Here, the geographical space distribution of the oil and gas industry in China is comprehensively investigated using the overal Moran’s I index and local Moran’s I index. We found that China’s oil and gas industry ...Here, the geographical space distribution of the oil and gas industry in China is comprehensively investigated using the overal Moran’s I index and local Moran’s I index. We found that China’s oil and gas industry development from 2000 to 2010 has a differentiated geographical space distribution upstream （extractive industry） but not downstream （reifning industry）. To analyze upstream and downstream states a spatial econometrics model （SEM） was used to identify inlfuential factors resulting from the spatial concentration of the oil and gas industry. An external effect is the key factor promoting the spatial concentration of the upstream industry in China;governmental economic policy is another important factor.展开更多
Estimation of soil organic carbon （SOC） pools and fluxes bears large uncertainties because SOC stocks vary greatly over geographical space and through time. Although development of the U.S. Soil Survey Geographic Da...Estimation of soil organic carbon （SOC） pools and fluxes bears large uncertainties because SOC stocks vary greatly over geographical space and through time. Although development of the U.S. Soil Survey Geographic Database （SSURGO）, currently the most detailed level with a map scale ranging from 1：12 000 to 1：63 360, has involved substantial government funds and coordinated network efforts, very few studies have utilized it for soil carbon assessment at the large landscape scale. The objectives of this study were to 1） compare estimates in soil organic matter among SSURGO, the State Soil Geographic Database （STATSGO）, and referenced field measurements at the soil map unit; 2） examine the influence of missing data on SOC estimation by SSURGO and STATSGO; 3） quantify spatial differences in SOC estimation between SSURGO and STATSCO, specifically for the state of Louisiana; and 4） assess scale effects on soil organic carbon density （SOCD） estimates from a soil map unit to a watershed and a river basin scale. SOC was estimated using soil attributes of SSURGO and STATSGO including soil organic matter （SOM） content, soil layer depth, and bulk density. Paired t-test, correlation, and regression analyses were performed to investigate various relations of SOC and SOM among the datasets. There were positive relations of SOC estimates between SSURGO and STATSGO at the soil map unit （R2 = 0.56, n = 86, t = 1.65, P = 0.102; depth： 30 cm）. However, the SOC estimated by STATSGO were 9%, 33% and 36~ lower for the upper 30-cm, the upper l-m, and the maximal depth （up to 2.75 m） soils, respectively, than those from SSURGO. The difference tended to increase as the spatial scale changes from the soil map unit to the watershed and river basin scales. Compared with the referenced field measurements, the estimates in SOM by SSURGO showed a closer match than those of STATSCO, indicating that the former was more accurate than the latter in SOC estimation, both in spatial and temporal resolutions. Further applications of SSURGO in SOC estimation for the entire United States could improve the accuracy of soil carbon accounting in regional and national carbon balances.展开更多
基金the Key Project of the National Social Science Foundation of China(Grant No.11AJL007)The Ministry of education of Humanities and Social Science project(Grant No.12YJC790082)
文摘Here, the geographical space distribution of the oil and gas industry in China is comprehensively investigated using the overal Moran’s I index and local Moran’s I index. We found that China’s oil and gas industry development from 2000 to 2010 has a differentiated geographical space distribution upstream （extractive industry） but not downstream （reifning industry）. To analyze upstream and downstream states a spatial econometrics model （SEM） was used to identify inlfuential factors resulting from the spatial concentration of the oil and gas industry. An external effect is the key factor promoting the spatial concentration of the upstream industry in China;governmental economic policy is another important factor.
基金Supported by the U.S. Louisiana Board of Regents (No. LEQSF (2004-2007)-RD-A-04)
文摘Estimation of soil organic carbon （SOC） pools and fluxes bears large uncertainties because SOC stocks vary greatly over geographical space and through time. Although development of the U.S. Soil Survey Geographic Database （SSURGO）, currently the most detailed level with a map scale ranging from 1：12 000 to 1：63 360, has involved substantial government funds and coordinated network efforts, very few studies have utilized it for soil carbon assessment at the large landscape scale. The objectives of this study were to 1） compare estimates in soil organic matter among SSURGO, the State Soil Geographic Database （STATSGO）, and referenced field measurements at the soil map unit; 2） examine the influence of missing data on SOC estimation by SSURGO and STATSGO; 3） quantify spatial differences in SOC estimation between SSURGO and STATSCO, specifically for the state of Louisiana; and 4） assess scale effects on soil organic carbon density （SOCD） estimates from a soil map unit to a watershed and a river basin scale. SOC was estimated using soil attributes of SSURGO and STATSGO including soil organic matter （SOM） content, soil layer depth, and bulk density. Paired t-test, correlation, and regression analyses were performed to investigate various relations of SOC and SOM among the datasets. There were positive relations of SOC estimates between SSURGO and STATSGO at the soil map unit （R2 = 0.56, n = 86, t = 1.65, P = 0.102; depth： 30 cm）. However, the SOC estimated by STATSGO were 9%, 33% and 36~ lower for the upper 30-cm, the upper l-m, and the maximal depth （up to 2.75 m） soils, respectively, than those from SSURGO. The difference tended to increase as the spatial scale changes from the soil map unit to the watershed and river basin scales. Compared with the referenced field measurements, the estimates in SOM by SSURGO showed a closer match than those of STATSCO, indicating that the former was more accurate than the latter in SOC estimation, both in spatial and temporal resolutions. Further applications of SSURGO in SOC estimation for the entire United States could improve the accuracy of soil carbon accounting in regional and national carbon balances.
