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The role of smart technologies can become very important and useful to solve the main population issues nowadays and provide foundations for a sustainable future. A smart approach is an opportunity for knowledge integration, necessary to solve crucial problems of contemporary societies. Today, the main challenge is to reduce the effects of global warming and ensure a balanced economic development of society. The close collaboration of all involved engineering professions is mandatory to achieve interdisciplinary synergies and can bridge challenging engineering tasks. Intense research efforts should be directed towards balanced resource utilization, efficient energy conversion technologies, integration of renewable energy systems, effective approaches to enable circular economy framework, effective process integration as well as other issues important to the population. This review editorial is primarily focused on the contributions presented at the 3rd International Conference on Smart and Sustainable Technologies held in Split, Croatia, in 2018 (SpliTech2018). The SpliTech2018 conference was a multidisciplinary event with research topics related to the main conference tracks, i.e. Smart City/Environment, Energy, Engineering Modelling and e-Health. The strategic focus of the conference was to help solve crucial issues of our times, mainly related to the sustainability and smart utilisation of limited and valuable resources. This contribution brings new ideas and discusses present issues as well as challenges that should lead towards a sustainable future based on the application of the smart technologies. The herein addressed papers bring together latest research progress into four main topic areas: (i) Green Buildings, Energy Use and Consumption, (ii) Solar Energy Utilisation, (iii) Efficiency and Waste Elimination, (iv) Smart Cities and Internet of Things. The main results of this introduction review article include a discussion of different concepts and technologies that bring further development on a broad range of topics focused on efficiency improvement, smart and sustainable resource management, cleaner production concepts and on the discussion of the various actions which would lead towards a sustainable future. [ABSTRACT FROM AUTHOR]

Investments in energy efficiency often remain undecided upon and there is a large potential for enhancing energy efficiency in the industrial and commercial sectors. Within this context, the aim of the research project M_Key "Management as a key driver of energy performance" is to better understand how large-scale energy consumer (LSEC) companies make energy-efficiency investment decisions. With about 35% of the total Swiss electricity consumption, LSECs are an important target group for energy-efficiency programmes in Switzerland. The goal of this paper is to present and describe the main findings of M_Key (Management as Key for Energy Performance) research project. For the first time, these findings extensively depict large-scale energy consumers in Switzerland. They highlight the low level of energy management in Swiss LSECs and of the resources allocated to tackle energy issues. Due to a lack of monitoring and control tools, companies have difficulty assessing their energy-efficiency investments. Energy issues are not generally a priority for companies, with a few exceptions, mainly in large firms. Requirements from national or cantonal energy policies have a significant role (and in the case of SMEs a key role) in inciting companies to action. When energy efficiency is perceived as strategic, companies tend to have a high level of energy management. The better the energy management system is, the more likely the chances are for a positive decision on energy-efficiency investment. Another important conclusion is that the more strategic a project or investment is considered to be, the less restrictive are the financial criteria applied. [ABSTRACT FROM AUTHOR]

This paper investigates the effect of the implementation of smart city policy (SCP) on energy efficiency (EE) in China. For this purpose, we have developed a non-convex metafrontier data envelopment analysis (DEA) approach to measure EE, and empirically examine the impacts of SCP on China's EE using Difference-in-Differences (DID) analysis, using a dataset of 251 cities between 2003 and 2016. Results show that there exists significant regional heterogeneity across cities, with the SCP exerting a significant positive effect on EE, indicating that the SCP can effectively improve EE during the study period. These findings are robust to different sub-sample, energy efficiency alternative measures, estimation strategies, and Baidu Search Index. Based on empirical results, the implications of SCP are outlined. • Energy efficiency measures of 251 Chinese prefectural-level cities are estimated. • Eastern region performs the best, followed by the central and western regions. • The effects of smart city policy on energy efficiency is investigated using DID method. • The smart city policy can effectively improve energy efficiency during the study period. • Empirical results are robust to different sub-sample, energy efficiency alternative measures, etc. [ABSTRACT FROM AUTHOR]

In recent years, various environmental assessment methods have been developed. The aim of this paper is to compare these methods to identify their advantages and disadvantages when used to analyze energy efficiency in agricultural production systems. A systematic review of information helped to identify six environmental assessment methods: ecological footprint, material flow analysis, ecological network analysis, life cycle analysis, exergy, and emergy. A multi-criteria comparison was carried out, taking into account the level of formalization, system modeling, spatial scale, inventoried flows, type of indicators, relationship with the concept of efficiency, and usability of each of the methods. This work allowed the strengths and weaknesses of each environmental assessment method to be highlighted. Proving that the Emergy approach could provide a relevant framework for the analysis of the multiple energy flows that interact in an agricultural production system, and for achieving an integral understanding of energy efficiency in the whole system. [ABSTRACT FROM AUTHOR]

For many years, China has invested effort into achieving resource conservation and environmentally friendly economic development. The measures implemented include promoting technological innovation, market-oriented reform and industrial structure adjustment. But there are open questions: What have the effects of these measures been? Have they helped China achieve win-win development in terms of its resources, environment, and economy? This paper aims to answer these questions by using a newly proposed global three-hierarchy meta-frontier Luenberger index that can quantify the effects of promoting technological innovation, industrial restructuring, etc., on economic growth, energy conservation and emissions reduction. The results show that (1) during 2001–2016, China's energy efficiency, economic efficiency, and emissions efficiency improved, indicating that China's efforts have brought about the win-win development of resources, the environment, and the economy; (2) the implemented measures have heterogeneous effects on economic growth, energy conservation, and emissions reduction. The implemented measures are generally beneficial to energy conservation and emissions reduction but potentially harmful to the economy; (3) the effects of these measures on economic growth, energy conservation and emissions reduction across China's regions and provinces are also quite different, indicating that local provincial governments should have a clear understanding of their own characteristics before implementing the relevant policies and measures. • A global three-hierarchy meta-frontier Luenberger index was proposed for the study. • It can quantify the effects of China's efforts on economy, energy, and emissions. • Results show that China's efforts have brought about a win-win development. • There are the heterogeneities of China's pathways to economic growth, energy conservations and emissions reductions. • The future strategies for China's win-win development should take the heterogeneities into account. [ABSTRACT FROM AUTHOR]

This paper estimates the linkages among total Sulphur dioxide (SO 2) emissions, total GDP and energy efficiency using China's provincial panel data from 2002 to 2015. We investigate total emissions rather than per capita emissions or ambient concentrations since it is total emissions that the environment cares about. Energy efficiency is estimated using stochastic frontier analysis and decomposed into both persistent and transient efficiency. We then investigate the long-run dynamics among SO 2 emissions, economic growth, and energy efficiency by employing the panel-based error correction model and taking the effects of cyclical variations into account. Our analysis shows that GDP has a positive impact on total SO 2 emissions in the short run and gains in energy efficiency have a significant negative effect on emissions in the long run. By controlling the effects of the business cycle, the effects of GDP on emissions remain positive in both the short and long run. The cross-sectional analysis provides similar insights. We argue that economic growth itself is an emission generator. Therefore, the government needs to establish a long-run strategy to curb emissions by improving energy efficiency. Image 1029 • We estimate the linkages among sulphur dioxide emissions, GDP and energy efficiency. • Energy efficiency is estimated by SFA and decomposed into persistent and transient efficiency. • We employ the panel-based error correction model with cyclical variations and structural break. • GDP has a positive impact on emissions in both the short and long run. • Gains in energy efficiency affects emissions negatively in the long run. [ABSTRACT FROM AUTHOR]

Environmental impact evaluations in manufacturing fields is a complicated decision-making problem, which involves wastes emission, resource consumption and energy utility, etc. This paper presents an intelligence method for environmental impact evaluation using kernel fuzzy clustering and a back-propagation neural network. The objective of this article is to apply artificial intelligence technology to evaluate environmental influence of cutting process and develop a decision support tool for selecting the optimal environmental solution from various alternative schemes. There are three stages to accomplish this assessment procedure. First, the evaluation index system of the cutting process was analyzed and established. Next, a training sample set which was used for learning the 'knowledge and experience' by neural networks, was acquired by kernel fuzzy clustering algorithm. Finally, an evaluation model based on a back-propagation neural network was constructed, and connection weights of the model were determined after training. A case research on the cutting processes of the automobile workpiece was conducted. Results showed that the proposed method is more concise and practicable than existing evaluation methods and provide a feasible and effective decision-making tool for selecting an optimal cutting process while manufacturing. [ABSTRACT FROM AUTHOR]

The fossil energy and the carbon market are closely connected, but few articles have applied the Granger causality method on quantiles to explore the bi-directional causality between carbon prices and fossil fuels. Most articles use conditional mean regression models to test linear Granger causality, but are unable to assess tail causality or nonlinear causality. In order to accomplish the objective of studying the interaction of carbon and fossil energy markets at different quartiles, we use the method of Granger causality at quartiles, which allows us to assess median and tail causality and to gain insights into how carbon and traditional fossil energy markets interact under different market conditions. This paper analyzes the causal relationships between carbon prices and coal, oil, and gas from June 1, 2015 to October 31, 2022 across all quantiles. We find no indication of Granger-causality from carbon to oil and gas at the middle quantile. However, at the lower and upper quantiles, we discover the causal relationship between changes in carbon prices and traditional energy prices. The asymmetric two-way causal relationship between the carbon market and the fossil energy market has prompted the government to carry out differentiated management, as well as to improve energy efficiency. • We study the bi-causality between coal, oil, gas markets and carbon market. • We test for Granger-causality for each quantile of the distribution. • The negative correlation between carbon prices and fossil energy prices under mean reversion analysis is unreliable. • The impact of carbon taxes on oil prices varies widely across different quantiles. • We make demands on governments to develop energy policies. [ABSTRACT FROM AUTHOR]

Excessive energy-related carbon emission intensity will lead to deterioration of environmental quality and hinder green economic growth. This paper proposes a novel self-adaptive fractional order grey generalized Verhulst model (SAFGGVM) to predict energy-related carbon emission intensity in China, America, India, Russia, and Japan with nonlinear and complex characteristics effectively. Firstly, self-adaptive fractional order and dynamic background value coefficient are introduced to capture the nonlinear evolutionary trends. Subsequently, the optimal nonlinear parameters are determined using Grey Wolf Optimization algorithm through comprehensive comparison. Furthermore, the flexibility of SAFGGVM is verified by presenting its relevance to existing models. Finally, the validity, applicability, and robustness of SAFGGVM are confirmed by comparing with two machine learning models and four grey prediction models. The empirical results exhibit that the overall precision of SAFGGVM model significantly prevails over the others in five cases, with MAPE values less than 6% in both the simulation and prediction intervals. The out-of-sample forecast results indicate that the energy-related carbon emission intensity of five countries will fall into 5.7024, 1.6030, 7.3668, 8.1633, 2.0399 tons/10,000 USD by 2025, and the decreasing rate of developing countries tends to catch up with, or even exceed, developed countries. Projection results can serve as a reference point for countries to achieve green and sustainable development. • A novel self-adaptive grey generalized Verhulst model is proposed. • Energy-related carbon emission intensity (ECEI) of five countries is predicted. • Optimal nonlinear parameters are determined by Grey Wolf Optimization algorithm. • The novel model is superior to other competing models in accuracy and roubutness. • Decline rate of ECEI in developing countries will catch up with developed countries. [ABSTRACT FROM AUTHOR]

Policies targeting energy efficiency and renewable energy are sometimes viewed as more politically feasible than carbon pricing in pursuit of emission reduction goals. This paper assesses underlying drivers of energy policy development. These factors include economic, social, environmental, and institutional variables. The between estimator for panel data is an appropriate method for our focus on some exogenous variables that vary more across countries than over time. We find that larger oil reserves per capita have a negative relationship with renewable energy policy development. Education and political globalisation have strong positive relationships with both energy-efficiency and renewable-energy policy development. These results suggest that greater participation in global political groups can be an indirect approach toward energy policy development, in cases where direct and immediate policies are hard to implement. OECD countries have higher policy scores by 33 and 25 points in energy efficiency and renewable energy respectively. Blinder-Oaxaca decomposition analysis shows that these higher scores are primarily due to social and political institution predictors rather than economics and physical endowments. • Large-n cross country international sample on continuous policy score measurement. • Political globalisation is positively linked with renewables and energy efficiency policies. • Education is positively linked to energy policy development, especially for efficiency. • Higher OECD countries' policy scores are primarily due to social and political predictors. • Higher incomes are unlikely to lead to automatic improvements in energy policies. [ABSTRACT FROM AUTHOR]

Hot embossing is an efficient technology for the fabrication of glass micro- and nanostructures. However, the long cycle time, high energy consumption, and high nitrogen emissions in traditional hot embossing approach hinders sustainable production of glass micro-optics. Therefore, this paper proposes a novel two-stage gravity-assisted hot embossing process that enables the fabrication of high-quality glass micro-pillar arrays in a rapid, green manner. In this approach, the two-stage gravity loading unit provides accurate force on glass preform through a simple control system, and the tailor-made heating module based on Si 3 N 4 ceramic heaters requires an energy consumption of just 700 kJ per molding cycle. As a result, the proposed approach is featured by low energy consumption and high manufacturing efficiency. In this study, the effects of the embossing temperature, force, and time on the replication accuracy of a K9 glass micro-pillar array were investigated through a series of hot embossing experiments. Afterward, the form accuracy, uniformity of microstructures, and warpage of glass replicas under the optimal process parameters were evaluated separately. Finally, repeated hot embossing experiments were conducted to demonstrate the reproducibility of the hot embossing process. The experimental results show that embossing temperature had the most significant effect on the replication accuracy of glass micro-pillars. By optimizing the process parameters, the embossed glass micro-pillars could achieve transfer ratios of 99.00% and 95.14% in height and diameter, respectively. Moreover, the surface roughness of glass micro-pillars was less than 5 nm, and the warpage amplitude of glass replicas in a 3.9 × 3.9 mm area was smaller than 1 μm. The small discrepancy in geometric features of glass replica in the repeated trails reflects the satisfactory reproducibility of this hot embossing process on the micro and nano scales. The results indicate the strong industrialization potential of the proposed two-stage gravity-assisted hot embossing machine and hot embossing process for fabricating high-quality glass microstructures more cleanly and sustainably. • A novel gravity-assisted rapid high-temperature hot embossing apparatus is developed. • A novel hot embossing process is designed for fabricating high-quality glass micro-pillar arrays in a cleaner manner. • Effects of process parameters on the replication accuracy of glass micro-pillar arrays are evaluated. • Form deviation, uniformity of microstructures as well as warpage of glass replica are analyzed. • The reproducibility in replicating geometric features in the novel hot embossing process is demonstrated. [ABSTRACT FROM AUTHOR]

The improvement of energy efficiency of products is a key pillar of climate and energy strategy in the European Union (EU). The first EU product policies were adopted in the late 1970s, and they have evolved to become a coherent set of implementing measures under framework directives that harmonise and refine the regulatory process. After years of weak implementation, considerable progress in terms of scale and ambition has been achieved in the last decade. In 2020, product mandatory measures covered 50% of the EU total final energy consumption, leading to 46 Mtoe energy savings. This paper describes the available policy instruments to promote energy efficiency and remove the market barriers hindering the penetration of the best performing technologies. It offers a review of the progress made over these last 40 years of EU product policies, describing the Energy Labelling, the Minimum Energy Performance Standards (MEPS), the Ecodesign Directive and the voluntary agreements (EU Ecolabel and Green Public Procurement). Moreover, it highlights the remaining challenges and provides policy recommendations to further exploit the EU potential to save energy from products. • Energy efficiency in products is a key pillar of EU climate and energy strategy. • Despite 40 years of EU product policies, their scale has only improved since 2010. • In 2020, mandatory measures covered 50% of the EU final energy consumption. • By 2030, savings expect to reduce the final consumption by 17%. • Policy orientation to further exploit the energy savings of products is provided. [ABSTRACT FROM AUTHOR]

The LNG market connects numerous export and import markets, and the global LNG trade has been growing steadily. Despite the availability of multiple alternatives, only a small fraction of this global cryogenic potential is currently utilized, and a negligible number of regasification terminals are equipped with cold energy recovery systems. This paper explores various options for recovering cold energy from LNG regasification processes including electricity production, refrigeration, cryogenic separation processes, CO 2 capture and liquefaction. The present study examines the industrial recovery options, proposing both conventional and innovative process schemes and analysing them in terms of their thermodynamic modelling and feasibility. Nine case studies were selected and simulated using Aspen Hysys software, considering material and energy balances. Specific energy consumptions are calculated, and an exergetic comparison methodology is adopted to provide a unified approach capable to embrace heterogeneous systems utilizing mechanical/electrical energy, thermal energy, and "chemical energy". The temperature profiles are examined, and sensitivity analyses are performed on key parameters. The integration of direct cooling and condensation brings high exergy efficiency of above 40% and economic advantages due to the absence of an external refrigeration cycle. Coupling regasification with CO 2 liquefaction reduces specific energy consumption by 53% compared to the conventional process. Similarly, in the case of an ethylene production plant, energy consumption is reduced by 95%. Simulations for electricity generation using an ORC cycle with LNG as the cold source achieve around 25% exergy efficiency, but the LCOE (Levelized Cost of Electricity) is attractive, ranging from 0.07 to 0.09 €/kWh. Integration with an Air Separation Unit (ASU) for cryogenic distillation leads to a 41% decrease in specific energy consumption. Further integration with Allam and cascade ORC cycles has been simulated. Globally, air separation shows exergy efficiencies of 37–38%, which is higher than the average 25% obtained from conventional thermodynamic cycles with low-temperature hot sources. Regarding cryogenic CO 2 capture, it exhibits lower specific energy consumption compared to traditional processes. The exergy efficiency is high at 22%, and the LCOP (Levelized Cost of CO2 Production) of 55 €/ton is attractive for carbon capture processes that can be coupled with regasification units and NG utilization (a.e. for hydrogen and electricity production). [ABSTRACT FROM AUTHOR]

The increasing energy demand in harbour areas, coupled with the need to reduce pollutant emissions, has led to the development of renewable energy-based polygeneration systems to face the carbon footprint of ports and ships at berth. In this way, in the coming years, ports can be converted into modern energy hubs. From this point of view, this paper presents a new dynamic simulation model for assessing and optimizing the energy and economic impact of ports. Here, energy systems and renewable sources can be designed to be connected to national electricity and natural gas grids and can include also alternative fuels (hydrogen, biomethane, etc.) and thermal energy networks, as well as different biomass fluxes (to be exploited for energy aims). Energy availability/demands of near towns and port buildings/infrastructures, as well as on-shore power supply are also included in the dynamic assessments. Hourly weather data and different prices for all the considered energy carriers are taken into account hour by hour. A multi-objective optimization approach is also implemented in the model considering energy and economic indexes to be optimized. The whole model is implemented in a computer tool written in MATLAB. For showing the capability of the developed model, a novel case study referred to the port of Naples (South-Italy) is presented. Here, several renewable energy sources are considered, including an anaerobic biodigester for producing biogas from the organic waste of docked cruise ships. A combined heat and power system (fed by biogas) is implemented in the port energy hub also for supplying absorption chillers. PV panels, and marine power generators are also included. In the conducted analysis, optimization targets are the maximization of system self-consumption and self-sufficiency as well as the minimum simple payback period. The proposed system can effectively contribute to the decarbonization of the port energy demand and reduce harmful pollutant emissions. Results showed that very high rate of renewable energy produced on-site can be exploited (up to 84%) by the considered port facilities, ensuring increasing independency from utility power grid (self-sufficiency index up to 40%). By the obtained results and through the developed simulation/optimization tool, novel design and operating criteria can be achieved for future port energy hubs featured by renewables and bi-directional energy exchange between ships and port. [ABSTRACT FROM AUTHOR]

Energy intensive industries can be classified into those that process metal, glass, ceramics, paper, cement, and bulk chemicals. They are associated with significantly high proportions of carbon emissions, consume a lot of energy and raw materials, and cause energy wastage as a result of heat escaping from furnaces, reheating of products, and rejection of parts. In alignment with UN sustainable development goals of industry, innovation, infrastructure and responsible consumption and production, it is important to ensure that the energy consumption of EIIs are monitored and reduced such that their energy efficiency can be improved. Towards this aim, it is possible to employ the concepts of digitalisation and smart manufacturing to identify the critical areas of improvement and establish enablers that can help improve the energy efficiency. The aim of this research is to review the current state of digitalisation in energy-intensive industries and propose a framework to support the realisation of sustainable smart manufacturing in Energy Intensive Industries (EIIs). The key objectives of the work are (i) the investigation of process mining and simulation modelling to support sustainability, (ii) embedding intelligence in EIIs to improve energy and material efficiency and (iii) proposing a framework to enable the digital transformation of EIIs. The proposed five-layer framework employs data acquisition, process management, simulation & modelling, artificial intelligence, and data visualisation to identify and forecast energy consumption. A detailed description of the various phases of the framework and how they can be used to support sustainability and smart manufacturing is demonstrated using business process data obtained from a machining industry. In the demonstrated case study, the process management layer utilises Disco for process mining, the simulation layer utilises Matlab SimEvent for discrete-event simulation, the artificial intelligence layer utilises Matlab for energy prediction and the visualisation layer utilises grafana to dashboard the e-KPIs. The findings of the research indicate that the proposed digital life-cyle framework helps EIIs realise sustainable smart manufacturing through better understanding of the energy-intensive processes. The study also provided a better understanding of the integration of process mining and simulation & modelling within the context of EIIs. [Display omitted] • Framework to support the digital life cycle management of Energy Intensive Industries. • Investigate the interplay between process mining and simulation modelling to support sustainability. • Intelligence needs to be embedded to improve energy efficiency and material efficiency. [ABSTRACT FROM AUTHOR]

The construction industry is one of the most energy-intensive industries in China, which contributes to high carbon emissions and hinders efforts to reduce the emissions. The high energy consumption and emission have seriously constrained the growth of low carbon economy in China's construction industry. And reducing the carbon emission from construction industry (CECI) has become the key to sustainable development and the achievement of "double carbon target". The paper firstly decomposes the main influencing factors and calculates their effects by LMDI model. Then, the Tapio decoupling index is used to explore the decoupling relationship between carbon emissions and economic output. Finally, the spatial-temporal evolution of carbon emissions is analyzed through spatial autocorrelation theory. The results show that the carbon emissions from construction industry are highly increased by the construction employment rate (EM), and may be reduced by the energy intensity (EI) and carbon emission efficiency (CI). And the regions with better decoupling status are the eastern and central regions, which have relatively developed construction industry. Moreover, the spatial-temporal characteristic of carbon emissions is "high in the east and low in the west, high in the south and low in the north," showing obviously regional differences. [Display omitted] • Factors about production technology are taken into account, reflecting the characteristics of the construction industry. • Developed regions have better decoupling potential and more responsibility for CO 2. • The spatial distribution is "high in the east and low in the west, high in the south and low in the north". • The provinces in a region have similar decoupling status and carbon emission distribution. [ABSTRACT FROM AUTHOR]

Promoting the coupled development of water, energy, and food (WEF) subsystems is a critical step to enhance synergies and increase efficiencies in the WEF nexus. However, the evolution and obstacles of coupled development are largely ignored. This study developed a framework dividing links in the nexus to select indicators and integrated models for the degree of coordination and obstacle diagnosis to explore the coupled development of the WEF nexus from 2000 to 2020 in the water-scarce megacities of Beijing and Tianjin. The results show that the average coordination degree of the WEF nexus in Beijing (0.315) and Tianjin (0.317) is at a low level, indicating a limited interaction between the WEF subsystems, while the coordinated development degree of the WEF nexus is increasing. The order degree of the WEF subsystems shows a two-level hierarchical structure, indicating that both similarities and differences are included in place-specific characteristics. Furthermore, obstacle factors are identified from water and energy subsystems, in which environmental water use (W3) and the energy consumption per gross domestic production unit (E4) played the most significant roles and require a higher priority in policy response. The results in this paper complement obstacle factor analysis in WEF nexus practice, and provide operational indicators for nexus governance. [Display omitted] • Linkages are divided into one-, two-, and three-resource scales. • The number of related official documents is used to define the weight of subsystems. • The water subsystem dominated the coupled development of the water-energy-food nexus. • The order degree of subsystems show a two-level hierarchical structure. • Key obstacle factors are identified from water and energy subsystems. [ABSTRACT FROM AUTHOR]

Over the recent decades, the escalating expenses associated with energy provision alongside the implementation of rigorous ecological regulations have engendered a noteworthy concern pertaining to energy consumption within the water sector. The reduction of energy consumption and the improvement of effluent quality to meet more stringent discharge limits pose significant challenges for wastewater treatment plants (WWTPs). Most of the energy consumption in WWTPs is attributed to aeration, especially in secondary treatment processes where microorganisms rely on a significant supply of oxygen to sustain their life activities and degrade pollutants. This paper reviews the common secondary treatment processes (oxygen ditch, biological aerated filter, sequential batch reactor, membrane bioreactor) and their energy consumption, and examines the energy consumption and efficiency of aeration in WWTPs in different countries and regions. To address the high energy consumption of aeration, methods for energy saving and consumption reduction are discussed from three aspects: aerator design, mass transfer enhancement, and aeration control strategies. The aerator assumes a pivotal function in determining the characteristics of gas-liquid distribution characteristics in the reactor, which then affects the mass transfer. Mass transfer enhancement in the reactor can improve the oxygen transport efficiency between gas, liquid, and activated sludge. Based on the actual operation conditions, aeration control strategies can adjust the aeration rate to provide accurate air supply and achieving dissolved oxygen (DO) control, thereby reducing the high energy consumption caused by excess aeration. Finally, this study proposes research directions for optimizing the aeration process in WWTPs, including investigating the mechanism of DO transfer to microorganisms, exploring novel oxygen supply modes, and improving energy management of plant-wide consumption to enhance the plant's energy self-sufficiency. Future WWTPs should be low-energy, high-efficiency, intelligent, and environmentally friendly to address the energy shortages and accommodate the large-scale development and unmanned operation of WWTPs. [Display omitted] [ABSTRACT FROM AUTHOR]

Energy Internet (EI) is typically characterized by digitalization and clean energy that seeks to revolutionize the energy system and reduce carbon emissions. Even though several scholars conclude that EI accelerates the clean energy transition of energy system, since the controversial relationship between digitalization and energy consumption in academia, current studies on the carbon emission reduction advantages of the EI are still in dispute. This paper employs the effect of EI's construction on the carbon emission peak goal of China's energy system based on the system dynamics model and different scenarios. The simulation results find that: (1) the construction of the EI could enable China's energy system to meet the carbon reduction commitment in 2028, with a carbon emission peak of 11.61 billion tons; (2) As digitalization and clean energy become more extensively employed, rising energy demand is satisfied by the manner of consuming low-carbon electricity energy. This is thought to be the primary factor that could promote China to achieve the carbon peak goal earlier than expected; (3) The advancement of digitalization has further promoted the decoupling of China's economic growth from carbon emissions. It is projected that China's energy system will still achieve the carbon peak goal by 2030 when the expected annual GDP growth rate is increased to 6.32%, 5.75%, and 4.6% for the years 2020–2025, 2026–2030, and 2031–2035, respectively. These findings might provide a new perspective for quantitative research on the environmental outcomes of the EI and the relationship between environmental improvement and economic growth. [ABSTRACT FROM AUTHOR]

Buildings are one of the major sectors responsible for high energy demand and GHG emissions worldwide, which is driving expectations for implementing net-zero emission buildings (NZEBs) in many countries. Specifically, most developed countries have a carbon-neutral target set up for 2050, mandating all new buildings from then to be constructed as net-zero emission. Therefore, this paper investigated the economic feasibility of implementing NZEBs in the United States residential sector by combining a heat pump system with two renewable energy sources that are heavily supported by the federal government: solar and geothermal technologies. First, this study clearly demonstrates that "net-zero emission" is the most challenging task among neutralizing the energetic, economic, and environmental by-products from building operations. Moreover, this study analyzed the payback periods of multiple NZEB scenarios by considering the potential future changes in technology and policy necessarily required to meet the net-zero emission target by 2050. For reference, the technological and institutional factors were represented by "PV energy conversion rate" and "CO 2 equivalent price of emission trading scheme (ETS)", respectively. The results show that improving the PV energy conversion rate is much more effective in reducing the payback period of NZEBs compared to raising the CO 2 equivalent price of ETS. However, the growing public awareness for this CO 2 regulation policy will soon bring synergistic effect with the advancement of PV technology. In conclusion, this research framework clarifies the rather vague technological and institutional challenges that should be addressed to allow NZEBs to become economically feasible in the United States residential sector. Therefore, many building engineers, PV technicians, and policymakers should actively contribute to improving the techno-economic drivers for implementing NZEBs by 2050. • Most developed countries are mandating all new buildings from 2050 to be constructed as net-zero emission. • Net-zero emission target is hardly achievable without the simultaneous energy supply from solar and geothermal. • PV energy conversion rate should reach 43.50% to meet the net-zero critical point. • Improving the PV energy conversion rate is highly essential for reducing the payback period of NZEBs. • The growing public awareness of ETS will bring synergistic effect with the advancement of PV technology. [ABSTRACT FROM AUTHOR]

Additive manufacturing (AM) has been recognized as a promising technology for achieving more secure and resilient supply chains to support social transitions to sustainable post-fossil carbon societies. AM is considered to be efficient and sustainable and its environmental impact is mainly from energy consumption. Therefore, it is of great significance to accurate predict and thus reduce the energy consumption of AM. However, current models to predict AM energy consumption are not sufficiently accurate. In this paper, a hybrid mechanism-based and data-driven approach is proposed to forecast the AM energy consumption. This approach accurately modelled the working time and power of each energy-consuming component of the FDM machine. Firstly, the physical model was used to establish the energy consumption prediction framework which documents the total energy into energy consumed by each component. Secondly, the data-driven method was leveraged via data fitting and deep learning to obtain the power and temporal model of the nozzle and the platform, considering their working states and interactions between them and the cooling fan. Thirdly, experimental measurements and G-code parsing were utilized to obtain data of the power model and the temporal model of the remaining components. In this work, the different types of parts were fabricated by the fused deposition modelling (FDM) printer and the energy consumption was measured and compared with the predicted values to verify the accuracy and effectiveness of the proposed method. Finally, the proposed method was compared with other methods to document the benefits of the proposed method. The results showed that the prediction accuracy of the proposed method for these four experiments was more than 94% accurate and it outperformed other methods. The proposed method can be used to guide process design to more accurately predict and thus reduce the energy consumption of AM parts during their manufacturing processes. [ABSTRACT FROM AUTHOR]

There is no one-size-fits-all solution for global energy sustainability. It is affected by a country's policies, resources, financing opportunities, the attention given to it, and the degree to which other pressing issues, such as poverty and access to power, are addressed in each country. This paper used 28 indicators to assess a country's energy sustainability. These indicators ranged from CO 2 per capita emissions to the share of electricity production from wind. The indicators were adjusted so that the lower values were more sustainable. 31 countries were selected. Kohonen's self-organizing maps were used to present this 28-dimensional model in a 2D map. This algorithm is a type of artificial neural network trained by unsupervised learning. Eight clusters of countries were found through the analysis using the four-light traffic panel, where green is the most sustainable, followed by blue, yellow, and red. The closeness on the map does not guarantee that two countries will be in the same cluster. Three color matches and closeness were the criteria for the creation of clusters. The energy transition from the map's lower to upper sides is plainly visible. Red clusters make up the lower side, while green clusters make up the top. One blue cluster and two yellow clusters are located between them. The algorithm converged in the 133rd iteration; the quantization error reached 0 and the topological error reached a value of 25. This method can be used to visualize how far or close a country is to the best achievers. It shows which parameters can or should be improved and what the world trend is. If there is a trend, cost-effective solutions are frequently present. [ABSTRACT FROM AUTHOR]

The annual consumption of petroleum products in India was about 221 million metric tons in 2015. Of this, 84% was imported. The Indian industrial sector accounts for about 16%-20% of the total fuel consumption for thermal energy for different heating applications in the temperature range of 50°C--250 °C. Solar collectors can meet these temperature requirements and offer the possibility to mitigate the consumption of oil. This study highlights the fact that conversion efficiency from solar energy is much higher for process heating than for electricity generation and that process heating applications constitute a significant share of industrial energy consumption. In this paper, a methodology has been developed to estimate the potential for integration of solar collectors for process heating. The methodology employs process operating temperatures to select the type of solar collectors. The size of the solar field is estimated taking into account the thermal heat loads, working fluid and temperatures of these processes, the efficiency of the chosen solar collectors, location-based solar irradiance and capacity utilization of the solar collectors. The proposed methodology has been validated with a software tool called System Advisor Model (SAM). The techno-economic analysis will indicate the viability of solar systems for integration in industries. Therefore, the consociated parameters on economic (capital cost, fuel oil savings, monetary benefits), financial (Payback periods, Rates of Returns) and environmental (Carbon savings) are estimated. Further, the methodology has been applied to select Indian industries to verify its potential quantitatively. The industries selected include Textile, Pulp & Paper, Dairy, Leather and Automobile. Process-wise energy demands are considered while estimating the potential as the fuel requirement offset by solar energy in terms of absolute fuel oil savings, monetary benefits and carbon savings. The other economic and financial parameters mentioned above were estimated to verify the capability and present the market position of solar systems. Further, sensitivity analyses have been performed with respect to solar energy penetration and fuel oil prices to address the viability of integration of solar energy for process heating. [ABSTRACT FROM AUTHOR]

Circular economy ambitions demand considering lifecycle of products within product development. From the perspective of the circular economy, environmental considerations should, for example, increasingly entail product's use phase not only to increase product's energy-efficiency, but also to prolong the time in use. This paper investigates how these considerations are addressed within information and communications technology (ICT) sector. Drawing from previous research in constructing the hypotheses, it is examined how companies report about their quantitative environmental targets for products, and what topical areas are in focus for their environmentally conscious practices related to products' reuse. Then, relationships with these published quantitative targets and environmental performance are assessed. Content analysis method is used to derive data from 43 sustainability reports. Our results suggest that being mature enough to publish quantitative environmental targets for products is positively associated with lifecycle thinking, considering the durability of products, and remanufacture. This exploratory study reveals among others, that although published, quantitative environmental targets for products are not yet dominantly present; they are positively associated with environmental performance measures. This includes capability to report quantitatively on flows of reclaimed products, and positions in external ranking. But, we also highlight the need for more discussion around reuse alternatives that are not commonly published among companies in this sample. The paper concludes by discussing the applicability of this research method. [ABSTRACT FROM AUTHOR]

Improved energy efficiency is one of the most effective strategies for achieving energy sustainability and independence locally, regionally and globally. Industry accounts for 40% of global energy usage each year, which suggests its potentially significant impact on overall energy use. The rapid development of standards for more efficient equipment and components are pushing manufacturers towards further improvements on a component level. However, it has been shown that the largest efficiency potential is actually found in higher system levels in which components serve, such as production processes, ventilation or hydraulic systems. Even though the importance of increasing energy efficiency at a systemic level has been widely acknowledged in recent years, practical approaches are seldom discussed in the literature. This gap between aspiration and achievement calls for the development of new approaches to foster system efficiency in industrial systems. This paper presents a systems analysis and corresponding model to increase the energy efficiency of industrial processes that involve intensive usage of electric motor systems. The model originates from traditional value stream mapping. Three case studies representing three different industrial processes were conducted to develop and validate the model. In total, 31 energy efficiency measures were identified, 29 of which address actions beyond component levels. Alongside identifications of energy efficiency measures, the model promotes the creation of conditions for cross-functional worker participation. By applying this model, the knowledge and skills gained by industrial personnel working with improved energy efficiency, maintenance and production processes can be used immediately to generate improvement suggestions. The paper concludes that this model, based on a simplified energy-focused value stream mapping, could broaden the scope of energy efficiency actions, engage a larger group of workers within the organisation and increase systems efficiency. This can be further used as a practical tool for finding continuous improvement possibilities that will result in strengthened competitiveness and more cost-effective and sustainable manufacturing. [ABSTRACT FROM AUTHOR]

With the advent of the era of big data, large-dimensional spatial panel data is gradually used to do empirical research in the macroeconomic field. This paper adopts a Data Envelopment Analysis(DEA) approach to calculate regional energy efficiency based on the perspective of total-factor energy efficiency using statistical data of 30 administrative regions in China. On basis of spatial effects, the paper assesses the convergence of regional energy efficiency in China using large-dimensional spatial panel data model. Our results indicate that (1) There is significant spatial autocorrelation and clear spatial effects in China's regional energy efficiency. Thus, spatial effects should not be ignored when assessing the convergence of regional energy efficiency in China; (2) During the period from 2000 to 2014 China's regional energy efficiency not only exhibits absolute β -convergence but also exhibits conditional β -convergence, the convergence rate is higher than the rate of absolute convergence after controlling for the initial conditions of the level of economic development, foreign direct investment and government influence; (3) The convergence rate of China's regional energy efficiency from 2004 to 2014 is higher than the convergence rate for the period of 2000–2004, which indicates that industry transfer has contributed to improvement in the convergence of regional energy efficiency in China since 2004. [ABSTRACT FROM AUTHOR]

This paper investigates CO 2 emissions produced by two different container terminal operating models (tire transtainers and rail transtainers) at the port of Kaohsiung, and seeks to determine energy saving and CO 2 reduction strategies for shipping companies and terminal operators in order to comply with green port requirements. This paper's research methodology involves carbon footprint analysis and gray relational analysis. Carbon footprint analysis is employed to calculate the CO 2 emissions per container of two different container terminal operating models employing data for various areas (such as the berthing area, container yard, and gate area), where the raw data is collected from shipping companies or terminal operating companies at the port of Kaohsiung. Gray relational analysis is then used to determine the ranking order of different container terminal operating models based on the green port assessment criteria of working time efficiency, energy consumption, and CO 2 emissions. The chief findings of this paper are that the ranking order of container terminal types based on carbon footprint is tire transtainer ahead of rail transtainer; optimal green port assessment criteria constituted working time efficiency, energy cost, and CO 2 emissions; and use of container handling equipment with high operating efficiency can not only accomplish work rapidly, lessening the berthing time of ships in port, but also reduce energy costs and CO 2 emissions. [ABSTRACT FROM AUTHOR]

Sustainable consumption (SC) is key for sustainable development, but only limited studies have examined how to motivate SC among consumers in developing countries. Younger consumers tend to more sustainable consumption while whether they really understand the SC concept is not clear. To explore the role of SC understanding for motivating SC behaviors, this paper develops a concept model with hypotheses to propose that SC understanding mediates relationships between influencing factors and SC behaviors. Based on 623 usable questionnaires collected from junior high schools and senior high schools in three eastern cities of China, five influencing factors for SC behaviors are identified, they are, education, awareness & attitude, social influence, effectiveness of energy saving and emission reduction for sustainable products, and differentiation of sustainable products from regular products. Statistic results through hierarchical regression analysis demonstrate that SC understanding is necessary to motivate SC behaviors with certain influencing factors. Chinese adolescents can understand the SC concept through education and then purchase sustainable products. Only if sustainable products can show benefits of energy saving and emission reduction, Chinese adolescent would like to use these products. Environmental awareness and attitude can bring environmental treatment and disposal of products, but SC understanding is necessary. This paper contributes to extend the TPB to develop a conceptual model including education as one influencing factor for SC behaviors. It also develops three factors for SC behaviors from the product life cycle perspective. Besides, it also contributes to extend the previous studies about motivation of SC consumption to adolescents in an emergent economy of China to examine the role of SC understanding. Statistic results reveal the low implementation level of SC behaviors, and the government should highlight sustainable education to improve sustainable awareness and SC understanding. It is also shown that adolescents would like to purchase sustainable products if environmental performance of such products can be demonstrated, which also provide insights for enterprises to achieve win-win opportunities. [ABSTRACT FROM AUTHOR]

In a multi-pass face milling process, cutting parameters for each pass and the total number of passes dramatically affect the electrical energy consumption and production cost of the final product. In this paper, the electrical energy consumption characteristics of multi-pass face milling are firstly analyzed. Then a multi-objective parameter optimization model for maximizing energy efficiency and minimizing production cost is proposed and solved by the Adaptive Multi-objective Particle Swarm Optimization algorithm. Finally, a case study is carried out to validate the proposed model and search for the trade-off solutions between maximum energy efficiency and minimum production cost. From the results of the case study, significant interaction effects between cutting parameters and number of passes are revealed. Moreover, it also can be found that the traditional multi-pass parameter optimization for minimizing production cost does not necessarily satisfy the maximum energy efficiency criterion. Simultaneously optimizing the cutting parameters of each pass and the total number of passes achieves a trade-off between maximum energy efficiency and minimum production cost. Based on the work presented in this paper, manufacturers can easily improve energy efficiency and reduce production cost in the multi-pass face milling process. [ABSTRACT FROM AUTHOR]

This paper compares the energy and environmental impacts of organic and conventional apples cultivated in the North of Italy, by applying the Life Cycle Assessment (LCA) methodology. The authors examined the supply chain of apples, including the input of raw materials and energy sources, the farming step, the post-harvest processes and the distribution of apples to the final users. The paper develops two original contributions: 1) it enhances the limited number of studies on LCA applied to apples; 2) it compares organic and conventional apples produced in lands characterized by the same climatic conditions, to evaluate which of the two products is more competitive from an energy and environmental point of view. The results showed that, despite a lower productivity, preferring organic apples versus conventional apples could help to reduce the environmental impacts for most of the examined impact categories. With a few exceptions, differences lower than 7% occur between the eco-profiles of the two examined products. A relevant share of the primary energy consumption and almost all of the examined environmental impacts are caused by the post-harvest processes and by transport to the final users, assuming that the products are distributed on local, national and international markets. Furthermore, a detailed analysis of the farming step showed that a significant share in the overall energy and environmental impacts is due to the use of fertilizers and pesticides and to diesel consumption of agricultural machines. [ABSTRACT FROM AUTHOR]

Adoption and improvement of energy efficiency is one of the recommended pathways to tackle the problems of growing energy demand and greenhouse gas emissions. However, studies show that rebound effects from energy efficiency improvements can erode anticipated energy savings. This paper elucidates the rebound effect arising from energy efficiency initiatives in Kenya. The study uses Index Decomposition Analysis to assess the drivers of Kenya's total primary energy supply. The paper then adopts the neoclassical growth model in the empirical analysis of energy and carbon rebound effects at the macro-economic level. Cobb-Douglas production function is further applied using Solow growth model and energy as a factor of production to approximate technological progress. Results of decomposition of energy supply show that energy intensity and affluence are the drivers with the most significant effect on energy supply while population has less critical effect. Average energy and carbon rebound effects show partial rebounds of 15.64% and 31.41%, respectively. These results imply that there is need to accelerate the pace of transitioning to green energy as the ultimate solution to minimizing environmental impacts arising from energy use and tackling climate change. • Carbon and energy rebound effects of energy efficiency initiatives is studied. • Decomposition analysis is used to investigate drivers of energy supply. • Energy intensity and affluence have most significant influence on energy supply. • Average estimates of carbon and energy rebound effects are partial rebound effects. [ABSTRACT FROM AUTHOR]

This paper argues that many past studies have only been concerned with efficiency and not with the efficiency achievement ratio, whereas information on Baltic Sea countries' energy or environmental efficiency has always been based on studies that focus on different research objectives, but do not include all the Baltic Sea countries. Hence, the main aim of this paper is to analyze the impact of the achievement ratios for energy consumption and carbon dioxide emissions on both energy consumption and carbon dioxide emissions by including all Baltic Sea countries as a case study and constructing new static and dynamic efficiency indices. The study's results show that the 2008 Global Financial Crisis resulted in low achievement ratios for energy consumption and carbon dioxide emissions, and the conclusion is that since a lot of attention has been paid to staving off an economic downturn, the Global Financial Crisis may in fact have stalled actions to combat carbon dioxide emissions and energy over-consumption. In addition, the Baltic Sea countries should strengthen energy management on the demand side. [ABSTRACT FROM AUTHOR]

Excessive energy consumption and carbon dioxide emissions are long lasting issues and need continuous research to improve efficiency evaluation and performance monitoring of economies. The static and dynamic aspects of efficiency need to be dealt with simultaneously to thoroughly analyze the performance of economies. This paper aims at conducting a static and dynamic analysis of energy and CO 2 emissions efficiency of major economies. In this paper, we applied slacks based model of Tone (2001) with the treatment of undesirable output in objective function and constraint assuming free disposability of undesirable output and built a dashboard. The results can be summarized by saying that the larger economies with intensive production strategy, larger secondary industry, and weaker carbon tax laws are more likely to be inefficient. China, India, and Russia have the greatest potential for improvement in both energy efficiency and carbon dioxide emissions efficiency. The suggested dashboard has the ability to dig out the potential for efficiency improvement of countries like the USA, which mostly appear at the efficient frontier with other techniques. Concluding it can be suggested that economies in lower ranks of dashboard need stricter policy measures to cut energy consumption, carbon dioxide emissions and encourage a shift from fossil fuels to other renewable resources of energy. [ABSTRACT FROM AUTHOR]

Economic growth and the development of global markets have been coupled with energy use, which have caused an increase in global energy demand and created pressure on the supply of energy resources. This special volume section reports advances being made towards sustainable cleaner production and sustainable energy options. The section presents a selection of papers that show leading examples of the application of sustainable management, green production, and renewable energy. An overview framework is proposed to categorise the papers and to show key actors, factors and technologies for resource efficiency, cleaner production and sustainable energy. The themes covered by the papers include drivers of such sustainable practices. Five of these papers focus on the role of technology, regulatory framework, and customers' efforts in fostering the development and adoption of greener technologies. Another group of seven papers gives examples of on how to use green chemistry and cleaner energy sources such as biomass to foster transition to sustainable production. The last paper addresses the effect of environmental practices on firm's performance. The special volume section highlights the importance of multidisciplinary approaches that integrate social and technological perspectives to solve current sustainability problems and to promote the development of sustainable energy and sustainable production. [ABSTRACT FROM AUTHOR]

The CRIMSON (Constrained Rapid Induction Melting Single Shot Up-Casting) method uses a rapid induction furnace to melt just enough metal for a single mould rather than bulk melting used in traditional casting process. The molten metal is then transferred to a computer – controlled platform to complete the counter-gravity up filling. The highly controlled metal flow is pushed into the mould to finish the pouring and solidification. In the present paper the energy saving capability of CRIMSON approach is compared with conventional sand casting process. The paper focuses on the energy and resource efficiency optimization of casting stages through simulation and life cycle assessment analysis simulation for proposing alternative means for the better performance of such processes. It is proven that the CRIMSON process can produce high quality castings with higher energy efficiency and lower environmental impact. [ABSTRACT FROM AUTHOR]

Home energy consumption is increasing, and renewable energy sources are being deployed. The use of renewable energy sources as an alternative to fossil fuels and nuclear energy is a significant opportunity for reducing CO 2 emissions. However, carbon emissions from electricity generation are dependent on the type of renewable energy source and the quantity of the electricity that is produced. With the emergence of the smart grid, residents are able to reduce their electricity consumption by shedding the power usage in their homes. The objective of this study is to smooth power peak demand and diminish CO 2 emissions. This paper describes the power shedding benefits of reducing energy consumption and CO 2 emissions. This reduction of CO 2 emissions eliminates the need for auxiliary power plants in the grid in the case of peak demand points and the need to switch to renewable energy sources. In this study, we prove that emerging information and communication technology can reduce energy use and CO 2 emissions. Wireless communication architecture is introduced in this study to increase grid flexibility and rapidity in the data flow among all of the components in the proposed system. Our proposed system with local renewable generation can reduce CO 2 emissions nearly 72% of all CO 2 emissions when all appliances are supplied with the grid. With our load shedding algorithm, the CO 2 emissions decrease nearly 91%. The yearly estimation shows that our system can reduce 62% of CO 2 emissions and 37% of consumed power. In this paper, we introduce an efficient architecture of a smart home energy management system and propose a shedding algorithm for home energy usage. This system is based on wireless communication among home appliances, a home management and control system, a grid management and control system and domestic renewable energy sources. Our proposed system, which employs wireless communication, domestic renewable generation and a power shedding algorithm, can operate in conjunction with future smart grids by applying real-time data, such as peak demand points. Numerous real data sets permitted the development of some of the statistics in the system results. [ABSTRACT FROM AUTHOR]

The industrial sector accounts for 35%–40% of total energy use in Sweden, where 30% of industrial energy use comes from non-energy-intensive small and medium-sized enterprises (SMEs). This sector has not historically received much attention when it comes to improving energy efficiency due to limited resources, lower priority given to energy issues, rather small energy-saving potential for a single company, and great heterogeneity of the SMEs. However, the accumulative energy-saving potential for SMEs can be quite high and achieved at very low costs, partly because most improvements are found in support processes and are relatively easy to implement. Various public policies, such as industrial energy audit programmes, serve as a means for overcoming barriers to energy efficiency in the sector of SMEs. One example is the Swedish Energy Audit Programme (SEAP), a stand-alone audit programme functioning between 2010 and 2014. The aim of this paper is to examine the programme by means of process and impact evaluation. The results show that the programme resulted in annual net energy efficiency savings equivalent to 340 GWh/year or 6% of the 713 participating companies' energy end use. The implementation rate in the audit programme was 53%. On average, the public cost of one implemented measure was € 700. Derived from the amount of energy saved, the audit programme's annual cost-effectiveness is €7/MWh saved energy. This paper adds a significant scientific contribution due to the method used for evaluation. Multiple company visits and availability of quantitative data from 713 companies gave the possibility to address the additionality effects and estimate net energy savings more precisely. [ABSTRACT FROM AUTHOR]

Laser cladding is one of the principal means of equipment remanufacturing and the environmental profiles of this technology has become a research focus. This paper examines the environmental impacts of cast iron cylinder head block remanufacturing through laser cladding using life cycle assessment (LCA), and compares it with the new cylinder head block manufacturing. Resource and energy consumptions of each manufacturing and remanufacturing processes are collected along the production line and then the results of six selected environmental impact categories are calculated. Consistency and sensitivity analysis is also conducted after life cycle impact assessment. The results reveal that cylinder head remanufacturing by laser cladding will achieve large environmental benefits, which can cut environment impact over the entire life cycle by 63.8% on average. This paper also discusses the trend of changes in environmental impacts using scenario analysis over different remanufacturing levels. By taking characterized global warming potential (GWP) as the assessment index, the result shows that remanufacturing will no longer be the preferred option if it needs to repair more than 16 cracks by laser cladding for the cylinder head. [ABSTRACT FROM AUTHOR]

The coherency of research, development, and innovation processes are vital in promoting a more resource efficient society. Sustainability-oriented innovation systems define specific kinds of innovation systems, i.e. those that are directed to the aims of sustainable development. This paper develops an integrated method with four layers of analysis to evaluate the priorities and performances of such systems. The method is applied to a sample of emerging and innovation based economies, namely Brazil, Russia, India, China, South Africa, Turkey, and Singapore. The analyses consider the 6 main activities or functional dynamics of innovation systems and a set of 19 keywords. The keywords relate to the thematic clusters of renewable energy technologies, energy efficiency, and environmental management. The priorities of the countries in each main interaction of the system are classified accordingly. The priorities are compared to the existing level of specialization in the keywords based on the intermediate outputs of the system. The analyses cover 153,838 papers and 15,138 patents between the years 2003 and 2014. A Sustainable Innovation Index is developed to aggregate the normalized values of country performance across all keywords. Singapore receives the highest value (21.17) and the average of Brazil, Russia, India, China, and South Africa is 14.91. The results determine the ability of countries to align priorities and performance towards more mature innovation systems for sustainable development. [ABSTRACT FROM AUTHOR]

Recognizing the industrial sector's high energy use and vast potential for energy saving, many national governments have established voluntary or negotiated agreements that set quantitative energy-saving targets for individual industrial enterprises. The most commonly used energy-saving targets worldwide are volume and energy intensity targets. Since 2006, the Chinese government has set mandatory, enterprise-level energy-saving targets in terms of the “amount of energy saved”; this represents a unique category that differs from both volume and energy intensity targets. Based on first-hand data collected at ten case enterprises, this paper represents one of the first attempts to verify the reliability of the amount of energy saved data reported by the Chinese government and to rigorously and systematically investigate the inherent weaknesses of the amount of energy saved targets through a comparison with the mainstream categories of energy-saving targets. The ten case enterprises reported four different types of amount of energy saved. While all of the case enterprises claimed full achievement of their amount of energy saved targets, four enterprises exaggerated their performance by violating the National Standard for Calculating the Amount of Energy Saved of Enterprises. The paper thus hypothesizes that the National Top-1000 Enterprise Program's alleged achievement of performance 65% higher relative to its amount of energy saved target is likely an overestimation. Although amount of energy saved targets constitute an innovative energy-saving indicator, they do not represent a step forward from conventional volume and intensity targets because of the following four weaknesses: 1) amount of energy saved targets provide limited potential for comparison given the different types of amount of energy saved and the different calculation methods, 2) they generate uncertain environmental outcomes as it is possible to meet a portion of an amount of energy saved target through production volume expansion, 3) they are difficult to enforce due to the complexity of data verification on the part of local government agencies, and 4) they are poorly correlated with the national target for reducing energy intensity. Amount of energy saved targets should therefore be replaced with “double-control” targets that impose both volume and intensity targets on industrial enterprises. [ABSTRACT FROM AUTHOR]

The growing demand for private vehicles associated with the rise in the number of middle-income consumers in Indonesia has led to an enormous energy demand and high-level CO 2 emissions. In order to increase the sustainability in the transportation sector, the Indonesian government has given special attention to electric vehicles as key future solutions to achieve clean and green transport and to improve energy supply security. This paper aims to investigate public perception in relation to the adoption of electric motorcycles (EMs) in Indonesia. We conduct a survey involving 514 participants and find that only 36% of participants agree to adopt EMs as their next motorcycle, and 40% are still in doubt (i.e. neutral). To identify factors influencing EMs adoption, we expand the Theory of Planned Behaviour (TPB) model and analyse the data collected using Partial-Least Squares Structural Equation Modelling (PLS-SEM). The findings show that an adequate perception of EMs, environmental benefits, economic benefits, and incentive policies positively affect the attitude toward the adoption of EMs, which reinforce the results of the previous studies. However, perceived cost and perceived risks appear to be insignificantly influential in our study. Furthermore, factors related to high-behavioural control and support from relatives and closest people are shown to significantly affect the adoption of EMs. This paper also presents the acceptable price range of EMs and the optimal price point (OPP) for segments differentiated by monthly income. All these findings provide valuable insights for the different stakeholders interested in enhancing the adoption of EMs in Indonesia. [ABSTRACT FROM AUTHOR]

Taiwan's per capita carbon emissions ranks first in Asia. Whether Taiwan's current "2025 Non-Nuclear Homeland" energy transition policy can achieve the goal of greenhouse gas reduction is an issue that needs to be studied urgently. On the basis of studying the status quo of energy supply and demand and carbon emissions in Taiwan, this paper first uses an econometric model to predict Taiwan's energy demand from 2021 to 2030, and then uses a Markov chain model to predict its energy structure changes and CO 2 emission trends under different scenarios, and evaluates the impact of its energy transition policies on carbon emission reduction goals. On this basis, by comparing the carbon trading markets on both sides of the Taiwan strait, the feasibility of and obstacles to the cross-strait cooperation in the carbon trading market are discussed in depth, and the opportunities for future cross-strait cooperation in the carbon trading market are proposed innovatively. The empirical results of this paper show that the carbon emissions in 2025 under the energy transition policy will be reduced by 59.8–61.1 million tons compared with those without planning constraints, but compared with the carbon reduction target (a reduction of 124.8–133 million tons), there is still a long way to go. The contribution potential of the annual carbon emission reduction target is only 45.9%–47.9%, and the contribution potential in 2030 is 57.2%–60.1%. The current "2025 Non-Nuclear Homeland" policy is not in line with Taiwan's current reality, and it is difficult to achieve the expected carbon reduction goal. In order to realize the energy transition in Taiwan in the future, it is necessary to make overall planning of energy policies, explore the cross-strait power cooperation model, and gradually try to build a cross-strait carbon trading market. [ABSTRACT FROM AUTHOR]

The growing demand for private vehicles associated with the rise in the number of middle-income consumers in Indonesia has led to an enormous energy demand and high-level CO 2 emissions. In order to increase the sustainability in the transportation sector, the Indonesian government has given special attention to electric vehicles as key future solutions to achieve clean and green transport and to improve energy supply security. This paper aims to investigate public perception in relation to the adoption of electric motorcycles (EMs) in Indonesia. We conduct a survey involving 514 participants and find that only 36% of participants agree to adopt EMs as their next motorcycle, and 40% are still in doubt (i.e. neutral). To identify factors influencing EMs adoption, we expand the Theory of Planned Behaviour (TPB) model and analyse the data collected using Partial-Least Squares Structural Equation Modelling (PLS-SEM). The findings show that an adequate perception of EMs, environmental benefits, economic benefits, and incentive policies positively affect the attitude toward the adoption of EMs, which reinforce the results of the previous studies. However, perceived cost and perceived risks appear to be insignificantly influential in our study. Furthermore, factors related to high-behavioural control and support from relatives and closest people are shown to significantly affect the adoption of EMs. This paper also presents the acceptable price range of EMs and the optimal price point (OPP) for segments differentiated by monthly income. All these findings provide valuable insights for the different stakeholders interested in enhancing the adoption of EMs in Indonesia. [ABSTRACT FROM AUTHOR]

The rapidly growing state of energy consumption and the urgency of mitigating the harmful effects of climate change have accelerated our transition to renewable energy. This paper proposes a multi-level framework for exploring the global competition to develop renewable energy technologies. Through a combination of text mining techniques and by analyzing a corpus of patents covering six renewable energy technologies from 1970 to 2019, we reveal the lifecycle of these technologies, the original sources of these technologies, their potential markets, and the different supply and demand patterns of the major countries/regions involved. The results show that solar and wind energy are currently the most widely used renewable energy sources, yet biomass is the only energy source still in its growing stage. China's patents have seen a rapid upward trend, spurred on by strong policy promotion, which runs counter to the decrease in patent applications seen in most other major countries in this area. However, China's technology imports still far outweigh its exports, indicating that there is still a sizeable gap between China and some of the more developed countries like the United States, Germany, France, and the United Kingdom in terms of high-quality, high-value patents. Accordingly, China, the United States, and Japan are currently the leading in the world's renewable energy markets. That said, the market potential of Europe, Canada, and Australia should not be underestimated. These and more themes are discussed from a political, economic, environmental, and global governance perspective. This paper provides useful insights into the global renewable energy technology landscape, particularly the competitive dynamics at play. The aim is to guide policy makers or managers on the next steps in our progress toward applying and using renewable energy at scale. [ABSTRACT FROM AUTHOR]

In machining processes, there are contradictions between high efficiency and environmentally-friendly machining. This indicated that there is a major potential for sustainable machining to both increase efficiency and protect ecological balance. For this, the employment of effective methods has become imperative to reduce resource use. In this way, the use of analysis and optimization tools to select the tool material, cutting parameters, cooling/lubrication conditions, etc., will be a benefit for minimizing waste without sacrificing efficiency. In the literature, researchers have used various methods to reveal the requirements of sustainability. However, reaching the concept of sustainable machining by using one more than technique confuses the readers. Therefore, it was deemed necessary to present and discuss the current methods, models and analyzes in a comprehensive review paper. In this context, this paper reviewed the previously published works, especially focusing on sustainability and energy consumption modeling methods in machining operations. At the end of the study, it was seen that LCA provides a systematic and quantitative view of the system and thus can act as a decision support tool. Moreover, it can give an idea to the manufacturers about the improvement of the process and the areas of innovation. However, the method has several disadvantages such as relatively expensive and specific software, obtaining inventory data, impractical to use, time-consuming and requires large amounts of data. Various mathematical models and algorithms have been developed to deal with the complex situations and offer a more practical use compared to LCA. However, it is difficult to state that clear results have been achieved thanks to these models. It was seen that in complex machining processes, only approximate results can be found with models, which leads to questioning the reliability of the models. [ABSTRACT FROM AUTHOR]

Cleaner production is a green production way to minimize emissions and waste as well as maximize product output. Cleaner production has proven itself as an effective way to reduce energy consumption and improve material utilization during the whole manufacturing process. However, the implementation of cleaner production strategy is facing barriers due to the lack of applying advanced technologies such as cloud manufacturing, Internet of Things, and cyber-physical system. Based on these advanced technologies, this paper presents an architecture of energy-cyber-physical system enabled management for energy-intensive manufacturing industries to promote the implementation of cleaner production strategy. An energy-cyber-physical system enabled green manufacturing model for the future smart factory is proposed. Then the qualitative and quantitative synergetic models based on energy-cyber-physical system are developed for cleaner manufacturing. Finally, an application of a partner company is presented to demonstrate the proposed framework and synergistic models. The results show that energy consumption can be greatly reduced using synergistic evaluation models. Further managerial implications are summarized to increase the level of synergy of energy-cyber-physical system, which can achieve cleaner production strategy by making energy-efficient decisions from different departments. • An architecture of energy-CPS enabled management is proposed to save energy. • Energy-CPS enabled intelligent model is proposed for green manufacturing. • Qualitative and quantitative synergetic models are developed for cleaner production. • The energy consumption can be reduced through the synergistic evaluation models. [ABSTRACT FROM AUTHOR]

Demand response plays an important role in helping us achieve clean energy and climate goals. It offers a variety of environmental benefits, such as reducing energy usage, offsetting the need for fossil-fueled power plants, and helping to manage system challenges from increased wind and solar energy. However, DR suffers from difficulties associated with optimization model complexity. Especially, when the DR problem is formulated as a mixed integer nonlinear programming (MINLP) problem, which is NP-hard problem. In order to solve this problem, a global optimization approach that combines a particle swarm optimization (PSO) algorithm with a sequential quadratic programming (SQP) local optimizer is proposed in this paper. This allows us to incorporate the global search characteristics of PSO algorithms with the local search capabilities of SQP algorithms. This unique combination increase the possibility of a successful global solution by balancing the exploration-exploitation trade-off and is particularly effective atsolving MINLP problems. Application of the approach in residential energy management indicates that the proposed algorithm is effective atresolving the MINLP DR problem. • A complex mixed nonlinear integer models is proposed and applied to a residential scenario. • A hybrid algorithm was implemented to solve the non-convex MINLP, and can obtain near optimal solution. • The performance of proposed algorithm is better for solving demand response related MINLP than commercial solver Knitro. [ABSTRACT FROM AUTHOR]

Energy is the material basis for the development of national economy. However, energy shortages become increasingly serious as a result of the growing population and the modernization of the global economy. Therefore, investigating drivers of energy consumption is crucial to coordinate the relationship between energy and national economic growth. This study aimed to uncover the effects of energy prices (EP), urbanization (URN) and GDP on per capita energy consumption (EC) with considering the income gaps between countries. The paper adopted the Granger causality test approach and the impulse response function analysis by using long-term time series data on EC, EP, URN and GDP during 1980–2015 in 186 countries divided into three groups (high-income, upper- and lower-middle income groups). The results indicated a long-term co-integration relationship among these variables. Granger causality test showed a bidirectional causality between URN and EC in high and lower-middle income countries but no causality in the upper-middle income group. In all groups, there is a bidirectional causal link between GDP and EC. The results of IRF showed that as urbanization level improves, its role in promoting EC become less significant. Moreover, EP affected EC negatively both in high and lower-middle income countries, but it has a positive impact on EC in upper-middle income countries. The study supports the finding that urbanization is an important factor affecting energy consumption per capita, although its contributions vary across income groups, which offers a new pathway to control the excessive growth of energy consumption. • Analyzing energy, urbanization, economy and prices in 186 countries for 1980–2015. • An extra analysis for 87 individuals through integration techniques with panel data. • Economic growth and urbanization took a different role in different income groups. • Energy prices was a tiny effect on per capita energy consumption. [ABSTRACT FROM AUTHOR]

The paper takes the industrial coupling symbiosis network (ICSN) of oil and gas resource-based urban eco-industrial parks (EIPs) as the research object. The purpose is to judge the stability of a symbiotic, one-way relying and two-way symbiosis model applied to oil and gas resource-based cities through constructing a logistic growth symbiotic network model. Through identifying and analyzing the stability model, the research concludes the stability conditions. The analytic results are as follows: In the symmetric ICSN system, it is important to maintain equal status and balanced benefit distribution among the coupled symbiotic enterprises. In the one-way-based ICSN system, the core enterprise significantly promotes the improvement of ecological efficiency the satellite enterprise while the satellite enterprise less effectively promotes the improvement of ecological efficiency of the core enterprise. In the two-way ICSN system, the core enterprise and the satellite enterprise are easily affected by the symbiotic enterprises within the network, and the stability is extremely poor. These conditions and analytic results will serve as a basis for constructing an efficient closed-loop system for ICSN of the oil and gas resource-based urban EIPs.Through the stability analysis of the industrial coupling symbiotic network of the oil and gas resource-based urban eco-industrial park, it can determine the stability conditions that affect the overall performance of the industrial park. Studies have shown that increasing network stability and improving energy efficiency in the park can improve the overall sustainability of the eco-industrial park and accelerate the effective transformation of resource-based cities. • Constructing an industrial coupling symbiotic network model. • Given stability conditions for three symbiotic modes. • Extremely poor stability of Two-way relying industry coupling symbiotic network. • Logistic growth model for network stability analysis. [ABSTRACT FROM AUTHOR]

This paper presents the application of the Discrete Event Simulation (DES) in an industrial plant to analyze the behavior of the consumption and generation of electrical energy in conjunction with other variables of the process. In other words, the process modeling was conducted considering the energy consumption in equipment as well as the local photovoltaic (PV) energy generation. A medium-sized plant was the reference base used herein, and the main variables considered were the time of supply of tanks and processing of raw material, active power of equipment and energy tariffs. A simulation model with resolution in seconds was designed, enabling the simulation of consumption curves of each equipment of a given part of the production process. The equipment of variable active demand was modeled by a mathematical regression method, and the others were assumed with constant power. The same model was altered to unit in minutes for simulating the monthly electricity consumption, so as to obtain financial results. The contractual rules for electricity supply were included, and the use of a diesel-powered generator was taken into account. The application of a freeware was employed as a low cost alternative for small and medium-sized companies, even though the lack of graphic resources has increased the complexity of the system modeling process. The model with resolution in seconds proved to be efficient to simulate the consumption profile of a specific equipment that alters the power in a short-time period, such as the pumps. The model, on the other hand, in minutes allowed a more effective analysis in terms of finance, enabling the verification of alternative scenarios throughout a month. The monthly cost reduction perceived by considering self-generation in this model was about 36%. Detailed diagram of the main elements of modeling of a manufacturing process of an industry concentrating on the consumption and generation of electric energy. Image 1 • The electricity consumption in an industrial process can be approached in a simulation model. • PV energy can be simulated together with variables of industrial processes. • The discrete event simulation can be used in industrial processes to optimize the electricity management. [ABSTRACT FROM AUTHOR]