GDP and Temperature (Climate) Change

Describes and motivates a real-world problem where data science may provide helpful insights. Your description should be easily understood by a casual reader and include citations to motivating sources or relevant information

Impacting all aspects of society, climate change is becoming an increasingly serious problem. Increasing temperatures have posed negative impacts on society to wildlife. To account for the impact of temeprature changes, from a broader view, temperature change can be viewed through the lens of an economic indicator known as GDP. GDP is the total value of finished services and goods during a period of time within a specific country.

Motivating this real-world problem, in recent news, economists have raised questions about China's emphasis on GDP growth over "wellbeing" (Stanway 1). In other news, a Sydney-based research firm states that by 2050 China's GDP will be endangered as environmental-related disasters are becoming more frequent, causing danger to economies worldwide (Xue 1).

Therefore, although GDP growth is positive towards economies, at what cost does climate change play a role?

Explicitly load and show your dataset. Provide a data dictionary which explains the meaning of each feature present. Demonstrate that this data is sufficient to make progress on your real-world problem described above.

import pandas as pd

df_1 = pd.read_csv('Countries_GDP_updated.csv')
print(df_1)

df_1.head()

                       Country Name Country Code          1960          1961  \
0       Africa Eastern and Southern          AFE  1.931311e+10  1.972349e+10   
1        Africa Western and Central          AFW  1.040428e+10  1.112805e+10   
2                         Australia          AUS  1.860679e+10  1.968306e+10   
3                           Austria          AUT  6.592694e+09  7.311750e+09   
4                           Burundi          BDI  1.960000e+08  2.030000e+08   
..                              ...          ...           ...           ...   
115  St. Vincent and the Grenadines          VCT  1.306656e+07  1.399988e+07   
116                           World          WLD  1.390000e+12  1.440000e+12   
117                    South Africa          ZAF  7.575397e+09  7.972997e+09   
118                          Zambia          ZMB  7.130000e+08  6.962857e+08   
119                        Zimbabwe          ZWE  1.052990e+09  1.096647e+09   

             1962          1963          1964          1965          1966  \
0    2.149392e+10  2.573321e+10  2.352744e+10  2.681057e+10  2.915216e+10   
1    1.194335e+10  1.267652e+10  1.383858e+10  1.486247e+10  1.583285e+10   
2    1.992272e+10  2.153993e+10  2.380110e+10  2.597715e+10  2.730989e+10   
3    7.756110e+09  8.374175e+09  9.169984e+09  9.994071e+09  1.088768e+10   
4    2.135000e+08  2.327500e+08  2.607500e+08  1.589950e+08  1.654446e+08   
..            ...           ...           ...           ...           ...   
115  1.452488e+07  1.370822e+07  1.475821e+07  1.510821e+07  1.609987e+07   
116  1.550000e+12  1.670000e+12  1.820000e+12  1.990000e+12  2.160000e+12   
117  8.497997e+09  9.423396e+09  1.037400e+10  1.133440e+10  1.235500e+10   
118  6.931429e+08  7.187143e+08  8.394286e+08  1.082857e+09  1.264286e+09   
119  1.117602e+09  1.159512e+09  1.217138e+09  1.311436e+09  1.281750e+09   

             1967  ...          2011          2012          2013  \
0    3.017317e+10  ...  9.430000e+11  9.510000e+11  9.640000e+11   
1    1.442643e+10  ...  6.710000e+11  7.280000e+11  8.210000e+11   
2    3.044462e+10  ...  1.400000e+12  1.550000e+12  1.580000e+12   
3    1.157943e+10  ...  4.310000e+11  4.090000e+11  4.300000e+11   
4    1.782971e+08  ...  2.235821e+09  2.333308e+09  2.451625e+09   
..            ...  ...           ...           ...           ...   
115  1.583518e+07  ...  6.761296e+08  6.929333e+08  7.212074e+08   
116  2.290000e+12  ...  7.370000e+13  7.530000e+13  7.740000e+13   
117  1.377739e+10  ...  4.580000e+11  4.340000e+11  4.010000e+11   
118  1.368000e+09  ...  2.345952e+10  2.550306e+10  2.803724e+10   
119  1.397002e+09  ...  1.410192e+10  1.711485e+10  1.909102e+10   

             2014          2015          2016          2017          2018  \
0    9.850000e+11  9.200000e+11  8.730000e+11  9.850000e+11  1.010000e+12   
1    8.650000e+11  7.610000e+11  6.910000e+11  6.840000e+11  7.420000e+11   
2    1.470000e+12  1.350000e+12  1.210000e+12  1.330000e+12  1.430000e+12   
3    4.420000e+11  3.820000e+11  3.960000e+11  4.160000e+11  4.550000e+11   
4    2.705783e+09  3.104395e+09  2.732809e+09  2.748180e+09  2.668496e+09   
..            ...           ...           ...           ...           ...   
115  7.277148e+08  7.554000e+08  7.744296e+08  7.921778e+08  8.113000e+08   
116  7.960000e+13  7.510000e+13  7.630000e+13  8.120000e+13  8.630000e+13   
117  3.810000e+11  3.470000e+11  3.240000e+11  3.810000e+11  4.050000e+11   
118  2.714102e+10  2.125122e+10  2.095841e+10  2.587360e+10  2.631159e+10   
119  1.949552e+10  1.996312e+10  2.054868e+10  1.758489e+10  1.811554e+10   

             2019          2020  
0    1.010000e+12  9.210000e+11  
1    7.950000e+11  7.850000e+11  
2    1.390000e+12  1.330000e+12  
3    4.450000e+11  4.330000e+11  
4    2.631434e+09  2.841786e+09  
..            ...           ...  
115  8.250407e+08  8.074741e+08  
116  8.760000e+13  8.470000e+13  
117  3.880000e+11  3.350000e+11  
118  2.330867e+10  1.811063e+10  
119  1.928429e+10  1.805117e+10  

[120 rows x 63 columns]

	Country Name	Country Code	1960	1961	1962	1963	1964	1965	1966	1967	...	2011	2012	2013	2014	2015	2016	2017	2018	2019	2020
0	Africa Eastern and Southern	AFE	1.931311e+10	1.972349e+10	2.149392e+10	2.573321e+10	2.352744e+10	2.681057e+10	2.915216e+10	3.017317e+10	...	9.430000e+11	9.510000e+11	9.640000e+11	9.850000e+11	9.200000e+11	8.730000e+11	9.850000e+11	1.010000e+12	1.010000e+12	9.210000e+11
1	Africa Western and Central	AFW	1.040428e+10	1.112805e+10	1.194335e+10	1.267652e+10	1.383858e+10	1.486247e+10	1.583285e+10	1.442643e+10	...	6.710000e+11	7.280000e+11	8.210000e+11	8.650000e+11	7.610000e+11	6.910000e+11	6.840000e+11	7.420000e+11	7.950000e+11	7.850000e+11
2	Australia	AUS	1.860679e+10	1.968306e+10	1.992272e+10	2.153993e+10	2.380110e+10	2.597715e+10	2.730989e+10	3.044462e+10	...	1.400000e+12	1.550000e+12	1.580000e+12	1.470000e+12	1.350000e+12	1.210000e+12	1.330000e+12	1.430000e+12	1.390000e+12	1.330000e+12
3	Austria	AUT	6.592694e+09	7.311750e+09	7.756110e+09	8.374175e+09	9.169984e+09	9.994071e+09	1.088768e+10	1.157943e+10	...	4.310000e+11	4.090000e+11	4.300000e+11	4.420000e+11	3.820000e+11	3.960000e+11	4.160000e+11	4.550000e+11	4.450000e+11	4.330000e+11
4	Burundi	BDI	1.960000e+08	2.030000e+08	2.135000e+08	2.327500e+08	2.607500e+08	1.589950e+08	1.654446e+08	1.782971e+08	...	2.235821e+09	2.333308e+09	2.451625e+09	2.705783e+09	3.104395e+09	2.732809e+09	2.748180e+09	2.668496e+09	2.631434e+09	2.841786e+09

5 rows × 63 columns

GDP: This dataset includes the name of each various countries (121 countries), country codes, years from 1960-2020, and a corresponding GDP calculation for the specific year.

df_2 = pd.read_csv('Temperature_Change_Data.csv')
print(df_2)

df_2.head()

      Country Code                             Country Name  year  tem_change
0              AFG                              Afghanistan  1961      -0.080
1              ALB                                  Albania  1961       0.631
2              DZA                                  Algeria  1961       0.186
3              ASM                           American Samoa  1961      -0.014
4              AND                                  Andorra  1961       0.749
...            ...                                      ...   ...         ...
16751          NaN        Low Income Food Deficit Countries  2019       1.244
16752          NaN  Net Food Importing Developing Countries  2019       1.412
16753          NaN                        Annex I countries  2019       1.627
16754          NaN                    Non-Annex I countries  2019       1.361
16755          NaN                                     OECD  2019       1.297

[16756 rows x 4 columns]

	Country Code	Country Name	year	tem_change
0	AFG	Afghanistan	1961	-0.080
1	ALB	Albania	1961	0.631
2	DZA	Algeria	1961	0.186
3	ASM	American Samoa	1961	-0.014
4	AND	Andorra	1961	0.749

Temperature Change: This dataset includes country code with the country name, country name, years from 1961-2019, and temperature change for that year

Write one or two sentences about how the data will be used to solve the problem

We can find conclusions by grouping the data in different ways. These datasets are sufficient to progress on this real-world problem by analyzing trends between GDP and how significant the temperature change is for that year. We can find conclusions based on specific areas regarding GDP and temperature change. We can also analyze if there is a correlation between the percent change in GDP with a lower temperature change.

Citations:

https://www.reuters.com/world/china/economists-urge-china-think-beyond-gdp-head-off-climate-risks-2023-02-23/

https://www.scmp.com/business/article/3210818/half-chinas-gdp-risk-climate-related-disaster-2050-sydney-based-research-firm-xdi-says