Wednesday, 27 January 2016

Community Knowledge, Perceptions and Behaviour for Climate Changein Drinkingwater Sector

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telah diterbitkan di proceeding the 13th international asian urbanization conference

Abstract

This study was conducted by observing the phenomenon of climate change issues in water sector, using community readiness and vulnerability theory. Community readiness is a major factor in determining whether a local program can be effectively implemented and community supported. The concept of vulnerability to climate change is a function of exposure, sensitivity, and adaptive capacity. Research locationshave characteristics: urban poor, high population density, intervention assistance programs related to water management, water abundance or water-deficient areas. This study is a part of community preparedness vulnerability model calculation in Solo, Makassar and Serang. Exposure levels in study area tend to lead to vulnerable, and further sensitivity determine presence or absence of institutional arrangements, programs related to clean water. In 2012, a study has found estimation of variable regression coefficients. In 2013, further experiments were testing the model. The model, tested by generalization, validity and reliable to build adaptive capacity index in research location. Index, one of good predictors for vulnerability assessment of climate change using data regression, can also be used as a gauge, a tool for the determining variable sensitivity and vulnerability. Model assessment index can be measured for regions using climate-change-events approach.The result show that relation between climate change behavior and perception is the highest. Perception chiefly formed by knowledge.

Keywords




1.      Introduction
Climate change and extreme rainfall patterns can cause the increasing of temperatures.It canalsoaffect the availability, management and distribution of water as well as  the water supplyespecialy perceived in water shortage areas. Need for water will increase sharply along with population growth.Impact ofclimate change impact varies in different regions influenced by geographical region location. Change in water availability is affected by rainfall pattern changes, stream and river flows, ground water level decreasing, and salt water intrusion in rivers and groundwater.
Phenomenon of climate change leads adaptation efforts, both on institutional and in community level. According to the IPCC, adaptation to climate change includes all action to reduce the system vulnerability urban areas for instance, population groups, specially vulnerable groups, or individuals and households against the negative impacts of climate change. The adaptation consists of series actions to reduce short-term climate shocks vulnerability.
Communities adaptation is affected by society adaptabilitywhich can be traced from the readiness of community itself. This study was conducted by observingclimate change issues phenomenonin water sector, using community readiness theory and vulnerability theory. Community readiness is a major factor in determining whether a local program can be effectively implemented and supported by community. Community readiness can be seen as arelation beetweenknowledge, perceptions and behavior for climate changeindrinkingwater sector.
This research question is: how is the relationship pattern between knowledge, perceptions and behaviors related to climate change in water sector?
This research is useful to show relationship patterns and response actions related to community readiness on knowledge, perceptions and behavior aspects of climate change in water sector.
Research Design
Quantitative approach is used to provide an overview of factors related to climate change vulnerability and adaptive capacity. The study was conducted by analysingthe relationship of performance indicators, for adaptive capacity index readiness, community drinking water sector to climate change. This study is necessary in order to improve water service efforts, related to climate change in research location.
The study was conducted by reviewing three aspects related to knowledge, perceptions and behavior, in drinking water sectors affected by climate change. To see phenomenon of climate change described by thoseaspects, the analysis as follows.
Knowledge, according to Notoatmodjo (2003),is obtained through education, others experience, mass media and environment surrounding. Knowledge is an important factor for shaping individual actions. Knowledge is also a factor to buildconfidence, attitude and daily behavior, hence can be stated that knowledge is facts that support one’sactions.
Perceptions play an important role in decision making, a psychological function which makes individualsbe able to observe senses stimulation and turn it into aserial action managed.
Perception (Gunawan I, 2006) include interpretation of objects, signs, and people from experience concerned point of view, stimulus acceptance, stimulus organizing and translations or interpretations that have been organized ultimately affect behavior and attitude formation .
Another scientist (Rogers, 1974 in E. Siahaan, 2010) provide relevant theories of behavior in a stage of process, namely: Awareness, knowledge of process when individual is aware ofstimulus (the object). Interest, the stage when people are becoming interested in stimulus. Evaluation, a state considering good and bad of stimulus for him. Nextstage is a trial, where people have begun to try new behaviors. Adaptation, individual has behaved in accordance with new knowledge and attitude awareness.
Perception is classified into three aspects, namely aspects of cognition, related expectations, how to gain knowledge or ways of thinking and past experience in individual perceives something. The second aspect of affection, which involves individual emotions. These three aspects related, regarding attitudes, behaviors, activities and motives, relates to views on anything related to motive or purpose.
Perception and understanding of climate change from all commuities vary due to information gap between researchers/scientists with policy makersand practitioners, so acceptable measuring instruments is necessary to all element.

2.      Methodology
The study was conducted using basic theory related to incorporation of community preparedness and vulnerability to climate change. Related readiness group theory divides the population into three levels, namely individual, community and institution. Theory of climate change related vulnerability indicator group divided into three parts, namely adaptive capacity, exposure and sensitivity.

2.1.             Location and Research Period
Research carried out using quantitative approach at multiple locations, involving respondents and interviewees were selected according to solved problem.The experiment was conducted in Solo city, Makassar and Serang, on February until November 2013.
The survey location chosen for Solo took 56 respondents represent individuals/families in Jebres and 63 respondents in Pasar Kliwon. Whilerespondents number representing Jebres community leaders in district is 18 people and 11 people in Pasar Kliwon.
Makassar is divided into 14 districts. Pabaengbaeng Tamalate and Ballaparang Rapocini are 2 among 14 districts taken in this study. Number of respondents in Rappocini is 64 people and Tamalate is 86 people. For public figures respondents in Rappocini is 21 people and Tamalate is 17 people.
A selected location in Serang is Singamerta Ciruas, Gembor Cikande, Kebuyutan Tirtayasa and Tanjungsari Pabuaran. Research to individuals / families in Singamerta Ciruas with respondents 60 people, Tirtayasa with 14 respondents, Binuang done with 36 people. Research to village figure in Binuang with 10 people, Tirtayasa with 5 respondents, Ciruas with 10 people, and Pabuaran done with 5 people.

2.2.             Data Collecting
Data was collected using the following techniques. The first isliterature study by collecting secondary data from various sources (books, journals, magazines, maps, newspapers, documents, research reports, data sources from the internet, etc.). The next step is to conduct interview, which conducted in an atmosphere of familiar and formal situations. Interviews can be started from introduction so that informant did not mind answering. Interviews can be conducted more than once according to informant free time. The next research step is field observation. Observations made ​​through direct observation in a location that will be mapped. The next step becomes dominant step in this research is by using questionnaires. The questionnaire distributed to a number of respondents to determine samples required number (representative of a population that will be mapped). Samples number determination considered by homogeneity and heterogeneity of population.
2.3.             Population
Populations to determine community readiness represented by board of household communities (RT/ RW) and community group (KSM) board as primary object andimplementation subject of adaptation models. This population was also seen to have a lot of information, strategic and social cultural processes that occur in a community, about the phenomenon.

2.4.             Sample
The sampling technique used in this study to be population representative which is purposive sampling based on characteristics or nature of known populations (Moleong, 2004).
a. Criteria for selecting community leaders defined by following inclusion atribut, ie
(1) Management member of household community board withminimum 1 year served.
(2) Live in study location and willing to beresearch subject.
(3) 20 years age minimum.
b. Criteria for selecting household respondent defined by following inclusion atribut, namely:
(1) One of family members who are considered to represent
(2) Live in study location and willing to be research subject.
(3) 20 years age minimum.
Questionnaire was distributed to community leaders and households respondent in each region by considering the homogeneity and heterogeneity of population. Location of study districts defined by four criteria, namely: Urban Poor Community, High population density, Interventions from government or private programs related to water, Region with an abundance of water or with water shortages.

2.5.             Analysis
Analysis was done by multivariate analysis process.This analysis was done by corelate several independent variables with dependent variable at the same time. From the multivariate analysis we can know:
a. Independent variable which have greatest influence on dependent variable?
b. Is an independent variable associated with other variables influenced dependent variable or not?
c. Some form of relationship with independent - dependent variable related, directly or indirectly?

3.      Result and Discussion
3.1.             Result
Region fenomenon describe with index, and formula that use to find is Arctic Water Resource Vulnarabilitu Index.
I=

Table 1. Vulnerable Indicator Index
Indicator
Ukuran
High Vulnerability
0,00 – 0,33
Moderate Vulnerability
0,34 – 0,66
Highly Resilient
0,67 – 1,00
Source: Arctic Water Resource Vulnerability Index (AWRVI)

Research location divide in three variable came from IPCC theory, that is capacity adaptive, exposure and sensitivity.

Table2. Indicator and Research Location
No
Indicator
Regional Index
Adaptive Capacity
Solo
Makassar
Serang
1
Education
0,80
0,80
0,40
2
Income
0,31
0,15
0,01
3
Job
0,80
0,80
0,13
4
Knowledge
0,64
0,60
0,50
5
Perseption
0,67
0,68
0,51
6
Local Genius
0,34
0,25
0,27
7
Community partisipation
0,59
0,75
0,38
8
Leadership
0,81
0,71
0,55
9
Network
0,68
0,44
0,63
10
Information Availability
0,55
0,59
0,33
11
Organization Availability
0,21
0,43
0,19
Index Average
0,58
0,56
0,35
Exposure



1
Individual managemant at water shortgate season
0,44
0,46
0,49
2
Regional managemant at water shortgate season
0,52
0,56
0,52
Index Average
0.48
0.51
0,51
Sensitivity



1
Water use daily behaviour
0,76
0,63
0,41
2
Climate change related behaviour
0,70
0,71
0,49
3
Program agreement
0,44
0,60
0,53
4
Benefit
0,47
0,39
0,62
Index Average
0,59
0,58
0,51
Source: research team analysis result, 2013

The table is based on vulnerability index in three location, that is: Solo, Makassar, Serang in 2013. Index can be explain with description:
1) Adaptive capacity indicators of in:
a) Solo: adaptive capacity condition in Solo is on moderate vulnerability. This variable consist of education, employment, leadership and networking among community, institutional and households withHighly Resilient condition. These variables can strengthen communities adaptive capacity to face climate change impacts, but for knowledge variable and perceptions about climate change, local knowledge, community involvement and the availability of information that still need to be improved because these variables remain at moderate susceptibility status (Moderate Vulnerability), while income variable and the existence of the organization are at high vulnerability status (high Vulnerability), so as to affect the susceptibility of adaptive capacity of society as a whole in the face of climate change .
b) Makassar City: indicators of adaptive capacity in Makassar at the upper limit of vulnerability status is moderate (Moderate Vulnerability), with details for the education, employment, community involvement and leadership in the community, institutional and households variables have been tough on the status (Highly Resilient), these variables can strengthen the adaptive capacity of communities to cope with the impacts of climate change, but for the variable knowledge and perceptions about climate change , the network , the availability of information , presence information , as well as the existence of the organization still needs to be improved because this variable is still at the moderate status (moderate Vulnerability), while the income variable and local knowledge are at high status (high Vulnerability) , so it can affect the adaptive capacity of society as a whole in the face of climate change .
c ) Serang City: indicators of adaptive capacity in Serang city is at the lower limit being vulnerable status (Moderate Vulnerability), where the adaptive capacity in the city is very weak compared to the other two regions , because it does not have variables that can strengthen the adaptive capacity of communities to deal with the impact of climate change and have a status indicator with high vulnerability (Highly Resilient) at most, namely: income, employment, local knowledge, local knowledge, community involvement, availability of information and the provision of information, so that it can increase the vulnerability and adaptive capacity of society weakens the face climate changes. Another variable in the status of being (Moderate Vulnerability) are in education indicators, knowledge and perceptions of climate change, leadership and networking.
2) Exposure indicator, in Solo, Makassar and Serang for management variable of scarce water season in both the individual and community levels are at moderate exposure (Moderate Vulnerability), which means scarce water season occurs in a region still lead to vulnerabilities in the community in the face of climate change .
3) Sensitivity Indicatorsin:
a) Solo : people in this city, has two variables that explain that status on sensitivity tough category (Highly Resilient) that the behavior of daily water usage and behavior on climate change at the household level, this behavior means that 2 must be retained to prepare the public resilient in overcoming climate change, but there is still a variable that has a medium sensitivity of vulnerability category (moderate vulnerability) is variable agreement programs and benefits, so that the two variables is feared to result in terms of the institutional community are still not ready to adapt to the impacts of climate change.
b ) Makassar: people in this city, only has 1 variable sensitivity explain that tough category (Highly Resilient) that the behavior of climate change at the household level, meaning that at least shows the variables that community resilience in the face of climate change is not strong enough to increase the ability of communities adapt to climate change because there are three other variables that have a sensitivity to moderate vulnerability category (moderate vulnerability) is agreement benefits programs variable, and conduct daily water use so that these variables will lead to public concern in terms of institutional and households are still not ready to adapt to the impacts of climate change.
c ) Serang City : people in this city, only has 1 variable sensitivity explain that tough category (Highly Resilient) that the behavior of climate change at the household level, meaning that at least shows the variables that community resilience in the face of climate change is not strong enough to increase the ability of communities adapt to climate change because there are three other variables that have a sensitivity to moderate vulnerability category (moderate vulnerability) is variable agreement benefits programs , and conduct daily water use so that these variables will lead to public concern in terms of institutional and households are still not ready to adapt to the impacts of climate change.

Vulnerable index andsub index: (adaptive capacity; exposure; sensitivity)
1)      Solo
2)      Makassar
3)      Serang

Analysis: based on indicators of vulnerability, namely adaptive capacity, exposure and sensitivity in Solo, Makassar, and Serang on average has been on the vulnerability index medium (Moderate Vulnerability), because most of the variables that exist in the three indicators are at moderate vulnerability to adapt to the impacts of climate change.
Model Summary
Model
R
R Square
Adjusted R Square
Std. Error of the Estimate
1
.307a
.094
.086
26.75012


a. Predictors: (Constant), climate change behaviour, climate change knowledge, climate change perseption




ANOVAb
Model
Sum of Squares
df
Mean Square
F
Sig.
1
Regression
25126.012
3
8375.337
11.704
.000a
Residual
241862.262
338
715.569


Total
266988.274
341



a. Predictors: (Constant), climate change behaviour, climate change knowledge, climate change perseption
b. Dependent Variable: climate change drink water use behaviour
Coefficients
Model
Unstandardized Coefficients
Standardized Coefficients
t
Sig.
B
Std. Error
Beta
(Constant)
18.554
8.779

2.114
.035
CC knowledge
.339
.098
.200
3.463
.001
CC perseption
.172
.150
.075
1.146
.252
CC behaviour
.230
.108
.128
2.126
.034
a. Dependent Variable: climate change drink water use behaviour

Positive and significant effect on the behavior of water everyday use. Magnitude simultaneous effect of 0,094 or 9.4 % is a variable contribution of knowledge, perceptions, and attitudes about climate change ondaily behavior water use, while 90.6 % remaining influenced by other factors outside model.
The model is significant happening simultaneously. It can be seen from the probability (sig) or < 0.000. Further significance testing followed by individual testing through statistical parameters. The results of individual tests showed only one variable that does not have a significant effect with the use of water everyday, namely: perception of climate change, taking into account the acquisition of a sig > 0.05. Furthermore, empirical causal effect between variables (X1) knowledge, (X2) perception, and (x) 3 behavior on climate change on the behavior of daily water use (Y) can be described by an equation of structural sub. Y = ρ + ρ YX1 YX2 + ρ YX3 , or Y = 0.200 X1 + 0.075 X2 + 0.128 X3.
1) In partial compensation of knowledge about climate change, but a positive and significant effect on behavior in daily water use. The magnitude partial effect and the direct compensation of 0.200 or 20%. Thus, behavior of high and low daily water use is influenced by knowledge of compensation by 20%, while remaining 90% is explained other factors outside the model.
2) In partial compensation perceptions about climate change but not significant positive effect on behavior in the use of water everyday. The magnitude of partial effect and direct compensation of only 0,075, or to 7.5 %. Thus, behavior of high and low daily water use is influenced by the perception of just compensation by 7.5 %, while the remaining 92.5% described other factors outside the model .
3) In partial compensation behavior on climate change and a significant positive effect on behavior in the use of water everyday. The magnitude of partial effect and direct compensation amounting to 0.128 or rounded to 12.8%. Thus, the behavior of the high and low daily water use is influenced by the behavior of oerubahan climate compensation amounted to only 12.8%, while the remaining 87.2 % described other factors outside of the model.
Model Summary
Model
R
R Square
Adjusted R Square
Std. Error of the Estimate
1
.436a
.190
.160
24.08753
a. Predictors: (Constant), climate change drink water use behaviour, climate change perseption, climate change knowledge, climate change behaviour

ANOVAb
Model
Sum of Squares
df
Mean Square
F
Sig.
1
Regression
14609.381
4
3652.345
6.295
.000a
Residual
62082.366
107
580.209


Total
76691.747
111



a. Predictors: (Constant), climate change drink water use behaviour, climate change perseption,climate change knowledge, climate change behaviour
b. Dependent Variable: climate change drink water use behaviour in water shorgate season

Coefficientsa
Model
Unstandardized Coefficients
Standardized Coefficients
t
Sig.
B
Std. Error
Beta
1
(Constant)
3.483
12.324

.283
.778
CC knowledge
-.121
.180
.068
.669
.505
CC perseption
.309
.242
.155
1.279
.204
CC behaviour
.256
.168
.176
1.525
.130
CC drink water use behaviour
.254
.089
.270
2.864
.005

Simultaneously knowledge, perception, attitudes about climate change and the behavior of water everyday use positive and significant effect on the behavior of water use during the summer water step. Magnitude is the simultaneous effect of 0.190 or 19% is a variable contribution of knowledge, perception, attitudes about climate change and the behavior of daily water use behavior to the behavior of water use during the summer the water move, while the remaining 98.1% influenced by other factors outside the model .
This occurs simultaneously model significantly. It can be seen from the probability (sig) or < 0.000. Further significance testing followed by individual testing through statistical parameters. The results of individual tests showed only one variable that has a significant influence to the behavior of water use season when water is scarce, ie: behavior of drinking water use behavior, taking into account the acquisition of sig < 0.05 . Furthermore, empirical causal effect between variables (X1) knowledge, (X2) perception, (X3) behavior on climate change and (X4) behavior of daily water usage to (Y) the behavior of water use season when water is scarce can be described through substructural equation. Y = ρ + ρ YX1 YX2 + ρ YX3 , or Y = 0.068 X1 + 0.1557 X2 + 0.176 X3 + 0.270 X4.
1) In partial compensation of knowledge about climate change is positive but it is not significant effect on the behavior of water use season when water is scarce. The magnitude of the partial effect and the direct compensation of only 0,068 , or 6.8 %. Thus , the behavior of high and low water use season when the water is influenced by compensation measures knowledge only by 6.8%, while the remaining 93.2% described other factors outside the model.
2) In partial compensation perceptions about climate change and a significant positive effect on the behavior of water use season when water is scarce. The magnitude of the effect is partial and only direct compensation of 0.155 or 15.5 %. Thus, the behavior of high and low water use season when water is scarce perception is influenced by the compensation amounted to only 15.5%, while the remaining 84.5 % described other factors outside the model.
3 ) In partial compensation behavior, it has a positive effect on climate change, but no significant effect on the behavior of water use season when water is scarce. The magnitude of the partial effect and the direct compensation of only 0.176 or 17.6 %. Thus, the behavior of high and low water use season when water is scarce compensation behavior is influenced by climate change only amounted to 17.6%, while the remaining 82.4% described other factors outside of the model

Relation between education (x 1), income (x 2), climate change knowledge (x 3)with climate change perseption
Model Summary
Model
R
R Square
Adjusted R Square
Std. Error of the Estimate
1
.567a
.321
.313
10.44843
a. Predictors: (Constant), education, income, climate change knowledge
ANOVAb
Model
Sum of Squares
df
Mean Square
F
Sig.
1
Regression
13577.579
3
4525.860
41.457
.000a
Residual
28711.620
263
109.170


Total
42289.199
266



a. Predictors: (Constant), education, income, climate change knowledge
b. Dependent Variable: climate change perseption


Coefficientsa
Model
Unstandardized Coefficients
Standardized Coefficients
T
Sig.
B
Std. Error
Beta
1
(Constant)
41.829
2.624

15.939
.000
CC knowledge
.275
.043
.356
6.433
.000
Income
7.329E-7
.000
.059
1.069
.286
Education
2.893
.567
.295
5.103
.000
a. Dependent Variable: climate change perseption


Simultaneously income, knowledge about climate change, and education last positive and significant effect on the perception of climate change. Magnitude is the simultaneous effect of 0.321 or 32.1 % is contributed by a variable income , knowledge about climate change, and education last, while the remaining 67.9 % influenced by other factors outside the model.
This occurs simultaneously model significantly. It can be seen from the probability (sig) or < 0.000. Further significance testing followed by individual testing through statistical parameters. Individual test results indicate there are two variables that have a significant effect on the perception of climate change, namely: education and knowledge about climate change, taking into account the acquisition of sig < 0.05. Furthermore, empirical causal effect between variables ( X1 ) income, ( X2 ) education, and ( X3 ) knowledge of climate change on the perception of climate change ( Y ) can be described by an equation of structural sub. Y = ρ + ρ YX1 YX2 + ρ YX3 , or Y = 0.059 X1 + 0.295 X2 + 0.356 X3.
1) In partial compensation income, it is positive but it is not significant effect on the perception of climate change . The magnitude of the partial effect and the direct compensation of only 0,059 or rounded to 5.9 % . Thus , the behavior of the high and low daily water use is influenced by compensation earnings amounted to only 5.9 % , while the remaining 94.1 % described other factors outside the model .
2 ) In partial compensation education and significant positive effect on the perception of climate change . The magnitude of the effect of partial and direct compensation amounting to 0.295 or 29.5 % . Thus , the level of perception of climate change is influenced by the perception of compensation by 29.5 % , while the remaining 70.5 % described other factors outside the model .
3 ) In partial compensation of knowledge about climate change and a significant positive effect on the perception of climate change . The magnitude of the effect of partial and direct compensation amounting to 0.356 or 35.6 % . Thus , the behavior of the high and low daily water use is influenced by knowledge about climate change compensation by 35.6 %, while the remaining 64.4 % described other factors outside the model .

Relation between local wisdom (x1), leadership (x2), Community Action Plan (x3),  information avaibility (x4), network (x5), and communication channel (x6) organization avaibility (y)
Model Summary
Model
R
R Square
Adjusted R Square
Std. Error of the Estimate
1
.900a
.810
.798
15.98166
a. Predictors: (Constant), information avalability, local wisdom, programme aggrement, leadership, network, community action plan
ANOVAb
Model
Sum of Squares
df
Mean Square
F
Sig.
1
Regression
97205.911
6
16200.985
63.430
.000a
Residual
22731.787
89
255.413


Total
119937.698
95



Predictors: (Constant), information avalability, local wisdom, programme aggrement, leadership, network, community action plan

Coefficientsa
Model
Unstandardized Coefficients
Standardized Coefficients
t
Sig.
B
Std. Error
Beta
1
(Constant)
3.863
9.196

.420
.675
Local wisdom
.013
.109
.006
.120
.905
Community action plan
.425
.065
.476
6.583
.000
Network
.276
.055
.347
5.066
.000
Program agreement
.131
.061
.113
2.149
.034
Leadership
.004
.137
.002
.029
.977
Information availability
.118
.055
.141
2.163
.033
a. Dependent Variable: organization availability


Simultaneously, local wisdom ( x1 ) , Leadership ( x2 ) , CAP ( x3 ) , program agreement ( x4 ) , network ( x5 ) , and the communication channel (x6) positive and significant effect on the existence of the organization (y). Magnitude is the simultaneous effect of 0,810 or 81.0 % is the contribution of local knowledge (x1), Leadership (x2), CAP (x3) , program agreement (x4) , network (x5) , and the communication channel (x6), while the remaining 19,0% influenced by other factors outside the model. This occurs simultaneously model significantly. It can be seen from the probability (sig) or < 0.000. Further significance testing followed by individual testing through statistical parameters. Individual test results showed that there are two variables that do not have a significant effect with the existence of the organization, namely: local knowledge and leadership with siq value > 0.05. Furthermore, empirical causal effect between local knowledge (x1), Leadership (x2), CAP (x3), the availability of information (x4), network (x5), and the program agreement (x6) with the organization's existence (y) can be described by the equation structural sub. Y = ρ + ρ YX1 YX3 YX2 + ρ , ρ + + ρ YX4 YX5 YX4 + ρ + ρ YX6 or Y = 0.006 X1 + 0.002 X2 + 0.476 X3 + 0.141 X4 + 0.347 + 0.113 X5 X6
1) In partial compensation of local wisdom is positive but not significant effect on the existence of the organization. The magnitude of the partial effect and the direct compensation of only 0,006, or 0.6%. Thus, whether or not the existence of an organization in a region affected by local genius only by 0.6%, while the remaining 99.4% described other factors outside the model.
2) In partial compensation of leadership positive but it does not have significant effect on the existence of the organization. The magnitude of the partial effect and the direct compensation of only 0,002, or 0.2%. Thus, whether or not the existence of an organization in a region affected by the leadership only by 0.2%, while the remaining 99.98% is explained other factors outside the model.
3) In partial compensation the availability of information is positive but it does not have significant effect on the existence of the organization. The magnitude of the partial effect and the direct compensation of only 0.141 or 14.1%. Thus whether or not the existence of an organization in a region affected by the availability of information compensation of 14.1%, while the remaining 85.9% described other factors outside the model.
4) In partial compensation Community Action Plan ( CAP ) and a significant positive influence on the existence of the organization. The magnitude of the effect of partial and direct compensation amounting to 0.476 or 47.6%. Thus whether or not the existence of an organization in a region affected by CAP compensation amounting to 47.6%, while the remaining 52.4% described other factors outside of the model
5) In partial compensation network availability and a significant positive influence on the existence of the organization. The magnitude of the effect of partial and direct compensation amounting to 0.347 or 34.7%. Thus whether or not the existence of an organization in a region affected by the network in a region of 34.7%, while the remaining 65.3% described other factors outside of the model
6) In partial compensation program agreement positive and significant effect on the existence of the organization. The magnitude of the effect of partial and direct compensation amounting to 0.113 or 11.3%. Thus whether or not the existence of an organization in a region affected by the compensation program agreement of 11.3%, while the remaining 88.7% described other factors outside of the model

4.      Discussion
Adaptation Model Vulnerable Local Communities Water and Sanitation Related Impacts of Climate Change
a. line diagram: knowledge of climate change, perceptions on climate change, climate change behavior, behavior with daily water usage, and behavior usage in water rare season
 









5.      Conclusion
From the results of this study found evidence that there is chained relationship - related between climate change knowledge, perception, behaviour. Climate change knowledge and behaviour have relationship related wih water consumption daily behaviour. The result show that relation between climate change behavior and perception is the highest. From the results of same study found a strong reciprocal relationship between income and education. Additionally proved again that knowledge will influence the perception of climate change. Compared with income level turns out to have a greater influence on shaping knowledge.



References

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Book chapter: Tim Peneliti, 2012. Laporan Akhir Mitigasi dan Adaptasi Perubahan Iklim oleh Masyarakat dalam Ketersediaan Air Minum. Balai Litbang Sosekling Bidang Permukiman. Yogyakarta.
Tim Peneliti, 2013. Laporan Akhir Peningkatan Kapasitas Adaptasi Masyarakat Daerah Rentan Air Minum dan Sanitasi terkait Dampak Perubahan Iklim. Balai Litbang Sosekling Bidang Permukiman. Yogyakarta

Journal Article: Elza Surmaini, Eleonora Runtunuwu, dan Irsal Las, Upaya Sektor Pertanian Dalam Menghadapi Perubahan Iklim, 27 Oktober 2011, Jurnal Litbang Pertanian
Gunawan Indra, 2006, Pengetahuan Masyarakat tentang Pengelolaan Sanitasi Berbasis Masyarakat, Tesis, Universitas Diponegoro
Pratiwi HU dan Rahayu E., 2011, Perilaku Mengkonsumsi Air Putih Ditinjau Dari Persepsi Terhadap Perilaku Kesehatan, Fakultas Psikologi Universitas Katolik Soegijapranata Semarang





[*] Corresponding author. Tel.: +62274-555205; fax:+62274-564978.
E-mail address:yudha.ph@pu.go.id


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