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

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          Abstract

          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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          Technical Change and the Aggregate Production Function

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            Matching As An Econometric Evaluation Estimator

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              Efficiency Estimation from Cobb-Douglas Production Functions with Composed Error

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                Author and article information

                Journal
                Int J Environ Res Public Health
                Int J Environ Res Public Health
                ijerph
                International Journal of Environmental Research and Public Health
                MDPI
                1661-7827
                1660-4601
                05 July 2019
                July 2019
                : 16
                : 13
                Affiliations
                College of Economics and Management, Shandong University of Science and Technology, Qingdao 266590, China
                Author notes
                [* ]Correspondence: xin@ 123456tju.edu.cn (B.X.); 15610016397@ 123456163.com (Y.Q.); Tel.: +86-136-8766-0373 (B.X.)
                Article
                ijerph-16-02396
                10.3390/ijerph16132396
                6650913
                31284528
                © 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/).

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