Identifying optimal carbon neutrality pathway of passenger transport in megacities: A case study of Beijing, China

The high-emitting passenger transport sector is a major contributor to megacity carbon emissions due to rapid motorization and urbanization. Achieving carbon neutrality in these hubs requires innovative strategies to address the complex energy and emission patterns of passenger transport. However, current research typically treats transportation planning and energy supply optimization in isolation, failing to capture dynamic cost-transmissions between upstream clean power generation and downstream electric mobility. To bridge this gap, this study develops a novel demand-driven dual-layer nested energy system optimization model. This framework uniquely achieves a sequential coupling where travel mode choices impose rigid constraints on the upstream optimization of renewable resource potential and energy supply costs. Empirical results from Beijing indicate that under a deep low-carbon transition (DLCT) scenario, the share of subway and bus systems rises to 67.88% by 2060, while petroleum consumption drops to 6.9%. Electricity becomes the dominant energy source (92.06%), supported by solar and wind power increasing to 30.11% and 31.09%, respectively. Consequently, cumulative emissions decrease by 82.73% compared to the Business-as-Usual (BAU) scenario. Notably, since the model accounts for direct emissions for fossil fuels versus life-cycle emissions for electricity, these results represent a conservative estimate of the actual decarbonization potential. Based on marginal abatement cost analysis, the study underscores a phased strategy: prioritizing green mobility in the near term, scaling non-fossil power in the medium term, and deploying hydrogen as a strategic long-term supplement for hard-to-abate niches. This framework offers a replicable methodology for designing cost-effective, system-wide low-carbon transitions in megacities.