Analyzing the asymmetric FinTech services under natural resources, and renewable energy in the future environmental performance: New insights from STIRPAT model framework

Shu X., Usman M., Ahmad P., Irfan M.

Resources Policy, vol.92, 2024 (SSCI) identifier

  • Publication Type: Article / Article
  • Volume: 92
  • Publication Date: 2024
  • Doi Number: 10.1016/j.resourpol.2024.104984
  • Journal Name: Resources Policy
  • Journal Indexes: Social Sciences Citation Index (SSCI), Scopus, International Bibliography of Social Sciences, Aerospace Database, Aquatic Science & Fisheries Abstracts (ASFA), Business Source Elite, Business Source Premier, Communication Abstracts, Compendex, EconLit, Index Islamicus, INSPEC, Metadex, PAIS International, Pollution Abstracts, Public Affairs Index, Civil Engineering Abstracts
  • Keywords: Dynamic ARDL model, Environmental degradation, FinTech development, Natural resources, Renewable energy
  • Azerbaijan State University of Economics (UNEC) Affiliated: No

Abstract

Increasing environmental degradation and energy crises have pushed economies to adopt and impliment more sustainable technologies. In this way, financial technologies (FinTech) and natural resources have played a significant role in diminishing environmental pollution by integrating economic growth and urbanization. Both of these factors can boost renewable energy sources and diminish environmental pollution. To achieve this goal, this study estimates the short run, and long run relationship between FinTech services, natural resources, and greenhouse gasses (GHG) emissions in China from 2010Q1–2022Q4, while applying economic growth, renewable energy use, and urbanization as control variables using novel dynamic autoregressive distributive lag (DARDL) simulations. This study discovers a novel FinTech development index through principal component analysis (PCA) comprising four imperative factors that signify the FinTech development and employes a reliable and robust environmental framework recognized as the Stochastic Impacts by Regression on Population, Affluence, and Technology (STIRPAT) model. The estimated findings reveal that FinTech development and renewable energy significantly reduce GHG emissions in the long run. At the same time, renewable energy consumption reduces environmental pollution in the short run. Conversely, natural resources and urbanization significantly boost GHG emissions in the long run, and short run. Moreover, economic growth increases GHG emissions in the long run but adversely influences environmental degradation in the short run. Finally, this study suggests that China implements sustainable progress tactics to accomplish a harmonious equilibrium between FinTech services, natural resources, energy use, economic growth, and ecological fortification, efficiently addressing the encounters of ecological deprivation, encouraging balance FinTech services and natural resource management, and achieving Sustainable Development Goals (SDGs) targets.