Measuring the integrated risk of China’s carbon financial market based on the copula model

Wang, Xiping; Liu, Yan

doi:10.1007/s11356-022-19679-w

Cited by 12 publications

(5 citation statements)

References 27 publications

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“…Secondly, these studies focus on the pricing and factors that influence carbon emission rights and their derivatives [17][18][19][20]. Lastly, research delves into the risks associated with the carbon market, encompassing policy, price, and market risks [21][22][23][24].Undoubtedly, research on carbon finance is increasingly receiving attention and many studies have been conducted. Most experts have concentrated on empirical and qualitative studies, including some review articles.…”

Section: Introductionmentioning

confidence: 99%

Global Trends of Carbon Finance: A Bibliometric Analysis

Zhongxuan

2023

Sustainability

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Based on the Web of Science core database, this paper used bibliometric analysis to analyze 4408 academic works related to carbon finance from 1992 to 2021. This work shows that the number of publications on carbon finance-related research has increased rapidly; the Journal of Cleaner Production is the most productive journal. China boasts the most significant number of publications, while the United States has the most intensive cooperation with other countries. The Chinese Academy of Sciences is the most productive and influential independent institution, and Chinese authors make up the highest proportion of the top 20 most prolific authors. We can summarize the main research directions in carbon finance through keyword analysis: carbon capture, economic growth, carbon sequestration, financial development, modeling, and carbon price forecasting. Research related to the impact of energy consumption, renewable energy, and urbanization on carbon emissions may become a hot spot for future research in carbon finance. Through an in-depth analysis of the development over the past 30 years and future trends of carbon finance, this paper can provide some reference for future research, which is of great significance in promoting carbon emission reduction.

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Section: Introductionmentioning

confidence: 99%

Global Trends of Carbon Finance: A Bibliometric Analysis

Zhongxuan

2023

Sustainability

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“…Finally, the output is mapped to the target variable through a fully connected layer to obtain prediction results. Given input X = {(p (1) , p (2) , . .…”

Section: Mftsformermentioning

confidence: 99%

“…In the context of extreme weather events and climate change, reducing carbon emissions has become a pressing global issue [1]. To limit corporate carbon emissions, China has established pilot carbon trading markets in major cities such as Beijing, Shanghai, and Guangzhou [2]. China's carbon market is now transiting from regional pilot projects to national unification [3].…”

Section: Introductionmentioning

confidence: 99%

Urban Carbon Price Forecasting by Fusing Remote Sensing Images and Historical Price Data

Mou,

Xie,

et al. 2023

Forests

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Under the strict carbon emission quota policy in China, the urban carbon price directly affects the operation of enterprises, as well as forest carbon sequestration. As a result, accurately forecasting carbon prices has been a popular research topic in forest science. Similar to stock prices, urban carbon prices are difficult to forecast using simple models with only historical prices. Fortunately, urban remote sensing images containing rich human economic activity information reflect the changing trend of carbon prices. However, properly integrating remote sensing data into carbon price forecasting has not yet been investigated. In this study, by introducing the powerful transformer paradigm, we propose a novel carbon price forecasting method, called MFTSformer, to uncover information from urban remote sensing and historical price data through the encoder–decoder framework. Moreover, a self-attention mechanism is used to capture the intrinsic characteristics of long-term price data. We conduct comparison experiments with four baselines, ablation experiments, and case studies in Guangzhou. The results show that MFTSformer reduces errors by up to 52.24%. Moreover, it outperforms the baselines in long-term accurate carbon price prediction (averaging 15.3%) with fewer training resources (it converges rapidly within 20 epochs). These findings suggest that the effective MFTSformer can offer new insights regarding AI to urban forest research.

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“…They found that the price volatility in EU-ETS exhibits characteristics, such as peakedness in the tails, autocorrelation, and volatility clustering, indicating an extreme risk. Wang et al (2022) [34] quantitatively studied the comprehensive risk in the Chinese carbon market through the Copula-EVT-VaR model. Zhao et al (2023) [35] used EVT to study the risk spillover of international oil price volatility on the Chinese stock market, and the backtest illustrated that EVT can effectively measure the risk between these two markets.…”

Section: Introductionmentioning

confidence: 99%

Research on Risk Measurement of China’s Carbon Trading Market

Duan,

He,

Yao

et al. 2023

Energies

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In today’s environmentally conscious world, carbon trading has emerged as a widely accepted economic instrument to mitigate the externalities resulting from deteriorating environmental problems. Consequently, the use of market-based mechanisms to address environmental issues has reached a global consensus. Many countries are implementing progressive steps by establishing carbon markets to promote low-carbon development and meet their carbon reduction targets. However, the inherent risks in carbon trading markets may hamper the formation of a reasonable carbon price signal, leading to inadequate stimulation of low-carbon technology investments and potential failure to achieve national emission reduction goals. Therefore, managing the risks associated with carbon trading markets is crucial. This study focuses on measuring the risk of China’s carbon market, with the primary aim of exploring carbon price fluctuation patterns and precisely measuring market risks. The risks associated with China’s carbon market are quantified and analyzed using the exponential generalized autoregressive conditional heteroskedasticity (EGARCH) model, extreme value theory (EVT), and the value at risk (VaR) method. Results show that (1) the effect of external shocks on each carbon market is asymmetrical, and positive shocks exert considerable leverage effects on carbon price fluctuations. (2) EVT can be used to effectively fit the risks in the carbon markets. The risks of each carbon market show different characteristics. The risk of Hubei and Guangdong carbon markets is relatively small, and the dynamic VaR is nearly ±0.2. (3) Compared with the performance of the Chinese carbon market, the performance of the European Union Emission Trading Scheme is more stable, and its dynamic VaR for most of the period is within ±0.1, which is considerably lower than the VaR of other Chinese carbon markets. This study also proposes suitable policy implications to ensure the healthy and sustainable development of China’s carbon market.

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Measuring the integrated risk of China’s carbon financial market based on the copula model

Cited by 12 publications

References 27 publications

Global Trends of Carbon Finance: A Bibliometric Analysis

Global Trends of Carbon Finance: A Bibliometric Analysis

Urban Carbon Price Forecasting by Fusing Remote Sensing Images and Historical Price Data

Research on Risk Measurement of China’s Carbon Trading Market

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