China Policy Journal Volume 1, Number 1, Fall 2018 | Page 35

China Policy Journal
economic growth in China. Besides, the
intensity-based cap setting can make
better adjustments for the emergence of
new entrants and unexpected changes
of emission reduction cost. However,
as the intensity-based cap allows rapid
economic growth to continue, its effectiveness
on emission abatement has a
higher uncertainty than using the absolute
emission cap.
Compared to more mature ETS
such as EU ETS, Chinese ETS is still at
the trial stage. After more than fouryear
operation, the problems of the
emission trading markets have become
apparent, including low liquidity and
high volatility, even though some pilots
are a little better than others. China’s
ETSs generally had a poor performance
because of the absence of legal binding
forces, excessive allowance allocation,
market segmentation, and lack of active
investments (Tan and Wang 2017a).
The price of emission allowance
is often used for analyzing an emission
trading market, as it theoretically
responds to the market supply and
demand. Factors affecting the price of
the CO 2
emission allowance that are
commonly identified in the literature
include energy prices, macroeconomic
indicators, extreme temperature events
and institutional events. A summary of
this group of studies is shown in Table
2. In these studies, the words such as
“impact”, “influence”, “effect” etc. mostly
refer to Granger causality in the sense of
inter-temporal precedence, rather than
causality “in the colloquial sense of an
unavoidable logical link” (Keppler and
Mansanet-Bataller 2010). The empirical
literature concentrates on the analysis of
the EU CO 2
emission allowance (EUA)
prices, while there are a small number
of studies on the ETS in the United
States (Hammoudeh, Nguyen, and Sousa,
2014a; Hammoudeh et al. 2015; Kim
and Koo 2010). Literature has examined
the Granger causality from crude oil
price, natural gas price, and coal price
to the price of CO 2
emission allowances,
using time series techniques such
as GARCH, Vector Auto-regression
(VAR), Newey–West Ordinary Least
Squares (NW-OLS), Autoregressive
Distributed Lag (ADL), Vector Error
Correction Model (VECM) and quantile
regressions. Alberola, Chevallier,
and Chèze (2008), Hammoudeh, Nguyen,
and Sousa (2014a,b), Hammoudeh
et al. (2015), and Keppler and Mansanet-Bataller
(2010) also include electricity
price in the analysis in addition
to energy prices, assuming that changes
in electricity price may affect CO 2
emission
allowance price due to the resulting
changes in the consumption of electricity,
a secondary energy source. This may
not happen in China since Chinese electricity
price is highly regulated and the
price changes are not frequent.
According to the substitution effect
theory, the increase in oil price (or
natural gas price) would contribute to
an increase in CO 2
emission allowance
price through a fuel substitution from
oil (or natural gas) to more carbon-intensive
fuels such as coal. For instance,
Boersen and Scholtens (2014) found
that oil price, as well as natural gas price,
were positive drivers of EUA futures
price during the second phase of EU
ETS. Alberola, Chevallier, and Chèze
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