Introduction Many of the development challenges faced today—from plastic waste management to energy transitions—are shaped by complex systems. They comprise many actors, feedback loops, and time delays. Waste management, for example, involves interactions between consumer behavior, collection infrastructure, and market demand for recycled materials. Energy transitions, meanwhile, account for how policy incentives, technology adoption, and market forces influence one another over time. Traditional analysis tools often fail to capture these complexities, which can lead to unintended policy consequences. In the transition toward a circular economy, another layer of complexity is added. Circular economy is built on designing out waste, keeping materials in use, and regenerating natural systems. Implementing these principles requires understanding how materials, energy, and information move across multiple interconnected systems, such as waste, water, energy, and manufacturing. Each intervention, whether it is introducing recycling incentives, changing consumer behavior, or reforming production processes, can lead to multiple feedback effects over time. What is System Dynamics Modeling? Why is it Important for Development? System dynamics modeling is a way to make complexities visible. It maps material and resource flows, simulates how policies influence outcomes, and identifies leverage points to guide effective circular economy strategies that can deliver long-term benefits. The model uses causal loop diagrams (a representation of the variables and their interrelationships within the model), stock-and-flow diagrams (which show how things accumulate and how the speed of change determines their levels over time), and simulations (a virtual imitation of a system or process) to show how different parts of a system interact over time. For example, it can map how rising plastic consumption increases waste, how recycling reduces landfill pressure, and how policies ripple across the entire system. This way, system dynamics modeling supports evidence-based decision-making and helps development partners design more effective and integrated circular economy interventions. Figure 1: Example of a Causal Loop Diagram Source: Adapted from A Simulation Model to Analyze the Efficacy of Plastic Waste Management Policies. Chemical Engineering Transactions. In short, it allows development professionals to see the bigger picture, safely test scenarios, and anticipate outcomes when planning interventions. What Benefits Do System Dynamics Modeling Bring to Development Projects? Together with managing complex systems, development practitioners often need to make decisions in environments of uncertainty, with limited data, and under pressure to balance multiple goals at once. System dynamics modeling can help navigate these challenges by making relationships, trade-offs, and long-term impacts visible before costly interventions are implemented. Benefits of system dynamics modeling include: Holistic understanding: Captures feedback loops, time delays, and cross-sector interactions often overlooked in linear analysis. Policy testing without risk: Functions like a “flight simulator” for policy, allowing decision-makers to experiment with scenarios before implementation. Works with both data and expert input: Useful in data-scarce environments where stakeholder knowledge fills gaps. Supports collaboration: Visual diagrams make it easier for diverse stakeholders, from ministries to local communities, to engage in discussion. Long-term perspective: Highlights how short-term actions play out over decades, critical for climate, circular economy, and health systems planning. Multiresource optimization: Helps visualize how interventions in one resource stream (water, waste, or energy) can create opportunities or challenges in others, which can particularly support circular economy approaches, such as showing how recovering heat from waste processing can cut energy costs and emissions. How Do You Choose a Suitable System Dynamics Tool? There are many software platforms and approaches for building models, from commercial tools such as Vensim and Stella Architect, to open-source options in Python or R, to hybrid platforms such as AnyLogic that combine multiple modeling methods. Choosing the right one depends less on the tool itself and more on the purpose and context of the project. Here are 7 steps for choosing a suitable system dynamics tool. Step 1: Define the purpose When defining your model's purpose, consider whether you're looking to optimize specific resource flows, maximize circularity metrics, or demonstrate how circular approaches compare to linear ones. For circular economy applications, models that can track material flows, embedded emissions, and resource regeneration rates will be most valuable. Being clear on the purpose ensures that the model serves decision-making rather than becoming a purely academic exercise. Step 2: Identify stakeholders and users Consider who will engage with the model and what they need from it. A visual, user-friendly tool like Stella Architect works well in workshops with government partners while technical research teams may prefer more customizable, code-based platforms. Matching the tool to the audience increases buy-in and usability. Step 3: Match complexity to capacity Models range from simple causal loop diagrams to highly complex hybrid simulations. In many development contexts, simpler models are more practical since they can be maintained and understood by local partners. Overly complex tools may require skills and resources unavailable once donor support ends. Step 4: Assess data needs and availability Consider data requirements such as resource inflow/outflow measurements, recovery rates, freshwater saved through recycling, emissions erased through material substitution, or waste diverted from disposal. Some modeling methods work better in data-scarce environments using expert knowledge to fill gaps, while others require detailed time-series, survey, or geospatial datasets. Assessing data quality and availability prevents teams from committing to a tool that cannot be sustained. Step 5: Evaluate cost and sustainability Software licenses, training time, and technical support influence tool practicality. Open-source options may reduce costs but often require stronger coding skills. Sustainability means ensuring local partners can continue using the model after project funding ends. Tools such as Vensim and Stella can be used, but Stella is more user-friendly and lower-cost for novices. Step 6: Consider integration Ask whether the tool can connect with other analytical platforms such as geographic information systems, enterprise resource planning, life cycle assessment, or monitoring dashboards. Integrated tools allow decision-makers see cross-sector connections. For circular economy, linking life cycle assessment tools helps quantify resource efficiency and environmental impact (e.g., carbon dioxide is avoided when organic waste is converted to biogas rather than landfilled). Step 7: Pilot before scaling Run a small pilot exercise to test usability and output relevance. Piloting allows refinement of assumptions, input validation, and stakeholder trust-building. It avoids costly mistakes and demonstrates early wins that build momentum for scaling. How Can System Dynamics Modeling Help Analyze Plastic Waste Management Policies? A system dynamics model was developed (Figure 1) in a study to analyze the long-term effectiveness of various strategies and policies in reducing total plastic waste. Using historical data, they simulated the business-as-usual scenario and alternative strategies with varying results. Figure 2 shows the possible reductions over a 100-year period by implementing circular initiatives to increase awareness. Figure 2: Simulation of the Total Plastic Waste Reduction with Circular Economy Initiatives (tons) Source: A Simulation Model to Analyze the Efficacy of Plastic Waste Management Policies. Chemical Engineering Transactions. Results showed that continuing the current path leads to high waste accumulation, while upstream interventions are most effective at reducing plastic waste. The study demonstrates system dynamics modeling’s potential for policy design and informed decision-making. What Opportunities and Challenges Exist When Applying System Dynamics Modeling? System dynamics modeling presents major opportunities. It can transform decision-making by showing policymakers the “whole system,” revealing relationships and trade-offs, and clarifying long-term impacts of short-term choices. It also helps donors and governments align strategies across ministries, ensuring interventions in one sector, such as agriculture, do not harm another, such as water or energy. With digitalization advances—including machine learning integration, Monte Carlo simulations for uncertainty, and Digital Twins for real-time monitoring—make these tools increasingly powerful and accessible. Obstacles remain. System dynamics modeling requires specialized expertise, and many organizations lack in-house modelers. Insight quality depends on data and assumptions, which can be challenging in data-scarce contexts. Tools can be resource-intensive, with steep learning curves and sustainability risks once project funding ends. Models can also be misunderstood as forecasts rather than explorations of possible futures, requiring careful communication. For development practitioners, the message is clear: system dynamics modeling is a powerful tool. Structured use of these models enables smarter policies, better risk anticipation, and more resilient and impactful outcomes. Ask the Experts Piya Kerdlap Founder and Managing Director, PXP Sustainability Piya Kerdlap is a sustainability scientist and international development professional. He has 10 years of experience in life cycle assessment, financial modeling, and circular economy in Southeast Asia. He is an ADB consultant on the Promoting Action on Plastic Pollution from Source to Sea in Asia and the Pacific technical assistance project. He obtained his bachelor’s degree in environmental science from the University of North Carolina at Chapel Hill and PhD in mechanical engineering from the National University of Singapore. Marios Kostis Environmental Technology Specialist, Operational Tools and Digital Initiatives Hub, Seureca Marios Kostis specializes in integrating advanced technologies into environmental operations—from AI-powered utilities management to circular economy digital strategies. He has deployed sensor networks and predictive tools and carried out extensive technology monitoring while coordinating cross-functional teams. Building on this hands-on experience, he designs and implements digital transformation road maps that optimize operations and reduce costs across international projects. Vincent Aloysius Team Leader and Circular Economy Specialist, Seureca Vincent Aloysius is the team leader for Seureca, the consulting firm for ADB’s technical assistance project, Promoting Action on Plastic Pollution from Source to Sea in Asia and the Pacific. Previously, he was program management officer for the United Nations Environment Programme’s SEA Circular Project, focusing on markets and value chains. He led initiatives on innovation, city-level actions, capacity building, finance and business models, and policy advancement. He has extensive private sector experience in environmental services, resource recovery, and waste management. Asian Development Bank (ADB) The Asian Development Bank is a leading multilateral development bank supporting sustainable, inclusive, and resilient growth across Asia and the Pacific. Working with its members and partners to solve complex challenges together, ADB harnesses innovative financial tools and strategic partnerships to transform lives, build quality infrastructure, and safeguard our planet. Founded in 1966, ADB is owned by 69 members—49 from the region. Follow Asian Development Bank (ADB) on Leave your question or comment in the section below: View the discussion thread.