The effort to impact matrix for life, work, and environmental accounting

I have to credit my friend Jon for the initial inspiration for this post. Along with being a talented software engineer, he’s an incredible musician, and in quite a few groups. He was complaining to me the other day about an orchestra he was in that was requiring him to struggle through a lot of really esoteric charts with complex key and time signatures. It was needing significant rehearsal time to get the songs right and really wasn’t that much fun for him. It went against his golden ratio, or in his words, the effort:fun ratio. It’s not that he is lazy or unwilling to put in the time practicing and learning new things, just that when he exerts any significant amount of effort, he thinks the payout should be better: especially for someone who has played really fun gigs with not that much effort.

I was thinking about Jon’s golden optimization ratio when I was out to dinner with my team earlier this month and we were sketching concepts on the back of the napkin. It was one of those evenings where we were having fun realizing that anything worth explaining can be distilled down to be put on the back of the napkin. Being in the climate impact space we decided to swap out “fun” for “impact” and put it onto a matrix to see where the discussion took us as we collectively brainstormed on the napkin.

The four quadrants (with examples that relate to environmental impact accounting) are as follows:

Low Effort | High Impact

This quadrant, we decided, was optimal. My colleague Nick pulled out his scientist card and related this to having a high catalytic effect. This is essentially the work that enzymes do whereby they lower the amount of energy required to initiate a reaction needed for cells to perform critical functions. This is the ultimate efficiency to achieve the greatest amount of benefit for the least amount of input.

Example: Implementing a user-friendly automated data tracking system that leverages operational data for MMRV (measurement, monitoring, reporting and verification) to track environmental improvement and integrate into existing data schema to run GHG simulations. The data is being collected anyway, and has an inherent need to be accurate, allowing to report out more efficiently on GHG reductions or removals.

High Effort | High Impact

This quadrant — keeping with the chemistry terms — requires high activation energy. In other words, for the impact (chemical reaction) to take place, a great deal of energy needs to be exerted to get the reaction started. In nature, this is a positive aspect for complex high energy molecules, because this creates a stable environment for living things. Also, wasn’t it Theodore Roosevelt who once said “nothing worth having, ever comes easy”?

Example: Developing a sophisticated supply chain tracking system to calculate the carbon footprint of a specific consumer product, such as a gallon of milk. This involves meticulous data collection from fields upstream of dairy farms, dairy farms, processing plants, distribution networks, and retail outlets. Rigorous verification processes ensure the accuracy of emissions data at each stage of the supply chain. The result is a transparent, cradle-to-shelf carbon footprint assessment for milk products, enabling consumers to make eco-conscious choices and motivating the supply chain to make lower carbon intensive choices.

High Effort | Low Impact

This quadrant, we considered to be the path to burn out. Or, if you find yourself stuck in this quadrant you might find yourself evoking Einstein: “the definition of insanity is to do the same thing over and expect different results”. To be fair, one might find oneself stuck in this quadrant not by one’s own will but mired in bureaucratic systems put in place by those who are perhaps unaware of the ineffective ratio here, or with restrictions that could have been avoided by considering the total amount of effort and potential of the project beforehand.

Example: Complex MMRV schemes with significant data collection requirements and cumbersome administration that are not sure how to use the data, or even whether the data is valuable to provide accurate reporting for a small community with limited potential for scale.

Low Effort | Low Impact

This quadrant might be tempting, but it guarantees a low reward, with low chances of success. Stay away from this one unless it is to just calibrate your efforts. The bottom line on applying low effort, is that you should really only do so if you know that it will be the right degree of effort to create your impact.

Example: Self-reported data using a spreadsheet for an emissions tracking system that glosses over key details. While it might be easy, and simple to set up, the data and results will not be all that useful.

So what?

Zooming out from the MMRV examples, it’s worth recognizing that one rarely gets to the optimal quadrant when doing something for the first time. Sometimes it is quite necessary to do things “incorrectly” or do too little, or too much of an activity just to find out how something is going to work out. We often have no idea how much effort something is going to take, or what kind of impact it will have until we exert ourselves. It often takes trial and error, learning from informed stakeholders, putting in thousands of hours of practice, and maintaining a continual learning mindset to get to a place where we can enjoy our golden ratio. It’s also worth noting that this construct is really quite subjective because effort and impact mean different things to different people. For some, an effort may be necessary or valuable, because it yields important co-benefits and important elements that can’t be easily characterized into what is effort, or impact.

So perhaps this matrix isn’t really much more useful than being a scribble on a napkin to think through some interesting examples over a dinner conversation, or disagree with the premise entirely. But if this way of thinking is useful for you, try asking yourself these questions:

• What impact is my (or my company’s) effort having? How do I know? What can I test?
• What do effort and impact mean to you?
• Considering your industry, what are the high-effort, high-impact projects worth pursuing?
• How do you benchmark your own efforts / impacts to your peers?
• What steps can you take to avoid the high effort / low impact trap?
• Can you move beyond a balance sheet and optimizing production to measure true impact?
• What do I need to acquire (knowledge, skills, materials, capacity) to reduce my effort and increase my impact?
• What policy changes would lower your effort and raise your impact?
• Wave a magic wand, what spell did you cast for low-effort, high impact activities?

And if you did find this way of thinking at all useful, and have come up with your own answers, we’d love to hear from you, please comment below!