Interpreting low-carbon transition at the subnational level: Evidence from China using a Natural Language Processing approach

Abstract: Subnational governments play an important role in low-carbon transitions around the world. However, the concept of low-carbon transition requires detailed interpretations. Our study sheds light on this important gap by drawing evidence from China's low-carbon provincial and city pilots (LCPCs) using three natural language processing algorithms. Our study found that low-carbon transition was interpreted collectively as an economy-centered process. Five primary foci and the corresponding clusters were identified, which indicate the preferred transition pathways of the LCPCs and imply that the pilots recognized no “one size fits all” transitions. Innovative interpretations were found among LCPCs, which were largely embedded in local contexts and tied to unique local resources and actors. However, innovative interpretations with local embeddedness might not translate into effective policies. And "just transition" has been largely missing from the interpretation. The concept of low-carbon transition may still remain vague to the subnational policymakers at a more detailed level.