impact of climate change on economy essay

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How Climate Change Impacts the Economy

The Fourth National Climate Assessment , published in 2018, warned that if we do not curb greenhouse gas emissions and start to adapt, climate change could seriously disrupt the U.S. economy. Warmer temperatures, sea level rise and extreme weather will damage property and critical infrastructure, impact human health and productivity, and negatively affect sectors such as agriculture, forestry, fisheries and tourism. The demand for energy will increase as power generation becomes less reliable, and water supplies will be stressed. Damage to other countries around the globe will also affect U.S. business through disruption in trade and supply chains.

A recent report  examined how climate change could affect 22 different sectors of the economy under two different scenarios: if global temperatures rose 2.8˚ C from pre-industrial levels by 2100, and if they increased by 4.5˚ C. The study projected that if the higher-temperature scenario prevails, climate change impacts on these 22 sectors could cost the U.S. $520 billion each year. If we can keep to 2.8˚ C, it would cost $224 billion less. In any case, the U.S. stands to suffer large economic losses due to climate change, second only to India, according to another study .

We are already seeing the economic impacts of the changing climate. According to Morgan Stanley, climate disasters have cost North America $415 billion in the last three years, much of that due to wildfires and hurricanes.

In 2017, Texas’s estimated losses from Hurricane Harvey were $125 billion; Hurricane Sandy caused about $71 billion of damages in 2012. And while it’s not yet possible to directly link climate change to hurricanes, warmer temperatures and higher sea levels are known to enhance their intensity and destructiveness.

“Science advances also give us more detailed spatial information to say which assets and operations are in harm’s way with climate change—for example say, just how many buildings will be inundated due to sea level rise,” said climatologist Radley Horton, associate research professor at Lamont-Doherty Earth Observatory. But the indirect economic impacts may be felt long before an actual disaster.

“For example, it’s not just whether a building is underwater or not,” he said. “What’s important are the harder-to-define things like when does societal risk perception shift? It may be that buildings lose their value before the water actually arrives, once people realize that eventually the water’s going to arrive. We need deeper thinking about the interconnection between physical and social systems.”

Here are some of the many ways that climate change will likely affect our economy, both directly and indirectly.

Agriculture

The sector most vulnerable to climate risk is agriculture.

Environmental economist Geoffrey Heal, a professor in the Columbia Business School, explained that although agriculture makes up a fairly small part of the total U.S. economy, “locally these effects could be big. There are about a dozen states in the Midwest that are very dependent on agriculture and they could take quite a big hit.”

They already have. Extreme rainfall events have increased 37 percent in the Midwest since the 1950s, and this year, the region has experienced above normal amounts of rain and snowmelt that have caused historic flooding.

Many fields have washed away and livestock have drowned; Nebraska alone lost $440 million worth of cattle, and as of March, Iowa had suffered $1.6 billion in losses.

The National Oceanic and Atmospheric Administration (NOAA) expects the coming months to bring even more flooding, which could impact our food supply. To date, farmers have only planted 67 percent of their corn crop compared to last June, when they had planted 96 percent. This lost yield could cause prices for animal feed and ethanol to rise, and potentially disrupt marketplaces at home and abroad. As a result of climate change impacts, the Midwest is projected to lose up to 25 percent of its current corn and  soybean yield by 2050.

In addition to flooding, increased heat and drought will likely reduce crop yields. According to a 2011  National Academy of Sciences report , for every degree Celsius the global thermostat rises, there will be a 5 to 15 percent decrease in overall crop production. Many commodity crops such as corn, soybean, wheat, rice, cotton, and oats do not grow well above certain temperature thresholds. In addition, crops will be affected by less availability of water and groundwater, increased pests and weeds, and fire risk. And as farmers struggle to stay afloat by finding ways to adapt to changing conditions, prices will likely increase and be passed along to consumers.

Infrastructure

Much of our society’s critical infrastructure is at risk from flooding. “Sea level rise could potentially cause a loss of value of assets in the trillions of dollars—probably anywhere from two to five trillion dollars—by the end of the century,” said Heal. “That’s loss from damage to housing, damage to airports on the coasts, damage to docks, the railway line that runs up and down the East Coast all of which is within a few feet of sea level, damage to I-95 which runs also along the coast. And that’s just the East Coast. If you take a global perspective, this is repeated around the world.” Much of this infrastructure will likely need to be repaired or replaced.

Military bases are also vulnerable. According to a  2016 report published by the Center for Climate and Security policy institute, sea level rise could flood parts of military bases along the East and Gulf coasts for up to three months a year as soon as 2050. Inland military installations near rivers are also vulnerable, because they can overflow with heavy precipitation, which is expected to become more common as the atmosphere warms. Extreme weather will necessitate more maintenance and repair for runways and roads, infrastructure and equipment.

In addition, our communication systems will be affected. A 2018 study   found that over 4,000 miles of fiber optic cable as well as data centers, traffic exchanges and termination points — the lifeblood of the global information network — are at risk from sea level rise. According to NOAA’s sea level rise projections, this infrastructure could be underwater by 2033 because most of it is buried along highways and coastlines. When it was built 25 years ago, climate change was not a concern, so while the cables are water resistant, they are not waterproof. New York, Miami and Seattle and large service providers including CenturyLink, Intelliquent and AT&T are most at risk. Threats to the internet infrastructure could have huge implications for businesses in the U.S.

Human health and productivity

If temperatures rise 4.5˚ C by 2090, 9,300 more people will die in American cities due to the rising heat. The annual losses associated with extreme temperature-related deaths alone are projected to be $140 billion.

Increasing warmth and precipitation will also add to the risk of waterborne and foodborne diseases and allergies, and spur the proliferation of insects that spread diseases like Zika, West Nile, dengue and Lyme disease into new territories. Extreme weather and climate-related natural disasters can also exacerbate mental health issues. The most vulnerable populations, such as the elderly, children, low-income communities and communities of color, will be most affected by these health impacts.

Temperature extremes are also projected to cause the loss of two billion labor hours each year by 2090, resulting in $160 billion of lost wages. Because of heat exposure, productivity in the Southeast and Southern Great Plains regions is expected to decline by 3 percent, and some counties of Texas and Florida could lose more than 6 percent of labor hours each year by 2100. According to a 2014 Rhodium Group study, the largest climate change-related economic losses in the U.S. will be from lost labor productivity.

Two billion dollars could be lost in winter recreation due to less snow and ice. For example, rapid warming in the Adirondack Mountains could decimate the winter activity sector, which makes up 30 percent of the local economy.

In addition, as water temperatures increase, water quality could suffer due to more frequent and more intense algae blooms, which can be toxic, thus curtailing recreational water activities and freshwater fishing. More frequent and severe wildfires will worsen air quality and discourage tourism. Sea level rise could submerge small islands and coastal areas, while deforestation and its destructive impacts on biodiversity could make some tourist destinations less attractive.

Businesses and the financial market

Climate change and its impacts across the globe will threaten the bottom line of businesses in a variety of ways. The frequency and intensity of extreme weather, both in the U.S. and in other countries, can damage factories, supply chain operations and other infrastructure, and disrupt transport. Drought will make water more expensive, which will likely affect the cost of raw materials and production. Climate volatility may force companies to deal with uncertainty in the price of resources for production, energy transport and insurance. And some products could become obsolete or lose their market, such as equipment related to coal mining or skiing in an area that no longer has snow.

Whether in the U.S. or abroad, new regulations such as carbon pricing and subsidies that favor a competitor may affect a business’s bottom line. A company’s reputation could also suffer if it’s seen as doing something that hurts the environment. And investors and stakeholders are increasingly worried about the potential for “stranded assets”—those that become prematurely obsolete or fall out of favor, and must be recorded as a loss, such as fossil fuels that many believe should stay in the ground or real estate in a newly designated flood plain.

In 2018, the Carbon Disclosure Project asked more than 7,000 companies to assess their financial risks from climate change. The CDP found that, unless they took preemptive measures, 215 of the world’s 500 biggest companies could lose an estimated one trillion dollars due to climate change, beginning within five years. For example, Alphabet (Google’s parent company) will likely have to deal with rising cooling costs for its data centers. Hitachi Ltd.’s suppliers in Southeast Asia could be disrupted by increased rainfall and flooding. Some companies have already been impacted by climate change-related losses. Western Digital Technologies, maker of hard disks, suffered enormous losses in 2011 after flooding in Thailand disrupted its production.

PG&E became liable for fire damages and had to file for bankruptcy after its power lines sparked California’s deadliest wildfire last fall. And GE cost its investors $193 billion between 2015 and 2018 because it overestimated demand for natural gas and underestimated the transition to renewable energy.

“The movement away from fossil fuels will have a big impact which could affect banks and investment firms that have relationships with the fossil fuel industry,” said Heal. “For example, the stock market value of the U.S. coal industry in 2011 was something like $37 billion. Today it’s about $2 billion. So anybody that lent a lot of money to the coal industry 10 years back would be in trouble. One of the things worrying those in the financial field is that this could happen to the oil and gas industry. So people who have invested in them or lent money to them are potentially at risk.”

Climate change and opportunity

The good news is that climate change also presents business opportunities. The Carbon Disclosure Project reported that 225 of the world’s 500 biggest companies believe climate change could generate over $2.1 trillion in new business prospects.

There will be more opportunity in clean energy, resilient and green buildings, and energy efficiency. Hybrid and electric vehicle production and the electric public transit sector are expected to grow. Construction of green infrastructure and more resilient coastal infrastructure could create many new jobs. Carbon capture and sequestration and uses of captured CO2  present opportunities, especially in light of the new 45Q federal tax credits. In addition, there are forward-thinking new businesses—witness the dramatic rise of Beyond Meat, the company selling plant-based burgers at Carl’s Jr. and A&W.

As the Arctic sea ice melts, new shipping lines will open up for trade, substantially cutting transport time. The warming Arctic could also offer more prospects for oil and gas drilling. Weather satellites and radar technology will be in demand to monitor extreme weather. Air conditioning and cooling products will be needed around the world. Biotech companies are developing new crops that are resistant to climate change impacts. Pharmaceutical companies expect increased demand for drugs to combat diseases such as malaria and dengue and other infectious diseases. And the market for military equipment and private security services may expand because the scarcity of resources could trigger civil unrest and conflict.

What individuals, businesses and governments can do to protect themselves

How much climate change will hurt the economy depends on what measures we take to adapt to and prepare for it.

Individuals

Individuals need to consider the implications of climate change when choosing where to spend and invest their money. And be aware that while a particular risk may not seem to be factored into prices yet, things could turn on a dime when the realization of risk sinks in, resulting in a massive redistribution of wealth. So it’s best not to buy or move to an area near wild lands, which have a higher risk of wildfires. Don’t move into a flood zone or buy real estate in an area that’s vulnerable to sea level rise. And in any case, purchase flood and fire insurance, and diversify your investments.

Individuals should also think about different opportunities in terms of new places that people are moving to. And, if possible, people who work outdoors in construction, agriculture or tourism should consider alternative jobs within the sector or new industries to work in.

Businesses and financial entities

Businesses need to scrutinize their operations carefully. “There’s a groundswell towards the view that any companies that fail to study their exposure to extreme weather and fail to disclose the types of vulnerabilities, including indirect ones, are going to have a hard time in the future,” said Horton. “Are companies looking at what’s coming down the road and making strategies to deal with it? I think investors are going to demand that and the companies that don’t do that are going to have trouble getting underwriting, getting infrastructure funded by the Moody’s of the world, and getting insurance.” He added that he’s seen a change in the last three or four years in what his students are demanding and believes that young people in the future will not work for companies that are not thinking about climate change.

Banks and funds need to analyze where their investments are and see if they are vulnerable to climate change. Have they invested in someone who has coastal property, or given a loan to a fossil fuel company or in agriculture operations that might be affected by climate change? Sixty-three percent of financial risk managers surveyed now believe climate change is a major concern. As a result, “The total value of funds that have integrated environmental, social and governance factors into their investment process has more than quadrupled since 2014, rising to $485 billion as of April,” reported the Wall Street Journal .

Governments

Governments should proactively think about the risks their communities face before disaster strikes.

They should be investing in resiliency measures such as hardening infrastructure, improving water resources, building redundancy into important systems, moving people out of harm’s way and improving health care services. “You want to do it before the disaster but you also need to be cognizant that the only time people will listen seems to be right after a disaster,” said Horton. “Those are also the times when money’s available to rebuild.”

Government leaders are currently debating whether the country can afford the Green New Deal (an ambitious plan to address climate change) or something like it. The question should be, ‘can we afford not to afford it?’ Nobel Prize-winning economist Joseph Stiglitz, a professor at Columbia University, wrote in an op ed , “We will pay for climate breakdown one way or another, so it makes sense to spend the money now to reduce emissions rather than wait until later to pay a lot more for the consequences… It’s a cliché, but it’s true: An ounce of prevention is worth a pound of cure.”

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Recent record-breaking heat waves have affected communities across the world. The Extreme Heat Workshop will bring together researchers and practitioners to advance the state of knowledge, identify community needs, and develop a framework for evaluating risks with a focus on climate justice. Register by June 15

Degrowth is the most effective solution. Eco-sufficiency and life quality are more important than profit maximization. Please read https://www.degrowth.info or https://en.wikipedia.org/wiki/Degrowth

how os quality of life gonna go up when people have less resources from degrowth

Don’t state the obvious. You said the quite part out loud.

Well, many people will have the chance to grow and adapt, making this obvious statment stupid. Many have already died, and many more will, but we can eventually get better. I don’t have much faith though.

Hey there, this was a very informative and we’ll written article. Thank you kindly

Right. Helps a lot in an essay!

I used this for a essay and this helped a lot! Thanks!

Hello, This is a big problem economically and globally. Climate change has impacted us in so many ways.

https://www.cbsnews.com/video/climate-refugees-the-quest-for-a-haven-from-extreme-weather-events/#x

Is this all rights reserved? Can I use some info from here?

why was this made, and how the hell does texas have enough buildings to cost 71 billion dollars in damages.

because they built buildings

To Renee Cho,

I first want to start off by saying thank you for sharing your knowledge of this subject with the world. It’s extremely important to share these types of ideas publicly, and it’s helpful when trying to formulate an opinion on this subject when you aren’t an expert on it.  I agree with your article. I think Climate change, if not dealt with, can have a bigger effect on our lives than we often think. Yes, the climate and earth would suffer, but so will our economy in the years leading up to the point of no return. The damage to the supply chain and factories, which you mentioned, is a huge deal. If our supply can’t withstand the strength of the demand in the future, then we will have more problems than just climate change. Because of the genera; nature of the market however, businesses will start to see that renewable energy is more profitable, and the market will start to shift. If fossil fuels become obsolete, companies wont run the risk of receiving a bad reputation for using them. I hope that this is what our economy will look like soon, instead of companies holding onto fossil fuels and other things that are harmful to our earth. They can’t make money if the world isn’t safe to make it on.  Another thing I found interesting is how instead of just focusing on what businesses need to do to mitigate this issue, you also target the individual consumer. Individuals play a big part in the market and economic health, so the choices they make can really make a difference in how climate change affects our economy.  Everyone needs to read this article, or articles like this; it’s crucial that you understand how not only the world is affected by this issue, but how you as an individual are affected as well. 

Can i use the above stated information for an article to be published in our college Magazine

This article is great! very informative can i use some of the content for my assignment?

All people must start to learn to control and reduce emission of greenhouse gases.

This was incredibly helpful, thank you!!

I like the article! it’s very descriptive.

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• Published: 17 April 2024

The economic commitment of climate change

• Maximilian Kotz   ORCID: orcid.org/0000-0003-2564-5043 1 , 2 ,
• Anders Levermann   ORCID: orcid.org/0000-0003-4432-4704 1 , 2 &
• Leonie Wenz   ORCID: orcid.org/0000-0002-8500-1568 1 , 3  

Nature volume  628 ,  pages 551–557 ( 2024 ) Cite this article

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• Environmental economics
• Environmental health
• Interdisciplinary studies
• Projection and prediction

Global projections of macroeconomic climate-change damages typically consider impacts from average annual and national temperatures over long time horizons 1 , 2 , 3 , 4 , 5 , 6 . Here we use recent empirical findings from more than 1,600 regions worldwide over the past 40 years to project sub-national damages from temperature and precipitation, including daily variability and extremes 7 , 8 . Using an empirical approach that provides a robust lower bound on the persistence of impacts on economic growth, we find that the world economy is committed to an income reduction of 19% within the next 26 years independent of future emission choices (relative to a baseline without climate impacts, likely range of 11–29% accounting for physical climate and empirical uncertainty). These damages already outweigh the mitigation costs required to limit global warming to 2 °C by sixfold over this near-term time frame and thereafter diverge strongly dependent on emission choices. Committed damages arise predominantly through changes in average temperature, but accounting for further climatic components raises estimates by approximately 50% and leads to stronger regional heterogeneity. Committed losses are projected for all regions except those at very high latitudes, at which reductions in temperature variability bring benefits. The largest losses are committed at lower latitudes in regions with lower cumulative historical emissions and lower present-day income.

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Projections of the macroeconomic damage caused by future climate change are crucial to informing public and policy debates about adaptation, mitigation and climate justice. On the one hand, adaptation against climate impacts must be justified and planned on the basis of an understanding of their future magnitude and spatial distribution 9 . This is also of importance in the context of climate justice 10 , as well as to key societal actors, including governments, central banks and private businesses, which increasingly require the inclusion of climate risks in their macroeconomic forecasts to aid adaptive decision-making 11 , 12 . On the other hand, climate mitigation policy such as the Paris Climate Agreement is often evaluated by balancing the costs of its implementation against the benefits of avoiding projected physical damages. This evaluation occurs both formally through cost–benefit analyses 1 , 4 , 5 , 6 , as well as informally through public perception of mitigation and damage costs 13 .

Projections of future damages meet challenges when informing these debates, in particular the human biases relating to uncertainty and remoteness that are raised by long-term perspectives 14 . Here we aim to overcome such challenges by assessing the extent of economic damages from climate change to which the world is already committed by historical emissions and socio-economic inertia (the range of future emission scenarios that are considered socio-economically plausible 15 ). Such a focus on the near term limits the large uncertainties about diverging future emission trajectories, the resulting long-term climate response and the validity of applying historically observed climate–economic relations over long timescales during which socio-technical conditions may change considerably. As such, this focus aims to simplify the communication and maximize the credibility of projected economic damages from future climate change.

In projecting the future economic damages from climate change, we make use of recent advances in climate econometrics that provide evidence for impacts on sub-national economic growth from numerous components of the distribution of daily temperature and precipitation 3 , 7 , 8 . Using fixed-effects panel regression models to control for potential confounders, these studies exploit within-region variation in local temperature and precipitation in a panel of more than 1,600 regions worldwide, comprising climate and income data over the past 40 years, to identify the plausibly causal effects of changes in several climate variables on economic productivity 16 , 17 . Specifically, macroeconomic impacts have been identified from changing daily temperature variability, total annual precipitation, the annual number of wet days and extreme daily rainfall that occur in addition to those already identified from changing average temperature 2 , 3 , 18 . Moreover, regional heterogeneity in these effects based on the prevailing local climatic conditions has been found using interactions terms. The selection of these climate variables follows micro-level evidence for mechanisms related to the impacts of average temperatures on labour and agricultural productivity 2 , of temperature variability on agricultural productivity and health 7 , as well as of precipitation on agricultural productivity, labour outcomes and flood damages 8 (see Extended Data Table 1 for an overview, including more detailed references). References  7 , 8 contain a more detailed motivation for the use of these particular climate variables and provide extensive empirical tests about the robustness and nature of their effects on economic output, which are summarized in Methods . By accounting for these extra climatic variables at the sub-national level, we aim for a more comprehensive description of climate impacts with greater detail across both time and space.

Constraining the persistence of impacts

A key determinant and source of discrepancy in estimates of the magnitude of future climate damages is the extent to which the impact of a climate variable on economic growth rates persists. The two extreme cases in which these impacts persist indefinitely or only instantaneously are commonly referred to as growth or level effects 19 , 20 (see Methods section ‘Empirical model specification: fixed-effects distributed lag models’ for mathematical definitions). Recent work shows that future damages from climate change depend strongly on whether growth or level effects are assumed 20 . Following refs.  2 , 18 , we provide constraints on this persistence by using distributed lag models to test the significance of delayed effects separately for each climate variable. Notably, and in contrast to refs.  2 , 18 , we use climate variables in their first-differenced form following ref.  3 , implying a dependence of the growth rate on a change in climate variables. This choice means that a baseline specification without any lags constitutes a model prior of purely level effects, in which a permanent change in the climate has only an instantaneous effect on the growth rate 3 , 19 , 21 . By including lags, one can then test whether any effects may persist further. This is in contrast to the specification used by refs.  2 , 18 , in which climate variables are used without taking the first difference, implying a dependence of the growth rate on the level of climate variables. In this alternative case, the baseline specification without any lags constitutes a model prior of pure growth effects, in which a change in climate has an infinitely persistent effect on the growth rate. Consequently, including further lags in this alternative case tests whether the initial growth impact is recovered 18 , 19 , 21 . Both of these specifications suffer from the limiting possibility that, if too few lags are included, one might falsely accept the model prior. The limitations of including a very large number of lags, including loss of data and increasing statistical uncertainty with an increasing number of parameters, mean that such a possibility is likely. By choosing a specification in which the model prior is one of level effects, our approach is therefore conservative by design, avoiding assumptions of infinite persistence of climate impacts on growth and instead providing a lower bound on this persistence based on what is observable empirically (see Methods section ‘Empirical model specification: fixed-effects distributed lag models’ for further exposition of this framework). The conservative nature of such a choice is probably the reason that ref.  19 finds much greater consistency between the impacts projected by models that use the first difference of climate variables, as opposed to their levels.

We begin our empirical analysis of the persistence of climate impacts on growth using ten lags of the first-differenced climate variables in fixed-effects distributed lag models. We detect substantial effects on economic growth at time lags of up to approximately 8–10 years for the temperature terms and up to approximately 4 years for the precipitation terms (Extended Data Fig. 1 and Extended Data Table 2 ). Furthermore, evaluation by means of information criteria indicates that the inclusion of all five climate variables and the use of these numbers of lags provide a preferable trade-off between best-fitting the data and including further terms that could cause overfitting, in comparison with model specifications excluding climate variables or including more or fewer lags (Extended Data Fig. 3 , Supplementary Methods Section  1 and Supplementary Table 1 ). We therefore remove statistically insignificant terms at later lags (Supplementary Figs. 1 – 3 and Supplementary Tables 2 – 4 ). Further tests using Monte Carlo simulations demonstrate that the empirical models are robust to autocorrelation in the lagged climate variables (Supplementary Methods Section  2 and Supplementary Figs. 4 and 5 ), that information criteria provide an effective indicator for lag selection (Supplementary Methods Section  2 and Supplementary Fig. 6 ), that the results are robust to concerns of imperfect multicollinearity between climate variables and that including several climate variables is actually necessary to isolate their separate effects (Supplementary Methods Section  3 and Supplementary Fig. 7 ). We provide a further robustness check using a restricted distributed lag model to limit oscillations in the lagged parameter estimates that may result from autocorrelation, finding that it provides similar estimates of cumulative marginal effects to the unrestricted model (Supplementary Methods Section 4 and Supplementary Figs. 8 and 9 ). Finally, to explicitly account for any outstanding uncertainty arising from the precise choice of the number of lags, we include empirical models with marginally different numbers of lags in the error-sampling procedure of our projection of future damages. On the basis of the lag-selection procedure (the significance of lagged terms in Extended Data Fig. 1 and Extended Data Table 2 , as well as information criteria in Extended Data Fig. 3 ), we sample from models with eight to ten lags for temperature and four for precipitation (models shown in Supplementary Figs. 1 – 3 and Supplementary Tables 2 – 4 ). In summary, this empirical approach to constrain the persistence of climate impacts on economic growth rates is conservative by design in avoiding assumptions of infinite persistence, but nevertheless provides a lower bound on the extent of impact persistence that is robust to the numerous tests outlined above.

Committed damages until mid-century

We combine these empirical economic response functions (Supplementary Figs. 1 – 3 and Supplementary Tables 2 – 4 ) with an ensemble of 21 climate models (see Supplementary Table 5 ) from the Coupled Model Intercomparison Project Phase 6 (CMIP-6) 22 to project the macroeconomic damages from these components of physical climate change (see Methods for further details). Bias-adjusted climate models that provide a highly accurate reproduction of observed climatological patterns with limited uncertainty (Supplementary Table 6 ) are used to avoid introducing biases in the projections. Following a well-developed literature 2 , 3 , 19 , these projections do not aim to provide a prediction of future economic growth. Instead, they are a projection of the exogenous impact of future climate conditions on the economy relative to the baselines specified by socio-economic projections, based on the plausibly causal relationships inferred by the empirical models and assuming ceteris paribus. Other exogenous factors relevant for the prediction of economic output are purposefully assumed constant.

A Monte Carlo procedure that samples from climate model projections, empirical models with different numbers of lags and model parameter estimates (obtained by 1,000 block-bootstrap resamples of each of the regressions in Supplementary Figs. 1 – 3 and Supplementary Tables 2 – 4 ) is used to estimate the combined uncertainty from these sources. Given these uncertainty distributions, we find that projected global damages are statistically indistinguishable across the two most extreme emission scenarios until 2049 (at the 5% significance level; Fig. 1 ). As such, the climate damages occurring before this time constitute those to which the world is already committed owing to the combination of past emissions and the range of future emission scenarios that are considered socio-economically plausible 15 . These committed damages comprise a permanent income reduction of 19% on average globally (population-weighted average) in comparison with a baseline without climate-change impacts (with a likely range of 11–29%, following the likelihood classification adopted by the Intergovernmental Panel on Climate Change (IPCC); see caption of Fig. 1 ). Even though levels of income per capita generally still increase relative to those of today, this constitutes a permanent income reduction for most regions, including North America and Europe (each with median income reductions of approximately 11%) and with South Asia and Africa being the most strongly affected (each with median income reductions of approximately 22%; Fig. 1 ). Under a middle-of-the road scenario of future income development (SSP2, in which SSP stands for Shared Socio-economic Pathway), this corresponds to global annual damages in 2049 of 38 trillion in 2005 international dollars (likely range of 19–59 trillion 2005 international dollars). Compared with empirical specifications that assume pure growth or pure level effects, our preferred specification that provides a robust lower bound on the extent of climate impact persistence produces damages between these two extreme assumptions (Extended Data Fig. 3 ).

Estimates of the projected reduction in income per capita from changes in all climate variables based on empirical models of climate impacts on economic output with a robust lower bound on their persistence (Extended Data Fig. 1 ) under a low-emission scenario compatible with the 2 °C warming target and a high-emission scenario (SSP2-RCP2.6 and SSP5-RCP8.5, respectively) are shown in purple and orange, respectively. Shading represents the 34% and 10% confidence intervals reflecting the likely and very likely ranges, respectively (following the likelihood classification adopted by the IPCC), having estimated uncertainty from a Monte Carlo procedure, which samples the uncertainty from the choice of physical climate models, empirical models with different numbers of lags and bootstrapped estimates of the regression parameters shown in Supplementary Figs. 1 – 3 . Vertical dashed lines show the time at which the climate damages of the two emission scenarios diverge at the 5% and 1% significance levels based on the distribution of differences between emission scenarios arising from the uncertainty sampling discussed above. Note that uncertainty in the difference of the two scenarios is smaller than the combined uncertainty of the two respective scenarios because samples of the uncertainty (climate model and empirical model choice, as well as model parameter bootstrap) are consistent across the two emission scenarios, hence the divergence of damages occurs while the uncertainty bounds of the two separate damage scenarios still overlap. Estimates of global mitigation costs from the three IAMs that provide results for the SSP2 baseline and SSP2-RCP2.6 scenario are shown in light green in the top panel, with the median of these estimates shown in bold.

Damages already outweigh mitigation costs

We compare the damages to which the world is committed over the next 25 years to estimates of the mitigation costs required to achieve the Paris Climate Agreement. Taking estimates of mitigation costs from the three integrated assessment models (IAMs) in the IPCC AR6 database 23 that provide results under comparable scenarios (SSP2 baseline and SSP2-RCP2.6, in which RCP stands for Representative Concentration Pathway), we find that the median committed climate damages are larger than the median mitigation costs in 2050 (six trillion in 2005 international dollars) by a factor of approximately six (note that estimates of mitigation costs are only provided every 10 years by the IAMs and so a comparison in 2049 is not possible). This comparison simply aims to compare the magnitude of future damages against mitigation costs, rather than to conduct a formal cost–benefit analysis of transitioning from one emission path to another. Formal cost–benefit analyses typically find that the net benefits of mitigation only emerge after 2050 (ref.  5 ), which may lead some to conclude that physical damages from climate change are simply not large enough to outweigh mitigation costs until the second half of the century. Our simple comparison of their magnitudes makes clear that damages are actually already considerably larger than mitigation costs and the delayed emergence of net mitigation benefits results primarily from the fact that damages across different emission paths are indistinguishable until mid-century (Fig. 1 ).

Although these near-term damages constitute those to which the world is already committed, we note that damage estimates diverge strongly across emission scenarios after 2049, conveying the clear benefits of mitigation from a purely economic point of view that have been emphasized in previous studies 4 , 24 . As well as the uncertainties assessed in Fig. 1 , these conclusions are robust to structural choices, such as the timescale with which changes in the moderating variables of the empirical models are estimated (Supplementary Figs. 10 and 11 ), as well as the order in which one accounts for the intertemporal and international components of currency comparison (Supplementary Fig. 12 ; see Methods for further details).

Damages from variability and extremes

Committed damages primarily arise through changes in average temperature (Fig. 2 ). This reflects the fact that projected changes in average temperature are larger than those in other climate variables when expressed as a function of their historical interannual variability (Extended Data Fig. 4 ). Because the historical variability is that on which the empirical models are estimated, larger projected changes in comparison with this variability probably lead to larger future impacts in a purely statistical sense. From a mechanistic perspective, one may plausibly interpret this result as implying that future changes in average temperature are the most unprecedented from the perspective of the historical fluctuations to which the economy is accustomed and therefore will cause the most damage. This insight may prove useful in terms of guiding adaptation measures to the sources of greatest damage.

Estimates of the median projected reduction in sub-national income per capita across emission scenarios (SSP2-RCP2.6 and SSP2-RCP8.5) as well as climate model, empirical model and model parameter uncertainty in the year in which climate damages diverge at the 5% level (2049, as identified in Fig. 1 ). a , Impacts arising from all climate variables. b – f , Impacts arising separately from changes in annual mean temperature ( b ), daily temperature variability ( c ), total annual precipitation ( d ), the annual number of wet days (>1 mm) ( e ) and extreme daily rainfall ( f ) (see Methods for further definitions). Data on national administrative boundaries are obtained from the GADM database version 3.6 and are freely available for academic use ( https://gadm.org/ ).

Nevertheless, future damages based on empirical models that consider changes in annual average temperature only and exclude the other climate variables constitute income reductions of only 13% in 2049 (Extended Data Fig. 5a , likely range 5–21%). This suggests that accounting for the other components of the distribution of temperature and precipitation raises net damages by nearly 50%. This increase arises through the further damages that these climatic components cause, but also because their inclusion reveals a stronger negative economic response to average temperatures (Extended Data Fig. 5b ). The latter finding is consistent with our Monte Carlo simulations, which suggest that the magnitude of the effect of average temperature on economic growth is underestimated unless accounting for the impacts of other correlated climate variables (Supplementary Fig. 7 ).

In terms of the relative contributions of the different climatic components to overall damages, we find that accounting for daily temperature variability causes the largest increase in overall damages relative to empirical frameworks that only consider changes in annual average temperature (4.9 percentage points, likely range 2.4–8.7 percentage points, equivalent to approximately 10 trillion international dollars). Accounting for precipitation causes smaller increases in overall damages, which are—nevertheless—equivalent to approximately 1.2 trillion international dollars: 0.01 percentage points (−0.37–0.33 percentage points), 0.34 percentage points (0.07–0.90 percentage points) and 0.36 percentage points (0.13–0.65 percentage points) from total annual precipitation, the number of wet days and extreme daily precipitation, respectively. Moreover, climate models seem to underestimate future changes in temperature variability 25 and extreme precipitation 26 , 27 in response to anthropogenic forcing as compared with that observed historically, suggesting that the true impacts from these variables may be larger.

The distribution of committed damages

The spatial distribution of committed damages (Fig. 2a ) reflects a complex interplay between the patterns of future change in several climatic components and those of historical economic vulnerability to changes in those variables. Damages resulting from increasing annual mean temperature (Fig. 2b ) are negative almost everywhere globally, and larger at lower latitudes in regions in which temperatures are already higher and economic vulnerability to temperature increases is greatest (see the response heterogeneity to mean temperature embodied in Extended Data Fig. 1a ). This occurs despite the amplified warming projected at higher latitudes 28 , suggesting that regional heterogeneity in economic vulnerability to temperature changes outweighs heterogeneity in the magnitude of future warming (Supplementary Fig. 13a ). Economic damages owing to daily temperature variability (Fig. 2c ) exhibit a strong latitudinal polarisation, primarily reflecting the physical response of daily variability to greenhouse forcing in which increases in variability across lower latitudes (and Europe) contrast decreases at high latitudes 25 (Supplementary Fig. 13b ). These two temperature terms are the dominant determinants of the pattern of overall damages (Fig. 2a ), which exhibits a strong polarity with damages across most of the globe except at the highest northern latitudes. Future changes in total annual precipitation mainly bring economic benefits except in regions of drying, such as the Mediterranean and central South America (Fig. 2d and Supplementary Fig. 13c ), but these benefits are opposed by changes in the number of wet days, which produce damages with a similar pattern of opposite sign (Fig. 2e and Supplementary Fig. 13d ). By contrast, changes in extreme daily rainfall produce damages in all regions, reflecting the intensification of daily rainfall extremes over global land areas 29 , 30 (Fig. 2f and Supplementary Fig. 13e ).

The spatial distribution of committed damages implies considerable injustice along two dimensions: culpability for the historical emissions that have caused climate change and pre-existing levels of socio-economic welfare. Spearman’s rank correlations indicate that committed damages are significantly larger in countries with smaller historical cumulative emissions, as well as in regions with lower current income per capita (Fig. 3 ). This implies that those countries that will suffer the most from the damages already committed are those that are least responsible for climate change and which also have the least resources to adapt to it.

Estimates of the median projected change in national income per capita across emission scenarios (RCP2.6 and RCP8.5) as well as climate model, empirical model and model parameter uncertainty in the year in which climate damages diverge at the 5% level (2049, as identified in Fig. 1 ) are plotted against cumulative national emissions per capita in 2020 (from the Global Carbon Project) and coloured by national income per capita in 2020 (from the World Bank) in a and vice versa in b . In each panel, the size of each scatter point is weighted by the national population in 2020 (from the World Bank). Inset numbers indicate the Spearman’s rank correlation ρ and P -values for a hypothesis test whose null hypothesis is of no correlation, as well as the Spearman’s rank correlation weighted by national population.

To further quantify this heterogeneity, we assess the difference in committed damages between the upper and lower quartiles of regions when ranked by present income levels and historical cumulative emissions (using a population weighting to both define the quartiles and estimate the group averages). On average, the quartile of countries with lower income are committed to an income loss that is 8.9 percentage points (or 61%) greater than the upper quartile (Extended Data Fig. 6 ), with a likely range of 3.8–14.7 percentage points across the uncertainty sampling of our damage projections (following the likelihood classification adopted by the IPCC). Similarly, the quartile of countries with lower historical cumulative emissions are committed to an income loss that is 6.9 percentage points (or 40%) greater than the upper quartile, with a likely range of 0.27–12 percentage points. These patterns reemphasize the prevalence of injustice in climate impacts 31 , 32 , 33 in the context of the damages to which the world is already committed by historical emissions and socio-economic inertia.

Contextualizing the magnitude of damages

The magnitude of projected economic damages exceeds previous literature estimates 2 , 3 , arising from several developments made on previous approaches. Our estimates are larger than those of ref.  2 (see first row of Extended Data Table 3 ), primarily because of the facts that sub-national estimates typically show a steeper temperature response (see also refs.  3 , 34 ) and that accounting for other climatic components raises damage estimates (Extended Data Fig. 5 ). However, we note that our empirical approach using first-differenced climate variables is conservative compared with that of ref.  2 in regard to the persistence of climate impacts on growth (see introduction and Methods section ‘Empirical model specification: fixed-effects distributed lag models’), an important determinant of the magnitude of long-term damages 19 , 21 . Using a similar empirical specification to ref.  2 , which assumes infinite persistence while maintaining the rest of our approach (sub-national data and further climate variables), produces considerably larger damages (purple curve of Extended Data Fig. 3 ). Compared with studies that do take the first difference of climate variables 3 , 35 , our estimates are also larger (see second and third rows of Extended Data Table 3 ). The inclusion of further climate variables (Extended Data Fig. 5 ) and a sufficient number of lags to more adequately capture the extent of impact persistence (Extended Data Figs. 1 and 2 ) are the main sources of this difference, as is the use of specifications that capture nonlinearities in the temperature response when compared with ref.  35 . In summary, our estimates develop on previous studies by incorporating the latest data and empirical insights 7 , 8 , as well as in providing a robust empirical lower bound on the persistence of impacts on economic growth, which constitutes a middle ground between the extremes of the growth-versus-levels debate 19 , 21 (Extended Data Fig. 3 ).

Compared with the fraction of variance explained by the empirical models historically (<5%), the projection of reductions in income of 19% may seem large. This arises owing to the fact that projected changes in climatic conditions are much larger than those that were experienced historically, particularly for changes in average temperature (Extended Data Fig. 4 ). As such, any assessment of future climate-change impacts necessarily requires an extrapolation outside the range of the historical data on which the empirical impact models were evaluated. Nevertheless, these models constitute the most state-of-the-art methods for inference of plausibly causal climate impacts based on observed data. Moreover, we take explicit steps to limit out-of-sample extrapolation by capping the moderating variables of the interaction terms at the 95th percentile of the historical distribution (see Methods ). This avoids extrapolating the marginal effects outside what was observed historically. Given the nonlinear response of economic output to annual mean temperature (Extended Data Fig. 1 and Extended Data Table 2 ), this is a conservative choice that limits the magnitude of damages that we project. Furthermore, back-of-the-envelope calculations indicate that the projected damages are consistent with the magnitude and patterns of historical economic development (see Supplementary Discussion Section  5 ).

Missing impacts and spatial spillovers

Despite assessing several climatic components from which economic impacts have recently been identified 3 , 7 , 8 , this assessment of aggregate climate damages should not be considered comprehensive. Important channels such as impacts from heatwaves 31 , sea-level rise 36 , tropical cyclones 37 and tipping points 38 , 39 , as well as non-market damages such as those to ecosystems 40 and human health 41 , are not considered in these estimates. Sea-level rise is unlikely to be feasibly incorporated into empirical assessments such as this because historical sea-level variability is mostly small. Non-market damages are inherently intractable within our estimates of impacts on aggregate monetary output and estimates of these impacts could arguably be considered as extra to those identified here. Recent empirical work suggests that accounting for these channels would probably raise estimates of these committed damages, with larger damages continuing to arise in the global south 31 , 36 , 37 , 38 , 39 , 40 , 41 , 42 .

Moreover, our main empirical analysis does not explicitly evaluate the potential for impacts in local regions to produce effects that ‘spill over’ into other regions. Such effects may further mitigate or amplify the impacts we estimate, for example, if companies relocate production from one affected region to another or if impacts propagate along supply chains. The current literature indicates that trade plays a substantial role in propagating spillover effects 43 , 44 , making their assessment at the sub-national level challenging without available data on sub-national trade dependencies. Studies accounting for only spatially adjacent neighbours indicate that negative impacts in one region induce further negative impacts in neighbouring regions 45 , 46 , 47 , 48 , suggesting that our projected damages are probably conservative by excluding these effects. In Supplementary Fig. 14 , we assess spillovers from neighbouring regions using a spatial-lag model. For simplicity, this analysis excludes temporal lags, focusing only on contemporaneous effects. The results show that accounting for spatial spillovers can amplify the overall magnitude, and also the heterogeneity, of impacts. Consistent with previous literature, this indicates that the overall magnitude (Fig. 1 ) and heterogeneity (Fig. 3 ) of damages that we project in our main specification may be conservative without explicitly accounting for spillovers. We note that further analysis that addresses both spatially and trade-connected spillovers, while also accounting for delayed impacts using temporal lags, would be necessary to adequately address this question fully. These approaches offer fruitful avenues for further research but are beyond the scope of this manuscript, which primarily aims to explore the impacts of different climate conditions and their persistence.

Policy implications

We find that the economic damages resulting from climate change until 2049 are those to which the world economy is already committed and that these greatly outweigh the costs required to mitigate emissions in line with the 2 °C target of the Paris Climate Agreement (Fig. 1 ). This assessment is complementary to formal analyses of the net costs and benefits associated with moving from one emission path to another, which typically find that net benefits of mitigation only emerge in the second half of the century 5 . Our simple comparison of the magnitude of damages and mitigation costs makes clear that this is primarily because damages are indistinguishable across emissions scenarios—that is, committed—until mid-century (Fig. 1 ) and that they are actually already much larger than mitigation costs. For simplicity, and owing to the availability of data, we compare damages to mitigation costs at the global level. Regional estimates of mitigation costs may shed further light on the national incentives for mitigation to which our results already hint, of relevance for international climate policy. Although these damages are committed from a mitigation perspective, adaptation may provide an opportunity to reduce them. Moreover, the strong divergence of damages after mid-century reemphasizes the clear benefits of mitigation from a purely economic perspective, as highlighted in previous studies 1 , 4 , 6 , 24 .

Historical climate data

Historical daily 2-m temperature and precipitation totals (in mm) are obtained for the period 1979–2019 from the W5E5 database. The W5E5 dataset comes from ERA-5, a state-of-the-art reanalysis of historical observations, but has been bias-adjusted by applying version 2.0 of the WATCH Forcing Data to ERA-5 reanalysis data and precipitation data from version 2.3 of the Global Precipitation Climatology Project to better reflect ground-based measurements 49 , 50 , 51 . We obtain these data on a 0.5° × 0.5° grid from the Inter-Sectoral Impact Model Intercomparison Project (ISIMIP) database. Notably, these historical data have been used to bias-adjust future climate projections from CMIP-6 (see the following section), ensuring consistency between the distribution of historical daily weather on which our empirical models were estimated and the climate projections used to estimate future damages. These data are publicly available from the ISIMIP database. See refs.  7 , 8 for robustness tests of the empirical models to the choice of climate data reanalysis products.

Future climate data

Daily 2-m temperature and precipitation totals (in mm) are taken from 21 climate models participating in CMIP-6 under a high (RCP8.5) and a low (RCP2.6) greenhouse gas emission scenario from 2015 to 2100. The data have been bias-adjusted and statistically downscaled to a common half-degree grid to reflect the historical distribution of daily temperature and precipitation of the W5E5 dataset using the trend-preserving method developed by the ISIMIP 50 , 52 . As such, the climate model data reproduce observed climatological patterns exceptionally well (Supplementary Table 5 ). Gridded data are publicly available from the ISIMIP database.

Historical economic data

Historical economic data come from the DOSE database of sub-national economic output 53 . We use a recent revision to the DOSE dataset that provides data across 83 countries, 1,660 sub-national regions with varying temporal coverage from 1960 to 2019. Sub-national units constitute the first administrative division below national, for example, states for the USA and provinces for China. Data come from measures of gross regional product per capita (GRPpc) or income per capita in local currencies, reflecting the values reported in national statistical agencies, yearbooks and, in some cases, academic literature. We follow previous literature 3 , 7 , 8 , 54 and assess real sub-national output per capita by first converting values from local currencies to US dollars to account for diverging national inflationary tendencies and then account for US inflation using a US deflator. Alternatively, one might first account for national inflation and then convert between currencies. Supplementary Fig. 12 demonstrates that our conclusions are consistent when accounting for price changes in the reversed order, although the magnitude of estimated damages varies. See the documentation of the DOSE dataset for further discussion of these choices. Conversions between currencies are conducted using exchange rates from the FRED database of the Federal Reserve Bank of St. Louis 55 and the national deflators from the World Bank 56 .

Future socio-economic data

Baseline gridded gross domestic product (GDP) and population data for the period 2015–2100 are taken from the middle-of-the-road scenario SSP2 (ref.  15 ). Population data have been downscaled to a half-degree grid by the ISIMIP following the methodologies of refs.  57 , 58 , which we then aggregate to the sub-national level of our economic data using the spatial aggregation procedure described below. Because current methodologies for downscaling the GDP of the SSPs use downscaled population to do so, per-capita estimates of GDP with a realistic distribution at the sub-national level are not readily available for the SSPs. We therefore use national-level GDP per capita (GDPpc) projections for all sub-national regions of a given country, assuming homogeneity within countries in terms of baseline GDPpc. Here we use projections that have been updated to account for the impact of the COVID-19 pandemic on the trajectory of future income, while remaining consistent with the long-term development of the SSPs 59 . The choice of baseline SSP alters the magnitude of projected climate damages in monetary terms, but when assessed in terms of percentage change from the baseline, the choice of socio-economic scenario is inconsequential. Gridded SSP population data and national-level GDPpc data are publicly available from the ISIMIP database. Sub-national estimates as used in this study are available in the code and data replication files.

Climate variables

Following recent literature 3 , 7 , 8 , we calculate an array of climate variables for which substantial impacts on macroeconomic output have been identified empirically, supported by further evidence at the micro level for plausible underlying mechanisms. See refs.  7 , 8 for an extensive motivation for the use of these particular climate variables and for detailed empirical tests on the nature and robustness of their effects on economic output. To summarize, these studies have found evidence for independent impacts on economic growth rates from annual average temperature, daily temperature variability, total annual precipitation, the annual number of wet days and extreme daily rainfall. Assessments of daily temperature variability were motivated by evidence of impacts on agricultural output and human health, as well as macroeconomic literature on the impacts of volatility on growth when manifest in different dimensions, such as government spending, exchange rates and even output itself 7 . Assessments of precipitation impacts were motivated by evidence of impacts on agricultural productivity, metropolitan labour outcomes and conflict, as well as damages caused by flash flooding 8 . See Extended Data Table 1 for detailed references to empirical studies of these physical mechanisms. Marked impacts of daily temperature variability, total annual precipitation, the number of wet days and extreme daily rainfall on macroeconomic output were identified robustly across different climate datasets, spatial aggregation schemes, specifications of regional time trends and error-clustering approaches. They were also found to be robust to the consideration of temperature extremes 7 , 8 . Furthermore, these climate variables were identified as having independent effects on economic output 7 , 8 , which we further explain here using Monte Carlo simulations to demonstrate the robustness of the results to concerns of imperfect multicollinearity between climate variables (Supplementary Methods Section  2 ), as well as by using information criteria (Supplementary Table 1 ) to demonstrate that including several lagged climate variables provides a preferable trade-off between optimally describing the data and limiting the possibility of overfitting.

We calculate these variables from the distribution of daily, d , temperature, T x , d , and precipitation, P x , d , at the grid-cell, x , level for both the historical and future climate data. As well as annual mean temperature, \({\bar{T}}_{x,y}\) , and annual total precipitation, P x , y , we calculate annual, y , measures of daily temperature variability, \({\widetilde{T}}_{x,y}\) :

the number of wet days, Pwd x , y :

and extreme daily rainfall:

in which T x , d , m , y is the grid-cell-specific daily temperature in month m and year y , \({\bar{T}}_{x,m,{y}}\) is the year and grid-cell-specific monthly, m , mean temperature, D m and D y the number of days in a given month m or year y , respectively, H the Heaviside step function, 1 mm the threshold used to define wet days and P 99.9 x is the 99.9th percentile of historical (1979–2019) daily precipitation at the grid-cell level. Units of the climate measures are degrees Celsius for annual mean temperature and daily temperature variability, millimetres for total annual precipitation and extreme daily precipitation, and simply the number of days for the annual number of wet days.

We also calculated weighted standard deviations of monthly rainfall totals as also used in ref.  8 but do not include them in our projections as we find that, when accounting for delayed effects, their effect becomes statistically indistinct and is better captured by changes in total annual rainfall.

Spatial aggregation

We aggregate grid-cell-level historical and future climate measures, as well as grid-cell-level future GDPpc and population, to the level of the first administrative unit below national level of the GADM database, using an area-weighting algorithm that estimates the portion of each grid cell falling within an administrative boundary. We use this as our baseline specification following previous findings that the effect of area or population weighting at the sub-national level is negligible 7 , 8 .

Empirical model specification: fixed-effects distributed lag models

Following a wide range of climate econometric literature 16 , 60 , we use panel regression models with a selection of fixed effects and time trends to isolate plausibly exogenous variation with which to maximize confidence in a causal interpretation of the effects of climate on economic growth rates. The use of region fixed effects, μ r , accounts for unobserved time-invariant differences between regions, such as prevailing climatic norms and growth rates owing to historical and geopolitical factors. The use of yearly fixed effects, η y , accounts for regionally invariant annual shocks to the global climate or economy such as the El Niño–Southern Oscillation or global recessions. In our baseline specification, we also include region-specific linear time trends, k r y , to exclude the possibility of spurious correlations resulting from common slow-moving trends in climate and growth.

The persistence of climate impacts on economic growth rates is a key determinant of the long-term magnitude of damages. Methods for inferring the extent of persistence in impacts on growth rates have typically used lagged climate variables to evaluate the presence of delayed effects or catch-up dynamics 2 , 18 . For example, consider starting from a model in which a climate condition, C r , y , (for example, annual mean temperature) affects the growth rate, Δlgrp r , y (the first difference of the logarithm of gross regional product) of region r in year y :

which we refer to as a ‘pure growth effects’ model in the main text. Typically, further lags are included,

and the cumulative effect of all lagged terms is evaluated to assess the extent to which climate impacts on growth rates persist. Following ref.  18 , in the case that,

the implication is that impacts on the growth rate persist up to NL years after the initial shock (possibly to a weaker or a stronger extent), whereas if

then the initial impact on the growth rate is recovered after NL years and the effect is only one on the level of output. However, we note that such approaches are limited by the fact that, when including an insufficient number of lags to detect a recovery of the growth rates, one may find equation ( 6 ) to be satisfied and incorrectly assume that a change in climatic conditions affects the growth rate indefinitely. In practice, given a limited record of historical data, including too few lags to confidently conclude in an infinitely persistent impact on the growth rate is likely, particularly over the long timescales over which future climate damages are often projected 2 , 24 . To avoid this issue, we instead begin our analysis with a model for which the level of output, lgrp r , y , depends on the level of a climate variable, C r , y :

Given the non-stationarity of the level of output, we follow the literature 19 and estimate such an equation in first-differenced form as,

which we refer to as a model of ‘pure level effects’ in the main text. This model constitutes a baseline specification in which a permanent change in the climate variable produces an instantaneous impact on the growth rate and a permanent effect only on the level of output. By including lagged variables in this specification,

we are able to test whether the impacts on the growth rate persist any further than instantaneously by evaluating whether α L  > 0 are statistically significantly different from zero. Even though this framework is also limited by the possibility of including too few lags, the choice of a baseline model specification in which impacts on the growth rate do not persist means that, in the case of including too few lags, the framework reverts to the baseline specification of level effects. As such, this framework is conservative with respect to the persistence of impacts and the magnitude of future damages. It naturally avoids assumptions of infinite persistence and we are able to interpret any persistence that we identify with equation ( 9 ) as a lower bound on the extent of climate impact persistence on growth rates. See the main text for further discussion of this specification choice, in particular about its conservative nature compared with previous literature estimates, such as refs.  2 , 18 .

We allow the response to climatic changes to vary across regions, using interactions of the climate variables with historical average (1979–2019) climatic conditions reflecting heterogenous effects identified in previous work 7 , 8 . Following this previous work, the moderating variables of these interaction terms constitute the historical average of either the variable itself or of the seasonal temperature difference, \({\hat{T}}_{r}\) , or annual mean temperature, \({\bar{T}}_{r}\) , in the case of daily temperature variability 7 and extreme daily rainfall, respectively 8 .

The resulting regression equation with N and M lagged variables, respectively, reads:

in which Δlgrp r , y is the annual, regional GRPpc growth rate, measured as the first difference of the logarithm of real GRPpc, following previous work 2 , 3 , 7 , 8 , 18 , 19 . Fixed-effects regressions were run using the fixest package in R (ref.  61 ).

Estimates of the coefficients of interest α i , L are shown in Extended Data Fig. 1 for N  =  M  = 10 lags and for our preferred choice of the number of lags in Supplementary Figs. 1 – 3 . In Extended Data Fig. 1 , errors are shown clustered at the regional level, but for the construction of damage projections, we block-bootstrap the regressions by region 1,000 times to provide a range of parameter estimates with which to sample the projection uncertainty (following refs.  2 , 31 ).

Spatial-lag model

In Supplementary Fig. 14 , we present the results from a spatial-lag model that explores the potential for climate impacts to ‘spill over’ into spatially neighbouring regions. We measure the distance between centroids of each pair of sub-national regions and construct spatial lags that take the average of the first-differenced climate variables and their interaction terms over neighbouring regions that are at distances of 0–500, 500–1,000, 1,000–1,500 and 1,500–2000 km (spatial lags, ‘SL’, 1 to 4). For simplicity, we then assess a spatial-lag model without temporal lags to assess spatial spillovers of contemporaneous climate impacts. This model takes the form:

in which SL indicates the spatial lag of each climate variable and interaction term. In Supplementary Fig. 14 , we plot the cumulative marginal effect of each climate variable at different baseline climate conditions by summing the coefficients for each climate variable and interaction term, for example, for average temperature impacts as:

These cumulative marginal effects can be regarded as the overall spatially dependent impact to an individual region given a one-unit shock to a climate variable in that region and all neighbouring regions at a given value of the moderating variable of the interaction term.

Constructing projections of economic damage from future climate change

We construct projections of future climate damages by applying the coefficients estimated in equation ( 10 ) and shown in Supplementary Tables 2 – 4 (when including only lags with statistically significant effects in specifications that limit overfitting; see Supplementary Methods Section  1 ) to projections of future climate change from the CMIP-6 models. Year-on-year changes in each primary climate variable of interest are calculated to reflect the year-to-year variations used in the empirical models. 30-year moving averages of the moderating variables of the interaction terms are calculated to reflect the long-term average of climatic conditions that were used for the moderating variables in the empirical models. By using moving averages in the projections, we account for the changing vulnerability to climate shocks based on the evolving long-term conditions (Supplementary Figs. 10 and 11 show that the results are robust to the precise choice of the window of this moving average). Although these climate variables are not differenced, the fact that the bias-adjusted climate models reproduce observed climatological patterns across regions for these moderating variables very accurately (Supplementary Table 6 ) with limited spread across models (<3%) precludes the possibility that any considerable bias or uncertainty is introduced by this methodological choice. However, we impose caps on these moderating variables at the 95th percentile at which they were observed in the historical data to prevent extrapolation of the marginal effects outside the range in which the regressions were estimated. This is a conservative choice that limits the magnitude of our damage projections.

Time series of primary climate variables and moderating climate variables are then combined with estimates of the empirical model parameters to evaluate the regression coefficients in equation ( 10 ), producing a time series of annual GRPpc growth-rate reductions for a given emission scenario, climate model and set of empirical model parameters. The resulting time series of growth-rate impacts reflects those occurring owing to future climate change. By contrast, a future scenario with no climate change would be one in which climate variables do not change (other than with random year-to-year fluctuations) and hence the time-averaged evaluation of equation ( 10 ) would be zero. Our approach therefore implicitly compares the future climate-change scenario to this no-climate-change baseline scenario.

The time series of growth-rate impacts owing to future climate change in region r and year y , δ r , y , are then added to the future baseline growth rates, π r , y (in log-diff form), obtained from the SSP2 scenario to yield trajectories of damaged GRPpc growth rates, ρ r , y . These trajectories are aggregated over time to estimate the future trajectory of GRPpc with future climate impacts:

in which GRPpc r , y =2020 is the initial log level of GRPpc. We begin damage estimates in 2020 to reflect the damages occurring since the end of the period for which we estimate the empirical models (1979–2019) and to match the timing of mitigation-cost estimates from most IAMs (see below).

For each emission scenario, this procedure is repeated 1,000 times while randomly sampling from the selection of climate models, the selection of empirical models with different numbers of lags (shown in Supplementary Figs. 1 – 3 and Supplementary Tables 2 – 4 ) and bootstrapped estimates of the regression parameters. The result is an ensemble of future GRPpc trajectories that reflect uncertainty from both physical climate change and the structural and sampling uncertainty of the empirical models.

Estimates of mitigation costs

We obtain IPCC estimates of the aggregate costs of emission mitigation from the AR6 Scenario Explorer and Database hosted by IIASA 23 . Specifically, we search the AR6 Scenarios Database World v1.1 for IAMs that provided estimates of global GDP and population under both a SSP2 baseline and a SSP2-RCP2.6 scenario to maintain consistency with the socio-economic and emission scenarios of the climate damage projections. We find five IAMs that provide data for these scenarios, namely, MESSAGE-GLOBIOM 1.0, REMIND-MAgPIE 1.5, AIM/GCE 2.0, GCAM 4.2 and WITCH-GLOBIOM 3.1. Of these five IAMs, we use the results only from the first three that passed the IPCC vetting procedure for reproducing historical emission and climate trajectories. We then estimate global mitigation costs as the percentage difference in global per capita GDP between the SSP2 baseline and the SSP2-RCP2.6 emission scenario. In the case of one of these IAMs, estimates of mitigation costs begin in 2020, whereas in the case of two others, mitigation costs begin in 2010. The mitigation cost estimates before 2020 in these two IAMs are mostly negligible, and our choice to begin comparison with damage estimates in 2020 is conservative with respect to the relative weight of climate damages compared with mitigation costs for these two IAMs.

Data availability

Data on economic production and ERA-5 climate data are publicly available at https://doi.org/10.5281/zenodo.4681306 (ref. 62 ) and https://www.ecmwf.int/en/forecasts/datasets/reanalysis-datasets/era5 , respectively. Data on mitigation costs are publicly available at https://data.ene.iiasa.ac.at/ar6/#/downloads . Processed climate and economic data, as well as all other necessary data for reproduction of the results, are available at the public repository https://doi.org/10.5281/zenodo.10562951  (ref. 63 ).

Code availability

All code necessary for reproduction of the results is available at the public repository https://doi.org/10.5281/zenodo.10562951  (ref. 63 ).

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Acknowledgements

We gratefully acknowledge financing from the Volkswagen Foundation and the Deutsche Gesellschaft für Internationale Zusammenarbeit (GIZ) GmbH on behalf of the Government of the Federal Republic of Germany and Federal Ministry for Economic Cooperation and Development (BMZ).

Open access funding provided by Potsdam-Institut für Klimafolgenforschung (PIK) e.V.

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All authors contributed to the design of the analysis. M.K. conducted the analysis and produced the figures. All authors contributed to the interpretation and presentation of the results. M.K. and L.W. wrote the manuscript.

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Extended data figures and tables

Extended data fig. 1 constraining the persistence of historical climate impacts on economic growth rates..

The results of a panel-based fixed-effects distributed lag model for the effects of annual mean temperature ( a ), daily temperature variability ( b ), total annual precipitation ( c ), the number of wet days ( d ) and extreme daily precipitation ( e ) on sub-national economic growth rates. Point estimates show the effects of a 1 °C or one standard deviation increase (for temperature and precipitation variables, respectively) at the lower quartile, median and upper quartile of the relevant moderating variable (green, orange and purple, respectively) at different lagged periods after the initial shock (note that these are not cumulative effects). Climate variables are used in their first-differenced form (see main text for discussion) and the moderating climate variables are the annual mean temperature, seasonal temperature difference, total annual precipitation, number of wet days and annual mean temperature, respectively, in panels a – e (see Methods for further discussion). Error bars show the 95% confidence intervals having clustered standard errors by region. The within-region R 2 , Bayesian and Akaike information criteria for the model are shown at the top of the figure. This figure shows results with ten lags for each variable to demonstrate the observed levels of persistence, but our preferred specifications remove later lags based on the statistical significance of terms shown above and the information criteria shown in Extended Data Fig. 2 . The resulting models without later lags are shown in Supplementary Figs. 1 – 3 .

Extended Data Fig. 2 Incremental lag-selection procedure using information criteria and within-region R 2 .

Starting from a panel-based fixed-effects distributed lag model estimating the effects of climate on economic growth using the real historical data (as in equation ( 4 )) with ten lags for all climate variables (as shown in Extended Data Fig. 1 ), lags are incrementally removed for one climate variable at a time. The resulting Bayesian and Akaike information criteria are shown in a – e and f – j , respectively, and the within-region R 2 and number of observations in k – o and p – t , respectively. Different rows show the results when removing lags from different climate variables, ordered from top to bottom as annual mean temperature, daily temperature variability, total annual precipitation, the number of wet days and extreme annual precipitation. Information criteria show minima at approximately four lags for precipitation variables and ten to eight for temperature variables, indicating that including these numbers of lags does not lead to overfitting. See Supplementary Table 1 for an assessment using information criteria to determine whether including further climate variables causes overfitting.

Extended Data Fig. 3 Damages in our preferred specification that provides a robust lower bound on the persistence of climate impacts on economic growth versus damages in specifications of pure growth or pure level effects.

Estimates of future damages as shown in Fig. 1 but under the emission scenario RCP8.5 for three separate empirical specifications: in orange our preferred specification, which provides an empirical lower bound on the persistence of climate impacts on economic growth rates while avoiding assumptions of infinite persistence (see main text for further discussion); in purple a specification of ‘pure growth effects’ in which the first difference of climate variables is not taken and no lagged climate variables are included (the baseline specification of ref.  2 ); and in pink a specification of ‘pure level effects’ in which the first difference of climate variables is taken but no lagged terms are included.

Extended Data Fig. 4 Climate changes in different variables as a function of historical interannual variability.

Changes in each climate variable of interest from 1979–2019 to 2035–2065 under the high-emission scenario SSP5-RCP8.5, expressed as a percentage of the historical variability of each measure. Historical variability is estimated as the standard deviation of each detrended climate variable over the period 1979–2019 during which the empirical models were identified (detrending is appropriate because of the inclusion of region-specific linear time trends in the empirical models). See Supplementary Fig. 13 for changes expressed in standard units. Data on national administrative boundaries are obtained from the GADM database version 3.6 and are freely available for academic use ( https://gadm.org/ ).

Extended Data Fig. 5 Contribution of different climate variables to overall committed damages.

a , Climate damages in 2049 when using empirical models that account for all climate variables, changes in annual mean temperature only or changes in both annual mean temperature and one other climate variable (daily temperature variability, total annual precipitation, the number of wet days and extreme daily precipitation, respectively). b , The cumulative marginal effects of an increase in annual mean temperature of 1 °C, at different baseline temperatures, estimated from empirical models including all climate variables or annual mean temperature only. Estimates and uncertainty bars represent the median and 95% confidence intervals obtained from 1,000 block-bootstrap resamples from each of three different empirical models using eight, nine or ten lags of temperature terms.

Extended Data Fig. 6 The difference in committed damages between the upper and lower quartiles of countries when ranked by GDP and cumulative historical emissions.

Quartiles are defined using a population weighting, as are the average committed damages across each quartile group. The violin plots indicate the distribution of differences between quartiles across the two extreme emission scenarios (RCP2.6 and RCP8.5) and the uncertainty sampling procedure outlined in Methods , which accounts for uncertainty arising from the choice of lags in the empirical models, uncertainty in the empirical model parameter estimates, as well as the climate model projections. Bars indicate the median, as well as the 10th and 90th percentiles and upper and lower sixths of the distribution reflecting the very likely and likely ranges following the likelihood classification adopted by the IPCC.

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Kotz, M., Levermann, A. & Wenz, L. The economic commitment of climate change. Nature 628 , 551–557 (2024). https://doi.org/10.1038/s41586-024-07219-0

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This is what the climate crisis is costing economies around the world

Failure to mitigate climate change is ranked as one of the key threats in the World Economic Forum’s Global Risks Report 2023. Image:  Unsplash/Jonathan Ford

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This article was first published in October 2023 and updated in November 2023.

• The economic toll of extreme weather has grown substantially, according to a World Meteorological Organization report.
• Extreme weather events and climate-related disasters have caused significant economic losses, reaching nearly $1.5 trillion in the decade to 2019.
• The international system has struggled to make the required progress on climate change, according to the World Economic Forum’s Global Risks Report 2023 .

Around the world we’re counting the cost of climate change, in every way, but what does this mean in terms of economic losses, what are the main causes and where is this being felt the most?

Extreme weather, climate and water-related events caused almost $1.5 trillion of economic losses in the decade to 2019 , up from $184 billion in the 1970s, according to a World Meteorological Organization (WMO) report. The real figures are likely to be even higher, as many losses go unreported.

While the good news contained in the report showed that improved warnings and disaster management has cut the number of lives lost, there is no getting away from the huge human and environmental cost of the climate crisis, and the impact it is having on livelihoods and businesses.

Failure to mitigate climate change is ranked as one of the key threats in the World Economic Forum’s Global Risks Report 2023 , with 70% of respondents rating existing measures to prevent or prepare for climate change as “ineffective” or “highly ineffective”.

“Despite 30 years of global climate advocacy and diplomacy, the international system has struggled to make the required progress on climate change,” the report says. “The potential failure to address this existential global risk first entered the top rankings of the Global Risks Report over a decade ago, in 2011.”

The Global Risks Report 2023 ranked failure to mitigate climate change as one of the most severe threats in the next two years, while climate- and nature- related risks lead the rankings by severity over the long term.

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The economic costs of the climate crisis

While the environmental losses and hazards are clear, the economic costs are also important since they have an uneven impact on communities, with some countries and regions disproportionately affected.

China suffered direct economic losses of more than $42 billion in the first nine months of 2023 from natural disasters including torrential rains, landslides, hailstorms and typhoons according to the nation’s government data.

Tropical cyclone damage was the largest proportion, according to the WMO data, with floods coming in second, and drought third.

In Africa, disasters from 1970-2021 caused $43 billion in economic losses, with droughts accounting for 95% of deaths, according to the WMO. Europe’s reported cost was $562 billion in losses, with 8% of global disaster deaths occurring in Europe, the data showed.

For South America, the losses amounted to $115.2 billion and for North America, Central America and the Caribbean it was $2 trillion. Meanwhile, the latest instalment of the US National Climate Assessment has concluded that extreme weather events currently cost the country $1 billion every three weeks (compared to every four months in the 1980s) and averaged $150 billion in damages each year between 2018-2022.

More frequent and severe

Climate-related extreme events are set to become more frequent and severe, according to the Intergovernmental Panel on Climate Change. It is against this backdrop the United Arab Emirates will host the 28th Conference of the Parties to the UN Framework Convention on Climate Change, known as COP28, from 30 November to 12 December.

The United Nations' global chief heat officer , Eleni Myrivili, has called for firm commitments at the summit to stem rapidly rising temperatures in cities.

As we head towards the meeting, you can follow the build-up on the World Economic Forum’s website.

“Our only ask is that you come with solutions, and real actions,” Majid Al Suwaidi, Director-General and Special Representative of the United Nations Climate Change Conference (COP28), Office of the Special Envoy for Climate Change of the UAE, told the Forum in a panel session. “That’s what we want our COP to be about, solutions, actions, real things that will get us back on track.”

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What Are the Effects of Climate Change?

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Climate change is our planet’s greatest existential threat . If we don’t limit greenhouse gas emissions from the burning of fossil fuels, the consequences of rising global temperatures include massive crop and fishery collapse, the disappearance of hundreds of thousands of species, and entire communities becoming uninhabitable. While these outcomes may still be avoidable, climate change is already causing suffering and death. From raging wildfires and supercharged storms, its compounding effects can be felt today, outside our own windows.

Understanding these impacts can help us prepare for what’s here, what’s avoidable, and what’s yet to come, and to better prepare and protect all communities. Even though everyone is or will be affected by climate change, those living in the world’s poorest countries—which have contributed least to the problem—are the most climate-vulnerable. They have the fewest financial resources to respond to crises or adapt, and they’re closely dependent on a healthy, thriving natural world for food and income. Similarly, in the United States, it is most often low-income communities and communities of color that are on the frontlines of climate impacts. And because climate change and rising inequality are interconnected crises, decision makers must take action to combat both—and all of us must fight for climate justice. Here’s what you need to know about what we’re up against.

Effects of climate change on weather

Effects of climate change on the environment, effects of climate change on agriculture, effects of climate change on animals, effects of climate change on humans, future effects of climate change.

As global temperatures climb, widespread shifts in weather systems occur, making events like droughts , hurricanes , and floods more intense and unpredictable. Extreme weather events that may have hit just once in our grandparents’ lifetimes are becoming more common in ours. However, not every place will experience the same effects: Climate change may cause severe drought in one region while making floods more likely in another.

Already, the planet has warmed 1.1 degrees Celsius (1.9 degrees Fahrenheit) since the preindustrial era began 250 years ago, according to the Intergovernmental Panel on Climate Change (IPCC) . And scientists warn it could reach a worst-case scenario of 4 degrees Celsius (7.2 degrees Fahrenheit) by 2100 if we fail to tackle the causes of climate change —namely, the burning of fossil fuels (coal, oil, and gas) .

Tokyo during a record-breaking heat wave, August 13, 2020

The Yomiuri Shimbun via AP Images

Higher average temperatures

This change in global average temperature—seemingly small but consequential and climbing—means that, each summer, we are likely to experience increasingly sweltering heat waves. Even local news meteorologists are starting to connect strings of record-breaking days to new long-term trends, which are especially problematic in regions where infrastructure and housing have not been built with intensifying heat in mind. And heat waves aren’t just uncomfortable—they’re the leading cause of weather-related fatalities in the United States.

Longer-lasting droughts

Hotter temperatures increase the rate at which water evaporates from the air, leading to more severe and pervasive droughts . Already, climate change has pushed the American West into a severe “megadrought”—the driest 22-year stretch recorded in at least 1,200 years—shrinking drinking water supplies, withering crops , and making forests more susceptible to insect infestations. Drought can also create a positive feedback loop in which drier soil and less plant cover cause even faster evaporation.

More intense wildfires

This drier, hotter climate also creates conditions that fuel more vicious wildfire seasons—with fires that spread faster and burn longer—putting millions of additional lives and homes at risk. The number of large wildfires doubled between 1984 and 2015 in the western United States. And in California alone, the annual area burned by wildfires increased 500 percent between 1972 and 2018.

Evacuation after Hurricane Harvey in Houston, August 28, 2017

David J. Phillip/AP Photo

Stronger storms

Warmer air also holds more moisture, making tropical cyclones wetter, stronger, and more capable of rapidly intensifying. In the latest report from the IPCC , scientists found that daily rainfall during extreme precipitation events would increase by about 7 percent for each degree Celsius of global warming, increasing the dangers of flooding . The frequency of severe Category 4 and 5 hurricanes is also expected to increase. In 2017, Hurricane Harvey, a devastating Category 4 storm, dumped a record 275 trillion pounds of rain and resulted in dozens of deaths in the Houston area.

From the poles to the tropics, climate change is disrupting ecosystems. Even a seemingly slight shift in temperature can cause dramatic changes that ripple through food webs and the environment.

The lake at Jökulsárlón, a glacial lagoon in Iceland, which has grown because of continued glacial melting

Eskinder Debebe/UN Photo

Melting sea ice

The effects of climate change are most apparent in the world’s coldest regions—the poles. The Arctic is heating up twice as fast as anywhere else on earth, leading to the rapid melting of glaciers and polar ice sheets, where a massive amount of water is stored. As sea ice melts, darker ocean waters that absorb more sunlight become exposed, creating a positive feedback loop that speeds up the melting process. In just 15 years, the Arctic could be entirely ice-free in the summer.

Sea level rise

Scientists predict that melting sea ice and glaciers, as well as the fact that warmer water expands in volume, could cause sea levels to rise as much as 6.6 feet by the end of the century, should we fail to curb emissions. The extent (and pace) of this change would devastate low-lying regions, including island nations and densely populated coastal cities like New York City and Mumbai.

But sea level rise at far lower levels is still costly, dangerous, and disruptive. According to the 2022 Sea Level Rise Technical Report from the National Ocean Service, the United States will see a foot of sea level rise by 2050, which will regularly damage infrastructure, like roads, sewage treatment plants, and even power plants . Beaches that families have grown up visiting may be gone by the end of the century. Sea level rise also harms the environment, as encroaching seawater can both erode coastal ecosystems and invade freshwater inland aquifers, which we rely on for agriculture and drinking water. Saltwater incursion is already reshaping life in nations like Bangladesh , where one-quarter of the lands lie less than 7 feet above sea level.

A waterlogged road, caused by rainstorm and upstream flood discharge, in the Shaoguan, Guangdong Province of China, June 21, 2022

Stringer/Anadolu Agency via Getty Images

In addition to coastal flooding caused by sea level rise, climate change influences the factors that result in inland and urban flooding: snowmelt and heavy rain. As global warming continues to both exacerbate sea level rise and extreme weather, our nation’s floodplains are expected to grow by approximately 45 percent by 2100. In 2022, deadly flooding in Pakistan—which inundated as much as a third of the country—resulted from torrential rains mixed with melting glaciers and snow.

Warmer ocean waters and marine heat waves

Oceans are taking the brunt of our climate crisis. Covering more than 70 percent of the planet’s surface, oceans absorb 93 percent of all the heat that’s trapped by greenhouse gases and up to 30 percent of all the carbon dioxide emitted from burning fossil fuels.

Temperature-sensitive fish and other marine life are already changing migration patterns toward cooler and deeper waters to survive, sending food webs and important commercial fisheries into disarray. And the frequency of marine heat waves has increased by more than a third . These spikes have led to mass die-offs of plankton and marine mammals.

To make matters worse, the elevated absorption of carbon dioxide by the ocean leads to its gradual acidification , which alters the fundamental chemical makeup of the water and threatens marine life that has evolved to live in a narrow pH band. Animals like corals, oysters, and mussels will likely feel these effects first, as acidification disrupts the calcification process required to build their shells.

Ecosystem stressors

Land-based ecosystems—from old-growth forests to savannahs to tropical rainforests—are faring no better. Climate change is likely to increase outbreaks of pests, invasive species, and pathogen infections in forests. It’s changing the kinds of vegetation that can thrive in a given region and disrupting the life cycles of wildlife, all of which is changing the composition of ecosystems and making them less resilient to stressors. While ecosystems have the capacity to adapt, many are reaching the hard limits of that natural capacity . More repercussions will follow as temperatures rise.

Climate change appears to be triggering a series of cascading ecological changes that we can neither fully predict nor, once they have enough momentum, fully stop. This ecosystem destabilization may be most apparent when it comes to keystone species that have an outsize- role in holding up an ecosystem’s structure.

Coffee plants destroyed by frost due to extremely low temperatures near Caconde in the São Paulo state of Brazil, August 25, 2021

Jonne Roriz/Bloomberg via Getty Images

Less predictable growing seasons

In a warming world, farming crops is more unpredictable—and livestock, which are sensitive to extreme weather, become harder to raise. Climate change shifts precipitation patterns, causing unpredictable floods and longer-lasting droughts. More frequent and severe hurricanes can devastate an entire season’s worth of crops. Meanwhile, the dynamics of pests, pathogens, and invasive species—all of which are costly for farmers to manage—are also expected to become harder to predict. This is bad news, given that most of the world’s farms are small and family-run. One bad drought or flood could decimate an entire season’s crop or herd. For example, in June 2022, a triple-digit heat wave in Kansas wiped out thousands of cows. While the regenerative agriculture movement is empowering rural communities to make their lands more resilient to climate change, unfortunately, not all communities can equitably access the support services that can help them embrace these more sustainable farming tactics.

Reduced soil health

Healthy soil has good moisture and mineral content and is teeming with bugs, bacteria, fungi, and microbes that in turn contribute to healthy crops. But climate change, particularly extreme heat and changes in precipitation, can degrade soil quality. These impacts are exacerbated in areas where industrial, chemical-dependent monoculture farming has made soil and crops less able to withstand environmental changes.

Food shortages

Ultimately, impacts to our agricultural systems pose a direct threat to the global food supply. And food shortages and price hikes driven by climate change will not affect everyone equally: Wealthier people will continue to have more options for accessing food, while potentially billions of others will be plummeted into food insecurity—adding to the billions that already have moderate or severe difficulty getting enough to eat.

The poison dart frog’s survival is currently threatened by habitat loss and climate change.

Chris Mattison/Minden Pictures

It’s about far more than just the polar bears: Half of all animal species in the world’s most biodiverse places, like the Amazon rainforest and the Galapagos Islands, are at risk of extinction from climate change. And climate change is threatening species that are already suffering from the biodiversity crisis, which is driven primarily by changes in land and ocean use (like converting wild places to farmland) and direct exploitation of species (like overfishing and wildlife trade). With species already in rough shape—more than 500,000 species have insufficient habitat for long-term survival—unchecked climate change is poised to push millions over the edge.

Climate change rapidly and fundamentally alters (or in some cases, destroys) the habitat that wildlife have incrementally adapted to over millennia. This is especially harmful for species’ habitats that are currently under threat from other causes. Ice-dependent mammals like walruses and penguins, for example, won’t fare well as ice sheets shrink. Rapid shifts in ocean temperatures stress the algae that nourishes coral reefs, causing reefs to starve—an increasingly common phenomenon known as coral bleaching . Disappearing wetlands in the Midwest’s Prairie Pothole Region means the loss of watering holes and breeding grounds for millions of migratory birds. (Many species are now struggling to survive, as more than 85 percent of wetlands have been lost since 1700). And sea level rise will inundate or erode away many coastal habitats, where hundreds of species of birds, invertebrates, and other marine species live.

Many species’ behaviors—mating, feeding, migration—are closely tied to subtle seasonal shifts, as in temperature , precipitation level, and foliage. In some cases, changes to the environment are happening quicker than species are able to adapt. When the types and quantity of plant life change across a region, or when certain species bloom or hatch earlier or later than in the past, it impacts food and water supplies and reverberates up food chains.

Wildfire smoke–filled air in Multnomah County, Oregon, September 16, 2020

Motoya Nakamura/Multnomah County Communications, CC BY NC-ND 4.0

Ultimately, the way climate change impacts weather, the environment, animals, and agriculture affects humanity as well. But there’s more. Around the world, our ways of life—from how we get our food to the industries around which our economies are based—have all developed in the context of relatively stable climates. As global warming shakes this foundation, it promises to alter the very fabric of society. At worst, this could lead to widespread famine, disease, war, displacement , injury, and death. For many around the world, this grim forecast is already their reality. In this way, climate change poses an existential threat to all human life.

Human health

Climate change worsens air quality . It increases exposure to hazardous wildfire smoke and ozone smog triggered by warmer conditions, both of which harm our health, particularly for those with pre-existing illnesses like asthma or heart disease.

Insect-borne diseases like malaria and Zika become more prevalent in a warming world as their carriers are able to exist in more regions or thrive for longer seasons. In the past 30 years, the incidence of Lyme disease from ticks has nearly doubled in the United States, according to the U.S. Environmental Protection Agency (EPA). Thousands of people face injury, illness , and death every year from more frequent or more intense extreme weather events. At a 2-degree Celsius rise in global average temperature, an estimated one billion people will face heat stress risk. In the summer of 2022 alone, thousands died in record-shattering heat waves across Europe. Weeks later, dozens were killed by record-breaking urban flooding in the United States and South Korea—and more than 1,500 people perished in the flooding in Pakistan , where resulting stagnant water and unsanitary conditions threaten even more.

The effects of climate change—and the looming threat of what’s yet to come—take a significant toll on mental health too. One 2021 study on climate anxiety, published in the journal Nature , surveyed 10,000 young people from 10 different countries. Forty-five percent of respondents said that their feelings about climate change, varying from anxiety to powerlessness to anger, impacted their daily lives.

A patient with dengue fever, a mosquito-borne disease, in Karachi, Pakistan, where the spread of diseases worsened due to flooding, September 2022

Fareed Khan/AP Photo

Worsening inequity

The climate crisis exacerbates existing inequities. Though wealthy nations, such as the United States, have emitted the lion’s share of historical greenhouse gas emissions, it’s developing countries that may lack the resources to adapt and will now bear the brunt of the climate crisis. In some cases, low-lying island nations—like many in the Pacific —may cease to exist before developed economies make meaningful reductions to their carbon emissions.

Even within wealthier nations, disparities will continue to grow between those rich enough to shield themselves from the realities of climate change and those who cannot. Those with ample resources will not be displaced from their homes by wars over food or water—at least not right away. They will have homes with cool air during heat waves and be able to easily evacuate when a hurricane is headed their way. They will be able to buy increasingly expensive food and access treatment for respiratory illness caused by wildfire smoke. Billions of others can’t—and are paying the highest price for climate pollution they did not produce.

Hurricane Katrina, for example, displaced more than one million people around the Gulf Coast. But in New Orleans , where redlining practices promoted racial and economic segregation, the city’s more affluent areas tended to be located on higher ground—and those residents were able to return and rebuild much faster than others.

Displacement

Climate change will drive displacement due to impacts like food and water scarcities, sea level rise, and economic instability. It’s already happening. The United Nations Global Compact on Refugees recognizes that “climate, environmental degradation and disasters increasingly interact with the drivers of refugee movements.” Again, communities with the fewest resources—including those facing political instability and poverty—will feel the effects first and most devastatingly.

A flood-damaged home in Queens, New York, December 1, 2021

K.C. Wilsey/FEMA

Economic impacts

According to the 2018 National Climate Assessment, unless action is taken, climate change will cost the U.S. economy as much as $500 billion per year by the end of the century. And that doesn’t even include its enormous impacts on human health . Entire local industries—from commercial fishing to tourism to husbandry—are at risk of collapsing, along with the economic support they provide.

Recovering from the destruction wrought by extreme weather like hurricanes, flash floods, and wildfires is also getting more expensive every year. In 2021, the price tag of weather disasters in the United States totaled $145 billion —the third-costliest year on record, including a number of billion-dollar weather events.

The first wave of impacts can already be felt in our communities and seen on the nightly news. The World Health Organization says that in the near future, between 2030 and 2050, climate change is expected to cause an additional 250,000 deaths per year from things like malnutrition, insect-borne diseases, and heat stress. And the World Bank estimates that climate change could displace more than 140 million people within their home countries in sub-Saharan Africa, South Asia, and Latin America by 2050.

But the degree to which the climate crisis upends our lives depends on whether global leaders decide to chart a different course. If we fail to curb greenhouse gas emissions, scientists predict a catastrophic 4.3 degrees Celsius , (or around 8 degrees Fahrenheit) of warming by the end of the century. What would a world that warm look like? Wars over water. Crowded hospitals to contend with spreading disease. Collapsed fisheries. Dead coral reefs. Even more lethal heat waves. These are just some of the impacts predicted by climate scientists .

Solar panel installation at a floating photovoltaic plant on a lake in Haltern am See, Germany, April 2022

Martin Meissner/AP Photo

Climate mitigation, or our ability to reverse climate change and undo its widespread effects, hinges on the successful enactment of policies that yield deep cuts to carbon pollution, end our dependence on dangerous fossil fuels and the deadly air pollution they generate, and prioritize the people and ecosystems on the frontlines. And these actions must be taken quickly in order to ensure a healthier present day and future. In one of its latest reports, the IPCC presented its most optimistic emissions scenario, in which the world only briefly surpasses 1.5 degrees of warming but sequestration measures cause it to dip back below by 2100. Climate adaptation , a term that refers to coping with climate impacts, is no longer optional ; it’s necessary, particularly for the world’s most vulnerable populations.

By following the urgent warnings of the IPCC and limiting warming, we may be able to avoid passing some of the critical thresholds that, once crossed, can lead to potentially irreversible, catastrophic impacts for the planet, including more warming. These thresholds are known as climate tipping points and refer to when a natural system "tips" into an entirely different state. One example would be Arctic permafrost, which stores carbon like a freezer: As the permafrost melts from warming temperatures, it releases carbon dioxide into the atmosphere.

Importantly, climate action is not a binary pass-fail test. Every fraction of a degree of warming that we prevent will reduce human suffering and death, and keep more of the planet’s natural systems intact. The good news is that a wide range of solutions exist to sharply reduce emissions, slow the pace of warming, and protect communities on the frontlines of climate impacts. Climate leaders the world over—those on major political stages as well as grassroots community activists—are offering up alternative models to systems that prioritize polluters over people. Many of these solutions are rooted in ancestral and Indigenous understandings of the natural world and have existed for millennia. Some solutions require major investments into clean, renewable energy and sustainable technologies. To be successful, climate solutions must also address intersecting crises—like poverty, racism, and gender inequality —that compound and drive the causes and impacts of the climate crisis. A combination of human ingenuity and immense political will can help us get there.

This NRDC.org story is available for online republication by news media outlets or nonprofits under these conditions: The writer(s) must be credited with a byline; you must note prominently that the story was originally published by NRDC.org and link to the original; the story cannot be edited (beyond simple things such as grammar); you can’t resell the story in any form or grant republishing rights to other outlets; you can’t republish our material wholesale or automatically—you need to select stories individually; you can’t republish the photos or graphics on our site without specific permission; you should drop us a note to let us know when you’ve used one of our stories.

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Impact of climate change on the economy of South Africa

Published on

What could potentially happen to the state of the South African economy due to the negative impacts of climate change and the sectors that will be hardest hit.

The impact of climate change on the economy of South Africa could cause severe damage to major sectors, affecting the gross domestic product of the country negatively. This would be due to losses attributed to climate change and its implications.

The concerning issue around climate change is that the impacts on the economy are most likely to only present themselves over time. After all, damage to the environment is a process that moves at a rate that is not easy to pick up on until it is too late, despite the signs being there all along.

One of the sectors known to be impacted severely by climate change is the agricultural sector. Agriculture is driven by the state of the environment, particularly when it comes to the farming industry. When the environment does not present suitable conditions for agriculture to thrive, the farming industry will suffer economically.

The tourism sector is also set to experience losses, as the impacts on the environment’s biodiversity and habitats will result in losses of these environmental essentials, which will no longer attract tourists into the country – a country known for its rich lands and natural habitats.

The country’s ecosystem will suffer due to changes in its function and the changes in temperature brought about by the unpredictable changes to the climate. Human health can also become susceptible to the impacts of the climate. As certain environmental factors start to change, these can start to affect our bodies, making us more prone to sicknesses.

The environment is therefore a key player and driver of the South African economy. It is important that it is well looked after in order to avoid the devastating impacts of climate change on the economy. Major sectors which bring in much-needed revenue are the ones that will suffer most.

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A pedestrian walks near the branch of Federal Reserve Bank of San Francisco, in Salt Lake City, Utah, U.S., April 7, 2023. REUTERS/Ann Saphir/ File Photo

SAN FRANCISCO (Reuters) - An increase in the number of hot days as climate change warms the globe would likely damage the U.S. economy over the long-term, according to research published on Tuesday by the Federal Reserve Bank of San Francisco.

"Our findings suggest that, under a scenario with no large-scale efforts to reduce carbon emissions, future increases in extreme heat would reduce the capital stock by 5.4% and annual consumption by 1.8% by the year 2200," wrote Stephie Fried, a senior economist at the San Francisco Fed, and co-authors Gregory Casey and Matthew Gibson, both professors at Williams College.

The researchers used scientists' best estimates for the number of days per year where working outdoors would cause heat stress, estimated to rise from 22 days in 2020 to 80 in 2100.

They then projected the likely drain on labor productivity in construction, where - unlike most of the services and manufacturing sectors - air-conditioning cannot counter the impact of hot days.

They focused on construction because it makes up a larger share of overall economic output and U.S. investment than other sectors like agriculture or mining where workers are also vulnerable to heat.

"Decreases in construction productivity slow capital accumulation and therefore have long-lasting effects on macroeconomic outcomes," they wrote.

Using a less-likely alternative scenario under which the number of extreme-heat days rises to 125 in 2100, the authors found much larger consequences from a decline in construction productivity, with capital accumulation projected to fall by 18% and consumption by 7% in 2200.

(Reporting by Ann Saphir; Editing by Andrea Ricci)

Copyright 2024 Thomson Reuters .

Tags: environment , United States , pollution

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Extreme events pose risk to global economy

According to a World Economic Forum study, related risks will increase in the coming years. A specialist from Brasil's Ministry of Finance suggests ways to mitigate the effects of the climate crisis and avoid economic losses.

Extreme weather events are becoming an increasingly significant threat to the global economy. Data from the World Economic Forum's (WEF) Global Risks Report 2024 show that the effects of climate change are one of the most pressing concerns for the next two years and could worsen over the decade, causing severe economic losses and affecting the growth of countries. The risks are particularly high for developing ones, more vulnerable to climate disasters.

The study also reveals that frequent storms, floods, and droughts can destroy infrastructure, impact financial stability, and disrupt supply chains by raising agricultural production costs and intensifying global inequality. The way forward is a concerted worldwide effort to mitigate these risks.

According to Cristina Reis, Undersecretary for Sustainable Economic Development at the Brazilian Ministry of Finance's Secretariat for Economic Policy, the global effort to face this scenario entails "preparing countries to avoid these climate risks, promoting economic development paths that are environmentally sustainable and socially just".

Reis emphasizes the importance of replacing the most polluting economic activities with those that can preserve, conserve, protect, and regenerate ecosystems. This proposal is consistent with the solutions presented in the Forum's study. She says the challenge is to provide mechanisms for ecological transformation capable of reacting to climate disasters and "providing the most efficient, swift, and humane assistance to the victims and the affected territories". According to the expert, the measures are central to the Ecological Transformation Plan being implemented in Brasil.

International cooperation

According to the Forum's analysis, global cooperation is under pressure, which may hinder the effectiveness of climate-related measures, mainly investments in resilient infrastructure, mitigation, and adaptation policies. Because the causes of extreme weather events are numerous, economies must plan for an uncertain future and invest in coordinated actions to avoid catastrophic consequences for the planet. 

Reis points out that collaboration between countries can spark a chain reaction of crisis-related actions. "In addition to participating in international funds, it is possible to use multilateral development banks to mobilize resources and guarantee the application of the amounts allocated to countries, both for projects with sovereign guarantees and those without guarantees," she stated. 

Advantages for developing countries

During the first half of 2024, at least half of the G20 member countries experienced extreme weather events such as floods, above-average rainfall, wildfires, high temperatures, or severe cold spells. India, Germany, France, Brasil, Indonesia, and the United States were among the countries that experienced climate emergencies. 

Developing economies have faced even more devastating impacts, but the prospect for these nations to cope with the challenges is encouraging. "Despite financial and technological constraints, most developing countries possess competitive advantages for a sustainable economy. Their vast biodiversity and clean energy matrix are valuable assets in promoting the ecological transformation we seek," said the economist. 

Actions to strengthen these countries’ capacities to deal with the crisis must be proportional to the economic damage and human lives. "We must strengthen local capacities and develop economic, environmental, climate and social policies for this purpose, engaging the productive sector, opening, expanding and strengthening markets that bring new economic, business and employment opportunities. In addition, countries can use these advantages for sustainability, to attract foreign investment, and develop domestic investment," said Reis.

Role of the Group of 20

The Undersecretary of the Ministry of Finance believes that it is critical to prioritize dialog. In this regard, the G20, an economic cooperation forum that brings together the world's largest economies, plays an important role in coordinating efforts among member countries to mitigate climate impacts. "The (G20) countries are diverse and unequal in economic, technological, environmental, and social terms. They bring a myriad of perspectives and pluralities that need to be respected and, at the same time, preserved," she points out. 

Cristiana Reis concludes that it is fundamental that the forum address measures to restructure the international economic agenda and seek to develop financial instruments for nature-based solutions. 

"These can be achieved, for example, through the expansion of initiatives that reward countries for conserving forests and ecosystems, such as biodiversity credit, green bonds, or debt for nature, a reduction in public debt with a conservation counterpart called SWAP (swapping the rate or profitability of financial assets), Debt for Nature. The reform of international climate funds to facilitate operations with developing countries also plays a significant part, setting the goal of issuing sustainable sovereign bonds, offering the possibility of contracting debts in local currencies, promoting exchange risk protection mechanisms, and so on," said Reis.

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Long-Term Macroeconomic Effects of Climate Change: A Cross-Country Analysis

We study the long-term impact of climate change on economic activity across countries, using a stochastic growth model where labour productivity is affected by country-specific climate variables—defined as deviations of temperature and precipitation from their historical norms. Using a panel data set of 174 countries over the years 1960 to 2014, we find that per-capita real output growth is adversely affected by persistent changes in the temperature above or below its historical norm, but we do not obtain any statistically significant effects for changes in precipitation. Our counterfactual analysis suggests that a persistent increase in average global temperature by 0.04°C per year, in the absence of mitigation policies, reduces world real GDP per capita by 7.22 percent by 2100. On the other hand, abiding by the Paris Agreement, thereby limiting the temperature increase to 0.01°C per annum, reduces the loss substantially to 1.07 percent. These effects vary significantly across countries. We also provide supplementary evidence using data on a sample of 48 U.S. states between 1963 and 2016, and show that climate change has a long-lasting adverse impact on real output in various states and economic sectors, and on labor productivity and employment.

We are grateful to Zeina Hasna, Ron Smith and participants at the International Monetary Fund (IMF), Bank of Lithuania, Bank of Canada, EPRG, Cambridge Judge Business School, the ERF 24th Annual Conference, and the 2018 MIT CEEPR Research Workshop for comments and suggestions. We also thank Matthew Norris for help with constructing the global climate dataset. We gratefully acknowledge financial support from the Keynes Fund. Part of this work was done while Jui-Chung Yang was a Postdoctoral Research Fellow at the USC Dornsife INET. The views expressed in this paper are those of the authors and do not necessarily represent those of the IMF or its policy, not those of the National Bureau of Economic Research.

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Climate change and its effects on local ecosystems, the importance of financial literacy for college students, evolution of online learning post-pandemic, the role of artificial intelligence in modern education.

The essay example below is crafted to help you with your academic assignments and needs. Whether you’re looking for inspiration, seeking to gather ideas, or wanting to understand the specific structure required for this type of writing, this sample is designed to guide you. Use it as a resource to improve your writing skills and better understand how to approach various essay formats.

Climate change is having profound effects on local ecosystems, altering the balance of nature and threatening biodiversity. Rising temperatures, changing precipitation patterns, and extreme weather events are some of the consequences impacting ecosystems. For instance, in coastal areas, rising sea levels are eroding habitats and endangering species like sea turtles and shorebirds.

Local ecosystems, such as forests, are also experiencing changes. Increased temperatures and prolonged droughts make forests more susceptible to wildfires, affecting plant and animal life. Additionally, changes in temperature and rainfall patterns disrupt the natural cycles of plant blooming and animal migration.

Communities are responding to these challenges through conservation efforts and sustainable practices. Restoration projects, such as reforestation and wetland restoration, aim to rebuild and protect habitats. Additionally, initiatives to reduce carbon footprints, like promoting renewable energy and sustainable agriculture, are essential in mitigating the effects of climate change.

In conclusion, climate change poses a significant threat to local ecosystems. Communities can take action to protect and preserve biodiversity by understanding and addressing these impacts. Sustainable practices and conservation efforts are crucial in safeguarding our environment for future generations.

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The Role of Renewable Energy in Combating Climate Change

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Wildfire smoke impacts more than our health — it also costs workers over $100B a year. Here's why.

By Irina Ivanova

Edited By Alain Sherter

Updated on: June 8, 2023 / 10:31 AM EDT / MoneyWatch

With the smoke from burning Canadian forests  enveloping the U.S. Northeast, major cities fell silent this week. Public schools canceled outdoor activities , companies sent workers home , performances were postponed, libraries shut their doors and professional baseball games were canceled.

Such disruptions in ordinary urban life illustrates the wide-ranging economic toll of climate change, which experts say is making wildfires more intense and contributing to air pollution.

"It's gray and the sun looked orange in the sky this morning, like Star Wars or something," Paul Billings, national vice president for public policy at the American Lung Association, told CBS MoneyWatch from Washington, D.C.

"It's really early in the season, we're still in the spring, and we're seeing these wildfires in Canada and the U.S. that are impacting air quality across the eastern United States. In New England, across the mid-Atlantic and into Minnesota, we're seeing elevated levels of particulate matter or soot," he added.

These tiny particles are especially dangerous for people with heart disease, asthma or chronic obstructive pulmonary disease (COPD), but they carry risks for everyone, including risks of asthma attacks, heart attack, stroke or early death.

"Some people need to take their medication more — others end up in the emergency room," Billings said.

• Map satellite images show Canadian wildfire smoke moving across the Northeast
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Because the kind of particles found iin smoke are so small, they get past the body's natural defenses, such as mucus membranes in the nose and throat as well as the body's coughing mechanism. 

"They penetrate deep in the lungs and where you have oxygen exchange systems," Billings said. "These particles actually get into your blood and create a wide range of poor health outcomes, including stroke, heart attacks and different kinds of cancer."

Forest fires aren't the only source of particulate matter — diesel trucks and coal-fired power have historically contributed the lion's share of air pollution. But wildfires are a growing factor. The increased frequency of wildfires in a hotter, drier climate has reversed some of the improvements in air quality since the 1970 Clean Air Act, the American Lung Association noted in an April report.

"Staggering" costs

The earth's warming climate is contributing to the problem, with temperatures in Canada unseasonably high this year. Lytton, British Columbia — typically a temperate town — hit a record high of 121 degrees last week, tying California's Death Valley. Hot, dry weather makes it more likely that a forest will catch fire and burn longer. Already, Canada's wildfire season is on track to be the most destructive in the country's history.

Globally, air pollution kills more than 3 million people a year, according to the World Health Association. In dollar terms, the costs are vast and reflected in increased hospitalizations, missed work and school days, and lower worker productivity. 

"The costs are staggering," Billings said

Air pollution adds $2,500 a year to a typical American's medical bills, a recent study from the Natural Resources Defense Council found. Across the U.S., smoke, factory output and car exhaust cost the economy $800 billion a year, or about 3% of the nation's total economic output, the NRDC found.

Perhaps unsurprisingly, high levels of air pollution also reduce earnings by making it harder and more unpleasant to work, adding a significant drag on the economy. Outdoor workers, such as delivery people, and landscapers and teachers are most affected , but office workers aren't necessarily safe. Even indoor air pollution spikes to three or four times safe levels during a wildfire event, studies have found .

$125 billion in lost pay

Researchers at Stanford who mapped wildfire plumes across the U.S. found that a single day of smoke exposure lowers a person's quarterly earnings by 0.1%, according to a recent working paper published by the National Bureau of Economic Research . Across the U.S. as a whole, workers lost $125 billion a year due to wildfire smoke, the paper found — about 2% of all labor income.  

Aside from smoke, hotter air also increases production of ozone , a major component of smog and a lung irritant. "Some researchers have likened it to sunburn on the lungs — your cells get irritated and weep," Billings said.

As with other kinds of pollution, the effects of ozone, smog and smoke aren't evenly distributed, with low-income people and people of color more likely to be exposed, according to the ALA. 

Businesses and governments can take some steps, like improving indoor filtration, not forcing workers to go outside and alerting issuing public service alerts about air quality. But reducing the toll of air pollution long-term means widespread electrification, Billings said. That would reduce emissions from transportation and factories.

"I think too often, people look at these as anomalous weather events," he said. "This is not some happenstance of a fire. It's early June. There have always been fires, but the big driver that is creating these hot, dry conditions that are creating the opportunities for these fires is climate change."

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Oil ticked higher on Friday, finding support from OPEC+ members Saudi Arabia and Russia indicating readiness to pause or reverse oil output increases, but crude was still headed for its third straight weekly loss on demand concerns.