This thesis analyzes the contribution of ESA and NASA's patenting activities to achieving the Sustainable Development Goals (SDGs) of the 2030 Agenda. A dataset of patent families from the two agencies was constructed and subjected to collection, filtering, and cleansing to ensure reliability and consistency. Subsequently, the technological outputs were classified using machine learning models based on Natural Language Processing (NLP) techniques. The empirical analysis was divided into three phases: descriptive statistics on patenting activity, study of collaboration networks between the agencies and external partners, and text mining of patent content. The results highlight both commonalities and significant differences between the two agencies in terms of technological priorities, collaboration methods, and alignment with the SDGs, offering useful insights for understanding how they orient their activities towards the Sustainable Development Goals.
La presente tesi analizza il contributo delle attività brevettuali dell'ESA e della NASA al raggiungimento degli Obiettivi di Sviluppo Sostenibile (SDGs) dell’Agenda 2030. È stato costruito un dataset di famiglie brevettuali delle due agenzie, sottoposto a operazioni di raccolta, filtraggio e pulizia al fine di garantirne l’affidabilità e la coerenza. Successivamente, gli output tecnologici sono stati classificati tramite modelli di machine learning basati su tecniche di Natural Language Processing (NLP). L’analisi empirica si è articolata in tre fasi: statistiche descrittive sull’attività brevettuale, studio delle reti di collaborazione tra le agenzie e partner esterni, e text mining sui contenuti dei brevetti. I risultati evidenziano sia aspetti in comune che differenze significative tra le due agenzie in termini di priorità tecnologiche, modalità di collaborazione e allineamento agli SDGs, offrendo spunti utili per comprendere come orientino le proprie attività rispetto agli Obiettivi di Sviluppo Sostenibile.
INNOVAZIONE SPAZIALE E SVILUPPO SOSTENIBILE: UN'ANALISI DEI BREVETTI ESA E NASA
SGOBBA, MATTEO
2024/2025
Abstract
This thesis analyzes the contribution of ESA and NASA's patenting activities to achieving the Sustainable Development Goals (SDGs) of the 2030 Agenda. A dataset of patent families from the two agencies was constructed and subjected to collection, filtering, and cleansing to ensure reliability and consistency. Subsequently, the technological outputs were classified using machine learning models based on Natural Language Processing (NLP) techniques. The empirical analysis was divided into three phases: descriptive statistics on patenting activity, study of collaboration networks between the agencies and external partners, and text mining of patent content. The results highlight both commonalities and significant differences between the two agencies in terms of technological priorities, collaboration methods, and alignment with the SDGs, offering useful insights for understanding how they orient their activities towards the Sustainable Development Goals.| File | Dimensione | Formato | |
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Descrizione: Tesi di laurea triennale in Business Data Analytics - Matteo Sgobba
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https://hdl.handle.net/20.500.14253/67802