International Business Machines Corporation

Ayush Jain

Jagabondhu Hazra

Ranjini Bangalore Guruprasad

Smitkumar Narotambhai Marvaniya

Shantanu Ravindra Godbole

Manikandan Padmanaban

Sujoy Kumar Roy Chowdhury

Divgian Sidhu

A Scope 3 emission system includes a controller with a microprocessor and a memory. The memory stores executable instructions that when executed by the microprocessor cause the microprocessor to perform a computer-executable method. The method comprises generating embeddings based on enterprise financial transaction data, enterprise metadata, and crowd source data, and generating sector wise carbon-aware spatio-temporal weights indicative of an estimated level of Scope 3 emissions produced by a corresponding commodity sector. The method further includes generating a carbon-aware natural language processing (NLP) foundation model (FM) based on the embeddings and the sector wise carbon-aware spatio-temporal weights. User-generated data indicating at least one target commodity sector and spend data associated with the target commodity sector is input into the NLP FM, and the NLP FM outputs an estimation of the Scope 3 emissions based on the least one target commodity sector and the spend data.

Carbon footprint estimation using foundation model

Media is streamed in accordance with carbon footprint considerations. A streaming history of a streaming plan is analyzed to determine a historical carbon footprint. At least one streaming plan is presented that includes target carbon footprint relative to the historical carbon footprint. A selection is received for one of the at least one streaming plan, wherein the streaming performance on the streaming plan is tracked for post streaming plan carbon emissions. The user streaming performance is modified for the post streaming plan carbon emissions to substantially match the target carbon footprint of the at least one streaming plan.

Optimizing carbon emissions from streaming platforms with artificial intelligence based model

A computer implemented method identifies a set of environmental projects. A number of processor units identifies a group in a plurality of groups for organization of interest using organization parameters for the organization of interest. The number of processor units determines environmental performance for the organizations in the group using environmental data and the organization parameters for the organizations in the group. The number of processor units identifies the set of environmental projects for the organization of interest based on the environmental performance determined for the organizations in the group.

Net zero acceleration for organizations

Growing community of inventors

Lucknow, India

Ayush Jain