This paper studies socioeconomic and environmental changes in the neighboring areas Bangladesh-Myanmar border from 2012 to 2019, thus covering the period before and after the 2017 Rakhine conflict in Myanmar and outflux of refugees across the border to Bangladesh. Given the scarcity and costliness of traditional data collection methods in such conflict areas, the paper uses a novel methodological model based on very-high-resolution satellite imagery, nighttime satellite imagery, and machine-learning algorithms to generate reliable and reusable data for comparative assessment of the impacts of the Rakhine conflict. Assessments of welfare and environmental risks using this approach can be accurate and scalable across different regions and times when other data are unavailable. Key findings are: the general livelihood situation has worsened and income sources shrunk in Rakhine; forced migration damaged the ecologically fragile regions in the two countries; the destruction of aquaculture wetland ecosystems is observed in Rakhine; the deforestation rate reached 20% in Rakhine and 13% on the Bangladeshi side of the border. The results can provide guidance to policymakers and international actors as they work to repatriate the victims of the conflict in Rakhine and minimize the conflict’s security and environmental consequences. The methodology can be applied to other data-poor conflict and refugee areas in the world.

本文研究了 2012 年至 2019 年孟缅边境周边地区的社会经济和环境变化，从而涵盖了 2017 年缅甸若开邦冲突之前和之后的时期以及跨境难民涌入孟加拉国的时期。鉴于此类冲突地区传统数据收集方法的稀缺性和成本高昂，本文使用基于超高分辨率卫星图像、夜间卫星图像和机器学习算法的新方法模型来生成可靠且可重复使用的数据以进行比较评估若开邦冲突的影响。当其他数据不可用时，使用这种方法对福利和环境风险的评估可以在不同地区和时间进行准确和可扩展。主要发现是：若开邦的总体生计状况恶化，收入来源减少；强迫移民破坏了两国生态脆弱地区；在若开邦观察到水产养殖湿地生态系统遭到破坏；若开邦的森林砍伐率达到 20%，边境的孟加拉国一侧达到 13%。结果可以为决策者和国际行动者提供指导，帮助他们遣返若开邦冲突的受害者，并将冲突对安全和环境的影响降至最低。该方法可以应用于世界上其他缺乏数据的冲突和难民地区。若开邦的森林砍伐率达到 20%，边境的孟加拉国一侧达到 13%。结果可以为决策者和国际行动者提供指导，帮助他们遣返若开邦冲突的受害者，并将冲突对安全和环境的影响降至最低。该方法可以应用于世界上其他缺乏数据的冲突和难民地区。若开邦的森林砍伐率达到 20%，边境的孟加拉国一侧达到 13%。结果可以为决策者和国际行动者提供指导，帮助他们遣返若开邦冲突的受害者，并将冲突对安全和环境的影响降至最低。该方法可以应用于世界上其他缺乏数据的冲突和难民地区。