碳排放峰值控制下的建设用地扩展规模研究
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摘要
建设用地作为最大的碳源用地类型,碳排放贡献率显著,因此,低碳调控是实现建设用地减量化的有效手段。已有研究多是通过优化土地利用结构、控制建设用地扩展来实现碳减排效果,而当中国政府做出2030年左右达到CO2排放峰值的承诺后,首先需要解决的是碳排放的达峰问题。因此,本文通过构建与修正Kaya恒等式、回归拟合、灰色预测等方法,在合肥市建设用地碳排放峰值预测的基础上,对该峰值管控下的建设用地扩展进行研究。得到以下结论:①提出了碳排放峰值对建设用地管控的研究思路与框架,认为基于碳排放峰值的科学预测,可以有效地控制建设用地扩展,并引导土地利用结构调整。②GDP、人口与建设用地、碳排放的关系密切,按照人均GDP高、中、低值三种情景的设定,认为中值情景更符合合肥市"十二五"以来的发展状况,即合肥市将在2030年达到CO2排放峰值1 862. 54万t,此后开始逐渐降低。③建设用地扩展与碳排放之间具有强相关性,根据中值情景下合肥市的CO2排放峰值预测结果,合肥市建设用地将在2030年达到最高值10. 81万hm~2,此后开始逐渐减少。最后,提出两点讨论:①对于模型构建、因素分解等方面可进一步深入研究,从而为决策、规划提供更全面的依据。②展望未来,"退建还耕"应当是中国城市精明增长的路径之一,城市周边建设用地复垦将是城镇建设用地整治的重要工作内容。
As the largest type of carbon-source land,construction land has a particularly significant contribution rate for carbon emission. It can be seen that low-carbon regulation is an effective means to realize the reduction of construction land. Most of the existing studies have achieved the carbon emission reduction effect by optimizing the land use structure and controlling the expansion of construction land. However,after Chinese government has made a commitment to reach the peak of CO2 emission around 2030,the first thing to be solved is to predict the peak of carbon emission. Therefore,by constructing and revising the Kaya identity,regression fitting,grey prediction and other methods,this paper first predicted carbon emission peak of construction land,then further studied the expansion scale of construction land under the peak in Hefei City. The results show that: ①It constructs a research framework on the control of construction land by carbon emission peak. Based on scientific prediction of carbon emission peak,it can effectively control the expansion of construction land and guide the adjustment of land use structure. ②GDP,population and construction land,carbon emissions are closely related. Due to the setting of the three scenarios of high,medium and low per capita GDP,the median scenario is more consistent with the development situation of Hefei,that is,Hefei will reach the peak of carbon emission of 18,625,400 tons in2030,and it will gradually decrease thereafter. ③There is a strong correlation between the expansion of construction land and carbon emissions. In view of the prediction results of carbon emission peak,construction land in Hefei will peak at 108,100 hm~2 in 2030,and it will gradually decrease thereafter. Finally,two points of discussion are proposed: ①Further research can be carried out on model construction,factor decomposition and so on,to provide more comprehensive basis for decision-making and planning. ② Looking forward to the future,it is proposed that‘withdrawing construction and returning cultivation'should be one of the smart ways of urban growth in China,and the reclamation of construction land around the city will be an important work content of urban construction land renovation.
As the largest type of carbon-source land,construction land has a particularly significant contribution rate for carbon emission. It can be seen that low-carbon regulation is an effective means to realize the reduction of construction land. Most of the existing studies have achieved the carbon emission reduction effect by optimizing the land use structure and controlling the expansion of construction land. However,after Chinese government has made a commitment to reach the peak of CO2 emission around 2030,the first thing to be solved is to predict the peak of carbon emission. Therefore,by constructing and revising the Kaya identity,regression fitting,grey prediction and other methods,this paper first predicted carbon emission peak of construction land,then further studied the expansion scale of construction land under the peak in Hefei City. The results show that: ①It constructs a research framework on the control of construction land by carbon emission peak. Based on scientific prediction of carbon emission peak,it can effectively control the expansion of construction land and guide the adjustment of land use structure. ②GDP,population and construction land,carbon emissions are closely related. Due to the setting of the three scenarios of high,medium and low per capita GDP,the median scenario is more consistent with the development situation of Hefei,that is,Hefei will reach the peak of carbon emission of 18,625,400 tons in2030,and it will gradually decrease thereafter. ③There is a strong correlation between the expansion of construction land and carbon emissions. In view of the prediction results of carbon emission peak,construction land in Hefei will peak at 108,100 hm~2 in 2030,and it will gradually decrease thereafter. Finally,two points of discussion are proposed: ①Further research can be carried out on model construction,factor decomposition and so on,to provide more comprehensive basis for decision-making and planning. ② Looking forward to the future,it is proposed that‘withdrawing construction and returning cultivation'should be one of the smart ways of urban growth in China,and the reclamation of construction land around the city will be an important work content of urban construction land renovation.
引文
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[17]郭杰,丁冠乔,刘晓曼,等.城镇景观格局对区域碳排放影响及其差别化管控研究[J].中国人口·资源与环境,2018,28(10):55-61.
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[21]朱永彬,王铮,庞丽,等.基于经济模拟的中国能源消费与碳排放高峰预测[J].地理学报,2009,64(8):935-944.
[22]郑海涛,胡杰,王文涛.中国地级城市碳减排目标实现时间测算[J].中国人口·资源与环境,2016,26(4):48-54.
[23]DILLING L,FAILEY E. Managing carbon in a multiple use world:the implications of land-use decision context for carbon management[J]. Global environmental change,2013,23(1):291-300.
[24]赵荣钦,黄贤金,钟太洋,等.区域土地利用结构的碳效应评估及低碳优化[J].农业工程学报,2013,29(17):220-229.
[25]FRIEDLINGSTEIN P,HOUGHTON R A,MARLAND G,et al.Update on CO2emissions[J]. Nature geoscience,2010,3(12):811-812.
[26]吕炜.美国产业结构演变的动因与机制——基于面板数据的实证分析[J].经济学动态,2010(8):131-135.
[27]杜强,陈乔,陆宁.基于改进IPAT模型的中国未来碳排放预测[J].环境科学学报,2012,32(9):2294-2302.
[28]赵会顺,陈超,胡振琪,等.天山北坡经济带城市土地集约利用评价及障碍因素分析[J].农业工程学报,2018,34(20):258-266.
[29]黄凌翔,张臣刚,卢静,等.土地供应结构对经济增长影响的空间效应研究——以长江中游城市群为例[J].中国土地科学,2018,32(9):51-58.
[30]陈占明,吴施美,马文博,等.中国地级以上城市二氧化碳排放的影响因素分析:基于扩展的STIRPAT模型[J].中国人口·资源与环境,2018,28(10):45-54.
[31]谭显春,赖海萍,顾佰和,等.主体功能区视角下的碳排放核算——以广东省为例[J].生态学报,2018,38(17):6292-6301.
[32]YUAN J H,XU Y,HU Z,et al. Peak energy consumption and CO2emissions in China[J]. Energy policy,2014,68:508-523.
[2]SATTERTHWAITE D. Cities’contribution to global warming:notes on the allocation of greenhouse gas emissions[J]. Environment and urbanization,2008,20(2):539-549.
[3]IPCC. Working Group I contribution to the IPCC Fifth assessment report, climate change 2013:the physical science basis[R].Cambridge:Cambridge University Press,2013.
[4]卢娜.土地利用变化碳排放效应研究[D].江苏:南京农业大学,2011:1.
[5]CHUAI X W,HUANG X J,WANG W J,et al. Spatial econometric analysis of carbon emissions from energy consumption in China[J].Journal of geographical sciences,2012,22(4):630-642.
[6]刘竹,关大博,魏伟.中国二氧化碳排放数据核算[J].中国科学:地球科学,2018,48(7):878-887.
[7]齐绍洲,林屾,王班班.中部六省经济增长方式对区域碳排放的影响——基于Tapio脱钩模型、面板数据的滞后期工具变量法的研究[J].中国人口·资源与环境,2015,25(5):59-66.
[8]CHUAI X W,HUANG X J,WANG W J,et al. Spatial simulation of land use based on terrestrial ecosystem carbon storage in coastal Jiangsu,China[J]. Scientific reports,2014(4):1-8.
[9]张蕊娇,刘振鸿.中国钢铁行业CO2排放核算[J].中国人口·资源与环境,2012,22(11):5-8.
[10]GAO T M,SHEN L,SHEN M,et al. Evolution and projection of CO2emissions for China’s cement industry from 1980 to 2020[J].Renewable and sustainable energy reviews,2017,74:522-537.
[11]YAN X Y,CROOKES R J. Energy demand and emissions from road transportation vehicles in China[J]. Progress in energy and combustion science,2010,36(6):651-676.
[12]刘海云,龚梦琪.要素市场扭曲与双向FDI的碳排放规模效应研究[J].中国人口·资源与环境,2018,28(10):27-35.
[13]MI Z F,ZHANG Y K,GUAN D B,et al. Consumption-based emission accounting for Chinese cities[J]. Applied energy,2016,184:1073-1081.
[14]SU B,ANG B W. Input-output analysis of CO2emissions embodied in trade:a multi-region model for China[J]. Applied energy,2014,114:377-384.
[15]HAN F,XIE R,LU Y,et al. The effects of urban agglomeration economies on carbon emissions:evidence from Chinese cities[J].Journal of cleaner production,2018,172:1096-1110.
[16]张苗,甘臣林,陈银蓉,等.中国城市建设用地开发强度的碳排放效率分析与低碳优化[J].资源科学,2016,38(2):265-275.
[17]郭杰,丁冠乔,刘晓曼,等.城镇景观格局对区域碳排放影响及其差别化管控研究[J].中国人口·资源与环境,2018,28(10):55-61.
[18]XIE X,SHAO S,LIN B Q. Exploring the driving forces and mitigation pathways of CO2emissions in China’s petroleum refining and coking industry:1995-2031[J]. Applied energy,2016,184:1004-1015.
[19]BAKHSH K,ROSE S,ALI M F,et al. Economic growth,CO2emissions,renewable waste and FDI relation in Pakistan:new evidences from 3SLS[J]. Journal of environmental management,2017,196:627-632.
[20]张乐勤,李荣富,陈素平,等.安徽省1995年—2009年能源消费碳排放驱动因子分析及趋势预测:基于STIRPAT模型[J].资源科学,2012,34(2):316-327.
[21]朱永彬,王铮,庞丽,等.基于经济模拟的中国能源消费与碳排放高峰预测[J].地理学报,2009,64(8):935-944.
[22]郑海涛,胡杰,王文涛.中国地级城市碳减排目标实现时间测算[J].中国人口·资源与环境,2016,26(4):48-54.
[23]DILLING L,FAILEY E. Managing carbon in a multiple use world:the implications of land-use decision context for carbon management[J]. Global environmental change,2013,23(1):291-300.
[24]赵荣钦,黄贤金,钟太洋,等.区域土地利用结构的碳效应评估及低碳优化[J].农业工程学报,2013,29(17):220-229.
[25]FRIEDLINGSTEIN P,HOUGHTON R A,MARLAND G,et al.Update on CO2emissions[J]. Nature geoscience,2010,3(12):811-812.
[26]吕炜.美国产业结构演变的动因与机制——基于面板数据的实证分析[J].经济学动态,2010(8):131-135.
[27]杜强,陈乔,陆宁.基于改进IPAT模型的中国未来碳排放预测[J].环境科学学报,2012,32(9):2294-2302.
[28]赵会顺,陈超,胡振琪,等.天山北坡经济带城市土地集约利用评价及障碍因素分析[J].农业工程学报,2018,34(20):258-266.
[29]黄凌翔,张臣刚,卢静,等.土地供应结构对经济增长影响的空间效应研究——以长江中游城市群为例[J].中国土地科学,2018,32(9):51-58.
[30]陈占明,吴施美,马文博,等.中国地级以上城市二氧化碳排放的影响因素分析:基于扩展的STIRPAT模型[J].中国人口·资源与环境,2018,28(10):45-54.
[31]谭显春,赖海萍,顾佰和,等.主体功能区视角下的碳排放核算——以广东省为例[J].生态学报,2018,38(17):6292-6301.
[32]YUAN J H,XU Y,HU Z,et al. Peak energy consumption and CO2emissions in China[J]. Energy policy,2014,68:508-523.