Exploratory modelling forecasts CO2 emissions trends in urban travel demand, indicating the need for enhancing green transport strategies.
Vehicle electrification, automation, and sharing are reshaping urban travel demand. This study utilizes a system dynamics model to forecast travel demand in downtown Shanghai through 2035, incorporating these emerging technologies. 5,000 scenarios are evaluated using Exploratory Modelling and Analysis, and scenario discovery is conducted using the Patient Rule Induction Method. Modelling results reveal two opposite CO2 emissions trends driven by shifts in travel mode shares. (1) 433 scenarios exhibit a desirable trend, with the largest reduction to 83% of the 2022 emission by 2035; (2) Approximately half of the scenarios show a concerning trend, with the largest increase to 145% of the 2022 level by 2035. These opposing trends are attributed to variations in the pace and acceptance of autonomous vehicle adoption. Our findings underscore the necessity for strategies that enhance non-motorised transport while accelerating the development of emerging mobility technologies and encourage green travel choices among residents.
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Yang et al. (2025) studied this question.
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