Energy Economics最新文献_第7页

Research on the impact of ultra-high voltage transmission on urban carbon neutral technology innovation: An empirical test based on double machine learning method 超高压输电对城市碳中和技术创新的影响研究——基于双机器学习方法的实证检验

IF 14.2 2区经济学 Q1 ECONOMICS

Energy Economics

Pub Date : 2025-12-30 DOI: 10.1016/j.eneco.2025.109120

Dongri Han , Ruiqi Wang , Yijia Yuan , Deheng Xiao

Energy infrastructure is a pivotal driver in reshaping the development trajectory of low-carbon technology as global carbon neutrality and the significant alteration of energy systems. Its innovation-driven efficacy has not been thoroughly investigated. This paper examines the “ultra-high voltage (UHV) transmission project,” which encompasses 270 Chinese cities at the prefecture level and above, as a quasi-natural experiment from 2006 to 2023. The difference-in-differences model and double machine learning are integrated to provide a causal inference framework that systematically reveals the multifaceted mechanism of energy infrastructure's impact on carbon neutral technology innovation. The findings indicate that UHV transmission project significantly increased carbon neutral technology innovation in pilot cities, enabling the optimal allocation of energy across regions. This supports the hypothesis of a network effect-innovation response mechanism driven by the dynamic adaptation of energy infrastructure. Further mechanism tests identify three transmission paths: government green development attention, informal environmental regulation, and energy consumption structure. Heterogeneity analysis reveals that these effects vary by region: energy-rich areas utilize UHV networks to break the resource curse; old industrial bases utilize it for green transitions; and small and medium-sized cities benefit from collaborative innovation. UHV transmission project reduces regional development gaps and weakens conventional geographic advantages. The paper provides precise policy targets for the energy revolution and regional coordination to support carbon neutrality, while providing practical guidance for infrastructure investment decisions.

随着全球碳中和和能源系统的重大变革，能源基础设施是重塑低碳技术发展轨迹的关键驱动力。其创新驱动的功效尚未得到彻底调查。本文将“特高压（UHV）输电项目”作为2006年至2023年的准自然实验进行研究，该项目涵盖了中国270个地级及以上城市。将差中差模型和双机器学习相结合，提供了一个因果推理框架，系统地揭示了能源基础设施对碳中和技术创新影响的多方面机制。研究结果表明，特高压输电项目显著促进了试点城市的碳中和技术创新，实现了区域间能源的优化配置。这支持了能源基础设施动态适应驱动的网络效应-创新响应机制假说。进一步的机制检验确定了三条传导路径：政府绿色发展关注度、非正式环境规制和能源消费结构。异质性分析表明，这些效应因地区而异：能源丰富地区利用特高压网络打破资源诅咒；老工业基地利用它进行绿色转型；中小城市从协同创新中受益。特高压输电工程缩小了区域发展差距，削弱了传统的地理优势。本文为能源革命和支持碳中和的区域协调提供了精确的政策目标，同时为基础设施投资决策提供了实用指导。

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引用次数: 0

Strategic sector coupling? Market power in heat and power markets 战略部门耦合？供热和电力市场的市场力量

IF 14.2 2区经济学 Q1 ECONOMICS

Energy Economics

Pub Date : 2025-12-30 DOI: 10.1016/j.eneco.2025.109106

Afzal S. Siddiqui , Sebastian Maier

Power-sector decarbonisation envisages extensive uptake of variable renewable energy (VRE) technologies. Although VRE output is intermittent, coupling between heat and power sectors via combined heat and power (CHP) plants could provide the requisite flexibility. However, strategic CHP plants could use the link between the two energy sectors to manipulate electricity prices. We use a bi-level model to investigate the incentives for the exercise of such market power. At the upper level, a firm with both heat-only and CHP plants is a Stackelberg leader when determining its heat output and is constrained by power-market operations at the lower level. Such a strategic firm produces more (less) heat from its CHP (heat-only) plant vis-à-vis the social optimum to constrain its power output, thereby boosting the electricity price. Additional market power at the lower level from power-only generation induces the strategic heat producer to reduce distortions to its operations as long as the electricity market is relatively large. In order to attenuate welfare losses from such strategic behaviour, we devise an incentive-based regulatory mechanism consisting of a subsidy to or a tax on CHP heat output. Numerical examples illustrate the properties of our analytical results, which can inform future negotiations over CHP cost allocations between regulators and producers.

电力部门的脱碳设想了可变可再生能源（VRE）技术的广泛采用。尽管VRE的输出是间歇性的，但通过热电联产（CHP）工厂实现热电部门之间的耦合可以提供必要的灵活性。然而，战略性的热电联产电厂可以利用这两个能源部门之间的联系来操纵电价。我们使用一个双层模型来研究这种市场力量的激励机制。在上层，同时拥有供热和热电联产电厂的公司在确定其热量输出时是Stackelberg的领导者，而在下层则受到电力市场运作的限制。这样的战略企业通过其热电联产（仅供热）工厂生产更多（更少）的热量，从而达到-à-vis社会最优，以限制其电力输出，从而提高电价。只要电力市场规模相对较大，仅发电所产生的较低水平的额外市场力量，就会促使战略产热企业减少对其运营的扭曲。为了减少这种战略行为造成的福利损失，我们设计了一种基于激励的监管机制，包括对热电联产的热输出进行补贴或征税。数值例子说明了我们分析结果的性质，这可以为未来监管机构和生产商之间关于热电联产成本分配的谈判提供信息。

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引用次数: 0

A data-driven merit order: Learning a fundamental electricity price model 数据驱动的价值排序：学习基本电价模型

IF 14.2 2区经济学 Q1 ECONOMICS

Energy Economics

Pub Date : 2025-12-30 DOI: 10.1016/j.eneco.2025.109114

Paul Ghelasi, Florian Ziel

Electricity price forecasting approaches generally fall into two categories: data-driven models, which learn from historical patterns, or fundamental models, which simulate market mechanisms. We propose a novel and highly efficient data-driven merit order model that integrates both paradigms. The resulting supply stack framework embeds the classical expert-based merit order as a nested special case, allowing all key parameters, such as plant efficiencies, bidding behavior, and available capacities, to be estimated directly from historical data, rather than assumed. We further enhance the model with critical embedded extensions such as hydro power, cross-border flows and corrections for underreported capacities, which considerably improve forecasting accuracy. Applied to the German day-ahead market, our model outperforms both classic fundamental and state-of-the-art machine learning models. It retains the interpretability of fundamental models, offering insights into marginal technologies, fuel switches, and dispatch patterns, elements which are typically inaccessible to black-box machine learning approaches. This transparency and high computational efficiency make it a promising new direction for electricity price modeling.

电价预测方法一般分为两类：从历史模式中学习的数据驱动模型，或模拟市场机制的基本模型。我们提出了一种新颖且高效的数据驱动的绩效排序模型，该模型集成了这两种范式。由此产生的供应堆栈框架将经典的基于专家的绩效排序嵌入为嵌套的特殊情况，允许所有关键参数，如工厂效率、投标行为和可用容量，直接从历史数据中进行估计，而不是假设。我们通过关键的嵌入式扩展，如水电、跨境流量和对低报容量的修正，进一步增强了模型，这大大提高了预测的准确性。应用于德国日前市场，我们的模型优于经典的基本模型和最先进的机器学习模型。它保留了基本模型的可解释性，提供了对边缘技术、燃料开关和调度模式的见解，这些元素通常是黑盒机器学习方法无法访问的。这种透明性和较高的计算效率使其成为电价建模的一个有前景的新方向。

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引用次数: 0

Spatial and socioeconomic factors of distributed photovoltaic generation in Minas Gerais 米纳斯吉拉斯州分布式光伏发电的空间和社会经济因素

IF 14.2 2区经济学 Q1 ECONOMICS

Energy Economics

Pub Date : 2025-12-30 DOI: 10.1016/j.eneco.2025.109121

Guilherme Perobelli Salgueiro , Fernando Salgueiro Perobelli , Edson Paulo Domingues , Victor Eduardo de Melo Valério

This article analyzes the determinants and spatial diffusion of residential distributed photovoltaic (PV) adoption in Minas Gerais, Brazil. Comprising 853 municipalities with deep socioeconomic disparities, the state serves as a unique case study for technology diffusion. The hypothesis of spatial randomness for the density of PV installations (N/Km²) was tested and rejected using Moran's I, revealing distinct clusters of high and low adoption. Employing a Spatial Durbin Model (SDM) to capture both direct and spillover effects, the results demonstrate that socioeconomic factors, specifically average wage, tertiary education, and urbanization are the primary drivers of diffusion. Crucially, the study identifies an ‘efficiency gap’ in the northern region of the state: while possessing the highest solar irradiation resources, these municipalities exhibit low adoption rates due to structural socioeconomic barriers, indicating that physical potential is a necessary but insufficient condition for diffusion. These findings suggest that public policies must go beyond technical incentives to address financial and educational constraints to promote equitable energy transition.

本文分析了巴西米纳斯吉拉斯州居民分布式光伏（PV）采用的决定因素和空间扩散。该州由853个城市组成，社会经济差距很大，是技术扩散的独特案例研究。使用Moran’s I对光伏装置密度（N/Km2）的空间随机性假设进行了测试和否定，揭示了不同的高采用率和低采用率集群。利用空间德宾模型（SDM）捕捉直接效应和溢出效应，结果表明，社会经济因素，特别是平均工资、高等教育和城市化是扩散的主要驱动因素。至关重要的是，该研究确定了该州北部地区的“效率差距”：虽然拥有最高的太阳辐照资源，但由于结构性的社会经济障碍，这些城市的采用率较低，这表明物理潜力是扩散的必要条件，但不是充分条件。这些研究结果表明，公共政策必须超越技术激励，解决财政和教育方面的限制，以促进公平的能源转型。

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引用次数: 0

How can China's healthy city pilot policy improve energy efficiency? Insights from difference-in-differences and double/debiased machine learning approaches 中国健康城市试点政策如何提高能源效率？来自差异中的差异和双重/去偏见机器学习方法的见解

IF 14.2 2区经济学 Q1 ECONOMICS

Energy Economics

Pub Date : 2025-12-30 DOI: 10.1016/j.eneco.2025.109119

Zihao Zhou , Shanlang Lin , Zhan He , Xiaoming Zhang , Sutong Chen , Junpei Huang

Improving energy efficiency (EE) is a critical pathway toward resource conservation and urban environmental sustainability. While healthy city initiatives have been acknowledged for their positive environmental and public health outcomes, their implications for urban EE have received limited empirical attention. This study takes China's healthy city pilot (HCP) policy as a quasi-natural experiment to investigate how the HCP policy affects EE. Using panel data from 282 prefecture-level cities between 2006 and 2023, we employ a difference-in-differences (DID) approach and a double/debiased machine learning (DDML) method to ensure robust causal identification. The relevant results are threefold. (1) The HCP policy significantly improves EE in pilot cities, which is driven by increased clean energy adoption, enhanced green technology innovation capabilities, improved public transportation services, and raised public environmental awareness. (2) The result of moderating effect analysis reveals that government environmental regulation can amplify pilot cities' energy transition effect. (3) HCP policy implementation widens regional disparities in EE across pilot cities, exhibiting a Matthew effect, with greater benefits observed in cities with lower resource dependence, better healthcare, stronger digital economies, and more developed green foundations. We propose that alongside expanding healthy city initiatives informed by policy experience, governments should strengthen policy support for underdeveloped cities with weak environmental and economic foundations to advance the Healthy China strategy.

提高能源效率是实现资源节约和城市环境可持续发展的重要途径。虽然健康城市倡议因其积极的环境和公共卫生成果而得到认可，但其对城市EE的影响却受到有限的实证关注。本研究以中国健康城市试点（HCP）政策为准自然实验，探讨健康城市试点政策对情感表达的影响。利用2006年至2023年间282个地级市的面板数据，我们采用了差分法（DID）和双/去偏机器学习（DDML）方法来确保稳健的因果识别。相关的结果有三个方面。(1) HCP政策显著提高了试点城市的能效，这主要得益于清洁能源采用率的提高、绿色技术创新能力的增强、公共交通服务的改善和公众环保意识的提高。(2)调节效应分析结果表明，政府环境规制可以放大试点城市的能源转型效应。(3) HCP政策的实施扩大了试点城市环境友好度的区域差异，呈现马太效应，资源依赖度较低、医疗条件较好、数字经济实力较强、绿色基础较发达的城市环境友好度受益更大。我们建议各国政府在总结政策经验的基础上，在扩大健康城市倡议的同时，加强对环境和经济基础薄弱的欠发达城市的政策支持，以推进“健康中国”战略。

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引用次数: 0

Time segmentation in tanker freight markets: The role of risk and relative freight rates in switching decisions 油轮货运市场的时间分割：风险和相对运价在转换决策中的作用

IF 14.2 2区经济学 Q1 ECONOMICS

Energy Economics

Pub Date : 2025-12-26 DOI: 10.1016/j.eneco.2025.109110

Manolis G. Kavussanos, Stergiani (Stella) A. Moysiadou, Dimitris A. Tsouknidis

This paper investigates the influence of macroeconomic and market-related factors on the chartering decisions of shipowners in the tanker segment of the ocean-going maritime industry. It introduces a methodological framework, which for a particular vessel type, highlights segmentation in the freight market based on contract duration. According to this framework, shipowners select charter contracts aligned with their risk preferences and strategic objectives, leading them to operate within a preferred “habitat” time-segment of the freight market -namely, the short-term spot market (single-voyage contracts), medium-term, or long-term time charters. A novel contribution of this study is the incorporation of global as well as shipping industry-specific risk factors. They include the risk aversion index developed by Bekaert et al. (2022), freight and crude oil price volatilities. The paper demonstrates that both global and industry specific risk factors in conjunction with relative freight rate differentials across contract durations significantly influence shipowners' selection of charter length and their potential transitions between different time-segments of the market. Using a unique dataset of 33,564 individual freight fixtures for crude oil tankers over a nine-year period, the study finds that the dominant “habitat” for VLCC and Suezmax tanker operators is the spot market, revealing a low level of risk aversion among market participants. However, changes in perceived risk and in relative freight rates can prompt temporary deviations from their preferred time-segment. The findings are in line with standard economics and finance theories and common shipping practice.

本文研究了宏观经济因素和市场相关因素对远洋航运业油轮行业船东租船决策的影响。它引入了一个方法框架，该框架针对特定的船舶类型，突出了基于合同期限的货运市场细分。根据这一框架，船东根据其风险偏好和战略目标选择租船合同，从而使他们在货运市场的首选“栖息地”时间段内运营，即短期现货市场（单次航程合同）、中期或长期租船。本研究的一个新颖贡献是纳入了全球以及航运业特定的风险因素。它们包括Bekaert et al.（2022）开发的风险规避指数、运费和原油价格波动。本文表明，全球和行业特定的风险因素，以及不同合同期限的相对运费差异，都显著影响了船东对租船期限的选择，以及他们在不同市场时间段之间的潜在转变。该研究使用了一个独特的数据集，该数据集包含了9年期间33564艘原油油轮的单独货运固定设备，研究发现，VLCC和苏伊士型油轮运营商的主要“栖息地”是现货市场，这表明市场参与者的风险厌恶程度较低。然而，感知到的风险和相对运费的变化可能促使他们暂时偏离他们喜欢的时间段。研究结果与标准的经济学和金融学理论以及常见的航运实践相一致。

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引用次数: 0

Can policy achieve environmental improvement? Evidence from the whole county photovoltaic project in China 政策能改善环境吗？来自中国全县光伏项目的证据

IF 14.2 2区经济学 Q1 ECONOMICS

Energy Economics

Pub Date : 2025-12-26 DOI: 10.1016/j.eneco.2025.109100

Wanhai You , Chenyao Fang , Yawei Guo , Shijing Nan

To facilitate the transition to clean energy and promote sustainable development, China's National Energy Administration issued the “Notice on the Publication of the County Wide Rooftop Distributed Solar Photovoltaic Development Pilot Project List” (also known as China's Whole County Photovoltaic Project). Using county-level panel data spanning May 2014 to September 2022, this study treats the initiative as an exogenous policy shock and employs a Difference-in-Differences (DID) design to assess its impact on environmental pollution. Empirical results indicate that the pilot policy significantly mitigates environmental pollution; this finding is further confirmed by a battery of robustness tests. Additionally, the mechanism analysis indicates that the project mitigates environmental pollution by facilitating public investment and driving green technological innovation. Furthermore, the heterogeneity analysis reveals that the pollution reduction effect is more pronounced in non-provincial capital, non-resource-based and western counties. Finally, we assess the heterogeneous effects using unconditional quantile regression (UQR). The results demonstrate that the significant impact is driven entirely by counties at the highest quantiles of pollution. Our findings offer valuable insights for policymakers in other countries aiming to advance energy transitions and mitigate environmental pollution.

为促进向清洁能源转型，促进可持续发展，国家能源局发布了《关于公布全县屋顶分布式太阳能光伏发展试点项目名单的通知》（又称全县光伏项目）。本研究使用2014年5月至2022年9月的县级面板数据，将该倡议视为外生政策冲击，并采用差分法（DID）设计评估其对环境污染的影响。实证结果表明，试点政策显著缓解了环境污染；一系列稳健性测试进一步证实了这一发现。机制分析表明，该项目通过促进公共投资和推动绿色技术创新来缓解环境污染。异质性分析表明，非省会城市、非资源型和西部地区的污染减排效果更为显著。最后，我们使用无条件分位数回归（UQR）评估异质性效应。结果表明，显著影响完全是由污染最高分位数的县驱动的。我们的研究结果为旨在推进能源转型和减轻环境污染的其他国家的政策制定者提供了有价值的见解。

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引用次数: 0

Interactions between multiple environmental markets: addressing contamination bias in overlapping policies 多重环境市场之间的相互作用：解决重叠政策中的污染偏差

IF 14.2 2区经济学 Q1 ECONOMICS

Energy Economics

Pub Date : 2025-12-24 DOI: 10.1016/j.eneco.2025.109094

Tiantian Yang , Richard S.J. Tol

To address the dual environmental challenges of pollution and climate change, China has established multiple environmental markets, including pollution emissions trading, carbon emissions trading, energy-use rights trading, and green electricity trading. Previous empirical studies suffer from known biases arising from time-varying treatment and multiple treatments. To address these limitations, this study adopts a dynamic control group design and combines Difference-in-Differences (DiD) and Artificial Counterfactual (ArCo) empirical strategies. Using panel data on A-share listed companies from 2000 to 2024, this study investigates the marginal effects and interactive impacts of multiple environmental markets implemented in staggered and overlapping phases. Existing pollution emissions trading mitigates the negative effects of carbon emission trading. Carbon trading suppresses (improves) financial performance (if implemented alongside energy-use rights trading). The addition of energy-use rights or green electricity trading in regions already covered by carbon or pollution markets has no significant effects.

为应对污染和气候变化的双重环境挑战，中国建立了多种环境市场，包括污染排放权交易、碳排放权交易、能源使用权交易和绿色电力交易。以往的实证研究存在时变处理和多重处理的已知偏差。为了解决这些局限性，本研究采用动态控制组设计，并结合差分中的差分（DiD）和人工反事实（ArCo）实证策略。本文利用2000 - 2024年a股上市公司的面板数据，研究了在交错和重叠阶段实施的多重环境市场的边际效应和交互影响。现有的污染排放权交易缓解了碳排放权交易的负面影响。碳交易抑制（改善）财务绩效（如果与能源使用权交易一起实施）。在已经被碳排放或污染市场覆盖的地区增加能源使用权或绿色电力交易没有显著效果。

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引用次数: 0

Market structure and technology adoption in renewable energy 可再生能源的市场结构和技术采用

IF 14.2 2区经济学 Q1 ECONOMICS

Energy Economics

Pub Date : 2025-12-24 DOI: 10.1016/j.eneco.2025.109109

Gaurav Doshi , Sarah Johnston

We study the effect of market structure on technology adoption in the U.S. solar and wind power industries. We compare adoption across two market types: restructured markets, which are designed to promote competition, and regulated markets, which are dominated by regulated monopolists. Solar projects in restructured markets are 32 percent less likely to adopt frontier technology. We also find negative effects of restructuring on adoption for wind projects. We provide evidence that this negative relationship between competition and technology adoption is explained by differences in financing costs across the two market types.

我们研究了市场结构对美国太阳能和风能产业技术采用的影响。我们比较了两种市场类型的采用情况：旨在促进竞争的重组市场和受监管的市场，后者由受监管的垄断者主导。重组市场中的太阳能项目采用前沿技术的可能性降低了32%。我们还发现了重组对风电项目采用的负面影响。我们提供的证据表明，竞争与技术采用之间的这种负相关关系可以通过两种市场类型之间融资成本的差异来解释。

引用次数: 0

Economic emission dispatching strategy considering dynamic parameter effects: A novel approach based on projection neural networks and deep learning 考虑动态参数影响的经济排放调度策略：基于投影神经网络和深度学习的新方法

IF 14.2 2区经济学 Q1 ECONOMICS

Energy Economics

Pub Date : 2025-12-24 DOI: 10.1016/j.eneco.2025.109104

Xueying Liu, You Zhao, Xing He

The economic emission dispatch (EED) problem is influenced by dynamic parameters such as power demand and climatic factors affecting renewable energy (RES) generation, which adds to the complexity of the dispatch process. Constrained by the serial iterative computing architecture, conventional optimization algorithms often face the challenges such as long computation time and computational inefficiency caused by repeated solving when dealing with EED involving continuous changes in dynamic parameters. To address the problem, this paper combines projection neural network (PNN) and deep learning to cope with the effect of dynamic parameters on the EED. First, a deep PNN (DPNN) is proposed by embedding PNN in deep learning. Then, the dynamic parameters in the EED are taken as input variables to the DPNN. Compared to PNN, DPNN do not require iterations and can respond immediately to dynamic parameter changes to directly provide predicted solutions for EED, which allows the DPNN reduce computation time and improve computational efficiency. Simulation results show that compared with PNN and convex solvers, DPNN can significantly reduce the computation time with good computational performance and can be adapted to EED problems containing dynamic parameters.

经济排放调度问题受电力需求和影响可再生能源发电的气候因素等动态参数的影响，增加了调度过程的复杂性。传统优化算法在处理动态参数连续变化的动态环境时，受串行迭代计算架构的限制，往往面临计算时间长、反复求解效率低的挑战。为了解决这一问题，本文将投影神经网络（PNN）与深度学习相结合，以应对动态参数对EED的影响。首先，通过将PNN嵌入到深度学习中，提出了一种深度PNN （DPNN）。然后，将EED中的动态参数作为DPNN的输入变量。与PNN相比，DPNN不需要迭代，可以立即响应动态参数变化，直接为EED提供预测解，从而减少了计算时间，提高了计算效率。仿真结果表明，与PNN和凸求解器相比，DPNN能显著缩短计算时间，具有良好的计算性能，能够适应包含动态参数的EED问题。

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引用次数: 0