Carbon Footprint
The carbon footprint is a measure of the total amount of greenhouse gases produced to directly and indirectly support human activities, usually expressed in equivalent tons of carbon dioxide (CO2).
The carbon footprint is a subset of the ecological footprint and of the more comprehensive Life Cycle Assessment (LCA). An individual, nation, or organization’s carbon footprint can be measured by undertaking a GHG emissions assessment or other calculative activities denoted as carbon accounting.
The data is sourced from the greenhouse gas emissions per capita indicator of the OECD’s How’s Life? well-being database (dataset DSD_HSL@DF_HSL_FWB, indicator 12_9), so the figures are expressed in tonnes of CO2 equivalent per person, on an annual basis.
This series currently ends in 2020, so a date range that starts after that returns an empty DataFrame and later years are absent rather than NaN.
Also known as: CO2 emissions, carbon emissions, greenhouse gas.
Carbon Footprint in Python or via the MCP
Install the Finance Toolkit with:
pip install financetoolkit -U
Then call get_carbon_footprint as shown below.
from financetoolkit import Economics
economics = Economics(start_date="2010-01-01", end_date="2020-01-01")
economics.get_carbon_footprint(countries=['Germany', 'United States', 'Poland'])
Or, after setting up the Finance Toolkit MCP, ask:
How did greenhouse gas emissions per person in Germany, the United States and Poland develop from 2010 to 2020?
Which returns:
| Germany | United States | Poland | |
|---|---|---|---|
| 2010 | 14.574 | 22.85 | 10.557 |
| 2011 | 14.512 | 22.081 | 10.479 |
| 2012 | 13.938 | 21.212 | 10.14 |
| 2013 | 14.139 | 21.404 | 9.749 |
| 2014 | 13.74 | 21.551 | 9.703 |
| 2015 | 13.318 | 21.253 | 9.55 |
| 2016 | 13.37 | 20.577 | 9.772 |
| 2017 | 13.205 | 20.353 | 10.228 |
| 2018 | 13.267 | 20.8 | 10.377 |
| 2019 | 12.582 | 20.191 | 9.838 |
| 2020 | 11.498 | 18.34 | 9.451 |
Parameters
get_carbon_footprint accepts the following parameters.
| Parameter | Type | Description | Example |
|---|---|---|---|
countries | list[str] | str | None | The countries to include in the data. Defaults to None. | ["United States", "Germany"] |
rolling | int | The rolling window size to use for smoothing the data (simple moving average). Defaults to None. | 4 |
trailing | int | The trailing window size to use for summing the data over trailing periods (e.g. a trailing-4-quarter sum). Defaults to None. | 4 |
growth | bool | Whether to return the growth data or the actual data. Defaults to False. | True |
lag | int | The number of periods to lag the data by. Defaults to 1. | 4 |
standardize | bool | Whether to standardize (Z-Score) the result. When combined with growth=True, standardizes the growth values instead of the raw values. Defaults to False. | True |
rounding | int | None | The number of decimals to round the results to. Defaults to None. | 2 |