Graph:
16000
14000
12000
10000
8000
EU
USA
6000
4000
China
2000
0
0
HK
1000000 2000000 3000000 4000000
GDP (in billions of US dollars)
5000000 6000000 7000000
Analysis:
The graph doesn’t really show any pattern at all, except as we all know there’re few countries at the top & extreme level and all other countries are all at the bottom. It is shown that the polarity between rich and poor countries, or more developed and less developed countries, are becoming greater and yet more significant. There are wide gaps between the rich and poor poles. As if there’re two poles, one at the bottom, where hundreds of countries gather together.
Whereas there’s the other pole on the top, less dense, with only several ‘big countries’: EU, USA,
China and that’s it. It’s said to have no pattern because we couldn’t find a way to plot a trend that fits. One major reason is that there’re not any countries that lie between the two extreme poles, in other words the middle of the graph. This furthermore convinces the polarity of the results.

Conclusion:
As we have analyzed, there is no trend for the results at all. Countries are all sorted into 2 distinct poles. Poor countries such as the African countries contribute to the lower pole of countries. However, there’re still a number of small but more developed countries that stay at the bottom. For instance, Singapore. More developed countries with fair territory size come after them. Without even looking at the table of data, we can predict by looking at the graph that the part where the countries start scatter from each other a bit could possibly be the countries that are economically powerful but has relatively smaller population such as Japan,
Canada, UK, and Russia. They have a high GDP per capita, but in total as they have a smaller population, the countries would have lower GDP. Same for the CO2 emission level. Therefore it is impossible to draw a trend line that fits and in fact, the results are not fair. China has a greater GDP than USA, in total, but it doesn’t mean China is more developed than USA. Since averagely each person in USA has a higher GDP than China, China only beats USA by its massive population. Hence, in order to obtain fair result that ought to display some sort of trend, I decided to investigate on the GDP and CO2 emission values per capita.
90 000
80 000
70 000
60 000
50 000
40 000
30 000
20 000
10 000
0
0 5 10 15
CO2 emissions per capita
20 25 30
The graph for per capita turned out to be similar as the summed ones we did earlier, except that the polarization is less of a problem, and the countries are more evenly distributed.
However, it’s true that at least there’re some countries located in the middle of graph now, but

still, there’re lots of countries down the bottom and few countries at top. It is suggested that more and more countries want to be rich while there’re still a huge number of countries that suffer poverty. In addition, while countries are more developed, they have higher carbon dioxide emissions. The reason the world’s total CO2 emissions is increasing is mainly because as mentioned, more countries want to be rich and thus develop rapidly. Currently the Earth won’t collapse yet due to the balance out of the massive number of undeveloped countries, resulting in extreme low carbon dioxide emissions. But this is going to be a great issue everybody has to be aware of and concern as the world is developing at an unbelievable rate and the same for its emissions of CO2. Dozens of years later, even the countries with the lowest CO2 emissions would possible produce lots of CO2 like China today.
Accuracy:
Actually, the results I collected for GDP and CO2 emissions are from different years. For CO2 emissions, due to the lack of information available, the latest data available on the Internet which I found is only from year 2006. When searching about GDP, there are only year 2009 and
2008’s data available, so I decided to pick the year closest to 2006 (which is 2008) and just put them together as one year. This might affect the accuracy and validity of the data, but I figured out the ratio for each country wouldn’t change a lot during the 2 years so it wouldn’t cause a big problem.
Evaluation:
If we are to do the investigation again, I would select a number of typical countries representing different groups of countries among all of them to investigate, instead of getting a graph in the end a bunch of scattered countries all around it. For data collection, I’d try to obtain the latest results and ensure the two types of results collected are from the same year. My aim is to achieve a best accuracy after all. Remember, everything we do carefully and precisely contribute to the ease of more likely getting a perfect trend!