基于LMDI模型的广东省产业用水驱动力和驱动效应分析
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摘要
经济快速发展下,区域产业用水变化异常复杂且具高度不确定性。为探讨区域用水演变规律及其驱动成因,选取广东省为研究区,基于扩展Kaya恒等式和对数平均迪氏分解法(LMDI)分析了经济规模、产业结构、用水强度和人口规模等要素对广东省产业用水的驱动成因。研究结果表明:经济规模效应和用水强度效应对广东省用水产生较强的驱动力,而产业结构效应和人口规模效应强度对该省的用水影响逐渐减弱;经济规模效应和人口规模效应对广东省用水产生正向驱动力,累计效应值分别为654.90亿、87.90亿m~3;用水强度效应和产业结构效应则产生负向驱动力,累积效应值分别为-587.48亿、-167.99亿m~3。
Under the rapid economic development,the change of regional industrial water use is extremely complex and highy uncertain.In order to analyze the main factors of industrial water use, the paper builds the industrial water use identity based on the extended Kaya identity, and then adopts the Logarithmic Mean Divisia Index model(LMDI)to analyze the driving forces of the economic scale, water use intensity, industrial structure and population size effects. Results show that: ①economic scale and water use intensity effects are the most important factors, while the industrial structure and population size effects are relatively weak;②the decomposition analysis shows that the economic scale and population size effects have positive incremental force, while water intensity and industrial structure effects have reducing force, and the cumulative effect of those four factors are 654.90 billion,87.90 billion,-587.48 billion and-167.99 billion m~3 respectively.
Under the rapid economic development,the change of regional industrial water use is extremely complex and highy uncertain.In order to analyze the main factors of industrial water use, the paper builds the industrial water use identity based on the extended Kaya identity, and then adopts the Logarithmic Mean Divisia Index model(LMDI)to analyze the driving forces of the economic scale, water use intensity, industrial structure and population size effects. Results show that: ①economic scale and water use intensity effects are the most important factors, while the industrial structure and population size effects are relatively weak;②the decomposition analysis shows that the economic scale and population size effects have positive incremental force, while water intensity and industrial structure effects have reducing force, and the cumulative effect of those four factors are 654.90 billion,87.90 billion,-587.48 billion and-167.99 billion m~3 respectively.
引文
[1] 戴小文,何艳秋,钟秋波.中国农业能源消耗碳排放变化驱动因素及其贡献研究——基于Kaya恒等扩展与LMDI指数分解方法[J].中国生态农业学报,2015,23(11):1445-1454.
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[9] 白夏,戚晓明,潘争伟,等.区域产业用水需求变化驱动效应测度及空间分异分析[J].华北水利水电大学学报(自然科学版),2018,39(4):89-96.
[10] 秦福兴,耿雷华,陈晓燕.确定万元GDP取水量定额方法的探索[J].水利学报,2004,35(8):119-122.
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[12] XU S C,HE Z X,LONG R Y.Factors that influence carbon emissions due to energy consumption in China:Decomposition analysis using LMDI[J].Applied Energy,2014,127:182-193.
[13] 吴丹.中国经济发展与水资源利用的演变态势、“脱钩”评价与机理分析——以中美对比分析为例[J].河海大学学报(哲学社会科学版),2016,18(1):47-53.
[14] 鲍超.中国城镇化与经济增长及用水变化的时空耦合关系[J].地理学报,2014,69(12):1799-1809.
[15]杨硕.北京市用水结构演变分析及驱动因子研究[J].人民珠江,2018,39(5):53-56.
[2] 杨武,王贲,项定先,等.武汉市能源消费碳排放因素分解与低碳发展研究[J].中国人口·资源与环境,2018,28(7):13-16.
[3] ANG B W,ZHANG F Q.A survey of index decomposition analysis in energy and environmental studies[J].Energy,2000,25(12):1149-1176.
[4] 张礼兵,徐勇俊,金菊良,等.安徽省工业用水量变化影响因素分析[J].水利学报,2014,45(7):837-843.
[5] 张陈俊,章恒全,陈其勇,等.中国用水量变化的影响因素分析——基于LMDI方法[J].资源科学,2016(7):1308-1322.
[6] SHANG Y Z,LU S B,SHANG L,et al.Decomposition methods for analyzing changes of industrial water use[J].Journal of Hydrology,2016,543:808-817.
[7] 何艳虎,李深林,杨洁,等.我国不同区域用水结构变化及其驱动因素分析[J].水资源与水工程学报,2016,27(4):1-6.
[8] 吴昊,华骅,王腊春,等.区域用水结构演变及驱动力分析[J].河海大学学报(自然科学版),2016,44(6):477-484.
[9] 白夏,戚晓明,潘争伟,等.区域产业用水需求变化驱动效应测度及空间分异分析[J].华北水利水电大学学报(自然科学版),2018,39(4):89-96.
[10] 秦福兴,耿雷华,陈晓燕.确定万元GDP取水量定额方法的探索[J].水利学报,2004,35(8):119-122.
[11] 孙才志,谢巍.中国产业用水变化驱动效应测度及空间分异[J].经济地理,2011,31(4):666-672.
[12] XU S C,HE Z X,LONG R Y.Factors that influence carbon emissions due to energy consumption in China:Decomposition analysis using LMDI[J].Applied Energy,2014,127:182-193.
[13] 吴丹.中国经济发展与水资源利用的演变态势、“脱钩”评价与机理分析——以中美对比分析为例[J].河海大学学报(哲学社会科学版),2016,18(1):47-53.
[14] 鲍超.中国城镇化与经济增长及用水变化的时空耦合关系[J].地理学报,2014,69(12):1799-1809.
[15]杨硕.北京市用水结构演变分析及驱动因子研究[J].人民珠江,2018,39(5):53-56.