APRIL 2024 2024/131 A KNOWLEDGE NOTE SERIES FOR THE ENERGY & EXTRACTIVES GLOBAL PRACTICE Adapting Spatial Frameworks to Guide Energy Access Interventions in Urbanizing Africa The bottom line. The extension of electricity into rural areas has been the main focus of efforts to achieve universal access to reliable, affordable, and modern energy by 2030. On the African continent and elsewhere, however, rapid urbanization has produced new patterns of human settlement that blur the distinction between rural and urban. As a case study of Kenya demonstrates, access metrics aggregated at the rural or urban level do not equip governments and their partners to properly identify or target sites for electrification. Spatialized frameworks and data that define space along a rural–urban continuum or as urban catchment areas can improve policy makers’ understanding of the specific barriers to access that communities face. Why is categorizing areas as simply Patterns of urbanization on the African continent are rapid, rural or urban insufficient for planning unevenly distributed, and complex. Contrary to popular electrification? belief, African urbanization is not simply migration from Official statistics based on a simple rural-urban a rural area to a primary city. Informal and spontaneous distinction fail to capture the complexity of Africa’s urbanization has also become the norm in many African cit- urbanization patterns ies (Awan 2023). Examples of observed urbanization trends include: Labeling locales as either urban or rural assumes that there are clear and measurable differences between the two types of spaces. This idea is losing empirical meaning, given the 3 The in situ emergence of new cities and town centers rapid and sustained urbanization occurring across human (OECD, UNECA, and AfDB 2022) societies. 3 Growth and densification in urban peripheries (McGee 2021) In Sub-Saharan Africa, the urban population grew by a fac- 3 The emergence of satellite cities and urban corridors tor of more than 20 between 1950 and 2015 (OECD, UNECA, (OECD, UNECA, and AfDB 2022) and AfDB 2022). Between now and 2050, it will grow by 560 3 The merging of existing urban centers into dense, poly- million to reach 1.3 billion (UN DESA 2022). nucleated conurbations/mega-agglomerations (OECD, UNECA, and AfDB 2022). Jessica Kersey is a consultant at ESMAP and a PhD candidate in the Energy and Resources Authors Group at the University of California, Berkeley. Bryan Bonsuk Koo is an energy specialist at ESMAP. 2 Adapting Spatial Frameworks to Guide Energy Access Interventions in Urbanizing Africa Statistics that are spatially aggregated into rural or urban How does the classification of an area affect categories level do not characterize these newly urban and efforts to electrify it? urbanizing communities, for several reasons (Brenner and A coarse classification may not allow policy makers and Schmid 2014). The official definition of urban varies from researchers to spot barriers to energy access, model country to country, and most projections are based on data and evaluate appropriate technologies, or support the from censuses conducted in the 1990s or 2000s. It can take effective governance of electrification decades for national governments to recognize new cities and towns administratively. During this time, many experi- A rural-urban binary framing limits electrification efforts by ence rapid growth without the support and resources that making it difficult to: an “urban” designation would provide. 3 Understand the barriers to energy access and the energy Informal settlements are also often omitted from or under- transition pathways of diverse communities represented in official statistics (Mitlin and Satterthwaite 3 Adapt electrification strategies that better meet commu- 2013). It is unclear how peri-urban communities are captured nities’ present and future needs within a rural-urban division; their needs tend to be differ- 3 Perform accurate geospatial energy modelling ent from those of both their urban and rural counterparts. 3 Establish coherent institutional jurisdictions. A significant number of communities are thus hidden in or missing from official statistics—by design or because they are Understanding barriers to energy access obscured within data aggregated along a binary rural-urban According to the SDG 7 Tracking Report, in 2021 over 83 per- categorization (Van Duijne 2019; Onda and others 2019). cent of the 674 million unelectrified people in Sub-Saharan Africa were rural (IEA and others 2023). The general under- Improving the ability to measure and track urbanization standing is that the main barrier in rural areas is that the grid effects is important because urban areas—and the infra- does not reach them (Fall and others 2008). In contrast, urban structure that powers them—play a crucial role in economic environments are viewed as well-electrified (albeit with recog- development and poverty reduction. Thanks to economies of nized challenges with service quality), because of the density agglomeration, these areas facilitate the efficient provision of of grid infrastructure (Blimpo, Postepska, and Xu 2020). basic services, the concentration and specialization of labor, innovation, and the availability of a wider range of goods There is a rural bias in the understanding of how and where and services (UN-Habitat 2011). These benefits extend far energy poverty manifests itself (Castán-Broto and others beyond the formal boundaries of the city. Linkages between 2017; Singh and others 2015b). This argument does not imply urban areas and their surroundings provide opportunities for that rural communities do not face pressing challenges in employment and income diversification as well as access to accessing electricity that deserve to be addressed. Rather, services deep in the countryside (Satterthwaite and Tacoli it highlights that many “rural” communities are actually in 2002). Spatial frameworks that capture these dynamics locations that are newly urban, densifying, within the urban can guide efforts to provide basic infrastructure to where periphery, or strongly linked to neighboring cities. The pat- it can best support broader urban societal and economic terns of electricity access in these spaces between, within, transitions. and around cities are complex (Silver 2023). Understanding these broader processes of urbanization can be instructive in understanding the nature of the energy access challenges these communities face. In a period of rapid urbanization and In urban areas, households are thought to transition from demographic transition, barriers to energy traditional biomass-based energy sources to electricity and access are evolving in ways that do not gas through several stages as their incomes rise and cleaner sources become more readily available (Barnes, Krutilla, cleanly map to binary rural-urban divisions. and Hyde 2004). These transitions take place haltingly and Adapting Spatial Frameworks to Guide Energy Access Interventions in Urbanizing Africa 3 unevenly, however, especially for the lowest-income subset of The ability to better spatialize economic the urban population (Aung and others 2022; Fall and others 2008; Mahumane and Mulder 2022). Lack of affordability and demographic dynamics and model their is the most widely recognized barrier. One study finds that trajectories can help governments and their urbanization is correlated with more energy consumption and higher levels of access to clean fuels—but with increased partners make strategic decisions about energy poverty in terms of energy expenditures as a percent how to prioritize and target electrification of total income (Mahumane and Mulder 2022). resources. There is considerable spatial variation in the challenges that unelectrified or under-electrified populations in Sub- cleanly into simple rural-urban divisions. Statistics and narra- Saharan Africa face in accessing electricity (Falchetta and tives that rely on these designations poorly equip practitioners others 2019). Within cities, infrastructure tends to be con- to understand—and efficiently address—these challenges. centrated in commercial districts, high-income residential As more and more of the population in Sub-Saharan Africa zones, and other formal areas (Lall and others 2017). The moves to or is born in urban and urbanizing areas, their needs quality and coverage of services also worsen moving down will be increasingly difficult to understand and meet without the urban hierarchy; electricity may not be available at all a more spatially informed understanding of their challenges. in smaller cities or towns. The electricity access challenges of informal or “slum” communities within cities are also substan- Adapting electrification strategies to urbanization tial but often underrepresented (Buyana 2022). Bureaucratic patterns requirements to prove land tenure are a documented barrier Urbanization produces fundamental transformations in the in these communities ( Singh and others 2015b). socioeconomic structure of communities. Areas of high popu- lation density benefit from agglomeration economies, which Access to infrastructure declines rapidly moving into the are associated with increased incomes, better access to urban periphery (Lall and others 2017). In some peri-urban health and education, and increased economic productivity. areas, communities that lack electricity, sewerage, and piped Surrounding areas benefit from rural–urban linkages, through water access exist alongside formally planned neighborhoods which goods, people, finance, and other resources flow. that have access to full modern services (Silver 2015). Other peri-urban communities are “under grid,” and their barrier to Energy is a critical input to these processes. Provided at the access is not the physical availability of distribution infrastruc- right quantity, quality, and price, electricity can provide a ture but the high cost of domestic wiring and the connection clean, low-carbon development pathway for these emerg- application (Lee, Miguel, and Wolfram 2020). ing economies (Goldemberg and others 1985). Spatialized economic and demographic data can help policy makers Many others are connected but face issues with reliability make strategic decisions about how to prioritize and target or power quality that limit when and how electricity can be electrification resources. used (Ayaburi and others 2020; Jacome and others 2019; Klugman and others 2019). Connections are sometimes pre- A designation of rural or urban does not yield insight into carious, meaning that an intermediary can disconnect the the following factors, which are interlinked with urbanization end user. In Kampala’s informal settlements, for example, dynamics and consequential for electrification efforts: landlords can and often do disconnect their tenants because of unpaid rent, personal conflicts, or as a way to control 3 The types of productive economic activities communities costs. Intermediaries sometimes impose limitations on when engage in electricity is available and the types of appliances that can 3 The scale of these activities and expected changes over be used (Kersey and others 2023; Yaguma and others 2024). time 3 The existence and strength of linkages with nearby urban In a period of rapid urbanization and demographic transition, centers. energy access barriers are evolving in ways that do not map 4 Adapting Spatial Frameworks to Guide Energy Access Interventions in Urbanizing Africa In urbanizing areas, demand for electricity will grow as liveli- daytime loads that can serve as anchor customers (Guillou hoods improve and become more diversified. Electricity sys- and Girard 2022). tems must be able to support the shifting demand growth of their users. Unreliable or poor-quality supply can constrain The Utilities 2.0 Twaake pilot in Uganda is an example of a economic growth and create or increase reliance on expen- mini grid whose business model is premised on realizing the sive and polluting fuel sources. How demand grows—which latent demand of economically diverse peri-urban commu- will depend in large part on the nature of localized eco- nities. The objective of the pilot is to demonstrate that mini nomic activities—has implications for the types of systems grids can be used to stimulate demand before the arrival of that will best meet users’ needs. A recent study argues that the central grid. the electricity demand of agricultural activities has been overlooked and that its inclusion could significantly alter cur- Under Twaake, a 40 kilowatt (kW) peak mini grid was con- rent least-cost electrification planning (Korkovelos, Koo, and structed in the village of Kiwumu. The livelihoods of residents Malik 2022). there are strongly linked to commerce in Kampala, which is 45 minutes away by truck or bus. Over half of business cus- Extending this argument to the context of urbanizing com- tomers received dedicated asset financing for commercial munities, local economies are rapidly growing and diver- and agricultural productive-use appliances. The mini grid sifying into manufacturing, service, and trade. How these was connected to the central grid in June 2023. Despite transitions are occurring, and the extent to which they can be early challenges with institutional coordination, this model electrified, affects the rate of demand growth and the shape appears to have been effective in improving the cost eco- of the demand profile. Both are consequential for selecting nomics of electrification (Rockefeller Foundation 2021). technologies and business models capable of matching out- put to time of use and accommodating future growth (and From the supply side, the cost-effectiveness of technology shifts) in demand while remaining financially sustainable. options is strongly influenced by the form and density of These dynamics are well-understood by mini grid developers, human settlement patterns. The cost of electrification per who often seek urbanizing sites with niche industries, such as capita varies significantly depending on the technology type light manufacturing and agricultural processing, with high and population density (figure 1). Figure 1. Capital cost per capita of electrification via a central grid, mini grid, and solar home system, based on settlement density 1,000 Capital cost per capita of infrastructure 900 Categorization and density (people/km2) 800 Large city (30,000) Large city (20,000) provision ($/capita) 700 Large city (10,000) 600 Large city (5,008) 500 Large city (3,026) Large city (1,455) 400 Secondary city (1,247) 300 Rural hinterland (38) 200 Deep rural (13) 100 0 Grid Mini grid Solar PV Technology Source: Adapted from Foster and Briceno-Garmendia (2010). Adapting Spatial Frameworks to Guide Energy Access Interventions in Urbanizing Africa 5 Mini grids are often assumed to be a relevant option for iso- by about 25 percent (Foster and Briceno-Garmendia 2010). lated and unelectrified rural areas. In higher-density areas, These findings underscore the need to increase coordination they are also cost-competitive with grid expansion. Through between entities responsible for urban planning and electric- fieldwork in India, Senegal, and Tanzania, Guillou and Girard ity service provision. Through effective urban planning, cities (2022) find that mini grid developers “tend to favor larger can grow in ways that minimize the costs of basic service villages and places that, though officially rural, have eco- provision, avoid lock-in of high-carbon fuel sources, and align nomic and social dynamics that bring them closer to urban with other objectives of sustainable urban growth. areas.” This finding is consistent with a growing body of work that advocates for the potential of under-grid mini grids Modeling least-cost electrification accurately and other hybridized or grid-interactive systems to improve Most electrification models explicitly or implicitly incorporate access in areas where the grid will soon reach or is present rural-urban binary classifications. Depending on how these but offers poor service (Graber, Mong, and Sherwood 2018). assumptions factor into the model, they may yield recom- mendations for technologies that are inappropriate to the Energy demand, the cost of service provision, and urban on-the-ground realities of urbanizing communities. growth patterns are interlinked processes. While we have discussed matching energy services to fit the needs of Demand estimation is highly sensitive to forecasting meth- urbanizing areas, the reverse – shaping planning to enable ods and input assumptions, which can produce dramatic high-efficiency forms of urban living – is also possible differences in modeling outcomes (Kemausuor 2014). Two (Madlener and Sunak 2011). Urban planning can be an of the most commonly used least-cost electrification mod- important tool in promoting growth patterns that are energy els base several key demand modeling assumptions on a and cost-efficient. Healthy urban expansion is a particular rural-urban split (table 1). In some of the early applications area of concern. Urban sprawl often leads to the inefficient of the Open Source Spatial Electrification Tool (Onset), for use of resources and a rise in the per capita cost of service example, demand was based on estimates of per capita delivery. Doubling urban density has been estimated to rural and urban consumption (Moksnes and others 2017). reduce the per capita cost of infrastructure improvements Table 1. Assumptions in least-cost electrification models that are informed by binary rural-urban classifications Assumptions that depend on rural or Technology output Model Urban classification urban designation options Source Network Planner User defines threshold below which Population growth rate, a Grid, mini grid, Kemausuor and an area is considered rural and average household size, off-grid others (2014); above which it is considered urban. fraction of total demand Ohiare (2015) during peak hours Reference No explicit urban-rural assumptions within modeling parameters, Small home solar, Ciller and others Electrification but some input assumptions are informed by national datasets mini grids, grid- (2019); Borofsky Model (REM) that are disaggregated by rural or urban. connected systems (2015) Open Source Spatial User defines threshold below which Average household size, Grid connection, mini Mentis and others Electrification Tool an area is considered rural and population growth rates, grid systems, stand- (2017); Korkovelos (OnSSET) above which it is considered urban. access tiers b alone systems and others (2019) a. After a “rural” community surpasses the set threshold, it upgrades to “urban” growth rates. b. Demand estimation has improved as these models become more sophisticated and higher-resolution socioeconomic data become more widely available. The Global Electrification Platform, for example, which is based on OnSSET, allows for a bottom-up estimation mode that uses a spatialized poverty index and income data to estimate demand. 6 Adapting Spatial Frameworks to Guide Energy Access Interventions in Urbanizing Africa Rural-urban assumptions also enter demand forecasting Senegal provides an extreme example of this institutional algorithms as average population growth rates and/or disconnect. In the 1990s, a reform in the electricity sector household size. These assumptions are often based on aver- designated a new electrification agency, the Senegalese aged historical values and usually do not account for the Rural Electrification Agency (ASER) for rural areas; urban location-specific dynamics that drive these demographic areas remained under the jurisdiction of the national utility, shifts. Population growth is usually much higher in “rural” SENELEC. Under this framework, peri-urban areas fell into areas that are near large cities. Areas may also experience neither rural nor urban classifications and were not targeted population declines as people resettle in areas with more by either agency’s policies (Singh and others 2015a). economic opportunity or political and environmental stabil- ity (Black and others 2008). Models that do not account for Reevaluating Kenya’s Multi-Tier Framework these dynamics may not be able to identify areas of high Survey: What insights does a continuum potential growth and densification and could therefore rec- approach offer? ommend systems that are not well-suited to local economic Spatial disaggregation of Kenya’s Multi-Tier Framework trajectories. Survey reveals large access deficits in peri-urban communities, driven primarily by high connection costs A diverse ecosystem of electricity provision models now exists, including the central grid, mini grids, mesh grids, metro grids, Rural-urban as a continuum and solar home systems. Many of these systems are com- Several established datasets can be operationalized in lieu mercially available at sizes and price points that are increas- of a binary rural-urban classification. Table 2 outlines four of ingly tailored to income-constrained users. Most models the most widely used or recently developed datasets; fig- include just three possibilities: solar home systems, mini grids, ure 2 shows images based on them. and grid extension. They do not incorporate evolving hybrid technologies such as mesh grids and under-grid mini grids, The analysis presented here uses the Urban-Rural Catchment which may be cost-effective in densifying rural and peri-ur- Area (URCA), which disaggregates space into 30 categories. ban communities or areas that are grid-connected but A particular strength of the URCA methodology is its ability experience poor reliability and service quality (Menon 2022). to identify economic linkages. URCA is based on Central Future iterations of these models could introduce penalties Place Theory, which posits that people living closer to a for poor grid reliability and quality and allow grid-tied or city or town have better access to its goods, services, and under-grid systems to compete against grid extension within opportunities and that bigger cities offer a broader range optimization algorithms. of urban benefits (Christaller 1933). Under the URCA frame- work, space is understood as a catchment area surrounding Establishing coherent institutional jurisdictions an urban center (Cattaneo, Nelson, and McMenomy 2021). Rural-urban designations can present a challenge to effec- Catchment areas are defined by the length of time it would tive governance of electricity provision. In many countries, a take someone living in that area to reach the nearest city rural electrification agency or similar institution is tasked with or town of a particular size. It is estimated by calculating expanding access into rural areas. Within cities, the munic- the least-cost path over a cost surface. This cost surface ipality or the utility is often responsible for electrification. quantifies the ease and speed of travel over different routes, Peri-urban areas may not fall cleanly under the jurisdiction of accounting for transport networks, land cover data, and either institution. Emerging urban zones must wait until the international borders (Weiss and others 2018). An important next census cycle to be formally incorporated and receive limitation is that this algorithm does not explicitly account the resources necessary to plan and administer urban ser- for the mode of travel. Catchment area designations may vices. These and other rapid demographic shifts have gone vary across country and regional contexts, as different con- largely unrecognized in the official statistics that are used for texts favor different transportation modes. planning and policy making. Adapting Spatial Frameworks to Guide Energy Access Interventions in Urbanizing Africa 7 Table 2. Features of datasets that can be used in geospatial analysis and planning Dataset Description Type Area covered Years covered Custodian Dataset address Africapolis Provides extents of urban Vector African 2015, 2022 OECD /oecd.us4.list-manage. https:/ agglomerations; includes continent com/subscribe?u=5aa4680 statistics on population and 998eddebe5f4ce7065&id= settlement density. 17db0c31dd Degree of Classifies space as cities, Raster Global 1975–2030, European /ghsl.jrc.ec.europa.eu/ https:/ Urbanisation towns and suburbs, and at five-year Commission download.php?ds=smod (DEGURBA) rural areas based on a intervals (UN endorsed) combination of geographical contiguity and population density. This first level of the classification may be complemented by a range of more detailed concepts, such as metropolitan areas, commuting zones, dense towns, semi-dense towns, suburban or peri-urban areas, villages, dispersed rural areas, and mostly uninhabited areas. Open Includes outlines of buildings Vectors Central and Varies by Google /sites.research. https:/ Buildings and characterizes the and South America, location (based google/open- density of settlements; points Africa, Indian on availability of buildings/#download footprints of buildings are Subcontinent, satellite data) determined from high- Southeast Asia resolution satellite imagery via a deep learning model. Urban-Rural Thirty categories show Raster Global 2015 Authors /figshare.com/ https:/ Catchment catchment areas around and articles/dataset/ Area (URCA) cities and towns, defined Urban-rural_ in terms of travel time to continuum/12579572 closest city or town (less than 1 hour, 1–2 hours, 2–3 hours, more than 3 hours) of different sizes. Sources: Florczyk and others (2019); OECD, UNECA, and AfDB (2022); Sirko and others (2021); Cattaneo, Nelson, and McMenomy (2021). The URCA framework segments populations into categories This analysis re-evaluates the results of Kenya’s Multi-Tier with similar socioeconomic demographics and quality of Framework (MTF) survey using an adapted version of the infrastructure services. This type of spatial disaggregation is URCA framework. The objective is to reveal the additional necessary to understand and address the needs of popula- insights that a more sophisticated spatial framework can tion groups across an increasingly urban global landscape. offer in interpreting the MTF results. 8 Adapting Spatial Frameworks to Guide Energy Access Interventions in Urbanizing Africa Figure 2. Representative images from four datasets used to analyze development challenges and progress a. Africapolis b. DEGURBA (Degree of Urbanisation) c. Open Buildings d. URCA (Urban-Rural Catchment Area) Sources: Authors, using data from respective dataset. Adapting Spatial Frameworks to Guide Energy Access Interventions in Urbanizing Africa 9 The MTF survey is a nationally representative sample of Table 3. Definition of urban catchment energy access metrics, conducted in 2016. The adapted categories URCA framework used here contains the five categories Category Description shown in table 3. They were derived by aggregating the 30 URCA categories into new categories, each of which con- City or town City or town with 20,000–5 million tains at least 100 of the original MTF survey points. Figure 3 inhabitants shows a map of Kenya using the adapted URCA framework Less than one hour Locale within one hour travel time to a to large city city of 250,000–5 million inhabitants The aggregation of the original URCA categories into the Less than one hour Locale within one hour travel time to a adapted framework is a necessary limitation of this study to medium-size city of 100,000–250,000 inhabitants that is rooted in the spatial distribution of the underlying city sample points. In particular, peri-urban communities within Less than one hour Locale within one hour travel time one hour of a city or town are not sufficiently disaggregated to small city or to a city or town of 20,000–100,000 within the adapted or original URCA framework. Future work town inhabitants could disaggregate these populations based on 15-, 30-, Non-urban Locale more than one hour travel time to and 45-minute commute times. a city or town of any size Spatial distribution of people without access to Source: Dubey and others 2019 Note: Definitions are adapted from the Urban-Rural Catchment Area electricity framework. Panel a of figure 4 shows the distribution of Kenya’s popula- tion across the five categories of the adapted URCA frame- work in terms of their electrification status.1 About 69 percent Figure 3. Map of Kenya based on the of Kenya’s population lives within an hour’s travel of a large, adapted URCA framework medium-size, or small city or town; they make up 81 percent of the unelectrified. Panel b shows how these survey points are categorized in the original survey, which uses a binary rural/urban classification. Across these three categories, 57 percent of the population is considered urban. This means that when survey results are aggregated by rural or urban, the characteristics of the pop- ulations represented by these three categories are not well represented within either. Areas less than one hour from a large or medium-size city contain roughly half of the population (51 percent). These categories correspond to the peri-urban zones surrounding Nairobi, Kisumu, Kisii, and Nakuru, shown in figure 3. This distribution makes sense given recent demographic trends in Kenya. In 2022, an extensive geospatial analysis of urban- ization on the African continent identified Kisii and Kisumu as “spontaneous mega-agglomerations,” with over 3.5 and 5.0 million inhabitants, respectively (OECD, UNECA, and AfDB 2022). Both are part of a rapidly densifying conurbation in 1. For the purposes of this analysis, respondents are considered electrified if they have a grid connection. 10 Adapting Spatial Frameworks to Guide Energy Access Interventions in Urbanizing Africa Figure 4. Grid electrification status of Kenya’s population a. Based on adapted URCA framework Based on original categorization as rural or b.  urban in the Multi-Tier Framework survey 3,500 100 3,500 100 Electrified Rural 3,000 Unelectrified 3,000 80 Urban 80 Electrification rate (%) Population (1,000s) Population (1,000s) 2,500 2,500 Percent urban 60 60 2,000 2,000 1,500 40 1,500 40 1,000 1,000 20 20 500 500 0 0 0 0 City or <1 hour to <1 hour to <1 hour to Non-urban City or <1 hour to <1 hour to <1 hour to Non-urban town large city medium city small city or town large city medium city small city or town town southwestern Kenya whose linkages extend into neighbor- insights into socioeconomic characteristics relevant to elec- ing Uganda and Tanzania. Figure 5 shows the population trification planning. Conventionally, living conditions and density of the unelectrified population in Kenya by adjusted socioeconomic status (such as income or occupation) are URCA category. assumed to be similar among rural and urban households. Figure 6 shows annual household income and the percent- Socioeconomic indicators and the choice of technology age of heads of household participating in farm and non- Disaggregating household economic indicators from the farm sectors by spatial category. MTF survey using the adapted URCA framework also yields Rates of employment in the non-farming sectors are highest in urban areas and decrease steadily with distance from a city or town. Employment in farm sectors is highest in more Figure 5. Density of unelectrified population remote areas but still significant in areas close to cities, in Kenya (people per km2) suggesting that these populations are engaging in signifi- cant agricultural activities, likely driven by demand from nearby urban centers. The significant amount of non-farm income shows that economic activities are diversified in these areas. Annual household income is highest for people living in cities and towns and decreases steadily as the distance from the nearest urban center increases. The correlation between income levels and electricity consumption is well-established. It can reasonably be assumed that communities located less than an hour from a large city, for example, would have greater willingness and ability to pay for electricity than non-urban populations. These dynamics are apparent in Kisumu, in both the city (Kenya’s third-largest) and the surrounding county. As Kenya Adapting Spatial Frameworks to Guide Energy Access Interventions in Urbanizing Africa 11 Figure 6. Annual household income and employment in farm and non-farm sectors in Kenya, by distance from city a. Annual household income b. Employment in farm and nonfarm sectors 450 70 Annual average household expenses Employment of head of households Non-farm 60 Farm 400 (1,000s Ksh per year) 50 by sector (%) 350 40 300 30 20 250 10 200 0 City or <1 hour to <1 hour to <1 hour to Non-urban City or <1 hour to <1 hour to <1 hour to Non-urban town large city medium city small city or town large city medium city small city or town town Note: Categories are based on the URCA framework. has become denser and its population has grown, its econ- the primary barrier to electrification. Of the estimated 3.1 omy has expanded from traditional fishing and agricultural million people in Kenya who cite connection cost as the rea- activities to industrial activities, such as paint manufactur- son for non-connection, 87 percent live within an hour of a ing, sugar processing, and soft drink bottling and distribu- city or town. tion. Electricity access rates are just 53 percent, well below the national average of 75 percent (Buma 2020; Dubey and These findings align with local policy realities. In 2015, Kenya others 2019). The National Electrification Strategy has slated announced the Last Mile Connectivity Project, which sought Kisumu for grid extension and densification. Off-grid devel- to electrify four million under-grid households (Ministry of opers have seen this combination of low electrification rate Energy 2018). The program targeted communities that were and high economic activity as an opportunity, and several physically close to the existing grid but not yet connected mini-grids have been deployed across the county of Kisumu to it. It reached its target number of connections. Demand (Ministry of Energy 2018; Powerhive n.d.; Kenya GMG Facility remained low among new customers, however, and many 2021). Kenyans were reluctant to pay for a connection even at highly subsidized rates (Lee, Miguel, and Wolfram 2020). Identifying barriers to energy access Effective and well-targeted interventions hinge on under- standing the barriers different demographic groups face to Sustained urbanization is driving permanent accessing and using electricity from the grid. Spatial analysis of these barriers can be helpful for programmatic planning changes in the way people and economies and implementation. are spatially distributed. Policy makers and Figure 7 shows the main barriers to grid connection across practitioners need to begin engaging in the five adapted URCA categories in Kenya. For people living critical discussions about how assumptions within an hour of a city or town, the high cost of connection, rather than the physical availability of grid infrastructure, is about rural/urban affect their work. 12 Adapting Spatial Frameworks to Guide Energy Access Interventions in Urbanizing Africa Figure 7. Reasons Kenyan survey respondents cite for not being connected to the grid a. By rural-urban division b. By distance from cities and towns 4,000 2,500 Number of people facing barrier Number of people facing barrier 2,000 3,000 1,500 (1,000s) (1,000s) 2,000 1,000 1,000 500 0 0 Rural City or <1 hour to <1 hour to <1 hour to Non-urban Urban town large city medium city small city or town Grid is too far/not available Submitted application and waiting for connection Renting, landlord decision Administrative procedure is too complicated High cost of electricity High cost of connection Note: Categories in panel b are based on the URCA framework. More work needs to be done to understand the drivers of urban distinctions and may not be adequately targeted by electricity uptake in these under-grid, peri-urban com- existing institutions. This analysis might point to the need for munities (Blimpo, Postepska, and Xu 2020). Using spatial new institutions (such as a Weak Grid Electrification Agency), techniques to isolate the responses of specific communities new initiatives or task forces within existing institutions, or within larger datasets would be a helpful starting point. increased coordination between national agencies and municipal governments. Such a review should seek to exam- How can policy makers operationalize the ine the rural-urban definitions used by different institutions new spatial frameworks? and to understand how outdated or poor-quality underlying data may influence these definitions and their application in More disaggregated spatial frameworks can policy and legal processes. immediately be integrated into survey design, modeling, and governance Second, energy access surveys should not rely exclusively More sophisticated spatial frameworks and data inputs can on government-level rural/urban designations for their be immediately integrated into electrification planning and sampling strategies; disaggregated spatial frameworks policy making in several ways. should complement them. Although the URCA dataset is an important step forward in efforts to deconstruct the rural-ur- First, governments should review the jurisdictions of the ban binary, adaptations are required to make it accessible national and municipal institutions responsible for electric and useful for energy access policy makers. Future iterations service provision. Certain populations, such as people in should integrate energy-specific indicators such as the prox- informal settlements, rapidly growing rural towns, or areas in imity of existing grid infrastructure, using geospatial data. the peri-urban periphery, do not fall cleanly between rural/ They should also seek ways to disaggregate peri-urban Adapting Spatial Frameworks to Guide Energy Access Interventions in Urbanizing Africa 13 communities still further, as the less than one hour commute frameworks such as the URCA and continue to integrate time used in the URCA framework is too coarse. Spatial emerging high-resolution, spatialized socioeconomic and frameworks should also be adapted to account for differ- demographic data inputs. It is also important to incorporate ences in how populations are distributed within and across urban-rural dynamics and patterns in the development of cities and towns of various size categories, across relevant the least-cost planning model. local, national, or regional scales. Awareness is important to driving change in the tools, dis- Third, geospatial least-cost electrification models should course, and frameworks used to guide efforts to achieve be adapted to avoid defining parameters and constraints SDG 7. Practitioners and policy makers must recognize that based on a binary rural/urban distinction. Demand estima- sustained urbanization is driving permanent changes in tion, for example, is an important modeling assumption that the way people and economies are spatially distributed—in is often based (explicitly or implicitly) on rural or urban sta- ways that are consequential for electrification efforts—and tus. This assumption can have particularly counterproduc- begin engaging in critical discussions about how rural/urban tive impacts on electrification planning, as higher-density, assumptions affect their own work. urban-proximate areas with strong access to productive appliance supply chains and financing that are designated The authors are grateful to Samuel Miles of the University of as having “rural” levels of demand may not receive systems California, Berkeley, for his help in developing the concepts of appropriate capacity to support their economic activ- reported in this brief. ity. Demand estimation techniques should incorporate References Aung, Ther, Pamela Jagger, Kay Thwe Hlaing, Khin Khin Han, Policy Implications for Migration. Development Research and Wakako Kobayashi. 2022. “City Living but Still Energy Centre on Migration, Globalisation and Poverty, Sussex, United Poor: Household Energy Transitions under Rapid Urbanization /www.unscn.org/layout/modules/resources/ Kingdom. https:/ in Myanmar.” Energy Research & Social Science 85 (March): files/Demographics_and_CC_future_trends.pdf. /doi.org/10.1016/j.erss.2021.102432. 102432. https:/ Blimpo, Moussa P., Agnieszka Postepska, and Yanbin Xu. 2020. Awan, Nishat. 2023. “Informal Settlements of the Global “Why Is Household Electricity Uptake Low in Sub-Saharan South.” In Architectural Borders and Territories, ed, Gihan / Africa?” World Development 133 (September): 105002. https:/ Karunaratne. London: Routledge. doi.org/10.1016/j.worlddev.2020.105002. Ayaburi, John, Morgan Bazilian, Jacob Kincer, and Todd Moss. Borofsky, Yael. 2015. Towards a Transdisciplinary Approach 2020. “Measuring ‘Reasonably Reliable’ Access to Electricity to Rural Electrification Planning for Universal Access in India. /doi. Services.” The Electricity Journal 33 (7): 106828. https:/ Massachusetts Institute of Technology, Cambridge, MA. org/10.1016/j.tej.2020.106828. /dspace.mit.edu/handle/1721.1/98731. https:/ Barnes, Douglas F., Kerry Krutilla, and William Hyde. 2004. The Brenner, Neil, and Christian Schmid. 2014. “The ‘Urban Age’ Urban Household Energy Transition: Energy, Poverty, and the in Question.” International Journal of Urban and Regional Environment in the Developing World. Washington, DC: Energy /doi.org/10.1111/1468-2427.12115. Research 38 (3): 731–55. https:/ Sector Management Assistance Program. Buma, Carine. 2020. Initial Energy Status Report: Kisumu Black, Richard, Dominic Kniveton, Ronald Skeldon, Daniel /renewablesroadmap.iclei.org/ Country, Kenya. ICLEI. https:/ Coppard, Akira Murata, and Kerstin Schmidt-Verkerk. 2008. resource/kisumu-county-kenya-initial-energy-status-report/. Demographics and Climate Change: Future Trends and Their 14 Adapting Spatial Frameworks to Guide Energy Access Interventions in Urbanizing Africa Buyana, Kareem. 2022. “Transgression in the Energy Foster, Vivien, and Cecilia Briceno-Garmendia. 2010. Africa’s Infrastructure Landscapes of Cities.” Landscape Research Infrastructure: A Time for Transformation. Africa Development /doi.org/10.1080/01426397.2022.2039108. (March): 1–13. https:/ Forum. Washington, DC: World Bank. Castán-Broto, Vanesa, Lucy Stevens, Emmanuel Ackom, Goldemberg, José, Thomas B. Johansson, Amulya K. N. Julia Tomei, Priti Parikh, Iwona Bisaga, Long Seng To, Joshua Reddy, and Robert H. Williams. 1985. “Basic Needs and Much Kirshner, and Yacob Mulugetta. 2017. “A Research Agenda More with One Kilowatt per Capita.” Ambio 14 (4/5): 190–200. for a People-Centred Approach to Energy Access in the Graber, Sachiko, Patricia Mong, and James Sherwood. 2018. / Urbanizing Global South.” Nature Energy 2 (10): 776–79. https:/ Under the Grid: Improving the Economics and Reliability doi.org/10.1038/s41560-017-0007-x. of Rural Electricity Service with Undergrid Minigrids. Rocky Cattaneo, Andrea, Andrew Nelson, and Theresa McMenomy. /rmi.org/insight/ Mountain Institute, Washington, DC. https:/ 2021. “Global Mapping of Urban–Rural Catchment Areas under-the-grid/. Reveals Unequal Access to Services.” Proceedings of the Guillou, Emmanuelle, and Bérénice Girard. 2022. “Mini-Grids /doi. National Academy of Sciences 118 (2): e2011990118. https:/ at the Interface: The Deployment of Mini-Grids in Urbanizing org/10.1073/pnas.2011990118. Localities of the Global South.” Journal of Urban Technology. Christaller, W. 1933. “Die Zentralen Orte in Süddeutschland.” /doi.org/10.1080/10630732.2022.2087170. https:/ In Central Places in Southern Germany, translated by C.W. IEA (International Energy Agency), IRENA (International Baskin. Wissenschaftliche Buchgesellschaft. Renewable Energy Agency), UNSD (United Nations Statistics Ciller, Pedro, Douglas Ellman, Claudio Vergara, Andres Division), World Bank, and WHO (World Health Organization). Gonzalez-Garcia, Stephen J. Lee, Cailinn Drouin, Matthew 2023. Tracking SDG 7: The Energy Progress Report. Brusnahan, and others 2019. “Optimal Electrification Planning Washington, DC: World Bank. Incorporating On- and Off-Grid Technologies: The Reference Jacome, Veronica, Noah Klugman, Catherine Wolfram, Electrification Model (REM).” Proceedings of the IEEE 107 (9): Belinda Grunfeld, Duncan Callaway, and Isha Ray. 2019. /doi.org/10.1109/JPROC.2019.2922543. 1872–1905. https:/ “Power Quality and Modern Energy for All.” Proceedings of the Dubey, S., E. Adovor, D. Rysankova, E. Portale, and B. Koo. 2019. /doi. National Academy of Sciences 116 (33): 16308–13. https:/ Kenya: Beyond Connections: Energy Access Diagnostic Report org/10.1073/pnas.1903610116. Based on the Multi-Tier Framework. Washington, DC: World Kemausuor, Francis, Edwin Adkins, Isaac Adu-Poku, Abeeku Bank. Brew-Hammond, and Vijay Modi. 2014. “Electrification Falchetta, Giacomo, Shonali Pachauri, Simon Parkinson, and Planning Using Network Planner Tool: The Case of Ghana.” Edward Byers. 2019. “A High-Resolution Gridded Dataset to / Energy for Sustainable Development 19 (April): 92–101. https:/ Assess Electrification in Sub-Saharan Africa.” Scientific Data 6 doi.org/10.1016/j.esd.2013.12.009. /doi.org/10.1038/s41597-019-0122-6. (1): 110. https:/ Kenya GMG Facility. 2021. Providing Energy Access to Kenya’s Fall, Abdoulaye, Sécou Sarr, Touria Dafrallah, and Abdou /storymaps.arcgis.com/stories/ Rural Population. https:/ Ndour. 2008. “Modern Energy Access in Peri-Urban Areas d6ff0a856b4b4ccabf3f58bf9816ee7f. of West Africa: The Case of Dakar, Senegal.” Energy for Kersey, Jessica, Judith Mbabazi, Civian Massa, Elena van /doi.org/10.1016/ Sustainable Development 12 (4): 22–37. https:/ Hove, June Lukuyu, Stellamaris Wavamunno Nakacwa, S0973-0826(09)60005-3. Lydia Letaru, Esther Mburu, May Kabiri, and Sirezi Bulenza. Florczyk, A. J., C. Corbane, D. Ehrlich, S. Freire, T. Kemper, L. 2023. Spotlight Kampala Illuminating Energy Inequities in Maffenini, M. Meichiorri, and others. 2019. GHSL Data Package Informal Urban Communities: Main Findings Report. Spotlight 2019. EUR 29788 EN. Luxembourg: Publications Office of the Kampala, Kampala. /data.europa.eu/doi/10.2760/290498. European Union. https:/ Adapting Spatial Frameworks to Guide Energy Access Interventions in Urbanizing Africa 15 Klugman, Noah, Joshua Adkins, Emily Paszkiewicz, Molly G. Menon, Nithya. 2022. “Mini-Grids vs. Mesh-Grids.” Okra Hickman, Matthew Podolsky, Jay Taneja, and Prabal Dutta. /www.okrasolar.com/blog/ Solar (blog). 2022. https:/ 2021. “Watching the Grid: Utility-Independent Measurements mini-grids-vs-mesh-grids. of Electricity Reliability in Accra, Ghana.” In Proceedings of Mentis, Dimitrios, Mark Howells, Holger Rogner, Alexandros the 20th International Conference on Information Processing Korkovelos, Christopher Arderne, Eduardo Zepeda, Shahid in Sensor Networks (co-located with CPS-IoT Week 2021). Siyal, and others 2017. “Lighting the World: The First Nashville, TN, USA: ACM. Application of an Open Source, Spatial Electrification Tool Korkovelos, Alexandros, Babak Khavari, Andreas Sahlberg, (OnSSET) in Sub-Saharan Africa.” Environmental Research Mark Howells, and Christopher Arderne. 2019. “The Role of /doi.org/10.1088/1748-9326/ Letters 12 (8): 085003. https:/ Open Access Data in Geospatial Electrification Planning and aa7b29. the Achievement of SDG7. An OnSSET-Based Case Study Ministry of Energy. 2018. “Kenya National Electrification /doi.org/10.3390/ for Malawi.” Energies 12 (7): 1395. https:/ Strategy: Key Highlights.” Government of Kenya, Nairobi. en12071395. /pubdocs.worldbank.org/en/413001554284496731/ https:/ Korkovelos, Alexandros, Bryan Bonsuk Koo, and Kabir Malik. Kenya-National-Electrification-Strategy-KNES-Key- 2022. “Agrodem: An Open-Source Model That Quantifies the Highlights-2018.pdf. Electricity Requirements of Irrigation.” Live Wire 2022/120. Mitlin, Diana, and David Satterthwaite. 2013. Urban Poverty in World Bank, Washington, DC. the Global South: Scale and Nature. New York: Routledge. Lall, Somik Vinay, J. Vernon Henderson, Anthony J. Venables, Moksnes, Nandi, Alexandros Korkovelos, Dimitrios Mentis, and Paolo Avner. 2017. Africa’s Cities: Opening Doors and Mark Howells. 2017. “Electrification Pathways for /doi. to the World. Washington, DC: World Bank. https:/ Kenya–Linking Spatial Electrification Analysis and Medium to org/10.1596/978-1-4648-1044-2. Long Term Energy Planning.” Environmental Research Letters Lee, Kenneth, Edward Miguel, and Catherine Wolfram. 12(9):095008. doi: 10.1088/1748-9326/aa7e18. 2020. “Experimental Evidence on the Economics of Rural OECD (Organisation for Economic Co-operation and Electrification.” Journal of Political Economy 128 (4): 1523–65. Development), UNECA (United Nations Economic Commission /doi.org/10.1086/705417. https:/ for Africa), and AfDB (African Development Bank). 2022. Madlener, Reinhard, and Yasin Sunak. 2011. “Impacts of Africa’s Urbanisation Dynamics 2022: The Economic Power of Urbanization on Urban Structures and Energy Demand: What /doi. Africa’s Cities. West African Studies. Paris: OECD. https:/ Can We Learn for Urban Energy Planning and Urbanization org/10.1787/3834ed5b-en. Management?” Sustainable Cities and Society 1 (1): 45–53. Ohiare, Sanusi. 2015. “Expanding Electricity Access to All /doi.org/10.1016/j.scs.2010.08.006. https:/ in Nigeria: A Spatial Planning and Cost Analysis.” Energy, Mahumane, Gilberto, and Peter Mulder. 2022. “Urbanization /doi.org/10.1186/ Sustainability and Society 5 (1): 8. https:/ of Energy Poverty? The Case of Mozambique.” Renewable s13705-015-0037-9. /doi. and Sustainable Energy Reviews 159 (May): 112089. https:/ Onda, Kyle, Parmanand Sinha, Andrea E. Gaughan, org/10.1016/j.rser.2022.112089. Forrest R. Stevens, and Nikhil Kaza. 2019. “Missing Millions: McGee, Terry G. 2021. “The Emergence of Desakota Undercounting Urbanization in India.” Population and Regions in Asia: Expanding a Hypothesis.” In Implosions/ /doi.org/10.1007/ Environment 41 (2): 126–50. https:/ /doi. Explosions, ed. Neil Brenner, 121–37. De Gruyter. https:/ s11111-019-00329-2. org/10.1515/9783868598933-010. /repp. Powerhive. n.d. “Powerhive Project Summary.” https:/ energy/wp-content/uploads/2022/12/Powerhive-2022.pdf. 16 Adapting Spatial Frameworks to Guide Energy Access Interventions in Urbanizing Africa Rockefeller Foundation. 2021. “Utilities 2.0 Twaake Pilot: UN-Habitat (United Nations Human Settlements Programme). New Integrated Energy Approach Could Deliver Universal 2011. The Economic Role of Cities. HS/067/11E. Global Urban Electrification in Uganda for Half the Cost, Fraction of the Economic Dialogue Series. Nairobi. /www.rockefellerfoundation.org/ Time as Grid-Only.” https:/ Van Duijne, Robbin Jan. 2019. “Why India’s Urbanization Is news/utilities-2-0-twaake-pilot-new-integrated-energy-ap- Hidden: Observations from ‘Rural’ Bihar.” World Development proach-could-deliver-universal-electrification-in-uganda-for- /doi.org/10.1016/j. 123 (November): 104610. https:/ half-the-cost-fraction-of-the-time-as-grid-only/. worlddev.2019.104610. Satterthwaite, David, and Cecilia Tacoli. 2002. “Seeking an Weiss, D. J., A. Nelson, H. S. Gibson, W. Temperley, S. Peedell, Understanding of Poverty That Recognizes Rural–Urban A. Lieber, M. Hancher, and others 2018. “A Global Map of Differences and Rural–Urban Linkages.” In Urban Livelihoods, Travel Time to Cities to Assess Inequalities in Accessibility in 1st ed., 19. London: Routledge. /doi.org/10.1038/ 2015.” Nature 553 (7688): 333–36. https:/ Silver, Jonathan. 2015. “Disrupted Infrastructures: An nature25181. Urban Political Ecology of Interrupted Electricity in Accra: Yaguma, Penlope, Federico Caprotti, Muhamad Rosyid Jazuli, Disrupted Infrastructures.” International Journal of Urban Priti Parikh, and Yacob Mulugetta. 2024. “‘Don’t Cook or Iron /doi. and Regional Research 39 (5): 984–1003. https:/ with It’: Heterogeneities and Coping Strategies for Accessing org/10.1111/1468-2427.12317. and Using Electricity in the Informal Settlements of Kampala, ———. 2023. The Infrastructural South: Techno-Environments Uganda.” Energy Research & Social Science 108 (February): of the Third Wave of Urbanization. Infrastructures Series. /doi.org/10.1016/j.erss.2023.103395. 103395. https:/ Cambridge, MA: MIT Press. Singh, Rozita, Xiao Wang, Emmanuel Ackom, and Juan Reyes. 2015a. Energy Access Realities in Urban Poor Communities of Make further connections Developing Countries: Assessments and Recommendations. Live Wire 2022/120. “Agrodem: An Open-Source Model That GNESD-SPM-UPEA III. Global Network on Energy for Quantifies the Electricity Requirements of Irrigation, “by Sustainable Development. Alexandros Korkovelos, Bryan Bonsuk Koo, and Kabir Malik. Singh, Rozita, Xiao Wang, Juan Carlos Mendoza, and Live Wire 2019/100. “Where and How Slum Electrification Emmanuel Kofi Ackom. 2015b. “Electricity (in)Accessibility Succeeds: A Proposal for Replication,” by Rutu Dave, Connie to the Urban Poor in Developing Countries: Electricity (in) Smyser, and Fabian Kohrer. Accessibility in Developing Countries.” Wiley Interdisciplinary Live Wire 2014/16. “Capturing the Multi-Dimensionality of /doi. Reviews: Energy and Environment 4 (4): 339–53. https:/ Energy Access,” by Mikul Bhatia and Nicolina Angelou. org/10.1002/wene.148. Live Wire 2014/35. “Planning for Electricity Access,” by Sirko, Wojciech, Sergii Kashubin, Marvin Ritter, Abigail Annkah, Debabrata Chattopadhyay. Rahul Kitchlu, and Rhonda L. Yasser Salah Eddine Bouchareb, Yann Dauphin, Daniel Keysers, Jordan. Maxim Neumann, Moustapha Cisse, and John Quinn. 2021. “Continental-Scale Building Detection from High Resolution /doi.org/10.48550/ARXIV.2107.12283. Satellite Imagery.” https:/ UN DESA (United Nations Department of Economic and Social Affairs, Population Division). 2022. World Population Prospects 2022: Summary of Results. UN DESA/POP/2022/TR/NO. 3. New York.