Modeling, analysis, and evaluation of a carbon tax policy based on the emission factor

Almutairi, Hossa; Elhedhli, Samir

doi:10.1016/j.cie.2014.09.003

Cited by 30 publications

(5 citation statements)

References 27 publications

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“…This efficient use contributes a relative advantage to developed countries such as India and China, which are encouraged to renovate their production processes. In terms of CO 2 emissions, DEA and eco-efficiency, some outstanding recent studies can be addressed in the literature (Grigoroudis and Petridis, 2019; Wang et al , 2011; Almutairi and Elhedhli, 2014; Bi et al , 2014; Zeng et al , 2016a; Li and Lin, 2016; Jiang et al , 2016; Zeng and Chen, 2016; Zeng et al , 2016b; Mahmoudi et al , 2019b; Jin et al , 2018; Khoshroo et al , 2018; Arabi et al , 2017; Boyd and Pang, 2000; Wei et al , 2007; Azadeh et al , 2007; Angulo-Meza et al , 2019; Balitskiy et al , 2016; Bian et al , 2016; Moya et al , 2016; Mirmozaffari, 2019; Mirmozaffari, 2020; Mirmozaffari et al , 2017; Mirmozaffari et al , 2020; Lee and Park, 2017; Taleghani and Taleghani, 2020; Yu et al , 2014; Malakoutian and Khalsar, 2020; Khaksar and Malakoutian, 2020).…”

Section: Background and Literature Reviewmentioning

confidence: 99%

A novel artificial intelligent approach: comparison of machine learning tools and algorithms based on optimization DEA Malmquist productivity index for eco-efficiency evaluation

Mirmozaffari

Shadkam

Khalili

et al. 2021

IJESM

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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.

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Section: Background and Literature Reviewmentioning

confidence: 99%

A novel artificial intelligent approach: comparison of machine learning tools and algorithms based on optimization DEA Malmquist productivity index for eco-efficiency evaluation

Mirmozaffari

Shadkam

Khalili

et al. 2021

IJESM

View full text Add to dashboard Cite

show abstract

“…According to data from the International Maritime Organization (IMO), in 2020, there will be about 90,000 seaborne ships in the world, consuming about 350 million metric tons of fuel, and CO₂emissions as high as 1.4 billion tons [3] . In order to achieve carbon emission reduction in the shipping industry, the IMO passed two mandatory performance and efficiency standards related to carbon emission reduction of all ships for the first time at the 2011 meeting [4] .…”

Section: Fig 2co2 Emissions From the Shipping Industry 2012-2020mentioning

confidence: 99%

Optimization of liner route allocation considering carbon emissions

Shen

2021

E3S Web Conf.

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With the general awareness of energy and environmental issues in the whole society, the issue of greenhouse gas emission reduction has gradually become the focus of attention. Ocean transport undertakes more than 90% of the world's cargo transportation and also produces a large amount of carbon dioxide. In order to achieve carbon emission reduction in the shipping industry, in June 2012, the ECOFIN proposed to impose a Maritime Carbon Tax, which will increase the operating costs of shipping companies. In view of this, it is of great significance to consider carbon emission factors in the optimization of liner routes. This paper considers the carbon emissions of maritime transportation, establishes a mathematical model that minimizes operating costs such as maritime carbon tax, ship management fees, and fuel costs, verifies the effectiveness of the algorithm through calculation examples, and provides a plan for the carrier to make optimization decisions on liner route allocation.

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“…Studies on carbon tax are shown in Table 1. Almutairi and Elhedhli [30] and Kuo et al [31] examined impacts of a carbon tax on enterprise strategies, by solving the problem of minimizing costs with a carbon tax. However, both studies did not consider the supply chain decisions including supplier selection and order quantity.…”

Section: Literaturementioning

confidence: 99%

Green Procurement Decisions with Carbon Leakage by Global Suppliers and Order Quantities under Different Carbon Tax

2019

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Manufactures have been pressed to reduce greenhouse gas (GHG) emissions by environmental regulations and policies. Towards to reduction of GHG emissions, a carbon tax has been already introduced in 40 countries. Owing to different carbon prices among countries, there are potential risks of carbon leakage, where manufacturers transfer production operations to the countries with lower taxes to pursue lower costs. Moreover, procurement costs and GHG emissions vary by country because of economic conditions and electric energy mixes. Therefore, total GHG emissions could be globally reduced if manufactures relocate their production bases or switch suppliers in the country with lower GHG emission levels. This study proposes a green procurement decision for the supplier selection and the order quantity for minimizing GHG emission and costs considering the different carbon taxes in different countries. First, a bill of materials for each part is constructed through the life cycle inventory database with the Asian international input/output tables for a case study. Second, a green procurement decision considering the different carbon prices is formulated using integer programming. Finally, the results, including carbon leakage, are analyzed from the viewpoint of manufacturers, governments, and global perspectives.

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Modeling, analysis, and evaluation of a carbon tax policy based on the emission factor

Cited by 30 publications

References 27 publications

A novel artificial intelligent approach: comparison of machine learning tools and algorithms based on optimization DEA Malmquist productivity index for eco-efficiency evaluation

A novel artificial intelligent approach: comparison of machine learning tools and algorithms based on optimization DEA Malmquist productivity index for eco-efficiency evaluation

Optimization of liner route allocation considering carbon emissions

Green Procurement Decisions with Carbon Leakage by Global Suppliers and Order Quantities under Different Carbon Tax

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