标题： 透过绿色：基于文本的分类与绿色专利的公司收益 显示英文标题

标题： Seeing Through Green: Text-Based Classification and the Firm's Returns from Green Patents

摘要： 本文介绍了用于从官方支持文件中识别“真正”绿色专利的自然语言处理方法。 我们在大约1240万项专利上进行训练，这些专利之前文献中已被分类为绿色专利。 因此，我们训练一个简单的神经网络，通过与环境技术相关的表达式的向量表示来扩展基准词典。 经过测试，我们发现“真正”绿色专利约占之前文献中被分类为绿色专利总数的20%。 我们展示了技术类别的异质性，然后检查发现“真正”绿色专利被后续发明引用的次数少约1%。 在论文的第二部分，我们测试了专利与欧盟企业层面财务报表仪表板之间的关系。 在控制了反向因果关系后，我们显示持有至少一项“真正”绿色专利会提高销售额、市场份额和生产率。 如果我们把分析限制在高新颖性“真正”绿色专利上，我们会发现它们还能带来更高的利润。 我们的研究结果强调了使用文本分析来评估更细粒度的专利分类的重要性，这些分类对于不同领域的政策制定是有用的。 显示英文摘要

摘要： This paper introduces Natural Language Processing for identifying ``true'' green patents from official supporting documents. We start our training on about 12.4 million patents that had been classified as green from previous literature. Thus, we train a simple neural network to enlarge a baseline dictionary through vector representations of expressions related to environmental technologies. After testing, we find that ``true'' green patents represent about 20\% of the total of patents classified as green from previous literature. We show heterogeneity by technological classes, and then check that `true' green patents are about 1\% less cited by following inventions. In the second part of the paper, we test the relationship between patenting and a dashboard of firm-level financial accounts in the European Union. After controlling for reverse causality, we show that holding at least one ``true'' green patent raises sales, market shares, and productivity. If we restrict the analysis to high-novelty ``true'' green patents, we find that they also yield higher profits. Our findings underscore the importance of using text analyses to gauge finer-grained patent classifications that are useful for policymaking in different domains.