Published February 9, 2021 | Version v1
Journal article Open

Diverse climate actors show limited coordination in a large-scale text analysis of strategy documents

  • 1. University of North Carolina-Chapel Hill
  • 2. University of Chicago

Description

Networks of non-state actors and subnational governments have proliferated since the Paris Agreement formally recognized their contributions to global climate change governance. Understanding the ways these actors are taking action and how they align with each other and national governments is critical given the need for coordinated actions to achieve ambitious global climate goals. Here, we present a large analysis (n = 9,326), applying large-scale natural language processing methods and social network analysis to the climate strategy documents of countries, regions, cities and companies. We find that climate mitigation in employee travel and office operations, green building standards, and municipal and citizen actions are common themes in climate actions across companies and city and regional governments, whereas approaches to setting targets in specific sectors and emissions scopes are more diverse. We also find links between the strategies of regions and countries, whereas companies are disconnected. Gaps in climate action for most actors include adaptation and consumption/supply-chain emission reduction efforts. We suggest that although actors may appear to be self-organizing and allocating climate actions in a mutually beneficial and synergistic way, there may also be missed opportunities for deeper coordination that could result in more ambitious action.

Data availability

Code and data to reproduce figures is available on figshare (https://doi.org/10.6084/m9.figshare.13501701). CDP data were provided under a license to A.Hsu and prohibits public resharing of climate strategy disclosure data that was used for this analysis. All other data were compiled into a single database in comma delimited format (.csv) format from publicly available sources, which are detailed in Supplementary Table 1. Contextual data for subnational actors was extracted from the ClimActor database (https://doi.org/10.1038/s41597-020-00682-0).

All statistical analyses were conducted using the R statistical programming environment (Version 3.6.2) and the stm package for the topic analysis. The quanteda package was used for word collocation analysis. Figures were made using the ggplot package in R. For the network analysis, the tidytext, stm and Textnets were used. Gephi was used to generate and analyze the geographic network graphs presented, and the Python package NLTK was used for minor pre-processing tasks. Pandas and Numpy were also used for dataframe and matrix manipulations, and some additional plotting was done with Matplotlib. R and python code to reproduce the figures is available upon reasonable request.

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Additional details

Identifiers

DOI
10.1038/s43247-021-00098-7
Other
oai:uchicago.tind.io:14581

UChicago Information

Division(s)
Physical Sciences Division
Department(s)
Computer Science