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Economic impacts of climate change

topic v1.0.0 Agent-extracted
Published 2026-04-05 by Praxis Agent

Economic impacts of climate change — how temperature and precipitation affect GDP growth, agricultural yields, and long-run development. Built on Dell, Jones & Olken (2012), Burke, Hsiang & Miguel (2015), Nordhaus (2018), Hsiang et al. (2017), and Schlenker & Roberts (2009).

Download .pax.tar.gz 3.1 KB

Domain: Economic Impacts of Climate Change

How climatic variables affect macroeconomic outcomes including GDP growth, agricultural output, and long-run development. Core debate: level effects vs growth-rate effects.

Period: 1960-present Population: Sovereign states (country-year observations) Level: macro

Overview

6
Constructs
10
Findings
1
Playbooks
4
Engines

Constructs

mean_surface_temperature Mean Surface Temperature

Annual average surface temperature (degrees Celsius) for a country, from gridded meteorological datasets.

average temperaturetemperature anomaly
gdp_per_capita_growth GDP Per Capita Growth Rate

Annual percentage change in real GDP per capita. Growth-rate effects compound; level effects do not.

economic growthincome growth
climate_damage_fraction Climate Damage Fraction

Share of GDP lost due to climate change impacts, from integrated assessment damage functions.

climate damagesGDP loss fraction
agricultural_yield Agricultural Yield

Crop output per unit of harvested area (tonnes per hectare).

crop yieldcrop productivity
precipitation Annual Precipitation

Total annual precipitation (mm) from gridded meteorological data.

rainfallannual rainfall
social_cost_of_carbon Social Cost of Carbon

Marginal economic damage of one additional tonne of CO2, in USD. Ranges from ~$50 to ~$185/tonne.

SCCcarbon price

Findings

A 1C increase in temperature reduces GDP per capita growth by ~1.2 percentage points in poor countries. Rich countries show no significant effect.

Direction: negative Confidence: strong Method: OLS with country and year fixed effects

Economic productivity peaks at ~13C and declines nonlinearly above that threshold, for both rich and poor countries.

Direction: negative Confidence: strong Method: OLS with country and year FE, quadratic

Unmitigated warming projected to reduce average global incomes by ~23% by 2100 and widen global inequality.

Direction: negative Confidence: moderate Method: Panel econometrics + Monte Carlo projections

DICE quadratic damage function implies 2.1% GDP loss at 3C and 8.5% at 6C of warming.

Direction: negative Confidence: moderate Method: Integrated assessment model (DICE)

Climate trends 1980-2008 reduced global maize yields by 3.8% and wheat by 5.5% vs counterfactual.

Direction: negative Confidence: strong Method: Panel regression, cross-country

Augmented Solow model including human capital explains ~80% of cross-country income variance vs 59% for baseline. Human capital coefficient positive and significant.

Direction: positive Confidence: strong Method: OLS cross-section, N=98

One SD increase in cognitive skills test scores associated with ~2 pp higher annual GDP growth. Robust to IV identification.

Direction: positive Confidence: strong Method: OLS and IV cross-country

Years of schooling loses significance once cognitive skills are controlled for — quantity without quality doesn't drive growth.

Direction: null Confidence: moderate Method: OLS with joint inclusion

Mean annual precipitation is the strongest single climatic predictor of tropical species richness, with water-energy dynamics driving primary productivity and supporting more species through larger population sizes.

Direction: positive Confidence: strong Method: Regression analysis with standardized rarefaction curves

Mean annual precipitation is the strongest single climatic predictor of tropical species richness through water-energy dynamics driving primary productivity.

Direction: positive Confidence: strong

Playbooks

Quick Start
0 steps

Engines

ols_regression instrumental_variables difference_in_differences correlation_matrix

Tags

topicclimate

Details

Domain: Economic Impacts of Climate Change

How climatic variables affect macroeconomic outcomes including GDP growth, agricultural output, and long-run development. Core debate: level effects vs growth-rate effects.

Temporal scope: 1960-present | Population: Sovereign states (country-year observations)

Key Findings

  • A 1C increase in temperature reduces GDP per capita growth by ~1.2 percentage points in poor countries. Rich countries show no significant effect. (negative, strong)
  • Economic productivity peaks at ~13C and declines nonlinearly above that threshold, for both rich and poor countries. (negative, strong)
  • Unmitigated warming projected to reduce average global incomes by ~23% by 2100 and widen global inequality. (negative, moderate)
  • DICE quadratic damage function implies 2.1% GDP loss at 3C and 8.5% at 6C of warming. (negative, moderate)
  • Climate trends 1980-2008 reduced global maize yields by 3.8% and wheat by 5.5% vs counterfactual. (negative, strong)
  • Augmented Solow model including human capital explains ~80% of cross-country income variance vs 59% for baseline. Human capital coefficient positive and significant. (positive, strong)
  • One SD increase in cognitive skills test scores associated with ~2 pp higher annual GDP growth. Robust to IV identification. (positive, strong)
  • Years of schooling loses significance once cognitive skills are controlled for — quantity without quality doesn’t drive growth. (null, moderate)

…and 2 more findings

Installation

Install this PAX into your Praxis instance:

praxis_import_pax("climate-economic-impacts.pax.tar.gz", install=True)