Energy Economics最新文献_第9页

Carbon price volatility in the New Zealand Emission Trading Scheme 新西兰排放交易计划中的碳价格波动

IF 14.2 2区经济学 Q1 ECONOMICS

Energy Economics

Pub Date : 2025-12-22 DOI: 10.1016/j.eneco.2025.109107

Yudou Yang, Le Wen, Basil Sharp, Sholeh Maani

The design of an emission trading scheme (ETS) critically influences its efficacy in reducing carbon emissions. This study enhances the international discourse by analyzing carbon price volatility in the New Zealand Emission Trading Scheme (NZ ETS), a system distinct for its greenhouse gas emissions targets, direct trade of forestry entitlements by forestry participants in the secondary market, and the allowance for unlimited surrender of these entitlements to meet emission obligations. Utilizing daily time series data from 1 July 2010 to 31 December 2022, we apply ARIMA (p, d, q)-EGARCH (m, n)-X models to evaluate the effects of supply-side, demand-side, and regulatory factors on carbon price volatility in the NZ ETS. The findings reveal: (1) Entitlements exert significant long-term effects on volatility, initially positive due to increased supply followed by negative impacts, consistent with mean reversion theory; (2) Demand-side factors influence carbon price volatility only in the short term due to the stable demand for allowances; (3) Successful auctions and policy announcements related to the supply and demand of tradeable units significantly affect volatility; and (4) Regulatory adjustments to entitlement supply notably alter price dynamics in the NZ ETS. These insights assist policymakers in managing ETSs with substantial potential supplies of carbon credits, helping mitigate volatility risks. The evidence-based conclusions also serve as a valuable reference for Southeast Asian countries and those with abundant forest resources establishing or operating ETSs to meet their climate goals.

碳排放交易机制（ETS）的设计对其减少碳排放的效果有着至关重要的影响。本研究通过分析新西兰排放交易计划（NZ ETS）的碳价格波动，增强了国际话语权。新西兰排放交易计划（NZ ETS）是一个独特的体系，其温室气体排放目标、林业参与者在二级市场上的林业权利直接交易，以及为履行排放义务而无限放弃这些权利的许可。利用2010年7月1日至2022年12月31日的每日时间序列数据，我们应用ARIMA (p, d, q)-EGARCH (m, n)-X模型来评估供给侧、需求侧和监管因素对新西兰碳排放交易体系碳价格波动的影响。结果表明：(1)应享权益对波动性具有显著的长期影响，最初是由于供给增加而产生的正影响，随后是负影响，符合均值回归理论；(2)由于配额需求稳定，需求侧因素仅在短期内影响碳价格波动；(3)与可交易单位供需相关的成功拍卖和政策公告显著影响波动性；(4)对权利供应的监管调整显著改变了新西兰排放交易体系的价格动态。这些见解有助于政策制定者管理具有大量潜在碳信用供应的碳排放交易体系，帮助降低波动性风险。基于证据的结论也为东南亚国家和森林资源丰富的国家建立或运营碳排放交易体系以实现其气候目标提供了有价值的参考。

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引用次数: 0

Complexity analysis of automotive supply chains considering online reputation under dual policies 双重政策下考虑网络声誉的汽车供应链复杂性分析

IF 14.2 2区经济学 Q1 ECONOMICS

Energy Economics

Pub Date : 2025-12-20 DOI: 10.1016/j.eneco.2025.109105

Xiaobin Wang , Abudureheman Kadeer , Huanying He

Facing dual pressures from sluggish consumer demand and the “dual‑carbon” goals, this study constructs a synergistic policy framework that integrates carbon quota trading and trade-in subsidies, while innovatively incorporating the dynamic effects of online reputation. Based on game theory and complex systems theory, we establish a manufacturer-led Stackelberg game model and a multi-period dynamic recursive model to unravel the coupling mechanisms through which policies and digital reputation influence supply chain decision-making. Our findings reveal that differentiated pricing strategies are a key driver of complex system dynamics: when traditional fuel vehicles (TFV) adopt conservative pricing

(v_{1} < 0.02)

while new energy electric vehicles (NEEV) implement aggressive strategies

(v_{3} > 0.16)

, the system exhibits asymmetric profit oscillations, with the Largest Lyapunov Exponent of 0.48, and the volatility of NEEV direct sales prices far exceeds that of retail channels. To address this, we propose a time-delayed feedback control method, which effectively suppresses chaotic phenomena and enhances the robustness of the supply chain system. These findings provide crucial theoretical foundation and practical insights for policy coordination, dynamic pricing, and stability management in automotive supply chains under the dual‑carbon targets.

面对消费者需求疲软和“双碳”目标的双重压力，本研究构建了碳配额交易与以旧换新补贴相结合的协同政策框架，并创新地纳入了网络声誉的动态效应。基于博弈论和复杂系统理论，建立了制造商主导的Stackelberg博弈模型和多周期动态递归模型，揭示了政策和数字声誉影响供应链决策的耦合机制。研究结果表明，差异化定价策略是复杂系统动力学的关键驱动因素：当传统燃油车（TFV）采用保守定价v1<；0.02，而新能源电动汽车（NEEV）采用激进定价v1& gt；0.16时，系统呈现非对称利润波动，最大Lyapunov指数为0.48，且新能源电动汽车直销价格的波动幅度远远超过零售渠道。为了解决这一问题，我们提出了一种时滞反馈控制方法，有效地抑制了混沌现象，增强了供应链系统的鲁棒性。这些发现为双碳目标下汽车供应链的政策协调、动态定价和稳定性管理提供了重要的理论基础和实践见解。

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引用次数: 0

Distributional impacts of heterogenous carbon prices in the EU 欧盟异质性碳价格的分布影响

IF 14.2 2区经济学 Q1 ECONOMICS

Energy Economics

Pub Date : 2025-12-18 DOI: 10.1016/j.eneco.2025.109102

Magnus Merkle , Geoffroy Dolphin

We analyse the impact of carbon price heterogeneity on households in the EU from 2010 to 2020 using a novel dataset that combines carbon pricing policies with household budget survey data and a global multiregional input-output framework. Accounting for both heterogeneity in carbon pricing across emission sources and the indirect effects from inter-industry linkages, we obtain two key findings. First, due to incomplete carbon pricing coverage, household burdens have been lower than previously estimated. Second, carbon pricing incidence across income groups differs not only due to varying carbon intensities of demand but also due to differing expenditure shares on products that benefit from exemptions. In the majority of EU countries, low-income households have on average paid higher prices for the carbon embodied in their consumption than high-income households. Closing the carbon pricing gaps, particularly with regard to emissions embodied in imports, can help equalise burdens.

我们使用一个新的数据集，将碳定价政策与家庭预算调查数据和全球多区域投入产出框架相结合，分析了2010年至2020年欧盟碳价格异质性对家庭的影响。考虑到不同排放源间碳定价的异质性和行业间联系的间接影响，我们得到了两个关键发现。首先，由于碳定价覆盖范围不完整，家庭负担低于之前的估计。其次，不同收入群体的碳定价发生率不同，不仅是因为需求的碳强度不同，还因为受益于豁免的产品的支出份额不同。在大多数欧盟国家，平均而言，低收入家庭为其消费中包含的碳支付的价格高于高收入家庭。缩小碳定价差距，特别是在进口碳排放方面的差距，有助于平衡负担。

引用次数: 0

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

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

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

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