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In late 2023, the United Nations conference on climate change (COP28), which was held in Dubai, encouraged a quick move from fossil fuels to renewable energy. Solar energy is one of the most promising forms of energy that is both sustainable and renewable. Generally, photovoltaic systems transform solar irradiance into electricity. Unfortunately, instability and intermittency in solar radiation can lead to interruptions in electricity production. The accurate forecasting of solar irradiance guarantees sustainable power production even when solar irradiance is not present. Batteries can store solar energy to be used during periods of solar absence. Additionally, deterministic models take into account the specification of technical PV systems and may be not accurate for low solar irradiance. This paper presents a comparative study for the most common Deep Learning (DL) and Machine Learning (ML) algorithms employed for short-term solar irradiance forecasting. The dataset was gathered in Islamabad during a five-year period, from 2015 to 2019, at hourly intervals with accurate meteorological sensors. Furthermore, the Grid Search Cross Validation (GSCV) with five folds is introduced to ML and DL models for optimizing the hyperparameters of these models. Several performance metrics are used to assess the algorithms, such as the Adjusted R2 score, Normalized Root Mean Square Error (NRMSE), Mean Absolute Deviation (MAD), Mean Absolute Error (MAE) and Mean Square Error (MSE). The statistical analysis shows that CNN-LSTM outperforms its counterparts of nine well-known DL models with Adjusted R2 score value of 0.984. For ML algorithms, gradient boosting regression is an effective forecasting method with Adjusted R2 score value of 0.962, beating its rivals of six ML models. Furthermore, SHAP and LIME are examples of explainable Artificial Intelligence (XAI) utilized for understanding the reasons behind the obtained results. [ABSTRACT FROM AUTHOR]

Waste management is a difficult and complicated issue. Since this waste may constitute a threat to persons and the environment, it is vital to guarantee that it is adequately collected. Therefore, new waste collection technologies that adopt modern technology and the Internet of Things (IoT) are the appropriate alternative. Determining the optimal intelligent technology for waste management and tuning its priorities is a complicated task that requires taking into account the dimensions of environmental, economic, and social sustainability. Thus, this paper introduces a hybrid methodology for multi-criteria decision-making (MCDM) that assesses intelligent waste management technologies utilizing IoT, taking into account multiple criteria. First, eleven decision-making criteria are determined to give a realistic approach. Second, the researchers demonstrated the innovative decision approach established on the combination of the Measurement of Alternatives and Ranking according to the COmpromise Solution (MARCOS) method and the Indifference Threshold-based Attribute Ratio Analysis (ITARA) method, called T2NN-ITARA, under a type-2 neutrosophic numbers (T2NNs) environment. This approach has been used to define the criteria's relative significance. Also, T2NN-MARCOS approach has been established to evaluate and classify intelligent waste management technologies based on IoT and to reveal the most sustainable solution. An illustrative case study evaluating four intelligent waste management technologies based on IoT is presented to prove the validity of the applied methodology. The findings show that the criteria of sustainability and standardization are the two most influential criteria in the evaluation and classification of intelligent waste management technologies based on IoT. It has also been determined that the RFID and GPRS blend for waste management is the most suitable intelligent technology for garbage management. Sensitivity and comparison analyzes were also accomplished to illustrate the stability, strength, and robustness of the suggested approach. The research provides significant information for government and waste practitioners. [ABSTRACT FROM AUTHOR]

The autonomous vehicle (AV) is one of the emerging technologies of the new age that has the potential to restructure transportation infrastructure. AVs are able to sense their surroundings and move around with control and self-sufficiency. AVs can contribute towards reducing traffic congestion on the roads, improving the quality of life, and achieving the highest levels of traffic safety. Thus, this type of vehicle can be integrated into the logistics industry. Due to the presence of several AVs, selecting a standard and efficient AV for logistics planning is a great challenge. The selection of an AV depends on many conflicting and essential criteria. Given its efficiency and reliability in dealing with conflicting criteria, a comprehensive multi-criteria decision-making (MCDM) approach was applied to solve the problem of selecting the optimal AV. However, the MCDM selection process is based on human judgment, which can be ambiguous. Accordingly, uncertainty was handled using type-2 neutrosophic numbers (T2NN). Initially, the method based on the removal effects of criteria (MEREC) was extended under T2NN and employed to assess and prioritize criteria. Then, the combined compromise solution (CoCoSo) method was extended under T2NN and applied to rank the candidate substitutions. To confirm the feasibility of the applied approach, an illustrative case study of four AVs was introduced. A sensitivity analysis was performed by changing the weights of the criteria and some other parameters to confirm the validity and stability of the proposed approach. In addition, a comparison analysis with other MCDM approaches was conducted to show the effectiveness and reliability of the applied approach. This research provides useful information for policymakers in the field of logistics. Finally, the results indicate that the velocity of AVs criterion is the most influential criterion in the selection of an intelligent AV. [ABSTRACT FROM AUTHOR]

Most studies in recent decades focused on transforming linear economics into circular through recovering and remanufacturing the products. Circular Economies (CE) aim to minimize the usage of resources by utilizing the waste in production as new or raw materials. Interconnectivity between parties in the industrial system provides decision-makers with rich information and anticipation of failure. Industry 4.0 technologies (I4.0) allow for handling such issues, protecting the environment by utilizing resources efficiently, and restructuring the industry to be smarter as well. This paper contributes to achieving cleaner production (CP), CE, and social for manufacturers through the linkage between 6R methodology with new technologies of I4.0 such as Blockchain technology (BCT) and big data analytical technology (BDA). In this paper, the authors proposed a Multi-criteria decision-making (MCDM) decision framework based on the best-worst method (BWM), Decision-Making trial and evaluation laboratory (DEMATEL), Technique for order of preference by similarity to ideal solution (TOPSIS), and Complex Proportional Assessment (COPRAS). The authors contributed to addressing the weaknesses and problems of these subjective MCDM methods through the cooperation of the neutrosophic theory with the usage of MCDM methods in this work. In the first stage, all criteria that influence sustainable manufacturer selection are specified using literature research on this topic. BWM-based neutrosophic theory was combined to get the criteria's weights with the aid of DEMATEL-based neutrosophic to obtain the least and best criteria used in BWM in the second stage. The optimal sustainable manufacturer was selected based on TOPSIS and COPRAS under neutrosophic theory in the third and fourth stages, respectively. Furthermore, a case study performed indicated manufacturer 2 (A2) is an optimal sustainable manufacturer in two ranking methods otherwise, manufacturer 4 (A4) is the worst sustainable manufacturer. The contribution of this work is to propose a hybrid MCDM with an uncertainty theory of neutrosophic for sustainable manufacturer selection based BDA-BCT with 6R. Sensitivity analyses were carried out to show the decision's flexibility in various scenarios. Finally, the consequences for management viewpoints were considered. [ABSTRACT FROM AUTHOR]

A blockchain, as a form of distributed ledger technology, represents the unanimity of replication, synchronization, and sharing of data among various geographical sites. Blockchains have demonstrated impressive and effective applications throughout many aspects of the business. Blockchain technology can lead to the advent of the construction of Digital Twins (DTs). DTs involve the real representation of physical devices digitally as a virtual representation of both elements and dynamics prior to the building and deployment of actual devices. DT products can be built using blockchain-based technology in order to achieve sustainability. The technology of DT is one of the emerging novel technologies of Industry 4.0, along with artificial intelligence (AI) and the Internet of Things (IoT). Therefore, the present study adopts intelligent decision-making techniques to conduct a biased analysis of the drivers, barriers, and risks involved in applying blockchain technologies to the sustainable production of DTs. The proposed model illustrates the use of neutrosophic theory to handle the uncertain conditions of real-life situations and the indeterminate cases evolved in decision-makers' judgments and perspectives. In addition, the model applies the analysis of Multi-criteria Decision Making (MCDM) methods through the use of ordered weighted averaging (OWA) and the Technique of Order Preference Similarity to the Ideal Solution (TOPSIS) to achieve optimal rankings for DT production providers based on consistent weighted decision-maker's judgments in order to maintain and to assure sustainability. An empirical study is applied to the uncertain environment to aid decision-makers in achieving ideal decisions for DT providers with respect to various DT challenges, promoting sustainability and determining the best service providers. The Monte Carlo simulation method is used to illustrate, predict, and forecast the importance of the weights of decision-makers' judgments as well as the direct impact on the sustainability of DT production. [ABSTRACT FROM AUTHOR]

Cyber-physical systems (CPS) and their Supervisory Control and Data Acquisition (SCADA) have attracted great interest for automatic management of industrial infrastructures, such as water and wastewater systems. A range of technologies can be employed for wastewater treatment CPS to manage risks and protect the infrastructures of water systems and their wastewater against cyberattacks. In this paper, we develop a novel risk assessment framework, named RAF-CPWS, which perfectly estimates the risks of water and wastewater technologies. To do this, a multi-criteria group decision-making (MCGDM) approach is designed by neutrosophic theory to assess the risks of wastewater treatment technologies (WWTTs). The proposed approach evaluates the best WWTTs, considering various economic, environmental, technological and cybersecurity, and social factors. A decision-making trial and evaluation laboratory (DEMATEL) is employed to evaluate the significance of the adopted factors in a real testbed setting. The proposed approach contributes to a comprehensive measure of WWTTs through several factors, revealing its high sustainability and security in assessing the risks of cyber-physical water and wastewater systems. [ABSTRACT FROM AUTHOR]

The demand for energy in Egypt has increased dramatically due to the steady increase in economic and societal development. To meet this need, the use of more renewable energy resources is an essential part of the solution to the ultimate shortage of energy. Because of the multitude of factors involved, the selection of the most suitable renewable energy systems (RESs) is a multi-criteria decision-making (MCDM) problem. There are a good number of works associated with the design of MCDM methods, particularly under uncertain and ambiguous situations. However, efficient incorporation of ambiguity and uncertainty in decision making is still a challenging task, and thus this work proposes a hybrid MCDM approach for selecting the components of a sustainable RES under uncertain environments, utilizing different triangular neutrosophic numbers to deal with unclear information. This proposed hybrid approach starts with defining the relative importance of the selected sustainable criteria by using an Analytic Hierarchy Process (AHP). Later, in the second phase, this hybrid approach combines VIKOR and the TOPSIS to rank the RESs for a real-life case study. The results obtained indicate that concentrated solar power is the most suitable source of renewable energy for Egypt, with photoelectric power the second most suitable. • Development of an evaluation model to select the most appropriate RES in Egypt. • A novel hybrid MCDM (AHP-VIKOR-TOPSIS) approach is proposed for RES selection. • Five criteria and their related sub-criteria are considered for such decision making. • The triangular neutrosophic numbers are introduced to dispose uncertainties. • A case study is proposed to illustrate the rationality of the proposed method. [ABSTRACT FROM AUTHOR]

Hydrogen has a considerable number of uses in society so researchers continue to seek sustainable production methods in terms of the materials involved in the reaction at an appropriate and reasonable cost. In addition to its uses in a wide variety of chemical processes, hydrogen is also one of the new alternatives to energy because its combustion has a very low environmental impact and the heat content of combustion is almost three times the heat content of the same mass of oil fuel. Unfortunately, the majority of hydrogen production systems rely on fossil fuels, so considerable research is required to ensure that such production systems are more sustainable. Sustainability in hydrogen production entails not harming the environment and producing in a clean, safe, reliable, and affordable manner. Traditionally, hydrogen production options have not received much attention from researchers with regard to assessing environmental, economic, and social dimensions. Consequently, we seek in this study to complete a comprehensive evaluation of the sustainability of hydrogen production options through the use of a multi-criteria decision making model. Further, decisions taken by experts in the energy field are subject to uncertainty so this paper suggests a model to evaluate hydrogen production options by a group of experts based on a set of criteria under the neutrosophic environment. The relative importance of the selected criteria is evaluated by the Analytic Hierarchy Process and Complex Proportional Assessment and Evaluation based on Distance from Average Solution are adopted to rank the hydrogen production options. An illustrative case considering seven hydrogen production options was studied to verify the feasibility of the proposed framework, while results comparisons were implemented to indicate the advantages of the hybrid MCDM methods. All the results obtained indicate that the wind electrolysis method is the key to sustainable hydrogen production in the near future, followed by the biomass gasification approach. • A novel MCDM framework is proposed for evaluating sustainable HPOs. • Uncertainties were incorporated into the sustainability assessment framework. • Five main criteria and their seventeen sub-indicators have been used. • The AHP method was used to determine the weights of the criteria and sub-indicators. • A case study with seven hydrogen production options are considered to verify. [ABSTRACT FROM AUTHOR]

The development of sustainable green buildings (GBs) is a major contribution to the preservation of the environment. Sustainable thinking in GB construction is not a supplementary element, but rather necessary to achieve the building's functional, economic, and environmental efficiency in order to preserve resources and meet current and future needs. In particular, developing countries can apply the idea of sustainability in GBs by following international policies and standards, combined with their local characteristics, to construct GBs that are aligned with the environment and are in line with the available local capabilities and resources. The paper focuses on the dimensions and indicators of sustainable design for GBs in developing countries to achieve the positive dimensions of building sustainability, such as preserving energy and natural resources, water management, adaptation to the surrounding environment, and respecting the needs of its users. We assess and prioritize the dimensions and indicators of GBs through the use of a multi-criteria decision-making (MCDM) method under a neutrosophic environment. Initially, the Delphi method is employed to capture preference and to determine the dimensions and their indicators in addition to provide preference among sub-indicators. The relative importance of the selected dimensions and indicators is assessed through the analytical hierarchy method (AHP) method. The results indicate that the water efficiency dimension is the most significant, with a weight of 0.330, while the energy efficiency dimension is the least significant for GBs in developing countries, with a weight of 0.100. The paper concludes with a set of administrative implications for applying sustainable development strategies in GBs. [ABSTRACT FROM AUTHOR]

Reverse logistics (RL) is considered the reverse manner of gathering and redeploying goods at the end of their lifetime span from consumers to manufacturers in order to reutilize, dispose, or remanufacture. Whereas RL has many economic benefits, it presents compromises to businesses that wish to remain competitive but be responsible global citizens in terms of social, environmental, risk, and safety aspects of sustainable development. Managing RL systems therefore is considered a multifaceted mission that necessities a significant level of technology, infrastructure, experience, and competence. Consequently, various commerce institutions are looking to outsourcing their RL actions to third-party reverse logistics providers (3PRLPs). In this work, a novel hybrid multiple-criteria decision-making (MCDM) framework is proposed to classify and choose 3PRLPs, which comprises the analytic hierarchy process (AHP) technique, and technique for order of preference by similarity to ideal solution (TOPSIS) technique under neutrosophic environment. Accordingly, AHP is availed for defining weights of key dimensions and their subindices. In addition, TOPSIS was adopted for ranking the specified 3PRLPs. The efficiency of the proposed approach is clarified through application on a considered car parts manufacturing industry case in Egypt, which shows the features of the combined MCDM methods. A comparative and sensitivity analyses were performed to highlight the benefits of the incorporated MCDM methods and for clarifying the effect of changing weights in selecting the sustainable 3PRLP alternative, respectively. The suggested framework is also shown to present more functional execution when dealing with uncertainties and qualitative inputs, demonstrating applicability to a broad range of applications. Ultimately, the best sustainable 3PRLPs were selected and results show that social, environmental, and risk and safety sustainability factors have the greatest influence when determining 3PRLPs alternatives. [ABSTRACT FROM AUTHOR]

Many countries that derive their energy from fossil fuel sources are turning to renewable, and environmentally friendly energy sources to alleviate the growing concerns about global warming and environmental issues. Bioenergy technology is one of the potential alternatives for renewable energy systems. This paper evaluates sustainable bioenergy production technologies through a case study in Egypt. A comprehensive methodology has been developed in which experts and decision makers are able to use linguistic terms to express their opinions and participate in the decision making required to prioritize the dimensions that affect the sustainability of bioenergy production technologies. Identification of the optimum bioenergy production technology and setting its priorities is a difficult task; many dimensions must be taken into account in the evaluation process, such as the environmental, technical, economic, and social dimensions and their sub-indicators. This paper therefore applies a hybrid multi-criteria decision-making (MCDM) approach that takes into account many conflicting dimensions. In addition, handling of uncertainty was conducted under a neutrosophic environment using trapezoidal neutrosophic numbers (TNNs). Initially, the Decision-Making Trial and Evaluation Laboratory (DEMATEL) method was employed to identify the relative importance of the dimensions and their sub-indicators. Then, Evaluation based on Distance from Average Solution (EDAS) was employed to rank alternatives. An illustrative case considering seven bioenergy production technologies was studied to confirm the feasibility of the suggested approach, and comparisons were performed to show the advantages of the hybrid MCDM techniques. Sensitivity analysis is conducted to prove the validity and stability of the developed framework with variations in weights. The results of this work provide useful information for energy policy decision makers and the results of the case study indicate that the conversion of agricultural and municipal wastes to biogas is the most suitable sustainable bioenergy technology with a weight of 0.996 followed by oil crops to biodiesel technology with weight 0.539. Image 1 • A new MCDM is proposed for the assessment of bioenergy production technologies. • The proposed approach can address uncertainties and imprecision. • 4 main dimensions and 14 sub-indicators are used in the sustainability assessment. • DEMATEL ranked the identified dimensions and EDAS prioritized the alternatives. • A case study is proposed to illustrate the rationality of the proposed approach. [ABSTRACT FROM AUTHOR]

The development of economic activities and social progress index leads to the governmental considerations for the environmental challenge's issues. The Green Credit Policy (GCP) in China for manufacturing, as a part of a sustainable finance package, initiatives restrictions with suppliers to reduce harmful pollution for the environment. The study mainly validates the impact of GCP on manufacturing for diminishing the emerged pollution to the environment. The study develops Neutrosophic Multiple-Criteria Decision-Making Framework (N-MCDMF) according to neutrosophic theory and various MCDM methods of grey relational analysis (GRA), analytic network process (ANP), the Decision-Making Trial and Evaluation Laboratory technique (DEMATEL), and the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) to support the decision-makers with highly systematic procedures in the uncertain and inconsistent environmental conditions. The N-MCDMF evaluates the conditions of GCP and recommends the optimal Supply Chain Management (SCM) in manufacturing alternatives. A case study is presented for the validation of the issues of applicability and flexibility for the proposed N-MCDMF. The results obtained from the implementation of the N-MCDMF indicates the applicability and flexibility of the proposed approach. In addition, results show that SCM in manufacturing can provide more cooperation for the environment to reduce harmful pollution and to attain sustainability for achieving motivations under the restrictions of GCP. [ABSTRACT FROM AUTHOR]

The trend of considering supply chain sustainability with an absence of attention to sustainability risks may disturb the business future. The role of risk management is concentrated in identifying and analyse the influence of loss to business, social and environment, get ready by coverage budget, and derive strategies to protect supply chain sustainability against these risks. Risk management assists the company's performance to be more confident in supply chain sustainability decisions. The extent of the risk is based on the organization's magnitude, so the sustainable supply chain risk management strategies of large firms require to be more advanced. The purpose of this research is the estimation of sustainable supply chain risk management (SSCRM). The proposed methodology in this paper is a combination of plithogenic multi-criteria decision-making approach based on the Technique in Order of Preference by Similarity to Ideal Solution (TOPSIS) and Criteria Importance Through Inter-criteria Correlation (CRITIC) methods. In order to evaluate the proposed model, we present a real-world case study of the Telecommunications Equipment Company. The results show the importance of each criterion to evaluate SSCRM and the ranking of the three telecommunications equipment categories. [ABSTRACT FROM AUTHOR]

Supply chain sustainability has become one of the most attractive decision management topics. There are many articles that have focused on this field presenting many different points of view. This research is centred on the evaluation of supply chain sustainability based on two critical dimensions. The first is the importance of evaluation metrics based on economic, environmental and social aspects, and the second is the degree of difficulty of information gathering. This paper aims to increase the accuracy of the evaluation. The proposed method is a combination of quality function deployment (QFD) with plithogenic aggregation operations. The aggregation operation is applied to aggregate: Firstly, the decision maker's opinions of requirements that are needed to evaluate the supply chain sustainability; secondly, the evaluation metrics based on the requirements; and lastly, the evaluation of information gathering difficulty. To validate the proposed model, this study presented a real world case study of Thailand's sugar industry. The results showed the most preferred and the lowest preferred metrics in order to evaluate the sustainability of the supply chain strategy. [ABSTRACT FROM AUTHOR]

Agriculture is essential for many countries that depend on crops for food production and security. So, evaluating irrigation systems and selecting the best one is critical and gives various benefits such as water use efficiency, productivity, and agricultural development. This paper proposed a framework for conserving water and increasing effectiveness by using a suitable irrigation system. We used the multi-criteria decision-making (MCDM) methodology to deal with conflict criteria in the evaluation process. We used two MCDM methods such as Criteria Importance Through Inter-Criteria Correlation (CRITIC) method to compute the weights of the irrigation system criteria, and the spherical fuzzy double normalization-based multiple aggregations (DNMA) method to rank the irrigation systems (alternatives). The main advantage of CRITIC method computes the conflict and variability of the criteria by calculate weights of them. The main advantage of DNMA is used the two normalization method to rank the alternatives. These methods are integrated with spherical fuzzy set (SFS) fuzzy information in the assessment process. It has three values: membership, non-membership, and hesitant degrees to overcome uncertainty in the assessment steps. The proposed methodology is applied to a case study to show its performance. This study used 20 criteria of irrigation systems and 10 irrigation systems (alternatives) to select the best alternative. The results are discussed from the perspective of five experts. The sensitivity analysis is conducted to show the stability of the results. The comparative analysis is performed to show the validity and effectiveness of the proposed methodology. The results show the proposed methodology is more robust compared to other methods. • Irrigation systems are evaluated under uncertainty model. • CRITIC and DNMA methods are used to evaluate the irrigation systems. • The spherical fuzzy set used to deal with uncertainty information in the evaluation process. • Intensive verifications with other challenging approaches are discussed. • A case study is proposed to illustrate the rationality of the proposed model. [ABSTRACT FROM AUTHOR]