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Since early 2010s, the Green Routing Problem (GRP) has dominated the literature of logistics and transportation. The problem itself consists of finding a set of vehicle routes for a set of customers while minimizing the detrimental effects of transportation activities. These negative externalities have been intensively tackled in the last decade. Operations research studies have particularly focused on minimizing the energy consumption and emissions. As a result, the rich literature on GRPs has already reached its peak, and several early literature reviews have been conducted on various aspects of related vehicle routing and scheduling problem variants. The major contribution of this paper is that it represents a comprehensive review of the current reviews on GRP studies. In addition to that, it is an up-to-date review based on a new chronological taxonomy of the literature. The detailed analysis provides a useful framework for understanding the research gaps for the future studies and the potential impacts for the academic community. [ABSTRACT FROM AUTHOR]

The present paper addresses a novel two-echelon multi-product Location-Allocation-Routing problem (LARP). It also considers the integration of issues such as disruption, environmental pollution, and energy-efficient vehicles as currently critical issues in a Supply Chain Network (SCN) that includes production plants, central warehouses, and retailers. The aim of this study is to minimize the total cost, which involves costs related to the establishment, shipment processes, environmental pollution, travelling, vehicle usage, and fuel consumption, in a way to cover the total demand of retailers. The problem is NP-hard; thus, to solve it approximately, we developed Grey Wolf Optimization (GWO) and Particle Swarm Optimization (PSO) algorithms. The numerical analysis showed that the proposed algorithms yielded high-quality results in a short computational time where the average gaps of GWO and PSO against CPLEX are 0.78% and 0.9%, respectively. Then, a case study of a dairy factory in Iran is conducted to evaluate the applicability of the proposed methodology and find the optimal policy. Finally, a set of sensitivity analyses is carried out to suggest managerial insights and decision aids. [ABSTRACT FROM AUTHOR]

Since the 1970s, Brazil has carried out the most successful world program of commercial biomass for use and production of energy by stimulating its sugarcane industry and promoting the large-scale production of ethanol nationwide in response to the first oil shock. Today, the technologies behind ethanol production are well established. Brazil is the world's largest sugarcane supplier, producing its ethanol at a competitive price. If other sugarcane producing countries decided to join Brazil's move toward the production of this biofuel, what impacts might there be for each country's economy and employment? This is what we investigate in this paper. Prime candidates for ethanol production include Australia, China, Colombia, India, Indonesia, Mexico, Pakistan, the Philippines, Thailand, and the United States of America. We evaluated the potential socioeconomic impacts of developing this promising industry by using an input–output approach. More specifically, we adapted the Brazilian method of producing ethanol to these countries' distinct economies. We augmented the input–output table of each country, inserting a new ethanol industry based on the Brazilian ethanol production model. We also augmented their new ethanol industry's sales following a hypothetical hydrous and anhydrous ethanol consumption scenario. Thereafter, we reconcile the national accounts, concluding our analysis by quantifying and comparing the different net effects of this new industry for each of the assessed countries for the year of 2009. We demonstrate which industries would be expected to be positively or negatively impacted by this substitution in each country; and find that not all of the countries we assessed would experience positive socioeconomic results from emulating Brazil's production of ethanol. [ABSTRACT FROM AUTHOR]

Research on the development of sustainable supply chain models is highly active nowadays. Merging the concept of supply chain management with sustainable development goals, leads to simultaneous consideration of all economic, environmental and social factors. This paper addresses the design of a sustainable closed-loop supply chain including suppliers, manufacturers, distribution centers, customer zones, and disposal centers considering the consumption of energy. In addition, the distribution centers play the roles of warehouse and collection centers. The problem involves three choices of remanufacturing, recycling, and disposing the returned items. The objectives are including the total profit, energy consumption and the number of created job opportunities. As far as we know, these objectives are rarely considered in a sustainable closed-loop supply chain model. The proposed model also responds to the customer demand and also addresses the real-life constraints for location, allocation and inventory decisions in a closed-loop supply chain framework. Another novelty of this research is to develop a set of efficient Lagrangian relaxation reformulations and fast heuristics for solving a real-world numerical example. The results have revealed that the obtained solution is feasible and the developed solution algorithm is highly efficient for solving supply chain models. Finally, a comprehensive discussion is provided to highlight our findings and managerial insights from our results. [ABSTRACT FROM AUTHOR]

China has launched energy quota right trading system, as one of the sustainable operations, since 2016 to cap the total energy consumption, however the economic and energy saving effect of this new policy is unclear. Based on the input and output panel data of China's 29 manufacturing sub-industries, this paper constructs the non-parametric optimization model under the command-and-control and the energy quota right trading scenario, to compare their potential economic gains and energy savings of these two polices for Chinese manufacturing industries. Results show that, whether at overall level or at the sub-industries level, the economic potential and energy-saving potential from energy quota right trading are higher than those from command-and-control. However, a part of energy-saving potential will be squeezed out due to the negative externality of the energy market trading. We suggest the well integration of both command-and-control and the market mechanism to achieve a Win–Win development. [ABSTRACT FROM AUTHOR]

To address the gap in energy performance studies regarding the role of information entropy in feedback processes between input and output slacks, the paper develops a novel Stochastic-Entropic Analysis for Ideal Solutions (SEA-IS) model. The non-linear stochastic optimization model is then applied to assess the potential information gains that may arise from energy slacks minimization given the different optimal reduction quantiles in US states. The model relies on Beta distributed priors to model the odds-ratio of learning feedback and takes advantages of numerous strengths present in DEA and TOPSIS approaches for performance management. Machine learning methods are also employed to predict information gains in terms of contextual variables. We find that California is the only U.S. state that has indicate strong mutual information feedback and continuous improvements in efficiency. The results indicate there is ample scope for harnessing the power of information gains in improving energy efficiency, particularly in 37 U.S. states, which indicates scope for a public–private partnership to achieve this goal. [ABSTRACT FROM AUTHOR]

In this paper, we analyze the temporal dependence in energy prices and demand using daily data of Portugal and Spain over the period 2007–2017. The methodology used is based on a stochastic Hidden Markov Model and the results indicate first that all significant relationships between energy prices and demands were found to be positive; second, spot prices are only time dependent on future prices and spot energy, while future energy is solely time dependent on spot energy behavior; third, future prices are not only autocorrelated but also time-dependent with spot energy and future energy demands level; and finally, spot energy is autocorrelated and time-dependent with future prices and future energy. Policy implications of the results obtained are presented at the end of the article. [ABSTRACT FROM AUTHOR]

Data envelopment analysis (DEA) is an analytical method for measuring, benchmarking, and improving efficiency performance. While DEA has traditionally been a deterministic ex-post approach, when operational data are not available, it can be positioned to represent an ex-ante perspective, if it is feasible to simulate production behaviors. However, there are two limitations of ex-ante DEA. First, is the representation of uncertainties associated with the contextual conditions and their impact on production. Second, is the inability to incorporate risk preferences that relate to the organizations' attitude towards production uncertainty. This paper addresses these two gaps through a method that integrates Monte-Carlo simulations, the Subjective Expected Utility Theory, and ex-ante DEA. Our method allows to explicitly model and incorporate the uncertainties associated with the contextual variables and the production behavior, while facilitating the representation of risk preferences. We demonstrate our method through a case study that is framed around a hypothetical community evaluating Micro-Grid (MG) design alternatives. We utilize the National Renewable Energy Lab data to model the stakeholder energy demand and environmental uncertainties; and use the engineering specifications for the MG technology to populate the production possibility set. We then investigate the distribution of efficiency scores by using various stakeholder risk attitudes and find that DEA rankings are insensitive to risk preferences. The proposed method and the documented risk insensitivity are significant contributions as they allow to reposition ex-ante DEA as a nonparametric alternative to multi-criteria decision-making, which can be generalized across different application areas, including uncommon DEA applications such as engineering. [ABSTRACT FROM AUTHOR]

A wireless sensor network (WSN) consists of a large number of unattended sensors with limited storage, battery power, computation, and communication capabilities, where battery power (or energy) is the most crucial resource for sensor nodes. The information sensed by sensors needs to be transmitted to sink quickly especially for the applications with delay restriction. However, it is difficult to achieve optimal energy efficiency and source-to-sink delay simultaneously. So it is very necessary to find a power control solution based tradeoff between energy and delay. In this paper, a cooperative differential game model is proposed, and a power solution is obtained which determines a fair distribution of the total cooperative cost among sources. [ABSTRACT FROM AUTHOR]

The fiercely competitive business environment may require the manufacturing layouts to be modified across the entire planning horizon owing to addition or deletion of new/existing products, machines or processes. The existing layout may not be appropriate for the next time periods as product combination and part demand tend to vary under multi-time scenario. Also the increased awareness of environmental concerns and paucity of vital resources like electric energy has led organizations to rethink about their manufacturing strategies and design layouts which are both cost effective as well as environmentally sustainable. An appropriately planned layout not only helps in reduction of material handling distance but can also greatly contribute to enhancement of the energy efficiency of manufacturing systems and contribute to resource productivity and sustainable value creation. To address these issues in the facility layout design, this paper models a dynamic cellular facility layout problem incorporating the sustainability aspect by considering the minimization of net electric energy consumption along with material handling and rearrangement costs. The model presented in this work is a mixed integer non-linear program. The model aims to minimize the aggregated cost of overall material handling for both the inter and intra-cell movements simultaneously. Additionally the model also minimizes the net electrical energy consumption across the entire time horizon. Twenty five data sets corresponding to varying combinations of machines, time periods and cells have been taken from extant literature to validate the proposed model. LINGO 10 optimization software has been used to solve the proposed model. However, due to NP-Hard nature of cellular facility layout problem, the proposed model is computationally difficult to be solved in reasonable time using LINGO 10, particularly, for layouts pertaining to larger dimensions. To overcome these complexities, a meta-heuristic based on simulated annealing (SA) is also employed to solve the model. It is discerned from the experimental results that LINGO is not able to optimally solve the model whereas the SA optimally solves the model for larger dimensions as well in reasonable computational time. [ABSTRACT FROM AUTHOR]

Forecasting energy demand has been a critical process in various decision support systems regarding consumption planning, distribution strategies, and energy policies. Traditionally, forecasting energy consumption or demand methods included trend analyses, regression, and auto-regression. With advancements in machine learning methods, algorithms such as support vector machines, artificial neural networks, and random forests became prevalent. In recent times, with an unprecedented improvement in computing capabilities, deep learning algorithms are increasingly used to forecast energy consumption/demand. In this contribution, a relatively novel approach is employed to use long-term memory. Weather data was used to forecast the energy consumption from three datasets, with an additional piece of information in the deep learning architecture. This additional information carries the causal relationships between the weather indicators and energy consumption. This architecture with the causal information is termed as entangled long short term memory. The results show that the entangled long short term memory outperforms the state-of-the-art deep learning architecture (bidirectional long short term memory). The theoretical and practical implications of these results are discussed in terms of decision-making and energy management systems. [ABSTRACT FROM AUTHOR]

We consider a problem where different classes of customers can book different types of services in advance and the service company has to respond immediately to the booking request confirming or rejecting it. Due to the possibility of cancellations before the day of service, or no-shows at the day of service, overbooking the given capacity is a viable decision. The objective of the service company is to maximize profit made of class-type specific revenues, refunds for cancellations or no-shows as well as the cost of overtime. For the calculation of the latter, information of the underlying appointment schedule is required. Throughout the paper we will relate the problem to capacity allocation in radiology services. Drawing upon ideas from revenue management, overbooking, and appointment scheduling we model the problem as a Markov decision process in discrete time which due to proper aggregation can be optimally solved with an iterative stochastic dynamic programming approach. In an experimental study we successfully apply the approach to a real world problem with data from the radiology department of a hospital. Furthermore, we compare the optimal policy to four heuristic policies, of whom one is currently in use. We can show that the optimal policy significantly improves the currently used policy and that a nested booking limit type policy closely approximates the optimal policy and is thus recommended for use in practice. [ABSTRACT FROM AUTHOR]

Chinese industry has developed greatly since China implemented its 'reform and opening-up' policy in 1978. With the rapid development of industry, the problems of growing energy consumption and environmental pollution are drawing increasing attention from government managers and scholars. This paper divides industrial systems into two stages, an energy utilization stage and a pollution treatment stage, for accurately evaluating the total-factor energy efficiency as well as the overall efficiency. We build a new two-stage data envelopment analysis model with shared inputs to open the 'black box' of efficiency measurement in traditional energy efficiency methods. Applying the model to data for Chinese regions, we can display the advantages and disadvantages of these two stages of industry. The results show that (1) the performance of Chinese industry improved during the years 2006-2010; (2) the energy utilization stage performance was better than that of the pollution treatment stage, but the gaps reduced year by year; and (3) energy efficiency increased during this period. Based on these results, some policy recommendations are given. [ABSTRACT FROM AUTHOR]

Recovery energy has been one of important components of energy consumption structure in China. Recovery energy is a type of secondary energy, which is generated from various sources. Since the estimation of recovery energy from some sources such as coke oven gas and other coal gas contains some uncertainties, recovery energy can be represented as an interval variable. The extant studies in the literature ignore recovery energy in the evaluation of energy efficiency or carbon dioxide ( $$\text {CO}_{2}$$ ) emissions efficiency. As a part of energy supply in China, recovery energy may influence regional primary energy consumption and $$\text {CO}_{2}$$ emissions significantly. In this context, this paper proposes an interval slacks-based measure approach to evaluate energy efficiency and $$\text {CO}_{2}$$ emissions efficiency in the presence of recovery energy in China. In the described approach, the indicator of $$\text {CO}_{2}$$ emissions is incorporated based on the weak disposability assumption, and recovery energy is modeled as a dual factor. The optimal exact data on recovery energy of each region can be obtained. Based on the proposed approach, the measures of energy saving potential and $$\text {CO}_{2}$$ emissions reduction potential are derived. Findings resulting from the model application show that there are great disparities in regional efficiencies, and the inefficiency of Chinese regions is largely driven by those inefficient regions in the central and western areas. Notably, the development of recovery energy can help to improve regional energy efficiency and $$\text {CO}_{2}$$ emissions efficiency in China. [ABSTRACT FROM AUTHOR]

Total-factor energy efficiency (TFEE) assessment has received increasing attention in both operations research and energy economics communities. Earlier TFEE studies implicitly assume that production activity lies in the economic area, which precludes the possibility that the production activity lies in the non-economic area and thus suffers from congestion. This paper contributes to develop TFEE index by taking into account congestion effect. It starts with the definition of congested production technology, based on which several data envelopment analysis models are proposed to construct TFEE index with congestion. The models for quantifying energy inefficiency caused by congestion effect are also developed. We apply the proposed index to evaluate the energy efficiency performance of Chinese industrial sectors at province level in 2010-2012. It is found that TFEE with congestion can yield useful insights about the choice of proper ways to achieve energy efficiency improvement. A comparison with the empirical results under congestion-free production technology indicates that ignoring congestion effect may lead to significantly different TFEE scores when congestion effect does exist. [ABSTRACT FROM AUTHOR]

In data envelopment analysis (DEA), it is usually assumed that all data are continuous and not restricted by upper and/or lower bounds. However, there are situations where data are discrete and/or bounded, and where projections arising from DEA models are required to fall within those bounds. Such situations can be found, for example, in cases where percentage data are present and where projected percentages must not exceed the requisite 100 % limit. Other examples include Likert scale data. Using existing integer DEA approaches as a backdrop, the current paper presents models for dealing with bounded and discrete data. Our proposed models address the issue of constraining DEA projections to fall within imposed bounds. It is shown that Likert scale data can be modeled using the proposed approach. The proposed DEA models are used to evaluate the energy efficiency of 29 provinces in China. [ABSTRACT FROM AUTHOR]

This study uses two empirical approaches to explore the asymmetric effects of oil and coal prices on renewable energy consumption (REC) in China from 1970 to 2019. As a conventional approach, we used the nonlinear autoregressive distributed lags (NARDL) model, while machine learning was used as a non-conventional approach. The empirical findings of the NARDL indicate that oil and coal price fluctuations have a significant effect on REC for both the short and long term. The results of the non-conventional approaches based on machine learning indicated that the SVM model was more efficient than the KNN model in terms of accuracy, performance, and convergence. Referring to the SVM model findings, the results show that an increase in the coal price has a higher ability to predict REC than the oil price. As a robustness check, we also find that an increase in Brent prices significantly decreases REC. The findings of this study support the view that there is a substitution effect from oil to coal before initiating the use of renewable energy in China. [ABSTRACT FROM AUTHOR]

We introduce the BDA dynamics and explore the associated applications in renewable energy sector with a focus on data-driven innovation. Our study draws on the exponential growth of renewable energy initiatives over the last decades and on the paucity of literature to illustrate the use of BDA in the energy industry. We conduct a qualitative field study in the UK with stakeholder interviews and analyse our results using thematic analysis. Our findings indicate that no matter if the importance of the energy sector for 'people's well-being, industrial competitiveness, and societal advancement, old fashioned approaches to analytics for organisational processes are currently applied widely within the energy sector. These are triggered by resistance to change and insufficient organisational knowledge about BDA, hindering innovation opportunities. Furthermore, for energy organisations to integrate BDA approaches, they need to deal with challenges such as training employees on BDA and the associated costs. Overall, our study provides insights from practitioners about adopting BDA innovations in the renewable energy sector to inform decision-makers and provide recommendations for future research. [ABSTRACT FROM AUTHOR]

Wireless Sensor Networks (WSNs) are autonomous wireless systems consists of a variety of collaborative sensor nodes forming a self-configuring network with or without any pre-defined infrastructure. The common challenges of a WSN are network connectivity, node mobility, energy consumption, data computation and aggregation at sensor nodes. In this paper we focus on intermittency in network connectivity due to mobility of sensor nodes. We propose a new mathematical model to capture a given entire WSN as is with intermittency introduced between the communication links due to mobility. The model involves open GI/ G/1/ N queuing networks whereby intermittency durations in communication links are captured in terms of mobility models. The analytical formulas for the performance measures such as average end-to-end delay, packet loss probability, throughput, and average number of hops are derived using the queuing network analyzer and expansion method for models with infinite- and finite-buffer nodes, respectively. For models with 2-state intermittency, we analyze the performance measures by classifying these models into three types: namely, model with intermittent reception, model with intermittent transmission and/or reception, and model with intermittent transmission. We extend the analysis to multi-state intermittency models. We demonstrate the gained insight of WSNs through extensive numerical results. [ABSTRACT FROM AUTHOR]

The contradiction between economic development, resources and environment is becoming more and more important every day. Based on the proposed energy conservation, this paper will study whether reducing energy consumption hinders economic growth. This article considers the spatial heterogeneity factors and the different energy consumption levels between different areas. Through empirical analysis, we discuss the quantitative relationship between energy consumption and economic growth from different areas. Finally, we propose long- and short-term strategic arrangements for energy consumption in order to facilitate sustainable development. [ABSTRACT FROM AUTHOR]

China becomes the largest energy consumer in 2010 but its energy productivity is well below the world average. To meet China's fast growing energy using, energy efficiency should be especially emphasized under China's energy policy. This paper focuses on the regional level of energy efficiency change in China. And we analyze total factor energy efficiency for 30 Chinese provinces over the period 1998-2009 using Malmquist index method and Tobit analysis. The Malmquist estimation results suggest there is a dropping change trend of energy productivity growth. Chinese energy efficiency still faces with huge regional disparity, but the energy technical efficiency reflects convergence in the nationwide and west region. As a result of Tobit regression, we find that industrial structure, energy consumption structure and institutional factor have different influences on energy efficiency. [ABSTRACT FROM AUTHOR]

In this paper, we review the energy efficiency in China, and the following topics are discussed. (i) Some useful empirical analyses applied on the energy efficiency based on some expanded models, (ii) the relationship among environment, energy and economic growth, (iii) some important environmental efficiency evaluation indexes, and (iv) some latest researches on the emission system. Finally, some policy recommendations are made. [ABSTRACT FROM AUTHOR]

Estimating the true consumption of appliances in use is important both for modeling and short term forecasting of electricity demand, as well as for planning and assessing Demand Side Management (DSM). This paper applies the new technique known as Conditional Demand Analysis (CDA) to estimate consumption of these appliances. This was made possible due to a major survey carded out for the Brazilian National Electricity Conservation Program by the Brazilian electric utility companies while they were still state owned. Results were checked against traditional appliance energy consumption figures, and investigations carried out to ascertain if differences were due to regional variations in consumption patterns from northern to southern Brazil. [ABSTRACT FROM AUTHOR]

We address the problem of minimizing the fuel consumption incurred by compressor stations in steady-state natural gas transmission networks. In the practical world, these type of instances are very large, in terms of the number of decision variables and the number of constraints, and very complex due to the presence of non-linearity and non-convexity in both the set of feasible solutions and the objective function. In this paper we present a study of the properties of gas pipeline networks, and exploit them to develop a technique that can be used to reduce significantly problem dimension, without disrupting problem structure, making it more amenable to solution. [ABSTRACT FROM AUTHOR]

We consider the problem of determining an optimal driving strategy in a train control problem with a generalised equation of motion. We assume that the journey must be completed within a given time and seek a strategy that minimises fuel consumption. On the one hand we consider the case where continuous control can be used and on the other hand we consider the case where only discrete control is available. We pay particular attention to a unified development of the two cases. For the continuous control problem we use the Pontryagín principle to find necessary conditions on an optimal strategy and show that these conditions yield key equations that determine the optimal switching points. In the discrete control problem, which is the typical situation with diesel-electric locomotives, we show that for each fixed control sequence the cost of fuel can be minimised by finding the optimal switching times. The corresponding strategies are called strategies of optimal type and in this case we use the Kuhn-Tucker equations to find key equations that determine the optimal switching times. We note that the strategies of optimal type can be used to approximate as closely as we please the optimal strategy obtained using continuous control and we present two new derivations of the key equations. We illustrate our general remarks by reference to a typical train control problem. [ABSTRACT FROM AUTHOR]

Promoting energy efficiency in iron and steel production provides opportunities for mitigating environmental impacts from this energy-intensive industry. Energy efficiency technologies differ in investment costs, fuel-saving potentials, and environmental performance. Hence the decision-making of the adoption strategy needs to prioritize technological combinations concerning these multi-dimensional objectives. To address this problem, this study proposes a hybrid multi-criteria decision-support model for adopting energy efficiency technologies in the iron and steel industry. The modeling framework integrates a linear programming model that determines the optimal technology adoption rates based on the techno-economic, energy, and environmental performance details and an interactive multi-criteria model analysis tool for diverse modeling environments. A real case study was performed in which a total number of 56 energy efficiency technologies were investigated against various criteria concerning economics, energy, and environmental performances. The results examine the tradeoffs and synergies were examined with regard to seven criteria. A balanced solution shows that a total investment of 13.4 billion USD could save 2.51 Exajoule fuel consumption, cut 67.4 million tons (Mton) CO2 emissions, and reduce air pollution of 1.5 Mton SO2, 1.41 Mton NOx, and 0.86 Mton PM, respectively. The case study demonstrates the effectiveness and applicability of the proposed multi-criteria decision-making support framework. [ABSTRACT FROM AUTHOR]

Range anxiety of electric vehicles (EVs) still poses a major barrier in their adoption in the logistics operations despite the advancements in the battery technology. The need for recharging the battery during the day brings additional complexities to the operational planning of commercial EVs in last mile deliveries. The driving range of an EV may vary according to different factors including ambient temperature, weight, speed, acceleration/deceleration, and the road profile. In this study, we revisit the well-known electric vehicle routing problem with time windows by taking into account the weight of the load carried. Cargo weight may play a crucial role in the operational efficiency of the EVs since it may affect the energy consumption significantly. We first present two alternative mathematical formulations of the problem and test their performances on small-size instances that can be solved using a commercial solver. Next, we develop a matheuristic approach that integrates an optimal repair procedure in the large neighbourhood search method and validate its performance. Then, we present an extensive numerical study to investigate the influence of load on the routing decisions. Our results show that cargo weight may create substantial changes in the route plans and fleet size, and neglecting it may cause severe disruptions in service and increase the costs. [ABSTRACT FROM AUTHOR]

We identify factors influencing energy efficiency and the role of price instruments such as tax and technology use in reducing energy intensity at the firm level. We use data from 2001 to 2015 for India's manufacturing sector from the Centre for Monitoring Indian Economy. Our result strongly suggests that R&D and productivity have a positive impact on achieving energy efficiency. In such a case, at least one-to-one correspondence between the tax and energy intensity may help promote renewable energy use if they are subsidized and allowed to come under the provision of tax credit or tax exemption. Since price instruments do not produce any revenue recycling effect, policymakers can trade-off between increasing corporate tax and generating employment. Therefore, environmental regulations should strictly relate to increase energy efficiency and bring the manufacturing sector out of the productivity dilemma. Also, as evidence from the empirical analysis, there is an urgent need to substitute vintage capital with new capital and better technology. In addition to the existing liberalization policies, the Government must design green domestic policies for the manufacturing sector and map them with FDI and trade. As the polluted firms are energy-intensive, "Performance, Achievement and Trade" (PAT) policies need to focus on these firms. [ABSTRACT FROM AUTHOR]

Using province-level data for South Korea, we analyze the dynamic relationship between economic growth and several energy parameters. Specifically, we decompose the growth effect into scale, composition, and technique effects, and control for regional spillovers through the use of a dynamic GMM estimator for spatial panel data models. The analyzed period, ranging from 2000 to 2017, allows us to look for changes in the regional growth effects following the implementation of the National Strategy for Green Growth in 2009. Our estimates show that the scale and composition effect tended to increase both per capita final energy use and energy intensity, outweighing reductions through the technique effect. In contrast, when considering renewable energy production, the scale and technique effect increased and the composition effect decreased the corresponding figures. Thereby, the technique effect was the main driver of increases in renewable energy production. Despite the larger, yet comparatively small share of renewables in Korea's energy mix, no considerable change of the growth effects can be observed since 2009. Therefore, to reduce the risks for the economy and achieve the political objectives of the green growth strategy throughout the whole country and in a timely manner, a stronger commitment seems to be required. [ABSTRACT FROM AUTHOR]

Renewable energy is significant for addressing climate change and energy security. This study focused on the drivers of China's renewable energy consumption (REC) by an extended production-theoretical decomposition analysis and emphasized REC technical efficiency and technological change in 28 provinces during 1997–2017. We then projected China's REC to 2030 based on nine scenarios using a Monte Carlo simulation approach and specifically considering the impacts of the COVID-19 pandemic on the national economy. The decomposition results showed that economic growth and population scale generally contributed to an increase in REC at national and provincial levels over the period while the overall technical efficiency and technological change in REC played limited roles in prompting REC nationally. The projection results indicated that the target that generates 50% of its electricity from renewable energy sources for China, could be achieved by 2030 if enough actions are taken to accelerate renewable energy development. Finally, we provided policy proposals that support our findings. [ABSTRACT FROM AUTHOR]

Distributed welding flow shop scheduling problem is an extension of distributed permutation flow shop scheduling problem, which possesses a set of identical factories of welding flow shop. On account of several machines can process one job simultaneously in welding shop, increasing the amount of machines can short the processing time of operation while waste more energy consumption at the same time. Thus, energy-efficient is of great significance to take total energy consumption into account in scheduling. A multi-objective mixed integer programming model for energy-efficient scheduling of distributed welding flow shop is presented based on three sub-problems with allocating jobs among factories, scheduling the jobs in each factory and determining the amount of machines upon each job. A multi-objective whale swarm algorithm is proposed to optimize the total energy consumption and makespan simultaneously. In the proposed algorithm, a new initialization method is designed to improve the quality of the initial solution. And various update operators, as well as local search, are designed according to the feature of the problem. To conduct the experiment, diversified indicators are applied to evaluate the proposed algorithm and other MOEAs performance. And the experiment results demonstrate the effectiveness of the proposed method. The proposed algorithm is applied in the real-life case with great performance compared with other MOEAs. [ABSTRACT FROM AUTHOR]

With the increase in energy consumption and carbon dioxide emissions, promoting an energy-saving lifestyle among residents has become an urgent environmental and social need. Studying factors that influence household daily energy-saving behaviors may help the government draft policies for reducing the energy consumption and promoting the sustainable development of the human economic society. In this study, we investigate the features of energy-related situational factors, individual/household energy consumption behavioral norms, and energy-saving behavioral intentions by performing a questionnaire survey in Beijing. We examine the relationships among the mentioned aspects by applying factor analysis and structural equation modeling. Results show that among the three aspects assessed, situational factors most significantly and effectively influence the residents to assume energy-saving behaviors. Energy-saving behavioral norms partly mediate the relationship between situational factors and behavioral intention. We propose practice policy implications on the basis of the results. In particular, the positive influences of situational factors should be strengthened, and relevant policy measures should be emphasized to establish a situational background beneficial for accelerating the formation or transformation of energy-saving behaviors. [ABSTRACT FROM AUTHOR]

We examine the problem of managing a server farm in a way that attempts to satisfy the conflicting objectives of high performance and low power consumption. A subset of servers is designated as 'reserve'. The reserves are powered up when the number of jobs in the system is sufficiently high, and are powered down when that number is sufficiently low. Powering up takes an interval of time during which the reserves consume power but do not serve jobs. The question of how to choose the number of reserves, and the up and down thresholds, is answered by analyzing a suitable queueing model and minimizing an appropriate cost function. Heuristics and numerical results are also presented. [ABSTRACT FROM AUTHOR]