Raw metal needs and supply risks for the development of wind energy in Germany until 2050

Shammugam, Shivenes; Gervais, Estelle; Schlegl, Thomas; Rathgeber, Andreas W.

doi:10.1016/j.jclepro.2019.02.223

Cited by 42 publications

(23 citation statements)

References 37 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…It was concluded that wind energy companies cannot find new loans when interest rates are high. Moreover, Shammugam et al [6] studied the supply risks for the development of wind energy in Germany until 2050 by using a regression methodology. It was determined that interest rate risks are one of the most significant risks for these projects.…”

Section: Discussionmentioning

confidence: 99%

“…Besides this, the increasing interest rate will harm the customers and suppliers of the company. This will cause the wind power company to experience difficulties in the supply chain [6].…”

Section: Literature On the Risk Factors Of Wind Energy Investmentsmentioning

confidence: 99%

“…Volatility in the exchange rate (C1) [3,5,9] High inflation (C2) [1,10] Interest rate risk (C3) [6,11] Socio-political factors (D2)…”

Section: Macroeconomic Factors (D1)mentioning

confidence: 99%

“…Since the wind is available at all times during the day, it is possible to obtain electricity continuously with the help of wind turbines. In contrast, regarding solar energy, electricity cannot be obtained after sunset [6]. In addition, wind energy does not take up much space like other types of renewable energy, which is one of the important advantages of this energy type.Considering these issues, it can be understood that wind energy investments are very important for the social and economic development of the country.…”

mentioning

confidence: 99%

See 3 more Smart Citations

Multi-Faceted Analysis of Systematic Risk-Based Wind Energy Investment Decisions in E7 Economies Using Modified Hybrid Modeling with IT2 Fuzzy Sets

Qiu¹,

et al. 2020

View full text Add to dashboard Cite

This study aimed to analyze the systematic risks of wind energy investments. Within this framework, E7 countries are included in the scope of the examination. A large literature review was carried out and 12 different systematic risk factors that could exist in wind energy investments were identified. The analysis process of the study consisted of two different stages. First, the specified risk criteria were weighted with the help of the interval type 2 (IT2) fuzzy decision-making trial and evaluation laboratory (DEMATEL) method. Second, E7 countries were ranked according to the risk management effectiveness in wind energy investments. In this process, the IT2 fuzzy Vlsekriterijumska Optimizacija I Kompromisno Resenje (VIKOR) approach was taken into consideration. The findings show that volatility in exchange rates and interest rates were the most important risks in wind energy investments. In addition, it was determined that China and Indonesia were the most successful countries in managing risks in wind energy investments. In contrast, India, Russia, and Turkey were determined to be the least successful. Additionally, the IT2 fuzzy technique for order preference by similarity to ideal solution (TOPSIS) method was applied as a robustness check of the extended VIKOR method. It was concluded that the ranking results of the IT2 fuzzy TOPSIS method were similar to the results of the IT2 fuzzy VIKOR. It can be understood that the proposed ranking method was consistent with the comparative analysis results. From this point of view, it was observed that countries should take measures regarding their exchange rate and interest rate risks in order to increase the efficiency in wind energy investments. In this context, companies should first ensure that they do not have a foreign exchange short position in their balance sheets by conducting an effective financial analysis. In addition, it is important to use financial derivatives to minimize the exchange rate and interest rate risks. Using these results, it will be possible to manage this risk by taking the reverse position for the existing foreign currency and interest risk. In this way, it will be possible to increase the efficiency of wind energy investments, which will contribute to the social and economic development of each respective country.Energies 2020, 13, 1423 2 of 25 their own energy resources can benefit from these resources [1]. This situation gives these countries a serious cost advantage. On the other hand, countries that do not have sufficient energy resources have to supply the energy they need from outside, which makes these countries dependent on energy. Because of this problem, countries are developing several strategies to produce their own energy [2].Renewable energy sources are an important alternative that allows countries to produce their own energy. The most important feature of these energy alternatives, which come in different forms, such as wind, solar, biomass, geothermal, and hydroelectricity, is that they obtain their resources from natur...

show abstract

Section: Discussionmentioning

confidence: 99%

“…Besides this, the increasing interest rate will harm the customers and suppliers of the company. This will cause the wind power company to experience difficulties in the supply chain [6].…”

Section: Literature On the Risk Factors Of Wind Energy Investmentsmentioning

confidence: 99%

“…Volatility in the exchange rate (C1) [3,5,9] High inflation (C2) [1,10] Interest rate risk (C3) [6,11] Socio-political factors (D2)…”

Section: Macroeconomic Factors (D1)mentioning

confidence: 99%

mentioning

confidence: 99%

See 2 more Smart Citations

Multi-Faceted Analysis of Systematic Risk-Based Wind Energy Investment Decisions in E7 Economies Using Modified Hybrid Modeling with IT2 Fuzzy Sets

Qiu¹,

et al. 2020

View full text Add to dashboard Cite

show abstract

“…This issue has aroused worldwide concern. For the German wind industry, Shammugam et al (2019) sees copper and dysprosium as the most critical materials for development, both of which clearly exceed Germany's quota for renewable energy technologies and will face stiff competition from other sectors for access to raw materials. In the United States, renewable energy (mainly wind and solar) is particularly constrained by Te and Dy (Nassar et al, 2016).…”

Section: Introductionmentioning

confidence: 99%

Renewable Energy and Material Supply Risks: a Predictive Analysis Based on An LSTM Model

et al. 2020

View full text Add to dashboard Cite

In response to climate change and continued reliance on traditional high-carbon fossil fuels, promoting the transition toward sustainable energy systems by development of low-carbon energy resources has been seen as the main strategy for mitigating and solving global climate change. However, the promotion of low-carbon energy also faces material supply risks. To provide a reference for the steady and rapid development of renewable energy and other energy in the future energy market, this paper considers renewable energy prediction based on a long-and short-term memory network model as well as the growth rate changes of crude oil, natural gas, nuclear energy, financial revenues, and expenditure. In the prediction process, it is found that natural gas will be a strong competitor for the development of renewable energy in the future. When natural gas grows too quickly, the growth of renewable energy will be negative. On the other hand, when the monthly growth rate of natural gas and crude oil is smaller than that of nuclear energy, renewable energy will display a growth trend, and the rate will increase with the growth of natural gas and nuclear energy. What is more, wind and solar energy will be limited by metallic materials, such as Dy, Nd, Te, and In. Improving the energy density of metals plays a key role in China's transition to a low-carbon energy structure.

show abstract

Dynamic modeling of global fossil fuel infrastructure and materials needs: Overcoming a lack of available data

et al. 2022

View full text Add to dashboard Cite

Raw metal needs and supply risks for the development of wind energy in Germany until 2050

Cited by 42 publications

References 37 publications

Multi-Faceted Analysis of Systematic Risk-Based Wind Energy Investment Decisions in E7 Economies Using Modified Hybrid Modeling with IT2 Fuzzy Sets

Multi-Faceted Analysis of Systematic Risk-Based Wind Energy Investment Decisions in E7 Economies Using Modified Hybrid Modeling with IT2 Fuzzy Sets

Renewable Energy and Material Supply Risks: a Predictive Analysis Based on An LSTM Model

Dynamic modeling of global fossil fuel infrastructure and materials needs: Overcoming a lack of available data

Contact Info

Product

Resources

About