Testing the Economic Impact of Climate Change in En-ROADS

By Dr. Sibel EKER
March 4, 2021

In the March 2021 release of En-ROADS you can now find settings in the Assumptions to adjust the level of impact that climate change has on economic growth. There are also a number of new graphs that have been added to support testing of this new feature.

Watch this webinar to learn more about the economic impacts of climate change in En-ROADS

Background

Climate change is expected to cause a swath of environmental damages, as well as impacts on human health and well-being, and it won’t leave the economy unaffected. Extreme weather events, sea level rise, droughts and flooding are expected to lead to substantial economic losses, exacerbated by the decreased investment in goods and services due to the cost of responding to these events.

Economists have long studied the climate impacts on economy, and their estimates have increased over time as evidence accumulated. According to the 5th Assessment Report of Intergovernmental Panel on Climate Change (IPCC) published in 2014, 0.2-2% loss of global annual income was expected at 2 °C warming. The report stressed that these estimates are highly uncertain and actual losses are more likely to be greater. In 2016, William Nordhaus, the Nobel laureate economist who pioneered the studies on the economic impact of climate change, estimated 2.1% of global income to be lost at a 3 °C warming, and 8.5% loss at a 6 °C. Several other economists have higher estimates not only for high temperatures (e.g. 50% loss at 4 °C), but also at lower temperatures, for instance, 15%–25% loss by 2100 for 2.5–3 °C warming. News outlets have reported things like, 7.22% reduction in world’s GDP by 2100, and global adaptation costs reaching $280 - $500 billion by 2050. Moreover, despite their wide coverage of economic sectors from agriculture to infrastructure, these estimates still do not include the cost of natural capital loss, which is very hard to quantify yet might constitute a large portion of the economic impacts of climate change according to a recent Dasgupta review on the economics of biodiversity.

Economists often represent the relationship between global income and climate change as a damage function, which maps the global mean temperature increase from preindustrial times to an estimate for global GDP loss. As outlined above and illustrated in the figure below, such estimates span a wide variety of values and forms.  These assessments underlie not only scientific research but also analysis of climate policies that rely on social cost of carbon (a measure of economic loss caused by each ton of CO2 emitted).

How to explore the economic impact of climate change on En-ROADS?

You can now explore the GDP loss caused by temperature increase, and its cascading effects on energy demand, supply, and emissions in the En-ROADS simulator under the Assumptions settings. In En-ROADS, economic growth is one of the key drivers of energy demand and emissions, hence the positive link from economy to temperature increase was already present. With this new feature, you can add the link from temperature to economy and see the implications of the closed feedback between economy and climate. Note that this link refers to the climate impact only, not the cost of climate policies, such as a carbon tax.

Step-by-step guide for using this new feature:

Step 1: Check out the Assumptions menu for Economic impact of climate change

Go to the Simulation menu on the top menu of the En-ROADS simulator and select Assumptions. Scroll down through the list and select Economic impact of climate change.

You will find a unique slider type which enables setting two parameter values simultaneously. To the right there is a dropdown menu Preset that can be used to recreate the findings of one of the studies from the graph above. Then there is a selection of Related Graphs that can be used to understand and explore the assumptions you have made.

Step 2: Create a damage function

There are two ways to create a damage function in En-ROADS:

1. The easiest way is to replicate the estimates of the economists, using the Preset dropdown menu on the right-hand side of the slider.
2. You can also create a customized estimate of GDP loss by using the two sliders. These sliders are discussed in more detail below.

Whether you create your own custom damage function or choose one of the presets, you can see how this compares to the available estimates by selecting the Reduction in GDP vs Temperature graph from the Related Graph dropdown menu.

Sliders

The two parameters are Reduction in GDP at 2 °C from climate impacts controlled by the round slider handle on the left-hand side, and Maximum reduction in GDP controlled by the triangular slider handle on the right. With these two sliders, you can specify a damage function that aligns with one of the existing studies shown above or is customized to fit your own expectations.

To set Reduction in GDP at 2 °C, think about to what extent global GDP might decline, as a percentage of what it could be, if the temperature increase reaches 2 °C. Burke et al. (2015), for instance, estimated this to be approximately 13%, with an uncertainty range of ~5-35%. The default value of the Reduction in GDP at 2 °C is zero, meaning that the baseline scenario of En-ROADS assumes no economic loss due to temperature increase. Any non-zero value of this slider activates the damage function feature, and you can start exploring the impact of temperature increase on GDP.

Maximum reduction in GDP stands for maximum economic loss due to climate change as a percentage of global GDP, regardless of the temperature change. Whether we reach 3 °C, 5 °C or more, do you think we could lose 20% of all economic production in the world (like Burke et al. estimate), or do you think it might go up as high as 100%? Note that Maximum reduction in GDP is an upper limit on the Reduction in GDP at 2 °C values you can choose.

You can always click on the grey triangle button next to the slider title and read a brief description.

Reduction in GDP vs Temperature Graph

This graph is different from the other graphs you see on En-ROADS, because it has the temperature change on the x-axis, not time. On the y-axis, you see the percent reduction in GDP due to climate impacts (see below). The colored lines show the four estimates from the literature, like Nordhaus and Burke et el., and the blue line shows the current scenario you are creating. If we choose Dietz & Stern as the preset damage function, our current scenario overlaps with the orange line that shows the estimate of Dietz and Stern (2015). Remember that this graph shows the relation between temperature and economic impact, hence the temperature values on the x-axis go up to 5 °C. In your current scenario, temperature increase by 2100 might be lower than this.

If you would like to create a stronger damage function, for instance with very high reduction in GDP even at lower temperatures, keep in mind that the highest estimates in the scientific literature so far are from Burke et al. (2015), corresponding to  ~35% damage at 2 °C warming, and around 75% damage at 5 °C. To create such an extreme damage scenario as in the picture below, you can set the sliders Reduction in GDP at 2 °C and Maximum reduction in GDP to 35% and 75%, respectively.

Step 3: Observe the impact of your choice on GDP

Explore the dynamics of the assumptions you have set by exploring related graphs. Those graphs that are most closely related to this function can be accessed via the Related graph menu next to the impacts in the Assumptions, and can also be found in the graphs menus.

One relevant graph to explore is the Reduction in GDP from Climate Impact graph (found in Graphs > Impacts). This shows the percentage reduction in GDP over time, tightly related to the temperature trajectory in the current scenario.

Through the graphs you can find under the Population & GDP group, you can also see how your damage assumption affects global average GDP per capita and Gross World Product (global GDP) projections. In the Dietz & Stern scenario, for instance, 30% reduction in GDP by 2100 corresponds to a loss of up to $215 trillion annually by 2100. This is almost 10 times of the current GDP of the US. Cumulative loss between today and 2100, compared to the En-ROADS baseline scenario, amounts to approximately $3900 trillion with Dietz & Stern’s damage assumption.

Step 4: Observe the wider implications of your choice

Since economic growth is a major driver of energy demand, the GDP loss due to climate impacts reduces energy demand, supply, and eventually the GHG emissions. You can browse through the graphs menu, choose for instance Total Primary Energy Demand or Greenhouse Gas Total Emissions, and Temperature Change to observe the wider implications of your damage assumption. The stronger your assumption about the economic damage, the higher the reduction in emissions, hence the lower the temperature change.

Considerations

This balancing feedback loop between climate and economy could surely offset some carbon emissions yet it cannot solve the climate change problem itself. A future world where emissions are reduced due to disasters would impact most vulnerable populations first, and intensify poverty and inequality. An extreme scenario that you might create could prevent substantial temperature rise, but harsh economic conditions of this extreme scenario would likely lead the world away from global equity goals. Therefore, while exploring how the world could look with different assumptions of the economic impact of climate change, please consider the multiple dimensions of sustainability humanity is striving for, and don’t forget to have a multisolving attitude.

Take a look at the En-ROADS simulator now to create your own scenario of climate action. If you would like to read about the mathematical implementation of economic impacts, visit the Frequently Asked Questions about model assumptions, or consult the En-ROADS Technical Reference to review the equations behind this new structure. Please get in touch with us if you have questions, comments or ideas.