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This study aims to examine the performance indicators (PIs) for assessing environmental management plan (EMP) implementation in road construction projects. The specific objectives are to compare the key PIs between environment auditors and environment officers and among project stakeholders, develop components to categorize interrelated key PIs and evaluate the effectiveness of interrelated key PIs and components.

Thirty-nine PIs were identified through a systematic literature review and in-depth interviews with environmental professionals. Subsequently, a questionnaire survey was designed based on this list of PIs and distributed to industry professionals. Sixty-one responses were collected in Malaysia and analyzed using the mean score ranking, normalization, agreement analysis, overlap analysis, factor analysis and fuzzy synthetic evaluation.

The analyses identified 18 key PIs: soil erosion, dust appearance, spill of chemical substance, construction waste, clogged drainage, overflowed silt trap, oil/fuel spills, changes in the colour of bodies of water, excessive cut and fill, vegetation depletion, changes in the colour of the runoff water, landslide occurrence, slope failures, irregular flood, public safety, deforestation, open burning and increased of schedule waste. Also, the key PIs can be grouped and ranked into the following four components: geological, pollution, environmental changes and ecological. Finally, the overall importance of the key PIs is between important and very important.

This study is a pioneer in quantitively examining the key PIs for EMP implementation in road construction projects. Researchers, industry practitioners and policymakers can use the findings to develop strategies and tools to allow public monitoring of EMP implementation.

The purpose of this paper is to report on the results of a study carried out to identify and analyse which potential subject areas may have impact on developments in the field of building maintenance (BM). That is, it is intended to contribute to the integration of new approaches so that building maintenance management (BMM) becomes as automated, digital and intelligent or smartness as possible in the near future.

The research approach has resulted in a theory that is essentially based on a qualitative design. The route followed was a literature review, involving the collection, analysis and interpretation of carefully selected information, mostly from recently published records. The data assembled and the empirical experience itself made it possible to present a comprehensive viewpoint and some future outlooks.

Five thematic areas considered as potentially impactful for BM developments have been highlighted, analysed and generically labelled as thematic base words, which are monitoring, automation, digitalisation, intelligence and smart. It is believed that these may be aspects that will lay the groundwork for a much more advanced and integrated agenda, featured by a high-tech vision.

This is thought to be a different way of looking at the problem, as it addresses five current issues together. Trendy technological aspects are quite innovative and advantageous for BMM, providing opportunities not yet widely explored and boosting the paradigm shift.

Accurate prediction of the structural condition of urban critical infrastructure is crucial for predictive maintenance. However, the existing prediction methods lack precision due to limitations in utilizing heterogeneous sensing data and domain knowledge as well as insufficient generalizability resulting from limited data samples. This paper integrates implicit and qualitative expert knowledge into quantifiable values in tunnel condition assessment and proposes a tunnel structure prediction algorithm that augments a state-of-the-art attention-based long short-term memory (LSTM) model with expert rating knowledge to achieve robust prediction results to reasonably allocate maintenance resources.

Through formalizing domain experts' knowledge into quantitative tunnel condition index (TCI) with analytic hierarchy process (AHP), a fusion approach using sequence smoothing and sliding time window techniques is applied to the TCI and time-series sensing data. By incorporating both sensing data and expert ratings, an attention-based LSTM model is developed to improve prediction accuracy and reduce the uncertainty of structural influencing factors.

The empirical experiment in Dalian Road Tunnel in Shanghai, China showcases the effectiveness of the proposed method, which can comprehensively evaluate the tunnel structure condition and significantly improve prediction performance.

This study proposes a novel structure condition prediction algorithm that augments a state-of-the-art attention-based LSTM model with expert rating knowledge for robust prediction of structure condition of complex projects.

This research investigates whether focal firms employ strategic supply chain information disclosure, focusing on the concealment of supplier and customer identities, as part of their supply chain environmental risk management strategies (supplier sustainability risk and customer loss risk, respectively).

Using a panel dataset of Chinese listed firms from 2009 to 2019 and utilizing the suppliers’ environmental punishment of peer firms (peer events) as an exogenous shock and employing ordinary least squares (OLS) estimation, this study conducts a regression analysis to test how focal firms disclose the identities of their suppliers and customers.

Our results indicate that focal firms prefer to hide the identities of their suppliers and customers following the environmental punishment of peer firms’ suppliers. In addition, supplier concentration weakens the effect of withholding supplier identities, whereas customer concentration strengthens the effect of hiding customer identities. Mechanism analysis shows that firms hide supplier identities to avoid their reputation being affected and hide customer identities to prevent the deterioration of customers’ reputations and thus impact their market share.

Our study reveals that reputation spillover is another crucial factor in supply chain transparency. It is also pioneering in applying the anonymity theory to explain focal firms’ information disclosure strategy in supply chains.

Gas transmission pipelines are at constant risk of gas leakage or fire due to various atmospheric environments, corrosion on pipe metal surfaces and other external factors. This study aims to reduce the human and financial risks associated with gas transmission by regularly monitoring pipeline performance, controlling situations and preventing disasters.

Facility managers can monitor the status of gas transmission lines in real-time by integrating sensor information into a building information modeling (BIM) 3D model. Using the Monitoring Panel plugin, coded in C# programming language and operated through Navisworks software, the model provides up-to-date information on pipeline safety and performance.

By collecting project information on the BIM and installing critical sensors, this approach allows facility manager to observe the real-time safety status of gas pipelines. If any risks of gas leakage or accidents are identified by the sensors, the BIM model quickly shows the location of the incident, enabling facility managers to make the best decisions to reduce financial and life risks. This intelligent gas transmission pipeline approach changes traditional risk management and inspection methods, minimizing the risk of explosion and gas leakage in the environment.

This research distinguishes itself from related work by integrating sensor data into a BIM model for real-time monitoring and providing facility managers with up-to-date safety information. By leveraging intelligent gas transmission pipelines, the system enables quick identification and location of potential hazards, reducing financial and human risks associated with gas transmission.

This study examines the effects of corporate governance mechanisms on voluntary corporate carbon disclosure in Bangladeshi firms.

To investigate the association between corporate governance mechanisms and corporate carbon disclosures, this study employs ordinary least square (OLS) methods. To mitigate the potential endogeneity concerns, the authors also introduce firm fixed effect (FE) and random effect (RE). Primarily, the study sample includes 250 firm-year observations over the period 2015–2019 for listed companies on the Dhaka Stock Exchange (DSE) in Bangladesh. Subsequently, corporate governance mechanisms that influence voluntary carbon disclosure were examined using both univariate and OLS models.

The findings of this study suggest that firms with a larger board size and more independent directors have a positive impact on the firm's intensity to disclose carbon-related information. However, no evidence has been found of the existence of an environmental committee, and the presence of female directors on the board tends to be associated with a higher level of voluntary corporate carbon disclosure.

The study offers necessary evidence of the determinants of corporate carbon disclosures, which will be useful for managers, senior executives, policymakers and regulatory bodies. To improve corporate governance practices and formulate separate sets of regulations and reporting criteria, disclosing extensive and holistic carbon-related information obligatory. Further, the outcomes of this study based on Bangladeshi firms can be comprehensive for other developing countries to take precautions to tackle the effect of global climate change.

Evaluating the implementation of environmental management plans (EMPs) in highway construction projects is essential to avoid climate change. Public evaluations can help ensure that the EMP is implemented correctly and efficiently. To allow public evaluation of EMP implementations, this study aims to investigate performance indicators (PIs) for assessing EMP implementation in highway construction projects. To that end, the study objectives are to compare the critical PIs between environment auditors (EAs) and environment officers (EOs) and among the main project stakeholders (i.e. clients, contractors and consultants), create components for the critical PIs and assess the efficiency of the components.

The paper identified 39 PIs from interviews with environmental professionals and a systematic literature review. Then a questionnaire survey was developed based on the PIs and sent to EAs and EOs. The data were analyzed via mean score ranking, normalization, agreement analysis, factor analysis and fuzzy synthetic evaluation (FSE).

The analyses revealed 21 critical PIs for assessing EMP implementation in highway construction projects. Also, the critical PIs can be grouped into four components: ecological, pollution, public safety and ecological. Finally, the overall importance of the critical PIs from the FSE is between important and very important.

To the best of the authors’ knowledge, this paper is the first-of-its-kind study on the critical PIs for assessing EMP implementation in highway construction projects.

Technology acceptance is a measure of that technology’s usefulness. Oil palm is one of the biggest contributors to Indonesia’s revenues, thus fueling its economy. Using remote sensing would allow a plantation to monitor and forecast its production and the amount of fertilizer used. This review aims to provide a policy recommendation in the form of a strategy to improve the added value of Indonesia’s oil palm and support the government in increasing oil palm production. This recommendation needs to be formulated by determining the users’ acceptance of remote sensing technology (state-owned plantations, private plantation companies and smallholder plantations).

This review’s methodology used sentiment analysis through text mining (bag of words model). The study’s primary data were from focus group discussions (FGDs), questionnaires, observations on participants, audio-visual documentation and focused discussions based on group category. The results of interviews and FGDs were transcribed into text and analyzed to 1) find words that can represent the content of the document; 2) classify and determine the frequency (word cloud); and finally 3) analyze the sentiment.

The result showed that private plantation companies and state-owned plantations had extremely high positive sentiments toward using remote sensing in their oil palm plantations, whereas smallholders had a 60% resistance. However, there is still a possibility for this technology’s adoption by smallholders, provided it is free and easily applied.

Basically, technology is applied to make work easier. However, not everyone is tech-savvy, especially the older generations. One dimension of technology acceptance is user/customer retention. New technology would not be immediately accepted, but there would be user perceptions about its uses and ease. At first, people might be reluctant to accept a new technology due to the perception that it is useless and difficult. Technology acceptance is the gauge of how useful technology is in making work easier compared to conventional ways.

Therefore, technology acceptance needs to be improved among smallholders by intensively socializing the policies, and through dissemination and dedication by academics and the government.

The social implications of using technology are reducing the workforce, but the company will be more profitable and efficient.

Remote sensing is one of the topics that people have not taken up in a large way, especially sentiment analysis. Acceptance of technology that utilizes remote sensing for plantations is very useful and efficient. In the end, company profits can be allocated more toward empowering the community and the environment.

Modern human society has continuous advancements that have a negative impact on the quality of the air. Daily transportation, industrial and residential operations churn up dangerous contaminants in our surroundings. Addressing air pollution issues is critical for human health and ecosystems, particularly in developing countries such as Egypt. Excessive levels of pollutants have been linked to a variety of circulatory, respiratory and nervous illnesses. To this end, the purpose of this research paper is to forecast air pollution concentrations in Egypt based on time series analysis.

Deep learning models are leveraged to analyze air quality time series in the 6th of October City, Egypt. In this regard, convolutional neural network (CNN), long short-term memory network and multilayer perceptron neural network models are used to forecast the overall concentrations of sulfur dioxide (SO₂) and particulate matter 10 µm in diameter (PM₁₀). The models are trained and validated by using monthly data available from the Egyptian Environmental Affairs Agency between December 2014 and July 2020. The performance measures such as determination coefficient, root mean square error and mean absolute error are used to evaluate the outcomes of models.

The CNN model exhibits the best performance in terms of forecasting pollutant concentrations 3, 6, 9 and 12 months ahead. Finally, using data from December 2014 to July 2021, the CNN model is used to anticipate the pollutant concentrations 12 months ahead. In July 2022, the overall concentrations of SO₂ and PM₁₀ are expected to reach 10 and 127 µg/m³, respectively. The developed model could aid decision-makers, practitioners and local authorities in planning and implementing various interventions to mitigate their negative influences on the population and environment.

This research introduces the development of an efficient time-series model that can project the future concentrations of particulate and gaseous air pollutants in Egypt. This research study offers the first time application of deep learning models to forecast the air quality in Egypt. This research study examines the performance of machine learning approaches and deep learning techniques to forecast sulfur dioxide and particular matter concentrations using standard performance metrics.

Organizations experience various issues with the optimum use of data. This study is qualitative research to identify and provide a helpful pattern for increasing the performance of sustainable supply chain management (SSCM).

The statistical population in the qualitative section includes managers and experts in the supply chain (SC) and food production. The data were collected via semi-structured interviews, and data saturation happens after the tenth interview. Then, the data were coded using grounded theory and qualitative research analysis. 384 questionnaires were distributed among employees via random sampling. SmartPLS software is used to investigate and analyze the relationships in the mentioned model through 13 core categories.

The findings indicate that organizational productivity and SC deficiencies are among the effective factors in the SSCM primarily identified by this study. Moreover, the findings propose that industry SC, macro policies, organizational performance, social factors, economic factors, organizational factors, political factors, technological factors, production and customer are likely to positively impact the SSCM, which have previously been documented by studies.

The model and concepts extracted from the responses of research participants show well that there are reasons and motivations for increasing the performance of SSCM. Also, the designed model shows well that the motives and reasons for turning to this system are satisfied due to its implementation.