Choose a State on the Map
| All Emissions (MtCO2e) in 2005 (state/U.S.) | |
| 2030 All Emissions (MtCO2e) Target(state/U.S.) | |
| Transportation Emissions (MtCO2e) in 2005 (state/U.S.) | |
| 2030 Transportation Emissions (MtCO2e) Target (state/U.S.) | |
| Laws & Incentives - Alternative Fuels & Advanced Vehicles | |
| Fueling Stations with Alternative Fuels | |
| Available Charging Stations | |
| Available Charging Outlets |
Building on Past U.S. Leadership, including Efforts by States, Cities, Tribes, and Territories, the New Target Aims at 50-52 Percent Reduction in U.S. Greenhouse Gas Pollution from 2005 Levels in 2030." - President Joe Biden, White House press release, April 22, 2021.
Greenhouse gas emissions (GHG) are measured in metric tons of carbon dioxide equivalent (MtCO2e) and is a metric measure used to compare the emissions from different greenhouse gases based upon their global warming potential (GWP). GHG emissions recorded in MtCO2e does not take into account changes in atmospheric levels of greenhouse gases attributed to forest and land-use activities. Data for 2020 reflects the impact of the COVID-19 pandemic on emissions in all sectors. Predictions are based on features with publicly available data with values between 1995 and 2018.
Linear Equation: Y = 38.875 + -1.1206*GDP + 36.597*Population + -6.6565*Transit + 6.3047*VMT + 4.4656*SQMI + 3.1102*Temp
The worldwide predicted global warming increase above pre-industrial levels of 1.5°C may have a substantial impacts and risks by 2050.
Overview
This project focuses on the measured CO2 in the transportation sector of the U.S. and is used to predict CO2 levels into the future.
Transportation makes up 29% of the total U.S. greenhouse gas emissions. In April of 2021, President Biden announced a target of 50-52% reduction in emissions from 2005 levels. The Environmental Protection Agency (EPA) has recommendations for what can be done to reduce pollution from vehicles.
Are we on track for meeting this goal in the transportation sector and what are the factors that have the biggest impact on meeting this goal?
This project uses machine learning to determine the predictability of carbon emissions in the transportation sector if trends in person vehicle and public transportation continues.
Data from various sources was used to test-train a dataset to determine the predictability of greenhouse gas (GHG) emissions due to transportation.
Process
This project showcases using machine learning to make a prediction and uses visualizations to provide the user an interactive means to explore data relevant to the prediction.
View the project Github Repo README.md for more information on the process and methodologies.
The project methodology focused on:
- Working as a team, demonstrated by using branches in GitHub to manage the push/merge/pull of the repository, acquiring datasets, coordinating efforts to develop the machine learning model, and preparing information for display/presentation.
- Acquiring the datasets from multiple sources.
- ETL of chosen datasets.
- Developing a machine learning model using multi-linear regressions to predict an outcome.
- Creating visualizations used to demonstrate the project's intent.
- Creating a website and deploying to GitHub Pages.
- Languages and libraries used:
- Python
- Pandas
- GeoPandas
- Numpy
- Matplotlib
- Seaborn
- Scikit-learn
- Beautiful Soup
- HTML
- CSS
- Bootstrap
- D3
- Javascript / Requests (JSON)
- Leaflet
- Tableau
Data & Resources
Data for this project was derived from various sources used in both the machine learning model and for the visualizations.
Sources:
Team
Nick Buller
github.com/nbullerds
nabuller@gmail.com
Business analyst consultant with 6+ years of experience and a background in data analysis, process improvement, and business facilitation. Proven ability to work cross-functionally across business disciplines and roles for the purpose of gathering business objectives and software/data requirements. Driven by personal satisfaction derived from building data models and visualizations to provide insights into business and process improvement decision making.
Kerry Harp
github.com/klharp
kharp@umn.edu
Promoting sustainability in the built environment and the health/wellbeing of occupants. With degrees in architecture and accreditations in LEED and WELL, Kerry’s value is being a bridge between the technical and design fields to promote and deploy resilient, sustainability, and wellbeing strategies.
Matt Killeen
github.com/matthewkilleen0830
matthew.killeen0830@gmail.com
Analyst with more than 10 years of experience in financial services analyzing investments, market trends and forecasts, and financial plans. Utilizes technical skill sets to analyze dynamic data and develop reports. A critically thinking problem solver with a passion for organizing and presenting data to provide objective analytical solutions.
Ciera Morris
github.com/cieranmorris
cieranmorris@gmail.com
Developing Data Analyst with relevant biomedical research experience who is interested in using a multi-disciplinary approach towards analyzing data in industry.