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

TL;DR

This paper employs NLP to accurately identify genuine green patents, revealing their economic impact and heterogeneity, thereby improving patent classification for policymaking and firm strategy.

Contribution

It introduces a neural network-based NLP method to distinguish true green patents from classified ones, enhancing the accuracy of green patent identification.

Findings

Only 20% of patents classified as green are truly green.

True green patents are about 1% less cited by subsequent inventions.

Holding true green patents correlates with higher firm sales, market share, and productivity.

Abstract

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.