Background:
In studying the era we currently are contributors of, called the Anthropocene, we first researched its defining of our assertive stance on overarching climate and nature. We explored this in looking at studies and readings, as well as carrying out panels and field work to learn about local ecosystem disruptions caused by humans (see labs 1-5 here). However, in looking at human’s influence, as a whole, on Earth, we neglect to consider how capitalism is (or is not) a large player in this disruption of our ecosystem. To include capitalism in the equation, and perhaps narrow particular human influences, we may also find ourselves in what could be called, the Capitalocene. In order to measure the Capitalocene, aka how capitalism has played a role in our environmental influence, I researched the data that goes into the Environmental Performance Index, better known as the EPI (see here for more information on the EPI). The EPI ranks 180 countries based on 24 different indictors that influence either environmental health (HLT) or ecosystem vitality (ECO). I also pulled data from the World Bank on each country’s income group, allowing myself to organize and compare data from countries of similar income groups, and/or regions.
Procedure:
1. I downloaded data from World Bank, and on EPI, onto two different spreadsheets, one of every countries EPI scores, as well as the scores that went into that decision, and a second of which region and income level each country belongs in.
2. I then compiled all the data onto one sheet, so that I could compute the averages for each region and income level using the individual indictors from each country.
3. Through the creation of a pivot table, said calculations were made and placed on a new sheet, summarizing all data of averages on two new tables (one for region and one for income level).
4. Using these tables, I selected a few points of data comparison that I found to have results representative of the general trend that most analyzed data seemed to follow in relation to the overall EPI, including the EPI itself, and created visual charts to better visualize said trends and compare data.
Results:
In analyzing the Environmental Performance Index (EPI) of every country accounted for (180), it was apparent that a grouping of some sort would have to take place in order to effectively understand the immense amount of data. At first, I organized each country into 1 of 7 regions (information retrieved from the World Bank), allowing to see any patterns in environmental progress for specific regions (see below). While this does not give any sense of GDP or how each region relates on the scale of capitalism, I can use previous knowledge to see a trend in which regions that contain more affluent countries (ie: North America, then Europe and Central America), seem to have a better average EPI overall, while those with greatest economic disparity (South Asia, Sub- Saharan, etc) rank lower.
After seeing this and noticed the similar trend among each of the seven regions, I decided it would be best to analyze data by splitting countries into income group instead. In doing so (see below), the trend of EPI increasing with income was obvious. In each of
the four income groups, a jump of around 10% variation occurs between each (give or take a few percent).
The two main indictors used to decipher the EPI of each country by grouping 24 subcategories, the Environmental Health, or HLT(40%), and Ecosystem Vitality, or ECO, (60%), also contained rather consistent findings with the previous in relation to income group
The last piece of data I decided to look at, in response to researching Land Cover Use and Change in my surrounding ecosystem (see labs 1-5 here), is the Tree Cover Loss (TCL) average for each country, organized by income group. Tree cover loss is the measure for forests’s health, which makes up 10% of Ecosystem Vitality.
(Below is a chart of averages, as well as standard deviations, for averaged variables I took into consideration in the analyzation of how capitalism influences the EPI).
Discussion:
In the 1950’s and 1960’s the Kuznet’s Curve became a well-known hypothesis in the field of economics, specifically in this case, to show the relation between environmental impact and affluence/advancements. The curve hypothesizes that in order for economic development to occur, environmental degradation must get worse first, before getting better. It can also equate the way in which inequality will first get worse, before getting better throughout economic development. While the EPI scores (and those two contributors) did not contribute to supporting the hypothesis entailed in the Kuznet’s curve (as all three linearly increased with income), the overall tree cover loss followed a pattern similar to that of the Kuznet’s curve. While the lowest and highest income countries held the least tree cover loss, the median countries suffered most from this.
Aside from the Kuznet’s curve, my immediate findings represent a trend in which the poorer a country is, the more they suffer from environmental degradation. This inequality based on income group, not only by country but by overall region, represents an inequitable valance of externalities. In order to get a better understanding, I would do a more in-depth study into all 24 categories, in order to diversify and pin-point major sources of lower EPI’s