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It is important for Chinese cities to advance sustainable energy consumption and ensure that they achieve their carbon emission peak and carbon neutrality. This paper develops a comprehensive assessment of the spatial variability and influencing factors of the quantity of household energy consumption per capita in mega-cities. Based on microlevel household survey data from Guangzhou, Guangdong Province, China (N = 1082), we analyze the spatial effects and factors of urban residents' energy consumption. A spatial econometric model with four spatial weight matrices is employed, and the nested spatial weight matrix, which holds practical economic and geographical significance, is set. We find that (1) the quantity of energy consumption per capita of all households in Guangzhou is 189.57 kgce/year and shows a spatial difference, with 177.5 kgce/year in the central urban area and 203.47 kgce/year in the outskirts urban area. In particular, households in the outskirts of urban areas present a higher percentage of "high" and "medium-high" energy consumption per capita than those in central urban areas. (2) The quantity of household energy consumption per capita of urban residents is characterized by significant spatial correlation and agglomeration, with "high-high" and "low-low" agglomeration in central urban areas. (3) The household labor force, the housing area and household income are the core factors that drive the quantity of household energy consumption per capita, while the region and educational level are also main factors. We observe the diversity of the energy transition by comparing the patterns of the quantity of household energy consumption per capita within the city. A comprehensive picture of the diversity underlying the energy transition applied across cases can be distilled. [ABSTRACT FROM AUTHOR]

Ammonia is a very important chemical industry product. It has a wide range of applications in refrigeration, energy, and fertilizers, and 80% of ammonia is used as a nitrogen fertilizer. China mainly produces ammonia and urea from coal gasification, which leads to difficult CO 2 capture and high energy consumption. This study proposed a new process for the production of ammonia and urea by coal direct chemical looping hydrogen production process (CDCLTU). The coal direct chemical looping hydrogen production replaces the air separation unit and water gas conversion unit of the traditional coal-to-urea process (CTU), which effectively separates and captures CO 2 and significantly reduces the energy consumption. A model of the CDCLTU was established using Aspen Plus. Parameter optimization, technoeconomic, and environmental analysis were conducted through simulations. The results showed that CDCLTU could completely replace the air separation system that the CTU relies on. Compared with the traditional process, the utilization rate of C increased from 53.46% to 67.95% in the new process. The energy consumption of CO 2 separation and capture was reduced by 97.04%. The CDCLH had a 79.7% energy efficiency in the hydrogen production process and 99% capture rate of CO 2. The total capital investment was dropped by 41%, unit urea production cost dropped by 12.1%, and the payback period dropped from 7.5 to 3.5 years. This paper provides a new direction for clean and efficient utilization of coal resources. [Display omitted] • Proposed a novel coal-to-urea process by chemical looping technology. • Conducted main parameters optimization and performance analysis for the new process. • The new process reduced 97.04% energy consumption of CO 2 separation and capture. • Production cost of the new process is 12.1% lower than that of the conventional process. • The new process achieved 67.95% C utilization rate and 1.39 t/t lower CO 2 emission. [ABSTRACT FROM AUTHOR]

The non-metallic minerals industry is the third-largest industrial energy consumer, and it constitutes around 7% of global CO 2 emissions due to the high energy intensity of production processes. Although the substantial improvement of the energy efficiency of this industry in the EU observed since 1995, the energy intensity of this sector is still relatively high. To reach the goals of the Paris Agreement, it is necessary to implement further steps towards the decarbonization of this sector. Previous works do not provide information about research trends in this field or the current development of technologies. To fill this gap, the following research questions have been asked: (Q1) which decarbonization measures are already applied or under development in the non-metallic minerals sector?; (Q2) which of these measures might bring breakthrough to this sector?; (Q3) what is the impact of these measures on costs, energy consumption and GHG emissions? To this end, this paper performs a systematic literature review of GHG emission mitigation options of the non-metallic minerals industry by screening 2964 articles and performing an in-depth analysis of 48 full-text relevant articles following the PRISMA approach. The results show that the carbon capture, utilization and storage (CCUS) is the most popular research topic and has the highest decarbonization potential for the industry (up to 60%). However, more research is needed for the full-scale implementation. Further lowering the clinker to cement ratio to at least 75% is also of major importance, however, it is a mature technology, and not much research is required in this field. Despite the relatively low representation in scientific literature, heat electrification is an unavoidable direction for the industry in the long term, resulting from the global shift towards sustainable energy from renewables. The article also sets directions for future research, especially in terms of further development of the most promising technologies and on the industry transition pathways that would be legally adopted at the international level. • The non-metallic minerals industry constitutes around 7% of global CO 2 emissions. • A systematic literature review of GHG emission mitigation options of the industry. • 2964 articles screened and 48 full-text articles analyzed. • CCS is the most discussed mitigation option, yet not ready for commercialization. • Heat electrification is an unavoidable direction, resulting from the climate policy. [ABSTRACT FROM AUTHOR]

The spatial distribution of biomass resources is not uniform. At present, the zoning of biomass energy development suitability is primarily based on resource potential, whereas energy demand is ignored as an important factor affecting the degree of local energy self-sufficiency. Under the constraints of high transportation costs, solar energy with low geographical constraints can be a supplementary energy source for biomass despite its instability. Based on the energy utilization potential of solar energy and biomass resources, this paper evaluates the energy surplus factor (ratio of renewable resource potential to energy demand) of each city in the study area, takes meeting the specific energy surplus factor as the construction prerequisites of biomass combined heat and power (CHP) plants, calculates the biomass resource utilization rate and energy surplus factor in the study area under different conditions, and provides zoning suggestions and data reference for the unified planning of biomass CHP plants. The minimum energy surplus factor that is suitable for the development of biomass energy is set as the construction threshold of biomass CHP plants (The construction threshold is the minimum condition for the construction of biomass CHP plants. Only areas where the energy surplus factor reaches the preset construction threshold are allowed to build biomass CHP plants). The results show that the construction threshold is negatively correlated with the utilization rate of biomass resources. When the construction threshold is less than 1.4, the utilization rate of biomass resources decreases rapidly with the increase of the construction threshold; when the construction threshold is greater than 1.4, the utilization rate of biomass resources decreases slowly and tends to be flat. It is suggested that biomass resources should be developed in different regions and in stages, and priority should be given to developing areas with an energy surplus factor greater than 1.4. Areas with an energy surplus factor of 0.3–1.4 are considered secondary development areas, and areas with an energy surplus factor of less than 0.3 are not recommended for development; these can meet 31% and 59% of the total energy demand and reduce 50319380 tons and 180524400 tons carbon emissions so that the limited construction cost can meet higher energy demand and biomass resource utilization. • Priority development areas are identified based on solar and biomass potential. • Energy surplus factor is calculated to determine the development suitability. • Strategies for zoning and phased planning of biomass resources are proposed. [ABSTRACT FROM AUTHOR]

Better digging the sustainable development path of the top five energy consumers (China, the United States, India, Japan and Russia) can serve both domestic economy and global economy to achieve coordinated development with the ever-increasing energy demand. This work has made the following efforts: (1) the Tapio decoupling model was employed to determine the decoupling states between energy consumption and economy; (2) based on the extended Kaya formula and the LMDI method, this work focused on measuring the impact of technological factors and urbanization factor on energy consumption; (3) the LMDI method and the Tapio decoupling model were combined to measure the impact degree of influencing factors on decoupling indicators. The main findings show that from 2001 to 2018: (1) Japan was the country with the most year of strong decoupling, followed by the United States, which indicates that the decoupling status of developed countries was better than that of developing countries. (2) The driving forces behind the energy consumption growth are mainly the effects of R&D scale and urbanization, while the main factors that hinder energy consumption growth were technological factors, including energy intensity effect and R&D efficiency effect. (3) Energy intensity effect was the main driving force for the decoupling process, and the R&D efficiency effect was the biggest obstacle to decoupling energy consumption from economic growth. Finally, this paper proposes that the government can give priority to relying on technological innovation to accelerate the development and promotion of energy-saving and emission-reduction technologies. • Exploring the coordinated development path of energy consumption and economic growth in top five energy consumers. • Extending the traditional LMDI method to measure the impact of technological progress and urbanization on energy consumption. • R&D scale and urbanization promote energy consumption growth, while technological progress inhibits energy consumption growth. • Governments can prioritize technological innovation to speed up energy conservation and emissions reduction. [ABSTRACT FROM AUTHOR]

Achieving the peak of carbon dioxide (CO 2) emissions requires a large amount of green and low-carbon investment. Whether the green finance system can efficiently support the capital need for achieving the CO 2 emissions target in the context of the COVID-19 epidemic is a matter of concern. This paper constructs a system dynamics model (SD model) to illustrate the quantitative relationship between the green finance system and CO 2 emissions and introduce the COVID-19 epidemic as a variable to analyze ten simulation scenarios regarding the carbon emissions commitment realization under different green finance and economic growth status. It is shown that: (1) Regardless of the impact of COVID-19, China can meet its commitment by reaching its CO 2 emissions peak in 2030 and realizing a 20% non-fossil energy proportion in 2025; (2) Under the impact of the epidemic, the goal can not be obtained in all energy consumption scenarios when the government expenditure on the environment is low. The target year of reaching CO 2 emissions peak becomes 2033, 2037, and 2040. The results indicate that reducing government expenditure on environment protection makes the CO 2 emissions peak target less likely to be achieved within a given time frame. We also concluded with important policy implications according to the result of the simulations. Overall, this study makes a reference for other economies and researchers to quantitatively predict the interaction relationship between the green finance system and CO 2 emissions in the context of COVID-19, which provides policymakers with insights into a joint power of energy consumption upgrading and green capital guidance. • Providing empirical studies regarding the mechanism of the inhibitory effect of green finance on CO 2 emissions. • Using systems dynamics model to address the missing data problem. • Making solid suggestions under COVID-19 Epidemic. • Building the relationship between the green finance system and CO 2 emissions and making forecasts for "3060" mission result. [ABSTRACT FROM AUTHOR]

In the natural stone sector, rock blocks are cut as a plate using block-cutting machines (BCM), and the plates are polished in the polishing machines. Energy consumption is the primary operating cost in both the cutting and polishing processes. The energy consumption of BCMs is affected by many factors, with the most influential being traverse speed. The energy efficiency of BCMs can be improved by regulating the traverse speed throughout the cutting process, and the roughness of the cut surface can be reduced by keeping the ratio between the traverse speed and peripheral speed constant as an additional benefit. This also reduces energy consumption and waste production in the polishing process. In this paper, two controllers used for the regulation of traverse and peripheral speeds are applied to a laboratory-scale BCM, one of which regulates only the traverse speed, while the other regulates both the traverse and peripheral speeds. A limped hierarchical fuzzy logic controller (LHFLC) approach is used in the design of the controllers for the first time on the BCM. In the proposed LHFLC method, the design procedure of the controller is simplified, and its memory usage, computational load, and response time are decreased. The performance of the controllers (a three-input/one-output (3I1O) FLC, 3I1O LHFLC, and a three-input/two-output (3I2O) LHFLC) was tested with cutting experiments performed on three rock samples (Bilecik Beige limestone, Uşak Green marble, and Afyon travertine), and the acquired results reveal that the proposed controllers (3I1O LHFLC and 3I2O LHFLC) are more efficient than the previous controller (3I1O FLC), with over 21%. A statistical analysis shows that to improve the energy efficiency of the BCM, the 3I1O LHFLC could be used rather than the 3I1O FLC. It is further noted that statistically, the cutting rate can be kept more constant with the 3I2O LHFLC than with the 3I1O LHFLC, meaning that the surface roughness of the plates produced by the BCM can be reduced using the 3I2O LHFLC. [Display omitted] • The energy efficiency of the block-cutting machine (BCM) was improved by regulating traverse speed throughout the process. • A limped hierarchical fuzzy logic controller (LHFLC) was used to regulate the traverse speed for the first time on a BCM. • With the LHFLC, the energy consumption of the BCM was reduced by at least 20%. • The cutting rate of the BCM was kept constant through the regulation of both the traverse and saw blade speeds. • Energy consumption and waste production can be reduced in the polishing process keeping the constant cutting rate of the BCM. [ABSTRACT FROM AUTHOR]

In the German industry Heating, Ventilation and Air Conditioning (HVAC) systems account for 11 %–20 % of the final energy consumption. Usually HVAC systems are dimensioned based on static extreme values and thus during most of the operation time, HVAC systems tend to be overdimensioned. In addition, the extensive literature review of 40 papers shows that often topics on model-based energy efficiency improvement are commonly performed for office or residential buildings, but rather rarely for production environments. Furthermore, the performance of control schemes for energy efficiency improvement are rarely compared with each other or incompletely compared with each other. In this publication, we use the framework of a Cyber–Physical Production System (CPPS) to compare the performance of four main control schemes, ranging from simple time-based control to model-predictive control, which has not been done in the context of battery production before. Over a fixed observation period, the four control schemes are transparently compared with each other in a case study using the example of the Battery LabFactory Braunschweig (BLB). Compared to the initial state, a significant reduction in final energy demand can be achieved with all examined control approaches, which is even up to 37.29% in case of the model-predictive control. Surprisingly, even simple control approaches, without any predictions, have a good energetic saving potential of up to 20.22% compared to the initial state, which is why they are initially recommended for practical implementation. Moreover, we publish all source code of the control schemes, in order to encourage the implementation of energy efficiency measures in further applications not restricted to battery production. • Comparison of control schemes for energy efficiency improvement of HVAC systems. • Considering scheduled to model-predictive control applied to battery production. • Best control scheme achieves energetic reduction of up to 37.29% in case study. • Control scheme approaches transferable to other cases outside of battery production. • Publication of source code to encourage implementation of efficiency measures. [ABSTRACT FROM AUTHOR]

With the improvement of occupants' requirements for quality of life, the functions and regions of buildings are becoming more and more refined. Large-scale buildings with multi-regional function come out increasingly. The traditional uniform constant temperature design and operation management technology can no longer meet the needs of occupants, as the dynamic thermal comfort level of human body in different regions has great discrepancy. This paper conducts the whole chain regulation model for large multi-regional buildings from demand level, application level and control level. Aiming to reduce the load reasonably on the premise of thermal comfort, a model of dynamically adjusting the set point temperature in different regions is established on demand level. As load demand refers to the energy provided by the operation of HVAC system, the controlled parameters values are obtained by adopting the intelligent optimization strategy for the application level. On the bottom control level, the optimized parameters are managed by model predictive control (MPC), which has great advantage of rapid response. It is found that the strategy of dynamically adjusting the temperature set point in typical day can reduce the load demand by 6.16% without sacrificing the comfort of indoor personnel. Based on the load demand optimization, the operation optimization and MPC strategy is further adopted for application and control level by simulation, and can realize the total energy saving of HVAC system by 12.78%. • A unified whole chain regulation model for multi-regional building is provided. • The multi-regional dynamic indoor set point temperature model is established. • The intelligent optimization regulation model is obtained on application level. • A fast parameter regulation control model is established on control level. • The strategy achieves multi-objective optimization of energy and thermal comfort. [ABSTRACT FROM AUTHOR]

Electric vehicles are promoting sustainable developments in the automotive industry. But the short driving range has been an inconvenience to the electric vehicle (EV) users. This paper evaluates the potential of Photovoltaic integrated into EV in real-world conditions to assess energy consumption, range and EV's charging frequency for battery and fuel cell powertrain configurations. A simulation model is developed, which estimates the energy production through onboard Photovoltaics, energy consumption, and range under diverse driving profiles for five different vehicle types, ranging from Micro-car, 5 seaters light-duty vehicle, Shuttle and heavy-duty vehicles. The results showed that the range increased with reduced energy consumption and charging frequency with onboard Photovoltaics for battery powertrain configuration. The range improved by 30–50% for Microcar and 30–100% for the 5-seater vehicle with the private driving profile. Simultaneously, for Shuttle, an increase in the range between 5 and 15% is reported. On the contrary, the results indicated the fuel cell powertrain configuration does not present an added value with onboard Photovoltaics. Further analysis showed that the higher irradiance rates improved the operational period of Photovoltaic integrated into EV by 5 more days than standard EV. Finally, the economic analysis revealed that Photovoltaics integrated into EV is profitable, reaching the break-even point on the additional photovoltaics expense before the half-life of the vehicle, which makes the total ownership cost lower than a standard EV for its lifetime. • Assessment of PVEV in real-world driving for battery and fuel cell powertrain configurations. • Simulation model developed to estimate energy production through onboard PV. • Range is boosted by 30–100% in moderate urban private use driving profiles. • Higher irradiance rates improved operational capacity by 5 more days. • PVEV is profitable, reaching the breakeven point before the vehicle half-life. [ABSTRACT FROM AUTHOR]

The paper deals with the reuse of fibers from textile waste for the development of innovative and sustainable materials applied in the construction field. Currently, managing the high volumes of textile waste produced, and reducing the damage this waste does to the ecosystem, involves finding solutions for its reuse. Meanwhile, the construction sector has a great impact on the environment, and there is an urgent need to design innovative, sustainable building materials to reduce energy consumption and prevent the depletion of resources. In this context, the objective of the MOBIOTEX project is to develop new materials to replace the petrochemical-made vapor-barrier (VB) and rain-screen (RS) generally used in timber-frame construction (TFC). For that, nonwoven (NW) fabrics from textile waste were manufactured. Then, measurements based on standards for VB and RS were carried out on untreated and functionalized NW fabrics, to test their sealing and mechanical properties. Lamination of NW gives satisfactory results and some modified materials meet the requirements for both VB and RS applications and have similar properties as commercial products. These first results are quite promising as they show that, with some treatment, nonwoven from textile waste can be as technically efficient as commercial products. The different processes used during the MOBIOTEX project made it possible to build up the system boundary for the production of functionalized nonwoven in order to carry out future Life Cycle Assessment (LCA) and to compare with commercial products' LCA. In a circular economy approach, other characteristics such as durability, fire resistance and recyclability of such materials should be studied and optimized in order to reduce energy consumption and environmental impacts. Still, the main benefit of the developed materials, compared to commercial petrochemical rain-screens and vapor barriers, is on the production of raw materials, because it involves less exploitation of non-renewable resources, as an important part of materials come from waste, and the application in timber-framed construction can be a positive way in which to reuse a non-negligible volume of waste from the textile industry. [ABSTRACT FROM AUTHOR]

Limiting the impact of human use of energy on the environment is necessary to maintain the Earth's ecosystems such that they can continue to provide the ecosystem services on which humans and all other living organisms depend. Because the methods available to limit the impact on the environment of energy production and use require energy, energy used for this purpose—termed ecosystem maintenance energy (ESME)—reduces the net energy available. A capacity to estimate the energy that can be produced while sustaining ecosystem services, termed green energy, is critical to future energy use. This paper sets out a method to determine ESME by assigning energy consumption to the processes needed to remove the source of the impact on the ecosystem that results from the energy system lifecycle. We illustrate the method by the use of a case study based on a concentrated solar power plant (CSP) with natural gas back up. Green energy production is assessed in terms of the green energy return on energy invested, EROIg. CSP ecosystem impact is determined via a lifecycle assessment of the plant's operations for varying degrees of natural gas back-up. ESME is then determined by calculating the energy consumption of the impact source removal (ISR) process needed to remove the ecosystem impact. We show that, although the CSP plant produces increasing amounts of net energy with increasing natural gas back-up, the capacity to produce net green energy reduces with increasing natural gas back-up once ESME is included. For the CSP plant examined, EROI increases, while EROIg decreases for increasing use of natural gas. This indicates the large impact of ESME on the plant's energy return and the necessity of its inclusion in energy assessments. • Ecosystem maintenance energy (ESME) should be counted as energy cost. • For green energy production, EROI is greatly affected by ESME. • Even renewable energies have large ESME costs affecting their EROIg. • ESME is computed using the ISR energy associated with the lifecycle impact. [ABSTRACT FROM AUTHOR]

Despite the substantial effort on almost all levels during the last decades, the buildings' renovation rate needs to be at least doubled from the current 1% to meet ambitious energy efficiency goals. In the same way, the energy-intensive material replacement did not reach yet the desired grade in terms of sustainability measures and outlined goals heading to a low-carbon economy. This paper summarizes principles of sustainable development together with the current methodological framework relevant to the civil engineering and construction industry. The main part is devoted to the identification and understanding of principal factors preventing faster adoption of energy efficiency measures. High initial financial costs, investment risk over a long-term period, poor acceptance of sustainable measures, lack of information, and limited methodological framework for a reliable evaluation of environmental projects with the intergeneration context or understanding of externalities of human activities are identified as the main barriers to the sustainable building retrofits. These barriers are closely related each other and can be merged into several groups according to the stakeholders or scientific disciplines to amplify their primary impact. Notwithstanding, solution strategies based on narrow boundary conditions and limited multidisciplinary approach prevent substantial advances towards the sustainable building sector. In this regard, the major obstacles preventing the achievement of energy efficiency goals can be remedied by interdisciplinary cooperation. • Lacking multidisciplinary approach hinders sustainable measures adoption. • Limited public acceptance prevents a stronger political commitment. • Methods of social measures need to be studied for decision support. • Sustainable building industry transition is not a technical issue only. [ABSTRACT FROM AUTHOR]

This paper examines the nexus between renewable energy consumption (REC) and Gross Domestic Product (GDP) in G7 countries. To this end, we analyse data from 1980 to 2020 employing Toda and Yamamoto (1995) time causality test, Lemmens et al. (2008) frequency causality test and Hatemi-J's (2012) asymmetric causality approach. The findings differ across countries and are as follows. The conservative hypothesis is validated for the US and Canada. Specifically, we find significant and positive causal effect from GDP to REC in the long-term horizons (periods greater than 15 years for the US and 7 years for Canada). The growth hypothesis is confirmed for Germany. Precisely, significant and positive causal impact is shown from REC to GDP in the long-run horizons (periods longer than 7 years). The neutrality hypothesis is supported for France, the UK, Italy and Japan as there is no significant causal link at any time horizon. This is likely because the clean energy consumption does not reach a threshold beyond which it will start to have considerable interaction with economic growth. This study provides policymakers a better understanding of the REC-economic growth nexus to formulate appropriate policies. • We examine the nexus between renewable energy consumption and economic growth in G7 countries. • We use Toda and Yamamoto (1995) and Lemmens et al. (2008) tests and Hatemi-J's (2012) asymmetric causality approach. • For the US and Canada, significant and positive causal effect is shown from GDP to REC in the long-term horizons. • For Germany, significant and positive causal impact is displayed from REC to GDP in the long-run horizons. • For France, the UK, Italy and Japan, no significant causal link is found between REC and economic growth. [ABSTRACT FROM AUTHOR]

The increasing energy and material consumption associated with global economic growth has resulted in the need for more severe efforts at mitigating global climate change. The iron and steel industry consumes 8% of energy and emits 7% of total CO 2 globally. China's iron and steel industry contributes to 15% of that country's total CO 2 emissions. Therefore, there is an urgent need to explore the possibility of net zero emissions in the iron and steel industry in China to meet China's goal of carbon neutrality before 2060. In the study presented in this paper, the MESSAGEix–China iron and steel model was developed by integrating the process-based technology of the sector into the IIASA's MESSAGEix framework to explore zero CO 2 emission pathways and their associated impacts on resources, energy, and water in China's iron and steel industry up to 2100. We found that there are multiple pathways to achieving zero CO 2 emissions in the Chinese iron and steel industry by the end of the 21st century. More specifically, in all the pathways developed in this study, CO 2 emissions decreased significantly between 2030 and 2060 due to the rapid application of 100% scrap-based Electric Arc Furnaces (EAFs) and hydrogen-based Direct Reduced Iron (DRI)-EAFs steel-making technologies. However, by 2060, there will still be 70–360 Mt of CO 2 emissions from China's iron and steel industry; consequently, carbon sink or negative emission technologies are required to offset this and achieve the country's carbon neutrality goal. Furthermore, technologies for achieving zero emissions differ widely in terms of their impacts on the consumption of materials and energy. Compared to the electric (ELE) scenarios, 25–40% of extra iron ore is consumed in the current and new national policy (NPS) scenarios and the DRI scenarios, but 25–220% of scrap is required. At the same time, 20–150% more energy will be saved in the ELE scenarios than in the NPS and DRI scenarios. Finally, we recommend that policy makers design a cross-cutting strategy to achieve zero CO 2 emissions and enhance efforts for material recycling and the provision of clean energy and water. • There are multiple pathways to achieving net zero emissions in China's iron and steel industry. • Steel outputs are essential for reducing CO 2 emissions and conserving materials, energy, and water. • Hydrogen-based DRI and 100% scrap-EAFs technologies are key to achieving zero emissions. • CO 2 mitigation measures have limited impacts on water consumption. [ABSTRACT FROM AUTHOR]

Sustainable design of pavements is necessary for achieving carbon neutrality, which is the important goal for green pavements. In order to investigate the impacts of major design parameters on the environmental and economic performances of asphalt pavement, process life cycle assessment (LCA) and process life cycle cost (LCC) methods were used. And this paper mainly focuses on the effects of pavement type, resilient modulus of subgrade, thickness of adjustable layer, traffic level and the bidirectional annual average daily traffic volume of two-axis six-wheel and above vehicles (AADTT) on environmental and economic performances of asphalt pavement. Then energy consumption, greenhouse gas (GHG) emission, construction budget and environmental economy cost of heavy traffic and medium traffic asphalt pavement were quantitatively analyzed during construction period. The results indicate that the energy consumption, GHG emission, construction budget and environmental economy cost of each layer of semi-rigid and flexible base asphalt pavement are the largest in the materials preparation stage, followed by the transportation stage and the construction stage. The above environmental and economic indicators are mainly influenced by AADTT, adjustable layer thickness and pavement type of asphalt pavement during construction period, while the resilient modulus of subgrade has minor effect on environmental and economic indicators. Overall, the energy consumption and GHG emission of the flexible base asphalt pavement are lower than that of the semi-rigid base asphalt pavement, although flexible base asphalt pavement has higher construction budget and environmental economy cost. [ABSTRACT FROM AUTHOR]

Residential and commercial buildings consume around 40% of the total energy of the world. The building sector is also responsible for 30% of the global greenhouse gas emissions. There is an urgent need to assess the environmental carbon footprint (CFP) of global building industry. This study reviews the related studies and calculates the CFP of a residential building constructed in Turkey by considering the type of construction materials, energy consumption habits, maintenance and demolition stages in a 50 year of life cycle. The CFP of a common type of residential building constructed in Turkey found to be 48.87 kgCO 2 eq/m2/year. The CFP of structural elements used during the construction phase has been calculated to be 6.94 kgCO 2 eq/m2/year, which is about 14.21% of the total footprint. It is investigated that; the operational stage is the foremost contributor, representing 64.11% of CFP. The CO 2 emissions have been decreased by 38.05% by the implementation of basic methods. Unlike other studies, this paper revealed that the CFP from transportation of habitants is 6.5% more than the construction stage contributing 15.13% of overall emissions. The comparative studies of constructions in different countries show that CFP significantly depend on the behavioral factors of citizens of a country and the system boundaries selected. The results exposed that the effects of transportation should not be disregarded in the future studies. [Display omitted] • The total life cycle CFP of the building found to be 48.87 kgCO 2 eq/m2/year. • The CFP of construction materials is calculated to be 6.94 kgCO 2 eq/m2/year. • CFP from transportation of habitants is 6.5% more than the construction stage contributing 15.13% of overall emissions. • The daily energy use of the habitants is the main contributor to the CFP, representing the 64.11% of CFP. • The CFP of the building has been decreased by 38.05% by the implementation of low-carbon strategies. [ABSTRACT FROM AUTHOR]

Responding to the increased interest in addressing organisational sustainability issues using behaviour change strategies, this paper aims to propose a methodology for doing so from a different perspective – namely, sociology and social practice theory. Firstly, the background of behaviour change approaches and practice theory are discussed. Then a methodology for conducting a pro-environmental organisational change project is proposed. The methodology involves five key elements: detailed analysis of context, outlining a theoretical framework, establishing project boundaries, acknowledging connectivity of practices and choosing data collection methods. We illustrate the application of methodology by using examples of everyday consumables, energy and waste in a hospital trust in the South East of England. This approach has been effective for analysing routine and inconspicuous consumption within an organisation, as it considers individual attitudes and motivations as well as the structural and habitual nature of communities of practices. It allows researchers and managers to understand workplace consumption issues from several perspectives and identify the best angle from which to approach potential resolutions. • A five-step methodology for an organisational change management project is proposed. • Practice theory helps analyse routine inconspicuous consumption in an organization. • Practice theory helps establish project boundaries and connectivity between practices. • Organisational context and structure can facilitate sustainable workplace consumption. • Energy and waste consumption practices in workplaces rely on habits and routines. [ABSTRACT FROM AUTHOR]

Room air conditioners (RACs) are one of the high energy-consuming home appliances. Developing a smart solution to evaluate and track the efficiency of RACs is essentially useful for residents to make necessary maintenance or replacement decisions. While smart meters are increasingly installed to monitor electricity use, the application of smart meter data to evaluate the RAC efficiency remains inadequate. In this paper, we present a data-driven framework to identify non-inverter window RACs with low energy efficiency by analyzing smart meter data from the university student hall rooms. In this framework, we first applied the extreme gradient boosting (XGBoost) method to predict a RAC's hourly electricity consumption. Then we measured the effect of outdoor temperature on the XGBoost prediction of hourly RAC electricity consumption using the Shapley Additive Explanation method to interpret the RAC's efficiency. We conjectured that the RAC efficiency is normal if the predicted hourly electricity consumption is significantly correlated with the outdoor temperature. In contrast, the RAC efficiency is low if the outdoor temperature changes have little impact on the predicted electricity consumption. Finally, we applied the K-Means clustering algorithm to separate the RACs into the "low efficiency" and "normal efficiency" categories based on each's pattern of outdoor temperature's impact on electricity consumption. Our cross-validation result showed that the XGBoost model can achieve an average R2 score of 0.50 and an average root mean squared error of 0.20 kWh. We used RAC replacement records to validate our framework of interpreting the RAC's efficiency. On average, RACs having low efficiency consumed 25.69% more electricity per hour. Overall, our data-driven framework can contribute to extending the value of smart meters for RAC efficiency evaluation. Meanwhile, the smart meter data-driven framework can be improved, and more validation is needed in the future. • We created a data-driven framework to assess the room air conditioner's efficiency. • An XGBoost model was developed for hourly electricity consumed by air conditioners. • We clustered air conditioners based on Shapley value of outdoor temperature. • Air conditioners with low efficiency were identified and validated from clustering. • Identifying air conditioners with low efficiency can save electricity. [ABSTRACT FROM AUTHOR]

Battery demand is estimated to growth at a 25% annual rate, to reach a volume of 2600 GWh in 2030. Recovering critical raw materials from end-of-life batteries is now mandatory to limit the need of primary raw materials in the long-term. However, recycling of lithium-ion batteries (LIBs) is still in its early stage. Many technologies have been proposed, that consist of a combination of mechanical processing, often after thermal pre-treatment, ended up to hydrometallurgical or pyrometallurgical processing. All these methods have several pros and cons in terms of applicability, safety, environmental impact, eventual recovery efficiency of the components, and economic factors. In this frame, the evaluation of the sustainability of the proposed technologies plays a fundamental role in selecting the most suitable approaches able to support material supply for batteries, identifying the major solutions to optimize the end-of-life management strategies for LIBs, in the perspective of a circular economy context. However, LCA was developed to evaluate commercially mature technologies. This paper aims to propose and discuss, for the first time, the sustainability of 33 different technologies available in literature for LIBs recovery, even if developed only at laboratory level, by a screening approach, preliminary to LCA analysis. For this purpose, ESCAPE (standing for Evaluation of Sustainability of material substitution using CArbon footPrint by a simplified approach) tool based on the calculation of embodied energy and carbon footprint is used. Based on ESCAPE analysis results, this work shows and discusses the parameters that affect the technologies sustainability (thermal and mechanical treatments and chemicals and water use). The results, in accord with preliminary available LCA data, indicate that chemicals and ultrapure water consumption are the most energy-intensive processes drivers, showing that pyrometallurgy can limit carbon footprint and energy consumption in comparison to hydrometallurgy. Finally, by using the ESCAPE index, it is shown that some technologies may be improved for example by using industrial water instead of ultrapure water and/or by recycling the chemicals used for leaching steps. In summary it is shown that ESCAPE tool can be used in supporting eco-design strategies with the aim to facilitate the understanding of environmental implications of proposed LIBs recovery technologies and to focalize the research toward the more promising ones. [Display omitted] • Sustainability analysis of 33 technologies to recover spent LIBs is proposed. • The processes steps are evaluated in terms of embodied energy and carbon footprint. • LIBs recycling processes should aim for a better water management and leachants recovery. [ABSTRACT FROM AUTHOR]

This paper estimated the environmental performance and primary energy consumption of a newly installed large-scale solar photovoltaic system of 37.57 MW dc in Bidor, Malaysia using the process-based life-cycle assessment approach. The mid-point and end-point impact categories were evaluated, and seven relevant indicators related to energy and global warming were determined. The global warming impact and energy payback time obtained were 30.95 g CO 2 -eq/kWh and 3.43 years, respectively. Furthermore, the global warming impact mitigation potential attained was 811 kilo metric tonne (ktonne) CO 2 -eq throughout the system lifetime. Analyses of contribution, sensitivity and uncertainty were performed to i) identify photovoltaic system components that substantially consumed primary energy and impacted the environment, ii) evaluate the effect of four key parameters, namely irradiation, performance ratio, system lifetime and electricity mix on the energy and global warming-related indicators, and iii) estimate the uncertainty range of the evaluated indicators. The results highlighted that the main contributor to the indicators is the photovoltaic module. Other significant contributors are the civil work and operational and maintenance but the contribution from these components is much lower than the photovoltaic module. Besides that, the cumulative energy demand, energy return on investment, global warming impact and global warming impact mitigation potential showed an improvement when extending system lifetime from 15 to 40 years, i.e. from 0.8136 to 0.3677 MJ, 4.61 to 11.25, 55.37 to 24.99 g CO 2 -eq/kWh and 407.36 to 1055.78 g CO 2 -eq respectively, except for energy payback time and global warming payback time, i.e. from 3.26 to 3.55 years and 1.42 to 1.71 years respectively. • A life-cycle assessment of a real large-scale solar photovoltaic system is performed. • Extending lifetime of the system marginally increases the energy payback time. • The GW impact is about 19 times smaller than the Malaysian grid emission factor. • PV module is the major contributor to total primary energy consumption and GW impact. • BOS and O&M have a moderate share for primary energy consumption and GW impact. [ABSTRACT FROM AUTHOR]

With the advantages of avoiding land-use competition, biomass from agricultural residues is a promising solution to alleviate the energy crisis and environmental problems. In the traditional biomass utilization management problem, few studies have considered that land and water are key resources for the sustainable development of biomass from a life cycle view. Concerning the complex food-water-energy nexus, this paper focuses on the synergetic management of crop planting structure and biomass utilization as a whole system. An integrated framework is proposed for the co-optimization of cropland distribution and biomass utilization pathway under multiple uncertainties. Base on interval programming and fuzzy set theory, a type-2 fuzzy interval linear programming model is developed to handle multiple uncertainties with various characteristics and provide the optimum strategies for decision makers with different risk preferences. The proposed framework is verified by a case study of Hebei Province, China. Moreover, the impacts of water shortage and the implementation of the carbon price on the optimized strategies, economic and environmental benefits are investigated to provide deeper management insights when facing complex external factors. The obtained results suggest that corn and wheat will still be the primary crops, and bioethanol production will gain priority in the biomass utilization pathways for economic purposes. Water resources availability will greatly affect the crop planting allocation as well as total benefits but barely influence the biomass utilization pathways. Furthermore, with the implementation of the carbon trade market, a higher carbon price will stimulate biomass pellets production to replace fossil fuel consumption and improve the economic benefits directly. • Synergetic management of crop planting and biomass utilization is proposed. • A novel hybrid inexact programming approach is developed to address uncertainties. • The impacts of water resources availability are analyzed from a nexus perspective. • The influences of carbon price on the biomass utilization pathway are explored. [ABSTRACT FROM AUTHOR]

Under the downward pressure of the economy, China's extensive economic growth that relies on factor input is difficult to sustain. Scientific analysis of the industrial restructuring and energy consumption has become the key to realizing China's economic growth from high-speed to high-quality. Based on the fully-modified least square method and panel vector error correction model, this paper explores the equilibrium relationship and Granger causality among the industrial restructuring, energy consumption and economic growth at aggregated and disaggregated levels. The results show that: (1) The effect of industrial structure rationalization on economic growth is higher than that of industrial structure upgrading. When emphasizing industrial structure upgrade, the more reliable industrial policy to achieve China's sustainable economic growth is to pay attention to the industrial structure rationalization, especially in western China. (2) The impacts of natural gas and renewable energy consumption on economic growth are much smaller than coal and electricity consumption; more energy policies should be designed to encourage clean energy development. • The industrial structure rationalization has highest contribution to the economic growth of eastern China, followed by central and western China. • There exists unidirectional causality from industrial structure upgrading to structure rationalizing in western China and bidirectional causality between them in central China. • Effects of natural gas and renewable energy consumption on economic growth are much smaller than coal and electricity consumption. • Unidirectional long-term causality from renewable energy to economic growth exist in each sub-region. [ABSTRACT FROM AUTHOR]

All economies need energy for growth, particularly emerging ones with continued demand for fossil fuels that move them to unconventional energy sources such as heavy oil. However, the presence of large quantities of heteroatoms -sulfur, nitrogen, oxygen, and metals – in heavy oil is a major contributor to environmental pollution during the combustion phase. This requires the crude and residue to be upgraded and fractionated within tight specifications. Hot compressed water (HCW), at or beyond the critical point (374 °C, 22.1 MPa), has emerged as a highly promising environmentally benign and efficient method for heavy oil upgrading to replace conventional toxic solvents. The synergistic effect of HCW with energy generated from renewable sources is a new avenue into a cleaner carbon energy system for fuel production with the benefits of reduced energy consumption and lower carbon dioxide emission. This paper provides a comprehensive review in multi-scale which underlines the recent evolution and the challenges in this field. In addition, the critical factors and emerging gaps influencing the heavy oil upgrading in HCW are appended. Eventually, a set of suggestions for integrating renewable energy technologies with heavy oil upgrading in HCW and future perspective has been reported. [Display omitted] • The synergistic effect between HCW and heavy oil as a cleaner carbon conversion system is reviewed. • A comprehensive view in multi-scale which underlines the recent evolution and the challenges in this field. • Critical factors and emerging gaps influencing the heavy oil upgrading in HCW are appended. • A logical integration of renewable technologies with carbon-based energy conversion system in HCW is suggested. • Possible research suggestions and directions on this field have been proposed. [ABSTRACT FROM AUTHOR]

Energy shortage, greenhouse effect and environmental pollution are the three major crises faced by mankind. How to solve these three problems effectively has become the focus of all countries. Therefore, this paper proposes an innovative LNG cold energy utilization method that can simultaneously alleviate these three problems. This method includes an improved structure of the organic Rankine cycle, CO 2 capture cycle and supercritical CO 2 extraction of wastewater. In addition, automated machine learning based on data-driven approach is effectively used for modeling the wastewater extraction. Then, after verifying the accuracy of the proposed model, some parameters have great influence on the system performance. For instance, the temperature of supercritical CO 2 and working fluid water affect the local exergy efficiency and their regression coefficients are −0.29%/°C and 0.13%/°C, respectively. These parameters in wastewater extraction process are studied. Finally, under the optimization of genetic algorithm, the maximum wastewater treatment can reach 62.7 t/h increasing by 14.0%, and the LNG cold energy generation can reach 110.1 kWh/tLNG with an increase of 28.9%, while the exergy efficiency of the system is 50.4% increasing by 24.1%. Thus, organic Rankine cycle power generation, carbon capture and wastewater treatment play the roles of energy saving, emission reduction and environmental protection. At the same time, the results are compared with those of other scholars to verify the superiority of the proposed scheme. [Display omitted] • LNG cold energy reuse covering electricity, CO 2 capture and extraction is proposed. • Automated machine learning approach is used to build wastewater extraction model. • Reasonable arrangement of regenerative heat exchanger enhances system performance. • The maximum wastewater treatment reaches 62.7 ton/h utilizing genetic algorithm. [ABSTRACT FROM AUTHOR]

Recovering, recycling and reusing are some processes whose popularity is intense nowadays due to the increasing concern about sustainability and environmental issues. These processes are composed by some input variables that can be adjusted to optimize related relevant responses. The present paper, focusing on multiobjective optimization, proposes the Two-Phased Optimization Methodology based on the use of factor analysis, the Normal Boundary Intersection method and stochastic programming. A real application is developed in a cladding process of ABNT 1020 carbon steel plate using austenitic ABNT 316L stainless steel cored wire to exemplify the approach. The first stage of the methodology focuses on optimizing the geometric characteristics of the weld bead in order to improve the quality of the final product. The achieved values for the input variables were wire feed rate = 8.96 m/min, arc voltage = 29.38 V, welding speed = 24.21 cm/min, contact tip to the workpiece distance = 17.90 mm. From the comparison of the optimized geometry from Phase 1 with the real DoE experiments geometry, the scrap and rework areas are measured through a computer graphics software. Then, in the Phase 2, which focuses on a sustainability aspect, it is solved the multiobjective stochastic problem aiming the minimization of the scrap and rework jointly with the energy consumption. In this case, the optimized values for the input variables were wire feed rate = 9.95 m/min, arc voltage = 28 V, welding speed = 33.51 cm/min, contact tip to the workpiece distance = 25.41 mm. The methodology provides consistent results when dealing with a large number of responses considering the quality of the product and the environmental issues. • Quality and sustainability on the cladding process were investigated. • Two-Phased Optimization (TPO) methodology was proposed. • TPO results for quality were applied for the sustainability approach. • The final results shown that quality can still be hold in a sustainable process. [ABSTRACT FROM AUTHOR]

Biofuel manufacturing from renewable biomass through a smart manufacturing system is an important alternative to fossil fuels, which helps to decrease dependability on conventional fuel and decrease carbon emanations. By utilizing efficient labors, smart machines, and minimized energy utilization, the conventional biofuel manufacturing framework can be converted into a smart sustainable manufacturing framework. The existing conventional fuel demand can be replaced by biofuel. This study efforts to make pure biofuel with less amount of carbon emanations and energy utilization through a smart multi-type biofuel manufacturing framework, where the demand of the biofuel is selling price dependent. The different energy costs including air handling cost, lighting cost are calculated in this work-in-process inventory. The carbon emissions cost is included in every stage of this model. A variable demand has introduced for the maximization of profit. To reduce energy consumption and carbon emissions, a two-stage inspection cost with a variable manufacturing rate is taken to make the manufacturing process flexible such that the amount of impure biofuel is minimized. Although a random manufacturing rate is applied through a smart manufacturing system, still impure biofuel is manufactured. The impure biofuel is remanufactured again through refining just after the well-planned manufacturing ends. In this model, the multi-delivery technique is used such that a fixed amount of biofuel of n segments of the pure biofuel is transported to market places at predetermined intervals at the time of delivery frame. The classical optimization technique is used for continuous and differentiable variables and the mixed integer programming technique is utilized for discrete variables to maximize the total profit globally. To validate the model's usefulness, four numerical observations are studied. It is shown that by utilizing the said procedure, the percentage of impure biofuel can be decreased truly through the minimized energy consumption. The graphical representation and the sensitivity experiment show the impact of every parameter with the whole cost of the research paper. Numerical results assist to get the maximum profit and the optimum selling price of biofuel globally in a sustainable smart multi-type biofuel manufacturing system. [Display omitted] [ABSTRACT FROM AUTHOR]

Cutting energy consumption prediction gives decision supports for the energy-saving operation to realise green manufacturing. However, there are challenges when predicting aviation parts due to the machining features causing tool wear and expensive data labelling. Consequently, this paper builds a prediction model and emphasises training with limited experimental data by proposing an ensemble transfer learning approach. The approach incorporates transfer learning, i.e., TrAdaBoost-R2 (TR) algorithm, and calibration, i.e., Bayesian and Markov chain Monte Carlo calibration (MCMC). Firstly, a cutting energy consumption prediction model considering tool wear is formulated with cutting and tool parameters as the inputs. Secondly, a dataset including experiment and simulation data for training is constructed, where TR is used to identify the valuable data from the simulation model calibrated by MCMC. Then random forest regression (RFR) is introduced as a base learner to train the prediction model on the hybrid dataset. Finally, a case study of the aluminium alloy 7075 parts milling process shows that the proposed method is accurate in cutting energy consumption prediction. Compared with RFR and TR-RFR, the proposed method's coefficient of determination (R2) increases by 11.60% and 3.55%, indicating high goodness of fit under the same small samples of the experiment. Therefore, the proposed method could help determine the most efficient process plan without excessive time, materials and energy, significantly contributing to green manufacturing. [Display omitted] • Cutting energy consumption of aviation parts is predicted for green manufacturing. • The model considers tool wear with cutting and tool parameters as inputs. • It handles costly data acquisition problems using ensemble transferring learning. • The model exhibits satisfactory performance in a comparative case study. • It provides sustainable practical implications for society and industry. [ABSTRACT FROM AUTHOR]

Energy efficiency based on behavioral change has attracted increasing interest in recent years, although, solutions in this area lack much needed techno-economic analysis. That is due to the absence of both prospective studies and consumer awareness. To close such gap, this paper proposes the first techno-economic assessment of a behavioral change-based building energy efficiency solution, to the best of the authors' knowledge. From the one hand, the technical assessment is conducted through (i) introducing a novel edge-based energy efficiency solution; (ii) analyzing energy data using machine learning tools and micro-moments, and producing intelligent, personalized, and explainable action recommendations; and (iii) proceeding with a technical evaluation of four application scenarios, i.e., data collection, data analysis and anomaly detection, recommendation generation, and data visualization. On the other hand, economic assessment is performed by examining the marketability potential of the proposed solution via a market and research analysis of behavioral change-based systems for energy efficiency applications. Also, various factors impacting the commercialization of the final product are investigated before providing recommended actions to ensure its potential marketability via conducting a Go/No-Go evaluation. In conclusion, the proposed solution is designed at a low cost and can save up to 28%–68% of the consumed energy, which results in a Go decision to commercialize the technology. • Techno-economic assessment of behavioral based energy saving systems. • Edge-based energy saving using artificial intelligence and micro-moments. • Anomaly detection and explainable recommender systems for energy monitoring. • Market assessment and commercialization strategy considerations. • Market barriers and market drives of buildings energy efficiency solutions. [ABSTRACT FROM AUTHOR]

Global energy interconnection (GEI) is vital for countries to achieve clean energy production and carbon neutrality. To improve the construction efficiency of GEI projects, capital allocation efficiency and its driving factors must be analyzed. Therefore, this study uses a three-stage data envelopment analysis (DEA) model to evaluate the capital allocation efficiency of 161 countries. The results demonstrate that there is spatial heterogeneity in global capital allocation efficiency and that most countries have low levels of efficiency. The average pure efficiency is 0.364 and the average scale efficiency is only 0.176. To determine the fastest possible method of improving efficiency in different countries, this paper combined an adjusted three-stage DEA model with a geographically weighted regression (GWR) model to quantify the driving factors of capital allocation efficiency. The level of economic development is the main driving factor, whereas government support has the least influence on capital allocation efficiency. Furthermore, the coefficients of the influence of each driving factor vary significantly across different regions. In conclusion, this study quantitatively estimates the efficiency of capital allocation for GEI projects and explores the key driving factors of efficiency from the perspective of heterogeneity, and makes targeted recommendations for countries to improve efficiency. • Environmental variables generally masks the inefficiency of GEI capital allocation. • The average pure efficiency is 0.364 and the average scale efficiency is only 0.176. • Capital allocation efficiency are spatially correlated globally. • The economic development level is the main driving factor of capital allocation. [ABSTRACT FROM AUTHOR]

The increasing global consumption of energy and its negative impacts on the environment has been an issue discussed both by academics and practitioners, including in the health sector. Hospitals are major consumers of energy, which leads to increased operating costs, decreased competitiveness, and negative environmental impacts. Energy management in hospitals must be understood as a way to contribute to environmental sustainability and as a way to improve competitiveness. Therefore, the objective of this paper is to propose guidelines for efficient and sustainable energy management in hospitals, developed based on the literature review, the challenges faced and the good practices of sustainable energy management in the hospitals studied, the cross analysis of the case studies, and the authors' expertise. As a result, the eight proposed guidelines are related to culture change, energy consumption management, energy management standard, purchase of renewable energy, green technologies, sustainable hospital buildings, social responsibility programs focused on energy use, and sustainable energy public policies. These guidelines contribute in an applied/managerial way to help managers of public and private hospitals overcome the challenges of energy management and contribute to UN's SDG7 due to its potential to improve energy efficiency and environmental performance, therefore, being a movement towards clean energy. This study also theoretically/scientifically contributes to the academic debate since the empirical findings found in the hospitals studied and the proposition of guidelines deepen and expand the key elements identified in the literature on efficient energy management in hospitals, providing the basis for developing new research on the subject. Energy management in hospitals is discussed from the perspective of sustainability. Challenges need to be overcome for sustainability benefits to be achieved. This work can assist public and private hospitals in developing sustainability. Guidelines for sustainable energy management in hospitals are proposed. [ABSTRACT FROM AUTHOR]

Despite the fact that research on multigeneration and polygeneration systems are available, more efficient and effective systems that fulfill several energy demands at the same time are still needed. Furthermore, efficient use and harvesting of renewable energy sources are critical for global emission reductions. Accordingly, this paper proposes a novel geothermal-solar-based hybrid system for simultaneous power and freshwater production. The overall objective of the study is to model the proposed cogeneration system with energy and exergy exergoeconomic, and economic viewpoints, and obtain the optimum performances of the system using non-dominated sorting genetic algorithm II (NSGA-II) method and LINMAP decision-making approach. According to the obtained results, at the base operating conditions, the exergy destruction rate for the CPVT is the highest with 311.3 k W amongst other components, followed by the RO unit with a value of 159 k W. Also, the net output electricity of the total system and freshwater generation rate are computed to be 775.3 k W and 10.14 k g. s − 1 . Also, for the first optimization scenario (S U C P − η e x ), the exergy efficiency and sum unit cost of the products are obtained by 42.31 % and 16.32 $. G J − 1 , respectively. And, the produced electricity and freshwater are computed to be 755.3 k W and. 16.44 k g. s − 1 • Proposal and study of a newly designed electricity/freshwater co-production system. • Smart use of solar and geothermal energies for the performance enhancement. • Energy, exergy, exergy costing, and NPV methods together with NSGA-II optimization. • The optimum exergy efficiency and unit cost are separately 42.31% and 16.32 $/GJ. • The optimum payback period of the system is estimated to be 1.07 years. [ABSTRACT FROM AUTHOR]

Process Integration techniques are powerful in analysing the energy usage of industrial plants and designing retrofit solutions for increasing their energy efficiency. However they are far from constituting the industrial practice. One primary cause for this is the severe time consumption they entail, especially in acquiring the necessary process data. A recently developed method, the Required Data Reduction Analysis (RDRA), aims at reducing this time consumption. It makes use of uncertainty and sensitivity analysis techniques, together with optimisation tools, in order to systematically identify: (i) a reduced number of process parameters, whose information need to be acquired with high detail, and (ii) the maximum acceptable uncertainty of each of them. The paper presents recent additions to the method, expanding its capabilities and addressing two open questions highlighted in previous work. In particular: (i) three sensitivity analysis techniques are compared, based on computational effort and precision, (ii) a novel algorithm for minimising the number of parameters needing detailed data acquisition is proposed and (iii) the impact of the uncertainties characterisation on the outcome of the analysis is assessed. The results of the analysis considering four different case studies provided evidence that the Standardised Regression Coefficient method for sensitivity analysis should be preferred in the Required Data Reduction Analysis, and that the uncertainty characterisation did not jeopardise the results. Moreover, the proposed minimisation algorithm proved to be effective and fast in reducing the number of parameters. A reduction of 5 to 13 parameters out of 19 to 22 was achieved on the four case studies by requiring 14000 to 22000 model simulations. • The Required Data Reduction Analysis (RDRA) method is strengthened and extended. • A novel algorithm for minimising the number of parameters to be measured is proposed. • The main open questions related to the RDRA method are investigated and answered. • The method is tested on 4 industrial case studies generalising the validity of the results. [ABSTRACT FROM AUTHOR]

The super efficiency slack-based measure (SBM) with undesirable outputs was used in this paper to determine the energy efficiency of the service industry in various provinces from 2007 to 2017. The different characteristics of the energy efficiency of service industry in different provinces were obtained from the perspective of heterogeneous resource endowment. The findings show that: ⅰ) China's service industry's overall energy efficiency fluctuates in a 'W' shape. Provinces with varying resource endowments demonstrated significant variation in energy efficiency. Provinces with a high concentration of technology and energy had the highest and lowest levels, respectively, indicating an upward and downward trend. ⅱ) Energy efficiency improvements in China's service sector were primarily a result of technological advancements. Technological advancement and technical efficiency were the primary causes in technology- and labor-intensive provinces, respectively, whereas the combined effect of both factors was the primary cause in energy-intensive provinces. ⅲ) Foreign investment and environmental regulation may both contribute to the improvement of energy efficiency in the service industry, while energy structure and government intervention may work against it. Additionally, a U-shaped relationship was observed between the service industry's development level and energy efficiency. As a result, policymakers should develop targeted promotion strategies based on the resource endowment and development stage of the service industry in various provinces. • Undesirable super efficiency SBM model and Malmquist–Luenberger index are used. • China's service industry's overall energy efficiency fluctuates in a 'W' shape. • Static and dynamic analysis of the service industry's energy efficiency is provided. • The service industry's energy efficiency in provinces with different resource endowment is heterogeneous. [ABSTRACT FROM AUTHOR]

As a result of the increased demand for energy combined with the limitations of traditional energy sources, it is necessary to turn to renewable energy sources such as solar energy, which is one of the most promising technologies to assist the transition towards sustainable development. With the increasing interest in renewable energies in general and solar energy in particular, attempts have been made to ensure that solar energy technologies provide an amount of energy equal to or close to the amount of energy currently consumed. Solar photovoltaic farm applications have become so widespread that they are now relied upon to produce massive amounts of energy for use in factories and homes. In this regard, identifying and prioritizing convenient areas for the construction of expensive solar photovoltaic farms is an important issue. Additionally, determining the suitable geographical locations for photovoltaic farms is not only related to the amount of solar radiation received, but many dimensions must be taken into accounts such as technical, economic, environmental, socio-political dimensions, risks, and their sub-indicators. To overcome the problem of prioritizing and selecting the best location for solar photovoltaic farms to achieve sustainability factors resulting from the presence of many different indicators, this paper applies a hybrid multi-criteria decision-making approach under a neutrosophic environment. Initially, the Delphi method is employed to capture preference and to determine the dimensions and their indicators in addition to provide preference among sub-indicators and available alternatives. The proportional importance of the designated dimensions and their sub-indicators was evaluated by the Decision-Making Trial and Evaluation Laboratory. Then, Vlse Kriterijumska Optimizacija Kompromisno Resenje is adopted to rank seven solar photovoltaic farm locations in Egypt. The Benban solar photovoltaic farm is shown to be the most sustainable alternative among the solar photovoltaic farm locations followed by Darb Al Arbaeen solar photovoltaic farm and so has priority for increased installation investments in solar energy production. To validate the proposed approach, results comparisons were implemented to indicate the advantages of the hybrid multi-criteria decision-making methods. Sensitivity analysis reveals that changing criteria weightings does not affect the ranking order. [ABSTRACT FROM AUTHOR]

At present, wet spraying is the main process for treatment of exhaust gas emitted by the concentrated sulfuric acid roasting method for decomposition of rare earth concentrates, which not only consumes a large amount of resources but also wastes the residual heat corresponding to temperatures of approximately 300 °C. In this paper, an exhaust gas dry processing technology that is completely different from the original wet spray process is proposed and successfully implemented in a rare earth roasting kiln. The dry process has successfully realized dust removal, cooling and acid condensation from exhaust gas through a cyclone separator and heat exchange-condensing acid system, and the waste heat can be converted into steam through a steam drum, which greatly simplifies the process for treatment of roasting exhaust gas. Compared with wet spraying, the dry process has the advantages of operational simplicity, small footprint, low energy consumption, low maintenance cost and no wastewater production, which makes it a promising process for the treatment of exhaust gas from rare earth roasting. • Dry process can recycle the waste heat of roasting exhaust gas to produce steam. • Dry system can produce higher concentration of condensed acid. • Dry defluorination process further realizes the recycle of residual fluoride. • Dry process has the advantages of simple system, water/energy conservation. [ABSTRACT FROM AUTHOR]

For the sake of alleviating the pressure of energy and environment, compressed natural gas vehicle has become an important development direction of the automobile industry in the world with its advantages of low cost and restraining greenhouse effect. However, the operation of liquid-compressed natural gas station will inevitably produce boil off gas and gasoline vapor, which will cause great pollution to the environment if discharged into the atmosphere. Furthermore, in the face of increasing number of liquid-compressed natural gas stations, how to efficiently recover the cold energy released in the process of liquid natural gas regasification is a huge challenge. In this paper, a novel design integrating boil off gas and gasoline vapor recovery, liquid air energy storage and parallel Rankine cycle is proposed to recover cold energy of LNG in different temperature ranges for liquid-compressed natural gas station. The optimal ratio of binary mixtures are selected by genetic algorithm for parallel Rankine cycle, and the result shows that compared with pure working fluid, the total net power output of binary mixtures are increased by 6.62% to 3028.01 kW. Besides, an advanced peak-shaving strategy is proposed to satisfy the electricity demand of users at different time periods. The result of economic analysis exhibits that the payback period of the system is 5.24 years. This novel design not only provides a new idea for the recovery of boil off gas and gasoline vapor, but also complements the blank in the filed of cold energy utilization of liquid-compressed natural gas station. [Display omitted] • A new design of cold energy recovery is proposed for liquid-compressed natural gas station. • This system can not only recover boil off gas and gasoline vapor, but also generate electricity. • Peak-shaving strategy integrated liquid air energy storage is proposed for allocating electricity. • Total net work is increased by 6.62% through the optimization of genetic algorithm. [ABSTRACT FROM AUTHOR]

The real-world gaseous and particulate emission characteristics of an agricultural tractor retrofitted with a catalysed diesel particulate filter (CDPF) with different catalyst loadings were investigated. The tests were carried out using a portable emission measurement system (PEMS) under idling, running and working conditions. The results showed that the emissions of the tractor strongly depended on the operating conditions. From idling to running then to working condition, the emission rates increased but emission factors based on fuel consumption decreased, and the proportion of nucleation particles and geometric mean diameter presented a slight downward trend. The reduction effect of CDPF on the CO and total hydrocarbon (THC) improved from idling to running then to working condition, and it was enhanced by increasing catalyst loading. The particle number (PN) and particle mass (PM) reduction performance of CDPF was less affected by operating condition but enhanced with the increase of the catalyst loading. After the tractor was equipped with the CDPF, the particle size distribution maintained a bimodal shape, but the decrease of nucleation particles was significantly higher than that of accumulation particles, causing a decrease in the nucleation particles proportion and a shift of the peak towards smaller particle size. With the increase of the CDPF catalyst loading, the proportion of the accumulation particles increased, and the geometric mean particle size also increased. The findings of this paper can provide scientific references to design and optimize CDPF for non-road mobile machinery to achieve clean emissions. [Display omitted] • Tractor retrofitted by CDPF with different PGM loadings were studied using PEMS. • CDPF reduction performance strongly depended on the operation conditions. • CDPF had better reduction effect on nucleation particles than accumulation ones. • CDPF reduction performance enhanced with increasing catalyst loading. • Geometric mean diameter of particles increased with CDPF catalyst loading. [ABSTRACT FROM AUTHOR]

Climate change, natural resource depletion and waste problems reveal the importance of natural resource saving. Resource-saving behaviour is necessary in order to enhance energy and water efficiency. By applying various theories of pro-environmental behaviour, researchers have recommended the main tools, such as financial, social and informational for the promotion of this behaviour. However, none of those works explored the impact of the proposed tools on the resource-saving behaviour. Therefore, this study showed the most effective tools, which can be regarded as the most important for policymakers. Furthermore, considering statements about the current level in respect of these tools, the aim of this study was to analyse the influence of declared financial, social and informational tools and real situations on resource-saving behaviour. Using data of the survey performed in Lithuania at the beginning of 2020, and by applying a new theory defined as "Assumptions and reflections of real situations", the results revealed that according to citizens' opinion, only the informational tool significantly determined the resource-saving behaviour. Social and financial tools had insignificant impact on this behaviour. However, the current level in respect of social and informational tools significantly affected resource-saving behaviour. Considering the financial tool, the results showed that only the money-saving aspect significantly determined resource-saving behaviour. A high price of resources did not motivate people to save natural resources. Therefore, this paper provided practical insights how effectively promote the resource-saving behaviour. [ABSTRACT FROM AUTHOR]

Industrial robots have been widely utilized in factories to replace or help employees performing tasks such as handling, welding, and assembly. With the increasing breadth of installation and deployment of industrial robots, it is critical to predict and optimize their energy consumption in order to assure their environmentally friendly characteristics. While the data-driven modeling method based on multi-layer perception has been demonstrated to be a viable way for revealing quantitative relationships between operating parameters and energy consumption for industrial robots regardless of physical process or case-specific information, there are still some limitations in terms of prediction range, modeling efficiency, and sample size requirement. To accelerate model rebuilding and improve model accuracy without collecting a large number of samples simultaneously, this paper proposes a transfer-learning-based model creation method for energy consumption of industrial robots, where the associated operating parameters are analyzed, and structure adjustment strategies for multi-layer perception are planned for various industrial robot systems, then, schemes for the reuse of parameters in well-trained networks on source domain and the fine-tunning of multi-layer perception model are formulated to efficiently build the accurate energy consumption model on target domains. Experiments are conducted on two distinct robot systems, the Epson C4 and the Siasun SR10C, with nonidentical operating parameter structures and sample sizes. The results demonstrate that transfer learning models with fine-tuning strategy outperform previous data-driven modeling methods in terms of model accuracy and modeling efficiency in both of these situations. [Display omitted] • The transfer-learning based model creation method for predicting the energy consumption of industrial robots. • Analyze related operating parameters and design MLP's structure adjustment strategies to help knowledge transfer among domains. • Create EC models for target domains by reusing and fine-tunning parameters on case-related transferred layers. • Boost the energy consumption modeling accuracy on small scale dataset and enhance the efficiency of modeling process. [ABSTRACT FROM AUTHOR]

Energy storage system (ESS) is essential for wind power integration, and it has become more and more important to optimize the wind-energy storage system (WESS) for keeping power grid safe and stable. In this paper, a novel energy storage sizing approach which can improve the performance of WESS is put forward. Firstly, the hinges model is introduced into the power spectral density (PSD) analysis to obtain the low-pass decomposition frequency (LPDF), which is then used for energy storage sizing optimization based on the single-day energy balance. Secondly, a comprehensive evaluation criteria system based on cloud model is proposed to assess various characteristics of WESS. Thirdly, a case study based on a 30 MW wind farm indicates that the power curve of the proposed method meets the conditions for wind power integration, and the size and stability of ESS are optimized. Compared with the traditional filtering algorithms, the WESS performance of the proposed algorithm is improved by 77.55% than wavelet packet decomposition (WPD) and 65.84% than empirical mode decomposition (EMD), and its applicability in different time spans is also verified. • An approach to determine energy storage size is proposed by improved power spectral density analysis. • An evaluation system based on cloud model is designed to assess wind-energy storage system. • The system performance is improved by at least 65.84% through this method. • The effectiveness and applicability in different time spans are proved by case study. [ABSTRACT FROM AUTHOR]

With the popularization and promotion of electric vehicles (EVs), their interactions with power grids and traffic networks have increasingly deepened. Accurate modelling of EV behaviour can faithfully depict the characteristics of EV driving and charging. However, most existing modelling researches fail to adopt real-world travel data and consider realistic perceptual decision-making psychology of owners. Thus, this paper proposes a novel behavioural modelling for EVs based on a data-driven approach combined with behavioural economics theory. To characterize the driving behaviour of owners using actual data, a systematic data mining and modelling approach is firstly proposed based on the open-source 'Didi' traffic travel data set, which obtains the traffic operation rules and the regenerative behaviour characteristics data. According to the subjective perceptual characteristics of social economic man, a Cumulative Prospect Theory-based modelling framework is further developed to quantify the uncertain and stochastic charging decision-making behaviour of EV users. Moreover, the user's preferences and attitudes are evaluated by calculating their cumulative prospect value when choosing charging stations. Finally, the most suitable charging station is recommended for EVs with charging requirements. Case studies are conducted within a practical zone in Nanjing, China. The results demonstrate that the traffic travel rules of vehicle owners have typical date types and functional area distribution characteristics. And the travel time and space of private and commercial vehicles are relatively regular, whereas the travel rules of public vehicles are random. Besides, this proposed methodology can not only effectively capture the irrational decision-making characteristics of EV users' charging behaviour, but also achieve promising performance in terms of reducing the charging waiting cost. The user's decision-making regarding charging behaviour exhibits a higher risk-seeking preference than a risk-aversion preference. Proposed a comprehensive and systematic analysis methodology of traffic travel data mining and modelling Depicted the driving behaviour portrait of urban vehicle owners based on the open-source 'Didi' traffic travel dataset Developed a Cumulative Prospect Theory-based modelling framework to quantify the stochastic uncertain charging decision-making behaviour of Electric Vehicle users. Considered the subjective perceptual characteristics of social economic man, the irrational behaviour modelling approach more accurately perceive Electric Vehicle charging energy demand. [ABSTRACT FROM AUTHOR]

This paper presents a theoretical novel approach to a marine infrastructure, which jointly uses an OWC (Oscillating Water Column) and hydrogen electrolysis as a clean source of primary conversion for energy management, capable to satisfy a continuous designed demand. A case study in the Port of Motril (Southern Spain) is proposed in order to analyze the performance of the power plant. The assessment covers the sale and management profits from the harvested energy from waves in the electricity market, and from the electrolysis of the oceanic water H 2 O plus dissolved salts μ into hydrogen H 2 , oxygen O 2 and salts μ components, also known as green hydrogen. The investment analysis of the project is conducted through the dynamic financial index Net Present Value (N P V), using Monte Carlo techniques to determine the repayment period. The results are consistent, showing that the plant could supply the annual electricity demand of 356 people with a short repayment period. The amount of energy depends on peak periods T p and wave heights H s , which means that other locations could be found so as to improve the performance of the plant in terms of productions of energy and demand supply. • Wave energy harvested through oscillating water column technology can reach an efficiency of 75%. • Harvested Energy is used for energy storage purposes through hydrogen electrolysis. • The civil infrastructure can effectively supply the demand according to the case study. • The recovery of investment take a short period due to positive environmental impacts. [ABSTRACT FROM AUTHOR]

The carbon trading mechanism is considered an effective method for improving energy efficiency, and reducing pollution. In this regard, two methods of considering reward-penalty on the supply and demand sides as well as user satisfaction are the most effective ways to improve the carbon trading mechanism. Herein, this paper proposes a bilayer optimization model of the community-integrated energy service system. The population in the community is mainly divided into office workers and retirees, and their preferences are fully considered. The user considers electric energy reduction and substitution modes to optimize the energy consumption behavior in the reward stage. And the service provider considers the full consumption of wind and photovoltaic power and optimizes the output of each unit in the penalty stage. The effect of reward-penalty factors on the supply and demand sides is analyzed. The algorithm used to achieve bilayer optimization is the hybrid solution algorithm, which adopts the improved particle swarm optimization algorithm and CPLEX solver. The experimental results show that the costs of office workers and retirees are reduced by an average of 18.2% and 12.2%, respectively. And the service provider's profit is increased by 15.28%. The results also provide site selection strategies for energy supply. • A carbon trading mechanism that considers the reward-penalty is proposed. • An integrated energy service system that considers user satisfaction is proposed. • The total cost of users has dropped by an average of 12.72%. • The profit of the energy service provider has increased by an average of 15.28%. • The conclusions provide certain help of site location for energy supply. [ABSTRACT FROM AUTHOR]

Spinning is one of the major steps in textiles to convert staple fibers from either natural or synthetic sources into continuous and twisted yarns. Since its invention, ring spinning has become the most widely used spinning technology due to the high yarn quality and applicability. However, the productivity is limited by its twisting and winding mechanism. Viable approaches to substantially improve the throughput of ring spinning are crucial. Therefore, this paper presents a systematic review of the literature on the state-of-the-art of disruptive technologies aiming at improving the throughput of ring spinning. Based on the governing equation of ring yarn productivity, two approaches of increasing spindle speed via reducing or eliminating ring-traveler friction and reducing yarn twist have been scrutinized. Increasing the spindle speed directly increases the centrifugal force and air drag of the yarn in the spinning balloon and exacerbates the traveler's wear. By reducing or eliminating the ring-traveler friction, it is possible to achieve a balance between improved productivity, similar energy consumption, and reasonable traveler wear. Since the yarn productivity is inversely proportional to the yarn twist, yarn productivity can be enhanced by reducing yarn twist with less energy consumption. However, with far less twist, the yarn strength is going to suffer. Therefore, both approaches improve ring yarn yield to some extent, and the level of improvement is subjected to the yarn count, fiber material, spinning conditions, etc. This review provides a clear view of the combination of both approaches to improve yarn productivity and energy efficiency. In the future, it is expected to see more studies of new alternatives which provide the ring spinning process with a cleaner and higher throughput compared to previous or current spinning technologies. • Analysis of limitations of further increasing the throughput of ring spinning. • Review of current approaches by reducing or eliminating ring-traveler friction and reducing yarn twist. • Discussion of yarn spinnability, quality, energy consumption, and production cost. • Suggestions to future research directions towards a cleaner production. [ABSTRACT FROM AUTHOR]

Given their increasing number and the implementation of more energy-intensive treatment methods, wastewater treatment plants (WWTPs) expect to increase energy consumption. In addition, climate change presents new challenges to the operation of these facilities, thus being critical to understand how to improve their energy performance and environmental sustainability while ensuring the quality of service provided. This paper reviews the latest publications on the energy performance of municipal WWTPs, particularly on the different phases of the treatment process and the impacting factors. The contextual and underlying factors that influence energy performance were identified, categorized, and analyzed through a broad survey. The most significant factors are the plant size, load factor (plant capacity utilization), and dilution factor. The implementation of anaerobic-anoxic-oxic systems is considered suitable in sensitive areas requiring high pollutant and nutrient removal rates, presenting, in some cases, moderate energy consumption (0.267 kWh/m3), being similar to the conventional activated sludge (0.269 kWh/m3). In more stringent effluent quality requirements, such as wastewater reuse, membrane bioreactors are advised, despite higher consumption (0.33 kWh/m3). Energy improvements can also be achieved by implementing automatization, inverters, and strategies that increase flexibility and adaptability in the operational process. Lastly, given the multidimensional characteristics of the WWTPs assessment, further improvements may be identified if the energy performance of these plants is compared using holistic and multi-criteria approaches, integrating multiple inputs and outputs simultaneously. • Long-term operation of low installed power equipment deserves more attention. • Energy intensity is mainly influenced by plant's size, load, and influent dilution. • Anaerobic-anoxic-oxic systems are a good option for impoverished areas. • Energy performance must be assessed in a holistic and multi-criteria approach. [ABSTRACT FROM AUTHOR]

The prospect of enabling more sustainable industries through energy efficiency innovations has received increased attention in policy and research. In Norway, previous studies have identified a techno-economic potential of 12 TWh reduction in direct energy use and an additional 10 TWh utilization of surplus heat, in industries. However, the novelty and complexity of energy efficient technologies can make their adoption, and hence utilizing this potential, difficult. This paper explores the development and implementation of two high-temperature heat pumps and one heat recovery concept in three R&D projects in the Norwegian manufacturing industry. Our qualitative research finds and explores three implementation paradoxes : 1) While the novelty of innovations partly explains why they are difficult to implement, novelty is also a motivator for firms' technology development strategies. 2) Both innovations close to core production technologies, as well as seemingly incremental innovations, can have system-wide consequences in the organizations, making them difficult to implement. 3) While implementation windows (changes to organizations and technical systems) positively affect firms' motivation, ability, and opportunity to develop and adopt innovations, these situations introduce time constraints, putting pressure on less mature technical solutions and R&D processes. Through unpacking these seemingly opposing dynamics, we find that the same factors can both promote and inhibit adoption of technologies during different stages of innovation processes. We discuss the managerial implications on how firms can align R&D collaborations with implementation opportunities, to enhance adoption of radical energy efficiency innovations. In conclusion, we discuss how these contributions can translate into future quantitative research. [Display omitted] • Case study of three radical energy efficiency innovations in Norwegian industry. • Implementation paradoxes promote and hinder adoption of technologies. • Novelty of innovations is both a motivator and hindrance towards implementation. • Implementation windows induce time constraints making adoption difficult. • Alignment between innovation processes and implementation windows is crucial. [ABSTRACT FROM AUTHOR]

Lean manufacturing has already proven to be an effective management philosophy for eliminating waste in production processes, leading to operational gains. However, nowadays, it is not enough just to obtain economic results, with the environmental aspect being a concern that organizations have been incorporating into their production processes due to stakeholder's pressure in order to increase their sustainable performance. As such, a bibliometric and a structured analysis of the literature were carried out focusing on the connection between lean, green and sustainability concepts. The Web of Science platform was used to collect data, encompassing the period from 1900 to 2020. The carried analyses covered the identification of the most relevant papers, including their authors, as well as their temporal and geographical distribution. There was also focus on the draft of a map to observe an underlying intellectual structure through VOSviewer. The novelty of this research is that, through the gathering of relevant data related to the three concepts considered and their analysis, a conceptual model was proposed consisting of lean-green and sustainability (LGS) tools to achieve superior triple bottom line (TBL) performance. This study pinpointed that in most of the cases where LGS tools have been applied (TPM - Total Productive Maintenance - and 6R, e.g.), there was a positive impact mostly on the economic and environmental pillars of the targeted companies. Although there were also a small number of other cases where it was found that an improved efficiency induced an increased consumption of energy and water, namely by lean tools (SMED - Single Minute Exchange of Dies, e.g.), the results demonstrated the capability of lean and green, especially their combination, to enhance sustainability outcomes. Future research should focus on quantifying the effects that LGS tools, in a single way or combined, bring to all TBL pillars, especially the social one, e.g., by developing key performance indicators. The testing of the proposed conceptual model, including its completion, is also encouraged, namely through the evaluation of what is the best combination of LGS tools, considering specific sectors of activity. [ABSTRACT FROM AUTHOR]

Connected battery electric buses (CBEBs) equipped with Vehicle-to-Infrastructure (V2I) have great potential for improving bus performance, through aspects such as energy saving and travel time reduction. For CBEBs, intelligent speed planning determines the energy consumption, which directly affects the charging or discharging of current to the battery and furthermore affects the battery degradation. This paper develops a speed planning model, aiming to minimize the total battery capacity loss of a battery electric bus by optimizing the bus speed of driving intervals and avoiding stopping delays at intersections along a bus line with a bus lane. Motor modeling, battery modeling, bus operation modeling, and constraints such as cruise time, arrival time, travel speed, acceleration and deceleration are all involved. A Q-Learning-based solution algorithm is proposed to solve the developed model. A case study based on a specific bus line with a bus lane is conducted. Results indicate that the developed method has an obvious advantage in reducing the total battery capacity loss compared with the greedy policy strategy. This study provides potential for future CBEB operation design. • The speed planning of connected battery electric buses (CBEBs) is taken as the research object. • A Q-Learning-based solution algorithm is proposed to solve the developed model. • A case study is carried out to demonstrate the computational efficiency. [ABSTRACT FROM AUTHOR]

As one of the environmentally friendly technologies, heat pumps have been widely utilized in residential and commercial buildings all over the world. The use of renewable energy sources and environmentally friendly refrigerants are two major challenges associated with heat pumps applications. Due to the advantages of cascade heat pumps over single-stage heat pumps and the higher energy demand met by cascade heat pumps, investigation of the effects of geographic conditions (weather and soil conditions) as well as the simultaneous use of two refrigerants for high and low temperature cycles on performance of cascade heat pumps would provide critical information for practical applications. This paper compares the performance of air source and ground source cascade heat pumps using refrigerants by low global warming potential and zero ozone depletion potential in top 10 coldest cities of Russia. The effects of variations in temperature of evaporator and cascade heat exchanger on optimal operational conditions are investigated based on energy, exergy, economic and environmental analysis using Pareto front curve. Optimization results show that there is an optimal point for evaporator temperature and cascade heat exchanger temperature where the maximum COP, maximum exergy efficiency and minimum total cost rate for ASHP and GSHP systems are obtained. It is found that at low air temperature, since the underground temperature is higher than ambient air, the efficiency of GSHP system is better than efficiency of ASHP system. The maximum COP of 2.357 and 2.44 are respectively obtained for ASHP and GSHP systems in Saint Petersburg. In addition, Yakutsk city has the maximum exergy efficiency of 40.7% and 43.2% and maximum total cost rate of 14714 $ year−1 and 13693 $ year−1 for ASHP and GSHP systems, respectively. Finally, it is concluded that Saint Petersburg is the best city to employ ASHP system while GSHP system is suggested for Khabarovsk city. • Performance of ASHP and GSHP is compared in 10 coldest cities of Russia, thermoeconomically. • Maximum COP of 2.357 and 2.44 are respectively obtained for ASHP and GSHP in Saint Petersburg. • Maximum exergy efficiency of 40.7% and 43.2% are achieved for ASHP and GSHP in Yakutsk, respectively. • Yakutsk also has the maximum total cost rate of 14714 $ year−1 and 13693 $ year−1 for ASHP and GSHP. • Saint Petersburg and Khabarovsk are the best cities to employ ASHP and GSHP. [ABSTRACT FROM AUTHOR]