Quantifying the Socio-Economic Impacts of EV Chraging Stations

PRESENTED BY;- M. Mavin De Silva

PhD student at Nagaoka University of technology

Supervisors:

Prof. Takahiro Yabe

Prof. Siqin Wang

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Problem Statement & Objective

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Designing community-centric EV infrastructure

“In the United States, the Federal Government has committed to ensuring that half of all new vehicles sold in 2030 are zero-emission vehicles..” - pwc.org

“It is proposed to establish a convenient and equitable network of 500,000 chargers, enhancing EV accessibility for Americans across both local and long-distance travel.” - dot.gov

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The Necessity of Equitable and Efficient Allocation of EVCSs (Gap)

Recent studies conducted by EV charging companies rely on qualitative or survey data and therefore are difficult to scale and less predictable.

Studies have failed to differentiate the effects of EV charger installations on local businesses from the influence of other confounding factors, such as changes in economic, demographic, and business conditions.

One recent study published in Nature (earlier this month) has quantified the impact of installing EVCS on customer counts and spending in nearby businesses in California but it uses less representative control group which shows only small increase in economic benefits.

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Problem Statement

How can we design EV charging infrastructure networks and behavioral incentives for drivers that benefit the community & local enterprises?

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Hypothesis; As EV drivers park their vehicles to recharge, they often find themselves with spare time, creating an opportunity in activities such as shopping or dining in nearby establishments.

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Objective

Thorough literature reviews of Existing Computaional Models that are used to quantify the impacts of EVCSs.

Leverage big data sources (mobile phone location data) to understand human behavior changes with the EVCS installation.

Experiment with DiD+RDD approach to measure and predict the causal impacts of EVCS placement on surrounding businesses.

To develop computational models that predict the cascading impacts of mobility behavior changes triggered by EV charging station placement, on local businesses.

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Understanding the Data

Data Analysis and Interpretation

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The Approach - NYC

Source: Alternative Fuels Data Center

Source: Placer.AI

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Analyzing the Data

file:///C:/Users/Dreamtech%20Computers/Downloads/evcs_map.html

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Descriptive Analytics

(Avg Z Score)

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Descriptive Analytics

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Experimental Design

Time Lagged Diff-in-Diff Regression/ Difference-in-Discontinuities design (DiDC)

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Time Lagged DiD Setup

• 1765 Matched pairs were obtained.

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Model Result

Difference-in-Discontinuities results (DiDC)

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Thank you very much for listening!