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Environmental Phillips curve: OECD and Asian NICs perspective


This research aims to explore the existence of a new concept known as “environmental Phillips curve” (EPC) developed by the authors. Taking annual data of 30 countries for 26 years, a panel data estimation method is applied. The invented function shows an inverse relationship between pollution and unemployment. In most of the cases, the industrialized countries show that the relationship is valid. The notion is proved effective in every format of investigation. It seems that curbing pollution in the world is only possible at the cost of human employment. Therefore, if countries want to curb environmental pollution without affecting the generation of employment and reducing poverty, they should contemplate both innovation and enforcement alternative technologies that would be less polluting but employment friendly. Moreover, this research also suggests that if a country can treat pollution efficiently, it can increase the national income without a deteriorating unemployment level.

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Author information




M.A. Kashem: Study plan; literature review; data collection; writing main sections of the paper; econometric estimation, and data and result analysis.

M.M. Rahman: Conceptual and methodological development; variable selection; result analysis, polishing and editing, and improving the quality of the manuscript; and undertaking the responsibility of corresponding author.

Corresponding author

Correspondence to Mohammad Mafizur Rahman.

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Appendix 1

Table 4 The results of cross-sectional dependence tests
Table 5 The results of unit root test

Appendix 2. Bi-variate graphs (unemployment vs CO2 emissions)


Appendix 3. All estimated models

Table 6 Model 1: the results of FEM
Table 7 Model 2: the results of REM
Table 8 Model 3: the results of PCSEs
Table 9 The results of Hausman test

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Kashem, M.A., Rahman, M.M. Environmental Phillips curve: OECD and Asian NICs perspective. Environ Sci Pollut Res 27, 31153–31170 (2020).

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  • Environmental Phillips curve (EPC)
  • CO2 emissions
  • Unemployment
  • Panel data
  • OECD

JEL codes

  • Q53
  • E24
  • C23