1. Development of a Methodology to Evaluate Waste and Recycling Rates Debra L. Kantner Bryan Staley, PhD, PE

2. Our History and Mission Founded in 1992 as a 501(c)3 charity Mission: To direct research studies & educational initiatives for sustainable waste management practices via: –Research grants –Scholarships/Internships –General education

3. Key Programs RESEARCH Nearly $11 million in projects that help shape policy, develop sustainable practices, bring value, and direct the future of solid waste management SCHOLARSHIPS Educating the next generation of solid waste researchers and technical personnel – nearly 50 scholars thus far, totaling almost $0.75 million CONTINUING EDUCATION Informing policymakers, practitioners, and others regarding cutting edge research and solid waste management practices via meetings and online continuing education INTERNAL RESEARCH Conduct state of practice research and trend analyses to benefit to solid waste industry, while providing research experience for talented college undergraduates.

4. Strategic Focus Areas EREFs strategic plan addresses all areas of the integrated waste management infrastructure. 1) Landfills 2) Equipment/Safety 3) Transport/Collection 4) Policy/Economics 5) Recycling/Waste Minimization (includes packaging) 6) Combustion/WTE 7) Conversion Technologies (includes composting) 8) Life Cycle Analysis/Inventory

5. Development of Waste and Recycling Rate Methodology

6. Current methods (e.g. US EPA) result in: –Estimation of recycling rates on a citywide basis –‘Blanket-style’ approach to management and incentives Such methods are not specific enough to assist cities/haulers in identifying rate trends. Recent improved tracking techniques allow for a data-driven approach to provide better information to the end user (e.g. city, hauler) Background

7. Overview Methodology developed by EREF provides two sets of analyses: 1)Rates Analysis for specific areas within a city using available technology or operations data (e.g. RFID, ‘clicker’/driver collected, etc) 1)Correlation Analysis that indicates why particular areas may have depressed or elevated rates Can be applied to BOTH waste and recycling data

8. Basic Methodology 1)Obtain collection data –Weekly participation based on collection events –Mass per route based on weigh tickets 2)Quantify rates geospatially based on: –Truck route –Census tract/block –Individual residence –Custom boundaries (neighborhood, street, etc.)

9. 3)Determine high/low participation areas –Set-out rates –Average mass per home per route High or Low participation are typically assigned in reference to the city average 4)Analyze fraction of service area in high/low participation areas –How much of area affected, # carts, # homes, etc –How far below average for each area Basic Methodology (continued)

10. 5)Determine why certain areas may have high/low participation based on: –Demographics –Housing –Other characteristics 6)Develop recommendations Can include: –Target incentive programs based on demographics –Determining size of targeted incentive groups Basic Methodology (continued)

11. Results

12. Assessing Rates via a Data Driven Approach Colors indicate individual truck service area Each icon is an individual pickup RFID data for recycling carts from March 1, 2011 to Feb 24, 2012

13. Rates Analysis

14. Rate Definitions Set-Out Rate: The percentage of set- out opportunities that were utilized by program participants during a defined period of time. Mass Rate: The amount of recyclables set at the curb, based on route average. This is determined using scale tickets

15. Set-Out Rates (based on Census tracts) Citywide weekly set-out rate: 63.6% –41 tracts –62,440 carts –4.2M collection events –Varied 43% to 78% 35 point difference Set-Out Rate

16. Observed Cart Mass Average28.1 lb/cart Min11.5 lb/cart Max49.6 lb/cart

17. Mass Rates (based on block group) Citywide average mass rate: 400 lb/HH-yr –82 block groups –62,440 carts –4.2M collection events –Varied 231 to 639 lb/HH-yr 408 pound difference Mass Rate (lb/HH-yr) 231 – 334 334 – 435 435 – 537 537 – 640

18. Defining High/Low The citywide average weekly participation rate and standard deviation are used to define “high” and “low” groups Defining “High” and “Low” Groups Citywide Average 63% Standard Deviation 9% “High”> 72% “Low”< 54% LowHighAverage

19. High/Low Comparison (based on census tracts) HIGH: >72% set-out rate –6 tracts –9,615 carts –16% of carts LOW: < 54% set-out rate –8 tracts –9,163 carts –15% of carts

20. Correlation Analysis

21. Participation v. mass

23. Examined 96 Census Bureau variables to find possible relationships to participation rate. Performed statistical analysis to determine which variables were significant. Result: 5 primary variables were most important and correlated to participation Correlation Variables

24. Set-Out Rate

25. Correlation Plots

26. (continued)

27. Correlation Plots (continued)

28. Correlation Plots (continued)

29. Correlation Plots (continued)

30. Mass Rate

31. Correlation Plots

34. Household Income Education Level Home ValueHousehold Size Owner Occupancy Set-Out % YES Mass Lb/HH-yr YES NO Correlation Summary

35. Townhouse Analysis

36. Sampled within city to test observed participation differences between single family homes and townhomes. –7 tracts –2% to 57% townhouses Townhome v. Single Family

37. Townhome v. Single Family (continued) Townhomes Single Family Homes Set-Out Rate Average 45%64% Set-Out Rate Range 38% - 53%55% - 77% Mass Rate Average 370 lb/HH-yr 507 lb/HH-yr Mass Rate St. Dev. 177 lb/HH-yr 207 lb/HH-yr Average Set-Out Rate Townhomes v. Single Family Tracts for townhouse comparison include high and low: –Set-out rate –Income –Education –Owner Occupancy Both downtown units and neighborhood complexes

38. Methodology Benefits

39. Identify key differences within the low tracts Tailor programs to tract demographics –Incentivize high and low income areas differently (A and B) –Examine bin size and collection frequency in C Census Tract Low Education Low Income Low Home Value Individual Resident Renter Occupied Townhouse A XX B XXX C XX D X Recommendations –Townhouses may benefit from education/awareness

40. Benefits 1)Targeted spending of program budget 2)Allows for more effective implementation of awareness and incentive programs 3)Provides a means to track performance over time that is coupled with demographics and program data (e.g. before and after analyses) 4)Data provides opportunity for further analysis

41. Further Analysis

42. Participation Estimate

44. Thank You