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.

验证经济模型的准确性对于复杂的政策问题尤其困难，例如能源法规如何影响受监管企业的劳动力。对劳动力的经济影响是寻求实施清洁能源政策同时保留就业机会的政策制定者所关注的问题。该项目旨在探索一种经常用于政策的经济模型的性能，以确定中国拟议的能源消费法规的环境和经济影响，清洁能源和经济增长是中国的首要政策重点。这项研究将使用新颖的社会科学方法来验证模型结果，以融入当地知识和专业知识。该项目将在清华大学与中国著名的气候能源政策专家滕飞教授合作进行。该项目的研究结果将有助于经济学家和政策制定者寻求准确的模型和精心设计的能源政策。中国优先发展清洁高效能源，包括最近建立能源消费交易体系的努力。可计算一般均衡（CGE）模型经常用于模拟整个经济的政策影响，包括排放和劳动力影响。然而，CGE模型预测的外部验证研究不足。估计中国的劳动分配效应？的能源消费交易计划，本研究将使用贸易一体化全球能源和资源（TIGER）模型，现有的全球动态CGE模型的中国水平的版本，以产生一组初始的结果，该模型可以验证。这些结果将通知一个新的专家启发过程中产生的主观概率分布的敏感性分析。他们还将通知分配的影响，通过预测发生的机制，如果这些与模型的讨论？该奖项是在东亚和太平洋暑期研究所项目下设立的，旨在支持美国研究生的暑期研究，由NSF和中国科技部共同资助。