Abstract
In recent years, the study of the factors affecting the carbon trading price plays an important role in promoting the carbon trading markets and the sustainable development of green economy. However, due to the short establishment time of China’s carbon trading market, the carbon trading price data of the pilot markets were not complete and have the typical characteristics of poor information. The traditional grey correlation model cannot effectively identify the volatility and the grey correlation coefficient of trading data. In this paper, an inscribed cored grey relational analysis model (IC-GRA) is constructed by extracting the values of the triangle inscribed center of the time series sample. Through numerical examples and empirical analysis, it is verified that IC-GRA not only satisfies the four axioms of traditional grey correlation but also avoids the influence of outliers of time series fluctuation and improves the discriminability of the grey correlation coefficient. The empirical results of the IC-GRA model in China’s seven pilot carbon trading markets show that: 1. among international carbon trade factor, the biggest influence factor carbon trade price is different in pilot markets. The price of natural gas has a greater correlation with the carbon price of carbon trading markets in Shenzhen, Guangzhou, and Chongqing. The futures price of Certified Emission Reduction (CER) has a strong correlation with the carbon price of Shanghai and Beijing carbon trading markets; the price of Hubei carbon trading market is the largest related to crude oil future price in the New York Mercantile Exchange ( NYMEX). 2. Air Quality Index (AQI) is most relevant to the market carbon price of carbon trading, followed by the trading turnover and trading volume of the carbon trading market. Therefore, studying the carbon trading price of the carbon trading market plays a positive role in improving the sustainable development in those areas.
Highlights
Many scholars have followed this train of thought and put forward various grey relational analysis models, including absolute relational model [13], T type relational model [14], B type and C type relational degree [15,16], and slope relational degree [17]
This paper proposes a relational model based on center coordinates of an inscribed circle of a triangle
In order to effectively distinguish the grey relational coefficient generated by two different time series, two comparative variables have been introduced into the Formula (7), which are the subtraction variable
Summary
Introduction of Factors Influencing Carbon Emissions Trading and Carbon Pricing. Our lives and economy have improved immensely with industrialization Such developments are at the cost of using vast amounts of fuel energy, increasing carbon dioxide emissions, and endangering the ecosystem and environment. Carbon emission’s trading is a market mechanism aimed at reducing emissions of greenhouse gas. The carbon emissions trading system improves global improves globaland energy-environment-economic issues. Different scholars hold different views on relationship between carbon trade price and scholars hold different views between carbon trade and AQI [4,5]. In this price will increase as the result In this way, the emission of carbon dioxide can be controlled, which way,improve the emission of carbon can be which improve the air quality.
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