Does the Impact of Carbon Price Determinants Change with the Different Quantiles of Carbon Prices? Evidence from China ETS Pilots

Abstract

<jats:p>Since carbon price volatility is critical to the risk management of the CO2 emissions trading market, research has focused on energy prices and macroeconomic drivers which cause changes in carbon prices and make the carbon market more volatile than other markets. However, they have ignored whether the impact of carbon price determinants changes when the carbon price is at different levels. To fill this gap, this paper applies a semiparametric quantile regression model to explore the effects of energy prices and macroeconomic drivers on carbon prices at different quantiles. The model combines the advantages of parameter estimation, nonparametric estimation and quantile regression to describe the nonlinear relationship between carbon price and its fundamentals, which do not need to make any assumptions about the random error. Carbon prices are high–tailed and exhibit higher kurtosis, the traditional models which tend to assume that data are normally distributed can’t perform well. Furthermore, the semiparametric model doesn’t need to assume that the data are normally distributed. Therefore, the semiparametric model can effectively model the data. Some new evidence from China’s emission trading scheme (ETS) pilots shows that energy prices and macroeconomic drivers have different effects on carbon prices at high or low quantiles. First, the negative impact of coal prices on carbon prices was greater at the lower quantile of carbon prices in the Shenzhen ETS pilot. However, the effects of coal prices were positive in the Beijing ETS pilot, which may be attributed to great demand for coal. Second, oil prices had greater negative effects on carbon prices at higher quantiles in Beijing and Hubei ETS pilots. This can be attributed to the fact that businesses use less oil when carbon prices are high. For the Shenzhen ETS pilot, the effects of oil prices were positive. Third, natural gas prices have a stronger effect on carbon prices as quantiles increased in the Beijing and Hubei ETS pilots. Lastly, the effects of macroeconomic drivers on carbon prices at low quantiles were stronger in the Shenzhen ETS pilots and higher at the medium quantiles in Beijing and Hubei ETS pilots. These findings suggest that the impact of determinants on the carbon prices at different levels is not constant. Ignoring this issue will lead to a missed warning about the risks of the carbon market. This study will be of positive significance for China’s emission trading scheme (ETS) pilots, in order to accurately monitor the effects of carbon prices determinants and effectively avoid carbon market risks.</jats:p>

Carbon emissions of Provincial Administrative Regions from the airlines in China

“…In 2018, China carried out carbon trading pilot projects in seven provinces and cities, and the carbon trading prices of these seven pilot projects are different. According to statistics 14,15 As shown in Fig. 4, the ranking of many PARs has changed a lot compared with Fig.…”

“…In 2018, China carried out carbon trading pilot projects in seven provinces and cities, and the carbon trading prices of these seven pilot projects are different. According to statistics 14,15 , the average costs of the seven pilot projects in 2018 are 16.35 yuan/ton in Guangdong, 19.22 yuan/ton in Hubei, 25.25 yuan/ton in Shanghai, 27.89 yuan/ton in Shenzhen, 13.70 yuan/ton in Tianjin, 4.01 yuan/ton in Chongqing, 52.72 yuan/ton in Beijing, and 23.38 yuan/ton in Fujian. This study sets the trading price of the other provinces as the average price of the seven pilots, which is 22.82 yuan/ton.…”

Energy sector impact on country’s economic security: Baltic sea region EU countries’ case

“…Shows the total number of GWh generated by power plants. DOI: 10.14807/ijmp.v11i9.1399-Chu et al (2020 in their research used the carbon price volatility to the risk management of the CO2 emissions trading market; - Figaj et al (2020) proposed a hybrid geothermal-solar-wind system that was modelled and simulated by adopted software. Researchers' designed system is managing adequately the thermal energy flows in order to match the thermal energy demand of the user; -in Arumägi and Kalamees (2020) analysis detailed energy performance-related costs of the actual solution components compared with the current practice are included, as well as the costs due to operational energy use and renewable energy harvesting are calculated; -Masip Macía et al ( 2019) stated, that the mining industry is characterized by high consumption of energy due to the wide diversity of processes involved, specifically the transportation of ore slurry via pipeline systems; -An energy usage indicator was used to establish a metric to rank the buildings of each typology according to their energy efficiency in Bernardo and Oliveira (2018) article.…”

Low‐carbon economic operation for integrated energy system considering carbon trading mechanism

“…This mechanism contributing to the energy transformation and upgrading will make more use of the clean energy such as natural gas. 15 Since the first global carbon trading market was launched in EU in 2005, the scale of the international carbon market has been expanding. 16 China's national carbon trading system has been pilot since 2013 and is expected to become the world's largest carbon trading market.…”