消费总量控制下的能源空间配置路径及优化策略
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摘要
本文尝试性将能源集中化配置模型引入到能源消费总量控制的研究框架,使用规模报酬不变与可变DEA模型,对山东省17地市2005—2015年的工业能源利用效率进行测度,进而结合各地市的边际节能成本(边际能源贡献)分析对能源进行优化配置,找出最优能源利用路径。研究发现:(1)山东省工业并未出现能源利用的规模报酬递减趋势,各地市实现了工业发展与能源优化利用的同步。(2)济南、青岛、烟台工业具有较高的能源效率,能够为其它地市的产业转型升级提供技术、经验与资金支持,形成技术扩散效应。(3)位于省区边界的滨州、聊城、临沂、枣庄能源利用效率不高,与其靠近内陆且缺乏中心城市辐射带动有关。(4)各地市最优消费路径比较实际消费路径,都呈现不同程度的偏移。偏移最大的为能源效率较高的青岛、烟台、威海等与能源效率较低的枣庄、日照、淄博与莱芜,特别是能源利用效率较低的上述四地市,必须缩减原始能源消费量20%以上,此分配结果与这些地区工业能源利用效率较低的表现相一致。据此可知,经济发达地区实际能源消费量普遍低于最优能源消费量,为负向偏离,可采用持续宽松的能源消费约束;经济次发达地区实际能源消费量相对最优能源消费量偏离较小,应维持现有能源消费量,保持能源消费低速增长的路径;经济不发达地区实际能源消费量普遍高于最优能源消费量,为正向偏离,应改变其能源依赖的发展模式,形成规制型的能源消费路径。
This paper developed a centralized DEA model for energy consumption quotas allocation,given that the total energy consumption can be restricted. We explored the energy use efficiency for 17 cities in Shandong Province from 2005 to 2015 and investigated the marginal energy saving cost according to the energy use efficiency measured above. Furthermore,the optimal paths for energy utilization have been investigated with the lowest total energy saving cost,yielding the greatest total GDP output for 17 cities.The results showed that:(1) The decrease of returns to scale did not occur up to the time of study. Simultaneously,the damage to environment could be disposed more effective with the benefit from industrialization.(2)The energy use efficiency of industrialization in Jinan,Qingdao and Yantai were higher than other cities; the technology,experience and capital could be exported to adjacent cities to support those cities' economic transformation and upgrade of industry.(3)The cities near the border of Shandong province,such as Binzhou,Liaocheng,Linyi and Zaozhuang,had the low energy use efficiency,for the reason of lacking cooperation with central cities.(4)Comparing actual paths with optimal paths of energy use,the deviations from optimal to actual paths could be observed obviously for17 cities. Best performers( i. e. Qingdao,Yantai and Weihai which had high energy use efficiency) and worst performers( i. e.Zaozhuang,Rizhao,Zibo and Laiwu which had low energy use efficiency) had the biggest degrees of deviation. In addition,according to the levels of the energy use efficiency,worst performers must reduce 20% of their energy consumption at least. Therefore,the developed cities consumed less energy than optimal level and the weak regulation should be imposed. The sub-developed cities consume equal energy with optimal level and the moderate regulation should be imposed. On the contrary,the underdeveloped cities consumed more energy than optimal level and the strong regulation should be imposed.
This paper developed a centralized DEA model for energy consumption quotas allocation,given that the total energy consumption can be restricted. We explored the energy use efficiency for 17 cities in Shandong Province from 2005 to 2015 and investigated the marginal energy saving cost according to the energy use efficiency measured above. Furthermore,the optimal paths for energy utilization have been investigated with the lowest total energy saving cost,yielding the greatest total GDP output for 17 cities.The results showed that:(1) The decrease of returns to scale did not occur up to the time of study. Simultaneously,the damage to environment could be disposed more effective with the benefit from industrialization.(2)The energy use efficiency of industrialization in Jinan,Qingdao and Yantai were higher than other cities; the technology,experience and capital could be exported to adjacent cities to support those cities' economic transformation and upgrade of industry.(3)The cities near the border of Shandong province,such as Binzhou,Liaocheng,Linyi and Zaozhuang,had the low energy use efficiency,for the reason of lacking cooperation with central cities.(4)Comparing actual paths with optimal paths of energy use,the deviations from optimal to actual paths could be observed obviously for17 cities. Best performers( i. e. Qingdao,Yantai and Weihai which had high energy use efficiency) and worst performers( i. e.Zaozhuang,Rizhao,Zibo and Laiwu which had low energy use efficiency) had the biggest degrees of deviation. In addition,according to the levels of the energy use efficiency,worst performers must reduce 20% of their energy consumption at least. Therefore,the developed cities consumed less energy than optimal level and the weak regulation should be imposed. The sub-developed cities consume equal energy with optimal level and the moderate regulation should be imposed. On the contrary,the underdeveloped cities consumed more energy than optimal level and the strong regulation should be imposed.
引文
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[19]王同庆,杨蕙馨,基于DEA方法的山东规模以上工业企业全要素生产率分析[J].山东社会科学,2012(2):158-162.
[20]崔百胜,朱麟.基于内生增长理论与GVAR模型的能源消费控制目标下经济增长与碳减排研究[J].中国管理科学,2016,24(1):11-20.
[21]阮加,雅倩.能源消费总量控制对地区“十二五”发展规划影响的约束分析[J].科学学与科学技术管理,2011,32(5):86-91.
[22]沈镭,刘立涛,王礼茂,等.2050年中国能源消费的情景预测[J].自然资源学报,2015,30(3):361-373.
[23]孙作人,周德群,周鹏,等.基于环境ZSG-DEA的我国省区节能指标分配[J].系统工程,2012(1):84-90.
[24]王平,刘致秀,朱帮助,等.能源结构优化对广东省碳强度目标的贡献潜力[J].中国人口·资源与环境,2013,23(4):49-54.
[25]吴健生,牛妍,彭建,等.基于DMSP/OLS夜间灯光数据的1995-2009年中国地级市能源消费动态[J],地理研究,2014,33(4):625-634.
[2]马丽梅,张晓.中国雾霾污染的空间效应及经济、能源结构影响[J].中国工业经济,2014(4):19-31.
[3]冷艳丽,杜思正.能源价格扭曲与雾霾污染---中国的经验证据[J].产业经济研究,2016(1):71-79.
[4]张军,王圣.我国长江流域中三角区域大气污染物排放特征研究[J].中国环境管理,2017,9(3):83-88.
[5]刘志雄.能源消费是否引起环境污染?---基于我国1991-2011年数据的实证研究[J].生态经济,2015,31(3):19-24.
[6]高湘昀,安海忠,郝晓晴.京津冀能源消费结构对环境的动态效应研究[J].资源与产业,2016,18(6):59-67.
[7]王姗姗,徐吉辉,邱长溶.能源消费与环境污染的边限协整分析[J].中国人口·资源与环境,2010,20(4):69-73.
[8]林伯强,李江龙.环境治理约束下的中国能源结构转变---基于煤炭和二氧化碳峰值的分析[J].中国社会科学,2015(9):84-107.
[9]吴凡.中国节能目标分解与分配研究[D].北京:对外经济贸易大学,2017.
[10]戴彦德,吕斌,冯超.“十三五”中国能源消费总量控制与节能[J].北京理工大学学报(社会科学版),2015(1):1-7.
[11]齐绍洲,李锴.区域部门经济增长与能源强度差异收敛分析[J].经济研究,2010(2):109-122.
[12]魏一鸣,廖华.“十二五”中国能源和碳排放预测与展望[J].中国科学院院刊,2011(2):150-153.
[13]秦鹏,代霞.中国能源消费总量:时序演变、影响因素与管控路径[J].求索,2015(1):111-115.
[14]刘冰,孙华臣.能源消费总量控制政策对产业结构调整的门限效应及现实影响[J].中国人口·资源与环境,2015,25(11):75-81.
[15]刘冰,孙华臣,卢华.“双约束”条件下经济结构调整的缺口测度及路径选择[J].东岳论丛,2014(4):99-106.
[16]LOZANO S,VILLA G.Centralized resource allocation using data envelopment analysis[J].Journal of productivity analysis,2004,22(1):143-161.
[17]SUN Z R,LUO R D,ZHOU D Q.Optimal path for controlling sectoral CO2emissions among China’s regions:a centralized DEAapproach[J].Sustainability,2016,8(1):28-47.
[18]ZHOU P,SUN Z R,ZHOU D Q.Optimal path for controlling CO2emissions in China:a perspective of efficiency analysis[J].Energy economics,2014,45:99-110.
[19]王同庆,杨蕙馨,基于DEA方法的山东规模以上工业企业全要素生产率分析[J].山东社会科学,2012(2):158-162.
[20]崔百胜,朱麟.基于内生增长理论与GVAR模型的能源消费控制目标下经济增长与碳减排研究[J].中国管理科学,2016,24(1):11-20.
[21]阮加,雅倩.能源消费总量控制对地区“十二五”发展规划影响的约束分析[J].科学学与科学技术管理,2011,32(5):86-91.
[22]沈镭,刘立涛,王礼茂,等.2050年中国能源消费的情景预测[J].自然资源学报,2015,30(3):361-373.
[23]孙作人,周德群,周鹏,等.基于环境ZSG-DEA的我国省区节能指标分配[J].系统工程,2012(1):84-90.
[24]王平,刘致秀,朱帮助,等.能源结构优化对广东省碳强度目标的贡献潜力[J].中国人口·资源与环境,2013,23(4):49-54.
[25]吴健生,牛妍,彭建,等.基于DMSP/OLS夜间灯光数据的1995-2009年中国地级市能源消费动态[J],地理研究,2014,33(4):625-634.
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