Deteksi Greenwashing dalam Pengungkapan ESG: Analisis Berbasis NLP pada Perusahaan Perkebunan Kelapa Sawit

Rinaldi  Sinaga; Enny Susilowati  Mardjono; Nila  Tristiarini

doi:10.33395/owner.v10i4.3790

Authors

Rinaldi Sinaga
✉ Corresponding author: p33202400033@mhs.dinus.ac.id Universitas Dian Nuswantoro
Enny Susilowati Mardjono
Nila Tristiarini Universitas Dian Nuswantoro

DOI:

https://doi.org/10.33395/owner.v10i4.3790

Keywords:

greenwashing, ESG, palm oil, NLP, sustainability disclosure

Abstract

The rapid expansion of corporate sustainability reporting in the palm oil sector has intensified demands for transparency, however, while simultaneously raising concerns about greenwashing driven by selective narrative disclosure. This study examines the credibility of Environmental, Social, and Governance (ESG) communication by comparing corporate sustainability reports with external media coverage across 198 palm oil plantation companies operating in eight major producing countries Indonesia, Malaysia, Thailand, Colombia, Nigeria, Papua New Guinea, Guatemala, and Honduras for the year 2025. Employing Natural Language Processing (NLP) techniques, the study constructs a Greenwashing Severity Index (GSI) that measures the divergence between corporate ESG self-representation and independent external narratives drawn from news media coverage and NGO reports. The index integrates ESG-specific focus measures, sentiment analysis, TF-IDF word weighting, and topic modelling to quantify imbalances in ESG communication. Findings reveal moderate yet widespread greenwashing across countries, company types, and plantation sizes, with substantial heterogeneity linked to regulatory maturity, certification adoption (RSPO, ISPO, MSPO), and stakeholder oversight. Greater narrative alignment is observed among RSPO-certified companies and exchange-listed plantation groups. Propensity score matching analysis further indicates that companies exhibiting unbalanced emphasis across ESG dimensions display significantly higher GSI values, consistent with strategic disclosure behaviour. Overall, findings suggest that formal transparency alone is insufficient to ensure credible ESG communication in the global palm oil industry.

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References

Austin, P. C. (2011). Optimal caliper widths for propensity-score matching when estimating differences in means and differences in proportions in observational studies. Pharmaceutical Statistics, 10(2), 150-161. https://doi.org/10.1002/pst.433

Bernini, F., Giuliani, M., & La Rosa, F. (2024). Measuring greenwashing: A systematic methodological literature review. Business Ethics, the Environment & Responsibility, 33, 649-667. https://doi.org/10.1111/beer.12631

Blei, D. M., Ng, A. Y., & Jordan, M. I. (2003). Latent Dirichlet Allocation. Journal of Machine Learning Research, 3, 993-1022.

Carlson, K. M., Heilmayr, R., Gibbs, H. K., Noojipady, P., Burns, D. N., Morton, D. C., Walker, N. F., Paoli, G. D., & Kremen, C. (2018). Effect of oil palm sustainability certification on deforestation and fire in Indonesia. Proceedings of the National Academy of Sciences, 115(1), 121-126. https://doi.org/10.1073/pnas.1704728114

Cattau, M. E., Marlier, M. E., & DeFries, R. (2016). Effectiveness of Roundtable on Sustainable Palm Oil (RSPO) for reducing fires on oil palm concessions in Indonesia from 2012 to 2015. Environmental Research Letters, 11(10), 105007. https://doi.org/10.1088/1748-9326/11/10/105007

Dorfleitner, G., & Utz, S. (2024). Green, green, it's green they say: A conceptual framework for measuring greenwashing on firm level. Review of Managerial Science, 18, 3463-3486. https://doi.org/10.1007/s11846-023-00718-w

Furumo, P. R., & Lambin, E. F. (2020). Scaling up zero-deforestation initiatives through public-private partnerships: A look inside post-conflict Colombia. Global Environmental Change, 62, 102055. https://doi.org/10.1016/j.gloenvcha.2020.102055

Garrett, R. D., Levy, S., Carlson, K. M., Gardner, T. A., Godar, J., Clapp, J., & Ayre, B. (2019). Criteria for effective zero-deforestation commitments. Global Environmental Change, 54, 135-147. https://doi.org/10.1016/j.gloenvcha.2018.11.003

Gaveau, D. L. A., Locatelli, B., Salim, M. A., Husnayaen, Manurung, T., Descals, A., Angelsen, A., Meijaard, E., & Sheil, D. (2022). Slowing deforestation in Indonesia follows declining oil palm expansion and lower oil prices. PLOS ONE, 17(3), e0266178. https://doi.org/10.1371/journal.pone.0266178

Heilmayr, R., Carlson, K. M., & Benedict, J. J. (2020). Deforestation spillovers from oil palm sustainability certification. Environmental Research Letters, 15(7), 075002. https://doi.org/10.1088/1748-9326/ab7f0c

Ho, D. E., Imai, K., King, G., & Stuart, E. A. (2007). Matching as nonparametric preprocessing for reducing model dependence in parametric causal inference. Political Analysis, 15(3), 199-236. https://doi.org/10.1093/pan/mpl013

Kathan, M. C., Utz, S., Dorfleitner, G., Eckberg, J., & Chmel, L. (2025). What you see is not what you get: ESG scores and greenwashing risk. Finance Research Letters, 74, 106710. https://doi.org/10.1016/j.frl.2024.106710

Lubloy, A., Kereszturi, J. L., & Berlinger, E. (2025). Quantifying firm-level greenwashing: A systematic literature review. Journal of Environmental Management, 373, 123399. https://doi.org/10.1016/j.jenvman.2024.123399

Lyons-White, J., & Knight, A. T. (2018). Palm oil supply chain complexity impedes implementation of corporate no-deforestation commitments. Global Environmental Change, 50, 303-313. https://doi.org/10.1016/j.gloenvcha.2018.04.012

Pacheco, P., Schoneveld, G., Dermawan, A., Komarudin, H., & Djama, M. (2020). Governing sustainable palm oil supply: Disconnects, complementarities, and antagonisms between state regulations and private standards. Regulation & Governance, 14(3), 568-598. https://doi.org/10.1111/rego.12220

Sajid, M., Zakkariya, K. A., Suki, N. M., & Islam, J. U. (2024). When going green goes wrong: The effects of greenwashing on brand avoidance and negative word-of-mouth. Journal of Retailing and Consumer Services, 78, 103773. https://doi.org/10.1016/j.jretconser.2024.103773

Saxena, S. (2024). Using natural language processing for detecting greenwashing indicators and constructing impact-focused index portfolio. SSRN Electronic Journal. https://doi.org/10.2139/ssrn.5024113

Schimanski, T., Reding, A., Reding, N., Bingler, J., Kraus, M., & Leippold, M. (2024). Bridging the gap in ESG measurement: Using NLP to quantify environmental, social, and governance communication. Finance Research Letters, 61, 104979. https://doi.org/10.1016/j.frl.2024.104979

Spaniol, M. J., Danilova-Jensen, E., Nielsen, M., Rosdahl, C. G., & Schmidt, C. J. (2024). Defining greenwashing: A concept analysis. Sustainability, 16, 9055. https://doi.org/10.3390/su16209055

Sparck Jones, K. (1972). A statistical interpretation of term specificity and its application in retrieval. Journal of Documentation, 28(1), 11-21. https://doi.org/10.1108/eb026526

Xu, T., Sun, Y., & He, W. (2024). Government digitalization and corporate greenwashing. Journal of Cleaner Production, 452, 142015. https://doi.org/10.1016/j.jclepro.2024.142015

Zhao, Y., Kroher, L., Engler, M., & Schnattinger, K. (2023). Detecting greenwashing in the Environmental, Social, and Governance domains using natural language processing. In Proceedings of the 15th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management (IC3K 2023) - KDIR (Vol. 1, pp. 175-181). SCITEPRESS. https://doi.org/10.5220/0012155400003598