Spatial-Temporal Changes and Associated Determinants of Global Heating Degree Days

Yuanzheng Li, Jinyuan Li, Ao Xu, Zhizhi Feng, Chanjuan Hu and Guosong Zhao
Additional contact information
Yuanzheng Li: School of Resources and Environment, Henan University of Economics and Law, Zhengzhou 450046, China
Jinyuan Li: School of Resources and Environment, Henan University of Economics and Law, Zhengzhou 450046, China
Ao Xu: School of Resources and Environment, Henan University of Economics and Law, Zhengzhou 450046, China
Zhizhi Feng: School of Resources and Environment, Henan University of Economics and Law, Zhengzhou 450046, China
Chanjuan Hu: Institute of Geographical Sciences, Henan Academy of Sciences, Zhengzhou 450052, China
Guosong Zhao: School of Geography and Information Engineering, China University of Geosciences, Wuhan 430074, China

IJERPH, 2021, vol. 18, issue 12, 1-15

Abstract: The heating degree days (HDDs) could indicate the climate impact on energy consumption and thermal environment conditions effectively during the winter season. Nevertheless, studies on the spatial-temporal changes in global HDDs and their determinants are scarce. This study used multi-source data and several methods to explore the rules of the spatial distribution of global HDDs and their interannual changes over the past 49 years and some critical determinants. The results show that global HDDs generally became larger in regions with higher latitudes and altitudes. Most global change rates of HDDs were negative ( p < 0.10) and decreased to a greater extent in areas with higher latitudes. Most global HDDs showed sustainability trends in the future. Both the HDDs and their change rates were significantly partially correlated with latitude, altitude, mean albedo, and EVI during winter, annual mean PM 2.5 concentration, and nighttime light intensity ( p = 0.000). The HDDs and their change rates could be simulated well by the machine learning method. Their RMSEs were 564.08 °C * days and 3.59 °C * days * year ?1 , respectively. Our findings could support the scientific response to climate warming, the construction of living environments, sustainable development, etc.

Keywords: climate change; energy consumption; thermal environment; Hurst exponents; influence factors; enhanced vegetation index; PM 2.5; albedo; remote sensing; general regression neural network (search for similar items in EconPapers)
JEL-codes: I I1 I3 Q Q5 (search for similar items in EconPapers)
Date: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
https://www.mdpi.com/1660-4601/18/12/6186/pdf (application/pdf)
https://www.mdpi.com/1660-4601/18/12/6186/ (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:18:y:2021:i:12:p:6186-:d:570798

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 ().