spreadsheet decision modeling

General Instructions – PLEASE READ THEM CAREFULLY
· The Assignment must be submitted on Blackboard (WORD format only) via allocated folder.
· Assignments submitted through email will not be accepted.
· Students are advised to make their work clear and well presented, marks may be reduced for poor presentation. This includes filling your information on the cover page.
· Students must mention question number clearly in their answer.
· Late submission will NOT be accepted.
· Avoid plagiarism, the work should be in your own words, copying from students or other resources without proper referencing will result in ZERO marks. No exceptions.
· All answered must be typed using Times New Roman (size 12, double-spaced) font. No pictures containing text will be accepted and will be considered plagiarism).
· Submissions without this cover page will NOT be accepted.
MGT
ATTACHED FILE(S)

Kingdom of Saudi Arabia
Ministry of Education
Saudi Electronic University

المملكة العربية السعودية
وزارة التعليم
الجامعة السعودية الإلكترونية
College of Administrative and Financial Sciences
Assignment 3
Spreadsheet Decision Modeling (MGT 425)
Due Date:30/04/2022 @ 23:59 (End of Week 13)

Course Name: Spreadsheet Decision Modeling

Student’s Name:

Course Code: MGT 425

Student’s ID Number:

Semester: 2

CRN:

Academic Year: 2021-22-2nd

For Instructor’s Use only

Instructor’s Name:

Students’ Grade:
Marks Obtained/Out of 10

Level of Marks: High/Middle/Low
General Instructions – PLEASE READ THEM CAREFULLY
· The Assignment must be submitted on Blackboard (WORD format only) via allocated folder.
· Assignments submitted through email will not be accepted.
· Students are advised to make their work clear and well presented, marks may be reduced for poor presentation. This includes filling your information on the cover page.
· Students must mention question number clearly in their answer.
· Late submission will NOT be accepted.
· Avoid plagiarism, the work should be in your own words, copying from students or other resources without proper referencing will result in ZERO marks. No exceptions.
· All answered must be typed using Times New Roman (size 12, double-spaced) font. No pictures containing text will be accepted and will be considered plagiarism).
· Submissions without this cover page will NOT be accepted.
Learning Outcomes:

No.

Course Learning Outcomes (CLOs)

Question Number

CLO1

Find some structured ways of dealing with complex managerial decision problems.

Question 1

CLO2

Explain simple decision models and management science ideas that provide powerful and (often surprising) qualitative insight about large spectrum of managerial problems.

Question 2, 3

CLO3

Demonstrate the tools for deciding when and which decision models to use for specific problems.

Question 4

CLO4

Build an understanding of the kind of problems that is tackled using spreadsheet modeling and decision analysis.

Question 5
Assignment Instructions:
· Log in to Saudi Digital Library (SDL) via University’s website
· On first page of SDL, choose “English Databases”
· From the list find and click on EBSCO database.
· In the Search Bar of EBSCO find the following article:
Title: “Modeling Autonomous Decision-Making on Energy and Environmental Management Using Petri-Net: Case Study”.
Author: Niken Prilandita, Benjamin McLellan, Tetsuo Tezuka.
Assignment Questions: (Marks 10)
Read the above case study and answer the following Questions:
1. Explain the Autonomous Decision Making that can be taken by managers in business organizations (150-200 words) 2 Marks
2. What are the reasons that many countries around the world are gradually shifting towards sustainable energy options? (150-200 words) 2 Marks
3. Elaborate the global agenda and technological challenges for creating a more sustainable environment. (150-200 words) 2 Marks
4. Why energy decision-making functions are occurred at various stakeholder levels, and how the decisions made by one stakeholder may affect others in the total energy system? (150-200 words) 2 Marks
5. What you have learned from this study and how it is beneficial for you as a management professional (150-200 words) 2 Marks.
Answers:

challenges
Article
Modeling Autonomous Decision-Making on
Energy and Environmental Management Using
Petri-Net: The Case Study of a Community in
Bandung, Indonesia
Niken Prilandita *, Benjamin McLellan and Tetsuo Tezuka
Graduate School of Energy Science, Kyoto University, Yoshida-Honmachi, Sakyo-ku, Kyoto 606-8501, Japan;
b-mclellan@energy.kyoto-u.ac.jp (B.M.); tezuka@energy.kyoto-u.ac.jp (T.T.)
* Correspondence: nikenpri@sappk.itb.ac.id; Tel.: +81-75-753-4739; Fax: +81-75-753-9189
Academic Editor: Palmiro Poltronieri
Received: 28 December 2015; Accepted: 5 April 2016; Published: 14 April 2016
Abstract: Autonomous decision-making in this study is defined as the process where decision-makers
have the freedom and ability to find problems, select goals, and make decisions for achieving the
selected problems/goals by themselves. Autonomous behavior is considered significant for achieving
decision implementation, especially in the context of energy and environmental management, where
multiple stakeholders are involved and each stakeholder holds valuable local information for making
decisions. This paper aims to build a structured process in modeling the autonomous decision-making.
A practical decision-making process in waste-to-energy conversion activities in a community in
Bandung, Indonesia, is selected as a case study. The decision-making process here is considered as
a discrete event system, which is then represented as a Petri-net model. First, the decision-making
process in the case study is decomposed into discrete events or decision-making stages, and the
stakeholders’ properties in each stage are extracted from the case study. Second, several stakeholder
properties that indicate autonomous behavior are identified as autonomous properties. Third,
presented is a method to develop the decision-making process as a Petri-net model. The model is
utilized for identifying the critical points for verifying the performance of the derived Petri-net.
Keywords: autonomy; decision-making; Petri-net; energy; environmental; community; Indonesia
1. Introduction
The recent global agenda and technological challenges for creating a more sustainable
environment have encouraged countries around the world to gradually shift towards sustainable
energy transitions. Upon the new global agreement of Sustainable Development Goals, every country
is now highly anticipated to direct their efforts towards realizing a more sustainable energy system and
environment [1]. From the technology side, the emergence of new technologies, such as smart grids
and source-centered renewable energies, have expanded the potential and requirements of energy
generation and management in ways that have not been available previously. These facts suggest that
the energy system is likely to become more distributed and localized, thus the decision-making and
policy-making process in the energy sector should be adjusted to follow this future tendency [2].
Most decisions made on energy and environmental management affect a large number of people
and, thus, are of public interest. Decision-making in this sector usually becomes complicated since
various interests need to be accommodated in the process. Moreover, once a consensus has been
successfully reached, it does not guarantee successful implementation. Various decision-making
approaches for reaching an easy consensus, as well as for achieving successful implementation,
have been proposed. Two common approaches in decision-making are with the centralized and the
Challenges 2016, 7, 9; doi:10.3390/challe7010009 www.mdpi.com/journal/challenges
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Challenges 2016, 7, 9 2 of 26
decentralized approaches [3,4]. The quest of balancing between the centralized and the decentralized
systems for decision-making is often an issue in organizational management. Easy access to
information with the advancement of information technology, the internet, and other means today,
have made the decision-making style in organizations lean towards a more decentralized style [5,6].
However, this approach may not be entirely applicable for cases in energy and environmental
management that occur in the public domain.
This study puts more focus on autonomy in decision-making processes regardless of whether they
are conducted under a centralized or a decentralized system. Two ways of understanding the concept
of autonomy are considered here. Firstly, autonomy in the political or public administration field,
which is often seen as one of the traits of a more decentralized system [4]. Secondly, as understood
in the current study, autonomy can be considered as a property of persons regardless of the systemic
context [7–9]. Therefore, we argue that autonomy can exist in both centralized and decentralized
approaches because autonomy is the property of each decision-maker.
The hypothesis of this study is that decisions made autonomously are more likely to achieve
successful outcomes. Autonomy in making decisions is believed to be related to an increase in quality
of life. Research from neuroscience has found that actively making decisions can boost pleasure and
increase the decision-makers’ happiness, satisfaction, and perceived control [10]. Furthermore, high
levels of happiness and satisfaction are causal influences on success and achievement, not the other
way around [11]. Simply stated, if a decision-maker has made an autonomous decision, without being
coerced or forced, it is considered more likely that the decision-maker will achieve the decision goal
and benefit from that.
Normatively, stakeholders’ autonomy in making decisions is important, though its important
role in decision-making may not been objectively examined [7]. The fact that we have not found
studies that objectively examined the role of autonomy in decision-making in energy-environmental
management showed that this theme has to date been insufficiently examined. We argue that the
recent global agenda and technological advances in the energy-environmental sector (e.g., smart-grid
technologies, decentralized energy, and market liberalization) expect decision-makers to become more
autonomous. This situation has created the necessity to develop a framework that can represent and
identify the role of stakeholders’ autonomy in the decision-making process. Such a framework would
consist of several elements employed for specific tasks, and is the purpose of the current research.
This paper discusses one of the important elements of the framework, a model that aims to represent,
analyze, and simulate the autonomous decision-making process.
The autonomous decision-making model in this paper is developed as a discrete event system,
and this paper presents the method to build such a model. The decision-making process is
decomposed into discrete events that we call decision-making stages. Afterwards, the properties
of stakeholders involved in each stage are identified; thus, the concept of a discrete event system
for autonomous decision-making is established. Petri-net is utilized to represent the discrete event
system of the autonomous decision-making process. Each decision-making stage, the stakeholders’
properties, and the state after decisions are made; corresponding to a small Petri-net model
consisting of a few transitions and places. The autonomous decision-making model is constructed
by combining all of these small Petri-net models of each event/stage. As an addition, we conducted
analysis of the Petri-net model’s behavior for identifying the stages which are indispensable for an
autonomous decision-making system. These stages are called the critical points in the autonomous
decision-making process.
2. The Definition of Autonomous Decision-Making
This section explains the definition of autonomous decision-making. The term, autonomous
decision-making is defined by dissecting it into the root words comprising it, which are “autonomy”
and “decision-making”. The development of the concept of autonomy as a political and personal
property is historically explained, followed by a brief explanation on various scopes of the
Challenges 2016, 7, 9 3 of 26
decision-making process, and various types of energy decision-making. Based on this information, we
construct the definition of autonomous decision-making used in this study.
2.1. The Concept of Autonomy
The definition of autonomy has been through several changes throughout the course of history.
As mentioned above, there are at least two different concepts of autonomy explained in this paper.
Autonomy originated from the Greek words “auto” which means self, and “nomos” which means law.
This concept was firstly coined referring to the city states in ancient Greece that were self-governing.
Originally, autonomy was defined in a political manner, which was the right of the states (or city-states,
in that instance) to administer their own affairs [9]. In the context of public administration management,
territorial or local autonomy is the result of a decentralization process [12]. In the Indonesian context
for example, the Law of Decentralization number 22/1999, was the beginning of the country’s journey
towards a more decentralized political structure. This law has since become the legal basis for
providing more autonomy to local governments in making decisions regarding their own territory
and environment. The spirit of the law has had a side effect, however, in that it caused the Indonesian
people to gain greater awareness of autonomy, knowing that they had more freedom in choosing
among options. This has promoted decision-making processes to be performed more autonomously
in various levels of society’s hierarchical structure, including at the lower authority levels, such as
villages and sub-districts [13]. Looking at this fact, the term autonomy in Indonesia has gradually
become understood not only as the property of a state or territory, but also as a personal trait.
One of the most important moments in the history of the concept of autonomy was when the
definition of autonomy was transformed from the property of a state in the ancient Greek era, into a
property of persons during the Renaissance era [7,8]. Since then, the concept of autonomy has been
understood in both ways. However, autonomy in the majority of contemporary works is seen as a
property of persons, or personal autonomy [7]. Although the concept of autonomy mainly revolves
around these two definitions, the dimensions of autonomy are understood in many different ways,
depending on which field of study is viewing it. Mackenzie, for example, defined three dimensions
of autonomy, namely self-determination, self-governance, and self-authorization [14]. Other studies
focus on the self-directedness and resoluteness dimensions of autonomy [9]. Meanwhile, the computer
science and information technology fields view the ability to continuously learn or self-learning traits
in the emergence of autonomous machines or artificial intelligence as one of the most important
characteristics of autonomy [15].
2.2. Decision-Making Process
The definition of decision-making has been long established, and since decision-making is
understood as a process of making decisions, then the definitions mostly evolved on the scope
of the process. There are two predominately different views in decision theory regarding the extent
of the decision-making scope. Firstly, decision-making is defined as a process started by identifying
problems or goals, and ended after a decision has been made. One of the main supporters of this
concept was Herbert Simon (1960) [16]. Later, Huber (1980) expanded the concept of decision-making
by defining it as “the process through which a course of action is taken” [17], and the process by
which the decision is implemented is considered as part of the problem-solving process. Most of the
studies that defined the decision-making process came from the field of organizational management.
Meanwhile when decisions need to be made in the public domain, the decision-making process is often
regarded as the whole cycle from problem identification up to decision implementation and evaluation,
and then feeding-back to problem identification. This is known as a generic decision cycle [18], or a
planning process [19]. An example of a decision-making cycle is presented in Figure 1. In this study,
we investigate the decision-making process extended to the implementation stages.
Challenges 2016, 7, 9 4 of 26
Challenges 2016, 7, 9 4 of 27

Figure 1. Example of a decision-making cycle [18,19].
2.3. Energy-Environmental Decision-Making at Various Stakeholder Levels
The following section explains decision-making in energy and environment by various
stakeholders, such as national government, local government (provincial/city/regency governments,
and formal agencies/bodies within these local governments), community, household and individual
(households and individuals are considered as a single decision-maker), and non-governmental
institutions (i.e., international and local NGOs, business or private sector stakeholders, media,
experts and academicians). As mentioned earlier, decision-making in energy and environmental
management often becomes complex because it occurs in the public domain and, therefore, various
stakeholders are involved in it. According to Sexton, et al. [20], the main stakeholders that are usually
involved in environment-related decision-making are national governments, regional or local
government bodies, business associations, environmental advocacy groups, community or
neighborhood groups, and affected or interested individuals. The relationships between these
stakeholders can be classified into two types of relationship, which are vertical (hierarchical) and
horizontal (parallel) relationships with each other [21,22]. Decision-making for individual
stakeholders and groups of stakeholders is influenced both by the structure of relationships and the
characteristics of the individual stakeholders.
Energy related decision-making and policy-making (We use the phrase “energy (and
environmental) decision-making and policy-making” or “decision-making in energy sector”
interchangeably in this paper because the research object is related with both energy and
environmental sector.) at the national level tends to occur in a top-down manner, following the
hierarchical structure of the country’s institutions. In the UK, for example, energy decision-making
functions have historically been performed mainly by the central government and large corporations
in the private sector. This situation began to change after the Localism Bill was stipulated in 2010
aiming to shift decision-making power from central governments to individuals, communities, and
local government [23,24]. Another example is from a developing country, Indonesia, where for more
than two decades since the first national energy policy was introduced in 1981, the key strategic
energy decisions and policies are made centrally by the national government [25]. The role of local
government in the energy sector was recognized after the promulgation of the Energy Act in 2007.
The act mandates each local government to formulate its own local energy masterplan, based on the
targets outlined by the national energy masterplan.
Recent experiences from both countries have shown that the local authorities are mandated and
expected to have more capacity in energy decision-making functions. The long period of centralized
energy decision-making experience in both countries has created a great challenge for the local
authorities to pick up the task. Lack of capacity of the local government with regards to energy
planning, and limited guidelines on how to formulate the masterplan itself, are some of the
Figure 1. Example of a decision-making cycle [18,19].
2.3. Energy-Environmental Decision-Making at Various Stakeholder Levels
The following section explains decision-making in energy and environment by various
stakeholders, such as national government, local government (provincial/city/regency governments,
and formal agencies/bodies within these local governments), community, household and individual
(households and individuals are considered as a single decision-maker), and non-governmental
institutions (i.e., international and local NGOs, business or private sector stakeholders, media, experts
and academicians). As mentioned earlier, decision-making in energy and environmental management
often becomes complex because it occurs in the public domain and, therefore, various stakeholders
are involved in it. According to Sexton, et al. [20], the main stakeholders that are usually involved in
environment-related decision-making are national governments, regional or local government bodies,
business associations, environmental advocacy groups, community or neighborhood groups, and
affected or interested individuals. The relationships between these stakeholders can be classified
into two types of relationship, which are vertical (hierarchical) and horizontal (parallel) relationships
with each other [21,22]. Decision-making for individual stakeholders and groups of stakeholders is
influenced both by the structure of relationships and the characteristics of the individual stakeholders.
Energy related decision-making and policy-making (We use the phrase “energy (and environmental)
decision-making and policy-making” or “decision-making in energy sector” interchangeably in this paper
because the research object is related with both energy and environmental sector.) at the national level
tends to occur in a top-down manner, following the hierarchical structure of the country’s institutions.
In the UK, for example, energy decision-making functions have historically been performed mainly by the
central government and large corporations in the private sector. This situation began to change after the
Localism Bill was stipulated in 2010 aiming to shift decision-making power from central governments to
individuals, communities, and local government [23,24]. Another example is from a developing country,
Indonesia, where for more than two decades since the first national energy policy was introduced in
1981, the key strategic energy decisions and policies are made centrally by the national government [25].
The role of local government in the energy sector was recognized after the promulgation of the Energy
Act in 2007. The act mandates each local government to formulate its own local energy masterplan,
based on the targets outlined by the national energy masterplan.
Recent experiences from both countries have shown that the local authorities are mandated
and expected to have more capacity in energy decision-making functions. The long period of
centralized energy decision-making experience in both countries has created a great challenge for
the local authorities to pick up the task. Lack of capacity of the local government with regards to
energy planning, and limited guidelines on how to formulate the masterplan itself, are some of
the challenges faced by the locals. Despite the limited capacity and experience, local governments
around the world have developed various energy-environmental measures and local action plans, as a
Challenges 2016, 7, 9 5 of 26
form of participation in global initiatives such as the International Council for Local Environmental
Initiatives (ICLEI) and the Climate Alliance [26]. Aside from having a mandate to implement
energy-environmental measures at the local level, local authorities are also expected to involve and
nurture the community or grassroots levels in local energy initiatives [27].
Energy decision-making functions at the community level have been empirically observed in
North America [28–30]. Most of the decisions and measures taken are on climate change mitigation
planning, considered as the re-emergence of the energy planning efforts which increased after the oil
crisis in the 1970s, but later declined in the 1980s due to lower energy prices [28]. Although the number
of local actions for energy measures in USA were increased after 2006, all of the decision-making
processes identified were initially developed at the municipality level first [28]. The municipalities then
involved the community in their plans to reduce community-wide energy use and GHG emissions.
Although similar, the Canadian experience with its community energy management or community
energy planning program is slightly different from what happened in the USA. Observations of the
Community Energy Plans (CEPs) that emerged during 2003–2007 [29,30] have shown the potential
of community roles in formulating action plans specifically related to energy efficiency, energy
conservation, and application of renewable energies [30]. However, since CEP is part of a broader
commitment of the municipalities on forming local action plans for GHG reduction, the content of
the CEP is often written in accordance to what the municipality or municipal council needs [29].
These practices are somewhat different from what was conceived by Jaccard, et al. [31] as community
energy management.
The practices of energy related decision-making at the community level is also evident in European
countries, such as in the UK and Germany [27,32,33]. Often referred to as grassroots initiatives [27,34]
or community (renewable) energy [33,35], it is defined as projects where communities exhibit a high
degree of ownership and control, and collectively benefit from the outcomes [35]. The term community
in this literature is relatively broad, referring to a group of people who share the same geographical
location (neighborhood communities) or the same interest (non-governmental organizations) [33].
The recent practices of community energy in Europe are gradually shifting as part of socio-political
movements from the grassroots level [27] and, thus, they are more likely to be considered as bottom-up
initiatives when compared to the CEPs in North America.
Energy decision-making at the individual level is traditionally studied as a part of consumer
behavior studies which view the individual as the energy customer or end-user [36,37]. Individuals
as consumers make everyday decisions related to energy; therefore, they are becoming the target of
various energy measures [37], such as the behavior change programs in energy consumption and
energy technology adoption [38]. The high potential of new energy systems and technologies such as
renewable energy systems and smart grids have shifted the focus of individual energy decision-making.
In the light of these technologies, individuals’ energy decisions are not only shaped by the energy
system and policy, but can also shape the system [39]. The social foundation of smart grids consists
of “decentralized socio-technical networks that underpin the electricity consumption of groups of
consumers who are increasingly becoming autonomous” [40]. However, for effective technology
adoption, it is suggested to no longer view the individual solely as a consumer of energy, but also as a
citizen, part of a community or society [37].
From the research related with energy decision-making above, it is found that energy
decision-making functions occur at various stakeholder levels, and the decisions made by one
stakeholder may affect others in the total energy system. The challenge of shifting towards a more
localized and distributed energy system creates a need for every stakeholder not only to actively
participate in energy decision-making, but also to become more autonomous.
2.4. Definition of Autonomous Decision-Making
In this research, we put more focus on autonomy as the property of persons, not as a property of
the system or environment. This study considers that each decision-maker is seen as an autonomous
Challenges 2016, 7, 9 6 of 26
system, or in other words, autonomy is a property of each stakeholder who participates in the
decision-making process. This means that every decision-maker or stakeholder has their own goal to
achieve and has the autonomy to decide by themselves. Thus, as mentioned earlier, this study views
that autonomy can exist in both centralized and decentralized approaches.
In light of this, we define the autonomous decision-making as the process where decision-makers
have the freedom and ability to find problems, select goals, and make decisions for achieving the
selected problems/goals by themselves in a responsible manner based on available information.
It follows that persons having the ability to self-determine, self-govern, show self-control, and
self-learning are persons who exhibit autonomous behavior. The definition for each autonomous
behavior used in this paper is presented in Table A1 in the Appendix.
3. Methodology for Modeling an Autonomous Decision-Making Process
The aim of this study is to develop the autonomous decision-making model for the energy
and environmental management process by using Petri-net. For this aim, an energy-environmental
management project in Indonesian community (Rukun Warga) is selected as a case study. The steps
performed for modeling in this paper are: (1) case selection and data collection; (2) decomposing
the decision-making process and extraction of the stakeholders’ properties; (3) identification of
stakeholders’ autonomous properties; and (4) modeling the decision-making process from the observed
case using Petri-net and analysis of the model.
3.1. Case Study Selection and Data Collection
This paper undertook one decision-making process as a case study to be modeled, and there is a
strong indication to select this particular case. The selected case study was included and investigated
along with other five community decision-making processes in our previous work [41]. These cases
were, in turn, selected from a broader set of around 20 case studies. The five cases were selected due to
their success in project implementation and the availability of detailed documentation and information.
Among the five cases, the community presented in this study was considered to have utilized both
centralized (top-down) and decentralized (bottom-up) decision-making approaches. Since we argued
that autonomous decision-making can occur under both approaches, by selecting this case we can
investigate and model autonomous decision-making under both approaches using the same case.
In addition to that, by using the same case study which exhibits two different decision-making
approaches over a period of time, the behavior change and improved capability of the community in
making decision were observed.
The model developed here is based on a case study of a practical decision-making process for a
waste management system project in a community in Bandung City, Indonesia. The waste management
technique utilized in the community project is a bio-digester installation to transform household waste
to energy (biogas). This case was selected because a considerable number of stakeholders were
involved in the activities with relatively even inputs to the project. Various stakeholders’ involvement
in a project is a rare occasion, especially when almost all stakeholders can contribute relatively evenly
in the project. This situation occurred because the project developed in two phases. The first phase
started as one project and then changed to another project after the first went through a stagnant phase.
The second phase achieved quite a successful outcome and is still in operation at the time of writing.
The stakeholders that were involved in each phase are different, which is one reason why there were
various stakeholder contributions. This unique situation is considered useful for understanding the
possible outcomes from various stakeholders’ engagement when the project changed course.
A thorough data collection is necessary for understanding the case study well. Information
about the community activities and decision-making process were collected using secondary and
primary sources. Various secondary records used were project reports, academic reports, journal
articles, newspaper articles, and web-based articles. Interviews, informal discussions, observation, and
demonstration of the biogas installation were also undertaken during site visits. The primary sources
Challenges 2016, 7, 9 7 of 26
interviewed are the chief of the community, the former community chief, bio-digester operators, and
the recycling center operator.
3.2. Decomposing the Decision-Making Process and Extraction of the Stakeholders’ Properties
The decomposition of the case study is important for constructing the autonomous
decision-making model as a discrete event system. There are two steps involved in this decomposition,
which yield two major results that become the foundation of the discrete event system for modeling
autonomous decision-making. Firstly, the community decision-making process is decomposed into
decision-making stages. Secondly, the properties of each stakeholder involved in each stage are
identified. Utilizing the framework developed in our previous work [41], the decision-making process
is decomposed. Modified from Simon [16], Huber [17], and Petrie [18], the framework consists of four
important phases, namely: (1) problem finding; (2) knowledge and information; (3) consensus building;
and (4) decision and implementation (see Figure 2). The points or questions in each phase function as
guidance in decomposing decision-making stages and identifying the stakeholders’ involvement.
Challenges 2016, 7, 9 7 of 27

sources interviewed are the chief of the community, the former community chief, bio-digester
operators, and the recycling center operator.
3.2. Decomposing the Decision-Making Process and Extraction of the Stakeholders’ Properties
The decomposition of the case study is important for constructing the autonomous
decision-making model as a discrete event system. There are two steps involved in this
decomposition, which yield two major results that become the foundation of the discrete event
system for modeling autonomous decision-making. Firstly, the community decision-making process
is decomposed into decision-making stages. Secondly, the properties of each stakeholder involved in
each stage are identified. Utilizing the framework developed in our previous work [41], the
decision-making process is decomposed. Modified from Simon [16], Huber [17], and Petrie [18], the
framework consists of four important phases, namely: (1) problem finding; (2) knowledge and
information; (3) consensus building; and (4) decision and implementation (see Figure 2). The points
or questions in each phase function as guidance in decomposing decision-making stages and
identifying the stakeholders’ involvement.

Figure 2. The decision-making decomposition framework [41].
The procedure for extraction of stakeholders’ general properties was performed based on our
previous work which utilized five case studies of community energy-environmental projects, of
which the present case study was one [41]. The five different cases of community projects selected
(from a set of around 20) exhibit various types of decision-making processes, ranging from
centralized to decentralized approaches. All of the five cases were considered as successful in
reaching the project goals. From analysis of these successful cases, the role and properties of the
stakeholders’ are extracted by utilizing the framework in Figure 2, with the properties and the
framework development itself based on the decision-making literature.
Figure 2. The decision-making decomposition framework [41].
The procedure for extraction of stakeholders’ general properties was performed based on our
previous work which utilized five case studies of community energy-environmental projects, of which
the present case study was one [41]. The five different cases of community projects selected (from
a set of around 20) exhibit various types of decision-making processes, ranging from centralized to
decentralized approaches. All of the five cases were considered as successful in reaching the project
goals. From analysis of these successful cases, the role and properties of the stakeholders’ are extracted
by utilizing the framework in Figure 2, with the properties and the framework development itself
based on the decision-making literature.
Challenges 2016, 7, 9 8 of 26
3.3. Identifying the Stakeholders’ Autonomous Properties
The structured method for identifying the stakeholders’ autonomous properties from the
stakeholders’ general properties is explained here. The list of stakeholders’ general properties which
contributed to the success of the community project was derived from a thorough literature review into
decision-making processes, cross-checked with successful case studies. In order to determine which of
these properties are aligned with autonomy in decision-making, a further analysis was undertaken.
The decision-making process, as a whole, is considered to be autonomous decision-making if the
stakeholders in the system are making decisions autonomously. In other words, the stakeholders need
to exhibit an autonomous behavior. Therefore, the stakeholders’ autonomous properties are identified
by cross-comparing the stakeholders’ general properties with elements of autonomous behavior.
The cross-comparison process was performed qualitatively using content analysis of the
autonomous behaviors and stakeholders’ properties definitions. The stakeholders’ general properties
are identified in the previous step, while the elements of autonomous behavior are identified in
Section 2, namely: (1) self-governance; (2) self-control; (3) self-learning; and (4) self-determination.
Upon defining each stakeholders’ property and autonomous behavior, each property is examined.
Those which comply with at least one definition of autonomous behavior are identified as stakeholders’
autonomous properties. Utilizing this method, the stakeholders’ autonomous properties can be
objectively identified.
3.4. Developing and Analyzing the Autonomous Decision-Making Model Using Petri-Net
The method for constructing the autonomous decision-making process using Petri-net is presented
in this section. The justification of Petri-net utilization is explained, followed by the Petri-net history
and its utilization. Afterwards, a brief explanation of a standard Petri-net model. The autonomous
decision-making model developed in this paper is built as a discrete event system by compiling the
results from previous steps, which are the decision stages and the stakeholders’ properties. The method
to represents the discrete events into a Petri-net model is also explained in this section.
In this paper, we consider the decision-making process as a system built upon discrete events
which perform and interact with each other sequentially and in parallel. Energy-environmental
decision-making is of public interest, therefore the decision-making involves many and various
stakeholders. In our research, the stakeholders are autonomous. They are being shaped by, and can also
shape, the system. Therefore, an interrelated bi-directional connection between stakeholders and the
decision-making process is expected. Petri-net has an advantage of representing the model in two-ways:
graphically and mathematically. Therefore, we consider that Petri-net is a suitable tool to represent the
complexity of multiple autonomous stakeholders in energy-environmental decision-making. Moreover,
the utilization of Petri-net enables a simple simulation of autonomous decision-making model to be
performed further.
Petri-net is one of the tools often utilized for modeling a discrete event system, and nowadays its
application has been employed on a very broad field of study, including decision-making. The history
of Petri-net is established by its development by Carl Adam Petri in 1962. Petri-net is useful for
modeling the flow of information and control in systems, especially those which exhibit asynchronous
and concurrent events [42–44]. Petri-net is commonly applied to model various kinds of dynamic
discrete-event systems such as computer networks, manufacturing plants, communication systems,
logistic networks, and command and control systems [45]. In recent years, the utilization of Petri-net
has reached far beyond computer science and manufacturing studies. For example, Petri-net has
been used to model decision-making processes in a legal case [46] and modeling the story plot
for games [47,48]. In the energy-environmental field, several studies have employed Petri-net in
modeling: a more energy efficient machine tool [49], multisource energy conversion systems [50],
energy management system for autonomous micro-grids [51], municipal waste management [52], and
environmental effects of biofuel utilization [53]. The advantage of utilizing Petri-net in this study is
that it can describe objectively a decision-making process with multi-stakeholder involvement.
Challenges 2016, 7, 9 9 of 26
A standard Petri-net consists of P, T, I, O, µ (places, transitions, inputs, outputs, marking/token).
In detail, P is a finite set of places, which are represented by circles; T is a finite set of transitions, which
are represented by rectangles/bars; I is an input function which represents connection from P to T;
O is an output function which represents a connection from T to P; and µ is the initial marking which
is represented by a small dot called a token [54].
In order to transform the discrete events of autonomous decision-making into Petri-net
accordingly, the results from previous steps are compiled. First, the result from decomposing the
decision-making process are the decision stages. These decision-making stages, which are considered
as discrete events, are transformed into “transitions” in the Petri-net model, whereas the result from
the autonomous properties extraction is the stakeholders’ properties. The state or the combinations of
the stakeholders’ properties, are represented as “places”. Likewise, the results or outputs from each
event/stage are also represented as “places”. The relationship between the state and the stages are
represented with inbound and outbound arcs. In short, the decision-making stages can be transformed
into the Petri-net by:
1. Describing the state of affairs or a condition experienced by the stakeholder as a Place (P).
2. Describing the decision-making process, or event, or action conducted by the stakeholder as a
Transition (T).
3. Describing the relationship of Place(s) and Transition(s) and the movement of the token (µ) with
inbound and outbound arcs.
The token moves from one place to another by “firing” through a transition. A place has a token
if a particular stakeholders’ condition or property is satisfied, thus firing the transition. The existence
or the absence of the condition is the key factor that determines whether a transition in the Petri-net is
enabled or not.
The decision-making model is constructed by combining all of the transitions and places
representing the decision-making stages into one Petri-net model. For simplification purposes, several
decision stages are represented as simple Petri-net models, which are drawn hierarchically in another
layer under the main model. These lower layers of Petri-net models do not affect the purpose of the
whole model, which tries to show the relationship between stakeholders’ autonomous properties in
each decision-making stage and decision outcomes.
The utilization of Petri-net to describe the decision-making process made the autonomous part of
the decision-making more prominent and easier to be identified. Therefore, we can identify the critical
points in the decision-making process, where the existence or absence of autonomous properties will
lead to a different decision or achieve different outcomes. The performance of autonomous properties
in the success of the decision-making is going to be evaluated by analyzing the combinations of the
conditions resulting from the simulation.
4. Results
This section presents the results obtained from each method aforementioned. A brief description
of the selected case study is presented prior to the results from decomposing the case study
decision-making process into stages. The stakeholders’ autonomous properties are identified
afterwards. Later on, the development and analysis of the decision-making model using Petri-net
are explained.
4.1. Overview of the Case Study
As described earlier, the case study project had two phases, and each phases is briefly explained
here. The initial project was called a Community-based Basic Infrastructure Improvement Program
(CBIIP), with the final goal to improve the sanitation situation in the community. The case study consists
of two related projects, which are a composting center and bio-digester installation. The bio-digester
Challenges 2016, 7, 9 10 of 26
installation project was an improvement to an existing composting project in the RW 11 community (RW
are often identified by number. RW 11 means it is the 11th community to exist in the particular village).
This community, inhabited by 3000 people, or roughly 800 households, is one of the low-income slum
areas in Bandung. It is one of the densest districts in the city. Recognizing the need for improvement
of community life, CBIIP was initiated by the Ministry of Public Works in the Bandung Branch
with assistance from the Bandung City government in 1996. Focusing on economic, social, and
environmental aspects, one of the projects conducted was the construction of a composting center
located in RW 11 to improve the poor sanitation and waste situation [55]. After the project term was
finished and the budget terminated, the composting center operation became stagnant, and was then
replaced by a bio-digester installation.
The second project, a biogas production project in the form of a bio-methane digester installation,
was initiated by the community in collaboration with academicians, the private sector, and
community-based organizations (CBO). After the composting system was not as successful as planned,
especially in terms of profit, it was terminated around 2009–2010. However, views on waste and
garbage in the RW 11 community had changed. They maintained the waste segregation activities, and
the women’s organization (My Darling) began selling plastic waste and tried to reuse it for handicrafts.
Moreover, the existing CBO tried to seek financial support by submitting proposals to international and
national non-governmental organizations (NGOs) [56]. Eventually, with assistance and consultation
from academic scholars, the Environmental Agency and a local NGO, and financial help from the local
bank, the composting system was changed to the bio-methane system, which produces biogas for
households and liquid fertilizer.
One recent study about the biogas production in this community has been conducted
thoroughly [57]. The outcomes from the biogas production project were studied from socio-economic
perspectives. It was found that the biogas production at RW 11 is currently not economically feasible
due to limited market reach for the bio-slurry products. Meanwhile from the social point of view, the
study identified that the community was relatively accepting of the project despite a mix of responses
found among RW 11 community members. It can be concluded that this pilot project in biogas
production is still operating because of the social acceptance factors rather than economic factors.
4.2. The Decision-Making Stages and Stakeholders’ Properties
The decomposition of the decision-making process resulted into two major outputs. The first
output are the decision-making stages, and the second are the stakeholders’ general properties. These
outputs are the foundation in establishing an autonomous decision-making model as a discrete event
system. The case history and other related information obtained from various sources are analyzed
qualitatively to decompose the decision-making process of the case study into decision stages. Utilizing
the framework in Figure 2, we decomposed the decision-making process of the biogas production
project in RW 11 into six stages, which are:
1. Find or define the problem
2. Design the solution alternatives
3. Agreement/consensus building
4. Implementation and construction of the Waste Management System (WMS)
5. Management (O and M)
6. Termination of the project
Even though the framework suggested four major phases, the number of stages drawn in the
Petri-net model may vary and, thereby, be more than four. The biogas production project in RW 11
has been established for a long time, therefore, it has been gone through the stages of “management”
and “project termination”. Moreover, the project has been regenerated into another project, which is
still running. Depending on the case study, the decomposition of the decision-making process may
Challenges 2016, 7, 9 11 of 26
result in various numbers of stages. These stages are represented in the Petri-net model as transitions.
The relationships between each transition are drawn by combining it with the stakeholders’ properties.
As mentioned in Section 3.2, the stakeholders’ properties are extracted from the successful case
studies by applying the same framework (Figure 2) and based on literature on various decision-making
processes. These properties were taken from various energy-environmental decision-making studies,
as presented in Table A2 in the Appendix. This process resulted in the stakeholders’ general
properties, listed in Table 1. These extracted properties are considered to be those which contributed
to successful community decision-making implementation. The stakeholders’ general properties
are further examined using autonomous behavior elements in Section 2 to identify the stakeholders’
autonomous properties.
Table 1. Stakeholders’ general properties.
Stakeholders’ General Properties
1 Self-control 11 Trust
2 Initiative 12 Interaction
3 Self-learning 13 Collaboration
4 Motivation 14 Openness
5 Ability to organize 15 Commitment
6 Leadership 16 Local culture
7 Self-governance 17 Networking ability
8
Ability to collect and
understand information
18 Creativity
9 Communication ability 19 Innovativeness
10 Responsibility 20 Proximity
4.3. The Stakeholders’ Autonomous Properties
Identification of the stakeholders’ autonomous properties is one of the important process
conducted in this paper. The properties extracted in previous step are general stakeholder properties
that contributed to the success of the project goal. These properties are cross-compared with the
autonomous behaviors mentioned in Section 2. Among the 20 general properties listed above, three of
them are already included as autonomous behaviors (self-control, self-learning, and self-governance).
The remaining 17 properties were cross-checked with the autonomous behaviors.
The method for identifying stakeholders’ autonomous properties explained in Section 3.3 requires
each autonomous behavior and the general properties listed in Table 1 to be clearly defined. From
these definitions (see Appendix, Tables A1 and A2), the stakeholders’ general properties are objectively
identified as to which autonomous behavior they exhibit (if any). The results of this cross-comparison
are presented in Table 2.
From Table 2, eleven out of seventeen properties are considered as exhibiting stakeholders’
autonomous behavior. The other six are not marked as autonomous behavior of the stakeholder, for at
least two reasons. First, they are not a property of persons or individuals. The properties, such as local
culture, trust, and proximity are categorized as a system or environment property. Therefore, even
though they exhibit some traits of autonomy, they are not included as stakeholder properties. Second,
the properties of creativity and innovativeness, by definition, are not regarded as corresponding with
the autonomy definition or dimensions.
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Table 2. Extraction of Stakeholders’ autonomous properties.
No
General
Decision-Making
Property
Autonomous Behavior
Self-Governance Self-Control Self-Learning Self-Determination
1 Initiative X X X O
2 Motivation X X X O
3 Ability to organize O O X X
4 Leadership O O X X
5
Ability to collect and
understand information
X X O X
6 Communication ability O X X X
7 Responsibility X O X O
8 Trust X X X X
9 Interaction X X O X
10 Collaboration X X O X
11 Openness X X X X
12 Commitment X O X O
13 Local culture X X X X
14 Networking ability X X O X
15 Creativity X X X X
16 Innovativeness X X X X
17 Proximity X X X X
After correlating these properties with the autonomy dimensions, selected properties are further
classified into seven points based on definitional similarity, and they are as follows:
1. Motivation, initiative; selected because the decision-makers need to have motivation or initiative,
or ability to think by themselves in order to be considered as autonomous.
2. Leadership, ability to organize; selected because autonomy also requires self-governance and
self-control. In order to have the ability to govern or organize themselves, the decision-makers
need to have some level of leadership and ability to coordinate and communicate their goal with
their subordinates or members.
3. Self-learning, ability to manage information; selected because an autonomous decision-maker needs
to have the willingness and ability to learn, to manage and collect information, and to understand
the information necessary to make decisions.
4. Interaction between the community members; one of the results of the analysis conducted on the
five cases was that the interaction among community leaders and members has an important role
in reaching a consensus or decision, as well as in decision implementation, and sustaining the
operation and maintenance of the project. A decision that is reached through group interaction
performs better when compared to a decision reached by a group of people that does not interact
at all [58].
5. Networking and collaboration between stakeholders; this property is linked with the previous
property. We differentiate it because, in this property, the community (leaders and members) is
considered as one stakeholder. The networking and collaboration between the community and
other stakeholders outside the community, such as government agencies, officials, local NGOs,
private sectors, and others, was seen in the five cases and contributed to the success of the project.
6. Persuasion and negotiation ability; this property is closely related with the leadership level of the
stakeholder. This property was also very useful in reaching a consensus or decision, especially
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when the project involved multiple stakeholders. This property is found predominantly in the
cases where the initiative does not come from governments.
7. Responsibility and commitment; this property is especially important when the decision is ready to
be implemented. In order for the project to be constructed, each stakeholder involved needs to be
responsible for their duty and commit to the decision that has been made.
4.4. The Petri-Net Model
The autonomous decision-making model, which in this paper is regarded as a discrete event
system, is represented using Petri-net. The principal process of modeling the decision-making into
Petri-net can be described as follows. Each stage of the decision-making in Section 4.2 is transformed
into transition for the Petri-net model. For graphical purpose, we provide two version of Petri-net
graphs. The simplified Petri-net diagram for this case study is presented in Figure 3, meanwhile the
complete Petri-net graph using Yasper is presented in Figure A1 in the Appendix.
The conditions for each stakeholder involved are given at the beginning of the net, and are not
changed during the course of the process. There are three subnets added (for detail see Appendix,
Figure A2–A4). Each subnet is designed for one autonomous property, namely the Motivation Subnet
(T1), the Leadership Subnet (T2), and the ability to manage information, shortened as the Information
Subnet (T3). The reason behind the subnets’ creation is because the model will be simulated by
changing the number of stakeholders involved and changing the combination of their properties.
Therefore, it is important to show the process of how each stakeholder becomes autonomous or
non-autonomous in detail. However, we realized that this process can make the whole decision-making
process model more visually complex. Therefore, we added several subnets in the Petri-net model,
hence, making it a hierarchical model. The subnets show the process of every stakeholder in becoming
autonomous or non-autonomous. Autonomous stakeholders will have a token in the corresponding
places, whereas those who do not have autonomous properties will have no token. The other reason is
because the content of these hierarchical transitions are fluid, depending on how many stakeholders
are involved. This makes it inefficient to draw directly on the primary layer.
The results from these hierarchical transitions from the subnets are shown on the primary layer as
one single place, which is a simplification of the number of places corresponding to each stakeholder
involved (in Figure 3, these are designated by blue coloring, hereafter they are called “blue” places).
If the number of stakeholders is more than one, then each blue place consists of a combination of
stakeholders’ conditions. This simplification is purely for graphical purposes. These blue places are
drawn as several single places in the complete version of the Petri-net model. The example given
in Figure A1 of the Appendix shows that if there are five autonomous stakeholders involved in the
decision-making process, this would result in each of the hierarchical transitions (T1, T2, and T3)
producing five tokens in each of the corresponding places. Therefore, each blue place in primary layer
(P2, P3, and P4) actually consists of five single places with a token in it. For simulation purposes, it
is not possible to simply put five tokens in each of P2, P3, and P4. This is because at later transitions
(T6 and T8), the rules are specifically differentiated based on the stakeholder types. Decision-making
processes may require certain specific stakeholders to make an autonomous decision—in this case
a token from these stakeholders will be compulsory. In addition to this, the specific direction that
a decision takes may be designated by which, or how many, other stakeholders have autonomous
properties (a token, in this case).
In Figure 3, there are three variations of transition. First, is the standard transition, which is
marked by a black box. Second, the orange diamond-shape transition, which represents an XOR
transition. An XOR transition consumes one token from one of its input places and produces a token
in one of its output places. This means that this transition can be fired if there is at least one token in
one of its input places. The third transition is a hierarchical transition (T1–T3). As mentioned before,
the Petri-net model in this paper is a hierarchical one, meaning there is another process or another set
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of Petri-net models under the primary layer. A detail explanation on the variations of transitions with
the corresponding decision type used in this paper is presented in Table 3.
There are three other important elements of the Petri-net model shown in Figure 3, namely
the inbound arcs, outbound arcs, and tokens. The inbound and outbound arcs between places and
transitions show the direction of token movement. In addition, they also show the relationship
between places and transitions. Since places represent the conditions needing to be fulfilled for firing
the transitions, it is easy to identify what kind of conditions are required for an action or event to occur.
The bidirectional arc represents a simplification of a situation in which whenever a transition is fired,
then the transition will produce a token in the output place and also put the token back in the input
place. The legend for the Petri-net in Figure 3 is presented in Table 4.
The Petri-net developed in this paper shows that the discrete event system consists of
decision-making stages and the role of stakeholders involved in a decision-making process can be
objectively and logically modeled. Utilizing the procedures explained above, other decision-making
cases can also be represented using Petri-net. Although the model might be different in detail, the
decision-making stages are relatively similar.
Table 3. Type of transitions with its corresponding decisions type.
Type of Transition Type of Decision
Standard transition
Used if the condition(s) to reach a particular action/decision is unnegotiable, or if the
number of states resulted from a particular action/decision are definite.
XOR transition
Used if there are two or more states that possible as inputs or outputs of the particular
action/decision. This type of transition is usually applied to decisions that branches
subject to certain conditions.
Hierarchical transition
Used as a representative of a sub-layer in the Petri-net. The sub-layer contains another set
of transitions-places which is deliberately hidden to simplify the main Petri-net model.
Table 4. Legend for places and transitions in the Petri-net model.
Place Description Transition Description
P1: Waste and sanitation problem situation T1: Motivation subnet
P2: Set of stakeholders’ motivation level T2: Information subnet
P3:
Set of stakeholders’ ability to manage
information level
T3: Leadership subnet
P4: Set of stakeholders’ leadership level T4: Problem finding process
P5: Problem is defined T5: Designing alternatives process
P6: Alternatives are designed T6: Decision-making
P7:
WMS technique is selected
(decision is made)
T7: Construction of WMS
P8:
WMS is constructed
(decision is implemented)
T8: Operation & Maintenance
P9: Waste is reduced T9: Termination of the project
P10: Project stopped
P11: Project continued
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Figure 3. Hierarchical Petri-net (simplified) describing the decision-making process of a WMS case study. Figure 3. Hierarchical Petri-net (simplified) describing the decision-making process of a WMS case study.
Challenges 2016, 7, 9 16 of 26
5. Analysis and Discussions
Two main steps were performed through the methods explained in this paper. First is the method
to decompose the community decision-making process into discrete events. This process resulted into
two outputs, which are the decision-making stages (Section 4.2) and the stakeholders’ autonomous
properties (Section 4.3). The second step is the method to build the discrete event system in the form
of the Petri-net model, which generalizes the decision-making in a case of energy and environmental
management (Section 4.4).
The decomposition process produces decision-making stages and the stakeholders’ autonomous
properties. The decision-making stages are performed utilizing the framework in Figure 2. As a result,
six decision-making stages are obtained; namely, problem definition, alternatives design, agreement or
consensus building, implementation and construction, management, and project termination, whereas
the stakeholders’ properties are identified by qualitatively cross-comparing the stakeholders’ general
properties with the autonomous behaviors. There are seven autonomous properties identified here;
namely, (1) motivation and initiative; (2) leadership and ability to organize; (3) self-learning or the
ability to manage information, (4) interaction; (5) networking and collaboration; (6) persuasion and
negotiation ability; and (7) responsibility and commitment. Among these, properties (4) and (5)
are considered as more a property of groups of people, meaning they exist if there are at least two
types of stakeholders involved in the decision-making, whereas the other properties belong to an
individual stakeholder.
The results from the decomposition process are then represented by Petri-net. The model is
constructed by combining the decision-making stages that already converted into transitions and
places. Analysis of the Petri-net provide a further understanding that there are several transitions that
would yield different outcomes if the conditions at the blue places are changed. These transitions are
identified as critical points, which are identified from Petri-net graph in Figure 3.
As discussed in Section 4.4, a blue place contains the result from the hierarchical transitions
and each blue place can represent more than one “standard” place. A token in one of the sub-places
contained in a blue place represents the particular stakeholders’ autonomous properties and it will not
be changed during the course of the simulation. For example, if a stakeholder is set since the beginning
as not having motivation properties, then it will continue to lack motivation until the end of the model
or the termination of the model. A critical point in this study refers to a certain transition in the Petri-net
model that is influenced by the conditions set in the blue places, which have particular influence on
autonomy. From the model, the critical points identified in this decision-making process are:
1. Problem finding process (T4). At this critical point, there are two determining properties, which
resulted from motivation subnet (T1) and information management capability (T2). T4 fires if
there is at least one token in one of its input places (P2 and P3). This means that at this stage, any
stakeholder, regardless the type, can contribute in finding the problem as long they have high
motivation or strong leadership.
2. Designing alternatives (T5), fires depending on the property of information management
capability (T2). T5 fires if there is a token in P5 and there is at least one token in P3. This means
that in order to design decision alternatives, at least one stakeholder must have the capability to
manage information.
3. Decision-making process or consensus-building process (T6), which is determined by the
property of leadership level (T3). T6 fires if there is a token in P6 and at least one token in
P4. This means that in order to reach a decision or a consensus together, at least one stakeholder
needs to have strong leadership. The output of this transition is differentiated by the specific
stakeholders’ conditions.
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4. Operation and maintenance phase (T8), is determined by all three properties of: leadership level
(P4), motivation level (P2), and information management capability (P3). Basically, T8 fires if
there is a combination between the properties of several stakeholders together. This means that
collaboration, networking, and interaction between stakeholders plays an important role in this
Operation and Maintenance stage. However, since the leadership property (P4) is already given
in T6, therefore there is no need to connect T8 with the inbound arc from P4. The result of T8
will be differentiated based on the properties from blue places based on types and properties
of stakeholders.
5. Termination of the project (T9), determining property: result from the O and M phase (T8).
The outputs from the previous transition (T8) are differentiated based on the stakeholder types
and properties. In the simulation, the rule will be imposed on T9 as to whether to produce a
token for P10 or P11, based on the token condition in P9. For example, if the token produced from
T8 shows a condition of autonomous local people (community leaders or interested individuals),
then the project will be more likely to go beyond project termination, and therefore T9 will
produce a token in P11. Since T9 is an XOR transition, the firing of T9 can only be produced in
one of P10 or P11.
Among these critical points, the first two points (T4 and T5) determine whether the process will
reach a decision or fail to reach a decision. The latter three points (T6, T8, and T9) determine the variety
of success levels in achieving the project goal. Meanwhile T7 is not identified as a critical point because
the transition only depends on one input place.
The Petri-net model in this paper represents the decision-making process as it occurred in
community case studies. The common traits of community decision-making should not be neglected.
For example, in a community, if a certain problem exists and a stakeholder proposes some solution
alternatives but the remaining stakeholders are not able to reach a consensus or decision, then the
whole decision-making process fails/stagnates and the problem will persist. This means that, for
solving the same problem, the decision-making process needs to be started from the beginning again.
In the simulation, this trait will be represented by the instant termination of the model simulation
every time a transition is not fired.
The critical points in this paper are identified by developing the Petri-net model which resulted
from carefully decomposing the case study. Therefore, the most important part is decomposing the
case’s story into decision-making stages, which can only be performed if the case study or project
history is well understood. This made the data collection procedure holds an important role in
understanding the context under which the decision was taken. Although complete information might
be available in the form of reports and secondary records, direct field visits and observations are highly
recommended to obtain a thorough understanding of the targeted community, and also to avoid bias
from previous researchers. Another important point is the selection of principal informants to be
interviewed. It is best to interview stakeholders that are involved directly at the beginning of the
project even though they might already be very old or have already stepped down from their position
if the project has been conducted for a number of years.
The method explained throughout this paper comprises of decomposing the community
decision-making process, extraction of the stakeholders’ autonomous properties, and modeling the
autonomous decision-making process. The results of these steps are complemented by the results
obtained from various literature and data collection. The structured method utilized in this paper can
be summarized in Figure 4 below.
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Challenges 2016, 7, x 18 of 27

Figure 4. General method for developing autonomous decision-making model.
6. Conclusions
This paper presents a method to build an autonomous decision-making model, which is
considered to be important within the development of decentralized generation and
demand-centered projects in energy and environmental beneficiation. However, the approach is
generalizable to other fields and case studies beyond that presented here. The specific Petri-net must
be designed given the understanding of the stakeholders and project elements involved in decision
making, which is performed by decomposing a decision-making process into discrete events or
decision-making stages as shown in Figure 4. Using Petri-net as a tool, the decision-making stages
are transformed into a set of place-transitions or simple Petri-net models, and these small models are
compiled to construct the autonomous decision-making model. The utilization of Petri-net to
represent decision-making models helps the decision-making process to be analyzed objectively and
important stages of autonomous decision-making are prominently shown. These important stages
are identified as critical points of autonomous decision-making. A critical point is influenced by the
stakeholders’ properties and determines the output of the model, or whether the model can reach
the end of the network or not.
The results of this paper are going to be further employed for simulations based on the Petri-net
model. The utilization of Petri-net in building the autonomous decision-making model is considered
as one of the effective ways to perform the model simulation in the future study. Some of the
stakeholders’ autonomous properties identified above such as motivation, leadership, and ability to
manage information, are going to be assigned deterministically to each stakeholder involved in the
decision-making process and various outcomes from the simulations will be observed in order to
identify the key conditions suitable for successfully achieving goals.
Acknowledgments: The authors are grateful for the comments and suggestions from three anonymous
reviewers. The first author would like to extend her gratitude to the Ministry of Education, Culture, Sports,
Science and Technology (MEXT), Japan, for supporting this study.
Author Contributions: The manuscript is prepared by Niken Prilandita, under the supervision of Tetsuo Tezuka
and Benjamin McLellan, who assisted in co-authoring and improving paper.
Conflicts of Interest: The authors declare no conflict of interest.
Figure 4. General method for developing autonomous decision-making model.
6. Conclusions
This paper presents a method to build an autonomous decision-making model, which is
considered to be important within the development of decentralized generation and demand-centered
projects in energy and environmental beneficiation. However, the approach is generalizable to other
fields and case studies beyond that presented here. The specific Petri-net must be designed given
the understanding of the stakeholders and project elements involved in decision making, which is
performed by decomposing a decision-making process into discrete events or decision-making stages
as shown in Figure 4. Using Petri-net as a tool, the decision-making stages are transformed into a set
of place-transitions or simple Petri-net models, and these small models are compiled to construct the
autonomous decision-making model. The utilization of Petri-net to represent decision-making models
helps the decision-making process to be analyzed objectively and important stages of autonomous
decision-making are prominently shown. These important stages are identified as critical points
of autonomous decision-making. A critical point is influenced by the stakeholders’ properties and
determines the output of the model, or whether the model can reach the end of the network or not.
The results of this paper are going to be further employed for simulations based on the Petri-net
model. The utilization of Petri-net in building the autonomous decision-making model is considered
as one of the effective ways to perform the model simulation in the future study. Some of the
stakeholders’ autonomous properties identified above such as motivation, leadership, and ability to
manage information, are going to be assigned deterministically to each stakeholder involved in the
decision-making process and various outcomes from the simulations will be observed in order to
identify the key conditions suitable for successfully achieving goals.
Acknowledgments: The authors are grateful for the comments and suggestions from three anonymous reviewers.
The first author would like to extend her gratitude to the Ministry of Education, Culture, Sports, Science and
Technology (MEXT), Japan, for supporting this study.
Author Contributions: The manuscript is prepared by Niken Prilandita, under the supervision of Tetsuo Tezuka
and Benjamin McLellan, who assisted in co-authoring and improving paper.
Conflicts of Interest: The authors declare no conflict of interest.
Challenges 2016, 7, 9 19 of 26
Appendix
Table A1 below listed the behaviors or characteristics that commonly associated with autonomous
individual, or even used to define the concept of autonomy. The second column shows that these
behaviors appeared or even mentioned as a prerequisite for succeeding a decision implementation
based on various literature in decision-making, especially in the energy and environmental sector.
The third column contain general definition of each behavior, which are useful for the cross-comparing
process in Section 4.3.
Table A1. Definition of elements of autonomous behavior.
Autonomous Behavior Definition Mentioned in
Self-governance
Governance refers to the processes of interaction and
decision-making among the actors involved in a collective
problem that lead to the creation, reinforcement, or
reproduction of social norms and institutions [59]. Therefore,
self-governance defined as the capability of an individual or
group to develop their own way to establish the governance
and running it without intervention.
[40]
Self-control
Refers to a set of processes that enable individuals to guide
their goal directed activities over time and across changing
contexts [60]. Often used interchangeably with
self-regulation [61].
[61–63]
Self-learning
The capability to perform the act of learning by oneself.
Learning here defined as the acquisition of knowledge and/or
skills that serve as an enduring platform for adaptive
development and to comprehend and navigate novel
problems [61].
[15]
Self-determination
The capacity to choose and to have those choices, rather than
reinforcement contingencies, drives, or any other forces or
pressures, be the determinants of one’s actions.
Self-determination often involves controlling one’s
environment or one’s outcomes, but it may also involve
choosing to give up control [64].
[2,65]
Table A2 below are the observed stakeholders’ properties existed in the successful cases of
community project in energy-environmental management. The second column shows that these
properties appeared or even mentioned as a prerequisite for succeeding a decision implementation
based on various literature in decision-making, especially in the energy and environmental sector.
The third column contain general definition of each properties, which are useful for the cross-comparing
process in Section 4.3.
Table A2. Definition of stakeholders’ general properties.
Properties Mentioned in Definition
Initiative [4]
Behavior characterized by self-starting nature, proactive approach, and
by being persistent in overcoming difficulties that arise in the pursuit of
a goal [61].
Motivation [3,4]
Refers to the set of psychological processes governing the direction,
intensity, and persistence of actions that are not due solely to
overwhelming environmental demands that coerce or force action [61].
Challenges 2016, 7, 9 20 of 26
Table A2. Cont.
Properties Mentioned in Definition
Initiative [4]
Behavior characterized by self-starting nature, proactive approach, and
by being persistent in overcoming difficulties that arise in the pursuit of
a goal [61].
Motivation [3,4]
Refers to the set of psychological processes governing the direction,
intensity, and persistence of actions that are not due solely to
overwhelming environmental demands that coerce or force action [61].
Ability to
organize
[4]
Capacity to coordinate, manage, facilitate, a particular object/tasks
among group of people to reach a certain goal [61].
Leadership [66,67]
A set of role behaviors by individuals in the context of the group or
organization to which they belong.
The exercise of influence over others by utilizing various bases of social
power, tactics, and so on in order to elicit the group members’
compliance with certain norms and their commitment to achieve the
group’s objectives [61].
Ability to
collect and
understand
information
[3]
Capacity to collect and understand information without help from
other parties.
Communication
ability
[4]
Capacity to exchange in exchange information, form understandings,
coordinate activities, exercise influence, socialize, and generate and
maintain systems of beliefs, symbols, and values among members of
institution/organizations [61].
Responsibility [68,69]
An attribute that an adult person is duty-bound to undertake [70].
In environmental behavior, it defined as an individual sense of
obligation or duty to take measures against environmental
degradation [71].
Trust [3]
A generalized expectancy held by an individual or group that the word,
promise, verbal, or written statement of another individual or group
can be relied on [61].
Interaction [72–74]
A particular kinds of social relationship that are different from, but
constitutive of, groups, organizations, and networks. Interaction occurs
when two or more participants are in each other’s perceptual range and
orient to each other through their action and activity [75].
Collaboration [76,77]
Collective action or effort performed by a group of people to solve
problem or adjust environments in order to discover new mutually
beneficial options [77].
Openness [4]
Referred as transparency to access information within organization,
institution, or society [78]
Commitment [79,80]
Referred as the level of identification with, and attachment and loyalty
to, an organization, an occupation, or some other feature of work [61].
Local culture [61]
Some shared set of characteristics in common to a particular group of
people [61].
Networking
ability
[73,81]
Capacity to perform a process of contacting and being contacted by
people in one’s social or technical/professional world and maintaining
these linkages and relationships [61].
Creativity [4,82,83]
The generation of ideas or products that are both novel and appropriate
(correct, useful, valuable, or meaningful) [61].
Innovativeness [83,84]
The degree to which an individual is relatively earlier in adopting new
ideas than the other members of a system [85].
Proximity [86,87]
Referred to the spatial distance or familiarity of a certain object or
problem to a person or group of person.
Challenges 2016, 7, 9 21 of 26
Challenges 2016, 7, x 21 of 27

Figure A1. The Petri-net model drawn using Yasper (no simplification). Figure A1. The Petri-net model drawn using Yasper (no simplification).
Challenges 2016, 7, 9 22 of 26
Challenges 2016, 7, x 22 of 27

Figure A2. The “Motivation” Subnet.
Figure A2. The “Motivation” Subnet.
Challenges 2016, 7, x 23 of 27

Figure A3. The “Leadership” Subnet.

Figure A4. The “Information” Subnet.
Figure A3. The “Leadership” Subnet.
Challenges 2016, 7, 9 23 of 26
Challenges 2016, 7, x 23 of 27

Figure A3. The “Leadership” Subnet.

Figure A4. The “Information” Subnet.
Figure A4. The “Information” Subnet.
References
1. International Council for Science (ICSU) and International Social Science Council (ISSC). Review of Targets for
the Sustainable Development Goals: The Science Perspective; ICSU: Paris, France, 2015.
2. Scheer, H. Energy Autonomy: The Economic, Social and Technological Case for Renewable Energy, 2nd ed.;
Earthscan: London, UK, 2007; Volume 41.
3. Malone, T.W. Is Empowerment Just a Fad? Control, Decision Making, and IT. MIT Sloan Manag. Rev. 1997,
38, 22–35.
4. Malone, T.W. The Future of Work: How the New Order of Business Will Shape Your Organizations, Your Management
Style, and Your Life; Harvard Business School Press: Boston, MA, USA, 2004.
5. Malone, T.W.; Laubacher, R.; Scott Morton, M.S. Inventing the Organizations of the 21st Century; MIT Press:
Cambridge, MA, USA, 2003.
6. Kralewski, D. Bottom-up decentralized approach to innovation strategy. In Proceedings of the 1st Workshop
on New Generation Enterprise and Business Innovation Systems (NGEBIS-2012), Gdansk, Poland, 26 June 2012;
Missikoff, M., Smith, F., Eds.; CEUR-WS: Gdansk, Poland, 2012; Volume 864, pp. 55–61.
7. Piper, M. Autonomy: Normative. Available online: http://www.iep.utm.edu/aut-norm (accessed on
20 November 2015).
8. Dryden, J. Autonomy. Available online: http://www.iep.utm.edu/autonomy/ (accessed on 21 November 2015).
9. Prause-Stamm, J. Self-Directedness and Resoluteness: The Two Dimensions of Autonomy; The Humboldt
University of Berlin: Berlin, Germany, 2013.
10. Korb, A. The Upward Spiral: Using Neuroscience to Reverse the Course of Depression, One Small Change at a Time;
New Harbinger Publications, Inc.: Oakland, CA, USA, 2015.
11. Achor, S. The Happiness Advantage: The Seven Principles of Positive Psychology that Fuel Success and Performance
at Work; Crown Business: New York, NY, USA, 2010.
12. Zaharia, P. Autonomy and decentralization: Current priorities in the local public administration management.
USV Ann. Econ. Public Admin. 2011, 11, 288–292.
Challenges 2016, 7, 9 24 of 26
13. Beard, V.A.; Miraftab, F.; Silver, C. Planning and Decentralization: Contested Spaces for Public Action in the Global
South; Routledge: New York, NY, USA, 2008.
14. Mackenzie, C. Three dimensions of autonomy: A relational analysis. In Autonomy, Oppression, and Gender;
Veltman, A., Piper, M., Eds.; Oxford University Press: Oxford, UK, 2014; pp. 15–41.
15. Di Paolo, E.A.; Iizuka, H. How (not) to model autonomous behaviour. Biosystems 2008, 91, 409–423.
[CrossRef] [PubMed]
16. Simon, H.A. The New Science of Management Decision; Harper and Brothers: New York, NY, USA, 1960.
17. Braunstein, D.N. Review of managerial decision making/model building for decision analysis. Acad. Manag.
Rev. 1981, 6, 515–517.
18. Petrie, J.; Cohen, B.; Stewart, M. Decision support frameworks and metrics for sustainable development of
minerals and metals. Clean Technol. Environ. Policy 2007, 9, 133–145. [CrossRef]
19. Montana, P.J.; Charnov, B.H. Management; Barron’s Educational Series, Inc.: New York, NY, USA, 2000.
20. Sexton, K.; Marcus, A.F.; Easter, K.W.; Burkhardt, T.D. Better Environmental Decisions: Strategies for
Governments, Businesses and Communities; Island Press: Washington, DC, USA, 1999.
21. Jollands, N.; Gasc, E.; Pasquier, S.B. Innovations in Multi-Level Governance for Energy Efficiency; IEA: Paris,
France, 2009.
22. Organization for Economic Cooperation and Development. Cities, Climate Change and Multilevel Governance;
OECD: Paris, France, 2009.
23. Bale, C.S.E.; Foxon, T.J.; Hannon, M.J.; Gale, W.F. Strategic energy planning within local authorities in the
UK: A study of the city of Leeds. Energy Policy 2012, 48, 242–251. [CrossRef]
24. Bale, C.S.E.; Abuhussein, A.M.; Foxon, T.J.; Hannon, M.J.; Gale, W.F. Delivering national policy at the
local level: The role for local authorities in the implementation of the UKs flagship Green Deal policy. In
Proceedings of the International Sustainable Development Research Conference, Hull, UK, 24–26 June 2012.
25. Indonesia Ministry of Energy and Mineral Resources (Kementrian ESDM). Blueprint Pengelolaan Energi
Nasional 2006–2025; Kementrian ESDM: Jakarta, Indonesia, 2006.
26. Bulkeley, H.; Kern, K. Local Government and the Governing of Climate Change in Germany and the UK.
Urban Stud. 2006, 43, 2237–2259. [CrossRef]
27. Blanchet, T. Struggle over energy transition in Berlin: How do grassroots initiatives affect local energy
policy-making? Energy Policy 2014, 78, 246–254. [CrossRef]
28. Pitt, D. Harnessing community energy: The keys to climate mitigation policy adoption in US municipalities.
Local Environ. Int. J. Justice Sustain. 2010, 15, 717–729. [CrossRef]
29. St Denis, G.; Parker, P. Community energy planning in Canada: The role of renewable energy. Renew. Sustain.
Energy Rev. 2009, 13, 2088–2095. [CrossRef]
30. Tozer, L. Community energy plans in Canadian cities: Success and barriers in implementation. Local Environ.
Int. J. Justice Sustain. 2013, 18, 20–35. [CrossRef]
31. Jaccard, M.; Failing, L.; Berry, T. From equipment to infrastructure: Community energy management and
greenhouse gas emission reduction. Energy Policy 1997, 25, 1065–1074. [CrossRef]
32. Walker, G.; Hunter, S.; Devine-Wright, P.; Evans, B.; Fay, H. Harnessing Community Energies: Explaining
and Evaluating Community-Based Localism in Renewable Energy Policy in the UK. Glob. Environ. Polit.
2007, 7, 64–82. [CrossRef]
33. Seyfang, G.; Park, J.J.; Smith, A. A thousand flowers blooming? An examination of community energy in the
UK. Energy Policy 2013, 61, 977–989. [CrossRef]
34. Seyfang, G.; Haxeltine, A. Growing grassroots innovations: Exploring the role of community-based initiatives
in governing sustainable energy transitions. Environ. Plan. C Gov. Policy 2012, 30, 381–400. [CrossRef]
35. Walker, G.; Devine-Wright, P. Community renewable energy: What should it mean? Energy Policy 2008, 36,
497–500. [CrossRef]
36. Axsen, J.; Kurani, K.S. Social Influence, Consumer Behavior, and Low-Carbon Energy Transitions. Annu. Rev.
Environ. Resour. 2012, 37, 311–340. [CrossRef]
37. Heiskanen, E.; Johnson, M.; Robinson, S.; Vadovics, E.; Saastamoinen, M. Low-carbon communities as a
context for individual behavioural change. Energy Policy 2010, 38, 7586–7595. [CrossRef]
38. Moloney, S.; Strengers, Y. “Going Green”? The Limitations of Behaviour Change Programmes as a Policy
Response to Escalating Resource Consumption. Environ. Policy Gov. 2014, 24, 94–107. [CrossRef]
http://dx.doi.org/10.1016/j.biosystems.2007.05.016
http://www.ncbi.nlm.nih.gov/pubmed/17709174
http://dx.doi.org/10.1007/s10098-006-0074-3
http://dx.doi.org/10.1016/j.enpol.2012.05.019
http://dx.doi.org/10.1080/00420980600936491
http://dx.doi.org/10.1016/j.enpol.2014.11.001
http://dx.doi.org/10.1080/13549839.2010.509388
http://dx.doi.org/10.1016/j.rser.2008.09.030
http://dx.doi.org/10.1080/13549839.2012.716406
http://dx.doi.org/10.1016/S0301-4215(97)00091-8
http://dx.doi.org/10.1162/glep.2007.7.2.64
http://dx.doi.org/10.1016/j.enpol.2013.06.030
http://dx.doi.org/10.1068/c10222
http://dx.doi.org/10.1016/j.enpol.2007.10.019
http://dx.doi.org/10.1146/annurev-environ-062111-145049
http://dx.doi.org/10.1016/j.enpol.2009.07.002
http://dx.doi.org/10.1002/eet.1642
Challenges 2016, 7, 9 25 of 26
39. Naus, J.; Spaargaren, G.; van Vliet, B.J.M.; van der Horst, H.M. Smart grids, information flows and emerging
domestic energy practices. Energy Policy 2014, 68, 436–446. [CrossRef]
40. Wolsink, M. The research agenda on social acceptance of distributed generation in smart grids: Renewable
as common pool resources. Renew. Sustain. Energy Rev. 2012, 16, 822–835. [CrossRef]
41. Prilandita, N.; McLellan, B.C.; Tezuka, T. The Framework for Identifying Autonomous Decision-Making
Process on Energy and Environmental Issues: The Case Studies in Indonesian Communities (Rukun Warga).
Available online: http://www.j-sustain.com (accessed on 4 January 2016).
42. Hillion, H.P. Performance Evaluation of Decision-Making Organizations Using Timed Petri Nets; Massachusetts
Institute of Technology: Cambridge, MA, USA, 1986.
43. Shukla, A.; Robbi, A.D. A Petri net simulation tool. In Proceedings of the 1991 IEEE International Conference
on Systems, Man, and Cybernetics, “Decision Aiding for Complex System”, Charlottesville, VA, USA, 13–16
October 1991; pp. 361–366.
44. Bullers, W.I. A Tripartite Approach to Information Systems Development. Decis. Sci. 1991, 22, 120–135.
[CrossRef]
45. Wang, J. Petri nets for dynamic event-driven system modeling. In Handbook of Dynamic System Modeling;
Fishwick, P., Ed.; CRC Press: Boca Raton, FL, USA, 2007; pp. 1–17.
46. Sileno, G.; Boer, A. Legal knowledge conveyed by narratives: Towards a representational model.
In Proceedings of the Workshop on Computational Models of Narrative (CMN 2014), Quebec City, QC,
Canada, 31 July–2 August 2014; pp. 182–191.
47. Balas, D.; Brom, C.; Abonyi, A.; Gemrot, J. Hierarchical petri nets for story plots featuring virtual humans.
In Proceedings of the 4th Artificial Intelligence and Interactive Digital Entertainment Conference, Palo Alto,
CA, USA, 22–24 October 2008; Association for the Advancement of Artificial Intelligence: Stanford, CA,
USA, 2008; pp. 2–9.
48. Araújo, M.; Roque, L. Modeling games with petri nets. In Breaking New Ground: Innovation in Games, Play,
Practice and Theory; Digital Games Research Association (DiGRA): London, UK, 2009.
49. Xie, N.; Duan, M.; Chinnam, R.B.; Li, A.; Xue, W. An energy modeling and evaluation approach for machine
tools using generalized stochastic Petri Nets. J. Clean. Prod. 2015, 113, 523–531. [CrossRef]
50. Sava, A.; Adjallah, K.H.; Lagaza, H. Hybrid Petri nets for modeling and control of multi-source energy
conversion systems. In Proceedings of the 2014 International Conference on Control, Decision and
Information Technologies (CoDIT), Metz, France, 3–5 November 2014; pp. 516–521.
51. Kyriakarakos, G.; Dounis, A.I.; Arvanitis, K.G.; Papadakis, G. A fuzzy cognitive maps–petri nets energy
management system for autonomous polygeneration microgrids. Appl. Soft Comput. 2012, 12, 3785–3797.
[CrossRef]
52. Bat’a, R.; Obršálová, I.; Volek, J.; Jordão, T.C. Petri nets application for management of biodegradable
components of municipal waste. WSEAS Trans. Environ. Dev. 2008, 4, 1057–1066.
53. Bat’a, R.; Kárník, J. Modeling of environmental effect of biofuels by Petri Nets. In Advances in Environment,
Biotechnology and Biomedicine; WSEAS Press: Zlin, Czech Republic, 2012; pp. 69–74.
54. Zhou, M. Petri Nets in Flexible and Agile Automation; Kluwer Academic Publishers: New York, NY, USA, 1995.
55. Indonesia Ministry of Public Works. Best Practices of Solid Waste Management in Indonesia (in Bahasa Indonesia);
Departemen Pekerjaan Umum: Jakarta, Indonesia, 2007.
56. Hidayat, M.; Tanuwidjaja, G. Proposal Revitalisasi Permukiman dan Sistem Lingkungan Kelurahan Cibangkong
Bertumpu kepada Masyarakat; Green Impact Indonesia: Bandung, Indonesia, 2009.
57. Amir, E.; Hophmayer-Tokich, S.; Kurnani, T. Socio-Economic Considerations of Converting Food Waste into Biogas
on a Household Level in Indonesia: The Case of the City of Bandung. Recycling 2015, 1, 61–88. [CrossRef]
58. Plous, S. The Psychology of Judgment and Decision Making; McGraw-Hill: New York, NY, USA, 1993.
59. Hufty, M. Investigating policy processes: The Governance Analytical Framework (GAF). In Research
for Sustainable Development: Foundations, Experiences, and Perspectives; Wiesmann, U., Hurni, H., Eds.;
Geographica Bernensia: Bern, Switzerland, 2011; pp. 403–424.
60. Ameriks, J.; Caplin, A.; Leahy, J.; Tyler, T. Measuring Self-Control Problems. Am. Econ. Rev. 2007, 97, 966–972.
[CrossRef]
61. Nicholson, N.; Audia, P.G.; Pillutla, M.M. The Blackwell Encyclopedia of Management: Organizational Behavior,
2nd ed.; Blackwell Publishing Ltd.: Oxford, UK, 2005.
http://dx.doi.org/10.1016/j.enpol.2014.01.038
http://dx.doi.org/10.1016/j.rser.2011.09.006
http://dx.doi.org/10.1111/j.1540-5915.1991.tb01266.x
http://dx.doi.org/10.1016/j.jclepro.2015.09.100
http://dx.doi.org/10.1016/j.asoc.2012.01.024
http://dx.doi.org/10.3390/recycling1010061
http://dx.doi.org/10.1257/aer.97.3.966
Challenges 2016, 7, 9 26 of 26
62. Schweizer-Ries, P. Energy sustainable communities: Environmental psychological investigations.
Energy Policy 2008, 36, 4126–4135. [CrossRef]
63. Barr, S.; Gilg, A.; Shaw, G. Citizens, consumers and sustainability: (Re)Framing environmental practice in an
age of climate change. Glob. Environ. Chang. 2011, 21, 1224–1233. [CrossRef]
64. Deci, E.; Ryan, R.M. Intrinsic Motivation and Self-Determination in Human Behavior, 1st ed.; Springer Science &
Business Media: New York, NY, USA, 1985.
65. Wittayapak, C.; Dearden, P. Decision-Making Arrangements in Community-Based Watershed Management
in Northern Thailand. Soc. Nat. Resour. 1999, 12, 673–691.
66. Getimis, P.; Heinelt, H. Leadership and Community Involvement in the European Cities: Conditions
of Success and/or Failure. Available online: http://www.sampac.nl/EUKN2015/www.eukn.org/
dsresourcefaad.pdf?objectid=148461 (accessed on 14 May 2015).
67. Fahmi, F.Z.; Prawira, M.I.; Hudalah, D.; Firman, T. Leadership and collaborative planning: The case of
Surakarta, Indonesia. Plan. Theory 2015. [CrossRef]
68. Wenshun, W.; Xiaohua, L.; Hualong, L. Empirical Research of the Environmental Responsibility Affected
on the Urban Residential Housing Energy Saving Investment Behavior. Energy Procedia 2011, 5, 991–997.
[CrossRef]
69. Barr, S.; Gilg, A.; Shaw, G. “Helping People Make Better Choices”: Exploring the behaviour change agenda
for environmental sustainability. Appl. Geogr. 2011, 31, 712–720. [CrossRef]
70. Williams, G. Responsibility. Available online: http://www.iep.utm.edu/responsi/ (accessed on 30 January 2016).
71. Fransson, N.; Gärling, T. Environmental concern: Conceptual definitions, measurement methods, and
research findings. J. Environ. Psychol. 1999, 19, 369–382. [CrossRef]
72. Bale, C.; McCullen, N.; Foxon, T.; Rucklidge, A.; Gale, W. Local Authority Interventions in the Domestic
Sector and the Role of Social Networks: A Case Study from the City of Leeds. Available online:
http://opus.bath.ac.uk/33041 (accessed on 21 January 2016).
73. Bale, C.S.E.; McCullen, N.J.; Foxon, T.J.; Rucklidge, A.M.; Gale, W.F. Harnessing social networks for
promoting adoption of energy technologies in the domestic sector. Energy Policy 2013, 63, 833–844. [CrossRef]
74. Seyfang, G.; Smith, A. Grassroots innovations for sustainable development: Towards a new research and
policy agenda. Environ. Polit. 2007, 16, 584–603. [CrossRef]
75. Ritzer, G. The Blackwell Encyclopedia of Socology, 4th ed.; Blackwell Publishing Ltd.: Oxford, UK, 2009.
76. Innes, J.E.; Booher, D.E. Consensus Building and Complex Adaptive Systems. J. Am. Plan. Assoc. 1999, 65,
412–423. [CrossRef]
77. Innes, J.E.; Booher, D.E. Planning with Complexity: An Introduction to Collaborative Rationality for Public Policy,
1st ed.; Routledge: New York, NY, USA, 2010.
78. Peters, M.A. The Idea of Openness: Open Education and Education for Openness. Available online:
http://eepat.net/doku.php?id=open_education_and_education_for_openness (accessed on 30 January 2016).
79. Abrahamse, W.; Steg, L.; Vlek, C.; Rothengatter, T. A review of intervention studies aimed at household
energy conservation. J. Environ. Psychol. 2005, 25, 273–291. [CrossRef]
80. Spaargaren, G.; Oosterveer, P. Citizen-Consumers as Agents of Change in Globalizing Modernity: The Case
of Sustainable Consumption. Sustainability 2010, 2, 1887–1908. [CrossRef]
81. Booher, D.E.; Innes, J.E. Network Power in Collaborative Planning. J. Plan. Educ. Res. 2002, 21, 221–236. [CrossRef]
82. Amabile, T.M. Creativity in Context; Westview Press: Boulder, CO, USA, 1996.
83. De Vries, M.S. The rise and fall of decentralization: A comparative analysis of arguments and practices in
European countries. Eur. J. Polit. Res. 2000, 38, 193–224. [CrossRef]
84. Monger, R.F. Managerial Decision Making with Technology, 1st ed.; Pergamon Press: New York, NY, USA, 1987.
85. Rogers, E.M. Diffusion of Innovations, 5th ed.; Simon and Schuster: New York, NY, USA, 2003.
86. Devine-Wright, P. Renewable Energy and the Public: From NIMBY to Participation; Earthscan: London, UK, 2011;
Volume 32.
87. Hodson, M.; Marvin, S. Can cities shape socio-technical transitions and how would we know if they were?
Res. Policy 2010, 39, 477–485. [CrossRef]
© 2016 by the authors; licensee MDPI, Basel, Switzerland. This article is an open access
article distributed under the terms and conditions of the Creative Commons Attribution
(CC-BY) license (http://creativecommons.org/licenses/by/4.0/).
http://dx.doi.org/10.1016/j.enpol.2008.06.021
http://dx.doi.org/10.1016/j.gloenvcha.2011.07.009
http://dx.doi.org/10.1177/1473095215584655
http://dx.doi.org/10.1016/j.egypro.2011.03.175
http://dx.doi.org/10.1016/j.apgeog.2010.12.003
http://dx.doi.org/10.1006/jevp.1999.0141
http://dx.doi.org/10.1016/j.enpol.2013.09.033
http://dx.doi.org/10.1080/09644010701419121
http://dx.doi.org/10.1080/01944369908976071
http://dx.doi.org/10.1016/j.jenvp.2005.08.002
http://dx.doi.org/10.3390/su2071887
http://dx.doi.org/10.1177/0739456X0202100301
http://dx.doi.org/10.1111/1475-6765.00532
http://dx.doi.org/10.1016/j.respol.2010.01.020

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Approximate price: $22

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550 words
We'll send you the first draft for approval by September 11, 2018 at 10:52 AM
Total price:
$26
The price is based on these factors:
Academic level
Number of pages
Urgency
Basic features
  • Free title page and bibliography
  • Unlimited revisions
  • Plagiarism-free guarantee
  • Money-back guarantee
  • 24/7 support
On-demand options
  • Writer’s samples
  • Part-by-part delivery
  • Overnight delivery
  • Copies of used sources
  • Expert Proofreading
Paper format
  • 275 words per page
  • 12 pt Arial/Times New Roman
  • Double line spacing
  • Any citation style (APA, MLA, Chicago/Turabian, Harvard)

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Money-back guarantee

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Zero-plagiarism guarantee

Each paper is composed from scratch, according to your instructions. It is then checked by our plagiarism-detection software. There is no gap where plagiarism could squeeze in.

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Free-revision policy

Thanks to our free revisions, there is no way for you to be unsatisfied. We will work on your paper until you are completely happy with the result.

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