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Introduction: Ethiopian rural households primarily rely on fuelwood as their main energy Q7 source, yet the country lacks precise data on fuelwood harvesting and its economic significance. Consequently, there is limited understanding of CO2 emissions resulting from fuelwood use. Methods: This study aimed to estimate the annual amount of fuelwood collected and the associated CO2 and carbon (C) emissions. Using simple random sampling for household selection, data were analyzed with Excel and Stata. Results: The results reveal that fuelwood dependency is a major driver of deforestation and CO2 emissions, with households consuming approximately 2,725 kg of firewood and 26 kg of charcoal annually. Each household also extracts an average of 3,909.3 kg of firewood and 516.5 kg of charcoal annually for sale. Among sampled households, fuelwood constitutes 904,261 kg of energy, with 51% used for household consumption and 96% allocated for income generation. The inefficient burning of this fuel results in significant emissions, adding 974,000 kg of CO2 or 265,600 kg of carbon annually to the household carbon footprint. On average, each household emits 7,740 kg of CO2 and 1,960 kg of carbon per year. Discussion: The study emphasizes that, while fuelwood plays a critical role in household energy and income, its unsustainable use accelerates emissions and deforestation. To mitigate these effects, the adoption of alternative energy sources like electricity and forest conservation through local plantations is essential for climate resilience. [ABSTRACT FROM AUTHOR]

Microgrid can realize energy saving and emission reduction and multi‐energy complementation, but the fluctuation of renewable energy output and the error of day‐ahead scheduling will threaten the stability of microgrid operation. For this reason, this article proposes a microgrid multi‐timescale optimal scheduling method based on new energy output scenario generation. First, the microgrid framework of this article is introduced, and an energy cycle emission reduction model taking into account electricity‐to‐gas conversion is designed; second, a new energy prediction error model is established based on the prediction box and Gaussian hybrid model, and the scenario of wind and photovoltaic power generation is generated by correcting the sampling intervals for error sampling; and then, based on the day‐ahead scheduling plan, an intraday cooling, heating and electricity two‐layer rolling optimization model is established to correct the day‐ahead scenario. Finally, the example analysis shows that the cyclic emission reduction model can realize the recycling of resources, reduce the fuel cost and carbon emission, and the generation of scenarios for wind power can smooth out the fluctuation of new energy power, which, together with the intra‐day two‐layer rolling optimization, can reduce the day‐ahead scheduling error and improve the stability of microgrid operation. [ABSTRACT FROM AUTHOR]

The urgency of mitigating climate change and the challenges it poses to ecosystems and human systems are highlighted in the Intergovernmental Panel on Climate Change's (IPCC) Sixth Assessment Report (AR6). In order to achieve sustainable development, it is imperative to adopt a series of adaptive measures to enhance the resilience of various sectors to climate change and reduce greenhouse gas emissions. This article analyzes the driving mechanism behind the reduction in electricity consumption by urban residents based on 302 valid questionnaires from 18 communities in nine districts in B City. Using a method that combines qualitative and empirical research, the study proposes policy recommendations aimed at guiding urban residents toward reducing their electricity consumption. These recommendations serve as a policy reference for cities striving to achieve sustainability and low-carbon targets. The primary innovations and conclusions of the study are as follows: (1) this study summarizes the primary factors and processes influencing the reduction in electricity consumption among urban residents, examined from the following three perspectives: residents' characteristics, psychological understanding, and external environment. (2) On the basis of the research data, empirical analysis and hypothesis testing are conducted using a variety of mathematical and statistical methods. The results indicate significant differences in the electricity consumption reduction behavior of heterogeneous urban residents in both public and private areas. Subjective norms, perceived behavioral control, and knowledge of electricity conservation have significant direct influences on residents' willingness to reduce their electricity consumption. Among these factors, subjective norms have the most significant impact, while the impact of attitude is negligible. Economic incentive policies have a significant positive regulatory effect on the relationship between "willingness (intention)" and "private area electricity consumption reduction behavior". [ABSTRACT FROM AUTHOR]

Understanding how smart city investment influences the environment has valuable implications for increasing the environmental performance of smart cities. This study adopted spatial econometric models to explore the environmental impacts of smart city investments using data from 100 smart cities in China for the period 2014–2019. Smart city investment includes investment in information and communication technologies (ICT) and human capital. Environmental performance was measured in terms of air quality, green space and energy consumption. The results reveal that smart city investment can produce spatial spillover effects and that the environmental quality of cities is spatially correlated. ICT reduces air quality but increases green space and electricity consumption. In addition, human capital contributes to electricity conservation but does not influence air quality and green space. The impacts of smart city investments on the environment vary according to economic development level. Moreover, policy implications for the implementation of smart city investments that increase environmental performance are proposed. [ABSTRACT FROM AUTHOR]

In the last two decades, Brazil has undergone a major transformation in terms of access to electricity. With a policy of universal electrification of Brazilian homes called Luz para Todos, millions of Brazilians were joined to the national electricity system. Almost twenty years after the creation of the program, there are around 700 thousand people without formal electricity. This article brings approach to identify the geographic variables that led to the maintenance of the situation of exclusion in the system. Therefore, it was necessary to identify the uses of the territory by the power of the State and the interests of the market, taking as an example the Enseada de Palmas, in Ilha Grande, Angra dos Reis (RJ). The variables discussed here deal with the selective uses aimed at the functionalization of the territory for tourism on the island, while the main public policy is the installation of Conservation Units in multiple spheres of power. Vertical territorial practices fragmented the insular space and isolated Palmas beach. As a result, public policies of ecological conservation, which in the face of reality are seen as necessary, combined with market interests, have become instruments of impoverishment of the population not included in such policies. [ABSTRACT FROM AUTHOR]

This research conducted focused on the Grati Hospital Building, which is an effort to mitigate global warming by evaluating the building in terms of green building principles. This is because the building is newly constructed and located in an industrial area with a dense population, making it suitable for a case study. The aim is to evaluate the RSUD Grati Hospital building based on the Green Building concept by applying Greenship assessment simulations. This research is classified as quantitative research, focusing on direct observational methods with reference to the Greenship Rating Tools, specifically version 1.2 for newly constructed buildings. The simulation results of the evaluation conducted on the RSUD Grati Hospital building are as follows: (1) Land Use Efficiency scores 7 points, (2) Energy Efficiency and Conservation scores 8 points, (3) Water Conservation scores 10 points, (4) Material Resources and Cycle scores 8 points, (5) Indoor Health and Comfort scores 6 points, (6) Building Environmental Management scores 4 points. Based on the points obtained, the overall rating is Bronze, with a score of 43 points or 42.57%, ranking fourth out of four ranks available, thus qualifying the building as a green building. The distinctive feature of the RSUD Grati Hospital building in implementing green building principles lies in its electricity and water conservation efforts, as reflected in the Energy Efficiency and Water Conservation categories. [ABSTRACT FROM AUTHOR]

Copyright of Gazi Üniversitesi Fen Bilimleri Dergisi Part C: Tasarım ve Teknoloji is the property of Gazi University and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Background and aims: As a part of the framework of the EU-funded Energy efficiency through Behavior CHANge Transition (ENCHANT) project, the present paper intends to provide a "Research Protocol" of a web-based trial to: (i) assess the effectiveness of behavioral intervention strategies––either single or in combination––on electricity saving, and (ii) unravel the psychological factors contributing to intervention effectiveness in households across Europe. Methods and materials: Six distinct interventions (i.e., information provision, collective vs. individual message framing, social norms, consumption feedback, competitive elements, and commitment strategies) targeting electricity saving in households from six European countries (i.e., Austria, Germany, Italy, Norway, Romania, and Türkiye) are evaluated, with an initial expected samples of about 1500 households per country randomly assigned to 12 intervention groups and two control groups, and data is collected through an ad-hoc online platform. The primary outcome is the weekly electricity consumption normalized to the last seven days before measurement per person per household. Secondary outcomes are the peak consumption during the last day before measurement and the self-reported implementation of electricity saving behaviors (e.g., deicing the refrigerator). The underlying psychological factors expected to mediate and/or moderate the intervention effects on these outcomes are intentions to save electricity, perceived difficulty of saving energy, attitudes to electricity saving, electricity saving habit strength, social norms to save electricity, personal norms, collective efficacy, emotional reaction to electricity consumption, and national identity. The intervention effectiveness will be evaluated by comparing psychological factors and consumption variables before and after the intervention, leading to a 14 (groups including 2 control groups) × 6 (time) mixed factorial design, with one factor between (group) and one factor within subjects (time)–6 measurements of the psychological factors and 6 readings of the electricity meters, which gives then 5 weeks of electricity consumption. Results: Data collection for the present RCT started in January 2023, and by October 2023 data collection will conclude. Discussion: Upon establishing feasibility and effectiveness, the outcomes of this study will assist policymakers, municipalities, NGOs, and other communal entities in identifying impactful interventions tailored to their unique circumstances and available resources. Researchers will benefit from a flexible, structured tool that allows the design, implementation and monitoring of complex interventions protocols. Crucially, the intervention participants will benefit from electricity saving strategies, fostering immediate effectiveness of the interventions in real-life contexts. Trial registration: This trial was preregistered in the Open Science Framework: https://osf.io/9vtn4. [ABSTRACT FROM AUTHOR]

This study examines how electric utilities and regulators can encourage residential consumers to conserve electricity during the hottest summer days and shift electricity load from the day to off-peak, nighttime hours. We analyze a two-year field experiment involving 280 Texas households that explores approaches to conservation and load-shifting to enable emission reductions and reduce generation costs. Our critical peak pricing intervention reduces electricity consumption by 14% on the peak hours of the hottest days, leading to greenhouse gas emission reductions of about 16%. A key contribution of this study is the use of high-frequency appliance-level data. We show that 74% of the critical peak response is from reducing air conditioning. In a complementary nighttime pilot program, consumers respond strongly to lower prices by programming the timing of electric vehicle charging. Our work highlights how automation can influence the consumer tradeoffs relating to effort costs, discomfort, monetary incentives, and warm glow. This paper was accepted by Rajesh Chandy, Special Section of Management Science on Business and Climate Change. Supplemental Material: The data and online appendices are available at https://doi.org/10.1287/mnsc.2020.02074. [ABSTRACT FROM AUTHOR]

The incentive-based emergency demand response measure serves as an important regulatory tool during energy system operations. However, whether people will sacrifice comfort to respond to it during heatwave and what the effect on heat vulnerable populations will be are still unclear. A large-scale emergency demand response pilot involving 205,129 households was conducted in southwestern China during continuous extreme high temperatures in summer. We found that the incentive-based emergency demand response causes a statistically significant decline in electricity use with no additional financial burden on vulnerable groups. The electricity conservation potential of urban households was higher than that of rural households. Households with children did not respond to the emergency demand response, while the response of households with elderly individuals proved to be more positive. The repeated and frequent implementation of this policy did not result in an attenuation of the regulatory effect. This research can serve as a reference for countries with similar regulated power markets. Co-lead authors Wang, Zhang, Qiu, Lu and their colleagues model an incentive-based emergency demand response to counter heatwaves. The modelled responded leads to the peak load reduction of 7.32% for the covered households, and can achieve a 1.02% peak load reduction when reaching a wide audience with no additional financial burden on vulnerable groups. [ABSTRACT FROM AUTHOR]

In the framework of fuel reduction and energy conservation, the electric vehicles (EV's) has been identified as a promising option in contrast to fuel-driven vehicles. EV's battery limits to require visiting a greater number of times to the recharging stations, which must be viewed as in the route planning to keep away from inefficient vehicle routes with lengthy diversions. These problems have to consider, we propose an Efficient Electric Vehicle Route Optimization with Time-of-Use Electricity Pricing using Bat algorithm. Which can reduce the used vehicles as well as electricity-cost and total travel distance. Additionally, functional model and collective models are used to minimize the objectives: distance and cost. The computational assessment in light of the notable benchmarking test instances exhibits, proposed optimization algorithm electricity cost conservation on average 12.17% with Learnable Partheno-Genetic Algorithm (Yang et al. in IEEE Trans Smart Grid 6:657–666, 2015) 8.45% with VNS/TS Algorithm (Lin et al. in Trans Res Part-C 130:103285, 2021) and 5.15% with Mixed Integer Programming model (Ham and Park in IEEE Access 9:37220–37228, 2021). [ABSTRACT FROM AUTHOR]

We conduct a field experiment on electricity conservation to study whether revealing both the competitive state and the social state in a group contest affects individual beliefs and efforts. Our experiment randomizes group composition, participation, and types of information received in the contest. We find that contestants without feedback about relative performance had difficulty assessing their group's competitive status, and laggards within a group tended to be overconfident about their relative contribution. In addition, we find that contestants receiving both competitive and social information were more likely to have correct beliefs about their positions during the contest and exerted the most effort. Meanwhile, contestants receiving no performance feedback did not behave differently from those who did not participate in the contest. Overall, contestants reduced their energy use by 10% during the contest. Our results support the notion that providing feedback is important in a group contest. [ABSTRACT FROM AUTHOR]

In order to encourage energy saving and the adoption of renewable sources, this study provides a comprehensive experimental framework that integrates socioeconomic and behavioral objectives for the local energy community. The experiment aims to find out how successfully using behavioral interventions might encourage customers to save electrical energy and encourage them to adopt renewable energy, e.g., solar photovoltaic energy, in the present case. Using this method, we can calculate the causal impact of the intervention on consumer participation in the local electricity sector. The study uses consumer data on the import and export of electrical power from retailer electricity utilities at a predetermined power exchange price and a midmarket price for local energy community power transactions. The local energy community model simulates the consumption, storage, and export of 20 residential customers who, in different scenarios, are the test subjects of an empirical experiment and embrace electricity conservation and renewable energy. We address the optimization issue of calculating the power exchange cost and revenue in various scenarios and comparing them with the base case cost. The cases are built on the customers' behavioral interventions' empirical response. The findings demonstrate that the interaction of socioeconomic and behavioral objectives leads to impressive cost savings of up to 19.26% for energy utility customers. The policy implication is suggested for local energy utilities. [ABSTRACT FROM AUTHOR]

In response to escalating global climate change, governments and international organizations were engaged in identifying effective strategies to mitigate carbon emissions. Residential photovoltaic systems emerged as a prominent, clean energy solution, drawing attention for their carbon-neutral electricity generation. Despite this, concerns persisted over whether technological advancements and policy incentives could sustain efforts to conserve energy and reduce emissions, particularly in light of the Jevons Paradox, which posits that technological progress might increase energy consumption and counteract emission reductions. This study, grounded in self-identity theory, examined the influence of residential PV installation on household electricity conservation behavior and assessed the paradox's relevance in this context. Through a survey across three representative Chinese provinces, collecting 996 responses, the research utilized Partial Least Squares Structural Equation Modeling (PLS-SEM) to analyze the data. Findings revealed that PV installation and usage among residents bolstered self-consistency, social identity, and identity affirmation, indirectly encouraging electricity conservation. Economic incentives and environmental campaigns were both shown to increase residential PV adoption and subsequent electricity-saving actions. However, economic incentives were found to be more effective, suggesting that economic benefits primarily influenced residents' energy consumption behaviors. Interestingly, a lower inclination for electricity conservation among higher-income residents hinted at the Jevons paradox (or a rebound effect). The study's findings underscore the importance of crafting energy and environmental policies that balance economic incentives with long-term environmental awareness strategies. It also suggests that reinforcing residents' identities as environmental advocates could enhance engagement in green energy practices, offering valuable insights for China and other nations striving to promote residential PV systems and meet emission reduction goals. [ABSTRACT FROM AUTHOR]

Household electricity conservation is a non-negligible aspect of environmental pollution with growing importance for an eco-friendly economy and society. However, debates regarding household electricity consumption have placed more emphasis on the physical attributes of the dwellings, demographic characteristics and the socioeconomic behaviour of households; few studies have directly discussed the awareness of environmental pollution. Based on the 'China Family Panel Studies' surveys with an extracted 8249 households in 25 provinces from 2014 to 2018, we analyse whether or not and to what extent the awareness of environmental pollution impacts household electricity consumption. The study finds that the awareness of environmental pollution rather than actual environmental pollution increases household electricity consumption. The finding is robust under various model specifications. Given a 1% increase in the awareness of environmental pollution, households living in southern China, located in eastern China or living in an urban area were found to have higher electricity consumption. To address possible estimation bias due to self-selection, we design a quasi-policy-shock variable to describe the severity of the perceived environmental pollution and run the propensity score matching regression (PSM). The finding still holds. [ABSTRACT FROM AUTHOR]

Copyright of Labor & Engenho is the property of Universidade Estadual de Campinas - Portal de Periodicos Eletronicos Cientificos and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Unsustainable use of electricity has severe implications on the environment and human well-being. With an estimated consumption of about 20% of total global electricity demand, the household sector is a key player in efforts for crafting interventions for reducing electricity consumption. Despite increasing calls for behavioural solutions to electricity conservation at the household level, more attention has been paid to technical than behavioural interventions. Yet a deeper understanding of electricity use behaviour is needed to design interventions and engender integration of behavioural interventions into demand-side management and decision making. Although South Africa is energy insecure and a major greenhouse gas emitter, less attention has been paid to household electricity use using behavioural lenses. Using a scoping review approach, this study inductively reviewed publications to examine the state of research on household electricity use in South Africa, focussing on (1) research trends and contexts, (2) conceptual focus, (3) proposed interventions for reducing electricity consumption and (4) future research needs. Very few publications considered reported and actual electricity use behaviour. Most publications (65%) paid attention to technical dimensions for reducing household electricity consumption such as economic nudges and technical retrofits, rather than behavioural strategies. Of the publications that focussed on behaviour, very few explicitly examined reported electricity use behaviour. Most publications did not consider the role of partnerships in designing interventions for reducing electricity consumption but rather employed individualistic perspectives. Overall, the results suggest that calls for behaviour change research have not been fully heeded. More studies on electricity use behaviour in different contexts, including across an income heterogeneity gradient, and the role of context dependent collective settings in drafting interventions, are required to better inform pathways to sustainable electricity use. [ABSTRACT FROM AUTHOR]

As drought worsens in the western United States, the Colorado River--the "lifeline of the Southwest"--is in dire straits. In the Colorado River basin, it is important to allocate water wisely. We propose a plan for allocating water from the river to serve water and a water hydroelectric power needs under varying environmental conditions. We analyze the river's reservoirs at the Glen Canyon Dam and Hoover Dams, finding the relationship between water height and volume of water in each reservoir. To allocate water and hydropower, we set up a transportation model. Its objective is to supply from the two dams the minimum amount of water to meet needs. The constraints are mainly water demand for general use and the water flow necessary for generating electricity. We analyze the relationship between water flow direction and water resource transfer between the two dams. We account for the loss of water and electricity during transportation. We find the longest time to be able to meet the demands without addition of water. We extend our transportation model to balance the competing interests of industry, agriculture, and residents. The supply of water has social, economic, and environmental effects; the supply of electricity has social and economic effects. We consider the economic effect of industrial, agricultural, and residential use objects in five states when there is no water shortage. When there is one, we combine the three effects and carry out multi-objective planning. Additionally, we propose a water allocation plan for Mexico. Finally, we conduct sensitivity analysis on the influence of changes over time in the demands for water and electricity, the development of renewable energy technology, and water and electricity conservation measures. [ABSTRACT FROM AUTHOR]

With the rapid economic growth and improved living standards, electricity has become an indispensable energy source in our lives. Therefore, the stability of the grid power supply and the conservation of electricity is critical. The following are some of the problems facing now: 1) During the peak power consumption period, it will pose a threat to the power grid. Enhancing and improving the power distribution infrastructure requires high maintenance costs. 2) The user's electricity schedule is unreasonable due to personal behavior, which will cause a waste of electricity. Controlling load as a vital part of incentive demand response (DR) can achieve rapid response and improve demand-side resilience. Maintaining load by manually formulating rules, some devices are selective to be adjusted during peak power consumption. However, it is challenging to optimize methods based on manual rules. This paper uses SoftActor-Critic (SAC) as a control algorithm to optimize the control strategy. The results show that through the coordination of the SAC to control load in CityLearn, realizes the goal of reducing both the peak load demand and the operation costs on the premise of regulating voltage to the safe limit. [ABSTRACT FROM AUTHOR]

Solar energy is crucial to the transformation of the energy system, and it is important to promote 'green' energy technologies to achieve the 2015 UN Paris Agreement targets. This study takes a holistic view of consumers' energy-related behaviour by identifying the factors driving the adoption of rooftop solar photovoltaic and by distinguishing the electricity conservation practices of solar and non-solar households. We reveal a nuanced understanding of the rebound effect from a behavioural perspective, which may help solar energy policy formulation and efficient decarbonization pathways in Australia and elsewhere. A face-to-face survey of 325 households in a regional city, principal component analysis and binary probit analysis were used to investigate the intention to install rooftop solar photovoltaic panels. The results show that home ownership, unwillingness to sacrifice personal comforts to save electricity, being pro-energy efficiency, and income are positively related to rooftop solar installation. In addition, different segments, such as the 'energy-efficiency advocate', the 'digital tool lovers' and the 'behaviour change advocates' were identified. Finally, recommendations for policy and practice are made to promote rooftop solar and more careful use of electricity in the residential sector. [ABSTRACT FROM AUTHOR]

This study investigates the different impacts of monetary and nonmonetary incentives on energy‐saving behaviors using a field experiment conducted in Japan. We find that the average reduction in electricity consumption from the rebate is 4%, whereas that from the nudge is not significantly different from zero. Applying a novel machine learning method for causal inference (causal forest) to estimate heterogeneous treatment effects at the household level, we demonstrate that the nudge intervention's treatment effects generate greater heterogeneity among households. These findings suggest that selective targeting for treatment increases the policy efficiency of monetary and nonmonetary interventions. [ABSTRACT FROM AUTHOR]

Based on data from a household survey completed in 2016, this paper estimated a model for residential electricity consumption in Kuwait. The main objective of estimating the model was to examine the impact of curtailment and efficiency energy conservation measures on household electricity consumption. Given Kuwait's demographic composition and variations in electricity consumption across months, the model was estimated for two population groups (nationals and expatriates) and three periods (annual, summer months, and winter months). Prior to estimating the model, tests were conducted to determine if the observed differences in consumption across the population groups were sufficiently different to warrant separate treatment of groups. The survey data revealed that despite electricity consumption being heavily subsidized, households made efforts to conserve electricity by investing in efficient technologies as well as changing habits and practices. While the curtailment measures reported to be adopted by households were found to lower electricity consumption of the national households, the efficiency measures were found to impact electricity consumption of expatriate households. Awareness of conservation campaigns and conservation issues were found to lower electricity consumption, particularly in the case of expatriate households. [ABSTRACT FROM AUTHOR]

Electricity conservation systems are designed to conserve electricity to manage the bridge between the high raising demand and the production. Such systems have been so far using sensors to detect the necessity which adds an additional cost to the setup. Closed-circuit Television (CCTV) has been installed in almost everywhere around us especially in commercial places. Interpretation of these CCTV images is being carried out for various reasons to elicit the information from it. Hence a framework for electricity conservation that enables the electricity supply only when required, using existing resources would be a cost effective conservation system. Such a framework using a deep learning model based on Faster-RCNN is developed, which makes use of these CCTV images to detect the presence or absence of a human in a place. An Arduino microcontroller is embedded to this framework which automatically turns on/off the electricity based on human's presence/absence respectively. The proposed approach is demonstrated on CHOKE POINT dataset and two real time datasets which images from CCTV footages. F-measure, Accuracy scores (AUC score) and training time are the metrics for which the model is evaluated. An average accuracy rate of 82% is obtained by hyper-parameter tuning and using Adam optimization technique. This lays the underpinning for designing automatic frameworks for electricity conservation systems using existing resources. [ABSTRACT FROM AUTHOR]

Human practices are at the centre of unsustainable electricity use at the household level, which means behaviour change strategies can form pathways towards electricity conservation. Although behaviour change interventions are useful in promoting electricity savings at the household level, they are rarely inclusive and informed by the users of electricity. Using a field-based experiment, this study examines the impact of co-designed interventions on household electricity consumption among low-income households in Makhanda, South Africa. The results show that co-designed interventions yielded significantly more electricity savings (5%) than prescriptive interventions (2%) over the intervention period. Participant households exposed to the co-designed interventions showed electricity savings of up to 14% post the intervention period, suggestive of positive persistent effects. Household size and employment status had a significant negative effect on electricity consumption while age, employment status, and baseline electricity consumption yielded positive effects. Electricity savings were positively correlated with involvement and trust, perceived behavioural control, and the intention to act pro-environmentally. The results highlight that co-designed interventions can yield significant electricity savings, which can in turn, result in grid stability, and reduced electricity expenditures and carbon emissions. A key contribution of this study lies in advancing our understanding of the effects of user-driven approaches in yielding persistent electricity-saving behaviour, which to date has not been a focus of intervention studies. In particular, the findings lend support to notions of supporting the agency of electricity users in co-developing solutions for local sustainability challenges. [ABSTRACT FROM AUTHOR]

After more than three decades in a row, Brazil, the only equatorial country to adopt a DST policy, forsook it. The reduction delivered in electricity consumption, which was supposed to average out 4% per year, became ineffective and inefficient, because it focused mainly on lighting and residential consumers, rather than on cooling and the industrial sector. Moreover, the burden of reducing electricity consumption was only placed on the Southern regions of Brazil. The shutdown of this policy has raised questions about the country's commitment to its pledges on energy efficiency to the Paris Agreement. Based on data available from 2006 to 2017, this article compares the achievements of a command-and-control energy policy, such as DST, with those that would have arisen from an ETS (Energy Trading Scheme). The results show that, per year, society's net benefits might have been over 3 times greater: 12.58% rather than 4.1% for the reduction target of electricity consumption. Applying these targets to an ETS framework, net cost savings would be 104.09% rather than 33.64%, whereas mean electricity savings would make up 22.78% rather than 7.42%. Therefore, with ETS, mean electricity savings might turn out to be 5 times greater if compared with 4.1% savings reported yearly by DST. Furthermore, the Northern regions, which have historically been left out of DST policies, could have additionally earned up to € 16.8 billion per year from reducing their electricity consumption jointly with their Southern counterparties. • Brazil's DST policy rendered negligible electricity savings over more than 30 years. • Permit trading could deliver cost and energy savings 3 and 5 times greater. • With permit trading, non-DST regions profit from larger electricity savings. • Brazil's pledges on energy savings to the Paris Agreement might be doubled. [ABSTRACT FROM AUTHOR]

This paper presents a novel water reservoir cover using semi-transparent Organic Photovoltaics (OPV), that addresses water, energy and land shortages in addition to water quality improvements, by creating a dual-purpose usage of land resources by both generating electricity and reducing evaporation, whilst at the same time preserving or enhancing water quality. A simulation of the effect of the OPV cover on water temperature and evaporation is presented, as well as a discussion on the different variables influencing its viability. The filtering out of parts of the IR spectrum and 400–550 nm parts of the spectrum reduced both water temperatures and evaporation, which could thereby improve water quality. [ABSTRACT FROM AUTHOR]

While numerous studies examine the causal relationships of electricity rates on conservation and of conservation on variable costs, little work has been done on the missing links of conservation on fixed costs and on overall electricity rates. This article presents a new model to scientifically quantify these two gaps and complete the economic picture of conservation. This knowledge can equip government policymakers and conservation program designers at utilities to create more efficient and effective programs. New mathematical models of the full system-level rate impact of different forms of conservation are presented. Four common forms of conservation were analyzed for their impacts on total fixed and variable costs and rates: peak shaving (S1), off-peak reduction (S2), peak shifting (S3), and time-independent conservation (S4). These were all measured against the base case without conservation (S5). A test system based on the electricity system in Ontario, Canada, was created and analyzed over a 21-year period. The results show that different forms of conservation have different impacts on fixed and variable costs and rates, and the most useful metric for the economic impact of conservation is the change in utility rates, inclusive of fixed and variable components. For conservation programs to lower rates, they must decrease the peak demand, which will lower fixed costs by deferring capital investments, and increase utilization, which will lower rates by increasing consumption during off-peak times. [ABSTRACT FROM AUTHOR]

The average electricity consumption per capita in China still has significant room for growth. Under the current rigid residential electricity pricing, it is very important to study how factors other than price can promote electricity conservation among residents. This paper uses micro-survey data of 2018 and 2020 to analyze energy efficiency and the rebound effect through a stochastic energy demand frontier model, as well as to examine the role of news propaganda in this context. The research conclusions are as follows: (1) News propaganda promoting electricity saving can weaken the rebound effect of electricity use, which can lead to better electricity savings. (2) The energy efficiency of residential electricity has improved significantly compared to previous years, and the rebound effect has decreased. (3) Electricity prices have a negative impact on electricity consumption, while income, household size, home area, and extreme temperatures have a positive impact. Based on these conclusions, we propose policy recommendations to address the current inflexibility in residential electricity pricing and to improve the efficiency of electricity use among residents. • News propaganda promoting electricity saving can weaken the rebound effect. • The energy efficiency of residential electricity has improved. • The rebound effect of residential electricity has decreased. • News propaganda can be a complement to the inflexibility of pricing policy. [ABSTRACT FROM AUTHOR]

Visual feedback, which refers to the provision of energy information through visual interfaces to households, has emerged as a crucial approach for promoting energy conservation and addressing climate change. However, existing experimental studies lack a unified standard framework design for visual feedback, and have significant divergences in its effectiveness. Hence, we first propose a design framework for visual feedback on household electricity consumption, including information, time and display. On this basis, this study provides comprehensive meta -analytic evidence on the effectiveness of visual feedback from 156 articles between 1976 and 2024, and finds that visual feedback has a moderate energy-saving effect. Further considering the interaction of 3D influencing factors, we explore the optimal combination of visual feedback using multilevel meta -regression. The results show that information, time and display are all important factors for visual feedback. When designing informational interventions, it is important to choose effective feedback frequency and feedback media. The optimal strategy of 3D visual feedback combination is "self-comparison + monetary incentives + low frequency + digital media". These findings can help policy makers and utilities to develop effective feedback measures to promote household energy conservation. [ABSTRACT FROM AUTHOR]

Summary: High‐performance spintronic water‐splitting devices with high applied power, spintronic population under room temperature have been demonstrated by the Ce2O3 small polaron surface state‐based spintronic injection system. Spintronic entanglement tomography decrypts the information about ultrahigh‐energy conversion efficiency as high as 140% and the optimal applied voltage 0.682 V. Ce+3 ions demonstrate unpaired d orbital for achieving spin‐orbital coupling by accepting the quantum information of angular momentum, spin, and orbital from incident laser light which modulated by ħ + ħ (↑↑), ħ + (− ħ) (↓↑), ħ ‐ (−ħ) (↑↓) and (−ħ) + (−ħ) (↓↓) angular momentum. A practical way is been provided for achieving the spintronic entanglement in water splitting devices by describing the correlations between spintronic that interact and then separate. Thus, the quantum computing algorithm is encrypted and well decrypted by spin‐orbital coupling. Besides, in this research, we demonstrated an ultra‐high energy conversion water splitting cells with spintronic injection and spintronic entanglement. Comparing with theory power for water splitting, the spintronic entanglement water splitting devices in our research saved 30% power to achieve water splitting and hydrogen generation. [ABSTRACT FROM AUTHOR]

Power and energy consumption are becoming key challenges for the supercomputers’ exascale race. HPC systems’ processors waist active power during communication and synchronization among the MPI processes in large-scale HPC applications. However, due to the time scale at which communication happens, transitioning into low-power states while waiting for the completion of each communication may introduce unacceptable overhead. In this article, we present COUNTDOWN, a run-time library for identifying and automatically reducing the power consumption of the CPUs during communication and synchronization. COUNTDOWN saves energy without penalizing the time-to-completion by lowering CPUs power consumption only during idle times for which power state transition overhead is negligible. This is done transparently to the user, without requiring labor-intensive and error-prone application code modifications, nor requiring recompilation of the application. We test our methodology on a production Tier-1 system. For the NAS benchmarks, COUNTDOWN saves between 6 and 50 percent energy, with a time-to-solution penalty lower than 5 percent. In a complete production—Quantum ESPRESSO—for a 3.5K cores run, COUNTDOWN saves 22.36 percent energy, with a performance penalty below 3 percent. Energy saving increases to 37 percent with a performance penalty of 6.38 percent, if the application is executed without communication tuning. [ABSTRACT FROM AUTHOR]

Automated demand response (ADR) is a utility program that is designed to achieve electricity conservation. An ADR program is regarded as the problem of controlling the power consumption of a set of consumers. In this article, we propose a control‐theoretic approach for an ADR program. First, a mathematical model of the power consumption is proposed. This model can express complex behavior by switching a Markov chain. Its effectiveness is illustrated by modeling the power consumption of an air‐conditioner. Next, a new method of model predictive control for a set of consumers is developed using the proposed model. The control strategy at each time is chosen from a given finite set by solving a mixed integer linear programming (MILP) problem. The advantage of the proposed method is that the MILP problem is scalable with respect to the number of consumers. To show its effectiveness, we present a numerical example. [ABSTRACT FROM AUTHOR]

Electricity conservation techniques have gained more importance in recent years. Many smart techniques are invented to save electricity with the help of assisted devices like sensors. Though it saves electricity, it adds an additional sensor cost to the system. This work aims to develop a system that manages the electric power supply, only when it is actually needed i.e., the system enables the power supply when a human is present in the location and disables it otherwise. The system avoids any additional costs by using the closed circuit television, which is installed in most of the places for security reasons. Human detection is done by a Modified-single shot detection with a specific hyperparameter tuning method. Further the model is pruned to reduce the computational cost of the framework which in turn reduces the processing speed of the network drastically. The model yields the output to the Arduino micro-controller to enable the power supply in and around the location only when a human is detected and disables it when the human exits. The model is evaluated on CHOKEPOINT dataset and real-time video surveillance footage. Experimental results have shown an average accuracy of 85.82% with 2.1 seconds of processing time per frame. [ABSTRACT FROM AUTHOR]

Over the previous two decades, Chinese economic development presented a rapid growth. However, with continuous industrialization and urbanization, China is confronted with great challenges of energy security and environmental issues. These problems are closely related to the current accounting method of economic growth to a certain extent. In order to meet these challenges, it is imperative to establish a green accounting system of economic growth and measure China's green GDP and its changing trend based on the industrial perspective. Using the System of Environmental Economic Accounting (SEEA) and industry data, this paper estimates China's green GDP and green value added by industry sectors in 2005, 2007, 2010, 2012, 2015, and 2017. The results reveal the following: First, the ratio of green GDP to traditional GDP gradually increases from 89.85 to 95.83% during 2005–2017, which means that the negative externalities of economic growth of the resource and environment are gradually weakened. Second, the difference between traditional GDP and green GDP during 2005–2017 is about 6.96%, with the carbon emissions accounting for 70.71% of environmental impact. Third, due to more than 80% of the environmental impact coming from three sectors: manufacturing (49.99%), electricity industry (22.63%), and other services (11.37%), these three sectors should be key sectors for energy conservation and emission reduction; fourth, the green GDP of the mining, electricity industries, and manufacturing accounts for the lowest proportion of GDP, which means that the development patterns of these three industries in recent years should be adjusted and optimized step by step. [ABSTRACT FROM AUTHOR]

Electrical pylons are frequently used by birds for perching, roosting, and nesting. These behaviors can lead to electrocutions, particularly when pylons are constructed of grounded concrete and steel crossarms, as they were in our study area near Arak, Iran. To address electrocutions of Eurasian Kestrels (Falco tinnunculus) and a variety of passerines nesting on a 20-kV distribution line, we documented construction and use of nests on pylons on a 34-km segment of power line in 2018. Following removal of nests by the electric utility operating the line, we installed nest boxes on pylons that previously supported nests, and in 2019 we documented use of those nest boxes. We predicted that after installation of nest boxes, numbers of Eurasian Kestrel nests would increase, and numbers of nests outside of nest boxes, avian electrocutions, and faults (electric current arcing from conductors to pylons) would decrease. All four predictions were validated. We documented 31 nests in 2018, including 3 Eurasian Kestrel nests. In 2019, we documented 6 nests outside of nest boxes, and 31 nests in nest boxes, including 16 Eurasian Kestrel nests. In 2018, we found 1 electrocuted Eurasian Kestrel, and 39 electrocuted passerines. In 2019, we found 3 electrocuted Eurasian Kestrels, 11 passerines, 2 Eurasian Eagle-Owls (Bubo bubo), and 1 Steppe Eagle (Aquila nipalensis). The rate of electrocutions per nest for Eurasian Kestrels decreased from 0.33 to 0.19, and the electrocution count for all birds decreased by 57.5%. Electrical faults, an indirect measure of potential avian electrocutions, decreased from 173 in 2018 to 120 in 2019. Although there is some risk that nest boxes on power poles could create ecological traps, our results suggest that in this study electrocutions and electrical faults were reduced, supporting conservation goals and electric power reliability goals while simultaneously reducing electric utilities' maintenance obligations. [ABSTRACT FROM AUTHOR]

Due to the growth of demand for electric power, electric power loss reduction takes great attention for the power utility. In this paper, a low-level generation or distributed generation (DG) has been used for transmission power losses reduction. Karbala city transmission network (which is the case study) has been represented by using MATLAB m-file to study the load flow and the power loss for it. The paper proposed the particle swarm optimization (PSO) technique in order to find the optimal number and allocation of DG with the objective to decrease power losses as possible. The results show the effect of the optimal allocation of DG on power loss reduction. [ABSTRACT FROM AUTHOR]

Electrooxidation (EO) is a promising wastewater treatment technology that is plagued with high energy consumption. This study simulates the energy recovery potential from EO by process integration with recuperated micro gas turbine (RMGT). Gainful utilization of hydrogen produced at cathode is key to it. Percent recoverable energy (PRE) from EO would be strongly influenced by the cathodic current efficiency (CCE) and interelectrode potential (IP) and was likely to vary from a high of 46% to a low of 14%. Simulations suggested that process integration with RMGT could potentially recover 72.4% of this energy – 30.7% as electricity and 41.7% as thermal energy in the form of hot water at 353 K. This translated to 6.3%–9.7% electricity saved in EO besides recovered thermal energy. Energy dynamics was found to be highly sensitive to recuperator performance of the RMGT. Better recuperator performance made energy recovery through process integration more electricity driven at the cost of recovered heat and total energy recovery. • 14–46% input energy in electrooxidation recoverable as hydrogen. • Process integration with recuperated Micro Gas Turbine can recover 72.4% energy. • 30.7% energy can be recovered as electricity and 41.7% as hot water. • Overall 10% electricity saving in electrooxidation possible. • ACE, IP, and recuperator performance are critical. [ABSTRACT FROM AUTHOR]

Reducing energy consumption can bring various benefits, such as saving power, reducing operating costs and improving system reliability. Data centers afford huge energy costs. Reducing energy consumption and providing efficient quality of service (QoS) are the goals pursued by data centers. This paper aims to develop the scheduling strategy to reduce the energy consumption for latency-critical workload with the Dynamic Voltage Frequency Scaling technique. In this paper, we propose a two-stage strategy that dynamically schedules the CPU to run at the optimal frequency level and provides satisfactory QoS during the latency-critical workload execution. The two-stage strategy includes a static stage and a dynamic stage. In the static stage, a heuristic algorithm is developed to determine an optimal frequency level for different loads, at which the CPU consumes lower energy. However, due to the dynamic characteristics of the load, the determined frequency in the static stage may not guarantee QoS. Therefore, in the dynamic stage, a threshold method is proposed to fine-tune the frequency to ensure the QoS. The two stages are worked together to reduce energy consumption for latency- critical workload which has QoS constraint. A case study on the Web search application shows that our scheduling strategy is effective for energy-saving, with a reduction of more than 13% compared with the baseline strategy. [ABSTRACT FROM AUTHOR]

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The CCHP system driven by biomass energy is essential for mitigating the fossil energy crisis and reducing CO 2 emission. But few research work focuses on the micro-scale system due to the limited efficiency. In this paper an efficient household CCHP system is designed, which is driven by the water steam (100 °C-120 °C) from the biomass boiler and is consisted of an R245fa power cycle with a gravity assisted thermal driven "pump", a single-pressure R134a/DMF/He diffusion absorption refrigeration cycle, and several heat exchangers for heating supply. The models of the system are constructed, and the energy, economic and environmental indexes are evaluated. The results reveal that the higher temperature of heat source and the lower temperature of ambient could improve the thermal efficiency of the CCHP system, and realize the lower cost and CO 2 emission. The highest thermal efficiency of the CCHP system can reach to 55.26%. The quarterly energy saving for summer of a 100 m2 isolated house of a rural family can reach to 5059 kW ⋅ h (497 kW ⋅ h for cooling, 3154 kW ⋅ h for heating and 1408 kW ⋅ h for power generation), which can save about 3121.40 RMB electricity bill and reduce 0.15 t CO 2 emissions. Image 1 • A gravity assisted thermal driven "pump" is designed to replace the electric pump in power cycle. • A single-pressure R134a/DMF/He diffusion absorption refrigeration cycle is adopted. • Performance of the CCHP system under different seasons is analyzed. • The highest thermal efficiency of the CCHP system can reach to 55.26%. • The quarterly energy saving (summer, 100 m2) is about 5059 kW•h. [ABSTRACT FROM AUTHOR]

Home energy management requires accurate information about the appliances’ consumption pattern. This information can help consumers save energy, control their usage by shifting their usage to off-peak hours and reduce their electricity costs. Non-intrusive load monitoring (NILM) in which the power consumption profile of appliances are extracted from the aggregated signal of a household, provides this information. For the NILM problem, machine learning approaches as the training-based solutions require large training datasets for an accurate disaggregation and the optimization-based approaches employs prior information about the characteristics of appliances. This paper proposes a novel event-based optimization algorithm. In its first stage, the prior information about appliances is extracted from the events of the consumption profiles of appliances by means of clustering. Then, a new event-based down-sampling method and transition filtering are designed for decreasing the computation time of optimization. At the last stage of the proposed algorithm, post-processing considering ON duration of appliances and varying states are proposed to increase the accuracy of the power profile reconstruction. The proposed approach was successfully tested for the low-frequency dataset of a house from the REDD. Numerical results show the advantages of the proposed algorithm, marked improvement over classification-based NILM considering small training dataset and its applicability in disaggregating the power consumption measured by the smart meter. [ABSTRACT FROM AUTHOR]

The development of technology and design of light management systems remains dynamic. Among all the benefits offered by these systems, the most valuable might definitely be the possibility of saving energy consumption. Knowing the value of energy savings is the key factor that users need to know before deciding to use a lighting management system (the type of light management system). For this purpose, it is useful to simulate the operation of the lighting control system, for example in the DIALux program. Such simulation helps evaluate potential savings in electricity consumption using the proposed lighting control system. In the DIALux program, it is possible to change the luminous flux value of luminaires. In such a case, it becomes possible to semi-simulate the light management system's operation as we don't receive actual information on reducing installed power of the lighting system during reduction of the luminous flux value of luminaires. This article shows what type of technical data are important to use for the DIALux program to properly and accurately simulate light management systems and to receive accurate data on energy saving. It also presents the results of photometrical and electric parameter measurements (F - luminous flux, P - power, PF - power factor, THDi - total harmonic distortion of current). The article discusses the power control characteristics obtained on the basis of these measurements and explores the source of differences between simulation of energy saving calculations and real measured energy savings. An existing lighting control system installed in an office reception area was used to compare calculations with the real value of energy consumption reduction. The impact of electronic power and control systems on electrical network parameters is also an important problem mentioned in this article. It also explores the effect of power regulation of LED luminaires and LED modules on the value of the power factor and total harmonic distortion (current) value (THDi). [ABSTRACT FROM AUTHOR]