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全球温室气体和一氧化碳排放分析:哪些国家呈现出特殊模式?通过推特进行深入观察。

Analysis of Worldwide Greenhouse and Carbon Monoxide Gas Emissions: Which Countries Exhibit a Special Pattern? A Closer Look via Twitter.

作者信息

Mouronte-López Mary Luz, Subirán Marta

机构信息

Higher Polytechnic School, Universidad Francisco de Vitoria, Carretera de Pozuelo a Majadahonda km. 1.800, 28223 Pozuelo de Alarcón, Madrid Spain.

出版信息

Int J Environ Res. 2023;17(1):19. doi: 10.1007/s41742-023-00510-4. Epub 2023 Jan 20.


DOI:10.1007/s41742-023-00510-4
PMID:36694839
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC9853490/
Abstract

UNLABELLED: There is significant global concern about the harmful effects of greenhouse gas and carbon monoxide emissions (deforestation, air pollution, global warming, etc.). The 2015 Paris Agreement on climate change aspires to reduce global warming by achieving a climate-neutral world. Research has been carried out to calculate and diminish the aforementioned emissions in waste, power industry, transport, building, in addition to other areas. The aim of this paper is to analyse the carbon and greenhouse gas emissions across countries around the globe in order to find patterns and correlate them to socio-economic indicators [gross national income (GNI), industrial production (IPI) and human development indexes (HDI)] as well as Twitter interactions regarding climate change. For this purpose, time series and socio-economic data have been downloaded from different repositories including EDGAR (Emissions Database for Global Atmospheric Research), World Bank and UNDP (United Nations Development Programme). Although classical clustering algorithms have already been used in the examination of some environmental issues, we use a non-parametric time series clustering method, which has been suggested in certain scientific literature as a more flexible approach, since any ad hoc parametric assumptions are required. The chosen socio-economic indicators have also demonstrated their relevance in pieces of research related to various fields. With respect to Twitter, which is one of the most popular social networks nowadays, significant analysis has also been performed on the basis of capturing citizens' perceptions on a multitude of matters. We found that several countries such as Brazil, India, China, Nigeria, Russia, United States, Spain, Andorra, Greece, and Qatar show differences in carbon and greenhouse gas emissions patterns. Besides, there does not seem to be a correlation between GNI, IPI and HDI as well as the above mentioned emissions Regarding Twitter interactions, a dissimilarity in the distribution of hashtags was detected between the aforementioned countries and the rest of the world. This research can help to identify countries in which more governmental measures are needed to reduce the type of emissions analysed in certain industrial sectors. In addition, it points out the topics related to climate change that seem to generate the most debate on Twitter for countries with an unusual pattern. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1007/s41742-023-00510-4.

摘要

未标注:全球对温室气体和一氧化碳排放的有害影响(森林砍伐、空气污染、全球变暖等)极为关注。2015年的《巴黎气候变化协定》旨在通过实现气候中和的世界来减少全球变暖。除其他领域外,已开展研究以计算和减少废物、电力行业、交通、建筑等领域的上述排放。本文的目的是分析全球各国的碳和温室气体排放,以找出模式并将其与社会经济指标[国民总收入(GNI)、工业生产指数(IPI)和人类发展指数(HDI)]以及关于气候变化的推特互动相关联。为此,已从不同数据库下载了时间序列和社会经济数据,包括全球大气研究排放数据库(EDGAR)、世界银行和联合国开发计划署(UNDP)。尽管经典聚类算法已用于某些环境问题的研究,但我们使用一种非参数时间序列聚类方法,在某些科学文献中该方法被认为是一种更灵活的方法,因为它不需要任何特殊的参数假设。所选的社会经济指标在与各个领域相关的研究中也已证明其相关性。关于推特,它是当今最受欢迎的社交网络之一,也基于捕捉公民对众多问题的看法进行了大量分析。我们发现,巴西、印度、中国、尼日利亚、俄罗斯、美国、西班牙、安道尔、希腊和卡塔尔等几个国家在碳和温室气体排放模式上存在差异。此外,国民总收入、工业生产指数和人类发展指数与上述排放之间似乎没有相关性。关于推特互动,在上述国家与世界其他国家之间检测到标签分布的差异。这项研究有助于确定哪些国家需要更多政府措施来减少某些工业部门所分析的排放类型。此外,它指出了对于模式异常的国家,在推特上似乎引发最多辩论的与气候变化相关的话题。 补充信息:在线版本包含可在10.1007/s41742-023-00510-4获取的补充材料。

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本文引用的文献

[1]
Climate change responses benefit from a global food system approach.

Nat Food. 2020-2

[2]
Greenhouse gases emission reduction for electric power generation sector by efficient dispatching of thermal plants integrated with renewable systems.

Sci Rep. 2022-7-20

[3]
Using Clustering, Statistical Modeling, and Climate Change Projections to Analyze Recent and Future Region-Specific Compound Ozone and Temperature Burden Over Europe.

Geohealth. 2022-4-16

[4]
Exploring the relationships between socio-economic indicators and student enrollment in higher education institutions of Pakistan.

PLoS One. 2021-12-22

[5]
Carbon emissions rapidly rebounded following COVID pandemic dip.

Nature. 2021-11-4

[6]
Bots and Misinformation Spread on Social Media: Implications for COVID-19.

J Med Internet Res. 2021-5-20

[7]
Life-Cycle Greenhouse Gas Emissions and Human Health Trade-Offs of Organic Waste Management Strategies.

Environ Sci Technol. 2020-7-21

[8]
Global mismatch between greenhouse gas emissions and the burden of climate change.

Sci Rep. 2016-2-5

[9]
Economic development and multiple air pollutant emissions from the industrial sector.

Environ Sci Pollut Res Int. 2016-2

[10]
Climate Change Sentiment on Twitter: An Unsolicited Public Opinion Poll.

PLoS One. 2015-8-20

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