Does Greenwashing Pay Off? Understanding the Relationship Between Environmental Actions and Environmental Legitimacy

Abstract

Do firms gain environmental legitimacy when they conform to external expectations regarding the natural environment? Drawing on institutional logic and signaling theory, we investigate sources of heterogeneity in the impacts of environmental actions on environmental legitimacy. Longitudinal data (1997–2001) about 325 publicly traded U.S. firms in polluting industries support the notion that environmental actions help firms gain environmental legitimacy. However, some actions instead can harm this legitimacy if environmental performance deteriorates and the firm is subject to intense scrutiny from nongovernmental organizations. Thus, an important contribution of this research is to identify conditions under which greenwashing can backfire.

Appendix

Environmental Legitimacy Measure

$$\begin{aligned} {\text{Environmental legitimacy}}_{\text{it}} &= \frac{{(p_{\text{it}}^{ 2} - p_{\text{it}} n_{\text{it}} )}}{{T_{\text{it}}^{2} }}\quad{\text{if}}\quad p_{\text{it}} > n_{\text{it}} \hfill \\ &= \frac{{(p_{\text{t}} n_{\text{t}} - n_{\text{t}}^{2} )}}{{T_{\text{it}}^{ 2} }}\quad{\text{if}}\quad p_{\text{it}} < n_{\text{it}} \hfill \\ &= 0 \qquad \qquad{\text{ if}}\quad p_{\text{it}} = n_{\text{it}} \hfill \\ \end{aligned} ,$$

where p _it is the number of legitimating environmental articles about firm i in year t, n _it is the number of legitimacy-challenging environmental articles about firm i in year t, and T _it is the total number of articles about firm i in year t (T _t = p _t + n _t).

Environmental Performance Measure

We obtained a weighted waste score for each facility:

$$ww_{\text{jt}} = \sum\limits_{\text{k}} {\sum\limits_{\text{l}} {E_{\text{klt}} } } \times f_{\text{kl}},$$

where $E_{\text{klt}}$ is the emissions of chemical l to medium k in year t by facility j, and $f_{\text{kl}}$ is the weighting factor corresponding to chemical l emitted to medium k. Next, we predicted waste generation using the reported production ratios:

$$\text{P} {\text{redicted\_waste}}_{\text{it + 1}} = \sum\limits_{\text{j}} {ww_{\text{jt}} } \times PR_{\text{jt + 1}}.$$

Finally, we computed the difference between actual waste and predicted waste and aggregated the values at the parent firm level to obtain a firm measure of pollution prevention:

$${\text{Pollution prevention}}_{{{\text{it}} + 1}} = \sum\limits_{\text{j}} {ww_{\text{jt}} } \times PR_{\text{jt + 1}} - \sum\limits_{\text{j}} {ww_{\text{jt + 1}} } ,$$

where j are facilities that belong to firm i. If the actual waste level is lower than the predicted level, pollution prevention_it+1 yields positive values, indicating a reduction of waste generation. Thus, bigger values are associated with better environmental performance.

Environmental Patents Index

$${\text{Environmental patents}}_{\text{it}} = w_{\text{it}}\times {\text{Patents}}_{\text{it}}{,} \quad{\text{with }}w_{\text{it}} = \frac{{({\text{Citations}} \div {\text{patents}})_{\text{it}} }}{{\frac{{\sum_{i = 1}^{i = N} {({\text{Citations}} \div {\text{patents)}}_{\text{it}} } }}{{N_{\text{t}} }}}}$$

and where patents_it denotes total environmental patents of firm i granted during year t, citations_it is all the citations received by the patents granted to firm i during year t, and N _t denotes the total number of firms in the sample for year t.