能源强度与碳强度约束下中国经济增长优化研究
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摘要
为了应对国际减排压力和适应国内经济发展方式的转变，中国政府在“十二五”规划中提出适度放慢经济增长速度、提高服务业增加值比重、优化产业结构、大幅降低能源强度和碳强度、优化能源消费结构等。为了实现“十二五”期间全国能源强度和碳强度分别降低16%和17%的双重目标，政府制定了《“十二五”节能减排综合性工作方案》和《“十二五”控制温室气体排放工作方案》，分别对各省区设定了能源强度和碳强度的降低目标。如何在保证经济平稳增长的同时，尽可能地降低节能减排成本，提高能源效率，优化能源消费结构，降低能源强度和碳强度，减慢能源消耗和碳排放的增速，促进碳排放与经济增长之间的进一步脱钩，寻找最优的经济增长道路，是关系发展全局的重大战略问题。
本文首先预测了双强度及其影响因素的未来变化趋势；其次，综合考虑了产业结构和能源消费结构的历史演变和未来规划约束，结合参数规划和多目标规划思想，研究了全国及省区的产业结构、能源强度、能源消费结构的调整对经济增长的影响问题。
（1）分析了全国能源强度和碳强度的影响因素；并基于历史演变轨迹，预测了双强度及其影响因素的未来变化趋势。全国层面上，预测了三大产业结构与一次能源消费结构的未来变化趋势。省区层面上，预测了各省区能源强度和能源消费结构的未来变化趋势。
结果表明：“十二五”期间，若产业结构遵循历史演变规律，则全国产业结构得到优化，第一产业和第二产业比重有不同程度的下降，第三产业比重有所上升，但是没有达到目标要求，说明“十二五”期间需要加大产业结构的调整力度。绝大多数省区能源强度的降幅能够完成规划目标；但是海南、青海、宁夏、新疆的能源强度的下降幅度较小，并且新疆的能源强度不降反升，没有完成规划目标。若不考虑中长期能源发展规划，仅基于能源消费结构的历史演变规律，则“十二五”期间，煤炭和石油的消费比例均有所下降，天然气和非化石能源的消费比例均有所上升，其中非化石能源的消费比例为10.32%，没有达到11.4%的规划要求。
（2）建立了双强度约束下行业经济增长的多目标优化模型，分析了行业能源强度、能源消费结构和碳强度的调整对经济增长的影响。结果表明，调整行业能源强度和优化能源消费结构对各行业经济增长的影响较大，但是影响程度存在差异。若“十二五”期间各行业能源强度和一次能源消费结构遵循“十一五”期间的变化趋势，则对农业、工业和建筑业的经济增长不利，对其他行业有利。在此基础上，若减少工业和建筑业能源强度的降幅，增加其他行业能源强度的降幅，可以提高农业和建筑业的经济增长速度；优化能源消费结构能减缓工业和建筑业能源强度的下降压力，并且非化石能源比例可达到11.4%。若在优化能源消费结构基础上，进一步减少工业和建筑业能源强度的降幅，对各行业经济增长影响不大，但是会增加全国能源消耗和二氧化碳排放。各种情景中服务业增加值比重均可达47%，但是进一步上升的潜力不大；全国及各行业能源消耗、二氧化碳排放与经济增长之间均将处于弱脱钩状态。
（3）建立了双强度约束下省区经济增长的多目标优化模型，分析了在全国及省区能源强度和碳强度约束下的经济增长优化问题，预测了各省区碳排放、能源消耗与经济增长之间的脱钩状态。结果表明，“十二五”期间，若各省区能源强度与碳强度均能够实现政府制定的降低目标，将会阻碍山西、宁夏、内蒙古和贵州的经济增长，但是能促进其他省区的经济增长；提高北京、河北、上海、浙江和广东的经济增长有利于带动山西、内蒙古和贵州的经济发展，但是会提高部分省区的能源结构碳强度；全国能源强度和碳强度的最优降幅分别为18.19%和19.56%。除海南和青海的碳排放、能源消耗与经济增长之间均处于增长连接状态外，其他省区均处于弱脱钩状态，说明在经济增长最优路径上各省区节能减排效果明显。
（4）结合省区能源强度和能源消费结构的历史演变和未来规划要求，建立了经济增长中的省区能源消费结构多目标优化模型，在各省区能源强度恰好实现政府制定的降低目标、遵循历史演变趋势和调整各省能源强度三种情景中，分别分析了各省区能源消费结构的优化问题，得到了各区省的能源强度和碳强度的最优降幅，预测了各省区碳排放与经济增长之间的脱钩状态和脱钩程度。
结果表明：全国能源消费结构得到优化，煤炭、石油和天然气的消费比例有不同程度的下降，非化石能源的消费比例上升幅度超过“十二五”规划要求。所有省区的非化石能源消费比例的最优值均为其扩张约束的上限，而多数省区的煤炭消费比例取值为上限或中间值而非下限，说明各省区需尽可能地提高非化石能源消费比例，但是短期内这些省区能源消费结构优化的速度不易过快。需要加快上海、山东、湖南、海南、贵州、云南、青海、宁夏、新疆的能源消费结构优化速度，放慢黑龙江、内蒙古、吉林、河南、湖北、陕西的能源消费结构优化速度。
能源强度和碳强度的降幅存在区域差异。北京、山西、内蒙古、吉林、黑龙江、安徽、山东的能源强度的降幅超过20%，青海和新疆的能源强度的降幅小于2%。各省区的碳强度均恰好或超额完成政府制定的降低目标，天津、浙江、广东、甘肃、青海、新疆的碳强度恰好实现了降低目标，其他省区的碳强度均超额实现了降低目标。
在碳排放脱钩方面，除了海南、青海、新疆外，其他省区的能源消耗与经济增长之间均处于弱脱钩状态。北京、天津、山东、河北、内蒙古、山西、安徽、江西、河南、贵州、陕西、黑龙江、吉林、辽宁的能源消耗与经济增长之间的弱脱钩程度大于全国平均水平。除了青海外，其他省区的碳排放与经济增长之间均处于弱脱钩状态。
In order to response the international pressure of carbon emissions reduction and adapt to theway change of domestic economic growth, the chinese government proposes some of measuresincluding slowing down the economic growth rate moderately, and raising the proportion of the addedvalue of service industry, and optimizing the industrial structure, and reducing energy intensity andcarbon intensity greatly, and optimizing energy consumption structure in the “12th Five-Year PlanOutline”. In order to achieve the rate of energy intensity reduction by16%and the rate of carbonemission intensity reduction by17%, the government drew up the comprehensive work plans aboutenergy conservation and the comprehensive work plans about emission reduction during the “12thFive-Year” period. The government set the target of the rate of provincial energy intensity reductionand provincial carbon intensity reduction. In guarantee of steady economic growth at the same time,how to reduce the cost of emissions reduction and energy conservation, and to improve the efficiencyof energy utilization, and to optimize energy consumption structure, and to reduce energy intensityand carbon intensity, and to slow down the growth rate of energy consumption and carbon emissions,and to promote further decoupling of carbon emission and economic growth, and to search theoptimal economic growth path, become a major issues of the long run developing strategy.
In this paper, the futrue change trend of energy intensity and carbon emissions intensity and thechange trend of the influence factors of the double intensity were predicted. The evolution historytrend of energy consumption structure and industrial structure and the planning constraints were allconsidered. Parametric programming and multi-objective programming ideas were combined with.How the adjustment of energy consumption structure and industrial structure, and energy intensity ofthe whole nation and every province affect the low-carbon economic growth path were analyzed.
（1）The influence factors of energy intensity and carbon emissions intensity of the whole nationwere analyzed. The futrue change trend of the double intensity and their influence factors werepredicted based on the history data. The futrue change trend of energy consumption structure andindustrial structure of the nation were predicted. The futrue change trend of energy consumptionstructure and energy intensity of every province were also predicted.
The results show that, the national industrial structure would be optimized following historicalevolution trajectory. The proportion of added value of first industry and secondary industry wouldboth drop from2010to2015. The proportion of added value of third industry would rise from2010to2015, which could not reach47%. The industrial structure need to be adjusted further during the “12thFive-Year” period. The rate of energy intensity reduction of some provinces could reach the target of“12th Five-Year Planning”. If don’t consider the medium and long-term energy plan but based on thehistorical evolution from2010to2015, the energy consumption structure of the whole nation wouldbe optimized. The proportion of coal consumption and oil consumption would both drop respectively.The proportion of gas consumption and non-fossil energy consumption would both rise respectively. The proportion of non-fossil energy consumption could not reach the target in the “12th Five-YearPlanning”.
（2）The multi-objective programming models of the industrial economic growth under therestriction of double intensity were established. It also analyzed the impact of industrial energyconsumption intensity, energy consumption structure and carbon dioxide emissions intensityadjustment on the economic growth path. The results show that, the economic growth rate ofagriculture, industry and construction would all slow down if the changing tendency of energyintensity of each industry and the changing tendency of primary energy consumption structure from2010to2015follow the tendency from2005to2010in the first multi-objective scenario.Nevertheless, the economic growth rate of other sectors would be improved. On the basis of the firstmulti-objective scenario, lessening the reduction rate of energy consumption intensity of constructionand industry, and enlarging the reduction rate of other industrial energy consumption intensity couldimprove the economic growth rate of agriculture and construction. Optimizing energy consumptionstructure could decelerate the pressure of reducing energy consumption intensity of industry andconstruction. The share of non-fossil energy in its energy mix would reach11.4%by2015. Based onthe optimization of energy consumption structure, lessening the reduction rate of energy consumptionintensity of construction and industry further more has little effect on the economic growth of allsectors, but could increase energy consumption and carbon dioxide emissions of the whole nation. Inall scenarios, the share of service industry would all reach47%by2015, but the additional potential toincrease it would be small. The decoupling relationship between energy consumption and economicgrowth of the whole nation and every sector would be in weak decoupling relationship. Thedecoupling relationship between carbon dioxide emissions and economic growth of the whole nationwould also be in weak decoupling relationship.
（3）An optimal model of provincial economic growth under the restriction of double intensitywas established form the global optimal angle. Under the constraint of energy intensity and carbonemissions intensity of the nation, the optimal path of provincial economic growth was found. Thedecoupling relationship between carbon dioxide emissions and economic growth of each provincewas predicted. If each provincial government achieves the target of energy intensity and carbondioxide emissions intensity in two blue prints, the economic growth rate of Shanxi, Ningxia, InnerMongolia and Huizhou would be reduced, but the economic growth rate of other provinces would bepromoted. Increasing economic growth rate of Beijing, Hebei, Shanghai, Zhejiang, and Guangdong isbenefit to promote economic development of Shanxi, Inner Mongolia, and Huizhou. But theenergy-carbon intensity of several provinces would be increased. From2010to2015, the nationalenergy intensity and carbon emissions intensity respectively would be about down18.19%and19.56%. The decoupling relationship between energy consumption and economic growth of Hainanand Qinghai would be a growth connection, but the decoupling relationship of energy consumptionand economic growth of other provinces would be all weak decoupling. The decoupling relationshipof carbon dioxide emissions and economic growth of Hainan would be a growth connection, but this decoupling relationship of other provinces would be all weak decoupling. The results show that theeffect of energy conservation and emission reduction would be remarkable along the optimaleconomic growth path. The growth rate of carbon dioxide emissions would be lower than the growthrate of energy consumption in each province. It means that energy consumption structure of eachprovince would be gradually optimizing.
（4）The evolution history trend of provincial energy intensity and energy consumption structureand planning constraints were all considered. The multi-objective optimization models of provincialenergy consumption structure were established. The optimal adjustment of provincial energyconsumption structure were analyzed in the three scenarios. The first scenario is just realizingprovincial energy intensity reduction goals. The second scenario is the predictive value of theprovincial energy intensity reduction to follow evolution history trend. The third scenario of isadjusting provincial energy intensity reduction. The optimal rate of provincial energy intensityreduction and provincial energy intensity reduction were searched. The decoupling relationshipbetween carbon dioxide emissions and economic growth of each province was also predicted.
The energy consumption structure of the whole nation would be optimized. The proportion ofcoal consumption, oil consumption, and gas consumption would drop respectively. The proportion ofnon-fossil energy consumption would rise. But the proportion of non-fossil energy consumption couldnot reach the target in the “12th Five-Year Planning”. The optimal proportion of non-fossil energyconsumption of all provinces were upper limit of its expansion constraints, But the optimal proportionof coal consumption of most of provinces were lower limit of its expansion constraints. These resultsmeans that the non-fossil energy consumption should rise as far as possible, but the pace of optimizeenergy consumption structure of these provinces could not fast in the short term.
The pace of optimize energy consumption structure of shanghai, shandong, hunan, hainan,guizhou, yunnan, qinghai, ningxia, xinjiang need be speeded up. The pace of optimize energyconsumption structure of Heilongjiang, Jilin, Inner Mongolia, henan, Hubei, Shanxi need slow down.The optimal rate of energy intensity reduction and carbon intensity reduction existed regionaldifferences. The optimal rate of energy intensity reduction of beijing, shanxi, Inner Mongolia, jilin,heilongjiang, anhui, and Shandong were more than20%. However, The optimal rate of energyintensity reduction of Qinghai and Xinjiang were less than2%.
The optimal rate of carbon emissions intensity reduction of each province was more than thetarget of planning constraints. The optimal rate of carbon emissions intensity reduction of Tianjin,Zhejiang, Guangdong, Gansu, Qinghai, and Xinjiang was just its target. However, The optimal rate ofcarbon emissions intensity reduction of other provinces was more than its target.
本文首先预测了双强度及其影响因素的未来变化趋势；其次，综合考虑了产业结构和能源消费结构的历史演变和未来规划约束，结合参数规划和多目标规划思想，研究了全国及省区的产业结构、能源强度、能源消费结构的调整对经济增长的影响问题。
（1）分析了全国能源强度和碳强度的影响因素；并基于历史演变轨迹，预测了双强度及其影响因素的未来变化趋势。全国层面上，预测了三大产业结构与一次能源消费结构的未来变化趋势。省区层面上，预测了各省区能源强度和能源消费结构的未来变化趋势。
结果表明：“十二五”期间，若产业结构遵循历史演变规律，则全国产业结构得到优化，第一产业和第二产业比重有不同程度的下降，第三产业比重有所上升，但是没有达到目标要求，说明“十二五”期间需要加大产业结构的调整力度。绝大多数省区能源强度的降幅能够完成规划目标；但是海南、青海、宁夏、新疆的能源强度的下降幅度较小，并且新疆的能源强度不降反升，没有完成规划目标。若不考虑中长期能源发展规划，仅基于能源消费结构的历史演变规律，则“十二五”期间，煤炭和石油的消费比例均有所下降，天然气和非化石能源的消费比例均有所上升，其中非化石能源的消费比例为10.32%，没有达到11.4%的规划要求。
（2）建立了双强度约束下行业经济增长的多目标优化模型，分析了行业能源强度、能源消费结构和碳强度的调整对经济增长的影响。结果表明，调整行业能源强度和优化能源消费结构对各行业经济增长的影响较大，但是影响程度存在差异。若“十二五”期间各行业能源强度和一次能源消费结构遵循“十一五”期间的变化趋势，则对农业、工业和建筑业的经济增长不利，对其他行业有利。在此基础上，若减少工业和建筑业能源强度的降幅，增加其他行业能源强度的降幅，可以提高农业和建筑业的经济增长速度；优化能源消费结构能减缓工业和建筑业能源强度的下降压力，并且非化石能源比例可达到11.4%。若在优化能源消费结构基础上，进一步减少工业和建筑业能源强度的降幅，对各行业经济增长影响不大，但是会增加全国能源消耗和二氧化碳排放。各种情景中服务业增加值比重均可达47%，但是进一步上升的潜力不大；全国及各行业能源消耗、二氧化碳排放与经济增长之间均将处于弱脱钩状态。
（3）建立了双强度约束下省区经济增长的多目标优化模型，分析了在全国及省区能源强度和碳强度约束下的经济增长优化问题，预测了各省区碳排放、能源消耗与经济增长之间的脱钩状态。结果表明，“十二五”期间，若各省区能源强度与碳强度均能够实现政府制定的降低目标，将会阻碍山西、宁夏、内蒙古和贵州的经济增长，但是能促进其他省区的经济增长；提高北京、河北、上海、浙江和广东的经济增长有利于带动山西、内蒙古和贵州的经济发展，但是会提高部分省区的能源结构碳强度；全国能源强度和碳强度的最优降幅分别为18.19%和19.56%。除海南和青海的碳排放、能源消耗与经济增长之间均处于增长连接状态外，其他省区均处于弱脱钩状态，说明在经济增长最优路径上各省区节能减排效果明显。
（4）结合省区能源强度和能源消费结构的历史演变和未来规划要求，建立了经济增长中的省区能源消费结构多目标优化模型，在各省区能源强度恰好实现政府制定的降低目标、遵循历史演变趋势和调整各省能源强度三种情景中，分别分析了各省区能源消费结构的优化问题，得到了各区省的能源强度和碳强度的最优降幅，预测了各省区碳排放与经济增长之间的脱钩状态和脱钩程度。
结果表明：全国能源消费结构得到优化，煤炭、石油和天然气的消费比例有不同程度的下降，非化石能源的消费比例上升幅度超过“十二五”规划要求。所有省区的非化石能源消费比例的最优值均为其扩张约束的上限，而多数省区的煤炭消费比例取值为上限或中间值而非下限，说明各省区需尽可能地提高非化石能源消费比例，但是短期内这些省区能源消费结构优化的速度不易过快。需要加快上海、山东、湖南、海南、贵州、云南、青海、宁夏、新疆的能源消费结构优化速度，放慢黑龙江、内蒙古、吉林、河南、湖北、陕西的能源消费结构优化速度。
能源强度和碳强度的降幅存在区域差异。北京、山西、内蒙古、吉林、黑龙江、安徽、山东的能源强度的降幅超过20%，青海和新疆的能源强度的降幅小于2%。各省区的碳强度均恰好或超额完成政府制定的降低目标，天津、浙江、广东、甘肃、青海、新疆的碳强度恰好实现了降低目标，其他省区的碳强度均超额实现了降低目标。
在碳排放脱钩方面，除了海南、青海、新疆外，其他省区的能源消耗与经济增长之间均处于弱脱钩状态。北京、天津、山东、河北、内蒙古、山西、安徽、江西、河南、贵州、陕西、黑龙江、吉林、辽宁的能源消耗与经济增长之间的弱脱钩程度大于全国平均水平。除了青海外，其他省区的碳排放与经济增长之间均处于弱脱钩状态。
In order to response the international pressure of carbon emissions reduction and adapt to theway change of domestic economic growth, the chinese government proposes some of measuresincluding slowing down the economic growth rate moderately, and raising the proportion of the addedvalue of service industry, and optimizing the industrial structure, and reducing energy intensity andcarbon intensity greatly, and optimizing energy consumption structure in the “12th Five-Year PlanOutline”. In order to achieve the rate of energy intensity reduction by16%and the rate of carbonemission intensity reduction by17%, the government drew up the comprehensive work plans aboutenergy conservation and the comprehensive work plans about emission reduction during the “12thFive-Year” period. The government set the target of the rate of provincial energy intensity reductionand provincial carbon intensity reduction. In guarantee of steady economic growth at the same time,how to reduce the cost of emissions reduction and energy conservation, and to improve the efficiencyof energy utilization, and to optimize energy consumption structure, and to reduce energy intensityand carbon intensity, and to slow down the growth rate of energy consumption and carbon emissions,and to promote further decoupling of carbon emission and economic growth, and to search theoptimal economic growth path, become a major issues of the long run developing strategy.
In this paper, the futrue change trend of energy intensity and carbon emissions intensity and thechange trend of the influence factors of the double intensity were predicted. The evolution historytrend of energy consumption structure and industrial structure and the planning constraints were allconsidered. Parametric programming and multi-objective programming ideas were combined with.How the adjustment of energy consumption structure and industrial structure, and energy intensity ofthe whole nation and every province affect the low-carbon economic growth path were analyzed.
（1）The influence factors of energy intensity and carbon emissions intensity of the whole nationwere analyzed. The futrue change trend of the double intensity and their influence factors werepredicted based on the history data. The futrue change trend of energy consumption structure andindustrial structure of the nation were predicted. The futrue change trend of energy consumptionstructure and energy intensity of every province were also predicted.
The results show that, the national industrial structure would be optimized following historicalevolution trajectory. The proportion of added value of first industry and secondary industry wouldboth drop from2010to2015. The proportion of added value of third industry would rise from2010to2015, which could not reach47%. The industrial structure need to be adjusted further during the “12thFive-Year” period. The rate of energy intensity reduction of some provinces could reach the target of“12th Five-Year Planning”. If don’t consider the medium and long-term energy plan but based on thehistorical evolution from2010to2015, the energy consumption structure of the whole nation wouldbe optimized. The proportion of coal consumption and oil consumption would both drop respectively.The proportion of gas consumption and non-fossil energy consumption would both rise respectively. The proportion of non-fossil energy consumption could not reach the target in the “12th Five-YearPlanning”.
（2）The multi-objective programming models of the industrial economic growth under therestriction of double intensity were established. It also analyzed the impact of industrial energyconsumption intensity, energy consumption structure and carbon dioxide emissions intensityadjustment on the economic growth path. The results show that, the economic growth rate ofagriculture, industry and construction would all slow down if the changing tendency of energyintensity of each industry and the changing tendency of primary energy consumption structure from2010to2015follow the tendency from2005to2010in the first multi-objective scenario.Nevertheless, the economic growth rate of other sectors would be improved. On the basis of the firstmulti-objective scenario, lessening the reduction rate of energy consumption intensity of constructionand industry, and enlarging the reduction rate of other industrial energy consumption intensity couldimprove the economic growth rate of agriculture and construction. Optimizing energy consumptionstructure could decelerate the pressure of reducing energy consumption intensity of industry andconstruction. The share of non-fossil energy in its energy mix would reach11.4%by2015. Based onthe optimization of energy consumption structure, lessening the reduction rate of energy consumptionintensity of construction and industry further more has little effect on the economic growth of allsectors, but could increase energy consumption and carbon dioxide emissions of the whole nation. Inall scenarios, the share of service industry would all reach47%by2015, but the additional potential toincrease it would be small. The decoupling relationship between energy consumption and economicgrowth of the whole nation and every sector would be in weak decoupling relationship. Thedecoupling relationship between carbon dioxide emissions and economic growth of the whole nationwould also be in weak decoupling relationship.
（3）An optimal model of provincial economic growth under the restriction of double intensitywas established form the global optimal angle. Under the constraint of energy intensity and carbonemissions intensity of the nation, the optimal path of provincial economic growth was found. Thedecoupling relationship between carbon dioxide emissions and economic growth of each provincewas predicted. If each provincial government achieves the target of energy intensity and carbondioxide emissions intensity in two blue prints, the economic growth rate of Shanxi, Ningxia, InnerMongolia and Huizhou would be reduced, but the economic growth rate of other provinces would bepromoted. Increasing economic growth rate of Beijing, Hebei, Shanghai, Zhejiang, and Guangdong isbenefit to promote economic development of Shanxi, Inner Mongolia, and Huizhou. But theenergy-carbon intensity of several provinces would be increased. From2010to2015, the nationalenergy intensity and carbon emissions intensity respectively would be about down18.19%and19.56%. The decoupling relationship between energy consumption and economic growth of Hainanand Qinghai would be a growth connection, but the decoupling relationship of energy consumptionand economic growth of other provinces would be all weak decoupling. The decoupling relationshipof carbon dioxide emissions and economic growth of Hainan would be a growth connection, but this decoupling relationship of other provinces would be all weak decoupling. The results show that theeffect of energy conservation and emission reduction would be remarkable along the optimaleconomic growth path. The growth rate of carbon dioxide emissions would be lower than the growthrate of energy consumption in each province. It means that energy consumption structure of eachprovince would be gradually optimizing.
（4）The evolution history trend of provincial energy intensity and energy consumption structureand planning constraints were all considered. The multi-objective optimization models of provincialenergy consumption structure were established. The optimal adjustment of provincial energyconsumption structure were analyzed in the three scenarios. The first scenario is just realizingprovincial energy intensity reduction goals. The second scenario is the predictive value of theprovincial energy intensity reduction to follow evolution history trend. The third scenario of isadjusting provincial energy intensity reduction. The optimal rate of provincial energy intensityreduction and provincial energy intensity reduction were searched. The decoupling relationshipbetween carbon dioxide emissions and economic growth of each province was also predicted.
The energy consumption structure of the whole nation would be optimized. The proportion ofcoal consumption, oil consumption, and gas consumption would drop respectively. The proportion ofnon-fossil energy consumption would rise. But the proportion of non-fossil energy consumption couldnot reach the target in the “12th Five-Year Planning”. The optimal proportion of non-fossil energyconsumption of all provinces were upper limit of its expansion constraints, But the optimal proportionof coal consumption of most of provinces were lower limit of its expansion constraints. These resultsmeans that the non-fossil energy consumption should rise as far as possible, but the pace of optimizeenergy consumption structure of these provinces could not fast in the short term.
The pace of optimize energy consumption structure of shanghai, shandong, hunan, hainan,guizhou, yunnan, qinghai, ningxia, xinjiang need be speeded up. The pace of optimize energyconsumption structure of Heilongjiang, Jilin, Inner Mongolia, henan, Hubei, Shanxi need slow down.The optimal rate of energy intensity reduction and carbon intensity reduction existed regionaldifferences. The optimal rate of energy intensity reduction of beijing, shanxi, Inner Mongolia, jilin,heilongjiang, anhui, and Shandong were more than20%. However, The optimal rate of energyintensity reduction of Qinghai and Xinjiang were less than2%.
The optimal rate of carbon emissions intensity reduction of each province was more than thetarget of planning constraints. The optimal rate of carbon emissions intensity reduction of Tianjin,Zhejiang, Guangdong, Gansu, Qinghai, and Xinjiang was just its target. However, The optimal rate ofcarbon emissions intensity reduction of other provinces was more than its target.
引文
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