System and method for estimating CO2 emission

Patent metadata

Field	Value
Publication number	US-10489793-B2
Application number	US-201715452511-A
Country	US
Kind code	B2
Filing date	Mar 7, 2017
Priority date	Feb 13, 2009
Publication date	Nov 26, 2019
Grant date	Nov 26, 2019

Abstract

Estimations of carbon dioxide (“CO2”) emission of an entity upon the condition of incomplete or missing data uses one or more algorithms implemented in a machine having a processor and a memory and data concerning the entity. The data is applied to an algorithm implemented as code executable in the processor. The algorithm produces a result that comprises an estimate of the CO2 emission of the entity. The CO2 emission estimate can be output to a user, and the underlying formula and data can inspected and optionally modified by users with suitable permissions. The CO2 emission estimate can be applied as a factor in a formula to compute a rating for the entity which can be output from the machine. Error estimates associated with the data used by the algorithm can be generated to provide improved estimates.

First claim

We claim:

1. A method comprising:
computing, by at least one estimation module of a machine having a processor and a memory, using incomplete data relating to an environmental factor for an entity, at least one component of an estimate of the environmental factor, wherein the computing is performed by applying a heuristic model algorithm associated with the at least one estimation module to the incomplete data, wherein the at least one estimation module is one of a plurality of estimation modules, each estimation module of the plurality of estimation modules being associated with a respective model algorithm for computing a respective component of the at least one component of the estimate;
generating, by the processor, a rating of the entity based on the estimate, wherein the generating includes applying the estimate as a factor in a formula to generate the rating for the entity; and
outputting the rating from the machine.

2. A method as in claim 1 , further comprising:
receiving at the machine data relating to an industry, sector, or sub-sector to which the entity belongs, wherein the data relating to the industry, sector, or sub-sector includes the data relating to the environmental factor;
determining by the processor one or more circumstances in which the data relating to the environmental factor for the entity is at least one of missing and incomplete, wherein the at least one estimation module includes a plurality of estimation modules, and wherein each estimation module of the plurality of estimation modules encodes a respective heuristic model algorithm; and
computing, by each estimation module of the plurality of estimation modules, using the respective heuristic model algorithm, a different component of the estimate of the environmental factor of the entity.

3. A method as in claim 2 , wherein the step of computing, by each estimation module of the plurality of estimation modules, the different component of the estimate of the environmental factor of the entity is performed in parallel.

4. A method as in claim 2 , further comprising combining the results of the plurality of estimation modules.

5. A method as in claim 4 , wherein the combining the results of the plurality of estimation modules includes computing a simple average that is provided as the environmental factor in the form of a single value.

6. A method as in claim 2 , wherein the respective heuristic model algorithms have a hierarchical accuracy order, and wherein the computing, by each estimation module of the plurality of estimation modules, of the different component of the estimate is performed in accordance with the hierarchical accuracy order.

7. A method as in claim 2 , including the additional steps of:
computing an error associated with each respective heuristic model algorithm by applying each respective heuristic model algorithm to at least one additional entity having a known value concerning the environmental factor; and
calculating a difference between the estimate for the additional entity concerning the environmental factor and the known value.

8. A method as in claim 7 , including the additional step of correcting for any calculated difference concerning the additional entity so as to account for such error in the estimate for the entity.

9. A method as in claim 1 , further comprising making the estimate available to a user through a user interface.

10. A method as in claim 9 , wherein the making step is selectively enabled based upon operation of a data-fault module executing in the machine, the data-fault module being operative to configure the processor to enable the making step if the entity is determined to have missing or incomplete data concerning the environmental factor.

11. A method as in claim 1 , further comprising a threshold step of determining, via a data-fault module executing in the machine, whether the entity has missing or incomplete data relating to the environmental factor for the entity, and performing the remaining steps if the entity is determined to have missing or incomplete data relating to the environmental factor for the entity.

12. A method as in claim 2 , including the additional steps of:
providing a user interface to the machine; and
permitting users, through the user-interface, to customize at least one of the respective heuristic model algorithms.

13. A method as in claim 1 , wherein the entity is selected from the group consisting essentially of a company, a corporation, a limited liability company, a limited liability corporation, a partnership, a limited liability partnership, a self-regulated organization, and a joint venture.

14. A method comprising:
computing, by at least one estimation module of a machine having a processor and a memory, using incomplete data relating to an environmental factor for an entity, at least one component of an estimate of the environmental factor, wherein the computing is performed by applying a heuristic model algorithm associated with the at least one estimation module to the incomplete data, wherein the at least one estimation module is one of a plurality of estimation modules, each estimation module of the plurality of estimation modules being associated with a respective model algorithm for computing a respective component of the at least one component of the estimate;
estimating by the processor the environmental factor of the entity based on the at least one component of the estimate computed by the at least one estimation module; and
outputting the estimate from the machine.

15. A method as in claim 14 , further comprising:
receiving at the machine data relating to an industry, sector, or sub-sector to which the entity belongs, wherein the data relating to the industry, sector, or sub-sector includes the data relating to the environmental factor;
determining by the processor one or more circumstances in which the data relating to the environmental factor for the entity is at least one of missing and incomplete, wherein the at least one estimation module includes a plurality of estimation modules, and wherein each estimation module of the plurality of estimation modules encodes a respective heuristic model algorithm; and
computing, by each estimation module of the plurality of estimation modules, using the respective heuristic model algorithm, a different component of the estimate of the environmental factor of the entity.

16. A method as in claim 15 , wherein the step of computing, by each estimation module of the plurality of estimation modules, the different component of the estimate of the environmental factor of the entity is performed in parallel.

17. A method as in claim 15 , further comprising combining the results of the plurality of estimation modules, wherein the combining the results of the plurality of estimation modules includes computing a simple average that is provided as the environmental factor in the form of a single value.

18. A method as in claim 15 , including the additional steps of:
computing an error associated with each respective heuristic model algorithm by applying each respective heuristic model algorithm to at least one additional entity having a known value concerning the environmental factor;
calculating a difference between the estimate for the additional entity concerning the environmental factor and the known value; and
correcting for any calculated difference concerning the additional entity so as to account for such error in the estimate for the entity.

19. A method as in claim 14 , further comprising making the estim

Assignees

• Refinitiv Us Organization Llc

Inventors

• Ohnemus Peter
• Greenwald Christopher
• Pircher Peter

Classifications

• G06Q30/018 Primary

  Certifying business or products · CPC title

• G06Q30/0282

  Rating or review of business operators or products · CPC title

• G06Q10/06393

  Score-carding, benchmarking or key performance indicator [KPI] analysis · CPC title

• G06Q10/06

  Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling · CPC title

• G06Q10/10

  Office automation; Time management · CPC title