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A statistical evaluation of UK energy policy: The climate change levy and climate change agreements

英国能源政策的统计评估：气候变化税和气候变化协议

基本信息

批准号：
2088643
负责人：
金额：
--
依托单位：
University College London
依托单位国家：
英国
项目类别：
Studentship
财政年份：
2018
资助国家：
英国
起止时间：
2018 至无数据
项目状态：
已结题

来源：
https://gtr.ukri.org/projects?ref=studentship-2088643
关键词：
statistical evaluation UK energy policy

项目摘要

The Climate Change Levy (CCL) is an energy tax introduced by the government in 2001. Firms in the industrial, agricultural, commercial, and public sector are levied on each kw/h of energy consumed; tax rates vary between electricity, gas, and solid fuels. Fearing a loss of competitiveness for energy intensive businesses, the Climate Change Agreements (CCA) scheme was designed to lighten the burden of CCL. The current iteration, phase 2, began in April 2013 and reduces the CCL by 90% for electricity and 65% for all other fuels in exchange for meeting negotiated emissions targets. The literature studying CCA and CCL focuses on ex-ante evaluations of the policies. Limitations in data that prevented ex-post evaluation of the policy are diminishing due to advances in computer science. Using confidential administrative data provided by the Department for Business, Energy, Industry, and Strategy (BEIS) this PhD thesis aims to study the effects of the policies through modern econometric and machine learning methods. The core investigation will aim to answer the following questions:1. What was the direct impact of the CCA on energy demand?2. How did firms achieve the required reductions in energy consumption for schema participation?3. What kind of characteristics were common among firms that strongly reacted to CCA? What kind of firms ceased operations?The results of the thesis can be used by policy makers to inform forward-going energy policy.The paper currently in progress answers the first question and studies the effect of the CCA on observed energy consumption in policy participants. Using the novel Changes-in-Changes estimator developed in Athey and Imbens, 2006, it becomes possible to investigate heterogeneous impacts of the policy across the distribution of outcomes. In essence, one can see how each firm reacted to participating in CCA, regardless of size. Schema participant and electricity data were provided by BEIS, however, the data was not ready to use due to not having shared uniquely identifying variables. A substantial amount of time was spent to devise a matching algorithm that utilizes "edit distance" metrics, i.e. algorithms that measure word similarity, to match addresses of facilities and electricity meters in the scheme. Preliminary results indicate that small facilities increased energy demand while it decreased for large facilities. The estimates combined with elasticity calculations imply that emissions targets were tight and difficult to satisfy for large facilities. The results of this study are submitted for presentation at the Energy Evaluation Europe 2020 conference, part of the CCA evaluation conducted by BEIS, and part of my upcoming upgrade seminar.The second question will be answered using the innovative causal forest method developed in Wager and Athey, 2018, which builds on random forest algorithms to estimate heterogeneous treatment effects. In this application, the aim is to study how firms satisfied the emissions targets. Using data from the Annual Business Survey (ABS), which contains variables such as turnover, energy expenditure, and employment, it becomes possible to estimate whether firms raised efficiency or decreased output to meet targets. To gain access to the ABS data hosted by the Office of National Statistics, I obtained the required accredited researcher certification.The last question will be answered by using a support-vector machine, a classification algorithm, to group data in the ABS depending on their responsiveness to CCA estimated in the first question. Ultimately, the goal is to understand what kind of firm metrics, such as investment in capital/technology or time-trends in energy efficiency, strongly predict the response to the policies. In a similar vein, the methodology will be used to understand what type of firms in CCA and CCL experienced facility closure post policy.

气候变化税（Climate Change Levy，CCL）是政府于2001年推出的一项能源税。工业、农业、商业和公共部门的企业按每千瓦时消耗的能源征税;税率因电力、天然气和固体燃料而异。由于担心能源密集型企业失去竞争力，气候变化协议计划旨在减轻CCL的负担。目前的迭代，第二阶段，从2013年4月开始，将电力的CCL降低90%，所有其他燃料的CCL降低65%，以换取达到谈判的排放目标。研究共同国家评估和共同国家赔偿责任的文献侧重于对政策的事前评估。由于计算机科学的进步，阻碍对政策进行事后评估的数据限制正在减少。本博士论文使用商业，能源，工业和战略部（BEIS）提供的机密行政数据，旨在通过现代计量经济学和机器学习方法研究政策的影响。核心调查将旨在回答以下问题：1。共同国家评估对能源需求的直接影响是什么？2.企业如何实现所需的减少能源消耗的模式参与？3.对共同国家评估反应强烈的公司有哪些共同特征？什么样的公司停止了业务？本文的研究结果可以为政策制定者提供信息，为未来的能源政策提供参考。目前正在进行的论文回答了第一个问题，并研究了CCA对政策参与者观察到的能源消费的影响。使用Athey和Imbens在2006年开发的新的Changes-in-Changes估计器，可以调查政策在结果分布中的异质性影响。从本质上讲，人们可以看到每个公司对参与共同国家评估的反应，无论规模大小。模式参与者和电力数据由BEIS提供，但是，由于没有共享唯一标识变量，数据尚未准备好使用。花费了大量时间来设计一种匹配算法，该算法利用“编辑距离”指标（即测量单词相似性的算法）来匹配该方案中的设施和电表的地址。初步结果表明，小型设施增加了能源需求，而大型设施则减少了能源需求。估计与弹性计算相结合意味着排放目标是严格的，难以满足大型设施。本研究的结果将提交给2020年欧洲能源评估会议，这是BEIS进行的CCA评估的一部分，也是我即将举行的升级研讨会的一部分。第二个问题将使用Wager和Athey，2018年开发的创新因果森林方法来回答，该方法基于随机森林算法来估计异质处理效果。在这个应用程序中，目的是研究企业如何满足排放目标。利用年度商业调查（ABS）的数据，其中包含营业额、能源支出和就业等变量，可以估计企业是否提高了效率或减少了产出，以实现目标。为了访问国家统计局的ABS数据，我获得了所需的认证研究员证书。最后一个问题将通过使用支持向量机（一种分类算法）来回答，根据第一个问题中估计的CCA的响应性对ABS中的数据进行分组。最终，目标是了解什么样的公司指标，如资本/技术投资或能源效率的时间趋势，强烈预测对政策的反应。同样，该方法将用于了解CCA和CCL中哪些类型的公司经历了设施关闭后政策。