How can food waste management research and data support the UN Sustainable Development Goals?

1 The link between food waste and the SDGs

Food waste management is not only an issue of efficiency and profitability, but also of sustainability and social responsibility. Through reducing food waste, you can contribute to the SDGs, such as Zero Hunger, Responsible Consumption and Production, Climate Action, and Partnerships for the Goals. For instance, by preventing food loss and waste, you can increase the availability of food for those in need, especially in developing countries. Additionally, optimizing food production and consumption can reduce the environmental impact of the food system and minimize waste generation. Moreover, avoiding food waste can lower greenhouse gas emissions associated with food production, processing, transport, and disposal - which account for 8% of global emissions. Finally, collaboration with other stakeholders - such as farmers, processors, retailers, consumers, policymakers, researchers, and civil society - can foster innovation and collective action to tackle food waste.

2 The role of research and data in food waste management

To effectively address food waste, it is important to understand its causes, patterns, and impacts across the food supply chain. Research and data can help identify hotspots and drivers of food waste in different contexts, quantify and measure the amount and value of food waste, evaluate costs and benefits of food waste prevention, reduction, recovery, and recycling options, design evidence-based solutions tailored to the needs and opportunities of each sector and region, and monitor the progress and outcomes of food waste management initiatives.

3 The sources and types of food waste data

When making food waste management decisions and actions, there are many sources and types of data that can be useful. Primary data is data that you collect yourself, such as through a food waste audit. Secondary data is data you obtain from other sources, like reports or databases. Qualitative data describes the characteristics, opinions, perceptions, or experiences of people or phenomena related to food waste. And quantitative data expresses the magnitude, frequency, or distribution of food waste or its impacts in numerical terms. For example, you can use statistics or indicators to calculate the economic or environmental costs of food waste.

4 The tools and methods for food waste data analysis

Once you have collected or accessed the relevant data, you need to analyze it to extract meaningful insights and recommendations. There are various tools and methods that can help with this task, such as data visualization which presents data in graphical or pictorial forms like charts, maps, or dashboards. This can be used to communicate complex or large data sets in a clear and engaging way, as well as to identify patterns, trends, or outliers. Additionally, data mining is the process of discovering hidden or unknown information from large or complex data sets. Data modeling is the process of creating a representation or simulation of a system or scenario related to food waste, while data evaluation assesses the quality, validity, reliability, or usefulness of the data or its analysis. All of these techniques can help to uncover relationships, correlations, or causalities among food waste variables and factors; test hypotheses; predict outcomes; optimize solutions; verify findings; address limitations; and improve methods.

5 The benefits and challenges of food waste data

Using food waste data can bring many benefits to your food cost management and sustainability goals, such as enhancing your awareness of the food waste problem, improving efficiency and profitability, increasing competitiveness and reputation, supporting decision making and planning, and contributing to global efforts to achieve the SDGs. However, there are some challenges associated with using food waste data, including accessing reliable data, managing large or complex datasets, protecting privacy and security of the data and its sources, ensuring consistency and comparability of the data across contexts and scales, and communicating the data effectively to different audiences.

6 Here’s what else to consider

This is a space to share examples, stories, or insights that don’t fit into any of the previous sections. What else would you like to add?