Mitigation Efforts Calculator (MEC)
- 170 Downloads
The Mitigation Efforts Calculator (MEC) has been developed by the International Institute for Applied Systems Analysis (IIASA) as an online tool to compare greenhouse gas (GHG) mitigation proposals by various countries for the year 2020. In this paper, first we introduce the MEC conceptual model, i.e. the methodology and system architecture. We then discuss the abstract formulation of four different international greenhouse gas trading regimes that are conceivable. Hereafter, the optimization process and its output results, namely cost curves are presented. Finally, we illustrate the MEC as a tool for interactively evaluating complex cost curve information in the context of GHG mitigation targets as currently discussed in international climate policy circles.
KeywordsBusiness intelligence Decision model Interactive system Optimisation Cost curves
- Amann, M., Bertok, I., Borken, J., Cofala, J., Heyes, C., Hoglund, L., et al. (2008). GAINS—Potentials and costs for greenhouse gas mitigation in annex i countries. Tech. rep., Laxenburg, Austria.Google Scholar
- Amann, M., Rafaj, P., & Hoehne, N. (2009). GHG mitigation potentials in annex i countries comparison of model estimates for 2020. Tech. Rep. IR-09-34, International Institute for Applied Systems Analysis, Laxenburg, Austria.Google Scholar
- Cofala, J., Amann, M., & Mechler, R. (2006). Scenarios of world anthropogenic emissions of air pollutants and methane up to 2030. IIASA Interim Report IR-06-023, Laxenburg, Austria.Google Scholar
- Gangadharan, G., & Swami, S. (2004). Business intelligence systems: design and implementation strategies. In 26th international conference on information technology interfaces (pp. 139–144).Google Scholar
- Hoeglund-Isaksson, L., Winiwarter, W., Wagner, F., Klimont, Z., & Amann, M. (2010). Potentials and costs for mitigation of non-CO2 greenhouse gas emissions in the European Union until 2030 results.Google Scholar
- Hordijk, L., & Amann, M. (2007). How science and policy combined to combat air pollution problems. Environmenal Policy and Law, 37(4), 336–340.Google Scholar
- Nguyen, T. B., Schoepp, W., & Wagner, F. (2008). GAINS-BI (p. 332). ACM Press. doi: 10.1145/1497308.1497369.
- Tvrdikova, M. (2007). Support of decision making by business intelligence tools (pp. 364–368). IEEE. doi: 10.1109/CISIM.2007.64.
- UNFCCC (2007). Bali action plan. Tech. rep., United Nations Framework Convention on Climate Change. internal-pdf://06a01-1090503685/06a01.pdf.
- Wagner, F., Schoepp, W., & Heyes, C. (2006). The RAINS optimization module for the Clean Air for Europe (CAFE) programme. Tech. rep., International Institute for Applied Systems Analysis (IIASA), Laxenburg, Austria.Google Scholar
- Wei, X., Xiaofei, X., Lei, S., Quanlong, L., & Hao, L. (2001). Business intelligence based group decision support system. In Proceedings of the international conferences on info-tech and info-net (Vol. 5, pp. 295–300). ICII 2001-Beijing.Google Scholar
- Zeng, L., Xu, L., Shi, Z., Wang, M., & Wu, W. (2006). Techniques, process, and enterprise solutions of business intelligence. In IEEE international conference on systems, man and cybernetics (Vol. 6, pp. 4722–4726). SMC’06.Google Scholar