Pub Date : 2024-11-20DOI: 10.1016/j.eneco.2024.108055
Zhenhua Liu , Yushu Wang , Xinting Yuan , Zhihua Ding , Qiang Ji
Geopolitical risk, as a key determinant of energy supply, greatly influences the vulnerability of energy markets. This study develops a novel energy market vulnerability index—which measures the level and dynamics in vulnerability of energy markets from market risk perspective—using a quantile connectedness approach for the first time. Then, by introducing a generalized autoregressive conditional heteroskedasticity–mixed-data sampling (GARCH-MIDAS) model, we explore the impact and predictive role of geopolitical risk on the vulnerability of energy markets. We find that the vulnerability of energy markets showed an upward trend and fluctuated considerably during 2007–2024. Moreover, geopolitical risk positively affects the vulnerability of energy markets. Finally, the vulnerability of energy markets can be forecasted better by the predictor, geopolitical risk. Our results offer useful insights for investors and policy-makers in the energy markets.
{"title":"Geopolitical risk and vulnerability of energy markets","authors":"Zhenhua Liu , Yushu Wang , Xinting Yuan , Zhihua Ding , Qiang Ji","doi":"10.1016/j.eneco.2024.108055","DOIUrl":"10.1016/j.eneco.2024.108055","url":null,"abstract":"<div><div>Geopolitical risk, as a key determinant of energy supply, greatly influences the vulnerability of energy markets. This study develops a novel energy market vulnerability index—which measures the level and dynamics in vulnerability of energy markets from market risk perspective—using a quantile connectedness approach for the first time. Then, by introducing a generalized autoregressive conditional heteroskedasticity–mixed-data sampling (GARCH-MIDAS) model, we explore the impact and predictive role of geopolitical risk on the vulnerability of energy markets. We find that the vulnerability of energy markets showed an upward trend and fluctuated considerably during 2007–2024. Moreover, geopolitical risk positively affects the vulnerability of energy markets. Finally, the vulnerability of energy markets can be forecasted better by the predictor, geopolitical risk. Our results offer useful insights for investors and policy-makers in the energy markets.</div></div>","PeriodicalId":11665,"journal":{"name":"Energy Economics","volume":"141 ","pages":"Article 108055"},"PeriodicalIF":13.6,"publicationDate":"2024-11-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142722382","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 : 2024-11-17DOI: 10.1016/j.eneco.2024.108048
Minhan Zhai , Wenqing Wu , Sang-Bing Tsai
The development of Artificial Intelligence (AI) has brought both opportunities and challenges for energy enterprises to make investment decisions. This paper considers an Artificial intelligence orientation (AIO) indicator that reflects AI introduction and deployment to analyze whether and how AIO affects inefficient investment in energy enterprises. By using machine learning methods to construct AIO indicators, this paper finds that AIO can effectively alleviate ineffective investments in energy enterprises. Furthermore, this paper explores the moderating effects of the absorbed slack resources and conducts heterogeneity analysis based on enterprises ownership and lifecycle. The research results indicate that absorbed slack resources can weaken the alleviating effect of AIO on investment inefficiency. Besides, heterogeneity analysis also reveals that AIO can significantly alleviate investment inefficiency in the non-state-owned energy enterprises and those in the growth stage. These findings are important for energy enterprises to adopt and deploy AI technologies.
{"title":"The effects of Artificial intelligence orientation on inefficient investment: Firm-level evidence from China's energy enterprises","authors":"Minhan Zhai , Wenqing Wu , Sang-Bing Tsai","doi":"10.1016/j.eneco.2024.108048","DOIUrl":"10.1016/j.eneco.2024.108048","url":null,"abstract":"<div><div>The development of Artificial Intelligence (AI) has brought both opportunities and challenges for energy enterprises to make investment decisions. This paper considers an Artificial intelligence orientation (AIO) indicator that reflects AI introduction and deployment to analyze whether and how AIO affects inefficient investment in energy enterprises. By using machine learning methods to construct AIO indicators, this paper finds that AIO can effectively alleviate ineffective investments in energy enterprises. Furthermore, this paper explores the moderating effects of the absorbed slack resources and conducts heterogeneity analysis based on enterprises ownership and lifecycle. The research results indicate that absorbed slack resources can weaken the alleviating effect of AIO on investment inefficiency. Besides, heterogeneity analysis also reveals that AIO can significantly alleviate investment inefficiency in the non-state-owned energy enterprises and those in the growth stage. These findings are important for energy enterprises to adopt and deploy AI technologies.</div></div>","PeriodicalId":11665,"journal":{"name":"Energy Economics","volume":"141 ","pages":"Article 108048"},"PeriodicalIF":13.6,"publicationDate":"2024-11-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142705468","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 : 2024-11-17DOI: 10.1016/j.eneco.2024.108040
Lingbing Feng , Jiajun Qi , Yuhao Zheng
This study examines the impact of a recent regional artificial intelligence pilot zone (AIPZ) policy in China on firms' carbon performance using a quasi-natural experiment. Using the Difference-in-Differences (DID) methodology, the findings reveal that the AIPZ policy significantly reduces firms' carbon emissions. This effect is most pronounced for firms with high talent levels, positive media sentiment, and strong internal control, while heavily polluting firms experience a relatively minor effect. A variable importance analysis using the generalized random forest approach identifies return on assets (ROA) and Tobin's Q as significant contributors to the variation in firms' responses. Specifically, when ROA is negative, the treatment effect is relatively large and increases slowly. In contrast, when ROA is positive, the treatment effect decreases rapidly, showing a zero-boundary effect. Additionally, Tobin's Q exhibits an inverted U-shaped relationship with the treatment effect. The findings of this study offer valuable insights for policymakers in China and beyond, highlighting the importance of considering firm-specific characteristics to achieve effective and sustainable environmental management alongside economic development.
本研究采用准自然实验的方法，考察了中国近期出台的人工智能试验区（AIPZ）政策对企业碳绩效的影响。利用差分法（DID），研究结果表明，人工智能试验区政策显著降低了企业的碳排放量。这种效应对人才水平高、媒体情绪好、内部控制强的企业最为明显，而对重污染企业的影响相对较小。利用广义随机森林方法进行的变量重要性分析表明，资产收益率（ROA）和托宾 Q 值是导致企业反应差异的重要因素。具体而言，当投资回报率为负数时，治疗效果相对较大，且增长缓慢。相反，当投资回报率为正值时，治疗效果迅速下降，呈现出零边界效应。此外，托宾 Q 值与治疗效果呈倒 U 型关系。本研究的结论为中国和其他国家的政策制定者提供了宝贵的启示，强调了在经济发展的同时考虑企业具体特征以实现有效和可持续环境管理的重要性。
{"title":"How can AI reduce carbon emissions? Insights from a quasi-natural experiment using generalized random forest","authors":"Lingbing Feng , Jiajun Qi , Yuhao Zheng","doi":"10.1016/j.eneco.2024.108040","DOIUrl":"10.1016/j.eneco.2024.108040","url":null,"abstract":"<div><div>This study examines the impact of a recent regional artificial intelligence pilot zone (AIPZ) policy in China on firms' carbon performance using a quasi-natural experiment. Using the Difference-in-Differences (DID) methodology, the findings reveal that the AIPZ policy significantly reduces firms' carbon emissions. This effect is most pronounced for firms with high talent levels, positive media sentiment, and strong internal control, while heavily polluting firms experience a relatively minor effect. A variable importance analysis using the generalized random forest approach identifies return on assets (ROA) and Tobin's Q as significant contributors to the variation in firms' responses. Specifically, when ROA is negative, the treatment effect is relatively large and increases slowly. In contrast, when ROA is positive, the treatment effect decreases rapidly, showing a zero-boundary effect. Additionally, Tobin's Q exhibits an inverted U-shaped relationship with the treatment effect. The findings of this study offer valuable insights for policymakers in China and beyond, highlighting the importance of considering firm-specific characteristics to achieve effective and sustainable environmental management alongside economic development.</div></div>","PeriodicalId":11665,"journal":{"name":"Energy Economics","volume":"141 ","pages":"Article 108040"},"PeriodicalIF":13.6,"publicationDate":"2024-11-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142705472","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 : 2024-11-17DOI: 10.1016/j.eneco.2024.108050
Sheng-Hao Zhang , Jun Yang , Jixin Cheng , Xiaoming Li
Digitalization offers tremendous potential for low-carbon development in China, yet the carbon rebound effect it triggers remains controversial. This paper develops China's Digital-Economy-Energy-Environment Analysis/Computable General Equilibrium (CDEEEA/CGE) model, which assesses the actual input of ICT and its factor characteristics for the first time. On this basis, the digitalization process of China is modeled based on the endogenous drive of digital industrialization, and the carbon emission effect of digitalization is innovatively decomposed, thereby revealing the formation mechanism of the carbon rebound effect. Research results indicate that in the digital industrialization scenario, through the substitution effect, the share of ICT factor input and the share of the tertiary industry increase, which leads to a favorable performance of carbon intensity (−3.61 % in 2060). However, the extra carbon emissions (256.64 Mt. in 2060) resulting from the output effect and the income effect completely counteract the expected emission reductions (116.4 Mt. in 2060), triggering a backfire effect. Nevertheless, policymakers should not narrowly pursue a low rebound effect, as its essence represents the redistribution of the digitalization dividend. This paper further points out that the complementary environmental policy can largely retain the economic benefits of digitalization while eliminating the environmental impact of the carbon rebound effect. This research offers novel theoretical grounds and practical routes for sustainable development in the digitalization backdrop.
{"title":"Exploring the carbon rebound effect of digitalization and policy responses: A CDEEEA/CGE based analysis","authors":"Sheng-Hao Zhang , Jun Yang , Jixin Cheng , Xiaoming Li","doi":"10.1016/j.eneco.2024.108050","DOIUrl":"10.1016/j.eneco.2024.108050","url":null,"abstract":"<div><div>Digitalization offers tremendous potential for low-carbon development in China, yet the carbon rebound effect it triggers remains controversial. This paper develops China's Digital-Economy-Energy-Environment Analysis/Computable General Equilibrium (CDEEEA/CGE) model, which assesses the actual input of ICT and its factor characteristics for the first time. On this basis, the digitalization process of China is modeled based on the endogenous drive of digital industrialization, and the carbon emission effect of digitalization is innovatively decomposed, thereby revealing the formation mechanism of the carbon rebound effect. Research results indicate that in the digital industrialization scenario, through the substitution effect, the share of ICT factor input and the share of the tertiary industry increase, which leads to a favorable performance of carbon intensity (−3.61 % in 2060). However, the extra carbon emissions (256.64 Mt. in 2060) resulting from the output effect and the income effect completely counteract the expected emission reductions (116.4 Mt. in 2060), triggering a backfire effect. Nevertheless, policymakers should not narrowly pursue a low rebound effect, as its essence represents the redistribution of the digitalization dividend. This paper further points out that the complementary environmental policy can largely retain the economic benefits of digitalization while eliminating the environmental impact of the carbon rebound effect. This research offers novel theoretical grounds and practical routes for sustainable development in the digitalization backdrop.</div></div>","PeriodicalId":11665,"journal":{"name":"Energy Economics","volume":"141 ","pages":"Article 108050"},"PeriodicalIF":13.6,"publicationDate":"2024-11-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142705467","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 : 2024-11-16DOI: 10.1016/j.eneco.2024.108044
Lei Hu , Min Song , Fenghua Wen , Yun Zhang , Yunning Zhao
This study initially develops a risk spillover network within the energy futures market, subsequently analyzing the impacts of climate attention on the risk spillovers associated with individual contracts in this network. We construct a high-dimensional network of 19 futures contracts CoVaR based on the LASSO-VAR method. Furthermore, we construct a climate attention index using the search volume of the climate-related Baidu Index during the same period and use a random forest (RF) model to study its impact on the energy futures market. We find that the energy futures market has a significant risk spillover effect, and climate attention has a significant non-linear effect on risk spillover. In light of increasing climate attention, our RF regression analyses reveal a notable shift in the risk spillover of energy futures. Based on these findings, we recommend tailored management strategies to address this evolving trend effectively.
{"title":"The impact of climate attention on risk spillover effect in energy futures markets","authors":"Lei Hu , Min Song , Fenghua Wen , Yun Zhang , Yunning Zhao","doi":"10.1016/j.eneco.2024.108044","DOIUrl":"10.1016/j.eneco.2024.108044","url":null,"abstract":"<div><div>This study initially develops a risk spillover network within the energy futures market, subsequently analyzing the impacts of climate attention on the risk spillovers associated with individual contracts in this network. We construct a high-dimensional network of 19 futures contracts CoVaR based on the LASSO-VAR method. Furthermore, we construct a climate attention index using the search volume of the climate-related Baidu Index during the same period and use a random forest (RF) model to study its impact on the energy futures market. We find that the energy futures market has a significant risk spillover effect, and climate attention has a significant non-linear effect on risk spillover. In light of increasing climate attention, our RF regression analyses reveal a notable shift in the risk spillover of energy futures. Based on these findings, we recommend tailored management strategies to address this evolving trend effectively.</div></div>","PeriodicalId":11665,"journal":{"name":"Energy Economics","volume":"141 ","pages":"Article 108044"},"PeriodicalIF":13.6,"publicationDate":"2024-11-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142696376","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 : 2024-11-16DOI: 10.1016/j.eneco.2024.108042
Alexander Hoogsteyn , Jelle Meus , Kenneth Bruninx , Erik Delarue
This paper explores various policies to support climate-neutral hydrogen production, focusing on their interaction with energy markets and cap-and-trade systems such as the EU emission trading scheme. We develop and deploy a state-of-the-art equilibrium model to examine the effect of hydrogen support policies on the interactions between hydrogen, electricity and emission markets. Our analysis shows that mechanisms remunerating hydrogen production can distort spot prices of electricity and hydrogen more strongly than mechanisms that remunerate hydrogen production capacity. Hydrogen support mechanisms furthermore promote renewable electricity production and deter investment in conventional generation assets. The associated decrease in emissions in the power sector leads to an increase of emissions in the industrial and hydrogen sector due to the waterbed effect in the EU emission trading scheme. Our case study on an emission-capped area inspired by the EU shows that the operational distortions that production-based mechanisms exhibit, typically increase costs more than the investment distortions that capacity-based mechanisms entail.
{"title":"Interactions and distortions of different support policies for green hydrogen","authors":"Alexander Hoogsteyn , Jelle Meus , Kenneth Bruninx , Erik Delarue","doi":"10.1016/j.eneco.2024.108042","DOIUrl":"10.1016/j.eneco.2024.108042","url":null,"abstract":"<div><div>This paper explores various policies to support climate-neutral hydrogen production, focusing on their interaction with energy markets and cap-and-trade systems such as the EU emission trading scheme. We develop and deploy a state-of-the-art equilibrium model to examine the effect of hydrogen support policies on the interactions between hydrogen, electricity and emission markets. Our analysis shows that mechanisms remunerating hydrogen production can distort spot prices of electricity and hydrogen more strongly than mechanisms that remunerate hydrogen production capacity. Hydrogen support mechanisms furthermore promote renewable electricity production and deter investment in conventional generation assets. The associated decrease in emissions in the power sector leads to an increase of emissions in the industrial and hydrogen sector due to the waterbed effect in the EU emission trading scheme. Our case study on an emission-capped area inspired by the EU shows that the operational distortions that production-based mechanisms exhibit, typically increase costs more than the investment distortions that capacity-based mechanisms entail.</div></div>","PeriodicalId":11665,"journal":{"name":"Energy Economics","volume":"141 ","pages":"Article 108042"},"PeriodicalIF":13.6,"publicationDate":"2024-11-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142759221","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 : 2024-11-15DOI: 10.1016/j.eneco.2024.108047
Ying Liu , Hongyun Huang , William Mbanyele , Zhixing Wei , Xin Li
Whether and how green industrial policy effectively nudges corporate sustainable performance remains an ongoing debated topic in the academic. In this study, we take the first step and examine the link between green industrial policy and corporate green innovation. We utilize the staggered adoption of the Green Factory Identification (GFI) in China as a plausibly exogenous shock. The Staggered Difference-in-Difference analysis demonstrates a significant positive association between the GFI and green innovation. It remains robust even after conducting various tests to ensure its validity. Additionally, we find that government research and development (R&D) subsidies exhibit an inverted U-shaped effect on this relationship. Furthermore, we elucidate two potential mechanisms that underlie the augmentation of green innovation facilitated by the GFI: alleviating financing constraints and fostering external supervision. Moreover, the positive impact of the GFI appears to be more pronounced among non-state-owned firms, those with superior managerial abilities, and those without political connections. More importantly, the economic consequences of the GFI implementation indicate improved corporate financial performance. As a noteworthy example of green industrial policy, green factories also demonstrate regional spillover effects, driving green development within the same region.
绿色产业政策是否以及如何有效地促进企业的可持续绩效，仍是学术界一直争论不休的话题。在本研究中，我们首先考察了绿色产业政策与企业绿色创新之间的联系。我们将中国交错采用绿色工厂认定（GFI）作为一个看似外生的冲击。交错差分分析表明，GFI 与绿色创新之间存在显著的正相关关系。为确保其有效性，我们进行了各种检验，但结果依然稳健。此外，我们还发现政府研发补贴对这一关系产生了倒 U 型的影响。此外，我们还阐明了 GFI 促进绿色创新的两个潜在机制：缓解融资限制和促进外部监督。此外，GFI 的积极影响似乎在非国有企业、具有卓越管理能力的企业和没有政治关系的企业中更为明显。更重要的是，实施 GFI 所带来的经济后果表明，企业财务业绩得到了改善。作为绿色产业政策的一个值得注意的例子，绿色工厂还表现出区域溢出效应，推动了同一区域内的绿色发展。
{"title":"How does green industrial policy affect corporate green innovation? Evidence from the green factory identification in China","authors":"Ying Liu , Hongyun Huang , William Mbanyele , Zhixing Wei , Xin Li","doi":"10.1016/j.eneco.2024.108047","DOIUrl":"10.1016/j.eneco.2024.108047","url":null,"abstract":"<div><div>Whether and how green industrial policy effectively nudges corporate sustainable performance remains an ongoing debated topic in the academic. In this study, we take the first step and examine the link between green industrial policy and corporate green innovation. We utilize the staggered adoption of the Green Factory Identification (GFI) in China as a plausibly exogenous shock. The Staggered Difference-in-Difference analysis demonstrates a significant positive association between the GFI and green innovation. It remains robust even after conducting various tests to ensure its validity. Additionally, we find that government research and development (R&D) subsidies exhibit an inverted U-shaped effect on this relationship. Furthermore, we elucidate two potential mechanisms that underlie the augmentation of green innovation facilitated by the GFI: alleviating financing constraints and fostering external supervision. Moreover, the positive impact of the GFI appears to be more pronounced among non-state-owned firms, those with superior managerial abilities, and those without political connections. More importantly, the economic consequences of the GFI implementation indicate improved corporate financial performance. As a noteworthy example of green industrial policy, green factories also demonstrate regional spillover effects, driving green development within the same region.</div></div>","PeriodicalId":11665,"journal":{"name":"Energy Economics","volume":"141 ","pages":"Article 108047"},"PeriodicalIF":13.6,"publicationDate":"2024-11-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142705469","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 : 2024-11-14DOI: 10.1016/j.eneco.2024.108039
Peigong Li , Mingchen Li , Wanwan Zhu , Brian M. Lucey
In light of the Conference of Parties 26, carbon information reporting has become ever-increasingly important. Prior studies presented much evidence on whether environmental disclosure could reliably reflect environmental performance. However, very limited evidence has been provided on if environmental disclosure could drive firms to improve future environmental performance. Based on the competing theoretical predictions from the legitimacy theory and the “outside-in” management perspectives, this study provides new international insight into if carbon disclosure improvements could motivate future carbon performance improvement based on a change analysis. Particularly, the investigation uses a recently available carbon data set of both developed economies and developing economies from the Carbon Disclosure Project and other publicly available media platforms. We find that an improvement in carbon disclosure indicates a future carbon performance deterioration in developed economies, however, carbon disclosure changes are not related to future carbon performance changes in developing economies when using performance data from the Carbon Disclosure Project. When using performance data from other publicly available media platforms, carbon disclosure changes are not related to future carbon performance changes at all internationally. This indicates that the carbon information disclosed on other public media platforms has been intentionally beautified. Thus, firms' carbon performance changes from these platforms lose track of the prior changes in firms' carbon disclosure.
{"title":"Whether voluntary GHG disclosure could help improve subsequent GHG performance-new global evidence","authors":"Peigong Li , Mingchen Li , Wanwan Zhu , Brian M. Lucey","doi":"10.1016/j.eneco.2024.108039","DOIUrl":"10.1016/j.eneco.2024.108039","url":null,"abstract":"<div><div>In light of the Conference of Parties 26, carbon information reporting has become ever-increasingly important. Prior studies presented much evidence on whether environmental disclosure could reliably reflect environmental performance. However, very limited evidence has been provided on if environmental disclosure could drive firms to improve future environmental performance. Based on the competing theoretical predictions from the legitimacy theory and the “outside-in” management perspectives, this study provides new international insight into if carbon disclosure improvements could motivate future carbon performance improvement based on a change analysis. Particularly, the investigation uses a recently available carbon data set of both developed economies and developing economies from the Carbon Disclosure Project and other publicly available media platforms. We find that an improvement in carbon disclosure indicates a future carbon performance deterioration in developed economies, however, carbon disclosure changes are not related to future carbon performance changes in developing economies when using performance data from the Carbon Disclosure Project. When using performance data from other publicly available media platforms, carbon disclosure changes are not related to future carbon performance changes at all internationally. This indicates that the carbon information disclosed on other public media platforms has been intentionally beautified. Thus, firms' carbon performance changes from these platforms lose track of the prior changes in firms' carbon disclosure.</div></div>","PeriodicalId":11665,"journal":{"name":"Energy Economics","volume":"141 ","pages":"Article 108039"},"PeriodicalIF":13.6,"publicationDate":"2024-11-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142696348","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}
This study investigates the influence of the green credit policy on firm de-zombification in China using the listed firm samples. This paper presents a comparative analysis of the efficacy of two distinct policy implementation methods within the green credit policy framework. The results reveal that the implementation of the Green Credit Guidelines does not yield a statistically significant improvement in the de-zombification of green credit-restricted firms if they are implemented voluntarily. Only by including green credit performance in the assessment of bank officials, the de-zombification of such firms can be markedly promoted. Overall, the green credit policy acts as a catalyst for de-zombification by exerting dual effects: aggravating financial constraints and stimulating green innovation. The positive effect of green credit policy on de-zombification mainly comes from SOEs, firms with weak external supervision and firms located in areas with a lower degree of marketization. The results of the empirical study offer crucial insights for policymaking in China. It is imperative for the Chinese government to continue adhering to and further enhancing the green credit policy, with a particular focus on mandatory measures.
{"title":"From voluntary to mandatory implementation: The impact of green credit policy on de-zombification in China","authors":"Ruipeng Tan , Wenjun Zhu , Mengmeng Xu , Zixuan Zhang","doi":"10.1016/j.eneco.2024.108045","DOIUrl":"10.1016/j.eneco.2024.108045","url":null,"abstract":"<div><div>This study investigates the influence of the green credit policy on firm de-zombification in China using the listed firm samples. This paper presents a comparative analysis of the efficacy of two distinct policy implementation methods within the green credit policy framework. The results reveal that the implementation of the Green Credit Guidelines does not yield a statistically significant improvement in the de-zombification of green credit-restricted firms if they are implemented voluntarily. Only by including green credit performance in the assessment of bank officials, the de-zombification of such firms can be markedly promoted. Overall, the green credit policy acts as a catalyst for de-zombification by exerting dual effects: aggravating financial constraints and stimulating green innovation. The positive effect of green credit policy on de-zombification mainly comes from SOEs, firms with weak external supervision and firms located in areas with a lower degree of marketization. The results of the empirical study offer crucial insights for policymaking in China. It is imperative for the Chinese government to continue adhering to and further enhancing the green credit policy, with a particular focus on mandatory measures.</div></div>","PeriodicalId":11665,"journal":{"name":"Energy Economics","volume":"141 ","pages":"Article 108045"},"PeriodicalIF":13.6,"publicationDate":"2024-11-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142704358","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 : 2024-11-14DOI: 10.1016/j.eneco.2024.108004
Linh Pham , Kuang-Chung Hsu
This paper investigates the effect of geopolitical risks on the idiosyncratic and systemic risks of the cleantech metal stock markets in China, a major producer of these metals and consumer of renewable energy technologies. Cleantech metals are defined as those fundamental to the production of clean energy technologies, such as wind turbines, solar panels, and batteries for electric vehicles. Using data on cleantech metal stocks between 2010 and 2024, we show that extreme upward movements in geopolitical risks significantly Ganger cause extreme movements in individual cleantech metal stocks. In addition, we find that geopolitical risks also drive common volatility in cleantech metal markets, particularly at extreme quantiles. Our results indicated a less significant effect of geopolitical risks on cleantech metal markets at the median quantile.
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