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Peer-Review Record

Value Relevance of Corporate Environmental Performance: A Comprehensive Analysis of Performance Indicators Using Korean Data

Sustainability 2020, 12(17), 7209; https://doi.org/10.3390/su12177209
by Hyunwoo Choi 1, Ingoo Han 2 and Jaywon Lee 3,*
Reviewer 1: Anonymous
Reviewer 2:
Reviewer 3: Anonymous
Reviewer 4: Anonymous
Reviewer 5: Anonymous
Sustainability 2020, 12(17), 7209; https://doi.org/10.3390/su12177209
Submission received: 24 July 2020 / Revised: 31 August 2020 / Accepted: 1 September 2020 / Published: 3 September 2020

Round 1

Reviewer 1 Report

Please see the attached referee report.

Comments for author File: Comments.pdf

Author Response

Please see the attachment.

Author Response File: Author Response.pdf

Reviewer 2 Report

Comments on

Value Relevance of Corporate Environmental Performance:

A Comprehensive Analysis of Performance Indicators

 

 

             This paper examines the value relevance of corporate environmental performance using individual environmental performance indicators and multidimensional constructs. The empirical results indicate that only a few individual environmental performance indicator variables are value relevant while most of environmental performance constructs have significant impact on firm value - suggesting that firm value significantly increases with improved environmental management or operational performance.

 

Comments:

 

  1. I complement the authors in constructing a thorough comparison in value relevance of corporate environmental performance (CEP) using individual environmental performance indicators and multidimensional constructs. The prior literature is well summarized and the study is forthcoming in noting its limitations.
  2. However, I have issues with the research design and the features of the data and institutional background. Please, see below for detailed comments.
  3. There must be some information regarding the change in laws and regulations surrounding the environmental performance and how this affects firms.
  4. In line with my comment on number 3, there are endogeneity issues regarding this matter. Is it that environmental performance leads to higher value or is it higher valued firms being associated with improvements in environmental performance? To alleviate this problem, the study should engage in statistical methods such as the Granger Causality Analysis (Granger 1969). The Granger Causality Approach can assess the causal relationship between a firm’s CEP and market value. Granger causality tests generally include lagged values of the dependent variable in the equation and investigate whether prior independent variables provide information over and above what is provided by prior values of the dependent variable.
  5. There must be detailed explanations of ASSET4 database. In addition, the study should provide an appendix where Trumpp (2015)’s indicators are explained in detail. In the current draft, the readers do not know how environmental indexes are coded.

 

 

 

Author Response

Please see the attachment.

Author Response File: Author Response.docx

Reviewer 3 Report

see the attachment

Comments for author File: Comments.pdf

Author Response

Please see the attachment.

Author Response File: Author Response.docx

Reviewer 4 Report

I would like to preface these comments by stating up front I am not an accounting or econometrics expert. My area is largely strategy. Having said that I was impressed with the care you took in the analytical part of the paper. In fact, in my view, that is the real strength of the manuscript. 

The hypotheses in and of themselves are not very insightful. In fact I am not sure why they are needed.

I would suggest you make this a purely methodological piece. One way you could improve on that element is to see if you can replicate your findings around issues related to multicollinearity and relative value of individual vs. composite measures of environmental sustainability in another dataset. This could be a newer dataset in Korea or a dataset from a similar timeframe in Europe or Japan (I doubt you can get the data in the U.S. or China).

Author Response

Please see the attachment.

Author Response File: Author Response.docx

Reviewer 5 Report

Review Report

Thanks for the opportunity to read this paper.

According to the authors, this paper “examines the value relevance of corporate environmental performance (CEP) using individual environmental performance indicators and multidimensional constructs derived from Trumpp et al. (2015)”.

Properly introduced and developed, the topic approached in this paper fits well in the theme of this journal (contributing to the literature of environmental performance), and the paper is, well organized and the reading flows.

Overall, my opinion is that this paper does rises to the standards expected by the journal, and thus I think that the paper may be accepted for publication.

Author Response

Thank you very much for your constructive comments and encouragement. We think we were able to improve the quality of paper substantially by responding to the issues raised by five reviewers and believe that our revised version of the manuscript is now more suitable for publication.

Round 2

Reviewer 3 Report

Review 24/8/2020

 

                As with previous review, my comments will not be exhaustive.  While I do appreciate the work done by the author(s) to clarify their work, the manuscript (MS) is still deficient in many ways.  As such, I cannot recommend the MS for publication.

 

                I still cannot determine what exactly are the contributions of this paper.  There are no new methods proposed, nor are the methods being applied in a novel way.  Because of this, the MS should probably be reframed as a case study.  As stated previously, there is no rationale or comparative assertions regarding the methods utilized.  For example, why do we use both Pearson correlations and VIFs?  What are the issues that necessitate this?

 

                The paper’s organization is still incorrect.  For example, the Granger causality methods are after the main findings of the regression model!  The writing is still not on par with a peer-reviewed paper.  The writing is highly condensed and lacking in clarity (the main findings of the MS are summarized in a single page).  For example, there are no definitions or discussion of what is meant by environmental management performance (EMP) and environmental operational performance (EOP).  Repeatedly, the author(s) us the term “value relevance” without any clarification as to what this means.  “Income” is used instead of “revenue.”  Multicollinearity and “Variance Inflation Factors (VIF)” are never explained.  The MS claims “The composite measure of CEP may alleviate some concerns of multicollinearity since the composite measure (EPC) is a standardized measure from sum of individual measures (EPI) (p.6, lines 209-210).” How does this solve the problem of multicollinearity?  What is the source(s) of this claim?  These types of problems are continuous throughout the text.  Hence, the author(s) are incorrectly assuming that their readership are already familiar with the concepts and theory behind their MS, although that should never be assumed to be the case.  Rather, any MS of this sort should be approachable even for the lay person with a genuine interest in the paper’s results. 

 

 

                Some passages seem to internally conflict.  For example, “This study does not test the non-linear relationship since the main independent variable is a standardized composite measure of CEP.”  [First of all, which study? The MS?  Trumpp and Guenther (2015)?  Secondly, what is meant by “standardized?”  Mathematically standardized?  Or, standard impact categories?]  Nevertheless, later the MS states that only three of the twelve variables of are composite: “columns 10–12 show the results using the composite EPC variables” (p.12, lines 307-308).  This makes for very confusing reading.

 

                Some of the claims made by the author(s) are untrue.  For example, “sensitive industries” [why not simply say “high pollution industrial sectors”?] are more likely to make their environmental performance known to the public (p.2, lines 64-66).  That is hardly the case as many “sensitive industries” either do not make their reports public (e.g., Koch Industries) or only disclose very limited information (e.g., U.S. Steel Corporation only report GHGs).

 

                The observations are still highly confusing.  The author(s) claim in one instance that “[t]hese results imply that the bulk of firm market values are explained by assets, liabilities and net income as argued by Ohlson (1995) while other factors such as corporate environmental performance seldom incrementally add to value relevance in significant amounts (p.12, lines 323-325).” This implies that there were no results from this research in terms of a predictor model.  This also contradicts some of the claims made elsewhere in the MS: “Panel A of Table 6 reveals that for firms in environmentally sensitive industries, POL1, ENERGY, WATER, and WASTE have additional value relevance among the individual EPI variables” (p. 12, lines 334-335).”  And, then later with the Granger causality: “CEP [environmental performance] does not “Granger cause” market value in a consistent way (lines 425-426).”

 

                I do not understand why the author(s) shifted from an APA formatted referencing style to a pseudo-Chicago style. I simply said in the previous review that the referencing was inconsistent.

 

                Lastly, I do not agree with the author(s) contention that the similar adjusted R-squared values “would imply that the bulk of firm market values are explained by assets, liabilities and net income” (fr. Response to Reviewer). Rather, the similarity of adjusted R-squared values suggests that the regression model variables are too interconnected, as confirmed by the high VIF values.  The composite variable regressions do see reductions in VIFs but this is probably due to a “loss of information” issue (e.g., see Song et al., 2013) since, again, the adjusted R-squared values are relatively the same, before and after using composite variables.  Thus, this model is not very useful as a predictor.

 

 

Reference

Song, M-K., Lin, F-C., Ward, S.E., and Fine, J.P. Composite variables: when and how. Nurs Res, 65(1), 45–49.

Author Response

Please see the attachment

Author Response File: Author Response.docx

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