1 Emissions Trading, Electricity Industry Restructuring, and Investment in Pollution Abatement Meredith Fowlie UC Berkeley UCEI March 24, 2006

2 Research Questions Does the structure of the electricity market affect pollution control technology adoption decisions? If so, are there implications for pollution permit market outcomes/efficiency?

3 Cap and Trade Programs Traditional approach to regulating industrial sources of pollution more heavy handed. “Cap and Trade” regulation now the centerpiece of the current policy response to problems caused by industrial air pollution.

4 Preview of key findings Two factors distort investment in pollution control away from first best: 1.Interstate variation in electricity market structure/regulation 2. The pollution permit market’s failure to reflect spatial variation in damages from pollution Simulation results indicate that a relatively simple pollution permit market design change could have significantly improved the efficiency of pollution permit market outcomes.

5 Transport winds and ozone patterns on high ozone days in the Eastern US (source: “Telling the OTAG Ozone Story with Data”, OTAG Air Quality Analysis Workgroup, June 1997) hour standard: 80 ppb

6 Estimated NOx Control Costs for a 512 MW T-Fired Boiler (Post-retrofit NOx rate reductions: lbs/mmBtu) Capital Costs ($/kW) Strategies incorporating SCR 75-80% reduction Low NOx Burner Technologies: 28-38% Combustion Modification: 23% SNCR: 25% reduction No retrofit: 0% reduction The higher the capital: variable cost ratio, the larger the emissions reductions.

7 EPA promulgates the SIP Call SIP Call held up in court by state challenges Court upholds SIP Call Permit trading begins in anticipation of SIP NOx market SIP Compliance deadline for 8 states affected by OTC and SIP All 19 states and D.C. must comply with SIP Call By 1999, restructuring bills have been passed in 12 SIP states By 2000, the remaining 7 states have all officially resolved not to move forward with restructuring Electricity Restructuring Timeline SIP Call Timeline All states hold hearings to consider restructuring :

8 VARIABLEREGULATEDRESTRUCTURED # Units # Facilities10899 Capacity (MW) 268 (258) 275 (243) Pre-retrofit NOx rate (lbs/mmBtu) 0.54 (0.22) 0.50 (0.21) Summer capacity factor 67 (13) 64 (16) Pre-retrofit heat rate (kWh/btu) 11,509 (1685) 11,379 (2153) Unit age (years) 43 (11) 42 (11) # Burners 36 (17) 36 (17) A Preliminary Look at the Data

9 Preliminary summary statistics Regulated electricity markets (87,828 MW; 286 Units) Restructured electricity markets (88,370 MW; 302 Units) Data sources: EPA CEMS, EIA 767/860, ICAC, MJ Bradley and Associates.

Technology Type Capital Cost ($/kW) Variable Operating Cost (cents/kWh) REGULATEDRESTRUCTUREDREGULATEDRESTRUCTURED Combustion Modification (4.24) (4.87) 1.10 (0.39) 0.94 (0.38) LNBO (15.95) (13.83) 0.64 (0.16) 0.64 (0.20) SNCR (21.88) (14.41) 1.03 (0.38) 0.97 (0.41) SCR (25.52) (21.20) 0.54 (0.19) 0.52 (0.31) Control Technology Estimated Costs and Efficiencies by Electricity Market Type (means and standard deviations)

11 Random Parameter Logit Specification m: indexes plant manager. t: indexes boiler or “choice situation”. T m : number of boilers operated by plant manager m: t=1,..,T m. i : indexes compliance option available to boiler t operated by manager m. J mt : number of compliance options available to boiler mt: i=1,…,J mt. y : T m * 1 vector denoting m th manager’s observed choices. X m : matrix of observable characteristics of choices faced by manager m (variable operating costs, capital costs, technology fixed effects). b, : unknown parameters that characterize the distribution of .  m : manager specific coefficients.

REGULATEDRESTRUCTURED Post-combustion controls indicator-3.39** (0.59) -1.35** (0.35) Combustion modification indicator-2.48** (0.32) -1.87** (0.30) LNB technology indicator-2.48** (0.31) -1.55** (0.37) Annual operating costs (v) ($100,000) -1.00** (0.05) -1.21** (0.26) Capital costs (K) ($100,000) (0.10) -0.53** (0.12) Capital costs * Age-0.011* (0.005) ** (0.006) Cholesky 1 (  v ) 0.51** (0.16) 1.42** (0.30) Cholesky 2 (  F ) 0.14** (0.05) 0.30** (0.08) Cholesky 3 (off diagonal) 0.04 (0.07) 0.04 (0.11) Log Likelihood LR Test Compare to logit specification Chi-square **143.66** Random Coefficient Logit Model Estimation Results 1 1 Robust standard errors are in parentheses. * Denotes significant at 5%; ** Denotes significant at 1%.

13 Summary of Estimation Results Empirical evidence suggests that managers in restructured electricity markets were more likely to choose less capital intensive compliance strategies.

14 Economic Efficiency Implications 43% above cost minimizing level cap

15 Health and Environmental Implications hour standard: 80 ppb

Predicted Conditional on Predicted Choices (BASELINE) 1:5 Trading Ratio HIGH DAMAGE AREA NOx Emissions (tons/day) 2053 (55) 1596 = tons/day (146) LOW DAMAGE AREA NOx Emissions (tons/day) 2295 (55) 2750 =+ 455 tons/day (146) TOTAL NOx Emissions (tons/day) 4347 (9) 4346 (12) % NOx emissions in high damage area 47% (1%) 37% (3%) Exposure based trading simulation results (standard deviations are in parentheses)

17 Conclusions Plants in restructured electricity markets less likely to adopt cleaner, more capital intensive compliance strategies. Exposure based permit trading could have significantly improved permit market efficiency by reducing the proportion of the permitted emissions that occurred in high damage areas.