A novel artificial intelligent approach: comparison of machine learning tools and algorithms based on optimization DEA Malmquist productivity index for eco-efficiency evaluation
| Autor: | Kamyar Kabirifar, Elham Shadkam, Mirpouya Mirmozaffari, Tayyebeh Asgari Gashteroodkhani, Seyyed Mohammad Khalili, Reza Yazdani |
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| Přispěvatelé: | University of Texas at Arlington [Arlington], Khayyam University, Ferdowsi University Mashhad, University of New South Wales [Sydney] (UNSW), Islamic Azad University, University of Guilan |
| Rok vydání: | 2021 |
| Předmět: |
Artificial Intelligent
Optimization [SPI.OTHER]Engineering Sciences [physics]/Other Association rule learning Eco-efficiency Process (engineering) Computer science 020209 energy Strategy and Management 02 engineering and technology 010501 environmental sciences Association rules Machine learning computer.software_genre 7. Clean energy 01 natural sciences [INFO.INFO-AI]Computer Science [cs]/Artificial Intelligence [cs.AI] 0202 electrical engineering electronic engineering information engineering Data envelopment analysis Additive model Productivity 0105 earth and related environmental sciences business.industry [INFO.INFO-RO]Computer Science [cs]/Operations Research [cs.RO] Classification Slack variable Statistical classification General Energy Data Envelopment Analysis 13. Climate action Two-stage additive models Artificial intelligence business computer Algorithm |
| Zdroj: | International Journal of Energy Sector Management International Journal of Energy Sector Management, Emerald, 2021, 15 (3), pp.523-550. ⟨10.1108/IJESM-02-2020-0003⟩ |
| ISSN: | 1750-6220 |
| DOI: | 10.1108/ijesm-02-2020-0003 |
| Popis: | Purpose Cement as one of the major components of construction activities, releases a tremendous amount of carbon dioxide (CO2) into the atmosphere, resulting in adverse environmental impacts and high energy consumption. Increasing demand for CO2 consumption has urged construction companies and decision-makers to consider ecological efficiency affected by CO2 consumption. Therefore, this paper aims to develop a method capable of analyzing and assessing the eco-efficiency determining factor in Iran’s 22 local cement companies over 2015–2019. Design/methodology/approach This research uses two well-known artificial intelligence approaches, namely, optimization data envelopment analysis (DEA) and machine learning algorithms at the first and second steps, respectively, to fulfill the research aim. Meanwhile, to find the superior model, the CCR model, BBC model and additive DEA models to measure the efficiency of decision processes are used. A proportional decreasing or increasing of inputs/outputs is the main concern in measuring efficiency which neglect slacks, and hence, is a critical limitation of radial models. Thus, the additive model by considering desirable and undesirable outputs, as a well-known DEA non-proportional and non-radial model, is used to solve the problem. Additive models measure efficiency via slack variables. Considering both input-oriented and output-oriented is one of the main advantages of the additive model. Findings After applying the proposed model, the Malmquist productivity index is computed to evaluate the productivity of companies over 2015–2019. Although DEA is an appreciated method for evaluating, it fails to extract unknown information. Thus, machine learning algorithms play an important role in this step. Association rules are used to extract hidden rules and to introduce the three strongest rules. Finally, three data mining classification algorithms in three different tools have been applied to introduce the superior algorithm and tool. A new converting two-stage to single-stage model is proposed to obtain the eco-efficiency of the whole system. This model is proposed to fix the efficiency of a two-stage process and prevent the dependency on various weights. Converting undesirable outputs and desirable inputs to final desirable inputs in a single-stage model to minimize inputs, as well as turning desirable outputs to final desirable outputs in the single-stage model to maximize outputs to have a positive effect on the efficiency of the whole process. Originality/value The performance of the proposed approach provides us with a chance to recognize pattern recognition of the whole, combining DEA and data mining techniques during the selected period (five years from 2015 to 2019). Meanwhile, the cement industry is one of the foremost manufacturers of naturally harmful material using an undesirable by-product; specific stress is given to that pollution control investment or undesirable output while evaluating energy use efficiency. The significant concentration of the study is to respond to five preliminary questions. |
| Databáze: | OpenAIRE |
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