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

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.