UNSW Institute for Climate Risk & Response Industry Seminar: Dr Yucheng Wang

Steering Adaptation: Firm Cost-Sharing, Moral Hazard, and Driver Responses to Heat in the Trucking Industry

August UNSW windfarms

Join UNSW for the upcoming ICRR Seminar commencing at 10:30am, followed by light refreshments at 11:30am. This is a hybrid event, you can join online.

Abstract: We study how firms steer workers’ adaptation behaviors to the negative impact of climate risks. Using unique data from real-time GPS and AI-enhanced driver-monitoring systems tracking 10,000 truck drivers over 15 months, we show that exposure to extreme heat increases dangerous driving behaviors and reduces productivity. Labor contracts play a central role in determining drivers’ adaptation choices. Independent contractors—who own their trucks and bear their own operating costs—are more sensitive to extreme heat than company-employed drivers, largely because they reduce air-conditioning use to save on fuel. As a substitute, contractors adapt by taking more rest and adjusting their labor supply. Firms also directly mitigate negative productivity shocks by dispatching additional trucks. Our calculation suggests that one additional hour of temperature above 33 Degrees Celsius results in $0.77 million daily economic losses in China, with 78% borne by other road users as externalities. Comparing adaptation costs to social benefits, we show that private adaptation efforts fall short of socially optimal levels.

Bio: Yucheng Wang a Lecturer (Assistant Professor) at the School of Economics at the University of Sydney. He received his Ph.D. from the University of Pittsburgh. Before joining the University of Sydney, I worked as Postdoctoral Fellow at Cornell, CICER and Visiting Fellow at the University of Wisconsin Madison. Yucheng's research fields include Environmental, Urban, Transportation Economics and Industrial Organization, with specific focus on environmental and welfare consequences of urban transportation policies; global EV market, EV adoption and usage; heavy-duty transportation industry; application of AI in transportation and other industries.

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