Reducing global emissions will require a global cosmopolitan culture built from detailed attention to conflicting national climate change frames (interpretations) in media discourse. The authors analyze the global field of media climate change discourse using 17 diverse cases and 131 frames. They find four main conflicting dimensions of difference: validity of climate science, scale of ecological risk, scale of climate politics, and support for mitigation policy. These dimensions yield four clusters of cases producing a fractured global field. Positive values on the dimensions show modest association with emissions reductions. Data-mining media research is needed to determine trends in this global field.

Sub‐national governments are a crucial non‐state actor for mitigating climate change. This importance has recently strengthened as increasing numbers of municipalities declare net‐zero carbon emission goals to support the Paris Agreement, often well ahead of national governments. In Japan, net‐zero declarations have also diffused widely, with nearly 800 declarations appearing in domino‐like fashion over 2019–2022. To elucidate the factors that propelled this rapid diffusion, we used an event‐history analysis based on data from a survey to develop an integrated statistical model. We then deepen understanding of diffusion mechanisms through seven brief case studies informed by interviews and document analysis. We find that the drivers of policy diffusion varied over time. During the early stage, internal factors drove the spread of declarations; namely participation in transnational city networks, endowed human and financial resources, and political leadership. But in later stages, diffusion was mostly propelled by external factors; namely declarations by neighboring cities and the affiliated prefectural government. Through these findings, we contribute to scholarship through two novel perspectives. First, we reveal the factors driving policy diffusion across both early adopters and the ordinary majority. Second, we show how the influence of well‐known factors can vary during different stages of policy diffusion.

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