Harmonised rules for the calculation of the carbon footprint of photovoltaic modules in the context of the EU Ecodesign Directive.
Date: 2025-05-15
Institutions: Joint Research Centre
Subjects: approximation of laws, directive (EU), ecodesign, ecological footprint, photovoltaic cell, reduction of gas emissions, report

ISSN 1831-9424 

Harmonised rules for the calculation of 
the carbon footprint of photovoltaic 
modules in the context of the EU 
Ecodesign Directive 

Ardente, F., Eynard, U., Leccisi, E., Mathieux, F., Wolf, K. 

2025 

 EUR 40302 

 
 
  
  
 
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JRC141275 

EUR 40302 

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How to cite this report: European Commission: Joint Research Centre, Ardente, F., Eynard, U., Leccisi, E., Mathieux, F. and Wolf, K., Harmonised 
rules for the calculation of the carbon footprint of photovoltaic modules in the context of the EU Ecodesign Directive, Publications Office of the 
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Contents 

Abstract ....................................................................................................................................................................................................................................................................... 3 

Acknowledgments.............................................................................................................................................................................................................................................. 4 

Executive Summary ......................................................................................................................................................................................................................................... 5 

1.  Introduction..................................................................................................................................................................................................................................................... 7 

1.1  Environmental Footprint (EF) method developed by the European Commission (EC Recommendations 
2279/2021) .................................................................................................................................................................................................................................................... 9 

1.2  Preparatory study for solar photovoltaic modules, inverters and systems ........................................................................ 9 

2.  Background concepts underpinning the harmonised rules for the calculation of the carbon footprint of 
photovoltaic panels ....................................................................................................................................................................................................................................... 11 

3.  Suggestion for potential improvements of the harmonised rules for the calculation of the carbon footprint 
of photovoltaic modules........................................................................................................................................................................................................................... 13 

3.1 Product category in scope for regulatory intervention ............................................................................................................................ 13 

3.2 Hotspot analysis for identification of potential areas for regulatory intervention ...................................................... 13 

3.3 Product Environmental Footprint Category Rules (PEFCR) “Photovoltaic modules used in photovoltaic 
power systems for electricity generation” ..................................................................................................................................................................... 14 

3.4 PEFCR adaptation ........................................................................................................................................................................................................................ 15 

3.4.1 Functional unit and reference flow ............................................................................................................................................................... 17 

3.4.2 System boundary ........................................................................................................................................................................................................... 17 

3.4.3 Impact assessment ...................................................................................................................................................................................................... 19 

3.4.4 Use of company-specific activity data ...................................................................................................................................................... 19 

3.4.5 Non-mandatory company-specific activity data .............................................................................................................................. 22 

3.4.6 Data quality rating (DQR) of default secondary datasets ...................................................................................................... 23 

3.4.7 Life cycle stages ............................................................................................................................................................................................................. 25 

3.4.8 Modelling of recycled content and materials recycling ............................................................................................................. 27 

3.4.9 Distribution ........................................................................................................................................................................................................................... 29 

3.4.10 Analysis of electricity modelling approaches .................................................................................................................................. 29 

3.4.10.1.1 On-site generated electricity ............................................................................................................................................... 30 

3.4.10.1.2 Supplier-specific electricity product .............................................................................................................................. 31 

3.4.10.1.3 How to model the residual consumption mix ...................................................................................................... 33 

3.4.10.1.4 How to model the average consumption mix ...................................................................................................... 33 

4.  Modelling of the PV technologies ........................................................................................................................................................................................... 34 

4.1 Life cycle inventories of PV technologies ............................................................................................................................................................. 34 

4.2 Additional potential materials needed for key and innovative PV technologies ............................................................. 38 

5.  Carbon footprint results and discussion .......................................................................................................................................................................... 39 

5.1 Carbon footprint results ........................................................................................................................................................................................................ 39 

5.2 Key findings ....................................................................................................................................................................................................................................... 45 

5.3 Data gaps ............................................................................................................................................................................................................................................ 46 

5.4 Description of the scenarios under analysis and differences with PEFCR ............................................................................ 48 

5.4.1 Amount of Silicon in panel .................................................................................................................................................................................... 49 

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5.4.2 Electricity in production ............................................................................................................................................................................................ 49 

5.4.3 Silicon mix ............................................................................................................................................................................................................................. 50 

5.5 Interpretation of carbon footprint results for Mono-Si: .......................................................................................................................... 50 

6.  Stakeholder Consultations ............................................................................................................................................................................................................ 55 

7.  Support to a PV dedicated tool for calculating carbon footprint ............................................................................................................. 57 

8.  Conclusions .................................................................................................................................................................................................................................................. 59 

References ............................................................................................................................................................................................................................................................. 60 

List of abbreviations .................................................................................................................................................................................................................................... 62 

List of figures ..................................................................................................................................................................................................................................................... 63 

List of tables ........................................................................................................................................................................................................................................................ 64 

Annex A – “Harmonised rules for the calculation of the carbon footprint of photovoltaic modules” version of 
30 March 2023 ................................................................................................................................................................................................................................................. 65 

Annex B – Energy efficiency index and energy yield over the lifetime ....................................................................................................101 

Annex C – Preliminary screening of additional potential datasets needed for key and innovative PV 
technologies ......................................................................................................................................................................................................................................................104 

Annex D – Details on carbon footprint results according to the scenarios under analysis ..................................................109 

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Abstract  

The decarbonisation of EU energy systems is critical to reach climate objectives in 2030 and 2050 
as announced in the European Green Deal and, in this context, photovoltaics (PV) is called to have a 
prominent contribution, according to the REPowerEU plan. 

In this direction, it is crucial to ensure that newly installed PV panel capacities in the EU are 
affordable and competitive, but also that they are manufactured in a sustainable manner. 

The aim of this Science for Policy Report is to outline a set of harmonized rules for accounting the 
carbon footprint of PV panels, designed to be applicable in the regulatory context and in particular 
for the potential development of Ecodesign requirements (as in line with Directive 2009/125/EC for 
the Ecodesign of energy related products). 

The harmonized rules proposed in the present report are based on the Environmental Footprint 
method developed by the European Commission (EC Recommendations 2279/2021)1 and the 
Product Environmental Footprint Category Rules (PEFCR) for PV2. In particular, the PEFCR for PV 
have been used as pillar for building these harmonized rules with adaptations due to the application 
in a regulatory context.  

The method has been the outcome of a consensus building process based on various positions from 
different stakeholders received at different stages.   

1 Available at : https://eur-lex.europa.eu/eli/reco/2021/2279/oj/eng 
2 PV PEFCR (Note that the time validity is 2021). Available at: https://wayback.archive-it.org/org-

1495/20221006222253mp_/https:/ec.europa.eu/environment/eussd/smgp/pdf/PEFCR_PV_electricity_feb2020_2.pdf 

3 

 
 
 
 
  
 
 
 
Acknowledgments 

We would like to thank Susanna Andreasi Bassi, Jens F. Peters, Daniele Candelaresi, Nicola Ferrara 
from JRC unit D.3, for their support on discussing and reviewing some of the methodological 
aspects in the report. We would like to thank also Ewan Dunlop and Nigel Taylor from JRC C2 for 
providing technical information on PV technologies. 

We would like to thank Marc-Andree Wolf of maki Consulting who has provided support on data and 
modelling. 

We are grateful to thank Davide Polverini and Inés Pina Garcia- Galán from DG GROW and Mauro 
Cordella and Alicia Boyano Larriba from DG ENV for their support and inputs on the report and 
guidance on the policy developments and the regulatory aspects of this study. 

Finally we acknowledge the detailed feedback received from several stakeholders, which helped us 
to improve the method for the accounting of carbon footprint of PV panels. 

Authors 

Ardente Fulvio a, Eynard Umberto a, Leccisi Enrica b, Mathieux Fabrice a, Wolf Kirana c  

a European Commission, Joint Research Centre (JRC), Ispra, Italy   

b Piksel S.r.l, 20126 Milan, Italy (the work has been carried out at and on behalf of JRC)  

c maki Consulting, Germany  

Contributions:  

Ardente Fulvio: Contribution to the entire report: conceptualisation, writing – review and editing, 
supervision, project administration  

Eynard Umberto: Contribution to the entire report: conceptualisation, methodology, data analysis, 
writing – review and editing, supervision  

Leccisi Enrica: Contribution to the entire report:  sector analysis, data collection, data analysis, data 
preparation and visualisation, LCA modelling, writing of technical documentation and methodology, 
review and editing  

Mathieux Fabrice: Contribution to the entire report: conceptualisation, review, supervision, project 
administration  

Wolf Kirana: Contribution to Section 5 of the report: LCA modelling, data collection, data analysis, 
writing original draft of section 5  

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Executive Summary  

This Science for Policy Report illustrates a proposal of harmonized rules for accounting the carbon 
footprint of photovoltaic (PV) panels, which have been conceived to be applicable to regulatory 
contexts - in particular for the development of Ecodesign requirements as in line with Directive 
2009/125/EC for the Ecodesign of energy related products.  

The work has been performed by JRC for supporting DG GROW in the definition of a carbon 
footprint method for Ecodesign implementing measures for PV panels. External 
collaborators have been involved in this process, especially for what concern the LCA modelling of 
PV scenarios. Also, JRC colleagues involved in the preparatory study on PV panels3 have been 
consulted to provide information related to the analysis of PV technology types and formulation of 
energy yield needed for calculating the carbon footprint.  

The method proposed in the present report is based on the Environmental Footprint method 
developed by the European Commission (EC Recommendations 2279/2021)4 and the Product 
Environmental Footprint Category Rules (PEFCR) for PV5 have been used as pillar for building these 
harmonized rules with adaptations due to the application in a regulatory context.  

The method has been the outcome of a consensus building process based on various positions 
from different stakeholders received at different stages.  The first stakeholder consultation at the 
“Consultation Forum Meeting on potential Ecodesign and Energy labelling requirements for 
photovoltaic modules, inverters and systems” was held on June 27th 2022 and the second at the 
“Technical stakeholder meeting on PV module carbon footprint calculation rules and related 
conformity assessment procedure” on March 30th 2023, in which a total number of 80 written 
comments were received by 6 commenting stakeholders, including public and private entities. 
Differences and analogies with the PV PEFCR are presented and discussed in this report. In 
particular, compared to PEFCR, adaptations to the method regarded: the system boundary; the use 
of company-specific activity data and non-mandatory company-specific activity data; the inclusion 
of bifacial PV panels and innovative silicon-based multi-junction PV modules; the review of data 
quality rating of default secondary datasets; the modelling of recycling content and materials 
recycling; the electricity modelling. 

Findings presented in this work are intended to inform the technical and political debate on the 
carbon footprint PV panels and to provide a scientific method that can be applied for supporting 
entry market Ecodesign requirements. This represents one of the first attempt (together with 
requirements in the EU Regulation on Batteries6) to introduce a mandatory carbon footprint 
requirement for products in a regulatory context.    

This report discusses also how to model PV technologies and their life-cycle inventories under 
analysis (mono-crystalline silicon, multi-crystalline silicon, cadmium telluride), including the default 
secondary datasets and the associated activity data to be used. The inventories are detailed in an 
excel file that JRC has developed7, based on the original input from the PV PEFCR. Moreover, this 

3 https://publications.jrc.ec.europa.eu/repository/handle/JRC122431 

4 https://eur-lex.europa.eu/eli/reco/2021/2279/oj/eng 
5 PV PEFCR (Note that the time validity is 2021) available at: https://wayback.archive-it.org/org-

1495/20221006222253mp_/https:/ec.europa.eu/environment/eussd/smgp/pdf/PEFCR_PV_electricity_feb2020_2.pdf 

6 https://eur-lex.europa.eu/legal-content/EN/TXT/PDF/?uri=CELEX:02023R1542-20230728 

7 https://ec.europa.eu/docsroom/documents/46532 

5 

 
 
 
 
 
 
 
report describes the support by JRC for a dedicated tool, currently under development by the EC, for 
facilitating the calculation of the carbon footprint of PV modules according to the harmonized 
methodological rules.  

Results reported in this report show the carbon footprint of PV panels among different scenarios. 
The range of carbon footprint outcomes, spanning from the most favourable (10.8  gCO2 eq per kWh) 
to the least favourable scenarios (44  gCO2 eq per kWh)..  

The primary contributors to the carbon footprint of mono- and multi-crystalline silicon PV panels 
include: 

—  electricity for manufacturing the silicon, 

—  silicon content  

—  aluminium frame,  

—  glass.  

The product lifetime plays a significant role in terms of the carbon footprint per kilowatt-hour.  

Future research could also focus on extending current rules to other relevant impact categories 
beyond carbon footprint - including for example resource use (fossils), resource use (minerals and 
metals) acidification and particulate matter. Finally, the proposed method should be adapted and 
extended to innovative PV technologies (e.g. single-junction perovskite panels) that will be 
potentially scaled up from laboratory to commercial scale as well as on existing PV panel 
technologies with technological improvements in their manufacturing (e.g. updated life cycle 
inventories). 

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1. 

Introduction  

The European Union (EU) is fostering grid decarbonisation by requiring 1 TW of installed solar 
photovoltaics (PV), up from ~130 GW in 2021 (European Commission, 2022). 

Also, the rapid deployment of renewable energy and PV is at the core of the REPowerEU plan – the 
EU initiative to put an end to its dependency from fossil fuels. By the end of 2023, the EU reached 
271GW of solar PV installed generation capacity, having added more than 50 GW that year. It 
delivered around 5% of total EU electricity generation. The REPowerEU plan strategy aims to bring 
online over 320 GW of solar photovoltaic by 2025 (more than doubling compared to 2020) and 
almost 600 GW by 2030.  

Facing the climate change challenge requires societies and economies to transform, thus phasing 
out unsustainable practices of production and consumption. Photovoltaic systems are considered a 
green or low-carbon technology, but their manufacturing is an energy intensive process and cause 
impacts on the environment. Consequently, PV systems and modules cannot be designed without 
considering also environmental criteria and estimating their impacts with the aim of reducing them 
as much as possible. With the aim of maximising the emission reduction, PV modules and inverters 
must not only have a high conversion efficiency and long lifetime, but materials and components 
should have been sourced, manufactured, used and disposed of, in an environmentally sensible 
manner. 

Given that solar energy is going to play a relevant role among the energy sources of the future, it is 
timely to consider and to steer this production as much as possible towards a sustainable 
technology.  

Globally, many national decarbonisation goals, have been declared; for example, the United States 
aspires to decarbonize the electricity grid by 2035 (White House, 2021). France and Korea have 
already identified solutions to start cutting emissions from PV, by including greenhouse gas (GHG) 
emissions targets in their national tenders. (Commission de régulation de l’énergie, 2021 & Korean 
Ministry of Environment, 2021). 

Then, it is of paramount importance to estimate the carbon footprint of the PV sector, assessing 
and quantifying the material and energy flows of PV manufacturing.  

With the aim of improving the sustainability of PV, not only voluntary goals have to be taken along, 
but compulsory regulations could potentially set requirements for the next PV modules generations 
to come to the market. It will then be necessary to set the rules for the clear quantification of the 
emissions.  

The quantification of the carbon footprint of a product (PCF) is one of the first steps to complete in 
order to maximise the GHG emissions reduction. A number of companies in the photovoltaic sector 
are aiming to reduce significantly GHG emissions along their value chain and closely aligning 
towards the United Nation’s sustainable development goals (SDGs) (Energy Global, 2022). 

CFP applied to PV is generally expressed as ‘gCO2 eq/kWh’, i.e. grams of carbon dioxide equivalent per 
kilowatt-hour of electricity generated. Alternatively, it may be expressed in ‘kgCO2 eq/kWc‘. 
Commission de Régulation de l’Energie Carbon dioxide is one of the most significant GHG and is 
produced, for example, when fossil fuels are burnt. GHGs other than carbon dioxide, such as 
methane, are quantified as equivalent amounts of carbon dioxide. This is done by calculating their 
global warming potential relative to carbon dioxide over a specified timescale, usually 100 years. 

7 

 
 
 
In many cases, emissions of “low-carbon” or “clean” electricity generation technologies do not arise 
directly from the operation and use of the generators, but they may be dominated by indirect 
emissions, such as those produced during construction of the plant or the production of fuels (where 
applicable). For solar PV energy, the geographical location-specific energy resource also has an 
important influence on the impacts (e.g. specific irradiation). This is because higher electricity 
outputs may cause lower footprints, as total emissions are spread over a greater amount of 
electricity (Polverini et al., 2023). 

Ecodesign (European Union, 2009) and Energy Labelling (European Union, 2017) are European 
Union (EU) legislative acts that aim to contribute to a clean and less costly energy transition for 
business, citizens and governments and overall improve the EU’s energy efficiency and support the 
wider ‘European Green Deal’ objectives (European Commission, 2019), including the Circular 
Economy agenda. They create business opportunities and increase resilience by setting harmonised 
rules for energy-related products on aspects such as energy consumption, water consumption, 
emission levels and material efficiency. These measures also foster demand for and supply of more 
sustainable products whilst reducing energy user expenditure significantly: estimates indicate that 
savings in 2021 exceeded EUR 120 billion and could reach the double in 2022 (European 
Commission, 2021). Furthermore, these policies will help achieve the EU’s target of reducing GHG by 
at least 55% by 2030 (European Commission, 2020), compared to 1990 levels. 

Within this policy framework, the European Commission established a third Ecodesign Working Plan 
(European Commission, 2016), identifying PV modules and inverters as one of the non-regulated 
product groups with the largest potential for environmental savings and indicating the need for 
investigating in more detail the possible environmental improvements.  

Findings presented in this work are intended to inform the technical and political debate on the 
carbon footprint PV panels for supporting entry market Ecodesign requirements. This represents one 
of the first attempt (together with requirements in the EU Regulation on Batteries) to introduce 
mandatory carbon footprint requirement in a regulatory context.     

The aim of this report is to discuss harmonised rules for the calculation of the carbon footprint of 
photovoltaic modules and potentially applicable in the context of the EU Ecodesign Directive.  
Overall, these rules have been elaborated based on the Environmental Footprint method, with a 
special focus on the accounting of the carbon footprint occurring during the PV manufacturing as 
choices of materials and components in terms of their amount/mass, origin, their quality affect the 
overall carbon intensity of the module. Updated life cycle inventories based on PVPS International 
Energy Agency have also considered for what concerns the electricity consumption for the silicon. 

It is essential to clarify that the publication of this report does not imply that Ecodesign 
requirements for PV modules and inverters will be adopted, nor does it suggest that the proposed 
methodology will be incorporated into any future legislation. Policy discussions regarding the 
possible introduction of Ecodesign requirements for PV modules and inverters are still ongoing. This 
report is meant to inform the decision-making process, showing possible carbon footprint values 
associated to practical cases of country specific and/or worldwide manufacturing value chains. 

The report is organised in eight sections. Specifically, Section 2 describes the background concepts 
underpinning the harmonised rules for the calculation of the carbon footprint of photovoltaic panels; 
Section 3 illustrates suggestions for future improvements including discussion on certain open 

8 

 
 
 
 
 
methodological aspects. It includes also discussions on adaptations and alignments with the Product 
Environmental Footprint Category Rules (PEFCR) of PV8; Section 4 shows the life-cycle inventories of 
the PV technologies under analysis (mono-crystalline silicon, multi-crystalline silicon, cadmium 
telluride), including default secondary datasets provided and the associated activity data; Section 5 
shows the analysis of some reference scenarios - including different geographical countries, 
different silicon mass, different panel lifetime, different recycled content, different electricity yields 
- providing carbon footprint results and their interpretation; Section 6 is focused on the stakeholder 
consultations; Section 7 describes the support provided to the realisation of a dedicated IT online 
tool for calculating the carbon footprint of PV panels, based on the method previously introduced. 
Finally, Section 8 presents the conclusions of the report. 

1.1  Environmental Footprint (EF) method developed by the European 

Commission (EC Recommendations 2279/2021) 

The Environmental Footprint method have been built after a long-standing process, which involved 
LCA practitioners and hundreds of companies in different sectors, and tested on a number of 
different products, including PV, over 4 years of pilot phase. The EF methods are also reflecting the 
newest advancements in line with progresses of the LCA scientific community (including taking into 
considerations recommendations Life Cycle Initiative). Specifically, the EF method is also in line with 
the latest updates of IPCC 2021 on characterization factors for Global Warming Potential. The 
prominent role of the EF method has been recognized in several other policies including the Farm to 
Fork Strategy (F2F), the Taxonomy Regulation, EU Ecolabel and Green Public Procurement (GPP), the 
Green Claims Directive for quantifying environmental life-cycle perspective, ensuring comparability 
through different products and using a consistent database. This role of the EF is expected to 
increase in the future, in order to grant more methodological coherence within different EU 
instruments. New examples of implementations of the EF method in recent policies include also the 
Ecodesign Directive (through the revised Methodology for Ecodesign of Energy related Products 
(MEErP) and related tool) and the Ecodesign for Sustainable Product Regulation (ESPR) proposal. 

The EC Recommendations 2279/2021 states “If a PEFCR exists, this should be used for calculating 
the environmental footprint of a product belonging to that product category”. 

Generally, Product Environmental Footprint Category Rules (PEFCRs) provide specific guidance for 
calculating products' life cycle potential environmental impacts. In other words, they complement 
general methodological guidance by providing further specification at the level of a specific product 
category, contributing to increased relevance, reproducibility and consistency of the results. 

1.2  Preparatory study for solar photovoltaic modules, inverters and systems  

Following the inclusion of PV products in an Ecodesign working plan, the European Commission 
launched in 2017 the Preparatory study on sustainable product policy instruments to assess the 
feasibility of applying Ecodesign, Energy Label, Ecolabel and Green Public Procurement instruments 
to solar photovoltaic modules, inverters and systems. The four policy instruments assessed by the 

8 PEFCR_PV_electricity_feb2020_2.pdf - Note that the time validity is 2021 

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study are either mandatory - in the case of Ecodesign and the Energy label – or voluntary in nature 
- in the case of the EU Ecolabel and Green Public Procurement criteria. 

The main objective of the study was to provide technical support for the development of a 
preparatory study to assess the feasibility of Ecodesign and/or Energy labelling requirements, as 
well as the potential for Ecolabel and Green Public Procurement criteria, for the PV panels, inverters 
and systems product group.  

In 2020 it was published the report on the “Preparatory study for solar photovoltaic modules, 
inverters and systems” (Dodd et al. 2020). One of the main indications provided by this report was 
that the carbon footprint of the manufacturing phase of photovoltaic modules is among the most 
salient aspects of this product group (Dodd et al. 2020).  

Indeed, for all photovoltaic modules, the carbon footprint is largely determined at the design stage. 
First, the manufacturer’s choice of materials and components in terms of their amount/mass, origin, 
their quality affect the overall carbon intensity of the module. Second, a comparison of the output 
of the module (e.g. kWh produced), which is also largely dependent on its design, with the carbon 
intensity of these material inputs then determines the carbon footprint. Apart from these design 
factors, the carbon intensity of the energy mix used during the manufacturing process also 
influences the carbon footprint, as also confirmed by many studies (Leccisi et al., 2016, Fthenakis 
and Leccisi, 2021, Muller et al. 2021). The preparatory study (Dodd, 2020) therefore showed that 
there is significant improvement potential that could be attained by means of such design choices. 

The policy relevance at EU level of potential carbon footprint requirements for PV modules has been 
also announced in the EU Solar Energy Strategy (European Commission, 2020): ‘the Commission is 
also assessing options covering [..] the carbon footprint of PV modules.’ and ‘these measures are 
also expected to foster innovation and provide a common reference for potential buyers to compare 
different products’. 

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2.  Background concepts underpinning the harmonised rules for the 
calculation of the carbon footprint of photovoltaic panels  

As described in Polverini et al., 2024, there are plethora of methods, guidance documents and 
standards that can be applied to calculate the carbon footprint. As an example, they are listed in 
Table 1. 

Among all the methods and standard mentioned above, there is a need of harmonizing the carbon 
footprint specifically of PV products at the European level.  

Table 1 Non-exhaustive list of standards, documents and guidelines that can be used for calculating the 
carbon footprint of photovoltaics. 

Standards and other 
guidelines/ reference 
documents 

ISO 14067: 2018 Greenhouse 
gases — Carbon footprint of 
products — Requirements and 
guidelines for quantification 

GHG Protocol Product Standard 

Description 

Method 

Requirements and guidelines for quantification 
of CFP 

ISO 14067:2018.  

Product Life Cycle Accounting and Reporting 
Standard to evaluate the full life cycle GHG 
emissions of a product 

GHG Protocol Product 
Standard 

EU Member states PCR (Italy, 
France, Norway, Finland, 
Netherlands) 

Databases and Product Category Rules for 
construction products/services where PV 
modules and inverters are part of new and 
renovated buildings  

EN 15804 

European PEFCR Guide for PV 
modules 

Guidance for calculating and reporting products' 
life cycle environmental impacts 

PEF method 

Italy's LCA legislation Promotion 
of the Green Economy  

Legislation fully based on the Environmental 
Footprint methods. Voluntary "Made Green in 
Italy" label 

PEF method 

NSF/ANSI 457 Sustainability 
Leadership Standard for PV 
Modules and PV Inverters 

Standard to establish product sustainability 
performance criteria and corporate performance 
metrics exemplifying sustainability leadership in 
the market. Basis of conformity assessment, 
such as third-party certification. 

NSF ANSI 457 -2019 

France's public tenders for utility 
scale PV plants 

Public tenders include carbon footprint 
requirements to prioritize projects with low-
carbon manufacturing processes. 

ADEME guidelines 

Korean carbon footprint 
assessment regulation for PV 
modules 

Carbon footprint assessment method and 
requirements to prioritize projects with low-
carbon manufacturing processes 

French Methodology CRE3 

LCA according to ISO 14040 

Electronic Product Environmental 
Assessment Tool (EPEAT) 

Carbon footprint ranking system for evaluating, 
comparing and selecting products 

ISO standards 14040-4 2, 3, 
IEA PVPS 12 ‘Methodology 
Guidelines for LCA on PV’ 

Source: JRC elaboration (Adapted from Polverini et al., 2023) 

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As detailed in Polverini et al., 2023 the method presented in this report uses as pillar the PEFCR of 
PV module and define the harmonised calculation rules for the carbon footprint evaluation under a 
regulatory context.  

It focuses on products that are under the scope of the Ecodesign Directive (European Union, 2009).  

These rules are applied to PV modules, with the objective to be conceptually applicable to different 
technologies and product groups.  

Figure 1 shows a flow chart of the method summary for regulatory intervention, depicting the main 
phases of the definition of the harmonised rules for the calculation of the carbon footprint of 
photovoltaic modules. Each step is described and detailed in this report sections. 

Figure 1 Flow chart of the method summary for regulatory intervention. 

Source: JRC elaboration: Adapted from Polverini et al., 2023) 

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3.  Suggestion for potential improvements of the harmonised rules 
for the calculation of the carbon footprint of photovoltaic 
modules  

In this section suggestions for future improvements of the harmonised rules for the calculation of 
the PV carbon footprint are presented. Alignments and differences compared to the PV PEFCR, 
discussed in Section 4, are discussed. Also, potential technical improvements compared to the Annex 
A (attached to this report and shared with stakeholders during the consultation held on March 
2023) with a policy proposal draft of the harmonized rules are illustrates and explained. 

3.1 Product category in scope for regulatory intervention 

As shown in Figure 1, the first step of the harmonized rule is the definition of the product category 
in scope for regulatory intervention. In this case, it consists of a complete photovoltaic panel that 
includes the aluminium frame and glass layers. Differently compared to PV PEFCR, the mechanical 
and electrical balance of system are excluded from the scope mainly because these represent 
different products systems compared to the PV module. 

In terms of PV module technological coverage, the following technologies can be included: 

—  mono-crystalline silicon PV modules 

—  multi-crystalline PV modules,  

—  cadmium telluride (CdTe) PV modules 

—  silicon based PV modules with extra-layers (e.g. silicon perovskite tandem PV modules)) 

CIGS (copper, indium, gallium, (di) selenide) and micro-silicon PV panels were included in the PEFCR, 
however now they do not have currently a relevant market share and therefore they have been 
excluded from the scope of this analysis (Fraunhofer 2024).  

Mono-crystalline silicon PV category includes different technological types such as passivated 
Emitter and Rear Cell (PERC), tunnel oxide passivated contact (TOPCon), interdigitated back contact 
(IBC) and heterojunction (HJT) PV modules (IEA PVPS Task 12, 2024).  

Silicon-based PV modules with extra-layers (e.g. silicon perovskite tandem PV modules) are not 
listed in the PEFCR, but they could enter in the market in the not-to-distant future (Leccisi et al., 
2021) and then they could be added to the scope with the aim of covering innovative types of PV 
panels I.  

3.2 Hotspot analysis for identification of potential areas for regulatory 
intervention 

The second step of the harmonized rules, as illustrated in Figure 1, is the hotspot analysis. 

13 

 
 
 
 
 
 
 
 
 
The calculation of the carbon footprint of photovoltaic modules is based on the bill of material, the 
energy, and auxiliary materials used to produce the specific product. The hotspot analysis highlights 
that, in particular for both mono- and multi-silicon PV panels, the electricity for manufacturing the 
silicon, aluminium frame, glass, and silicon have to be accurately identified as they are the primary 
contributors to the carbon footprint of a photovoltaic panel. These key findings are confirmed and 
explained in Section 5 of this report as well as the influence of electricity contribution that varies 
depending on the electricity mix of the country that manufactures the panel. Moreover, the 
product's lifespan plays a significant role in terms of the carbon footprint per kilowatt-hour 
produced. 

3.3 Product Environmental Footprint Category Rules (PEFCR) “Photovoltaic 
modules used in photovoltaic power systems for electricity generation”  

The step 3 of Figure 1 consists of the ‘Product Environmental Footprint category rules’ (PEFCR) 
(Product Environmental Footprint Category Rules, 2020) of PV power systems for electricity 
generation, which was developed in the framework of the Product Environmental Footprint (PEF) 
pilot phase. 

It establishes the methodology to calculate environmental impacts for the main PV technologies 
and for a representative (virtual) product.  

PEFCRs reflect the international agreements and technical/scientific progress in the area of life 
cycle assessment.  

The Technical secretariat who developed the PEFCR has been composed of manufacturers of PV 
modules, energy research centres, LCA consultants, and PV industry associations who were part of 
stakeholder consultations, overall representing more than 50% of the EU market player for this 
product group at the time of the study. Also, the PV PEFCR has been prepared taking into account 
relevant schemes at the time of the study, including several established international standards and 
guidelines such as IEA PVPS Task 12 Methodology Guidelines on Life Cycle Assessment of 
Photovoltaic Electricity (2015b); Product Category Rules within the International EPD System IES 
(PCR CPC 171 & 173 2013); Product Category Rules within the European Standards on 
Environmental Product Declaration of construction works (EN 15804 2013) (IEA PVPS Task 12). 
Specifically, the PV PEFCR has been developed with reference to products sold in the European 
Union + EFTA. The product category corresponds to the production of photovoltaic modules used in 
photovoltaic power systems for electricity generation and the product analysed is a photovoltaic 
module. “Photovoltaic module” is used as general term for panels (framed modules) and laminates 
(unframed modules).  

The PV PEFCR includes five subcategories representing the following PV technologies:  

—  Cadmium-Telluride photovoltaic modules (CdTe)  

—  Copper-Indium-Gallium-Selenide photovoltaic modules (CIS / CIGS)  

—  Micromorphous Silicon photovoltaic modules (micro-Si)  

—  Multicrystalline Silicon photovoltaic modules (multi-Si)  

—  Monocrystalline Silicon photovoltaic modules (mono-Si)  

PECFCR is a useful tool for having consistent comparisons between different PV manufacturing 
technologies and various products. However, given that PV technology has developed fast in a 

14 

 
 
 
 
relatively short time, many different technologies for panel production exist today. They differ in 
material consumption/efficiency, power conversion efficiency (PCE), life expectancy, degradation 
ratio and costs but also in their environmental performance. 

In the PV PEFCR, the FU is 1 kWh (kilowatt hour) of DC electricity generated by a photovoltaic 
module. The product system of the electricity production with a photovoltaic module consists of five 
life cycle stages: 

—  raw material acquisition and pre-processing,  

—  distribution and storage,  

—  production of the main product,  

—  use and  

—  end of life.  

The manufacturing of PV modules shall cover raw material extraction to wafer, cell and module 
production in case of crystalline silicon modules, the supply chain of semiconductors (e.g. 
micromorphous silicon, cadmium sulphide, cadmium telluride, gallium and other materials used in 
thin film technologies) in case of thin film PV modules and the supply chain of carrier and 
connection materials (such as glass, silver, junction box and frame in case of PV panels). In the PV 
PEFCR, the product system also includes the mounting system required to fix the PV modules on a 
slanted roof because its demand depends on the conversion efficiency of the module. The electric 
installation is also taken into account. The inverter and the AC cabling is not be part of the product 
system. The transport of the PV modules, the mounting system and the electric installation to the 
place of installation of the PV system is included in a separate life cycle stage. The production of 
the main product shall comprise the assembly of the PV system. The use phase includes electricity 
production and the maintenance efforts (cleaning). The end of life covers the transport of the PV 
modules to a recycling facility or to a landfill, the dismantling of the modules and the recycling / 
landfilling process itself. 

These rules help to place the focus on those aspects and parameters that matter the most, and 
hence contribute to increased relevance, reproducibility and consistency of the results. 

The Climate Change impact category is expressed in kgCO2eq/kWh. PV PEFCR also discusses that the 
‘climate change’ impact (i.e. the impact assessment method used to quantify the carbon footprint) 
is one of the most relevant impact category for the manufacturing phase. The contribution analysis 
of carbon footprint impacts of PV panels as well as the major contributors to the total score depend 
on the underlying assumptions and the technology types. Moreover, according to the PV PEFCR, the 
most relevant processes (cumulatively contributing at least 80% to the characterised results) for 
the representative product (PV technology mix) of in the climate change impact category includes 
electricity, aluminium, solar glass, and steel. 

3.4 PEFCR adaptation 

According to the step 4 in Figure 1 the PV PEFCR can be adapted according to the policy purpose of 
setting Ecodesign requirements. Specifically, each section of the PEFCR have been checked and 
modified according to the scope of Ecodesign requirement on carbon footprint of PV modules. 

All the adaptations should be reported in a public version of a supporting study. 

15 

 
 
 
 
 
A summary of technical aspects that have been considered for the adaptation of the PV PEFCR for 
the purpose of the harmonized rules for calculating carbon footprint is shown in Table 2. 

Table 2 Main aspects considered for adapting the PV PEFCR for the purpose of using it for harmonized rules 
for calculating carbon footprint. 

PEFCR sections 

Aspects to be considered 

Scope 

The technologies under analysis have been updated based on current market 
share: 

CIGS and micro-Si can be deleted 

Silicon based technologies with additional layers can be added 

Innovative PV technologies should be considered 

System boundaries have been adapted to the regulatory context: 

Balance of system components can be excluded from the analysis, including 
only the PV panel (including glass and aluminum frame if present) 

Functional unit is aligned to that of PV PEFCR for comparability of the 
results. It consists of 1 kWh (kilowatt hour) of DC electricity generated during 
a photovoltaic module's service life. 

The reference flow is the amount of product needed to fulfil the defined 
function and shall be measured in m2 of photovoltaic module. All 
quantitative input and output data collected by the manufacturer to quantify 
the carbon footprint shall be calculated in relation to this reference flow. 

Impact assessment method for calculating results (based on EF method) 
have been updated based on the latest one available 

Application of Circular Footprint Formula (CFF) and default values of CFF: 

The proportion of material in the input to the production that has been 
recycled from a previous system (R1) can be set equal to 0 by default for all 
materials and can be changed by the declarant in case of different values  

The proportion of the material in the product that will be recycled (or reused) 
in a subsequent system (R2) can be updated according to the current state 
of the art and fix by default for all materials  

Life Cycle Inventory 

EF datasets and their universally unique identifier (uuids) have been updated 

Company-specific data for mandatory processes shall be used  

Company-specific data for non mandatory processes may be used when the 
declarant has access to specific information 

if the applicant has access to company-specific data for other processes 
along the supply chain (e.g. electricity mix), the applicant may use such data  

Time representativeness, geographical representativeness and technological 
representativeness of default secondary datasets shall be evaluated for 
using them  

Data Quality Rating 

Precision has been deleted 

Data Need Matrix 

Data Need Matrix has been deleted 

Life Cycle Stages and 
Distribution 

The distance and mass transported shall be characterised with company-
specific activity data.  

Source: JRC elaboration 

16 

 
 
 
 
3.4.1 Functional unit and reference flow 

In the harmonized rules for calculating the carbon footprint of PV, the functional unit is defined as 
one kWh (kilowatt-hour) of the total DC electric energy generated over a photovoltaic module’s 
service life, measured in kWh, and that is aligned with the PV PEFCR. 

The selected functional unit (i.e. kWh) allows to consider the effect of the lifetime of the 
photovoltaic module, its power conversion efficiency, its degradation ratio as well as the level of 
irradiation of the place of installation.  

Moreover, it allows the potential comparison with electricity generated by other electricity 
generation technologies. 

The reference flow is the amount of product needed to fulfil the defined function and shall be 
measured in m2 of photovoltaic module. All quantitative input and output data collected by the 
manufacturer to quantify the carbon footprint shall be calculated in relation to this reference flow.  

The reference flow (m2) is linked to the functional unit (kWh) by the “DC total energy yield over the 
service life", calculated according to the Annex III of the policy proposal that is reported in the Annex 
B attached to this report. 

3.4.2 System boundary 

Considering the scope of the analysis detailed in Section 3.1, the following life cycle stages and 
processes of the PV modules need to be included in the system boundary: 

—  Raw materials acquisition, pre-processing and manufacturing of photovoltaic modules. 

—  mining  and  pre-processing,  up  to  the  manufacturing  of  silicon  ingot,  wafers  and. 

photovoltaic cells;  

o 

the supply chain of semiconductors (cadmium sulphide, cadmium telluride, and 
other materials used in thin film technologies) in case of thin film modules; and  

—  the supply chain of carrier and connection materials (such as glass, silver, junction box 

and frame) in case of all modules.  

o  cell and module production in case of crystalline silicon modules;  

—  assembly of photovoltaic cells; and  

—  assembly of modules with the frame and the electric/electronic components. However, in 
the case of photovoltaic modules that do not require a frame for the installation and are 
supplied without one, the frame shall not be accounted in the carbon footprint calculation.  

The distribution should cover:  

—  the transportation of PV modules from the manufacturing plants to the point of placing 

the PV panel on the market (to be identified and justified by the manufacturer).  

Compared to PEFCR, the distribution rationale may be revised and specifically it may include also 
the distribution within Europe and the activity data may be company-specific, while default 
secondary datasets are provided. 

17 

 
 
 
 
 
 
The following processes may be excluded from the life cycle calculation in alignment with the 
product in scope:  

—  Manufacturing of equipment (capital goods) for modules assembly and recycling.  

—  All processes belonging to the subsequent life cycle stages (i.e., distribution to the final 
consumer, assembly of the system, use and disposal, dismantling and recycling of the 
photovoltaic modules) due to the adapted scope for the Ecodesign requirement on carbon 
footprint of PV modules.  

—  Benefits  and  burdens  of  recycled  materials  in  input  and  of  manufacturing  wastes  of 
specific materials are considered in the application of the CFF to the module production 
– as detailed in Section 3.4.8.  

A common schematic of the process supply chain of mono-Si, multi-Si, and CdTe PV modules is 
shown in Figure 2 and Figure 3. 

Figure 2 System diagram for the PV technologies included in scope (Adapted from PV PEFCR). 

Source: JRC elaboration (adapted from PV PEFCR) 

18 

 
 
 
 
 
 
Figure 3 Flow diagram of the supply chain for the PV technologies included in scope. (Adapted from PV 
PEFCR). 

Source: JRC elaboration (adapted from PV PEFCR) 

3.4.3 Impact assessment 

The impact assessment shall be done by applying the Environmental Footprint (‘EF’) impact 
assessment method available in the Life Cycle Data Network “LCDN" on the European Platform on 
LCA for the impact category “climate change” and be reported in grams CO2-eq/kWh. 

3.4.4 Use of company-specific activity data 

—  Mandatory company-specific activity data 

Company-specific activity data are required in particular for the manufacturing of PV modules and 
may also be used to model the semiconductor material supply chain.  

As listed in Figure 2 and Figure 3, for mono-crystalline and multi-crystalline silicon PV modules, 
mandatory company-specific activity data are required for cell and panel production, while for thin 
film CdTe PV modules, company-specific activity data are required for laminate PV production. 

As stated in the PV PEFCR, several companies producing crystalline silicon cells or modules are 
vertically integrated and have control over wafer production, silicon crystallization and partly also 
solar grade silicon production. 

The following paragraphs describe a technical approach that may be used as a guide for potential 
improvements on the use of company-specific activity data. 

The manufacturer shall use company-specific activity data for the processes listed in Table 3. 

Also, the activity data to be collected and the default secondary datasets to be used for each 
process are listed in the Excel spreadsheet with PV life-cycle Inventories “CF_Annex_PV_modules-
Life_cycle_inventory" described in Section 4.1.  

19 

 
 
 
 
 
 
The data quality of the default secondary datasets used to model the product shall be reassessed, 
based on the rules in Section 3.4.6, by the manufacturer to ensure a certain level of 
representativeness:  

If the default secondary dataset is representative of the process (i.e. TeR≤2 and GeR≤3), the default 
secondary dataset shall be used in combination with company-specific activity data.  

If the default secondary dataset is not representative (TeR>2 or GeR>3) of the process, the 
manufacturer shall adapt the dataset to the manufacturing context (i.e.g. to what concerns 
electricity consumption) and recalculate the DQR following the rules in Section 3.4.6. 

If the recalculated DQR is higher than 1.6, the manufacturer shall use a secondary dataset 
respecting the following hierarchy:  

—  A secondary dataset respecting the following hierarchy:  

o 

the  most  representative  EF-compliant  dataset  commissioned  by  the  European 
Commission  available  in  LCDN.  If  the  EF-compliant  dataset  is  a  partially 
disaggregated, the electricity dataset or datasets connected to the core process 
one level down the supply chain at -1 level may be changed for the representative 
average electricity consumption mix of the country where the process is occurring, 
modelled in accordance with section 3.4.10. Such choice shall be duly justified in 
the carbon footprint study; 

o  a representative ILCD entry-level compliant dataset 

—  a company-specific dataset with a DQR equal to or lower than 1.6.  

If a default secondary dataset is not available, such as but not limited to innovative materials, the 
manufacturer may use a proxy dataset (selected from the Excel spreadsheet with life-cycle 
inventories “CF_Annex_PV_modules-Life_cycle_inventory) discussed in Section 4.1, if after 
reassessing its quality (TeR, GeR) the recalculated DQR of the dataset is ≤1.6, according to the rules 
in Section 3.4.6. 

If the recalculated DQR of the proxy dataset is higher than 1.6, the manufacturer shall choose one 
of the following options:  

—  A secondary dataset respecting the following hierarchy:  

o 

the  most  representative  EF-compliant  dataset  commissioned  by  the  European 
Commission  available  in  LCDN.  If  the  EF-compliant  dataset  is  a  partially 
disaggregated, the electricity dataset or datasets connected to the core process 
one level down the supply chain at -1 level may be changed for the representative 
average electricity consumption mix of the country where the process is occurring, 
modelled in accordance with section 3.4.10. Such choice shall be duly justified in 
the carbon footprint study; 

o  a representative ILCD entry-level compliant dataset 

—  a company-specific dataset with a DQR equal to or lower than 1.6.  

20 

 
 
 
 
 
 
Table 3 List of processes for which the use of company-specific activity data is mandatory. 

Technology 

Life cycle stage 

Process 

Cadmium-Telluride PV modules 

Multicrystalline Silicon PV 
modules 

Raw materials acquisition, pre-
processing and PV module 
manufacturing 

Distribution 

Raw materials acquisition, pre-
processing and PV module 
manufacturing 

Raw materials acquisition, pre-
processing and PV module 
manufacturing 

Distribution 

Photovoltaic laminate, CdTe, at 
plant 

Photovoltaic laminate, CdTe, at 
regional storage 

Photovoltaic cell, multi-Si, at plant 

Photovoltaic panel, multi-Si, at 
plant 

Photovoltaic panel, multi-Si, at 
regional storage 

Monocrystalline Silicon PV modules  Raw materials acquisition, pre-

processing and PV module 
manufacturing 

Photovoltaic cell, mono-Si, at 
plant 

Raw materials acquisition, pre-
processing and PV module 
manufacturing 

Distribution 

Photovoltaic panel, mono-Si, at 
plant 

Photovoltaic panel, mono-Si, at 
regional storage 

Source: JRC elaboration (adapted from PV PEFCR) 

3.4.4.1 Bifacial PV panels  

The harmonized rules for calculating carbon footprint of PV panels have also the aim of proving 
guidance for innovative and emerging PV technologies (e.g. bifacial, innovative silicon-based multi-
junction PV modules), which was not taken into the account in the PV PEFCR but are important to 
consider for updating PV technological aspects. 

In particular, bifacial PV receives sunlight to DC electricity on both the front and back of the PV 
modules and usually generates more energy than mono-facial ones. 

The manufacturer shall use company-specific activity data for modelling the glass used in the front 
and rear sides of bifacial PV panels, including the additional amount needed for the rear glass. 

The manufacturer shall follow the rules described for the default secondary datasets to be used for 
modelling the glass of bifacial PV panels. 

In case of bifacial PV panels, some materials (e.g. backsheet in polyvinylfluoride - PVF) may not be 
needed anymore in the product. In that case, the manufacturer of bifacial PV panels may disregard 
the corresponding material or component in the modelling of the carbon footprint. 

3.4.4.2 Innovative silicon-based single- and multi-junction PV modules 

The photovoltaic energy conversion efficiency of solar cells and solar panels based on silicon is 
fundamentally limited to 29.4 percent. This limitation can be overcome by coating the solar cells 
with additional materials to create a “multi-junction” solar cell. In such a geometry, multiple light 
absorption layers are stacked on top of each other, so that each layer effectively absorbs a specific 

21 

 
 
 
part of the sunlight’s color spectrum. This multi-layer concept can strongly enhance the cell 
efficiency. An example of multi-junction architecture could be a silicon perovskite tandem PV 
module, which reached a laboratory energy conversion efficiency of 33.7% in 20249.  

An example of innovative silicon-based single-junction PV module consists of heterojunction (HJT) 
PV modules that differs from standard solar cells due to different materials used for the n-type and 
p-type layers of the absorbing layer.  

Specifically, HJT PV technology combines crystalline silicon (c-Si) wafer-based PV technology with 
thin-film technology (i.e. amorphous silicon) and indium tin oxide (ITO) is one of the applied 
materials for the transparent conductive oxide (TCO) layer of the heterojunction solar cell.  

The absorber layer of the HJT PV module encloses a c-Si wafer-based layer placed between two 
thin intrinsic amorphous silicon layers with doped amorphous silicon layers placed on top. The 
number of TCO layers varies depending on the HJT cell being mono-facial or bifacial PV, with the 
rear layer being a metal layer acting as the conductor for mono-facial heterojunction cells. 

In this context, the manufacturer shall use company-specific activity data for the modelling of any 
extra-layers (e.g. amorphous silicon) that may be added to the silicon-based PV cell.  

The manufacturer shall follow the rules described for the default secondary datasets to be used for 
modelling those extra-layers.  

As for bifacial PV, these innovative silicon-based single-junction PV modules were not in the scope 
of PV PEFCR, but they are relevant for assessing PV technologies with improved power conversion 
efficiencies. 

3.4.5 Non-mandatory company-specific activity data   

Below it is described a technical approach that may be used as a guide for the use of non-
mandatory company-specific activity data. These refer to all processes that are not included in the 
list of processes for which company-specific activity data are mandatory. Differently, from PV 
PEFCR the data need matrix shall not be applied for these data because it has been replaced with 
specific requirements as described in this chapter. 

Case 1: The manufacturer has access to (company) specific information  

If the manufacturer has access to company-specific information - including activity data of the 
processes - the data quality of the default secondary datasets used to model the product should be 
reassessed by the manufacturer to ensure a certain level of representativeness based on the rules 
set out in Section 3.4.6, to combine them with the specific company activity data.  

If the default secondary dataset is representative for the manufacturer (i.e. TeR≤2 and GeR≤3), the 
default secondary dataset may be used in combination with company-specific activity data.  

If the default secondary dataset is not representative (TeR>2 or GeR>3) of the process, the 
manufacturer may adapt the dataset to the manufacturing context (i.e.g. to what concerns 
electricity consumption) and recalculate the DQR following the rules in Section 3.4.6.  

If the recalculated DQR is higher than 1.6, one of the following options shall be chosen:  

9 Photovoltaics-Report (14).pdf 

22 

 
 
 
 
 
—  A secondary dataset respecting the following hierarchy:  

o 

the  most  representative  EF-compliant  dataset  commissioned  by  the  European 
Commission  available  in  LCDN.  If  the  EF-compliant  dataset  is  a  partially 
disaggregated, the electricity dataset or datasets connected to the core process 
one level down the supply chain at -1 level may be changed for the representative 
average electricity consumption mix of the country where the process is occurring, 
modelled in accordance with section 3.4.10. Such choice shall be duly justified in 
the carbon footprint study; 

o  a representative ILCD entry-level compliant dataset 

—  a company-specific dataset with a DQR equal to or lower than 1.6.  

Case 2: The manufacturer does not have access to (company) specific information.  

If the manufacturer does not have access to company-specific information, the default secondary 
datasets and default activity data listed in the Excel spreadsheet with life-cycle inventory of PV 
technologies named “CF_Annex_PV_modules-Life_cycle_inventory" shall be used. 

3.4.6 Data quality rating (DQR) of default secondary datasets  

With the aim of ensuring an accurate representation of each PV process manufacturing and its 
components/stages, the default secondary datasets provided in the Excel spreadsheet with life-
cycle inventories should be evaluated based in specific criteria described below, which is an 
adaptations of the Data Quality Rating (DQR) used in PV PEFCR.  

Specifically, a DQR should be calculated for the default secondary datasets used for PV modelling 
according to the following procedure, which has been adapted for being more accurate for policy 
purpose: 

Assign the DQR criteria for each of the default secondary datasets to ensure an accurate 
representation of the context:  

—  Technological Representativeness (TeR),  

—  Geographical Representativeness (GeR).  

The values of each criterion shall be assigned based on Table 4.  

Calculate the DQR score of the declared value as the sum of the values of the two DQR criteria 
divided by two  (TeR + GeR)/2.  

The DQR score and the values of the single DQR criteria (TeR and GeR) for each of the default 
secondary datasets shall be provided in the public version of the supporting study.  

23 

 
 
 
 
 
 
 
 
Table 4 Evaluation of the DQR criteria. 

Quality rating 

TeR dataset 

GeR dataset 

1 

2 

.3 

4 

5 

The technology modelled and the 
pathway of production* in the PV 
carbon footprint declaration is 
exactly the same as the one in 
the scope of the default 
secondary dataset. 

The technology modelled in the 
PV carbon footprint declaration is 
included in the scope of the 
default secondary dataset, with 
some yet limited differences in 
the pathway of production*. 

The technology modelled in the 
PV carbon footprint declaration is 
included in the scope of the 
default secondary dataset with 
substantial but not critical 
differences in the pathway of 
production*. 

The technology modelled in the 
PV carbon footprint declaration is 
similar (i..e, technological proxy) 
to those included in the scope of 
the dataset  

The technology modelled in the 
PV carbon footprint declaration is 
different from those included in 
the scope of the dataset 

The process modelled in the PV carbon 
footprint declaration takes place in the 
country for which the dataset is valid. 

The process modelled in the PV carbon 
footprint declaration takes place in the 
region (e.g., Europe, Asia, North America, 
Africa) for which the dataset is valid. 

The process modelled in the PV carbon 
footprint declaration takes place in one of 
the geographical regions where the dataset 
is valid (e.g., global – GLO, ROW). 

The process modelled in the PV carbon 
footprint declaration takes place in a 
country that is not included in the 
geographical region(s) for which the dataset 
is valid, but it is estimated that there are 
sufficient similarities based on expert 
judgement 

In all the other cases not listed on 1-4 

Source: JRC elaboration 

*The pathway of production for the modelled technologies shall be checked and evaluated against the default secondary 
dataset description and, if provided there, under consideration of process parameters and major process inputs (e.g. 
efficiencies, fuel types, temperature, pressure, scale). 

Adjustments have been made in the data quality criteria definition compared to PV PEFCR. In 
particular, precision is not included for evaluating the representativeness in the mandatory 
company-specific processes because the activity data shall be provided by the declarant. For the 
non-mandatory company-specific processes, the activity data may be default values in case the 
declarant has no access to specific information. In this case, the source of default activity data is 
the PEFCR (and it is one source for all the stages). The data are already verified since they were in 
the PEFCR.  

If the electricity is changed in the ‘–1 level’ of a default secondary dataset, the GeR of the default 
secondary dataset shall be recalculated as follows:  

GeR = GeRoriginal − (GeRoriginal – GeRmodified, −1) * Contributionoriginal, −1  

24 

 
 
 
where:  

GeR is the GeR of the default secondary dataset after changing the dataset describing the 
electricity consumption in the –1 level, based on Table 4. 
GeRoriginal is the GeR of the default secondary dataset before changing the dataset describing 
the electricity consumption in the –1 level, based on Table 4. 
GeRmodified, -1 is the GeR of the dataset describing the electricity consumption in the ‘-1 level’ 
after the adjustment based on Table 4. GeRnew,-1 represents the geographical 
representativeness of the electricity production referring to where the process happens in 
reality. 
Contributionoriginal,−1 is the contribution, expressed as a percentage, of the carbon footprint of 
the electricity consumption in the ‘-1 level’ compared to the total carbon footprint of the 
secondary dataset. 

3.4.7 Life cycle stages 

Raw material acquisition, pre-processing and module manufacturing 

This life cycle stage includes raw material acquisition and pre-processing, as well as the 
manufacturing of the photovoltaic modules. The supply chain of the modules shall include the 
production of the modules, the cells and wafers (if applicable) and the supply chain of the materials 
required in the module and in manufacturing (such as working materials and process gases, energy 
carriers), including raw material extraction and refining towards the directly employed material. The 
supply chain of the frame (if applicable), shall similarly include raw material extraction and refining. 

Compared to PV PEFCR, the mounting system and the electric installation as well as their life stages 
are excluded as detailed in section 3.4.2. 

The processes taking place in the life cycle stage raw material acquisition and pre-processing, the 
inputs and outputs as well as the default secondary datasets to be used are listed in the Excel file 
named “CF_Annex_PV_modules-Life_cycle_inventory”, sheet “Raw-Materials&Pre-Processing” 
discussed in Section 4.  

Transport of raw materials and intermediate products 

Transport of raw materials and intermediate products to the production site shall be included in this 
life-cycle stage. 

Table 5 shows the default transport distances by train and lorry (lorry >32 t, EURO 4) for some 
frequently used raw materials and intermediate products, as listed in PV PEFCR. These default 
values may also be used to estimate default transport distances for similar products required in the 
supply chain of PV modules. The default transport distances shown in Table 5 shall be used in case 
company-specific information is not available. For transports by lorry, a default utilization ratio of 
64% shall be used if specific data are not available. This utilization ratio includes empty return trips. 

For suppliers located outside Europe, the default transport scenario described in the Excel file with 
life-cycle inventories named “CF_Annex_PV_modules-Life_cycle_inventory” and discussed in Section 
4, sheet Transport-Scenarios should be used. This scenario includes the transport of raw materials 
or intermediate products between the harbour or airport and the factories in and outside Europe, 
which is estimated to 1’000 km by lorry (>32 t, EURO 4). The intercontinental transport to Europe 
occurs either by transoceanic container ship (18,000 km) or by cargo airplane (10,000 km). If the 

25 

 
 
 
 
location of the supplier is known, specific data may be used to calculate the transport distances and 
modelling the applicable transport modes to the production site. 

Air cargo shipping of semi-finished products such as wafers and cells shall be included according to 
its share in supply logistics in a three year period. 

Table 5 Default distances for transport of raw materials and intermediate products. 

Density [kg/m3] 

Consumption in Europe 

Mineral products 

gravel/sand 

metals 

Steel/cast iron 

copper 

aluminium 

plastics 

PVC 

PE 

PP 

Wood products 

Particle board 

2000 

7900 

8900 

2700 

1400 

950 

900 

680 

km train 

- 

200 

200 

200 

200 

200 

200 

200 

Basic chemicals, inorganic (carrier substance to be considered additionally) 

1045 

2532 

909 

1840 

1383 

1685 

993 

Caustic soda 

Soda (sodium 
carbonate) 
Hydrochloric acid 

Sulphuric acid 

Nitric acid 

Phosphoric acid 

Hydrofluoric acid 

Basic chemicals, organic 

ethylene 

naphta 

refrigerants 

Organ. solvents 

600 

600 

200 

600 

600 

600 

600 

600 

600 

600 

600 

km lorry 32t 

50 

100 

100 

100 

100 

100 

100 

50 

100 

100 

100 

100 

Gases (if not produced on the spot) if bought in cylinders: doubling of transport distances (due to tare 
weight) 
oxygen 

100 

50 

nitrogen 

hydrogen 

helium 

For all other materials 

Source: JRC elaboration (adapted from PV PEFCR) 

50 

50 

50 

130 

100 

100 

100 

240 

26 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
Packaging materials  

The use and disposal of packaging materials shall be considered for the entire product system and 
modelled as part of the raw material acquisition stage. The raw material consumption of reusable 
packaging shall be calculated by dividing the actual weight of the packaging by the reuse rate. The 
reuse rate affects the quantity of transport that is needed per functional unit. The transport impact 
shall be calculated by dividing the one-way trip impact by the number of times this packaging is 
reused.  

For reusable packaging, the default reuse rates provided below shall be used, unless data of better 
quality are available:  

—  Plastic pallets: 50 trips  

—  Wooden pallets: 25 trips  

3.4.8 Modelling of recycled content and materials recycling  

The recycled content of materials used in PV modules as well as their recycling potential at the end-
of-life shall be modelled according to the following circular footprint formula (CFF): 

(1 −  𝑅_1 ) ∙ 𝐸𝑣 +  𝑅_1  ∙   (𝐴𝐸_𝑟𝑒𝑐𝑦𝑐𝑙𝑒𝑑 +   (1 −  𝐴) ∙ 𝐸𝑣  ∙  𝑄𝑆𝑖𝑛/𝑄𝑝 ) + (1 − 𝐴) ∙ 𝑅2

∙ (𝐸_𝑟𝑒𝑐𝑦𝑐𝑙𝑖𝑛𝑔𝐸𝑜𝐿 − 𝐸𝑣∗   ∙  𝑄𝑆𝑜𝑢𝑡/𝑄𝑝 ) 

A: allocation factor of burdens and credits between supplier and user of recycled materials.  

R1: proportion of material in the input to the production that has been recycled from a previous 
system.  

R2: proportion of the material in the product that will be recycled (or reused) in a subsequent 
system. R2 shall therefore take into account the inefficiencies in the collection and recycling (or 
reuse) processes. R2 shall be measured at the output of the recycling plant  

Ev: specific emissions and resources consumed (per functional unit) arising from the acquisition 
and pre-processing of virgin material.  

Erecycled (Erec): specific emissions and resources consumed (per functional unit) arising from the 
recycling process of the recycled (reused) material, including collection, sorting and transportation 
process.  

ErecyclingEoL (ErecEoL): specific emissions and resources consumed (per functional unit) arising 
from the recycling process at EoL, including collection, sorting and transportation process.  

E*v: specific emissions and resources consumed (per functional unit) arising from the acquisition 
and pre-processing of virgin material assumed to be substituted by recyclable materials.  

Qsin: quality of the ingoing secondary material, i.e. the quality of the recycled material at the point 
of substitution.  

27 

 
 
 
 
 
 
QSout: quality of the outgoing secondary material, i.e. the quality of the recyclable material at the 
point of substitution.  

Qp: quality of the primary material, i.e. quality of the virgin material.  

The default parameter values for applying the CFF and the default values for R1 and R2 are listed 
in the Excel spreadsheet “CF_Annex_PV_modules-Life_cycle_inventory” discussed in Section 4, 
sheet CFF-parameters. An exerpt is shown in Figure 4. 

Adjustments have been made compared to PV PEFCR for policy purpose. In particular, in the 
harmonized rules for calculating carbon footprint of PV, if default values are provided for R2 and 
these are not possible to be changed. For all materials not listed in the sheet CFF-parameters, it is 
assumed that R2=0.  

The default secondary datasets for modelling the CFF (for glass, copper, aluminium, aluminium 
alloys, steel) are listed in the same Excel spreadsheet “CF_Annex_PV_modules-
Life_cycle_inventory", sheet “Raw-Materials&Pre-Processing".  

If a default secondary dataset of a specific recycled material (i.e. silicon) is not available in the 
Excel list “CF_Annex_PV_modules-Life_cycle_inventory", sheet “Raw-Materials&Pre-Processing", the 
manufacturer shall develop an EF compliant dataset with a DQR ≤ 1.6 following the Guide for EF 
compliant datasets. This EF compliant dataset shall be implemented in the CFF to model the impact 
of the recycled content of such material based on the R1 value declared by the manufacturer.  

The R1 values are by default equal to 0, unless differently declared and proved. Specifically, the 
applied R1 values shall be subject to verification and provided in the supporting study.  

The following general guidelines shall be followed when using supply-chain specific R1 values:  

—  The supplier information (through e.g., statement of conformity or delivery note) shall be 

maintained during all stages of production and delivery at the converter.  

—  Once the material is delivered to the converter for production of the end products, the 
converter shall handle information through their regular administrative procedures.  

—  The  converter  for  production  of  the  end  products  claiming  recycled  content  shall 
demonstrate through his management system the [%] of recycled input material into the 
respective end product(s).  

—  The latter demonstration shall be transferred upon request to the user of the end product.  

—  Company-owned traceability systems can be applied as long as they cover the general 

guidelines outlined above 

28 

 
 
 
 
 
 
 
 
 
 
 
 
Figure 4: CFF parameters. 

Source: JRC elaboration (adapted from PV PEFCR) 

The CFF applied to manufacturing waste  

All manufacturing waste shall be considered in the modelling.  

The total amount of manufacturing waste shall be consistent with the BoM (total input to the 
manufacturing process), the mass balance of the final product and the yield rates of the 
manufacturing processes.  

Specifically, the waste of products used during the manufacturing stage shall be included in the 
overall modelling and reported at the life cycle stage where the waste occurs.  

3.4.9 Distribution 

The transportation of photovoltaic modules from factory to the market point shall be modelled 
within this life cycle stage. The distance and mass transported shall be characterised with company-
specific activity data.  

The transport from regional storage to the photovoltaic power system where the photovoltaic 
modules are installed is excluded from the system boundary. If photovoltaic modules are produced 
in several production sites, the share of each facility in the European supply mix shall be accounted 
for in the life cycle stage distribution.  

The processes taking place in the life cycle stage distribution, the inputs and outputs as well as the 
default datasets are listed in the Excel spreadsheet named “CF_Annex_PV_modules-
Life_cycle_inventory” and discussed in Section 4, sheet “Distribution”.  

3.4.10 Analysis of electricity modelling approaches  

In this section there is an analysis of potential alternative modelling approaches for accounting the 
electricity demand for manufacturing PV panels.  

29 

 
 
 
 
 
Two alternative approaches are presented in the following sub-sections 3.4.10.1 and 3.4.10.2.  

The first approach allows the use of contractual instruments within a set of minimum criteria (as 
described below) as well as the use of residual consumption mix. This first approach is elaborated 
based on PEF/PEFCR rules with additional clarifications and stricter requirements intended for policy 
purpose. Specifically, PEF Recommendations (Commission Recommendations (EU) 2021/2279 on 
the use of the Environmental Footprint methods to measure and communicate the life-cycle 
environmental performance of products and organisations) allows the use of contractual 
instruments under certain criteria based on GHG Protocol Guidelines. The first approach includes two 
stricter requirements compared to PEF related to the transparency of the tracking systems and it 
avoids double counting of claims.   

The second approach is based on the discussion with stakeholders and includes only the national 
average electricity consumption mix unless there is a direct electricity connected.  

3.4.10.1 Electricity modelling approach  

Specifically, in the first approach, the following electricity mix shall be used in hierarchical order:  

1.  On-site  generated  electricity  modelled  according  to  section  3.4.10.1.1  if  it  meets  the 

conditions set in the same section.  

2.  Supplier-specific electricity product modelled according to section 3.4.10.1.2 if the contractual 

instrument meets the set of minimum criteria described in the same section.  

3.  Residual consumption mix modelled according to section 3.4.10.3 if the activity occurs in a 
grid/country  where  a  contractual  instrument  respecting  the  criteria  described  in  section 
3.4.10.1.2 exists. The residual consumption mix shall be used even if the declarant itself did 
not claim any supplier-specific electricity product. The residual consumption mix characterizes 
the unclaimed, untracked or publicly shared electricity and prevents double counting with the 
use of supplier-specific electricity product in the point (2) of the hierarchy.  

4.  Average consumption mix modelled according to section 3.4.10.1.4 if the activity occurs in a 
grid/country  where  no  contractual  instruments  meeting  the  criteria  described  in  section 
3.4.10.1.2 exist (i.e., no residual consumption mix is available). The average consumption mix 
reflects the total electricity mix including claimed or tracked electricity.  

The use of carbon intensity factors values provided by a grid operator or certificate-issuing entity is 
not permitted.  

The way the electricity is modelled shall be reported in the supporting study. 

3.4.10.1.1 On-site generated electricity  

The on-site generated electricity shall be claimed if the electricity is supplied to the plant from a 
production asset within the premises of the energy-consuming plant and if the production asset is 
connected to the energy-using plant by means of a direct and dedicated connection.  

If the energy-consuming plant is also connected to the electricity grid and electricity is sourced from 
the grid in addition to on-site generation (e.g., during times of low on-site generation), all energy 
sourced from the grid shall be accounted and modelled following the points (2), (3), or (4) of the 
hierarchy described in section 3.4.10.1. The maximum amount of electricity that may be claimed in 
a year is the difference between the yearly total amount of energy produced and the yearly amount 

30 

 
 
 
 
 
of energy injected in the grid. The manufacturer shall provide evidences in the supporting study of 
the values of on-site generated electricity considered in the calculation.  

If contractual instruments of any type, related to the on-site generated electricity, have been sold to 
a third party, then the on-site generated electricity cannot be claimed. If such electricity is 
consumed in the plant, then it shall be modelled following the points (2), (3) or (4) of the hierarchy 
described in section 3.4.10.1.  

No credit shall be modelled if the amount of electricity produced exceeds the amount consumed on-
site within the defined system boundary and it is sold to, e.g., the electricity grid.  

Datasets describing medium-voltage may be used for low-voltage, neglecting the conversion losses.  

3.4.10.1.2 Supplier-specific electricity product 

The environmental integrity of the use of supplier-specific electricity products depends on ensuring 
that the related contractual instruments (for tracking) are reliable and unique. Without this, the 
carbon footprint calculation lacks the accuracy and consistency needed to drive claims on the 
electricity procurement. Therefore, the contractual instrument shall be claimed if the respect of the 
following five minimum criteria is proved in the supporting study.  

Datasets describing medium-voltage may be used for low-voltage, neglecting the conversion losses.  

Criterion 1 – Convey attributes  

To satisfy the criterion, the contractual instrument shall:  

—  Convey the energy source mix and complementary attributes of the product associated 

with the unit of electricity produced.  

—  Include an explanation of the calculation method used to determine the energy source 

mix of the product.  

Criterion 2 – Be a unique claim  

To satisfy the criterion, the contractual instrument shall:  

—  Have  mechanisms  in  place  that  ensure  it  is  the  only  instrument  that  carries  the 
environmental attribute claim associated with that quantity of electricity generated.  

—  Have mechanisms in place to ensure the instrument can be claimed only once.  

—  Be tracked and redeemed, retired, or cancelled by or on behalf of the company (e.g., by 
an audit of contracts, third party certification), or handled automatically through other 
disclosure registries, systems, or mechanisms.  

—  Be associated with a quantity of generated electricity that is reported and considered for 
the  determination  of  the  country-specific  residual  consumption  mix,  and  this  unique 
residual  consumption  mix  is  disclosed  publicly  by  a  competent  authority.  Sometimes 
national laws and regulations may define a residual consumption mix for a geographical 
area  that  is  different  from  the  country.  The  manufacturer  shall  report  the  residual 
consumption mix and its source in the supporting study.  

—  Allow for the unambiguous identification of the technology type, age and location and 

capacity of the energy generation facility to which it refers.  

31 

 
 
 
 
—  Refer to an energy generation facility that is located in a country with a tracking system 

in place that meets the minimum criteria for tracking systems. 

Criterion 3 – Be issued from a tracking system that fulfils specific criteria  

To satisfy the criterion, the contractual instrument shall be issued by a tracking system that fulfils 
the following criteria:  

—  Is  based  on  objective,  non-discriminatory,  and  transparent  criteria  for  the  issuing 

certificates.  

—  Is a unique entity per geographical area and per type of energy production and it shall be 

governmentally appointed.  

—  Relies on accurate, reliable, and fraud-resistant mechanisms for the issuance, transfer 

and cancellation of certificates.  

—  Is independent from the verifier.  

—  Entrusts  the  issuance  of  certificates,  as  well  as  the  supervision  of  their  transfer  and 
cancellation  of  certificates,  to  a  legal  entity  or  entities  who  are independent  from  the 
production, trade of energy, and the corresponding certificates.  

—  Whose activities are governed by transparent rules and procedures.  

—  Whose decisions may be challenged and reviewed in the context of proceedings before 

an independent judiciary.  

—  Whose use is enforceable by national legislation for claims on the origin of consumed 

energy.  

—  Works in interaction with the authority publishing the residual consumption mix in a way 
that  prevents  double  claims  of  renewable  energy  sources  and  other  environmental 
attributes.  

Criterion 4 – Be as close as possible to the period to which the contractual instrument is applied  

To satisfy the criterion, the contractual instrument shall:  

—  Ensure  that  certificates  are  valid  no  longer  than  12  months  after  the  represented 
electricity was generated. Certificates shall be used (hence cancelled/redeemed/retired) 
within 18 months after the electricity was generated.  

Criterion 5 – Be sourced from the same market in which the reporting entity’s electricity-consuming 
operations are located and to which the instrument is applied  

The electricity to which the contractual instruments refer to and the company claiming the 
contractual instrument shall be within the same market boundaries.  

The “market boundary” refers to an area in which:  

—  There  is  a  physical  interconnection  between  the  point  of  generation  and  the  point  of 
consumption  of  renewable  electricity.  When  interconnection  happens  across  different 
grids, there shall be an entity that coordinates and tracks the exchange between such 
grids.  

32 

 
 
 
  
  
 
—  The  countries’  utilities/energy  suppliers  recognize  each  other’s  energy  source  tracking 

instruments and have a system in place to prevent double counting of claims  

3.4.10.1.3 How to model the residual consumption mix  

If a default secondary dataset modelling the residual consumption mix in a specific country (e.g., 
residual consumption mix in Estonia) is present in the Excel spreadsheet named 
““CF_Annex_PV_modules-Life_cycle_inventory” discussed in Section 4, then this dataset shall be 
used. Otherwise, the manufacturer shall model its own residual consumption mix for the considered 
country using the following approach: 

—  Use the composition of the residual consumption mix (e.g., X% of MWh produced with 
hydro energy, Y% of MWh produced with coal power plant). The most recent composition 
shall be used (e.g. from the issuing body). Then the background processes per energy tyoe 
and  country/region  (e.g.  production  of  1MWh  solar  energy  in  the  corresponding 
country/region) shall be combined. 

If the residual consumption mix is modelled with own data, the source, the year, the geographical 
boundaries, the percentage from each electricity source, and the background datasets shall be 
provided in the supporting study. 

3.4.10.1.4 How to model the average consumption mix   

The average consumption mix shall be modelled using default secondary datasets listed in the Excel 
spreadsheet named “CF_Annex_PV_modules-Life_cycle_inventory", discussed in Section 4. 

If in the spreadsheet “CF_Annex_PV_modules-Life_cycle_inventory" there is a dataset modelling the 
average consumption mix in the country, or in the region (EU) of interest, that dataset shall be used 
giving priority first to the country, and then to the region. 

3.4.10.2 Alternative approach 

Alternatively, the carbon footprint of the consumption of electricity may be calculated according to 
the following hierarchy:  

—  On-site  generated  electricity  modelled  according  to  section  3.4.10.1.1  if  it  meets  the 

conditions set in the same section.  

—  Average consumption mix modelled according to section 3.4.10.1.4  

33 

 
 
 
 
4.  Modelling of the PV technologies  

The life cycle inventories of PV technologies under analysis (mono-crystalline silicon, multi-
crystalline silicon, CdTe) are listed in an Excel spreadsheet10 linked to the Annex A, which was part 
of a policy proposal dated March 2023. In that Excel spreadsheet, for each PV stage (e.g. panel, cell, 
wafer) the default secondary datasets are provided with their associated uuid. In addition, for all 
the non-mandatory processes, the associated activity data are provided for each default secondary 
datasets. In terms of geographical locations, four areas are listed (i.e. European Union, US, China, 
APAC). Also, CFF parameters are provided in the tab “CFF parameters” with a dedicated Table. 

In the following section 4.1, additional suggestions for complementing the life-cycle Inventory of PV 
technologies are discussed. Additions and adjustments in that Excel file with life-cycle inventories 
were needed to allow applicability to the harmonized rules for calculating carbon footprint of PV 
(including all the PEFCR adaptations described in Section 3). 

4.1 Life cycle inventories of PV technologies  

In this section, potential suggestions implemented in the excel spreadsheet with the life-cycle 
inventories of PV are discussed in comparison with the version shared with stakeholders on the 
consultation held in March 2023.   

Below a summary of the major identified changes: 

—  Distribution 

o  distribution  from  extra  EU  to  EU  to  be  implemented  as  company-specific 

information;  

o  distribution from EU to EU to be implemented as company-specific information 

—  Elementary flows 

o  Elementary flows to be added for all the processes according to the model of the 

representative product of the PEFCR PV 

—  UUID 

o  UUIDs of EF 2.0 datasets to be replaced with UUIDs of EF3.1 datasets 

—  Update silicon production mix  

o  electronic grade and electronic off grade silicon processes to be deleted because 
these technological processes are not yet used in the manufacturing of PV wafer; 

o  solar grade silicon to be kept as it is still used in commercial production; 

o 

the geographical market share of silicon production mix (kg of each solar grade 
silicon) to be updated according to the values in Table 6. 

—  Electricity consumption 

o  As  shown  in  Table  7,  In  the  mono-crystalline  silicon  PV,  the  activity  data  of 
electricity consumption (MJ per kg and MJ per m2) to be updated according to the 
latest values listed in the IEA PVPS Task 12 2020 for the following processes: 

▪  Silicon, solar grade, modified Siemens process in EU, CN, US and APAC 

10 https://ec.europa.eu/docsroom/documents/46532 

34 

 
 
 
 
 
▪  CZ single crystalline silicon, at plant in EU, CN, US and APAC 

▪  Wafer, mono-Si, at plant in EU, CN, US and APAC 

o  Residual grid mix default datasets may be deleted and replaced with electricity 
consumption mix default datasets depending on the approaches adopted;  

o  Default  dataset  of  electricity  consumption  mix  in  DE  to  be  replaced  with  the 
default dataset of electricity consumption mix in EU (average mix) in the solar 
grade production 

o  Electricity  consumption  mix  dataset  for  US  to  be  replaced  with  RNA  (Regional 

Nord America) because it was not present in the EF 3.1 database. 

—  Default activity data 

o  Default activity data for mandatory processes to be deleted (e.g. panel and cell 
in mono-Si and multi-Si for EU, CN, US and APAC and laminate in CdTe for MY 
and US) 

—   Default values of Data Quality  

o  Default values of DQR, GeR, TeR and P to be deleted 

Table 6 shows the production mix of silicon that have been implemented for updating the scenarios 
of silicon production described in PV PEFCR. In particular, electronic grade and electronic off grade 
silicon processes are obsolete and not yet used in the manufacturing of PV wafer, according to data 
listed in the IEA PVPS Task 12 2020. Also, the geographical area has been updated with the aim of 
being more realistic as possible. 

35 

 
 
 
 
Table 6 Update of silicon production mixes (JRC elaboration: adapted from PEFCR PV and based on IEA PVPS Task 12 2020) 

Items 

EF datasets name 

unit 

amount 

UUID EF dataset 

Silicon, 
production mix, 
photovoltaics, at 
plant (EU) 

Silicon, 
production mix, 
photovoltaics, at 
plant (CN) 

Silicon, electronic grade, at plant (DE)* 

Silicon, electronic grade, off-grade, at plant (DE) 

Silicon, solar grade, modified Siemens process, at plant (EU) 

Silicon, production mix, photovoltaics, at plant (EU) 

Silicon, electronic grade, at plant (APAC) 

Silicon, electronic grade, at plant (CN) 

Silicon, electronic grade, at plant (DE) 

Silicon, electronic grade, at plant (US) 

Silicon, electronic grade, off-grade, at plant (APAC) 

Silicon, electronic grade, off-grade, at plant (CN) 

Silicon, electronic grade, off-grade, at plant (DE) 

Silicon, electronic grade, off-grade, at plant (US) 

Silicon, solar grade, modified Siemens process, at plant (APAC) 

Silicon, solar grade, modified Siemens process, at plant (CN) 

Silicon, solar grade, modified Siemens process, at plant (EU) 

Silicon, solar grade, modified Siemens process, at plant (US) 

Silicon, 
production mix, 
photovoltaics, at 
plant (APAC) 

Silicon, production mix, photovoltaics, at plant (CN) 

Silicon, electronic grade, at plant (APAC) 

Silicon, electronic grade, off-grade, at plant (APAC) 

Silicon, solar grade, modified Siemens process, at plant (CN) 

Silicon, solar grade, modified Siemens process, at plant (APAC) 

36 

kg 

kg 

kg 

kg 

kg 

kg 

kg 

kg 

kg 

kg 

kg 

kg 

kg 

kg 

kg 

kg 

kg 

kg 

kg 

kg 

kg 

0 

0 

0e38bfe5-fa5b-3377-ad1f-c4682c194843 

868aa61b-a71b-3819-b059-e19147908962 

1.00E+00 

78e55a85-2481-320f-b923-ba53fea5b217 

1.00E+00 

7cf88a38-da63-3a31-9a0d-233f0a1b13d1 

0 

0 

0 

0 

0 

0 

0 

0 

1.30E-02 

9.87E-01 

0 

0 

b9883a06-1487-39c8-a840-10619cf4c8fb 

83091162-30c3-36fb-83b1-5f7de4eb5a47 

0e38bfe5-fa5b-3377-ad1f-c4682c194843 

7d01962d-8443-39d1-bf23-7935bda83fcd 

9f1ac96d-7b85-3521-b5f0-e97ab0522898 

178ff0ec-d9b0-357f-9dc9-eef4509665ba 

868aa61b-a71b-3819-b059-e19147908962 

c2a2c742-e991-3179-a7fb-0d91ba5e451b 

e8889cc9-1305-38be-8bba-842a507ad4e2 

3ce0bf75-144f-3454-be68-7f2bbd9afbdd 

78e55a85-2481-320f-b923-ba53fea5b217 

f23b1307-6416-3819-8818-7b28dcb24dd8 

1.00E+00 

88b711e1-8138-304e-a723-1afa259ac633 

0 

0 

b9883a06-1487-39c8-a840-10619cf4c8fb 

9f1ac96d-7b85-3521-b5f0-e97ab0522898 

9.87E-01 

3ce0bf75-144f-3454-be68-7f2bbd9afbdd 

1.30E-02 

e8889cc9-1305-38be-8bba-842a507ad4e2 

 
 
 
 
 
Items 

EF datasets name 

unit 

amount 

UUID EF dataset 

Silicon, 
production mix, 
photovoltaics, at 
plant (US) 

Silicon, production mix, photovoltaics, at plant (APAC) 

Silicon, electronic grade, at plant (US) 

Silicon, electronic grade, off-grade, at plant (US) 

Silicon, solar grade, modified Siemens process, at plant (US) 

Silicon, production mix, photovoltaics, at plant (US) 

Source: JRC elaboration: adapted from PEFCR PV and based on IEA PVPS Task 12 2020 

kg 

kg 

kg 

kg 

kg 

1.00E+00 

d373023c-aceb-311c-9bab-78f035750547 

0 

0 

7d01962d-8443-39d1-bf23-7935bda83fcd 

c2a2c742-e991-3179-a7fb-0d91ba5e451b 

1.00E+00 

f23b1307-6416-3819-8818-7b28dcb24dd8 

1.00E+00 

e2b44dee-717e-3785-b3ac-6ba2bd173633 

37 

 
 
 
Table 7 shows the activity data of electricity consumption (MJ per kg and MJ per m2) that have been 
implemented according to the values listed in the IEA PVPS Task 12 2020. 

Table 7. Updated activity data for electricity to be implemented in the modelling and in the Excel file. 

Process 

Unit 

Old data in the 
Excel (based on 
PV PEFCR) 

New data as  
implemented in 
the modelling 
and in the Excel 
for EU, APAC, US 
and CN  

New data 
potentially to be 
implemented in 
the Excel for EU, 
APAC, US and CN 
with an increase 
of 30% for being 
convervative 

MG-Si 

SOG-Si 

Cz ingot 

Wafer 

Cell 

Panel 

Source: JRC elaboration 

MJ/kg 

MJ/kg 

MJ/kg 
MJ/m2 
MJ/m2 

MJ/m2 

39.6 

129 (EU) and 396 
(CN, US, APAC) 
246.0 

92.5 

51.8 

13.4 

39.6 

176.4 

115.2 

17.1 

63.7 

50.4 

n.a*. 

229.3 

149.8 

22.3 

n.a.* 

n.a.* 

*n.a. in case the values have not been changed, or the values are related to company specific information  

4.2 Additional potential materials needed for key and innovative PV 
technologies 

Compared to conventional PV technologies, key and emerging PV panels (e.g. TOPCON, HJT, IBC, 
Silicon-perovskite tandem) may need additional materials and processing for manufacturing them, 
compared to what listed in the Excel spreadsheet with life-cycle inventories of PV technologies. 

With the aim of addressing this, we conducted a preliminary screening of additional potential 
datasets needed for key and innovative PV technologies. Results of this analysis are summarized in 
Annex C.  

In particular, for each mapped material it is associated the PV technology in which it may be used 
(such as PERC, TOPCON, CdTe, Silicon-perovskite tandem) and the reference source with the 
associated link.  

Annex C also lists the amount of material (activity data) used for producing such PV panels (as 
retrieved from inventories in the references).  

The "Recycling Silicon" table in Annex C is intended as a preliminary screening of key documents, 
projects and sources mapping recycled silicon processes, indicating the technologies used and the 
reference scale which they refer to. 

38 

 
 
 
 
5.  Carbon footprint results and discussion  

5.1 Carbon footprint results  

In this section carbon footprint results, scenarios and interpretation are presented. In particular, 
carbon footprint results have been calculated according to the harmonized rules described in 
Section 3 of this report and the life-cycle inventories used are aligned to what described in Section 
4 of this report.  

The energy yield used for the calculation are illustrated in Annex III of the policy proposal and 
summarized in Annex B of this report. 

The scenarios selected for the analysis were chosen based on their electricity grid mixes (fossil 
fuels based, renewable energies based, balanced presence of fossil fuels and renewable energies). 
Sensitivity analysis in terms of different parameters have been conducted (as described in Section 
5.4) and specifically those are: 

—  Operational years 

—  Electricity yield 

—  Silicon content 

—  Conservative and updated activity data for key electricity processes 

—  Circular Footprint Formula parameters  

In terms of results, the results across all scenarios for mono-crystalline silicon PV range from the 
worst-case scenario, which is 4.40 E-02 kgCO2 eq per kWh, to the best-case scenario, which is  
1.08E-02 kgCO2 eq per kWh.  

For multi-crystalline silicon PV panel, the results across all scenarios range from the worst-case 
scenario, which is 5.01E-02 kgCO2 eq per kWh, to the best-case scenario, which is 1.78E-02 kgCO2 eq 
per kWh.  

In other words, the range of carbon footprint outcomes, spanning from the most favourable to the 
least favourable scenarios, is a factor of 4 for mono-crystalline silicon and a factor of 2.8 for multi-
crystalline silicon PV panel. 

In Table 8 and Table 9, the results are presented for mono-crystalline and multi-crystalline 
scenarios with variations in the grid carbon intensity. For mono-crystalline PV results reported in 
Table 8, information about the use of updated activity data for the electricity process of silicon and 
in terms of silicon production mix (including technological process and geographical location) are 
reported in the column named “activity data”. Within this column, “old data” represents the values of 
the PV PEFCR, while “new data” are the updated values based on Table 6 and Table 7. 

The carbon footprint of CdTe PV panel calculated with the assumptions and the inventories 
described below would be 1.20E-02 kgCO2 eq per kWh.  

39 

 
 
 
Table 8 Summary carbon footprint results of all mono-crystalline photovoltaic panel scenarios. 

Sc. 
No. 

PV scenario 

Countr
y 

Operational 
year [yrs] 

Electricity 
yield per 
m2 

Silicon 
[kg] 
per m2 

activity 
data  

R1 
Al 

R1 Si 

R2 
Al 

R2 Si 

Climate Change – 
Global Warming 
Potential [kgCO2 eq. 
per kWh] 

0.92 

0 

1.082E-02 

1 

2 

3 

4 

5 

6 

7 

8 

9 

PV produced in country 1  

PV produced in country 1 
except wafer, crystalline in 
country 2 

PV produced in country 2 
except wafer, crystal 

PV produced in country 2 
with default silicon mix 

 Country3, 6,733 kWh, 
Si0.58kg, 25%PV for all in 
supply chain 

 Country3, 6,733 kWh, 
Si0.58kg, Country3 supply 
except MG from Country2 

 Country4, 6,733 kWh, Si 
0.58kg, Country4 supply 
with Country2 electricity) 

 Country4, 6,733 kWh, Si 
0.58kg, 25% PV for all 
processes 

 Country4, 6,733 kWh, Si 
0.58kg, R1 PEFCR 

10 

 Country4, 6,733 kWh, Si 
0.58kg, 25% PV for panel 
and cell 

1 

1 

2 

2 

3 

3 

4 

4 

4 

4 

30 

30 

30 

30 

30 

30 

30 

30 

30 

30 

6,733 kWh 

0.58 

old data* 

0 

6,733 kWh 

0.58 

old data* 

0 

6,733 kWh 

0.58 

old data* 

0 

6,733 kWh 

0.58 

old data* 

0 

6,733 kWh 

0.58 

old data* 

0 

6,733 kWh 

0.58 

old data* 

0 

6,733 kWh 

0.58 

old data* 

0 

6,733 kWh 

0.58 

old data* 

0 

0 

0 

0 

0 

0 

0 

0 

0 

0.92 

0.92 

0.92 

0 

0 

0 

0.92 

0 

0.92 

0 

0.92 

0 

0.92 

 0 

6,733 kWh 

0.58 

old data* 

0.77 

0 

0.92 

0 

6,733 kWh 

0.58 

old data* 

0 

0 

0.92 

0 

41 

1.44E-02 

1.77E-02 

1.82E-02 

1.84E-02 

2.08E-02 

2.10E-02 

2.11E-02 

2.39E-02 

2.40E-02 

 
 
 
Sc. 
No. 

PV scenario 

Countr
y 

Operational 
year [yrs] 

Electricity 
yield per 
m2 

Silicon 
[kg] 
per m2 

activity 
data  

R1 
Al 

R1 Si 

R2 
Al 

R2 Si 

Climate Change – 
Global Warming 
Potential [kgCO2 eq. 
per kWh] 

11 

12 

13 

14 

15 

16 

17 

18 

19 

20 

21 

22 

 Country4, 6,733 kWh, Si 
0.58kg,  

 Country4, 6,733 kWh, Si 
0.58kg, 25 years 

 Country4, 6,733 kWh, Si 
1.080kg 

 Country4, 6,733 kWh, Si 
0.58kg, 20 years 

 Country4, 6,733 kWh, Si 
1,580kg,  

 Country4, 6,733 kWh, Si 
0.58kg,  

Country2 : 0_MonoSi,6,733 
kWh, Si0.58kg 

 Country4: 0_MonoSi, 
6,733 kWh, Si 1,580kg 

Country 2: 0_MonoSi 
,6,733 kWh, Si1,58kg, 

 Country4: 6,733 kWh, Si 
0.58kg, EF3.1, Country4 
supply,SiR10.5, R20.50)  

 Country4, 6,733 kWh, Si 
0.58kg, EF3.1, Country4 
supply,SiR10.5) 

 Country4, 6,733 kWh, Si 
0.58kg, SiR10.33)  

4 

4 

4 

4 

4 

4 

2 

4 

2 

4 

4 

4 

30 

25 

30 

20 

30 

30 

30 

30 

30 

30 

30 

30 

6,733 kWh 

0.58 

old data* 

0 

5686,71 
kWh 

0.58 

old data* 

0 

6,733 kWh 

1.08 

old data* 

0 

4610.03 
kWh 

0.58 

old data* 

0 

6,733 kWh 

1.58 

old data* 

0 

6,733 kWh 

0.58 

6,733 kWh 

0.58 

6,733 kWh 

1.58 

6,733 kWh 

1.58 

6,733 kWh 

0.58 

6,733 kWh 

0.58 

6,733 kWh 

0.58 

0 

0 

0 

0 

0 

0 

0 

New 
data* 

New 
data* 

New 
data* 

New 
data* 

New 
data* 

New 
data* 

New 
data* 

42 

0 

0 

0 

0 

0 

0 

0 

0 

0 

0.92 

0.92 

0.92 

0.92 

0.92 

0.92 

0.92 

0.92 

0.92 

0 

0 

0 

0 

0 

0 

0 

0 

0 

0.5 

0.92 

0.5 

0.5 

0.92 

0.33 

0.92 

0 

0 

2.44E-02 

2.93E-02 

3.42E-02 

3.65E-02 

4.40E-02 

1.87E-02 

1.62E-02 

2.99E-02 

2.53E-02 

1.85E-02 

1.866E-02 

1.869E-02 

 
 
 
Sc. 
No. 

PV scenario 

Countr
y 

Operational 
year [yrs] 

Electricity 
yield per 
m2 

Silicon 
[kg] 
per m2 

activity 
data  

R1 
Al 

R1 Si 

R2 
Al 

R2 Si 

23 

PV produced in country 1 

24 

PV produced in country 1 

25 

26 

27 

28 

Country 2: 0_MonoSi 
,6,733 kWh, Si1,58kg, 

 Country3, 6,733 kWh, 
Si0.58kg, Country3 supply 
except MG from Country2 

 Country3, 6,733 kWh, 
Si0.58kg, Country3 supply 
except MG from Country2 

 Country4, 6,733 kWh, Si 
0.58kg, silicon mix as 
defined in PEFCR 

Source: JRC elaboration 

1 

1 

2 

3 

3 

4 

30 

30 

30 

30 

30 

30 

6,733 kWh 

0.58 

6,733 kWh 

1.08 

6,733 kWh 

1.08 

6,733 kWh 

0.58 

6,733 kWh 

1.08 

New 
data* 

New 
data* 

New 
data* 

New 
data* 

New 
data* 

6,733 kWh 

0.58 

NA 

0 

0 

0 

0 

0 

0 

0 

0 

0 

0.92 

0.92 

0.92 

0 

0 

0 

0 

0.92 

0 

0 

0.92 

0 

0 

0.92 

0 

Climate Change – 
Global Warming 
Potential [kgCO2 eq. 
per kWh] 

1.080E-02 

1.25E-02 

2.08E-02 

1.86E-02 

2.33E-02 

2.33E-02 

*detailed information about “old data” and “new data” are available in Table 6 and Table 7 
Note: R1Al is recycled content of Aluminium;R2Al is recyclability of aluminium at the end of life; R1Si is recycled content of silicon; R2Si is recyclability of silicon at the end of life; R1 PEFCR scenario include 
recycled content of aluminium according to PEFCR; SiR1 0.5, R2 0.50 Recycled content of Silicon 50% in the input and recyclability at the end of life 50%; SiR10.33 Recycled content of Silicon 33% in the input 

43 

 
 
 
Table 9 Summary carbon footprint results of all multi-crystalline photovoltaic panels scenarios. 

Sc. 
No. 

1m 

2m 

3m 

4m 

5m 

6m 

7m 

8m 

9m 

10m 

11m 

12m 

13m 

14m 

15m 

PV scenario 

Description 

Country 1 Multi-Si (646g Si)  

All manufacturing processes are located in country1, with 
30 years operation. 

Silicon 
[kg} 
per m2 

Climate 
Change 
[kg CO2 eq. 
per kWh] 

0.646 

1.78E-02 

Country2:Multi-Si (646g Si)  

All manufacturing processes are located in country 2, with 
30 years operation. 

0.646 

2.63E-02 

Country3: Multi-Si (646g Si 
25%PV all) 

All manufacturing processes are located in country3, with 
25% electricity from PV in production, with 30 years 
operation. 

0.646 

2.65E-02 

Country4: Multi-Si (Si646g) 

All manufacturing processes are located in Country4, with 
lower electricity consumed in production, with 30 years 
operation. 

0.646 

2.74E-02 

Country4: Multi-Si 
(Si646g,25%PV all processes) 

All manufacturing processes are located in Country4, with 
25% electricity from PV in production, with 30 years 
operation. 

0.646 

2.92E-02 

Country3: Multi-Si (646g Si) 
except MG) 

All manufacturing processes are located in country3, with 
the exception of MG in country 2, with 30 years operation. 

0.646 

2.93E-02 

Country4: Multi-Si (Si646g, 
25%PV for panel and cell) 

All manufacturing processes are located in Country4, with 
25% electricity from PV in panel an cell production, with 
30 years operation. 

0.646 

3.27E-02 

Country4: Multi-Si (Si646g, 
R1PEFCR) 

All manufacturing processes are located in Country4 with 
30 years operation, with recycled aluminium 77% as input 

0.646 

3.29E-02 

 Country4: Multi-Si (Si646g) 

All manufacturing processes are located in Country4 with 
only 30 years operation 

0.646 

3.34E-02 

 Country4: Multi-Si (Si833g) 

All manufacturing processes are located in Country4 with 
only 30 years operation 

0.833 

3.74E-02 

 Country4: Multi-Si (Si646g, 
25Yrs) 

All manufacturing processes are located in Country4 with 
only 25 years operation 

0.646 

4.01E-02 

 Country4: Multi-Si (Si1020g) 

All manufacturing processes are located in Country4 with 
only 30 years operation 

1.020 

4.14E-02 

 Country4: Multi-Si (Si646g, 
20Yrs) 

All manufacturing processes are located in Country4 with 
only 20 years operation 

0.646 

5.01E-02 

Country 1: Multi-Si (646g Si)  

All manufacturing processes are located in country1, with 
the exception of the casting and wafer procedures, which 
are based in country 2 with 30 years operation. 

0.646 

2.06E-02 

 Country4, Si 646g with 
default countries mix silicon 
production 

All manufacturing processes are located in Country4 
except silicon production that some silicon produce in 
other countries e.g. Europe with only 30 years operation  

0.646 

3.16E-02 

Source: JRC elaboration 

44 

 
 
 
 
5.2 Key findings  

The main contributors to the carbon footprint of a photovoltaic panel, both mono- and multi-silicon 
PV, are: 

—  Electricity for manufacturing silicon,  

—  Silicon content. 

—  Glass content, 

—  Aluminium content,  

The percentage of contribution from electricity varies, depending on the carbon footprint per 
kilowatt-hour of the electricity mix of the country that manufactures the panel. 

For monocrystalline silicon PV panels, the product's lifespan plays a significant role in terms of the 
carbon footprint per kilowatt-hour. For instance, in the scenario of country 4, when the lifespan of 
the PV panel decreases from 30 years to 25 years, the carbon footprint increases by 20%. If the 
lifespan is further reduced to 20 years, the carbon footprint increases by 50%. In this context, it is 
relevant to ensure the verifiability of the lifetime and the degradation ratio provided by the PV 
panel producer (e.g. through a linked technical guaranty). 

Furthermore, when the amount of silicon increases from 0.58 kg per square meter of ingot to 1.58 
kg per square meter of ingot, the carbon footprint per kilowatt-hour increases by 80% for the 
country 4 scenario. It should be noted that the silicon content mainly depends on the silicon wafer 
thickness and on the kerf losses in the manufacturing processes. Both factors are constantly 
decrease over the year11 .The high amount of silicon (i.e. 1.58 kg/m2) implemented in the modelling 
is a conservative default value that can be used by the declarant in absence of specific information 
on its specific silicon usage because it represents obsolete PV production processes. Currently, the 
silicon wafer thickness reported by industries is about 150 µm12 and the silicon content is below 
0.5 kg/m213.  

The assumption on energy production is based on the formula provided in the Annex III of the policy 
proposal as described in Annex B of this report. Specifically, it has been used the following equation:  

EEIM_30y = EEIM x (1 – DR x (30x 0.5))  

where: 

—  EEIM is the Energy Efficiency Index as the ratio of the DC energy yield delivered by one 
module (EYm(DC) over one year (considered the first year of installation) expressed in 
kWh, delivered by the module area (Am) in m2. 

—  EEIM_30y the Energy Efficiency Index over 30 years of panel lifetime.  

—  DR.  Linear  annual  degradation  rate  for  mono-  and  multicrystalline  silicon  0.7%  per 

annum.  

11 Available at: https://www.ise.fraunhofer.de/en/publications/studies/photovoltaics-report.html 
12 Photovoltaics-Report (14).pdf 
13 Life Cycle Inventories and Life Cycle Assessments of Photovoltaic Systems 

45 

 
 
 
 
 
Energy Efficiency Index for modules (EEIM) kWh*m-2 per year are calculated without considering 
age degradation and assuming operation at the default measurement temperature of 25 °C (i.e., 
the temperature-dependent, typically lower yield is not considered).  

Monocrystalline N-type Silicon 

EEIM_30y: 6733.0 kWh*m-2 per 30-year lifetime  

Multicrystalline   

EEIM_30y: 5917.4 kWh*m-2 per 30-year lifetime  

CdTe 

EEIM_30y: 4820.0 kWh*m-2 per 30-year lifetime 

5.3 Data gaps  

Below is the list of the data gaps of this study, same as in PEFCR; these gaps are within the 
permitted cut-off criteria of the PEF method:  

—  Adhesive for metals  

—  Charcoal  

—  Chemicals inorganic  

—  Chemicals organic  

—  Disposal, building, electric wiring, to final disposal  

—  Disposal, waste, Si waferprod., inorg, 9.4% water, to residual material landfill  

—  Disposal, waste, silicon wafer production, 0% water, to underground deposit  

—  Graphite, at plant  

—  Helium, at plant  

—  Metal working machine, unspecified, at plant  

—  Nitrogen trifluoride, at plant  

—  Petroleum coke, at refinery  

—  Solvents, organic, unspecified, at plant  

—  Water supply network 

Table 10 lists where proxy datasets have been used as the required process or product was not 
available in the official EF compliant database EF 3.1 the same as in the PV PEFCR Version: 1.2 
Date of publication: February 2020:  

46 

 
 
 
 
 
 
 
 
 
 
 
 
Table 10 Use of proxies. 

Process or product 
needed in model 

Proxy data set used  

Solder production plant 

Building, steel frame construction (1 m³ gross volume = 125 kg), single route, at 
plant, steel frame construction, material quantities adjustable 

Disposal, slag from MG silicon 
production, 0% water, to inert 
material landfill 

Landfill of inert (construction materials) landfill including leachate treatment and 
with transport without collection and pre-treatment production mix (region 
specific sites), at landfill site 

Disposal, sludge, pig iron 
production, 8.6% water, to 
residual material landfill 

Landfill of inert (construction materials) landfill including leachate treatment and 
with transport without collection and pre-treatment production mix (region 
specific sites), at landfill site 

Silicon tetrachloride, at plant 

silicon tetrachloride 

Aluminium, production mix, 
wrought alloy, at plant 

Aluminium ingot mix (high purity) primary production, aluminium casting single 
route, at plant 2.7 g/cm3, >99% Al 

Brass, at plant 

Brick, at plant 

Building, hall 

Diesel, burned in building 
machine 

Disposal, building, brick, to 
sorting plant 

Brass die-casting die casting, from copper and zinc, primary production single 
route, at plant 8.41- 8.86 g/cm3 

Bricks vertically perforated (EN15804 A1-A3), production mix, at plant, 
technology mix, vertically perforated 

Building, reinforced concrete frame construction (1 m³ gross volume = 242 kg), 
single route, at plant, reinforced concrete frame production, material quantities 
adjustable 

Diesel combustion in construction machine, diesel driven 

Landfill of inert (construction materials) landfill including leachate treatment and 
with transport without collection and pre-treatment production mix (region 
specific sites), at landfill site 

Disposal, building, reinforced 
concrete, to sorting plant 

Landfill of inert (construction materials) landfill including leachate treatment and 
with transport without collection and pre-treatment production mix (region 
specific sites), at landfill site 

Disposal, building, reinforcement 
steel, to sorting plant 

Landfill of inert (steel) landfill including leachate treatment and with transport 
without collection and pre-treatment production mix (region specific sites), at 
landfill site 

Ethylvinylacetate, foil, at plant 

Gallium, semiconductor-grade, 
at regional storage 

Glass fibre reinforced plastic, 
polyamide, injection moulding, 
at plant 

Light fuel oil, burned in 
industrial furnace 1MW, non-
modulating 

Packaging film, High barrier PE/EVOH/PE raw material production, extrusion, 
blowing, flattening single route, at plant grammage: 0.066 kg/m2 outer, 0.042 
kg/m2 inner; thickness: 135 µm (outer film: 90 µm, inner film: 45 µm) 

Gallium technology mix production mix, at plant 5.9 g/cm3 

Glass fibres  production mix  at plant  per kg glass fibres 

Thermal energy from light fuel oil (LFO) technology mix regarding firing and flue 
gas cleaning production mix, at heat plant MJ, 100% efficiency 

47 

 
 
 
Process or product 
needed in model 

Proxy data set used  

Natural gas, burned in boiler 
condensing modulating >100kW 

Thermal energy from light fuel oil (LFO) technology mix regarding firing and flue 
gas cleaning production mix, at heat plant MJ, 100% efficiency 

Natural gas, burned in boiler 
modulating >100Kw 

Thermal energy from light fuel oil (LFO) technology mix regarding firing and flue 
gas cleaning production mix, at heat plant MJ, 100% efficiency 

Natural gas, burned in industrial 
furnace low-NOx >100kW 

Thermal energy from light fuel oil (LFO) technology mix regarding firing and flue 
gas cleaning production mix, at heat plant MJ, 100% efficiency 

Polyvinylbutyral foil, at plant 

Polyvinyl Butyral Granulate (PVB), single route, at plant, polymerisation of 
polyvinyl alcohol, 142 g/mol per repeating unit 

Polyvinylfluoride film, at 
plant/US 

Polyvinyl fluoride polymerisation of vinyl fluoride production mix, at plant 1.77 
g/cm3 

Reinforcing steel, at plant 

Reinforced steel (wire) EAF route production mix, at plant wire 

Sheet rolling, steel 

Steel hot dip galvanised steel sheet hot dip galvanization single route, at plant 
1.5 mm sheet thickness, 0.02 mm zinc thickness 

Silicone plant 

(Proxy) Building Hall for Silicone plant 

Solar collector factory 

Building, steel frame construction (1 m³ gross volume = 125 kg), single route, at 
plant, steel frame construction, material quantities adjustable 

Solar collector factory 

Building, reinforced concrete frame construction (1 m³ gross volume = 242 kg), 
single route, at plant, reinforced concrete frame production, material quantities 
adjustable 

Steel, low-alloyed, at plant 

Steel hot dip galvanised steel sheet hot dip galvanization single route, at plant 
1.5 mm sheet thickness, 0.02 mm zinc thickness 

Treatment, PV cell production 
effluent, to wastewater 
treatment, class 3 

Treatment, sewage, from 
residence to wastewater 
treatment, class 2 

Treatment, sewage, unpolluted, 
from residence, to wastewater 
treatment, class 2 

Treatment, sewage, unpolluted, 
to wastewater treatment, class 
3 

Source: JRC elaboration 

Treatment of effluents from glass production wastewater treatment including 
sludge treatment production mix, at plant 1m3 of wastewater treated 

Treatment of residential wastewater, small plant wastewater treatment 
including sludge treatment production mix, at plant 1m3 of waste water treated 

Treatment of residential wastewater, small plant wastewater treatment 
including sludge treatment production mix, at plant 1m3 of wastewater treated 

Treatment of residential wastewater, small plant wastewater treatment 
including sludge treatment production mix, at plant 1m3 of wastewater treated 

5.4 Description of the scenarios under analysis and differences with PEFCR 

Model configurations of PV associated with the results described in section 5.1 have several aspects 
that differ from the representative product (RP) that is described in the PV PEFCR. For example, the 
amount of silicon per area of ingot is higher in the PEFCR than the PV that is modelled in this 
scenario; more recent electricity mix have been considered; and the RP in the PEFCR is the average 
product unlike in this project, in which different specific products in different location have been 

48 

 
 
 
assessed. The reason for such difference was to capture the carbon footprint of different product in 
the market, representing particular supply chains. Moreover, EF 2.0 datasets referenced in the PV 
PEFCR, which are no longer valid, have been replaced by EF 3.1 datasets. The silicon mix was also 
assumed as different, as explained in  section 4.1 and section 5.4.3.  

Scenario analysis for mono-Si  

A three-step scenario analysis has been conducted. In the first step, the electricity consumption in 
the production of panel, cell, solar grade silicon, off grade silicon, electronic grade silicon, wafer 
data is derived from the Excel spreadsheet that was shared with the stakeholders during the 
consultation held in March 2023 and linked to the policy proposal attached to the Annex A (“Old 
data based on PV PEFCR” as explained and listed in Table 7). This initial scenario considers four 
countries where panel is manufactured, the utilization of green electricity for the production phase, 
alterations to the quantity of silicon per panel area, and the product's lifespan.  

In the second step, some of the activity data have been updated to reflect the advancements in 
current technology. This includes the integration of the most recent data on electricity consumption 
in production and silicon usage. In the third and final step, the focus shifts towards anticipating 
future technological developments, such as enhancements in the silicon recycling process.  

5.4.1 Amount of Silicon in panel  

The amount of silicon usage in mono-crystalline PV panels is documented in Table 11 with an 
assumed range from the lower to a high content. As explained above, the current silicon usage 
reported by industries is below 0.5 kg/m2 and then the high silicon content represents a 
conservative value that can be used by the declarant in absence of specific information. 

Table 11 Amount of silicon per area. 

Technology 

Si (min) 
[kg/m2] 

Si (mean) 
[kg/m2] 

Si (max) [kg/m2] 

Mono 

Multi 

Source: JRC elaboration 

0.588 

1.08 

0.646 

0.833 

1.58 

1.02 

5.4.2 Electricity in production  

The new data of electricity usage is based on the report “Life Cycle Inventories and Life Cycle 
Assessments of Photovoltaic Systems” in December 2020 (Report IEA-PVPS T12-19:2020) that 
published by the International Energy Agency (IEA), as documented in Table 7, were used to model 
the different scenarios for the mono-crystalline Si PV panels. For multi-crystalline Si PV panels, old 
data based on PV PEFCR were used.   

The approach adopted for modelling the electricity used in the PV panel manufacturing is in 
accordance with Section 3.10.2 “Alternative approach”.  

49 

 
 
 
 
5.4.3 Silicon mix  

The silicon mix is an artificial production mix for manufacturing the polysilicon that have been 
implemented in the life cycle inventories for representing an average share from a geographical 
production and technological process perspectives. 

The new data for the amount of silicon mix is based on the same report as electricity scenario, as 
documented in Table 6. This represents a potential update compared to old values. 

5.5 Interpretation of carbon footprint results for Mono-Si: 

In the analysis conducted on monocrystalline silicon photovoltaic, a total of 28 scenarios were 
examined. The findings, as presented in Table 12, span from the most unfavourable scenario, with a 
carbon footprint of 4.40 E-02 kgCO2 eq per kWh, to the most favourable scenario, which records a 
significantly lower emission of 1.08 E-02 kgCO2 eq per kWh. This represents a fourfold difference 
between the two extremes.  

In the context of photovoltaic production in Country 4, which has been selected due to its energy 
mix mainly based on fossil fuels, using 0.58 kg of Silicon per square meter, a reduction in the 
product's lifespan from 30 years to 20 years results in a 1.5-fold increase in the carbon footprint. 
As reported in Table 12, this means it increases from 2.44 E-2 kgCO2 eq to 3.65 E-2 kgCO2 eq. 
Furthermore, when the quantity of Silicon per square meter is increased from 0.58 kg to 1.58 kg, 
the carbon footprint escalates by 1.8 times, reaching 4.4 E-2 kgCO2 eq.  

The stages of transportation to regional storage and the end of life of the PV module yield the 
same results across all three scenarios. This is attributed to the assumption that these stages occur 
in the European Union, and the same data sets are utilized to analyse the carbon footprint. 
Photovoltaics manufactured in Country 4 with a default silicon mix have a slightly lower impact 
than those with an entirely supply chain based on Country 4. This can be explained by the fact that 
some of the silicon is produced in countries with a lower carbon footprint for electricity.   

Table 12 Example of Carbon footprint of mono Si photovoltaic panel per life cycle stage: Si 0.58 kg per m2. 

Sc. 
No. 

3 

28 

11 

Scenario 

Total 

raw 
material 
and 
productio
n 

transporta
tion to 
regional 
storage 

End of 
Life of PV 
Module  

Produced in Country 2 

1.77E-02 

1.89E-02 

3.16E-04 

-1.53E-03 

Produced in Country 4 with 
default silicon mix 

Produced in Country 4 with 
default silicon produced in 
Country 4 

2.33E-02 

2.45E-02 

3.16E-04 

-1.53E-03 

2.44E-02 

2.56E-02 

3.16E-04 

-1.53E-03 

Source: JRC elaboration 

The detailed outcomes of the mono silicon photovoltaic panel, under the country 2 (Scenario 3), are 
illustrated in Table 13 to Table 15. An analysis of the results from these tables reveals that 
materials significantly impact the carbon footprint. For instance, Aluminium ingot contributes 

50 

 
 
 
16.9%, glass accounts for 11.3%, and metal grade silicon makes up 6%. Additionally, electricity 
from the entire supply chain contributes a substantial 40.7%.   

Table 13 Carbon footprint of Monocrystalline silicon photovoltaic Panel process, Scenario 3.  

Process  

Total  

Electricity Mix 2022  

Climate Change - total kg CO2 eq. 
per 1 kWh  

Percentage [%]  

1.89E-02  

2.17E-04  

PHOTOVOLTAIC CELL. MONO-SI. AT PLANT (Silicon0.58)EF3.1   

1.07E-02  

Articulated lorry transport. Euro 4. Total weight >32 t EF 3.1  

5.19E-05  

Articulated lorry transport. Euro 4. Total weight >32 t EF 3.1  

3.78E-06  

Copper Cathode. production mix EF 3.1  

Diesel mix at refinery EF 3.1  

Flat glas. tempering EF 3.1  

Flat glass. uncoated EF 3.1  

Freight train. average EF 3.1  

glass fiber EF 3.1  

Lead (primary) EF 3.1  

Tap water EF 3.1  

Solar glass Quantis  

6.29E-05  

1.47E-08  

4.01E-04  

1.76E-03  

1.16E-04  

7.32E-05  

1.83E-07  

5.67E-07  

1.16E-03  

1.15%  

56.24%  

0.27%  

0.02%  

0.33%  

0.00%  

2.12%  

9.29%  

0.61%  

0.39%  

0.00%  

0.00%  

6.14%  

Aluminium ingot (silicon and magnesium main solutes)  

3.20E-03  

16.92%  

Copper Wire Drawing  

Corrugated board. uncoated  

HDPE granulates  

Packaging film. High barrier PE/EVOH/PE  

Pallet. wood (80x120)  

PET granulates. amorphous  

Silicone. high viscosity  

Treatment of residential wastewater. small plant  

Waste incineration of hazardous waste  

Waste incineration of municipal solid waste  

Waste incineration of plastics (unspecified)  

Diesel combustion in construction machine   

Glass SMD diode EF 3.1  

2.22E-06  

1.18E-04  

6.11E-06  

3.26E-04  

4.05E-05  

1.35E-04  

8.75E-05  

1.08E-06  

9.72E-08  

1.06E-07  

2.39E-04  

8.64E-08  

1.33E-04  

51 

0.01%  

0.62%  

0.03%  

1.72%  

0.21%  

0.71%  

0.46%  

0.01%  

0.00%  

0.00%  

1.26%  

0.00%  

0.70%  

 
 
 
  
Process  

Polyvinyl fluoride EF 3.1  

potassium hydroxide production  

Tin EF 3.1  

photovoltaic panel. single-Si  

1-propanol production  

hydrogen fluoride production  

isopropanol production  

soap production  

Source: JRC elaboration 

Climate Change - total kg CO2 eq. 
per 1 kWh  

Percentage [%]  

9.28E-05  

1.32E-05  

1.00E-05  

2.92E-06  

8.08E-06  

1.32E-05  

3.74E-08  

1.68E-06  

0.49%  

0.07%  

0.05%  

0.02%  

0.04%  

0.07%  

0.00%  

0.01%  

Table 14 Carbon footprint of Monocrystalline silicon photovoltaic Cell process. 

Process  

 Total  

 photovoltaic cell factory  

Climate Change - total kg CO2 eq. 
per 1 kWh  

Percentage [%]  

1.07E-02  

4.83E-05  

LC1: single-Si wafer. photovoltaics. at plant (Si0.588kg).  

9.38E-03  

Electricity Mix 2022  

Silane. at plant  

photovoltaic cell. single-Si. at plant  

Ammonia. as 100% NH3 production EF 3.1  

7.84E-04  

6.56E-05  

2.69E-05  

5.79E-06  

Articulated lorry transport. Euro 4. Total weight >32 t  

2.27E-06  

Freight train. average EF 3.1  

Tap water EF 3.1  

Thermal energy from natural gas  

Treatment of residential wastewater. small plant  

Waste incineration of hazardous waste  

Metallization paste. back side EF 3.1  

Metallization paste. back side. aluminium EF 3.1  

Metallization paste. front side EF 3.1  

tetrafluoroethane production  

Transoceanic ship. containers  

Hydrochloric acid production  

3.89E-06  

1.80E-05  

5.25E-07  

3.19E-05  

9.71E-06  

2.63E-05  

5.38E-05  

4.86E-05  

6.99E-08  

3.32E-08  

5.48E-08  

52 

0.5%  

88.1%  

7.4%  

0.6%  

0.3%  

0.1%  

0.0%  

0.0%  

0.2%  

0.0%  

0.3%  

0.1%  

0.2%  

0.5%  

0.5%  

0.0%  

0.0%  

0.0%  

 
 
 
  
hydrogen fluoride production  

isopropanol production  

Lime production  

Nitrogen liquid production  

phosphoryl chloride production  

Sodium hydroxide production  

Source: JRC elaboration 

1.28E-07  

4.21E-05  

2.21E-06  

3.55E-05  

6.01E-06  

5.81E-05  

0.0%  

0.4%  

0.0%  

0.3%  

0.1%  

0.5%  

Table 15 Carbon footprint of mono-crystalline silicon photovoltaic panels: single Crystalline process. 

Climate Change - total kg CO2 
eq. per 1 kWh  

Percentage [%]  

Process  

 Total  

Electricity grid mix  

CZ single crystalline silicon. at plant EF3.1  

Silicon carbide. reprocessed. at plant  

Triethylene glycol. reprocessed. at plant  

Tap water EF 3.1  

9.38E-03  

2.52E-03  

5.54E-03  

6.96E-05  

1.09E-04  

6.23E-10  

Articulated lorry transport. Euro 4. Total weight >32 t  

7.95E-06  

Flat glas. tempering EF 3.1  

Flat glass. uncoated EF 3.1  

 Freight train. average EF 3.1  

Steel cold rolled coil EF 3.1  

Steel wire drawing EF 3.1  

Brass die-casting  

Acetic acid production  

Acrylic binder production  

Alkylbenzene production  

De-ionised water production  

dipropylene glycol monomethyl ether production  

Hydrochloric acid production  

silicon carbide production  

Sodium hydroxide production  

triethylene glycol production  

Thermal energy from natural gas EF 3.1  

Source: JRC elaboration 

4.38E-07  

1.92E-06  

1.01E-05  

2.76E-04  

2.48E-05  

1.47E-06  

8.71E-06  

3.77E-07  

5.93E-05  

1.90E-06  

1.74E-04  

2.42E-07  

4.69E-04  

1.49E-06  

6.10E-05  

3.67E-05  

53 

26.9%  

59.1%  

0.7%  

1.2%  

0.0%  

0.1%  

0.0%  

0.0%  

0.1%  

2.9%  

0.3%  

0.0%  

0.1%  

0.0%  

0.6%  

0.0%  

1.9%  

0.0%  

5.0%  

0.0%  

0.7%  

0.4%  

 
 
 
  
Other results and contribution analysis of multi-crystalline and CdTe PV panels are reported into the 
Annex D. 

54 

 
 
 
 
 
6.  Stakeholder Consultations   

The harmonised rules for calculating the carbon footprint of PV modules and then the present 
report is the result of extensive exchange with several involved stakeholders.  

All feedback received regarding the proposed method have been analysed and considered.  

As such, the present report represents the outcome of a consensus building process based on 
various position from different stakeholder received at different stages of the development of the 
method. 

Specifically, a first stakeholder consultation has been organised within the “Consultation Forum 
Meeting on potential Ecodesign and Energy labelling requirements for photovoltaic modules, 
inverters and systems”, which was held on June 27th, 2022. A second consultation on “Technical 
stakeholder meeting PV module carbon footprint calculation rules and related conformity 
assessment procedure” was on March 30th, 2023. The Annex A attached to this report represents 
the version shared with stakeholders on the second consultation. 

These two stakeholder consultations were focused on the following topics: 

—  Description of the product group/policy framework;  

—  PV module carbon footprint calculation rules: 

o  scope,  

o 

functional unit,  

o  system boundaries,  

o  use of company-specific activity data, 

o  modelling requirements (electricity and recycled content and material recycling) 

—  Carbon footprint requirements;  

—  Comparison with other existing scheme (e.g. the FR case study); 

—  International standards on carbon footprint;  

—  Online tool for the PV module carbon footprint calculation. 

Following the second stakeholder consultation, a total number of 80 written comments were 
received by 6 commenting stakeholders, including public and private entities. Below there is an 
overview of the macro-category topics for which the presented harmonised rules for PV carbon 
footprint received comments and feedback from stakeholders: 

—  Circular Footprint Formula 

—  Conformity Assessment 

—  Data quality 

—  Definition 

—  Electricity modelling 

—  Energy Return on Investment 

—  PEFCR and alignments 

—  Kerf Recycling 

—  Reference flow 

55 

 
 
 
—  Scope of the regulation 

—  System boundaries 

—  Transport 

—  Verification 

In particular, feedback received on circular footprint formula have reflected in changes to the 
default values proposed for R2 of certain materials (e.g. aluminium); comments on data quality 
have been reflected in some default activity data proposed in the life cycle inventories (e.g. 
electricity consumption for manufacturing the silicon wafers); also, the data need matrix have been 
omitted and the electricity modelling approach has been revised by proposing two alternative 
scenarios.  

56 

 
 
 
 
7.  Support to a PV dedicated tool for calculating carbon footprint 

The development of a potential dedicated PV tool for calculating carbon footprint of PV panels has 
been identified as a strategy to facilitate the calculation by operators, based on the harmonized 
method described in the previous chapters. On such purpose, EC launched an external service 
contract to develop a dedicated PV tool, having JRC provided technical support. Currently, this 
dedicated PV tool is under development and a beta version is expected to be distributed in the short 
term for stakeholder checking. The JRC followed the development of the tool through meetings, 
delivery of information and data, and internal testing of the beta version. The main activities are 
summarized in this section.  

Several meetings, exchanges and analyses have been scheduled and conducted for monitoring and 
supporting the tool development.  

As a starting point, JRC provided IT tool developers with the revised Excel spreadsheet14 containing 
the life cycle inventories of the PV panel under analysis (including default datasets to be used, 
activity data, elementary and emission flows, mapping of datasets from EF 2.0 database to EF 3.1 
database). It was also provided guidance on how to implement the proposed method into the IT 
tool.  

Technical support has been given also in reviewing the deliverables provided by the IT tool 
developers (including the i) inception report and ii) interim report). In particular, the revision of the 
inception report was focused on:  

—  adding details on the differences between mandatory and non-mandatory processes for 

which rules are different;  

—  adding the data quality rating assessment for default secondary datasets;  

—  general alignment of nomenclatures and rules with the harmonized methodology;  

—  import of EF compliant datasets;  

—  adaptability of the tool to future reference packages; 

—  confidentiality  of  activity  data  and  company-specific  datasets  created  or  modified; 

inclusion of CFF parameters into the implementation;  

—  alignment with PV levels (e.g. panel, cell, Cz ingot, silicon wafer, metallurgic grade silicon).  

Preliminary results of a representative product calculated within the tool have been revised 
according to the carbon footprint method proposed in this report. Assumptions (e.g. lifetime, 
degradation, geographical location) behind results have been checked and refined as well as a 
preliminary comparison between various products (PV technologies) and different geographical 
scenarios. A contribution analysis of processes and materials contributing to the carbon footprint 
has been reviewed as well as a sensitivity analysis of electricity, glass content, aluminium content, 
and silicon content. In this context, silicon content was identified as a factor dominating results 
about the carbon footprint of silicon-based PV panels.  

Tests have been conducted on different beta versions of the IT tool for aligning the technical 
features. JRC analysis was focused on key aspects as: 

—  Information required 

14 https://ec.europa.eu/docsroom/documents/46532 

57 

 
 
 
 
 
—  Unit 

—  Nomenclature 

—  Electricity 

—  Dataset 

—  Geographical location 

—  Data Quality Rating 

—  Methodology 

—  Carbon footprint results 

—  Interface of the tool 

As for the information requirement, it has been highlighted the relevance of having an alignment of 
the field to be compiled by the user with the methodological rule, clarifying what information is 
needed and how the tool use it.  

In terms of units of measure, it has been suggested of aligning them with the definitions of 
reference flow and functional unit. The nomenclature used in the tool should be coherent with the 
methodological terms (e.g. for what concerns CFF parameters such as R1 and R2) as well as 
electricity choices and default datasets to be used.  

Other relevant methodological aspects were identified by JRC, as: 

—  Geographical  differences (e.g. a material  produced in Europe  or extra EU  countries) of 

default datasets should be visible allowing the users a proper selection of them. 

—  For the DQR evaluation, the IT tool should allow the assessment of the representativeness 
of the default datasets (e.g. GeR and TeR), as described in the methodological rules. 

—  The  IT  tool  should  allow  the  calculation  of  carbon  footprint  results  in  line  with  the 
harmonized methodology  using  kgCO2  eq/kWh  as  functional  unit  of  the  PV  panel  under 
analysis. Other environmental indicators that the tool may calculate are optional and not 
essential for the aim of the tool which remain the carbon footprint evaluation.  

—  Results should be possible to be exported in a Word report and in an Excel spreadsheet.  

—  The  IT  tool  interface  should  be  user  friendly  for  understanding  properly  the  correct 

function to be selected from the user. 

58 

 
 
 
 
 
8.  Conclusions 

Among all the methods and standard that can be applied to calculate the carbon footprint, there is 
a need of harmonizing the carbon footprint specifically of PV products at the European level.  

This science for policy report discusses a proposal of harmonized rules for accounting the carbon 
footprint of PV panels potentially applicable in the context of the EU Ecodesign Directive. 

Overall, these rules have been elaborated based on the Environmental Footprint method and uses 
as pillar the previous Product Environmental Footprint Category Rules of PV with adaptations for 
policy purpose that are explained and discussed in this report. The reasoning behind the main 
methodological and technical assumptions are presented in the report as well as some open points 
to be further addressed. 

In particular, the scope has been adjusted compared to PV PEFCR with the aim of including 
emerging and innovative PV technologies based on the current market share.  

According to this, life-cycle inventories of the PV technologies under analysis (mono-crystalline 
silicon, multi-crystalline silicon, cadmium telluride) have been adapted, including the associated 
default secondary datasets that are provided (e.g. for materials, energy, transports). The associated 
activity data have been updated in terms of electricity consumption of key processes. 

Moreover, applying the harmonized rules proposed and discussed in this report, carbon footprint 
results of PV panels have been calculated among different scenarios illustrated in this report (e.g. of 
production, various silicon content, electricity yield, recycled content, and operational lifetime). 

The primary contributors to the carbon footprint of mono- and multi-crystalline silicon PV panels 
include: 

—  electricity for manufacturing the silicon, 

—  silicon content  

—  aluminium frame,  

—  glass.  

The product lifetime plays a significant role in terms of the carbon footprint per kilowatt-hour.  

Inputs from stakeholder consultations has been considered during the elaboration of these 
harmonised rules for calculating the carbon footprint of PV modules. This report may be considered 
as an output built upon a large consensus building process based on various position from different 
stakeholder received at various stages of the development of the method. 

Finally, it is described the support provided to the realisation of a dedicated IT online tool for 
calculating the carbon footprint of PV panels, based on the harmonized rules proposed.  

Future research could also focus on extending current rules to other relevant impact categories 
beyond carbon footprint - including for example. resource use (fossils), resource use (minerals and 
metals) acidification and particulate matter. Finally, the proposed method should be adapted and 
extended to innovative PV technologies (e.g. single-junction perovskite panels) that will be 
potentially scaled up from laboratory to commercial scale as well as on existing PV panel 
technologies with technological improvements in their manufacturing (e.g. updated life cycle 
inventories). 

59 

 
 
 
 
References  

A. Mueller, L. Friedrich, C. Reichel, S. Herceg, M. Mittag, D. Holger Neuhaus A comparative life cycle 
assessment of silicon PV modules: Impact of module design, manufacturing location and inventory 
Solar Energy Materials and Solar Cells, 230 (2021), 10.1016/j.solmat.2021.111277. 

Chatzipanagi, A., Jaeger-Waldau, A., Letout, S., Mountraki, A., Gea Bermudez, J., Georgakaki, A., Ince, 
E. and Schmitz, A., Clean Energy Technology Observatory: Photovoltaics in the European Union - 
2024 Status Report on Technology Development, Trends, Value Chains and Markets, Publications 
Office of the European Union, Luxembourg, 2024, doi:10.2760/1812909, JRC139297. 

Commission de régulation de l’énergie (2021), Délibération de la Commission de régulation de 
l’énergie du 17 juin 2021 portant avis relatif aux sept projets de cahiers des charges d’appels 
d’offres pour le soutien à la production d’électricité d’origine renouvelable pour la période 
2021/2026, www.actu-environnement.com/media/pdf/news-38003-cre-deliberation-cahiers-des-
charges-2021-2026.pdf. 

Energy Global, Issue Spring 2022, www.energyglobal.com/magazine/energy-global/march-2022/. 
Retrieved 12 June 2022. 

European Commission (2019), Communication from the Commission – The European Green Deal, 
COM/2019/ 640 final of 11 December 2019. 

European Commission (2020), Communication from the Commission to the European Parliament, 
the Council, the European Economic and Social Committee and the Committee of the Regions – 
Stepping up Europe’s 2030 climate ambition: Investing in a climate-neutral future for the benefit of 
our people, COM(2020) 562 final of 17 September 2020. 

European Commission, Energy-efficient products, https://ec.europa.eu/info/energy-climate-change-
environment/standards-tools-and-labels/products-labelling-rules-and-requirements/energy-label-
and-ecodesign/energy-efficient-products_en European Commission (2016), Communication from 
the Commission – Ecodesign Working Plan 2016-2019. COM(2016) 773 final of 30 November 
2016. 

European Union (2009), Directive 2009/125/EC of the European Parliament and of the Council of 21 
October 2009 establishing a framework for the setting of ecodesign requirements for energy-
related products, OJ L 285, 31.10.2009, pp. 10–35. 

European Union (2017), Regulation (EU) 2017/1369 of the European Parliament and of the Council 
of 4 July 2017 setting a framework for energy labelling and repealing Directive 2010/30/EU, OJ 
L198, 28.7.2017, pp. 1–23. 

Fthenakis, V. and Leccisi, E., 2021. Updated sustainability status of crystalline silicon‐based 
photovoltaic systems: Life‐cycle energy and environmental impact reduction trends. Progress in 
Photovoltaics: Research and Applications, 29(10), pp.1068-1077. 

Korean Ministry of Economy and Finance (2021). Government Announces Korean New Deal 2.0, July 
14, 2021. https://english.moef.go.kr/pc/selectTbPressCenterDtl.do?boardCd=N0001&seq=5173. 

Leccisi, E. and Fthenakis, V., 2021. Life cycle energy demand and carbon emissions of scalable 
single‐junction and tandem perovskite PV. Progress in Photovoltaics: Research and Applications, 
29(10), pp.1078-1092. 

Leccisi, E., Raugei, M. and Fthenakis, V., 2016. The energy and environmental performance of 
ground-mounted photovoltaic systems—a timely update. Energies, 9(8), p.622. 

60 

 
 
 
N. Dodd, N. Espinosa, P. van Tichelen, K. Peeters, A. Soares. Preparatory study for solar photovoltaic 
modules, inverters and systems, EUR 30468 EN. Publications Office of the European Union, 
Luxembourg, Science for Policy (2020). 

PEFCR PV Technical Secretariat (2019), Product Environmental Footprint Category Rules (PEFCR) 
(2019): Photovoltaic Modules used in Photovoltaic Power Systems for Electricity Generation, 
PEFCR_PV_electricity_feb2020_2.pdf Polverini, D., Espinosa, N., Eynard, U., Leccisi, E., Ardente, F. and 
Mathieux, F., 2023. Assessing the carbon footprint of photovoltaic modules through the EU 
Ecodesign Directive. Solar Energy, 257, pp.1-9. 

R. Frischknecht, P. Stolz, L. Krebs, M. de Wild-Scholten, P. Sinha, V. Fthenakis, H. C. Kim, M. Raugei, M. 
Stucki, 2020, Life Cycle Inventories and Life Cycle Assessment of Photovoltaic Systems, 
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61 

 
 
 
 
 
List of abbreviations  

CFF 

 Circular Footprint Formula 

CdTe         Cadmium Telluride  

CI(G)S       Copper-Indium-Gallium-Selenide  

DNM 

Data Needs Matrix 

DQR 

Data Quality Rating 

EF 

EoL 

Environmental Footprint 

End-of-life 

FU            Functional Unit 

GeR          Geographical Representativeness 

ILCD         International Life-cycle Data 

JRC          Joint Research Centre 

kWh          Kilowatt Hour 

LCA           Life Cycle Assessment 

LCI 

 Life-cycle Inventory 

LCIA 

    Life-cycle Impact Assessment 

LCDN  

  Life-cycle Data Network 

LT              Lifetime 

micro-Si     Micromorphous Silicon  

mono-Si     Mono-crystalline Silicon  

multi-Si      Multi-crystalline Silicon  

PEF           Product Environmental Footprint  

PEFCR 

Product Environmental Footprint Category Rules 

PV 

Photovoltaic 

RF            Reference Flow 

RP            Representative Product 

SB            System Boundary 

TeR           Technological Representativeness 

62 

 
 
 
 
 
 
List of figures 

Figure 1 Flow chart of the method summary for regulatory intervention. .............................................................................. 12 

Figure 2 System diagram for the PV technologies included in scope (Adapted from PV PEFCR). ............................. 18 

Figure 3 Flow diagram of the supply chain for the PV technologies included in scope. (Adapted from PV 
PEFCR). ................................................................................................................................................................................................................................... 19 

Figure 4: CFF parameters. .......................................................................................................................................................................................... 29 

63 

 
 
 
 
List of tables 

Table 1 Non-exhaustive list of standards, documents and guidelines that can be used for calculating the 
carbon footprint of photovoltaics. ........................................................................................................................................................................ 11 

Table 2 Main aspects considered for adapting the PV PEFCR for the purpose of using it for harmonized rules 
for calculating carbon footprint. ............................................................................................................................................................................ 16 

Table 3 List of processes for which the use of company-specific activity data is mandatory. .................................. 21 

Table 4 Evaluation of the DQR criteria. ........................................................................................................................................................... 24 

Table 5 Default distances for transport of raw materials and intermediate products. ................................................... 26 

Table 6 Update of silicon production mixes (JRC elaboration: adapted from PEFCR PV and based on IEA PVPS 
Task 12 2020)................................................................................................................................................................................................................... 36 

Table 7. Updated activity data for electricity to be implemented in the modelling and in the Excel file. ............ 38 

Table 8 Summary carbon footprint results of all mono-crystalline photovoltaic panel scenarios. .......................... 41 

Table 9 Summary carbon footprint results of all multi-crystalline photovoltaic panels scenarios. ......................... 44 

Table 10 Use of proxies. ........................................................................................................................................................................................... 47 

Table 11 Amount of silicon per area. ............................................................................................................................................................... 49 

Table 12 Example of Carbon footprint of mono Si photovoltaic panel per life cycle stage: Si 0.58 kg per m2.50 

Table 13 Carbon footprint of Monocrystalline silicon photovoltaic Panel process, Scenario 3. .................................. 51 

Table 14 Carbon footprint of Monocrystalline silicon photovoltaic Cell process. ................................................................. 52 

Table 15 Carbon footprint of mono-crystalline silicon photovoltaic panels: single Crystalline process................ 53 

64 

 
 
 
 
Annex A – “Harmonised rules for the calculation of the carbon footprint of 
photovoltaic modules” version of 30 March 2023 

WORKING DOCUMENT ON PV carbon footprint 

ANNEX IV 

Harmonised rules for the calculation of the carbon footprint of photovoltaic 
modules 

Part 1 – Harmonised calculation rules 

1. Definitions  

For the purposes of this Annex, the following definitions shall apply: 

(a)  ‘Activity data’ means the information associated with processes while modelling Life 
Cycle  Inventories  (LCI).  The  aggregated  LCI  results  of  the  process  chains  that 
represent the activities of a process are each multiplied by the corresponding activity 
data and then combined to derive the carbon footprint associated with that process; 

(b)  ‘Aggregated dataset’ means life cycle inventory (LCI) of multiple unit processes (e.g. 
material or energy production) or life cycle stages (cradle-to-gate), but for which the 
inputs and outputs are provided only at the aggregated level. Aggregated datasets 
are also called "LCI results", “cumulative inventory” or “system processes” datasets; 

(c)  ‘Bill  of  materials’  means  list  of  the  materials,  sub-assemblies,  intermediate 
assemblies,  sub-components,  parts  and  the  quantities  of  each  needed  to 
manufacture the product in scope of the study; 

(d)  ‘carbon footprint’ (CF) means the sum of greenhouse gas (GHG) emissions and GHG 
removals  in  a  product  system,  expressed  as  carbon  dioxide  (CO2)  equivalents  and 
based  on  a  Product  Environmental  Footprint[1]  (PEF)  study  using  the  single  impact 
category of climate change; 

(e)  Company-specific information: it covers all kind of information (including company 
specific data) the applicant may have regarding either processes, materials or flows 
occurring during the phases under control of the company and along the supply chain 
that  can  have  an  impact  on  the  representativeness  of  the  dataset  (geographical, 
technological, time representativeness); 

(f)  ‘Company-specific data’ refers to directly measured or collected data from one or 
multiple facilities (site-specific data) that are representative for the activities of the 
company. It includes company-specific activity data and direct elementary flows. It is 
synonymous  to  'primary  data';  or  “supply-chain  specific  data”  or  “manufacturer-
specific” data;; 

(g)  ‘Functional  unit’  means  the  qualitative  and  quantitative  aspects  of  the  function(s) 

and/or service(s) provided by the product being evaluated; 

(h)  ‘Life cycle’ means the consecutive and interlinked stages of a product system, from 
raw material acquisition or generation from natural resources to final disposal; 

(i)  ‘Life  cycle  inventory  (LCI)’  means  the  combined  set  of  exchanges  of  elementary, 

waste and product flows in a LCI dataset; 

65 

 
 
 
 
(j)  ‘Life  cycle  inventory  (LCI)  dataset’  means  a  document  or  file  with  life  cycle 
information  of  a  specified  product  or  other  reference  (e.g.,  site,  process),  covering 
descriptive metadata and quantitative life cycle inventory. A LCI dataset could be a 
unit process dataset, partially disaggregated or an aggregated dataset; 

(k)  ‘Partially disaggregated dataset’ a dataset with a LCI that contains elementary flows 
and  activity  data,  and  that  only in  combination  with  its  complementing  underlying 
datasets yield a complete aggregated LCI data set 

(l)  ‘Reference flow’ means the measure of the outputs from processes in a given product 

system required to fulfil the function expressed by the functional unit; 

(m) ‘Secondary data’ means data not from a specific process within the supply-chain of 
the  company  performing  a  carbon  footprint  study.  This  refers  to  data  that  is  not 
directly collected, measured, or estimated by the company, but sourced from a third 
party LCI database or other sources. Secondary data includes industry average data 
(e.g.,  from  published  production  data,  government  statistics,  and 
industry 
associations),  literature  studies,  engineering  studies  and patents,  and  may  also  be 
based on financial data, and contain proxy data, and other generic data; 

(n)  ‘System boundary’ means the aspects included or excluded from the life cycle study; 

(o)  ‘Circular Footprint Formula’ (CFF) describes how burdens and benefits from disposal 
and recovery of the product or service assessed as well as use of secondary materials 
(i.e. recycled content) into that product or service are allocated to the system under 
study; 

(p)  ‘Data  Needs  Matrix’  (DNM)  describes  the  requirements  for  the  use  of  company 
specific data and secondary data, depending on the level of influence the applicant 
has on the processes along the value chain; 

(q)  ‘Manufacturer’ producer of the PV modules; 

(r)  ‘Unit process’ is the smallest element considered in the LCI for which input and output 

data are quantified (based on ISO 14040:2006); 

(s)  ‘Tracking  system’  (electricity)  means  a  system  applying  the  process  of  assigning 

electricity generation attributes to electricity consumption; 

(t)  ‘Regional storage’ physical place, located in the EU, where PV panels are stored before 

they are transported to the place of installation; 

(u)  “TiR “ is the Time Representativeness;  

(v)  “TeR” is the Technology representativeness;  

(w) “GeR” is Geographical Representativeness. 

2. Scope  

This Annex provides harmonised rules on how to calculate the carbon footprint of photovoltaic 
modules. It also applies to bifacial photovoltaic modules.  

The calculation of the carbon footprint shall be based on the bill of material, the energy, and 
auxiliary materials used to produce a specific photovoltaic module model. In particular, the 
photovoltaic cells, the glass, the frame materials and the electronic components (e.g. junction boxes, 
cabling) have to be accurately identified, for the specific product l, as they may become a relevant 
contributor to the carbon footprint of a photovoltaic module. 

The bill of materials shall be based on company-specific data for the specific product model that 
cover a time period of at least one year (12 months). If 12 month data are not available, shorter 

66 

 
 
 
periods and/or product design and pilot scale data may be used. In the latter situation, the carbon 
footprint calculation shall be updated once data covering 12 month production become available. 

The material losses during module manufacturing shall be taken into account.  

If the photovoltaic module is supplied without frame, and the photovoltaic module does not require 
any frame for being installed, no frame shall be accounted in the carbon footprint calculation. 

3. Functional unit and reference flow  

The functional unit is defined as one kWh (kilowatt-hour) of the total DC electric energy generated 
over a photovoltaic module’s service life. The total DC electric energy generated over a module’s 
service life is calculated according to Annex III, point 4.3 under ‘temperate continental’ climate 
conditions.  

The reference flow is the amount of product needed to fulfil the defined function and shall be 
measured in m2 of photovoltaic module per kWh of the total energy required by the application over 
its service life. All quantitative input and output data collected by the manufacturer to quantify the 
carbon footprint shall be calculated in relation to this reference flow.  

4. System boundary  

The following life cycle stages and processes of the PV modules shall be included in the system 
boundary:  

Life cycle stage  

Raw material acquisition, pre-processing 
and module manufacturing  

Distribution 

Short description of the processes 
included  

Includes mining and pre-processing, up to 
the manufacturing of silicon ingot, wafers, 
photovoltaic cells and the supply chain of 
electric/electronic components and other 
components such glass, silver, frame and 
encapsulant materials.  

Assembly of photovoltaic cells and assembly 
of modules with the frame (in case) and the 
electric/electronic components.  

Transportation of PV modules from 
manufacturing plants to a regional storage 
located in the EU (to be identified and 
justified). 

Manufacturing of equipment (capital goods) for modules assembly and recycling shall be excluded 
from the assessment. 

All other processes belonging to the subsequent life cycle stages, shall be excluded from the 
lifecycle calculations (i.e., distribution to the final consumer, assembly of the system, use and 
disposal, dismantling and recycling of the photovoltaic modules. Benefits and burdens of recycled 

67 

 
 
 
 
 
  
 
materials in input and of main materials recycled at the end of life are considered in the application 
of the CFF to the module production. 

The manufacturing of photovoltaic modules shall cover raw material extraction to wafer, cell and 
module production in case of crystalline silicon modules, the supply chain of semiconductors 
(micromorphous silicon, cadmium sulphide, cadmium telluride, gallium and other materials used in 
thin film technologies) in case of thin film modules, and the supply chain of carrier and connection 
materials (such as glass, silver, junction box and frame) in case of all modules. A more detailed 
depiction of the supply chain of mono-Si, multi-Si, micro-Si, CdTe and CIS / CIGS PV modules is 
shown in Fig. 1 and Fig. 2. 

Figure 1 – System diagram for the PV technologies included in scope. The supply chain of the individual PV 
technologies is shown in more detail in Fig. 2. 

Figure 2 – Excerpt of the supply chain for the PV technologies included in scope. 

According to this Annex, no cut-off is applicable to the processes involved in the system diagrams 
of Fig. 1 and 2, beyond the capital goods identified above. 

68 

 
 
 
 
 
 
 
 
5. Use of company specific activity data 

5.1 Mandatory company specific activity data  

The manufacturer shall use company specific data for the processes listed in table 1A and further 
detailed in Part 2 of this Annex. 

Secondary datasets are provided by default in this Annex to model the product (hereinafter they are 
defined as “default secondary dataset”).  

When the manufacturer is willing to use default secondary datasets (as listed in the spreadsheet 
named “CF_Annex_PV_modules-Life_cycle_inventory”[2]), their data quality criteria need to be 
reassessed, based on the rules in the EC recommendation 2021/2279 (Annex I, section 4.6.5.3). If 
the reassessed data quality criteria of the default secondary dataset are representative for the 
manufacturer (i.e. TiR≤2 or TeR≤2 or GR≤2), the default secondary dataset may be combined with 
company-specific activity data. 

If the reassessed quality of the dataset is not representative (TiR>2 or TeR>2 or GR>2) of the 
process, the manufacturer shall adapt the dataset to the manufacturing context (e.g. to what 
concerns electricity consumption) and recalculate the DQR following the rules in the EC 
recommendation 2021/2279 (Annex I, section 4.6.5.3). If the recalculated DQR is higher than 3, the 
manufacturer shall develop an EF compliant dataset following the Guide for EF compliant 
datasets[3]. 

If a default secondary dataset is not available (e.g. innovative materials) the manufacturer may use 
a proxy dataset (selected from the list “CF_Annex_PV_modules-Life_cycle_inventory8), if after 
reassessing its quality (TiR, TeR, GR) the recalculated DQR of the dataset is ≤3. 

If not, i.e. the recalculated DQR of the proxy dataset is higher than 3, the manufacturer shall 
develop an EF compliant dataset following the Guide for EF compliant datasets. To develop an EF 
compliant dataset, selection of appropriate datasets shall follow the hierarchy described in the 
spreadsheet “CF_Annex_PV_modules-Life_cycle_inventory8” sheet Data sources. 

Table 1A – List of processes (for each technology and life cycle stage) for which the use of company-specific 
activity data is mandatory. 

Technology 

Life cycle stage 

Process 

Cadmium-Telluride PV modules 

Copper-Indium-Gallium-Selenide 
PV modules 

Raw materials acquisition, pre-
processing and PV module 
manufacturing 

Distribution 

Raw materials acquisition, pre-
processing and PV module 
manufacturing 

Raw materials acquisition, pre-
processing and PV module 
manufacturing 

Distribution 

69 

Photovoltaic laminate, CdTe, at 
plant 

Photovoltaic laminate, CdTe, at 
regional storage 

Photovoltaic laminate, CIS, at 
plant 

Photovoltaic panel, CIS, at plant 

Photovoltaic panel, CIS, at 
regional storage 

 
 
 
 
Technology 

Life cycle stage 

Process 

Micromorphous Silicon PV 
modules 

Multicrystalline Silicon PV 
modules 

Monocrystalline Silicon PV 
modules 

Raw materials acquisition, pre-
processing and PV module 
manufacturing 

Distribution 

Raw materials acquisition, pre-
processing and PV module 
manufacturing 

Raw materials acquisition, pre-
processing and PV module 
manufacturing 

Distribution 

Raw materials acquisition, pre-
processing and PV module 
manufacturing 

Raw materials acquisition, pre-
processing and PV module 
manufacturing 

Distribution 

Photovoltaic panel, micro-Si, at 
plant 

Photovoltaic panel, micro-Si, at 
regional storage 

Photovoltaic cell, multi-Si, at 
plant 

Photovoltaic panel, multi-Si, at 
plant 

Photovoltaic panel, multi-Si, at 
regional storage 

Photovoltaic cell, mono-Si, at 
plant 

Photovoltaic panel, mono-Si, at 
plant 

Photovoltaic panel, mono-Si, at 
regional storage 

5.2 Non-mandatory company specific activity data 

For non-mandatory process the manufacturer shall apply the data need matrix (Section 7.2). 

6. Life cycle stages 

Raw material acquisition, pre-processing and module manufacturing 

This life cycle stage includes raw material acquisition and pre-processing, as well as the 
manufacturing of the photovoltaic modules. The supply chain of the modules shall include the 
production of the modules, the cells and wafers (if applicable) and the supply chain of the materials 
required in the module and in manufacturing (such as working materials and process gases, energy 
carriers), including raw material extraction and refining towards the directly employed material. The 
supply chain of the frame (if applicable), shall similarly include raw material extraction and refining. 

The processes taking place in the life cycle stage raw material acquisition and pre-processing, the 
inputs and outputs as well as the default secondary datasets to be used are listed in the file named 
“CF_Annex_PV_modules-Life_cycle inventory8, sheet “Raw-Materials&Pre-Processing”. Processes 
that are expected to be run by the company are written in capital letters (see column A in the 
spreadsheet).  

Transport of raw materials and intermediate products 

Transport of raw materials and intermediate products to the production site shall be included in this 
life-cycle stage. 

70 

 
 
 
 
 
Table 2B shows the default transport distances by train and lorry (lorry >32 t, EURO 4) for some 
frequently used raw materials and intermediate products. These default values may also be used to 
estimate default transport distances for similar products required in the supply chain of PV 
modules. The default transport distances shown in Table 2B shall be used in case company-specific 
information is not available. For transports by lorry, a default utilization ratio of 64 % shall be used 
if specific data are not available. This utilization ratio includes empty return trips. 

For suppliers located outside Europe, the default transport scenario described in the file named 
“CF_Annex_PV_modules-Life_cycle_inventory8”, sheet Transport-Scenarios should be used. This 
scenario includes the transport of raw materials or intermediate products between the harbour or 
airport and the factories in and outside Europe, which is estimated to 1’000 km by lorry (>32 t, 
EURO 4). The intercontinental transport to Europe occurs either by transoceanic container ship 
(18’000 km) or by cargo airplane (10’000 km). If the location of the supplier is known, specific data 
may be used to calculate the transport distances and modelling the applicable transport modes to 
the production site. 

Air cargo shipping of semi-finished products such as wafers and cells shall be included according to 
its share in supply logistics in a three years period. 

Table 2B – Default distances for transport of raw materials and intermediate products 

71 

km trainkm lorry 32tgravel/sand2000-50steel/cast iron7900200100copper8900200100aluminium2700200100PVC1400200100PE950200100PP900200100particle board68020050caustic soda1045600100soda (sodium carbonate2532600100hydrochloric acid909200100sulphuric acid1840600100nitric acid1383600100phosphoric acid1685600100hydrofluoric acid993600100ethylene600100naphta600100refrigerants600100organ. Solvents600100oxygen10050nitrogen10050hydrogen10050helium10050gases (if not produced on the spot) if bought in cylinders: doubling of transport distances (due to tare weight)Density [kg/m3]consumption in Europemineral productsmetalsplasticswood productsbasic chemicals, inorganic (carrier substance to be considered additionally)Basic chemicals, organic 
 
 
 
Packaging materials 

The use and disposal of packaging materials shall be considered for the entire product system and 
modelled as part of the raw material acquisition stage. The raw material consumption of reusable 
packaging shall be calculated by dividing the actual weight of the packaging by the reuse rate. The 
reuse rate affects the quantity of transport that is needed per functional unit. The transport impact 
shall be calculated by dividing the one-way trip impact by the number of times this packaging is 
reused. 

For reusable packaging, the default reuse rates provided below shall be used, unless data of better 
quality are available: 

—  Plastic pallets: 50 trips  

—  Wooden pallets: 25 trips  

Modelling recycled content and materials recycling 

The recycled content of materials used in PV modules as well as their recycling potential at the end-
of-life shall be modelled according to the following formula: 

(1 −   𝑅1) ∙ 𝐸𝑣 +   𝑅1   ∙   (𝐴𝐸𝑟𝑒𝑐𝑦𝑐𝑙𝑒𝑑 +   (1 −  𝐴) ∙ 𝐸𝑣   ∙  

𝑄𝑆𝑖𝑛
𝑄𝑝

) + (1 − 𝐴) ∙ 𝑅2 ∙ (𝐸𝑟𝑒𝑐𝑦𝑐𝑙𝑖𝑛𝑔𝐸𝑜𝐿 − 𝐸𝑣

∗   ∙  

𝑄𝑆𝑜𝑢𝑡
𝑄𝑝

) 

A: allocation factor of burdens and credits between supplier and user of recycled materials. 

R1: it is the proportion of material in the input to the production that has been recycled from a 
previous system. 

R2: it is the proportion of the material in the product that will be recycled (or reused) in a 
subsequent system. R2 shall therefore take into account the inefficiencies in the collection and 
recycling (or reuse) processes. R2 shall be measured at the output of the recycling plant 

Ev: specific emissions and resources consumed (per functional unit) arising from the acquisition 
and pre-processing of virgin material. 

Erecycled (Erec): specific emissions and resources consumed (per functional unit) arising from the 
recycling process of the recycled (reused) material, including collection, sorting and transportation 
process. 

ErecyclingEoL (ErecEoL): specific emissions and resources consumed (per functional unit) arising from the 
recycling process at EoL, including collection, sorting and transportation process. 

v: specific emissions and resources consumed (per functional unit) arising from the acquisition and 

E*
pre-processing of virgin material assumed to be substituted by recyclable materials. 

Qsin: quality of the ingoing secondary material, i.e. the quality of the recycled material at the point 
of substitution. 

QSout: quality of the outgoing secondary material, i.e. the quality of the recyclable material at the 
point of substitution. 

Qp: quality of the primary material, i.e. quality of the virgin material. 

72 

 
 
 
  
  
  
The R1 values applied shall be supply-chain specific or default ones. Material-specific values based 
on supply market statistics are not accepted as a proxy. The applied R1 values shall be subject to 
verification. 

When using supply-chain specific R1 values other than 0, traceability throughout the supply chain is 
necessary. The following general guidelines shall be followed when using supply-chain specific R1 
values: 

—  The supplier information (through e.g., statement of conformity or delivery note) shall be 

maintained during all stages of production and delivery at the converter. 

—  Once the material is delivered to the converter for production of the end products, the 
converter shall handle information through their regular administrative procedures. 

—  The  converter  for  production  of  the  end  products  claiming  recycled  content  shall 
demonstrate through his management system the [%] of recycled input material into the 
respective end product(s). 

—  The latter demonstration shall be transferred upon request to the user of the end product. 

—  Company-owned traceability systems can be applied as long as they cover the general 

guidelines outlined above. 

The default parameter values for applying the CFF and the default values for R1 and R2 are listed 
in the spreadsheet “CF_Annex_PV_modules-Life_cycle_inventory8”, sheet CFF-parameters. For all 
materials not listed in the sheet CFF-parameters, it is assumed that R2=0. 

Distribution 

The transportation of photovoltaic modules from factory to EU borders, shall be modelled within 
this life cycle stage. The distance and mass transported shall be characterised with company-
specific data. For producers located in EU Member States, this step is not applicable 

The transport from regional storage to the photovoltaic power system where the photovoltaic 
modules are installed is excluded from the system boundary. If photovoltaic modules are produced 
in several production sites, the share of each facility in the European supply mix shall be accounted 
for in the life cycle stage distribution. 

The processes taking place in the life cycle stage distribution, the inputs and outputs as well as the 
default datasets are listed in the spreadsheet named “CF_Annex_PV_modules-
Life_cycle_inventory8”, sheet “Distribution”. Processes that are expected to be run by the company 
applying the Annex are written in capital letters (see column A in the spreadsheet).  

7. Other modelling requirements 

7.1 Sampling 

Sampling is allowed in case of a high number of production sites are involved in the production of 
the module for which the carbon footprint is calculated. In case sampling used to collect data from 
different production sites, different sub-populations shall be identified by considering the 
geographical distribution, the technologies applied and the production capacity of all facilities. The 
number of sub-populations (Nsp) shall be identified as:  

Nsp=g∙t∙c 

where g is the number of countries in which the plants are located, t is the number of technologies 
and c is the number of classes of plant capacity.  

73 

 
 
 
Once the sub-populations have been identified, for each sub-population the size of sample shall be 
calculated (the sub-sample size) based on the total production of the sub-population. The total 
production shall represent the product under analysis (e.g. total amount of multi-Si PV cells or 
modules produced, measured in m2). The percentage of production to be covered by each sub-
population shall not be lower than 75 %. The data collected from different production sites shall be 
weighted based on the production volume of each facility.  

The company applying this Annex shall describe the sub-populations and report the percentage of 
the total production that is covered by the collected samples. 

7.2 Data needs matrix (DNM) 

All processes required to model the product that are not included in the list of  processes for which 
company-specific activity data are mandatory (Table 1A listed in section 5.1) shall be evaluated 
using the Data Needs Matrix (see Table 3). The following three situations are found in the DNM and 
are explained below: 

Situation 1: the process is run by the company applying this Annex 
Situation 2: the process is not run by the company applying this Annex but the company 
has access to (company-)specific information. 
Situation 3: the process is not run by the company applying this Annex and this company 
does not have access to (company-)specific information. 

Table 3 – Data Needs Matrix. 

Situation 

Requirement 

Situation 1: process run by the company applying 
this Annex 

When the manufacturer is willing to use default 
secondary datasets (listed in the spreadsheet 
named “CF_Annex_PV_modules-
Life_cycle_inventory”), their data quality criteria 
need to be reassessed, based on the rules in the EC 
recommendation 2021/2279 (Annex I, section 
4.6.5.3). 

If the reassessed data quality criteria of the default 
secondary dataset are representative for the 
manufacturer (i.e. TiR≤2 or TeR≤2 or GR≤2), the 
dataset may be combined with company-specific 
activity data. 

If the recalculated quality of the default dataset is 
not representative (TiR>2 or TeR>2 or GR>2) of the 
process, the manufacturer shall adapt the dataset 
to the manufacturing context (e.g. to what concerns 
electricity consumption) and recalculate the DQR 
following the rules in the EC recommendation 
2021/2279 (Annex I, section 4.6.5.3). If the 
recalculated DQR is higher than 3, the manufacturer 
shall develop an EF compliant dataset following the 
Guide for EF compliant datasets. 

If a default secondary dataset is not available (e.g. 
innovative materials) the manufacturer may use a 
proxy dataset (selected from the list 
“CF_Annex_PV_modules-Life_cycle_inventory), if 
after reassessing its quality (TiR, TeR, GR) the 
recalculated DQR is ≤3. 

74 

 
 
 
Situation 

Requirement 

Situation 2: process not run by the company 
applying this Annex but with access to company-
specific information 

If not, (i.e. the recalculated DQR of the proxy 
dataset is higher than 3, the manufacturer shall 
develop an EF compliant primary dataset following 
the Guide for EF compliant datasets. 

When the manufacturer is willing to use default 
secondary datasets (listed in the spreadsheet 
named “CF_Annex_PV_modules-
Life_cycle_inventory”), their data quality criteria 
need to be reassessed, based on the rules in the EC 
recommendation 2021/2279 (Annex I, section 
4.6.5.3). 

If the reassessed data quality criteria of the default 
secondary dataset are representative for the 
manufacturer (i.e. TiR≤2 or TeR≤2 or GR≤2), the 
dataset may be combined with company-specific 
activity data. 

If the recalculated quality of the default dataset is 
not representative (TiR>2 or TeR>2 or GR>2) of the 
process, the manufacturer shall adapt the dataset 
to the manufacturing context (e.g. to what concerns 
electricity consumption) and recalculate the DQR 
following the rules in the EC recommendation 
2021/2279 (Annex I, section 4.6.5.3). If the 
recalculated DQR is higher than 3, the manufacturer 
shall develop an EF compliant dataset following the 
Guide for EF compliant datasets.  

If a default secondary dataset is not available (e.g. 
innovative materials) the manufacturer may use a 
proxy dataset (selected from the list 
“CF_Annex_PV_modules-Life_cycle_inventory), if 
after reassessing its quality (TiR, TeR, GR) the 
recalculated DQR is ≤3. 

If not, (i.e. the recalculated DQR of the proxy 
dataset is higher than 3, the manufacturer shall 
develop an EF compliant primary dataset following 
the Guide for EF compliant datasets. 

Situation 3: process not run by the company 
applying this Annex and without access to company-
specific information 

Use default secondary datasets and default activity 
data listed in the spreadsheet named 
“CF_Annex_PV_modules-Life_cycle_inventory” 

7.3 Allocation rules 

There is one instance in the crystalline silicon PV supply chain, where a multiproduct process occurs 
(see Table 4). Cuttings (circular segments) from monocrystalline wafer production are fed into 
multicrystalline silicon casting. The cradle to gate efforts and impacts of the supply of solar grade 
silicon used in Czochralski monocrystalline production shall be fully allocated to the monocrystalline 
silicon wafers. The (internal) recycling efforts and impacts required to prepare the cuttings for an 
input into the multicrystalline casting process shall fully be allocated to the multicrystalline silicon 
wafers. 

75 

 
 
 
  
Table 4 – Allocation rules for the silicon supply chain. 

Allocation of electricity consumption 

Wherever possible, allocation should be avoided by subdivision of the process system. This means 
for instance, that the electricity demand of a production plant producing several products should be 
measured separately for each process or product. If this is not possible, the allocation rules for 
electricity described in Table 5 shall be followed.  

Table 5 – Allocation rules for electricity. 

Process 

PV cell 
production 

Physical 
relationship 
Area (m2 of PV 
cells) 

Modelling instructions 

The allocation of the electricity consumption of a specific production 
plant shall be based on the total amount of PV cells produced at this 
site, measured in m2 on a yearly basis. 

PV module 
production 

Area (m2 of PV 
modules) 

The allocation of the electricity consumption of a specific production 
plant shall be based on the total amount of PV modules produced at 
this site, measured in m2 on a yearly basis. 

PV cell and 
module 
production in 
the same 
plant 

Area (m2 of PV cells 
and modules), 
weighted by the 
default specific 
electricity 
consumption per 
m2 

The allocation of the electricity consumption of a specific production 
plant shall be based on the total amount of PV cells and modules 
produced at this site, measured in m2 on a yearly basis. The areas of 
PV cells and modules produced shall be weighted based on the specific 
electricity consumption per m2 (outer dimensions including production 
losses). The following formulae shall be used to calculate the 
allocation factors (AF): 

If the consumed electricity comes from more than one electricity mix, each mix source shall be used 
in terms of its proportion in the total kWh consumed. For example, if a fraction of this total kWh 
consumed is coming from a specific supplier a supplier-specific electricity mix shall be used for this 

76 

 
 
 
 
 
  
part; the requirements on “electricity modelling” below fully apply. See below for on-site electricity 
use. 

A specific electricity type may be allocated to one specific product in the following conditions: 

—  The production (and related electricity consumption) of a product occurs in a separate site 

(building), the energy type physical related to this separated site may be used. 

—  The production (and related electricity consumption) of a product occurs in a shared space 
with specific energy metering or purchase records or electricity bills, the product specific 
information (measure, record, bill) may be used. 

—  All the products produced in the specific plant are supplied with a public available Product 
Environmental Footprint (PEF) study. The company who wants to make the claim shall 
make all PEF studies available. The allocation rule applied shall be clearly documented, 
consistently applied in all PEF studies connected to the site and verified. An example is 
the 100% allocation of a greener electricity mix to a specific product. 

7.4 Electricity modelling 

The following section introduces two types of electricity mixes: (i) the average consumption grid mix 
which reflects the total electricity mix over a defined grid including green claimed or tracked 
electricity, and (ii) the residual consumption grid mix, which characterizes the unclaimed, untracked 
or publicly shared electricity only. 

The following electricity mix shall be used in hierarchical order: 

I. 

II. 

The on-site generated electricity shall be used according to the conditions described 
below. On-site electricity shall be modelled according to the DNM.  

Supplier-specific electricity product shall be used if: 

a.  available, and 

b. 

the set of minimum criteria referred to in the section below to ensure the 
contractual instruments are reliable is met. 

The supplier-specific electricity products shall be modelled according to the 
DNM. 

III. 

The supplier-specific total electricity mix shall be used if: 

a.  available, and 

b. 

the set of minimum criteria referred to in the section below to ensure the 
contractual instruments are reliable is met. 

The supplier-specific electricity mix shall be modelled according to the DNM. 

IV. 

The  country-specific  residual  consumption  grid  mix  shall  be  used.  Country-specific 
means  the  country  in  which  the  life  cycle  stage  or  activity  occurs.  The  residual 
consumption  grid  mix  characterizes  the  unclaimed,  untracked  or  publicly  shared 
electricity and prevents double counting with the use of supplier-specific electricity 
mixes. In the case of very large countries in which several electrical grids operate, the 
grid-specific residual consumption grid mix shall be used if available.  

V. 

As  a  last  option,  if  no  country-  or  grid-  specific  residual  consumption  grid  mix  is 
available, the country- or grid- specific average consumption mix shall be used. 

77 

 
 
 
 
The environmental integrity of the use of supplier-specific electricity mixes depends on ensuring 
that contractual instruments (for tracking) are reliable and unique. Without this, the carbon footprint 
lacks the accuracy and consistency needed to drive product/corporate electricity procurement 
decisions and accurate consideration of the supplier-specific mix by buyers of electricity. Therefore, 
a set of minimum criteria that relate to the integrity of the contractual instruments as reliable 
conveyers of environmental footprint information has been identified. 

The direct use of emission values from e.g., a grid operating or certificate-issuing entity is not 
permitted. The electricity mix used and the corresponding emission factors shall be reported. 

NOTE: certificates for the contractual instruments could include an estimation of the carbon 
footprint of the electricity delivered. However, there is no guarantee that such data are EF-
compliant. 

7.4.1 On-site electricity generation 

On-site electricity generation is given if electricity is supplied to the plant from a production asset 
within the premises of the energy-consuming plant or if the production asset is connected to the 
energy-using plant by means of a direct and dedicated connection. 

If the energy-consuming plant is also connected to the electricity grid and electricity is sourced from 
the grid in addition to on-site generation (e.g., during times of low on-site generation), all energy 
sourced from the grid shall be accounted for according to the rules laid out in section 7.4.  

The amount of on-site generated energy that may be accounted for is the difference between the 
total energy demand of the production site and the amount of energy sourced from the grid. 

Two situations apply to the on-site generated electricity: 

1.  No contractual instruments have been sold to a third party: the manufacturer shall model its 

own electricity mix for the amount of on-site generated electricity. 

2.  Contractual instruments have been sold to a third party: the manufacturer shall use ‘country-

specific residual consumption (grid) mix’. 

If the total amount of electricity produced on-site exceeds the amount consumed on-site within the 
defined system boundary and is sold to, e.g., the electricity grid, this system may be seen as a 
multi-functional situation. 

The system will provide two functions (e.g., product + electricity) and the following rules shall be 
followed. 

3. 

4. 

If  possible,  apply  subdivision.  This  applies  both  to  separate  electricity  productions  or  to  a 
common  electricity  production  where  you  may  allocate,  based  on  electricity  amounts,  the 
upstream and direct emissions to your own consumption and to the share you sell to a third 
party  (e.g.,  if  a  company  uses  a  windmill  on  its  production  site  and  exports  30%  of  the 
produced electricity, emissions related to 70% of produced electricity shall be accounted for). 

If not possible, direct substitution shall be used. The country-specific residual consumption 
(grid)  mix  shall  be  used  as  substitution.  Subdivision  is  considered  as  not  possible  when 
upstream impacts or direct emissions are closely related to the product itself. 

Electricity from facilities for which the attributes have been sold off (via contracts or certificates) 
shall be characterised as having the environmental attributes of the country residual consumption 
mix where the facility is located. 

Set of minimum criteria to ensure contractual instruments from suppliers are reliable: 

78 

 
 
 
A supplier-specific electricity product/mix may only be used when the applicant ensures that any 
contractual instrument used meets the criteria specified below. A contractual instrument used for 
electricity modelling shall meet the following criteria: 

—  Criterion 1 - Convey attributes: 

Convey the energy type mix associated with the unit of electricity produced and include 
an explanation of the calculation method used to determine this mix and the geographical 
location where the energy is generated. 

The  energy  type  mix  shall  be  calculated  based  on  delivered  electricity,  incorporating 
certificates  sourced  and  retired  on  behalf  of  the  relevant  company  (for  the  supplier-
specific  electricity  product)  or  on  behalf  of  the  supplier’s  customers  (for  the  supplier-
specific electricity mix).  

—  Criterion 2 - Be a unique claim: 

o  Be the only instruments that carry the environmental attribute claim associated 

with that quantity of electricity generated. 

o  Be tracked and redeemed, retired, or cancelled by or on behalf of the company 
(e.g, by an audit of contracts, third party certification, or handled automatically 
through other disclosure registries, systems, or mechanisms).  

o  The  quantity  of  generated  electricity  associated  with  the  instrument  and  its 
energy type mix is reported and considered for the determination of the country-
or grid- specific residual grid mix, and this residual grid mix is disclosed publicly. 

o  Allows  for  the  unambiguous  identification  of  the  type,  age  and  location  and 

capacity of the energy generation facility to which it refers.  

o  The energy generation facility  to which it refers is located  in a country with a 
tracking system in place that meets the minimum criteria for tracking systems 
specified in this section. 

o 

In case the energy generation facility to which it refers is located in a country 
with  a  multi-certificate  tracking  system,  it  is  accompanied  by  any  additional 
contractual instruments from the supplier necessary to show and ensure there is 
no double counting. 

o  Be issued by a tracking system that fulfils the following criteria: 

▪ 

Is based on objective, non-discriminatory and transparent criteria for the 
issuing certificates;  

▪  Allows  certificates  to  be  valid  no  longer  than  12  months  after  the 

production of the relevant energy unit;  

▪  Relies  on  accurate,  reliable  and  fraud-resistant  mechanisms  for  the 

issuance, transfer and cancellation of certificates;  

▪  Entrusts the issuance of certificates, as well as the supervision of their 

transfer and cancellation of certificates, to an entity or entities:  

▪ 

Is independent of energy production, trade and supply activities, and of 
any commercial interest of customers on whose behalf certificates are 
redeemed, retired, or cancelled;  

▪  Whose activities are governed by transparent rules and procedures laid 

down by law;  

79 

 
 
 
 
▪  Whose  decisions  may  be  challenged  and  reviewed  in  the  context  of 

proceedings before an independent judiciary. 

—  Criterion 3 - Be as close as possible to the period to which the contractual instrument is 

applied 

o  Ensure that certificates are valid no longer than 12 months after the production 

of the relevant energy unit; 

—  Criterion 4 - Be sourced from the same market in which the reporting entity’s electricity- 

consuming operations are located and to which the instrument is applied 

To claim the use of renewable electricity, companies shall source renewable electricity from within 
the boundary of the market in which they are consuming the electricity.  

The “market boundary” refers to an area in which: 

—  The  laws  and  regulatory  framework  governing  the  electricity  sector  are  consistent 

between the areas of production and consumption.  

—  There  is  a  physical  interconnection  between  the  point  of  generation  and  the  point  of 
consumption  of  renewable  electricity.  When  interconnection  happens  across  different 
grids, there shall be an entity that coordinates and tracks the exchange between such 
grids.  

—  The  countries’  utilities/energy  suppliers  recognize  each  other’s  energy  sourcing 

instruments and have a system in place to prevent double counting of claims 

7.4.2. How to model 'country-specific residual grid mix, consumption mix': 

The manufacturer shall use EF-compliant datasets for residual consumption grid mix .registered on 
the LCDN If no dataset for the specific country/grid is available, the following approach shall be 
used: 

—  Determine the country consumption mix (e.g. X% of MWh produced with hydro energy, 
Y% of MWh produced with coal power plant) as provided by the country-specific issuing 
body 

—  Combine them with EF-compliant datasets per energy type and country/region (e.g. LCI 
dataset for the production of 1MWh hydro energy in the corresponding country/region). 

If no country-specific residual consumption grid mix is available, use the country-specific average 
consumption mix: 

1.  Activity data related to non-EU country consumption mix per detailed energy type shall be 

determined based on: 

—  Domestic production mix per production technologies 

—  Import quantity and from which neighbouring countries 

—  Transmission losses 

—  Distribution losses 

—  Type of fuel supply (share of resources used, by import and / or domestic supply)). 

2.  Available  LCI  datasets  per  fuel  technologies  and  generation  technology.  The  LCI  datasets 

available are generally specific to a country or a region in terms of: 

80 

 
 
 
  
  
—  Fuel supply (share of resources used, by import and / or domestic supply), 

—  Energy carrier properties (e.g. element and energy contents) 

—  Technology  standards  of power  plants  regarding  efficiency,  firing  technology,  flue-gas 

desulphurisation, NOx removal and de-dusting. 

8. Carbon footprint assessment  

The carbon footprint shall be calculated according to the impact assessment method reported in 
Table 6. 

Table 6 – method for calculating the carbon footprint 

Impact category 

Indicator 

Climate change[4] 

Radiative forcing as 
Global Warming Potential 
(GWP100) 

Unit 

kgCO2 eq 

LCIA method 

Baseline model of 100 
years of the IPCC (based 
on IPCC 2013) 

The full list of characterization factors is available in the spreadsheet “CF_Annex_PV_modules-
Life_cycle_inventory9”, sheet GWP CF. 

The results shall be reported using the unit of measure gCO2 eq/kWh. 

The results shall be provided as “cradle-to-EU market” total CF value as well as separate values for 
the two life cycle stages described in section 4. 

Part 2 - List of mandatory company-specific activity data 

The following tables show the mandatory company-specific activity data for each of the PV 
technologies included in the Scope of this Annex.  

Cadmium-Telluride photovoltaic modules (CdTe) 

Photovoltaic laminate production 

Specific requirements (e.g. frequency, measurement standard, etc) 

Unit of 
measure 

Activity data to be 
collected 

INPUT 

Electricity 

Solar glass 

Measurement (e.g. electricity meter or electricity bills); Yearly average 

Bill of materials of the PV panel (including manufacturing losses) 
 Yearly average 

Flat glass tempering 

Bill of materials of the PV panel (including manufacturing losses) 
 Yearly average 

Flat glass (uncoated) 

Bill of materials of the PV panel (including manufacturing losses) 
 Yearly average 

81 

MJ 

kg 

kg 

kg 

 
 
 
 
  
  
  
Photovoltaic laminate production 

Activity data to be 
collected 

Specific requirements (e.g. frequency, measurement standard, etc) 

Unit of 
measure 

Copper 

Bill of materials of the PV panel (including manufacturing losses) 
 Yearly average 

Cadmium sulphide 

Bill of materials of the PV panel (including manufacturing losses) 
 Yearly average 

Cadmium telluride 

Bill of materials of the PV panel (including manufacturing losses) 
 Yearly average 

Ethylene vinyl acetate 
(EVA) 

Bill of materials of the PV panel (including manufacturing losses) 
 Yearly average 

Glass fibre reinforced 
plastic 

Bill of materials of the PV panel (including manufacturing losses) 
 Yearly average 

Silicone 

Silica sand 

Tap water 

Nitrogen 

Nitric acid 

Bill of materials of the PV panel (including manufacturing losses) 
 Yearly average 

Bill of materials of the PV panel (including manufacturing losses) 
 Yearly average 

Measurement (e.g. water meter or water bills); Yearly average 

Measurement; Yearly average 

Measurement; Yearly average 

Sulphuric acid 

Measurement; Yearly average 

Hydrogen peroxide 

Measurement; Yearly average 

Sodium hydroxide 

Measurement; Yearly average 

Sodium chloride 

Measurement; Yearly average 

Isopropanol 

Measurement; Yearly average 

Corrugated board 

Measurement; Yearly average 

kg 

kg 

kg 

kg 

kg 

kg 

kg 

kg 

kg 

kg 

kg 

kg 

kg 

kg 

kg 

kg 

Freight train transport 

Lorry transport 

Ship transport 

Calculation (mass x distance) 
 Transport of all input materials to the production site and transport of 
waste materials to treatment site 
 Yearly average 

Calculation (mass x distance) 
 Transport of all input materials to the production site and transport of 
waste materials to treatment site 
 Yearly average 

Calculation (mass x distance) 
 Transport of all input materials to the production site and transport of 
waste materials to treatment site 
 Yearly average 

kgkm 

kgkm 

kgkm 

82 

 
 
 
Photovoltaic laminate production 

Activity data to be 
collected 

Airplane transport 

OUTPUT 

Specific requirements (e.g. frequency, measurement standard, etc) 

Calculation (mass x distance) 
 Transport of all input materials to the production site and transport of 
waste materials to treatment site 
 Share in supply logistics in a 3 years period 

Photovoltaic laminate, 
CdTe, at plant 

Specify the size of the PV panel 

Plastic waste 
(incineration) 

Measurement; Yearly average 

Municipal solid waste 
(incineration) 

Measurement; Yearly average 

Unit of 
measure 

kgkm 

m2 

kg 

kg 

Distribution 

Activity data to be 
collected 

INPUT 

Photovoltaic laminate, 
CdTe, at plant 

Freight train transport 

Lorry transport 

Ship transport 

Airplane transport 

Specific requirements (e.g. frequency, measurement standard, etc) 

Unit of 
measure 

Specify the mass per m2 PV panel 
 Calculate the share of each facility in the European supply mix in case of 
several production sites 
 Yearly average 

m2 

Calculation (mass x distance) 
 Transport of PV panels from the production site to a regional storage in 
Europe 
 Use a weighted average in case of several production sites 
 Yearly average 

Calculation (mass x distance) 
 Transport of PV panels from the production site to a regional storage in 
Europe 
 Use a weighted average in case of several production sites 
 Yearly average 

Calculation (mass x distance) 
 Transport of PV panels from the production site to a regional storage in 
Europe 
 Use a weighted average in case of several production sites 
 Yearly average 

Calculation (mass x distance) 
 Transport of PV panels from the production site to a regional storage in 
Europe 
 Use a weighted average in case of several production sites 
 Share in supply logistics in a 3 years period 

kgkm 

kgkm 

kgkm 

kgkm 

83 

 
 
 
  
Distribution 

Activity data to be 
collected 

OUTPUT 

Photovoltaic laminate, 
CdTe, at regional storage 

Specific requirements (e.g. frequency, measurement standard, etc) 

Unit of 
measure 

m2 

Copper-Indium-Gallium-Selenide photovoltaic modules (CIS / CIGS) 

Photovoltaic laminate production 

Activity data to be 
collected 

Specific requirements (e.g. frequency, measurement standard, etc) 

Unit of 
measure 

INPUT 

Electricity 

Solar glass 

Measurement (e.g. electricity meter or electricity bills); Yearly average 

Bill of materials of the PV panel (including manufacturing losses) 
 Yearly average 

Flat glass tempering 

Bill of materials of the PV panel (including manufacturing losses) 
 Yearly average 

Flat glass (uncoated) 

Bill of materials of the PV panel (including manufacturing losses) 
 Yearly average 

Aluminium 

Copper wire 

Tin 

Zinc oxide 

Molybdenum 

Indium 

Gallium 

Selenium 

Bill of materials of the PV panel (including manufacturing losses) 
 Yearly average 

Bill of materials of the PV panel (including manufacturing losses) 
 Yearly average 

Bill of materials of the PV panel (including manufacturing losses) 
 Yearly average 

Bill of materials of the PV panel (including manufacturing losses) 
 Yearly average 

Bill of materials of the PV panel (including manufacturing losses) 
 Yearly average 

Bill of materials of the PV panel (including manufacturing losses) 
 Yearly average 

Bill of materials of the PV panel (including manufacturing losses) 
 Yearly average 

Bill of materials of the PV panel (including manufacturing losses) 
 Yearly average 

Cadmium sulphide 

Bill of materials of the PV panel (including manufacturing losses) 
 Yearly average 

84 

MJ 

kg 

kg 

kg 

kg 

kg 

kg 

kg 

kg 

kg 

kg 

kg 

kg 

 
 
 
  
  
Photovoltaic laminate production 

Activity data to be 
collected 

Specific requirements (e.g. frequency, measurement standard, etc) 

Unit of 
measure 

Diode 

Flux 

Bill of materials of the PV panel (including manufacturing losses) 
 Yearly average 

Bill of materials of the PV panel (including manufacturing losses) 
 Yearly average 

Ethylene vinyl acetate 
(EVA) 

Bill of materials of the PV panel (including manufacturing losses) 
 Yearly average 

Polyvinyl butyral (PVB) 

Bill of materials of the PV panel (including manufacturing losses) 
 Yearly average 

Polyethylene 
terephthalate (PET) 

Bill of materials of the PV panel (including manufacturing losses) 
 Yearly average 

High-density polyethylene 
(HDPE) 

Bill of materials of the PV panel (including manufacturing losses) 
 Yearly average 

Polyphenylene sulphide 
(PPS) 

Bill of materials of the PV panel (including manufacturing losses) 
 Yearly average 

Silicone 

Tap water 

Nitrogen 

Argon 

Ammonia 

Urea 

Bill of materials of the PV panel (including manufacturing losses) 
 Yearly average 

Measurement (e.g. water meter or water bills); Yearly average 

Measurement; Yearly average 

Measurement; Yearly average 

Measurement; Yearly average 

Measurement; Yearly average 

Hydrogen peroxide 

Measurement; Yearly average 

Sodium hydroxide 

Measurement; Yearly average 

Hydrochloric acid 

Measurement; Yearly average 

Sulphuric acid 

Measurement; Yearly average 

Hydrogen sulphide 

Measurement; Yearly average 

Butyl acrylate 

Measurement; Yearly average 

Diborane 

Measurement; Yearly average 

Freight train transport 

Calculation (mass x distance) 
 Transport of all input materials to the production site and transport of 
waste materials to treatment site 
 Yearly average 

85 

p 

kg 

kg 

kg 

kg 

kg 

kg 

kg 

kg 

kg 

kg 

kg 

kg 

kg 

kg 

kg 

kg 

kg 

kg 

m3 

kgkm 

 
 
 
Photovoltaic laminate production 

Specific requirements (e.g. frequency, measurement standard, etc) 

Unit of 
measure 

Activity data to be 
collected 

Lorry transport 

Ship transport 

Airplane transport 

OUTPUT 

Photovoltaic laminate, 
CIS, at plant 

Calculation (mass x distance) 
 Transport of all input materials to the production site and transport of 
waste materials to treatment site 
 Yearly average 

Calculation (mass x distance) 
 Transport of all input materials to the production site and transport of 
waste materials to treatment site 
 Yearly average 

Calculation (mass x distance) 
 Transport of all input materials to the production site and transport of 
waste materials to treatment site 
 Share in supply logistics in a 3 years period 

Specify the size of the PV laminate 

Inert waste (landfill) 

Measurement; Yearly average 

Inert waste (incineration) 

Measurement; Yearly average 

Plastic waste 
(incineration) 

Measurement; Yearly average 

Wastewater (wastewater 
treatment plant) 

Measurement; Yearly average 

Photovoltaic panel production 

kgkm 

kgkm 

kgkm 

m2 

kg 

kg 

kg 

kg 

Activity data to be 
collected 

INPUT 

Specific requirements (e.g. frequency, measurement standard, etc) 

Unit of 
measure 

Photovoltaic laminate, 
CIS, at plant 

Specify the size of the PV panel 

Thermal energy from light 
fuel oil 

Measurement (e.g. energy meter or energy bills); Yearly average 

Aluminium alloy 

Bill of materials of the PV panel (including manufacturing losses) 
 Yearly average 

Glass fibre reinforced 
plastic 

Bill of materials of the PV panel (including manufacturing losses) 
 Yearly average 

m2 

MJ 

kg 

kg 

Freight train transport 

Calculation (mass x distance) 
 Transport of all input materials to the production site and transport of 

kgkm 

86 

 
 
 
  
Photovoltaic panel production 

Activity data to be 
collected 

Specific requirements (e.g. frequency, measurement standard, etc) 

Unit of 
measure 

waste materials to treatment site 
 Yearly average 

Calculation (mass x distance) 
 Transport of all input materials to the production site and transport of 
waste materials to treatment site 
 Yearly average 

Calculation (mass x distance) 
 Transport of all input materials to the production site and transport of 
waste materials to treatment site 
 Yearly average 

Calculation (mass x distance) 
 Transport of all input materials to the production site and transport of 
waste materials to treatment site 
 Share in supply logistics in a 3 years period 

Lorry transport 

Ship transport 

Airplane transport 

OUTPUT 

Photovoltaic panel, CIS, at 
plant 

Specify the size of the PV panel 

Plastic waste 
(incineration) 

Measurement; Yearly average 

kgkm 

kgkm 

kgkm 

m2 

kg 

Distribution 

Activity data to be 
collected 

INPUT 

Specific requirements (e.g. frequency, measurement standard, etc) 

Unit of 
measure 

Photovoltaic panel, CIS, at 
plant 

Specify the mass per m2 PV panel 
 Calculate the share of each facility in the European supply mix in case of 
several production sites 
 Yearly average 

m2 

Freight train transport 

Lorry transport 

Ship transport 

Calculation (mass x distance) 
 Transport of PV panels from the production site to a regional storage in 
Europe 
 Use a weighted average in case of several production sites 
 Yearly average 

Calculation (mass x distance) 
 Transport of PV panels from the production site to a regional storage in 
Europe 
 Use a weighted average in case of several production sites 
 Yearly average 

Calculation (mass x distance) 
 Transport of PV panels from the production site to a regional storage in 
Europe 

kgkm 

kgkm 

kgkm 

87 

 
 
 
  
  
  
Distribution 

Activity data to be 
collected 

Airplane transport 

OUTPUT 

Photovoltaic panel, CIS, at 
regional storage 

Specific requirements (e.g. frequency, measurement standard, etc) 

Unit of 
measure 

 Use a weighted average in case of several production sites 
 Yearly average 

Calculation (mass x distance) 
 Transport of PV panels from the production site to a regional storage in 
Europe 
 Use a weighted average in case of several production sites 
 Share in supply logistics in a 3 years period 

kgkm 

m2 

Micromorphous Silicon photovoltaic modules (micro-Si) 

Photovoltaic Panel manufacturing 

Specific requirements (e.g. frequency, measurement standard, etc) 

Unit of 
measure 

Activity data to be 
collected 

INPUT 

Electricity 

Measurement (e.g. electricity meter or electricity bills); Yearly average 

Natural gas 

Measurement (e.g. energy meter or energy bills); Yearly average 

Aluminium alloy 

Bill of materials of the PV panel (including manufacturing losses) 
 Yearly average 

Copper wire 

Silver 

Solar glass 

Bill of materials of the PV panel (including manufacturing losses) 
 Yearly average 

Bill of materials of the PV panel (including manufacturing losses) 
 Yearly average 

Bill of materials of the PV panel (including manufacturing losses) 
 Yearly average 

Flat glass tempering 

Bill of materials of the PV panel (including manufacturing losses) 
 Yearly average 

Cable 

Bill of materials of the PV panel (including manufacturing losses) 
 Yearly average 

Ethylene vinyl acetate 
(EVA) 

Bill of materials of the PV panel (including manufacturing losses) 
 Yearly average 

High-density polyethylene 
(HDPE) 

Bill of materials of the PV panel (including manufacturing losses) 
 Yearly average 

88 

MJ 

MJ 

kg 

kg 

kg 

kg 

kg 

m 

kg 

kg 

 
 
 
  
  
Photovoltaic Panel manufacturing 

Activity data to be 
collected 

Specific requirements (e.g. frequency, measurement standard, etc) 

Unit of 
measure 

Injection moulding of 
HDPE 

Bill of materials of the PV panel (including manufacturing losses) 
 Yearly average 

Silicone 

Silane (silicon 
tetrahydride) 

Bill of materials of the PV panel (including manufacturing losses) 
 Yearly average 

Measurement; Yearly average 

Deionised water 

Measurement; Yearly average 

Decarbonised water 

Measurement; Yearly average 

Hydrogen 

Nitrogen 

Oxygen 

Measurement; Yearly average 

Measurement; Yearly average 

Measurement; Yearly average 

Carbon dioxide 

Measurement; Yearly average 

Argon 

Measurement; Yearly average 

Phosphane 

Measurement; Yearly average 

Diborane 

Measurement; Yearly average 

Trimethyl borate 

Measurement; Yearly average 

Bisphenol A 

Measurement; Yearly average 

Zeolite 

Measurement; Yearly average 

Compressed air 

Measurement; Yearly average 

Freight train transport 

Lorry transport 

Ship transport 

Airplane transport 

Calculation (mass x distance) 
 Transport of all input materials to the production site and transport of 
waste materials to treatment site 
 Yearly average 

Calculation (mass x distance) 
 Transport of all input materials to the production site and transport of 
waste materials to treatment site 
 Yearly average 

Calculation (mass x distance) 
 Transport of all input materials to the production site and transport of 
waste materials to treatment site 
 Yearly average 

Calculation (mass x distance) 
 Transport of all input materials to the production site and transport of 
waste materials to treatment site 
 Share in supply logistics in a 3 years period 

89 

kg 

kg 

kg 

kg 

kg 

kg 

kg 

kg 

kg 

kg 

kg 

kg 

kg 

kg 

kg 

m3 

kgkm 

kgkm 

kgkm 

kgkm 

 
 
 
Photovoltaic Panel manufacturing 

Activity data to be 
collected 

OUTPUT 

Specific requirements (e.g. frequency, measurement standard, etc) 

Unit of 
measure 

Photovoltaic panel, micro-
Si, at plant 

Specify the size of the PV panel 

Inert waste (incineration) 

Measurement; Yearly average 

Plastic waste 
(incineration) 

Hazardous waste 
(incineration) 

Measurement; Yearly average 

Measurement; Yearly average 

Wastewater (wastewater 
treatment plant) 

Measurement; Yearly average 

Nitrogen trifluoride (NF3)  Measurement; Yearly average 

m2 

kg 

kg 

kg 

kg 

kg 

Distribution 

Activity data to be 
collected 

INPUT 

Specific requirements (e.g. frequency, measurement standard, etc) 

Unit of 
measure 

Photovoltaic panel, micro-
Si, at plant 

Specify the mass per m2 PV panel 
 Calculate the share of each facility in the European supply mix in case of 
several production sites 
 Yearly average 

m2 

Freight train transport 

Lorry transport 

Ship transport 

Calculation (mass x distance) 
 Transport of PV panels from the production site to a regional storage in 
Europe 
 Use a weighted average in case of several production sites 
 Yearly average 

Calculation (mass x distance) 
 Transport of PV panels from the production site to a regional storage in 
Europe 
 Use a weighted average in case of several production sites 
 Yearly average 

Calculation (mass x distance) 
 Transport of PV panels from the production site to a regional storage in 
Europe 
 Use a weighted average in case of several production sites 
 Yearly average 

Airplane transport 

Calculation (mass x distance) 
 Transport of PV panels from the production site to a regional storage in 
Europe 

kgkm 

kgkm 

kgkm 

kgkm 

90 

 
 
 
  
Distribution 

Activity data to be 
collected 

OUTPUT 

Photovoltaic panel, micro-
Si, at regional storage 

Specific requirements (e.g. frequency, measurement standard, etc) 

 Use a weighted average in case of several production sites 
 Share in supply logistics in a 3 years period 

Unit of 
measure 

m2 

Multicrystalline (Multi-Si) Silicon PV modules 

Photovoltaic cell manufacturing 

Specific requirements (e.g. frequency, measurement standard, etc)  Unit of 

measure 

Activity data to be 
collected 

INPUT 

Electricity 

Thermal energy from 
natural gas 

Thermal energy from light 
fuel oil 

Measurement (e.g. electricity meter or electricity bills); Yearly average 

Measurement (e.g. energy meter or energy bills); Yearly average 

Measurement (e.g. energy meter or energy bills); Yearly average 

Wafer, multi-Si 

Bill of materials of the PV panel (including manufacturing losses) 
 Yearly average 

Metallization paste, front 
side 

Bill of materials of the PV panel (including manufacturing losses) 
 Yearly average 

Metallization paste, back 
side 

Bill of materials of the PV panel (including manufacturing losses) 
 Yearly average 

Metallization paste, back 
side, aluminium 

Bill of materials of the PV panel (including manufacturing losses) 
 Yearly average 

Ammonia 

Measurement; Yearly average 

Phosphoric acid 

Measurement; Yearly average 

Phosphoryl chloride 

Measurement; Yearly average 

Isopropanol 

Measurement; Yearly average 

Calcium chloride 

Measurement; Yearly average 

Hydrochloric acid 

Measurement; Yearly average 

Hydrogen fluoride 

Measurement; Yearly average 

91 

MJ 

MJ 

MJ 

m2 

kg 

kg 

kg 

kg 

kg 

kg 

kg 

kg 

kg 

kg 

 
 
 
  
  
  
Photovoltaic cell manufacturing 

Activity data to be 
collected 

Specific requirements (e.g. frequency, measurement standard, etc)  Unit of 

measure 

Nitric acid 

Measurement; Yearly average 

Sulphuric acid 

Measurement; Yearly average 

Sodium hydroxide 

Measurement; Yearly average 

Potassium hydroxide 

Measurement; Yearly average 

Hydrogen peroxide 

Measurement; Yearly average 

Ammonium sulphate 

Measurement; Yearly average 

Lime 

Measurement; Yearly average 

Tetrafluoroethane 
(R134a) 

Silane (silicon 
tetrahydride) 

Nitrogen 

Oxygen 

Measurement; Yearly average 

Measurement; Yearly average 

Measurement; Yearly average 

Measurement; Yearly average 

Tap water 

Measurement (e.g. water meter or water bills); Yearly average 

Freight train transport 

Lorry transport 

Ship transport 

Airplane transport 

Calculation (mass x distance) 
 Transport of all input materials to the production site and transport of 
waste materials to treatment site 
 Yearly average 

Calculation (mass x distance) 
 Transport of all input materials to the production site and transport of 
waste materials to treatment site 
 Yearly average 

Calculation (mass x distance) 
 Transport of all input materials to the production site and transport of 
waste materials to treatment site 
 Yearly average 

Calculation (mass x distance) 
 Transport of all input materials to the production site and transport of 
waste materials to treatment site 
 Share in supply logistics in a 3 years period 

Photovoltaic cell factory 

Estimation 
 Use a default demand of 4.00E-7 p if specific data are not available 

OUTPUT 

Photovoltaic cell, multi-Si 

92 

kg 

kg 

kg 

kg 

kg 

kg 

kg 

kg 

kg 

kg 

kg 

kg 

kgkm 

kgkm 

kgkm 

kgkm 

P 

m2 

 
 
 
  
Photovoltaic cell manufacturing 

Activity data to be 
collected 

Hazardous waste 
(incineration) 

Measurement; Yearly average 

Specific requirements (e.g. frequency, measurement standard, etc)  Unit of 

Wastewater (wastewater 
treatment plant) 

Measurement; Yearly average 

Photovoltaic panel manufacturing 

Activity data to be 
collected 

INPUT 

Electricity 

Diesel 

Specific requirements (e.g. frequency, measurement standard, etc) 

Measurement (e.g. electricity meter or electricity bills); Yearly average 

Measurement; Yearly average 

Photovoltaic cell, multi-Si 

Bill of materials of the PV panel (including manufacturing losses) 
 Yearly average 

Aluminium alloy 

Bill of materials of the PV panel (including manufacturing losses) 
 Yearly average 

Copper wire 

Bill of materials of the PV panel (including manufacturing losses) 
 Yearly average 

Tin 

Lead 

Diode 

Bill of materials of the PV panel (including manufacturing losses) 
 Yearly average 

Bill of materials of the PV panel (including manufacturing losses) 
 Yearly average 

Bill of materials of the PV panel (including manufacturing losses) 
 Yearly average 

Solar glass 

Bill of materials of the PV panel (including manufacturing losses) 
 Yearly average 

Flat glass tempering 

Bill of materials of the PV panel (including manufacturing losses) 
 Yearly average 

Ethylene vinyl acetate 
(EVA) 

Bill of materials of the PV panel (including manufacturing losses) 
 Yearly average 

Polyvinyl fluoride (PVF) 

Bill of materials of the PV panel (including manufacturing losses) 
 Yearly average 

Polyethylene 
terephthalate (PET) 

Bill of materials of the PV panel (including manufacturing losses) 
 Yearly average 

93 

measure 

kg 

kg 

Unit of 
measure 

MJ 

kg 

m2 

kg 

kg 

kg 

kg 

P 

kg 

kg 

kg 

kg 

kg 

 
 
 
  
Photovoltaic panel manufacturing 

Activity data to be 
collected 

Specific requirements (e.g. frequency, measurement standard, etc) 

Unit of 
measure 

High-density polyethylene 
(HDPE) 

Bill of materials of the PV panel (including manufacturing losses) 
 Yearly average 

Glass fibre reinforced 
plastic 

Bill of materials of the PV panel (including manufacturing losses) 
 Yearly average 

Silicone 

Bill of materials of the PV panel (including manufacturing losses) 
 Yearly average 

Tap water 

Measurement (e.g. water meter or water bills); Yearly average 

Hydrogen fluoride 

Measurement; Yearly average 

Potassium hydroxide 

Measurement; Yearly average 

1-propanol 

Measurement; Yearly average 

Isopropanol 

Measurement; Yearly average 

Soap 

Measurement; Yearly average 

Corrugated board 

Measurement; Yearly average 

Pallet, wood 

Default reuse rate: 25 trips 

Freight train transport 

Lorry transport 

Ship transport 

Airplane transport 

Calculation (mass x distance) 
 Transport of all input materials to the production site and transport of 
waste materials to treatment site 
 Yearly average 

Calculation (mass x distance) 
 Transport of all input materials to the production site and transport of 
waste materials to treatment site 
 Yearly average 

Calculation (mass x distance) 
 Transport of all input materials to the production site and transport of 
waste materials to treatment site 
 Yearly average 

Calculation (mass x distance) 
 Transport of all input materials to the production site and transport of 
waste materials to treatment site 
 Share in supply logistics in a 3 years period 

Photovoltaic panel factory 

Estimation 
 Use a default demand of 4.00E-6 p if specific data are not available 

OUTPUT 

Photovoltaic panel, multi-
Si, at plant 

Specify the size of the PV panel 

94 

kg 

kg 

kg 

kg 

kg 

kg 

kg 

kg 

kg 

kg 

kg 

kgkm 

kgkm 

kgkm 

kgkm 

P 

m2 

 
 
 
Specific requirements (e.g. frequency, measurement standard, etc) 

Unit of 
measure 

Photovoltaic panel manufacturing 

Activity data to be 
collected 

Plastic waste 
(incineration) 

Hazardous waste 
(incineration) 

Measurement; Yearly average 

Measurement; Yearly average 

Municipal solid waste 
(incineration) 

Measurement; Yearly average 

Wastewater (wastewater 
treatment plant) 

Measurement; Yearly average 

kg 

kg 

kg 

kg 

Distribution 

Activity data to be 
collected 

INPUT 

Specific requirements (e.g. frequency, measurement standard, etc) 

Unit of 
measure 

Photovoltaic panel, multi-
Si, at plant 

Specify the mass per m2 PV panel 
 Calculate the share of each facility in the European supply mix in case of 
several production sites 
 Yearly average 

m2 

Freight train transport 

Lorry transport 

Ship transport 

Airplane transport 

OUTPUT 

Photovoltaic panel, multi-
Si, at regional storage 

Calculation (mass x distance) 
 Transport of PV panels from the production site to a regional storage in 
Europe 
 Use a weighted average in case of several production sites 
 Yearly average 

Calculation (mass x distance) 
 Transport of PV panels from the production site to a regional storage in 
Europe 
 Use a weighted average in case of several production sites 
 Yearly average 

Calculation (mass x distance) 
 Transport of PV panels from the production site to a regional storage in 
Europe 
 Use a weighted average in case of several production sites 
 Yearly average 

Calculation (mass x distance) 
 Transport of PV panels from the production site to a regional storage in 
Europe 
 Use a weighted average in case of several production sites 
 Share in supply logistics in a 3 years period 

kgkm 

kgkm 

kgkm 

kgkm 

m2 

95 

 
 
 
  
  
Specific requirements (e.g. frequency, measurement standard, etc) 

Unit of 
measure 

Monocrystalline (Mono-Si) Silicon PV modules 

Photovoltaic Cell manufacturing 

Activity data to be 
collected 

INPUT 

Electricity 

Measurement (e.g. electricity meter or electricity bills); Yearly average 

Thermal energy from 
natural gas 

Measurement; Yearly average 

Wafer, mono-Si 

Bill of materials of the PV panel (including manufacturing losses) 
 Yearly average 

Metallization paste, front 
side 

Bill of materials of the PV panel (including manufacturing losses) 
 Yearly average 

Metallization paste, back 
side 

Bill of materials of the PV panel (including manufacturing losses) 
 Yearly average 

Metallization paste, back 
side, aluminium 

Bill of materials of the PV panel (including manufacturing losses) 
 Yearly average 

Ammonia 

Measurement; Yearly average 

Phosphoryl chloride 

Measurement; Yearly average 

Isopropanol 

Measurement; Yearly average 

Hydrochloric acid 

Measurement; Yearly average 

Hydrogen fluoride 

Measurement; Yearly average 

Sodium hydroxide 

Measurement; Yearly average 

Lime 

Measurement; Yearly average 

Tetrafluoroethane 
(R134a) 

Measurement; Yearly average 

Silane (silicon 
tetrahydride) 

Nitrogen 

Tap water 

Measurement; Yearly average 

Measurement; Yearly average 

Measurement (e.g. water meter or water bills); Yearly average 

Freight train transport 

Calculation (mass x distance) 
 Transport of all input materials to the production site and transport of 
waste materials to treatment site 
 Yearly average 

Lorry transport 

Calculation (mass x distance) 
 Transport of all input materials to the production site and transport of 

96 

MJ 

MJ 

m2 

kg 

kg 

kg 

kg 

kg 

kg 

kg 

kg 

kg 

kg 

kg 

kg 

kg 

kg 

kgkm 

kgkm 

 
 
 
  
Photovoltaic Cell manufacturing 

Activity data to be 
collected 

Specific requirements (e.g. frequency, measurement standard, etc) 

Unit of 
measure 

waste materials to treatment site 
 Yearly average 

Calculation (mass x distance) 
 Transport of all input materials to the production site and transport of 
waste materials to treatment site 
 Yearly average 

Calculation (mass x distance) 
 Transport of all input materials to the production site and transport of 
waste materials to treatment site 
 Share in supply logistics in a 3 years period 

Ship transport 

Airplane transport 

OUTPUT 

Photovoltaic cell, mono-Si 

Hazardous waste 
(incineration) 

Measurement; Yearly average 

Wastewater (wastewater 
treatment plant) 

Measurement; Yearly average 

Photovoltaic Panel manufacturing 

kgkm 

kgkm 

m2 

kg 

kg 

Activity data to be 
collected 

Specific requirements (e.g. frequency, measurement standard, etc) 

Unit of 
measure 

INPUT 

Electricity 

Diesel 

Measurement (e.g. electricity meter or electricity bills); Yearly average 

Measurement; Yearly average 

Photovoltaic cell, mono-Si 

Bill of materials of the PV panel (including manufacturing losses) 
 Yearly average 

Aluminium alloy 

Bill of materials of the PV panel (including manufacturing losses) 
 Yearly average 

Copper wire 

Bill of materials of the PV panel (including manufacturing losses) 
 Yearly average 

Tin 

Lead 

Diode 

Bill of materials of the PV panel (including manufacturing losses) 
 Yearly average 

Bill of materials of the PV panel (including manufacturing losses) 
 Yearly average 

Bill of materials of the PV panel (including manufacturing losses) 
 Yearly average 

MJ 

kg 

m2 

kg 

kg 

kg 

kg 

p 

97 

 
 
 
  
  
Photovoltaic Panel manufacturing 

Activity data to be 
collected 

Specific requirements (e.g. frequency, measurement standard, etc) 

Unit of 
measure 

Solar glass 

Bill of materials of the PV panel (including manufacturing losses) 
 Yearly average 

Flat glass tempering 

Bill of materials of the PV panel (including manufacturing losses) 
 Yearly average 

Ethylene vinyl acetate 
(EVA) 

Bill of materials of the PV panel (including manufacturing losses) 
 Yearly average 

Polyvinyl fluoride (PVF) 

Bill of materials of the PV panel (including manufacturing losses) 
 Yearly average 

Polyethylene 
terephthalate (PET) 

Bill of materials of the PV panel (including manufacturing losses) 
 Yearly average 

High-density polyethylene 
(HDPE) 

Bill of materials of the PV panel (including manufacturing losses) 
 Yearly average 

Silicone 

Bill of materials of the PV panel (including manufacturing losses) 
 Yearly average 

Glass fibre reinforced 
plastic 

Bill of materials of the PV panel (including manufacturing losses) 
 Yearly average 

Tap water 

Measurement (e.g. water meter or water bills); Yearly average 

Hydrogen fluoride 

Measurement; Yearly average 

Potassium hydroxide 

Measurement; Yearly average 

1-propanol 

Measurement; Yearly average 

Isopropanol 

Measurement; Yearly average 

Soap 

Measurement; Yearly average 

Corrugated board 

Measurement; Yearly average 

Pallet, wood 

Estimation 
 Default reuse rate: 25 trips 

kg 

kg 

kg 

kg 

kg 

kg 

kg 

kg 

kg 

kg 

kg 

kg 

kg 

kg 

kg 

kg 

Freight train transport 

Lorry transport 

Calculation (mass x distance) 
 Transport of all input materials to the production site and transport of 
waste materials to treatment site 
 Yearly average 

Calculation (mass x distance) 
 Transport of all input materials to the production site and transport of 
waste materials to treatment site 
 Yearly average 

Ship transport 

Calculation (mass x distance) 
 Transport of all input materials to the production site and transport of 

kgkm 

kgkm 

kgkm 

98 

 
 
 
Photovoltaic Panel manufacturing 

Activity data to be 
collected 

Specific requirements (e.g. frequency, measurement standard, etc) 

Unit of 
measure 

waste materials to treatment site 
 Yearly average 

Calculation (mass x distance) 
 Transport of all input materials to the production site and transport of 
waste materials to treatment site 
 Share in supply logistics in a 3 years period 

kgkm 

Airplane transport 

OUTPUT 

Photovoltaic panel, mono-
Si, at plant 

Specify the size of the PV panel 

Plastic waste 
(incineration) 

Hazardous waste 
(incineration) 

Measurement; Yearly average 

Measurement; Yearly average 

Municipal solid waste 
(incineration) 

Measurement; Yearly average 

Wastewater (wastewater 
treatment plant) 

Measurement; Yearly average 

m2 

kg 

kg 

kg 

kg 

Distribution 

Activity data to be 
collected 

INPUT 

Specific requirements (e.g. frequency, measurement standard, etc) 

Unit of 
measure 

Photovoltaic panel, mono-
Si, at plant 

Specify the mass per m2 PV panel 
 Calculate the share of each facility in the European supply mix in case of 
several production sites 
 Yearly average 

m2 

Freight train transport 

Lorry transport 

Calculation (mass x distance) 
 Transport of PV panels from the production site to a regional storage in 
Europe 
 Use a weighted average in case of several production sites 
 Yearly average 

Calculation (mass x distance) 
 Transport of PV panels from the production site to a regional storage in 
Europe 
 Use a weighted average in case of several production sites 
 Yearly average 

kgkm 

kgkm 

Ship transport 

Calculation (mass x distance) 
 Transport of PV panels from the production site to a regional storage in 

kgkm 

99 

 
 
 
  
  
Distribution 

Activity data to be 
collected 

Airplane transport 

OUTPUT 

Photovoltaic panel, mono-
Si, at regional storage 

Specific requirements (e.g. frequency, measurement standard, etc) 

Unit of 
measure 

Europe 
 Use a weighted average in case of several production sites 
 Yearly average 

Calculation (mass x distance) 
 Transport of PV panels from the production site to a regional storage in 
Europe 
 Use a weighted average in case of several production sites 
 Share in supply logistics in a 3 years period 

kgkm 

m2 

[1] https://ec.europa.eu/environment/publications/recommendation-use-environmental-footprint-methods_  
[2] https://ec.europa.eu/docsroom/documents/46532 -  
[3] https://eplca.jrc.ec.europa.eu/permalink/Guide_EF_DATA.pdf 
[4] The contribution of the sub-categories “Climate change – biogenic” and “Climate change – land use and 

land transformation” to the total climate change impacts is lower than 5 %. The environmental 
impacts shall therefore only be calculated for the category “Climate change” but not for the sub-
categories “Climate change – biogenic” and “Climate change – land use and land transformation”. 

100 

 
 
 
  
 
 
Annex B – Energy efficiency index and energy yield over the lifetime 

Specifically, as in the Annex III of the policy proposal, the EEIM is expressed in kWh/m2 and calculated 
as follows for each of the three European reference climatic conditions: ‘temperate coastal’, 
‘temperate continental’ and ‘subtropical arid’:  

where  
EEIM_c is the energy efficiency index for a module placed in a European reference climate c, 
expressed in kWh/m2.  
EYM(DC)Y1c 
conditions in the reference climate c, assuming no degradation, expressed in kWh.  

is the DC energy yield from a photovoltaic module over one year under the climatic 

AM is the area of the photovoltaic module, expressed in m2.  

The photovoltaic module is assumed to be ground-mounted on a rack that is fixed open and facing 
the equator with an angle of inclination of 20º. Degradation and other losses due to soiling or 
shadows from surrounding obstacles are not considered. Ground albedo is not considered for 
bifacial PV modules.  

For photovoltaic modules with integrated micro-inverters, the EEI shall be determined without the 
micro-inverter.  

For monofacial PV modules, the yearly DC energy yield (EYM(DC)_Y1,kWh) shall be calculated using the 
input data: 

—  matrix of Pmax versus irradiance (at AM1.5) and versus module temperature, which may 
be interpolated or extrapolated to obtain the instantaneous power at a given irradiance 
and module temperature. For linear modules, the Pmax dependence on irradiance and on 
temperature are independent;  

—  thermal  coefficients  u0  and  u1  describing  the  module  operating  temperature  as  a 
function of irradiance, ambient temperature and wind speed, which are used to calculate 
the instantaneous module temperature;  

—  the angle of incidence response ar, used to calculate the effective light transmission into 

the module at different incident angles;  

—  spectral responsivity, used to calculate spectral mismatch and hence to correct reference 

spectral conditions;  

—  standard  reference  climatic  profiles  for  the  reference  climatic  conditions  relevant  to 
Europe, defined as ‘subtropical arid’, ‘temperate continental’ and ‘temperate coastal’.  

Pmax is the DC power output of a module measured at specific temperatures and irradiances 
according to IEC 61853-1.  

The method does not include degradation or losses other than those due to the effect of the angle 
of incidence, spectral response or module efficiency dependence on irradiance and temperature.  

The DC energy yield of the module over its first year of installation EYM(DC)Y1 is calculated as follows:  

101 

 
 
 
 
 
 
 
 
where  

j ranges from 1 to 8 760 in the reference period (one year).  

EYM, j is the energy output of the module in the period j (1 hour), expressed in kWh, and is calculated 
as:  

𝐸𝑌𝑀,𝑗 =   𝑃𝑀,𝑗(𝐺𝑐𝑜𝑟𝑟,𝑗, 𝑇𝑀,𝑗)   ∙ 1 ℎ𝑜𝑢𝑟 

PM, j is the module power output for the jth hour (W)  

Gcorr,j is the corrected global in-plane irradiance for the jth hour (W/m2)  

TM,j is the module temperature for the jth hour (ºC)  

The EYM(DC)Y1 is one of the outputs along with the CSER (Climate Specific Energy Rating) 

They are related as follows:  

The DC energy yield of the module over is calculated as follows:  

where  

Pmax,STC is the DC power output of a module under STC or STC (air mass 1.5 spectrum, 1000 W/m2, 
25°C) measured according to IEC 61853-1.  

Gref is 1 000 W/m2, the irradiance used to measure the Pmax,STC , which is the maximum power output 
of the PV module under STC conditions.  

Hp is the total hourly global in-plane irradiance, expressed in kWh/m2 for the reference climatic 
period (1 year).  

from the yearly DC energy yield (EYM(DC)_Y1, kWh), by considering 30 years of service life (TLT) and 
factoring in the degradation rate (τdeg M, %/year). 

102 

 
 
 
 
 
 
 
 
 
 
 
 
 
Where 

—  EYM(DC)_Y1  is  the DC  energy  yield  delivered  by  one  PV module  over  the  first  year  of 

installation under the applicable reference climate conditions, expressed in kWh. 

—  TLT is lifetime of the PV module, which is assumed as 30 years. 

—  𝜏𝑑𝑒𝑔_M  is  the  PV  module  lifetime  performance  degradation  rate,  expressed  here  in 

decimal format. A degradation rate of 1% should be applied here as 0.01. 

The following prescribed values have to be applied for the corresponding technology:  

—  Degradation  rate  for  all  crystalline  silicon  wafer  technologies,  including  those  with 

heterojunction passivation layers: 0.5 % per year. 

—  Degradation rate for thin film and multijunction photovoltaic modules using a crystalline 

silicon absorber plus any thin film absorber: 

For monofacial and bifacial PV modules, the DC total energy yield over the service life (EYM(DC) LT), 
shall be calculated from the yearly DC energy yield (EYM(DC)Y1, kWh), multiplied by the years of 
service of service life (TLT) and factoring in the degradation rate (τdegM, %/year).  

where  

EYM(DC)Y1 is the DC energy yield delivered by the monofacial PV module over the first year of 
installation under the applicable reference climate conditions, expressed in kWh.  

EYMB(DC)Y1 
installation under the applicable reference climate conditions, expressed in kWh.  

is the DC energy yield delivered by the bifacial PV module over the first year of 

TLT is lifetime of the PV module. The lifetime of PV modules is determined by the number of years, 
indicated in the guarantee of the PV module, for which the guaranteed degradation rate of the 
photovoltaic module’s power output is not higher than the degradation rate given set out in Annex 
III.1.3, with a maximum limit of 30 years. If the PV module is sold without guarantee, or with a 
guaranteed degradation rate of the power output higher than the degradation rate given set out in 
Annex III.1.3, the assumed lifetime of the module will be 10 years.  

𝜏𝑑𝑒𝑔M is the PV module lifetime performance degradation rate, expressed here in decimal format. A 
degradation rate of 1% shall be applied here as 0.01.  

103 

 
 
 
 
 
Annex C – Preliminary screening of additional potential datasets needed for key 
and innovative PV technologies 

Name of materials 

PV technology 

Reference 

Link 

Phosphorus Oxychloride   PERC 

IEA PVPS 
TASK 12, 
2024 

Microsoft Word - IEA-PVPS-
LCA PERC technology 
v21.docx  

Amount of 
material  
0.000181 kg per 1 
m2 of PV  

Trimethylaluminum 
(TMA)  

PERC 

IEA PVPS 
TASK 12, 
2024 

Microsoft Word - IEA-PVPS-
LCA PERC technology 
v21.docx  

2.42E-04 kg per 1 
m2 of PV  

Boron trifluoride  

TOPCON 

Diborane  

TOPCON 

Phospine  

TOPCON 

Song et al., 
2024 

Song et al., 
2024 

Song et al., 
2024 

Iodine 

Si-perovskite tandem 
and single junction 
perovskite  

Leccisi et al., 
2021 

lead iodide 

Si-perovskite tandem 
and single junction 
perovskite  

Leccisi et al., 
2021 

0.0218 kg per 1 m2 
of PV  

n.a. 

n.a. 

0.933 per 1 kg PbI2 

0.00138 kg per 1 m2 
of PV  

Envirornmental and 
economic Assessment of 
China’s electricity sector for 
Carbon Neutrality (els-
cdn.com)  

Envirornmental and 
economic Assessment of 
China’s electricity sector for 
Carbon Neutrality (els-
cdn.com)  

Envirornmental and 
economic Assessment of 
China’s electricity sector for 
Carbon Neutrality (els-
cdn.com)  

Life cycle energy demand 
and carbon emissions of 
scalable single‐junction and 
tandem perovskite PV - 
Leccisi - 2021 - Progress in 
Photovoltaics: Research and 
Applications - Wiley Online 
Library  

Life cycle energy demand 
and carbon emissions of 
scalable single‐junction and 
tandem perovskite PV - 
Leccisi - 2021 - Progress in 
Photovoltaics: Research and 
Applications - Wiley Online 
Library  

104 

 
 
 
Name of materials 

PV technology 

Reference 

Link 

Hydrogen sulfide 

Si-perovskite tandem 
and single junction 
perovskite  

Leccisi et al., 
2021 

Methylammonium 
iodide 

Si-perovskite tandem 
and single junction 
perovskite  

Leccisi et al., 
2021 

Methylamine 

Si-perovskite tandem 
and single junction 
perovskite  

Leccisi et al., 
2021 

Ethanol 

Si-perovskite tandem 
and single junction 
perovskite  

Leccisi et al., 
2021 

Diethyl ether 

Si-perovskite tandem 
and single junction 
perovskite  

Leccisi et al., 
2021 

Life cycle energy demand 
and carbon emissions of 
scalable single‐junction and 
tandem perovskite PV - 
Leccisi - 2021 - Progress in 
Photovoltaics: Research and 
Applications - Wiley Online 
Library  

Life cycle energy demand 
and carbon emissions of 
scalable single‐junction and 
tandem perovskite PV - 
Leccisi - 2021 - Progress in 
Photovoltaics: Research and 
Applications - Wiley Online 
Library  

Life cycle energy demand 
and carbon emissions of 
scalable single‐junction and 
tandem perovskite PV - 
Leccisi - 2021 - Progress in 
Photovoltaics: Research and 
Applications - Wiley Online 
Library  

Life cycle energy demand 
and carbon emissions of 
scalable single‐junction and 
tandem perovskite PV - 
Leccisi - 2021 - Progress in 
Photovoltaics: Research and 
Applications - Wiley Online 
Library 

Life cycle energy demand 
and carbon emissions of 
scalable single‐junction and 
tandem perovskite PV - 
Leccisi - 2021 - Progress in 
Photovoltaics: Research and 
Applications - Wiley Online 
Library 

Amount of 
material  
0.139 kg per 1 kg 
CH3NH3I 

1.43E-04 kg per 1 
m2 of PV  

0.581 kg per 1kg of 
Methylammonium 
iodide 

7.31 kg per 1 kg 
CH3NH3I 

20.8 kg per 1 kg 
CH3NH3I 

105 

 
 
 
Name of materials 

PV technology 

Reference 

Link 

Ammonium thiocyanate   Si-perovskite tandem 

and single junction 
perovskite  

Leccisi et al., 
2021 

Monochlorobenzene 

Si-perovskite tandem 
and single junction 
perovskite  

Leccisi et al., 
2021 

N,N-
Dimethylformamide 

Si-perovskite tandem 
and single junction 
perovskite  

Leccisi et al., 
2021 

Toluene 

Si-perovskite tandem 
and single junction 
perovskite  

Leccisi et al., 
2021 

PCBM 

Si-perovskite tandem 
and single junction 
perovskite  

Leccisi et al., 
2021 

Life cycle energy demand 
and carbon emissions of 
scalable single‐junction and 
tandem perovskite PV - 
Leccisi - 2021 - Progress in 
Photovoltaics: Research and 
Applications - Wiley Online 
Library 

Life cycle energy demand 
and carbon emissions of 
scalable single‐junction and 
tandem perovskite PV - 
Leccisi - 2021 - Progress in 
Photovoltaics: Research and 
Applications - Wiley Online 
Library 

Life cycle energy demand 
and carbon emissions of 
scalable single‐junction and 
tandem perovskite PV - 
Leccisi - 2021 - Progress in 
Photovoltaics: Research and 
Applications - Wiley Online 
Library 

Life cycle energy demand 
and carbon emissions of 
scalable single‐junction and 
tandem perovskite PV - 
Leccisi - 2021 - Progress in 
Photovoltaics: Research and 
Applications - Wiley Online 
Library 

Life cycle energy demand 
and carbon emissions of 
scalable single‐junction and 
tandem perovskite PV - 
Leccisi - 2021 - Progress in 
Photovoltaics: Research and 
Applications - Wiley Online 
Library 

Amount of 
material  
0.0015 kg per 1 m2 
of PV Panel 

0.0004 per 1 m2 of 
PV Panel 

0.00283 kg per 1 m2 
of PV panel 

122.88 kg per 1 kg 
PCBM 

1.54E-04 kg per 1 
m2 of PV panel 

106 

 
 
 
Name of materials 

PV technology 

Reference 

Link 

Methyl cyclopentane 

Si-perovskite tandem 
and single junction 
perovskite  

Leccisi et al., 
2021 

Sodium hypochlorite 

Si-perovskite tandem 
and single junction 
perovskite  

Leccisi et al., 
2021 

Sulfur trioxide 

Si-perovskite tandem 
and single junction 
perovskite  

Leccisi et al., 
2021 

Nickel Oxide  

Si-perovskite tandem 
and single junction 
perovskite  

Leccisi et al., 
2021 

Life cycle energy demand 
and carbon emissions of 
scalable single‐junction and 
tandem perovskite PV - 
Leccisi - 2021 - Progress in 
Photovoltaics: Research and 
Applications - Wiley Online 
Library 

Life cycle energy demand 
and carbon emissions of 
scalable single‐junction and 
tandem perovskite PV - 
Leccisi - 2021 - Progress in 
Photovoltaics: Research and 
Applications - Wiley Online 
Library 

Life cycle energy demand 
and carbon emissions of 
scalable single‐junction and 
tandem perovskite PV - 
Leccisi - 2021 - Progress in 
Photovoltaics: Research and 
Applications - Wiley Online 
Library 

Life cycle energy demand 
and carbon emissions of 
scalable single‐junction and 
tandem perovskite PV - 
Leccisi - 2021 - Progress in 
Photovoltaics: Research and 
Applications - Wiley Online 
Library 

Amount of 
material  
0.85 kg per 1 kg 
PCBM 

0.344 kg per 1 kg 
PCBM 

0.479 per 1 kg PCBM 

0.0004 kg per 1 m2 
of PV panel 

zinc coating, pieces  

CdTe 

Fthenakis, 
2024 

Updated sustainability 
status of cadmium telluride 
thin‐film photovoltaic 
systems and projections 
(wiley.com)  

0.133 m2 per 1 m2 of 
PV panel 

107 

 
 
 
 
 
 
 
 
Details of the Silicon 
recycling process 
Innovative process for PV 
recycling, pyrometallurgic 
and idrometallurgic 
processes 
Innovative process 
allowing to separate the 
materials and remelt the 
high purity silicon 
contained in the kerf 
losses  
Technical feasibility 
laboratory test of thermal 
treatment 
Review of challenge and 
opportunities of silicon 
recycling processes and 
other materials 
The FORESi project aims 
at FOstering a Recycled 
European Silicon supply 
through an industrial 
demonstration of a 
cradle-to-cradle Silicon 
value chain 
The objective of the 
SIKELOR project was to 
develop an innovative 
electromagnetic 
technology for recycling 
silicon kerf loss which 
arises from slicing of 
silicon ingots into thin 
wafers. The technology 
proposed for reclaiming 
silicon from kerf loss 
combines methods for 
densification, purification, 
and casting of ingots.  
Review of the 
advancements in silicon 
recovery research and 
development over the last 
decade with key concepts, 
technologies, and 
challenges associated  

Reference 

Link 

Reference scale 

Latunussa et al., 
2016 Ardente et 
al., 2019 

https://www.sciencedirect.com/scienc
e/article/pii/S0927024816001227; 
https://www.sciencedirect.com/scienc
e/article/pii/S0956053X19302909 

Lab scale 

Rosi - Circular 
Economy for the 
PV industry 

Corcelli et al., 
2017 

https://www.rosi-solar.com/ 

Pilot - Industrial 
scale 

https://www.sciencedirect.com/scienc
e/article/pii/S0959652617309563 

Lab scale 

Tao et al., 2020 

https://onlinelibrary.wiley.com/doi/full
/10.1002/pip.3316 

Lab scale 

FORESi, FOstering 
a Recycled 
European Silicon 
supply 

https://sipow.no/foresi.html 

Industrial scale 

Lab scale 

Lab scale 

SIKELOR (Silicon 
kerf loss 
recycling) 

https://cordis.europa.eu/project/id/60
3718/reporting 

Yu et al., 2023 

https://www.sciencedirect.com/scienc
e/article/pii/S2452223623001189 

108 

 
 
 
 
 
 
Annex D – Details on carbon footprint results according to the scenarios under 
analysis  

In the study carried out on multi-silicon photovoltaic systems, we scrutinized a total of 15 different 
scenarios. The results, as depicted in the following Table, range from the least desirable scenario, 
which has a carbon footprint of 5.01 E-02 kgCO2 eq. per kWh, to the most desirable scenario, which 
shows a substantially reduced emission of 1.78 E-02 kgCO2 eq per kWh. This indicates a nearly 
threefold difference between the highest and lowest emission scenarios.  

In the context of photovoltaic production in country 4, using 0.646 kg of Silicon per square meter, a 
reduction in the product's lifespan from 30 years to 20 years results in a 1.5-fold increase in the 
carbon footprint. This means it increases from 3.34E-02 kgCO2 eq to 5.01 E-2 kgCO2 eq. Furthermore, 
when the quantity of Silicon per square meter is increased from 0.646 kg to 1.02 kg, the carbon 
footprint escalates by 1.2 times, reaching 4.14 E-2 kgCO2 eq. 

Description 

Photovoltaic scenario 

country1: Multi-Si 
(country1, 646g Si)  

All manufacturing processes are located 
in country1, with 30 years operation. 

Silicon 
[kg} 
per m2 

Climate 
Change 
[kg CO2 eq. 
per kwh] 

0.646 

1.78E-02 

country2:Multi-Si 
(country1, 646g Si)  

All manufacturing processes are located 
in country2, with 30 years operation. 

0.646 

2.63E-02 

country3: Multi-Si 
(country3, 646g Si 
25%PV all) 

All manufacturing processes are located 
in country3, with 25% electricity from PV 
in production, with 30 years operation. 

0.646 

2.65E-02 

country4: Multi-Si (CN, 
Si646g) 

All manufacturing processes are located 
in country4, with lower electricity 
consumed in production, with 30 years 
operation. 

0.646 

2.74E-02 

country4: Multi-Si 
(country4, Si646g,25%PV 
all processes) 

All manufacturing processes are located 
in country4, with 25% electricity from PV 
in production, with 30 years operation. 

0.646 

2.92E-02 

country3: Multi-Si 
(country3, 646g Si) 
except MG) 

country4: Multi-Si 
(country4, Si646g, 
25%PV for panel and 
cell) 

country4: Multi-Si 
(country4, Si646g, 
R1PEFCR) 

All manufacturing processes are located 
in country3, with the exception of MG in 
country2, with 30 years operation. 

All manufacturing processes are located 
in country4, with 25% electricity from PV 
in panel an cell production, with 30 years 
operation. 

All manufacturing processes are located 
in country4 with 30 years operation, with 
recycled aluminium 77% as input 

0.646 

2.93E-02 

0.646 

3.27E-02 

0.646 

3.29E-02 

1m 

2m 

3m 

4m 

5m 

6m 

7m 

8m 

9m 

country4: Multi-Si 
(country4, Si646g) 

All manufacturing processes are located 
in country4 with only 30 years operation 

0.646 

3.34E-02 

109 

 
 
 
 
Description 

Photovoltaic scenario 

country4: Multi-Si 
(country4, Si833g) 

All manufacturing processes are located 
in country4 with only 30 years operation 

Silicon 
[kg} 
per m2 

Climate 
Change 
[kg CO2 eq. 
per kwh] 

0.833 

3.74E-02 

country4: Multi-Si 
(country4, Si646g, 25Yrs) 

All manufacturing processes are located 
in country4 with only 25 years operation 

0.646 

4.01E-02 

country4: Multi-Si 
(country4, Si1020g) 

All manufacturing processes are located 
in country4 with only 30 years operation 

1.020 

4.14E-02 

country4: Multi-Si 
(country4, Si646g, 20Yrs) 

All manufacturing processes are located 
in country4 with only 20 years operation 

0.646 

5.01E-02 

country1: Multi-Si 
(country1, 646g Si)  

country4, Si 646g with 
default countries mix 
silicon production 

All manufacturing processes are located 
in country1, with the exception of the 
casting and wafer procedures, which are 
based in country2 with 30 years 
operation. 

All manufacturing processes are located 
in country4 except silicon production that 
some silicon produce in other countries 
e.g. Europe with only 30 years operation  

0.646 

2.06E-02 

0.646 

3.16E-02 

10
m 

11
m 

12
m 

13
m 

14
m 

15
m 

* Per kWh of the total DC electric energy generated over a photovoltaic module’s service life 

The following presents the carbon footprint of a multi-silicon photovoltaic panel at each stage of its 
life cycle for three different scenarios. The stages of transportation to regional storage and the end 
of life of the PV module yield the same results across all three scenarios. This is attributed to the 
assumption that these stages occur in Europe, and the same data sets are utilized to analyse the 
carbon footprint. Photovoltaics manufactured in country4 with a default silicon mix have a slightly 
lower impact than those with an entirely country4 supply chain. This can be explained by the fact 
that some of the silicon is produced in countries with a lower carbon footprint from electricity.  

110 

 
 
 
 
 
 
 
 
 
 
 
 
 
Example of Carbon footprint of mono silicon photovoltaic panel per life cycle stage: 
silicon content 646 g per m2 

No. 

Scenario 

Total  

raw 
material 
and 
production 

transporta
tion to 
regional 
storage 

End of Life 
of PV 
Module - 
Poly  

2m  Multi-Si, country2, Si646g, country2 

2.63E-02 

2.85E-02 

5.18E-04 

-2.72E-03 

supply Chain 

9m  Multi-Si, country4, Si646g, country4 

3.34E-02 

3.56E-02 

5.18E-04 

-2.72E-03 

supplyChain 

15
m 

Multi-Si, country4, Si 646g with 
default countries mix silicon 
production 

3.16E-02 

3.38E-02 

5.18E-04 

-2.72E-03 

The following Table presents the carbon footprint of CdTe PV photovoltaic panels. It is assumed that 
these systems generate 4,820.0 kWh*m2 of electricity over a 30-year lifespan, with the bill of 
materials conforming to the EC PV PEFCR. This assumption reflects the average performance of 
CdTe PV panels in the EU market during the development of the PEFCR. The stages of the life cycle 
that contribute most significantly to the carbon footprint are the acquisition of raw materials, pre-
processing, and module manufacturing, with electricity and glass being the primary contributors. 
The potential for material recycling offers a modest credit of 3%, as the major contributors to the 
carbon footprint are electricity and glass. It is, at present, only 9% of glass can be recycled. 

Carbon footprint of Cadmium-Telluride photovoltaic panels 

Overall, Carbon 
footprint 

[kgCO2 eq per kWh] 

Raw material acquisition, 
pre-processing, and 
module manufacturing 

[kgCO2eq per kWh] 

Distribution 

EoL 

[kgCO2eq per kWh] 

[kgCO2eq per kWh] 

1.18E-02 

1.15E-02 

6.87E-04 

-3.58E-04 

* Per kWh of the total DC electric energy generated over a photovoltaic module’s service life 

111 

 
 
 
 
 
 
 
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