Analyzing the Relationship between the PM2.5 Concentration and the Gini Coefficient Using the Grey Model

To explore the relationship between the PM2.5 concentration and the gap between the rich and the poor, the PM2.5 concentration in 26 provincial regions of China is predicted by using the Gini coefficient as the independent variable. The nonequigap fractional grey prediction model (CFNGM (1, 1)) is u...

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Main Authors: Lifeng Wu, Kai Cai, Yan Chen
Format: Article
Language:English
Published: Wiley 2021-01-01
Series:Journal of Mathematics
Online Access:http://dx.doi.org/10.1155/2021/8222993
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author Lifeng Wu
Kai Cai
Yan Chen
author_facet Lifeng Wu
Kai Cai
Yan Chen
author_sort Lifeng Wu
collection DOAJ
description To explore the relationship between the PM2.5 concentration and the gap between the rich and the poor, the PM2.5 concentration in 26 provincial regions of China is predicted by using the Gini coefficient as the independent variable. The nonequigap fractional grey prediction model (CFNGM (1, 1)) is used for data fitting and predicting. The validity of the model is verified by comparing with the traditional nonequidistant grey model. The predicting results show that the PM2.5 concentration in many provinces of China presents a roughly downward trend. In the past nine years, the Gini coefficients have declined in more than 70% of the 26 provinces. However, the development of the Gini coefficient in Northwest China fluctuates greatly and even has an upward trend in recent years. According to the predictive results, reasonable suggestions can be put forward for the effective control of PM2.5 emission in China.
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spelling doaj-art-48879bf4bd854cd984908de150cabd182025-02-03T07:23:54ZengWileyJournal of Mathematics2314-46292314-47852021-01-01202110.1155/2021/82229938222993Analyzing the Relationship between the PM2.5 Concentration and the Gini Coefficient Using the Grey ModelLifeng Wu0Kai Cai1Yan Chen2School of Management Engineering and Business, Hebei University of Engineering, Handan 056038, ChinaSchool of Management Engineering and Business, Hebei University of Engineering, Handan 056038, ChinaSchool of Management Engineering and Business, Hebei University of Engineering, Handan 056038, ChinaTo explore the relationship between the PM2.5 concentration and the gap between the rich and the poor, the PM2.5 concentration in 26 provincial regions of China is predicted by using the Gini coefficient as the independent variable. The nonequigap fractional grey prediction model (CFNGM (1, 1)) is used for data fitting and predicting. The validity of the model is verified by comparing with the traditional nonequidistant grey model. The predicting results show that the PM2.5 concentration in many provinces of China presents a roughly downward trend. In the past nine years, the Gini coefficients have declined in more than 70% of the 26 provinces. However, the development of the Gini coefficient in Northwest China fluctuates greatly and even has an upward trend in recent years. According to the predictive results, reasonable suggestions can be put forward for the effective control of PM2.5 emission in China.http://dx.doi.org/10.1155/2021/8222993
spellingShingle Lifeng Wu
Kai Cai
Yan Chen
Analyzing the Relationship between the PM2.5 Concentration and the Gini Coefficient Using the Grey Model
Journal of Mathematics
title Analyzing the Relationship between the PM2.5 Concentration and the Gini Coefficient Using the Grey Model
title_full Analyzing the Relationship between the PM2.5 Concentration and the Gini Coefficient Using the Grey Model
title_fullStr Analyzing the Relationship between the PM2.5 Concentration and the Gini Coefficient Using the Grey Model
title_full_unstemmed Analyzing the Relationship between the PM2.5 Concentration and the Gini Coefficient Using the Grey Model
title_short Analyzing the Relationship between the PM2.5 Concentration and the Gini Coefficient Using the Grey Model
title_sort analyzing the relationship between the pm2 5 concentration and the gini coefficient using the grey model
url http://dx.doi.org/10.1155/2021/8222993
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