Diverse climate actors show limited coordination in a large-scale text analysis of strategy documents
Diverse climate actors show limited coordination in a large-scale text analysis of strategy documents
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DOI：
10.1038/s43247-021-00098-7
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发表时间：
2021-02-09
影响因子：
7.9
通讯作者：
Rauber, Ross
中科院分区：
文献类型：
--
作者：
Hsu, Angel;Rauber, Ross
自《巴黎协定》正式承认非国家行为体和国家以下各级政府对全球气候变化治理的贡献以来，它们的网络迅速增加。了解这些行为体采取行动的方式以及它们如何相互协调和与各国政府协调一致至关重要，因为需要采取协调行动来实现雄心勃勃的全球气候目标。在这里，我们提出了一个大型分析（n= 9，326），将大规模自然语言处理方法和社会网络分析应用于国家，地区，城市和公司的气候战略文件。我们发现，员工出行和办公室运营中的气候减缓、绿色建筑标准以及市政和公民行动是企业和城市及地方政府气候行动的共同主题，而在特定部门和排放范围中设定目标的方法则更加多样化。我们还发现，地区和国家的战略之间存在联系，而公司之间则没有联系。大多数行为者在气候行动方面的差距包括适应和消费/供应链减排努力。我们认为，虽然各行为体可能似乎是以互利和协同的方式自我组织和分配气候行动，但也可能错过了更深入协调的机会，而这种协调可能会导致更雄心勃勃的行动。城市、地区、国家和公司等气候行动者在气候行动方面表现出多样性，在适应和消费供应链减排方面存在差距，这表明基于机器学习的自然语言处理和社交网络分析。
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. Climate actors such as cities, regions, countries and companies show diversity in climate actions with gaps in adaptation and consumption-supply chain emissions reductions, suggests a machine-learning based natural language processing and social network analysis.