DOI: 10.5281/zenodo.15545361
title: The anatomy of Green AI technologies: structure, evolution, and impact - Dataset and Replicability Material

description: 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.