This report examines the prospective role of nuclear and Small Modular Reactors (SMRs) within the complex energy transitions of Low and Middle-Income Countries (LMICs), offering a replicable energy planning framework and informing potential UK Government support. It underscores that while variable renewable energy (VRE) sources are foundational for decarbonisation, their high penetration can introduce significant system-level costs, often uncaptured by simplistic Levelised Cost of Electricity (LCOE) metrics, necessitating complementary firm, dispatchable capacity. Modelling indicates that SMRs could integrate meaningfully into fossil-fuel heavy systems (with limited hydro and geothermal as so-called “firm” renewable capacity) from approximately 2037–2045. This integration would be driven by carbon constraints and reliability needs, potentially contributing over 20% of generation by 2055. Conversely, in hydropower- dominated systems (that provide “firm” renewable capacity), SMRs are projected to play a more limited role, typically less than 2% of generation, as existing hydro and increasingly cost-effective battery storage largely meet demand. The viability of SMRs is contingent upon specific “triggers”, notably their capital costs falling below approximately $6,000–7,500 MUSD/GWe, alongside carbon prices exceeding $73–78 USD/ton, respectively. (At lower carbon prices, fossil-fuel alternatives provide the required “firm” capacity). Note that these numbers will change over time (as the energy system is dynamic) and will require updating annually. Construction delays significantly escalate total system costs, primarily by necessitating reliance on more expensive, often fossil-fuel-based, backup generation and incurring higher capital costs over time.

Developing nations encounter significant challenges in accessing the necessary finance to meet climate goals. The emerging ‘Data-to-Deal’ approach is a collaborative effort by 60 specialists, which aims to address this by providing a flexible framework of options, tailored to individual country circumstances, aiming to enhance core functions and capabilities; it serves as a basis for concrete action, informing capacity building, technical assistance, and research. This paper argues for the mainstreaming of a holistic approach to accessing climate finance by outlining the components of the Data-to- Deal pipeline and showing the effectiveness of Data-to-Deal through the demonstration of its successful implementation in Costa Rica.

A starter data kit for Papua New Guinea

A starter data kit for Republic Of Korea

A starter data kit for Cote D'Ivoire

Energy system modelling can be used to develop internally consistent quantified scenarios. These provide key insights needed to mobilise finance, understand market development, infrastructure deployment, the associated role of institutions, and generally support improved policymaking. However, access to data is often a barrier to starting energy system modelling, especially in developing countries, thereby causing delays to decision making. Therefore, this article provides data that can be used to create a simple zero-order energy system model for a range of developing countries in Africa, East Asia, and South America, which can act as a starting point for further model development and scenario analysis. The data are collected entirely from publicly available and accessible sources, including the websites and databases of international organisations, journal articles, and existing modelling studies. This means that the datasets can be easily updated based on the latest available information or more detailed and accurate local data. As an example, these data were also used to calibrate a simple energy system model for Kenya using the Open Source Energy Modelling System (OSeMOSYS) and three stylized scenarios (Fossil Future, Least Cost and Net Zero by 2050) for 2020-2050. The assumptions used and the results of these scenarios are presented in the appendix as an illustrative example of what can be done with these data. This simple model can be adapted and further developed by in-country analysts and academics, providing a platform for future work.

The Climate, Land, Energy and Water systems (CLEWs) approach guides the development of integrated assess-ments. The approach includes an analytical component that can be performed using simple accounting methods, soft-linking tools, incorporating cross-systems considerations in sectoral models, or using one modelling tool to represent CLEW systems. This paper describes how a CLEWs quantitative analysis can be performed using one single modelling tool, the Open Source Energy Modelling System (OSeMOSYS). Although OSeMOSYS was pri-marily developed for energy systems analysis, the tool's functionality and flexibility allow for its application to CLEWs. A step-by-step explanation of how climate, land, energy, and water systems can be represented with OSeMOSYS, complemented with the interpretation of sets, parameters, and variables in the OSeMOSYS code, is provided. A hypothetical case serves as the basis for developing a modelling exercise that exemplifies the building of a CLEWs model in OSeMOSYS. System-centred scenario analysis is performed with the integrated model example to illustrate its application. The analysis of results shows how integrated insights can be derived from the quantitative exercise in the form of conflicts, trade-offs, opportunities, and synergies. In addition to the modelling exercise, using the OSeMOSYS-CLEWs example in teaching, training and open science is explored to support knowledge transfer and advancement in the field.

A starter data kit for Equatorial Guinea

A starter data kit for South Africa

A starter data kit for Congo, Rep.

A starter data kit for Burkina Faso

Energy system modelling can be used to assess the implications of different scenarios and support improved policymaking. However, access to data is often a barrier to starting energy system modelling in developing countries, thereby causing delays. Therefore, this article provides data that can be used to create a simple zero order energy system model for Congo Republic, which can act as a starting point for further model development and scenario analysis. The data are collected entirely from publicly available and accessible sources, including the websites and databases of international organizations, journal articles, and existing modelling studies. This means that the dataset can be easily updated based on the latest available information or more detailed and accurate local data. These data were also used to calibrate a simple energy system model using the Open Source Energy Modelling System (OSeMOSYS) and two stylized scenarios (Fossil Future and Least Cost) for 2020-2050. The assumptions used and results of these scenarios are presented in the appendix as an illustrative example of what can be done with these data. This simple model can be adapted and further developed by in-country analysts and academics, providing a platform for future work.

Energy modelling is the process of using mathematical models to develop abstractions and then seek insights into future energy systems. It can be an abstract academic activity. Or, it can insert threads that influence our development. We argue therefore, that energy modelling that provides policy support (EMoPS) should not only be grounded in rigorous analytics, but also in good governance principles. As, together with other policy actions, it should be accountable. Almost all aspects of society and much of its impact on the environment are influenced by our use of energy. In this context, EMoPS can inspire, motivate, calibrate, and ‘post assess’ energy policy. But, such modeling is often undertaken by too few analysts under time and resource pressure. Building on the advances of ‘class leaders’, we propose that EMoPS should reach for practical goals — including engagement and accountability with the communities it involves, and those it will later affect. (We use the term Ubuntu, meaning ‘I am because you are’ to capture this interdependency). We argue that Ubuntu, together with retrievability, repeatability, reconstructability, interoperability and auditability (U4RIA) of EMoPS should be used to signal the beginnings of a new default practice. We demonstrate how the U4RIA principles can contribute in practice using recent modelling of aspirational energy futures by Costa Rica as a case study. This modelling effort includes community involvement and interfaces and integrates stakeholder involvement. It leaves a trail that allows for its auditing and accountability, while building capacity and sustainable institutional memory.

Energy system modelling can be used to assess the implications of different scenarios and support improved policymaking. However, access to data is often a barrier to starting energy system modelling in developing countries, thereby causing delays. Therefore, this article provides data that can be used to create a simple zero order energy system model for Namibia, which can act as a starting point for further model development and scenario analysis. The data are collected entirely from publicly available and accessible sources, including the websites and databases of international organizations, journal articles, and existing modelling studies. This means that the dataset can be easily updated based on the latest available information or more detailed and accurate local data. These data were also used to calibrate a simple energy system model using the Open Source Energy Modelling System (OSeMOSYS) and three stylized scenarios (Fossil Future, Least Cost and Net Zero by 2050) for 2020–2050. The assumptions used and results of these scenarios are presented in the appendix as an illustrative example of what can be done with these data. This simple model can be adapted and further developed by in-country analysts and academics, providing a platform for future work.