Abstract
Abstract. Framing sustainable environmental laws in regulating Natural Capital funds for Renewable Energy (RE) is central to the discussion on sustainability strategies. Natural Capital is that limited form of capital assets or service (tangible or intangible) that satisfies basic and social conditions for human existence and protection. This paper proposes an analytical regulatory model utilizing Neural Network (NN) of substantive and procedural issues framing the regulatory parameters associated with Natural capital funding. The model recognizes the fact that the purpose of any legal system is not only to assign duties and responsibilities in protecting rights of individuals and groups in their respective endeavors; but for effective modelling of natural structures as well. Through a preliminarydiscussion of European and USA markets’; regulatory systems with a focus on market and social values, it attempts to discern a practical model to formulate social and regulatory measures on financial structures and energy matters that are considered rights and obligations of individuals and organizations in conducting their businesses. As it has been a subject of academic, government, and public discussions with intense controversies, finding the differences of methodological, and analytical foundation will most probably lead to deeper insight into regulating funds for renewable energy.
Keywords. Natural capital, Sustainable, Entrepreneurial collaboration, ISO, Climate change, Neural Network (NN).
JEL. N70, O13, Q40.
References
Bartik, T. (2013). Social costs of jobs lost due to environmental regulations. [Retrieved from]. Available:
Berman, B., & Bui, L. (2001). Environmental regulation and labor demand: Evidence from the south coast air basin. Journal of Public Economics, 79(2), 265-295. doi. 10.1016/S0047-2727(99)00101-2
European Environment Agency, (2013). Our life insurance, our natural capital: an EU biodiversity strategy to 2020. [Retrieved from].
Haines-Young, R.H., & Potschin M.P. (2010). The links between biodiversity, ecosystem services and human well-being. in Ecosystem Ecology, doi. 10.1017/CBO9780511750458.007
Haines-Young, R., Potschin M., & Kienast, F. (2012). Indicators of ecosystem service potential at European scales: Mapping marginal changes and trade-offs. Ecological Indicators,, 21, 39-53. doi. 10.1016/j.ecolind.2011.09.004
Lavalle, C. et al., (2011). Implementation of the CAP Policy Options with the Land Use Modelling Platform. A first indicator-based analysis. Publications Office of the European Union, Luxembourg. [Retrieved from].
Maes, J., Paracchini, M.L., Zulian, G., Dunbar, M.B., & Alkemade, R. (2012). Synergies and trade-offs between ecosystem service supply, biodiversity and habitat conservation status in Europe. Biological Conservation, 155, 1-12. doi. 10.1016/j.biocon.2012.06.016
Mellit, A., Benghanem, M., & Bendekhis, M. (2005). Artificial neural network model for prediction solar power data: application for sizing standalone photovoltaic power system. Power Engineering Society Journal (IEEE), 13(1), 40-44. doi. 10.1109/PES.2005.1489526
Millennium Ecosystem Assessment, (2005). Ecosystems and Human Well-Being: Biodiversity Synthesis. World Resources Institute, Washington, D.C. (USA).
Sukhdev, P. (2011). Putting a price on nature: The economics of ecosystems and biodiversity. Solutions, 1(6), 34-43.
The Economics of Ecosystems and Biodiversity (TEEB), (2010). The Economics of Ecosystems and Biodiversity. Ecological and economic foundation, Earthscan, Cambridge. [Retrieved from].
