DYNAMO Literature Review Summarizing Instructions

This document defines the canonical format and conventions for summarizing academic and policy research articles relevant to the DYNAMO project. It ensures all summaries are consistent, comparable, and directly usable in analysis.

Version: 2025-09-17. Up to date DYNAMO index is here.

Output structure for each article

Each summary must include the following sections, in this order, using bold headings:

• Bibliographic info
  Full citation in APA style (authors, year, title, journal, DOI/link).
• Scope & system studied
  Sector, geography, and CE strategy addressed.
• Model type & structure (ODD 2020 headings)
  • Purpose and patterns
  • Entities, state variables, and scales
  • Process overview and scheduling
  • Design concepts
  • Initialization
  • Input data & calibration
  • Submodels / decision modules
• Policy / strategy levers examined
  Which interventions or scenarios are tested (e.g. subsidies, taxes, regulations, adoption incentives).
• Key findings
  Main dynamics, trade-offs, or emergent results reported.
• Relevance for toolkit development
  How the methods/results could inform development of CE profitability calculators, market simulators, or policy analysis models.
• Strengths & limitations
  Transparency, reproducibility, transferability to Finnish/EU contexts, integration with IO/LCA/EEIO where relevant.
• Classification for review
  Choose one:
  • Include in main review (directly relevant to CE ABM/SD/toolkits)
  • Background only (contextual but not methodologically central)
  • Exclude (not relevant)

Standardized terminology

• Always use “decision modules” for submodels/agent rules.
• Always report Input data & calibration together (data sources, parameters, estimation).
• Costs, subsidies, regulations, and incentives are always listed under Policy / strategy levers.
• If a model is not ABM/SD but e.g. IO, optimization, hybrid, still fit into the ODD headings to allow cross-comparison.
• Summaries must be concise but detailed enough for quick comparison.

Example classification

• Include in main review → Articles presenting ABM/SD/IO or hybrid modeling frameworks for CE markets, supply chains, policies, or investments.
• Background only → Articles with useful context (e.g. CE strategies, waste statistics) but not methodologically central.
• Exclude → Articles without clear relevance to modeling CE systems.

Example summary entry

Bibliographic info
Gan, V.J.L., & Cheng, J.C.P. (2015). Formulation and analysis of dynamic supply chain of backfill in construction waste management using agent-based modeling. Advanced Engineering Informatics, 29(4), 878–888. https://doi.org/10.1016/j.aei.2015.01.004

Scope & system studied

• Sector: Construction waste management (excavated backfill).
• Geography: Hong Kong.
• CE strategy: Reuse of backfill between construction sites to reduce landfill disposal and costs.

Model type & structure (ODD 2020 headings)

• Purpose and patterns: Identify suitable modeling approaches for dynamic supply chains and evaluate ABM vs centralized optimization for backfill recovery.
• Entities, state variables, and scales:
  • Agents: Construction sites (producers, consumers, or both), landfills, commercial suppliers.
  • State variables: Backfill stock, excavation/backfilling schedules, cost parameters, site capacity.
  • Temporal scale: Monthly phases of construction projects.
  • Spatial scale: Regional network of sites in Hong Kong.
• Process overview and scheduling: Producers and consumers negotiate backfill exchanges via a contract-net protocol (task announcement, bidding, awarding); decisions update each time step. Centralized optimization model solves same network as a global optimum benchmark.
• Design concepts:
  • Emergence: Supply chain material flow patterns.
  • Adaptation: Agents respond to local availability, demand, and costs.
  • Interaction: Negotiation and bidding between agents.
  • Stochasticity: Incorporated via demand variation using stochastic programming.
• Initialization: Excavation/backfilling schedules of six construction sites; landfill and one commercial supplier.
• Input data & calibration: Empirical data on project schedules, transportation costs, material prices, and landfill disposal charges; parameters sourced from local construction/waste policies.
• Decision modules:
  • Producer module: Optimize export rate considering holding and dumping costs.
  • Consumer module: Optimize import rate to minimize holding/procurement costs.
  • Negotiation module: Contract net protocol for producer–consumer agreements.

Policy / strategy levers examined

• Waste disposal charges (government landfill levy).
• Coordination mechanisms: centralized optimization vs distributed ABM.

Key findings

• Centralized optimization yields slightly lower costs but requires unrealistic full information sharing.
• ABM produces near-optimal solutions while better reflecting decentralized decision-making, limited information exchange, and dynamic adaptability.
• ABM reacts faster to disturbances (new projects, delays) than centralized approaches.
• Trade-off: global optimality vs realism and resilience.

Relevance for toolkit development

• Strong demonstration of ABM for decentralized CE supply chains.
• Negotiation algorithms could inform CE market simulators matching supply and demand of secondary materials.
• Highly relevant for designing decision-support tools in CE logistics.

Strengths & limitations

• Strengths: Realistic modeling of decentralized actors; transparent comparison of centralized vs distributed approaches; case-based calibration.
• Limitations: Narrow focus (backfill only); simple case (one landfill, one commercial source); no direct integration with IO/LCA; global optimality not guaranteed.
• Transferability: Concepts transferable to EU/Finland in construction waste and industrial symbiosis contexts.

Classification for review
Include in main review.