Economic and Environmental Costs of Regulatory Uncertainty for Coal

Jan 12, 2009 - Uncertainty about the extent and timing of CO2 emissions regulations for the electricity-generating sector exacerbates the difficulty o...
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Environ. Sci. Technol. 2009, 43, 578–584

Economic and Environmental Costs of Regulatory Uncertainty for Coal-Fired Power Plants D A L I A P A T I Ñ O - E C H E V E R R I , * ,† PAUL FISCHBECK,‡ AND E L M A R K R I E G L E R ‡,§ Duke University, A150 LSRC Box 90328, Durham, North Carolina 27708, Carnegie Mellon University, 5000 Forbes Avenue, Pittsburgh, Pennsylvania 15213, and Potsdam Institute for Climate Impact Research, PO Box 601203, 14412 Potsdam, Germany

Received January 10, 2008. Revised manuscript received October 16, 2008. Accepted November 21, 2008.

Uncertainty about the extent and timing of CO2 emissions regulations for the electricity-generating sector exacerbates the difficulty of selecting investment strategies for retrofitting or alternatively replacing existent coal-fired power plants. This may result in inefficient investments imposing economic and environmental costs to society. In this paper, we construct a multiperiod decision model with an embedded multistage stochastic dynamic program minimizing the expected total costs of plant operation, installations, and pollution allowances. We use the model to forecast optimal sequential investment decisions of a power plant operator with and without uncertainty about future CO2 allowance prices. The comparison of the two cases demonstrates that uncertainty on future CO2 emissions regulations might cause significant economic costs and higher air emissions.

Introduction Any serious effort to limit CO2 emissions in the United States will impose constraints on the electricity-generating sector. Uncertainty on the type, timing, and stringency of potential air emissions regulations coupled with uncertainties on fuel prices and future costs and performance of new technologies for electricity generation and pollution control, pose a serious challenge to decision makers in the industry who seek to comply with regulations at minimum cost. Significant emissions reductions from power plants require heavy capital investments for the installation of add-on emissions controls or for plant replacement. One investment decision that might be optimal for one regulatory scenario might be very expensive under another. For example, delaying the installation of controls for the three air pollutants, SO2, NOx, and mercury (3-P), puts a plant in the vulnerable position of relying on volatile allowances markets with rising prices. However, investing in costly emission controls for a coal plant that would have little to no economic value if a CO2 emissions constraint existed is potentially risky to the industry and to society at large. What is the expected cost to a plant of not knowing when and how CO2 will be regulated? What is the environmental effect of this regulatory uncertainty? Could * Corresponding author e-mail: [email protected]. † Duke University. ‡ Carnegie Mellon University. § Potsdam Institute for Climate Impact Research. 578

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expected emissions be lowered with uncertainty resolution on future CO2 regulations? In this paper, we provide a framework to answer these questions by representing the optimal sequential decisions of a power plant operator with and without uncertainty on CO2 regulations during a planning horizon of 30 years and then comparing the corresponding costs and emissions. Previous studies have explored the effect of uncertainty in CO2 emissions regulations on investment decisions in electricity generation. Patino-Echeverri et al. (1) estimate the economic cost of regulatory uncertainty (as the Expected Value of Perfect Information (EVPI)) for an existing 500 MW coal-fired plant as $40 M over 30 years which is equivalent to $0.36/MWh. Bergerson and Lave (2) estimate the social costs of uncertainty by comparing investment decisions in new coal-fired power generation based on a private discount rate of 10% to those based on a social discount rate of 3%. They conclude that the differences in costs are $0.011/kWh for a policy that imposes a carbon tax of $100 per ton of CO2 in years 1-10 and $0.0085/kWh for a policy that imposes a carbon tax of $100 in years 16-25. Reinelt and Keith (3) investigated the effects of regulatory uncertainty on investment decisions for an existent power plant and concluded that regulatory uncertainty always raises the cost of carbon abatement. Yang et al. (4) find that the CO2 price risks for coal, gas, and nuclear power generators under different scenarios for CO2, fuel, and electricity prices are in the ranges of $180-$400/kW, $160-$690/kW, and $210-$900/kW. In this analysis we follow the approach of refs 1 and 3 of comparing the investment and operating decisions that would be made with and without uncertainty in the price of CO2. Our analysis differs from that of ref 1 in that it explicitly compares the difference in air emissions that results from decisions made with and without uncertainty. It also differs from refs 2-4 in that it (1) represents the constraints imposed by emissions regulations on 3-P and CO2, (2) compares the differences in emissions for these pollutants and particulate matter (PM), and (3) represents in detail a large range of investment alternatives including flexibility in the order and timing of the retrofits as well as the possibility of mothballing unprofitable equipment. By using a simplified characterization of regulatory uncertainty with a number of CO2 policy driven scenarios, we are able to compare explicitly the economic and environmental outcomes of decisions made with and without uncertainty and to illustrate the negative consequences of delaying regulation.

Method: A Model to Represent Investment and Operating Decisions To estimate the costs and emissions caused by uncertainties on regulations for a power plant, we (1) determine the optimal sequential investment and operating decisions made at the plant with and without uncertainty, and (2) compare costs and emissions of the two. We assume that at each point in time the decision makers find the optimal sequence of investment and operating decisions by using a dynamic programming model that accounts for the possibility of installing a number of different emissions controls and/or a new plant, and of retrofitting, mothballing, and shutting-down the plant at any period of the planning horizon. By explicitly modeling the managerial flexibility regarding installation and use of controls and plants, and the irreversibility of the investment (represented with 10.1021/es800094h CCC: $40.75

 2009 American Chemical Society

Published on Web 01/12/2009

the full payment of the capital costs at installation and no salvage value at shutdown), the model effectively accounts for the “options” available to the decision maker (as real options analysis (5) does, but instead of assuming a continuous stochastic process for the variables, it assumes a discrete probability function as explained in the scenarios section). Investment Alternatives. In this paper we investigate the investment and operating decisions for a conventional coalfired power plant for which the quantity and sale price of electricity are given (e.g., a base-load plant in a regulated region). The decision maker is the plant operator who seeks to minimize the expected net present value (ENPV) of the cost of producing electricity over a planning horizon while complying with environmental regulations. We assume that the plant can be retrofitted with emissions controls for each of the 3-P, PM, and CO2, or be replaced with a new plant. The new plant can either be a Super Critical Pulverized Coal (SC) power plant, an Integrated Gasification Combined Cycle (IGCC) power plant, or a Natural Gas Combined Cycle (NGCC) power plant. The new plants can be installed with or without a Carbon Capture and Sequestration (CCS) system. We assume that by paying an extra cost, any of the new plants that were installed without CCS can later be retrofitted with CCS, and that retrofitting a plant for a particular pollutant does not eliminate future retrofit options. We also assume that any emission-control system or plant can be mothballed at no cost. The SC plant will include a Wet Flue Gas Desulfurization (WFGD) system to reduce SO2 emissions (as mandated by New Source Performance Standards (NSPS)) but might not include a Selective Catalytic Reduction System (SCR) to reduce NOx that could be added later. The IGCC has no mercury and very low SO2 and NOx emissions and therefore, does not need add-on controls. We also consider an IGCC “Capture-Ready” plant that has the performance of a plant whose design has been optimized to operate with CCS (and therefore, higher operating costs than the IGCC plant), and a lower cost of retrofitting CCS. Despite the cost and construction-time uncertainty of a “Capture-Ready” IGCC plant, we believe it necessary to include it in the analysis to account for the possibility of making a preinvestment in an IGCC plant and to cover the case of not using a CCS system on an IGCC plant when economical conditions make it suboptimal. There are a total of 21 resulting plant types (listed in Table 1) and a total of 40 different investments (listed in Table 2) that represent the different combinations and sequences in which emissions controls can be installed in each of the plants considered. Assumptions on Capital and O&M Costs. Capital costs, efficiencies, and energy penalties of emissions controls depend on plant size, technology type, coal type, and operating conditions. We analyze the investment and operating decisions of a large power plant that is a major emitter of the three regulated pollutants, PM, and CO2. The plant (with an electrostatic precipitator (ESP) installed) has the same capacity (1,780 MW) and similar performance of the Hatfield’s Ferry Power plant (as reported in eGRID (6)) and has been chosen to represent approximately 45 of the large (greater than 1 GW nameplate capacity) and dirty (with emissions above the national average for coal plants: 10 lbs SO2/MWh and 3 lbs NOx/MWh) U.S. plants that have recently been or currently are in the process of deciding which SO2 and/or NOx emission-control devices to install. Information on the performance (emissions, fuel consumption, energy penalties, and O&M costs) of each of the plant configurations considered is shown in Table 1 and was obtained from the IECM-CS model (7) using Appalachian-medium-sulfur coal type (also known as Pittsburgh #8), and capacity factors of 65% and 87% for the existent and new plants, respectively. We assume that the capital costs corresponding to each

investment are equal to the IECM values but increased by a retrofit-penalty-factor that accounts for the costs of installing a control on an existent plant. Retrofit penalty factors are very site-specific (8) and can vary over a wide range of values, exhibiting or not some economies of scale. We assume that there are economies of scale for simultaneous installations. For retrofits on the existent plant and a SC plant, we assume factors of 1.2 for one installation, 1.15 for two simultaneous installations, and 1.1 for three simultaneous installations (e.g., costs of installing one emissions control system on an existing PC or SC plant are 20% higher than the costs of installing the same equipment on a new plant). The NGCC retrofit factor (for installing a CCS system) is assumed to be 1.2. Because the installation of a CCS system on an IGCC requires a substantial change in the precombustion portion of the plant (see, e.g., 9-12), we assume a retrofit factor of 1.3. If the IGCC is “Capture Ready,” then we assume the retrofit factor is 1.1. For the base-case analysis, we assume that both capital and O&M costs (not including fuel) remain constant (in real terms) from period to period and that fuel prices evolve according to the base-case scenario of the AEO (13) (see Supporting Information (SI) section 1). Air Emissions Regulations and Scenarios Considered. Current emissions regulations for 3-P imply a price per unit of emissions. We assume that future CO2 emissions regulations will also impose a price on CO2 (e.g., a tax) and represent regulatory uncertainty with a set of eight scenarios that specify prices for CO2 for each year of a planning horizon reaching out to 2050 as presented in Table 3. All scenarios except 1 and 5 are characterized by a price of $10 per tonne (t) of CO2 or more starting in year 2010, and scenarios 5-8 include a sudden increase to $30/t CO2 or more in 2020. The starting price of $10/t CO2 corresponds roughly to estimates of the carbon price for 2010 and 2015 under the McCain-Lieberman legislation (14, 15), a price of $20/t CO2 is consistent with estimates of the allowance price for the Lieberman-Warner Climate Security Act for 2015 (16), and the higher prices of $30/t CO2 and $40/t CO2 are somewhat above estimates for 2020 but plausible considering the observed prices in the European Union Emissions Trading System (ETS) (17) and estimates for 2030. Each scenario is assigned a probability that represents a decision maker’s subjective belief about future regulations. We assume that a decision maker assigns equal likelihood to all the scenarios that are possible, and when it is clear that a scenario is no longer possible (because it is incompatible with what has been observed in reality), probabilities are reassigned so that the scenarios that are still possible are equally likely (see SI section 2). We assume that uncertainty is fully resolved in the year 2020 after the last price increase for CO2 emissions allowances has occurred. To focus on the uncertainty of CO2 regulation, we assume fixed scenarios for fuel prices and 3-P emissions allowances prices based on observations and forecasts reported in refs 13 and 18-20 as described in the SI. Our setting of a number of scenarios with attached probabilities assumes that there is uncertainty about which scenario (CO2 regulation) will be the reality faced by the firm but there is no uncertainty about the price of a unit of emissions once that scenario is realized. In reality in a capand-trade (CAT) system, the prices of allowances might fluctuate on a daily basis (e.g., following a stochastic process). While this daily volatility might have important effects on the value of each investment, it is not a product of regulatory uncertainty, and we have chosen not to model it to isolate the effects of CO2 regulatory uncertainty (see comment on alternative modeling approaches in the SI section 3). In CAT systems like the ones that currently exist for SO2 and NOx, the government allocates a number of free emissions allowances to the power plants. It can be shown (SI section VOL. 43, NO. 3, 2009 / ENVIRONMENTAL SCIENCE & TECHNOLOGY

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99,719 99,719 99,719 99,719 99,719 99,719 99,719 99,719 99,719 99,719 99,719 99,719 83,356 83,356 83,356 83,356 100,115 100,115 94,249 65,832 65,832

9,844 9,844 9,844 9,844 9,844 9,844 9,844 9,844 9,844 9,844 9,844 9,844 9,852 9,794 9,794 9,794 9,851 9,851 10,308 9,677 9,677

electricity output (GWh)a 9.23 9.30 9.23 9.23 9.30 9.30 0.93 9.23 9.30 0.93 0.93 0.93 7.79 7.79 0.78 0.78 8.99 0.84 8.46 3.55 0.36

CO2 (Mtonne) 155,263 30,060 154,116 155,263 30,265 30,060 17 154,116 30,265 18 17 18 2,783 3,044 14 15 15 254 3,184 -

SO2 (ton) 26,790 26,790 7,479 26,790 7,479 26,790 26,456 7,479 7,479 7,384 26,456 7,384 23,573 6,252 23,277 6,173 6,173 1,018 990 -

NOx (ton)

emissions

1,496 1,496 1,496 1,496 1,496 1,496 748 1,496 1,496 748 748 748 1,250 1,250 625 625 625 50 47 -

PM (ton) 550 184 550 239 56 184 184 239 56 56 184 56 154 47 154 47 47 56 520 -

Hg (lbs)

controls energy (GWh/yr)c 211.1 61.0 4.3 273.9 212.4 3,022.9 65.4 275.2 3,085.1 3,024.2 3,086.4 262.6 316.2 2,528.2 2,580.3 404.6 1,418.7

controls OM (M$2007)b 17.8 9.2 22.5 26.9 17.8 165.6 31.8 26.9 29.2 165.7 174.5 17.0 24.9 136.9 144.5 86.4 50.1

31.5 13.1 22.8 44.7 31.6 362.1 36.1 44.8 229.7 362.2 375.2 34.1 45.5 301.2 312.2 112.7 142.3

total OM controls (M$2007)d

OM costs

46.4 46.3 46.4 46.5 46.3 46.3 46.3 46.4 46.3 46.3 46.3 46.3 41.2 41.2 41.2 41.2 69.2 69.2 66.99 15.39 15.4

base plant OM (M$2007)

172.0 172.0 172.0 172.0 172.0 172.0 172.0 172.0 172.0 172.0 172.0 172.0 143.7 143.7 143.7 143.7 172.6 172.6 162.5 574.0 574.0

fuel costs (M$2007)e

a Total electricity output without subtracting controls’ energy usage. b Does not include costs of energy used. CO2 costs of sequestration based on pipeline transport distance of 161 km (100 miles); CO2 stream compressed to 13.7 MPa (2,000 psig) with no booster compressors. c Excludes the energy usage of the ESP and the NOx in-furnace controls which we take as part of the base plant. d Assumes the cost of energy penalty is $65/MWh. e Assumes coal and gas prices are as in the base-case scenario. f CCS includes a MEA system for CO2 capture. g Based on Texaco quench gasifier (2 + 1 spare), 2 GE 7FA gas turbine, 3-pressure reheat HRSG with steam parameters 1400 psig/1000 F/1000 F. Sulfur removal efficiency is 98% via hydrolyzer + Selexol system; Sulfur recovery via Claus plant and Beavon-Stretford tailgas unit. Note that installing a Carbon Injection System (CI) after a Wet Flue Gas Desulfurization System (WFGD) has been installed does not provide further reductions of mercury emissions.

1. original (includes ESP) 2. original + WFGD 3. original + SCR 4. original + CI 5. original + WFGD + SCR 6. original + WFGD + CI 7. original + WFGD + CCSf 8. original + SCR + CI 9. original + WFGD + SCR + CI 10. original + WFGD + SCR + CCSf 11. original + WFGD + CI + CCSf 12. original + WFGD + SCR + CI + CCSf 13. SC + WFGD 14. SC + WFGD + SCR 15. SC + WFGD + CCSf 16. SC + WFGD + SCR + CCSf 17. IGCC C-readyg 18. IGCC + CCSg 19. IGCC 20. NGCC 21. NGCC + CCSf

plant type

energy input (103ˆ MBTU/yr)

performance

TABLE 1. Performance and O&M Costs of Configurations Considered (All Given in 2007 U.S. Dollars)

TABLE 2. Alternative Investments Considered by Decision Maker and Resulting Configuration investment

investment number

capital cost (M$2007)

performance and OM costs of plant type no.a

original WFGD on original SCR on original CI on original (WFGD + SCR) on original (WFGD + CCS) on original (SCR + CI) on original (WFGD + SCR + CCS) on Original WFGD on (original + SCR) WFGD on (original + CI) SCR on (original + WFGD) SCR on (original + CI) CI on (original + SCR) CCS on (original + WFGD) (WFGD + CCS) on (original + SCR) (SCR + CCS) on (original + WFGD) WFGD + SCR on (original + CI) WFGD + SCR + CCS on (original + CI) WFGD on (SCR + CI) SCR on (WFGD + CI) SCR on (original + WFGD + CCS) CCS on (original + WFGD + SCR) CCS on (original + WFGD + CI) WFGD + CCS on (original + SCR + CI) CCS on (original + WFGD + SCR + CI) SCPC + WFGD SCPC + WFGD + SCR SCPC + WFGD + SCR + CCS SCPC + WFGD + CCS SCR on (SCPC + WFGD) SCR + CCS on (SCPC + WFGD) CCS on (SCPC + WFGD + SCR) IGCC IGCC + CCS CCS on IGCC IGCC-ready CCS on IGCC-ready NGCC NGCC + CCS CCS on NGCC

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40

0.00 175.38 111.02 15.26 274.47 1,112.75 119.50 1,156.77 175.38 175.38 111.02 111.02 13.68 986.90 1,102.96 1,052.17 274.83 1,167.54 187.14 111.02 111.02 986.90 985.75 1,114.22 987.28 1,516.60 1,390.69 1,910.79 2,036.07 77.86 672.73 624.12 2,231.14 2,982.34 951.82 2,338.66 708.06 910.55 1,283.33 447.34

1 2 3 4 5 7 8 10 5 6 5 8 8 7 10 10 9 12 9 9 10 10 11 12 12 13 14 16 15 14 16 16 19 18 18 17 18 20 21 21

a

See plant types, OM costs, and performance in Table 1.

TABLE 3. Prices of CO2 Allowances (in $2007/t) scenario

2007

2010-2020

Price 2020 f

1 2 3 4 5 6 7 8

0 0 0 0 0 0 0 0

0 10 20 30 0 10 20 30

0 10 20 30 30 40 40 40

5) that if emissions allowances from one period are not banked for use in future periods, then the initial allocation of permits is irrelevant for the optimization problem that seeks to minimize costs at the plant. To keep the analysis simple, we assume that plant owners do not bank allowances, so the only random variables that we include in the model as a consequence of a regulatory scenario are the prices of CO2 emissions allowances. The effects of different discount rates and fuel prices are examined in a sensitivity analysis. The amount of pollution emitted by a single plant does not mean much when there is a CAT regulatory mechanism that keeps overall emissions at the cap. In this paper, however, we are keeping the price of emissions allowances for 3-P constant across the different CO2 regulatory scenarios and are assuming that these are the prices that would exist absent a CO2 policy. Therefore, the difference in 3-P emissions (and

PM) under the different CO2 scenarios is an indicator of the changes in overall emissions that would emerge if we had a tax system imposing a fixed price per emission allowance. Multi-Period Decision Making Model (MPDM). Our Multi-Period Decision Making Model (MPDM) determines the optimal sequential decisions of the decision maker over a period of 13 years until uncertainty about future CO2 allowance prices is completely resolved in 2020 and there is no need to adjust the investment strategy. At each year, investments and operations are chosen so the ENPV of costs over the following T ) 30 years (planning horizon) is minimized, by solving a multistage Stochastic Optimization Model (SOM) described below (21). Once the investment and usage decisions for a given year are determined, the MPDM moves to the next year by updating (1) the “current” conditions, which are determined by the investments made in the “past,” and (2) the probabilities assigned to the “remaining” scenarios, which change as it becomes clear that some scenarios are no longer possible. The latter requires us to assume that one of the S scenarios for CO2 allowance prices describes the reality, and that it will be revealed to the decision maker in 2020. In the last year of the MPDM (i.e., 2020), the SOM has collapsed to a deterministic optimization problem describing investment and usage decisions until the end of the planning horizon (i.e., 2050). This formulation requires the specification of fuel prices, allowances prices, and capital and O&M costs for each scenario for each of the 43 years. Our analysis determines what a rational decision maker would do, given that each of the S scenarios actually occurs, VOL. 43, NO. 3, 2009 / ENVIRONMENTAL SCIENCE & TECHNOLOGY

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FIGURE 1. Emissions controls installed and used (a) when there is no regulatory uncertainty and (b) with regulatory uncertainty. Numbers in the figure indicate plant configuration as described in Table 2 (1, original plant; 2, WFGD; 5, WFGD+SCR; 11, SCR/WFGD; 27, SC+WFGD+SCR+CCS; 34, IGCC+CCS). in two information conditions: when the scenario is known from the beginning (certain conditions) and when it is only known for sure at the end (uncertain condition). Multistage Stochastic Optimization Model (SOM). The MPDM assumes that at each year, the decision maker will invest and use the available generation and emissions-control equipment as prescribed by the SOM. The SOM is a linear program that includes binary variables representing the installation/usage decisions (set to 1 if the control is installed/ used and set to 0 otherwise). The objective function represents capital and O&M costs for the current year plus the expected net present value of the capital and O&M costs of the plant for the following 30 years. Total O&M costs are constituted by O&M costs of the plant (including the energy penalties of the control devices), fuel costs, and emissions costs that are in turn determined by emissions levels and allowance prices. Expected future costs are the costs under each scenario weighted by the probability of occurrence of that scenario. We assume that decision variables are contingent on the scenario. See formulation of SOM in SI section 4.

Results and Discussion Investment and Operating Decisions with and without Uncertainty on CO2 Regulations. Figure 1 presents the investment and operating decisions under certainty and under uncertainty, for each of the scenarios, assuming a discount rate of 5% (real). When it is certain that CO2 prices will not be above $10/t (i.e., either scenario 1 or 2 occurs), then the optimal decision is to install and use both WFGD and SCR on the original plant. If scenario 3 occurs ($20/t CO2 in 2020), then it is optimal to install and use an SCPC with WFGD and SCR. For scenarios in which CO2 prices start at $20/t or higher in 2010 and are $30/t or more in 2020 (scenarios 4, 7, and 8), it is optimal to install an IGCC with CCS in year 1. For scenarios 5 and 6, the low price of CO2 allowances ($10/t or less) before 2020 does not justify the 582

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installation of the IGCC today. Instead both WFGD and SCR are installed on the original plant, and in year 2020, the plant is replaced with an IGCC+CCS. These results are consistent with the findings of refs 2 and 22 that a CO2 price of approximately $30/t is required to justify investment in IGCC. Uncertainty on the price of CO2 allowances delays the installation of emissions controls in all scenarios. For scenarios 1 and 2 (i.e., prices stay at $10/t CO2 or less but this is only known with certainty in 2020), it is optimal to install a WFGD in 2015 to be used years 2017-2021 and to install an SCR in 2020 to be used in years 2022 on. In this case, uncertainty makes decision makers wait before making any investment. For scenarios 3-8, the first investment is delayed until 2020 when the uncertainty is resolved. For scenario 3 ($20/t CO2 starting in 2010), a SC with WFGD and SCR is installed. For scenarios 4-8 (prices after 2020 are $30/tCO2 or more), an IGCC+CCS is installed. The delay in the installation of a new plant or environmental controls causes extra emissions and extra costs of compliance. For the base-case assumptions, both total costs and emissions are higher when investment and operating decisions are made when there is uncertainty on future CO2 allowance prices. Figure 2 shows the costs of CO2 regulatory uncertainty over a planning horizon of 30 years for each of the eight scenarios. The top panel shows the components of the costs of regulatory uncertainty. The costs due to regulatory uncertainty for scenario S are given by the costs, when the decision maker does not know this is the reality minus the costs, when the decision maker knows this scenario is the reality from the beginning. The expected value of perfect information (EVPI) can be calculated by summing the product of the cost of uncertainty for each scenario times the probability of occurrence of that scenario. Although investment decisions made under uncertainty result in lower capital and O&M costs for some scenarios, the costs of compliance with environmental regulations incurred through the pur-

FIGURE 2. Additional costs caused by regulatory uncertainty under each scenario. Top: breakdown of costs due to regulatory uncertainty. Bottom: total costs due to uncertainty (blue) and total costs due to regulation assuming certainty (red). Costs due to regulatory uncertainty are equal to the costs of making decisions under uncertainty minus the costs of making decisions under certainty. Costs of regulation are equal to the costs of the regulation scenario under certainty minus the costs of scenario 1 under certainty. chase of allowances makes the total costs higher than the costs when decisions are made with perfect foresight. The bottom panel shows the total costs of regulatory uncertainty (blue bars) and the costs of imposing a regulation to reduce CO2 emissions (red bars). The costs of regulation for scenarios 2-8 are calculated by the difference in costs when a scenario is the reality and the decision maker knows it, and the costs when scenario 1 (i.e., the scenario without CO2 emissions regulations) is the reality and the decision maker knows it. From the figure it is clear that the costs of regulatory uncertainty are significant when compared to the costs due to regulation. (See SI for more in Figure 2). Figure 3 presents the difference in emissions between the certain and uncertain cases (blue bars: uncertain minus certain) and the emissions reductions caused by each regulation scenario under certainty (red bars: emissions of scenario 1 minus emissions of scenario s under certainty). The red bars show that regulatory scenarios with a sufficiently high CO2 price (scenarios 3-8) also achieve reductions in SO2, NOx, and PM emissions when they are implemented under certainty. When there is uncertainty, the extra 3-P emissions are higher than the emissions reductions caused by regulation under certainty. More pollutant emissions occur when there is regulatory uncertainty. Exceptions to this are PM and CO2 in scenarios 1 and 2 where low prices of CO2 ($0/t CO2 in scenario 1 and $10/t CO2 in scenario 2) do not justify the investment in CCS. Sensitivity Analysis. Sensitivity of Results to Discount Rate. To explore some of the effects that the discount rate used by investors has on the optimal choice (e.g., the timing of installation of controls and the construction of new plants) and the costs of regulatory uncertainty, we ran the previous analysis for discount rates of 4% and 6% (see Figures 3-10 in SI).

FIGURE 3. Additional air emissions caused by uncertainty under each scenario (blue bars show emissions under uncertainty minus emissions under certainty) and emissions reductions due to regulation under certainty (red bars show emissions for each scenario under certainty minus emissions for scenario 1sno carbon policysunder certainty). There is no discounting (unlike the allowance cost comparisons). PM refers only to the directly emitted particulates. For scenario 2 there are no emissions reductions due to regulation because investments under scenario 2 are equal to those of scenario 1 under certainty (and also under uncertainty). Our results show that the higher the discount rate, the lower the incentives to make large capital investments, and therefore, the greater the amount of uncontrolled emissions. Although emissions are larger, those attributed to uncertainty are smaller as there is less difference between the actions taken with and without uncertainty. For example, when discount rates are 6%, the incentives to invest in a SC for scenario 2 ($10/t CO2 from 2010) in the certainty case disappear, which eliminates the extra emissions caused by uncertainty for this scenario. Similarly, we can see how extra emissions can be higher with a lower discount rate of 4%. In this case when there is no uncertainty, it is optimal to install a SC (with WFGD and SCR) under scenario 2. This investment does not happen under uncertainty and therefore, the difference in emissions between the uncertainty and certainty cases is larger than they were for discount rates of either 5% or 6%. Sensitivity of Results When Fuel Price Assumptions Change. To explore how different forecasts change the optimal installations, we assume that prices after 2013 move so they reach the AEO low and high estimates of $1.33 and $2.72 per million Btu for coal in 2030 (corresponding to alternative assumptions about mining productivity, labor costs, mine equipment costs, railroad productivity, and rail equipment costs ((13), p100)) and $6.18 and $8.43 per million Btu for natural gas in 2030. For these cases, we find that there is no change in the optimal strategies under certainty or uncertainty except for low coal prices and scenario 3 ($20/t CO2 starting in 2010) under uncertainty in which instead of installing an SC +WFGD+SCR as prescribed for the basecase analysis, a WFGD is installed in year 2015 and then an SCR is installed in 2020. This makes emissions for scenario 3 under uncertainty higher than in the base case (see Figures 11-13 in SI). To explore whether an even lower price of natural gas would motivate an investment in NGCC, we modeled a case where prices drop to $5.5 per million Btu and find that there is almost no change in the optimal decisions except that under uncertainty an NGCC plant is installed in 2013 for scenario 3 ($20/t CO2 in 2010) instead VOL. 43, NO. 3, 2009 / ENVIRONMENTAL SCIENCE & TECHNOLOGY

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of a SC when natural gas prices are higher (see Figures 14-16 in SI). The difference in total costs between the uncertainty and certainty cases is almost unchanged relative to the base case as a higher positive difference in fuel costs is offset by a lower positive difference in capital costs. However, the difference in emissions under uncertainty and certainty do differ from those of the base case. Because an NGCC has lower emissions of CO2 and PM than an SC, these emissions are lower under uncertainty than under certainty. However the difference in NOx emissions becomes less pronounced as an NGCC has higher emissions than a SC+WFGD+SCR.

Discussion Our analysis shows that delays in the announcement of a CO2 emissions regulation can cause higher costs of electricity generation and higher emissions. It highlights the effect of strong path dependencies in the power generation industry that make it difficult and/or very expensive to change the course of emissions. Although several factors not directly related to carbon policy (like capital costs, discount rates, and fuel prices) affect investors decisions and the associated extra costs and emissions, there is no doubt that resolution of CO2 climate uncertainty might generate both economic benefits and lower 3-P emissions. The costs of regulatory uncertainty might affect industrial actors and electricity customers in a different way in restructured states versus in traditionally regulated states. While in restructured states the costs will likely be borne by industrial actors who depend on market-revenues and not on regulated rates to cover their costs of business, in the regulated states these costs will be passed on to customers. This is not to say that consumers in restructured states are safe, since eventually the extra costs of regulatory uncertainty will be reflected in the competitive price of electricity. These results show that it is not only rational for electricity costumers (and their representatives) but also for electric utilities to lobby for CO2 emissions regulations (as some businesses already do, e.g., ref 23), to minimize the risks of making investment decisions that can prove to be wrong in the future. We believe that it is important that regulators weigh the costs and benefits of delaying new regulations (in a benefit-cost analysis (24)). While it seems sensitive to delay regulation until more about feasibility, performance, and costs of control technologies, and overall impact on the U.S. economy is known, it is important to keep in mind that waiting is not free and in fact can be costly to firms and society, and harmful to the environment.

Acknowledgments This research was supported by the U.S. National Science Foundation under grant SES-0345798 to the Carnegie Mellon University Center on Climate Decision Making under Uncertainty. E.K. is supported by a Marie Curie International Fellowship (MOIF-CT-2005-008758) within the sixth European Community Framework Program. We thank the editor and reviewers for their helpful insights.

Supporting Information Available Additional text, tables, and figures. This material is available free of charge via the Internet at http://pubs.acs.org.

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