Evaluating the effects of uncertainty on projections of greenhouse gas emissions : a biofuel case study in Brazil

Abstract

The use of projections of greenhouse gas emissions (GHG) estimates are fundamental to design appropriate policies to combat climate change, but the inherent complex nature of the climate system results in projections with a significant degree of uncertainty. An important source of uncertainty in GHG emissions estimates refers to land use changes (LUC) due to the complexity of the land system. As the land domain plays a relevant role in climate change mitigation, understanding the effects of uncertainty on projections of LUC-related GHG emissions estimates is crucial to better support the process of decision making. Based on a case study conducted by van der Hilst et al. (2018), this thesis evaluates the effects of uncertainty on the projections of LUC-related GHG emissions in Brazil towards 2030, given an expected increase in the global biofuel demand and distinct scenarios of LUC mitigation measures. With the use of Monte Carlo simulation technique, we developed a spatially explicit, stochastic model in Python programming language to perform the uncertainty analysis. As uncertainty can be derived from many sources, we focused on adding uncertainty in the model input data to assess its effects on the LUC-related GHG emissions estimates resulting from an increase in the global biofuel demand. As van der Hilst et al. (2018) performed an analysis of the same case study, but without uncertainty analysis, this thesis compares the stochastic results of the deterministic results. The comparison of the results obtained between the deterministic and the stochastic approach provides valuable insights about the effects of uncertainty in the final estimates of emissions. We run the model for six distinct LUC scenarios and computed the LUC-related GHG emission estimates given the changes in soil organic carbon (SOC) and biomass stocks, resulting in estimates with an associated uncertainty. We performed a statistical test to verify the existence of significant differences in the emission estimates between the scenarios and we run a sensitivity analysis to evaluate the contribution of the model components in the overall uncertainty of the emission estimates. The outcomes allows saying that adding uncertainty in the input data results in estimates with great uncertainty, specially in the emissions resulting from the changes in SOC stocks. The emission estimates obtained in this thesis have similar values when comparing to results of the deterministic approach of van der Hilst et al. (2018). The statistical test allows saying that the LUC-related GHG emission estimates resulting from an additional ethanol demand are significantly different between all scenarios, therefore the emission estimates could be used to support decision making e.g. to define or prioritize the implementation of a new LUC mitigation measure in Brazil.