Communicating LCA: Beyond the Bar Chart

EarthShift Global’s Data Visualization Specialist Tess Konnovitch discusses the different types of chart styles that are commonly used for life cycle assessment data visualization. Tess presented this information with colleague Gianni Guglielmi at the 2023 LCA Institute; they are currently working on a guidance document that shares the below information, along with survey results and research, to help progress data visualization in the LCA community.

As a data visualization specialist, I work with analysts and clients to determine how to communicate their LCA data findings and insights. One of the most important decisions we make is determining which chart style will best convey the information. In this post, we will explore some of the most popular chart styles, their strengths and weaknesses, and when to use them for LCA data visualization.  Effective visualization helps to more easily and accurately communicate LCA findings to a range of audiences.

The Bar Chart

To decide between a regular (vertical) bar chart and a stacked bar chart, consider if you want to compare the total values across categories or the individual values within each category. If you want to compare total values, a regular bar chart is the way to go, while a stacked bar chart is more appropriate if you want to show the composition of each category. 

In terms of units, using actual values is best when the absolute values matter while using 100% values is useful when you want to show the relative proportions of each category. Finally, if you want to compare the values among categories that have vastly different scales, displaying data relative to the largest value can help provide a clearer picture of the relationships between categories. 

In LCA results specifically, absolute values should only be used with one impact category. If you are including multiple impact categories, you must use relative values such as percentages or normalized values. Impact categories are not directly comparable. Excel will let you plot these categories, but that does not mean you should! Using absolute values to compare impact categories can be misleading and inaccurate, and it is important to use relative values and compare products or processes within the same category to get a more accurate picture of their environmental impact.

The Donut and Pie Charts

We recommend using a donut instead of a pie chart. The hole in the donut chart can be used to display additional information, such as the total of the data being represented, the impact category, the units, etc. In addition, the slices are more rectangular, allowing readers to estimate size instead of angle. Creating a grid of donut carts is a visually appealing and space-saving data visualization technique. Each level of the hierarchy is represented by a ring, and each ring is divided into segments proportional to the data it represents. The innermost ring represents the top level of the hierarchy (e.g., life cycle stages), and each subsequent ring represents a lower level (e.g., parts and other groups). 

The Treemap and Sunburst Charts

The treemap and sunburst charts both display hierarchical data. 

Treemaps, as the name suggests, use rectangles to represent different nodes of a tree. The size of each rectangle is proportional to the value it represents. 

A sunburst chart is a hierarchical donut chart. Sunburst charts are often used to show the relationship between different categories and sub-categories.  

Deciding between a sunburst and treemap depends on the number of levels in the hierarchy and size of each group. We recommend plotting your data in both visualizations, and deciding which plot is (1) more visually appealing (e.g., sunburst charts can sometimes have weird, unaesthetic gaps) and (2) most legible (i.e., data labels are clear, or can be made clear using manual data labels and connector lines).

The Violin Chart

The violin chart is a visually beautiful chart that is highly effective at showing uncertainty. Violins show the distribution of the data and its density, and can be drawn in different orientations, including vertical or horizontal, and mirrored, non-mirrored, or two-sided. 

There are two primary ways to display data in a violin chart. At EarthShift Global, we often start with a simple violin, in which there is a center mass that represents the 50% confidence interval, and the outer mass that represents the 95% confidence interval. This allows readers to visualize the uncertainty of the data. However, for those that wish to see summary statistics (e.g., mean, quartiles, etc.), it is possible to put a box plot inside of a violin. In our opinion and experience, the major benefit of a violin plot is that it does not rely on summary statistics. While summary statistics can provide a quick and easy way to summarize data, they can also be misleading, particularly in life cycle assessment where data is a range and uncertain.

The Boxplot Chart 

Boxplots, like violins, are a useful tool for displaying the distribution of a dataset and summary statistics. A boxplot, also known as a box-and-whisker plot, displays the median or mean, quartiles, and outliers of a dataset. As mentioned above, summary statistics should be used cautiously with LCA data.

The Heatmap Chart 

Heatmaps use color-coding to show relative magnitudes of values across a data set. In LCA studies, heat maps can be used to visually represent the impact of different life cycle stages or processes by using color to represent the magnitude of impact. A heat map is successful at giving an initial impression of “good” and “bad” but is not very informative compared to other charts. It does not typically show discrete data points, and the color scheme may be confusing to some (e.g., green is associated with “increase” but also “good”; in LCA, an increase in impact is not good). We recommend using a heat map cautiously, and including data labels when possible.

The Line Chart 

Line charts, in our experience, are less common in LCA studies. This could be because line charts are often associated with experiments over time. However, we argue that a life cycle is its own timeline, and therefore we can plot how an impact changes over the course of a life cycle. An advantage to line charts is that we can plot all of the data points for each stage, but emphasize the mean with a trend line, making this a very informative plot.

The Sankey Diagram

Sankey diagrams can help to illustrate the inputs and outputs associated with different stages of a product's life cycle, as well as the interconnections between and impacts of those stages. Sankey diagrams are less common than other visualizations, such as bar and donut charts, because they require additional visualization software or plug-ins in Excel. They may also not be familiar to most audiences.

The Blur Chart 

The blur chart is similar to a bar graph, but instead of a hard line at the mean it shows a gradient to demonstrate uncertainty. It is an important tool in LCA because it helps to visualize and communicate the level of uncertainty in the results of an LCA study. Tensa et al., 2022 found that the blur chart was effective in forcing users to acknowledge uncertainty, but not many people liked it. This touches on the previous topic of summary statistics; people like discrete numbers, but in reality LCA is full of uncertainty. Deciding between a bar and blur chart is dependent on audience and goal.

We hope that this blog post has been helpful in exploring the types of chart styles and guiding you toward the chart style that best suits your needs. For those who are interested in learning more about this topic, we will be sharing our full guidance document, which expands on the above information and includes research and survey results, in the near future. This document will provide even more insights into the best chart styles for visualizing life cycle assessment data.