Suchergebnisse

AbstractUsing 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 CO2 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.