NSGA-II is a stochastic optimization technique, so the found solution theoretically cannot guarantee the optimality unless infinite iterations are performed. To check whether the best solutions found in our case are robust or not, we evaluate two extreme scenarios regarding the selection of utilization pathways -fully employing either FT or MS. As shown in Figure , either way does not deliver better solutions than the found solution found by NSGA-II. On the one hand, the CO2 utilization via entirely FT tends to bring in a higher profit, but the potential for GHG emissions reduction is limited to 20%. On the other hand, fully MS can enhance GHG emissions reduction to 46% but dramatically lose the economic advantage compared to the original solution found by NSGA-II. Similarly, we evaluate another extreme scenario, where the heating is fully substituted by lowcarbon electricity. As shown in Figure , such a complete substitution brings in minor improvement on GHG reduction but significantly sacrifices the economic gain. In brief, multi-objective optimization can recommend moderate operating conditions for the industrial park. Relating the Pareto front to decision variables can offer an insight into how environmental and economic aspects are affected by operating conditions. Specifically, utilization to gasoline/diesel (FT path) can bring in more economic benefits, while utilization to methanol (MS path) and electrifying heating is more environmental-friendly. By contrast, the extreme operating conditions tend to significantly sacrifice either economic or environmental aspects.