Abstract:    With the coordinated action of global response to climate change, the relationship between trade and environmental policy has attracted more and more attention. The General Agreement on Tariffs and Trade (GATT) gives the members of the World Trade Organization (WTO) the "Environment Protection Exceptional Right". Each member has the right to take trade restrictive measures on the grounds of protecting human, animal and plant life and health, or protecting natural resources. There are also many provisions on environmental protection in international conventions and multilateral free trade agreements. Therefore, many countries take green trade barriers in the name of protecting natural resources and the lives and health of residents. Compared with developing countries, developed countries more often adopt green barriers. Among them, the United States is one of the main countries that frequently adopt green barriers, while Chinese enterprises are often the implementation object of green barriers. Based on the perspective of enterprise innovation, this research analyses the effect of American green barriers on Chinese listed companies′ R&D investment. 
    Compared with existing studies, the possible contributions and significance of this paper are as follows: Firstly, this paper enriches the existing literature of studying the impact of the implementation of green trade barriers from the perspective of R&D investment. Based on the name of environment or residents′ life and health, green barriers affect trade flow by imposing certain restrictions on import and export, but the impact on enterprise′ R&D investment is still lack of empirical research support. Secondly, based on the S&P global market intelligence database, the segmental industry data of A-share listed companies in Shanghai and Shenzhen stock markets are screened. By matching with the data of the U.S. environmental trade policy reviews (TPRs), the specific listed companies affected by the U.S. green barriers are identified, so as to provide micro evidence for the research on the impact of green barriers on enterprises′ R&D investment. Thirdly, the existing relevant studies mainly focus on the impact of green barriers on the export or import of some product. Based on the case data of green barriers of the United States, the research conclusions are more general and could also provide some guidance for green trade policy selection.
    Based on the data of American TPRs and R&D investment of Chinese listed companies from 2009 to 2019 and the difference-in-differences (DID) model, the results shows that green barriers significantly reduce the R&D investment of Chinese listed companies, and scale, profit and signal mechanism are the main channels. Further research found that compared with small enterprises and foreign-funded enterprises, the R&D investment of large enterprises, state-owned and private listed enterprises suffering from green barriers more.
    This paper drew the following policy implications: China need to speed up the construction of green trade development system, establish and improve environmental standards and technical specifications, participate in the mutual recognition of international standards actively, and improve the international competitiveness of domestic green products. On the one hand, policy makers should promote enterprises to develop green products, form green industrial chain, and strengthen green technology innovation and industrial upgrading. On the other hand, the government should also promote exchanges and cooperation on multilateral and regional green trade related issues, actively take part in the formulation of international rules for green trade, and make full use of trade dispute settlement mechanisms such as WTO and free trade agreement. In addition, the assistance policies for enterprises suffering from green barriers should focus on the firm heterogeneity and environmental characteristics, improve the response capacity of enterprises and stimulate their own R&D motivations.

Key words: green barrier, R&D investment, signaling mechanism, DID estimation