Predictability of the renewable energy market returns: The informational gains from the climate policy uncertainty

Xu, Yongan; Li, Ming; Ye, Wen; Bai, Jiancheng

doi:10.1016/j.resourpol.2022.103141

Cited by 18 publications

(6 citation statements)

References 38 publications

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“…In addition, the costs associated with producing energy from solar and wind sources are highly predictable, especially as there are no fuel costs subject to market fluctuations. This can lead to more stable energy prices in the long term 72–74 …”

Section: Resultsmentioning

confidence: 99%

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Unlocking renewable energy potential: A case study of solar and wind site selection in the Kasserine region, central‐western Tunisia

Rekik,

El Alimi

2023

Energy Science & Engineering

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Developing wind and solar photovoltaics on a large scale requires substantial financial investments, making it crucial to identify the most suitable locations beforehand. To address this issue, a spatial analysis is carried out to determine the most potential sites for hosting large‐scale solar photovoltaic and wind systems in the region of Kasserine, central‐western Tunisia. To this end, an integrated model based on Step‐wise Assessment Ratio Analysis (SWARA), Decision‐Making Trial and Evaluation Laboratory (DEMATEL), and Geographic Information System is proposed. An extensive literature survey is conducted to establish suitability criteria and constraints. The SWARA‐DEMATEL is used to assign weights and capture the interdependencies among the considered criteria, while the raster calculator tool from the ArcGis 10.8 software is utilized to extract the final suitability maps. The obtained results indicate that the region of Kasserine exhibits great solar and wind potential, with areas of 635 and 467 km2 extremely fit for installing solar and wind systems, respectively. Furthermore, 349 km2 are identified as potential locations for hosting solar‐wind hybrid systems. Considering these outcomes, policymakers can take the initiative to accelerate the deployment of these facilities, which would assist the country in achieving its plans by 2030.

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Section: Resultsmentioning

confidence: 99%

“…This can lead to more stable energy prices in the long term. [72][73][74] Second, Tunisia's geopolitical and social situations influence investment security and partnership opportunities. In addition, the legislative landscape shapes the practicalities of renewable energy projects.…”

Section: Potential Sitesmentioning

confidence: 99%

Unlocking renewable energy potential: A case study of solar and wind site selection in the Kasserine region, central‐western Tunisia

Rekik,

El Alimi

2023

Energy Science & Engineering

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“…Additionally, we aim to highlight studies that investigate the relationship between climate policy uncertainty and renewable energy prices [9,57]. In a thorough examination of the impact of the CPU index on global renewable energy market returns, significant insights have been uncovered.…”

Section: Literature Reviewmentioning

confidence: 99%

“…These countries have implemented climate policies aimed at limiting carbon emissions. Nonetheless, the uncertainty surrounding climate change poses challenges and uncertainty to the corresponding climate policy implementation within this process [9].…”

Section: Introductionmentioning

confidence: 99%

“…Climate policies have been implemented to encourage investments in measures that can encourage increased investment in renewable energy and reduce carbon emissions [10]. Due to the compelling empirical evidence highlighting the considerable effect of climate policy uncertainty (CPU) on economic and financial systems, scholars have displayed a notable inclination towards examining the ramifications of climate change on financial markets [9,11,12]. In this respect, Hsu et al [13] found evidence that uncertainty in environmental policies and regulations significantly impacts the returns of emissions' portfolios.…”

Section: Introductionmentioning

confidence: 99%

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Impact of Climate Policy Uncertainty, Clean Energy Index, and Carbon Emission Allowance Prices on Bitcoin Returns

Gürsoy,

Jóźwik,

Dogan

et al. 2024

Sustainability

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This research aimed to investigate the relationship between climate policy uncertainty (CPU), clean energy (ENERGY), carbon emission allowance prices (CARBON), and Bitcoin returns (BTC) for the period from August 2012 to August 2022. The empirical analysis strategies utilized in this study included the Fourier Bootstrap ARDL long-term coefficient estimator, the Fourier Granger Causality, and the Fourier Toda–Yamamoto Causality methods. Following the confirmation of cointegration among the variables, we observed a positive relationship between BTC and CARBON, a positive relationship between BTC and CPU, and a negative relationship between BTC and ENERGY. In terms of causal associations, we identified one-way causality running from CARBON to BTC, BTC to CPU, and BTC to the ENERGY variable. The study underscores the potential benefits and revenue opportunities for investors seeking diversified investment strategies in light of climate change concerns. Furthermore, it suggests actionable strategies for policymakers, such as implementing carbon taxes and educational campaigns, to foster a transition towards clean energy sources within the cryptocurrency mining sector and thereby mitigate environmental impacts.

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Forecasting Equity Premium in the Face of Climate Policy Uncertainty

Ali,

Naz

2024

Journal of Forecasting

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This study examines the role of the US climate policy uncertainty (CPU) index in forecasting the equity premium, employing shrinkage methods such as LASSO and elastic net (ENet) to dynamically select predictors from a dataset spanning April 1987 to December 2022. Alongside CPU, other uncertainty predictors like economic policy uncertainty (EPU), geopolitical risk (GPR), and the volatility index (VIX) are considered to assess their complementary roles in out‐of‐sample (OOS) equity premium forecasting. The results reveal that while CPU alone cannot consistently predict the equity premium, it provides crucial complementary information when combined with other predictors, leading to a statistically significant OOS of 1.231%. The relationship between CPU and the equity premium is time varying, with a stronger influence observed during periods of economic downturn or heightened uncertainty, as demonstrated by wavelet coherence analysis. This study also identifies CPU's significant impact on industry‐specific returns, particularly in climate‐sensitive sectors, offering valuable insights for investment strategies and risk management in an era of increasing CPU.

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Predictability of the renewable energy market returns: The informational gains from the climate policy uncertainty

Cited by 18 publications

References 38 publications

Unlocking renewable energy potential: A case study of solar and wind site selection in the Kasserine region, central‐western Tunisia

Unlocking renewable energy potential: A case study of solar and wind site selection in the Kasserine region, central‐western Tunisia

Impact of Climate Policy Uncertainty, Clean Energy Index, and Carbon Emission Allowance Prices on Bitcoin Returns

Forecasting Equity Premium in the Face of Climate Policy Uncertainty

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