Implications for Energy Generation

A) Energy Use Examples

Stored Energy Value

Stored value that is “volunteered” (given, leased, sold, etc.) to the local or national energy grid can be credited so that the value is not lost if energy has not been used.

Producing value wholly different from today’s systems, which require 100% production to obtain Return on Investment; even where there is zero or low demand for the energy produced: ie. creating incentive to conserve energy since this produces benefit (efficiencies, lower emissions, less waste, less contamination and less need to recycle waste products).

Tax Authority participation is not needed to use the method. It could employ carbon credits or similar structures in the markets, or in private arrangements between power producers, brokers, and users.

Renewable Energy Value

The fundamental idea here is that data is ephemeral. It is exactly like wind, solar, and hydro power. It must be constantly generated to have utility.

What if countries convert the definition of net generated value of renewable energy (which is ephemeral) to data (which is ephemeral) using the definitions and formulas in Whitepaper-01?

Could investors profit from Net ROI by writing off the Cost of Data Collected (all costs to develop and sell the power) which are distinct from Expenses (such as Labour to produce that power)?

Getting ahead of the emissions threat

Data Generating has six phase states [A to F]

Getting ahead of the threat means determining and acting to achieve highest anticipated net data value and the energy cost to gather the data we desire.

An important aspect of the data schema is (therefore) that State E & F invites “efficiency pre-analysis” to forecast and predict the data value and energy cost to gather the data tabulated from Stages A to D.

B) Example for Investment Managers

Profitable Greenhouse Gas Emissions Reduction

Supplementary Whitepaper [Link]

Changing our thinking about the nature and measures of underlying data (data that is assigned monetary value when recorded in the ledger) creates opportunity to create earning/investable/insurable spread rewards for activities producing “a social good” (benefits to society) and “desired outcomes” (private & public-private ROI).

Here, we can say that “Achieving lower GHG emissions” is the desired social good & desired ROI target. We use the framework to assess performance. We use the method to create incentives to cut the cost of data-generating emissions – which are by-products that come from harvesting, processing, and analyzing data flows.

: We tie emission rates to data-generation energy cost;
: Recording it as equivalent value to data-generation expense, to:

> Establish, measure, evaluate, and rate achievement;
> Comparing rates across all investment asset classes;
> Quantifying rolling targets to continuously improve operations.

The framework and method flows from the nature of new information used in accounting, not the particularities of any nation’s law. Making it usable in every tax jurisdiction. And usable to compare activities of all asset classes and activities down to divisions, units, and conceivably factories, departments, offices, and devices.

C) Using DVA to monetize benefits for society: Sample Use Case

Solar Mass Water (SMW) Corp* [Link]

SMW Corp.’s solar panels draw water from the air.

SMW may not be a cost-effective source of water supply using traditional accounting methods. But consider the bigger picture: SMW delivers proof-positive data to an App, demonstrating litres delivered per household per day while removing many waterborne threats to population health. Offering numerous benefits to society that ought to be profitably monetized.

Data Value Accounting offers the means to get paid for producing these benefits. And could make installations the centre of associated app startup ecosystems in those countries – an economic benefits’ driver.

Using DVA, what social benefits could SMW cost-effectively produce?

Shouldn’t these be monetized?

* Author: David Huer. I do not have a financial interest in the use case.