The spatial distribution of health vulnerability to heat waves in Guangdong Province, China

Abstract

BackgroundInternational literature has illustrated that the health impacts of heat waves vary according to differences in the spatial variability of high temperatures and the social and economic characteristics of populations and communities. However, to date there have been few studies that quantitatively assess the health vulnerability to heat waves in China.ObjectivesTo assess the spatial distribution of health vulnerability to heat waves in Guangdong Province, China.MethodsA vulnerability framework including dimensions of exposure, sensitivity, and adaptive capacity was employed. The last two dimensions were called social vulnerability. An indicator pool was proposed with reference to relevant literatures, local context provided by relevant local stakeholder experts, and data availability. An analytic hierarchy process (AHP) and a principal component analysis were used to determine the weight of indicators. A multiplicative vulnerability index (VI) was constructed for each district/county of Guangdong province, China.ResultsA total of 13 items (two for exposure, six for sensitivity, and five for adaptive capacity) were proposed to assess vulnerability. The results of an AHP revealed that the average VI in Guangdong Province was 0.26 with the highest in the Lianzhou and Liannan counties of Qingyuan (VI=0.50) and the lowest in the Yantian district of Shenzhen (VI=0.08). Vulnerability was gradiently distributed with higher levels in northern inland regions and lower levels in southern coastal regions. In the principal component analysis, three components were isolated from the 11 social vulnerability indicators. The estimated vulnerability had a similar distribution pattern with that estimated by AHP (Intraclass correlation coefficient (ICC)=0.98, p<0.01).ConclusionsHealth vulnerability to heat waves in Guangdong Province had a distinct spatial distribution, with higher levels in northern inland regions than that in the southern coastal regions.

Adaptation to Climate Change through Spatial Planning in Compact Urban Areas: A Case Study in the City of Thessaloniki

“…Vulnerability to the UHI effect is determined by a combination of factors, such as higher temperatures and climatological trends, demographic factors, quality of life and other socio-economic components, as well as by urban design characteristics [2,3,11,[38][39][40][41][42][43][44][45][46][47]. Therefore, data from the study area were classified according to three sets: climatic conditions/environmental data, including temperature, humidity, wind, rising sea levels, air pollutants; demographic data, including population change, demographic and socio-economic structure; and urban form data, including layout plan, building characteristics, land uses, densities, open spaces/Green Infrastructure.…”

“…However, both the municipality and the prefecture exhibit high unemployment rates, which has increased dramatically after 2009 due to the economic crisis. Unemployment indicates a sensitivity of the population; it can be associated with social isolation, low income and poor living conditions, thus leading to vulnerability [41,44,46].…”

“…The municipality featured a small increase in the population of small children (0-4 years old) in the period 2001-2011. Both of these groups are considered to be particularly exposed and sensitive to the impacts of climate change and find it harder to adapt [2,12,41,[43][44][45][46][47][51][52][53]. The composition of population by sex was not examined as a variable, since the relevant studies report conflicting evidence [40,45,47].…”

“…All of these features could have resulted in the observed intensity of UHI and climate change. Given that the felt effects of climate change depend on gender, age, education and social class [2,11,12,[38][39][40][41][44][45][46][47], the Municipality of Kalamaria has a high degree of so-called "generic vulnerability" (Table 1). This is due to the fact that it features an increased percentage of elderly (over the age of 65), higher than the average in the Prefecture of Thessaloniki.…”

Evolución del concepto de cambio climático y su impacto en la salud pública del Perú

“…Por ejemplo, para un evento específico como una ola de calor, se puede definir la vulnerabilidad al cambio climático como una función de: 1) el nivel de exposición a un evento de ola de calor (temperatura y humedad); 2) el nivel de sensibilidad a un evento de ola de calor (acceso a aire acondicionado), y 3) el nivel de capacidad de adaptación de las poblaciones (medidas para refrescarse y mantenerse hidratado) (23) . Los indicadores también pueden ayudar a distinguir entre, por ejemplo, todas las muertes durante una ola de calor y aquellas muertes "atribuibles" al cambio climático.…”