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The utility factor is calculated based on the expected longevity of the product compared to the average product life span in the industry. The longevity can either be defined by years in use or the amount of functional units (e.g. glow plugs have a longevity of about 60.000 km)
On a material basis, the expected lifespan of a material is compared to its category.
At the product level, the MCI assesses how long a product lasts compared to the industry average. In this context, the Linear Flow Index (LFI) will be calculated for each material used in the manufacture of the product. However, when assessing the Utility Factor and thus comparing the product’s lifetime to the industry average, the analysis will be conducted at the product level, not the material level.
At the material level, the MCI evaluates the durability of a material relative to its material group. For instance, the longevity of polyethylene (PE) is compared to the average longevity of all polymers. So for PE the expected Lifetime is 18 years, for Plastic in general it is 10 years. Therefore the Utility factor of PP would be 1,8=18/10. For this reason, the comparability of circularity between different materials of different category groups can be biased.
The Linear Flow Index (LFI) calculates the circular performance of a product or material by analyzing the share of reused, recycled, or sustainable biological feedstock as input and the recycled, reused, energy recovered or composted share at end-of-life as output. This helps understand how much unrecoverable waste the product creates in its lifetime in relation to its weight. The LFI ranges from 0 to 1, with 0 representing complete circularity and 1 representing complete linearity.
In recycling purposes, the calculation applies the "50:50 approach", meaning that it considers both the waste generated for the use of recycled materials (recycled feedstock) and the waste produced during the recycling process at the end-of-life stage. This is best explained using an example. Let's take two products (P1 and P2) with 50% recycled material. At the end of its life, P1 is recycled and has a recycling efficiency of 50%. This recycled material is used as the recycled feedstock for P2. If we were to take 100% of the waste generated from the use of recycled material into account, we would count the waste from the recycled material in P1 twice, because P2 is partly made of the materials of P1. One solution for this problem would be only taking into account recycling at end-of-life. As this would place unequal penalties at end of life stage in comparison to recycled feedstock, the 50:50 approach is applied. This provides equal emphasis on both recycling processes and a holistic perspective of the circular performance of a product.
Life Cycle Impact Assessment is the third step of an LCA - now it's getting interesting!
This phase is about calculating the environmental impact of the products. To do so, we must specify each product's background and foreground system.
Foreground system
The foreground system, also known as the system under study, represents the specific product or process assessed in the LCA. It includes all the activities and stages directly associated with the production, use, and disposal of the product or process.
The foreground system focuses on the unique characteristics and attributes of the product or process, such as material composition, manufacturing processes, distribution channels, and end-of-life scenarios.
Data related to the foreground system are typically collected through LCI phase.
Background system:
The background system refers to the broader context within which the product or process operates.
It encompasses all the upstream activities and inputs necessary to support the foreground system. These activities include raw material production, electricity or heat production for product's assembly.
Background data provides information about the environmental burdens associated with these upstream processes. This data is typically collected from databases, industry reports, scientific literature, and other secondary sources.
Now that we have specified the product system for conducting LCIA, we need to:
Choose background data (emission factors) from databases with high-reliability score
Map foreground- and background data (e.g., product materials and emission factors)
Use the PCF calculation engine to calculate the environmental impact
In most cases, primary supplier data about the environmental impact of purchased goods (e.g., LCAs or PCF studies) are unavailable. In those cases, we use secondary background data from widely used, third party reviewed databases. These include:
Ecoinvent 3.10
EF 3.1
Idemat 2024
Agribalyse
Base Empreinte V23
World of Steel 2022
published EPDs from suppliers
Published LCAs in scientific journals
Each product data point is assigned to an emission factor.
At this stage, our automated LCA engine maps product components to their attributed environmental data.
Now, let's delve into calculating the results.
Below, we'll illustrate this process using the example of a plastic chair in a simplified manner. In practice, this analysis is automated in our LCA engine. Our LCA engine enables the assessment of entire product portfolios and intricate items with numerous components and non-linear supply chains.
Let's go back to our example of the plastic chair.
CF (Materials): How to calculate the carbon footprint of materials?
For the recycled material, we apply the CFF method and use an EF from Ecoinvent for the PP.
For the steel screws, the supplier has published an EPD, so we can use supplier specific data here.
The plastic is injection molded, there we have an assumed loss rate of 3,5 %. Therefore, the material carbon footprint (CF) is calculated as follows:
CF (Production):
How to calculate the carbon footprint of production process?
In the next step, the environmental impacts of the production are calculated.
Here we have two steps: First, the PP is injection molded. It means that the plastic granulate is transformed into the desired form.The injection molding process requires 12kWh electricity. The producer reported that they use the Italian grid mix. there is a material loss rate of 3.5 %.
The next step is the chair assembly. This process is done in a semi-automated way in Germany. The amount of electricity required per chair is 2.5 kWh. The producer reported that they renewable electricity.
In most cases, these two phases account for the largest part of the overall PCF. Nevertheless, we still need to calculate the environmental impact of the transport and packaging. In a cradle-to-gate assessment, all upstream transport steps and packagings are considered until the product arrives at the production gate or the warehouse.
In our example, it's crucial to consider the journey of PP granulate from China to Italy via truck and ship, followed by its transit from Italy to Germany by truck.
We adjust for varying material weights due to the loss rate during the injection molding. Meanwhile, the Steel Screws travel from Poland to Germany. Notably, as we rely on the EPD provided by the Steel Screw Supplier, the raw material transport is already factored into the CF, ensuring we avoid duplicating these calculations.
For packaging, we include the product packaging of the final product, which is 1,2 kg corrugated carton (100 % recycled material) and a plastic bag which has a weight of 0.085 kg. The packaging of the steel screws is included in the EF from the supplier EPD and the PP raw material packaging is cut-off in this case.
Adding up all CFs we get the final PCF excluding Safety Margin.
-> Read more about how the Accuracy Score determines the Safety Margin here.
Life Cycle Inventory (LCI) is the second step of LCA. Here is where you need to collect all the data points required for LCA.
Your data doesn't need to be perfectly complete. Yook's software can work with every type of data. Whether complete or incomplete, whether automated data exchange via API or manual CSV exports - We have you covered. Data gaps are closed by our software and we guide you toward collecting the most relevant and vital data points with the help of our accuracy metric. make
We collect your product data using manual or automatic exports from your ERP/ PLM system. In general, we work with every data point available. But keep in mind that, the more and better data points are available, the more accurate the result is ( see the Accuracy Score).
Important data points over the product's life cycle are weight, material composition, origins, production methods, as well as supply chain data, and packaging information. We use sector-specific guidelines and industry-specific proxies in case of data gaps. Thus, most of the data points are already available in a BOM export. Additional information, such as the energy mix used in your production facilities or supplier-specific data, can be uploaded additionally.
The following table shows which data is required in the different life cycle stages, using the example of a plastic chair.
| Life Cycle Stage | Input data | Example: Plastic Chair |
|---|---|---|
Now it's on us: we check your product data, close data gaps, merge data from different sources, and clean it up. Then, we use your static data to replicate the product flow, accounting for non-linear relations and supply chains. This is what we call a virtual BOM.
Typically, your BOM comprises a multitude of components and sub-components. Among these are items that undergo processing or sub-assembly, details not directly included in the BOM. Therefore, we enrich this information to provide a more comprehensive overview.
Life Cycle Assessment (LCA) is a fundamental tool for calculating, managing, and mitigating the environmental impacts of products or services.
“LCA studies the environmental aspects and potential impacts throughout a product’s life cycle (i.e. cradle-to-grave) from raw material acquisition through production, use and disposal. The general categories of environmental impacts needing consideration include resource use, human health, and ecological consequences.” (ISO 14044:2006)
The concept of LCA emerged in the late 1960s and evolved into a structured methodology during the late 1980s. The first comprehensive framework for LCA was established with the publication of the ISO 14040 series in 1997.
Despite the robust framework provided by ISO standards, the practical application of LCA often encountered challenges, including:
Complexity: mapping the life cycle stages and assessing the impacts was not straightforward. The data collection, modeling, and analysis can be complex and time-consuming.
Unrealistic requirements: Meeting the exhaustive requirements of traditional LCA methodologies may not always be feasible within time and resource constraints.
Scalability: Traditional LCAs are not scalable because of the limitations of tools used by LCA practitioners.
There is a growing need for a technological solution to solve the complex issues and make LCA more accessible and scalable for the industry.
This is why at Yook, we strive for a pragmatic approach to LCA. Our focus is on crafting a software solution that is performing LCA at scale and looks into the entire expansive product portfolio. Our effort is to keep the balance between pragmatism and scientific precision.
LCA, as defined by ISO 14040, has four key stages:
Goal and Scope Definition: In this initial stage, we precisely define the purpose, functional unit, and boundaries of the assessment.
Life Cycle Inventory (LCI): It is now time to collect the relevant data points. This entails gathering data from the extraction of raw materials to manufacturing, distribution, utilization, and eventual disposal.
Life Cycle Impact Assessment (LCIA): We need to understand the environmental impact attributed to a product. Here is a step where we translate all the compiled data into the form of an environmental indicator (Environmental impact assessment indicator). Different categories such as Global warming potential, Primary energy consumption, Toxicity, Water usage, land usage, and many more are evaluated at this stage.
Interpretation: Time for analytics! This stage involves a thorough analysis and understanding of the results. Based on the results from LCIA, we make conclusions and recommendations for carbon reduction strategies, product modification, or better sourcing, to name a few.
Life cycle assessment is a complicated concept that is often subject to uncertainty. Assumptions often have to be made behind the scenes, and data of varying quality is processed.
This is why Yook developed the accuracy score to create transparency on the quality of every product's carbon footprint. It indicates limitations due to data quality and availability. On top of the accuracy score, we determine a safety margin to avoid underestimating the carbon footprint.
The accuracy score is the average of three sub-scores: product data quality, background data reliability, and mapping precision.
The accuracy score is derived from a weighted average of three sub-scores, each assessing different data layers for every life cycle stage. This score is calculated for each product part and life cycle stage.
For every sub score, we have a unique calculation process:
Product Data quality
As this reflects the product data availability and quality, we check whether all relevant product information needed for LCA is available. These include the following:
product weight
material type
material composition/ weights
material origin
material properties
process type
material loss rate
location
energy consumption
energy mix
start and destination locations
transport means
material type
material weight
material origin
material properties
Background data reliability
Background data refer to emission factors or other information about a product's environmental impact. If no primary information (e.g., a supplier specific LCA or EPD) are available, we use secondary data from LCA databases such as Ecoinvent, Idemat, Agribalyse, etc.
For the reliability assessment we evaluate the source with respect to its general reliability, transparency, documentation and plausibility. Additionally we flag controversial materials by adding them to our "material red list".
The secondary background data reliability is an average of
source_reliability: indicates the general trustworthiness of our EF database
activity_reliability: refers to the material being on the redlist of controversial materials
We use the PEF DQR rules to calculate the reliability of primary background data reliability. These include the following criteria:
whether the study is externally verified
whether the data refers to the same time period
whether the activities refer to the same technology
whether the activities reflect the same geography
Mapping precision
The mapping precision sub-score is aligned to the PEF DQR. This means, in order to have the DQR for PEF compliant studies, it can easily be extracted.
Three criteria for the mapping were assessed based on the ISO DQR system:
Technological representativeness
Geographical representativeness
Time representativeness
Next, in order to determine the overall accuracy score of the product's carbon footprint, we compute two weighted averages, each founded on a materiality assessment, specifically regarding the relative carbon impact. Hence, we proceed with the following three steps:
item accuracy score: equally weighted average of product data quality, background data reliability and mapping precision. Calculated per life cycle stage. This means, every part of a product gets its own accuracy score for materials, production, transport and packaging.
life cycle stage accuracy score: based on the relative impact of different materials/ production steps to the respective life-cycle stage, we weight the accuracy scores of each element to calculate a weighted average accuracy score per life cycle stage.
overall product accuracy score: as a last step we calculate the final accuracy score based on the relative impact of each life cycle stage to the final PCF.
The weighting of the accuracy score ensures that the materials, production steps or parts of the product with the highest carbon impact count more than small parts that have almost no significance. It also helps with targeted data collection and improvement where it matters most.
The accuracy score is given on a scale of 1 to 100, where 1 is the lowest and 100 is the highest. An accuracy score of up to 90 can be achieved, as no model can ever represent the full reality.
We have developed a system to assign the scores to a qualitative description for accuracy score.
The safety margin is a factor determined by the accuracy score by which the calculated product carbon footprint is multiplied in order to control for uncertainties and to avoid underestimating carbon emissions.
Remember the allocation topic from the?
As mentioned before, there are many methods to tackle the allocation situation.
The EU Parliament has established Product Environmental Footprint (PEF) regulations to provide a standardized approach for product environmental assessment.
Within the PEF framework, a specific method known as the Circular Footprint Formula (CFF) has been introduced. The CFF is designed to calculate the environmental footprint of recycled materials.
Let us walk you through this approach with an example:
When a plastic bottle undergoes recycling to produce a t-shirt using the recycled material, the emissions generated during the collection, cleaning, and processing stages (such as shredding and pelletizing to form new plastic granulate) are typically attributed to the End-of-Life phase of the plastic bottle.
But we can also argue that, since this recycled material serves as the raw material for the t-shirt, then the emissions of the recycling should be included in the material production for the new product.
As a result, the emissions associated with recycling may appear to be counted twice.
This underscores the necessity for proper allocation methods to distribute emissions between the two product systems accurately.
Similarly, the benefits derived from avoiding the use of virgin materials in t-shirt production should not solely be credited to the t-shirt itself; a portion of these credits is also allocated to the original product. This approach ensures that the preceding product receives recognition for its contribution to recycling efforts.
The CFF offers a specific allocation rule for recycling processes. Based on this rule, the burdens and credits are split between the virgin and recycled material based on specific allocation factors.
The CFF formula is divided into three parts:
Material: the emissions from the material input and material recycling are calculated. In the next step, the credit for the avoided material is also considered.
Energy: the emissions due to the energy recovery process are calculated. As the next step, the credit for the avoided primary energy source is taken into account.
Disposal: The emissions of the remaining waste and disposal are taken into account.
For cradle-to-gate studies, the formula is simplified. In this simplified approach, only the first part of the material part is relevant, as the end-of-life phase is not included in the calculations.
The CFF for cradle-to-gate studies considers the impact of the virgin material and the impact of the recycled material input. This includes emissions from the recycling process as well as allocated impacts from primary material production.
-> Now, the EF of 1.66 can be used as material EF for the PET that consists 80 % recycled and 20 % virgin material. It is a bit lower than the virgin material EF which is 2.05 in our example. Take care that depending on the material, different production processes need to be added.
Yook's approach at a glance:
Yook conducts streamlined Life Cycle Assessments with a focus on the products' -IPCC 2021- measured in Kg CO2e.
The products’ life cycle is analyzed on a cradle-to-gate basis, meaning that upstream activities, production, transport, and packaging are included while distribution, use, and end-of-life are excluded from the analysis.
We conduct and apply or the PEF Circular Footprint Formula () if applicable.
Our methodology follows the GHG Protocol Product Standard and follows sector-specific guidelines, for example, the EU PEFCR. In addition, we can follow sector-specific guidelines, for example, the EU PEFCR, or apply sector-specific requirements, for example for differentiating FLAG- from non-FLAG emissions or to be able to use the data for CBAM reporting
In the LCA journey, the first step is to define a clear goal and scope for the analysis.
As there is no single source of truth for LCA, the first phase, Goal and Scope Definition, plays an important role and determines the cooking recipe depending on the objectives.
Different LCA approaches can be applied depending on the objectives and data availability.
Some areas to consider are:
Depth of the analysis: screening LCA for hotspot analysis, or would you like product-level insights?
Scope and limitations: cradle-to-gate LCA with a single impact focus on global warming or cradle-to-grave with multiple indicators?
Compliance: Choose regulations and standards to follow. This will directly affect the modeling of the product.
The least complex but also the least granular approach is the spend-based carbon footprint calculation, using monetary emission factors depending on the product category.
The activity-based approach can be applied to enhance the assessment. This assessment is based on the physical flows within the product system boundaries. As the first step, the average data from databases can be used. In the following step, the results can be enriched by using supplier-specific data.
Let's dive into each approach:
The spend-based calculation uses general emission factors (EFs) and only considers a product's price and category. The relevant emission factors indicate [Kg CO2e/EUR spend]. As the required input data is mainly accessible, the assessment is relatively easy.
However, some disadvantages are:
No possibility to account for differences in one product category
Price or spending is not a sufficient proxy for climate impact due to the volatility of CO2e results because of externalities (e.g., inflation, price reductions)
When a price discount is offered, the PCF decreases, though it´s the same product
Higher-priced goods, e.g., luxury brands and sustainable options, result in a higher product carbon footprint compared to low-priced products
Based on our experience, retailers might be motivated to conduct LCA for carbon accounting reporting. In such cases, exact product data might be missing. Therefore, initially, a spend-based analysis can be a useful starting point to get an overview of hotspots and important reduction levers.
The case below shows that the granularity of a spend-based calculation is not sufficient to explain variation in one product category. As it's only determined by product category and the product's price.
The activity-based calculation is more complex but also as more accurate. Depending on the granularity of the study, three methods are available :
The average-data approach uses industry-specific default data for a defined product category. In this approach, the granularity depends on the available industry and product category average data.
The supplier-specific approach requires specific, primary data along a product's supply chain to conduct an LCA. This method helps to determine the precise environmental impact of a specific product.
The hybrid approach combines the methods mentioned above, using a mix of supplier-specific data whenever available and average data to fill gaps.
The functional unit specifies the unit of analysis for the LCA study, such as one kilogram of product, one kilometer of transportation, or one hour of service. It defines the reference quantity against which all inputs and outputs are measured and compared.
At Yook, the functional unit is specified as one product.
Environmental impact indicators are essential components of LCA, as they provide a structured framework for quantifying and assessing the environmental burdens across different stages of a product's life cycle.
Here are the key environmental impact indicators commonly measured in LCA studies:
By integrating these indicators into LCA studies, stakeholders can identify hotspots, prioritize improvement opportunities, and promote sustainable decision-making to minimize environmental impacts.
At Yook, we are currently calculating carbon footprints. We focus on the climate change impact category, which measures the global warming potential (GWP 100).
Whenever possible, we constantly use updated emission factors from reliable databases and primary data. We constantly use updated emission factors from reliable databases and primary data whenever possible.
Note: Additional environmental impact categories might have relevant effects and can lead to trade-offs or unwanted burden shifting.
The system boundary includes all life cycle stages of the product, process, or service, from raw material extraction and production, distribution, use, and end-of-life disposal (cradle to grave).
Raw Material Acquisition: Includes activities such as resource extraction, agricultural production, and material processing.
Manufacturing: Encompasses all processes involved in the production of goods or the provision of services, including assembly, fabrication, and packaging.
Distribution and Transportation: Involves the transportation of the raw materials, components, and finished products between different stages of the supply chain and to end-users.
Use: Covers the operational use of the product or service by consumers, including energy consumption, maintenance, and product performance.
End of life: Addresses the disposal, recycling, or recovery of materials and energy at the end of the product's life cycle, including waste management and recycling processes.
In some studies, the boundaries can be to the gate of the distribution (cradle to gate). At Yook, our current model is cradle-to-gate. This means we include all upstream and core activities while the downstream activities are excluded.
Here is an example of a product system with cradle-to-gate system boundaries.
Allocation refers to assigning environmental burdens or benefits among multiple co-products or processes within a life cycle system.
Allocation is necessary when a product system yields more than one product or service simultaneously or when a process produces the desired product and by-products or co-products.
According to ISO 14040/44, there are three choices in the case of multi-output processes:
Avoid allocation by system expansion: Employing this strategy, particularly in consequential Life Cycle Assessment (LCA), helps to sidestep the complexities associated with allocation.
Physical relationships allocation: Allocation factor based on dry mass or volume of the co-products: Simplifying the process, this method ensures a fair distribution of environmental impacts across the system based on the physical properties of the co-products.
Allocation by other relationships (e.g., economic): Use the price of the co-products to determine allocation factor: By considering economic factors, such as the price of co-products, this approach offers a straightforward and transparent method for determining allocation, enhancing the accuracy and reliability of LCA assessments.
Now, we are getting a bit deeper into LCA Theory. But let's start smoothly:
One case is when multiple products are interconnected or share standard processes. In such cases, also called multi-output processes, it can be challenging to precisely attribute the environmental impacts to a specific product.
Example is:
Another case is the recycling process.
Regarding recycled materials, we also have two interlinked products: The virgin material recycled in its end-of-life phase and the resulting recyclate. This raises two questions that require an allocation decision:
which material bears the burden of the recycling process? Is the virgin material in the End of Life (EOL) phase or the recyclate in the material phase?
how is the burden of virgin material extraction allocated? Is it only to the virgin material, or is a portion also allocated to the recyclate?
There are some alternatives as the solution.
Burdens or credits associated with materials from previous or subsequent life cycles are not considered.
For example, scrap input to the production process is considered to be free of burdens, but equally, no credit is received for scrap available for recycling at end-of-life.
Hence this approach rewards the use of recycled content but does not reward end-of-life recycling.
In the substitution approach, the environmental burdens or benefits of co-products are allocated based on their potential to replace other products or materials in the market.
This method assumes that the co-products can be used as substitutes for virgin materials or products, thereby avoiding the environmental impacts associated with their production.
Example: In steel production, if the slag generated during steel production is used as a substitute for virgin aggregate in construction materials (e.g., road base, concrete), the environmental impacts of producing the slag would be allocated based on the amount of virgin material it replaces. This approach considers the avoided impacts of using the co-products as substitutes.
Alright! Now that we know about multi-output processes and allocation, let's head over to the next topic: Attributional versus Consequential LCA.
The analysis would focus on comparing the direct environmental impacts of producing the two types of packaging over the life cycle (raw materials, production, etc.) for both packaging types.
Result: The LCA might show differences in carbon impacts between the two materials.
The analysis would go beyond the immediate impacts and consider the broader consequences of the material switch.
This assessment accounts for potential changes in market behaviors and technology adoption
Result: It would provide an understanding of the overall carbon impact due to market changes.
Here is a comparison of these two approaches:
You might wonder what is the most relevant method for your product. Currently, there is not a single LCA standard applicable across all product categories. Therefore, facilitating easy comparison of LCAs does not exist. Instead, there are numerous guidelines, standards, handbooks, and sector-specific initiatives, which can be daunting to navigate.
At Yook, we diligently monitor regulations within each industry sector and employ the widely accepted methodologies available.
Welcome to our Knowledge Hub! This section describes the core LCA embedded in Yook's software for calculating PCFs.
Here, you can download a pdf summary of our PCF calculation methodology.
Circularity in LCA? Well, strictly speaking, circularity is not really part of an LCA, except when applying the CFF to allocate credits and burdens of recycling materials. Nevertheless, the circularity of a product is considered to be just as important as its environmental impact and even if it's not part of a traditional LCA, circularity can be calculated independently. However, quantifying products' circularity is not as straightforward as quantifying products' environmental impact: There exist around 300 circularity indices, but none of them has really established itself yet. One of the most recognized ones is the Material Circularity Indicator (MCI).
There are two approaches for combining LCA and circular economy: by integrating circularity into LCA by using the CFF and by calculating an independent, additional metric to assess the product's circularity.
In the next two sub-pages we discuss the CFF and the MCI in more detail.
The European Commission introduced the Circular Footprint Formula (CFF) as part of the Product Environmental Footprint (PEF) assessment methodology to harmonize and standardize the analysis practices of recycling in LCA. It defines rules to allocate environmental burdens and credits for recycling, reusing, or energy recovering between supplier and user of recycled materials. Like this, emissions from recycling are not only accounted for in the End-of-Life phase of the first product but partly attributed to the subsequent product made with the recycled material. In the same way credits from the avoided virgin material are shared between the two product systems.
In addition to the Circular Footprint Formula (CFF), Circularity Indicators offer another method for evaluating the circularity of a product. Unlike the CFF, these indicators operate independently of LCA calculations and provide a distinct way to quantify a product's circularity. One notable indicator is the Material Circularity Index (MCI), developed by the McArthur Foundation.
In contrast to LCA-based approaches, the MCI does not involve the calculation of emissions, eliminating the need for the distribution of credits and burdens across multiple product systems. The MCI focuses on the analyzed product, assessing circularity aspects of material flows from raw materials to the end-of-life phase. The index calculates a circularity factor based on the proportion of recycled, reused, or sustainably produced biological material input and unrecoverable waste output generated at the end-of-life. Additionally, the specific product's longevity is a crucial factor in the calculations.
Next to its inherent challenges of complexity and uncertainty, LCA is often critizied for not being a sufficient tool for informed decision making since it is not fully comprehensive.
This is why we calculate the MCI as additional indicator for the environmental and circular performance of a product. Find more about the calculation details in the following sub-chapters.
Interested in reading more? The following links may be helpful:
The Material Circulator Indicator (MCI) assesses the circular performance of a product or material through three key metrics: firstly, the proportion of circular materials used (such as recycled or reused content); secondly, the end-of-life outcomes (determining whether a product is recycled or disposed of in a landfill); and thirdly, the durability of the product or material.
Non-recoverable waste
In essence, the circular performance of a product or material is assessed by comparing the weight of non-recoverable waste generated during both production and end-of-life phases to the total weight of the product or material.
We start by categorizing the input materials as virgin, recycled, reused, or biological. This classification is essential for calculating the amount of non-recoverable waste generated during the production of a product.
Then, we examine the end-of-life scenarios, such as recycling, reusing, composting, or energy recovery. This information is crucial for determining the amount of non-recoverable waste produced after a product's lifecycle.
More information on the assessment of the circular performance will be provided in the section .
The other aspect of the MCI is the longevity of a product. Here the longevity of a product will be compared to the industry average.This metric compares a product's lifespan to the industry average, with longer-lasting products positively influencing the MCI score. It's important to note that the assessment of longevity differs when calculating the MCI for a product versus a material. Further details can be found in the deep dive section .
Yook's MCI calculator is based on the 2019 revision of the methodology by the
Information that need to be provided for the calculation:
the share of reused materials
the share of recycled materials
the share of sustainable biological materials
Product lifetime or number of functional units achieved during the lifetime
Information that can help increase the accuracy of the calculation:
collection rate for recycling [%]
Efficiency of recycling [%] (how much recycled output will be created)
rate of biological material meeting criteria for energy recovery [%]
efficiency energy recovery
rate of component reuse
Industry average lifetime or average number of functional units achieved during the lifetime
Advantages
Provides another indicator of sustainability alongside the Product Carbon Footprint (PCF)
Promotes the use of recycled or reused materials and encourages recycling at the end of the product's life
Considers longevity; thus, materials with higher durability compared to the industry average have a positive impact on MCI
Limitations
Methodology can be unclear in some instances
Lack of data for average product lifetime and recycling/collection rates due to limited adaption
Despite its limitations, the Material Circularity Indicator (MCI) is a valuable tool for considering circularity and durability alongside the Product Carbon Footprint (PCF) in material and product selection.
| Product | Plastic chair |
|---|---|
| sub-score | criteria |
|---|---|
| Accuracy | Score | Description |
|---|---|---|
| Accuracy score | Safety margin |
|---|---|
| abbreviation | description |
|---|
is an interesting video to learn more about the CFF. Are you interested in learning more? Here are some helpful links:
| product | price (€) | spend-based category | EF | CF | Accuracy |
|---|
Curious to learn more? scientific article provides more detailed insights into each of these approaches.
| Indicator | What does it mean |
|---|
The EU PEF framework prescribes the application where the burdens are split between the virgin material and recyclate
(You can learn more.)
| Attributional LCA | Consequential LCA |
|---|
To learn more about the regulations, please check our . We have thoroughly reviewed and provided an overview of the different guidelines.
Weight
5,5 Kg
Composition
81% - PP (of which 50% is recycled)
19% - Steel
Production
Injection molded PP
Loss rate
3,5%
Materials
Weight (product + components)
materials
Name
Composition
Origin
Supplier properties
chair weight: 5,5 kg
4,5 kg Polypropylene (PP)
>origin: unknown, proxy: China
>50 % recycled
1,0 kg Steel Screws
>origin: Poland
Material Transformation
Process type
Material loss
Energy consumption
Type of energy supply
Additional input (e.g. chemicals)
Location
Injection Molding (PP)
>3,5 % loss rate
>12 kWh electricity
>Italy
Production
Process type
Energy consumption
Energy mix at production facility
Additional input (e.g. chemicals)
Location
Chair Assembly (semi-automated)
>2,5 kWh electricity
>Germany
>100 % renewables
Transport
1. Raw materials - Transformation
2. Transformation - Production
> Distances
> Transport means
PP: China (proxy) - Italy - Germany
>12.000 km ship + 1.500 km truck
>1.800 km truck
Screws: Poland - Germany
>1.500 km truck
Packaging
Weight and type of raw material packaging
Weight and type of component packaging
Weight and type of product packaging
corrugated carton: 1,2 kg
>100 % recycled
Plastic bag: 0,085 kg
Product data quality
Reflects the availability and quality of product data.
Relevant product data points such as weight, material composition, location, production, and supply chain information are assigned relative importance depending on the product category.
This enables targeted data collection and improvement where it matters most.
Background data reliability
Background data refer to emission factors or other information about a product's environmental impact
This sub-scores measures the quality of background data connected with the product data (i.e., the quality of emission factors).
Depending on whether suppliers' environmental impact information is available, background data can be primary or secondary. We evaluate the reliability and quality of such background data.
mapping precision
This reflects how precisely we could map a data point.
It corresponds to the DQR's main areas of technical representativeness (the degree of similarity) and temporal and geographical representativeness (the degree of precision and similarity).
Low
1 - 20
Very inaccurate Spend-based and Average data
No specific product primary data (e.g., material composition, weight) is available
Medium
21 - 40
Poor availability/ quality of primary data,
Used heuristics and proxies or very low secondary data quality
Elevated
41 - 60
Primary data available for some datapoints (e.g., product weight, main materials)
Good background data quality
(This level is usually obtained after the first iteration of PCF calculations)
High
61-80
Detailed primary data availability (e.g., as above + material origins + specifications, production details) Good background data quality (This level is usually obtained with some data collection effort)
Very high
81-90
Very good availability and quality of primary data
Supplier specific information on materials/ components.
Good background data quality Precise mapping of product and background data.
-
91 - 100
Since no model can perfectly reflect reality and climate sciences are always subject to uncertainty, an accuracy score of 100 can never be reached.
1 - 20
1.5
21 - 40
1.4
41 - 60
1.3
61 - 80
1.2
81 - 90
1.1
R1 | share of recycled material |
EF virgin | emission factor of virgin material |
EF recycled | emission factor of the recycling process |
A | pre-determined allocation factor depending on the material |
Q sin / Q out | pre-determined quality ratio determining the quality difference of the virgin and recycled material depending on the material |
paper, white | 1,49 | paper and cardboard | 1,09 | 1,62 | 9 |
paper, recycled | 3,99 | paper and cardboard | 1,09 | 4,35 | 9 |
Measures the potential of greenhouse gas emissions to contribute to global warming over a specified time horizon, typically 100 years. It is expressed in terms of carbon dioxide equivalents (CO2e) and encompasses emissions of carbon dioxide (CO2), methane (CH4), nitrous oxide (N2O), and other greenhouse gases. |
Quantifies the potential for sulfur dioxide (SO2) emissions and nitrogen oxides (NOx) to acidify the environment, leading to acid rain. It assesses the impacts on soil, water bodies, and vegetation, which can harm ecosystems. |
Evaluates the potential for nutrient emissions, such as nitrogen (N) and phosphorus (P), to cause eutrophication in water bodies. Excessive nutrient levels promote algae growth, leading to oxygen depletion and ecological imbalance in aquatic ecosystems. |
Measures the potential of substances, such as chlorofluorocarbons (CFCs) and halons, to deplete the ozone layer in the stratosphere. Ozone depletion increases the harmful UV radiation reaching the Earth's surface, posing risks to human health and ecosystems. |
Evaluates the potential for substances to cause harm to human health through various exposure pathways, including ingestion, inhalation, and dermal contact. It considers both acute and chronic toxicity effects and helps prioritize substances based on their hazardous properties. |
Assesses the potential for substances to cause harm to ecosystems and non-human organisms, including aquatic and terrestrial species. It considers the toxicity of substances to different organisms and ecosystems, helping identify environmentally sensitive areas. |
Quantify the amount of land occupied or transformed by a product or process, including direct land use (e.g., agriculture, infrastructure) and indirect land use (e.g., habitat destruction). They help assess the impacts on biodiversity, ecosystem services, and land availability. |
Water use | Assesses the consumption of water resources. |
Environmental impact in its current state | Environmental impact of changes |
Inputs and outputs are attributed to a product. | Activities are linked. Thus, the product system adapts to changes. |
Direct and indirect impacts associated with the production, use, and end-of-life of a product | Includes indirect effects beyond the direct system boundaries and considers the consequences of decisions |
Analysis of product environmental impact and product optimization within specific system boundaries | Broader analysis such as market changes or policy decisions e.g. changes in market prices and subsequent effects |
Activity-based PCF Calculation | A complex carbon footprint calculation method that uses activity data rather than spend-based data. It includes average-data, supplier-specific, and hybrid approaches for granularity. |
Allocation | In LCA, the process of assigning the environmental impact in multi-output processes where multiple products are interconnected or share common processes. |
Attributional LCA | A life cycle assessment approach focusing on the direct environmental impacts of producing a product over its life cycle, such as raw materials, production, etc. |
Average-data Approach | An activity-based PCF calculation method using industry-specific default data for a defined product( category). |
Background data | environmental impact or emission factor data that is connected with the product data in order to calculate PCFs. It can be primary (supplier-specific information, e.g. an EPD) or secondary (e.g., EFs from databases such as ecoinvent for a material). |
Carbon Footprint (CF) | A measure of the total amount of carbon dioxide equivalent emissions directly and indirectly caused by an activity or accumulated over the life stages of a product. |
Carbon Intensity of Electricity | A measure of how much CO2e is emitted per kilowatt-hour of electricity produced. It varies based on the energy mix of a region or manufacturer (e.g., renewables vs. fossil fuels). |
Circular Economy | An economic system aimed at eliminating waste and the continual use of resources through principles like reuse, sharing, repair, refurbishment, remanufacturing, and recycling. |
Circular Footprint Formula (CFF) | A formula published by the European Commission as part of the PEF methodology to allocate environmental burdens and credits for recycling, reusing, or recovering energy between supplier and user of recycled materials. |
CO2e (Carbon Dioxide Equivalent) | A standard unit for measuring carbon footprints. It expresses the impact of each different greenhouse gas in terms of the amount of CO2 that would create the same amount of warming. This is used to combine the impacts of various greenhouse gases (GHGs) such as methane (CH4) and nitrous oxide (N2O) into a single metric for easier comparison and aggregation. |
Compliance | Adherence to specific rules, standards, or laws in LCA, such as ISO guidelines, GHG Protocol, or EU PEF. |
Consequential LCA | An LCA approach that considers broader consequences beyond immediate impacts, such as market shifts and changes in behavior resulting from a measure. It provides a holistic view of environmental consequences including indirect effects. |
Cradle-to-Gate | An assessment approach that includes all upstream and core activities in the life cycle of a product but excludes downstream activities. |
Cradle-to-Grave | A comprehensive assessment approach considering the entire lifecycle of a product from raw material extraction (cradle) to disposal (grave). |
Cut-Off Allocation | An allocation method where burdens or credits associated with material from previous or subsequent life cycles are not considered, focusing instead on the use of recycled content. |
Data Quality | In LCA, the accuracy and completeness of data used, impacting the reliability of the assessment. |
Ecoinvent | A comprehensive database for life cycle inventory data, widely used in LCA studies. |
Emission Factor (EF) | A coefficient that quantifies the emissions per unit of activity, most often given in kg CO2e / kg. |
Environmental Impact Assessment (EIA) | A process of evaluating the likely environmental impacts of a proposed project or development, considering inter-related socio-economic, cultural, and human-health impacts. |
Environmental Impact Categories | Divisions in LCA used to categorize and quantify impacts on different aspects of the environment, such as air, water, soil, etc. |
Environmental Product Declaration (EPD) | A standardized document providing information about a product's environmental impact, based on an LCA. |
Foreground data | = product data, refers to specific, detailed information directly related to the product being assessed. Primary, foreground data is supplier-specific whereby secondary data refer to industry specific default values in case of missing data. Examples include product weight, its material composition, weight, production methods and supply chain information. |
Functional Unit (FU) | In LCA, the quantified performance of a product system for use as a reference unit in a life cycle assessment study. It can be 1 kg, 1 m3 or 1 product. |
Global Warming Potential (GWP) | A measure used to compare the ability of different greenhouse gases to trap heat in the atmosphere. It represents the total warming effect of one unit of a gas over a fixed period, often 100 years, compared to one unit of carbon dioxide (CO2). GWP is a critical concept in understanding and comparing the impact of different gases on global warming. |
Goal and Scope Definition | The initial stage in LCA involving the precise definition of the purpose, functional unit, and the boundaries of the assessment. |
Greenhouse Gas (GHG) Emissions | Emissions of gases that trap heat in the atmosphere, contributing to global warming and climate change. |
Greenhouse Gas (GHG) Protocol | A widely used international accounting tool and standard for government and business leaders to understand, quantify, and manage greenhouse gas emissions. |
Idemat Database | A database providing information on the environmental impact of materials, often used in LCA. |
ISO 14040/44 | International standards providing guidelines and principles for conducting and reporting life cycle assessment studies. |
Life Cycle Assessment (LCA) | A systematic analysis of the environmental aspects and potential impacts associated with a product throughout its life cycle. |
Life Cycle Costing (LCC) | An approach for assessing the total cost of ownership, considering all costs associated with the life cycle of a product. |
Life Cycle Impact Assessment (LCIA) | A phase in LCA where potential environmental impacts are assessed using inventory data. |
Life Cycle Inventory (LCI) | A phase in LCA involving the compilation of detailed inputs and outputs across various stages of a product's life cycle. |
Manual Data Collection | The process of gathering data by hand for use in LCA, as opposed to automatic data collection methods. |
Material Circularity Index (MCI) | An index developed by the McArthur Foundation to evaluate the circularity aspects of material flows of a product, from raw materials to end-of-life. |
Material Loss Rate | In manufacturing, the percentage of material that is wasted during the production process. |
Primary data | Supplier- or company specific foreground or background data. Examples include primary information about a product's weight or a PCF for a certain material. |
Product Environmental Footprint (PEF) | An EU initiative that quantifies the environmental performance of a product or service throughout its life cycle. |
Product Life Cycle | The stages a product goes through from raw material extraction to disposal or recycling. |
Product Lifecycle Management (PLM) | A strategic approach to managing the lifecycle of a product from inception, through engineering design and manufacture, to service and disposal. |
Raw Material Acquisition | The initial stage of a product's life cycle involving the extraction or harvesting of natural resources. |
Recycling Process Allocation | The assignment of environmental impacts in the recycling process, typically between the recycled material and the virgin material. |
Renewable Energy | Energy from sources that are naturally replenishing but flow-limited, such as solar, wind, hydroelectric, and geothermal. |
Safety Margin | In LCA, a factor used to control for uncertainties and avoid underestimating emissions, influenced by the accuracy score of data. |
Scalability | The ability of LCA methodology to be applied to large-scale systems or numerous products simultaneously. |
Scope 1 and 2 Emissions | Direct emissions from owned or controlled sources (Scope 1) and indirect emissions from the generation of purchased energy (Scope 2). |
Scope 3 emissions | Indirect emissions from a company’s value chain, including both upstream and downstream emissions. |
Secondary data | Default or average data for foreground or background information. Examples include an average weight for a product where primary information is missing or a general material emission factor from a database such as ecoinvent. |
Spend-based PCF calculation | A carbon footprint calculation method using general emission factors and considering a product's price and category. |
Streamlined LCA | A simplified approach to LCA, often software-based, used for analyzing a wide range of product categories in a less complex manner. |
Substitution Approach | An allocation method where the impact of recycled materials is equated to the impact of virgin materials, focusing on end-of-life recycling. |
Supplier-specific Approach | An activity-based PCF calculation method requiring detailed data from the supply chain of a product for a life-cycle assessment. |
System Boundaries | In LCA, the limits of what is included in the assessment, such as which stages of the life cycle and which processes are considered. |
Virtual Bill of Materials (BOM) | A digital representation of a product flow including components and materials as well as production and transport steps used in LCA to assess the environmental impact. |
The Material Circularity Indicator (MCI) is calculated using the formula:
MCI = 1 - LFI*(0.9/Utility Function)
Here, the factor "0.9" represents the weight of the product's longevity (utility function) relative to its circular performance (LFI). This factor aligns with the framework established by the Ellen MacArthur Foundation.
The MCI ranges from 0 to 1, where lower values indicate a more circular production process. The formulas utilised at Yook adhere to the guidelines set by the Ellen MacArthur Foundation.
