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Economics Department

Guide to reading economics papers, jeffrey parker, reed college.

In your Reed economics courses, you will often be asked to read papers from professional journals that were written for an audience of Ph.D. economists. Some of these will be fairly easy to understand; others will be rather opaque. The outline below suggests a method of extracting what I think are the essential (for class purposes) elements from papers.

• Who are the authors? Do they have an established point of view or position in a controversial debate that they would want to defend? If so, you should recognize this when interpreting the results of the paper. This may be difficult for you to know without an extensive knowledge of the literature and is often not an important issue, but when it is important it can be very important.
• What question is the paper addressing? This should be obvious from the title, abstract, and/or introduction. Identifying the target question as you begin reading should help you put the component parts of the paper into context.
• Is it a theoretical paper that deduces a conclusion from a set of theoretical assumptions? The internal logic of most theoretical papers can usually be assumed to be validated by the journal's referees and editors. The central questions about the paper may then revolve around its applicability: Are the model's assumptions plausible for the question being addressed? It is important to recognize that no economic model ever has assumptions that are strictly true. A "good" model captures some essential core characteristics of the world but ignores less important details. For you, the essential question is: Is this a good model for this question?
• Is it an empirical study that attempts to assess whether the evidence is consistent with a particular hypothesis or to estimate an important economic parameter? Empirical papers are open to question on myriad grounds because there are many valid ways to test most hypotheses. This is why there are many empirical papers addressing any important question. To understand an empirical paper, focus on its essential parts: the logic of the test, the data used, which are the dependent and which the independent variables, and the econometric methods used to estimate the model. As a student, it is most important to understand the logic of the test: What does it mean for a key estimated parameter to be positive or negative? How does the analysis distinguish between a state of the world in which the central hypothesis is true and a state where it is false? Are the estimates of the key parameters statistically significant? Economically significant?
• Is it a case study that looks in detail at one or more examples of the phenomenon under study? Is the case chosen typical of other important applications? Does the author interpret the case as an illustration of a particular theory? Are there other theories that might explain the case equally well or better?
• Is it a " meta-study " that surveys other papers addressing the question? These studies often have voluminous bibliographies that are more useful as reference tools than as objects for detailed study. Which of the studies being discussed are the most central or seminal? On what results do the central studies agree and where do they disagree? Does the preponderance of evidence support one conclusion or another?
• What are the paper's results and conclusions? How strongly are the conclusions supported by the results of the analysis? Do the authors present any "robustness checks" or "sensitivity analysis" that suggests how sensitive the conclusion is to assumptions, data, and methods used?
• How does the paper fit into the broader literature? Does this paper contradict others that find different answers to the same question? If so, what is the source of the difference in outcomes? Which, if either, paper seems to have established its case more strongly? Can we tentatively form a conclusion or are we left with considerable uncertainty about the answer to the question?

These elements can usually be extracted from a careful reading of specific sections of the paper without understanding all the details of other sections. Introduction, model specification, data, results, interpretation, and conclusion sections often contain the essential elements. As you read more economics papers, you will get better at extracting these elements.

If you are asked to summarize a paper for class, it is critical that you understand the paper's essential elements. Depending on the paper, there may be other details that are important as well, but most papers can be summarized concisely in a page (or in a five-minute presentation) by writing or talking about the answers to the questions listed above.

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Economics 272

Japan's modern economy, washington and lee university, how to read a paper.

Most of the paper I’ll assign this term are in a standard format. Once you learn that format, you’ll be able to “read” a paper quickly (indeed, potentially more quickly than me because you’ll learn how to skip technical material and so so, whereas sometimes I’ll go through those details).

• Read the conclusion and then the introduction (doing it the other way around is OK, too). Sometimes they’ll be the same, wasting space and the reader’s energy (hey, in a short paper do you really need to repeat things?). The better written the paper, the easier it is to skim.
• Next — and I will take you through the process — scan the tables. This won’t always be helpful — the variable names may be computer-generated gibberish, which I find inexcusable in the days of ready editing. And it may not be clear what the tables are trying to convey — the title may be clear, but you won’t know which variables are central, and which are there as controls.
• Then … there will be a literature survey, something that will place the paper in context. Sometimes it will be terse, assuming you’re generally familiar with the field, and hence not very helpful. But try…
• Then comes stuff on methodology — what is the model, and perhaps details of what they’ll do in terms of statistical tests. If the paper is well-written, you can still read the first and last paragraphs, and (sometimes) the first and last paragraphs of sub-sections.
• You will find a description of data, typically with a table of sample means and so on. Always worth scanning.
• The penultimate section (or two ) will be the results. You really ought to skim. When there are two sections, one will be the main results, the other will consist of robustness checks.

The goal? — reading a paper in 5 minutes. Hard to achieve, but you may be able to approach that for your term paper, when you’re reading [scanning!] multiple papers on the same topic.

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• 01 Apr 2024
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Navigating the Mood of Customers Weary of Price Hikes

Price increases might be tempering after historic surges, but companies continue to wrestle with pinched consumers. Alexander MacKay, Chiara Farronato, and Emily Williams make sense of the economic whiplash of inflation and offer insights for business leaders trying to find equilibrium.

• 29 Jan 2024
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Do Disasters Rally Support for Climate Action? It's Complicated.

Reactions to devastating wildfires in the Amazon show the contrasting realities for people living in areas vulnerable to climate change. Research by Paula Rettl illustrates the political ramifications that arise as people weigh the economic tradeoffs of natural disasters.

• 10 Jan 2024

Technology and COVID Upended Tipping Norms. Will Consumers Keep Paying?

When COVID pushed service-based businesses to the brink, tipping became a way for customers to show their appreciation. Now that the pandemic is over, new technologies have enabled companies to maintain and expand the use of digital payment nudges, says Jill Avery.

• 17 Aug 2023

‘Not a Bunch of Weirdos’: Why Mainstream Investors Buy Crypto

Bitcoin might seem like the preferred tender of conspiracy theorists and criminals, but everyday investors are increasingly embracing crypto. A study of 59 million consumers by Marco Di Maggio and colleagues paints a shockingly ordinary picture of today's cryptocurrency buyer. What do they stand to gain?

• 15 Aug 2023

Why Giving to Others Makes Us Happy

Giving to others is also good for the giver. A research paper by Ashley Whillans and colleagues identifies three circumstances in which spending money on other people can boost happiness.

• 13 Mar 2023

What Would It Take to Unlock Microfinance's Full Potential?

Microfinance has been seen as a vehicle for economic mobility in developing countries, but the results have been mixed. Research by Natalia Rigol and Ben Roth probes how different lending approaches might serve entrepreneurs better.

• 23 Jan 2023

After High-Profile Failures, Can Investors Still Trust Credit Ratings?

Rating agencies, such as Standard & Poor’s and Moody's, have been criticized for not warning investors of risks that led to major financial catastrophes. But an analysis of thousands of ratings by Anywhere Sikochi and colleagues suggests that agencies have learned from past mistakes.

• 29 Nov 2022

How Much More Would Holiday Shoppers Pay to Wear Something Rare?

Economic worries will make pricing strategy even more critical this holiday season. Research by Chiara Farronato reveals the value that hip consumers see in hard-to-find products. Are companies simply making too many goods?

• 21 Nov 2022

Buy Now, Pay Later: How Retail's Hot Feature Hurts Low-Income Shoppers

More consumers may opt to "buy now, pay later" this holiday season, but what happens if they can't make that last payment? Research by Marco Di Maggio and Emily Williams highlights the risks of these financing services, especially for lower-income shoppers.

• 01 Sep 2022
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Is It Time to Consider Lifting Tariffs on Chinese Imports?

Many of the tariffs levied by the Trump administration on Chinese goods remain in place. James Heskett weighs whether the US should prioritize renegotiating trade agreements with China, and what it would take to move on from the trade war. Open for comment; 0 Comments.

• 05 Jul 2022

Have We Seen the Peak of Just-in-Time Inventory Management?

Toyota and other companies have harnessed just-in-time inventory management to cut logistics costs and boost service. That is, until COVID-19 roiled global supply chains. Will we ever get back to the days of tighter inventory control? asks James Heskett. Open for comment; 0 Comments.

• 09 Mar 2022

War in Ukraine: Soaring Gas Prices and the Return of Stagflation?

With nothing left to lose, Russia's invasion of Ukraine will likely intensify, roiling energy markets further and raising questions about the future of globalization, says Rawi Abdelal. Open for comment; 0 Comments.

• 10 Feb 2022

Why Are Prices So High Right Now—and Will They Ever Return to Normal?

And when will sold-out products return to store shelves? The answers aren't so straightforward. Research by Alberto Cavallo probes the complex interplay of product shortages, prices, and inflation. Open for comment; 0 Comments.

• 11 Jan 2022
• Cold Call Podcast

Can Entrepreneurs and Governments Team Up to Solve Big Problems?

In 2017, Shield AI’s quadcopter, with no pilot and no flight plan, could clear a building and outpace human warfighters by almost five minutes. It was evidence that autonomous robots could help protect civilian and service member lives. But was it also evidence that Shield AI—a startup barely two years past founding—could ask their newest potential customer, the US government, for a large contract for a system of coordinated, exploring robots? Or would it scare them away? Harvard Business School professor Mitch Weiss and Brandon Tseng, Shield AI’s CGO and co-founder, discuss these and other challenges entrepreneurs face when working with the public sector, and how investing in new ideas can enable entrepreneurs and governments to join forces and solve big problems in the case, “Shield AI.” Open for comment; 0 Comments.

• 06 May 2021

How Four Women Made Miami More Equitable for Startups

A case study by Rosabeth Moss Kanter examines what it takes to break gender barriers and build thriving businesses in an emerging startup hub. Open for comment; 0 Comments.

• 20 Apr 2021
• Working Paper Summaries

The Emergence of Mafia-like Business Systems in China

This study sheds light on the political pathology of fraudulent, illegal, and corrupt business practices. Features of the Chinese system—including regulatory gaps, a lack of formal means of property protection, and pervasive uncertainty—seem to facilitate the rise of mafia systems.

• 02 Feb 2021

Nonprofits in Good Times and Bad Times

Tax returns from millions of US nonprofits reveal that charities do not expand during bad times, when need is the greatest. Although they are able to smooth the swings of their activities more than for-profit organizations, nonprofits exhibit substantial sensitivity to economic cycles.

• 01 Feb 2021

Has the New Economy Finally Arrived?

Economists have long tied low unemployment to inflation. James Heskett considers whether the US economic policy of the past four years has shaken those assumptions. Open for comment; 0 Comments.

• 06 Jan 2021

Aggregate Advertising Expenditure in the US Economy: What's Up? Is It Real?

We analyze total United States advertising spending from 1960 to 2018. In nominal terms, the elasticity of annual advertising outlays with respect to gross domestic product appears to have increased substantially beginning in the late 1990s, roughly coinciding with the dramatic growth of internet-based advertising.

• 11 Dec 2020

Economic Jitters Push Pandemic Job Seekers to Big Companies, Not Startups

Small companies are receiving fewer applications, particularly from experienced professionals, according to research by Shai Bernstein and colleagues. How can startups overcome pandemic fears and compete for talent? Open for comment; 0 Comments.

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The 20 Most Influential Economic Papers Of All Time

Columbia University Professor Michael Woodford's "Accommodation at the Zero Lower Bound" showed how central bankers' words and targets can be just as powerful as actual actions. 

Fed Chairman Ben Bernanke appears to have acted on Woodford's implicit conclusion that, as our Joe Weisenthal put it , you can use "Verbal Easing" to do  something , while waiting to see if things improve.

Economics papers have had tremendous influence on how decisions are made, how strategies are developed,  and how policy is determined.

Last year, the American Economic Review put together last year a list of its 20-most important papers during its 100-year existence . 

Read on to see that list (in no particular order).

"The Use of Knowledge In Society"

Publication date : 1945

Authors:  Friedrich Hayek

Conclusion : Prices must be the focal point of all economic decisions.

LINK TO PAPER

Source:  AER

"The Cost of Capital, Corporation Finance and the Theory of Investment"

Publication date : 1958

Authors:  Franco Modigliani and Merton Miller

Conclusion : A firm’s total market value is invariant to its borrowing behavior.

"Do Stock Prices Move Too Much to be Justified by Subsequent Changes in Dividends?"

Publication date : 1981

Authors:  Robert Shiller

Conclusion : Stock price volatility is far too great  to be simply attributed to new information about future real dividends.

"An Almost Ideal Demand System"

Publication date : 1980

Authors: Angus S. Deaton and John Muellbauer

Conclusion : You can predict household demand based on the relative costs of "subsistence" versus "bliss".

"Uncertainty and the Welfare Economics of Medical Care"

Publication date : 1963

Authors:  Kenneth J. Arrow

Conclusion : The health care market is too plagued by moral hazard and consumers' lack of information to be viewed as free.

LINK TO PAPER  

Source: AER

"Some International Evidence on Output-Inflation Tradeoffs"

Publication date : 1973

Authors:  Robert E. Lucas, Jr.

Conclusion : You can model what happens when consumers make decisions without knowing whether a  price change also signals a relative price change.

"The Role of Monetary Policy"

Publication date : 1968

Authors:  Milton Friedman

Conclusion : There exists a "natural rate of unemployment," or the number of jobs a given economy can support.

"Monopolistic Competition and Optimum Product Diversity"

Publication date : 1965

Authors:  Avinash K. Dixit and Joseph E. Stiglitz

Conclusion : Product variety can influence the way the market dictates resource allocation. The market  considers profits , while a social opti mum takes into account the consumer's sur plus.

"A Theory of Production"

Publication date : 1928

Authors:  Charles W. Cobb and Paul H. Douglas

Conclusion : You can figure out how much production a given amount of capital and labor can yield.

"Optimal Taxation and Public Production"

Publication date : 1971

Authors:  Peter A. Diamond and James A. Mirrlees

Conclusion : It is possible to set taxes that minimize distortions and disincentives, and eliminate production inefficiencies.

LINK TO PART I  & Part II

"Capital Theory and Investment Behavior"

Authors:  Dale W. Jorgenson

Conclusion : Cost-of-finance (interest rates and equity yields) and taxes should drive investment decisions.

"Production, Information Costs, and Economic Organization"

Publication date : 1972

Authors:  Armen A. Alchian and Harold Demsetz

Conclusion : Firms must continuously measure productivity. The market alone is not enough to generate peak output — strong management is also required, and firm's will benefit from devoting resources to it.

"On The Impossibility Of Informationally Efficient Markets"

Authors:  Sanford Grossman

Conclusion : In a world of dispersed information, the equilibrium price can itself be a source of information to consumers.

"Scale Economies, Product Differentiation and the Pattern of Trade"

Authors:  Paul Krugman

Conclusion : Standard comparative cost theory doesn't sufficiently explain trade.

"Migration, Unemployment and Development"

Publication date : 1970

Authors:  John R. Harris and Michael Todaro

Conclusion : In poor countries, individuals migrate from rural to urban areas.

"The Political Economy of the Rent-Seeking Society"

Publication date : 1974

Authors:  Anne O. Kreuger

Conclusion : Tariffs are better for countries to enact than import quotas.

"Economic Growth and Income Inequality"

Publication date : 1955

Authors:  Simon Kuznets

Conclusion : Earlier phases of economic development are characterized by increasing income inequality.

"National Debt In A Neoclassical Growth Model"

Authors:  Peter A. Diamond

Conclusion : External and internal government debt can potentially reduce capital stock in the long-run.

"A Theory of Optimum Currency Areas"

Publication date : 1961

Authors:  Robert A. Mundell

Conclusion : You can figure out the optimal number of entities for a given currency areas.

"The Economic Theory of Agency: The Principal's Problem"

Authors:  Stephen A. Ross

Conclusion : There is a way to avoid moral hazard when you're paying people a lot of money.

For more influencers...

Click here to learn about 13 of Bloomberg's most influential people in markets > 

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Searchable index of NBER working papers. The full text of working papers from November 1994-present are available online. To obtain copies of earlier papers, see NBER's  Out of Print Working Papers  instructions. NBER also provides a useful set of  data links  on their free web site.

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Bibliographic references to over 100,000 Economics working papers, plus full text for over half of the papers listed.

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Aarge clearinghouse of Economics paper repositories. Some searching is more advanced than others depending on the functionality of the repository in question (you can also search across all the repositories). Provides citation tracking, related articles, etc. 

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Aggregate Demand Externality and Self-Fulfilling Default Cycles

We develop a model of self-fulfilling default cycles with demand externality a la Dixit- Stiglitz to explain the recurrent clustered defaults observed in the data. The literature reports that observable fundamental factors alone are insufficient to explain the cluster. A decline in aggregate output reduces the value of firms and increases their probability of default. As defaults take more firms out of production, aggregate output declines further, creating a positive feedback loop that generates multiple equilibria and self-fulfilling default cycles. Our global analysis using Bogdanov-Takens bifurcation reveals the existence of multiple or even infinite paths that satisfy all equilibrium conditions. Moreover, a family of periodic orbits can emerge in the perfect foresight equilibrium. Our model is consistent with the view that business cycles arise largely because the economy’s internal forces tend to endogenously generate cyclical mechanisms (Beaudry et al., 2020).

Feng Dong acknowledges the financial support from the National Natural Science Foundation of China (72250064, 72122011) and Tsinghua University Initiative Scientific Research Program (2023THZWJC03). Pengfei Wang acknowledges the financial support from the National Natural Science Foundation of China (72150003, 72125007). The views expressed herein are those of the authors and do not necessarily reflect the views of the National Bureau of Economic Research.

I (Pengfei Wang) have read the NBER disclosure policy and attest that the acknowledgements and my supplemental disclosure statement together disclose all sources of funding and all material and relevant financial relationships.

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• Published: 13 March 2024

Global supply chains amplify economic costs of future extreme heat risk

• Yida Sun   ORCID: orcid.org/0000-0001-8160-1358 1   na1 ,
• Shupeng Zhu   ORCID: orcid.org/0000-0002-8446-9329 2 , 3   na1 ,
• Daoping Wang   ORCID: orcid.org/0000-0001-5221-4965 4 , 5   na1 ,
• Jianping Duan 6 ,
• Hui Lu   ORCID: orcid.org/0000-0003-1640-239X 1 , 7 ,
• Hao Yin 8 ,
• Chang Tan 1 ,
• Lingrui Zhang 9 ,
• Mengzhen Zhao 10 ,
• Wenjia Cai   ORCID: orcid.org/0000-0002-4436-512X 1 ,
• Yong Wang   ORCID: orcid.org/0000-0002-5831-9466 1 ,
• Yixin Hu   ORCID: orcid.org/0000-0002-3986-9059 11 ,
• Shu Tao   ORCID: orcid.org/0000-0002-7374-7063 12 &
• Dabo Guan   ORCID: orcid.org/0000-0003-3773-3403 1 , 13  

Nature volume  627 ,  pages 797–804 ( 2024 ) Cite this article

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Evidence shows a continuing increase in the frequency and severity of global heatwaves 1 , 2 , raising concerns about the future impacts of climate change and the associated socioeconomic costs 3 , 4 . Here we develop a disaster footprint analytical framework by integrating climate, epidemiological and hybrid input–output and computable general equilibrium global trade models to estimate the midcentury socioeconomic impacts of heat stress. We consider health costs related to heat exposure, the value of heat-induced labour productivity loss and indirect losses due to economic disruptions cascading through supply chains. Here we show that the global annual incremental gross domestic product loss increases exponentially from 0.03 ± 0.01 (SSP 245)–0.05 ± 0.03 (SSP 585) percentage points during 2030–2040 to 0.05 ± 0.01–0.15 ± 0.04 percentage points during 2050–2060. By 2060, the expected global economic losses reach a total of 0.6–4.6% with losses attributed to health loss (37–45%), labour productivity loss (18–37%) and indirect loss (12–43%) under different shared socioeconomic pathways. Small- and medium-sized developing countries suffer disproportionately from higher health loss in South-Central Africa (2.1 to 4.0 times above global average) and labour productivity loss in West Africa and Southeast Asia (2.0–3.3 times above global average). The supply-chain disruption effects are much more widespread with strong hit to those manufacturing-heavy countries such as China and the USA, leading to soaring economic losses of 2.7 ± 0.7% and 1.8 ± 0.5%, respectively.

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Research has been showing a trend in rising temperature and increasing occurrence of extreme heatwaves since the 1950s 1 , 2 . This continuous pattern raises concerns about the potential impacts of climate change and its associated socioeconomic costs. Notable effects of heat stress are on human health and labour productivity. On the one hand, global heat stress makes it difficult for the body to maintain its core temperature, thereby increasing morbidity and mortality from heat stroke 5 , 6 , 7 . Countries across all latitudes, including Russia 8 , the USA 9 , China 5 , Australia 10 and North Africa 11 have suffered from increased heat stress since the deadly European heatwave in 2003 12 , which caused considerable mortality and morbidity. On the other hand, biometeorological studies suggest that heat stress can seriously decrease labour productivity 13 , 14 , 15 , 16 , measured in terms of lost worktime from recommended work/rest ratios during heat stress, reduced work efficiency as estimated from exposure–response functions and self-reported reduced work efficiency 13 , 17 , 18 .

In the context of increasingly integrated global supply chains, the impacts of heat stress are not just confined to specific populations and industrial sectors in low latitudes but extend to wider regions and sectors 19 , 20 , 21 , 22 . For example, a Western European country such as the UK is rarely directly and severely affected by heat stress. However, consumption of beer or coffee in the UK can drop as a result of the severe impact of heat stress on wheat and coffee bean suppliers in Africa and South America 23 . This kind of spillover effect can have important consequences in terms of global food security 24 , 25 , 26 , energy supply 27 and the supply of various mineral products 28 .

The direct mortality and productivity loss resulting from heat stress have been extensively studied. However, the indirect losses due to supply-chain disruptions have not been fully analysed 29 , 30 , as previous literature has either devoted insufficient discussion to the indirect effects by only reporting the total/aggregated effects 31 , 32 , 33 or ignored the amplifying effect of the global trade system on direct losses. As climate change will make the impacts of heat stress worse over time, developing methodologies that allow comprehensive quantifications of both the direct and indirect impacts of heat stress on human systems can help policy-makers to develop more effective climate change mitigation and adaptation policies. In this study, a disaster footprint analytical framework, by integrating climate, epidemiological and hybrid input–output and computable general equilibrium global trade modules, was constructed to provide a comprehensive assessment of the impact of heat stress on socioeconomic systems to 2060, including health loss (excess mortality due to extreme heatwaves), labour productivity loss (decreased daily labour productivity due to higher temperature and humidity) and indirect loss (production stagnation due to lack of supply or demand) across 141 regions and 65 sectors worldwide. Details of our analytical approach are provided in the Methods . In summary, we use the sixth phase of the coupled model intercomparison project phase 6 (CMIP6) 34 , 35 , where 14 widely applied global climate models (GCMs) are averaged to assess future daily temperature and humidity parameters. Grid-scale daily excess mortality (health loss) and labour loss rates (labour productivity loss) are calculated on the basis of empirical functions and statistics from previous studies 36 . On the basis of the above labour constraints in different regions and industries, a hybrid input–output and computable general equilibrium global trade module was developed in the disaster footprint analytical framework to assess the pattern of heat-related economic losses transmitted through the global supply chain. By quantifying indirect effects that were hardly analysed before, this model provides insight into the far-reaching impacts of heat stress across global supply chains and how such impacts evolve spatially and over long time scales. The estimated results are based on static production and trade relationships which may not accurately address the dynamic nexus among industries and countries in the long-term.

This study examines three scenarios combining various representative concentration pathways (RCPs) and shared socioeconomic pathways (SSPs). RCPs represent greenhouse gas concentration trajectories as adopted by the Intergovernmental Panel on Climate Change (IPCC). Each RCP scenario implies different magnitudes of future heat stress. SSPs represent socioeconomic development pathways. Different SSP scenarios imply different amounts of risks of heat stress exposure and societal adaptive capacity. Three SSP–RCP scenarios were considered: SSP 585, SSP 245 and SSP 119. Scenario SSP 585 represents a world of rapid and unconstrained growth in economic output and energy use. Scenario SSP 245 represents the middle of the range of plausible future pathways 37 , reflecting the continuation of historical mitigation efforts 38 . In scenario SSP 119, the world shifts pervasively toward a more sustainable path, emphasizing more inclusive development that respects perceived environmental boundaries. These three scenarios, from high carbon to sustainable trajectories, allow the quantification of the potential economic benefits of ambitious emissions reduction policies that have previously received little attention.

Nonlinear growth trend of global heat-related losses

Figure 1a–d depicts the total global economic loss and the specific components. Under the SSP 119 scenario, the total global gross domestic product (GDP) loss is 0.9% (0.6–1.1%) in 2040 and each component is estimated as follows: health loss (0.5%), labour productivity loss (0.3%) and indirect loss (0.1%). In 2060, global GDP loss slightly decreases to 0.8% (0.4% health loss, 0.3% labour productivity loss and 0.1% indirect loss), amounting to about US $3.75 trillion (values are constant 2020 price). The number of global average heatwave days (definition and calculation detailed in the Methods ) would increase by 24% compared to 2022 and the average annual number of heatwave deaths would be around 0.59 million (0.44–0.74 million). In the case of a high-emissions, high-growth development path, SSP 585, economic losses in 2060 increase by 500% compared to the SSP 119 scenario, up to 3.9% (2.9–4.5%) (1.6% health loss, 0.8% labour loss and 1.5% indirect loss), with a value of about US $24.70 (18.36–28.80) trillion. The global annual heatwave days would be 104% higher compared to 2022 and the global average annual number of heat-induced deaths would increase to around 1.12 million (0.85–1.39 million). The labour and health loss on regional and global scales are close to the results of previous studies 39 , 40 , 41 .

a – d , Evolutionary trends of the four types of losses from 2040 to 2060 under different scenarios (health loss ( a ); labour productivity loss ( b ); indirect loss (supply-chain disruptions) ( c ); and the total losses ( d )). The colours from light to dark, represent the economic losses from the three scenarios SSP 119, SSP 245, SSP 585, respectively. e – p , The spatial distribution of global losses as a percentage of each country’s GDP at midcentury under the SSP 119 ( e – h ), SSP 245 ( i – j , l ) and SSP 585 ( m – p ) scenarios. The values shown are 10-year averages (for example, loss reported in 2060 represents the average loss calculated over the period between 2055 and 2065).

Global economic losses show a nonlinear growth trend with respect to time and degree of heat stress, driven by increased indirect losses. Over time, total losses grow from 1.5% of GDP in 2040 to 2.5% of GDP in 2050 and to 3.9% of GDP in 2060 (Fig. 1d ) under the SSP 585 scenario. However, the proportion of global GDP loss due to supply-chain disruptions is 0.1%, 0.3%, 0.7% and 1.5% per decade from 2030 onwards (Fig. 1c ), showing an exponential-like growth pattern (Extended Data Fig. 5 and Supplementary Figs. 7 and 8 ). Growing indirect losses gradually become the dominant contributor to total losses. Looking at the scenario scale, total GDP losses in 2060 are 0.8% under SSP 119, 2.0% under SSP 245 and 3.9% under SSP 585, of which the losses due to indirect effects are 0.1% of global GDP (13% of total) under SSP 119, 0.5% (25% of total) under SSP 245 and 1.5% (38% of total) under SSP 585. As the degree of heat stress increases progressively, the indirect effects gain more weight in the total losses.

Figure 1e–p explains the mechanism behind the growing weight of the indirect effect in the total losses as the degree of heat stress increases: in terms of spatial patterns, when direct losses are of low to medium magnitude, their impact on the supply network is limited to the regional area; however, when direct losses are severe, they have wider ramifications impacting the supply chain globally and giving rise to further, indirect, losses. Under the SSP 119 scenario, health losses are most significant in South-Central Africa and Eastern Europe (Fig. 1e ); labour productivity losses are concentrated in lower latitudes, including West Africa and South Asia ( Fig. 1f ); indirect losses are concentrated in Central America and East Asia ( Fig. 1g ); in general, Central and Southern Africa, Southeast Asia and Latin America have the most severe total losses ( Fig. 1h ). The spatial patterns of direct economic loss of labour and health under the three scenarios are similar. However, it is noteworthy that persistent and severe heat stress expected under the rapid growth SSP 585 scenario leads to substantial disruptions beyond the regional scale through to global value chains (GVCs). Countries such as Brazil, China and Norway all suffer substantial economic ripple losses. China’s indirect economic losses due to supply-chain disruptions soar from 0.4% under SSP 119 to 2.7% of GDP under SSP 585, Brazil from 0.2% to 2.5% and Norway from less than 0.1% to 2.1%. Although developed countries at high latitudes can mitigate most potential losses through adaptation strategies such as air conditioning under SSP 119 scenario, they remain exposed to risk of declining supply or demand in the GVCs under SSP 585 scenario (Fig. 1o and Supplementary Fig. 10 ). European Union (EU) countries will face considerable indirect losses due to their trading partners’ reduced production capacity of minerals and food products, especially developing countries. Although severely affected countries in South Asia or Africa are not core trading partners of the EU and trade volumes between such countries and the EU are relatively small, indirect economic losses in the EU will be amplified when many of those developing countries are affected by heat stress.

Different sensitivities to heat stress across countries

Different economies face different risk of losses from heat stress, depending on their geographical locations and the position they occupy in the global supply chain. First, countries whose densely inhabited districts are expected to suffer from severe future warming and temperature anomalies, are the most vulnerable to health losses in terms of excess mortality. Under the SSP 119 scenario, South-Central Africa’s GDP loss due to heatwave deaths is 1.8% (1.2–2.5%) in 2060, the highest in the world. It is followed by Trinidad and Tobago (1.7%), Sri Lanka (1.5%) and Indonesia (1.5%; Fig. 2a ). Vulnerability to health impacts depends on the frequency of extreme weather events and the amount of adaptive capacity. For example, Hungary and Croatia suffer considerable health losses, even though in these countries the climate is cooler than in the Middle East and North Africa. Unlike labour losses, which occur in regions with very high average temperature and humidity, health losses depend largely on the variance and abrupt changes in summer temperatures. As climate change will lead to more frequent and intense heatwaves, populations in cooler climatic zones will experience considerable loss of life if the adaptive capacity does not keep pace with the abrupt and sudden changes.

a – c , Top ten climate change-sensitive regions with the most severe health losses ( a ), labour productivity losses ( b ) and indirect losses ( c ) in 2060, under SSP 119 scenario. d – f , Top ten climate change-sensitive countries with the most severe health losses ( d ), labour productivity losses ( e ) and indirect losses ( f ) in 2060, under SSP 585 scenario. The countries marked with black triangles are newly ranked among the most vulnerable countries in 2060 under the SSP 585 scenario compared with SSP 119. The values shown are 10-year averages. Error bars represent 1 s.d. from the mean of decadal data. Upper and lower limits indicate mean + s.d. and mean − s.d., respectively. TTO, Trinidad and Tobago.

Second, low-income emerging economies in the warmest climatic zones are more likely to suffer labour productivity losses. Under the SSP 119 scenario for 2060, countries such as Botswana, Nepal and Nigeria suffer substantial labour productivity losses, up to 1.3%, 1.2% and 1.2% of GDP, respectively (Fig. 2b ). These emerging economies are predominantly located in southern and western Africa (except Nepal), where scorching climates combined with substantial warming over time result in labour-intensive activities during summer months being conducted under increasingly high temperatures. To add insult to injury, most of these countries depend on primary industries such as agriculture, forestry, mining and construction, where workers are mostly outdoors and will be severely affected by extreme heat. For example, agriculture accounts for 21.3% of Nepal’s GDP and 23.4% of Nigeria’s, whereas mining contributes to nearly 28% of Botswana’s GDP 42 . The widespread suspension and reduction of production in the agroforestry and extractive industries due to heat stress will have serious repercussions on national economies and international trade balances. Consequently, these countries are among the most affected by the loss of labour productivity.

Third, small to medium-sized economies with strong and diverse connections to the most affected regions in the GVC, are highly vulnerable to indirect effects. In the context of the SSP 119 scenario, value chains in Latin America and Southeast Asia are the most severely affected (Fig. 2c ). Puerto Rico suffered the highest losses, estimated at 0.8% (0.5–1.1%) of GDP, whereas Venezuela, Malaysia and other Latin American countries, including El Salvador, Panama and Dominican Republic, lost approximately 0.4–0.8% of GDP. Under the SSP 585 scenario, Southeast Asian economies such as Brunei, Malaysia, Singapore and Indonesia suffer the most. These losses stem from strong trade connections with highly vulnerable countries. For example, Brunei and Singapore are exposed to indirect effects as they import nearly 60% of their annual mineral and metal products from China, Malaysia and Indonesia. Caribbean countries like Puerto Rico and Panama generally have less economic diversity and depend heavily on the service sector and international trade. The complex mechanism of transmitting losses along the value chain necessitates thorough consideration by countries for managing future risk of instability across critical industries.

A comparison between the SSP 119 (Fig. 2a–c ) and SSP 585 (Fig. 2d–f ) scenarios shows that losses do not increase uniformly across developing and developed countries when faced with severe climate change impacts, indicating uneven exposures to climate risk. Under the high-emission SSP 585 pathway, a substantial portion of the rapidly escalating economic losses is shouldered by developing countries. Despite Africa contributing less than 5% of global greenhouse gas emissions, 12 countries in the continent, including Rwanda, Botswana, Uganda and Malawi, are projected to suffer some of the most substantial economic losses globally by the midcentury. Several East African countries such as Malawi, Madagascar and Tanzania are highly expected to suffer labour productivity losses of approximately 2.5–4.0% of GDP. Regarding health losses, South-Central Africa and Rwanda experience GDP losses of 8.6% and 7.2%, respectively, almost five times more compared to the SSP 119 scenario. In the SSP 585 scenario, indirect losses become more widespread, affecting both developed and developing economies. Brunei incurs the highest indirect losses at 4.7% (4.0–5.3%) of its GDP, whereas other emerging economies like Paraguay and Indonesia lose around 3.3% of their GDP. These findings demonstrated that the rapid growth of income and air-conditioning penetration in emerging economies under the SSP 585 scenario falls short of counteracting the immense impact of climate change on their economies.

Asymmetric effects of heat stress on global supply chains

Figure 3 highlights the three types of losses for sectors experiencing the highest losses across representative countries. The crop farming, construction and mining sectors are the most affected in most countries, especially in several African and Asian countries that rely on primary industries. For instance, an average summer wet bulb globe temperature (WBGT) above 30 in Tanzania challenges the ability of most outdoor workers to adapt in the midcentury. Sectors requiring workers to be directly exposed to sunlight, such as construction and farming, will suffer a loss of value-added (VA) of 1.9% in 2040 under the SSP 119 scenario. In 2060, rising incomes and a stable climate will result in a slightly reduced VA loss of 0.3% explained by lower labour productivity and health losses. However, under the high warming SSP 585 scenario, the same VA loss increases to 3.9% in 2040 and soars to 8.1% in 2060. In addition, most indoor manufacturing industries suffer a VA loss of 6.0–7.4% in 2060 under the SSP 585 scenario. As demonstrated in Extended Data Fig. 9 , countries like Tanzania, Zimbabwe and other African countries exhibit similar patterns of loss. Countries with comparable loss patterns tend to be situated at low latitudes, particularly in the Middle East, South Asia and Africa—regions most threatened by climate change. Most indoor manufacturing and service industries in developing countries have limited access to air conditioning and, as a result, labour capacity and economic development will be severely undermined by climate change.

a – l , Showing the top five most vulnerable sectors in Tanzania ( a – c ), India ( d – f ), Germany ( g – i ) and Sweden ( j – l ) in 2040 ( a , d , g , j ), 2050 ( b , e , h , k ) and 2060 ( c , f , i , l ). Sectors with absolute VA losses exceeding the median were ranked by percentage of VA losses, from highest to lowest. The length of the bar represents the 10-year average percentage VA loss of a sector. Sectors with the same percentage loss (for example, wheat, rice and cereals) were combined. Colours indicate the three categories of losses under SSP 119: health losses (yellow bars), labour productivity losses (purple bars) and indirect losses (azure bars). The orange and red bars represent the increment of the total loss under SSP 245 and SSP 585 (without differentiating by type of loss), respectively. The four types of countries were derived by machine-learning clustering based on sectoral patterns of economic loss (Supplementary Figs. 11 and 12 and Supplementary Table 3 ).

Non-metallic products and ferrous metals are vulnerable to climate change because of simultaneous supply-chain shocks from both upstream (supply) and downstream (demand). For example, a country such as India is affected directly by high temperatures and indirectly by the close links with countries severely impacted by heat stress. In 2040, losses in non-metallic manufacturing are second only to construction and agriculture sectors at 2.2% of sectoral VA, whereas ferrous metals industry loses 1.4% of VA. These losses can be attributed to both insufficient demand in the domestic construction sector and shortage of minerals and coal supplies from countries in Southeast Asia and Africa (for example, Indonesia and South Africa). In 2060, under SSP 585 scenario, with the increasingly frequent shutdowns in mining and construction industries under extreme summer heat stress, the ferrous metals industry in India suffers the most substantial VA loss at 5.0%, of which more than 70% is due to indirect losses, followed by the loss from non-metallic manufacturing industry at 3.9%. The sectoral patterns of loss in India are characterized by a combination of health, labour and indirect losses. The decline in labour productivity in the domestic construction and plantation industries leads directly to high economic losses in the country’s related value chains. As shown in Extended Data Fig. 10 , countries located at low and middle latitudes, such as China and Vietnam, exhibit similar patterns of loss.

Light manufacturing, including metal products, rubber and plastic products, food processing and beverages and tobacco, are vulnerable to indirect effects because of a lack of raw materials supply, such as minerals, metals, crops, oil seeds and vegetables. For example, under the SSP 119 scenario, metal products and tobacco and beverage manufacturing in Germany lose around 0.3% of VA in 2040. Under the SSP 585 scenario, the economic loss of beverages and tobacco would increase by more than six times in 2060, reaching 2.0% of VA as imports of plantation products (palm oil, soybeans, coffee, spices and so on) from South America, Southeast Asia and Africa decline by around 5% to 8% (Extended Data Fig. 8 ). Losses of metal products rise even faster, reaching 2.4% of VA in 2060. This is because the main producers of raw materials, such as coal and metals, which are essential for the metal products industry, are primarily located in regions that are vulnerable to climate change. This leads to higher losses in the metal product-related chain in most countries with developed manufacturing industries, including Germany, France and Australia (Extended Data Fig. 11 ). These countries have a relatively low share of agricultural GDP (less than 3%), with slight losses. Labour productivity losses are high only in the construction or mining sector, whereas indirect losses are higher in the metal-related manufacturing sector because of insufficient supply from foreign trading partners.

Similarly, high-end machinery, equipment and chemical products industries suffer indirect losses as a result of multilevel cascading effects, even in very cool climates. Losses in these industries, especially in developed countries such as European countries, emerge slowly and are not substantial under the SSP 119 scenario but increase sharply under SSP 245 and SSP 585 scenarios. For example, Sweden’s industry-wide production suffers mainly from indirect losses through supply-chain disruptions and excess mortality due to heatwaves. From 2040 to 2060 under the SSP 119 scenario, impacts on production activities are moderate given the cool climate and dependence on the stable EU supply chain. Sectors like electrical equipment and chemical products experience less than 1% of VA loss, mostly health loss due to sudden extreme heatwaves. However, sector VA losses soar under the SSP 585 scenario. Losses in the mechanical equipment sector increase rapidly, growing by approximately five times compared to the SSP 119 scenario. Ferrous metals (2.2%), electrical equipment (1.9%) and machinery and equipment (1.6%) experience the highest VA losses. Indirect losses become a main constraint in many sectors because national adaptive strategies or close regional trade flows (as in the EU) can no longer support production when heat stress becomes increasingly more severe globally. As shown in Extended Data Fig. 12 , developed economies located at high latitudes, such as Norway and the UK, are characterized by similar loss patterns.

We also analyse the mechanism through which indirect losses from disruptions in international trade flows propagate through national supply chains of specific sectors. Figure 4 illustrates how climate risk propagates through two supply chains, the Indian food production and the Dominican Republic tourism sectors, respectively (see Extended Data Fig. 8 for other typical supply chains). Each of these sectors is important to the respective economies of India (13% of GDP) and the Dominican Republic (18% of GDP) and each is largely dependent on international supply chains. In the case of India’s food sector, we see a pattern of ‘upstream constraint’ through which insufficient upstream supply of intermediates (such as palm oil from Indonesia) impacts the downstream sector and the entire value chain, whereas in the case of tourism in the Dominican Republic, we see a pattern of ‘downstream constraint’—the impact of insufficient downstream demand affects the upstream sector and the entire value chain.

a – d , Trade flows between India food production sector and upstream ( a , c ) and downstream ( b , d ) sectors in 2040 ( a , b ) and 2060 ( c , d ). e – h , Trade flows between Dominican Republic tourism sector and upstream ( e , g ) and downstream ( f , h ) sectors in 2040 ( e , f ) and 2060 ( g , h ). Each bar represents a key trading partner (sector with trade volume above the 50% quartile of trade volumes of the selected sector with all partner sectors) and the length represents the percentage decrease in product flow compared to the base period of 2014. The colours of the bars represent the cohesion level of the particular sector to the Indian food production sector from blue (weak) to red (strong), which is measured by the trade volume between the particular sector and the Indian food production sector. nec, not elsewhere classified.

The supply chain of the Indian food production industry relies heavily on its upstream suppliers, the oil and fat sectors of Indonesia and Malaysia, and as a result it is vulnerable to higher temperatures. The unmitigated warming under the SSP 585 scenario exacerbates the shortage of raw materials. By 2060, palm oil supplies from Malaysia and Indonesia fall by 5.3% and 4.9%. Additionally, Brazilian sugar, Southeast Asian and African vegetables, fruits and nuts are also less available, with a supply decreased by around 4–6%. Consequently, downstream countries, including India, Vietnam, Pakistan and other important trading partners, experience a contraction of imports between 3.7% and 5.1% (Extended Data Fig. 6 ). These impacts can negatively affect food prices and security in both developing and developed countries.

In contrast to the Indian food production industry, the Dominican Republic’s tourism industry is more constrained by downstream demands. Under the SSP 585 scenario, the wealth generated by tourism in the Dominican Republic could drop substantially, as the largest source of foreign visitors, the USA, is likely to reduce annual demand for tourism in the Dominican Republic on average by around 5.5%. Demand from Malaysia and Indonesia is likely to fall by 6.1% and 8.1%. A drop in tourism output, the backbone of the Dominican Republic’s economy, is likely to reduce demand for upstream business services and manufacturing industries by approximately 4.5–4.7%, causing an extra impact on the Dominican national economy. The decline in the tourism sector is also likely to lead to a 4.7% and 4.3% drop in the Dominican Republic’s demand for insurance and financial services from the USA, as well as 4.2% and 4.5% drop in the demand for electronic equipment and chemical products from China (Extended Data Fig. 7 ). Furthermore, a smaller tourism sector leads to the slowdown in the construction of tourism infrastructure and the supply of tourism supporting products, posing considerable risks for tourism investment.

Implication for targeted risk governance and regional cooperation

By coupling climate, epidemiological and economic models, this study investigates the direct impact of heat stress on human activities and the indirect losses across the broader global supply chain. Focusing on the indirect effects of heat stress addresses a substantial gap in the literature. Comprehending the indirect effects of heat stress is crucial for devising effective and targeted adaptation strategies in the context of increasingly complex global supply-chain networks.

Our findings show that supply chains amplify the risk of future heat stress by causing nonlinear economic losses worldwide. In other words, the considerable adverse indirect effects of heat stress across interconnected markets cannot be overlooked. The indirect losses of heat stress highlight the need for countries to strengthen collaboration across global relevant supply-chain stakeholders to achieve successful heat stress adaptation. For instance, our results demonstrate that the impact of a heatwave on the agriculture and food manufacturing industry in India can further lead to a 0.9–2.3% loss of VA in the US food manufacturing industry. If the USA were to support India’s adaptation efforts through technology transfer, they would indirectly be reducing their own losses. These considerations could guide policy-makers working towards global cooperation for future climate change mitigation and heat stress adaptation efforts.

We also illustrate the sensitivity of different countries and sectors to the three types of losses caused by heat stress. For example, Caribbean and Central African countries are more likely to suffer health losses, whereas for low-income countries in Africa and Southeast Asia labour losses are more likely. By contrast, small to medium-sized economies dependent on international trade, such as Brunei, are more exposed to indirect losses. The way heat stress-related costs emerge demonstrates how extensive and diverse impacts from heat stress are propagated through global supply chains, resulting in economic losses to a country or sector that may not be immediately apparent. Our quantitative results provide valuable information for designing more targeted and effective heat stress adaptation strategies.

Our developed model and estimations are subject to uncertainties and limitation (detailed description in Supplementary Information sections  1.1 – 1.3 ). For example, although the disaster footprint module is widely used and performs well for single-country/single-region analyses, the substitutability of products in a multicountry scenario requires further discussion to ensure robustness. To quantify some of the uncertainties, we conducted a comprehensive sensitivity analysis, with details available in the Methods and Supplementary Information section  1 . Specifically, we used different years and versions of the input–output database for comparison to analyse the uncertainty in production and trade structures (Extended Data Fig. 4 ).

Globally, the estimate of the total amount of indirect losses is robust to changes in the data used (GTAP 2011 and GTAP 2014) for the base period. The results of the loss assessment at global scale differ by less than 5% in 2060. Most countries are distributed around the y  =  x line, which suggests a consistent assessment across different trade structures. Regionally, for a few countries, indirect loss assessments can show larger differences. By comparison, we find that when using GTAP 2014 data for the base period, indirect economic losses in East and Southeast Asian countries, such as Singapore, Korea and Japan, are amplified (Extended Data Fig. 4 and Supplementary Fig. 6 ). This can be explained by the fact that, in GTAP 2014, those countries have closer economic ties with climate-sensitive markets, including Malaysia, China, India and Vietnam. For instance, trade between Singapore and emerging economies such as China and Vietnam had increased substantially from 2010 to 2014. According to the Singapore Department of Statistics ( https://www.singstat.gov.sg/ ) and the United Nations Commodity Trade Statistics Database ( https://comtrade.un.org ), China became the largest trading partner of Singapore in 2014, up from fourth place in 2011, whereas Vietnam rose to the 13th largest partner in 2014, from the 20th place in 2011. Conversely, Singapore’s total trade share with the EU and the USA decreased slightly over the same period. Similarly, Japan, Korea and Myanmar developed closer trade relationships with emerging markets such as China, India and Vietnam.

The assessment of indirect losses under different trade relationships offers important insights into the likely supply-chain risks posed by climate change. As Africa, South America and Southeast Asia become increasingly involved in GVCs, the resilience of GVCs to the impacts of climate change must be properly assessed, rather than merely considering scale effects and comparative advantage in terms of economic efficiency.

For parameters such as the maximum stock ratio and excess production capacity, we conducted the experiment several times in the range of possible values from previous studies. For trade substitutability, upper and lower bounds of perfect substitution and non-substitution (traditional static input–output model) were used. We elaborate in more detail about the uncertainty intervals of the parameter for the three main modules and perform a Monte Carlo analysis, including simulation of economic loss dynamics for 10,000 periods (Supplementary Table 2 ). We have also conducted an historical validation using several authentic data sources (robustness tests and validation in the Supplementary Information ), encompassing government statistics, empirical studies and institution reports 3 , 43 , 44 , 45 , 46 , 47 (Supplementary Tables 1 , 6 and 7 , and Supplementary Fig. 1 ), in addition to a comparative analysis of previous studies 33 , 39 , 48 concerning future periods based on CMIP5 data and similar RCP scenarios (Extended Data Fig. 5 ).

Despite the uncertainties, our conclusion that projected climate change will continue to increase heat-related risks globally in the coming decades and that global supply chains will amplify economic losses by spreading indirect losses to wider regions, remain robust. Therefore, in the future, the organization of global supply chains should gradually shift from an exclusive focus on efficiency to one that places equal emphasis on efficiency and resilience. A concerted global strategy to reduce emissions will not only directly protect many people in developing economies from direct economic losses of heat stress but will also maintain resilient and efficient global supply chains and contribute to the long-term, sound development of the global economy.

Our methodology, in essence, combines three modules of climate, health and economy with full validation (Extended Data Fig. 1 ). The integrated model links climate module (estimating future climate parameters including surface air temperature and relative humidity and so on), demographic and health module (simulating future world population dynamics and exposure–response functions to warming) and economic module (dynamic footprint of heat-induced labour loss on global economy and supply chain).

Climate module

Fourteen GCMs involved in the framework of CMIP6 (Extended Data Table 1 ) with ten bias-corrected models from ISIMIP3b 49 , 50 are used to estimate the modelled heat stress projection for the end of the twenty-first century. Five models were randomly averaged several times from the climate model ensemble as a Monte Carlo uncertainty analysis. ERA5 re-analysis data 51 from 1985 to 2022 are used for bias-correction and validation. Climatic parameters such as maximum and average temperature and relative humidity on a daily scale are integrated, which are closely related to future working environment (Supplementary Fig. 9 ).

Many institutes, including International Standards Organization (ISO) and US National Institute for Occupational Safety and Health (NIOSH), use WBGT to quantify different amounts of heat stress and define the percentage of a typical working hour that a person can work while maintaining core body temperature. To facilitate the long-term calculation, we use 18 simplified WBGT, which approximates WBGT well using temperature ( T a ) and relative humidity (RH) 52 , 53 as parameters such as solar radiation and wind speed have higher uncertainty and weaker effects at the global scale. To take into account indoor heat exposures for industrial and service sector workers, we used the approximation that indoor WGBT indoor  = WBGT outdoor  − 4, based on a deduction of the radiation exposure factor from the formula below 18 :

We also calculated the spatial and temporal evolutionary trends in the occurrence of future heatwaves to calculate excess mortality. There is no consistent definition for heatwave worldwide because people may have acclimatized to their local climatic zones and different studies have applied various temperature metrics 54 , 55 . Heatwaves are usually defined by absolute or relative temperature threshold in consecutive days 56 . There are various ways to define a heatwave. For example, the IPCC defines heatwave as “a period of abnormally hot weather, often defined with reference to a relative temperature threshold, lasting from two days to months”, whereas the Chinese Meteorological Administration defined heatwave as “at least three consecutive days with maximum temperature exceeding 35 °C”. Others 31 identified heatwave using the TX90p criterion, that is, when the 90th percentile of the distribution of regional maximum temperatures spanned by data from the period 1981–2010 was exceeded for at least three consecutive days. In our study, two or more consecutive days above the 95% threshold of the 1985–2015 ERA5 daily mean temperature 51 , 57 were defined as a heatwave, which is considered to be a moderate estimation and is widely used in epidemiological studies 36 , 58 , 59 . Several definitions, such as four or more consecutive days above the 97.5% threshold, are used as sensitivity analysis. Considering certain amounts of climate adaptation of the local resident along the warming climate, dynamic heatwave thresholds 60 are defined as part of the uncertainty analysis in this study; that is two or more consecutive days above the 95% threshold of the daily mean temperature between 1985 and the year before the target year were defined as a heatwave (ERA5 data are used for 1985–2014; climate projection data are used after 2015). The use of a dynamic threshold based on both historical and climate projections data helps to incorporate the human adaptation of heat stress in a long-term warming scenario, as reported in recent studies 61 , 62 , 63 , 64 .

Health costs related to heat exposure

Some studies have shown that the health impact of heatwaves could vary substantially with location 65 , 66 . Few studies have investigated the heatwave-induced mortality risk at a global scale 41 , 67 . A primitive health risk function associating heatwave mortality risks with four different climate zones was established by ref. 36 on the basis of a comprehensive study using data from 400 communities in 18 countries/regions across several years (1972–2012). Here, we used the relative risk coefficients (Extended Data Table 2 ) from figure 4 of ref. 36 for four different climate zones (Extended Data Fig. 3 ) to estimate potential heatwave-related death due to climate change on a global scale. The simplified four-climate-zone-based estimation may neglect subregional characters and should be interpreted with caution, as further factors affecting heat-induced death (such as air condition accessibility 68 , age 69 , 70 , 71 , 72 and humidity 73 ) are not included in this study.

The number of excess deaths D hw during a heatwave period was calculated at each grid cell level (0.5°) with the following equation:

POP is the population at the given location consistent with the SSPs 74 . MR is the average daily mortality rate (2009–2019) at the country level obtained from the World Bank 75 . For 37 countries with large territory and more refined data (for example, European Union (including UK), Russia, Ukraine, China, the USA, Canada, Brazil, South Africa, India and Australia), we used state/provincial statistics based on data from national statistical offices (Source, World Bank; state/province level data for European Union, Eurostat 76 ; Russia, The Russian Fertility and Mortality database 77 ; China, China Statistical Yearbook 2019 78 ; the USA, National Institutes of Health 79 ; Brazil, Fundação Amazônia de Amparo a Estudos e Pesquisas 80 ; Canada, Statistics Canada 81 ; Australia, Australian Bureau of Statistics 82 ; India, Ministry of Finance Economic Survey 83 ). RR is the relative risk of mortality caused by heatwaves. HWN is the number of heatwave days for the given year and location (Extended Data Fig. 2 ).

The calculated excess deaths are translated to a social-economic loss on the basis of the value of statistical life (VSL). The concept of VSL is widely used throughout the world to monetize fatality risks in benefit–cost analyses. The VSL represents the individual’s local money–mortality risk tradeoff value, which is the value of small changes in risk, not the value attached to identified lives. The country-based VSL estimation used in this research is adopted from the global health risks pricing study by ref. 84 . The estimation is based on the estimated VSL in the USA (US$ 2019 11 million) and coupled with an income elasticity of 1.0 to adjust the VSL to other countries using the fixed-effects specification. A similar health valuation method has been adopted in past studies 85 , 86 and was recommended in the report of the World Bank 87 . Moreover, a sensitivity test is conducted under the assumption that all life would be valued equally across the world (Supplementary Figs. 2 and 3 ). For such a test, an averaged VSL is calculated by summing up each country’s income-based VSL times its population then dividing by the total population of the world.

Expose function of labour productivity

The increase in daily temperatures affects the efficiency of workers and reduces safe working time. A compromise in endurance capacity due to thermoregulatory stress was already evident at 21 °C. Different studies used similar methods to evaluate the labour loss function. The form of logistic function with ‘S’ shape has become the consensus of the academic community but the specific functional equation and parameters are various in different studies. The loss functions used in mainstream research include exponential function 88 as equation ( 4 ), cumulative normal distribution function 5 , 41 as equation ( 5 ) and so on. In this research, we adopt the cumulative normal distribution function (equation ( 5 )) as our benchmark function because it was extensively applied and case proven in 3-year reports of the Lancet Countdown on health and climate change 5 , 41 , 89 , 90 . Because the Hothaps function (equation ( 4 )) is subject to parameter uncertainty as a result of being based on a few empirical studies, we use it to test for the sensitivity of our estimates (Supplementary Figs. 4 and 5 ). Our methodology identifies three ISO standard work intensity amounts: 200 W (assumed to be office workers in the service industry, engaged in light work indoors), 300 W (assumed to be industrial workers, engaged in moderate work indoors) and 400 W (assumed to be construction or agricultural workers, engaged in heavy work outside). For example, to calculate workability loss fraction in India’s food production sector (300 W, indoor), we bring the corresponding parameters (Extended Data Table 3 ) and WBGT indoor into equation ( 5 ). Previous studies have tended to ignore indoor workforce loss, assuming that the indoor workforce was very low under current climate condition or protected by air conditioning 91 . However, a growing number of studies have proved that future indoor labour losses cannot be underestimated 31 . For example, only 7% of households in India possess an air conditioner, despite having extremely high cooling needs. Considering the severe adaptation cooling deficit in emerging economies 92 , indoor labour losses must be fully considered in global-scale studies. This study uses the climate–income–air conditioner usage function published by ref. 93 to assess the rate of air conditioning protection in conjunction with the per capita income of each country under each SSP scenario. Higher per capita income in each country leads to higher air-conditioning penetration, whereas the climate base determines the rate and trend of increase in air-conditioning penetration (elasticity of penetration to income). In our study, we improved the function by replacing cooling degree days (CDDs) with indoor WBGT, as CDDs only consider temperature neglecting humidity. Only the indoor workforce under air conditioning, will be protected from heat-induced loss.

Of which the parameters for a given activity level (Prod mean and Prod SD , defined as the amount of internal heat generated in performing the activity) are given in Extended Data Table 3 , and ERF is the error function defined as:

To calculate average daily impacts, we use an approximation for hourly data based on the 4 + 4 + 4 method implemented by ref. 14 . We assume that 4 h per day is close to WBGT max and 4 h per day is close to WBGT mean (early morning and early evening). The remaining 4 h of a 12 h daylight day is assumed to be halfway between WBGT mean and WBGT max (labelled WBGT half ). The analysis above gives the summer daily potential workability lost in each grid cell at each amount of work intensity and environment (200–400 W, indoor or outdoor). By combining this with the dynamic population grid under each SSP scenario (see Supplementary Fig. 13 for comparison with static population setting), we aggregate to obtain country-scale labour productivity losses. In the disaster footprint model, we adopt the approach presented by ref. 5 which defines the timeframe for computing labour productivity losses as the warm season (June to 30 September in the Northern Hemisphere and December to 30 March in the Southern Hemisphere) to adjust the overestimation of the risk of moderate hot temperature, as the model is more applicable to sudden and strong shocks rather than moderate changes throughout the year.

Global disaster footprint analysis module

The global economic loss will be calculated using the following hybrid input–output and computable general equilibrium (CGE) global trade module. Our global trade module is an extension of the adaptive regional input–output (ARIO) model 20 , 94 , 95 , which was widely used in the literature to simulate the propagation of negative shocks throughout the economy 96 , 97 , 98 , 99 . Our model improves the ARIO model in two ways. The first improvement is related to the substitutability of products from the same sector sourced from different regions. Second, in our model, clients will choose their suppliers across regions on the basis of their capacity. These two improvements contribute to a more realistic representation of bottlenecks along global supply chains 100 .

Our global trade module mainly includes four modules: production module, allocation module, demand module and simulation module. The production module is mainly designed for characterizing the firm’s production activities. The allocation module is mainly used to describe how firms allocate output to their clients, including downstream firms (intermediate demand) and households (final demand). The demand module is mainly used to describe how clients place orders to their suppliers. And the simulation module is mainly designed for executing the whole simulation procedure.

Production module

The production module is used to characterize production processes. Firms rent capital and use labour to process natural resources and intermediate inputs produced by other firms into a specific product. The production process for firm i can be expressed as follows,

where x i denotes the output of the firm i , in monetary value; p denotes type of intermediate products; \({z}_{i}^{{\rm{p}}}\) denotes intermediate products used in production processes; va i denotes the primary inputs to production, such as labour ( L ), capital ( K ) and natural resources (NR). The production function for firms is f ( · ). There is a wide range of functional forms, such as Leontief 101 , Cobb–Douglas and constant elasticity of substitution production function 102 . Different functional forms reflect the possibility for firms to substitute an input for another. Considering that heat stress tends to be concentrated in a specific short period of time, during which economic agents cannot easily replace inputs as suitable substitutes, might temporarily be unavailable, we use Leontief production function which does not allow substitution between inputs.

where \({a}_{i}^{{\rm{p}}}\) and \({b}_{i}\) are the input coefficients calculated as

where the horizontal bar indicates the value of that variable in the equilibrium state. In an equilibrium state, producers use intermediate products and primary inputs to produce goods and services to satisfy demand from their clients. After a disaster, output will decline. From a production perspective, there are mainly the following constraints.

Labour supply constraints

Labour constraints during heat stress or after a disaster may impose severe knock-on effects on the rest of the economy 21 , 103 . This makes labour constraints a key factor to consider in disaster impact analysis. For example, in the case of heat stress, these constraints can arise from employees’ inability to work as a result of illness or extreme environmental temperatures beyond health threshold. In this model, the proportion of surviving productive capacity from the constrained labour productive capacity ( \({x}_{i}^{{\rm{L}}}\) ) after a shock is defined as:

Where \({\gamma }_{i}^{{\rm{L}}}(t)\) is the proportion of labour that is unavailable at each time step t during heat stress; \((1-{\gamma }_{i}^{{\rm{L}}}(t))\) contains the available proportion of employment at time t .

The proportion of the available productive capacity of labour is thus a function of the losses from the sectoral labour forces and its predisaster employment level. Following the assumption of the fixed proportion of production functions, the productive capacity of labour in each region after a disaster ( \({x}_{i}^{{\rm{L}}}\) ) will represent a linear proportion of the available labour capacity at each time step. Take heatwaves as an example; during extreme heatwaves that last for days on end, governments and businesses often shut down work to reduce the risk of serious illnesses such as pyrexia. This imposes an exogenous negative shock on the economic network.

Constraints on productive capital

Similar to labour constraints, the productive capacity of industrial capital in each region during the aftermath of a disaster ( \({x}_{i}^{{\rm{K}}}\) ) will be constrained by the surviving capacity of the industrial capital 30 , 96 , 104 , 105 , 106 . The share of damage to each sector is directly considered as the proportion of the monetized damage to capital assets in relation to the total value of industrial capital for each sector, which is disclosed in the event account vector for each region \(({\gamma }_{i}^{{\rm{K}}})\) , following ref. 107 . This assumption is embodied in the essence of the input–output model, which is hard-coded through the Leontief-type production function and its restricted substitution. As capital and labour are considered perfectly complementary as well as the main production factors and the full employment of those factors in the economy is also assumed, we assume that damage in capital assets is directly related with production level and, therefore, VA level. Then, the remaining productive capacity of the industrial capital at each time step is defined as:

Where, \({\bar{K}}_{i}\) is the capital stock of firm \(i\) in the predisaster situation and K i ( t ) is the surviving capital stock of firm \(i\) at time \(t\) during the recovery process

Supply constraints

Firms will purchase intermediate products from their supplier in each period. Insufficient inventory of a firm’s intermediate products will create a bottleneck for production activities. The potential production level that the inventory of the p th intermediate product can support is

where \({S}_{i}^{{\rm{p}}}(t-1)\) refers to the amount of p th intermediate products held by firm i at the end of time step t  − 1.

Considering all the limitation mentioned above, the maximum supply capacity of firm i can be expressed as

The actual production of firm i , \({x}_{i}^{{\rm{a}}}(t)\) , depends on both its maximum supply capacity and the total orders the firm received from its clients, \({{\rm{TD}}}_{i}(t-1)\) (see section on the ‘Demand module’),

The inventory held by firm i will be consumed during the production process,

Allocation module

The allocation module mainly describes how suppliers allocate products to their clients. When some firms in the economic system suffer a negative shock, their production will be constrained by a shortage to primary inputs such as a shortage of labour supply during extreme heat stress. In this case, a firm’s output will not be able to fill all orders of its clients. A rationing scheme that reflects a mechanism on the basis of which a firm allocates an insufficient amount of products to its clients is needed 108 . For this case study, we applied a proportional rationing scheme according to which a firm allocates its output in proportion to its orders. Under the proportional rationing scheme, the amounts of products of firm i allocated to firm j , \({{\rm{F}}{\rm{R}}{\rm{C}}}_{j}^{i}\) and household h , \({{\rm{H}}{\rm{R}}{\rm{C}}}_{h}^{i}\) are as follows,

where \({{\rm{F}}{\rm{O}}{\rm{D}}}_{i}^{j}(t-1)\) refers to the order issued by firm j to its supplier i in time step t − 1, and \({{\rm{H}}{\rm{O}}{\rm{D}}}_{i}^{h}(t-1)\) refers to the order issued by household h to its supplier j . Firm j received intermediates to restore its inventories,

Therefore, the amount of intermediate p held by firm i at the end of period t is

Demand module

The demand module represents a characterization of how firms and households issues orders to their suppliers at the end of each period. A firm orders its supplier because of the need to restore its intermediate product inventory. We assume that each firm has a specific target inventory level based on its maximum supply capacity in each time step,

Then the order issued by firm i to its supplier j is

Households issue orders to their suppliers on the basis of their demand and the supply capacity of their suppliers. In this study, the demand of household h to final products q , \({{\rm{HD}}}_{h}^{q}\left(t\right)\) , is given exogenously at each time step. Then, the order issued by household (HOD) h to its supplier j is

The total order received (TOD) by firm j is

Simulation module

At each time step, the actions of firms and households are as follows in Monte Carlo simulations.

Firms plan and execute their production on the basis of three factors: (1) inventories of intermediate products they have, (2) supply of primary inputs and (3) orders from their clients. Firms will maximize their output under these constraints.

Product allocation

Firms allocate outputs to clients on the basis of their orders. In equilibrium, the output of firms just meets all orders. When production is constrained by exogenous negative shocks, outputs may not cover all orders. In this case, we use a proportional rationing scheme proposed in the literature 20 , 108 (see section on ‘Allocation module’) to allocate products of firms.

Firms and households issue orders to their suppliers for the next time step. Firms place orders with their suppliers on the basis of the gaps in their inventories (target inventory level minus existing inventory level). Households place orders with their suppliers on the basis of their demand. When a product comes from several suppliers, the allocation of orders is adjusted according to the production capacity of each supplier.

This discrete-time dynamic procedure can reproduce the equilibrium of the economic system and can simulate the propagation of exogenous shocks, both from firm and household side or transportation disruptions, in the economic network. From the firm side, if the supply of a firm’s primary inputs is constrained, it will have two effects. On the one hand, the decline in output in this firm means that its clients’ orders cannot be fulfilled. This will result in a decrease in inventory of these clients, which will constrain their production. This is the so-called forward or downstream effect. On the other hand, less output in this firm also means less use of intermediate products from its suppliers. This will reduce the number of orders it places on its suppliers, which will further reduce the production level of its suppliers. This is the so-called backward or upstream effect. From the household side, the fluctuation of household demand caused by exogenous shocks will also trigger the aforementioned backward effect. Take tourism as an example, when the temperature is well beyond the comfort range of the visitor, the demand for tourism from households all over the world will decline significantly. This influence will further propagate to the accommodation and catering industry through supplier–client links.

Economic footprint

We define the VA decrease of all firms in a network caused by an exogenous negative shock as the disaster footprint of the shock. For the firm directly affected by exogenous negative shocks, its loss includes two parts: (1) the VA decrease caused by exogenous constraints and (2) the VA decrease caused by propagation. The former is the direct loss, whereas the latter is the indirect loss. A negative shock’s total economic footprint (TEF i,r ), direct economic footprint (DEF i,r ) and propagated economic footprint (PEF i,r ) for firm i in region r are,

Global supply-chain network

We build a global supply-chain network based on v.10 of the Global Trade Analysis Project (GTAP) database 109 and use GTAP 9 (ref. 110 ), EMERGING database 111 for robustness analysis. GTAP 10 provides a multiregional input–output (MRIO) table for the year 2014. Also, the database for the year 2011 was used for robustness testing. This MRIO table divides the world into 141 economies, each of which contains 65 production sectors (Supplementary Tables 4 and 5 ). If we treat each sector as a firm (producer) and assume that each region has a representative household, we can obtain the following information in the MRIO table: (1) suppliers and clients of each firm; (2) suppliers for each household and (3) the flow of each supplier–client connection under the equilibrium condition. This provides a benchmark for our model. We also used a dynamic CGE model consistent with the SSP scenarios for a parallel assessment and as part of the robustness check of the ARIO results. Specifically, the CGE model we used is a G-RDEM 112 with aggregated ten regions and ten sectors 113 , 114 , 115 (Supplementary Information section 1.3 ).

When applying such a realistic and aggregated network to the disaster footprint model, we need to consider the substitutability of intermediate products supplied by suppliers from the same sector in different regions 115 , 116 , 117 . The substitution between some intermediate products is straightforward. For example, for a firm that extracts spices from bananas it does not make much of a difference if the bananas are sourced from the Philippines or Thailand. However, for a car manufacturing firm in Japan, which uses screws from Chinese auto parts suppliers and engines from German auto parts suppliers to assemble cars, the products of the suppliers in these two regions are non-substitutable. If we assume that all goods are non-substitutable as in the traditional input–output model, then we will overestimate the loss of producers such as the case of the fragrance extraction firm. If we assume that products from suppliers in the same sector can be completely substitutable, then we will substantially underestimate the losses of producers such as the Japanese car manufacturing firm. To alleviate these shortcomings in the evaluation of losses under the two assumptions, we allow for the possibility of substitution for each sector depending on the region and sector of the supplier (Supplementary Information section 1.3 ).

Nonetheless, our estimates of economic damages from heat stress are subject to some important uncertainties 118 and our methods may not capture all types of economic damages. We only include economic losses caused by heat stress on human activities without considering the impacts on infrastructure, crop growth and other factors. Considering the challenges of predicting changes to socioeconomic systems globally, we have followed the approach from the literature 23 , 31 , 91 , 119 to simulate supply-chain indirect losses by considering the impact of future climate risks on current socioeconomic settings. We have not considered the potential substitution of labour with capital resulting from technological advances, such as mechanization. Our analysis ignores the different levels of trade openness and globalization among SSP narratives, as well as the role of dynamic factors such as technology and price. Again, although we have conducted robustness tests for different degrees of trade substitutability, the relevant parameter is set randomly in the Monte Carlo simulation rather than derived through a general equilibrium model. The results should therefore be interpreted with caution as indicating potential future climate change risks to the existing economy rather than as quantitative predictions, given that the static representation of the economic structure in our model inevitably skews the assessment in the long run.

Data availability

Data for the numerical results of this research are provided at https://zenodo.org/records/10032431 . The global trade dataset used to simulate the presented results are licensed by the Global Trade Analysis Project at the Centre for Global Trade Analysis, Department of Agricultural Economics, Purdue University. The GTAP v.10 can be obtained for a fee from its official website: https://www.gtap.agecon.purdue.edu/databases/v10/index.aspx . Owing to the restriction in the licensing agreement with GTAP, the authors have no right to disclose the original dataset publicly. Multimodal meteorological data are derived from World Climate Research Programme (WCRP CMIP6): https://esgf-node.llnl.gov/search/cmip6/ . Socioeconomic data for the different SSP scenarios are derived from IIASA: https://secure.iiasa.ac.at/web-apps/ene/SspDb/ . Global population projection grids are from Socioeconomic Data and Applications Center (SEDAC) ( https://sedac.ciesin.columbia.edu/data/set/popdynamics-1-8th-pop-base-year-projection-ssp-2000-2100-rev01/data-download ).

Code availability

The climate and epidemiological module processes daily surface temperature, dynamic population grid and baseline mortality data to determine heatwave days and the associated excess deaths. The economic module simulates changes of values and flows in global multiregional input–output table under shocks. All of the codes can be accessed at https://zenodo.org/records/10334260 . The minimal input for the code is multiregional input–output table. The sample code and test data for the minimal inputs are also provided.

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Acknowledgements

This study was supported by the National Natural Science Foundation of China (grant nos. 72242105, 72091514 and 72250710169) and the startup funding from Zhejiang University to S.Z.

Author information

These authors contributed equally: Yida Sun, Shupeng Zhu, Daoping Wang

Authors and Affiliations

Department of Earth System Science, Ministry of Education Key Laboratory for Earth System Modeling, Institute for Global Change Studies, Tsinghua University, Beijing, China

Yida Sun, Hui Lu, Chang Tan, Wenjia Cai, Yong Wang & Dabo Guan

Department of Atmospheric Sciences, School of Earth Sciences, Zhejiang University, Hangzhou, China

Shupeng Zhu

Advanced Power and Energy Program, University of California Irvine, Irvine, CA, USA

Department of Geography, King’s College London, London, UK

Daoping Wang

Centre for Climate Engagement, Department of Computer Science and Technology, University of Cambridge, Cambridge, UK

State Key Laboratory of Earth Surface and Ecological Resources, Faculty of Geographical Science, Beijing Normal University, Beijing, China

Jianping Duan

Tsinghua University (Department of Earth System Science)—Xi’an Institute of Surveying and Mapping Joint Research Center for Next-Generation Smart Mapping, Beijing, China

Department of Economics, University of Southern California, Los Angeles, CA, USA

Department of Economics, University of Waterloo, Waterloo, Ontario, Canada

Lingrui Zhang

School of Management and Economics, Beijing Institute of Technology, Beijing, China

Mengzhen Zhao

School of Economics and Management, Southeast University, Nanjing, China

College of Urban Environment, Peking University, Beijing, China

The Bartlett School of Sustainable Construction, University College London, London, UK

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Contributions

D.G. designed and supervised the study. Y.S., S.Z. and D.W. conducted the study, collected the data, analysed the results and drafted the paper. J.D. and Y.W. collected and processed the meteorological data. H.Y., M.Z. and W.C. provided guidance on the calculation of health and labour productivity losses. C.T., Y.H. and L.Z. participated in the writing of the manuscript. S.T. and H.L. guided the uncertainty analysis and validation.

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Correspondence to Dabo Guan .

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Extended data figures and tables

Extended data fig. 1 schematic diagram of the methodological framework..

Coupling mechanisms for climate, health and economic modules.

Extended Data Fig. 2 Heatwave days in the 2040 s and 2060 s under SSP585 Scenario.

The number of heatwave days in each cell was calculated from the ten-year average.

Extended Data Fig. 3 Climate zones classification of relative risk.

Cold area: mean temperature of hot season: ≤<= 20.7 °C; moderate cold areas: mean temperature of hot season: 20.7–24.1 °C; moderate hot areas: mean temperature of hot season: 24.1–27.6 °C; and hot areas: mean temperature of hot season: >27.6 °C, based on ERA5 1985–2010.

Extended Data Fig. 4 Estimates for the ten countries with the highest indirect losses under the SSP585 scenario using different base period trade data.

Estimates are displayed as 10-year averages for the year 2060, using the GTAP2011 (a), GTAP2014 (b) and EMERGING 2019 (c) databases separately. The colours of the bars represent GDP per capita from low to high. (d), indirect losses under different benchmark trade structures in each region. The horizontal axis measures the indirect losses as a percentage of GDP using the GTAP2014 trade structure and the vertical axis measures the indirect losses as a percentage of GDP using the GTAP2011 trade structure. Details of Extended Data Fig. 4d can be checked in Supplementary Fig. 6 .

Extended Data Fig. 5 Global economic losses for each scenario under Monte Carlo simulations.

The assessment results of existing studies are marked with symbols for comparison. None of the previous studies were based on CMIP6 SSP119 scenario, so we use RCP2.6 to compare with the SSP119 scenario in our study. The studies above did not simulate health loss and the mean values of the health loss simulations in this paper were added for consistency.

Extended Data Fig. 6 Impacts of heat stress on India food manufacturing supply chains.

( a ), ( c ) panels represent the upstream sectors of the India’s food production sector in 2040 and 2060, respectively. ( b ), ( d ) panels represent the downstream sectors. Each bar represents a key trading partner (i.e. sector with trade volume above the 50 percent quartile of trade volumes of the selected sector with all partner sectors) and the length represents the percentage decrease in product flow compared to the base period of 2014. The colours of the bars represent the cohesion level of the particular sector to the Indian food production sector from blue (weak) to red (strong), which is measured by the trade volume between the particular sector and the Indian food production sector.

Extended Data Fig. 7 Impacts of heat stress on Dominican Republic tourism supply chains.

( a ), ( c ) panels represent the upstream sectors of the Dominican Republic’s tourism sector in 2040 and 2060, respectively. ( b ), ( d ) panels represent the downstream sectors. Each bar represents a key trading partner (i.e. sector with trade volume above the 50 percent quartile of trade volumes of the selected sector with all partner sectors) and the length represents the percentage decrease in product flow compared to the base period of 2014. The colours of the bars represent the cohesion level of the particular sector to the Dominican Republic’s tourism sector from blue (weak) to red (strong), which is measured by the trade volume between the particular sector and the Dominican Republic’s tourism sector.

Extended Data Fig. 8 Impacts of heat stress on Germany beverages and tobacco products supply chains.

( a ), ( c ) panels represent the upstream sectors of the Germany’s beverages and tobacco products sector in 2040 and 2060, respectively. ( b ), ( d ), panels represent the downstream sectors.

Extended Data Fig. 9 Sectoral loss patterns of type 1 countries.

The top 5 most vulnerable sectors in Tanzania (a–c), Botswana(d–f) and Malawi (g–i). The column length represents each sector’s percentage loss of annual value-added. Sectors with the same loss percentage (e.g. wheat, rice, cereals, etc.) were combined. Colours indicate the three categories of losses in SSP119: health losses (Yellow bars), labour productivity losses (Blue bars) and supply-chain disruption losses (Green bars). The orange and red bars represent total loss increments for SSP245 and SSP585 (no distinction between types of loss in this part), respectively. The red dashed line indicates the mean value of losses for all sectors in the SSP585 scenario.

Extended Data Fig. 10 Sectoral loss patterns of type 2 countries.

The top 5 most vulnerable sectors in India (a–c), Vietnam (d–f) and China (g–i). The column length represents each sector’s percentage loss of annual value-added. Sectors with the same loss percentage (e.g. wheat, rice, cereals, etc.) were combined. Colours indicate the three categories of losses in SSP119: health losses (Yellow bars), labour productivity losses (Blue bars) and supply-chain disruption losses (Green bars). The orange and red bars represent total loss increments for SSP245 and SSP585 (no distinction between types of loss in this part), respectively. The red dashed line indicates the mean value of losses for all sectors in the SSP585 scenario.

Extended Data Fig. 11 Sectoral loss patterns of type 3 countries.

The top 5 most vulnerable sectors in Germany (a–c), France (d–f) and Australia (g–i). The column length represents each sector’s percentage loss of annual value-added. Sectors with the same loss percentage (e.g. wheat, rice, cereals, etc.) were combined. Colours indicate the three categories of losses in SSP119: health losses (Yellow bars), labour productivity losses (Blue bars) and supply-chain disruption losses (Green bars). The orange and red bars represent total loss increments for SSP245 and SSP585 (no distinction between types of loss in this part), respectively. The red dashed line indicates the mean value of losses for all sectors in the SSP585 scenario.

Extended Data Fig. 12 Sectoral loss patterns of type 4 countries.

The top 5 most vulnerable sectors in Sweden (a–c), Norway(d–f) and United Kingdom (g–i). The column length represents each sector’s percentage loss of annual value-added. Sectors with the same loss percentage (e.g. wheat, rice, cereals, etc.) were combined. Colours indicate the three categories of losses in SSP119: health losses (Yellow bars), labour productivity losses (Blue bars) and supply-chain disruption losses (Green bars). The orange and red bars represent total loss increments for SSP245 and SSP585 (no distinction between types of loss in this part), respectively. The red dashed line indicates the mean value of losses for all sectors in the SSP585 scenario.

Supplementary information

Supplementary information.

Supplementary sections 1–7, including Figs. 1–13, Tables 1–7 and references.

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Sun, Y., Zhu, S., Wang, D. et al. Global supply chains amplify economic costs of future extreme heat risk. Nature 627 , 797–804 (2024). https://doi.org/10.1038/s41586-024-07147-z

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DOI : https://doi.org/10.1038/s41586-024-07147-z

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When it comes to global economic governance, less can be more if you want increased cooperation, stability, and equitable growth

HKS Professor Dani Rodrik says his new paper shows that a “light model” of international trade governance can reduce U.S.-China tensions and lead to more equitable prosperity worldwide.

In an era of great power rivalry and escalating tensions between the United States and China, how should global economic policymakers approach governance to foster greater cooperation, reduce conflict, and promote equity and shared prosperity? Tread lightly, says Harvard Kennedy School Professor Dani Rodrik . In a new paper in the Journal of Government and Economics titled " How to smooth U.S.-China economic relations for the benefit of the global economy: A light model of global economic governance ," Rodrik argues for a less-is-more approach that emphasizes transparency and restricts trade rules to those that are truly necessary. Recent history, he writes, has shown that heavy-handed, one-size-fits-all global economic governance has disadvantaged developing countries and empowered nativist, populist politicians in regions that suffered job losses due to globalization. Rodrik is the Ford Foundation Professor of International Political Economy and co-director of the Reimagining the Economy project at HKS 

Q: In your paper, you argue for a “light” model of global economic governance to smooth U.S.-China economic relations and benefit the global economy. What measures does a light economic governance model include and what does it exclude? 

The basic distinction is between addressing the most egregious policy barriers at the border—especially beggar-thy-neighbor policies—versus a more ambitious agenda of trying to remove all obstacles to the flow of trade and finance even if these encompass domestic regulations. My colleague Robert Lawrence has called this the difference between “shallow integration” and “deep integration.” So countries might negotiate over import tariffs or export restrictions, but industrial policies or financial regulations, for example, would remain largely outside the remit of global rules or trade agreements.

Q: What evidence is there that more intensive global governance and trade rules—a “heavy” or “deep integration” model—have been unnecessary or counter-productive? 

The deep integration model had two undesirable side effects. First, it made it harder for developing countries to engage in industrial policies or other structural policies to diversify and upgrade their economies and to shelter themselves from the destabilizing effects of short-term capital flows, because of real (or self-imposed) constraints on their policy autonomy. It was countries such as China who disregarded such constraints and made full use of industrial policies and capital controls that performed the best. Others who followed the deep integration model and relied on trade agreements and openness to foreign capital as their sole growth strategy, such as Mexico, did quite poorly. 

Second, it prevented policy makers in advanced economies from taking seriously and addressing the adverse labor market effects of growing imports from China and elsewhere. During the 1970s and 1980s, before globalization got supercharged, it was common for countries to put up informal trade barriers—typically “voluntary” export restrictions administered by exporting countries—which limited the shock. These were safety valves for the trade system, and even though economists complained about “protectionism,” such measures did the job of regime maintenance (in the words of our late colleague John Ruggie). After the 1990s, policymakers’ response to these dislocations was to shrug their shoulders and tell the losers this is how globalization worked and there was nothing to be done. This was one of the key failings of mainstream politicians, which in turn empowered the populists. We have plenty of evidence now that regions that lost jobs to imports from China or Mexico became the breeding grounds for the nativist populists.

“The future of the global economic order depends first and foremost on how the bilateral relationship between the U.S. and China will evolve.”

Dani rodrik.

Q: One of the main building blocks of the light governance model is what you call a “transparency-enhancing process for policymaking.” What is that process and why is increased transparency important? 

In an interdependent global economy, it is inevitable that many policies that target national economic well-being as well as domestic social and environmental priorities will have some undesirable side effects on other nations. This is the case, for example, when nations engage in policies to fix important market failures or address national security concerns. Often such policies are needed and legitimate, and trade partners have to be permissive and understanding. Such policies have to be distinguished from those that are explicitly beggar-thy-neighbor—that is, policies that generate benefits at home because of the harms they produce for other nations.  

If we are going to live in a world where national policymakers have greater autonomy to address domestic priorities, as I think we have to, it will be important for them to communicate their motivations both to their domestic audiences and to other governments. This is to build trust and mutual understanding. For example, when the United States imposes export controls on “sensitive technologies” or imposes restrictions on Chinese investments on U.S. soil on the basis of national security, we need much better explanation on (a) what the national security objective really is, and (b) how the export or investment in question undermines the objective. Otherwise, national security can turn into a blanket justification for all kinds of policies that either do not really address national security or (as in the case of the U.S.) take too expansive view of it.

Q: How would a light governance model smooth U.S.-China relations? And how would it work in the context of the current slowdown in what had been China’s robust growth over the last three decades? 

It would be a good first step if each side were to give up on hypocrisy and recognize the similarity of their approaches. The United States continues to criticize China for allegedly pursuing mercantilist and protectionist policies and violating the norms of a “liberal” international order. For their part, Chinese policymakers accuse the United States of turning its back on globalization and waging economic warfare on China. Neither side seems to be aware of the irony that the United States has taken a page from the Chinese playbook, while U.S. departures from the “liberal order” are readily recognizable to Chinese policy makers from their own practices.   

Q: How would a light economic governance model achieve an overall global economic benefit? 

The future of the global economic order depends first and foremost on how the bilateral relationship between the United States and China will evolve. So anything that smooths this critical relationship would be very good news for the world economy. Second, as these two powers build a certain degree of trust and understanding, this would also contribute to an environment where they play a positive role in providing critical global public goods (such as decarbonizing the world economy and global public health).    

Photograph by STR/AFP/Getty Images

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