Using a statistical prediction model to combat climate change : the impacts taking a data driven approach

Abstract

Climate change is one of the most pressing challenges facing humanity, characterized by long-term shifts in temperatures and weather patterns. For developing economies like Uganda, which are highly reliant on climate-sensitive sectors, the issue is critical. This dissertation outlines a data-driven framework for building climate resilience in Uganda through the application of a statistical prediction model. The study's primary objective is to move climate action beyond reactive measures toward proactive, informed decision-making by enhancing strategic planning and enabling rigorous monitoring of policy efficacy. Initial analysis of time-series data reveals a critical divergence in Uganda's mitigation efforts: despite a highly decarbonized electricity generation sector (approximately 90% renewable sources as of 2021), total Greenhouse Gas (GHG) and Fossil CO₂ emissions continue a concerning upward trajectory. This evidence compels a shift in policy focus towards diffuse emission sources, namely land use change, transportation, and agriculture. The analytical findings identify several strategic policy imperatives, including the implementation of carbon pricing, which is estimated to reduce emissions by 18% while generating revenue to address up to one-fifth of the nation's financing requirements for the UN Sustainable Development Goals. However, policy effectiveness is undermined by a profound institutional failure at the local level, where funding for climate action represents only 0.93% of district budgets. The utilization of advanced econometric models, such as Vector Autoregression (VAR) and Long Short-Term Memory (LSTM) networks, is therefore necessary to provide stakeholders with accurate, tailored forecasts. These forecasts are required to mobilize capital, quantify the return on investment of green infrastructure, and target resilience technologies effectively, thereby bridging the gap between national policy and local implementation.