Assessing Environmental Management Plan Implementation in Water Supply Construction Projects: Key Performance Indicators

Abstract

Assessing the implementation of environmental management plans (EMPs) in construction projects is crucial for meeting environmental sustainability goals and reducing potential adverse impacts. By using performance indicators (PIs), stakeholders can objectively measure the performance of EMP implementation, identifying areas of success and areas that may require improvement. Therefore, this study aims to examine the PIs for assessing EMP implementation in water supply construction projects, using Saudi Arabia as a case study. Data from semi-structured interviews and a systematic literature review were used to develop a potential list of PIs. Then, the PIs were used to create a survey and distributed to industry professionals. Data from 112 respondents were analyzed using mean ranking analysis, the normalization method, exploratory factor analysis (EFA), and fuzzy synthetic evaluation (FSE). Eighteen critical PIs for assessing EMP implementation in water supply construction projects were identified, including public safety, road safety hazards, construction waste, clogged drainage, irregular flooding, the spilling of chemical substances, slope failures, soil erosion, landslide occurrence, increased schedule waste, changes in the color of bodies of water, oil/fuel spills, restricted site accessibility, the smell of run-off water, traffic accidents on construction sites, the spread of disease, changes in the color of run-off water, and overflowing silt traps. The EFA revealed that PIs can be grouped into three underlying constructs: fluid-related indicators, health and safety-related indicators, and site environment-related indicators. The FSE results confirmed that all PIs are between moderately critical to critical. This study’s significance lies in its examination of PIs that aim to improve the environmental performance of water supply construction projects. Understanding which indicators are most effective allows for targeted improvements, helping to minimize negative environmental impacts and ensuring sustainable practices. Finally, this study is a pioneer in examining the critical PIs for assessing EMP implementation in water supply construction projects.

Full text

Citation: Radzi, A.R.; Farouk, A.M.;
Romali, N.S.; Farouk, M.; Elgamal, M.;
Rahman, R.A. Assessing
Environmental Management Plan
Implementation in Water Supply
Construction Projects: Key
Performance Indicators. Sustainability
2024,16, 600. https://doi.org/
10.3390/su16020600
Academic Editor: Manuel Duarte
Pinheiro
Received: 28 October 2023
Revised: 30 December 2023
Accepted: 4 January 2024
Published: 10 January 2024
Copyright: © 2024 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 (https://
creativecommons.org/licenses/by/
4.0/).
sustainability
Article
Assessing Environmental Management Plan Implementation in
Water Supply Construction Projects: Key Performance Indicators
Afiqah R. Radzi
1
, Abdelrahman M. Farouk
2
, Noor Suraya Romali
2
, Mohamed Farouk
3, 4,
* , Mohamed Elgamal
3
and Rahimi A. Rahman 2, 5, *
1
Faculty of Built Environment, University of Malaya, Kuala Lumpur 50603, Malaysia; nurafiqah279@gmail.com
2Faculty of Civil Engineering Technology, Universiti Malaysia Pahang Al-Sultan Abdullah,
Kuantan 26300, Malaysia; abdelrahman.mfarouk@gmail.com (A.M.F.); suraya@ump.edu.my (N.S.R.)
3Civil Engineering Department, College of Engineering, Imam Mohammad Ibn Saud Islamic University
(IMSIU), Riyadh 11432, Saudi Arabia; mhelgamal@imamu.edu.sa
4Faculty of Engineering, Ain Shams University, Cairo 11517, Egypt
5Faculty of Graduate Studies, Daffodil International University, Dhaka 1341, Bangladesh
*Correspondence: miradi@imamu.edu.sa (M.F.); arahimirahman@ump.edu.my (R.A.R.)
Abstract: Assessing the implementation of environmental management plans (EMPs) in construction
projects is crucial for meeting environmental sustainability goals and reducing potential adverse
impacts. By using performance indicators (PIs), stakeholders can objectively measure the perfor-
mance of EMP implementation, identifying areas of success and areas that may require improvement.
Therefore, this study aims to examine the PIs for assessing EMP implementation in water supply
construction projects, using Saudi Arabia as a case study. Data from semi-structured interviews and a
systematic literature review were used to develop a potential list of PIs. Then, the PIs were used to
create a survey and distributed to industry professionals. Data from 112 respondents were analyzed
using mean ranking analysis, the normalization method, exploratory factor analysis (EFA), and fuzzy
synthetic evaluation (FSE). Eighteen critical PIs for assessing EMP implementation in water supply
construction projects were identified, including public safety, road safety hazards, construction waste,
clogged drainage, irregular flooding, the spilling of chemical substances, slope failures, soil erosion,
landslide occurrence, increased schedule waste, changes in the color of bodies of water, oil/fuel
spills, restricted site accessibility, the smell of run-off water, traffic accidents on construction sites,
the spread of disease, changes in the color of run-off water, and overflowing silt traps. The EFA
revealed that PIs can be grouped into three underlying constructs: fluid-related indicators, health and
safety-related indicators, and site environment-related indicators. The FSE results confirmed that all
PIs are between moderately critical to critical. This study’s significance lies in its examination of PIs
that aim to improve the environmental performance of water supply construction projects. Under-
standing which indicators are most effective allows for targeted improvements, helping to minimize
negative environmental impacts and ensuring sustainable practices. Finally, this study is a pioneer in
examining the critical PIs for assessing EMP implementation in water supply construction projects.
Keywords: environmental management plan; EMP; water supply construction projects; Saudi Arabia;
exploratory factor analysis; EFA; fuzzy synthetic evaluation; FSE
1. Introduction
Environmental management plans (EMPs) are developed to ensure that construction
projects are carried out in an environmentally responsible and sustainable manner [
1
].
Normally, construction projects result in substantial energy and natural resource consump-
tion [
2
,
3
], accompanied by the release of significant levels of air, water, noise, and land
pollution [
4
,
5
]. Furthermore, the construction and demolition debris generated by this
industry has detrimental consequences, contributing to environmental issues such as pollu-
tion in water, soil, and air [
6
–
8
]. These challenges also extend to economic repercussions,
Sustainability 2024,16, 600. https://doi.org/10.3390/su16020600 https://www.mdpi.com/journal/sustainability

Sustainability 2024,16, 600 2 of 20
including the depletion of essential resources and increased public health risks [
9
]. Hence,
EMPs are designed to protect the environment from the potential harm caused by project
activities by reducing or mitigating various adverse impacts. On the contrary, poor EMP
implementation can expose nearby communities to health risks such as waterborne dis-
eases, respiratory issues, and other health problems, particularly if pollutants or hazardous
materials are released into the environment [
10
]. Failure to implement EMP can result
in significant environmental damage, including habitat destruction, water pollution, soil
erosion, and biodiversity loss [
11
,
12
]. These environmental impacts can be long-lasting and
may harm ecosystems and natural resources. Therefore, it is essential to ensure effective
EMP implementation to minimize these risks and ensure responsible project management.
Construction project stakeholders may not correctly implement EMPs for several
reasons. Some stakeholders, especially those indirectly involved in environmental man-
agement, may have little awareness and not fully understand the importance of EMP
implementation [
13
]. Moreover, there is a lack of enthusiasm among project stakeholders
when it comes to implementing EMPs due to the perception that the associated benefits and
competitive advantages are relatively minimal [
14
]. In addition, implementing EMP mea-
sures often requires financial resources, technical expertise, and personnel. Stakeholders
may face budgetary constraints or a shortage of trained personnel, making it challenging
to execute the plan effectively. In addition, stakeholders may prioritize other aspects of
the project, such as cost and schedule, over environmental considerations. Stakeholders
who do not prioritize environmental sustainability or do not have a strong commitment
to responsible environmental management may not allocate the necessary resources and
attention to EMP implementation. Thus, there is a need to find appropriate approaches
to ensure the correct and effective EMP implementation by construction project stake-
holders. Despite this imperative, the existing body of literature in relation to indicators
that assess EMP implementation holds limited studies. While [
15
] made notable contri-
butions by developing a set of environmental operational indicators and performance
indicators, the focus primarily centered on environmental performance assessment, and the
perspectives were derived from project managers. Similarly, Ref. [
16
] delved into indicators
for EMP implementation but primarily within the context of road construction projects.
Additionally, Ref. [
17
] extended this inquiry to highway construction projects. Notably,
these works have concentrated on indicators specifically tailored to highway and road
construction, leaving a notable gap in the exploration of EMP indicators for a broader range
of construction projects.
PIs are often an integral part of multi-criteria decision-making (MCDM) processes.
In MCDM, decision makers evaluate and compare multiple criteria to make informed
decisions. PIs serve as measurable criteria that are used to assess the effectiveness or
performance of a system, process, or project. MCDM allows for the prioritization of the
critical aspects of EMP implementation. By assigning weights to different PIs, stakeholders
can focus on the most crucial elements, optimizing resource allocation, addressing the most
pressing environmental concerns, and ensuring a more targeted approach to improvement.
Moreover, PIs provide a structured way of monitoring the progress and effectiveness of
EMP implementation by offering measurable data points that allow stakeholders to gauge
how well environmental measures are being executed. These indicators are early warning
signs of potential issues and challenges in EMP implementation. The early identification of
problems allows for timely corrective action, preventing the escalation of issues that could
disrupt this project’s progress. In addition, project managers and environmental profession-
als can establish robust monitoring systems by understanding the indicators of poor EMP
implementation. This project’s environmental performance may be tracked more effectively,
ensuring that EMP measures are consistently applied. Moreover, recognizing indicators of
poor EMP implementation helps project stakeholders prioritize ecological protection by
focusing on areas where EMP may fail, leading to the improved protection of ecosystems,
water quality, and biodiversity. Thus, there is a need to investigate the appropriate PIs for
assessing EMP implementation to improve environmental management practices in water

Sustainability 2024,16, 600 3 of 20
supply construction projects, leading to more sustainable and environmentally friendly
construction practices.
Based on this background, this study aims to investigate the effectiveness of PIs in
assessing EMP implementation in water supply construction projects, using Saudi Arabia
as a case study. Specifically, the study objectives are to identify the critical PIs, group
the PIs, and evaluate the effectiveness of the PIs to assess EMP implementation in water
supply construction projects. To accomplish this goal, 112 environmental professionals
completed a questionnaire survey. The data were analyzed using the mean ranking analysis,
normalization method, exploratory factor analysis (EFA), and fuzzy synthetic evaluation
(FSE). Finally, a set of effective PIs for assessing EMP implementation in water supply
construction projects was established. This study contributes to a better knowledge of
PIs for assessing EMP implementation in water supply construction projects. Moreover,
the study findings can serve as a significant reference for industry practitioners and pol-
icymakers in assuring the success of EMP implementation in water supply construction
projects. Using the right set of PIs is crucial for assessing EMP implementation accurately
and improving environmental management practices in water supply construction projects,
leading to more sustainable and environmentally friendly construction practices. Also, this
study is a pioneer in examining the critical PIs for EMP implementation in water supply
construction projects. By identifying critical PIs and evaluating EMP implementation, this
study contributes to the protection and conservation of the environment, safeguarding
natural resources, ecosystems, and biodiversity, which directly impact the well-being of
the community. Moreover, this study uses of a combination of semi-structured interviews,
a systematic literature review, and multiple analytical methods (mean ranking analysis,
normalization method, EFA, and FSE) to provide a comprehensive methodological ap-
proach. Other researchers can learn from and potentially adopt these methods for future
research, especially in the context of identifying PIs for assessing EMP implementation in
construction projects. The list of critical PIs can serve as a starting point for researchers
working on similar topics, providing a foundation for developing other performance indi-
cators or refining existing ones. This study’s outcomes can also serve as a benchmark for
future comparative studies. Researchers may use the identified PIs and the categorization
of constructs as a reference point when comparing the effectiveness of implementing an
environmental management plan across different projects, regions, and time periods.
2. Literature Review
2.1. Performance Indicators for Construction Projects
PIs for construction projects are vital for monitoring and managing construction activi-
ties [
18
,
19
]. The construction industry has a long history of developing and using indicators,
particularly in the context of sustainable development [
20
]. These indicators are essential
tools for measuring and assessing the environmental, social, and economic impacts of con-
struction projects and for guiding efforts to make the industry more sustainable. Moreover,
as public awareness of environmental issues is paramount, educating the public about
these concerns, including their causes, consequences, and potential policy solutions, can
increase support for government initiatives addressing environmental challenges [
21
]. In
this broader context, PIs play a vital role in evaluating environmental performance and
monitoring progress toward sustainable development goals. They can also be applied at
the national level to assist in planning, setting policy goals, and establishing environmental
priorities. Through the measurement and analysis of environmental-related PIs, construc-
tion projects can minimize their environmental impact and contribute to the preservation
of the environment, ensuring that development activities align with broader environmental
conservation goals. In essence, PIs are integral to effective environmental management and
public involvement in environmental issues.
Prior work has recognized the existence of various environmental PIs across different
project types. The appropriate use of indicators can be a powerful tool in addressing the
sustainability of businesses both at a corporate-wide level and at a project level [
22
]. For

Sustainability 2024,16, 600 4 of 20
instance, Ref. [
20
] identified and evaluated performance indicators aimed at monitoring
and appraising the sustainable performance of construction projects in the execution phase
within the United Arab Emirates. Their findings underscore the significance of indicators
related to renewable energy and construction site safety. Furthermore, Ref. [
23
] delved into
environmental performance indicators to assess the sustainability of building projects. This
work highlighted key indicators, such as the project’s impact on water quality, air quality,
energy consumption, conservation, environmental compliance, and management. These
investigations contribute valuable insights to stakeholders, aiding in identifying the most
pertinent sustainability indicators for construction projects, thereby facilitating assessments
of sustainability performance.
2.2. Construction Industry in Saudi Arabia
The construction industry in Saudi Arabia is globally recognized as one of the largest
and most influential industries, mainly due to its substantial contributions to the devel-
opment of numerous mega projects [
24
]. In terms of economic significance, the construc-
tion industry accounts for approximately 6% of Saudi Arabia’s Gross Domestic Product
(GDP) [
25
]. Moreover, this contribution to the GDP is anticipated to continue increasing
in the coming years. The Saudi Arabian government has made a substantial commitment
to bolster the construction industry by earmarking a significant portion of the annual na-
tional budget for its growth over the next decade, with the aim of achieving the ambitious
targets outlined in the Vision 2030 initiative [
26
]. Vision 2030, coupled with the National
Transformation Programme 2020, aiming for a boost in private sector investments and
ongoing reforms, collectively serve as the driving forces behind the expansion of the Saudi
construction industry. These developments have yielded positive outcomes, particularly
in the advancement of housing and industrial construction activities throughout the na-
tion. Furthermore, Saudi Arabia has a multitude of megaprojects slated for completion
by the year 2030, reflecting the nation’s commitment to ambitious development goals and
economic diversification.
Several works have been conducted to enhance the construction industry in Saudi
Arabia. In a work by [
27
], critical success factors (CSFs) were identified to facilitate the
adoption of value management practices within Saudi Arabia’s construction industry. To
enhance the adoption of value management, 25 CSFs were defined with insights from
construction experts. Another work by [
24
] focused on understanding the constraints
and restrictions affecting construction projects in Saudi Arabia, particularly during the
planning stage. This work indicated that disputes over project timelines, cost overruns, and
project abandonment were significant factors contributing to project failures. In addition,
government officials and contractors were identified as contributors to project delays and
delivery issues. Additionally, Ref. [
28
] examined the implementation and design of safety
practices in the Saudi Arabian construction industry. This work highlighted that crucial
success factors for safety practices include legislation and stakeholder awareness, while
a major barrier involves clients’ and their representatives’ apprehension regarding cost
overruns. These prior findings collectively provide valuable insights into different facets
of the construction industry in Saudi Arabia, offering opportunities to enhance industry
practices and address critical challenges for improved performance.
2.3. Water Supply Construction Projects
The body of existing work on water supply construction projects is relatively limited,
but several noteworthy works have explored the critical aspects of these projects [
29
], for in-
stance, investigated the key risk factors associated with public–private partnerships (PPPs)
in water supply infrastructure projects. Their findings emphasized the importance of risk-
sharing between the government and private sector entities rather than solely transferring
the specific risks to one party. Ref. [
30
] delved into the CSFs for water infrastructure projects
delivered through public–private partnerships. Their work underscored the significance of
thorough planning to ensure project viability, a high degree of transparency and account-

Sustainability 2024,16, 600 5 of 20
ability, and the establishment of a legal framework ensuring policy continuity as CSFs for
the successful delivery of water infrastructure projects under PPP initiatives. Their findings
emerged from factor analysis, leading to the identification of grouped factors related to
public cooperation, project viability, and policy and legislation enhancement. Furthermore,
Ref. [
31
] explored the challenges associated with poor delivery and the underlying factors
contributing to inadequate outcomes in water infrastructure projects in South Africa. Their
work revealed major challenges, such as project completion delays, cost overruns, subpar
work quality, inefficient fund utilization, and unsatisfactory service delivery. They further
identified the factors contributing to these challenges across four critical dimensions of
infrastructure projects: project management, organization and management, construction
and construction management, and sociopolitical aspects.
2.4. Positioning This Study
The background information provided highlights a critical gap in the current body of
knowledge on EMP implementation. While various works have been conducted in this
country, there is a noticeable dearth of research on EMP implementation. Furthermore, it
is worth noting that existing works on water supply construction projects have primarily
centered around risk assessments, CSFs, and challenges. This study aims to proceed
beyond these aspects by comprehensively analyzing PIs, their interrelationships, and their
effectiveness in evaluating EMP implementation. This holistic approach can provide a
more nuanced and comprehensive understanding of the role of PIs in promoting effective
environmental management within water supply construction projects. Therefore, this
study aims to address this gap by focusing on the effectiveness of PIs. In doing so, this study
seeks to pinpoint areas where improvement is needed and propose tailored approaches
for assessing EMP implementation. This approach is crucial for refining environmental
management practices in water supply construction projects and ensuring sustainability
and environmentally friendly construction practices. In other words, this study endeavors
to fill a significant research gap, enhance an understanding of EMP implementation in
the context of water supply construction projects and contribute to more sustainable and
environmentally conscious construction practices.
3. Methodology
3.1. Survey Development
This study used a questionnaire survey to collect data on PIs quantitatively to assess
EMP implementation in water supply construction projects. Surveys are a proven and
efficient method for gathering diverse responses from professionals, especially when
random sampling is applied [
32
]. The subsequent sections provide insights into the survey
development process used in this study.
3.1.1. Systematic Literature Review
A systematic literature review (SLR) was conducted to identify the potential PIs
reported in the prior literature. The process commenced with a search conducted in the
Scopus database. Scopus was chosen as the database of choice due to its popularity and
relevance in the field of construction management [
33
]. The SLR began with an extensive
search for relevant articles, using the “title/abstract/keyword” function in the Scopus
database, resulting in the identification of 199 papers. To ensure the robustness of the
selected literature, only peer-reviewed journals were selected for their higher quality from
a more thorough peer review process [
34
]. Following the initial screening of abstracts,
26 articles were deemed suitable for further review. However, not all articles were directly
related to PIs in construction projects. Thus, articles that did not meet the subject matter
were eliminated following a thorough examination of their content. Consequently, a total of
13 articles were found to be valid for further investigation. Figure 1shows the SLR process
conducted in this study.

Sustainability 2024,16, 600 6 of 20
Sustainability 2024, 16, x FOR PEER REVIEW 6 of 20
relevance in the field of construction management [33]. The SLR began with an extensive
search for relevant articles, using the “title/abstract/keyword” function in the Scopus da-
tabase, resulting in the identification of 199 papers. To ensure the robustness of the se-
lected literature, only peer-reviewed journals were selected for their higher quality from
a more thorough peer review process [34]. Following the initial screening of abstracts, 26
articles were deemed suitable for further review. However, not all articles were directly
related to PIs in construction projects. Thus, articles that did not meet the subject matter
were eliminated following a thorough examination of their content. Consequently, a total
of 13 articles were found to be valid for further investigation. Figure 1 shows the SLR
process conducted in this study.
Figure 1. SLR process.
3.1.2. Interview
In addition to the SLR, interviews were conducted with environmental professionals
to identify the potential PIs that might not have been previously documented in the exist-
ing body of knowledge. This approach aligns with previous works that have used a com-
bination of SLR and interviews to comprehensively identify relevant variables during sur-
vey development [35,36]. The interview process commenced with an introductory phase,
during which the purpose of the interview and the topic of discussion were outlined. Sub-
sequently, interview questions were presented to the interviewee. In some instances, fol-
low-up questions were posed based on the interviewees’ responses to gain a deeper un-
derstanding of the information provided and ensure clarity. If needed, questions were
rephrased to facilitate accurate responses. At the conclusion of each session, the inter-
viewer expressed gratitude to the interviewees for their participation. To maintain trans-
parency and accuracy, a summary of each interview was prepared and shared with the
respective respondents for validation [37,38].
The interviews involved ten environmental professionals due to data saturation.
Data saturation is an important tool for evaluating qualitative data [39]. Data saturation
occurs when researchers reach a point where collecting more data does not provide addi-
tional information relevant to the research questions [40]. During the interviews, data sat-
uration was achieved with the tenth participant, signifying that further data collection
would likely yield redundant information. It is noteworthy that a qualitative inquiry em-
ploying interviews often advocates for a participant range between 5 and 25 [41]. Prior
works in the construction project management area have also used data saturation in qual-
itative studies [42,43].
The interview data were analyzed using the thematic analysis technique, as de-
scribed by [44]. The analysis aimed to identify and categorize the PIs that emerged from
the interviews, ultimately contributing to developing a comprehensive list of PIs for
Figure 1. SLR process.
3.1.2. Interview
In addition to the SLR, interviews were conducted with environmental professionals
to identify the potential PIs that might not have been previously documented in the
existing body of knowledge. This approach aligns with previous works that have used a
combination of SLR and interviews to comprehensively identify relevant variables during
survey development [
35
,
36
]. The interview process commenced with an introductory
phase, during which the purpose of the interview and the topic of discussion were outlined.
Subsequently, interview questions were presented to the interviewee. In some instances,
follow-up questions were posed based on the interviewees’ responses to gain a deeper
understanding of the information provided and ensure clarity. If needed, questions were
rephrased to facilitate accurate responses. At the conclusion of each session, the interviewer
expressed gratitude to the interviewees for their participation. To maintain transparency
and accuracy, a summary of each interview was prepared and shared with the respective
respondents for validation [37,38].
The interviews involved ten environmental professionals due to data saturation. Data
saturation is an important tool for evaluating qualitative data [
39
]. Data saturation occurs
when researchers reach a point where collecting more data does not provide additional
information relevant to the research questions [
40
]. During the interviews, data saturation
was achieved with the tenth participant, signifying that further data collection would
likely yield redundant information. It is noteworthy that a qualitative inquiry employing
interviews often advocates for a participant range between 5 and 25 [
41
]. Prior works in
the construction project management area have also used data saturation in qualitative
studies [42,43].
The interview data were analyzed using the thematic analysis technique, as described
by [
44
]. The analysis aimed to identify and categorize the PIs that emerged from the
interviews, ultimately contributing to developing a comprehensive list of PIs for assessing
EMP implementation in water supply construction projects. Table 1summarizes the
39 potential PIs for assessing EMP implementation that were identified through the SLR
and semi-structured interviews.

Sustainability 2024,16, 600 7 of 20
Table 1. PIs for assessing EMP implementation.
Code PIs Sources
PI01 Soil erosion Interview, [45,46]
PI02 Traffic accidents on the construction site Interview, [47–49]
PI03 The smells of run-off water Interview, [46,50]
PI04 Appearance of dust Interview, [51]
PI05 Spills of chemical substances [52]
PI06 Construction waste [15,46,53,54]
PI07 Clogged drainage [45]
PI08 Overflowing silt trap [Interview]
PI09 Oil/fuel spills Interview, [52]
PI10 Traffic accidents among public users [Interview]
PI11 Visibility drops Interview, [51]
PI12 Changes in the color of bodies of water [Interview]
PI13 Excessive cut and fill Interview, [55]
PI14 Traffic emission gas [51,52,56]
PI15 Vegetation depletion [45,53,55,57,58]
PI16 Wildlife appearance at construction sites [46,50,51,53,55,56,59,60]
PI17 Unpleasant air odors [46,51,52,60–62]
PI18 Changes in the color of run-off water [Interview]
PI19 Landslide occurrence Interview, [47]
PI20 Light pollution (during nighttime) [46,50]
PI21 Restricted site accessibility [51]
PI22 The smell of nearby bodies of water [Interview]
PI23 Slope failures Interview, [45,55,57,58]
PI24 Depletion of agricultural land [47]
PI25 Excessive noise [Interview]
PI26 Irregular flooding [Interview]
PI27 Destruction of animal habitats [46,50,51,55,56,60]
PI28 Public safety [45]
PI29 Deforestation [47,55]
PI30 Changes in the color of silt traps Interview
PI31 Open burning Interview
PI32 Alteration of topography [58]
PI33 Spread of disease [63]
PI34 Vibration occurrences Interview
PI35 Traffic congestion Interview
PI36 Social disturbance [54]
PI37 Increased schedule waste [15,46,53,54]
PI38 Road safety hazard Interview
PI39 Proliferation of pest [63]

Sustainability 2024,16, 600 8 of 20
3.1.3. Survey Design
The front page of the survey included the study objectives and contact information.
The first section of the survey asked respondents about their backgrounds and experience
in relation to water supply construction projects to assess their applicability to answering
the survey. The PIs were listed and evaluated in the second section on a five-point Likert
scale (1 = not important; 2 = slightly important; 3 = moderately important; 4 = important;
5 = very
important). The Likert scale is commonly used in construction project management
research due to its precision [
64
,
65
]. At the end of the survey, respondents were given
spaces to describe and rank any additional PIs to assess EMP implementation in water
supply construction projects.
3.1.4. Pilot Test
Detecting design and instrumentation issues in a survey is possible through a pilot
test [
66
]. Additionally, feedback from the pilot test is vital for enhancing the survey’s
quality and estimating the time needed for its completion [
67
]. A pilot test involving five
environmental professionals and five academics possessing over a decade of experience
in water supply construction projects was executed. This pilot study aimed to eliminate
unclear language and ensure the correct usage of technical terms within the survey. Conse-
quently, this survey was refined based on feedback from the pilot test participants and then
deemed finalized.
3.2. Data Collection
The target population for the survey was environmental professionals involved in
water supply construction projects in Saudi Arabia. Purposive sampling was adopted
to achieve the target population. Purposive sampling entails identifying and choosing
knowledgeable and skilled individuals [
68
]. In addition, the respondents must be avail-
able, willing to participate, and able to express their experiences and opinions clearly,
expressively, and reflectively [69]. Finally, a total of 112 valid responses were obtained.
Table 2shows that more than half of the respondents, accounting for 58.9% of the total
sample, possessed 10 or more years of experience in water supply construction projects.
Regarding the number of water supply construction projects they had been involved
in, only 27.7% of those surveyed reported having experience with just one project. In
contrast, the remaining 72.3% of the sample had experience with at least two water supply
construction projects. This suggests that the data collected can be regarded as reliable
for further analysis, given the respondents’ substantial level of professional experience in
water supply construction projects.
Table 2. Respondent profile.
Characteristics Categories Frequency Percent (%)
Years of experience in water
supply construction projects
Fresh graduate 17 15.2
Less than 3 years of experience 12 10.7
3 to 10 years 17 15.2
10 to 15 years 17 15.2
More than 15 years of experience 49 43.8
Number of water supply
construction projects involved in
Only one project 31 27.7
2 to 5 projects 23 20.5
6 to 10 projects 14 12.5
11 to 20 projects 17 15.2
More than 20 projects 27 24.1

Sustainability 2024,16, 600 9 of 20
3.3. Data Analysis
3.3.1. Reliability of Data
Reliability analysis was undertaken to assess the survey’s reliability and consistency.
The internal consistency of the 39 PIs was tested using Cronbach’s alpha (
α
) coefficient. The
α
value ranged from 0 to 1, where 0 denotes no consistency, and 1 denotes internal consis-
tency. An acceptable level of consistency was indicated by an
α
value of 0.70 or above [
70
].
The 39 PIs obtained an overall score of 0.944 at the 5% significance level, confirming data
reliability. As a result, the acquired data are suitable for further investigation.
Outliers were then identified by screening the dataset with a two-standard deviation
technique [
36
]. Data points that deviated from the norm significantly and that could have
a major impact on the outcome were considered outliers. In the two-standard deviation
approach, two separate standard deviation intervals were calculated. Variables were
considered outliers if their means were outside any of these two standard deviation ranges.
Intervals of 2.504 and 4.178 were determined using the two standard deviation approaches.
As “Deforestation” (PI29) and “Public Safety” (PI28) were outside the two SD interval
values, they were considered outliers. “Deforestation” (PI29) was not included in the
further analysis. given that practitioners deemed it to be less important than other variables.
However, “Public safety” (PI28) was still included in subsequent analysis because it was
considered to potentially be very important. The importance or relevance of an indicator
may outweigh its deviation from the mean. In some cases, a particular indicator may have
unique significance or may be considered critical by stakeholders or experts, even if it
exhibits outlier characteristics. For example, “Public safety” (PI28) might indicate a high
level of importance in a water supply construction project, justifying its retention despite
being an outlier.
3.3.2. Mean Ranking Analysis and Normalization Method
This study employed a mean score ranking method to establish the relative rankings
of the PIs. In cases where multiple PIs had identical mean values, the PI with the lowest
standard deviation (SD) was accorded the highest rank. A smaller SD indicates that the
differences in responses are not statistically significant, making the mean value a more
reliable representation for most respondents [70].
After ranking the PIs, a normalization technique was applied to facilitate a more
meaningful interpretation of the data, particularly when identifying crucial variables. This
normalization approach was adapted from the work of [
71
]. This procedure involved
setting the minimum mean value to 0 and the maximum mean value to 1. Subsequently,
the other mean values were converted into decimal values within the range of 0 to 1.
PIs with normalized values of at least 0.50 were identified as critical for evaluating EMP
implementation in water supply construction projects.
3.3.3. Exploratory Factor Analysis
This study employed EFA as a method that condenses a large number of interrelated
variables into more manageable and relevant sets or constructs [
72
]. The ratio of the
sample size to the number of variables was calculated to determine the sample size for
the EFA. Eighteen critical PIs were identified based on the normalization method. The
calculated ratio of the sample size to the number of variables was 6.22, which exceeded the
recommended value of 5.00 [73], indicating that the sample size was sufficient for EFA.
Two assessments were conducted to determine the appropriateness of the data for
EFA. The Kaiser–Meyer–Olkin (KMO) measure of sample adequacy compared the squared
correlation between variables to the squared partial correlation between variables; a good
EFA typically had a KMO value higher than 0.50 [
74
]. Additionally, Bartlett’s test of
sphericity was used to assess the relationships between variables. EFA is considered
appropriate if the original correlation matrix is not an identity matrix, indicating significant
relationships between the variables [75].

Sustainability 2024,16, 600 10 of 20
Principal Component Analysis (PCA) was utilized as the method for factor extraction
to uncover the underlying constructs within the dataset. Variables with eigenvalues greater
than one, indicating their substantial contribution to the principal constructs, were retained
for further analysis. Following this, a varimax rotation was applied to the PIs to uncover any
latent constructs. Variables with factor loadings surpassing 0.50 were deemed significant
and valuable for interpreting these constructs [76].
3.3.4. Fuzzy Synthetic Evaluation
Finally, this study employed the FSE technique to assess the effectiveness of each PI
and construct. This method has been used in previous construction project management
research to evaluate different types of variables, including strategies and impacts [
65
,
77
,
78
].
The steps for executing the FSE are as follows:
Step 1: Weightings for each performance indicator
The effectiveness of the FSE method relies on the weights assigned to each component
and subcomponent. Equation (1) was used to calculate the weightings for each critical PI.
Wi=Mi
∑5
i=1Mi
, 0 ≤wi ≤1, 0 ≤i≤1 (1)
where
Wi
is the weighting;
Mi
is the mean score, and
∑Mi
is the summation of the mean
score of all critical PIs.
Step 2: The membership function for each component
FSE employs grading alternatives to generate membership functions (MFs) for the
critical PIs. A five-point Likert grading scale was used, ranging from 1 (very low) to 5 (very
high), denoted as E = (1, 2, 3, 4, 5). Equation (2) was used to calculate the MF of each critical
PI based on the survey responses.
MFuin =x1uin
E1
+x2uin
E2
+x3uin
E3
+x4uin
E4
+x5uin
E5
(2)
where
uin
is the PIs;
MFuin
is the MF of a given PI;
xjuin
(j = 1, 2, 3, 4, 5) is the percentage
of respondents who were rated j for the significance of a specific PI, which measures the
degree of MF;
xjuin
E1
is the relationship between
xjuin
and its grade alternative; and ‘+’ is a
notation in a fuzzy set. Using Equation (3), the MF of a specific critical PI could be indicated
as follows:
MFuin =x1uin +x2uin +x3ui n +x4uin +x5uin (3)
Equation (4) is used to process and can be adopted when multiple components are
considered, and the difference in their weight is minimal.
M(·,⊕)bj=min1, ∑wi×rij min =1∀bj∈B(4)
where
wi
represents the weightings of each PI;
rij
is the membership function of each critical
PI; and ⊕is the sum of the weighting and membership function product.
Step 3: Overall effectiveness level
The overall effectiveness level (OEL) of critical PIs was computed using Equation (5).
This equation considers the weightings (W), the degree of the MF (R), and L as the linguistic
variables (1—very low, 2—low, 3—neutral, 4—high, 5—very high) to determine the OEL.
The OEL of the critical PIs can be computed as follows:
OEL =Σni=1(W×Ri)×L (5)

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4. Result
4.1. Result of Mean Ranking Analysis and Normalization Method
The results of ranking the PIs for assessing EMP implementation in water supply
construction projects are presented in Table 3. The mean value of the PIs extends from 2.571
to 4.214. The critical PIs have normalized mean values of at least 0.50. Eighteen PIs were
found to have normalized values of 0.50 or above, making them critical PIs.
Table 3. Results of mean ranking analysis and normalization method.
Code Mean Standard Deviation Normalized Value
PI28 4.214 1.043 1.000 *
PI38 4.098 0.910 0.929 *
PI4 3.955 0.874 0.842 *
PI7 3.839 1.205 0.772 *
PI26 3.813 1.061 0.755 *
PI6 3.795 1.428 0.745 *
PI25 3.759 1.042 0.723 *
PI1 3.732 1.147 0.707 *
PI19 3.696 1.130 0.685 *
PI37 3.661 0.982 0.663 *
PI12 3.652 1.213 0.658 *
PI9 3.634 1.464 0.647 *
PI21 3.571 1.145 0.609 *
PI13 3.545 1.030 0.592 *
PI2 3.500 1.208 0.565 *
PI33 3.482 1.259 0.554 *
PI18 3.455 1.222 0.538 *
PI8 3.429 1.063 0.522 *
PI36 3.375 0.978 0.489
PI10 3.313 1.186 0.451
PI3 3.304 1.177 0.446
PI24 3.286 1.196 0.435
PI22 3.205 1.164 0.386
PI5 3.161 1.070 0.359
PI35 3.152 1.117 0.353
PI32 3.116 1.184 0.332
PI34 3.107 1.126 0.326
PI11 3.089 0.954 0.315
PI23 3.071 1.029 0.304
PI20 3.036 1.154 0.283
PI16 3.027 1.069 0.277
PI17 3.009 1.143 0.266
PI14 2.964 1.039 0.239
PI30 2.955 1.181 0.234
PI31 2.866 1.284 0.179
PI15 2.768 1.208 0.120
PI27 2.688 1.446 0.071
PI39 2.571 1.299 0.000
Note: * = critical PIs.

Sustainability 2024,16, 600 12 of 20
4.2. Result of EFA
The Kaiser–Meyer–Olkin (KMO) value for the PIs stands at 0.855, surpassing the mini-
mum threshold of 0.50 [
74
]. Conversely, Bartlett’s test of sphericity returned a significant
value of 0.000, indicating that the dataset is not an identity matrix. Consequently, the data
are deemed suitable for EFA.
As illustrated in Table 4, 14 PIs are successfully loaded into three underlying constructs,
each possessing factor loadings greater than 0.50. These three constructs collectively account
for 60.414% of the total variance. To determine the appropriate label for each construct,
one can consider variables with higher factor loadings or the entire set of variables. Hence,
the constructs are categorized as fluid-related indicators (F), health and safety-related
indicators (HS), and site environment-related indicators (SE).
Table 4. Results of EFA.
Constructs PIs Code Factor
Loadings
Variance
Explained
Cronbach
Alpha
Fluid-related indicators
Changes in the color of bodies of water
PI12 0.843 28.561 0.891
Spills of chemical substances PI6 0.774
Changes in the color of run-off water PI18 0.758
Overflowed silt trap PI8 0.702
Clogged drainage PI7 0.682
Oil/fuel spills PI9 0.681
Soil erosion PI1 0.621
Health and safety-related indicators
Public safety PI28 0.771 17.118 0.713
Traffic accidents on construction sites PI2 0.691
Spread of disease PI33 0.671
Landslide occurrence PI19 0.583
Site environment-related indicators Restricted site accessibility PI21 0.785 14.735 0.691
Road safety hazard PI38 0.731
Irregular flooding PI26 0.713
Additionally, Cronbach’s alpha reliability test was conducted to ensure the accuracy
of the grouping of factors. As indicated in Table 4, Cronbach’s alpha coefficients exceeded
the minimum threshold of 0.60 [
79
]. This implies that each construct exhibits good internal
consistency.
4.3. Result of FSE
Tables 5–7display the FSE results for key PIs, including MFs for levels 3, 2, and 1,
along with an OEL value of 3.72. Meanwhile, site environment-related indicators have the
highest criticality at 3.84. This is followed by health and safety-related indicators (3.75)
and fluid-related indicators (3.65). In other words, all constructs have values between
“moderately critical” to “critical”. Policymakers should consider focusing on the key PIs
in site environmental-related indicators. However, the other constructs cannot be ignored
when assessing EMP implementation in water supply construction projects.

Sustainability 2024,16, 600 13 of 20
Table 5. Description of performance indicator input variables.
Code MI SD NV CI OR CR TM CW
FL - - - uf l - - 25.54 0.492
PI7 3.84 1.20 0.52 ufl1 3 1 - -
PI6 3.80 1.43 0.47 ufl2 5 2 - -
PI1 3.73 1.15 0.39 ufl3 6 3 - -
PI12 3.65 1.21 0.28 ufl4 8 4 - -
PI9 3.63 1.46 0.26 ufl5 9 5 - -
PI18 3.46 1.22 0.03 ufl6 13 6 - -
PI8 3.43 1.06 0.00 ufl7 14 7
HS - - - uhs - - 14.89 0.287
PI28 4.21 1.04 1.00 uhs1 1 1 - -
PI19 3.70 1.13 0.34 uhs2 7 2 - -
PI2 3.50 1.21 0.09 uhs3 11 3 - -
PI33 3.48 1.26 0.07 uhs4 12 4 - -
SE - - - use - - 11.48 0.221
PI38 4.10 0.91 0.85 use2 2 1 - -
PI26 3.81 1.06 0.49 use2 4 2 - -
PI21 3.57 1.14 0.18 use2 10 3 - -
Total 51.91 1.000
Note: MI = mean index SD = standard deviation; NV = normalized value = (mean e minimum mean)/(maximum
mean x minimum mean); CI = codes for index system; OR = overall rank; CR = construct rank; TM = total mean;
and CW = construct weighting.
Table 6. Results from the fuzzy synthetic evaluation.
Code Level MI Weightings MF Value
Overall 1 - - 0.07, 0.09, 0.19, 0.34, 0.31
FL 2 - 0.492 0.09, 0.11, 0.18, 0.31, 0.31
PI7 3 3.84 0.150 0.04, 0.13, 0.14, 0.29, 0.38
PI6 3 3.80 0.149 0.13, 0.09, 0.12, 0.21, 0.46
PI1 3 3.73 0.146 0.05, 0.11, 0.18, 0.38, 0.29
PI12 3 3.65 0.143 0.08, 0.10, 0.19, 0.36, 0.28
PI9 3 3.63 0.142 0.15, 0.08, 0.16, 0.20, 0.41
PI18 3 3.46 0.135 0.10, 0.13, 0.17, 0.41, 0.19
PI8 3 3.43 0.134 0.04, 0.14, 0.31, 0.34, 0.16
HS 2 - 0.287 0.07, 0.07, 0.21, 0.31, 0.33
PI28 3 4.21 0.283 0.04, 0.05, 0.08, 0.32, 0.51
PI19 3 3.70 0.248 0.06, 0.07, 0.24, 0.36, 0.27
PI2 3 3.50 0.235 0.10, 0.08, 0.27, 0.33, 0.22
PI33 3 3.48 0.234 0.10, 0.10, 0.29, 0.24, 0.27
SE 2 - 0.221 0.04, 0.07, 0.17, 0.43, 0.29
PI38 3 4.10 0.357 0.02, 0.04, 0.15, 0.42, 0.38
PI26 3 3.81 0.332 0.04, 0.08, 0.16, 0.45, 0.27
PI21 3 3.57 0.311 0.07, 0.11, 0.21, 0.41, 0.21
Table 7. Effectiveness index of the constructs.
No. Construct Construct Code Weighting
1 Fluid-related indicators FL 3.65
2 Health and safety-related indicators HS 3.75
3 Site environment-related indicators SE 3.84
5. Discussions
5.1. Fluid-Related Indicators
The first construct identified through factor analysis is referred to as “Fluid-Related
Indicators.” This construct encompasses indicators that pertain to fluid elements used to

Sustainability 2024,16, 600 14 of 20
assess the implementation of an EMP in water supply construction projects. It comprises
the following seven specific indicators: PI12, PI18, PI6, PI9, PI8, PI7, and PI1.
Changes in the color of bodies of water (PI12) are an indicator of the poor implementa-
tion of an EMP in water supply construction projects. Water bodies changing color, typically
to a murky or unnatural hue, can be a visible and easily noticeable sign of pollution or
contamination. It serves as an early warning signal that something may be wrong with
the water quality. Another indicator of poor EMP implementation is changes in the color
of run-off water (PI18). Run-off water is water that flows over the surface of the ground
rather than soaking into it. It occurs when precipitation, such as rain or melted snow, falls
onto the Earth’s surface and cannot be absorbed by the soil or vegetation. Changes in the
color of run-off water, especially if it becomes discolored or murky, can be a clear sign of
pollution or contamination. Run-off water should ideally be clear or match the natural color
of the surrounding environment. Color changes can indicate the presence of sediments,
chemicals, or other pollutants.
In addition, the spill of chemical substances (PI6) is one of the indicators of poor EMP
implementation in water supply construction projects. When chemical spills occur despite
the presence of an EMP, it may suggest shortcomings in the plan’s implementation and
management. Water environments are often highly sensitive to chemical contamination.
Even small chemical spills can have significant adverse effects on aquatic ecosystems, water
quality, and the health of aquatic organisms. Furthermore, oil/fuel spills (PI9) are also
one of the indicators to assess the implementation of an EMP. Oil and fuel spills can lead
to water pollution, affecting the quality of water bodies. Monitoring and mitigating such
spills are essential to prevent contamination and ensure that water remains safe for various
uses, including drinking water supply and recreational activities.
Also, an overflowing silt trap (PI8) indicates poor EMP implementation in water
supply construction projects. When a silt trap overflows, erosion control measures may
not effectively prevent sediment run-off, potentially leading to increased water pollution.
Sediment pollution is the most significant pollutant from construction sites as it could
potentially simultaneously affect the economy, environment, and society [
80
]. Clogged
drainage (PI7) is another indicator of poor EMP implementation. Drainage systems are
crucial for managing the flow of water on construction sites. When drainage systems
become clogged, it can disrupt the intended flow of water, potentially leading to localized
flooding, erosion, or other water-related issues. Soil erosion (PI11) is another indicator to
assess the implementation of an EMP in water supply construction projects. Soil erosion is a
primary concern in construction projects, especially those near water bodies. Uncontrolled
erosion can result in the loss of topsoil, the sedimentation of water bodies, and the release
of contaminants, which can negatively impact water quality and aquatic ecosystems.
5.2. Health and Safety-Related Indicators
The second construct identified through factor analysis is referred to as “Health and
Safety-Related Indicators”. This construct comprises indicators that are associated with
health and safety considerations and are used to assess the implementation of an EMP in
water supply construction projects. It has the following five specific indicators: PI28, PI33,
PI19, PI2, and PI25.
Public safety (PI28) is one of the indicators to assess the implementation of an EMP
in water supply construction projects. Public safety incidents, such as accidents or in-
juries involving nearby residents or visitors, indicate that safety measures outlined in
the EMP may not be adequately enforced or effective. Ensuring the safety of workers,
nearby residents, and the general public is a top priority in any construction project. Also,
the spread of disease (PI33) is another indicator of poor EMP implementation in water
supply construction projects. Construction activities can pose a risk of contributing to the
growth and spread of waterborne pathogens in building water systems [
81
]. Moreover,
construction sites near water bodies can pose health risks due to the potential for the spread

Sustainability 2024,16, 600 15 of 20
of waterborne diseases. Monitoring the spread of disease is critical for safeguarding the
health and safety of workers and nearby communities.
Another indicator of poor EMP implementation is the occurrence of landslides (PI19).
Landslides pose a significant safety risk to workers on the construction site and nearby
communities. Construction activities near water bodies can alter the stability of slopes and
increase the likelihood of landslides [
82
]. Traffic accidents on construction sites (PI2) are
also an indicator to assess the implementation of an EMP in water supply construction
projects. Traffic accidents on construction sites can lead to injuries or fatalities among
construction workers. Traffic accidents can disrupt nearby communities, lead to complaints,
and negatively impact community relations, especially if the public perceives that the
construction project is causing avoidable accidents. In addition, slope failures (PI25) are
one of the indicators of the poor implementation of an EMP. Slope failures pose a significant
safety risk to construction workers on the site, as well as to nearby communities and the
public. Slope failures can result in soil erosion, the sedimentation of water bodies, or the
release of contaminants, harming aquatic ecosystems, disrupting habitats, and degrading
water quality.
5.3. Site Enviroment-Related Indicators
The third construct identified through factor analysis is referred to as “Site Environment-
Related Indicators”. This construct encompasses indicators that are associated with the
site’s environment and are used to assess the implementation of an EMP in water sup-
ply construction projects. It includes the following three specific indicators: PI21, PI26,
and PI38.
The first indicator in this construct is restricted site accessibility (PI21). Limited site
accessibility can impact the safety of construction workers and site visitors. Restricted
access routes can lead to delays in emergency response, potentially jeopardizing the safety
of workers and the public. Irregular flooding (PI26) is also one of the indicators to assess
the implementation of an EMP in water supply construction projects. Irregular flooding
can pose a severe safety risk to construction workers on the site, as well as to nearby
communities and the public. Road safety hazards (PI38) are another indicator of poor EMP
implementation. Construction sites near water bodies, including heavy construction vehi-
cles, often involve increased traffic. Road safety hazards can also affect nearby communities
and the general public. Additionally, injuries and fatalities associated with construction
accidents impose a huge cost on the industry [83].
5.4. Comparison with Prior Works
A comparison of this study’s results with prior findings offers a nuanced understand-
ing of the PIs for assessing EMP implementation. Specifically, this study’s outputs were
compared with PIs identified in the context of road construction [
16
] and highway con-
struction projects [
17
], revealing both commonalities and disparities. Table 8captures the
essence of this comparative analysis, indicating that out of the 18 critical PIs identified in
this study, 6 were not deemed critical in prior works. Notably, in road construction projects,
indicators such as restricted site accessibility, road safety hazards, the smell of runoff wa-
ter, traffic accidents on construction sites, and the spread of disease were not considered
critical. Similarly, in highway construction projects, increased schedule waste, restricted
site accessibility, the smell of runoff water, and the spread of disease did not emerge as
critical indicators. Despite these variations, a noteworthy observation is the substantial
overlap in critical PIs between this study and prior research. Most of the PIs identified as
critical in the current study were consistently reported as critical in the context of road and
highway construction projects. This consistency underscores the pivotal role that these
particular indicators play in assessing EMP implementation across diverse construction
scenarios, implying that certain environmental considerations encapsulated in these critical
PIs have universal relevance and importance in evaluating the sustainability of construction
projects. In essence, the persistent identification of these shared critical PIs underscores

Sustainability 2024,16, 600 16 of 20
their centrality in gauging the effectiveness of EMP implementation. Recognizing and
acknowledging these indicators is not only crucial but forms an integral part of ensuring
sustainable construction practices, fostering a comprehensive and standardized approach
toward achieving environmentally responsible construction outcomes.
Table 8. Comparison with prior works.
Performance Indicators Road Projects 1Highway Projects 2
Water Supply Projects (This Study)
Public safety • • •
Road safety hazards - • •
Construction waste • • •
Clogged drainage • • •
Irregular flood • • •
Spills of chemical substances • • •
Slope failures • • •
Soil erosion • • •
Landslide occurrence • • •
Increased schedule waste •-•
Changes in the color of bodies of water • • •
Oil/fuel spills • • •
Restricted site accessibility - - •
The smell of run-off water - - •
Traffic accidents on construction site - • •
Spread of disease - - •
Changes in the color of run-off water • • •
Overflowed silt trap • • •
Note: 1= [16]2= [17].
6. Conclusions
This study focuses on identifying and assessing the effectiveness of PIs for assessing
the implementation of EMPs in water supply construction projects. This study identified a
total of 39 potential PIs based on insights from interviews with environmental professionals
and a comprehensive review of the existing literature. This study collected data from 112 en-
vironmental professionals who completed surveys to evaluate the effectiveness of these PIs.
The analysis employed several techniques, including mean ranking, normalization, EFA,
and FSE. This study identified 18 critical PIs for assessing EMP implementation through
these analyses. These critical PIs encompass a wide range of factors, including public safety,
road safety hazards, construction waste, clogged drainage, irregular flooding, spilling
chemical substances, slope failures, soil erosion, landslide occurrence, increased schedule
waste, changes in the color of bodies of water, oil/fuel spills, restricted site accessibility,
the smell of run-off water, traffic accidents on construction site, spread of disease, changes
in the color of run-off water, and overflowing silt traps. Additionally, the result of EFA
grouped these PIs into the following three underlying constructs: fluid-related indicators,
health and safety-related indicators, and site environment-related indicators. The FSE
results confirmed that all PIs are between moderately critical to critical in their importance.
This study’s findings have several implications. First, the results can serve as a valuable
resource for academics and researchers interested in developing frameworks for improved
EMP implementation in water supply construction projects. Second, the results can enhance
environmental management practices in water supply construction projects, ultimately
leading to more sustainable and eco-friendly construction practices. In conclusion, this

Sustainability 2024,16, 600 17 of 20
study provides valuable insights into PIs for assessing EMP implementation in water
supply construction projects, offering a foundation for further research and potential
improvements in environmental management practices in the construction industry.
Despite its contributions, this study has certain limitations. The sample size of 112 sur-
vey respondents might be considered relatively small. Thus, future scholars can replicate
this study with a larger sample size. Also, the findings are specific to the local context
of the case study. Therefore, caution should be exercised when applying these results to
other countries or regions. Additionally, future research could expand upon this study by
employing more advanced statistical techniques, such as structural equation modeling, to
explore causal relationships among the identified PIs. Additionally, the application of ma-
chine learning techniques could contribute to external validation and bolster the robustness
assessment of the current study. Moreover, this study uses FSE as one of its data analysis
methods. FSE is known for its simplicity and ease of interpretation. In assessing EMP
implementation in water supply construction projects, where stakeholders may include
industry professionals with diverse backgrounds, a straightforward and easily understand-
able method can be more effective. FSE clearly indicates the criticality of performance
indicators without the need for the complex probabilistic reasoning inherent in Bayesian
networks. Also, FSE is particularly useful when dealing with imprecise or uncertain data.
Data may not always be precise or quantifiable in construction projects, and FSE allows for
a flexible and adaptive approach to handling such uncertainties. Bayesian networks, while
powerful in handling probabilistic relationships, might require a more precise dataset and
may be less accommodating to vagueness in the data. Thus, future studies could explore
the use of Bayesian networks to model the complex interdependencies among performance
indicators, providing a more nuanced understanding of how these indicators influence
each other in the context of EMP implementation. Nevertheless, this study’s findings still
provide significant knowledge about the PIs that can assess EMP implementation in water
supply construction projects.
Author Contributions: Conceptualization, A.M.F., M.F., M.E. and R.A.R.; methodology, A.R.R.,
A.M.F., M.F., M.E. and R.A.R.; validation, R.A.R.; formal analysis, A.R.R.; resources, A.M.F., M.F.,
M.E. and R.A.R.; data curation, A.M.F., M.F. and M.E.; writing—original draft preparation, A.R.R.;
writing—review and editing, A.M.F., N.S.R., M.F., M.E. and R.A.R.; visualization, A.R.R.; supervision,
N.S.R., M.F., M.E. and R.A.R.; project administration, M.F. and R.A.R.; funding acquisition: M.F. All
authors have read and agreed to the published version of the manuscript.
Funding: This study is supported and funded by the Deanship of Scientific Research at Imam
Mohammad Ibn Saud Islamic University (IMSIU), grant number IMSIU-RG23107.
Institutional Review Board Statement: Not applicable.
Informed Consent Statement: Not applicable.
Data Availability Statement: The data presented in this study are available upon request from the
corresponding authors. The data are not publicly available due to some data being proprietary or
confidential in nature. Therefore, data may only be provided with restrictions.
Conflicts of Interest: The authors declare no conflicts of interest.
References
1.
Worldbank. 2012. Available online: https://www.worldbank.org/content/dam/Worldbank/document/Kyrgyzstan-EMF-
health-project.pdf (accessed on 19 September 2023).
2.
Maund, K.; Gajendran, T.; Brewer, G. Key issues for implementation of environmental planning policy: Construction management
practice. Sustainability 2018,10, 2156. [CrossRef]
3.
Udawatta, N.; Zuo, J.; Chiveralls, K.; Zillante, G. Attitudinal and behavioural approaches to improving waste management on
construction projects in Australia: Benefits and limitations. Int. J. Constr. Manag. 2015,15, 137–147. [CrossRef]
4. Ofori, G. The environment: The fourth construction project objective? Constr. Manag. Econ. 1992,10, 369–395. [CrossRef]
5.
United Nations Environment Programme. 2021 Global Status Report for Buildings and Construction: Towards a Zero-Emission, Efficient
and Resilient Buildings and Construction Sector; UN: New York, NY, USA, 2021.

Sustainability 2024,16, 600 18 of 20
6.
Esin, T.; Cosgun, N. A study conducted to reduce construction waste generation in Turkey. Build. Environ. 2007,42, 1667–1674.
[CrossRef]
7.
Xing, J.; Ye, K.; Zuo, J.; Jiang, W. Control dust pollution on construction sites: What governments do in China? Sustainability 2018,
10, 2945. [CrossRef]
8.
Wieser, A.A.; Scherz, M.; Passer, A.; Kreiner, H. Challenges of a healthy built environment: Air pollution in construction industry.
Sustainability 2021,13, 10469. [CrossRef]
9.
Marzouk, M.; Abdelkader, E.M.; El-zayat, M.; Aboushady, A. Assessing environmental impact indicators in road construction
projects in developing countries. Sustainability 2017,9, 843. [CrossRef]
10.
Cheriyan, D.; Khamraev, K.; Choi, J.H. Varying health risks of respirable and fine particles from construction works. Sustain.
Cities Soc. 2021,72, 103016. [CrossRef]
11.
Li, X.; Zhu, Y.; Zhang, Z. An LCA-based environmental impact assessment model for construction processes. Build. Environ. 2010,
45, 766–775. [CrossRef]
12.
Yan, H.; Ding, G.; Li, H.; Wang, Y.; Zhang, L.; Shen, Q.; Feng, K. Field evaluation of the dust impacts from construc-tion sites on
surrounding areas: A city case study in China. Sustainability 2019,11, 1906. [CrossRef]
13.
Yuan, H. Barriers and countermeasures for managing construction and demolition waste: A case of Shenzhen in China. J. Clean.
Prod. 2017,157, 84–93. [CrossRef]
14.
Nielsen, K.J. A comparison of inspection practices within the construction industry between the Danish and Swedish Work
Environment Authorities. Constr. Manag. Econ. 2017,35, 154–169. [CrossRef]
15.
Tam, V.W.; Tam, C.M.; Shen, L.Y.; Zeng, S.X.; Ho, C.M. Environmental performance assessment: Perceptions of project managers
on the relationship between operational and environmental performance indicators. Constr. Manag. Econ. 2006,24, 287–299.
[CrossRef]
16.
Dahalan, N.H.; Rahman, R.A.; Ahmad, S.W.; Che Ibrahim, C.K.I. Public monitoring of environmental management plan
implementation in road construction projects: Key performance indicators. J. Eng. Des. Technol. 2023,ahead-of-print. [CrossRef]
17.
Dahalan, N.H.; Rahman, R.A.; Hassan, S.H.; Ahmad, S.W. Performance indicators for public evaluation of environmental
management plan implementation in highway construction projects. Int. J. Disaster Resil. Built Environ. 2023,ahead-of-print.
[CrossRef]
18.
Cleary, J.P.; Lamanna, A.J. Correlation of Construction Performance Indicators and Project Success in a Portfolio of Building
Projects. Buildings 2022,12, 957. [CrossRef]
19.
Shaawat, M.E.; Alqahtani, S.M.S.; Qasem, A.; Jamil, R.; Almohassen, A.S.; Bongwirnso, U.M. A Performance Quality Index to
Assess Professional Conduct of Contractors at Sustainable Construction Projects in Saudi Arabia. Sustainability 2023,15, 7500.
[CrossRef]
20.
Rajabi, S.; El-Sayegh, S.; Romdhane, L. Identification and assessment of sustainability performance indicators for construction
projects. Environ. Sustain. Indic. 2022,15, 100193. [CrossRef]
21.
Smeets, E.; Weterings, R. Environmental Indicators: Typology and Overview; European Environment Agency: Copenhagen,
Denmark, 1999.
22.
Keeble, J.J.; Topiol, S.; Berkeley, S. Using indicators to measure sustainability performance at a corporate and pro-ject level. J. Bus.
Ethics 2003,44, 149–158. [CrossRef]
23.
Agyekum, K.; Botchway, S.Y.; Adinyira, E.; Opoku, A. Environmental performance indicators for assessing sus-tainability of
projects in the Ghanaian construction industry. Smart Sustain. Built Environ. 2022,11, 918–950. [CrossRef]
24.
Alsulamy, S. Investigating critical failure drivers of construction project at planning stage in Saudi Arabia. Front. Eng. Built
Environ. 2022,2, 154–166. [CrossRef]
25.
Abdulmoneim, A.M.; Samadony, A.A.; Nosair, I. Identification and ranking the most significant risks of the mega construction
projects in Saudi Arabia. Saudi J. Civ. Eng. 2021,5, 35–49.
26.
Moshashai, D.; Leber, A.M.; Savage, J.D. Saudi Arabia plans for its economic future: Vision 2030, the National Transformation
Plan and Saudi fiscal reform. Br. J. Middle East. Stud. 2020,47, 381–401. [CrossRef]
27.
Alsolami, B.M. Identifying and assessing critical success factors of value management implementation in Saudi Arabia building
construc-tion industry. Ain Shams Eng. J. 2022,13, 101804. [CrossRef]
28.
Hassanain, M.A.; Al-Harogi, M.; Sanni-Anibire, M.O. Design for safety in the construction industry: A study of architecture and
engineering firms in Saudi Arabia. Facilities 2022,40, 895–911. [CrossRef]
29.
Ameyaw, E.E.; Chan, A.P. A fuzzy approach for the allocation of risks in public–private partnership water-infrastructure projects
in developing countries. J. Infrastruct. Syst. 2016,22, 04016016. [CrossRef]
30.
Dithebe, K.; Aigbavboa, C.O.; Thwala, W.D.; Oke, A.E. Factor analysis of critical success factors for water infra-structure projects
delivered under public–private partnerships. J. Financ. Manag. Prop. Construc.-Tion. 2019,24, 338–357. [CrossRef]
31.
Aiyetan, A.O.; Das, D.K. Evaluation of the factors and strategies for water infrastructure project delivery in South Africa.
Infrastructures 2021,6, 65. [CrossRef]
32.
Krosnick, J.A. Questionnaire design. In The Palgrave Handbook of Survey Research; Palgrave Macmillan: Cham, Switzerland, 2018;
pp. 439–455.
33.
Shirowzhan, S.; Sepasgozar, S.M.; Edwards, D.J.; Li, H.; Wang, C. BIM compatibility and its differentiation with interoperability
challenges as an innovation factor. Autom. Constr. 2020,112, 103086. [CrossRef]

Sustainability 2024,16, 600 19 of 20
34.
Osobajo, O.A.; Oke, A.; Omotayo, T.; Obi, L.I. A systematic review of circular economy research in the construction industry.
Smart Sustain. Built Environ. 2022,11, 39–64. [CrossRef]
35.
Munianday, P.; Radzi, A.R.; Esa, M.; Rahman, R.A. Optimal strategies for improving organizational BIM capabilities: PLS-SEM
approach. J. Manag. Eng. 2022,38, 04022015. [CrossRef]
36.
Radzi, A.R.; Rahman, R.A.; Doh, S.I.; Esa, M. Construction readiness for highway projects: Key decision criteria. J. Constr. Eng.
Manag. 2022,148, 04021196. [CrossRef]
37.
Rani, H.A.; Farouk, A.M.; Anandh, K.S.; Almutairi, S.; Rahman, R.A. Impact of COVID-19 on construction pro-jects: The case of
India. Buildings 2022,12, 762. [CrossRef]
38.
Farouk, A.M.; Zulhisham, A.Z.; Lee, Y.S.; Rajabi, M.S.; Rahman, R.A. Factors, challenges and strategies of trust in BIM-Based
construction projects: A case study in Malaysia. Infrastructures 2023,8, 13. [CrossRef]
39.
Kerr, C.; Nixon, A.; Wild, D. Assessing and demonstrating data saturation in qualitative inquiry supporting patient-reported
outcomes research. Expert Rev. Pharmacoecon. Outcomes Res. 2010,10, 269–281. [CrossRef] [PubMed]
40.
Seale, C. Grounding theory. In The Quality of Qualitative Research; Seale, C., Ed.; SAGE Publications: London, UK, 1999; pp. 87–105.
41. Moustakas, C. Phenomenological Research Methods; Sage Publications: New York, NY, USA, 1994.
42.
Chan, D.W.; Choi, T.N. Critical analysis of the application of the safe working cycle (SWC) interview findings from Hong Kong. J.
Facil. Manag. 2015,13, 244–265. [CrossRef]
43.
Oraee, M.; Hosseini, M.R.; Edwards, D.; Papadonikolaki, E. Collaboration in BIM-based construction networks: A qualitative
model of influential factors. Eng. Constr. Archit. Manag. 2022,29, 1194–1217. [CrossRef]
44. Braun, V.; Clarke, V. Using thematic analysis in psychology. Qual. Res. Psychol. 2006,3, 77–101. [CrossRef]
45.
Kaufman, M.M.; Wigston, D.L.; Perlman, E.B. Environmental evaluation of subdivision site developments. Environ. Manag. 2002,
29, 801–812. [CrossRef]
46.
Niemeijer, D.; de Groot, R.S. A conceptual framework for selecting environmental indicator sets. Ecol. Indic. 2008,8, 14–25.
[CrossRef]
47.
Dangi, M.B.; Chaudhary, R.P.; Rijal, K.; Stahl, P.D.; Belbase, S.; Gerow, K.G.; Fernandez, D.; Pyakurel, B. Impacts of environmental
change on agroecosystems and livelihoods in Annapurna Conservation Area, Nepal. Environ. Dev. 2018,25, 59–72. [CrossRef]
48.
Gilchrist, A.; Allouche, E.N. Quantification of social costs associated with construction projects: State-of-the-art review. Tunn.
Undergr. Space Technol. 2005,20, 89–104. [CrossRef]
49.
Ramani, T.; Zietsman, J.; Eisele, W.; Rosa, D.; Spillane, D.; Bochner, B. Developing Sustainable Transportation Performance Measures
for TXDOT’s Strategic Plan: Technical Report (No. FHWA/TX-09/0–5541-1); TX Transportation Institute: San Antonio, TX, USA, 2009.
50.
Anderson, J.L.; Muench, S.T. Sustainability trends measured by the greenroads rating system. Transp. Res. Rec. 2013,2357, 24–32.
[CrossRef]
51.
Fernandez-Sanchez, G.; Rodríguez-Lopez, F. A methodology to identify sustainability indicators in construction project
management—Application to infrastructure projects in Spain. Ecol. Indic. 2010,10, 1193–1201. [CrossRef]
52.
Gallego, I. The use of economic, social and environmental indicators as a measure of sustainable development in Spain. Corp. Soc.
Responsib. Environ. Manag. 2006,13, 78–97. [CrossRef]
53.
Park, J.W.; Ahn, Y.H. Development of a green road rating system for South Korea. Int. J. Sustain. Build. Technol. Urban Dev. 2015,6,
249–263. [CrossRef]
54.
Shen, L.; Lu, W.; Peng, Y.; Jiang, S. Critical assessment indicators for measuring benefits of rural infrastructure investment in
China. J. Infrastruct. Syst. 2011,17, 176–183. [CrossRef]
55. Chang, A.S.; Tsai, C.Y. Sustainable design indicators: Roadway project as an example. Ecol. Indic. 2015,53, 137–143. [CrossRef]
56.
Umer, A.; Hewage, K.; Haider, H.; Sadiq, R. Sustainability assessment of roadway projects under uncertainty using Green
Proforma: An index-based approach. Int. J. Sustain. Built Environ. 2016,5, 604–619. [CrossRef]
57.
Wang, Z.; He, X.; Zhang, C.; Xu, J.; Wang, Y. Evaluation of geological and ecological bearing capacity and spatial pattern along
du-wen road based on the analytic hierarchy process (AHP) and the technique for order of preference by similarity to an ideal
solution (TOPSIS) method. ISPRS Int. J. Geo-Inf. 2020,9, 237. [CrossRef]
58.
Yang, H.Z.; Wang, Z.F.; Dai, Q.M.; Feng, Z. Ecological impact assessment method of highways in Tibetan Plateau: A case study of
Gonghe-Yushu Expressway. J. Mt. Sci. 2020,17, 1916–1930. [CrossRef]
59.
Sun, C.; Xu, S.; Qi, W.; Chen, C.; Deng, Y.; Pei, N.; König, H.J. Biodiversity constraint indicator establishment and its optimization
for urban growth: Framework and application. Environ. Res. Lett. 2019,14, 125006. [CrossRef]
60. Bell, S.; Morse, S. Sustainability Indicators: Measuring the Immeasurable? Routledge: London, UK, 2012.
61. Goedkoop, M.; Spriensma, R. The Eco-Indicator 95; PRéConsultants: Amersfoort, The Netherlands, 1995; p. 85.
62.
Mihyeon Jeon, C.; Amekudzi, A. Addressing sustainability in transportation systems: Definitions, indicators, and metrics. J.
Infrastruct. Syst. 2005,11, 31–50. [CrossRef]
63.
Burns, J.; Cutshall, C.; Harper-Lore, B.; Knowles, C.; Lucas, D.W.; Peda, R.; Ryan, M.M.; Sanderson, L.; Walters, R.L.; White, T.;
et al. Environmental Stewardship Practices, Procedures, and Policies for Highway Construction and Maintenance; American
Association of State Highway and Transportation Officials (AASHTO) Standing Committee on the Environment; 2004. Available
online: https://trid.trb.org/view/745847 (accessed on 19 September 2023).
64.
Rajabi, M.S.; Radzi, A.R.; Rezaeiashtiani, M.; Famili, A.; Rashidi, M.E.; Rahman, R.A. Key Assessment Criteria for Organizational
BIM Capabilities: A Cross-Regional Study. Buildings 2022,12, 1013. [CrossRef]

Sustainability 2024,16, 600 20 of 20
65.
Zamani, S.H.; Rahman, R.A.; Fauzi, M.A.; Mohamed Yusof, L. Government pandemic response strategies for AEC enterprises:
Lessons from COVID-19. J. Eng. Des. Technol. 2022,ahead of print. [CrossRef]
66. Cooper, D.R.; dan Schindler, P.S. Business Research Methods, 8th ed.; Mc-Graw Hill: New York, NY, USA, 2003.
67. Fellow, R.; Liu, A. Research Methods for Construction, 2nd ed.; Blackwell: Oxford, UK, 2003.
68.
Cresswell, J.W.; Plano Clark, V.L. Designing and Conducting Mixed Method Research, 22nd ed.; Sage: Thousand Oaks, CA, USA, 2011.
69.
Bernard, H.R. Research Methods in Anthropology: Qualitative and Quantitative Approaches, 3rd ed.; Alta Mira Press: Walnut Creek,
CA, USA, 2002.
70.
Staplehurst, J.; Ragsdell, G. Knowledge sharing in SMEs: A comparison of two case study organisations. J. Knowl. Manag. Pract.
2010,11, 1–16.
71.
Chan, A.P.; Lam, P.T.; Wen, Y.; Ameyaw, E.E.; Wang, S.; Ke, Y. Cross-sectional analysis of critical risk factors for PPP water projects
in China. J. Infrastruct. Syst. 2015,21, 04014031. [CrossRef]
72. Norusis, M. SPSS 16.0 Advanced Statistical Procedures Companion; Prentice Hall Press: Hoboken, NI, USA, 2008.
73. Gorsuch, R.L. Factor Analysis; Erlbaum: Hillsdale, NJ, USA, 1983.
74.
Field, A. Correlation: Bivariate Correlation. Field: A Discovering Statistics Using SPSS; British Library: London, UK, 2009; pp. 175–179.
75.
Pallant, J. SPSS Survival Manual: A Step by Step Guide to Data Analysis Using IBM SPSS; McGraw-Hill Education: London, UK, 2020.
76. Osborne, J.W. What is rotating in exploratory factor analysis? Pract. Assess. Res. Eval. 2019,20, 2.
77.
Farouk, A.M.; Omer, M.M.; Rahman, R.; Romali, N.S. Effective approaches to water distribution network rehabilitation: Fuzzy
synthetic evaluation. In AIP Conference Proceedings; AIP Publishing: Melville, NY, USA, 2023; Volume 2688.
78.
Algahtany, M.; Radzi, A.R.; Al-Mohammad, M.S.; Rahman, R.A. Government Initiatives for Enhancing Building Information
Modeling Adoption in Saudi Arabia. Buildings 2023,13, 2130. [CrossRef]
79.
Churchill, G.A., Jr. A paradigm for developing better measures of marketing constructs. J. Mark. Res. 1979,16, 64–73. [CrossRef]
80.
Harbor, J. Engineering geomorphology at the cutting edge of land disturbance: Erosion and sediment control on construction
sites. Geomorphology 1999,31, 247–263. [CrossRef]
81.
Gamage, S.D.; Ambrose, M.; Kralovic, S.M.; Roselle, G.A. Water safety and Legionella in health care: Priorities, policy, and
practice. Infect. Dis. Clin. 2016,30, 689–712. [CrossRef]
82.
Bai, M.; Du, Y.; Chen, Y.; Xing, Y.; Zhao, P. Risk assessment of long gas and oil pipeline projects inducing landslide disasters
during construction. J. Perform. Constr. Facil. 2017,31, 04017063. [CrossRef]
83.
Manu, P.; Ankrah, N.; Proverbs, D.; Suresh, S. The health and safety impact of construction project features. Eng. Constr. Archit.
Manag. 2014,21, 65–93. [CrossRef]
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