Modifications
of BEES
As has already been pointed out BEES was developed for water resources development projects of the United States Bureau Reclamation. Many attempts have been made to modify BEES. Depending on the nature of the project and the geographical and environmental setting modifications have been proposed in the categorisation of environment into various components parameters. As a logical consequence the parameter importance units have also been assigned afresh. At some instances new value function curves also have been added. In most of the cases, however, the overall scheme of the BEES has remained more or less unchanged.
Lohani and Soo (1982) designed an environmental evaluation system for water resources projects in Thailand. They reduced the number of parameters to 51. According to the scheme the 51 parameters are organised into 3 categories (ecological, physicochemical and human-interest) and 9 components. Modifications and refinements have also been made in value function curves to suit local conditions. One interesting feature is that the scheme increases the range of environmental quality values to 0 to 10. Together with 51 parameters this makes the total possible environmental score to be 10000! This high a resolution is meaningless and confusing. From a case study published (Lohani and Halim 1983), it is apparent that the EES proposed by Lohani and Soo (1982) does not require all the parameters to be considered. Thus the total environmental score does not possess any qualitative significance. Furthermore this methodology does not mention any criteria for assessment of major and minor red flags, and data needs.
Kundu (1993) modified BEES with the objective of using it for mining projects in India. Under the scheme of modification, environment has been sub-divided into the following four categories:
1. Ecosystem
2. Physical Environment
3. Aesthetics
4. Socio-Economics
These categories are further subdivided into 8 components and then finally into 39 parameters. Kundu's modification allows relatively fare representation of land, air and water. However, too much weightage (36%) has been given to aesthetics and socio-economics, while land, - the most significant from the point of view of mining-, has been assigned only 9.6% weightage. One important improvement that can be observed from Kundu (1993) is that double counting of parameters has been carefully avoided. Moreover the same list of parameters has been recommended for both underground and opencast mines. Certain phenomena, e.g. 'land subsidence', have been erroneously included as parameter. Value function curves have been developed for Indian conditions (Kundu and Banerjee 1994) so that its applicability can be increased.
NEERI (1994) proposed another modification of BEES to make it applicable to industrial projects. Under the proposed scheme 'Environment' has been categorised into Biological Environment, Environmental Pollution Aesthetics and Socio-Economics. However, instead of accepting the pre-assigned set of parameter importance units (PIU), NEERI (1994) proposed sensitivity analysis to be carried out on the PIUs of all the four categories taken together. Pre assigned values of PIUs were estimated by using paired comparison technique and were found to be 400, 300, 100 and 200 respectively for the above mentioned categories. From the sensitivity analysis carried on these values it has been found that a 5% increase in the above value of PIU for the 'socio-economics" category will make the project favourable under all conditions. The NEERI study further revealed that if only bio-physical environment was considered (i.e. if the categories of aesthetics and socio-economics are excluded) the project studied would be acceptable when and only when more that 65% weight is assigned to biological environment. That would mean assignment of only 35% or less weight to the category, 'environmental pollution'.
Several other applications of BEES have been reported. BEES approach has been successfully applied to a rapid transit system (Smith 1974) and to highway projects, pipeline projects and wastewater treatment plants (Dee et al. 1973).
Optimum Pathway Matrix
Optimum pathway matrix analysis approach to environmental decision making is a scaling weighting checklist developed by Odum (1971). This methodology has many features similar to BEES. A checklist of 56 environmental parameters is incorporated and for each of the parameters, measurable indicators are specified. Developed specifically for highway projects this methodology groups the environmental parameters into four classes of factors, namely,
* economic and highway engineering factors;
* environmental and land use considerations;
* recreation considerations; and,
*social and human considerations.
As may be seen factors not directly related to the biophysical environment received greater attention.
Odum (1971) presented a test case for evaluation of eight highway alternatives. They gathered necessary data from existing reports, documents and other secondary sources. Impact on a parameter due to an alternative is represented by following a scheme of unitisation. When there are 'n' parameters under consideration for 'm' number of alternatives the unitisation factor for the ith parameter is calculated by the following mathematical formation:
Where
Sij = unitization factor for ith parameter for the jth alternative.
Xij = estimated value for ith parameter for the jth alternative
i = 1,2,3,…....n
j = 1,2,3,……..m
The unitised value for ith parameter and jth alternative can now be calculated by the following expression.
UVij= Sij . Xij
Where
UVij= Unitised value for ith parameter and jth alternative
Odum (1971) cited eight alternative highway routes for which the 'area to be greatly affected by noise' (parameter number 23) for each alternative were as follows.
A significant difference between BEES and Optimum Pathway Matrix is that the later does not assign parameter importance index beforehand. Relative importance weights of the parameters are determined separately for long-term and initial effects. Relative importance weights of the parameters are assigned through subjective assessment by an interdisciplinary team. Odum et al. (1971) suggested a range of value between -20 to +50.
As has already been pointed out BEES was developed for water resources development projects of the United States Bureau Reclamation. Many attempts have been made to modify BEES. Depending on the nature of the project and the geographical and environmental setting modifications have been proposed in the categorisation of environment into various components parameters. As a logical consequence the parameter importance units have also been assigned afresh. At some instances new value function curves also have been added. In most of the cases, however, the overall scheme of the BEES has remained more or less unchanged.
Lohani and Soo (1982) designed an environmental evaluation system for water resources projects in Thailand. They reduced the number of parameters to 51. According to the scheme the 51 parameters are organised into 3 categories (ecological, physicochemical and human-interest) and 9 components. Modifications and refinements have also been made in value function curves to suit local conditions. One interesting feature is that the scheme increases the range of environmental quality values to 0 to 10. Together with 51 parameters this makes the total possible environmental score to be 10000! This high a resolution is meaningless and confusing. From a case study published (Lohani and Halim 1983), it is apparent that the EES proposed by Lohani and Soo (1982) does not require all the parameters to be considered. Thus the total environmental score does not possess any qualitative significance. Furthermore this methodology does not mention any criteria for assessment of major and minor red flags, and data needs.
Kundu (1993) modified BEES with the objective of using it for mining projects in India. Under the scheme of modification, environment has been sub-divided into the following four categories:
1. Ecosystem
2. Physical Environment
3. Aesthetics
4. Socio-Economics
These categories are further subdivided into 8 components and then finally into 39 parameters. Kundu's modification allows relatively fare representation of land, air and water. However, too much weightage (36%) has been given to aesthetics and socio-economics, while land, - the most significant from the point of view of mining-, has been assigned only 9.6% weightage. One important improvement that can be observed from Kundu (1993) is that double counting of parameters has been carefully avoided. Moreover the same list of parameters has been recommended for both underground and opencast mines. Certain phenomena, e.g. 'land subsidence', have been erroneously included as parameter. Value function curves have been developed for Indian conditions (Kundu and Banerjee 1994) so that its applicability can be increased.
NEERI (1994) proposed another modification of BEES to make it applicable to industrial projects. Under the proposed scheme 'Environment' has been categorised into Biological Environment, Environmental Pollution Aesthetics and Socio-Economics. However, instead of accepting the pre-assigned set of parameter importance units (PIU), NEERI (1994) proposed sensitivity analysis to be carried out on the PIUs of all the four categories taken together. Pre assigned values of PIUs were estimated by using paired comparison technique and were found to be 400, 300, 100 and 200 respectively for the above mentioned categories. From the sensitivity analysis carried on these values it has been found that a 5% increase in the above value of PIU for the 'socio-economics" category will make the project favourable under all conditions. The NEERI study further revealed that if only bio-physical environment was considered (i.e. if the categories of aesthetics and socio-economics are excluded) the project studied would be acceptable when and only when more that 65% weight is assigned to biological environment. That would mean assignment of only 35% or less weight to the category, 'environmental pollution'.
Several other applications of BEES have been reported. BEES approach has been successfully applied to a rapid transit system (Smith 1974) and to highway projects, pipeline projects and wastewater treatment plants (Dee et al. 1973).
Optimum Pathway Matrix
Optimum pathway matrix analysis approach to environmental decision making is a scaling weighting checklist developed by Odum (1971). This methodology has many features similar to BEES. A checklist of 56 environmental parameters is incorporated and for each of the parameters, measurable indicators are specified. Developed specifically for highway projects this methodology groups the environmental parameters into four classes of factors, namely,
* economic and highway engineering factors;
* environmental and land use considerations;
* recreation considerations; and,
*social and human considerations.
As may be seen factors not directly related to the biophysical environment received greater attention.
Odum (1971) presented a test case for evaluation of eight highway alternatives. They gathered necessary data from existing reports, documents and other secondary sources. Impact on a parameter due to an alternative is represented by following a scheme of unitisation. When there are 'n' parameters under consideration for 'm' number of alternatives the unitisation factor for the ith parameter is calculated by the following mathematical formation:
Where
Sij = unitization factor for ith parameter for the jth alternative.
Xij = estimated value for ith parameter for the jth alternative
i = 1,2,3,…....n
j = 1,2,3,……..m
The unitised value for ith parameter and jth alternative can now be calculated by the following expression.
UVij= Sij . Xij
Where
UVij= Unitised value for ith parameter and jth alternative
Odum (1971) cited eight alternative highway routes for which the 'area to be greatly affected by noise' (parameter number 23) for each alternative were as follows.
A significant difference between BEES and Optimum Pathway Matrix is that the later does not assign parameter importance index beforehand. Relative importance weights of the parameters are determined separately for long-term and initial effects. Relative importance weights of the parameters are assigned through subjective assessment by an interdisciplinary team. Odum et al. (1971) suggested a range of value between -20 to +50.