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Department of Spice


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Evidence of Work

Driving Towards an Electric Future: ESG Analysis on EV Uptake in Australia

Project Info

Department of Spice thumbnail

Team Name


Department of Spice


Team Members


Harri S and 3 other members with unpublished profiles.

Project Description


Exploring the adoption of Electric Vehicles (EVs) in Australia, this project delves deep into the intricate web of Environmental, Social, and Governance (ESG) factors. Utilizing extensive vehicle registration data, our primary focus is to decipher the factors determining the increasing uptake of EVs and the policies required to achieve net-zero.

From an Environmental perspective, we aim to showcase the tangible benefits of EVs, such as reductions in CO2 emissions and improved air quality. The Social segment elaborates on Australian consumer preferences, underscored by statistical analysis, indicating a shift towards sustainable transport. Through a series of informative graphs, we've highlighted correlations like the influence of income on EV adoption. Lastly, the Governance section paints a vivid picture of how policy changes drive market dynamics. Overlaying new EV registrations with policy shifts, we've mapped out the significant role of government interventions.

By dissecting these ESG pillars, our endeavour is to stimulate informed conversations, promote change, and drive Australia closer to an eco-friendlier transport future.


#ev #esg

Data Story


Journeying Through Australia's EV Landscape

In Australia, a quiet revolution is unfolding on the roads. Gone are the days when the environmental impact of cars was a secondary thought. Today, with carbon footprints being mapped to postcodes, the need for Electric Vehicles (EVs) has never been more evident. As smoke-belching engines make way for silent, electric counterparts, the skies have begun to breathe easier. The Australian pulse echoes this sentiment — a remarkable 62% advocate for governmental EV subsidies, and a vision for a fossil fuel-free vehicular future by 2035 is shared by over half. Yet, it isn't just environmental consciousness that's driving this shift. A dive into demographics highlights the unexpected: income isn't the barrier many believed it to be. But perhaps the most compelling catalyst? Governance. As policies shift and evolve, so does the Australian road. It's clear: the path to a greener future is electric.


Evidence of Work

Video

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Project Image

Team DataSets

SEFIA Scores - ABS.gov.au

Description of Use Looking at SEFIA scores to identify trends with EV uptake. https://colab.research.google.com/drive/1MgiCcKTUpmfE2OJW9LbyvCau9gceby_M?usp=sharing

Data Set

taxation statistics - Data.gov

Description of Use Used to look at income and EV adoption. https://colab.research.google.com/drive/1MgiCcKTUpmfE2OJW9LbyvCau9gceby_M?usp=sharing

Data Set

EVs Available - data.wa.gov

Description of Use Combined with the EV council list of EVs to form a collection of known EVs

Data Set

EVs Available - electricvehiclecouncil

Description of Use This list of EVs in combined with another list from wa.gov

Data Set

New Vehicle Registrations 2018-2022

Description of Use Used in combination with other ESG data to draw insights. Preprocessing methods are described in this google colab notebook: https://colab.research.google.com/drive/1UaDBCbELcTgTcf4qdnSwjl0fdiCkVXJk?usp=sharing

Data Set

Challenge Entries

Explore the relationship between vehicle types and the uptake of EVs

The challenge is to analyse vehicle registration data and explore the relationship between vehicle types and the uptake of EVs. Identify and analyse factors that may influence the adoption of electric vehicles.

Eligibility: Use at least 1 dataset from Data.Vic list.

Go to Challenge | 12 teams have entered this challenge.

Best Creative Use of Data in Response to ESG

How can you showcase data in a creative manner to respond to ESG challenges? How can we present and visualise data to stimulate conversation and promote change?

Eligibility: Must use a least one Australian relevant dataset - combining datasets preferred.

Go to Challenge | 33 teams have entered this challenge.