Pub Date : 2024-12-12DOI: 10.1016/j.eneco.2024.108132
Farhad Billimoria, Jacob Mays, Rahmat Poudineh
One of the persistent concerns in scarcity-based electricity market designs is that markets for long-term contracts are highly illiquid or ‘missing’. In the context of decarbonisation, a key question arises as to whether this phenomenon will persist or improve as markets transition to greater proportions of zero-marginal cost renewables and storage. Using a stochastic equilibrium model and insights from insurance theory, we consider long-term hedging in the context of credit and financing constraints. For electricity markets dominated by thermal generation, the deliverability of long-term hedges can be significantly impacted by the volatility of thermal fuels and the co-dependence between them under extreme conditions. Our results demonstrate the importance of fuel hedging as an underlying driver of the cost and deliverability of electricity hedging. Where the underlying fuel exposure cannot be contracted, generators may need to price contracts at multiples of the expected value of spot prices. The results provide guidance for discourse on policy and market design in relation to tail risk. One interpretation of the results in this paper is that the lack of contracting for tail risks given a volatile raw commodity is not a market failure per se, but a rational response of market participants due in part to the expense of hedging generation when fuel exposures are unable to be hedged. Counterintuitively, in the context of the energy transition, our results show that, ceteris paribus, increasing the penetration of low carbon resources like wind, solar, and energy storage can add diversity to the risk exposures of the underlying hedge contract.
{"title":"Hedging and tail risk in electricity markets","authors":"Farhad Billimoria, Jacob Mays, Rahmat Poudineh","doi":"10.1016/j.eneco.2024.108132","DOIUrl":"https://doi.org/10.1016/j.eneco.2024.108132","url":null,"abstract":"One of the persistent concerns in scarcity-based electricity market designs is that markets for long-term contracts are highly illiquid or ‘missing’. In the context of decarbonisation, a key question arises as to whether this phenomenon will persist or improve as markets transition to greater proportions of zero-marginal cost renewables and storage. Using a stochastic equilibrium model and insights from insurance theory, we consider long-term hedging in the context of credit and financing constraints. For electricity markets dominated by thermal generation, the deliverability of long-term hedges can be significantly impacted by the volatility of thermal fuels and the co-dependence between them under extreme conditions. Our results demonstrate the importance of fuel hedging as an underlying driver of the cost and deliverability of electricity hedging. Where the underlying fuel exposure cannot be contracted, generators may need to price contracts at multiples of the expected value of spot prices. The results provide guidance for discourse on policy and market design in relation to tail risk. One interpretation of the results in this paper is that the lack of contracting for tail risks given a volatile raw commodity is not a market failure per se, but a rational response of market participants due in part to the expense of hedging generation when fuel exposures are unable to be hedged. Counterintuitively, in the context of the energy transition, our results show that, ceteris paribus, increasing the penetration of low carbon resources like wind, solar, and energy storage can add diversity to the risk exposures of the underlying hedge contract.","PeriodicalId":11665,"journal":{"name":"Energy Economics","volume":"32 1","pages":""},"PeriodicalIF":12.8,"publicationDate":"2024-12-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142873881","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 : 2024-12-11DOI: 10.1016/j.eneco.2024.108130
Jin Boon Wong, Qin Zhang
We investigate the impact of firm-level political risks on carbon emissions. Our results show that companies decrease their total emission footprint in response to higher political risks. However, this is driven predominantly by the reduction of scope 2 emissions, which “involves purchased energy consumed by the firm.” With increasing policymakers, investors, and stakeholders' emphasis on positive climate change actions, our findings indicate that corporations may view reducing carbon emissions as an efficient strategy to draw attention away from higher political risks. Further analyses reveal that this tactic is adopted mainly by resource-constrained companies that are smaller, underperforming, with lower cash reserves and cashflow from operations. Using a channel test, we also provide empirical evidence that reducing scope 2 emissions in response to higher political risks may have helped firms avoid lower market valuation. Our findings are robust to a series of sensitivity and endogeneity checks. Overall, this study advances the literature by highlighting the interplay between politics and carbon emission in an increasingly climate change-focused environment.
{"title":"The impact of political risks on carbon emissions","authors":"Jin Boon Wong, Qin Zhang","doi":"10.1016/j.eneco.2024.108130","DOIUrl":"https://doi.org/10.1016/j.eneco.2024.108130","url":null,"abstract":"We investigate the impact of firm-level political risks on carbon emissions. Our results show that companies decrease their total emission footprint in response to higher political risks. However, this is driven predominantly by the reduction of scope 2 emissions, which “involves purchased energy consumed by the firm.” With increasing policymakers, investors, and stakeholders' emphasis on positive climate change actions, our findings indicate that corporations may view reducing carbon emissions as an efficient strategy to draw attention away from higher political risks. Further analyses reveal that this tactic is adopted mainly by resource-constrained companies that are smaller, underperforming, with lower cash reserves and cashflow from operations. Using a channel test, we also provide empirical evidence that reducing scope 2 emissions in response to higher political risks may have helped firms avoid lower market valuation. Our findings are robust to a series of sensitivity and endogeneity checks. Overall, this study advances the literature by highlighting the interplay between politics and carbon emission in an increasingly climate change-focused environment.","PeriodicalId":11665,"journal":{"name":"Energy Economics","volume":"151 1","pages":""},"PeriodicalIF":12.8,"publicationDate":"2024-12-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142889332","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 : 2024-12-11DOI: 10.1016/j.eneco.2024.108114
Mariya Gubareva, Muhammad Shafiullah, Tamara Teplova
{"title":"Corrigendum to “Cross-quantile risk assessment: The interplay of crude oil, artificial intelligence, clean tech, and other markets” [Energy Economics Volume 141, January 2025, 108085]","authors":"Mariya Gubareva, Muhammad Shafiullah, Tamara Teplova","doi":"10.1016/j.eneco.2024.108114","DOIUrl":"https://doi.org/10.1016/j.eneco.2024.108114","url":null,"abstract":"","PeriodicalId":11665,"journal":{"name":"Energy Economics","volume":"38 1","pages":""},"PeriodicalIF":12.8,"publicationDate":"2024-12-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142825373","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 : 2024-12-07DOI: 10.1016/j.eneco.2024.108095
Sai Bravo-Melgarejo, Carole Haritchabalet
We present a stylized microeconomic model to analyze solar panels and storage investment decisions of a representative consumer under either grid (credit regulation) or individual (price regulation) storage. We identify the conditions under which prosumers become storers. We show that solar technology must be more competitive under credit than price regulation for consumers to invest in storage. We calibrate our model using French data from 2023 and 2030 and consider batteries and fuel-cells as potential individual storage technologies. France’s current price regulation incentivizes investment in solar capacity, but not in storage. Conversely, credit regulation would immediately encourage energy storage. Looking ahead to 2030, both regulatory frameworks would incentivize energy storage. The distribution system operator prefers credit regulation, provided the regulator allows full energy recovery. Overall, energy storage enhances welfare under both regulatory frameworks.
{"title":"Prosumers: Grid vs. individual storage","authors":"Sai Bravo-Melgarejo, Carole Haritchabalet","doi":"10.1016/j.eneco.2024.108095","DOIUrl":"https://doi.org/10.1016/j.eneco.2024.108095","url":null,"abstract":"We present a stylized microeconomic model to analyze solar panels and storage investment decisions of a representative consumer under either grid (credit regulation) or individual (price regulation) storage. We identify the conditions under which prosumers become storers. We show that solar technology must be more competitive under credit than price regulation for consumers to invest in storage. We calibrate our model using French data from 2023 and 2030 and consider batteries and fuel-cells as potential individual storage technologies. France’s current price regulation incentivizes investment in solar capacity, but not in storage. Conversely, credit regulation would immediately encourage energy storage. Looking ahead to 2030, both regulatory frameworks would incentivize energy storage. The distribution system operator prefers credit regulation, provided the regulator allows full energy recovery. Overall, energy storage enhances welfare under both regulatory frameworks.","PeriodicalId":11665,"journal":{"name":"Energy Economics","volume":"21 1","pages":""},"PeriodicalIF":12.8,"publicationDate":"2024-12-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142825374","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 : 2024-12-07DOI: 10.1016/j.eneco.2024.108096
Mohamed Amin Benatia, Fouad Ben Abdelaziz, M’hammed Sahnoun
Edge sensor nodes are used to ensure informed decisions in several fields, including smart buildings, supply chain management, sustainability, mobile robotics in industry and logistics, and applications, including the Internet of Things (IoT). However, designing an optimal and cost-effective deployment of edge sensor nodes in a complex environment with different types of walls and interferences poses a significant challenge. Traditional methodologies rely on trial and error, which can lead to non-optimal solutions and ignore the network’s efficiency and sustainability issues, such as energy consumption and quality of service (QoS). This paper proposes a two-stage strategy for deploying edge sensor nodes using multi-objective evolutionary algorithms (MOEA) that consider the topology of the environment, including walls and doors, which impact the network’s QoS. The first stage involves using a single-solution-based metaheuristic (S-metaheuristic) to generate an initial population. The second stage involves integrating the population into a population-based metaheuristic (P-metaheuristic) to find the optimal sensor positioning and communication strategy. The computational experiments demonstrate the superiority of the proposed approach compared to traditional methods that rely on random generation of the initial population in terms of energy consumption and area coverage.
{"title":"Energy savings and coverage optimization in edge WiFi sensor deployment for buildings: A multi-objective evolutionary approach","authors":"Mohamed Amin Benatia, Fouad Ben Abdelaziz, M’hammed Sahnoun","doi":"10.1016/j.eneco.2024.108096","DOIUrl":"https://doi.org/10.1016/j.eneco.2024.108096","url":null,"abstract":"Edge sensor nodes are used to ensure informed decisions in several fields, including smart buildings, supply chain management, sustainability, mobile robotics in industry and logistics, and applications, including the Internet of Things (IoT). However, designing an optimal and cost-effective deployment of edge sensor nodes in a complex environment with different types of walls and interferences poses a significant challenge. Traditional methodologies rely on trial and error, which can lead to non-optimal solutions and ignore the network’s efficiency and sustainability issues, such as energy consumption and quality of service (QoS). This paper proposes a two-stage strategy for deploying edge sensor nodes using multi-objective evolutionary algorithms (MOEA) that consider the topology of the environment, including walls and doors, which impact the network’s QoS. The first stage involves using a single-solution-based metaheuristic (S-metaheuristic) to generate an initial population. The second stage involves integrating the population into a population-based metaheuristic (P-metaheuristic) to find the optimal sensor positioning and communication strategy. The computational experiments demonstrate the superiority of the proposed approach compared to traditional methods that rely on random generation of the initial population in terms of energy consumption and area coverage.","PeriodicalId":11665,"journal":{"name":"Energy Economics","volume":"114 1","pages":""},"PeriodicalIF":12.8,"publicationDate":"2024-12-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142889309","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 : 2024-12-06DOI: 10.1016/j.eneco.2024.108082
Vincenzo Pacelli, Caterina Di Tommaso, Matteo Foglia, Maria Melania Povia
This paper investigates the connection between energy uncertainty and banking credit risk within the Eurozone. To analyze this relationship, we first apply a Bayesian time-varying VAR model to examine how shocks in energy uncertainty influence financial risk. Next, we use the impulse response function to assess how these shocks propagate through the banking sector. Further, long-run Granger causality is employed to investigate the causal pathways of shock transmission. Our empirical findings show clear patterns: banking credit risk increases in response to energy uncertainty shocks. Over time, these shocks show a progressively rising impact on credit risk, highlighting the growing influence of energy uncertainty on the spread of financial risk.
本文研究了欧元区能源不确定性与银行信贷风险之间的关系。为了分析这种关系，我们首先应用贝叶斯时变 VAR 模型来研究能源不确定性的冲击如何影响金融风险。接下来，我们使用脉冲响应函数来评估这些冲击如何通过银行业传播。此外，我们还利用长期格兰杰因果关系来研究冲击传播的因果途径。我们的实证研究结果显示了清晰的模式：银行业信贷风险会随着能源不确定性冲击而增加。随着时间的推移，这些冲击对信贷风险的影响逐步上升，凸显出能源不确定性对金融风险扩散的影响越来越大。
{"title":"Spillover effects between energy uncertainty and financial risk in the Eurozone banking sector","authors":"Vincenzo Pacelli, Caterina Di Tommaso, Matteo Foglia, Maria Melania Povia","doi":"10.1016/j.eneco.2024.108082","DOIUrl":"https://doi.org/10.1016/j.eneco.2024.108082","url":null,"abstract":"This paper investigates the connection between energy uncertainty and banking credit risk within the Eurozone. To analyze this relationship, we first apply a Bayesian time-varying VAR model to examine how shocks in energy uncertainty influence financial risk. Next, we use the impulse response function to assess how these shocks propagate through the banking sector. Further, long-run Granger causality is employed to investigate the causal pathways of shock transmission. Our empirical findings show clear patterns: banking credit risk increases in response to energy uncertainty shocks. Over time, these shocks show a progressively rising impact on credit risk, highlighting the growing influence of energy uncertainty on the spread of financial risk.","PeriodicalId":11665,"journal":{"name":"Energy Economics","volume":"22 1","pages":""},"PeriodicalIF":12.8,"publicationDate":"2024-12-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142825375","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 : 2024-12-06DOI: 10.1016/j.eneco.2024.108097
Dimitris Anastasiou, Zied Ftiti, Waël Louhichi, Anastasios Rizos, Artemis Stratopoulou
In recent years, inflation has surged significantly, and uncertainty surrounding energy prices has increased worldwide. This uncertainty, which has led to demand shocks during the post-pandemic recovery phase and has been exacerbated by recent geopolitical tensions, has impacted investor behaviour, leading to shifts in investment strategies and market sentiment. This study aimed to explore the connection between these two trends by examining whether investor sentiment related to oil markets affects inflation in the European Union (EU) utilising a comprehensive dataset and advanced econometric techniques. Our findings reveal that oil sentiment significantly affects inflation, with implications for both demand and supply dynamics. A positive relationship is observed between oil sentiment and headline Harmonised Index of Consumer Prices (HICP) inflation, and a negative relationship between oil volatility and inflation over a 12-month horizon. Additionally, our findings reveal that a model incorporating fundamentals and oil sentiment provides superior forecasting performance for EU inflation, suggesting that oil sentiment offers valuable insights into the future behaviour of EU inflation. Finally, our results demonstrate that EU inflation reacts positively and significantly to a positive oil sentiment shock only during low-sentiment periods, whereas an insignificant response is observed during high-sentiment periods. Our study highlights the importance of incorporating investor sentiment into policy frameworks, suggesting that understanding these psychological factors can enhance inflation management strategies. Overall, this research contributes to a deeper understanding of the complex interplay between investor sentiment and macroeconomic variables, offering valuable insights for policymakers and investors alike.
{"title":"The influence of oil investors' sentiment on inflation dynamics and uncertainty","authors":"Dimitris Anastasiou, Zied Ftiti, Waël Louhichi, Anastasios Rizos, Artemis Stratopoulou","doi":"10.1016/j.eneco.2024.108097","DOIUrl":"https://doi.org/10.1016/j.eneco.2024.108097","url":null,"abstract":"In recent years, inflation has surged significantly, and uncertainty surrounding energy prices has increased worldwide. This uncertainty, which has led to demand shocks during the post-pandemic recovery phase and has been exacerbated by recent geopolitical tensions, has impacted investor behaviour, leading to shifts in investment strategies and market sentiment. This study aimed to explore the connection between these two trends by examining whether investor sentiment related to oil markets affects inflation in the European Union (EU) utilising a comprehensive dataset and advanced econometric techniques. Our findings reveal that oil sentiment significantly affects inflation, with implications for both demand and supply dynamics. A positive relationship is observed between oil sentiment and headline Harmonised Index of Consumer Prices (HICP) inflation, and a negative relationship between oil volatility and inflation over a 12-month horizon. Additionally, our findings reveal that a model incorporating fundamentals and oil sentiment provides superior forecasting performance for EU inflation, suggesting that oil sentiment offers valuable insights into the future behaviour of EU inflation. Finally, our results demonstrate that EU inflation reacts positively and significantly to a positive oil sentiment shock only during low-sentiment periods, whereas an insignificant response is observed during high-sentiment periods. Our study highlights the importance of incorporating investor sentiment into policy frameworks, suggesting that understanding these psychological factors can enhance inflation management strategies. Overall, this research contributes to a deeper understanding of the complex interplay between investor sentiment and macroeconomic variables, offering valuable insights for policymakers and investors alike.","PeriodicalId":11665,"journal":{"name":"Energy Economics","volume":"43 1","pages":""},"PeriodicalIF":12.8,"publicationDate":"2024-12-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142935929","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 : 2024-12-06DOI: 10.1016/j.eneco.2024.108113
Dora Almeida, Paulo Ferreira, Andreia Dionísio, Faheem Aslam
This study delves into the complexities of energy commodity futures and clean energy indexes, analyzing their responses to geopolitical risk. The detrended fluctuation analysis was applied, and the efficiency index was estimated to assess energy market behavior better. This approach allows the evaluation of long-range dependence and market efficiency. The findings show evolving patterns influenced by significant geopolitical events such as the COVID-19 pandemic and geopolitical conflicts. Transfer entropy analysis also uncovers directional dependence between energy markets and geopolitical risk, highlighting energy commodities' influential (or anticipated) role on geopolitical indexes. The dynamic analysis emphasizes time-varying relationships, with fluctuations notably impacted by global events like the European sovereign debt crisis and escalating geopolitical tensions. Additionally, clean energy indexes exhibit sensitivity to geopolitical risk, offering valuable insights into market behavior and informing risk management strategies. The study highlights the complex and dynamic relationships between energy markets and geopolitical factors and provides useful information for investors and policymakers on energy markets.
{"title":"Exploring the connection between geopolitical risks and energy markets","authors":"Dora Almeida, Paulo Ferreira, Andreia Dionísio, Faheem Aslam","doi":"10.1016/j.eneco.2024.108113","DOIUrl":"https://doi.org/10.1016/j.eneco.2024.108113","url":null,"abstract":"This study delves into the complexities of energy commodity futures and clean energy indexes, analyzing their responses to geopolitical risk. The detrended fluctuation analysis was applied, and the efficiency index was estimated to assess energy market behavior better. This approach allows the evaluation of long-range dependence and market efficiency. The findings show evolving patterns influenced by significant geopolitical events such as the COVID-19 pandemic and geopolitical conflicts. Transfer entropy analysis also uncovers directional dependence between energy markets and geopolitical risk, highlighting energy commodities' influential (or anticipated) role on geopolitical indexes. The dynamic analysis emphasizes time-varying relationships, with fluctuations notably impacted by global events like the European sovereign debt crisis and escalating geopolitical tensions. Additionally, clean energy indexes exhibit sensitivity to geopolitical risk, offering valuable insights into market behavior and informing risk management strategies. The study highlights the complex and dynamic relationships between energy markets and geopolitical factors and provides useful information for investors and policymakers on energy markets.","PeriodicalId":11665,"journal":{"name":"Energy Economics","volume":"45 1","pages":""},"PeriodicalIF":12.8,"publicationDate":"2024-12-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142825380","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 : 2024-12-05DOI: 10.1016/j.eneco.2024.108111
Xiqiang Xia, Jiangwen Li, Wei Wei, Ramzi Benkraiem, Mohammad Zoynul Abedin
Considering the policies surrounding carbon trading, decarbonization plans have been regarded as imperative choices for the manufacturing industry. However, there has been little research into combining the concrete carbon quota allocation methods with the low-carbon supply chain. Still, the distinction between ordinary and low-carbon manufacturers has been scarcely investigated. To fill these gaps, drawing on two quota allocation methods—grandfathering and benchmarking, we model the supply chains under two production modes, which consists of an ordinary manufacturer, a low-carbon manufacturer and a hybrid manufacturer. Our primary conclusions are listed here. The carbon emission reduction level (CERL) shall fluctuate within an acceptable scope to prevent adverse consequences on total social welfare. Additionally, independent of the production mode, manufacturers' profits will peak when the gross carbon quotas meet certain values under grandfathering. Meanwhile, under benchmarking, the environmental performance and consumer surpluses are better when the benchmark quota reaches a certain value.
{"title":"Emission reduction levels of manufacturers under carbon trading policies","authors":"Xiqiang Xia, Jiangwen Li, Wei Wei, Ramzi Benkraiem, Mohammad Zoynul Abedin","doi":"10.1016/j.eneco.2024.108111","DOIUrl":"https://doi.org/10.1016/j.eneco.2024.108111","url":null,"abstract":"Considering the policies surrounding carbon trading, decarbonization plans have been regarded as imperative choices for the manufacturing industry. However, there has been little research into combining the concrete carbon quota allocation methods with the low-carbon supply chain. Still, the distinction between ordinary and low-carbon manufacturers has been scarcely investigated. To fill these gaps, drawing on two quota allocation methods—grandfathering and benchmarking, we model the supply chains under two production modes, which consists of an ordinary manufacturer, a low-carbon manufacturer and a hybrid manufacturer. Our primary conclusions are listed here. The carbon emission reduction level (CERL) shall fluctuate within an acceptable scope to prevent adverse consequences on total social welfare. Additionally, independent of the production mode, manufacturers' profits will peak when the gross carbon quotas meet certain values under grandfathering. Meanwhile, under benchmarking, the environmental performance and consumer surpluses are better when the benchmark quota reaches a certain value.","PeriodicalId":11665,"journal":{"name":"Energy Economics","volume":"14 1","pages":""},"PeriodicalIF":12.8,"publicationDate":"2024-12-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142825376","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}
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