There’s a quiet crisis unfolding inside the sustainability movement.
On the surface, everything looks active: new ESG policies, climate risk tools, SDG dashboards, decarbonization roadmaps.
But underneath it all, a fundamental question often goes unasked:
Is any of this actually working?
Are emissions going down because of these strategies—or in spite of them?
Are portfolios outperforming because they’re green—or because of unrelated macro factors?
Are we seeing impact, or just… activity?
If you come from a hard science background, like I do, these questions feel obvious. In physics, you’re taught to model, test, falsify. Precision isn’t optional. Hypotheses aren’t trusted—they’re broken until proven.
In sustainable finance? Not so much.
From Feel-Good to Evidence-Based
When I left physics for sustainability and finance, I kept encountering tools that looked rigorous but weren’t. ESG scores. Materiality matrices. Impact ratings.
They were full of numbers. But they weren’t grounded in causality.
And that’s the issue. We’re often measuring correlation—what happened—not causation—what drove it.
If a climate policy is followed by fewer floods, was it effective? Or was the rainy season just mild?
If a company scores well on ESG and performs well financially, is there a causal link—or just coincidence?
Without asking these questions, we risk building a movement on sand.
The Hidden Cost of Not Knowing
When we don’t evaluate what works, we:
Waste capital on ineffective strategies
Create false confidence in metrics that don’t drive results
Lose time, which is the most irreplaceable asset in the climate crisis
And we miss an opportunity to improve.
Because what we don’t know is actually where the power lies. It’s the entry point to learning, strategy, and scale.
Physics as a Lens for Sustainability
At Wangari, we’re trying to change the equation—literally. We’re building tools to embed causal intelligence into sustainability data and decision-making.
The methods we use come from econometrics, data science, and even theoretical physics. We’re asking questions like:
Did this ESG investment causally reduce risk exposure?
Did biodiversity policy X actually slow land degradation—after controlling for external shocks?
Which variables drive the strongest signal across our entire sustainability portfolio?
We’re not chasing impact stories. We’re building feedback loops.
Why This Isn’t Standard (Yet)
So why isn’t everyone doing this?
Cultural Lag: Sustainability grew from a place of values. It emphasized what matters, not always what works. As the field matures, we need a new synthesis—where values meet verifiability.
Fear of Uncertainty: Many stakeholders are afraid to say, “We don’t know.” But that’s how science—and progress—works.
The irony is: when we admit uncertainty, we gain credibility.
It opens the door to better questions, clearer investments, smarter strategy.
A New Role for Analysts and Institutions
In this new paradigm, financial analysts become more than spreadsheet jockeys. They become sustainability detectives—testing hypotheses, measuring counterfactuals, communicating confidence levels.
Meanwhile, institutions shift from performance theater to performance truth.
They stop asking, “How do we look?” and start asking, “What’s working—and how do we know?”
This isn’t slower. It’s smarter.
And it’s how real change happens.
The Road Ahead
We’re entering a historic moment of capital reallocation—toward climate solutions, social equity, and ecological regeneration.
But if we don’t know what works, we risk wasting this opportunity.
So let’s build new habits:
Ask causal questions.
Welcome uncertainty.
Test what matters.
Share what we’re learning—not just what looks good.
Because if we want to build a better world, we can’t just do more.
We need to know which actions actually move the needle.
That’s the next frontier.
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