An Analysis of the Interactions between Electricity, Fossil Fuel andCarbon Market Prices in Guangdong, China(◯) Xianbing LIU 1 and Zhen JIN 2
1. Introduction
The formation of substantial carbon market prices and the pass-through of price signals to the end users ofelectricity are essential for achieving the goals of a greenhouse gases emissions trading scheme (GHG ETS).
There remains a long way for the true carbon costs of China’s power companies to flow through electricity prices
since the establishment of a nationwide carbon market and the new round reform of power sector are still underadvancement in parallel. Within such a process, it is necessary to clarify the unique institutions of power sector in
China for understanding the reform complex. Giving an analysis of the interactions between the pilot carbon
market and electricity forward market, which have been operated, i.e., in Guangdong province for several years, ismeaningful to seek the opportunity for promoting the effectiveness of national GHG ETS in the future.
2. Research method
Various methods were applied for this analysis. The related information was gathered to figure out the latest
progress of the pilot ETS and the reform of electricity market in Guangdong. The historical data of price series
were collected as much as possible from the accessible sources. After the information collection and datacompilation, Stata was applied for the statistical tests and regressions with a standard procedure for the analysis oftime-series data. Different with the previous researches in the regions of EU-ETS and the U.S., the forward
electricity prices were confirmed to be stationary in this study. The multivariate OLS was thus applied byconverting all the non-stationary input price series into the stationary variables. Since all the input prices are
non-stationary and their first differences are stationary, the co-integrated vector error correlation model (VECM)
was performed to analyze the linkage between carbon price and fossil fuel prices.
3. Analysis results•

Statistics of various prices

Statistics of the five price series, available from the beginning of 2016 to April, 2019, indicates that distribution ofelectricity and coal prices is skewed to the left with a negative skewness, while distribution of carbon and diesel
prices is skewed to the right with a positive skewness and LNG prices are distributed symmetrically with askewness of nearly 0. The kurtosis value of electricity and carbon prices is greater than 3.00, confirming theirheavy tailed distribution. The distribution of the other prices are light tailed with a kurtosis less than 3.00.
•The relationships between carbon and fossil fuel prices

With the co-integration rank and optimum number of lags determined, the VECM was run and the estimation
results are reported in the table below. The co-integrated vector shows that coal and diesel prices have a positive
1

Climate and Energy Area, Institute for Global Environmental Strategies (IGES)
2108-11, Kamiyamaguchi, Hayama, Kanagawa, 240-0115, Japan
E-mail: liu@iges.or.jp
2 Affiliation and address are the same as above.
E-mail: jin@iges.or.jp-1-impact, while LNG price indicates a negative impact on carbon prices from the long run in Guangdong. If looking
at the short term parameters, the lagged carbon and coal prices indicate significant impacts on their own current
market prices. This implies that the prices of carbon and coal are essentially exogenous in the short run.
Nevertheless, the lagged coal, diesel and LNG prices have no effect on the short term dynamics of carbon prices.
Therefore, the carbon market investors can’t use the historical information of fossil fuel prices to improve theirshort run forecast of the spot carbon prices in Guangdong pilot market.
Table: The result of VECM model analysis of carbon and fossil fuel pricesA) Co-integration relationship
Endogenous variables

P -Carbon

P -CoalP -LNGP -Diesel

1.00-0.1696***

58.7006***-31.2394***

Error correction△P -Carbon-0.1026**△P -Coal1.8629***△P -LNG
0.0008△P -Diesel△P -Carbon (-1)-0.5816***-0.0538-0.0061-0.0326-0.4267**-0.8089-0.0106-0.0249△P -Coal (-1)

0.01760.7481***0.00110.00150.0272-0.4602***-0.0008

0.0007△P -LNG (-1)-1.7428-42.6142

0.34640.7758-8.3444-115.0822**-0.1037

0.0028△P -Diesel (-1)

2.289357.1524**-0.0018-0.0228△P -Diesel (-2)

5.062883.3590***0.1015-0.0001

Constant-0.1941-0.0107

0.00790.0113R-squared

0.51180.80500.29070.2696

CoefficientsB) Short run dynamics△P -Carbon (-2)
△P -Coal (-2)

△P -LNG (-2)-0.0008

Log likelihood: -95.16; AIC: 9.87; HQIC: 10.49; SIC: 11.91
Note: *** Significant at 1% level; ** Significant at 5% level.

•Regression result with electricity forward price as the dependent

Using electricity forward price as the dependent, multivariate OLS regressions were carried out with the firstdifferences of other prices as the independents. The result indicates that electricity price in Guangdong issignificantly and positively associated with the first difference of coal price, but has no significant relationshipwith the first difference of the other two fuels, including diesel and LNG. There reveals no significant relationshipbetween the electricity price and the first difference of carbon prices. This implies that the change in carbon prices
of Guangdong is still not a factor influencing power company’s bidding strategy in the monthly forward market.
4. Conclusions
This analysis provides some implications for the synergy of power sector reform and nationwide carbon market
development in China. The liberalization of power industry shall be further advanced to create the conditions forcarbon cost pass-through in the electricity markets. The national ETS shall apply simpler and stricter benchmarks,
and auctioning to allocate the emissions allowances of power sector for the formation of substantial carbon market
prices and the shift to a low carbon power supply portfolio.
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