Economic Sensitivities for Carbon Capture

Carbon capture, utilization and storage (CCUS) is a proven solution for reducing carbon dioxide (CO₂) emissions from industries such as power, cement, steel, and oil and gas. However, making the decision to proceed with a carbon capture project can be challenging due to high costs and uncertain revenue streams.

When evaluating a CCUS project, project owners need to understand key factors, such as capital and operating costs, carbon pricing, and government incentives, that influence project economics. Analyzing how these factors impact the net present value (NPV) of a project helps guide risk mitigation strategies, optimize economic returns and make key design decisions, including technology selection and operational integration.

Sensitivity analysis is a valuable tool for testing the financial robustness of carbon capture projects under varying conditions. Economic sensitivities refer to how changes in various factors impact a project’s profitability. For example, what happens to the value of a project if carbon prices rise or fall? How dependent are project economics on government incentives?

There is limited publicly available information about CCUS projects’ sensitivity to economic inputs. For Canada in particular, there is limited analysis on how carbon pricing regimes and uncertainties in financial incentives impact project viability. Similarly, key capture system design features like combined heat and power (CHP) plants have been investigated as a way to improve capture project economics. In addition to providing heat and power needed for the capture system, excess power can be sold to the grid; however, there is little analysis on their sensitivity to utility pricing and Canadian policy factors.

A probabilistic model has been developed to assess economic sensitivities and their influence on a project’s NPV. This model is based on a hypothetical carbon capture plant in Alberta. The following economic inputs were used:

1. Capital and operating costs:

Carbon capture projects are expensive to build and operate (sometimes upwards of billions of dollars). If capital and/or operating costs change (i.e., go up or down), this can significantly change profitability.

2. Fuel and power pricing:

CCUS is energy-intensive, meaning it requires a lot of fuel (i.e., natural gas) and/or power (i.e., electricity) to operate, and these prices are not static. Natural gas prices are influenced by global energy markets, pipeline constraints or supply issues, and seasonal demands. Power prices are influenced by market demands, carbon pricing or emissions regulations, and weather impacts.  These fluctuations in fuel and power pricing can have a significant impact on operating costs and revenue.

3. Carbon pricing:

Carbon pricing puts a cost on CO₂ emissions to encourage companies to reduce emissions. In Alberta, large emitters are subject to emissions reductions targets under the Technology Innovation and Emissions Reduction Regulation (TIER). For emissions above this target, facilities are required to pay into the TIER fund at a fixed carbon tax price, or submit carbon credits. Carbon credits can be generated by reducing facility emissions below the target or by conducting approved emission reduction offset projects, including CCUS. By reducing emissions with CCUS, emitters can avoid paying carbon taxes (set at a fixed value under TIER) and earn revenue through the sale of carbon credits at market prices. This model evaluates the impacts of fluctuating carbon taxes and carbon credit prices on project viability.

4. Government Incentives:

The governments of Canada and Alberta have introduced financial incentives to encourage the development of CCUS projects. The federal CCUS Investment Tax Credit (ITC) will refund up to 50% of a point source capture project’s eligible capital costs. The Alberta Carbon Capture Incentive Program (ACCIP) is tied to the CCUS ITC, and will repay up to 12% of eligible capital costs (visit Canadian CCUS Incentives Tool | CCS Knowledge Centre for details). As government funding has a big impact on the profitability of a CCUS project, any changes to government policies and priorities introduce uncertainty into the economics of future projects.

Model Assumptions

To understand the relative sensitivity of carbon capture economics to various inputs, we simulated a post-combustion amine-based carbon capture plant on a natural gas-fired power plant in Alberta. Two case studies were examined.

1. Auxiliary Boiler: The capture plant has a natural gas-fired auxiliary boiler to generate the steam required for the capture process (i.e., solvent regeneration).
2. Combined Heat and Power Plant: The capture plant has a natural gas-fired combined heat and power (CHP) plant which supplies both steam and electricity to the capture facility, eliminating the need to purchase external electricity. Any excess power generated by the CHP plant is sold to the grid, creating a revenue stream for the project.

To calculate the NPV of the project, the Alberta carbon capture incentives (i.e., CCUS ITC and ACCIP) were used in this model. Capital and operating costs, including natural gas prices, power prices and carbon prices were calculated at high, nominal (middle) and low ranges for these two facilities.

Disclaimer: This is a hypothetical, stand-alone project: actual results will depend on project parameters, owner’s existing assets and tax obligations, and carbon pricing schemes.

Click here to learn more about the study’s methodology and assumptions.

Interpreting the Results

What is a tornado chart?

Tornado charts represent the relative impact of the input variables on the project’s NPV. It is important to understand that these charts do not show which variables will create a positive or negative outcome for the project, but rather the most important variables to consider when evaluating this scenario.

How to read a tornado chart:

Vertical axis: This axis accounts for all of the input variables that were used in the evaluation. These variables are displayed in the order of the impact they have on the NPV (i.e. variables that are at the top of the list have a greater impact on NPV than those at the bottom of the list).

Horizontal axis: This axis outlines the percent change in NPV when a single variable is set to its high, nominal, or low value, while all other variables remain constant at their nominal value.  The centre (or nominal) point represents the baseline NPV, calculated when all input variables remain unchanged at their nominal value (i.e., 0% variation). Bars extending to the left indicate negative impacts to the NPV, while bars extending to the right show a positive impact. The length of each bar reflects the sensitivity of the NPV to that variable (i.e., longer bars indicate a greater influence, and shorter bars suggest less impact).