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Future prospects of Carbon Capture technologies: an expert elicitation

International Energy WorkshopParis, 19 June 2013

Valentina BosettiFondazione Eni Enrico Mattei ,Centro Euro-Mediterraneo per I Cambimenti Climatici, and Università Commerciale Luigi BocconiElena C. RicciFondazione Eni Enrico Mattei, Centro Euro-Mediterraneo per I Cambimenti Climatici andUniversità degli Studi di Milano

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• Motivation

• Our Study

• Expert Elicitation Methodology Results

• Integrated assessment modelling Assumptions Scenarios Results

• Conclusions

Outline

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• In a carbon-constrained world CCS will play a crucial role

• Compared to other options: Smoother transition to low-carbon economy Allows use of fossil fuels “Bridge technology” Would not be adopted otherwise

• We focus on Carbon Capture: Obstacles: costs.

• Public R&D investments have high opportunity costs (many technologies)important to compare effectiveness

• Lack of data on future costs (innovation is an uncertain process) Use of expert judgments is increasing

Motivation

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• Aim: evaluating future prospects of carbon capture technologies, under different policy scenarios

• Two phases:

EXPERT ELICITATION: Data collection and analysis on future costs (EP) 6 carbon capture technologies European sample Data usable by other modellers

INTEGRATED ASSESSMENT MODELLING: to evaluate optimality of investments in the long-term and compared to other mitigation options Coal+ccs, Gas+ccs, Woody-Bio+ccs More than one scenario of EP (not only on/off) Policy indications

Our Study

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• Protocol developed with Erin Baker (Umass) and Karen Jenny (Insight decisions)

• Expert judgments collected between Dec 2011 and May 2012

• Web-based + telephone follow-up Easier for location & agenda, multiple sessions, time to get information, visual support, result

standardization

• 6 technologies:

Part I – Expert elicitation: methodology (1)

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• 12 experts

• European sample (mainly)

• 58% Academia/Research – 42% Industry

Part I – Expert elicitation: methodology (2)

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• Probabilistic information on future values of Energy Penalty (EP) given different scenarios

Part I – Expert elicitation: methodology (3)

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Part I – Expert elicitation: scenarios

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Collected data:

Part I – Expert elicitation: results (1)

Panels: technologyE#: expert IDColour: ScenarioSymbol: 50th perc.Wiskers: 5th and 95th

Range: 7-78%

IQR > for ads, membr, other-PC

S3 < S2 < S1

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Technology ranking (mean, median, maximum values of EP):

•If we look at minimum values of EP the best case scenario (5th percentile): Other post-combustion technologies become the best performing technology for scenario 2 and 1

Part I – Expert elicitation: results (2)

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Input data: 3 evolution paths

Worst: least optimist expert in scenario 2 – carbon tax chilled-ammonia Best: most optimist expert in scenario 2 – carbon tax molden carbonate fuel-cells Best-R&D: most optimist expert in all scenarios (R&D) pre-combustion

Part II – Integrated assessment modelling: input

Most extreme cases of our sample

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WITCH: World Induced Technical Change Hybrid model

Hybrid I.A.M.: Economy: Ramsey-type optimal growth (inter-temporal) Energy: Energy sector detail (technology portfolio) Climate: Damage feedback (global variable)

12 Regions (“where” issues) Intertemporal (“when” issues) Game-theoretical set-up (free-riding incentives)

A dynamic integrated model of the world economy that provides normative information on the optimal response of the economic system to climate change damage and policy

Economic Activity

Energy Use

emissions

AtmosphereBiosphere

Deep Oceans

temperature

Part II – Integrated assessment modelling: The WITCH model

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We test different climate policy scenarios: Tax equivalent to a Stabilization 450 ppm-CO2eq Tax equivalent to a Stabilization 500 ppm-CO2eq Business as usual (no tax on emissions)

Part II – Integrated assessment modelling: Climate policy

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The scenarios we analyse:

Part II – Integrated assessment modelling: Scenarios

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Electricty generation (TWh/y)

Part II – Integrated assessment modelling: Results (1)

COAL with CCS

Worst: India, Te and Mena / +EasiaBest: +USA / +USA, China, E-euroBestRD: as above

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Electricty generation (TWh/y)

Part II – Integrated assessment modelling: Results (2)

WBIO with CCS

Worst: Laca, SSABest: china,BestRD: as above

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Electricty generation (TWh/y)

Part II – Integrated assessment modelling: Results (3)

GAS with CCS

Worst: Mena, Easia, India / SasiaBest: chinaBestRD: china

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Policy costs (% loss of GDP wrt BAU)

Range: 450: 1-40% wrt 450 no-ccs; 450: 0.2-43% wrt 450 no-ccs (China – Sasia).

Part II – Integrated assessment modelling: Results (4)

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Conclusions

• We evaluate the probabilistic impacts of climate policies and R&D on the efficiency of carbon capture via expert informed opinions/judgements

• Our results highlight that pre-combustion is in general the most promising technology, though other post-combustion technologies can perform very well or very badly

• EP values range quite a lot therefore it is important to investigate different technologies

• R&D initiatives seem to perform better than market mechanisms (but slightly)

• Both policy scenarios lower the expected values of EP in 2025

• We build three possible evolution paths for the most extreme values of EP and simulate them to compare coal+ccs, wbio+css and gas+css to other mitigation and electricity generation options

• Different paths impact both the size and the timing of investment and deployment of electricity generation with CCS, therefore it is important to study different paths and not just on/off option

• Policy costs can be reduced from 0.2% to 40% wrt to same scenarios without CCS

Thank You

elena.ricci@feem.it

C.so Magenta 63, 20123 Milano, Italy - www.feem.it

http://www.witchmodel.org/index.html

http://www.icarus-project.org/