Future rooftop photovoltaics will weaken carbon mitigation but offer promising water and land benefits

Domain: neuroscience
Published: 2026-03-27
Editorial outcome: major_revision

Abstract
Rooftop photovoltaics are widely recognized for their carbon mitigation benefits, yet uncertainties persist regarding their future dynamics and broader impacts on water and land. Here we develop a city-level integrated framework to quantify rooftop photovoltaics potential across 349 Chinese cities, and evaluate evolving carbon-water-land tradeoffs under shared socioeconomic and representative concentration pathways. By 2050, projections across 15 combined scenarios indicate China’s rooftop photovoltaic areas will increase by 9.2 to 34.8 percent relative to 2020 levels. Installed capacity ranges from 7.19 to 9.05 terawatts in 2050, expanding rapidly in eastern coastal cities. Under a moderate socioeconomic and climate scenario, the national carbon mitigation benefits peak during 2035 to 2040, accumulating 2.04 to 2.18 gigatons of carbon dioxide equivalent by 2050. In contrast, the water and land saving benefits continue to rise, reaching 16.5 to 17.4 cubic kilometers and 281 to 297 thousand square kilometers by 2050. These findings underscore the critical need for multidimensional planning to optimize future sustainable photovoltaics deployment.

Introduction
Cities are pivotal engines of future population growth, innovation, and economic opportunity worldwide1. Yet, a significant share of global urban areas still faces acute energy shortages, with approximately 760 million people globally lacking access to electricity in 2023, predominantly in developing countries and emerging economies2. Conventional power generation systems, often located far from demand centers3, thus remain out of sight and mind for the public. Rooftop photovoltaic (PV) systems, a hallmark of distributed energy, utilize idle urban rooftop spaces to enable localized power generation4, which can offer remarkable spatial adaptability and energy self-sufficiency5,6. Compared with centralized power plants, rooftop PV not only reduces long-distance transmission losses by an estimated 6–8%7 but minimizes land requirements, making it an effective strategy for alleviating urban land use conflicts and enhancing the resilience of energy systems8,9. Globally, the development potential of rooftop resources is vast. Estimates suggest that rooftops cover 200,000 km2 of the planet’s 13 million km2 land surface, with Asia accounting for a substantial share10. In China alone, the total rooftop area across 354 cities reached 66,000 km2 by 2020, signaling enormous untapped capacity for rooftop PV deployment11.

Amid intensifying global warming, the carbon mitigation benefits of rooftop PV have garnered widespread attention12. Research has largely centered on carbon emission mitigation during the operational phase13–18. Yet recent insights suggest that ignoring emissions across the full life cycle—from raw material extraction through manufacturing, transport, installation, and maintenance—may inflate the perceived climate benefits of rooftop PV19–22. Moreover, a singular focus on carbon mitigation may obscure the multidimensional impacts of rooftop PV and miss opportunities to embrace broader sustainability development goal (SDG). Recognizing the close interlinkages between energy (SDG7), carbon (SDG13), water (SDG6), and land (SDG15)23,24, urban policymakers should integrate a holistic view that considers not just energy generation, but also the indirect resource consumption (e.g., materials) and its cascading implications for water and land systems25.

In terms of water use, though rooftop PV consumes water during material production and operation26,27, its life cycle water footprint remains lower than that of fossil fuel-based generation mix. In California, for example, residential rooftop PV saves an average of 200 cubic meters of water annually, around 60% of the typical American household’s consumption28. Regarding direct land use, rooftop PV capitalizes on existing urban building landscapes, and avoids the need for additional ground space29,30. While utility-scale PV stations conversely depend on vast tracts of farmland, grassland, or ecological zones, potentially triggering land use conflicts and ecological pressures31–33. Coal-based power compounds this issue with extensive land demands for mining, storage, and transmission34. Seen from the entire lifespan, rooftop PV entails indirect land occupation that amounts to only a fraction of that associated with coal-fired power26,35. Nevertheless, whether rooftop PV consistently offers land saving advantages over power generation mix warrants further quantification.

As ambitious calls for net-zero power systems grow, the large-scale integration of renewables promises to sharply lower carbon emission factors of power generation mix36–38. However, the carbon mitigation benefit of rooftop PV will inherently change, when considering the generation mix decarbonizes and rooftop PV’s technological advancement. Meanwhile, little is known about the future performance of rooftop PV regarding water and land dimensions. These uncertainties complicate efforts to evaluate the energy-carbon-water-land tradeoffs between rooftop PV and power generation mix, particularly under varying future socio-economic-climate trajectories. Bridging these knowledge gaps can help refine contributions of rooftop PV to the SDGs and inform tailored, evidence-based urban energy strategies.

To tackle these challenges, we constructed a city-level methodological framework for quantifying the rooftop PV’s tradeoffs (Fig. 1), blending machine learning, life cycle assessment, and forward-looking scenario simulations. The model can map out city-level rooftop PV potential and quantify the associated carbon mitigation, water saving, and land saving, projecting their evolutionary trends from 2025 to 2050. By weaving in socio-economic-climate scenarios, provincial generation mix evolution, and PV technological advancement from 2025 onward, we illuminate the nexus dynamics of rooftop PV across multiple dimensions. Applied to 349 Chinese cities, we uncover the notable spatial variations of rooftop PV in tradeoffs against local power generation mix. This study equips stakeholders with a view of rooftop PV’s untapped value and arms urban decision-makers with context-specific evidence for designing effective, locally tailored energy transitions.

Results
Rooftop photovoltaic potential within city boundaries
This study developed a machine learning model to predict the rooftop PV potential across 349 Chinese cities, including built-up areas and hinterlands within city administrative boundaries. To assess the robustness and generalizability, the cross-validation procedure was applied to subsets of data stratified by China’s seven geographical regions and by city-level gross domestic product (GDP) classifications (see Methods and Supplementary Figs. 1–2). We estimated that China possesses a total of 33,290 km2 of rooftop PV potential across 349 cities. However, this resource is distributed unevenly, with the majority located in densely built and highly urbanized eastern regions. At the city scale, rooftop PV potential varies substantially, ranging from 2.4 km2 in Wuzhishan to 533 km2 in Chongqing. The leading cities include Chongqing, Weifang, Linyi, Shijiazhuang, and Baoding (Fig. 2a). Comparing built-up areas to hinterlands, Shenzhen, Shantou, and Wuzhishan offer higher rooftop PV potential in built-up areas. While the other 99% of cities exhibit greater potential in hinterlands, ranging from 1.2 to 68 times higher than built-up areas, which is consistent across all provinces (Supplementary Fig. 6). Shandong province exemplifies this spatial structure, as its extensive rooftop areas in hinterlands, combined with strong policy support, have enabled it to emerge as a national leader in distributed rooftop PV development.

We compared the rooftop PV generation with total electricity demand in city administrative boundaries (Supplementary Fig. 7) to evaluate the theoretical urban electricity self-sufficiency (UESS). The UESS ranges from 16% to 417% (Supplementary Fig. 8a). It was further found that 87% of cities (302 of 349), primarily concentrated east of the Hu Huanyong Line (Fig. 2b), could theoretically generate more electricity than they consume. In built-up areas, rooftop PV can meet a small portion of demand, ranging from 0.8% to 58.5% (Fig. 2c). Conversely, rooftop PV in hinterland can satisfy 11%–403% of city electricity demand (Fig. 2d), and 82% of cities (286 of 349) can fully achieve the electricity self-sufficiency.

Our assessment of China’s five largest cities by GDP (Shanghai, Beijing, Shenzhen, Chongqing, and Guangzhou) shows significant variation in the potential for rooftop PV to meet local electricity demand (Supplementary Fig. 8b). Among them, Chongqing exhibits the greatest theoretical self-sufficiency (134.5%), with generation capacity far exceeding its demand. This is largely driven by its hinterland (126.8%), while its built-up area contributes only 7.7%. In contrast, Shenzhen (53.8% total) follows a built-up area–dominated mode, where 40.1% of potential arises from the urban core compared to only 13.7% from the hinterland, consistent with its compact urban form. For Shanghai, Beijing, and Guangzhou, they rely significantly on their hinterlands. Shanghai has a total UESS of 58.2%, with 41.0% coming from the hinterland and 17.2% from the built-up area. In Beijing and Guangzhou, built-up areas contribute around 18.5% and 12.8%, respectively, to their total UESS, while the hinterlands provide the larger share. These profiles underscore the importance of aligning rooftop PV deployment strategies with the spatial configurations and morphological characteristics of different megacities.

Energy-carbon-water-land nexus of rooftop photovoltaics
By integrating local solar irradiation, PV conversion efficiency, and performance ratio, we evaluated the rooftop PV generation potential across cities (Fig. 3a). Since rooftop PV systems offer a viable alternative to local power generation mix, the comparison between rooftop PV and generation mix is crucial to determine the practical benefits and limitations of each. We defined the life cycle system boundaries for rooftop PV, encompassing material production, manufacturing, transportation, installation, operation, decommissioning, disposal, and recycling (Supplementary Fig. 9). We included the direct impacts (energy and water input, carbon emissions), and accounted for the embodied impacts (by materials and resources) of rooftop PV (Supplementary Methods). In particular, we incorporated the direct cleaning water usage during operational stage, informed by company interviews on rooftop PV practices (Supplementary Table 2), which cover diverse techniques (e.g., robots, drones, handheld brushes) with varying water intensities and cleaning frequencies. The life cycle assessment results per kilowatt-hour (kWh) indicate that rooftop PV systems generate carbon emissions (52.2–99.3 g kWh-1), and require water consumption (0.34–0.65 L kWh-1) as well as land occupation (0.0034–0.0064 m2 kWh-1). For comparison, we benchmarked the life cycle impacts of rooftop PV against the local power generation mix on a per kWh electricity basis. The life cycle carbon emission, water consumption, and land occupation of power generation mix (Supplementary Table 5) were computed based on provincial generation structures (Supplementary Table 6) and the corresponding life cycle impacts of each generation technology (see Methods for details). Our analysis reveals that rooftop PV substantially outperforms generation mix across the carbon, water, and land impact dimensions from a life cycle perspective. Specifically, for each kWh of electricity generated compared with generation mix, city-level rooftop PV in China provides median benefits of 759 g in carbon mitigation, 3.4 L in water saving, and 0.05 m2 in land saving. These benefits exhibit significant spatial heterogeneity, with city-level values ranging from 81.6–1362 g kWh-1 for carbon mitigation, 2.4–15.1 L kWh-1 for water saving, and 0.02–0.11 m2 kWh-1 for land saving.

From a carbon perspective, rooftop PV generation in provinces such as Inner Mongolia (1348–1362 g kWh-1), Jilin (1070–1076 g kWh-1), Heilongjiang (1064–1074 g kWh-1), Shanxi (1041–1049 g kWh-1), and Zhejiang (1041–1046 g kWh-1) showcase high carbon mitigation benefits (Fig. 3b). This is primarily due to the high proportion of coal power in the power structures of these provinces and the high life cycle carbon intensity of coal power generation. However, regions like Sichuan (82–124 g kWh-1) and Yunnan (100–121 g kWh-1) exhibit poor carbon mitigation benefits (Fig. 3b) and long carbon payback periods (Supplementary Fig. 3a). The main reasons for this are twofold: First, these areas benefit from low-carbon generation structures dominated by hydropower, indicating that the electricity substituted by rooftop PV is already clean, which results in minimal mitigation. Second, weak solar radiation (especially for some cities in Sichuan) due to basin topography reduces the lifetime energy generation of PV systems. This effectively increases the PV system’s life cycle carbon emissions per kWh generation, diminishing its overall life cycle environmental benefits. However, Sichuan (14.9–15.1 L kWh-1), Yunnan (14.3–14.3 L kWh-1), Qinghai (11.3–11.34 L kWh-1), and Hubei (10.5–10.6 L kWh-1) exhibit the most substantial water saving benefits through rooftop PV generation (Fig. 3c), leading to the lowest water payback periods (Supplementary Fig. 3b). This is because their provincial generation mixes contain a high share of hydropower, whose life cycle water consumption is comparatively large, thereby leading to greater water saving when substituted by rooftop PV. Heilongjiang and Hainan stand out for their substantial land saving benefits, with values of 0.105–0.106 m2 kWh-1 and 0.962–0.967 m2 kWh-1, respectively (Fig. 3d). This stems from the higher proportion of biomass in the power structure of these two provinces. Biomass has a greater life cycle land occupation, and thus elevate the land factors of generation mix. The rooftop PV in these regions thereby yields more pronounced land saving benefits, resulting in shorter land payback periods (Supplementary Fig. 3c). Conversely, Qinghai (0.023–0.024 m2 kWh-1), Shaanxi (0.033–0.035 m2 kWh-1), Guizhou (0.033–0.034 m2 kWh-1), Xinjiang (0.028–0.029 m2 kWh-1), and Gansu (0.0275–0.0289 m2 kWh-1) present the least significant land saving benefit (Fig. 3d) and, therefore, have longer land payback periods (Supplementary Fig. 3c).

Beyond resource and environmental impacts, we evaluated the economic performance of urban rooftop PV using the levelized cost of electricity (LCOE), explicitly accounting for rooftop rental costs (Supplementary Table 7) in addition to initial capital investment, operation and maintenance costs. The levelized cost of electricity (LCOE) of rooftop PV is estimated at 0.041–0.077 USD kWh-1 (Fig. 3e), ensuring user-side grid parity across 349 cities (Supplementary Fig. 5), that is, rooftop PV electricity costs are no higher than the retail tariffs paid by consumers. The payback periods, however, exhibit pronounced heterogeneity (Supplementary Fig. 3d), mainly driven by differences in local electricity tariffs, rooftop rents as well as PV generation. Due to low electricity tariff in Xinjiang and Inner Mongolia, and low generation per unit area in Sichuan, Guizhou, and Gansu, Xinjiang (13.8–16.9 years), Inner Mongolia (11–14.5 years), Sichuan (8.4–16.2 years), Guizhou (12.9–16.9 years), Gansu (11.1–16.6 years), and parts of Shaanxi (9.7–14.4 years) require longer periods to offset their economic costs. We further examined the spatial distribution of the economic cost required per unit of benefit (based on the normalized sum of carbon mitigation, water saving, and land saving) for urban rooftop PV (Fig. 3f). Gansu, Shaanxi, Guizhou, Fujian, and Guangdong challenge high costs when generating carbon-water-land benefits, while Heilongjiang, Jilin, Hebei, Yunnan, and parts of Inner Mongolia and Sichuan have low costs, driven by favorable LCOE and positive environmental performances in carbon mitigation, water saving, and land saving. These heterogeneities suggest that deployment strategies should be tailored to specific regions. Areas with high benefits and low costs should prioritize the adoption of rooftop PV, while regions with high costs require policy and technological support to enhance the viability of deploying urban rooftop PV.

Future scenarios reshape rooftop photovoltaics’ nexus
Under the framework of Shared Socioeconomic Pathways (SSPs), we projected the expansion of China’s rooftop PV area from 2025 to 2050, based on the maximum exploitable potential. Our results indicate substantial variations in future rooftop PV expansion, with growth trajectories diverging markedly among SSPs. By 2050, the fossil-fueled development scenario (SSP5) shows the largest increase, with the national rooftop PV area reaching a median of 44,877 km2, representing a 34.8% rise (95% CI: 31.9%–37.5%) relative to 2020 levels. In contrast, the sustainability scenario (SSP1) yields the smallest increase, reaching 36,346 km2, and a 9.2% rise (95% CI: 7.3%–11.1%). The intermediate scenarios fall between these two extremes: the middle-of-the-road scenario (SSP2) results in a 24.8% increase (95% CI: 22.2%–27.3%), while the regional rivalry (SSP3) and inequality (SSP4) scenarios show moderate growth of 14.9% (95% CI: 12.3%–17.5%) and 14.5% (95% CI: 12.1%–17.1%), respectively (Fig. 4a).

We further combined the SSP-based rooftop PV area projections with radiative forcing estimates under three Representative Concentration Pathways (RCPs), including RCP2.6, RCP4.5, and RCP8.5. This integration enabled Monte Carlo simulations of China’s rooftop PV installed capacity potential from 2025 to 2050, covering 15 SSP–RCP combinations. Under the maximum rooftop PV potential, the median national installed capacity by 2050 is projected to increase by 74.4% (95% CI: 67.3%–81.6%) under the SSP185 scenario and by 119.6% (95% CI: 98.6%–141.2%) under the SSP545 scenario, corresponding to 7.19 TW (95% CI: 6.89–7.49 TW) and 9.05 TW (95% CI: 8.19–9.92 TW), respectively (Fig. 4b). Under the middle-of-the-road SSP245 pathway, the national installed capacity will to reach a median of 8.2 TW by 2050, a 103% increase (95% CI: 87.4%–119.4%) relative to year 2020 (Fig. 4b). Under the SSP245 scenario, rooftop PV generation across China’s 349 cities will increase substantially, with the national average growth reaching 87.5% by 2030 and 193.6% by 2050 relative to 2020 levels (Fig. 4c). However, this potential is highly uneven, the eastern coastal cities are primary hotspots for future rooftop PV deployment, with Shandong projected to reach 786.7 GW by 2050 (Supplementary Table 8). A broad cohort of economically developed cities or those with abundant solar resources in this region, including Huizhou, Wenzhou, Ningde, and Qingdao, consistently exhibit strong long-term potential, with projected growth rates commonly exceeding 200%. In addition, cities like Panjin (448.8%), Mianyang (406.5%), and Longyan (347.9%) are projected to see their generation increase more than four-fold, indicating significant local drivers. Whereas cities in the northwest or less-developed regions, such as Jiayuguan and Wuzhishan, show significantly more modest, though still positive, growth trajectories.

We incorporated anticipated advances in rooftop PV to project the future life cycle carbon, water, and land intensities of rooftop PV systems, including improved conversion efficiency, enhanced recycling rate, and decreased embodied impacts of key materials. These life cycle evolution of rooftop PV was integrated with provincial power generation structure optimization scenarios to assess the future dynamics among carbon, water, and land benefits. Under policy-driven constraints, coal- and gas-fired generation in the power structure is projected to peak around 2030 and 2040, respectively, while renewable penetration increases, with solar and wind power progressively dominating China’s electricity mix, complemented by hydropower, nuclear, and biomass (Supplementary Fig. 10). Within the power generation mix, the life cycle carbon, water, and land intensities of solar and wind power are projected to evolve over time, reflecting anticipated technological advancement, and the intensities of other generation technologies (coal, gas, nuclear, hydro, and biomass) are assumed constant. The results indicate that the life cycle carbon intensity of rooftop PV steadily decreases with technological advancement, and the carbon intensities of power generation mix also decline due to a rising share of renewables (Supplementary Fig. 11). For water and land, however, the trajectories of provincial generation mix factors are more heterogeneous. Some provinces exhibit decreases, whereas others remain relatively stable or fluctuate depending on their generation structure (Supplementary Fig. 11). In contrast, rooftop PV shows a consistent downward trend in water and land intensities. These dynamics highlight that the relative benefits of rooftop PV are shaped jointly by PV technological advancement and generation structure evolution differences. Results show that the unit carbon mitigation benefit is projected to decrease over time, with the median value declining from 713.6 g kWh-1 in 2025 to 388 g kWh-1 in 2050 (Supplementary Fig. 12a). Similarly, the unit water saving benefit shows a slight downward trend, with the median decreasing from 3.9 L kWh-1 in 2025 to 3.5 L kWh-1 in 2050 (Supplementary Fig. 12b). In contrast, the unit land saving benefit demonstrates an increase, rising from 0.043 m2 kWh-1 in 2025 to 0.056 m2 kWh-1 in 2050 (Supplementary Fig. 12c), with the growing efficiency of life cycle land occupation of rooftop PV. The unit benefits and payback periods, with and without technological advancements, are additionally shown in Supplementary Fig. 12d–f.

We further developed economically-constrained deployment scenarios to reflect the possibilities of market attractiveness on city-level rooftop PV rollout. To achieve this, we ranked and grouped all 349 cities into four quartiles based on their LCOE. We then applied tiered deployment rates to these quartiles to simulate a market-driven pathway where more profitable regions are prioritized. Specifically, the deployment rates were set at 80%, 60%, 30%, and 10%, corresponding to the quartiles from the most economically attractive (lowest LCOE) to the least. The final deployment level for each city under a given SSP scenario was then determined by multiplying its maximum technical potential by its assigned quartile-specific rate. This approach allows for the evaluation of future carbon, water, and land benefits under large-scale rooftop PV development schemes. Results show that at the national scale, the carbon mitigation benefit of rooftop PV will first rise to a peak around 2035–2040 and then decline (Fig. 5a), while the water and land saving benefits will consistently increase over time (Fig. 5b&c). Under the SSP245 scenario, the carbon mitigation benefit in 2050 is 2.1 (95% CI: 2.04–2.18) Gt CO2, equivalent to 44.5% of China’s 2020 electricity sector emissions. In contrast, both national water and land saving exhibit a continuous upward trend through 2050 (Fig. 5b&c). By then, the national water saving is projected to reach 17 (95% CI: 16.5–17.4) km3, approximating 29.2% of the Yellow River’s groundwater reserves. The land saving will reach 289 thousand km2 (95% CI: 281–297), equivalent to about 18 times the administrative area of Beijing, and accounting for 20.1% of China’s planetary boundary for land-system change. This fundamental divergence underscores the evolving strategic value of rooftop PV, transitioning it from a primary climate mitigation tool in the near term to a crucial instrument for long-term water and land resource security.

Analysis of the top 20 GDP cities in 2050 reveals distinct tradeoffs between rooftop PV generation and its associated benefits (Fig. 5d). Beijing emerges as the strongest performer in co-benefits, achieving the highest water and land saving benefits nationwide, while Qingdao stands out for its exceptional carbon mitigation benefit. By contrast, Chongqing, despite hosting the largest rooftop PV generation potential, delivers only modest carbon, water, and land benefits, and Chengdu registers the lowest carbon mitigation among all major cities. Shanghai, although economically dominant, shows only moderate performance across all metrics. These contrasting patterns highlight that GDP scale does not necessarily translate into proportional PV co-benefits. To maximize national outcomes, urban PV deployment should follow differentiated strategies, expanding capacity in cities with both high potential and strong co-benefits, while offering targeted support and technological innovation in cities where benefits remain limited.

Methods
City-level potential of rooftop photovoltaics
This study established a machine learning model to estimate the city-level rooftop PV potential for 349 Chinese cities for the baseline year of 2020. The model was trained using a rooftop PV area dataset18 derived from a 124-city analysis of individual buildings (Supplementary Fig. 1 and Supplementary Notes). The 124-city baseline dataset was first disaggregated to the county level. This granular, county-level data was then used to train the machine learning model. After the model generated predictions for all counties nationwide, these results were re-aggregated to their respective city-level administrative boundaries to obtain the rooftop PV potential within built-up area in 2020 for all 349 cities. Our primary data collection effort then focused on compiling a set of city-level predictor variables for all target cities, including nighttime light, population, GDP, road length, and built-up area. The data sources, statistical characteristics and correlations of the variables are presented in Supplementary Table 9–10. To enhance model performance, we engineered additional features from this initial set, such as the ratio of urban area to GDP and road density. The LightGBM model was selected based on its performance in predicting the potential rooftop PV area. To handle the right-skewed distribution of the target variable, it was log-transformed prior to training. The model’s key hyperparameters, such as the maximum depth of the tree and the learning rate, were optimized using grid search techniques. The model performance and hyperparameters were assessed using a 10-fold cross-validation scheme applied to the county-level training samples, with the coefficient of determination (R2) as the primary evaluation metric and the L2 loss function (mean squared error, MSE) as the objective. The held-out fold in each split was used exclusively for evaluation and was not used for model fitting or hyperparameter selection, thereby minimizing information leakage during model development. The final LightGBM model achieved an R2 of 0.88 based on 10-fold cross-validation. To further examine the model’s stability, we conducted a stratified validation to assess its performance across China’s seven major geographical regions and different economic levels (Supplementary Fig. 2). The results indicate that the seven analyzed geographical regions exhibited R2 values above 0.83, ranging from 0.831 in Southwest China to 0.912 in Northern China (Supplementary Table 4). Additionally, it was revealed the stable predictive performance across different levels of economic development. When stratified into four tiers based on GDP (Low, Lower-Mid, Upper-Mid, and High), the model achieved R2 of 0.863, 0.872, 0.880, and 0.867, respectively (Supplementary Table 11). The mean absolute error (MAE) ranges from 0.045 km2 to 0.091 km2 and the root mean square error (RMSE) ranges from 0.389 km2 to 0.756 km2 across the quartiles.

To extend our predictions from the built-up area to the entire city administrative boundary, we implemented an LightGBM model, in a parallel process, to predict the total rooftop area within built-up areas, which achieved an R2 of 0.87 based on 10-fold cross-validation. We estimated total rooftop area in the surrounding hinterlands by subtracting the model-predicted total rooftop area of built-up areas from total rooftop area of entire city administrative boundaries11. The total rooftop PV areas in city boundary can be formulated as equation (1):
RPA = RPAML + \eta(RA - RAML) (1)
i i,b i i,b
Wherein, RPA denotes the rooftop PV area within administrative boundary of city i. RPAML and RAML respectively stand for the rooftop PV area and rooftop area within built-up areas of city i, based on this study’s machine learning. RA is the total rooftop area within city administrative boundaries of city i. \eta represents the proportion of suitable rooftops for PV deployment within hinterlands, it is assumed that half of the rooftops within hinterlands can be utilized for rooftop PV development. The justification for rooftop utilization factor in hinterlands is available in Supplementary Notes.

Energy-water-land-carbon effects
A whole life cycle inventory of urban rooftop PV per meter square is established integrating field investigation, literature, and reports from International Energy Agency (IEA). We first imported the Ecoinvent v3.11 database into the OpenLCA software, and ran the life cycle assessment methods including Recipe Midpoint (H) v1.13 and Cumulative Energy Demand to account for the life cycle impacts of rooftop PV. The life cycle stages include the stage of material production, manufacturing, installation, operation, decommission, recycling, and disposal. The associated transportation is also included in the corresponding life cycle stages. The material production includes rooftop PV panels (metallurgical-grade silicon, solar-grade silicon, polysilicon ingots, polysilicon wafers, PV cells, and PV modules) and the balance of system (inverters and brackets). See Supplementary Table 1 for the material inventory. The required clean water consumption of the PV panels during the operation stage is also considered, including different rooftop PV cleaning techniques and frequencies (Supplementary Table 2). For the recycling stage, silica sand, soda powder, limestone, copper, and aluminum are considered. Disposal processes incorporate incineration and landfill of solid waste (wafer scrap, polytetrafluoroethylene, circuit boards, etc.), as well as effluent treatment. The detailed information of life cycle assessment is available in Supplementary Methods. The lifetime of rooftop PV system in this study is regarded as 30 years, thus two supporting inverters are required. The life cycle impact of rooftop PV per kWh of electricity generation can be expressed using equations (2) – (4):
E = \sum G g \sum S sADs,tBDc,s,t (2)
c,i,t RPGi,t
E = \sum G g \sum S sADs,tBDw,s,t (3)
w,i,t RPGi,t
E = \sum G g \sum S sADs,tBDl,s,t (4)
l,i,t RPGi,t
Where E refers to the life cycle carbon emission of urban rooftop PV per kWh generation in year t. G is each life cycle stage. S represents life cycle inventory material of rooftop PV per meter square. ADs,t is the activity data of life cycle inventory material s of urban rooftop PV in year t. RPGi,t is the annual rooftop PV generation per meter square in city i and year t. BDc,s,t is the background carbon emission factor of material s of urban rooftop PV in year t. E refers to the life cycle water consumption of urban rooftop PV per kWh generation in year t. BDw,s,t is the background water consumption factor of material s of urban rooftop PV in year t. El,i,t refers to the life cycle land occupation of urban rooftop PV per kWh generation in year t. BDl,s,t is the background land occupation factor of material s of urban rooftop PV in year t.

The rooftop PV generation potential of urban rooftop PV per meter square can be computed as equation (5):
RPG = GHI * \eta * PR * RPA (5)
i,t i,t i,t
Wherein, GHI is the annually average solar irradiance of city i in year t, which is obtained from SolarGIS55. \eta is the conversion efficiency between solar energy and electricity, which is set to 16% in this research for the current year. PR is the PV module performance ratio and specifically set to 70%. RPAi,t is the rooftop PV area within administrative boundary of city i in year t, here we use one meter square to reflect rooftop PV generation per unit area. Based on rooftop PV power generation, the theoretical urban electricity self-sufficiency rate (UESS) was calculated according to urban electricity consumption (Supplementary Fig. 7), which were extracted based on grid-resolution data56.

Guided by the IEA PVPS Task 12 methodology57, we established the life cycle impact factors for electricity generation in each provincial mix. The factor quantifies the environmental burdens of the local generation portfolio, calculated by weighting the life cycle intensity of each generation technology (including coal, gas, biomass, nuclear, hydro, wind, and solar) according to its respective share in the provincial power generation mix58. Crucially, to ensure a consistent generation to generation comparison with rooftop PV, this boundary explicitly excludes transmission and distribution (T&D) infrastructure, T&D losses, and energy storage. Regarding the life cycle intensities of generation technologies, we sourced carbon emission and land occupation data from Ecoinvent v3.11, supplemented by water consumption coefficients derived from previous literature59. The life cycle factors of power generation mix can be mathematically formulated as equations (6) – (8):
GM = CFx * Gx j,t (6)
c,i,t c,j,t TGj,t
GM = WFx * Gx j,t (7)
w,i,t w,j,t TGj,t
GM = LFx * Gx j,t (8)
l,i,t l,j,t TGj,t
Wherein, GM denotes generation mix’s life cycle carbon emission per kWh generation of city i in year t. CFx is the life cycle carbon emission of power type x per kWh generation in province j in year t, j is the province that city i belongs to. Gx j,t is the generation of power type x in province j in year t. TGj,t is the total generation of province j in year t. GM denotes generation mix’s life cycle water consumption per kWh generation of city i in year t. WFx is the life cycle water consumption of power type x per kWh generation in province j in year t. GM denotes generation mix’s life cycle land occupation per kWh generation of city i in year t. LFx is the life cycle land occupation of power type x per kWh generation in province j in year t.

In China, electricity planning and dispatch operate primarily at the provincial level, indicating that the granular city-level data is unavailable. Given that inter-regional transmission capacity has historically represented only 5% of national generation capacity, using the provincial generation mix as a proxy is statistically justifiable60. Furthermore, since distributed rooftop PV functions by substituting local power supply, we benchmark the life cycle impacts of rooftop PV against the provincial power generation mix portfolio to estimate the physical environmental benefits of rooftop PV systems, including carbon mitigation (kg CO2e), water saving (m3), and land saving (m2) benefits. Methodological details of the carbon, water, and land payback periods, as well as the grid parity level, are provided in the Supplementary Methods.
CM = GM - E (9)
i,t c,i,t c,i,t
WS = GM - E (10)
i,t w,i,t w,i,t
LS = GM - E (11)
i,t l,i,t l,i,t
Wherein, CM is the carbon mitigation benefit of city i in year t. WS is the water saving benefit of city i in year t. LS is the land saving benefit of city i in year t.

Further, we assess the cost-effectiveness of urban rooftop PV by normalizing its three key physical benefits (carbon mitigation, water saving, and land saving) against its economic cost. This is used to calculate the economic cost required per unit of synergistic carbon-water-land benefit.
EB = 1 / LCOE * ( (CMi,t - CMt,min) / (CMt,max - CMt,min) + (WSi,t - WSt,min) / (WSt,max - WSt,min) + (LSi,t - LSt,min) / (LSt,max - LSt,min) ) (12)
i,t i,t
LCOE = (IC + \sum T OM) / \sum T (RPGi,t * (1-d)t) / (1+t)t (13)
i,t
Where EB denotes the cost-effectiveness ratio, representing the levelized cost required per unit of normalized, synergistic benefits for city i in year t. CMt,max and CMt,min are the maximum and minimum values of carbon mitigation (kg CO2e) among 349 cities in year t, respectively. WSt,max and WSt,min are the maximum and minimum values of water saving (m3) among 349 cities in year t, respectively. LSt,max and LSt,min are the maximum and minimum value of land saving (m2) among 349 cities in year t, respectively. LCOE refers to the levelized cost of electricity of urban rooftop PV of city i in year t. T is the lifetime of the PV system. t denotes a certain year, t\inT. IC is the investment cost. OM is the operation and maintenance cost, it is taken as 2% of the investment cost. p refers to the discount rate, which is taken as 8%. d is the decay rate of the PV system, and in this study, it is taken as 2% in the first year and 0.55% for the second year and thereafter.

Future scenario simulation
The Shared Socioeconomic Pathways (SSPs) are scenario frameworks for describing and analyzing future socio-economic development trends, which are mainly used for research in the fields of climate change, environment, resources, and policy, and are especially widely used in global climate change modelling61. The core purpose of SSP scenarios is to provide multiple socio-economic development pathways for climate change research and to help predict the impacts of climate change under different socio-economic development scenarios. To project the future rooftop PV potential, we employed a delta (change factor) method. This method scales our 2020 baseline potential based on the assumption that rooftop PV areas grow proportionally to the total rooftop areas. The future projections for global rooftop area were sourced from Joshi et al.62, where the data were generated using a machine learning model driven by SSP-specific factors. As the surface-downwelling shortwave radiation is the main cause of the variability in solar power generation, we computed average annual rooftop PV power generation using daily estimates of surface-downwelling shortwave radiation rendered by GCMs from the Coupled Model Intercomparison Project 6 (CMIP6). The data are obtained from the World Climate Research Program’s Working Group. By combining the five SSPs with these three Representative Concentration Pathways (RCPs)—RCP2.6, RCP4.5, and RCP8.5, our study evaluates a matrix of 15 future SSP-RCP scenarios. This combination follows the standard design of the CMIP6 framework and was deliberately chosen to systematically explore a wide range of plausible futures. We note that our analysis does not cover all possible variants, such as SSP370, which represents a future of heightened regional rivalry, and the exploration of which is beyond the scope of this study. Our scenario framework encompasses a spectrum from a sustainable, stringent mitigation future aligned with the Paris Agreement (e.g., SSP126), to a middle-of-the-road trajectory (e.g., SSP245), and a high-emission, fossil-fueled development pathway (e.g., SSP585). Within this framework, scenarios such as SSP126 and SSP245 are particularly relevant to China’s policy context and long-term carbon neutrality goals. The delta downscaling method (deviation-corrected change factor method) was further used to correct SSP and RCP scenario data from 2025 to 2050. Downscaling future scenario data of Chinese cities can be calculated as equations (14) – (16):
RPA = RPA * (RAi,SSP,tn / RAi,SSP,tref) (14)
i,SSP,tn i,tref
GHI = GHI * (GIi,RCP,tn / GIi,RCP,tref) (15)
i,RCP,tn i,tref
CAP = (GHIi,RCP,tn * \eta * PR * RPAi,SSP,tn) / (CFi * h) (16)
i,SSP-RCP,tn
Where RPAi,SSP,tn refers to the projected rooftop PV area of city i in future year t under the SSP scenario. RPAi,tref is the total rooftop PV area of city i in reference year 2020. RAi,SSP,tn and RAi,SSP,tref are the rooftop areas of city i under the SSP scenario, respectively in future year t and reference year. GHIi,RCP,tn refers to the projected annual solar radiation of city i in future year t under the RCP scenario. GHIi,tref is the annual solar radiation of city i in reference year. GIi,RCP,tn and GIi,RCP,tref are the annual radiation of city i under the RCP scenario, respectively in future year t and reference year. CAPi,SSP-RCP,tn is the rooftop PV capacity potential of city i in future year t under the combined SSP and RCP scenario. CFi is the capacity factor of city i, which is shown in Supplementary Fig. 13. h denotes the annual hours.

To represent the future evolution of provincial power generation mix, we adopted optimized generation structures for 2025–2050 derived from a province-level energy modeling framework63. Based on the dynamic mixes, we calculated the carbon, water, and land intensities of provincial power generation. The assessment covered a full portfolio of generation technologies, including coal, gas, biomass, hydro, wind, solar, and nuclear power. In our research, with a specific focus on wind and solar as the key drivers of decarbonization, the onshore wind and utility-scale PV in the power mix were selected as the representative technologies to reflect technological advancement, while the life cycle intensities of other generation technologies were held constant. To be specific, we estimated the future annual generation of wind and utility-scale PV based on technology-specific drivers (improving capacity factors for wind64, and changing solar radiation and conversion efficiency for PV). We further updated the future life cycle inventories of wind and utility-scale PV, including embodied material impact decrease and recycling rate increase. See Supplementary Fig. 14 for the details.

Concurrently, we developed a dynamic life cycle inventory framework specifically for rooftop PV systems to simulate the distinct technological trajectories. Key assumptions were as follows: recycling rates for major module materials (limestone, soda, silica sand, copper, and aluminum) were assumed to increase at a conservative annual rate of 2%, reflecting progressive material recovery policies; the embodied environmental intensities of non-electrical components, including glass, aluminum frames, and mounting brackets, were assumed to decline by 1% per year, representing cleaner material production65; PV module degradation was incorporated, with a rate of 2% in the first year and 0.55% annually thereafter, based on field investigation; and PV module conversion efficiency was projected to improve at an annual rate of 0.35%, starting from a 2020 baseline of 16%, according to the Energy Research Institute of the National Development and Reform Commission of China66. Notably, throughout the whole life cycle, the electricity for rooftop PV comes from the evolving provincial power generation mix from 2025 to 2050. To capture parametric uncertainties, we also implemented a Monte Carlo simulation (10,000 iterations) on critical variables, including cleaning water usage, solar irradiation, and provincial grid mix.

We further evaluated future rooftop PV deployment schemes that incorporate economic feasibility to reflect realistic deployment constraints. Specifically, the 349 cities were ranked by their LCOE from low to high and divided into four quartiles. Development rates of 80%, 60%, 30%, and 10% were assigned to the first through fourth quartiles, respectively, capturing the decreasing likelihood of deployment with lower economic attractiveness. The economically adjusted rooftop PV potential for each city and SSP pathway was then calculated by multiplying the maximum technical potential by the corresponding development rate. Based on the city-level rooftop PV deployment schemes, we quantified the future carbon, water, and land benefits of rooftop PV. Simulation results are reported using the median (50th percentile) as the central estimate, with 95% confidence intervals (2.5th–97.5th percentiles) provided to characterize the uncertainty bounds. To further evaluate the broader significance of the carbon, water, and land benefits of rooftop PV, we benchmarked them against China’s national shares of planetary boundaries67, thereby assessing the extent to which rooftop PV can contribute to mitigating environmental pressures within safe operating limits.

Peer review rounds
[{"round": 1, "reviewer_comments": {"reviewer_1": "This study presents a systematic evaluation of the carbon mitigation, water savings, and land occupation benefits of rooftop\nPV systems at the city scale across China. It integrates LCA, SSP scenario modeling, and machine learning. The study\naddresses a timely issue using an interdisciplinary methodological framework. However, the\nmanuscript currently suffers\nfrom substantial issues regarding the applicability of key methodological assumptions, the completeness of result\ninterpretation, and the reliability and transparency of data sources. These limitations may lead to overstatements or even\nmisinterpretations of the conclusions. The manuscript cannot be considered for publication unless the authors resolve the\nfollowing major concerns:\n1. Data foundation and model training\n(1) The authors mention using web-scraping techniques to collect building data from 124 cities, but the original data sources,\ninterpretation methods, spatial resolution, or labeling accuracy are not disclosed. The spatial distribution of the sampled\ncities is also not provided.\n(2) A machine learning model trained on the 124-city sample is used to extrapolate rooftop PV deployment potential to 349\ncities. However, the authors do not report the generalization error across different geographic or urban typologies. It is\nrecommended to provide spatially resolved model performance metrics and identify areas with large uncertainties to improve\nthe credibility of extrapolated results.\n2. Assessment of carbon, water, and land benefits\n(3) The manuscript notes that previous studies may have overestimated rooftop PV carbon mitigation by 13–23% due to\nusing only operational emissions. While the authors adopt an LCA framework to estimate the lifecycle carbon, water, and\nland footprints of rooftop PV, the grid-side comparison benchmark (i.e., CE_grid,z,t) is based solely on direct emissions from\nelectricity generation, without incorporating the full lifecycle emissions of different energy types. This inconsistency in system\nboundaries introduces systematic bias. How do the lifecycle emissions of rooftop PV compare with those of conventional\nenergy sources under a consistent framework? The authors are advised to align system boundaries or at least conduct\nsensitivity analyses to quantify the impact of this assumption.\n(4) For estimating water and land savings, the authors compare provincial average grid water and land intensity with city-\nlevel rooftop PV indicators. However, electricity generation is spatially heterogeneous within provinces, and interprovincial\npower transfers are common. Comparing provincial averages directly with city-scale rooftop PV values introduces potential\nspatial mismatches. The authors should clarify how such uncertainty is addressed.\n3. Future scenario modeling\n(5) The manuscript reports that in certain cities, rooftop PV may increase emissions in the future, due to faster\ndecarbonization of the grid than the carbon savings from rooftop PV systems. While future grid structures are projected under\nSSP-based scenarios, the rooftop PV lifecycle parameters (carbon, water, and land) remain static. No explanation is given\nas to whether rooftop PV parameters are assumed to evolve over time (e.g., improved module efficiency, cleaner\nmanufacturing). The authors are encouraged to introduce at least two technology development paths (e.g., “no improvement”\nvs. “technical advancement”) and assess their impact on the conclusions. Without this, the “net-negative mitigation” claim\nmay be overstated.\n(6) Although the authors evaluate both the environmental benefits (carbon, water, land) and economic feasibility (e.g., LCOE,\ninvestment payback period) of rooftop PV deployment, these assessments are conducted in isolation. The scenario\nmodeling assumes widespread rooftop PV deployment regardless of regional differences in economic feasibility. It is\nrecommended that the authors integrate economic feasibility as a constraint in the future scenario simulations, to construct a\nmore realistic and policy-relevant cost–benefit trade-off framework.", "reviewer_2": "This study offers an impressive city-scale assessment of rooftop-PV deployment and its energy–carbon–water–land nexus\nfor 349 Chinese cities, weaving together machine-learning roof-area estimates, a seven-stage life-cycle assessment (LCA),\nand SSP–RCP scenario modeling through 2050. Its most striking insight is that once provincial grids decarbonize, rooftop\nPV can raise net-life-cycle CO₂ in several hydro-dominated provinces while water- and land-saving benefits continue to\ngrow. The work, therefore, moves the debate beyond “more solar = less carbon,” reminding policy-makers that the value\nproposition for distributed PV will shift from carbon to other resource metrics. The geographic resolution and multi-\ndimensional framing add real value to the literature. The methods are generally sound and rest on reputable data sets\n(CMIP6, Ecoinvent v3.7, Feng et al. 2014). Still, several simplifying assumptions —static PV parameters, deterministic\noutputs, a theoretical self-sufficiency index, and full-roof uptake—limit operational credibility. Addressing the issues below\nwill strengthen the science and its usefulness to decision-makers.\nMajor comments\n1. UESS is calculated as annual PV generation divided by annual demand, with no modeling of hourly load mismatch,\nstorage, or distribution network hosting limits. Please clarify the metric’s theoretical nature and discuss how diurnal\nmismatch and grid hosting limits could affect the headline results and policy relevance. Current wording risks being\ninterpreted as operational autonomy.\n2. Embodied-carbon factors: one size does not fit all. The “negative-mitigation” result rests on a single national LCA profile\nfor PV modules and the balance-of-system. Please discuss if region-specific module footprints could alter the conclusion.\n3. Module efficiency is fixed at 15 %, degradation is ignored, and life-cycle inputs stay constant. Model at least one learning-\ncurve scenario (higher efficiency, lower embodied CO₂, improved recycling) or bound the likely impact qualitatively.\n4. Roof-area prediction error, CMIP6 climate spread, and future grid-mix assumptions are all uncertain, yet outputs are\ndeterministic. Provide Monte-Carlo bands or simple high/low envelopes for key results such as pay-back times and nexus\nbenefits. Robustness cannot be judged otherwise.\n5. Assumption of full rooftop deployment: The core scenarios presume near-complete utilization of all “suitable” roofs. Add\nillustrative adoption rates (e.g., 30 % and 60 %) or explicitly discuss socio-economic barriers so readers can judge the\nresilience of the conclusions.\n6. Multi-gigawatt rooftop PV in dense urban networks will face hosting-capacity limits and potential curtailment. Include a\nconcise subsection summarising Chinese distribution-code limits, storage targets, and typical curtailment ranges to ground\nthe findings in operational reality.\n7. Explain why specific SSP–RCP pairs were chosen, note which combinations are most policy-relevant for China, and\ndiscuss scenarios not covered. Readers need to know what scenarios do—and do not—cover.\n8. Uniform water- and land-use factors: Cleaning frequency, dry-clean technologies, and the urban opportunity cost of roof\nspace vary widely. Flag these caveats or add a sensitivity range to avoid over-generalization across diverse climates.\nMinor comments\n1. Figures – add axis labels, R² / p-values where regression lines are shown, and uncertainty ribbons where feasible.\n2. Terminology – use “theoretical self-sufficiency” consistently; distinguish “built-up” from “hinterland” throughout.\n3. Units check – land-occupation values are reported as 33.8–64.3 ha kWh⁻¹, which seems dimensionally improbable;\nplease verify units (perhaps ha GWh⁻¹).\n4. Discussion – the section linking technical findings to policy is excellent; retain this strength."}, "author_response": "Thank you very much for your time involved in reviewing this\nmanuscript. We are grateful for your instructive comments that helped us improved\nthe clarity and breadth of the research. We hope that our modifications can address\nyour concerns. In this letter, we focus on each of your comments individually. To\nfacilitate your check, we restate your comments in black, our responses are\nprovided in blue, and the revised versions in the manuscript are shown in red\nitalic fonts.\n[C1] Data foundation and model training. The authors mention using web-scraping\ntechniques to collect building data from 124 cities, but the original data sources,\ninterpretation methods, spatial resolution, or labeling accuracy are not disclosed. The\nspatial distribution of the sampled cities is also not provided.\nResponse: We thank the reviewer for pointing out the need for more transparency\non data foundation. In the revised version, we showed the sampled city distribution\nand clarified the data processing in Supplementary Fig. 12. We specifically reported\nthe original data sources, interpretation methods, spatial resolution, and labeling\naccuracy in Lines 389-406 in Supplementary Information.\nSupplementary Fig. 10 | Sampled city distribution and rooftop PV potential area\nscreening process.\nSources: The dataset was constructed by integrating multiple sources. Vectorized\nbuilding footprints were primarily sourced from the Resource and Environment\nScience and Data Center7, Chinese Academy of Sciences. Building age and\nheight data were obtained via web-scraping from major real estate8 and map\nwebsites9 (amap.com). (2) Interpretation Methods and Spatial Resolution: The\nanalysis was conducted at the individual building level (i.e., vector footprints).\nA key interpretation method was the use of a random forest classification model\nto distinguish between flat and pitched roofs based on high-resolution Google\nMap imagery. The model classified roof types based on RGB color values. (3)\nLabeling Accuracy: The reliability of the foundational data was validated in the\noriginal study. The building footprint data was found to cover >95% of buildings\naccurately when compared with high-resolution maps. The random forest model\nfor roof type classification achieved a prediction accuracy of over 87% on the\ntest dataset. (4) Spatial Distribution of Sampled Cities for Model Training: For\nthe roof-type classification model, the training data consisted of over 10,000 roof\nsamples from seven representative cities selected via stratified random sampling\nbased on population across China’s different regions. The detailed descriptions\nof data processing are available in previous publication10.\n[C2] A machine learning model trained on the 124-city sample is used to extrapolate\nrooftop PV deployment potential to 349 cities. However, the authors do not report the\ngeneralization error across different geographic or urban typologies. It is recommended\nto provide spatially resolved model performance metrics and identify areas with large\nuncertainties to improve the credibility of extrapolated results.\nResponse: Thank you for the comment regarding the machine learning model’s\nperformances across different geographic or urban typologies. In response, we\nimplemented a 10-fold cross-validation framework based on both GDP quartiles and\nthe seven major geographical regions of China. Specifically, after the model\ngenerated predictions for each validation set, these predictions and their\ncorresponding true values were grouped based on the samples’ predefined GDP\nquartiles and geographical regions. Standard evaluation metrics were then\ncalculated independently for each subgroup. This granular evaluation allows for a\nrobust assessment of the model’s performance consistency across diverse data\nsubsets. The modifications are shown as follows.\nLines 573-578: To further examine the model’s stability, we conducted a\nstratified validation to assess its performance across China’s seven major\ngeographical regions and different economic levels (Supplementary Fig. 13).\nThe results indicate that the seven analyzed geographical regions exhibited R2\nvalues above 0.83, ranging from 0.831 in Southwest China to 0.912 in Northern\nChina (Supplementary Table 10). Notably, Northeast China recorded the highest\nerror metrics, with an MAE of 0.089 km2 and an RMSE of 0.671 km2. Additionally,\n2\nit was revealed the stable predictive performance across different levels of\neconomic development. When stratified into four tiers based on GDP (Low,\nLower-Mid, Upper-Mid, and High), the model achieved R2 of 0.863, 0.872, 0.880,\nand 0.867, respectively (Supplementary Table 2). The MAE ranges from 0.045\nkm2 to 0.091 km2, and RMSE ranges from 0.389 km2 to 0.756 km2 across the\nquartiles.\nSupplementary Table 1 | 10-fold cross-validation performance for rooftop PV\narea prediction in built-up areas across China’s seven geographical regions.\nRegion R2 MAE (km2) RMSE (km2)\nSouthwest China 0.831 0.054 0.134\nNorthwest China 0.839 0.025 0.276\nEastern China 0.867 0.049 0.523\nSouthern China 0.874 0.077 0.189\nCentral China 0.898 0.037 0.253\nNortheast China 0.910 0.089 0.671\nNorthern China 0.912 0.022 0.096\nSupplementary Table 2 | 10-fold cross-validation performance for rooftop PV\narea prediction in built-up areas, stratified by city-level GDP.\nGDP class R2 MAE (km2) RMSE (km2)\nLow GDP 0.863 0.091 0.481\nLower-Mid GDP 0.872 0.057 0.756\nUpper-Mid GDP 0.880 0.062 0.389\nHigh GDP 0.867 0.045 0.413\nSupplementary Fig. 11 | Geographical and economic stratification of Chinese\ncities for machine learning performance testing. a, Distribution of cities across\nseven geographical regions. b, Classification of cities into four categories based\n3\non GDP, from low to high.\n[C3] Assessment of carbon, water, and land benefits. The manuscript notes that\nprevious studies may have overestimated rooftop PV carbon mitigation by 13–23% due\nto using only operational emissions. While the authors adopt an LCA framework to\nestimate the lifecycle carbon, water, and land footprints of rooftop PV, the grid-side\ncomparison benchmark (i.e., CE_grid,z,t) is based solely on direct emissions from\nelectricity generation, without incorporating the full lifecycle emissions of different\nenergy types. This inconsistency in system boundaries introduces systematic bias. How\ndo the life cycle emissions of rooftop PV compare with those of conventional energy\nsources under a consistent framework? The authors are advised to align system\nboundaries or at least conduct sensitivity analyses to quantify the impact of this\nassumption.\nResponse: We appreciate the reviewer’s valuable comment regarding system\nboundary consistency. In the revised manuscript, we have undertaken a systematic\nand significant revision of the methodology to establish a harmonized life cycle\nassessment framework for both rooftop PV and the grid electricity. Specifically, we\ndeveloped life cycle impact factors for each provincial grid. This was accomplished\nusing a weighted average method, which combines each province’s electricity\ngeneration mix—that is, the respective shares of coal, gas, biomass, nuclear, hydro,\nwind, and solar—with the life cycle impact coefficients for each of those\ntechnologies. This approach ensures that our comparison between rooftop PV and\nthe grid begins from a consistent foundation. Crucially, the life cycle assessment\nframework was dynamically integrated into our future scenarios (2025–2050). The\nlife cycle impact factor for each provincial grid was recalculated for future years to\naccount for two concurrent dynamics: the projected evolution of the provincial\nelectricity generation mix, and the changing life cycle impacts of the generation\ntechnologies themselves, notably incorporating updated life cycle inventories for\nwind and solar power to reflect their rapid technological advancement.\nMethodological revisions of grid’s life cycle factors are shown as follows. As for\nthe consideration of future life cycle changes, please see our response of comment\n5.\nLines 639-647: Following the guidelines from the IEA PVPS Task 12 on the Life\nCycle Assessment of PV electricity57, we employed a weighted average method\nto establish the life cycle impact factors for each provincial power grid. This\napproach calculates the provincial grid factor by multiplying the life cycle\ncoefficient for each generation technology by its respective share in that\nprovince’s electricity mix. The analysis incorporated coefficients for three impact\ncategories: carbon emissions and land occupation sourced from the Ecoinvent\nv3.11 database, and water consumption58. These factors were applied to the full\nportfolio of generation technologies, including coal, gas, biomass, hydro, wind,\nsolar, and nuclear power59.\n4\n[C4] For estimating water and land savings, the authors compare provincial average\ngrid water and land intensity with city-level rooftop PV indicators. However, electricity\ngeneration is spatially heterogeneous within provinces, and interprovincial power\ntransfers are common. Comparing provincial averages directly with city-scale rooftop\nPV values introduces potential spatial mismatches.\nResponse: Thank you for this important point regarding spatial scale. We agree that\nthis is a critical aspect of grid analysis. First, we acknowledge that interprovincial\npower transfers are a reality of China’s power system. However, we point out that it\nis difficult to obtain detailed, city-level electricity supply and flow data in China, as\nsuch information is not publicly available. We consequently made two assumptions\nin this study: (1) A city’s electricity consumption is assumed to be sourced directly\nfrom its provincial grid. (2) We assume that the provincial grid prioritizes meeting\nits own supply, without considering interprovincial imports or exports.\nBased on these two assumptions, we believe that the electricity generated by rooftop\nPV in a city directly offsets the electricity supply from its provincial grid. Hence,\nusing the provincial average grid intensity as the substitution benchmark is a\nreasonable approach. Although this does introduce a potential spatial mismatch, it\nis a practical and defensible method given the current data availability, and it is\nconsistent with the resolution of available official data (e.g., from the Ministry of\nEcology and Environment), which is provided at the provincial and regional level.\nFurthermore, electricity dispatch and planning in China are managed primarily at\nthe provincial level. Distributed PV generation within a city displaces electricity\ndrawn from this integrated provincial grid, making the provincial average factor the\nappropriate proxy for the actual electricity being displaced.\nLines 660-672: When estimating the environmental benefits (e.g., carbon, water,\nand land savings) of rooftop PV systems, we compared city-level rooftop PV\nindicators with provincial-level grid intensities. Due to the lack of publicly\navailable, detailed city-level electricity supply and flow data, we made a core\nassumption in this research: a city’s electricity consumption is assumed to be\nsourced directly from its provincial grid, and the provincial grid is treated as an\nindependent supply-demand balancing system, without explicitly considering\ninterprovincial power imports or exports. This is based on the operational reality\nof China’s power system and the highest resolution of available official data. In\npractice, electricity dispatch and planning are primarily managed at the\nprovincial level, that is, the distributed PV generation within a city offsets power\nfrom the provincial grid, which is supplied by a mixture of in-province generation\nand potential imports. Under these data constraints, the provincial average\nintensity is considered the proxy for the electricity being displaced.\n[C5] Future scenario modeling. The manuscript reports that in certain cities, rooftop\nPV may increase emissions in the future, due to faster decarbonization of the grid than\nthe carbon savings from rooftop PV systems. While future grid structures are projected\nunder SSP-based scenarios, the rooftop PV lifecycle parameters (carbon, water, and\nland) remain static. No explanation is given as to whether rooftop PV parameters are\nassumed to evolve over time (e.g., improved module efficiency, cleaner manufacturing).\nThe authors are encouraged to introduce at least two technology development paths\n(e.g., “no improvement” vs. “technical advancement”) and assess their impact on the\nconclusions. Without this, the “net-negative mitigation” claim may be overstated.\nResponse: Thank you for your constructive comment, which has significantly\nstrengthened our analysis. In response, we applied the full life cycle assessment\nframework. We have revised the methodology to concurrently model the evolving\nlife cycle impacts for both the future power grid and rooftop PV systems from 2025\nto 2050 (Supplementary Fig. 13). This has led to a crucial refinement of our\nconclusions. The previous finding of potential “net-negative mitigation,” an\noverstatement resulting from the static PV assumption, has now been corrected.\nSpecifically, the regions previously identified as potentially net-negative are now\ncorrectly shown to have the lowest positive mitigation benefits. This finding is\nattributable to their hydropower-dominant, low-carbon grid electricity, which offers\nless room for carbon displacement by PV. This updated overall result stems from\ntwo key methodological enhancements: applying a full life cycle assessment to the\ngrid and accounting for the technological advancement of rooftop PV. Crucially, our\ncentral thesis about the shifting role of rooftop PV not only holds but is now on\nstronger footing. Additionally, as you suggested, we have conducted the analysis for\ntwo technology pathways (“with technological advancement” vs “without\ntechnological advancement”), and the comparisons are now provided in\nSupplementary Fig. 9.\nSupplementary Fig. 13 | Dynamic life cycle assessment framework of future\nrooftop PV and power grid.\nLines 746-773: To represent the future evolution of provincial power grids, we\nadopted optimized generation structures for 2025–2050 derived from a\ncomprehensive energy modeling framework62. Based on these dynamic mixes, we\n6\ncalculated the grid intensities for carbon, water, and land through lifespans. The\nassessment covered a full portfolio of generation technologies, including coal,\ngas, biomass, hydro, wind, solar, and nuclear power. With a specific focus on\nwind and solar as the key drivers of decarbonization, the onshore wind and\nutility-scale solar PV were selected as the representative technologies to reflect\ntechnological advancement in the power grid system, while the life cycle\nintensities of other generation technologies were held constant. To be specific,\nwe estimated the future annual generation of wind and solar based on\ntechnology-specific drivers (improving capacity factors for wind63, and changing\nsolar radiation and conversion efficiency for PV). We also updated the future life\ncycle inventories of wind and solar, including embodied material impact\ndecrease and recycling rate increase. See Supplementary Fig. 13 for the details.\nConcurrently, we developed a dynamic life cycle inventory framework\nspecifically for rooftop PV systems to model their distinct technological\ntrajectory. Key assumptions were as follows: recycling rates for major module\nmaterials (limestone, soda, silica sand, copper, and aluminum) were assumed to\nincrease at a conservative annual rate of 2%, reflecting progressive material\nrecovery policies; the embodied environmental intensities of non-electrical\ncomponents, including glass, aluminum frames, and mounting brackets, were\nassumed to decline by 1% per year, representing cleaner material production64;\nPV module degradation was incorporated, with a rate of 2% in the first year and\n0.55% annually thereafter, based on field investigation; and PV module\nconversion efficiency was projected to improve at an annual rate of 0.35%,\nstarting from a 2020 baseline of 16%, according to the Energy Research Institute\nof the National Development and Reform Commission of China65. To capture\nparametric uncertainties, we implemented a Monte Carlo simulation (10,000\niterations) on critical variables, including solar irradiation, and the provincial\ngrid mix.\nwith and without technological advancement of rooftop PV. The panels show the\ntemporal evolution of unit life cycle benefits (top row) and environmental\npayback periods (bottom row) for rooftop PV across 349 Chinese cities. The top\nrow displays the benefits for a, carbon mitigation, b, water saving, and c, land\nsaving. The bottom row shows the corresponding payback periods in years for d,\ncarbon, e, water, and f, land. Each panel uses a hybrid violin-boxplot, where the\nviolin shape shows the distribution density of city-level results, and the embedded\nboxplot indicates the median and interquartile range. Individual grey dots\nrepresent the results for each city, and the city values are sourced from the\nmedian in 95% confidential intervals.\n[C6] Although the authors evaluate both the environmental benefits (carbon, water, land)\nand economic feasibility (e.g., LCOE, investment payback period) of rooftop PV\ndeployment, these assessments are conducted in isolation. The scenario modeling\nassumes widespread rooftop PV deployment regardless of regional differences in\neconomic feasibility. It is recommended that the authors integrate economic feasibility\nas a constraint in the future scenario simulations, to construct a more realistic and\npolicy-relevant cost–benefit trade-off framework.\nResponse: We agree that integrating economic feasibility is essential for creating\nrealistic and policy-relevant deployment scenarios. Our revised process is as follows:\nFirst, we calculate the Levelized Cost of Electricity (LCOE) for rooftop PV in each\ncity. We then rank all cities based on their economic attractiveness, specifically from\nthe lowest LCOE (most attractive) to the highest (least attractive). Based on this\nranking, we group the cities into four quartiles and assign differentiated, accelerated\ndeployment rates to the more economically favorable quartiles (i.e., those with\nlower LCOE). This ensures our scenarios now reflect market-driven investment\nlogic, where more profitable regions are developed first. To further enhance the\nrobustness of this new framework, we incorporated a 10,000-iteration Monte Carlo\nsimulation to address uncertainties in the underlying potential area. Consequently,\nour final results for economically-adjusted deployment are now presented as a\nmedian (50th percentile) estimate with a 95% confidence interval (2.5th–97.5th\npercentiles). We believe this probabilistic and economically-constrained approach\ndirectly addresses your comment and provides a much more realistic and policy-\nrelevant assessment.\nLines 365-373: We further developed economically-constrained deployment\nscenarios to reflect the benefits of market attractiveness on city-level rooftop PV\nrollout. To achieve this, we ranked and grouped all 349 cities into four quartiles\nbased on their Levelized Cost of Electricity (LCOE). We then applied tiered\ndeployment rates to these quartiles to simulate a market-driven pathway where\nmore profitable regions are prioritized. Specifically, these rates were set at 80%,\n60%, 30%, and 10%, corresponding to the quartiles from the most economically\nattractive (lowest LCOE) to the least. The final deployment level for each city\nunder a given SSP scenario was then determined by multiplying its maximum\ntechnical potential by its assigned quartile-specific rate.\nFig. 5 | Future energy-carbon-water-land nexus performance of urban rooftop\nPV systems. a,b,c, National carbon mitigation, water saving, and land saving of\nurban rooftop PV. The results were reported as the median (50th percentile), with\n95% confidence intervals (2.5th–97.5th percentiles) to capture uncertainty\nbounds. Note that the benefits refer to the economic feasibility-based deployment\npathway.\nThis study offers an impressive city-scale assessment of rooftop-PV deployment and\nits energy–carbon–water–land nexus for 349 Chinese cities, weaving together machine-\nlearning roof-area estimates, a seven-stage life-cycle assessment (LCA), and SSP–RCP\nscenario modeling through 2050. Its most striking insight is that once provincial grids\ndecarbonize, rooftop PV can raise net-life-cycle CO₂ in several hydro-dominated\nprovinces while water- and land-saving benefits continue to grow. The work, therefore,\nmoves the debate beyond “more solar = less carbon,” reminding policy-makers that the\nvalue proposition for distributed PV will shift from carbon to other resource metrics.\nThe geographic resolution and multi-dimensional framing add real value to the\nliterature. The methods are generally sound and rest on reputable data sets (CMIP6,\nEcoinvent v3.7, Feng et al. 2014). Still, several simplifying assumptions —static PV\nparameters, deterministic outputs, a theoretical self-sufficiency index, and full-roof\nuptake—limit operational credibility. Addressing the issues below will strengthen the\nscience and its usefulness to decision-makers.\nResponse: We sincerely thank the Reviewer for the positive evaluation of our\nmanuscript and for the constructive feedback. Your comments have significantly\nimproved the clarity and breadth of our analysis. We have revised the manuscript to\naddress your concerns and below we provide detailed responses to each of the points\nraised. To facilitate your check, we restate your comments in black, our responses\nare provided in blue, and the revised versions in the manuscript are shown in red\nitalic fonts.\nMajor comments\n[C1] UESS (urban electricity self sufficiency) is calculated as annual PV generation\ndivided by annual demand, with no modeling of hourly load mismatch, storage, or\ndistribution network hosting limits. Please clarify the metric’s theoretical nature and\ndiscuss how diurnal mismatch and grid hosting limits could affect the headline results\nand policy relevance. Current wording risks being interpreted as operational autonomy.\nResponse: Thank you for this point. We have revised the manuscript to\nexplicitly state that UESS reflects a theoretical maximum under idealized conditions.\nAdditionally, we have added the discussion on how temporal load-generation\nmismatch and grid hosting limits could reduce the practical feasibility of achieving\nthe reported UESS values and influence the policy implications. The revised text is\nprovided below.\nLines 529-538: Second, the estimated urban electricity self-sufficiency in this\nstudy is a theoretical maximum, based on annual generation and demand. It does\nnot account for two key grid integration challenges, namely the diurnal mismatch\nbetween midday solar generation and evening peak demand, which may lead to\n10\nenergy curtailment without sufficient storage, and the limited grid hosting\ncapacity of urban networks, which may need significant upgrades to support\ndense rooftop PV. As a consequence, while our study maps the available strategic\nbenefits, translating this theoretical potential into tangible benefits is contingent\nupon concurrent policy and investment in energy storage and smart grid\nmodernization.\n[C2] Embodied-carbon factors: one size does not fit all. The “negative-mitigation”\nresult rests on a single national LCA profile for PV modules and the balance-of-system.\nPlease discuss if region-specific module footprints could alter the conclusion.\nResponse: Thank you for this important point on spatial heterogeneity. We agree\nthat this is a critical aspect and have revised the manuscript to clarify our\nmethodology and its justification. Our study employs a robust hybrid approach that\ncombines a detailed, national-representative Life Cycle Inventory (LCI) with the\nlocalized data in operational stage.\nTo be specific, we first established a comprehensive LCI representing the\nmainstream of China’s PV industry, covering the full life cycle of the PV system.\nThis includes not only upstream material production and module manufacturing, but\nalso installation, transportation from production hubs to installation sites, and the\nend-of-life stages of decommissioning, recycling and disposal. This national-level\napproach is justified because China’s PV manufacturing is highly standardized and\ngeographically concentrated (e.g., in Jiangsu, Anhui), while installation, transport\nlogistics, and waste management practices typically follow national norms, patterns,\nand policies. Therefore, a national average LCI serves as a reasonable\napproximation for these stages.\nCrucially, for the operational phase: We calculated city-specific life cycle impacts\nby considering the localized lifetime electricity generation in each of the 349 cities.\nThis step is vital as local solar irradiation than affect PV generation is the dominant\ndriver of spatial variability in the final life cycle performance.\nNevertheless, we acknowledge that our model does not capture all local variabilities.\nTherefore, we have added the following paragraph to the Discussion section to\nexplicitly state these limitations.\nLines 538-545: In addition, our model does not fully parameterize all local\nvariabilities of different cities, such as diverse rooftop uses, specific maintenance\ntechnologies, or the explicit link between operational practices and regional\nconditions. For instance, while our uncertainty analysis included a wide range\nof variables (e.g., rooftop rental fees and varied cleaning practices), these factors\nwere not formally modeled against specific geographic characteristics like local\ndust and rainfall patterns, which influence optimal cleaning frequency. Therefore,\n11\nfuture project-scale and region-specific studies will be essential to further refine\nthese life cycle estimates.\n[C3] Module efficiency is fixed at 15 %, degradation is ignored, and life-cycle inputs\nstay constant. Model at least one learning-curve scenario (higher efficiency, lower\nembodied CO₂, improved recycling) or bound the likely impact qualitatively.\nResponse: Thank you for your constructive comment, which has significantly\nstrengthened our analysis. In response, we have revised the methodology to\nconcurrently model the evolving life cycle impacts for both the future power grid\nand rooftop PV systems from 2025 to 2050 (Supplementary Fig. 15). This has led\nto a crucial refinement of our conclusions. The previous finding of potential “net-\nnegative mitigation,” an overstatement resulting from the static PV assumption, has\nnow been corrected. Specifically, the regions previously identified as potentially\nnet-negative are now correctly shown to have the lowest positive mitigation benefits.\nThis finding is attributable to their hydropower-dominant, low-carbon grid\nelectricity, which offers less room for carbon displacement by PV. This updated\noverall result stems from two key methodological enhancements: applying a full life\ncycle assessment to the grid and accounting for the technological advancement of\nrooftop PV. Crucially, our central thesis about the shifting role of rooftop PV not\nonly holds but is now on stronger footing. Additionally, as you suggested, we have\nconducted the analysis for two technology pathways (“with technological\nadvancement” vs “without technological advancement”), and the comparisons are\nnow provided in Supplementary Table 11.\nSupplementary Fig. 13 | Dynamic life cycle assessment framework of future\nrooftop PV and power grid.\nLines 746-773: To represent the future evolution of provincial power grids, we\nadopted optimized generation structures for 2025–2050 derived from a\ncomprehensive energy modeling framework62. Based on these dynamic mixes, we\n12\ncalculated the grid intensities for carbon, water, and land through lifespans. The\nassessment covered a full portfolio of generation technologies, including coal,\ngas, biomass, hydro, wind, solar, and nuclear power. With a specific focus on\nwind and solar as the key drivers of decarbonization, the onshore wind and\nutility-scale solar PV were selected as the representative technologies to reflect\ntechnological advancement in the power grid system, while the life cycle\nintensities of other generation technologies were held constant. To be specific,\nwe estimated the future annual generation of wind and solar based on\ntechnology-specific drivers (improving capacity factors for wind63, and changing\nsolar radiation and conversion efficiency for PV). We also updated the future life\ncycle inventories of wind and solar, including embodied material impact\ndecrease and recycling rate increase. See Supplementary Fig. 13 for the details.\nConcurrently, we developed a dynamic life cycle inventory framework\nspecifically for rooftop PV systems to model their distinct technological\ntrajectory. Key assumptions were as follows: recycling rates for major module\nmaterials (limestone, soda, silica sand, copper, and aluminum) were assumed to\nincrease at a conservative annual rate of 2%, reflecting progressive material\nrecovery policies; the embodied environmental intensities of non-electrical\ncomponents, including glass, aluminum frames, and mounting brackets, were\nassumed to decline by 1% per year, representing cleaner material production64;\nPV module degradation was incorporated, with a rate of 2% in the first year and\n0.55% annually thereafter, based on field investigation; and PV module\nconversion efficiency was projected to improve at an annual rate of 0.35%,\nstarting from a 2020 baseline of 16%, according to the Energy Research Institute\nof the National Development and Reform Commission of China65. To capture\nparametric uncertainties, we implemented a Monte Carlo simulation (10,000\niterations) on critical variables, including cleaning water usage, solar\nirradiation, and the provincial grid mix.\nwith and without technological advancement of rooftop PV. The panels show the\ntemporal evolution of unit life cycle benefits (top row) and environmental\npayback periods (bottom row) for rooftop PV across 349 Chinese cities. The top\nrow displays the benefits for a, carbon mitigation, b, water saving, and c, land\nsaving. The bottom row shows the corresponding payback periods in years for d,\ncarbon, e, water, and f, land. Each panel uses a hybrid violin-boxplot, where the\nviolin shape shows the distribution density of city-level results, and the embedded\nboxplot indicates the median and interquartile range. Individual grey dots\nrepresent the results for each city, and the city values are sourced from the\nmedian in 95% confidential intervals.\n[C4] Roof-area prediction error, CMIP6 climate spread, and future grid-mix\nassumptions are all uncertain, yet outputs are deterministic. Provide Monte-Carlo bands\nor simple high/low envelopes for key results such as pay-back times and nexus benefits.\nRobustness cannot be judged otherwise.\nResponse: We agreed the necessity of providing uncertainty ranges to enhance the\nresult robustness. In the revised manuscript, we explicitly accounted for key sources\nof uncertainty in our analysis, including roof area prediction errors, variation across\nCMIP6 climate models, and future electricity grid mix. We incorporated these\nuncertainties into a Monte Carlo simulation framework, generating probabilistic\ndistributions for future rooftop PV area and installed capacity in Fig. 4. Future\nprojections of city-level rooftop PV generation, nexus benefits of carbon mitigation,\nwater saving, and land saving, as well as payback periods with 95% confidence\nintervals are provided in the Supplementary Fig. 11, as shown in last response.\nFig. 4 | Trends of urban rooftop PV potential under future SSP-RCP scenarios.\na, Changes in national rooftop PV area under the SSP scenarios. Solid lines\nrepresent the median (50th percentile) estimates, and shaded ribbons indicate\nthe 95% confidence intervals. b, Changes in national rooftop PV installed\ncapacity potential under the SSP-RCP scenarios. The capacity potential is\ncomputed based on the rooftop PV area from SSP scenarios and total power\ngeneration from RCP scenarios (see Methods). Solid lines show the median\nvalues, and shaded ribbons represent the 95% confidence intervals.\n14\n[C5] Assumption of full rooftop deployment: The core scenarios presume near-\ncomplete utilization of all “suitable” roofs. Add illustrative adoption rates (e.g., 30 %\nand 60 %) or explicitly discuss socio-economic barriers so readers can judge the\nresilience of the conclusions.\nResponse: We agree that integrating economic feasibility is essential for creating\nrealistic and policy-relevant deployment scenarios. Our revised process is as follows:\nFirst, we calculate the Levelized Cost of Electricity (LCOE) for rooftop PV in every\ncity. We then rank all cities based on their economic attractiveness, specifically from\nthe lowest LCOE (most attractive) to the highest (least attractive). Based on this\nranking, we group the cities into four quartiles and assign differentiated, accelerated\ndeployment rates to the more economically favorable quartiles (i.e., those with\nlower LCOE). This ensures our scenarios now reflect market-driven investment\nlogic, where more profitable regions are developed first. To further enhance the\nrobustness of this new framework, we incorporated a 10,000-iteration Monte Carlo\nsimulation to address uncertainties in the underlying potential area. Consequently,\nour final results for economically-adjusted deployment are now presented as a\nmedian (50th percentile) estimate with a 95% confidence interval (2.5th–97.5th\npercentiles). We believe this probabilistic and economically-constrained approach\ndirectly addresses your comment and provides a much more realistic and policy-\nrelevant assessment. In addition, we supplemented the related discussion on the\nsocio-economic barriers for deploying rooftop PV, as you commented.\nLines 365-373: We further developed economically-constrained deployment\nscenarios to reflect the benefits of market attractiveness on city-level rooftop PV\nrollout. To achieve this, we ranked and grouped all 349 cities into four quartiles\nbased on their Levelized Cost of Electricity (LCOE). We then applied tiered\ndeployment rates to these quartiles to simulate a market-driven pathway where\nmore profitable regions are prioritized. Specifically, these rates were set at 80%,\n60%, 30%, and 10%, corresponding to the quartiles from the most economically\nattractive (lowest LCOE) to the least. The final deployment level for each city\nunder a given SSP scenario was then determined by multiplying its maximum\ntechnical potential by its assigned quartile-specific rate.\nFig. 5 | Future energy-carbon-water-land nexus performance of urban rooftop\nurban rooftop PV. The results were reported as the median (50th percentile), with\n95% confidence intervals (2.5th–97.5th percentiles) to capture uncertainty\nbounds. Note that the benefits refer to the economic feasibility-based deployment\npathway.\nLines 520-529: We further note several key limitations and directions for future\nresearch. First, while this study quantifies technical and environmental potential,\nactual realization depends on socio-economic and institutional factors: upfront\ninvestment and financing constraints, electricity tariff structures, permitting\nrequirements, availability of skilled labor, and heterogeneity in local policy\nsupport. Socioeconomic barriers such as rooftop ownership disputes and varying\npublic acceptance can significantly affect adoption rates53. Future research\nshould incorporate these dynamics into scenario modeling, possibly through\nsurvey-based data on policy effectiveness and public attitudes to better capture\nthe gap between technical potential and feasible implementation26.\n[C6] Multi-gigawatt rooftop PV in dense urban networks will face hosting-capacity\nlimits and potential curtailment. Include a concise subsection summarising Chinese\ndistribution-code limits, storage targets, and typical curtailment ranges to ground the\nfindings in operational reality.\nResponse: Thank you for the remark. In the revised manuscript, we now added\ndiscussion that addresses operational constraints relevant to rooftop PV deployment,\nincluding Chinese distribution-code limits, current and planned storage targets, and\ntypical curtailment ranges observed in practice.\nLines 481-491: Beyond economic hurdles, large-scale grid integration faces\nphysical and operational constraints. National technical codes strictly regulate\nPV penetration to prevent voltage excursions and equipment overload, while\ncomplex interconnection standards create additional hurdles44. Furthermore,\nChina’s goal to install 30 GW of energy storage by 2025 is insufficient to support\nthe vast rooftop PV potential, and deployment often lags behind targets45. While\nsome provinces now mandate 10% storage with 2-hour duration, curtailment\nrates of 5–10% or higher are unavoidable in some regions without adequate\nstorage and grid upgrades46–52. Maximizing the value of rooftop PV will therefore\ndepend on flexible interconnection standards, accelerated storage integration,\nand improved regional balancing.\n[C7] Explain why specific SSP–RCP pairs were chosen, note which combinations are\nmost policy-relevant for China, and discuss scenarios not covered. Readers need to\nknow what scenarios do—and do not—cover.\nResponse: Thank you for the comment, please check the revision as follows.\nConcentration Pathways (RCPs)—RCP2.6, RCP4.5, and RCP8.5, our study\nevaluates a matrix of 15 future scenarios. This combination follows the standard\ndesign of the CMIP6 framework and was deliberately chosen to systematically\nexplore a wide range of plausible futures. We note that our analysis does not\ncover all possible variants, such as SSP370, which represents a future of\nheightened regional rivalry, and the exploration of which is beyond the scope of\nthis study. Our scenario framework encompasses a spectrum from a sustainable,\nstringent mitigation future aligned with the Paris Agreement (e.g., SSP126), to a\n“middle-of-the-road” trajectory (e.g., SSP245), and a high-emission, fossil-\nfueled development pathway (e.g., SSP585). Within this framework, scenarios\nsuch as SSP126 and SSP245 are particularly relevant to China’s policy context\nand long-term carbon neutrality goals.\n[C8] Uniform water- and land-use factors: Cleaning frequency, dry-clean technologies,\nand the urban opportunity cost of roof space vary widely. Flag these caveats or add a\nsensitivity range to avoid over-generalization across diverse climates\nResponse: We thank the reviewer for the insightful comment regarding the regional\nheterogeneities of key operational and economic parameters. To address the\nvariability in operational cleaning practices, we adjusted the life cycle inventories\nby considering diverse operational cleaning techniques and frequencies, through the\ncompany interview towards different rooftop PV cleaning techniques, including\ncleaning robots, cleaning drones, and handheld cleaning brushes. We integrated the\ncleaning frequency and technology dimensions into the life cycle assessment\nframework, and the uncertainties are reflected into the PV life cycle results and\nbenefits. As the PV cleaning techniques are new-born industry, we cannot get\nsufficient information on future development, hence we assumed the investigated\nPV cleaning parameters maintain in the future. In addition, we regarded rooftop\nrental fee as opportunity cost and incorporated it into the economic assessment. To\nbetter reflect the current industry practices, we conducted a survey of rooftop PV\noperators. The results showed that there were significant market differences in\nrooftop rental rates. The overall rent for rooftop PV systems ranges from 0.42 to 2.8\nUSD per meter square. We selected 0.42 USD, 1.4 USD, and 2.8 USD within this\nrange for uncertainty analysis. Furthermore, the PV deployment scenarios were set\nbased on rent-combined economic feasibility deployment scenario in the response\nof comment 5.\nFollowing the reviewer’s suggestion, we have revised the Discussion section to\nexplicitly flag the caveats related to regional heterogeneity. We now directly discuss\nthe variability in the opportunity cost of roof space, acknowledging that factors like\nproperty ownership and local regulations create significant regional differences.\nFurthermore, our scenario-based analysis of cleaning frequencies and technologies\nis designed to capture a plausible range of real-world operational conditions. We\n17\nhave also added a statement acknowledging that these cleaning practices are heavily\ninfluenced by diverse climates. While our study does not formally parameterize\nthese climate-specific variations, the uncertainty analysis on cleaning frequency and\ntechnology serves to represent the potential magnitude of this variability, thereby\nmitigating the over-generalization of using a single, uniform factor.\nSupplementary Table 3 | Water usage and energy demand of operational cleaning\ntechniques, based on authors’ interview.\nPV Water Energy Life Life cycle Life Life cycle\ncleaning usage (L demand cycle cleaning cycle cleaning\ntechniqu kW-1 (kWh cleanin energy cleanin energy\ne each kW-1 g water consumpti g water consumpti\ncompan cleanin each usage on (kWh m- usage on (kWh m-\ny g) cleanin (L m-2, 2, 12 times (L m-2, 2, 24 times\ng) 12 a year) 24 a year)\ntimes a times a\nyear) year)\n#1 4.41 0.00135 270 0.08 540 0.17\n#2 1.51 0.131 92.5 8.02 184.9 16.04\n#3 4 0.00325 278 0.20 556 0.4\n#4 4.7 0.00116 287.8 0.07 575.5 0.14\n#5 4.7 0.00135 287.8 0.08 575.5 0.17\n#6 9.88 0.0078 605 0.48 1210.3 0.96\nSupplementary Table 6 | Rooftop PV rent summary from authors’ investigation.\nRooftop PV Annual rent (USD PV panel size (m2 each Rooftop rent\noperating each panel) panel) (USD per meter\ncompany square)\n#1 2.8 3.15 0.889\n#2 2.8 3.15 0.889\n#3 3.5 3.15 1.1116\n#4 3.5 2.79 1.2544\n#5 3.5 2.79 1.2544\n#6 3.5 2.79 1.2544\n#7 5.6 2.79 2.0076\n#8 7.0 3.15 2.2218\n#9 8.4 3.15 2.667\n#10 2.1 3.15 0.6664\n#11 1.4 3.15 0.4438\nrooftop PV in China. The analysis covers 349 cities, which are ranked on the x-\naxis from the lowest to the highest baseline Levelized Cost of Electricity (LCOE).\na, City-level LCOE under three annual rooftop rent scenarios: 0.42, 1.4, and 2.8\nUSD (representing low, medium, and high cost assumptions, respectively). Lines\nrepresent the median LCOE from a Monte Carlo simulation, and the surrounding\nribbons show the 95% confidence interval. b, User-side grid parity level,\nshowing the ratio of rooftop PV LCOE to the local commercial and industrial\nelectricity price. Values below the dashed line (parity = 1) indicate economic\nviability for the end-user. c, Plant-side grid parity level, showing the ratio of\nrooftop PV LCOE to the provincial benchmark price for coal-fired power. Values\nbelow the dashed line indicate cost-competitiveness with wholesale electricity\ngeneration.\nLines 538-545: In addition, our model does not fully parameterize all local\nvariabilities of different cities, such as diverse rooftop uses, specific maintenance\ntechnologies, or the explicit link between operational practices and regional\nconditions. For instance, while our uncertainty analysis included a wide range\nof variables (e.g., rooftop rental fees and varied cleaning practices), these factors\nwere not formally modeled against specific geographic characteristics like local\ndust and rainfall patterns, which influence optimal cleaning frequency. Therefore,\nfuture project-scale and region-specific studies will be essential to further refine\nthese life cycle estimates.\n[C9] Figures – add axis labels, R²/p-values where regression lines are shown,\nand uncertainty ribbons where feasible.\nResponse: The figure has been updated in the revised manuscript.\n[C10] Terminology – use “theoretical self-sufficiency” consistently; distinguish “built-\nup” from “hinterland” throughout.\nResponse: We have clarified the theoretical self-sufficiency through the manuscript\nand distinguish “built-up” from “hinterland”.\n[C11] Units check – land-occupation values are reported as 33.8–64.3 ha kWh⁻¹,\nwhich seems dimensionally improbable; please verify units (perhaps ha GWh⁻¹).\nResponse: Thank you for the kind reminder, we now corrected the unit in the revised\nversion.\n[C12] Discussion – the section linking technical findings to policy is excellent;\nretain this strength.\nResponse: Thank you very much for the positive comment and encouragement, we\npolished the discussion and whole manuscript further.\n20"}, {"round": 2, "reviewer_comments": {"reviewer_1": "The authors have done considerable work in this revision. The manuscript is now\nsubstantially improved, and the major concerns from the previous round have been\naddressed. There are just a few points, detailed below, that require further clarification.", "reviewer_2": "The authors have made substantial and thoughtful revisions to the manuscript. All major\nconcerns raised in the previous round, particularly regarding static PV assumptions,\ndeterministic outputs, full rooftop deployment, and embodied carbon sensitivity, have\nbeen carefully addressed through solid methodological improvements.\nThe revised paper now offers a rigorous and policy-relevant analysis of the city scale’s\nrooftop PV–energy–water–land–carbon nexus. Its technical quality, clarity, and framing\nare fully consistent with the standards of Nature Communications, and the results will\nbe valuable to researchers and policy makers working on urban energy and\nsustainability.", "reviewer_3": "[C1] Major Concern: A previous publication\n(https://www.sciencedirect.com/science/article/pii/S2666916125000660?via=ihub)\nexamines the rooftop potential for urban agriculture using the same rooftop database\nfor the same 124 cities. Although the current study expands the spatial scope to 349\ncities, it appears that both studies are “competing” for the same rooftops. This overlap\nshould be explicitly acknowledged and discussed in detail within the manuscript.\nReviewer noted, PV installation is feasible on sloped rooftops when their orientation\nis favorable. As stated in the last comment, 39% of the pitched-rooftop areas, those\noriented toward the south, southeast, or southwest, were retained in the analysis due\nto its adequate solar exposure. The remaining pitched rooftops with unfavorable\norientations were omitted from the potential calculation.\n[C7] In addition to the criteria in Supplementary Fig. 10, the evaluation of rooftop\npotential should consider building ownership. It cannot be assumed that all privately\nowned buildings will install PV systems. A correction coefficient should be introduced,\nalong with a discussion to support this assumption."}, "author_response": ""}]

Decision letter