DATA SOURCE AUTHORITY ANALYSIS FOR SUSTAINABILITY ACTION PLAN CONFIDENCE

1 . An enterprise system comprising:

one or more devices configured to perform one or more respective operations of an enterprise; and

a sustainability platform system configured to:

obtain input data from one or more input data sources;

determine a respective authority level for each of the one or more input data sources;

determine uncertainty data associated with the input data based on the respective authority level for each of the one or more input data sources;

determine confidence parameters associated with the input data based on the uncertainty data;

generate one or more sustainability action plans for improving one or more sustainability parameters of the enterprise based on the input data and the confidence parameters; and

send one or more commands to the one or more devices to adjust the one or more respective operations according to the one or more sustainability action plans.

2 . The enterprise system of claim 1 , wherein the sustainability platform system is configured to determine the respective authority level for each of the one or more input data sources based on a perceived reliability of each of the one or more input data sources.

3 . The enterprise system of claim 1 , wherein the sustainability platform system is configured to determine the uncertainty data based on the respective authority level for each of the one or more input data sources and obtained uncertainty data in data values of the input data from the one or more input data sources.

4 . The enterprise system of claim 3 , wherein the obtained uncertainty data comprises ranges in the data values of the input data based on one or more confidence intervals defined in the input data.

5 . The enterprise system of claim 3 , wherein the data values comprise forecasted values for associated with a future time period, and the confidence parameters are based on an uncertainty in the future time period.

6 . The enterprise system of claim 1 , wherein the confidence parameters are based on a sentiment analysis of a context of the input data.

7 . The enterprise system of claim 1 , wherein the sustainability platform system is configured to obtain the input data from the one or more input data sources by:

querying one or more databases of the one or more input data sources for the input data;

scraping the input data from the one or more input data sources via a large language model machine learning algorithm; or

both.

8 . The enterprise system of claim 7 , wherein the one or more input data sources comprise government regulatory websites, social media websites, news publication websites, product catalogs corresponding to the one or more devices, or any combination thereof.

9 . The enterprise system of claim 1 , wherein the sustainability platform system is configured to generate the one or more sustainability action plans by:

determining at least one abatement technology estimated to improve the one or more sustainability parameters based on the input data and a sustainability model representative of a state of operations of the enterprise, wherein the one or more sustainability parameters comprise a carbon footprint of the one or more devices, a water usage of the one or more devices, a waste output of the one or more devices, a greenhouse gas emission of the one or more devices, or any combination thereof; and

generating the one or more sustainability action plans based on the at least one abatement technology.

10 . A method comprising:

obtaining, via a computing system, input data from one or more input data sources;

determining, via the computing system, a respective authority level for each of the one or more input data sources;

determining, via the computing system, uncertainty data associated with the input data based on the respective authority level for each of the one or more input data sources;

determining, via the computing system, confidence parameters associated with the input data based on the uncertainty data;

generating, via the computing system, one or more sustainability action plans for improving one or more sustainability parameters of an enterprise based on the input data; and

sending, via the computing system, one or more commands to one or more devices of the enterprise to adjust one or more respective operations of the one or more devices according to the one or more sustainability action plans.

11 . The method of claim 10 , comprising determining, via the computing system, the uncertainty data based on the respective authority level for each of the one or more input data sources and obtained uncertainty data in data values of the input data from the one or more input data sources.

12 . The method of claim 11 , wherein the obtained uncertainty data comprises ranges in the data values of the input data based on one or more confidence intervals defined in the input data.

13 . The method of claim 10 , wherein determining the respective authority level for each of the one or more input data sources is based on a perceived reliability of each of the one or more input data sources.

14 . The method of claim 10 , wherein obtaining the input data from the one or more input data sources comprises:

querying one or more databases of the one or more input data sources for the input data, wherein the one or more input data sources comprise government regulatory websites, social media websites, news publication websites, product catalogs corresponding to the one or more devices, or any combination thereof;

scraping the input data from the one or more input data sources via a large language model machine learning algorithm; or

both.

15 . The method of claim 10 , wherein generating the one or more sustainability action plans comprises:

determining at least one abatement technology estimated to improve the one or more sustainability parameters based on the input data and a sustainability model of the enterprise, wherein the sustainability model is representative of a state of operations of the enterprise; and

generating the one or more sustainability action plans based on the at least one abatement technology.

16 . The method of claim 15 , comprising:

generating, via the computing system, the sustainability model based on the input data;

simulating, via the computing system, an effect of the one or more sustainability action plans on the one or more sustainability parameters over a period of time based on the input data to generate one or more simulated sustainability parameters; and

in response to determining that the one or more simulated sustainability parameters are within one or more thresholds, sending, via the computing system, the one or more commands to the one or more devices.

17 . A non-transitory, machine-readable medium comprising instructions that, when executed by one or more processors, cause the one or more processors to perform operations comprising:

obtaining input data from one or more input data sources;

determining a respective authority level for each of the one or more input data sources;

determining uncertainty data associated with the input data based on the respective authority level for each of the one or more input data sources;

determining confidence parameters associated with the input data based on the uncertainty data;

generating one or more sustainability action plans for improving one or more sustainability parameters of an enterprise based on the input data; and

sending one or more commands to one or more devices of the enterprise to adjust one or more respective operations of the one or more devices according to the one or more sustainability action plans.

18 . The non-transitory, machine-readable medium of claim 17 , wherein obtaining the input data from the one or more input data sources comprises:

querying one or more databases of the one or more input data sources for the input data, wherein the one or more input data sources comprise government regulatory websites, social media websites, news publication websites, product catalogs corresponding to the one or more devices, or any combination thereof;

scraping the input data from the one or more input data sources via a large language model machine learning algorithm; or

both.

19 . The non-transitory, machine-readable medium of claim 18 , wherein the operations comprise determining the uncertainty data based on the respective authority level for each of the one or more input data sources and obtained uncertainty data in data values of the input data from the one or more input data sources, wherein the government regulatory websites comprise a higher authority level than the social media websites regarding regulation data of the input data.

20 . The non-transitory, machine-readable medium of claim 19 , wherein the data values comprise forecasted values for associated with a future time period, and the confidence parameters are based on an uncertainty in the future time period.