associated with _SCRUBBER_ is much smaller compared to those from other models. This result again confirms the fact that because most of the scrubbers were installed before the enactment of the Acid Rain Program, the installation of scrubbers is not a major factor that drives the decreasing trend in emission rates.

(33) Overall, results from alternative specifications closely resemble the basic fixed effects estimation in column (1). In all cases, the relationship between electricity price volatility and emission rate, shown in the first row of Table IV, is statistically negative with an estimated elasticity around 0.8% - 0.9%. All four sets of alternative specifications produced qualitatively similar results on coefficients of other independent variables as well.

The columns in Table V are structured in a manner similar to those in Table IV. Specifically, the first two columns report the results from the fixed effects and random effects models. A Hausman test does not reject the null hypothesis that the cross-sectional error term _uit_ is not correlated with the other independent variables. Therefore, both the fixed and random effects models provide consistent estimations. Nonetheless, in the following, I still report results from fixed effects estimation, which are not systematically different from those of a random effects model. Table V shows that the coefficients on price volatility are positive and statistically distinguishable from zero. Alternative specifications have almost no effect on the estimation results on the coefficient of price volatility, except for specification in column (5) which corresponds to estimations of the balanced panel dataset. The lower estimated coefficient of Δ<sup>_P_</sup> _e_ in column (5) indicates that there could be differences in the elasticity of allowance savings between divestitured and non-diversitured units. Recall that the size of a unit’s allowance bank ( _Bi(t+1)_ ) is determined by both levels of emissions ( _eit_ ) and trading with other units ( _xit_ ). Given that the emission elasticities of these two types of units are similar as suggested by Table IV, the difference in estimated coefficients of Δ<sup>_P_</sup> _e_ could be explained by different trading strategies: diverstitured units could be more aggressive in purchasing allowances in response to higher electricity price fluctuations.

Based on column (2) in Table V, a one percent increase in electricity price volatility is on average associated with an increase of 2.46% in the size of the allowance bank. This implies that when electricity price volatility increases by a one-standard deviation, a sample “average” unit will carry an additional 1027 tons of allowances forward to the next period. The 95% confidence interval is between 137 to 1918 tons. Coefficients of other independent variables generally remain consistent with prior expectations.

Considering the potential endogeneity of delivered coal prices, I control for units’ location in relation to PRB coal (miles) and the observed price difference between low- and high-sulfur coal prices. Table VI reports estimation results for emission rate (columns [1] and [2]) and the percentage change in annual allowance stocks (columns [3] and [4]) based on these specifications. Regressions (2) and (4) reported in Table VI also instrument SO2 allowance prices 23