Geographic and Financial Impact of Carbon Tax on USA
This is the repository for our Big Data Project at Columbia University (EECS E6893).
The United States has a much greater geographic distribution of people than other industrialized nations and a significant percentage of commuters (people who travel long distances to work). This research will look at the geographic and financial impact of a carbon tax on suburban areas. We will look at commuting distance data utilizing standard online mapping tools. We will then construct multiple distance-dependent commuting models combined with population date from the census to estimate commuters.
We will then use apply a set of carbon tax measures to the data including to ones similar to Washington State's Initiative 732 (https://en.wikipedia.org/wiki/Washington_Initiative_732) and Kyoto protocal levels (http://www.ipcc.ch/ipccreports/tar/wg3/index.php?idp=37). The financial impact of the tax will then be investigated by comparing to income data (also from the census).
The expected outcome is that carbon tax will disproportionately impact large cities with high commuting populations. Additionally, the impact of in terms of % of income will be even more significant. Or not.
This research was inspired by: http://pubs.acs.org/doi/pdf/10.1021/es4034364
Languages: Python/Spark or System G Dataset: Combine multiple data sets. Scrape distance data using Google Maps and other mapping software. Population and income data from census sources. Analytics: Spark and/or System G http://www.ee.columbia.edu/~cylin/course/bigdata/getprojectinfo.html