trialling the pas 2050 on dulux...

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Trialling the PAS 2050 on Dulux Paint Environmental Resources Management A research report completed for the Department for Environment, Food and Rural Affairs

March 2010

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Published by the Department for Environment, Food and Rural Affairs

Department for Environment, Food and Rural Affairs Nobel House 17 Smith Square London SW1P 3JR Tel: 020 7238 6000 Website: www.defra.gov.uk © Queen's Printer and Controller of HMSO 2010 This publication is value added. If you wish to re-use this material, please apply for a Click-Use Licence for value added material at: http://www.opsi.gov.uk/click-use/value-added-licence-information/index.htm Alternatively applications can be sent to Office of Public Sector Information, Information Policy Team, St Clements House, 2-16 Colegate, Norwich NR3 1BQ; Fax: +44 (0)1603 723000; email: [email protected] Information about this publication is available from: SCP&W Evidence Base Defra Zone 5D, 5th Floor, Ergon House c/o Nobel House, 17 Smith Square London SW1P 3JR Email: [email protected]

http://www.defra.gov.uk/

http://www.opsi.gov.uk/click-use/value-added-licence-information/index.htm

mailto:[email protected]

mailto:[email protected]

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EV0426 Trialling the PAS 2050 on priority products

Final Report to the Department for Environment, Food and Rural Affairs

March 2010

This research was commissioned and funded by Defra. The views expressed reflect the research findings and the authors‟ interpretation; they do not necessarily reflect Defra policy or opinions.

ERM Ltd 2nd Floor Exchequer Court

33 St Mary Axe London EC3A 8AA

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TABLE OF CONTENTS

Executive Summary ................................................................................................. 2

Background ......................................................................................................................................... 2 Summary Findings .............................................................................................................................. 3 Ease/Difficulty of PAS Steps ............................................................................................................... 3 Areas for Potential Improvement in Guidance .................................................................................... 4 Consistency of the Footprint with PAS 2050 ....................................................................................... 5

1 Background and objectives ............................................................................ 6

2 Methods and approach .................................................................................... 8

3 Findings .......................................................................................................... 11

AkzoNobel‟s experience – Step 1 ..................................................................................................... 14 ERM commentary – Step 1 ............................................................................................................... 15 AkzoNobel‟s experience – Step 2 ..................................................................................................... 15 ERM commentary – Step 2 ............................................................................................................... 15 AkzoNobel‟s experience – Step 3 ..................................................................................................... 17 ERM commentary – Step 3 ............................................................................................................... 19 AkzoNobel‟s experience – Step 4 ..................................................................................................... 24 ERM‟s experience – Step 4............................................................................................................... 24

4 Summary ......................................................................................................... 30

Ease/Difficulty of PAS Steps ............................................................................................................. 31 Areas for Potential Improvement in Guidance .................................................................................. 32 Consistency of the Footprint with PAS 2050 ..................................................................................... 33

5 References ...................................................................................................... 35

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Executive Summary

Background

The PAS (Publically Available Specification) 2050 provides a standard method for calculating the greenhouse gas emissions embedded within the life cycle of a product or service. The method was developed by British Standards Institute (BSI), at the request of Defra and the Carbon Trust. It was launched in October 2008, following a period of consultation and testing. To assist the use and uptake of PAS 2050, Defra is running trials with products and services not tested to date, in order to identify the lessons learnt from the initial experiences of using the PAS 2050. This trial was to undertake a carbon footprint of an ICI paint product using the PAS 2050 method. During the course of the trial ICI became part of the AkzoNobel group of companies so the abbreviation "AkzoNobel " should be read as "ICI Paints AkzoNobel". The process was undertaken with a view to achieving the following objectives:

calculation of a cradle to grave carbon footprint for the paint product;

recording AkzoNobel‟s experiences in implementing the PAS 2050 and, in particular, difficulties encountered at each stage in the process; and

documenting the lessons learnt from AkzoNobel‟s experiences, in order to inform future revisions of the PAS 2050 and guidance documentation.

AkzoNobel has already undertaken some carbon footprinting of paint products and raw materials, resulting in the development of the Environmental Impact Analyser (EIA) streamlined LCA tool (http://www.Akzonobelpaints.com/news/pr-steward.jsp). As such, this trial was undertaken with the expectation of a different kind of insight to the others completed. As the AkzoNobel team are abreast of footprinting concepts and have already compiled a database of information for raw materials and paint production, the trial‟s learnings were, in particular, focused on:

the calculation of anticipated „post-production‟ greenhouse gas emissions, from factory gate through distribution, retail, consumer use and eventual disposal;

assessing the usability/utility of the PAS 2050 in comparison with AkzoNobel‟s own EIA tool; and

understanding divergences between the PAS 2050 and EIA, and potential barriers to aligning the PAS 2050 with existing tools.

The trial was undertaken by an R&D-based team from AkzoNobel, with assistance from an MSc student at Manchester Business School and input from AkzoNobel 's logistics supplier, TDG and one of their major retail customers, B&Q. ERM acted in an advisory capacity, assuming the role of Life Cycle Assessment (LCA) specialist.

http://www.icipaints.com/news/pr-steward.jsp

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Summary Findings

In general, the AkzoNobel team found the PAS 2050 process well structured, easy to follow and, given the internal work already completed on raw material data acquisition, relatively easy to apply. The process also yielded valuable insights into the full carbon footprint of a can of paint. The relative ease/difficulty of respective PAS Steps are summarised below, and Figure 4.1 shows the hours spent on each activity in the footprinting process. As expected, given the focus of the trial, the majority of AkzoNobel‟s effort was centred around the collection of data for post-production life cycle stages. The majority of ERM‟s effort was spent in reviewing the data used in footprint calculations for conformance with the PAS 2050 standard.

Figure1 Time Spent on Footprinting Exercise – Task by Task

Ease/Difficulty of PAS Steps

Step 1 – Process Map

AkzoNobel found Step 1 of the footprinting process to be relatively easy, as the team had a good knowledge of the life cycle of the product and prior experience of „life cycle thinking‟.

Both the Guide to PAS 2050 and external support were found to be useful in „clarifying thinking‟.

Step 2 – Boundaries and Prioritisation

As a result of the decision processes already undertaken during the development of the EIA tool, this step was also a simple one for AkzoNobel.

Data collection from suppliers has been carried out since 2007, beginning with the most important „core‟ chemicals and expanding across suppliers over time. As such,

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the best available data for raw material production is already included within tool, alongside generic datasets for smaller, less significant chemical inputs. Thus a prioritisation process has, in effect, already been undertaken

Step 3 – Data Collection

Data collection for raw materials and production processes was relatively straightforward for the AkzoNobel team, as the vast majority of information had already been both collected and analysed.

Data collection for post production life cycle stages was the most difficult aspect of the product assessment for the AkzoNobel team. Given the lack of existing and publically available information, new and specific information needed to be collected. In order to do this, a number of key contacts in the downstream supply chain were consulted, by means of a series of interviews.

It was found that the PAS 2050 and supporting guidance contained relatively little information to help to structure these interviews, and that this would have been welcome. Support from ERM was sought to identify key information for each stage in the post-production chain.

Step 4 - Calculation

The AkzoNobel team undertook the footprint calculations very well and understood the principles behind the multiplication of activity data and emission factors, and the flows between process stages. This was, in part, due to the effort already expended by team members in the development of the EIA tool.

Post production stages of the life cycle were, following initial guidance, very well, and thoroughly, addressed.

Support from ERM was used to verify the calculations undertaken, for peace of mind, but it was not felt that external assistance, or any other guidance, was required for this Step.

It appeared that there was benefit in discussing the footprinting process with customers and it was interesting to learn that one of the major retailers of consumer paint products had also undertaken a similar analysis for paint. This was used as a sense-check of the calculated footprint and served to increase confidence in the results.

Areas for Potential Improvement in Guidance

Difficulties encountered in undertaking the footprint were relatively few – and, as expected, centred on the collection of data for post-production life cycle stages. Based on the observations from this trial, suggested improvements/extensions to the PAS 2050 Guide would be focused on the structure of information provided. The Guide to PAS contains detailed examples of how post-production stages in the life cycle should be calculated, and the type of information needed. But this information did not appear to be in an accessible format for AkzoNobel when embarking on data collection activities. The PAS guidance may be more useful in this respect if presented in a more „practical‟ arrangement, such as a list of prompts for things to consider for each area, questions that might be asked of suppliers and wider data sources.

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Consistency of the Footprint with PAS 2050

A detailed review of the emissions data used was carried out by ERM, in order to understand where differences between the PAS 2050 and EIA lie. The differences found could be categorised into two groups, as follows.

The scope of primary data for raw materials contained within the EIA tool. Primary data have been collected for a number of chemicals as part of the ongoing development of the EIA. Suppliers provide information on raw materials, wastes, energy and transport. Energy and transport burdens are converted into CO2e emissions using Defra conversion factors (or factors provided by the supplier where specifically relevant). However, raw material inputs and wastes are not converted in CO2e emissions. This means that, whilst the data used are very detailed and specific to the raw material in question, they do not consider the entirety of „upstream‟ emissions that occur in the extraction and pre-processing of ingredients and so are not consistent with PAS 2050 boundaries.

The use of Defra emission factors to convert fuel and energy consumptions into CO2 emissions. Defra GHG reporting factors (2008) have been used in the majority of cases to translate information on fuels, energy and transportation into CO2 emissions. These factors only account for the CO2 associated with burning fuel, and so are not compliant with PAS 2050 boundaries.

The likely significance of these divergences from PAS 2050 were also considered. It was found that, in combination, the implication of using Defra GHG reporting factors to convert fuel consumption into emissions across the life cycle of paint was found to be in the region of 100 g CO2e per 5 litre can. This equates to approximately 1-2% of the total, cradle-to-grave, footprint – ie a relatively insignificant amount. The data review undertaken also suggests that the majority of data used in the footprint calculations were either consistent with values reported elsewhere, or insignificant to the footprint. The one exception is the dataset used for titanium dioxide production. Emissions associated with titanium dioxide production, as extracted from the EIA, are relatively low in comparison with proprietary datasets for this process. AkzoNobel are aware of this deviation, but understandably prefer to use primary data from their specific supplier in calculating a footprint. Given the importance of this chemical to the product footprint (30% of total emissions), this is the right choice. However, it is recommended that the dataset is examined in more detail to understand the reason for it being lower than generic estimates, and to confirm that it conforms with PAS 2050.

Barriers to the Use and Alignment of PAS 2050

The main barrier to the use and alignment of PAS 2050 with AkzoNobel‟s EIA tool is in the scope/boundaries of the data contained within the EIA. In some cases, this was found not significantly to affect the footprint, but in others further analysis would be required to ensure PAS 2050 compliance. This would not be an insurmountable task, as the majority of the effort – the collection of primary data from suppliers – has already been undertaken.. A further barrier is in the availability of a consistent set of PAS-compliant GHG conversion factors for use by companies in reporting product footprints (as opposed to corporate reporting). Although the use of Defra‟s corporate reporting factors was not found significantly to affect the footprint results in this trial, for other products this could be reversed.

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1 Background and objectives

Background

The PAS (Publically Available Specification) 2050 provides a standard method for calculating the greenhouse gas emissions embedded within the life cycle of a product or service. The method was developed by British Standards Institute (BSI), at the request of Defra and the Carbon Trust. It was launched in October 2008, following a period of consultation and testing. During this development phase, a range of products and services were tested against the draft method and the resulting experiences were used to revise requirements and to inform supporting documentation. To assist the use and the uptake of PAS 2050, Defra are running further trials with products and services not tested to date, in order to identify the lessons learnt from the initial experiences of using the PAS 2050. The assessment of AkzoNobel‟s Dulux white matt paint is one of these trials.

Project Aim & Objectives

The aim of this project was to undertake a carbon footprint of an AkzoNobel paint product using the PAS 2050 method. The process was undertaken with a view to achieving the following objectives:

calculation of a cradle to grave carbon footprint for the paint product;

recording AkzoNobel‟s experiences in implementing the PAS 2050 and, in particular, difficulties encountered at each stage in the process; and

documenting the lessons learnt from AkzoNobel‟s experiences, in order to inform future revisions of the PAS 2050 and guidance documentation.

AkzoNobel has already undertaken some carbon footprinting of paint products and raw materials, resulting in the development of the Environmental Impact Analyser (EIA) streamlined life cycle assessment (LCA) tool (http://www.Akzonobelpaints.com/news/pr-steward.jsp). As such, this trial was undertaken with the expectation of a different kind of insight to the others completed. As the AkzoNobel team are abreast of footprinting concepts and have already compiled a database of information for raw materials and paint production, the trial‟s learnings were, in particular, focused on:

the calculation of anticipated „post-production‟ greenhouse gas emissions, from factory gate through distribution, retail, consumer use and eventual disposal;

assessing the usability/utility of the PAS 2050 in comparison with AkzoNobel‟s own EIA tool; and

understanding divergences between the PAS 2050 and EIA, and potential barriers to aligning the PAS 2050 with existing tools.



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This Report

The scope of this report is not specifically to document the results of footprint calculations, but to record AkzoNobel‟s experience of using the PAS 2050 and accompanying guidance materials. The report sets out:

a summary of steps and tools used to undertake the trial;

the AkzoNobel team and ERM‟s experiences in undertaking each step in the process;

resources expended in calculating the footprint;

specific technical issues encountered, and how they were resolved;

the usefulness of supporting guidance documentation to the PAS 2050; and

recommendations as to how guidance could be improved.

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2 Methods and approach

Consistent with PAS 2050 guidance, a stepwise approach to the trial was undertaken. This involved the steps set out in Figure 2.1, extracted from the Guide to PAS 2050. This process provided a structure for the trial, but given AkzoNobel‟s experience with carbon footprinting to date, some steps were not carried out at as great a level as detail as for the other Defra trials. Further detail on the approach to each step taken is set out below.

Figure 2.1 Steps to Calculating a Carbon Footprint (Guide to PAS 2050, 2008)

© Crown 2008 and Carbon Trust 2008

Start up

An initial meeting between Defra, ERM (the LCA specialist) and the AkzoNobel team was held in order to:

clarify the objectives of the trial;

discuss the trial product and supply chain;

run through the PAS 2050 process and potential differences with the approach to carbon footprinting currently undertaken within AkzoNobel;

discuss the information available in the Environmental Impact Analyser; and

identify additional data needs – predominantly for the post-production stages of the life cycle of paint.

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Calculating the footprint

Step 1 – Process map Following the start–up meeting, the AkzoNobel team agreed the specific product and route to market to be footprinted and drew up a process map of the product‟s life cycle, from raw material production through to final disposal. Step 2 – Boundaries & Prioritisation The process map defined in Step 1 also outlined the boundaries of the assessment. This included all raw materials, transport and processing steps involved in the manufacture of the consumer product, its distribution, retail, use and disposal. As a result of AkzoNobel‟s existing database of carbon footprint information for raw materials, paint and packaging production, there was not any need to exclude any materials from the assessment. Data prioritisation efforts were instead focused on identifying the information required for post-production impacts, and potential data sources. Step 3 – Data Primary data for raw materials, paint and packaging production have been collated by AkzoNobel as part of the ongoing development of the EIA tool. Data questionnaires are issued to suppliers via the purchasing team and the information received is converted into an estimate of greenhouse gas emissions for each material or process. This information was extracted from the EIA for all raw materials, transport steps and production processes in the manufacture of the paint and product packaging. Additional primary information was collected for the distribution, storage and retail steps of the product life cycle via interviews with internal logistics personnel, key logistics contractors and retail outlets. Secondary data were sourced to inform estimates of the use and end of life stages of the product life cycle. Step 4 – Calculation The AkzoNobel team used the process map drawn up in Step 1 as the basis for the footprint calculation exercise. Each box in the process map was identified with a unique number and a set of supporting footprint calculations was subsequently presented for each numbered item/process. ERM reviewed the calculations/assumptions made at various stages in the project and provided advice on gap filling and any inconsistencies with the PAS 2050.

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Step 5 – Uncertainty The uncertainty analysis step is an optional one and was not carried out in this trial. This step may be adopted by AkzoNobel for PAS 2050 calculations in the future, but has not been undertaken as yet.

Recording of experiences

In order to support this report and to document the experiences of using the PAS 2050 in the AkzoNobel, and other, trials, a PAS 2050 „Understanding and Learnings‟ template was developed. This template comprised a standard set of questions and metrics to appraise the company‟s implementation of the specification, in terms of:

experiences;

feedback;

learnings with regard to the pros and cons of the PAS 2050;

additional support and material needs; and

resources expended throughout the trial.

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3 Findings

This section records the experiences of using the PAS 2050 in the AkzoNobel Dulux paint trial, specific lessons learnt throughout each footprint calculation step and any specific technical issues encountered. The section is split into findings for each PAS Step, documenting the tasks undertaken, reasoning, outcomes and resource needs (time). This is followed by a summary of AkzoNobel‟s experience in carrying out the Step and ERM‟s commentary on any issues encountered.

Start up

A number of AkzoNobel personnel attended the initial project meeting, including representatives from Sustainability, Marketing and R&D functions. The meeting served to provide background to the aims of the trial for the AkzoNobel team; and an outline of the paint supply chain and EIA tool for Defra and ERM. AkzoNobel were aware of the PAS 2050 and recognised the need to align existing footprinting efforts with the PAS as it becomes established as the UK standard for product carbon footprinting. The EIA tool was developed in partnership with Carillion and Forum for the Future. Data collection for the tool commenced in 2007, when information for around 20 core materials was compiled. Now, the tool contains data for all raw materials (>100) used in paint production. Suppliers are provided with a data questionnaire by the procurement team as a matter of course. The questionnaire asks suppliers to provide information on quantities and fate of wastes, total raw materials, water use, greenhouse gas emissions (directly or indirectly through energy consumption) and transport distances/modes for product and raw materials transport. The EIA currently supports all AkzoNobel paint products on the market in Europe with an extension to US and Chinese markets being investigated. It is used by product teams to compare and to contrast the potential environmental impacts of paints with different formulations. An estimate of margins of error is also provided. The tool produces a carbon footprint (as well as other environmental indicators) for each product from „cradle‟ - ie raw material production - to „factory gate‟ ie the point at which a finished, packaged product is ready to leave the production site for onwards distribution. It does not contain any information on the emissions associated with distributing the product to a retail outlet, any storage or consolidation points along the way, storage at a retail outlet, use by the consumer or eventual disposal. This is with the exception of the greenhouse gas emissions associated with the release of VOCs (volatile organic compounds) when the paint is used. The AkzoNobel team was, in particular, interested in using the trial to expand the boundaries of the product footprint calculation to include these post-production stages. Furthermore, it was considered an important step to investigate to the consistency of the EIA approach in comparison with PAS 2050.

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A full project initiation was delayed for a period following the start-up meeting due to changes in the personnel responsible for overseeing the footprint calculations. The project was then subsequently re-initiated by means of a series of telephone conferences and meetings. The AkzoNobel team identified to members of the R&D function to lead the project, to enable overlap with those involved in the ongoing use of the EIA tool. An MSc student from the Manchester Business School was also invited to work alongside the team and undertake key data collection tasks.

Calculating the footprint

Step 1 – Process map Following the start–up meeting, the AkzoNobel team confirmed the specific product to be assessed as one 5 litre tin of Dulux UK Retail Pure Brilliant White Matt. This is the biggest volume paint product sold in the UK. The functional unit calculated was one 5 litre tin. AkzoNobel didn't, for this trial, consider any other functional unit, as the product chosen is their biggest seller and representative of many products across the range. Within the EIA, account is taken of differences in spreading rate when making comparisons between products. This comparative form of functional unit is used to inform consumers as to which product offers the lowest carbon footprint per unit of wall covered (at acceptable coverage). For this trial, no product comparisons were made, but AkzoNobel considers that the same logic would apply to PAS 2050 product footprints when communicating to consumers. A process map of the product life cycle was drawn up, and is shown in Figure 3.1. A summary of the tasks undertaken and effort expended as part of this PAS step is shown in Table 3.1.

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Figure 3.1 Process Flow Map for Dulux UK Retail White Matt Paint (1 x 5 litre)

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Table 3.1 Footprinting Activities – Step 1

PAS Step

Task/ Activity

Process Undertaken to Complete this Task

Time Taken – AKZONOBEL (hours)

Support from ERM (hours)

Key Assumptions/ Reasoning Made

Outcome from Task

Step 1

Defining functional unit for study

The product was identified following the kick off meeting between Defra,

AkzoNobel and ERM

5 (meetings)

5 (meetings)

The product selected was considered to be the most representative of

AkzoNobel‟s UK

paint business. The unit assessed reflects the unit in which the product is consumed by the end user.

Product selection and unit of assessment.

Building process map

The process map was developed by

AkzoNobel using

knowledge of the full paint journey from cradle to grave. ERM reviewed for completeness and consistency with the PAS 2050. Only one element was amended – the exclusion of the transport step from retail to consumer.

3 2 The process map included all inputs, processes, waste flows, transport steps and other possible drivers of emissions.

Process map

AkzoNobel’s experience – Step 1

AkzoNobel found Step 1 of the footprinting process to be relatively easy, as the team had a good knowledge of the life cycle of the product and prior experience of „life cycle thinking‟. Both the Guide to PAS 2050 and external support were found to be useful. “It was useful to see suggested boundaries and how it should be laid out. Having an example made it easier to understand, the guidance was clear and helpful”. “Mostly, I used the guidelines to understand the overall purpose, and this was clear. The example given for the flow chart was very useful”. “Without external support we may have missed some of the waste issues and mistakenly included transportation from retail to consumer in the assessment”. Overall the process of undertaking Step 1 was considered to be a very useful in “clarifying thinking”.

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ERM commentary – Step 1

No difficulties were observed in the AkzoNobel team undertaking this task. The team primarily completed the process map themselves, with only very minimal steer given at the start-up and review stage. The guidance given in the PAS was found to be helpful and, as for other trials, this Step in general was found to be a useful scene-setting one. Step 2 – Boundaries & Prioritisation The process map defined in Step 1 was used as an outline of the boundaries of the assessment. This included all raw materials, transport and processing steps involved in the manufacture of the consumer product, its distribution, retail, use and disposal. As a result of AkzoNobel‟s existing database of carbon footprint information for raw materials, paint and packaging production, there was not any need to exclude any materials from the assessment. A list of raw materials and processing operations involved in the production of Pure Brilliant White Matt was obtained from development, manufacturing and supply teams as relevant. Data prioritisation efforts were mainly focused on identifying the information required in order to characterise post-production impacts associated with paint distribution, retail, use and disposal. No specific elements were excluded from the analysis. In total, the time taken to complete this step was only around 1-2 hours, with a similar amount of time expended by ERM in discussing available data and defining post-production stages.


Having already characterised and collected data for upstream stages in the life cycle, the AkzoNobel team found this step relatively easy. There was no need to exclude any raw materials or process steps from the assessment, because representative data were available. External support from ERM was relied upon in order to characterise data needs and boundaries for the post-production stages of the life cycle, and in particular the approach taken to dealing with use and end-of-life. For this reason, it was AkzoNobel‟s consideration that, in all likelihood, they couldn‟t have undertaken this step without external help.


As a result of the decision processes already undertaken during the development of the EIA tool, this step was a simple one. Data collection from suppliers has been carried out since 2007, beginning with the most important „core‟ chemicals and expanding across suppliers over time. As such, the best available data for raw material production is already included

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within the tool, alongside generic datasets for smaller, less significant chemical inputs. Thus a prioritisation process has, in effect already been undertaken. All relevant post-production stages were selected for inclusion in the assessment. Two aspects are of particular interest for paint are discussed below.

The use of paint is a passive process, not incurring any energy for application. There are, however, other consumables used in the application of paint that require consideration. For example, brushes/rollers to apply the paint; and water to wash the brushes/rollers. PAS 2050 guidance on the inclusion or otherwise of such consumables within the footprint is that they should be included (Section 6.4.4). A „consumable‟ is then defined in Section 3.13 as an “ancillary input that is necessary for a process to occur but does not form a tangible part of the product… Consumables differ from capital goods in that they have an expected life of one year or less”. In this assessment, water (and subsequent wastewater treatment) was included in the paint footprint, but hardware like brushes/rollers, which can have a longer lifespan and multiple uses, were not.

With regard to the fate of applied paint at end-of-life, there is obvious uncertainty around the length of time the product will remain „in-situ‟, or be removed for disposal. The temporal scope of the PAS 2050 is 100 years and, as such, consideration should be given to the onwards transport and management of any paint that is removed within that period. Given the uncertainty surrounding this, and the fact that the greenhouse burden associated with any onwards transport or management will be minimal (it will most likely end up in landfill or recycled along with other building waste), this aspect was excluded from the assessment.

Step 3 – Data Specific data for the raw materials, production processes and packaging materials that comprise Dulux Pure Brilliant White Matt paint were extracted from the EIA tool for use in the footprint calculations. These were predominantly in the form of kg CO2e per kg of material or process. During the data collection stage, ERM undertook a high level review of the data extracted from the EIA with regard to alignment with PAS boundaries. A summary of this review is presented in Table 3.3. Additional primary information was sought for the distribution, storage and retail steps of the product life cycle via interviews with internal logistics personnel, TDG – AkzoNobel‟s provider of logistics services, and B&Q, a major paint retailer. Secondary data and assumptions were compiled in order to inform estimates of the use and end of life stages of the product life cycle. A summary of the tasks undertaken and effort expended during data collection activities is shown in Table 3.2.

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Raw materials and production processes Data collection for raw materials and production processes was relatively straightforward for the AkzoNobel team, as the vast majority of information had already been both collected and analysed. The main tasks were to collate information relating to the composition of the product under assessment and to extract the relevant data from the EIA tool. The team members managing the footprint calculations were well versed with EIA and so obtaining and interpreting the relevant information was not difficult. Post-production life cycle stages Data collection for post production life cycle stages was the most difficult aspect of the product assessment for the AkzoNobel team. Given the lack of existing and publically available information, new and specific information needed to be collected. In order to do this, a number of key contacts in the downstream supply chain were consulted:

internally, AkzoNobel‟s logistics function:

TDG – AkzoNobel‟s external logistics contractor; and

B&Q – one of AkzoNobel‟s largest customers and a major consumer paint retailer. Consultation was predominantly by means of a series of interviews held by the MSc student assisting AkzoNobel‟s team. It was found that the PAS 2050 and supporting guidance contained relatively little information to help to structure these interviews, and that this would have been welcome. Support from ERM was sought to identify key information for each stage in the post-production chain. For example, the specific data points needed for the assessment, the appropriate questions to ask in order to obtain them, and the format in which they might typically be available/not available. The Guide to PAS 2050 does contain the necessary information to formulate these kinds of interview questions. The „croissants example‟ shows in detail the types of information required to calculate distribution, storage and retail impacts for a product. However, this would be perhaps more accessible if presented in a „practical‟ arrangement, such as a list of prompts for things to consider for each area, and some advice on the types of format that information might be available in. For example, when considering distribution stages, a first step would be to establish whether the organisation in question operates its own fleet, or if transportation is sub-contracted. If an in-house fleet is operated, then it is likely that an estimate of litres of fuel consumed per volume of product can be determined (either simply total fuel purchased divided by total product output, or adjusting for differences in vehicle or destination). If sub-contracted, then an emissions calculation may need to be based on distances travelled, types of vehicles used and an estimate of how optimised the delivery network is (eg how much empty space on average). The AkzoNobel team considered that if they had not had external assistance in this area, the data gathering stage would have been more difficult and taken longer.

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PAS Step

Task/ Activity


Time Taken

AkzoNobel (hours)


Key Assumptions/ Reasoning Made

Outcome from Task

Step 3

Primary data collection

Extraction of CO2 impacts for the relevant raw materials, transport steps and production processes from the Environmental Impact Analyser.

5 - Specific formulation and mass flows for the product under assessment were obtained and relevant data extracted from the EIA.

Emission factors (kg CO2e per kg) for all relevant materials and processes.


Scoping of data requirements for post-production stages

2 2 Energy and fuel inputs were considered to be the key data points required for distribution and retail steps. Water consumption and wastewater treatment were considered during use and end-of-life

List of data requirements and interview questions


Interviews with logistics personnel, TDG and B&Q plus subsequent collation of information provided

22.5 3 n/a Data to calculate distribution and retail stages in the life cycle

Data checking/ verification

High-level review of EIA data sources undertaken by ERM.

Review of information provided by TDG and B&Q undertaken internally

within the AkzoNobel project team.

5 6 + meetings

EIA data sources reviewed for PAS compliance using expert judgement

Reviewed datasets and amendments

Secondary data collection

Sourcing emission factors for water, transportation and waste management to fill data gaps and inform use and end of life assumptions.

22.5 3 Key data sources were Defra GHG conversion factors, Water UK literature, Defra waste research and contact with WRAP (paint programme)

Emission factors (kg CO2e per litre water /per tkm transport etc) to fill data gaps.

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Raw materials and production processes

The AkzoNobel team were already well advanced in the collection of primary data for paint manufacture and raw materials. Where gaps in primary data existed, representative secondary datasets had also been collated within the EIA tool. As a first step in the data collection process, relevant data availability and sources were extracted from the EIA database and ERM undertook a high level review of the information to hand, and its likely alignment to PAS 2050. A summary of this review is presented in Table 3.3.

Table 3.3 Initial Data Review

Process Source and Likely Consistency with PAS

Production of chemical raw materials and water

Primary data have been collected for a number of chemicals as part of the ongoing development of the EIA. For the Dulux Pure Brilliant White Matt product, datasets for Titanium dioxide and extenders were extracted from the EIA database of primary information. Suppliers provide information on raw materials, wastes, energy and transport. Energy and transport burdens are converted into CO2e emissions using Defra conversion factors (or factors provided by the supplier where specifically relevant). However, raw material inputs and wastes are not converted in CO2e emissions. This means that, whilst the data used are very detailed and specific to the raw material in question, they do not consider the entirety of „upstream‟ emissions that occur in the extraction and pre-processing of ingredients and so are not consistent with PAS boundaries. In some cases, additional, generic information is added – for example mining data from Anglo American are used to supplement the titanium dioxide production data. It is likely that these omissions in the life cycle/inconsistencies with PAS have greater significance for some raw materials and processes than others. Further consideration was given to this in Step 4 – footprint calculations.

Generic data for the production of chemical monomers and co-solvents are sourced from the Plastics Europe database. This is a widely used life cycle inventory database that contains data representative of chemicals production in Europe. It is the source most typically used in life cycle and carbon footprinting studies and is entirely suitable for use in this footprint.

Alongside production data for solvents, information from the Defra report on Climate Change Consequences of VOC Emission Controls (2007) was used to quantity the CO2e emissions associated with the release of solvent volatile organic compounds (VOCs) when the paint is used. It is assumed that all of the solvents will be vaporised and released when the paint is used, which is an appropriately conservative approach.

Data for water production are sourced from Anglian water. Although unlikely significantly to affect the final footprint, a relatively high quantity of water is used across production processes and so it was recommended that this emission factor be cross-checked against the values published by Water UK.

Production of latex

Data relating to materials and energy consumption and wastes from latex

production were sourced from AkzoNobel‟s Slough site, reflecting 2007

production. Materials and waste balances were determined from actual consumptions. Process energy was allocated to latex production using an estimated % of site energy use for this specific process. This is consistent with the allocation method of preference, as stated in PAS Section 8.1.1: to separate individual processes wherever possible and determine inputs and outputs for each.

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Process Source and Likely Consistency with PAS

Production of paint

As for latex production, data relating to materials and energy consumption and

wastes from paint production were sourced from AkzoNobel‟s Slough site,

reflecting 2007 production. Materials and waste balances were determined from actual consumptions. Process energy was allocated to paint production using and estimated % of site energy use for this specific process.

Production of packaging

Data for packaging polymer production were sourced from an Australian life cycle calculator tool www.greenflyonline.org. This enables specific emission factors for polymers with different recycled content to be calculated. Average UK plastic packaging recycling rates for 2005 were used in this context to

calculate an emission factor for the polypropylene used in AkzoNobel‟s paint packaging. This approach is consistent with the PAS guidance, which states that an approach “which factors in the recycling rate across the entire material system” is applicable.

Transportation Defra GHG reporting factors (2008) have been used to translate information on transport distances into CO2 emissions.

http://www.defra.gov.uk/environment/business/reporting/pdf/ghg-cf-guidelines-annexes2008.pdf. These factors only account for the CO2 associated with burning fuel. They do not include the burdens of producing the fuel, or transporting it to point of use. As such, these emission factors underestimate life cycle emissions associated with transportation and are not compliant with PAS 2050 boundaries. The PAS states that upstream emissions from producing fuels/energy and transporting fuels should be included in the assessment. The likely significance of the use of these factors with regard to the final footprint estimate is considered further in Step 4.

Management of wastes

Data to describe the management of solid wastes are not available in the EIA

tool. This was a data gap that the AkzoNobel team found difficult to fill.

However, solid waste landfill is relatively insignificant in the paint production process, as the majority of wastes are discharged in the wastewater stream, and any solid wastes (eg upstream in the supply chain or associated with product packaging) are predominantly comprised of inert materials that do not degrade in landfill and so are not a significant source of greenhouse gas emissions.

Data for wastewater treatment are sourced from Water UK. This is the data source also commonly used in many life cycle studies and carbon footprints and is considered to be complete and appropriate for use in this study.

Post-production life cycle stages

As discussed earlier, it was found that there was some difficulty on the AkzoNobel team‟s part in identifying the information required to characterise post-production impacts, and how they might go about compiling it. In terms of the types of difficulty/barrier encountered, initial questions (and responses) were as follows.

How do we average the distribution impacts over different customer routes (direct to store or via a distribution centre) and different distances. Is there an established method?

o In terms of distribution route, the approach that should be taken is to estimate – for the product in question - the % that are distributed direct to store and the % that are distributed via a warehouse. You will need to calculate a CO2e impact for each route and then calculate a weighted average across all route based on the % split.

http://www.greenflyonline.org/

http://www.defra.gov.uk/environment/business/reporting/pdf/ghg-cf-guidelines-annexes2008.pdf


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o To characterise a route that involves multiple end points – ie in this case a nationwide network of stores – is an estimate, but there are several ways to tackle it, for example total vehicle miles/fuel litres allocated across all products (if you operate your own fleet), or maximum/ minimum/ midpoint, or a high level analysis of where hubs are distributed across the country.

o Most important is clearly to record the assumption and then consider its impact on the final footprint. If it is identified as an important contribution, then this would be an area that you might want to look into further, with a view to optimisation.

How do we calculate our RDC impacts per litre of paint?

o Do you operate your own RDCs? If so, you just need high level data on energy per year, and throughput (volume or number of pallets/products per pallet) per year. If not, then you would ideally ask your distributor for this information.

How can we work out how far on average the customer travels from the store?

o This step isn‟t included in the PAS footprint boundary.

How do we work out how much waste a customer produces on average, what is done with this and how far the waste travels?

o A first port of call would be to look for published work in this area – eg WRAP, Defra, AkzoNobel internal? There may have been some research on household paint recovery/ reuse rates.

o For many products, it is necessary to make some assumptions for the use and end-of-life profile of the product. This is fine, as long as assumptions are sensible, and it is important, as ever, to record assumptions and to be aware of their significance.

o In terms of the distance waste travels, it is often appropriate to make a simplifying assumption here (eg 20 km travel) and to consider its significance on the final footprint.

Having talked through the information required in more detail, and undertaken interviews with key actors in the downstream supply chain, a good dataset for these life cycle stages was collected by the AkzoNobel team. The interview-based approach was very forthcoming; and the meetings seemed to be also useful in the context of working with the supply chain and discussing different footprinting work underway.

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Step 4 – Calculation The AkzoNobel team used the process map drawn up in Step 1 as the basis for the footprint calculation exercise. Each box in the process map was identified with a unique number and a set of supporting footprint calculations was subsequently presented for each numbered item/process.

Figure 3.2 Paint Life Cycle – numbered process steps

Each set of calculations identified the material or process „emission factor‟ extracted from the EIA or from secondary data; the mass flow information relevant to the product being assessed; and showed the workings used to combine these in order to calculate the carbon footprint for the process step. The emissions for each process step were summed to generate both a „cradle to gate‟ and „cradle to grave‟ footprint. At this stage, ERM undertook a more detailed data review, looking not just at data sources, but the emission factors used. This step was undertaken in order to inform the likely significance of deviations from the PAS 2050 earlier identified. The outcome of this process is described further in this Section. A summary of the tasks undertaken and effort expended as part of this PAS step in general is shown in Table 3.4.

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PAS Step

Task/ Activity


Time Taken – Akzo Nobel (hours)


Key Assumptions/ Reasoning Made Outcome from Task

Step 4

Structuring footprint calculations

A process flow chart was developed in PowerPoint with each process/activity allotted a number. Subsequent slides for each process/activity set out the data source and any calculations and assumptions used to determine the emissions estimate for the footprint. A separate slide summed the emissions for each process step to generate both a „cradle to gate‟ and „cradle to grave‟ footprint.

20 - Calculations at each stage followed the general format of emission factor x mass flow = emissions. Setting out calculations made at each stage is useful for transparency and will enable the re-visiting of assumption/data sources in future.

PowerPoint slides detailing each life cycle process step, calculation and data sources for each and the resulting footprint.

Review ERM reviewed the emission factors used and subsequent calculations and provided advice on addressing any inconsistencies.

Cross-checks were also made with the EIA and evaluation undertaken independently by B&Q.

2 6 Checks were made in order to ensure the following:

- mass balances correct (inputs and wastes, water and wastewater);

- material and energy inputs of the right order (with reference to previous experience or literature);

- secondary emission factors selected of the right order (with reference to previous experience or literature and a preference for conservative values); and

- all appropriate steps accounted for (eg waste management and transport of wastes for each process).

Reviewed model and recommended updates.

Recommendations were based on importance/ significance. For example, minor omissions such as waste transport were noted, but not recommended to be amended (balance of effort and output).

Clarification and amendment

1

-

[at this stage discussions are ongoing to some degree to finalise the footprint]

Final footprint

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Compiling data from the EIA and the information collected on post-production stages was found to be a relatively easy task and was undertaken without any external support. It was also not felt that the PAS 2050 guidance was specifically needed for this task. Cross-checks were undertaken internally, in particular with regard to post-production stages of the life cycle. Calculations were completed in parallel by AkzoNobel‟s internal team and the MSc student supporting the team. This was found to be a useful exercise and highlighted areas in which suppliers‟ data were ambiguous and could be interpreted in different ways. A cross-check of the final footprint could also be undertaken, as B&Q has been involved in a University of Manchester project, CCaLC, to calculate the life cycle impacts of paint. The emissions for a litre of paint calculated using the CCaLC tool (for a generic, solvent-free paint) is within 10 grams of the final value calculated by AkzoNobel in this trial. Given the uncertainties involved in any footprint calculation, this is a very strong correlation and gave the AkzoNobel team confidence in their findings. In addition to calculations for the life cycle stages including within the PAS 2050 scope (those outlined in the process map), an exercise was carried out to estimate the potential emissions associated with consumer transport of paint, from retail outlet to household. It was found that this stage in the life cycle could be a potentially significant source of emissions. Under a hypothetical scenario where a consumer trip from store to home is dedicated to one can of paint, a total of 5.7kg CO2e per product was estimated.

ERM’s experience – Step 4

The AkzoNobel team undertook the footprint calculations very well and understood the principles behind the multiplication of activity data and emission factors, and the flows between process stages. This was, in part, due to the effort already expended by team members in the development of the EIA tool. Post production, as well as raw material and manufacture, stages of the life cycle were, following initial guidance, very well, and thoroughly, addressed. It appeared that there was benefit in discussing the footprinting process with customers and it was interesting to learn that one of the major retailers of consumer paint products had also undertaken a similar analysis for paint. Specific difficulties were few, but those encountered are outlined in Table 3.5. A review of the emissions data used in AkzoNobel‟s footprint calculations was carried out. The results of this review are summarised below.

Production of surfactants and monomers used in latex production

o Emissions associated with surfactant production as extracted from the EIA are relatively low. Using a typical value for a fatty alcohol sulphonate from the Ecoinvent database, and taking into account dilution rate, emissions associated with the production of this chemical input would increase by approximately 10 grams CO2e. This is less than 1% of the total footprint and so can be considered insignificant.

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o Emissions associated with monomer production as extracted from the EIA are high in comparison with equivalent process data in the Ecoinvent database. However, this is due to the inclusion of the potential global warming impacts resulting from emissions of these chemicals as volatile organic compounds (VOCs) during painting. These additional emissions equate to approximately 1 kg CO2e for a 5 litre tin of paint.

Latex production

o Emissions associated with latex production (not including raw materials) are predominantly associated with energy consumption. Defra GHG conversion factors were used to translate electricity and fuel consumptions for the process into CO2e. In using this dataset, this excludes the upstream extraction, processing and transport of energy carriers and so is not entirely consistent with the PAS 2050. However, it is a publicly available source commonly used in footprint assessments. Emissions associated with upstream processes in electricity production contribute approximately 15% to the total life cycle emissions. If the emissions estimate for latex production were scaled according to this, the footprint per 5 litres of paint would be increased by 17 grams.

Production of other paint ingredients

o Emissions associated with titanium dioxide production, as extracted from the EIA, are relatively low in comparison with proprietary datasets for this process. Using the value for titanium dioxide production from the Ecoinvent database, emissions associated with the production of this chemical input would increase by approximately 1.8 kg CO2e for a 5 litre can. This is clearly a significant difference. AkzoNobel are aware of this deviation from generic estimates for titanium dioxide production, but understandably prefer to use primary data from their specific supplier in calculating a footprint. Given the importance of this chemical to the product footprint (30% of total emissions), this is the right choice. However, it is recommended that the dataset is examined in more detail to understand the reason for it being lower than generic estimates, and to confirm that it conforms to PAS 2050, or amend as such.

o Emissions associated with the primary VOC-related ingredients, as extracted from the EIA, are high in comparison with approximately equivalent process data in the Ecoinvent database. However, as for latex monomers, this is due to the inclusion of the potential global warming impacts resulting from subsequent VOC emissions during painting. These additional emissions equate to approximately 500 g CO2e for a 5 litre tin of paint.

o Emissions estimates for all other chemical inputs are either broadly consistent with other life cycle inventory sources, or make only very minor contributions to the final footprint.

Paint manufacture

o Emissions associated with paint manufacture (not including raw materials) are predominantly associated with energy consumption. Defra GHG conversion factors were again used to translate electricity and fuel consumptions for the process into CO2e. As earlier noted, using this dataset excludes the upstream extraction, processing and transport of energy carriers and so is not entirely consistent with the PAS 2050. If the emissions estimate for paint production were scaled to include these, the footprint per 5 litres of paint would be increased by 33 grams CO2e.

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Production of packaging materials

o The emission factor for polypropylene pellet production was sourced from the material supplier and amended to reflect „average recycled content‟ for UK polypropylene production. The resulting emission factor suggests that this recycled content is very small, based on the differential between the emission factor extracted from the EIA and life cycle emissions for a similar process from the Ecoinvent database. As earlier noted, the approach taken is a suitable one, and consistent with PAS. It would, of course, be more representative to have a specific recycled content from the can manufacturer. However, packaging raw materials contribute around 6% to the total footprint and so a generic approach is both reasonable and consistent with PAS 2050.

o Data relating to packaging manufacture (moulding, coating and printing) were sourced from RPC and converted into CO2e using Defra GHG conversion factors. As such, the same issue as earlier noted is relevant and if emissions were scaled to include upstream fuel production emissions, the footprint per 5 litres of paint would be increased by 41 grams CO2e.

Distribution and retail

o Data provided by AkzoNobel‟s internal logistics team and TDG with regard to paint transport from production site to customer warehouse were in the form „litres of diesel per 5 litre can‟. This was then converted into greenhouse gas emissions using Defra conversion factors.

o For transport from warehouse to retail, B&Q provided an estimated average distance from central warehouse to an average store. A corresponding fuel consumption for this distance/mass of goods travelled was calculated with reference to Defra‟s greenhouse gas reporting guidelines and then converted into greenhouse gas emissions.

o Fuel consumption data were found to be reasonable when cross-referenced against other sources. However, the use of Defra conversion factors for fuel consumption excludes upstream extraction burdens, as noted earlier. If an estimate of these burdens were included, then the footprint of distribution transport stages would be increased by 22 g CO2e for a 5 litre can.

o B&Q provided information on gas and electricity consumption at their central warehouse and an estimate of the area of this warehouse occupied by paint delivered by AkzoNobel. These were combined with total quantities of paint delivered by TDG to the B&Q warehouse in order to determine gas and electricity consumption per litre, and per 5 litre can of paint. Defra greenhouse gas reporting factors were used to convert these to CO2e.

o With regard to retail impacts, B&Q provided information on the average yearly CO2 emissions per m2 of shop floor. Total sales space was then sourced from B&Q‟s website and total yearly deliveries of paint, plus estimated residence time, were used to estimate the % of sales space occupied by AkzoNobel paint, and one 5 litre can.

o Resultant emissions from these calculations were found to be reasonable when cross-referenced against other sources – although warehouse emissions were approximately twice those reported elsewhere. However, we would consider that a reasonable estimate has been made. The data source for B&Q‟s store emissions estimate is unknown, but likely to be consistent with the PAS scope (ie including upstream burdens) if calculated as part of the CCaLC project.

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Use

o An estimate of the amount of water used in washing brushes/rollers for a 5 litre can of paint was made by AkzoNobel. Water consumption and wastewater treatment burdens were quantified using emission factors published by Water UK. There is a good deal of uncertainty around the estimated water consumption, but the total emissions associated with water delivery and wastewater management for this life cycle stage contribute less than 1% to the life cycle total - even if the consumption estimate was doubled, or quadrupled. As such, we can be confident that the assumptions made for this life cycle stage are not unduly influencing the footprint calculated.

End of life

o The fate of all packaging materials at end of life was assumed to be landfill/residual waste, as a worst case scenario for household management. However, the greenhouse gas accounting implications of this assumption are negligible, as landfill of non-degradable materials does not incur a greenhouse gas burden. Likewise, recycling and incineration do not incur greenhouse burdens or benefits for the Dulux product - they are assumed to generate useful products (recyclate and energy) and so any process burdens are allocated to these products, and not the paint life cycle.

o It was assumed that approximately 25% of paint, on average, is unused by consumers (a value determined by AkzoNobel in previous work). There are services offered to collect and re-use/recycle this waste paint, such as „Community rePaint‟, but few take advantage of these schemes. As such, it was assumed that the paint would be disposed via a household drain, diluted approximately 10 times in water. Wastewater treatment burdens were quantified using the emission factor published by Water UK. This is a fairly generic factor, and it is reasonable to believe that paint might have a higher treatment demand than normal household dirty water. An alternative, and more specific, option would be to consider the relative chemical oxygen demand (COD) for the wastewater stream and estimate the energy burden required to remove this COD loading. ERM has carried out a similar task when calculating the carbon footprint of household detergents. This exercise showed that there is some difference in the two approaches to calculating emissions, but that these are relatively small. End of life contributes less than 1% to the paint life cycle and so we would consider that the generic approach taken gives a reasonable estimation of potential greenhouse gas impacts.

In combination, the implication of using non-PAS aligned Defra GHG reporting factors to convert fuel consumption into emissions across the life cycle of paint was found to be in the region of 100 g CO2e per 5 litre can. This equates to approximately 1-2% of the total, cradle-to-grave, footprint – ie a relatively insignificant amount. The detailed review undertaken suggests that the majority of data used in the footprint calculations were consistent with other life cycle inventories. The only element that would need to be addressed further for the analysis to more fully align with PAS 2050 is the dataset collated for titanium dioxide production. Primary data from AkzoNobel‟s supplier have been used in the assessment. However, the scope and boundaries of these data need to be examined in more detail in order to confirm conformance with PAS 2050, or amend as such. A number of assumptions were required in the calculation of emissions for post-production stages in the life cycle of paint. However, in all cases these assumptions were considered to be reasonable and conservative (a requirement of the PAS 2050). The total contribution of post-production stages to the life cycle of paint was found to be approximately 2%.

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Table 3.5 Specific Difficulties Encountered by Technical Issue

Technical Issue Footprint Calculation Considerations

Allocation of process emissions to co-products

The AkzoNobel team had addressed the need to allocate production site emissions between different processes during the development of the EIA tool. The approach taken was to estimate the proportion (%) of energy consumed by specific operations across the site. As such, the need to allocate energy consumption by more arbitrary means was avoided (eg the relative quantity/value of different outputs from the site).

Rectification of process data to functional unit

This was not a difficultly for the paint footprint, as material and process data were extracted from the EIA on a „per kg‟ basis and only needed a density value to be converted to litres. Accounting for mass loss and wastage can be an accounting difficulty in some process chains, but the yield for latex and paint production is high and the vast majority of wastes can be internally recycled.

Accounting for post production impacts

This was the main time-consuming aspect of the data collection stage of the footprint calculation. However, following meetings with suppliers, a good data set was complied for post-production impacts.

Assessing data quality (and suitability for use)

In order to determine the suitability of data used in the footprint and alignment with PAS 2050, an external source was relied on (ERM). However, within the EIA tool there is also a consideration given to uncertainty, based on

AkzoNobel‟s judgement of data quality (gold, silver, bronze and unclassified). Reported carbon reductions (% savings) are down-played according to whether the assessed formulations are calculated with predominantly gold/silver/bronze data. For post-production stages, internal cross-checks were made to ensure the correct interpretation of data provided.

Filling data gaps The approach taken to filling data gaps has been outlined in each instance in

this report. The AkzoNobel team did not encounter any major issues that could not be addressed, with the exception of waste management impacts. However, these are of low significance for the paint footprint.

Accounting for 100-year temporal boundary (eg weighting/ carbon storage)

The stage at which 100-year accounting rules became relevant for the paint life cycle was at end-of-life (proportion remaining in-situ, or requiring further management). However, this was considered to be a very minor element of the footprint and so was not addressed in detail.

Distinguishing between treatment of fossil and biogenic carbon

This was not applicable in the paint life cycle.

Handling of non-CO2 GHG emissions

VOCs are the major non-CO2 emissions sources relevant to the paint life cycle. The burdens associated with solvent VOC emissions were quantified by assuming that all solvents in the paint formulations are ultimately released into the atmosphere. Subsequent VOC conversions to CO2 were not assessed in detail, but the method used was in line with Defra guidance and appropriate factors are assumed to have been used.

Double-counting of emissions?

The burdens of VOC emissions were accounted as part of the raw material impacts of paint production. If also accounted during paint use (where arguably they are best placed) there would have been overlap. However, the

AkzoNobel team were aware of this issue.

Accounting for land use change


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Technical Issue Footprint Calculation Considerations

Accounting for variable supply chains or markets

Data from variable suppliers is already accounted, appropriately, in the primary datasets collated in the EIA tool. With regard to variable markets, this was not considered for this assessment, and it would be difficult to accurately do so. The major route to market was selected for study and, based on the relatively small contribution of these stages to total life cycle emissions, this was considered to be representative for the product assessed.

Accounting for onsite generation of electrAkzonobelty


Emissions scaling This was not applicable in the paint life cycle, as no raw material inputs were omitted. No account was taken of potential for wastage in the distribution and retail chain (eg damaged or unsold product), but this is likely to be very small for this type of non-perishable, non-fragile product.

Step 5 – Uncertainty The uncertainty analysis step is an optional one and was not carried out in this trial. A cross-check of the final footprint was undertaken by comparing the value with a footprint for a generic, solvent-free paint provided by B&Q. The latter estimate was calculated in conjunction with the University of Manchester, as part of the CCaLC project. Both footprints were very closely aligned, giving the AkzoNobel team greater confidence in their findings.

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4 Summary

Overall, the trial was a very successful one. AkzoNobel had already undertaken some carbon footprinting of paint products and, as such, the anticipated insight was of a slightly different form to the PAS 2050 other trials. The focus was on understanding:

the relative ease of undertaking the PAS 2050 process where AkzoNobel had existing footprinting methods in place;

insights, in particular, from the calculation of „post-production‟ impacts during paint distribution, retail, use and eventual disposal;

the divergences between AkzoNobel‟s internal EIA tool and the PAS 2050‟s requirements; and

any barriers to the use and alignment of PAS 2050 with AkzoNobel‟s existing tool. In general, the AkzoNobel team found the PAS 2050 process very useful to deliver a view of the full carbon footprint of a can of paint, which complimented the in-house AkzoNobel work. The dataset compiled for the post-production stages of the paint life cycle can be directly used in the EIA tool to characterise impacts for other paint products. The good agreement in the output of PAS 2050, AkzoNobel's Environmental Impact Analyser and some proprietary data supplied by B&Q on a generic paint gave confidence as to the validity of the results. Completing the PAS process also highlighted areas of data sourcing - raw materials and transport - where some additional validation was needed to ensure total PAS compliance of the footprint calculation and AkzoNobel's Environmental Impact Analyser. This is now underway and is due for completion shortly. The relative ease/difficulty of respective PAS Steps are summarised below, and Figure 4.1 shows the hours spent on each activity in the footprinting process. As expected, given the focus of the trial, the majority of AkzoNobel‟s effort was centred on the collection of data for post-production life cycle stages. The majority of ERM‟s effort was spent in reviewing the data used in footprint calculations for conformance with the PAS 2050 standard. It should be noted that the AkzoNobel team were also supported by an MSc student from Manchester Business School, without whose help they would have struggled with resources to carry out the full study.

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Figure 4.1 Time Spent on Footprinting Exercise – Task by Task

Ease/Difficulty of PAS Steps

Step 1 – Process Map

AkzoNobel found Step 1 of the footprinting process to be relatively easy, as the team had a good knowledge of the life cycle of the product and prior experience of „life cycle thinking‟.

Both the Guide to PAS 2050 and external support were found to be useful in „clarifying thinking‟.

Step 2 – Boundaries and Prioritisation

As a result of the decision processes already undertaken during the development of the EIA tool, this step was also a simple one for AkzoNobel.

Data collection from suppliers has been carried out since 2007, beginning with the most important „core‟ chemicals and expanding across suppliers over time. As such, the best available data for raw material production is already included within the tool, alongside generic datasets for smaller, less significant chemical inputs. Thus a prioritisation process has, in effect, already been undertaken

Step 3 – Data Collection

Data collection for raw materials and production processes was relatively straightforward for the AkzoNobel team, as the majority of information had already been both collected and analysed.

Data collection for post production life cycle stages was the most difficult aspect of the product assessment for the AkzoNobel team. Given the lack of existing and publically available information, new and specific information needed to be collected.

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In order to do this, a number of key contacts in the downstream supply chain were consulted, by means of a series of interviews.

It was found that the PAS 2050 and supporting guidance contained relatively little information to help to structure these interviews, and that this would have been welcome. Support from ERM was sought to identify key information for each stage in the post-production chain.

The AkzoNobel team considered that if they had not had external assistance in this area, the data gathering stage would have been more difficult and taken longer.

Step 4 - Calculation

The AkzoNobel team undertook the footprint calculations very well and understood the principles behind the multiplication of activity data and emission factors, and the flows between process stages. This was, in part, due to the effort already expended by team members in the development of the EIA tool.

Post production stages of the life cycle were, following initial guidance, very well, and thoroughly, addressed.

Support from ERM was used to verify the calculations undertaken, for peace of mind, but it was not felt that external assistance, or any other guidance, was required for this Step.

It appeared that there was benefit in discussing the footprinting process with customers and it was interesting to learn that one of the major retailers of consumer paint products had also undertaken a similar analysis for paint. This was used as a sense-check of the calculated footprint and served to increase confidence in the results.

An exercise was carried out to estimate the potential emissions associated with consumer transport of paint, from retail outlet to household. It was found that this stage in the life cycle could be a potentially significant source of emissions. Under a hypothetical scenario where a consumer trip from store to home is dedicated to one can of paint, a total of 5.7kg CO2e per product was estimated.

Areas for Potential Improvement in Guidance

Difficulties encountered in undertaking the footprint were relatively few – and, as expected, centred on the collection of data for post-production life cycle stages. Based on the observations from this trial, suggested improvements/extensions to the PAS 2050 Guide would be focused on the structure of information provided. The Guide to PAS contains detailed examples of how post-production stages in the life cycle should be calculated, and the type of information needed. But this information did not appear to be in an accessible format for AkzoNobel when embarking on data collection activities. Support from ERM was sought to understand the specific data points needed for the assessment, the appropriate questions to ask to obtain them, and the format in which they might typically be available/not available. The PAS guidance may be more useful in this respect if presented in a more „practical‟ arrangement, such as a list of prompts for things to consider for each area, questions that might be asked of suppliers and wider data sources. For example, when considering distribution stages, a first step would be to establish whether the organisation in question operates its own fleet, or if transportation is sub-contracted. If an in-house fleet is operated,

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then it is likely that an estimate of litres of fuel consumed per volume of product can be determined (either simply total fuel purchased divided by total product output, or adjusting for differences in vehicle or destination). If sub-contracted, then an emissions calculations may need to be based on distances travelled, types of vehicles used and an estimate of how optimised the delivery network is (how much empty space on average). It was also reported that one of the most useful aspects of the Guide to PAS was the „what not to include‟ boxes. This is a useful means of communicating key information.

Consistency of the Footprint with PAS 2050

A detailed review of the emissions data used in AkzoNobel‟s footprint calculations was carried out by ERM, in order to understand where differences between the PAS 2050 and EIA lie. The differences found could be categorised into two groups:

The scope of primary data for raw materials contained within the EIA tool. Primary data have been collected for a number of chemicals as part of the ongoing development of the EIA. Suppliers provide information on raw materials, wastes, energy and transport. Energy and transport burdens are converted into CO2e emissions using Defra conversion factors (or factors provided by the supplier where specifically relevant). However, raw material inputs and wastes are not converted in CO2e emissions. This means that, whilst the data used are very detailed and specific to the raw material in question, they do not consider the entirety of „upstream‟ emissions that occur in the extraction and pre-processing of ingredients and so are not consistent with PAS 2050 boundaries.

The use of Defra emission factors to convert fuel and energy consumptions into CO2 emissions. Defra GHG reporting factors (2008) have been used in the majority of cases to translate information on fuels, energy and transportation into CO2 emissions. These factors only account for the CO2 associated with burning fuel (in an engine or power plant). They do not include the burdens of producing the fuel, or transporting it to point of use. As such, these emission factors underestimate life cycle emissions associated with fuel use and are not compliant with PAS 2050 boundaries. The PAS states that upstream emissions from producing fuels/energy and transporting fuels should be included in the assessment.

The likely significance of these divergences from PAS 2050 was also considered. It was found that in combination, the implication of using Defra GHG reporting factors to convert fuel consumption into emissions across the life cycle of paint was found to be in the region of 100 g CO2e per 5 litre can. This equates to approximately 1-2% of the total, cradle-to-grave, footprint – ie a relatively insignificant amount. The data review undertaken also suggests that the majority of data used in the footprint calculations were either consistent with values reported elsewhere, or insignificant to the footprint. For example, a number of assumptions were required in the calculation of emissions for post-production stages. However, the total contribution of post-production stages to the life cycle of paint was found to be approximately 2%. The one exception is the dataset used for titanium dioxide production. Emissions associated with titanium dioxide production, as extracted from the EIA, are relatively low in comparison with proprietary datasets for this process. AkzoNobel are aware of this deviation, but understandably prefer to use primary data from their specific supplier in calculating a

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footprint. Given the importance of this chemical to the product footprint (30% of total emissions) this is the right choice. However, it is recommended that the dataset is examined in more detail to understand the reason for it being lower than generic estimates, and to confirm that it conforms to PAS 2050. AkzoNobel are in the process of doing this.

Barriers to the Use and Alignment of PAS 2050

The main barrier to the use and alignment of PAS 2050 with AkzoNobel‟s EIA tool is in the scope/boundaries of the data contained within the EIA. In some cases, this was found not significantly to affect the footprint, but in others further analysis would be required to ensure PAS 2050 compliance. However, this would not be an insurmountable task, as the majority of the effort – the collection of primary data from suppliers – has already been undertaken by AkzoNobel. Some additional validation is already underway as a result of these trial findings. A further barrier is in the availability of a consistent set of PAS-compliant GHG conversion factors for use by companies in reporting product footprints (as opposed to corporate reporting). Although the use of Defra‟s corporate reporting factors was not found significantly to affect the footprint results in this trial, for other products this could be reversed.

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5 References

BSI (2008). PAS 2050:2008. Specification for the Assessment of the Life Cycle Greenhouse Gas Emissions of Goods and Services. BSI, London. BSI (2008). Guide to PAS 2050. How to Assess the Carbon Footprint of Goods and Services. BSI, London. Defra (2008) GHG reporting factors - http://www.defra.gov.uk/environment/business/reporting/pdf/ghg-cf-guidelines-annexes2008.pdf Eco-profiles of the European Plastics Industry - http://lca.plasticseurope.org/index.htm Ecoinvent database v2.1 - www.ecoinvent.ch



http://lca.plasticseurope.org/index.htm

http://www.ecoinvent.ch/

trialling the pas 2050 on dulux...

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