How Digital Tools Accelerate Circular Economy Transitions in Asia and the Pacific

Circular economy is built on designing out waste, keeping materials in use, and regenerating natural systems. Achieving this vision requires data, foresight, and the ability to test interventions before scaling. Digitalization provides these capabilities. Tools such as sensors, mobile platforms, artificial intelligence (AI), and modeling software give governments and businesses visibility into the use of resources and material flows—allowing them to ask “what if” questions on interventions such as banning single-use plastics or investing in new recycling infrastructure and get evidence-based answers before committing scarce resources.

Digital tools are especially powerful when applied across interconnected resource systems such as water, waste, and energy. Sensors monitoring water quality in treatment facilities can simultaneously track resource recovery potential, while waste management platforms can identify opportunities to transform discarded materials into energy inputs, creating a truly integrated circular resource model.

In many developing member countries, policies and strategies for transitioning to a circular economy exist, but implementation and monitoring lag behind. Digital tools bridge this gap by turning policy ambition into measurable action. For example, waste tracking platforms can ensure that materials collected for recycling are actually processed, while digital twins can simulate resource flows in real time, helping policymakers and businesses test different interventions before scaling them.

Using digital modeling and simulation, donors can strengthen accountability, reduce program delivery leakages, and align circular economy interventions with broader goals such as inclusive growth, resilience, and climate action.

Dynamic systems modeling has become a powerful way to understand circular economy challenges and opportunities. Several approaches are widely used:

• System Dynamics models how different factors (e.g., population, consumption, and recycling) influence each other over time—ideal for testing long-term policy effects on resource use or pollution.
• Agent-Based Models simulate the behavior of individuals (e.g., consumers or firms) to see how micro decisions drive systemwide outcomes.
• Discrete Event Simulation models operational processes, such as recycling plant operations or waste collection routes.
• Hybrid Models combine methods for a holistic view (e.g., linking consumer adoption with national policy impacts).

When integrated with machine learning, artificial intelligence, or digital twins, these models provide real-time updates and predictive insights that support responsive policymaking.

Figure 1: Approaches for Dynamic Systems Modeling and Their Applications

Source: Authors.

Several case studies illustrate how modeling is already shaping circular strategies.

• Marine debris in Taipei,China. A hybrid system dynamics and Monte Carlo model showed that no single intervention is sufficient. Complementary policy bundles—raising public awareness, restricting plastics production, and improving waste management—produce the most effective outcomes.
• Plastic waste policies. Simulation models found that reactive measures such as cleanup campaigns slow pollution but do not reverse it. Proactive interventions, such as reforming plastic production and eco-design, achieve lasting reductions in waste accumulation.
• Grassroots circular economy in Indonesia. A causal loop diagram helped a dairy farmer group turn cattle waste into fertilizer. Modeling revealed which support measures, such as farmer training and consistent government support, were critical to sustaining the system.

These cases demonstrate that digital tools can help developing member countries test strategies, identify leverage points, and avoid costly trial-and-error approaches.

Digital tools add value in three ways:

• Efficiency and cost savings. AI-assisted waste sorting increases recycling yields, while simulation models help optimize waste collection routes or recycling plant operations. Studies show a 50% increase in efficiency from manual to AI-sorting and up to 44% cost savings through waste collection routes optimization.
• Inclusivity and market creation. Digital platforms for secondary materials connect small businesses with buyers, creating new income opportunities. For example, 65,273 metric tons of secondary raw materials were traded on SECONTRADE.
• Accountability and transparency. Donors and governments can track outcomes in real time, making it easier to measure results and justify policy actions and investments. United Kingdom’s 2026 Digital Waste Tracking system aims to reduce waste related crimes of up to £1 billion per year.

In development contexts, digital tools help projects become more efficient, inclusive, and credible. Advanced digital applications further support resource management across sectors:

• Hypervision systems centrally monitor and optimize multiple resource flows (energy, water, waste) simultaneously, achieving energy savings.
• AI-powered leak detection in water networks combines satellite imagery with machine learning to identify potential leaks early, preserving precious water resources and reducing infrastructure damage.
• Digital material marketplaces connect waste generators with potential users, turning previously discarded materials into valuable inputs for other processes.

Challenges include limited digital infrastructure, high upfront costs, data gaps, lack of interoperability, and limited technical capacity.

Without strong governance frameworks, even the best tools may fail to achieve impact. Donors and multilateral organizations are critical in addressing institutional barriers by providing technical assistance, governance support, and long-term capacity building.

Development partners are uniquely positioned to address the gaps. They can:

• Invest in digital infrastructure and skills training, ensuring that developing member countries can access and apply advanced tools.
• Fund pilot projects that showcase the value of digital circular economy solutions and catalyze wider adoption.
• Integrate modeling tools into project monitoring and evaluation systems to track circular economy performance and development outcomes over time.
• Introduce requirements for digital tools early in the procurement and project preparation process. Embedding modeling and digitalization into the earliest stages of policy support or investment projects ensures that donors and governments can test scenarios, select the best interventions, and set up performance metrics from the outset.
• Promote regional cooperation, harmonize data standards, and support cross-border trade in secondary materials.
• Strengthen stakeholder engagement and align interventions with broader development priorities.