Anthropogenic changes to non-anthropogenic carbon fluxes are a primary driver of climate change. Yet no comprehensive metric currently exists to measure and value anthropogenic changes in carbon flux between all states of carbon.
As our understanding of climate change progresses, focusing on CO2 as a scalar metric for the plethora of anthropogenic activity and carbon compounds will prove inadequate, convoluted, and unmanageable. A broader, more basic vector metric is needed to capture the entirety of carbon-related anthropogenic activity.
We propose a new metric to measure changes in the temporal proximity of any form or state of carbon in the biosphere from one state to another, for example the temporal proximity of any carbon compound to a CO2 sink such as the atmosphere. The effect of any human activity can be measured by the difference between the anthropogenic and non-anthropogenic temporal proximity of any form of carbon. The unit for the new metric would be time.
We also propose a logarithmic vector scale for temporal proximity as a measure of anthropogenic changes in carbon flux. The distance between the starting and ending temporal proximities would be a vector representing the change in temporal proximity. A base-10 logarithmic scale would allow the addition and subtraction of exponents to calculate approximate changes in temporal proximity.
Results/Conclusions
The empirical tables of temporal proximity data for the relevant forms of carbon will be developed over time once the standards for measuring or otherwise determining temporal proximity have been established. The temporal proximity data would, when practical, be empirically derived for a standard set of environmental conditions including temperature, pressure, humidity, solarization, wind speed, etc.
The data for short-cycle forms of carbon would be the first candidates for determination. With longer-cycle forms, such as with fossil carbon, when empirical determination is not feasible, temporal proximity could be determined first through models set forth by the scientific and academic communities, augmented by actual data as time progresses.
By assessing embodied carbon within imports for carbon parity with domestic production, temporal proximity would eliminate the incentives to use spatial shifts in carbon emissions to avoid carbon fees. Similarity, temporal proximity would modulate the incentives to use temporal displacement of carbon emissions, such as the exhausting of carbon cognates and carbon sequestration, while eliminating the need for carbon credits.