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This Guidance helps scale product carbon footprint assessments by reducing the manual effort involved in emission factor mapping steps. Environmental impact factors (EIFs) measure activity into metrics to assess potential environmental impacts, such as carbon dioxide equivalent (CO2e). This Guidance provides the ability to automate EIF selection from the v1.2 US Environmentally-Extended Input-Output (USEEIO) database, a database developed by the United States Environmental Protection Agency (US EPA) for product carbon footprinting. It is built on Amazon's CaML model, an algorithm that automates Economic Input-Output based Life Cycle Assessment (EIO-LCA) factor selection using semantic text similarity matching for text descriptions of the product and the industry sector.
Please note: [Disclaimer]
Architecture Diagram
[Architecture diagram description]
Step 1
Existing systems collect transaction data containing product descriptions and cost information.
Step 2
Amazon Simple Storage Service (Amazon S3) ingests the transaction data. AWS Glue cleans incoming data.
Step 3
Amazon SageMaker Batch Transform jobs runs Amazon’s CaML model to map text descriptions to emission factors and calculate spend-based footprints. Explore the sample code for an example.
Step 4
Store the processed data containing calculated footprints in an S3 bucket. AWS Glue Data Catalog then catalogs the processed data.
Step 5
With object metadata stored in the AWS Glue Data Catalog, you can build insightful dashboards in Amazon QuickSight or query your data directly in Amazon S3 using Amazon Athena.
Step 1
Existing systems collect transaction data containing product descriptions and cost information.
Step 2
Amazon Simple Storage Service (Amazon S3) ingests the transaction data. AWS Glue cleans incoming data.
Step 3
Amazon SageMaker Batch Transform jobs runs Amazon’s CaML model to map text descriptions to emission factors and calculate spend-based footprints. Explore the sample code for an example.
Step 4
Store the processed data containing calculated footprints in an S3 bucket. AWS Glue Data Catalog then catalogs the processed data.
Step 5
With object metadata stored in the AWS Glue Data Catalog, you can build insightful dashboards in Amazon QuickSight or query your data directly in Amazon S3 using Amazon Athena.
Well-Architected Pillars
The AWS Well-Architected Framework helps you understand the pros and cons of the decisions you make when building systems in the cloud. The six pillars of the Framework allow you to learn architectural best practices for designing and operating reliable, secure, efficient, cost-effective, and sustainable systems. Using the AWS Well-Architected Tool, available at no charge in the AWS Management Console, you can review your workloads against these best practices by answering a set of questions for each pillar.
The architecture diagram above is an example of a Solution created with Well-Architected best practices in mind. To be fully Well-Architected, you should follow as many Well-Architected best practices as possible.
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Operational ExcellenceRead the Operational Excellence whitepaper
Amazon CloudWatch provides centralized logging with metrics and alarms across all deployed services to raise alerts for operational anomalies.
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SecurityRead the Security whitepaper
Resources are protected using AWS Identity and Access Management (IAM) policies and principles. Use least-privilege access and role-based access to grant permissions to operators. Data at rest is encrypted using AWS Key Management Service (KMS). HTTPS endpoints with transport layer security (TLS) provide encryption in transit for service endpoints.
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ReliabilityRead the Reliability whitepaper
This Guidance uses serverless AWS services whenever possible, such as Amazon S3, AWS Glue, and Athena, which automatically adapt to changes in demand. The SageMaker Batch Transform jobs can be provisioned according to expected demand.
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Performance EfficiencyRead the Performance Efficiency whitepaper
SageMaker is purpose-built for running ML models such as CaML. This Guidance uses serverless managed services, such as Amazon S3, Athena, and AWS Glue, that automatically scale in response to changing demand, reducing resource overhead. Storing data in Amazon S3 allows consumers to bring various tools and services to their data, dependent on their needs.
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Cost OptimizationRead the Cost Optimization whitepaper
With the exception of SageMaker Batch Inference jobs, this Guidance relies entirely on serverless services such as Amazon S3 and Athena. These serverless services scale automatically to meet demand, which helps reduce overall resource utilization and costs.
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SustainabilityRead the Sustainability whitepaper
Amazon S3 lifecycle policies can be configured to automatically move data to more energy-efficient storage classes and to enforce deletion timelines. These lifecycle policies can help minimize overall storage requirements and energy usage.
Implementation Resources
The sample code is a starting point. It is industry validated, prescriptive but not definitive, and a peek under the hood to help you begin.
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Disclaimer
The sample code; software libraries; command line tools; proofs of concept; templates; or other related technology (including any of the foregoing that are provided by our personnel) is provided to you as AWS Content under the AWS Customer Agreement, or the relevant written agreement between you and AWS (whichever applies). You should not use this AWS Content in your production accounts, or on production or other critical data. You are responsible for testing, securing, and optimizing the AWS Content, such as sample code, as appropriate for production grade use based on your specific quality control practices and standards. Deploying AWS Content may incur AWS charges for creating or using AWS chargeable resources, such as running Amazon EC2 instances or using Amazon S3 storage.
While this Guidance serves as a starting point to help streamline EIF mapping for Life Cycle Assessment (LCA) experts, the results should be vetted by LCA experts prior to considering specific actions. The user should also supplement the results with appropriate information for greater accuracy. This Guidance uses v1.2 USEEIO database as a sample for demonstration purposes only. We recommend that the users apply the most recent version of the database.
The model was tested on products in the US retail sector and does not cover services. The user assumes responsibility to ensure accuracy of the emission factor mapping and to validate the results. Using this Guidance does not guarantee verified greenhouse gas (GHG) disclosures. AWS does not assume any legal liability or responsibility for any errors or omissions or for the results obtained from the use of this Guidance.