as dynamic response experiments. The GP-MPC achieves the best outcomes in the dynamic comparison tests. The root mean square error (RMSE) is just 2.42 kPa, and the overshoot is less than 59.9% of the Proportional-Integral-Derivative (PID) controller and 68.5% of the sliding mode controller (SMC). Although the compressed air consumption of GP-MPC is basically the same as that of PID, it is significantly better than SMC, with a total saving of about 70.2%. All the experiments prove that the controller proposed in this paper can effectively improve the energy efficiency and tracking accuracy of the pressure servo control system. [Display omitted] • A Gaussian process model for the high-speed on-off valve is developed which is able to predict uncollected data. • A model predictive controller is established by combining the Gaussian process model with rolling optimization method. • The pressure tracking and energy-saving performance are intended to be improved by using the objective function. [ABSTRACT FROM AUTHOR]

The utilization of renewable energy has attracted much attention with the deterioration of the environment. Solar energy is widely distributed and has huge reserves. But, solar energy is also volatile and intermittent. This brings difficulties to the large-scale grid connection of solar energy and the distribution of power resources. The key to solve this problem is to provide accurate and stable short-term prediction for solar power generation. For this purpose, we present an attention-based Bayesian sequence to sequence (Seq2Seq) model within decomposition-ensemble strategy. Firstly, an influential factors analysis is performed on the data about meteorological conditions, operation status of the photovoltaic panels and historical solar power generation sequences to obtain the optimal combination of the influential factors. Secondly, this paper proposes a novel decomposition-ensemble framework based on complete ensemble empirical mode decomposition with adaptive noise and independent component analysis to mine the intrinsic modes of the solar power generation time series. Thirdly, this paper presents an attention-based Bayesian Seq2Seq method for modeling the relationships between solar power generation and influential factors. Using a real-world dataset from State Administration of Electricity Investment Science and Technology of China, a case study, comparative analysis and robustness checks are conducted, through which the contributions of the methods in the proposed model and the superior performance of the model are demonstrated. • Short-term solar power generation prediction model established. • An influential factors analysis is designed to obtain the optimal combination of the influential factors. • A novel decomposition-ensemble framework is proposed to mine the intrinsic modes of the solar power generation sequence. • An attention-based Bayesian Seq2Seq method is presented. [ABSTRACT FROM AUTHOR]

Sustainability pertains to the interplay between environmental, economic, and social systems at varying scales. In transportation systems, vehicles use different fuels to fulfill their energy requirements. However these fuels emit green house gases that pollute the environment. Taking into account this fact, the present paper delves into the investigation of a multi-objective multi-item fixed cost solid transportation problem with various driving styles. Also, three types of fuels are incorporated in the proposed model. The primary focus of this paper is to maximize profit by reducing breakage of the items along with reducing carbon emission and time of transportation. In real-life situations, the parameters of multi-objective multi-item fixed cost solid transportation problem may not be tackled crisply. Hence triangular intuitionistic fuzzy numbers are introduced in the proposed model. The score function is employed to convert intuitionistic fuzzy problem to deterministic one. Thereafter, a methodology is developed to solve the proposed model involving different driving styles and is extended to consider the optimistic, pessimistic, and mixed view of the decision maker. Comparisons are made among the compromise solutions that are derived from different approaches and are shown by various charts and graphs. A case study is included to illustrate the applicability and validity of the proposed methodology. • A sustainable multi-objective 4DTP with multiple items and fixed charge is modeled. • Three types of fuels: diesel, petrol and CNG are incorporated in the proposed model. • Driving style is included to analyze the fuel consumption and breakability of items. • The decision-maker's optimistic, pessimistic, and mixed viewpoints are discussed. [ABSTRACT FROM AUTHOR]

Alternative fuels are the subject of growing interest among both researchers and practitioners. In this aspect, to properly handle the problem of proper evaluating alternative fuels, appropriate methods, and tools are undoubtedly needed. Since the evaluation of sustainable alternative fuels similarly, like most sustainability problems, has a multi-criteria character, multi-criteria methods have the potential to be employed here. Analysis of the alternative fuels domain indicates that classical multi-criteria methods are not exactly suitable for use in cases where, instead of a single matrix for evaluating alternatives, we have a set of them collected for particular periods. Therefore, developing a dedicated approach that considers the specifics of a given problem is necessary. For this purpose, the paper proposes a new Temporal Preference Ranking Organization Method for Enrichment Evaluations II (PROMETHEE II) method for multi-criteria temporal evaluation. Hence, the presented method solves the limitations occurring in previously developed multi-criteria methods. In addition to the classical aspect of evaluating the options under consideration, the proposed method makes it possible to accurately grasp the dynamics of the outcomes and put them into an easy-to-interpret result. A practical contribution of the paper is applying the developed method to a multi-criteria temporal evaluation of sustainable consumption of alternative fuels in road transport. For this purpose, an assessment model was built based on the data for 32 European countries acquired from the European Union's statistical office (Eurostat). Preference imprecision modeling and model sensitivity analysis demonstrated the analytical potential of the developed sustainability assessment model and, simultaneously, the Temporal PROMETHEE method's superiority. The comparative analysis of the Temporal PROMETHEE II method clearly shows that the results are robust and reliable, and the developed method is suitable for evaluating various temporal sustainability problems. The research demonstrated that a model alternative fuel mix and its shape as well as progression is maintained by Scandinavian countries such as Norway and Sweden. [Display omitted] • A new temporal method for multi-criteria sustainability assessment. • Public available algorithm and code of method developed. • An evaluation of sustainable consumption of alternative fuels. [ABSTRACT FROM AUTHOR]

Warehouses are major contributors to the rise of greenhouse gas emissions in supply chains. Thus, it is not surprising that the attention of academic research to green and sustainable warehousing has been growing in recent years. This attention has led to an increasing number of publications in this field, which is why a systematic literature review on the topic of green warehousing is proposed in the paper at hand. This work provides a comprehensive overview and classification of the existing research on green warehousing, summarizes and synthesizes the available knowledge on this topic, and identifies key trends. Based on the evaluation of the literature, promising ideas for future research are proposed. Citation and network analyses are carried out to evaluate the relationships among the topics covered. The results show an increasing interest in sustainability topics within the warehousing literature, where energy saving has been the most frequently studied objective, followed by environmental impact of warehouse buildings, and green warehouse management in general. The green warehousing literature, however, lacks case studies and empirical data. The main contribution of this paper is an exhaustive summary of the state of knowledge on green warehousing in terms of the macro-themes addressed, the specific topics investigated and the methodological approaches, including a comprehensive and systematic classification of the relevant literature. An outline of managerial guidelines about green warehouse management and the propositions of future research ideas contribute to the further development of this emerging research field. • A literature review on green warehousing is presented. • A classification framework for the state-of-the-art is developed. • A thematic analysis highlights the key research topics on green warehousing. • Bibliometric and network analyses are used to corroborate the review results. • Future research in this field are outlined. [ABSTRACT FROM AUTHOR]

The objective of the traditional dispatching optimization model is to find the most economical scheduling mode, while ignoring the efficient use of renewable energy. To properly arrange thermal power generation and renewable energy power generation, this paper constructs an optimization model of the combined wind–photovoltaic (PV)–thermal dispatching system under a carbon emissions trading (CET) mechanism. The contribution of this paper is twofold. First, a dispatching optimization model is proposed, which not only considers the total cost but also takes into account the impact of the renewable energy spillage rate on the dispatching system. As a factor affecting renewable energy consumption, a carbon emissions trading mechanism has also been introduced into the dispatching system. The second contribution is to develop a model for predicting the output of renewable energy generation. Then, the optimization model is applied to the auxiliary power dispatching system of the Tianzhong ±800 kV UHV DC transmission channel in Xinjiang. The results indicate that under a carbon emissions trading mechanism, thermal power generation will be limited by the carbon emissions cost, thereby reducing the total cost of the system. Optimizing the spillage rate increases the share of renewable energy in the dispatching system, resulting in a 3.5% reduction in the actual spillage rate. The proposed model alleviates the serious problem of electricity being abandoned in the existing scheduling mode and helps to realize economical low-carbon dispatching of the power system. [ABSTRACT FROM AUTHOR]

Abstract Currently, wind and solar energy have not been well utilized in China, and the rate of abandoned wind and solar facilities in China is increasing annually. Renewable portfolio standards (RPS) are an effective system for promoting large-scale renewable energy integration and consumption and realizing the goal of renewable energy development. To clarify the interactive relationship between the different levels and multiple actors in RPS, this paper uses policy network theory to analyze the interactive relationship of stakeholders from the three aspects of network subject, network structure and network interaction. The policy network of the RPS consists of the policy community, professional network, intergovernmental network, producer network and issue network. Under the RPS, to meet their own needs, participants affect the formulation and implementation of the RPS through the actions of confrontation, alliance, competition and cooperation. In different stages of developing the RPS, the openness and integration of policy networks change accordingly. This study overcomes the limitations of past simple analyses of the actor relationship in the policy process and thereby offers certain reference significance. Highlights • The actors of RPS policy networks are analyzed. • This paper analyze the game relationship of the stakeholders from three aspects. • This research reveals the network relationship between multiple stakeholders. • This study breaks through the limitations of past analysis of actor relationship. [ABSTRACT FROM AUTHOR]

Abstract Railways are a rather efficient transport mean, and yet there is increasing interest in reducing their energy consumption and making them more sustainable in the current context of climate change. Many studies try to model, analyse and optimise the energy consumed by railways, and there is a wide diversity of methods, techniques and approaches regarding how to formulate and solve this problem. This paper aims to provide insight into this topic by reviewing up to 52 papers related to railways energy consumption. Two main areas are analysed: modelling techniques used to simulate train(s) movement and energy consumption, and optimisation methods used to achieve more efficient train circulations in railway networks. The most used methods in each case are briefly described and the main trends found are analysed. Furthermore, a statistical study has been carried out to recognise relationships between methods and optimisation variables. It was found that deterministic models based on the Davis equation are by far (85% of the papers reviewed) the most common in terms of modelling. As for optimisation, meta-heuristic methods are the preferred choice (57.8%), particularly Genetic Algorithms. Highlights • Modelling and optimisation methods for railways energy efficiency are reviewed. • Modelling methods provide reliable simulations of the trains' energy consumption. • Optimisation methods help obtaining the most efficient solutions for train traffic. • Most used modelling methods are deterministic, based on Davis equation. • Most used optimisation methods are meta-heuristics, mainly genetic algorithms. [ABSTRACT FROM AUTHOR]

Abstract The Chinese government conducted a policy experiment on real estate-led economic growth on such a massive scale, which is unprecedented. This paper empirically analyzes the impact of real estate investment on energy efficiency. Provincial level panel data of China during 2004–2012 was used for the analysis. Two-way Fixed Effect model was applied to test the impact of real estate investment on total-factor energy efficiency (TFEE). The findings in this paper are: a certain degree of the real estate-led model of economic development can improve TFEE of the economy. Developing countries can gain economic development and energy efficiency improvement simultaneously by developing the real estate industry to a certain extent. But the impact of real estate investment on TFEE exhibits a nonlinear characteristic, that is, when the real estate investment proportion exceeds the threshold value, the impact of real estate investment on TFEE will take a reversal; Except Beijing, Shanghai, Hainan and Zhejiang provinces, to some extent, improving real estate investment can be helpful to improve TFEE in most of China's provinces. Highlights • The Chinese government conducted a policy experiment of real estate-led economic growth and the scale is unprecedented. • The impact of real estate investment on TFEE exhibits an "inverted-U shaped" curve. • A certain degree of the real estate-led model of economic development can improve TFEE of the economy. • Except few provinces, most of the Chinese province are lying on the left side of the curve. • Panel data is used to assess the effect of real estate investment on TFEE. [ABSTRACT FROM AUTHOR]

Abstract Green and sustainability issues are being considered extensively in industry and in the literature. Most of the green-related effects in the manufacturing sector can be modeled by the costs associated with the production machines due to their resource consumption and/or pollutant emission, as a part of the operation cost. Accordingly, taking into account the machine processing costs alongside the other production criteria is one of the main approaches to consider green issues in the production management problems. In the same way, scheduling as one of the major operational problems in the factories can benefit this point of view to involve sustainability aspects. In this paper, the aforementioned modeling approach is taken one more time to establish a formulation for a green scheduling problem in uniform parallel machine environments. Here, it is assumed that the machines have different processing cost rates, i.e. they are different with regard to sustainability. The considered problem objectives are the total green cost and the makespan which are minimized simultaneously with the aim of earning Pareto optimal solutions. To this end, the ε -constraint method is first used to convert the problem into single objective problems. Then an existing heuristic is improved to solve these problems and estimate the Pareto solutions that show the trade-offs between the green cost objective and the ordinary time objective. Moreover, having a rigorous analyze in a theorem, some useful upper bounds for the gap of the green cost of the heuristic solution from optimality is derived. Finally, conducting numerical experiments at the end of the paper, the superiority of the improved heuristic to the previous one and its effectiveness in approximating the Pareto optimal solutions is demonstrated. Graphical abstract Image 1096644 Highlights • A green scheduling model in uniform parallel machine environments is developed. • The machines are assumed to have different sustainability costs. • The aim is to minimize both the total costs and the makespan, simultaneously. • The ε-constraint method is first used to convert the problem into single objective problems. • An improved version of an existing heuristic is utilized to solve the problems. [ABSTRACT FROM AUTHOR]

Abstract Water pumping systems are widely used in industrial and civil applications. During their operational life, they consume energy and materials (mainly for installation and replacement of components). The aim of this paper is to optimize the pump operations in order to save energy and enlarge the operational life of the pumps and their components. This paper addresses the multi-period (e.g. 24-hours time horizon) optimization of pumping systems. To this end, we have developed a simulation-based optimization approach including novel relevant technical features of a generic but realistic pumping system, namely cavitation and overflow. The proposed multi-period optimization differs further from the classic static optimization (i.e. at the design stage), proposing a new dynamic approach in which pump activation is steered dynamically for an optimal management of the variability of water inflow. Furthermore, the problem includes constraints on the number of pump activations allowed to reduce material strain. The performances of different meta-heuristic optimization algorithms (e.g. genetic algorithm, simulated annealing and particle swarm optimization) for solving the problem are compared. The numerical results show that energy savings are possible with the dynamic approach and that particle swarm optimization and simulated annealing algorithms provide the most suitable solutions for this problem. Highlights • Three algorithms are compared to minimize energy pump consumption. • The pump model includes energy, cavitation, overow and activation frequency. • Static and dynamic optimizations are investigated. • The dynamic optimization can reduce the energy consumption by 6.5%. [ABSTRACT FROM AUTHOR]

Abstract The extensive use of energy-saving appliances can promote household energy conservation, which, to a certain extent, can help to alleviate haze pollution. Based on the Theory of Planned Behavior and Normative Activation Model, a research framework is developed in this paper to explore residents' intention to purchase energy-saving appliances under the influence of severe haze pollution. This paper takes the residents in Xuzhou as the object of survey. Then, the structural equation model and Bootstrap method are used to analyze the valid measurement questionnaires. The results demonstrate that residents had a very strong intention to purchase energy-saving appliances under the influence of haze pollution from the perspective of self-interest and altruism, respectively. Attitude, subjective norm, perceived behavior control and personal norm are the key factors influencing residents' purchasing intention of energy-saving appliances under frequent haze pollution, of which attitude is the most powerful influence. In addition, ascription of responsibility is a partial mediator between the relationship of awareness of consequences and personal norm, and together with awareness of consequences can positively influence personal norm. Finally, based on the empirical results, this paper proposes measures to guide consumers to buy energy-saving appliances. [ABSTRACT FROM AUTHOR]

Abstract As an effective way to reduce power consumption, coasting has been widely used during the operation of high-speed train. However, as a typical electromechanical coupling system, the different speed of inert point may lead to complex dynamic behavior of high-speed train and affect the safety operation. Based on this, this paper proposes an optimization methodology of the coasting point to simultaneously ensure the safety operation and energy saving of the train. A mathematical model of permanent magnet synchronous motor (PMSM) during coasting is established, and the local stability and bifurcation conditions are derived based on Routh-Hurwitz criterion and the bifurcation theory. And the influence of load torque variation on bifurcation characteristics of equilibrium point and stable operation domain of the system is studied. Moreover, detailed numerical simulations are carried out to analyze the effect of load torque on the coasting point, and an optimization methodology is proposed. Experimental platform is set up to verify the correctness and the effectiveness of the proposed optimization methodology. The results show that the variation of the motor parameters may the occurrence of Fold and Hopf bifurcations, and the increase of load torque may change the bifurcation characteristics and lead the system to transit into a chaotic state and lose stability. The experimental results show that the optimization method proposed in this paper can effectively reduce the energy consumption by 7.7 percent. Highlights • A dynamic model of PMSM for high-speed train during coasting is established. • The influence of the change of load torque on the stable operation domain of high-speed is studied. • Under the premise of ensuring the safe operation of high-speed train, an optimization methodology of the coasting point is proposed. [ABSTRACT FROM AUTHOR]

Abstract Onshore and offshore wind turbines may have different environmental sustainability due to their own characteristics, and this information is important for future growth of wind power. The paper uses life cycle assessment (LCA) to estimate the life-cycle greenhouse gas (GHG) emissions of onshore and offshore wind turbines with the nominal capacity of 2 MW, to advance our understanding of onshore and offshore wind energy and to inform policy, planning, and investment decisions for future growth of wind power. Results show that the life-cycle GHG emission intensity is 0.082 kg CO 2 -equivalent (eq)/Megajoule (MJ) for onshore wind turbine and is 0.130 kg CO 2 -eq/MJ for offshore wind turbine, respectively. Offshore wind turbine has larger life-cycle GHG emissions than onshore wind turbine, owing to the floating platform fixed in sea. Onshore and offshore wind turbines have much smaller life-cycle GHG emission intensity than coal power plants. If the installed wind turbines in 2014 displace coal, the saved GHG emissions can roughly reach 5.08 × 107 t CO 2 -eq, accounting for 0.09% of global GHG emissions in 2012. The sensitive analysis shows that the lifetime and energy production of wind turbine have large influences on the GHG emission intensity of both onshore and offshore wind turbines, implying that it is an effective way to prolong the lifetime of wind turbine and increase the energy production of wind turbine to reduce the GHG emission intensity of wind turbine. The sensitivity analysis further shows that the distance from wind turbine factory to wind farm site has more significant influence on the life-cycle GHG emission intensities of both onshore and offshore wind turbines than that from wind farm site to the recycling and landfill locations, suggesting that the wind farm site and the wind turbine factory should be as close as possible. Highlights • The paper presents a first attempt to analyse the life-cycle GHG emission of onshore and offshore wind turbines with same capacity size. • Offshore wind turbine has larger life-cycle GHG emissions than onshore wind turbine. • Onshore and offshore wind farms have much smaller life-cycle GHG emission intensity than coal power plants. • Lifetime and energy production have large influences on GHG emission intensity of onshore and offshore wind turbines. • If the installed wind turbines in 2014 displace coal, the saved GHG emissions can roughly reach 5.08 × 107 t CO 2 -eq. [ABSTRACT FROM AUTHOR]

Abstract Environmental issues and cleaner production are getting increasing attentions currently, making the clean production and sustainable consumption with low emissions significant. Traditional energy vehicles are increasingly unsuitable for the development of current society. It's a trend in the current vehicle market to accelerate the substitution of new energy vehicles for traditional energy vehicles. This paper makes a theoretical study on the development of new energy vehicle market supported by the government. First, this paper establishes a Cournot duopoly model which includes domestic and imported new energy vehicle manufacturers. Through the analysis of this model, this paper argues that government support policies such as subsidies and tariffs can effectively expand the market share of domestic new energy vehicles with less technology. Yet this approach is unsustainable, because domestic new energy vehicles with less technology can only survive in the market by shortening the gap with imported brands. Secondly, this paper establishes a Stackelberg model with product price as the decision variable and find that when there is a technical gap, the first mover advantage in the market will no longer exist. Finally, this paper compares two kinds of subsidies for domestic new energy vehicles and shows that the two subsidies are not different from subsidizing consumers or enterprises in expanding market share, and the consumer can obtain higher social welfare level as the subsidy object. Highlights • New energy vehicles boom with obvious substitution effect on traditional ones. • Both subsidies and tariffs can expand the production of domestic new-energy cars. • First move of domestic brands into the market can narrow the price difference. • First mover advantage no longer exists in the Stackelberg game. • Consumer subsidy is superior to enterprise subsidy in new energy auto industry. [ABSTRACT FROM AUTHOR]

Abstract The governance of industrial energy use in recent times has been driven by the quest for greater energy security and sustainable industrial growth. Sustainable use of energy necessitates a rethink in the production, use and governance of energy resources that stimulates and supports transition to a more effective and efficient energy system. However, how do we ensure an effective transition in industrial energy use in the midst of gross energy poverty within a developing country context? This paper explores the motives and drivers of changes in energy use within the industrial sector. Statistical data from published reports, as well as informal interviews of stakeholders in Nigeria's industrial sector were used. Following analysis of data, four important salient phases (eras) of industrial energy transition in Nigeria's manufacturing sector was established. These distinct eras are: (1) Grid-dependent era; (2) Self-generation era; (3) Industrial energy outsourcing era; (4) Industrial energy conservation era. The study reveals that: outsourcing of industrial services; cost reduction; and business realignment motives are key drivers of transitions in Nigeria's industrial sector. This paper concludes by highlighting the implications of these changes for the future of energy and sustainable industrial growth in Nigeria. Highlights • Industrial energy transitions in Nigeria is happening but rather slow. • There is high dependence on biofuels and wastes for certain industrial processes. • Cost reduction and business realignment motives are key drivers of transitions. • Outsourcing industrial energy services is viewed as one way to drive transitions. [ABSTRACT FROM AUTHOR]

Abstract With energy shortage and environmental pollution becoming increasingly severe problems, energy-efficient scheduling is attracting much more attention than before. This paper addresses the flexible job shop scheduling problem (FJSP) with the objective of minimizing total energy consumption. Firstly, the total energy consumption of the workshop is discussed and modelled. Then, based on two different modeling ideas namely idle time variable and idle energy variable, six new mixed integer linear programming (MILP) models with turning Off/On strategy are proposed. The original objective function of the model based on idle time variable is nonlinear and linearization is needed. For the linearization, additional decision variables and constraints are added. The objective function of the model based on idle energy variable is originally linear and concise. Lastly, those six proposed models and the existing one are detailedly compared and evaluated under both the size and computational complexities. The correctness and effectiveness of all MILP models are verified by using CPLEX SLOVER to carry out numerical experiments. The results show that the MILP models based on different modeling ideas vary remarkably in both size and computational complexities, and all the six models proposed in this paper outperform the existing model significantly. The proposed models will help the enterprises rationalize production so as to reduce energy consumption and costs. [ABSTRACT FROM AUTHOR]

Abstract In this paper, we adopt a non-radial directional distance function to analyze the efficiency and shadow price of CO 2 emissions of coal-fired power plants. Using a sample of Shanghai's 20 coal-fired power plants, we find that the efficiency performance of power plants remained steady during the period 2007–2010. On average, the carbon abatement costs of Shanghai's power plants increased from 20.58 yuan/ton to 39.67 yuan/ton during 2007–2010. Although the average shadow price of the power plants increased during the period, the price was still well below shadow prices for the energy consumption of the industries, and a little lower than the trading price in the ETS markets. In addition, the paper discusses the implications of the findings. [ABSTRACT FROM AUTHOR]