Systematic Bias in Green Patent Classification: Silent Green and False Green

Hamid Bekamiri, Jan Auernhammer, Milad Abbasiharofteh and Jesper Lindgaard Christensen
Additional contact information
Hamid Bekamiri: Aalborg University Business School, The IKE Research Group, Aalborg University, Denmark
Jan Auernhammer: Center for Design Research, ME Design Group, Stanford University, USA
Milad Abbasiharofteh: Aalborg University Business School, The IKE Research Group, Aalborg University, Denmark
Jesper Lindgaard Christensen: Aalborg University Business School, The IKE Research Group, Aalborg University, Denmark

Papers from arXiv.org

Abstract: Green-patent indicators built on Cooperative Patent Classification Y02 tags are widely used in research, policy, and investment, yet their construct validity has not been audited at corpus scale. We assess whether Y02 is systematically biased and whether that bias may reinforce the ESG innovation disconnect. We introduce an Error-as-Signal framework that treats disagreement between an administrative label and an independent model as diagnostic evidence of measurement error. Screening 9,075,421 USPTO granted patents (1962-2024) with a fine-tuned domain model against Y02 yields 517,772 disagreements. Two independent open-weight large language models then judge by consensus whether each flagged invention has a direct climate-mitigation or adaptation function. We identify 180,384 administrative Type I errors (False Green), concentrated in digital data processing, wireless networks, digital communications, and semiconductors, and 29,465 Type II errors (Silent Green), concentrated in separation processes, catalysis, exhaust control, heat pumps, power systems, and batteries. Correcting consensus-attributed errors reduces the measured green-patent population by 25.5% (592,387 to 441,468 patents; sensitivity bounds 390,540-508,126), with ICT energy efficiency (Y02D) falling 67.6%. Omission follows an inverted-U relationship with technological atypicality, making complex and unconventional inventions especially likely to go unlabelled. Event tests show no discrete jump in misclassification when green classification became salient and only a small rise in green framing after the 2013 CPC launch. The bias primarily reflects classification capacity rather than applicant strategy and structurally under-recognizes heavy industry.

Date: 2026-08
References: Add references at CitEc
Citations:

Downloads: (external link)
https://arxiv.org/pdf/2608.23420 Latest version (application/pdf)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:arx:papers:2608.23420

Access Statistics for this paper

More papers in Papers from arXiv.org
Bibliographic data for series maintained by arXiv administrators ().