Food Waste

Food Waste

1. Artificial Intelligence

   • Issues with AI
     • Make AI more Sustainable
       • Make algorithms more efficient so that less resources are needed to complete tasks.
       • Encourage companies to make their data centers more sustainable
         • Power using renewable energy
         • Offset Emissions
         • Reuse resources like water when possible
     • Knowledge Gaps: Lack of understanding about the environmental impacts of AI
       • Need to establish standardized procedures for measuring the environmental impact of AI
       • Are the resources needed to utilize AI more than the resources saved from avoiding food waste?
     • Data Centers
       • Produce electronic waste containing hazardous materials like mercury and lead
       • Consume large volumes of water to cool electrical components
         • Globally, AI infrastructure may soon consume six times more water than the country of Denmark (UN Environment Programme, 2024).
       • Rely on critical minerals and rare elements that are mined unsustainably
       • Use massive amounts of electricity, usually from burning fossil fuels
   • Ways to Use AI to avoid food waste
     • Automate waste tracking and get insight on waste patterns
     • Identify substandard products before reaching consumers
       • The company ImpactVision uses machine learning to analyze composition of food products and ensure quality standards are met, avoiding waste further down the supply chain. (Onyeaka et al., 2023)
     • Interpret feedback from consumers to identify where demand is for food
       • The company Brightloom uses AI to optimize menu options based on customer preferences, avoiding the overproduction of food items (Onyeaka et al., 2023).
     • Optimize growing conditions to ensure quality products
     • Analyze data about food storage conditions to prevent spoilage
     • Optimize delivery routes and times to ensure fresh, quality produce
     • Suggest recipes based on available ingredients
     • Assess effectiveness of educational interventions
       • AI-driven programs identify the most impactful strategies for increasing awareness of food waste (Ahmad Wafi & Tumiran, 2024).
   • Future of AI for Food Waste
     • 5 Years
       • 10 Years
         • Beyond
           • Existing AI technologies will continue to be normalized and made more accessible to all with more user-friendly design and more affordable costs, largely reducing overall food waste.
           • Computer scientists will continue developing new ways to use AI for sustainability.
         • AI technology will be so accessible that smaller, less innovative food production companies will implement it as well, lowering food waste at the production level.
         • Appliances with built in AI will be available to consumers, reducing food waste in homes.
         • Using AI to track food waste will be standard practice for all eateries (and will be more affordable)
       • Restaurants will start using AI-powered automatic waste tracking at a larger scale, which will cut down their food waste.
       • Large, tech-savvy food companies will begin using AI to better predict consumer trends and optimize delivery routes and storage conditions.
       • People interested in tech will start exploring uses of AI to avoid food waste at the consumer level.

2. Food waste is a problem

   • One third of food intended for human consumption in the United States goes to waste (Buzby, 2022).
     • Producing food requires the use of resources at every stage: production, processing, transportation, etc. When food gets wasted, all of those resources go to waste as well.
       • Food waste that is sent to landfills rots and releases methane into the atmosphere.
         • Outside of the landfill emissions, food loss in the United states produces 170 million tons of carbon dioxide equivalent greenhouse gas emissions each year (Buzby, 2022).

Imagens e arquivos do mapa