Energy Strategy Reviews最新文献

{"title":"Evaluating global progress towards Sustainable Development Goal 7 over space and time by a more comprehensive energy sustainability index","authors":"Qi Zhao ,&nbsp;Xi Lu ,&nbsp;Rachael Marie Fleming","doi":"10.1016/j.esr.2024.101626","DOIUrl":"10.1016/j.esr.2024.101626","url":null,"abstract":"<div><div>Quantifying progress towards achieving the 7th Sustainable Development Goal (SDG7) is essential for tracking global efforts on sustainable energy transition as well as guiding policy development. However, there is a lack of comprehensive assessment frameworks with consideration of temporal-and-regional dynamics. Targeting this research gap, we developed an Energy Sustainability Index (ESI) with 5 dimensions and 12 indicators to measure the progress of SDG7 in 140 countries around the world from 2011 to 2020, with a particular emphasis on its geographical inequality. Results show that while the global average ESI score has increased by 6.7 % from 2011 to 2020, this growth was largely dependent on the rapid development of a few emerging economies, with the growth rate in the rest of the world still lagging. In addition, most of the regions have failed to balance development across the 5 dimensions of the ESI, causing a regression in some dimensions, notably energy reliability. Analysis of inter-population inequality shows that although global inequality in energy development declined by 14.3 % in the period of 2011–2020, the extreme low scores of the Least-Developed Countries (LDCs) remained virtually unchanged, implying that energy development in LDCs needs more attention. This study provides a new paradigm for further exploration in the spatial and temporal characteristics of energy sustainability and highlights the urgency of promoting SDG7 globally in a holistic and synergistic approach.</div></div>","PeriodicalId":11546,"journal":{"name":"Energy Strategy Reviews","volume":"57 ","pages":"Article 101626"},"PeriodicalIF":7.9,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143152869","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"The health impacts of renewable energy consumption in sub-Saharan Africa: A machine learning perspective","authors":"Mwoya Byaro ,&nbsp;Anicet Rwezaula","doi":"10.1016/j.esr.2024.101621","DOIUrl":"10.1016/j.esr.2024.101621","url":null,"abstract":"<div><div>Recently, countries in sub-Saharan Africa (SSA) have shown increasing interest in transitioning to renewable energy due to climate change. However, the health impacts of renewable energy use are not thoroughly studied in the African context. This study investigates the impacts of renewable energy use on health outcomes, including life expectancy, maternal, and under-five mortality, in 26 SSA countries selected during the period 2000 to 2022. The main contribution of our study is the use of novel machine learning techniques known as Kernel-based Regularized Least Squares (KRLS) to fill gaps in the existing literature. Our study controlled for health expenditure, income, carbon dioxide (CO₂) emissions, and tuberculosis cases. The findings show that (i) renewable energy use significantly improves health outcomes in SSA, including increased life expectancy and reduced maternal and under-five mortality; (ii) carbon dioxide (CO₂) emissions and tuberculosis incidence have negative impacts on health outcomes, leading to decreased life expectancy and increased maternal and under-five mortality rates; (iii) the impact of renewable energy use on life expectancy and maternal mortality is nonlinear at the 25th and 75th percentiles, respectively. The study discusses potential pathways through which renewable energy use impacts health outcomes. The practical policy implication is that African governments and their collaborative partners adopt and implement renewable energy policies and programs, linking them to national health policies, development plans, and budget cycles to improve public health in the region.</div></div>","PeriodicalId":11546,"journal":{"name":"Energy Strategy Reviews","volume":"57 ","pages":"Article 101621"},"PeriodicalIF":7.9,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143152871","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"The energy-food nexus: Exploring the effects of biofuel policy on crude oil and corn market connectivity","authors":"Jin Guo ,&nbsp;Tetsuji Tanaka ,&nbsp;Xiufang Wang","doi":"10.1016/j.esr.2024.101607","DOIUrl":"10.1016/j.esr.2024.101607","url":null,"abstract":"<div><div>The rapid growth of US ethanol production, driven by the Energy Policy Act of 2005 and the Energy Independence and Security Act of 2007, has raised questions about intensifying price linkages between energy and food markets. This is based on the premise that higher crude oil prices incentivize ethanol producers to increase production, thereby boosting corn demand. This study explores the impact of biofuel policies by testing the hypothesis that increased U.S. ethanol production has strengthened these linkages. Previous literature overlooked the fact that both fuel and food goods tend to move together with the commodity price index, which can often change in response to interest rates, monetary supply, or the U.S. dollar index. To overcome this deficiency, we filter out the 'noises' from the commodity price index by controlling for the index with the partial wavelet transform and present more accurate relationships between crude oil and corn prices. Our primary finding reveals significant mid- and long-term correlations between crude oil and corn prices from 2007. This suggests that U.S. biofuel policies could strengthen the connectivity between crude oil and corn prices.</div></div>","PeriodicalId":11546,"journal":{"name":"Energy Strategy Reviews","volume":"57 ","pages":"Article 101607"},"PeriodicalIF":7.9,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143152876","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Agent-based modeling: Insights into consumer behavior, urban dynamics, grid management, and market interactions","authors":"Sambeet Mishra ,&nbsp;Thiago Lima Silva ,&nbsp;Lars Hellemo ,&nbsp;Stefan Jaehnert ,&nbsp;Lars Even Egner ,&nbsp;Sobah Abbas Petersen ,&nbsp;Tim Signer ,&nbsp;Florian Zimmermann ,&nbsp;Chiara Bordin","doi":"10.1016/j.esr.2024.101613","DOIUrl":"10.1016/j.esr.2024.101613","url":null,"abstract":"<div><div>A future sustainable energy system is expected to be digital, de-central, de-carbonized, and democratized. As the transition unfolds, new and diverse actors of various sizes will emerge in different segments. Thereby, the future energy system could shift its attention to the actors’ behavior than finding an optimum based on the physical system. Agent based modeling tools can reflect decisions from several actors in a decentralized and digital market setting. Then, such tools can enable a sustainable energy transition.</div><div>This work sets out to investigate how agent-based models could tackle various challenges in energy transition. This investigation covers four segments of the energy system — consumer, city, microgrid, and market. It starts with the consumer where consumer behavior is modeled. From there, expands to a city level where the dynamic characteristics of a city are simulated. The next step is distributed microgrids, particularly how to optimally plan the grid expansions. The final step in the investigation is simulating an energy market with national and international stakeholders. The selection of models presents how agent-based models can be applied to decision-making processes in the aforementioned segments. Then a novel framework with metrics for characterization is proposed and validated that addresses the challenge — which are the characteristics that make an agent-based model a better fit to tackle a modeling objective? Additionally, the framework identifies the existing knowledge gaps and the scope for further developments.</div><div>In summary, this work outlines how far agent-based models have come to tackle energy system challenges to sustain the energy transition. This work specifically highlights the scope, advantages, challenges, and trends of the agent-based models in energy sector applications. Moreover, this study finds that agent-based models reflect what a solution could be more than the traditional modeling practice that focuses on what a solution should be.</div></div>","PeriodicalId":11546,"journal":{"name":"Energy Strategy Reviews","volume":"57 ","pages":"Article 101613"},"PeriodicalIF":7.9,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143151465","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Energy consumption prediction for households in a society with an ageing population","authors":"Yan Zou ,&nbsp;Chen Wang ,&nbsp;Hina Najam ,&nbsp;Abdelmohsen A. Nassani ,&nbsp;Gozal Djuraeva ,&nbsp;David Oscar","doi":"10.1016/j.esr.2024.101622","DOIUrl":"10.1016/j.esr.2024.101622","url":null,"abstract":"<div><div>Social aging significantly impacts household energy consumption patterns and demand, particularly in megacities like Shanghai. This study addresses the gap in understanding high-frequency impacts of aging on energy use by employing advanced machine learning techniques. Using Gaussian Mixture Models (GMM) and Finite Mixture Models (FMM), we analyze high-frequency hourly energy consumption data from 14,000 households in Shanghai (2016–2023) to identify distinct consumption patterns and their relationship with household characteristics. The study also simulates future scenarios incorporating demographic aging and income growth. The results reveal that an aging society not only increases overall energy demand but also significantly alters hourly consumption patterns, amplifying disparities between peak and non-peak hours. These shifts, compounded by income growth, highlight the need for tailored energy policies addressing demographic transitions. This research contributes to sustainable energy planning by providing actionable insights into the intersection of aging demographics, economic development, and urban energy consumption. The findings align with the United Nations Sustainable Development Goals (SDGs) by promoting efficient and inclusive energy strategies.</div></div>","PeriodicalId":11546,"journal":{"name":"Energy Strategy Reviews","volume":"57 ","pages":"Article 101622"},"PeriodicalIF":7.9,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143152385","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Evaluation of spatial clustering methods for regionalisation of hydrogen ecosystems","authors":"Friedrich Mendler ,&nbsp;Barbara Koch ,&nbsp;Björn Meißner ,&nbsp;Christopher Voglstätter ,&nbsp;Tom Smolinka","doi":"10.1016/j.esr.2024.101627","DOIUrl":"10.1016/j.esr.2024.101627","url":null,"abstract":"<div><div>Spatially resolved modelling of local hydrogen ecosystems can help to identify optimal sizing and locations for plants and infrastructure along the value chain. Spatial clustering to identify the subregions can lead to a better representation of important features compared to administrative units or uniform grids. Several algorithms are available for regionalisation, but an evaluation of their suitability for hydrogen ecosystems or similar applications is missing in the literature. This paper presents a holistic evaluation of different spatial algorithms based on existing and newly developed statistical indicators. Although the best algorithm depends on the focus of the regionalisation process, the method REDCAP proved to be the best overall, especially with higher intra-cluster homogeneity compared to the widely used k-means algorithm. The developed indicators and their evaluation regarding different objectives are seen to be transferable to other clustering and regionalisation applications, like energy system analysis or general supply chain analysis.</div></div>","PeriodicalId":11546,"journal":{"name":"Energy Strategy Reviews","volume":"57 ","pages":"Article 101627"},"PeriodicalIF":7.9,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143152386","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Does income growth affect renewable energy or carbon emissions first? A Fourier-based analysis for renewable and fossil energies","authors":"Ugur Korkut Pata ,&nbsp;Umit Bulut ,&nbsp;Daniel Balsalobre-Lorente ,&nbsp;Jana Chovancová","doi":"10.1016/j.esr.2024.101615","DOIUrl":"10.1016/j.esr.2024.101615","url":null,"abstract":"<div><div>Environmental issues and global warming continue to drive researchers to investigate the validity of hypotheses regarding the environment. The environmental Kuznets curve (EKC) is the most popular hypothesis in the environmental economics, prompting researchers to propose a new hypothesis based on it. In this framework, the renewable energy Kuznets curve (RKC) hypothesis was proposed as a prerequisite for the EKC. According to the RKC, at the beginning of the economic growth process, an economy tends to use fossil fuels and reduce the use of renewable energy (RE) because fossil fuels are cheap. Then, as economic growth process continues and income reaches a threshold/turning point, the economy begins to rely more on RE sources. Such RE use indicates a U-shaped association between income and RE (the RKC hypothesis). Based on this information, the study compares the validity of EKC and RKC for the United States (USA) and aims to answer the question of whether the increase in per capita income initially increases RE or decreases carbon emissions. To test and compare the EKC and the RKC simultaneously and to capture smooth structural shifts, this paper uses time series techniques based on the Fourier method from 1973 to 2022. This paper presents results that support the validity of RKC and EKC. The outcomes also illustrate that the turning point of income is lower for the RKC hypothesis than for the EKC model. This suggests that the RKC hypothesis is a prerequisite for the EKC hypothesis. In other words, a higher income first helps in the deployment of renewable energies and then in the reduction of carbon emissions.</div></div>","PeriodicalId":11546,"journal":{"name":"Energy Strategy Reviews","volume":"57 ","pages":"Article 101615"},"PeriodicalIF":7.9,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143152872","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Persistence of emissions in selected African countries: Energy consumption and population growth dynamics","authors":"Obindah Gershon ,&nbsp;Joseph Kwasi Asafo ,&nbsp;Job Nagarmi Sowah ,&nbsp;Fauzia Tanko","doi":"10.1016/j.esr.2024.101628","DOIUrl":"10.1016/j.esr.2024.101628","url":null,"abstract":"<div><div>This paper relates to the United Nations Sustainable Development Goals (SDGs) 7 and 13. We uniquely investigate a population growth-energy-emissions persistence trilemma in Africa. This study contributes to the literature and policymaking by determining the effect of population growth and energy consumption on emissions and whether this interaction dims or enhances the effect of energy consumption on emissions persistence using static panel estimation techniques with annual data from 2000 to 2022. The panel data analyses of twenty-four countries in sub-Saharan Africa reveal that an increase in energy consumption results in decreased carbon emissions. However, population increase reduces the positive impact of energy consumption on carbon emissions and the persistence of carbon emissions. The empirical outcomes engender a new argument that the population growth rate in Africa interacts distinctly with the rate of clean and conventional energy consumption to determine the persistence of carbon emissions. Furthermore, the study provides empirical justification for extending clean energy to rural African communities, where reproduction occurs faster than in urban areas. Relevant policy implications are discussed, and some recommendations are made.</div></div>","PeriodicalId":11546,"journal":{"name":"Energy Strategy Reviews","volume":"57 ","pages":"Article 101628"},"PeriodicalIF":7.9,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143152873","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Dust mitigation methods and multi-criteria decision-making cleaning strategies for photovoltaic systems: Advances, challenges, and future directions","authors":"M. Gandomzadeh ,&nbsp;A.A. Yaghoubi ,&nbsp;A. Hoorsun ,&nbsp;A. Parsay ,&nbsp;A. Gholami ,&nbsp;M. Zandi ,&nbsp;R. Gavagsaz-Ghoachani ,&nbsp;H.A. Kazem","doi":"10.1016/j.esr.2024.101629","DOIUrl":"10.1016/j.esr.2024.101629","url":null,"abstract":"<div><div>This review consolidates four decades of research (1983–2024) on dust mitigation for photovoltaic systems, categorizing strategies into four key areas: preventive measures, dust monitoring systems, active cleaning methods, and multi-criteria decision-making strategies for cleaning schedules. A systematic content analysis was employed to critically evaluate methodologies, findings, and emerging trends. Preventive measures, notably super-hydrophobic coatings, can reduce dust accumulation by up to 50 %. However, integrating monitoring systems remains complex. The review highlights the potential of hybrid cleaning methods combining manual, mechanical, electromechanical, and electrostatic approaches to balance their benefits and limitations for higher efficiency and practicality. It also emphasizes a shift from fixed-interval cleaning schedules to dynamic, AI-driven decisions based on real-time data, potentially reducing the levelized cost of energy by 8 % for large-scale photovoltaic plants and 4.3 % for smaller systems. Unmanned aerial vehicle-based cleaning methods are recognized as a promising future solution for large-scale photovoltaic systems. The review identifies critical research gaps and provides recommendations for advancing dust mitigation technologies and optimizing photovoltaic cleaning and maintenance strategies to minimize soiling effects. This comprehensive analysis aims to guide future research toward more sustainable and economically viable soil detection and mitigation systems as well as photovoltaic system management.</div></div>","PeriodicalId":11546,"journal":{"name":"Energy Strategy Reviews","volume":"57 ","pages":"Article 101629"},"PeriodicalIF":7.9,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143151460","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Capturing complex electricity load patterns: A hybrid deep learning approach with proposed external-convolution attention","authors":"Mohammad Sadegh Zare ,&nbsp;Mohammad Reza Nikoo ,&nbsp;Mingjie Chen ,&nbsp;Amir H. Gandomi","doi":"10.1016/j.esr.2025.101638","DOIUrl":"10.1016/j.esr.2025.101638","url":null,"abstract":"<div><div>Short-term electricity load forecasting is a critical factor in optimizing power systems, minimizing operating costs, and securing reliable energy resources. There are various approaches for short-term electricity load forecasting, but handling complex dependencies and sudden changes in load data remains challenging. This study introduces a hybrid deep learning model to improve load forecasting accuracy. The model combines the strengths of various deep learning architectures such as Convolutional Neural Network, Temporal Convolutional Network, and Bidirectional Long Short-Term Memory with a proposed attention mechanism. This approach helps to extract temporal relations and learn long-term patterns. Furthermore, the proposed External-Convolution Attention technique effectively captures global and temporal patterns within the input sequences. Three data sets are used to conduct experiments and validate the proposed model. The proposed model is compared against several machine learning and deep learning models across five evaluation metrics. The findings show the strength of the proposed model by outperforming other models. Our load forecasting method achieves improvements ranging from 2% to 21% across different evaluation metrics. The study also evaluates the effects of datasets, features, and prediction horizons. The presented hybrid deep learning model and a novel attention mechanism improve load forecasting accuracy, contributing to the advancement of artificial intelligence in energy optimization techniques. Our model demonstrates superior performance through extensive experimentation and diverse scenarios by identifying complex load patterns and adjusting to various datasets. This indicates its practical applicability in engineering for optimizing power systems and minimizing operational costs.</div></div>","PeriodicalId":11546,"journal":{"name":"Energy Strategy Reviews","volume":"57 ","pages":"Article 101638"},"PeriodicalIF":7.9,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143151902","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}