Econometrics measures the relation between two or more variables, running statistical analysis of historical data and finding correlation between specific selected variables. Econometric exercises include three stages specification, estimation and forecasting. The structure of the system is specified by a set of equations, describing both physical relations and behavior, and their strength is defined by estimating the correlation among variables such as coefficients relating changes in one variable to changes in another using historical data (UNEP, 2014). There were many factors included in this case to assess tradeoffs between household’s consumption and green environment problems. The logistic models, therefore, fitted with recruited indicators. However, it was assumed that respondents would have a binomial response whether the tradeoffs between consumption growth and green environment existed or not. This study used binary logistic regression model would be identified the significant factors on the resource consumption growth and green environment tradeoffs.
In previous studies, for instance, BASF (2005 & 2009), sailing et al. (2013), and ESCAP (2011&2014) indicator analysis did not employ econometric model to regress the effect of social, economic and environment indicators in the course of company’s productions. Instead, this literature and institution reports revealed quantitative computation of resource consumption efficiency and the value add on product portfolio and quality along with product life cycle assessment. However, this study, therefore, filled the identified literatures and methods gaps using distinct econometric models for each object mentioned in chapter one along with the nature of indicators. In addition, descriptive and inferential statistics were used to calculate the effect of independent factors on the dependent variable using SPSS 20 and STAT 14 software version.
Koskela, et al. (2000), who studies an overlapping generation model, with a renewable resource served as a store of value and as an input factor in the production of the consumption good. They find that indeterminacy and cycles result in their model depend on the value of the intertemporal elasticity of consumption. The analysis of the dynamics of model by Alfred and Willi (2008) demonstrated that it is characterized by local and global determinacy. However, they point out that the results may be due to the
assumptions made, especially concerning the utility function of the household and then give a complete characterization of the dynamic model and contribute competitive economies with externalities (Greiner, 2007). Among examples of such studies is the contribution by Benhabib, et al. (2000). The difference of other findings is that they consider negative external effects of production. That is, pollution as a byproduct of production, in contrast to the aforementioned papers, which assume positive externalities associated with production or capital.
This study used mixed approach and methodologies to assess household’s green perception and behavioural affect between consumption and green environment tradeoffs. More importantly, household demographic characteristics: age, sex, education, family size, marital status, and etc. were recruited to portray the household’s perceptions and behavioral effects. The rationality of this study stood from households have distinct perception and behaviours along with their socio-demographic characters, which were independent factors. Even so, the resource consumption and green environment tradeoff was dependent factor. Accordingly, this study identified an association between dependent and independent factors using a binary logistic regression. This model was managed the probable effect of multiple independent variables and determined their association and a relationship between dummy dependent variables.
Along with this, variables namely, household’s income, employment status, education level, perception, attitudes, behaviour, ability and willingness to pay, culture, awareness, sensitive and emotionality were major explanatory variables included in the working hypothesis. The dependent variable was household’s consumption growth and green environment tradeoffs (CONENVTRD). This tradeoff would be affected by household’s employment status (HHEMP), perception (HHPRC), behaviour (HHBEH), Attitudes (HHATT), Awareness (HHAWR), Income (HHINC), Education level (HHEDU), sensitivity and emotionality (HHSEMO), ability to pay (HHABI), willingness to pay (HHWPA), and etc. Meanwhile, it would be formulated a relationship between the explained and explanatory factors.
In other words, resource consumption growth and green environment tradeoff (CONEVTRD) is a function of independent variables in the following ways:
CONENVTRD = f(HHEMP, HHPRC, HHBEH, HHATT, HHAWR, HHINCom, HHEDU, QWA, HHSEMO,HHABI, HHWPA, and etc)
Where;
CONEVTRD = Resource Consumption growth and green Environment Tradeoff.
EMP, PRC, BEH, ATT, AWR, INCOME, EDU, QWA, SEMOE, ABP, WPA, SOW respectively presents household’s employment, perception, behavior, attitude, awareness, income, education level, quantity of water consumed and recycled, sensitivity and emotionality, ability and willingness to pay.
After specifying this tradeoff function in linear form including error term (ei), it was formulated a multiple
linear regression model as follow:
CONEVTRD = β0 + β1HHEMP + β2HHPRC + β3HHBEH + β4HHATT + β5HHAWR + β6HHINC +
β7HHEDU + β8QWA + β9QWAS + β10HHSEMOE + β11HHABP + β12HHWPA + …+ and
etc + ei
Where, it is possible to present CONVETRD = Yi and the explanatory factors = Xi . The model would be;
Yi = β0 + β1X1 + β2 X2 + β3 X3 + β4Xi+ …+ ei
The rationality of constructing binary logistic regressions was the fact that it helped to hold multiple factors and showed association between binary response factors and measurements. Based on the constructed model, which shows association between dependent and independent factors, hypothesis for each explanatory variable was proposed and represented by Hi. Where, i= 1, 2...,n.
3.9.1.1Variable proposition and Hypothesis
H1: Household’s employment status has no significant effect on water resource consumption to
protect environment
H2: Household’s sex has no significant effect on water resource consumption to protect
environment
H3: Household’s perception has no significant effect on water resource consumption to protect
environment
H4: Household’s consumption Behaviour has no significant effect on water consumption to protect
environment
H5: Household’s attitude has no significant effect on water resource consumption to protect
environment
H6: Household’s Awareness has no significant effect on water resources consumption to protect
environment
H7: Household’s income has no significant effect on water resource consumption to protect
environment
H8: Household’s education level has no significant effect on water resource consumption to protect
environment protect environment
H9: Household’s quantity of water use has no significant effect on water resource consumption
patterns
H10: Household’s sensitivity and emotionality has no significant effect on water resource
consumption to protect environment
H11: Household ability and willingness to pay money has no significant effect on water resource
consumption to protect environment
To test the multicollinearity problem during result analysis variance inflation factors (VIF) was used and tested. VIF greater or equal to 10 was an indicator for the existence of serious problem of multi collinearity. Contingency coefficient was calculated during the analysis section variable have not multicollinearity effect despite it was showed the degree of association between the dummy variables. Contingency coefficient is a chi-square based measure of association. Value of 0.75 shows strong relationship. Heteroscedasticity was detected by using Breusch-Pagen test (Httest) in STATA 14 software version. Furthermore, the reliabilities
and validity of data were checked using Cronbach alpha method. When the alpha result was greater than 0.7, the data is more valid. Accordingly, the Cronbach value calculated 0.84 and presenting valid. This depicts the collected data were sufficient to portray the association between consumption growth and green environmental tradeoffs.