The Impact of Carbon Pricing on the Performance of Energy Firms: A Comparative Analysis Between Renewable and Traditional Energy

The transition from traditional energy to cleaner energy sources has raised concerns from companies and investors regarding, among other things, the impact on financial downside risk. This article implements backtesting techniques to estimate and validate the value-at-risk (VaR) and expected shortfall (ES) in order to compare their performance among four renewable energy stocks and four traditional energy stocks from the WilderHill New Energy Global Innovation and the Bloomberg World Energy for the period 2005-2016. The models used to estimate VaR and ES are AR(1)-GARCH(1,1), AR(1)-EGARCH(1,1), and AR(1)-APARCH(1,1), all of them under either normal, skew-normal, Student’s t, skewed-t, Generalized Error or Skew-Generalized Error distributed innovations. Backtesting performance is tested through traditional Kupiec and Christoffersen tests for VaR, but also through recent backtesting ES techniques. The paper extends these tests to the skewed-t, skew-normal and Skew-Generalized Error distributions and applies it for the first time in traditional and renewable energy markets showing that the skewed-t and the Generalized Error distribution are an accurate tool for risk management in those markets. Our findings have important implications for portfolio managers and regulators in terms of capital allocation in renewable and traditional energy stocks, mainly to reduce the impact of possible extreme loss events.

Purpose This study aims to examine the dynamic risk-return characteristics of renewable energy (RE) stocks, compare them with traditional energy companies and the overall equity market index (NIFTY50) in India and provide a suitable investment strategy using these assets. Design/methodology/approach This study uses the Markov regime-switching model to identify high- and low-volatile market regimes to provide insights into the time-varying nature of returns, volatility and correlations across these asset classes. Findings Despite being more volatile, RE stocks do not provide negative mean returns irrespective of the market regimes. The NIFTY50 index provides consistent and profitable returns during the high-variance regimes, but it produces negative average returns in less volatile markets. On the contrary, conventional energy stocks offer the highest returns in the low-volatile market, but they also show negative average returns during the high-variance regime. The portfolio diversification strategy suggests that combining renewable and conventional energy indices provides the most effective hedge in both regimes. Research limitations/implications This research contributes to the understanding of RE stocks as a viable asset class for diversification and risk management in emerging markets like India, emphasizing the importance of adaptive investment strategies in response to market volatility. Practical implications The results underscore the necessity for investors, portfolio managers and policymakers to adopt regime-specific and dynamically adaptive strategies tailored to varying market states to optimize returns and manage risks. Originality/value This study represents a pioneering effort to explore the dynamic risk-return profile of conventional, renewable and aggregate market indexes within the Indian equity market. By incorporating regime-switching techniques and a tailored RE index, it addresses a key gap in emerging market energy finance and offers actionable insights for dynamic investment strategies.

This chapter studies how a well-defined environmental concerns factor influences the equity performance of US energy firms and whether or not this influence differs for renewable and non-renewable energy firms. We construct the environmental sentiment factor and seven sub-group environment-related risk factors using the Dynamic Factor Model econometric method. Then, we estimate stock abnormal returns from well-established asset pricing models to measure firm equity performance. We use an unbalanced panel of 448 US energy firms at the monthly frequency from January 2004 to October 2016. First, we find that all US energy companies show negative abnormal returns in the sampled period, that is the actual returns were lower than expected. Renewable energy firms had higher company value, since renewable energy stocks had lower abnormal returns. We also find that the abnormal returns of renewable energy stocks mainly come from environment regulation risks, while the abnormal returns of non-renewable energy stocks are more sensitive to the measure of public environmental concerns and extreme weather conditions. Consequently, we find that holding the level of environmental related risks constant, non-renewable energy stocks command relative lower abnormal returns.

Carbon trading volume and price forecasting in China using multiple machine learning models

Reducing anthropogenic greenhouse gas emissions is a critical element to keep global warming below 2 °C. In terms of the IEA report, the largest sources of emission in 2016, which approaches to 42% of global total, is generated by power sector and heat sector. This indicates that reducing the emission in the power sector can play a crucial role to limit global warming. Large shares of low-carbon generators such as renewables, power plants with carbon capture and storage and implementing a sustainable environmental tax or carbon price are the possible approaches to reduce the emissions from the power sector. The paper investigates how carbon prices affect the Northern European power system. The power system model is net transfer capacity-based model which aims to minimize economic performance, such as operational cost and environmental cost, with the common power system constraints and large expansions of sustainable energy development, i.e., solar and wind energy. The carbon prices are based on scenarios of the Shared Socioeconomic Pathways (SSPs) which aims to limit global warming to below 2 °C with a probability greater than 66%. Four scenarios are conducted based on SSPs carbon prices. Results show that the carbon prices have a great impact on the economic performance of the power system, i.e., the higher carbon price, the higher power prices. Increasing carbon prices result in decreasing of coal production including hard coal and lignite coal production but increasing the gas production. This is due to different fuel carbon prices. Furthermore, renewable energy such as wind production continues to increase. This implies a positive relationship between renewable energy and carbon prices, such as the higher the carbon prices, the higher renewable energy production.

Renewable energy tokens allow investors to diversify their portfolios while supporting renewable energy projects. While renewable energy tokens and renewable energy stocks are different investment instruments with different characteristics, there can be important links between them, as both offer the opportunity to invest in renewable energy sources. The aim of the study is to investigate the return connectedness between renewable energy tokens and renewable energy stock indices. EWT, PWR, SNC, and WPR are used to represent renewable energy tokens, and WilderHill Clean Energy Index, S&P Global Clean Energy Index, and European Renewable Energy Index indices are used to represent renewable energy stock markets. In the study, return connectedness between assets is investigated with the QVAR model. The return spillovers between renewable energy tokens and renewable energy stock indices show that they vary under different market conditions. Under normal market conditions, renewable energy tokens and renewable energy indices are significantly unconnected. During extreme market downturns and upturns, the return interconnectedness among these assets significantly increases. Moreover, the return spillovers between renewable energy tokens and renewable energy stock indices are asymmetric and time-varying. The return connectedness between these assets is affected by extreme events such as COVID-19, the Russia-Ukraine war, and the collapse of the cryptocurrency market. Since the net return spillover relationships between assets are time-varying and under different market conditions, investors and portfolio managers should constantly review the net return spillovers between assets and adjust their positions accordingly.

With the pressing need for green finances to combat climate change, there has been a growing advocacy for investing in renewable energy stocks. However, the investment landscape continues to favor non-renewable energy stocks. To understand this financial decision, which seems to contradict the global push for sustainability, we employ the GARCH-MIDAS model to compare the resilience of renewable and non-renewable energy stocks to climate-related risks. Our findings indicate that both RE and NRE are vulnerable to the effects of climate change. However, our results suggest that investors may perceive RE stocks as more resilient and stable considering climate-induced shocks.

Impact of carbon pricing on the integrated green supply chain of the semiconductor industry

This paper investigates the predictive relationships among climate policy uncertainty (CPU), oil prices, and renewable energy (RE) stock market returns, particularly highlighting the challenges posed by the varying data frequencies of these variables. The study utilizes a comprehensive dataset comprising monthly CPU, daily oil prices, and RE stock returns, sourced globally. By applying a mixed-frequency causality test (MFCT), the analysis reveals significant predictability across different time horizons, particularly highlighting the strong influence of oil prices on RE stock returns over short-term horizons, while CPU demonstrates a more pronounced effect over medium to long-term horizons. In contrast, the application of the classical Granger causality test on low-frequency (monthly) data indicates an insignificant relationship between CPU and RE stocks, suggesting that traditional models may overlook important predictive dynamics. The analysis was conducted using Matlab code, and the findings provide valuable insights for policymakers in designing effective climate policies and for investors in optimizing portfolio strategies and hedging against risks.

Cross-quantilogram-based correlation and dependence between renewable energy stock and other asset classes

Does climate policy uncertainty predict renewable energy stocks? A quantile-based (a)symmetric causality analysis

The Case against Carbon Prices

Under the carbon trading mechanism, renewable energy projects can gain additional benefits through Chinese Certified Emission Reduction transactions. Due to the uncertainty of carbon trading system, carbon prices will fluctuate randomly, which will affect the investment timing of renewable energy projects. Thus, the value of the option will be generated. Therefore, renewable energy power generation project investment has the right of option. However, the traditional investment decision-making method can no longer meet the requirements of renewable energy investment in the current stage. In this paper, a real option model considering carbon price fluctuation is proposed as a tool for renewable energy investment. Considering optimal investment timing and carbon price, the model introduces a carbon price fluctuation as part of the optimization, studies the flexibility of enterprises’ delayed investment under the fluctuation of carbon price. A case study is carried out to verify the effectiveness of the proposed real option model by selecting a wind farm in North China. The model is expected to help investors to assess the volatility and risk of renewable energy projects more accurately, and help investors to make a complete plan for the project investment, thus promoting the efficient allocation of resources in the energy industry.

PurposeThe outbreak and the spreading of the COVID-19 pandemic have impacted the global financial sector, including the alternative clean and renewable energy sector. This paper aims to assess the impact of the pandemic, COVID-19 on the stock market indices of the clean energy sector using quantile regression methods.Design/methodology/approachThis study utilized daily data sets on the four major categories of stocks: (1) Morgan Stanley Capital International Global Alternative Energy Index, (2) WilderHill Clean Energy Index, (3) Renewable Energy Industrial Index (RENIXX) and (4) the S&P 500 Global Clean Index. The study adopts a multifactor capital asset pricing model.FindingsClean and alternative energy stocks are powerful instruments for diversification. However, the impact of the volatility index induced by infectious disease is negative and significant across quantiles.Practical implicationsFor investors and policymakers, considering how the uncertainty caused by COVID-19 and the geopolitical index influences renewable energy markets is of great practical importance. For investors, it throws insights into portfolio diversification. For policy makers, it helps to devise strategies to reboot the economy along the lines of the deployment of renewables. This study sheds light on a global green-energy transition and has practical implications for renewable energy resilience in post-pandemic times.Originality/valueThis paper can be considered as a pioneer that explores the nexus between oil prices, interest rates, volatility index, and geopolitical risk upon the stock indices of clean and alternative sources of (renewable) energy in the COVID-19 pandemic situation. The results have important insights into the area of energy and policy decision-making. Additionally, the paper's novelty lies in using the explanatory variables associated with the Covid 19 pandemic.

The current paper focusses on the co-movement between oil prices and renewable energy stock markets in a multivariate framework. The vine copula approach that offers a great flexibility in conditional dependence modelling is used. More specifically, we investigate the issue of the average dependence and co-movement between oil prices (West Texas Intermediate [WTI]) and renewable energy stock prices (Wilder Hill New Energy Global Innovation Index [NEX], Wilder Hill Clean Energy Index [ECO] and S and P Global Clean Energy Index [SPGCE]) by applying the vine copula based threshold generalised autoregressive conditional heteroskedasticity (TGARCH) model. Over the period 2003–2016, empirical findings reveal significant and symmetric dependence between the considered markets. Therefore, there is symmetric tail dependence, indicating the evidence of upper and lower tail dependence. This means that movements in oil prices and renewable energy indices are coupled to the same direction. These empirical insights are of particular interest to policymakers, risk managers and investors in renewable energy sector.