In the second piece in our How We Work series, we walk you through how we built our climate report, step by step.
By Enrico Soddu
When Jonas Jølle read my first draft of the 2026 IFSWF and OPSWF Climate Survey, Hidden Currents, he called it a good start. The implication was clear.
I spent weeks cleaning the raw SurveyMonkey data and making it ready for Tableau. Unfortunately, AI doesn’t like to do the grunt work of prepping the data for me; it prefers to summarise my findings or ask me if I want it “board-ready”. After six years of running the survey, we certainly had an abundance of information. I mean, it is not Big Data (what happened to this buzzword?), but still: over 20 questions, seven regions, and almost half of the sovereign wealth fund universe. The analysis was descriptive, but it held up. My co-author from the One Planet SWF Network (OPSWF), Jonas Jølle, asked, "How would an investment committee read this?"
When a senior advisor at OPSWF and former global co-head of corporate governance at Norway’s sovereign wealth fund points out a flaw in your analysis, you listen and then rethink the entire approach. How do you take what 31 sovereign wealth funds tell you and turn it into something that influences decisions? How do you move people, not just describe what they said? That’s where the real work began, not the analysis itself, but the translation.
The challenge was comparability: five survey waves, questions that shifted between them, and a mix of multiple-choice and Likert-scale answers (1–5 agreement scales) that didn't fit on the same chart.
Jonas suggested we try something like Net Promoter Score, a tool for measuring customer satisfaction. My first instinct was no, since we'd never used it before and didn’t want a climate report for sovereign wealth funds to read like a customer survey. But as I kept staring at the charts, I started to see his point. He wanted to solve a real problem: how do you present complex data without pretending it is more precise than it is? In the end, I came around.
Why Net Score
Look at the images below. When I finally ran the data both ways, side by side, the difference jumped out. One shows the full Likert scale, the other shows the Net Scores.
Twenty climate topics, each with a five-point scale and a wall of numbers, fail the “five-second test”, as the reader cannot understand them in less than five seconds. The Net Score version feels like a conversation. One number for each topic. Rankings you can scan in seconds. Yearly changes that stand out instead of getting lost in columns.
Let me be clear. It is not a Net Promoter Score. We borrowed the idea of positives minus negatives, but the tool, the scale, and the question are all different. Jonas brought the idea from customer research. I recast it for an entirely different job.
From Index to Action: Visualising the Rankings
The charts had to earn their place. Each one answers a specific question that an investment committee would actually ask.
For regional attractiveness, we used horizontal bars (see chart above) that start at zero. Anything above zero means people feel positive. In 2025, for the first time in four years, every region landed on the positive side. The bold zero line makes that shift clear. The jumps are big: West Asia up by 57 points, East and Southeast Asia by 62.
For segment attractiveness, with over twenty climate topics, we switched to a bump chart. Here, the y-axis shows rank rather than raw score. Clean hydrogen, which dropped from the middle in 2022 to the bottom by 2025, saw its Net Score go negative. Energy storage climbs from the lower half to second place. The lines crossing tell the story.
For questions about the climate risk assessment pipeline, we used stacked bars to show change over time, with answers “done”, “in progress”, “under review”, or “ruled out”. The chart shows the pipeline getting narrower. The share for in-progress and considering has dropped sharply from 2023 to 2025. The chart shows a system that has stopped expanding and started choosing.
What We Don’t Hide
We highlighted the main biases in the data before publication: central tendency (people picking the middle answer), acquiescence (tendency to agree with whatever is being asked), social desirability (picking an answer to look good), and non-response (choosing not to answer). Being open about them does not weaken the work.
The professionals completing this survey manage over $10 trillion in public funds.
The professionals reading it decide where their organisations go next. Both deserve numbers they can trust, with their caveats clearly stated.
Most reports don't mention any of this. We figured you'd rather know.