1 Economics 331b Spring 2010 Week of April 5 Integrated Assessment Models: Modeling Mitigation (Abatement)

Agenda This week (Monday and Wednesday): - Review on term paper - How to calculate SCC - Mitigation Monday: Add last module to your little model: mitigation. 2

How to estimate SCC 1.Numerical derivative: - Calculate PV income - Recalculate PV income with 1 additional unit of E - Take difference 2. Analytical: - Have Damage=D=f(T); T = g(RF); RF=h(C); C=z(E). - Therefore D’(E)=f’ g’ h’ z’ 3

Model estimate 4

5 Fossil fuel use generates CO2 emissions Carbon cycle: redistributes around atmosphere, oceans, etc. Climate system: change in radiation warming, precip, ocean currents, etc.. Impacts on ecosystems, agriculture, diseases, skiing, golfing, … Measures to control emissions (limits, taxes, subsidies, …) The emissions -climate- impacts- policy nexus

6 Price of carbon emissions Social cost of carbon The basic analytical structure Abatement P carbon * Marginal Cost 0 Abatement*

Mitigation (abatement) We have examined the damage side. For a full cost-benefit analysis, we need the cost side. “Mitigation” involves analyses of the policies involving the reduction of emissions CO2 and other GHGs There are four major issues involved: 1. Projecting the emissions 2. Estimating the costs of emissions reductions 3. Designing policies to reduce emissions 4. Encouraging low-carbon technological change This set of tasks is generally much easier that impacts because we have extensive information on impacts of energy taxes, regulations, etc. 7

1. Projecting emissions For this we need an integrated assessment model. As an example, the following shows the projected emissions to 2105 in the Yale-RICE model and in several other models examined in EMF-22. 8

Projections CO2 emissions various models (with no emissions reductions policies) 9 EMF-22 and Yale-RICE model

2. Estimating Costs of Reducing Emissions Analysts use different strategies to model abatement: –Some use econometric analysis (“top-down”) –Some use engineering/mathematical programming estimates (“bottom up”) Econometric: Look for some kind of “experiment” in which energy or carbon prices vary. Then estimate impact of higher prices on carbon emissions: - Some examples of CO2 taxes or European Trading System. - More useful are energy taxes. - Some rely on production functions and simulations. 10

11 Example from McKinzey Study

Example of econometric (“top-down”) approach to mitigation Assume that the demand for gasoline is Q = Bp -λ Supply of gasoline is perfectly elastic with tax τ: p = q + τ CO 2 emissions are proportional to consumption: E = kQ So we have: E = kB -λ (q + τ) -λ =c (q + τ) -λ [Numbers are calibrated to Actual US data.] 12

Survey of multiple models from IPCC FAR 13 Source: IPCC, AR4, Mitigation.

Summary of estimates 14 Source: IPCC, AR4, Mitigation, p. 77.

Summary from IPCC 15

16 Further discussion There has been a great deal of controversy about the McKinsey study. The idea of “negative cost” emissions reduction raises major conceptual and policy issues. For the DICE/RICE models, we have generally relied on more micro and engineering studies. The next set of slides shows estimates based on the IPCC Fourth Assessment Report survey of mitigation costs. The bottom line is that the cost using the top-down approaches are generally higher than bottom-up.

What are your views on top down v. bottom up? 17 There is a very lively controversy about the role of "negative cost" mitigation. The McKinsey report (Reducing US Greenhouse Emissions, p. xiii) has a very substantial number of such mitigation possibilities. Other modelers are sharply critical of the MK report and believe that (aside from external costs) there are very few negative cost options. You should take a specific example from the report. Make a case for whether the negative cost finding is correct or not. I will call on some of you at the beginning of class for a short report.

Nordhaus house survey 18

Derivation of mitigation cost function in RICE model Start with a reduced-form cost function: (1) C = Qλμ  where C = mitigation cost, Q = GDP, μ = emissions control rate, λ,  are parameters. Take the derivative w.r.t. emissions and substitute σ = E 0 /Q (2) dC/dE = MC emissions reductions = Qλβμ  -1 [dμ/dE] = λβμ  -1 /σ Note that MC(0) = 0; MC(1) = λβ/σ = price of backstop technology*; and C/Q = λ with zero emissions. *”Backstop technology” is technology at which get 100 emissions reduction (say solar/nuclear/fusion/wind for everything). 19

The 2 °C target Current policy has focused on a target of 2 °C rise from pre-industrial times. Copenhagen Accord of December 2009, which “recognized … the scientific view that the increase in global temperature should be below 2 degrees Celsius.” Sources of “scientific view”: –Climate history over long run –Possible tipping points in climate system –Thresholds for ecosystems 20