Economic Efficiency of Renewable Portfolio Standards in

the Presence of Cap-and-Trade

Kenneth GillinghamYale School of Forestry & Environmental Studies

Arthur van BenthemStanford University

Motivation• Throughout the world, there has been a push for a cap-

and-trade (CAT) system on carbon emissions at the same time as a renewable portfolio standard (RPS)– Recent US Senate and House climate bill proposals– Current EU climate policy (binding cap; voluntary RPS)

Question: Why would we ever want an RPS if we already have a CAT?

Specific Questions

• What would we have to believe for an RPS to be economic-efficiency improving if we already have CAT?– What market failures would need to be present?– How important would we have to believe those market

failures are? – Does the RPS in current/proposed U.S. climate policy improve

economic efficiency?– What is the loss in economic efficiency from not adopting a

first-best policy?

Outline of Research Project

• Theoretical Model– Modeling market failures

• Simulation Model– Partial-equilibrium model calibrated to the U.S. economy– Market failures made more explicit than in previous work

Proposed ACES in the U.S.

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Tg C

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total emissions covered cap level 2005 emissions

Many state-level RPS policies already

• An RPS was proposed in concert with ACES

• Already numerous state-level RPS policies

Source: US DOE EERE (2011)

Market Failures

• Three Market Failures1. Externality from carbon dioxide (and other) emissions2. R&D spillovers3. Learning-by-doing (LBD) spillovers

• Starting point intuition:– If we only have an environmental externality and we

internalize it, RPS must be efficiency-reducing– But RPS may help address R&D and LBD spillovers…

R&D Market Failure Intuition

• Two period model– Cost functions

– Profits

– Competitive equilibrium vs. social optimum

R&D Market Failure Intuition

• Competitive equilibrium underprovides R&D investment• Degree of appropriability is given by

• By symmetry, r0i=R0,-i/(N-1), and we can specify

– aR is fraction of R&D investment that is appropriable– This enables representative firm approach

versus

Learning-by-doing Market Failure

Quantity of electricity (q0)

Pri

ce

of

ele

ctr

icit

y (p0) t = 0

p0*

MC0(q0)

q0 q0,soc

A

Quantity of electricity (q1)

Pri

ce

of

ele

ctr

icit

y (p1) t = 1

p1*

MC0(q0)

q1 q1,soc

MC1(q1)

MC1,soc(q1)

B C

Partial-Equilibrium Model

• Simple partial equilibrium model– Two sectors: electricity (E) and industry (I)– Electricity can be renewable (R) or fossil (F)– Quantities qF, qR and qI

– Carbon intensities bF, bI (bR = 0)– Marginal environmental damage t– Cost functions Ci(qi) (i = R,F,I)

– Demand pE(qF+qR) and pI(qI)

• For simplicity, assume:𝐶𝑅 (𝑞𝑅 ,𝑅 )=𝛾 (𝑅 )𝑐𝑅 (𝑞𝑅 )

Modeling Cap-and-trade and RPS

• Cap-and-trade – emissions must be less than

• RPS – fraction of electricity generation by fossil fuels must be less than

Social Planner vs Decentralized

• More general social planner’s problem:

• Compare to the representative agent problem with a cap-and-trade and an RPS:

RPS helps to address innovation market failures – but imperfectly

• Let’s compare the first order conditions of the two problems:

R&D underinvestment

R&D overinvestment

Several Propositions

If no innovation market failures exist:1. Adding a binding RPS to an existing cap reduces the

optimal permit priceWith both environmental and innovation market failures:2. In the absence of R&D subsidies, the permit price

should be set higher than the marginal damages3. In the absence of a CAT, a small binding RPS will always

be economic efficiency-improving4. With a CAT, a small binding RPS will always be

economic-efficiency improving

What Does this Mean for Policy?

• This is where the simulation model comes in…– We parameterize and calibrate the model to the US economy

*PRELIMINARY FINDINGS* • Under our base case set of assumptions:– We find that it is going to be very difficult to justify any large-

scale RPS policy– Without a CAT, it appears that the socially optimal RPS policy

may not be too far from some of the state-level policies

Acknowledgments

• We would like to thank Larry Goulder of Stanford University for his thoughtful comments about this project