Pub Date : 2024-10-12DOI: 10.1016/j.eneco.2024.107954
Xiang Deng, Fang Xu
With the growing prominence of global environmental concerns, the interplay between the oil and new energy industries has become increasingly vital. We employ a connectedness approach based on the TVP-VAR model to explore the dynamic connectedness in both time and frequency domains between the oil and various industries within the new energy industry chains. Empirical findings reveal total connectedness of approximately 70 %, primarily manifested as inter-industry associations within the new energy industry and total connectedness predominantly emerges in short term and is sensitive to extreme events. Additionally, the oil and wind power industries have consistently played roles as net recipients of risk. Conversely, the photovoltaic, energy storage, and new energy battery industries have consistently acted as net risk propagators. The roles of the hydroelectric, nuclear power, and new energy vehicle sectors in risk propagation vary with different frequency components. Thirdly, we identify six pairs of industry combinations exhibiting significant two-way spillover effects. Finally, after two robustness tests, the above conclusions remain valid. These research findings offer valuable insights for policymakers and investors.
{"title":"Connectedness between international oil and China's new energy industry chain: A time-frequency analysis based on TVP-VAR model","authors":"Xiang Deng, Fang Xu","doi":"10.1016/j.eneco.2024.107954","DOIUrl":"10.1016/j.eneco.2024.107954","url":null,"abstract":"<div><div>With the growing prominence of global environmental concerns, the interplay between the oil and new energy industries has become increasingly vital. We employ a connectedness approach based on the TVP-VAR model to explore the dynamic connectedness in both time and frequency domains between the oil and various industries within the new energy industry chains. Empirical findings reveal total connectedness of approximately 70 %, primarily manifested as inter-industry associations within the new energy industry and total connectedness predominantly emerges in short term and is sensitive to extreme events. Additionally, the oil and wind power industries have consistently played roles as net recipients of risk. Conversely, the photovoltaic, energy storage, and new energy battery industries have consistently acted as net risk propagators. The roles of the hydroelectric, nuclear power, and new energy vehicle sectors in risk propagation vary with different frequency components. Thirdly, we identify six pairs of industry combinations exhibiting significant two-way spillover effects. Finally, after two robustness tests, the above conclusions remain valid. These research findings offer valuable insights for policymakers and investors.</div></div>","PeriodicalId":11665,"journal":{"name":"Energy Economics","volume":"140 ","pages":"Article 107954"},"PeriodicalIF":13.6,"publicationDate":"2024-10-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142532891","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 : 2024-10-12DOI: 10.1016/j.eneco.2024.107960
Jie Zhang , Huiru Wei , Kuiran Yuan , Xiaodong Yang
The Green Credit Policy (GCP) is a vital governmental practice promoting green development through financial support. This study employs a Difference-in-Differences method to investigate the impact of GCP on the digital transformation of firms (DT) using data from Chinese A-share listed companies spanning 2007 to 2022. Results reveal that the DT is significantly inhibited after the government implements GCP. This inhibitory effect is mainly produced by reducing technological innovation, increasing environmental protection investment, and strengthening financing constraints. This study also identifies that increased government investment in digital infrastructure, increased marketization, and enhanced R&D backgrounds of executives can potentially diminish the negative impact of GCP on DT. Our findings contribute to a better response to the climate challenge and provide valuable references for accelerating DT.
{"title":"New industrial policy and corporate digital transformation: Empowering or impairing? Emerging evidence from green credit policy","authors":"Jie Zhang , Huiru Wei , Kuiran Yuan , Xiaodong Yang","doi":"10.1016/j.eneco.2024.107960","DOIUrl":"10.1016/j.eneco.2024.107960","url":null,"abstract":"<div><div>The Green Credit Policy (GCP) is a vital governmental practice promoting green development through financial support. This study employs a Difference-in-Differences method to investigate the impact of GCP on the digital transformation of firms (DT) using data from Chinese A-share listed companies spanning 2007 to 2022. Results reveal that the DT is significantly inhibited after the government implements GCP. This inhibitory effect is mainly produced by reducing technological innovation, increasing environmental protection investment, and strengthening financing constraints. This study also identifies that increased government investment in digital infrastructure, increased marketization, and enhanced R&D backgrounds of executives can potentially diminish the negative impact of GCP on DT. Our findings contribute to a better response to the climate challenge and provide valuable references for accelerating DT.</div></div>","PeriodicalId":11665,"journal":{"name":"Energy Economics","volume":"140 ","pages":"Article 107960"},"PeriodicalIF":13.6,"publicationDate":"2024-10-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142532807","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 : 2024-10-10DOI: 10.1016/j.eneco.2024.107943
Xiaojun Sun , Yee Van Fan , Yalin Lei , Ting Pan , Petar Sabev Varbanov
The mechanisms and effects of technological investment on energy productivity and energy structure in the petrochemical industry remain unclear due to the directional nature of technological progress. This study proposes a unified theoretical framework for the impact of directed technological investment on energy productivity and energy structure by incorporating energy factors into the theory of technological progress bias. The aim is to elucidate the impact of technological progress on energy productivity and energy structure, and to unravel the underlying effect mechanisms. A fixed effects model that includes moderating effects is also developed to support the assessment. The study found that the petrochemical industry's technological investment in China was initially biased towards enhancing labour-augmenting technological progress. The mechanism analysis revealed that technological investment, under the moderating effects of price and environmental governance, preferred a capital-energy bias, leading to insignificant improvements in energy productivity but a substantial increase in labour productivity. In addition, the technological investment, influenced by the moderating effect of environmental governance, led to some improvement in the energy structure during the sample period. This study integrates the mechanisms of directed technological investment on energy productivity and energy structure into a unified analytical framework, systematically investigating the reasons, effect mechanisms, and consequences of bias, while providing empirical evidence that supports low-carbon development in the petrochemical industry.
{"title":"Mechanism of directed technological investment on energy productivity and energy structure: A unified theoretical framework","authors":"Xiaojun Sun , Yee Van Fan , Yalin Lei , Ting Pan , Petar Sabev Varbanov","doi":"10.1016/j.eneco.2024.107943","DOIUrl":"10.1016/j.eneco.2024.107943","url":null,"abstract":"<div><div>The mechanisms and effects of technological investment on energy productivity and energy structure in the petrochemical industry remain unclear due to the directional nature of technological progress. This study proposes a unified theoretical framework for the impact of directed technological investment on energy productivity and energy structure by incorporating energy factors into the theory of technological progress bias. The aim is to elucidate the impact of technological progress on energy productivity and energy structure, and to unravel the underlying effect mechanisms. A fixed effects model that includes moderating effects is also developed to support the assessment. The study found that the petrochemical industry's technological investment in China was initially biased towards enhancing labour-augmenting technological progress. The mechanism analysis revealed that technological investment, under the moderating effects of price and environmental governance, preferred a capital-energy bias, leading to insignificant improvements in energy productivity but a substantial increase in labour productivity. In addition, the technological investment, influenced by the moderating effect of environmental governance, led to some improvement in the energy structure during the sample period. This study integrates the mechanisms of directed technological investment on energy productivity and energy structure into a unified analytical framework, systematically investigating the reasons, effect mechanisms, and consequences of bias, while providing empirical evidence that supports low-carbon development in the petrochemical industry.</div></div>","PeriodicalId":11665,"journal":{"name":"Energy Economics","volume":"140 ","pages":"Article 107943"},"PeriodicalIF":13.6,"publicationDate":"2024-10-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142532892","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 : 2024-10-09DOI: 10.1016/j.eneco.2024.107944
Michał Gradzewicz , Janusz Jabłonowski , Michał Sasiela , Zbigniew Żółkiewski
The aim of this paper is to assess the impact on the Polish economy of energy price shocks arising after the Russian invasion of Ukraine. We computed both the impact of the energy shocks (separately for gas, oil and coal prices) on the real side of the economy, and the pass-through of energy prices to the overall price level. The former part of the analysis was simulated using a computable general equilibrium (CGE) model of the Polish economy while the price effects of the shocks were simulated using a dual Leontief price model. Additionally, the price model was augmented with the mechanism of nominal wage adjustment suggested by the theory. This methodological novelty is our original contribution to empirical economics. Our simulations indicate that the price shock for all energy goods of the magnitude observed in 2022 resulted in a decrease in GDP of about 2.8% relative to the baseline solution. Moreover, we document a strong pro-inflationary effect of rising energy prices. After a combined shock to energy prices the consumption deflator increases by 10.3% (when we include the spreading the price increases across the industries), but the effect is simulated at 15.4%, when we account for an additional nominal wage adjustments (ensuring no real wage changes). We show that due to the differences in forward and backward propagation of shocks, the oil price shock had the strongest impact on real aggregates, whereas prices were hit the strongest by the gas price shock.
{"title":"The impact of energy price increases on the Polish economy","authors":"Michał Gradzewicz , Janusz Jabłonowski , Michał Sasiela , Zbigniew Żółkiewski","doi":"10.1016/j.eneco.2024.107944","DOIUrl":"10.1016/j.eneco.2024.107944","url":null,"abstract":"<div><div>The aim of this paper is to assess the impact on the Polish economy of energy price shocks arising after the Russian invasion of Ukraine. We computed both the impact of the energy shocks (separately for gas, oil and coal prices) on the real side of the economy, and the pass-through of energy prices to the overall price level. The former part of the analysis was simulated using a computable general equilibrium (CGE) model of the Polish economy while the price effects of the shocks were simulated using a dual Leontief price model. Additionally, the price model was augmented with the mechanism of nominal wage adjustment suggested by the theory. This methodological novelty is our original contribution to empirical economics. Our simulations indicate that the price shock for all energy goods of the magnitude observed in 2022 resulted in a decrease in GDP of about 2.8% relative to the baseline solution. Moreover, we document a strong pro-inflationary effect of rising energy prices. After a combined shock to energy prices the consumption deflator increases by 10.3% (when we include the spreading the price increases across the industries), but the effect is simulated at 15.4%, when we account for an additional nominal wage adjustments (ensuring no real wage changes). We show that due to the differences in forward and backward propagation of shocks, the oil price shock had the strongest impact on real aggregates, whereas prices were hit the strongest by the gas price shock.</div></div>","PeriodicalId":11665,"journal":{"name":"Energy Economics","volume":"140 ","pages":"Article 107944"},"PeriodicalIF":13.6,"publicationDate":"2024-10-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142532893","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 : 2024-10-05DOI: 10.1016/j.eneco.2024.107940
Leonardo Iania , Marco Lyrio , Liana Nersisyan
We study the effect of oil price shocks on bond risk premia. Based on Baumeister and Hamilton (2019), we identify the different sources of oil price shocks using a structural vector autoregressive (SVAR) model of the global market for crude oil. These structural factors are then used as unspanned factors in an affine term structure model based on the representation of Joslin et al. (2014). This is done for a total of 15 countries. Unspanned factors are responsible for most of the variability in bond risk premia for short holding periods, while spanned factors dominate the variance decomposition for longer holding periods. In both cases, global oil supply and global economic activity are clearly the most important unspanned shocks. A historical decomposition around the outbreak of the COVID-19 crisis shows the clear influence of global economic activity shocks during the months of February and March 2020, increasing bond risk premia significantly.
{"title":"Oil price shocks and bond risk premia: Evidence from a panel of 15 countries","authors":"Leonardo Iania , Marco Lyrio , Liana Nersisyan","doi":"10.1016/j.eneco.2024.107940","DOIUrl":"10.1016/j.eneco.2024.107940","url":null,"abstract":"<div><div>We study the effect of oil price shocks on bond risk premia. Based on Baumeister and Hamilton (2019), we identify the different sources of oil price shocks using a structural vector autoregressive (SVAR) model of the global market for crude oil. These structural factors are then used as unspanned factors in an affine term structure model based on the representation of Joslin et al. (2014). This is done for a total of 15 countries. Unspanned factors are responsible for most of the variability in bond risk premia for short holding periods, while spanned factors dominate the variance decomposition for longer holding periods. In both cases, global oil supply and global economic activity are clearly the most important unspanned shocks. A historical decomposition around the outbreak of the COVID-19 crisis shows the clear influence of global economic activity shocks during the months of February and March 2020, increasing bond risk premia significantly.</div></div>","PeriodicalId":11665,"journal":{"name":"Energy Economics","volume":"139 ","pages":"Article 107940"},"PeriodicalIF":13.6,"publicationDate":"2024-10-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142420850","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 : 2024-10-05DOI: 10.1016/j.eneco.2024.107942
Jing Ma , Qing Li , Qiuyun Zhao , Jennhae Liou , Chen Li
A significant share of corporate carbon emissions stems from the supply chain, necessitating an analysis of how supply chain digitalization influences green innovation in the digital age. This paper examines this impact using data from Chinese listed firms (2012−2022). Theoretically, the study posits that supply chain digitalization facilitates green innovation through two primary mechanisms: enhancing upstream and downstream integration and boosting the internal efficiency of supply chain management at nodal enterprises. Empirically, a quasi-natural experiment leveraging the Supply Chain Innovation and Application Pilot Program serves as an exogenous shock. Key findings include: (1) Supply chain digitalization enhances corporate green innovation, with robust results across various tests. (2) The effect is mainly driven by enhanced supply chain integration—more from supplier concentration than customer concentration—and improved internal supply chain management efficiency. (3) The impact has three characteristics: Quality-first Effect, Crowding-in Effect and Persistence Effect. Specifically, supply chain digitalization mainly boosts high-quality green invention patent applications without crowding-out other non-green innovation, while also positively influences sustained green innovation. (4) Supply chain digitalization primarily enhances green innovation in End-of-Pipe and Process Control Technologies, with limited effects on Pollution Prevention at Source.
{"title":"From bytes to green: The impact of supply chain digitization on corporate green innovation","authors":"Jing Ma , Qing Li , Qiuyun Zhao , Jennhae Liou , Chen Li","doi":"10.1016/j.eneco.2024.107942","DOIUrl":"10.1016/j.eneco.2024.107942","url":null,"abstract":"<div><div>A significant share of corporate carbon emissions stems from the supply chain, necessitating an analysis of how supply chain digitalization influences green innovation in the digital age. This paper examines this impact using data from Chinese listed firms (2012−2022). Theoretically, the study posits that supply chain digitalization facilitates green innovation through two primary mechanisms: enhancing upstream and downstream integration and boosting the internal efficiency of supply chain management at nodal enterprises. Empirically, a quasi-natural experiment leveraging the Supply Chain Innovation and Application Pilot Program serves as an exogenous shock. Key findings include: (1) Supply chain digitalization enhances corporate green innovation, with robust results across various tests. (2) The effect is mainly driven by enhanced supply chain integration—more from supplier concentration than customer concentration—and improved internal supply chain management efficiency. (3) The impact has three characteristics: Quality-first Effect, Crowding-in Effect and Persistence Effect. Specifically, supply chain digitalization mainly boosts high-quality green invention patent applications without crowding-out other non-green innovation, while also positively influences sustained green innovation. (4) Supply chain digitalization primarily enhances green innovation in End-of-Pipe and Process Control Technologies, with limited effects on Pollution Prevention at Source.</div></div>","PeriodicalId":11665,"journal":{"name":"Energy Economics","volume":"139 ","pages":"Article 107942"},"PeriodicalIF":13.6,"publicationDate":"2024-10-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142432681","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}
Operational decisions relying on predictive distributions of electricity prices can result in significantly higher profits compared to those based solely on point forecasts. However, the majority of models developed in both academic and industrial settings provide only point predictions. To address this, we examine three postprocessing methods for converting point forecasts of day-ahead electricity prices into probabilistic ones: Quantile Regression Averaging, Conformal Prediction, and the recently introduced Isotonic Distributional Regression. We find that while the latter demonstrates the most varied behavior, it contributes the most to the ensemble of the three predictive distributions, as measured by Shapley values. Remarkably, the performance of the combination is superior to that of state-of-the-art Distributional Deep Neural Networks over two 4.5-year test periods from the German and Spanish power markets, spanning the COVID pandemic and the war in Ukraine.
{"title":"Postprocessing of point predictions for probabilistic forecasting of day-ahead electricity prices: The benefits of using isotonic distributional regression","authors":"Arkadiusz Lipiecki , Bartosz Uniejewski , Rafał Weron","doi":"10.1016/j.eneco.2024.107934","DOIUrl":"10.1016/j.eneco.2024.107934","url":null,"abstract":"<div><div>Operational decisions relying on predictive distributions of electricity prices can result in significantly higher profits compared to those based solely on point forecasts. However, the majority of models developed in both academic and industrial settings provide only point predictions. To address this, we examine three postprocessing methods for converting point forecasts of day-ahead electricity prices into probabilistic ones: Quantile Regression Averaging, Conformal Prediction, and the recently introduced Isotonic Distributional Regression. We find that while the latter demonstrates the most varied behavior, it contributes the most to the ensemble of the three predictive distributions, as measured by Shapley values. Remarkably, the performance of the combination is superior to that of state-of-the-art Distributional Deep Neural Networks over two 4.5-year test periods from the German and Spanish power markets, spanning the COVID pandemic and the war in Ukraine.</div></div>","PeriodicalId":11665,"journal":{"name":"Energy Economics","volume":"139 ","pages":"Article 107934"},"PeriodicalIF":13.6,"publicationDate":"2024-10-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142420854","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"经济学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-10-04DOI: 10.1016/j.eneco.2024.107952
Kun Yang, Yuying Sun, Yongmiao Hong, Shouyang Wang
This paper proposes a novel Multi-scale Interval-valued Decomposition Ensemble (MIDE) framework for forecasting European Union Allowance (EUA) carbon futures prices, which integrates Noise-assisted Multivariate Empirical Mode Decomposition (NAMEMD), Interval-valued Vector Auto-Regressive (IVAR) model, Interval Event Analysis (IEA) method, and Interval Multi-Layer Perceptron (IMLP). First, the original interval-valued carbon prices with other interval-valued control variables are decomposed and integrated into high, medium, and low-frequency components by NAMEMD. Second, IVAR is used to investigate the dynamics of the interval-valued vector system in low-frequency components, while IMLP is employed to characterize the high-frequency components. Besides, the interval event analysis investigates typical events that significantly impact carbon prices in the medium-frequency component. Furthermore, empirical findings indicate that our proposed MIDE learning approach significantly outperforms some other benchmark models in out-of-sample forecasting.
本文提出了一种用于预测欧盟配额（EUA）碳期货价格的新型多尺度区间值分解集合（MIDE）框架，该框架集成了噪声辅助多变量经验模式分解（NAMEMD）、区间值矢量自回归（IVAR）模型、区间事件分析（IEA）方法和区间多层感知器（IMLP）。首先，利用 NAMEMD 将原始区间值碳价格与其他区间值控制变量分解并整合为高、中、低频成分。其次，利用 IVAR 研究区间值向量系统在低频成分中的动态变化，同时利用 IMLP 描述高频成分的特征。此外，区间事件分析研究了在中频成分中对碳价格产生重大影响的典型事件。此外，实证研究结果表明，我们提出的 MIDE 学习方法在样本外预测方面明显优于其他一些基准模型。
{"title":"Forecasting interval carbon price through a multi-scale interval-valued decomposition ensemble approach","authors":"Kun Yang, Yuying Sun, Yongmiao Hong, Shouyang Wang","doi":"10.1016/j.eneco.2024.107952","DOIUrl":"10.1016/j.eneco.2024.107952","url":null,"abstract":"<div><div>This paper proposes a novel Multi-scale Interval-valued Decomposition Ensemble (MIDE) framework for forecasting European Union Allowance (EUA) carbon futures prices, which integrates Noise-assisted Multivariate Empirical Mode Decomposition (NAMEMD), Interval-valued Vector Auto-Regressive (IVAR) model, Interval Event Analysis (IEA) method, and Interval Multi-Layer Perceptron (IMLP). First, the original interval-valued carbon prices with other interval-valued control variables are decomposed and integrated into high, medium, and low-frequency components by NAMEMD. Second, IVAR is used to investigate the dynamics of the interval-valued vector system in low-frequency components, while IMLP is employed to characterize the high-frequency components. Besides, the interval event analysis investigates typical events that significantly impact carbon prices in the medium-frequency component. Furthermore, empirical findings indicate that our proposed MIDE learning approach significantly outperforms some other benchmark models in out-of-sample forecasting.</div></div>","PeriodicalId":11665,"journal":{"name":"Energy Economics","volume":"139 ","pages":"Article 107952"},"PeriodicalIF":13.6,"publicationDate":"2024-10-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142420800","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 : 2024-10-04DOI: 10.1016/j.eneco.2024.107931
Micah Fields, David Lindequist
International climate policy risk spillovers occur when expected changes to climate policy stringency in one country affect expected climate policy stringency in another country. We develop an event study procedure to identify such spillovers in emissions trading systems, specifically examining the impact from the United States (US) to the European Union (EU). Distinguishing between policy events likely to reduce US commitment to climate action (‘brown events’) and those likely to increase it (‘green events’), we find that the average brown US policy event is associated with an anticipated increase in future EU carbon permit supply, leading to a cumulative 7.1% drop in EU carbon prices over the event window. Conversely, green US policy events are linked to an expected decrease in future EU permit supply, resulting in a cumulative 4.7% rise in EU carbon prices. These findings suggest that financial markets anticipate EU regulators to align with the direction of US climate policy. Our results underscore the significance of regulatory risk spillovers in global climate policy coordination.
{"title":"Global spillovers of US climate policy risk: Evidence from EU carbon emissions futures","authors":"Micah Fields, David Lindequist","doi":"10.1016/j.eneco.2024.107931","DOIUrl":"10.1016/j.eneco.2024.107931","url":null,"abstract":"<div><div>International climate policy risk spillovers occur when expected changes to climate policy stringency in one country affect expected climate policy stringency in another country. We develop an event study procedure to identify such spillovers in emissions trading systems, specifically examining the impact from the United States (US) to the European Union (EU). Distinguishing between policy events likely to reduce US commitment to climate action (‘brown events’) and those likely to increase it (‘green events’), we find that the average brown US policy event is associated with an anticipated increase in future EU carbon permit supply, leading to a cumulative 7.1% drop in EU carbon prices over the event window. Conversely, green US policy events are linked to an expected decrease in future EU permit supply, resulting in a cumulative 4.7% rise in EU carbon prices. These findings suggest that financial markets anticipate EU regulators to align with the direction of US climate policy. Our results underscore the significance of regulatory risk spillovers in global climate policy coordination.</div></div>","PeriodicalId":11665,"journal":{"name":"Energy Economics","volume":"139 ","pages":"Article 107931"},"PeriodicalIF":13.6,"publicationDate":"2024-10-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142420851","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"经济学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-10-04DOI: 10.1016/j.eneco.2024.107946
Cristina Peñasco , Laura Diaz Anadon
In our previous publication “Assessing the effectiveness of energy efficiency measures in the residential sector gas consumption through dynamic treatment effects: Evidence from England and Wales”, we analyzed the impact of the implementation of energy efficiency (EE) measures, in particular loft insulation and cavity walls, on household gas consumption up to five years after installation. Upon review, we realized that our phrasing, specifically the term “energy savings disappear,” might have led to misunderstandings regarding our findings. In this commentary, we clarify that our results indicate reductions in the level of energy (gas) savings achieved, two to four years after the implementation of the energy efficiency measures. The adoption of EE measures is associated with significant reductions in household residential gas consumption one year after their implementation, as we expressed in Peñasco and Anadon (2023). However, the level of savings decreases four years after the retrofitting of cavity wall insulation measures and two years after the installation of loft insulation, generating increases in consumption with respect to the maximum level of savings achieved, i.e., rebounds in consumption. We find that, after five years, energy savings from loft installations are still positive, in the range of 4–5 % compared to the control group—a level of savings that represents a rebound of about 20–25 %, when compared to the maximum level of savings that occurs two years after installation. For cavity walls, after five years gas savings are in the range of 6–9 % compared to the control group, with rebounds of about 10–13 % compared to the maximum savings in year two. This clarification is crucial to prevent a misinterpretation of the results in future research and policy making.
{"title":"A comment on “Assessing the effectiveness of energy efficiency measures in the residential sector gas consumption through dynamic treatment effects: Evidence from England and Wales”","authors":"Cristina Peñasco , Laura Diaz Anadon","doi":"10.1016/j.eneco.2024.107946","DOIUrl":"10.1016/j.eneco.2024.107946","url":null,"abstract":"<div><div>In our previous publication “Assessing the effectiveness of energy efficiency measures in the residential sector gas consumption through dynamic treatment effects: Evidence from England and Wales”, we analyzed the impact of the implementation of energy efficiency (EE) measures, in particular loft insulation and cavity walls, on household gas consumption up to five years after installation. Upon review, we realized that our phrasing, specifically the term “energy savings disappear,” might have led to misunderstandings regarding our findings. In this commentary, we clarify that our results indicate reductions in the level of energy (gas) savings achieved, two to four years after the implementation of the energy efficiency measures. The adoption of EE measures is associated with significant reductions in household residential gas consumption one year after their implementation, as we expressed in Peñasco and Anadon (2023). However, the level of savings decreases four years after the retrofitting of cavity wall insulation measures and two years after the installation of loft insulation, generating increases in consumption with respect to the maximum level of savings achieved, i.e., rebounds in consumption. We find that, after five years, energy savings from loft installations are still positive, in the range of 4–5 % compared to the control group—a level of savings that represents a rebound of about 20–25 %, when compared to the maximum level of savings that occurs two years after installation. For cavity walls, after five years gas savings are in the range of 6–9 % compared to the control group, with rebounds of about 10–13 % compared to the maximum savings in year two. This clarification is crucial to prevent a misinterpretation of the results in future research and policy making.</div></div>","PeriodicalId":11665,"journal":{"name":"Energy Economics","volume":"139 ","pages":"Article 107946"},"PeriodicalIF":13.6,"publicationDate":"2024-10-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142432680","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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