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Effects of Smart City Policies on Green Total Factor Productivity: Evidence from a Quasi-Natural Experiment in China

When cities develop rapidly, there are negative effects such as population expansion, traffic congestion, resource shortages, and pollution. It has become essential to explore new types of urban development patterns, and thus, the concept of the “smart city” has emerged. The purpose of this paper is...

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Detalles Bibliográficos
Autores principales: Xin, Baogui, Qu, Yongmei
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2019
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6650913/
https://www.ncbi.nlm.nih.gov/pubmed/31284528
http://dx.doi.org/10.3390/ijerph16132396
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author Xin, Baogui
Qu, Yongmei
author_facet Xin, Baogui
Qu, Yongmei
author_sort Xin, Baogui
collection PubMed
description When cities develop rapidly, there are negative effects such as population expansion, traffic congestion, resource shortages, and pollution. It has become essential to explore new types of urban development patterns, and thus, the concept of the “smart city” has emerged. The purpose of this paper is to investigate the links between smart city policies and urban green total factor productivity (GTFP) in the context of China. Based on panel data of 200 cities in China from 2007–2016 and treating smart city policy as a quasi-natural experiment, the paper uses a difference-in-differences propensity score matching (PSM-DID) approach to prevent selection bias. The results show: (a) Smart city policies can significantly increase urban GTFP by 16% to 18%; (b) the larger the city, the stronger and more significant this promotion.
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spelling pubmed-66509132019-08-07 Effects of Smart City Policies on Green Total Factor Productivity: Evidence from a Quasi-Natural Experiment in China Xin, Baogui Qu, Yongmei Int J Environ Res Public Health Article When cities develop rapidly, there are negative effects such as population expansion, traffic congestion, resource shortages, and pollution. It has become essential to explore new types of urban development patterns, and thus, the concept of the “smart city” has emerged. The purpose of this paper is to investigate the links between smart city policies and urban green total factor productivity (GTFP) in the context of China. Based on panel data of 200 cities in China from 2007–2016 and treating smart city policy as a quasi-natural experiment, the paper uses a difference-in-differences propensity score matching (PSM-DID) approach to prevent selection bias. The results show: (a) Smart city policies can significantly increase urban GTFP by 16% to 18%; (b) the larger the city, the stronger and more significant this promotion. MDPI 2019-07-05 2019-07 /pmc/articles/PMC6650913/ /pubmed/31284528 http://dx.doi.org/10.3390/ijerph16132396 Text en © 2019 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Xin, Baogui
Qu, Yongmei
Effects of Smart City Policies on Green Total Factor Productivity: Evidence from a Quasi-Natural Experiment in China
title Effects of Smart City Policies on Green Total Factor Productivity: Evidence from a Quasi-Natural Experiment in China
title_full Effects of Smart City Policies on Green Total Factor Productivity: Evidence from a Quasi-Natural Experiment in China
title_fullStr Effects of Smart City Policies on Green Total Factor Productivity: Evidence from a Quasi-Natural Experiment in China
title_full_unstemmed Effects of Smart City Policies on Green Total Factor Productivity: Evidence from a Quasi-Natural Experiment in China
title_short Effects of Smart City Policies on Green Total Factor Productivity: Evidence from a Quasi-Natural Experiment in China
title_sort effects of smart city policies on green total factor productivity: evidence from a quasi-natural experiment in china
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6650913/
https://www.ncbi.nlm.nih.gov/pubmed/31284528
http://dx.doi.org/10.3390/ijerph16132396
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