Project title: Landfill Audits
Industry partner: Sustainability Victoria (SV)
Sustainability Victoria supports Victorians to be more sustainable in their everyday life; in homes and in jobs, schools and communities and in the systems and infrastructure that support a thriving Victorian economy and lifestyle. SV aims to improve the way Victoria manages its resources and help communities to take action on climate change. SV provide expert advice and guidance in energy, materials and waste. SV conducts research and demonstrate what is possible and inspires people to make sustainable change above and beyond legal requirements.
Background: SV undertakes ad-hoc landfill audits every 5 to 10 years in Victoria. The audits are visual audits and consist of auditors undertaking visual inspections of the waste composition deposited at landfills for a period of 1 week at a select number of landfills (approx. 6).
Landfill audits are very expensive and time consuming.
SV now has datasets from the last 2 landfill audits conducted in 2005 and 2009.
SV uses this information to provide the evidence base to enable government to make decisions on infrastructure planning and investment opportunities in resource recovery.
SV is currently undertaking new audits for the 2018 period at 3 landfill sites.
SV does not know how accurate or representative the data from these audits are.
Project aim/expected outcomes:
A statistical analysis of the datasets to calculate the margin of errors and confidence interval for all levels of the disaggregate data.
- Margin of error and CI at the 95% CI for each landfill audited for each year;
- Margin of error and CI at the 95% CI for the total of each year of the landfill audited;
- Margin of error and CI at the 95% CI for each material type audited for each landfill audited;
- Margin of error and CI at the 95% CI for each material type audited for the year;
- Develop a matrix showing which parts of the aggregate data are reliable and which are not;
- Recommendation on how audits might be improved;
- Recommendation of sample size for next audits.
Analytics or Data Science students