---
# System prepended metadata

title: Mortality Cost of Carbon

---

# Mortality Cost of Carbon

Some links:
- [GitHub Issue](https://github.com/alan-turing-institute/TuringDataStories/issues/158)
- [Media coverage](https://www.theguardian.com/environment/2021/jul/29/carbon-emissions-americans-social-cost)
- [Nature Paper](https://www.nature.com/articles/s41467-021-24487-w#Sec15)
- [2019 Talk](https://forum.effectivealtruism.org/posts/Np3B45Gf29SwqZ5c6/r-daniel-bressler-climate-change-deaths-increase-the-social)
- [Drive folder](https://drive.google.com/drive/folders/1gBiFCdJUkCGBjWam7B6m8a_26a_-E7A9?usp=sharing)

### Option 1 - more complicated

The DICE-EMR model (found in the [Drive folder](https://drive.google.com/drive/folders/1gBiFCdJUkCGBjWam7B6m8a_26a_-E7A9?usp=sharing)) consists of Matlab code, Stata code, and Excel spreadsheets. The model seems a bit tricky to use. We could re-implement the model in Python and improve the documentation, making it more accessible to other researchers.
- Would let us tweak the model, different emissions senarios or add / modify mortality rates.
    - Current estimates for intergroup conflict are "conservative" (as described by the author). Could modify these based on user specified senarios.
- Final story results: reproduce the main results from the paper, with some additional senarios.

**Todo**
1. Scope out how long this would take.
2. Try and convert some of the Matlab or Stata code to Python, check the data in the excel spreadsheets.
3. Discuss potential extensions.
4. Have a discussion around framining and outcomes of the story

### Option 2 - more simple

We could take the outputs of the model (found in [this](https://docs.google.com/spreadsheets/d/1cimuF1LtokeV7S8FP2eN1U10xDllNSAG/edit?usp=sharing&ouid=105418150182455695014&rtpof=true&sd=true) Excel file) and build from it.
- More info about the combined mortality rate. 
- More info about long term projections.
- Personalisation feature: function that allows someone to calculate their impact bassed on [calculate carbon footprint](https://www.carbonfootprint.com/calculator.aspx) (or CLI calculator [here](https://github.com/protea-earth/carbon_footprint), python package [here](https://github.com/eillarra/carbonize)). Needs some discussion on whether this is a useful framing. Discussion on [origins](https://www.theguardian.com/commentisfree/2021/aug/23/big-oil-coined-carbon-footprints-to-blame-us-for-their-greed-keep-them-on-the-hook) of carbon footprint, whether it's useful to think in this way, discussions ([here](https://slate.com/technology/2018/10/carbon-footprint-climate-change-personal-action-collective-action.html) and [here](https://theconversation.com/climate-change-yes-your-individual-action-does-make-a-difference-115169)) around individual vs collective impact.
    - Probably better alternative: take some common lifestyle modifications (changing diet, flying less, etc) and quantify the impacts of these in terms of excess deaths.
- Discuss in more detail some of the supplementary results that we not discussed in detail in the main paper, for example this plot. ![](https://i.imgur.com/MY60PdO.png)

**Todo**
1. Look at the [Excel file](https://docs.google.com/spreadsheets/d/1cimuF1LtokeV7S8FP2eN1U10xDllNSAG/edit?usp=sharing&ouid=105418150182455695014&rtpof=true&sd=true) and work out what the plots are, what the outputs we can use are. Add axis titles to plots
2. Investigate some API or find some database about the emissions of different lifestyle activities, that we can plug into.
3. Have a discussion around framining and outcome of the story