相关实验视频
Updated: Feb 10, 2026

13:19
Deep Neural Networks for Image-Based Dietary Assessment
Published on: March 13, 2021
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[基于拉索变压器神经网络模型的海南省碳排放预测]
Yu-Jie Jin1,2, Xiao-Bin Jin1,2, Xing-Ming Hong3
1School of Geography and Ocean Science, Nanjing University, Nanjing 210023, China.
Huan jing ke xue= Huanjing kexue
|February 9, 2026
概括
海南省 海南省
科学领域:
- 环境科学 环境科学
- 生态经济学生态经济学
- 气候变化研究 气候变化研究
背景情况:
- 海南省是中国一个重要的生态区和自由贸易港.
- 该省面临着在国家碳中和目标下平衡碳减排和经济发展的挑战.
- 了解碳排放驱动因素对于可持续发展至关重要.
研究的目的:
- 分析海南省 (2004-2023) 的碳排放趋势和影响因素.
- 使用先进的建模预测未来的碳排放 (2024-2030年).
- 为协调的低碳经济发展提供见解.
主要方法:
- 碳来源,碳汇和净排放的计算.
- 对于因子分解的逻辑平均分数指数 (LMDI) 模型.
- 拉索分析用于因素选.
- 拉索转换器神经网络 (PatchTST) 用于预测.
主要成果:
- 碳汇保持稳定;净排放跟踪碳来源 (2004-2023年).
- 确定了关键驱动因素:能源强度,土地碳强度,经济效率,土地利用结构,人口和土地利用效率.
- 预计2030年碳排放量:43,455,300,土地利用效率增长最快,人口增长最慢.
结论:
- 优化工业结构,资源效率和生态系统保护至关重要.
- 这些战略可以促进海南的协调碳减排和经济增长.
- 该研究为海南的低碳发展提供了决策参考.
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