| 黑龙江省碳达峰预测及实现路径分析 |
| 摘要点击 225 全文点击 18 投稿时间：2024-04-11 修订日期：2024-07-01 |
| 查看HTML全文
查看全文 查看/发表评论 下载PDF阅读器 |
| 中文关键词 黑龙江省 碳达峰 碳排放预测 人工智能模型 情景分析 脱钩分析 |
| 英文关键词 Heilongjiang Province carbon peak carbon emission prediction artificial intelligence model scenario analysis decoupling analysis |
|
| 中文摘要 |
| 黑龙江省作为中国重要的重工业基地和商品粮食主产区,近年受到气候变化影响较大.为更好地预测碳排放趋势,耦合贝叶斯优化算法、等度量映射算法和支持向量机算法构建了碳排放量预测模型.结果显示,该集成模型在训练集阶段相关性可达到0.99,而在碳排放波动阶段仍能保持0.81的相关性,可以保证一定的预测精度.对黑龙江省碳排放量预测结果表明,通过低碳发展该省有望在2035年实现碳达峰,为381×106 t,预计早于基准水平5 a实现碳达峰,减少碳排放量6.39%,早于高速发展情景10 a实现碳达峰,减少碳排放量17.06%.同时,通过Tapio脱钩指数模型对2012~2019年黑龙江省碳排放波动趋势进行分析,发现该地区经济与碳排放量尚未进入稳定的脱钩阶段,但能源消费强度与碳排放量已呈现出脱钩的趋势.并基于碳达峰预测与脱钩指数对黑龙江省实现路径进行探讨,有望推进黑龙江省实现碳达峰愿景. |
| 英文摘要 |
| Heilongjiang Province, as a Chinese heavy industry base and major grain producing area, has faced notable challenges in recent years due to climate change. To effectively predict the trend of carbon emissions, the Bayesian optimization algorithm, isomap algorithm, and support vector machine algorithm were integrated to construct a carbon emission prediction model. The results demonstrated that the integrated prediction model exhibited excellent generalization ability, with a correlation coefficient of 0.99 during the training phase, and maintained generalization ability with a correlation of 0.81 during carbon emission fluctuations for satisfactory prediction accuracy. Carbon emissions in Heilongjiang Province were forecast to peak at 381 million tons by 2035 through low-carbon development, five years ahead of baseline projections with a 6.39% cut. This peak would come ten years sooner than that under high-speed growth, reducing emissions by 17.06%. Analysis with the Tapio Decoupling Index Model （2012~2019） showed an unstable decoupling between economic growth and carbon emissions but a positive trend in energy intensity vs. emissions. Discussions are underway based on these forecasts and decoupling insights to outline pathways for Heilongjiang to reach its carbon peaking goals earlier. |
|
|
|
