Optimizing the Informal-to-Formal Recycling Transition: A Multi-Objective Decision-Support Framework for Urban Waste Governance

The informal waste recycling sector provides livelihoods for roughly 0.2% of the urban population in developing economies, yet its formalization amid economic development creates trade-offs among social welfare, environmental performance, and fiscal sustainability. This study formulates the informal-to-formal recycling transition as a three-objective problem: minimizing the residual informal waste-picker headcount (an exit/displacement proxy rather than a measure of integration success), maximizing recycling rates, and minimizing government expenditure. We solve this problem with NSGA-II and SPEA2 on a stylized model whose parameters are calibrated for order-of-magnitude consistency with publicly documented Chinese municipal waste-management benchmarks, a directional sanity check rather than a formal parameter fit; each algorithm runs 30 times. SPEA2 attains a modestly higher hypervolume than NSGA-II (0.733 vs. 0.714; two-sided Mann–Whitney U rank test, p≈3×10−11), and both algorithms converge within roughly 50 generations on this tractable two-variable problem. An unconstrained random search remains highly competitive, underscoring how readily this low-dimensional frontier can be approximated. The decision space comprises two policy levers, the formalization target α and the enforcement intensity E; a subsidy dimension is excluded because it would enter the fiscal objective only as a cost without a benefit channel. All fiscal values are expressed in abstract model currency units. A one-at-a-time sensitivity analysis identifies the formalization gain coefficient γ1 as the parameter with the highest hypervolume coefficient of variation (CV = 13.2%), pinpointing where empirical estimation effort matters most within this model. Scenario simulations under boom, recession, and transition conditions shift the Pareto frontier in distinct ways. A bi-objective ablation indicates that the three objectives are largely independent, since only a small share of bi-objective solutions is strictly dominated in the full three-objective space; a quantitative hidden-cost penalty is not established. A representative compromise strategy (α≈0.97, E≈0.33) reaches a 44.5% recycling rate with near-complete formalization at modest fiscal cost. The framework thus serves as a transparent, reproducible decision-support tool for urban waste governance in developing economies, making explicit the trade-offs that the model implements.