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Converting Sustainable into Financial Benefits: Applying the ROSI Methodology to Advance the Russian Decentralized Energy System

ФИО студента: Benedikt Naeke

Руководитель: Yulia Leevik

Кампус/факультет: St.Petersburg School of Economics and Management

Программа: Finance (Master)

Оценка: 8

Год защиты: 2021

This study aims to establish a profound framework to assess and compare the initial and operational costs for renewable energy systems (RES) and diesel generators in the decentralized energy system of Russia. Under special consideration are costs that arise when taking sustainable aspects into account. The Return on Sustainability Investment (ROSI™) methodology of the NYU Stern Center for Sustainable Business is the foundation of this study. Drawing on many and diverse sources a specific and authentic business case for the energy supply of an average Russian village is created. The findings indicate that the costs for the implementation of RES in regions isolated from the Unified Power System are many times lower than for the current energy production. These results are even surpassed if one includes the costs incurred by the environmentally and socially damaging supply chain and power generation with diesel. Existing research agrees that Russia has not only a high potential for RES but the transition to alternative energy generation plays an essential role in the long-term strategic positioning of the Russian economy. Hence, the positive results from this variously applicable and adjustable framework intends to incentivize any Russian energy corporation to break new grounds. Generally, the ROSI methodology is applied by a team of the NYU Stern Center in cooperation with a company. Therefore, input data and outcome are related to firm specifics. This study adopts a generalist approach away from the company-centred perspective. The starting point is a current situation with critical issues on multiple levels. By monetizing each of these issues and demonstrating the benefits of remedying, respective companies ought to be attracted and incorporate own data to refine the model.

Full text (added May 11, 2021)

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