Energy mix with technological innovation to abate carbon emission: fresh evidence from Mexico applying wavelet tools and spectral causality

The global warming issue arises from climate change, which draws scientists' attention toward cleaner energy sources. Among clean sources, renewables and nuclear energy are getting immense attention among policymakers. However, the significance of nuclear energy in reducing CO2 emissions has remained ambiguous, necessitating further research. Therefore, the present study draws impetuous attention to the United Nations Sustainable Development Goals-7 (affordable clean energy) & 13 (climate change mitigation) by looking at the relationship between energy mix (fossil fuels, renewables, and nuclear), economic growth, technological innovation, and CO2 emissions in Mexico from 1980 to 2019 using the autoregressive distributed lag (ARDL) model. In addition, to assess the direction of causality, this study applied wavelet techniques and spectral causality. The findings affirm that renewable and nuclear energy use and technological innovation tend to curb CO2 emissions, whereas fossil fuel consumption and economic expansion trigger CO2 emissions. The study lends support to the environmental Kuznets curve (EKC) phenomenon in Mexico. The FMOLS and DOLS tests show that our long-run estimates are reliable. In different time scales, the wavelet coherence result is also consistent. Finally, the results of the spectral causality approach demonstrate a significant causal association between the variables tested at various frequencies. As a result, in order to achieve SDGs 7 and 13 and support an environmentally friendly ecosystem, Mexico's energy mix must be changed to renewables and nuclear.

Automated summary

The issue of global warming arises from climate change, which has led scientists to focus on cleaner energy sources. Among clean sources, renewables and nuclear energy have gained significant attention from policymakers. However, the importance of nuclear energy in reducing CO2 emissions remains uncertain, requiring further research. Therefore, this study examines the relationship between energy mix, economic growth, technological innovation, and CO2 emissions in Mexico from 1980 to 2019, using the autoregressive distributed lag (ARDL) model, to address the United Nations Sustainable Development Goals-7 (affordable clean energy) & 13 (climate change mitigation). The findings suggest that the use of renewable and nuclear energy, as well as technological innovation, can help reduce CO2 emissions, while fossil fuel consumption and economic expansion contribute to higher emissions. The study supports the environmental Kuznets curve (EKC) phenomenon in Mexico and provides reliable long-term estimates through FMOLS and DOLS tests. Wavelet coherence analysis confirms consistency across different time scales, and the spectral causality approach reveals significant causal associations between the variables at various frequencies. Therefore, Mexico needs to transition its energy mix to renewables and nuclear in order to achieve SDGs 7 and 13 and promote an environmentally friendly ecosystem.