Production
https://prod.org.br/article/doi/10.1590/0103-6513.20220053
Production
Research Article

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

Federico Gabriel Camargo

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Abstract

Paper aims: This paper compares the economic valuation (Intrinsic Cost or IC) of carbon dioxide emissions (carbon price) of the externality taxes (Kyoto Protocol) of the Argentine Production Chain (APC).

Originality: The experimental IC index and this novel combination allow incorporating objective (incremental) and subjective (acceptance, hierarchy and uncertainty) evaluation of the APC. This method is extended in this paper (to other operators).

Research method: The ICs are applied to attribute graphs in two scenarios (APC and society), that are obtained from the combination of Fuzzy Decision Making (FMD), Analytic Hierarchy Process (AHP) and PSO with the Algebraic (AP), Generic Hamacher (GHP and PHP) and Einstein (EP) products.

Main findings: (i) The coherency of the new ICs is demonstrated by mathematical and graphical analysis; (ii) The most demanding operators are the EP and HP.

Implications for theory and practice: It contributes to establish mechanisms for regulation of the APC.

Keywords

Carbon price (externality taxes) of Argentine Productive Chain (APC). Fuzzy Decision Making (FDM), Intrinsic Cost (IC) index. Variants of the fuzzy operators, Analytic Hierarchy Process (AHP). Algebraic Product (AP). Generic Hamacher (GHP). Particular Hamacher (PHP), Einstein (EP) Products

References

Amenta, P., Lucadamo, A., & Marcarelli, G. (2021). On the choice of weights for aggregating judgments in non-negotiable AHP group decision making. European Journal of Operational Research, 288(1), 294-301. http://dx.doi.org/10.1016/j.ejor.2020.05.048.

Argentina. Ministerio de Economia. Secretaría de Energía. (2022). Datos Energía. Ministerio de Energía y Minería. Retrieved in 2022, October 12, from http://datos.energia.gob.ar/

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.

Camargo, F. G., & Schweickardt, G. A. (2014). Estimación de la tasa de retorno energético: análisis comparativo de las metodologías disponibles en la actualidad. MASKANA, I+D+ingeniería, 65-73. Retrieved in 2022, October 12, from https://publicaciones.ucuenca.edu.ec/ojs/index.php/maskana/article/view/575

Camargo, F. G. (2021). Survey and calculation of the energy potential and solar, wind and biomass EROI: application to a case study in Argentina. Dyna, 88(219), 50-58. http://dx.doi.org/10.15446/dyna.v88n219.95569.

Camargo, F. G. (2022a). Dynamic modeling of the energy returned on invested. Dyna, 89(221), 50-59. http://dx.doi.org/10.15446/dyna.v89n221.97965.

Camargo, F. G. (2022b). Fuzzy multi-objective optimization of the energy transition towards renewable energies with a mixed methodology. Production, 32, e20210132. http://dx.doi.org/10.1590/0103-6513.20210132.

Camargo, F. G., Schweickardt, G. A., & Casanova, C. A. (2018). Maps of Intrinsic Cost (IC) in reliability problems of medium voltage power distribution systems through a Fuzzy multi-objective model. Dyna, 85(204), 334-343. http://dx.doi.org/10.15446/dyna.v85n204.65836.

Camargo, F. G., Casanova Pietroboni, C. A., Pérez, E., & Schweickardt, G. A. (2019). Metodología regulatoria para propiciar la eficiencia energética desde el lado de la oferta con penetración de fuentes primarias de energías renovables. Parte 1: Descripción y alcance del modelo de optimización. Investigacion Operativa, 27(45), 5-24. Retrieved in 2022, October 12, from https://ria.utn.edu.ar/handle/20.500.12272/5349

Camargo, F. G. (2019). Metodología regulatoria para propiciar la eficiencia energética desde el lado de la oferta en sistemas de distribución de energía eléctrica [Tesis doctoral]. Universidad Tecnológica Nacional, Santa Fe. Retrieved in 2022, October 12, from http://hdl.handle.net/20.500.12272/7010

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.

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.

Ichihashi, S. (2021). The economics of data externalities. Journal of Economic Theory, 196, 105316. http://dx.doi.org/10.1016/j.jet.2021.105316.

Investing.com. (2022). Materias Primas. Futuros emisiones de carbono. Ministerio de Energía y Minería. Retrieved in 2022, October 12, from https://es.investing.com/commodities/carbon-emissions-historical-data

Janiesch, C., Zschech, P., & Heinrich, K. (2021). Machine learning and deep learning. Electronic Markets, 31(3), 685-695. http://dx.doi.org/10.1007/s12525-021-00475-2.

Jeanne, O., & Korinek, A. (2019). Managing credit booms and busts: A Pigouvian taxation approach. Journal of Monetary Economics, 107, 2-17. http://dx.doi.org/10.1016/j.jmoneco.2018.12.005.

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.

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.

Liu, Y., Eckert, C. M., & Earl, C. (2020). A review of fuzzy AHP methods for decision-making with subjective judgements. Expert Systems with Applications, 161, 113738. http://dx.doi.org/10.1016/j.eswa.2020.113738.

Maamoun, N. (2019). The Kyoto protocol: empirical evidence of a hidden success. Journal of Environmental Economics and Management, 95, 227-256. http://dx.doi.org/10.1016/j.jeem.2019.04.001.

Miyamoto, M., & Takeuchi, K. (2019). Climate agreement and technology diffusion: Impact of the Kyoto Protocol on international patent applications for renewable energy technologies. Energy Policy, 129, 1331-1338. http://dx.doi.org/10.1016/j.enpol.2019.02.053.

Pradhan, B. K., & Ghosh, J. (2022). A computable general equilibrium (CGE) assessment of technological progress and carbon pricing in India’s green energy transition via furthering its renewable capacity. Energy Economics, 106, 105788. http://dx.doi.org/10.1016/j.eneco.2021.105788.

Robson, E. N., Wijayaratna, K. P., & Dixit, V. V. (2018). A review of computable general equilibrium models for transport and their applications in appraisal. Transportation Research Part A, Policy and Practice, 116, 31-53. http://dx.doi.org/10.1016/j.tra.2018.06.003.

Saaty, T. L. (2003). Decision-making with the AHP: why is the principal eigenvector necessary. European Journal of Operational Research, 145(1), 85-91. http://dx.doi.org/10.1016/S0377-2217(02)00227-8.

Schweickardt, G., & Miranda, V. (2009). A two-stage planning and control model toward economically adapted power distribution systems using analytical hierarchy processes and fuzzy optimization. International Journal of Electrical Power & Energy Systems, 31(6), 277-284. http://dx.doi.org/10.1016/j.ijepes.2009.03.003.

Schweickardt, G., & Pistonesi, H. (2010). Un modelo posibilístico para estimar el costo intrínseco de la energía no suministrada en sistemas de distribución eléctrica. Dyna, 77(162), 249-259.

Shahzadi, G., Akram, M., & Al-Kenani, A. N. (2020). Decision-making approach under Pythagorean fuzzy Yager weighted operators. Mathematics, 8(1), 70. http://dx.doi.org/10.3390/math8010070.

Silajdzic, S., & Mehic, E. (2018). Do environmental taxes pay off? The impact of energy and transport taxes on CO2 emissions in transition economies. South East European Journal of Economics and Business, 13(2), 126-143. http://dx.doi.org/10.2478/jeb-2018-0016.

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.

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.

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.

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.

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.

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.

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.

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.
 


Submitted date:
05/10/2022

Accepted date:
12/09/2022

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