Reducing greenhouse gas emissions is hard. So is counting and showing them.

One popular part of the data-related courses I teach through Wellington Uni Professional involves participants critiquing various forms of data communication created by others. The idea is that it's easier to spot things that are confusing, misleading, inaccurate, or just could be better when they were made by other people because we're all too close to our own work and know what we mean even if that's not what comes across to others.

I thought I would share some publicly available examples of data communication and visualisation and comment on what works, what doesn't, and what's okay but could be improved. The idea isn't to disparage the people or organisations that have produced less than ideal data communications – we've all done that – but rather to show how hard it is to craft truly effective data communication, and to share some lessons and tips toward that end.

Let's examine this chart from Danyl McLauchlan's thoughtful column in this week's Listener to see what broader lessons we can draw about effective data communication. I want to start by pointing out that the chart has little to do with the points being made in the column itself.

The chart appears in 'Increasingly common tragedy' by Danyl McLauchlan on page 11 of the Listener, February 7, 2026. Image reproduced for purposes of education, criticism and commentary.

A quick glance at the chart would lead you to believe that the column is about what sources contribute most to New Zealand's greenhouse gas emissions; however that is only touched on briefly in the text. Mainly, the column offers a game-theoretic explanation for the collective failure of the country and the world to reduce emissions sufficiently to avoid the types of weather extremes we are already starting to experience, comments on New Zealand's lack of resilience in the face of those extremes, and discusses the benefits clean energy offers above and beyond its contribution to reducing emissions. Someone just flipping through the magazine might look at the chart and take away the message that climate is really just a problem for farmers, which is not the point of the column. This data visualisation detracts from the important, but nuanced, arguments being made rather than helping to communicate them clearly.

Lesson: Data visualisations are not decorations. They should only be used when they help enhance viewers' understanding of the topic being discussed.

Besides not being particularly relevant to the text of the article, the data shown in the chart is unlikely to be clearly understood by the audience – in this case readers of the Listener. The data comes from The Ministry for the Environment. It is based on measurement standards developed as part of international agreements. Climate professionals would be familiar with those standards, but the average reader of the Listener is unlikely to be.

That's a crucial difference because data communication created for subject matter experts should be different from data communication created for the general public. In this case, a couple important things that climate experts know, but most of the magazine reading public does not are that: 1) Greenhouse gas emissions can be measured on either a production or consumption basis, and 2) The standard way of reporting greenhouse gas emissions excludes international travel and transport.

The data in the chart shows greenhouse gas emissions generated by source in the goods and services New Zealand produces (not consumes). That makes even that first, dramatic, bar hard to interpret because New Zealand exports a lot of animal products. That first bar being the longest doesn't tell us if livestock is farmed more or less efficiently in terms of greenhouse gas emissions in New Zealand than elsewhere. We also can't tell if farming is more or less efficient in terms of greenhouse gas emissions than other industries because we have no information about the outputs associated with the emissions.

With regard to the second point, a reader unfamiliar with the fact that international travel and transport are not included in the data shown might reasonably conclude that their twice yearly international trips are not a problem given domestic aviation is at the bottom of the chart and international aviation does not appear at all. Yet if such a reader were to calculate his or her personal consumption-based emissions and include all sources, they might be surprised to discover that those international flights are likely to be their greatest personal source of greenhouse gas emissions.

Lesson: Data communications should be tailored to the intended target audience.

These two principles of using visualisations purposefully rather than decoratively, and tailoring them to the intended target audience can make the difference between data communication that clarifies and data communication that confuses. In future posts, I'll explore more examples of what works, what doesn't, and what could be better with some thoughtful improvements.