Strategizing sustainability and profitability in electric Mobility-as-a-Service (E-MaaS) ecosystems with carbon incentives: A multi-leader multi-follower game

Electric Mobility-as-a-Service (E-MaaS) emerges as a promising solution for environmentally-friendly mobility in the future, yet MaaS operators have been struggling to achieve profitability. We introduce a novel E-MaaS ecosystem where platforms can leverage carbon credits revenue from the government’s emissions reduction fund (ERF) by incentivizing travelers to choose more E-MaaS services, thereby reducing carbon emissions. In such an E-MaaS ecosystem, travelers can select either electric (E)-MaaS or traditional (T)-MaaS services and submit heterogeneous requests, such as distance, service time, tolerance for inconvenience, and travel delay budget, which are modeled as inputs. We propose a multi-leader multi-follower game (MLMFG) model where each leader (MaaS platform) competes to maximize its profits by making operational decisions such as pricing, EV acquisition ratio, and E(T)-MaaS bundle allocation while anticipating travelers’ participation levels. In response to the platforms’ decisions, each follower (traveler) aims to minimize her travel costs by determining the participation levels for E(T)-MaaS services via multiple MaaS platforms. We develop a customized alternating direction method of multipliers (ADMM) algorithm to solve the proposed MLMFG efficiently. Comprehensive numerical experiments based on real-life data in Australia demonstrate the convergence and robustness of the proposed ADMM algorithm. Further, experimental results reveal how factors such as market size, travel demand, ERF budget, subsidy rate, and unit price boundaries impact the profits and operational strategies of different MaaS platforms. Overall, the proposed MLMFG model for the E-MaaS ecosystem provides valuable insights for MaaS operators aiming to balance profitability with environmental responsibility, navigating a future where sustainability and profitability goals could converge.