About The Datasets
The Contains modified Copernicus Climate Change Service information (2025) dataset from Our World in Data offers monthly average global surface temperature data from 1950 to 2025.
The Global Carbon Budget (2024) dataset, also from Our World in Data, provides data on annual CO₂ emissions by country and industry from 1750 to 2023.
Data Preparation
To facilitate better analysis, the data was grouped into periods of 10 years, with the exception of the last period (2020-2023). The final period was excluded from correlation analysis to avoid potential misleading conclusions.
This approach enabled a straightforward comparison of total CO2 emissions with the average global surface temperature over time. A new table, named "co2emissions_and_temperature_decades" was created using these variables to compute the Pearson correlation.
Given the temperature rise in the 1980s (following the third industrial revolution), we also calculated the total CO2 emissions by country and industry from 1970 to 2019.
Pearson Correlation
With the newly created table, we calculated the Pearson correlation and p-value using the following formulas:
This resulted in a strong positive correlation value of 0.95 and a p-value of less than 0.05, indicating a significant relationship between CO2 emissions and global temperature.
Linear Regression
With this data, a linear regression was performed to determine how much CO2 emissions are required to increase the global temperature. The following formulas were used:
Slope:
Slope = VAR N = COUNTROWS('co2emissions and temperature_decades') -- Number of data points VAR SumX_ = SUM('co2emissions and temperature_decades'[Total CO₂ emissions]) VAR SumY = SUM('co2emissions and temperature_decades'[Avg. Global Surface Temperature (Celsius)]) VAR SumXY = SUMX('co2emissions and temperature_decades', 'co2emissions and temperature_decades'[Total CO₂ emissions] * 'co2emissions and temperature_decades'[Avg. Global Surface Temperature (Celsius)]) VAR SumX2 = SUMX('co2emissions and temperature_decades', 'co2emissions and temperature_decades'[Total CO₂ emissions] * 'co2emissions and temperature_decades'[Total CO₂ emissions]) RETURN (N * SumXY - SumX_ * SumY) / (N * SumX2 - SumX_ * SumX_)
Intercept:
Intercept = VAR N = [N] VAR SumX_ = SUM('co2emissions and temperature_decades'[Total CO₂ emissions]) VAR SumY = SUM('co2emissions and temperature_decades'[Avg. Global Surface Temperature (Celsius)]) VAR m = [Slope] -- Use the previously calculated Slope RETURN (SumY - m * SumX_) / N
Thus, the analysis showed that every 100 billion units of CO2 emissions results in a 0.25°C increase in the average global surface temperature.
Important Considerations
We observed that the third industrial revolution may significantly contribute to rising average temperatures. However, it's also important to remember that correlation does not always imply causation. For example, ice cream sales and swimming pool drownings are strongly correlated, but stopping ice cream sales won’t reduce drownings. Both are likely linked to summer weather, increasing pool activity and ice cream consumption.
While CO2 emissions are a likely cause of global warming, other factors like deforestation and methane emissions should also be considered in further analyses.
Created by @lps_solutions
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