Abstract
The world is confronted with a slew of environmental issues, one of which is attenuating the detrimental impacts of carbon dioxide (CO2) emission-induced climate change. The ever-increasing use of energy is eroding natural resources to the point that our economic future may be jeopardized. The Tunisian economic growth indicates the excellent performance in the industrial sector as the minimum required input for these developments which necessitate additional energy consumption, resulting in increased CO2 emissions and environmental degradation. This study explores the role of energy efficiency, urbanization, economic growth, and natural gas energy usage in the industrial sector on the CO2 emissions of Tunisia. The research mainly employs the vector autoregressive model (VAR) to examine the factors driving the evolution of CO2 emissions through the industrial sector from 2000 to 2018. The findings assess that natural gas as an energy source and efficiency is crucial for reducing CO2 emissions. The study has shown the existence of the environmental Kuznets curve (EKC), which demonstrates that economic development in Tunisia has an inverted U-shape connection with CO2 emissions. The findings show that energy consumption and GDP have a considerable impact on CO2 emissions due to large-scale population changes and industrial structure alteration. In contrast, energy efficiency is a key factor in lowering CO2 emissions. Based on the study’s results, the article will enable economic decision-makers and relevant authorities to develop an appropriate energy strategy for the industrial sector to safeguard environmental deterioration in the long term by lowering carbon emissions.
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Availability of Data
The datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request.
Abbreviations
- VAR:
-
Vector autoregressive model
- EKC:
-
Environmental Kuznets curve
- IEA:
-
International Energy Agency
- IRENA:
-
International Renewable Energy Agency
- REN21:
-
Renewable Energy Policy Network for the 21st Century
- CAIT:
-
The climate analysis indicators tool
- ND-GAIN Index:
-
Notre Dame Global Adaptation Index
- IAEA:
-
International Atomic Energy Agency
- GIZ:
-
Deutsche Gesellschaft für Internationale Zusammenarbeit
- ANME:
-
National Agency for Energy Management
- BMUB:
-
Building and nuclear safety
- LMDI:
-
Log-mean divisia index
- STIRPAT:
-
Stochastic impacts by regression on population, affluence, and technology
- PVAR:
-
Panel vector autoregressive
- AIC:
-
Akaike information criterion
- SC:
-
Schwartz criteria
- INS:
-
National Institute of Statistics
- ADF:
-
Augmented Dickey-Fuller
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Muhammad Ramzan: introduction, methodology, data analysis interpretations, supervision, conclusion and policy implications. Hafiz Arslan Iqbal: abstract, introduction, editing and revision. Besma: main theme, data collection. Buhari: supervision.
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Talbi, B., Ramzan, M., Iqbal, H.A. et al. Appraisal of CO2 emission in Tunisia’s industrial sector: a dynamic vector autoregression method. Environ Sci Pollut Res 29, 38464–38477 (2022). https://doi.org/10.1007/s11356-022-18805-y
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DOI: https://doi.org/10.1007/s11356-022-18805-y