five

Replication Data for: Intergovernmental engagement on health impacts of climate change

收藏
DataONE2022-01-16 更新2024-06-08 收录
下载链接:
https://search.dataone.org/view/sha256:8617e3305cd24558c7cfeac6ebb9878bc1c09947b5a27e07ab6ae4a091785dfd
下载链接
链接失效反馈
官方服务:
资源简介:
Objective. To examine countries’ engagement with the health impacts of climate change in their formal statements to intergovernmental organizations, and the factors driving engagement. Methods. We obtained the texts of countries’ annual statements in United Nations (UN) general debates from 2000 to 2019 and their nationally determined contributions at the Paris Agreement in 2016. To measure countries’ engagement, we used a keyword-in-context text search with relevant search terms to count the total number of references to the relationship of health to climate change. We used a machine learning model (random forest predictions) to identify the most important country-level predictors of engagement. The predictors included political and economic factors, health outcomes, climate change-related variables and membership of political negotiating groups in the UN. Findings. For both UN general debate statements and nationally determined contributions, low- and middle-income countries discussed the health impacts of climate change much more than did high-income countries. The most important predictors of engagement were health outcomes (infant mortality, maternal deaths, life expectancy), countries’ income levels (gross domestic product per capita), and fossil fuel consumption. Membership of political negotiating groups (such as Group of 77 and Small Island Developing States) was a less important predictor. Conclusion. Our analysis indicated a global North–South division in engagement with health and climate change. Countries who carry the heaviest health burdens but lack necessary resources to address the impacts of climate change are shouldering responsibility for reminding the global community of the implications of climate change for people’s health.
创建时间:
2023-11-23
5,000+
优质数据集
54 个
任务类型
进入经典数据集
二维码
社区交流群

面向社区/商业的数据集话题

二维码
科研交流群

面向高校/科研机构的开源数据集话题

数据驱动未来

携手共赢发展

商业合作