UBC's Urban Predictive Analytics Lab is helping ensure communities planning ZEV charging policies will get the biggest bang for their buck.
Many cities are keen to move forward on climate-friendly transportation and land-use policies and regulations—but that agenda can still be tough sledding, politically-speaking.
A PICS-funded project might help change that by giving stakeholders and elected officials a richer understanding of the costs, savings, and carbon-cutting payoff of a given battery-electric vehicle policy, action, or infrastructure investment.
“We are trying to understand how lifestyles are changing over time, and how those shifts will impact mobility needs, and ultimately show how specific technologies can play a role in the overall picture,” explains Martino Tran, who directs the University of British Columbia’s Urban Predictive Analytics Lab and is part of the PICS Transportation Futures in BC project.
The Condo Conundrum
In consultation with Metro Vancouver municipal leaders, Tran and his colleagues have been developing a mathematical computational model that stakeholders will eventually use to understand the payback of a given investment in zero-emission vehicle (ZEV) charging in multiple-unit residential buildings, or MURBs.
At present, Vancouver residents who live in condos or apartments and who also own battery-electric vehicles must charge up away from home, as strata councils and landlords have proven largely disinterested in installing Level 1 or 2 charging stations in common spaces such as parking garages. This creates a barrier to adoption.
Enter Tran’s model. “We will be able to run a scenario that says, ‘If we implement a subsidy to the construction sector to install an X percent share of Level 1 chargers in MURBs, and a Y percent share of Level 2 chargers, then here is the potential consumer uptake [on ZEVs] you will see,” he explains.
And that’s where the magic happens. “This model will be able to assess the consequences of that policy, the impact it will have on ZEV adoption, and from there, the quantity of carbon averted over a given region or time,” Tran says.
The team is currently improving the model based on stakeholder analysis work, and also scoping out the building requirements and technical infrastructure that will inform it, such as power capacity needs, metering, and so on. They’ll begin beta testing by the end of 2019; a planned dashboard will give stakeholders ready access to the tool.
Based on the model, the team has also devised a set of policy recommendations most likely to reinforce ZEV uptake. These range from providing cash rebates to help ZEV owners purchase charging equipment, to requiring the installation of charging stations in all new MURBs across BC, to updating codes to target the issue of oversized electrical systems.
Solution Seekers Set the Agenda
And it all emerged from initial conversations with city leaders who were looking for solutions. It’s an example of the new approach that is not only guiding research questions at the Urban Predictive Analytics Lab, but also the overarching strategy at PICS.
“In the past, research was very isolated,” explains Tran. “Many tried to develop problems that were aligned with the solutions they had on hand, instead of identifying real-world problems.”
Now researchers must develop the questions that guide their work in collaboration with the institutions and governments best positioned to act on the insights that emerge. “We see this shift reflected in the changing demands from students,” he says. “They want to be involved in meaningful work.”
“They don't want to publish a paper that just sits there.”
Diana Lopez-Behar, Martino Tran, Jerome R. Mayaud, Thomas Froese, Omar E. Herrera, Walter Merida, “Putting electric vehicles on the map: A policy agenda for residential charging infrastructure in Canada,” Energy Research & Social Science, Volume 50, April 2019, pages 29-37.
Lopez-Behar, Diana & Tran, Martino & Froese, Thomas & Mayaud, Jerome R. & Herrera, Omar E. & Merida, Walter, 2019. “Charging infrastructure for electric vehicles in Multi-Unit Residential Buildings: Mapping feedbacks and policy recommendations,” Energy Policy, Elsevier, Volume 126(C), pages 444-451.