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空气污染、服务业发展与创新:来自中国的证据。

Air pollution, service development and innovation: Evidence from China.

机构信息

School of Economics, Zhejiang University, Hangzhou, China.

出版信息

PLoS One. 2022 May 24;17(5):e0263883. doi: 10.1371/journal.pone.0263883. eCollection 2022.

DOI:10.1371/journal.pone.0263883
PMID:35609022
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC9129014/
Abstract

The environment is crucial in economic development. However, the impact of the environment on cities, especially, on urban innovation still needs more attention. Based on city-level and PM2.5 data from 280 prefecture-level cities and municipalities in China from 2003 to 2016, we empirically studied the influence of haze on urban innovation and its mechanism. The results show that (1) haze has a significant negative impact on urban innovation, which varies with regions, resource-dependent cities, provincial capital cities, and sub-provincial cities. (2) This result appears to be driven by industrial structure. By influencing the development of the service industry, haze affects the level of urban innovation. (3) In particular, advanced producer services and basic producer services are heterogeneous. Advanced producer services have a stronger impact on innovation, while basic producer services are more sensitive to haze. We used the Instrumental Variable (IV) regression to address endogeneity, the conclusion remains robust.

摘要

环境对经济发展至关重要。然而,环境对城市的影响,尤其是对城市创新的影响,仍需要更多关注。基于 2003 年至 2016 年中国 280 个地级及以上城市和直辖市的城市层面和 PM2.5 数据,我们实证研究了雾霾对城市创新的影响及其机制。结果表明:(1)雾霾对城市创新有显著的负向影响,这种影响因地区、资源依赖型城市、省会城市和副省级城市而异。(2)这一结果似乎是由产业结构驱动的。雾霾通过影响服务业的发展,进而影响城市创新水平。(3)特别是,高级生产性服务业和基础性生产性服务业存在异质性。高级生产性服务业对创新的影响更强,而基础性生产性服务业对雾霾更为敏感。我们使用工具变量(IV)回归来解决内生性问题,结论仍然稳健。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a23c/9129014/f4ab08736f05/pone.0263883.g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a23c/9129014/7ddab86b5ae1/pone.0263883.g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a23c/9129014/f4ab08736f05/pone.0263883.g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a23c/9129014/7ddab86b5ae1/pone.0263883.g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a23c/9129014/f4ab08736f05/pone.0263883.g002.jpg

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本文引用的文献

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Regional Estimates of Chemical Composition of Fine Particulate Matter Using a Combined Geoscience-Statistical Method with Information from Satellites, Models, and Monitors.利用卫星、模型和监测数据的综合地球科学-统计方法估算细颗粒物的化学组成的区域估计。
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