Exploring Public Sentiment on Green Economy Policy: A Natural Language Processing-Based Analysis

Authors

  • Widya Rizki Eka Putri Department of Accounting, Faculty of Economic and Business, Universitas Lampung, Lampung, Indonesia
  • Saring Suhendro Department of Accounting, Faculty of Economic and Business, Universitas Lampung, Lampung, Indonesia
  • Rialdi Azhar Department of Accounting, Faculty of Economic and Business, Universitas Lampung, Lampung, Indonesia
  • Neny Desriani Department of Accounting, Faculty of Economic and Business, Universitas Lampung, Lampung, Indonesia
  • Andy Chandra Pramana Department of Public Sector Accounting, Faculty of Economic and Business, Universitas Kadiri, Jawa Timur, Indonesia

DOI:

https://doi.org/10.32479/ijeep.18360

Keywords:

Green Economy, Public Sentiment, Natural Language Processing, Bidirectional Encoder Representations from Transformers

Abstract

This study employs a natural language processing (NLP) methodology to examine public sentiment toward green economy policies in Indonesia, utilizing data collected via direct surveys. Green economic policies, including carbon taxes and investments in renewable energy, are gaining significance in addressing environmental and economic concerns. Sentiment analysis and topic modeling are employed to discern trends in public opinion, encompassing positive, negative, and neutral sentiments regarding diverse policy elements. The research findings indicate that 79.34% of public answers endorse green economic initiatives, notably those associated with renewable energy investments and carbon emission reductions, whereas 20.66% exhibit a negative stance, primarily due to apprehensions regarding short-term economic effects. This study offers critical insights for policymakers to improve communication and execution tactics to bolster public support for green economy initiatives in Indonesia.  

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Published

2025-02-25

How to Cite

Putri, W. R. E., Suhendro, S., Azhar, R., Desriani, N., & Pramana, A. C. (2025). Exploring Public Sentiment on Green Economy Policy: A Natural Language Processing-Based Analysis. International Journal of Energy Economics and Policy, 15(2), 560–565. https://doi.org/10.32479/ijeep.18360

Issue

Vol. 15 No. 2 (2025)

Section

Articles

License

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