Abstract
Energy devolution in Kenya has had mixed and fragmented progress with over 15 counties yet to begin their County Energy Plan development. Yet, with the Ministry of Energy and Petroleum (MoEP) upcoming deadline for CEP production, and the following attempts to begin the first iteration of the integrated national energy plan (INEP), focus on county to national planning, data dialogues and modelling practices are of key critical current importance. Additionally, questions remain on how to translate the rich, bottom up, qualitative and narrative needs-based insights outlined within county energy plans, to an aggregate quantitative integrated national energy model. Here we apply mixed methodologies to energy modelling to overcome limitations of a strictly quantitative approach. We propose developing the existing national demand modelling socio-economic scenarios of baseline, ambitious, and reserved, to include a narrative driven county specific additional scenario, co-developed with 26 local county energy experts and decision-makers. Through the co-development of energy demand projections for integrated national energy planning using the case study of Taita Taveta, we present energy demand projections to assist policy makers at the regional, national, and international levels in energy modelling and policy pipelines to support both county energy planning and integrated national energy planning. Here we show how qualitative narratives can be integrated into modelling processes, alongside looking beyond technical and economic approaches to consider social, cultural, and behavioural factors. The produced energy demand projections provide a novel exploration of translating county energy plans into a standardised format for county-to-national integrated and inclusive energy modelling.
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Title
Whole Energy System Demand Models for Taita Taveta County, Kenya using the Model for Analysis of Energy Demand (MAED)
Description
These MAED models contain historic annual data on whole energy system demand (2018 to 2023) segregated by sector (industry, services, households) and fuel type. Additionally, historic social and economic data on population (total, growth, urban-rural split, household size), GDP (total, sectoral split, growth) and electrification are included within this data note. This historic data has been used to create three demand scenarios alongside a baseline demand projection from 2018 to 2070.
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