China Policy Journal Volume 1, Number 1, Fall 2018 | Page 44

Assessing the Implementation of Local Emission Trading Schemes in China
the regulated enterprises. It may reflect
deeper problems of the two ETSs: loose
enforcement and over-supply of CEA.
Note: Full sample period started from the first operation dates of the ETS pilots till 30 June 2017.
Figure 1. Average Weekly Trading Volume of CEA in Seven ETS Pilots
In each ETS, the trading activities
are more frequent and involve larger
trading volumes toward the compliance
deadline. In most time of the year, the
trading activities are relatively sporadic
and with smaller trading volumes. Thus,
the trading volume variables all exhibit
the asymmetric and leptokurtic distribution,
with high-level positive skewness
and pronounced excess kurtosis.
The standard deviation of CEA
price returns (lnReturn t
) can be considered
as a measure of price volatility.
We use it to compare the market risks
between local ETSs. Figure 2 shows the
price volatility over different time periods,
launching dates -June 2014 (1 st
Period), July 2014–June 2015 (2 nd Period),
July 2015–June 2016 (3 rd Period),
and July 2016–June 2017 (4 th Period).
We can see that each ETS pilot exhibits
a variation of price volatility in different
sub-periods. Although Hubei ETS
had low CEA price on average, its price
volatility was the lowest compared to
other piloting ETSs, but it became more
and more volatile over time. Beijing
ETS had a relatively low volatility but
experienced a higher level of volatility
during July 2015–June 2016. Chongqing
CEA price had the highest volatility,
and it was the most volatile during July
2016–June 2017.
We can see that different local
ETSs perform so differently that the
operation of the national ETS can be
expected to be more challenging. Overall,
Beijing and Shenzhen ETSs seem
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