GAINING CREDIT POINT in Green Building: SOLIDWASTE REDUCTION
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GAINING CREDIT POINT IN GREEN BUILDING: SOLIDWASTE REDUCTION
Abstract—previous research about green building discuss more about construction phase instead of operational phase in a building life cycle. Based on the criteria set by the LEED (Leadership in Energy and Environment Design) a building declared as a green building and earn credits 1 point if able to do the reduction of waste at source as much as 50% of the waste generated . This study determines potential reduction in solid waste generate by single houses. Interviews with 55 respondents own house about 50-600 m2 were conducted to explore dominant factor that would lead to sustainable consumption: whether income, education, or awareness. Logit models and multi linear regression were employed to show what dominates changes in consumption and how correlation each of factors in consumption patterns. Impact to production of carbon footprint from solid waste generation was predicted based on the model. The model lead to a conclusion that implementation of green building concept to by reducing waste generation still has many obstacles related to social economic condition of the occupant. Keywords—consumption pattern, carbon footprint, solid waste generation, green building.
I. INTRODUCTION In the context of global climate change mitigation, building sector is accepted to be the most effective sector to reduce energy consumption and greenhouse gas emissions (GHGs) (Metz, 2007). This sector contributes to 30% of the total annual GHGs and consumes up to 40% of all energy worldwide (UNEP, 2009). The ratio of building sector’s energy consumption is: (i) 15-25% comes from the construction phase, and (ii) 75-85% comes from the operating phase (Nassen, 2007). However, previous studies discussed more about the construction phase of a building instead of the latter which is key element to the succees of green building concept. Therefore, an analysis of the successful implementation of green building should be conducted within its whole life cycle. This paper suggests that due to the importance of the operating phase of green building, sustainable consumption of its residents is the key element of solving problems, i.e. reducing the carbon footprint (i.e. electricity usage). This study aims to determine the main factors that influence the change of consumption patterns of the green building occupants. Particularly, this article focuses on ‘single houses’ (instead of commercial buildings) as they are the dominant buildings in Indonesian’s urban area.
So if operating phase is the key, then sustainable consumption will be the answer of the problems. There is a sort of general consensus in the field of ecological economics which states that the pattern of consumption is unsustainable. Some studies indicate that this is the case in scale activities such as energy consumption and also food (Myers & Kent, 2003). This problem is getting worse by the fact that consumption is controlled by a wasteful attitude and consumers tend not to care about the impact of their consumption choices on the environment (Brown & Cameron, 2000), so that it accelerates the consumption style scenario "tragedy of the commons". Traditional economic approach suggests taxes and subsidies to encourage sustainable consumption (Wagner, 2006), but there are objections on the assumption that consumer preferences are fixed (Stigler, 1977) and studies have begun to explore the implications of a policy based on a more realistic view of the preferences. Norton in 1998 has called on policy makers to encourage lifestyle with minimal use of materials and found a democratic society should develop a process to evaluate consumer preferences (Norton, 1998).
Economic and sociological approaches are commonly used and will complement each other (Rostow, 1960). The study of consumption and lifestyle choices of households are connected to the input-output modeling, energy and emissions flow analysis in a single integrated framework started many terms (Duchin, 1998). The main idea of these studies typically connect information household characteristics such as education level, number of children, socioeconomic status and others in the analysis and used as explanatory variables in quantitative modeling. Social economic factors is a major determinant in successful green building implementation. Many cases are pointed out that if technology is good, there is no guarantee that socially or economically would be accepted by the users. Similarly, the green building concept is required appropriate occupant social economic condition which support the sustainability of this concept. We will taking up this statement with analysis of social and economic condition of single houses occupants which increasing sustainability in electricity consumption.
II. LITERATURE REVIEW Sustainability has become priority issue since the World Summit in Rio in 1992 but with advancement in sustainable lifestyle is still dull (Thøgersen, 2005). Social aspect is the most ignored dimension of the sustainability concept. Many times people consider a building, its impact and effects in different ways. This one is challange to establish general sustainable requirements between people (Dempsey N. , Bramley, Power, & Brown, 2011). It would lead to different points of view in sustainability priorities, as consequences, identification and the characteristics of a sustainable building dependent on the point of view (du Plessis & Cole, 2011).
Many studies define social aspects of a building as quality of life or sustainable lifestyle (Dempsey N. , Bramley, Power, & Brown, 2011); (Vallance, Perkins, & Dixon, 2011). These attempts have increased the uncertainty of social sustainability. The relativity of the concept of sustainability gives a social character to the meaning of sustainable. From point of view, social dependence of sustainability in the building sector could be achieved through a participative process by different stakeholders express and contribute with their own idea of sustainability (Moffat & Kohler, 2008). This requires a social context with knowledge sharing between individuals, so the sustainability achieved through participative decisions (Bagheri & Hjorth, 2007). It is important to consider sustainable building concept as a path. Sustainable building definition is has many constrain because time, space, domains and social would increase the uncertainties in identifying sustainability in the built environment. These factors implies a consistent rate of uncertainty and suggest that it is more a transition path than the label given to a building (Berardi, 2013).
Considering all of these studies, greater importance has to be given to the social and economic context of a building in implementing green building concept to single houses. According to (Building Maintenance: A Path towards Sustainability, 2010), there is the need for sustainable maintenance for green buildings. People's behaviour and way of living is influenced by their cultural background. There are many problems faced by the maintenance management team to maintain and operate the building due to the influence of culture practices (Al-Arjani, 1995). This facts bring to the urgency of study about occupant social economic condition that would lead to behaviour in consumption pattern as one of green building indicator.
III. RESEARCH AREA GIS analysis shown that major area of Tangerang Municipality dominate by settlement area, and then followed by industrial area and commercial area as describe in Figure 1. Building population is used to analize how the implementation of green building policy would affect to city sustainability in the future and describe how the settlement patterns spatially located in Tangerang Municipality area. GIS analysis also shown that there are about 430.094 building unit in 2013. Table 1. Building Composition in Tangerang No Type of Buildings Unit Percentage 1 Historical Building 18 0.0042 2 Public Facilities 5,065 1.18 3 Government Office 364 0.08 4 Industries 5,938 1.38 5 Settlement 417,581 97.09 6 Schools 1,128 0.26 TOTAL 430,094 100.00 Source: GIS analysis, 2014 For big population (Dessel, 2013) consider formula to estimate sample size with certain accuracy. Settlement unit in Tangerang is 417,581 units in 2013, with confidence interval 10%, confidence level 95% and proportion 50% then the sample size would be 55 unit of housing.
IV. METHODS 4.1. Survey Design Identifying some key variables in electricity consumption is the first step we took in this research. These variables were described in number of questions for respondents (stated in red number on the scheme). From this scheme we explored relations between variables with logit model. Table 2. List of Question in Questionairre 1 Gender 2 Age 3 Location: District 4 Location: Subdistrict 5 Level of Education 6 Occupation 7 Monthly income 8 Number of people in house 9 Type of the house 10 Building area 11 Number of room 12 How long have you been live here 13 How much waste you produce in a day? (in kgs) 14 Can you estimate how much of your waste were organic? 15 Is there any pre treatmet to your waste before you dispose it? 16 If yes, what kind of pre treatment you do? 17 What is the methode you use to dispose the waste? (to transfer dipo, by burn it, landfill, composting the organic, etc) 18 Do you use waste services? 19 If yes, how much the cost for the service per month? 20 Do you think it's too expensive? 21 Do you have any plan to reduce or reuse your waste? 22 How many percent do you think you can reduce your waste generate today? 23 If there is reuseable waste collection willing to buy your waste such as plastic, can, paper etc, would you involve in this program to raise income?
These 23 questions would be classified as main variables that connected each other in describing electricity consumption pattern. Figure 2. show that these variables would have lead to path of analysis.
In order to distribute samples evenly, housing would be categorized in three classes. Housing was classified into three type: simple housing,
Figure 2. Path Analysis of key Variables middle class housing and high class housing. This classification is based on Decree of Minister of Home Affairs, Minister of Public Works and Ministry and State Minister of Public Housing No. 548-384/1992 classify housing into three specification as follow: 1. Simple housing with area around 54-200 m2 2. Middle class housing with area around 200 - 600 m2 3. High class housing with area around antara 600 - 2,000 m2
GIS analysis show that in Tangerang Municipality simple housing dominate the settlement area for more than 50%. Besides, there is a lot of uncategorized specification housing unit that stated on the decree which has area less than 54 m2. Table 3. GIS Analysis of Housing Classification Classification Area (m2) Total Area (m2) Percentage Smaller than simple housing <54 2,393,907.12 5.66 Simple housing 54 - 200 23,150,217.33 54.70 Middle class housing 200-600 6,359,894.48 15.03 High class housing 600 - 2000 10,415,981.07 24.61 Source: GIS Analysis, 2015
Sample distribute in each district depend on housing density and their classification. Figure 3 show sample distribution in the study area. Number of sample in each district was calculated based on housing intensity in each area.
Source: GIS Analysis, 2015 4.2. Data Analysis Logit Model for Binary Data Considering type of data collected from the questionairre we used logistic regression as analysis tools. Logistic regression is used to predict a categorical (usually dichotomous) variable from a set of predictor variables. With a categorical dependent variable, discriminate function analysis is usually employed if all of the predictors are continuous and nicely distributed; logistic analysis is usually employed if all of the predictors are categorical; and logistic regression is often chosen if the predictor variables are a mix of continuous and categorical variables and/or if they are not nicely distributed (logistic regression makes no assumptions about the distributions of the predictor variables). (0) Multiple Linear Regression A linear regression model that contains more than one predictor variable is called a multiple linear regression model. The following model is a multiple linear regression model with two predictor variables, x1 and x2. (2) V. RESULT AND DISCUSSION 5.1. Profile of Respondents Socio-economic characteristics In Figure 4a and Figure 4b, we can see that most of the respondent are male (72%) and between 40-50 years old (36%) followed with the age range of 30-40 years old (29%). Most of respondent level education is bachelor degree (68%) and the remaining is graduated from high school (30%) while post graduate only 2% of all respondent (Figure 4d). In this survey we categorized level of incone in 5 class: (1)
30% 28% 11% 6% 18% 29% 72% 68% 36%
<= High School Bachelor degree Postgraduate
Figure 4. Socio Economic of Respondents
Most of the respondent has two storey house (31 respondents) and number of people live in a house is between 2-6 people with almost 50% of them has 3 bedrooms. Figure 5. shows that number of people in house would be related to number of rooms and size of the house. In this figure size of the house categorize based on the building area (1)House that smaller than 36m 2; (2)house between 36-70m2; (3)house 70-120m2; (4)House 120-200 m2 and; (5)House more than 200m2. Number of people in the house in average is about 5 persons. The increasing of people in a house would be followed by demand of space, that is why house with more than 5 persons usualy has two storey. Space availability for one person in a hous could be an indicator to know socio-economic condition of the family. The more space available for one person show that the family has better socio-economic condition compare to family with less space availability for each person (Galobardes, Shaw, & Lynch, 2006);
(Banguero, 1984); (Kuate‐Defo, 1994).
Figure 7a. describe about size of the house and most of respondent's housing has area between 70-120 m 2. GIS analysis and survey has similar result that only few of people has house with size more than 600m2. This survey show that only 2% of respondent with housing size more than 200 m2.
5.2. Statistic Model Figure 2 shows that there is 3 main sub model that will lead to electricity consumption model. The sub model are awareness model, living cost model and also electricity usage pattern model. We conducted the analysis based on these three models.
3.2.1. Correlation between Awareness and Education In this part we tried to know how education would reflect in the awareness of respondent about waste management. There are three questions that would reflect level of awreness: 15. Is there any pre treatmet to your waste before you dispose it? 16. If yes, what kind of pre treatment you do? If there is reuseable waste collection willing to buy your waste such as plastic, can, paper etc, would you involve in this program to 23. raise income? Questions no. 15 and no. 23 answered in two option yes/no. Respondents who answer all of them with 'yes' would be categorized as respondents with awareness in waste management. Question no. 16 has different answer of pre treatment is waste selection, it will considered as respondent with awareness also. Education of respondent classified in two categorized: respondent with education higher than high school and respondents that has education level of high school or lower. Chi-square test was employed to examine dependence between awareness and education with hypotesis: H0 : awareness and education has no corelation Ha : awareness and education has correlation The result show that significance is 0.028, lower than alpha 5% and 10% so the conclusion is awareness and education has correlation. Further examination of the correlation between awareness and education using binary logistic regression. Omnibus test of model coefficient using hypotheses as folow: Ho : Model is significant Ha : Model is not significant The result show that chi-square value about 0.027, lower than alpha 5% and 10%. Model summary only show R = 0.114, it means that education has ability to explain awareness variable for only 11.4%. The final result of correlation between awareness and education from binary logistic regression is: AWARENESS = 0.613 – 1.306 EDUCATION (4) The model has Odds Ratio (OR) about 0.271, which means that respondent with education level higher than high school tend to have higher awareness in waste management about 0.271 times rather than to be unaware the importance of waste management or respondent with education higher than high school tend to unaware 3.69 times than aware.
3.2.2. Correlation of Consumption Pattern and Income As describe before, education of respondent classified in two categorized: respondent with education higher than high school and respondents that has education in level of high school or lower. Income categorized in to five level: (1) < Rp 1,000,000 (2) Rp 1,000,000 - 3,000,000 (3) Rp 3,000,000 - 5,000,000 (4) Rp 5,000,000 - 7,000,000 (5) > Rp 7,000,000. Data of consumption pattern is categoric, so we employed multiple linear regression model to know relation between those variables. Result of calculation show that R square is 0.004 which indicate weak and positive correlation between consumption pattern and income. Because of the data is categoric, we employed binary logistic regresion for Y variable with two categorized: code 1 as aware and code 0 as unaware. First step of this analysis is to examin independency of Y as dependent variable and X as independent variable by Chi-Square methode with these hypothesis: Ho: consumption pattern and income has no relation H1: consumption pattern and income has relation The result show that asyimp sig Pearson Chi-square is 0.884 atau 88.4%, much more higher than alpha 5% and 10%, so the the conclusion is income would affect to consumption pattern. Even the correlation is not significant, but still income has ability to affect awareness of respondent in waste management, so analysis cold be continued to next step (logistic regression) to figure out how much income would affect the awareness. The result of simultaneuos test by Omnibus test show that model is not significant (Chi Square Significant is 0.898) and Hosmer & Lemeshow Test resultshow that the model is fit (Chi Square Significant is 1).
The model from binary logisti regression is CONSUMPTION PATTERN = -1.099 – 1.099 INCOME1 + 0.288 INCOME2 + 0.511 INCOME 3 + 0.0 INCOME4 Significant is alway higher than alpha 5% and10%, which means statistically the conclusion is income has no affect to consumption pattern. But if theoritecaly income affected consumption pattern, interpretation of the odds ratios are as folow: Odds Ratio is 0.333 for Income 1 (< Rp 1,000,000) compare to Income 5 (> Rp 7,000,000), it means that Income 1 would have less waste generated 0.271 times rather than has more waste generated. Odds Ratio 1.667 for income 3 (Rp 3,000,001-Rp 5,000,000) compare to Income 5 (> Rp 7,000,000), it means that Income 3 would have less waste generated 1.667 times rather than has more waste generated. Odds Ratio 1 for income 4 (Rp 5,000,001-Rp 7,000,000) compare to Income 5 (> Rp 7,000,000), it means that Income 4 would have the same chance to have less waste generated or more waste generated.
3.2.3. Correlation of Living Cost, Income, Consumption Pattern In this analysis we used 2 categorized for income variable, income-1 is higher than Rp 5,000,000 and income-0 is less than Rp 5,000,000. Predictors are consumption pattern and income, and the result of R square is 42.5% which means independent variables (consumption pattern and income) has ability to explain living cost for about 42.5%. Goodness of fit test) show that significant is 0.000 much less than alpha 5% and 10%, so the conclusion is model is fil (there is minimum one independent variable affect the dependent variable. Partial test show that income has influence to living cost (sig. 0.000) and amount of waste generated is not influence living cost (sig. 0.639), the model would be:
LIVING COST = 3,442,629 + 3,116,202 INCOME + 266,002.7 CONSUMPTION PATTERN It means that in average, responden living cost is about Rp 3,442,629 and if respondent income > Rp 5,000,000 living cost would increase about Rp 3,116,202. If respondent generated less waste, living cost would be higher about Rp 266,002.7.
VI. CONCLUSION Considering all the fact explored above, the implementation of green building concept in single houses would face to significant obstacles. Awareness of the occupant is still low and it's influenced by level of education. on the other hand, the more income the less waste their generated. In this case we think it's because of some people with higher income in Indonesia, has ability to buy some ready to use product aout side their house, such as eat in restaurant or bring some instant food which has less waste left from producing food at home. Some model formed by stastistical analysis show that education is the most important thing for increasing awarenes in reducing waste generated in order to gain 1 point of LEED.
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