Table 2: Descriptive Statistics
Variables |
Unit |
Mean |
Std. Dev |
Min |
Max |
Electricity (Y) |
Mwh |
1874281 |
1541744 |
141000 |
6686101 |
Capital (K) |
MW |
469.64 |
341.52 |
67.50 |
1260 |
Labour (L) |
Number |
1308 |
792.48 |
104 |
2946 |
Fuel (F) |
Toe |
887848.20 |
735710.10 |
68720.71 |
3197387 |
CO2 (P) |
TCO2 |
2413491 |
2182987 |
139013.60 |
9169197 |
Note: Sample size is 76; toe = tonnes of oil equivalent; t CO2 = tonnes of carbon
dioxide; Mwh = Megawatt hour; MW = Megawatt; Fuel comprises both coal and
oil consumption.
Electricity Prices: In order to derive the shadow prices of the outputs,
market price of at least one of the output is necessary. As there exists no
market for the undesirable outputs, we do not get the prices for these.
Therefore, to derive the shadow prices of the undesirable outputs we
need to know the price of the desirable output, which in the present case
is electricity. The data on electricity tariffs i.e., the sale price of electricity
is taken as the price of electricity and is obtained from CESC, DVC, and
WBPDCL separately for the different years under consideration.
It should be noted here that the data on CO2 emission is
generated from the consumption of fossil fuels by the thermal plants. As
the data on CO2 emissions is related to the consumption of fossil fuels,
one cannot use these for econometrically estimating the output distance
function. The unavailability of consistent and reliable plant-wise data on
CO2 emissions for the years under consideration does not permit us to
estimate the stochastic output distance function by the econometric
method. Hence only deterministic linear programming technique is used
in the present study to derive the shadow prices of undesirable output.
The study considers carbon dioxide, which is one of the
important greenhouse gases, as the only undesirable output in the
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