A Dynamic Recursive Analysis of A Carbon Tax Including Local Health Feedback

An ancillary benefit of Greenhouse Gas (GHG) mitigation refers to a benefit derived from GHG mitigation that is in addition to the reduction in adverse impacts of global climate change. One type of ancillary benefit of GHG mitigation is reduced local air toxics, which is associated with improved health. Middle-income countries, as defined by the World Bank, like Thailand are in a unique position to obtain large ancillary health gains from reduced local air toxics when GHG is mitigated by curbing fossil fuel consumption. The author assesses whether by capturing the local health effects of reduced air toxics as an ancillary effect of GHG mitigation, and by allowing this benefit to feed back into the economy, the desirability of policies aimed at GHG mitigation will change, from the standpoint of macroeconomic and welfare indicators. The author uses a multi-period comprehensive cost/benefit framework - a Dynamic Recursive Computable General Equilibrium (CGE) model - for the assessment. A health effects sub-model takes the PM10 emissions (volume) information from the CGE model to assess the implications for ambient PM10 concentration, local health, labor supply and medical expenditures. The saved labor is exogenously fed back to the CGE model to find the economy-wide repercussions whereas the adjustment of medical expenditures due to improved environmental quality is endogenized in the model. To illustrate this methodology, the methodology is applied to the country of Thailand, a middle-income country, for the period of 1998-2010. The base year was calibrated to a 1998 Social Accounting Matrix originally obtained from the Thai Development Research Institute. Findings include: (1) average GDP growth with the carbon tax relative to the no policy scenario turns positive when the health feedback is included, and (2) the welfare of households with the carbon tax relative to the no policy scenario improve by a factor of two when health feedback in incorporated. An extensive sensitivity analysis over these results was then carried out, using upper and lower bound values instead of the central or default values for 11 key parameters. A tornado diagram was used to identify the parameters whose uncertainties influence key results the most. Three parameters were identified as the most influential parameters - the distribution of source term contributions to ambient PM10 (KCOEFF), the capital-to-output ratio (KSCALE), and the elasticity of substitution for top level CES production technology (AGGINP). The key results corresponding with alternative assumptions about these three parameters were then evaluated more closely. Under three alternative scenarios - low bound KCOEFF, low bound KSCALE, and high bound AGGINP - the key results or findings alluded to earlier no longer hold. Although the author does not have the information about the probability distributions of the occurrence of alternative values for these parameters, she assesses the likelihoods of these alternative values’ being closer to reality than their default values.