Effect of AI-related patents, energy transition, environmental policy stringency, income, and energy consumption sub-types on the environmental sustainability: Evidence from China by KRLS approach

Kartal M. T., Kim E., Muxtarov Ş., Taşkın Yeşilova F. D., Kirikkaleli D., KILIÇ DEPREN S., ...More

Journal of Environmental Management, vol.395, 2025 (SCI-Expanded, Scopus) identifier identifier identifier

  • Publication Type: Article / Article
  • Volume: 395
  • Publication Date: 2025
  • Doi Number: 10.1016/j.jenvman.2025.127924
  • Journal Name: Journal of Environmental Management
  • Journal Indexes: Science Citation Index Expanded (SCI-EXPANDED), Scopus, BIOSIS, Compendex, EMBASE, Environment Index, Geobase, Greenfile, Index Islamicus, Public Affairs Index, Social Sciences Abstracts
  • Keywords: AI-Related patents, China, Energy consumption, Energy transition, Environmental policy stringency, Environmental sustainability, Income
  • Open Archive Collection: Article
  • Azerbaijan State University of Economics (UNEC) Affiliated: Yes

Abstract

Due to the increasing negative effects on humanity, searching for potential solutions to combat environmental problems has been developing. Accordingly, the study examines the effect of a set of critical factors on environmental sustainability (ES) proxied by ecological footprint (EFP) and load capacity factor (LCF) in China. In this context, the study considers AI-related patents, energy transition, environmental policy stringency (EPS), income, and energy consumption (EC) sub-types and applies the Kernel Regularized Least Squares (KRLS) approach on data from 2000 to 2020 within the context of marginal effect analysis. The outcomes show that (i) AI-related patents and energy transition are completely ineffective to ensure ES; (ii) EPS are marginally effective only at 0.25th and 0.75th percentiles to support ES; (iii) economic growth as well as oil, gas, and coal EC are not good for ES across all percentiles; (iv) nuclear EC is only helpful at 0.25th percentiles, whereas renewable EC is completely unbeneficial; (v) KRLS approach presents successful prediction outcomes around 99.7 % (vi) some variables (i.e., nuclear and renewable EC as well as EPS); have marginal and varying effects across percentiles, whereas some others have not. Thus, the study empirically demonstrates the inefficiency of AI-related patents and energy transition on the ES, whereas EPS and nuclear EC can be helpful to develop ES in the Chinese case.