nonlinearity and uncertainty, necessitating the solution of stochastic nonlinear programming (SNLP) problems.
However, the existing algorithms to solve such problems suffer from various limitations. L-shaped BONUS
algorithm, an integration of BONUS algorithm and sampling based L-shaped method, has been recently proposed to
overcome some of these problems. It is shown to have desirable computational properties. This work further
investigates the properties of the algorithm by applying it to the environmental problem of pollutant
(nutrient) trading in Christina River Basin. Environmental concerns being heightened, pollution abatement
related decisions are important for the industries, making the efficient solution techniques invaluable. The
results confirm the computational efficiency of the L-shaped BONUS algorithm. Simultaneously, interesting
aspects of the environmental trading problem are also explored, pointing towards the scope of implementing
stochastic programming techniques for better decision making in the field of industrial ecology.