A surreal fishing net with illuminated knots representing key industries in LCA.

Beyond the Supply Chain: Why 'Complete' LCA Models Miss the Mark

Mira Elwood in Climate & Environment August 2025 • 4 min read.

"Rethinking environmental assessments for real-world impact"

Life Cycle Assessment (LCA) is a powerful tool for evaluating the environmental impacts of a product or service throughout its entire life cycle. Traditionally, process-based LCAs have been used, but they often face criticism for having 'incomplete' system boundaries. This has led to the development of hybrid LCAs, which combine process-based data with input-output (IO) models to capture a broader range of economic activities.

The core idea behind hybrid LCA is appealing: by including more and more processes, we can get closer to a truly 'complete' picture of environmental impacts. But is this pursuit of completeness actually worthwhile? Does adding every possible economic activity really improve the accuracy and relevance of our assessments? Some researchers are beginning to question whether expanding the system boundary is truly the key to better environmental decision-making.

This article unpacks the debate around system boundaries in LCA, exploring the limitations of both process-based and hybrid approaches. We'll delve into why the quest for 'completeness' might be a distraction from more critical issues, and how a more focused, market-aware approach can lead to more effective strategies for reducing environmental impact.

The Illusion of Completeness: Why More Isn't Always Better

A surreal fishing net with illuminated knots representing key industries in LCA.

The idea of a perfectly complete LCA model, one that accounts for every single economic activity on Earth, seems intuitively appealing. However, the practical value of such a model is questionable. The decisions LCA seeks to inform are usually about specific changes: altering a product system, promoting an alternative, or reducing impacts in a particular area. A model that includes everything may end up obscuring the relevant factors and providing little actionable insight.

The limitations of traditional, process-based LCAs aren't primarily due to 'incomplete' system boundaries. Instead, the focus is on supply chain and the reliance on unrealistic assumptions. These include the omission of price effects and other market dynamics, which can significantly influence the actual environmental outcomes of a product or service.

Narrow Focus: Concentrating solely on the supply chain can blind us to broader economic effects.
Unrealistic Assumptions: Ignoring price signals and market constraints creates a distorted picture of reality.
Doubling Down on Limitations: Hybrid LCA, by incorporating IO models, inadvertently reinforces these problematic assumptions.

Consider the example of corn ethanol. Researchers show that expanding the system boundary to include unrelated industries, such as Chinese stuffed animal production, doesn't necessarily make the LCA results more accurate or relevant. The theoretical link is tenuous, and there's no evidence that US corn ethanol expansion has a tangible impact on those industries. Instead, focusing on actual market mechanisms, such as indirect land use change, is crucial for predicting whether promoting corn ethanol will truly reduce carbon emissions.

Reimagining LCA: Flexibility and Market Awareness

Future LCA studies should shift away from simply 'completing' the system boundary within a conventional supply chain and linear framework. A move towards more realistic modeling of our complicated human-environment system is important. Instead of trying to include everything, the study argues for flexible, market-based system boundaries tailored to the specific decision at hand. This involves considering the scale of potential changes and how they may affect the economy. A change at larger scales is likely to have a broader impact, thus justifying a broader system boundary, while a broad system boundary for a small change will likely result in overestimates. Ultimately, more is not necessarily better when it comes to LCA.

About this Article -

This article was crafted using a human-AI hybrid and collaborative approach. AI assisted our team with initial drafting, research insights, identifying key questions, and image generation. Our human editors guided topic selection, defined the angle, structured the content, ensured factual accuracy and relevance, refined the tone, and conducted thorough editing to deliver helpful, high-quality information.See our About page for more information.

This article is based on research published under:

DOI-LINK: 10.1007/s11367-018-1532-y, Alternate LINK

Title: Moving From Completing System Boundaries To More Realistic Modeling Of The Economy In Life Cycle Assessment

Subject: General Environmental Science

Journal: The International Journal of Life Cycle Assessment

Publisher: Springer Science and Business Media LLC

Authors: Yi Yang, Reinout Heijungs

Published: 2018-09-17

Everything You Need To Know

What is Life Cycle Assessment, and how are hybrid models used to address the limitations of traditional approaches?

Life Cycle Assessment is used to evaluate the environmental impacts of a product or service throughout its entire life cycle. Traditionally, process-based LCAs have been employed, but hybrid LCAs have emerged to address criticisms about incomplete system boundaries. These hybrid approaches combine process-based data with input-output models, aiming to capture a broader range of economic activities. While the intention is to create a more complete picture of environmental impacts, this pursuit of completeness is being questioned for its actual value in improving the accuracy and relevance of assessments.

Does adding more economic activities to a Life Cycle Assessment model guarantee a more accurate environmental impact assessment?

While expanding system boundaries by including more processes aims for a 'complete' picture of environmental impacts, it doesn't always enhance accuracy or relevance. The decisions Life Cycle Assessment informs are usually about specific changes, such as altering a product system or reducing impacts in a particular area. A model that includes everything may obscure relevant factors and offer little actionable insight. The focus on supply chain and the reliance on unrealistic assumptions are the core limitations.

What are the key limitations of traditional, process-based Life Cycle Assessments, and how do these limitations affect the reliability of their findings?

Traditional, process-based Life Cycle Assessments often fall short due to a narrow focus on the supply chain and the use of unrealistic assumptions. These include omitting price effects and other market dynamics, which significantly influence the actual environmental outcomes of a product or service. This narrow focus can blind us to broader economic effects, and ignoring price signals and market constraints creates a distorted picture of reality.

In what specific instances does expanding the system boundary in Life Cycle Assessment fail to improve the accuracy or relevance of the results?

Expanding the system boundary to include unrelated industries, such as Chinese stuffed animal production, doesn't necessarily make Life Cycle Assessment results more accurate or relevant when assessing something like corn ethanol. Instead, focusing on actual market mechanisms, such as indirect land use change, is crucial for predicting the true impact of promoting corn ethanol on carbon emissions.

How should future Life Cycle Assessment studies adapt to better reflect real-world environmental impacts and inform effective strategies for improvement?

Future Life Cycle Assessment studies should move away from simply completing the system boundary within a conventional supply chain and linear framework. A shift towards more realistic modeling that considers the scale of potential changes and how they may affect the economy is crucial. A market-aware approach involves using flexible system boundaries tailored to the specific decision at hand. Larger scale changes justify broader system boundaries, while broad system boundaries for small changes likely result in overestimates.