Harun Kınacı

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.

Sınıflandırma problemi araştırmacılar tarafından uzun zamandır incelenen bir konu olmasına rağmen güncelliğini hala korumaktadır. Özellikle görüntü işleme ve hastalık tanısının belirlenmesi problemleri güncel uygulama alanlarından bazılardır. Bu çalışma destek vektör regresyon ve matematiksel programlamaya dayalı yeni bir veri sınıflandırma yöntemi sunmaktadır. Önerilen yöntem iki aşamalı hibrit bir yapıdan oluşmaktadır. İlk aşamada, destek vektör regresyon denklemi ile her bir birim için sınıflandırma skoru elde edilirken ikinci aşamada ise birimlerin sınıflandırma skorları kullanılarak bir matematiksel model yardımıyla sınıflandırma kuralı oluşturulur ve birimlerin sınıflandırılması sağlanır. Önerilen yöntem geleneksel yöntemlere alternatif bir yenilik sunmaktadır. Geleneksel matematiksel programlamaya dayalı yöntemler sınıfları doğrusal bir fonksiyon ile ayırır. Bu durum ise matematiksel programlamaya dayalı algoritmalarının kullanımını kısıtlar. Önerilen yöntem, doğrusal veya doğrusal ayrılamayan veri yapılarının tamamında kullanılabilir olmasının yanı sıra ikiden fazla grup sayısının olduğu problem türlerine de kolaylıkla dönüştürülebilmektedir. Model önce simülasyon ile irdelenmiş sonrasında Elektroensefalograf (EEG) sinyallerinin sınıflandırılması probleminde uygulanmış ve sınıflandırma performansı mevcut yöntemlerle karşılaştırılmıştır. Elde edilen sonuçlar tablolarda verilmiş ve önerilen modelin mevcut algoritmalara alternatif olabileceğini gösterilmiştir.

Waste polymers are widely used in asphalt modification, especially, in order to reduce their detrimental effect on the environment. This study discusses the usability of waste expanded polystyrene (EPS) foam in asphalt modification. In this respect, the EPS foam, which produces waste in both production and use stages, was mixed with bitumen by weight at the ratios of 2%, 4%, 6%, and 8%, and dry modification method was employed. The penetration, ductility, softening point, flash point, and specific gravity values of virgin bitumen were compared with waste EPS foam modified bitumen. The comparison was made by both graphical and statistical methods. A strong correlation between the physical properties of modified asphalt and the ratio of EPS foam was observed. A drop was observed in penetration and ductility values. Besides, it was found by statistical analyses that 2% of the increase in the additive ratio is not sufficient to make a significant difference in the physical properties of bitumen, therefore, the increase should be at least 4%. Overall, it is concluded that EPS foam additive can be used in bitumen modification for hot regions where low penetration is required.

Two essential topics of Sustainable Development Goals (SDGs) are accessible which are clean energy (SDG-7) and climate change action (SDG-13). Developments and innovations in energy technologies play an essential role in achieving these goals. Therefore, any country should use energy R&D expenditures, which are the primary source of energy innovation, most optimally. This paper aims to investigate the environmental efficiency of R&D expenditures for energy efficiency, renewable energy, hydro and fuel cells, fossil energy, nuclear energy, and other power and storage technologies in OECD countries using data envelopment analysis (DEA) and bootstrap DEA. Estimation findings indicate that only the USA ensures the environmental efficiency in energy R&D expenditures among OECD countries. Although Japan, Canada, France, Germany, and Italy cannot provide environmental efficiency in energy R&D, their scores are very close to the efficiency frontier. Portugal, Hungary, and Slovak Republic are the countries with the lowest environmental efficiency in energy R&D expenditures. At the end of the investigation, this paper also provides an empirical estimation of the extent to which inefficient countries should change their R&D spending to achieve efficiency.