لباب-العدد 32

207 |

(9) Andreas Gros et al., “Conflict in Yemen: From Ethnic Fighting to Food Riots,” arXiv Preprint (2012), pp. 8–9. (10) Rafael Reuveny, “Climate Change-Induced Migration and Violent Conflict,” Political Geography, Vol. 26, No. 6 (2007), pp. 657–660, 664–670. (11) Ge et al., “Modelling Armed Conflict Risk under Climate Change with Machine Learning and Time-Series Data,” pp. 2–4. (12) Schyler C. Sun et al., “Revealing the Excitation Causality between Climate and Political Violence via a Neural Forward-Intensity Poisson Process,” arXiv Preprint (2022), pp. 5171–5173. (13) Markus Reichstein et al., “Deep Learning and Process Understanding for Data-Driven Earth System Science,” Nature, Vol. 566, No. 7743 (2019), pp. 196–198. (14) Erwan Monier et al., “Toward a Consistent Modeling Framework to Assess Multi-sectoral Climate Impacts,” Nature Communications, Vol. 9 (2018), pp. 3–6. (15) Yoo-Geun Ham et al., “Deep Learning for Multi-Year ENSO Forecasts,” Nature, Vol. 573, No. 7775 (2019), pp. 568–572. (16) Peijin Li et al., “Using Deep Learning to Generate Key Variables in Global Mitigation Scenarios,” Nature Climate Change, Vol. 15 (2025), pp. 762–765, 767–768. (17) Reichstein et al., “Deep Learning and Process Understanding for Data- Driven Earth System Science,” pp. 195–196. (18) Josh Cowls et al., “The AI Gambit: Leveraging Artificial Intelligence to Combat Climate Change—Opportunities, Challenges, and Recommendations,” AI & Society, Vol. 38, No. 1 (2023), pp. 296–298. (19) Kyungmee Kim and Vincent Boulanin, Artificial Intelligence for Climate Security: Possibilities and Challenges (Stockholm: Stockholm International Peace Research Institute [SIPRI], 2023), pp. 4–6, 22, 25–28. (20) Henrik Skaug Sætra and Evan Selinger, “Technological Remedies for Social Problems: Defining and Demarcating Techno-Fixes and Techno- Solutionism,” Science and Engineering Ethics, Vol. 30 (2024), pp. 2–10.

Made with FlippingBook Online newsletter