基于生态足迹模型的区域可持续发展评价研究
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摘要
中国社会正处于经济高速发展和城市化进程的重要阶段,也正处于可持续发展的转折点。如何实现社会经济与自然生态系统的和谐运转,是关系到中华民族乃至全人类永续生存与长远发展的重大课题。生态足迹分析是目前可持续发展评估中最受关注和广泛应用的方法之一,它以形象直观的生物生产性土地面积为计量单位,通过测度和比较人类社会发展的物质需求与自然生态系统所能提供的生态承载力之间的盈亏状况,来定量表征区域可持续发展状态,从而有助于监测可持续性发展进程和实施可持续发展管理。本研究在对生态足迹模型方法进行部分改进的基础上,以江苏省为案例进行了实证分析,以期为促进生态足迹方法体系的完善,以及为区域生态经济可持续发展设定目标、确定行动和追踪既定目标的进展提供依据。主要研究结果如下：
1.江苏省农田碳足迹时空变化特征
根据农田碳素收支平衡分析构建农田碳足迹计算模型,全面核算了1996-2009年江苏全省及各地市农田生态系统的碳排放、碳吸收及碳足迹。结果表明：近15年来,江苏省农作物碳吸收量和碳吸收强度呈“V”字型变化,变化范围分别为2933.6-3896.9×104t.a-1和6.04-7.71t·hm-2·a-1;水稻和小麦两种作物的碳吸收量占全省农作物碳吸收总量的70%左右。农业投入碳排放呈逐渐上升趋势,碳排放强度从1.43t·hm-2·a-1上升至1.88t·hm-2·a-1,增长了31.5%,增速超过作物碳吸收强度,化肥碳排放始终占据主导地位。受碳吸收与碳排放的共同影响,农田碳足迹呈波动增长,变化在13.68-17.56×105hm2之间,约相当于同期耕地面积的27.0%-36.1%,农田系统碳生态盈余则呈明显降低趋势。各地市农田碳吸收、碳排放和碳足迹大体上均呈现自北部向南部递减的格局,其中碳足迹以徐州为最高、常州为最低,两者相差6.62倍。
2.基于区域土地利用变化的江苏省能源碳足迹变化特征
为了全面反映区域各类生产性土地对C02的吸纳能力,本研究根据区域土地利用实际变化和各类土地净初级生产力(NPP)来估算区域土地的平均碳吸收速率,对区域能源消费碳足迹算法进行改进。结果表明：在1999-2009年期间,江苏省生产性土地的平均碳吸收速率变化在4.77-5.78t·hm-2·a-1。人均能源足迹由0.1771hm2增长到0.4194hm2,较传统算法的计算结果年均降低68.5%,但仍显著超出了区域空间负荷,每年约需要相当于1.39-3.34个江苏省总土地面积的空间来吸纳。人均能源足迹与人均GDP之间存在显著的抛物线回归关系和环境库兹涅茨现象,并处于逐渐加强的相对脱钩状态,表明当前江苏省经济增长对能源消费的依赖减弱,能源利用效率有所提高。改进算法的能源足迹结果可以更真实客观地反映能源消费对本区域环境的实际压力。
3.江苏省生态足迹变化趋势与可持续发展评价
传统生态足迹模型测算显示,江苏省近十年来人均生态足迹呈不断上升的趋势,从1.9536hm2增加至2.9993hm2,增长幅度高达53.20%;而生态承载力稳中略降,由0.5727hm2降为0.5068hm2。生态赤字与生态足迹基本同步上升,从1.4461hm2增至2.5501hm2。化石能源用地在人均总生态足迹中的比重超过了50%,成为江苏省生态足迹上升的主要原因。
采用改进算法的能源足迹,使研究期间的人均生态足迹增长势头明显减弱,由1.5649hm2上升至1.8266hm2,增长幅度仅为16.72%,较传统生态足迹每年降低19.9%-39.1%。由于生态承载力基本稳定,所以改进后的生态赤字也将显著降低。两种算法得出的生态足迹,均远远高于生态承载力,表明江苏省社会经济活动对生态资源的消耗远远超出生态承载力的范围,目前的经济发展是以耗竭自身资源或其他区域的自然资源为基础的。
4-江苏省社会经济发展要素对生态足迹的驱动效应
综合应用回归分析、协整分析和脱钩分析等多种方法,分析了江苏省生态足迹与人均GDP、工业化率、城镇化率、固定资产投资额、恩格尔系数、工业总产值和人口总量等7个主要社会经济发展要素之间的关系,结果表明：江苏省近十年人均生态足迹与7个社会经济指标之间均表现为显著的二次曲线回归关系,其中与工业化率和恩格尔系数之间的密切程度相对较低。多元回归分析显示社会经济发展要素对人均生态足迹增长具有显著的驱动作用。脱钩指数分析显示,人均总生态足迹及各组分与人均GDP、工业总产值和固定资产投资额之间的脱钩指数均小于1,表现出明显的脱钩状态。而能源用地与城镇化率之间的脱钩指数在2002年后大于1,呈明显的挂钩状态。表明当前江苏省经济增长对资源消耗依赖程度降低,资源利用效率有所提高,城镇化的快速发展是导致当前江苏省生态足迹尤其是能源碳足迹增长的主要原因。
Nowadays, Chinese society is at an important stage of rapid development of economy and urbanization process, and, is at turning point of sustainable development. It is a momentous subject which respect to survival and development of Chinese nation and even humanity that how to make social economy and ecosystem working harmoniously. Ecological Footprint analysis is one of methods of sustainable development assessment which attracts many attentions and has extensive use; it measures human appropriation of ecosystem products and services in terms of the amount of bioproductive land and sea area needed to supply these products and services. It can express quantitatively regional sustainable development situation by comparing humanity demand on ecosystem services and biological capacity that ecosystem could provide, so as to monitor sustainable developmental process and implement sustainable developmental management. This research partially modifed Ecological Footprint methodology, exemplified by Jiangsu Province, aim to perfect Ecological Footprint method and provide a basis for setting goals, identifying options for action, and tracking progress toward stated goals in regional sustainable development.
1. Temporal and spatial variation in carbon footprint of farmland ecosystem in Jiangsu Province
In this research, we built a carbon footprint calculation model of farmland ecosystem based on carbon payment balance analysis and the parameter values depended on latest domestic researches, and then analyzed the trends of carbon emission, carbon absorption, and carbon footprint of farmland ecosystem in Jiangsu province from1996to2009. The results showed that the dynamic of crop carbon absorption amount and intensity shaped like a "V", carbon absorption intensity changed in the range of6.04to7.71t·hm-2·a-1. The carbon absorption of Rice and wheat was70%of total crop carbon absorption amount in Jiangsu Province. Carbon emission intensity of farmland input showed a trend of increase from1.43t·hm-2·a-1up to1.88t·hm-2·a-1during the period, the increase rate was higher than that of carbon absorption intensity, and the carbon emission from chemical fertilizer was always dominant. Under the influence of carbon emission and absorption, carbon footprint of farmland ecosystem kept increasing with a fluctuation range of13.68×10to17.56×10hm2, the amount approximated27.0%to36.1%of farm area at the same time, while carbon ecological surplus showed a trend of decrease during the period. In general, carbon emission, carbon absorption and carbon footprint showed a trend of decrease from north to south in Jiangsu Province, the minimum of carbon footprint appeared in Changzhou, while the maximum appeared in Xuzhou, which increased6.62times.
2. Change characteristics of energy carbon footprint based on regional land use change in Jiangsu Province
In order to reflect absorbency for CO2of difference kind of regional productive area across-the-board, this study estimated regional land average carbon absorption rate based on regional actual land use change and net primary productivity (NPP) of all kinds of land, and modified the algorithm of energy carbon footprint (ECF). The results showed that average carbon absorption rate of productive land area changed in the range of4.77to5.78t·hm-2·a-1in Jiangsu Province and ECF per capita increased from0.1771hm2to0.4194hm2. Compared with traditional algorithm, the ECF per capita reduced68.5%on average a year, but the amount still overstepped regional burthen ability observably,1.39to3.34times of total area of Jiangsu Province, needed for carbon absorption per year. The relationship between GDP per capita and ECF per capita was remarkable parabola regression and did prove the environmental Kuznets curve hypothesis, and, they were in the state of gradual increasing relative decoupling. These reflect the dependence of current economic growth on energy consumption gradually reduces and energy utilization efficiency improves. The results of ECF calculated by the improved algorithm could reflect regional environmental pressure by energy consumption more veritably and objectively.
3. Change trends of ecological footprint and sustainable development appraisal in Jiangsu Province
The results calculated by traditional ecological footprint (EF) model showed that EF per capita increased by53.20%, from1.5649hm2to1.8266hm2, in recent ten years in Jiangsu. But ecosystem carrying capacity had a little drop, decreasing from0.5727hm2to0.5068hm2, and ecological deficit increased from1.4461hm2to2.5501hm2in Jiangsu Province as the same rate as ecological footprint. Fossil energy land accounted for more than50%of EF per capita, it has become the primary cause of the increasing of EF in Jiangsu Province. It made the growth of EF per capita slow-up obviously during the study that using the improved model of ECF, the amount just increased from1.5649hm2to1.8266hm2, having a growth rate of16.72%, and it decreased from19.9to39.1percents, compared to the traditional method. EF Results calculated by the two methods were both above ecosystem carrying capacity, indicating that biological resources consumed by social economic activities had far beyond ecosystem carrying capacity in Jiangsu Province, at the present time, economic development is based on depletion of natural resources in Jiangsu Province.
4. Driving effects of socio-economic factors on ecological footprint in Jiangsu Province
By combining regression analysis, principal component analysis, co-integration analysis, and decoupling analysis, we analyzed the relationships between ecological footprint per capita of Jiangsu and social economic factors, such as GDP per capita, industrialized rate, urbanization rate, fixed investment, Engelian coefficient, total industrial output value and total population. In recent ten years, there were remarkable quadric curvilinear regression relationships between EF per capita and seven socio-economic indicators, and among them, the relationships with industrialized rate and Engelian coefficient had lower dependence. The result of multiple regression analysis and principal component analysis indicated that these socio-economic factors were strong driving force for regional EF per capita. The decoupling index between total ecological footprint per capita and GDP per capita, total industrial output value, and fixed investment were all less than1, showing a visible decoupling state, but the index between energy land and urbanization rate was greater than1after2002, which showing a clear coupling state, indicating that economic gain was less depend on depletion of natural resources, level of resources utilization was higher, rapid growth of urbanization was the leading cause of increasing of EF especially energy carbon footprint in Jiangsu Province.
1.江苏省农田碳足迹时空变化特征
根据农田碳素收支平衡分析构建农田碳足迹计算模型,全面核算了1996-2009年江苏全省及各地市农田生态系统的碳排放、碳吸收及碳足迹。结果表明：近15年来,江苏省农作物碳吸收量和碳吸收强度呈“V”字型变化,变化范围分别为2933.6-3896.9×104t.a-1和6.04-7.71t·hm-2·a-1;水稻和小麦两种作物的碳吸收量占全省农作物碳吸收总量的70%左右。农业投入碳排放呈逐渐上升趋势,碳排放强度从1.43t·hm-2·a-1上升至1.88t·hm-2·a-1,增长了31.5%,增速超过作物碳吸收强度,化肥碳排放始终占据主导地位。受碳吸收与碳排放的共同影响,农田碳足迹呈波动增长,变化在13.68-17.56×105hm2之间,约相当于同期耕地面积的27.0%-36.1%,农田系统碳生态盈余则呈明显降低趋势。各地市农田碳吸收、碳排放和碳足迹大体上均呈现自北部向南部递减的格局,其中碳足迹以徐州为最高、常州为最低,两者相差6.62倍。
2.基于区域土地利用变化的江苏省能源碳足迹变化特征
为了全面反映区域各类生产性土地对C02的吸纳能力,本研究根据区域土地利用实际变化和各类土地净初级生产力(NPP)来估算区域土地的平均碳吸收速率,对区域能源消费碳足迹算法进行改进。结果表明：在1999-2009年期间,江苏省生产性土地的平均碳吸收速率变化在4.77-5.78t·hm-2·a-1。人均能源足迹由0.1771hm2增长到0.4194hm2,较传统算法的计算结果年均降低68.5%,但仍显著超出了区域空间负荷,每年约需要相当于1.39-3.34个江苏省总土地面积的空间来吸纳。人均能源足迹与人均GDP之间存在显著的抛物线回归关系和环境库兹涅茨现象,并处于逐渐加强的相对脱钩状态,表明当前江苏省经济增长对能源消费的依赖减弱,能源利用效率有所提高。改进算法的能源足迹结果可以更真实客观地反映能源消费对本区域环境的实际压力。
3.江苏省生态足迹变化趋势与可持续发展评价
传统生态足迹模型测算显示,江苏省近十年来人均生态足迹呈不断上升的趋势,从1.9536hm2增加至2.9993hm2,增长幅度高达53.20%;而生态承载力稳中略降,由0.5727hm2降为0.5068hm2。生态赤字与生态足迹基本同步上升,从1.4461hm2增至2.5501hm2。化石能源用地在人均总生态足迹中的比重超过了50%,成为江苏省生态足迹上升的主要原因。
采用改进算法的能源足迹,使研究期间的人均生态足迹增长势头明显减弱,由1.5649hm2上升至1.8266hm2,增长幅度仅为16.72%,较传统生态足迹每年降低19.9%-39.1%。由于生态承载力基本稳定,所以改进后的生态赤字也将显著降低。两种算法得出的生态足迹,均远远高于生态承载力,表明江苏省社会经济活动对生态资源的消耗远远超出生态承载力的范围,目前的经济发展是以耗竭自身资源或其他区域的自然资源为基础的。
4-江苏省社会经济发展要素对生态足迹的驱动效应
综合应用回归分析、协整分析和脱钩分析等多种方法,分析了江苏省生态足迹与人均GDP、工业化率、城镇化率、固定资产投资额、恩格尔系数、工业总产值和人口总量等7个主要社会经济发展要素之间的关系,结果表明：江苏省近十年人均生态足迹与7个社会经济指标之间均表现为显著的二次曲线回归关系,其中与工业化率和恩格尔系数之间的密切程度相对较低。多元回归分析显示社会经济发展要素对人均生态足迹增长具有显著的驱动作用。脱钩指数分析显示,人均总生态足迹及各组分与人均GDP、工业总产值和固定资产投资额之间的脱钩指数均小于1,表现出明显的脱钩状态。而能源用地与城镇化率之间的脱钩指数在2002年后大于1,呈明显的挂钩状态。表明当前江苏省经济增长对资源消耗依赖程度降低,资源利用效率有所提高,城镇化的快速发展是导致当前江苏省生态足迹尤其是能源碳足迹增长的主要原因。
Nowadays, Chinese society is at an important stage of rapid development of economy and urbanization process, and, is at turning point of sustainable development. It is a momentous subject which respect to survival and development of Chinese nation and even humanity that how to make social economy and ecosystem working harmoniously. Ecological Footprint analysis is one of methods of sustainable development assessment which attracts many attentions and has extensive use; it measures human appropriation of ecosystem products and services in terms of the amount of bioproductive land and sea area needed to supply these products and services. It can express quantitatively regional sustainable development situation by comparing humanity demand on ecosystem services and biological capacity that ecosystem could provide, so as to monitor sustainable developmental process and implement sustainable developmental management. This research partially modifed Ecological Footprint methodology, exemplified by Jiangsu Province, aim to perfect Ecological Footprint method and provide a basis for setting goals, identifying options for action, and tracking progress toward stated goals in regional sustainable development.
1. Temporal and spatial variation in carbon footprint of farmland ecosystem in Jiangsu Province
In this research, we built a carbon footprint calculation model of farmland ecosystem based on carbon payment balance analysis and the parameter values depended on latest domestic researches, and then analyzed the trends of carbon emission, carbon absorption, and carbon footprint of farmland ecosystem in Jiangsu province from1996to2009. The results showed that the dynamic of crop carbon absorption amount and intensity shaped like a "V", carbon absorption intensity changed in the range of6.04to7.71t·hm-2·a-1. The carbon absorption of Rice and wheat was70%of total crop carbon absorption amount in Jiangsu Province. Carbon emission intensity of farmland input showed a trend of increase from1.43t·hm-2·a-1up to1.88t·hm-2·a-1during the period, the increase rate was higher than that of carbon absorption intensity, and the carbon emission from chemical fertilizer was always dominant. Under the influence of carbon emission and absorption, carbon footprint of farmland ecosystem kept increasing with a fluctuation range of13.68×10to17.56×10hm2, the amount approximated27.0%to36.1%of farm area at the same time, while carbon ecological surplus showed a trend of decrease during the period. In general, carbon emission, carbon absorption and carbon footprint showed a trend of decrease from north to south in Jiangsu Province, the minimum of carbon footprint appeared in Changzhou, while the maximum appeared in Xuzhou, which increased6.62times.
2. Change characteristics of energy carbon footprint based on regional land use change in Jiangsu Province
In order to reflect absorbency for CO2of difference kind of regional productive area across-the-board, this study estimated regional land average carbon absorption rate based on regional actual land use change and net primary productivity (NPP) of all kinds of land, and modified the algorithm of energy carbon footprint (ECF). The results showed that average carbon absorption rate of productive land area changed in the range of4.77to5.78t·hm-2·a-1in Jiangsu Province and ECF per capita increased from0.1771hm2to0.4194hm2. Compared with traditional algorithm, the ECF per capita reduced68.5%on average a year, but the amount still overstepped regional burthen ability observably,1.39to3.34times of total area of Jiangsu Province, needed for carbon absorption per year. The relationship between GDP per capita and ECF per capita was remarkable parabola regression and did prove the environmental Kuznets curve hypothesis, and, they were in the state of gradual increasing relative decoupling. These reflect the dependence of current economic growth on energy consumption gradually reduces and energy utilization efficiency improves. The results of ECF calculated by the improved algorithm could reflect regional environmental pressure by energy consumption more veritably and objectively.
3. Change trends of ecological footprint and sustainable development appraisal in Jiangsu Province
The results calculated by traditional ecological footprint (EF) model showed that EF per capita increased by53.20%, from1.5649hm2to1.8266hm2, in recent ten years in Jiangsu. But ecosystem carrying capacity had a little drop, decreasing from0.5727hm2to0.5068hm2, and ecological deficit increased from1.4461hm2to2.5501hm2in Jiangsu Province as the same rate as ecological footprint. Fossil energy land accounted for more than50%of EF per capita, it has become the primary cause of the increasing of EF in Jiangsu Province. It made the growth of EF per capita slow-up obviously during the study that using the improved model of ECF, the amount just increased from1.5649hm2to1.8266hm2, having a growth rate of16.72%, and it decreased from19.9to39.1percents, compared to the traditional method. EF Results calculated by the two methods were both above ecosystem carrying capacity, indicating that biological resources consumed by social economic activities had far beyond ecosystem carrying capacity in Jiangsu Province, at the present time, economic development is based on depletion of natural resources in Jiangsu Province.
4. Driving effects of socio-economic factors on ecological footprint in Jiangsu Province
By combining regression analysis, principal component analysis, co-integration analysis, and decoupling analysis, we analyzed the relationships between ecological footprint per capita of Jiangsu and social economic factors, such as GDP per capita, industrialized rate, urbanization rate, fixed investment, Engelian coefficient, total industrial output value and total population. In recent ten years, there were remarkable quadric curvilinear regression relationships between EF per capita and seven socio-economic indicators, and among them, the relationships with industrialized rate and Engelian coefficient had lower dependence. The result of multiple regression analysis and principal component analysis indicated that these socio-economic factors were strong driving force for regional EF per capita. The decoupling index between total ecological footprint per capita and GDP per capita, total industrial output value, and fixed investment were all less than1, showing a visible decoupling state, but the index between energy land and urbanization rate was greater than1after2002, which showing a clear coupling state, indicating that economic gain was less depend on depletion of natural resources, level of resources utilization was higher, rapid growth of urbanization was the leading cause of increasing of EF especially energy carbon footprint in Jiangsu Province.
引文
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