Coal is the principal form of energy used in China. Hence, coal price variations are expected to have some influence on merchandise prices. Monthly data from January, 2002, to October, 2010, were used to construct a v...Coal is the principal form of energy used in China. Hence, coal price variations are expected to have some influence on merchandise prices. Monthly data from January, 2002, to October, 2010, were used to construct a varying-parameter state space model, and an error correction model, to estimate the influence of coat prices on Chinese merchandise prices. The time lag and the dynamic relationship were determined from the data. A long term equilibrium relationship between coal price and the PPI, and the CPI, can be observed. The long term influence of coal price fluctuations on the PPI is 0.263%. The corresponding value for the CPI is 0.157%. The PPI shows an influence from coal price change in the first period of observation： by eight periods the influence is obvious, after which it diminishes. The effect of coal price change on the CPI is rather weak and has no long term memory. Analysis of variance shows a similar situation. The elas- ticity coefficient of coal prices on the CPI, or the PPI, fluctuates over the 2002-2004 period. From 2002 to 2007 the influence elasticity on the CPI declined and subsequently levelled off after 2009.展开更多
To reveal the ecological mechanism of spatial patterns of plant phenology and spatial sensitivity of plant phenology responses to climate change,we used Ulmus pumila leaf unfolding and leaf fall data at 46 stations of...To reveal the ecological mechanism of spatial patterns of plant phenology and spatial sensitivity of plant phenology responses to climate change,we used Ulmus pumila leaf unfolding and leaf fall data at 46 stations of China's temperate zone during the period 1986-2005 to simulate 20-year mean and yearly spatial patterns of the beginning and end dates of the Ulmus pumila growing season by establishing air temperature-based spatial phenology models,and validate these models by extensive spatial extrapolation.Results show that the spatial patterns of 20-year mean and yearly February-April or September-November temperatures control the spatial patterns of 20-year mean and yearly beginning or end dates of the growing season.Spatial series of mean beginning dates shows a significantly negative correlation with spatial series of mean February-April temperatures at the 46 stations.The mean spring spatial phenology model explained 90% of beginning date variance(p<0.001) with a Root Mean Square Error(RMSE) of 4.7 days.In contrast,spatial series of mean end dates displays a significantly positive correlation with spatial series of mean September-November temperatures at the 46 stations.The mean autumn spatial phenology model explained 79% of end date variance(p<0.001) with a RMSE of 6 days.Similarly,spatial series of yearly beginning dates correlates negatively with spatial series of yearly February-April temperatures and the explained variances of yearly spring spatial phenology models to beginning date are between 72%-87%(p<0.001),whereas spatial series of yearly end dates correlates positively with spatial series of yearly September-November temperatures and the explained variances of yearly autumn spatial phenology models to end date are between 48%-76%(p<0.001).The overall RMSEs of yearly models in simulating beginning and end dates at all modeling stations are 7.3 days and 9 days,respectively.The spatial prediction accuracies of growing season's beginning and end dates based on both 20-year mean and yearly models are close to the spatial simulation accuracies of these models,indicating that the models have a strong spatial extrapolation capability.Further analysis displays that the negative spatial response rate of growing season's beginning date to air temperature was larger in warmer years with higher regional mean February-April temperatures than in colder years with lower regional mean February-April temperatures.This finding implies that climate warming in winter and spring may enhance sensitivity of the spatial response of growing season's beginning date to air temperature.展开更多
基金support for this work, provided by the National Natural Science Foundation of China (No. 71003097)Jiangsu Province Social Science Foundation (No. 10EYD025)2008 China University of Mining and Technology Youth Foundation Program (No.2008W04)
文摘Coal is the principal form of energy used in China. Hence, coal price variations are expected to have some influence on merchandise prices. Monthly data from January, 2002, to October, 2010, were used to construct a varying-parameter state space model, and an error correction model, to estimate the influence of coat prices on Chinese merchandise prices. The time lag and the dynamic relationship were determined from the data. A long term equilibrium relationship between coal price and the PPI, and the CPI, can be observed. The long term influence of coal price fluctuations on the PPI is 0.263%. The corresponding value for the CPI is 0.157%. The PPI shows an influence from coal price change in the first period of observation： by eight periods the influence is obvious, after which it diminishes. The effect of coal price change on the CPI is rather weak and has no long term memory. Analysis of variance shows a similar situation. The elas- ticity coefficient of coal prices on the CPI, or the PPI, fluctuates over the 2002-2004 period. From 2002 to 2007 the influence elasticity on the CPI declined and subsequently levelled off after 2009.
基金supported by National Natural Science Foundation of China (Grant Nos.40871029 and 41071027)
文摘To reveal the ecological mechanism of spatial patterns of plant phenology and spatial sensitivity of plant phenology responses to climate change,we used Ulmus pumila leaf unfolding and leaf fall data at 46 stations of China's temperate zone during the period 1986-2005 to simulate 20-year mean and yearly spatial patterns of the beginning and end dates of the Ulmus pumila growing season by establishing air temperature-based spatial phenology models,and validate these models by extensive spatial extrapolation.Results show that the spatial patterns of 20-year mean and yearly February-April or September-November temperatures control the spatial patterns of 20-year mean and yearly beginning or end dates of the growing season.Spatial series of mean beginning dates shows a significantly negative correlation with spatial series of mean February-April temperatures at the 46 stations.The mean spring spatial phenology model explained 90% of beginning date variance(p<0.001) with a Root Mean Square Error(RMSE) of 4.7 days.In contrast,spatial series of mean end dates displays a significantly positive correlation with spatial series of mean September-November temperatures at the 46 stations.The mean autumn spatial phenology model explained 79% of end date variance(p<0.001) with a RMSE of 6 days.Similarly,spatial series of yearly beginning dates correlates negatively with spatial series of yearly February-April temperatures and the explained variances of yearly spring spatial phenology models to beginning date are between 72%-87%(p<0.001),whereas spatial series of yearly end dates correlates positively with spatial series of yearly September-November temperatures and the explained variances of yearly autumn spatial phenology models to end date are between 48%-76%(p<0.001).The overall RMSEs of yearly models in simulating beginning and end dates at all modeling stations are 7.3 days and 9 days,respectively.The spatial prediction accuracies of growing season's beginning and end dates based on both 20-year mean and yearly models are close to the spatial simulation accuracies of these models,indicating that the models have a strong spatial extrapolation capability.Further analysis displays that the negative spatial response rate of growing season's beginning date to air temperature was larger in warmer years with higher regional mean February-April temperatures than in colder years with lower regional mean February-April temperatures.This finding implies that climate warming in winter and spring may enhance sensitivity of the spatial response of growing season's beginning date to air temperature.
