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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]

Buildings have significantly contributed to high energy demand (30.3%) and GHG emissions (26.1%) worldwide. Consequently, many developed countries have set carbon-neutral targets for 2050, mandating that all new constructions thereafter be designed as net-zero emission buildings (NZEBs). To achieve this goal, one of the most practical sources of renewable energy, solar power, is employed in this study to estimate the economic feasibility of implementing NZEBs in the non-residential sector of the United States. While past studies have typically estimated the economic payback period of solar power technology by assuming fixed values for "PV energy conversion rates (%)" and "investment costs ($USD)", this paper carefully investigated the economic feasibility of "solar-based NZEBs" by precisely tracking these dynamic future changes in PV panels. These techno-economic variables were carefully predicted using a statistical technique known as the five-parameter logistic (5 PL) function. The results show that Site 4 is the only solar region suitable for implementing "PV-integrated NZEBs", reaching a payback period of 8.44 years in 2040 (Scenario 1). Furthermore, with small technological improvements in PV energy efficiency (%), buildings located in Site 3 and Site 4 can meet the net-zero emission target with payback periods of less than 10 years in 2034 (Scenario 2). Finally, starting in 2044, the "PV-integrated system" will have a payback period of approximately 7 years in most study locations, even without federal support for the solar investment tax credit (SITC). Accordingly, federal support for solar power generation will be more effective if the SITC rate is gradually reduced by 2.5% annually starting in 2033. In conclusion, this paper suggests the economic feasibility of implementing solar-based NZEBs in the United States non-residential sector by considering the synergetic effect of technological improvement and cost reduction in PV-integrated systems. • This study predicted the cost and efficiency of PV panels to estimate the economic feasibility of solar-based NZEBs. • The cost reduction and technical advancement in PV panels can remarkably improve the economics of solar power. • The 5 PL function is systematically modeled to predict the total investment cost and efficiency of PV panels. • In most study locations, solar-based NZEBs are projected to achieve a 7-year payback period beginning in 2044. • The federal support for the SITC rate (%) is recommended to be gradually lowered starting in 2033. [ABSTRACT FROM AUTHOR]

Harmonizing the cleanliness and economics of energy is an effective means of integrating economic growth with carbon peaking goals, and realizing sustainable development in China. However, due to the existence of the energy trilemma, a worthwhile research question is how to fulfill the carbon peaking commitment while guaranteeing China's stable national development. Based on this, this paper not only discusses the nonlinear relationship between clean energy consumption transition and economic growth through threshold regression, but also simulates several scenarios of energy transition dynamics with different growth rates by using Oracle Crystal Ball software to forecast the future trend of China's carbon emissions up to 2030, aiming to find a transition scenario that can make the peak of carbon emissions appear without significant economic burden, so as to put forward countermeasures and suggestions to avoid falling into the energy trilemma in the process of fulfilling the commitment of carbon peaking. This paper finds that: firstly, there is an inverted U-shaped non-linear relationship between clean energy consumption transition and economic growth, with the inflection point of the inverted U-shape being 0.595; and secondly, only when energy transition exceeds 0.535 in 2030, the peak of carbon emissions (about 12,922.28 Mt) will occur. In summary, this paper argues that China should steadily advance energy transition, avoiding either too fast or too slow a pace (bring clean energy consumption transition to within the 0.535–0.595 range by 2030), in order to secure economic growth while honoring its commitment to peak carbon. Therefore, China should prudently design a coal exit strategy to avoid falling into the energy trilemma when promoting energy transition. Moreover, if China wants to further fulfill its commitment to carbon neutrality by 2060, it needs to continue to deepen the construction of green financial markets in order to better balance the relationship between energy cleanliness and economy. [ABSTRACT FROM AUTHOR]

Climate emergency is widely acknowledged. However, our institutions are struggling to find new intervention types to accelerate the transition. This paper analyses this struggle by combining agency theories and discursive approaches to study transition intermediaries, i.e, organizations seeking to foster the transition activities of others. The internal meaning structures of intermediaries are described as interlocking shared action frames, i.e., beliefs and meanings underpining their activities. These frames are characterized through the definition of pairs of contrasting frame elements along eleven framing dimensions. This conceptualization allows for a semi-quantitative mapping of internal structures. The method is developped and illustrated with the in-depth case study of a Swiss regional utility department running an energy efficiency programme. Analysing a series of exploratory workshops, we find that established frames revolve around technology-oriented, managerial approaches to the transition, ultimately narrowing the range of imaginable interventions. While these are well-studied shortcomings of energy efficiency centred approaches, further observations suggest that these frames underpin the perception of intervention impacts, helping keep staff and recipients involved. To strike a balance between energy saving targets and transformative ambitions, this paper suggests revising programme evaluation logics and reframing technological solutions as responses, rather than substitutes, to practice changes. [Display omitted] • A discursive approach is used to explore the agency of transition intermediaries. • Internal meaning structures are described as interlocking shared agency frames. • A utility department running an energy efficiency programme is taken as case study. • An established perspective revolves around technical, managerial approaches. • Emergent perspectives tend to be linked this coherent established perspective. [ABSTRACT FROM AUTHOR]

Recently, the widespread adoption of artificial intelligence, particularly generative AI technology, has surged across various industries. However, a notable drawback of this technology is its significant energy consumption during model training and operation, which poses challenges to sustainability goals and the environment. Consequently, various initiatives have emerged to promote what is termed "green artificial intelligence," aiming to mitigate these environmental impacts. Nevertheless, research discussing these initiatives remains scarce. Hence, this study aims to identify green artificial intelligence initiatives that contribute to environmental friendliness. This paper has comprehensively reviewed the existing literature, professional websites, and expert blogs to identify and analyze available green AI initiatives. This paper has identified 55 such initiatives, broadly categorized into six themes: cloud optimization, model efficiency, carbon footprinting, sustainability-focused AI development, open-source initiatives, and green AI research and community. This study discusses the strengths and limitations of each initiative to offer a comprehensive overview. The findings provide valuable insights, particularly for industries interested in green artificial intelligence and green technology in general. While some tools have been recognized and studied, comprehensive research and analysis are still required to empirically evaluate the majority of other tools due to their early stages of development in this field. • Study explores the growing awareness and concern about environmental impact of AI. • It investigates development of tools and approaches to address Green AI. • Significance of collaboration, open-source initiatives, in Green AI development. • Examine importance of ethical considerations in green AI development. • Provide insights for industries in green AI and green technology in general. [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]

Iron & Steel, Pulp & Paper, Non-Metallic Minerals and Chemical & Petrochemical are the most energy intensive subsectors, even though they utilise only a limited range of production processes compared to other sectors like Machinery or Food & Beverages. To support future efforts for decarbonising the European industry, this study aims to develop a methodology to correctly and dynamically depict all relevant production processes of the mentioned subsectors and to generate synthetic load profiles (LP) 1 1 LP ... Load Profile. based upon their consumption and generation behaviour. In a first step, the energy intensive subsectors and their main production processes are identified. A standardised research approach is used to correctly depict their characteristics e.g. runtime, energy consumption and generation, unit sizes etc. Next, a methodology for modelling the timely behaviour of these production processes and for generating synthetic LPs is developed. This method is based upon the bottom-up approach of discrete-event simulation combined with stochastics. The developed methodology is then implemented into the simulation software Ganymed. Finally, the results of this methodology are validated via a case study, modelling the primary steel production route of an Austrian steel mill. In overall, the synthetic electricity LP shows good approximations to the measured one with a mean absolute percentage error of 6.08% for the simulated five days in total. However, a stronger deviation of the generated LP compared to the measured counterpart can be noted at the last two days. This deviation results from a reduction of the capacity during the real life production. This, however, can be taken into account in the synthetic generation given a more extensive data basis. Consequently, Ganymed can be deemed as a suitable software for generating energy consumption and generation behaviour of processes and production chains of energy intensive industries. • Production processes of energy intensive industries are classified and structured. • A methodology for generating load profiles of these industries is presented. • Discrete-event simulation is enhanced extensively to accurately depict industries. • Load profiles of various energy carriers for these industries can be generated. • Various system dimensions from single process to overall plant can be depicted. [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 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]

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]

Digital transformation has the potential to significantly impact household energy consumption (HEC) by promoting the adoption of energy-efficient technologies. This paper utilizes the time-varying Difference-in-Differences (DID) model to evaluate the causal effect of digital transformation on HEC within China's households. We find that HEC is positively impacted by digital transformation, as embodied by the "Broadband China" strategy; and the results remain reliable when many robustness tests are taken into account. Specifically, digital transformation has the potential to indirectly increase HEC by consumption upgrade and internet development mechanisms. The heterogeneity analysis reveals that the impact of digital transformation on HEC varies significantly across different household and regional. High-income, low skilled, rural areas, as well as households in areas with higher levels of development and marketization, are more likely to be affected by the digital revolution in production and consumption, which will inevitably lead to an increase in HEC. These insights are crucial for developing effective strategies to reduce HEC and promote sustainable development. [Display omitted] • Assesses digital transformation's impact on China's household energy use. • Identifies significant increase in energy consumption due to digitalization. • Examines mechanisms of how digital transformation affects energy use. • Provides insights for harmonizing digital and green transformations. [ABSTRACT FROM AUTHOR]

Ground-coupled heat pump (GCHP) systems can provide comfortable indoor environments, but also inevitably contribute to greenhouse gas emissions and other impacts on human health, ecosystems, and resources. Life cycle assessment (LCA) methodology has been widely adopted to estimate the environmental impacts associated with GCHP systems, with operational electricity consumption being the largest contributor among most categories. Given the data-intensive nature of LCA, operational electricity consumption should be prioritised to refine the accuracy of LCA results. Previous studies, however, have relied on static COP (Coefficient of Performance) and annual average data to obtain the environmental impacts and have disregarded the effect of long-term performance degradation caused by building thermal load imbalances. In this paper, to bridge this research gap, a dynamic operational environmental impact assessment (DOEIA) method was proposed to improve the precision of LCA results by incorporating higher temporal resolution data of electricity mix and real-time performance modelling of the reversible GCHP system. Impacts of different temporal resolutions (i.e. monthly, trimestral, and annual) on the accuracy of LCA results was examined and the operational performance degradation resulting from imbalanced building heating and cooling loads was considered. Results demonstrate that while performance degradation effects are evenly distributed across impact categories, gaps due to temporal resolutions vary significantly between different categories. Subsequent analysis of spatial variations in the latter further emphasises the importance of accounting for higher temporal resolutions. Finally, life cycle impact assessment (LCIA) results for reversible GCHP systems in different locations were obtained with the support of DOEIA method and the results underscore the necessity of a cleaner energy mix. The proposed DOEIA method can be applied to other energy systems for comparative analyses. It is replicable in other countries and regions and therefore is expected to provide methodological guidance for future decision-makers. • Development of a dynamic method to improve the accuracy of LCA of GCHP systems. • Evaluated long-term performance degradation and low-temporal resolution data effects. • Performance degradation affects categories consistently; low-resolution data varies. • Spatial analysis clarifies parameters affecting environmental impacts. • The proposed method is applicable to other energy systems for comparative analyses. [ABSTRACT FROM AUTHOR]

The coordinated operation of concentrating solar power (CSP) and traditional thermal power can facilitate the integration of variable wind and solar renewable energy (VRE) into the grid supported by ultra-high voltage (UHV) transmission line, forming a novel HRES. The optimal sizing of VRE in this novel HRES has been rarely investigated. This paper thus proposed a framework serving the capacity configuration optimization for VRE in aforementioned HRES. The lifecycle economic and technical performance of the HRES was evaluated to guide capacity planning. The operational characteristics of the thermal power and CSP were investigated via short-term scheduling optimization. The methodology was applied to a HRES base located in Qinghai Province, China. Results indicate a maximum capacity ratio of VRE to flexible power of 1.74:1 without power curtailments. Further expansion of VRE leads to an escalation in power curtailments. The optimal VRE sizing is determined as a combination of 4000 MW solar power and 1000 MW wind power, which achieves a levelized cost of energy as low as 0.363 CNY/kWh, corresponding to a capacity factor of 0.32. The role of CSP in mitigating VRE fluctuations in HRES is more significant than that of thermal power due to its superior flexibility and low operating costs. The inter-day heat transfer of CSP can further facilitate the stability of the HRES output between consecutive days. These findings provide valuable insights for capacity configuration and operation of the new HRES, which will promote the utilization of renewable energy and cleaner production of the power system. [Display omitted] • Optimal wind and solar sizing of a novel hybrid power system is researched. • Short-term daily scheduling model of the novel hybrid power system is proposed. • The maximum ratio of VRE to flexible power is 1.74:1 without power curtailment. • The HRES demonstrates a LCOE of 0.363 CNY/kWh under optimal VRE sizing. • Concentrating solar power proves superior flexibility compared to thermal power. [ABSTRACT FROM AUTHOR]

Renewable energy has demonstrated a significantly greater increase in its share of total energy consumption than carbon emissions in recent decades. This rise in renewable energy consumption did not effectively mitigate climate change, as evidenced by the accelerated rate of climate change. This paper aims to determine the cause of this failure by examining the total effect centrality and mediating effects of renewable energy in economic systems in the short run and long run. The total effect centrality and mediating effects of renewable energy illustrate the evolution of its overall impact on the major variables of an economic system and its role in mediating the indirect effects of these variables on carbon emissions, respectively. The study employs a recently developed network analysis that is a pioneer in the estimation of the time-varying total effect centrality and time-varying mediating effects of renewable energy, while also accounting for its cross-associations among all variables in a network. The study derives three conclusions by examining the three countries with the highest carbon emissions in their respective regions: the United States in North America, China in Asia, and Germany in Europe. First, renewable energy has become an essential element of economic systems, having a substantial total effect centrality on macroeconomic systems and, by way of its mediating effects, substantially mitigating the indirect effects of major macroeconomic variables on carbon emissions in the short run. Second, the total effect centrality and mediating effects of renewable energy diminish in the long run, indicating that its capacity to effectively mitigate climate change remains constrained in structural terms. Third, the long-term decline in total effect centrality rises by 0.3, 0.7, and 0.4 for the United States, Germany, and China, respectively. In the same nations, the long-term decline of mediating effects rises by 0.2, 0.4, and 0.2, respectively. The study provides pertinent policy recommendations on how to augment the total effect centrality and mediating effects of renewable energy in economic systems. [Display omitted] • Explains why renewable energy failed in curbing the accelaration of climate change. • Introduces the 'total effect centrality' of renewable energy in economic systems. • Illustrates the time-varying mediation role of renewable energy in economic systems. [ABSTRACT FROM AUTHOR]

The time-varying dynamics of the transition to renewable energy reflect the effectiveness and efficiency of energy policy. This paper investigates renewable energy transition behavior in China using Markov regime-switching models. Two distinct phases, expansion and contraction, are identified based on the growth rates of renewable energy consumption share. We find that since China started its new-energy national strategy in 2005, there has been an extended period in which it has been in the expansion state, although it occasionally reverts to a contraction state. Intriguingly, we uncover that the timing of transition state changes can be explained by the changes in policy-related, market-based instruments and risk-related indexes. These findings present implications for policymakers in developing more precise and effective policies, that drives investment choices to align with the goal of a sustainable energy system. [Display omitted] [ABSTRACT FROM AUTHOR]

Establishing an intuitive link between driving factors of household energy consumption activities and inequalities is important for the understanding of household heterogeneity in energy consumption behaviours. This paper proposes a novel typology framework based on machine learning approaches and data from 3637 Chinese households in 2014 from 85 cities. Activity-based energy consumption was measured, highlighting inequalities across activities, regions and household types. The results showed significant energy consumption disparities between urban/rural and north/south households, especially in cooking, space heating and vehicle activities. By identifying driving factors of energy consumption, a new household typology classified samples into 6 (all), 6 (urban) and 7 (rural) types. Within these types, households with similar demographic structures, lifestyles and energy consumption habits were clustered. Demographic structure, region, and primary energy demand were used as the basis for the typology. The findings demonstrated how household lifestyle differences explained the cause and underlying driving factors of urban-rural energy consumption inequalities and provided suggestions for city-by-city and type-by-type measurements to support effective low-carbon transformation in cities. • Activity-based household energy consumption (HEC) is measured and calibrated. • Household energy consumption inequalities exist in urban/rural and the north/south groups. • A four-step machine-learning approach well classifies households into reasonable clusters. • Activity-based variables are driving factors for HECs in a LASSO model. • Inequality is narrowed in household clusters and type-by-type policies are needed. [ABSTRACT FROM AUTHOR]

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

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

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

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

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

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

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

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

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]

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]

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]