| 碳排放约束下城市长期脱碳趋势预测 |
| 摘要点击 253 全文点击 18 投稿时间：2024-04-08 修订日期：2024-07-08 |
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| 中文关键词 碳排放约束政策 城市碳排放 长期预测模型 BP神经网络模型 大型城市 |
| 英文关键词 carbon emission constraint policies urban carbon emissions long-term prediction model BP neural network model large cities |
| 作者 | 单位 | E-mail | | 袁启恒 | 国家电网有限公司大数据中心, 北京 100052 | 6053995@qq.com | | 刘贵贤 | 清华大学环境学院, 北京 100084 | guixianliu1990@163.com | | 周春雷 | 国家电网有限公司大数据中心, 北京 100052 | | | 鲁玺 | 清华大学环境学院, 北京 100084 | | | 薄宇 | 清华大学环境学院, 北京 100084 | | | 李燕溪 | 国家电网有限公司大数据中心, 北京 100052 | | | 陈翔 | 国家电网有限公司大数据中心, 北京 100052 | | | 江鹏 | 国家电网有限公司大数据中心, 北京 100052 | | | 黄昱杰 | 清华大学环境学院, 北京 100084 | | | 王宇博 | 清华大学环境学院, 北京 100084 | | | 郑佳琳 | 清华大学环境学院, 北京 100084 | | | 王旭东 | 国网天津市电力公司, 天津 300010 | | | 王林 | 国网天津市电力公司, 天津 300010 | |
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| 中文摘要 |
| 随着气候变化对环境、经济发展和人类健康构成的威胁日益严峻,全球关注点逐渐聚焦于CO2排放.作为全球能源活动碳排放的主要来源,城市成为削减碳排放的关键战场.为准确预测城市长期碳排放趋势,首先按照“政策目标-政策体系-政策执行-市场机制”逻辑构建了碳排放约束政策指标体系；随后,利用BP神经网络模型,结合GDP、产业结构、人口规模、能源结构和能源强度等构建城市长期碳排放趋势预测模型；并对4个典型超大型城市的2021~2060年碳排放进行长期预测.结果表明：①北京、上海和重庆的范围1、范围2和范围3总碳排放均呈现显著下降趋势,而天津呈现先增后降趋势,2025年达到峰值6.19亿t；②总体下降趋势下,北京和上海存在平台期,即在特定时间段内,碳排放量相对稳定,没有显著下降；③过于复杂的政策体系可能抑制碳减排效率,合理的政策执行强度是确保碳排放持续下降的关键；④适当放慢碳市场覆盖范围扩张的速度,对于促进碳排放的进一步降低具有积极作用. |
| 英文摘要 |
| With climate change posing an increasingly serious threat to the environment, economic development, and human health, global attention is gradually focusing on CO2. As the main source of carbon emissions from global energy activities, cities have become a key battlefield for reducing carbon emissions. To accurately predict the long-term carbon emission trend of cities, this study first constructed a carbon emission constraint policy indicator system according to the logic of “policy objectives policy system policy execution market mechanism.” Subsequently, using the BP neural network model, a long-term carbon emission trend prediction model for cities was constructed by combining GDP, industrial structure, population size, energy structure, and energy intensity, and long-term carbon emissions forecasts were made for Beijing, Tianjin, Shanghai, and Chongqing from 2021 to 2060. The results showed that： ① The total carbon emissions of Scope 1, Scope 2, and Scope 3 of Beijing, Shanghai, and Chongqing all showed a significant downward trend, whereas Tianjin showed a trend of first increasing and then decreasing, reaching a peak of 619 million tons in 2025； ② under the overall downward trend, there was a plateau period between Beijing and Shanghai, in which carbon emissions remained relatively stable during a specific time without a significant decrease； ③ an overly complex policy system may have suppressed the efficiency of carbon reduction and a reasonable intensity of policy implementation is the key to ensuring a sustained decrease in carbon emissions； and ④ slowing down the expansion of carbon market coverage appropriately had a positive effect on promoting further reduction of carbon emissions. |
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