The language of climate resilience: A value chain that is inclusive of human experiences

This article is sponosred by Booz Allen Hamilton

The impacts of climate change — an increasing number of more intense hurricanes, droughts, heatwaves and so on — traverse freely across geographic, social and political hierarchies. To create resilient communities, climate action needs to transcend the limits of current thinking, where the solutions get to the heart of the core issues and human experiences.

Communities and organizations alike are increasingly accepting that the climate is changing. Climate change is causing an increase in extreme weather events at the macro-scale, and it will incrementally affect key climate measures, such as temperature and precipitation, which affect the quality of life, the resilience of built infrastructure and our multi-sector economies. Ultimately, these impacts are felt by people and are often disproportionately distributed across communities and socioeconomic boundaries.

For many, this is triggering an "all hands-on deck" mentality, and there has been an influx in action. At the state and local levels, organizations are establishing new leadership roles, such as the chief resilience officer, to increase their focus on resilience planning and implementation. These groups are developing their own formalized resilience plans and playbooks, while alliances to address these challenges are forming across diverse groups at local, national and global levels.

To effectively anticipate future challenges, mitigate risk and improve resilience, these groups will have to solve nontrivial coordination and collaboration problems. Across the world, groups are often left asking: Who else is facing similar challenges? What are others doing to solve these problems? Climate change is a daunting multi-generational problem — one that can only be alleviated when we achieve true economies of scale and get to the heart of our needs for improved resilience, sustainability and justice.

These broad questions lead to more precise questions for the resilience leaders to address the root causes: What other localities around the globe have similar geographic, cultural, economic and resource characteristics? What partnerships and procurements optimize my resilience plan, particularly considering my budgetary and locality constraints? What did we learn from the last set of extreme weather events and disaster responses? While we can answer some of these questions with traditional climate datasets powered by ground systems, advanced sensors and models, we still need to understand people’s narratives. The language of climate resilience is more comprehensive than we think.

To extract actionable insights from our stories, past experiences and individual plans, we must contextualize them against the backdrop of others. This is where advanced technologies such as artificial intelligence (AI) and machine learning (ML) can help.

Large language models, headlined by the now infamous ChatGPT, have garnered significant attention. These are deep learning algorithms and machine learning algorithms that use deep neural networks, trained on unstructured data. These models can then be used for a wide range of tasks, including content summarization, translation and prediction.

These and other models within the field of natural language processing (NLP) have massive implications for climate resilience. As each new climate challenge is identified, communities

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