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# High resolution global spatiotemporal assessment of rooftop solar photovoltaics potential for renewable electricity generation

## Abstract

Rooftop solar photovoltaics currently account for 40% of the global solar photovoltaics installed capacity and one-fourth of the total renewable capacity additions in 2018. Yet, only limited information is available on its global potential and associated costs at a high spatiotemporal resolution. Here, we present a high-resolution global assessment of rooftop solar photovoltaics potential using big data, machine learning and geospatial analysis. We analyse 130 million km2 of global land surface area to demarcate 0.2 million km2 of rooftop area, which together represent 27 PWh yr−1 of electricity generation potential for costs between 40–280 $MWh−1. Out of this, 10 PWh yr−1 can be realised below 100$ MWh−1. The global potential is predominantly spread between Asia (47%), North America (20%) and Europe (13%). The cost of attaining the potential is lowest in India (66 $MWh−1) and China (68$ MWh−1), with USA (238 $MWh−1) and UK (251$ MWh−1) representing some of the costliest countries.

## Introduction

### Global technical potential and cost assessment

Our assessment shows a total global technical potential of 27 PWh yr−1, of which Asia (13 PWh yr−1), North America (5.5 PWh yr−1) and Europe (3.6 PWh yr−1) represent a majority of the potential followed by Africa (2.9 PWh yr−1) and South America (1.7 PWh yr−1). The hotspots for the potential are concentrated in and around the densely populated nucleated settlements globally (Fig. 5a). Nearly 20% (5 PWh yr−1) of the global potential is located within the areas with a high population density (>1500 people/km2), with 55% (15 PWh yr−1) of the potential being dispersed within the low-density areas (<500 people/km2). Amongst the countries, China (4.3 PWh yr−1), the USA (4.2 PWh yr−1), and India (1.7 PWh yr−1) have the highest yearly potential (Table 1). A ±1% deviation can be observed in the yearly global potential due to the aggregation methodology of the CF factor (Supplementary Fig. 2).

Although the African region has a good solar insolation endowment, the RTSPV potential is assessed as being the third lowest due to low building stock. Amongst the African Region, the largest potential is concentrated in the West African region followed by the North African Region. The combined West and North African regions have more potential than India, highlighting the importance that low-cost RSTPV can play in future energy systems. Future population growth and a corresponding increase in the building stock may increase the overall RTSPV potential for Africa. Both North American and European regions have similar assessed rooftop areas (~30,000 km2), yet North America has nearly 1.5 times the potential of Europe due to higher solar insolation during the year especially during the winter months.

Along with spatial variability due to on-ground building distributions, seasonal variability of the RTSPV potential is also observed due to the variation in intra annual solar insolation. The seasonal variability of the monthly global potential is between 1.84 and 2.61 PWh, with December and January representing the months with the lowest global potential (Fig. 5a). Globally the highest seasonal variability of the potentials is observed above the 45° north latitude covering Europe, Russia, the USA, and Canada. Within regions, the highest intra annual variability of the potentials (Fig. 6) is observed in the West European region (EUW) with monthly potentials between 94 and 255 TWh. There is a variability of ±40% around the average monthly potential of 183 TWh in EUW, with the highest monthly potential being observed in the summer and the lowest monthly potentials in the winter. The lowest regional intra annual variability of potentials is observed in the West African region (AFW) with monthly potentials between 97 and 119 PWh. There is a variability of ±1% around the average monthly potential of 109 TWh in the AFW region, with the maximum monthly potential observed in December and January.

To analyze the cost of attaining the potentials, we generated supply cost curves for seven world regions and also at an aggregated global level (Fig. 7a). Nearly 10 PWh yr−1 (40%) of the global potential can be achieved below 100 $MWh yr−1, with the majority of the potential being realized below 200$ MWh−1 (Fig. 7b). At a global level, nearly 40% of the potential can be achieved with an investment equivalent to 10% of the 2015 global GDP value, and with an investment equivalent to 30% of the 2015 GDP value, nearly 100% of the global potential can be realized (Fig. 7c). We found that realizable potential doubles with each subsequent doubling of capital investment in RTSPV until invested capital is equivalent to 20% of the global GDP value in 2015. An increase of investment from 20 to 30% of the global 2015 GDP value increases the realizable potential by only 27% indicating the areas where the cost of implementation of RSTV is very high. These areas are represented by large warehouse/industrial complexes in Alaska and Canada, where yearly solar insolation is low and CAPEX investment is high to cover the entire large rooftops with solar panels.

At a global level, spatial variability in the LCOE is also observed (Fig. 5b). In the northern hemisphere, the LCOE values gradually increase from 40 $MWh−1 to 280$ MWh−1 with increasing latitude. Here North-East China is an exception which has shown a decrease in LCOE values with increasing latitudes. For Asia, a majority of the potential can be realized between 40 and 100 $MWh−1 making the RTSPV competitive with fossil fuel technologies. The cost of attaining the country-specific potential is lowest in India at 66$ MWh−1 compared to China (68 $MWh−1). For Europe, Africa, South America, and Island Nations a majority of total potential can be realized below 180$ MWh−1. Within Europe, Spain has the lowest LCOE cost of 90 $MWh−1, with an increasing trend in cost being observed when moving towards the higher latitudes. Within each country of the European region, further variability in LCOE is also observed with some regions observing cheaper costs than neighboring regions in the same latitude. In the African region, the majority of the total potential can be realized between 110 and 160$ MWh−1. Within Africa, Nigeria, Gabon, and Cameroon have the highest costs (around 150 $MWh−1) to achieve their respective potentials. North America, UK, and Japan have shown the highest cost to realize the potential. This can be attributed to the high CAPEX costs in the countries, which are expected to reduce in the future due to technological innovations and reduction in import tariffs. In North America, Canada and the northeastern states of the U.S.A around the Great Lakes have the highest LCOE costs. The cost to achieve the potential in these countries ranges from 200 to 280$ MWh−1. U.K (251 $MWh−1) has the highest country-specific costs to achieve its potential. Globally, CAPEX required to access potential varies both with respect to the size of the GDP and with respect to the LCOE values (Fig. 8a). To realize the complete potential in their respective countries, low-income countries would need to invest capital which is multiple times (up to 3.5 times) their 2015 GDP value even at relatively low LCOE of between 80 and 150$ MWh−1 to cover for high upfront costs. For similar LCOE values, high-income countries (World Bank Income Classification) can achieve their complete potentials with a capital investment equivalent to a fraction (up to half) of their 2015 GDP value.

We categorized countries into groups based on their GDP per capita (GDPC) and on their yield factor (Fig. 8b). In this study, we defined the yield factor as the yearly potential that can be realized from 1 TW of installed capacity. Based on this categorization, we found out that emerging economics including India, Brazil, and Mexico have high yield factors (1.5–2) that would favor the deployment of RTSPV in these countries. Even though the LCOE difference between the two most populous countries (India and China) is minimal, with the greater solar endowment and with high yield factor, RTSPV technology rollout becomes more favorable in India compared to China. However, the uptake of RTSPV is still very low in these countries due to lack of credit and the inability to pay the high upfront cost of the RTSPV system. This highlights the need for global cooperation, technology transfer, and green financial instruments to accelerate the deployment of low carbon RTSPV technology in low-income and medium-income countries.

## Discussion

The study showcases how a framework based on big data and an ML model in conjunction with cloud computing platforms can be used to undertake a planetary scale resource potential assessment. We analyzed 130 million km2 of global land surface area by utilizing learnings from global samples containing 300 million buildings with 16 million km of roads. Using Google Earth Engine and a ML model we demarcated 1.2 million km2 of the built-up area containing 0.2 million km2 of rooftop area. As part of the assessment, we generated (1) a global rooftop area dataset (2) a global RTSPV potential dataset at a monthly temporal resolution, (3) costs of attaining the technical potential. The datasets were further used to generate high-resolution global maps of the potential and costs. We have also advanced the current state of art by combining the top-down and bottom-up approaches at a global scale to develop a hybrid framework for resource potential assessment which can also be used in advancing the assessment of global wind and bioenergy potentials. The assessment shows that a sizeable RTSPV potential of 27 PWh yr−1 exists at a global level that can be attained for costs between 40 and 280 $MWh−1. The potential is highest in Asia followed by North America and Europe. A capital investment of around 7 trillion dollars is required at current prices to achieve a global RTSPV based electricity generation of 10 PWh yr−1 below the LCOE of 100$ MWh−1, covering 3.72 billion people globally.

At the EU-27 regional level, our estimated rooftop area of 7596.4 km2 is similar to the 7935 km2 calculated in the Bodis et al. study when incorporating a rooftop scaling factor of 0.3. For the USA, our estimated rooftop area and annual potential of 8827 km2/1.9 PWh yr−1 compare well with the estimates of 8130 km2/1.4 PWh yr−1 presented in the Gagnon et al. study when incorporating a rooftop scaling factor of 0.32. On a city level basis, our potential of 1 TWh yr−1 is in alignment with the 1 TWh yr−1 calculated in a study by Hong et al.36 where they have used advanced hillshade analysis to capture the effects of building induced shadows in a dense urban topography. A detailed global/regional/country and city level comparison of our results with selected research work is documented in Supplementary Table 7. From the comparison with other studies, we can conclude that the results from our framework demonstrate high veracity as they are within the margin of error of values present in the literature. In addition, the good estimation accuracy of our framework at high spatial resolution feeds into higher accuracies at aggregated lower resolutions.

Our assessment has important implications for addressing the twin challenges of sustainable development and climate change with co-benefits in advancing SDG 3 and SDG 7. First, the analysis of spatial RTSPV potential presented in this study shows that 55% of the global RSTPV potential is spread across low-density areas. This highlights an important aspect of solar transition, where the majority of its benefits in providing cost-effective and fast deployable electricity can be realized in rural areas. RTSPV can thus aid in mitigating the energy poverty being experienced in the less developed and sparsely populated areas in a country where extensive grid integration may be costly or where competition for land may exist. Nearly 20% of the global potential lies in the high-density areas where the deployment of RTSPV can aid in displacing fossil fuel-derived electricity with less polluting electricity generation thereby reducing local air pollution60. Second, from a perspective of energy equality and “leaving no one behind” agenda of the SDG, the most disadvantaged areas with respect to access to electricity are currently the low-income countries3 which require the rapid and cost-effective deployment of clean electricity generation infrastructure. Our assessment shows that low-income countries may need significant capital investments as steep upfront RTSPV installation costs in the order of magnitude 2–3 times their 2015 GDP value to achieve their country-specific potentials. At current costs, governments may need to provide subsidies and seek external investments to improve the prospects of deployment of RTSPV in these areas. This highlights the vital role that the developed economies may play in enabling the deployment of RTSPV in these countries by the means of financial flows to realize the climate change co-benefits. With maturing of the technology and the emergence of an economy of scale, costs will further go down to enable a solar revolution in these areas and aid in their low carbon energy future.

Third, countries that are currently reaping their demographic dividends like India and China are better suited for rapid deployment of RTSPV. We showed that these countries have high potential with low seasonal potential variability along with the low cost of deployment. As these countries have the largest population share globally with large building stocks, they can be the first movers in decarbonizing their electricity generation infrastructure by substantially deploying a decentralized electricity generation portfolio, further aiding in climate change mitigation. Along with climate mitigation, RSTPV deployment in these countries can gain a lot from the high workforce percentage in the population in the form of cost-effective manufacturing and operational maintenance. Fourth, the high-resolution assessment can aid the local governments in identifying suitable locations for the rapid deployment of energy generation infrastructure. This way bottom-up formulation of energy policy can lead to inclusive designing of nationwide policies to provide energy justice to the citizens. Fifth, businesses and financial institutions like World Bank and International Monetary Fund can analyze in-depth the investment opportunities and risks in implementing RSTPV infrastructures leading to local job creation and sustainable development of manufacturing industries. Sixth, the information contained in the assessment along with supply cost curves is a measurable step forward and fills in a significant information gap that is present in current integrated assessment models where solar PV potentials are often represented as aggregated potentials for utility and rooftop installations. Our assessment provides insightful findings and technical potential datasets that will certainly aid in accurately modeling the future carbon-neutral scenarios to inform national energy policies61,62,63,64,65,66. This will no doubt aid in exploring sustainable and inclusive low-carbon future possibilities.

Our study shows pronounced variability of seasonal potentials in the higher latitudes for countries covering Europe, North America, and Australian regions. These regions have high electricity consumption per capita along with the financial capacity to introduce significant VRE in the electricity generation mix. To mitigate the variability of the potentials over the year, smart grids that optimize the generation portfolio and the introduction of regulator-driven mechanisms that balance the generation market becomes important. Further, the introduction of new market mechanisms67 is needed to effectively integrate the prosumers and utility operators in competitive electricity generation markets. With falling prices of electricity storage technologies and smart management of interconnected grids, RSTPV technology will play a critical role in these markets by undertaking generation, storage, and system balancing roles.

In conclusion, our assessment shows that the current electricity generation potential of RTSPV exceeds the current (2018) yearly aggregated global electricity demand68. Our assessment also shows that a minimum of 50% of the total global rooftop area is required to meet the yearly global aggregated electricity demand. Due to the diurnal cycles of solar insolation and to balance the seasonal and daily variability of the RTSPV generation, the role of storage solutions to compliment RTSPV electricity generation is critical in realizing the maximum potential of this technology and to meet the peak daily demand. Hence, the practical realizable potential of RTSPV will depend on the future cost trajectory of storage technologies, capital expenditure related to the technology, and the overall configuration of the energy system.

Even with its limitations and shortcomings the current assessment is still the state of art and provides researchers with global analysis datasets. The underlying methods and datasets have been peer-reviewed and are of the highest quality currently available and represent a generation advancement over the datasets used in the current state of art methods. The current dataset can be improved by using a next-generation 10 m resolution landcover dataset and with an increase in the spatial resolution of the population and solar data at a global scale. Better data processing platforms can enable further work to be accomplished at a 1 km resolution providing a 100 times increase in the spatial representation of the potentials. In addition, inputs in the form of realistic regional variation in the rooftop availability will aid in narrowing down the uncertainty in potentials and cost and should be the next logical research step in model improvement.

## Methods

### Top–down method

We generated a total of 3,521,120 fishnets using the ArcGIS PRO desktop application for all the global landmass except for the continent of Antarctica. The FN grid is the lowest unit of data aggregation in our method. The FN’s at the boundary of two countries have a common Fishnet identifier but unique country attribution with FN being split at the boundary. Next, the fishnet polygons were uploaded to Google Earth engine platform69 (GEE) to calculate satellite-derived Built Area (BAFN), Population (PPLNFN), and conversion factors (CFFN) for each fishnet cell.

We utilized the global land cover (LC) layer from Copernicus Global Land Service v2.0 to calculate BAFN values within each FN. This landcover classification layer was chosen for its robustness and near-global coverage and it is backed by exhaustive testing and validation. The LC has many categories of classifications, amongst which built-up area is one of the classifications. The built-up classification is in turn derived from European Space Agency’s World Settlement Footprint 2015 layer that is derived from a 10 m resolution sentinel mission’s radar and optical imagery. To calculate the total built-up area in each Fishnet cell, the LC raster file was first cut into smaller sizes based on each fishnet’s geographical bounds. The individually cut LC dataset was then aggregated using the following:

$${{{{{{\mathrm{BA}}}}}}}_{{{{{{\mathrm{FN}}}}}}}={\sum }^{}({{{{{\mathrm{P}}}}}}{{{{{{\mathrm{X}}}}}}}_{{{{{{\mathrm{V}}}}}}}\times {{{{{\mathrm{P}}}}}}{{{{{{\mathrm{X}}}}}}}_{{{{{{\mathrm{A}}}}}}})$$
(1)

where BAFN is the Built-up area in each fishnet cell, PXV is the pixel value (0–100) representing the percent of the built area in each pixel, PXA is the area occupied by each pixel.

To map the number of people living in each fishnet cell, we utilized a global population raster file at 100 m resolution provided by the WorldPop project. The population raster disaggregates the recorded United Nations population counts in an administrative unit to a finer resolution using ML-based methodology. The population raster file was split into smaller entities based on each fishnet cell’s geographical bounds. Then, a masking file containing areas covered by the LC layer within each fishnet cell is used to mask the population outside the bounds of the BAFN area in each fishnet. The individual small population dataset was aggregated for each fishnet cell using the following:

$${{{{{{\mathrm{PPLN}}}}}}}_{{{{{{\mathrm{FN}}}}}}}={\sum }^{}{{{{{\mathrm{PX}}}}}}{({{{{{\mathrm{NM}}}}}})}_{{{{{{\mathrm{V}}}}}}}$$
(2)

where, PPLNFN is the total count of people living in each fishnet, and PX(NM)V is the pixel value of each pixel that is not masked by the overlay LC layer. The population raster file was masked to remove any population count data that is not part of the LC pixel. The masked population raster pixel can be attributed to artifacts induced due to the downscaling algorithm in the original dataset or extra built-up areas that were external to the LC layer. To maintain homogeneity in the analysis, the LC layer was taken as the base for all analysis, and any area not covered by (BAFN) was assumed to be not present on the ground, even if it actually exists as ground truth.

The CF factors were calculated using World Bank’s SolarGIS raster datasets. The dataset is provided as a 1 km resolution raster dataset with each pixel providing daily kWh generation for each kWp (peak) installed capacity within that pixel at a monthly resolution. The dataset has been generated using extensive simulation and validation of solar insolation, power conversion losses, effects of atmosphere, and panel aging using 20 years of documented data. For each fishnet cell, the CF factors were aggregated using the following:

$${{{{{{\mathrm{CF}}}}}}}_{{{M}},{{{{{\mathrm{FN}}}}}}}=\frac{1}{n}\mathop{\sum }\limits_{1}^{n}{{{{{\mathrm{P}}}}}}{{{{{{\mathrm{X}}}}}}}_{{{{{{\mathrm{V}}}}}}}$$
(3)

where, CFM,FN is the CF factor for each month for each FN, n is the number of CF pixels in the FN and PXV is the pixel value of each pixel in the FN geographical bound. All three datasets (PPLNFN, CFFN, BAFN) and FN geometries were first processed on the ArcGIS Pro desktop application to harmonize coordinate reference systems and then uploaded to the GEE platform for processing. On GEE, we split the datasets based on individual FN geometries and aggregated the three datasets based on the rules highlighted above. GEE’s cloud computing architecture can process a large number of datasets in a relatively shorter time frame and outputs a tabulated dataset for further processing.

### Bottom-up method

To generate ground truth building footprints, we collected building polygon shapes as vector layers from big data sources. For building footprint samples, we used AI-generated building footprints by Microsoft AI and Ecopia AI teams. These two datasets cover the entire USA, Canada, and 39 African countries. The samples from Microsoft AI and Ecopia AI (>300 million individual buildings) were split up based on the FN layer for each FN cell overlapping the sample countries, further masked to remove building footprints outside of the BAFN layer. The unmasked building footprints were aggregated based on the following:

$${{{{{{\mathrm{BF}}}}}}}_{{{{{{\mathrm{FN}}}}}}}={\sum }^{}{{{{{\mathrm{BP}}}}}}{({{{{{\mathrm{NM}}}}}})}_{{{{{{\mathrm{V}}}}}}}$$
(4)

where BFFN is the aggregated building footprint for each sample FN and BP(NM)V is the unmasked individual building footprint polygon area within the FN that is overlapping the BAFN layer. The masking removed buildings constructed after the 2015 reference year. Although the BAFN layer covers the entire extent of the built-up area globally, it can still miss some built-up areas due to artifacts in satellite imagery. However, this is negligible and for the purpose of our study considered as the reference layer on which to base our analysis. Overlapping building footprints were dissolved into a single polygon before splitting and polygons being intersected by an FN boundary were split up at the line of intersection. For the rest of the world, OSM-derived building footprints (accessed April 2020) were analyzed and aggregated based on Eq. (4). A total of 4000 global FN samples were selected from the OSM building dataset. The sampling strategy to generate the 4000 OSM samples was to extract buildings bound by FNs that had BFFN/BAFN ratio of between 0.15 and 0.11. These ratios correspond to the 75th percentile and 50th percentile of data processed from Microsoft AI and Ecopia AI datasets. In total, we were able to successfully collect samples from nearly all the global countries covering different stages of socio-economic development, cultural spread, and geographical locations.

The road length metric was derived entirely from the OSM datasets. To process the RL dataset, OSM’s planetary dataset file (accessed April 2020) was used. The planetary file has roads represented in the form of lines attributed by the different types e.g., residential, highway, footpath, etc. The line feature was split up based on each global FN, masked using BAFN layer, and aggregated based on the following:

$${{{{{{\mathrm{RL}}}}}}}_{{{{{{\mathrm{FN}}}}}}}={\sum }^{}{{{{{\mathrm{L}}}}}}{({{{{{\mathrm{NM}}}}}})}_{{{{{{\mathrm{V}}}}}}}$$
(5)

where RLFN is the aggregated road length for each global FN and L(NM)V is the individual length of all the roads within each FN overlapping BAFN layer. It was observed during the aggregation of the RLFN dataset, that road endpoints in the OSM dataset overlap in some locations, these overlaps were dissolved into single line features prior to aggregation. Also, some roads extend beyond the BAFN layer within each FN. These roads were clipped at the BAFN boundary to maintain a homogenous extent for the region of interest for all the datasets used in the analysis. In total, we were successful in processing the road infrastructure for nearly all the countries of the world.

Loading, splitting, and geometry processing for the building footprints and the road lines were performed using ArcGIS PRO’s multicore support. The aggregation of the datasets and mapping of the aggregated dataset with FN boundaries was performed using custom python scripts built on DASK70 parallel compute module. Due to the sheer size of the data being processed, we found that multicore architectures and parallel computing frameworks developed in recent years can be of great use in designing and executing planetary-scale analysis with minimal cost and time requirements.

### Downscaling model

The ML model was trained on PPLNFN, RLFN, BAFN as independent variables and BFFN as the dependent variable for each sample FN. The first step in model preparation was to impute the missing data in the independent variables. The imputations are necessary as some FNs have either missing population or road length data due to the global scale of the analysis. This discrepancy is expected and is present due to OSM roads not being mapped for every road on the planet and also due to the downscaling methodology used in generating the original population raster by WorldPop.

Data imputation was handled using a custom python script utilizing the Scikit-Learn71 module’s iterative impute function. Further, we generated the downscaling model using XGBoost72 framework. When choosing between Neural Network-based framework or Gradient boosting frameworks, we used the latter as XGBoost framework has shown superior performance, while using considerably less computation time to reach an optimum model state. Also, being run on CPU-only architecture, the XGBoost framework can generate repeatable results in each subsequent run, which is difficult to achieve on a GPU-based framework like Neural Networks due to inherent uncertainty induced by the massive parallel compute architecture of a GPU.

The base XGBoost model was customized for our task by hyper-tuning the parameters of the model using 5-fold cross-validation. Each fold of the cross-validation generated a mean square error (MSE) loss metric at the end of its run. The mean of all five MSE was chosen as the metric to reduce during the hyper-tuning process (final model parameters are present in Supplementary Table 1). The trained model was then used to estimate building footprint values for each global FN cell using PPLNFN, RLFN, BAFN values. The final output of the downscaling (BFEFN) was stored as a global 10 km resolution raster file where each pixel represents the estimated aggregated building footprint for each FN.

### Technical potential estimation

To calculate RTSPV potential from BFEFN, we made some generalizing assumptions to maintain uniformity in our calculations. We assumed that the estimated building footprint is representative of the available rooftop area in each FN i.e., 100% of the estimated rooftop is available for solar panel installation. To install 1 kWp of roof-mounted solar PV, 10 m2 of rooftop area is required, which is in line with the thin film technology currently in use. The roof-mounted solar PV is installed at the optimum angle for each latitude and is sun-facing and shade-free to generate maximum electricity output. The building rooftops are flat in design leading to the utilization of the entire rooftop for the installation of solar panels.

Based on the assumptions i.e., 10 m2 area for a 10% efficient panel, the technical solar potential is calculated for all the global FNs for 12 months using the following:

$${{{{{{\mathrm{SP}}}}}}}_{{{M}},{{{{{\mathrm{FN}}}}}}}={{{{{\mathrm{BF}}}}}}{{{{{{\mathrm{E}}}}}}}_{{{{{{\mathrm{FN}}}}}}}\times {{{{{\mathrm{C}}}}}}{{{{{{\mathrm{F}}}}}}}_{{{M}},{{{{{\mathrm{FN}}}}}}}\times \frac{{{{{{\mathrm{Day}}}}}}{{{{{{\mathrm{s}}}}}}}_{{{M}}}}{10}$$
(6)

where SP is the technical solar potential, BFEFN is the estimated roof area in m2, CF is the conversion factor, M is the month and FN is the unique fishnet cell, and DaysM is the number of days in the respective month. The World Bank’s solar conversion factors are available for regions between 60°N and 45°S. For regions beyond 60°N and 45°S, we assumed a constant conversion factor of 3.5 kWh/kWp/day. This has led to a slight over-assessment of the potential for countries like Sweden, Norway. However, as the density of built-up area reduces significantly beyond the 60°N and 45°S, the total global error due to this assumption remains small. The calculations for generating the solar potential are processed using custom python scripts using the DASK module to handle massive arithmetic operations. Further, the processed solar potential raster dataset is stored as a geopackage file for visualizations and economic calculation.

### Cost calculation

LCOE provides an easy and robust method to compare the economic viability of a project within a specific FN. It was assumed that the capital cost of the installation (CAPEX) will be staggered to the first year of commissioning and the installed panels will have a lifetime of 25 years. The geo-mapping for CAPEX, operating expenditure (OPEX), and discount rate (DR) was sourced from IRENA’s renewable energy cost report 2019. The LCOE for each FN is calculated using the following:

$${{{{{{\mathrm{LCOE}}}}}}}_{{{{{{\mathrm{FN}}}}}}}=\frac{\frac{{\sum }_{t=1}^{25}{{{{{\mathrm{CAPE}}}}}}{{{{{{\mathrm{X}}}}}}}_{{{{{{\mathrm{FN}}}}}}}\,+\,{{{{{\mathrm{OPE}}}}}}{{{{{{\mathrm{X}}}}}}}_{{{{{{\mathrm{FN}}}}}},t}}{{(1\,+\,{{{{{\mathrm{DR}}}}}})}^{t}}}{\frac{{\sum }_{t=1}^{25}{\sum }_{M=1}^{12}{{{{{\mathrm{S}}}}}}{{{{{{\mathrm{P}}}}}}}_{{{M}},{{{{{\mathrm{FN}}}}}}}}{{(1\,+\,{{{{{\mathrm{DR}}}}}})}^{t}}}$$
(7)

where, CAPEXFN (2019 $/kW) is the capital expenditure in installing the RTSPV system for the given FN, OPEXFN,t (2019$/kW) is the operational and maintenance expenditure for the given FN and for the specific year (t), t is the year number, DR is the discount rate, M is the month number, and SPM,FN (kWh/month) is the potential generation for the given month and FN. We have used CAPEX data for 17 different countries and allocated the average CAPEX value to the rest of the countries based on the continent they are in. OPEX and DR have values based on OECD and Non-OECD country classifications. Country-wise aggregation of LCOE, SP from each FN from their respective high-resolution intra country values has been done using the following rule

$${{{{{{\mathrm{LCOE}}}}}}}_{{{{{{\mathrm{country}}}}}}}:\left\{\begin{array}{c}{{{{{\mathrm{CAPEX}}}}}}:{{{{{\mathrm{aggregation}}}}}}\_{{{{{\mathrm{of}}}}}}\_{{{{{\mathrm{individual}}}}}}\_{{{{{\mathrm{FN}}}}}}\_{{{{{\mathrm{values}}}}}}\\ {{{{{\mathrm{OPEX}}}}}}:{{{{{\mathrm{aggregation}}}}}}\_{{{{{\mathrm{of}}}}}}\_{{{{{\mathrm{individual}}}}}}\_{{{{{\mathrm{FN}}}}}}\_{{{{{\mathrm{values}}}}}}\\ {{{{{\mathrm{SP}}}}}}:{{{{{\mathrm{aggregation}}}}}}\_{{{{{\mathrm{of}}}}}}\_{{{{{\mathrm{individual}}}}}}\_{{{{{\mathrm{FN}}}}}}\_{{{{{\mathrm{values}}}}}}\\ {{{{{\mathrm{DR}}}}}}:{{{{{\mathrm{mean}}}}}}\_{{{{{\mathrm{of}}}}}}\_{{{{{\mathrm{indiviual}}}}}}\_{{{{{\mathrm{FN}}}}}}\_{{{{{\mathrm{values}}}}}}\end{array}\right\}.$$
(8)

It should be noted that we did not consider the cost of additional grid expansion or storage infrastructure to attain the full technical rooftop solar PV potential. Also, the cost of decommissioning and scrap metal value of the installation was not considered at the end of the 25-year lifetime of the projects. While calculating the SP and LCOE, it was assumed that no rooftop solar PV installation exists globally, and all the additional capacities will start their commissioning from the year 2019.

### Limitations

Our assessment is based on the accuracy of the global landcover layer, which with its 100 m resolution can in some locations overestimate the built-up area extents. In addition, the landcover classifies roads, parking lots, boundaries of green areas, tennis courts, and archeologically significant areas as built-up areas with misclassification varying between different regions. Our assumption that the rooftops being flat, shadow-free and sun-facing with a full rooftop available for installation adds to the methodological limitations. Next, the big data related to building footprints and global roads have inherent methodological limitations like the simplified representation of a complex rooftop with a square polygon, overlapping roads, etc.

For technical potential calculations, we assumed that 100% of the estimated rooftop is available for installing solar panels i.e., orientation and slope of the building are not accounted for the 100% rooftop availability assumption-based results in our main analysis. These assumptions can lead to limitations in the real-world interpretation of main results as a fraction of rooftop may be available for the installation of solar panels. To account for this, we have documented regional change in potential as an uncertainty analysis for a combination of rooftop scaling factors and panel efficiencies. In the current literature, reduction of total rooftop area to available rooftop area is generally done through a rooftop scaling factor which is a proxy for loss of rooftop area due to orientation, slope, and roof superstructures like chimneys, etc. Although, some studies exist at the country level where the rooftop scaling factor is documented, on a global scale no authoritative dataset exists that can demarcate country-wise rooftop scaling factors. Further work is required to document the country-specific rooftop scaling factors which are outside the scope of the research aim of this study.

Our cost assumptions cover 17 different countries across continents with average values for the rest of the countries. These assumptions can assign increased or decreased LCOE values to a certain region like Africa and South America. Another limitation of the cost assumptions is the inability of the LCOE metric to capture intra country variation of LCOE to a high degree due to lack of high-resolution cost data. Also, cost variability due to additional grid rollout, tariff mechanisms, and global change in prices due to trade protectionism practices are beyond the scope of the current assessment. Finally, we calculated all the cost and potential metrics assuming that no installed capacity exists for the ROI, where in the present time horizon, some installed capacity does exist.

A majority of limitations can be attributed to the underlying data used in our assessment, which can be improved with subsequent advancement in the methodology of the data providers. Further research can be undertaken to reduce the methodological limitations that are currently bootstrapped by the data availability and lack of homogenous global data.

## Data availability

The data that support the findings of this study are available from the corresponding author upon reasonable request. The global road map is based on OpenStreetMap (OSM), which can be freely downloaded. The planet file used in this study is downloaded on April 1, 2020. The landcover map is based on Copernicus Global Land Service: Land Cover 100 m: collection 3: epoch 2015: Globe (https://doi.org/10.5281/zenodo.2583745). Other data sources that are free to use are provided in the main text and in the “Methods” section.

## Code availability

Pseudocode to undertake this analysis can be found in the supplementary material (Supplementary Note 1) and should be read in conjunction with the “Methods” section. Base XGBoost Model is available at https://xgboost.readthedocs.io. The python script for plotting and data aggregation is available from the corresponding author upon reasonable request.

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## Acknowledgements

S.J., B.O.G. and J.G. are supported by a research grant from Science Foundation Ireland (SFI) and the National Natural Science Foundation of China (NSFC) under the SFI-NSFC Partnership Programme Grant Number 17/NSFC/5181. S.M. acknowledges support from the H2020 European Commission Project ‘PARIS REINFORCE’ under Grant Agreement no. 820846.

## Author information

Authors

### Contributions

S.J. and J.G. conceived the research idea. S.J. designed and developed the framework and model along with the codes. S.J., S.M., and P.H. designed the GIS and data analysis frameworks. S.J. created the figures and drafted the manuscript. P.R.S. and B.O.G. provided valuable insights on the results. All authors discussed the results and contributed to the manuscript.

### Corresponding author

Correspondence to Siddharth Joshi.

## Ethics declarations

### Competing interests

The authors declare no competing interests.

Peer review information Nature Communications thanks the anonymous reviewer(s) for their contribution to the peer review of this work.

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Joshi, S., Mittal, S., Holloway, P. et al. High resolution global spatiotemporal assessment of rooftop solar photovoltaics potential for renewable electricity generation. Nat Commun 12, 5738 (2021). https://doi.org/10.1038/s41467-021-25720-2

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• DOI: https://doi.org/10.1038/s41467-021-25720-2

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