Sensitivity Analysis and Uncertainty Parameter Quantification in a Regression Model: The Case of Deforestation in Tanzania

Abstract

Sep 2020, Published Oct 2020
Abstract
In this paper a multiple regression model for the economic factors and policy that influence the
rate of deforestation in Tanzania is formulated. Sensitivity analysis for parameters of explanatory
variables using one-at-a time and direct methods is carried out and the model is fitted by classical
least square (LSQ) and Markov Chain Monte Carlo (MCMC) methods. Uncertainty quantification
of parameters by adaptive Markov Chain Monte Carlo methods is performed. The coefficient of
determination indicates that 87% of deforestation rate is explained by explanatory variables
captured in the model. Household poverty rate is found to be the most sensitive factor to
deforestation, while purchasing power is the least sensitive in both methods. Model validation
indicates a good agreement between the collected data and the predicted data by the model and
Markoc Chain Monte Carlo method yielded a good sample mix. Thus, the study recommends that
since economic activities tend to increase the rate of deforestation, then policy and decisionmaking
processes should link the country’s desire for economic growth and environmental
management.

Keywords: deforestation; economic factors; Markov Chain Monte Carlo methods; regression
model; sensitivity;