summarizing lessons learned, including comprehensive details of the seven pilots,
are given chronologically in (Wang 2016). China was not the first country to build a
carbon market, and market planners can learn from the experiences of many international schemes (Dong et al. 2016). The specific aspects of ETS pilot projects have
been discussed, including the choices of allocation methods of carbon emissions
trading (Feng et al. 2018), management of the carbon emissions trading market
(Chang et al. 2018), and reference to the experiences of developed countries (Jotzo
and Löschel 2014).
Most quantitative analyses have used computable general equilibrium (CGE)
models (Tang et al. 2016; Mu et al. 2018) to clarify the impacts of ETS in terms
of economic and environmental performance. CGE models are useful for policy
forecasting, allowing analysis of future impacts of ETS policy. As pilot ETS policies
have already been running for several years, it is essential to understand current
implementation impacts. Comparative analysis of changes in carbon emissions and
carbon intensity in pilot areas or industries over time has allowed discussion of the
effects of pilot ETS in China (Zhang et al. 2017a, b). Concerns have been raised
about using DID methods based on panel data from pilot regions to estimate the
effect of ETS policy (Jin et al. 2018, Zhang et al. 2017b, 2019). However, due to
2-year delays in data collection, ETS policies must be revised before the launch of
the national market in the power sector, based on only limited results from the pilots.
Furthermore, prevailing trend hypothesis and robustness tests have not previously
been conducted.
In this chapter, we combine qualitative and quantitative analyses to clarify the
impact of ETS policy from 2013 to 2017, based on provincial level panel data from
2008 to 2017. We use carbon emissions, carbon intensity, energy consumption, and
energy intensity as target indicators to evaluate whether the ETS policy is attaining
its low-carbon goals. As Shenzhen is a part of Guangdong, we take the 6 pilot
regions as the treatment group and the remaining 24 provinces as the control group
(excluding Tibet, Hong Kong, Macao, and Taiwan).
The rest of this chapter is organized as follows: Section 15.2 describes the eight
pilot regions from policy and market performance perspectives. Section 15.3
describes data collection and the methodology applied. Section 15.4 shows the
empirical results, discussion, and a robustness test of the DID model. Section 15.5
concludes with policy implications for promoting the development of a national
carbon trading market based on the experiences of the pilot schemes.
15.2 ETS Pilot Projects in China
There are many reasons that the eight regions were chosen to serve as pilots. The
regions cover the north (Beijing, Tianjin), the mid-west (Hubei, Chongqing), and the
southeast coastal area (Shanghai, Shenzhen, Guangdong, Fujian) of China, so can
reflect geographical disparities. Furthermore, they include the national political
center (Beijing), the national economic center (Shanghai), and a city with prior
15 Design and Analysis of a Carbon Emissions Trading System for Low-Carbon. . .
275
are given chronologically in (Wang 2016). China was not the first country to build a
carbon market, and market planners can learn from the experiences of many international schemes (Dong et al. 2016). The specific aspects of ETS pilot projects have
been discussed, including the choices of allocation methods of carbon emissions
trading (Feng et al. 2018), management of the carbon emissions trading market
(Chang et al. 2018), and reference to the experiences of developed countries (Jotzo
and Löschel 2014).
Most quantitative analyses have used computable general equilibrium (CGE)
models (Tang et al. 2016; Mu et al. 2018) to clarify the impacts of ETS in terms
of economic and environmental performance. CGE models are useful for policy
forecasting, allowing analysis of future impacts of ETS policy. As pilot ETS policies
have already been running for several years, it is essential to understand current
implementation impacts. Comparative analysis of changes in carbon emissions and
carbon intensity in pilot areas or industries over time has allowed discussion of the
effects of pilot ETS in China (Zhang et al. 2017a, b). Concerns have been raised
about using DID methods based on panel data from pilot regions to estimate the
effect of ETS policy (Jin et al. 2018, Zhang et al. 2017b, 2019). However, due to
2-year delays in data collection, ETS policies must be revised before the launch of
the national market in the power sector, based on only limited results from the pilots.
Furthermore, prevailing trend hypothesis and robustness tests have not previously
been conducted.
In this chapter, we combine qualitative and quantitative analyses to clarify the
impact of ETS policy from 2013 to 2017, based on provincial level panel data from
2008 to 2017. We use carbon emissions, carbon intensity, energy consumption, and
energy intensity as target indicators to evaluate whether the ETS policy is attaining
its low-carbon goals. As Shenzhen is a part of Guangdong, we take the 6 pilot
regions as the treatment group and the remaining 24 provinces as the control group
(excluding Tibet, Hong Kong, Macao, and Taiwan).
The rest of this chapter is organized as follows: Section 15.2 describes the eight
pilot regions from policy and market performance perspectives. Section 15.3
describes data collection and the methodology applied. Section 15.4 shows the
empirical results, discussion, and a robustness test of the DID model. Section 15.5
concludes with policy implications for promoting the development of a national
carbon trading market based on the experiences of the pilot schemes.
15.2 ETS Pilot Projects in China
There are many reasons that the eight regions were chosen to serve as pilots. The
regions cover the north (Beijing, Tianjin), the mid-west (Hubei, Chongqing), and the
southeast coastal area (Shanghai, Shenzhen, Guangdong, Fujian) of China, so can
reflect geographical disparities. Furthermore, they include the national political
center (Beijing), the national economic center (Shanghai), and a city with prior
15 Design and Analysis of a Carbon Emissions Trading System for Low-Carbon. . .
275