Background: Cooking with traditional fuels can lead to severe health issues caused by household air pollution, and can also affect gender equality and drive environmental degradation. In Nepal, despite government efforts to promote electric cooking, more than half of the population still uses traditional fuels, with electric cooking adoption remaining below 1%. Several of the barriers to and enablers of clean cooking vary geographically; however, few studies have considered spatial explicit information in planning national-scale transitions to clean cooking. In this study we provide a spatially explicit roadmap to estimate the required investments and benefits gained from the transition across Nepal. Methods: This study uses geospatial modelling methods to evaluate strategies to achieve the Government of Nepal's vision for a national-scale transition to clean cooking. We integrate the open-source clean cooking geospatial assessment tool OnStove and a spatial multicriteria analysis model. With OnStove, we evaluate which cooking technologies and fuels maximise the net benefits of a clean-cooking transition across each km2 of the region. With the multicriteria analysis, we weigh stakeholder preferences and prioritise areas of action where policy should be implemented. We used the most up-to-date geospatial data to the year 2023, such as the High Resolution Settlement Layer, Open Street Maps’ road networks, the Global Human Settlement Layer, NASA/USGS forest cover maps, and Facebook's Relative Wealth Index, among others. We also relied on data from the Nepal Oil Corporation, the Nepal Electricity Agency, the Central Bureau of Statistic's 2021 national census, and the Alternative Energy Promotion Center. We evaluate four scenarios capturing advances on clean cooking policy up to the year 2022, current market inefficiencies, and the potential effects of new policies for clean-cooking transition in Nepal. Findings: Our results show that transitional and clean cooking technologies provide higher net benefits than traditional options everywhere across Nepal in all scenarios. Our net-benefit analysis shows that around 9563 deaths could be averted yearly if benefits and externalities were perceived and valued correctly. Furthermore, substantial benefits could be achieved in regard to greenhouse gas emissions avoidance, time saved, and health-cost reductions. Our results also show that the current subsidy strategy from the Government of Nepal is well aligned with the benefits achieved under a cost–benefit analysis. In this context, electric cooking can bring the highest benefits to the largest part of the population. The analysis showed how high subsidies for liquefied petroleum gas in Nepal can present trade-offs with energy security and independence, and how this could be avoided by transferring part of the subsidy to cover differentiated electric cooking tariffs. Accounting for stakeholder preferences and sociodemographic and geographical differences to prioritise areas of focus can balance affordability constraints and target the most vulnerable people first, thus achieving integrated and inclusive planning. Interpretation: Using spatially explicit modelling approaches to evaluate strategies for a clean cooking transition can provide more nuanced results that have not been possible before. This approach can enable data-driven and integrated planning to help to understand which locations of a study area should be prioritised for policy application. Integrated planning can help to reduce affordability constraints on the population and design strategies for a sustainable and inclusive transition. These strategies allow financial institutions, donors, impact investors, development organisations, and government agencies to use their resources, funds, and assistance to create a large impact. Funding: Clean Cooking Alliance.

In 2015 the United Nations (UN) General Assembly agreed on the Sustainable Development Goals (SDGs), a set of 17 goals defined by 169 targets to be reached by 2030. Amongst them is SDG 7. SDG 7 states “Ensure access to affordable, reliable, sustainable and modern energy for all”. The first target of SDG 7 mentions access to electricity and clean cooking specifically. Access to electricity brings with it myriad benefits across several sectors, including residential, health and education. Access to clean cooking can help reduce adverse health and environmental effects, as well as high opportunity costs related to current cooking practices, amongst other benefits. 

While SDG 7 has been recognized as a key pillar to achieve sustainable development, its achievement has remained elusive. As of 2021, 675 million people worldwide were estimated to lack access to electricity. The largest access gap is found in Sub-Saharan Africa (SSA) where only 50% of the population had electricity access. For clean cooking the situation is worse, with around 2.3 billion people globally lacking access. Again, the access gap is most pronounced in SSA with only 18% of the population in the region using clean cooking. The situation in SSA is further exacerbated by the fact that the population increases faster than the clean cooking access rate.

For electricity access modelling Geographic Information Systems (GIS) and the use of geospatial data is being increasingly leveraged. As every case in a study area is unique and requires context-specific information, the spatial dimension of GIS can help to more effectively model towards universal electricity access. Resource availability, fuel costs and access to infrastructure change spatially and a geospatial approach helps to capture this. Such reasoning can also be applied to clean cooking. Yet, at the time of writing this thesis, there was no geospatial tool comparing the relative costs and benefits of different cooking solutions. This work aims to advance the state-of-the-art in geospatial modelling approaches to support integrated energy planning towards universal electricity and clean cooking access.

Geospatial electrification modeling, while proven useful, is still a new field with many on-going developments. One such significant development was the move from raster population datasets to aggregated vector settlements. Raster datasets divide an area into uniform units with each unit including some piece of information about the area. It can be beneficial to have uniform units in modelling, but for this reason rasters fail to capture the size and shape that population settlements naturally have. On the other hand, aggregating raster datasets to vector settlements may impact modelling results. With this in mind, the first research question explores how the aggregation of data changes modelling results in geospatial electrification models. Paper I presents an open-source algorithm for the creation of aggregated vector settlements from raster data. In Paper I this algorithm is applied to 44 countries in SSA. As part of the algorithm, night-time lights are used to assess electrification rates within settlements and population density is used to assess the urban-rural divide of each country. The electrification rates and urban-rural divide is subsequently validated against survey data and compared to previous results. Following this, Paper II compares results produced by the Open Source Spatial Electrification Tool (OnSSET) as the level of population aggregation changes. This is done for three case studies (Benin, Malawi and Namibia), by producing 26 population bases for each country. Two of the population bases are rasters with different resolutions, three use the method developed in Paper I and 21 are clustered using the Density-Based Spatial Clustering of Applications with Noise (DBSCAN) algorithm. Paper II also presents the first Global Sensitivity Analysis (GSA) conducted for geospatial least-cost electrification models. The GSA enables comparisons between the importance of parameters that previous research has identified as important and the importance of population aggregation. 

The second research question explores if, and how, GIS can be used to develop clean cooking tools comparing different cooking solutions. To this end, Paper III presents OnStove. OnStove is the first geospatial tool comparing the relative costs and benefits of different cooking solutions. In Paper III the tool is described and applied for the first time to 44 countries in SSA. The tool is open-source and all data used to run the analysis (as well as the results) are published and made available for a broader public. Two main scenarios are developed for SSA assessing differences between current cooking practices and potential pathways for maximizing net-benefits (defined as total benefits minus total costs). In addition to the main scenarios, 680 additional scenarios are developed as part of a GSA to assess the impact of uncertainty of 33 parameters on key outputs.

Finally, the last research question assesses how integrated energy access planning impacts the results of existing geospatial electrification (OnSSET) and clean cooking (OnStove) tools. This is done by combining the two aforementioned tools in Paper IV in a case study of Kenya. The results describe how the least-cost technology mix and Levelized Cost of Electricity (LCoE) changes across Kenya as the increased electricity load following the inclusion of electric cooking is accounted for in OnSSET. On the cooking side the paper outlines how the competitiveness of electric stoves change as the electrification rate increase and the LCoE change. Paper IV also deepens the insights on research questions one and two as a new resolution is used to generate the population clusters using the algorithm developed in Paper I and new developments are done to OnStove.

År 2015 kom Förenta nationernas (FN) generalförsamling överens om en uppsättning av 17 hållbarhetsmål, definierade av 169 delmål. Avsikten är att dessa ska uppnås senast 2030. Bland dem finns mål 7. Mål 7 ämnar att "Säkerställa tillgång till ekonomiskt överkomlig, tillförlitlig, hållbar och modern energi för alla". Det första delmålet för mål 7 nämner specifikt tillgång till elektricitet och ren teknik för matlagning. Tillgång till elektricitet medför många fördelar över olika sektorer, inklusive hushåll, sjukvård och skola. Tillgång till ren teknik för matlagning är också viktig och kan bland annat bidra till att minska skadliga hälso- och miljöeffekter samt höga alternativkostnader relaterade till nuvarande matlagningspraxis.

Även om mål 7 har visat sig vara viktigt, har dess uppnående förblivit flyktigt. År 2021 uppskattades 675 miljoner människor världen över sakna tillgång till el. Det största tillgångsgapet återfinns i Afrika söder om Sahara (SSA), där endast 50 % av befolkningen hade tillgång till el. När det gäller ren teknik för matlagning är situationen desto värre, med cirka 2,3 miljarder människor globalt som förlitar sig på icke-rena matlagningsbränslen. Återigen är tillgångsgapet mest påtagligt i SSA, där endast 18 % av befolkningen använder ren matlagning. Situationen i SSA förvärras ytterligare av att befolkningen ökar i snabbare takt än tillgången till ren teknik för matlagning.

För modellering av tillgång till elektricitet används alltmer geografiska informationssystem (GIS) och geospatial data. Eftersom varje fall i ett område är unikt och kräver kontextspecifik information, kan geospatial data effektivisera modelleringen mot universell tillgång till elektricitet. Tillgänglighet av resurser, bränslekostnader och tillgång till diverse infrastruktur förändras över ett undersökningsområde och geospatiala metoder hjälper till att inkludera dessa förändringar. Det finns ingen anledning att tro att detta inte gäller även för frågan om ren teknik för matlagning. Ändå finns det hittills inga geospatiala verktyg som jämför de relativa kostnaderna och fördelarna med olika matlagningslösningar.

GIS i modellering av tillgång till elektricitet, även om bevisat användbart, är fortfarande en relativt ny företeelse och mycket utveckling sker fortfarande inom området. En sådan betydande utveckling var övergången från rasterbaserade populationskartor till aggregerade vektorkartor. Ett raster delar in ett område i enhetliga delar där varje del innehåller information om studieområdet. Det kan vara fördelaktigt med enhetliga enheter vid modellering, men detta gör att rasters ofta misslyckas med att fånga den storlek och form som samhällen naturligt har. Å andra sidan kan aggregering av rasters till vektorssamhällen påverka modelleringsresultat. Med detta i åtanke utforskar den första forskningsfrågan hur aggregering av data förändrar modelleringsresultaten i geospatiala elektrifieringsmodeller. I Publikation I presenteras en algoritm med öppen källkod för skapandet av aggregerade vektorsamhällen utifrån rasterdata. Denna kod tillämpas på 44 länder i SSA. Som en del av publikationen används även ljus under nattetid för att bedöma elektrifieringsgraden inom samhällen och befolkningstäthet används för att bedöma den urbana-rurala uppdelningen för varje land. Elektrifieringsgraden och den urbana-rurala uppdelningen valideras därefter mot publicerad data och jämförs med tidigare resultat. Därefter jämför Publikation II resultat producerade av Open Source Spatial Electrification Tool (OnSSET) med olika nivåer av populationsaggregering. Detta görs för tre fallstudier (Benin, Malawi och Namibia), genom att producera 26 populationsbaser för varje land. Två av baserna är raster med olika upplösningar, tre använder metoden utvecklad i Publikation I och 21 är aggregerade med hjälp av Density-Based Spatial Clustering of Applications with Noise (DBSCAN) -algoritmen. Utöver detta presenterar Publikation II den första globala känslighetsanalysen för geospatiala elektrifieringsmodeller. Den globala känslighetsanalysen möjliggör jämförelser av betydelsen av parametrar som tidigare forskning har identifierat som viktiga och betydelsen av populationsaggregering.

Den andra forskningsfråga utforskar om, och hur, GIS kan användas för att utveckla verktyg för modellering av olika matlagningslösningar. För att svara på denna fråga presenteras OnStove i Publikation III. OnStove är det första geospatiala verktyget som jämför de relativa kostnaderna och fördelarna med olika matlagningslösningar. Publikation III beskriver verktyget och tillämpar det för första gången på 44 länder i SSA. Källkoden är öppen och all data som används för att köra analysen (samt resultaten) är publicerade och tillgängliga för en bredare allmänhet. Två huvudscenarion utvecklas för SSA för att bedöma potentiella skillnader mellan den nuvarande situationen och en situation där nettovärdet (definierat som totalt värde minus total kostnad) i regionen maximeras. Utöver de två huvudscenarierna utvecklas 680 scenarion som en del av en global känslighetsanalys för att bedöma 33 parametrars påverkan av viktiga resultat.

Den sista forskningsfrågan utvärderar hur resultaten från ett geospatialt elektrifieringsverktyg (OnSSET) och ett geospatialt verktyg för att modellera matlagning (OnStove) förändras när målen för universell elektrifiering och tillgång till ren matlagning modelleras samtidigt. Detta görs genom att länka de två tidigare nämnda verktygen i Publikation IV genom en fallstudie i Kenya. Resultaten beskriver hur den minst kostsamma teknikfördelningen och nivån på elpriset förändras i Kenya när elförbrukningen ökar till följd av att elektrisk matlagning inkluderas. På matlagningsfronten redogör publikationen för hur konkurrenskraften för elektriska spisar förändras när elektrifieringen ökar och nivån på elpriset förändras. Publikation IV fördjupar också insikterna om forskningsfråga ett och två då en ny upplösning används för att generera populationskluster med koden utvecklad i Publikation I och nya utvecklingar görs för OnStove.

Universal clean cooking is a key target under Sustainable Development Goal (SDG) 7, with implications for several other SDGs, such as good health, gender equality and climate. Yet, 2.4 billion people globally still lack access to clean cooking. The situation is especially dire in sub-Saharan Africa (SSA), where only 17% use clean options. We develop OnStove, an open-source spatial tool comparing the relative potential of different cookstoves on the basis of their costs and benefits, and apply it to SSA. Our results suggest a severe market failure as the currently most used solution, traditional biomass, produces the lowest social net-benefits nearly everywhere in SSA. Correcting this failure, which stems from multiple market and behavioural obstacles, would deliver significant health, time and emission benefits but requires identification and promotion of policies to transform cooking energy use. Spatial mapping offers a more nuanced understanding of the costs needed to deliver cleaner cooking transitions than was previously possible, which is useful for improved targeting of intervention strategies.

As of 2021, 675 million people globally lack access to electricity. Geospatial electrification tools can be used to identify the mix of grid-extension, mini-grids and stand-alone technologies that can supply currently unelectrified areas at the lowest cost. Several such tools have been developed, at different levels of modelling detail and complexity. In this paper, we improve the Open Source Spatial Electrification Tool (OnSSET) to develop a flexible geospatial electrification tool that can still run lighter rapid assessments for a first estimate of the technology split, but now also more detailed analysis with higher spatial and temporal resolution used for grid routing, distribution network design and optimization of hybrid mini-grid generation introduced through new algorithms. We compare the existing light and new detailed versions of the tool through a case study of the north-western parts of the Democratic Republic of the Congo. We find that the new grid routing algorithm lead to more off-grid technologies, and that the detailed design of distribution networks lead to a reduction in stand-alone technologies. The detailed optimization of hybrid mini-grids display varying effects at different demand levels. Given the increased computational effort that is observed with higher modelling detail, we discuss the implications for scenario design and selection of geospatial electrification tool for future analyses aiming to support the achievement of SDG 7.

Access to electricity is a prerequisite for development, included in both the Agenda for Sustainable Development and the African Union’s Agenda 2063. Still, universal access to electricity is elusive to large parts of the global population. In Somalia, approximately one-third of the population has access to electricity. The country is unique among non-island countries as it has no centralized grid network. This paper applies a geospatial electrification model to examine paths towards universal access to electricity in Somalia under different timelines and with regard to different levels of myopia in the modeling process. This extends the previous scientific literature on geospatial electrification modeling by studying the effect of myopia for the first time and simultaneously presenting the first geospatial electrification analysis focused on Somalia. Using the Open Source Spatial Electrification Tool (OnSSET), the least-cost electrification options towards 2030 and 2040, respectively, are compared. We find that under the shorter timeline, a deployment of mini-grids and stand-alone PV technologies alone provides the least-cost option under all but one scenario. However, under the longer timeline, the construction of a national transmission backbone would lower overall costs if there is high demand growth and/or low cost of centralized grid electricity generation. We also compare different levels of myopia in the modeling process. Here, OnSSET is first run directly until 2040, then in five-year time-steps and annual time-steps. We find that running the model directly until 2040 leads to the lowest costs overall. Running the model myopically leads to a sub-optimal, more costly technology mix, with a lock-in effect towards stand-alone systems. On the other hand, the myopic approach does provide additional insights into the development of the system over time. We find that longer-term planning favors the centralized grid network, whereas short-sighted myopic planning can lead to higher costs in the long term and a technology mix with a higher share of stand-alone PV.

This paper presents the first application of the scenario discovery approach in geospatial electrification modelling. 1944 electrification simulations were constructed for Burkina Faso from a combination seven input levers, including four grid-extension strategies. The scenario discovery analysis identifies a scenario described by a high grid electricity generation cost in combination with an intensification strategy for grid-extension, as most likely to lead to a high cost of electricity in Burkina Faso. Thus, to avoid such a high cost, decisions in the country could be targeted either at lowering grid electricity generation costs or to choose one of the other two gridextension strategies, or both. For each of the grid-extension strategies, a number of drivers causing a high LCOE were identified. Common drivers for all strategies were the grid electricity generation cost and discount rate. The scenario discovery approach was used to identify the key drivers of high electrification costs and their interactions, providing useful information that might not be gained from a traditional scenario-axes approach. This approach provided a structured way to analyze more parameters than found in previous electrification studies for Burkina Faso. The paper discusses on the pros compared to a traditional scenario-axes approach, such as reduced risk of perceived bias and improved ability to deal with multiple uncertain parameters, but also notes the additional computational requirements.

Human settlements are usually nucleated around manmade central points or distinctive natural features, forming clusters that vary in shape and size. However, population distribution in geo-sciences is often represented in the form of pixelated rasters. Rasters indicate population density at predefined spatial resolutions, but are unable to capture the actual shape or size of settlements. Here we suggest a methodology that translates high-resolution raster population data into vector-based population clusters. We use open-source data and develop an open-access algorithm tailored for low and middle-income countries with data scarcity issues. Each cluster includes unique characteristics indicating population, electrification rate and urban-rural categorization. Results are validated against national electrification rates provided by the World Bank and data from selected Demographic and Health Surveys (DHS). We find that our modeled national electrification rates are consistent with the rates reported by the World Bank, while the modeled urban/rural classification has 88% accuracy. By delineating settlements, this dataset can complement existing raster population data in studies such as energy planning, urban planning and disease response.

The introduction of geospatial data into modelling efforts carries many advantages but also introduces numerous challenges. A common challenge is the Modifiable Areal Unit Problem (MAUP), describing how results change as the spatial aggregation of data changes. Here, we have studied MAUP in geospatial least-cost electrification modelling. We do this by assessing the effects of using 26 different population bases each for Benin, Malawi and Namibia. We use the population bases to generate 2080 electrification scenarios per country and conducting a global sensitivity analysis using the Delta Moment-Independent Measure. We identify population aggregation to be highly influential to the model results with regards to method of aggregation (delta values of 0.06-0.24 depending on output studied), administrative division (0.05-0.14), buffer chosen in the clustering process (0.05-0.32) and the minimum number of neighbours within the buffer required for clustering (0.05-0.19). Based on our findings, we conclude that geospatial electrification studies are not robust concerning the choice of population data. We suggest, that modelers put larger emphasis on different population aggregation methods in their sensitivity analyses and that the methods chosen to conduct sensitivity analysis are global in nature (i.e. moving all inputs simultaneously through their possible range of values).

The authors wish to make a change in author names (adding new author—Dimitrios Mentis) to this paper [1]: Author Contributions On page 19, author contributions are updated as follows: Conceptualization, A.K., D.M. and M.H.; Methodology, A.K., A.S., B.K. and D.M.; Software, A.K., B.K., A.S. and C.A.; Validation, M.H.; Formal Analysis, A.K.; Investigation, A.K.; Resources, A.K. and B.K.; Data Curation, A.K., B.K. and A.S.; Writing—Original Draft Preparation, A.K.; Writing—Review and Editing, A.K., D.M., M.H., C.A.; Visualization, A.K. and B.K.; Supervision, M.H.; Project Administration, M.H.; Funding Acquisition, M.H., D.M. and A.K. All authors have read and agreed to the published version of the manuscript. Funding On page 19, funding sources are updated as follows: This research was funded by the World Bank under the contract number 7185716 and partially by (a) the Swedish Center for Smart Grids and Energy Storage (SweGRIDS-ABB) under grant VF-2015-0018 and (b) the ÅForsk Foundation under grant 17-604. The authors would like to apologize for any inconvenience caused to the readers and contributors by these changes. The changes do not a ect the scientific results. The manuscript will be updated, and the original will remain online on the article webpage, with a reference to this correction.

Universal electrification by 2030 is an important goal of Sustainable Development Goal (SDG) 7. Electricity provision no longer relies only on centralized grid expansion, but also on off-grid and mini-grid systems. Although this technological diversity holds promise, the technologies differ both physically and institutionally in electricity delivery. These differences raise equity and justice concerns around how they are implemented. For example, how can electricity be kept affordable for all consumers when access is provided by various technologies operated under different business models? This paper addresses this aspect of affordability and sheds light on how the SDG7 target could be met more equitably and fairly. We use a novel analytical methodology to apply two different principles of justice - equality and equity - to incorporate affordability into electricity pricing. Using a geospatial electrification model and Tanzania as a case study, we first arrive at price levels based on the principle(s) of justice. Then, we produce location-specific recommendations for subsidy levels needed to ensure those price levels. We find that the equity approach benefits a bigger section of the population than the equality approach. Moreover, the former costs significantly less per capita than the latter. Having said that, the equity approach is complex and therefore harder to implement. The methodological framework proposed in this study acts as a proof-of-concept for examining concerns around distributive justice using quantitative energy modelling tools and drawing policy relevant insights for energy planning in developing countries. Additionally, by focusing on the spatial aspects of energy access and the issue of fairness, the study also contributes to the growing conceptualizations of energy justice.