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Scientific Reports volume 15, Article number: 24516 (2025)
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Global climate change is an urgent environmental challenge demanding solutions that address both greenhouse gas emissions and local temperature regulation. In this study, we utilize a numerical simulation approach to investigate how novel photovoltaic (PV) materials with selective transmission and reflection capabilities can simultaneously lower surface temperatures and reduce CO2 emissions. By conducting a parametric analysis that varies coverage ratios, reflectivity, and climate sensitivity, we quantify the potential for local cooling and emission reductions under different deployment scenarios. Results indicate that large-scale adoption of these advanced PV systems can substantially mitigate heat buildup while decreasing CO2 levels, thereby highlighting the dual climate benefits of solar radiation reflection and clean energy generation. This work underscores the importance of optimizing both thermal and carbon footprints in future PV installations to effectively contribute to global warming mitigation.
Large-scale photovoltaic (PV) systems play a crucial role in the global energy transition. They significantly reduce greenhouse gas emissions and influence the global climate through changes in surface albedo and energy balance1. Beyond their role in reducing emissions, PV panels modify the energy distribution on the Earth’s surface by absorbing and reflecting solar radiation2. This effect is particularly notable in high-radiation areas, such as deserts or open ocean regions. In these regions, the reflectivity and transmissivity of PV panels significantly impact local temperature regulation3. Recent innovations in PV materials focus on increasing reflectivity and decreasing heat absorption4, thereby mitigating local heat island effects. Such advancements not only improve energy efficiency but also enhance surface cooling via the albedo effect, effectively reducing localized warming trends5.
In addition to regulating surface temperatures, PV systems indirectly mitigate global warming by reducing atmospheric CO2, the primary driver of long-term climate change. Lower fossil fuel consumption translates into lower CO2 concentrations, which in turn reduces radiative forcing (RF). By decreasing RF, PV systems help slow the rate of global temperature increase. However, the overall impact of CO2 reduction varies by deployment scenario; coastal and high-latitude regions, with greater climate sensitivity, may experience more pronounced benefits.
To capture these multifaceted effects, a numerical model is required that accounts for solar radiation, atmospheric CO2, diurnal temperature variations, and heat capacity differences between land and ocean. Finite element analysis and other numerical methods can then simulate surface temperature changes for different PV coverage ratios. Incorporating radiative forcing and climate sensitivity parameters is crucial for understanding the long-term implications of large-scale PV deployment on global warming. This study provides such an approach, aiming to predict how PV installations may influence both surface temperatures and CO2 levels over extended timescales.
Global warming has already profoundly altered Earth’s climate. Since 1880, global temperatures have risen by about 1.2 (^{circ })C, with acceleration noted in recent decades. According to recent observations, 2023 was the hottest year on record, exceeding pre-industrial temperatures by approximately 1.45 (^{circ }) C. This warming trend stems primarily from anthropogenic greenhouse gas emissions, especially CO2, which has increased from 280 ppm in the pre-industrial era to over 420 ppm today. Human activities-including fossil fuel combustion, industrial processes, and land-use changes-continue to drive this upward trend. As global temperatures rise, extreme weather events such as heatwaves, floods, and droughts are becoming more frequent. The Arctic is warming at twice the global average, accelerating ice melt and sea level rise. These developments underscore the urgent need for effective climate mitigation strategies, and large-scale PV deployment offers a promising pathway to reduce emissions and stabilize global temperatures.
Seasonal temperature changes from 1880 to 2024. Data: NASA/GISS/GISTEMP.
As shown in Fig. 1, global temperatures have trended upward since the late 19th century. While seasonal variations persist, the overarching increase is predominantly attributed to human activities, especially fossil fuel use. The sustained rise in greenhouse gases has accelerated global warming in recent decades. This backdrop provides a critical context for assessing PV system deployment, as higher temperatures can affect both the efficiency of PV panels and their net climatic impact.
Global mean temperature anomalies from 1880 to 2020.
Figure 2 reveals a clear, long-term increase in both land and ocean temperatures, reinforcing the necessity of renewable energy solutions, including PV systems, to help mitigate climate change. By reducing CO2 emissions, PV technologies can slow the observed upward trend.
Land (orange) vs. ocean (blue) temperature anomalies, 1880–2020.
As shown in Fig. 3, land surfaces have warmed faster than oceans, especially since the mid-20th century. This disparity highlights how land-based PV systems could be strategically important for mitigating regional warming and addressing heat island effects in urban and desert environments.
Atmospheric CO2 concentrations (1960–2023).
Figure 4 shows a notable rise in atmospheric CO2 from roughly 320 ppm to over 420 ppm, indicating a 30% increase in just a few decades. This escalation intensifies the greenhouse effect, underlining the pressing need for renewable energy solutions such as PV, which reduces CO2 emissions and helps stabilize global temperatures.
To address the challenges outlined above, this study aims to:
Develop a comprehensive numerical model that incorporates advanced PV material properties (e.g., reflectivity, conversion efficiency) and climate data from NASA/GISS and Mauna Loa.
Investigate how different PV coverage ratios and material designs influence surface temperatures and CO2 levels under various climate sensitivity scenarios.
Quantify the dual benefits of reduced warming and lower carbon emissions, highlighting their relevance for both regional and global climate strategies.
Provide guidance for policy and decision-makers on optimizing PV deployment to maximize net climate benefits.
To simulate the impact of large-scale photovoltaic (PV) deployment on climate, we developed a comprehensive numerical model that integrates radiative forcing (RF) and climate sensitivity (CS) to evaluate long-term temperature changes and CO2 emission reductions6. Following the guidelines set by the Intergovernmental Panel on Climate Change (IPCC)7, our model also incorporates the albedo effect, which is crucial for assessing how PV systems influence local temperature regulation8. Additionally, advancements in PV technology-particularly selective reflection and transmission-are included to capture their potential for mitigating climate change4.
The key parameters used in our numerical simulation are summarized in Table 1, which details the solar constant, albedo coefficients for different surfaces, and heat capacity values. These parameters are essential for accurately modeling the energy balance and evaluating temperature changes under various PV deployment scenarios.
Radiative forcing (RF) refers to the alteration of Earth’s energy balance caused by anthropogenic or natural factors-such as greenhouse gases, aerosols, or land-use changes-and is measured in watts per square meter (W/m2). Positive RF contributes to global warming, while negative RF promotes cooling. In this work, we focus on the positive RF from greenhouse gases (particularly CO2) and the negative or reduced RF effects arising from increased albedo due to PV installations.
Climate sensitivity (CS) gauges how responsive the climate system is to changes in RF. It is commonly expressed as the global mean temperature increase resulting from a doubling of atmospheric CO2, with typical values ranging from 1.5(^{circ }) C to 4.5(^{circ }) C. A higher CS implies a greater temperature response to the same forcing. By incorporating different CS values in our simulations, we evaluate how sensitive the overall climate is to reductions in CO2 emissions and to changes in surface albedo from PV systems.
Several simplifying assumptions are made in this model. First, PV panels are assumed to be uniformly distributed across the surface, without accounting for local terrain or land-use constraints. Second, albedo adjustments depend solely on temperature changes, while other factors such as humidity or vegetation shifts are not considered. Third, the atmosphere is treated as a single-layer system, and radiative forcing is applied uniformly across the globe. Lastly, the model calculates reductions in CO2 emissions based solely on PV-generated electricity, excluding other renewable sources.
We use standard international references for the solar constant (1361 W/m2)7, while albedo values for deserts, oceans, and land are derived from satellite measurements (e.g., NASA MODIS)9. The initial atmospheric CO2 level (400 ppm) is based on Mauna Loa Observatory records from 202310, and the climate sensitivity (4(^{circ }) C) aligns with IPCC Fifth Assessment Report findings. These parameters ensure consistency with widely recognized climate datasets and enhance the credibility of the simulation results.
We perform a sensitivity analysis by varying key parameters such as the albedo change rate ((k)), the PV coverage fraction ((A_{textrm{covered}})), the climate sensitivity ((S)), and the CO2 reduction efficiency per MWh. Each parameter is tested across a predefined range to evaluate its effect on the total temperature change. This analysis identifies the most influential parameters, revealing how variations in PV deployment or environmental conditions can significantly alter climate outcomes.
In this model, the total temperature change reflects two primary processes: (i) the albedo-driven shift in absorbed solar radiation caused by PV panels, and (ii) the lowered greenhouse gas emissions from replacing fossil fuels with PV-generated electricity. The combined effect is expressed as:
where the first term reflects changes in absorbed solar energy (albedo effect), and the second term captures the impact of reduced CO2 on radiative forcing.
When photovoltaic panels are installed, the surface albedo changes, thereby altering how much solar radiation is absorbed. We quantify this difference as:
where
Here, (S_0) is the solar constant, (alpha _{textrm{new}}) and (alpha _{textrm{original}}(t)) are the modified and baseline albedos, (f_{textrm{atm}}) represents atmospheric absorption, and (f_{textrm{adjust}}) accounts for day-night and seasonal variations. The resulting temperature change from albedo alteration is calculated as:
Beyond albedo effects, PV systems reduce CO2 emissions by generating electricity that replaces fossil fuels. The total electricity ((E_{textrm{electricity,total}}(t))) is computed as:
where (T) is the transmissivity of the PV material and (eta) is the PV conversion efficiency. The amount of CO2 avoided is:
We then update the atmospheric CO2 level (C(t)) each time step:
where (M_{textrm{atm}}) is the mass of the atmosphere. The corresponding radiative forcing ((F_{textrm{CO2}}(t))) is:
and the associated temperature change from reduced greenhouse gases is:
To ensure reliability, we validated our model by comparing results against smaller-scale observational data on PV farms in desert regions and findings from established climate models. Our simulations aligned well with observed trends in both surface temperature changes and CO2 offsets, supporting the model’s applicability to large-scale scenarios. Furthermore, although our focus is on electricity substitution from fossil sources, the methodology can be adapted to include other renewable energy technologies or hybrid systems. All temperature changes reported are calculated as absolute deviations from a baseline scenario, ensuring clarity on cooling effects. Future work may incorporate region-specific data on land-use, vegetation, and cloud-cover dynamics to refine local impact assessments.
The net temperature outcome at each timestep is the sum of temperature changes from albedo modifications and CO2 reduction:
By capturing both radiative and emission-based contributions, this framework provides a comprehensive means of evaluating how large-scale PV deployment influences global and regional temperatures over time.
Figure 5 highlights the relationship between CO2 reductions and global temperature change, illustrating that each 1% decrease in CO2 leads to approximately a 0.07 (^{circ }) C drop in global temperature. Although temperature change is logarithmically related to CO2 concentration, the near-linear trend in this figure arises from the relatively modest range of CO2 reductions considered.
Relationship between CO2 reduction and global temperature change, indicating a near-linear segment where a 1% decrease in CO2 corresponds to about 0.07 (^{circ }) C of cooling.
These results underscore how even moderate cuts in CO2 can yield tangible temperature benefits, emphasizing the importance of global efforts to reduce greenhouse gas emissions.
Figure 6 presents a 20-year simulation of surface temperature variations under different PV materials (new, traditional, and mirror-like) and coverage ratios. The simulations are run for three representative surface types (average land, ocean, and desert). Surfaces employing highly reflective materials experience the most pronounced cooling, highlighting the potential of mirror-like PV systems to mitigate localized warming.
Simulated 20-year temperature changes for various PV materials (new, traditional, and mirror-like) and coverage ratios across different surface types.
These findings suggest that reflective PV panels can effectively reduce surface temperatures, offering a viable strategy for localized heat island mitigation, especially in regions with intense solar irradiance. All temperature differences reported here are expressed as absolute changes relative to a no-PV baseline scenario.
Table 2 quantifies how incremental CO2 reductions translate into corresponding decreases in radiative forcing and temperature. The values are derived from the standard radiative forcing formula,
where (Delta F) is the change in radiative forcing (W/m2), (C) is the current CO2 concentration (ppm), and (C_0) is the baseline concentration (pre-industrial, often taken as 280 ppm). Although the relationship is logarithmic, the modest reduction range considered in our scenarios yields near-linear temperature responses.
For instance, a 10% reduction in CO2 yields roughly a 0.56 W/m2 decrease in radiative forcing and about 0.75 (^{circ }) C of cooling. These results illustrate the value of aggressive emission cuts for slowing global warming. Moreover, combining emissions reductions with high-reflectivity PV materials delivers a dual benefit: lowered CO2 levels and diminished surface heat absorption, reinforcing the importance of integrated approaches to climate mitigation.
Our simulations confirm that cutting CO2 emissions remains a primary driver for global temperature reduction: each 1% decrease in CO2 lowers global mean temperature by approximately 0.07 (^{circ }) C. This near-linear trend, in line with logarithmic forcing at modest reduction levels, underscores the high impact of collaborative efforts to curb greenhouse gas emissions. For instance, a 30% CO2 reduction could yield a 2.54 (^{circ }) C drop in global temperatures, supporting international goals to keep warming under 2 (^{circ }) C.
Beyond emission cuts, introducing highly reflective photovoltaic (PV) materials augments cooling by increasing surface albedo. In high-irradiation regions such as deserts, these materials reflect significant solar energy back into space, thereby reducing local heat buildup. When combined with emission reductions, reflective PV systems offer a dual mechanism for global and regional climate mitigation.
A key finding of this study is the dual functionality of next-generation PV panels. While they produce clean energy, they also mitigate warming by lowering the absorption of incoming solar radiation. Our model indicates that mirror-like surfaces exhibit the strongest cooling, whereas conventional PV materials-though beneficial for decarbonization-reflect less solar energy and thus yield a smaller cooling effect. In areas with pronounced heat stress, such as urban heat islands or deserts, deploying these reflective materials could provide both local temperature relief and substantial carbon offsets. This synergy underscores how selective-reflective PV technologies can align energy objectives with climate adaptation measures.
This work reinforces earlier conclusions that reducing CO2 emissions is crucial for stabilizing global temperatures,7 but it also expands on the role of reflective PV coatings in achieving additional cooling benefits.6 While conventional PV research often focuses on energy conversion efficiency,11 our results emphasize that altering albedo characteristics can be just as critical. Geographic and climatic variations remain essential considerations:12 although deserts maximize reflective gains, areas with lower solar irradiance may see reduced effectiveness. Nevertheless, compared to more experimental Solar Radiation Management (SRM) methods like stratospheric aerosol injection,13 reflective PV arrays offer a more controllable, lower-risk, and scalable solution.
Several assumptions may limit the real-world applicability of our model. First and foremost, our analysis focuses on the operational phase and does not incorporate a full life-cycle assessment (LCA). This omits several critical upstream and downstream factors, such as the energy and environmental costs associated with large-scale material synthesis, manufacturing, transportation of materials, and installation logistics. Second, the model assumes stable performance over time, whereas all real-world PV materials experience some degree of performance degradation, which would affect the cumulative energy yield and long-term climate benefits.
Furthermore, we assume an even distribution of PV systems across Earth’s surface, overlooking local topography, vegetation, and urban structures. Climate feedbacks such as changes in cloud cover, soil moisture, or atmospheric circulation are not dynamically modeled, potentially affecting local or regional cooling estimates. We also treat CO2 concentrations as uniformly mixed, whereas actual atmospheric transport processes can create spatial heterogeneities. Lastly, our focus on PV-generated electricity means we do not account for other renewables, such as wind or hydro, which can further reduce greenhouse gas emissions. Addressing this comprehensive set of factors in future models could yield more precise predictions of climate impacts and cooling potentials.
To refine our understanding of photovoltaic materials in climate mitigation, several avenues merit exploration. Real-world field tests deploying reflective PV systems under diverse land types-urban, arid, and forested-would validate our model’s assumptions and highlight site-specific constraints. Moreover, integrating other greenhouse gases (e.g., methane, nitrous oxide) would provide a more comprehensive assessment of total radiative forcing reductions. Research on advanced PV materials with tunable optical properties could further optimize the balance between energy generation and thermal regulation. Lastly, examining synergistic effects with other interventions-such as afforestation, carbon capture, or additional SRM techniques-may reveal holistic approaches to climate mitigation that combine carbon, albedo, and other feedback mechanisms.
In conclusion, while our study demonstrates the significant potential of reflective PV materials to reduce surface temperatures and CO2 emissions, further empirical data and refined modeling are needed to guide large-scale implementation. By addressing the limitations noted here, future work can better unlock the dual benefits of advanced PV systems-supplying clean energy and mitigating climate change at both local and global scales.
The key resources used for numerical simulations and analysis are listed in Table 3. This includes information on software versions, data repositories, and any specialized PV materials or climate models referenced in this work.
For further information, requests for materials, or inquiries regarding the simulation code, please contact the lead author, Xiang Gao (25245613@qq.com). The in-house developed photovoltaic materials and associated software are available upon request for non-commercial academic research purposes. All requests may be subject to a review process and may require a material transfer agreement (MTA), where applicable.
We employed a high-resolution numerical model to evaluate how large-scale photovoltaic (PV) deployments influence surface temperature regulation and CO2 emission reductions. The model integrates multiple climate parameters-such as radiative forcing (RF), surface albedo, and climate sensitivity (CS)-to capture both direct and indirect effects of PV systems on global and regional temperatures. Python (version 3.7) served as the primary environment for code development, with NumPy used for matrix operations and Matplotlib for data visualization. Publicly available datasets, including NASA’s CERES solar radiation data and Mauna Loa CO2 records, provided baseline climate conditions.
Two primary simulation periods-20 years (short-term) and 100 years (long-term)-were chosen to capture both immediate and sustained effects of PV deployment. The short-term horizon focuses on near-instantaneous temperature responses and CO2 offsets, while the long-term horizon accounts for accumulating radiative forcing changes and more gradual climate feedbacks.
Three types of photovoltaic materials were considered, each with distinct optical and thermal properties:
New photovoltaic material: Balances high visible-light conversion efficiency with selective infrared (IR) reflection. By reflecting IR wavelengths, it reduces surface heating while generating clean electricity.
Traditional photovoltaic material: A standard commercial option, primarily absorbing solar radiation for electricity generation. Although cost-effective, it offers lower reflectivity and may have reduced cooling benefits.
Mirror-like reflective material: Reflects up to 95% of incoming solar radiation but does not generate electricity. Ideal for areas where cooling is paramount, such as deserts or urban heat island mitigation.
Reflectance, transmittance, and absorptance values were derived from empirical measurements and existing literature. By encompassing these diverse materials, the model evaluates how each technology can contribute differently to climate objectives.
We simulated PV coverage on three representative surface categories:
Desert surfaces (albedo (approx 0.40)): characterized by intense solar irradiance and low vegetation, making them highly suitable for large-scale PV deployment and noticeable albedo-driven cooling.
Ocean surfaces (albedo (approx 0.15)): examined through floating solar farms, focusing on potential thermal modifications and the feasibility of ocean-based PV systems.
Average land surface (albedo (approx 0.33)): a composite of diverse terrains (urban, agricultural, forested), providing a baseline to compare deployment scenarios in mixed-use regions.
Coverage ratios ranged from 0.00001 to 0.2, reflecting minimal to extensive PV installations. These ranges allowed us to explore scenarios from early-stage implementations to large-scale expansions.
Radiative forcing (RF) from CO2 was computed as:
where (C(t)) is the current CO2 concentration and (C_0) is the baseline. Climate sensitivity (CS)-ranging from 1.5(^{circ }) C to 4.5(^{circ }) C-translates forcing into temperature changes. Higher CS values imply stronger warming responses, underscoring the importance of testing various sensitivity scenarios.
Surface albedo was dynamically updated based on PV material deployment. The difference in absorbed solar energy before and after PV installation constitutes the albedo-driven temperature change:
Here, (Delta E_{textrm{albedo}}) represents the change in absorbed energy due to modified reflectivity, while (A_{textrm{covered}}), (M_{textrm{surface}}), and (c_{textrm{surface}}) characterize coverage area, surface mass, and heat capacity, respectively.
By replacing fossil fuel consumption, PV-generated electricity lowers atmospheric CO2 levels. The model calculates electricity output based on solar irradiance, transmissivity, and conversion efficiency. CO2 reduction is then computed as:
We update the atmospheric CO2 concentration (C(t)) each timestep, capturing the cumulative impact of PV adoption.
To test the model’s robustness, we varied key parameters such as climate sensitivity, coverage ratio, and albedo coefficients over plausible ranges. Each parameter set was run through the simulation to produce distributions of temperature changes and CO2 reduction outcomes. We then derived confidence intervals using standard error estimates, identifying the most influential parameters and evaluating how uncertainties might propagate through the model.
We tracked the following metrics across our scenarios:
Temperature change: Combined effect of albedo modifications and reduced radiative forcing due to lower CO2.
CO2 reduction: Annual and cumulative decreases in CO2 levels attributed to PV-generated electricity.
Radiative forcing: Calculated via changes in CO2 concentration and albedo, serving as a direct indicator of climate system perturbations.
Analyses were performed in Python with NumPy, and visualization used Matplotlib. We computed confidence intervals for key variables, ensuring that any variations in coverage or climate sensitivity could be interpreted alongside their statistical significance.
For further information, requests for materials, or inquiries regarding the simulation code, please contact the lead author, Xiang Gao (25245613@qq.com). The in-house developed photovoltaic materials and associated software are available upon request for non-commercial academic research purposes. All requests may be subject to a review process and may require a material transfer agreement (MTA), where applicable. All background climate datasets (e.g., NASA CERES solar radiation, Mauna Loa CO2 observations) are publicly accessible. The custom simulation code and photovoltaic material parameters are available from the lead contact (25245613@qq.com) for non-commercial research purposes, subject to approval and potential MTAs.
Boamah, Eric et al. Carbon footprint of photovoltaic energy systems. Sustain. Energy Rev. 34, 02–215 (2017).
Google Scholar
Anwar, M. et al. Industrialization and solar energy: Perspectives. Renew. Energy 154, 1065–1080 (2020).
Google Scholar
Bilgili, M. et al. Hydropower and solar energy integration: A global perspective. Renew. Sustain. Energy Rev. 138, 110490 (2021).
Google Scholar
Roberts, D. et al. Innovations in photovoltaic materials for energy conversion and climate mitigation. Mater. Today 55, 78–91 (2023).
Google Scholar
Leach, M. et al. Next-generation solar energy solutions for a warming planet. Energy Environ. Sci. 16, 220–240 (2023).
Google Scholar
Forster, P.M. et al. Revisiting solar radiation management in climate strategies. Sci. Adv. 9 (2023).
IPCC. Climate change 2022: Impacts, adaptation and vulnerability. In Intergovernmental Panel on Climate Change (2022).
Brown, S. et al. Urban heat islands and photovoltaic system efficiency. J. Urban Plan. 23, 156–172 (2021).
Google Scholar
MODIS Satellite Data. Global Albedo Data from Modis. In NASA MODIS (2023).
Mauna Loa Observatory. CO2 Concentrations from Mauna Loa Observatory. (NOAA, 2023).
Brown, S. et al. Cooling effects of novel photovoltaic materials. Renew. Energy 176, 95–112 (2023).
Google Scholar
Cao, L. et al. Pathways to sustainable energy transition: The role of photovoltaic technologies. J. Energy Res. 67, 101–120 (2023).
Google Scholar
Smith, R. et al. Global photovoltaic energy impacts on climate change. Nat. Clim. Change 12, 812–823 (2022).
Google Scholar
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Northwestern Polytechnical University, Xi’an, China
Peng Zhang & Xiang Gao
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Peng Zhang: Conceptualization, Methodology, Investigation, Writing – original draft. Xiang Gao: Supervision, Formal analysis, Writing – review & editing, Funding acquisition, Corresponding author.
Correspondence to Xiang Gao.
The authors declare no competing interests.
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Zhang, P., Gao, X. The role of novel photovoltaic materials in climate change mitigation based on numerical simulations. Sci Rep 15, 24516 (2025). https://doi.org/10.1038/s41598-025-10327-0
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