Household’s perception and behavioural effect between consumption and green environment tradeoffs, inequality on resource consumption and resilient green environment was computed using an environment Kuznet Curve Model. This was done using household’s monthly income and poverty status. Household’s poverty measured using WHO (2012) income threshold line. Accordingly, household’s monthly income, which is found below 1.5 dollar/day, is poor. Otherwise, non-poor. Based on this poverty line, household’s perception and behavioural towards keeping green environment associated with income inequality. EKC model computed income inequality effects on household’s consumption and green environment tradeoffs. This concept is supported by different hypothesis and economic theories. For instance, household’s perception to purchase green goods and consumption activities were varied across their income level, ceteris paribus. Expenditure and consumption of green goods were assumed unequal besides to respondent’s willingness to keep the green environment. Hence, it was vital to explore consumption behaviours, perception, and awareness, inequality between households etc., which resilient the green environment, along with income measurement. To do so, it was assumed that respondents were rational and thinks at the margin to balance an economic costs and benefits during consumption. Based on this, there was respondent’s expenditures inequality during resource consumption and recycles, holding other factors being constant.
To investigate income inequality and green environment association, variable categorization, identification and determination were done and used logistic regression model. With this respect, the independent variables were household’s income, perception, and consumption behavior. Dependent variable was the tradeoffs between resource consumption and green environment problems. In pursuit of this, it was considered the following major assumptions: binary or dichotomous response dependent variables, which take 1 for existence of income inequality between households. Otherwise, zero. Household’s consumption
behavior was assumed to be nonlinear along with the resource elasticity of demand. There is omitted variables called latent effect of household’s perception and behaviours. For instance, household’s sensitivity, emotionality and preference to consume resource efficient varied across the economic, environment and societal reason.
Based on these assumptions, among independent factors, household’s monthly income was measured in birr. Other independent variables were household’s perception; consumption behaviors; willingness and ability to pay money, and etc measured using five-point Likert scales. It was appropriate to use Logistic regression model for binary dependent variables, which has either 1 or zero values. In other words, if green environment problems existed due income inequality = 1, otherwise, 0. Pertinent to the issue in hand and assumption for binary logistic regression model, independent variables were household’s perception (HHperc), behaviour (HHbehav), income (HHincom). However, the dependent factor was tradeoffs between consumption growth and green environment. Logarithmic of household’s income was independent factors besides to qualitative characters mentioned. It was assumed that disturbance term was logistically distributed with these factors.
Based on Kuznet’s model application, this study proposed income inequality, which was associated with household’s perception and behaviors, during resource consumption. This inequality also associated with green environment and resource consumption tradeoffs. In pursuing so, household’s incomes categorized into low, middle, and high income groups. The household’s low income category comprised of less than 500; middle income from 500-2000 and high income above 2000 Birr. Other independent variables were assumed binomially distributed. That is the functional relationship between the variables and binary logistic model formulated as follow:
The variable association refers to measure income inequality and its impact on green environment awareness and perception as well as its resilience. To do so, two major hypotheses were mentioned as follows;
i. The probability that household’s behaviour would be affected by their income and in turn influenced the greening environment, ‘Yes’ = P(HHbehav), p) = 1
ii. Otherwise, the probability that household’s behaviourstwould not be affected by income and influenced the green environment, ‘No’ response = P(HHbehav),1-p) = 0
The same dummy factor presentation was proposed and worked for the household’s perception, awareness, and etc factors association with the consumption growth and green environment tradeoffs. Nevertheless, income inequality created difference on household’s behaviour, and perception to resilient the green environment. In other words, income inequality determined household’s consumption behaviours, awareness, perception and widen the consumption growth and greening environment tradeoffs. To elaborate these relationships, it was proposed that household’s behaviors and perception were dependent and affected by income coefficient by𝛽𝑖.
By assuming binomial response between respondents, binary logistic regression was formulated. That is 𝐻𝐻𝐵𝑒ℎ𝑎𝑣𝑖𝑜𝑢𝑟𝑜𝑟𝑝𝑒𝑟𝑐𝑒𝑝𝑡𝑖𝑜𝑛 = 𝛽0+ 𝛽𝑖𝑙𝑜𝑔𝑖𝑛𝑐𝑜𝑚𝑒 + 𝑒𝑖
Where,
HHBEHAV = household’s behaviour HHPERCE = household’s perception LOGINCOME = logarithm of monthly income ei = disturbance term
This model computed the marginal effect of each explanatory factors mentioned and their association. For instance, as incomes of households were changed by one birr, their consumption behaviours and perception to keep green environment were changed positively by 𝛽𝑖. Moreover, there is a probability that
there would be income inequality between households to keep green environment during water consumption process. Meanwhile, there was a possible chance of household’s behavior and perception independently would have shown inequality towards greening environment gets a p value = 1. Otherwise, 1-P = 0. The household’s income and its logs were computed by EKC model and inserted into the logistic regression.
Water consumption and waste emission (Wt) inequality between households was computed along with
principles of environmental Kuznet model. This was computed and regressed with respect to household’s monthly income. The dependent variable Et is liquid waste emissions per income. The choice of this
the emergence of green environment problem. Accordingly, variable Wt is a dummy that takes on the value
1 for factories or households, who discharged wastes to Borkena river. Otherwise, 0. This variable was used to check if respondents were signatory of reducing their waste emissions through recycling processes.
In this sense, these variables were measured household’s waste reduction inequality and prone. Consumption per capita (WNt) is the ratio between water consumption quantity and average monthly
income. The water consumption (m3) comes from the municipality and water supply and sewerage
enterprise office (2016). One expects theoretically that there is a positive relationship between water consumption and waste emission. Hence, this variable was aimed at demonstrating that wide income inequality leads to a greater social conscience about environment problems and a pressure in favor of green regulation.