Maximising environmental savings from silicon photovoltaics manufacturing to 2035

Domain: physics
Published: 2026-02-03
Editorial outcome: minor_revision

Abstract
The silicon photovoltaics market is transitioning from the incumbent passivated emitter rear cell to the higher efficiency tunnel oxide passivated contact technology and it is crucial to understand the environmental impact of this change. Here, we conduct life cycle assessment to compare both technologies quantitatively and identify environmental savings in 15 of 16 environmental impact categories for tunnel oxide passivated contact. This includes a 6.5% reduction in carbon dioxide equivalent emissions, per watt peak at the expense of 15.2% increase in metal resource use, for photovoltaic modules manufactured in China and transported to central Europe. A critical factor in photovoltaics manufacturing is the carbon intensity of the electricity mix. We model the impact of photovoltaics production across different global regions, incorporating future electricity mix scenarios and a projection for photovoltaics deployment. Our model provides a forecast of the environmental impact of global photovoltaics manufacturing and identifies a potential reduction of 8.2 gigatonnes of carbon dioxide equivalent emissions by 2035, depending on manufacturing location.

Introduction
Rapid, global decarbonisation requires swift transitions towards higher-performing, sustainable, renewable energy technologies. Photovoltaics (PV) offers enormous promise as a low-carbon, versatile and relatively inexpensive technology. Recently, PV has been deployed at an unprecedented scale, reaching over 1 terawatt peak (TWp) of cumulative installed capacity by the end of 2023. This growth is expected to continue at scale such that total installed capacity could reach 80 TW by 2050. However, this growth requires additional resources and causes environmental impact. Previous work has quantified resources required for terawatt-scale PV production to 2100, finding that material demand could exceed capacity depending on silicon architecture, though this doesn’t consider the environmental impact of material extraction. Other work seeks to quantify material demand whilst considering electricity demand and greenhouse gas emissions, finding up to 11% of the 1.5 °C greenhouse gas emission budget could be spent on terawatt PV production. Whilst much existing PV research focuses on greenhouse gas emissions, it neglects broader environmental impacts such as ecosystem health, other atmospheric impacts, and human health, which need to be considered to truly evaluate the environmental sustainability. This type of analysis can be achieved using Life Cycle Assessment (LCA). LCA has been applied to PV to identify areas of high environmental impact (“hotspots”), investigating factors such as silicon feedstock, wafer size or module design. LCA has also been used to compare different technologies and evaluates supply chains. Fewer studies compare silicon cell architectures, with the majority focusing on general comparisons between mono-crystalline and multi-crystalline (which is no longer a timely issue) technologies, usually without stating cell architecture. Although comparisons of aluminium back surface field to passivated emitter rear cell (PERC) technology exist, these are quickly becoming outdated due to rapid technological advancements. Our work considers the environmental impact of currently leading silicon architectures, which will dominate the PV market over the next decade.

Recently, mainstream silicon PV technology has shifted from PERC to tunnel oxide passivated contact (TOPCon) cells. Manufacturing TOPCon cells is similar to PERC, though there are significant structural differences resulting in higher efficiency. Firstly, PERC tends to use a gallium-doped p-type wafer, whereas TOPCon uses an n-type dopant, such as phosphorus or antimony. At the front, PERC uses a phosphorus diffused emitter and silicon nitride (SiNx) as a combined passivation layer and anti-reflection coating, whilst TOPCon uses a boron-doped emitter with an aluminium oxide (AlOx) passivation layer and SiNx antireflective coating. At the rear, PERC uses AlOx to passivate the p-type surface of the wafer before being capped with SiNx, whereas TOPCon uses a tunnel oxide layer, improving passivation, coated by a phosphorus-doped poly-silicon layer and capped with SiNx, improving contact conductivity. Finally, the contacts differ: silver is used for the front in both PERC and TOPCon, but the former uses a combination of silver and aluminium for the rear contact whereas the latter uses just silver.

Unlike PERC, the environmental sustainability of TOPCon modules is relatively underexplored in the literature. The only other assessment of TOPCon manufacturing using LCA focuses on manufacturing solely in China. The work investigated environmental differences between mono and bi-facial modules, p-type and n-type technologies, wafer size and carbon emissions from PV manufacturing between 2023 and 2060. Although the work provides a detailed comparison between module designs within China, TOPCon manufacturing outside China remains unexplored.

Here, using LCA, we explore the sustainability of TOPCon manufacturing, suitably addressing the literature gap, by identifying hotspots regarding future PV manufacturing and performing a quantitative comparison to PERC. The impact is considered on a global scale by uniquely including projections for technology and materials consumption improvements from the International Technology Roadmap for Photovoltaics (ITRPV), as well as electricity mix scenarios from the US Energy Information Administration, into our LCA. Additionally, we provide a readily accessible and industrially validated life-cycle inventory for TOPCon cell manufacturing as a basis for other researchers to build upon and develop further modelling. A cradle-to-gate approach is adopted, considering raw material extraction, module assembly, and transportation to central Europe. Conducting Monte Carlo uncertainty analysis provides additional confidence in our conclusions. We evaluate the impact of TOPCon manufacturing up to 2035 whilst considering technological developments and future electricity mix scenarios for different manufacturing locations: India, China, the United States of America (US) and Europe. Sensitivity of TOPCon manufacturing to the electricity mix composition is investigated, demonstrating its critical relevance. Subsequently, we forecast the cumulative impact of PV deployment over the coming decade and compare its benefits to the environmental cost of manufacturing using LCA.

Results
State-of-the-art module manufacturing
A manufactured PV module comprises a wafer, cell, module components and transportation of the module to the deployment location (in this case, central Europe). The TOPCon module life-cycle inventory is created by adapting a PERC inventory to account for different doping and include primary cell data from an international TOPCon manufacturer. The contribution of each production stage is investigated for 16 environmental impact categories, described in a glossary (Supplementary Table 2).

Here, a baseline scenario compares a 1 Wp PERC and TOPCon module manufactured in China and transported to central Europe, considering the technological state in 2023. LCA results (Supplementary Table 3) show TOPCon modules exhibit lower impacts than PERC for 15 of 16 investigated environmental impact categories (per Wp). The exception is Resource use (minerals and metals), which is 15.2% higher for TOPCon, due to increased silver used for TOPCon contacts, compared to the silver and aluminium mix adopted in PERC. Results are normalised to the annual impact of an average European, using the Environmental Footprint v3.1 methodology, to identify the six highest value impact categories and simplify the presentation of results. Normalisation is only done for the “PERC vs TOPCon” comparison and hotspot analysis. After normalisation, the six highest value impact categories are identified as: Climate change, Particulate matter, Eutrophication (freshwater), Photochemical ozone formation, Resource use (fossils) and Resource use (minerals and metals) as shown in Fig. 1, together with the relative percentage change between PERC and TOPCon modules. For simplicity, Resource use (fossils) and Resource use (minerals and metals) will be referred to as “Fossil use” and “Metal use”, respectively. All 16 categories are normalised and presented in Supplementary Fig. 1. A key cause for improved environmental impact is the reduced amount of material per Wp of TOPCon due to higher cell conversion efficiency.

Figure 2 presents a breakdown of the TOPCon module manufacturing stages, shown in Fig. 1, for the hotspot analysis. For TOPCon, the wafer dominates 12 of 16 environmental impact categories. These categories have large contributions from the electricity consumed during silicon purification, providing >85% of the wafer impact in 10 categories. A breakdown of the wafer impact for the six highest impact categories in Fig. 2a shows that electricity consumption dominates all six. High impact categories in wafer production correlate with increased impact for overall module production: Fossil use, Climate change and Particulate matter, where electricity consumption during wafer production accounts for 88.2%, 89.9% and 93.5%, respectively. This highlights electricity consumption during wafer production as a hotspot, particularly the production of poly-silicon (poly-Si) and Czochralski-silicon (Cz-Si). These high impacts stem from the use of fossil-fuel sources for electricity generation highlighting the importance of increasing the share of renewables in the electricity mix to reduce these impacts. However, the use of renewable energy still contributes towards various other environmental impacts, although not as large as fossil fuels, so efforts should also target minimising electricity consumption during silicon purification.

Metal use has the highest value and is the only category dominated by cell fabrication, as shown in Fig. 1. The TOPCon cell fabrication impact is broken down in Fig. 2b. High Metal use values are from silver use during metallisation of (front and rear) contacts, representing 53.0% of the whole module Metal use impact, and 98.3% of the cell Metal use impact. The magnitude of Metal use is four times larger than the next highest impact category, Photochemical ozone formation. This sets a quantitative target for research and development activities to reduce silver consumption and adoption of alternative contact materials, such as copper. Emissions and waste treatment during cell production make up 83.4% of the Photochemical ozone formation category and 48.6% of Particulate matter, notably non-methane volatile organic compounds (NMVOC) emissions and particulates <2.5 μm. The cause of these emissions is not explicit since inventory items for direct emissions have been taken from the literature though NMVOC emissions are typically associated with using solvents and cleaning processes, suggesting that optimising or minimising solvent use would reduce these emissions. Other hotspots include poly-Si deposition (specifically silane usage) and annealing (specifically electricity consumption), which contribute >15% of the impact in 11 impact categories. Silane also dominates silicon nitride deposition during plasma-enhanced chemical vapour deposition (PECVD) at the cell front and rear. Rear PECVD contributes >15% to two impact categories, whereas the Front PECVD impact is greater, contributing >15% to six impact categories due to trimethylaluminum usage. Recovering unused silane or minimising its use through reducing poly-Si thickness are routes to reducing this impact. However, changes to the device structure, such as poly-Si thinning, may adversely affect the cell performance. This trade-off should be carefully considered by performing further LCA that considers these structural changes and consequent performance to ensure synergistic impact reduction and performance improvement.

The module components’ production is the largest contributor to two impact categories: Human toxicity (non-cancer), and Land use (see Supplementary Fig. 1). A breakdown of the module components’ impact in Fig. 2c identifies hotspot materials as copper and solar glass, in addition to tin, which impacts Metal use due to its scarcity. Copper is a hotspot for multiple categories, providing >50% of the overall module impact in three categories. Solar glass also contributes highly, representing >25% of the module components’ impact in eight categories and >50% in two further categories. Upstream solar glass production analysis identifies uncoated flat glass as high impact, specifically using soda ash, electricity and heavy fuel oil during production. Reducing the associated impact through recycling key materials used during PV production, such as copper, glass, silver, and silicon, has been investigated previously.

Module transportation from China to Europe requires freight lorries, trains and ships. LCA results in Fig. 2d show that the freight ship dominates all six high-impact categories, though it transports modules over 100 times the distance transported by lorry and train. Comparing 1 tkm (tonne-kilometre) of each transportation method (see Supplementary Table 4) demonstrates the smaller impact of transoceanic freight relative to other forms of transportation. Transport has the largest impact on photochemical ozone formation, due to hydrocarbon fuels used for shipping and lorry transport and construction of rail infrastructure. Additionally, the Fossil use category has a large impact resulting from the use of fossil fuels (e.g., petroleum, diesel, hard coal and heavy fuel oil) to power the vehicles.

Technology development and manufacturing location
The environmental impact of manufacturing PERC is sensitive to the module performance and manufacturing location. For the following results, we assume that the processing yield and manufacturing capacity of PV in China can be replicated in the other investigated locations. We investigate the impact of 1 Wp TOPCon manufacturing for India, China, the US and Europe. Different locations are considered by changing the electricity mix, such that it represents the composition of each location and modifying transport inputs accordingly, from the manufacturing location to central Europe. We consider low zero-carbon technologies cost scenario from the Energy Information Administration (EIA) to represent decarbonisation of each location’s future electricity mix. Additionally, the results encompass effects of performance and material advancements on TOPCon manufacturing to 2035 – according to ITRPV 2024, these values are provided in Supplementary Table 5. The impact of manufacturing TOPCon (per Wp) reduces over time due to these improvements as shown in Fig. 3 for the six highest impact categories.

In 2023, TOPCon manufacturing in India has the highest Climate change impact (0.95 kg CO2 equivalent (eq.) Wp−1), whilst the lowest occurs in Europe (0.40 kg CO2 eq. Wp−1), shown in Fig. 3a. By 2035, assuming electricity mixes remain constant (solid lines), Climate change decreases on average by 0.10 kg CO2 eq. Wp−1 across all locations due to improved performance, reduced silver during metallisation and reduced poly-Si for Cz-Si production. This reduction for Climate change is more sensitive to poly-Si reduction (and related electricity reduction) than silver reduction, which instead greatly effects Metal use.

Considering all scenarios in 2023–2034, TOPCon manufacturing can result in 0.31–0.95 kg CO2 eq. Wp−1, with the lowest impact in Europe. Analysis of dashed lines shows the impact of TOPCon manufacturing reducing further when considering future electricity mix scenarios which account for progress towards decarbonisation.

The five other high-impact categories (Fig. 3b–3f) show similar trends to Climate change, with India and Europe tending to have the highest and lowest impact, respectively. Particulate matter (Fig. 3b) differs, where China’s impact is greater than India (for current and future scenarios) despite India having a larger share of coal in its mix. This is due to high quantities of particulate emissions released during electricity production for internal use during coal mining in China. Also, the US has the highest Metal use impact (considering future electricity mixes) by 2035 (Fig. 3f). This is a consequence of an increased share of renewable resources, where more scarce materials, such as silver, are required for renewable energy systems. Scarce materials have higher Metal use values than abundant materials. This increased proportion of renewable energy decreases the proportion of fossil-based resources, causing a complementary reduction in US Fossil use, shown by the blue dashed line in Fig. 3e.

When considering future electricity mixes (dashed lines), all impact categories in Fig. 3 exhibit greater reductions in TOPCon manufacturing, except Metal use (Fig. 3f), which slightly increases due to an increasing share of renewables. Europe has the lowest Metal use impact for the future mix because the proportion of PV in the mix is lowest relative to other locations. There is still a significant overall reduction in Metal use by 2035 due to decreasing silver consumption, emphasising its criticality for reducing this impact.

It should be noted that current electricity mix models use the Ecoinvent database, where India’s inventory is over five years old, whilst other countries were updated at the end of 2023. To account for this discrepancy, future scenarios also include a 2023 data point. China shows a higher Eutrophication (freshwater) impact for the future electricity mix than the current mix, though both scenarios show agreement that the impact will decrease over time. This suggests that there are differences between modelled electricity mixes such that electricity sources that contribute more to Eutrophication (freshwater) are greater in the future scenario than the current (Ecoinvent) scenario. This could be from the use of hard coal in the mix, which was found to significantly contribute to this category.

Global deployment
Confirming that the benefits of global PV deployment outweigh the environmental costs of manufacturing is essential. The environmental cost of manufacturing is modelled based on PERC and TOPCon deployment to 2035, taken from ITRPV 2024, for Climate change and Metal use impacts shown in Fig. 4. The shaded region acts as an indicator of the difference in impact between current and future electricity mixes. The benefits of this global PV deployment are described in the following section, where CO2 emissions associated with each kWh are evaluated.

Climate change (Fig. 4a) shows that PV deployment to 2035 can result in up to 13.8 Gt CO2 eq., but changes to manufacturing location and decarbonising, future electricity mixes can reduce this by 8.2 Gt CO2 eq. For context, this saving is equivalent to 13.9% of global net anthropogenic greenhouse gas emissions in 2019. Additionally, our results suggest that changing manufacturing location from China to Europe could reduce this impact by 49.5% to 2035, assuming EIA scenarios hold true. However, the renewable technology deployment rate in various locations, particularly China, presents a high level of uncertainty which should be addressed through collective development of more detailed and accessible electricity mix scenarios.

Metal use (Fig. 4b) increases with the decarbonising nature of future electricity mixes for all locations due to increased critical minerals and metals used for the higher share of renewable energy systems within the electricity mix. In the future, Europe is the most beneficial location for Metal use, with the US becoming the least, despite having the lowest impact considering current mixes. This suggests the US will experience the greatest increase in renewables' share in the mix. Unlike Climate change, which differs considerably between locations, Metal use has a much smaller range. Greater changes to Climate change are caused by higher contributions from the wafer (and consequent high electricity contribution), this contribution is much less significant for Metal use, causing the smaller range. Expected silver consumption for this deployment is ~0.1 Mt.

Carbon dioxide emissions, per kilowatt-hour
The CO2 equivalent emissions are investigated for PERC and TOPCon deployment up to 2035 and compared to future electricity mixes for each location. This comparison, in Fig. 5, shows that deployment of PERC and TOPCon (Solar PV) results in 0.017 kg CO2 eq. kWh−1 by 2035, whereas the carbon intensity of electricity mixes for the investigated locations is substantially higher (e.g., China and the US will emit 0.608 kg and 0.210 kg CO2 eq. kWh−1, respectively).

The PV deployed in this study will contribute 94,602 TWh of electricity generated between 2023 and 2035. This assumes an isolation of 1000 kWh m−2 yr−1 and a performance ratio 0.827, whilst considering the degradation of the PV module over time in line with ITRPV predictions. As a reminder, the assumptions are displayed in Supplementary Table 1. Carbon emissions related to this electricity generated from PERC and TOPCon are 2.26 Gt CO2 eq. Generating this amount of electricity, considering future mixes, would result in 62.10, 66.75, 32.69, and 27.56 Gt CO2 eq., from China, India, the US and Europe, respectively. Comparing this to the value from Solar PV shows >25 Gt CO2 eq. avoided emissions. This period considers less than half the expected lifetime of PV modules (12 of 30 years), meaning the avoided emissions from this PV deployment are even greater than suggested here, providing strong incentives for mass PV deployment over the coming decade by demonstrating significant reductions in net CO2.

Sensitivity and uncertainty analysis
Electricity consumption is shown as a hotspot for PERC and TOPCon manufacturing. The work so far uses a single, average electricity mix for each investigated location as taken from the Ecoinvent database. However, the electricity grid within each location can differ and these sub-grids are also available in Ecoinvent and provided in Supplementary Table 6. A sensitivity analysis is conducted to consider this variation for different sub-grid electricity mixes and their effect on the Climate change impact of manufacturing 1 Wp PERC and TOPCon. Variation is measured by using the highest and lowest carbon intensity sub-grids for each location. Results are shown in Fig. 6. The representative value of the electricity mix for each location, as used throughout the work, is shown by the black line. This analysis shows that the Climate change impact of PV manufacturing can vary significantly depending on which sub-grid electricity mix is used. The variation ranges between 0.32 and 0.58 kg CO2 eq. Wp−1, with the largest variation observed for China. There is an overlap of all investigated locations and, more interestingly, it is shown that PV manufacturing in the lowest carbon intensity sub-grid location of China results in comparable CO2 eq. emissions to manufacturing in (Reference) Europe. This not only demonstrates the variability of impact within each region but also the high variability in the results due to modelling choices. Another useful observation is that the same conclusions are drawn when comparing the lowest carbon intensity sub-grids to the reference electricity mix.

The sensitivity analysis is developed to investigate how the contribution of individual electricity sources to the overall electricity mix affects the impact of manufacturing a 1 Wp TOPCon, for all 16 impact categories. The main contributing source in the electricity mix is varied by 5% and is compared to a Reference scenario where each of the contributing electricity sources is assumed to contribute equally. Results are summarised using relative percentage changes in Fig. 7. The raw data are found in Supplementary Table 7.

Results show that the environmental impact can change by up to 35.9% (not shown on the radar) for Ionising radiation when a nuclear-dominant mix is used, though typically the categories are affected by ~5%. Coal has the highest impact on TOPCon manufacturing for nine categories and increasing the proportion of coal in the mix increases the impact in 12 categories, notably by +4.8% for Climate change. Excluding Ionising radiation, the impact of TOPCon manufacturing is found to be the most sensitive to coal, with an average absolute change of 2.1% and the least sensitive to PV, hydropower and biogas, all with an absolute average change 0.9%. This analysis suggests that the impact of TOPCon manufacturing is sensitive to electricity mix compositions: a relative change of 5% in source contribution can result in average absolute changes >1% for 1 Wp TOPCon manufacturing for 10 impact categories. This is due to the high electricity consumption during wafer fabrication. Increasing the share of renewable energy resources in the mix can reduce the impact in most categories, as demonstrated in Fig. 7b, showing that increasing the share of any renewable electricity source decreases the impact for nine categories compared to the Reference scenario. Increased hydropower is the only scenario that experiences a decrease for all 16 impact categories, implying that hydro storage may be the most suitable, low-carbon partner to complement solar PV. This sensitivity analysis is useful for providing guidance to reduce specific impact categories whilst identifying potential burden shifting.

Sensitivity analysis was also conducted on the inventory of PERC and TOPCon modules to quantify the change in environmental impact when considering technological improvement for: efficiency improvement, silver use, wafer electricity reduction and silane usage reduction. The magnitude of the improvement and benchmark is given in Supplementary Table 8 and the raw results are available in Supplementary Table 9. The percentage change for the six highest environmental impact categories is presented in Fig. 8. Information on the modelling is outlined in the methodology. Briefly, to aid in understanding these results: the efficiency is modified to assume the module efficiency equal to the stabilised cell efficiency in 2034 (Fig. 8a); silver use is assumed as 5 mg Wp−1 for both technologies (Fig. 8b); electricity consumption during wafer fabrication is assumed to reduce proportionally to expected wafer thickness reductions in 2034 (Fig. 8c); and silane use is assumed to reduce (Fig. 8d) by 14.4% based on enhanced deposition rate via inductively coupled plasma-PECVD (ICP-PECVD).

The percentage change due to module efficiency improvements, Fig. 8a, shows identical values for each environmental impact category. This is because the area per Wp, for the functional unit, is inversely proportional to the efficiency – thus the increase in efficiency will proportionally reduce the value of all impacts. Further, the scope of the LCA is limited to the manufacturing of modules, meaning there is no impact considered during the use phase, which would affect this correlation. The difference in TOPCon impact due to the efficiency improvement is 2.5% larger than the change in impact for PERC caused by the larger, relative improvement in TOPCon module efficiency (+15.9%) than for PERC efficiency (+12.6%). This suggests that the impact is sensitive to module efficiency changes and that improving module efficiency is very effective for the simultaneous reduction of PV manufacturing environmental impact across multiple categories.

Reducing silver usage, Fig. 8b, is shown to be most effective for reducing the Metal use impact category (<41.3%) and least effective for the Particulate matter impact (<0.4%). Changing silver use to 5 mg Wp−1 has a greater percentage reduction in silver quantity for TOPCon (78.0%) than PERC (66.5%), causing larger reductions to TOPCon categories in this figure.

Only Metal use and Eutrophication, freshwater exhibit impact changes >1% suggesting silver use reduction is not effective for simultaneous impact reduction across categories and that most impact categories are not very sensitive to variations in silver usage.

Wafer electricity consumption reductions are assumed equal for PERC and TOPCon (26%) resulting in impact reductions >10% in four of the six presented impact categories, shown in Fig. 8c. From these six impact categories, Metal use shows the smallest reductions of 0.6-0.7% for TOPCon and PERC, respectively whilst the other five presented impact categories show changes >9.6%. This is due to the higher contribution of the wafer towards these five categories and the dominance of the impact from electricity consumption, as shown by the results in Figs. 1 and 2a. The difference in Metal use is smaller for TOPCon than PERC, but the other five impact categories demonstrate larger impact reductions for TOPCon than PERC. This is due to the lower impact of TOPCon relative to PERC in all these impact categories, except Metal use, where TOPCon is higher, making the differences from wafer electricity reduction more significant in PERC manufacturing than in TOPCon. Sensitivity to silane reduction was investigated due to its identification as a hotspot during cell fabrication and its common use in silicon cell fabrication. A 14.4% reduction in both PERC and TOPCon silane usage causes reductions >0.3% for the six presented impact categories, Fig. 8d. This change is minor because the cell fabrication’s contribution towards the module manufacturing is small compared to the wafer and module components. Although these changes are small, suggesting the impact is not sensitive to silane reduction, it is also shown that most of the impact categories – excluding Metal use – experience similar magnitudes of impact reduction (within 0.2%), demonstrating simultaneous impact category reductions.

Lastly, Monte Carlo uncertainty analysis was conducted. Results are shown in Fig. 9 and all Monte Carlo output data are accessible in Supplementary Table 10. The probability that manufacturing 1 Wp PERC has a larger impact than TOPCon is >70% in most impact categories (11/16), suggesting a high level of confidence in the results. This includes Climate change (71.5%), human toxicity, cancer (85.0%), ozone depletion (98.7%) and Eutrophication, freshwater (70.21%). In contrast, Metal use shows 95.8% confidence that TOPCon has a higher impact than PERC. The lowest level of confidence is for Water use, which shows a probability of 59.3% that the Water use impact of PERC is larger than the impact of TOPCon. These results show that the uncertainty associated with the inventory for PERC and TOPCon does not significantly affect the overall comparison or the conclusions drawn from it.

Methods
Lifecycle assessment modelling
The results and conclusions stated in this work have been obtained using lifecycle assessment (LCA). The goal of the LCA is to quantitatively investigate the environmental impact of manufacturing dual-glass, TOPCon modules and assess the environmental benefits of this compared to manufacturing dual-glass PERC modules through considering the extraction of raw materials and manufacturing stages of the life cycle only, known as a “cradle-to-gate” approach. All manufacturing process stages are considered, from quartz mining to production of module components and transportation, as shown in the system boundary, Fig. 10. For all scenarios, the shipping location was fixed as central Europe. A functional unit of 1 Wp was chosen to include the difference in performance of each module type. The area used to provide the functional unit is shared in Supplementary Table 11, calculated using Supplementary Equation (1) in Supplementary Data 3.

Performance and material development values of PV modules are taken from the 2024 International Technology Roadmap for Photovoltaics (ITRPV), which includes projections for efficiency, degradation, silver consumption and poly-Si consumption (shared in Supplementary Table 5). The foreground data used for the PERC module inventory has been taken from literature and updated accordingly for current manufacturing as reflected in the ITRPV. The TOPCon module inventory is comparable to the PERC module inventory, using the same wafer and module component inventories (although changing wafer dopant to account for the n-type doping as opposed to the p-type doping used for PERC), but includes primary TOPCon cell manufacturer’s data provided by an international TOPCon manufacturer. All assumptions used for the completion of this work are available in Supplementary Table 1 and the inventory for TOPCon and PERC are provided in Supplementary Table 2 and 3, respectively. The background data is provided by the Ecoinvent v3.10 database, which is used with LCA modelling software SimaPro v9.6.0.1.

Environmental Footprint (EF) v3.1, as recommended by the European Union is chosen to assess impact and provides a thorough analysis of 16 environmental impact categories which considers a broad spread of environmental aspects including terrestrial and aquatic ecosystems, human health, resource use and the atmosphere. This is crucial when considering a system like a photovoltaic module because it contains a wide variety of flows, including electricity consumption, heavy metals, solvents and potentially toxic chemicals, where understanding the impact of these inputs on the environment is essential for comprehensive sustainability investigations. These 16 impact categories are: Acidification, Climate change, Ecotoxicity, (freshwater), Particulate matter, Eutrophication (marine), Eutrophication (freshwater), Eutrophication (terrestrial), Human toxicity (cancer), Human toxicity (non-cancer), Ionising radiation, Land use, Ozone depletion, Photochemical ozone formation, Resource use (fossils), Resource use (minerals and metals) and Water use. The results are calculated within the SimaPro modelling software to provide numerical data for midpoint impact categories, which involves using emission information from the inventory and characterisation factors from the EF v3.1 impact assessment method to calculate the LCA results. The results have also been normalised to the average annual environmental impact of a European person using assumptions from the EF v3.1 impact assessment methodology. Normalising the data allows for the identification of the high impact categories, which have been shown in the figures throughout the study. More information on the EF v3.1 impact assessment methodology can be found at and a glossary for each of the impact categories can be found in Supplementary Table 2.

Technological developments and manufacturing location considerations
Investigating alternative global regions requires changes to the electricity mix and transport inputs. The transport is considered as transporting the constructed module from the manufacturing location to a common destination in central Europe. The transport for China and Europe is taken from the same literature as the PERC inventory for consistency and US transportation is taken from Grant and Hicks. For India, we assume that modules are manufactured in Mundra before being transported via lorry to Gujarat for shipping to a European port (Rotterdam) before being transported by lorry over a distance equal to transport inventories for other locations. The electricity mixes are initially taken from the Ecoinvent V3.10 database from medium voltage, market inputs for China, the US, India and Europe to provide the baseline scenario, which does not consider decarbonisation. These inventories were last updated in December 2023 (except India, updated in March 2017), so are assumed to be accurate for the current electricity mix. Decarbonisation of each location’s electricity mix is modelled on electricity generation projections taken from the most recent (2023) International Energy Outlooks’ “Low zero-carbon technology costs” case, which assumes a 40% reduced capital cost of zero-carbon technologies. We base the analysis on this scenario due to ongoing efforts to reduce the price of renewable electricity generating sources. When considering European projections, the model for “Western Europe” has been used. These have then been modelled in SimaPro LCA software such that the contribution of each electricity generation source matches the projected generation contribution mix for each year investigated: 2023, 2024, 2026, 2028, 2031 and 2035, in line with ITRPV technological projections. For years falling between EIA projections, a linear growth was assumed within each 5-year period. The electricity mix models used for future electricity mixes are provided in Supplementary Data 4.

Global environmental impact projections
Projections for global PV deployment have also been taken from the ITRPV. This information is provided in Supplementary Table 12. The market to 2035 has been assumed to consist of only PERC and TOPCon modules, with PERC modules being completely phased out by 2034. This assumption has been made because the majority of the market (53%) is held by PERC and TOPCon modules up to the end of the ITRPV’s forecasted period, hence it is expected that the impact from global PV deployment will be predominantly from PERC and TOPCon modules. Beyond 2035, and the forecast included in this study, the impact of other silicon technologies (e.g., silicon heterojunction (SHJ), interdigitated back contact (IBC)) and tandem perovskite-based devices should be integrated into the global deployment scenarios due to their higher expected shares within the PV market. This section also calculates the benefits of global PV deployment, as shown in Supplementary Data 4, and explained in Supplementary Note 1.

Sensitivity and uncertainty analysis
Sensitivity analysis is conducted on the electricity mix used in the LCA modelling and on various variables considered in the inventories. First, the sensitivity of the electricity mix taken from Ecoinvent v3.10 is analysed for the Climate change impact category, in order to consider the variation in impact due to the choice of electricity mix. In Ecoinvent, there are sub-grid electricity mixes available for each of the investigated locations: China, India, US and Europe. The highest and lowest carbon-intensive sub-grid mixes are taken for China, India and US, whereas Poland and Switzerland are selected to represent the highest and lowest carbon-intensive sub-grid mixes, respectively, within Europe. The selected inventory items from Ecoinvent are available in Supplementary Table 6. The chosen sub-grid electricity mixes were input into the LCA modelling, to replace the “average” electricity grid mixes for each location and the LCA results were recalculated for both PERC and TOPCon for the Climate change impact category. The sensitivity analysis on the electricity mix used was further developed to investigate how the electricity mix composition affects the impact of 1 Wp TOPCon manufacturing. This is done by creating a Reference scenario where each of the investigated contributing electricity sources: coal, nuclear, gas, oil, wind, photovoltaic, biogas, hydropower and geothermal are set to 10% contribution towards the generation of 1 kWh electricity – each representing an equal contribution. The contribution of an individual electricity source is increased by 5% (whilst the other 9 contributing resources decrease proportionally – maintaining the 1 kWh output) and the impact of 1 Wp TOPCon manufacturing is measured again. This allows for a comparison of TOPCon manufacturing in each of the impact categories whilst considering changes to individual contributing electricity-generating resources within the electricity mix. This is useful when looking to target reductions for specific impact categories during PV manufacturing, with an understanding of the potential burden shift onto another impact category. All electricity mix calculations are available in Supplementary Data 4 and 6 and the source data used to create the figures are also available in the Source Data file.

Sensitivity of the following variables has also been investigated: efficiency, silver use, electricity consumption during wafer fabrication and silane use, for the six identified highest value impact categories – as determined during normalisation. The values of each variable have been modified such to represent achievable targets demonstrating the potential percentage reduction in impact as a result of these technological developments. Values used for the sensitivity analysis are available in Supplementary Table 8. The sensitivity is conducted on the baseline model, which is the assumption that the PV module is manufactured in China, using an average grid electricity mix, and transported to central Europe in the year 2023. The values for efficiency were updated from the ITRPV 2024 from the initial stabilised module efficiency in 2023, to the values of stabilised cell efficiency in 2034, stated in the ITRPV 2024. This change increases PERC efficiency by a relative 12.6% and TOPCon efficiency by a relative 15.9%. Values for the silver use were initially obtained from the ITRPV 2024. For the sensitivity analysis, it is assumed that the silver consumption is decreased to just 5 mg Wp−1 for both technologies since this value has been stated as a target for achieving sustainable multi-terawatt PV production. The reduction is by 66.5% for PERC and 78.0% for TOPCon technology. The electricity consumption values, per m2, have been taken from the PERC inventory available in the literature, the sensitivity analysis used new electricity values, which, for both technologies, have been reduced by 26% to represent reductions in wafer thickness by 2034 as outlined in the ITRPV 2024. The values for silane have been taken from the PERC inventory in the literature for the PERC module and industry verified inventory data for the TOPCon module, both of these values are reduced by 14.4%, is a conservative value based on the findings by Yoon et al., who investigated inductively coupled plasma-PECVD (ICP-PECVD), which found that the introduction of H2 and reduction in the silane atmospheric fraction by 14.4%, can enhance the deposition rate compared to a standard Ar/SiH4 baseline.

Finally, a Monte Carlo uncertainty analysis is conducted via the built-in Monte Carlo simulator in LCA software, SimaPro v9.6.0.1. A pedigree method is used to consider the completeness, reliability, temporal, geographical and technological quality of the foreground inventory data and calculate geometric standard deviations for each, assuming a lognormal distribution. Background inventory items used Ecoinvent v3.10’s built-in uncertainty parameters. The results are obtained from 10,000 runs, to ensure consistent replicability between subsequent analysis trials, with a 95% confidence level. All Monte Carlo output results are available in Supplementary Table 10 and the pedigree matrix values are provided alongside the TOPCon and PERC cell inventories in Supplementary Data 1 and 2, respectively. The pedigree matrix for the wafer and module components inputs has been taken from the PVPS LCI publication.

Peer review rounds
[{"round": 1, "reviewer_comments": {"reviewer_1": "This is a high-quality manuscript focusing on a really important topic of environmental impact projection of silicon PV manufacturing. The methodology was described clearly, data sources and results were provided sufficiently. However, the novelty and the research gap of this work was not presented due to lacking comparing with other published work in this area. There are also some improvements needed regarding clarifications and precision, please see the detailed comments attached.", "reviewer_2": "The paper presents a large volume of data that is rapidly changing, making it difficult for reviewers to verify each piece individually. It is requested to provide detailed sources and years for the data presented.\nAdditionally, the most significant logical inconsistency in this paper lies in the fact that the price of TOPCon solar cell modules produced in China is much lower than in other parts of the world, so why would their CO2 emissions be significantly higher than in other regions? The production efficiency in Europe and America is low, with very limited capacity, yet how can they achieve lower CO2 emissions? Clearly, the aforementioned contradictions are illogical.", "reviewer_3": "The paper evaluates the environmental impact with transitioning from PERC to TOPCon modules. I think the paper is interesting and useful to ensure future technologies do not create new concerns. However, I do find the impact of the work rather limited and potentially misleading due to the 3 main reasons below:\n1) it relies mostly on already published inventory data that have been updated for the wafer only based on ITRPV values.\n2) Assumptions and scenarios considered are not clear. Typical LCA require a clear system boundary with information about input/output, what is included/excluded and what is compared. In this case the comparison is between PERC and TOPCon where most of the upstream process is considered the same (up to cell) and only modified for efficiency. The reader might be able to figure it out by reading at the same time the paper + supporting info + references but it certainly lacks transparency. A table summarizing assumptions for each cell would be required to make sure there is no bias in the approach. Also, that would be required if the author wants the study to be reused. For example, the final assumption assume transporting the module from China to central Europe (which I assume is where the authors are). If there was a system diagram and/or clear assumption table it would be easier to see how to modify the study to be more broadly applicable.\n3) There is no uncertainty analysis conducted even though monte carlo analysis is mentioned. All results are deterministic. The only figures that show various scenarios are Fig 3 and 4 where the only variable that is changed is either the grid remains the same or change over time. Sensitivity analysis is only done on grid mix, not technology input. That’s neither sensitivity or uncertainty analysis. A reduction of 6.5% in carbon footprint is small – looking at the impact of changing some of the input in the LCI would have been useful before claiming that TOPCon are better than PERC. This conclusion is also only valid if GHG is more important than resource depletion.\nIn addition, minor comments:\n4) Following up on comments #2 about lack of clarity about assumptions – Fig 2 ends up with a mix of transport form Europe and China while the original scope in the introduction mention comparing India, China, US and Europe. The abstract only mentions reduction of 6.5% in CO2 / Wp – is that for this China to Europe scenario?\n5) Fig 1 + 2 – what are the units for each indicator?\n6) The authors said they run monte carlo analysis – how? None of the results show any range. All analysis seems to be deterministic. There is no information about this was done. I don't see any input with uncertainty in the LCI supporting info.\n7) Fig 3f – use of axis is misleading – Fig F looks like there is a huge decrease but it’s only because of axis choice.\n8) A lot of the results are related to grid environmental impact. Europe is not a country and large country such as US, India and China all have sub regions grid in Ecoinvent. It easy to make Europe looks good and China bad when using this approach. Again - this could be answered with better sensitivity or scenarios analysis.\n9) Comparison with other studies is very difficult because results are normalized. How does the new carbon footprint of PERC compare with the original reference paper with the ITRPV update? How does the results compare with carbon standards like EPEAT (goal is to be under 400 kg CO2/kWp for ultra-low carbon) or EU Ecodesign directive ?"}, "author_response": "Maximising Environmental Savings from Silicon Photovoltaics Manufacturing to 2035\nNCOMMS-25-13362-T\nResponse to Reviewers’ Comments\nWe wish to thank all the Reviewers for their valuable feedback on our manuscript. This feedback is\nextremely useful, and we have carefully considered and incorporated all the Reviewers’ comments into\nour revised submission.\nOur work reports on the environmental impact of TOPCon PV manufacturing compared to PERC and\nprovides an evaluation of this impact to 2035 for different manufacturing locations. To the best of our\nknowledge, this is the first time that an industrially validated TOPCon PV cell inventory has been\nsubmitted for publication into the public domain and will therefore have considerable impact. We also\nreport the first insight into TOPCon manufacturing outside of China and the cumulative environmental\nimpact of worldwide PV deployment to 2035 considering technology innovation and electricity mix\ndecarbonisation in various locations.\nWe note that the Reviewers were positive about our work, with Reviewer 1 commenting that “This is a\nhigh-quality manuscript focusing on a really important topic of environmental impact projection\nof silicon PV manufacturing.” We were also pleased that Reviewer 1 found that the “methodology\nwas described clearly, data sources and results were provided sufficiently.” We also highlight that\nReviewer 3 indicates that “the paper is interesting and useful to ensure future technologies do not\ncreate new concerns”.\nIn addition to these positive comments, all reviewers provided useful suggestions to improve our work.\nWe have significantly revised the manuscript accordingly and a summary of the significant changes is\nas follows:\n• addition of a more comprehensive literature review which sets a clearer context for the work and\nhighlights its distinctiveness relative to other studies;\n• addition of a “Sensitivity and uncertainty” section including three new figures;\n• restructuring and reformatting of Supplementary Information and Supplementary Data documents\nwith associated signposting in the manuscript to enhance clarity and navigation; and\n• addition of descriptions and further information to the manuscript, Supplementary Information and\nSupplementary Data to enhance transparency.\nWe provide a point-by-point response to the Reviewer’s comments in the tables that follow.\nContents\nPage 2 – Response to Reviewer 1\nPage 7 – Response to Reviewer 2\nPage 8 – Response to Reviewer 3\nNCOMMS-25-13362-T\nResponse to Reviewer 1\n# Comment Response\nwork was not presented due to this more clearly including a comparison to relevant\nlacking comparing with other published work in the area. We have revised the introduction\npublished work in this area. and literature review to make this distinction clearer. In brief,\nthe key novelty we present is a unique combination of (a)\ninternationally accepted projections for PV technology\ndevelopment with (b) global electricity mix scenarios. When\nboth factors are used in LCA modelling, they reveal\nsignificant variations in environmental impact depending on\nwhere the PV modules are manufactured. This is the first\ntime that this has been achieved for TOPCon outside of\nChina. Furthermore, our work is the first to provide an\nindustrially validated TOPCon PV cell inventory for other\nresearchers and policy makers to use. This is important as\nTOPCon cell technology is expected to dominate solar\nelectricity for the next decade.\nWe have added a comparison to other published work in the\nintroduction and highlighted the novelty of the present work\nmore strongly. These changes can be found on: Page 1, Line\n26-54; Page 1, 84-92; and Page 2, Line 1-3.\n2 “Reduction of 8.2 Gt CO eq. by We thank the reviewer for this suggestion. We have put this\n2\n2035” – indicate the significance number in the context of 2019 global greenhouse gas\nof this compared to the global emissions reported by the Intergovernmental Panel on\ngreen-house gas emission or to Climate Change. Silicon PV deployment to 2035 can result\nother matrix to reflect the in up to 13.8 Gt CO eq. emissions, but changes to\n2\nimpact of this number. manufacturing location and grid decarbonisation can\nreduce this by 8.2 Gt CO eq. This corresponds to an\n2\nastounding 13.9% of global net anthropogenic greenhouse\ngas emissions in 2019 [IPCC report]. We have made this\nclear in the text as it provides a very strong measure of\nimpact.\nThe change in the manuscript can be found on Page 6, Line\n21-23.\nmissing part so the research gap more detailed insight into published work would be useful\nis missing. As some work on to readers. We are aware of just one previous publication\ncomparing the environmental detailing an LCA of TOPCon (Z. Wang et al., J. Clean. Prod.,\nimpact of PERC vs TOPCon (2024), 470, 143187). Importantly, this other work is limited\ntechnology has been published, to manufacturing in China only. In contrast, our work is the\nplease add a short session first to explore the environmental impact of manufacturing\nabout this. TOPCon in China, India, the EU and the USA. Further, our\nLCA models uniquely incorporate the effects of: (i) projected\nimprovements in materials consumption and technology;\nand (ii) decarbonising the electricity mix in these locations\nto demonstrate that this can result in a wider range of\nenvironmental impacts. Both of these effects are sourced\nfrom internationally accepted sources (the International\nNCOMMS-25-13362-T\nTechnology Roadmap for Photovoltaics and the US Energy\nInformation Administration) that are freely accessible in the\npublic domain. Another important difference is that our\nwork is the first to provide an open-access and industrially\nvalidated TOPCon life-cycle inventory. This is significant\nbecause it will allow other researchers to build on the work\nand develop further valuable LCA modelling.\nAs well as the additional literature added in response to the\nReviewer’s first comment, we have also revised the\nmanuscript text to describe the work of Wang et al. in more\ndetail and subsequently identify the research gap that our\nwork occupies. This can be found on Page 1, Line 75 – 83.\nat an unprecedented scale, been corrected to be aligned with reference [4], Page 1,\nreaching 1 TWp in 2022 and Line 23-24.\ndoubling within just two years”:\nplease correct the statement to\nbe more precise on the\ndescription. It is supposed to be\n“reaching over 1TWp of\ncumulative PV installed\ncapacity by the end of 2023”\naccording to reference [4]\n5 “TOPCon cells incorporate a We agree with the Reviewer that a more accurate and\nsilicon oxide tunnel layer at the detailed explanation and comparison between TOPCon and\nrear, as well as the front, and PERC architecture is important for setting the context for\nrear silver contacts to improve this work. We have provided a more comprehensive\npassivation, resistance and description of TOPCon compared to PERC. The new text is in\ncarrier collection”. If it aims to the manuscript Page 1, Line 57–73.\nexplain it in details, the\nexplanation of TOPCon cells\nshould be more accurate and\ncomplete. Topcon cells\nincorporated thin silicon oxide\nlayers on both front and rear\nsides to improve surface\npassivation, a heavily-dop poly\nsilicon layer at rear side to\nreduced the contact resistance\nunder the rear silver contacts.\n“sustainability of TOPCon and we thank them for bringing it to our attention. We have\nmodules” the authors are added text to the introduction to clarify that this is\nreferring to, as sustainability environmental sustainability (Page 1, Line 74) and due to the\ninvolves 3 pillars, eg. importance of this point we have also added additional text\nenvironmental, social, and to the “Assumptions and Future Opportunities” section,\neconomic, please make the Page 14, Line 12–17.\nstatement more clear.\n7 “stresses the importance of The Reviewer is correct in commenting that it is important to\nminimising electricity use…”, I reduce the amount of electricity produced from fossil fuels\nexpect that the authors are however, we also note that reducing the amount of\nNCOMMS-25-13362-T\nreferring to reduce the “dirty” electricity use – regardless of source – for example through\nelectricity powered by fossil process efficiency, will also result in lower environmental\nfuels instead of the amount of impacts. We have sought to clarify this by revising the\nelectricity, please correct it if manuscript on Page 2, Line 73–80.\nthis is agreed.\n8 “only” was repeated Thanks for spotting this. The repetition has been removed,\nPage 2, Line 81.\n9 “reducing poly-Si thickness” This is another very important point that the Reviewer has\nwas highlighted as a way to highlighted and one that we agree should be addressed.\nreduce the environmental We have added text at the end of this section to highlight\nimpact of TOPCon, however, the trade-off between performance and environmental\nreducing the thickness of poly- impact, Page 2, Line 111-116.\nlayer will have adverse impact\non the cell performance, and\nsome PV manufacturing has\nalso demonstrated that\nincluding the poly-Si layer at\nfront side as well can improve\nthe TOPCon performance\nfurther. So please modify the\nlast sentence of this paragraph\nto explain, maybe good to bring\nthe point that even though\nreducing the thickness of poly-Si\nmay adversely impact the\nTopcon cell performance, trade-\noff between cell performance\nand its environmental impact\nshould be carefully considered\n(just an example of expression\nwhat I meant)\nmanufacturing to 2035”, it says presentation of the projected impacts of TOPCon\n“We investigate the impact of manufacturing and bringing it to our attention. To help clarify\nTOPCon manufacturing for we have renamed the section “TOPCon technology\nIndia, China, the US and Europe development and manufacturing location”, where here we\nby changing the electricity mix to present LCA results identifying the impact of manufacturing\nrepresent each location and 1 W of TOPCon PV in different geographical locations and\np\nmodifying the transport inputs accounting for the forecasted improvements in cell\naccordingly.”, but the actual technology.\nresults in Fig. 3 were obtained\nonly considering the percentage The modelling of locations involves changing the\nof solar over the overall transportation inputs (from the manufacturing location to\nelectricity generations if I am not central Europe) and the electricity mix (the composition of\nwrong? If so, please clarify this the electricity generating resources used for solar panel\nin the texts to avoid misleading, production). Electricity mixes are taken from the LCA\nparticularly as “electricity mix” Ecoinvent v3.10 database (representing “Current”\nwas highlighted in many electricity mixes) and projections to 2035 use the US Energy\nsections of the paper. The Information Administration (representing “Future”\nreason is that when considering electricity mixes). The details of this methodology are\nelectricity mix where other clarified in the Supplementary Information,\nfactors should be included, eg. Supplementary Note 1.\nNCOMMS-25-13362-T\nother metals like steel and\ncopper in addition to silver All materials used for the various electricity generating\nshould also be considered for resources – such as wind, coal and gas – have been\nwind energy. considered through this LCA approach.\nWe have added text to the beginning of this section to\ndescribe the results and provide a smoother introduction\nmaking sure this explanation is well communicated, Page 3,\nLine 39–46.\ndifficult to be seen with the the figure to include an inset which magnifies the difference\ncurrent scale, it might be good to more clearly towards 2034 whilst still presenting the range\ninclude a magnified version to of values over time. The new Figure 4 is shown below and\nshow the difference clearer. added to the manuscript, Page 6.\n12 “The PV deployed in this study We thank the reviewer for this suggestion. For clarification,\nwill contribute 94,602 TWh of the following list provides details on the assumptions used\nelectricity generated between to obtain 94,602 TWh electricity generation value:\n2023-2035.”, what is the\nassumptions for the PV • the energy output was calculated using equation 1\ninstallation and exposed from A. Müller et al. (2021, Sol. Energy Mater. Sol.\nelectricity generation to obtain Cells, 230 111277);\n94,602 TWh? Please add. • the insolation was 1000 kWh m-2yr-1 (representative\nof Northern Europe);\n• the lifetime, degradation (both in year 1 and\nannually) and efficiency were taken from the 2024\nInternational Roadmap for Photovoltaics;\n• the performance ratio was assumed to be 0.8,\nfollowing International Energy Agency LCA of PV\nmethodology guidelines; and\n• the number of years was taken from installation to\n2035. (i.e. for modules in 2023, 12 years of output\nare considered but for modules in 2034 just 1 year\nof their output is considered).\nWhen the whole lifetime of the PV is considered, the\nelectricity output would be 499,723 TWh. However, to keep\nthe focus of impact to 2035 this is not mentioned in the\nmanuscript and the emissions avoided over this longer time\nperiod have not been considered (this would also involve\nconsidering changing electricity mixes beyond 2034).\nNCOMMS-25-13362-T\nTo clarify these assumptions in the manuscript, we have\nupdated the manuscript text, Page 6, Line 59–63, and\nprovided information on calculating this value in\nCalculation 3 (Supplementary Data, Dataset 3) along with a\nwritten description in the Supplementary Information,\nSupplementary Note 1.\n“CC”. CC has been changed to Climate change. Page 10, Line 33.\nTable 4 Inventory table for identified. After careful checking, we have identified that\nTOPCon and PERC cell there were two typographical errors in (now) Table 2 and\nmanufacturing per m2: the silver Table 3 in the Supplementary Information for which we\nmetallisation for TOPCon (1.2e- apologise and are grateful to the Reviewer for identifying.\n4 kg) is 1 order of magnitude\nlower than that of the PERC Firstly, the value for TOPCon silver metallisation (1.2E-4 kg\n(4.5e-3 kg)? This seems m-2) was not scaled for area and this should in fact be 3.62E-\ninconsistent with the results 3 kg m-2. Secondly, the value for PERC (4.5E-3 kg m-2) is from\nabout impact of metal use Müller et al. (2021, Sol. Energy Mater. Sol. Cells, 230\ndiscussed in the manuscript in 111277) and was not updated with data from the\npage 7 “The only increased International Roadmap for Photovoltaics. This should in fact\nimpact for TOPCon modules is be 2.26E-3 kg m-2.\nMetal use,”. Please check and\nexplain. These have both been corrected in the manuscript and we\nwish to reassure the Reviewer that the correct values were\nused in the LCA modelling and therefore this typographical\nerror does not affect the results. We have also re-checked\nthe other values in Supplementary Tables 2 and 3 and\nconfirm that they are now correct.\nEnd of Reviewer 1’s Comments\nNCOMMS-25-13362-T\nResponse to Reviewer 2\n# Comment Response\nvolume of data that is rapidly Reviewer has highlighted and agree that more details are\nchanging, making it difficult for required to help with verifying the data. To improve the\nreviewers to verify each piece accessibility of the data we have added the sources and\nindividually. It is requested to years for the data presented, as requested, and have re-\nprovide detailed sources and structured the Supplementary Information and\nyears for the data presented. Supplementary Data to make navigation of this data more\nstraightforward which includes a contents page for both\ndocuments and all data used to plot Figures.\nsignificant logical inconsistency between cost and CO emissions and is correct in stating\n2\nin this paper lies in the fact that that PV modules manufactured in China are lower\nthe price of TOPCon solar cell economic cost. Yet, it is worth noting that the electricity grid\nmodules produced in China is in China is relatively carbon intensive (in the Ecoinvent\nmuch lower than in other parts database) and this is the reason for high CO emissions. We\n2\nof the world, so why would their emphasise that our work does not make any correlation\nCO2 emissions be significantly between the cost of PV manufacturing or production\nhigher than in other regions? The efficiency on CO emissions. We have added a comment to\n2\nproduction efficiency in Europe the “Assumptions and Future Opportunities” section to\nand America is low, with very clarify this and highlight that future work could expand the\nlimited capacity, yet how can modelling to account for these important points raised by\nthey achieve lower CO2 the Reviewer. This can be found on Page 14, Line 1-6.\nemissions? Clearly, the\naforementioned contradictions Our work models the environmental impact of\nare illogical. manufacturing of TOPCon PV modules in other regions,\nbased on the electricity mixes in those locations. The\nmodelling implicitly uses identical manufacturing\nprocesses and capacity that are independent of location.\nThe results show that the environmental impact of PV\nmanufacturing is sensitive to location through the carbon\nintensity of the electricity mixes used in manufacturing. We\nhave added this to a new table of assumptions to the\nSupplementary Information (Supplementary Table 1).\nWe also stress that our goal is not to express a preference\nfor one manufacturing location over another but rather to\nshow how this can result in a wide range of emissions (and\nrelated savings opportunities). We realise that the electricity\nmix composition in each location can vary significantly,\naffecting the carbon intensity of the grid. Motivated by this\nunderstanding, a new sensitivity analysis has been added to\nthe manuscript comparing the highest and lowest carbon\nintensity sub-grid regions for each location. Interestingly,\nthis analysis (Fig. 6) shows that PV manufacturing in the\nlowest carbon intensity sub-grid location in China results in\ncomparable CO eq. emissions to the reference\n2\nmanufacturing in Europe. This is added to: Page 6, Line 77-\n78 and Page 7; Line 5-28; and Fig 6.\nEnd of Reviewer 2’s Comments\nNCOMMS-25-13362-T\nResponse to Reviewer 3\n# Comment Response\nlimited and potentially updated a previously published PERC life cycle inventory\nmisleading due to the 3 main (LCI) according to International Technology Roadmap for\nreasons below:\nPhotovoltaics (ITRPV) values, this is not the essential part of\nthe manuscript nor the primary focus of the work. Instead,\n1) it relies mostly on already our work brings a completely new and industrially validated\npublished inventory data that TOPCon cell LCI (available in Supplementary Table 2). We\nhave been updated for the wafer think that this is valuable, particularly because we are not\nonly based on ITRPV values. aware of any other published TOPCon LCI, and therefore\nimpactful because it can be used directly by other\nresearchers (we apologise that this wasn’t clear in the\noriginal submission and have signposted the LCI and raw\nLCA results in the revised manuscript).\nWith our new TOPCon LCI, it is useful to make a comparison\nbetween the LCA results of the updated PERC cell and the\nTOPCon cell however, the novel part of the work is that we\ncombine both projections for evolution of these two\ntechnologies with global electricity mix scenarios into our\nLCA models to identify significant variation in environmental\nimpact.\nconsidered are not clear. Typical apologise for the ambiguity. This was a weakness in the\nLCA require a clear system original submission which we have sought to rectify with\nboundary with information significant changes throughout the manuscript.\nabout input/output, what is Specifically, we have now added:\nincluded/excluded and what is - a system boundary diagram to the Methods section\ncompared. In this case the of the manuscript;\ncomparison is between PERC - a table summarising our assumptions in the\nand TOPCon where most of the Supplementary Information with appropriate\nupstream process is considered signposting in the manuscript (Supplementary Table\nthe same (up to cell) and only 1); and\nmodified for efficiency. The - clarification on the transport throughout the\nreader might be able to figure it manuscript.\nout by reading at the same time\nthe paper + supporting info + The system boundary (new Figure 10) is shown here for the\nreferences but it certainly lacks Reviewer’s reference. (Page 12)\ntransparency. A table\nsummarizing assumptions for\neach cell would be required to\nmake sure there is no bias in the\napproach. Also, that would be\nrequired if the author wants the\nstudy to be reused. For example,\nthe final assumption assume\ntransporting the module from\nChina to central Europe (which I\nassume is where the authors\nare). If there was a system\nNCOMMS-25-13362-T\ndiagram and/or clear\nassumption table it would be\neasier to see how to modify the\nstudy to be more broadly\napplicable.\nconducted even though monte We acknowledge the issue with sensitivity and uncertainty\ncarlo analysis is mentioned. All analysis and have addressed this by developing and re-\nresults are deterministic. The writing the “Sensitivity and Uncertainty” section of the\nonly figures that show various manuscript. Page 6, Line 76 - Page 10, Line 29. The methods\nscenarios are Fig 3 and 4 where are described, Page 13, Line 1-91.\nthe only variable that is changed\nis either the grid remains the This is a major revision which includes the addition of three\nsame or change over time. new figures (Fig. 6, Fig. 8, Fig. 9) to the manuscript which for\nSensitivity analysis is only done reference, are shown here. All the data are accessible in the\non grid mix, not technology Supplementary Data and Supplementary Information.\ninput. That’s neither sensitivity\nor uncertainty analysis. A\nreduction of 6.5% in carbon\nfootprint is small – looking at the\nimpact of changing some of the\ninput in the LCI would have been\nuseful before claiming that\nTOPCon are better than PERC.\nThis conclusion is also only valid\nif GHG is more important than\nresource depletion.\nNCOMMS-25-13362-T\nabout lack of clarity about manufacturing in China with transport to Europe and we\nassumptions – Fig 2 ends up have added text to the abstract (Page 1, Line 13) to clarify\nwith a mix of transport form this. We have clarified the transportation in the introduction\nEurope and China while the and throughout the relevant places in the Results and\noriginal scope in the Methods sections.\nintroduction mention\ncomparing India, China, US and In the Results section, we initially compare PERC and\nEurope. The abstract only TOPCon (for manufacturing in China only) and then\nmentions reduction of 6.5% in investigate the effect of manufacturing location for TOPCon\nCO2 / Wp – is that for this China only. We have updated the first two subsection headings in\nto Europe scenario? the Results section (at Page 2, Line 22 and Page 3, Line 35-\n36) to make this distinction clearer.\neach indicator? arbitrary units (now stated in the axis title) because the\nresults have been normalised to make the data\ndimensionless and allow for easier comparison across the\nset of impact categories.\nWe have added an explanation of the normalisation process\nto the manuscript (Page 2, Line 41–47) before the\npresentation of the results and note that the raw data\ntogether with the individual units are available in\nSupplementary Table 5. The data used to create all the\nfigures are given in Supplementary Data.\nNormalised results are only used for the presentation of\nthe results within the PERC vs TOPCon comparison and the\nhotspot analysis (Fig. 1 and Fig 2).\nmonte carlo analysis – how? transparency is a feature which we cherish and aim to\nNone of the results show any achieve when conducting an LCA. We agree that there was\nrange. All analysis seems to be insufficient information to support the Monte Carlo analysis\ndeterministic. There is no in the original manuscript and apologise for this. We have\ninformation about this was looked to improve the transparency of the work by:\ndone. I don't see any input with\nuncertainty in the LCI • adding pedigree matrix values to the life cycle\nsupporting info. inventory (PERC and TOPCon Cells in\nSupplementary Tables 2 and 3),\nNCOMMS-25-13362-T\n• sharing the raw data from the LCA software over\n10,000 runs (Supplementary Table 9),\n• moving the presentation of the Monte Carlo results\nfrom the Supplementary Information into the main\nmanuscript (Fig. 9 - Shown in Response 3)\n• providing a more in-depth discussion of the results.\nThe additional text discussing the Monte Carlo results is in\nthe sensitivity and uncertainty analysis section, Page 10,\nLine 14-29. The method is described in the methodology,\nPage 13, Line 76-91.\n– Fig F looks like there is a huge changed the axis on Fig. 3f to reflect that this is indeed a\ndecrease but it’s only because small decrease. The new Figure is shown here for the\nof axis choice. Reviewer’s reference.\ngrid environmental impact. that the differences in impact for various sub-regions can\nEurope is not a country and large affect the results and we have addressed this by conducting\ncountry such as US, India and additional sensitivity analysis which compares the highest\nChina all have sub regions grid in and lowest carbon intensive sub-grid mixes for each of the\nEcoinvent. It easy to make investigated locations. For Europe, we used Switzerland and\nEurope looks good and China Poland for low and high intensity respectively. As the\nbad when using this approach. Reviewer notes, a comparison between Europe and China is\nAgain - this could be answered more nuanced and we have described this and the new\nwith better sensitivity or figure in the revision to the Sensitivity and Uncertainty\nscenarios analysis.\nNCOMMS-25-13362-T\nsection, Page 6, Line 77 – Page 7, line 28. The additional Fig.\n6 is shown in Response 3.\nis very difficult because results Reviewer and have looked to appropriately address this\nare normalized. How does the comment. To allow for readers to compare our work with\nnew carbon footprint of PERC others we have provided the raw results in the\ncompare with the original Supplementary Information (with appropriate signposting in\nreference paper with the ITRPV the manuscript) and the data used to plot the figures in the\nupdate? How does the results Supplementary Data. We have also added a comparison of\ncompare with carbon standards our results to the original reference paper (Muller et al.,\nlike EPEAT (goal is to be under 2021) and the EPEAT carbon standards as suggested by the\n400 kg CO2/kWp for ultra-low Reviewer. This has been added to the “Discussion” section\ncarbon) or EU Ecodesign of the manuscript, Page 11, Line 20-46.\ndirective?\nEnd of Reviewer 3’s Comments\n12"}, {"round": 2, "reviewer_comments": {"reviewer_1": "The auhtors has done a great job in addressing my comments. I am also glad to see we share the common understanding in few imporant aspects which have been corrected/imporved in the revised manufacrpt, including enhanced description of novelty, added comparison with other research work, more cleared and presise explanation in numbers, accumption and so forth. I am happy to accept this improved manuscript for publication.", "reviewer_3": "Thank you for addressing the comments, in particular the contribution of this work is a lot more clear. I think for LCA being transparent and providing sufficient details to be reproducible is important and in that sense the paper is greatly improved.\nI have only 3 small comments regarding the figures:\nFig. 5 shows up to 2034 but legend say up to 2035, this is confusing\nFig 8 – it makes sense that efficiency has the same impact on all impact categories since LCA is based on manufacturing and there is no impact during the use phase – it might not be necessary to show this one but maybe mention it in the text only\nFig 9 – y axis is % change compared to reference? It’s not in the figure or the caption"}, "author_response": "Maximising Environmental Savings from Silicon Photovoltaics Manufacturing to 2035\nNCOMMS-25-13362A\nResponse to Reviewers’ Comments\nWe thank the Reviewers again for their valuable and constructive feedback throughout the\nreview process and also for their comments on our revised manuscript.\nReviewer 1’s comments\nThe authors has done a great job in addressing my comments. I am also glad to see we share\nthe common understanding in few important aspects which have been corrected/improved in\nthe revised manuscript, including enhanced description of novelty, added comparison with\nother research work, more cleared and precise explanation in numbers, assumption and so\nforth. I am happy to accept this improved manuscript for publication.\nResponse to Reviewer 1’s comments\nWe are grateful for the Reviewer’s feedback and positive comments.\nReviewer 3’s comments\nThank you for addressing the comments, in particular the contribution of this work is a lot more\nclear. I think for LCA being transparent and providing sufficient details to be reproducible is\nimportant and in that sense the paper is greatly improved.\nI have only 3 small comments regarding the figures:\n1. Fig. 5 shows up to 2034 but legend say up to 2035, this is confusing\n2. Fig 8 – it makes sense that efficiency has the same impact on all impact categories since\nLCA is based on manufacturing and there is no impact during the use phase – it might not be\nnecessary to show this one but maybe mention it in the text only\n3. Fig 9 – y axis is % change compared to reference? It’s not in the figure or the caption\nResponse to Reviewer 3’s comments\nWe appreciate the Reviewer’s feedback which has made our research more transparent,\nreproducible and robust, adding significant value to the work.\n1. Thank you for identifying this. The legend for Fig. 5 has been changed to 2034 to be\nconsistent with the graph axis.\n2. We understand the Reviewer’s reasoning for removing Fig 8a showing the percentage\nchange due to efficiency differences in the sensitivity analysis however, we believe that the\nfigure provides a useful visual aid for a non-specialist audience to clearly reflect the\nstatements made in the text and so we have made the decision not to remove Fig. 8a. We\nfound the Reviewer’s explanation useful and have added this to the manuscript text\n(Sensitivity and Uncertainty Analysis section>paragraph 5>third line), which we think will\nfurther improve the clarity of this finding to the audience. “Further, the scope of the LCA is\nlimited to the manufacturing of modules meaning there is no impact considered during the\nuse phase which would affect this correlation.”\n3. Thank you for spotting this. The y-axis on Fig. 9 is the percentage of Monte Carlo runs that\ndeliver a particular output. For example, 71.49 % of 10,000 runs had a result where the\nClimate Change impact of manufacturing 1W PERC was greater than that of manufacturing\np\np\nexplanation in the caption: “This is shown by the percentage of runs where the impact of\nmanufacturing TOPCon is less than, or otherwise greater than the impact of manufacturing\nPERC.”"}]

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