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This paper analyzes the determinants of demand for Nordic Swan eco-labeled paper products and whether the factors determining the discrete decision to buy a Swan-labeled product are different from those that determine the quantity purchased. Using observed consumer purchase data from Denmark, we find that prices and socio-demographic characteristics have a strong impact on the decision to purchase a Swan-labeled good. The quantity consumed of Swan-labeled goods, on the other hand, is strongly affected by the availability of Swan goods, discounts, and household size. [Copyright &y& Elsevier]

Energy consumption forecasting plays a pivotal role in modern resource management and sustainable development. This paper presents a concise overview of state-of-the-art techniques and methodologies employed in the field of energy consumption forecasting, with a particular emphasis on the application of Machine Learning (ML) models. The paper surveys recent advancements, addresses key challenges, and identifies promising directions for future research in this critical domain. By examining the current landscape of energy consumption forecasting through the lens of machine learning, this review aims to offer researchers and practitioners valuable insights and guidance for enhancing the accuracy and efficiency of energy consumption pattern prediction. [ABSTRACT FROM AUTHOR]

The purpose of the paper is to examine the factor that influence the productivity in the EU Member States. In the paper a multi-level analysis was applied. The research showed that the systematic transformation of the economy towards modern branches in favor of the traditional ones caused an increase in productivity. On the other hand, it is impossible to completely replace the former. Hence, the most appropriate direction seems to be a gradual increase in efficiency in underdeveloped sectors of the economy without abandoning them entirely. What is especially interesting, it was found that positive changes in final consumption expenditure of general government negatively affect productivity. Thus, there is a dilemma related to the scope of the impact of fiscal policy and regulations in the economy. [ABSTRACT FROM AUTHOR]

In this paper, a real-time chemi-thermomechanical pulping (CTMP) process was investigated, where opportunities were explored to increase energy consumption efficiency with a guarantee of expected pulp properties as a priority. Due to the process instrumentation limitations, such as the lack of advanced regulatory controllers and optimization units, the advanced process controllers and real-time optimization strategies can hardly be implemented in the existing CTMP processes. Therefore, a novel visualization-based process evaluation methodology is proposed to aid the mill operators in making further decisions to improve the process operations. Additionally, to determine the desirable pulp properties for production, an inferential sensor model is developed that estimates the handsheet properties from pulp properties. Also, to assess the model prediction accuracy in real-time, a reliability index model is designed. The proposed methodologies have been validated on a practical CTMP process, and the results show the satisfactory performance of the system. • Developed a strategy to evaluate the CTMP performance using visualization methodology. • A novel inferential sensor model is implemented to determine the desirable pulp properties. • A reliability index model classifying reliability levels and assessing model accuracy is developed. • The proposed model adapts to model updates, uncertainties, and computation resource limitations. [ABSTRACT FROM AUTHOR]

Steel demand forecasting is crucial for steel industries, enabling effective production planning, inventory management, resource optimization, and strategic decision-making. This also holds true for steel plant manufacturers, who require insights into future demand trends to assess market potential, evaluate project feasibility, and identify growth regions. This paper addresses the predictability of steel demand across countries and regions worldwide, employing a comparative analysis of linear and non-linear modeling approaches to forecast steel demand per capita. A global model is trained in order to integrate information from diverse countries at varying stages of development, allowing the model to learn from comparable conditions and historical patterns. For each modeling approach hyperparameter tuning and selection of lagged input variables was performed. The performance of different model configurations was evaluated and compared for individual countries. This evaluation reveals that the predictability varies across countries and regions and shows for which countries a simple regression model based on past data is sufficient and for which countries more sophisticated models are needed. The developed models are useful in providing insights into consumption patterns, comparative risk assessment, and development of steel consumption. [ABSTRACT FROM AUTHOR]

Improving energy efficiency in public passenger transport is key to achieving sustainability goals. In many developing countries, public passenger transport relies heavily on fossil fuels, which necessitates a strong focus on reducing emissions from energy consumption. Surprisingly, few studies have addressed this issue in the context of daily operations, with most research focusing on operational and infrastructural changes. This paper aims to fill this gap by introducing detailed energy consumption variables into a public transport simulation model using SUMO, a tool known for its seamless integration with Python. The development of two Python scripts underpins this endeavour. The first script facilitates the automatic generation of scenarios, allowing the manipulation of parameters such as station spacing, line length, passenger capacity and waiting times, among others. The second script is dedicated to estimating energy consumption for each scenario. Together, these tools allow us to calculate average energy consumption for different scenarios and modes of public transport. As a result, they provide valuable insights for the formulation of public policies aimed at saving energy in different public transport environments and lay the foundation for the integration and development of intelligent transport systems and simulation-based digital twins. [ABSTRACT FROM AUTHOR]

Due to the mobile and random nature of services in cyber–physical–social systems (CPSSs), developing service composition approaches that ensure high availability, minimal energy consumption, and high quality of service (QoS) remains a complex challenge. Over the last two decades, several service composition approaches have been proposed in the literature to deal with this challenge. Nevertheless, the existing approaches have certain limitations, particularly in situations where services may move from one location to another, become unavailable due to intensive battery usage, encounter failures, or undergo a decline in quality. These limitations often arise because these approaches do not simultaneously integrate mobility, energy, and QoS constraints while defining the user's movement in a random manner. In this paper, the learning-based swarm optimization-aware service composition algorithm (LS-SCA) is proposed to overcome the aforementioned shortcoming. This approach surpasses existing ones by accounting simultaneously for the user's mobility, energy, and QoS criteria during the service composition process. First, the Small World in Motion (SWIM) mobility model is employed in this study to determine the user's mobility traces, avoiding the random generation of users' traces. Second, an energy consumption model is proposed to increase the energy efficiency by avoiding the overuse of the devices' batteries that can reduce the availability of services and lead to the composition failure. Third, the two-phase learning-based swarm optimizer (TPLSO) method is used in the composition process to find the sub-optimal composition that satisfies the global QoS constraints with the highest utility in terms of mobility, energy, and QoS. Unlike the most existing metaheuristic-based service composition approaches where the overall composition population is improved over a given number of iterations, the TPLSO method is exploited in this paper to improve only a subset of compositions, which reduces the composition time and increases the QoS utility of the composition. The simulation scenarios using two real datasets demonstrate that the LS-SCA approach outperforms six baselines in terms of energy consumption, QoS utility, and availability of composition. This notable performance makes the proposed approach more suitable for real-world applications where energy efficiency, QoS, and availability are crucial factors to consider. [ABSTRACT FROM AUTHOR]

China's carbon emissions from households have increased with the growth of the consumption scale. To accomplish the ambitious goals of reducing emissions, carbon emission reduction on the household side should not be neglected. Based on the 2012–2018 CFPS database, This paper examines the relationship between household carbon emissions(HCEs) and the digital economy by analyzing data on household consumption expenditure and carbon emission coefficients. Utilize instrumental variables and the "Broadband China" natural experiment to conduct robustness tests, and further clarify how the digital economy influences direct and indirect household carbon emissions. The study finds that (1) Direct HCEs witness an increase while indirect HCEs experience a decrease due to the digital economy. (2) The digital economy increases direct HCEs by expanding the consumption scale, but surprisingly, the digital economy has not made a significant impact on enhancing residents' environmental awareness. (3) When analyzing indirect HCEs, we introduced a macro perspective, and it is found that the digital economy results in a decline in indirect HCEs primarily due to a reduction in the energy intensity of the whole industry. This paper provides new evidence on the digital economy and its impact on carbon emissions at the household level, which is instructive for understanding the interaction between the production and consumption sides in achieving carbon neutrality and how to promote emission reduction on the consumption side. • Household carbon emissions are measured from direct and indirect perspectives. • The impact of digital economy on household carbon emissions is heterogeneous. • Digital economy increases household carbon emissions through consumption scale. • Digital economy has no significant impact on environmental awareness. • Digital economy reduces indirect household carbon emissions through energy intensity. [ABSTRACT FROM AUTHOR]

• This paper studies the new problem, EVs' provision of flexible ramping products (FRPs). • The dispatching capability of EV fleets is disclosed through the feasible operating region in variable spaces. • A cloud-edge collaborated market structure for the electricity market with FRPs is proposed to facilitate a large amount of EVAs integrated with protected privacy. • A distributed optimal model combining the EVA's bidding subproblem and the market clearing subproblem is established. • The distributed optimal model is solved based on the modified analytical target cascading (ATC), in which the convergence performance is improved through heuristic constraints. • Time-coupled multi-period (TCMP) operation mode is implemented in the market to maximize the dispatching potential of EV fleets. More flexible ramping service is required due to the increase of renewable power generation in power systems. Electric vehicles (EVs) could provide such flexible ramping products (FRPs) at low cost while participating in the electricity market through aggregation. However, EVs' dispatching capability cannot be fully utilized without the right incentives. This paper addresses a distributed optimal model developed between EV aggregators (EVAs) and the independent system operator (ISO). To make such concept, a cloud-edge collaborated market structure is adopted. At the edge level, EVAs assess the dispatching capability and solve the market bidding subproblem. At the cloud level, ISO solves the market clearing subproblem considering system economy and security. The overall problem is solved by the analytical target cascading (ATC) method. Heuristic constraints are also introduced into the model to improve convergence performance. The model is tested on a modified IEEE 30-bus system. Results demonstrate that the proposed method can incentivize EVAs with different owners to shift load and provide FRPs accurately, meanwhile reducing the cost and increasing the consumption of renewable energy effectively. [ABSTRACT FROM AUTHOR]

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

The widespread use of digital technology has driven a huge change in the way payments are made. Based on the micro-data of the China Family Panel Studies (CFPS), this paper explores the relationship between mobile payment and carbon emissions from the consumption side. With the exogenous impact of Alipay's "Collecting Five Blessings" campaign, this paper finds that mobile payment significantly increases household consumption carbon emissions, but the marginal effect is gradually decreasing. Mechanism analysis shows that mobile payment mainly realizes the above impact through promoting the increase of consumption expenditure and upgrading of consumption structure. Further analysis indicates that carbon reduction policies introduced by the Chinese government in the personal consumption sector can mitigate the carbon emissions growth associated with mobile payments. Still, there is a need to strengthen policy measures further to effectively address the issue of rising carbon emissions from household consumption. [ABSTRACT FROM AUTHOR]

Abstract: This article contributes to the theoretical and empirical discussions on how it could be possible to transform technical eco-efficiency into reduced environmental impact of consumption. The rebound effect is an important concept in this context. The relevance of consumer behaviour is addressed within three important sectors for the Zero Emissions Techniques and Systems: shopping of paper products, energy use in household and waste management. It is not only important how much the consumers buy, but also what kinds of products they buy and how they use and dispose of them. However, the responsibility for a sustainable development should not entirely be put on the shoulders of the individual consumer. [Copyright &y& Elsevier]

Understanding consumers' purchase behaviors is fundamental to the success of the dairy industry. With its economic importance, the Chinese market is critical to dairy producers in most countries around the world. However, understanding consumers in this market is particularly challenging, as these consumers often have a different relationship with dairy products than consumers elsewhere in the world, given the country's historical dairy-related scandals. This special relationship can be characterized by what consumer behavior researchers call "high involvement," indicating that Chinese dairy consumers often attempt to reduce the level of risk associated with buying dairy products. Surprisingly, although this relationship affects several important aspects of purchase behavior, examination of the concept of involvement in the dairy sector has not been widely considered. Of note, there is no understanding of how Chinese consumers vary in their involvement levels and their implications on their dairy purchase behaviors. Nor have there been involvement-based insights provided on how dairy companies can position their marketing strategy to suit the needs of these consumers better. Thus, this study proposes a new approach to understanding Chinese consumer dairy decisions by introducing "consumer involvement" as a segmentation tool through which individuals' behaviors can be predicated according to their involvement profile. Based on an online survey administered in Shanghai using 1,073 dairy consumers, principal component analysis confirmed involvement with dairy is a multidimensional construct with the following 4 factors: pleasure value, symbolic value, risk importance, and risk probability. A 2-step cluster analysis identified 4 consumer clusters based on their involvement profile: face-concerned dairy lover, carefree dairy consumer, cautious dairy lover, and confused dairy consumer. According to a one-way ANOVA test and cross-tabulation with χ2 test, these consumer segments behave differently in relation to the extensiveness of decision making, cue utilization, trust of information sources, and consumption behavior. The outcomes in this paper further explain why efforts to restore consumer trust for dairy products do not work among some consumers, as individuals may exhibit diverse attitudes toward such information due to their heterogeneous involvement levels. The study also provides suggestions for market practitioners and organizations to develop effective target market strategies and policies according to different consumer clusters. [ABSTRACT FROM AUTHOR]

With advances in AI (Artificial Intelligence) and the surge of Big Data, Deep learning (DL) has a remarkable impact on businesses in various domains including finance, marketing, healthcare, manufacturing, and agriculture. From sales prediction to detection and monitoring customer behaviors, its use in business analytics is proven to help grow and improve business functions. However, execution of DL can instigate high cost during both model training and deployment. Time and energy are two essential cost factors of DL computation as they imply not only monetary but also environmental consequences. Moreover, different DL architecture incurs different costs. Selecting the best model to train and deploy is hence important to the business. Most existing work focuses on building a DL model that obtains the highest accuracy or similar measures to evaluate quality of data analytics. In this paper, we argue that in practice, such performance criteria may not be adequate, for example, the most accurate model may not be the most cost-effective. This paper studies a practical approach that includes energy consumption during training and deploying the trained DL in deciding what DL model should be deployed. The paper describes how energy consumption can be estimated and illustrates the approach for cost analysis of DL application on the smart agriculture business where plant disease classification model is trained and deployed. Lessons learned from the study is included. In summary, our contributions are: (1) an approach to analyze cost of using DL model in terms of time, accuracy and energy consumption to support model selection, and (2) application of the approach on plant disease classification in smart agriculture business. [ABSTRACT FROM AUTHOR]

Electricity theft has emerged as a notable concern for smart grids, as fraudulent users illicitly access electricity from utilities without a contractual agreement or manipulate meter readings to evade bill payments. Consequently, the importance of electricity theft detection (ETD) in preserving the cost-effectiveness of smart grids has increased. In recent years, the utilization of deep learning models in electricity theft detection has surged in popularity, owing to their capacity to capture the periodic patterns within time-series data on electricity consumption. Nonetheless, deep learning techniques for electricity theft detection encounter several challenges, such as data imbalance, higher false positive rates, and the determination of periodic cycle timings. In this paper, we present an innovative approach to tackle these challenges: the Multi-Objective Evolutionary Hybrid Deep Learning (MOE-HDL) architecture, specifically designed to identify electricity theft. In MOE-HDL techniques, first, a novel multi-objective evolutionary algorithm based on ranking scheme (RMOE) is proposed to optimize architectural model parameters and the detection window size, with precision and recall as conflicting objectives. Next, an optimized dynamic detection window is generated to effectively capture the periodic patterns in consumption data and convert the one-dimensional data into a grid structure. Finally, a hybrid deep learning model is designed with the help of optimized model parameters to identify abnormal patterns in electricity consumption data. We conducted a comprehensive set of experiments, comparing our approach with eight baseline methods for electricity theft detection. The experimental results demonstrate that the proposed MOE-HDL approach outperforms these alternatives in terms of performance. • In this paper, a novel multi-objective evolutionary hybrid deep learning approach was developed for theft detection in smart grids. • Precision and recall are two conflicting yet effective objective functions designed to optimize the model parameters and detection window. • A dynamic detection window is obtained through optimization to transform electricity consumption data. • The hybrid deep learning model is designed according to the model parameters obtained through evolutionary multi-objective optimization. [ABSTRACT FROM AUTHOR]

• This paper proposes a comprehensive optimization program to increase economic efficiency and improve the resiliency. • A demand response program integrated with home energy storage systems is presented to optimize energy consumption. • To modify the consumption pattern of household consumers, a real-time pricing algorithm is proposed. • A self-healing system reconfiguration program integrated with distributed energy resources is presented. • Real data of California households are considered to model the home appliances and home energy storage systems. This paper proposes a comprehensive optimization program to increase economic efficiency and improve the resiliency of the Distribution Network (DN). A Demand Response Program (DRP) integrated with Home Energy Storage Systems (HESSs) is presented to optimize the energy consumption of household consumers. Each consumer implements a Smart Home Energy Management System (SHEMS) to optimize their energy consumption according to their desired comfort and preferences. To modify the consumption pattern of household consumers, a Real-Time Pricing (RTP) algorithm is proposed to reflect the energy price of the wholesale market to the retail market and consumers. In addition, a Self-Healing System Reconfiguration (SHSR) program integrated with Distributed Energy Resources (DER), reactive power compensation equipment, and Energy Storage Systems (ESSs) is presented to manage the DN energy and restore the network loads in disruptive events. The reconfiguration operation is performed by converting the isolated part of the DN from the upstream network to several self-sufficient networked virtual microgrids without executing any switching process. Real data of California households are considered to model the home appliances and HESSs. The proposed comprehensive program is validated on the modified IEEE 123-bus feeder in normal and emergency operating conditions. [ABSTRACT FROM AUTHOR]

Demand Side Management (DSM) plays a significant role in the smart grid to minimize Electricity Cost (EC). Home Energy Management Systems (HEMSs) have recently been studied and proposed explicitly for HEM. In this paper, we propose a novel nature-inspired hybrid Genetic Flower Pollination Algorithm (GFPA) to minimize cost with an affordable delay in appliance scheduling. Our proposed GFPA algorithm combines elements of the Genetic Algorithm (GA) and Flower Pollination Algorithm (FPA) to create a hybrid approach. To assess the effectiveness of the proposed algorithm, we consider a scalable town consisting of 1, 10, 30, and 50 homes, respectively. The proposed solution finds an optimal scheduling pattern that simultaneously minimizes EC and Peak to Average Ratio (PAR) while maximizing User Comfort (UC). We assume that all homes are homogeneous in terms of appliances and power consumption patterns. Simulation results show that our proposed scheme GFPA performs better when applying Critical Peak Pricing (CPP) signal using different Operational Time Intervals (OTIs) and compared with unscheduled, GA, and FPA-based solutions in terms of reducing cost since they achieve on average 98%, 36%, 23%, and 22%, respectively. Similarly, PAR averages 98%, 36%, 59%, and 55%, respectively. While, UC comparing to GA and FPA, are around 88%, 48%, and 63%, respectively. Our proposed scheme achieves better results by applying Real Time Pricing (RTP) signals and different OTIs. As these schemes, i.e., unscheduled, GA, FPA, and GFPA, achieve cost on average 92%, 50%, 29%, and 28%, respectively. While PAR on average 94%, 39%, 62%, and 56%, and UC for GA, FPA, and GFPA on average 98%, 52%, and 49%, respectively. Overall, our proposed GFPA algorithm offers a more effective solution for minimizing EC with an affordable delay in appliance scheduling while considering PAR and UC. [ABSTRACT FROM AUTHOR]

It is challenging to handle the non-linear power consumption model, complex workflow structures, and diverse user-defined deadlines for energy-efficient workflow scheduling in sustainable cloud computing. Although metaheuristics are very attractive to solve this problem, most of the existing work regards the problem as a black-box and ignores the use of domain knowledge. To make up for their shortcomings, this paper tailors an energy-aware intelligent scheduling algorithm (EIS) with three new mechanisms. First, we derive the optimal execution time that minimizes energy consumption for each task on a given resource. Second, based on the optimal execution time of each workflow task, the EIS distributes the workflow slack time (difference between its completion time and deadline) to reduce the voltages and frequencies of task executions for energy saving. Third, the EIS mines the idle time gaps caused by task precedence constraints to further reduce dynamic energy consumption whilst satisfying workflows' deadline constraints. To measure the performance of the EIS, we conduct extensive comparison experiments based on actual workflow applications. The results demonstrate that the energy consumption of the EIS is much lower than that of the competitors under different deadlines, and has a faster descend rate with the evolution process. [ABSTRACT FROM AUTHOR]

New technologies that are applied in many areas of social and economic life, make the processes of creating, distributing and communicating value to customers continuously transform. It is also connected with redefining the roles performed by market entities, including producers, service providers, distributors and consumers. New technologies expand the possibilities of engaging the consumer in processes that have been conducted before only by companies. In this context, technology management is important. It translates into creating a spiral of values, in which what is possible in the field of new technologies will inspire what is indicated in the area of value management for the customer and vice versa. This is reflected in the concept of technological determinism, as well as in the ongoing changes in consumer behaviour and consumption virtualization. The aim of the paper is to present the scope of Internet applications in the processes of managing value for customers in Poland. The paper is based on literature studies and the results of a quantitative empirical study, which was performed on a sample of 156 companies operating on the Polish market. The research included people responsible for the use of the Internet in marketing or for maintaining contacts with customers with the use of new technologies. The analysis of the research results shows that the Internet is used to a greater extent to communicate and distribute value rather than to involve customers in its creation. [ABSTRACT FROM AUTHOR]

Consumer behavior, by definition, is concerning with various actions adopted by a person to acquire, use and dispose of consumer goods and services, including the decision-making process that precedes and determines these actions. In real practice, such behavior can be altered with the introduction of various internet-based financing services. Especially for college students, who have relatively weak knowledge of personal finance and insufficient vigilance of excessive consumption, their consumption behavior can be easily manipulated by internet-based financing service providing firms. In this paper, the objective is to determine the dominant factors adopted by internet-based financing service providers to modify the consumption behavior of college students. From 571 college students' questionnaires, the collected data are subjected to factor and logistic regression analysis to verify what factors are likely to stimulate the willingness of college students to actively employing the internet-based financing services to fulfill their advanced consumption demands. The obtained result indicates the service and convenience factors are likely to influence and alter their consumption behavior, and the corresponding recommendations on how to refine and regulate such market are proposed at the end of this paper. [ABSTRACT FROM AUTHOR]

In the last decade, with the introduction of smart meters to smart grids, demand response programs (DRPs) have been widely adopted to establish a generation and consumption balance. DRPs provide many benefits for efficient grid management. However, these programs are conducive to higher levels of dissatisfaction by changing grid customers' consumption patterns. This paper aims to investigate the effects of DRPs on social welfare (SW). To this end, the paper presents a mathematical model for SW during the implementation of DRPs. In the proposed model, the level of customer satisfaction is assumed the main factor contributing to SW. This mathematical model considers different types of DRPs in terms of their impacts on SW. The paper also seeks to obtain linear and nonlinear models of DRPs and the coefficient of participation (CoP). CoP as an indicator shows the percentage of customers who actively participate in each DRP and plays a significant role in the assessment of the SW level. Moreover, owing to the sparsity and variety of distribution network customers, load patterns are classified into different clusters to take the load types into account. As a matter of fact, this process aims to identify similar patterns and thus, the same level of satisfaction for each separate cluster. The classification process is performed by using a machine learning-based clustering method known as the Affinity Propagation (AP) algorithm. Then, the model calculates the level of SW for the clusters based on the usage of electrical equipment and the time of day when they are turned on. The obtained levels of SW help operators select the best programs for every cluster in terms of customer satisfaction, and achieve the highest performance of DRPs. Lastly, the model is evaluated using real data of a distribution network to ensure the effectiveness and accuracy of the model. [Display omitted] • Development of a mathematical model for assessing Social Welfare during DRPs execution, considering customer satisfaction. • Integration of Coefficient of Participation (CoP) as an important factor in evaluating SW during DRP implementation. • Development of Affinity Propagation algorithm to cluster load patterns, enabling tailored DRPs to maximize effectiveness. [ABSTRACT FROM AUTHOR]

• Derivatives have applications in biodiversity conservation, both direct and indirect. • Derivatives can help hedge the risk of biodiversity loss. • By addressing biodiversity conservation, derivatives can also contribute to sustainable development goals. Increase in human activities owing to the rise in consumption demand is increasing the risk of biodiversity loss. To address this concern, several conservation activities are being undertaken, including those that find their basis in finance. This paper highlights the role of finance towards biodiversity conservation, which is illustrated by taking into consideration one of the tools commonly used in financial risk management, namely, derivatives. The paper infers that derivatives can be directly applied to biodiversity conservation. In addition, it also highlights the possibility of specific derivatives such as energy and weather derivatives to indirectly contribute to conservation. Despite the challenges involved, such as pricing of biodiversity elements and assessment of loss, the possibility of applying derivatives to minimize the risk of biodiversity loss exists. [ABSTRACT FROM AUTHOR]

The paper introduces a robust methodology for evaluating complex electricity system efficiency, making several contributions. It systematically decomposes the electric power system into various processes, including power generation, transmission, distribution, and power consumption, with parallel sub-processes for production and domestic power consumption. Employing data envelopment analysis (DEA), the paper develops a network directional slacks-based (DSB) model to evaluate the whole system and the internal processes. The model is theoretically proven to be feasible, providing efficiency scores and identifying inefficiencies in the system and its processes. A noteworthy addition is the introduction of the DSB green Luenberger productivity index and its decomposition, offering an analysis of productivity changes in electricity systems. Empirical analysis covering 30 Chinese provinces from 2013 to 2019 uncovers the performance variations among provinces and processes, pointing out inefficiencies and changes in technology and efficiency over time. The empirical findings aim to support efforts in enhancing the overall efficiency of China's interprovincial power system. • We decompose the complex electric power system into four processes with dependence. • We build a network directional slacks-based (DSB) model for the complex power system. • We give a DSB green Luenberger productivity index (DPI) for our network model. • The efficiency score and DPI of the system are decomposed into those of processes. • We apply the model and indexes to electric power systems in Chinese provinces. [ABSTRACT FROM AUTHOR]

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

A shared economy is a model of collaborative consumption by obtaining, giving, or sharing access to goods and services through online platforms. It is a marketplace that provides users with access to product benefits with temporary ownership. Digitalization has introduced new possibilities and conditions for sharing and exchanging goods and services, allowing users to compare offers and connect dynamically with the seller in real time. The paper presents the results of the survey conducted on a group of 1,620 prosumers and the practical applications of the analysis of the results. The research aimed to identify the main components of the co-sharing decision-making process and their importance in this process. A diagnostic survey was used in the study, and the collected data were statistically analyzed. The main findings are that there are common criteria for evaluating the model of sharing goods and services for the research population. They are: sharing preference, trust, realized benefits, propensity to propagate, risk awareness, aversion to sharing, willingness to share, and the limits of anonymity. Moreover, subjective satisfaction with the achieved income influences the perception of factors that are important in the decision-making process of sharing goods and services. [ABSTRACT FROM AUTHOR]

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

Increasing of consumed energy is the dominating challenge in many industrial fields, especially in computer numerical controlled (CNC) machines. Therefore, reducing even a small amount of the consumed energy can lead to a significant reduction in the world's energy consumption. Control techniques, which are used to manufacture industrial parts using higher controller gains can generally enhance motion accuracy of feed drive systems; however they increase the energy consumption. This paper proposes a new adaptive sliding mode control (ASMC), in which energy saving and motion accuracy of industrial machines can be effectively enhanced. The adaptation strategy allows control gains to be adaptively updated based on tracking performance. Once the resultant errors increase due to disturbance, the control gains are simultaneously modified to generate adequate control signals and achieve the precise track to the desired reference. The proposed adaptive gains can be flexibly changed during reaching and sliding phases, and therefore motion accuracy and energy reduction are improved. In order to confirm the effectiveness, the proposed adaptive law was compared with different adaptation approaches in several previous studies using a circular motion trajectory. Experimental results show a significant enhancement on the motion accuracy and energy saving for the feed drives. • This paper proposed a new adaptation law for the sliding mode control. • The adaptation gains could be updated through the reaching and sliding phases. • The effectiveness was verified with previous studied using industrial feed drives. • Experiment was repeated five times to ensure the repetitiveness. • The proposed approach reduced errors by 55.98% under the same control condition. • Control variance was largely reduced by 7.87% under the same tracking performance. • Energy consumption was reduced by 2.53% under the same motion accuracy. [ABSTRACT FROM AUTHOR]

Given the optimization problem of agriculture products logistics distribution path in the city, the demand of retail terminals is uncertain because consumer demand is affected by environmental and social factors, and the agriculture products urban distribution optimization model based on the determined demand cannot effectively solve the distribution problem with uncertain demand. Based on the demand determination model, this paper constructs a robust optimization research model based on scenario sets and reallocates distribution paths according to the change of demand. A robust optimization model of the agriculture products urban distribution path under multiple scenarios is built on the basis of meeting the mixing time window and using the objective function of total cost agriculture products urban distribution optimization model. To solve the problem, an improved genetic algorithm was designed by introducing elite retention strategy and migration strategy, which improved the defect of the traditional genetic algorithm which was easy to precocious convergence. Compared with the genetic algorithm, the proposed algorithm achieved the minimum total cost in all scenarios, the average total cost was reduced, the average convergence time of the algorithm was reduced by 5%, and the average total vehicle running distance was reduced by 3%. [ABSTRACT FROM AUTHOR]

Effective control of electricity consumption is crucial to achieving China's dual carbon goals, as it can significantly reduce coal consumption and associated carbon emissions. Using the Logarithmic Mean Divisa Index (LMDI) method, this paper constructs a two-stage decomposition model, which can divide total electricity consumption into industry and domestic electricity consumption and further examine the differential driving mechanism of the two types of electricity consumption separately. Taking the Yangtze River Delta as the research object, this paper analyzes the drivers of its temporal differences in total electricity consumption from 2000 to 2019 and fills the research gap in the identification of drivers of spatial differences in electricity consumption. The results showed: (1) Industry electricity consumption is the main driver of the increase in total electricity consumption, with economic growth and domestic electricity intensity being the primary and secondary factors behind this trend. The population scale also contributes to the increase. However, the effects of industrial structure, industry electricity intensity, and urbanization on total electricity consumption are not constant over time. This paper further decomposes the factors influencing changes in total electricity consumption in the provinces of Shanghai, Jiangsu, Zhejiang, and Anhui. (2) The spatial difference of electricity consumption between the other three provinces and Shanghai mainly comes from industry electricity consumption. (3) There are two kinds of conversion relations for the spatial-temporal differences of total electricity consumption. According to the conclusion, the targeted countermeasures and suggestions for total electricity consumption control are obtained. • We use the LMDI model to study the driving factors of total electricity consumption. • Production is the main reason for the increase of total power consumption. • Economic growth is the first factor to promote the total electricity consumption. [ABSTRACT FROM AUTHOR]

In this paper we critically assess common perceptions of work to inform current debates on work in ecological economics. Work is usually conceived as (1) a productive activity (2) that satisfies consumer demand, (3) is conducive to health and well-being, and (4) ensures social inclusion and personal development. Drawing on the burgeoning literature of postwork or critiques of work, we argue that work may rather be understood as a biophysically intense, consumption-causing, heteronomous institution with ambivalent health impacts that stabilises societies in environmentally and socially unsustainable ways. Therefore, work should be radically reduced and organised differently so that it is no longer the main mechanism for livelihood provisioning and social inclusion. Based on our fourfold critique of work developed in this paper, we sketch out a postwork research agenda for ecological economics. [ABSTRACT FROM AUTHOR]

Repair is a critical contributor towards a more sustainable circular economy by prolonging the lives of consumer products. However, compared with purchasing new products, consumers perceive repair as expensive and inconvenient. Consequently, repair demand is low. To overcome this problem and stimulate demand, public authorities have introduced financial incentives in the form of different funding schemes for electric and electronic products. Clearly the design of these schemes will affect how repair companies set repair prices and how this affects consumer demand for repair. However, at a micro-level, this relationship between repair funding and repair prices as well as demand has not been studied before. In this paper we analyze and compare two designs of a funding scheme for electric and electronic products as implemented in Austria. Using a stylized model, we show that while such schemes lead to increasing prices, they can indeed enhance demand and improve repair company profits. Moreover, we find that preferences of consumers and repair companies with respect to the design of the funding scheme may diverge. Thus, public authorities need to prioritize demand or supply side benefits of the funding scheme. • To stimulate repair different incentive schemes have been implemented in practice. • We analyze different designs of repair funding schemes based on a subsidy. • We find that a subsidy can increase demand and repair company profit simultaneously. • Subsidy design is critical for ensuring its efficiency. [ABSTRACT FROM AUTHOR]

• A novel spatiotemporal graph attention-enabled Transformer for multivariate residential load forecasting. It differs from traditional Transformer-based models that ignore spatial correlations among multiple households. • A novel Transformer with graph Seq2Seq structure for multi-step residential load forecasting. It differs from typical multi-step forecasting methods that may suffer from obvious performance degradation with the increasing number of forecasting steps. • Extensive experiments conducted on three real-world load datasets show that the proposed method significantly outperforms several state-of-the-art benchmarks in both single- and multi-step spatiotemporal residential load forecasting. Short-term residential load forecasting (STRLF) holds great significance for the stable and economic operation of distributed power systems. Different households in the same region may exhibit similar consumption patterns owing to the analogous environmental parameters. Incorporating the spatiotemporal correlations can enhance the load forecasting performance of individual households. To this end, a spatiotemporal graph attention (STGA)-enabled Transformer is proposed for multivariate, multi-step residential load forecasting in this paper. Specifically, the multiple residential loads are cast to a graph and a Transformer with a graph sequence-to-sequence (Seq2Seq) structure is employed to model the multi-step load forecasting problem. Gated fusion-based STGA blocks are embedded in the encoder and decoder of the Transformer to extract dynamic spatial correlations and non-linear temporal patterns among multiple residential loads. A transform attention block is further designed to transfer historical graph observations into future graph predictions and alleviate the error propagation between the encoder and decoder. The embedding of multiple attention modules in the Seq2Seq framework allows us to capture the spatiotemporal correlations between residents and achieve confident inference of load values several steps ahead. Numerical simulations on residential data from three different regions demonstrate that the developed Transformer method improves multi-step load forecasting by 14.7% at least, compared to the state-of-the-art benchmarks. [ABSTRACT FROM AUTHOR]

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

Many studies have investigated the distribution of greenhouse gas emissions associated with household consumption, highlighting that richer households have a substantially larger carbon footprint than poorer households. While these studies have typically used income or household expenditures as a proxy of households' living standards, the association between emissions and wealth has hardly been studied. Wealth is not just an important component of households' material living standards, known to be imperfectly correlated with income and expenditures, but it is also a very relevant dimension for climate policies. For instance, affluent households have more scope to invest in reducing emissions (e.g. insulation of their homes). Therefore, in this paper, we compare the inequality of emissions over wealth groups as well as groups defined by other living standard concepts such as income, expenditure and the joint distribution of income and wealth to add a focus on wealth to the analysis of emission inequality. Our study focuses on Belgium and the United Kingdom, which differ considerably in their level of wealth inequality. Our results highlight that the social distribution of emissions varies between expenditures, income, wealth and the joint distribution of income and wealth, with per capita emissions being more strongly associated with the joint distribution of income and wealth than with income alone. We also show that a significant share of households has both low income and low wealth, and may thus be lacking the means to prepare for a carbon-neutral future. Although increasing wealth taxation could provide much-needed revenues for low carbon investments, our results suggest that it would have a lower direct impact on the level of emissions and emission inequality compared to increasing taxes on high incomes. • Wealth is positively correlated with emissions. • Emission inequality according to wealth is higher than according to income. • People with low income and low wealth face a cumulative disadvantage. • Wealth inequality is a relevant dimension when designing climate policies. [ABSTRACT FROM AUTHOR]

European agro-environmental policy aims to reduce agricultural emissions of nutrients and pesticides into water, yet these goals remain unmet. Voluntary direct payments schemes aim to steer farmers towards emission-reducing practices. We assessed the environmental impacts of stricter direct payment regulations: Farmers who want to receive direct payments a) have to produce pesticide-free and b) have to reduce their livestock numbers to their own farm's feed production capacity. We compared the environmental impacts of the Swiss agricultural sector in three direct payment scenarios with the status quo. We addressed the trade-offs between domestic production and imports, and between target and other environmental impacts. While freshwater ecotoxicity within Switzerland would decrease considerably due to reduced pesticide use, aquatic eutrophication caused by domestic agriculture would remain similar to the status quo. However, increased import amounts would more than offset the domestic environmental improvements. Eutrophication, particularly caused by imported meat, would increase strongly, as would deforestation and water scarcity. Our paper shows that the improvement of water quality in Switzerland has to be bought with partly considerable trade-offs in the countries of origin of imported products, showing the need for complementary measures such as reducing food waste or changing consumption patterns. • Policy measures to reduce agricultural water pollution were evaluated using LCA. • A ban on pesticides and reducing livestock decrease pollution within Switzerland. • Water quality in Switzerland has to be bought with considerable trade-offs in the countries of origin of imported products. • There are considerable trade-offs with non-target impacts, e.g. water scarcity and deforestation. • Extensification measures in agriculture must be accompanied by other levers such as changing consumption patterns. [ABSTRACT FROM AUTHOR]

Consumers' knowledge of electricity prices and its effect on their behaviors have been widely studied, yet the comparison and generalization of these findings are challenging due to variations in definition and survey items. This paper proposes a comprehensive framework to assess Electricity Price Literacy by introducing a functional representation that encompasses various electricity pricing systems and categorizing into three knowledge domains to systematically organize previous literature's knowledge measures. To demonstrate the practicality of this framework, a survey was administered to a nationwide sample of 4214 electricity consumers in South Korea. The survey results highlight variations in electricity price literacy across different domains and reveal varying relationships between domain-specific knowledge and behaviors, thereby highlighting the need for a decomposed measure of price knowledge. [ABSTRACT FROM AUTHOR]

Currently, building energy consumption prediction models are usually based on a large amount of historical operational data in high demands of building operating hours and monitoring systems. However, many buildings may lack operational data due to relatively limited monitoring systems, causing the failure to use data-driven methods to characterize the energy profile. In this context, transfer learning is a promising method to establish the knowledge transfer between many high-quality building operation datasets and a small amount of target building data, and to help predict energy consumption in the target building. This paper studies the possibility of employing transfer learning to achieve both short and long-term building energy consumption prediction. Firstly, a similarity analysis method, based on variable modal decomposition and dynamic time warping, is proposed for identifying the source buildings with the most similar energy features to the target building. Then, transfer learning for long-term prediction air-conditioning energy consumption is developed with weather parameters generated by the Morphing method as inputs. For the short-term single-step prediction, the proposed model CV-RMSE improves 81.3% (AEC) and 77.4% (EEC), respectively, compared to the prediction model that does not implement the transfer learning strategy and directly uses the target BEC data. As for the short-term multi-step prediction, the proposed model CV-RMSE improves 62.0% (AEC) and 65.5% (EEC), respectively. For the long-term prediction, the average CV-RMSE for the whole year is 6.62% and 11.15% for the proposed and directly target domain-based model, respectively. The proposed method explores the practicality of transfer learning in building energy forecasting, contributing to the use of existing building operation data for energy management at different timespan. • A similarity analysis method for building energy consumption data based on VMD-DTW is proposed. • The Seq2Seq-Transformer model is applied to short-term multi-step prediction of building energy consumption. • Transfer learning idea is first introduced to the long-term building energy consumption prediction. [ABSTRACT FROM AUTHOR]

Regional data center clusters have flourished in recent years to serve customers in a major city with low latency. The optimal coordination of data centers in a regional cluster has become a pressing issue because of their rising energy consumptions. In this paper, an online algorithm based on Lyapunov optimization framework is developed for the combined computing workload and energy coordination of data centers in a regional cluster. The proposed online algorithm is prediction-free and easy to implement. We prove that the workload queues and battery energy level will be within their physical limits, though their related time-coupling constraints are not considered explicitly in the proposed algorithm. The previous online algorithms do not have such a guarantee. A theoretical upper bound on the optimality gap between the online and offline results is derived to provide a performance guarantee for the proposed algorithm. To enable distributed implementation, an accelerated distributed coordination algorithm is developed based on the alternating direction method of multipliers (ADMM) with iteration truncation and follow-up well-designed adjustments, whereby a nearly optimal solution is attained with much enhanced computational efficiency. Case studies show that the proposed algorithm reduces the operational costs and saves computation time compared to online benchmarks. • The combined computing workload and energy coordination of data centers is studied. • A prediction-free online coordination algorithm is built with proven properties. • An accelerated ADMM algorithm is developed for distributed implementation. • The proposed coordination method greatly reduces the total cost of data centers. • The computation time is largely reduced compared to the traditional ADMM. [ABSTRACT FROM AUTHOR]

• A classification model of electricity consumption based on sparse denoising autoencoder and spectral clustering is proposed. • The SDAE and the manually defining electricity consumption characteristic indicators are adopt to improve classification. • The t -SNE is employed to visualize the classification results to achieve the secondary classification. • The Gaussian distance weighting are employed to depict the typical electricity consumption behaviors. The development of electrical measurement technology has brought high latitude residential electricity consumption data to power companies, which contains the characteristics of users' electricity consumption behavior and provides data support for the behavior classification. In order to improve the efficiency of data feature extraction and the accuracy of electricity consumption behavior identification, a classification model based on sparse denoising autoencoder feature dimensionality reduction and spectral clustering is proposed in this paper. Firstly, the sparse denoising autoencoder (SDAE) and the manually defining electricity consumption characteristic indicators are deployed to extract features from the residential daily electricity consumption data, and then the spectral clustering is employed to classify the extracted electricity consumption characteristics. Secondly, the t-distributed stochastic neighbor embedding (t-SNE) is applied to visualize and analyze the classification results, and on this basis, the secondary classification is implemented to fix the issue of the confused electricity consumption behaviors. Finally, the typical consumption behavior curves are calculated by Gaussian distance weighting method, and the characteristics of power consumption behavior are analyzed and summarized. The proposed approach is evaluated and verified by using the electricity dataset in Fujian, China. [ABSTRACT FROM AUTHOR]

Negative trends of climate change and biodiversity loss are closely linked with farming practices, and it is therefore essential to re-think how agricultural systems can sequester more carbon, and simultaneously create vital ecosystems. The overall aim of this research is to imagine Swedish farms as carbon sinks rather than sources, and how to re-design the current farm- and food system to also address other social, economic, and environmental sustainability challenges. This paper is the outcome of two visioning workshops together with participants in an ongoing initiative called Swedish Carbon Sequestration [Svensk Kolinlagring]. Participants discussed what alternative futures might look like, how they would function, and how to get there. The farm-level visions include perennial crops, keyline design, online farmers markets, increased collaboration between farms, and increased knowledge about soil health. The participants highlight complex interactions between animals, trees, leys, and crops that can support carbon sequestration. They also emphasize the need to increase both farmer's and society's knowledge about soil health and its multiple positive effects on carbon sequestration. In addition, a transformation of the farm- and food system would also contribute with positive effects on farmers income and their autonomy over decision making and long-term planning, in turn also improving farmers' and consumers' health. The participants highlight that the food system will be transformed by changes in consumer demand, increased knowledge and awareness, shortened value chains, and by changing policies and financial support systems to favor farmers who engage with agroecological principles of farming. • We develop future visions of a farm system that supports carbon sequestration. • Increased use of perennials will have multiple societal and environmental benefits. • Farmers envision a future with increased collaboration between farms. • Future policies and support systems need to favor the unconventional farmer. • Farmers envision a transformation of the food system that prioritizes soil health. [ABSTRACT FROM AUTHOR]

Highlights • Analyzes European household's willingness to mitigate climate change through changes in consumption behavior. • New analytical perspective through Qualitative Comparative Analysis in large-N design. • Highlights the contribution of health information for willingness to mitigate. • Demonstrates limitations of financial incentives to motivate behavioural change. • Points to need for different policy design to motivate household mitigation of GHG emissions. Abstract The climate change mitigation potential of the production side has been discussed very frequently, but private citizens and households have received less systematic attention. Thus, this paper focuses on the willingness of private households to reduce their greenhouse gas emissions. While many scholars study the socio-economic characteristics of people to then recommend policies, we change the analytical perspective and look at the characteristics of climate mitigation actions that affect households' willingness to adopt these measures: Costs, emission reductions, behavioral change, and consumption sector. The paper uses data from HOPE (HOusehold Preferences for reducing greenhouse gas emissions in four European high-income countries), an interdisciplinary project investigating households' preferences for reducing greenhouse gas in four countries (France, Germany, Norway and Sweden). We use a fuzzy-set QCA (fsQCA) to analyze which attributes are necessary or sufficient conditions for specific actions to be selected by participants of HOPE. The results of the fsQCA show that Food & Recycling actions are particularly popular. The general pattern that was found shows that people prefer actions that are easy to implement even though they often do not reduce the CO2e-Footprint by much. Therefore, the condition Behavioral Change is the most important lever in improving households' willingness to act on climate change. Moreover, the analysis of Health Information suggests that there is an urgent need to go beyond financial arguments in future research. [ABSTRACT FROM AUTHOR]

Abstract The world food price crisis in 2007/08 has aroused worldwide attention to the global food price volatility and food self-sufficiency issues. This paper modelled the entire environment of food production and transaction from a holistic view by a Food-Energy-Water (FEW) nexus in order to reveal the hidden connections related to the food self-sufficiency issue, including the interdependencies of food production with its restraining factors (hybrid energy, hybrid water), other production sectors, and international exchanges. This paper mapped all direct and indirect flows in the FEW nexus and projected a potential Emission Trading Scheme (ETS) to figure out the impacts of policies on FEW nexus flows, nexus robustness, total input of all sectors, and household expenditures in Japan and China. The results show that the pattern of food-related extraction flows was more imbalanced than the pattern of hybrid energy flows, due to the high dependence of Japan on the food supply of China (16.11% of total food-related extractions). An ETS may increase Japan's total household expenditure on imported goods from China in the fields of sugar refining (1.3096%), processing vegetable oils and fats (0.1164%), processing of meat cattle (0.1010%), as well as slightly decrease the system robustness of the total nexus. [ABSTRACT FROM AUTHOR]

Abstract Sustainable consumption and production is identified as one of the essential requirements for sustainable development. Due to different economic conditions and socio-cultural factors, sustainable consumption and production requires a diverse focus in developing and developed economies. To date, few efforts have been made to systematically compare the status of sustainable consumption and production and its direction from the perspective of developing and developed economies. This paper provides a literature review of published articles in international scientific journals related to sustainable consumption and production between 1998 and 2018 inclusive. Three carefully designed questions are proposed and answered in this article, forming the basis for conducting a comprehensive comparative analysis of the differences and challenges in sustainable consumption and production practices within developed and developing economies. The findings strongly suggest that countries in Europe hold international leadership in sustainable consumption and production practices. This finding, alongside others, is analyzed and discussed in greater detail in this paper, resulting in the articulation of gaps and future research opportunities in the current body of the literature. Highlights • Sustainable consumption and production (SCP) in two distinct economies is studied. • A three-dimension taxonomy is designed for performing the content analysis. • It is found that Europe hold international leadership in SCP practices. • SCP is a complicated and slow process with uncertain grounds in both economies. • Current trends and future research opportunities in SCP practices regarding different economies' status are articulated. [ABSTRACT FROM AUTHOR]

Abstract Most of the sustainable scheduling research deals with some environmental issues such as electricity consumption or carbon emissions although there are many other environmental and social indicators. Whereas, more and more manufactures are executing many solutions not only via implementation of environmentally-oriented but also socially responsible economically sound and management program. This proposed research has an objective to get a comprehensive view of the state and progress of sustainable scheduling by considering more sustainable indicators to assist many manufacturers to apply environmentally sound technological solutions. To achieve this objective, we, first, create a pool of sustainable manufacturing indicators that refers to some previous research that focus on applicable-proven indicators. Then, we employ an iterative process that is adapted from the general guidelines for literature review. We conceptualize, search, evaluate, analyze, and synthesize all 50 relevant papers resulting a completely new research framework to characterize the research. We use mathematical model components, i.e. manufacturing model, the system of objectives, objective function, constraints, model type, and optimization method, as the dimensions to classify the papers that were included in our analysis. We also apply the triple bottom line pillars – economic, environmental, and social to specify attributes and categories for the objective function and constraints. In literature classification step, the framework seems very sensitive since it can identify 49 different sustainable scheduling configurations from 50 reviewed papers. It means the framework can easily grasp the contributions of each paper. Then, in the analyses phase, we use 'sustainable link' to indicate if one research can be classified as sustainable scheduling research. The results show that energy cost and greenhouse gas indicators become the most frequently used indicators in sustainable scheduling. On the other hand, the mapping shows that some links have been rarely or never included, indicating potential areas for further research. We also list four main directions for future research which are: implementing other optimization methods; adding sustainability indicators; extending the model to a larger scale of manufacturing system; and loosening some assumptions. [ABSTRACT FROM AUTHOR]

There are two axioms that have shaped the degrowth discussion, namely one on the reduction of production and consumption and one on the necessity of value and attitude change in society to respect environmental limits. Surprisingly little research has focused on the role of technology in connection to value change, consumption behavior and degrowth. This gap is linked to a widely held assumption of technological innovation acting contrary to degrowth. In contrast, this paper suggests that technological innovation needs to be connected to degrowth. In particular, the fast evolution of information and communication technologies in recent years points to their importance for the global civil society. Given the scope of sustainability challenges connected to these technologies and smartphones in particular, this paper argues that a degrowth movement going hand in hand with technological development is only made possible, if sustainable alternatives for highly demanded products are offered. Specifically, this study draws on lifestyle movements as the nexus between consumption, degrowth and technology. Three core concepts are found to influence the attendance to degrowth-related consumer movements: a sustainable lifestyle, alternative forms of consumption and social commitment, all of which are assumed to have a positive effect. Empirically this paper draws on the case of the Fairphone, a company that applies sustainable characteristics to a smartphone. Data collected in a survey via online fora of the Fairphone from November 4–29, 2015 are analyzed using a structural equation modeling with partial least squares. Results indicate that a sustainable lifestyle represents the dominant factor explaining the involvement with the Fairphone. Surprisingly, the findings show that alternative consumption seems to negatively influence the involvement with the Fairphone and social commitment seems to play a minor role in the model. These aspects point to the Fairphone as a technical artifact, centered on a choice for a sustainable lifestyle. Future research needs to reach clarification on the relationship of alternative consumption and degrowth related movements and whether the negative effect is case specific or beyond. Furthermore, future studies need to delve deeper into lifestyle decisions as a leverage point towards degrowth, especially on lifestyles understood as a from of activism to change contemporary society. [ABSTRACT FROM AUTHOR]

Energy conservation and preventive maintenance of equipment require the ability to accurately predict future trends in shop floor power consumption to keep track of equipment operation and abnormalities. Due to the non-linearity and uncertainty of the workshop electrical energy consumption data, it is very difficult to establish an accurate energy consumption prediction model. For the above problems, an energy consumption prediction model based on Prophet-EEMD-LSTM was proposed. To identify the period features in the energy consumption time series data, the Prophet algorithm was introduced to identify and extract the period and trend features of the energy consumption data. Then for the feature of the uncertainty of the remaining data, this paper used the ensemble empirical mode decomposition (EEMD) method to decompose the data to form the components with specific modalities. Finally, all the extracted feature data were put into the input layer of the Long short-term memory (LSTM), and the final energy consumption values were predicted using the excellent prediction performance of the LSTM on time-series data. The experimental results showed that the model proposed in this paper has high prediction performance for energy consumption data. • Energy consumption prediction models guide energy saving and predictive maintenance in paint workshop. • The model uses only energy consumption data for predictions, eliminating the need for other data. • The complexity of the data is reduced by Prophet and EEMD, and the LSTM captures the temporal characteristics of the data. • The Prophet-EEMD-LSTM model shows higher performance with an average R2 value of 0.94006. [ABSTRACT FROM AUTHOR]

• The paper reviews 33 on-field residential feedback studies. Only occupied homes where feedback was provided and energy consumption was monitored were considered for the review. • Our study proposes a taxonomy for energy feedback based on different characteristics of feedback such as frequency, type, presentation style and mode of access. • Understand the different ways in which field studies were conducted and assess a few parameters to establish their relationship with energy savings achieved. Residential energy feedback is about providing personalized information on household energy use to consumers to encourage energy savings. This paper conducts a review of field-based studies that have evaluated the impact of energy feedback on residential energy consumption. The review includes studies in real occupied homes that have deployed feedback intervention(s) and measured energy savings. Our study builds a taxonomy for energy feedback based on different characteristics of feedback such as frequency, type, presentation style, and methods of access. Energy savings from similar feedback types were found to differ depending on how the study was conducted. The reviewed studies deployed a range of feedback information including energy units, energy cost and tailored information conducted across diverse audiences (ethnicity, geographical positioning), varying experimental types (longitudinal, Randomized Control Trial) and, size and duration of the studies. The duration of studies varied widely, ranging from one month to three years and demonstrated energy savings between 5% and 20%. While most studies achieved energy savings due to energy feedback, a few of them reported an increase in energy consumption which could be due to rebound effect. Most of the studies in literature adopted a longitudinal design where the current household energy consumption was compared with the past consumption to arrive at the savings. Others used Randomized Control Trial (RCT) design where energy consumption of intervention group was compared with control group. Most of the studies were conducted in developed countries with cold climates.. There are fewer studies done on large-scale residential energy feedback especially in emerging economies where there is a rise in air conditioning use. We propose future studies to also consider the human behavior and cultural influences while evaluating impact of energy feedback. Our recommendation is that the academic and policy community address this gap since energy feedback is likely to stimulate positive behavior change amongst householders leading to energy savings. [ABSTRACT FROM AUTHOR]

Continuously increasing consumption of material goods drives current resource and environmental crises, including climate change and loss of biodiversity. Although technology offers solutions, their development and adoption is not at the speed required to address these crises. Therefore, demand side responses have to be triggered using policies, with economists suggesting mainly the use of price signals. However, both the effectiveness and the political acceptability of taxation have been questioned, especially since increases in fuel prices during the last decade in both Europe and North America have not yielded the expected reductions in the fuel economy and after the vigorous opposition to the ambitious increases in fuel taxes, for example in France. The present paper offers an explanation for the reduced effectiveness of environmental taxation by focusing on relatively high-income individuals whose consumption of highly polluting material goods is driven by motivations to improve their social status. Furthermore, the paper shows that complementing the tax with information provision aiming at moderating status seeking overconsumption improves social welfare. Decoupling consumption of highly polluting material goods from social status in individuals' well-being, through informative advertisement campaigns, could have a substantial environmental effect directly and also indirectly by improving the effectiveness of taxation. [ABSTRACT FROM AUTHOR]

As more people send information to the cloud-fog infrastructure, this brings many problems to the management of computer energy consumption. Therefore, energy consumption management of servers, fog devices and cloud computing platform should be investigated to comply with the Green IT requirement. In this paper, we propose an energy consumption prediction model consisting of several components such as hardware design, data pre-processing, characteristics extraction and selection. Our main goal is to develop a non-invasive meter based on a network of sensors that includes a microcontroller, the MQTT communication protocol and the energy measurement module. This meter measures voltage, current, power, frequency, energy and power factor while a dashboard is used to present the energy measurements in real-time. In particular, we perform measurements using a workstation that has similar characteristics to the servers of a Datacenter locate at the Information Technology Center in ESPOL, which currently provide this type of services in Ecuador. For convenience, we evaluated different linear regression models to select the best one and to predict future energy consumption based on the several measurements from the workstation during several hours which enables the consumer to optimize and to reduce the maintenance costs of the IT equipment. The supervised machine learning algorithms presented in this work allow us to predict the energy consumption by hours and by days. [ABSTRACT FROM AUTHOR]