Policy-driven municipal solid waste network optimization under carbon regulation: a risk-informed MILP framework for...

Municipal solid waste planning in a rebuilding city is not only a question of cost or technology choice. It also depends on access, operational feasibility, environmental exposure, and the policy conditions under which the system can be rebuilt. This study develops a policy-driven mixed-integer linear programming (MILP) model for designing a low-carbon municipal solid waste (MSW) network that includes transfer stations, mechanical-biological treatment (MBT) facilities, waste-to-energy (WtE) plants, and landfills. The model evaluates four planning concerns together: total cost, carbon emissions, implementation feasibility risk, and site environmental risk. In addition to the four-goal baseline formulation, the study explicitly examines three carbon-policy regimes: carbon-tax internalization, carbon tax combined with processing subsidy, and cap-and-trade. The model is tested in Idlib Governorate in northwest Syria, where formal municipal data are scarce. For this reason, the case-study dataset combines field-based information, input from eleven experts, geographic information system (GIS)-based risk assessment, fuzzy Full Consistency Method (Fuzzy-FUCOM) weighting, and weighted goal programming. The baseline solution opens three transfer stations, two MBT facilities, one WtE plant, and two landfills, generating about 141,128 MWh of electricity per year at an annual cost of USD 216.8 million. The sensitivity results show that waste supply has the largest effect on cost, while the MBT-to-WtE fraction has the strongest effect on emissions and energy recovery. The policy scenarios show a mainly financial effect under the tested settings: carbon tax increases the monetized cost of emissions, processing subsidy reduces part of the treatment cost, and cap-and-trade creates either permit costs or trading revenue depending on the cap. Overall, the framework provides a practical planning structure for MSW network design where data, infrastructure, and governance capacity are limited.