Risk assessment of financing renewable energy projects: A case study of financing a small hydropower plant project in Serbia

Spasenić, Željko; Makajić-Nikolić, Dragana; Benković, Slađana

doi:10.1016/j.egyr.2022.06.065

Cited by 14 publications

(3 citation statements)

References 54 publications

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“…There is significant research directed toward finding an optimal renewable energy source for a specific region, considering the potential of that region [12][13][14][15]. Some efforts have also been directed toward analyzing the renewable energy potential of the Western Balkans [30][31][32][33][34][35]. However, these research studies provide either a very general overview of the topic or analyze an alternative renewable energy source, such as biomass.…”

Section: Introductionmentioning

confidence: 99%

Renewable Energy-Powered Traffic Signalization as a Step to Carbon-Neutral Cities (The Case of Western Balkans)

et al. 2023

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Global warming and its negative effect on the environment pose a great threat to the future of mankind. In order to overcome these challenges, EU countries have set the target of becoming climate neutral by 2050, a path defined through the policies of the European Green Deal. The Republic of Serbia and Bosnia and Herzegovina also agreed upon the same goal through the endorsement of the Green Agenda for the Western Balkans. One of the means to achieve this goal is by increasing the share of renewable energy sources. In this paper, we analyze the feasibility, challenges, and potential issues of substituting conventional traffic light signalization with signalization based on renewable energy sources on a national scale. An ad hoc questionnaire was used to collect data from representatives of most of the municipalities in the Republic of Srpska entity in Bosnia and Herzegovina and representatives of the city of Novi Sad in Serbia. In the city of Novi Sad, personal interviews were also carried out to collect additional information. The results of this research show that the implementation of solar-powered traffic lights is economically and environmentally viable. These results will provide the basis for understanding the benefits and challenges in the case of the application of traffic light signalization based on renewable energy sources.

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Section: Introductionmentioning

confidence: 99%

Renewable Energy-Powered Traffic Signalization as a Step to Carbon-Neutral Cities (The Case of Western Balkans)

et al. 2023

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“…From the perspective of risk, Wu et al [26] constructed an asymmetric duopoly model of two competitive supply chains with different carbon emission technologies and studied the influence of financing risk of investment in carbon emission technologies in supply chains on pricing. Spasenic et al [27] adopted the qualitative analysis method to conduct a credit risk assessment on the financing of small hydropower station projects in Serbia to identify and describe risk events that may lead to loan default. From the perspective of the financing effect, Gu et al [28] established a climate-economic-technology comprehensive assessment model and studied the implementation effect of low-carbon technology financing on climate mitigation, technology extension and economic development in the context of climate change cooperation.…”

Section: Literature Reviewmentioning

confidence: 99%

Study on Low-Carbon Technology Investment Strategies for High Energy-Consuming Enterprises under the Health Co-Benefits of Carbon Emission Reduction

Liu

Sun

2023

Sustainability

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The excessive use of fossil energy has led to a yearly increase in carbon dioxide and atmospheric pollutant emissions, and climate change has become increasingly prominent, seriously affecting people’s daily lives and physical and mental health. According to statistics, rising temperatures and extreme weather phenomena due to climate change have led to a 68% increase in heat-related deaths today compared to the period between 2000 and 2004, and a 61% increase in the number of days humans face high fire risks in the same period. Currently, in order to achieve synergistic economic and environmental development and enhance the health co-benefits of carbon emission reduction, it is urgent for high-energy-consuming enterprises to make sound low-carbon technology investment decisions. Therefore, in this paper, under the carbon quota and trading policy and carbon tax policy, and considering the existence of low-carbon preferences of consumers, the financial constraints of upstream high energy-consuming enterprises and sufficient funds of downstream retailers, a low-carbon technology investment decision model under intra-supply chain financing is constructed using Stackelberg game theory. Moreover, by applying the inverse induction method, we solve the optimal decision of low-carbon technology investment with three different subsidy methods: no subsidy, cost subsidy and product subsidy. Finally, the validity of the model is verified by numerical simulation, and the effects of different influencing factors on low-carbon technology investment are analyzed. The results show that: (1) the reasonable formulation of carbon trading price, carbon tax rate, cost subsidy ratio and product subsidy coefficient are important factors to promote enterprises’ low-carbon technology investment; (2) the improvement of consumers’ low-carbon preference level and the reduction in repayment interest rate can promote enterprises’ investment; (3) compared with no subsidy, cost subsidy and product subsidy can effectively improve enterprises’ low-carbon technology investment enthusiasm, and the effect of product subsidy is better than that of cost subsidy. The effect of product subsidies is better than that of cost subsidies. This paper aims to provide suggestions for the government to refine low-carbon technology investment incentive policies and for enterprises to optimize low-carbon technology investment decisions, so as to enhance the healthy co-benefits of carbon emission reduction and achieve green and sustainable economic development.

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“…In order to conduct the plate bearing test, a horseshoe-shaped adit was dug with the dimensions being 22.2 m 2 (width × height). To compute the rock mass deformation modulus under natural conditions, the authors followed the method given by Spasenic et al [81] and Hua, Jiang, Liu, Gao, and Yu [29], to determine the in situ rock mass deformation modulus. This investigation relies on Boussinesq-based plate bearing testing.…”

Section: In Situ Rock Mass Deformation Modulus (Em)mentioning

confidence: 99%

An Appropriate Model for the Prediction of Rock Mass Deformation Modulus among Various Artificial Intelligence Models

et al. 2022

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The rock mass deformation modulus (Em) is an essential input parameter in numerical modeling for assessing the rock mass behavior required for the sustainable design of engineering structures. The in situ methods for determining this parameter are costly and time consuming. Their results may not be reliable due to the presence of various natures of joints and following difficult field testing procedures. Therefore, it is imperative to predict the rock mass deformation modulus using alternate methods. In this research, four different predictive models were developed, i.e., one statistical model (Muti Linear Regression (MLR)) and three Artificial Intelligence models (Artificial Neural Network (ANN), Random Forest Regression (RFR), and K-Neighbor Network (KNN)) by employing Rock Mass Rating (RMR89) and Point load index (I50) as appropriate input variables selected through correlation matrix analysis among eight different variables to propose an appropriate model for the prediction of Em. The efficacy of each predictive model was evaluated by using four different performance indicators: performance coefficient R2, Mean Absolute Error (MAE), Mean Squared Error (MSE), and Median Absolute Error (MEAE). The results show that the R2, MAE, MSE, and MEAE for the ANN model are 0.999, 0.2343, 0.2873, and 0.0814, respectively, which are better than MLR, KNN, and RFR. Therefore, the ANN model is proposed as the most appropriate model for the prediction of Em. The findings of this research will provide a better understanding and foundation for the professionals working in fields during the prediction of various engineering parameters, especially Em for sustainable engineering design in the rock engineering field.

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Risk assessment of financing renewable energy projects: A case study of financing a small hydropower plant project in Serbia

Cited by 14 publications

References 54 publications

Renewable Energy-Powered Traffic Signalization as a Step to Carbon-Neutral Cities (The Case of Western Balkans)

Renewable Energy-Powered Traffic Signalization as a Step to Carbon-Neutral Cities (The Case of Western Balkans)

Study on Low-Carbon Technology Investment Strategies for High Energy-Consuming Enterprises under the Health Co-Benefits of Carbon Emission Reduction

An Appropriate Model for the Prediction of Rock Mass Deformation Modulus among Various Artificial Intelligence Models

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