Abstract The paper deals with the problem of determining the optimal emission abatement policy in a region. The criterion used consists of looking for the policy which minimizes the overall abatement cost under the constraint of meeting a given ambient standard. This policy is determined by a formal optimization model (mathematical program). Pollutant dispersion is affected by uncertainty, related to the meteorological situation in the region, thus the satisfaction of the ambient standard constraint in the program can be regarded as a random event (‘stochastic programming approach’). Two solutions for the stochastic program are suggested: the distribution approach (which leads to the definition of Pareto alternatives between abatement cost and risk of violating the standard) and chance-constraints programming (the satisfaction of the standard is required with a given probability, at least).
[1]
V. V Kolbin,et al.
Stochastic Programming
,
1977
.
[2]
Jati Kumar Sengupta,et al.
Stochastic programming: Methods and applications
,
1972
.
[3]
Scott E. Atkinson,et al.
A cost-effectiveness analysis of alternative air quality control strategies
,
1974
.
[4]
L B Lave,et al.
Air pollution and human health.
,
1977,
Science.
[5]
Douglass J. Wilde,et al.
Foundations of Optimization.
,
1967
.
[6]
S. Vajda,et al.
Probabilistic Programming
,
1972
.
[7]
Peter Kall,et al.
Stochastic Linear Programming
,
1975
.
[8]
Sanford V. Berg,et al.
Distributional analysis of regional benefits and cost of air quality control
,
1979
.