Stochastic Modeling Applied to the Expanded Brazilian Agriculture Sector: CO₂ Emissions Analysis and Reduction Proposals for Climate Mitigation
Brazilian Expanded Agriculture; CO2 emissions; Probabilistic distributions; Climate mitigation; Zero Carbon Index.
This thesis presents a statistical and integrated approach to analyzing the impacts of climate change on the Brazilian agricultural sector, with a focus on quantifying CO2 emissions along the expanded production chain, including the industrial and direct and indirect services sectors. Based on secondary data from the Greenhouse Gas Emissions and Removals Estimation System (SEEG) and the Center for Advanced Studies in Applied Economics (CEPEA), stochastic modeling was applied, adjusting probabilistic distributions and regression analysis, making it possible to construct future scenarios based on reduction targets. The research proposes a multi-criteria metric for evaluating climate impact models, as well as the development and application of the Expanded Agricultural Zero Carbon Index (ICZAE). Factor analysis and Pareto analysis tools were also used to identify and prioritize the categories with the greatest variability in emissions, guiding more efficient mitigation strategies. The comparison between the Phase I (historical data) and Phase II (adjusted distributions) models shows the effect of climate policies on slowing down emissions. The research provides relevant input for the design of public policies and mitigation strategies aimed at Carbon neutrality in the agricultural sector.