Investment Now or Pay Later: How Climate Uncertainty Drives Up Green Transition Costs

In a groundbreaking collaboration between the University of Illinois Urbana-Champaign and the World Bank Group, researchers Adam Michael Bauer, Florent McIsaac, and Stéphane Hallegatte present a compelling case for rethinking global climate investment strategies in their 2025 paper Decarbonization Investment Strategies in an Uncertain Climate. With the Paris Agreement's goal of limiting global warming to well below 2°C, and ideally to 1.5°C, becoming increasingly urgent, the study argues that the path to a low-carbon future is far more complex and costly when climate uncertainty and economic adjustment costs are factored in. Their detailed modeling framework sheds light on the true economic stakes of delayed learning about the planet’s remaining carbon budget and shows how the pace, timing, and distribution of climate investments must evolve to match an unpredictable climate future.

Climate Uncertainty: The Hidden Cost Multiplier

At the heart of the research lies a core dilemma: climate uncertainty significantly amplifies the cost of climate action. Adjustment costs, economic barriers like scarce skilled labor or limits in production capacity, already raise the price tag of a rapid green transition. When these are combined with uncertainty about how much carbon can still be emitted before breaching warming thresholds, the result is a steep increase in near-term investment requirements. Policymakers are forced to prepare for the worst-case scenario, even when it might not materialize, simply because they don’t know when, or if, the safe threshold will be crossed. This uncertainty makes it economically rational to act sooner and more aggressively, particularly in sectors that take longer to transition.

The authors develop two contrasting models: one that realistically captures adjustment costs and capital accumulation across multiple sectors, and a simplified “strawman” model that assumes instantaneous, frictionless abatement. While both models perform similarly under certainty, the differences grow stark under uncertainty. The full model shows that uncertainty causes investment paths to shift dramatically toward the present, driving up total costs. Meanwhile, the strawman model underestimates the cost and urgency of early action, offering an overly optimistic picture of the transition.

Front-Loading as a Hedge Against Risk

The study highlights how climate uncertainty reshapes the timing of investments. In particular, it shows that decarbonization investments must be heavily front-loaded to hedge against the risk of a smaller-than-expected carbon budget. If the world waits too long to learn how much CO₂ can still be safely emitted, it risks having to decarbonize too quickly later, at much higher costs. For example, under a 1.7°C target, if the worst-case budget is just 300 GtCO₂ and current emissions are roughly 40 GtCO₂ per year, the carbon budget could be exhausted in under 8 years. This forces early and costly interventions to avoid locking in infrastructure that would later need to be abandoned or replaced.

Investment in hard-to-abate sectors, such as agriculture, heavy industry, and transportation, is especially affected. These sectors have high emissions intensities and slower technological turnover, meaning they require more time and capital to decarbonize. In uncertain scenarios, the model finds their investment pathways switch from bell-shaped to U-shaped: rising sharply before information is revealed, and adjusting once the actual carbon budget becomes known. Easy-to-abate sectors like energy and forestry also see earlier, steeper declines in investment patterns, reinforcing the need for an urgent push in all directions.

The Carbon Price Tells a Skewed Story

Carbon pricing lies at the core of economic climate policy. In the models presented, the presence of climate uncertainty skews the distribution of optimal carbon prices upward. When learning is delayed, the carbon price distribution inherits a “heavy tail,” meaning extreme values on the high end become more likely. This reflects the reality that, if policymakers only learn in 2030 or later that the carbon budget is much smaller than expected, they will have to impose extremely high carbon prices to stay on track. Conversely, if the news is good, prices can only be relaxed slightly. This asymmetry results in higher average carbon prices and justifies greater precautionary investments today.

The researchers emphasize that the earlier this information is known, the more manageable and stable carbon prices remain. Learning even one year earlier can reduce overall policy costs by billions of dollars. The study quantifies the "value of learning" and finds it substantial, up to $1 trillion annually, especially for tighter warming targets like 1.7°C. This finding elevates the importance of investing in climate science, carbon monitoring, and transparent reporting systems alongside emissions reduction strategies.

Adjustment Costs Make Everything Worse

One of the most crucial takeaways from the paper is the underestimated role of adjustment costs in climate policy models. When these frictions are excluded, as is common in many integrated assessment models, results suggest a smoother, more forgiving path to net-zero emissions. But in reality, transitioning economic sectors takes time, money, and resources that aren’t instantly available. Adjustment costs make worst-case climate scenarios far more expensive to manage. This effect is especially pronounced in the presence of delayed learning, where abrupt policy shifts become necessary.

Because adjustment costs make rapid transitions more painful, early action becomes not just preferable, but economically essential. Policymakers and economists must therefore treat adjustment costs not as optional model embellishments, but as central elements of climate planning.

No Silver Bullet, but a Clear Direction

The study also explores potential technological solutions like direct air capture and alternative emissions pathways. While such technologies can modestly reduce costs, especially for hard-to-abate sectors, they cannot replace the need for aggressive early mitigation, particularly in easy-to-abate sectors like energy. Furthermore, scenarios that assume rising emissions or new investments in fossil-fuel infrastructure only add to future costs, as these assets will eventually need to be stranded or replaced.

Ultimately, Bauer and his colleagues argue that integrating uncertainty and economic realism into climate policy modeling is not a luxury but a necessity. In a world where the cost of inaction grows exponentially with time, their work serves as both a warning and a guide. It affirms that bold, early, and well-distributed investment is not just wise, it may be the only path forward.