Pub Date : 2026-03-01Epub Date: 2026-02-13DOI: 10.1016/j.esr.2026.102139
Sensheng Li , Houjian Li
Under the background of geopolitical risk and economic policy uncertainty, understanding the dynamic interaction between key commodity markets is essential. This study examines the time–frequency relationship between China's crude oil and gold under multiple sources of uncertainty. Using a comprehensive dataset spanning March 2018 to December 2025, we employ a wavelet-based time-varying parameter vector autoregression framework to derive several key insights. First, the wavelet correlation between crude oil, gold, and uncertainty is mainly dominated by medium and long-term scales. Second, the risk contagion among crude oil, gold, and uncertainty increases with the increase of the time scale, and extreme events will aggravate the risk contagion among variables. Third, EPU acts as a net contributor to risk in the short term, while GPR shows strong long-term risk spillover. Finally, there is a two-way risk contagion relationship between oil and EPU, and gold is mainly the recipient of uncertain shocks. These findings are highly relevant for designing effective hedging and risk-management strategies in the face of heightened uncertainty, particularly through positions in crude oil and gold.
{"title":"Dual safe havens during turbulence? Wavelet-based TVP-VAR evidence from oil–gold dynamics under uncertainty shocks","authors":"Sensheng Li , Houjian Li","doi":"10.1016/j.esr.2026.102139","DOIUrl":"10.1016/j.esr.2026.102139","url":null,"abstract":"<div><div>Under the background of geopolitical risk and economic policy uncertainty, understanding the dynamic interaction between key commodity markets is essential. This study examines the time–frequency relationship between China's crude oil and gold under multiple sources of uncertainty. Using a comprehensive dataset spanning March 2018 to December 2025, we employ a wavelet-based time-varying parameter vector autoregression framework to derive several key insights. First, the wavelet correlation between crude oil, gold, and uncertainty is mainly dominated by medium and long-term scales. Second, the risk contagion among crude oil, gold, and uncertainty increases with the increase of the time scale, and extreme events will aggravate the risk contagion among variables. Third, EPU acts as a net contributor to risk in the short term, while GPR shows strong long-term risk spillover. Finally, there is a two-way risk contagion relationship between oil and EPU, and gold is mainly the recipient of uncertain shocks. These findings are highly relevant for designing effective hedging and risk-management strategies in the face of heightened uncertainty, particularly through positions in crude oil and gold.</div></div>","PeriodicalId":11546,"journal":{"name":"Energy Strategy Reviews","volume":"64 ","pages":"Article 102139"},"PeriodicalIF":7.9,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147399014","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: 2026-02-20DOI: 10.1016/j.esr.2026.102103
Katharina Wildgruber , Cameron Thompson , Florian Egli
Reaching climate targets requires a massive build-out of renewable energy (RE), the cost of which is critical to the feasibility and speed of this build-out. As demonstrated in energy system modelling, the weighted average cost of capital (WACC) of RE is an important factor in determining the cost competitiveness of RE compared to fossil fuel-based alternatives. Research has found substantial differences in WACCs between countries, technologies, and across time with implications for the cost and feasibility of the energy transition. However, obtaining up-to-date WACC values for modelling remains challenging because information is typically constrained to investors, and the elicitation is time-consuming and costly. In this paper, we develop an open-access tool to calculate the WACC by country and year for utility-scale onshore wind, offshore wind, and solar photovoltaics (PV) investments. Using two different approaches, we calibrate the tool with the most extensive available empirical WACC dataset for renewables. Our tool can help improve energy system modelling, which is particularly important in data-scarce regions of the world, many of which are expected to see considerable energy demand growth over the coming years.
{"title":"An empirically validated open-access approach for calculating the cost of capital of renewables","authors":"Katharina Wildgruber , Cameron Thompson , Florian Egli","doi":"10.1016/j.esr.2026.102103","DOIUrl":"10.1016/j.esr.2026.102103","url":null,"abstract":"<div><div>Reaching climate targets requires a massive build-out of renewable energy (RE), the cost of which is critical to the feasibility and speed of this build-out. As demonstrated in energy system modelling, the weighted average cost of capital (WACC) of RE is an important factor in determining the cost competitiveness of RE compared to fossil fuel-based alternatives. Research has found substantial differences in WACCs between countries, technologies, and across time with implications for the cost and feasibility of the energy transition. However, obtaining up-to-date WACC values for modelling remains challenging because information is typically constrained to investors, and the elicitation is time-consuming and costly. In this paper, we develop an open-access tool to calculate the WACC by country and year for utility-scale onshore wind, offshore wind, and solar photovoltaics (PV) investments. Using two different approaches, we calibrate the tool with the most extensive available empirical WACC dataset for renewables. Our tool can help improve energy system modelling, which is particularly important in data-scarce regions of the world, many of which are expected to see considerable energy demand growth over the coming years.</div></div>","PeriodicalId":11546,"journal":{"name":"Energy Strategy Reviews","volume":"64 ","pages":"Article 102103"},"PeriodicalIF":7.9,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147399038","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: 2026-02-25DOI: 10.1016/j.esr.2026.102173
Jacob Mannhardt, Lukas Hegner, Giovanni Sansavini
Myopic decision-making jeopardizes a successful energy transition. Indeed, the operation of energy assets is planned with much shorter foresight than energy investment decisions, which are planned years in advance. These conflicting foresight horizons can lead to a carbon-intensive operation despite accelerating renewable investments. Here, we investigate the diverse impacts of operational and investment myopic foresight on the European energy transition. For this purpose, we present a novel two-level rolling horizon framework, where the investment decisions are optimized on the first level and then passed to the operational optimization on the second level. With the help of the presented optimization framework, we demonstrate that myopic decision-making in operating the energy system can hamper the energy transition, even when the investment decisions are optimized with a long foresight. Furthermore, we analyze how policies can help to respect the long-term climate goals under short-sighted operational decision-making. Specifically, we investigate three archetypal policies: investment targets and bans, operational targets and generation bans, and an annual emission policy. The results show that operational policies can be effective in safeguarding the energy transition against short-term operational decision-making. In contrast, investment policies prove to be ineffective, and thus their real-world impact may be overstated when neglecting myopic operation.
{"title":"Two-level rolling horizon optimization to separate investment and operational myopic planning of the energy transition","authors":"Jacob Mannhardt, Lukas Hegner, Giovanni Sansavini","doi":"10.1016/j.esr.2026.102173","DOIUrl":"10.1016/j.esr.2026.102173","url":null,"abstract":"<div><div>Myopic decision-making jeopardizes a successful energy transition. Indeed, the operation of energy assets is planned with much shorter foresight than energy investment decisions, which are planned years in advance. These conflicting foresight horizons can lead to a carbon-intensive operation despite accelerating renewable investments. Here, we investigate the diverse impacts of operational and investment myopic foresight on the European energy transition. For this purpose, we present a novel two-level rolling horizon framework, where the investment decisions are optimized on the first level and then passed to the operational optimization on the second level. With the help of the presented optimization framework, we demonstrate that myopic decision-making in operating the energy system can hamper the energy transition, even when the investment decisions are optimized with a long foresight. Furthermore, we analyze how policies can help to respect the long-term climate goals under short-sighted operational decision-making. Specifically, we investigate three archetypal policies: investment targets and bans, operational targets and generation bans, and an annual emission policy. The results show that operational policies can be effective in safeguarding the energy transition against short-term operational decision-making. In contrast, investment policies prove to be ineffective, and thus their real-world impact may be overstated when neglecting myopic operation.</div></div>","PeriodicalId":11546,"journal":{"name":"Energy Strategy Reviews","volume":"64 ","pages":"Article 102173"},"PeriodicalIF":7.9,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147399073","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.2026.102106
Xin Fang, Li Yang
As one of China's most economically active, open and innovative regions, the Yangtze River Delta (YRD) boasts strategic significance in the country's modernization and further opening-up. Exploring its medium-to long-term carbon emission trends and mitigation potential is crucial to achieving China's “Dual Carbon” goals. To systematically evaluate its complex multi-sectoral and cross-regional energy-economy-emission system, this study develops the LEAP-SJZA model. Endowed with the advantage of flexible model structure and data framework configuration, this model enables systematic simulation of dynamic impacts under diverse policy scenarios. From three dimensions—primary energy, end-use industries, and emission contributions—we predict and analyze the YRD's carbon emissions and mitigation potential. Results demonstrate: (1) The YRD can successfully achieve the goal of “carbon peaking by 2030″ under baseline, low-carbon, integrated, and blueprint scenarios. (2) Industry remains the dominant contributor to medium- and long-term carbon emissions in the YRD, with industrial carbon emissions accounting for 49 %–85 % of the total by 2060. (3) Industrial collaborative innovation exerts a significant short-term emission reduction effect; clean energy substitution serves as the core driver, while cross-regional low-carbon technology sharing acts as a long-term booster. Finally, we propose medium-to long-term countermeasures focusing on low-carbon transformation of energy structure, industrial collaborative emission reduction, and cross-regional low-carbon technology sharing, providing actionable references for the YRD to advance high-quality regional integrated development under the “Dual Carbon” goals. The LEAP-SJZA model's scenario simulation capability and multi-dimensional analysis results allow policymakers to quantitatively assess the effectiveness of different emission reduction measures, thereby supporting targeted and evidence-based decision-making for the YRD's integrated low-carbon governance.
{"title":"Scenario simulation of CO2 emissions and mitigation pathways in the Yangtze River Delta under the “Dual Carbon” goals: Medium-to long-term projections with the LEAP-SJZA model","authors":"Xin Fang, Li Yang","doi":"10.1016/j.esr.2026.102106","DOIUrl":"10.1016/j.esr.2026.102106","url":null,"abstract":"<div><div>As one of China's most economically active, open and innovative regions, the Yangtze River Delta (YRD) boasts strategic significance in the country's modernization and further opening-up. Exploring its medium-to long-term carbon emission trends and mitigation potential is crucial to achieving China's “Dual Carbon” goals. To systematically evaluate its complex multi-sectoral and cross-regional energy-economy-emission system, this study develops the LEAP-SJZA model. Endowed with the advantage of flexible model structure and data framework configuration, this model enables systematic simulation of dynamic impacts under diverse policy scenarios. From three dimensions—primary energy, end-use industries, and emission contributions—we predict and analyze the YRD's carbon emissions and mitigation potential. Results demonstrate: (1) The YRD can successfully achieve the goal of “carbon peaking by 2030″ under baseline, low-carbon, integrated, and blueprint scenarios. (2) Industry remains the dominant contributor to medium- and long-term carbon emissions in the YRD, with industrial carbon emissions accounting for 49 %–85 % of the total by 2060. (3) Industrial collaborative innovation exerts a significant short-term emission reduction effect; clean energy substitution serves as the core driver, while cross-regional low-carbon technology sharing acts as a long-term booster. Finally, we propose medium-to long-term countermeasures focusing on low-carbon transformation of energy structure, industrial collaborative emission reduction, and cross-regional low-carbon technology sharing, providing actionable references for the YRD to advance high-quality regional integrated development under the “Dual Carbon” goals. The LEAP-SJZA model's scenario simulation capability and multi-dimensional analysis results allow policymakers to quantitatively assess the effectiveness of different emission reduction measures, thereby supporting targeted and evidence-based decision-making for the YRD's integrated low-carbon governance.</div></div>","PeriodicalId":11546,"journal":{"name":"Energy Strategy Reviews","volume":"64 ","pages":"Article 102106"},"PeriodicalIF":7.9,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147399384","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.2026.102125
Ivan Mariuzzo, Bernadette Fina, Carolin Monsberger, Tara Esterl
Following the adoption of EU directives by the European Parliament and the Council, energy communities gained increased attention in the last years due to their socially-centric governance in shaping members’ welfare in local energy systems. Although being already (economically) attractive for citizens, there are still barriers on the regulatory side locking the development of advanced business models and thus additional revenue streams for community’s members. Coordinating different energy communities beyond their individual dimensions could pave the way towards opportunities for members, stakeholder, and financing institutions. Therefore, in this paper different use cases regarding the exploitation of energy communities’ flexibility potentials are developed, including their integration in electricity markets. A top-down approach has been used to screen all the relevant options from literature and adapt them into the Austrian regulatory framework. Therefore, use cases have been framed taking into account the most promising options for internal flexibility use and/or within consolidated energy markets. In addition, different control strategies are included. The results are discussed from a holistic perspective, taking into account the interests of all stakeholders involved. The proposed use cases are further discussed, with their own strengths and weaknesses, and compared in terms of immediate applicability and potential. As a result, it is identified how EC members could already operate their flexible units in response to dynamic grid tariffs or energy prices, with no additional aggregator nor additional technical requirements. However, a community-centric control strategy would unlock potential towards more coordinated flexibility provision. The latter includes not only a better self-consumption optimization (EC-oriented), but also a better planning for day-ahead, intraday, and balancing markets (market oriented) and more transparent information exchange with energy suppliers (market oriented). However, this would require further investments and rollout of IoT and communication enabling technologies. The presence of multiple or a single supplier for EC members is also found to be an important factor.
{"title":"From self-consumption to energy markets: Pathways to unlock energy community flexibility potentials in the Austrian framework","authors":"Ivan Mariuzzo, Bernadette Fina, Carolin Monsberger, Tara Esterl","doi":"10.1016/j.esr.2026.102125","DOIUrl":"10.1016/j.esr.2026.102125","url":null,"abstract":"<div><div>Following the adoption of EU directives by the European Parliament and the Council, energy communities gained increased attention in the last years due to their socially-centric governance in shaping members’ welfare in local energy systems. Although being already (economically) attractive for citizens, there are still barriers on the regulatory side locking the development of advanced business models and thus additional revenue streams for community’s members. Coordinating different energy communities beyond their individual dimensions could pave the way towards opportunities for members, stakeholder, and financing institutions. Therefore, in this paper different use cases regarding the exploitation of energy communities’ flexibility potentials are developed, including their integration in electricity markets. A top-down approach has been used to screen all the relevant options from literature and adapt them into the Austrian regulatory framework. Therefore, use cases have been framed taking into account the most promising options for internal flexibility use and/or within consolidated energy markets. In addition, different control strategies are included. The results are discussed from a holistic perspective, taking into account the interests of all stakeholders involved. The proposed use cases are further discussed, with their own strengths and weaknesses, and compared in terms of immediate applicability and potential. As a result, it is identified how EC members could already operate their flexible units in response to dynamic grid tariffs or energy prices, with no additional aggregator nor additional technical requirements. However, a community-centric control strategy would unlock potential towards more coordinated flexibility provision. The latter includes not only a better self-consumption optimization (EC-oriented), but also a better planning for day-ahead, intraday, and balancing markets (market oriented) and more transparent information exchange with energy suppliers (market oriented). However, this would require further investments and rollout of IoT and communication enabling technologies. The presence of multiple or a single supplier for EC members is also found to be an important factor.</div></div>","PeriodicalId":11546,"journal":{"name":"Energy Strategy Reviews","volume":"64 ","pages":"Article 102125"},"PeriodicalIF":7.9,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147399381","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: 2026-02-03DOI: 10.1016/j.esr.2026.102092
Shiqi Tan , Liming Yao , Zhongwen Xu , Yin Long , Hongming Xie
This study addresses the urgent need for technological solutions to combat climate change by examining the development of green and low-carbon technology (GLCT) patents in China from 2000 to 2020. Given China's dual-carbon goals to peak carbon emissions by 2030 and achieve carbon neutrality by 2060, the nation's energy and environmental challenges are critical. Using LMDI decomposition, the study reveals significant growth in GLCT patents, driven by R&D investment, renewable energy adoption, and carbon reduction policies, while factors like energy intensity inhibit further advancements. Regional analysis highlights the diversity in GLCT development across China's power grids, where grids like North China lead in fossil energy carbon reduction due to high fossil fuel dependency, and East and South China excel in clean and renewable energy patents. Future scenarios aligned with a 2.0 °C target show that robust climate policies can substantially accelerate GLCT innovation, underscoring the need for region-specific strategies to support China's low-carbon transition and global climate commitments.
{"title":"Deciphering drivers and pathways of green and low-carbon technology innovation in China: An extended LMDI decomposition and policy-scenario analysis","authors":"Shiqi Tan , Liming Yao , Zhongwen Xu , Yin Long , Hongming Xie","doi":"10.1016/j.esr.2026.102092","DOIUrl":"10.1016/j.esr.2026.102092","url":null,"abstract":"<div><div>This study addresses the urgent need for technological solutions to combat climate change by examining the development of green and low-carbon technology (GLCT) patents in China from 2000 to 2020. Given China's dual-carbon goals to peak carbon emissions by 2030 and achieve carbon neutrality by 2060, the nation's energy and environmental challenges are critical. Using LMDI decomposition, the study reveals significant growth in GLCT patents, driven by R&D investment, renewable energy adoption, and carbon reduction policies, while factors like energy intensity inhibit further advancements. Regional analysis highlights the diversity in GLCT development across China's power grids, where grids like North China lead in fossil energy carbon reduction due to high fossil fuel dependency, and East and South China excel in clean and renewable energy patents. Future scenarios aligned with a 2.0 °C target show that robust climate policies can substantially accelerate GLCT innovation, underscoring the need for region-specific strategies to support China's low-carbon transition and global climate commitments.</div></div>","PeriodicalId":11546,"journal":{"name":"Energy Strategy Reviews","volume":"64 ","pages":"Article 102092"},"PeriodicalIF":7.9,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147399385","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}
The rapid integration of Artificial Intelligence (AI) into business operations is transforming global investment dynamics, particularly within the renewable energy sector. This study investigates how AI adoption influences private investment in renewable energy firms, using a comprehensive firm-level panel of 1550 companies across twelve developed and emerging economies from 2016 to 2023. A novel AI adoption index is created using text-mining techniques applied to firms' annual reports, capturing both the breadth and intensity of AI engagement. Employing panel fixed-effects and instrumental variable (IV–2SLS) estimations to address endogeneity concerns, the findings demonstrate that AI adoption significantly enhances firms’ ability to attract private capital by serving as both a productivity tool and a strategic signal of innovation and technological readiness. Strong digital infrastructure and institutional quality amplify the effect, which is more pronounced in technologically dynamic sectors such as solar and wind energy, and among larger firms in developed economies. Moreover, the study identifies a nonlinear (inverted U-shaped) relationship, suggesting that excessive AI intensity may yield diminishing investment benefits. The results remain robust across alternative model specifications and during the COVID-19 period. By linking digital transformation with sustainable finance, the study contributes new theoretical and policy insights, highlighting AI as both a technological enabler and a strategic instrument for accelerating private investment in the global clean energy transition.
{"title":"Artificial intelligence adoption and private investment in renewable energy: Evidence from firm-level data across developed and emerging economies","authors":"Abdulazeez Y.H. Saif-Alyousfi, Turki Rashed Alshammari","doi":"10.1016/j.esr.2026.102054","DOIUrl":"10.1016/j.esr.2026.102054","url":null,"abstract":"<div><div>The rapid integration of Artificial Intelligence (AI) into business operations is transforming global investment dynamics, particularly within the renewable energy sector. This study investigates how AI adoption influences private investment in renewable energy firms, using a comprehensive firm-level panel of 1550 companies across twelve developed and emerging economies from 2016 to 2023. A novel AI adoption index is created using text-mining techniques applied to firms' annual reports, capturing both the breadth and intensity of AI engagement. Employing panel fixed-effects and instrumental variable (IV–2SLS) estimations to address endogeneity concerns, the findings demonstrate that AI adoption significantly enhances firms’ ability to attract private capital by serving as both a productivity tool and a strategic signal of innovation and technological readiness. Strong digital infrastructure and institutional quality amplify the effect, which is more pronounced in technologically dynamic sectors such as solar and wind energy, and among larger firms in developed economies. Moreover, the study identifies a nonlinear (inverted U-shaped) relationship, suggesting that excessive AI intensity may yield diminishing investment benefits. The results remain robust across alternative model specifications and during the COVID-19 period. By linking digital transformation with sustainable finance, the study contributes new theoretical and policy insights, highlighting AI as both a technological enabler and a strategic instrument for accelerating private investment in the global clean energy transition.</div></div>","PeriodicalId":11546,"journal":{"name":"Energy Strategy Reviews","volume":"64 ","pages":"Article 102054"},"PeriodicalIF":7.9,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147399450","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: 2026-01-23DOI: 10.1016/j.esr.2026.102050
Rahul Prasad Singh , Prince Kumar Singh , Indrajeet Kumar , Manish Kumar , Vivek Kumar Gaur , Amit Kaushik , Aditi Arya , Mahaswetta Saikia , Sergio de los Santos-Villalobos , Ajay Kumar , Laurent Dufossé
Microalgal bioenergy shows great potential for meeting global energy needs but faces economic limits due to low biofuel precursor yields. Optimizing microalgal biomass and lipid accumulation is vital for sustainable bioenergy production; however, the trade-off between growth and lipid synthesis remains a major challenge. Therefore, this review examines the integration of genetic engineering and artificial intelligence (AI) strategies to address these challenges within a circular bioeconomy framework aimed at maximizing the bioenergy potential of microalgae. Key advancements in genetic transformation approaches targeting lipid biosynthetic pathways and associated enzymes [acetyl-CoA carboxylase (ACCase), malonyl-CoA ACP transacylase (MAT), acyl-ACP thioesterase (TE), glycerol phosphate acyltransferase (GPAT), lysophosphatidic acid acyltransferase (LPAAT), and diacylglycerol acyltransferase (DGAT)] are discussed in detail to enhance lipid productivity. Furthermore, strategies to remove stumbling blocks such as suppressing carbohydrate biosynthesis, inhibiting lipid degradation, and modulating acetyl-CoA pathways along with photosynthetic engineering (reduction of antenna size and manipulation of Calvin cycle) approaches were explored to more effectively channel carbon flux toward lipid biosynthesis. The review also examines lipid engineering approaches aimed at modifying fatty acid composition and enhancing lipid secretion, along with the manipulation of lipogenic transcription factors (Dof-type, bZIP, NRR, and MYB) to facilitate transcriptomic reprogramming. Additionally, AI algorithms have been introduced for their potential to optimize biorefinery systems by enhancing microalgal species selection, biomass harvesting, and the optimization of cultivation and biorefinery conversion processes, while simultaneously minimizing operational costs, risks, and environmental impacts. Thus, this review highlights the potential of genetic engineering and AI in microalgae to enhance bioenergy precursors, thereby advancing sustainable biofuel production within a circular bioeconomy framework for future development.
{"title":"Sustainable bioenergy from microalgal lipid remodeling: An AI and genetic engineering approach for the circular economy","authors":"Rahul Prasad Singh , Prince Kumar Singh , Indrajeet Kumar , Manish Kumar , Vivek Kumar Gaur , Amit Kaushik , Aditi Arya , Mahaswetta Saikia , Sergio de los Santos-Villalobos , Ajay Kumar , Laurent Dufossé","doi":"10.1016/j.esr.2026.102050","DOIUrl":"10.1016/j.esr.2026.102050","url":null,"abstract":"<div><div>Microalgal bioenergy shows great potential for meeting global energy needs but faces economic limits due to low biofuel precursor yields. Optimizing microalgal biomass and lipid accumulation is vital for sustainable bioenergy production; however, the trade-off between growth and lipid synthesis remains a major challenge. Therefore, this review examines the integration of genetic engineering and artificial intelligence (AI) strategies to address these challenges within a circular bioeconomy framework aimed at maximizing the bioenergy potential of microalgae. Key advancements in genetic transformation approaches targeting lipid biosynthetic pathways and associated enzymes [acetyl-CoA carboxylase (ACCase), malonyl-CoA ACP transacylase (MAT), acyl-ACP thioesterase (TE), glycerol phosphate acyltransferase (GPAT), lysophosphatidic acid acyltransferase (LPAAT), and diacylglycerol acyltransferase (DGAT)] are discussed in detail to enhance lipid productivity. Furthermore, strategies to remove stumbling blocks such as suppressing carbohydrate biosynthesis, inhibiting lipid degradation, and modulating acetyl-CoA pathways along with photosynthetic engineering (reduction of antenna size and manipulation of Calvin cycle) approaches were explored to more effectively channel carbon flux toward lipid biosynthesis. The review also examines lipid engineering approaches aimed at modifying fatty acid composition and enhancing lipid secretion, along with the manipulation of lipogenic transcription factors (Dof-type, bZIP, NRR, and MYB) to facilitate transcriptomic reprogramming. Additionally, AI algorithms have been introduced for their potential to optimize biorefinery systems by enhancing microalgal species selection, biomass harvesting, and the optimization of cultivation and biorefinery conversion processes, while simultaneously minimizing operational costs, risks, and environmental impacts. Thus, this review highlights the potential of genetic engineering and AI in microalgae to enhance bioenergy precursors, thereby advancing sustainable biofuel production within a circular bioeconomy framework for future development.</div></div>","PeriodicalId":11546,"journal":{"name":"Energy Strategy Reviews","volume":"64 ","pages":"Article 102050"},"PeriodicalIF":7.9,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146026297","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: 2026-02-16DOI: 10.1016/j.esr.2026.102136
Manman Ge, Cisheng Wu
With the rapid expansion of the new energy vehicle market, retired power batteries from new energy vehicles have entered a stage of large-scale replacement, and their safe and environmentally sound recycling has attracted widespread attention. Drawing on behavioral economics and game theory, this paper incorporates consumer behavioral preferences and the market participation of informal recyclers to construct a four-party evolutionary game model involving the government, consumers, formal recyclers, and informal recyclers. Combined with simulation, this study explores how government policies, market prices, and consumer behavioral preferences—including irrational factors such as mental accounting, social norms, and loss aversion—affect the evolutionary dynamics of the recycling system. The results show that: Fines on informal recyclers have a stronger impact than subsidies for formal recyclers and exhibit a threshold effect: when fines are below 1200 yuan, their deterrent effect is limited; when fines exceed this level, informal recyclers oscillate between “cooperation” and “counteraction,” with higher fines causing greater strategic fluctuations. In addition, differences in recycling prices also show a threshold effect on consumers' choice of recycling channels: when the price difference between informal and formal recyclers does not exceed 200 yuan, consumers prefer formal recyclers; once it surpasses 200 yuan, they tend to turn to informal recyclers. Moreover, consumers’ loss aversion has a stronger influence on their strategies than environmental protection utility, guilt, or social norms. Specifically, environmental protection utility, guilt, and social norms can only slow the speed at which consumers shift to informal channels, but cannot alter their ultimate choice; in contrast, loss aversion amplifies the effect of price differences, accelerating the switch to informal recyclers. Finally, the loss aversion of informal recyclers intensifies their oscillation and uncertainty between “cooperation” and “counteraction.” With increasing loss aversion, their evolutionary trajectories display stronger fluctuations and greater instability.
{"title":"Recycling of retired power batteries: A four-party evolutionary game considering consumer behavior preferences and informal recyclers’ participation","authors":"Manman Ge, Cisheng Wu","doi":"10.1016/j.esr.2026.102136","DOIUrl":"10.1016/j.esr.2026.102136","url":null,"abstract":"<div><div>With the rapid expansion of the new energy vehicle market, retired power batteries from new energy vehicles have entered a stage of large-scale replacement, and their safe and environmentally sound recycling has attracted widespread attention. Drawing on behavioral economics and game theory, this paper incorporates consumer behavioral preferences and the market participation of informal recyclers to construct a four-party evolutionary game model involving the government, consumers, formal recyclers, and informal recyclers. Combined with simulation, this study explores how government policies, market prices, and consumer behavioral preferences—including irrational factors such as mental accounting, social norms, and loss aversion—affect the evolutionary dynamics of the recycling system. The results show that: Fines on informal recyclers have a stronger impact than subsidies for formal recyclers and exhibit a threshold effect: when fines are below 1200 yuan, their deterrent effect is limited; when fines exceed this level, informal recyclers oscillate between “cooperation” and “counteraction,” with higher fines causing greater strategic fluctuations. In addition, differences in recycling prices also show a threshold effect on consumers' choice of recycling channels: when the price difference between informal and formal recyclers does not exceed 200 yuan, consumers prefer formal recyclers; once it surpasses 200 yuan, they tend to turn to informal recyclers. Moreover, consumers’ loss aversion has a stronger influence on their strategies than environmental protection utility, guilt, or social norms. Specifically, environmental protection utility, guilt, and social norms can only slow the speed at which consumers shift to informal channels, but cannot alter their ultimate choice; in contrast, loss aversion amplifies the effect of price differences, accelerating the switch to informal recyclers. Finally, the loss aversion of informal recyclers intensifies their oscillation and uncertainty between “cooperation” and “counteraction.” With increasing loss aversion, their evolutionary trajectories display stronger fluctuations and greater instability.</div></div>","PeriodicalId":11546,"journal":{"name":"Energy Strategy Reviews","volume":"64 ","pages":"Article 102136"},"PeriodicalIF":7.9,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147399011","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: 2026-02-19DOI: 10.1016/j.esr.2026.102161
Cheng Lai , Yiding Wu , Dan Lai , Shijie Wen
Driven by the dual imperatives of resource security and technological upgrading, countries worldwide are accelerating the construction of renewable energy–related metals supply chains, exemplified by the integration of non-ferrous metals and renewable energy manufacturing sectors. This process has triggered a new wave of vertical integration among enterprises. However, whether vertical integration strategies can effectively enhance technological innovation acrosssupply chains remains a topic of theoretical debate and empirical uncertainty. To assess the effectiveness of this strategy, we conducted a specialized study of non-ferrous metal and renewable energy manufacturing enterprises listed on China's A-share market during the period 2009–2024, adopting an supply chain perspective. The findings reveal:Vertical integration has become an important strategic choice for firms along the energy-related metals supply chain in the context of energy transition. Empirical results indicate that vertical integration is positively associated with firms technological innovation performance, operating mainly through enhanced organizational stability and more efficient resource allocation. The innovation-enhancing effect is stronger for forward integration led by non-ferrous metal enterprises than for backward integration implemented by new energy firms. Moreover, this effect is amplified under conditions of larger supply–demand mismatches, higher levels of digital transformation, lower financing constraints, and within state-owned enterprises. The research findings offer a reference for decision-making in the non-ferrous metal industry, therenewable energy industry, and other strategic emerging industries seeking to explore patterns of technological innovation.
{"title":"Does vertical integration enhance technological innovation? Evidence from China's energy-related metals supply chain","authors":"Cheng Lai , Yiding Wu , Dan Lai , Shijie Wen","doi":"10.1016/j.esr.2026.102161","DOIUrl":"10.1016/j.esr.2026.102161","url":null,"abstract":"<div><div>Driven by the dual imperatives of resource security and technological upgrading, countries worldwide are accelerating the construction of renewable energy–related metals supply chains, exemplified by the integration of non-ferrous metals and renewable energy manufacturing sectors. This process has triggered a new wave of vertical integration among enterprises. However, whether vertical integration strategies can effectively enhance technological innovation acrosssupply chains remains a topic of theoretical debate and empirical uncertainty. To assess the effectiveness of this strategy, we conducted a specialized study of non-ferrous metal and renewable energy manufacturing enterprises listed on China's A-share market during the period 2009–2024, adopting an supply chain perspective. The findings reveal:Vertical integration has become an important strategic choice for firms along the energy-related metals supply chain in the context of energy transition. Empirical results indicate that vertical integration is positively associated with firms technological innovation performance, operating mainly through enhanced organizational stability and more efficient resource allocation. The innovation-enhancing effect is stronger for forward integration led by non-ferrous metal enterprises than for backward integration implemented by new energy firms. Moreover, this effect is amplified under conditions of larger supply–demand mismatches, higher levels of digital transformation, lower financing constraints, and within state-owned enterprises. The research findings offer a reference for decision-making in the non-ferrous metal industry, therenewable energy industry, and other strategic emerging industries seeking to explore patterns of technological innovation.</div></div>","PeriodicalId":11546,"journal":{"name":"Energy Strategy Reviews","volume":"64 ","pages":"Article 102161"},"PeriodicalIF":7.9,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147399041","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}
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