Table 4: Estimated Efficiency Loss (million) under two scenarios
|
Year 1 |
Year 2 |
Year 3 |
Year 4 |
Year 5 |
Total | |
|
Scenario 1- 10% Taiwan Power(LNB) |
0.06 |
0.06 |
0.05 |
0.17 | ||
|
Taiwan Plastic(LNB) |
0.01 |
0.01 |
0.02 | |||
|
Total |
0.06 |
0.01 |
0.06 |
0.01 |
0.05 |
0.19 |
|
Scenario 1- 20% Taiwan Power(LNB) |
0.003 |
0.003 |
0.002 |
0.008 | ||
|
Taiwan Plastic(LNB) |
0.001 |
0.001 |
0.002 | |||
|
Total |
0.003 |
0.001 |
0.003 |
0.001 |
0.002 |
0.01 |
|
Scenario 2 - 10% Taiwan Power(LNB) |
0.63 |
0.17 |
0.80 | |||
|
Taiwan Plastic(LNB) |
0.04 |
0.04 | ||||
|
Total |
0.63 |
0.04 |
0.17 |
0.83 | ||
|
Scenario 2 - 20% China Petroleum(SCR) |
1.12 |
1.12 | ||||
|
Total |
1.12 |
1.12 |
Incorporating smaller firms into KPERMS might be another solution for the
reducing efficiency loss, as this would increase the effective demand. The firms
excluded in the trading program analysis here are currently subject to the air pollution
fee regulation. If smaller firms can be incorporated in the market, then the relatively
smaller demand for ERCs from these firms can successfully reduce the excess ERCs.
Therefore, the government may consider a more flexible regime that allows smaller
firms to switch conveniently between the permit trading system and the air pollution
fee system when the price of those excess permit is lower than the fee they face.
13