FACTS: Fuzzy Assessment and Control for Temperature Stabilization - Regulating Global Carbon Emissions with a Fuzzy Approach to Climate Projections

Bernardo A. Bastien Olvera, Carlos Gay y García

Abstract

This work presents a new approach for assessing the climate system and for stabilizing the temperature and other climate parameters. FACTS, as we call it, is a fuzzy inference system that overview certain climate state, and is able to generate the CO2 emissions reduction needed to implement in order to stabilize the temperature. FACTS was constructed using a neural network optimization process along with data generated by a classical emissions pathfinder. Then, it was embedded in MAGICC6, a simple climate model that was forced by the four Representative Concentration Pathways until and ultimately stabilized by the proposed methodology.

References

  1. Belenky, M. (2015). Achieving the u.s. 2025 emissions mitigation target. Available at: http://www.climateadvisers.com/wpcontent/uploads/ 2013/12/US-Achieving-2025-Target May-20151.pdf .
  2. Garg, A., Shukla, P., and K., B. (2014). India report - alternate development pathways for india: Aligning copenhagen climate change commitments with national energy security and economic development. low climate impact scenarios and the implications of required tight emission control strategies [limits]. Ahmedabad, India: Indian Institute of Management, Ahmedabad.
  3. Jang, J.-S. R., Sun, C.-T., and Mizutani, E. (1997). Neurofuzzy and soft computing. Prentice Hall, Englewood Cliffs, N.J.
  4. Meinshausen, M., Bill, H., Wigley, T., Vuuren, D. V., Elzen, M. D., and Swart, R. (2005). Multi-gas emissions pathways to meet climate targets. Climatic Change, 17(27).
  5. Meinshausen, M., Raper, S., and Wigley, T. (2011). Emulating coupled atmosphere-ocean and carbon cycle models with a simpler model, magicc6 - part 1: Model description and calibration. Atmos. Chem. Phys., 11:1417-1456.
  6. Rogelj, J., McCollum, D. L., ONeill, B. C., and Riahi, K. (2013). 2020 emissions levels required to limit warming to below 2c. Nature Climate Change, 3.
  7. Shing, J. and Jang, R. (1993). Anfis: Adaptive networkbased fuzzy inference system. IEEE, Transactions on systems, man, and cybernetics, 23.
  8. Takagi, T. and Sugeno, M. (1985). Fuzzy identification of system and its application to modeling and control. IEEE, SMC, 15:199-249.
  9. UNEP (2015). The emissions gap report. United Nations.
  10. UNFCC (2015). Paris agreement. Conference of the Parts.
  11. Wigley, T. (2003). MAGICC/SCENGEN 4.1: Technical Manual. UCAR-Climate and Global Dynamics Division, Boulder, Colorado.
  12. Zadeh, L. A. (1965). Fuzzy sets. Information and Control, 8:338-353.
  13. Zadeh, L. A. (1975). The concept of a linguistic variable and its application to approximate reasoning. Inf. Sci, 8:199-249.
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Paper Citation


in Harvard Style

Bastien Olvera B. and Gay y García C. (2016). FACTS: Fuzzy Assessment and Control for Temperature Stabilization - Regulating Global Carbon Emissions with a Fuzzy Approach to Climate Projections . In - MSCCES, (SIMULTECH 2016) ISBN , pages 0-0. DOI: 10.5220/0006011603570362


in Bibtex Style

@conference{mscces16,
author={Bernardo A. Bastien Olvera and Carlos Gay y García},
title={FACTS: Fuzzy Assessment and Control for Temperature Stabilization - Regulating Global Carbon Emissions with a Fuzzy Approach to Climate Projections},
booktitle={ - MSCCES, (SIMULTECH 2016)},
year={2016},
pages={},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0006011603570362},
isbn={},
}


in EndNote Style

TY - CONF
JO - - MSCCES, (SIMULTECH 2016)
TI - FACTS: Fuzzy Assessment and Control for Temperature Stabilization - Regulating Global Carbon Emissions with a Fuzzy Approach to Climate Projections
SN -
AU - Bastien Olvera B.
AU - Gay y García C.
PY - 2016
SP - 0
EP - 0
DO - 10.5220/0006011603570362