Dealing with uncertainty in the European climate policy

Although uncertainty is widely recognised as a key issue in the climate change issue, most economic models used to assess the greenhouse gas (GHG) abatement policy mix are deterministic. This report aims at filling this gap by developing a simple stochastic model of the European energy sector featuring the most important uncertainty sources in this context, including the economic activity in CO2-intensive sectors and the cost of key technologies, as well as the interaction between climate policy and renewable energy subsidies. The report finds that uncertainty changes the (ex ante) optimal policy choice and shows that incentives to renewable energies and energy savings should be maintained even if emissions are covered by an emissions trading scheme.

Attachment:

Lecuyer and Quirion (2015)_Dealing with uncertainty in the European climate policy.pdf

Citation:

Lecuyer, Oskar; Quirion, Philippe, 2015. Dealing with uncertainty in the European climate policy. CECILIA2050 WP4 Deliverable 4.4. Bern: University of Bern, Paris: SMASH, CIRED, CNRS.

Funding:

European Commission

Authors:

Oskar Lecuyer, University of Bern, Philippe Quirion, SMASH, CIRED, CNRS

Year of publication:

2015

Number of pages:

1	Executive summary	5
2	Introduction	7
3	Uncertainty in climate policy-making: key insights from economic studies	8
3.1	Global analyses	8
3.2	Policy-making at the domestic level	9
3.2.1	Carbon pricing	9
3.2.2	Renewable energy support	10
3.2.3	Combination between carbon pricing and renewable energy support	12
4	Uncertainty and interaction of climate and energy policies in the European context	13
4.1	Motivation and scenarios	13
4.2	A simple model: uncertainty on electricity demand and a single fossil-fuel technology	15
4.2.1	The producer’s profit maximisation problem	15
4.2.2	The social planner’s expected welfare maximisation problem	17
4.3	Results of the simple model	18
4.3.1	Optimal policy levels when uncertainty is low	18
4.3.2	Optimal policy levels when uncertainty is high	19
4.3.3	Ordering renewable support instruments with uncertainty when combined to an emission cap	20
4.4	Alternative model settings: uncertainty in the cost of renewable technologies and multiple fossil technologies	21
4.4.1	Uncertainty in the cost of renewable technologies	21
4.4.2	Multiple fossil technologies, uncertain future demand	22
4.5	Numerical application to the European electricity market for 2030	23
4.5.1	Model setting	23
4.5.2	Ordering instruments with demand uncertainty	23
4.5.3	Technology uncertainty	28
5	Conclusions	30
	References	31