Abstract
Are private voluntary environmental actions by firms a sign of mismanagement, or a profitable “win-win” replacement for regulation? Empirical evidence is decidedly mixed. In this study, we use 19 years of monthly stock price returns, from 1991 to 2009, to examine the profitability of participation in CCX, a large voluntary greenhouse gas mitigation program. After controlling for systemic market risk as well as industry-specific shocks, we find statistically significant and positive excess returns for firms that announce their decision to join CCX. In addition, the progress of proposed greenhouse gas legislation (the Waxman–Markey bill) had a positive and large impact on excess returns for CCX member firms, suggesting that a major incentive for firms to join CCX may be to prepare for future regulation. Marginal abatement costs (proxied by the carbon price), on the other hand, were unrelated to excess returns. Our results imply that voluntary approaches should play a role in combating climate change, but that relying on them alone is not enough.
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Notes
Although the EPA changed its policy and now includes CO2 as an air pollutant, there are no federal standards, taxes or other regulation that puts a price on CO2 emissions. Regional and local initiatives exist, however, such as the Regional Greenhouse Gas Initiative (RGGI) and California’s Global Warming Solutions Act (AB 32).
A review of the CCX and other voluntary programs can be found in Kollmuss et al. (2008).
There are 3 classes of membership on CCX: Members, participant members, and associate members. Participant members establish a registry and get their emissions verified, but don’t make any commitment on emissions reduction. Associate members have negligible direct emissions, but pledge to report and fully offset their indirect emissions. We focus here solely on full members: Those with sizeable direct emissions who pledge to reduce them.
CCX was founded by Climate Exchange PLC, a publicly traded company. In July 2010, Intercontinental Exchange (ICE) acquired CCX and its global affiliates. Members reduced emissions by more than the required amount by the end of 2010, explaining the virtually zero carbon price in the last year. In July 2011, ICE launched the CCX Offset Registry Program, a platform to generate, register and trade carbon credits based on verified emission reductions (CCX fact sheet by ICE, available at www.theice.com/ccx, last accessed in June 2012).
There is a separate literature that focuses specifically on the environmental effectiveness of various voluntary programs. In the current context, Matisoff (2012) finds that emissions from firms that participated in the CCX decreased, but that this decrease was due to a reduction in output rather than a reduction of carbon intensity.
If the market correctly prices securities, stock prices are equal to the discounted expected stream of dividends, which in turn reflect firm profits. If the efficient market hypothesis does not hold and stock prices are also affected by variables other than future profits, such as self-fulfilling expectations or investor ”herding” behavior, our approach remains valid as long as the deviation between price and fundamental equally applies to CCX member firms and their rivals. Other researchers use Tobin’s Q (equity value divided by market replacement cost of assets) which can be interpreted as a measure of intangible firm value. But like stock prices, Tobin’s Q tends to be non- stationary, which complicates statistical inference in our multivariate regression approach. Taking first differences solves the non-stationarity problem, but since the market replacement cost presumably does not change month-to-month, the information contained in first-differenced Tobin’s Q is identical to the information contained in returns.
See Fama and French (2004) for a review of its history and application. The main reason why we use the CAPM is that it is frequently applied in finance, and its parsimonious nature. Rather than making stock prices depend on a range of (usually unobserved) firm characteristics, the CAPM relies on aggregate market information to price stock.
For a balanced panel with \(\text{ N} \times \text{ T}\) observations, regressing individual stock returns on time-varying common factors yields the same point estimates as regressing cross-section averages (i.e. portfolio returns) on the same factors. In our case of an unbalanced panel, the point estimates will slightly differ because the portfolio approach equally weighs time averages, whereas the individual stock regression gives equal weight to all observations, implying a greater weight for months with more data. The interpretation of \(\alpha \) and \(\upbeta \) remains unchanged.
The book-to-market ratio is defined as book equity divided by market equity. Book equity captures a firm’s total assets minus liabilities and is defined as the value of stockholders’ equity plus the value of deferred taxes and investment tax credit, minus the value of preferred stock; market equity is the stock price times the number of shares.
The actual calculation is the difference between the average of two winning portfolios (formed from small firms and big firms), minus the average from two losers (small and big). The winners and losers are formed above the 70 % percentile form the previous month, and below the 30 % percentile, respectively.
http://mba.tuck.dartmouth.edu/pages/faculty/ken.french/datalibrary.html (last access on 8/16/2011).
Prospects for a federal cap-and-trade system decreased somewhat when the bill was tabled in the Senate and attention was shifted to the recession and the deficit. This undoubtedly postponed the advent of a mandatory program, but mandatory permit trading remains the cornerstone of action against climate change in most other OECD countries, making it at least possible that the US will eventually institute a mandatory system as well. Because there was no clearly identifiable moment at which the probability of a mandated program decreased, we did not include an “anti-Waxman” dummy in the analysis. If such a moment existed, we would expect negative excess returns for CCX members.
The weak inequality follows directly from stepwise marginal abatement cost curves. A second reason for our assumption is related to perception: The voluntary nature of the market should not change the basic arbitrage condition that firms abate as long as this is cheaper than buying permits. The reverse, however, may not be true if firms’ main incentive to join CCX is to generate an image of greenness: They could choose to abate even if this is costlier than covering all of their emissions by purchasing permits. There remains a stigma of absolution from environmental sins associated with buying offsets, if not among economists then in the general public.
We set the announcement dummy to one for the month of the announcement for all firms. To address the fact that some firms joined late in the month, giving the market much less time to react than for firms joining early, we set the announcement dummy equal to one for the month following the announcement month for all firms that announced their decision to join after the 15th of each month. This means that the period during which we measure excess returns after the announcement varies from a minimum of 15 days (for firms announcing on the 15th) to a maximum of 44 days (for firms announcing on the 16th).
In specification (4), the effect of joining CCX is captured by a one-time effect on returns, which is equivalent to a permanent effect on prices. To control for the possibility of lagged and/or permanent effect on returns, we introduced lagged versions of the announcement dummy, but they were statistically insignificant (results available on request).
As in Kim and Lyon (2011), the voluntary program can be viewed as a treatment, and here the effect of joining CCX is treatment effect on the treated. Complicating the analysis is the fact that different firms joined at different times. In theory, the average treatment effect on the treated (ATT) would have to be constructed from several distinct effects occurring at different times. Rather than explicitly computing the ATT for each firm and then form an average, we construct a single control group by matching and use a D-in-D-type estimation procedure.
The CRSP is a database of daily and monthly stock prices for publicly-listed firms in the United States going back to 1925. CRSP is maintained at the University of Chicago; for more information, see: http://www.crsp.com/.
We use the effective federal funds rate as the risk-free rate of interest. The effective federal funds rate is a weighted average of rates on brokered trades. Monthly figures include each calendar day in the month, and are annualized using a 360-day year or bank interest. Information and data are available at http://www.federalreserve.gov/econresdata/releases/statisticsdata.htm.
The CCX was comprised of a mix of municipalities, non-profits, and for-profit corporations. Among the for-profit firms (the object of this study), over 14 major (SIC) industry groups were represented, including agricultural, manufacturing, financial services, and consumer products. The most frequent industry represented in our sample was energy utilities (\(\text{ n} = 17\)), followed by pulp/paper and packaging (\(\text{ n} = 8\)), and petrochemicals (\(\text{ n} = 7\)).
We do not include excess returns on the industry level for the full sample regressions because this would amount to including a time average of the LHS variable as a regressor, which would then “explain” almost the entire variation of excess returns. This is not the case for the matched-sample regressions because the industry averages are computed using the full sample.
This point was made by a reviewer. It implies that the CFI market is not efficient in the sense that the CFI price is not equal to marginal abatement costs. Although not consistent with theory, this is a valid point in practice, especially considering the low carbon price and the different impact that reducing emissions and buying offsets may have on consumers and investors. Whereas reducing emissions sends a message of “greenness”, buying offsets (which is physically equivalent, conditional on additionality of the offset project) may be perceived less favorably.
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Appendix: Derivation of the Regression Specification
Appendix: Derivation of the Regression Specification
In order to derive a theoretically consistent regression specification, we start by considering the price equation that gives rise to the CAPM while suppressing the subscripts on the coefficients:
or, equivalently,
Taking logs and differentiating w.r.t time gives
where a dot represents a time derivative. Rearranging and discretizing in order to accommodate market data results in
where \(\Delta \) is the first-difference operator. Since proportional first differences are equal to returns, () is equivalent to Eq. (2) in the main text. We now add average prices for “small” and “big” (in terms of market value), “high” and “low” (in terms of book-to-market ratio) and “winners” and “losers” (in terms of the past month’s market performance), which are the basis for the SMB, HML and MOM factors. This leads to the following equation in prices:
Following the same steps as above and using the definitions of the four factors, this becomes Eq. (3) defined in returns.
The RHS of () does not include any firm-specific information but only depends on aggregate market data. We now introduce firm-specific information. Specifically, we include industry-level stock prices \(P_{it}^{sic}\), the CCX carbon price \(P_{t}^{C}\), and dummies indicating CCX membership \(M_{it}\) (1 for CCX members, 0 otherwise) and the passing of the Waxman–Markey bill \(W_{t}\) (1 for June 2009, 0 otherwise). Since the effect of the carbon price and Waxman–Markey can be expected to differ between members and nonmembers, we also include interaction terms. In order to be consistent (both in terms of theory and math) with the four-factor model, we add these terms multiplicatively to the price equation:
Taking logs and differentiating w.r.t time:
Substituting for \(\Gamma _{t}\), discretizing and using the fact that all member firms joined CCX before June 2009 (implying that \(\Delta M_{it}W_{t-1}=0\,\forall t\)) leads to the following specification:
Writing the proportional first differences as returns and substituting the definitions of the SMB, HML and MOM factors, this becomes Eq. (4).
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Gans, W., Hintermann, B. Market Effects of Voluntary Climate Action by Firms: Evidence from the Chicago Climate Exchange. Environ Resource Econ 55, 291–308 (2013). https://doi.org/10.1007/s10640-012-9626-7
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DOI: https://doi.org/10.1007/s10640-012-9626-7