Exploring the short-term and long-term linkages between carbon price and influence factors considering COVID-19 impact.

Because of global lock-downs caused by the unexpected COVID-19, the interactions between emission trading and related markets have changed significantly compared to the pre-COVID-19 period. Considering the pandemic effect, this paper established an integrated system to identify the relationship trajectories between carbon trading market and impact factors. A noise-assisted multivariate empirical mode decomposition (N-A MEMD) method was utilized to simultaneously decompose the original multi-dimensional time series into intrinsic mode functions (IMFs), after which the Lempel-Ziv (LZ) complexity algorithm was applied to reconstruct the IMFs into high-frequency (HF), low-frequency (LF), and trend modules. Vector autoregression (VAR) and vector error correction (VEC) models were then used to systematically simulate the correlations. The time span was split into pre-COVID-19 and post-COVID-19 periods for comparison, and the mobility trends data during the outbreak period released by the Apple company was chosen to reflect the pandemic effects. The empirical analysis results revealed the energy prices, macroeconomic index, and exchange rate are the main external impact factors of carbon price in the short term. Summarizing from the cointegration models over the long term, the market stability reserve (MSR) mechanism was found to have ability on stabilizing the carbon price under the epidemic shock. Furthermore, the COVID-19 was found to complicate the relationships between carbon price and influence factors, which resulted in fluctuating markets.

ENLIGHTENMENT OF INTERNATIONAL CARBON EMISSION TRADING PRACTICE TO CHINA

In the era of "Post-epidemic" and "Dual-carbon targets", the focus of research on China's carbon trading market has changed from basic framework design to problem solving and development paths in the process of practice. Foreign carbon trading markets have developed for many years, and have experienced the financial crisis and the impact of the coronavirus epidemic. By analyzing the important problems and countermeasures encountered in the process of carbon trading market by representative organizations such as EU, USA, New Zealand, Korea and Japan, the valuable experience and reference significance of foreign carbon trading practice were summarized. At the same time, comparing the similarities and differences between Chinese and foreign carbon trading national conditions, and taking into account the current development of China's carbon trading market, this paper put forward some carbon trading strategies with Chinese characteristics and absorbing foreign advanced experience, such as choosing appropriate emission caps, balancing regulation, formulating price stabilization mechanism, and leaving interfaces for international cooperation. © 2023 Research Institute of Petroleum Processing, SINOPEC. All rights reserved.

Carbon trading and COVID-19: a hybrid machine learning approach for international carbon price forecasting.

Accurate carbon price forecasting can better allocate carbon emissions and thus ensure a balance between economic development and potential climate impacts. In this paper, we propose a new two-stage framework based on processes of decomposition and re-estimation to forecast prices across international carbon markets. We focus on the Emissions Trading System (ETS) in the EU, as well as the five main pilot schemes in China, spanning the period from May 2014 to January 2022. In this way, the raw carbon prices are first separated into multiple sub-factors and then reconstructed into factors of 'trend' and 'period' with the use of Singular Spectrum Analysis (SSA). Once the subsequences have been thus decomposed, we further apply six machine learning and deep learning methods, allowing the data to be assembled and thus facilitating the prediction of the final carbon price values. We find that from amongst these machine learning models, the Support vector regression (SSA-SVR) and Least squares support vector regression (SSA-LSSVR) stand out in terms of performance for the prediction of carbon prices in both the European ETS and equivalent models in China. Another interesting finding to come out of our experiments is that the sophisticated algorithms are far from being the best performing models in the prediction of carbon prices. Even after accounting for the impacts of the COVID-19 pandemic and other macro-economic variables, as well as the prices of other energy sources, our framework still works effectively.

ENLIGHTENMENT OF INTERNATIONAL CARBON EMISSION TRADING PRACTICE TO CHINA

In the era of "Post-epidemic" and "Dual-carbon targets", the focus of research on China's carbon trading market has changed from basic framework design to problem solving and development paths in the process of practice. Foreign carbon trading markets have developed for many years, and have experienced the financial crisis and the impact of the coronavirus epidemic. By analyzing the important problems and countermeasures encountered in the process of carbon trading market by representative organizations such as EU, USA, New Zealand, Korea and Japan, the valuable experience and reference significance of foreign carbon trading practice were summarized. At the same time, comparing the similarities and differences between Chinese and foreign carbon trading national conditions, and taking into account the current development of China's carbon trading market, this paper put forward some carbon trading strategies with Chinese characteristics and absorbing foreign advanced experience, such as choosing appropriate emission caps, balancing regulation, formulating price stabilization mechanism, and leaving interfaces for international cooperation. © 2023 Research Institute of Petroleum Processing, SINOPEC. All rights reserved.

Air traffic flow management under emission policies: Analyzing the impact of sustainable aviation fuel and different carbon prices

As part of the global efforts to make aviation activities more environmentally friendly, the worldwide goal is to achieve a 50% reduction in the 2005 emissions by 2050. In this context, aviation emissions represent a critical challenge to aviation activities, especially with the increasing travel demand up to the beginning of the COVID-19 crisis, starting in 2020. One of the potential drivers that would help the aviation industry reduce its emissions is the use of sustainable aviation fuel (SAF). In this study, we analyzed the impact of SAF from an air traffic flow management (ATFM) perspective, considering delay and re-routing costs. We developed an optimization model that considers, in addition to the traditional ATFM costs, fuel costs and carbon dioxide emissions. We investigated the impact of accounting for these two new aspects, that is, fuel costs and emissions, on ATFM performance, and we compared SAF with conventional fuel. The analysis of a real case study revealed that, in addition to delay and re-routing costs, fuel cost should be included in the ATFM model so that the resulting solution becomes economically and environmentally realistic for airlines. The increase in the fuel cost and network delays when using SAF requires setting an appropriate carbon price under an emission policy, such as the carbon offsetting and reduction scheme for international flights policy, to make SAF more attractive. Furthermore, flexible re-routing programs for flights operated using SAF make it advantageous from an ATFM perspective.

The impact of cryptocurrencies on China's carbon price variation during COVID-19: A quantile perspective

We aim to document the impact of cryptocurrencies on China's carbon price variation using some quantile techniques during COVID-19 with the daily data spanning from August 7, 2015 to April 30, 2021. In this paper, we show that cryptocurrencies have a very strong explanation power for carbon market with the non-parametric causality-in-quantiles method. In addition, cryptocurrencies can work as a good hedging candidate for carbon market at different investment horizons with the quantile coherency approach. Using hedging effectiveness measure, we further show that COVID-19 can reverse the optimal hedging ratios in our portfolio specification in cryptocurrencies‑carbon emission trading pairs while this pandemic does not have effects on the trading effectiveness. Finally, the heterogeneity and asymmetry features in the dynamic quantile-on-quantile effects are detected and the effects on carbon efficient index show relatively strong fluctuation while on carbon emission trading market are relatively strong in magnitude. Our empirical results conclude with many potential applications for policymakers and investors.

Key factors affecting carbon prices from a time-varying perspective.

For humankind to sustain a livable atmosphere on the planet, many countries have committed to achieving carbon neutralization. Countries mainly reduce carbon emissions by regulations through a carbon tax or by establishing a carbon market using economic stimuli. In this paper, we use the least absolute shrinkage and selection operator (LASSO) method to select the key determinants of a carbon market and then use the Markov switching vector autoregression (MSVAR) model to study the market's driving factors and analyze its time-varying characteristics. The results show that there are perceptible time-varying characteristics and notable differences among markets. During COVID-19, energy factors had a long-term shock on the carbon market, economic factors had a short-term shock on the carbon market, and the economic recession has led to fluctuations in the carbon market. In addition, through MSVAR, the results show that the energy market has a negative effect on the carbon market, and the stock market has a positive effect on the carbon market. In periods of low volatility, compared with the natural gas market and coal market, the oil market has a stronger shock on the carbon market. In periods of high volatility, the coal market has a stronger shock on the carbon market. In terms of emission reduction, countries around the world would be wise to change their energy consumption structure, reduce coal use, and shift to a cleaner energy consumption structure.

‘Technology not taxes’: A viable Australian path to net zero emissions?

In the absence of a price on carbon and a renewable energy target supported by emissions credit trading, the Australian Government is relying on financial support for early-stage technologies to reduce emissions: the ‘technology not taxes’ ‘principle’. Recent studies of global implementation indicate that pricing carbon is the most economically efficient way to achieve economy-wide emissions reductions. Nonetheless, financial support for early-stage technology can be a useful complementary or ‘second best’ approach. Australia's Technology Investment Roadmap and Net Zero Plan could be strengthened by detailed emissions plans on a sectoral basis, supported by interim targets set in legislation. Projected fossil fuel exports in coming decades in the Net Zero Plan are consistent with the Australian Government's ‘gas led’ Covid 19 economic recovery strategy. This underpins reliance on carbon capture and storage (CCS), despite cost and technical barriers that have led to minimal development compared to renewable energy. In the absence of interim emissions targets, Australia risks locking in fossil fuel developments on the basis of uncertain future CCS capacity. A Net Zero Plan focused primarily on scale-up of renewable energy and its use to produce hydrogen, reflecting several influential reports, state government initiatives and private sector mega projects, would have better prospects for achieving net zero emissions.

System-level effects of increased energy efficiency in global low-carbon scenarios: A model comparison

Supporting investments in energy efficiency is considered a robust strategy to achieve a successful transition to low-carbon energy systems in line with the Paris Agreement. Increased energy efficiency levels are expected to reduce the need for supply-side investments in controversial technologies, such as carbon dioxide capture and storage (CCS) and nuclear energy, and to induce a downward push on carbon prices, which may facilitate the political and societal acceptance of climate policies, without adversely affecting living comfort and sustainable development. In order to fully reap these potential benefits, economies need to design policy packages that balance emission reduction incentives on both the demand and the supply side. In this paper we carry out a model-comparison exercise, using two well-established global integrated assessment models, PROMETHEUS and TIAM-ECN, to quantitatively analyze the global system-level effects of increased energy efficiency in the context of ambitious post-COVID climate change mitigation scenarios. Our results confirm the expected benefits induced by higher energy efficiency levels, as in 2050 global carbon prices are found to decline by 10%–50% and CO2 storage from CCS plants is 13%–90% lower relative to the “default” mitigation scenarios. Similarly, enhanced energy efficiency reduces the additional average yearly system costs needed globally in 2050 to achieve emission reductions in line with the Paris Agreement. These additional costs are estimated to be of the order of 2 trillion US$ – or 1% of global GDP – in a well-below-2 °C scenario, and can be reduced by 6–30% with the adoption of higher energy efficiency standards. While the two models project broadly consistent future trends for the energy mix in the various scenarios, the effects may differ in magnitude due to intrinsic differences in how the models are set up and how sensitive they are to changes in energy efficiency and emission reduction targets. © 2022

The price fluctuation in Chinese carbon emission trading market: New evidence from adaptive Fourier decomposition

This paper studies the carbon price fluctuation in China through the adaptive Fourier decomposition (AFD). Apart from the transient time-frequency distribution of the original AFD model, we also reconstruct the mono-components of this model to obtain the components in different time-frequency scales. Our empirical results based on the carbon price in Hubei Province demonstrate that there are three periods when the price fluctuates dramatically, mainly affected by the governmental policies about carbon emission and the development of clean energies, as well as the outbreak of COVID-19. Furthermore, the fluctuations of the price in the three identified periods are reflected in different scales. The comparison of the decomposition results and those of EMD and VMD shows that the AFD performs best in absorbing the price's useful information extracted through all these methods. © 2021 The Authors. Published by Elsevier B.V.

A VaR-Based Methodology for Assessing Carbon Price Risk across European Union Economic Sectors

The latest European Union measures for combating climate adopted in the “Fit for 55 package” envisage the extension of the Emissions Trading System, the first “cap-and-trade” system in the world created for achieving climate targets, which limits the amount of greenhouse gas emissions by imposing a price on carbon. In this context, our study provides an integrated assessment of carbon price risk exposure of all economic sectors in the European Union Member States, thus supporting decision making in determining the energy transition risk. We propose a novel approach in assessing carbon risk exposure using the Value at Risk methodology to compute the carbon price under the EU ETS, based on historical price simulation for January–August 2021 and ARMA-GARCH models for the October 2012–August 2021 period. We further built a value erosion metric, which allowed us to establish each sector’s exposure to risk and to identify differences between Eastern and Western EU countries. We find that the refining sector appears to be highly vulnerable, whereas there is higher potential for large losses in the energy supply and chemical sectors in Eastern EU Member States, given a different pace of industry restructuring.

Carbon price prediction for China's ETS pilots using variational mode decomposition and optimized extreme learning machine.

With the national goal of "carbon peak by 2030 and carbon neutral by 2060 in China", studies on carbon prices of China's Emissions Trading System (ETS) pilots have shown growing interest in the related fields. Carbon price fluctuations reflect the scarcity of carbon resources, and accurate prediction can improve carbon asset management capabilities. Therefore, in order to clarify the dynamics of carbon markets and assign carbon emissions allocation rationally, we propose a hybrid feature-driven forecasting model with the framework of decomposition-reconstruction-prediction-ensemble. In this paper, the non-stationary, nonlinear and chaotic characteristics of carbon prices in China's ETS pilots have been verified, and then the prediction model is built based on the tested features. Firstly, the original carbon price series are decomposed by Variational Mode Decomposition (VMD), and then reconstructed by Sample Entropy (SE). Next, Extreme Learning Machine (ELM) optimized by Particle Swarm Optimization (PSO) is conducted to predict the subsequences. Lastly, the forecasting series of every subseries are summed to obtain the final results. The empirical results based on carbon prices of China's ETS pilots proved that the proposed model performs more efficiently than the current benchmark models. As carbon prices are expected to increase across all ETS during the post-COVID-19 recovery stage, the new prediction model will be useful for improving the guiding principles of the existing government policies including the likely introductions of Border Carbon Adjustment (BCA) in the EU and the US, and governing the large global public companies to deliver their "net zero" commitments.

Exploring volatility of carbon price in European Union due to COVID-19 pandemic.

The European Union (EU) Emissions Trading System is the most important means for the EU to achieve carbon neutrality, but it has been severely affected by the outbreak of COVID-19 in Europe, and carbon price have fluctuated sharply. Research on the driving factors of carbon price during this period will help maintain the stability of the carbon emissions trading market and promote the realization of carbon neutrality. This study selected the EU carbon allowance futures price as the research object and applied the Bai-Perron structural break test to analyze the factors that influences carbon price fluctuations using the Johansen cointegration technique and the Newey-West regression estimation. Studies have shown that the outbreak of COVID-19 and the "€750 billion green recovery plan" both had a significant impact on EU carbon price. Carbon price has also undergone significant structural changes. Under the influence of these two factors, the relationship between the level of economic development and carbon price displayed a short-term negative correlation. At the same time, oil price and interbank dismantling rates were also important factors affecting carbon price, while the impact of the clean development mechanism on carbon price was not significant. The study confirmed the effectiveness of the EU's "green recovery plan" in stabilizing the carbon market during the COVID-19 pandemic and will provide a reference for the formulation of economic recovery policies of countries around the world.

The dynamics of carbon on green energy equity investment: quantile-on-quantile and quantile coherency approaches.

We analyze the dynamic correlation between the carbon price and the stock returns of green energy companies and calculate the hedging effect of the carbon price on stock returns in green energy sectors. The results show that the coefficients of the carbon price change with time and are vulnerable to extreme events like the COVID-19. The quantile-on-quantile (QQ) model results reveal a dynamic effect from the carbon price to the stock returns of green energy sectors. The quantile coherency (QC) approach results show that investors can benefit more in the short term with high-frequency trading to hedge between carbon trading and the green energy stock market. What is more, the hedging effects are heterogenetic and investors should adjust their hedging strategies in different quantiles.