The American Power Act, a proposed U.S. cap and trade energy law which was put forth in May 2010, specifies that 72 GW of Carbon Capture and Storage (CCS) technology would be installed by 2034.
As an economist, I had a strong curiousity about what it would take to achieve this 72 GW objective.
To fullfill such curiousity, I studied the proposed cap and trade energy bill and analyzed how different input parameters (such as carbon price, technological process, and carbon allowance auction process) will affect the progress of CCS technology deployment.
The biggest challenge was how to translate 1000 pages of legal language of American Power Act into colloquial English. I was lucky that the process was relatively easy with great assistance from Jonas Monast, an environmental lawyer associated with the Nicholas Institute for Environmental Policy Solutions at Duke University, and Mike Nolan, a Nicholas Institute’s energy intern, who currently pursues dual-degree MBA-MEM at both Nicholas School and Fuqua Business School. Additionally, I had an intensive brainstorming with my good friend Gurpreet Neeraj Singh, an energy consultant at Resource System Group.
I transformed these observations (in collquial English) into an economic model called “CCSDeploy” in which I run different possible scenarios (slow, moderate, and rapid CCS deployment progress). Prof. Bruce McCarl, an expert in economic modeling and climate policy, reviewed the early manuscript in which I am very grateful. (However, remaining mistakes left are mine and mine alone.)
By developing this model, I can demonstrates that the 72 GW objective can only be achieved in a rapid deployment scenario with quick learning-by-doing and high carbon price starting at $25/ton in 2013 with a 5% annual increase. I also proposed an economic indicator, bonus ratio, that helps determine CCS economic plausibility.
By the time I completed the model and analysis the American Power Act had stalled in congress. Hence, I did not publish this piece. Nevertheless, I believe the analysis reveals new information that no other economists have uncovered.
Darmawan Prasodjo, PhD
Modeling CCS Deployment under Cap and Trade
by Darmawan Prasodjo, Ph.D
This study analyzes Carbon Capture Storage (CCS) deployment under the features in a piece of legislation – the draft of cap and trade American Power Act (APA) – that was proposed in 2010 which contained a goal of CCS capacity for emissions from 72 Gigawatt (GW) by 2034. A model was developed that simulates CCS deployment while considering different combinations of carbon price trajectories, technology progress, and assumed auction prices. The model shows that the deployment rate of CCS technology under APA is affected by the available bonus allowances, carbon price trajectory, CCS incentive, technological adaptation, and auction process. Furthermore it demonstrates that the 72 GW objective can only be achieved in a rapid deployment scenario with quick learning-by-doing and high carbon price starting at $25 in 2013 with a 5% annual increase. Furthermore under the slow and moderate deployment scenarios CCS capacity falls short of achieving the 72 GW objectives.
Keywords: CCS; Carbon Capture and Storage; Climate Change; Cap and Trade; Economic Model; Energy Policy;
As a reliable and affordable source of domestic energy, coal has in large part powered the American economy for decades. Coal-fired power plants account for 50% of the electricity generated and 27% of total fossil fuel energy consumed in the United States.This trend will likely continue. According to the Energy Information Agency (EIA), the U.S. has the world’s largest supply of recoverable coal reserves totaling about 275 billion tons, which is enough to accommodate demand for 250 years at the current rate of consumption. Coal is also an advantageous energy source because of its unquestionable thrift. It provides energy at a cost between $1 and $3 per million Btu with the next most inexpensive fuel source being natural gas at $4-$6 per million Btu. Given this current and future economic advantage, it’s all but certain that coal will remain a dominant part of the U.S. energy mix for decades to come. Although, it is likely that business as usual for coal-fired power plants will be impacted by future possibility of carbon constrained economy.
Practical experience and detailed technical and engineering cost studies demonstrate that CCS is both technologically and potentially economically feasible. According to a report by the Interagency Task force on Carbon Capture and Storage published this August (Interagency Task Force on Carbon Capture and Storage 2010) the required technologies (capture, transmission, and storage) to perform CCS already exist. The real-world barrier to CCS development is the lack of a supportive environmental regulation. A supportive national policy will assist utility companies in overcoming the incremental costs of adopting CCS and creating stable and reliable frameworks for private investments.
In the past, environmental law and regulation was dominated by command-and-control approaches. In the 1990s this approach shifted and policy makers increasingly explored environmental policy instruments which provided economic incentives for firms and individuals to reach environmental goals. For instance, the 1990 Clean Air Act Amendment that proposed controlling acid rain with a cap and trade program is cited by “The Economist” magazine as the greatest green success story of that decade. Following the success of the 1990 Clean Air Act, the U.S. has attempted to take actions to reduce carbon dioxide and other greenhouse gas emissions linked with global climate change using market-based instruments.
In June, 26, 2009, the U.S. House of Representatives approved a comprehensive climate energy legislation known as the American Clean Energy and Security Act (ACES) or HR 2454. However, the bill died in the Senate and never became law. On May, 2010, Sen. John Kerry (D-MA) and Sen. Joe Lieberman (I-CT) introduced similar comprehensive energy legislation, the American Power Act. This proposed legislation also stalled in the national legislature and failed to become law. To analyze the economic feasibility of CCS technologies under cap and trade instruments I developed a model to simulate the different scenarios of CCS deployment by considering different combinations of carbon price trajectories, technological progress, and allowance auction. The model is based on the premise that CCS costs is high ($80-$150/ton) and that the expected carbon price is much lower ($15-$25/ton with a 5% annual increase). Under these assumptions, installing CCS technology without a government subsidy is economically unfeasible. The real constraint of CCS deployment under cap and trade policy is the availability of CCS bonus allowances, a form of government subsidy. However, the relationship between bonus allowances and tons of CO2 captured is not linearly one-to-one. The amount of CO2 captured (and permanently sequestered) is recognized by a quantity of bonus allowances that are awarded with respect to the current carbon price.
Based on this premise, I propose, design, and develop a model that simulates the distribution of a guaranteed CCS bonus allowance by considering the dynamics of carbon pricing, the progress of CCS technology, and the availability of CCS bonus allowances under cap and trade policy. This study uses the American Power Act (APA) for a case study in systematic modeling. First I identify the features of the APA such as the available bonus allowance and the characteristics of each phase. Second, I identify the empirical interrelationship between available bonus allowance and the amount of CO2captured coupled with carbon price trajectories and CCS technological progress. Third, I translate this understanding (model requirement) into an economic model. I consider three scenarios: rapid, moderate, and slow deployments. I aim to model, explain, and assess the likelihood of achieving the goal of 72 GW of deployment by 2034.
This study is able to achieve these objectives:
- identifying the CCS economic path in reaching 72 GW net CCS capacity
- quantifying net CCS capacity under different scenarios
- quantifying the amount of CO2 captured
- quantifying the bonus ratio
- identifying the deployment stage schedule
2. Cap and Trade Literature Review
Cap and trade can be traced back to Coase’s idea of negotiated solutions to externality problems (Coase 1960). Crocker (Cocker 1966) and Dales (Dales 1968) independently developed the idea of using transferable discharge permits (TDP) to allocate the pollution-control burden among pollution sources. Dales’ work focuses on water pollution permitting while Crocker’s focuses on air pollution permitting (even though they use the same system). Montgomery (Montgomery 1972) provides the extension of proof that such a system could provide a cost-effective policy instrument by defining an emission based permit system (EPS). Tietenberg (Tietenberg 1985) suggests that a cost-effective solution may be achieved by the EPS approach, which allows for unit-for-unit trades among any sources in the same airshed (in the case of air pollution).
From these literature sources, it can be deduced that the allocation method of allowances (free distribution or auction) does not influence firms’ production and emission reduction decisions. Montgomery (Montgomery 1972) emphasizes that firms face the same emissions cost regardless of the allocation method. When using an allowance, whether it was received for free or purchased through auction, a firm loses the opportunity to sell that allowance, and thereby recognizes this “opportunity cost” in deciding whether to use the allowance. Consequently, the allocation choice will not influence a cap’s overall costs.
This section revisits the economic theory of cap and trade which is an adaptation of the basic cap and trade theory discussed in Hanley et al. (Hanley, Shogren and White 1997). Let’s consider a CO2-polluting coal-fired power plant that faces an increasing marginal pollution abatement cost curve (Figure 1). Without any regulation, the plant will choose to abate zero units of carbon and avoid the abatement costs represented by the area underneath the marginal abatement cost curve: B + C + D. Suppose a benefit-cost analysis has determined that optimal abatement occurs at the blue dot where the marginal benefit and marginal cost curves intersect. The resulting level of emissions is e* (Figure 1).
Using cap and trade, the government is to set a cap where marginal benefit equals marginal abatement cost. The efficient abatement level is achieved at e*, and the total abatement cost to the pollution firm is the area of B (Figure 1).
Alternately, in the case of a carbon tax, the government is to set a tax where marginal benefit equals marginal abatement cost. Firms operating coal fired power plants will notice that it is cheaper to abate carbon emissions as long as the marginal abatement cost is lower than the tax. Since the tax bill (A + B) is greater than the marginal abatement cost bill (B) to the left of the vertical “cap” line, the coal-fired power plant firms will choose to abate. To the right of the “cap” line, the marginal abatement cost bill (C + D) is greater than the tax bill (D) so the firm will choose to pay the tax and continue to pollute. As a result, the efficient abatement level is achieved at e*. The total abatement cost to firms operating coal-fired power plants is the total area of B + D, with total government revenue being D (Figure 1).
To understand the logic of trading carbon allowances between coal-fired power plants, a two-panel diagram is needed to illustrate the increasing marginal abatement costs of two coal-fired power plants (Figure 2). One plant utilizes Pulverized Coal (PC) technology with higher abatement cost (in blue) that goes right to left with abatement. The other plant uses Integrated Gasification Combined Cycle (IGCC) technology which has a lower abatement cost (in green) that goes left to right with abatement. The width of the horizontal axis is the abatement that must be achieved to reduce the overall emissions to an efficient level.
The intersection of the two marginal abatement costs is where economic efficiency is achieved. The total costs of achieving the efficient abatement/emissions level is C + G + K. The efficient emissions level, e*, shows that the low-abatement-cost power plant should allow for greater reductions in emissions than the high-abatement-cost power plant (Figure 2).
Under a cap and trade framework, a government body sets a carbon cap by issuing carbon permits to coal-fired power plants. Each permit gives the plant the right to emit one unit of carbon. For simplicity’s purpose, I can do it “fairly” by giving each plant the same amount of permits. The abatement cost for the low-abatement-cost (IGCC) plant is equal to area C. The abatement cost for the high-abatement-cost (PC) plant is D + F + G + K (Figure 2). At some point, the high-cost power plant might rather have a permit than pay those high costs. If it recognizes that the marginal abatement cost is higher than the marginal abatement cost of the low-cost plant, it could propose a trade. In effect, the blue line over area D, F and G is a demand curve for permits, and the green line is a supply curve for permits. Anywhere in between the blue and green line is a permit price that is mutually agreeable between both plants. A competitive permit market will result in a permit price equivalent to the efficient carbon tax. Trading reduces overall abatement costs by the area of D + F. The efficient abatement level is achieved at e*. The abatement cost to the polluting plants, C + G + K, is minimized (Figure 2).
5. An Overview of CCS Technology Development
A coal plant with CCS will cost more than a similar plant due to the additional cost to build and operate the capture facility. The capture process also requires additional energy which is referred to as an “energy penalty” (Al-Juaied and Whitmore 2009). The high cost and energy penalty burdens, as well as the lack of full-scale experience with CO2 capture at coal-fired plants are two of the many obstacles to the adoption of CCS. As time and technology progress, the reliability of the technology will improve, decreasing the costs of deploying CO2 capture with coal-fired power plants along with the attendant energy penalties.
The CCS community agrees that the first several full-scale operations at coal-fired power plants will perform inconsistently as CCS technology will still be in early development. The Energy Information Agency uses the National Energy Modeling System (NEMS) to configure a network of only 4 power plants for an initial deployment of CCS to minimize risk and to maximize the learning process (U.S. Energy Information Administration 2010). Kuuskraa (Kuuskraa 2007) refers to this initial stage as the “Smaller Scale Program” and Thompson et al. (Thompson, et al. 2010) call it the “Pioneer Phase.” Both agree that a larger-scale CCS project is needed to reduce the cost of CCS and Thompson et al. labels this subsequent stage as the “Cost Reduction Phase.” Our CCS technology development framework builds upon and refines these earlier efforts. Our model categorizes the development of the CCS industry as moving through four key phases: The CCS Startup Phase, the Early Adopter Phase, The Cost Reduction Phase, and The Full Scale CCS Deployment Phase (Figure 3).
5.1 The CCS Startup Phase
Prior to the startup phase, CCS technology has been developed on a laboratory scale or pilot project scale but has not been implemented on a full commercial scale. The first few commercial-scale CCS plants can be categorized as the 1st generation of CCS technology and they may face uncertainty regarding reliability and performance. The emphasis during this phase is to focus resources on critical reliability issues by identifying significant risks to CCS implementation as early as possible. The CCS startup phase should be designed to ensure that the incursion into new full-scale CCS technology territory will be a successful venture.
5.2 The Early Adopter Phase
The early adopter phase is characterized by technical improvements that lead to a better generation of CCS technology with fewer questions about reliability and performance. This phase is aimed at building confidence and experience in selecting, designing and operating integrated CO2 capture and storage systems. At the end of the early adopter phase, reliable cost and performance expectations for CO2 capture, transport and injection technology will be achieved.
5.3 The Cost Reduction Phase
In the cost reduction phase, experience gained from the early adopter phase, along with the on-going development of CCS technology, will contribute to further reductions in the high capital costs of installing CO2 capture technologies. Technological progress will likely reduce high energy requirements and loss of power generation output. The cost reduction will take place by traversing learning curves and expanding infrastructure such as pipelines and storage sites to support CCS growth. The cost reduction phase could transform CCS from a technology only affordable to a select few coal-fired plants to a cost-effective GHG mitigation option with global impact.
5.4 The Full Scale CCS Deployment Phase
Due to the progress achieved during the previous phases, CCS technology becomes technically reliable, cost-effective, and widely accepted resulting in full-scale CCS deployment. By this time the technology is ready to face rapid commercial deployment and expansion to a scale that makes deep reductions in carbon dioxide emissions possible by mid-century.
6. Components of CCS Abatement Cost
The components of CCS – capture, transport, and injection – already exist in mature markets for certain industrial applications but the technology has yet to be used in commercial-scale coal-fired power plants. For example, oil companies capture CO2 from the ground, use pipelines to transport it to mature oil fields, and then inject it into wells in order to perform enhanced oil recovery.
Table 1 gives the current capture cost, which ranges from $45 to $130 per ton of CO2 depending on the generating technology and type of coal used (Al-Juaied and Whitmore 2009). By 2030, after several generations of technological advancement, the future cost of capture is expected to drop by $25-$80 per ton of CO2.
The transportation cost varies based on the distance to available reservoirs and the scale of the pipeline. Using economies of scale, costs can be reduced by creating a network of pipelines that funnel into increasingly large pipes as shown in the previous part of this study. In this way CO2 can be piped as far as 1,000 km at a cost of less than $8/ton (Chandel, Pratson and Williams 2010).
For sequestration, the last leg of the CCS process, costs can range from $1-$1,000 per ton of CO2 depending on the nature of geological reservoir sites (Eccles, et al. 2009). Palo Alto Research Center estimates the sequestration cost component must be less than $5 per ton of CO2 in order for CCS to be cost-effective (Palo Alto Research Center 2008). Geological reservoirs with injection costs less than this sequestration cost of $5 per ton of CO2 will be utilized first with higher cost reservoirs becoming more feasible as technology matures.
The high cost of CO2 capture and uncertainty surrounding sequestration make CCS unfeasible for market penetration under current conditions. Given the right incentives, though, certain coal-fired power plants will be able to invest in and develop technologies to be implemented in the future by the industry at large.
7. American Power Act Features
Now I review features of the APA that must be considered in its analysis.
7.1 Bonus Allowance and CCS Technology Stages
To facilitate the deployment of carbon capture and storage (CCS) technology the APA provides for a bonus allowance to create incentives for operators to install CO2 capture facilities in retrofit power plants or to build new plants with carbon capture capabilities. The percentage of the national cap for CCS starts at 0.8% in 2017 and reaches 10% in 2034 (Figure 6). Since the national cap (Figure 4) decreases yearly, the actual value of the allowance for CCS also varies yearly, starting at about 50 million tons per year in the beginning and stabilizing between 300 and 350 million tons per year after 2022 (Figure 7).
The allocation of CCS bonus allowances in the APA will greatly impact CCS deployment and the progress of CCS technology. Hence it is important to analyze the availability of CCS bonus allowance allocations (Figure 7) under the framework of CCS technology development put forth in the CCS Technology Development Framework.
7.1.1 APA – The CCS Startup Phase
The APA recognizes the high technological risk related to the startup phase by providing less than 50 million tons of bonus allowance during the period of 2017 to 2019. The limited number of bonus allowances coupled with a low carbon price (range between $18/ton to $30/ton) during this period will restrict the number of pioneer plants that are able to participate in the program to only two or three mid-size power plants (300-500 MW). A small number of power plants installing CCS will minimize the risk and at the same time maximize the learning process. It is expected that critical reliability issues with mid-size (300-500) power plants will be resolved during the CCS startup phase in the span of three years.
7.1.2 APA – The Early Adopter Phase
In the startup phase, the CCS community will learn a great deal about 1st generation technology across the spectrum of capture, transportation, and sequestration. However, many technological uncertainties will remain due to different sized power plants, different generation technologies, and different geologic formations. The APA increases the amount of bonus allowance from 50 million tons to around 250 million tons during the period of 2020 to 2022. The additional bonus allowance allows additional power plants with different sizes and technologies to join the CCS program. These additional power plants will enable the process to improve the reliability of CCS technology, making it dependable for different combinations of power plant sizes, generation technologies, capture technologies and sequestration sites.
7.1.3 APA – The Cost Reduction Phase
CCS cost eventually has to be reduced in order for CCS to play a big role in mitigating CO2 emission in a cost-effective way. The APA facilitates such a cost reduction by allocating more bonus allowances – 300 to 350 million tons – from 2023 to 2034. The additional bonus allowance coupled with the higher carbon price during the same period will add additional power plants to the system and expand the CCS infrastructure further. The speech of Secretary Steven Chu in Charleston, West Virginia, captures the essence of this development, when he states “Engineers and scientists do remarkable things and costs are driven down.” (Chu 2010). The cost reduction phase could transform CCS from a technology only affordable to a select few coal fired plants to a cost-effective GHG mitigation option with global impact.
7.1.4 APA – The Full Scale CCS Deployment Phase
The APA expects that the CCS technology will reach maturity at the end of this program, creating broad public acceptance of CCS and enabling a transition to full commercial deployment to achieve GHG reduction objectives. The start of full-scale CCS deployment is contingent on both the success of the previous “cost reduction phase” and on carbon pricing. Full-scale deployment could start before 2030 if the program is successful from a technological, political, and societal perspective, or it could be delayed beyond 2034 if the progress of CCS deployment is slow.
7.2 APA CCS Deployment Phases
To progressively adopt CCS technology the CCS deployment program under the APA is divided into two phases:
7.2.1 Phase I
Phase one is mainly designed to overcome CCS technological challenges and is further divided into two tranches.
184.108.40.206 Tranche 1
In tranche one, the pioneer firms are rewarded with a CCS incentive in the form of bonus allowances. Starting from $50 per ton of equivalent bonus allowance for 50% CO2 captured and sequestered, a company could receive at most $96 per ton for 90% or above of CO2 avoided. The goal of tranche one is to eliminate the potential risks associated with CCS early deployment (Table 2). The pioneer power plants are expected to establish reliable cost and performance expectations of CCS, building confidence and experience in designing and operating CCS.
220.127.116.11 Tranche 2
Once the electric generating units reach the capacity of 10 GW, Phase one will move forward into tranche 2. The second tranche is similar to the first tranche, the only difference is the CCS incentive is $85 per ton instead of $96 per ton of CO2 emissions avoided. With ten more GWs of electric generating capacity the plants in the second tranche could further lower the high capital costs through learning, thus extending CCS infrastructure and market penetration while establishing even more reliable expectations of cost and performance (Table 2).
7.2.2 Phase II
During the second phase, the more mature technology will give operators an accurate expectation of cost and performance for building plants with CCS capability. On the policy side, instead of giving CCS incentives with fixed value, the central authority will distribute the bonus allowances based on reverse auction, meaning that only the companies with lowest bid can acquire the allowance. This procedure will favor the plants with efficient CCS technology which are able to capture and sequester carbon emissions with the lowest cost. Reverse auction will facilitate the adoption of efficient CCS technology and assist the utility sector in transitioning to commercial CCS deployment (Table 2).
7.3 Reverse Auction: APA Second Phase
Phase II of the APA sets the amount of CCS incentive per ton of CO2 emissions avoided through reverse auction. The reverse auction allows fossil fuel plants with CCS technology to offer bids to capture and store a ton of CO2 for a price. While traditional auctions involve a single seller and many buyers, a reverse auction generally involves many sellers, which in this case are the power companies, and one buyer, in this case the authority that distributes bonus allowances. In a first price reverse auction, the winner is the bidder who submits the lowest bid, and is paid the bonus price equal to his or her bid (Milgrom and Weber 1982). Alternately, in a second price reverse auction, the winner is the bidder who submits the lowest bid, and is paid a bonus price equal to the next lowest submitted bid (Milgrom and Weber 1982).
Power plants that can offer the lowest $/ton bids will win the auction and receive the associated bonus allowances. This policy ensures that incentive is available to enhance the likelihood of commercial CCS deployment. However, bonus allowances are only issued to those projects which can sequester CO2 at the lowest cost. Without a reverse auction, plants are likely to hold out on installing or retrofitting plants with CCS technology until the price of carbon reaches a “breakeven price” where the cost of capturing and sequestering a ton of CO2 is equal to the cost of one allowance. Most studies have indicated that this is around $40/ton of CO2, a price not reached until 2030 for most of the APA allowance price trajectories.
When power companies abate one ton of CO2, they have to incur the marginal cost of capturing, transporting, and injecting it noted below in equation (1) as CCSCost. During Phase II of a reverse auction, the APA provides a CCS subsidy in the amount of the Auction Price. In this scenario the total cost born by power companies is (CCScost – AuctionPrice). On the other hand, power companies have the option to keep emitting CO2 by purchasing CO2 permits at the prevailing market price. Power companies are indifferent in regards to choosing between going online with CCS or emitting CO2 by buying carbon permits when the total cost of CCS born by utility companies is equal to carbon price as dictated in equation (1):
With simple math we can rearrange equation (1) to become equation (2) and determine the minimum bids that a power plant is willing to submit. Equation (2) implies that the auction price should be higher than the total of CCS cost minus the carbon price.
Each coal-fired power plant has a unique CCS cost depending on their coal technology, the amount of CO2 capture, the spatial arrangement of the facility, and the sequestration site. It is pointed out in game theory that, during a bidding process, bidders have a dominant strategy to bid their true values which can be derived from equation (12) (Milgrom and Weber 1982). This is an important fact when firms are submitting their bids in a reverse auction, because those with the lowest costs are more likely to submit a lower price to win the bidding process. Since the winning bidder’s value is the minimum among all the values, the winning bid conveys a low bound on all the loser’s signaling the least incentive needed to install CCS technology at that point in time. Reverse auction ensures that the bonus allowance is only issued to the most efficient plant with the lowest capture and sequestration cost (Kuuskraa 2007). In this way, the APA can achieve a low-carbon economy with the lowest societal cost.
8. The CCS Deployment Model
My CCS deployment model factors in real-world considerations for CCS implementation. We achieve this by considering how the interaction of carbon price, CCS technology progress, the reverse auction process, and constraint of CCS bonus allowance availability will affect the progress of CCS deployment under cap and trade. I use the American Power Act as a case study.
The model is developed in two stages. In the first stage I translate the legal language of American Power Act into colloquial English by having extensive discussions with Jonas Monast, an environmental lawyer associated with The Nicholas Institute for Environmental Policy Solutions at Duke University. The resulting text is a summary of this collaboration:
- This model analyzes CCS deployment under a cap and trade climate policy framework. In this study, the model is specifically applied to the Kerry-Lieberman American Power Act (Kerry and Lieberman 2010).
- The model iterates each year from 2017 until 2034 to select eligible power plants. Allocation of the available CCS bonus allowance set by the APA is made to the greatest possible number of coal-fired power plants based on the cost of CCS technology and carbon price trajectory.
- There is a 90% carbon capture rate for all installed CCS units.
- The model simulates reverse auction allocations which are dependent on CCS cost, carbon price, and the resulting auction price. Remaining bonus allowances after allocation are added to the following year. No new CCS installations will receive allowances after 2034.
- The model is contract-based, which means that for each power plant participating in the program, it predicts the amount CO2 captured for the next 10 years and the amount of allowance needed to cover those captured emissions based on the predicted bonus ratio. This is necessary because the APA framework treats each contract differently depending on when power companies implement CCS (first phase, first or second tranche, second phase, etc).
- The participation of additional power plants in any given year will depend on the number of allowances made available under the emissions cap, the bonus price or auction price, the aggregate amount of CO2 emitted that year, and the carbon price at that time.
- 10 years of allowances are reserved for each additional plant to cover captured emissions. Power plants will be withdrawn from the CCS program after ten years of receiving bonus allowance and the bonus allowances tied to the plants are available for other power plants.
- Allowance allocation is optimized with perfect knowledge of future carbon price trajectory and future technological progress.
- The model stops picking an additional plant when remaining bonus allowances for that year are allocated; or remaining allowances until 2034 cannot cover the amount necessary for 10 years of capture.
In the second stage I translate this model requirement into an economic simulation program (Appendix A and B). In this study, I run the model with different combinations of carbon price trajectories, technological progress, CCS costs, and auction prices.
9. Empirical Model Setup
Now empirical assumptions to set up the APA model are reviewed including 1) relationship between bonus allowance and CO2 captured, 2) carbon price trajectories, 3) the relationship between carbon price and deployment scenarios, and 4) indicator of CCS economic plausibility.
9.1 Relationship Between Bonus Allowance and CO2 Captured
Our model is based on the premise that the relationship between bonus allowances and tons of CO2 captured is not one to one. CO2 that is captured and permanently sequestered is recognized by a quantity of bonus allowances that are awarded with respect to the current carbon price. The purpose of this conversion is so that the administrator can issue bonus allowances that will have a market value equivalent to the bonus price promised by the legislation (i.e. $96/ton during first tranche, $85/ton during second tranche and auction price during second phase.). For example, if the price of a ton of CO2 emission on the open market is $12/ton and the CCS bonus price is $96/ton (the bonus price promised by the legislative framework during first tranche) then the administrator would be required to give 8 bonus allowances (to match $96) for each ton captured, this is referred to as the “bonus ratio”.
Similarly, if the price of a carbon permit is $36/ton and the bonus price is $96/ton then 3 bonus allowances would be issued (to match the $96 bonus price) per ton of CO2 captured and sequestered. Hence the same quantity of available CCS bonus allowance may cover different amount of CO2 captured depending on different carbon price trajectories as described in Figure 8 and 9.
9.2 Carbon Price Trajectories
Our CCS deployment model uses allowance prices of $15, $20, and $25 per ton of CO2 starting in 2013 and increasing annually by 5% (Figure 10). This rate is consistent with other energy-economic models and with the U.S. Environmental Protection Agency’s (EPA) recent analysis of the American Power Act (U.S. Environmental Protection Agency 2010). In their base case scenario of the bill they use $16 and $17 per tCO2 in 2013, which falls between our slow and moderate CCS deployment scenarios. Initial allowance prices in the EPA’s analysis range from $18-40 for their scenarios.
9.3 Carbon Price and Deployment Scenarios
My CCS deployment model combines three carbon permit price trajectories with three CCS technology development trajectories in order to represent slow, moderate, and rapid CCS deployment scenarios (Table 3). The difference between CCS cost and carbon price directly influences the auction price of an allowance during the reverse auction of Phase II (equation 2). The implication of this relationship is that if CCS technology progresses quickly costs and auction prices are minimized. On the other hand, if technology develops slowly CCS costs and auction prices will remain high.
9.3.1 Slow Deployment Scenario
The initial carbon price is set low at $15/ton in 2013 and slow technological progress results in high CCS costs. This scenario leads to a large difference between CCS costs and carbon prices and a high auction price. The model sets the auction price at $70/ton (Table 3).
9.3.2 Moderate Deployment Scenario
The initial carbon price is set at $20/ton in 2013, a midrange value, and moderate technological progress results in moderate CCS costs. Because this scenario produces less of a difference between CCS costs and carbon prices than the slow deployment scenario a lower auction price of $50/ton is set (Table 3).
9.3.3 Rapid Deployment Scenario
The initial carbon price is set high at $25/ton in 2013, and rapid technological progress results in significant efficiency gains and greatly reduced CCS costs. This scenario leads to a small difference between CCS costs and carbon prices and a low auction price. Our model sets the auction price at $30/ton (Table 3).
9.4 Indicator of CCS Economic Plausibility
During the process of CCS deployment, the energy modeling community would greatly benefit from a CCS economic plausibility index to allow analysis of the economic performance of CCS technology and facilitate predictions of future performance. In this study, I propose the use of bonus ratio which is the ratio between the CCS incentive and carbon price (equation 3).
Bonus ratio also represents the quantity of bonus allowance issued to cover one ton of CO2. In this section I explain why the ratio between CCS incentive and carbon price conveys the degree of economic plausibility of CCS technology and can be very informative.
With the assumption of a CO2 capture rate of 90%, the APA sets the CCS incentive for tranche 1 and tranche 2 at $96/ton and $85/ton respectively (Table 4). It is clear that the bonus ratio during the first phase is purely determined by the market carbon price because the bonus price is fixed.
In the second phase, the amount of CCS incentive is not mandated. Instead it is determined through the process of reverse auction and is called auction price (Table 4). This reflects the more dynamic nature of the bonus ratio at this time as it will be affected by both the auction price and the carbon price. Additionally, according to equation (2), the auction price is dictated by the difference of CCS cost and carbon price. This means that the bonus ratio during the reverse auction framework of phase 2 is indirectly influenced by CCS cost and directly influenced by the carbon price.
One indication of full-scale CCS commercialization is when CCS technology is a cost- effective solution in mitigating climate change. It also means that CCS technology will be installed regardless of whether there is a CCS incentive. Equation (4) characterizes conditions when a power plant is indifferent whether to install CCS technology.
If there is no CCS incentive and a power plant is still indifferent whether to install CCS technology, we can derive equation(4) to become equation(5) by replacing CCS incentive by zero.
The full-scale CCS commercial deployment is dictated by equation(5) where marginal CCScost is less than carbon price. This can be achieved with a successful CCS technology development and in the same time high carbon price.
When CCS incentive is small (or close to zero), equation(3) can be derived to become equation(6).
Equation (6) holds only when equation (5) also holds. These two conditions signal that CO2 source operators are willing to install CCS technology without any form of CCS subsidy. Hence, the signal of full scale CCS commercial deployment is indicated by low value of bonus ratio. Bonus ratio less than one signals a progressive situation for CCS deployment. However, bonus ratio equal to zero means that CCS technology is trully a cost-effective solution to mitigate CO2 even without government incentive/subsidy.
10. APA Analysis
Now an analysis will be done on aspects of the APA including 1) the discussion of how the output of bonus ratio will affect the timing of APA phases, and 2) the discussion of how the bonus ratio will affect cumulative CCS Net Capacity and Amount of CO2 Captured.
10.1 Bonus Ratio and Timing
This model is able to identify different path of bonus ratio depending on different scenarios. Using the framework of bonus ratio analysis (discussed in previous section), we can explain how different scenarios will affect the different path of bonus ratio which will affect the timing of each phase of CCS deployment.
The slow CCS deployment scenario is characterized by a low carbon price and high CCS cost which will dictate a high bonus ratio. A higher bonus ratio will enable CCS participants to receive more bonus allowance for each ton of CO2 emission avoided. Therefore, the bonus allowance available each year will be used up quickly, limiting the number of plants that are able to join the program. A higher bonus ratio will require more time to complete each tranche and proceed to the second phase (Figure 11, figure 12). Considering that the CCS program in the APA ends in 2034, the prolonged first phase will shorten the amount of time the second phase has to enforce a reverse auction that is able to further decrease the bonus ratio. A shorter second phase leaves little time for the market to transition to full-scale deployment, and the bonus ratio remains high (Figure 11). The result is that auction price remains high and technological progress is still slow, which further impedes the implementation of the APA CCS program. A high bonus ratio also signals that there is not enough incentive to install CCS technology because of a low carbon price and high CCS cost. As a result, the plausibility that any power plant will install CCS technology without the guaranteed bonus incentive is low.
On the contrary, rapid CCS deployment is characterized by a high carbon price and low CCS cost due to rapid CCS technology development. As a result, bonus ratio is low, and CCS participants will receive less bonus allowance for each ton of CO2 emission avoided (Figure 11 and figure 12). This will allow more plants to participate in the program which, in turn, will allow for more learning by doing. This rapid deployment will expedite the completion of the 1st and 2nd tranches and give more time to perform the reverse auction in phase two (Figure 11 and figure 12). The longer second phase will give the market more time to transition to full-scale commercial deployment, which could further lower the auction price and contribute to rapid technological progress. As explained in the previous section, low bonus ratio also means that there is a good incentive for power plants to install CCS technology even without a guaranteed bonus incentive.
Full scale CCS commercial deployment will be achieved when CCS technology can serve as a cost-effective solution for capturing CO2 with little or no additional government incentive. Absolute full-scale CCS commercial deployment means that the technology deployment is self-propelled and bonus allowance is not needed, meaning that the bonus ratio is equal to zero (see previous section). Rapid deployment scenario results in low bonus ratio (less than one) which indicates that CCS deployment is progressing well, demonstrating that there is a strong incentive for industries to adopt the technology. On the other hand, slow deployment scenario results in high bonus ratio (greater than one) which indicates that there are little incentives for utility companies to adopt CCS technology unless there is a high subsidy.
10.2 Cumulative CCS Net Capacity and Amount of CO2 Captured
Now an analysis will be done on aspects of the cumulative CCS net capacity and the amount of CO2 captured.
10.2.1 Cumulative CCS Net Capacity
The APA sets an objective of having 72 GW of CCS net capacity deployed by 2034. This is in agreement with the energy modeling community consensus that full-scale CCS commercial deployment is reached when the CCS capacity is around 62-72 GW. This study is able to identify the capacity of CCS installed depending on different scenarios. Our analysis also explains how bonus ratio (as the indicator of CCS deployment plausibility) drives the final CCS capacity installed.
Under slow deployment, APA has a slow start since the number of power plants participating is constrained by the number of bonus allowances available (and high bonus ratio is high as demonstrated in the previous section) and bonus allowance is used up quickly. The slow start in the beginning also slows down the learning-by-doing process, which means that the CCS costis likely to stay high. During the reverse auction, the auction price and bonus ratio are high, which also limits the number of additional power plants that can participate in the program. It is not a surprise that under slow deployment, our model estimates only 24.5 GW of CCS net capacities by 2034 (Table 5 and figure 13).
Under the rapid deployment scenario, the bonus ratio is low, enabling APA to cover more CO2 and get a quick start during the 1st and 2nd tranche. This smooth implementation of CCS technology during the first phase makes the CCS cost drop further during the second phase. At the same time, carbon prices continue to increase. The combination of low CCS cost and high carbon prices make the auction price low (around $30) which further decreases the bonus ratio to less than one. As a result, a lot of net CCS capacity is installed during the period after 2030. The cumulative CCS net capacity under rapid deployment according to our model scenario could reach 77 GW by 2034, which is in line with APA’s objective of 72 GW by 2034. However, the slow and moderate deployment scenarios come up short, with 24.5 GW and 41 GW CCS net capacity at the end of 2034 (Figure 27, Table 18).
10.2.1 Amount of CO2 Captured
This model is able to identify the exact amount of CO2 captured depending on the predicted scenario. The amount of CO2 captured is linearly comparable to the total CCS net capacity. Additionally, the amount of CO2 captures depends on the time span CCS systems operate. The longer the CCS system operates, the larger the amount of CO2 captured. We assume that the CCS technology installed will keep capturing CO2 for the time span of 40 years.
While the APA commits to bonus allowances for only ten years, we assume that CCS technology will operate for the next 40 years. Hence, this study divides the CO2 captured into two different categories, as follows:
- CO2 captured under bonus allowance
Refers to the CO2 captured while the coal-fired power plant still receives bonus allowance under the first ten years of CCS operation.
- CO2 captured beyond bonus allowance
Refers to the CO2 captured over the following 30 years, while the coal-fired power plant is not receiving bonus allowance.
Because the length of time covered by the second category (CO2 captured beyond bonus allowance) is three times that of the first (CO2 captured under bonus allowance), the amount of CO2 captured under the second category is likewise three times the amount captured under the first.
As depicted in our model output, the graphs show that under the slow deployment scenario (Figure 14 and Table 6), the total CO2 captured under bonus allowance and beyond bonus allowance are 1.8 billion tons and 5.3 billion tons respectively. The total of CO2 captured under slow deployment scenario is 7.1 billion tons in the span of 2017 to 2070 (Figure 14 and Table 6). Our model does not take into account CO2 captured without the incentive of bonus allowance. Under the slow deployment scenario where carbon price is low and CCS cost is high, the chance that commercial power plants will install CCS technology without bonus incentive is slim.
Under the rapid deployment scenario, the CO2 captured under bonus allowance and beyond bonus allowance are 5.5 billion tons and 16.6 billion tons respectively, with a total of 22.1 billion tons of CO2 in the time span between 2017 and 2070 (Figure 15 and Table 6). The rapid deployment scenario dictates a low bonus ratio (less than one), especially after 2030 (Figure 11). A low bonus ratio signals that the CCS technology is in the process of reaching full-scale commercial deployment. Hence, under the rapid deployment scenario, there might be a possibility that there are several coal-fired power plants that are willing to install efficient CCS technology even without APA bonus allowance assistance. Our model does not take into account these additional quantities of CO2 captured which means that our rapid deployment estimate is a low estimate.
The significant wealth invested in fossil fuel infrastructure combined and the stong and growing energy demand coupled with the currently limited inventory of alternative energy resources (e.g. solar power, wind, and biomass) indicate that the world’s economies will continue to consume significant fossil fuel resources in the foreseeable future. Efforts to stabilize CO2 have been widely called for and if pursued must be done in an economically efficient manner. The availability of CCS in the wide portfolio of options for reducing greenhouse gas emissions may help facilitate the achievement of GHG emission reduction goals.
The future economic feasibility of CCS is critically dependent on CCS cost, future energy policy (e.g. CO2 tax or cap and trade) and its relative economic competitiveness over other mitigation options. The IPCC report on CCS indicates that CCS systems will be competitive with other large-scale mitigation options such as nuclear power and renewable energy technologies (Intergovernmental Panel on Climate Change 2005)
According to the consensus within CCS community, 62 – 72 GW of installed CCS capacity is the milestone which, when reached, signals full commercial CCS deployment. It is simply impossible, however, for there to be instantaneous large commercial adoption of CCS. Carbon price alone will be insufficient to support large scale CCS deployment due to the initial CCS technology barrier. The APA uses a combination of incentives for research and development, demonstration projects, and CCS incentives to overcome these barriers. The APA deployment policy is designed to focus on spurring innovation in addition to increasing CCS deployment. According to our model, full CCS commercial deployment can only be reached under rapid a deployment scenario, with cumulative capacity reaching 77GW by 2034. Under this scenario, five years are required to complete the first phase and about 12 years are required in a reverse auction. However, the scenario requires a carbon price starting at $25 in 2013. For utility companies, this assumption is harsh and will increase operating costs by $75 million in order to purchase CO2 allowances for a plant with 500MW capacity in the first year alone. Although the slow and medium-deployment scenarios may put less stress on the operators, they will fall short of meeting the APA CCS programmatic goal of 72GW of CCS deployment by 2034.
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