Sustainable transport through agent-based modeling

Individual choices and environmental awareness are an essential part of achieving sustainability; not least because people are more likely to make sustainable choices if those around them do. These so-called peer effects were the focus of work as part of the Alternative Pathways to Sustainable Development and Climate Stabilization project, a joint effort between the TNT and Energy (ENE) programs, and colleagues at the Research Institute for Innovative Technologies for the Earth, Japan. After much model development in previous years, the novel agent-based models were sufficiently developed in 2016 to be tested in empirical calibrations with a focus on vehicle choice adoption and transportation systems transitions, which are a traditional weakness of highly aggregated integrated assessment models.

As a calibration exercise, TNT researchers used an agent-based model to replicate the results of a discrete-choice model of the vehicle market in North America, which had been developed by ENE and colleagues. The excellent congruence between these two contrasting modeling approaches enabled the researchers to isolate the effects of social network interactions and peer effects in agent-based modeling scenarios. The model simulations meant they could quantify the market impact of social network and peer effects: by “switching them off,” they could determine how much earlier and higher traditional economic incentives such as carbon taxes would have to be to yield comparable market outcomes.

The results are highly instructive for climate policy. Social network and peer effects—which can be enhanced by new information and communication technologies—can compensate for a decade’s delay in carbon tax introduction and allow the tax to be 30% lower. Motivating environmentally conscious consumers can therefore be an effective climate policy, especially in cases where early and sufficiently stringent economic climate policy is not possible.

In a second calibration exercise, TNT researchers modified the existing agent-based model of vehicle choice and tested it using real-world data on the vehicle market (conventional and electric) in Shanghai, China. The city was chosen because the electric vehicle market is particularly large and dynamic (rivalling markets such as California, USA, or Norway).

The researchers used historical data for both conventional and electric vehicles and then performed simulations of future market growth under a range of policy scenarios. Currently, economic incentives for electric vehicle purchases in Shanghai are unparalleled, including both federal and local government subsidies, as well as a waiver on a car registration fee that is roughly the price of a medium-sized car.

If these strong economic incentives continue, a market penetration of electric vehicles of 80% by 2040 is possible in the scenarios modeled. Social network and peer effects can compensate, to a degree, for a possible weakening of the substantial (and costly) economic incentives to adopt zero-emission vehicles. Alternatively, continued strong policy incentives could yield a complete transformation to zero-emissions urban mobility based on non-motorized mobility (i.e., walking and cycling), electrified public transport, and electric vehicles.