Citizen Science: How Big Plastic Count, RECLAIM’s Recycling Data Game use this approach to tackle plastic waste

Mobilizing citizen science can play a pivotal role, generating data, raising awareness, engaging communities and driving policy action. 

Urgent actions by multiple stakeholders are needed across the plastic value chain to tackle plastic
pollution, addressing both the production of plastic and sustainable management of waste. One crucial
step could be mobilising citizen science as a decisive tool to generate data, raising awareness, fostering
community engagement, and advocating for policy action.

Global plastic production is staggering, with close to 350 million tons produced each year. But what
happens to this plastic once it is thrown away? According to the World Bank, nearly 70 per cent of this
plastic volume ends up in landfills or is incinerated, merely 10 per cent is recycled, while the remaining
20 per cent is released into the environment.

Shockingly, nearly 1.7 million tons of this plastic have found its way into the oceans, as reported by the
OECD. The urgency of addressing this issue is underscored by projections from the latest “Global Waste
Management Outlook (GWMO) 2024
” report, launched at the #UNEA6 in Nairobi, Kenya, indicating that
municipal solid waste (MSW) generation is expected to increase from 2.3 billion tonnes in 2023 to 3.8
billion tonnes by 2050.

To tackle this plastic waste crisis and hold governments and organisations accountable for their
contribution to the mess, the UK’s week-long Big Plastic Count initiative, between 11 and 17 March,
encourages citizens, civil society, and educational institutions to join forces in counting and
characterising plastic waste they toss into their bins for a week. The initiative’s very own plastic ID tool
educates participants about how to categorise a piece of plastic, thereby increasing the level of public
knowledge and later use this knowledge to advocate for better policies and regulations to stem plastic

By employing a citizen science approach, the initiative aims to gather data on plastic consumption and
waste, to advocate for more evidence-based policies that promote sustainable waste management
practices to foster a circular economy.

Citizen science methodology can inform evidence-based policy decisions and drive positive
environmental outcomes. Initiatives, such as the Big Plastic Count UK, and RECLAIM’s Recycling Data
Game (RDG)
are steps in that direction.

RECLAIM Project’s RDG, much like the Big Plastic Count, employs citizen science methodology to collect
scientific data to train Artificial Intelligence (AI) for effective material recovery, thereby promoting better
material sorting based on accurate identification of stuff.

Also read: RECLAIM’s role in sustainable waste manangement (LINK)

(Focus on RDG game specifics) In the RDG, developed by the University of Malta, citizens take part in
project activities by participating in a game to identify different types of post-consumer waste, training
the AI-module to become more effective in identifying plastic waste for recycling and waste
management. With the help of gaming and human inputs, player interaction is used to generate data,
that feeds in new knowledge to the AI algorithm in turn generating new challenges for users and this
iterative feedback loop helps retrain the AI Module. But this process is also used to introduce users to
the fundamental concepts related to deep learning and ethical AI.

The RDG has three primary aims: fostering awareness and heightening social sensitivity toward recycling
processes, implications, and challenges; engaging citizens in data analysis and tackling scientific
challenges; and finally, involving citizens in material recovery research and project activities.

Read: Recycling as we know today (LINK)

In doing so, RECLAIM Project is actively cognisant of the equity, inclusivity, diversity and transparency in
relation to data collection and ethical AI. For instance, the project will incorporate training programs for
non-expert users of AI, data and robotics systems, including the basic concepts of trustworthy and
ethical AI. It will also pay special attention to including users of diverse age, gender and background to
reduce biases and increase fairness to ensure diverse and representative datasets.

WATCH VIDEO: RECLAIM’s initial prMRF tests are successful

RECLAIM’s citizen-science approach empowers citizens to actively engage in material recovery tasks,
leveraging their intelligence and knowledge to train AI modules for more precise post-consumer waste
sorting. While accelerating the transition to a circular economy requires departing from business-asusual practices, it’s crucial to recognise that various strategies are needed to work for different contexts.
While AI can enhance sorting methods, it’s not intended to replace human labour but rather
complement the process. No single solution fits all contexts; rather, inclusivity is key, employing
strategies that best suit each specific context.

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