A review of artificial intelligence as a tool for sustainability in Mexico
保存先:
| 著者: | , |
|---|---|
| フォーマット: | artículo original |
| 状態: | Versión publicada |
| 出版日付: | 2026 |
| その他の書誌記述: | Artificial Intelligence (AI) is emerging as a strategic tool to address sustainability challenges in Mexico. This study aims to evaluate the current state of research on AI applied to sustainability in the country, identify impact patterns, and highlight critical areas of opportunity. Employing a methodological review of recent scientific literature from key repositories and selecting 11 studies published after 2020, the analysis reveals a marked dichotomy of “two AIs.” On the one hand, “green AI,” focused on environmental monitoring and ecosystem conservation, demonstrates growing maturity and successful outcomes, which are attributed to strong institutional support and the availability of public satellite data. On the other hand, “brown AI,” applied to urban and industrial problems such as waste management, is determined to be incipient, theoretical, and facing practical failures. It is concluded that this divergence is fundamentally due to data types and availability: while “green AI” thrives on abundant public data, “brown AI” is hindered by the scarcity of granular, high-quality data. Furthermore, the study identifies significant gaps in areas such as renewable energy, urban planning, and the circular economy. It proposes pragmatic adaptation strategies to overcome the data barrier as an initial step toward future context-specific innovations in Mexico. |
| 国: | Portal de Revistas TEC |
| 機関: | Instituto Tecnológico de Costa Rica |
| Repositorio: | Portal de Revistas TEC |
| 言語: | Español |
| OAI Identifier: | oai:ojs.pkp.sfu.ca:article/8522 |
| オンライン･アクセス: | https://revistas.tec.ac.cr/index.php/tec_marcha/article/view/8522 |
| キーワード: | Artificial intelligence sustainability Mexico machine learning environmental monitoring Inteligencia artificial sustentabilidad México aprendizaje automático monitoreo ambiental |