scholarly journals Carbon Market Regulation Mechanism Research Based on Carbon Accumulation Model with Jump Diffusion

2014 ◽  
Vol 2014 ◽  
pp. 1-7 ◽  
Author(s):  
Dongmei Guo ◽  
Yi Hu ◽  
Bingjie Zhang
Keyword(s):  
Jump Diffusion ◽  
Carbon Price ◽  
Carbon Market ◽  
Carbon Accumulation ◽  
High Price ◽  
Regulation Mechanism ◽  
Market Regulation ◽  
Accumulation Model ◽  
Actual Operation ◽  
Carbon Finance

In order to explore carbon market regulation mechanism more effectively, based on carbon accumulation model with jump diffusion, this paper studies the carbon price from two perspectives of quantity instrument and price instrument and quantitatively simulates carbon price regulation mechanisms in the light of actual operation of EU carbon market. The results show that quantity instrument and price instrument both have certain effects on carbon market; according to the comparison of the elasticity change of the expected carbon price, comparative advantages of both instruments rely on the price of carbon finance market. Where the carbon price is excessively high, price instrument is superior to quantity instrument; where carbon price is excessively low, quantity instrument is better than price instrument. Therefore, in the case of carbon market regulation based on expected carbon price, if the carbon price is too high, price instrument should prevail; if the carbon price is excessively low, quantity instrument should prevail.

scholarly journals Mechanism of Carbon Finance’s Influence on Radical Low-Carbon Innovation with Evidence from China

2020 ◽  
Vol 12 (18) ◽  
pp. 7708
Author(s):  
Limei Sun ◽  
Jinyu Wang ◽  
Zhicheng Wang ◽  
Leorey Marquez
Keyword(s):  
Carbon Dioxide Emissions ◽  
Carbon Market ◽  
Carbon Intensity ◽  
Chinese Government ◽  
Market Regulation ◽  
Low Carbon ◽  
Agent Simulation ◽  
Carbon Finance ◽  
Reduce Carbon Dioxide ◽  
Low Carbon Technologies

Radical low-carbon innovations have considerable technological and revolutionary influences. These key technologies considerably reduce carbon dioxide emissions. This study examines the role of carbon finance development in China’s radical low-carbon innovations. The paper identifies the key entities involved, constructs a network model of the interaction between carbon finance and radical low-carbon innovation, and uses multi-agent simulation modeling to analyze the associated influence mechanism. The results demonstrate that the carbon market can promote radical low-carbon innovation by (1) regulating the number of enterprises participating subject to carbon emission regulations, (2) regulating the number of market intermediaries, (3) establishing the market regulation level, and (4) setting the carbon intensity reduction level. The paper concludes that the Chinese government can formulate novel carbon market-related policies and regulations that, in a timely manner, influence the relationship between the carbon market and participating entities to promote the development of radical low-carbon technologies.

scholarly journals Evolutionary Analysis of a Three-Dimensional Carbon Price Dynamic System

2018 ◽  
Vol 11 (1) ◽  
pp. 116 ◽  
Author(s):  
Xinghua Fan ◽  
Ying Zhang ◽  
Jiuli Yin
Keyword(s):  
Dynamic System ◽  
Three Dimensional ◽  
Carbon Price ◽  
Carbon Market ◽  
Energy Price ◽  
Evolutionary Analysis ◽  
Government Control ◽  
System Parameters ◽  
Price Dynamic

The carbon market is the least-cost tool to reduce carbon emissions. This study explores the evolution of the carbon price in the carbon market from a dynamic system perspective. A three-dimensional carbon price dynamic system is established to quantify the interactions among the carbon price, energy price, and economic growth. The system built in this study presents various dynamic characteristics including chaotic attractors and stable equilibria. Specifically, the existence of chaos in the system is verified by Lyapunov exponents spectrum and bifurcation diagram. In contrast, the system tends to be stable in the case of China after identifying the system parameters through the genetic algorithm. Furthermore, evolutionary trends of the carbon price are analyzed when the system parameters are perturbed. The results show that the carbon price is positively correlated with energy price as well as energy price policy. Besides, the level of the carbon price is negatively correlated with government control in the short term and positively correlated in the long term. This study can help analyze trends in the carbon price in the mid-term to long-term.

scholarly journals Carbon Price Prediction Based on Ensemble Empirical Mode Decomposition and Extreme Learning Machine Optimized by Improved Bat Algorithm Considering Energy Price Factors

Energies ◽  
2020 ◽  
Vol 13 (13) ◽  
pp. 3471
Author(s):  
Wei Sun ◽  
Junjian Zhang
Keyword(s):  
Extreme Learning Machine ◽  
Empirical Mode Decomposition ◽  
Bat Algorithm ◽  
Carbon Price ◽  
Ensemble Empirical Mode Decomposition ◽  
Carbon Market ◽  
Energy Price ◽  
Price Prediction ◽  
Mode Decomposition ◽  
Learning Machine

In response to climate change and environmental issues, many countries have gradually optimized carbon market management and improved the carbon market trading mechanism. Carbon price prediction plays a pivotal role in promoting carbon market management when investors are guided by prediction to conduct rational carbon trading. A novel carbon price prediction methodology is constructed based on ensemble empirical mode decomposition, improved bat algorithm, and extreme learning machine (EEMD-IBA-ELM) in this study. Firstly, the carbon price is decomposed into multiple regular intrinsic mode function (IMF) components by the ensemble empirical mode decomposition, and partial autocorrelation analysis (PACF) is used to find IMF historical data affecting the current value of IMF. Secondly, the improved bat algorithm (IBA) is used to heighten extreme learning machine (ELM) while adaptive parameters are obtained. Finally, EEMD-IBA-ELM was established to predict carbon price. Simultaneously, energy price fluctuation is introduced into the carbon price prediction model. As a consequence, EEMD-IBA-ELM carbon price prediction ability is further improved. In the empirical analysis, the historical carbon price of European Climate Exchange (ECX) and Korea Exchange (KRX) markets are used to examine the effectiveness and stability of the model. Errors of carbon price prediction in ECX and KRX is 2.1982% and 1.1762%, respectively. The results show that the EEMD-IBA-ELM carbon price prediction model can accurately predict carbon price when prediction effect shows strong stability. Furthermore, carbon price prediction accurateness was significantly enhanced by using energy price fluctuation as an influencing factor of carbon price prediction.

Estimating the Portfolio Risk with Copula-GARCH-EVT Method: Empirical Study of Carbon Market

2013 ◽  
Vol 791-793 ◽  
pp. 2175-2178 ◽  
Author(s):  
Ting Li ◽  
Zhi Gang Zhang ◽  
Lu Tao Zhao
Keyword(s):  
Emission Reduction ◽  
Value At Risk ◽  
Marginal Distribution ◽  
Carbon Price ◽  
Carbon Market ◽  
Portfolio Risk ◽  
Distribution Models ◽  
Investor Confidence ◽  
Market Participants ◽  
Better Than

With the rapid growth of the carbon market, carbon price fluctuations are increasingly important for market participants. Carbon market risk directly affects the investor confidence and emission reduction results. In this paper we use Copula-GARCH-EVT model to calculate the Value-at-Risk of carbon futures via Monte Carlo method and demonstrate that GARCH-EVT model is the proper marginal distribution, it has higher accuracy than other marginal distribution models. The method of Copula is better than the traditional covariance metric method.

scholarly journals Forecasting Carbon Dioxide Price Using a Time-Varying High-Order Moment Hybrid Model of NAGARCHSK and Gated Recurrent Unit Network

2022 ◽  
Vol 19 (2) ◽  
pp. 899
Author(s):  
Po Yun ◽  
Chen Zhang ◽  
Yaqi Wu ◽  
Yu Yang
Keyword(s):  
Carbon Dioxide ◽  
Hybrid Model ◽  
Carbon Dioxide Emissions ◽  
Effective Means ◽  
Carbon Price ◽  
Carbon Market ◽  
High Order ◽  
Order Moment ◽  
Time Varying ◽  
High Order Moment

The carbon market is recognized as the most effective means for reducing global carbon dioxide emissions. Effective carbon price forecasting can help the carbon market to solve environmental problems at a lower economic cost. However, the existing studies focus on the carbon premium explanation from the perspective of return and volatility spillover under the framework of the mean-variance low-order moment. Specifically, the time-varying, high-order moment shock of market asymmetry and extreme policies on carbon price have been ignored. The innovation of this paper is constructing a new hybrid model, NAGARCHSK-GRU, that is consistent with the special characteristics of the carbon market. In the proposed model, the NAGARCHSK model is designed to extract the time-varying, high-order moment parameter characteristics of carbon price, and the multilayer GRU model is used to train the obtained time-varying parameter and improve the forecasting accuracy. The results conclude that the NAGARCHSK-GRU model has better accuracy and robustness for forecasting carbon price. Moreover, the long-term forecasting performance has been proved. This conclusion proves the rationality of incorporating the time-varying impact of asymmetric information and extreme factors into the forecasting model, and contributes to a powerful reference for investors to formulate investment strategies and assist a reduction in carbon emissions.

scholarly journals The Long-Term Trend Analysis and Scenario Simulation of the Carbon Price Based on the Energy-Economic Regulation

2020 ◽  
Vol 12 (5) ◽  
pp. 653-668
Author(s):  
Zhao-Peng Li ◽  
Li Yang ◽  
Si-Rui Li ◽  
Xiaoling Yuan
Keyword(s):  
Economic Development ◽  
Energy Consumption ◽  
Clean Energy ◽  
Carbon Price ◽  
Energy Development ◽  
Carbon Market ◽  
Content Type ◽  
Price Trends ◽  
The Impact

Purpose China’s national carbon market will be officially launched in 2020, when it will become the world’s largest carbon market. However, China’s carbon market is faced with various major challenges. One of the most important challenges is its impact on the social and economic development of arid and semi-arid regions. By simulating the carbon price trends under different economic development and energy consumption levels, this study aims to help the government can plan ahead to formulate various countermeasures to promote the integration of arid and semi-arid regions into the national carbon market. Design/methodology/approach To achieve this goal, this paper builds a back propagation neural network model, takes the third phase of the European Union Emissions Trading System (EU ETS) as the research object and uses the mean impact value method to screen out the important driving variables of European Union Allowance (EUA) price, including economic development (Stoxx600, Stoxx50, FTSE, CAC40 and DAX), black energy (coal and Brent), clean energy (gas, PV Crystalox Solar and Nordex) and carbon price alternatives Certification Emission Reduction (CER). Finally, this paper sets up six scenarios by combining the above variables to simulate the impact of different economic development and energy consumption levels on carbon price trends. Findings Under the control of the unchanged CER price level, economic development, black energy and clean energy development will all have a certain impact on the EUA price trends. When economic development, black energy consumption and clean energy development are on the rise, the EUA price level will increase. When the three types of variables show a downward trend, except for the sluggish development of clean energy, which will cause the EUA price to rise sharply, the EUA price trend will also decline accordingly in the remaining scenarios. Originality/value On the one hand, this paper incorporates driving factors of carbon price into the construction of carbon price prediction system, which not only has higher prediction accuracy but also can simulate the long-term price trend. On the other hand, this paper uses scenario simulation to show the size, direction and duration of the impact of economic development, black energy consumption and clean energy development on carbon prices in a more intuitive way.

scholarly journals Influencing factors of price fluctuation in China’s carbon market

2020 ◽  
Vol 218 ◽  
pp. 01044
Author(s):  
Qi Wei ◽  
Yuanyuan Bian ◽  
Xuejuan Yang
Keyword(s):  
Negative Impact ◽  
Positive Impact ◽  
Emission Trading ◽  
Carbon Price ◽  
Carbon Market ◽  
Coal Consumption ◽  
Factors Affecting ◽  
Price Signals ◽  
Carbon Emission Trading ◽  
Carbon Prices

Carbon emission trading is an important countermeasure for countries around the world to cope with the challenge of climate change. Price signals in the carbon market play an important stabilizing role. Therefore, research on the factors affecting carbon price fluctuations is of great significance. Based on this, an empirical study on the fluctuation factors of carbon price in China’s pilot carbon market showed that: gross industrial output, coal consumption and the number of extreme weather have a positive impact on carbon prices, while the technology innovation index has a negative impact on carbon prices. This article puts forward suggestions on the construction of the carbon market, stabilizes carbon prices, and promotes the development of China’s carbon market.

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