constraints or subject to other minor objectives. Decision-makers must stipulate in
advance what weightages must be given, if any, to the parameters. It must be noted
that the inherent properties of the general linear programming model necessitate that
the functional relationships described in the problem to be linear and additive,
divisible and deterministic.
When linear programming is used to solve environmental problems, the objective
is to maximise the economic benefits of production, subject to the constraints of
conserving or enhancing environmental duality or minimising the regional incremental capital cost of controlling emissions. The dual-primal property of every linear
programming problem is a most valuable feature since it produces shadow prices for
the constraints in the primal problem. The values obtained for the shadow prices are
particularly useful for sensitivity analysis.
A shadow price on a constraint specifies by how much the value of the objective
function varies if the constraint is changed by one unit. Shadow prices are acquired
from the dual problem and permit policy-makers to examine which particular
constraints exercise the greatest restriction on the achievement of primary goals.
This is mostly pertinent to environmental quality management and planning, since
shadow prices usually replace actual market prices as indicators in the evaluation of
unpriced environmental goods and services.
Environmental quality effects can be integrated into linear programming applications in many ways, depending on:
1. the nature of the goals in environmental management,
2. the available technologies for pollution reduction, and
3. the proposed incentives for implementation.
If economically feasible technologies for control are unavailable, the environmental quality impacts can be regulated by “structural” methods.
If economically feasible alternative technologies for control are available, then
“technical” means for pollution reduction can be implemented.
8.9.7 Limitations of Linear Programming
However, it is not easy to apply linear programming to environmental problems.
Inherent linear programming problems such as the difficulty of incorporating joint
costs (see Chap. 19) and economies of scale do not allow replicating real life
situations precisely. If relative weightages are required for economic and environmental quality parameters are needed, this may present difficulties to planners and
decision makers.
The solution to linear programming problems is influenced by the constraints,
because the number of variables in the problem cannot exceed the number of
constraints. An insufficient number basic of constraints leads to oversimplicity, but
if further, arbitrary constraints are included, this might undermine the true
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8 Analysis of Environmental Impacts of Infrastructure
advance what weightages must be given, if any, to the parameters. It must be noted
that the inherent properties of the general linear programming model necessitate that
the functional relationships described in the problem to be linear and additive,
divisible and deterministic.
When linear programming is used to solve environmental problems, the objective
is to maximise the economic benefits of production, subject to the constraints of
conserving or enhancing environmental duality or minimising the regional incremental capital cost of controlling emissions. The dual-primal property of every linear
programming problem is a most valuable feature since it produces shadow prices for
the constraints in the primal problem. The values obtained for the shadow prices are
particularly useful for sensitivity analysis.
A shadow price on a constraint specifies by how much the value of the objective
function varies if the constraint is changed by one unit. Shadow prices are acquired
from the dual problem and permit policy-makers to examine which particular
constraints exercise the greatest restriction on the achievement of primary goals.
This is mostly pertinent to environmental quality management and planning, since
shadow prices usually replace actual market prices as indicators in the evaluation of
unpriced environmental goods and services.
Environmental quality effects can be integrated into linear programming applications in many ways, depending on:
1. the nature of the goals in environmental management,
2. the available technologies for pollution reduction, and
3. the proposed incentives for implementation.
If economically feasible technologies for control are unavailable, the environmental quality impacts can be regulated by “structural” methods.
If economically feasible alternative technologies for control are available, then
“technical” means for pollution reduction can be implemented.
8.9.7 Limitations of Linear Programming
However, it is not easy to apply linear programming to environmental problems.
Inherent linear programming problems such as the difficulty of incorporating joint
costs (see Chap. 19) and economies of scale do not allow replicating real life
situations precisely. If relative weightages are required for economic and environmental quality parameters are needed, this may present difficulties to planners and
decision makers.
The solution to linear programming problems is influenced by the constraints,
because the number of variables in the problem cannot exceed the number of
constraints. An insufficient number basic of constraints leads to oversimplicity, but
if further, arbitrary constraints are included, this might undermine the true
244
8 Analysis of Environmental Impacts of Infrastructure