  Climate change is threatening human health in unprecedented orders and many
ways. These threats are expected to grow unless effective and evidence-based
policies are developed and acted upon to minimize or eliminate them. Attaining
such a task requires the highest degree of the flow of knowledge from science
into policy. The multidisciplinary, location-specific, and vastness of
published science makes it challenging to keep track of novel work in this
area, as well as making the traditional knowledge synthesis methods inefficient
in infusing science into policy. To this end, we consider developing multiple
domain-specific language models (LMs) with different variations from Climate-
and Health-related information, which can serve as a foundational step toward
capturing available knowledge to enable solving different tasks, such as
detecting similarities between climate- and health-related concepts,
fact-checking, relation extraction, evidence of health effects to policy text
generation, and more. To our knowledge, this is the first work that proposes
developing multiple domain-specific language models for the considered domains.
We will make the developed models, resources, and codebase available for the
researchers.
