针对能源发展中的高能耗问题和碳排放问题，以山东省沿海城镇带为研究区域，将二氧化碳排放因子纳入能源消费结构优化之中，采用多目标规划的方法构建了以碳排放成本最小、能源消费产生的污染物治理费用最小为目标函数，以经济发展规划、能源消费总量、能源消费结构及碳排放量为约束函数的山东省沿海城镇带能源消费结构多目标优化模型。研究区2035年的能源发展多目标优化结果为：煤炭消费量占能源消费总数下降为36%；油类能源消费量占能源消费总数的17%；天然气消费量占能源消费总数的25%；清洁能源占能源消费总数的比例提高到22%，各项指标均满足山东省能源消费的多个目标和约束。同时，针对山东省沿海城镇带低碳情景下的未来发展，提出了加快能源供给侧和能源消费侧的结构性改革、实施清洁能源和电能替代等建议，以加快转变高耗能生产方式，促进经济结构优化升级，实现节能减排、低碳环保的可持续发展。
This paper studies the problems of the high energy consumption and the carbon emissions in the energy development, focusing on the coastal urban belt of Shandong Province, putting the factor of the carbon dioxide emission into the optimization of the energy consumption structure, and building a multi-objective optimization model of the energy consumption structure of the coastal urban belt of Shandong Province by using the method of the multi-objective planning, with the objective function as to minimize the cost of the carbon emission and the cost of the pollutant treatment from the energy consumption and with the constraint functions based on the economic development planning, the total energy consumption, the energy consumption structure and the carbon emissions. The results of the multi-objective optimization of the energy development in 2035 are as follows:the coal consumption accounts for 36% of the total energy consumption; the oil consumption accounts for 17% of the total energy consumption; the natural gas consumption accounts for 25% of the total energy consumption; and the clean energy accounts for 22% of the total energy consumption. All indicators are in line with the multiple objectives and the constraints of the energy consumption of Shandong Province. Finally, suggestions are made for the future development of the coastal cities and towns within the low carbon scenario:speeding up the structural reform of the energy supply side and the energy consumption side, and implementing effective measures such as clean energy and electricity substitution, in order to accelerate the transformation of the high-energy-consuming production mode, promoting the optimization and the upgrading of the economic structure, to achieve a sustainable development of the energy saving, the emission reduction and the low-carbon environmental protection.
[1] 克莱尔. 资源战争:全球冲突的新场景[M]. 上海:上海译文出版社, 2002:10-15.
[2] Bilgen S. Structure and environmental impact of global energy consumption[J]. Renewable & Sustainable Energy Reviews, 2014(38):890-902.
[3] Brantley, Liddle. Impact of population, age structure, and urbanization on carbon emissions/energy consumption:Evidence from macro-level, cross-country analyses[J]. Population and Environment, 2014, 35(3):286-304.
[4] 王锋, 冯根福, 吴丽华. 中国经济增长中碳强度下降的省区贡献分解[J]. 经济研究, 2013, 48(8):143-155.
[5] 邵敏, 郝晴, 闫桂焕, 等. 山东省能源消费碳排放影响因素分析[J]. 科学与管理, 2019(4):64-68.
[6] Mensah I A, Sun M, Gao C X, et al. Analysis on the nexus of economic growth, fossil fuel energy consumption, CO2 emissions and oil price in Africa based on a PMG panel ARDL approach[J]. Journal of Cleaner Production, 2019(228):161-174.
[7] Sadorsky. Renewable energy consumption, CO2 emissions and oil prices in the G7 countries[J]. Energy Econ, 2009, 3(31):21-28.
[8] 陆家亮, 赵素平. 中国能源消费结构调整与天然气产业发展前景[J]. 天然气工业, 2013, 33(11):9-15.
[9] 黄华. 煤矿节能减排多目标优化研究[J]. 工矿自动化, 2017(6):7714.
[10] 崔欣欣. 火力发电企业节能减排管理研究[D]. 太原:太原理工大学, 2019.
[11] Yang X, Wang S J, Zhang W Z, et al. Impacts of energy consumption, energy structure, and treatment technology on SO2 emissions:A multi-scale LMDI decomposition analysis in China[J]. Applied Energy, 2016(184):714-726.
[12] 王飞. 基于碳排放约束的浙江省能源消费结构优化研究[D]. 杭州:浙江财经大学, 2018.
[13] 张春莲. 基于遗传算法的河北省能源结构多目标优化研究[D]. 北京:华北电力大学, 2013.
[14] 任艳. 基于遗传算法的能源结构多目标优化模型的研究[D]. 北京:中国石油大学, 2007.
[15] 杜广巍. 能源消费总量约束下的青海产业结构优化研究[J]. 青海环境, 2019, 29(1):28-31.
[16] 孙小强, 张求明. 一种基于粒子群优化的多目标优化算法[J]. 计算机工程与应用, 2006, 42(18):40-42.
[17] 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.
[18] Feng T W, Sun L Y, Zhang Y. The relationship between energy consumption structure, economic structure and energy intensity in China[J]. Energy Policy, 2009, 37(12):5475-5483.
[19] Liu Y Q, Zhao G H, Zhao Y S. An analysis of Chinese provincial carbon dioxide emission efficiencies based on energy consumption structure[J]. Energy Policy, 2016(96):524-533.
[20] 仇方道, 张纯敏, 唐晓丹. 基于能源约束的江苏省工业结构优化及空间重组[J]. 地理与地理信息科学, 2014, 30(5):54-59.
[21] 李爽. 低碳目标约束下我国能源消费结构优化研究[J]. 山西大学学报(哲学社会科学版), 2015, 38(4):108-115.
