A Study on Selecting Greenhouse Gas Reduction Options: A Simulation Analysis for Vietnam

When it comes to greenhouse gas (GHG) mitigation, both bottom-up and top-down policies have limitations. Bottom-up policies are region-specific and cannot be applied at the national level. Top-down policies may not balance the considerations of economic growth and the environment. Therefore, a combined approach is necessary. This Vietnamese case study investigates optimal GHG mitigation options for both economic development and emission reduction by simulating four scenarios characterized by the different carbon tax and subsidy rates. Interventions, like replacing old buses with low-carbon buses and conventional electricity generation with solar power, are considered in a dynamic input-output framework. The objective function is Green GDP-industries' total value added reflecting GHG emissions' social cost. The simulation model comprises four cases: business as usual, low subsidy rate (up to 10%), medium subsidy rate (up to 20%), and high subsidy rate (up to 30%), which are analyzed on parameters, including economic development, GHG emissions, and development of innovative sectors, like transportation and electricity. In three cases with different subsidy rates, the optimal carbon tax is simulated at the rate of USD 1/tCO(2) equivalent, the lowest rate among the world's current carbon prices. In addition, the medium subsidy (up to 20%) option yields the most competent scheme, with the highest GHG emission reduction and economic development effectiveness.

Automated summary

This study investigates optimal GHG mitigation options for both economic development and emission reduction by simulating four scenarios characterized by different carbon tax and subsidy rates. The optimal carbon tax is simulated at a rate of USD 1/tCO2 equivalent, the lowest rate among current global carbon prices. The medium subsidy option (up to 20%) shows the most effective scheme with the highest GHG emission reduction and economic development effectiveness.