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Does China's carbon emissions trading policy improve the technology innovation of relevant enterprises?
Author(s) -
Zhang YueJun,
Shi Wei,
Jiang Lin
Publication year - 2020
Publication title -
business strategy and the environment
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 2.123
H-Index - 105
eISSN - 1099-0836
pISSN - 0964-4733
DOI - 10.1002/bse.2404
Subject(s) - china , marketization , propensity score matching , matching (statistics) , industrial organization , endogeneity , business , sample (material) , panel data , technology innovation , economics , econometrics , statistics , chemistry , mathematics , chromatography , political science , law
Abstract China's carbon emissions trading (CET) policy aims to force relevant enterprises to implement low‐carbon technology innovation and address environmental challenges through marketization means. However, how China's CET policy may affect enterprise technology innovation and whether this effect may differ in industries remain to be further investigated. Therefore, based on the panel data of listed enterprises covered by the CET policy in China during 2009–2017, this paper employs the difference‐in‐difference (DID) and DID‐based propensity score matching models to evaluate the effect of CET on technology innovation. The empirical results indicate that the effect of China's CET on the technology innovation of related enterprises is generally not significant during the sample period, but this effect presents evident industrial heterogeneity. Specifically, among the eight CET‐covered industries, the CET policy helps to improve technology innovation for power and aviation enterprises but not in the other six industries (i.e., steel, chemical, building material, petrochemical, nonferrous metals, and paper), which implies that China's CET policy still has great potential for promoting the technology innovation of related enterprises. In addition, the central findings remain robust when the system generalized method of moment and DID‐based coarsened exact matching models are applied to consider the influence of omitted variables, unobservable confounders, and different matching methods.