The anatomy of Green AI technologies: structure, evolution, and impact - Dataset and Replicability Material

Accompanying material for the paper "The anatomy of Green AI technologies: structure, evolution, and impact" (2025). Dataset Construction The Green AI Patent Dataset comprises 63 326 unique U.S. patents that intersect environmental (“green”) technologies with artificial‐intelligence components, spanning from 1976 to 2023. It was assembled by combining: PatentsView (USPTO) – U.S. patents (snapshot of January 2025) labelled under Cooperative Patent Classification classes Y02 and Y04S for climate‐change mitigation/adaptation and smart‐grid technologies. Artificial Intelligence Patent Dataset (AIPD 2023 - most recent update) – USPTO’s machine‐learning–validated classification of AI‐related patents (predict50_any_ai = 1). Available here: Pairolero, N. et al. The artificial intelligence patent dataset (aipd) 2023 update. USPTO Economic Working Paper 2024-4,USPTO (2024). Available at https://www.uspto.gov/sites/default/files/documents/oce-aipd-2023.pdf. Variables Variable Description Completeness (non-null count) patent_id Unique USPTO patent identifier. 63 326 cpc_subclass Subclasses of "green" CPC taxonomy Y02 / Y04S. Refer to the USTPO's website for more details: https://www.uspto.gov/web/patents/classification/cpc/html/cpc-Y.html 63 326 patent_date Grant date of the patent (YYYY-MM-DD). 63 326 patent_title Title of the patent. 63 326 assignee Disambiguated assignee organization name. 59 479 country Disambiguated assignee country. 59 155 forward_citations Number of times this patent is cited by later patents (forward citations). 63 326 tech_domain BERTOPIC‐derived technology domain (integer 0–15; –1 marks outliers). 62 337 real_value Market‐value proxy associated with the patent, derived from the updated dataset of Kogan, L., Papanikolaou, D., Seru, A. & Stoffman, N. Technological innovation, resource allocation, and growth. The Q. J.Econ. 132, 665–712, DOI: 10.1093/qje/qjw040 (2017). 26 306 BERTOPIC Topic Mapping Each patent was assigned to one of 16 topics (tech_domain), numbered 0–15 (with –1 for outliers). Below is the label, example keywords (with their topic cohesion scores), and the number of patents in each topic: ID Label Top Keywords (score) Count 0 Data Processing & Memory Management processing (0.516), computing (0.461), process (0.449), systems (0.443), memory (0.421) 27 435 1 Microgrid & Distributed Energy Systems microgrid (0.487), electricity (0.421), utility (0.401), power (0.380), energy (0.370) 5 378 2 Vehicle Control & Autonomous Powertrains vehicle (0.477), vehicles (0.468), control (0.416), driving (0.387), engine (0.386) 3 747 3 Irrigation & Agricultural Water Mgmt irrigation (0.511), systems (0.431), flow (0.353), process (0.348), water (0.333) 2 754 4 Photovoltaic & Electrochemical Devices semiconductor (0.518), photoelectric (0.509), electrodes (0.487), electrode (0.473), photovoltaic (0.470) 2 599 5 Clinical Microbiome & Therapeutics microbiome (0.481), clinical (0.371), physiological (0.321), therapeutic (0.320), disease (0.314) 2 286 6 Combustion Engine Control combustion (0.423), engine (0.373), control (0.342), fuel (0.338), ignition (0.318) 2 179 7 Battery Charging & Management charging (0.485), charger (0.449), charge (0.425), battery (0.386), batteries (0.377) 1 541 8 HVAC & Thermal Regulation hvac (0.515), heater (0.474), cooling (0.471), heating (0.464), evaporator (0.455) 1 523 9 Lighting & Illumination Systems lighting (0.621), illumination (0.601), lights (0.545), brightness (0.526), light (0.488) 1 219 10 Exhaust & Emission Treatment exhaust (0.464), catalytic (0.446), purification (0.444), catalyst (0.366), emissions (0.365) 1 064 11 Wind Turbine & Rotor Control turbines (0.498), turbine (0.488), windmill (0.464), wind (0.418), rotor (0.300) 988 12 Aircraft Wing Aerodynamics & Control wing (0.450), aircraft (0.448), wingtip (0.424), apparatus (0.423), aerodynamic (0.418) 697 13 Meteorological Radar & Weather Forecasting radar (0.541), meteorological (0.511), weather (0.412), precipitation (0.391), systems (0.372) 542 14 Fuel Cell Systems & Electrodes fuel (0.375), cell (0.313), systems (0.295), cells (0.291), controls (0.262) 377 15 Turbine Airfoils & Cooling airfoils (0.584), airfoil (0.572), turbine (0.433), engine (0.333), axial (0.321) 352 –1 Outliers – 7 656 Code availability This Zenodo entry contains topic_modeling.ipynb, a fully documented jupyter notebook containing Python code for uncovering latent themes in patent abstracts using BERTopic. It walks through text preprocessing (lowercasing, standard English stopwords plus “herein” and “invention,” tokenization, and boilerplate removal), embedding with the all-MiniLM-L6-v2 SentenceTransformer, dimensionality reduction via UMAP, clustering with HDBSCAN, and topic extraction through class-based TF-IDF. The script also executes a grid search over UMAP and HDBSCAN hyperparameters, computes UMass coherence and topic diversity for each configuration, and saves a CSV of evaluation metrics, enabling straightforward reproduction of our topic-modeling workflow. **Note on Patent Abstracts** The BERTopic analysis in this notebook was performed on the full text of U.S. patent abstracts. To save space and comply with memory constraints, the abstracts themselves are not included in this repository. However, they can be downloaded directly from the PatentsView portal (see “g_patent_abstract” in the data tables at https://patentsview.org/download/data-download-tables). Each record is linked to our processed dataset via the `patent_id` field, so you can seamlessly merge the raw abstracts with your local copy of the Green AI dataset before running or inspecting the topic model. Additional analyses, such as data cleaning, merging, aggregation, and the generation of summary tables and plots, were also performed but are not included here by default, as they consist of straightforward operations using standard open-source libraries (e.g., pandas, NumPy, matplotlib, and seaborn). The full code for these steps can be made available upon request.