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Can using energy resources productively and promoting good governance boost carbon productivity? An economic growth-environmental degradation decoupling analysis on 116 global countries

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Abstract

Decoupling economic growth from environmental pollution for promoting low-carbon growth has become a global objective. Though the previous studies have mostly analyzed how environmental pollution can be reduced, not much emphasis was given to assessing how economic growth can be enhanced while limiting environmental damages in tandem. Hence, this study examines how carbon productivity is determined by energy productivity improvement, good governance, financial development, financial globalization, and international trade using data from 116 global economies. Overall, the analytical findings reveal that energy productivity improvement initially cannot decouple economic growth from environmental pollution by inhibiting carbon productivity. However, later on, using energy productively does manage to decouple economic growth from environmental pollution by boosting carbon productivity. Accordingly, the U-shaped nexus between these variables is confirmed by these statistical findings. Besides, the results also endorse the carbon productivity-boosting effects of good governance, financial development, and international trade while foreign direct investment receipts are not found to exert any significant impact on carbon productivity. On the other hand, the robustness tests' results affirm that the carbon productivity-influencing impacts are heterogeneous across countries belonging from different categories of national income, carbon productivity, energy productivity, governance, and regional locations, as well. Nevertheless, the results overall confirm that countries having comparatively higher levels of energy productivity and governance are more likely to decouple the growth of their respective economies from environmental pollution. Based on these findings, some decoupling policies are recommended.

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Data availability

The sources of data sets are mentioned in the study.

Notes

  1. For a full list of these selected countries, see Table 12 in the Appendix.

  2. For more information on PHE and PH hypotheses, see Mert and Caglar (2020).

Abbreviations

CO2 :

Carbon dioxide

COP27:

27th Conference of Parties

CRSD:

Cross-sectional dependency

D-CCE:

Dynamic common correlated effects

EAP:

East Asia and the Pacific

ECA:

Europe and Central Asia

EKCH:

Environmental Kuznets curve hypothesis

ERE:

Energy rebound effect

EUP:

Energy use productivity

FDI:

Foreign direct investment

HSC:

Heterogeneous slope coefficients

IV-D-CCE:

Instrumental Variable-augmented D-CCE

LAC:

Latin America and the Caribbean

MENA:

Middle East and North Africa

PH:

Pollution haven

PHE:

Pollution halo effect

RET:

Renewable energy transition

SA:

South Asia

SSA:

Sub-Saharan Africa

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Authors and Affiliations

  1. Department of Economics, School of Business and Economics, North South University, Dhaka, 1229, Bangladesh

    Muntasir Murshed

  2. Department of Journalism, Media and Communications, Daffodil International University, Dhaka, Bangladesh

    Muntasir Murshed

  3. Bangladesh Institute of Development Studies (BIDS), E-17 Agargaon, Sher-E-Bangla Nagar, Dhaka, 1207, Bangladesh

    Muntasir Murshed

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Muntasir Murshed — conceptualization; methodology; formal analysis; investigation; data curation; writing — original draft; writing — review and editing.

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Appendix

Appendix

See Tables 12, 13, 14, 15, 16, and 17

Table 12 List of selected countries
Full size table
Table 13 Descriptive statistics for full panel and income-group-specific sub-panels
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Table 14 Descriptive statistics for sub-panels of carbon productivity rate cohorts
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Table 15 Descriptive statistics for sub-panels of EUP rate cohorts
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Table 16 Descriptive statistics for sub-panels of good governance cohorts
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Table 17 Descriptive statistics for regional sub-panels
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Murshed, M. Can using energy resources productively and promoting good governance boost carbon productivity? An economic growth-environmental degradation decoupling analysis on 116 global countries. Environ Sci Pollut Res 30, 84537–84562 (2023). https://doi.org/10.1007/s11356-023-28215-3

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