2014 International Conference on Management Science & Engineering (21th)
August 17-19, 2014
Helsinki, Finland
The Construction and Study of Assessment System on Intersubjective
Cognitive Differences of NIMBY Conflict
—A Case Study of Julu Landfill in Hebei Province
YANG Tuo
School of Public Management, Beihang University, Beijing 100191, P.R.China
Abstract: NIMBY conflicts surface compensation or
benefits due to poor communication and conflicts
triggered, but it is the essence of government officials,
experts, scholars and the public to the conflict
surrounding the policy response to cognitive bias, this
bias affects not only the behavioral intentions
surrounding populace, but also actions that directly affect
their behavior. Existing literature focuses on research
priorities NIMBY conflicts origin, the causes and coping
strategies, and research on the cognitive differences
between the different actors in coping strategies less.
Therefore, this study uses fuzzy Delphi method to
construct a comprehensive set of actors in the conflict
NIMBY cognitive differences assessment framework, in
order to reveal the different actors of the NIMBY attitude
of cognitive strategies to deal with the conflict, seeking
to resolve the different strategies a true reflection of the
surrounding population, and for the construction of the
main conflict between the NIMBY behavior norms laid
research base.
Keywords: cognitive differences, expert judgment,
fuzzy delphi, indicator system
1 Introduction
NIMBY (Not In My Backyard) is the facility that
residents do not wish to set near their homes since they
think that it should service publics but not threaten their
living environment, health and other properties. [1] The
construction of NIMBY facilities can meet the social
needs, but it will cause the negative impact on the
surrounding population health and property, which
makes the publics accept inside, but oppose the
construction of this facility in the vicinity of their own.
NIMBY conflict triggered by NIMBY first appeared in
the United States in the 1970s, mainly in opposition to
polluting facilities. In recent years, along with social and
economic development and improving environmental
awareness of citizens, the construction of non-polluting
facilities in order to improve the quality of urban life
such as nursing homes, disease prevention and control
Supported by the National Social Science Fund of China
(11&ZD173, 11AGL009)
978-1-4799-5376-9/14/$31.00 ©2014 IEEE
center has also become the focus of public protest.
NIMBY conflict is superficially a conflict caused by
unsatisfied benefits compensation or coordination with
NIMBY as the carrier, but it is in essence cognitive
opposition and bias in NIMBY between government
officials, experts, scholars and the surrounding populace,
and if this cognitive bias should not be promptly and
effectively resolved, the public will vent their
dissatisfaction and take action to prevent the facilities
construction to show their stance, attitudes and
expectations. Theory of planned behavior believes that
behavioral cognition not only affects the behavioral
intention of actors, but also directly affects the behavior
(Notani, 1998). [2] We can say that cognitive differences
of NIMBY conflict actors are the deep-seated cause of
the generation and intensification of NIMBY conflicts,
and the manner and means to process the conflicts have
direct impact on social stability and harmony. Therefore,
this research summarizes coping strategies proposed by
the scholars for NIMBY conflict through literature
review and in-depth interviews, explores how to
construct a set of consensus-based integrated evaluation
framework for cognitive differences of NIMBY conflict
actors through Fuzzy Delphi expert questionnaire, and
takes Hebei Julu landfill as an example to analyze the
cognitive differences of government officials, experts,
scholars and surrounding populace in ways to resolve the
NIMBY conflict, so as to reveal different cognitive
attitudes of actors on measures to resolve the NIMBY
conflict and seek a true reflection of the populace on
different measures, laying research foundation for the
subsequent construction of the behavior norms for the
NIMBY conflict actors.
2 Literature review
The generation of NIMBY conflicts is caused by
both irrational and rational factors. For the former, Lake
(1987) observed the 81 waste treatment plants planned to
build in United States from 1980 to 1987, only eight of
which were successfully completed, mainly because of
people’s NIMBY plot to be against the construction of
environmental protection facilities for no reason. [3]
Hunter & Leyden (1995) also pointed out that the
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NIMBY plot is a completely selfish, ideological and
politicized behavioral tendency, which is difficult to
persuade rationally, and many countries all view it as an
obstacle to the public construction.[4] In contrast,
McAvoy (1999) believes that the lack of public
participation in the process of siting NIMBY facilities
are the key factor that constitutes the NIMBY
phenomenon, and if public participation is strengthened
so that participants can effectively challenge the science
and technology assumptions, it will help understand and
accept different views from each other. [5] It is this
intertwined emotion that makes the NIMBY conflict
resolution extremely complex. As for ways to resolve the
NIMBY conflict, experts and scholars in different fields
have analyzed from different perspectives. Qiu Changtai
(2007), starting from the environmental justice, believed
that the government decision-making process is arbitrary,
offering no channel for public participation, while
NIMBY lacks security control facilities for
environmental pollution, at the expense of people's
environmental right and healthy living right, ignoring the
ethics of environmental justice. [6] Li Yongzhan (1997)
analyzed the conflict awareness, resources and interests
of both sides from the point of view of conflict
management and public policy, focusing on the
controversies and the causes among policy stakeholders,
solutions of which mainly consist of risk mitigation and
feedback compensation. Zhang Xianghe (2010) analyzed
from the point of view of spatial planning, and pointed
out the NIMBY conflict resolution should be controlled
at source, with scientific risk prevention planning
through strengthening public participation, determining
reasonable compensation, and other aspects like the
facility siting. [7] Xiong Yan (2011) started from the
perspective of risk assessment and risk analysis and
proposed that responsible NIMBY units should improve
its responsiveness to ensure the third party involvement
in technical issues, and set various forms of citizen
participation mechanisms to reduce the build resistance.
[8]
Li Jianhua (2001) studied the conflict resolving
mechanism from public policy, and emphasized that,
starting from aspects like civic engagement and
economic incentives, NIMBY public participation and
financial compensation improvement should be early
planned to enhance the public trust in government and
reduce construction resistance. [9]
In the existing literatures, scholars studied on the
origin, causes and solution paths of NIMBY conflicts
from many different perspectives and made a useful
exploration to improve the mechanisms for resolving
NIMBY conflicts. However, the current scholars tend to
focus on the formation and coping strategies of the
NIMBY conflict, while few analyzes the cognitive
differences between actors in different coping strategies.
Cognition of NIMBY conflict actors on the same thing is
influenced by complicating factor including knowledge
structure, cultural background, work experience and
information channels, and there exist discrepancies in
cognitive perspective and understanding of measures
taken in building facilities, while large opinion difference
is easier to expand the scale of the conflict and impact
the control effectiveness. Therefore, this study will be
centered on cognitive differences to reveal the different
cognitive attitudes of NIMBY conflict actors on the
conflict resolving measures through the analysis of
cognitive difference level of government officials,
experts, scholars and the surrounding populace.
3 Research design
NIMBY conflict resolution need to concern multiple
value goals, for there are differences in the cognitive
style between different participants. The multi-objective
value can not be measured by traditional analysis
methods, because they can not fully reflect semantic
cognitive differences of the government officials, experts,
scholars and the surrounding populace in NIMBY
conflict solutions. To solve the semantic cognitive
difference problem of the respondents in the evaluation
items, this study applies fuzzy semantic variables to
design the questionnaire, and have experts and scholars
judge the project indicators. Fuzzy Delphi method is
used to inspect the constructed cognitive differences
evaluation system framework of NIMBY conflict actors
and the applicability of indicators, and AHP is used to
calculate and analyze the cognitive differences of
NIMBY actors.
3.1 Research methods
3.1.1 Fuzzy Delphi Method
The Delphi method seeks for expert advice through
communication-feedback in the form of inquiry letters,
capturing the advantages of both interviews and
questionnaires. As a subjective and qualitative method,
the Delphi method not only can be used in the prediction
field, but also can be widely used in the construction of
various evaluation index systems and the determining
process of specific indicators. [10] However, the
traditional Delphi method has shortcomings like
time-consuming, high cost, low recoveries and easy to
distort the expert opinions. Ishikawa (1993) used the
concept of cumulative frequency distribution and fuzzy
integral to integrate expert opinion as fuzzy numbers and
determined fuzzy Delphi method. [11] Compared with the
traditional Delphi method, fuzzy Delphi method has the
following advantages as an indicator screening tool: 1)
The number of investigations and survey costs are
reduced; 2) A more complete expression of expert
opinion is achieved; 3) With the introduction of fuzzy
theory, expert knowledge can better achieve the binding
of quantitative calculation and qualitative analysis. [12]
3.1.2 Analytic Hierarchy Process
AHP is to use the experience of decision-makers to
determine the relative importance of measurement
indicators, which expresses people's subjective judgment
in the form of number to reflect the importance of
indicators and give standard weights of each decision
scheme reasonably, achieving quantitative and qualitative
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combination and laying the foundation for a fair and
scientific risk assessment. Wu Dianting and Li Dongfang
(2005) summarized the contribution of AHP into the
following three points: a) it provided a framework for
thinking level, easy to organize ideas to be structured and
clear; b) scaling by contrast increased the objectivity of
judgments; c) combination of qualitative inference and
quantitative judgments enhanced the scientificity and
practicability. Therefore, this study uses AHP assess
NIMBY conflict actors framework and evaluation
indicator weights through pairwise comparison.
3.2 Research procedures
(1) Primary exploration on indicator system.
According to the study purpose, literature review and
expert interviews, develop a preliminary evaluation
system for NIMBY conflict actors and establish an
evaluation framework.
(2) Fuzzy semantic questionnaire design and survey.
Use semantic fuzziness to design the questionnaire and
invite experts to give their assessment on the appropriate
level of assessment framework and evaluation factors
proposed in this study. When the questionnaires are
collected, use fuzzy Delphi calculation to confirm
assessment framework and evaluation indicators for
NIMBY conflict actors.
(3) Index weights analysis of the evaluation
framework. Use AHP to get decision-making groups and
project weights and identify the cognitive differences
between decision-making groups.
(4) Empirical results analysis. Analyze the results
and organize discussions on the results.
4 Primary exploration on evaluation index
system
In the literature review, scholars study NIMBY
conflicts resolving mechanism from different angles, but
no scholar has conducted empirical quantitive analysis of
cognitive differences of stakeholders in different
resolving mechanisms. This study, in accordance with
the foregoing literature review, divides the conflict
resolution mechanisms mentioned more by the scholars
First class
indicators
A. Public
participation
B. Benefit
compensation
into four dimensions as public participation, benefit
compensation, information disclosure and risk
prevention. These four dimensions basically reflect
current basic means and measures adopted to resolve
NIMBY conflict. To facilitate the empirical research, the
dimensions and evaluation indicators of the evaluation
system of cognitive differences between NIMBY conflict
actors should not be too many, but the content to be
detected need indicating accurately in written expression
the majority of respondents are able to understand. After
reduction and reorganization, this study proposes
operational assessment framework of cognitive
differences between NIMBY conflict actors as shown in
Tab.1, first class indicators are the above four dimensions,
the second class indicators are evaluation and
measurement indicators of each dimension. Though the
evaluation indicators are supposed to be expressed with
simplified wording, there are still a small number of
evaluation indicators which are difficult to explain with
clear and brief words. Therefore, the survey phase of the
investigation requires a semi-open way, the relevant
indicators being interpreted by the investigators face to
face.
5 Indicator system construction
To construct an appropriate evaluation framework
of cognitive differences between NIMBY conflict actors,
this
study
adopts
semi-enclosed
anonymous
questionnaire and repeated communication-feedback
process to gather expert opinions. For the number of
evaluation experts, Delbecq et al (1975) consider that the
number of members can be controlled from 15 to 30
when the Delphi panelists are of high homogeneity,
while 5-10 is appropriate if they are of high
heterogeneity. [13] Xiao Shuhui (2008) believes that the
number of experts and scholars should be at least 8-10
people, and the group error can be reduced to a minimum
with the increase of the members of the experts, but the
number of expert members is over 30, its decision
quality will not rise due to the number increase. [14] This
study focuses on universities and government
administrative agencies which are of high homogeneity,
Tab.1 Primary exploration and literature sorting table on evaluation indicators
First class
Second class indicators
Second class indicators
indicators
C.1 Environmental impact assessment disclosure
A.1 Community participation
C. Information C.2 Basic environmental information disclosure
A.2 Hearing, forum
disclosure
C.3 Public involvement disclosure
A.3 Public questionnaire
C.4 Public opinion disposal disclosure
A.4 Social organization intervention
A.5 Expert advice argument
D.1 Legal construction for risk prevention
D.2 Environmental risk accountability system
B.1 Monetary indemnity
D.3 Environmental risk emergency system
D. Risk
B.2 Tax deduction and exemption
D.4 Environmental risk evaluation system
prevention
B.3 Material compensation
D.5 Environmental risk supervision system
B.4 Labor compensation
D.6 Environmental risk prediction system
B.5 Protective compensation
D.7 Risk troubleshooting system
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so the number of experts is controlled between 10 and 15.
Because NIMBY conflict involves many fields, different
perspectives will produce different conclusions. In this
study, respondents are divided into university academics
and government substantive staff two categories. The
selection criteria are as follows: 1) those who are
engaged in study-related teaching and research work; 2)
those whose work field related or similar to the research
topic; 3) those who have published papers similar or
related to this study topic. According to the above criteria,
this study selects totaling 11 university academics and
government departments staff, university scholars
including teachers and doctoral students from Peking
University (1), Beijing University of Aeronautics and
Astronautics (4) and the Chinese University of Mining
and Technology(1); members from government practical
departments include staff in Environmental Engineering
Assessment Center of the Environmental Protection
Department (2), CPC Hebei Provincial Party Committee
Propaganda Department (1), Xingtai City People's
Congress Standing Committee (1) and cadres form
Daliangzhaung Township, Qiaodong District, Xingtai
City (1). The questionnaire is divided into two phases,
and the beginning and ending dates are October 10, 2013
and November 20, 2013, with 21 questionnaires issued
and 20 returned, a return rate of 95%. After collecting the
questionnaire data at each stage, the researchers calculate
the index data and feed the result of this survey back as
an annex to experts and scholars for reference until the
indicator items achieve convergence and reach a
consensus. If the expert consensus value Gi after
convergence is below the threshold value S, then remove
the indicator from the assessment framework.
5.1 Recycling and disposal of the first round expert
questionaire
The first phase of the survey was completed from
October 10, 2013 to October 31, 2013. 11 questionnaires
were distributed, and 11 were recovered, with a recovery
rate of 100%. The views of experts and scholars in the
first stage are shown in the following table.
Through the calculation of test value Mi-Zi and
consensus value Gi of 21 evaluation indicators of
cognitive differences between NIMBY actors by
Microsoft Excel 2007, the results are shown in Table.3.
Tab.3 shows that the test value of index A3, B2, B5,
C2, C4 and D3 Mi-Zi <0, failing to reach convergence
and expert opinions failing to converge, so they need
another round of expert investigation.
5.2 Recycling and treatment of the second round of
expert questionnaire
The second phase of the survey was completed from
November 8, 2013 to November 20, 2013. The results of
the first phase and views of experts and scholars are
attached for reference. 11 questionnaires were distributed
and 10 recovered, with a recovery rate of 91%.
Compared with the first survey we can find that the
revised test value of the index Mi-Zi are all greater than
Tab.2 Opinion reorganization of the first phase Fuzzy
Delphi (FDM) expert questionnaire
Expert
Expert opinion reorganization
number
The character of fuzzy analysis method is to analyze
the fuzzy values of the questionnaire, but there is no
Expert
cross fuzzy zone among the indicators of the sample
No.1
questionnaire, which will inevitably affect the
semantic thinking level.
Expert
As for public participation, group involvement and
No.2
individual involvement should be differentiated.
There exist cognitive errors of material
compensation in benefit compensation, so material
Expert
compensation should be interpreted with notes,
No.3
while the interest compensation subjects should be
distinguished.
……
……
The description of protective examination is not
Expert
accurate, and there are mutual contents in material
compensation and protective examination, such as
No.10
drugs.
There exists much divergence in basic
environmental
information
of
information
disclosure, the name of which is less precise.
Expert
Basic environmental information should include
No.11
sketch of construction projects, overview of the
impacts the construction projects may have on the
environment, policies and measures to prevent or
mitigate adverse environmental impact.
zero, in the state of convergence, showing that experts
and scholars reach a consensus on the revised evaluation
system of cognitive differences between NIMBY conflict
actors, laying foundation for further analysis of expert
consensus value Gi.
Threshold value S can directly impact assessment
indicator selection, while as for the way to determine the
threshold value S, the existing literatures are generally
according to subjective judgment of experienced
researchers. Chen Zhaohong (2001) considered that
under the consideration of practical efficiency and cost,
indicator items obtaining above 80% or 90% of expert
reorganization can be used. By analyzing the relevant
literature, the author sets the threshold value of the
expert consensus Gi ≥ 7. The index test value Mi-Zi and
expert consensus value Gi in the second phase are shown
in Fig.1.
The results show that, in addition to the tax
compensation index B2 which does not obtain a majority
decision group identity, the other indicators are greater
than the threshold value S. By comparing the change of
the expert consensus value Gi in two stages, in addition
to indicators A1, C3, D2 and D5 which have declined in
the second stage, the rest of the indicators have increased
by varying degrees, the overall average value of expert
consensus Gi also increase from 7.48 in the first phase to
7.83 in the second stage, reflecting there is a tendency to
increase the score after the expert members refer to the
calculation results and feedback of the first stage to the
second phase of investigation.
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NO.
1
2
3
Tab.3 Statistical analysis results of the first phase of the questionnaire
Conservative
Optimistic cognitive
Assessment indicators
Expert
ognitive value
value
Mi-Zi
consensus
First class
Second class
value Gi
indicators
indicators
A1
3
6.010
9
7
8.432
10
0.422
7.648
A2
4
6.150
8
6
8.446
10
0.296
7.139
A3
1
5.523
9
4
8.014
10
-2.510
6.679
4
5
6
A4
A5
B1
4
4
5
6.125
6.796
7.504
9
8
9
7.5
7.5
7.5
8.354
9.082
9.502
10
10
10
0.728
1.786
0.498
7.843
7.784
8.358
7
B2
0.1
3.070
9
2
6.317
10
-3.753
4.949
B3
B4
1
4
4.351
5.577
7.5
8
5
6
7.148
7.809
10
10
0.297
0.233
6.014
6.855
10
B5
0.1
3.807
8
2
6.947
10
-2.860
5.247
11
C1
6
7.785
9
7.5
9.651
10
0.366
8.459
12
C2
4
6.751
9
6
8.847
10
-0.904
7.676
C3
5.5
7.525
9
8
9.513
10
0.989
8.506
14
C4
4
6.339
9
6
8.446
10
-0.893
7.437
15
16
D1
D2
5
5
7.065
7.267
9
9
7.5
7.5
9.071
9.268
10
10
0.505
0.500
8.172
8.257
17
D3
4
7.045
9
6
9.071
10
-0.975
7.833
D4
D5
D6
D7
4
4
6
6
6.990
7.163
7.703
7.703
8
9
8
8
8
7.5
7.5
7.5
9.279
9.311
9.607
9.607
10
10
10
10
7.48
2.289
0.648
1.404
1.404
8.000
8.245
7.938
7.938
8
9
13
18
19
20
21
A
B
C
D
Average value of Gi
No
convergence
No
convergence
No
convergence
No
convergence
No
convergence
No
convergence
Fig.1 Index test value and expert consensus value in the second phase
5.3 Evaluation indicator determination
From the analysis of the results, as for the four first
class indicators of public participation, benefit
compensation, information disclosure and risk
prevention, the expert identifications of the information
disclosure and risk prevention are the most consistent.
Monetary compensation is the most remarkable one
among second class indicators, the expert consensus
value of which is the highest of all 20 secondary
indicators. Conversely the two secondary indicators of
public survey and protective examination get low scores,
indicating that experts disagree on this view. After
deleting the evaluation index B2 which does not meet the
threshold value S, this study constructs an assessment
framework of cognitive differences between NIMBY
conflict actors (Fig.2), including four first class
indicators, eight second class indicators and 20 third
class indicators.
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Fig.2 Evaluation indicators of cognitive differences between NIMBY conflict actors
6 Cognitive differences of actors in Julu
landfill
Frequent incidents of garbage disposal bring
unprecedented social pressure from public opinion to
construction companies and government departments. As
odors, dioxin due to garbage incineration, landfill
leachate and many other issues become the focus of
public concern and attention, government departments
have to devote more resources to balance and coordinate
the interests of all parties, and the normal operation and
stable development of waste disposal companies also
face enormous risks and challenges. NIMBY effect and
social conflict in the waste disposal industry can be
summed to the conflict between a wide range of waste
production and centralized processing, and conflict
between minority environmental quality deterioration
and majority environmental benefits, thus studies on
NIMBY effect of the landfill and its social conflict
resolution mechanism have certain practical significance.
Therefore, this study selects Hebei Julu landfill as
research object, which is a Class Ⅳ landfill located 5
km northeast of Julu Town, with an area of 120 acres and
a total capacity of 115.07 cubic meters. This study
expects to understand cognitive differences between
NIMBY actors through the analysis of the Julu landfill
and provide a reference for the follow-up studies.
6.1 Evaluation system and indicator weights
assignment
After establishing evaluation framework, this study
tries to use Analytic Hierarchy Process (AHP) to
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integrate the views of relevant experts and scholars,
pairwise compare the relative importance of assessment
indicators and calculate the weight of each indicator. The
questionnaire is divided into two phases, and the
beginning and ending dates are October 10, 2013 and
April 27, 2014. The first phase of the survey was
completed from December 20, 2013 to January 28, 2014.
26 questionnaires were distributed, and 24 were
recovered, with a recovery rate of 92%. The second
phase of the survey was completed from April 8, 2014 to
April 27, 2014. 38 questionnaires were distributed and
36 recovered, with a recovery rate of 95%. Local
government officials and township staff are increased
into questionnaire respondents on the basis of the fuzzy
semantic respondents in the previous stage. Meanwhile,
to assess the cognitive differences of different
decision-making groups in the risk perception impact,
populace surrounding Julu landfill are also involved as
respondents, mainly residents within 5 km radius of the
landfill. A total of 64 questionnaires were issued and 60
questionnaires were returned, with a return rate of 94%,
involving 18 government officials, 16 experts and
scholars and 26 surrounding people. After the
questionnaire this study calculates the weights of
cognitive differences between various actors in the whole
sample. For comparison, the cognitive weight values will
be shown in radar map in this research (Fig.3 to 7) for
visual analysis.
Fig.4 Public participation dimension of cognitive
differences of actors
most recognized dimension by surrounding people,
followed by risk prevention, showing that starting from
economy can get most popular support and reduce the
construction resistance. Meanwhile, they all think
information disclosure less important, showing that
information disclosure system all sectors of
contemporary society are calling for has not been
accepted by various actors. There are a lot of cognitive
differences in public participation, the acceptance of
experts and scholars is significantly higher than other
groups, which may be related with their knowledge and
professional background, expecting to resolve conflicts
through communication and coordination.
6.2.2 Cognitive differences analysis of second class
evaluation dimension
1) Public participation. In this dimension
community participation is the most important evaluation
indicator no matter in terms of the whole dimension or
actors. The main indicators with large differences are
mainly expert argument meetings and public surveys, the
former accounting for a larger proportion in experts and
scholars, while the latter being recognized more by the
surrounding people, the reasons of which may be related
with the representativeness of actors.
Fig.3 First class evaluation dimension of cognitive
differences between actors
6.2 The results of analysis of the weight assignment
6.2.1 Cognitive differences analysis of first class
evaluation dimension
The overall opinion score shows that risk prevention
is the most important dimension to assess, followed by
interest compensation, public participation and
information disclosure. Down to government officials,
experts and scholars, both consider that risk prevention is
the most important and starting from system, early
warning and emergency response helps maximize access
to the building support. The benefit compensation is the
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Fig.5 Benefit compensation dimension of cognitive
differences of actors
2) Benefit compensation. From the weights of each
evaluation index, the various actors reach a consensus
that money is the best way of compensation. Both
experts and the surrounding people put labor
compensation as the second choice, expecting to obtain
stable income by obtaining employment opportunities to
participate in project facilities. Government officials list
material compensation as the second choice, desiring to
get public support through disposable compensation
method.
3) Information disclosure. Overall comments
display the indicator environmental impact assessment
accounts for the largest share. As for the individual
opinions of the three actors, the government officials,
experts and scholars hold that the environmental impact
assessment is the most important, while the surrounding
people pay more attention to public opinion disposal
results, and this difference may be in close contact with
their own interest demands of the surrounding populace.
cognitive differences of research actors.
Fig.7 Risk prevention dimension of cognitive differences of
actors
7 Conclusion
Fig.6 Information disclosure dimension of cognitive
differences of actors
4) Risk prevention. This overall opinion of this
assessment dimension shows that the legal construction
is the most important indicator. Cognitive differences
among various groups focus on the two indicators of the
legal system and accountability system. The former is
valued by government officials, experts and scholars,
hoping to solve the NIMBY risk problem through
institutionalized means, while the latter is valued by the
surrounding people, expecting to restraint the safety
awareness of government officials and NIMBY
construction unit through accountability system.
In conclusion, government officials pay more
attention to the system, the policy and the
implementation feasibility, experts and scholars are more
concerned about their professional background
considerations, hoping to strengthen communication and
mitigate conflict through public participation, and the
surrounding populace pays more attention to actual needs
related to self-interest. The above analysis lays certain
foundation for the follow researches on the causes of
The study carried out fuzzy Delphi questionnaire
survey and cognitive level analysis survey in two stages
through literature review and questionnaire survey, the
main purpose of which is to construct the evaluation
framework for cognitive differences of NIMBY conflict
actors, and analyze the cognitive differences between
actors in ways to resolve NIMBY conflicts with an
example of Hebei Julu landfill. The indicator system
construction involved in this research is only one aspect
of cognitive differences evaluation of NIMBY conflict
actors, and the follow-up studies will organize empirical
researches on the impact of NIMBY on the public,
government officials, experts and scholars to analyze the
cognitive differences in the three groups, to verify the
effectiveness of the indicator system.
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