This dataset is used to calculate the number of electric vehicles (EV) in the future. It includes the number of vehicles in use, population, and GDP per capita. Using Gompertz model fitting, it forecasts the number of vehicles in the future based on the IPCC AR6 regions.
task_ev_interp.py: interpolation for vehicle and vehicle_per_cap
task_ev_merge.py: merge gdp and vehicle per cap data
task_ev_fit.py: using Gompertz model fitting for gdp and vehicle, and using population to forecast future vehicle volume
vehicle_in_use_2015_2020.xlsx: just 2015 and 2020; Vehicles in use | www.oica.net
vehicle_in_use_2005_2015.xlsx: from 2005 to 2015; Number of Vehicles in Use — KAPSARC Data Portal
pdf: oica.net/wp-content/uploads/Total_in-use-All-Vehicles.pdf
IEA Global EV Data 2023.csv: https://www.iea.org/data-and-statistics/data-product/global-ev-outlook-2023
import data from other dvc repositories pop_medium_r10.csv: population data from dataset-population, here r10 refers to category r10, not data from ar6 gdp_per_cap_r10.csv: gdp per capita data from dataset-population, here r10 refers to category r10, not data from ar6 gdp_per_cap_future.parquet: gdp per capita data from dataset-ar6
vehicle_in_use_r10.csv: vehicle_in_use data in AR6 r10 category vehicle_per_capita_r10.csv: vehicle_per_capita data (caculated) gdp_vehicle.parquet: 2005-2100, merge gdp_per_cap and vehicle_per_cap data EV_penetration_rate_interp: 2005-2060 interpolated gdp_vehicle_fitting.parquet: 2005-2100, vehicle_per_cap and gdp_percap fitting, and vehicle and ev calculation
task_ev.duckdb: join tables from gdp_vehicle_fitting.parquet and category.csv
r10_list.csv: regions' name for r10 vehicle_saturation.csv: regional settings for vehicle_saturation by cateogry (median across model scenario) EV_penetration_rate: 2005-2060, regional settings for EV penetration rate (historical and forecasting), based on data_raw/IEA Global EV Data 2023.csv (historical and APS scenario's cars type) vehicle_in_use_2005_2015_clean.xlsx: clean version for data_raw/vehicle_in_use_2005_2020.xlsx (two files)