Abstract
We study stabilization targets: common environmental policy recommendations that specify a maximum probability of an environmental variable exceeding a fixed target (e.g., limit climate change to at most 2°C above pre-industrial). Previous work generally considers stabilization targets under certainty equivalence. Using an integrated assessment model with uncertainty about the sensitivity of the temperature to greenhouse gas (GHG) concentrations (the climate sensitivity), learning, and random weather shocks, we calculate the optimal GHG emissions policy with and without stabilization targets. We characterize the range of feasible targets and show that the climate is difficult to control in the short run, although as learning resolves the planner eventually achieves the target with a sustained reduction in emissions over time. We find that uncertainty exacerbates the welfare cost of stabilization targets. First, the targets are inflexible and do not adjust to new information about the climate system. Second, the target forces the emissions policy to overreact to transient shocks. These effects are present only in a model with uncertainty. Introduction of a stabilization target into the baseline model with uncertainty results in a welfare loss of 4.7%, which is 66% higher than the cost of introducing the target in the certainty version of the model.
| Original language | English (US) |
|---|---|
| Pages (from-to) | 611-657 |
| Number of pages | 47 |
| Journal | Journal of the Association of Environmental and Resource Economists |
| Volume | 4 |
| Issue number | 2 |
| DOIs | |
| State | Published - Jun 1 2017 |
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Keywords
- Climate change
- Learning
- Probabilistic stabilization targets
- Stabilization targets
- Uncertainty
ASJC Scopus subject areas
- Economics and Econometrics
- Nature and Landscape Conservation
- Management, Monitoring, Policy and Law
Cite this
Probabilistic stabilization targets. / Fitzpatrick, Luke G.; Kelly, David L.
In: Journal of the Association of Environmental and Resource Economists, Vol. 4, No. 2, 01.06.2017, p. 611-657.Research output: Contribution to journal › Article
}
TY - JOUR
T1 - Probabilistic stabilization targets
AU - Fitzpatrick, Luke G.
AU - Kelly, David L.
PY - 2017/6/1
Y1 - 2017/6/1
N2 - We study stabilization targets: common environmental policy recommendations that specify a maximum probability of an environmental variable exceeding a fixed target (e.g., limit climate change to at most 2°C above pre-industrial). Previous work generally considers stabilization targets under certainty equivalence. Using an integrated assessment model with uncertainty about the sensitivity of the temperature to greenhouse gas (GHG) concentrations (the climate sensitivity), learning, and random weather shocks, we calculate the optimal GHG emissions policy with and without stabilization targets. We characterize the range of feasible targets and show that the climate is difficult to control in the short run, although as learning resolves the planner eventually achieves the target with a sustained reduction in emissions over time. We find that uncertainty exacerbates the welfare cost of stabilization targets. First, the targets are inflexible and do not adjust to new information about the climate system. Second, the target forces the emissions policy to overreact to transient shocks. These effects are present only in a model with uncertainty. Introduction of a stabilization target into the baseline model with uncertainty results in a welfare loss of 4.7%, which is 66% higher than the cost of introducing the target in the certainty version of the model.
AB - We study stabilization targets: common environmental policy recommendations that specify a maximum probability of an environmental variable exceeding a fixed target (e.g., limit climate change to at most 2°C above pre-industrial). Previous work generally considers stabilization targets under certainty equivalence. Using an integrated assessment model with uncertainty about the sensitivity of the temperature to greenhouse gas (GHG) concentrations (the climate sensitivity), learning, and random weather shocks, we calculate the optimal GHG emissions policy with and without stabilization targets. We characterize the range of feasible targets and show that the climate is difficult to control in the short run, although as learning resolves the planner eventually achieves the target with a sustained reduction in emissions over time. We find that uncertainty exacerbates the welfare cost of stabilization targets. First, the targets are inflexible and do not adjust to new information about the climate system. Second, the target forces the emissions policy to overreact to transient shocks. These effects are present only in a model with uncertainty. Introduction of a stabilization target into the baseline model with uncertainty results in a welfare loss of 4.7%, which is 66% higher than the cost of introducing the target in the certainty version of the model.
KW - Climate change
KW - Learning
KW - Probabilistic stabilization targets
KW - Stabilization targets
KW - Uncertainty
UR - http://www.scopus.com/inward/record.url?scp=85031105498&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85031105498&partnerID=8YFLogxK
U2 - 10.1086/691687
DO - 10.1086/691687
M3 - Article
AN - SCOPUS:85031105498
VL - 4
SP - 611
EP - 657
JO - Journal of the Association of Environmental and Resource Economists
JF - Journal of the Association of Environmental and Resource Economists
SN - 2333-5955
IS - 2
ER -