Zero Waste offers a systematic approach to avoiding or reusing waste with the aim of minimizing its disposal into landfills. It's a strategic resource management paradigm that is becoming increasingly relevant in today's world, where the need for sustainable development and efficient resource utilization is evident.
_Be aware that the full description of this milestone you can find by refer to README_
Explore the problem statement based on our team's personal experiences, categorized by the countries where we reside.
Throughout the project, we refined our research questions for greater specificity:
Our project addresses challenges in implementing Zero Waste programs, with a particular focus on cultural and geographical factors hindering the adoption of new consumption practices and waste management strategies within the food industry.
Our team works with global data and analysis, with a focus on countries where team members currently reside.
_Be aware that the full description of this milestone you can find by refer to README_
Domain modeling in our Zero Waste project involves creating a simplified representation of key aspects related to waste management. It's a visual guide, akin to a map, helping us understand how economic indicators, cultural differences, and geography influence waste generation and management practices.
This non-technical model serves as a blueprint, aiding communication, identifying challenges, and guiding decision-making for effective waste reduction strategies.
For transparency and replication, we provide all scripts for data collection and cleaning, covering the entire process, including data partitioning.
Engaged in Data Science research, our team focuses on implementing zero waste programs, leveraging practical experience and modern data analysis methods to contribute significantly to the field.
For more details on our experience, refer to our_experience and README.
_Be aware that the full description of this milestone you can find by refer to README_
The project analyzes the relationship between economic indicators and waste generation using data from the 2021 UNEP Food Waste Index Report.
Non-technical explanation of our findings
Our exploration of the relationship between economic indicators and global waste generation patterns yielded several insights. The dataset, covering diverse countries, allowed us to observe trends and variations in economic factors and waste production.
Key Findings
The overall model does not explain a significant proportion of the variance in the total waste estimate (R-squared = 0.156). Additionally, individual economic indicators do not exhibit strong statistical significance in predicting the total waste production.
Technical description of analysis
Results of the data analysis conducted using Jupyter Notebook can be found here.
Technical explanation of our findings
Household Food Waste Across Regions:
Global Economic Impact of Food Waste:
Relationship Between Economic Prosperity and Food Waste Reduction Initiatives:
_For a detailed analysis, refer to the Full Project Analysis in the project repository._
Conclusion:
Adapting methods based on ongoing discoveries ensures a dynamic and thorough analysis approach. Answers for Actionable Research Questions.
Please be aware that the analysis is currently underway, and any updates or improvements will be incorporated into future milestones.
_Be aware that the full description of this milestone you can find by refer to README_
Our focus is on Nonprofit Organizations dedicated to addressing social and environmental issues, specifically in the realm of food waste. We aim to engage organizations with significant influence on groups contributing to food waste, such as households, food service, and retail.
Promoting Conscious Consumption: Tailor information campaigns based on country-specific waste patterns identified through analysis of the relationship between living standards and food waste.
Supporting Restaurants and Catering Establishments: Advocate for tailored programs in regions exhibiting higher waste estimates.
Developing Technology to Track and Manage Leftover Food: Emphasize the importance of technology by aligning proposals with the machine learning modeling ideas for waste reduction demonstrated in the project analysis.
Monitoring and Evaluation: Encourage organizations to adopt analytical approaches for waste reduction, considering regional waste disparities.
Our project analysis was carried out based on the latest public information found in various sources. The limit year of information found is 2021. We need the latest data from 2022 onwards, namely:
Detailed Yearly Waste Breakdown: Requesting country-specific, year-wise waste production data to enhance the precision of our analysis and predictions.
Recent Emission Reduction Programs Data: Soliciting information on recent emission reduction programs in the food industry to understand their potential influence on waste reduction predictions.
Socio-Cultural Factors Influencing Food Waste: Requesting data related to socio-cultural factors affecting food waste behaviors in different countries for a more nuanced analysis.
Machine Learning Model Training Data: Requesting additional training data for our machine learning model, to enhance its predictive capabilities.
Important message:
Based on the analysis, we have developed set of strategies that will lead to a reduction in the amount of food waste. Also, to provide more accurate and extensive analysis, we need more data presented here, and any food waste data after 2022. By collaboration, we will make significant progress in the area of food waste.
We welcome contributions from individuals and organizations passionate about waste management and sustainability. To contribute, please follow these guidelines:
Thank you for helping us make a positive impact on waste management!