<p>Environmental legislations, including carbon tax, carbon cap, and the cap-and-trade carbon regulation, as the most adopted carbon emissions reduction schemes, aim to diminish carbon emissions in different sectors. The effectiveness of these mechanisms in controlling carbon emissions in the logistics sector, as one of the largest contributors to carbon emissions, especially under uncertainty, has been neglected by previous scholars. To this end, this study investigates a location-routing problem of cross-docking logistics under various carbon policies and demand and carbon price uncertainty, aiming to evaluate the efficiency of carbon policies. For the introduced problem, a robust mixed-integer linear program is developed to minimize the total cost while considering environmental constraints. The developed model, on one side, helps logistics to optimize their decisions regarding various carbon policies and demand variation, and on the other side, reflects the effectiveness of carbon policies in practice. In a numerical example of a cross-docking system in the retail chain sector, the proposed model is validated. The research compared the efficiency of different carbon emission policies under various perturbation levels of uncertainty and explored the impact of key regulation parameters, such as the emission cap and the carbon tax rate, through a sensitivity analysis. The results show that the carbon tax and cap-and-trade policies are more cost-effective, while the carbon cap performs better in terms of carbon emissions. Carbon tax lowers costs by 12.57% versus cap-and-trade in mild uncertainty, while cap-and-trade gains 13.9% under higher uncertainty; both outperform the carbon cap, with reductions of 15.77% and 18.9%.</p>