Suppr超能文献

全球气候与能源行动数据集(GCoM数据集):一系列包含缓解、适应和能源获取承诺的气候与能源行动计划集合。

GCoM datasets: a collection of climate and energy action plans with mitigation, adaptation and energy access commitments.

作者信息

Franco Camilo, Melica Giulia, Treville Aldo, Baldi Marta Giulia, Palermo Valentina, Bertoldi Paolo, Pisoni Enrico, Monforti-Ferrario Fabio, Crippa Monica

机构信息

European Commission - Joint Research Centre (JRC), Ispra, 21027, Italy.

European Dynamics, Luxembourg, Luxembourg.

出版信息

Sci Data. 2024 Sep 5;11(1):969. doi: 10.1038/s41597-024-03613-5.

Abstract

This paper presents a collection of datasets holding information on the energy and climate action plans of 6,850 municipalities, taking part in the transnational initiative of the Global Covenant of Mayors (GCoM). This collection includes commitments for reducing net GHG emissions by at least 20% by 2020, 55% by 2030 and becoming climate neutral by 2050. The signatories commit to addressing any of the three pillars of the initiative, namely climate change mitigation, adaptation and energy access. Following two previous releases, the third release of the GCoM collection is introduced, with closing date September 2022. The datasets include information on the action plans and monitoring reports as they are self-reported by signatories, undergoing a quality-harnessing procedure before publication. Additionally, an external comparison is developed with the Emissions Database for Global Atmospheric Research (EDGAR v7), controlling for comparable sources and activity sectors, ensuring the usability of the GCoM datasets for relevant research on local policies and their effects on reducing the impact of climate change.

摘要

本文展示了一系列数据集,这些数据集包含了参与全球市长盟约(GCoM)跨国倡议的6850个城市的能源和气候行动计划信息。该集合包括到2020年将温室气体净排放量至少减少20%、到2030年减少55%以及到2050年实现气候中和的承诺。签署方承诺致力于该倡议的三个支柱中的任何一个,即气候变化缓解、适应和能源获取。继前两次发布之后,介绍了GCoM集合的第三次发布,截止日期为2022年9月。这些数据集包括行动计划和监测报告的信息,这些信息由签署方自行报告,并在发布前经过质量控制程序。此外,还与全球大气研究排放数据库(EDGAR v7)进行了外部比较,控制了可比来源和活动部门,确保GCoM数据集可用于有关地方政策及其对减少气候变化影响的相关研究。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/98cd/11377815/f194d1fb96b3/41597_2024_3613_Fig1_HTML.jpg

文献AI研究员

20分钟写一篇综述,助力文献阅读效率提升50倍。

立即体验

用中文搜PubMed

大模型驱动的PubMed中文搜索引擎

马上搜索

文档翻译

学术文献翻译模型,支持多种主流文档格式。

立即体验