Assessment of Human Impacts on Vegetation in Built-up Areas in China Based on AVHRR,MODIS and DMSP_OLS Nighttime Light Data,1992–2010

Since the reform and opening-up program started in 1978,the level of urbanization has increased rapidly in China.Rapid urban expansion and restructuring have had significant impacts on the ecological environment especially within built-up areas.In this study,ArcGIS 10,ENVI 4.5,and Visual FoxPro 6.0 were used to analyze the human impacts on vegetation in the built-up areas of 656Chinese cities from 1992 to 2010.Firstly,an existing algorithm was refined to extract the boundaries of the built-up areas based on the Defense Meteorological Satellite Program Operational Linescan System(DMSP_OLS)nighttime light data.This improved algorithm has the advantages of high accuracy and speed.Secondly,a mathematical model(Human impacts(HI))was constructed to measure the impacts of human factors on vegetation during rapid urbanization based on Advanced Very High Resolution Radiometer(AVHRR)Normalized Difference Vegetation Index(NDVI)and Moderate Resolution Imaging Spectroradiometer(MODIS)NDVI.HI values greater than zero indicate relatively beneficial effects while values less than zero indicate proportionally adverse effects.The results were analyzed from four aspects:the size of cities(metropolises,large cities,medium-sized cities,and small cities),large regions(the eastern,central,western,and northeastern China),administrative divisions of China(provinces,autonomous regions,and municipalities)and vegetation zones(humid and semi-humid forest zone,semi-arid steppe zone,and arid desert zone).Finally,we discussed how human factors impacted on vegetation changes in the built-up areas.We found that urban planning policies and developmental stages impacted on vegetation changes in the built-up areas.The negative human impacts followed an inverted′U′shape,first rising and then falling with increase of urban scales.China′s national policies,social and economic development affected vegetation changes in the built-up areas.The findings can provide a scientific basis for municipal planning departments,a decision-making reference for government,and scientific guidance for sustainable development in China.

Disparities between Explanatory Models of Health Clients, Healthcare Providers and Health Insurer

We present qualitative data from a study in Ghana （2011）, where the National Health Insurance Scheme （NHIS） was introduced to improve access to health care. In 2011 membership enrolment and retention in the scheme was stalling. To obtain better insights into socio-cultural factors that influence utilization of healthcare services and the NHIS this study compared Explanatory Models of healthcare clients with those of primary healthcare providers and the NHIS regarding illness, the need for, the quality of, and the control over heaithcare and health insurance services. We found critical disparities in socio-cultural beliefs and perceptions of healthcare and health insurance between these three stakeholder groups, such as the clients＇ holistic view on illness versus healthcare providers＇ bio-medical view; the clients＇ inter-relational focus in perceiving quality of services versus the providers＇ medical technical focus. These differences are leading to misconceptions, blame practice, poor services, non-adherence and low trust. The findings increase our understanding of clients＇ behavior and that of their service providers. We conclude with key messages for policy leaders and operational managers that can guide them in improving services and facilitating client trust and interest to participate in health insurance and utilize healthcare services.

Characterizing China＇s energy consumption with selective economic factors and energy-resource endowment： a spatial econometric approach

Coupled with intricate regional interactions, the provincial disparity of energy-resource endowment and other economic conditions in China have created spatially complex energy consumption patterns that require analyses beyond the traditional ones. To distill the spatial effect out of the resource and economic factors on China＇s energy consumption, this study recast the traditional econometric model in a spatial context. Several analytic steps were taken to reveal different aspects of the issue. Per capita energy consumption （AVEC） at the provincial level was first mapped to reveal spatial clusters of high energy consumption being located in either well developed or energy resourceful regions. This visual spatial autocorrela- tion pattern of AVEC was quantitatively tested to confirm its existence among Chinese provinces. A Moran scatter- plot was employed to further display a relatively centralized trend occurring in those provinces that had parallel AVEC, revealing a spatial structure with attraction among high-high or low-low regions and repellency among high-low or low-high regions. By a comparison between the ordinary least square （OLS） model and its spatial econometric counterparts, a spatial error model （SEM） was selected to analyze the impact of major economic determinants on AVEC. While the analytic results revealed a significant positive correlation between AVEC and economic development, other determinants showed some intricate influential patterns. The provinces endowed with rich energy reserves were inclined to consume much more energy than those otherwise, whereas changing the economic structure by increasing the proportion of secondary and tertiary industries also tended to consume more energy. Both situations seem to underpin the fact that these provinces were largely trapped in the economies that were supported by technologies of low energy efficiency during the period, while other parts of the country were rapidly modernized by adopting advanced technologies and more efficient industries. On the other hand, institutional change （i.e., marketization） and innova- tion （i.e., technological progress） exerted positive impacts on AVEC improvement, as always expected in this and other studies. Finally, the model comparison indicated that SEM was capable of separating spatial effect from the error term of OLS, so as to improve goodness-of-fit and the significance level of individual determinants.