Energy Economics最新文献_第5页

From synergy to spread: How environmental regulation is helping Chinese firms achieve low-carbon expansion — and take their supply chains with them 从协同到传播：环境监管如何帮助中国企业实现低碳扩张——并带动其供应链

IF 14.2 2区经济学 Q1 ECONOMICS

Energy Economics

Pub Date : 2025-12-18 DOI: 10.1016/j.eneco.2025.109103

An Pan , Puyu Mi , Xunpeng Shi

The synergy between carbon emission reduction and green expansion (CERGE) serves as a strategic orientation for firms to achieve green transformation. As environmental regulations are increasingly employed to promote such green growth, an important question arises as to how these policies can foster CERGE synergy—particularly by extending their influence from large enterprises to the vast number of smaller firms through supply chain linkages. This study aims to examine how the T10000P, as a command-and-control environmental regulation, influences the synergy between CERGE in the context of China's dual carbon goals and green transition. Using a sample of China's A-share listed companies from 2008 to 2020, this paper examines the impact of the top 10,000 enterprise energy saving program (T10000P) on the synergy between CERGE, measured through a coupling coordination model and assessed with a difference-in-differences approach. Results show that T10000P significantly enhances firms' synergy between CERGE, with stronger effects observed in high-tech and non-heavy-pollution industries. Financing constraints negatively moderate this impact, while alleviation of such constraints strengthens the synergy. Additionally, the supply chain spillover effect of T10000P is asymmetric: upstream firms' participation significantly improves downstream firms' synergy between CERGE, whereas downstream participation does not significantly affect upstream firms. This one-way spillover effect is more pronounced when upstream firms have greater bargaining power and closer ties with downstream firms. The findings provide valuable insights for leveraging key enterprises' leading role in supply chains and optimizing environmental regulations to promote industrial green transformation and sustainable development.

碳减排与绿色扩张的协同效应是企业实现绿色转型的战略取向。随着环境法规越来越多地被用于促进这种绿色增长，一个重要的问题出现了，即这些政策如何能够促进ge的协同作用——特别是通过供应链联系将其影响力从大企业扩展到大量的小公司。本研究旨在探讨在中国双碳目标和绿色转型背景下，T10000P作为一种命令与控制的环境调控如何影响ge的协同效应。本文以2008 - 2020年中国a股上市公司为样本，采用耦合协调模型和差中差法，考察了企业节能项目10000强（T10000P）对企业节能协同效应的影响。结果表明，T10000P显著增强了企业间的协同效应，且在高技术产业和非重污染产业的协同效应更强。融资限制对这种影响起到消极的缓和作用，而这种限制的缓解则加强了协同作用。此外，T10000P的供应链溢出效应是不对称的：上游企业的参与显著提高了下游企业在ge之间的协同效应，而下游企业的参与对上游企业的协同效应没有显著影响。当上游企业的议价能力更强，与下游企业的联系更紧密时，这种单向溢出效应更为明显。研究结果为发挥重点企业在供应链中的主导作用，优化环境法规，促进工业绿色转型和可持续发展提供了有价值的见解。

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引用次数: 0

The economics of negative price phenomenon in renewable-integrated electricity markets 可再生整合电力市场负价格现象的经济学

IF 14.2 2区经济学 Q1 ECONOMICS

Energy Economics

Pub Date : 2025-12-17 DOI: 10.1016/j.eneco.2025.109086

Nima Rafizadeh

Understanding how renewable energy integration affects electricity market efficiency and price formation is an important challenge in energy economics and environmental policy. Negative electricity prices, where generators pay to produce power, now occur with increasing frequency across wholesale markets, yet their economic drivers require better understanding. This paper addresses this gap by developing a theoretical framework linking generator behavior to market outcomes, then testing it empirically using over 14 million observations from New York’s wholesale markets (2010–2022). The theoretical analysis demonstrates that negative prices can achieve welfare-maximizing allocations under operational constraints and production subsidies. The empirical analysis, using binary response and count data models with high-frequency data, identifies a clear hierarchy of drivers: renewable energy integration emerges as primary, with solar energy reducing negative price occurrences while wind energy increases them. Weather conditions rank second in importance, while grid constraints show limited influence, contrary to policy focus on transmission expansion. These findings can inform policy discussions by suggesting that rather than suppressing negative prices through regulatory constraints, policymakers should preserve these efficient price signals while prioritizing technology-specific renewable policies and weather-responsive mechanisms over transmission expansion to enhance investment signals and market stability.

了解可再生能源整合如何影响电力市场效率和价格形成是能源经济学和环境政策的一个重要挑战。负电价（发电商为发电付费）现在在批发市场越来越频繁地出现，但其经济驱动因素需要更好地理解。本文通过建立一个将发电机行为与市场结果联系起来的理论框架来解决这一差距，然后使用来自纽约批发市场（2010-2022）的1400多万次观察结果对其进行实证检验。理论分析表明，在经营约束和生产补贴条件下，负价格可以实现福利最大化的分配。实证分析使用二元响应和高频数据计数数据模型，确定了驱动因素的明确层次：可再生能源整合成为主要因素，太阳能减少了负价格的发生，而风能增加了负价格的发生。天气条件的重要性排在第二位，而电网约束的影响有限，这与政策关注的输电扩张相反。这些发现可以为政策讨论提供信息，建议政策制定者不应通过监管约束来抑制负价格，而应保留这些有效的价格信号，同时优先考虑针对技术的可再生能源政策和天气响应机制，而不是输电扩张，以增强投资信号和市场稳定。

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引用次数: 0

Intertemporal hedging and the carbon beta premium: Insights from Chinese corporate bonds 跨期套期保值与碳贝塔溢价：来自中国公司债券的启示

IF 14.2 2区经济学 Q1 ECONOMICS

Energy Economics

Pub Date : 2025-12-17 DOI: 10.1016/j.eneco.2025.109096

Wei Wan , Chien-Chiang Lee , Hao Liu

By estimating the covariance between bond returns and carbon emission rights returns—referred to as the carbon beta—this paper examines whether and how carbon price risk is incorporated into bond pricing. The findings reveal a negative carbon beta premium in the Chinese bond market. This negative premium becomes more pronounced during periods of rising carbon returns and heightened climate-related attention, providing empirical support for the intertemporal hedging theory. Moreover, the negative pricing effect is stronger for bonds with longer maturities and for firms with better environmental performance, offering a clear basis for selecting target bonds for intertemporal hedging. These results provide new insights into the carbon beta premium and its transmission mechanism in fixed-income markets.

通过估算债券收益与碳排放权收益之间的协方差（即碳beta），本文考察了碳价格风险是否以及如何被纳入债券定价。研究结果显示，中国债券市场的碳贝塔溢价为负。在碳回报上升和气候相关关注加剧的时期，这种负溢价变得更加明显，为跨期对冲理论提供了实证支持。此外，对于期限较长的债券和环境绩效较好的公司，负定价效应更强，为选择跨期对冲的目标债券提供了明确的依据。这些结果为研究固定收益市场的碳溢价及其传导机制提供了新的视角。

引用次数: 0

Optimal flexible power deployment strategies in China considering the emission trading system 考虑排放权交易制度的中国最优柔性电力配置策略

IF 14.2 2区经济学 Q1 ECONOMICS

Energy Economics

Pub Date : 2025-12-17 DOI: 10.1016/j.eneco.2025.109099

Zhiwei Liu , Boqiang Lin

The rapid expansion of renewable energy sources (RES) in China has heightened the need for flexible power solutions to mitigate the intermittency of wind and photovoltaic generation. Two promising clean options are battery energy storage systems (BESS) coupled with RES and coal power retrofitted with carbon capture and storage (CCS). This study develops an optimization model to determine the cost-effective deployment of flexible power sources under China's Emission Trading System (ETS), evaluating trade-offs among energy security, transition efficiency, and system costs. The analysis reveals that (1) while BESS offers near-term economic advantages, CCS-equipped flexible coal power becomes increasingly competitive as RES penetration grows. (2) ETS plays a pivotal role in shaping deployment strategies, (3) Sensitivity analysis shows that cost variations in BESS and CCS can affect the optimal deployment strategy of flexible power sources. The results suggest that existing coal units should be retained in the near term to meet flexibility needs under the ETS, with the focus shifting to BESS in the medium term and to Coal power with CCS in the long term. Well-designed ETS mechanisms can reduce transition costs and improve efficiency, while technological advances may enhance the long-term viability of BESS.

中国可再生能源（RES）的迅速发展，提高了对灵活电力解决方案的需求，以缓解风能和光伏发电的间歇性。两种有前景的清洁能源选择是电池储能系统（BESS）与可再生能源相结合，以及采用碳捕获和储存（CCS）改造的煤电。本研究建立了一个优化模型，以确定中国碳排放交易体系（ETS）下柔性电源的成本效益部署，评估能源安全、转型效率和系统成本之间的权衡。分析表明：(1)尽管BESS具有短期经济优势，但随着可再生能源普及率的提高，配备ccs的柔性煤电的竞争力越来越强。(3)灵敏度分析表明，BESS和CCS的成本变化会影响柔性电源的最优部署策略。结果表明，短期内应保留现有的燃煤机组，以满足碳排放交易体系下的灵活性需求，中期重点转向BESS，长期重点转向采用CCS的煤电。设计良好的碳排放交易机制可以降低过渡成本并提高效率，而技术进步可能会提高BESS的长期可行性。

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引用次数: 0

Price vs policy: The impact of cost uncertainty on decarbonization pathways 价格与政策：成本不确定性对脱碳途径的影响

IF 14.2 2区经济学 Q1 ECONOMICS

Energy Economics

Pub Date : 2025-12-17 DOI: 10.1016/j.eneco.2025.109088

Nathan de Matos, Madeleine McPherson

The future costs of many low-carbon power generation technologies are highly uncertain. Capturing these uncertainties for current and emerging technologies can help to understand potential policy impacts and the roles that emerging technologies could play. Energy system studies often use a scenario-based approach, which requires modelers to choose specific values from the range of possibilities, which can bias results and report unlikely scenarios. This paper combines a stochastic capital-cost forecasting methodology, based on Wright’s law of experiential learning, with a range of cost values for emerging technologies. The set of inputs generated is linked with the COPPER power system capacity expansion model to generate a database of 400 model runs, using 100 sampled combinations of cost input parameters and four policy scenarios. The impacts of policy on future emissions, system costs and generation mixes are presented. The incorporation of uncertainty into the model demonstrates the consistent deployment of transmission across all scenarios, in contrast with the inconsistent deployment of emerging technologies. This study finds that neither the carbon tax nor proposed clean electricity regulations achieve power-system decarbonization by 2050 across all scenarios. The results from this study are consistent with the results of national and provincial energy studies in Canada, however some results were outliers compared to the full distribution of potential model outcomes. These findings underscore the critical need to incorporate uncertainty into power system models, particularly when discussing policies and emerging technologies.

许多低碳发电技术的未来成本是高度不确定的。捕捉当前技术和新兴技术的这些不确定性有助于了解潜在的政策影响以及新兴技术可能发挥的作用。能源系统研究通常使用基于场景的方法，这需要建模者从一系列可能性中选择特定的值，这可能会使结果产生偏差，并报告不太可能的场景。本文结合了一种基于赖特经验学习定律的随机资本成本预测方法，以及一系列新兴技术的成本值。生成的输入集与COPPER电力系统容量扩展模型相关联，使用100个成本输入参数的抽样组合和四个政策情景，生成一个包含400个模型运行的数据库。提出了政策对未来排放、系统成本和发电组合的影响。将不确定性纳入模型表明，与新兴技术的不一致部署相比，传输在所有情况下的部署是一致的。这项研究发现，无论是碳税还是拟议的清洁电力法规，都无法在2050年实现所有情景下的电力系统脱碳。本研究的结果与加拿大国家和省级能源研究的结果一致，但与潜在模型结果的完整分布相比，有些结果是异常值。这些发现强调了将不确定性纳入电力系统模型的迫切需要，特别是在讨论政策和新兴技术时。

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引用次数: 0

Sentiments and risks: A spillover tale under climate policy uncertainty 情绪与风险：气候政策不确定性下的溢出效应

IF 14.2 2区经济学 Q1 ECONOMICS

Energy Economics

Pub Date : 2025-12-17 DOI: 10.1016/j.eneco.2025.109101

Yingying Huang , Weizhong Liang , Kun Duan , Mamata Parhi , Tapas Mishra

The investor psyche that finds expression in sentiments has been centralized given its crucial spillovers with risk perceptions, eliciting clear interpretation of the spillover impact and its dynamics under the ongoing climate vulnerability. This paper analyzes the time-varying industrial spillover between sentiments and risks, and its asymmetric evolutions when facing climate policy uncertainty (CPU). Our results, drawn based on a historical dataset in the U.S., show that the risk of technology sector and the sentiment of financial sector are the two largest information providers. Sectors related to the real economy are found to be the greatest information receivers. Moreover, sectoral spillovers are less affected by CPU in normal market conditions but are influenced with a larger magnitude during extreme periods. Dirty and clean sectors exhibit similar roles in forming the spillover, while their roles show distinct responses when facing shocks to CPU. Our findings for the dynamic spillovers of sentiments and risks should be of interest to various stakeholders toward financial stability and green transition.

考虑到其风险感知的关键溢出效应，投资者的情绪已经集中起来，从而对持续的气候脆弱性下的溢出效应及其动态进行了清晰的解释。本文分析了气候政策不确定性下情绪与风险之间的时变产业溢出效应及其不对称演化。我们基于美国历史数据集得出的结果表明，科技行业的风险和金融行业的情绪是两个最大的信息提供者。与实体经济相关的部门是最大的信息接收者。此外，在正常市场条件下，部门溢出效应受CPU影响较小，但在极端时期受到的影响更大。肮脏和清洁行业在形成溢出效应方面表现出相似的作用，而它们的作用在面对CPU冲击时表现出不同的反应。我们关于情绪和风险的动态溢出效应的研究结果应该引起金融稳定和绿色转型的各个利益相关者的兴趣。

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引用次数: 0

Green transformation in the era of intelligence: How artificial intelligence affects disruptive green innovation in firms 智能时代的绿色转型：人工智能如何影响企业的破坏性绿色创新

IF 14.2 2区经济学 Q1 ECONOMICS

Energy Economics

Pub Date : 2025-12-14 DOI: 10.1016/j.eneco.2025.109093

Jie Chen , Lisha Yang , Huahuang Su

Artificial intelligence (AI) drives disruptive technological breakthroughs and fosters paradigm shifts in firms' green innovation (GI). This study constructs a Green Citation Disruption Index based on green patent citation networks to measure disruptive green innovation (DGI) in Chinese listed manufacturing firms. The results show that about 12.43 % of firms have achieved DGI. In our empirical analysis, we decompose firms' DGI into two dimensions: the probability of achieving DGI and the innovation capability of firms that have already achieved it. The results show that a one-unit increase in AI level raises the probability of a firm achieving DGI by about 4.33 percentage points and enhances its DGI capability by about 9.71 percentage points, after controlling for firm and year fixed effects as well as other covariates. The mechanism analysis reveals that AI facilitates disruptive breakthroughs in green technologies through capability enhancement and capability compensation. The capability enhancement effect operates as AI attracts patient capital and overcomes organizational inertia, thereby strengthening firms' intrinsic ability to undertake DGI. The capability compensation effect arises as AI reinforces inter-firm collaboration, enabling firms to draw on external capabilities and resources. Moreover, we find evidence of a gradual deepening pattern in AI-enabled collaboration: AI first strengthens relationship-based collaboration formed through managerial interlocks, and subsequently enhances capital-based collaboration via shared ownership ties over a longer horizon, providing increasingly substantive external support for DGI. Heterogeneity analyses suggest that AI more strongly enhances the DGI capability of firms located in regions with environmental tribunals. The effect is also more pronounced for high-pollution firms. Moreover, state-owned firms exhibit both a higher probability of achieving DGI and stronger improvements in their innovation capability through AI. This study extends the measurement framework for DGI, clarifies the mechanisms through which AI enables such innovation, and provides new empirical evidence on how AI drives firms' engagement in it.

人工智能（AI）推动了颠覆性的技术突破，促进了企业绿色创新（GI）的范式转变。本文构建了基于绿色专利引文网络的绿色引文中断指数来衡量中国制造业上市公司的颠覆性绿色创新。结果表明，12.43%的企业达到了DGI。在实证分析中，我们将企业DGI分解为实现DGI的概率和已经实现DGI的企业创新能力两个维度。结果表明，在控制了企业和年份固定效应以及其他协变量之后，人工智能水平每提高一个单位，企业实现DGI的概率就会提高约4.33个百分点，其DGI能力提高约9.71个百分点。机制分析表明，人工智能通过能力增强和能力补偿促进了绿色技术的颠覆性突破。能力增强效应是通过人工智能吸引耐心资本，克服组织惯性，从而增强企业承担DGI的内在能力。当人工智能加强了企业间的合作，使企业能够利用外部的能力和资源时，能力补偿效应就会出现。此外，我们发现了人工智能支持的协作模式逐渐深化的证据：人工智能首先加强了通过管理联锁形成的基于关系的协作，随后通过更长期的共享所有权关系增强了基于资本的协作，为DGI提供了越来越实质性的外部支持。异质性分析表明，人工智能更强地增强了环境法庭所在地区企业的DGI能力。对于高污染企业，这种影响也更为明显。此外，国有企业通过人工智能实现DGI的可能性更高，其创新能力也有更强的提高。本研究扩展了DGI的测量框架，阐明了人工智能实现这种创新的机制，并为人工智能如何推动企业参与创新提供了新的经验证据。

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引用次数: 0

The effect of external assurance on corporate carbon disclosures: Empirical evidence from Europe 外部保证对企业碳披露的影响：来自欧洲的经验证据

IF 14.2 2区经济学 Q1 ECONOMICS

Energy Economics

Pub Date : 2025-12-13 DOI: 10.1016/j.eneco.2025.109084

Georgios Papadopoulos

Company carbon disclosures are a fundamental piece of information for assessing firms’ environmental impact, and many policy actions are associated with them. Responding to a rising demand for transparency and regulatory requirements, firms disclose information on their greenhouse-gas (GHG) emissions and voluntarily engage with external assurance of the reported data. However, the possible existence of systematic differences in reported emissions with respect to their assurance status is under-explored. This study investigates the causal effect of external assurance on carbon disclosures in a sample of European companies. Findings suggest that, on average, non-assuring firms under-report their direct GHG emissions by a share similar to the largest annual emissions reduction in the EU, compared to the assured reports of their peers. Instead, assurance’s effect is much weaker in indirect emissions. These results demonstrate that third-party assurance could provide more prudent estimates of corporate GHG emissions which are important for all stakeholders, including companies, investors and policymakers, to mitigate climate change.

公司碳信息披露是评估公司环境影响的基本信息，许多政策行动都与之相关。为响应日益增长的透明度需求和监管要求，企业披露其温室气体（GHG）排放信息，并自愿参与报告数据的外部保证。但是，报告的排放量与它们的保证状况之间是否可能存在系统性差异，尚未得到充分探讨。本研究以欧洲公司为样本，探讨外部保证对碳披露的因果效应。研究结果表明，平均而言，与同行的有保证的报告相比，无保证的公司少报其直接温室气体排放量的比例与欧盟最大的年度减排量相似。相反，保险对间接排放的影响要弱得多。这些结果表明，第三方担保可以提供更谨慎的企业温室气体排放估计，这对包括公司、投资者和政策制定者在内的所有利益相关者都很重要，有助于减缓气候变化。

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引用次数: 0

The predictive effects of Fintech-ESG dynamic interdependence: A global perspective on Cleantech energy transition risk 金融科技与esg动态相互依赖的预测效应：清洁技术能源转型风险的全球视角

IF 14.2 2区经济学 Q1 ECONOMICS

Energy Economics

Pub Date : 2025-12-13 DOI: 10.1016/j.eneco.2025.109090

Martin Enilov , Edna Delantar , Mamata Parhi

Fintech plays an instrumental role in advancing global ESG objectives, leveraging a more inclusive, transparent, and accountable financial system. Our paper explores the occurrence of dynamic linkages between Fintech and ESG across various dimensions, examining how the strength of their interconnectedness drives the energy transition towards clean technology. Using daily data from 31st May 2018 to 1st August 2024, we apply a time-varying parameter robust Granger causality method coupled with quantile technique to provide the first attempt in the literature on the dynamic causal patterns between the strength of Fintech-ESG connection and Cleantech energy transition risk (CETR). We find asymmetry in the connectedness across different quantiles, with Fintech sectors acting primarily as shock transmitters, while most ESG indexes are receivers. The 2022 Russia-Ukraine conflict reduces the connectedness between Fintech and ESG, with minimal effects on spillover direction. Our results show a heterogeneous response to shocks in developed markets, while developing ones tend to react more homogeneously. Additionally, we find strong evidence of a time-varying causal relationship between Fintech-ESG connectedness and CETR, with the conflict exacerbating asymmetry, especially at the lower quantile. Recent trends suggest a modest resurgence in this connection, signalling a re-emergence of the Fintech-ESG connection influence on CETR. The impact of extreme events tends to taper-off over time, suggesting that the prolonged conflict-driven market environment may have stabilized sufficiently to restore Fintech's role in promoting ESG initiatives, thereby supporting the ongoing transition to clean technology.

金融科技在推进全球ESG目标、推动建立更加包容、透明和负责任的金融体系方面发挥着重要作用。我们的论文探讨了金融科技和ESG之间在各个维度上的动态联系，研究了它们相互联系的强度如何推动能源向清洁技术的过渡。利用2018年5月31日至2024年8月1日的日常数据，我们采用时变参数鲁棒格兰杰因果关系方法结合分位数技术，首次在文献中尝试了金融科技- esg连接强度与清洁技术能源转型风险（CETR）之间的动态因果关系模式。我们发现不同分位数之间的连通性不对称，金融科技行业主要充当冲击发射器，而大多数ESG指数都是接收器。2022年俄罗斯-乌克兰冲突降低了金融科技与ESG之间的联系，对溢出方向的影响微乎其微。我们的研究结果显示，发达市场对冲击的反应各不相同，而发展中市场的反应往往更为均匀。此外，我们发现强有力的证据表明，金融科技- esg连通性与ccr之间存在时变因果关系，这种冲突加剧了不对称性，尤其是在较低的分位数。最近的趋势表明，这一联系略有复苏，标志着金融科技- esg联系对ctr的影响再次出现。随着时间的推移，极端事件的影响往往会逐渐减弱，这表明长期冲突驱动的市场环境可能已经足够稳定，可以恢复金融科技在促进ESG倡议方面的作用，从而支持正在进行的向清洁技术的过渡。

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引用次数: 0

Navigating green innovation: The role of carbon regulation and green finance in corporate strategy 引导绿色创新：碳监管和绿色金融在企业战略中的作用

IF 14.2 2区经济学 Q1 ECONOMICS

Energy Economics

Pub Date : 2025-12-11 DOI: 10.1016/j.eneco.2025.109089

Qingying Zheng , Handan Li , Guochao Bu , Hongxun Liu

This paper first distinguishes green innovation into substantive innovation (GI) and strategic innovation (SI). It then employs evolutionary game and panel econometric models to examine firms' green innovation choices under varying intensities of carbon emission regulation, as well as the moderating role of green finance support. The results show that carbon regulation mitigates the insufficient incentive problem for green innovation. However, different levels of regulatory intensity induce firms to adopt distinct green innovation strategies. Using the comprehensive carbon price (TCP) as a proxy for regulation intensity, we identify an inverted U-shaped relationship between TCP and GI. When the TCP is below the critical value of 19.08 yuan/ton, an increase in carbon price effectively promotes GI, whereas beyond this threshold, further increases significantly inhibit GI. In contrast, TCP exhibits a significant positive linear relationship with SI, indicating that for each one-yuan increase in carbon price raises strategic green innovations by approximately 0.76 %. Green finance support alleviates the cost and risk pressures imposed by carbon emission regulation, raising the threshold to 20.56 yuan/ton and playing a significant moderating role. Moreover, heterogeneity analysis reveals that the inverted U-shaped relationship between TCP and GI varies across firms: the threshold shifts rightward for large-scale firms and those facing intense industry competition, but leftward for heavily polluting firms. These findings provide policy implications for optimizing carbon regulation and green finance mechanisms, and for addressing firms' overemphasis on the quantity rather than quality of green innovation.

本文首先将绿色创新分为实质性创新（GI）和战略性创新（SI）。运用演化博弈模型和面板计量经济模型考察了不同碳排放监管强度下企业的绿色创新选择，以及绿色金融支持的调节作用。研究结果表明，碳调控缓解了绿色创新激励不足的问题。然而，不同的监管强度会促使企业采取不同的绿色创新策略。利用综合碳价格（TCP）作为监管强度的代表，我们发现TCP与GI之间存在倒u型关系。当TCP低于19.08元/吨临界值时，碳价的上涨有效促进了GI，而超过该临界值后，进一步上涨则显著抑制GI。相比之下，TCP与SI表现出显著的正线性关系，表明碳价格每增加1元，战略绿色创新就会增加约0.76%。绿色金融支持缓解了碳排放监管带来的成本和风险压力，将碳排放监管门槛提高到20.56元/吨，起到了显著的调节作用。此外，异质性分析表明，TCP和GI之间的倒u型关系在不同企业之间存在差异：大型企业和面临激烈行业竞争的企业的阈值向右移动，而重污染企业的阈值向左移动。这些发现为优化碳监管和绿色金融机制，以及解决企业过度强调绿色创新的数量而不是质量的问题提供了政策启示。

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引用次数: 0