Climate change poses severe and unequal economic risks, especially for developing countries – where evidence gaps in mitigation, adaptation, and labour transitions highlight urgent research and policy priorities.
Editor's note: This episode of VoxDevTalks is also available on Spotify, Apple Podcasts, and YouTube.
In this episode of VoxDevTalks, Adrien Bilal discusses a new, wide-ranging review of macroeconomics and climate change (with co-author James Stock). The conversation surveys what economists now know about climate damages, mitigation and adaptation, why LMICs face distinctive risks, and where the biggest evidence gaps remain.
As Bilal puts it, the aim was to produce “a comprehensive guide to help economists get started in this field” – spanning damages, extreme events, risk, decarbonisation costs and strategies, and adaptation.
Measuring loss and damage: From country temperature to global mean warming
A central contribution of recent work is better quantification of how temperature changes affect output. Using the ‘classic’ country-level approach, modern estimates imply that a 1°C increase in a given country’s annual temperature leads to output changes between −5% and +5%, depending on baseline climate. Hot countries tend to lose, while colder countries may gain.
But Bilal stresses that country temperature is not the same as global mean temperature, which better captures ocean dynamics and proxies for extreme events.
“When you instead evaluate the impact of global mean temperature on output, you find larger effects… the global mean is also quite a bit larger. It's around 20% loss per degree Celsius.”
For LMICs, this finding is sobering: many are already hot and agriculture-intensive, meaning global warming translates into larger expected output losses than simple country-temperature models suggest.
Extreme events, spatial granularity, and aggregation challenges
Extreme events – droughts, storms, heatwaves – do not respect borders and strike at sub-national scales. Two empirical strategies are emerging:
- Country-year exposure indices to weather extremes (blunt but scalable).
- High-resolution climate datasets (daily temperature, wind speed, precipitation) linked to local economic measures (e.g. night-lights, local output, employment).
This finer lens reveals mechanisms and behavioural responses – migration, occupational shifts, firm closures – but it re-raises the classic aggregation problem when scaling local effects to macro-outcomes.
“At the regional or household group level, eventually we're still interested in adding it all back up to understand what it implies for the macro economy… When we go more granular, we learn much more about mechanisms and possible policy responses to climate shocks.”
Beyond GDP: Non-market damages and why monetising them is hard
Non-market impacts – mortality, crime, political instability – matter for welfare and policy design. Mortality is comparatively well studied, aided by good data and established valuation methods, though thorny ethical issues remain. For other domains, evidence exists but monetisation is the sticking point.
“Nonmarket effects are very important… The fundamental challenge, however, is how to monetize these effects.”
Until economists can place these damages on a common scale with output losses, cost-benefit analyses will underweight important channels – again, particularly relevant for LMICs where social stability and public health risks from climate shocks can be acute.
Mitigation economics: Carbon pricing, transition costs, and worker relocation
In regards to mitigation, Bilal reviews macro evidence on carbon taxes and emissions trading. Micro- to meso-level comparisons (across countries or firms) often find that carbon pricing has modest effects on output and employment. Yet time-series evidence exploiting EU Emissions Trading Scheme price shocks shows larger activity responses, closer to classic energy-price shock estimates.
“The jury is still out on how much the energy transition exactly costs.”
A major unknown is reallocation: which workers and places bear the adjustment, and how quickly do jobs and capital move from carbon-intensive to cleaner activities? Lessons from past trade shocks suggest transitions can be slow and locally painful without targeted support.
“What we know even less about is how the relocation of workers away from carbon intensive industries is going to play out… this relocation can be slow and difficult for communities that are heavily exposed and that have few other options.”
Carbon leakage, CBAM, and trade with LMICs
Leakage – emissions shifting abroad due to domestic climate policy – remains difficult to measure. Not all emissions are trade-exposed: transport and power generation are less leak-prone than industry and agriculture. Bilal notes that aggregated leakage estimates to date are modest, but evidence is sparse.
Policy design matters. The EU’s Carbon Border Adjustment Mechanism (CBAM) equalises the carbon price on imports with the domestic price, limiting incentives to offshore emissions and potentially encouraging partner countries to adopt their own carbon pricing to retain revenue.
Adaptation: Why evidence lags and what LMICs can do now
Adaptation research lags mitigation and damages for four reasons: (i) earlier uncertainty about climate impacts; (ii) identification is hard because adaptation is usually inferred, not directly observed; (iii) future, high-warming adaptation may involve large fixed-cost infrastructure (levees, sea walls) that past variation cannot fully inform; and (iv) few datasets track adaptation actions directly.
“There are few instances in which we can directly observe whether economic agents are taking measures to adapt.”
For agriculture-intensive economies, three pathways recur:
- Protective investment (e.g. irrigation): administratively feasible but limited against extreme heat.
- Crop switching: promising with extension support, but agronomic and market constraints bind.
- Structural change: moving workers out of agriculture and importing food; feasible only with alternative comparative advantage.
“It's actually not that easy to adapt if your agricultural share is high, except through the normal process of development.”
Insurance can cushion household-level shocks if premiums reflect risk, but that requires robust institutions – often a constraint in LMICs.
Migration, data, and the road ahead
Despite headlines, mass cross-border climate migration is not yet evident in the data; within-country moves respond more visibly to shocks.
“We actually don't have evidence of massive international migration in response to climate shocks… If anything, it seems that migration is happening within borders.”
New, granular, often private data (e.g. property values geocoded to elevation and flood maps) can sharpen aggregate risk estimates – crucial for both public investment and insurance pricing.
“More data, more detailed data is always good. It lets us measure new channels that we couldn’t before.”
Looking forward, Bilal highlights two high-priority research fronts: labour and capital market effects of the energy transition, and adaptation – specifically, its behaviours, costs, and macro consequences.
Why this matters for policy and economics education
LMIC policymakers need credible, macro-relevant estimates of damages, mitigation costs, and adaptation returns – grounded in the realities of extreme events, trade exposure, and labour market frictions. The review is a step towards that decision toolkit. Encouragingly, universities are responding to student demand with more environmental economics courses and cross-disciplinary sustainability programmes.
Finally, in a polarised policy environment, the role of research is to keep building an objective evidence base.
“We just have to keep doing our job, and maybe the science eventually finds a receptive ear.”