中国省域农田生态系统碳排放时空差异及公平性研究
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摘要
近些年全球变暖的情况愈演愈烈气候变化成为了全世界普遍关注的问题,本研究利用2003—2016年我国31个省(市、区)农田生态系统碳排放量、碳吸收量数据,分析了碳排放总量、省域碳排放强度、人均、地均碳排放量的时空变化,从生态承载力系数、经济贡献系数两个方面,分析了各省域间农田生态系统碳排放的公平性。研究结果表明:①2003—2016年我国农田生态系统碳排放并没有得到很好的控制,碳排放量大的地区已经由中南、西南、华北地区逐步蔓延到了西北、东北地区。②从碳排放强度角度分析,我国农田生态系统GDP增加速度大于碳排放量的增加速度,说明我国经济的快速增长并未引起农田生态系统CO2的大量排放;从人均碳排放量来看,由2003年的1.63 t/人增加到2016年的2.21 t/人,其中增长较快的地区主要集中在东北地区和西北地区;增长量最大的新疆,增长了99.34 kg/人,上海市出现了减少现象,人均碳排放减少了11.17 kg/人;从地均碳排放来看,由2003年的1.95 kg/m2增加到2016年的2.43 kg/m2,呈现下降趋势的只有北京、上海两市,研究区内其他29个省(市、区)的农田生态统地均碳排放均呈上升趋势,其中增长量最大的广东省,增加了45.55 g/m2;③我国西南和东北地区生态承载系数相对其它地区较高,研究区的经济贡献系数在0.230～16.752之间,基于2016年生态承载系数和经济贡献系数之间存在差异,将我国31个省(市、区)可以分为3种类型:北京等4市5省1区属于"高—低"型地区;吉林等4省1区属于"低—高"型地区;河北等13个省3区属于"低—低"型地区。
In recent years,global warming has become more and more serious,and climate change has become a worldwide concern. Based on the data of carbon emissions and carbon absorption of farmland ecosystems in31 provinces(cities and districts) in China from 2003 to 2016,in this work,the total amount of carbon emissions,intensity of carbon emissions,per land and per capita carbon emissions are analyzed. Temporal and spatial variations in carbon emissions from farmland ecosystems in different provinces were analyzed from the aspects of ecological carrying capacity coefficient and economic contribution coefficient. Our main conclusions are as follows.(1)Carbon emissions from farmland ecosystems in China were not well controlled from 2003 to 2016,and the areas with large carbon emissions were mainly from central,south-west and north China to the northwest and northeast China.(2) From the viewpoint of the carbon emission intensity,the growth rate of farmland ecosystem GDP in China is faster than that of carbon emissions,indicating that the rapid economic growth in China has not caused a large amount of CO2 emissions from the farmland ecosystem. Compared to the per capita carbon emissions in 2003 and2016,the per capita carbon emissions of other provinces(cities and regions) showed an increasing trend,except Shanghai and other six provinces(cities) whose per capita carbon emissions was decreased. The fastest-growing regions were mainly in the northeast and northwest regions. From the per land carbon emissions point of view,the carbon emissions in Beijing and Shanghai were both the same,and showed a downward trend. The other 29 provinces(cities,districts) in the studied area exhibited an upward trend of carbon emissions in the farmland ecosystem,among which Guangdong provive ranks the first with and increase of 45.55 g/m2.(3) Based on the differences between ecological carrying capacity coefficient and economic contribution coefficient in 2016,31 provinces(cities,districts) in China can be divided into three types. Beijing,four other cities,five provinces and one District belong to the"high-low"type; Areas such as Jilin,four other provinces,and one region are the"low-high"; Hebei,other 13 provinces,and three remaining regions can be categorized as the "low-low"type.
In recent years,global warming has become more and more serious,and climate change has become a worldwide concern. Based on the data of carbon emissions and carbon absorption of farmland ecosystems in31 provinces(cities and districts) in China from 2003 to 2016,in this work,the total amount of carbon emissions,intensity of carbon emissions,per land and per capita carbon emissions are analyzed. Temporal and spatial variations in carbon emissions from farmland ecosystems in different provinces were analyzed from the aspects of ecological carrying capacity coefficient and economic contribution coefficient. Our main conclusions are as follows.(1)Carbon emissions from farmland ecosystems in China were not well controlled from 2003 to 2016,and the areas with large carbon emissions were mainly from central,south-west and north China to the northwest and northeast China.(2) From the viewpoint of the carbon emission intensity,the growth rate of farmland ecosystem GDP in China is faster than that of carbon emissions,indicating that the rapid economic growth in China has not caused a large amount of CO2 emissions from the farmland ecosystem. Compared to the per capita carbon emissions in 2003 and2016,the per capita carbon emissions of other provinces(cities and regions) showed an increasing trend,except Shanghai and other six provinces(cities) whose per capita carbon emissions was decreased. The fastest-growing regions were mainly in the northeast and northwest regions. From the per land carbon emissions point of view,the carbon emissions in Beijing and Shanghai were both the same,and showed a downward trend. The other 29 provinces(cities,districts) in the studied area exhibited an upward trend of carbon emissions in the farmland ecosystem,among which Guangdong provive ranks the first with and increase of 45.55 g/m2.(3) Based on the differences between ecological carrying capacity coefficient and economic contribution coefficient in 2016,31 provinces(cities,districts) in China can be divided into three types. Beijing,four other cities,five provinces and one District belong to the"high-low"type; Areas such as Jilin,four other provinces,and one region are the"low-high"; Hebei,other 13 provinces,and three remaining regions can be categorized as the "low-low"type.
引文
[1]张小平,王龙飞.甘肃省农业碳排放变化及影响因素分析[J].干旱区地理,2014,37(5):1029-1030.
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[8]杨钧.中国农业碳排放的地区差异和影响因素分析[J].河南农业大学学报,2012,46(3):336-342.
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[10]黄宇轩.碳排放权初始分配及公平性评价体系研究[D].大连:大连理工大学,2016.
[11]陈罗烨,薛领,雪燕.中国农业净碳汇时空演化特征分析[J].自然资源学报,2016,31(4):596-607.
[12] MUSOLESI A,MAZZANTI M,ZOBOLI R. A panel data heterogeneous Bayesian estimation of environmental Kuznets curves for CO2emissions[J]. Appl Econ,2010,42(18):2275-2287.
[13] BRUVOLL A,MEDIN H. Factors behind the environmental Kuznets curve.A decomposition of the changes in air pollution[J].Environ Res Econ,2003,24(1):27-48.
[14]田云,张俊彪,尹朝静,等.中国农业碳排放分布动态与趋势演进———基于31个省(市,区)2002-2011年的面板数据分析[J].中国人口-资源与环境,2014,24(7):91-98.
[15]宋德勇,卢忠宝.中国碳排放影响因素分解及其周期性波动研究[J].中国人口-资源与环境,2009,19(3):18-24.
[16]田云,张俊彪.农业碳排放国内外研究进展[J].中国农业大学学报,2013,18(3):203-208.
[17]赵荣钦,刘英,马林,等.基于碳收支核算的河南省县域空间横向碳补偿研究[J].自然资源学报,2016,31(10):1675-1687.
[18]茹艺.黑龙江省农田生态系统碳源/汇变化特征及影响因素分析[J].哈尔滨师范大学自然科学学报,2015,31(6):109-113.
[19]卢俊宇,黄贤金,戴靓,等.基于时空尺度的中国省级区域能源消费碳排放公平性分析[J].自然资源学报,2012,27(12):2006-2017
[2]田云,张俊彪.中国省级区域农业碳排放公平性研究[J].中国人口-资源与环境,2013,23(11):42-43.
[3]庞丽.我国农业碳排放的区域差异与影响因素分析[J].干旱区资源与环境,2014,28(12):1-7.
[4]李波,张俊飚,李海鹏.中国农业碳排放时空特征及影响因素分解[J].中国人口-资源与环境,2011,21(8):80-85.
[5]文清,田云,王雅鹏.中国农业碳排放省域差异与驱动机理研究———基于30个省(市,区)1993~2012年的面板数据分析[J].干旱区资源与环境,2015,29(11):1-2.
[6]李虎,邱建军,王立刚,等.中国农田主要温室气体排放特征与控制技术[J].生态环境学报,2012,21(1):159-165.
[7]张悦,段华平,赵建波,等.中国农田生态系统的碳足迹分析[J].水土保持学报,2011,25(5):203-208.
[8]杨钧.中国农业碳排放的地区差异和影响因素分析[J].河南农业大学学报,2012,46(3):336-342.
[9]李国志,李宗植.中国农业能源消费碳排放因素分解实证分析———基于LMDI模型[J].农业技术经济,2010(10):66-72.
[10]黄宇轩.碳排放权初始分配及公平性评价体系研究[D].大连:大连理工大学,2016.
[11]陈罗烨,薛领,雪燕.中国农业净碳汇时空演化特征分析[J].自然资源学报,2016,31(4):596-607.
[12] MUSOLESI A,MAZZANTI M,ZOBOLI R. A panel data heterogeneous Bayesian estimation of environmental Kuznets curves for CO2emissions[J]. Appl Econ,2010,42(18):2275-2287.
[13] BRUVOLL A,MEDIN H. Factors behind the environmental Kuznets curve.A decomposition of the changes in air pollution[J].Environ Res Econ,2003,24(1):27-48.
[14]田云,张俊彪,尹朝静,等.中国农业碳排放分布动态与趋势演进———基于31个省(市,区)2002-2011年的面板数据分析[J].中国人口-资源与环境,2014,24(7):91-98.
[15]宋德勇,卢忠宝.中国碳排放影响因素分解及其周期性波动研究[J].中国人口-资源与环境,2009,19(3):18-24.
[16]田云,张俊彪.农业碳排放国内外研究进展[J].中国农业大学学报,2013,18(3):203-208.
[17]赵荣钦,刘英,马林,等.基于碳收支核算的河南省县域空间横向碳补偿研究[J].自然资源学报,2016,31(10):1675-1687.
[18]茹艺.黑龙江省农田生态系统碳源/汇变化特征及影响因素分析[J].哈尔滨师范大学自然科学学报,2015,31(6):109-113.
[19]卢俊宇,黄贤金,戴靓,等.基于时空尺度的中国省级区域能源消费碳排放公平性分析[J].自然资源学报,2012,27(12):2006-2017