| Citation: | YANG J Q,FAN X J,ZHAO Y H,et al.Prediction of carbon emissions in Shanxi Province based on PSO-BP neural network[J].Journal of Environmental Engineering Technology，2023，13（6）：2016-2024 doi: 10.12153/j.issn.1674-991X.20230190
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Shanxi, as a major province of energy use and carbon emission, has an important demonstration significance for the whole country to promote the "dual carbon" strategy. The carbon emissions of Shanxi Province from 2000 to 2020 were calculated based on IPCC emission coefficient method. Tapio decoupling model was used to analyze the decoupling relationship between carbon emissions and economic development, LMDI method was used to decompose the factors affecting carbon emission changes, and PSO-BP neural network model was used to simulate and forecast the carbon emissions of Shanxi Province. The results showed that the carbon emission in Shanxi Province increased during 2000-2020, while the carbon emission intensity decreased, and the decoupling coefficient was 0.585, indicating a weak decoupling state as a whole. Economic growth was the determining factor of carbon emission growth, and the optimization and adjustment of industrial structure and energy intensity was the leading factor to restrain carbon emission. The introduction of particle swarm optimization (PSO) improved the prediction accuracy of BP neural network effectively. The predicted results showed that carbon emissions in Shanxi Province would peak in 2032, 2029 and 2027 under three scenarios: baseline scenario, low carbon scenario and intensive low carbon scenario, respectively. In view of the forecast results, relevant policy suggestions were put forward.
| [1] |
ZHAO L T, ZHAO T, YUAN R. Scenario simulations for the peak of provincial household CO2 emissions in China based on the STIRPAT model[J]. Science of the Total Environment,2022,809:151098. doi: 10.1016/j.scitotenv.2021.151098
|
| [2] |
韩楠, 罗新宇.多情景视角下京津冀碳排放达峰预测与减排潜力[J]. 自然资源学报,2022,37(5):1277-1288. doi: 10.31497/zrzyxb.20220512
HAN N, LUO X Y. Carbon emission peak prediction and reduction potential in Beijing-Tianjin-Hebei region from the perspective of multiple scenarios[J]. Journal of Natural Resources,2022,37(5):1277-1288. doi: 10.31497/zrzyxb.20220512
|
| [3] | |
| [4] |
刘茂辉, 翟华欣, 刘胜楠, 等.基于LMDI方法和STIRPAT模型的天津市碳排放量对比分析[J]. 环境工程技术学报,2023,13(1):63-70.
LIU M H, ZHAI H X, LIU S N, et al. Comparative analysis of carbon emissions in Tianjin based on LMDI method and STIRPAT model[J]. Journal of Environmental Engineering Technology,2023,13(1):63-70.
|
| [5] |
左朝晖, 李绍康, 杨津津, 等.基于GA-BP神经网络的页岩气开发区域水资源承载力研究[J]. 环境工程技术学报,2021,11(1):194-201.
ZUO Z H, LI S K, YANG J J, et al. Research on water resources carrying capacity of shale gas development area based on GA-BP neural network[J]. Journal of Environmental Engineering Technology,2021,11(1):194-201.
|
| [6] |
张梦迪, 徐庆, 刘振鸿, 等.基于动态滑动窗口BP神经网络的水质时间序列预测[J]. 环境工程技术学报,2022,12(3):809-815.
ZHANG M D, XU Q, LIU Z H, et al. Prediction of water quality time series based on the dynamic sliding window BP neural network model[J]. Journal of Environmental Engineering Technology,2022,12(3):809-815.
|
| [7] | |
| [8] |
董聪, 董秀成, 蒋庆哲, 等.《巴黎协定》背景下中国碳排放情景预测: 基于BP神经网络模型[J]. 生态经济,2018,34(2):18-23.
DONG C, DONG X C, JIANG Q Z, et al. Scenarios prediction of the carbon dioxide emissions in China under the background of the Paris agreement based on BP neural network[J]. Ecological Economy,2018,34(2):18-23.
|
| [9] |
习立洋, 吴娜, 吉永军, 等.基于PSO-BP神经网络的船体分段任务包工时定额模型[J]. 船舶工程,2020,42(2):135-141.
XI L Y, WU N, JI Y J, et al. Man-hour quota model of hull block working package based on PSO-BP neural network[J]. Ship Engineering,2020,42(2):135-141.
|
| [10] |
田野. 基于神经网络和粒子群优化算法的供热负荷预测研究[D]. 北京: 北京建筑大学, 2022.
|
| [11] |
赵江燕, 朱宇恩, 马建超, 等.山西省能源消费碳排放清单和影响因素研究[J]. 太原理工大学学报,2022,53(6):989-996.
ZHAO J Y, ZHU Y E, MA J C, et al. Research on carbon emission inventory based on energy activities and the driving factors in Shanxi[J]. Journal of Taiyuan University of Technology,2022,53(6):989-996.
|
| [12] |
郭沛, 连慧君, 丛建辉.山西省碳排放影响因素分解: 基于LMDI模型的实证研究[J]. 资源开发与市场,2016,32(3):308-312.
GUO P, LIAN H J, CONG J H. Decompose of carbon emissions influence factors in Shanxi Province: empirical research based on LMDI model[J]. Resource Development & Market,2016,32(3):308-312.
|
| [13] |
关敏捷, 袁艳红, 冉木希, 等.基于STIRPAT模型的山西省能源碳排放影响因素及峰值预测[J]. 中国煤炭,2021,47(9):48-55.
GUAN M J, YUAN Y H, RAN M X, et al. Influencing factors and peak forecast of energy carbon emission in Shanxi based on STIRPAT model[J]. China Coal,2021,47(9):48-55.
|
| [14] |
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.
DU J H, ZHANG K Y, ZHANG X J. Study on influence factors of carbon emission and prediction of peak value in Shanxi Province[J]. Journal of North University of China (Natural Science Edition),2018,39(3):334-343.
|
| [15] |
朱宇恩, 李丽芬, 贺思思, 等.基于IPAT模型和情景分析法的山西省碳排放峰值年预测[J]. 资源科学,2016,38(12):2316-2325.
ZHU Y E, LI L F, HE S S, et al. Peak year prediction of Shanxi Province’s carbon emissions based on IPAT modeling and scenario analysis[J]. Resources Science,2016,38(12):2316-2325.
|
| [16] |
IPCC. 2006 IPCC guidelines for national greenhouse gas inventories[EB/OL]. (2019-09-12)[2023-03-01]. http://www.ipcc-nggip.iges.or.jp/public/2006gl/chinese/index.html.
|
| [17] |
蔡博峰, 朱松丽, 于胜民, 等.《IPCC 2006年国家温室气体清单指南2019修订版》解读[J]. 环境工程,2019,37(8):1-11.
CAI B F, ZHU S L, YU S M, et al. The interpretation of 2019 refinement to the 2006 IPCC guidelines for national greenhouse gas inventory[J]. Environmental Engineering,2019,37(8):1-11.
|
| [18] |
PETRI T. Towards a theory of decoupling: degrees of decoupling in the EU and the case of road traffic in Finland between 1970 and 2001 [J]. Transport Policy, 2005, 12 (2).
|
| [19] |
张型芳, 罗宏, 吕连宏.碳排放与经济增长的协调性分析[J]. 环境工程技术学报,2017,7(4):517-524.
ZHANG X F, LUO H, LÜ L H. Coordination analysis on carbon emission and economic growth[J]. Journal of Environmental Engineering Technology,2017,7(4):517-524.
|
| [20] |
谌伟, 李荷华.LMDI分解方法在碳排放领域用法探讨[J]. 生态经济,2015,31(8):93-96.
CHEN W, LI H H. Discussion on application of LMDI for carbon emissions[J]. Ecological Economy,2015,31(8):93-96.
|
| [21] |
ANG B W. Decomposition analysis for policy making in energy: which is the preferred method[J]. Energy Policy,2004,32(9):1131-1139. doi: 10.1016/S0301-4215(03)00076-4
|
| [22] |
杨存满, 鞠佳伟, 袁芳, 等.基于PSO-BP神经网络的水厂智能消毒预测模型[J]. 中国给水排水,2022,38(3):57-61.
YANG C M, JU J W, YUAN F, et al. Research on intelligent disinfection prediction model of waterworks based on PSO-BP neural network[J]. China Water & Wastewater,2022,38(3):57-61.
|
| [23] |
唐晓灵, 刘嘉敏.基于PSO-LSTM网络模型的建筑碳排放峰值预测[J]. 科技管理研究,2023,43(1):191-198.
TANG X L, LIU J M. Forecast of peak carbon emissions of buildings based on PSO-LSTM model[J]. Science and Technology Management Research,2023,43(1):191-198.
|
| [24] |
PAO H T, TSAI C M. Modeling and forecasting the CO2 emissions, energy consumption, and economie growth in Brazil[J]. Energy,2011,36(5):2450-2458. doi: 10.1016/j.energy.2011.01.032
|
| [25] |
李心萍, 苏时鹏, 张雅珊, 等.福建省碳排放预测与碳达峰路径分析[J]. 资源开发与市场,2023,39(2):139-147.
LI X P, SU S P, ZHANG Y S, et al. Carbon emission prediction and carbon peak path analysis in Fujian Province[J]. Resource Development & Market,2023,39(2):139-147.
|
| [26] |
秦艳, 王东燕, 杨美艳, 等.山西省“十四五”煤炭消费总量控制目标与对策研究[J]. 中国煤炭,2021(9):41-47.
QIN Y, WANG D Y, YANG M Y, et al. Discussion on the goals and countermeasures of total coal consumption control in Shanxi during the 14th Five-Year Plan[J]. China Coal,2021(9):41-47. ⊕
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