The study utilizes a panel dataset from the World Bank’s World Development Indicators, covering 45 Sub-Saharan African countries from 2001 to 2023. This dataset comprises 1,035 observations, providing a robust foundation for modeling electrification trajectories. Ethiopia serves as the primary case study, allowing for a detailed examination of a country with significant electrification challenges and potential. The framework compares several machine learning algorithms, including Linear Regression, Random Forest, XGBoost, and LightGBM, to determine which model offers the best balance of accuracy and interpretability for policy application.

Model validation was conducted using both leave-one-country-out and temporal-split methods to ensure robustness. The results indicated that Linear Regression achieved the lowest error rates, with a mean absolute error of 1.93 percentage points across the 45 held-out countries. For Ethiopia specifically, the error was 2.04 percentage points in the cross-country validation and 1.33 in the temporal split. This performance outperformed a persistence baseline in 33 of the 45 countries, with a median improvement in mean absolute error of 15.9 percent. The simplicity and accuracy of the linear model suggest that complex non-linear relationships may not be necessary for capturing the primary drivers of electrification in this context.

A key component of the framework is the use of SHapley Additive exPlanations, or SHAP, to provide interpretability. SHAP values reveal that prior-year access is the dominant feature in predictions, a finding consistent with the autoregressive specification and the concept of infrastructure path dependence. This means that a country’s current level of electrification is the strongest predictor of its future progress. While other socioeconomic factors play a role, the inertia of existing infrastructure creates a strong momentum effect. Understanding this path dependence is crucial for policymakers, as it highlights the importance of maintaining and expanding existing grids rather than starting from scratch in new areas.

The selection of Linear Regression as the scenario model was further justified through multicollinearity screening, coefficient-sign verification, and scenario-sensitivity checks. These diagnostic assessments ensure that the model’s predictions are not driven by spurious correlations or unstable coefficients. By using an interpretable model, the study allows policymakers to understand exactly how changes in input variables affect the forecasted electricity access rates. This transparency is essential for building trust in the model’s outputs and for designing targeted interventions that address specific bottlenecks in the electrification process.

Under three distinct policy scenarios—Slow, Business-as-Usual, and Accelerated—the model projects that Ethiopia will reach between 67.5 and 69.9 percent electricity access by 2030. This projection leaves a significant gap of 30 to 32 percentage points to achieve universal access. The analysis suggests that macroeconomic improvements alone are unlikely to close this gap within the remaining timeframe. Instead, structural interventions are required, including the extension of the national grid, reduction of connection costs, and the deployment of off-grid solar solutions. These findings align with broader trends in the region, where economic growth has not always translated directly into expanded energy access without deliberate policy action.

The framework’s open and potentially transferable nature offers a valuable resource for other regions facing similar challenges. By providing a reproducible workflow, the study enables researchers and policymakers to adapt the methodology to different contexts, pending validation in other areas. The use of publicly available data from the World Bank ensures that the model can be replicated and updated as new data becomes available. This accessibility is particularly important for developing countries that may lack the resources to develop proprietary forecasting tools. The open-source approach also facilitates collaboration and peer review, enhancing the reliability and applicability of the findings.

The implications of this research extend beyond Ethiopia to the broader Sub-Saharan African region. The 45 countries included in the panel represent a wide range of electrification trajectories, from near-zero access in countries like Somalia and South Sudan to near-universal access in Mauritius and South Africa. This diversity allows the model to capture the full spectrum of challenges and opportunities in continental-scale policy analysis. The findings suggest that a one-size-fits-all approach is insufficient; instead, policies must be tailored to the specific path dependence and structural constraints of each country. The framework provides a tool for such tailored analysis, enabling policymakers to simulate the impact of different interventions and prioritize those with the highest potential for closing the access gap.

As the world moves closer to the 2030 deadline for Sustainable Development Goal 7, the need for accurate and interpretable forecasting tools becomes increasingly critical. This study demonstrates that machine learning can be effectively applied to development policy when combined with rigorous validation and explainability techniques. The emphasis on interpretability ensures that the model’s insights are actionable, providing clear guidance on the structural changes needed to achieve universal electricity access. By bridging the gap between advanced data science and practical policy analysis, the framework offers a promising path forward for addressing one of the most pressing development challenges of our time.

Subject of Research: An interpretable machine learning framework for SDG 7 electricity access forecasting and policy scenario analysis in sub-Saharan Africa

Article Title: An interpretable machine learning framework for SDG 7 electricity access forecasting and policy scenario analysis in sub-Saharan Africa

Article References: File, S. G. (2026). An interpretable machine learning framework for SDG 7 electricity access forecasting and policy scenario analysis in sub-Saharan Africa. Discover Sustainability. https://doi.org/10.1007/s43621-026-05053-0

Image Credits: AI Generated

DOI: 10.1007/s43621-026-05053-0

Keywords: interpretable, machine, learning, framework, electricity, access, forecasting, policy, scenario, analysis, sub-Saharan, Africa