LAB SEVEN: Income and Environmental Influence

Background-

In studying the current Capitalocene era that is unfolding through urbanization and environmental disruption (see Lab Six for more information), many indicators of environmental and societal advancements present viable information in the world-wide distribution of such. After previously comparing the correlation between average income and measurements of environmental bioversity (the Environmental Performance Index, environmental health, and tree cover loss), my findings displayed a trend in which countries/regions with greater incomes displayed greater EPI’s, while tree cover loss presented results similar to that of the Kuznet’s Curve (see Lab Six results). While averaging and graphing these correlations gave general ideas, I decided from this to look into two other indicators of the Capitalocene form the World Bank Repository, nitrous oxide emissions and access to electricity. In looking at these two other variables of income status and environmental influence, I then present visuals of world-wide data through mapping, on ARC GIS, to look for further evidence of patterned correlations occurring due to the Capitalocene.

Procedure-

After merging data last week on country level EPI scores and income levels into one spreadsheet (see Lab Six for details), I used similar methods to add on to last weeks data. Using the World Bank repository, I first collected the percentage of people with access to electricity, as well as nitrous oxide emissions, onto a single spreadsheet. Using the elaborated data of each individual country from last week, I merged data from these two onto a single spreadsheet. I then used said chart to insert and convert into data on ARC GIS, an online mapping tool. This allowed me the opportunity to present any level of mapping and any number of variables on a world-wide map to find noticeable trends/correlations in indicators of the Capitalocene era.

Results-

While each map contains a various display of correlation, the following three has each of 180 countries color coded based on one of four income groups to the right, thus allowing for visual correlations to seen between income and an environmental indicator.

The below is a visual mapping of data graphed in Lab Six. Here is a display of each country's ECO (numerical) and income group (color-coded)

Figure One: ECO vs Income

The below is also a visual mapping of data graphed in Lab Six. Here is a display of each country's Environmental Health (numerical) and income group (color-coded). Environmental health is one of the two main indicators in the overall EPI. Again, the higher the environmental health (HLT), the better.

Figure Two: HLT vs. Income

Also utilized was a visual mapping of each country's emissions of nitrous oxides (numerical) and income group (color-coded). Each country's overall emissions of nitrous oxides are measured by a thousand metric tons of CO2 equivalent. While the initial correlation noticed is that larger countries produce greater emissions, look at the proportions in relation to income. If all countries were of equal size, it seems as though countries of highest and lowest incomes seem to produce the most emissions.

Figure Three: Nitrous Oxide Emissions vs. Income

Using data gathered through previous and current research, created was a display of each country's population percentage with access to electricity (numerical) and income group (color-coded). While access to electricity is often a measure of income, it can be be an overall measurement of urbanization as well, thus allowing us to see how countries that are further urbanized, are more or less advanced environmentally.

Figure Four: Access to Electricity vs. Income

While each map contains various displays of correlation, the following two has each of 180 countries color coded based on the amount of nitrous oxide being emitted (thousand metric tons of CO2 equivalent)

It addition to comparing income levels with various measures of environmental wellness, the following two maps display each of 180 countries color-coded based on the amount of nitrous oxide being emitted (thousand metric tons of CO2 equivalent), thus contributing to further overall pollution.

The above is a visual mapping allowing us to find any possible correlations between the data collected in Labs 6 and current research. Here is a display of each country's EPI (numerical) and Nitrous Oxide emissions (color-coded). The same two measures of data are displayed in the previous map as well, only with a reverse of numerical and color-coded data in order to allow for a different perspective in understanding.

The above is a visual mapping displaying possible correlations between the data collected in the previous and current labs. Here is a display of each country's ECO (numerical) and Nitrous Oxide emissions (color-coded).

Discussion-

I personally found the compounding of averages in my previous research to tell a more compelling story in finding correlations between income and biodiversity. By presenting averages on a chart, I was able to see noticeable changes in each of four income groups. In presenting the data through visual mapping of individual mapping, I found it harder to decipher correlations between the two, as country population/size is not taken into account (averages decrease influences of such variations). I was still presented with similarly linear results, where an increase in income resulted in an increase in nitrous oxide emissions. This most likely reflects the increased levels of urbanization that results in more production sources of such. While tree cover loss represented a shape similar to the of the Kuznet’s Curve, my current findings failed to support such a hypothesis. While I enjoyed the visual representation of individual mapping of data, it presented no further insight into regional relations. I would revert to averaging and graphing before pursuing any further findings.