Catalyzing virtuous cycles of climate action: an empirical model of polycentric climate governance

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Nations and international governing bodies are traditionally viewed as the primary actors working to mitigate global climate change. Yet subnational jurisdictions, such as states and cities, and non-state entities, such as businesses and civic organizations, have become increasingly important actors in efforts to reduce climate-warming greenhouse gas emissions and stem climate impacts. This research project uses new data science methods to answer essential questions about this emerging diverse climate landscape, analyzing which subnational government climate policies and strategies translate to measurable emissions reductions; determining where and how these policies and initiatives perform regarding emissions; isolating the conditions that allow urban climate actions to create virtuous cycles of interaction and raise ambition nationally and internationally. This research also creates new data science methodologies and informational frameworks that strengthen the scientific basis of non-state and subnational contributions to global climate governance and help answer fundamental questions regarding the efficacy of current frameworks to address climate change. Subnational (i.e., cities and states) and private actors’ engagement in international processes and frameworks devoted to mitigate climate change (e.g., the 2015 Paris Agreement) represents a shift in the global climate governance paradigm from a predominantly top-down, nation-state centric approach to a polycentric network of actors. Subnational and non-state actors’ ability to catalyze and enhance climate actions towards global goals is unknown, largely due to a lack of relevant empirical data. This research project produces new evidence describing subnational actors’ contributions to global climate mitigation and governance, lending empirical bases for multi-level, polycentric climate governance theories that are so far untested. Three broad questions are examined: what subnational government climate policies and strategies translate to measurable emissions reductions? Where and how are these policies and initiatives performing, considering the total value (i.e., upstream and downstream) and embedded carbon chains? What conditions enhance urban climate actions’ ability to create virtuous cycles of interaction and raise ambition nationally and internationally? The project develops large-scale, spatially-explicit, open datasets using innovative Machine Language techniques to collect policy data and earth observation (EO)—the practice of collecting data on Earth's biological, physical and chemical processes typically through the use of satellite remote sensing technologies. The new data on climate change policies and practices and emission reductions used to estimate the effect of subnational governments and organizations seeking to mitigate the climate effects of emissions; only limited similar data currently exists, and that which does tends to neglect the Global South. Special considerations are given so that methods and models are scalable, reproducible, and adaptable to various locales and political contexts. Each research component incorporates equity and justice considerations, with continuing evaluation of where each dataset, case study, and model may not be attuned to policy inclusiveness and promoting equity.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.