The urgency to limit global temperature rise to 1.5°C necessitates significant CO2 emission reductions, with road transport being a major contributor. Many countries are announcing timelines for phasing out fossil fuel vehicles (FFVs), aiming for transport sector decarbonization. The COVID-19 pandemic temporarily reduced transport demand and emissions, but the post-pandemic rebound highlights the need for sustainable solutions. China's commitment to carbon neutrality by 2060, coupled with its significant oil import dependency and air pollution challenges, makes this transition particularly crucial. The paper investigates the macroeconomic effects of different emission reduction policies in China, comparing the emissions of China to other major economies such as the United States, Japan, the European Union, and India. The concept of economic and environmental resilience, especially relevant post-COVID-19, is central to the analysis, examining the impact of policies on urban areas and regions.

The introduction cites numerous studies supporting the need for reducing transportation emissions and the importance of phasing out fossil fuel vehicles. Studies are referenced on the impact of transport on PM2.5, the contribution of transport to overall CO2 emissions, policy mixes for deep CO2 mitigation, the economic analysis of banning gasoline vehicles, the post-COVID-19 emission rebound effect, and policy mixes for sustainable mobility. The literature also informs on the concept of economic resilience and studies on city resilience, particularly in the context of COVID-19. The authors highlight existing literature focusing on national-level analysis while emphasizing the importance of studying regional heterogeneity in urban areas.

The study utilizes a two-pronged approach combining a Markov-chain model and a DSGE model. The Markov-chain model predicts future transport demand changes by mode (rail, road, waterway, aviation) and vehicle powertrain (FFVs, BEVs, PHEVs, FCVs) using data from 2013 to 2018, projecting to 2060. This provides insights into the potential shifts in fuel demand based on anticipated changes in transportation habits and the market share of different vehicle types. The DSGE model assesses the macroeconomic effects of various policy instruments (no policy, emission intensity target, emission tax, emission cap) under both deterministic and stochastic conditions. The model incorporates factors such as capital stock, labor supply, consumption, and emissions intermediate inputs (CO2 and SO2), allowing for an analysis of the economic consequences of different policy choices, including the impact of exogenous shocks, such as changes in energy prices. The models employ specific equations to represent economic dynamics, using parameters calibrated to reflect the Chinese economy. For instance, equations for carbon emission intensity (CEI), sulfur dioxide emissions and the Markov-chain model's probability transition matrix are presented. The DSGE model uses equations for utility, output, capital accumulation, emissions constraints, and incorporates the impact of policies via the emissions price or emission cap, analyzing responses to exogenous energy price shocks.

The Markov-chain model forecasts a significant decrease in FFV share and a corresponding increase in NEVs, particularly BEVs and PHEVs, by 2060. Analysis of regional carbon emission intensity (CEI) reveals significant heterogeneity across Chinese provinces and cities, with Yunnan, Inner Mongolia, and Ningxia showing the highest CEI values when using transportation sector GDP as the denominator, and Yunnan, Xinjiang, and Guangxi when using total provincial GDP. Analysis of SO2 emissions intensity per capita shows a significant increase across provinces from 2004 to 2019, highlighting the environmental health consequences of FFVs, especially in urban areas. The analysis then shifts to comparing SO2 emissions intensity using road mileage and land area as denominators, revealing that Shanghai and Guangzhou have the highest SO2 emissions among the five largest private car-owning cities. The static comparison of the DSGE model across different policy scenarios (no policy, emission intensity target, emission cap, emission tax) shows that all climate policies dampen economic growth relative to the business-as-usual scenario. However, under an energy tax, the model shows that road transport energy consumption fluctuates less than under the business-as-usual scenario indicating a positive impact on economic resilience in response to energy price shocks. Finally, international comparisons show that the United States has the highest total road transport CO2 and SO2 emissions, while China and India exhibit the fastest growth rates in these emissions. The implications of these findings suggest that while a ban on FFVs will initially decrease economic activity, it will provide more resilience to energy price shocks.

The findings highlight the significant potential of phasing out FFVs for both environmental and economic benefits. The regional heterogeneity in CEI and SO2 emissions underscores the need for tailored policy instruments that account for variations in economic development levels and FFV ownership across different regions. While all the investigated policies dampen economic development in the short run, the energy tax scenario shows a positive effect on economic resilience under exogenous shocks. The international comparison emphasizes the urgency of this transition, particularly for rapidly developing economies with large populations.

Phasing out FFVs offers a substantial opportunity to mitigate climate change and improve air quality, particularly in densely populated urban areas. While this transition requires careful consideration of the potential short-term economic impact, the analysis emphasizes the long-term economic and environmental benefits, including increased resilience to energy price shocks. Future research could investigate the distributional effects of these policies, delve deeper into the technological advancements required for the transition, and further analyze the complex interactions between economic growth, energy consumption, and environmental sustainability at a more granular level.

The study's reliance on specific models (Markov-chain and DSGE) limits the generalizability of the findings. Data limitations, particularly regarding regional-level data, might influence the accuracy of the analysis. The DSGE model is a simplified representation of a complex economy, making it crucial to acknowledge its limitations in capturing all the nuances of the transition. Further, the study uses specific parameters calibrated to China's economy, so the conclusions may not necessarily be extrapolated to different economies.