The authors study the solution robustness of the Risk Explicit Interval Linear Programming (REILP) model using numerical experimentations, investigating whether optimal solutions of a REILP would vary under various preferences to different constraints. The first numerical experiment deals with an optimal land use planning subject to nutrient loading constraints. The second one deals with an optimal water resource allocation subject to pollutant loading constraints. The results show that REILP solutions have different sensitivities to constraint preferences in different cases. This phenomenon suggests that in practice it is necessary to conduct thorough analysis on the robustness of REILP solutions to constraint preferences before reaching reliable decision support. In addition, the variability of REILP solutions with regard to various constraint preferences makes it possible to efficiently generate alternative management schemes within the frame work of REILP.

Multivariate statistical analysis and hedonic pricing method were employed to analyze the effects of structural variables, including land transaction price, bus lines, distance to downtown area, central business district, railway station and hospital, floor area ratio (FAR), and dichotomous variables, including nearness to rail transit, recreational facilities and parks on housing transaction price in Beijing city. Results indicate that the main determinants of real estate price in Beijing are land transaction price, distance to downtown area, FAR, existence of nearby rail transit and recreational facilities. Among which, land transaction price have a considerably positive effect on transaction price, while FAR and distance from housing to the downtown area are the main negative driving forces for housing transaction price in Beijing.