LAB 2

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Geography

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Dec 6, 2023

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Sustainability Metrics Assignment Complete the following individually and in your own words , using complete sentences unless otherwise indicated. See the rubric on eLC (linked to assignment Dropbox) for point values and assessment criteria. From Activity B: 1. Write the sustainability question you developed in Activity B: Does a country’s percentage of people living in urban areas affect that country’s material footprint per capita in tonnes? 2. List the units for your sustainability question’s Impact and Technology metrics: ● Impact: Material footprint per capita (tonnes) ● Technology: Percent of the population living in urban areas 3. Paste a screenshot of your GapMinder graph, showing your two highlighted countries, below:

Answer #4 and #5 in ~3-4 sentences each. 4. Among all the countries shown in the figure, what pattern(s) do you see in how population, affluence, and/or level of technology influence environmental impact? Does the IPAT framework seem to apply? Why or why not? The country’s national GDP has a massive effect on their percent of urban population. Generally, as seen from the graph, the higher percent of urban population out of total population, the higher the material footprint per capita. The impact on the environment (being material footprint per capita) follows perfectly along the lines of the IPAT framework because the countries with higher footprint are much more developed and more technologically advanced nations than those with lower ones. 5. Using specific values from your graph and your two focal countries to support your inferences, answer the sustainability question you posed above. How do the data allow you to answer your sustainability question (or not)? If the data do not allow you to answer your original question, write a statement about sustainability that your data do support. The countries highlighted are Burundi and Luxembourg. Luxembourg had the second highest material footprint at 104 tonnes per capita while having an urban population percentage of 90.4%. Although Luxumbourg is a small country 80% or higher is still a very large percentage of population in comparison to the other countries. Burundi has a material footprint of 1.59 tonnes per capita while having the lowest percentage of urban population in the chart at 12.4%. These stats clearly follow the IPAT correlation framework because the more “technology” (percent of population living in urban areas) the more that country has an impact on the environment. From Activity C: 6. Paste a screenshot of your GapMinder graph, showing the time “trails” for your four focal countries, below:

7. Complete the table below to show the data collected from your four focal countries. Double-click inside the table to edit the cell values. Columns for % change should update automatically. (If you have trouble entering your data directly into the embedded Excel sheet below, complete the table in the Handout and paste it into your assignment). Answer #8 and #9 in ~3-4 sentences each. 7. Using specific details from your graph and/or data table, describe the patterns of change in your focal countries over the selected time period. (e.g., What was your focal time period? How were changes in each variable similar/different among countries? Which countries/variables changed the most/least?) The time trails shown in the graph are from the years 1989 to 2016. Almost 3 decades means a lot of change is being shown. The wealthiest countries, Japan and Mexico, had very similar trends with both having around a 20 percent increase in impact and Japan having a tech. change of 18.4 percent while Mexico’s was 11.4 percent. Both Egypt and Ethiopia prove to be outliers in comparison to the rest of the countries. Ethiopia, even though enduring a more than doubled population over the course of the time period, managed to lower its impact by -64%. Egypt also almost doubled their population in the same amount of time, but had a slight increase to their impact at 18.7 percent. However the most interesting thing of all is that Egypt tech. value actually was -1.88%, which is baffling. 8. For your focal countries, reflect on what might have driven observed changes in I and T over time. Spend a few minutes using online sources to look for information about potential drivers that may have been behind the data pattern you observed (e.g., catastrophic weather events, major policy, social, or economic changes, etc.), and briefly summarize what you found about country- specific drivers of the changes for at least 2 of your focal countries. At the bottom of your answer, list the sources of information you consulted that informed your response. Urbanization in Eygpt has somehow gone down in the past 30 years from the data, only its been

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going slightly downward since the beginning of the 1970s. This is due to the severe amounts of labor migration that Egypt has/had. Not only that, but sociopolitical instability in Cairo is also a contributing factor. On the other hand there is Ethiopia, which has seen a 61 percent positive change in urban population percentage. Ethiopia is one of the least urbanized countries in Africa, however, it is also one that is growing the fastest. The main driver to this is that the Ethiopian workforce is gradually moving away from the agricultural industry and many more citizens are moving to cities to find various other jobs. This trend is currently present in many other countries around the world. Sources of information: Julia Zinkina, and Andrey Korotayev. “Urbanization Dynamics in Egypt: Factors, Trends, Perspectives.” Arab Studies Quarterly , vol. 35, no. 1, 2013, pp. 20–38. JSTOR , https://doi.org/10.13169/arabstudquar.35.1.0020. Accessed 8 Sep. 2022. Gebrekristos Mezgebo, Tsega. (2021). Urbanization and Development in Ethiopia: Policy Issues, Trends and Prospects. 7. List at least one thing you see as a strength of using the Gapminder tool to understand environmental impacts, and one thing you see as a limitation. A strength of using the Gapminder tool is definitely the time trails function. Being able to observe the data across different years and seeing that change on one chart is very amazing and interesting. I think one of the limitations is that it doesn’t have a broad range of variables for you to compare and to truly go in-depth with on multiple topics. Also, a lot of the variables have very few countries in that given dataset which proves unusable if you want to discuss a topic across different continents.

LAB 2

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