The impact mechanism of human activities over climate suitability based on social network data: evidence from China
Abstract
The impact mechanism of human activities on climate suitability is critical for understanding the human-environment nexus. In this study, social network data from Sina Weibo Platform was collected to quantitatively examined the relationship between the seven major types of human activities and climate suitability. The results indicated that the impacts of entertainment, tourism and daily life related human activities on climate suitability are significant (p-value < 0.05). With one-unit (one check-in record/km2) increase of entertainment and tourism related human activities, the coverage rate of climate suitable zone and the length of climate suitable period increase by 0.003% and 0.026 months, respectively. In contrast, one-unit of increase of daily life activities made the Theil entropy index of climate inequity and the length of climate suitable period increase 0.00035 units and shorten 0.014 months, respectively. Moreover, the impact mechanism of human activities on climate suitability showed a significant spatial heterogeneity within regions at different economic level or topographical conditions, which could be explained by the discrepancy of environmental policies, urban form and urban ventilation channel design strategies in China. This work exhibited a further step to new possibilities in clarifying the climate effect of human activities using open-sourced social network data.
Keyword : human activities, check-in data, climate suitability, spatial regression models
How to Cite
Ren, Y., Tang, X., Guo, N., & Du, M. (2022). The impact mechanism of human activities over climate suitability based on social network data: evidence from China. Journal of Environmental Engineering and Landscape Management, 30(1), 135-150. https://doi.org/10.3846/jeelm.2022.15219
This work is licensed under a Creative Commons Attribution 4.0 International License.
References
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Zhen, F., & Wei, Z. (2008). Influence of information technology on social spatial behaviors of urban residents – Case of Nanjing City in China. Chinese Geographical Science, 18(4), 316–322. https://doi.org/10.1007/s11769-008-0316-x
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Azimi, M., Feng, F., & Zhou, C. (2019). Air pollution inequality and health inequality in China: An empirical study. Environmental Science and Pollution Research, 26(12), 11962–11974. https://doi.org/10.1007/s11356-019-04599-z
Block, A., Keuler, K., & Schaller, E. (2004). Impacts of anthropogenic heat on regional climate patterns. Geophysical Research Letters, 31(12), 1–4. https://doi.org/10.1029/2004GL019852
Boer, E. P., de Beurs, K. M., & Hartkamp, A. D. (2001). Kriging and thin plate splines for mapping climate variables. International Journal of Applied Earth Observation and Geoinformation, 3(2), 146–154. https://doi.org/10.1016/S0303-2434(01)85006-6
Bulkeley, H. (2001). No regrets?: Economy and environment in Australia’s domestic climate change policy process. Global Environmental Change, 11(2), 155–169. https://doi.org/10.1016/S0959-3780(00)00064-9
Chen, S. L. (2012). The research of wind environment in near ground layer for mountainous city planning [Doctoral dissertation]. Chongqing University.
Cowell, F. A. (2000). Measurement of inequality. In Handbook of income distribution (Vol. 1, pp. 87–166). Elsevier. https://doi.org/10.1016/S1574-0056(00)80005-6
Fanger, P. O. (1970). Thermal comfort: Analysis and applications in environmental engineering. Danish Technical Press.
Feng, J., Wang, J., & Yan, Z. (2014). Impact of anthropogenic heat release on regional climate in three vast urban agglomerations in China. Advances in Atmospheric Sciences, 31(2), 363–373. https://doi.org/10.1007/s00376-013-3041-z
Gong, X., Wang, Y., & Lin, B. (2021). Assessing dynamic China’s energy security: Based on functional data analysis. Energy, 217, 119324. https://doi.org/10.1016/j.energy.2020.119324
Gu, C., Hu, L., Zhang, X., Wang, X., & Guo, J. (2011). Climate change and urbanization in the Yangtze River Delta. Habitat International, 35(4), 544–552. https://doi.org/10.1016/j.habitatint.2011.03.002
Höppe, P. (1999). The physiological equivalent temperature – a universal index for the biometeorological assessment of the thermal environment. International Journal of Biometeorology, 43(2), 71–75. https://doi.org/10.1007/s004840050118
Houghten, F. C. (1923). Determining lines of equal comfort. ASHVE Transactions, 29, 163–176.
Huang, Y., Deng, J., Li, J., & Zhong, Y. (2008). Visitors’ attitudes towards China’s national forest park policy, roles and functions, and appropriate use. Journal of Sustainable Tourism, 16(1), 63–84. https://doi.org/10.2167/jost720.0
Hunecke, M., Blöbaum, A., Matthies, E., & Höger, R. (2001). Responsibility and environment: Ecological norm orientation and external factors in the domain of travel mode choice behavior. Environment and Behavior, 33(6), 830–852. https://doi.org/10.1177/00139160121973269
Ichinose, T., Shimodozono, K., & Hanaki, K. (1999). Impact of anthropogenic heat on urban climate in Tokyo. Atmospheric Environment, 33(24–25), 3897–3909. https://doi.org/10.1016/S1352-2310(99)00132-6
Jendritzky, G., de Dear, R., & Havenith, G. (2012). UTCI – why another thermal index? International Journal of Biometeorology, 56(3), 421–428. https://doi.org/10.1007/s00484-011-0513-7
Jensen, J. R., & Jensen, R. R. (2012). Introductory geographic information systems. Pearson Higher Ed.
Kong, F. (2020). Multi-temporal scale assessment of climate comfort of habitat environment and spatial differences in China. Journal of Arid Land Resources and Environment, (3), 102–111.
Kong, F.-h., Yin, H.-w., Liu, J.-y., Yan, W.-j., & Sun, C.-f. (2013). A review of research on the urban green space cooling effect. Journal of Natural Resources, 28(1), 171–181. http://www.jnr.ac.cn/EN/Y2013/V28/I1/171
Li, J., & Wang, J. (2016). Simulation analysis on relationship between spatial form and microclimate of pedestrian street in Nanjing. Journal of Southeast University (Natural Science Edition), 46(5), 1103–1109. https://doi.org/10.3969/j.issn.1001-0505.2016.05.035
Li, L. J., Jiang, D. J., Li, J. Y., Liang, L. Q., & Zhang, L. (2007). Advances in hydrological response to land use/land cover change. Journal of Natural Resources, 22(2), 211–224. https://doi.org/10.11849/zrzyxb.2007.02.008
Lu, Y. P. (2018). Research on the influence of urban block morphology on the surface urban heat Island: A case study in Wuhan [Doctoral dissertation]. Huazhong University of Science and Technology.
Ma, L. J., Sun, G. N., Ma, Y. F., & Wang, J. J. (2011). An analysis on the influence of climate comfortable degree on temporal and spatial variation of inbound tourists in China’s hot cities. Tourism Tribune, 26, 45–50.
Ma, L., Sun, G., & Wang, J. (2009). Evaluation of tourism climate comfortableness of coastal cities in the Eastern China. Progress in Geography, 28(5), 713–722.
Miller, J. D., & Hutchins, M. (2017). The impacts of urbanisation and climate change on urban flooding and urban water quality: A review of the evidence concerning the United Kingdom. Journal of Hydrology: Regional Studies, 12, 345–362. https://doi.org/10.1016/j.ejrh.2017.06.006
Minard, D., Belding, H. S., & Kingston, J. R. (1957). Prevention of heat casualties. Journal of the American Medical Association, 165(14), 1813–1818. https://doi.org/10.1001/jama.1957.02980320043010
Moran, P. A. (1950). Notes on continuous stochastic phenomena. Biometrika, 37(1/2), 17–23. https://doi.org/10.1093/biomet/37.1-2.17
Offerle, B., Grimmond, C. S. B., & Fortuniak, K. (2005). Heat storage and anthropogenic heat flux in relation to the energy balance of a central European city centre. International Journal of Climatology: A Journal of the Royal Meteorological Society, 25(10), 1405–1419. https://doi.org/10.1002/joc.1198
Peng, S., He, X., Zhang, J. T., Ma, X., Sun, F., & Liu, S. (2015). Current status, problems and recommendations on climate change adaptation policies in China. China Population, Resources and Environment, 9, 1–7.
Qiu, H. L. (2014). Study on urban heat island effect and cooling effect of Greenland in Beijing [Doctoral dissertation]. Beijing Forestry University.
Quah, A. K., & Roth, M. (2012). Diurnal and weekly variation of anthropogenic heat emissions in a tropical city, Singapore. Atmospheric Environment, 46, 92–103. https://doi.org/10.1016/j.atmosenv.2011.10.015
Schor, J. (2015). Climate, inequality, and the need for reframing climate policy. Review of Radical Political Economics, 47(4), 525–536. https://doi.org/10.1177/0486613415576114
Shi, X., & Xu, X. (2012). Progress in the study of regional impact of aerosol and related features of heavy fog in Beijing City. Chinese Journal of Geophysics, 55(10), 3230–3239.
Siple, P. A., & Passel, C. F. (1945). Measurements of dry atmospheric cooling in subfreezing temperatures. Proceedings of the American Philosophical Society, 89(1), 177–199. http://www.jstor.org/stable/985324
Sun, F., He, X., Rummy, P., & Lauzon, K. (2015). Global progress in climate change adaptation policies and its implication for China. Chinese Journal of Population Resources and Environment, 13(1), 21–31. https://doi.org/10.1080/10042857.2015.1006190
Tang, Y., Feng, Z. M., & Yang, Y. Z. (2008). Evaluation of climate suitability for human settlement in China. Resources Science, 30(5), 648–653.
Thom, E. C. (1959). The discomfort index. Weatherwise, 12(2), 57–61. https://doi.org/10.1080/00431672.1959.9926960
Wang, J., Zhou, W., Pickett, S. T., Yu, W., & Li, W. (2019). A multiscale analysis of urbanization effects on ecosystem services supply in an urban megaregion. Science of the Total Environment, 662, 824–833. https://doi.org/10.1016/j.scitotenv.2019.01.260
Wang, Y., & Gong, X. (2020). Does financial development have a non-linear impact on energy consumption? Evidence from 30 provinces in China. Energy Economics, 90, 104845. https://doi.org/10.1016/j.eneco.2020.104845
Wen, F., Wu, N., & Gong, X. (2020). China’s carbon emissions trading and stock returns. Energy Economics, 86, 104627. https://doi.org/10.1016/j.eneco.2019.104627
Yan, L., Duarte, F., Wang, D., Zheng, S., & Ratti, C. (2019). Exploring the effect of air pollution on social activity in China using geotagged social media check-in data. Cities, 91, 116–125. https://doi.org/10.1016/j.cities.2018.11.011
Yan, Y., Yue, S., Liu, X., Wang, D., & Chen, H. (2013). Advances on assessment of bioclimatic comfort conditions at home and abroad. Advances in Earth Science, 28(10), 1119–1125.
Zhen, F., & Wei, Z. (2008). Influence of information technology on social spatial behaviors of urban residents – Case of Nanjing City in China. Chinese Geographical Science, 18(4), 316–322. https://doi.org/10.1007/s11769-008-0316-x
Zheng, S., Wang, J., Sun, C., Zhang, X., & Kahn, M. E. (2019). Air pollution lowers Chinese urbanites’ expressed happiness on social media. Nature Human Behaviour, 3(3), 237–243. https://doi.org/10.1038/s41562-018-0521-2
Zheng, X. B., Luo, Y. X., Zhao, T. L., Chen, J., & Kang, W. (2012). Geographical and climatological characterization of aerosol distribution in China. Scientia Geographica Sinica, 32(3), 265–272.