REGIONAL CARBON EMISSION PEAKING BASED ON THRESHOLD-STIRPAT EXTENSION MODEL: A CASE STUDY ON EAST CHINA
-
摘要: 中国政府提出要力争在2030年前实现碳达峰,因此积极探索不同发展模式下典型地区的碳达峰时间和峰值十分必要。将门限回归模型与STIRPAT模型相结合,构建门限-STIRPAT扩展模型,分析华东地区7个省市2005—2019年的碳排放,结合情景分析法,分别预测华东地区各省市2020—2040年的碳排放。结果表明:在基准情景的发展模式下,华东地区各省市均可在2030年实现碳达峰目标;在节能发展情景和绿色发展情景的模式下,各省市可以提前4~5年实现碳达峰目标。对于华东地区的7个省份,建议上海、江苏和浙江应以节能发展情景为标准进行产业发展规划;安徽、福建、江西和山东适合选择基准情景,并力争以节能发展情景为标准,布局产业发展。Abstract: The Chinese government proposes to strive to achieve peak carbon dioxide emissions by 2030, so it is necessary to actively explore the time and peak of carbon emissions in typical regions under different development models. Combining the threshold regression model with the STIRPAT model, a threshold-STIRPAT extension model was constructed to analyze the carbon emissions of seven provinces and cities in East China from 2005 to 2019, and combined with the scenario analysis method, the carbon emissions of provinces and cities in East China from 2020 to 2040 were predicted respectively. The forecast results show that: under the development model of the benchmark scenario, all provinces and cities in East China can achieve the peak of carbon emissions target in 2030; under the model of the energy-saving development scenario and the green development scenario, all provinces and cities can achieve the peak of carbon emissions target 4 to 5 years ahead of schedule. It is suggested that the 7 provinces in East China: Shanghai, Jiangsu and Zhejiang should use the energy-saving development scenario as the standard for industrial development planning; while Anhui, Fujian, Jiangxi and Shandong should select the benchmark scenario and strive to use the energy-saving development scenario as the standard, to layout their industrial development in the future.
-
Key words:
- carbon emissions /
- peak forecast /
- threshold regression model /
- STIRPAT model /
- scenario analysis
-
[1] National Development and Reform Commission of China.China's Intended Nationally Determined Contribution Document[R/OL].http://www.china.org.cn/english/china_key_words/2016-01/20/content_37622524.htm. 2023-07-18. [2] 中央人民政府.中国共产党第二十次全国代表大会上的报告[R/OL].https://www.gov.cn/xinwen/2022-10/25/content_5721685.htm.2023-07-18. [3] DONG K, JIANG H D, SUN R J, et al. Driving forces and mitigation potential of global CO2 emissions from 1980 through 2030: evidence from countries with different income levels[J]. Science of the Total Environment,2019,649:335-343. [4] MA X J, WANG C X, DONG B Y, et al. Carbon emissions from energy consumption in China: its measurement and driving factors[J]. Science of the Total Environment,2019,648:1411-1420. [5] LI H N, QIN Q D. Challenges for China’s carbon emissions peaking in 2030: a decomposition and decoupling analysis[J]. Journal of Cleaner Production,2019,207:857-865. [6] 张丽峰,刘思萌. 碳中和目标下京津冀地区碳排放影响因素研究:基于分位数回归和VAR模型的实证分析[J]. 资源开发与市场,2021,37(9):1025-1031. [7] 吴翔,夏天. 基于ARDL模型的吉林省碳排放影响因素实证研究[J]. 生态经济,2019,35(7):44-48. [8] 荣蓉,王凡. 山东省碳排放现状及影响因素研究:基于灰色关联分析[J]. 中外能源,2021,26(7):92-97. [9] 张露,宋媛. 京津冀产业结构优化对碳排放的影响研究:基于动态面板的系统GMM模型和VAR模型[J]. 资源与产业,2020,22(6):18-28. [10] DINDA S. Environmental kuznets curve hypothesis: a survey[J]. Ecological Economics,2004,49:431-455. [11] WANG Y, ZHANG C, LU A T, et al. A disaggregated analysis of the environmental Kuznets curve for industrial CO2 emissions in China[J]. Applied Energy,2017,190:172-180. [12] 曲越,秦晓钰,汪惠青,等. 中国"碳中和"的城市协同路径研究:基于"碳达峰"异质性的门限模型[J]. 中国地质大学学报(社会科学版),2022,22(4):50-63. [13] BAEK J. Environmental Kuznets curve for CO2 emissions: the case of Arctic countries[J]. Energy Economics,2015,50:13-17. [14] YANG G F, SUN T, WANG J L, et al. Modeling the nexus between carbon dioxide emissions and economic growth[J]. Energy Policy,2015,86:104-117. [15] WANG Q, JIANG R. Is China’s economic growth decoupled from carbon emissions?[J]. Journal of Cleaner Production,2019,225:1194-1208. [16] 周德群,邓海东,王义忠,等. 传统工业园区实现"双碳"目标路径研究:以江北新材料科技园为例[J]. 北京理工大学学报(社会科学版),2022,24(4):37-51. [17] 张丽峰,潘家华. 中国区域碳达峰预测与"双碳"目标实现策略研究[J]. 中国能源,2021,43(7):54-62,80. [18] 渠慎宁,郭朝先. 基于STIRPAT模型的中国碳排放峰值预测研究[J]. 中国人口·资源与环境,2010,20(12):10-15. [19] 林大荣. 福建省温室气体排放趋势峰值预测研究[J]. 能源与环境,2018,(6):2-4,8. [20] 江志高. 基于不同情景的莆田市碳排放峰值测算研究[J]. 环境科学与管理,2021,46(6):29-33. [21] 杜俊慧,张克勇,张雪姣. 山西省碳排放影响因素分解及峰值预测[J]. 中北大学学报(自然科学版),2018,39(3):334-343. [22] 范德成,张修凡.基于PSO-BP神经网络模型的中国碳排放情景预测及低碳发展路径研究[J]. 中外能源,2021,26(8):11-19. [23] ZHANG X L, VALERIE K, QI T Y, et al. Carbon emissions in China: How far can new efforts bend the curve?[J]. Energy Economics,2016,54:388-395. [24] 曹丽斌,李明煜,张立,等. 长三角城市群CO2排放达峰影响研究[J]. 环境工程,2020,38(11):33-38,59. [25] 臧宏宽,杨威杉,张静,等. 京津冀城市群二氧化碳排放达峰研究[J]. 环境工程,2020,38(11):19-24,77. [26] HANSEN B E. Inference in TAR models[J]. Studies in Nonlinear Dynamics & Economics. 2010, 2(1): 1-1. [27] EHRLISH P R, HOLDREN J P. Impact of population growth[J]. Science, New Series, 1971(171): 1212-1217. [28] YORK R, ROSA E A, DIETZ T. STIRPAT, IPAT and ImPACT: analytic tools for unpacking the driving forces of environmental impacts[J]. Ecological Economics, 2003, 46: 351-365. [29] 赵慈,宋晓聪,刘晓宇,等. 基于STIRPAT模型的浙江省碳排放峰值预测分析[J]. 生态经济,2022,38(6):29-34. [30] 张腾飞,杨俊,盛鹏飞. 城镇化对中国碳排放的影响及作用渠道[J]. 中国人口·资源与环境,2016,26(2):47-57. [31] 王少剑,莫惠斌,方创琳. 珠江三角洲城市群城市碳排放动态模拟与碳达峰[J]. 科学通报,2022,67(7):670-684. [32] 张哲,任怡萌,董会娟. 城市碳排放达峰和低碳发展研究:以上海市为例[J]. 环境工程,2020,38(11):12-18. [33] 上海市人民政府. 上海市国民经济和社会发展第十四个五年规划纲要和二〇三五年远景目标[EB/OL]. https://www.shanghai.gov.cn/nw22403/index.html.2023-07-18. [34] 江苏省人民政府. 江苏省国民经济和社会发展第十四个五年规划纲要和二〇三五年远景目标[EB/OL].http://fzggw.jiangsu.gov.cn/art/2021/8/26/art_83783_9993955.html.2023-07-18. [35] 浙江省人民政府. 浙江省国民经济和社会发展第十四个五年规划纲要和二〇三五年远景目标[EB/OL].http://kjt.zj.gov.cn/art/2021/4/15/art_1229247518_4595225.html.2023-07-18. [36] 安徽省人民政府. 安徽省国民经济和社会发展第十四个五年规划纲要和二〇三五年远景目标[EB/OL]. https://www.ah.gov.cn/public/1681/553978211.html.2023-07-18. [37] 福建省人民政府. 福建省国民经济和社会发展第十四个五年规划纲要和二〇三五年远景目标[EB/OL]. http://www.fujian.gov.cn/zwgk/ztzl/sswfjzhxlt/.2023-07-18. [38] 江西省人民政府. 江西省国民经济和社会发展第十四个五年规划纲要和二〇三五年远景目标[EB/OL]. http://www.jiangxi.gov.cn/col/col61150/index.html.2023-07-18. [39] 山东省人民政府. 山东省国民经济和社会发展第十四个五年规划纲要和二〇三五年远景目标[EB/OL]. http://www.shandong.gov.cn/art/2021/9/7/art_97902_429688.html.2023-07-18. [40] 中国碳核算数据库. 2019年30个省份排放清单[EB/OL]. https://www.ceads.net.cn/data/province/.2023-07-18.
点击查看大图
计量
- 文章访问数: 74
- HTML全文浏览量: 10
- PDF下载量: 7
- 被引次数: 0