ENERGY PLANNING AND MODELLING THROUGH INTEGRATED ASSESSMENT OF CLIMATE LAND ENERGY WATER NEXUS IN SUB-SAHARAN AFRICA

Achieving clean energy access and food security targets in Sub-Saharan Africa (SSA) will require significant infrastructure expansion.

Improved policy environments and governance structures are recognised as vital in scaling up funding for climate-resilient investments in renewable energy infrastructures.

The efficient exploitation of land, energy and water resources and their synergised use for sustainable economic development, as well as their robustness to stressors from climate change require integrated optimisation and assessment of strategic plans in these sectors.

Working with existing, widely adopted, open-source modelling tools and data, the EPIC Africa project will build on the transparent integrated assessment, and resource modelling, connecting to the already existing community of developers and users.

The project will create a network of African experts to train, co-create and sustain the tools developed in the long term, which is lacking at the moment.

The project will build advanced and spatially contextualised water-energy-food (WEF) models for long-term infrastructure planning.

Operational WEF models with sufficient temporal and spatial resolution will be developed and used to verify cost-optimised infrastructure investment plans under different climate and socioeconomic scenarios.

EPIC Africa sets out to support governance of sustainable development in SSA by forming and connecting a transition arena with stakeholders in dialogue, leading to the development of a WEF nexus digital engagement tool. Optimal use of shared resources will be exemplified for Africa using the cases of the Volta and Tana river basins.

Here, we will design specific policy, investment and infrastructure plans for the coming decades in the agricultural, water and energy access sectors.

The project will produce a set of institutional, regulatory and technical recommendations for sustainable management of optimised transition pathways.