The reduction of the negative impact from climate change¶

Description:¶

Climate change is a complex and multi-faceted problem that affects many different aspects of our lives. For example, the climate change influenced on crops adapations (Raze et al., 2019), energy production, and economic (Tol, 2018), making governments have introduced legislation to mitigate the effects of environmental change universally. Using Data science analysis from a variety of datasets can help us to understand the causes and effects of climate change and develop effective strategies to mitigate its impact.

Reference:¶

  1. Impact of Climate Change on Crops Adaptation and Strategies to Tackle Its Outcome: A Review
  2. The Economic Impacts of Climate Change

Dataset¶

Demonstration:¶

  • The data below shows the temperature change in celsius, which includes the country name, unit, change object, and change data per year from 1970-2020.
  • By analyzing the data from 50 years, we have the ability to predict the trand of climate change, informing the government to make some strategies to protect the current environment, and prevent people's loss.
In [1]:
import pandas as pd
import pprint
In [2]:
df = pd.read_csv('all countries global temperature.csv')

df
Out[2]:
ObjectId Country Name Unit Change 1970 1971 1972 1973 1974 1975 ... 2012 2013 2014 2015 2016 2017 2018 2019 2020 2021
0 1 Afghanistan, Islamic Rep. of Degree Celsius Surface Temperature Change 0.898 0.652 -1.089 0.262 -0.470 -0.468 ... 0.234 1.308 0.457 1.101 1.607 1.568 1.580 0.960 0.544 1.421
1 2 Albania Degree Celsius Surface Temperature Change -0.119 -0.200 -0.077 -0.299 -0.134 -0.203 ... 1.568 1.444 1.322 1.665 1.601 1.269 2.146 1.823 1.623 1.682
2 3 Algeria Degree Celsius Surface Temperature Change 0.114 -0.380 -0.342 -0.028 -0.502 -0.554 ... 1.128 1.173 1.676 1.101 1.736 1.498 1.211 1.094 1.913 2.317
3 4 American Samoa Degree Celsius Surface Temperature Change -0.036 -0.473 -0.070 0.322 -0.317 -0.128 ... 0.646 0.883 0.554 0.394 0.924 0.820 0.574 0.924 0.815 0.653
4 5 Andorra, Principality of Degree Celsius Surface Temperature Change 0.081 -0.355 -0.526 -0.010 -0.412 0.207 ... 1.196 0.757 1.857 1.546 1.830 1.771 1.761 1.813 2.401 1.367
... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ...
222 223 Western Sahara Degree Celsius Surface Temperature Change 0.547 -0.620 -1.104 -0.013 -0.600 -0.267 ... 1.234 1.282 1.244 1.277 1.636 2.078 0.851 1.430 2.026 1.557
223 224 World Degree Celsius Surface Temperature Change 0.153 -0.089 -0.193 0.271 -0.179 0.091 ... 1.058 1.007 1.042 1.406 1.658 1.424 1.284 1.449 1.713 1.442
224 225 Yemen, Rep. of Degree Celsius Surface Temperature Change 0.388 -0.199 0.049 0.333 -0.108 0.031 ... NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN
225 226 Zambia Degree Celsius Surface Temperature Change 0.354 -0.249 -0.146 0.386 -0.393 -0.116 ... 0.926 0.746 0.638 1.486 1.474 0.721 0.727 1.295 1.252 1.002
226 230 Zimbabwe Degree Celsius Surface Temperature Change 0.427 -0.033 -0.367 0.627 -0.471 -0.253 ... 0.329 0.102 -0.008 0.808 1.051 0.116 0.405 0.939 0.415 -0.101

227 rows × 56 columns

In [5]:
dict_cc = {'Country name': 'each country names', 'Unit': 'Degree Celsius', 'Change': 'surfance temperature change', '1970-2020': 'temperature change data per year'}
pprint.pprint(dict_cc)

{'1970-2020': 'temperature change data per year',
 'Change': 'surfance temperature change',
 'Country name': 'each country names',
 'Unit': 'Degree Celsius'}

Sovling strategies:¶

  • We can categorize the countries by the main territorical features, and compare their climate change.
  • We also can compare the climate change for each decade of the world, and predict the temperature change for the next 10 years, alarming the world organization to make some tactics and solutions on it.