Renewable energy development can enable climate-compatible growth in low- and middle-income countries, particularly given the substantial opportunities for energy export to high-income countries ...seeking to decarbonise their energy systems. However, this also comes with significant risks, including the potential to trigger a resource curse of adverse social, environmental, and economic effects resulting in paradoxically slowed growth. Here, we propose a novel framework to assess potential risks associated with renewable energy development in low- and middle-income countries rooted in the resource curse literature. Eighteen symptoms of the resource curse are evaluated in terms of relevance to renewable energy, and their potential risks and benefits during renewable energy development are established. We find that context-specific factors are key in determining whether resource developments will provoke adverse impacts or positive opportunities; so, preemptive context-specific risk assessment is needed to implement prevention and mitigation strategies. For example, while fossil fuel development has been seen in some circumstances to increase dependence on external capital and technology, where adequate education and financing strategies are implemented, it can instead enhance autonomy and development. Similar risks can apply to renewable energy development, and must be evaluated. The proposed resource curse risk assessment framework can be applied to individual contexts to help countries, companies, sectors, or projects maximise the positive outcomes of renewable energy development and avoid a renewable energy resource curse.
•We propose a framework to assess resource curse risk in renewable energy.•It is tailored for low- and middle-income countries developing renewable energy.•Negative impacts of the traditional resource curse are identified and analysed.•Using this framework, preventative and mitigating policies can be designed.•This risk is important as global electricity grids decarbonise via energy trade.
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.
A core tenet of citizen science is mutual benefit to the professional researcher and the citizen scientist. While the impacts on the citizen scientist are often implicitly assumed to be positive, ...this is infrequently studied directly. Here, we evaluate the impacts of the Power to the People remote mapping citizen science project on volunteers to explore best practices for positive impact. We analyze beta feedback collected before project launch, discussion board posts made during the project, an end-of-project evaluation survey, and mapping data generated during the project. We found that this project attracted a diverse global community who were motivated to contribute to research with the potential to create real-world impact. 87% of respondents had a "good" or "excellent" experience with the project, and 66% learned something by participating. Best-practices identified through this evaluation are to: (1) account for the intersectionality of contributor demographics; (2) emphasize project interdisciplinarity and real-world impact potential; (3) provide learning opportunities at multiple levels of depth; (4) remember that the most vocal contributors do not represent the entire community; and (5) evaluate data quality regularly to identify silent issues.
This article presents a geolocated dataset of rural home annotations on very high resolution satellite imagery from Uganda, Kenya, and Sierra Leone. This dataset was produced through a citizen ...science project called “Power to the People”, which mapped rural homes for electrical infrastructure planning and computer-vision-based mapping. Additional details on this work are presented in “Power to the People: Applying citizen science to home-level mapping for rural energy access” 1. 578,010 home annotations were made on approximately 1,267 km2 of imagery over 179 days by over 6,000 volunteers. The bounding-box annotations produced in this work have been anonymized and georeferenced. These raw annotations were found to have a precision of 49% and recall of 93% compared to a researcher-generated set of gold standard annotations. Data on roof colour and shape were also collected and are provided. Metadata about the sensors used to capture the original images and the annotation process are also attached to data records. While this dataset was collected for electrical infrastructure planning research, it can be useful in diverse sectors, including humanitarian assistance and public health.
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).
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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.
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.
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.
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