Perceived values and climate change resilience dataset in Siaya County, Kenya.

This dataset presents perceived values and socioeconomic indicators collected in Siaya, a rural county in Kenya in 2022. The data was obtained from 300 household surveys and group interviews conducted in six sub-counties across eleven villages. Socioeconomic data were collected with a special focus on climate change vulnerability. Information on housing, health, water accessibility and usage, electricity accessibility and usage, extreme weather events, community service, and information accessibility were mapped across survey questions. The user-perceived value (UPV) game - a perception-based surveying approach - was used to elicit local communities' needs and perceptions of climate change challenges. The UPV game involves asking interviewees to select which graphically depicted items would be most necessary in different situations and probing them for the reasons behind their choices (why-probing). The data was collected in two languages (Dholuo and English) and then translated into English. These surveys and interviews were conducted to better understand the needs of rural Kenyan communities and their perceptions of climate change, with the aim to identify ways to build resilience. Kenyan policymakers can use the dataset to inform county-level energy and development plans, while researchers and development practitioners can use the dataset to better design their research and programmes to reflect local needs and values.

GeoH2 model: Geospatial cost optimization of green hydrogen production including storage and transportation.

This paper presents GeoH2, a geospatial model that optimizes the cost of green hydrogen production, storage, transport, and conversion. This model calculates the cost of producing green hydrogen in a specified location to meet demand in another location by: •Optimizing hydrogen conversion and transport from production site to demand site•Optimizing green hydrogen production and storage based on spatially-specific wind and solar generation temporal availability This method allows users to map production costs throughout a region to identify the lowest-cost location of green hydrogen production to meet demand using a specified end-state for transportation and storage (i.e., pressurized hydrogen, ammonia, or liquefied hydrogen). These modeled costs can be compared to current or projected prices for energy and chemical feedstock in the region to assess the cost-competitiveness of green hydrogen. The model is designed to run at a country or regional scale. A case study application is provided for the context of Namibia.

Second-life battery systems for affordable energy access in Kenyan primary schools.

As the world transitions to net zero, energy storage is becoming increasingly important for applications such as electric vehicles, mini-grids, and utility-scale grid stability. The growing demand for storage will constrain raw battery materials, reduce the availability of new batteries, and increase the rate of battery retirement. As retired batteries are difficult to recycle into components, to avoid huge amounts of battery waste, reuse and repurposing options are needed. In this research, we explore the feasibility of using second-life batteries (which have been retired from their first intended life) and solar photovoltaics to provide affordable energy access to primary schools in Kenya. Based on interviews with 12 East African schools, realistic system sizes were determined with varying solar photovoltaic sizes (5-10 kW in 2.5 kW increments) and lithium-ion battery capacities (5-20 kWh in 5 kWh increments). Each combination was simulated under four scenarios as a sensitivity analysis of battery transportation costs (i.e., whether they are sourced locally or imported). A techno-economic analysis is undertaken to compare new and second-life batteries in the resulting 48 system scenarios in terms of cost and performance. We find that second-life batteries decrease the levelized cost of electricity by 5.6-35.3% in 97.2% of scenarios compared to similar systems with new batteries, and by 41.9-64.5% compared to the cost of the same energy service provided by the utility grid. The systems with the smallest levelized cost of electricity (i.e., 0.11 USD/kWh) use either 7.5 kW or 10 kW of solar with 20 kWh of storage. Across all cases, the payback period is decreased by 8.2-42.9% using second-life batteries compared to new batteries; the system with the smallest payback period (i.e., 2.9 years) uses 5 kW solar and 5 kWh storage. These results show second-life batteries to be viable and cost-competitive compared to new batteries for school electrification in Kenya, providing the same benefits while reducing waste.

Spatial Data Starter Kit for OnSSET Energy Planning in Kitui County, Kenya.

The 2019 Energy Act requires each of Kenya's 47 counties to independently develop energy plans. As county energy planning accelerates, it is important to understand the availability and readiness of data required to facilitate it. This article identifies, evaluates, and pre-processes openly available data to facilitate county-level energy planning using the Open Source Spatial Electrification Tool (OnSSET) in Kitui County, Kenya. In this way, it provides a ready-to-use starter kit of data inputs for county-level OnSSET analysis, and guidance to replicate this work in other counties. We classify the readiness level of each data type for county energy planning on a traffic light scale (i.e. green, amber, red) based on availability, accessibility, recency, accuracy, spatial resolution, and format (i.e. whether processing is required before use). Of the 25 core data inputs for OnSSET at the county-level, we find that 14 have a green, six have an amber, and five have a red readiness-level. Data processing requirements are documented, and the processed data for Kitui county are made available as a ready-to-use set of input parameters for OnSSET. While this data was collected for Kitui, the data sources and processing steps are largely applicable in other counties.

Selected 'Starter kit' energy system modelling data for selected countries in Africa, East Asia, and South America (#CCG, 2021).

Energy system modeling can be used to develop internally-consistent quantified scenarios. These provide key insights needed to mobilise finance, understand market development, infrastructure deployment and the associated role of institutions, and generally support improved policymaking. However, access to data is often a barrier to starting energy system modeling, especially in developing countries, thereby causing delays to decision making. Therefore, this article provides data that can be used to create a simple zero-order energy system model for a range of developing countries in Africa, East Asia, and South America, which can act as a starting point for further model development and scenario analysis. The data are collected entirely from publicly available and accessible sources, including the websites and databases of international organisations, journal articles, and existing modeling studies. This means that the datasets can be easily updated based on the latest available information or more detailed and accurate local data. As an example, these data were also used to calibrate a simple energy system model for Kenya using the Open Source Energy Modeling System (OSeMOSYS) and three stylized scenarios (Fossil Future, Least Cost and Net Zero by 2050) for 2020-2050. The assumptions used and the results of these scenarios are presented in the appendix as an illustrative example of what can be done with these data. This simple model can be adapted and further developed by in-country analysts and academics, providing a platform for future work.

Perceived value interviews and socio-economic survey data for communities in rural Uganda.

This article describes a dataset of perceived values and socioeconomic indicators collected in rural Ugandan communities. The data were collected in interviews which employed: (1) the User-Perceived Value game, which solicits verbal data using graphical prompts and 'why'-probing; and (2) socio-economic surveys, which collected demographic data. The dataset constitutes 119 interviews conducted between 2014 and 2015 in seven rural Ugandan villages. Interviews were conducted in various settings (e.g. individual/group, women/men/mixed) and in seven different local languages (which were subsequently translated into English). These interviews were part of a research project aiming to better understand what is important to rural communities in Uganda, and to investigate decision-making as a function of different demographics. This dataset can be used by researchers and practitioners in various fields such as sustainable development (e.g. to analyze how development initiatives may be designed to match community values) and natural language processing (e.g. to automatically perform perceived value classification from the expert-annotated interviews).