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Predicting recycling efficiency - Multiple regression modeling of the recycling rates of Tetra Pak’s beverage Cartons

Person, Caroline LU (2011) MVEK02 20111
Studies in Environmental Science
Abstract
Tetra Pak is the world’s largest producer of beverage cartons with an annual worldwide production of about 150 billion packages. They work actively with improving their recycling efficiency by setting recycling rate goals for the next three, six and nine years for each market. These goals have so far been educated guesses and there is a need for investigating what drives and predicts recycling efficiency. What factors affect how good a country is at recycling its paper packaging? And, if these factors are known, would it be possible to predict the development of recycling rates by looking at the development of these factors?
In this thesis I investigate these “predictors” influencing recycling efficiency and build models using multiple... (More)
Tetra Pak is the world’s largest producer of beverage cartons with an annual worldwide production of about 150 billion packages. They work actively with improving their recycling efficiency by setting recycling rate goals for the next three, six and nine years for each market. These goals have so far been educated guesses and there is a need for investigating what drives and predicts recycling efficiency. What factors affect how good a country is at recycling its paper packaging? And, if these factors are known, would it be possible to predict the development of recycling rates by looking at the development of these factors?
In this thesis I investigate these “predictors” influencing recycling efficiency and build models using multiple regression analysis with the aim of explaining as much as possible of the inbetween countries variation of recycling rates. Socio-economic predictors such as Gross Domestic Product (GDP), Education (EDU) and Urban population size (URB), together with Environmental concern (ENV), were tested against recycling rates measured for Tetra Pak´s beverage cartons. The model with the best fit (R2=0.382) consisted of GDP and Environmental concern, although only GDP turned out to be significant. Conclusively, GDP is a good predictor of recycling efficiency where the others were not. I encountered considerable problems with multicollinearity and lack of good quality data, leaving me quite critical towards the possibility of creating trustworthy models in the future. (Less)
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author
Person, Caroline LU
supervisor
organization
course
MVEK02 20111
year
type
M2 - Bachelor Degree
subject
language
English
id
2365832
date added to LUP
2012-02-28 13:12:26
date last changed
2012-02-28 13:12:26
@misc{2365832,
  abstract     = {Tetra Pak is the world’s largest producer of beverage cartons with an annual worldwide production of about 150 billion packages. They work actively with improving their recycling efficiency by setting recycling rate goals for the next three, six and nine years for each market. These goals have so far been educated guesses and there is a need for investigating what drives and predicts recycling efficiency. What factors affect how good a country is at recycling its paper packaging? And, if these factors are known, would it be possible to predict the development of recycling rates by looking at the development of these factors?
In this thesis I investigate these “predictors” influencing recycling efficiency and build models using multiple regression analysis with the aim of explaining as much as possible of the inbetween countries variation of recycling rates. Socio-economic predictors such as Gross Domestic Product (GDP), Education (EDU) and Urban population size (URB), together with Environmental concern (ENV), were tested against recycling rates measured for Tetra Pak´s beverage cartons. The model with the best fit (R2=0.382) consisted of GDP and Environmental concern, although only GDP turned out to be significant. Conclusively, GDP is a good predictor of recycling efficiency where the others were not. I encountered considerable problems with multicollinearity and lack of good quality data, leaving me quite critical towards the possibility of creating trustworthy models in the future.},
  author       = {Person, Caroline},
  language     = {eng},
  note         = {Student Paper},
  title        = {Predicting recycling efficiency - Multiple regression modeling of the recycling rates of Tetra Pak’s beverage Cartons},
  year         = {2011},
}