About The Program
Data on plastic production and waste management across South and Southeast Asia is missing, inconsistent, or unreliable because the region’s complex plastics value chains are fragmented and reliant on the informal economy. The result is limited visibility and transparency as well as an inability to effectively track, monitor, forecast, or optimize material flows and reduce ocean leakage. Technologies, models and data science being applied in other complex systems can be adopted into plastic waste management and revolutionise Asia’s circular economy.
The Plastic Data Challenge, The Incubation Network, and the members of the Semi-Finalist cohort below have achieved the following:
Built a diverse, collaborative global community around plastics data that included an advisory council of 23 thought-leaders across the data science and circular economy, 27 Mentors & Local Advisors from 11 countries, 20+ design-support partners, 30+ Private Sector Partners, 92+ Innovators from 39 Countries currently tackling this issue, and 15+ NGOs, Government and University Partners. More awe-inspiring than these numbers is the collaboration among both likely and unlikely teams that occurred across these groups — from mentors, experts, entrepreneurs, capital providers, partners, coaches, and corporate leaders across sectors.
Increased awareness of the data gap in Waste Management & Recycling in Asia and available solutions to address these challenges by bringing attention to this cause through the ever-expanding community as well as curated storytelling on this topic. The challenge will also be producing a market research report shared later this year to share key insights, best practices, and opportunities identified throughout in this space.
Supported the development and implementation of ten innovative pilots for data-driven solutions across the region. The Incubation Network team and a design partner developed a custom framework for pilot-readiness that was used to guide the cohort of Semi-Finalists through a three-month pilot-readiness program. This program focused on developing these company’s pilot’s market fit and design, resilience and risk awareness, as well as their connectivity to local and strategic partners through an assessment process, virtual innovation summit, and six-week mentorship program. All companies graduated this program with enhanced networks of support, clarity of their pilot design and action plan, and the confidence to implement their pilot in the region.
While The Incubation Network will continue to support the cohort of innovators in their piloting journey, the challenge selected three finalists that displayed strong “pilot-readiness”. These three innovators have the most actionable and market-ready pilot models, and will receive an additional US$10,000 in addition to further technical support from us:
At Clearbot, our vision is to empower local governments and communities with cutting-edge technology in order to keep plastic waste out of our waterways.
Clearbot is a swarm of trash collecting robots that use AI-Vision to detect and collect trash from bodies of water. Clearbot is 15x cheaper, has 5x more reach and removes 2x more trash daily to current methods.
The recovery of post-consumer waste in the developing world is driven by the informal waste ecosystem.
Currently, municipalities, multi-national brands and waste management companies struggle to integrate these stakeholders into the formal waste management system.
Kabadiwalla Connect helps leverage a city’s existing informal waste infrastructure in the collection, processing and management of municipal waste-streams.
Siklus delivers consumer products via refill tricycles to low-income communities with the objective to minimize packaging and plastic waste.
Our mission is to reduce plastic waste and make everyday necessities more affordable to low-income customers. We use an app to collect data on plastic waste leakage, optimize our supply chain and guide our expansion to maximize impact and aim to be the new way to Warung: mobile, sustainable and data-driven.