Propensity score matching model used to evaluate indicators impact on green environment resiliency. Social, economic and environmental indicators were identified and integrated by using instrumental variable model and built socio-eco efficiency framework. These indicators were varied across the household’s awareness, perception and behaviours regarding adopt the green mind (i.e. increasing consciousness about safe the living and working condition), technology and job use (i.e. choose safe technology and jobs that keep safe the living and working condition). Moreover, indicators would be different between the household’s poverty status (poor and non-poor), income level, sex, family sizes, education level and etc. With this respect, the green environment resilience (balanced resource consumption growth and the green
environment tradeoffs) was an outcome factor; the water consumption and recycling efficiency was atreated dependent factor, the socio-eco efficiency and sub indicators were treated independent factors. After propensity score estimation, this indicators impact evaluation paved was to develop the resource model.
With this respect, in the first step of PSM, according to Rosenbaum and Rubin (1983); Heckman, et al. (1997), Dehejia and Wahba (1999), andBecker & Ichino (2002) propensity score performed conditions and probability of matchings between variables. This study evaluated social, economic and environmental indicators (treated independent factors) impact on the green environment resilience (outcome factor) via water consumption and recycling efficiency (treated dependent factor). In pursuing this, first, social indicators were included such as sex, family size (small and large family size), culture, and poverty status (poor and non-poor). Second, economic indicators used the household’s monthly income. Third, environment indicators were water quantity consumption and recycles. These factors were executed matching between probabilities of ‘Yes’ or ‘No’ discrete response that presented a binomial controlled and non-controlled responses respectively.
The treated dependent factor, which is water consumption and recycling efficiency, was represented by respondent’s binomial response “Yes”, which refers the consumption and recycling efficiency. Otherwise, “No” response. In this study context, water consumption and recycling efficiency was measured by household’s daily cubic metre water requirement consumption and reuse the waste for other purpose replied ‘’Yes’’. Otherwise, ‘’No’’ response. The controlled household’s response (yes), which integrated social, economic, and environmental, achieved the consumption and recycling efficiency that resilient the green environment. However, the non-controlled response (No) could not integrate social, economic and environmental indicators to ensure consumption and recycling efficiency. This propensity depicted score estimation household’s decision on two choices. The first choice reflected “Yes” response was equal to 1 value. Otherwise, “No” response was equal to 0 value. Regarding to respondent’s decision to choose either of this response required types of model to be used.
Furthermore, this study used the resource consumption growth and green environment tradeoffs (CONVETRD) was an outcome factors whereas water consumption and recycling efficiency was treated dependent factor. Nonetheless, the treated independents factors included the household’s poverty, sex,
family size, education level, income, culture, water quantity and etc. In addition to this, the household’s green behaviours, social, economic and environmental indicators and socio- eco efficiency framework were treated independent factors. Along this formulation, this study employed a binary treatment model(logit) in the period of propensity score matching estimation. Owing to complexity of the probit model estimation and procedures, this study used logit model to find out the reliable impact analysis, between treated and non- treated factors (Caliendo and Kopeinig, 2005).
Hosmer and Lemshow (1989), Guajarati (2004) and Greene (2011) model fitness exhibits that the binary logistic regression and distribution has advantages over the dichotomous response and interpreted them in precise ways. Based on the binary choices of the factors used, a matching strategy was built on the conditional independence assumptions referred in Gujarati (2004). Along with this line, the outcome variable in this case, poverty status, socio-eco-efficiency framework, was independent of treatment conditional on the propensity score. Using logit model in Gujarati (2004) and Greene (2011) assumptions, the independent factor (household’s water consumption and recycling efficiency) was coded by “Yes” and “No” response and presented by 1 and 0 values respectively.
With this respect, the dummy dependent factor, which takes 1 and 0 values, revealed the probability that a household said Yes (Pi = 1/Xi). Otherwise, No (Pi = 0/Xi). Where, Xi was treated independent factors that
directly and indirectly affected the treated dependent and outcome factor respectively. Accordingly, the logit model was formulated of which a probability of the households, who consumed water and recycled efficiently, were Pi written as:
(Pi)n = (e)Zi
1+eZi ……….………(1)
Where,
Pi indicates the probability that household’s water consumption and recycling efficiently. This was
out come factor in PSM estimation
Zi = β0+ βixi+ ei ………..……….………..…….(2) Where,
Zi = treated dependent factor such as household’s poverty status (poor and non-poor)
𝛽𝑖 = coefficients
𝑒𝑖 = disturbance term
𝑖 = 1,2, 3…n
The probability that households who were not consumed water and recycling efficiently, 1-Pi could be
written as;
(1 − Pi)n = (e)Zi
1+eZi ……….……….….………..……..(3)
The ration of households who used water efficiently and non-users was described by odd ration. Thus, this ratio becomes;
Pi 1−Pi =
1+eZi
1+e−Zi = (e)Zi ………..……….………(4)
As it is indicated above, the left side the odd ratio that referred household’s in favors of user vs non-user or water consumption and recycling efficiently or not to resilient the green environment. In other words, the probability of households who consumed water efficiently vs non-efficient users were odd ratio. So, the logarithmic of this odd ratio written:
Li = ln(odd ratio) = Zi = β0+ βi∑n xi
i=0 + ei………..……...………(5)
Where;
Li = natural logarithmic value of odd ratio = Pi/1-Pi
xi = poverty stratus, sex, family size, socio-eco efficiency indicators and etc
This Li used to find out propensity score estimation using logit model along with the above mathematical
formulation and results were computed using STATA 14 software. To minimize the probability of unobservable characteristics on water consumption and recycling efficiency using evaluating indicators, the following model proposition was done. In other words, water consumption and recycling efficiency was determined by household’s sex, poverty status, education level, awareness about green technology, socio eco efficiency indicators and etc. That is water consumption and recycling efficiency was formulated in equation form:
WCORECF = β0+ βi∑(poverty , educi
n
i=0
, culture, socio − eco effciecny, indicators) + ei
Where,
WCORECF, Yi = household’s consumed water and recycle efficient (if Yes =1. Otherwise,
No=0)
Household’s poverty status (if they are non-poor =1, poor =0 values) Socio-eco efficiency adoption (Yes =1, No=0)
Indicators includes such as social, economic and environmental.
3.9 Chapter Summary
This chapter attempted a descriptive research design and a triangulated methodology used in this study. It used a cross-sectional surveyed data collected from the factories and households. It was, outlined the proposed various specific objectives that would be addressed in the study. In pursuit of this, different analytical tools were employed to compute the social, economic and environmental indicator’s effect on water consumption and recycling efficiency. This chapter also integrated consumer’s exogenous (social aspects) into endogenous factors (economic and environmental aspect) to balance the water consumption and recycling efficiency. Particularly, the household’s social aspects were consisted of the consumption culture, behaviour, poverty, family size, attitude, perception, awareness, ability and willingness, sensitive and emotionality to practices the green mind, technology use, market and jobs, which were associated and determined the resource consumption growth and green environment tradeoffs.
The household’s social aspects and characters would be measured using the five-point Likert scales and Cronbach alpha values. However, quantitative factor’s significant effect on the resource consumption growth and environment tradeoff were measured by using descriptive statistics and econometric models. For instance, a binary logistic regression model; instrumental variable model; simultaneous equation model and propensity score matching estimation were used to measure the effects of each explanatory factor mentioned in this chapter. For each model, different assumption and propositions were placed to evaluate the various indicators impact on the consumption and green environment tradeoffs; consumption and recycle efficiency; water consumption and recycling intensity. The various econometric assumptions described the socio-eco efficiency consequence on water consumption and recycling efficiency. The
collected data and model results were computed using SPSS 24 and STATA 15 software version and discussed in chapter four.
CHAPTER FOUR