Apple Carbon Emissions Progress Report [Winner]

Tools used in this project
Apple Carbon Emissions Progress Report  [Winner]

About this project


The premise for this project was that I would be "working as an independent journalist and data viz enthusiast.... (My) task is to use the data provided by Apple in their Environmental Progress Reports to visualize their progress towards becoming carbon neutral in 2030."


The provided data contains:

  • sources of Apple's greenhouse gas emissions from 2015 to 2022, including
    • corporate
    • product life cycle
  • the product life cycle of every baseline iPhone model released between 2015-2022.
  • Apple's revenue, market cap, and employees during the same period.


Following the objective, my approach was to create a journalistic style report, with a structured narrative or commentary supported by appropriate visuals.

The structure would include a high level introduction setting the scene for what the report was about, along with some detail on the data and context over a key metric used in reporting, million metric tons of C02 equivalent (mmtC02e). I opted to use mCe as shorthand.

undefinedI then planned to look at an overall macro level analysis looking at high level trends to date and determining whether they appeared to be on track or not.

Then, I planned to look at a more micro level, to see if any particular sources of emissions were performing positively or negatively, and whether this would be a risk to achieving their ultimate goals.


As mentioned, the style of the report was set as as a journalistic or storytelling type report, so it is explanatory rather than exploratory.

The story needs to be well structured, easy to digest and flowing from top to bottom and left to right.

The visuals need to be thoughtful, purposeful and concise. Each part of the visual needs to have a reason to be there, and be related to the story in some way.

There should be a balance sought between "attractiveness" and clutter. A set colour palette and employing Gestalt principles such as proximity, enclosure, connection and white space can help reduce the cognitive load on the reader.

Macro Level

Moving back to the analysis, I opted to look at the progress to date at a macro level and see how it was trending towards 2030

undefinedConsidering there was a target to reach (9.6mCe), I calculated the required annual reduction rates in emissions and the required offsets, and drew dashed lines to represent these. I also then plotted the actual reductions and offsets on the same graph to show progress to date and indicate they are ahead of schedule. This was complemented by annotations on the graph

Separately, I thought it appropriate to bring in the company performance over the same period to highlight that while Apple were making these beneficial changes, they were also improving their revenue and market cap, perhaps a nod to the 'naysayers' that believe going green will impact the bottom line of a business.

So, at a macro level, things look on track...but could we simply use a high level regression to predict success?

I had my doubts, so I wanted to dig a level deeper to see what was critical to the underlying performance....

Micro Level

That is where a more micro analysis looked at individual emissions components in the report. There were 15 individual reporting categories for emissions and 2 for offsets. I found that Manufacturing, Product Transportation and Product Use accounted for over 95% of all emissions.

So instead of analysing 15 categories, I kept the main contributors as individual categories, and bundled the remaining 12 emissions categories into "Others".

I then wanted to look at the trend lines for each of these between 2012 and 2022. I used a combo slope chart. This helps show the overall trend through the coloured lines, and then the dashed lines show the intervening data points. This helped paint a picture that manufacturing was responsible for the majority of emission reductions.

undefinedHaving found that product transportation actually increased emissions to 2022 and product use emissions were stagnating, I wondered if this trend continued, could they still meet their targets in 2030?

Therefore, I extrapolated the current annual rate changes for product use and transportation, and found that together they would still be contributing 5.3mCe by 2030. Considering "Others" would account for around 0.1mCe, that would mean that manufacturing would need to further reduce emissions significantly to 4.2mCe in order to meet the overall target.

undefinedMy thoughts are that reducing manufacturing from 29.6mCe to 4.2mCe, while at the same time having a business target to increase sales and revenue might be a step to far.

If you consider industrial processes, and improving efficiencies they will often follow a type of pareto principle. The simpler steps will likely have been taken already (min effort with max effect), but as they progress they will likely find there is greater efforts required to achieve smaller reductions.

It is a major risk, therefore I believe they need to find a way to reduce transportation and product use emissions, or maybe prepare to raise the quota of the offsets that will be required to meet net zero.

Additional project images

Discussion and feedback(45 comments)
Thaer Saad
Thaer Saad
7 months ago
I must commend you on the exceptional quality of your report. Your clarity of purpose and meticulous approach to visualizing Apple's progress toward carbon neutrality is truly commendable.

Renuka Deshpande
7 months ago
Excellent work Gerard

Soraya van Henten
Soraya van Henten
7 months ago
Very inspiring!

Leo Reyna
Leo Reyna
7 months ago
Excellent work!

Ritu Rani
Ritu Rani
7 months ago
What an incredibly informative report, Sir! This inspires me to keep learning and present some excellent work :)

Daniels Favour
Daniels Favour
6 months ago
Good day Gerard, excellent work i did like to know when you will be making the video on how you extrapolated product use and transport and on what platform will the video be uploaded. thank you very much looking forward to your reply.

Muhammad   Refaey
Muhammad Refaey
6 months ago
great work

Remi Martinato
5 months ago
Hi Gerard, I am always impressed by your work, and it led me to wonder about the benefits of using Power BI for statistical visualization. What do you think are the main advantages of Power BI for such tasks? Thank you in advance for your insights! Remi
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