Last week I had a new paper come
out with some fantastic co-authors (Delavane Diaz, Tom Hertel, and Uris Baldos)
on new damage functions in the agricultural sector. Since this covers a number
of topics of interest to G-FEED readers (climate damages, agricultural impacts,
SCC etc), I thought I’d dig a bit into what I see as some of the main
contributions.
Firstly what we do. Essentially this is an exercise in
trying to link a damage function directly to the scientific consensus on
climate impacts as represented by findings in the IPCC. We therefore rely on a database
of over 1000 results on the yield response to temperature previously published
by Challinor et al.
and used to support conclusions in the food security chapter of Working Group
2. We do a bit of reanalysis (using a multi-variate regression, adding
soybeans, allowing for more geographic heterogeneity in response) to get
yield-temperature response functions that can be extrapolated to a global grid
to give productivity shocks at 1, 2, and 3 degrees of warming (available for
other researchers to use here).
As readers of G-FEED well know though, getting productivity
shocks is only half the battle because what we really want are welfare changes.
Therefore, we introduce our productivity shocks into the 140-region GTAP CGE
model, which has a particularly rich representation of the agriculture sector
and international trade. This then gives us welfare changes due to agricultural
impacts at different levels of warming, which is what we need for an IAM damage
function. Finally, we take our findings all the way through to the SCC by
replacing the agricultural damages from FUND with our new estimates. Our
headline result is that improving damages just
in the agricultural sector leads the overall
SCC to more than double.
There’s lots more fun stuff in the paper and supplementary
information including a comparison with results from AgMIP, some interesting
findings around adaptation effectiveness, and a sensitivity analysis of GTAP
parameters. But here I want to highlight what I see as three main
contributions.
Firstly I think a major contribution is to the literature on
improving the scientific validity of damages underlying IAM results. The importance
of improving the empirical basis of damage functions has been pointed out by numerous
people.
This is something Delavane and I have worked on previously using empirically-estimated
growth-rate damages in DICE, and something that Sol and co-authors have done a lot of work
on. I think what’s new here is using the existing biophysical impacts
literature and tracing its implications all the way through to the SCC. There
is an awful lot of scientific work out there on the effects of climate change
relevant to the SCC, but wrangling the results from a large number of dispersed
studies into a form that can be used to support a global damage function is not
straightforward (something Delavane and I discuss in a recent
review paper). In this sense the agriculture sector is probably one of the
more straightforward to tackle – we definitely relied on previous work from the
lead authors of the IPCC chapter and from the AgMIP team. I do think this paper
provides one template for implementing the recommendation of the National
Academy of Sciences SCC report around damage functions – that they be based
on current and peer-reviewed science, that they report and quantify uncertainty
wherever possible, and that calibrations are transparent and well-documented.
Secondly, an important contribution of this paper is on
quantifying the importance of general-equilibrium effects in determining
welfare changes. Since so much climate impacts work estimates local
productivity changes, it raises the question of what these can or can’t tell us
about welfare under global climate change. We address this question directly by
decomposing our welfare changes into three components: the direct productivity
effect (essentially just the local productivity change multiplied by crop value),
the terms-of-trade effect, and the allocative efficiency effect (caused by
interactions with existing market distortions). The last one is generally
pretty small in our work, so regional changes in welfare boil down to a
combination of local productivity changes and the interaction between a
region’s trade position and global price changes. This breakdown for 3 degrees
of warming is shown below.
For these productivity shocks, the terms-of-trade (ToT)
effect is regionally pretty important. In a number of cases (notably the US,
Australia, Argentina, and South Africa) it reverses the sign of the welfare
change of the productivity effect. In other words, a number of regions
experience yield losses but welfare gains because the increasing value of
exports more than compensates for the productivity losses (with the opposite in
South Africa). There are a few rules-of-thumb for thinking about how important
the ToT effects are likely to be. Firstly, the ToT effects cancel out at the
global level, so if you are only interested in aggregate global damages, you
don’t have to worry about this effect. By similar logic, the higher the level
of regional aggregation, the less important ToT effects are likely to be, since
larger regions will likely contain both importers and exporters. Secondly, the
importance of ToT effects depends on the magnitude of relative price changes which
in turn depends on the productivity shocks. If the effect of climate change is to
redistribute production around the globe rather than to increase or decrease
aggregate production, then ToT effects will be smaller. We see this in our
AgMIP results, where losses in the tropics are offset to a greater degree by
gains in temperate regions, leading to smaller price changes and
correspondingly smaller ToT effects.
A final point I’d like to highlight is the quantitative importance
of our findings for the SCC. We knew from
previous work by Delavane that agriculture is important in driving the SCC
in FUND. Nevertheless, such a large increase in the total SCC from updating just
one sector, in a model that contains 14 different impact sectors, is striking
and begs the question of what would happen with other sectors. Moreover, there
are suggestions that other models might also be underestimating agricultural
impacts. Neither PAGE nor DICE model the agricultural sector explicitly, but
both include agricultural impacts as part of more aggregate damage functions
(market impacts in PAGE09 and non-SLR damages in DICE 2013). By coupling damage
functions from these models to standardized socio-economic and climate modules,
we are able to make an apples-to-apples comparison of our agriculture-sector
damages with these damage functions. The graph below didn’t make it into the
paper, but I think is informative:
What you see is that our estimated
agricultural damages (from the meta-analysis of yield estimates) are actually
larger than all market damages
included in PAGE. This indicates either that there are very small negative impacts
and large off-setting benefits in other market sectors, or that PAGE is
substantially under-estimating market damages. Comparing our results to DICE would
imply that agricultural impacts constitute 45% of non-SLR damages. This seems
high to me, considering this damage function includes both market and non-market
(i.e. mortality) damages. For instance, in their analysis of US
damages, Sol and co-authors found agricultural impacts to be dwarfed by
costs from increased mortality, and to be smaller than effects on labor
productivity and energy demand, suggesting to me that agricultural damages
might also be currently under-estimated in DICE.
That’s my (excessively-long) take
on the paper. Thank you to the GFEEDers for the opportunity. Do get in touch if
you have any questions or comments on the paper – fmoore at ucdavis dot edu.