Exploring Low-Carbon Energy Transitions Through Energy System Modeling: Leveraging data, scenario and sensitivity analysis : Insights from case applications in Ecuador, Kenya, and the State of Goa (India)

Combating climate change and achieving a sustainable future are the main objectives of the Sustainable Development Goals, requiring low-carbon energy systems. The SDGs, adopted by the United Nations in 2015, aim to create a more equitable and sustainable world by addressing pressing global challenges such as climate change. This thesis explores and provides insights into reducing greenhouse gas (GHG) emissions by focusing on energy systems modeling for infrastructure planning, strategies, and targets in developing economies. By identifying key research gaps, this thesis offers practical methods, insights, and open data into GHG reductions through the integration of renewables in specific locations. This dissertation uses an open-source energy modeling system generator to develop long-term models addressing the challenges of transitioning to low-carbon energy systems in Ecuador, Kenya, and the State of Goa (India). These real-world case applications show how financial, technological, and policy factors influence the adoption of renewable energy in specific geographical locations. In addition, by utilizing an open-source framework, it is possible to integrate cross-sectoral systems, such as the Climate, Land, Energy, and Water systems (CLEWs), to analyze the interaction between energy, land, and water resources. Research article I focuses on the impact of discount rates on long-term energy planning and how variations in these rates can influence decision-making processes. In addition, the discount rate is separated into global/social and individual/hurdle rates for electricity supply technologies. Research article II presents a whole energy system model tailored for a peculiar location in India, the state of Goa. The state lacks local electricity supply capacity and relies on allocated coal power plants. The study emphasizes achieving emission reduction goals in one scenario, accounting for a high share of renewables by integrating local stakeholders’ knowledge. In the third research article, the geographic location is Kenya. A Climate, Land, Energy, and Water (CLEWs) model is developed focusing on land and energy systems, specifically the cooking and agricultural sectors. Special attention is given to clean cooking technologies and reducing crop imports, with the variability of input parameters being cataloged and discussed. This detailed cataloging is novel in this kind of models, as it extends beyond the energy sector to include parameters from land, agriculture, and water systems, and their interlinkages with the cooking and agricultural sectors. Such variability in inputs also highlights the associated uncertainties in these parameters. In contrast, in research article IV, the methodological approach goes beyond cataloging and discussing to identify the most influential and non-influential parameters driving modeling results through a global sensitivity analysis (GSA). This sensitivity analysis is performed on a CLEWs model for Kenya, which is the result of merging two existing models, one related to cooking and agriculture (research article III) and another related to the whole energy system. Combining these models addresses the limitation of each in isolation and provides a more comprehensive view of cross-system interlinkages. The GSA approach not only reveals which parameters are influential on model results but also helps to better understand model behavior and the interactions across energy, land, and water systems. Through these four scientific articles, this thesis highlights how financial barriers and cross-sectoral complexities can either hinder or enable low-carbon energy transitions. It catalogs and discusses the uncertainty surrounding the techno-economic representations of land, energy, and water systems and demonstrates the influence of specific parameters on energy systems optimization models via sensitivity analysis. Ultimately, this integrated approach offers a blueprint for policymakers and stakeholders in other developing economies searching to balance financial viability with resource interdependencies on the path to reduce emissions.