1. | Naeem et al. (2023) | 2000–2019 | 19 African Countries | Infrastructure, industrialization, innovation, environmental efficiency, growth, energy demand | Data Envelopment Analysis (DEA) and Driscoll &Kraay methods | Negative and significant correlation exist between infrastructure, industrialization, innovation and environmental efficiency. Growth and energy demand have both positive and negative effects on these relationships |
2. | Pata et al. (2023) | 1965–2018 | United States | Biomass energy, load capacity factor, carbon emissions, ecological footprints | Fourier ARDL | Biomass energy improves environmental quality,and per capita income reduces environmental quality(i.e., load capacity factor) |
3. | Pata and Ertugrul (2023) | 1955–2018 | India | Load capacity factor, income, urbanization, human capital | Augmented ARDL method | Load capacity curve (LCC) hypothesis is valid for India. Income increases load capacity factor in India and favors environmental friendly |
4. | Pata (2021a) | 1982–2016 | United States of America andJapaan | Health expenditure, load capacity factor, renewable energy, | ARDL model | Renewable energy has positive impact on environmental quality in USA,and insignificant factor on environmental quality in Japan |
5. | Pata and Kartal(2022) | 1977–2018 | South Korea | Nuclear energy consumption, income, renewable energy, ecological footprint, load capacity factor, | Autoregressive distributed lag (ARDL) | Renewable energy has no long term effects on environment. Income improves environmental sustainability. Nuclear energy increases load capacity factor |
6. | Pata and Samour(2022) | 1977–2017 | France | Nuclear energy, renewable energy, ecological footprints, CO2 emissions, load capacity factor | Cointegration and causality test | No inverted U-shaped relationship between co2 emissions and income. Nuclear energy increases load capacity factor and improves environmental sustainability.Renewable energy consumption has no impact on environmental sustainability. |
7. | Gyamfi et al. (2022) | 1990–2018 | Sub-Saharan Africa | Trade ,income, clean energy, Consumption based CO2 emissions. | Second-generation techniques including CS-ARDL | Trade flow and income have a positive impact on CO2 emissions. Clean energy reduces consumption based C02 emissions. |
8. | Maji et al. (2022) | 2008–2020 | 45 sub-Saharan Africa countries (SSA) | CO2 per capita, Renewable energy, GDP per capita, Trade openness, Population growth, Education | System Generalized Method of Moments (GMM) | Renewable energy consumption reduces carbon dioxide emission. Clean energy improves environmental sustainability in SSA |
9. | Hishan et al. (2019) | 1995–2016 | 35 SSA | Access to clean fuel, technologies for cooking, Access to electricity, Access to finance, access to food, GDP per capita, trade openness, CO2 emissions | Generalized Method of Moment (GMM) | Access to clean fuels and technologies for cooking, increase carbon emissions. Similarly, trade openness, income increases carbon dioxide emissions. |
10. | Ibrahim & Waziri (2020) | 2008–2016 | 45 SSA | ICT, renewable energy, economic growth, trade openness, population growth, CO2 emissions | The system generalized method of moments (GMM) | Renewable energy use reduce CO2 emissions and improves environmental sustainability. Economic growth also mitigate CO2 emissions while trade openness have a neutral impact |
11. | Mesagan (2021) | 2000–2019 | 31 SSA | Production activities (economic growth), trade, carbon dioxide emissions | System of the generalized methods of moments (GMM) | Production activities (economic growth) lead to environmental degradation, trade openness has a positive impact on environmental degradation. |
12. | Riti et al. (2022) | 1990–2018 | SSA countries | Real gross fixed capital, renewable energy, economic growth, emissions of green house gases (GHG) | Panel auto regression distributed lag (PARDL) | Real GDP and real gross fixed capital formation exert positive and significant impacts on GHG emissions while renewable energy reduces GHG emissions. |
13. | Voumikand Ridwan (2023) | 1972–2021 | Argentina | FDI, population growth, industrialization, Education, carbon dioxide emissions | ARDL approach | Population growth and industrialization harm the environment in Argentina in the long run. |
14. | Voumikand Sultana (2022) | 1972–2021 | Industrialized economies—Brazil, Russia, India, China, and South Africa (BRICS) | Industrialization, Urbanization, Renewable energy, environmental degradation | Cross-sectionally augmented autoregressive distributive lag (CS-ARDL) | Industrialization, urbanization, income, and electrification stimulate environmental degradation. Renewable energyconsumption lessens environmental degradation in the BRICS region. |
15. | Wang et al. (2022) | 1990–2020 | G-7 countries | Production, trade activities, ecological quality, industrialization, Ttechnology | CS-ARDL | Production and trade activities consume more resources, which have their negative impact on ecological quality. Technology has a beneficial effect on ecological quality. |
16. | Raihan et al., (2022) | 1990–2019 | Bangladesh | Economic growth, urbanization, industrialization, CO2 emissions, renewable energy use, technological innovation, forest area | Autoregressive distributed lag (ARDL) followed by the Dynamic Ordinary Least Squares (DOLS) | Economic growth, urbanization and industrialization increase CO2 emissions in Bangladesh. Use of renewable energy, technological innovation, and forest area help to achieve the environmental sustainability by reducing CO2 emissions. |
17. | Kahouli et al., (2022) | 1971–2019 | Saudi Arabia | Energy consumption, environmental degradation, economic growth, trade, industrialization | Autoregressive Distributed Lag and the Vector Error Correction Model | Energy consumption contributes significantly to environmental degradation. Trade and urbanization cause economic growth, which in turn harms the environment |
18. | Usman andBalsalobre-Lorente (2022) | 1990–2019 | Newly industrialized countries | Industrialization, Total reserves, expansion of financial, ecological footprint. | Augmented mean group (AMG) panel algorithm | Industrialization, aggregate reserves, and financial development drive pollution. The abundance of natural resources and renewable energies reduces the environmental impact in the long run |
19. | Opoku and Aluko (2021) | 2000–2016 | 37 African countries | Industrialization, ecological footprint, income. | Quantile regression model for panel data | Industrialization increase environmental degradation in the 10–30th quantiles, and at 40–90th quantiles, industrialization reduces environmental degradation |
20. | Mahmood et al. (2020) | 1968 − 2014 | Saudi Arabia | Industrialization, urbanization, CO2 emissions | ARDL and non-linear | Industrialization burdens the environment. Increasing industrialization has a greater environmental impact |
21. | Ahmed et al. (2022) | 1995–2020 | 55 countries of the Asia-Pacific region | FDI, industrialization, CO2 emissions | Autoregressive distributed lag (ARDL) model | Industrialization has a positive and significant impact on the environment. |
22. | Opoku andBoachie (2020) | 1980 − 2014 | 36 selected African countries | Environmental degradation, Foreign direct investment, Industrialization | Pooled Mean Group estimation technique | The effects of industrialization on the environment are generally insignificant |
23. | Tenaw and Beyene (2021) | 1990–2015 | 20 sub-Saharan African (SSA) | Energy consumption, Trade openness, Environment | The Common Correlated Effects version of Pooled Mean Group Estimator (CCE-PMG), panel Autoregressive Distributed Lag (ARDL) model | Energy consumption and trade openness have a long-term negative impact on the environment |
24. | Ang’u et al. (2023) | 2022 | Kenya | Household energy cooking access, socio-economic characteristics (income and education) | Probit model | Income, access to credit and higher education are the key factors influencing household decisions to use clean fuels and technologies |
25. | Agboola, Bekun&Joshua (2021) | 1971–2016 | Saudi Arabia | Energy consumption, environmental depletion, environmental degradation, economic growth, CO2 emissions | Modified Wald test of Toda-Yamamoto methodology | Increased energy consumption pollutes the environment. Increased economic growth leads to environmental degradation |
26. | Yue et al (2022) | 2001–2020 | 5 Island economies | Access to clean fuels technologies, carbon damage, renewable energy, Carbon pricing, nuclear energy | Quantile regression | Clean fuels technologies and renewable energy reduces carbon damage. Carbon pricing stimualates the use of clean fuels and renewable energy. Nuclear energy reduces carbon damage |
27. | Ahmed et al. (2022) | 1984–2017 | Pakistan | Ecological footprints, democracy, clean energy, economic growth | ARDL approach | Clean energy reduces ecological footprint in Pakistan. Democracy improves environmental sustainability |
28. | Shaheenet al.(2022) | 2000–2020 | China | Renewable energy, cleaner energy technology, greenhouse gas emissions | Granger causality and Least Square | Inadequate accesss to clean cooking technology has a negative impact on environmental sustainability. Clean energy technologies improves environmental sustainability. |
29. | Byaro et al. (2022) | 1990–2020 | Tanzania | CO2 emissions, trade, finance, urbanization, economic growth, industrilization | ARDL | Income, trade, urbanization, industrialization all contribute to carbon dioxide emissions in Tanzania |
30. | This study | 2002–2018 | 29- Sub-Saharan African countries | Load capacity factor (LCF), income, industrialization, trade, clean fuels for cooking, renewable energy consumption | Generalized Quantile regression, Bayesian panel regression. | Clean fuels for cooking and the use of renewable energy improve environmental sustainability in SSA. The study confirms the Load Capacity Curve (LLC) hypothesis in SSA. Income and industrialization have heterogeneous effects on environmental sustainability. Trade also contributes to environmental sustainability |