Application of Correlation Analysis Based on Principal Components in the Study of Global Temperature Changes

Document Type : Original Article


1 School of Mechanical and Electrical Engineering, Guilin University of Electronic Technology, Guangxi, 541004, China

2 School of Civil and Engineering Management, Guangzhou Maritime University, Guangzhou, 510725, China


The current issue of global warming is prominent, and there are many factors that affect global temperature changes. Therefore, how to correctly judge the relationship between each influencing factor and global temperature changes and accurately find out the main reasons of global temperature rise which are the problems that must be considered and solved to alleviate global warming at present. According to previous official data, this paper proposed a correlation analysis method based on principal components to comprehensively analyze the relationship between natural disaster factors, human factors, and global temperature changes, and find out the main reasons that affect global temperature rise. Compared with traditional research methods, the new method provided in this paper can still remain scientific and accurate calculation results while reducing computational dimensions. The experimental results showed that in the relationship between natural disasters and global temperature changes, the average correlation coefficient of the principal component represented by biological disasters and geological disasters was the highest at 0.6097 and a test value of p<0.05, indicating a significant positive correlation between them and global temperature. However, the correlation coefficient of the principal component represented by floods and storms was negative, indicating a negative correlation between them and global temperature. In exploring the main factors affecting global temperature rise, both the total global population and the total global CO2 emissions had a significant positive correlation with global temperature. Among them, the average correlation coefficient of the total global population was the highest at 0.9972, and its weight also was the highest at 26.42%. Therefore, this indicates that the total global population is the most important factor affecting global temperature rise. This study can provide reference for countries to make decisions in response to global warming.


  1. Hache, E., Simoën, M., Seck, G. S., Bonnet, C., Jabberi, A. and Carcanague, S., 2020. The impact of future power generation on cement demand: An international and regional assessment based on climate scenarios, International Economics, 163, pp. 114-133. Doi:10.1016/j.inteco.2020.05.002
  2. Ashrafizadeh, S. and Seifollahi, Z., 2020. Corona Virus Echoes of Earth Grumbling: A Review, Iranian (Iranica) Journal of Energy & Environment, 11(3), pp. 237-247. Doi:10.5829/IJEE.2020.11.03.10
  3. Grünig, M., Mazzi, D., Calanca, P., Karger, D. N. and Pellissier, L., 2020. Crop and forest pest metawebs shift towards increased linkage and suitability overlap under climate change, Communications Biology, 3(1), pp. 233. Doi:10.1038/s42003-020-0962-9
  4. Nowak, D. J., 2019. The atmospheric system: Air quality and greenhouse gases, Understanding Urban Ecology: An Interdisciplinary Systems Approach, pp. 175-199. Doi:10.1007/978-3-03011259-2_8
  5. Abbass, K., Qasim, M. Z., Song, H., Murshed, M., Mahmood, H. and Younis, I., 2022. A review of the global climate change impacts, adaptation, and sustainable mitigation measures, Environmental Science and Pollution Research, 29(28), pp. 42539-42559. Doi:10.1007/s11356-022-19718-6
  6. An, R., Ji, M. and Zhang, S., 2018. Global warming and obesity: a systematic review, Obesity Reviews, 19(2), pp. 150-163. Doi:10.1111/obr.12624
  7. Raihan, A. and Tuspekova, A., 2022. Toward a sustainable environment: Nexus between economic growth, renewable energy use, forested area, and carbon emissions in Malaysia, Resources, Conservation & Recycling Advances, 15, pp. 200096. Doi:10.1016/j.rcradv.2022.200096
  8. Sutherland, W. J., Atkinson, P. W., Butchart, S. H., Capaja, M., Dicks, L. V., Fleishman, E., Gaston, K. J., Hails, R. S., Hughes, A. C. and Le Anstey, B., 2022. A horizon scan of global biological conservation issues for 2022, Trends in Ecology & Evolution, 37(1), pp. 95-104. Doi:10.1016/j.tree.2021.10.014
  9. Dettinger, M., 2011. Climate change, atmospheric rivers, and floods in California–a multimodel analysis of storm frequency and magnitude changes 1, JAWRA Journal of the American Water Resources Association, 47(3), pp. 514-523. Doi:10.1111/j.17521688.2011.00546.x
  10. Wiens, J. J., 2016. Climate-related local extinctions are already widespread among plant and animal species, PLoS Biology, 14(12), pp. e2001104. Doi:10.1371/journal.pbio.2001104.
  11. Raj, S., Arulraj, P., Anand, N., Balamurali, K. and Gokul, G., 2021. Influence of various design parameters on compressive strength of geopolymer concrete: A parametric study by taguchi method, International Journal of Engineering, 34(10), pp. 2351-2359. Doi:10.5829/IJE.2021.34.10A.16
  12. Cuixiang, Z., 2016. Natural strategies to alleviate global warming Technology Perspective, 24(2). Doi:10.3969/j.issn.2095-2457.2016.24.086
  13. Xiangyang, C., 2021. The Impact of Population, Consumption Scale and Structure on Carbon Emissions: Theoretical Mechanism and Empirical Analysis, Environmental and Economic Research, 6(03), pp. 8-24. Doi:10.19511/j.cnki. jee.2021.03.002
  14. Fallah, M., Mohajeri, A. and Barzegar-Mohammadi, M., 2017. A new mathematical model to optimize a green gas network: A case study, International Journal of Engineering, 30(7), pp. 1029-1037. Doi:10.5829/ije.2017.30.07a.12
  15. Li, Y., Zhang, S. and Liu, C., 2022. Research on greenhouse gas emission characteristics and emission mitigation potential of municipal solid waste treatment in Beijing, Sustainability, 14(14), pp. 8398. Doi:10.3390/SU14148398
  16. Vilakazi, B. S. and Mukwada, G., 2023. Curbing land degradation and mitigating climate change in mountainous regions: a systemic review, Environmental Monitoring and Assessment, 195(2), pp. 275. Doi:10.1007/S10661-022-10906-Y
  17. Vishnoi, N. K., 2021. A review study on effect of global warming over mankind, Asian Journal of Research in Social Sciences and Humanities, 11(12), pp. 215-222. Doi:10.5958/2249-7315.2021.00339.7
  18. Mella, P., 2022. Global Warming: Is It (Im) Possible to Stop It? The Systems Thinking Approach, Energies, 15(3), pp. 705. Doi:10.3390/EN15030705
  19. Pandey, S. K., 2020. Nature and global warming, TERI Information Digest on Energy and Environment, 19(1), pp. 1-8.
  20. Bian, Q., 2022. Global Warming Thermodynamics, Open Access Library Journal, 9(7), pp. 1-37. Doi:10.4236/oalib.1108945
  21. Wu Junying, J. X., 2017. Analysis of global warming causes and improvement measures, Talent, 12(256). Doi:CNKI:SUN:CAIZ.0.2017-12-225
  22. Global Disaster Data Platform. Available at:
  23. World Meteorological Organization (WMO). Available at:
  24. National Aeronautics and Space Administration (NASA) of the United States. Available at:
  25. Xiao, R., Zhuang, Q., Jin, S., Liu, B. and Liu, G., 2023. Evaluation of influencing factors of pipeline wax deposition strength based on principal component analysis, Petroleum Science and Technology, 41(6), pp. 700-711. Doi:10.1080/10916466.2022.2064495
  26. Ibrahim, A., Ismail, A., Juahir, H., Iliyasu, A. B., Wailare, B. T., Mukhtar, M. and Aminu, H., 2023. Water quality modelling using principal component analysis and artificial neural network, Marine Pollution Bulletin, 187, pp. 114493. Doi:10.1016/J.MARPOLBUL.2022.114493
  27. Lubo-Robles, D., Bedle, H., Marfurt, K. J. and Pranter, M. J., 2023. Evaluation of principal component analysis for seismic attribute selection and self-organizing maps for seismic facies discrimination in the presence of gas hydrates, Marine and Petroleum Geology, pp. 106097. Doi:10.1016/J.MARPETGEO.2023.106097
  28. Mamine, N., Khaldi, F. and Grara, N., 2020. Survey of the physico-chemical and parasitological quality of the wastewaters used in irrigation (Souk Ahras, North-East of Algeria), Iranian (Iranica) Journal of Energy & Environment, 11(1), pp. 78-88. Doi:10.5829/ijee.2020.11.01.13
  29. Han Liu, L. M., 2023. Comparative Analysis of Comprehensive Strength of Each County (City) in Kashgar Region Based on Principal Component Analysis, Practice and Understanding of Mathematics, pp. 1-6. Available at: http://kns.
  30. Xu Xiangyu, L. X., Zhang Suxia, 2022. Identification and analysis of cable damage in cable-stayed bridges under moving loads based on principal component analysis, Journal of Applied Mechanics, pp. 1-11. Avaiable at:
  31. Khair, S. and Rafizul, I. M., 2018. Assessment on metal elements in soil of waste landfill at Khulna: a study based on multivariate statistics, GIS and BP-ANN, Journal of Engineering, 9(1), pp. 59-69. Doi:10.5829/IJEE.2018.09.01.09
  32. Alizadeh, A. and Nowzari, H., 2023. Assessment of Microbial Indicators of Water Resources in KooheHava and TangeKhoor Free Area, Iranian (Iranica) Journal of Energy & Environment, 14(3), pp. 240-251. Doi:10.5829/IJEE.2023.14.03.06
  33. Bai, C. and Yan, P., 2022. Dependence Analysis of PM2. 5 Concentrations in 295 Chinese Cities in the Winter of 2019–2020, Atmosphere, 13(11), pp. 1847. Doi:10.3390/ATMOS13111847
  34. Hu Jiakun, W. W., Xi Xiaoxin, 2020. Mineral element assemblage zoning and prospecting prediction of D copper deposits in Dolores, Bolivia, Geological Review, 66(04), pp. 942-963. Doi:10.16509/j.georeview.2020.04.012
  35. Oranrejawu, R., Olatunji, O. and Akpan, G., 2018. Impacts of Climate Variability on Hydroelectric Power Generation in Shiroro Station, Nigeria, Iranian (Iranica) Journal of Energy & Environment, 9(3), pp. 197-203. Doi:10.5829/ijee.2018.09.03.07
  36. Li Bohong, C. S., Zhang Yutao 2018. The optimal calibration method of CN value in the SCS model -- taking Picea chinensis forest on the north slope of Tianshan Mountain as an example, China Rural Water and Hydropower, 430(08), pp. 77-81. Doi:10.3969/j.issn.1007-2284.2018.08.016
  37. Liu, C., Zhang, A., Xue, J., Lei, C. and Zeng, X., 2023. LSTM-Pearson Gas Concentration Prediction Model Feature Selection and Its Applications, Energies, 16(5), pp. 2318. Doi:10.13199/j.cnki.cst. 2022-1618
  38. Tong, L., Zhang, C., Peng, Z. and Wang, L., 2022. Spatial–Temporal Distribution Characteristics and Correlation Analysis of Air Pollutants from Ships in Inland Ports, Sustainability, 14(21), pp. 14214. Doi:10.3390/SU142114214
  39. Wang, M. and Cao, J., 2022. An automatic identification method of marine magnetic anomalies based on the sliding window correlation coefficient method, Journal of Applied Geophysics, 205, pp. 104761. Doi:10.1016/J.JAPPGEO.2022.104761
  40. Ji Peng, C. F., Xu Tianqi, Qi Qi, 2022. Fault line selection based on Kendall correlation coefficient and CEEMD decomposition zero sequence current phase method, Journal of Yunnan University for Nationalities (Natural Science Edition), 31(05), pp. 595-601.