Global Climate Change Report

The purpose of this report is to analyze data sets (Burke, Hsiang, and Miguel (2015)),regarding the impact of climate change on economic success around the world, both current and projected. More informaiton regarding their work can be found at in Nature here: http://www.nature.com/nature/journal/v527/n7577/full/nature15725.html

Visualizing Data

Question: Is there a correlation between a country’s mean temerature, poverty headcount, and the value of gross domestic product (GDP)?

Hypothesis: A country’s mean temperature and poverty level are positively correlated.

In the plot above, we see the poverty rate and mean temeprature of a country appear to be slightly positively coorelated. This informaiton supports my hypothesis that areas with higher mean temperatures are more likeley to experience higher poverty rates; however, this data does not provide sufficient evidence or level of confidence to draw a conclusion that there definitely is a positive correlation. The country, Mauritania, with the highest mean temperature, 28.6041935, had a poverty ratio of 42. The country, Mongolia, with the lowest mean temperature, -0.7334483, had a poverty ratio of 21.6. The country, Equatorial Guinea, with the highest poverty ratio, 24.5316667, had a mean temperature 76.8. The country, Malaysia, with the lowest poverty ratio, 26.08, had a mean temperature 0.6.

In the plot above, we see that a country’s mean temperature and gross domestic product value appear to be slightly negatively correlated. This informaiton supports my hypothesis that mean temperatures are more likely to experience higher poverty rates by poining out a specific source of monetary discreptancy between the market value of goods produced in countries experiencing different mean temperatures. To further support our hypothesis, we should track GDP and mean temperature per country over time. The country, Mauritania, with the highest mean temperature, 28.6041935, had a GDP of 1370.9852125. The country, Mongolia, with the lowest mean temperature, -0.7334483, had a GDP of 3967.8293856. The country, Luxembourg, with the highest GDP, 1.014499710^{5}, had a mean temperature 9.1890323. The country, Burundi, with the lowest GDP, 277.0683092, had a mean temperature 20.5625806.

We see in the plots above that there is some correlation between temperature and poverty, along with temperature and GDP. We should analyze temperature, poverty, and GDP over time to generate greater confidence in the possible correlation.

Map Visualization

Breakdown

In the charts above, you will see the projected GDP change over 20 year periods with climate change impact. In general, as the color gets lighter over time, the county’s GDP rises, and vice versa. The color classes are based on a a logarithmic scale. A color class level of 2 relates to a GDP value below 1000, 3 relates to below 10,000, 4 relates to below 100,000, and 5 relates to belwo 1,000,000. It is necessary to divide the data logarithmically because of the apparent variance in dispertion of wealth around the world. Without a logrithmic scale, a low GDP country that increased or decreased by as much as ten fold would be more likely to have its change, even at a such a high rate, go unnoticed.

Analysis

We see in these charts that certain countries, like Canada and Russia, are the most likely, based on prediciton, to economically succeed in a future impacted by climate change. Similarly, countries like America that are predicted to have stagnant economic success in a world impacted by climate change.

At a first glance, I would intuitively want to disagree with this predicted data to argue that overall GDP would increase in already GDP high countries and decrease in already GDP low countries. It is important, however, to remember that this data is based on relative worth in US dollars, so the apparent future success of countries outside of the USA may be in relation to taking into account the US dollar weakening with climate change. In any case, a good take home message here is that America has nothing to gain from climate change, even economically.

Interactive Visualizations

In this seciton of the report, we will compare the GDP of all countries between 2010 and predicted 2090 and how the shape of this data differs with and without climate change.

In the scatter plots above, GDP representing a marker for economic sucess in countries with and without climate change, we can clearly see that economic success is predicted to be more variantly disperesed among select countries if climate change continues, compared to if there were not any climate change. With climate change, there is predicted to be some countries benefitting quite a bit ecomically, but most suffering. When ther is no climate change, we see all countries steadily rising together in economic success.