绿色专利分类中的系统性偏差：隐性绿色与虚假绿色

中文摘要 AI 辅助

基于合作专利分类（Cooperative Patent Classification，简称CPC）Y02标签构建的绿色专利指标，广泛应用于研究、政策制定及投资领域，但其构念效度尚未在语料库规模上得到审计。本文评估Y02是否存在系统性偏差，以及该偏差是否会加剧ESG创新脱节问题。我们引入“误差即信号”框架，将行政标签与独立模型之间的分歧视为测量误差的诊断证据。通过微调领域模型，对1962-2024年间美国专利商标局（USPTO）授权的9,075,421件专利进行筛选，与Y02标签比对后产生517,772处分歧。随后，两个独立的开放权重大语言模型通过共识判断，确定每个被标记的发明是否具备直接的气候减缓或适应功能。我们识别出180,384个行政I类错误（虚假绿色），集中于数字数据处理、无线网络、数字通信及半导体领域；以及29,465个II类错误（隐性绿色），集中于分离工艺、催化、排气控制、热泵、电力系统及电池领域。修正共识判定的错误后，测得的绿色专利总量减少25.5%（从592,387件降至441,468件，敏感性区间为390,540-508,126件），其中信息通信技术（ICT）能效（Y02D）类别下降67.6%。遗漏与技术非典型性呈倒U型关系，使复杂且非常规的发明尤其容易被漏标。事件检验显示，当绿色分类变得突出时，误分类未出现离散跃升，且2013年CPC推出后，绿色框架仅小幅上升。该偏差主要反映分类能力而非申请人策略，且在结构上低估了重工业的贡献。

英文摘要

Research, policy, and capital rely on the Cooperative Patent Classification's Y02 tag to locate climate invention, yet whether Y02 measures what it is used to measure has never been tested at corpus scale. We ask three questions: what types of error does Y02 make, are those errors idiosyncratic, and what are their main causes? Because neither Y02 nor a text classifier trained on it is ground truth, our Error-as-Signal framework treats their disagreements as evidence, not model failure: in a construct-validity test, two independent large language models judge whether the primary function of each of 517,772 disputed inventions among 9,075,421 USPTO patents has a direct climate mechanism. Y02 makes two types of error, which we term False Green (180,384 inclusions) and Silent Green (29,465 omissions). The errors are not idiosyncratic but form a systematic bias: False Green concentrates in ICT, Silent Green in energy, transport, chemistry, and industrial engineering, so raw counts overstate ICT energy efficiency threefold. The evidence points away from applicant strategy, misclassification did not jump when green labels became salient, and toward bounded classification capacity. Yet complexity does not simply make classification harder; its two forms push errors in opposite directions, a complexity paradox. A one-standard-deviation increase in reflection complexity (capability scarcity) is associated with 2.45 times the odds that an error is an omission rather than an inclusion, whereas structural complexity (combinatorial diversity) tilts errors toward inclusion. What matters is not how complex an invention is but whether its complexity is legible to the taxonomy.

发表机构

• Aalborg University Business School（奥尔堡大学商学院）
• Stanford University（斯坦福大学）

机构由 AI 辅助整理，请以论文原文为准。