A hybrid novel method to economically evaluate the carbon dioxide emissions in the productive chain of Argentina
Federico Gabriel Camargo
Abstract
Keywords
References
Amenta, P., Lucadamo, A., & Marcarelli, G. (2021). On the choice of weights for aggregating judgments in non-negotiable AHP group decision making.
Argentina. Ministerio de Economia. Secretaría de Energía. (2022).
Aydin, C., & Esen, Ö. (2018). Reducing CO2 emissions in the EU member states: Do environmental taxes work?
Camargo, F. G., & Schweickardt, G. A. (2014). Estimación de la tasa de retorno energético: análisis comparativo de las metodologías disponibles en la actualidad.
Camargo, F. G. (2021). Survey and calculation of the energy potential and solar, wind and biomass EROI: application to a case study in Argentina.
Camargo, F. G. (2022a). Dynamic modeling of the energy returned on invested.
Camargo, F. G. (2022b). Fuzzy multi-objective optimization of the energy transition towards renewable energies with a mixed methodology.
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.
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.
Camargo, F. G. (2019).
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.
Hao, Y., Tian, C., & Wu, C. (2020). Modelling of carbon price in two real carbon trading markets.
Ichihashi, S. (2021). The economics of data externalities.
Investing.com. (2022).
Janiesch, C., Zschech, P., & Heinrich, K. (2021). Machine learning and deep learning.
Jeanne, O., & Korinek, A. (2019). Managing credit booms and busts: A Pigouvian taxation approach.
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.
Lin, B., & Jia, Z. (2019). How does tax system on energy industries affect energy demand, CO2 emissions, and economy in China?
Liu, Y., Eckert, C. M., & Earl, C. (2020). A review of fuzzy AHP methods for decision-making with subjective judgements.
Maamoun, N. (2019). The Kyoto protocol: empirical evidence of a hidden success.
Miyamoto, M., & Takeuchi, K. (2019). Climate agreement and technology diffusion: Impact of the Kyoto Protocol on international patent applications for renewable energy technologies.
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.
Robson, E. N., Wijayaratna, K. P., & Dixit, V. V. (2018). A review of computable general equilibrium models for transport and their applications in appraisal.
Saaty, T. L. (2003). Decision-making with the AHP: why is the principal eigenvector necessary.
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.
Schweickardt, G., & Pistonesi, H. (2010). Un modelo posibilístico para estimar el costo intrínseco de la energía no suministrada en sistemas de distribución eléctrica.
Shahzadi, G., Akram, M., & Al-Kenani, A. N. (2020). Decision-making approach under Pythagorean fuzzy Yager weighted operators.
Silajdzic, S., & Mehic, E. (2018). Do environmental taxes pay off? The impact of energy and transport taxes on CO2 emissions in transition economies.
Simshauser, P. (2018). Price discrimination and the modes of failure in deregulated retail electricity markets.
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.
Sun, W., & Huang, C. (2020). A carbon price prediction model based on secondary decomposition algorithm and optimized back propagation neural network.
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.
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.
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.
Zhang, J., & Zhang, Y. (2018). Carbon tax, tourism CO2 emissions and economic welfare.
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.
Submitted date:
05/10/2022
Accepted date:
12/09/2022