About this CFP
Amazon Sustainability works to make Amazon one of the most environmentally and socially responsible places to buy or sell goods and services. We conduct research to map, model and measure the end-to-end environmental and social impact of the company and vet sustainability topics that will have the greatest future impact to Amazon to inform business planning and resilience. We develop and test strategies that support revenue growth while reducing negative environmental and social impact. We work with the external science community to drive our vision and mission. We accelerate sustainability practices at Amazon by guiding critical decision makers with crisp recommendations backed by scientific rigor. We remove ambiguity around sustainability and provide them scientifically credible mechanisms, data, tools and solutions that they can use to make informed decisions.
We welcome proposals in the following research tracks:
Validating sustainability data at scale
Accurate and verifiable greenhouse gas (GHG) emissions data across the supply chain is critical for organizations to make informed procurement decisions, set meaningful carbon and other environmental impacts reduction targets, and drive meaningful progress towards their climate goals. However, the current process of validating supplier-reported GHG metrics is often manual, costly, and lacks consistency. Proving the accuracy of abatement data is further complicated by the complex and ever-changing nature of business operations. Key challenges include verifying that supplier-reported GHG emissions reductions adhere to established standards of being real, additional, and permanent, as well as socially-beneficial. We invite proposals for innovative, open-sourced projects that leverage machine learning (ML) and artificial intelligence (AI) techniques to improve data resolution and validate GHG emissions and carbon accounting data by harnessing data from diverse sources, including data shared by suppliers, with the goal of streamlining the process and lowering the overall cost of verification for all organizations. The validations should be sufficient for GHG emissions and carbon accounting claims. Where possible, we encourage proposals to incorporate current standards for producing (e.g., Product Category Rules) and sharing carbon data (e.g., WBCSD Pathfinder Initiative). Additional challenges include the difficulty in aggregating accurate, comparable GHG emissions data across complex, global supply chains due to inconsistent or costly data sharing practices, and the limited ability for organizations to quickly identify and address discrepancies or anomalies in supplier-reported carbon performance.
Machine learning applications for life cycle assessment
Life cycle assessment (LCA) is an instrumental method for corporations disclosing their environmental footprint. The primary challenges associated with corporate footprinting are scalability, automation, transparency, and lack of appropriate data to measure impacts of a wide range of products and services. Currently, much of the LCA work remains manual, and requires subject matter expertise. We solicit proposals that primarily focus on machine learning application in life cycle assessment ranging from to automating assessment and validation, completing life cycle inventories using approximation, computing product carbon footprint (PCF) in supply chain and BOM data, use of large language models (LLMs) and ontologies / knowledge graphs in LCA settings, and building tools to conduct scenario analysis and assess emissions abatement potential at a web-scale. As lack of groundtruth data is a perennial challenge in this field, proposals are encouraged to contribute open-source benchmark datasets and reduce reliance on large-scale, expensive data collection.
Data-driven sustainable product design and manufacturing
There is a lack of methods, tools, and systems to enable product manufacturers to incorporate sustainability performance metrics into decisions made across the product’s life cycle, from product development to manufacturing to post-use recovery and treatment. We are welcoming research proposals focused on innovative approaches to create, test, and implement decision support capabilities for multiple sustainability criteria (e.g., carbon, waste, and water) to increase the velocity and lower the cost of more sustainable product development. Proposals that demonstrate broad applicability across different product sectors, supply chain complexity, and manufacturing types (discrete and continuous) are highly encouraged.
Climate risk assessment
We invite proposals that leverage novel methods and modeling approaches to advance climate risk assessment and resilience at scale. Traditional methods for monitoring impacts/damages from climate hazards to point assets (e.g. buildings, infrastructure), linear assets (e.g. roads), and supply chains often require expert assessment and are limited in their ability to assess risk at a local level. We seek innovative proposals that utilize artificial intelligence, remote sensing (e.g. pre- and post-disaster imagery), and new modeling techniques to enhance the assessment of vulnerabilities (damage functions). Projects should demonstrate how the proposed approaches can enable scalable, high-resolution risk evaluation without relying on traditional expert assessments. Moreover, proposals investigating the application of emerging technologies to better assess climate-related risks to nature and forests are highly encouraged. Climate risks to forests threaten permanence of carbon storages, durability of nature-based solutions, biodiversity, and supply of commodities within supply chains. We are interested in proposals that use new methodologies to quantify climate-related reversal risks and risks to ecosystem services, for example the inter-connections between carbon, biodiversity, and climate risks. We strongly encourage open-source contributions.
Biodiversity
We request proposals that advance biodiversity measurement, monitoring, and impact assessment. Despite growing recognition of biodiversity risks, critical gaps remain in our ability to systematically quantify changes in ecosystems, species populations, and genetic diversity across spatial scales. Traditional methods for biodiversity assessment have limited scalability, often relying on sparse validation data and expert-driven scoring systems. We invite projects that harness in-situ and remote sampling, artificial-intelligence, and new statistical techniques to enable continuous, high-resolution, and reliable biodiversity tracking at local levels. Additionally, we encourage proposals that advance biodiversity impact quantification and attribution. Innovative approaches are needed to translate the tangible interactions between biodiversity and ecosystems, human systems, and organizations. We are interested in approaches that quantify biodiversity co-benefits of nature-based solutions and climate change mitigation strategies. We encourage open-source contributions and pathways enabling real-world implementation.
Lower-carbon cement and concrete
Amazon seeks research proposals to address a critical gap in validating lower-carbon cement and concrete innovations. Cement and concrete production is highly carbon-intensive, contributing significantly to global emissions. While new solutions emerge, a key challenge is the lack of standardized methods to confirm these new materials can be manufactured, transported, and placed as easily as existing products. We are interested in research that comprehensively evaluates the performance, workability, and constructability of lower-carbon cement and concrete mixes across the value chain. The goal is to generate data-driven evidence supporting broad adoption of sustainable alternatives. Proposals demonstrating collaborative industry partnerships and practical, scalable solutions are encouraged.
Responsible supply chain
Corporate Social Responsibility (CSR) within supply chains is a critical area of research, addressing the ethical, environmental, and social impacts of global supply networks. Traditional supply chain auditing practices, while prevalent, face significant challenges related to scalability, transparency, and the absence of universal evaluation standards. These audits often rely on manual data collection processes, limiting their effectiveness in addressing complex and dynamic social risks.
This call for papers seeks to explore fundamental and academic problems in CSR within supply chains. We invite research that advances the theoretical foundations of CSR in supply chains, particularly through the lens of data-driven approaches and machine learning. Topics of interest include, but are not limited to:
- Development of universal standards and frameworks for CSR evaluation in global supply chains.
- Methodologies for real-time social risk detection and hotspot analysis.
- Predictive modeling for supplier risk assessment and compliance.
- AI to support humans in performing audits, such as generating strategies and guidance.
- Innovative strategies for automating and enhancing the transparency of social responsibility audits.
- Theoretical exploration of the ethical implications of AI in CSR decision-making processes.
CO2 Mineralization
Carbon capture, utilization, and storage (CCUS) is a critical decarbonization lever across several hard-to-abate industrial sectors. However, the potential of carbon capture and storage (CCS) is constrained by the availability of suitable CO2 pipeline infrastructure and nearby geological storage sites. Carbon capture and utilization (CCU) technologies, such as ex-situ mineral carbonation, offer a viable alternative for industrial sites that lack underground storage infrastructure. Nevertheless, the potential of ex-situ carbon mineralization is also limited by the cost of carbonation and the availability of suitable feedstocks besides industrial waste materials. This call for proposals aims to identify solutions that can maximize the impact of mineral carbonation for permanent CO2 sequestration, for example the identification/development of direct carbonation of Mg-rich minerals, processes to broaden the application of magnesium carbonate (MgCO3) produced through mineral carbonation, or AI-driven models for optimization of ex-situ/superficial mineralization.
Timeline
Submission period: September 25, 2024 - November 13, 2024 (11:59PM Pacific Time)
Decision letters will be sent out February 2025
Award details
Selected Principal Investigators (PIs) may receive the following:
- Unrestricted funds, from $50,000 to $100,000 USD
- AWS Promotional Credits, up to $40,000 USD
- Training resources, including AWS tutorials and hands-on sessions with Amazon scientists and engineers
Awards are structured as one-year unrestricted gifts. The budget should include a list of expected costs specified in USD, and should not include administrative overhead costs. The final award amount will be determined by the awards panel.
Eligibility requirements
Please refer to the ARA Program rules on the Rules and Eligibility page.
Proposal requirements
Proposals should be prepared according to the proposal template. In addition, to submit a proposal for this CFP, please also include the following information:
- Description of the proposed solution and its innovative aspects
- Explanation of how the project addresses the specified challenges
- Plan for the development and implementation of the methodology or dataset
- Potential impact on sustainability in the targeted sectors
- Inclusion of Category Rules across Environmental Product Declarations and Product Environmental Footprints where applicable
- List of open-source tools, datasets, or methodologies you plan to contribute to.
- List of AWS ML tools you will use.
Selection criteria
Proposals will be reviewed by a panel of experts in machine learning, LCA, and sustainability. Proposals will be evaluated on the following:
- Immediate and sizeable impact on carbon abatements (i.e., reducing greenhouse gases)
- Practicality and scalability of the solutions that can support measurement validations
- Feasibility and clarity of the proposed approach
- Potential for widespread adoption and implementation
- Feasibility to open source
Expectations from recipients
To the extent deemed reasonable, Award recipients should acknowledge the support from ARA. Award recipients will inform ARA of publications, presentations, code and data releases, blogs/social media posts, and other speaking engagements referencing the results of the supported research or the Award. Award recipients are expected to provide updates and feedback to ARA via surveys or reports on the status of their research. Award recipients will have an opportunity to work with ARA on an informational statement about the awarded project that may be used to generate visibility for their institutions and ARA.
