The impact of energy poverty on the aggregate and disaggregate material footprints in BRICS

Abstract

This study aims to examine the impact of energy poverty on the aggregate and disaggregate material footprint components such as biomass, fossil fuel, metal ores and non-metallic minerals while considering the economic growth and tourism development during 2000–2014 for the BRICS countries (Brazil, Russia, India, China, and South Africa). By applying econometric tools, the study confirms a positive and significant impact of energy poverty on aggregate material footprint and its components. The same finding has been reached for tourism development. Moreover, this study finds a U-shaped Environmental Kuznets Curve (EKC) for all indicators used for material footprints. Based on the findings, this study proposes a set of policies for energy poverty alleviation to attain a sustainable environment and inclusive economic growth in the BRICS region.

Keywords:

• EKC
• material footprint
• energy poverty
• BRICS

JEL Codes:

• Q56
• Q59
• Q49

Disclosure statement

No potential conflict of interest was reported by the authors.

Supplemental data

Supplemental data for this article can be accessed here.

Notes

1 Please see Section 3.2 for the procedure followed in the construction of the energy poverty index in this study.

2 The index of energy poverty comprises the negative values. Hence, it is impossible to conduct a logarithmic transformation. The elasticity of economic growth (Y) can be obtained as (α₁+2α₂Y)*X/Y.

3 Our study does not incorporate a trade variable in the material footprint function since the material footprint is itself a trade adjusted indicator (Khan et al. 2023; Arshad Ansari, Haider, and Khan 2020; Razzaq et al. 2022). We are thankful to one of the reviewers in helping us to clarify this issue.

4 The study by Nguyen and Nasir (2021) nicely explains the three approaches of energy poverty measurement: physical, economic, and technological thresholds. Further, they discuss the merits and drawbacks of each approach. From the evaluation, the study found the superiority of the technological threshold approach over other approaches based on the indicators in energy poverty measurement.

5 Please see the link for further reading about the FMOLS and DOLS estimation even amid cross sectional dependence in the series. https://www.sciencedirect.com/science/article/abs/pii/S1364032117304410?casa_token=9-QT_8yWZBoAAAAA:gdB-nc5NjrzcuxIHyB4YBQYQWJ_x8_Ll5OWAVTPcEo1eJIhYtSRetHn87-xaJmoT8Q4NAoG9o0_x

6 Similar results are also found from using the Cross-Sectional Augmented Dickey-Fuller (CADF) unit root proposed by Pesaran (2007), as reported in Table A1 of the Appendix (online supplemental material). Moreover, slope coefficients across all the estimated models are heterogenous (please see Table A2 of the Appednix [online supplemental material]).

7 Similar cointegration results are also found by using the Westerlund Error Correction Model (ECM) panel cointegration test method proposed by Westerlund (2007), as reported in Table A3 of the Appendix (online supplemental material).

8 Please see the link for more insights on the extraction of resources and its implications. (https://www.wider.unu.edu/publication/it-or-not-poor-countries-are-increasingly-dependent-mining-and-oil-gas)

9 Please visit the following website to understand the different challenges for climate change mitigation in BRICS (https://www.orfonline.org/research/a-stocktaking-of-brics-performance-in-climate-action/)

10 Please see a detailed discussion related to tourism in BRICS (https://brics2021.gov.in/tourism)