On the efficacy of the "sniff test" for understanding climate impacts

Economists (and other high-statured people) often pride themselves on having good noses.  When encountering a research finding, they can often apply a quick "sniff test" to evaluate whether the results of this finding are likely true or false.

We encounter the sniff test frequently when we discuss our estimates of the potential impacts of future warming on domestic or global economic output. Economic orthodoxy has has long held that 3-4C of warming will reduce global GDP by a few percentage points over the next century relative to a world were temperature was held fixed.  These estimates are based largely on earlier cross-sectional studies relating average output to average temperature, and have provided critical input to the construction of "damage functions" used in benchmark integrated assessment models.  Output from these models, in turn, have played a pivotal role in policy-making around the social cost of carbon (e.g. see here).

Newer analysis done by your G-FEED bloggers and others using panel based methods have found much larger potential effects of warming on output (e.g. see here), e.g. ~20% loss of global GDP in a +4C world, relative to a world where temperature had stayed fixed.  Because these estimates are so much larger than previous estimates, they have frequently failed the sniff test, both publicly and in referee reports we've received.  Some people have told us the results just seem "too big".  No way can climate change make us 20% poorer than we would have been without it.

Rather than adjudicating the methods underlying these newer results, I want to evaluate this sniff test itself by simply looking at some historical data.  This is in the spirit of Sol's last post: before we argue so strongly for a particular approach or result based on our priors, how bout we just look at some data.  Is there historical evidence for some regions performing 20% better than others over century or shorter time scales?

Exhibit 1:  let's just look at income growth in US states.  Below is a plot of real per capita income growth since 1980 in selected US states, with the y-axis normalized to show the % change in income relative to 1980.  Even in this relatively short 35-year time span (a third of a century, for those scoring at home), there is huge variation in performance: income in Massachusetts grew >130% over this period, while incomes in Nevada grew only 30%.  Even the difference between coastal-elite Massachusetts and California over this 35-year period exceeds 20%.  So variation in performance of around 20% tis clearly within our very recent historical experience in this country, on a time scale much shorter than a century.

Data: Bureau of Labor Statistics

Ah, you say, but this is just the US, and we know we have rampant inequality in this country, etc etc.  This variation can't be representative of the rest of the world, right?

Okay:  below is the plot for a small sample of OECD countries, using real GDP/cap from World Bank WDI.  Again, plotting data since only 1980, there's been a ton of variation in observed growth in per capita incomes, with neighboring high-income countries varying by way more than 20% in how much they've grown.  The plot is of course way more stark if you include developing countries, with some countries only growing a few percentage points over the period, and others (e.g. China) growing by >1000% (and screwing up the axes of any plot you try to make...).  So:  massive variation at the country level within just a few decades.

Data: World Bank World Development Indicators

Ah, you say, this is just ~35 years of data -- way too short a time scale to look at these long run trends.  Can't possibly be true for longer time scales, right? Don't countries converge on longer time scales?

Okay:  below is a plot of real per capita growth rates over multiple centuries, based on the Maddison data, for a random selection of countries with data back to 1800.  At this time scale, you see absolutely gargantuan differences in changes in per capita incomes.  German incomes grew nearly 5000% since 1800, Portuguese incomes grew 2000%, while South African incomes grew only about 500%.

Data: Maddison Project

These century scale differences in income over time are roughly two orders of magnitude larger than what our climate impact estimates would predict to be the difference between unmitigated climate change, and a world where warming didn't happen.

So in light of these historical data, can we please do away with the vaunted "sniff test"?  Our estimates or climate impacts are small relative to the variation in historical performance we've seen, and that's true whether you look within countries over recent decades, or (even more so) across countries over very long time periods.  None of this, of course, tells us whether climate change will reduce economic output by 20%;  it just tells us that we cannot reject a 20% estimate out of hand for being "way too big".

But this begs the question:  why are our noses so bad?  Why is the sniff test so un-efficacious? Perhaps we continue to under-appreciate the power of compounding.  A key result from our earlier paper (building on key earlier work by Dell, Jones, and Olken, and shown by multiple other groups as well, see here, here, here), is that changes in temperature can affect the growth rate of GDP.  And small effects on the growth rate can have large effects on the level of income over long time scales.

E.g. consider an economy growing at 2% for a century (the US, roughly).  Knocking only a quarter of a percent off the growth rate -- i.e. growing at 1.75% instead of 2% -- leads to an economy that's >20% poorer than it would have been after 100 years.  Very small growth effects can lead to large level effects over long times scales.

Another analogy (h/t to my colleague and co-author Noah Diffenbaugh) is the continued success of managed mutual funds, despite the clear evidence that the small management fees charged by these funds leads to huge reductions in your accumulated investment over longer time scales, relative to investing in index funds with no fees.  Just as small fees eat away at your total portfolio earnings, small hits to the growth rate cumulate into large level effects down the line.  But the mutual fund industry in the US is $18 trillion (!!).

So let's continue to argue about methodological issues and about how best to understand the response of aggregate output to warming.  More posts from us on that soon.  But, please, can we stop applying the sniff test to these results?  Our noses work less well than we think.

Congressional testimony on economic consequences of climate change

Since it's basically a blog post, here's the oral testimony I gave last week to the House Budget Committee during the Hearing on "The Costs of Climate Change: Risks to the U.S. Economy and the Federal Budget." If you have hours to spare, you can watch the whole hearing or read the full (referenced) written testimony. It's not every day that someone asks you summarize a decade of research progress from your field in 800 words, so it seems worth documenting it somewhere.

Thank you Chairman Yarmuth, Ranking Member Womack, and members of the Committee for inviting me to speak today. 

My name is Solomon Hsiang, and I am the Chancellor’s Professor of Public Policy
at the University of California, Berkeley and currently a Visiting Scholar at Stanford. I was trained in both economics and climate physics at Columbia, MIT, and Princeton. My research focuses on the use of econometrics to measure the effect of the climate on the economy. 

The last decade has seen dramatic advances in our understanding of the economic value of the climate.  Crucially, we now are able to use real-world data to quantify how changes in the climate cause changes in the economy. This means that in addition to being able to project how unmitigated emission of greenhouse gasses will cause the physical climate to change, we can now also estimate the subsequent effect that these changes are likely to have on the livelihoods of Americans. 

Although, as with any emerging research field, there are large uncertainties and much work remains to be done. Nonetheless, I’d like to describe to you some key insights from this field regarding future risks if past emissions trends continue unabated. 

First, climate change is likely to have substantial negative impact on the US economy.  Expected damages are on the scale of several trillions of dollars, although there remains uncertainty in these numbers. For example, in a detailed analysis of county-level productivity, a colleague at University of Illinois and I estimated that the direct thermal effects alone would likely reduce incomes nation-wide over the next 80 years, a loss valued at roughly 5-10 trillion dollars in net present value. In another analysis, a colleague from University of Chicago and I computed that losses from intensified hurricanes were valued at around 900 billion dollars. Importantly, these numbers are not a complete accounting of impacts and other notable studies report larger losses. 

Second, extreme weather events are short-lived, but their economic impact is long-lasting.  Hurricanes, floods, droughts, and fires destroy assets that took communities years to build.  Rebuilding then diverts resources away from new productive investments that would have otherwise supported future growth. For example, a colleague at Rhodium Group and I estimated that Hurricane Maria set Puerto Rico back over two decades of progress; and research from MIT indicates that communities in the Great Plains have still not fully recovered from the Dustbowl of the 1930s. As climate change makes extreme events more intense and frequent, we will spend more attention and more money replacing depreciated assets and repairing communities. 

Third, the nature and magnitude of projected costs differs between locations and industries.  For example, extreme heat will impose large health, energy, and labor costs on the South; sea level rise and hurricanes will damage the Gulf Coast; and declining crop yields will transform the Plains and Midwest. 

Fourth, because low income regions and individuals tend to be hurt more, climate change will widen existing economic inequality.  For example, in a national analysis of many sectors, the poorest counties suffered median losses that were 9 times larger than the richest.  

Fifth, many impacts of climate change will not be felt in the marketplace, but rather in homes where health, happiness, and freedom from violence will be affected.  There are many examples of this. Mortality due to extreme heat is projected to rise dramatically.  Increasingly humid summers are projected to degrade happiness and sleep quality. Research from Harvard indicates that warming will likely elevate violent crime nationwide, producing over 180,000 sexual assaults and over 22,000 murders across eight decades. Colleagues at Stanford and I estimate that warming will generate roughly 14,000 additional suicides in the next thirty years. Increasing exposure of pregnant mothers to extreme heat and cyclones will harm fetuses for their lifetime. These impacts do not easily convert to dollars and cents, but they still merit attention. 

Sixth, populations across the country will try to adapt to climate change at substantial cost.  Some adaptions will transform jobs and lifestyles, some will require constructing new defensive infrastructure, and some will involve abandoning communities and industries where opportunities have deteriorated. In all cases, these adaptations will come at real cost, since resources expended on coping cannot be invested elsewhere. 

Lastly, outside of the US, the global consequences of climate change are projected to be large and destabilizing.  Unmitigated warming will likely slow global growth roughly 0.3 percentage points and reduce political stability throughout the tropics and subtropics. 

Together, these findings indicate that our climate is one of the nation’s most important economic assets. We should manage it with the seriousness and clarity of thought that we would apply to managing any other asset that also generates trillions of dollars in value for the American people. 

Thank you.

The SHCIT List

Just like George Lucas, I write my literature reviews out of order. But I'm happy to say that after several years of messing around in this field, in collaboration with great coauthors, I've finally finished the tetralogy that I've always wanted to complete.  The latest installment (Episode I) just came out in the JEP (it's designed to be a soft on-ramp for economists who are unfamiliar with climate change science to get acquainted with the problem).

Sol Hsiang's Climate Impacts Tutorial reading list:

1. An Economist’s Guide to Climate Change Science  (what is the physical problem?)
2. Using Weather Data and Climate Model Output in Economic Analyses of Climate Change (how do we look at the data for that problem?)
3. Climate Econometrics (how does one analyze that data to learn about the problem?)
4. Social and Economic Impacts of Climate (what did we learn when we did that?)

This addition completes the box set that can get any grad student up to speed on the broader climate impacts literature.

I hope this is helpful. I think I'm going to go and do more research on elephant poaching now...

Building a better damage function

Bob Kopp, Amir Jina, James Rising, and our partners at the Climate Impact Lab, Princeton, and RMS, have a new paper out today.  Our goal was to construct a climate damage function for the USA that is "micro-founded," in the sense that it is built up from causal relationships that are empirically measured using real-world data (if you're feeling skeptical, here's are two videos where Michael Greenstone and I explain why this matters).

Until now, the "damage function" has been a theoretical concept. The idea is that there should be some function that links global temperature changes to overall economic costs, and it was floated in the very earliest economic models of climate change, such as the original DICE model by Nordhaus where, in 1992, he described the idea while outlining his model:

from Nordhaus (1992)

The "extremely elusive" challenge was figuring out what this function should look like, e.g. what should theta_1 and theta_2 be? Should there something steeper than quadratic to capture really catastrophic outcomes? Many strong words have been shared between environmental economists at conferences about the shape and slope of this function, but essentially all discussions have been heuristic or theoretical.  We took a different approach, instead setting out to try and use the best available real world empirical results to figure out what the damage function looks like for the USA.  Here's what we did.

We started out by recognizing that a lot of work has already gone into modeling climate projections for the world, by dozens of teams of climate modelers around the world. So we took advantage of all those gazillions of processor-hours that have already been used and simply took all the CMIP5 models off the shelf, and systematically downscaled them to the county level.

Then we indexed each model against the global mean surface temperature change that it exhibits. Not all models agree on what warming will happen given a certain level of emissions. And even among models that exhibit the global mean temperature change, not all models agree on what will happen for specific locations in the US.  So it's important that we keep track of all possible experiences that the US might have in the future for each possible level of global mean temperature change.  Here's the array of actual warming patterns in maps, where each map is located on the horizontal axis based on the projected warming under RCP8.5 ("business as usual"). As you can see, the US may experience many different types of outcomes for any specific level of global mean warming.

Then, for each possible future warming scenario, we build a projection of what impacts will be in a whole bunch of sectors, using studies that meet a high empirical standard (pretty much the same one Marshall and I used in our conflict meta-analysis, plus a few other additional criteria). This was relying on a lot of previous work done by colleagues here, like Mike/Wolfram/David's crop results and Max's electricity results. We project impacts in agriculture, energy, mortality, labor and crime using empirical response functions. For energy, this got a little fancy because we hooked up the empirical model to NEMS and ran it as a process model. For coastal damages, we partnered with RMS and restructured their coastal cyclone model to take Bob's probabilistic SLR projections and Kerry Emanuel's cyclone projections as inputs---their model is pretty cool since it models thousands of coastal flood scenarios, and explicitly models damages for every building along the Atlantic coast. The energy and coastal models were each big lifts, using process models with empirical calibration, as were the reduced form impacts since we resampled daily weather and statistical uncertainty for each impact in each RCP in each climate model; this amounted to tracking 15 impacts across 3,143 counties across 29,000 possible states of the world for every day during 2000-2099. These are maps of the median scenarios for the different impacts:

Then, within each possible state of the world, we added up the costs across these different sectors to get a total cost. Doing this addition first is important because it accounts for any cross-sector correlations that might emerge in the future due to the spatial correlations in economic activity (across sectors) and their joint spatial correlation with the future climate anomaly (a bad realization for energy, ag, and mortality all might happen at the same time).  We then take these total costs and plot them against the global mean temperature change that was exhibited by the climate model that generated them. There ended up being 116 climate models that we could use, so there are only 116 different global temperature anomalies, but each model generated a whole distribution of possible outcomes due to weather and econometric uncertainty. Plotting these 116 distributions gives us a sense of the joint distribution between overall economic losses and global temperature changes: 

We can then just use normal statistics on these data to describe this joint distribution succinctly, getting out some equations that other folks can plug into their cost calculations or IAMS.  Below is the 5-95th intervals for the probability mass, as well as the median. To our knowledge, this is basically the first micro-founded damage function:

It turns out that Nordhaus was right about the functional form, it is quadratic. In the paper we try a bunch of other forms, but this thing is definitely quadratic. And if you are happy with the conditional average damage, we can get you the thetas: 

E[damage | T] = 0.283 x T + 0.146 x T^2

Now, of course, as we say several times in the paper, this function will change as we learn more about the different parts of the economy that the climate influences (for example, since we submitted the paper, we've learned that sleep is affected by climate). So for any new empirical study, as long as it meets our basic criteria, we can plug it in and crank out a new and updated damage function.

Beyond the damage function, there is one other finding which might interest the G-FEED crowd. First, because the South tends to be both hotter, it is disproportionally damaged by nonlinear climate impacts where high temperatures impose higher marginal damages (ag, mortality, energy & labor). Also, along the Gulf and southern Atlantic coast, coastal damages get large. The South also happens to be poorer than the North, which is impacted less heavily (or benefits on net, in many cases). This means that damages are negatively correlated with incomes, so the poor are hit hardest and the rich lose less (or gain). On net, this will increase current patterns of economic inequality (a point the press has emphasized heavily). Here are are whisker plots showing the distribution of total damage for each county, where counties are ordered by their rank in the current income distribution:

Note that nothing about this calculation takes into account the possibility that poor counties have fewer resources with which to cope, this is just about interaction of geography and the structure of the dose-response function.

This widening of inequality probably should matter for all sorts of reasons, including the possibility that it induces strong migration or social conflict (e.g. think about current rural-to-urban migration, the last election, or the Dust Bowl). But it also should matter for thinking about policy design and calculations of the social cost of carbon (SCC).  Pretty much all SCC calculations (e.g. DICE, FUND, PAGE) think about climate damages in welfare terms, but they compute damages for a representative agent that either represents the entire word, or enormous regions (e.g. the USA is one region in FUND). This made sense, since most of the models were primarily designed to think about the inter-temporal mitigation-as-investment problem, so collapsing the problem in the spatial dimension made it tractable in the inter-temporal dimension.  But it makes it really hard, or impossible, to resolve any inequality of damages among contemporary individuals within a region (in the case of FUND) or on the planet (in the case of DICE). Our analysis shows that there are highly unequal impacts within a single country, and this inequality of damages can be systematically incorporated into the damage function above, which as its shown is simply aggregate losses (treating national welfare as equal to average GDP only).  David Anthoff and others have thought about accounting for inequality between the representative agents of different FUND regions, and shown that it matters a lot.  But as far as I know, nobody has accounted for it within a country, and this seems to matter a lot too.

In the online appendix [Section K] (space is short at Science) we show how we can account for both inequality and risk, capturing both in a welfare-based damage function. Using our data and a welfare function that is additive in CRRA utilities, we compute inequality-neutral certainty-equivelent damage functions. These are the income losses that, if shared equally across the entire US population with certainty, would have the same welfare impact as the uncertain and unequal damages that we cover (i.e. shown in the dot-whisker plot above).  Two things to note about this concept. First, this adjustment could theoretically make damages appear smaller if climate changes were sufficiently progressive (i.e. hurting the wealthy and helping the poor). Second, there are two ways to compute this that are not equivelent; one could either compute the (i) inequality in risks borne by different counties or (ii) risks of inequality across counties. We chose to go with the first option, which involves first computing the certainty-equivelent damage for each county, then computing the inequality-neutral equivalent damage for that cross-sectional distribution of risk. (We thought it was a little too difficult for actual people to reasonably imagine all possible unequal states of the future world before integrating out the uncertainty.)

We compute these adjusted damages for a range of parameters that separately describe risk aversion and inequality aversion, since these are value judgements and we don't have strong priors on what the right number ought to be. Below is a graph of what happens to the damage function as you raise both these parameters above one (values of one just give you back the original damage function, which is the dashed line below). Each colored band is for a single inequality aversion value, where the top edge is for risk aversion = 8 and the lower edge is risk aversion = 2:

National inequality-neutral certainty-equivalent loss equal in value to direct damages under different assumptions regarding coefficients of inequality aversion and risk aversion. Shaded regions span results for risk aversion values between 2 and 8 (lower and upper bounds).  Dashed line is same as median curve above.

What we see is that adjustment for inequality starts to matter a lot pretty quickly, more so than risk aversion, but the two actually interact to create huge welfare losses as temperatures start to get high. For a sense of scale, note that in the original DICE model, Nordhaus defined "catastrophic outcomes" as possible events that might lower incomes by 20%.

Bob, David Anthoff and I have debated a bit what the right values for these parameters are, and I'll be the first to say I don't know what they should be. There are several estimates out there, but I think we really don't talk about inequality aversion much so there's not a ton to draw on. But, just like the discount rate (which has received a lot of attention/thought/debate), these ethical parameters have a huge influence on how we think about these damages. And looking at this figure, my guess is that inequality aversion may be just as influential on the SCC as the discount rate---especially once we start having global estimates with this kind of spatial resolution.  I think this is one of the most important directions for research to go: figuring out how we are supposed to value the inequality caused by climate change and accounting for it appropriately in the SCC. 

Trump's climate gift to Russia

Trump's recent announcement that the US would withdraw from the Paris Accords was hailed as a monumental political, environmental, and economic mistake.  But given all the theater surrounding the announcement, others also saw it as an effort to distract the public from the ongoing investigation of the Administration's ties to Russia.

It's hard to see how this latter claim could actually be evaluated.  But it got me thinking:  what are the benefits to Russia of the US withdrawing from the Paris accords?  Was the US withdrawal a climate gift to Russia?

Now, I'm guessing Trump has not read our paper showing that warming temperatures will have unequal economic effects around the world (unlike Obama, to repeat my shameless self promotion from last week).  In that paper, and consistent with a huge microeconomic literature, we see clear evidence in the historical data that cold high-latitude countries tend to experience higher GDP growth when temperatures warm, with the reverse being true in most of the rest of the world where average temperatures are already warmer (the US included).  Basically, if you're currently cold, you do better when it warms up;  if you're already warm, additional warming hurts you.  Pretty intuitive, and also shows up very clearly in the half century of data we have on economic growth from around the world.

Here's the original plot from our paper, with the figure on the left showing the historical relationship between temperature and GDP growth for all countries in the world.  If you're average temperature is below about 13C, historically your economy grows faster when annual temperatures warm.  If your at or above 13C, growth slows as temperatures warm.  The US has a population-weighted annual average temperature of just over 13C.  Russia has a population-weighted average temperature of just under 5C.  Russia is cold!

Figure 2 from Burke, Hsiang, Miguel 2015 Nature. Effect of annual average temperature on economic production. a, Global non-linear relationship between annual average temperature and change in log gross domestic product (GDP) per capita (thick black line, relative to optimum) during 1960–2010 with 90% confidence interval (blue, clustered by country, N= 6,584). Model includes country fixed effects, flexible trends, and precipitation controls. Vertical lines indicate average temperature for selected countries. Histograms show global distribution of temperature exposure (red), population (grey), and income (black). b, Comparing rich (above median, red) and poor (below median, blue) countries. Blue shaded region is 90% confidence interval for poor countries. Histograms show distribution of country–year observations. c, Same as b but for early (1960– 1989) and late (1990–2010) subsamples (all countries). d, Same as b but for agricultural income. e, Same as b but for non-agricultural income.

Last week we calculated the potential harm done to the economy of withdrawing from Paris.  The idea was this:  withdrawing from the Paris accords would make global temperatures rise relative to what they would have been if the US had met its Paris obligations (an additional +0.3C by 2100, according to these guys).  For reasons already stated, warming temperatures are bad for overall economic output in the US. So we can then calculate, what's the difference in output between now and 2100 that would occur in a withdrawal versus a non-withdrawal world?  For the US, the effects were pretty big:  I calculated that, in present value (i.e. discounting future losses at 3%), the US economy would lose about $8 trillion between now and 2100 due to the extra temperature increase induced by withdrawing from Paris.

What about Russia?  Again, Russia is cold, so extra warming is likely to help the Russian economy, all else equal.  You can actually see this really clearly in the Russian data.  Below is the plot of Russian GDP growth rates versus Russian temperatures, using data 1990-2010 (1990 being the first post-Soviet-collapse year that "Russia" shows up in the national accounts data).  Specifically, these are growth deviations from trend versus temperature deviations from trend;  we are detrending the data since you don't want to conflate trends in temperature that could be correlated with other trending factors that also affect growth.

This is just 20 data points, but the estimated effects are HUGE.  Basically, Russian GDP growth is multiple percentage points higher when temperatures warm by a degree C.  And the Russia-specific estimate is even higher than what we would predict the effect would be for Russia using the global response function pictured in blue above.

Anyway...  Basically what I did is to re-do the same calculation we did last week for the US, but now focusing on effects on the Russian economy and calculating what happens to Russian GDP in the scenario where the US withdraws from Paris versus the scenario where the US stays in.  To be conservative, I use estimates from the global response function, not the Russia-specific mega-response just noted.

Here's the main finding:  Trump's decision to withdraw the US from Paris is a $2.2 trillion dollar gift to Russia (paid out over the next 85 years).  Below is the figure showing what happens to Russian GDP under a withdrawal versus a no-withdrawal scenario (left), and the annual gains in GDP in each year (to 2100).  By 2100, Russia is ~10% richer than it would have been otherwise, and the (discounted) sum of these GDP gains is about $2.2 trillion dollars.

Given that there's no evidence that Trump has read our paper, I don't think we can claim that this climatic gift to Russia was purposeful.  But it's sadly ironic that an announcement that might have been meant to distract us from Russian meddling was simultaneously a monumental economic gift to that country.

The cost of Paris withdrawal

Lots of discussion today about the potential ramifications of the US withdrawing from the Paris Accords.  Folks have already done some nice calculations looking at the climate consequences of US withdrawal, but there's a lot of interest in the potential economic consequences and I hadn't seen anyone take a heroic stab at that yet.  So.....

The clearest picture I (read: google) could find on the climate implications was this nice website here from ClimateInteractive.org, where they find that a Paris-minus-US world and a Paris-including US world is the difference between +3.6C of warming and +3.3C of warming by 2100.  There are clearly a lot of assumptions that go into this calculation (e.g., what the hell happens after 2030 when the INDCs run out, what happens if Trump's successor (Kamala Harris?  Zuck?  Steph Curry?) re-signs us up, etc etc), but let's take this calculation as God's truth.  Withdrawal gives the world +0.3C of additional warming.

So I wanted to figure out: what is the cost to the US in terms of additional damages that are wrought by this extra warming that withdrawal would bring about?  A difference of +0.3C might not sound like much, but we've got this paper (cited by Obama, so it must be right) that suggests that changes in temperature can affect the growth rate of GDP in rich and poor countries alike.  So the current administration might be right that meeting the US's Paris commitments would have economic costs, but these need to be weight against the benefits of the reduced warming that we would get.  So what are those benefits?

So I took the basic setup we had in that paper, and ran the world forward to 2100 under +3.3C warming versus +3.6C warming, and I looked at what our results in that paper said would happen to US GDP in those different worlds.  See here for more info on how we do these sorts of calculations in this framework.  Basically, we have a function that tells us how growth rates change as temperatures change, derived from historical data.  Then we walk countries along this function as you crank up the temperature to your desired level. To get GDP in levels, you apply these changes in the growth rate to some baseline growth scenario, which we take off-the-shelf from the Shared Socioeconomic Pathways (SSPs, see here).  We also need population numbers, and take those from the SSPs as well.

Below is what I get when I run the US forward under +3.3C warming versus +3.6C warming.  Under SSP5 (the baseline scenario we use), the US clips along at an average per capita growth rate of above 2%/year.  Once you crank up the temperature by 3.5C or so, historical data tells us that we should shave between about 0.5-1 percentage point off of annual US growth.  So this means by 2100, instead of growing at 2%/year under a no warming scenario, the US would be growing at less than 1.5%/year in this much warmer world.  The effects earlier in the century are smaller, of course.

Our comparison is between +3.3C and +3.6C, and the effects on the growth rate are of course smaller.  But even small effects on the growth rate can add up to big cumulative effects on GDP over time.  The left plot below compares total US GDP in the "no withdrawal" world versus the "withdrawal" world, and the right plot gives you the amount of GDP that's lost in each year from withdrawing.  To be crystal clear, we're again only thinking about the differences brought about by the change in temperature between the two scenarios -- we're not thinking about what it would cost the US to meet its Paris commitments.

If my calculations are right, the numbers are large.  By 2100, withdrawing from Paris makes us (i.e. people in the US) about 5% poorer than we would have been otherwise.  The cumulative US GDP losses over time from withdrawal, discounted back to 2010 at 3%, are also impressive - I calculate them to be $8.2 trillion dollars (right plot above).  That is, withdrawing from the Paris agreement costs the US economy $8.2 trillion dollars in present discounted value.  That is a large pile of money.  Even if I'm off by a factor of 5, we're still talking low trillions.  

And to be clear, my calculations do not take into account many other near-term benefits of reducing our own emissions, such as the 'co-benefits' of better health outcomes from cleaner air.  These could be quite big as well.

The key policy question for the Trump administration is:  do we think the costs of meeting our obligations under Paris are going to run more than $8 trillion?  Put another way, are they going to amount to almost half of current US GDP?

To generate $8 trillion in costs between now and 2030 (after discounting at 3%), annual costs would have to be somewhere around $750 billion.  The compliance cost estimates for the Clean Power Plan that I've seen are about two orders of magnitude smaller than that, so even if the CPP only got us 10% of the way to our Paris commitment (which is very conservative), these costs do not come close to the overall benefits -- even if other reductions are many times as expensive as the CPP.  (Hopefully somebody can correct me if I'm way off on these cost numbers -- definitely not my specialty).

With benefits this big, withdrawal seems like bad policy.  

Chat with Nature editor Michael White about publishing, interdisciplinary research, and the state of climate economics

I recently had a long discussion with Dr. Michael White (the editor at Nature who handles climate science and economics) about a whole bunch of issues that probably interest g-feeders: working in an interdisciplinary space, deciding when to publish in econ vs. general interest journals, what we're all doing in climate econ these days, and the things he thought were funny (as a non-economist) about the AEA meetings. (There's also a few stories about my oddball childhood in there for Marshall to laugh at.)

You can listen here (1 hr).

He thought he was interviewing me for his podcast, but really I was interviewing him for our blog;) The session was an episode of Michael's podcast Forecast, which he produces to cover climate science.  Michael has been a regular attendee at our climate economics lunch and it's been helpful to get his take on the recent developments in our field.

Better than the real thing

Why read a stack of research papers when you can watch a 3 min cartoon?

Thanks to Eric Roston of Bloomberg for putting this together.

Measuring temperature from space

[We are going to try to bring G-FEED out of its fall/winter hibernation with a few posts on papers that various of us have coming out.  Below is the first of these, written by our excellent post-doc Sam Heft-Neal.]

Can we use satellites instead of ground stations to measure temperature and estimate climate response functions?

Guest post by Sam Heft-Neal

Temperature data are commonly used to estimate the sensitivity of societally relevant outcomes to ongoing climate changes. In order to derive a temperature measure, researchers typically interpolate nearby ground station data or use one of the publicly available global gridded data products put out by groups like CRU or UDel. However, Max and Sol and Wolfram and Adam have shown that there are a number of pitfalls associated with using interpolated station data to measure temperature in areas with sparse coverage. Temperature estimates far from weather stations, or in areas where stations go on and offline frequently, are subject to substantial measurement error that can bias impact estimates. This problem is particularly acute in tropical regions, where many of the largest climate impacts are expected to occur, but where we still have a limited understanding of key climate/society relationships. 

David, Marshall and I have a new paper that evaluates whether using temperature measures derived only from satellites can be used to estimate climate response functions. Several satellites measure surface emission of thermal energy, which can be converted into estimates of skin surface temperature (Ts) – a product that MODIS has provided at 1km resolution daily for nearly a decade. Past studies have evaluated agreement between MODIS and air temperature measured by weather stations (Ta) on daily time scales, often finding weak correlations for daytime temperatures because factors other than Ta, such as cloudiness and soil moisture, can affect Ts. However, these results could be of limited relevance for our purpose, since the climate response functions we care about usually rely on year-to-year variations in seasonally aggregated measures of temperature exposure, and correlations between station and satellite data tend to increase as the period of aggregation lengthens.

                                                                                     

In order to test whether Ts could be used in place of Ta to study temperature impacts, we revisited three previous studies (each led by a different G-Feed blog contributor) that had used standard measures of Ta to study climate impacts. For each study we replicated the analysis with both Ta and Ts and compared model performance. The first study examined temperature effects on maize yields in Africa using historical field trial data from plots under optimal management and plots under drought management. The second study looked at county level maize yields in the U.S. and the third study, which we selected to look at an application outside of agriculture, estimated temperature effects on county-level GDP in the U.S..

                                                                    

In order to assess model performance we calculated out-of-sample prediction error by repeatedly estimating the models on randomly selected 75% subsets of locations, predicting values for the 25% of locations that had been excluded from estimation, and calculating the RMSE of out-of-sample predicted values relative to actual values. In each case we found similar relationships between temperature and the outcome of interest (panels 1 and 2 below). For both types of African maize field trials we actually found that models with satellite temperature had lower prediction error than models with ground station temperature. However, when we compared models in the U.S. (panel 3) with its high density of stations, the models had very similar predictive powers. This finding suggests that Ts is more useful in regions with poor station coverage where Ta is measured with significant levels of error.               

 

Response functions for maize yields in Africa (left 2 plots) and maize yields in US (right plots).  Orange is surface temperature from MODIS, blue is air temperature from stations. 

While Ts predicts crop yields well, it is less clear whether it could be used to estimate response functions for non-agricultural applications like GDP. The GDP study also differs from the agricultural studies because instead of using seasonal averages for temperature we used temperature bins. The satellite data we used were 8-day composites so for every observation that fell into a given temperature bin we assigned eight days to that bin (in other words we assumed constant – or at least within a constant bin - temperature for each 8-day period). Even with this assumption, the model with Ts (somewhat surprisingly to us) reproduced a similar non-linear response function over most of the temperature support, particularly at the upper end of the temperature distribution where income appears to be most sensitive to temperature.  

 

Replication of Deryugina and Hsiang (2014), using ground stations (left) and MODIS (right).

Lastly, as a final comparison, we compared the aggregated impacts from 1◦C warming estimated with both models. In doing so we again find similar estimates for all applications.

 

Impact of +1C warming for different models. 

This overall consistency is perhaps somewhat surprising, given the often low correlations between anomalies in Ts and Ta at the daily or 8-day time scale. In the paper we argue that there are at least four reasons Ts could outperform Ta:

1.   Some of the “noise” in Ts vs. Ta relationships stems from errors in the Ta measures, particularly in regions such as Africa where Ta is often interpolated from anomalies at stations tens of kilometers away. So just because Ts and Ta don’t always agree it doesn’t necessarily mean Ts is wrong.

2.   Much of the noise likely cancels out when aggregating temperatures to the monthly or seasonal time scales that are used in regressions that relate outcomes to temperature. For applications that require finer temporal resolution of temperature measures, the noise in Ts may become more important – although again, whether it is larger than noise in high-temporal-resolution Ta remains an empirical question.

3.  Unlike ground measurements, satellite data come from a consistent sensor. Relative spatial variations could therefore be captured more precisely with satellites than with ground measurements from different instruments.

4.   There is reason to believe that Ts could be more appropriate than Ta for agricultural applications. In vegetated areas much of the noise in the daytime Ts vs. Ta relationship arises from anomalous canopy transpiration rates, with stressed canopies often several degrees warmer than Ta whereas healthy canopies are typically several degrees below Ta. Thus, Ts provides a more direct measure of crop condition than Ta, and this represents an advantage of Ts for agricultural applications that may compensate for some of its deficiencies.

Overall this exercise increased our optimism that Ts can serve as a replacement for Ta in some applications. One of the primary downsides of using satellite data is that the records do not go back as far as ground station records so this approach will not be appropriate for many studies with longer timescales. There are also some issues surrounding the transformation of Ts units into Ta units that we discuss in the paper. Despite these caveats, for studies covering recent years our results suggest that Ts is a viable option for replacing Ta, and that in areas with poor ground station coverage, using satellites to measure temperature may in fact be the better option.