Life Cycle Assessments (LCAs) are pivotal in corporate sustainability, providing a comprehensive view of a product’s environmental footprint from material acquisition to end-of-life. Process-based LCAs, a specific type of LCA, offer a detailed approach by examining individual processes within a product’s life cycle, allowing for accuracy, granularity, and informed decision-making. However, they can be resource-intensive and challenging to scale.
This article explains how a process-based LCA works, its advantages over the commonly used spend-based LCAs, and how it can be applied at scale to inform corporate sustainability strategies.
A Life Cycle Assessment (LCA), or Life Cycle Analysis, is a systematic method to evaluate the environmental inputs and outputs of a product or service through its life cycle stages—from material acquisition to manufacturing, distribution, use, and end-of-life management. The evaluation assesses environmental impacts such as global warming potential, resource use, and other factors. The International Organization for Standardization (ISO) provides standards for four phases of an LCA:
Inventory analysis can be carried out using process-based modeling, where data is collected for each activity in the life cycle, or a top-down approach using an economic model of activities. Using a combination of these two methods is called a hybrid LCA.
LCAs are pivotal for corporate sustainability success. They provide comprehensive insights into the environmental footprint of products or services, supporting ESG reporting, risk assessment, mitigation strategies, and customer communications.
A process-based Life Cycle Assessment (LCA) evaluates the environmental impacts of a product, service, or system by examining individual stages and subprocesses throughout its life cycle. The key steps of a process-based LCA are explained below with the example of a beef patty as shown in the figure.
Example of a process-based LCA for a beef patty’s life cycle
A process-based LCA defines its scope by identifying the starting and ending points of the life cycle, as well as the processes to include in the assessment.
The system boundary for a cradle-to-grave LCA on a beef patty, for example, includes processes starting from the initial land/water use and production of resource, feed, and energy, through to the beef production, packaging, distribution, storage, and consumption of the patty.
For each process within the system boundary, data is collected and quantified on all the inputs and outputs from primary and secondary sources. All processes of a product’s life cycle, from raw material extraction to manufacturing, transportation, use, and disposal or recycling are mapped out.
In the beef patty case, essential inputs for cattle feed production and rearing include land, water, energy, fertilizer, and pesticides. The slaughtering and grinding stages primarily require energy and water, whereas distribution, storage, and consumption primarily utilize energy. The outputs include intermediate products, such as feed, cattle, and raw beef, as well as the final product, beef patties.
After compiling the inventory data, the environmental impacts of the inputs (e.g., fertilizers, pesticides, crude oil, packaging materials), outputs (e.g., feed, cattle, beef patties), and natural resources are evaluated. Common impacts include global warming potential, acidification, eutrophication, land and water use, and more.
For a beef patty, methane emissions from cattle and manure, nitrous oxide from fertilizers, and carbon dioxide from fossil fuels are significant. Greenhouse gas emissions from direct land use changes, such as deforestation, are also critical, as outlined in the GHG protocol. Additional impacts include water stress, habitat and biodiversity loss, waste, and water and air pollution.
Some process-based LCAs may include normalization and weighting steps to aggregate the impact assessment results into a single score or set of scores. This allows for easier comparison of different environmental impacts.
In this example, normalization of environmental burdens across all categories per burger patty can help to identify the categories most important in the comparison.
Process-based LCAs offer many advantages. When conducted rigorously, they are a valuable tool for understanding corporate impacts and creating targeted carbon reduction strategies. However, they can also be resource-intensive and require access to detailed data, which can pose challenges in data collection and modeling.
An environmentally extended input-output (EEIO) LCA model, often referred to as a spend-based LCA, is a top-down approach to analyzing the environmental impacts of products or services—a method based on Wassily Leontief’s input-output analysis of economies. It can provide quick and high-level estimates of emissions from the production and upstream supply chain by allocating national emissions to finished products based on economic transactions. However, the results lack granularity and transparency, inhibiting actionable strategies for achieving corporate sustainability targets.
| Process-based LCA | Spend-based LCA | |
|---|---|---|
| Attributes | ||
| – System boundary | Key processes that drive impacts | All economic sectors |
| – Data needs | Detailed product inputs & activity data Granular emission factors Product inventories | Expenditure on purchased goods or services National/industry average emission factors Economic IO tables |
| – Outputs | Emissions drilled down by specific line items, facilities, and products | Emissions per unit of revenue in an industry sector or product category |
| – Time to complete | A few months on average | A few days to a couple of months |
| – Transparency | Assumptions and use of data are documented in finer detail | Often relies on assumptions and estimations |
| Corporate decision support | ||
| – Target setting | Provides a full process inventory (recommended by SBTi). | Used as high-level screening to prioritize data collection. Vulnerable to rebaselining. |
| – Reporting | Accurately tracks and reports year-on-year progress for Scope 1, 2, and 3. | Supports high-level corporate reporting. |
| – Supplier data collection | Targets high-impact suppliers with specific data requests. | Prioritizes sectors for further engagement. |
| – Mitigation strategy | Enables custom interventions and offset credits to achieve targets. | Unable to inform actionable strategies. |
| – Product development & communication | Identifies materials & design choices, and provides detailed, credible claims for individual products. | Identifies areas for further evaluation, and provides broad claims about sustainability efforts. |
The SBTi encourages companies to develop full value chain inventories when setting science-based targets, requiring the use of process-based LCA methods. Although spend-based methods are acceptable for a target submission, they are considered a screening process for prioritizing data collection among the 15 Scope categories.
The SBTi requires recalculating the baseline when an organization’s total base-year emissions vary by 5% or more. Spend-based LCA approaches that rely on high-level assumptions and expenditure data are more vulnerable to frequent rebaselining because the estimates often significantly deviate from the primary data. Process-based LCA approaches, based on life cycle activities and mass data, offer greater stability in reporting.
Process-based LCAs, when applied across the entire value chain, can offer detailed insights into Scope 1, 2, and 3 emissions and enable accurate tracking of year-on-year progress toward science-based targets. In contrast, spend-based LCAs, which rely on national production data and industry-average emission factors, cannot capture emission changes from specific production or sourcing practices. While limited in granularity, spend-based LCAs serve well for high-level corporate reporting.
With increasing pressures on companies to disclose their environmental footprints, process-based LCAs provide a solid foundation for tracking and reporting their emissions under regulatory and voluntary sustainability frameworks.
Collecting primary data from suppliers has become essential for Scope 3 disclosures and decarbonization initiatives. However, obtaining reliable, detailed supplier data for corporate needs remains a significant challenge.
Process-based LCAs help companies develop a granular understanding of their value chain footprints, even with limited primary data. This allows companies to identify “hotspots” and collaborate with suppliers with the highest impacts. The LCA models also facilitate specific data requests to complement modeled data, eliminating the need for lengthy, generic questionnaires for suppliers.
In the beef patty example, a process-based LCA pinpoints supplier facilities with the highest carbon emissions in each stage, from cattle rearing to beef and patty production. A spend-based LCA fails to capture variations in production practices and sub-regional environmental conditions, limiting effective hotspot identification and targeted data requests.
The granular insights from process-based LCAs enable companies to develop actionable interventions and verify credits required to offset supply chain emissions.
In the beef patty example, a process-based LCA informs decisions such as sourcing beef from a different state or using renewable energy for burger production in specific facilities. However, a spend-based model, which is tied to expenditure, limits strategies to purchasing fewer goods and scaling down production, thus hampering the ability to achieve zero goals.
Moreover, processed-based LCA models provide precise estimates for necessary carbon credits. For instance, when purchasing “deforestation-free” soy credits to reduce land use change emissions in the supply chain, a company needs to estimate the amount and location of deforestation caused by their soy purchase, which can only be derived from process-based LCAs.
For most companies, over 70% of the decisions affecting their corporate footprint are made during product development. The granularity of a process-based LCA enables product designers and developers to understand, early on, the impacts of various materials and design choices both environmentally and financially. In contrast, a spend-based LCA typically identifies key areas throughout a product’s life cycle based on expenditure, informing further investigation into reducing environmental impacts.
Consumers increasingly prefer products aligned with sustainability values, driving demand for LCAs. Business-to-business customers are the strongest drivers. Large institutional buyers in government, education, and finance rely on granular product data for environmentally preferable purchasing. Manufacturers of consumer products are also increasingly requesting LCA data for components and materials from their upstream suppliers. Compared to spend-based LCAs, process-based LCAs provide more in-depth information about sustainable design and production to support credible communications with various consumer groups.
Although process-based LCAs provide granular insights and enhanced transparency, implementing them at a corporate scale introduces two main challenges.
LCA software, such as Simapro, OpenLCA, and Gabi, is built to approach modeling at the level of a single product, with support for product variations or scenarios. However, it lacks the scalability required to model the number of products for a company’s full portfolio. In commercially available product sustainability software, changes to a product model can be applied to evaluate 10 to 20 versions of that product but not the hundreds or thousands of product variations necessary for corporate Scope 3 accounting.
The other primary challenge of scaling process-based LCAs is the ability to collect data for hundreds or thousands of product types sold by a company—with a reasonable amount of time and resources. Obtaining, cleaning, and organizing data for a full portfolio of products from various software systems and business departments demands considerable effort. Effective data collection also requires expertise to know which data points are most necessary to collect and how to address gaps in primary data.
Aligned Incentives employs a cutting-edge approach, which combines an extensive LCA database, top-tier LCA expertise, and advanced data and computing infrastructures, to enable custom process-based LCAs at scale.
Aligned Incentives’ approach for developing custom process-based LCAs at scale
Expertise
Our industry-leading LCA experts possess in-depth experience across sectors like agriculture, food, materials, electronics, energy, and industrial processes. They collaborate with companies closely to offer support in data collection and management, model customization, and strategy development.
During primary data collection, our experts assist in filling data gaps, building client-specific data structures and protocols, as well as creating automated pipelines to ensure consistency in future modeling or reporting iterations.
Data
Our ever-growing core LCA database covers complete supply chain inventories for 400K industrial, energy, and agricultural unit processes developed from over 200 sources, including:
The inventory data associated with each process is stored in our system using a modular structure, which allows for rapid customization even for highly complex multi-layer modeling.
Software
Using our core background database and primary data collected, our AITrack software can quickly develop life cycle inventories for a company’s entire product portfolio, customized to their unique materials, technology, timeframe, and region of production. With granular impact assessments, our system provides visualizations of emissions by product, procurement line item, facility, and source.
The hotspots identified enable targeted supplier engagement to collect additional data, which is incorporated to continuously optimize custom LCA inventories.
Following the example of LCA for beef patty, our unique approach shows three key strengths:
Want to learn more about our LCA approach and how it can support your sustainability journey? Speak to our team today.
