Modelling Key Performance Indicators in a Gamified Waste Management Tool
Abstract
:1. Introduction
2. Prior Art
Simulation Games and Gamified Tools
3. Factors Affecting Waste—Key Performance Indicators
3.1. Approaching the Waste Management Problem
3.2. Key Performance Indicators
3.2.1. Waste Compositional Analysis (MSW-C)
3.2.2. Municipal Solid Waste Production (MSW-P)
3.2.3. Municipal Solid Waste Recycling (MSW-R)
3.2.4. Waste Production Rate (WPR)
3.2.5. Waste Recovery Rate (WRR)
3.2.6. Waste Generation Rate (WGR)
3.2.7. Waste Infrastructure (WI)
3.2.8. Clean Index (CI)
- 1(A): Outstanding waste collection, defined as at least three times daily (morning, midday, afternoon). Waste bins exist at least every 100–200 m as well as other waste infrastructure (such as separated recycling bins, collection of hazardous waste, or increased waste awareness and informational signs). Mechanical cleaning of roads two times weekly, with waste bin capacity at least in the range of 1.5–2.5 L/guest.
- 2(B): Acceptable collection of waste, three to four times/week and twice/day for some areas (e.g., morning and afternoon collection, average waste bin separation of 200–400 m with other waste infrastructure (e.g., recycling bins). Waste bins capacity is at least 1.0–1.4 L/visitor. Informational signs are visible, and mechanical road cleaning occurs at least weekly.
- 3(C): Average collection of waste twice weekly and once per day in some areas. On average, waste bins are spaced ever 500 m with some other waste infrastructure available. Waste bin capacity varies from 0.5–1.4 L/visitor with limited awareness and informational signs, with only periodic road cleaning.
- 4(D): Periodic collection of waste–approximately once weekly, with limited public waste bins or other infrastructure, zero implementation of mechanical cleaning of the roads and waste bin capacity 0.5–1.4 L/visitor.
- 5(E): Zero formal cleaning programs in the area, no waste infrastructure and no mechanical cleaning of the roads. Little to no waste awareness or signage.
3.2.9. Accumulation Rate (AR)
3.2.10. Accumulation Index (AI)
3.2.11. Air Pollution
3.2.12. Mobility
- [A] Public Transportation must line with existing EU regulations regarding Transportation means (i.e., 80% Electric Cars by 2050 [36])
- [B] They must cover existing National plans and targets.
- [C] Satisfy the needs of Public Transportation for the maximum population density requirements.
- [D] Contribute to the existing National Plans to reduce Carbon Dioxide emissions.
3.2.13. Green Space
3.2.14. Renewable Energy Sources (RES)
3.2.15. Waste Water Treatment (WWT)
- [A] The wastewater treatment plant will perform all primary, secondary, and tertiary treatment [50].
- [B] Agglomerations and collection systems are 100% treated in the wastewater treatment plant.
- [C] The P.E of each area will always be within the limits provided by WWTD (≥2000 P.E).
- [D] Wastewater Treatment plants will protect environment and surface waters as well as human health [49]:
3.2.16. Strategies
3.2.17. Correlations among KPIs
4. Design and Development of Waste Management Tool
Current Status of the Tool
5. Conclusions and Future Work
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
References
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Commercial Games /Research Tools | Information about Waste Management Systems |
---|---|
SimCity 4 | All waste lumped as “garbage” |
Multiple disposal avenues (Landfill, Recycling, Waste-to-Energy) | |
Waste accumulation reported though desirability reports, Mayor Rating | |
Cities: Skylines | All waste lumped as “garbage” |
Multiple disposal avenues (Landfill, Recycling, Incinerator, Waste Processing, …) | |
Waste accumulation reported though feedback bubbles | |
Wu and Huang’s Research Tool [6] | All waste lumped as “garbage” |
Multiple disposal avenues (Waste Product Dump, Incinerator, Environment Factory, Trading Companies) | |
Waste accumulation reported though reports of garbage-driven natural disasters | |
Wood of War | Multiple waste monsters found with varied garbage piles |
Waste disposed of by defeating monsters Real-world waste reported through GPS tags | |
Real-world waste build-up is communicated to developers, authorities | |
NetLogo | All waste types lumped |
Sociotechnical approach for complex waste management and decision-making | |
Waste management parameters (agents) executed serially. Empirical calibration necessary to mirror real-world scenarios. |
Categories of Waste | Range (%) | Scaled Estimated Amount (tn) |
---|---|---|
PMD | 5–30 | 7639 |
Plastic Film | 1–7 | 3588 |
Plastics Non-Recyclable | 1–3 | 1835 |
Aluminium/Ferrous | 0.5–2 | 682 |
Paper | 5–20 | 8572 |
Glass | 1–7 | 4327 |
Toilet and Kitchen Paper | 1–15 | 9652 |
Food Waste (edible) | 2–20 | 12,055 |
Food Waste (inedible) | 2–20 | 4091 |
Organic Waste(Green Waste, Yard Waste) | 2–20 | 22,243 |
Others | 1–10 | 6494 |
Total | 81,178 (figures per [13]) |
Categories | Range (%) | Estimated Amount (tn) |
---|---|---|
PMD | 50–100 | 3819 |
Paper | 50–100 | 4286 |
Glass | 80–100 | 3461 |
Total | 5172 |
Categories | Range of Recovery (%) | Estimated Waste Recovered (tn) | |
---|---|---|---|
Min | Max | ||
PMD | 50 | 100 | 3819 |
Aluminium/Ferrous | 50 | 100 | 341 |
Paper | 50 | 100 | 4286 |
Glass | 80 | 100 | 3461 |
Food waste | 50 | 100 | 8073 |
Green Waste | 80 | 100 | 17,794 |
37,776 |
Categories | Bin Colour | Proposed Range |
---|---|---|
Recycle Bin for: | ||
Plastics | Yellow | |
Paper | Brown | |
PMD | Blue | min: W.I ⩾ 0.5–1.5 L/capita |
Mixed waste | Green | max: W.I ⩾ –25 L/capita |
Food Waste | Pink | |
Green Waste | Light Green | |
Batteries | Transparent | |
Glass Packaging | Grey | |
Aluminium | Perforated |
Quality | Level of Sevices (LOS) | CI | Identification |
---|---|---|---|
Very Clean | 1(A) | 0–2 | No litter seen |
Clean | 2(B) | 2–5 | No litter seen over a large area |
Moderate | 3(C) | 5–10 | A few pieces of litter visible |
Dirty | 4(D) | 10–20 | A lot of litter visible |
Very Dirty | 5(E) | 20+ | Most of the area is covered in litter |
LOS | WAI | WAR (Items/m/Day) | WAR (Items/km/Day) | |
---|---|---|---|---|
Extremely Low | 1 | ≤1 | 0.000001 | 1 |
Very Low | 2 | 1–2 | 0.00001 | 10 |
Low | 2–3 | 2–3 | 0.0001 | 100 |
Moderate | 3 | 3–4 | 0.001 | 1000 |
High | 4 | 4–5 | 0.01 | 10,000 |
Very High | 4–5 | 5–6 | 0.1 | 100,000 |
Extremely High | 5 | ≥6 | 1 | 1,000,000 |
Categories | Range (%) | Low | Moderate | High | Very High |
---|---|---|---|---|---|
0–100 | 0–50 | 50–100 | 100–200 | >200 | |
0–100 | 0–25 | 25–50 | 50–100 | >100 | |
Carbon Dioxide (CO) | To be defined | ||||
Carbon Monoxide (CO) | 0–100 | 0–7000 | 7000–15,000 | 15,000–20,000 | >20,000 |
Nitrogen Monoxide (NO) | 0–100 | ||||
Nitrogen Dioxide (NO) | 0–100 | 0–100 | 100–150 | 150–200 | >200 |
Sulphur Dioxide (SO) | 0–100 | 0–150 | 150–250 | 250–350 | >350 |
Ozone (O) | 0–100 | 0–100 | 100–140 | 140–180 | >180 |
Benzene (CH) | 0–100 | 0–5 | 5–10 | 10–15 | >15 |
Transportation |
---|
Buses |
Trams/Trolleys |
Trains/Underground Metro |
Bike lanes |
Walk lanes |
Electric Cars |
Green Space Categories | Green Space Requirements (m/Capita) |
---|---|
Green Space (Parks, Playgrounds, Open-air Sport Facilities) | min: 9 m/capita |
optimum: 50–57 m/capita |
Category | RES Share in Total Energy Production (MWh) |
---|---|
RES (P/V Parks, P/V houses, Wind Parks) | min: 11.7% (63,176 MWh) |
max ≥ 32% (≥114,760 MWh) |
Categories |
---|
Circular Economy |
European Green Deal (EGD) |
United Nations Sustainability Goals (UNSDGs) |
Zero Waste Policy |
Low Carbon Society |
Waste Prevention |
Energy Recovery |
Smart City |
Environmental Management Systems (i.e., ISO14001, EMAS) |
R-strategies |
Categories |
---|
Use of leftovers |
Use of reusable Grocery Bags |
Reusable Coffee Cup |
Home Composting |
Food Waste Campaign |
Donations where possible (i.e., Toys, Books, clothes etc) |
Book exchanges |
Smart shopping list (buying exact necessities from stores) |
Keeping vegetables and fruits in loosely tied bags |
Reuse of electrical appliances |
Electrical Appliances repair stores to extend life time of devices |
MSW-C | MSW-P | MSW-R | WPR | WRR | WGR | WI | CI | AR | AI | Air Pollution | Mobility | Green Space | Renewable Energy | Waste Water Treatment Plant | Strategies | |
MSW-C | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | ||||||||||
MSW-P | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | ||||||||||
MSW-R | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | |||||||||
WPR | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | ||||||||
WRR | ✓ | ✓ | ✓ | ✓ | ||||||||||||
WGR | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | ||||||
WI | ✓ | ✓ | ✓ | ✓ | ✓ | |||||||||||
CI | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | ||||||||
AR | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | ||||||||||
AI | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | ||||||||||
Air Pollution | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | ||||||||
Mobility | ✓ | ✓ | ✓ | |||||||||||||
Green Space | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | |||||||||
Renewable Energy | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | ||||||||||
Waste Water Treatment Plant | ✓ | |||||||||||||||
Strategies | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | |
Acronyms and Terms | ||||||||||||||||
MSW-C | Waste Compositional Analvsis | |||||||||||||||
MSW-P | Municipal Solid Waste Production | |||||||||||||||
MSW-R | Municipal Solid Waste Recycling | |||||||||||||||
WPR | Waste Production Rate | |||||||||||||||
WRR | Waste Recovery Rate | |||||||||||||||
WGR | Waste Generation Rate | |||||||||||||||
WI | Waste Infrastructure | |||||||||||||||
Cl | Clean lndex | |||||||||||||||
AR | Accumulation Rate | |||||||||||||||
Al | Accumulation Index |
Categories of Waste | Scaled Est. Amount (tn) | Range (tn) |
---|---|---|
PMD | 7639 | 5000–10,000 |
Plastic Film | 3588 | 2000–5000 |
Plastics Non-Recyclable | 1835 | 1000–3000 |
Aluminium/Ferrous | 682 | 500–1000 |
Paper | 8572 | 6000–10,000 |
Glass | 4327 | 3000–5000 |
Toilet and Kitchen Paper | 9652 | 8000–11,000 |
Food Waste (edible) | 12,055 | 10,000–14,000 |
Food Waste (inedible) | 4091 | 3000–5000 |
Organic Waste (Green Waste, Yard Waste) | 22,243 | 20,000–25,000 |
Others | 6494 | 5000–7000 |
Population: 1500 | Population: 2000 | Population: 5000 | Population: 6500 | Population: 7000 | Population: 10,000 | Population: 18,000 | Population: 20,000 | Population: 30,000 | ||||||||||
Lower Bound | Upper Bound | Lower Bound | Upper Bound | Lower Bound | Upper Bound | Lower Bound | Upper Bound | Lower Bound | Upper Bound | Lower Bound | Upper Bound | Lower Bound | Upper Bound | Lower Bound | Upper Bound | Lower Bound | Upper Bound | |
PMD | 750 | 1500 | 100 | 200 | 250 | 500 | 325 | 650 | 350 | 700 | 500 | 1000 | 900 | 1800 | 1000 | 2000 | 1500 | 3000 |
Plastic Film | 300 | 750 | 40 | 100 | 100 | 250 | 130 | 325 | 140 | 350 | 200 | 500 | 360 | 900 | 400 | 1000 | 600 | 1500 |
Plastic Non Recyclable | 150 | 450 | 20 | 60 | 50 | 150 | 65 | 195 | 70 | 210 | 100 | 300 | 180 | 540 | 200 | 600 | 300 | 900 |
Aluminun/ Ferrous | 75 | 150 | 10 | 20 | 25 | 50 | 32 | 65 | 35 | 70 | 50 | 100 | 90 | 180 | 100 | 200 | 150 | 300 |
Paper | 900 | 1500 | 120 | 200 | 300 | 500 | 390 | 650 | 420 | 700 | 600 | 1000 | 1080 | 1800 | 1200 | 2000 | 1800 | 3000 |
Glass | 450 | 750 | 60 | 100 | 150 | 250 | 195 | 325 | 210 | 350 | 300 | 500 | 540 | 900 | 600 | 1000 | 900 | 1500 |
Toilet and Kitchen paper | 1200 | 1650 | 160 | 220 | 400 | 550 | 520 | 715 | 560 | 770 | 800 | 1100 | 1440 | 1980 | 1600 | 2200 | 2400 | 3300 |
Food Waste Edible | 1500 | 2100 | 200 | 280 | 500 | 700 | 650 | 910 | 700 | 980 | 1000 | 1400 | 1800 | 2520 | 2000 | 2800 | 3000 | 4200 |
Food Waste Inedible | 450 | 750 | 60 | 100 | 150 | 250 | 195 | 325 | 210 | 350 | 300 | 500 | 540 | 900 | 600 | 1000 | 900 | 1500 |
Organic Waste | 3000 | 3750 | 400 | 500 | 1000 | 1250 | 1300 | 1625 | 1400 | 1750 | 2000 | 2500 | 3600 | 4500 | 4000 | 5000 | 6000 | 7500 |
Others | 750 | 1050 | 100 | 140 | 250 | 350 | 325 | 455 | 350 | 490 | 500 | 700 | 900 | 1260 | 1000 | 1400 | 1500 | 2100 |
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Pappas, G.; Papamichael, I.; Zorpas, A.; Siegel, J.E.; Rutkowski, J.; Politopoulos, K. Modelling Key Performance Indicators in a Gamified Waste Management Tool. Modelling 2022, 3, 27-53. https://doi.org/10.3390/modelling3010003
Pappas G, Papamichael I, Zorpas A, Siegel JE, Rutkowski J, Politopoulos K. Modelling Key Performance Indicators in a Gamified Waste Management Tool. Modelling. 2022; 3(1):27-53. https://doi.org/10.3390/modelling3010003
Chicago/Turabian StylePappas, Georgios, Iliana Papamichael, Antonis Zorpas, Joshua E. Siegel, Jacob Rutkowski, and Konstantinos Politopoulos. 2022. "Modelling Key Performance Indicators in a Gamified Waste Management Tool" Modelling 3, no. 1: 27-53. https://doi.org/10.3390/modelling3010003