ReCircuit Research Whitepaper

Global e-waste reached 62 billion kg in 2022, with only 22.3 percent documented as formally collected and recycled in an environmentally sound manner. The same monitor projects 82 billion kg of e-waste by 2030 under current trajectories [1]. These figures describe more than a waste stream; they describe a global information failure around products, materials, accountability, and recovery choices.

Critical minerals make the recovery problem strategically important. The International Energy Agency identifies copper, lithium, nickel, cobalt, graphite, and rare earth elements as central to clean energy supply chains and notes that market transparency, supply concentration, and ESG risk are policy priorities [2]. Recycling and urban mining cannot remove the need for primary mining, but they can create secondary supply, reduce waste, and improve resilience [3].

Circular economy theory emphasizes keeping products and materials in circulation at their highest value through maintenance, reuse, refurbishment, remanufacture, and recycling [9]. Electronics recovery is difficult because product condition, component value, hazardous material risk, repairability, data security, and market demand must be evaluated quickly and consistently.

Policy is also moving toward product-level data. The European Commission describes the Digital Product Passport as a digital identity card for products, components, and materials, intended to support sustainability, circularity, and legal compliance [4]. ReCircuit's research direction aligns with that shift: recovery systems need structured records that can move with products and materials.

The end-of-life electronics ecosystem is not one system. It is a chain of partial systems: consumer drop-off, corporate asset disposition, local collection, informal aggregation, repair shops, refurbishers, data sanitization providers, logistics vendors, recyclers, smelters, compliance teams, and auditors. Each node may know something important, but the evidence often fails to travel.

Visibility is weak because device identity and condition are not consistently captured at intake. Traceability is weak because the record of movement, custody, and processing is often separated from the physical asset. Decision making is weak because repair, resale, parts harvesting, recycling, and destruction are evaluated through local heuristics rather than a shared intelligence layer.

Compliance is becoming harder because sustainability claims require evidence. The GHG Protocol Scope 3 Standard provides a global methodology for value chain emissions accounting and identifies upstream and downstream categories beyond a company's direct operations [5]. ISO 14064-1 specifies principles and requirements for organizational GHG quantification and reporting [6]. Recovery platforms must therefore separate measured facts from estimates, confidence from proof, and operational convenience from audit evidence.

The ReCircuit platform vision is organized around a simple object: the recoverable asset. A recoverable asset may be a phone, laptop, PCB, battery pack, accessory, server component, or mixed electronic item. The platform does not treat the asset as anonymous waste. It builds a structured record around identity, photos, condition, component signals, safety flags, ownership context, location, market pathway, estimated emissions effect, and compliance evidence.

The intelligence layer then compares possible circular pathways: repair, reuse, resale, refurbishment, component harvesting, certified recycling, or secure destruction. Each pathway has a different economic, carbon, safety, data security, and compliance profile. ReCircuit's proposed contribution is to make that comparison explicit.

The long-term platform is not a black-box sustainability score. It is a decision system with inputs, outputs, assumptions, confidence levels, evidence requirements, and human review.

Computer vision can classify device category, visible condition, corrosion, cracked glass, missing components, battery swelling risk indicators, and component candidates. Forecasting can estimate route demand, collection density, seasonal inflows, and market movement for recovered parts. Recommendation systems can compare circular pathways using rules, model outputs, price signals, and processor constraints.

Large language models can assist with document understanding, policy search, user guidance, record summarization, and ESG narrative drafting, but they must not become the source of truth for measured facts. In ReCircuit's design, LLM outputs are advisory and cite the underlying asset record, policy source, or user-provided evidence.

Carbon estimation is treated as a model-backed estimate, not a proof claim. It can use device category, weight, transport route, recovery pathway, emissions factors, and confidence intervals to help compare options. Any public or customer-facing claim would require methodology review, factor provenance, and audit-ready documentation.

Consumers need simple intake, pickup guidance, device preparation instructions, and trust signals about where a device is going. Businesses need bulk upload, asset traceability, data security evidence, reporting exports, and workflow approvals. Recyclers need operational triage, batch planning, contamination and safety flags, route prioritization, and downstream records. Governments need anonymized program visibility, policy metrics, compliance views, and infrastructure planning.

ReCircuit's proposed enterprise platform is therefore multi-sided but not unfocused. The shared primitive is the recoverable asset record. Each stakeholder sees a different surface over that record: intake, operations, compliance, public program performance, or research analytics.

The platform should be designed for conservative claims. Dashboards must distinguish measured data, modeled estimates, pending evidence, human overrides, and unavailable data. This is especially important for ESG reporting, where weak evidence can create reputational and regulatory risk.

A realistic ReCircuit business model could combine software subscriptions, per-asset processing fees, API usage, compliance report generation, analytics modules, and implementation services for organizations with high electronics turnover. Government or campus programs could use procurement or grant-supported pilots where the objective is infrastructure learning rather than immediate enterprise sales.

The model should avoid dependency on unverified carbon credit revenue or inflated materials arbitrage. Early revenue, if pursued, should be tied to measurable utility: intake automation, operational prioritization, evidence generation, reporting time reduction, and improved recovery routing.

Technical risk includes poor image quality, dataset bias, device diversity, safety-critical misclassification, weak materials estimation, and model drift. Financial risk includes long enterprise sales cycles, low willingness to pay in waste operations, and high integration costs. Regulatory risk includes changing definitions of sustainability claims, waste handling rules, product passport requirements, and cross-border movement controls.

Execution risk is significant because ReCircuit is founder-led and early-stage. The platform vision spans AI, logistics, compliance, climate accounting, and enterprise workflow design. That scope must be sequenced carefully. Security risk includes sensitive asset information, serial numbers, location data, business inventory, and potentially personal data on devices. AI limitation risk includes hallucinated policy summaries, overconfident recommendations, and false precision in emissions estimates.

The mitigation strategy is staged development: narrow prototype, transparent assumptions, human review, source-linked records, conservative claims, security-by-design, and independent validation before any public performance claim.

Mohnish M is the founder of ReCircuit and House of Mohny. He is a Computer Science Engineering student building at the intersection of AI systems, product engineering, climate technology, and circular economy infrastructure.

ReCircuit reflects a research-led founder thesis: the circular economy needs better intelligence infrastructure before recovery systems can scale with trust. The current work focuses on public research, product architecture, prototype development, and clear communication of what has and has not been built.