*Names in bold indicate Presenter
Carbon capture and storage (CCS) is potentially one of the most important energy technologies to address climate change. CCS frequently accounts for more than 10% of future emissions reductions in integrated assessment modeling exercises. However, CCS is only likely to play such a large role in climate change mitigation if its costs are near or below the marginal cost of emissions abatement. The future costs of CCS depend in part on government actions, such as pricing pollution externalities and funding research. The effects of any of these specific policies on technology performance are highly uncertain. For example, whether and how much increased R&D funding will improve the performance of a specific technology is unknown, in part due to the inherent ex ante ignorance about the outcomes of investing in technology development. Even though future costs are highly uncertain, we are not completely ignorant; recent research has developed tools and produced data that, in combination, provide the basis for probabilistic estimates of future improvements in technology.
We address this uncertainty in technological change in two ways. First, in 2011, we conducted an expert elicitation in which individuals reported estimates of the efficiency of CCS technologies under 3 policy scenarios:
Scenario 1: No further U.S. government funded research and development (R&D) in CCS; current worldwide carbon policies are unchanged,
Scenario 2: No further U.S. government funded R&D in CCS, worldwide carbon policy equivalent to $100/t CO2 starting in 2015 and continuing indefinitely, and
Scenario 3: “High” US government investment in R&D 2015 through 2025; current worldwide carbon policies unchanged.
We use these elicitation results and additional data to model the future costs of 7 types of CCS technology applied to coal power plants. Second, we conduct extensive sensitivity analysis to assess the effects of various parameters on the cost of emissions reductions ($/tCO2) in 2025. Using data from a variety of sources, we explicitly characterize uncertainty in eight parameters including: energy penalties, capital costs, maintenance, discount rates, as well as CO2 transport and storage costs.
Although the expert elicitation of energy penalties under various policy conditions spans a considerable range, our initial results show that costs are more sensitive to assumptions about overnight capital costs and discounting. Assuming that several, but not all, of these sources of uncertainty are independent across technologies, we run Monte Carlo simulations to specify a distribution of the minimum costs of capture across these 7 CCS technologies.
We also introduce 2 mechanisms of technological change in our model of the future costs of CCS. Using the elicitation results, public R&D funding can increase the efficiency and feasibility of each technology. In the longer term, technologies also improve via production related effects, such as learning by doing and economies of scale.
3. Policy Experiments:
We use this model to evaluate the effects on the future costs of CCS from combinations of 3 policy instruments: (1) public R&D funding, (2) carbon prices, and (3) subsidies for early stage deployment.