Greening the BRICS: How Green Innovation Mitigates Ecological Footprints in Energy-Hungry Economies
Abstract
:1. Introduction
2. Literature Review
3. Theoretical Framework
Model Specification
4. Data Description and Methodology
4.1. Data Description
4.2. Methodology
4.2.1. Construction of Institutional Quality Index
4.2.2. Cross-Section Dependence Test
4.2.3. Slope Heterogeneity Test
4.2.4. Panel Unit Root Analysis
4.2.5. Panel Co-Integration Test
4.2.6. Driscoll–Kraay Standard Error
4.2.7. Panel Quantile Regression
5. Results and Discussion
6. Conclusions, Policy Implications, and Limitations
Limitations
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
Appendix A
Component | Eigenvalue | Difference | Proportion | Cumulative |
1 | 4.522430 | 3.639180 | 0.7437 | 0.7537 |
2 | 0.883248 | 0.669169 | 0.1472 | 0.9009 |
3 | 0.214078 | 0.017863 | 0.0357 | 0.9366 |
4 | 0.196215 | 0.081412 | 0.0327 | 0.9693 |
6 | 0.069230 | 0.0115 | 1.0000 | |
Demo Indicators | Factor Loadings | Unexplained | FD Indicators | KMO |
CC | 0.4405 | 0.1226 | Overall | 0.8698 |
GE | 0.4392 | 0.1276 | ||
PSAV | 0.4252 | 0.1824 | ||
RQ | 0.4478 | 0.0929 | ||
RL | 0.4419 | 0.1167 | ||
VA | 0.1909 | 0.8353 | ||
Bartlett’s test for sphericity: 1085.771 (0.000) |
Authors | Region and Time | Variables | Methodology | Findings |
---|---|---|---|---|
Yasin, Ahmad, Amin, Sattar and Hashmat [3] | BRICS, 1995–2017 | Agricultural production, renewable energy, energy consumption, financial development, forest rent, domestic investment | Panel Spatial Correlation Consistent Least-Squares Dummy Variables (PSCC-LSDV) estimation and Panel Quantile Regression (PQR) | Financial development, renewable energy, and agricultural production help to mitigate the carbon emissions and ecological footprint |
Saqib, Ozturk, Usman, Sharif and Razzaq [81] | United States,1990Q1–2018Q4 | Climate technologies, trade, and consumption-based carbon emissions (CCE) | Novel QARDL and Granger Causality-in-Quantiles | The use of recycling and climate-related technology effectively reduces the impact of CCE in the long term. |
Islam, Shahbaz, Sultana, Wang, Sohag and Abbas [36] | Bangladesh, 1980–2020 | Net savings, Natural resource depletion, Technological innovation (TI), democracy, CO2 emissions | NARDL | TI negative shock can raise CO2 emissions |
Yang, Jahanger and Ali [37] | BRICS countries, 1990–2016 | Remittance Inflows, Financial Development, Technological Innovations, Ecological Footprint (EF) | Robust econometric (second-generation) techniques | BRICS countries’ EFs have decreased due to technological advancement. |
Kihombo, Ahmed, Chen, Adebayo and Kirikkaleli [30] | MENA (Middle East and North Africa) and West Asian nations, 1990–2017 | Technological innovation, Financial Development, Economic growth, Ecological Footprint | FMOLS technique | Modern technologies have the potential to enhance environmental quality |
Ahmad, Jiang, Majeed, Umar, Khan and Muhammad [38] | Emerging economies, 1984–2016 | Natural Resources, Technological Innovations, Economic Growth, Ecological Footprint (EF) | CS-ARDL method | Technical progress is detrimental to EF |
Koengkan and Fuinhas [39] | Latin American and Caribbean (LAC), 1990 to 2014 | Carbon dioxide emissions (CO2), Renewable Energy Transition (RET), Gross domestic production (GDP), Trade openness, Urbanization index | Panel Autoregressive Distributed lags (PARDL) model | RET in the short and long-run has a negative impact on emissions of CO2 |
Usman, Alola and Sarkodie [41] | United States, 1985Q1–2014Q4 | Renewable Energy Consumption, Economic Growth, Biocapacity, Trade Policy, Ecological Footprint | ARDL model | Increased use of renewable energy sources hurts the ecological footprint, which may be linked to a decrease in environmental deterioration. |
Shahzad, Fareed, Shahzad and Shahzad [43] | United States (US), 1965Q1–2017Q4 | Economic Complexity (EC), Energy Consumption (FFEC), Ecological Footprint (EF) | QARDL model | EC and FFEC substantially augment the EF in the US. |
Usman, Akadiri and Adeshola [44] | USA, 1985Q1 to 2014Q4 | Renewable Energy (REN), Globalization (GLOB), Ecological Footprint (EF), Financial Development (FD), Real Output (REO) | ARDL estimation approach | REN and REO exhibit a detrimental influence on EF, while FD and GLOB exert a positive influence on EF. |
Umar, Ji, Kirikkaleli and Alola [45] | USA, 1981Q1 and 2019Q4 | Biomass Energy Consumption (BEC), Fossil Fuel (FF), Energy Consumption (EC), Economic Growth (GDP), CO2 emissions | FMOLS, DOLS, and CCR | The reduction in transportation-related emissions may be attributed to both BEC and real GDP, as opposed to FF. |
Yang, Ali, Hashmi and Jahanger [48] | Forty Two Developing countries, 1984–2016 | Income Inequality, Institutional Quality (IQ), carbon dioxide | Driscoll–Kraay regression, FMOLS, Pooled Mean Group (PMG) | High pollution levels and poor economic performance are associated with low IQ in developing countries. |
Jahanger, Usman and Ahmad [50] | Seventy Three Developing nations, 1990–2018 | Natural Resources, Institutional Quality, carbon emissions, Globalization | Two-Stage least square, Panel threshold techniques | Institutions with greater degrees of excellence had reduced CO2 emissions. |
Warsame, Sheik-Ali, Mohamed and Sarkodie [51] | Somalia, 1990–2017 | Arable Land (Environmental Degradation), Renewable Energy, Population, Institutional Quality, Economic Growth, Gross Capital Formation | ARDL | Sustainable ecosystems are the result of high-quality institutions, and they also found a link between environmental degradation and institutional quality. |
Hussain and Mahmood [52] | Pakistan, 1984–2019 | Institutional Quality (IQ), Ecological Footprint (EF), Energy Consumption (EC), GDP | Non-linear ARDL | Positive shocks harm EF while negative shocks have a good effect. EF increases with GDP and EC but decreases significantly with better IQ |
Rehman, Gill and Ali [54] | ASEAN, 1990–2018 | ICT, Institutional Quality (IQ), GDP, Energy Consumption (EC), Ecological Footprint (EF) | Pooled Mean Group (PMG) estimator | A clean atmosphere and lowered EF are the results of high-quality institutions |
Emmanuel, et al. [55] | 101 developing nations, 1995–2017 | Foreign Capital, Domestic Capital Formation, Institutional Quality (IQ), Democracy, Ecological Footprint (EF) | Dynamic Common Correlation Effect (DCCE) technique | Raising the IQ of different estimators raises the atmosphere’s level. |
Sun, Tian, Mehmood, Zhang and Tariq [56] | N-11 nations, 1990–2018 | Income Inequality (IINEQ), Natural Resources (NAT), Human Development (HD), and quality of institutions (IQ), Ecological Footprint (EF) | CS-ARDL method | Improvements in NAT, HD, and IQ have a positive effect on the environment. IINEQ exacerbates social disparities, thereby exerting a detrimental impact on the ecosystem. |
Alola, Bekun and Sarkodie [58] | 16-EU countries,1997–2014 | Ecological Footprint (EF), Real GDP, Trade Openness, Fertility Rate, Renewable (REN), Non-renewable Energy Consumption (NON-REN) | PMG-ARDL model | The role of NON-REN in depleting environmental quality while REN was found to improve environmental sustainability. |
Le and Ozturk [60] | 47 Emerging Market and Developing Economies (EMDEs), 1990–2014 | Globalization (GLOB), Financial Development (FD), Government Expenditures (GOVEXP), Institutional Quality (IQ), CO2 emissions, Energy Consumption (EC), GDP per capita | CCEMG, AMG, DCCE | GLOB, FD, and EC increase CO2 emissions |
Hassan, Xia, Khan and Shah [62] | Pakistan, 1970–2014 | GDP, Natural Resources (NR), Ecological Footprint (EF) | ARDL model | Pakistan’s NR and GDP boost its EF |
Majeed, Wang, Zhang and Kirikkaleli [63] | Gulf Cooperation Council (GCC) nations, 1990 to 2018 | Natural Resources (NR), Economic Globalization (EGLOB), Energy Consumption (EC), CO2, Urbanization (URBAN), Economic Growth (EG) | CS-ARDL approach | Renewable Energy Sources (RES), NR, and EGLOB may all contribute to better environmental conditions. |
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Variables | Symbol | Descriptions | Expected Signs |
---|---|---|---|
Ecological Footprint | EF | EF is measured in global hectors | |
Institutional Quality | Ins | With the use of Principal Component Analysis (PCA), the following variables are included in the Composite Index: CC, RL, GE, VA, SAV, and RQ. | +/− |
Urbanization | UR | Urban Population | +/− |
Total Natural Resource Rent | NRR | Percentage of GDP | − |
Green technological innovation | GTI | Patents (residents and non-residents) | +/− |
Energy Consumption | EC | The unit of energy consumption is the kilogram of oil equivalent per capita. | − |
Renewable Energy Consumption | REC | Percentage of total final energy consumption | + |
ln EF | Ins | ln UR | ln NRR | ln GTI | ln EC | ln REC | |
---|---|---|---|---|---|---|---|
Pesaran | 5.1613 a | −2.693 b | 8.4461 a | 10.990 a | 13.580 a | 10.804 a | 6.4822 b |
Breusch–Pagan LM | 81.607 a | 83.421 a | 173.35 a | 134.24 a | 188.67 a | 161.47 a | 101.41 a |
Pesaran scaled LM | 16.011 a | 16.471 a | 36.527 a | 27.782 a | 39.953 a | 33.870 a | 20.440 a |
Bias-corrected scaled LM | 15.919 a | 16.325 a | 36.434 a | 27.689 a | 39.860 a | 33.777 a | 20.347 a |
CIPS | ln EF | Ins | ln UR | ln NRR | ln GTI | ln EC | ln REC |
---|---|---|---|---|---|---|---|
@ Level | −1.781 | −1.423 | −2.378 b | −1.726 | −2.644 b | −1.217 | −1.853 |
Δ (FD) | −4.924 a | −3.957 a | −2.228 a | −5.348 a | −4.618 a | −3.247 a | −3.125 b |
Test | Delta | p-Value |
---|---|---|
Δ | 9.352 | 0.0000 |
Δ adj. | 11.065 | 0.0000 |
Modified Dickey–Fuller t | −5.1768 a |
Dickey–Fuller t | −3.0435 a |
Augmented Dickey–Fuller t | −1.6671 b |
Unadjusted modified Dickey–Fuller t | −6.0667 a |
Unadjusted Dickey–Fuller t | −3.2298 a |
Driscoll–Kraay | Panel QR | |||||
---|---|---|---|---|---|---|
Model 1 | Model 2 | Model 3 | Model 1 | Model 2 | Model 3 | |
−0.3232 a | −0.3336 a | −0.3721 c | −0.0549 c | −0.0523 b | −0.0453 c | |
0.6585 a | 0.5936 a | 0.6547 b | 0.1383 b | 0.0320 a | 0.1511 a | |
0.1156 a | 0.0654 c | 0.1155 c | 0.0236 a | 0.0297 a | 0.0240 a | |
−0.3537 a | −0.4865 a | −0.3515 a | −0.1020 a | −0.0101 a | −0.1013 a | |
1.2735 a | 2.3480 a | 1.2717 a | 0.0785 a | 0.1191 b | 0.0859 a | |
−0.2491 a | −0.3136 a | −0.2459 a | −0.3535 a | −0.3593 a | −0.3573 b | |
−0.1108 b | −0.0141 a | |||||
−0.0048 c | −0.0996 a | |||||
C | −16.437 a | −23.1021 a | −16.422 c | |||
Diagnostic Tests | ||||||
Obs. | 140 | 140 | 140 | 140 | 140 | 140 |
Cross-Sections | 5 | 5 | 5 | 5 | 5 | 5 |
Hausman | 235.35 a | 267.29 a | 256.18 a |
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Zhang, J.; Yasin, I. Greening the BRICS: How Green Innovation Mitigates Ecological Footprints in Energy-Hungry Economies. Sustainability 2024, 16, 3980. https://doi.org/10.3390/su16103980
Zhang J, Yasin I. Greening the BRICS: How Green Innovation Mitigates Ecological Footprints in Energy-Hungry Economies. Sustainability. 2024; 16(10):3980. https://doi.org/10.3390/su16103980
Chicago/Turabian StyleZhang, Junmei, and Iftikhar Yasin. 2024. "Greening the BRICS: How Green Innovation Mitigates Ecological Footprints in Energy-Hungry Economies" Sustainability 16, no. 10: 3980. https://doi.org/10.3390/su16103980