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Cluster Computing

, Volume 22, Supplement 3, pp 6425–6433 | Cite as

Partial least squares regression analysis to factor of influence for ecological footprint

  • Zhiqi HuangEmail author
Article

Abstract

Along with the increasing conflicts between the population growth, economic development and the degeneration of resource environment, sustainable development has become the thinking focus of human to get out of the trouble and is included in the basic starting points of establishing policies of all countries in the 21st century. The partial least squares regression method is applied for analysis on degree of importance of all factors that affect the Shaanxi ecological footprint based on the calculation of ecological footprint per person of Shaanxi from the year of 2006 to 2016 by taking Shaanxi as the survey region. Through the variable-drop importance analysis, singular point analysis and predictive analysis, the partial least squares regression model acquired is proved to have good accuracy. The research results are: From the year of 2006 to 2016, the ecological footprint per person of Shaanxi increased to 2.894958909 from 1.818103793 hm2. The ecological deficit increased to 2.42316627 from 1.28352051 hm2. And the ecological carrying capacity decreased to 0.47179264 from 0.53458328 hm2; the whole-district GDP, the per capita nonproductive expenditure of towners, the second industrial output and the first industrial output are the remarkable factors that affect the Shaanxi ecological footprint.

Keywords

Ecological footprint Partial least squares regression method Factor of influence Shaanxi 

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Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2018

Authors and Affiliations

  1. 1.School of Management and EconomyNorth China University of Water Resources and Electric PowerZhengzhouChina

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