EAPSI: New Approaches to Ground-Truthing Economic Models for Energy Policy Analysis

EAPSI：能源政策分析的真实经济模型的新方法

• 批准号: 1714083
• 负责人: Cecilia Springer
• 金额: $ 0.54万
• 依托单位: Springer Cecilia
• 依托单位国家: 美国
• 项目类别: Fellowship Award
• 财政年份: 2017
• 资助国家: 美国
• 起止时间: 2017-06-01 至 2018-05-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1714083&HistoricalAwards=false
• 关键词: EAPSI; New; Approaches; Ground; Truthing

Validating the accuracy of economic models is particularly difficult for complex policy questions, such as the question of how energy regulations affect the labor force of regulated firms. Economic impacts on the workforce are a concern for policymakers everywhere seeking to implement clean energy policies while preserving jobs. This project seeks to explore the performance of a type of economic model frequently used for policy to identify the environmental and economic impacts of a proposed energy consumption regulation in China, where clean energy and economic growth are top policy priorities. This research will validate the model results using novel social science methods to incorporate local knowledge and expertise. This project will be conducted at Tsinghua University in collaboration with Professor Teng Fei, a noted expert on climate-energy policy in China. The findings from this project will be useful for economists and policymakers seeking accurate models and well-designed energy policy. China's prioritization of clean and efficient energy includes a recent effort to establish an energy consumption trading system. Computable general equilibrium (CGE) models are frequently used to simulate economy-wide policy effects, including emissions and labor impacts. However, external validation of CGE model predictions is understudied. To estimate the distributional labor effects from China?s energy consumption trading scheme, this research will use a China-level version of the Trade Integrated Global Energy and Resources (TIGER) model, an existing global dynamic CGE model, to generate an initial set of results against which the model can be validated. These results will inform a novel expert elicitation process to generate subjective probability distributions for sensitivity analysis. They will also inform discussion of the mechanisms through which distributional impacts are predicted to occur and if these comport with the model?s structure, enhancing ongoing efforts to improve energy policy and modeling.This award, under the East Asia and Pacific Summer Institutes program, supports summer research by a U.S. graduate student and is jointly funded by NSF and the Chinese Ministry of Science and Technology.

对于复杂的政策问题，验证经济模型的准确性特别困难，例如能源法规如何影响受监管公司的劳动力的问题。经济对劳动力的影响是各地寻求实施清洁能源政策的政策的问题，同时保留工作。该项目旨在探索经常用于政策的经济模型的表现，以确定中国提议的能源消耗调节的环境和经济影响，在那里，清洁能源和经济增长是主要的政策优先事项。这项研究将使用新颖的社会科学方法来验证模型结果，以纳入本地知识和专业知识。该项目将与芬华大学与中国气候能源政策专家Teng Fei教授合作进行。该项目的发现将对寻求准确模型和精心设计的能源政策的经济学家和政策制定者有用。中国的清洁和高效能源的优先级包括最近建立能源消耗交易系统的努力。可计算的一般平衡（CGE）模型经常用于模拟整个经济政策的影响，包括排放和劳动影响。但是，对CGE模型预测的外部验证进行了研究。为了估计中国能源消耗计划的分配劳动效应，本研究将使用中国级版本的全球能源和资源综合版本（Tiger）模型（一种现有的全球动态CGE模型）来生成一组初始的结果，该结果可以验证该模型。这些结果将为一个新颖的专家启发过程提供信息，以生成主观概率分布以进行灵敏度分析。他们还将告知讨论预计会发生分配影响的机制，以及如果这些作用与模型结构相提并论，并加强了正在进行的改善能源政策和建模的努力。根据东亚和太平洋夏季研究所计划，该奖项支持美国研究生的夏季研究，并由NSF和中国科学和科学部共同资助。