The contradiction between China's economic development, its need for resources and the protection of the environment is ctitical. Scarce water resources have resulted in a considerable bottleneck restricting the economic development of water-deficient areas. An objective evaluation of the decoupling state of water consumption and economic development has become an important indicator of regional economic sustainable development. Based on panel data from 2000 to 2017 for six provinces in the arid and semiarid regions of Northwest China, the Logarithmic Mean Divisia Index method is employed to decompose the factors of the decoupling index and establish a decoupling relationship model between water consumption and economic development. The reasons that affect the decoupling state of water resource utilization and economic development are herein discussed, and the stability of the decoupling trend is analysed. Based on the overall regional trend, the decoupling state of the arid and semiarid regions in Northwest China improved from weak to strong, but the high decoupling stability index varied among the provinces. The intensity and structure were promotional factors for decoupling water consumption and economic development, and the contribution rate of the intensity factor was higher than that of the structure factor. Income and population were inhibiting factors for decoupling water consumption and economic development, and the contribution rate of the income factor was higher than that of the population factor. Based on these results, corresponding policy recommendations are provided. 相似文献
This paper quantifies a decomposition analysis of energy-related CO2 emissions in the industrial sectors of Shanghai over the period 1994-2007.The Log-Mean Divisia Index(LMDI) method is applied to this study in terms of six factors:labor force,labor mobility,gross labor productivity,energy intensity,fuel mix,and emission coefficient.In addition,the decoupling effect between industrial economic growth and CO2 emissions is analyzed to evaluate CO2 mitigation strategies for Shanghai.The results show that all labor productivity has the largest positive effect on CO2 emission changes in the industrial sectors,whereas labor mobility and energy intensity are the main components for decreasing CO2 emissions.Other factors have different effects on CO2 mitigation in different sub-periods.Although a relative decoupling of industrial CO2 emissions from the economic growth in Shanghai has been found,Shanghai should keep pace with the industrial CO2 emissions reduction by implementing low-carbon technology.These results have important policy implications:Plan C is the reasonable choice for Shanghai. 相似文献
Objective: Vehicle crashes that involve pedestrians at intersections have been reported occasionally. Pedestrian injury severity in these crashes is significantly related to driver and pedestrian attributes, vehicle characteristics, and the geometry of intersections. Identifying factors associated with pedestrian injury severity (PIS) is critical for reducing crashes and improving safety. For developing the proposed probit models, drivers involved in crashes are classified into 3 groups: young drivers (16 ≤ age ≤ 24), middle-aged drivers (25 ≤ age ≤ 64), and older drivers (age ≥ 65). This study determines that PIS is significantly but differently affected by these grouped drivers with different sets of explanatory variables.
Methods: A total of 2,614 crash records (2011–2012) at intersections in Cook County, Illinois, were collected. An ordered probit modeling approach was employed to develop the proposed model and examine factors influencing PIS. The likelihood ratio test was used to assess model performance. Elasticity analysis was conducted to interpret the marginal effect of contributing factors on PIS associated with different driver groups by age.
Results: The results show that 4 independent variables, including pedestrian age, vehicle type, point of first contact, and weather condition, significantly affect PIS at intersections for all drivers. Two additional independent variables (i.e., number of vehicles and traffic type) affect PIS for young and middle-aged drivers, and 2 other variables (i.e., divided type and hit-and-run related) are significant to PIS for both young and older drivers.
Conclusions: The independent variables significant to PIS at intersections for young, middle-aged, and older driver groups were identified and the marginal effect of each variable to the likelihood of PIS were assessed. 相似文献