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implications of looking for practical methods for defining measures to fit the form of the thing being
measured and checking to see what common measures do and do not. The origin of the discovery,
though, is interesting. It seems to be a result of pursuing an accounting problem that required finding a
way to treat complex units of organization in the environment as physical subjects, sufficiently well
defined to physically measure. Most of the extensive branching trees of energy use needed to bring
products to market are hidden from view, and so have gone unobserved and unrecorded. That was the
problem that made the difference, identifying a classic “fat tail” distribution that was not visible from
the available data. So from the view of information models those energy uses disappeared, until we
considered the physical causations involved as “receipts” for the real costs of the services provided and
had the luck of finding what seems like a good way of estimating them.
Part of the reason to mention these complex issues, but also keep the discussion short, is that people
are accustomed to thinking of physical processes in the environment as following formulas, and they
don’t. Our cultural awareness of how complexly organized natural systems work is very undeveloped.
The systems of nature seem much better described as local developmental processes, having parts that
change everywhere at once, following their own emerging dynamics as they respond to local
conditions. Environmental systems mostly have actively adaptive parts, and their collective behaviors
reflect how new directions of contagious development emerge, first opening up ever greater and then
less opportunity for themselves. Human interests and inventions are like that, “stormy”, but then even
the weather is too. So they only appear to follow formulas when their ways of changing are steady for
a while, but can also change direction fairly quickly sometimes with little notice for those not knowing
what to watch for. For our measures and theories to fit nature better we would need to both shift our
focus from considering natural systems by where the information collects, their “symptoms”, to
assessing their working processes of “development” that show how they work, while also of course
keeping watch for how their regular behaviors may quickly change direction.
- Conclusions
Methods for determining the energy costs of business and the energy needed to produce energy,
EROI, have suffered from the difficulty of defining what to measure. The standard LCA method is
well defined for assessing the traceable energy costs of business, associated with technology. Most of
the energy costs of businesses are not readily traced so LCA has not counted them. SEA corrects that
using a combination of “bottom up” and “top down” approaches for assigning shares of world energy
use, based on shares of world economic product. That revealed a large discrepancy between the two
methods, and many categories of instrumental energy use that were going uncounted.
Using SEA and starting with data categorized by where the records were found, we defined a
method of tracing causal links and reassigning energy uses found elsewhere, to “disaggregate” the
original categories and reconstruct the functional energy needs of the business operations paying for
them. Combining the two kinds of energy information required combining precise but incomplete
measures with imprecise but comprehensive measures. We used hybrid accounting to do that, not
unlike that used for LCA, but asking a different question. Instead of identifying the task as collecting
information from predefined categories, we identified the task as objectively defining a whole system
of connected operations so they could be physically measured.