Climate Change Policies and the Carbon Tax Effect on Meat and Dairy Industries in Brazil

This study analyzes the impacts of reducing greenhouse gas (GHG) emissions on the meat and dairy industries. To achieve this goal, the Global Trade Analysis Project (GTAP) database was used in a Computable General Equilibrium (CGE) setting, which allows for the inclusion of carbon taxes and the defi...

Full description

Bibliographic Details
Main Authors: Augusto Mussi Alvim, Eduardo Rodrigues Sanguinet
Format: Article
Language:English
Published: MDPI AG 2021-08-01
Series:Sustainability
Subjects:
Online Access:https://www.mdpi.com/2071-1050/13/16/9026
Description
Summary:This study analyzes the impacts of reducing greenhouse gas (GHG) emissions on the meat and dairy industries. To achieve this goal, the Global Trade Analysis Project (GTAP) database was used in a Computable General Equilibrium (CGE) setting, which allows for the inclusion of carbon taxes and the definition of four alternative environmental policies scenarios using both Global Warming Potential (GWP) and Global Temperature Potential (GTP) as GHG emissions measures. All scenarios analyze the main effects of carbon-based tax economic instruments on the industry and national production, trade, and emissions, comparing the results for different measures of GHG, GWP, and GTP from the Greenhouse Gas Emissions Estimation System (SEEG) sectoral Brazilian emissions database. In contrast with other industries, relatively lower taxes on the meat and dairy industries seem to be the most adequate in terms of cost distribution in the Brazilian economic structure when only the GWP measure is considered. Urban activities and less-methane-intensive industries benefit from climate change policies designed using GWP-based rather than GTP-based carbon taxes. The article also highlights the importance of a gradual introduction of carbon taxes, allowing the most vulnerable industries a transition moment to adopt clean technologies and/or redirect economic activity to less-GHG-emitting segments.
ISSN:2071-1050