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Natural Resources, Urbanisation, Economic Growth and the Ecological Footprint in South Africa: The Moderating Role of Human Capital

Solomon Prince Nathaniel
Solomon Prince Nathaniel
   | 30. Juni 2021
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Online veröffentlicht: 30. Juni 2021
Seitenbereich: 63 - 76
Eingereicht: 22. Okt. 2020
DOI: https://doi.org/10.2478/quageo-2021-0012
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© 2021 Solomon Prince Nathaniel, published by Sciendo
This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Fig. 1

Plots of the series. EF – ecological footprint; HC – human capital; NR – natural resources; UB – urbanisation.Source: own compilation.
Plots of the series. EF – ecological footprint; HC – human capital; NR – natural resources; UB – urbanisation.Source: own compilation.

Fig. 2

Causality relationship schema.EF – ecological footprint; HC – human capital; NR – natural resources; UB – urbanisation.
Causality relationship schema.EF – ecological footprint; HC – human capital; NR – natural resources; UB – urbanisation.

Fig. 3

Cusum and Cusumsq plots for Model 1.Source: own compilation.
Cusum and Cusumsq plots for Model 1.Source: own compilation.

Fig. 4

Cusum and Cusumsq plots for Model 2.Source: own compilation.
Cusum and Cusumsq plots for Model 2.Source: own compilation.

Fig. 5

Cusum and Cusumsq plots for Model 3.Source: own compilation.
Cusum and Cusumsq plots for Model 3.Source: own compilation.

Bounds test results.

Models Lower bound Upper bound Significance level
Model 1
Fc (lngr, lnnr, lnhc, lnub). F = 8.3425 2.252.863.69 3.593.764.46 10%5%1%
Model 2
Fc (lngr, lnnr, lnhc, lnub, lnin). F = 7.6069 1.812.142.82 2.933.344.21 10%5%1%
Model 3
Fc (lngr, lnnr, lnhc, lnub, lngr2). F = 8.5647 2.292.713.46 3.384.564.76 10%5%1%

BH test results.

Estimated models EG − JOH EG − JOH − BO − BDM Cointegration
lnEF = f(lnGR,lnNR,lnHC,lnUB) 14.651 38.854 Yes
lnEF = f(lnGR,lnNR,lnHC,lnUB,lnIN) 21.361 44.537 Yes
lnEF = f(lnGR,lnNR,lnHC,lnUB,lnGR2) 15.101 36.342 Yes
5% critical value (for Model 1) 10.576 20.143
5% critical value (for Models 2 and 3) 10.419 19.888

Descriptive statistics.

EF GR GR NR HC UB IN
Mean 1.070 8.772 14.51 1.657 0.727 3.991 0.549
Max. 3.930 8.933 15.05 2.678 1.016 4.179 0.801
Min. 1.100 8.615 14.00 0.650 0.584 3.867 0.426
Std. D 0.245 0.093 0.309 0.460 0.131 0.104 0.114
Skewness −0.117 0.265 0.281 0.062 0.862 0.311 0.836
Kurtosis 2.198 2.126 2.128 2.707 2.384 1.669 2.350
Prob. 0.505 0.359 0.348 0.905 0.037 0.120 0.042

ZA unit root results.

Variables ZA unit root test
Level Difference
t-value Break year t-value Break year
EF −4.191 2003 −8.473*** 2008
GR −3.116 2004 −5.155** 1994
NR −3.659 1987 −9.614** 1981
HC −1.726 2001 −5.336*** 2001
UB −4.991 1986 −7.031*** 1985
IN −3.581 2001 −3.989*** 2001
GR2 −3.024 1985 −5.206** 1994

Measurement and source of data.

S/N Indicator name Measurement Source
1 Urbanisation Urban population (% of total population) WDI (2019)
2 Natural resources Total natural resource rent (% of GDP) ✓
3 GDP per capita In constant 2010 USD ✓
4 Interaction term (Human capital × Urbanisation) ✓
5 GDP per capita2 In constant 2010 USD ✓
6 Ecological footprint Global hectares per capita GFN (2019)
7 Human capital Human capital index Penn World Table

Results of the DF-GLS and NG-Perron unit root tests.

Variables DF-GLS NG-Perron
At level Difference At level Difference
t-statistic t-statistic t-statistic MSB 5% t-statistic MSB 5%
EF 0.091 −6.811*** 0.829 0.233 0.148** 0.233
GR −0.846 −4.228*** 0.346 0.233 0.162** 0.233
HC −0.318*** −0.208 0.244** 0.233 0.559 0.233
NR −2.426 −8.647*** 0.234 0.233 0.154*** 0.233
UB −1.702*** −0.797 0.143** 0.233 0.519 0.233
IN −0.528*** −0.474 0.228** 0.233 0.551 0.233
GR2 −0.920 −4.152*** 0.338 0.233 0.164*** 0.233

Robustness check.

Variables FMOLS DOLS CCR
GR (log) 0.056** 0.202** 0.326***
(2.276) (2.581) (4.768)
HC (log) −0.064*** −1.667*** −0.621***
(−5.835) (−9.673) (−8.289)
NR (log) 0.024*** 0.241*** 1.046***
(3.768) (4.573) (9.231)
UB (log) 0.348*** 0.271*** 0.2286***
(3.987) (9.482) (7.271)

Toda-Yamamoto test results.

Null Hypotheses MWALD Stat. Probability Causality
GR→EF 6.015 0.021 Yes
HC→EF 5.892 0.044 Yes
NR→EF 4.042 0.120 No
UB→EF 8.763 0.007 Yes
EF→GR 7.382 0.035 Yes
HC→GR 9.702 0.031 Yes
NR→GR 5.819 0.042 Yes
UB→GR 8.281 0.018 Yes
EF→HC 1.745 0.650 No
GR→HC 0.619 0.723 No
NR→HC 1.891 0.634 No
UB→HC 2.817 0.380 No
EF→NR 0.483 0.066 Yes
GR→NR 0.581 0.001 Yes
HC→NR 1.291 0.035 Yes
UB→NR 9.382 0.491 No
EF→UB 0.173 0.849 No
GR→UB 0.481 0.341 No
HC→UB 1.461 0.635 No
NR→UB 9.903 0.045 Yes

ARDL results.

Long-run results
Variables Model 1 Model 2 Model 3
Constant 4.162** 4.432** 2.465
(2.478) (3.441) (1.047)
GR (log) 0.032** 0.546*** 0.129***
(2.675) (4.567) (3.657)
HC (log) −0.044** −0.146** −0.198***
(−2.987) (−2.286) (−3.892)
NR (log) 0.275*** 0.297*** 0.140***
(2.979) (3.486) (3.287)
UB (log) 0.450*** 0.228*** 0.052***
(7.657) (6.836) (6.679)
IN (log) – −0.231*** –
– (−4.675) –
GR2 (log) – – −0.056***
– – (−7.546)

Studies on NR, human capital, energy consumption, and the EF.

Author(s) Time period Methodology Variables considered Country(ies) Key finding(s)
Nathaniel (2021) 1990–2016 AMG NRR, GDP, HC, EF ASEAN bloc NRR does not hurt environment in Thailand and Laos PDR. Bidirectional causality exists between HC and GDP, and between NRR and GDP.
Ulucak et al. (2020) 1995–2016 DOLS, FMOLS. EF, GDP, NRR, URB, REN BRICS REN, URB, and NRR decrease EF. GDP enhances environmental degradation.
Ahmed et al. (2020a) 1970–2016 ARDL NRR, HC, GDP, URB, EF China URB, NRR, and GDP drive EF in China.
Ahmed et al. (2020b) 1971–2014 CUP-FM, CUP-BC. FDI, GDP, NRE, URB, HC, EF G7 countries. GDP, NRE, and URB increase EF, while HC and FDI reduce it.
Nathaniel (2020) 1971–2014 ARDL GDP, NRE, URB, TRD, EF Indonesia NRE, GDP, and URB increase EF in Indonesia.
Baloch et al. (2019a) 1990–2016 Driscoll-Kraay panel regression FDI, GDP, FDV, NRE, URB, EF 59 Belt and Road countries. FDV, FDI, NRE and URB have negative influence on environment.
Hassan et al. (2019a) 1971–2014 ARDL NRR, GDP, BIO, GDP2 EF Pakistan Long-run causality exists between BIO and EF. NRR has positive impact on EF.
Hassan et al. (2019b) 1971–2014 ARDL HC, GDP, BIO, EF Pakistan BIO increases EF. GDP declines EF by 0.60%. HC exerts negative effect on EF. GDP Granger causes EF.
Dogan et al. (2019) 1971–2013 ARDL Fossil fuel energy, URB, Export, FDV, REN, EF Mexico, Indonesia, Nigeria, and Turkey. URB is chief cause of environmental degradation.
Nathaniel et al. (2020c) 1990–2016 AMG REN, NRE, URB, EF, FDV, GDP MENA FDV, GDP, NRE, and URB increase EF in MENA. One-way causality flows from NRE and URB to EF.
Nathaniel et al. (2020d) 1980–2016 Panel Quantile Regression FDI, EF, NRE, URB, GDP, carbon footprint, CO2 emissions Coastal Mediterranean countries NRE degrades environment. Effects of GDP and URB on environment were mixed for different indicators.
Ulucak et al. (2020) 1992–2016 FMOLS, DOLS. NRR, URB, REN, GDP, GDP2, EF BRICS EKC is validated in individual BRICS countries. NRR, URB, and REN reduce EF.
Destek, Sinha (2020) 1980–2014 MG. FMOLS-MG, DOLS-MG. GDP, GDP2, EF, REN, TRD, NRE 24 OECD countries. EKC hypothesis does not hold. REN reduces EF.
Wang, Dong (2019) 1990–2014 AMG REN, URB, GDP, GDP2, EF, NRE 14 SSA countries. Feedback causality runs among NRE, URB, GDP, and EF. NRE, GDP, and URB exert positive effects on EF.
Sharma et al. (2020) 1990–2015 Panel ARDL REN, URB, POP, FOR, NRE, GDP, EF Asia URB, GDP, NRE, FOR, and POP drive EF. REN restores environmental quality.
Ansari et al. (2020) 1991–2017 PMG EF, URB, Material footprint, GDP, GLO, NRE 37 Asian countries URB and GLO increase EF. GDP and NRE also increase EF.
Sharif et al. (2020) 1965Q1–2017Q4 Quantile ARDL NRE, EF, REN, GDP Turkey Feedback causality exists among listed variables; RE, GDP, NRE, and EF.
Altıntaş, Kassouri (2020) 1990–2014 CCEMG, IFE. CO2 emissions, EF, RE, GDP, NRE Europe RE is environmentally friendly. NRE exerts positive impact on EF.
Baz et al. (2020) 1971–2014 NARDL GDP, NRE Capital, EF Pakistan EF Granger causes NRE. GDP does not cause EF.
Aziz et al. (2020) 1990–2018 QARDL GDP, EF, FOR, RE Pakistan FOR and REN minimise EF. GDP increases EF thereby encouraging environmental degradation.
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