National green GDP assessment and prediction for China based on a CA-Markov land use simulation model

Green Gross Domestic Product (GDP) is an important indicator to reflect the trade-off between the ecosystem and economic system. Substantial research has mapped historical green GDP spatially. But few studies have concerned future variations of green GDP. In this study, we have calculated and mapped...

Full description

Saved in:
Bibliographic Details
Main Authors: YU, Yuhan, YU, Mengmeng, LIN, Lu, CHEN, Jiaxin, LI, Dongjie, ZHANG, Wenting, CAO, Kai
Format: text
Language:English
Published: Institutional Knowledge at Singapore Management University 2019
Subjects:
Online Access:https://ink.library.smu.edu.sg/sis_research/5425
https://ink.library.smu.edu.sg/cgi/viewcontent.cgi?article=6428&context=sis_research
Tags: Add Tag
No Tags, Be the first to tag this record!
Institution: Singapore Management University
Language: English
id sg-smu-ink.sis_research-6428
record_format dspace
spelling sg-smu-ink.sis_research-64282020-12-11T06:21:16Z National green GDP assessment and prediction for China based on a CA-Markov land use simulation model YU, Yuhan YU, Mengmeng LIN, Lu CHEN, Jiaxin LI, Dongjie ZHANG, Wenting CAO, Kai Green Gross Domestic Product (GDP) is an important indicator to reflect the trade-off between the ecosystem and economic system. Substantial research has mapped historical green GDP spatially. But few studies have concerned future variations of green GDP. In this study, we have calculated and mapped the spatial distribution of the green GDP by summing the ecosystem service value (ESV) and GDP for China from 1990 to 2015. The pattern of land use change simulated by a CA-Markov model was used in the process of ESV prediction (with an average accuracy of 86%). On the other hand, based on the increasing trend of GDP during the period of 1990 to 2015, a regression model was built up to present time-series increases in GDP at prefecture-level cities, having an average value of R square (R2 ) of approximately 0.85 and significance level less than 0.05. The results indicated that (1) from 1990 to 2015, green GDP was increased, with a huge growth rate of 78%. Specifically, the ESV value was decreased slightly, while the GDP value was increased substantially. (2) Forecasted green GDP would increase by 194,978.29 billion yuan in 2050. Specifically, the future ESV will decline, while the rapidly increased GDP leads to the final increase in future green GDP. (3) According to our results, the spatial differences in green GDP for regions became more significant from 1990 to 2050 2019-01-01T08:00:00Z text application/pdf https://ink.library.smu.edu.sg/sis_research/5425 https://ink.library.smu.edu.sg/cgi/viewcontent.cgi?article=6428&context=sis_research http://creativecommons.org/licenses/by-nc-nd/4.0/ Research Collection School Of Computing and Information Systems eng Institutional Knowledge at Singapore Management University Green GDP Ecosystem service value Gross Domestic Product Land Use CA-Markov Asian Studies Databases and Information Systems
institution Singapore Management University
building SMU Libraries
continent Asia
country Singapore
Singapore
content_provider SMU Libraries
collection InK@SMU
language English
topic Green GDP
Ecosystem service value
Gross Domestic Product
Land Use
CA-Markov
Asian Studies
Databases and Information Systems
spellingShingle Green GDP
Ecosystem service value
Gross Domestic Product
Land Use
CA-Markov
Asian Studies
Databases and Information Systems
YU, Yuhan
YU, Mengmeng
LIN, Lu
CHEN, Jiaxin
LI, Dongjie
ZHANG, Wenting
CAO, Kai
National green GDP assessment and prediction for China based on a CA-Markov land use simulation model
description Green Gross Domestic Product (GDP) is an important indicator to reflect the trade-off between the ecosystem and economic system. Substantial research has mapped historical green GDP spatially. But few studies have concerned future variations of green GDP. In this study, we have calculated and mapped the spatial distribution of the green GDP by summing the ecosystem service value (ESV) and GDP for China from 1990 to 2015. The pattern of land use change simulated by a CA-Markov model was used in the process of ESV prediction (with an average accuracy of 86%). On the other hand, based on the increasing trend of GDP during the period of 1990 to 2015, a regression model was built up to present time-series increases in GDP at prefecture-level cities, having an average value of R square (R2 ) of approximately 0.85 and significance level less than 0.05. The results indicated that (1) from 1990 to 2015, green GDP was increased, with a huge growth rate of 78%. Specifically, the ESV value was decreased slightly, while the GDP value was increased substantially. (2) Forecasted green GDP would increase by 194,978.29 billion yuan in 2050. Specifically, the future ESV will decline, while the rapidly increased GDP leads to the final increase in future green GDP. (3) According to our results, the spatial differences in green GDP for regions became more significant from 1990 to 2050
format text
author YU, Yuhan
YU, Mengmeng
LIN, Lu
CHEN, Jiaxin
LI, Dongjie
ZHANG, Wenting
CAO, Kai
author_facet YU, Yuhan
YU, Mengmeng
LIN, Lu
CHEN, Jiaxin
LI, Dongjie
ZHANG, Wenting
CAO, Kai
author_sort YU, Yuhan
title National green GDP assessment and prediction for China based on a CA-Markov land use simulation model
title_short National green GDP assessment and prediction for China based on a CA-Markov land use simulation model
title_full National green GDP assessment and prediction for China based on a CA-Markov land use simulation model
title_fullStr National green GDP assessment and prediction for China based on a CA-Markov land use simulation model
title_full_unstemmed National green GDP assessment and prediction for China based on a CA-Markov land use simulation model
title_sort national green gdp assessment and prediction for china based on a ca-markov land use simulation model
publisher Institutional Knowledge at Singapore Management University
publishDate 2019
url https://ink.library.smu.edu.sg/sis_research/5425
https://ink.library.smu.edu.sg/cgi/viewcontent.cgi?article=6428&context=sis_research
_version_ 1712301834190716928