| BackgroundClimate change is a major concern all over the world.There are plenty of evidences proving the trend of global warming,and there is also an increasing trend for the intensity and frequency of heat and heat wave.Heat has adverse health impact and the health impact of heat varies among different populations.For example,infants and the elderly are at significantly higher risk of death due to high temperatures than the general population.Black races are significantly higher than those of other ethnic groups.In addition,there is a difference in the degree of increase in death risk in different regions due to temperature increase,which indicates that the vulnerability of heat in different regions is different.Therefore,assessing the heat vulnerability of different regions,understanding the ability of each region to withstand and respond to heat,and identifying vulnerable areas and vulnerable populations are of practical significance in adapting to climate change.At present,many countries and regions have conducted heat vulnerability assessment studies to effectively identify vulnerable areas and vulnerable populations in the study area.In some parts of China,heat vulnerability assessments have been conducted,but there is a lack of coverage across the country.Heat vulnerability assessment is still unknown for the most areas in China.ObjectivesThrough the investigation of heat vulnerability indicators,we aim to establish a preliminary heat vulnerability index system of China’s.Based on the establishment of a nationwide county-scale heat vulnerability index,we assess China’s heat vulnerability,and analyze the spatial distribution characteristics of heat vulnerability.According to regional features of heat vulnerability,we make policy recommendations and effective protection measures for different regions and groups.MethodsIn this study,we reviewed the relevant literature and summarized the heat vulnerability assessment indicators used in previous studies,and then investigated China’s existing national county-scale indicators data.For the screening of heat vulnerability indicators,we classify the indicators according to the factors of sensitivity and adaptability,and finally obtain the heat vulnerability index system applicable to China.Because China currently lacks the index of "Air Conditioning Owning rate",a key indicator of heat vulnerability assessment,we used the available data of 196 districts and counties that contain air conditioning indicators as basic data to establish a random forest prediction model to estimate the air conditioning data for other counties.In addition,the data format of the indicators such as "Normalized Difference Vegetation Index(NDVI)","Green Space Area","Gross Domestic Product(GDP)" and "Land Use" are spatial grid data.We use the regional statistics function in ArcGIS software to convert these data into numeric data table and match them with other data by district/county code.Based on the established heat vulnerability index system,firstly,the data of each index was standardized,and then we used the principal component analysis method to reduce the dimension of heat vulnerability indicators and extracted the principal components.The heat vulnerability index of each county was calculated by a summation of the principal component scores of the corresponding districts and counties.In order to understand more directly the distribution of China’s heat vulnerability index,we displayed the results of the index through a spatial map.The ArcGIS software was used to combine the results table of normalized heat vulnerability index for each county with the Chinese county map.The index was graded according to quartiles and reflected in the maps through different colors.At last,hotspot analysis of ArcGIS software is used to figure out the aggregating tendency of heat vulnerability index in China.Results1.Through the investigation of the China’s national public datasets,we found that the data in 2010 was relatively comprehensive and complete.The dataset includes data from the 6th demographic census of China in 2010,the Resource and Environment Data Cloud Platform(run by the Resource and Environment Data Center of the Chinese Academy of Sciences),and the National Geospatial Data Cloud.Combined with the indicators in the literature,we finally established the indicators of heat vulnerability assessments,including a total of 20 sensitive and adaptive indicators.2.The forecast results of county-scale air-conditioner owning rate across the country show that the counties with the least amount of air-conditioners are in Gansu Province;the counties with the highest air-conditioner owning rate is in Fujian Province.The results of spatial analysis of air conditioning forecast results show that there is no aggregating tendency in the air conditioner owning rate in most regions of China,but there is an aggregating tendency of higher air conditioner owning rate in the southeastern coastal areas of China,and an aggregating tendency of lower air conditioner owning rate in the central and northern regions as well as the southwest regions.Some coastal regions in the northeast have an aggregating tendency of higher air-conditioning owning rate and some regions in the northwest have a tendency of lower air-conditioning owning rate.3.The final indicators included in the principal component analysis include the population density,the proportion of the ethnic minorities,the proportion of the non-agricultural population,the proportion of the elderly population,the mortality rate,the illiteracy rate,the GDP per capita,the per capita living space,the NDVI,the air-conditioner owning rate of each county.There are 10indicators in total.The principle components extracted through the principal component analysis include geographic factors,urbanization factors,economic factors,and health factors.The accumulation of four principal components can explain the 71%variability of the original 10 variables.4.The heat vulnerability index in each county was obtained by calculating the score of each principal component.The county with the minimum value of heat vulnerability index(11)is a total of 67 counties;the counties with the maximum value(23)are in Tibet Autonomous Region.The average value of heat vulnerability index is 13.75,with a median of 14,and a standard deviation of 1.48.The heat vulnerability index is classified by quartiles,with a 25%quantile of 13;areas with index below 13 are low vulnerability areas;50%are quantiles at 14,and areas with index 14 are moderately vulnerable areas;The 75%quantile is 15,the index is in the high vulnerability area of 15-23,there are 706 districts and counties.5.The spatial pattern map of the heat vulnerability index indicates that there are many areas with high heat vulnerability in the mid-east coastal areas and mid-west regions,and there are many areas with low heat vulnerability in the northeast,mid-north and mid-south regions;heat vulnerability in the southwestern regions like Yunnan Province is low.ConclusionThis study established a national county-scale heat vulnerability index system in China,which established a foundation for China’s heat vulnerability assessment and provided a basis for further research on China’s heat vulnerability.There is a certain spatial heterogeneity of heat vulnerability in China,with high vulnerability in mid-east coastal areas and mid-west regions and the low vulnerability in the northeast,mid-north and mid-south regions.When heat weather occurs,measures should be taken to strengthen protection in these more vulnerable areas.Urbanization factors,ethnic factors,health factors and economic factors are the influencing factors of China’s heat vulnerability.The elderly populations,the lower income groups,the minority populations,and the low-educated populations are with higher heat vulnerability in China.Protection of these groups should be strengthened,and high-temperature warning measures should be taken before extreme heat occurs. |
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