基于STIRPAT模型的黑龙江省工业碳排放情景分析与峰值预测

doi:10.13776/j.cnki.resourcesindustries.20231212.003

摘要/Abstract

摘要： 在碳达峰、碳中和背景下，进行黑龙江省工业部门碳排放因素分析与情景预测研究，对实现黑龙江省工业绿色低碳发展具有重要意义。首先，根据IPCC准则测算黑龙江省历年工业碳排放量，基于拓展STIRPAT模型，从人口因素、经济发展水平、技术水平3个方面，确定人均生产总值的平方、人口规模、工业总产值、工业能源消耗、能耗效率、能源结构6个自变量，借助岭回归方法消除自变量间的多重共线性，建立黑龙江省工业部门的碳排放影响因素模型；然后，从经济发展、人口规模、能源消耗、能耗效率4个方面分析黑龙江省社会经济实际运行情况，并结合宏观政策，分别对自变量进行变量涨幅确定，对2020—2050年黑龙江省工业部门碳排放量设定基准情景、低碳情景、高能耗情景进行预测分析。研究发现：1）黑龙江省工业低碳发展面临化石能源需求大、能源转化效率不足的问题，在影响工业碳排放的6个变量中，人均生产总值的平方、工业总产值、工业能源消耗、能源结构对黑龙江省工业部门碳排放量起到促进作用，人口规模、能耗效率对黑龙江省工业部门碳排放起到抑制作用，其中工业能源消耗正向促进作用最为显著；2）黑龙江省工业碳排放量在各情景下均呈现先增加后减少的演变趋势，而在峰值出现的时间及高度上有所差异，低碳情景、基准情景、高能耗情景下黑龙江省工业部门碳排放量达峰时间分别为2030年、2035年和2045年，碳排放峰值分别为7 135万t、8 997万t、12 368万t。对此提出加大工业能源结构调整力度，重视能源转化技术升级，完善绿色低碳发展相关政策等建议。

关键词: 工业碳排放, 情景分析, STIRPAT模型, 黑龙江省, 碳达峰

Abstract:

Study on carbon emission factors and scenario prediction of Heilongjiang’s industrial sectors is of significance to reach Heilongjiang’s green low-carbon development under the background of carbon peaking and carbon neutralization. This paper uses IPCC to estimate Heilongjiang’s historical industrial carbon emission, and applies extended STIRPAT model to determine the six variables from population, economy and technology, square of GDP per capita, population scale, gross industrial production, industrial energy consumption, energy consuming efficiency and energy structure, and employes ridge regression to establish a carbon emission factor model by removing the multi-collinearity of independent variables. This paper also studies Heilongjiang’s social and economic reality from economic development, population scale, energy consumption and energy consumption efficiency, and determines the increment of independent variables combined with macroscopic policies, and predicts its 2020 to 2050 Heilongjiang’s appropriate industrial carbon emission under three scenarios, benchmark, low-carbon and highly-energy-consuming. Heilongjiang’s industrial low-carbon development is facing a huge demand for fossil energy and insufficient energy conversion efficient. Among the six factors impacting industrial carbon emission, square of GDP per capita, gross industrial production, industrial energy consumption and energy structure promote its industrial carbon emission, of which industrial energy consumption works the most, while population scale and energy consuming efficiency play on the contrast. Heilongjiang’s industrial carbon emission shows an increasing-then-decreasing evolutionary trend under the all three scenarios, varying in peaking time and heights, 71.35 millions tons in 2030 under the low-carbon scenario, 89.97 millions tons in 2035 under the benchmark scenario, and 123.68 millions tons in 2045 under the highly-energy-consuming scenario. This paper presents suggestions on largely adjusting industrial energy use structure, focusing on energy technical conversion and upgrade, and perfecting low-carbon green policies.

Key words: industrial carbon emission, scenario analysis, STIRPAT model, Heilongjiang province; carbon peaking

中图分类号:

F205
F403.3

何乐天, 杨泳琪, 李蓉, 等. 基于STIRPAT模型的黑龙江省工业碳排放情景分析与峰值预测[J]. 资源与产业, 2024, 26(1): 162-172.

HE Letian, YANG Yongqi, LI Rong, et al.. HEILONGJIANG’S INDUSTRIAL CARBON EMISSION SCENARIOS AND PEAKING PREDICTION BASED ON STIRPAT MODEL[J]. Resources & Industries, 2024, 26(1): 162-172.

参考文献

陈涛, 李晓阳, 陈斌, 2022. 中国碳排放影响因素分解及峰值预测研究[J/OL].安全与环境学报,[2022-12-28](2023-11-10) https://doi.org/10.13637/j.issn.1009-6094.2022.1448.

董莹, 许宝荣, 华中, 等, 2020. 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), 56(5): 606-614.〔DONG Y, XU B R, HUA Z, et al., 2020. Factor decomposition of carbon emission in Gansu province based on LMDI[J]. Journal of Lanzhou University(Natural Sciences), 56(5): 606-614.〕

方琦, 钱立华, 鲁政委, 2021. 我国实现碳达峰与碳中和的碳排放量测算[J]. 环境保护, 49(16): 49-54.〔FANG Q, QIAN L H, LU Z W, 2021. Measure carbon emission amount of China in the context of carbon peak and carbon neutrality[J]. Environmental Protection, 49(16): 49-54.〕

高新伟, 朱源, 2020. 科研投入抑制碳排放了吗?: 基于LMDI模型和STIRPAT模型的碳排放影响因素分析[J]. 资源与产业, 22(6): 37-45.〔GAO X W, ZHU Y, 2020. Do research inputs constrain carbon emission from carbon emission factors based on LMDI model and STIRPAT model?[J]. Resources & Industries, 22(6): 37-45.〕

纪建悦, 姜兴坤, 2012. 我国建筑业碳排放预测研究[J]. 中国海洋大学学报(社会科学版), 118(1): 53-57.〔JI J Y, JIANG X K, 2012. Carbon emissions prediction study in China’s construction industry[J]. Journal of Ocean University of China(Social Sciences), 118(1): 53-57.〕

蒋昀辰, 钟苏娟, 王逸, 等, 2022. 全国各省域碳达峰时空特征及影响因素[J]. 自然资源学报, 37(5): 1289-1302.〔JIANG Y C, ZHONG S J, WANG Y, et al., 2022. Spatio-temporal characteristics and influencing factors of carbon emission peak by province of China[J]. Journal of Natural Resources, 37(5): 1289-1302.〕

兰延文, 郭丽君, 李森, 2021. 碳排放驱动因素分解及碳排放达峰情景分析：以河南省为例[J]. 能源与环境(6): 7-11.

李江元, 丁涛, 2020. 我国碳排放增长的驱动因素分解：基于LMDI模型[J]. 煤炭经济研究, 40(6): 47-56.〔LI J Y, DING T, 2020. Driving factors for the growth of China’s carbon emissions based on LMDI model[J]. Coal Economic Research, 40(6): 47-56.〕

李心萍, 苏时鹏, 张雅珊, 等, 2023. 福建省碳排放预测与碳达峰路径分析[J]. 资源开发与市场, 39(2): 139-147.〔LI X P, SU S P, ZHANG Y S, et al., 2023. Carbon emission prediction and carbon peak path analysis in Fujian province[J]. Resource Development & Market, 39(2): 139-147.〕

林伯强, 蒋竺均, 2009. 中国二氧化碳的环境库兹涅茨曲线预测及影响因素分析[J]. 管理世界(4): 27-36.

刘淳森, 曲建升, 葛钰洁, 等, 2023. 基于LSTM模型的中国交通运输业碳排放预测[J]. 中国环境科学, 43(5): 2574-2582.〔LIU C S, QU J S, GE Y J, et al., 2023. LSTM model-based prediction of carbon emissions from China’s transportation sector[J]. China Environmental Science, 43(5): 2574-2582.〕

鲁万波, 仇婷婷, 杜磊, 2013. 中国不同经济增长阶段碳排放影响因素研究[J]. 经济研究, 48(4): 106-118.〔LU W B, QIU T T, DU L, 2013. A study on influence factors of carbon emissions under different economic growth stages in China[J]. Economic Research Journal, 48(4): 106-118.〕

渠慎宁, 郭朝先, 2010. 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, 20(12): 10-15.〔QU S N, GUO C X, 2010. Forecast of China’s carbon emissions based on STIRPAT model[J]. China Population, Resources and Environment, 20(12): 10-15.〕

宋府霖, 韩传峰, 滕敏敏, 2022. 长三角地区能源消费碳排放驱动因素分析及优化策略[J]. 生态经济, 38(4): 21-28.〔SONG F L, HAN C F, TENG M M, 2022. Analysis of driving factors and optimization strategies of energy consumption carbon emissions in the Yangtze River delta[J]. Ecological Economy, 38(4): 21-28.〕

孙蒙, 李长云, 邢振方, 等, 2023. 碳中和目标下中国碳排放关键影响因素分析及情景预测[J]. 高电压技术, 49(9): 4011-4021.〔SUN M, LI C Y, XING Z F, et al., 2023. Analysis of key influencing factors and scenario prediction of China’s carbon emission under carbon neutrality[J]. High Voltage Engineering: 49(9): 4011-4021.〕

唐志鹏, 于浩杰, 陈明星, 等, 2022. 基于函数极值条件下的中国碳达峰碳中和情景分析[J]. 自然资源学报, 37(5): 1247-1260.〔TANG Z P, YU H J, CHEN M X, et al., 2022. The scenario analysis of China’s carbon emission peak and carbon neutrality based on extremum condition of function[J]. Journal of Natural Resources, 37(5): 1247-1260.〕

田泽, 张宏阳, 纽文婕, 2021. 长江经济带碳排放峰值预测与减排策略[J]. 资源与产业, 23(1): 97-105.〔TIAN Z, ZHANG H Y, NIU W J, 2021. Peak prediction and reduction strategy of carbon emission in Yangtze River economic zone[J]. Resources & Industries, 23(1): 97-105.〕

王大会, 李泰君, 2023. 贵州省碳达峰现状与路径分析[J]. 中国资源综合利用, 41(1): 185-187.〔WANG D H, LI T J, 2023. Analysis on the status and path of carbon peak in Guizhou province[J]. China Resources Comprehensive Utilization, 41(1): 185-187.〕

王丽萍, 刘明浩, 2018. 基于投入产出法的中国物流业碳排放测算及影响因素研究[J]. 资源科学, 40(1): 195-206.〔WANG L P, LIU M H, 2018. Carbon emission measurement for China’s logistics industry and its influence factors based on input-output method[J]. Resources Science, 40(1): 195-206.〕

王利兵, 张赟, 2021. 中国能源碳排放因素分解与情景预测[J]. 电力建设, 42(9): 1-9.〔WANG L B, ZHANG Y, 2021. Factors decomposition and scenario prediction of energy-related CO₂ emissions in China[J]. Electric Power Construction, 42(9): 1-9.〕

王利军, 庞雅倩, 陈梦冬, 2022. 湖北省交通业碳排放影响因素及情景预测[J]. 资源与产业, 24(3): 106-113.〔WANG L J, PANG Y Q, CHEN M D, 2022. Carbon emission factors and scenario forecast of Hubei’s transportation industry[J]. Resources & Industries, 24(3): 106-113.〕

王梦凯, 白艳萍, 2022. 基于LMDI模型的江苏省碳排放强度影响因素分解研究[J]. 宁夏大学学报(自然科学版), 43(1): 109-114.〔WANG M K, BAI Y P, 2022. Decomposition of factors affecting carbon emission intensity in Jiangsu province based on LMDI model[J]. Journal of Ningxia University(Natural Science Edition), 43(1): 109-114.〕

王韶华, 赵暘春, 张伟, 等, 2022. 京津冀碳排放的影响因素分析及达峰情景预测：基于供给侧改革视角[J]. 北京理工大学学报(社会科学版), 24(6): 54-66.〔WANG S H, ZHAO Y C, ZHANG W, et al., 2022. Analysis on influencing factors of carbon emission and scenario forecast of carbon peak in Beijing-Tianjin-Hebei: a perspective of supply-side reform[J]. Journal of Beijing Institute of Technology(Social Sciences Edition), 24(6): 54-66.〕

王勇, 毕莹, 王恩东, 2017. 中国工业碳排放达峰的情景预测与减排潜力评估[J]. 中国人口·资源与环境, 27(10): 131-140.〔WANG Y, BI Y, WANG E D, 2017. Scene prediction of carbon emission peak and emission reduction potential estimation in Chinese industry[J]. China Population, Resources and Environment, 27(10): 131-140.〕

尹龙, 杨亚男, 章刘成, 2021. 中国居民消费碳排放峰值预测与分析[J]. 新疆社会科学(4): 42-50, 168.〔YIN L, YANG Y N, ZHANG L C, 2021. Prediction of the peak carbon emission of Chinese residents’ consumption and analysis[J]. Social Sciences in Xinjiang(4): 42-50, 168.〕

袁晓玲, 郗继宏, 李朝鹏, 等, 2020. 中国工业部门碳排放峰值预测及减排潜力研究[J]. 统计与信息论坛, 35(9): 72-82.〔YUAN X L, XI J H, LI C P, et al., 2020. A study on carbon emission peak forecast and emission reduction potential of China’s Industrial sector[J]. Journal of Statistics and Information, 35(9): 72-82.〕

曾贤刚, 余畅, 2023. 中国农业农村碳排放结构与碳达峰分析[J]. 中国环境科学, 43(4)：1906-1918.〔ZENG X G, YU C , SUN Y Q, 2023. Carbon emission structure and carbon peak of agriculture and rural areas in China[J]. China Environmental Science, , 43(4)：1906-1918.〕

赵慧卿, 黄先运, 2022. 基于情景分析的中国碳达峰预测[J]. 无锡商业职业技术学院学报, 22(3): 1-7.〔ZHAO H Q, HUANG X Y, 2022. Prediction of China’s carbon peaking based on scenario analysis[J]. Journal of Wuxi Vocational Institute of Commerce, 22(3): 1-7.〕

郑蕊, 刁书琪, 2022. 基于LMDI-PDA-MMI分解法的我国产业体系碳排放驱动因素研究[J]. 生态经济, 38(5): 33-39.〔ZHENG R, DIAO S Q, 2022. Investigating the driving factors of carbon emissions in China’s industrial system using the LMDI-PDA-MMI method [J]. Ecological Economy, 38(5): 33-39.〕

钟兴菊, 龙少波, 2016. 环境影响的IPAT模型再认识[J]. 中国人口·资源与环境, 26(3): 61-68.〔ZHONG X J, LONG S B, 2016. Rethinking the environmental impact of the IPAT model[J]. China Population, Resources and Environment, 26(3): 61-68.〕

朱海, 王立国, 贺焱, 等, 2023. 多情景下中国省域旅游业碳达峰的时空特征研究[J]. 干旱区资源与环境, 37(1): 169-176.〔ZHU H, WANG L G, HE Y, et al., 2023. Spatial and temporal characteristics of carbon peak of provincial tourism in China under multiple scenarios[J]. Journal of Arid Land Resources and Environment, 37(1): 169-176.〕

NING L, PEI L, LI F, et al., 2021. Forecast of China’s carbon emissions based on ARIMA method[J]. Discrete Dynamics in Nature & Society, 2021:1-12.

PU X, YAO J, ZHENG R, 2022. Forecast of energy consumption and carbon emissions in China’s building sector to 2060[J]. Energies, 15(14): 4950.

ZHANG X, ZHAO X R, JIANG Z J, et al., 2017. How to achieve the 2030 CO₂ emission-reduction targets for China’s industrial sector: retro-spective decomposition and prospective trajectories[J]. Global Environmental Change, 44: 83-97.

ZHOU Y, ZHANG J, HU S, 2021. Regression analysis and driving force model building of CO₂ emissions in China[J]. Scientific Reports, 11(1): 1-14.

ZHU L, HE L C, SHANG P P, et al., 2018. Influencing factors and scenario forecasts of carbon emissions of the Chinese power industry: based on a generalized divisia index model and monte carlo simulation[J]. Energies, 11(9): 2398.

[1]	关伟, 王勇, 许淑婷. 中国工业碳排放的网络结构及影响因素研究[J]. 资源与产业, 2023, 25(5): 40-49.
[2]	高珊, 曹明霞, 李丹. 江苏省碳排放特征分析及达峰预测[J]. 资源与产业, 2023, 25(3): 1-9.
[3]	朱智洺, 桂梦婷, 李红艳. “双碳”目标下江苏省碳平衡潜力预测研究[J]. 资源与产业, 2023, 25(1): 1-13.
[4]	杜焱, 胡鑫杨. 我国2030年实现碳达峰路径研究——基于经济、能源、碳排放系统的SD模型[J]. 资源与产业, 2022, 24(5): 19-28.
[5]	陆佳勤, 甘信华. 江苏省工业碳排放时空分异及减排策略[J]. 资源与产业, 2022, 24(4): 150-156.
[6]	王利军, 庞雅倩, 陈梦冬. 湖北省交通业碳排放影响因素及情景预测[J]. 资源与产业, 2022, 24(3): 106-113.
[7]	孙涵, 张红艳, 付晓灵. 中国经济增长与能源消费的多部门脱钩预测[J]. 资源与产业, 2021, 23(5): 11-20.
[8]	王怡, 郝秀辉, 姚丽霞. 区域经济与生态环境耦合协调发展的时空演化研究——以黑龙江省为例[J]. 资源与产业, 2021, 23(5): 21-30.
[9]	林向义, 牛艺, 罗洪云, 等. 黑龙江省工业企业能源效率测算及区域差异研究[J]. 资源与产业, 2021, 23(2): 39-47.
[10]	田泽, 张宏阳, 纽文婕. 长江经济带碳排放峰值预测与减排策略[J]. 资源与产业, 2021, 23(1): 97-105.
[11]	高新伟, 朱源. 科研投入抑制碳排放了吗？——基于LMDI模型和STIRPAT模型的碳排放影响因素分析[J]. 资源与产业, 2020, 22(6): 37-45.
[12]	朱灵伟，李冬青，罗罡辉，等. 农地碳排放影响因素空间差异性研究-基于STIRPAT和GWR模型的实证分析[J]. 资源与产业, 2019, 21(6): 82-91.
[13]	查奇芬，成鑫. . 技术进步对中国区域碳排放的影响研究[J]. 资源与产业, 2017, 19(6): 71-77.
[14]	沈明,沈镭,张艳,刘立涛,张超,陈枫楠. 陕西省能源供给系统稳定性动态情景预测[J]. 资源与产业, 2015, 17(5): 53-59.
[15]	赵映慧,盖兆雪,李艳芳,包喜利,王杨,薛睿. 黑龙江省生态环境质量空间分布与演变特征[J]. 资源与产业, 2014, 16(3): 107-112.