Energy planning under uncertainty remains a critical challenge in developing economies, where data limitations and climate variability complicate long, term investment decisions. In Sierra Leone, these challenges are particularly acute given low electricity access 36% and heavy reliance on hydropower with strong seasonal variability. This study addresses the limitations of deterministic energy modelling, which typically assumes perfect foresight and produces single optimal pathways that may not remain valid under uncertain futures. To overcome this, we integrate an OSeMOSYS, based technoeconomic optimization model with a Robust Decision Making (RDM) framework to evaluate resilient energy strategies. The approach is applied to the case of the Bumbuna Hydroelectric Expansion, including the proposed upstream Yiben reservoir. Key contributions include the systematic validation of least, cost model outputs under deep uncertainty through hundreds of parameter variations, and the identification of critical uncertainty drivers using scenario discovery techniques (PRIM). Results show that scenarios incorporating the Yiben reservoir (Scenarios 6 and 7) achieve up to ,100% renewable electricity generation by 2030, reduce reliance on heavy fuel oil generation, and deliver lower system costs and Levelised Cost of Electricity compared to alternatives without storage expansion. Furthermore, these scenarios consistently demonstrate robustness across a wide range of futures, clustering in low, cost, high, renewable outcome spaces. The findings highlight hydropower storage as a key enabler of system reliability and decarbonization. From a policy perspective, the study underscores the importance of integrating uncertainty analysis into national energy planning and supports prioritising cascade hydropower development to enhance energy security

This best practice brief presents Component 6 of the Data-to-Deal (D2D) framework, which addresses how to strengthen the enabling environment for investment. This component focuses on the coherent policy, regulatory, and institutional reforms needed to reduce risk, create clear routes to market, and build investor confidence in clean energy and sustainable transport systems. Stable legal frameworks, predictable pricing and incentive structures, and transparent procurement processes are essential to mobilise capital at scale. Component 6 on successful enabling environments comprises three sub-components: (i) operationalising cross-cutting enablers; (ii) identifying and designing policy reforms; and (iii) developing an implementation plan to prioritise and sequence reforms. These sub-components are embedded across the D2D pipeline, linking political commitment, technical planning, and finance mobilisation into a coherent reform agenda. The brief draws on case studies from Zambia, South Africa, and the UK to illustrate how targeted reforms can expand renewable energy, reduce costs, and create investor-ready markets, while also highlighting the role of domestic capital market reforms in unlocking long-term sustainable financing. By operationalising this approach, countries can develop integrated, context-specific reform packages that advance development priorities, meet climate commitments, and strengthen resilience.

Achieving the energy transition in Low- and Middle-Income Countries (LMICs) requires translating national energy plans into financially viable, investment-ready projects. Although many LMICs have developed long-term energy transition strategies, a persistent gap remains between high-level planning and actual investment mobilisation. The Data-to-Deal (D2D) framework offers a practical approach to support this alignment. Developed through international research and applied in real-world settings, D2D identifies seven interlinked steps to convert decarbonisation pathways into bankable investment pipelines. While all steps are critical, this consultation was focused on step 6 on Policy and Step 7 on Finance, which are most focused on implementation. This brief summarises key themes and discussion points from a high-level roundtable held during London Climate Action Week on 23 June 2025.

Energy systems modelling plays a pivotal role in understanding and optimizing complex energy systems. By integrating various factors such as energy demand, supply, infrastructure, and environmental considerations, energy systems modelling provides valuable insights for policymakers, industry stakeholders, and researchers. This can be key to informing stakeholder and policy decisions and facilitate the mobilisation of capital and market development to support the development of the energy sector. This article presents the data, assumptions, and related calculations used for the development of a national scale power system model for Sierra Leone. The focus of the model was a techno-economic analysis of specific hydropower expansion plans. Where possible, this data has been collected from publicly available sources such as scientific literature, feasibility studies, international databases, data from existing modelling efforts in Sierra Leone, and local stakeholders. The collection of the data, development of the model, and creation of scenarios was done in collaboration with the Ministry of Energy in Sierra Leone and verified by a broad range of stakeholders. The following paper outlines the key data presented in the full database which can be accessed through the URL found in the Specifications Table. The URL also contains the Reference Energy System (RES) used in this study.

Sierra Leone is an electricity-poor country with one of the lowest electricity consumption per capita rates across sub-Saharan Africa. Yet, with ambitious targets to transform and stimulate its economy in the coming decades, energy demand forecasting becomes an integral component of successful energy planning. Through applying the MAED-D (version 2.0.0) demand software, this research study aims to generate Sierra Leone’s electricity demand forecasts from 2023 to 2050. Three novel scenarios (baseline-, high-, and low-demand) are developed based on socio-economic and technical parameters. The baseline scenario considers the current electricity sector as business-as-usual; the high-demand scenario examines an ambitious development future with increased economic diversification and mechanisation, and the low-demand scenario examines more reserved future development. The modelled scenario results project an increase in electricity demand ranging from 7.32 PJ and 12.23 PJ to 5.53 PJ for the baseline-, high-, and low-demand scenarios, respectively, by 2050. This paper provides a base set of best-available data needed to produce an electricity demand model for Sierra Leone which can be used as a capacity-building tool for in-country energy planning alongside further integration into data modelling pipelines.

Energy systems modelling plays a pivotal role in understanding and optimizing complex energy systems. By integrating various factors such as energy demand, supply, infrastructure, and environmental considerations, energy systems modelling provides valuable insights for policymakers, industry stakeholders, and researchers. This can be key to informing stakeholder and policy decisions and facilitate the mobilisation of capital and market development to support the development of the energy sector. This article presents the data, assumptions, and related calculations used for the development of a national scale power system model for Sierra Leone. The focus of the model was a techno-economic analysis of specific hydropower expansion plans. Where possible, this data has been collected from publicly available sources such as scientific literature, feasibility studies, international databases, data from existing modelling efforts in Sierra Leone, and local stakeholders. The collection of the data, development of the model, and creation of scenarios was done in collaboration with the Ministry of Energy in Sierra Leone and verified by a broad range of stakeholders.The following paper outlines the key data presented in the full database which can be accessed through the link found in the Specifications Table. The link also contains the Reference Energy System (RES) used in this study.

Zero‑carbon electricity is a pre-requisite for decarbonisation of the wider economy and many scenarios envisage rapid expansion of renewables. IEA estimates a need to double the annual rate of investment to 2030. A rapid transition to low carbon electricity creates a risk to investors because projects being financed now will generate their revenue in a zero‑carbon electricity system for which there is no track record of price formation. Although the cost of renewable power has fallen greatly, future revenues from renewables are still affected by market price risks that are often outside the control of generators. If risks are difficult to quantify, they may be mis-priced, leading to inefficient premiums being added to the cost of capital, which could increase the overall cost of the transition. In this context many countries around the world provide support for low carbon energy, such as the UK Contracts for Difference. In this paper we investigate exposure to price risk caused by uncertainty over the mix of technologies used to achieve decarbonisation, which we term 'transition risk', and how this varies under different technology mixes, and for different policy regimes. We show that exposing investors to transition risk could increase the cost of delivering the renewables needed for a zero‑carbon electricity system by around 25% or £7bn/year compared to policy options that reduce exposure to wholesale market price risk. The paper concludes that as long as transition risks remain high, de-risking policy mechanisms can help to minimise the overall cost of achieving net-zero.

The study investigates the impact of climate action on energy security in West Africa using an autoregressive distributed lag and an error correction model. Empirical results for Burkina Faso showed that total carbon emissions and climate finance improved energy security performance in the country in the short and long run, while capacity building and energy efficiency impaired energy security performance in the short run. In the long run, capacity-building efforts are estimated to improve energy security performance in Burkina Faso. For Ghana, results showed that total carbon emissions improved energy security performance in Ghana while energy efficiency impaired energy security performance in the short run. Results for climate finance and capacity building are found to be insignificant, and no long-run results are found for Ghana. Lastly, results for Nigeria showed that total carbon emissions improved energy security performance in the long run. The short-run result for total carbon emissions and climate finance is found to be insignificant, while capacity building and energy efficiency impaired energy security performance in the long and short run. Considering the need to reduce carbon emissions during full implementation of Nationally Determined Contributions, policies should aim at decoupling carbon emissions from energy security by exploring options for low-carbon energy development.

As current production and consumption patterns of humanity exceed planetary boundaries, many opinion leaders have stressed the need to adopt green economic stimulus policies in the aftermath of the COVID-19 pandemic. Here, we provide an integrated multi-stakeholder framework to design an economic recovery strategy aligned with sustainability objectives. We first employ quantitative energy and economic models and then design a multi-criteria decision process in which we engage social actors from government, enterprises, and civil society. As a case study, we select green recovery measures that are relevant for a European Union country and assess their appropriateness with numerous criteria related to socio-economic and environmental sustainability and resilience. Results highlight trade-offs between immediate and long-run effects, between economic and environmental objectives, and between expert evidence and societal priorities. Importantly, we find that a ‘return-to-normal’ economic stimulus is not only environmentally unsustainable but also economically inferior to most green recovery schemes.

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.

The current literature on energy access highlights energy deprivation on a regional or country basis, but frequently neglects those outside of national energy agendas such as refugees and displaced people. To fill this gap and to help inform future analysis, this paper presents an end-use accounting model for energy consumption for cooking and lighting by displaced populations. We present initial estimates for the overall scale of energy poverty and three high-level scenarios for improving access to energy for cooking and lighting. Key findings suggest that as many as 7 million displaced people in camps have access to electricity for less than 4 h a day and that the widespread introduction of improved cookstoves and basic solar lanterns could save $303 million a year in fuel costs after an initial capital investment of $334 million. We conclude that there is a strong human, economic, and environmental case to be made for improving energy access for refugees and displaced people, and for recognising energy as a core concern within humanitarian relief efforts. (C) 2016 The Authors. Published by Elsevier Ltd.

The decarbonisation of electricity generation presents policy-makers in many countries with the delicate task of balancing initiatives for technological change whilst maintaining a commitment to market liberalisation. Despite the theoretical attractions, it has become debatable whether carbon markets by themselves can offer a complete solution. We address this through a modelling framework, stylised for the GB power market within the EU ETS, which includes three distinct components: (a) a long-term least-cost capacity planning model, similar in functionality to many used in policy analysis, but innovative in providing the endogenous calculation of carbon prices; (b) a short-term price risk model producing hourly dispatch and pricing outputs, which are used to test the annual financial performance risks implied by the longer-term investments; (c) an agent-based model which uses a computational learning algorithm to derive pricing behaviour in imperfect markets. The results indicate that the risk/return profile of electricity markets deteriorates substantially as a result of decarbonisation, reducing the propensity of companies to invest in the absence of increased government support. Markets may adjust, if allowed, by deferring investment until conditions improve, or by consolidating to increase market power, or by operating in a tighter market with reduced spare capacity. To the extent that each of these ‘market-led’ solutions may be politically unpalatable, policy design will need to sustain a delicate regulatory regime, moderating the increasing market power of companies whilst maintaining low-carbon subsidies for longer than expected.

The UK power generation sector faces a major new round of investment: the coincidence of asset retiring and ambitious goals for decarbonisation is not unique, but is particularly acute in the UK. The UK government has put in place a raft of new policies that seek to promote new, low carbon investment and ensure security of supply. The traditional channel for financing the sector has been through large utility companies, but this now looks challenging for various reasons. The UK therefore offers an interesting case study on several counts; the scale of the challenge, effectiveness of new policies, and the availability of alternative finance. We find that the link between the finance sector and the electricity sector is not ‘broken’, but the flow of money to the sector is threatened by the current weakness of the utilities’ business model. This paper compares estimates of the scale of investment required in the UK with historical investment rates. It summarises contemporary finance industry views of conditions and trends, and potential policy interventions that might be needed to bridge the investment gap. The potential for channelling institutional investor funds directly into energy assets is reviewed.

There is a growing focus on the economics of adaptation as policy moves from theory to practice. However, the techniques commonly used in economic appraisal have limitations in coping with climate change uncertainty. While decision making under uncertainty has gained prominence, economic appraisal of adaptation still uses approaches such as deterministic cost-benefit analysis. Against this background, this paper provides a critical review and assessment of existing economic decision support tools (cost-benefit analysis and cost-effectiveness analysis) an uncertainty framework (iterative risk management) and alternative tools that more fully incorporate uncertainty (real options analysis, robust decision making and portfolio analysis). The paper summarises each method, provides examples, and assesses their strengths and weaknesses for adaptation. The tools are then compared to identify key differences, and to identify when these approaches might be appropriate for specific applications in adaptation decision making.

Power generation companies are among the biggest emitters of greenhouse gases and are, therefore, potentially among the most exposed companies when it comes to regulatory risk and uncertainties in climate change policy. In practice, however, their risk exposure is reduced by the ability of power companies to pass through the additional costs to the price of electricity. Uncertainties in climate change policy create a financial incentive for power generation companies to delay new build and to keep old plant running for longer. This may, in turn, lead to greenhouse gas emissions remaining higher for longer than would otherwise be the case. This paper considers the actions that need to be taken by policy makers to address the issues caused by policy uncertainty, and to accelerate investment in new build, low carbon generation.

This Technology and Policy Assessment Report looks at the evidence for net job creation from policy support for energy efficiency and renewable energy technologies.

Nations and regions need to share lessons about the best ways to create enabling policies, regulations, and markets that get the most social benefit out of power systems and incent the necessary investments.

Within the EU, there have been calls for governments to provide greater certainty over carbon prices, even though it is evident that their price risk is not entirely due to policy uncertainty. We develop a stochastic simulation model of price formation in the EU ETS to analyse the coevolution of policy, market and technology risks under different initiatives. The current situation of a weak (20%) overall abatement target motivates various technology-support interventions, elevating policy uncertainty as the major source of carbon price risk. In contrast, taking a firm decision to move to a more stringent 30% cap would leave the EU–ETS price formation driven much more by market forces than by policy risks. This leads to considerations of how much risk mitigation by governments would be appropriate, and how much should be taken as business risk by the market participants.

Whether companies invest in new power facilities at a particular point in time, or delay, depends upon the perceived evolution of uncertainties and the investors’ attitudes to risk and return. With additional risks emerging through climate change mitigation mechanisms, the propensity to invest may increasingly depend upon how each technology and company is exposed to carbon price uncertainty. We approach this by estimating the cumulative probabilities of investment over time in various technologies as a function of behavioral, policy, financial and market assumptions. Using a multistage stochastic optimization model with exogenous uncertainty in carbon price, we demonstrate that detailed financial analysis with real options and risk constraints can make substantial difference to the investment propensities compared to conventional economic analysis. Further, we show that the effects of different carbon policies and market instruments on these decision propensities depend on the characteristics of the companies and may induce market structure evolution.