Comparative analysis of carbon emissions in Tianjin based on LMDI method and STIRPAT model
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摘要:
通过梳理天津市2000—2019年碳排放量变化，基于LMDI方法和STIRPAT模型分别构建碳排放量模型，对比分析碳排放量影响因素，并预测基准情景、低碳情景和超低碳情景下的天津市碳达峰和碳中和情况。结果显示：天津市能源结构的不断优化和能源强度的持续降低是导致碳排放量减缓的主要因素，但天津市富裕程度的增加和城镇化率的提高促进碳排放量的增加；在基准情景下，天津市很难在2025年实现碳达峰、在2060年之前实现碳中和；在低碳情景下，天津市可在2025年之前实现碳达峰，但在2060年之前实现碳中和难度较大；在超低碳情景下，天津市更易在2025年之前实现碳达峰，且在碳汇工程的实施下可在2060年之前实现碳中和。
Abstract:By sorting out changes in Tianjin's carbon emissions from 2000 to 2019, the carbon emission models were constructed based on LMDI method and STIRPAT model, respectively. The influencing factors of carbon emissions were compared and analyzed, and the carbon peak and carbon neutrality situation in Tianjin under three scenarios, including baseline scenario, low-carbon scenario, and ultra-low-carbon scenario, were predicted. The results showed that the continuous optimization of Tianjin's energy structure and the continuous reduction of energy intensity were the main factors leading to the reduction of Tianjin's carbon emissions, but the increase in Tianjin's wealth and the increase in urbanization rate had promoted the increase in Tianjin's carbon emissions. Under the baseline scenario, it was difficult for Tianjin to achieve carbon peak in 2025 and carbon neutrality before 2060. In the low-carbon scenario, Tianjin could achieve carbon peak before 2025, but it was more difficult to achieve carbon neutrality before 2060. Under the ultra-low-carbon scenario, Tianjin was more likely to achieve carbon peak before 2025, and under the implementation of the carbon sink project, it could achieve carbon neutrality before 2060.
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Key words:
- carbon peak /
- carbon neutral /
- LMDI method /
- STIRPAT model /
- Tianjin City
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图 1 LMDI各影响因素累计贡献率
Figure 1. Cumulative contribution rate of each influencing factor of LMDI
图 2 LMDI方法和STIRPAT模型对天津市碳排放量的拟合分析
Figure 2. Fitting analysis of LMDI method and STIRPAT model for Tianjin's carbon emissions
图 3 LMDI方法和STIRPAT模型对天津市碳达峰预测分析
Figure 3. Prediction and analysis of carbon peak in Tianjin by LMDI method and STIRPAT model
图 4 LMDI方法和STIRPAT模型对天津市碳中和预测分析
Figure 4. Prediction and analysis of carbon neutrality in Tianjin by LMDI method and STIRPAT model
表 1 情景设置
Table 1. Scenarios setting
情景 参数 2020年 2021—2025年 2026—2060年 基准情景 地区生产总值 增长1.5% 2021年增长6.5%，其他年增长6% 年增长6% 能源强度 5年累计下降17% 5年累计降低15% 5年累计降低15% 碳排放强度 5年累计降低20.5% 5年累计降低20.5% 5年累计降低20.5% 城镇化率 83.60% 年均增长0.28个百分点 年均增长0.28个百分点 第三产业占比 64.40% 年均增长1.8% 年均增长1.8%，增长至90%不再增长 人口总数 年均增长0.159% 年均增长0.159% 年均增长0.159% 低碳情景 地区生产总值 增长1.5% 2021年增长6.5%，其他年增长6% 年增长3.6% 能源强度 5年累计下降17% 5年累计降低15% 5年累计降低21% 碳排放强度 5年累计降低20.5% 5年累计降低28.7% 5年累计降低28.7% 城镇化率 83.60% 年均增长0.28个百分点 年均增长0.168个百分点 第三产业占比 64.40% 年均增长1.8% 年均增长2.52%，增长至90%不再增长 人口总数 年均增长0.159% 年均增长0.159% 年均增长0.095 4% 超低碳情景 地区生产总值 增长1.5% 2021年增长6.5%，其他年增长6% 年增长1.2% 能源强度 5年累计降低17% 5年累计降低15% 5年累计降低27% 碳排放强度 5年累计降低20.5% 5年累计降低36.9% 5年累计降低36.9% 城镇化率 83.60% 年均增长0.28个百分点 年均增长0.056个百分点 第三产业占比 64.40% 年均增长1.8% 年均增长3.24%，增长至90%不再增长 人口总数 年均增长0.159% 年均增长0.159% 年均增长0.031 8% 
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[1] 中央人民政府. 中华人民共和国国民经济和社会发展第十四个五年规划和2035年远景目标纲要[A/OL]. (2021-03-13)[2021-05-20]. http://www.gov.cn/xinwen/2021-03/13/content_5592681.htm. [2] 生态环境部. 关于碳达峰碳中和, 中央财经委员会第九次会议作出了哪些部署[EB/OL]. (2021-03-18)[2021-05-20]. http://www.mee.gov.cn/ywgz/ydqhbh/wsqtkz/202103/t20210318_825146.shtml. [3] 天津市人民政府. 天津市国民经济和社会发展第十四个五年规划和二〇三五年远景目标纲要[A/OL]. (2021-02-08)[2021-05-21]. http://www.tj.gov.cn/zwgk/szfwj/tjsrmzf/202102/t20210208_5353467.html. [4] 天津市生态环境局. 2021年天津市生态环境保护工作会议召开明确“十四五”生态环境保护工作重点任务[A/OL]. (2021-02-22)[2021-05-21]. http://sthj.tj.gov.cn/ZWXX808/HBYW1316/202102/t20210222_5363750.html. [5] 孙志威, 廖红英, 宋雨燕.基于对数平均迪氏指数法的天津市能源消费碳排放分解分析[J]. 环境污染与防治,2011,33(1):83-86. doi: 10.3969/j.issn.1001-3865.2011.01.019 [6] 袁玥, 齐宇.基于灰色关联分析的天津市碳排放驱动因素研究[J]. 环境污染与防治,2013,35(9):101-106. doi: 10.3969/j.issn.1001-3865.2013.09.021YUAN Y, QI Y. The drivers of Tianjin CO2 emission identified base on the grey relational analysis[J]. Environmental Pollution & Control,2013,35(9):101-106. doi: 10.3969/j.issn.1001-3865.2013.09.021 [7] 任晓松, 赵涛.基于广义费雪模型的天津市碳排放影响因素分析[J]. 干旱区资源与环境,2014,28(6):8-12.REN X S, ZHAO T. Impact factor decomposition analysis for carbon emissions change in Tianjin City based on the GFI model[J]. Journal of Arid Land Resources and Environment,2014,28(6):8-12. [8] 李健, 王孟艳, 高杨.基于STIRPAT模型的天津市低碳发展驱动力影响分析[J]. 科技管理研究,2014,34(15):66-71. doi: 10.3969/j.issn.1000-7695.2014.15.015LI J, WANG M Y, GAO Y. Analysis of the driving forces' impact on the low-carbon development of Tianjin based on STIRPAT model[J]. Science and Technology Management Research,2014,34(15):66-71. doi: 10.3969/j.issn.1000-7695.2014.15.015 [9] 王媛, 贾皎皎, 赵鹏, 等.LMDI方法分析结构效应对天津市碳排放的影响及对策[J]. 天津大学学报(社会科学版),2014,16(6):509-514.WANG Y, JIA J J, ZHAO P, et al. Effect of structure on carbon emission and countermeasures in Tianjin based on LMDI[J]. Journal of Tianjin University (Social Sciences),2014,16(6):509-514. [10] 李健, 王铮, 朴胜任.大型工业城市碳排放影响因素分析及趋势预测: 基于PLS-STIRPAT模型的实证研究[J]. 科技管理研究,2016,36(7):229-234. doi: 10.3969/j.issn.1000-7695.2016.07.042LI J, WANG Z, PIAO S R. Influence factors analysis and trend forecasting of large industrial cities' carbon emissions based on the PLS-STIRPAT model[J]. Science and Technology Management Research,2016,36(7):229-234. doi: 10.3969/j.issn.1000-7695.2016.07.042 [11] 李雪梅, 张庆.天津市能源消费碳排放影响因素及其情景预测[J]. 干旱区研究,2019,36(4):997-1004.LI X M, ZHANG Q. Factors affecting carbon emission from energy consumption in Tianjin[J]. Arid Zone Research,2019,36(4):997-1004. [12] 孙钰, 李泽涛, 姚晓东.天津市构建低碳城市的策略研究: 基于碳排放的情景分析[J]. 地域研究与开发,2012,31(6):115-118. doi: 10.3969/j.issn.1003-2363.2012.06.023SUN Y, LI Z T, YAO X D. The strategy of building low-carbon city in Tianjin: based on scenario analysis of carbon emission[J]. Areal Research and Development,2012,31(6):115-118. doi: 10.3969/j.issn.1003-2363.2012.06.023 [13] 苑清敏, 刘琪, 刘俊.基于系统动力学的城市碳排放及减排潜力分析: 以天津市为例[J]. 安全与环境学报,2016,16(6):256-261.YUAN Q M, LIU Q, LIU J. On the reduced urban carbon emission and an analysis of such reduced emission potential based on the dynamic system by taking Tianjin as a case sample[J]. Journal of Safety and Environment,2016,16(6):256-261. [14] 李健, 王尧, 王颖.天津市碳排放脱钩态势及碳减排潜力分析: 基于2007—2016年的面板数据[J]. 生态经济,2019,35(4):26-32.LI J, WANG Y, WANG Y. Tianjin carbon emissions decoupling analysis and carbon emission reduction potential: based on panel data from 2007 to 2016[J]. Ecological Economy,2019,35(4):26-32. [15] KAYA Y. Impact of carbon dioxide emission control on GNP growth: interpretation of proposed scenarios IPCC energy and industry subgroup, response strategies working group[R]. Paris: Presentation to the Energy and Industry Subgroup, Response Strategies Working Group, IPCC, 1989. [16] LI J X, CHEN Y N, LI Z, et al. Low-carbon economic development in Central Asia based on LMDI decomposition and comparative decoupling analyses[J]. Journal of Arid Land,2019,11(4):513-524. doi: 10.1007/s40333-019-0063-0 [17] ANG B W, LIU N. Handling zero values in the logarithmic mean Divisia index decomposition approach[J]. Energy Policy,2007,35(1):238-246. doi: 10.1016/j.enpol.2005.11.001 [18] 李健, 李海霞.产业转移视角下京津冀石化产业碳排放因素分解与减排潜力分析[J]. 环境科学研究,2020,33(2):324-332. doi: 10.13198/j.issn.1001-6929.2019.05.05LI J, LI H X. Analysis of carbon emission factors decomposition and emission reduction potential of Beijing-Tianjin-Hebei regional petrochemical industry from the perspective of industrial transfer[J]. Research of Environmental Sciences,2020,33(2):324-332. doi: 10.13198/j.issn.1001-6929.2019.05.05 [19] 庄颖, 夏斌.广东省交通碳排放核算及影响因素分析[J]. 环境科学研究,2017,30(7):1154-1162. doi: 10.13198/j.issn.1001-6929.2017.02.43ZHUANG Y, XIA B. Estimation of CO2 emissions from the transport sector in Guangdong Province, China and analysis of factors affecting emissions[J]. Research of Environmental Sciences,2017,30(7):1154-1162. doi: 10.13198/j.issn.1001-6929.2017.02.43 [20] ZHANG L, LEI J, ZHOU X, et al. Changes in carbon dioxide emissions and LMDI-based impact factor decomposition: the Xinjiang Uygur autonomous region as a case[J]. Journal of Arid Land,2014,6(2):145-155. doi: 10.1007/s40333-013-0242-3 [21] 余明成, 徐占军, 余健.山西省CO2排放影响因素研究及情景分析[J]. 环境科学研究,2018,31(8):1357-1365.YU M C, XU Z J, YU J. Influencing factors of CO2 emission and scenario analysis in Shanxi Province[J]. Research of Environmental Sciences,2018,31(8):1357-1365. [22] 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(3):351-365. doi: 10.1016/S0921-8009(03)00188-5 [23] 张乐勤, 陈素平.基于PLS方法的高技术产业发展对工业污染影响效应测度与分析[J]. 环境工程技术学报,2018,8(5):563-570.ZHANG L Q, CHEN S P. Measurement and analysis of influence effect of high-tech industry development on industrial pollution based on PLS method[J]. Journal of Environmental Engineering Technology,2018,8(5):563-570. [24] 张剑, 刘景洋, 董莉, 等. 中国能源消费CO2排放的影响因素及情景分析[J/OL]. 环境工程技术学报.doi:10.12153/j.issn.1674-991X.20211229.ZHANG J, LIU J Y, DONG L, et al. Influencing factors and scenario analysis of China's energy consumption CO2 Emissions[J/OL]. Journal of Environmental Engineering Technology. doi:10.3969/j.issn.1674-991X.20210563. [25] 刘晓燕.基于STIRPAT模型的工业能源消费碳排放影响因素分析[J]. 生态经济,2019,35(3):27-31.LIU X Y. Study on influencing factors of carbon emissions from industrial energy consumption based on STIRPAT model[J]. Ecological Economy,2019,35(3):27-31. [26] 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.ZHANG Z, REN Y M, DONG H J. Research on carbon emissions peaking and low-carbon development of cities: a case of Shanghai[J]. Environmental Engineering,2020,38(11):12-18. [27] 颜伟, 黄亚茹, 张晓莹, 等.基于STIRPAT模型的山东半岛蓝色经济区碳排放预测[J]. 济南大学学报(自然科学版),2021,35(2):125-131. doi: 10.13349/j.cnki.jdxbn.20201012.002YAN W, HUANG Y R, ZHANG X Y, et al. Prediction of carbon emission in Shandong Peninsula Blue Economic Zone based on STIRPAT model[J]. Journal of University of Jinan (Science and Technology),2021,35(2):125-131. doi: 10.13349/j.cnki.jdxbn.20201012.002 [28] 天津市统计局. 2020年天津市国民经济和社会发展统计公报[A/OL]. (2021-03-17)[2021-05-28]. http://stats.tj.gov.cn/tjsj_52032/tjgb/202103/t20210317_5386752.html. [29] 天津市人民政府. 天津市2021年政府工作报告[A/OL]. (2021-02-01)[2021-06-01]. http://www.tj.gov.cn/zwgk/zfgzbg/202102/t20210201_5343672.html. [30] 董莹, 许宝荣, 华中, 等.基于LMDI的甘肃省碳排放影响因素分解研究[J]. 兰州大学学报(自然科学版),2020,56(5):606-614.DONG Y, XU B R, HUA Z, et al. Factor decomposition of carbon emission in Gansu Province based on LMDI[J]. Journal of Lanzhou University (Natural Sciences),2020,56(5):606-614. [31] 王建雄, 吕沅姝, 李晨曦.LMDI法与STIRPAT模型下京津冀碳排放影响因素研究[J]. 中国经贸导刊(中),2021(1):114-116. [32] 黄蕊, 王铮, 丁冠群, 等.基于STIRPAT模型的江苏省能源消费碳排放影响因素分析及趋势预测[J]. 地理研究,2016,35(4):781-789.HUANG R, WANG Z, DING G Q, et al. Trend prediction and analysis of influencing factors of carbon emissions from energy consumption in Jiangsu Province based on STIRPAT model[J]. Geographical Research,2016,35(4):781-789. [33] 徐西蒙.昆明市二氧化碳排放峰值研究[J]. 环境科学导刊,2015,34(4):47-52. doi: 10.3969/j.issn.1673-9655.2015.04.010XU X M. CO2 emission peak prediction of Kunming[J]. Environmental Science Survey,2015,34(4):47-52. doi: 10.3969/j.issn.1673-9655.2015.04.010 [34] 张立, 谢紫璇, 曹丽斌, 等.中国城市碳达峰评估方法初探[J]. 环境工程,2020,38(11):1-5. doi: 10.13205/j.hjgc.202011001ZHANG L, XIE Z X, CAO L B, et al. Discussion on evaluation method on carbon dioxide emissions peaking for Chinese Cities[J]. Environmental Engineering,2020,38(11):1-5. doi: 10.13205/j.hjgc.202011001 [35] 孔佑花, 王丽, 郭志玲, 等.基于系统动力学的甘肃省碳排放峰值预测[J]. 环境工程技术学报,2018,8(3):309-318. doi: 10.3969/j.issn.1674-991X.2018.03.041KONG Y H, WANG L, GUO Z L, et al. Carbon emissions peak prediction in Gansu Province based on system dynamics[J]. Journal of Environmental Engineering Technology,2018,8(3):309-318. doi: 10.3969/j.issn.1674-991X.2018.03.041 [36] 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. doi: 10.13205/j.hjgc.202011004ZANG H K, YANG W S, ZHANG J, et al. Research on carbon dioxide emissions peaking in Beijing-Tianjin-Hebei City agglomeration[J]. Environmental Engineering,2020,38(11):19-24. doi: 10.13205/j.hjgc.202011004 [37] 孙旭东, 张蕾欣, 张博.碳中和背景下我国煤炭行业的发展与转型研究[J]. 中国矿业,2021,30(2):1-6. doi: 10.12075/j.issn.1004-4051.2021.02.034SUN X D, ZHANG L X, ZHANG B. Research on the coal industry development and transition in China under the background of carbon neutrality[J]. China Mining Magazine,2021,30(2):1-6. □ doi: 10.12075/j.issn.1004-4051.2021.02.034 -
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