Overview of Solutions Database
In 2016, ReFED launched its landmark Roadmap to Reduce U.S. Food Waste by 20%. That initial report became a touchstone for those in the food waste space, but there was a growing need for more - and more granular - data about the issue to fill in knowledge gaps and move the food system from awareness about the issue to insight-driven action. The newly developed ReFED Insights Engine is the next generation of data, insights, and guidance on U.S. food waste. This online data and solutions hub for food loss and waste is designed to provide anyone interested in food waste reduction with the information and insights they need to take meaningful action to address the problem and move a step forward towards achieving national and international goals of reducing food waste by 50 percent by 2030.
- Current ReFED Insights Engine tools include:
Food Waste Monitor: Centralized, trusted repository of information built with data from more than 50 public and proprietary datasets that shows how much food is being wasted in the U.S., why it’s happening, and where it goes.
Impact Calculator: Quantifies the impact of wasted food on the climate, natural resources, and recoverable meals.
Solutions Database: Provides a stakeholder-specific, comprehensive analysis of 40+ food waste reduction solutions based on impact goals, along with detailed fact sheets on each.
Solution Provider Directory: Connects users with a vetted list of 700+ nonprofit and for-profit organizations ready to help implement food waste reduction solutions.
The Solutions Database quantifies the potential financial, environmental, and social benefits of actionable solutions to reduce food waste in the U.S. This document describes the methodology used to quantify the Tons Diversion Potential, Net Financial Benefit, Greenhouse Gas Emissions (GHG) Reduction Potential, Water Savings Potential, Meal Equivalents, and Job Creation Potential of each solution. ReFED included solutions that have been demonstrated as feasible to implement and having a measurable impact on food waste reduction. The data analysis was limited to solutions that ReFED was able to model using available data. For each solution, ReFED researched publicly available sources and consulted experts to find the best available data. Some solutions were excluded from the analysis because the available data was proprietary and could not be publicly disclosed. Others were excluded because there was no available data or because they were deemed to be best practices that are already widely adopted. To make sure that solutions with data gaps are prioritized for future research, ReFED maintains a list of unmodeled solutions in the Solutions Database. These solutions have qualitative fact sheets available, but they are not included in the data modeling. While the list of modeled solutions is not exhaustive and is intended to be continuously improved and expanded, the proposed solutions provide a practical roadmap to achieve the national goal to cut food waste in half by 2030. Before starting development, the ReFED team sought feedback from its network of industry professionals from businesses, capital providers, government, nonprofits, and academia. The Solutions Database was designed to incorporate this feedback and maintain the strengths of the 2016 Roadmap report while filling previous information gaps with new data and models in a continuously improved, digital format. The following thematic areas summarize the major additions and improvements made:
- Roadmap to 50% Reduction by 2030
Aligned with national and international goals: The previous Roadmap outlined a path to reduce U.S. food waste by 20%. This new solutions Roadmap provides a path to 50% reduction by 2030, in alignment with U.S. and international goals. This assumes, however, that there is 100% adoption of all the solutions in the database.
- New and More Granular Information
Quantified causes of food waste: Quantifying the reasons why food waste is happening is a necessary precursor to calculating the potential benefit of food waste solutions. Until now, this causal information has not been quantified. ReFED applied solutions only to the portions of surplus where the solution applied. For instance, a donation solution was only applied to overproduced food in restaurant kitchens, not the waste left on customers plates. By gaining this understanding, ReFED is now able to more accurately estimate the potential impact of solutions.
Results tailored to specific sectors and stakeholders: Stakeholders can now quickly filter and view information that is relevant specifically to them. The previous Roadmap aggregated the costs and benefits of solutions across all stakeholders involved. It was not always clear when misaligned incentives existed (e.g., When implementing a solution required one stakeholder to bear most of the cost while others benefited). Now users are able to break out the costs and benefits for each stakeholder involved, providing a better understanding of the misaligned incentives and financial barriers that still exist for many solutions. This allows misaligned incentives to be identified and collectively addressed.
Food type specific data: Improved decision making requires food type specific information (e.g., developing a strategy to increase donations of produce specifically). In the past, much of the modeling was not food type specific. ReFED’s models now take food type into account at much more granular levels, leading to more accurate insights.
Geographically specific (state-level) data: ReFED data now reflects major differences between states (e.g., California has a large agricultural produce sector, Wisconsin has a large dairy manufacturing sector, Hawaii has a large foodservice and hospitality sector). This analysis now enables state-level actors to filter and prioritize different solutions based on their state’s local economy and food waste patterns.
- Interactivity and Automation
Interactive digital format: Different audiences have different needs. ReFED has moved to interactive online tools that allow stakeholders to quickly obtain data tailored to their specific needs. Some materials will still be provided in PDF format as well.
Quick updates and rapid feedback loop: A custom, automated web application allows the models to be rerun and the platform to be quickly updated with the latest information. This reduces the time required to produce new results to hours instead of months or years. This rapid feedback loop allows solutions to be quickly reprioritized according to the latest learnings as solutions are implemented and scaled. ReFED is planning to update results once or twice annually.
Data quality scores: ReFED developed data quality scores to communicate how confident ReFED is in the data being shared based on the quality of the underlying data sources and how they were used. These scores are now displayed front-and-center on the website rather than only in the documentation. This addition allows ReFED to share newly emerging data while maintaining transparency about the data confidence.
Open source data: Raw data and documentation is now made publicly available as much as legally possible. Confidential data is only used in cases where it yielded significant advantages over publicly available data.
- Research Opportunities
Setting a research agenda: ReFED’s new models and data quality scores are able to succinctly highlight what data is most critical and where it is lacking. ReFED hopes that this information will be used to prioritize research funding and advance new research projects.
- Adaptable Framework
Platform can be expanded to other countries if needed: Because the first version of the Roadmap served as inspiration for many other food waste initiatives at the international level, this platform was intentionally designed to be expanded to other countries using geographically specific data.
NOTICE AN ISSUE WITH THE DATA?
Send us an email! The Insights Engine was designed to be radically transparent so that the community of people using this work can help spot issues and identify opportunities to continually improve the data over time. If you see any mistakes, have additional information, or have recommendations for how to improve this resource, please let us know.