Traffic management has traditionally focused on toll-based road pricing. However, road pricing often raises concerns about accessibility and public dissatisfaction, leading to its prohibition in some regions, such as Finland. This study optimises the dynamic allocation of incentives to drivers, encouraging them to reroute onto alternative (potentially longer) paths to achieve greater societal benefit, namely reduced total travel time and total emissions in the transportation network, contributing to climate change mitigation. We employ a multi-agent reinforcement learning approach to dynamically assign incentives to drivers to reduce both total travel time and emissions, with travel times estimated using traffic simulation software. We demonstrate that, with an unlimited budget and an objective of minimising travel time, the incentive scheme reduces total travel time (TTT) by 16% compared to the dynamic UE. With a budget equivalent to about 11% of the UE total time, a 16% reduction in TTT is achieved. When the goal is to minimise emissions, a 9% reduction in CO2 emissions is observed under an unlimited budget. We demonstrate a critical trade-off: minimising TTT leads to an increase in emissions, while prioritising emission reductions raises TTT. However, with the right combination of weights in the multi-objective function, both TTT and total emissions are improved beyond the baseline.