Browsing by Subject "Carbon Pricing"

Carbon pricing is a cost-effective instrument of climate change mitigation policy. Its implementation is, however, limited by various political constraints. The goal of this thesis is to examine what factors empirically explain cross-country variation in carbon pricing policy. Understanding the political constraints limiting carbon pricing may have implications for policy design. Previous literature on the empirical determinants of carbon pricing policy has focused mostly on determinants based on political economy theory, such as variation in domestic interests, and been conducted with data only on explicit carbon pricing. Implicit carbon prices created by fuel excise taxes are, however, a significant part of the total price on emissions. This thesis contributes to existing literature by introducing two new determinants in public finance considerations and country-level social cost of carbon as well as utilizing broader carbon pricing data. Empirical methods used include regression based on maximum-likelihood estimation of censored data and multiple linear regression. The size of the public sector is found to have a statistically significant positive association with carbon pricing regardless of the model used. This supports the hypothesis that cross-country variation in carbon pricing is empirically explained by a need to finance public spending and by the double-dividend hypothesis. Other factors that are found to have a clear positive association with carbon pricing are level of democracy, administrative capacity, and GDP per capita. The results are somewhat mixed concerning the effect of other political institutions related factors as well as factors related to carbon intensity. The hypothesis that country-level social cost of carbon positively affects carbon pricing is clearly rebuked which suggests that a competitive game does not describe national-level policymakers’ decision-making. The results of the thesis should not, however, be interpreted as causal because of omitted variable bias, reverse causality, and a lack of time-series data.