Closing the Gap Between Energy Models and Energy Realities in Sub-Saharan Africa

By Tiruwork B. Tibebu

The energy future of Sub-Saharan Africa's (SSA) is shaped by interactions of various factors, each shifting the very landscape of energy pathways in the region.  For example, the costs of energy technologies depend on global learning and scale, but their relevance for SSA is dependent on trade dynamics, financial availability, and the evolution of domestic policy. Simultaneously, renewable energy investments are capital intensive and securing finance for such projects is subject to changing eligibility criteria, donor priorities, and macroeconomic conditions that are beyond national control. Growing clean technology capacity in global markets also has the potential to accelerate deployment in SSA, yet these benefits are contingent on access to supply chains. At the same time, as governments navigate the balancing act of economic development goals—driving ambitions for industrialisation—with sustainability objectives, domestic policy frameworks continue to evolve.

These circumstances call for scenario analyses that explicitly treat uncertainty as a key analytical dimension. Scenario modeling plays a crucial role in exploring future energy pathways and informing decision-making, particularly under conditions of significant uncertainty. By examining how strategies perform across a range of possible conditions, scenario analysis enables comparisons that extend beyond a single projected future. Rather than embedding all assumptions within one pathway, it explores multiple plausible outcomes based on varying assumptions.

Our recent paper, Decision-Informative and Policy-Relevant Scenario Modeling for the Energy Sector in Sub-Saharan Africa, published in Progress in Energy, offers a practical scenario design approach for energy pathways in SSA that can be integrated within established modelling platforms. We argue for these dimensions because energy modelling must extend beyond technology optimisation alone and explicitly reflect the development priorities, demand realities, institutional conditions and financing constraints that shape energy transitions. The four dimensions are: (i) Development trajectories: Energy-climate-development models should link energy demand and investment needs to realistic development pathways, including industrial growth, urbanisation and productive economic activity. (ii) Renewable energy and environmental goals: Models should assess not only least-cost energy supply, but also how renewable energy choices support emissions reduction, environmental protection and long-term sustainability. (iii) Electricity access and demand realism: Impactful modelling should represent real and latent electricity demand more accurately, especially for households, enterprises, agriculture and emerging productive sectors. (iv) Governance and financial feasibility: As a necessary condition, models should test whether proposed pathways are implementable under real-world governance, financing and institutional conditions, rather than assuming ideal policy execution. 

Development trajectories

The first dimension—development trajectories—reflects SSA’s development goals towards achieving industrialisation which requires a substantial improvement in the energy infrastructure. Incorporating these dynamics into energy scenario models is essential for developing strategies that are realistic, policy relevant, and aligned with national priorities. Growing industrial activity, agro-processing, productive energy use, and electrified cooking and transport are all expected to increase electricity demand beyond current consumption patterns. These demands can be represented through sector-specific demand models and incorporated as exogenous scenarios within energy system analyses. Comparing alternative trajectories with baseline pathways can provide valuable insights into how development pathways shape energy demand, infrastructure needs, and progress toward universal energy access and broader development goals. 

Alternative pathways

The second dimension constitutes renewable energy, emerging resources, and environmental goals. SSA aims to expand access to clean, affordable, and reliable energy to meet growing development and electricity demand. Across the region, national and regional energy strategies are increasingly including ambitious clean energy goals. As a result, energy models must align with these strategies and assess the implications of alternative pathways. Furthermore, models should explore the uncertain role of natural gas and compare gas-intensive, renewable-led, and mixed development pathways. Such scenarios can illuminate trade-offs by quantifying differences in infrastructure requirements, employment effects, stranded asset risks, and exposure to fuel price volatility.

Demand trajectories

The third component of the framework accounts for electricity access and demand. Conventional access metrics overlook critical factors such as affordability, reliability, and service quality, while binary urban–rural classifications fail to capture the complex spatial dynamics of rapid urbanization. Much of SSA’s urban growth is concentrated in peri-urban and informal settlements where infrastructure expansion frequently lags population growth, creating substantial unmet demand. Demand projections based solely on historical consumption trends may significantly underestimate future electricity needs. Energy scenarios should incorporate spatially disaggregated demand modeling, latent demand, and alternative access pathways, including both grid expansion and off-grid systems. Sensitivity analysis across different demand assumptions can help identify plausible futures while accounting for uncertainties in affordability, reliability, and economic development. It is also important to acknowledge that demand overestimation carries risks. Oversized infrastructure may face financial difficulties if projected demand fails to materialise. Sensitivity analysis alongside comparison across different modeling approaches can be used to identify plausible ranges and the conditions under which different demand trajectories emerge. 

Feasibility constraints

Governance, institutional capacity, and financial feasibility constitute the fourth dimension of the framework. These elements are often overlooked in SSA energy models despite their strong influence on implementation outcomes. Factors such as policy instability, limited administrative capacity, and high financing costs can slow energy deployment and create gaps between modeled and realized pathways. These constraints can be incorporated through scenario parameters such as differentiated costs of capital, limits on annual deployment rates, and technology-specific implementation delays. Exploring such factors can help assess how feasibility constraints shape energy transition outcomes without requiring political processes to be modeled endogenously.

 Implications for Energy-Climate-Development Modelling

We present three illustrative scenario archetypes designed to demonstrate how the proposed four-dimensional framework can be applied to address current policy-relevant decision-making problems in SSA. Each case is framed around a policy or decision question, namely what choice is at stake, an appropriate modelling boundary, including which model class is appropriate or what is treated as exogenous, key assumptions across the four framework dimensions, including what parameters vary across scenario variants, and insights into what the scenario analysis can reveal, including what trade-offs or implications arise. These cases illustrate how scenario modelling for SSA can move towards adaptive and context-sensitive policy analysis.

The key implication is that energy-climate-development modelling must be adjusted to reflect not only technology costs and emissions pathways, but also the development trajectories, demand realities, governance capacity and financing conditions that determine whether scenarios are politically relevant and practically implementable. Two modelling actions follow from this. First, models should explicitly define development-sensitive assumptions; including industrialisation pathways, productive energy demand, access deficits and latent demand, so that scenarios reflect the scale and structure of Africa’s development needs. Second, models should incorporate feasibility constraints, including investment mobilisation, institutional capacity, implementation timelines and financing conditions, so that scenario outputs move beyond technically optimal pathways towards decision-informative and policy-relevant pathways.