青藏高原东缘生态过渡带碳中和评估与预测  

Assessment and prediction of carbon neutrality in the eastern margin ecotone of Qinghai-Tibet Plateau

作　　者：高峰 律可心 乔智 马丰魁 姜群鸥[1,2] GAO Feng;LÜKexin;QIAO Zhi;MA Fengkui;JIANG Qun′ou(School of Soil and Water Conservation,Beijing Forestry University,Beijing 100083,China;Key Laboratory of Soil and Water Conservation and Desertification Prevention,Beijing Forestry University,Beijing 100083,China)

机构地区：[1]北京林业大学水土保持学院,北京100083 [2]北京林业大学水土保持与荒漠化防治教育部重点实验室,北京100083

出　　处：《生态学报》2022年第23期9442-9455,共14页Acta Ecologica Sinica

基　　金：国家重点研发计划(2019YFE0116500);国家自然科学基金项目(41901234)。

摘　　要：青藏高原东缘生态过渡带是我国重要的生态功能区和碳库,对该区域碳中和的评估和预测对于中国乃至亚洲的碳排放管理具有重要意义。基于率定的CASA模型估算了2001—2019年青藏高原东缘生态过渡带栅格尺度碳汇量,结合中国碳排放数据库分析近20年碳排放时空演变规律;然后,采用STIRPAT模型和岭回归建立碳排放与人口等6个社会经济指标的弹性关系,并讨论库兹涅兹曲线对碳排放的影响。之后采用情景分析法,设计包括绿色发展等5种不同经济发展情景预测研究区2020—2060年碳排放变化特征;最后,提出假性碳中和并进行定义,结合GM(1,1)模型预测所得碳汇量,探究青藏高原东缘生态过渡带净碳汇量未来不同情景演变趋势,预测与评估不同发展情景研究区碳中和状况。结果表明:研究区碳汇量在2001—2019年间呈波动缓慢上升趋势,研究区碳汇量东南部高西北部较低;而碳排放量增长速率迅速,于2019年达到108Mt左右,是2001年的3.07倍;近20年,研究区碳汇量均大于碳排放量,但二者差距呈减少趋势。STIRPAT模型岭回归系数表明,研究区内存在城镇化率环境库兹涅兹曲线(EKC)效应,随着城镇化率的提升,区域碳排放呈先增加后减少趋势,而对于富裕度无显著EKC效应;在6个影响因素中,人口变量对碳排放的影响最显著,每增加1%的人口,碳排放将增加1.03%左右;在预测的五种不同发展情景中,可持续发展情景(ST)与基准情景(BL)、节能情景(ES)与绿色发展情景(GD)分别在2050年、2040年实现碳达峰,碳达峰时间随着能耗的减少逐渐提前。粗放情景(ETS)在2060年仍未实现碳达峰,并且其碳排放将于2040年左右超过碳汇量,而其余四种情景预测2020—2060年碳汇量始终大于碳排放量,但其净碳汇量均呈先减少后增加的趋势。因此,青藏高原东缘生态过渡带具有较强固碳能力,但如采用不加管制的发展模式,其碳�The Eastern Margin Ecotone of Qinghai-Tibet Plateau is recognized both as a crucial ecological function zone and as a carbon pool of China. The assessment and prediction of carbon neutrality in this region are of great significance to the carbon emission control of China and even Asia. The calibrated CASA model was applied to estimate the carbon sink(CS) from 2001 to 2019. Combined with COemission data acquired from Carbon Emission Accounts & Datasets(CEADs), this paper analyzed the temporal and spatial change of CS and COemission in the Eastern Margin Ecotone of Qinghai-Tibet Plateau in the past 20 years. STIRPAT and Ridge regression were employed to establish the elasticity between COemission and six socioeconomic factors: population, secondary and tertiary industries, affluence, urbanization rate and stock farming. Environmental Kuznets Curve(EKC) effect was added to the equation to discuss the reflection on COemission. Five different scenarios were set up to predict COemission in 2020—2060. Finally, false-carbon neutrality was proposed and defined, combining the CS data predicted by the GM(1,1) model to study the net carbon sink(NCS) trends and carbon neutrality in the study area. The results show that the CS in the study area has been fluctuating upward about 265 million tons, the highest area is in the southeast region. While COemission increased rapidly, reaching about 108 million tons in 2019, more than triple the amount in 2001. In the past 20 years, CS was greater than COemission, but the gap between them has been diminishing. The regression coefficients show that population has the greatest impact on COemission. For every 1% of population growth, COemission will increase by about 1.03%. The urbanization rate has an EKC effect in the study area, which means regional COemission increases firstly and then decreases with the growth of the urbanization rate, and there is no significant EKC effect on affluence. Among the five scenarios, the sustainable(ST) and the baseline(BL) scenarios, the energy-saving(

关 键 词：CASA模型 灰色预测模型 STIRPAT模型 碳中和 

分 类 号：X321[环境科学与工程—环境工程]