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author:

Jiang, Y. (Jiang, Y..) [1] | Zhang, X. (Zhang, X..) [2] | Yang, L. (Yang, L..) [3] | Duan, K. (Duan, K..) [4]

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Scopus

Abstract:

Land resources are crucial yet limited inputs in economic activities, with significant implications for both economic development and public sector behaviour. This study investigates how land resource supply affects local government actions, using the place-biased land supply policy (PBLP) introduced in 2004 as a quasi-natural experiment. Through a propensity score matching and difference-in-differences (PSM-DID) approach, this study finds that cities facing land supply restrictions under PBLP experienced slower GDP growth alongside higher pollutant emissions. These effects remain robust across alternative variable measures, accounting for other policies, and varying matching techniques. The slowdown in economic growth is primarily driven by reduced fixed assets and foreign investment, prompting local governments to relax environmental regulations to boost short-term growth. These findings highlight the unintended consequences of administrative land policies on social welfare, revealing the complex dynamics between land supply, government behaviour, and socio-economic outcomes. © 2024 Informa UK Limited, trading as Taylor & Francis Group.

Keyword:

Biased land policy China economic growth environmental quality

Community:

  • [ 1 ] [Jiang Y.]College of Business, Shanghai University of Finance and Economics, Shanghai, China
  • [ 2 ] [Zhang X.]School of Economics and Management, Tongji University, Shanghai, China
  • [ 3 ] [Yang L.]School of Economics and Management, Tongji University, Shanghai, China
  • [ 4 ] [Yang L.]School of Business, Jinggangshan University, Ji’an, Jiangxi, China
  • [ 5 ] [Duan K.]School of Economics and Management, Fuzhou University, Fuzhou, China

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Source :

Applied Economics

ISSN: 0003-6846

Year: 2024

1 . 8 0 0

JCR@2023

Cited Count:

WoS CC Cited Count:

SCOPUS Cited Count: 1

ESI Highly Cited Papers on the List: 0 Unfold All

WanFang Cited Count:

Chinese Cited Count:

30 Days PV: 0

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