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Impacts on CO_{2} Emission Allowance Prices in China: A Quantile Regression Analysis of the Shanghai Emission Trading Scheme

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## Abstract

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_{2}) emission allowance prices provides guidance for price-making in 2017 when the nation-wide ETS of China will be established. This paper adopts a quantile regression approach to estimate the impacts of different factors in Shanghai emission trading scheme (SH-ETS), namely, economic growth, energy prices and temperature. The empirical analysis shows that: (i) the economic growth in Shanghai leads to a drop in the carbon allowance prices; (ii) the oil price has a slightly positive effect on the allowance prices regardless of the ordinary least squares (OLS) or quantile regression method; (iii) a long-run negative relationship exists between the coal price and the Shanghai emission allowances (SHEA) prices, but a positive interaction under different quantiles, especially the 25%–50% quantiles; (iv) temperature has a significantly positive effect at the 20%–30% quantiles and a conspicuous negative impact at the right tail of the allowances prices.

## 1. Introduction

_{2}emission allowance prices.

_{2}emission allowance prices, respectively. The former found that the main determinant of allowance trading was coal price in the long-run, but the latter found asymmetric and nonlinear relationships among variables. Hammoudeh et al. [12] emphasized the short-term dynamics by a Bayesian Structural vectorial autoregression (VAR) approach. In light of the abatement options on large installations, Keppler and Mansanet-Bataller [13] reported that coal and gas prices, through clean dark spreads (CDS) and clean spark spreads (CSS), had an influence on carbon prices, which in turn Granger caused electricity prices for Phase I of the EU-ETS. Bredin and Muckley [14] investigated additional fundamentals such as equity prices, temperature deviations, and CDS and CSS by using the Johansen cointegration test. Wang and Bai [15] found that CDS and CSS had a positive and negative effect on allowance prices in EU-ETS, respectively and other studies took account of the switching behavior between gas and coal [16,17]. Relying on the cointegration method, Creti et al. [17] investigated the relationships between carbon price and its drivers (oil price, the switching price between gas and coal, equity price index) during Phases I–II of the EU-ETS and showed that the equilibrium relationships existed in both phases, especially in Phase II. The switching price did not work in Phase I. Additionally, weather conditions have been incorporated into studies and shown important influence on the allowance prices [18,19,20,21,22]. Rickels et al. [18] acknowledged that the allowance prices react to changes in energy prices and weather when the allowances are regarded as a scarce input factor. Mansanet-Bataller et al. [19] aimed to consider the underlying rationality of pricing behavior and found that that energy resources are the principal factors to determine the carbon price levels, which were also affected by extreme temperatures. Chen et al. [21,22] separately assessed the impacts of changes in energy prices and climate factors (precipitation, weed and temperature) on allowance prices in EU-ETS and CCX, and pointed out that only temperature had a significant negative effect among climate factors in CCX. The equal importance of industrial production and macro-economy in relation to carbon price has been shown by many researchers [23,24,25,26]. Christiansen et al. [23] showed the role of two proxies (industrial production and equity price movements) on the price of emission allowances in the EU-ETS in 2005–2007. Chevallier [25] used the Markvo-switching model to assess the impacts of economic activity (aggregated industrial production) and energy prices (Brent, natural gas and coal prices) on European Union Allowances. Conraria and Sousa [26] found that a shock in economy index prices had an effect on carbon prices, but there was no significant impact of the substitution for carbon licenses in Phases II–III of the European market.

_{2}emission allowance prices by the quantile regression. Quantile regression has been widely employed in empirical studies [28,29,30,31,32,33,34,35,36,37,38]. Many scholars used the quantile regression approach to investigate the dynamics of changes in different wage distribution [28,29,30] and dynamic relationship between exchange rate and stock price [31,32,33]. Recently, a growing number of studies have applied quantile regression to explore issues of carbon emissions [37,38]. For instance, Zhang et al. [37] utilized a quantile regression method to analyze the relationship between corruption and carbon emissions in Asia-Pacific Economic Cooperation (APEC) countries. Xu and Lin [38] estimated the impacts on China’s provincial carbon emissions by using quantile regression.

_{2}emission allowances and different impact factors (macroeconomic, energy and climate factors). The contributions of this paper lie in three main areas. First, compared with available literature, there is little information about the impact factors on allowance prices in China’s carbon emission market. Second, we utilize the quantile regression to estimate whether the drivers consistently influence the CO

_{2}emission allowance prices through the conditional distribution in SH-ETS, providing a more complete picture. In particular, we reinforce the study by Hammoudeh et al. [27] and take into account other probable driving forces besides energy factors by summarizing previous literature.

## 2. Materials and Methods

#### 2.1. Data

_{2}emission allowance prices in SH-ETS and makes an analysis of daily time-series covering the dates from 26 November 2013 to the end of 2015. There are one dependent variable and four explanatory variables in our empirical analysis, namely SHEA price, Shanghai Composite Index (SZZS), fuel oil price, steam coal price and temperature, respectively. All variables were summarized in Table 1.

#### 2.2. Methodology

_{2}emission allowances and different impact factors (macroeconomic, energy and climate factors), we account for the impacts on allowances prices by a quantile regression approach, which was first proposed by Koenker and Bassett [41]. The advantages of quantile regression are mainly three-fold. Firstly, the quantile regression approach is insusceptible to influences of outlier observations, skewness, and heterogeneity on explaining variables [42]. It achieves better robustness of the outliers. Secondly, quantile regression requires no error terms distribution and can make a more efficient estimation. Finally, it obtains nonlinear relationships and estimates the impacts for several quantiles of the distribution for the dependent variable [35]. Hence, it describes the full picture of conditional distribution on a dependent variable, especially the tail location, vis-a-vis the OLS method.

## 3. Results and Discussion

_{2}emissions allowance prices in SH-ETS, the result indicates that there is a significantly negative effect at the 25% and 90% quantiles. Also, as is shown in Figure 2, the slope coefficients of SZZS fluctuate slightly. This means that the economic growth leads to a drop in allowance prices in Shanghai. This is unexpected and inconsistent with Wang and Bai [15]. However, Creti et al. [17] showed a negative relationship between economic growth and carbon prices in Phase I of the EU-ETS, which suggested that CO

_{2}allowances increased the diversification of a financial portfolio in a context of low environmental constraint [4]. Bredin and Muckley [14] found that equity price has a negative impact in Phase II of the EU-ETS and is likely due to the dramatic events on stock markets. The finding in this paper may be attributed to relatively low CO

_{2}intensities in Shanghai [50,51]. Hence, it achieves the CO

_{2}emissions mitigation with economic growth, leading to higher allowances prices.

## 4. Conclusions and Policy Implications

_{2}emission allowance prices through a quantile regression model, with a case study of SH-ETS because of the deficiency in research on China’s carbon emission markets at present. The empirical results for the SH-ETS demonstrate that economic growth has a significantly negative effect at the 25% and 90% quantiles, which means that economic growth in Shanghai generates a drop in carbon allowance prices. This may be due to relatively low CO

_{2}intensities in Shanghai and imply that Shanghai has taken some measures to decrease carbon emissions. This finding suggests that China should maximize the potential of the domestic carbon emissions trading market and encourage firms to develop more CDM projects. Trading of CCERs is a significant measure for emission allowance price adjustment, especially when the latter can only increase by a limited amount.

## Supplementary Files

Supplementary File 1## Acknowledgments

## Author Contributions

## Conflicts of Interest

## Abbreviations

SH-ETS | Shanghai Emission Trading Scheme |

SHEA | Shanghai Emission Allowances |

CDS | Clean Dark Spreads |

CSS | Clean Spark Spreads |

VAR | Vectorial Autoregression |

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**Figure 2.**The regression estimation at different quantiles. Note: The blue line expresses the slope coefficients by the quantile regression approach on the distribution of the allowances prices and the red line is the 95% confidence interval.

**Figure 3.**The trading volumes ratios of Shanghai Emission Allowances (SHEAs) and China certified emission reductions (CCERs) in various sectors.

Variable | Unit | |
---|---|---|

Dependent variable | SHEA | Yuan |

Explanatory variable | SZZS | Percent |

Fuel oil | yuan/ton | |

Steam coal | yuan/ton | |

Temperature | degree Centigrade |

Variable | Obs. | Mean | Max | Min | Std. Dev. | Skewness | Kurtosis |
---|---|---|---|---|---|---|---|

SHEA | 341 | 30.35 | 48.00 | 9.50 | 8.38 | −0.86 | 0.11 |

SZZS | 341 | 2936.52 | 5166.35 | 1991.25 | 907.99 | 0.68 | −0.71 |

Fuel oil | 341 | 3480.07 | 4809.29 | 2007.00 | 604.31 | −0.06 | −0.59 |

Steam coal | 341 | 477.56 | 595.60 | 290.04 | 68.46 | −0.83 | 0.48 |

Temperature | 341 | 14.78 | 34.00 | 1.50 | 7.84 | 0.22 | −1.08 |

Variable | ADF Value | 1st Difference of ADF |
---|---|---|

lnSHEA | −1.064833 | −22.58128 *** |

lnSZZS | 1.211579 | −14.48983 *** |

lnoil | −1.217713 | −17.76263 *** |

lncoal | −2.305738 ** | −27.39583 *** |

lntemp | −0.603371 | −13.30092 *** |

Hypothesized No. of CE(s) | Eigenvalue | Trace Statistic | 0.05 Critical Value | Prob.** |
---|---|---|---|---|

None * | 0.124383 | 70.73127 | 69.81889 | 0.0422 |

At most 1 | 0.037331 | 25.83582 | 47.85613 | 0.0945 |

Variable | Ols | 10% | 25% | 50% | 75% | 90% |
---|---|---|---|---|---|---|

α | 9.21008 *** | −5.825944 | −6.110953 *** | −6.664879 ** | 0.034741 | 5.215025 |

lnSZZS | −0.221336 | −0.248885 | −0.222590 *** | −0.141646 | −0.256483 | −0.442152 *** |

lnoil | 0.112685 | 0.639462 | 0.205251 | 0.244556 * | 0.173301 | 0.127672 |

lncoal | −0.830799 *** | 0.893984 * | 1.481077 *** | 1.501282 *** | 0.679181 | 0.148548 |

lntemp | −0.074253 *** | 0.047651 | 0.143950 *** | −0.008275 | −0.029189 | −0.043573 *** |

AR(1) | 0.990766 *** | - | - | - | - | - |

D.W | 2.309793 | - | - | - | - | - |

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**MDPI and ACS Style**

Zhang, J.; Zhang, L.
Impacts on CO_{2} Emission Allowance Prices in China: A Quantile Regression Analysis of the Shanghai Emission Trading Scheme. *Sustainability* **2016**, *8*, 1195.
https://doi.org/10.3390/su8111195

**AMA Style**

Zhang J, Zhang L.
Impacts on CO_{2} Emission Allowance Prices in China: A Quantile Regression Analysis of the Shanghai Emission Trading Scheme. *Sustainability*. 2016; 8(11):1195.
https://doi.org/10.3390/su8111195

**Chicago/Turabian Style**

Zhang, Jie, and Lu Zhang.
2016. "Impacts on CO_{2} Emission Allowance Prices in China: A Quantile Regression Analysis of the Shanghai Emission Trading Scheme" *Sustainability* 8, no. 11: 1195.
https://doi.org/10.3390/su8111195