Huang Chaoqing, Wu Qian, Chen Yujie, Nguyen MinhThu, Chen Bin, Hong Song, He Chao
School of Resource and Environmental Sciences, Wuhan University, Wuhan 430079, China.
Key Laboratory of Geographic Information System, Ministry of Education, Wuhan University, Wuhan 430079, China.
Eco Environ Health. 2025 Apr 26;4(2):100150. doi: 10.1016/j.eehl.2025.100150. eCollection 2025 Jun.
Understanding regional carbon emissions from human activities, particularly their spatio-temporal patterns, is essential for implementing decarbonization strategies and cultivating a low-carbon economy. This study develops a spatial visualization model to estimate carbon emissions in Southeast Asia using calibrated nighttime light data, with DMSP-OLS (Defense Meteorological Satellite Program Operational Linescan System) and NPP-VIIRS (National Polar-orbiting Partnership Visible Infrared Imaging Radiometer Suite) standardized through polynomial regression and machine learning to ensure consistency. Emissions in Southeast Asia increased by 2.51 times from 1992 to 2022, shifting from gradual to rapid growth. Validation against Open-source Data Inventory for Anthropogenic CO (ODIAC) and Emissions Database for Global Atmospheric Research (EDGAR) shows strong agreement in high-emission urban areas but discrepancies in low-emission rural regions due to data sparsity and satellite sensor limits. Spatial analysis reveals that major Southeast Asian cities and their peripheries exhibit robust, sustained growth, while rural, less-developed areas show slower trends, highlighting persistent urban-rural disparities. These urban regions demonstrate a "circular economy advantage", where per-unit-area carbon emissions steadily rise in economically advantageous zones. Despite high model accuracy, uncertainties persist due to variations in regional economic activities and the limitations of satellite-based emission proxies. Forecasts suggest elevated emission levels in major cities will continue, while changes in other areas remain relatively minimal. Consequently, achieving a low-carbon economy in Southeast Asia requires a top-down approach, emphasizing infrastructure enhancement, resource and energy optimization, and fostering a sustainable, circular socio-economic system.
了解人类活动产生的区域碳排放，尤其是其时空模式，对于实施脱碳战略和培育低碳经济至关重要。本研究开发了一种空间可视化模型，利用校准后的夜间灯光数据估算东南亚的碳排放量，通过多项式回归和机器学习对国防气象卫星计划业务线扫描系统（DMSP - OLS）和国家极地轨道伙伴关系可见红外成像辐射计套件（NPP - VIIRS）进行标准化，以确保一致性。从1992年到2022年，东南亚的排放量增长了2.51倍，从逐渐增长转变为快速增长。与人为CO开源数据清单（ODIAC）和全球大气研究排放数据库（EDGAR）的验证表明，在高排放城市地区有很强的一致性，但在低排放农村地区由于数据稀疏和卫星传感器限制存在差异。空间分析表明，东南亚主要城市及其周边地区呈现强劲、持续的增长，而农村和欠发达地区的增长趋势较慢，突出了持续存在的城乡差距。这些城市地区展现出一种“循环经济优势”，即在经济优势区域单位面积碳排放量稳步上升。尽管模型准确性较高，但由于区域经济活动的变化和基于卫星的排放代理的局限性，不确定性仍然存在。预测表明，主要城市的排放水平将继续上升，而其他地区的变化相对较小。因此，东南亚实现低碳经济需要一种自上而下的方法，强调加强基础设施、优化资源和能源，并培育可持续的循环社会经济系统。
