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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
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
Book: IPCC’s (2001) Third
Assessment Report (TAR) being the third supplementary submission to the Joint
Standing Committee on Treaties Inquiry into The Kyoto Protocol by RJ Foster.
Notoatmodjo. 2003. Pendidikan dan Perilaku Kesehatan. Jakarta : Rineka Cipta
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
Websites: http://www.ugm.ac.id/id/berita /8488minim.
kebijakan. menghadapi.risiko. ancaman. perubahan.iklim
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