碳排放税
温室气体
显著性差异
差异中的差异
经济
碳纤维
排放交易
帧(网络)
自然资源经济学
控制(管理)
骨料(复合)
农业经济学
环境科学
计量经济学
工程类
计算机科学
统计
材料科学
数学
管理
算法
复合数
复合材料
生物
生态学
电信
标识
DOI:10.1007/s10640-022-00679-w
摘要
Abstract Using difference-in-differences, synthetic control, and introducing a new break-detection approach, I show that the introduction of North America’s first major carbon tax has reduced transportation emissions but not ‘yet’ led to large statistically significant reductions in aggregate CO 2 emissions. Proposing a new method to assess policy based on breaks in difference-in-differences using machine learning, I demonstrate that neither carbon pricing nor trading schemes in other provinces are detected as large and statistically significant interventions. Instead, closures and efficiency-improvements in emission-intense industries in untaxed provinces have reduced emissions. Overall, the results show that existing carbon taxes (and prices) are likely too low to be effective in the time frame since their introduction.
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