A hybrid novel method to economically evaluate the carbon dioxide emissions in the productive chain of Argentina

Camargo, Federico Gabriel

doi:10.1590/0103-6513.20220053

Federico Gabriel Camargo ^* * camargof@frcu.utn.edu.ar

Universidad Tecnológica Nacional - UTN, Facultad Regional de La Rioja - FRLA y Concepción del Uruguay - FRCU, Electromechanical Department, La Rioja, Argentina

https://orcid.org/0000-0002-8685-1900

* camargof@frcu.utn.edu.ar

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Figure 1
Life Cycle Analysis (LCA) model applied to the Argentine Productive Chain (APC), Analytic Hierarchy Process (AHP) and marginal evaluation model by means of intrinsic cost. Source: The author.

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Figure 2
Analytic Hierarchy Process (AHP) applied to the Argentine productive chain. Source: The author.

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Figure 3
Investment Cost vs. Emissions for the Algebraic Product and Particular Hamacher Product. Source: The author.

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Figure 4
Investment Cost vs. Emissions for the General Hamacher's Product (

p = 0.5

) and Einstein's Product. Source: The author.

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Figure 5
Investment Cost vs. Emissions for the Particular Hamacher's Product (PHP). Source: The author.

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Figure 6
Investment Cost vs. Emissions for the Einstein's Product. Source: The author.

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Figure 7
Investment Cost vs. Emissions for the Algebraic Product and Particular Hamacher’s Product. Source: The author.

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Figure 8
Investment Cost vs. Emissions for the General Hamacher’s Product and Einstein's Product. Source: The author.

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Figure 9
Left graph: Investment Cost (increase) vs.

C O_{2}

(decrease). Right graph: Investment Cost (decrease) vs.

C O_{2}

(decrease). Source: The author.

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Figure 10
Intrinsic Cost vs.

C O_{2}

Emissions (both cases: ). Source: The author.

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Table 1
Search results in the Scopus database (conducted in October 2022).

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Table 2
Intrinsic cost ratio for the different t-norms: Algebraic Product - AP, Particular Hamacher's Product - PHP (with null p), General Hamacher's Product - GHP and Einstein's Product - EP.

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Author (year)	Country	Uncertainty	Modelling	Indices	Comment
Cavallaro et al. (2018)Cavallaro, F., Danielis, R., Nocera, S., & Rotaris, L. (2018). Should BEVs be subsidized or taxed? A European perspective based on the economic value of CO2 emissions. Transportation Research Part D, Transport and Environment, 64, 70-89. http://dx.doi.org/10.1016/j.trd.2017.07.017. http://dx.doi.org/10.1016/j.trd.2017.07....	Europe	Deterministic	Well-to-Wheel emission	$C O_{2}$ emission and Economic value	Analysis from statistical records
Zhang & Zhang (2018)Zhang, J., & Zhang, Y. (2018). Carbon tax, tourism CO2 emissions and economic welfare. Annals of Tourism Research, 69, 18-30. http://dx.doi.org/10.1016/j.annals.2017.12.009. http://dx.doi.org/10.1016/j.annals.2017....	China	Deterministic	Computable General Equilibrium model	Carbon tax and economic welfare	Analysis from statistical records
Lin & Jia (2019)Lin, B., & Jia, Z. (2019). How does tax system on energy industries affect energy demand, CO2 emissions, and economy in China? Energy Economics, 84, 104496. http://dx.doi.org/10.1016/j.eneco.2019.104496. http://dx.doi.org/10.1016/j.eneco.2019.1...	China	Deterministic and probabilistic	Computable General Equilibrium model	Carbon tax, energy demand in industry	Analysis from statistical records
Li et al. (2018)Li, Z., Dai, H., Sun, L., Xie, Y., Liu, Z., Wang, P., & Yabar, H. (2018). Exploring the impacts of regional unbalanced carbon tax on CO2 emissions and industrial competitiveness in Liaoning province of China. Energy Policy, 113, 9-19. http://dx.doi.org/10.1016/j.enpol.2017.10.048. http://dx.doi.org/10.1016/j.enpol.2017.1...	Liaoning (China)	Deterministic	Impacts of regional unbalanced carbon price	Carbon tax and industrial competitiveness	Analysis from statistical records
Simshauser (2018)Simshauser, P. (2018). Price discrimination and the modes of failure in deregulated retail electricity markets. Energy Economics, 75, 54-70. http://dx.doi.org/10.1016/j.eneco.2018.08.007. http://dx.doi.org/10.1016/j.eneco.2018.0...	Great Britain	Deterministic	Price discrimination and the modes of failure in deregulated retail electricity markets	Carbon price and $C O_{2}$ emissions	Analysis from statistical records
Aydin & Esen (2018)Aydin, C., & Esen, Ö. (2018). Reducing CO2 emissions in the EU member states: Do environmental taxes work? Journal of Environmental Planning and Management, 61(13), 2396-2420. http://dx.doi.org/10.1080/09640568.2017.1395731. http://dx.doi.org/10.1080/09640568.2017....	Europe	Deterministic	Dynamic panel threshold regression model	Environmental taxes and $C O_{2}$ emissions	Prediction from statistical records
Sun & Huang (2020)Sun, W., & Huang, C. (2020). A carbon price prediction model based on secondary decomposition algorithm and optimized back propagation neural network. Journal of Cleaner Production, 243, 118671. http://dx.doi.org/10.1016/j.jclepro.2019.118671. http://dx.doi.org/10.1016/j.jclepro.2019...	Beijing and Shanghai	Deterministic	Secondary decomposition algorithm and back propagation neural network	Carbon price and $C O_{2}$ emissions	Analysis from statistical records
Sun & Zhang (2018)Sun, W., & Zhang, C. (2018). Analysis and forecasting of the carbon price using multi—resolution singular value decomposition and extreme learning machine optimized by adaptive whale optimization algorithm. Applied Energy, 231, 1354-1371. http://dx.doi.org/10.1016/j.apenergy.2018.09.118. http://dx.doi.org/10.1016/j.apenergy.201...	China and Europe	Deterministic	Multi—resolution singular value decomposition and extreme learning machine	Carbon price and $C O_{2}$ emissions	Prediction from statistical records
Wei et al. (2018)Wei, S., Chongchong, Z., & Cuiping, S. (2018). Carbon pricing prediction based on wavelet transform and K-ELM optimized by bat optimization algorithm in China ETS: the case of Shanghai and Hubei carbon markets. Carbon Management, 9(6), 605-617. http://dx.doi.org/10.1080/17583004.2018.1522095. http://dx.doi.org/10.1080/17583004.2018....	Shanghai and Hubei	Deterministic	Wavelet transform and Kernel-Extreme Learning Machine	Carbon price and $C O_{2}$ emissions	Prediction from statistical records
Hao et al. (2020)Hao, Y., Tian, C., & Wu, C. (2020). Modelling of carbon price in two real carbon trading markets. Journal of Cleaner Production, 244, 118556. http://dx.doi.org/10.1016/j.jclepro.2019.118556. http://dx.doi.org/10.1016/j.jclepro.2019...	China and Europe	Deterministic	Extreme learning machine using a multi-objective algorithm	Carbon price and $C O_{2}$ emissions	Optimal prediction from statistical records
Zhao et al. (2021)Zhao, L. T., Miao, J., Qu, S., & Chen, X. H. (2021). A multi-factor integrated model for carbon price forecasting: market interaction promoting carbon emission reduction. The Science of the Total Environment, 796, 149110. http://dx.doi.org/10.1016/j.scitotenv.2021.149110. PMid:34328877. http://dx.doi.org/10.1016/j.scitotenv.20...	Global	Deterministic	Multi-factor decomposition and integration carbon price forecasting model	Carbon price and $C O_{2}$ emissions	Prediction from statistical records
Song et al. (2019)Song, Y., Liu, T., Liang, D., Li, Y., & Song, X. (2019). A fuzzy stochastic model for carbon price prediction under the effect of demand-related policy in China’s carbon market. Ecological Economics, 157, 253-265. http://dx.doi.org/10.1016/j.ecolecon.2018.10.001. http://dx.doi.org/10.1016/j.ecolecon.201...	China	Fuzzy -stochastic	Fuzzy stochastic model for carbon price prediction	Carbon price and $C O_{2}$ emissions	Prediction from statistical records
Tsao et al. (2021)Tsao, Y.-C., Nugraha Ridhwan Amir, E., Thanh, V.-V., & Dachyar, M. (2021). Designing an eco-efficient supply chain network considering carbon trade and trade-credit: a robust fuzzy optimization approach. Computers & Industrial Engineering, 160, 107595. http://dx.doi.org/10.1016/j.cie.2021.107595. http://dx.doi.org/10.1016/j.cie.2021.107...	Taiwan	Fuzzy-deterministic	Eco-efficient supply chain network and fuzzy optimization	Carbon trade and trade-credit	Prediction from statistical records

μ_{m} = \{\begin{matrix} β \\ {((\frac{U_{m}^{U p} - U_{m}}{U_{m}^{U p} - U_{m}^{L o w}}) \cdot (1 - β) + (\frac{U_{m} - U_{m}^{L o w}}{U_{m}^{U p} - U_{m}^{L o w}}) \cdot β)}^{E W_{m}} \\ 1 - β \end{matrix} \begin{matrix} , U_{m}^{L o w} \geq U_{m} \\ , U_{m}^{L o w} \leq U_{m} \leq U_{m}^{U p} \\ , U_{m}^{U p} \leq U_{m} \end{matrix}

I C_{i j} = \begin{matrix} \pm \\ ⇓ \\ s i g n \end{matrix} \begin{matrix} (\frac{E W_{i}}{E W_{j}}) \\ ⇓ \\ a_{i j} \end{matrix} \begin{matrix} (\frac{U_{j}^{U p} - U_{j}^{l o w}}{U_{i}^{U p} - U_{i}^{l o w}}) \\ ⇓ \\ b_{i j} \end{matrix} \begin{matrix} (\frac{μ_{i}^{1 - \frac{1}{E W_{i}}}}{μ_{j}^{1 - \frac{1}{E W_{j}}}}) \\ ⇓ \\ c_{i j} \end{matrix} \begin{matrix} (\frac{\partial μ_{j}}{\partial μ_{i}}) \\ ⇓ \\ d_{i j} \end{matrix}

I C_{i j} = \pm \frac{E W_{i}}{E W_{j}} \cdot \frac{U_{j}^{U p} - U_{j}^{l o w}}{U_{i}^{U p} - U_{i}^{l o w}} \cdot \frac{μ_{i}^{1 - \frac{1}{E W_{i}}}}{μ_{j}^{1 - \frac{1}{E W_{j}}}} \cdot \frac{μ_{j}}{μ_{i}} \cdot \frac{μ_{j} - 2}{μ_{i} - 2}

I C_{i j} = \pm \frac{E W_{i}}{E W_{j}} \cdot \frac{U_{j}^{U p} - U_{j}^{l o w}}{U_{i}^{U p} - U_{i}^{l o w}} \cdot \frac{μ_{i}^{1 - \frac{1}{E W_{i}}}}{μ_{j}^{1 - \frac{1}{E W_{j}}}} \cdot \frac{μ_{j}}{μ_{i}}

I C_{i j} = \frac{E W_{i}}{E W_{j}} \cdot \frac{U_{j}^{U p} - U_{j}^{l o w}}{U_{i}^{U p} - U_{i}^{l o w}} \cdot \frac{μ_{j}^{\frac{1}{E W_{j}}}}{μ_{i}^{\frac{1}{E W_{i}}}}

I C_{i j} = \pm \frac{E W_{i}}{E W_{j}} \cdot \frac{U_{j}^{U p} - U_{j}^{l o w}}{U_{i}^{U p} - U_{i}^{l o w}} \cdot \frac{μ_{i}^{1 - \frac{1}{E W_{i}}}}{μ_{j}^{1 - \frac{1}{E W_{j}}}} \cdot {(\frac{μ_{j}}{μ_{i}})}^{2}

I C_{i j} = \frac{E W_{i}}{E W_{j}} \cdot \frac{U_{j}^{U p} - U_{j}^{l o w}}{U_{i}^{U p} - U_{i}^{l o w}} \cdot \frac{μ_{j}^{1 + \frac{1}{E W_{j}}}}{μ_{i}^{1 + \frac{1}{E W_{i}}}}

\frac{1}{μ_{i}} + \frac{1}{μ_{j}} \cdot \frac{\partial μ_{j}}{\partial μ_{i}} - \frac{(1 - p)}{p + (1 - p) \cdot (μ_{i} + μ_{j} - μ_{i} \cdot μ_{j})} \cdot (1 - μ_{j} + (1 - μ_{i}) \cdot \frac{\partial μ_{j}}{\partial μ_{i}}) = 0

\frac{\partial μ_{j}}{\partial μ_{i}} = \{\begin{matrix} - \frac{μ_{j}}{μ_{i}}, p = 1 \\ - {(\frac{μ_{j}}{μ_{i}})}^{2}, p = 0 \\ - (\frac{p + (1 - p) \cdot μ_{j}}{p + (1 - p) \cdot μ_{i}}) \cdot \frac{μ_{j}}{μ_{i}}, a n o t h e r c a s e \end{matrix}

I C_{i j} = \pm \frac{E W_{i}}{E W_{j}} \cdot \frac{U_{j}^{U p} - U_{j}^{l o w}}{U_{i}^{U p} - U_{i}^{l o w}} \cdot \frac{μ_{i}^{1 - \frac{1}{E W_{i}}}}{μ_{j}^{1 - \frac{1}{E W_{j}}}} \cdot (\frac{p + (1 - p) \cdot μ_{j}}{p + (1 - p) \cdot μ_{i}}) \cdot \frac{μ_{j}}{μ_{i}}

I C_{i j} = \frac{E W_{i}}{E W_{j}} \cdot \frac{U_{j}^{U p} - U_{j}^{l o w}}{U_{i}^{U p} - U_{i}^{l o w}} \cdot \frac{μ_{j}^{\frac{1}{E W_{j}}}}{μ_{i}^{\frac{1}{E W_{i}}}} \cdot (\frac{p + (1 - p) \cdot μ_{j}}{p + (1 - p) \cdot μ_{i}})

	AP	PHP	GHP	EP
AP	1	$\frac{μ_{i}}{μ_{j}}$	$\frac{p - μ_{i} \cdot (p - 1)}{p - μ_{j} \cdot (p - 1)}$	$\frac{μ_{i} - 2}{μ_{j} - 2}$
PHP	$\frac{μ_{j}}{μ_{i}}$	1	$(\frac{μ_{j}}{μ_{i}}) (\frac{p - μ_{i} \cdot (p - 1)}{p - μ_{j} \cdot (p - 1)})$	$(\frac{μ_{j}}{μ_{i}}) (\frac{μ_{i} - 2}{μ_{j} - 2})$
GHP	$\frac{p - μ_{j} \cdot (p - 1)}{p - μ_{i} \cdot (p - 1)}$	$(\frac{μ_{i}}{μ_{j}}) (\frac{p - μ_{i} \cdot (p - 1)}{p - μ_{j} \cdot (p - 1)})$	1	$(\frac{p - μ_{j} \cdot (p - 1)}{p - μ_{i} \cdot (p - 1)}) (\frac{μ_{i} - 2}{μ_{j} - 2})$
EP	$\frac{μ_{j} - 2}{μ_{i} - 2}$	$(\frac{μ_{i}}{μ_{j}}) (\frac{μ_{j} - 2}{μ_{i} - 2})$	$(\frac{p - μ_{j} \cdot (p - 1)}{p - μ_{i} \cdot (p - 1)}) (\frac{μ_{i} - 2}{μ_{j} - 2})$	1

[1] * camargof@frcu.utn.edu.ar

Brasil

Brasil

A hybrid novel method to economically evaluate the carbon dioxide emissions in the productive chain of Argentina

Abstract

Paper aims

Originality

Research method

Main findings

Implications for theory and practice