Long-Run Effects of Technological Innovation, Green Innovation, and Green Energy on CO2 Emissions in Pakistan: An Augmented ARDL Approach

• Technological and green innovation, and green energy impact is examined. • Augmented ARDL approach is applied on data between 1990 and 2020 in Pakistan. • Technological and green innovation have a declining impact on CO 2 emissions. • Green energy utilization does not provide a decrease on CO 2 emissions. • ICT and GDP cause an increase in CO 2 emissions. Pakistan's vulnerability to climate change has intensified, prompting urgent efforts to identify the drivers of carbon emissions. This study explores the long-run impact of technological innovation (TNI), green innovation (GIN), and green energy utilization (GEU) on CO₂ emissions while accounting for the roles of ICT and GDP. Data from 1990 to 2020 is used, and the analysis employs the Augmented Autoregressive Distributed Lag (AARDL) model to capture long-term relationships. To ensure robustness, Fully Modified Ordinary Least Squares (FMOLS) are applied, and the Granger causality test is used to assess directional relationships. The results reveal that TNI and GIN reduce CO₂ emissions, confirming their effectiveness in mitigating environmental degradation. GEU, however, exhibits mixed effects across models, suggesting variability in its environmental benefits due to infrastructural or policy limitations. Granger causality analysis indicates that TNI, ICT, and GDP all causally influence CO₂ emissions. The FMOLS results support the consistency and reliability of the primary model, which explains approximately 97 % of the variation in emissions. These findings demonstrate the validity and completeness of the study's approach and contribute to a deeper knowledge of sustainable development pathways for Pakistan. Based on the evidence, the study recommends strengthening technological capabilities, supporting green innovation ecosystems, and implementing effective policies to enhance the impact of green energy. Such strategies are vital for advancing Pakistan’s climate goals and transitioning toward a low-carbon economy.

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Publication Details

Journal
Sustainable Futures
Published
2025-09-23
DOI
https://doi.org/10.1016/j.sftr.2025.101300
Citations
2
Primary Topic
Energy, Environment, Economic Growth
Type
article
Field-Weighted Citation Impact
6.26
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article

Long-Run Effects of Technological Innovation, Green Innovation, and Green Energy on CO2 Emissions in Pakistan: An Augmented ARDL Approach

Mustafa Tevfik Kartal, Sajid Ali, Sami Ullah
2 citations
Sustainable Futures
Energy, Environment, Economic Growth
6.26
article

Long-Run Effects of Technological Innovation, Green Innovation, and Green Energy on CO2 Emissions in Pakistan: An Augmented ARDL Approach

Mustafa Tevfik Kartal, Sajid Ali, Sami Ullah
article en
2 citations

Abstract

• Technological and green innovation, and green energy impact is examined. • Augmented ARDL approach is applied on data between 1990 and 2020 in Pakistan. • Technological and green innovation have a declining impact on CO 2 emissions. • Green energy utilization does not provide a decrease on CO 2 emissions. • ICT and GDP cause an increase in CO 2 emissions. Pakistan's vulnerability to climate change has intensified, prompting urgent efforts to identify the drivers of carbon emissions. This study explores the long-run impact of technological innovation (TNI), green innovation (GIN), and green energy utilization (GEU) on CO₂ emissions while accounting for the roles of ICT and GDP. Data from 1990 to 2020 is used, and the analysis employs the Augmented Autoregressive Distributed Lag (AARDL) model to capture long-term relationships. To ensure robustness, Fully Modified Ordinary Least Squares (FMOLS) are applied, and the Granger causality test is used to assess directional relationships. The results reveal that TNI and GIN reduce CO₂ emissions, confirming their effectiveness in mitigating environmental degradation. GEU, however, exhibits mixed effects across models, suggesting variability in its environmental benefits due to infrastructural or policy limitations. Granger causality analysis indicates that TNI, ICT, and GDP all causally influence CO₂ emissions. The FMOLS results support the consistency and reliability of the primary model, which explains approximately 97 % of the variation in emissions. These findings demonstrate the validity and completeness of the study's approach and contribute to a deeper knowledge of sustainable development pathways for Pakistan. Based on the evidence, the study recommends strengthening technological capabilities, supporting green innovation ecosystems, and implementing effective policies to enhance the impact of green energy. Such strategies are vital for advancing Pakistan’s climate goals and transitioning toward a low-carbon economy.

Sustainable FuturesVol. 10
Khazar University (AZ), European University of Lefke (TR), Huaqiao University (CN), Azerbaijan State University of Economics (AZ), Korea University of International Studies (KR), Northeast Petroleum University (CN)
Openalex Percentile: Top 4%
Energy, Environment, Economic Growth
6.26
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