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Dr. Gernot Wagner, NYU Department of Environmental Studies: “Declining CO2 Price Paths”
November 7, 2019 | 3:30 pm - 5:00 pm
Date & Time: November 7th, 3:30-5:00 PM
Location: Talley Student Union 4270
Please join us for a special guest lecture by Dr. Gernot Wagner, Clinical Associate Professor at New York University’s Department of Environmental Studies and Associated Clinical Professor at the NYU Wagner School of Public Service. Dr. Wagner wrote the books Climate Shock, joint with Dr. Martin Weitzman, in 2015; and But Will The Climate Notice? in 2011. He will present his recent research, “Declining CO2 Price Paths” (abstract below). All students, faculty, and staff are welcome to attend!
Abstract: Pricing greenhouse-gas (GHG) emissions involves making tradeoffs between consumption today and unknown damages in the (distant) future. While decision making under risk and uncertainty is the forte of financial economics, important insights from pricing financial assets do not typically inform standard climate–economy models. Here, we introduce EZ-Climate, a simple recursive dynamic asset pricing model that allows for a calibration of the carbon dioxide (CO2) price path based on probabilistic assumptions around climate damages. Atmospheric CO2 is the “asset” with a negative expected return. The economic model focuses on society’s willingness to substitute consumption across time and across uncertain states of nature, enabled by an Epstein–Zin (EZ) specification that delinks preferences over risk from intertemporal substitution. In contrast to most modeled CO2 price paths, EZ-Climate suggests a high price today that is expected to decline over time as the “insurance” value of mitigation declines and technological change makes emissions cuts cheaper. Second, higher risk aversion increases both the CO2 price and the risk premium relative to expected damages. Lastly, our model suggests large costs associated with delays in pricing CO2 emissions. In our base case, delaying implementation by 1 y leads to annual consumption losses of over 2%, a cost that roughly increases with the square of time per additional year of delay. The model also makes clear how sensitive results are to key inputs.