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The pulp and paper industry faces several challenges linked to climate and environmental impact, resource efficiency, rising energy prices, increased competition for biomass resources, and declining demand for traditional printed paper products. However, these challenges also offer strategic opportunities for the industry to develop into a competitive, resource-efficient, and low-carbon industry in line with a biobased economy. Against this background, this paper aims to analyse current energy strategies in the pulp and paper industry in Sweden. Specifically, the paper analyses how companies combine continuous process efficiency to reduce energy costs with activities that could be developed into new energy-related products to increase revenue. Most of the analysed companies work to various degrees with both these strategies, employing methods that include improving energy efficiency, energy security, and energy conversion, as well as developing a wide range of biobased energy products. However, our study indicates that there is an untapped potential associated with energy product development, and we conclude that energy efficiency measures can free up resources, enabling the development of new energy products. Finally, several potential managerial outcomes and implications are outlined. • The paper analyses the energy strategies in the pulp and paper industry in Sweden. • Energy strategies focus both to improve energy efficiency and develop new energy-products. • The companies' energy strategies combined exploitation and exploration. • A number of managerial implications are presented. [ABSTRACT FROM AUTHOR]

Deploying renewable energy to unit design energy-efficient technologies may fulfill the additional demand of various energy-intensive industries for their sustainability and resiliency. Multiple Stage Evaporator (MSE) is one of the most energy-intensive units used to extract the water content of the weak liquor in various industries, including the paper industry. This energy intensiveness may be reduced by integrating various Energy Reduction Schemes (ERSs). Hence, this work proposes a ERSs integrated MSE model to analyze its energy efficiency. These ERSs includes Thermo-Vapor Compressor, Steam-, Feed- Split, Feed Preheater, and Flash Tanks. Further, the performance of the proposed model is investigated under two important real-time plant complexities: Boiling Point Elevation and Fouling to achieve more realistic results. The performance analysis is initiated with the formulation of a nonlinear constrained optimization problem to increase the steam economy (SE). Also, the simulation is extended to validate at two different product concentrations (52% and 65%) by employing state-of-art optimization algorithms: CONOPT, and SCA in GAMS and MATLAB respectively. The simulated results shows an increment 1.64% and 1.37% of SE for both 52% and 65% concentration of weak liquor respectively in case of CONOPT than SCA. Also, the waste heat of the condensate, feed, and product may be further utilized for the heat recovery by incorporating the flash tanks which leads to a countable energy saving. Eventually, integrating the solar fields: PTC and LFR ensures a notable reduction in conventional heat utilization by 85.96% and 92.85%, respectively and hence, enhance the energy efficiency. • Expounded energy modeling of ERSs based MSE with BPE and Fouling. • Implementation of NLP and metaheuristic approaches for performance optimization. • Reutilization of waste heat to maximize the energy efficiency. • Integration of solar fields (PTC and LFR) to reduce conventional fuel utilization. • Prospecting the challenges and direction of energy management optimization. [ABSTRACT FROM AUTHOR]

Current management of solid waste from pulp and paper activities represents an environmental and economic burden worldwide due to pollution emissions. This study investigates the potential of hydrothermal carbonisation (HTC) treatment as a sustainable alternative for producing cleaner and energy-dense solid fuel from paper mill sludge. The effect of process parameters (temperature, reaction time and solid load) on hydrochar fuel formation from paper sludge was evaluated and, for the first time, the paper sludge-derived hydrochar was optimised to maximise the mass yield and calorific value using response surface methodology (RSM). The physicochemical characteristics, thermal fuel behaviour, energy recycling efficiency and electricity generation potential were assessed by proximate and ultimate analysis, thermogravimetry, bomb calorimeter, scanning electron microscopy and process energy assessment. Results showed that hydrochar fuel formation and properties were mainly influenced by the process temperature and residence time, and governed by dehydration and decarboxylation reactions which reduced the atomic H/C and O/C ratios by 35.5% and 64%, respectively. The produced hydrochars presented low sulphur, nitrogen and ash content with a maximum calorific value (HHV) of 22.9 MJ/kg, equivalent to the HHV of coal for commercial utility in South Africa. The HHV of the hydrochar corresponded to a 49.80% increase over the HHV of the initial feedstock. The optimum operating conditions were 231 ± 1 °C and 1.99 h for a hydrochar yield of 74.4% and calorific value of 18.5 MJ/kg. The energy assessment showed that up to 58.34% of the energy produced by hydrochar fuel combustion may be recycled as heat or power, while the remaining 41.66% of the combustion energy could be utilised to sustain the HTC treatment of paper sludge. The substantial water demand was concluded to be a drawback. Thus, water recirculation and the potential to catalyse the HTC reactions to increase overall process efficiency will constitute a future study to make the process more environmentally friendly for industrial-scale application. [Display omitted] • Dehydration and decarboxylation were predominant. • Optimum energy yield of 18.5 MJ/kg and 74.4% hydrochar was achieved under 231 ± 1 °C and 1.99 h. • Higher HTC reaction severity enhanced the physicochemical properties of the hydrochar. • Hydrochars produced have low nitrogen and sulphur (<1%) content. [ABSTRACT FROM AUTHOR]

Water pollution and freshwater scarcity are growing problems not only in Europe but all over the world. Water reuse contributes significantly to water sustainability and brings many environmental, economic, and social benefits. The European Union (EU) has recently been addressing water-related problems with great commitment. Unfortunately, the total production of wastewater in the European Union is unknown. The situation in the EU is not satisfactory, as only 2.5% of treated wastewater is reused. The first contribution of this study is the estimation of the total amount of wastewater produced annually in the EU. It is estimated that half of this amount comes from industry, namely over 25,500 million m3. Therefore, this study aims to review and analyze the current state of the industrial wastewater (IWW) issue in the EU and to put it in the context of current EU legislation, targets, and water reuse policy. Moreover, since water reuse technologies are energy intensive, the focus is given to the techno-economic impact of IWW reuse. The energy consumption and investment costs in relation to the amount of water treated have been determined for filtration only, minimum liquid discharge (MLD), and zero liquid discharge (ZLD) systems. Reclaiming 80% of IWW would increase industrial energy consumption by only 4.1%, and investment costs would amount to only 1.5% of the EU GDP. Further, the assessment was focused on the paper & pulp industry, which is among the largest wastewater producers. Implementing efficient water reuse technology in this industry could mean up to 3,449 Mm3/year of reclaimed water. [Display omitted] • Overview of EU legislation with recommendations for future EU policy is presented. • Total amount of wastewater from industry was determined as over 25,500 million m3. • Significant water savings in the industry are possible (80%) using MLD. • Water savings in the paper and pulp industry can be up to 3449 Mm3/year. • 80% of IWW reuse would cost only 1.5% of EU GDP and increase ECI only by 4.1%. [ABSTRACT FROM AUTHOR]

This paper presents an integrated mathematical decision-making model to reduce environmental footprints using process integration. By doing so, we address the research question: How can supplier selection, ergonomic factors, and green practices be integrated into mathematical decision-making models based on the vendor-managed inventory policy ? We use the Branch and Bound method to provide optimal solutions and a metaheuristic optimization approach in computational intelligence for large-scale problems. This paper employs a multi-criteria decision-making method to compare proposed algorithms results based on six criteria. Our findings reveal that integrating the sourcing process with inventory decisions significantly improves cost performance. Furthermore, our results demonstrate that the proposed model effectively balances environmental and economic objectives using a carbon tax. Moreover, sensitivity analysis results indicate that optimizing water and energy consumption can significantly improve carbon footprints. In addition, the study shows that ergonomic factors, including lighting and temperature, have a substantial impact on performance. The primary contributions of this paper include the incorporation of a multi-sourcing strategy, green practices, ergonomic factors, and supplier selection into vendor-managed inventory decision models. [Display omitted] • This paper presents a process integration for sustainable supply chains. • An analytical model is proposed to reduce environmental footprints using a carbon tax. • Water and energy consumption are included in the decision-making processes. • The impacts of ergonomic factors, such as light and temperature, are investigated. • Exact solvers and computational intelligence techniques optimize the proposed model. [ABSTRACT FROM AUTHOR]

Blockchain technology, with its peer-to-peer trading feature, influences the management of energy consumption by offering the potential to transform transparency, efficiency, and sustainability within the energy sector. Nonetheless, there is a need to develop analytical decision-making models tailored for managing peer-to-peer energy transactions to improve energy resilience. Therefore, the purpose of this paper is to address the research question: How can energy distribution systems be protected via blockchain technology to enhance energy resilience and mitigate vulnerabilities to disruptions ? This paper employs a conceptual research model design and a mathematical decision-making model to address the research question by capturing the peer-to-peer trading capability of blockchain technology. The theory of planned behavior provides theoretical explanations for the proposed model. The sample includes longitudinal energy consumption data from 2015 to 2023 in Texas. The findings indicate a significant improvement in energy efficiency along with a considerable decrease in total electricity consumption. Post hoc analysis results reveal that the seasonal autoregressive integrated moving average algorithm is effective as a reliable input for the proposed mathematical model. The significant implications are to implement blockchain-based smart grids in which energy systems become more resilient to disruptions, as the peer-to-peer capability enables users to trade energy. The proposed model suggests that energy will be used more efficiently and effectively. This paper contributes to prior works by introducing a mathematical model that captures the trading behavior of energy consumers. Moreover, this paper proposes the SARIMA algorithm to predict energy demand. • A mathematical peer-to-peer trading model is presented to improve resilience. • A blockchain-based smart grid is presented for consumer energy consumption. • Five machine learning algorithms are presented in energy demand management. • Longitudinal energy consumption data of Texas is used in the model. • Theoretical support is provided using the theory of planned behavior. [ABSTRACT FROM AUTHOR]

Since General Secretary Xi Jinping pledged the "30·60" targets to the world, the importance of pollution reduction and carbon reduction has become increasingly prominent. The previous focus on the pollution reduction effects of emission trading systems, while overlooking their carbon reduction impacts, as well as the limitations of research methods and scope, is no longer suitable for the needs of the new era. Therefore, this paper constructs a CGE model to study the synergistic effects, simulating the synergistic emission reduction effects of sulfur dioxide emission trading policies, and comes to the following conclusions: After the implementation of the sulfur dioxide emission trading mechanism, under different sulfur price scenarios, the synergistic reduction amounts of SO 2 and CO 2 vary greatly. As the sulfur price increases, the reduction amount becomes larger. In the five sulfur price scenarios set in this paper, the maximum reduction of SO 2 can reach about 111,400 tons, and the maximum for CO 2 is about 399 million tons. The CO 2 reduction rate is approximately 1.0767 times that of SO 2. This indicates that the sulfur dioxide emission trading policy is a very effective path for synergistic pollution and carbon reduction, with good reduction effects. In addition, sulfur dioxide emission trading will reduce the consumption of fossil energy, decrease industry output, raise product prices, cause GDP loss, and lead to reduced social welfare to varying degrees. [ABSTRACT FROM AUTHOR]

Energy development in industrial parks promotes both industrial and inter-park economies. However, as the number of industrial parks increases and the economic level of the parks rises, energy consumption is also increasing rapidly. The rapid expansion of inter-park trading markets is accompanied by a surge in carbon emissions, making industrial parks increasingly significant contributors to carbon emissions within the industrial sector. In response to this environmental challenge, this paper proposes an integrated trading approach within industrial parks that considers carbon constraints. By implementing a spot trading method for surplus industrial energy, the goal is to mitigate the carbon emission pressure within these parks. Firstly, the paper conducts an analysis of the characteristics of combined heat and power units in industrial parks. It introduces an integrated analysis method within the target region, consolidating information from various park units to enhance flexibility in energy utilization. Secondly, an energy storage model is established. This model efficiently leverages energy storage capacity to balance fluctuations in energy supply and demand within industrial parks, thereby alleviating carbon emission pressure. Finally, the study and analysis of an industrial park in Liaoning Province were conducted using the Yalmip + Gurobi commercial software on the MATLAB platform. Compared with the traditional industrial park, the carbon dioxide emission of the industrial park is reduced by 91.49t, and the energy cost is saved by 11.2 million RMB. Numerical results from instances indicate that this method surpasses traditional trading approaches, aligning with the rigorous standards of scientific research. • An energy storage model is proposed to address the shortage of energy storage in waste heat trading in industrial parks. • A coordinated scheduling program is presented to reduce energy scheduling imbalances between multiple campuses. • The carbon-constrained energy storage device is integrated to improve the decarbonization capacity. [ABSTRACT FROM AUTHOR]

Energy and water are interdependent. Water is essential for energy production, and energy is indispensable for water processes. This paper develops a Stackelberg game based bi-level multi-objective model for optimal water allocation under uncertainties from an energy-water nexus perspective, which considers energy-related water consumption and water-related energy production. It handles the hierarchical decision-making problem between the river basin authority and the subareas, as well as the tradeoffs between social equality and ecological damage. The upper level of the model makes schemes on water allocation for multiple subareas, and the lower level decides on water allocation for different types of power generation. To generate reasonable and practical strategies for water allocation, ɛ -constraint method and KKT conditions are used. A case study of Minjiang River, China is given to demonstrate the applicability and practicality. Scenarios analysis is also conducted to demonstrate the effectiveness of the model and the effects of the control parameter changes. The results indicate that the river basin authority would allocate larger water resources to the subareas with higher energy demand. It is concluded that the proposed allocation method is advantageous to the subareas with higher water utilization efficiency. The proposed bi-level model demonstrates its reasonableness and superiority to the single-level model. Policy implications are finally provided to support the sustainable development of energy-water nexus. • A bi-level model for water allocation in energy-water nexus system is proposed. • The hierarchical decision sequence between the river basin and subareas is solved. • The tradeoffs between social equality and ecological damage are made. • Optimal water allocation for multiple subareas and energy resources are quantified. • Uncertainties in decision-making environment are handled by fuzzy set theory. [ABSTRACT FROM AUTHOR]

The battery electric vehicle (BEV) industry, commanding over half of the global market share in China, has garnered significant attention in response to escalating concerns about climate change. NCM (LiNi x Co y Mn z O 2) batteries, prized for their high energy density, are pivotal in this industry. However, their production relies on critical metals, posing environmental challenges. Recycling processes aim to address this issue by recovering critical metals from end-of-life batteries. The industry is updating from NCM 111 to NCM 811 batteries to enhance energy density and reduce costs, but the key factors influencing the material flows and environmental impacts remain uncertain. This paper aims to identify the key factors contributing to the potential differences in material flows and environmental impacts introduced by the NCM battery upgrading in China. Following the new energy vehicle sales projections in China until 2035, a dynamic material flow analysis was employed to calculate material demands, recovered amounts, and substitution rates for various upgrading scenarios. The study reveals that the majority of retired batteries until 2035 were sold between 2022 and 2029. Material substitution rates prove to be more sensitive to the upgrading scenario than the lifetime and growth rate of BEVs. Faster NCM 811 penetration results in over 2.5 times more saved cobalt demand compared to additional nickel demand. A life cycle assessment of NCM batteries reveals a 39% lower global warming impact for NCM 811 compared to NCM 111, highlighting environmental benefits from increased energy density and reduced cobalt consumption. The energy density improvement contributed 85%, while the alteration in material composition contributed 15% to the global warming reduction in NCM 811. Recycling emerges as a crucial strategy in mitigating environmental impacts across all categories, particularly addressing eutrophication, previously deemed challenging due to the difficulty in recovering Li 2 CO 3. [ABSTRACT FROM AUTHOR]

[Display omitted] Ghana has been a pioneer in energy efficiency in the sub-Saharan region, becoming the first country to introduce minimum energy performance standards and energy labelling for lighting, air conditioners and refrigerating appliances in 2008. It also introduced an innovative and not yet widely adopted policy of banning the import of used air conditioners and refrigerating appliances, mostly from Europe. While the impact of this policy package has already been assessed in terms of energy savings in Ghana, it is still unclear whether the import ban on used products makes sense in terms of environmental impact. Ghana's ban may seem surprising given that current European product policies in the context of the Green Deal aim to extend the lifetime of products and encourage their reuse to make them more sustainable. In this paper, different scenarios covering new and used refrigerating appliances as well as different end-of-life management options were examined in a life cycle analysis. The results show that exporting used refrigerators from Europe to West Africa increases their environmental impact per functional unit in many categories, even if it extends the lifetime of the product. The informal end-of-life treatment of refrigerators by open burning has a massive impact on human toxicity. As energy consumption during the use phase is the main determinant of most environmental impacts, energy efficiency policies (minimum energy efficiency performance standards and labelling) combined with an import ban on used refrigerating appliances are the best approach to reduce these impacts. It is therefore recommended that the Ghanaian policy package be replicated in other countries in the region, but also that exporting countries take action, as the fight against environmental dumping is a shared responsibility. • Used refrigerators exported to West Africa increase greenhouse gas emissions. • Energy efficiency reduces the environmental impact of refrigerators. • Import ban on used refrigerating appliances is justified. • Informal end-of-life treatment of refrigerators has a major impact on human toxicity. [ABSTRACT FROM AUTHOR]

The energy demand in Sweden is increasing due to the growing electrification, and to meet this need, the generation must be expanded. Wind power generation is a very attractive renewable power production alternative for Sweden because of the favorable weather conditions. Research looks at how much wind energy may be deemed sustainable. Increased energy demand puts pressure on the government and industry to build more wind farms and, as a result, produce more wind turbines. Raw materials are necessary for wind turbines to provide a secure transition to green energy technologies. To meet these demands, the materials from various countries should efficiently contribute towards the Sustainable Development Goals (SDGs). This paper gathers information about raw resources from diverse nations worldwide. All materials are mapped to the country where they are produced using social science criteria. A few nations are chosen based on a sample approach for further analysis, and the implications of mining operations are investigated. Finally, the direct and indirect effects of the SDGs are considered. Based on the data gathered, recommendations and considerations are given to avoid or mitigate the repercussions of raw materials mining and make wind power generation more socially and environmentally sustainable. • Evaluation of side effects of wind turbine raw materials. • The extraction of raw materials often violates human rights. • Mining of raw materials for wind turbines negatively affects the environment. • Extraction of wind turbine materials affects several Sustainable Development Goals. [ABSTRACT FROM AUTHOR]

The 17 Sustainable Development Goals (SDGs) defined by the United Nations represent a blueprint for the adoption of global practices and policies to foster sustainability in societies and increase efficiencies in resources uses. The Italian National Trust, FAI, has embraced these goals, developing a holistic and transdisciplinary methodology to promote a more sustainable approach to preserve its natural and built heritage. This methodology combines heritage protection with the SDGs in various macro-areas of interventions, including energy, water, biosphere, responsible consumption, and sustainable communities. The aim is to increase access to affordable and clean energy, reduce water, waste, and soil footprints, promote responsible consumption, and develop human capital. The paper proposes a set of strategies to achieve the SDGs in heritage, involving the collaboration of several stakeholders, ranging from institutions to citizens. The methodology is applied to a specific Italian case study, Casa Macchi in Morazzone, to highlight the challenges in achieving the dual objective of preserving the historic building and promoting sustainability in the built environment. [Display omitted] • We propose a trans-disciplinary methodology for sustainable heritage preservation. • The heritage preservation project is analyzed in five macro-areas of intervention. • Assessment of effects on energy, water, biosphere, resources, and sustainability. • Casa Macchi preservation project to apply methodology and prove effectiveness. [ABSTRACT FROM AUTHOR]

Understanding the connection between water supply and energy consumption in a circular economy is crucial for managing the demands of these resources. Sankey diagrams have the potential to enhance the visualisation of resource flows. However, there is a knowledge gap regarding the effective use of Sankey diagrams to capture the water-energy nexus and circularity of water. This study focused on effectively utilizing Sankey diagrams to model the nexus of water and energy flows and to assess the circularity of water. A comprehensive literature review was conducted on major studies on water material flows and modelling. This was supported by a review of approaches for modelling circularity, which have evolved from the material circularity index. The newly developed framework in this paper models water resource flows at all stages of the water supply chain, embedding the nexus as an interaction and considering resource intensity and material circularity index at each node. A city-level case study in South Africa was then used to apply the recommendations, model circularity, and the water-energy nexus using Sankey diagrams. In terms of circularity, the results indicate that water loss in the study area was very high, accounting for up to 40% of the total water supply and the water recycle rate was still very low, ranging from 3% to 7%. In terms of the nexus, the residential sector was found to have the highest energy intensity for water supply, primarily due to energy demand for water heating. Exploring the use of renewable or solar energy for water heating will be of interest and value. The new approach presented in this study allows for the simultaneous consideration of objectives, enabling a more robust analysis of energy-effective water use and water circularity along the water supply chain. [Display omitted] • A new framework for modelling the water energy nexus enriched by circularity options, energy intensity factors and confidence modelling. • Options for embedded circularity through the water supply chain. • The urgent challenge to address massive water loses in water supply systems for water scarce regions. • New Sankey diagrams showcasing how to model nexus and circularity. [ABSTRACT FROM AUTHOR]

China's climate and environmental problems remained prominent. Energy transition was the key to improve climate and pollution, and it was at a critical period. Previous studies mostly focused on the impact of climate policies on the reduction of air pollutants and the environmental health impacts of individual sector or pathway policies. Thus, focusing on the synergistic abatement of CO 2 and air pollutants and health effects of energy transition in carbon neutrality was significant for building beautiful and healthy China. LEAP-HEA model was constructed to project energy demand, CO 2 and air pollutants, and assess energy transition's environmental health effects from 2020 to 2060. LMDI model was adopted to decompose the abatement contribution. Synergistic assessment index was used to estimate the coordinated abatement effects. Cost-effectiveness of energy transition and regional heterogeneity of PM 2.5 toxicity were not considered. The results indicated: (1) energy demand would decrease and structure would be optimized significantly under deep energy transition scenario. (2) Deep energy transition could promote earlier realization of carbon neutrality. It would contribute significantly to CO 2 and air pollutants abatement with strong synergistic effects. (3) Energy transition would reduce health risks with large economic benefits. Recommendations were proposed based on the conclusions. The framework of LEAP-HEA model. [Display omitted] • The synergistic abatement and health effect of energy transition were explored. • The models of LEAP, LMDI, exposure-response and the data of 2020 were used. • Energy transition would have significant synergistic abatement and health effect. • The paper has significance for promoting carbon neutrality and healthy China. [ABSTRACT FROM AUTHOR]

COVID–19 has disrupted human life considerably since the beginning of 2020. The transport sector is one of the most affected sector due to restrictions imposed to curb COVID–19 pandemic. This paper evaluates the environmental impacts of COVID–19 response scenarios using life cycle assessment approach. Six regions selected for the study are Australia, China, European Union (EU), India, United Kingdom (UK), and United States of America (USA). Data for vehicle population, vehicle kilometers travelled, fleet type, and fuel efficiency were used for 2015–2020. The environmental impacts were calculated for five scenarios; Baseline–2019, Business As Usual (BAU) and COVID–19 Response Scenario (CRS) for 2020 and 2030 namely, 2020–BAU, 2020–CRS, 2030–BAU, and 2030–CRS. The results indicate that COVID–19 occurrence and related response measures did not contribute to significant environment benefits in terms of Global Warming Potential (GWP) and other pollutants in short term and long term. This implies that although immediate reductions in emissions were observed in many studies, the long term effects were minimal due to irregular policies and unmanaged demand. Further, to achieve environment benefits sustained measures should be implemented in long term with proper planning and supporting infrastructure. Although electric vehicle strategies will provide benefits; emphasis on vehicle production and fuel production technologies is also needed to achieve the emission reduction targets. The findings are relevant for policy makers while devising policies to achieve the targets. • Vehicle stock will grow significantly in 2030 for developing countries • China's 2030 transport sector GWP will be highest, 1772 MT CO 2 eq, surpassing USA • Only USA showed reductions for all pollutants in 2030 COVID-19 response scenario • All countries had similar trends for most pollutants at different life cycle stages • COVID–19 occurrence didn't contribute to significant benefits in short & long term [ABSTRACT FROM AUTHOR]

Enhancing vehicle utilization through a collaborative logistics approach allows businesses to contribute to the three pillars of sustainability by reducing fuel consumption and emissions, enhancing profit margins, and increasing customer satisfaction. This paper aims to introduce an Integer Linear Programming model for solving a vehicle allocation problem within a proposed vertical logistics collaboration setting. In this collaboration setup, production capacity allocation decisions are jointly made by collaborating customers and the logistics service provider, in order to create a more balanced and profitable logistics plan for all involved parties. Through the application of the proposed mathematical model, our numerical analyses illustrate the effects of the vertical logistics collaboration on the performance of transportation operations, including improved vehicle utilization rate and increased satisfied demand amount. Besides, we analyze the outputs of this operation related to the economic (by focusing on profit growth), environmental (by measuring the reduction in emissions), and social (by assessing the discounted transportation costs for customers) aspects of sustainability. To the best of our knowledge, no current study in the literature has established the vehicle allocation problem in the context of vertical logistics collaboration across all three sustainability pillars at the same time. The model can also serve as a tool for evaluating the potential economic, environmental, and social benefits arising from vertical collaboration and joint projects with customers, along with the support offered to logistics decisions. [ABSTRACT FROM AUTHOR]

Recovery and re-utilization of high-quality exhaust vapor are one of the most important technical means to improve the performance of a potassium Rankine cycle system. The recyclable potential of the 300-kW potassium Rankine conceptual design is evaluated with the grand composite curve, indicating that the organic Rankine cycle (ORC) placed under the pinch point to form a potassium top-organic bottom energy cascade utilization scheme is meaningful. To make the system performance concrete, a comprehensive performance evaluation system is established, which consists of thermal, economic, and weight. Several important contents are performed, for working medium selection, thermal process analysis, and cycle parameter matching on the organic bottom cycle. In addition, this paper also preliminarily analyzes the effect of the electricity market environment on the power system. The results show that MD 4 M in the siloxane series has remarkable comprehensive performance advantages. The bottom cycle running an evaporation temperature of 626 K and condensing temperature of 390 K has great potential. The total output power of the binary coupled system exceeds 500 kW, the thermal efficiency is 25.32%, and the growth rate is 73.54%. The high thermal performance not only breaks through the upper limit of the single alkali metal system but also has a stronger ability to resist the fluctuations of the market environment. • The alkali metal thermal system with high enthalpy properties has great recovery potential. • K-MD 4 M binary coupling Rankine, an energy cascade utilization scheme, is investigated. • The influence of the operating temperature on the system performance is non-unidirectional. • The high power contradicts the economy and lightweight of the multi-cycle system. • The resilience to market economy fluctuations shock is enhanced by improving performance. [ABSTRACT FROM AUTHOR]

A gliding arc plasma has the advantages of strong reactivity, high energy density, and easy scale-up, so it is very competitive in the field of plasma-based N 2 fixation. In this work, we designed an underwater transfer arc discharge plasma N 2 fixation system based on a reverse vortex flow gliding arc. In full contact with air and water, the plasma could realize the efficient conversion of N 2 , O 2 , and H 2 O, obtaining gaseous NO, NO 2 , and activated water rich in NO x *. The experiment showed that an underwater transfer arc discharge was formed when the current was greater than 0.6 A. Under this operating condition, the conversion rate of N 2 reached 1.63%, the concentration of NO x reached 2.4%, and the production rate of nitrogen-containing products was about 3.41 mol/h. In addition, the energy consumption was as low as 2.21 MJ/mol, the lowest reported value for atmospheric-pressure rotary gliding arc plasma-based N 2 fixation. The generated activated water had abundant NO 3 − and NO 2 − ions, which could be directly applied as N 2 fertilizer in agriculture, promoting plant germination and growth. This paper describes an environmentally friendly and efficient technical route toward developing industrial N 2 fixation/green agriculture, strongly supporting the carbon neutrality concept. • A reverse vortex flow gliding arc under water was developed for N 2 fixation. • The transfer or non-transfer arc can discharge stably in air or under water. • The N 2 fixation energy consumption was reduced to 2.21 MJ/mol. • The plasma-activated water was rapid produced while N 2 fixation. [ABSTRACT FROM AUTHOR]

Shared e-scooters are considered a sustainable urban travel solution, yet their environmental impact is notable, primarily due to a short lifespan and the use of Li-ion batteries. The objective of this paper is to explore the possible factors with an influence on e-scooter energy consumption. These influential factors include trip and rider attributes, built environment characteristics, road geometry, surface materials, light and meteorological conditions. This study empirically investigates factors influencing e-scooter battery energy consumption, utilizing real usage data from GPS-equipped free-floating e-scooters in four European cities at the individual trip level. GPS positions allowed for the specification of local road characteristics and ambient conditions. For this purpose Shuttle Radar Topography Mission, Open Street Maps and the Meteostat application were used. Energy consumption was explained with the use of linear regression models as a function of those parameters, while controlling user experience and battery usage intensity. An extensive dataset covering over 150,000 trips in Bordeaux, Hamburg, Malaga, and Paris and two months of observations was used to estimate the models. Results indicate that, while travel distance is the most influential factor, other usage aspects play a role. Cold weather, rain, wind, as well as stone-paved streets increase energy consumption. In addition there seems to be a battery age threshold beyond which energy consumption rises. [Display omitted] • Distance influences energy consumption the most, but not exclusively. • Riding in stone-paved streets seems to increase energy consumption. • Cold weather, rainy days and wind increase energy consumption. • Riding in the night leads to increased energy consumption. • Energy consumption model parameters differ significantly per city. [ABSTRACT FROM AUTHOR]

International shipping is crucial for freight transport, but it relies primarily on fossil fuels, contributing 3% of global greenhouse gas emissions. This calls for urgent action to decarbonize the shipping industry. While renewable-based e-fuels are a strong candidate for decarbonization of this sector in the long run, deploying these to the required scale will take significant time, considering technical modifications onboard the vessels, as well as the changes in fuel production and infrastructure for distribution. Carbon capture from ships is another route to emission reduction that can be implemented faster due to the relatively high maturity of the technology. Tankers, dry bulk carriers, and container vessels contribute a majority of global shipping emissions and are therefore prime candidates for carbon capture and storage. Solvent-based post-combustion capture is chosen for this study as it is mature and suitable for marine applications, though technical, economic, environmental, and practical challenges remain. This paper assesses the technical feasibility of the capture system for ships; both retrofit and newbuild vessels. While achieving zero-emissions presents a significant challenge, it is feasible to attain 70%–90% CO 2 reduction through carbon capture in the near term. This reduction is crucial for transforming the industry into a more sustainable and environmentally friendly state. The limitation of space onboard is identified as a key factor in determining the viability, and the 70%–100% increase in energy consumption between existing ships and newbuild will be a substantial operational challenge. However, the high fuel consumption of the capture system could be economically acceptable if the price of alternative fuels remains high. [ABSTRACT FROM AUTHOR]

Minimising energy consumption in today's industrial sector is a crucial objective for achieving established climate goals. One effective strategy to enhance efficiency is optimising energy system operations within industries. The initial step in establishing operational optimisation involves developing a comprehensive model of the energy system. This model necessitates a specific structure to meet optimisation requirements. However, creating a model from scratch incurs substantial effort. While numerous companies possess energy models, they often lack the requisite structure for optimisation. Consequently, simplifying existing models can significantly reduce the effort needed to implement operational optimisation. This paper investigates the simplification of intricate industrial energy system models for optimisation purposes. The subsequent sections analyse two distinct approaches. One approach involves linearisation, while the other utilises neural networks. To facilitate a comparative analysis of these approaches, a reference model is developed. The assessment of these methodologies includes an investigation into optimisation robustness, computation time, accuracy concerning the reference model, and the effort required for developing and maintaining the simplified models. It proved that both approaches are suitable for operational optimisation. Linearisation exhibits superior computational efficiency compared to the neural network approach. The linearisation modelling approach together with the optimisation only required a few milliseconds for the calculation. The neural network approach needed 3 h for the calculation of the optimum with the genetic algorithm. The simulation of the neural network itself only required a few milliseconds. Hence, an improvement of the genetic algorithm is needed. However, the accuracy of linearisation falls short of that achieved by neural networks. The linearisation achieves a mean average percentage error from only 13%. In comparison the neural network's mean average error is 2.3%. Therefore, the linearisation must be improved. The impact using a piecewise linearisation on the results will be analysed in further research. [ABSTRACT FROM AUTHOR]

Our paper analyzes the optimal capacity investment in solar panels system for consumers. During the daytime, solar panel system absorbs energy from the sun and supplies the energy generated to the building structure. Our model takes into consideration the variations in the daily consumption of electricity and the variations in the daily available sun energy. We model these variations in our paper as uncertainty in energy demand and uncertainty in energy supply. Excessive usage of electricity that is beyond the energy generation of the solar panels are bought at a higher price from the utility company than the payment received from the utility company for excess energy sold to the grid. We analytically solve for the optimal quantity of solar panels to purchase and install. Our paper fills the research gap in quantifying the impact changes in solar energy yield rate, consumption, and sell-back rate has on the consumer's optimal investment decision in solar panels. We find that the optimal quantity of solar panels increases with the cost of energy during the daytime and payment for excess energy sold to the grid. Interestingly, when solar yield increases, the optimal solution only increases initially, and then it will eventually decrease. Furthermore, our analytical models provide consumers with a decision support system to determine the optimal number of solar panels to purchase and install while taking into consideration the random yield of solar panels, variations in energy consumption, and potential for sell back of excess unused energy generated under complex billing structures from the utility company. Our mathematical models are adaptable to policy changes such as rebates and purchase and sell back rates for electricity. Our numerical study validates the mathematical models and further analyzes the impact changes in solar energy yield rate, energy consumption, and sell-back coefficient have on the consumer's capacity investment decision in solar panels. Our numerical studies find that the lower the reliability factor of solar yield, the more impact the daytime energy cost has on the optimal quantity. When the sell-back coefficient exceeds a certain threshold, the optimal quantity of solar panels increases exponentially. [Display omitted] • We model the optimal quantity of solar panels to purchase and install for consumers. • Optimal quantity increases with consumption, but the rate of increase is decreasing. • Optimal quantity increases with energy daytime cost and sell-back rate. • When solar yield is high, the impact of payment coefficient is significant. • Increased variability in consumption, optimal quantity increases. [ABSTRACT FROM AUTHOR]

This research paper analyses the potential of multi-angled façade systems to achieve the sustainability goals of reduced energy consumption and improved indoor climate quality, hence aligning with UN SDGs 3, 11, 12 and 13. The objective of this research paper is to define the suitable glass properties for the two windows of the multi-angled façade. The concept of a multi-angled façade system is based on proposing using two different window orientations in each façade on a vertical axis (right and left), but not tilted up and down. The large part of the multi-angled façade is oriented more to the north to optimise the use of daylight and the small part of the multi-angled façade more to the south to optimise the use of solar radiation through the façades. One can optimise using daylight and solar heat inside the office room through the multi-angled façade design concept, but with less complexity in the design, manufacture, and transport phases. This is the gap between 3D façades and the new multi-angled façade system concept, which indicates its novelty. To evaluate the energy consumption, the researcher used the software program IDA ICE version 4.8. to analyse the energy behaviour through the façade and the building's indoor climate, to compare the glass property impacts. The researcher modelled five scenarios for a multi-angled façade with different glass properties in both parts of the multi-angled façade. Researchers have used this software in simulations to validate, test, and compare statistics for more than 25 years according to ASHRAE 140, 2004, and CEN Standard EN 15255 and 15265, 2007. To conduct the analysis for the material's embodied energy in the multi-angled façade, the researcher used the software LCAbyg version 5 and compared it with the situation for a flat façade. The results for the energy consumption results revealed that the energy behaviour in the first scenario's glass type is recommended because the illumination was better and offered lower total yearly energy consumption and the accepted number of overheating hours inside the office room. The result of the embodied energy for the materials of the multi-angled façade shows that the area-weighting of the sum of the total energy consumption through the building's lifetime and embodied energy is higher when one renovates with a flat façade by about 11%. • Discussing the potential of multi-angled façade systems as a new concept for office buildings. • Defining the suitable glass properties for the multi-angled façade's windows. • Explaining a good visual, optical, and thermal indoor climate; and. • Evaluating the embodied energy amount for the multi-angled façade materials. [ABSTRACT FROM AUTHOR]

This paper presents an analysis of the water-energy-food nexus (WEFN) system, focusing on its interlinked structure and dynamic characteristics. This paper proposes a dynamic series-loop data envelopment analysis (DEA) model with a directional distance function for measuring WEFN efficiency in China. A spatial Durbin model is constructed to investigate the impact of extreme temperatures on WEFN efficiency. The results are as follows. (1) Compared with the black-box DEA model and series network DEA model, a series-loop network DEA model can reflect the "networking effect" and "looping effect" in the WEFN system. (2) The WEFN system performs well with an average series-loop efficiency of 0.875, but the efficiency of the water subsystem scores low and needs improvement. (3) At the national level, extremely high and low temperatures have no significant effect on the WEFN efficiency in local or adjacent provinces, whereas average temperature promotes the WEFN efficiency in local provinces but hinders it in neighboring provinces. In the regions with a low temperature difference, average temperature has no impact on the WEFN efficiency of both the province itself and its neighboring provinces. Overall, this study provides valuable insights into the complex interlinkages within the WEFN system and highlights the importance of considering these relationships when improving the system efficiency. [ABSTRACT FROM AUTHOR]

The OECD community consumes excessive amounts of raw materials and materials, which leads to pollution and increases the material footprint. The material footprint is directly linked to the Sustainable Development Goals (SDGs)-8 and 12. The main reason for the material footprint is the usage of fossil fuels, and the dependence of OECD countries on fossil fuels continues to this day. By expanding renewable energy utilization due to rising energy security risk, a community can take steps to improve ecological quality and reduce its material footprint. In this context, the paper focuses to examine the impact of energy security, green innovation, economic policy stringency and income on the fossil fuel material footprint for 24 OECD countries over the period 1995–2018. To this end, the paper uses the Augmented Mean Group (AMG) method and novel Half Panel Jackknife (HPJ) causality and estimation approach. The study adds value to existing knowledge by empirically testing the determinants of fossil fuel material footprint for OECD countries for the first time using the novel HPJ approach. The results of the AMG show that increasing energy security risk index reduces the material footprint, while green innovation and economic policy stringency have no impact on environmental degradation. The environmental Kuznets curve (EKC) in the form of an inverted U and an inverted N are valid for OECD countries. The HPJ approach also illustrates that there is a causality from green innovation and energy security risk to material footprint, and innovation and risk are support footprint reduction. Based on these results, the study recommends that OECD policymakers pay attention to ensuring energy security to reduce the material footprint by expanding renewable resources and allocate more economic resources to green innovations with higher returns, considering the validity of the EKC. [Display omitted] • Applies second-generation panel data methods. • Investigates the fossil fuel material footprint for 24 OECD countries. • The EKC hypothesis holds for the fossil fuel material footprint of OECD countries. • Green innovation and energy security risk reduce fossil fuel material footprint. • Increasing risks and technological development contribute to SDGs 8 and 12. [ABSTRACT FROM AUTHOR]

With the fragmentation of global production, the share of intermediate trade in international trade has increased significantly. Nevertheless, more research must be done on how intermediate trade factors influence energy consumption. This paper utilized the multi-regional input-output (MRIO) model to categorize China's energy utilization into six parts based on its position within the global production chain. Additionally, it examined the impact of intermediate factors on China's energy consumption through index decomposition analysis (IDA). The results revealed that: (1) from 2000 to 2014, intermediate factors led to an increase of 1380.96Mtces in China's energy consumption, accounting for around 37% of the national total; (2) the reduction in efficiency is the primary cause of the surge in domestic intermediate energy consumption, while the increase in market proportion is the dominant driver promoting international intermediate energy consumption growth; (3) the international intermediate proportion contributed the most among all intermediate factors. Drawing upon the findings above, this paper proposed policy recommendations to regulate China's rising energy consumption, precisely the escalation of intermediate process energy usage, focusing on the global production chain. [Display omitted] • Energy consumption is divided into six parts based on the intermediate destination. • Fifteen factors is decomposed based on index decomposition analysis. • The contributions of the structure, proportion, and efficiency changes is considered. • This paper shows a way of identifying the composition of energy consumption. [ABSTRACT FROM AUTHOR]

Environmental issues are closely related to the economic development of human society, and reducing environmental pollution during production is of great significance for environmental protection. This paper utilizes data from Chinese industrial enterprises (2001–2010), and establishes a fixed effects model to examine whether minimum wage standards are important in reducing non-clean energy consumption by enterprises. The results indicate that an increase in the monthly minimum wage leads to a significant reduction in coal consumption by firms, while the change in the hourly minimum wage do not exert a statistically significant influence on coal consumption by firms. The above empirical results remain robust after conducting a series of endogeneity tests. The mechanism test shows that innovation and exports serve as mediating variables between the minimum wage and coal consumption. Specifically, raising the minimum wage can incentivize enterprises to innovate in production and expand their exports, resulting in a decrease in the consumption of non-clean energy. Finally, through the heterogeneity test, we observe that increases in the minimum wage have varying effects on coal consumption among firms with different numbers of employees and different ages. Based on the research findings of this paper, we propose that the government should comprehensively consider and improve relevant systems, formulate scientifically-based minimum wage standards, take into account both workers' rights and interests as well as enterprises' profits, actively promote energy transformation in enterprises, and ultimately achieve long-term development of the social environment. [ABSTRACT FROM AUTHOR]

Cities have emerged as critical and challenging places for promoting the green and low-carbon transformation of the economy and society in the context of the "dual carbon" targets. Meanwhile, fiscal policy is also an indispensable component of the public policy framework supporting the green and low-carbon transformation. This paper uses the pilot project of "Comprehensive Demonstration Cities for Energy Conservation and Emission Reduction Fiscal Policies" ("ECER-CD" policy) implemented in China since 2011 as a quasi-natural experiment. This research theoretically analyzes and empirically tests the energy conservation and emission reduction effects of fiscal policies supporting green and low-carbon transformation utilizing methodologies such as time-varying DID and multiple periods and multiple groups of DID. The study finds that the pilot cities effectively reverse the behavioural bias of local governments and promote energy conservation and emission reduction efforts. This conclusion holds after undergoing robustness tests such as parallel trend analysis and heterogeneity treatment effects. The effectiveness of the pilot policy is attributed to the upgrade of industrial structure, improvement of energy efficiency, and the effects of green technology innovation. However, cities' geographic location and resource dependency can interfere with the effective implementation of policies. This paper also further explores the environmental dividends and economic consequences of the policy and finds that the pilot cities have certain environmental dividends and do not significantly restrain economic and social development. This study broadens the research perspective on fiscal policies in energy conservation and emission reduction and clarifies the pathways through which fiscal policies promoting green, low-carbon transformation drive energy conservation and emission reduction in cities. Moreover, it also improves the use of the Difference-in-Differences method in evaluating policy effects in scenarios such as the overlapping implementation of pilot policies and policy withdrawal. Finally, this paper proposes policy recommendations to provide more empirical evidence for China to accelerate the green and low-carbon transformation of the economy and society. [ABSTRACT FROM AUTHOR]

Energy-saving construction has promoted the sustainable development of cities and is an important measure to achieve carbon neutrality and peak carbon. This paper uses the synthetic control method(SCM), propensity score matching difference-in-difference model(PSM-DID), spatial Durbin difference-in-difference model (SDM-DID) and other methods to explore the policy effect of energy-saving policy on the green innovation efficiency of Chinese cities based on the New Energy Demonstration City(NEDC) policy as a quasi-natural experiment. The paper has found that establishing NEDC can significantly improve the green innovation efficiency of pilot cities, and its positive impact continues to increase over time. The effect of energy-saving policy has regional heterogeneity, and NEDC can promote the improvement of urban green innovation efficiency more in eastern and central China. The results of PSM-DID further verify the robustness of the previous conclusion. The results of SDM-DID show that NEDC has a positive spatial spillover effect on the improvement of urban green innovation efficiency. NEDC policy can effectively promote the improvement of green innovation efficiency in neighboring cities of pilot cities. Mechanism analysis shows that NEDC affects urban green innovation efficiency by optimizing industrial structure, reducing resource dependence and gathering innovative elements. This paper enriches the research on the effect of urban energy-saving policies and provides policy references for improving urban green innovation capabilities. [Display omitted] [ABSTRACT FROM AUTHOR]

Implementing sustainable or green strategies in warehouse management has become increasingly important. The environmental impact can be assessed by evaluating resource usage and productivity. A proper method for assessing the sustainability of internal processes can provide a valuable tool for companies to understand what is being done correctly and what needs to be improved to comply with new guidelines. For this paper, a case study at Arvato warehousing provider for end consumer products was conducted, and a rating mechanism model for warehouses was developed. Data on energy consumption and productivity were collected from output of 13 warehouse units (modules). From the collected data, a set of KPIs, an index system, an automation categorization system, and a ranking system were created to help with decision making, problem identification, and solving. As result, this paper demonstrates the impact of different material handling systems on energy consumption and found that a higher level of automation does not always translate into operating more efficiently in terms of sustainability. Instead of reporting absolute numbers, this study created an index that displays relative values, making it much easier to identify the most efficient modules. Additionally, this study takes the automation index a step further by creating three categories of automation levels in addition to the percentage that indicates how advanced the technology in a warehouse is, allowing for the creation of groups of warehouses and the comparison of their performance. • Increased automation does not always translate into sustainable operational efficiency. • In general, warehouse units employing less technology perform worse. • Supply chain transparency can increase by grouping and scoring warehouses efficiency. • Electricity and productivity index enable measuring environmental impact. • Energy performance and productivity are related at various levels of automation. [ABSTRACT FROM AUTHOR]

Environmental issues have become global challenges, so seeking an economic model to reach environmental sustainability is essential. As an emerging economic model, green finance is considered the most effective tool in promoting low-carbon economic transition. China, the largest economy among developing countries, faces environmental inferiority and increasing energy demand. This paper aims to discuss the effects of green finance on the low-carbon economic transition. This paper makes the policy of green financial reform and innovation pilot zones a quasi-natural experiment. It employs difference-in-differences (DID) model to analyze the impact of green finance on the low-carbon economic transition. This paper found that the low-carbon economic transition achieved good results within experimental zones after establishing a pilot zone for green finance reform and innovation. Furthermore, the parallel trend test results, complicatedly disturbing facts mitigating provincial differences, and placebo tests are still valid. Finally, we found that low-carbon economic transition solely takes effect significantly in eastern and central areas but is not significant in western areas. The paper suggests that green finance has played an apparent role in promoting low-carbon economic transition and provides essential reference and experimental evidence for future economic transition after analyzing the pilot zone for green finance reform and innovation policy. • Green finance is a crucial tool for achieving low-carbon economic transition. • Eastern and middle regions experienced significant effects. • The study provides valuable experimental evidence for future economic transition. [ABSTRACT FROM AUTHOR]

The additive manufacturing (AM) paradigm, especially in the distributed manufacturing setting, is a novel paradigm that can bring production of components closer to the consumers, at potentially lower economic and environmental costs. In this paper, we present a comparative analysis of AM with conventional manufacturing (CM) from the energy consumption perspective to assess under what conditions AM is better—a key gap in the area. Leveraging an energy consumption survey between CM and AM supply chains, this paper analyzes the significance of transportation in a cradle-to-gate partial life cycle analysis. Though the decentralization of AM has been discussed in energy consumption assessments, it has not been discussed in terms of how the distributed components and their transportation would affect the two supply chains' scenarios. This gap motivated this research to conduct energy efficiency assessment of AM and its comparison with CM in a distributed supply chain. Our analysis showed that transportation contributed 3x to 4.5x increase in the overall energy impact in CM compared to corresponding transportation in the AM setting. In effect, from our analysis, for every three CM parts, the transportation energy consumption approximately equaled the energy needed to manufacture one similar part in AM. Our analysis leverages the solid-to-envelope ratio "α", defined as the volume of solid material within a part's envelope. Based on our analysis, AM would be the preferred production method for α values falling within the range of 0.08–0.22 (8–22% of raw material efficiency in CM). Hence part geometry, where AM has greater potential for energy savings by reduction in the amount of material waste, is a good indicator for using AM over CM for energy efficiency. We also discuss several future directions for better assessment of AM versus CM under different settings and upcoming innovations such as Industry 4.0. [ABSTRACT FROM AUTHOR]

The rapid development of urbanization in China inevitably leads to a sharp increase in building energy consumption, and Passive House (PH) have received much attention in China in recent years. In this paper, the annual energy demand (AED) and summer discomfort hours (SUH) of PH are used as optimization objectives to establish a set of design solutions for multi-objective optimization (MTO) and economic analysis of a PH in Shandong Province, China. It is found that (1) the algorithm based on nearest neighbor component analysis (NCA) feature extraction applies to the sensitivity analysis of multivariate nonlinear high-dimensional data, and the NCA-GA-RBF neural network technique is proposed, which has significantly improved the goodness of fit and generalization ability for AED and SUH. (2) The NSGA-II genetic algorithm is used for the optimization search, and the results show that the overall improvement rates of AED and SUH are 25.5% and 21.6%, respectively, among the nine solutions after comprehensive evaluation; (3) Finally, the optimal scheme of energy saving and the optimal scheme of the economy are selected for economic evaluation. By comparing the economics of the two options and the improvement rates of AEDs and SUHs, the guidelines for design decision-makers are provided. In summary, the design scheme of this paper is forward-looking and reference-oriented and provides some reference suggestions for the development of PH in China. • The NCA algorithm is suitable for multivariate nonlinear sensitivity analysis. • NCA-GA-RBF has stronger and more stable generalization ability and accuracy. • In this paper, climate is the main factor affecting thermal parameters. • Global costs have a stronger economic interpretation. [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]