Emissions Trading, Electricity Industry Restructuring and Investment in Pollution Abatement

V_ni = V_ni + vm_ni■ The estimated capital costs of installing the technology is K_ni. I
assume that the manager chooses the compliance strategy that minimizes expected
compliance costs. As it is likely that the compliance choice characteristics that are
relevant to the compliance decision are not limited to the attributes we observe, tech-
nology specific constants a_i are included to improve the fit of the model. These fixed
effects capture unobserved, intrinsic technology preferences or biases, such as widely
held perceptions regarding the reliability of a particular NOx control technology. Be-
cause this decision depends in part on unobserved factors, it is impossible to say with
certainty which compliance strategy a firm will choose. An extreme value stochas-
tic component ε_ni is included in the model to capture the idiosyncratic effect of the
unobserved factors.

I first estimate a conditional logit (CL) model of the compliance choice. Let β'x_ni
represent the deterministic component of C_ni. Let Y_n denote the nth firm’s chosen
alternative. The ε_ni are assumed to be iid extreme value and independent of β and
x_ni. The probability (conditional on β) that the nth firm chooses compliance strategy
i is the standard logit probability (McFadden) :

Jn
e^.

J=

The most restrictive specification of this CL model imposes homogeneity in re-
sponses to changes in capital and variable compliance costs; the β coefficients are not
allowed to vary across plants. A second specification captures systematic variation
in the β parameters by interacting observed plant characteristics with compliance
choice attributes. To facilitate a test of the hypothesis that firms in different types of
electricity markets weigh cost components differently in their compliance decisions,

18

<<   16   17   18   19   20   21   22   >>

More intriguing information