Pub Date : 2026-03-01Epub Date: 2026-02-11DOI: 10.1016/j.esr.2026.102115
Godswill Osuma , Akindele John Ogunsola , Talent Thebe Zwane , Ntokozo Nzimande
This study investigates whether corruption dampens the impact of foreign direct investment on energy access in Sub-Saharan Africa. The study adopted panel data of 49 SSA nations from 2000 to 2023. Methodologically, the study employs Panel OLS, Generalized Linear Models, Fully Modified OLS, and the Dumitrescu-Hurlin causality approach to analyse three basic hypotheses: firstly, does corruption significantly moderates the impact of FDI on access to electricity and clean cooking fuels, secondly, does institutional effectiveness conditions FDI inflows into the energy sector, and thirdly, whether causal feedback exists among institutions, FDI, and energy access. Empirical findings from the study show that while FDI significantly increases electricity access, poor institutional environments limit its impact on access to clean fuels. The control of corruption significantly enhances the effect of FDI on energy outcomes, while government effectiveness attracts FDI. The interaction term between energy access and corruption provides evidence of bidirectional causality, supporting the co-evolution of institutions and infrastructure. The findings further suggest that while corruption may temporarily facilitate certain investment flows, sustained improvements in energy access require institutional effectiveness rather than reliance on informal or distortionary governance channels, thus recommending strengthening financial flows, institutional integrity, and governance reform to ensure sustainable energy access. The study provides policymakers and development partners with practical information on how to meet SDG 7 in the region.
{"title":"Does corruption undermine energy access? Investigating foreign direct investment and energy poverty dynamics in Sub-Saharan Africa","authors":"Godswill Osuma , Akindele John Ogunsola , Talent Thebe Zwane , Ntokozo Nzimande","doi":"10.1016/j.esr.2026.102115","DOIUrl":"10.1016/j.esr.2026.102115","url":null,"abstract":"<div><div>This study investigates whether corruption dampens the impact of foreign direct investment on energy access in Sub-Saharan Africa. The study adopted panel data of 49 SSA nations from 2000 to 2023. Methodologically, the study employs Panel OLS, Generalized Linear Models, Fully Modified OLS, and the Dumitrescu-Hurlin causality approach to analyse three basic hypotheses: firstly, does corruption significantly moderates the impact of FDI on access to electricity and clean cooking fuels, secondly, does institutional effectiveness conditions FDI inflows into the energy sector, and thirdly, whether causal feedback exists among institutions, FDI, and energy access. Empirical findings from the study show that while FDI significantly increases electricity access, poor institutional environments limit its impact on access to clean fuels. The control of corruption significantly enhances the effect of FDI on energy outcomes, while government effectiveness attracts FDI. The interaction term between energy access and corruption provides evidence of bidirectional causality, supporting the co-evolution of institutions and infrastructure. The findings further suggest that while corruption may temporarily facilitate certain investment flows, sustained improvements in energy access require institutional effectiveness rather than reliance on informal or distortionary governance channels, thus recommending strengthening financial flows, institutional integrity, and governance reform to ensure sustainable energy access. The study provides policymakers and development partners with practical information on how to meet SDG 7 in the region.</div></div>","PeriodicalId":11546,"journal":{"name":"Energy Strategy Reviews","volume":"64 ","pages":"Article 102115"},"PeriodicalIF":7.9,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147399234","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-03-01Epub Date: 2026-02-06DOI: 10.1016/j.esr.2026.102048
Arne Arens , Charlotte Sophia Bez
India’s coal sector plays a central role in debates on energy transitions, yet systematic evidence on how media frame this transition remains limited. We address this gap by analysing ten years of English-language news coverage using a combined pipeline of dynamic topic modelling, sentiment analysis, and named entity recognition. Across more than 6 thousand articles, we map the evolution of topics, tones, and actor prominence. Environment-related narratives account for only about 13% of coverage, and governance and social issues around 12%, indicating their secondary position in the broader discourse. The energy transition appears largely marginal, typically embedded within frames of coal supply and energy security. Attention spikes around environmental or governance events are short-lived and do not shift narrative weight. Positive tones cluster around topics tied to state and corporate actors emphasising continuity and expansion. We show the coal complex’s discursive dominance and find China to be the most prominent external actor in transition-related coverage. The results provide an evidence-based foundation for subsequent analyses of policy windows and framing strategies around India’s coal phase-down and clean-energy transition.
{"title":"India’s long shadow of coal: Media framing of coal amidst transition calls","authors":"Arne Arens , Charlotte Sophia Bez","doi":"10.1016/j.esr.2026.102048","DOIUrl":"10.1016/j.esr.2026.102048","url":null,"abstract":"<div><div>India’s coal sector plays a central role in debates on energy transitions, yet systematic evidence on how media frame this transition remains limited. We address this gap by analysing ten years of English-language news coverage using a combined pipeline of dynamic topic modelling, sentiment analysis, and named entity recognition. Across more than 6 thousand articles, we map the evolution of topics, tones, and actor prominence. Environment-related narratives account for only about 13% of coverage, and governance and social issues around 12%, indicating their secondary position in the broader discourse. The energy transition appears largely marginal, typically embedded within frames of coal supply and energy security. Attention spikes around environmental or governance events are short-lived and do not shift narrative weight. Positive tones cluster around topics tied to state and corporate actors emphasising continuity and expansion. We show the coal complex’s discursive dominance and find China to be the most prominent external actor in transition-related coverage. The results provide an evidence-based foundation for subsequent analyses of policy windows and framing strategies around India’s coal phase-down and clean-energy transition.</div></div>","PeriodicalId":11546,"journal":{"name":"Energy Strategy Reviews","volume":"64 ","pages":"Article 102048"},"PeriodicalIF":7.9,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147399244","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
This study proposes and optimizes a novel hybrid renewable energy system (HRES) designed for achieving near-zero energy performance in coastal buildings. The selected case is a large hotel located on Kish Island, Iran, where electricity demand is supplied through a synergistic combination of photovoltaic (PV) panels, vertical-axis wind turbines (VAWTs), and oscillating water column (OWC) wave energy converters (WECs) integrated with a hydrogen-based energy storage system (HESS). The system is modeled using TRNSYS for dynamic simulation and coupled with a multi-objective optimization framework employing genetic algorithm. The optimization minimizes the total cost rate, loss of power supply probability (LPSP), and CO2 emissions simultaneously. The main innovation of this study lies in the comprehensive integration of solar, wind, and wave energy with hydrogen storage within a single building-scale system, which has not been previously explored in such detail for coastal environments. The results reveal that the optimized configuration, consisting of 500 PV modules, 13 VAWTs, and 35 OWCs, achieves a CO2 emission of 84.45 tons per year, an LPSP of 0.179, and an operational cost rate of 472 EUR/hour, representing an 81% reduction in annual carbon emissions compared to grid-dependent operation. The findings demonstrate that combining diverse renewable resources with hydrogen storage not only enhances system reliability and autonomy but also offers a scalable framework for developing cost-effective and low-carbon near-zero energy buildings in coastal and island regions.
{"title":"Integration of solar, wind, and wave energy for sustainable hotel operation in coastal regions: optimization of a building with hydrogen-supported hybrid renewable energy system","authors":"Mohsen Fathi , Zahra Mohammadi , Sevda Allahyari , Shayan Rahimi , Abolfazl Ahmadi , Alireza Zahedi","doi":"10.1016/j.esr.2026.102134","DOIUrl":"10.1016/j.esr.2026.102134","url":null,"abstract":"<div><div>This study proposes and optimizes a novel hybrid renewable energy system (HRES) designed for achieving near-zero energy performance in coastal buildings. The selected case is a large hotel located on Kish Island, Iran, where electricity demand is supplied through a synergistic combination of photovoltaic (PV) panels, vertical-axis wind turbines (VAWTs), and oscillating water column (OWC) wave energy converters (WECs) integrated with a hydrogen-based energy storage system (HESS). The system is modeled using TRNSYS for dynamic simulation and coupled with a multi-objective optimization framework employing genetic algorithm. The optimization minimizes the total cost rate, loss of power supply probability (LPSP), and CO<sub>2</sub> emissions simultaneously. The main innovation of this study lies in the comprehensive integration of solar, wind, and wave energy with hydrogen storage within a single building-scale system, which has not been previously explored in such detail for coastal environments. The results reveal that the optimized configuration, consisting of 500 PV modules, 13 VAWTs, and 35 OWCs, achieves a CO<sub>2</sub> emission of 84.45 tons per year, an LPSP of 0.179, and an operational cost rate of 472 EUR/hour, representing an 81% reduction in annual carbon emissions compared to grid-dependent operation. The findings demonstrate that combining diverse renewable resources with hydrogen storage not only enhances system reliability and autonomy but also offers a scalable framework for developing cost-effective and low-carbon near-zero energy buildings in coastal and island regions.</div></div>","PeriodicalId":11546,"journal":{"name":"Energy Strategy Reviews","volume":"64 ","pages":"Article 102134"},"PeriodicalIF":7.9,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147399292","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-03-01Epub Date: 2026-02-09DOI: 10.1016/j.esr.2026.102102
Sangida Akter , Md Shafiqul Islam
The cost of nuclear power is a decisive factor in planning and building reactors, particularly in developing countries, where uncertainties in financial structures, key technical parameters, potential construction delays, and supply-chain constraints complicate economic assessment. This study provides a probabilistic techno-economic evaluation of the Rooppur Nuclear Power Project (Rooppur NPP) with two VVER-1200 reactors in Bangladesh, combining deterministic discounted cash flow modeling with 10,000-run Monte Carlo simulations and Sobol global sensitivity analysis. The novelty lies in integrating uncertainty quantification with policy-relevant scenarios, including concessional financing and capital subsidies, to assess impacts on the levelized cost of electricity (LCOE), net present value (NPV), and internal rate of return (IRR). Results indicate a mean LCOE of $84/MWh (95% CI: $57–123), with best- and worst-case values of $51/MWh and $147/MWh, respectively. Weighted average cost of capital is the most sensitive driver, followed by fixed O&M costs and overnight capital costs. Construction delays of 4–8 years beyond the baseline 8-year schedule increase LCOE by 10–20%, highlighting the compounding effects of extended capital immobilization and deferred revenue. Policy simulations show that a 20% capital subsidy and/or 2% concessional financing rate reduce the LCOE to $44–62/MWh and improve both NPV and IRR, underscoring nuclear power's potential cost competitiveness relative to fossil fuels under carbon pricing. These findings provide actionable guidance for energy planners and policymakers in developing economies, informing project feasibility studies, power purchase agreements, and demonstrating that strategic financing and policy design are critical to unlocking low-carbon, reliable baseload nuclear power.
{"title":"Cost modeling and policy insights for deploying two VVER-1200 reactors in the newcomer nuclear country of Bangladesh","authors":"Sangida Akter , Md Shafiqul Islam","doi":"10.1016/j.esr.2026.102102","DOIUrl":"10.1016/j.esr.2026.102102","url":null,"abstract":"<div><div>The cost of nuclear power is a decisive factor in planning and building reactors, particularly in developing countries, where uncertainties in financial structures, key technical parameters, potential construction delays, and supply-chain constraints complicate economic assessment. This study provides a probabilistic techno-economic evaluation of the Rooppur Nuclear Power Project (Rooppur NPP) with two VVER-1200 reactors in Bangladesh, combining deterministic discounted cash flow modeling with 10,000-run Monte Carlo simulations and Sobol global sensitivity analysis. The novelty lies in integrating uncertainty quantification with policy-relevant scenarios, including concessional financing and capital subsidies, to assess impacts on the levelized cost of electricity (LCOE), net present value (NPV), and internal rate of return (IRR). Results indicate a mean LCOE of $84/MWh (95% CI: $57–123), with best- and worst-case values of $51/MWh and $147/MWh, respectively. Weighted average cost of capital is the most sensitive driver, followed by fixed O&M costs and overnight capital costs. Construction delays of 4–8 years beyond the baseline 8-year schedule increase LCOE by 10–20%, highlighting the compounding effects of extended capital immobilization and deferred revenue. Policy simulations show that a 20% capital subsidy and/or 2% concessional financing rate reduce the LCOE to $44–62/MWh and improve both NPV and IRR, underscoring nuclear power's potential cost competitiveness relative to fossil fuels under carbon pricing. These findings provide actionable guidance for energy planners and policymakers in developing economies, informing project feasibility studies, power purchase agreements, and demonstrating that strategic financing and policy design are critical to unlocking low-carbon, reliable baseload nuclear power.</div></div>","PeriodicalId":11546,"journal":{"name":"Energy Strategy Reviews","volume":"64 ","pages":"Article 102102"},"PeriodicalIF":7.9,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147399293","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-03-01Epub Date: 2026-02-05DOI: 10.1016/j.esr.2026.102055
Ali Razzaghi, Sayyed Majid Miri Larimi, Hamid Reza Baghaee
Active distribution networks (ADN) can effectively handle faults or interruptions, reduce downtime, and restore power more quickly by employing flexibility strategies such as advanced automation, self-healing capabilities, and multiple supply routes. This paper initially addresses the categorization of distribution network expansion planning (DNEP), encompassing its concepts and models. This paper examines various subjects, including uncertainty and its diverse implications, modernizing the distribution system, incorporating distributed generation (DG) units, deploying energy storage, and addressing electric vehicle (EV) charging. The significance of integrating flexibility into the distribution system is discussed, and technological solutions to enhance it are presented. Additionally, this paper examines dynamic load management, particularly for EVs, and the procedures for charging and discharging, along with battery storage. Contemporary DNEP aims to enhance the dependability, safety, and flexibility of distribution systems. A risk-aware planning framework that leverages conditional value-at-risk (CVaR) criteria is introduced to improve resilience against low-probability, high-impact events. The review is structured to serve researchers (methodological synthesis and identification of gaps), practitioners/DSOs (actionable planning guidance), and policymakers (regulatory levers that shape reliability and flexibility outcomes).
{"title":"Reliability-, security-, and flexibility-oriented distribution network expansion planning: Comprehensive review and future trends","authors":"Ali Razzaghi, Sayyed Majid Miri Larimi, Hamid Reza Baghaee","doi":"10.1016/j.esr.2026.102055","DOIUrl":"10.1016/j.esr.2026.102055","url":null,"abstract":"<div><div>Active distribution networks (ADN) can effectively handle faults or interruptions, reduce downtime, and restore power more quickly by employing flexibility strategies such as advanced automation, self-healing capabilities, and multiple supply routes. This paper initially addresses the categorization of distribution network expansion planning (DNEP), encompassing its concepts and models. This paper examines various subjects, including uncertainty and its diverse implications, modernizing the distribution system, incorporating distributed generation (DG) units, deploying energy storage, and addressing electric vehicle (EV) charging. The significance of integrating flexibility into the distribution system is discussed, and technological solutions to enhance it are presented. Additionally, this paper examines dynamic load management, particularly for EVs, and the procedures for charging and discharging, along with battery storage. Contemporary DNEP aims to enhance the dependability, safety, and flexibility of distribution systems. A risk-aware planning framework that leverages conditional value-at-risk (CVaR) criteria is introduced to improve resilience against low-probability, high-impact events. The review is structured to serve researchers (methodological synthesis and identification of gaps), practitioners/DSOs (actionable planning guidance), and policymakers (regulatory levers that shape reliability and flexibility outcomes).</div></div>","PeriodicalId":11546,"journal":{"name":"Energy Strategy Reviews","volume":"64 ","pages":"Article 102055"},"PeriodicalIF":7.9,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147399383","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-03-01Epub Date: 2026-01-31DOI: 10.1016/j.esr.2026.102060
Vítor Manuel de Sousa Gabriel , María Mar Miralles-Quirós , José Luis Miralles-Quirós
This study examines the connection between geopolitical risk (GPR) and two categories of exchange-traded funds (ETFs) centered on energy. The aim is to assess how risks from wars, political conflicts, and terrorist activities influence the behavior of these financial assets. We analyze the performance of funds representing clean energy and carbon energy sectors, alongside two geopolitical risk indices, over roughly fifteen and a half years. This timeframe includes various market conditions, particularly highlighting the pandemic crisis, the Ukraine war and the continuing conflict in Palestine. By employing a quantile connectivity approach, we identified secondary effects during turbulent financial periods, especially during downturns in energy asset prices linked to shifts in the geopolitical threat index. The findings provide valuable insights into how GPR impacts energy investments and are relevant for a wide range of market participants.
{"title":"Navigating tail risks: The influence of geopolitical uncertainty on clean and carbon energy","authors":"Vítor Manuel de Sousa Gabriel , María Mar Miralles-Quirós , José Luis Miralles-Quirós","doi":"10.1016/j.esr.2026.102060","DOIUrl":"10.1016/j.esr.2026.102060","url":null,"abstract":"<div><div>This study examines the connection between geopolitical risk (GPR) and two categories of exchange-traded funds (ETFs) centered on energy. The aim is to assess how risks from wars, political conflicts, and terrorist activities influence the behavior of these financial assets. We analyze the performance of funds representing clean energy and carbon energy sectors, alongside two geopolitical risk indices, over roughly fifteen and a half years. This timeframe includes various market conditions, particularly highlighting the pandemic crisis, the Ukraine war and the continuing conflict in Palestine. By employing a quantile connectivity approach, we identified secondary effects during turbulent financial periods, especially during downturns in energy asset prices linked to shifts in the geopolitical threat index. The findings provide valuable insights into how GPR impacts energy investments and are relevant for a wide range of market participants.</div></div>","PeriodicalId":11546,"journal":{"name":"Energy Strategy Reviews","volume":"64 ","pages":"Article 102060"},"PeriodicalIF":7.9,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147399449","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-03-01Epub Date: 2026-01-31DOI: 10.1016/j.esr.2026.102053
Aidin Shaghaghi , Mohammad Taghitahooneh , Sajad Qezelbigloo , Rahim Zahedi , Vahid Rezaei , Mir Hamidreza Moosavi
This paper introduces a dual-optimization framework designed to integrate Electric Vehicle (EV) charging stations into distribution networks, aiming to maximize economic returns while simultaneously improving network performance. Initially, a GA-based optimal scheduling framework is developed to maximize the collective profit for both EV owners and charging stations, incorporating financial penalties for service failures. Subsequently, a robust statistical simulation is utilized to account for uncertainties in vehicle availability and generate realistic station load profiles. These loads are then applied to the IEEE 33-bus system to evaluate key network performance indicators, including loss costs and voltage drops at the buses. The findings demonstrate that the proposed planning strategy, combined with strategic bus placement, effectively reduces network loss costs and enhances voltage profiles. Specifically, the loss-to-energy ratio improves from 4.36 % to 4.309 %, highlighting the framework's capability to balance economic objectives with enhanced grid efficiency.
{"title":"Optimal scheduling and uncertainty-aware planning of electric vehicles charging stations for enhanced power distribution network performance","authors":"Aidin Shaghaghi , Mohammad Taghitahooneh , Sajad Qezelbigloo , Rahim Zahedi , Vahid Rezaei , Mir Hamidreza Moosavi","doi":"10.1016/j.esr.2026.102053","DOIUrl":"10.1016/j.esr.2026.102053","url":null,"abstract":"<div><div>This paper introduces a dual-optimization framework designed to integrate Electric Vehicle (EV) charging stations into distribution networks, aiming to maximize economic returns while simultaneously improving network performance. Initially, a GA-based optimal scheduling framework is developed to maximize the collective profit for both EV owners and charging stations, incorporating financial penalties for service failures. Subsequently, a robust statistical simulation is utilized to account for uncertainties in vehicle availability and generate realistic station load profiles. These loads are then applied to the IEEE 33-bus system to evaluate key network performance indicators, including loss costs and voltage drops at the buses. The findings demonstrate that the proposed planning strategy, combined with strategic bus placement, effectively reduces network loss costs and enhances voltage profiles. Specifically, the loss-to-energy ratio improves from 4.36 % to 4.309 %, highlighting the framework's capability to balance economic objectives with enhanced grid efficiency.</div></div>","PeriodicalId":11546,"journal":{"name":"Energy Strategy Reviews","volume":"64 ","pages":"Article 102053"},"PeriodicalIF":7.9,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146076138","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
<div><div>The increasing urgency to address climate change has intensified the need for a deeper understanding of how renewable energy adoption, environmental pollution, and economic growth interact, particularly within spatially interconnected regions where cross-border externalities and policy spillovers fundamentally shape environmental outcomes. This study investigates the dynamic and spatially distributed relationships among renewable energy consumption, carbon dioxide emissions, and economic development across 31 Provincial administrative Units(Including provinces, autonomous regions, municipalities, and SAR hereinafter refered to as provinces) over the period 2000 to 2023, employing advanced spatial econometric techniques that explicitly account for geographical interdependencies often overlooked in conventional panel data analyses. Utilizing both static and dynamic Spatial Durbin Models alongside comprehensive robustness checks including alternative spatial weight matrices and model specifications, the analysis captures direct within-country effects, cross-border spillover effects, and feedback mechanisms that traditional non-spatial approaches fail to identify. The empirical findings reveal that renewable energy adoption generates substantial emissions reductions both domestically and regionally, with a one percent increase in renewable energy share reducing a country's own CO<sub>2</sub> emissions by 0.259 percent while simultaneously decreasing emissions in neighboring countries by 0.047 percent through mechanisms including technology diffusion, cross-border electricity trade, and policy emulation. Economic growth exhibits a nonlinear inverted U-shaped relationship with pollution consistent with the Environmental Kuznets Curve hypothesis, with high-income Provinces having surpassed the turning point at approximately $8,000 to $12,000 GDP per capita, beyond which additional economic development contributes to emissions reduction rather than environmental degradation. Industrial activities demonstrate a negative association with emissions when integrated with decarbonization technologies and cleaner production processes, challenging conventional assumptions about the inevitable environmental costs of industrialization. Cross-border trade, contrary to the Pollution Haven Hypothesis, exerts a negative total effect on emissions of 0.493 percent, reflecting China's structural shift toward high-value, low-emission goods and services alongside harmonized environmental standards that prevent the outsourcing of pollution-intensive activities. Spatial diagnostic tests including Moran's I statistics and LISA cluster maps confirm persistent positive spatial autocorrelation in emissions throughout the study period, validating the necessity of spatial modeling approaches, while the spatially lagged dependent variable coefficient of 0.074 demonstrates that emissions levels in neighboring countries directly influence domestic environmental outcomes. Ef
{"title":"Towards sustainability: Spatial analysis of renewable energy, environmental pollution, and economic growth in Chinese regions","authors":"Jiapei Wei , Yangbin Wu , Terane Shirvanova , Nilufar Umarova , Ju Jing","doi":"10.1016/j.esr.2026.102062","DOIUrl":"10.1016/j.esr.2026.102062","url":null,"abstract":"<div><div>The increasing urgency to address climate change has intensified the need for a deeper understanding of how renewable energy adoption, environmental pollution, and economic growth interact, particularly within spatially interconnected regions where cross-border externalities and policy spillovers fundamentally shape environmental outcomes. This study investigates the dynamic and spatially distributed relationships among renewable energy consumption, carbon dioxide emissions, and economic development across 31 Provincial administrative Units(Including provinces, autonomous regions, municipalities, and SAR hereinafter refered to as provinces) over the period 2000 to 2023, employing advanced spatial econometric techniques that explicitly account for geographical interdependencies often overlooked in conventional panel data analyses. Utilizing both static and dynamic Spatial Durbin Models alongside comprehensive robustness checks including alternative spatial weight matrices and model specifications, the analysis captures direct within-country effects, cross-border spillover effects, and feedback mechanisms that traditional non-spatial approaches fail to identify. The empirical findings reveal that renewable energy adoption generates substantial emissions reductions both domestically and regionally, with a one percent increase in renewable energy share reducing a country's own CO<sub>2</sub> emissions by 0.259 percent while simultaneously decreasing emissions in neighboring countries by 0.047 percent through mechanisms including technology diffusion, cross-border electricity trade, and policy emulation. Economic growth exhibits a nonlinear inverted U-shaped relationship with pollution consistent with the Environmental Kuznets Curve hypothesis, with high-income Provinces having surpassed the turning point at approximately $8,000 to $12,000 GDP per capita, beyond which additional economic development contributes to emissions reduction rather than environmental degradation. Industrial activities demonstrate a negative association with emissions when integrated with decarbonization technologies and cleaner production processes, challenging conventional assumptions about the inevitable environmental costs of industrialization. Cross-border trade, contrary to the Pollution Haven Hypothesis, exerts a negative total effect on emissions of 0.493 percent, reflecting China's structural shift toward high-value, low-emission goods and services alongside harmonized environmental standards that prevent the outsourcing of pollution-intensive activities. Spatial diagnostic tests including Moran's I statistics and LISA cluster maps confirm persistent positive spatial autocorrelation in emissions throughout the study period, validating the necessity of spatial modeling approaches, while the spatially lagged dependent variable coefficient of 0.074 demonstrates that emissions levels in neighboring countries directly influence domestic environmental outcomes. Ef","PeriodicalId":11546,"journal":{"name":"Energy Strategy Reviews","volume":"64 ","pages":"Article 102062"},"PeriodicalIF":7.9,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147399010","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-03-01Epub Date: 2026-02-14DOI: 10.1016/j.esr.2026.102095
Shi Yin , Mengxiao Sun , Ting Xu
Rural clean energy transformation (CET) is essential for achieving Chinese national carbon reduction goals and addressing climate change. However, rural areas face distinct challenges stemming from fragmented infrastructure, uneven economic development, and diverse energy demands. Using the Technology-Organization-Environment (TOE) framework and Fuzzy Set Qualitative Comparative Analysis (fsQCA) method, this study aims to explore the carbon emission reduction pathways of the CET across household and production sectors based on provincial-level data in China. The findings are as follows. (1) Carbon emission intensity (CMI) declined in 80% of provinces, with Hebei, Gansu, and Qinghai recording the largest reductions of 35.3%, 45.8%, and 40.7%, respectively. In contrast, more developed regions such as Guangdong and Zhejiang experienced increases in household energy CMI, with Guangdong showing a sharp rise of 216.1%. Meanwhile, CMI from production energy saw notable declines, particularly in Shanxi, Inner Mongolia, and Gansu. (2) Three distinct CET pathways were identified: i) policy- and market-driven models, ii) infrastructure-led transitions in less-developed regions, and iii) multi-stakeholder collaboration in agricultural and rural industries. (3) Household and production energy transitions exhibit distinct characteristics. The former centers on service enhancement and infrastructure coordination, while the latter is primarily driven by producer willingness and demand-side dynamics. These findings underscore the need for tailored, region-specific CET strategies that reflect local conditions and energy consumption patterns.
{"title":"Carbon emission reduction pathways in China's rural production and household sectors: A TOE and fsQCA-Based analysis","authors":"Shi Yin , Mengxiao Sun , Ting Xu","doi":"10.1016/j.esr.2026.102095","DOIUrl":"10.1016/j.esr.2026.102095","url":null,"abstract":"<div><div>Rural clean energy transformation (CET) is essential for achieving Chinese national carbon reduction goals and addressing climate change. However, rural areas face distinct challenges stemming from fragmented infrastructure, uneven economic development, and diverse energy demands. Using the Technology-Organization-Environment (TOE) framework and Fuzzy Set Qualitative Comparative Analysis (fsQCA) method, this study aims to explore the carbon emission reduction pathways of the CET across household and production sectors based on provincial-level data in China. The findings are as follows. (1) Carbon emission intensity (CMI) declined in 80% of provinces, with Hebei, Gansu, and Qinghai recording the largest reductions of 35.3%, 45.8%, and 40.7%, respectively. In contrast, more developed regions such as Guangdong and Zhejiang experienced increases in household energy CMI, with Guangdong showing a sharp rise of 216.1%. Meanwhile, CMI from production energy saw notable declines, particularly in Shanxi, Inner Mongolia, and Gansu. (2) Three distinct CET pathways were identified: i) policy- and market-driven models, ii) infrastructure-led transitions in less-developed regions, and iii) multi-stakeholder collaboration in agricultural and rural industries. (3) Household and production energy transitions exhibit distinct characteristics. The former centers on service enhancement and infrastructure coordination, while the latter is primarily driven by producer willingness and demand-side dynamics. These findings underscore the need for tailored, region-specific CET strategies that reflect local conditions and energy consumption patterns.</div></div>","PeriodicalId":11546,"journal":{"name":"Energy Strategy Reviews","volume":"64 ","pages":"Article 102095"},"PeriodicalIF":7.9,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147399012","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-03-01Epub Date: 2026-02-13DOI: 10.1016/j.esr.2026.102128
Yun Zhou , Xinran Yin , Yifan Chen , Jian Cao , Enjing Jiang , Yuxiu Si , Xuanlan Huang
Accelerating renewable energy development is critical for climate goals, with photovoltaic (PV) power emerging as a leading technology. However, PV's land-intensive nature triggers a “green-green dilemma” between climate targets and natural resource conservation, that is a globally prevalent challenge particularly acute in Zhejiang Province, China, where land constraints hamper large-scale PV expansion. To address this issue, this study proposes six PV-industry complementary modes including mountain-based agrophotovoltaics and offshore wind-solar hybrids, etc. Furthermore, we introduce the data-driven complementary development (DCD) framework, an innovative approach integrating K-means clustering, Mann–Whitney U test, and significance-based tier-searching (STS) algorithm to quantify subregional development potential for each mode. Unlike traditional clustering methods, the DCD framework enhances statistical robustness in potential identification. Applied to 90 subregions in Zhejiang, this framework reveals that most areas are suited for at least one complementary mode, providing actionable insights for local policymakers. This study makes three key contributions: enriching PV complementary development literature, proposing a replicable decision-support tool for resource-constrained regions, and offering targeted strategies to balance renewable energy growth and natural resource preservation in Zhejiang.
{"title":"Data-driven complementary policy development for photovoltaic industry facing resource constraints: A case study of Zhejiang Province in China","authors":"Yun Zhou , Xinran Yin , Yifan Chen , Jian Cao , Enjing Jiang , Yuxiu Si , Xuanlan Huang","doi":"10.1016/j.esr.2026.102128","DOIUrl":"10.1016/j.esr.2026.102128","url":null,"abstract":"<div><div>Accelerating renewable energy development is critical for climate goals, with photovoltaic (PV) power emerging as a leading technology. However, PV's land-intensive nature triggers a “green-green dilemma” between climate targets and natural resource conservation, that is a globally prevalent challenge particularly acute in Zhejiang Province, China, where land constraints hamper large-scale PV expansion. To address this issue, this study proposes six PV-industry complementary modes including mountain-based agrophotovoltaics and offshore wind-solar hybrids, etc. Furthermore, we introduce the data-driven complementary development (DCD) framework, an innovative approach integrating K-means clustering, Mann–Whitney <em>U</em> test, and significance-based tier-searching (STS) algorithm to quantify subregional development potential for each mode. Unlike traditional clustering methods, the DCD framework enhances statistical robustness in potential identification. Applied to 90 subregions in Zhejiang, this framework reveals that most areas are suited for at least one complementary mode, providing actionable insights for local policymakers. This study makes three key contributions: enriching PV complementary development literature, proposing a replicable decision-support tool for resource-constrained regions, and offering targeted strategies to balance renewable energy growth and natural resource preservation in Zhejiang.</div></div>","PeriodicalId":11546,"journal":{"name":"Energy Strategy Reviews","volume":"64 ","pages":"Article 102128"},"PeriodicalIF":7.9,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147399013","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
扫码关注我们
求助内容：
应助结果提醒方式：
京公网安备 11010802042870号