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Innovation, income, and waste disposal operations in Korea: evidence from a spectral granger causality analysis and artificial neural networks experiments

Author

Listed:
  • Marco Mele

    (Roma Tre University)

  • Cosimo Magazzino

    (Roma Tre University)

  • Nicolas Schneider

    (Paris 1 Panthéon-Sorbonne University)

  • Antonia Rosa Gurrieri

    (University of Foggia)

  • Hêriş Golpira

    (Islamic Azad University)

Abstract

The aim of this paper is to assess the causal relationship among innovation in environment-related technologies, per capita income, and three major waste disposal operations (landfill, recycling, and incineration) for Korea. A time-series analysis over the frequency domain (Breitung–Candelon Spectral Granger causality) is applied, followed by Artificial Neural Networks experiments over the 1985–2016 period. Empirical results highlight that economic growth is tightly linked both to the growth of recycled waste and to the increase of environment-related innovations. Findings also highlight that waste recycling operations can spur the level of economic activity.

Suggested Citation

  • Marco Mele & Cosimo Magazzino & Nicolas Schneider & Antonia Rosa Gurrieri & Hêriş Golpira, 2022. "Innovation, income, and waste disposal operations in Korea: evidence from a spectral granger causality analysis and artificial neural networks experiments," Economia Politica: Journal of Analytical and Institutional Economics, Springer;Fondazione Edison, vol. 39(2), pages 427-459, July.
    • Handle: RePEc:spr:epolit:v:39:y:2022:i:2:d:10.1007_s40888-022-00261-z
    DOI: 10.1007/s40888-022-00261-z
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    Cited by:

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    2. Li, Xiaobin & Sengupta, Tuhin & Si Mohammed, Kamel & Jamaani, Fouad, 2023. "Forecasting the lithium mineral resources prices in China: Evidence with Facebook Prophet (Fb-P) and Artificial Neural Networks (ANN) methods," Resources Policy, Elsevier, vol. 82(C).
    3. Hu, Bangyong & Guo, Man & Zhang, Shuwen, 2023. "The role of fiscal decentralization and natural resources markets in environmental sustainability in OECD," Resources Policy, Elsevier, vol. 85(PB).
    4. Jianing, Pang & Bai, Keke & Solangi, Yasir Ahmed & Magazzino, Cosimo & Ayaz, Kamran, 2024. "Examining the role of digitalization and technological innovation in promoting sustainable natural resource exploitation," Resources Policy, Elsevier, vol. 92(C).
    5. Fu, Rong & Liu, Jianmei, 2023. "Revenue sources of natural resources rents and its impact on sustainable development: Evidence from global data," Resources Policy, Elsevier, vol. 80(C).

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    More about this item

    Keywords

    Municipal solid waste; Waste recycling; Waste incineration; R&D; Artificial neural networks; Korea;
    All these keywords.

    JEL classification:

    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
    • C39 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Other
    • Q50 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Environmental Economics - - - General
    • Q53 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Environmental Economics - - - Air Pollution; Water Pollution; Noise; Hazardous Waste; Solid Waste; Recycling

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