The project ＂environmental conditions and behaviour depending on income and purchasing power＂ analysed the correlation between environmental impacts （e.g. noise, dust） or environmental behaviour （purchase of orga...The project ＂environmental conditions and behaviour depending on income and purchasing power＂ analysed the correlation between environmental impacts （e.g. noise, dust） or environmental behaviour （purchase of organic food, usage of public transport） and household income. Statistical matching was used to add income variables from EU-SILC to the the Micro Census （MC） environment dataset 2011. For each respondent of the MC environment a donor from the data from EU-SILC with minimal distance was found. Several matching options were tested to find the best option to link the variable ＂total disposable household income＂ to the environmental data. The machting options differed in the used distance function, which depended on the selected matching variables and their weight. The analysis provided an insight into advantages or problems of the method of statistical matching. Analyses of the environmental responses by household income terciles revealed significant differences in the assessment of quality of life, disturbance by noise, purchase of ecological products or the use of public transport.展开更多
文摘The project ＂environmental conditions and behaviour depending on income and purchasing power＂ analysed the correlation between environmental impacts （e.g. noise, dust） or environmental behaviour （purchase of organic food, usage of public transport） and household income. Statistical matching was used to add income variables from EU-SILC to the the Micro Census （MC） environment dataset 2011. For each respondent of the MC environment a donor from the data from EU-SILC with minimal distance was found. Several matching options were tested to find the best option to link the variable ＂total disposable household income＂ to the environmental data. The machting options differed in the used distance function, which depended on the selected matching variables and their weight. The analysis provided an insight into advantages or problems of the method of statistical matching. Analyses of the environmental responses by household income terciles revealed significant differences in the assessment of quality of life, disturbance by noise, purchase of ecological products or the use of public transport.
