Seeing Through Green: Text-Based Classification and the Firm's Returns from Green Patents
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| Format: | Preprint |
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2025
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| _version_ | 1866915558237143040 |
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| author | Santarlasci, Lapo Rungi, Armando Zinilli, Antonio |
| author_facet | Santarlasci, Lapo Rungi, Armando Zinilli, Antonio |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2507_02287 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Seeing Through Green: Text-Based Classification and the Firm's Returns from Green Patents Santarlasci, Lapo Rungi, Armando Zinilli, Antonio General Economics Economics Computation and Language 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. |
| title | Seeing Through Green: Text-Based Classification and the Firm's Returns from Green Patents |
| topic | General Economics Economics Computation and Language |
| url | https://arxiv.org/abs/2507.02287 |