The OneYearFast Environmental Experiment

Turning Fasting Into an Environmental Experiment

The OneYearFast project naturally lends itself to measurement.

Most fasting experiments focus on the body:

• Weight
• Body composition
• Blood markers
• Ketones
• Blood pressure
• Resting metabolic rate
• Exercise performance

These are the numbers the author of this site publishes openly across a year of fasting three full days a week and eating ordinary whole-food vegetarian meals on the remaining days. But why stop there? Food consumption also has an environmental dimension, and the same disciplined, publicly logged approach that works for biomarkers can be applied to what actually gets eaten, purchased and wasted.

What Could Be Measured?

A year-long environmental fasting experiment could record:

Fasting

• Fasting days
• Fasting duration
• Eating windows

Food

• Total calories
• Food weight
• Meat
• Dairy
• Eggs
• Fish
• Plant foods

Waste

• Food purchased
• Food consumed
• Food discarded

Environmental Estimates

• CO2e
• Land use
• Water use

Each of these categories can be logged in a simple spreadsheet alongside the biomarker data already being tracked, turning an otherwise abstract environmental question into a concrete, personal dataset built up week by week.

Establish a Baseline

Before changing anything, establish a baseline. What does a normal month of eating look like? How much food is consumed? What percentage is animal-based? How much is wasted?

Without this starting point, it becomes very difficult to say anything meaningful about change. A baseline month, ideally tracked before any fasting begins, provides the reference against which every later month can be compared. Then compare subsequent months, watching not just for overall reduction but for shifts in composition — does the proportion of plant-based food increase, does waste decline, does the variance between fasting weeks and non-fasting weeks narrow or widen over time.

The Scientific Caveat

These calculations will contain uncertainty. Food production varies enormously by:

• Country
• Farming system
• Climate
• Production method
• Season
• Supply chain

Global averages, of the kind published in datasets built on research from Poore and Nemecek and similar large-scale meta-analyses, are therefore useful for comparison but should not be interpreted as precise measurements of an individual farm or meal. A tomato grown in a heated greenhouse in winter can carry a very different footprint from the same tomato grown in season outdoors, yet most public datasets will report a single averaged figure. This is a genuine limitation of the exercise, not a reason to abandon it, but it means results should be described in terms of estimated trends rather than exact quantities.

It is also worth acknowledging the limits of self-tracking itself. Portion sizes are estimated, memory of what was eaten fades, and food diaries are known to under-report consumption, particularly around less structured eating days. None of this invalidates the exercise, but honest reporting means flagging these uncertainties rather than presenting a single tidy percentage as fact.

What Would Success Look Like?

Success isn't necessarily:

"I fasted 150 days."

A more interesting result would be:

"My annual food consumption changed by X percent, my estimated food-related emissions changed by Y percent, and I can explain why."

That kind of result requires connecting fasting behavior to actual consumption data, as discussed in Rebound Effect, and connecting consumption data to environmental estimates, as discussed in Personal Food Carbon Footprint. It also requires being willing to report a null or disappointing result if that is what the data show, in the same spirit that biomarker results are published regardless of whether they are flattering.

Why Publish It At All

Publishing biomarkers already invites scrutiny and accountability; publishing food and waste data does the same for the environmental side of the experiment. It also gives other people attempting similar fasting patterns a real reference point, rather than a vague impression that "fasting is probably good for the planet."

Turning Numbers Into a Narrative

Raw numbers alone rarely tell a complete story. A drop in estimated emissions of, say, 12 percent over a year means little without context about what changed. Did fasting reduce total calories, shift the balance of foods eaten, cut down on waste, or some combination of the three? Logging enough detail to answer that question afterward is more valuable than logging a single top-line figure.

This is similar to how biomarker data is presented on this site: a single blood pressure reading means little on its own, but a full year of readings alongside notes on fasting schedule and diet composition allows patterns to emerge. The same principle applies to environmental tracking. A monthly note describing what changed, alongside the raw figures, turns a spreadsheet into a genuine record of an experiment rather than a pile of numbers.

Realistic Expectations

It would be a mistake to expect dramatic, headline-grabbing results from this kind of personal tracking. Individual dietary changes, even sustained over a full year, are unlikely to produce enormous swings in estimated footprint, and the uncertainty in the underlying emissions data means small changes can be hard to distinguish from noise. The value of the experiment lies less in producing a striking number and more in building an honest, transparent record of what one person's fasting practice actually did to their food consumption and estimated environmental impact, for better or worse.

Key Takeaways

• A fasting experiment can be extended to track food consumption, composition and waste, not just biomarkers.
• Establishing a baseline month is essential before drawing conclusions from later months.
• Food emissions data is approximate and varies by region, season and farming method.
• Self-tracked food data carries real uncertainty and should be reported honestly.
• A meaningful result explains both the size and the cause of any change in footprint.
• Publishing the data, flattering or not, is part of what makes the experiment genuine.