Climate Risk Index and Relating it to Economic and Population Loss¶

Problem¶

Climate risk is an overlooked issue, that stems from natural phenomenons such as blizzards, tornadoes, flooding and tsunamis, etc. Alongside with these extreme weather cases comes many problems that drastically hurt a country, such as the fatalities caused and the economic loss a country faces as it struggles to recover from natural disasters, such as regaining power, providing its people with resources, and overseeing reconstruction and supporting families and businesses that may have suffered. Worldwide resource allocation also continues to be a problem, as several countries can be overlooked and many countries struggle to recover due to a high vulnerability to these weather threats. The Global Climate Risk Index is a scale that measures each country's risks to natural disasters.The dataframe below shows several country's climate risk scores, as well as the economic losses they have faced and fatalities caused by these natural climate phenomenons.

sources¶

  • https://data.world/dataworldadmin
  • https://www.kaggle.com/datasets/thedevastator/global-climate-risk-index-and-related-economic-l
In [68]:
import pandas as pd

df_economy = pd.DataFrame(pd.read_csv('climate-risk-index-1.csv'))
df_economy = df_economy.drop('the_geom', axis=1)
df_economy = df_economy.drop('the_geom_webmercator', axis=1)
df_economy = df_economy.drop('losses_usdm_ppp_rank', axis=1)
df_economy = df_economy.drop('losses_usdm_ppp_total', axis=1)
df_economy = df_economy.drop('rw_country_name', axis=1)

df_economy
Out[68]:
index cartodb_id country cri_rank cri_score fatalities_per_100k_rank fatalities_per_100k_total fatalities_rank fatalities_total losses_per_gdp__rank losses_per_gdp__total rw_country_code
0 0 1 Saudi Arabia 79 72.50 18 0.45 18 140 131 0.0001 SAU
1 1 2 Romania 61 61.50 112 0.01 102 1 16 0.6746 ROU
2 2 3 Spain 69 66.33 74 0.05 47 22 86 0.0394 ESP
3 3 4 Slovenia 135 124.50 114 0.00 114 0 135 NaN SVN
4 4 5 South Sudan 133 117.33 114 0.00 114 0 120 0.0021 SSD
... ... ... ... ... ... ... ... ... ... ... ... ...
177 177 178 Seychelles 135 124.50 114 0.00 114 0 135 NaN SYC
178 178 179 Gambia 135 124.50 114 0.00 114 0 135 NaN GMB
179 179 180 Togo 131 114.33 104 0.01 102 1 123 0.0012 TGO
180 180 181 Trinidad and Tobago 135 124.50 114 0.00 114 0 135 NaN TTO
181 181 182 Tonga 135 124.50 114 0.00 114 0 135 NaN TON

182 rows × 12 columns

In [69]:
project_dict = {'country': 'country name', 'cri_rank': 'Rank of the country on the Climate Risk Index', 
                'cri_score': 'Score of country on Climate Risk Index', 
                'fatalities_per_100k_rank' : 'Country Rank in terms of fatalities per 100,000 people', 
                'fatalities_per_10 0k_total' : 'Total number fatalities per 100,000 people',
                'fatalities_rank':'Country Rank in terms of total fatalities',
                'fatalities_total':'Total number of fatalities' ,
                'losses_per_gdp__rank':'Rank of the country in terms of losses per GDP',
                'losses_per_gdp__total':'Total losses per GDP',
                'rw_country_code': 'Country code'}

Demonstration¶

In [70]:
import matplotlib.pyplot as plt
import numpy as np

plt.scatter(df_economy['cri_rank'], df_economy['losses_per_gdp__rank'], c = df_economy['fatalities_rank'], cmap = 'plasma')
plt.colorbar( label = 'Fatalities Rank', orientation = 'vertical')
plt.xlabel('Climate Risk Index Rank')
plt.ylabel('GDP Loss Rank')
plt.title('CRI Rank vs GDP Loss Rank')

# line of best fit
model1 = np.poly1d(np.polyfit(df_economy['cri_rank'],df_economy['losses_per_gdp__rank'], 3))
polyline = np.linspace(0,140)
plt.plot(polyline, model1(polyline), c = 'black')
Out[70]:
[<matplotlib.lines.Line2D at 0x22a43ec8f40>]

Data Science Use¶

I intend to create a dictionary that associates different percentiles and countries that lie in it based on their fatalities and Climate Risk Index Score to highlight which countries are facing the most damage. This can help with resource and funding allocation, and to prioritize countries with greater fatalities.

I also plan to create several graphs that highlight any correlation between a country's climate risk and economic losses, as well as the correlations between climate risk and the countries fatalities, which may provide insight on how one factor may relate to others, in order to prevent loss, both population wise and GDP wise.

In [ ]: