Cross-cutting scenarios and strategies for designing decarbonization pathways in the transport sector toward carbon neutrality

China's rapid industrialization and urbanization have led to significant carbon emissions, making emission reduction crucial. China's commitment to carbon neutrality by 2060 necessitates significant changes across all sectors, with transportation being a key contributor. The transport sector's rapid growth, driven by economic development and urbanization, has resulted in a substantial increase in CO2 emissions. This study addresses the need for an integrated framework encompassing all possible options under the Avoid-Shift-Improve (A-S-I) approach, which has not been explicitly modeled in current generation Integrated Assessment Models (IAMs). Global IAMs offer a broad overview but lack detailed spatial and behavioral factors, while high-resolution transport models often simplify energy system representations. This research aims to bridge this gap by developing a regional transport-energy integrated model to project China's ground transport sector's energy consumption and emissions, considering regional characteristics.

Existing studies have utilized integrated assessment models (IAMs) to project future transport demand, energy use, and emissions, incorporating factors like mode choice and vehicle technologies. Global scenario studies have discussed the transport sector's role in climate change mitigation and long-term decarbonization pathways. Previous research has also examined the impacts of low-carbon policy measures, such as adopting more efficient vehicles and alternative energy sources. The Avoid-Shift-Improve (A-S-I) framework, widely used in transport planning, structures policy measures to reduce transport's environmental impact. Global IAMs focus on the 'Improve' strategy (reducing energy consumption and carbon intensity), while urban planners emphasize 'Avoid' and 'Shift' strategies (land-use regulations, mass transit development). However, global IAMs often lack the detailed spatial and behavioral resolution of transport models, which in turn lack the comprehensive energy system perspective of IAMs. This study bridges this methodological gap by integrating both approaches.

A regional transport-energy integrated model was developed to project energy consumption and emissions for China's ground transport sector across 31 provinces from 2015 to 2060. The model integrates a transport model and a bottom-up optimization energy system model (AIM/Enduse). The transport model simulates transport demand and modal choices based on socioeconomic factors (GDP, population, infrastructure), land use, and transport costs (device, fuel, time). Seven transport modes were considered (car, bus, two-wheeler, passenger rail, small truck, large truck, freight rail). Modal split was determined by mode-specific costs, calculated using device, fuel, and time costs. The transport model passed mode-specific service demands to the energy system model, which determined optimal technology and energy mixes, and corresponding emissions, using a detailed technology database. The technology mix and costs were fed back to the transport model iteratively until convergence. The model used panel data for parameters estimation (pooled regression, fixed effects, random effects), with socioeconomic data from the Chinese Statistical Yearbook, transport data from Yearbook of China Transportation & Communications, and cost data from existing studies. Future projections for socioeconomic factors, land use, and infrastructure were based on downscaled Shared Socioeconomic Pathways 2 data and national planning documents. Twelve scenarios were designed based on the A-S-I framework (Avoid, Shift, Improve, each with technology, regulation, information, and price instruments), along with a business-as-usual (BaU) scenario for comparison.

Scenario simulations revealed significant emission reduction potentials with all A-S-I policy interventions compared to the BaU scenario, where CO2 emissions peaked in 2030 and then decreased due to technological improvements and population changes. The 'Improve' strategy (especially regulation: petrol car ban) showed the most substantial emission reduction potential, followed by 'Shift', and then 'Avoid'. Cumulative emission reductions from 2015–2060 ranged from 3% (A_Regu) to 44% (I_Regu). Analyzing the combined scenarios, 'Improve' resulted in the largest emission reduction in 2060, while 'Avoid' and 'Shift' also contributed substantially. The 'Improve' strategy reduced emissions primarily by lowering energy and carbon intensity, while 'Avoid' reduced transport demand. Regional disparities were observed, with 'Avoid' and related instruments most effective in eastern developed regions, while 'Shift', 'Technology', and 'Regulation' were more potent in southern and southwestern regions. The 'Improve' strategy showed no significant correlation with GDP per capita, indicating its applicability across different economic levels. While the 'Improve' strategy offered the highest emission reduction, it also incurred the highest investment costs. 'Avoid' and 'Information' strategies resulted in substantial cost reductions. Synergies and trade-offs were observed; for instance, 'Improve' strategies may increase travel demand, offsetting the 'Avoid' strategy's benefits. Comprehensive policy packages integrating A-S-I strategies are crucial to maximize synergies and minimize trade-offs. Specifically, combining compact cities with good public transport, promoting clean vehicles with behavioral interventions, and considering regional disparities are essential for effectively achieving carbon neutrality.

The findings highlight the importance of a balanced approach to decarbonizing the transport sector, integrating strategies to maximize synergies and minimize trade-offs. The study underscores the need for a holistic approach that considers both demand-side management ('Avoid' and 'Shift' strategies) and supply-side improvements ('Improve' strategy). Regional disparities in policy effectiveness necessitate tailored strategies based on regional characteristics. While technological improvements are crucial, they must be implemented in conjunction with demand-side management to ensure effective and cost-efficient decarbonization. The integration of transport and energy systems modeling offers a valuable tool for assessing the complex interactions between various policy measures and their regional impacts. This approach facilitates evidence-based policymaking by comprehensively evaluating the potential of various decarbonization strategies.

This study provides a comprehensive assessment of decarbonization pathways for China's transport sector using an integrated transport-energy model and the A-S-I framework. The findings show that a balanced policy package integrating 'Avoid', 'Shift', and 'Improve' strategies, tailored to regional contexts, is crucial for achieving carbon neutrality by 2060. Future research should incorporate shared mobility, emerging technologies, and a more comprehensive cost analysis, including behavioral costs and infrastructure investments.

The cost analysis only considered financial investment costs, excluding the economic costs of behavioral interventions. Transport infrastructure costs were also not included in the model. The study focused on ground transport, excluding air and water transport. The scenarios didn't explicitly consider the impacts of the COVID-19 pandemic. Future research should address these limitations for a more complete understanding of decarbonization pathways.