Life Cycle Assessment (LCA) is a tool used to quantify the environmental impact of products. LCAs are used to calculate impacts of an individual product, like a T-shirt or a pair of sneakers, and can also be aggregated to support a brand’s Scope 3 greenhouse gas emissions reporting. Increasingly, LCA calculations are becoming the basis for regulatory reporting and product labelling, including frameworks like the EU Product Environmental Footprint (PEF) and France’s ADEME Eco-Score.
As a result, LCA results must now meet different rules and requirements depending on how and where they are applied. At the same time, expectations for fair and meaningful product comparisons are rising. Building product footprints that are suitable for making fair, meaningful comparisons across products requires a high level of standardization. This is especially challenging in the Apparel and Footwear industry, where products are highly complex and diverse, supply chains are global, and impacts can extend well past the factory gate.
Behind every LCA calculation/output there are several distinct, but closely connected parts, including:
These concepts can overlap in practice, but each plays a different role and each can meaningfully influence results, even when analyzing the same underlying product data. Understanding how these pieces fit together is essential for interpreting LCA outputs and understanding why different tools and frameworks can produce different results for the same product.
Life Cycle Inventory (LCI) - The Foundation
The Life Cycle Inventory (LCI) is the quantitative foundation of any LCA. It answers the most basic question: what goes into a product system, and what comes out of it?
An LCI represents a detailed accounting of all inputs and outputs associated with a product or process across its life cycle; from raw material extraction through manufacturing, transportation, use, and end-of-life. In the apparel and footwear industry, an LCI may include:
- Bill of Materials (e.g., quantities of cotton, polyester, leather, rubber)
- Energy use during processes like spinning, dyeing, and cut-and-sew
- Transportation distances between supply-chain stages
- Emissions, waste, and wastewater generated along the way
LCI data can be primary (directly measured from suppliers or facilities), or secondary (modeled using databases and literature). Importantly, what counts as “acceptable” data (how much primary data is required, and at what level of detail) is often defined by the LCA methodology (see Methodology Section below) being used.
Why this matters: If the LCI is incomplete or inconsistent, everything built on top of it will be affected. The LCI is best thought of as the ingredients list for an LCA. It is the foundation of the LCA.
LCA database - Building Blocks
LCA databases contain the datasets used to model environmental flows for common materials, processes, and activities. These datasets describe things like emissions per kilogram of fiber, electricity impacts by region, or transport emissions per kilometer.
In apparel and footwear, commonly used databases include:
- Higg Materials Sustainability Index (MSI) – Industry-specific material and process data that combines process-level datasets into material impact. Built on the LCA for Experts background database.
- ecoinvent and LCA for Experts – Multi-sector databases that provide background datasets for energy, chemicals, and transport and serve as the backbone for many LCA software tools (ex: SimaPro, OpenLCA, LCA for Experts software, etc.)
- Ecobalyse – database used in combination with the Ecobalyse calculation tool for French Ecoscores. Ecoinvent database used for background data.
- PEF database – Developed to align with the European Commission’s Product Environmental Footprint rules to support regulatory consistency; Version 3.1 was built on the ecoinvent background database.
Each database is built using its own models and assumptions including geography, technology, and energy mix. As a result, the choice of database alone can materially change LCA results, even if the same product data and methodology are applied.
Why this matters: Databases act like reference libraries. Two analysts can follow the same rules but still get different answers if they are pulling from different sources.
LCA Methodology - The rules of the system
While LCI data are the inputs, the LCA methodology defines the rules for how those inputs must be modeled and interpreted.The methodology determines what is in scope, what is out of scope, and how results should be calculated.
An LCA methodology specifies:
- System boundaries (e.g., cradle-to-gate vs. cradle-to-grave)
- Cradle-to-gate: The product's footprint only from raw material extraction up to the manufacturer's factory gate.
- Cradle-to-grave: Comprehensive analysis covering the entire lifecycle, extending through transportation, consumer use, and final disposal.
- Functional unit (what exactly is being measured)
- Allocation rules for shared processes
- Data quality and primary data requirements
- Which impact categories and models must be used
Some methodologies are database-agnostic, while others require or strongly prefer specific datasets. Examples include:
- Material Sustainability Index (MSI): Operates within Cascale’s defined methodological framework and uses industry specific process data that can be assembled into materials. Uses LCA for Experts database as a foundation, but for those who want to contribute data, different databases can be used with the proper documentation.
- Product Environmental Footprint (PEF): Requires product models to be built according to very detailed and prescriptive rules, defined in extensive technical documentation, using a specific PEF database
- French ADEME Eco-Score: Implemented through a tightly specified calculation engine (Ecobalyse), with a predefined database, impact models, and weighting factors.
Because methodologies differ in scope and rules, the same product can be modeled in very different ways depending on the framework used.
Why this matters: Methodology is the rulebook. Even with identical data, changing the rules can change the outcome.
Life Cycle Impact Assessment - Turning Data Into Impact
Once inventory data and modeling rules are set, the Life Cycle Impact Assessment (LCIA) phase translates physical flows into environmental impact indicators. Examples of these indicators are Climate Change (CO₂e), Water Scarcity, Eutrophication, Acidification, and Land Use. There are dozens of LCIA methodologies, each based on different scientific research and modeling assumptions. Common examples include IPCC climate characterization factors, CML, and EF-aligned impact models used in PEF. Because LCIA methods characterize the same emissions differently, the choice of impact assessment model adds yet another layer of variation across tools and frameworks.
Why this matters: LCIA is where data becomes meaning.
One Input, Multiple Outputs: Designing for a Multi-Framework World
We are entering a world where the same product needs to be assessed in multiple ways, depending on the context:
- Cradle-to-gate results to support design and sourcing decisions
- Scope 3-aligned calculations for corporate emissions reporting
- PEF-compliant results for EU regulatory use
- Consumer-facing scores such as France’s ADEME Eco-Score
While the outputs may differ, many of the inputs are the same. At a minimum, most frameworks require product weight, material composition, and basic manufacturing and processing information. Worldly is building a system where brands and suppliers enter their product data in one consistent place and Worldly handles the complexity behind the scenes. That means:
- Applying the appropriate database (such as Higg MSI or PEF-aligned datasets)
- Applying the correct methodology for each use case
- Running the right calculations and LCIA models
- Producing multiple outputs from the same underlying product data
In other words, the complexity should live in the system, not with brands and suppliers. By separating data collection from methodology and calculation, product footprinting becomes more scalable, adaptable, and future-proof, even in a world with multiple standards, labels, and regulatory expectations.
The future of LCA
As LCA becomes the foundation for product measurement, reporting, and regulation, differences in results are inevitable. In many cases, those differences may reflect how impacts are calculated, not what data is collected.
The future of LCA is not only collecting more high quality data; it is about using the same data intelligently across many frameworks. When one set of product inputs can support multiple outputs, LCA becomes more scalable, more comparable, and better suited for what comes next.