Volume 8 Issue 3
May  2018
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Article Navigation >  Journal of Environmental Engineering Technology  > 2018  >  8(3): 309-318
KONG Youhua, WANG Li, GUO Zhiling, JIANG Yunchao, WANG Bo. 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
Citation: KONG Youhua, WANG Li, GUO Zhiling, JIANG Yunchao, WANG Bo. 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
shu

Carbon emissions peak prediction in Gansu Province based on system dynamics

doi: 10.3969/j.issn.1674-991X.2018.03.041

  • 1. College of Earth Environmental Sciences, Lanzhou University, Lanzhou 730000, China

  • 2. Lanzhou Hydroelectric Survey and Design Institute, Lanzhou 730000, China

More Information
  • Corresponding author: Bo WANG E-mail: wangbo@lzu.edu.cn
  • Received Date: 2017-10-16
  • Publish Date: 2018-05-20
    Abstract
  • The carbon emissions peak was projected for Gansu Province which is mainly characterized by high energy-consuming industries and fragile ecology. Based on the analysis of the current situation, the carbon emissions in Gansu are divided into seven sectors, i.e. electricity, heat production and supply industry, ferrous metal smelting and rolling processing industry, petroleum processing, coking and nuclear fuel processing industry, non-ferrous metal smelting and rolling processing industry, non-metallic mineral products industry, chemical raw materials and chemical products manufacturing and transportation industry. The sub-sectors with largest carbon emission was chosen for each key energy-consuming sector, including electricity, heat industry, iron and steel industry, oil processing industry, aluminum and magnesium industry, cement industry, ammonia industry, and transportation industry. Then, the system dynamics models of sub-sector carbon emissions were established through Vensim PLE software, and eight different scenarios were set using scenario analysis method, being respectively fast-slow scheme, middle-slow scheme, slow scheme, fast-middle scheme, middle scheme, slow-middle scheme, fast scheme, middle-fast scheme, to forecast the carbon emissions peak in Gansu Province. The results show that the peak of carbon emission is 209-429 million tons, it will appear in 2028-2045. In consideration of the peak value, peak occurrence time and current development situation, middle scheme is the optimal way for achieving carbon emissions peak. According to the forecast results, it was proposed that Gansu Province should increase the industrial structure adjustment, energy structure optimization and production technology improvement.

     

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    • References(31)
  • [1]
    杜朝运, 马彧菲 . 试论我国碳排放权期货市场的构建[J].区域金融研究, 2011(9):48-52.
    doi: 10.3969/j.issn.1674-5477.2011.09.010

    DU C Y, MA Y F . Study on the establishment of carbon emissions futures market in China[J].Journal of Regional Financial Research, 2011(9):48-52. doi: 10.3969/j.issn.1674-5477.2011.09.010
    [2]
    WANG C, WEN B, WANG F , et al. Factors driving energy-related carbon emissions in Xinjiang:applying the extended STIRPAT model[J]. Polish Journal of Environmental Studies, 2017,26(4):1747-1755.
    doi: 10.15244/pjoes/68962
    [3]
    张乐勤, 许信旺, 许杨 . 基于Logistic模型的安徽省城镇化演进碳增量效应预测与分析[J]. 重庆工商大学学报(自然科学版), 2015,32(5):80-88.
    doi: 10.16055/j.issn.1672-058X.2015.0005.0020

    ZHANG L Q, XU X W, XU Y . Prediction and analysis on carbon incremental effect in Anhui urbanization evolution based on Logistic model[J]. Journal of Chongqing Technology and Business University(Natural Science Edition), 2015,32(5):80-88. doi: 10.16055/j.issn.1672-058X.2015.0005.0020
    [4]
    TIAN L, DING Z, WANG Y X , et al. Analysis of the driving factors and contributions to carbon emis-sions of energy consumption from the perspective of the peak volume and time based on LEAP[J]. Susta-inability, 2016,8:513-530.
    doi: 10.3390/su8060513
    [5]
    RITI J S, SONG D, SHU Y , et al. Decoupling CO2,emission and economic growth in China:is there consistency in estimation results in analyzing environmental Kuznets curve[J]. Journal of Cleaner Production, 2017,166:1448-1461.
    doi: 10.1016/j.jclepro.2017.08.117
    [6]
    WEN L, BAI L, ZHANG E . System dynamic modeling and scenario simulation on Beijing industrial carbon emissions[J]. Environmental Engineering Research, 2016,21(4):355-364.
    doi: 10.4491/eer.2016.049
    [7]
    郭志玲 . 甘肃省碳排放峰值预测与应对策略研究[D]. 兰州:兰州大学, 2015.

    GUO Z L . Study on peak prediction of carbon emission and control strategies in Gansu Province[D]. Lanzhou:Lanzhou University, 2015.
    [8]
    席细平, 谢运生, 王贺礼 , 等. 基于IPAT模型的江西省碳排放峰值预测研究[J]. 江西科学, 2014,32(6):768-772.
    doi: 10.13990/j.issn1001-3679.2014.06.005

    XI X P, XIE Y S, WANG H L , et al. Forecast of Jiangxi̓s carbon emissions to peak based on IPAT model[J]. Jiangxi Science, 2014,32(6):768-772. doi: 10.13990/j.issn1001-3679.2014.06.005
    [9]
    张巍 . 基于STIRPAT模型的陕西省工业碳排放量预测和情景分析[J]. 可再生能源, 2017,35(5):771-777.

    ZHANG W . Prediction and scenario analysis of industrial carbon emissions in Shaanxi Province based on STIRPAT model[J]. Renewable Energy Resources, 2017,35(5):771-777.
    [10]
    杜强, 陈乔, 杨锐 . 基于Logistic模型的中国各省碳排放预测[J]. 长江流域资源与环境, 2013,22(2):143-151.

    DU Q, CHEN Q, YANG R . Forecast carbon emissions of provinces in China based on logistic model[J]. Resources and Environment in the Yangtze Basin, 2013,22(2):143-151.
    [11]
    贾彦鹏, 刘仁志 . 基于LEAP模型的城市能源规划与CO2减排研究:以景德镇为例[J].应用基础与工程科学学报, 2010(1):75-83.

    JIA Y P, LIU R Z . Forecasting urban energy and CO2 emission using LEAP model:a case study in Jingdezhen City,China[J].Journal of Basic Science and Engineering, 2010(1):75-83.
    [12]
    陈文颖, 高鹏飞, 何建坤 . 用MARKAL-MACRO模型研究碳减排对中国能源系统的影响[J]. 清华大学学报(自然科学版), 2004,44(3):342-346.
    doi: 10.3321/j.issn:1000-0054.2004.03.023

    CHEN W Y, GAO P F, HE J K . Impact of carbon mitigation on China’s energy system using China MARKAL-MACRO model[J]. Journal of Tsinghua University(Science and Technology), 2004,44(3):342-346. doi: 10.3321/j.issn:1000-0054.2004.03.023
    [13]
    林伯强, 蒋竺均 . 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, 2009(4):27-36.
    [14]
    岳超, 王少鹏, 朱江玲 , 等. 2050年中国碳排放量的情景预测:碳排放与社会发展Ⅳ[J]. 北京大学学报(自然科学版), 2010,46(4):517-524.

    YUE C, WANG S P, ZHU J L , et al. 2050 carbon emissions projection for China:carbon emissions and social development:Ⅳ[J]. Acta Scientiarum Naturalium Universitatis Pekinensis, 2010,46(4):517-524.
    [15]
    LI L, XU J . Modelling and simulation of a system dynamics model for county cycle economy[J]. World Journal of Modelling and Simulation, 2006,2(3):150-159.
    [16]
    DONG R, XU J . The simulation of industry chain based on system dynamics model with grey forecasting[J]. World Journal of Modelling and Simulation, 2007,3(4), 252-261.
    [17]
    胡玉奎 . 系统动力学:战略与策略实验室[M]. 杭州: 浙江人民出版社, 1988.
    [18]
    王其潘 . 系统动力学[M]. 北京: 清华大学出版社, 1994.
    [19]
    钟永光, 贾晓菁, 李旭 . 系统动力学[M]. 北京: 科学出版社, 2009.
    [20]
    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(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(6):256-261.
    [21]
    鲁淳兮, 夏凌娟 . 中国钢铁产业能耗与碳排放关系的研究:基于系统动力学[J]. 中国管理信息化, 2017,20(4):119-121.
    [22]
    甘肃发展年鉴2016[M]. 北京: 中国统计出版社, 2017.
    [23]
    国家统计局能源司. 中国能源统计年鉴2012[M]. 北京: 中国统计出版社, 2013.
    [24]
    张肖, 吴高明, 吴声浩 , 等. 大型钢铁企业典型工序碳排放系数的确定方法探讨[J]. 环境科学学报, 2012,32(8):2024-2027.

    ZHANG X, WU G M, WU S H , et al. Determination of carbon dioxide emission factors in typical processes for large iron-steel companies[J]. Acta Scientiae Circumstantiae, 2012,32(8):2024-2027.
    [25]
    王思博 . 水泥行业温室气体排放核算方法研究[D]. 北京:中国社会科学院研究生院, 2012.

    WANG S B . Methodology research for greenhouse emissions from cement industry in China[D]. Beijing:Graduate School of Chinese Academy of Social Sciences, 2012.
    [26]
    赵晏强, 李小春, 白冰 , 等. 中国合成氨工业CO2排放现状及点源分布特征分析[J]. 环境污染与防治, 2011,33(10):101-105.
    doi: 10.3969/j.issn.1001-3865.2011.10.022
    [27]
    李志鹏 . 基于系统动力学的城市交通能源消耗与碳排放预测[D]. 天津:天津大学, 2012.

    LI Z P . The forecast of the energy consumption and carbon emission of urban traffic based on system dynamics:an empirical analysis on Tianjin[D]. Tianjin:Tianjin University, 2012.
    [28]
    王其藩 . 系统动力学[M]. 上海: 上海财经大学出版社, 2009.
    [29]
    张学才, 郭瑞雪 . 情景分析方法综述[J].理论月刊, 2005(8):125-126.
    [30]
    张小平, 方婷 . 甘肃省碳排放变化及影响因素分析[J]. 干旱区地理, 2012,35(3):487-493.

    ZHANG X P, FANG T . Influence factors of carbon emission in Gansu Province[J]. Arid Land Geography, 2012,35(3):487-493.
    [31]
    刘定惠, 杨永春 . 甘肃省碳排放变化的因素分解及实证分析[J]. 干旱区研究, 2012,29(3):510-516.

    LIU D H, YANG Y C . Factor decomposition and demonstration analysis of carbon emission variation in Gansu Province[J]. Arid Zone Research, 2012,29(3):510-516.
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