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基于因素分解模型的辽宁省用水变化驱动力测度及时空分异 被引量:12

Driving force measurement of water utilization change in Liaoning Province and analysis of their spatial-temporal difference based on factor decomposition model
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摘要 通过构建用水变化的因素分解模型,测度了1994-2006年辽宁省用水变化驱动力的经济增长效应、产业结构效应、用水强度效应及混合效应,并对各种效应进行了时间分异分析;测度了1994-2006年辽宁省14个地级市用水变化驱动力的四种效应,对各种效应进行空间聚类,进而对各种效应进行空间分异分析。结果表明:(1)经济增长拉动了水资源利用量的增长,1994-2006年平均效应为15.92×108m3/a;而用水强度和产业结构和混合效应的变化对水资源利用量的扩张起到了一定的遏制作用,同时期年平均效应分别为-12.44×108m3/a、-1.38×108m3/a和-1.88×108m3/a;(2)在1994-2006年间,经济增长效应的平均贡献率约为42.48%,用水强度效应的平均贡献率约为31.24%,产业结构效应的平均贡献率约为12.34%,混合效应的平均贡献率为4.72%。经济增长与用水强度变化是辽宁省用水变化的决定因素;(3)经济增长效应的波动性最小,对水资源利用量变化趋势起到稳定作用,产业结构效应的波动性最大,对水资源利用量变化趋势起到冲击作用,而用水强度效应和混合效应的波动性居于二者之间,是改变水资源利用量变化趋势的动力因素;(4)各种效应的空间分异反映出一个地区的经济发展水平、产业基础、资源条件与效应的发挥存在着密切的关系,因此水资源利用量的"零增长"与"负增长"目标需要在国民经济的不断发展中实现,水资源短缺问题也最终需要在国民经济的发展中解决。研究结果对于正确理解辽宁省水资源利用量变化与经济发展之间的关系,以及产业发展政策调整及可持续发展研究具有一定的参考价值。 The factor decomposition model has widely been used in the energy, economy and environment sciences, but there is not much existing literature on the quantitative analyses on water utilization change by the decomposition model, the driving force measurement of water utilization change in Liaoning Province and analysis of their spatialtemporal difference based on decomposition model were considered to be firstly developed by this research. Water shortage, especially in Liaoning Province, has become the bottle-neck factor for the regeneration and sustainable development of major old industry foundation in China. The factor decomposition model was constructed in this paper to measure the economy growth effect, industrial construction effect and water utilization intensity effect of driving mechanism for water utilization change in Liaoning Province from 1994 to 2006, and the temporal difference of these effects was analyzed. The factor decomposition model was also applied to measure the three kinds of effect of fourteen cities in Liaoning Province from 1994 to 2006, and combined the ISODATA cluster model to cluster the effect in space, and the spatial difference of effects was carried out. The results showed that : ( 1 ) Economy growth increased water utilization, whose average effect from 1994 to 2006 was 15.09 × 10^8 m^3/a; and water utilization intensity and industrial eonstruction decreased water utilization, whose average effects from 1994 to 2006 were - 13.32 × 10^8 m^3/a and - 12.55 ×10^8 m^3/a respectively in the same phase. In different period, the various effects were different; (2) The average contribution ratio of economy growth was 46.66% from 1994 to 2006, and the ones of water utilization intensity and industrial construction were 38.73% and 14.61% , so the economy growth and water utilization intensity were the determined factors for water utilization change, and the effect of industrial construction should not be neglected ; (3) The fluctuation degree of economy growth effect was least, which has a stable function of water utilization change, the fluctuation degree of industrial construction was most, which has a impact function of water utilization change, and the fluctuation degree of water intensity was middle, which was the impetus factor for water utilization change tendency; (4) The spatial difference of various effects reflected such a phenomenon that the effect function has a consanguineous relationship with the economy development level, industrial foundation and resources conditions, so the implement of "zero increase" and "negative increase " goal should be in the continuous development of national economy, and the water shortage problem also should be solved in the continuous development of national economy. The result of this research is of significance for exactly understanding the relation of water utilization and economy development, and also has certain reference significance for the regulation of industry policy and sustainable development.
作者 孙才志 王妍
出处 《干旱区地理》 CSCD 北大核心 2009年第6期850-858,共9页 Arid Land Geography
基金 国家自然科学基金(40501013)
关键词 辽宁省 用水变化驱动力测度 因素分解模型 时空分异 Liaoning Province driving mechanism for water utilization change the factor decomposition model spatial-temporal difference
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