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Prices and Quantities in a Climate Policy Setting. Svante Mandell. Observations and aim. In practice (the EU): Overarching quantitative target for CO 2 A dual regulation; CaT and emission taxes Under uncertainty, emissions taxes outperform CaT for handling GHG
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Prices and Quantities in a Climate Policy Setting Svante Mandell
Observations and aim • In practice (the EU): • Overarching quantitative target for CO2 • A dual regulation; CaT and emission taxes • Under uncertainty, emissions taxes outperform CaT for handling GHG • Q: (When) is a dual regulation justifiable?
The model • Starts in a classic Weitzman (-74) setting • Linear MAC- and MAB-functions • Uncertainty (additative, symmetric round zero) • Aggregate abatement benefits relevant • Answers if CaT or emission tax is preferable • CO2 causes a stock externality • A flat MAB • Variation in emissions ‘better’ than variation in price Use a tax
The model, cont. • Mandell (2008), JEEM: • Allow for dual regulation • Tax a subset of emitters, the rest CaT • Outcome closer to optimum, but not cost effective • Full CaT never optimal, full tax optimal for (sufficiently) flat MAB-functions
The model, cont. • This paper: • Flat (horizontal) MAB-function • A global cap that may never be exceeded • Two periods • Intuition: • The global cap may require high tax to be met full tax may not be optimal
Timing of the model STAGE 0 Policy maker decides on share to tax and tax level STAGE 1 Emitters choose emission volumes STAGE 2 Emitters choose emission volumes Uncertainty 1 is resolved Possible surplus is banked Tax level may be changed Uncertainty 2 is resolved
Policy goal • Policy maker strives to • Minimize present value of expected efficiency loss • S.t. the global cap must not be exceeded • Thus, we need an expression for E{DWLtot}
Period 1 Period 2 Allocation error Abatement efforts not distributed in a cost effective manner Volume error Actual emissions differ from efficient amount Two sources of eff. loss
The taxes • As low as global cap permits, but never below the MAB • Less stringent global cap lower taxes • Period 1 is ”sunk” when setting T2 • T2 typically lower than T1, due to surplus • Taxes increase in share of taxed emitters
n* / N = 0 = 0.5 = 1 Optimal share to tax (n*) = discount factor At =0 the model becomes a one-period model (outcomes in period 2 are given zero weight) ”Strict” global caps, i.e., a cap below expected efficient level ”Lenient” global caps, i.e., a cap above expected efficient level
Some intuition for n* • Start in a situation where • Global cap = expected efficient level • All emitters are in CaT • At low MAC realizations – too high emissions • At high MAC realizations – too low • Thus, an expected volume error • But no allocation error
Some intuition for n* (cont.) • Move some emitters to taxed sector • At low realizations; decreased error • At highest realization; emissions equal global cap • Other high realizations; increased error • And also an allocation error • Motivates a small taxed sector
Some intuition for n* (cont.) • Now, consider a higher global cap • A larger set of realizations will yield a decrease in efficiency loss • Motivates taxing a larger share • Thus, n* increases in the global cap
The role of the discount factor • Most likely a surplus in period 1 • Policy maker may not destroy permits – increased cap period 2 • Lenient cap period 1 even more so period 2 risk for large efficiency loss • Stringent cap period 1 less stringent period 2 may decrease efficiency loss
More weight on period 2 calls for a lower n* under leninet global cap… n* / N = 0 …but a higher n* under stricter global cap = 0.5 = 1 The role of the discount factor
Conclusions • Often, a dual regime is better than full emissions tax or CaT • Even accounting for not cost effective • This depends on • The global cap vs. E{eff. emissions} • Indirectly the slope of the MAC vs MAB • The discount factor
Actual EU policy • Contains both crucial elements • A quantitative target and a flat MAB, but: • The ’global cap’ is not entirely fixed, e.g., CDM • Suggests the model underestimates the optimal share to tax • Trading firms may bank ’individually’ • Suggests the model overestimates the optimal share to tax