Government and Local Authorities
Preliminary planning of collection coverage, awareness activities and infrastructure needs.
Version EWNC-EST-PILOT-1.0 · Pilot screening model
E-Waste Estimation and Prediction
Estimate annual e-waste from household and institutional activity data, compare product groups and explore future scenarios for planning and research.
Important model status
This public tool is a functional screening estimator. It uses transparent stock, weight and service-life assumptions to generate planning-level estimates. It is not yet a validated official inventory. Research deployment should replace default coefficients with locally verified data and, where historical cohorts are available, use the dynamic Weibull lifespan method described below.
Overview
What the model is designed to do
The estimator converts a small set of demographic and institutional activity variables into estimated electronic-product stock, annual e-waste generation, available waste after storage and formal collection potential.
Estimate at campus, town, tehsil, district or state scale.Separate major product groups and activity sectors.Explore conservative, business-as-usual and high-adoption scenarios.Generate 5, 10 or 20-year forecasts.Display an uncertainty range rather than a single exact figure.Download the result and assumption set as CSV.
Public screening mode
Current calculation
Estimated stock × average product weight ÷ average service lifeThe screening mode is suitable when historical product sales and disposal cohorts are unavailable. The research methodology section explains how the Centre can progress to a dynamic stock-and-flow model.
Who can use it
Designed for planning, research and institutional assessment
Educational Institutions
Campus, departmental, hostel and laboratory e-waste assessment.
Researchers and Students
Scenario development, method testing, sensitivity analysis and comparative studies.
Industry and EPR Stakeholders
Preliminary collection, reverse-logistics and material-availability planning.
Required data
Minimum activity variables
The public demo accepts a compact input set. Research versions may add product ownership surveys, historical procurement, sales, repair, storage and disposal records.
Household sector
Population and number of households.
Education sector
Schools, colleges, universities, laboratories and student counts where available.
Healthcare sector
Hospitals, clinics, beds, diagnostic units and medical-electronics inventory.
Government sector
Offices, departments, employees and electronic-asset records.
Commercial sector
Shops, offices, hotels, service establishments and industrial units.
Model assumptions
Product ownership, average weight, service life, storage rate, capture rate and growth scenario.
Model outputs
Planning indicators generated by the website
Annual Generation
Gross estimated e-waste in kilograms and tonnes per year.
Available E-Waste
Estimated amount available after accounting for storage or hibernation.
Formal Collection Potential
Quantity potentially captured through formal collection at the selected rate.
Product and Sector Breakdown
Category-wise and activity-driver contributions for prioritization.
Forecast Scenario
Projected e-waste after 5, 10 or 20 years under the selected growth assumption.
Uncertainty Range
Lower and upper planning bounds reflecting coefficient and lifespan uncertainty.
Collection Requirement
Indicative monthly volume that collection systems may need to accommodate.
Downloadable CSV
Inputs, assumptions and results for independent review and further analysis.
Methodology
Two levels of modelling
Level 1: Public screening estimator
The functional website demo estimates product stock from households and institutional activity drivers. For each product group, the estimated stock is multiplied by average unit weight and divided by average service life to approximate annual end-of-life outflow.
Estimated product stock
S(i) = Σ [activity driver(s) × ownership coefficient(i,s)]Annual screening estimate
E(i) = S(i) × average weight(i) ÷ average service life(i)Available and formally collectable quantities
Available = Generated × (1 − storage rate)Formal potential = Available × collection capture rateScenario forecasts apply the selected annual growth rate to the current estimate. The uncertainty range applies a planning margin to reflect uncertainty in ownership, weight, service life and storage assumptions.
Level 2: Research-grade dynamic material-flow model
Where annual product inflow or historical procurement cohorts are available, the Centre should estimate discard probability by product age using a Weibull lifespan distribution. Each historical cohort contributes to e-waste according to the probability that products of that age leave the in-use stock.
Weibull survival function
Rᵢ(a) = exp[-(a / ηᵢ)^βᵢ]Probability of discard at age a
pᵢ(a) = Rᵢ(a−1) − Rᵢ(a)Cohort-based e-waste
Eᵢ,t = wᵢ × Σ Iᵢ,t−a × pᵢ(a)The research implementation should retain model versions, data sources, parameter provenance and uncertainty analysis. It should be calibrated against actual institutional disposal, collection and recycler records.
Validation
How the model should be tested before official use
Backcast against verified institutional disposal and auction records.Compare estimated volumes with observed collection-drive and recycler receipts.Measure absolute, percentage, product-level and geographic errors.Conduct sensitivity and Monte Carlo uncertainty analysis.Review ownership and lifespan assumptions with sector experts.Version all parameter changes and preserve an audit trail.
Current status
Functional pilot · Under validation
The results are suitable for testing, planning discussion and method development. They are not an official e-waste inventory until local data and validation evidence are added.
Evidence base
Foundational research informing the methodology
The following papers support lifespan distributions, sales-stock-lifespan estimation, forecasting and transparent dynamic material-flow modelling.
Oguchi et al. — Lifespan of Commodities, Part II
Methodologies for estimating lifespan distributions and interpreting different lifespan definitions.
DOI: 10.1111/j.1530-9290.2010.00251.xWang et al. — Enhancing e-waste estimates
Supports sales-stock-lifespan logic and the importance of time-varying lifespan distributions.
DOI: 10.1016/j.wasman.2013.07.005Kim et al. — Estimating WEEE in South Korea
Uses a population-balance approach and product lifespan distributions for WEEE estimation.
DOI: 10.1016/j.wasman.2012.07.011Islam and Huda — E-waste in Australia
Combines Holt forecasting with dynamic Weibull lifespans for product and e-waste projections.
Journal of Cleaner Production, 237, 117787Pauliuk and Heeren — ODYM
An open framework for transparent and reproducible dynamic material-systems modelling.
DOI: 10.1111/jiec.12952Run the functional model
Pilot e-waste screening estimator
Enter planning data below. The model runs locally in the browser and does not save this demonstration run to the database.
Complete the inputs and run the model.
Your results, product breakdown, forecast and download option will appear here.