Introduction

In response to climate change, the global transition toward a low- or zero-carbon society is in progress to reduce greenhouse gas emissions and promote sustainable development (Sovacool et al. 2019a; Carley and Konisky 2020; Levenda et al. 2021; Solomon and Krishna 2011). Urban areas, which account for 75% of global energy consumption (IEA, 2021), are also experiencing the impact of this energy transition. However, the benefits of the energy transition are not evenly distributed among all cities; some cities receive greater benefits, while others bear a heavier burden (Carley et al., 2018). South Korea faces a similar challenge: although the energy transition is recognized as a national agenda, its benefits and burdens are not evenly distributed across the country. The Seoul Metropolitan Area illustrates this imbalance most clearly. Although it comprises over half of the country’s population and GDP, the region accounts for only 24% of national electricity generation while consuming 38% (KEEI, 2021). To meet this demand, the region relies heavily on electricity generated by nuclear and coal-fired power plants located in distant coastal regions, whose host communities bear localized environmental and health burdens while receiving a smaller share of the benefits (Kim et al., 2019; Park et al., 2024). Analyzing the spatial relationship between energy consumption and generation in South Korea therefore provides empirical insights into spatial energy imbalances.

The notion of energy justice has gained significant attention to address the uneven impacts of the energy transition across cities (Carley & Konisky, 2020; Heffron, 2022; Jenkins et al., 2016). As energy systems shift from fossil fuels to renewable energy sources, cities heavily reliant on carbon-intensive industries have encountered job losses and economic decline (Wang & Lo, 2021; Arent et al., 2022; Raimi et al., 2022). Moreover, the reduction in local tax revenue adversely impacts essential public services, such as education and transportation (Wang & Lo, 2021). In response, energy justice seeks to ensure equal benefits for everyone throughout all stages of the energy transition.

The issue of urban energy justice can be understood as a manifestation of geographical energy disparities. Energy systems are intricately tied to spatial dynamics, and cities source their energy from areas outside their urban centers (Bridge et al., 2013; Calvert, 2016). These energy sources could range from distant large energy complexes to relatively nearby decentralized micro-generation systems (Gui & MacGill, 2018). As the energy transition progresses, the spatial disparities of urban energy are likely to intensify (Bridge et al., 2013; Brockway et al., 2021).

To the best of the authors’ knowledge, DeRolph et al. (2019) is the only study that has explicitly addressed the measurement of the geographical energy imbalances of cities. The study introduced the concept of the city energyshed, defined as the network of power plants supplying given cities and the amount of energy allocated from each plant. This framework revealed power plant–city connections and enabled the estimation of cities’ energy mixes and the environmental footprints associated with their electricity consumption (DeRolph et al., 2019). However, their method relied on ArcGIS’s Target Market Share location-allocation solver, whose underlying algorithms remain proprietary and non-transparent. Because these commercial algorithms are not disclosed, researchers cannot easily integrate complex electrical grid variables such as line resistance, voltage ratings, or transmission capacity limits. Furthermore, the method requires setting spatial cutoff lengths to constrain allocation distances. While such cutoff limits may be practical for vast continental territories like the United States, they are unsuitable for compact, highly integrated national grids like South Korea, where the potential supply-demand reach spans the entire country. Thus, while previous research has provided a framework for tracing urban energy sources and estimating their environmental footprints, a transparent and territorially adaptable method for measuring geographical energy imbalances and related spatial energy injustices remains lacking. This study addresses this gap. The contribution of this paper is twofold: it proposes a transparent and territorially adaptable method for measuring geographical energy imbalances that does not depend on undisclosed algorithms or territory-specific cutoff assumptions, and it applies the method to South Korea to examine how spatial energy imbalances relate to the energy injustice arising from the energy transition.

Literature Review

Research on geographical energy justice has largely focused on injustice within cities, such as investigating disparities in the energy burden between affluent and rural areas (Bouzarovski and Tirado Herrero, 2017), analyzing spatial fuel poverty vulnerability (Reames, 2016), and assessing spatial accessibility to energy grids (Brockway et al., 2021). Beyond intra-city scales, scholars have conceptualized energy as a broader socio-environmental issue, demonstrating how vulnerable communities experience unequal burdens across energy supply chains (Hernández, 2015), and how low-carbon transitions can reinforce existing social, spatial, and environmental vulnerabilities (Sovacool, 2021; Sovacool et al., 2022). While these foundational works establish the multi-dimensional nature of energy justice, fewer studies empirically demonstrate intercity spatial energy imbalances.

To capture these intercity dynamics, recent scholarship has adopted the concept of the “energyshed”, the spatial extent where energy demand and supply mutually satisfy each other (Thomas & Erickson, 2021). The term was introduced by Hughes (2015) to extend energy management to a regional scale. Subsequent studies have attempted to explore the intertwined relationship with other systems, such as watersheds. For example, Brinkley et al. (2023) proposed a comprehensive “FEWshed” approach integrating Food, Energy, and Water systems. Similarly, McManamay et al. (2017, 2019) predicted changes in watersheds and energysheds in response to urban transformations. Thomas and Erickson (2021) proposed a broader perspective that expands the energyshed to include the areas involved in the production, refining, and transportation of energy sources; their energyshed therefore extends to a global scale, encompassing international imports and exports of energy resources.

The concept of the energyshed has been modeled on the natural physical environment, particularly the watershed, which has shaped its definition (Thomas & Erickson, 2021). While watersheds are largely constrained by physical features such as lakes, mountains, and hills, which undergo minimal changes over short periods, energyshed areas are more susceptible to alterations in energy infrastructure, such as power plants and transmission lines (DeRolph et al., 2019; Garrett et al., 2023, 2024). Therefore, an energyshed, the spatial extent of energy demand and supply, can change with the configuration of the energy network.

To understand the spatial continuum connecting energy production regions with energy consumption regions, a nuanced understanding of spatial energy flows within an energyshed framework is crucial (Leffel & Sovacool, 2026). Even when a national low-carbon energy transition succeeds as a whole, energy production regions often bear disproportionate “sacrifices”—such as localized environmental degradation, health risks, and land-use burdens—while distant energy consumption regions enjoy primary socio-economic benefits (Hernández, 2015). Tracing spatial energy flows through an energyshed framework makes these otherwise invisible, geographically disproportionate burdens traceable (DeRolph et al., 2019; Garrett et al., 2023, 2024; Leffel & Sovacool, 2026). In doing so, it provides a vital empirical tool to address energy justice across the whole energy system.

Analyzing energysheds—which map the power plants and transmission infrastructure required to meet a region’s energy supply—alongside energy flows that differentiate energy sinks and sources within those energysheds, enables us to illustrate how energy distribution changes across the electricity grid. DeRolph et al. (2019) and Garrett et al. (2024) explored this issue by visualizing city energysheds. However, as discussed in Sect. 1, these studies relied on the target market share location-allocation tool in commercial ArcGIS software, whose underlying allocation algorithms remain undisclosed.

Understanding the geographical energy imbalances of cities is therefore crucial, as it allows us to assess the three cores of energy justice: distributive justice, procedural justice, and recognition justice (Jenkins et al., 2016). It helps determine whether the benefits and harms arising from the energy transition process are equitably distributed across urban areas, informs the public when making energy policies, and acknowledges which cities are reaping the benefits and which ones are facing adverse consequences.

Measuring Geographical Energy (im)Balance

Study Area

This study applies the proposed method to mainland South Korea. South Korea provides an appropriate setting for this application because its relatively compact territory and highly interconnected national transmission grid allow electricity generated by power plants to potentially serve demand across the country. This characteristic is particularly suitable for evaluating the proposed threshold-free method, which does not impose a spatial limit on the geographic range of power-plant influence.

Building National Power Grids and Data Collection

First, we acquired maps of national power grids that are publicly available. As the maps were provided in images, we manually converted the images to networks. The national power grids include power stations, 765 kV and 345 kV substations, 765 kV and 345 kV routes, and DC ± 180 kV and 250 kV cable links. We include only the grid of the mainland of South Korea, as some islands independently support their own power needs. For the locations of the power plants and substations in the power grids, we collected publicly available addresses and set centroids of minimal administrative areas (in the Korean case, we used Dong for urban areas and Ri for rural areas). We connected the minimal administrative areas (Dong and Ri in Korean) to the nearest substation assuming a 154 kV route. We calculated the distance from the centroid of the minimal administrative areas to the substations using the Euclidean distance. If the centroid of the area and the location of the substations were the same, they were directly connected with no costs.

Then, we obtained the energy demands of local administrative units (Si, Gun and Gu in Korean) in 2020 (KEPCO 2023a) and separated the demands to the minimal administrative areas by proportions of building energy usage (MOLIT, 2023) of the areas. The energy dataset used for the analysis consisted of 123 power plants and 18,204 smallest administrative areas. We excluded the islands of Korea, including Jeju Island, which is the largest island in Korea, due to a lack of connection to the national grid, self-sufficiency in energy systems, and insufficiently available energy network data. The final dataset showed a total generation of 515,588 GWh of electricity and a consumption of 503,486 GWh of electricity. The difference between generation and consumption (approximately 2.3% of generation) corresponds mainly to transmission and distribution losses, which the allocation procedure does not model explicitly; generation that is not absorbed by any substation demand therefore remains unallocated (Sect. 3.3). For the demands of the minimal administrative areas, we multiplied the energy demands of local administrative units and the fraction of the building energy usage of the minimal administrative unit to the local administrative unit.

We gathered the power generation of nonrenewable energy resources and renewable energy resources. For nonrenewable energy resources, we collected the monthly power generation of each power plant (KEPCO 2023b). We merged power plants with the same minimal administrative area address. For renewable energy resources, we utilized renewable energy resource statistics of the local administrative units (KEA, 2021).

Matching Energy Demand and Supply

As renewable energy resources do not have precise locations, we first distributed electricity from renewable energy resources. Renewable energy from the local administrative units was distributed based on the proportion of energy demand from the minimal administrative unit to the corresponding local administrative unit. Any remaining renewable energy after fulfilling the energy demand of the local administrative units was allocated to the nearest substation as part of the renewable energy supply. This local-first rule was adopted because the renewable statistics are reported only at the level of local administrative units and because renewable capacity in South Korea largely consists of small-scale solar photovoltaic installations connected at the distribution level, whose output is typically consumed close to where it is generated (KEA, 2021).

Subsequently, we proceeded to distribute the generated power, considering the type and length of transmission lines. We calculated the relative costs of the power transmission lines as a proxy for transmission losses. For a given transmitted power P, the resistive loss of a line is I2R, where the current I equals P/V for a line voltage V and the resistance R is proportional to the line length L; the loss is therefore proportional to L/V2, that is, proportional to the length and inversely proportional to the square of the voltage. Normalizing by the shortest length (Lmin) and the lowest voltage (Vmin) in the network, the relative cost cl of transmission line l is defined as.

$$c_l=\left(L_l/L_{min}\right)\;\left(V_{min}/V_l\right)^2$$
(1)

where Ll and Vl are the length and voltage rating of line l. Lower values indicate lower transmission friction. For instance, the relative costs for lines of 100 m and 100 V (line A), 50 m and 50 V (line B), and 100 m and 50 V (line C) are 0.5, 1, and 2, respectively (Table 1). This indicator is a simplified proxy: it does not account for actual power flows, line resistance per unit length, or network capacity, and its implications are discussed in Sect. 5.

Table 1 Calculation of the relative costs of transmission lines
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We computed the relative costs of South Korea’s power grid and ordered the transmission lines from the lowest to the highest cost. Since the relative cost was determined based on the national power grid, we considered only the 765 kV and 345 kV transmission lines. Energy was then allocated along the transmission lines in the following iterative procedure. (i) Transmission lines were processed in ascending order of relative cost; for each line, the available supply at its upstream node (a power plant, or a substation holding excess power) was offered to the connected substation. (ii) If the remaining demand at the connected substation was greater than or equal to the available supply, the substation received all of the available power, and its remaining demand was met by other plants in subsequent iterations. (iii) If the available supply exceeded the remaining demand, the substation’s demand was fully satisfied and the excess power was passed to downstream links in order of relative cost. (iv) Steps (i)–(iii) were repeated until all substation demands were met or all generation had been allocated. Finally, the energy received at each substation was allocated to the minimal administrative units in proportion to their energy demands. In the current model, transmission lines are treated as uncapacitated and congestion is not represented; the output of each plant is bounded only by its observed monthly generation. Consequently, the resulting energyshed represents a modeled supply–demand allocation based on relative transmission cost rather than measured physical power flows in the interconnected grid, and the term energyshed is used in this sense throughout the paper.

The only previous attempt to delineate city energysheds, by DeRolph et al. (2019), employed a target market share location-allocation approach in ArcGIS, which requires setting cutoff lengths to limit the search space around demand points. This approach was developed to identify the location of a large store that captures a specified percentage of the market share within a defined cutoff length. While human mobility is limited by how far individuals are willing to travel to achieve their goals (Schläpfer et al., 2021; Barbosa et al., 2018; Yan et al., 2017), electricity transmission is not subject to such behavioral constraints. In the target market share approach, however, if no cutoff length is set, a single facility is designated to serve the entire demand. Consequently, applying the approach to an electricity grid without cutoff lengths results in the selection of only one power plant.

To systematically evaluate the performance of our proposed method, we conducted a comparative analysis with the method proposed by DeRolph et al. (2019) using identical datasets (Fig. 1). DeRolph et al. (2019) used ten equal-sized bins from 100 km to 1,000 km to assign cutoff lengths, and we used eight bins from 65 km to 520 km; the upper bound, 520 km, corresponds to the maximum supply distance obtained from our method. The comparison reveals substantial operational and spatial differences between the two approaches. First, when the cutoff length was set to 520 km within their framework, the number of selected facilities collapsed to a single power plant supplying the entire nation. Second, applying the cutoff specifications of DeRolph et al. (2019) to our study region results in the selection of only six power plants to meet national demand (Fig. 1), whereas our proposed method distributes energy across 112 of the 123 power plants (Sect. 4.1). Lastly, the target market share approach is highly sensitive to the choice of cutoff distance: minor adjustments in the distance bins cause substantial shifts in energyshed boundaries. In contrast, our threshold-free method eliminates the need for arbitrary cutoff distances. This advantage is particularly pronounced in countries or regions characterized by densely clustered power generation infrastructure, where subtle changes in distance thresholds can severely distort regional energy supply patterns.

Fig. 1
South Korea’s 16 metropolitan areas and provinces are connected by overlapping energyshed lines to six selected power plants. Many areas connect to multiple plants, and individual plants serve multiple areas. The map also marks other power plants and district boundaries.
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Application of the DeRolph et al. (2019) energyshed method to South Korea in 2020. Energysheds of the 16 metropolitan areas obtained with the target market share location-allocation approach using the cutoff specifications of DeRolph et al. (2019); only six power plants are selected. Basemap source: ESRI

Results

Energysheds of South Korea in 2020

Our proposed method revealed that 112 of the 123 power plants supplied at least one region (Fig. 2). Because the energysheds calculated using our method predominantly connect regions to nearby plants, the geographical energy balance of the country can be observed. We compared the Seoul Metropolitan Area, comprising Seoul, Gyeonggi Province, and Incheon, where almost half of the total population of Korea resides, with the Busan–Ulsan–Gyeongsangnam-do area, the second largest economic area in Korea (Fig. 2). The power plants within the Seoul Metropolitan Area are sufficient to supply Incheon, whereas Seoul and Gyeonggi Province additionally require plants located in other regions. A similar pattern is observed in the Busan–Ulsan–Gyeongsangnam-do area, where the required power plants are located in nearby areas.

Fig. 2
South Korea’s regional energysheds connect metropolitan areas to power plants across the country, with many connections crossing regional boundaries. Detailed maps show overlapping connections from Seoul, Gyeonggi and Incheon, and from Busan, Ulsan and Gyeongsangnam-do. Some connections extend to distant plants on the east and west coasts.
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Energysheds of South Korea in 2020. a Energysheds of 16 metropolitan areas in 2020. Lines are connected from centroids of the area to power plants. b Energysheds of the Seoul Metropolitan Area (Seoul, Gyeonggi Province, and Incheon) in 2020. c Energysheds of the Busan–Ulsan–Gyeongsangnam-do area in 2020. Basemap sources: ESRI (a); Carto (b and c)

The geographical influence of a region or power plants can be represented by the distance between the energy demand and the supply area. We found that energy demand has a positive relationship with the number of power plants serving the area and the total distance to the power plants (Fig. 3). However, the maximum distance, average distance, and minimum distance to the power plants do not exhibit a strong linear relationship with the energy demand. While higher electricity demand may necessitate more power plants, demand may also be met by constructing power plants in the surrounding regions. Therefore, to assess geographical energy equity effectively, various analytical methods should be employed, as the relationship between energy demand and the spatial distribution of power plants is multifaceted. Likewise, estimating the energysheds of power plants is important for analyzing geographical energy imbalance: a power plant with a longer energyshed serves more distant regions, which may indicate a spatial imbalance between generation and consumption. Figure 3 depicts the energysheds of the power plants. Plants generating more power tend to serve more regions. The other types of distance, such as the average, minimum and maximum distance to metropolitan areas, showed similar results. As the generated power increases, the average, minimum and maximum distances to the areas increase.

Fig. 3
Across ten scatterplots, higher regional demand generally corresponds to more supporting power plants and a greater sum of distances to them. Similarly, plants generating more power generally supply more regions and have greater summed distances to those regions. Maximum, average, and minimum distances vary widely, with no consistent trend across demand or generation levels. Most plants generate relatively little power and supply one to three regions.
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Characteristics of energysheds by energy demand and supply. a Number of power plants to support a region. b-e Maximum, average, minimum, and sum of distances to power plants to support a region. f Number of regions that a power plant supplies. g-j Maximum, average, minimum, and sum of distances to regions that a power plant supplies

Seasonal Change

We compared the seasonal changes in demand and energysheds over a period of 12 months in 2020 (Fig. 4). We can observe seasonal changes in the total distance and average distance to power plants with energy demands that show four distinct cluster patterns: vertically aligned, horizontally aligned, dispersed, and concentrated patterns. Each of these patterns provides insights into the geographical energy balance. For example, vertically aligned patterns indicate that the demand is stable while the set of supporting power plants varies. In contrast, horizontally aligned patterns indicate that the supporting power plants are stable, while the energy demand exhibits fluctuations. Dispersed patterns signify varying levels of both demand and energyshed, while a concentrated pattern indicates a stable energy demand and energyshed.

Fig. 4
Monthly distances vary across regions and power plants. Gyeonggi-do has the highest regional demand and consistently high total distances, while Gyeongsangbuk-do has the greatest month-to-month variation in total distance, followed by Chungcheongbuk-do. Among power plants, variation spans a wide range for coal, natural gas, and nuclear plants; renewable plants cluster at low supply levels.
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Seasonal variation of energysheds in 2020. a Monthly variation in average distances and sum of distances from a region to power plants supporting the region. b Standard deviation of monthly sum of distances. Energy demand is the summed energy use of a region in 2020. c Standard deviation of the monthly sum of distances from a power plant to the regions it supplies, by energy resource. Energy supply is the summed generation of a power plant in 2020. We excluded power plants that exhibited constant energysheds

Upon comparing the seasonal changes in the four types of energysheds from the perspective of power plants, we observed no clear patterns with respect to energy sources (Fig. 5). However, when we analyzed patterns by power plant, some power plants exhibited horizontally parallel patterns. These patterns suggest that structural grid topology and transmission connectivity may play a stabilizing role in maintaining consistent energyshed configurations, even during seasonal fluctuations in renewable energy output. Further study is needed to clarify the underlying mechanisms. Moreover, the presence of parallel patterns suggests that power plants have relatively stable spatial boundaries within which they supply electricity.

Fig. 5
Across all four distance measures, nuclear plants reach the highest supply levels and generally have the largest distances at high supply. Coal spans a wide range of distances, while natural gas, oil, pumped storage, and renewables cluster mostly at lower supply levels. The sum of distances reaches about 1,650 kilometers for nuclear and 1,000 kilometers for coal.
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Seasonal variation of energysheds of power plants by resources in 2020. Monthly variation in average distances, maximum, minimum, and sum of distances from a power plant to regions it supports

We further compared the total demands of cities with the variation in energyshed (Fig. 4). The variation in energyshed was calculated using the standard deviation of the total distance to the power plants over the 12-month period in 2020. A comparison of total energy demand with the magnitude of energyshed change shows that high energy consumption does not necessarily correspond to large fluctuations in energysheds (Fig. 4b). In contrast, total supply from power plants shows a more clearly proportional relationship with energyshed change (Fig. 4c). These relationships are descriptive and were not tested statistically.

We observed that the seasonal variations in the total distance to power plants by city had different patterns (Fig. 6). These seasonal fluctuations may be influenced not only by seasonal changes in demand but also by the surrounding network of power plants. Additionally, we observed that the energyshed of power plants appears to be influenced by the structure of the energy grid in addition to the energy source. This suggests that the energy network may play a role in shaping energyshed patterns alongside the energy mix.

Fig. 6
Normalized energysheds vary by month across regions and individual power plants, with no single monthly pattern shared by all. Regional values range from near zero to one; for example, Gwangju is high in April, May and October, while Ulsan is high in September. Power plants are grouped as coal, natural gas and nuclear, and show varied monthly patterns within each group.
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a Monthly variation of energysheds of regions. Energysheds are normalized values of the sum of monthly distances from a region to power plants. b Monthly variation of energysheds of power plants. Energysheds are normalized values of the sum of monthly distances from a power plant to regions. We excluded power plants that exhibited constant energysheds

Discussion and Conclusion

In the context of the low-carbon transition, it is vital to consider energy equity, which stems from the principles of environmental justice (Siciliano et al., 2021). We must acknowledge and address the inherent injustices and inequities that may arise during this transformative process. Energy justice provides a framework that examines fairness and inclusivity through dimensions such as distributive justice, procedural justice, and recognition justice (Jenkins et al., 2016; Carley & Konisky, 2020; McCauley et al., 2013). Distributive justice focuses on ensuring an equitable allocation of benefits and adverse effects for all populations. Procedural justice evaluates whether every stakeholder has participated in the decision-making process fairly, equitably, and inclusively. Recognition justice addresses the question of whose interests and concerns are accounted for and acknowledged. This approach helps us understand and address the fairness aspects of the transition more comprehensively.

Our proposed method offers a potential analytical framework to map spatial energy imbalances, which can serve as a diagnostic foundation for evaluating urban energy justice (Bouzarovski and Simcock 2017; Sovacool et al. 2019b; McCauley et al. 2019). In terms of distributive justice, our method enables the visualization of geographical energy imbalances, illustrating the inequalities between energy generation and consumption regions. This visualization can inform a more equitable allocation of resources by directing attention to energy-generating areas in need. Additionally, our method provides accessible and easily understandable spatial information, which can serve as a decision-support tool to promote inclusive stakeholder participation. By offering comprehensible materials, our approach can contribute to the active involvement of diverse stakeholders, including local residents, thereby enhancing procedural justice. In terms of recognition justice, the method helps identify areas whose energy generation is substantial but whose benefits are not commensurate.

Our proposed method can be applied to analyze spatial energy allocation across individual power sources. For instance, in the case of nuclear power generation in South Korea, which accounted for 30% of the country’s total electricity generation in 2020, nuclear power plants are primarily located along the coastline, with a significant concentration in the southeastern region (Fig. 7). The energysheds of nuclear power plants in the southeastern, southwestern, and northeastern regions differ, averaging 326 km, 266 km, and 1,484 km, respectively. Although a longer energyshed does not in itself demonstrate energy injustice, this spatial disparity reveals a geographic disconnect between host regions and the distant economic hubs they supply. Centralized nuclear power generation has also been associated with energy justice conflicts in other countries (Taebi et al., 2012; Jenkins et al., 2016, 2017).

Fig. 7
Nuclear energyshed connections converge on a few coastal power plants, mainly in the east and southwest. Coal energyshed connections link district centroids to plants along the west, east, and south coasts. Both maps distinguish maximum from minimum energysheds and mark power plants, district centroids, and district boundaries.
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Maximum and minimum energysheds of power plants in 2020. a Maximum and minimum energysheds of nuclear power plants in South Korea in 2020. b Maximum and minimum energysheds of coal-fired power plants in South Korea in 2020. Basemap source: Carto

Even in the case of coal power plants that produce greenhouse gases and air pollution, we can observe geographical energy imbalances (Fig. 7). Eight coal-fired power plants are needed to meet the energy demand of the Seoul Metropolitan Area; however, only one of them is located within the region, on an outlying island. Geographically, there are four coal power plants each in Chungcheongnam-do and Gangwon-do, with average energysheds of 482 km and 425 km, respectively. These figures differ significantly from those of other regions, such as Incheon (169 km), Gyeongsangnam-do (271 km), and Jeollanam-do (148 km), where the energysheds are considerably smaller. Such imbalances imply that distant consumers benefit from coal-fired generation while residents near the plants bear the associated air pollution (Fuller & McCauley, 2016; Chapman et al., 2018; Finley-Brook et al., 2018).

Furthermore, projecting and visualizing energysheds may help reduce conflicts during the low-carbon transition. In the energy transition, the construction of new high-voltage power lines is necessary to connect large-scale renewable energy generation to demand centers. However, the construction of these new power transmission lines often faces significant opposition due to concerns regarding visibility impacts and health risks (Hess et al., 2022; Komendantova & Battaglini, 2016). Nevertheless, research has shown that with the provision of comprehensive explanations of the benefits and risks associated with energy transition, opposition can be diminished (Lienert et al., 2015). Our energyshed analysis can serve as informative material for explaining the purpose and advantages of the energy transition, helping to mitigate opposition and foster a more informed and constructive dialogue.

Our proposed method offers a tool for measuring and comparing geographical energy imbalances, which can support efforts to address geographical energy injustice. It can also be applied to community energy projects and the RE100 initiative (RE100, 2024), which encourages the use of renewable energy to promote geographically proximate energy generation and consumption. Moreover, our method can be useful not only in energy policy but also in urban planning. It could facilitate the realization of benefits for energy sacrifice cities, where substantial renewable energy electricity is generated but the benefits are not fully realized. For instance, companies aspiring to engage in corporate activities fueled by renewable energy might choose to locate in these cities, thereby stimulating urban development. Politically, our method can be utilized as a reference material for all stakeholders, especially in areas with social conflicts surrounding aspects such as high-voltage transmission lines, facilitating a more informed and inclusive decision-making process.

Our research has several limitations. First, in terms of energy allocation, we employed a simplified relative-cost indicator based on the voltage and length of transmission lines as a proxy for transmission losses (Eq. 1). The resulting energyshed therefore reflects a modeled spatial supply–demand allocation rather than actual physical electricity flows, and the modeled allocation may diverge from actual flows depending on grid conditions. We did not test the robustness of the results to alternative cost specifications; doing so with power-flow-based cost measures is an important direction for future work. Second, the proposed method does not incorporate operational grid constraints such as transmission capacity, congestion, line resistance, or generator capacity. Because transmission lines are treated as uncapacitated, the model may route more energy through low-cost corridors than they can physically carry, and the resulting energysheds may be more compact than those that would arise under congestion. Future studies should incorporate detailed grid information, such as transmission line parameters and capacity constraints, using more comprehensive grid modeling approaches. Third, while our energyshed indicator quantifies spatial energy imbalances, spatial distance alone does not inherently define energy justice. To bridge spatial imbalance with multidimensional energy justice, future research should build upon the proposed energyshed methodology by incorporating empirical data on environmental impacts, local socio-economic benefits, regional health and safety costs, and the localized distribution of these benefits and burdens. Fourth, renewable generation was allocated first to local demand within the same local administrative unit, with any surplus assigned to the nearest substation. Because this simplified rule may affect the resulting spatial patterns, future studies should incorporate the actual grid connection points of renewable facilities to obtain more accurate energysheds. Fifth, the seasonal patterns reported in Sect. 4.2 are descriptive; formal statistical testing of the relative influence of grid structure and energy mix on energyshed variation remains for future work. Lastly, while this study focused on monthly energysheds, hourly analyses could capture short-term fluctuations in renewable energy output. Combining hourly analysis with detailed information about renewable energy sources, such as wind and solar energy, can improve the understanding of energy environments in a city.

While our analysis focused on geographical imbalance in metropolitan areas for simplicity, research on more detailed and specific regions could provide a more comprehensive understanding of energy geography. The results of this study can be used to envision more energy justice-oriented urban planning. For instance, in cases such as that of South Korea, where economic centers and energy generation are geographically distant, our findings can support the development of urban planning policies that bring economic urban areas closer to energy generation areas, promoting energy justice and sustainability in the planning process.