A comprehensive analysis of weighting and multicriteria methods in the context of sustainable energy

An integrated multicriteria decision making framework for the selection of waste cement dust filled automotive brake friction composites

This work discusses selecting optimal brake friction composite alternatives based on an integrated MABAC (multi-attributive border approximation area comparison) and AHP (analytic hierarchy process) approach. Therefore, non-asbestos automotive brake friction composites containing varying proportions of cement dust (50 to 0 wt%) and barium sulfate (0 to 50 wt%) were developed and tribo-evaluated on a Krauss machine following European regulations. Composite made up of 30 wt% cement dust and 20 wt% barium sulfate had the highest friction coefficient (0.361), lowest variability coefficient (0.598), and maximum recovery (123.27%). The composite with the least fading (15.36%) included 50 wt% cement dust, whereas the composite with the lowest wear (9.10 g) and the least frictional fluctuations (0.271) contained 50 wt% barium sulfate. By AHP, the friction coefficient (0.1989), fade (0.1696), recovery (0.1551), and wear (0.1412) were selected as the essential criteria in the performance assessment. Based on the MABAC ranking evaluation, the composite comprises 20 wt% barium sulfate and 30 wt% cement dust has the best tribological profile, whereas the composites of solely cement dust or barium sulfate have the poorest tribological profile. The acquired ranking results were confirmed using other decision-making models and subjected to sensitivity analysis to demonstrate their robustness.

Can digitalization effectively promote green energy efficiency? The linear and nonlinear relationship analysis

In light of the integration of digitalization and the energy revolution, digitalization can be integrated into the energy industry to develop energy-saving technologies and improve resource allocation efficiency. On the basis of 2013-2019 Chinese provincial panel data, this paper measures the level of green energy efficiency based on the super-EBM-DEA model and analyzes the linear relationship, nonlinear relationship, and potential mechanism between digitalization and green energy efficiency. The findings indicate that (1) overall, both China's digitalization and green energy efficiency formed a steady upward trajectory during the sample period. Digitalization showed a spatial characteristic of extending and spreading from the eastern region to the central and western regions. Green energy efficiency was characterized by obvious regional heterogeneity. (2) Progress in digitalization has a significant driving effect on green energy efficiency. Subdimensional analysis shows that this driving effect mainly comes from digital development and digital transactions. (3) The impact of digitalization on green energy efficiency presents a threshold effect of economic agglomeration (with a threshold of 0.0257 and a marginally increasing, positive driving trend) and population agglomeration (with a threshold of 4.2750 and a marginally decreasing, positive driving trend). (4) Decomposing changes in green energy efficiency into scale efficiency and pure technical efficiency, this study shows that pure technical efficiency gains due to digitalization are the main driver of green energy efficiency improvements. Finally, some specific policy recommendations are proposed.

Evaluating urban environmental quality using multi criteria decision making

In the urban environment, the quality refers to the capacity that provides and fulfills the material and spiritual needs of inhabitants. In order to improve the quality of urban life and standard of living for their citizens, planners and managers strive to raise Urban Environmental Quality. The objective of this study is to evaluate the quality of urban environment through the spatial analysis of a multi-criteria decision making (MCDM) method utilizing CRITIC. This research is conducted in district 4 and district 2 of the Tabriz Metropolis Municipality. In order to determine the quality of an urban environment, air pollution, vegetation coverage, land surface temperature, production of waste, population density, noise pollution, health care per capita, green spaces per capita, recreational spaces per capita, and distance from fault lines are used. After evaluating and producing environmental quality maps in two separate districts, 10 indicators were tested for significance and a comparative evaluation of two districts was conducted in order to determine which district was in better condition based on a statistical analysis of the T-test results. In accordance with the CRITIC method, there are significant differences between averages of waste production, population density, noise pollution, distance from fault lines, Land Surface Temperature, Normalized difference vegetation index, and distance from fault lines between the two districts. It appears that recreational space, air pollution, health care per capita, and green space per capita are not meaningfully different on averages. The preparation of environmental quality maps reveals the importance of meaningful indicators at the neighborhood level in two urban districts. In both districts by strengthening the continuity of the landscape through the development of ecological corridors and an increase in per capita can contribute to the improvement of the quality of the urban environment.

Prediction of soil erosion and sediment yield in an ungauged basin based on land use land cover changes

Soil erosion is a significant problem in the agriculture sector and the environment globally. Susceptible soil erosion zones must be identified and erosion rates evaluated to decrease land degradation problems and increase crop productivity by protecting soil fertility. Therefore, a research study has been carried out in the Ponnaniyar River basin, an ungauged tributary of the Cauvery basin in India, primarily used for agriculture. The main purpose of this study is to assess soil erosion (SE) and sediment yield (SY) for the future in an ungauged basin by utilizing the projected land use/land cover (LULC) map of the study area. Additionally, Landsat 8 satellite dataset was only used for the classification and prediction of LULC to eliminate the variation between the resolution, bands and its wavelength of different satellites datasets. To achieve the goals of this study, three phases were followed. First, the LULC of the study area was classified using a Random Trees Classifier (RTC), a machine learning technique, followed by the projection of land cover using a Cellular Automata-based Artificial Neural Network (CA-ANN) model. The driving factors for this model include digital elevation model (DEM), slope, distance to roads, settlements, and water bodies. The accuracy level of the projected LULC map was determined by comparing it with the classified LULC map of the study area, and the results showed an overall accuracy (OA) of 85.35 percentage and a kappa coefficient (K) of 0.74, respectively. Second, the projected LULC map was used in the land management factor (C) and conversation practice factor (P) of the Revised Universal Soil Loss Equation (RUSLE) model to assess soil erosion. The model was integrated with the sediment delivery ratio (SDR) to estimate sediment yield within the study area. The accuracy of the generated erosion map based on the classified and projected LULC for the year 2022 was determined using the receiver operating characteristic curve (ROC) curve, and it was found to be in satisfactory agreement. Finally, for effective soil and water conservation measures, the basin was divided into 13 sub-watersheds (SWs) using terrain analysis in geographical information system (GIS). The SWs were prioritized based on the mean soil loss in the 4-year interval from 2014 to 2030 and integrated using the weighted average method to determine the final prioritization. From these findings, SW 11, SW 9, SW 12, and SW 1 are extremely affected by soil erosion, and immediate implementation of water harvesting structures is required for soil conservation. Also, this research might be useful for decision-makers and policymakers in land management.

A GIS-based land suitability model for agricultural tractors in CALABARZON Region, Philippines

Agricultural machinery distribution programs are key components of the Philippine government to strengthen its economic productivity. However, concerns were reported that some of the distributed machines were under-utilized or unutilized in their respective farms. This study is focused on the development of a GIS-based model of determining suitable lowland rice areas for two-wheel and four-wheel tractors. A numerical rating system was designed that assigns a suitability score from three criteria, namely slope, road network proximity, and flood risk, based on established scales. Analytical Hierarchy Process was used as an approach to determine the relative influence in which results show that slope has the greatest weight (65%) followed by flood risk and road network proximity with 20% and 15%, respectively. The total suitability score (TSS) is then determined by getting the weighted average of the individual suitability scores. Land suitability mapping on the study area, the CALABARZON region, show that majority of the lowland rice areas are highly suitable for two-wheel and four-wheel tractors (81.39%) while some areas fall under marginal (15.03%) and moderate suitability (3.58%). Geotagged locations of the distributed agricultural tractors from 2015 to 2020 reveal that 78% of the distributed two-wheel tractors and 80% of four-wheel tractors in the region are situated in highly suitable areas. The GIS-based suitability model generated in this study can be utilized by the government to improve its machine distribution programs of two-wheel and four-wheel tractors in lowland rice farms.

Optimization and Performance Evaluation of a Foam Plugging Profile Control Well Selection System

Most of the oilfields are currently experiencing intermediate to late stages of oil recovery by waterflooding. Channels were created between the wells by water injection and its effect on the oil recovery is less. The use of water plugging profile control is required to control excessive water production from an oil reservoir. First, the well selection for profile control using the fuzzy evaluation method (FEM) and improvement by random forest (RF) classification model is investigated. To identify wells for profile control operation, a fuzzy model with four factors is established; then, a machine learning RF algorithm was applied to create the factor weight with high accuracy decision-making. The data source consists of 18 injection wells, with 70% of the well data being utilized for training and 30% for model testing. Following the fitting of the model, the new factor weight is determined and decisions are made. As a consequence, FEM selects 7 out of 18 wells for profile control, and by using the factor weight developed by RF, 4 out of 18 wells are chosen. Then, the profile control is conducted through a foam system proposed by laboratory experiments. A computer molding group numerical simulation model is created to profile the wells being selected by both methods, FEM and RF. The impact of foam system plugging on daily oil production, water cut, and cumulative oil production of both methods are contrasted. According to the study, the reservoir performed better when four wells were chosen by the weighting system developed by RF as opposed to seven wells that were chosen using the FEM model during the effective period. The weighting model developed by RF accurately increased the profile control wells' decision-making skills.

Inter-sectoral prioritization of climate technologies: insights from a Technology Needs Assessment for mitigation in Brazil

Technological development is key for national strategies to cope with the Paris Agreement's goals. Technology Needs Assessments (TNAs) aim to identify, prioritize, and diffuse climate change mitigation and/or adaptation technologies in developing countries. Their methodology includes a multi-criteria decision analysis (MCDA) framework but, although many countries already conducted a TNA, literature lacks discussions on country-specific processes for a TNA, as it usually follows a one-size-fits-all approach. This paper provides empirical evidence on the importance of country-driven processes that help shaping international programmes into country-specific needs and capabilities. It presents lessons learned from a tailored process for identification, prioritization, and selection of mitigation technologies in the scope of a TNA project for Brazil, an exceptional case of a developing country with strong capacity in integrated assessment modelling (IAM) scenarios for guiding its climate strategies. A previous IAM scenario result allowed pre-selecting technologies in six key economic sectors, while other TNAs prioritized no more than three. This allowed the elaboration of an overall ranking from the MCDA, in contrast to sectoral rankings that are mostly employed in other countries' TNAs. The overall ranking serves not only as a basis for the selection of priority technologies but also provides information on the integrated innovations framework for climate technologies in the country. Further specific findings of the tailored Brazilian TNA approach are discussed in the paper in order to call for the importance that a technology transfer project should not only be country-driven but also conducted through a country-specific process.

SUPPLEMENTARY INFORMATION

The online version contains supplementary material available at 10.1007/s11027-022-10025-6.

Designing evaluation framework for the empirical assessment of COVID-19 mobile apps in Pakistan

The significant proliferation in the mobile health applications (Apps) amidst Coronaviruses disease 2019 (COVID-19) resulted in decision making problems for healthcare professionals, decision makers and mobile users in Pakistan. This decision making process is also hampered by mobile app trade-offs, multiple features support, evolving healthcare needs and varying vendors. In this regard, evaluation model for mobile apps is presented which completes in three different phases. In first phase, features-based criteria is designed by leveraging Delphi method, and twenty (20) mobile apps are selected from app stores. In second stage, empirical evaluation is performed by using hybrid multi criteria decision approaches like CRiteria Importance Through Inter-criteria Correlation (CRITIC) method has been used for assigning weights to criteria features; and Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) method has been used for assessment of mobile app alternatives. In last step, decision making is performed to select the best mobile app for COVID-19 situations. The results suggest that proposed model can be adopted as a guideline by mobile app subscribers, patients and healthcare professionals.

Risk Treatment for Energy-Oriented Production Plans through the Selection, Classification, and Integration of Suitable Measures

With rising electricity prices, industries are trying to exploit opportunities to reduce electricity costs. Adapting to fluctuating energy prices offers the possibility to save electricity costs without reducing the performance of the production system. Production planning and control play key roles in the implementation of the adjustments. By taking into account the price forecasts for the electricity markets in addition to machine utilization, work in process, and throughput time, an energy-oriented production plan is set up. The electrical energy is procured based on this plan and the associated load profile. Deviations from the forecast and the purchased amount of electricity lead to high penalties, as they can destabilize the energy system. For manufacturing companies, this means that machine failures and other unexpected events must be dealt with in a structured manner to avoid these penalty costs. This paper presents an approach to selecting, classifying, and integrating suitable measures from existing risk treatment paths into the production schedule. The selection of measures is based on a hybrid multi-criteria decision-making method in which the three relevant criteria, namely, cost, energy flexibility, and risk reduction, are weighted by applying both an analytic hierarchy process and entropy, and they are then prioritized according to multi-attribute utility theory. In the following, the subdivision into preventive and reactive measures is made in order to choose between the modification of the original plan or the creation of backup plans. With the help of mathematical optimization, the measures are integrated into the production schedule by minimizing the cost of balancing energy. The approach was implemented in MATLAB® and validated using a case study in the foundry industry.

A Hybrid Intuitionistic Fuzzy-MEREC-RS-DNMA Method for Assessing the Alternative Fuel Vehicles with Sustainability Perspectives

Alternative fuel vehicles (AFVs) offer opportunities to lower fuel costs as well as to reduce greenhouse gas emissions, and, therefore, they are a feasible option for customers in the market. Due to technological advancements, decisions about suitable alternative fuel vehicles are a challenging problem for fleet operators. This paper aims to introduce a multi-attribute decision-analysis framework to rank and select the “alternative fuel vehicles (AFVs)” for a private home healthcare service provider in Chandigarh, India. The selection of AFVs can be treated as a decision-making problem, because of the presence of various qualitative and quantitative attributes. Thus, the current work introduces an integrated decision-making framework based on intuitionistic fuzzy-“method based on the removal effects of criteria (MEREC)”, “ranking sum (RS)”, and the “double normalization-based multi-aggregation (DNMA)” framework for assessing the AFVs. The combination of MEREC and RS is applied to assess the objective and subjective weighting values of various parameters for AFV assessment. The DNMA approach is utilized to prioritize the different AFVs over various significant parameters. According to the outcomes, the most significant parameters for AFV assessment are social benefits, fueling/charging infrastructure, and financial incentives, respectively. In this context, globally existing AFVs for the sustainable transportation sector are identified, and then prioritized against fifteen different criteria relevant to the environmental, economic, technological, social, and political aspects of sustainability. It is distinguished that electric vehicles (G2), hybrid electric vehicles (G1), and hydrogen vehicles (G3) achieve higher overall performance compared to the other technologies available in India. The assessment outcomes prove that electric vehicles can serve as a valuable alternative for decreasing carbon emissions and negative effects on the environment. This technology contributes to transportation sector development and job creation in less developed areas of the country. Moreover, a comparison with existing studies and a sensitivity investigation are conferred to reveal the robustness and stability of the developed framework.

Decision-Making Techniques for Water Shutoff Using Random Forests and Its Application in High Water Cut Reservoirs

The major oil fields are currently in the middle and late stages of waterflooding. The water channels between the wells are serious, and the injected water does little effect. The importance of profile control and water blocking has been identified. In this paper, the decision-making technique for water shutoff is investigated by the fuzzy evaluation method, FEM, which is improved using a random forest, RF, classification model. A machine learning random forest algorithm was developed to identify candidate wells and to predict the well performance for water shutoff operation. A data set consisting of 21 production wells with three-year production history is used, where out of the mentioned well data, 70% of them are implemented for training and the remaining are used for testing the model. After fitting the model, the new weights for the factors are established and decision-making is made. Accordingly, 16 wells out of 21 wells are selected by the FEM where 8 wells out of 21 wells are selected by the new factor weight created by RF for water shutoff. A numerical simulation model is established to plug the selected wells by both methods after which the influence of plugging on water cut, daily oil production, and cumulative oil production is compared. The paper shows that the reservoir had a better performance after eight wells were selected using a new weighting system created by RF instead of the 16 wells that were selected using the FEM model. The paper also states that the new weighting model's accuracy improved the decision-making abilities of the wells.

Evaluating Operational Features of Three Unconventional Intersections under Heavy Traffic Based on CRITIC Method

Conventional four-legged intersections are inefficient under heavy traffic requirements and are prone to congestion problems. Unconventional intersections with innovative designs allow for more efficient traffic operations and can increase the capacity of the intersection, in some cases. Common unconventional designs for four-legged intersections include the upstream signalized crossover intersection (USC), continuous flow intersection (CFI), and parallel flow intersection (PFI). At present, an increasing number of cities are using such unconventional designs to improve the performance of their intersections. In the reconstruction of original intersections or the design of new intersections, the question of how to more reasonably select the form of unconventional intersection becomes particularly critical. Therefore, we selected a typical intersection in Xi’an for optimization and investigated traffic data for this intersection. The traffic operations, with respect to the four solutions of a conventional intersection, USC, CFI, and PFI, were evaluated using the VISSIM software. Then, we evaluated the suitability of each solution under different situations using the CRITIC (CRiteria Importance Through Intercriteria Correlation) method, which is a multi-criteria decision-making (MCDM) method that enables a more comprehensive and integrated evaluation of the four solutions by taking into account the comparative intensities and conflicting character among the indices. The results show that the conventional intersection is only applicable to the case of very low traffic volume; PFI has the advantage in the case of moderate and high traffic volume; CFI performs better in the case of high traffic volume; and USC is generally inferior to CFI and PFI, although it has greater improvement, compared with the conventional solution, in a few cases.