Table
5.9: Example showing calculation of unitised value in Optimum Pathway Matrix
The initial and long-term relative weights were combined to develop composite weighting value by using the expression.
CWi = IWi + 10 LTWi
Where
CWi = composite weight for the ith parameter,
IWi = initial weight for the ith parameter, and
LTWi = long-term weight for the ith parameter.
It may be seen that long-term effects are considered to be 10 times as important as the initial weight. This was done primarily because the method was developed for highway impact assessment and for highways the operational life of a project is at least 10 times longer than the construction period. The composite weights thus obtained are then normalised by dividing each composite weight by the sum of all weights. Hence for the ith parameter the normalised weight, Ni, may be computed by the formula;
(under original premise of Odum et al. (1971), n = 56)
Finally environmental indices are calculated for each of the alternatives under consideration, in accordance with the following mathematical expression.
Where Ij = environmental index for the jth alternative(j=1,2, ..........m)
And e = error term to allow for misjudgement on relative weights by ± 50%.
According to Canter (1977) 'the key feature of the Odum method is that an error term is included to allow for misjudgement on relative weights'. Since the relative weights are subjectively determined by the EIA study team bias cannot be removed and therefore the Optimum Pathway Matrix results in low replicability. This is however partly compensated by repeating the analysis with stochastically generated error terms. The re-iteration and subsequent averaging lead to a result which allows mean, standard deviation and confidence internal (say for 95% level of confidence) to be determined for the environmental index of the alternative under consideration. Environmental indices of various alternatives are then compared for ranking of alternatives and selection.
Odum (1971) developed the methodology keeping highway projects in mind and the parameters listed are not suitable for other types of projects. Moreover the parameters are 'mixture' of cost considerations along with environmental considerations. That is the bio-physical environmental parameters are allowed to be traded off with economic parameters. This clearly violates the conditions of environmental sustainability which allows only weak substitutability between man-made (e.g. economic) capital and natural capital (environmental resources). The relative weights assigned to various parameters are also questionable. For example, the relative weights for both initial and long-term effects of benefit cost ratio have been accepted as-10. Thus, even the most favourable benefit cost ratio contributes a negative impact to environmental quality. In fact assignment of a negative relative weight to an environmental parameter does not have any physical significance and, therefore, lacks logic.
Water Resources Assessment Methodology
Water resources assessment methodology (WRAM) is another scaling-weighting checklist developed for impact assessment and alternative evaluation of water resource development projects (Solomon et al. 1977, Canter 1979, Canter 1996). Principal steps involved in the methodology are as follows:
1. Selection of an interdisciplinary team;
2. Selection of decision factors and assemblage of basic information;
3. Evaluation of alternatives relative to decision factors; and,
4. Documentation of results.
The task of weighting and scaling of decision factors is accomplished in step 3. Usually the unranked pair-wise comparison technique is used in determining importance coefficients for each decision-factor (FIC). Predicted impacts are rated (scaled) through development of alternative choice coefficients (ACC) for evaluation of alternatives relative to decision factors. A final co-efficient matrix is developed by multiplying FIC values with the corresponding ACC values. Values in final co-efficient matrix are used as the basis for evaluation of impacts of alternatives and trade-offs between alternatives (Table 5.8).
The method involved in carrying out the evaluation of alternatives (step 3) has already been discussed. The WRAM does not attempt to aggregate separate impacts into a grand index; instead total scores for sectoral impacts are arrived at. This approach enables trade-offs between sectoral impacts to be shown explicitly and made by decision-makers (Bisset 1988). Canter (1979) suggested that the best choice might be represented by the alternative with the highest product summation (Table 5.8). Tables 5.2 through 5.8 illustrate the concept.
Mongkol (1982) developed a quantitative environmental impact assessment methodology for decision-makers. The methodology bears conceptual similarity with BEES. One of the important characteristics of the methodology is that instead of computing overall environmental impact score it attempts to rate alternatives in terms of environmental benefit-cost ration obtained from the expression; -
Environmental Benefit-Cost ratio =beneficial impact score/adverse impact score
Bisset (1988) and Majumdar (1996) have described the method. A few other weighting-scaling checklists developed for water-resources development projects are cited in Lahlou and Canter (1993) and Canter (1996).
|
Alternative
|
Area (sq km) affected greatly
by noise impact
|
Max. of column (2)
|
Unitisation factor 1/(3)
|
Unitised value (2)x(4)
|
|
(1)
|
(2)
|
(3)
|
(4)
|
(5)
|
|
G
|
86
|
.
|
.
|
0.35
|
|
G-1
|
86
|
.
|
.
|
0.35
|
|
T
|
130
|
.
|
.
|
0.53
|
|
T1
|
130
|
247
|
0.0049
|
0.53
|
|
F
|
218
|
.
|
.
|
0.88
|
|
F1
|
218
|
.
|
.
|
0.88
|
|
P
|
247
|
.
|
.
|
1
|
|
O
|
172
|
.
|
.
|
0.70
|
The initial and long-term relative weights were combined to develop composite weighting value by using the expression.
CWi = IWi + 10 LTWi
Where
CWi = composite weight for the ith parameter,
IWi = initial weight for the ith parameter, and
LTWi = long-term weight for the ith parameter.
It may be seen that long-term effects are considered to be 10 times as important as the initial weight. This was done primarily because the method was developed for highway impact assessment and for highways the operational life of a project is at least 10 times longer than the construction period. The composite weights thus obtained are then normalised by dividing each composite weight by the sum of all weights. Hence for the ith parameter the normalised weight, Ni, may be computed by the formula;
(under original premise of Odum et al. (1971), n = 56)
Finally environmental indices are calculated for each of the alternatives under consideration, in accordance with the following mathematical expression.
Where Ij = environmental index for the jth alternative(j=1,2, ..........m)
And e = error term to allow for misjudgement on relative weights by ± 50%.
According to Canter (1977) 'the key feature of the Odum method is that an error term is included to allow for misjudgement on relative weights'. Since the relative weights are subjectively determined by the EIA study team bias cannot be removed and therefore the Optimum Pathway Matrix results in low replicability. This is however partly compensated by repeating the analysis with stochastically generated error terms. The re-iteration and subsequent averaging lead to a result which allows mean, standard deviation and confidence internal (say for 95% level of confidence) to be determined for the environmental index of the alternative under consideration. Environmental indices of various alternatives are then compared for ranking of alternatives and selection.
Odum (1971) developed the methodology keeping highway projects in mind and the parameters listed are not suitable for other types of projects. Moreover the parameters are 'mixture' of cost considerations along with environmental considerations. That is the bio-physical environmental parameters are allowed to be traded off with economic parameters. This clearly violates the conditions of environmental sustainability which allows only weak substitutability between man-made (e.g. economic) capital and natural capital (environmental resources). The relative weights assigned to various parameters are also questionable. For example, the relative weights for both initial and long-term effects of benefit cost ratio have been accepted as-10. Thus, even the most favourable benefit cost ratio contributes a negative impact to environmental quality. In fact assignment of a negative relative weight to an environmental parameter does not have any physical significance and, therefore, lacks logic.
Water Resources Assessment Methodology
Water resources assessment methodology (WRAM) is another scaling-weighting checklist developed for impact assessment and alternative evaluation of water resource development projects (Solomon et al. 1977, Canter 1979, Canter 1996). Principal steps involved in the methodology are as follows:
1. Selection of an interdisciplinary team;
2. Selection of decision factors and assemblage of basic information;
3. Evaluation of alternatives relative to decision factors; and,
4. Documentation of results.
The task of weighting and scaling of decision factors is accomplished in step 3. Usually the unranked pair-wise comparison technique is used in determining importance coefficients for each decision-factor (FIC). Predicted impacts are rated (scaled) through development of alternative choice coefficients (ACC) for evaluation of alternatives relative to decision factors. A final co-efficient matrix is developed by multiplying FIC values with the corresponding ACC values. Values in final co-efficient matrix are used as the basis for evaluation of impacts of alternatives and trade-offs between alternatives (Table 5.8).
The method involved in carrying out the evaluation of alternatives (step 3) has already been discussed. The WRAM does not attempt to aggregate separate impacts into a grand index; instead total scores for sectoral impacts are arrived at. This approach enables trade-offs between sectoral impacts to be shown explicitly and made by decision-makers (Bisset 1988). Canter (1979) suggested that the best choice might be represented by the alternative with the highest product summation (Table 5.8). Tables 5.2 through 5.8 illustrate the concept.
Mongkol (1982) developed a quantitative environmental impact assessment methodology for decision-makers. The methodology bears conceptual similarity with BEES. One of the important characteristics of the methodology is that instead of computing overall environmental impact score it attempts to rate alternatives in terms of environmental benefit-cost ration obtained from the expression; -
Environmental Benefit-Cost ratio =beneficial impact score/adverse impact score
Bisset (1988) and Majumdar (1996) have described the method. A few other weighting-scaling checklists developed for water-resources development projects are cited in Lahlou and Canter (1993) and Canter (1996).