摘要
厘清区域碳排放及其与经济发展的关系对于区域绿色低碳发展具有重要意义。该研究利用多源遥感夜光影像构建了2000-2020年安徽省夜间灯光数据集,估算了2000-2020年安徽省碳排放,考察了碳排放在时间和空间上的变化趋势,同时探讨了碳排放与经济发展之间的相互作用机制。结果显示:(1)对比4种预测模型,CNN-BiLSTM深度学习估算模型精度最优,在显著性水平P<0.001情况下,R2为0.8823,MAE为23.0067,MSPE为16.39%,RMSE为33.6161;(2)安徽省区域碳排放空间分布存在显著差异,最高地区年均碳排放为897万t/km^(2),碳排放极热点区从10个降至9个,极冷点区从0增至3个;(3)2000-2020年安徽省碳排放呈增长趋势,增速最高值为6.15万t/(km^(2)·a),碳排放量与增长速率在空间分布上具有较高的相似性;(4)碳排放与经济发展之间脱钩状态逐渐改善,平均脱钩系数为0.4814,脱钩状态以弱脱钩为主;(5)安徽省的碳排放受到人均GDP和人口规模的正向影响,且正向贡献度逐渐减弱,而能源结构和能源强度则对其产生负向影响。
Clarifying regional carbon emissions and their relationship with economic development is of great significance for regional green and low-carbon development.In this study,the night lighting dataset of Anhui Province from 2000 to 2020 was constructed by using multi-source remote sensing luminous images,simulating the carbon emissions of the Province from 2000 to 2020,investigating the change trend of carbon emissions in different time and space,and discussing the interaction mechanism between carbon emissions and economic development.The results showed that compared with the four prediction models,the accuracy verification showed that the CNN-BiLSTM deep learning estimation model had the best accuracy,with R2 being 0.8823,MAE being 23.0067,MSPE being 16.39%,and RMSE being 33.6161 under the significance level P<0.001.There were significant differences in the spatial distribution of regional carbon emissions in the province,with the highest annual average carbon emissions being 8.97 Mt/km^(2),the number of carbon emission extreme hotspots decreased from 10 to 9,and the number of extreme cold spots increased from 0 to 3.From 2000 to 2020,carbon emissions showed an increasing trend,with the highest growth rate being 0.0615 Mt/(km^(2)·a),and carbon emissions and growth rates had high similarities in spatial distribution.The decoupling state between carbon emissions and economic development gradually improved,with an average decoupling coefficient of 0.4814,and the decoupling state was mainly weak decoupling.Per capita GDP and population size are positive factors of carbon emissions,and energy structure and energy intensity are negative factors.The positive contribu⁃tion gradually decreases,and the negative contribution gradually increases.
作者
徐建辉
马文浩
胡枫
李媛媛
杨梦蝶
朱冰怡
XU Jianhui;MA Wenhao;HU Feng;LI Yuanyuan;YANG Mengdie;ZHU Bingyi(Anhui Province Key Laboratory of Physical Geographic Environment,Chuzhou University,Chuzhou 239000,China;Anhui Engineering Laboratory of Geoinformation Smart Sensing and Services,Chuzhou 239000,China;Anhui Center for Collaborative Innovation in Geographical Information Integration and Application,Chuzhou 239000,China;School of Geographic Information and Tourism,Chuzhou University,Chuzhou 239000,China)
出处
《环境科学与技术》
CAS
CSCD
北大核心
2023年第10期198-208,共11页
Environmental Science & Technology
基金
安徽省高校自然重点项目(KJ2020A0724,zrjz2021021,KJ2021A1078)
安徽省滁州市科学技术局科技计划项目(2021ZD007)
滁州学院博士科研启动基金(2022qd003)。
