<jupyter_start><jupyter_text>Day 1<jupyter_code>item.gap_minder(1990)
item.gap_minder(2016)<jupyter_output><empty_output><jupyter_text>The two plots clearly show, that the overall word’s energy consumption as well as GDP has gone up over the recent years. For countries in the lower end, it looks like there GDP has slightly increased while energy consumption remained roughly the same. On the other hand, high economic development stands in conjunction with increased energy consumption. It is visible that countries with a higher population tend to be on the upper end of the consumption side. Also, the overall density of the map increased corresponding to a higher population.<jupyter_code>item.plot_consumption("Germany")
item.plot_consumption("Germany", True)
item.plot_consumption("India")
item.plot_consumption("India", True)
item.plot_consumption("China")
item.plot_consumption("China", True)<jupyter_output><empty_output><jupyter_text>Looking at the six plots, it is clearly evident that the total energy consumption of India and China vastly increased over the years corresponding to their strong development. This is backed by coal and oil as the main energy providers for both countries. In comparison, the overall energy consumption remained rather constant for Germany, and even seems to have slightly decreased in recent years. This corresponds to the fact that Germany can already be considered a developed country over the observation period whereas India and China only recently experienced exponential economic growth.As India and China, Germany is also still highly reliant on coal and oil. Additionally, it shows high dependance on gas and some dependency on nuclear. While coal, oil, and nuclear consumption slightly decreased over the past, gas consumption stayed at a rather constant level. However, it should be pointed out that Germany also tries so increasingly shift to renewable energy sources such as wind and solar from the 2000s on. Nevertheless, while their share is noticeably higher in Germany compared to India and China, it still only makes up a tiny portion of the energy mix. The EU carbon tax likely also contributes to this shift which will be further discussed below. Looking at China, the relative consumption amounts seem to be somehow similar to India whereas its absolute consumption skyrockets in comparison to both - India and Germany. This corresponds to the fact that it is the country with the highest population while simultaneously being the number one greenhouse emissioner.Overall, all plots clearly show that the current efforts of all countries are certainly not enough to meet the goal of 1.5 degrees by 2030, and neither net-zero by 2050.<jupyter_code>item.gdp("Germany", "India", "China")<jupyter_output><empty_output><jupyter_text>The plot shows that the GDP of all countries has risen since 1970. However, there are clear differences in the steepness of the curves. Germany shows a rather constant increase. While it had the highest GDP back in the 1970s, it has been overtaken by China around 1980 as well as by India in the 2000s. This corresponds to the huge developments of the Indian and Chinese economy over the recent years whereas Germany already has reached a high level of economic development. However, as especially apparent when looking at China in both - consumption and GDP plots - economic development also leads to an extreme increase of energy consumption which is currently majorly carried by non-sustainable energy sources. To conclude, it is evident that there is a clear connection between energy consumption, GDP and population. A higher GDP corresponds to a higher energy consumption as does an increasing population. This intuitively makes sense; however, the key issue is what energy sources are backing a country's development and how much emissions they are causing. Here, individual political frameworks and requirements come into play. Day 2 will shed some more light on recent developments, interdependencies and also tries to give some insights into possible ways ahead.   Day 2<jupyter_code>item.compare_consumption("Germany", "India", "China")<jupyter_output><empty_output><jupyter_text>This plot backs the insights of the previously plotted consumption patterns in absolute and relative terms. Furthermore, it gives some insights on the corresponding Co2 emissions which are a key factor when assessing the sustainability of energy sources. Evidently, wind, solar as well as nuclear energy consumption do not cause any Co2 emissions. In contrast, coal is the main driver of greenhouse gases followed by oil. Comparatively, gas consumption certainly leads to less Co2 emissions than oil or gas. Thus, while it clearly needs to be reduced and replaced by green solutions in the future, it might serve as an intermediate solution/buffer on the way to net zero. This is even more true for hydro consumption which shows even less Co2 emissions and, thus presents one of the hopes of future energy mixes besides the even preferred zero-emission sources. However, recent developments clearly showed that the wide expansion and adoption of those sources is hindered by the high investments in necessary infrastructure, bureaucracy and corresponding political unwillingness on a national and international level.<jupyter_code>item.scatter_plot()<jupyter_output>No handles with labels found to put in legend.<jupyter_text>This plot complements the interdependence of economic development and energy consumption by clearly showing that higher development alias higher consumption ultimately also leads to higher emissions, given countries do ot change their energy mix. Therefore, it emphasizes the urgent need for all countries to initiate a shift to renewable energy sources right now!Let's have a look how energy consumptions and emissions are predicted to evolve over the next 5 years based on the historical data and trends:<jupyter_code>item.arima_predict("Germany", 5)
item.arima_predict("India", 5)
item.arima_predict("China", 5)<jupyter_output><empty_output>
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