Global Climate Action: Evaluating City, Region, and Corporate Climate Contributions

This project tracks emissions reduction commitments and progress by non-state actors.

Objectives:

Mapping subnational climate mitigation strategies using meta-analysis approach
Quantifying the “urban wedge” using machine learning approaches to systematically analyze themes, patterns, and linkages in NSA policy strategies.
Evaluating the full supply chain climate mitigation actions
Establishing the ‘direct effect’ theory of NSAs to global climate mitigation to understand the virtuous cycle of subnational climate mitigation actions as catalytic agents

Partners: Utrecht University, CDP, NewClimate Institute, Oxford Net-Zero, German Institute of Development and Sustainability, Radboud University

Funders: IKEA Foundation, National Science Foundation

Contacts: Angel Hsu, PhD, Zhi Yi Yeo, Katherine Burley, Kaihui Song

The Global Climate Action project is an ongoing collaboration between researchers and policymakers to measure and track corporate and subnational actors’ (collectively, non-state actors) climate ambition and progress and the efficacy of varying climate initiatives. The goal of the Global Climate Action project is to give researchers and policymakers a clearer sense of how these non-state actors are contributing to global climate mitigation efforts through innovations in measurement, modeling, and aggregation.

The Paris Agreement in 2015 formally recognized multiple levels of government and private and civil society actors and their role in mitigating global climate change, representing a shift in the global climate governance paradigm from a predominantly top-down, nation-state centric approach to a polycentric network of actors. Many subnational efforts exceed their national regulatory counterparts, suggesting that these subnational jurisdictions are able to adopt better targets than national governments. Bottom-up policies and climate solutions are promising catalysts of virtuous cycles that enhance the ambition of policy efforts at all levels. Evaluating subnational actors’ contributions to global climate mitigation and mapping this polycentric mode of governance requires an empirical foundation. This research project builds on the PI’s prior work developing new data science methods, which frequently use large-scale datasets and machine-learning techniques to answer: 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? Each research component will incorporate 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. The project will develop large-scale, spatially-explicit, open datasets, and we will work to ensure methods and models are scalable, reproducible, and adaptable to various locales and political contexts.

Results: