Current economic models underestimate the cost of climate change by overlooking the connections between extreme global weather events and their potential impact on global supply chains.
Scientists have said climate change will be the greatest threat to humanity and global ecosystems in the coming years and that it is a “” to stabilise global warming at 1.5°C. While there is consensus about climate change being a serious problem, economists produce models showing relatively .
Dr Timothy Neal, a Scientia Senior Lecturer in the School of Economics at the Business School and the Institute for Climate Risk & Response (ICRR), says the models and data on which economists base their assumptions lead to a severe underestimation of the financial impact of future extreme weather events.
Recently, he led a workshop run by the Institute for Climate Risk & Response alongside several government and industry presenters, where he presented insights into why many current economic models used to predict the impacts of climate change are flawed.
“To fully understand how climate change affects economic growth, we need to look beyond local weather and consider global weather conditions,” said Dr Neal.
Dr Neal explained that climate scientists create models of how weather factors like temperature and rainfall will likely change under different emissions trajectories. Economists then take those predictions and run them in a model to translate this data into losses to economic growth, agricultural yield, and other outcomes.
For example, climate scientists characterise heating the planet beyond 3°C as . However, economic models predict that even this heat level will have on global economic growth (GDP) per capita by the end of the century. Most predict a hit of around 1% to , while the most pessimistic modelling suggests GDP .
Dr Neal explained that while climate models and economic models work together, they show different things. Climate models don’t predict economic impacts directly; they only show how the climate will change. Economic models take the climate predictions and estimate the effects on the economy, like GDP loss.
“Most existing studies predict what I would consider to be minor macroeconomic impacts, even from severe warming scenarios of over 3°C [which ], and the impacts range from perhaps -5% to -20% in global GDP per capita by 2100.”
Dr Neal explained that one key difference between economic modelling and climate modelling is that economists often only examine the relationship between a country’s economic growth and local weather conditions. For example, they might look at how local weather impacts crop growth. This makes sense when studying something like agriculture, where only the local weather directly affects crop yield. However, local weather isn't the only factor affecting overall GDP. In reality, economies rely on global supply chains, meaning the weather in other parts of the world also matters. If weather disrupts supplies or production elsewhere, it can impact a country’s overall economy.
The model below exemplifies one economic model that uses climate data to estimate economic impacts. The damage to GDP varies significantly depending on whether global weather is incorporated into the calculation.
“They [economists] estimate historical regression of GDP growth on weather and or weather shocks, and then they use that functional historical relationship between growth and weather, and then they use that to forecast future economic growth under different climate change scenarios,” said Dr Neal.
“When you're modelling something like GDP growth, it relies fundamentally on global supply chains. So, what's going on overseas matters a great deal, not just what's happening domestically.”
While Dr Neal acknowledged the difficulty in determining what will happen in the future, the fact that climate change will have an unprecedented impact on our society suggests the risks are far more significant than the predictions of current economic models, which inform our policies and government responses.
“In the past, during a given year there's a proportion of countries that have good weather and a proportion that have bad weather. If you're one of the countries that have had bad weather, you can trade with the countries that have had good weather to insulate yourself from the effects of bad weather,” explained Dr Neal.
“If you have crop failures, for instance, you can import food from countries who have enjoyed bumper harvests. One of the big benefits of international trade historically has been a more secure food supply.
“But future climate change is a change to the global weather distribution, and in any given year under these severe climate change scenarios, the proportion of countries that experience bad weather simultaneously, or what is considered now to be bad weather, is going to significantly increase.
“And if that happens, then the potential to use trade to mitigate the effects of local weather shocks becomes severely compromised. And that's why leaving global weather conditions out of the econometric models (used to identify the potential damage of climate change) causes a fundamental mischaracterisation of the counterfactual of future climate change."
Luckily, the economic models used to predict the impacts of climate change can be expanded to include global weather conditions. This will give a more accurate view of how global climate affects an economy.
Instead of relying on broad, yearly economic data, models could use more specific, local data to more accurately reflect economic activity. For instance, satellite data that measures nighttime light levels can serve as an indicator of economic activity. By using this real-time, granular data rather than traditional annual government statistics, models can better capture the intricate relationships between local and global weather conditions and their economic impacts.
By making these changes, he said forecasts would better align with climate scientists' warnings of the consequences of future warming, helping inform current policy advice and prepare for future challenges.
“It's not an easy exercise to forecast the impacts of counterfactuals like extreme warming, but at the very least, the models should be improved to the point that they're capable of capturing the types of global phenomena that climate change represents.”
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