An Analysis on Water Ecological Footprint in Guiyang City and Its Driving Factors Based on STIRPAT Model Using Partial Least Squares and Elastic Net Regression
|更新时间:2025-03-12
|
An Analysis on Water Ecological Footprint in Guiyang City and Its Driving Factors Based on STIRPAT Model Using Partial Least Squares and Elastic Net Regression
Bulletin of Soiland Water ConservationVol. 38, Issue 4, Pages: 220-227(2018)
XU Guoyu, REN Xiaodong, YANG Zhenhua, et al. An Analysis on Water Ecological Footprint in Guiyang City and Its Driving Factors Based on STIRPAT Model Using Partial Least Squares and Elastic Net Regression[J]. Bulletin of Soiland Water Conservation, 2018, 38(4): 220-227.
DOI:
XU Guoyu, REN Xiaodong, YANG Zhenhua, et al. An Analysis on Water Ecological Footprint in Guiyang City and Its Driving Factors Based on STIRPAT Model Using Partial Least Squares and Elastic Net Regression[J]. Bulletin of Soiland Water Conservation, 2018, 38(4): 220-227. DOI: 10.13961/j.cnki.stbctb.2018.04.036.
An Analysis on Water Ecological Footprint in Guiyang City and Its Driving Factors Based on STIRPAT Model Using Partial Least Squares and Elastic Net Regression
[Objective] Based on analyzing the time series of urban water ecological footprint in Guiyang City
this paper made use of the water ecological footprint model to calculate the water ecological footprint and evaluated the sustainable utilization of water resources and to provide some advice for rational utilization of water resources.[Methods] Using water ecological footprint theory
the water ecological footprint
water resources carrying capacity and sustainable utilization index of water resources in Guiyang City from 2002 to 2016 were calculated. Then
according to the environmental pressure model STIRPAT modeling and using partial least squares(PLS) regression and elastic net regression to back up and analyse the driving force factors which affect the water ecological footprint.[Results] ① From 2002 to 2015
as a whole
water ecological footprint in Guiyang City was on the rise. The water ecological carrying capacity was in a large fluctuating range. The water resources sustainable utilization index was lower than 1. The water resource in the whole city was in a insecure state. ② Using LS regression to analyze the STIRPAT model
the results showed that the four driving force factors in the model have multiple collinear. Then
using partial least squares(PLS) to modify the STIRPAT model
the total linearity between the driving force factors was eliminated. But the VIP value only selected two meaningful driver force factors:the quadratic term of per capita GDP and the population. ③ According to the analysis of STIRPAT by elastic net
the results showed that the population
per capita GPD
the first two industries accounted for the ratio of the total industry
and the urbanization rates affected the water ecological footprint of Guiyang City. The water ecological footprint between first two industries accounted for the ratio of the total industry and the urbanization rate both existed the environmental Kuzenets curve.[Conclusion] In the process of developing and promoting rapid urbanization
the water ecological footprint of Guiyang City must be reduced
which is the key to the sustainable development of water resources.
关键词
Keywords
references
Mathis W, William E R. Perceptual and structural barriers to investing in natural capital:Economics from an ecological footprint perspective[J]. Ecological Economics, 1997,20(1):3-24.
Wackernagel M, Rees W E. Our ecological footprint:Reducing human impact on the earth[J]. Population & Environment, 1998,1(3):171-174.
Hoekstra A Y, Hung P Q. Virtual water trade:A quantification of virtual water flows between nations in relation to international corp trade[J]. Value of Water Research Report Series, 2002,11:239-304.
Tang Zhong, Xiang Hao. Analysis of major driving forces of ecological footprint based on the STIRPAT model and RR method: A case of Sichuan Province, Southwest China[J]. Journal of Mountain Science, 2011,8(4):611-618.
De Mol C, De Vito E, Rosasco L. Elastic-net regularization in learning theory[J]. Journal of Complexity, 2009,25(2):201-230.
Zou H, Hastie T. Zou H, Hastie T. Regularization and variable selection via the elastic net[J]. Journal of the Royal Statistical Society, 2005, 67(2):301-320.