Deciphering the Environmental Consequences of Competition-Induced Cost Rationalization Strategies of the High-Tech Industry: A Synergistic Combination of Advanced Machine Learning and Method of Moments Quantile Regression Procedures

Salih Çağrı İlkay, Harun Kınacı and Esra Betül Kınacı ()
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Salih Çağrı İlkay: Department of Economics, Faculty of Economics and Administrative Sciences, Erciyes University, 38030 Kayseri, Türkiye
Harun Kınacı: Department of Business Administration, Faculty of Economics and Administrative Sciences, Erciyes University, 38030 Kayseri, Türkiye
Esra Betül Kınacı: Department of Statistics, Faculty of Sciences, Gazi University, 06500 Ankara, Türkiye

Sustainability, 2025, vol. 17, issue 15, 1-23

Abstract: This study intends to portray how varying degrees of environmental policy stringency and the growing pressure of global competition reflect on high-tech (HT) sectors’ cost rationalization strategies and lead to environmental consequences in 15 G20 countries (1992–2019). Moreover, we center the pattern of cost rationalization management regarding the opportunity cost of ecosystem service consumption and propose to test the fundamental hypothesis stating the possible transmission of competition-induced technological innovations to green economic transformation. Our new methodology estimates quantile-specific effects with MM-QR, while identifying the main interaction effects between regulatory pressure and trade competition uses an extended STIRPAT model. The results reveal a paradoxical finding: despite higher environmental policy stringency and opportunity costs of ecosystem services, HT sectors persistently adopt environmentally detrimental cost-reduction approaches. These findings carry important policy implications: (1) environmental regulations for HT sectors require complementary innovation subsidies, (2) trade agreements should incorporate clean technology transfer clauses, and (3) governments must monitor sectoral emission leakage risks. Our dual machine learning–econometric approach provides policymakers with targeted insights for different emission scenarios, highlighting the need for differentiated strategies across clean and polluting HT subsectors.

Keywords: HT export competitiveness; environmental sustainability; cost rationalization; machine learning; method of moments quantile regression; ecosystem service consumption; G20 countries (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2025
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Persistent link: https://EconPapers.repec.org/RePEc:gam:jsusta:v:17:y:2025:i:15:p:6867-:d:1712059

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