Carbon Emissions and Stock Returns: Evidence from the Chinese Pilot Emissions Trading Scheme

In response to mounting evidence of climate change and the Kyoto Protocol, in 2011, the Chinese central government decided to build a nationwide carbon emissions scheme, beginning with seven pilot schemes launched in 2013. These pilot schemes were based on the similar European Emissions Trading Scheme. Oestreich and Tsiakas [1] examine the European scheme, finding when carbon emission allowances were granted free of charge, firms who received them (defined as “dirty” firms) outperformed the firms who did not (defined as “clean firms”), indicating that there is a significant “carbon pre-mium.” This study follows the methodology of Oestreich and Tsiakas [1] with Chinese data from the largest of the pilot schemes, the Shenzhen Pilot Emissions Trading Scheme. We find no positive, significant carbon premium but do find weak evidence of a negative premium for a special group of “very dirty” firms.


Introduction
The European Union's Emissions Trading Scheme (EU ETS), the largest international greenhouse gases emissions trading system, works on a "cap-and-trade" principle. This means that a government sets a level to which factories and installations can emit greenhouse gases and these businesses must surrender the equivalent in emission allowances at the end of the compliance year. However, if they have more allowances than the cap, they can then keep them to use later or Theoretical Economics Letters to sell. If they have fewer allowances than the cap, they must buy them from other companies or receive a fine.
China, for the purpose of reaching its energy-intensity reduction goal, has relied mostly on costly administrative approaches that have had only limited success. Following the EU ETS, in 2011 China decided to build a nationwide Emissions Trading Scheme. The scheme takes into consideration that different districts in China are at varied levels of economic development. To begin, in 2013 the Chinese central government approved the formation of pilot carbon trading schemes in seven regions (extended to eight in 2016). Each region has been given a certain degree of leeway in designing their own principles in regard to the schemes. Of the regions, Shenzhen is the most economically mature as, in 1979, it was the first region to implement economic reforms and the opening-up policy. It is also the largest pilot ETS in China.
On 19 December 2017, China launched a nationwide program to be in 3 stages, with actual trading not to occur until about 2020 [2]. This study is positioned at an ideal time to examine the potential effects of the ETS while the nationwide scheme is still in development.
This research report focuses on an empirical analysis of the effect of the carbon emissions in the Shenzhen Pilot Emissions Trading Scheme. Specifically, the research question of this report is: is there a carbon premium generated by the free carbon allowances that exist in stock returns? Following Oestreich and Tsiakas (2015), we describe carbon premium as the difference in returns between firms receiving those allowances and those that do not. We address this question by using the CAPM and the Fama French three-factor model and by forming different portfolios. Carbon emission allowance data and carbon intensity information are not available; hence, we have created our own criterion to distinguish high and low carbon intensity firms. We have created a "dirty" portfolio to represent the firms that have received free carbon allowances over the sample period and a "clean" portfolio to represent firms that have not received free carbon allowances. We have also constructed a special portfolio, "dirty-minus-clean", which is the equivalent of going long on the "dirty" portfolio and going short on the "clean" portfolio. The abnormal excess return of this portfolio is defined as the "carbon premium".
As discussed very eloquently in Oestreich and Tsiakas [1], who in turn base their work on Goulder, Hafstead, and Dworsky [3], the empirical analysis of this research report explains the carbon premium based on two economic mechanisms: the effect of cash flow and the effect of carbon risk.
In regard to the effect of cash flow, the cap-and-trade principle leads to an increase in the marginal cost (either actual or opportunity) required by each unit which can lead to less output, meaning spare carbon allowances which then can be sold for profit. This mechanism attributes the carbon premium to the higher profits generated by the free carbon allowance.
In addition to the effect of higher cash flow there is a secondary mechanism M. Zhang, R. B. Gregory-Allen DOI: 10.4236/tel.2018.811136 2084 Theoretical Economics Letters related to carbon risk. This mechanism illustrates that carbon emitting is exposed to a risk due to price uncertainty of (future) carbon allowances. In addition, policy changes, such as when carbon allowances change from being free to being charged for, can lead to further risk exposure. According to Litterman [4] and Pindyck [5], catastrophic climate change in the future would cause carbon allowance prices to be very high, leading carbon emitting firms to face further financial risks. The higher carbon risk of carbon emitting firms should be reflected in higher expected returns of these firms, thus another reason for an existing carbon premium.
In this study, we examine whether this carbon premium exists. To control for other possible factors we use two different financial risk models: CAPM and the Fama-French 3-factor model. We do not find a positive and significant premium, although we find weak evidence for a negative effect, both contrary to the findings of Oestreich and Tsiakas [1] using German data.

Development of Regulatory Measures and the EU ETS
On 4 June 1992, an international environmental treaty was negotiated and signed by 154 nations at the Earth Summit, The United Nations Framework Convention on Climate Change (UNFCCC). The purpose of this treaty was to get greenhouse gas emissions to a level that will not damage the earth's climate system. In order to achieve the aims of the UNFCCC, representatives from various nations continued to meet at a number of conferences between 2007 and 2016. Perhaps the most influential of these was the Paris Agreement that was signed in 2016, and the Kyoto Protocol, which introduced the methods that the signed countries must follow in order to reach their targets [6].
The idea of the carbon market originated at the Kyoto Protocol which included two commitment periods: 2008 to 2012 and 2013 to 2020. It introduced three flexible, market-based mechanisms for countries to adopt in order achieve their carbon emission goals. The first, called Joint Implementation (JI), was designed for developed countries. This mechanism runs on a baseline-and-credit system in which participants can earn Emission Reduction Units (ERUs) from each of their emission reduction projects. ERUs, also called "carbon credits", can be used to offset emitting activities, comply with regulatory emissions targets, or as voluntary measures.
The second mechanism is the Clean Development Mechanism (CDM) and is similar to JI. It was designed for developing countries and is also run as a baseline-and-credit system. The units derived from CDM projects are called Certified Emission Reductions (CERs).
The third flexible mechanism is the Emissions Trading Scheme (ETS). This mechanism runs a cap-and-trade system which is quite different from the above two. A "cap" or "limit," also called an "allowance", is set for the amount of greenhouse gases (in metric tons) that can be emitted in a certain year. An allo- At the end of a given year, the participants must surrender their allowances to cover their emissions; if they cannot, they face a heavy fine. If they have reduced their emissions and the level falls below their cap, they can keep the spare allowances to cover their future emissions or sell them. Over time, the "cap" is reduced so that total carbon emissions fall.

European Union Emissions Trading Scheme
Among the three mechanisms, the ETS is the most widely used and has created the most liquid regulated carbon market, the European Union Emissions Trad- allowances are being traded at auction. Oestreich and Tsiakas [1] found in the first and second phases that companies were able to profit from the free allowances, but in third phase they were not.

Chinese Climate Policy Developments and the Chinese Pilot ETS
In

Literature Review
Research on emissions trading schemes can be divided into two general categories: first, carbon allowance price drivers, the relationship between carbon price and stock return, and carbon spot and future prices, and second, effects of ETSs on financial performance such as stock returns, firm values and profits.
In the first category, Oberndorfer [ ter than firms that did not. They explain that the reasons for this, following a model from Goulder et al. [3], are due to the fact that the free allocation of carbon allowances brings higher cash flow to companies. Meanwhile, these companies have exposure to high carbon risk and, therefore, exhibit a higher than expected return. This model and the methodology from Oestreich and Tsiakas [1] are used in our study of Chinese data.
In summary, though the results are somewhat mixed, there is clear evidence of some relationship between trading emissions schemes, carbon prices and firm performance. Temperature, carbon credit supply and carbon intensity are all factors in determining prices, and the existence of a trading scheme seems to have an impact on the market. We extend the "dirty" vs. "clean" portfolio method to the very large and growing Chinese market.

Data Description
The sample period employed in this analysis is from June 2013 to August 2015 (financial data were collected from Bloomberg), beginning when the Shenzhen Pilot ETS commenced operations and ending with the latest compliance report disclosed on the official website. It includes two compliance years from the Shenzhen carbon emissions exchange, during which time all firms were receiv- However, at the end of each compliance year, the Shenzhen carbon emissions exchange disclosed a list of the emitting firms that finished submitting allowances and those that did not 1 .
We identified 26 firms that participated in the Shenzhen pilot ETS, were listed on the Shenzhen Stock Exchange with a decent stock price history, and received free carbon allowances over the sample period. This group, equally weighted, forms our "dirty" portfolio. We further identified 464 listed firms that did not receive carbon allowances and did not participate in the Shenzhen pilot ETS. This set forms our "clean" portfolio in Approach 1 described below.
We use monthly stock returns, the Shenzhen Stock Exchange Component Index (SZSE Component Index) is our market proxy, and the risk-free rate is the rate of the 10-year Chinese government bond.

Portfolio Classification
In the absence of actual carbon intensity data for emitting firms, we have taken three approaches to compare firms that have carbon emissions with firms that do not.
In the first approach, we have created two portfolios-a "dirty" portfolio and a "clean" portfolio, as described above for all firms with sufficient data. As these 2 portfolios are very different in size (26 vs. 464 firms), we also take a second approach. The dirty portfolio is made up of the same 26 firms as in the first approach, however the clean portfolio consists of 26 firms from the large clean portfolio, but matched with the 26 firms in the dirty portfolio with regard to industry, sector and market capitalization.
The third approach is the most complex. From a government announcement identifying heavy polluters in Shenzhen during the sample period, we found that 11 firms are in our dirty portfolios above. Though we don't have actual allowance and intensity data, these 11 firms can be considered as having higher carbon emissions and, therefore, as having received more carbon allowances than other emitting firms. Thus, they form the "very dirty" portfolio for this approach. The remaining 15 emitting firms form the "medium" portfolio. The other firms in the SZSE Component Index form the clean portfolio.

Models
Again following Oestreich and Tsiakas [1], we examine equally weighted portfolio returns based on overall means, standard deviations, and risk models from (1) CAPM and the (2) Fama-French 3-factor model. In all cases, we examine dirty, clean and "clean-dirty" portfolios. is the market portfolio, t SMB is the ''small-minus-big'' size factor, and t HML is the ''high-minus-low'' value factor, and , j t ε is a normal error term. Table 2 shows the results of the first approach using all our available firms in which there are 26 dirty firms and 464 clean firms. The carbon premium is the alpha of the dirty-minus-clean portfolio. These alphas of 0.39% and 0.33% for the CAPM and Fama French three-factor models, respectively, are positive but insignificant.

Results and Analysis
In Table 3 are the results for the second approach with matched characteristics for the dirty and clean portfolios, resulting in 26 dirty firms and 26 clean firms. In this approach, the carbon premium is a much higher 16.5% for CAPM and 16.4% for the Fama French three-factor model, but again these results are not significant.
In the third classification approach (see Table 4), we segregate the previous dirty portfolio into a "medium" portfolio and a "very dirty" portfolio. The results show that the carbon premium (alpha of the dirty-minus-clean portfolio) is negative at −15.17% and significant, but just barely at the 10% level. Considering all the other ambiguity in our data, this is probably not sufficient to warrant a conclusion of "significant" [14]. However, we regard this as weak evidence. Table 2. Results of the first portfolio classification approach. The "Dirty" portfolio is a portfolio of 26 firms that are included in the SZSE Component Index and received the free carbon allowance over the sample period. The "Clean" portfolio is a 464-firm portfolio included in SZSE that did not receive any carbon allowance and did not participate in the Shenzhen pilot ETS. The return of Dirty-minus-Clean portfolio is the return of the "Dirty" portfolio minus the return of the "Clean" portfolio. All portfolios are equally weighted portfolios and results are annualized. CAPM-α is the alpha of a CAPM regression, FF3-α is the alpha of the Fama-French three-factor model regression, T-statistics are in parentheses. The asterisks *, **, *** denote statistical significance at the 10%, 5% and 1% level respectively.  Table 3. Results of the second portfolio classification approach. The "Dirty" portfolio is a portfolio of 26 firms that are included in the SZSE Component Index and received the free carbon allowance over the sample period. The "Clean" portfolio is another 26 firms with no carbon allowance allocation that matches the 26 firms in the "Dirty" portfolio by the same or the most similar industry, sector and market capitalization within the SZSE Component Index. The return of Dirty-minus-Clean portfolio is the return of the "Dirty" portfolio minus the return of the "Clean" portfolio. All portfolios are equally weighted portfolios and results are annualized. CAPM-α is the alpha of a CAPM regression, FF3-α is the alpha of the Fama-French three-factor model regression, T-statistics are in parentheses. The asterisks *, **, *** denote statistical significance at the 10%, 5% and 1% level respectively.  Table 4. Results of the third portfolio classification approach. The "Very Dirty" portfolio is a portfolio of 11 firms that were identified as heavy polluters among 26 firms that are included in the SZSE Component Index and receiving the free carbon allowance. The "Medium" portfolio is the remaining 15 firms among 26 firms excluding those 11 "dirty" firms. The "Clean" portfolio is a 464-firm portfolio included in SZSE that did not receive any carbon allowance and did not participate in the Shenzhen pilot ETS. The return of the Very Dirty-minus-Clean portfolio is the return of the "Very-Dirty" portfolio minus the return of the "Clean" portfolio. All portfolios are equally weighted portfolios and results are annualized. CAPM-α is the alpha of a CAPM regression, FF3-α is the alpha of the Fama-French three-factor model regression, T-statistics are in parentheses. The asterisks *, **, *** denote statistical significance at the 10%, 5% and 1% level respectively. of how the policy has been implemented in China, there may be high carbon intensity firms "hiding" in the clean portfolio and low carbon intensity firms "hiding" in the dirty portfolio, which will have distorted our results to a certain degree.

Conclusions
As a result of human activity creating huge greenhouse gas emissions and potentially catastrophic climate change, a number of measures have been taken including the Kyoto Protocol. This protocol introduced the Carbon Emissions Trading Scheme and created the carbon market that has been operating in many countries around the world. The EU ETS, which has been in operation since 2005, has finished its initial two stages and is now in its third stage in which carbon allowances are sold at auction. Oestreich and Tsiakas [1], examining the EU ETS found that, during the stages in which emitting firms received free car-  [1], with no premium found in some portfolios and negative premiums found in the "very dirty" portfolio.
Our results may have been distorted due to several factors: the classification approaches used, the absence of accurate carbon intensity data, and China's compulsory carbon regulation policy.
In summary, this paper fills a gap in the literature by providing an empirical evaluation of whether firms in the Chinese ETS that received carbon emissions allowances significantly outperformed those that did not. As China has just launched the construction phase of its nationwide carbon emission trading scheme, the carbon intensity data for each firm may be disclosed and, as a result, this research could be extended.