Methodology

WasteWise provides a systems-based framework for evaluating circular bioeconomy opportunities from resource availability to technology selection and business model evaluation.

A systems-based decision framework

The methodology integrates user-defined inputs and scenarios with spatial and non-spatial datasets, technology knowledge, local contextual information, and multi-criteria decision analysis to generate evidence-based recommendations for resource recovery and reuse.

The analytical framework is organized into four interconnected layers:

  1. User-defined inputs
  2. Integrated analytical and data architecture
  3. Decision analytics
  4. Data visualization and decision outputs

1

User-defined inputs

Every assessment begins with user-defined inputs that establish the analytical scope and decision context. These inputs determine the datasets, analytical processes and evaluation criteria used throughout the assessment.

Typical inputs include:

– Geographic location
– Waste category and waste stream
– Resource recovery objective and decision priorities
– Preferred technology options and desired product

2

Integrated analytical & data architecture

The analytical architecture combines user-defined inputs with an integrated knowledge base comprising technical, spatial and contextual information. Data are harmonized from multiple sources, including secondary datasets, published literature, technical reports, stakeholder consultations, expert knowledge, field observations, remote sensing and GIS, participatory mapping, and validated project and partner datasets. The resulting data architecture provides a consistent evidence base for evaluating technologies, waste streams and geographic contexts.

Core data layers include:

– Spatial datasets (waste generation and resource distribution).
– Waste composition database.
– Technology, product and implementation datasets, including technology performance, market, economic, policy and institutional information.

3

Decision analytics

WasteWise applies a Multi-Criteria Decision Analysis (MCDA) framework to evaluate and rank alternative circular bioeconomy technologies and business models across multiple sustainability dimensions. Each option is assessed against a comprehensive set of technical, environmental, financial, social, market, institutional and health criteria. Criteria weights are derived using the Analytic Hierarchy Process (AHP), which converts stakeholder preferences into quantitative weights through structured pairwise comparisons and consistency checks. The weighted criteria are then evaluated using the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS), which ranks alternatives according to their relative proximity to ideal and anti-ideal solutions. Sensitivity analysis is also conducted to evaluate the influence of changes in criterion weights on the final rankings, helping identify robust, context-appropriate resource recovery options.

4

Decision intelligence & visualization

Assessment results are translated into interactive decision-support products that enable users to interpret, compare and communicate analytical findings.

Outputs include:

– Comparative technology rankings
– Site suitability maps
– Domain-specific performance indicators
– Scenario and sensitivity analysis
– Downloadable technical reports

These outputs provide a transparent evidence base for evaluating trade-offs, comparing alternative resource recovery pathways and supporting planning, investment and policy decisions. The downloadable technical reports also document the underlying data sources, assumptions, evaluation criteria and analytical parameters used in each assessment, promoting transparency and reproducibility.

Data Quality and Validation

Data availability and quality vary considerably across countries, regions and waste streams. Where high-resolution local information is unavailable, WasteWise uses validated reference datasets, literature-derived parameters and expert-informed assumptions to support the assessment. Where appropriate, default values and proxy indicators are applied in accordance with established analytical protocols while maintaining transparency regarding the underlying assumptions. Model inputs, analytical methods and datasets are continuously reviewed and refined through stakeholder engagement, expert validation, case studies and field applications. As new evidence becomes available, datasets and analytical parameters are updated to improve the robustness, accuracy and relevance of the platform. Detailed information on the data sources, assumptions, default values, criteria weightings, and analytical parameters used for each assessment is provided in the downloadable technical report to support transparency and reproducibility.

Scroll to Top