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Free associations of citizens and scientists with economic and green growth: A computational-linguistics analysis

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  • Savin, Ivan
  • Drews, Stefan
  • van den Bergh, Jeroen

Abstract

The debate about the relationship between economic growth and environmental sustainability triggers a range of associations. Here we analyze open-ended textual responses of citizens and scientists concerning their associations with the terms “economic growth” and “green growth”. We derive from the responses a number of topics and examine how associations differ across distinct opinion segments of people, namely supporters of Green growth, Agrowth and Degrowth. The results indicate that the general public is more critical of the notion of economic growth than academic researchers. Citizens stress problems of corruption, social inequality, unemployment and poverty, with less variation among the three opinion segments compared to scientists. The latter more strongly emphasize the environmental consequences of economic growth. Concerning associations of scientists with the term “green growth”, we find topics questioning its feasibility to be more likely expressed by Degrowth supporters, while topics stressing the possibility of sustainable economic growth by Green growth supporters. We find that topic polarization is stronger for scientists than citizens. Our results provide further validation for opinion clusters identified in previous studies and uncover additional insights about related views on growth and sustainability.

Suggested Citation

  • Savin, Ivan & Drews, Stefan & van den Bergh, Jeroen, 2021. "Free associations of citizens and scientists with economic and green growth: A computational-linguistics analysis," Ecological Economics, Elsevier, vol. 180(C).
    • Handle: RePEc:eee:ecolec:v:180:y:2021:i:c:s0921800920309484
    DOI: 10.1016/j.ecolecon.2020.106878
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    3. Mohamed M. Mostafa, 2023. "A one-hundred-year structural topic modeling analysis of the knowledge structure of international management research," Quality & Quantity: International Journal of Methodology, Springer, vol. 57(4), pages 3905-3935, August.
    4. Loureiro, Maria L. & Alló, Maria, 2024. "Feeling the heat? Analyzing climate change sentiment in Spain using Twitter data," Resource and Energy Economics, Elsevier, vol. 77(C).
    5. Cafferata, Alessia & Dávila-Fernández, Marwil J. & Sordi, Serena, 2021. "Seeing what can(not) be seen: Confirmation bias, employment dynamics and climate change," Journal of Economic Behavior & Organization, Elsevier, vol. 189(C), pages 567-586.
    6. Savin, Ivan & Ott, Ingrid & Konop, Chris, 2022. "Tracing the evolution of service robotics: Insights from a topic modeling approach," Technological Forecasting and Social Change, Elsevier, vol. 174(C).
    7. Tomaselli, Maria Fernanda & Kozak, Robert & Gifford, Robert & Sheppard, Stephen R.J., 2021. "Degrowth or Not Degrowth: The Importance of Message Frames for Characterizing the New Economy," Ecological Economics, Elsevier, vol. 183(C).
    8. Appolloni, Andrea & Chiappetta Jabbour, Charbel Jose & D'Adamo, Idiano & Gastaldi, Massimo & Settembre-Blundo, Davide, 2022. "Green recovery in the mature manufacturing industry: The role of the green-circular premium and sustainability certification in innovative efforts," Ecological Economics, Elsevier, vol. 193(C).
    9. Ivan Savin & Stefan Drews & Sara Maestre-Andrés & Jeroen Bergh, 2020. "Public views on carbon taxation and its fairness: a computational-linguistics analysis," Climatic Change, Springer, vol. 162(4), pages 2107-2138, October.

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