M. SOLTANI Ahmed amin

The aim of this paper is to investigate agricultural sustainability as a collective issueinvolving multiple rather than individual farms. Through the utilization of energyconsumption as a proxy, we propose a novel methodology that evaluates theimpact of farm consolidations on agricultural sustainability while accounting forresource preferences. Our approach incorporates the ordered weighted averaging(OWA) operator within an inverse data envelopment analysis (IDEA) model toidentify post-merger farms that meet preset efficiency targets. We employ a DEAcross-efficiency (DEA-CE) procedure to select merger plans that maximizeagricultural sustainability for each preference scenario. By analysing a case study of43 tomato greenhouse farms in Biskra, northern Algeria, ourfindings demonstratethat mergers can significantly enhance agricultural sustainability, surpassing thepotential of individual farms by a factor of over 15. Additionally, the adoption ofthe most sustainable merger plan can lead to energy savings of more than 69%.Irrespective of the preference scenario, substantial energy savings in machinery,fertilizers, diesel, and electricity ranging from 22.92% to 73.73% were observed.These results emphasize the strategic role of merger processes in promotingagricultural sustainability and optimizing resource utilization.

In recent advancements in water quality assessment, a novel Water Quality Index (WQI) grounded in Data Envelopment Analysis (DEA) has been introduced, offering a more comprehensive and efficient tool for evaluating water resources. This presentation aims to delve into the conceptual underpinnings of this innovative WQI, highlighting its methodological framework and the specific advantages it brings compared to traditional assessment techniques. In addition, ongoing research efforts aimed at refining and expanding the application of this DEA-based index will be discussed, along with potential future directions for enhancing the robustness and scope of this tool, particularly in light of emerging environmental challenges and regulatory requirements.

Mergers and acquisitions (M&A) are strategic maneuvers that have long been intertwined with the hospitality sector, involving the asset consolidation among hotel groups through various partnerships. While extensive research has focused on applying data envelopment analysis (DEA) to assess hotel efficiency, the application of DEA to hotel M&A has been largely overlooked. In this paper, we introduce an inverse DEA (IDEA)-based methodology to evaluate optimal gains from pairwise consolidations among hotels. This approach effectively identifies all productive post-merger hotels, i.e., those mergers with substantial potential for generating resource gains. The relevance of our proposed methodology is demonstrated through a case study of 58 hotels in the Sultanate of Oman. The occurrence of inputs’ correlation in the case study prompted resorting to OWA aggregation of pairs of correlated inputs, which also enabled mimicking pessimistic, neutral and optimistic DMs. A particularly striking finding is that some productive post-merger combinations implicate strongly efficient hotels. Individually, these hotels do not require savings, yet when merged, they exhibit significant savings potential, demonstrating that sustainable resource management can be achieved through strategic consolidations. Specifically, our results show that potential post-merger gains can reach as high as 96.72% of the current resource usage. These figures are over three times the savings required for individual inefficient hotels. These insights underscore the potential of mergers not only within hospitality but across various resource-intensive industries to drive sustainable development by enhancing operational efficiency, reducing resource consumption, and informing policy that supports sustainable practices through strategic partnerships

Les barrages en Algérie se sont révélés être une solution efficace pour la collecte des eauxpluviales, contribuant ainsi à la gestion des problèmes liés à l'eau et à la sécurité alimentaire.Une stratégie de gestion a été mise en place pour optimiser la mobilisation, le stockage etl'exploitation de cette ressource vitale, particulièrement dans les régions à climat aride etsemi-aride, comme le sud-ouest du pays. Le bassin versant de Brézina, qui s'étend sur 3 678km², abrite le barrage de Kheneg Larouia, d'une capacité de 123 millions de mètres cubes. Cebarrage, mis en eau en 1991, est destiné principalement à l'irrigation de la palmeraie deBrézina, à un périmètre agricole de plus de 1 000 hectares, ainsi qu'à la pisciculture.L'étude menée a porté sur la qualité physico-chimique et bactériologique des eaux du barrage.Le 13 avril 2021, une mortalité massive de poissons a été observée dans l'oued Mouilah,l'affluent principal du barrage, où un étang s'est formé en raison de la séparation avec lebarrage, causée par une pénurie de pluies depuis plus de deux ans. Le volume d'eau dubarrage était alors de 3 millions de mètres cubes. Cependant, après de fortes pluies les 27 et28 avril, le barrage a reçu un apport de 25 millions de mètres cubes.Des prélèvements ont été effectués le 15 avril 2021 pour analyser la qualité de l'eau,notamment dans la zone impactée et non impactée. Les résultats ont révélé une minéralisationélevée dans la zone touchée, avec une salinité de 2,8 g/l, contre 0,9 g/l dans la zone nonimpactée, en raison de la géologie locale. Les matières en suspension (MES) étaientrelativement faibles, avec des valeurs comprises entre 36 et 136 mg/l.

Hotel quality rating systems play a crucial role in influencing tourists’ choices by shaping their expectations. This paper presents an innovative approach to objectively classify hotel quality within a stakeholder framework – considering perspectives of hotel management, market competitors, and virtual assessors. Our method combines the cross-efficiency paradigm of data envelopment analysis with the ordered weighted averaging operator for ranking the hotels, followed by K-means clustering for a robust five-star categorization. The efficacy of the approach is substantiated through the assessment of 58 hotels in Oman, showcasing its reliability and differentiation from the official hotel star-rating system.

Mergers and acquisitions (M&A) in the hospitality sector involve consolidating assets among hotels through partnerships. While data envelopment analysis (DEA) has been widely used for hotel efficiency analysis, little attention has been paid to hotel M&A. In this paper, a hybrid DEA methodology consisting of two stages is proposed to identify optimal matches among hotels to enhance sector performance. The initial stage employs an inverse Data Envelopment Analysis (IDEA) model to evaluate the maximum gains that could potentially be generated from pairwise consolidations among hotels. A DEA procedure that incorporates a standard DEA model and a greedy heuristic is developed in the second stage to identify the optimal pairs of hotel mergers. The optimal merger strategy for the entire hotel industry is determined from the complete set of hotels under consideration. The pertinence of the proposed methodology is shown through a sample of 58 hotels from the Sultanate of Oman.

This paper investigates the merits of Mergers & Acquisitions (M&A), as strategic decisions, in optimizing energy use. The impact of M&A decisions on reducing energy consumption and, as a result, GHG emissions, are evaluated through the inverse data envelopment analysis (DEA) approach. Moreover, a new index, identified as synergy merge index (SMI), is developed to measure the merger’s synergetic effect and determine accordingly the most productive merger plan. Although the proposed methodology could be applied in any sector where energy use optimization is of interest, the investigations were carried out in the greenhouse (GH) production through a sample of 30 GH farms from Al-Batinah region, Oman. The standard DEA model declared 40% of the GH farms efficient, with an average technical efficiency of 0.872, yet, the inverse DEA results revealed that nearly 45% of the productive mergers involve at least one efficient GH farm, i.e., energy gains are still possible even if the merging farms are presumably efficient. The post-merger GH farms showed a substantial potential for energy gains, ranging between 17.56 and 74.47%, on average, with the most significant proportions observed for electricity. The highest notable proportions of energy gains reached 81.26%, 78.13%, 89.74%, 75.60%, 90.36%, and 77.41% for fertilizers, machinery, water chemicals, electricity, and labor, respectively. The most productive merger plan
revealed that GH farm mergers farms can improve the energy savings by a factor of more than 4, where the share of electricity represents alone 94.92%, followed by 3.83% for fertilizers and only 0.60% for water. These findings unequivocally demonstrate that mergers can have a considerable impact on enhancing energy efficiency, which, along the way, provides strong support for the implementation of local policies that endorse mergers as available strategy to achieving optimal energy utilization.

In this paper, we examine, through an inverse data envelopment analysis (DEA) framework, theeffect of Mergers & Acquisitions (M&A) on improving energy savings. The investigations areconducted in the greenhouse (GH) agricultural production for pairwise consolidations of GH farmseven though the proposed approach can be effective in any industry where energy consumptionoptimization is of interest.Using a sample of 30 cucumber GH farms in the Batinah region of Oman, the standard DEA modelwas initially implemented, where 40% of the GH farms are found efficient, with an averagetechnical efficiency as high as 0.872. In the next stage, the inverse DEA model to pairwiseconsolidations among the GH farms and, in spite of the latter high level of efficiency, the resultsrevealed that almost 45% of the productive post-merger GH farms comprise at least one efficientGH farm, that is, energy savings are still conceivable even if the merging farms are seeminglyefficient. The average proportions of potential energy gains per post-merger GH farm fall between17.56% and 74.47%, with the highest figures reported for electricity. The post-mergers’ energygains for labor, machinery, fertilizers, chemicals, water and electricity reached proportions as highas 77.41%, 78.13%, 81.26%, 75.60%, 89.74% and 90.36%, respectively.In order to build a merger plan for the whole cucumber GH farm production sector, we developed asynergy merge index (SMI) to enable selecting the most productive post-merger GH farms based onrelated energy gains outcomes. The inclusion of the selected post-merger GH farms contributed toimproving the energy savings by a factor of more than 4, where the share of electricity representsalone 94.92%, followed by 3.83% for fertilizers and only 0.60% for water.These results leave no doubt that the contribution of mergers to energy efficiency can be substantial,which may support new policies to promote mergers as an established alternative for energy useoptimization.

Mergers and acquisitions (M&As) are strategic decisions that enable multiple firms’ consolidation in order to expand market share, gain access to new markets and enhance production capabilities. In this paper, we show that firms’ mergers can also have a sizable contribution to sustainability as opposed to individual firms. Using Inverse data envelopment analysis (InvDEA) as a methodological framework, we investigate the impact of mergers on enhancing sustainability in the agricultural production sector through optimizing energy requirements. The investigations are carried out on a sample of 51 tomato GH farms from the Biskra province in Algeria. Based on the data analysis, infrastructure, electricity and fertilizers emerged as major energy inputs, utilizing 21.76%, 21.13% and 17.86%, respectively, of the total energy inputs. With an energy use efficiency of 0.9506, an energy productivity of 1.188 kg/MJ and a proportion of energy loss of only 4.94%, the energy input-output analysis revealed a reasonable efficiency level of the energy usage. Meanwhile, the standard DEA model produced an average technical efficiency of 0.9784 with 70.59% of the GH farms declared efficient, leading to a percentage of energy saving index of only 2.08% for the tomato GH production. In the next major part of the study, the application of the inverse DEA approach to pairwise GH farms consolidations enabled the identification of 1023 productive mergers. The analysis of related outcomes indicated that the proportions of potential energy gains per merger vary, on average, between 20.68% and 78.33%, with the highest possible gain levels of 80.78%, 92.24%, 89.34%, 97.77%, 97.27%, 91.94%, 97.29% and 96.72% for labor, machinery, diesel, infrastructure, fertilizers, manure, pesticides and electricity, respectively. In order to build the most productive merger plan, a new DEA procedure is developed with as objective the maximization of the total merger energy gains. The application of the new approach enabled matching 48 out of 51 GH farms with the best likely partners. Under the best merger plan, the post-merger percentage of energy saving index for the tomato GH production was estimated to 70.23%, that is, 33 times the pre-merger value of the same index. Practically, these findings appear strategically important and may definitely support new policies that would promote mergers among GH farmers as a plausible option for enhancing agricultural sustainability through optimizing energy consumption and, hence, mitigating environmental impact. Though the proposed methodology is concerned with agricultural production, its application scope can be extended to other sectors where energy consumption might be a critical issue.

This paper examines the potential of Mergers & Acquisitions (M&As) as a novel approach to energy use optimization. The investigations are carried out through inverse data envelopment analysis (DEA). Contrary to traditional DEA approaches that restrict the energy savings to individual production units, the proposed methodology looks at the issue from the perspective of possible mergers among these units. The new methodology, which deploys over two stages, is applied to pairwise consolidations among 51 tomato greenhouse (GH) farms from Biskra, Algeria. An inverse DEA model is implemented in the first stage to discern all possibly productive post-merger GH farms, i.e., those mergers that are likely to generate energy gains. In the second stage, a new procedure is devised to find the best matchings among partners of potential mergers and derive the best merger plan out of the whole sample of GH farms. The results of the inverse DEA application revealed that potential gains per energy input can be substantial, reaching proportions as high as 80.78% and above. The derived optimal merger plan exhibited a post-merger energy saving index of 70.23%, that is, 33 times the index of the traditional DEA approach. Practically, these findings leave no doubt that mergers can contribute significantly to energy savings, enough to support new policies for promoting mergers as strategic options towards optimal energy consumption. The application scope of the proposed methodology can be duly extended to other sectors where energy optimization might be a critical issue.

The role of climate change in future streamflow is still very uncertain, especially over semi-arid regions. However, part of this uncertainty can be offset by correcting systematic climate models’ bias. This paper tries to assess how the choice of a bias correction method may impact future streamflow of the Cheliff-Mactaa-Tafna (CMT) rivers. First, three correction methods (quantile mapping (QM), quantile delta mapping (QDM), and scaled distribution mapping (SDM)) were applied to an ensemble of future precipitation and temperature coming from CORDEX-Africa, which uses two Representative Concentration Pathways: RCP4.5 and RCP8.5. Then, the Zygos model was used to convert the corrected time series into streamflow. Interestingly, the findings showed an agreement between the three methods that revealed a decline in future streamflow up to [−42 to −62%] in autumn, [+31% to −11%] in winter, [−23% to −39%] in spring, and [−23% to −41%] in summer. The rate of decrease was largest when using QM-corrected model outputs, followed by the raw model, the SDM-corrected model, and finally, the QDM-corrected model outputs. As expected, the RCP presents the largest decline especially by the end of the 21st Century.

This paper introduces a new index, identified as Composite Water Quality Index (CWQI), for assessing water quality. The novelty of CWQI is rooted in the practical significance of the methodological approach that is developed for its computation. The CWQI is computed within an inclusive framework that integrates data envelopment analysis (DEA) Cross Efficiency (CE) and the Ordered Weighted Averaging (OWA) operator, using Optimistic Closeness Values (OCVs) as input variables. The OCV, which measures the potential of a water quality parameter to reach its best quality status, sets a solid preliminary ground for the assessment process. The DEA-CE approach enables a collective evaluation of the water quality, which bestows more inclusiveness on the quality assessment process and, hence, more robustness of the CWQI. The OWA operator extends the standard role of CWQI, as solely a water quality measurement device, to incorporate the practical conditions of water treatment for future decision plans. The new methodology has been applied on a sample of 47 dams, described with 10 physicochemical parameters, located in Northern Algeria. Adopting a wide range of water treatment conditions, the results reveal “Kissir” and “Bougara” as the best and the worst water sources, respectively. Meanwhile, the ranking patterns of the dams are found almost the same. The k-means clustering identified the Oranie–Chott–Chergui (OCC) basin as the poorest water quality zone and Algerois–Hodna–Sommam (AHS) basin as the best

Algeria has always experienced a shortage of water resources in recent decades, and the situation has gotten worse when water quality has reached high levels of deterioration. The main objective of this thesis is to evaluate the surface water quality of a large number of Algerian dams in order to identify the most contaminated areas of the country and the factors that may lead to that. The present contribution has been divided into two-fold. The first study is devoted to assessing the water quality of forty-seven (47) dams, described with 10 Physico-chemical parameters, during 11 months (2019), and located over the four principal northern watersheds using a newly developed Water Quality Index based on the Data Envelopment Analysis (DEA-DQI) approach. The results of the proposed index revealed that 21.27%, 27.66%, 25.53%, 4.25%, and 21.27% of all dams are classified as " Poor", "Marginal", "Average", "Good", and "Excellent" water quality, respectively. The best water quality is found in the "Kissir" dam and the worst one in the "Bougara" dam. It is noteworthy that the dams with the worst water quality are located in the Oranie-Chott-Chergui watershed (OCC), in the western region, due to uncontrolled municipal-industrial discharges and agricultural fertilization practices. These results can also be interpreted by the agent of drought due to the impact of climate change. Among all the selected dams, Beni Haroun (BH) dam has been studied separately as a specific case in the second part of our contribution, given its major importance in the country. Under the same framework of an integrated approach with a dataset of 22 parameters observed during 11 years (2000-2010), several methods were employed, including the Canadian Council of Ministers of the Environment Water Quality Index (CCME-WQI), Principal Component Analysis and Factor Analysis (PCA/FA), K-means clustering and Ordinary Least Squares (OLS) to perform a comprehensive assessment of the water quality of BH dam and its upstream viz. Wadi Rhumel (WR). CCME-WQIs showed that BH dam is characterized by "Poor" water quality for drinking, irrigation, industry, and aquatic life, with indices of 17, 40, 42, and 32, respectively. Besides, K-means algorithm shows a clear similarity in water quality between BH dam and WR, which indicates it is the main pollution source of the dam's water. PCA/FA found that the water quality of BH dam is influenced by two major pollution factors: (i) natural processes and (ii) non-point source anthropogenic pollution. The most impressive finding of such a study is certainly the positive trend in WQIs using OLS, which is rather promising.

In this paper, we introduce a new approach, based on a unified framework incorporating Data Envelopment Analysis (DEA) and Ordered Weighted Averaging (OWA), for assessing water quality in contextual settings that involve a large number of hydrochemical parameters. In order to enhance discrimination among water sources, the DEA model is adopted with data-driven input variables, called “surrogate optimistic closeness values,” computed through an aggregation procedure that includes the observed values of the hydrochemical parameters with OWA weights. The proposed DEA-OWA methodology has been employed to assess the quality of 51 water samples, collected from irrigation wells in Sereflikochisar Basin, Turkey, by means of 19 hydrochemical parameters. Using different subjectivity levels, the Surrogate Water Quality Indices (SWQIs) that are produced are proven effective in enhancing discrimination among the water sources while enabling a more robust water quality-based ranking. The k-means analysis has been used for clustering the water quality of the wells into Excellent, Good, Permissible, and Unsuitable rather than using pre-set boundaries. Only one water source has been identified as Excellent, whereas 17.65%, 45.10%, and 35.29% of the sampled wells, respectively, are categorized with Good, Permissible, and Unsuitable water quality. Inferred from wells’ location, the results suggest that the groundwater might be drastically affected by saline water intrusion from Lake Tuz. The latter conclusion has been corroborated through a Tobit regression analysis.

The main goal of this paper is to study the water quality of the Beni Haroun (BH) Dam for different purposes using the Canadian Council Ministers Environment (CMME) index, which included 22 physicochemical parameters observed during 11 years. A principal component analysis (PCA) was performed to reduce the number of dimensions. To identify the sources of possible pollution, data from two other stations, Ain Smara (ST1) and Menia (ST2), situated upstream of the dam are also used. The results show that the calculated values of CCME indices at BH dam for drinking, irrigation, industry, and aquatic life purposes were 17, 40, 42, and 32, respectively, during the period from 2000 to 2010. These indices indicate a poor water quality according to CCME categorization scheme. In this context, Richards Diagram identified two kinds of irrigation water quality in the studied sites, including C3S1 (poor quality) and C4S1 (bad quality). Time series plots and Mann–Kendall test showed a positive trend in the water quality of the BH Dam. This study demonstrates the advantage of CCME index for interpreting spatial and temporal variations in surface water quality.

The present paper aims to develop a new Water Quality Index (WQI) based on Data Envelopment Analysis (DEA). Rather than using subjective weights of a judgmental process as inputs of the DEA model, we propose more objective variables, identified as “optimistic closeness values”, appropriately derived from the observed values of the hydrochemical parameters. The proposed approach was employed to assess the water quality of 47 dams in Algeria, defined with a dataset of 10 hydrochemical parameters. The results of the DEA-based WQI application revealed that (i) 21.27%, 27.66%, 25.53%, 4.25% and 21.27% of the total dams are categorized as “Poor”, “Marginal”, “Medium”, “Good” and “Excellent” water quality, respectively; (ii) the best water quality is found in “Kissir” and the worst one in “Bougara”; (iii) a priority scale on the hydrochemical parameters can be set for the treatment of water using the notion of slack value. Collectively, the new methodology has proven its effectiveness not only for categorizing or ranking sites based on water quality but also as an alternative tool to be used to assist decision-makers in allocating funds and managing water resources.

Given that northern Algeria is characterized by a significant rainfall level compared to the southern compartment (arid/desert climate), thus almost all Algerian dams are located in the north. From this starting point, data set of 10 Physicochemical parameters were used in the current study to assess the water quality of fifteen dams using Canadian Council ministers of Environment (CCME) Index for drinking purpose from January to November 2019. In addition, Unsupervised machine learning technique such as Fuzzy C-Means (FCM) clustering algorithm was performed to classify the dams' set into different groups (clusters). According to the obtained results, three main kinds of dams' water quality (WQ) were recorded i.e., Marginal, Fair and Good WQ with index values of 52- 64, 65-79 and 80-94 respectively. FCM algorithm has identified four distinct clusters, where "Ain Zada" dam represents the 3rd one, is characterized by hyper concentrations of (NH4 + , PO4 3- and NO3 - ) and (COD, BOD and OM) which reflecting non-point pollution from chemical agricultural fertilization and organic pollution of municipal wastewater discharges and industrial effluents respectively. In this survey, the application of WQI and FCM algorithm proved to be effective in interpreting the Big data of Spatio-Temporal water quality variation. Keywords: water quality; CCME WQI; FCM; dam; Algeria.

In this paper, we use an integrated approach to carry out a comprehensive evaluation of water quality in the Beni Haroun (BH) dam, the largest surface water resource in Algeria. Several techniques have been employed under the same framework, including the Canadian Council Ministers Environment Water Quality Index (CCME-WQI), principal component analysis and factor analysis (PCA/FA), the K-means clustering, and the ordinary least square (OLS) analysis. A data set of 22 physicochemical parameters has been collected, over a period of 11 years, from three sampling stations: Ain Smara (ST1) and Menia (ST2), both located upstream of “Wadi Rhumel,” and BH dam station (ST3), located at the dam site. The PCA/FA enables the identification of seven key factors that influence significantly BH dam water quality. The average values of CCME indices at the BH dam were 17, 40, 42, and 32 for drinking, irrigation, industry, and aquatic life purposes, respectively, which indicate poor water quality, according to the CCME categorization scheme. Besides, the K-means algorithm has been proven to be a very useful machine learning tool to detect that the major source of BH dam pollution is “Wadi Rhumel.” Finally, OLS analysis, along with the Mann-Kendall test, highlighted the positive trend of BH dam’s water quality.

The main objective of this paper is to evaluate the water quality of Beni Haroun (BH) Dam for different purposes using the Canadian Council Ministers Environment (CMME) index which included 22 physicochemical parameters for 11 years. Principal component analysis (PCA) was performed to reduce the involved data magnitude. In addition to the monitoring station of BH dami.e., “Grarem” (ST3), Ain Smara (ST1) and Menia (ST2) were also selected in order to know the water pollution sources. The calculated values of CCME indices at BH dam for drinking, irrigation, industry and aquatic life purposes were 17, 40, 42 and 32 respectively over the period of 2000 to 2010. These indices indicate a poor water quality according to CCME categorization scheme. In this context, Richards diagram identified two kinds of irrigation water quality in the studied sites, including C3S1 (poor quality) and C4S1 (bad quality). Time series plots and the Mann-Kendall test showed an increasing trend in the water quality of BH dam. This study demonstrates the advantage of CCME index for interpreting spatial and temporal variations in surface water quality.

The main goal of this paper is to study the water quality of Beni Haroun (BH) Dam for different purposes using Canadian Council Ministers Environment (CMME) index, which included 22 physicochemical parameters observed during 11 years. A principal component analysis (PCA) was performed to reduce the number of dimensions. To identify the sources of possible pollution, data from two other stations, Ain Smara (ST1) and Menia (ST2), situated upstream of the dam are also used. The results show that, the calculated values of CCME indices at BH dam for drinking, irrigation, industry and aquatic life purposes were 17, 40, 42 and 32; respectively during the period from 2000 to 2010. These indices indicate a poor water quality according to CCME categorization scheme. In this context, Richards Diagram identified two kinds of irrigation water quality in the studied sites, including: CS1 (poor quality) and CS1 (bad quality). Time series plots and Mann-Kendall test showed an increasing trend in the water quality of BH dam. This study demonstrates the advantage of CCME index for interpreting spatial and temporal variations in surface water quality.