Introduction
The purpose of the survey was to explore and give an insight into various variables which influence the efficiency, consumption rates, and water management in the Integrated Water Resource Management (IWRM) practices. The survey tested three hypotheses. First, there is no significant effect of IWRM practices on efficiency. Second, there is no significant influence of the variables on water consumption rates. Third, the variables have no significant influence on water management. In the study, the dependent variables used included: Treated Sewage Effluent (TSE), Public Awareness (PA), Smart Water Systems (SWS) and Green Building Code (GBC).
Data Analysis
The results of the survey were obtained from linear regression models using Statistical Packages for Social Sciences version 23 (SPSS 23.0) and Microsoft Excel. It is understood that there are various softwares which can be used to give statistical results. However, SPSS is capable of creating highly valuable information which can lead to more reliable conclusions and decision making (Paura & Arhipova, 2012). The results of the study areas discussed below.
Results and Discussion
As shown earlier, regression analysis was used to show how various factors influenced the respective endogenous variables. In addition, Analysis of Variance (ANOVA) was used to show the level of statistical significance of F statistic.
Regression Results on Efficiency (E)
In the regression model to determine influence of the independent variables on efficiency, the independent variables that were regressed are, Treated Sewage Effluent (TSE), Public Awareness (PA), Smart Water Systems (SWS) and Green Building Code (GBC). ANOVA results showed a statistically significant F test statistic with P value less than 0.001. also, from the results, it can be seen that the goodness of fit (R squared) was 0.9. this shows that 90% of the efficiency is explained by the variables in the model, thus only 10% depends on other residual factors or random shocks in the environment. Moreover, the P values of the coefficients of the regressors shows that TSE showed the highest statistically significant positive influence on Efficiency at 1% significance level (P= 0.000). PA and SWS also showed statistically significant positive influence on Efficiency at 5% significance level respectively. GBC showed a negative influence, however, this was not statistically significant. The Constant also showed positive influence with P-value showing Statistically significance influence on Efficiency (P= 0.038).
The findings conform to previous findings on quality water management practices. For instance, in a study on Impact of Combined Sewer Overflow on Wastewater Treatment and Microbiological Quality of Rivers for Recreation, Mascher, et al. (2017). Sewage effluents from industrial wastes, kitchen and other sources is a major source of water pollution. Proper treatment, therefore, showed a positive impact on water management efficiency. Consistent results were also obtained for PA and SWS respectively. For example, SWS was found in previous studies to show a positive correlation with Efficiency in water management by reducing monitoring cost and helping to predict future demand by giving a predictability of consumption changes (Kirsten, et al., 2014).
Regression Results for Consumption Rates
In the regression model for the determinants of consumption rates, variables CBG, PA, TSE, and SWS were run as the independent variables. The results of the analysis showed Goodness of Fit at 0.725. this means, approximately 72% of the consumption rate is dependent on the regressed factors. Thus, 28% of the consumption rate is left to be determined by other factors not reflected on the model. This is a good show of fit, however, it shows that there is a need for other insightful studies to come up with other factors not explained in the model. Nonetheless, from the results, the coefficient of TSE still shows the highest positive impact on the consumption rates of water at 0.316 compared to coefficients of other variables. The P value of the variable (TSE) is also lowest, thus showing a higher level of statistical significance of the variable on the Consumption rates.
The findings are consistent with other empirical studies which have revealed that, when wastewater is well treated, it boosts consumers' confidence, thus increases the likelihood of its consumption. However, this goes hand in hand with the level of public awareness of such mitigation measures (Wang, et al., 2018). From the results, public awareness also showed a positive impact on Consumption rate with P-value showing statistical significance level (P = 0.097). In the study conducted in China, multinomial logistic regression model revealed that there was a significant positive correlation between PA and levels of water consumption in a given residential area (Wang, et al., 2018). Also, there was a strong positive correlation between SWS and consumption rates with P-value showing statistically significant effect (P<0.005). This is a finding similar to work obtained in previous empirical studies which revealed that water consumption rates are sufficiently dependent on Smart Water Systems. For instance, in a study, "Watersaving impacts of Smart Meter technology: An empirical 5 year, the wholeofcommunity study in Sydney, Australia."A smart water system was found to be very important in monitoring consumer behavior in terms of consumption patterns and reduction of losses on consumption data (Kirsten, et al., 2014).
Finally, the constant coefficient also revealed a positive correlation with the consumption rate with P-value showing statistically significant level (p= 0.084). Just as in the case for determinants of efficiency, GBC did not show any statistically significant effect on consumption rates.
Regression Results on Water Management
Similar to the influence on efficiency and consumption rates, the four independent variables were again used in a regression model to show their impact on water management. From the ANOVA results, F statistic showed a statistically significant difference with P<0.001. R squared was relatively lower (0.578) compared to the other two models. The almost average R square is evident to the fact that water management is not so much dependent on the predictors on the model. Only 58% of water management is explained by determinants, the rest depends on residual factors not included. This calls for more exploratory study to come up with more factors that will strongly predict the dependent variable.
From the results, SWS showed statistically significant and strong positive correlation with Water Management (P=0.002). the results were also similar for TSE with the second highest positive coefficient among the variables (P=0.008). The constant also posted statistically significant positive correlation with P=0.007. Public awareness, as well as green Building Code, did not show any statistically significant influence on Water Management. The results on influence on SWS are plausible because previous studies have also produced similar information, that with smart meter system, water management becomes more efficient and cost-effective in terms of time and labour (Kirsten, et al., 2014).
The smart water systems provide households with information regarding their level of consumption in real time, thus better management of demand and supply patterns. In a study to determine the level of sustainability of water as a resource, Treatment of Sewage Effluent was found to have a significant impact in the reuse of water, thus minimizing wastage. In an experimental approach, the United States Environmental Protection Agency have revealed, TSE has a significant impact on environmental sustainability by saving water and reducing energy consumption, thus positive spillover effects on the management of resources (Farzaneh, et al., 2016). The findings, therefore, reveal consistency to other previous studies.
Conclusion
With the purpose of the survey being to determine the influence of various independent factors of Integrated Water Resource Management on Efficiency, Consumption Rates and Water Management, the results obtained from ANOVA and simple regression reveal mixed findings. TSE and SWS were found to show positive and statistically significant influence on the three dependent variables. Public awareness showed statistically significant influence on Efficiency and consumption rates, while GBC had no significant effect for all the dependent variables. In relation to the outcome the hypotheses that, there is no significant effect of TSE, SWS and PA on Efficiency, Consumption Rates are rejected. Also, the hypotheses that, there is no significant influence of TSE and SWS on water management are rejected. The hypothesis that there is no significant influence of GBC on efficiency, consumption rates and water management is accepted. Finally, the hypothesis that there is no significant effect of PA on water management is also accepted. From the results there is mixed consistency and contradiction with previous findings, thus need for more research to come up with conclusive information.
References
Farzaneh, H. et al., 2016. Reuse of treated sewage effluent (TSE) in Qatar and its impact on sustainability and the environment. Qatar, Qatar University.
Kirsten, D., Doolan, C., Shi, R. & Honert, R., 2014. Watersaving impacts of Smart Meter technology: An empirical 5 year, wholeofcommunity study in Sydney, Australia. Journal of Water Resource, 50(9), pp. 7348-7358.
Mascher, F., Wolfgang, M., Kittinger, C. & Zarfel, G., 2017. Impact of Combined Sewer Overflow on Wastewater Treatment and Microbiological Quality of Rivers for Recreation. Journal of Microbiology and Environmental Medicine, 9(906), pp. 1-10.
Paura, L. & Arhipova, I., 2012. Advantages and Disadvantages of Professional and Free Software for Teaching Statistics. Information Technology.
Wang, L., Zhang, L., Zhang, Y. & Ye, B., 2018. Public Awareness of Drinking Water Safety and Contamination Accidents: A Case Study in Hainan. Journal of Environmental Health, 10(446).
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