The Macroeconomic Impact of Climate Change: Global Versus Local Temperature

Mon, 01 Jan 0001 00:00:00 +0000

The paper shows that the macroeconomic impact of climate change is an order of magnitude larger than what standard country-level panel estimates suggest. The key identification innovation is to measure the effect of global mean temperature shocks using time-series local projections, rather than using cross-country variation in local temperatures as in the conventional panel literature. A shock to global mean temperature tracks extreme weather events (droughts, heat waves, wind, precipitation anomalies) that affect all countries simultaneously; a local temperature anomaly in one country does not.

Empirical approach: The authors estimate local projections of world GDP growth on exogenous global mean temperature shocks. The shock is the innovation to global mean temperature after removing a 2-year autoregressive component and a low-frequency trend, following Hamilton (2018). Two estimation samples: BU (Barro-Ursúa macro history, 43 countries, 1860–2019) and PWT (Penn World Tables, 173 countries, 1960–2019).

Key empirical results (Section 3): A 1°C shock to global mean temperature causes world GDP to fall by 14% after 6 years in the PWT sample (95% CI: 6%–22%); significant at the 5% level in years 2–8; does not mean-revert within the 10-year sample horizon. In the BU sample, the peak GDP decline is 18% after 5 years (95% CI: 6%–30%). Converting the cumulative IRF ratio to a permanent temperature change yields a 22–34% long-run GDP decline per 1°C of permanent global warming (PWT and BU respectively). By contrast, local temperature shocks — estimated from a standard cross-country panel with country and year fixed effects — generate effects of 1–3% per °C, not statistically significant at the 5% level.

Why global > local (Section 4): Four categories of extreme climatic events (heat waves, droughts, wind, precipitation anomalies) jointly account for roughly half of the estimated global temperature effect on GDP. None of these are strongly correlated with local temperature anomalies because extreme weather reflects ocean-atmosphere dynamics (El Niño/ENSO) that elevate global mean temperature rather than any single country’s local temperature. In addition, capital and investment both decline persistently after global temperature shocks (capital response significant at 5% level), and warm/low-income countries are disproportionately affected.

Structural model (Section 5): A parsimonious neoclassical growth model embeds climate change as aggregate TFP changes. Households maximize ∫e^{−ρt}U(C_t)dt; firms use Cobb-Douglas technology Z_t K_t^α L_t^{1−α}. The damage function governing TFP is:

Z_t = Z_0 exp( ∫0^t ζ_s T̂{t−s} ds )

where T̂_t is excess global mean temperature above baseline and ζ_s = A(e^{−Bs} − e^{−Cs}) is the structural damage function. When ζ_s → 0, shocks have level but not growth effects; no statistically significant evidence of growth effects is found in Figure 3 of the paper. The model is calibrated with: risk aversion γ = 1 (log utility), capital share α = 0.33, annual capital depreciation δ = 0.08, and pure time preference ρ = 0.02. Proposition 1 (model inversion) shows that, to first order, ŷ_t = ẑ_t + α ∫K_{t,s} ẑ_s ds, where K_{t,s} is the sequence-space Jacobian of the neoclassical growth model. This delivers identification: observed output impulse responses recover the structural TFP damage function ζ_s without imposing functional form on the capital channel.

Estimation results (Section 5.3, Figure 12): The estimated damage function implies a 4% peak short-run productivity decline 2 years after a 1°C transitory global temperature shock; the effect decays slowly and remains significant for up to 10 years. The capital response (non-targeted moment) closely matches its empirical counterpart, providing an overidentification check. The local temperature damage function, estimated by targeting the local-panel output IRF, peaks at only 0.5% and is more than 8× smaller in cumulative productivity effect; it is not statistically different from zero at the 5% level.

Business-as-usual counterfactual (Section 6.1–6.2): Temperature rises from 2024, reaching 3°C above preindustrial by 2100 (asymptoting to 3.3°C), equivalent to 2°C of additional warming since 2024. Under the global temperature damage function:

World output by 2050: −28% vs. no-warming baseline
World output by 2100: −53% (accumulated TFP losses reach −40%)
Capital by 2100: −51% (investment initially rises as households anticipate lower permanent income, then decumulates rapidly)
Consumption by 2100: −53%
2024 welfare loss (consumption equivalent): 35%; welfare continues declining as temperatures rise, eventually reaching 56%
95% CI for 2100 output loss: 29%–77%
All effects statistically significant at the 5% level

Under the local temperature damage function with the same warming scenario: long-run output declines only 9%, welfare loss is 5%, and neither is statistically significant at the 5% or 10% level — consistent with conventional estimates (Nordhaus 1992, Dell et al. 2012, Burke et al. 2015).

Social Cost of Carbon (Section 6.2, Panel F): The SCC is defined as the consumption-equivalent amount households would pay at time 0 to avoid one additional ton of CO2, using the temperature-response function from Dietz et al. (2021a). Baseline result: $1,207 per ton (2024 international dollars), more than 6× larger than the $185/ton estimate in Rennert et al. (2022). 95% CI: $399–$2,015 per ton. Climate sensitivity range (half/double median): $600–$2,400 per ton. BU sample (larger damage functions): >$1,500 per ton. Using the local temperature damage function yields an SCC of only $149/ton, consistent with conventional estimates.

Sensitivity (Section 6.4): Higher time preference ρ > 0.04 lowers welfare losses below 20% and the SCC below 3× conventional high-end estimates — the only scenario where results converge toward prior estimates. Near-Stern discount rates (ρ → 0): welfare loss >40% and SCC >$2,500/ton. A 6°C-by-2100 scenario yields welfare losses >60%.

Historical growth accounting (Section 6.3): Starting the model in 1960 and imposing the realized 1960–2019 warming path, then holding temperature constant at its 2019 level, reveals:

World GDP per capita would be 25% higher today without warming since 1960
By 2040, output is 32% below potential from past warming — one-quarter of losses from historical warming are yet to materialize (due to delayed damage function and transitional capital dynamics)
Climate change reduced the annual world growth rate by as much as a third of baseline by the 21st century

Policy implication: Most decarbonization interventions cost ~$80/ton on average (Bistline et al. 2023). Under conventional SCC estimates based on local temperature ($149/ton), the US Domestic Climate Cost (DCC) falls below policy cost, making unilateral emissions reduction prohibitively expensive. Under the paper’s global temperature SCC of $1,207/ton, the DCC of the United States exceeds $80/ton even accounting for the fraction of global climate benefits that accrue domestically — unilateral decarbonization becomes cost-effective for large economies such as the US.

Scope conditions: The neoclassical model abstracts from adaptation, mitigation, trade, urbanization, and endogenous emissions. The identification assumption requires that global mean temperature innovations are uncorrelated with other global economic confounders at business-cycle and trend frequencies; the paper checks robustness against alternative detrending, exclusion of WWII and COVID-19 years, El Niño/ENSO controls, and instrumental variables for temperature based on solar/volcanic forcing. The conversion from medium-run to long-run effects relies on the constrained ζ_s = A(e^{−Bs} − e^{−Cs}) functional form ruling out growth effects — consistent with the data but not formally testable beyond the 10-year horizon. Counterfactuals involve 2–3°C temperature changes substantially beyond the sample’s moderate perturbations; the model’s extrapolation may understate damages if nonlinearities exist at extreme temperatures (the authors note their conservative constrained-form approach).

Summary of a forthcoming paper, AI-assisted and human-reviewed. See the linked original for the authoritative claims and full conditions.

In depth

Q1. Why do global temperature shocks produce GDP effects an order of magnitude larger than local temperature panel estimates?

Global mean temperature shocks are strongly correlated with extreme weather events — heat waves, droughts, wind storms, and precipitation anomalies — that simultaneously affect all countries; these four event categories jointly account for roughly half of the global temperature effect on GDP. Local temperature anomalies in a given country (as measured in standard cross-country panels with year fixed effects absorbed) are not correlated with these same events, because El Niño/ENSO and related ocean-atmosphere dynamics elevate global mean temperature without proportionally elevating any one country’s local temperature. Local panel studies also implicitly allow economic activity to shift toward cooler regions within a given year — an option unavailable when global warming affects all locations simultaneously. The resulting bias in local-panel estimates is not “aggregation bias” in the sense of Jensen’s inequality, but rather an identification problem: local panels identify a different object (the effect of temperature relative to other countries in the same year) rather than the aggregate climate impact the paper measures.

Q2. What is the identification strategy and what are the main threats?

The global temperature shock is identified as the innovation to global mean temperature after removing a 2-year AR component and a Hamilton (2018) low-frequency trend, yielding a shock orthogonal to its own recent history and to long-run trends. The main threats are: (i) global business-cycle confounders (worldwide recessions that simultaneously lower activity and emissions), addressed by controlling for quadratic time trends and global aggregate demand proxies; (ii) reverse causality (economic expansion warming the atmosphere), addressed by IV estimates using solar/volcanic forcing as instruments; (iii) low-frequency correlation between climate trends and productivity growth, addressed by flexible detrending and robustness to sample period. All major specification checks generate quantitatively similar results, and the paper passes placebo tests for large global confounders (WWII, COVID-19).

Q3. How does the structural model translate medium-run shock responses into long-run warming effects?

Proposition 1 (model inversion) shows that the output impulse response decomposes into a direct TFP effect ẑ_t and a capital channel ŷ_t = ẑ_t + α ∫K_{t,s} ẑ_s ds, where K_{t,s} is the sequence-space Jacobian of the neoclassical growth model (Auclert et al. 2021); this allows recovery of the structural TFP damage function {ζ_s} from the observed 10-year output IRF by non-linear least squares, without having to observe TFP directly. The counterfactual for a gradually rising temperature path (BAU scenario with 2°C additional warming since 2024) is then solved via the full nonlinear model — not via the log-linearization used in estimation — because the 2–3°C excursion far exceeds the sample’s modest temperature perturbations. The capital response (non-targeted moment) closely tracks its empirical counterpart, providing a strong overidentification check that the model’s capital dynamics are correctly specified.

Q4. Why does capital initially rise in the BAU counterfactual before declining?

Following standard permanent-income logic, when households learn at date 0 that global temperatures will rise and future TFP will fall, they temporarily increase saving and investment to accumulate buffer capital before the productivity decline materializes; this front-loads some capital accumulation in the early transition years (2024–2030s), briefly pushing capital above baseline, before the accumulated TFP losses overwhelm the saving motive and capital begins an extended decline. The net effect is still a 51% capital shortfall by 2100 because persistently lower TFP reduces the marginal product of capital over decades, depressing investment and allowing the capital stock to drift far below its no-warming balanced growth path.

The SCC is defined as the dollar amount C such that households are indifferent between (a) a world where one additional ton of CO2 is emitted at time 0 and (b) a world in steady-state where the household has paid C at time 0 (equation 7: V^{ss}(K^{ss} − C) = V^{SCC}_0(K^{ss})). The temperature response to a 1-ton CO2 pulse is taken from Dietz et al. (2021a) — temperature peaks at 0.002°C after a 1-gigaton pulse and stabilizes. The model generates the productivity path {Z^{SCC}_t} via the structural damage function, solves for equilibrium capital and consumption paths, and computes the value function V^{SCC}_0. The resulting $1,207/ton exceeds prior estimates by 6× because the global-temperature damage function implies 4% peak TFP losses per 1°C transitory shock, compared to the ~0.5% peak implied by local temperature — and the SCC is essentially the capitalized sum of these future productivity losses, so the ratio scales proportionally.

Q6. Why are historical climate losses so large if year-to-year warming is small?

The key is cumulation: annual warming increments are individually small (tenths of a degree), but the damage function {ζ_s} is persistent (effects last 10+ years), so each year’s increment adds a flow of persistent TFP losses that stack on top of prior increments. The paper’s growth accounting shows that climate change reduced the world growth rate by up to one-third of baseline in the 21st century — a number that appears modest in any single year but, compounded over decades, translates into a 25% GDP per capita shortfall by 2019. Additionally, because the estimated damage function has a 2-year lag before peak TFP impact, a substantial share of past warming’s losses are yet to be realized — the paper estimates GDP will be 32% below its potential by 2040 even with no further warming.

Q7. What does the sensitivity analysis reveal about the robustness of the results?

The key sensitivity is the rate of time preference ρ: at ρ = 0.02 (baseline, consistent with secular interest rate decline), welfare loss is 35%; at ρ = 0.04 (above recent market rates), welfare loss is still above 20%; only at implausibly high discount rates does the welfare loss fall below 15%. The SCC is more sensitive to ρ than welfare because the SCC is a capitalized stock valuation while welfare is an annualized flow. BU sample damage functions (larger IRF) raise welfare loss to 42% and 2100 GDP loss to 61%; these represent the high end of the estimates. The climate sensitivity range ($600–$2,400/ton for the SCC) reflects uncertainty in the physics of CO2-to-temperature conversion, not in the estimated economic damage function. Across all these dimensions, the global-temperature estimates remain order-of-magnitude larger than local-temperature estimates.

Q8. What is the policy implication for large economies considering unilateral decarbonization?

The domestic decarbonization test compares the Domestic Climate Cost (DCC) — the fraction of the global SCC that accrues to the decarbonizing country — against the marginal cost of abatement (~$80/ton average, Bistline et al. 2023). Under conventional local-temperature estimates ($149/ton global SCC), the US DCC falls below $80/ton, implying unilateral action destroys domestic value. Under the paper’s $1,207/ton global SCC, the US DCC comfortably exceeds $80/ton even if the US only captures a fraction of world welfare gains — because global temperature extremes (hurricanes, heat waves, droughts) strike the US directly, the DCC/SCC ratio is much higher than under local estimates where the US appears less exposed. This fundamentally changes the cost-benefit calculus for large-economy unilateral climate policy.

Key concepts

global mean temperature shock: a time-series innovation to world average surface temperature, identified by Hamilton (2018) detrending; captures ocean-atmosphere climate variability (El Niño/ENSO) correlated with extreme weather events affecting all countries simultaneously; the paper’s key identification variable, distinct from local temperature variation used in standard cross-country panels.

global vs. local temperature effect: the paper’s central finding that the GDP effect per 1°C global mean temperature shock (14–18%) is an order of magnitude larger than the effect per 1°C local temperature shock (1–3%); the gap is explained by extreme climatic events (heat waves, droughts, wind, precipitation) that co-move with global mean temperature but not with individual countries’ local temperatures.

structural damage function (ζ_s): the kernel relating excess global mean temperature T̂_{t−s} to log TFP at time t, specified as ζ_s = A(e^{−Bs} − e^{−Cs}); estimated from the PWT output impulse response via model inversion (Proposition 1); implies a 4% peak TFP loss 2 years after a 1°C transitory shock, decaying slowly over 10 years; rules out permanent growth effects consistent with the data.

Social Cost of Carbon (SCC): the one-time dollar amount households would pay at time 0 to avoid one additional ton of CO2; equals (in the linear limit) the present discounted value of all flow consumption-equivalent welfare losses from the induced warming; paper estimates $1,207/ton (2024 international dollars), more than 6× prior estimates, because the global-temperature damage function implies much larger per-degree productivity losses than local-temperature estimates.

committed climate losses: future GDP shortfalls already locked in by past warming, arising because the estimated damage function has a delayed peak (year 2) and slow decay (10+ years) — temperature rises in recent years continue reducing productivity for the following decade; the paper estimates these committed losses alone will lower GDP 32% below potential by 2040 even with temperature held constant at 2019 levels.

BAU scenario: the business-as-usual warming path used for the main counterfactual — global mean temperature reaches 3°C above preindustrial by 2100 (asymptoting to 3.3°C), implying 2°C of additional warming since the 2024 baseline; under this scenario the model implies 53% GDP loss, 51% capital loss, 53% consumption loss, and a 35% consumption-equivalent welfare loss by 2100.

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