Better modeling to inform climate policy decision gaps

Better modeling to inform climate policy decision gaps

As climate change radically reshapes our environment, existing computational models and the data that underpin them for understanding trends and making decisions, are no longer sufficient.

Current models fail to account for the uncertainty introduced by people’s behavior, because they study environmental variables in isolation and are primarily developed with data from the Global North. This decision-making gaps could be addressed by integrating datasets from the social and behavioral sciences and policy datasets into climate simulations. However, there are still substantial data gaps about public preferences, concerns and opinions around climate policies or climate technologies. Collective intelligence methods like data observatories that capture real-time data about public behavior and attitudes could be used by decision-makers to improve mapping between climate policies and behavioral outcomes to fill decision gaps around adaptation planning.

Climate models can also be designed together with local stakeholders – this is particularly important when modeling the behavior of different stakeholders in relation to natural resource management. Participatory modeling is a promising collective intelligence method that uses models to create a shared understanding of complex climate policy issues. It involves convening different groups with competing priorities to agree on an action plan. This approach could be vital for getting buy-in for local level decisions around natural resource management.


How might collective intelligence address decision-making gaps with modeling?

COVID-19 demonstrated the value of real-time public data observatories for insights into population-level behaviors unfolding in real time. In Germany, the COVID-19 Snapshot Monitoring (COSMO) initiative used crowdsourcing, surveys and social media data to generate a dataset about perceptions of risk, as well as understanding and behaviors undertaken by the public during the pandemic.This was used within government modeling of outcomes to explore the potential impacts of different policy decisions on the spread of disease. Likewise, the COVID-ZOE app used citizen science to collect data about real-time changes in people’s behaviors and was used to inform pandemic policy in the UK. The same methods could be used to generate data about responses to acute environmental crises and simulate the behavioral impacts of different climate policies.

Agent based modeling (ABM) is a modeling technique that allows decision makers to explore intersecting environmental systems and behavioral data. To date, the use of ABMs for policy has mostly been confined to modeling pandemics and climate-related disasters but there is potential to expand its use to anticipate the interaction between public attitudes, behavior and climate policy. For example, a proof of concept initiative in Haiti used crowdsourced geographic information and other publicly available data to model population movements in the immediate aftermath of disasters. In the future, models like this could support governments and first responders to explore different scenario options for aid distribution to inform better adaptation planning.

Participatory modeling uses a combination of computer simulations, role-play and collective decision making to make future impacts of present-day actions more tangible. In the Philippines, an initiative delivered by the company Deltares used participatory modeling with decision makers from several government departments and organizations in charge of public utilities to design an integrated water management plan for the Tacloban river basin, a region increasingly at risk of water shortages. Another example is where the Muonde Trust, a community-based research organization, partnered with international researchers and local stakeholders to apply participatory modeling to land management in Mazvihwa Communal Area, Zimbabwe. In this initiative, smallholders, local villagers and officials contributed data and helped to design models that represent the current state of the land. This helped to simulate more realistic options for cropland management and ultimately, allowed the groups to reach agreement on future adaptation strategies.