From Data to Decision

From Data to Decision: Designing Life Cycle Assessment Results for Impact

Written by: Tess Konnovitch, Scientific Marketing Manager & Gianni Guglielmi, Engineer and Life Cycle Analyst

Life Cycle Assessment generates an extraordinary amount of information.

Multiple impact categories. Multiple scenarios. Sensitivity analyses. Trade-offs across climate change, water use, eutrophication, toxicity, and resource depletion. Often hundreds of pages of outputs.

But data volume does not automatically create clarity.

Too often, LCA findings are presented exactly as they are exported — dense tables, default software graphs, and page after page of impact category breakdowns. While technically accurate, these outputs can obscure the most important insights: What changed? What drives impact? What trade-offs matter? How does this translate to effective decisions?

Effective data visualization is not about making results “look better.” It is about structuring information so that insight becomes visible. But structure alone is not enough. Interpretation is shaped not only by what we show — but how we design, frame, and contextualize it.

In 2024, we explored this challenge in our guidance document, Understanding Effective Ways to Visualize Life Cycle Assessment Results. The document examined common visualization pitfalls in LCA, reviewed research on graphical interpretation, and offered practical recommendations for improving clarity, reducing misinterpretation risk, and supporting better decisions.

Many of those principles remain just as relevant today — especially as LCA outputs grow more complex and sustainability decisions carry greater financial and reputational implications. As LCA increasingly informs regulatory filings, capital investments, product redesign, and public sustainability claims, the responsibility to communicate results clearly — and defensibly — has only intensified.

“Given that life cycle assessments and subsequent findings are intertwined with environmental and societal change, it becomes even more critical that the results are presented in a clear and easy-to-understand manner.”

— Understanding Effective Ways to Visualize Life Cycle Assessment Results

The Density Problem in LCA

LCA is inherently multidimensional. Even a relatively straightforward comparative assessment may include:

• Multiple impact categories

• Multiple products

• Alternative scenarios or design options

• Multi-product systems

• Allocation choices

• Sensitivity analyses

• Uncertainty results

Presenting results independently can quickly become overwhelming. Decision-makers are left scanning dozens of tables or flipping through extensive appendices, trying to piece together patterns on their own.

When results are shown in isolation — especially without carefully selected grouped visuals — the risk of misinterpretation increases. Important relationships may be missed, and partial insights can be mistaken for the full story.

This approach shifts the cognitive burden onto the audience — something they should never be required to carry. It is the practitioner’s responsibility to understand what the audience needs and translate an abundance of data into a clear, prescriptive path forward.

Strong visualization and framing does the opposite. It reduces cognitive load by organizing complexity into coherent structure — transforming analysis into insight, and insight into action.

“The way LCA results are presented will have an impact on the audience’s interpretation of them…(and) it may be the one opportunity for a reader to interact with and understand your work!

— Understanding Effective Ways to Visualize Life Cycle Assessment Results

From Outputs to Insight

Default software exports are designed for documentation — not for interpretation.

They preserve completeness. They ensure traceability. But they are not inherently structured to reveal what matters most for a specific audience or decision context.

Software allows nearly any configuration of data to be visualized. In many cases, however, default visualizations are generated regardless of the type of scenario being evaluated. These outputs are designed for consistency and transparency, but they do not necessarily align with the diverse purposes and disciplines that now rely on LCA.

“Impact categories are not directly comparable. Excel will let you plot these categories, but that does not mean you should.”

— Understanding Effective Ways to Visualize Life Cycle Assessment Results

As a result, effective interpretation requires more than technical capability — it requires intentional design. The way results are structured, framed, and visually presented can strongly influence how they are understood. 

Consider the example below, in which a dominant impact compresses the smaller contributors in a typical Excel chart, making them appear equivalent, while an intentionally designed visualization reveals meaningful variation among them.

Communicating results effectively requires intentional design choices, such as:

• Applying color, typography, and scale intentionally to create hierarchy and direct attention

• Grouping related impact categories into a unified dashboard view

• Standardizing axis scales across comparisons to avoid visual distortion

• Using indexing or normalization to clarify trade-offs

• Highlighting key drivers rather than listing every value

• Applying consistent formatting across scenarios

• Being creative!

An intentionally designed visual can structure multiple complex datasets in a way that allows patterns, trade-offs, and relative performance differences to emerge clearly. Rather than navigating dozens of pages, a decision-maker can understand the structure of the results in a single view.

The goal is not to simplify the analysis — it is to simplify the experience of understanding it.

Framing Shapes Interpretation

In addition to how the data is visualized, how results are titled, described, and contextualized — whether presented in a slideshow, embedded in a report, or shared in an executive summary — directly shapes how they are understood.

A slide labeled “Results” provides little guidance.
A slide titled “Design Option B Reduces Climate Impact by 28%” provides immediate interpretive direction.

In a report, an effective visual will provide sufficient context so that the audience is not required to scroll back up to get necessary information on the goal, the scenarios being considered, and other important modeling conditions. In fact, it is best practice to have at least some qualitative explanation to help the user understand the initial takeaways from the visual as well as to remind them the context of the LCA.

To aid the audience in this way, visuals should also include the following:

• The functional unit and any co-products (if the system is multi-functional)

• The system boundary (cradle-to-gate; gate-to-gate; etc…)

• The name of any and all impact categories being depicted in the visual

• The type of Impact Assessment method being used

• The description of any/all acronyms

• The baseline scenario highlighted/described in some way distinctly from the other scenarios (in the case of comparative LCAs)

Consider the two examples below. The data itself has not changed — but the way it is framed dramatically alters how quickly and accurately the audience understands what matters.

Example One:

Example Two:

Framing determines what the audience notices, what they compare, and what they conclude.

Accessibility is equally important. Choices around color contrast, font size, layout hierarchy, and colorblind-safe palettes are not cosmetic decisions — they influence who can meaningfully engage with the results.

“When creating data visualizations, it’s important to ensure that all users, with all levels of ability, have access to the information being displayed.”

— Understanding Effective Ways to Visualize Life Cycle Assessment Results

The responsibility of an LCA practitioner does not end with analysis. It extends to ensuring that findings are interpreted accurately, responsibly, and in context.

Because interpretation is never neutral — it is shaped by how information is framed. And when interpretation shapes action, visualization becomes more than communication — it becomes decision architecture.

Interpretation Shapes Decision-Making

At its core, data visualization in LCA is not about charts — it is about how conclusions are formed and actions are taken.

The question is not “What charts can we use?”

The question is “What does this audience need to understand to act?”

Effective visualization:

• Reduces cognitive overload

• Makes trade-offs explicit

• Preserves methodological transparency

• Supports defensible decision-making

As practitioners continue to develop LCA models through typical LCA software, it can become easy to unconsciously assume that exported results are inherently sufficient. But interpretation is context-dependent. The way results are presented should be shaped by the goal of the study, the audience’s familiarity with LCA concepts, and the magnitude and relevance of uncertainty.

Before designing results, practitioners should be able to answer four key questions:

• What is the goal of the analysis/study?

• What is the audience’s level of understanding in LCA concepts?

• What is the level of data uncertainty in this scenario and how can it be properly communicated?

• How much qualitative explanation is needed to support accurate interpretation?

As practitioners, it may seem that the considerations listed above are already embedded within the LCA process. But how deliberately do we adapt our presentation of results based on an audience’s level of LCA literacy? Can we confidently determine how much qualitative explanation is needed to accompany a particular visual? And how consistently do we adjust our communication strategy when uncertainty meaningfully affects interpretation?

Designing for effective decision-making requires grappling with these questions — yet in practice, certainty is difficult. Audience familiarity varies. Context shifts. Stakes differ. What feels clear to an analyst may not feel clear to a decision-maker.

To explore how visualization choices are made within the LCA community, we conducted research and practitioner surveys during the American Center for Life Cycle Assessment (ACLCA) 2023 LCA Institute Conference. The study examined which visual formats practitioners prefer for comparison, hotspot identification, uncertainty communication, and multi-impact analysis. The details of this study can be found in our guidance document, Understanding Effective Ways to Visualize Life Cycle Assessment Results.

The results revealed meaningful variation in how practitioners interpret and select visuals depending on the goal of the analysis. In other words, there is no single “correct” visual for a dataset — visualization choices are inherently context-dependent.

This reinforces a broader conclusion: selecting visuals is not a cosmetic decision. It is part of the analytical responsibility.

When done well, visualization becomes a strategic tool — not a decorative afterthought.

From Analysis to Action

Sustainability decisions increasingly carry financial, operational, and reputational implications. The clarity of communication can directly influence how confidently an organization moves forward.

At EarthShift Global, our Branding & Communications services help organizations translate rigorous LCA results into messaging and visuals that are both defensible and actionable. From dashboard development to executive-ready presentations, we ensure that technical analysis supports informed, credible decision-making.

For those interested in exploring these principles in more depth, join our March 2026 Brown Bag Webinar, where we will expand on strategies for simplifying and visualizing LCA results.

Because sustainability impact is not created by data alone — it is created when data is understood well enough to drive action.

“Science isn’t finished until it’s communicated.”
— Mark Walport (as referenced in Understanding Effective Ways to Visualize Life Cycle Assessment Results)

About the Authors

Tess Konnovitch, Scientific Marketing Manager

Tess Konnovitch is the Scientific Marketing Manager at EarthShift Global, where she leads strategic communication of life cycle assessment (LCA) insights across global audiences. She joined EarthShift Global in 2022 as a Data Visualization Specialist and was promoted into marketing leadership, blending scientific rigor, visual design, and clear messaging to ensure complex sustainability analyses resonate with decision-makers.

Tess holds a B.S. in Environmental Science and Biology from La Salle University and a M.S. in Computational Biology from Rutgers University, where her research focused on how organisms to ecosystems respond to human-driven environmental change such as warming and eutrophication. Her background bridges ecology, quantitative analysis, and communication — allowing her to translate dense technical data into meaningful, accessible insight.

An artist at heart with a deeply analytical foundation, Tess has spent over three years at EarthShift refining how LCA is visualized, framed, and understood.

Gianni Guglielmi, Engineer and Life Cycle Analyst

Gianni is an Engineer and Life Cycle Analyst at KeyLogic, a System One Company, where he leads the development of various LCA models and reports for the National Energy and Technology Laboratory (NETL).

He has a background in Environmental Engineering and holds a M.S. in Civil, Environmental, and Sustainable Engineering from Arizona State University. His graduate research focused on understanding how to improve the visualization and interpretation of LCA results depending on the specific scenario one is involved in.

From these experiences, Gianni has spent years working to ascertain how LCA practitioners can translate and communicate the detailed LCA results into meaningful interpretations and actionable conclusions.