Discovering Geographical Flock Patterns of CO 2 Emissions in China Using Trajectory Mining Techniques

Pengdong Zhang (), Lizhi Miao, Fei Wang and Xinting Li
Additional contact information
Pengdong Zhang: School of Geographic and Biologic Information, Nanjing University of Posts and Telecommunications, Wenyuan Road 9, Nanjing 210023, China
Lizhi Miao: School of Geographic and Biologic Information, Nanjing University of Posts and Telecommunications, Wenyuan Road 9, Nanjing 210023, China
Fei Wang: East China Sea Fisheries Research Institute, Chinese Academy of Fishery Sciences, Shanghai 200090, China
Xinting Li: School of Geographic and Biologic Information, Nanjing University of Posts and Telecommunications, Wenyuan Road 9, Nanjing 210023, China

IJERPH, 2023, vol. 20, issue 5, 1-16

Abstract: Carbon dioxide (CO 2 ) emissions are considered a significant factor that results in climate change. To better support the formulation of effective policies to reduce CO 2 emissions, specific types of important emission patterns need to be considered. Motivated by the flock pattern that exists in the domain of moving object trajectories, this paper extends this concept to a geographical flock pattern and aims to discover such patterns that might exist in CO 2 emission data. To achieve this, a spatiotemporal graph (STG)-based approach is proposed. Three main parts are involved in the proposed approach: generating attribute trajectories from CO 2 emission data, generating STGs from attribute trajectories, and discovering specific types of geographical flock patterns. Generally, eight different types of geographical flock patterns are derived based on two criteria, i.e., the high–low attribute values criterion and the extreme number–duration values criterion. A case study is conducted based on the CO 2 emission data in China on two levels: the province level and the geographical region level. The results demonstrate the effectiveness of the proposed approach in discovering geographical flock patterns of CO 2 emissions and provide potential suggestions and insights to assist policy making and the coordinated control of carbon emissions.

Keywords: carbon emission; geographical flock pattern mining; attribute trajectory; bivariate time series; climate change (search for similar items in EconPapers)
JEL-codes: I I1 I3 Q Q5 (search for similar items in EconPapers)
Date: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
https://www.mdpi.com/1660-4601/20/5/4265/pdf (application/pdf)
https://www.mdpi.com/1660-4601/20/5/4265/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jijerp:v:20:y:2023:i:5:p:4265-:d:1082669

Access Statistics for this article

IJERPH is currently edited by Ms. Jenna Liu

More articles in IJERPH from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().