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This study employs a comprehensive database of Spanish homes and econometric modeling to assess the factors influencing electricity consumption in Spain. Using panel data covering the period from 1998 to 2023, we analyzed the average yearly electricity usage across the 18 Spanish regions. Our findings reveal that fluctuations in electricity prices have remained the primary driver of consumption over this period. The price elasticity of demand has been exceptionally high, exceeding levels observed in earlier research and periods. Additionally, factors such as income, hours of sunlight, and temperature fluctuations positively influence electricity usage, albeit to a lesser extent than household and family characteristics (e.g., whether a single parent heads the household or if it includes foreign residents). There is no significant correlation between demand and factors such as the age or employment status of family members. Based on these findings, policy actions that target power pricing (e.g., price-based instruments) are likely to be the most effective in reducing electricity consumption.

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Engagement with households to fully realize the potential of demand-side solutions has attracted policy attention. The potential of feedback has been understudied, especially regarding who engages more in electricity conservation. Furthermore, most studies have been limited to the Western context, with only a few that explore Asia. This study fills these gaps by investigating changes in household hourly electricity consumption patterns after its members receive high-resolution feedback. After data balancing, we partitioned 63 households into distinct groups using K-means clustering and investigated consumption changes after the provision of high-resolution electricity feedback through a mobile application. The results indicate mixed effectiveness of feedback: some households reduced consumption by about 13 %, while others increased it between 7 % and 20 %. In addition, statistical analysis using survey responses revealed that households with greater awareness of electricity costs and a stronger interest in climate change were more receptive to feedback. Demographic and housing attributes such as age, building type, and floor count also influenced the feedback effect. The findings recommend enhancing awareness of electricity costs and climate change and developing a better understanding of individuals' challenges with changing conservation behaviors based on their demographic and housing characteristics.

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The objective of the study is to determine the electricity use, and tourism industry environmental impacts, and increase in CO2 emissions in Pakistan. What are the linkages of foreign direct investment, intercountryal trade, gross domestict product, and CO2 emissions. The study has applied the Autoregressive distributed lag (ARDL) method to analysis the data set from 1985 to 2023. The robustness test is applied using Dynamic Ordinary Least Square (DOLS), and Fully modified ordinary least squares (FMOLS). The results reveal that the increase in the electricity use, and tourism industry has significant negative impacts on CO2 emissions in both short- and long-run. The increase in intercountry trade effects the Domestic Product growth (GDP) growth and causes to increase in use of fossil fuels, which are the major source of CO2 emissions. The increase in foreign direct investment (FDI) increase the GDP growth, and FDI also increase the CO2 emissions in Pakistan. The results suggests that the incresae in the renewal energy consumption for the electricity production and transportation can help to decrease the CO2 emissions in Pakistan.

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The benign coupling coordination between electricity (EL) and economy (EC) contributes to a better environment and sustainability. This study explores whether EL and EC can coordinate theoretically, how to evaluate their coordination, what the statuses are, and how to enhance coupling coordination levels (CCL). Specifically, we select the data from 2011 to 2020 of the 31 provincial regions of China, use information entropy weight and the technique for order preference by similarity to an ideal solution method, establish the theoretical coupling coordination mechanism and the evaluation index system to measure CCL temporally-spatially and propose policy implications based on prediction tendencies. We find that most regions' CCLs fluctuate temporally with mild upward trends, indicating the much more benign coupling coordination status; the spatial distributions are uneven with narrowing gaps. However, future CCL gaps may increase; therefore, differentiated policy implications are needed, such as encouraging balanced-coordination policies, innovating for higher-quality coordination, and cooperating for intelligence and wealth transfer. This study is conducive to theoretically describing the coupling coordination mechanism between EL and EC, providing new insights for CCL evaluation and the coordination practice for different regions.

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Moisture-induced electricity generation (MEG), which can directly harvest electricity from moisture, is considered as an effective strategy for alleviating the growing energy crisis. Recently, tremendous efforts have been devoted to developing MEG active materials from wood lignocellulose (WLC) due to its excellent properties including environmental friendliness, sustainability, and biodegradability. This review comprehensively summarizes the recent advances in MEG based on WLC (wood, cellulose, lignin, and woody biochar), covering its principles, preparation, performances, and applications. In detail, the basic working mechanisms of MEG are discussed, and the natural features of WLC and their significant advantages in the fabrication of MEG active materials are emphasized. Furthermore, the recent advances in WLC-based MEG for harvesting electrical energy from moisture are specifically discussed, together with their potential applications (sensors and power sources). Finally, the main challenges of current WLC-based MEG are presented, as well as the potential solutions or directions to develop highly efficient MEG from WLC.

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Power sector and energy systems models are widely used to explore the impacts of demographic, socio-economic or policy changes on the cost and emissions of electricity generation. Technology cost and performance data are essential inputs to such models. Despite the ubiquity and importance of these parameters, there is no standardised database which collates the variety of values from across the literature, so modellers must collect them independently each time they populate or update model inputs, leading to duplicated efforts and inconsistencies which can profoundly influence model results. Technology cost and performance varies between countries, regions and over time, meaning that data must be country- or region-specific and frequently updated. Values also vary widely between sources, so obtaining a broad consensus view is critical. Here, we present a database which collates historical, current, and future cost and performance data and assumptions for the six most prominent electricity generation technologies; coal, gas, hydroelectric, nuclear, solar photovoltaic (PV) and wind power, which together accounted for over 92 % of installed generation capacity in 2022. In addition, we provide the same data for utility-scale battery energy storage systems (BESS), regarded as critical to the integration of variable renewables such as wind and solar PV. The data are global in scope but with regional and national specificity, covers the years 2015 through to 2050, and span 5518 datapoints from 56 sources. The database enables modellers to select and justify model input data and provides a benchmark for comparing assumptions and projections to other sources across the literature to validate model inputs and outputs. It is designed to be easily updated with new sources of data, ensuring its utility, comprehensiveness, and broad applicability in future.

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Life-cycle assessment (LCA) is one of the most widely applied methods for sustainability assessment. A main application of LCA is to compare alternative products to identify and promote those that are more environmentally friendly. Such comparative LCA studies often rest on, explicitly or implicitly, an idealized assumption, namely, 1:1 displacement between functionally equivalent products. However, product displacement in the real world is much more complicated, affected by various factors such as the rebound effect and policy schemes. Here, we quantitatively review studies that have considered these aspects to evaluate the magnitude and distribution of realistic displacement estimates across several major product categories (biofuels, electricity, electric vehicles, and recycled products). Results show that displacement ratios concentrate around 40-60%, suggesting considerable overestimation of the benefits of alternative products if the 1:1 displacement assumption was used. Overall, there have been a small number of modeling studies on realistic product displacement and their scopes were limited. Additional research is needed to cover more product categories and geographies and improve the modeling of market and policy complexities. As such research accumulates, their displacement estimates can form a database that can be drawn upon by comparative LCA studies to more accurately determine the environmental impacts of alternative products.

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As a new type of energy storage, shared energy storage (SES) can help promote the consumption of renewable energy and reduce the energy cost of users. To this end, an optimization clearing strategy for a multi-region electricity-heat joint market is proposed with consideration of SES and integrated demand response (DR). Firstly, the concept of shared energy storage station (SESS) is proposed, its business operation model is analyzed and its advantages over traditional energy storage are compared. Secondly, to exploit the potential for user flexibility, an integrated DR model that includes shiftable, transferable, interruptible electricity and heat load is constructed. Then, a multi-region electricity-heat joint market clearing model is constructed considering SESS and integrated DR. Finally, a three-region electricity-heat joint market connecting the external power grid, gas grid, and the SESS is used as an example. A comparison is made between the configuration of independent energy storage in each region and the configuration of SESS, which concludes that the introduction of the SESS and integrated DR can reduce the energy cost of users and improve the utilization rate of the energy storage facilities.

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The deregulation of electricity market has led to the development of the short-term electricity market. The power generators and consumers can sell and purchase the electricity in the day ahead terms. The market clearing electricity price varies throughout the day due to the increase in the consumers bidding for electricity. Forecasting of the electricity in the day ahead market is of significance for appropriate bidding. To predict the electricity price the modified method of Exponential Smoothing-CNN-LSTM is proposed based on the time series method of Exponential Smoothing and Deep Learning methods of Convolutional Neural Network (CNN) and Long Short-Term Memory (LSTM). The dataset used for assessment of the forecasting algorithms is collected from the day ahead electricity market at the Indian Energy Exchange (IEX). The forecasting results of the Exponential Smoothing-CNN-LSTM method evaluated in terms of Mean Absolute Error (MAE) as 0.11, Root Mean Squared Error (RMSE) as 0.17 and Mean Absolute Percentage Error (MAPE) as 1.53 % indicates improved performance. The proposed algorithm can be used to forecast the time series in other domains as finance, retail, healthcare, manufacturing.•The method of Exponential Smoothing-CNN-LSTM is proposed for forecasting the electricity price a day ahead for accurate bidding for the short-term electricity market participants.•The forecasting results indicate the better performance of the proposed method than the existing techniques of Exponential Smoothing, LSTM and CNN-LSTM due to the advantages of the Exponential Smoothing to extract the levels and seasonality and with the CNN-LSTM methods ability to model the complex spatial and temporal dependencies in the time series.

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Photocatalytic fuel cells (PFCs) present a promising and environmentally friendly approach to simultaneously treat organic pollutants in wastewater and electricity generation. The development of photoanodes with high light absorption and carrier mobility is essential for enhancing the performance of PFCs but remains challenging. Herein, a one-step self-assembly strategy was adopted to develop flower-like WO3/rGO microspheres for PFC devices. Attributed to the abundant surface-active sites, enhanced light harvesting, and efficient separation of photogenerated charge carriers, the WO3/rGO photoanode demonstrated superior rhodamine B (RhB) degradation rate (90% in 2 h), maximum power density (4.74 µW/cm2), and maximum photocurrent density (0.096 mA/cm2), 1.4, 2.4, and 4.0 times higher than the corresponding pure WO3 photoanode, respectively. Density functional theory (DFT) calculations reveal that the built-in electric field formed between the interface of WO3 and rGO promotes the transfer of photogenerated electrons from WO3 to rGO, thus exerting a significant impact on improving the migration and separation of photoinduced charge carriers. Moreover, by combining experimental and theoretical results, a complete PFC operation mechanism for the PFC system was proposed. This study focuses on the strategy of constructing rGO-doped photocatalysts to enhance the interfacial charge transfer mechanism, providing a promising approach for the development of high-performance photoanodes in PFC systems.

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This study investigated the technical feasibility of using electrogermination to activate dormant cysts as an inoculum for subsequent 14-d photosynthetic astaxanthin production in Haematococcus lacustris. Electrotreatment affected the cell viability, surface charge, and morphology of H. lacustris cysts. At an optimal voltage of 2 V for 60 min, the cyst germination rate peaked at 44.6 % after 1 d, representing a 2.2-fold increase compared with that of the untreated control. Notably, electrogermination significantly enhanced both the astaxanthin content (44.9 mg/g cell) and productivity (13.2 mg/L/d) after 14 d of photobioreactor cultivation, corresponding to 1.7- and 1.5-fold increases compared with those in control, respectively. However, excessive electrotreatment, particularly at voltages exceeding 2 V or for durations beyond 60 min, did not enhance the astaxanthin production capability of H. lacustris. Proper optimization of renewable electrogermination can enable sustainable algal biorefinery to produce multiple bioactive products without compromising cell viability and astaxanthin productivity.

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Obtaining water and renewable energy from the atmosphere provides a potential solution to the growing energy shortage. Leveraging the synergistic inspiration from desert beetles, cactus spines, and rice leaves, here, a multibioinspired hybrid wetting rod (HWR) is prepared through simple solution immersion and laser etching, which endows an efficient water collection from the atmosphere. Importantly, benefiting from the bionic asymmetric pattern design and the three-dimensional structure, the HWR possesses an omnidirectional fog collection with a rate of up to 23 g cm-2 h-1. We further show that the HWR could be combined with a droplet-based electricity generator to convert kinetic energy from falling droplets into electrical energy with a maximum output voltage of 200 V and a current of 2.47 µA to light up 28 LEDs. Collectively, this research provides a strategy for synchronous fog collection and power generation, which is promising for environmentally friendly energy production.

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Developing countries struggle with water quality management owing to poor infrastructure, limited expertise, and financial constraints. Traditional water testing, relying on periodic site visits and manual sampling, is impractical for continuous wide-area monitoring and fails to detect sudden heavy metal contamination. To address this, plant-inspired robots capable of fully autonomous water quality monitoring are proposed. Constructed from paper, the robot absorbs surrounding water through its roots. This paper robot is controlled by paper-based microfluidic logic that sends absorbed water to petal-shaped actuators only when the water is polluted by heavy metals. This triggers the actuators to swell and bend like a blooming flower, visually signaling contamination to local residents. In tests with copper-contaminated water, the robotic flower's diameter increased from 4.69 cm to 14.89 cm, a more than threefold expansion (217.25 %). This significant blooming movement serves as a highly visible and easily recognizable indicator of water pollution, even for the public. Furthermore, the paper robot can be mass-produced at a low cost (∼$0.2 per unit) and deployed over large areas. Once installed, the paper robot operates autonomously using surrounding water as a power source, eliminating the need for external electrical infrastructure and expert intervention. Therefore, this autonomous robot offers a new approach to water quality monitoring suitable for resource-limited environments, such as Sub-Saharan Africa.

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One of the main current focuses of global economies and decision-makers is the efficiency of energy utilization in cryptocurrency mining and trading, along with the reduction of associated carbon emissions. Understanding the pattern of Bitcoin's energy consumption and its bubble frequency can greatly enhance policy analysis and decision-making for energy efficiency and carbon emission reduction. This research aims to assess the validity of the random walk hypothesis for Bitcoin's electricity consumption and carbon footprint. We employed both traditional methods (ADF and KPSS) and recently proposed unit root techniques that account for structural breaks and non-linearity in the data series. Our analysis covers daily data from July 2010 to December 2021. The empirical results revealed that traditional unit root techniques did not confirm the stationarity of both bitcoin's electricity consumption and carbon footprint. However, novel structural break (SB) and linearity tests conducted enabled us to discover five SB episodes between 2012 and 2020 and non-linearity of the variables, which informed our application of the newly developed non-linear unit root tests with structural breaks. With the new methods, the results indicated stationarity after accommodating the SB and non-linearity. Furthermore, based on Phillips and Shi (2019)'s test, we identified certain bubble episodes in the bitcoin energy and carbon variables between 2013 and 2021. The major drivers of the bubbles in bitcoin energy consumption and carbon footprint are variables relating to the bitcoin and financial markets activities and risks, including the global economic and political risks. The study's conclusion based on the above findings informs several policy implications drawn for energy and environmental management including the encouragement of green investments in cryptocurrency mining and trading.

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In this work, two kinds of primary batteries, both of which included a Zn anode, C rod cathode, copper wire and electrolyte composed of Cd2+-contaminated water or soil, were constructed in the first attempt to both remove Cd2+ and generate electricity. Unlike traditional technologies such as electrokinetic remediation with high energy consumption, this technology could realize Cd2+ migration to aggregation and solidification and generate energy at the same time through simultaneous galvanic reactions. The passive surface of Zn and C was proven via electrochemical measurements to be porous to maintain the relatively active galvanic reactions for continuous Cd2+ precipitation. Cd2+ RE (removal efficiency) and electricity generation were investigated under different conditions, based on which two empirical models were established to predict them successfully. In soil, KCl was added to desorb Cd2+ from soil colloids to promote Cd2+ removal. These systems were also proven to remove Cd2+ efficiently when their effects on plants, zebrafish, and the soil bacterial community were tested. LEDs could be lit for days by utilizing the electricity produced herein. This work provides a novel, green, and low-cost route to remediate Cd2+ contamination and generate electricity simultaneously, which is of extensive practical significance in the environmental and energy fields.

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The emergence of simulators and their integration into teaching practice in the world of education have offered us technological opportunities to enhance and promote learning. Science students' abilities to observe, measure, predict, control variables, formulate hypotheses, and interpret data can all be activated by including simulations into the curriculum. The aim of this work is to study the effects of integrating an "evolution of electrical systems" simulator in improving students' motivation, participation and school results in learning and teaching electricity lessons in Moroccan secondary schools. Two study groups of 34 and 35 students were chosen to examine the research hypothesis. They both meet the standards for this research (same teacher, same school level, coming from the same socio-economic environment, and almost similar results in their school careers). Before beginning the process of incorporating simulation sequences in teaching, a diagnostic test was administered to both groups to assess the prerequisites for the RC and RL dipoles, and the results were evaluated. Then we designated one of the two groups as the test group, which received instruction using simulation sequences, and the other group as the control group, which received traditional teaching. Both groups took an Achievement test to evaluate the impact of this integration on the learning of physics. After examining the test data (Charts Comparison and Student's t-test), we came to the conclusion that the use of simulation sequences in the classroom produced significantly more positive and satisfactory results than the traditional approach (Mt = 12,09 for the test group and Mc = 9,69 for the control group). We saw during the sessions that the experimental class students were more motivated and engaged in their learning than the control group. We collected this data by closely observing behavioral shifts, participation rates, and student involvement in the design of the course. These new techniques contribute at improving the experimental part of electricity in secondary schools.

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Understanding electrical hazards and implementing safety measures is paramount to protecting lives and property. Therefore, this research investigates electrical hazards in households and safety measures taken by residents in Sokode-Etoe, Ghana. The primary objective is to identify gaps in knowledge regarding electrical hazards among domestic electricity consumers and offer recommendations to enhance safety and mitigate the risks. The data were systematically collected from 200 participants, including both homeowners and tenants, using a structured questionnaire. The results were presented using Likert scale analysis, sample t-test, binary logistic regression analysis, involving statistical hypothesis testing of predictor variable coefficients, Importance-Performance Map Analysis (IPMA) and Necessary Condition Analysis (NCA). Participants showed a high awareness of electrical hazards, yet demonstrated a weaker grasp of safety practices, correct emergency procedures, and infrequent testing of wiring systems by homeowners. The predominant electrical accident that emerged was electrical shock. Most homeowners have not engaged certified electrical inspectors for a decade, reflecting uncertainty about the safety protocols in place. Furthermore, respondents expressed a degree of uncertainty regarding the safety measures implemented in their households concerning electricity usage. This study underscores the pressing need to raise awareness and promote safe electrical practices in residential environments. Such an educational initiative could utilize a variety of communication channels, social media influencers, renowned personalities, customised mobile applications and other platforms. This research stands out as the inaugural investigation offering a comprehensive examination of the hazards related to energy consumption and safety precautions in Ghana. It focuses on an often-overlooked demographic of electricity users in Ghana, shedding light on domestic electrical safety issues and the growing hazards.

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China's renewable energy industry is facing the challenge of overcapacity. The environmental management literature suggests that consumers' participation in the green electricity market holds immense potential in addressing renewable energy consumption concerns. However, the question of how payment policies influence China's consumers' willingness to pay for green electricity remains unresolved. Based on 2854 valid questionnaires from a survey conducted in China's four first-tier cities in 2023, our research findings reveal: (1) While 97.9% of consumers express a willingness to use green electricity, only 63.1% are willing to pay a higher cost, indicating the existence of a "value-action" gap between environmental awareness and actual willingness to pay. (2) China's consumers' willingness to pay for green electricity is approximately 38.4 RMB per month. This figure has decreased by 5.7 RMB compared to our survey in 2019. (3) Consumers' willingness to pay will be influenced by the attitudes of those around them. (4) The voluntary payment policy positively impacts consumers' willingness to pay for green electricity. (5) Male, younger, lower education level, higher income, and larger household size consumers exhibit a higher willingness to pay. (6) Electricity price sensitivity weakens the impact of payment policies on willingness to pay.

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The recent COVID-19 pandemic has precipitated drastic changes in economic and lifestyle conditions, significantly altering residual electricity demand behavior. This alteration has expanded the demand gap between actual and forecasted electricity usage based on pre-pandemic data, highlighting a critical global issue. Many studies in the pandemic have explored the features of this widening gap, which is impacted by major social events like fast virus spread and lockdowns. However, the influence of factors like economic shifts and lifestyle changes on this demand remains largely unexplored, primarily due to the pandemic's significant effects in these areas. Understanding the essential factors affecting the demand gap is crucial for stakeholders in the electricity sector to develop effective strategies. This study examines the hourly electricity consumption and related factors during the specified period. We present a method combining time-series forecasting and sparse modeling. This helps identify critical factors affecting the electricity demand gap during the pandemic, highlighting the most crucial variables. Utilizing this method, we identify the variables that have undergone significant changes during the pandemic and evaluate their effects on the electricity demand gap. The effectiveness is proven by applying it to the dataset collected in German.

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This study introduces an orbital monitoring system designed to quantify non-technical losses (NTLs) within electricity distribution networks. Leveraging Sentinel-2 satellite imagery alongside advanced techniques in computer vision and machine learning, this system focuses on accurately segmenting urban areas, facilitating the removal of clouds, and utilizing OpenStreetMap masks for pre-annotation. Through testing on two datasets, the method attained a Jaccard index (IoU) of 0.9210 on the training set, derived from the region of France, and 0.88 on the test set, obtained from the region of Brazil, underscoring its efficacy and resilience. The precise segmentation of urban zones enables the identification of areas beyond the electric distribution company's coverage, thereby highlighting potential irregularities with heightened reliability. This approach holds promise for mitigating NTL, particularly through its ability to pinpoint potential irregular areas.