Through our first five labs in class we have looked at how anthropocene affects the environment. Using this lense it was our job to inspect how human engagement has influenced our environment. Inspecting Land Use Cover Change, we looked at how human interaction in our own community may have altered the environment. Beginning with cooling data we also interviewed a panel of local residents and experts about change in the community. In this new lab we have shifted our mindset to look at the environment and its changes through a capitalocene view. This lense is an expansion of the more broad anthropocentric view. Here we still classify everything based on the age of man however, we look at how capital may have affected the decisions made by man. Different regions of the world began transforming one after another in a race to produce commodities. Nature became something outside of society and instead was turned into a set of objects that were available as commodities. Hartley explains this concept and its negative consequences by saying, “Its ultimate horizon is not the impending doom of ecological catastrophe and human extinction: it is the capitalist mode of production and its dismantlement,” (Hartley, 10). This reinforces the notion that a capitalist lense is to blame for intensified land use rather than just human nature. In this lab we will be using data from the Yale Environmental Performance Index (EPI) and merge data from the World Bank with it. The Yale EPI is a “careful measurement of environmental trends and progress provides a foundation for effective policymaking. The 2018 Environmental Performance Index (EPI) ranks 180 countries on 24 performance indicators across ten issue categories covering environmental health and ecosystem vitality.” (Welcome | Environmental Performance Index). Here each country is compared to the policy goal and a score is issued to the country based on their performance. Beginning by importing the Yale EPI data into a spreadsheet on google, I then imported the World Bank data. Since the country codes remained constant in both data tables it is possible to merge the two using a chrome extension. Combining the spreadsheets also allowed us to add a pivot table to track the averages per region, averages per income level, standard deviation, and country count in each group. As I looked at the date I decided to make 6 tables, Income compared to EPI, Region to EPI, and four Income Level to Region charts. Our first graph is looking at income level vs EPI score. A countries EPI consists of 60% ecosystem vitality, which include things like air pollution, water resources, biodiversity and habitat etc. and 40% environmental health which includes air quality, water sanitation etc. From this graph we can see as the income rises the EPI score also rises, showing a potential correlation in income and EPI score. Graph two is comparing the average EPI vs specific regions. I wanted to compare these different areas to see if it would show us which specific regions around the world have a higher or lower EPI. Regions like North America and Europe that are often known for being a leading developer in the larger global context possibly creating more progressive goals. These locations also have undergone the industrial revolution fully and consist of a more capitalistic system. The next four graphs break down each level of income into their own graph. I separated high income, upper middle income, lower middle income, and low income into regions. I did this to look at how each region preforms as each separate income level. The more developed nations that have undergone industrial revaluation tended to score better than regions that were still going through a capital transformation. In the low income region the Sub-Saharan Africa section remains higher than Latin America. This could possibly have to do with Kuznet's curve. The Kuznet's curve shows that as countries become more economically developed their negative impacts on the environment go up. Because of this the curve suggests that less industrialized countries have a lower impact on the environment. The curve also suggests that very industrialized areas may shift to being more sustainable. As you can see from our graphs, we did not see this trend too much. Instead we say that higher income sections boasted far better scores with scores getting lower the less income you made. The graphs suggest that more industrialized areas are having a better impact on the environment. The only issue I take with this is it may not accurately represent Kuznet's Curve. It is possible that the scores are set on environmental goals therefore some of these low income areas may not be meeting those goals but may still be sustainable regions. Hartley, Daniel. “Against the Anthropocene.” Salvage1, no. July (2015). http://salvage.zone/in-print/against-the-anthropocene/. “Welcome | Environmental Performance Index.” Accessed October 21, 2018. https://epi.envirocenter.yale.edu/.
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