Pub Date : 2026-01-06DOI: 10.1016/j.eneco.2026.109136
Ying Wang , Honghong Wei , Andreas Kontoleon
Urban agglomeration (UA), as a model of regional integration, provides a platform for advancing sustainable urban development and carbon emission reduction. Treating the urban agglomeration implementation in China as a quasi-natural experiment, we use the high-speed rail (HSR)-weighting spatial difference-in-differences (SDID) method to examine both the direct and indirect effects of UA on carbon emission reduction. Using a balanced panel data set of 195 cities from 2004 to 2019 in China, our empirical results show that UA directly impacts carbon emission abatement in core cities and indirectly affects neighboring cities through inter-city HSR connections. Furthermore, the mechanism analysis suggests that: (1) UA reduces carbon emissions by upgrading industrial structures, generating a “borrowed-size” effect that promotes structural optimization and reduces carbon emissions in neighboring cities; (2) UA reduces carbon emissions by decreasing energy intensity, but the resulting siphon effect increases energy consumption in neighboring cities; (3) UA promotes local carbon emission reduction by stimulating technological innovation and diversification agglomeration, but does not influence carbon emissions in neighboring cities through these mechanisms. These findings provide useful insights into how UA and inter-city HSR facilitate the transition towards a low-carbon society.
{"title":"How does urban agglomeration contribute to achieving carbon reduction targets? Evidence from an HSR-weighting spatial DID approach","authors":"Ying Wang , Honghong Wei , Andreas Kontoleon","doi":"10.1016/j.eneco.2026.109136","DOIUrl":"10.1016/j.eneco.2026.109136","url":null,"abstract":"<div><div>Urban agglomeration (UA), as a model of regional integration, provides a platform for advancing sustainable urban development and carbon emission reduction. Treating the urban agglomeration implementation in China as a quasi-natural experiment, we use the high-speed rail (HSR)-weighting spatial difference-in-differences (SDID) method to examine both the direct and indirect effects of UA on carbon emission reduction. Using a balanced panel data set of 195 cities from 2004 to 2019 in China, our empirical results show that UA directly impacts carbon emission abatement in core cities and indirectly affects neighboring cities through inter-city HSR connections. Furthermore, the mechanism analysis suggests that: (1) UA reduces carbon emissions by upgrading industrial structures, generating a “borrowed-size” effect that promotes structural optimization and reduces carbon emissions in neighboring cities; (2) UA reduces carbon emissions by decreasing energy intensity, but the resulting siphon effect increases energy consumption in neighboring cities; (3) UA promotes local carbon emission reduction by stimulating technological innovation and diversification agglomeration, but does not influence carbon emissions in neighboring cities through these mechanisms. These findings provide useful insights into how UA and inter-city HSR facilitate the transition towards a low-carbon society.</div></div>","PeriodicalId":11665,"journal":{"name":"Energy Economics","volume":"154 ","pages":"Article 109136"},"PeriodicalIF":14.2,"publicationDate":"2026-01-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145939745","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"经济学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-01-05DOI: 10.1016/j.eneco.2026.109132
Lei Zhou , Shaoxin Hong , Siyan Su
There are two types of Basin Ecological Compensation Policies (BECP) in China: a formal policy initiated by the central government in the Xin'an River Basin and an informal policy organized by local governments in the Wei River Basin. We use a difference-in-differences (DID) approach to identify and compare the effects of these two policy types. We find that the BECP in the Xin'an River Basin significantly reduces enterprises' water pollutant emissions but also decreases Total Factor Productivity (TFP), whereas the BECP in the Wei River Basin has no significant effect. In addition, enterprises in the Xin'an River Basin experience reduced output and increased investment in cleaner production practices, which serve as the main channels through which water pollutant emissions decline. We further show that the economic losses borne by upstream regions exceed the compensation they receive, indicating that the compensation funds are insufficient. Finally, heterogeneity analyses reveal that the effectiveness of the BECP depends on factors such as adjacency to provincial boundaries, river length within a county, the number of industrial enterprises, and enterprise tax levels. These findings provide useful insights for the broader application of BECPs and for negotiations over compensation funding.
{"title":"Effects of basin ecological compensation policies in China: Insights from policy design differences","authors":"Lei Zhou , Shaoxin Hong , Siyan Su","doi":"10.1016/j.eneco.2026.109132","DOIUrl":"10.1016/j.eneco.2026.109132","url":null,"abstract":"<div><div>There are two types of Basin Ecological Compensation Policies (BECP) in China: a formal policy initiated by the central government in the Xin'an River Basin and an informal policy organized by local governments in the Wei River Basin. We use a difference-in-differences (DID) approach to identify and compare the effects of these two policy types. We find that the BECP in the Xin'an River Basin significantly reduces enterprises' water pollutant emissions but also decreases Total Factor Productivity (TFP), whereas the BECP in the Wei River Basin has no significant effect. In addition, enterprises in the Xin'an River Basin experience reduced output and increased investment in cleaner production practices, which serve as the main channels through which water pollutant emissions decline. We further show that the economic losses borne by upstream regions exceed the compensation they receive, indicating that the compensation funds are insufficient. Finally, heterogeneity analyses reveal that the effectiveness of the BECP depends on factors such as adjacency to provincial boundaries, river length within a county, the number of industrial enterprises, and enterprise tax levels. These findings provide useful insights for the broader application of BECPs and for negotiations over compensation funding.</div></div>","PeriodicalId":11665,"journal":{"name":"Energy Economics","volume":"154 ","pages":"Article 109132"},"PeriodicalIF":14.2,"publicationDate":"2026-01-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145939744","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"经济学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-01-04DOI: 10.1016/j.eneco.2025.109118
Zhongzhu Chu , Weijie Tan , Boru Ren , Zhiyi Xia
Frequent extreme climate events have heightened climate policy uncertainty (CPU) and incorporating the social cost of carbon has become a key element for countries seeking to improve their institutions in response to climate risks. Focusing on corporate efforts, this study innovatively constructs a carbon cost leadership strategy (CCLS) index for Chinese listed companies from 2010 to 2024 using a text-based machine learning approach. Drawing on institutional theory, we examine the relationship between CPU and firms' adoption of CCLS. Our findings indicate that CPU significantly inhibits the implementation of CCLS, primarily because CPU increases firms' operational risks and undermines firms' capacity to respond to climate regulations. Heterogeneity analysis reveals that this negative effect is more pronounced for state-owned enterprises, firms with low climate risk perception, those in low carbon-exposure and non-technology-intensive industries, and firms located in regions with weak public–government climate engagement. This study enriches the understanding of the social impacts of climate policy from the perspective of corporate carbon cost management and provides new insights for emerging economies to improve their social cost of carbon assessment systems and enhance firms' climate response capabilities.
{"title":"Assessing the effect of climate policy uncertainty on corporate carbon cost leadership strategy: Evidence from China","authors":"Zhongzhu Chu , Weijie Tan , Boru Ren , Zhiyi Xia","doi":"10.1016/j.eneco.2025.109118","DOIUrl":"10.1016/j.eneco.2025.109118","url":null,"abstract":"<div><div>Frequent extreme climate events have heightened climate policy uncertainty (CPU) and incorporating the social cost of carbon has become a key element for countries seeking to improve their institutions in response to climate risks. Focusing on corporate efforts, this study innovatively constructs a carbon cost leadership strategy (CCLS) index for Chinese listed companies from 2010 to 2024 using a text-based machine learning approach. Drawing on institutional theory, we examine the relationship between CPU and firms' adoption of CCLS. Our findings indicate that CPU significantly inhibits the implementation of CCLS, primarily because CPU increases firms' operational risks and undermines firms' capacity to respond to climate regulations. Heterogeneity analysis reveals that this negative effect is more pronounced for state-owned enterprises, firms with low climate risk perception, those in low carbon-exposure and non-technology-intensive industries, and firms located in regions with weak public–government climate engagement. This study enriches the understanding of the social impacts of climate policy from the perspective of corporate carbon cost management and provides new insights for emerging economies to improve their social cost of carbon assessment systems and enhance firms' climate response capabilities.</div></div>","PeriodicalId":11665,"journal":{"name":"Energy Economics","volume":"154 ","pages":"Article 109118"},"PeriodicalIF":14.2,"publicationDate":"2026-01-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145939743","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"经济学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-01-04DOI: 10.1016/j.eneco.2026.109133
Atanu Manna , Debarun Chakraborty , Nicholas Apergis
The study extends knowledge on the determinants of user app ratings using energy service applications, namely IndianOil ONE and Hello BPCL. Therefore, applying the Expectation Disconfirmation Theory and the Diffusion of Innovation Theory, it explores how user-related variables, such as trusting expectations in the technology, intended performance, disconfirmation, and intention, as well as diffusion factors, such as relative advantage, complexity, compatibility, and observability can predict user satisfaction and rating. We applied machine learning to topic modelling and extract the topics from the Google reviews. After retrieving the topics, regression and fsQCA analyses are performed to arrive at the final findings. The results document that the app's perceived reliability, along with expectations from using it and already established behavior patterns, should be unified to retain and improve users' positive mental representation of the application. The final suggestions focus on the advantages the application should demonstrate to users, the key requirements of a properly functioning application, and simple interface navigation to gain users' trust and expectations. This provides guidelines to relevant app developers and concerned stakeholders regarding the design and interface of those apps. However, it provides further insights into energy users regarding enhancing services in the core sector.
{"title":"User-centric design for energy service apps: Integrating expectations disconfirmation and innovation theories","authors":"Atanu Manna , Debarun Chakraborty , Nicholas Apergis","doi":"10.1016/j.eneco.2026.109133","DOIUrl":"10.1016/j.eneco.2026.109133","url":null,"abstract":"<div><div>The study extends knowledge on the determinants of user app ratings using energy service applications, namely IndianOil ONE and Hello BPCL. Therefore, applying the Expectation Disconfirmation Theory and the Diffusion of Innovation Theory, it explores how user-related variables, such as trusting expectations in the technology, intended performance, disconfirmation, and intention, as well as diffusion factors, such as relative advantage, complexity, compatibility, and observability can predict user satisfaction and rating. We applied machine learning to topic modelling and extract the topics from the Google reviews. After retrieving the topics, regression and fsQCA analyses are performed to arrive at the final findings. The results document that the app's perceived reliability, along with expectations from using it and already established behavior patterns, should be unified to retain and improve users' positive mental representation of the application. The final suggestions focus on the advantages the application should demonstrate to users, the key requirements of a properly functioning application, and simple interface navigation to gain users' trust and expectations. This provides guidelines to relevant app developers and concerned stakeholders regarding the design and interface of those apps. However, it provides further insights into energy users regarding enhancing services in the core sector.</div></div>","PeriodicalId":11665,"journal":{"name":"Energy Economics","volume":"154 ","pages":"Article 109133"},"PeriodicalIF":14.2,"publicationDate":"2026-01-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145893916","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"经济学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-01-03DOI: 10.1016/j.eneco.2025.109122
Yajing Chen , Gaoxiang Xu , Yutian Shan , Yushan Wei , Saiquan Hu , Jie She
Promoting green innovation is strategically essential for sustainable development, requiring enhanced expected returns among innovation actors. As economic expectations are shaped by narratives, government-disseminated green narratives may serve as powerful policy levers for advancing green innovation. This study employs large language models and LDA topic modeling to extract green narrative-related variables from Chinese provincial Party newspapers, combining these measures with panel data from 288 Chinese cities spanning 2011–2022 to examine how green narratives influence urban green innovation and through what mechanisms. The findings reveal that green narrative exposure significantly promotes urban green innovation through two pathways: facilitating green finance development and enhancing public environmental concern. Both the economic relevance of narrative topics and the narrativity of formats positively moderate this relationship. Further analyses confirm that narrative effects extend to firm-level green innovation quality measured by patent citations. This study demonstrates narratives as effective policy instruments for green innovation, extends green narrative research from individual to regional outcomes, and provides insights for leveraging narratives to promote substantive technological progress.
{"title":"The power of storytelling: How green narratives shape urban green innovation","authors":"Yajing Chen , Gaoxiang Xu , Yutian Shan , Yushan Wei , Saiquan Hu , Jie She","doi":"10.1016/j.eneco.2025.109122","DOIUrl":"10.1016/j.eneco.2025.109122","url":null,"abstract":"<div><div>Promoting green innovation is strategically essential for sustainable development, requiring enhanced expected returns among innovation actors. As economic expectations are shaped by narratives, government-disseminated green narratives may serve as powerful policy levers for advancing green innovation. This study employs large language models and LDA topic modeling to extract green narrative-related variables from Chinese provincial Party newspapers, combining these measures with panel data from 288 Chinese cities spanning 2011–2022 to examine how green narratives influence urban green innovation and through what mechanisms. The findings reveal that green narrative exposure significantly promotes urban green innovation through two pathways: facilitating green finance development and enhancing public environmental concern. Both the economic relevance of narrative topics and the narrativity of formats positively moderate this relationship. Further analyses confirm that narrative effects extend to firm-level green innovation quality measured by patent citations. This study demonstrates narratives as effective policy instruments for green innovation, extends green narrative research from individual to regional outcomes, and provides insights for leveraging narratives to promote substantive technological progress.</div></div>","PeriodicalId":11665,"journal":{"name":"Energy Economics","volume":"154 ","pages":"Article 109122"},"PeriodicalIF":14.2,"publicationDate":"2026-01-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145893919","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"经济学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-01-03DOI: 10.1016/j.eneco.2025.109116
Liukai Yu , Mark Goh , Junjun Zheng
The resistance toward green energy infrastructure (GEI) often leads to a NIMBY (Not in My BackYard) event, and this can challenge green energy development. This paper proposes an approach for alleviating NIMBYism through strategic risk information disclosure. It involves the project developer of the GEI committing ex-ante (before risk investigation) to a signaling mechanism that strategically discloses informed signals about the risk of the GEI to the local community, who may hold heterogeneous risk priors and engage in preference-driven social learning within the community. For this, a signaling mechanism based on a network persuasion model coupled with communication learning dynamics is developed. Our results suggest that resident heterogeneity converge to divergent consensus unions (subgroups), manifesting a social stratification and segregation phenomenon in NIMBYism. The optimal signaling mechanism is a tiered threshold recommendation structure, setting tailored thresholds for each community subgroup that commits to recommending acceptance when the risk level investigated does not exceed the threshold. The effectiveness of strategic disclosure is moderated by the external benefits and the prior pessimism of the community. It may underperform or even fail under low compensation and GEI's positive externality when dealing with a conservative community. Segregation among the community subgroups is not necessarily unfavorable. Additionally, we make two extension analyses on private priors of the residents and differentiated compensation for the divergent unions. These findings can inform policy on crafting strategic risk disclosure to address NIMBYism in GEIs.
{"title":"NIMBY-ism and green energy infrastructure: A strategic risk disclosure approach","authors":"Liukai Yu , Mark Goh , Junjun Zheng","doi":"10.1016/j.eneco.2025.109116","DOIUrl":"10.1016/j.eneco.2025.109116","url":null,"abstract":"<div><div>The resistance toward green energy infrastructure (GEI) often leads to a NIMBY (Not in My BackYard) event, and this can challenge green energy development. This paper proposes an approach for alleviating NIMBYism through strategic risk information disclosure. It involves the project developer of the GEI committing ex-ante (before risk investigation) to a signaling mechanism that strategically discloses informed signals about the risk of the GEI to the local community, who may hold heterogeneous risk priors and engage in preference-driven social learning within the community. For this, a signaling mechanism based on a network persuasion model coupled with communication learning dynamics is developed. Our results suggest that resident heterogeneity converge to divergent consensus unions (subgroups), manifesting a social stratification and segregation phenomenon in NIMBYism. The optimal signaling mechanism is a tiered threshold recommendation structure, setting tailored thresholds for each community subgroup that commits to recommending acceptance when the risk level investigated does not exceed the threshold. The effectiveness of strategic disclosure is moderated by the external benefits and the prior pessimism of the community. It may underperform or even fail under low compensation and GEI's positive externality when dealing with a conservative community. Segregation among the community subgroups is not necessarily unfavorable. Additionally, we make two extension analyses on private priors of the residents and differentiated compensation for the divergent unions. These findings can inform policy on crafting strategic risk disclosure to address NIMBYism in GEIs.</div></div>","PeriodicalId":11665,"journal":{"name":"Energy Economics","volume":"154 ","pages":"Article 109116"},"PeriodicalIF":14.2,"publicationDate":"2026-01-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145893918","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"经济学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-01-03DOI: 10.1016/j.eneco.2026.109131
Xin Jin, Yang Gao
Automated monitoring provides an innovative solution to the persistent challenge of border pollution governance. Leveraging the quasi-natural experiment of China's nationwide deployment of automated air monitoring stations, this study systematically examines the impact of automated monitoring on air pollution in provincial border cities and its underlying mechanisms. Using a triple-difference model incorporating the border variable, we find that PM2.5 concentrations in border cities decreased by 2.43 % more than in non-border cities after the installation of monitoring stations, indicating that automated monitoring effectively mitigates the long-standing border effect of air pollution. The governance effect is achieved through three primary mechanisms: (1) legal-spatial expansion effect, characterized by a significant increase in environmental penalties for border firms; (2) regulatory avoidance effect, evidenced by a simultaneous reduction in the number of surviving industrial enterprises and polluting firms in border regions; (3) rent-seeking suppression effect, manifested as significantly reduced rent-seeking costs for border firms. Further analyses reveal that while automated regulation suppresses industrial activities in border regions, it generates significant fiscal revenue and health benefits, resulting in a net benefit of 50.04 billion yuan for these regions.
{"title":"Automated monitoring and air pollution in border regions","authors":"Xin Jin, Yang Gao","doi":"10.1016/j.eneco.2026.109131","DOIUrl":"10.1016/j.eneco.2026.109131","url":null,"abstract":"<div><div>Automated monitoring provides an innovative solution to the persistent challenge of border pollution governance. Leveraging the quasi-natural experiment of China's nationwide deployment of automated air monitoring stations, this study systematically examines the impact of automated monitoring on air pollution in provincial border cities and its underlying mechanisms. Using a triple-difference model incorporating the border variable, we find that PM2.5 concentrations in border cities decreased by 2.43 % more than in non-border cities after the installation of monitoring stations, indicating that automated monitoring effectively mitigates the long-standing border effect of air pollution. The governance effect is achieved through three primary mechanisms: (1) legal-spatial expansion effect, characterized by a significant increase in environmental penalties for border firms; (2) regulatory avoidance effect, evidenced by a simultaneous reduction in the number of surviving industrial enterprises and polluting firms in border regions; (3) rent-seeking suppression effect, manifested as significantly reduced rent-seeking costs for border firms. Further analyses reveal that while automated regulation suppresses industrial activities in border regions, it generates significant fiscal revenue and health benefits, resulting in a net benefit of 50.04 billion yuan for these regions.</div></div>","PeriodicalId":11665,"journal":{"name":"Energy Economics","volume":"154 ","pages":"Article 109131"},"PeriodicalIF":14.2,"publicationDate":"2026-01-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145893917","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"经济学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-01-01DOI: 10.1016/j.eneco.2025.109112
Yue Wang, Bangjun Wang, Linyu Cui
To reveal the dynamic process of multi-agent collaborative energy transition under the coupling of electricity, carbon, and green certificate markets, this study constructs a four-party evolutionary game model involving the government (G), coal power enterprises (CPEs), green power enterprises (GPEs), and power grids (PGs). This model is based on a policy-market-agent three-dimensional collaborative analysis framework and the equilibrium strategies of each agent is analyzed. Subsequently, based on the full life-cycle theory, the study explores four periods of system development: the market emergence, policy initiation, synergistic development, and market-driven periods, analyzing the policy focus and market behavior at each period. Finally, numerical simulations are employed to discuss the impact of initial strategy probabilities on system evolution, and sensitivity analysis is conducted across four levels: policy, market, technology, and society. Research findings include: (1) System evolution follows a developmental trajectory from policy-driven initiation to market-driven autonomy, with carbon price and technology costs serving as key parameters influencing this process; (2) Policy instruments can compensate for market incentive gaps in the early transition phase, but precautions must be taken against their potential crowding-out effects on specific market segments; In the mid-to-late stages, policy interventions should gradually yield to market mechanisms; (3) RPS, CET, and TGC exhibit synergistic complementarity, with TGC primarily incentivizing GPEs, while CET and RPS jointly drive the low-carbon transition of CPEs; (4) Regulators should balance oversight costs against performance gains, dynamically adjusting policy intensity and subsidy phase-out schedules. This research offers new insights into stakeholder behavior under policy coordination and market coupling, providing quantitative decision-making support for achieving a smooth transition between effective governments and efficient markets.
{"title":"Multi-agent collaboration-driven energy structure transition: A quadripartite evolutionary game analysis framework integrating carbon, electricity, and green certificate markets","authors":"Yue Wang, Bangjun Wang, Linyu Cui","doi":"10.1016/j.eneco.2025.109112","DOIUrl":"10.1016/j.eneco.2025.109112","url":null,"abstract":"<div><div>To reveal the dynamic process of multi-agent collaborative energy transition under the coupling of electricity, carbon, and green certificate markets, this study constructs a four-party evolutionary game model involving the government (G), coal power enterprises (CPEs), green power enterprises (GPEs), and power grids (PGs). This model is based on a policy-market-agent three-dimensional collaborative analysis framework and the equilibrium strategies of each agent is analyzed. Subsequently, based on the full life-cycle theory, the study explores four periods of system development: the market emergence, policy initiation, synergistic development, and market-driven periods, analyzing the policy focus and market behavior at each period. Finally, numerical simulations are employed to discuss the impact of initial strategy probabilities on system evolution, and sensitivity analysis is conducted across four levels: policy, market, technology, and society. Research findings include: (1) System evolution follows a developmental trajectory from policy-driven initiation to market-driven autonomy, with carbon price and technology costs serving as key parameters influencing this process; (2) Policy instruments can compensate for market incentive gaps in the early transition phase, but precautions must be taken against their potential crowding-out effects on specific market segments; In the mid-to-late stages, policy interventions should gradually yield to market mechanisms; (3) RPS, CET, and TGC exhibit synergistic complementarity, with TGC primarily incentivizing GPEs, while CET and RPS jointly drive the low-carbon transition of CPEs; (4) Regulators should balance oversight costs against performance gains, dynamically adjusting policy intensity and subsidy phase-out schedules. This research offers new insights into stakeholder behavior under policy coordination and market coupling, providing quantitative decision-making support for achieving a smooth transition between effective governments and efficient markets.</div></div>","PeriodicalId":11665,"journal":{"name":"Energy Economics","volume":"153 ","pages":"Article 109112"},"PeriodicalIF":14.2,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145822777","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"经济学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-01-01DOI: 10.1016/j.eneco.2025.109108
Marco Fruzzetti , Tiziano Ropele
We investigate the predictive power of industrial lubricant oils for nowcasting the month-on-month growth rate of the Italian industrial production, using a set of advanced econometric models and various robustness checks. Our analysis shows that the inclusion of industrial lubricant oil data significantly improves the nowcast accuracy and outperforms models that exclude them in the post-pandemic period characterized by increased economic volatility, energy market disruptions and evolving structural dynamics. These findings suggest that industrial lubricant oils may serve as a more reliable economic indicator than other commonly used energy-related predictors, such as industrial gas consumption. As such, industrial lubricants represent a promising economic indicator for improving the accuracy of nowcasts of industrial activity, also in the context of structural changes, including the ongoing green transition.
{"title":"Nowcasting Italian industrial production: The predictive role of lubricant oils","authors":"Marco Fruzzetti , Tiziano Ropele","doi":"10.1016/j.eneco.2025.109108","DOIUrl":"10.1016/j.eneco.2025.109108","url":null,"abstract":"<div><div>We investigate the predictive power of industrial lubricant oils for nowcasting the month-on-month growth rate of the Italian industrial production, using a set of advanced econometric models and various robustness checks. Our analysis shows that the inclusion of industrial lubricant oil data significantly improves the nowcast accuracy and outperforms models that exclude them in the post-pandemic period characterized by increased economic volatility, energy market disruptions and evolving structural dynamics. These findings suggest that industrial lubricant oils may serve as a more reliable economic indicator than other commonly used energy-related predictors, such as industrial gas consumption. As such, industrial lubricants represent a promising economic indicator for improving the accuracy of nowcasts of industrial activity, also in the context of structural changes, including the ongoing green transition.</div></div>","PeriodicalId":11665,"journal":{"name":"Energy Economics","volume":"153 ","pages":"Article 109108"},"PeriodicalIF":14.2,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145786068","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"经济学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-01-01DOI: 10.1016/j.eneco.2025.109092
Jin Wang , Bin Ye , Zhaoxuan He , Hongjiang Pu , Bin Su , Yaqi Lu
The power sector is central to achieving the climate goals of the United Nations Sustainable Development Agenda, as it faces the dual challenge of advancing a low-carbon transition while ensuring energy security. This paper examines the critical role of green finance in addressing energy security and achieving low-carbon transformation in China's electricity market. Using a novel regional low-carbon energy security index, we empirically investigate the impact of green finance pilot policies at both regional and firm levels from 2010 to 2023. The results indicate an increase in low-carbon energy security and a decline in carbon emissions from electricity firms over the sample period. At the regional level, green finance policies demonstrate significant effectiveness in high-energy-consumption areas and regions prone to natural disasters. However, the overall impact of the green finance pilot policies on low-carbon energy security is not statistically significant. At the firm level, green finance policies are associated with a statistically significant reduction in firms' estimated carbon emissions, particularly those arising from fossil fuel combustion and solid waste incineration. This effect is especially pronounced in state-owned firms with high ownership concentration, advanced innovation capacity, and strong digitization. Mechanism analysis from a corporate finance perspective suggests that institutional investor ownership, equity balance, and management ownership serve as moderating factors. In summary, this study provides valuable insights into the strategic role of green finance in balancing energy security with the imperative of low-carbon development in China's power sector.
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