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Investigating the linkages of female employer, education expenditures, renewable energy, and CO2 emissions: application of CS-ARDL

pmid: 35441288
This work investigates the impacts of female employers, renewable energy, and education expenditures on CO2 emission in Pakistan, India, Bangladesh, and Sri Lanka. The annual data of 1990-2020 has been analyzed to present the empirical results. This work uses cross-sectional autoregressive distributed lag (CS-ARDL) approach to know long- and short-run coefficient values. The findings reveal that 1% increase in female employers, renewable energy, and education expenditures will decrease 0.04%, 0.64%, and 0.03% CO2 emissions, respectively, in the long run, which means female employers, renewable energy, and education spending are useful for South Asian countries to lower environmental pollution. This means that increasing female employers, ratio of renewable energy, and education expenditures are very important for South Asian countries to lower environmental pollution. This work recommends that education spending is providing environmental awareness, which is compulsory for cleaner environment.
Carbon Dioxide, Cross-Sectional Studies, Humans, Female, Economic Development, Renewable Energy, Health Expenditures
Carbon Dioxide, Cross-Sectional Studies, Humans, Female, Economic Development, Renewable Energy, Health Expenditures
citations This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).23 popularity This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.Top 10% influence This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).Average impulse This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.Top 10%
