The transport sector is the second largest source of greenhouse gas emissions worldwide. Vehicle electrification is the proposed solution to these emissions, with India aiming to electrify 30% of vehicle kilometres by 2030, with potentially large co-benefits for local air quality. But the climate and pollution impacts of this EV transition remain uncertain, largely because the electricity grid is expected to be dominated by coal well into the next few decades, despite unprecedented renewable capacity addition. Overall grid capacity is also expected to double in the next decade to cater to increasing energy demand, with EVs being a major source of this demand. In this context, demand response - a mechanism where electricity consumers are incentivized to reduce or shift their energy usage - becomes an appealing alternative. Usually demand response aims to shift demand away from the peak to help match demand and supply on the grid. But there is a case to shift some EV charging into the daytime solar window rather than the late-night hours when coal sets the margin, to achieve net emissions reductions (“additionality”) from the EV transition. This project seeks to create evidence for which EV segments are more amenable to shifting through RCTs with firms to study the time-of-day (ToD) pricing. We also plan to understand additionality from EV demand response, and to engage with electricity regulators to help design ToD tariffs that can meet the twin goals of decarbonization and grid reliability.

About the speaker:

Anshuman is a postdoctoral scholar at the Energy Policy Institute at the University of Chicago. He is an applied economist who studies topics related to the environment, climate, and economic growth in developing countries. He has conducted research into the economics of air and water pollution, climate change, and groundwater depletion. The research questions of interest to him are strongly informed by the specific institutions and political economy of developing countries. He uses large geospatial and administrative datasets, and machine learning methods to overcome missing data challenges. He has utilized structural models of economic geography, causal inference techniques, and big data in his work.

https://anshuman-econ.github.io/

The event is hybrid, but you must register to attend.