Collective Intelligence in Climate Mitigation

Collective Intelligence in Climate Mitigation

Mitigation-based collective intelligence projects in the Global South tend to address doing and data gaps, and span across different geographical scales. For example, several forestry initiatives are international programs where common, well-tested protocols for environmental observation are applied by local level initiatives. Alongside a contribution to coordinating actions, these initiatives help to fill data gaps by monitoring progress on a single issue in a standardized way. In contrast, waste management initiatives tend to respond to specific local needs, helping to better coordinate actions between diverse stakeholders in cities. These examples also capture data about the scale of the problem, particularly plastic pollution, or bring additional value by surfacing data about invisible or informal contributions to the waste management ecosystem. 

Examples of climate mitigation initiatives:

Below is summary overview of the three climate mitigation areas where most current collective intelligence practice is concentrated, alongside the key methods and climate action gaps that are addressed.

IPCC mitigation categories enabled by collective intelligence

Collective intelligence methods being used

Main climate action gaps being addressed

Ecosystems restoration, reforestation, afforestation / Reduced conversion of forests and other ecosystems

Crowdsourcing and remote sensing for forest protection

  • Data gaps on real-time threats and long-term trends of forest loss.
  • Distance gap through volunteer-led data analysis to fast track scientific research.

Microtasking and digital tools to scale collective action

  • Doing gaps around piecemeal,  local actions that fail to connect to global tree-planting targets.

Combining sensor data and microtasking for intelligent networked actions

  • Doing gaps around uncoordinated community activities for forest and other land-use restoration.
  • Diversity gap (Indigenous and traditional knowledge) to make more locally-appropriate decisions about interventions.

Waste minimisation, reduction and management

Crowdsourcing and combining datasets to monitor global waste

  • Data gaps about the precise location, quantity, type and origins of plastic litter.
  • Doing gaps around lack of accountability and persistence of behaviors that cause waste build up.
  • Distance gap around lack of reliable open data about waste that can be compared between countries.

Remote sensing and citizen science to manage marine litter

  • Data gaps about the scale, types and origins of plastic litter on coastlines.
  • Data gaps about hotspots where marine litter is concentrated.
  • Doing gaps around accumulation of marine litter.
  • Distance gaps around the consequences of plastic waste.

Citizen generated data and coordinated actions to manage urban waste

  • Data gaps about the quantity of and categories of municipal waste at the street level, as well as hotspots of waste build-up.
  • Data gaps around contributions of informal waste pickers.
  • Doing gaps around how to prioritize limited waste services and waste mismanagement.


1 23 case studies out of 106 analyzed. 

2 Although activity in these areas has a direct impact on the reduction of carbon emissions, they also have clear complementarity to adaptation actions described in the previous section. For example, waste management practices are often coupled with localized disaster risk reduction activity while localized ecosystem restoration projects are often implemented alongside biodiversity management adaptation. 

3 We have grouped Ecosystems restoration, reforestation, afforestation and Reduced conversion of forests and other ecosystems together, as forest-based initiatives often make contributions to both of these IPCC categories.