, 26 tweets, 9 min read Read on Twitter
Some notes on #AERE2019 coming your way! Climate, economy, agriculture, social cost of carbon and more.
Kevin Rennert from @rff: social cost of carbon estimates need GDP estimates to ~2300. Based on estimated growth rates, you can get $10 billion/yr GDP per capita! So...elicitation in progress to constrain statistical estimates of GDP.
Cool intercomparison tool: mimiframework.org allows easy comparison of DICE FUND and PAGE IAMs and their SCC estimates.
David Anthoff from @UCBerkeley: Using SNEASY and BRICK models to get Bayesian/MCMC-calibrated model of climate and sea level. Much better than DICE/FUND for climate hindcasts and other tests. Produces much narrower PDFs of SCC, esp reducing 95th percentile by 30-50%.
Richard Newell/Billy Pizer: discount rates should vary w/ income/growth rates per capita. Lots more in the slides at bit.ly/npp2019aere
Qingran Li from @DukeU: the shadow price of capital can be used to more firmly bound the discount rate. Uncertainties decline with time. Working paper at bit.ly/liandpizer
Lawrence Goulder from @Stanford: China has upcoming emissions trading system with "tradable performance standard". TPS *can* increase electricity production; cap & trade decreases. Some coal sectors nearly shut down w TPS. But TPS limits price increases, adjusts to macroeconomy.
China's TPS is a huge step forward but has a 12% higher cost for a given emission reduction, compared to cap & trade. Still, TPS has benefits ~ 5x the policy costs. Enforcement remains an open question.
Carolyn Fischer from @rff: the EU ETS had a price collapse after launch. Should there be a carbon floor price? UK has one, at 20 euro/ton. Various ways to implement/set a floor price.
@ArielOrtizBobea from @Cornell: US ag still highly vulnerable to climate shocks even though yields are up. Is this the case worldwide? Work in progress, but will use total factor productivity with effects on inputs removed.
... make that effects OF inputs removed.
Jon McFadden from @USDA: drought tolerant corn now makes up about 22% of US crop, in 2016. Up from 2% in 2011. Uptake rates somewhat related to historical drought shocks and their duration.
Favorite quote so far at #AERE2019: "The data are not all that crappy".
Daniel Bigelow from @USDA : most Ricardian ag assessments rely on self-reported land valuations. Use of actual transaction data from CoreLogic tends to increase uncertainties in climate-land value relationship. Totally opposite patterns for summer precipitation.
Jesus Arellano Gonzalez from @ucdavis: ag-climate research in developing countries violate many Hedonic/Ricardian assumptions. "Shadow values" for land show expected negative response to heat stress but HUGE positive response to access to credit.
Eric Edwards from NC State: relative to nearby private land, limited access to capital leads to lower use of irrigation and lower value crops on tribal trust land in Uinta Reservation, UT.
Points from discussant @ClimateFran from @ucdavis: do the historical land boundaries bias the irrigation conclusions? Or, is the long history of racist lending practices behind the lack of capital?
Ellen Bruno from @UCBerkeley: In Coachella Valley, CA, unrestricted water trade would save about $14 million/year. Based on model tuned on amazing groundwater microdata. In general, water markets may substantially reduce the economic impacts of droughts.
Nick Hagerty from @UCBerkeley: variation in irrigation policy across space are a proxy for ag adaptation to climate change. Variations in water supply negate only ~ 25% short term weather impacts. So: you CAN use short-term weather impacts to estimate climate change impacts. Wow!
Matt Woerman from U Mass Amherst: congestion in Texas electricity production can lead to splintering of markets and regional markets (bc heat reduces efficiency of electricity transfers). 10% smaller market size > 2x markups; climate change will increase congestion and markups.
Hyeongyul Roh from @DukeU: self-organizing maps classify regional variation in electricity prices in Texas. Can be homogeneous or wildly variable. Recent expansion of transmission capacity sharply reduces the price heterogeneity associated with wind power.
Jonas Savelsberg presently at @UCBerkeley: heatwaves can increase European electricity prices by 4.5% on average. But can be much higher regionally: 40% increase in Austria.
Steve Cicala from @UChicago: if you look at weather effects only, RCP 8.5 means ~ 5% increase in US electricity use. Goes to >8% if you allow for adaptation practices. #AERE2019
@ClimateFran shows that water trading in CA could save $2 billion per year. #AERE219
Simon Dietz argues that we need to bring discussions of mitigation costs together with climate damages. At the moment, the two communities are largely separate. #AERE2019
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