The Effect of Energy Consumption, Income, and Population Growth on CO2 Emissions: Evidence from NARDL and Machine Learning Models
Abstract
:1. Introduction
2. Methodology
2.1. Dataset
2.2. NARDL Model
3. Results and Discussion
3.1. Summary of Unit Root Tests (NARDL Model)
3.2. Co-Integration Analysis
3.3. NARDL Short-Term and Long-Term Co-Integration Analysis in China
3.4. NARDL Short-Term and Long-Term Co-Integration Analysis in India
3.5. NARDL Short-Term and Long-Term Co-Integration Analysis in USA
3.6. Results of Machine Learning Models
3.7. Forecasting CO2e in China (2017 to 2025)
3.8. Forecasting CO2e in India (2017 to 2025)
3.9. Forecasting CO2e in USA (2017 to 2025)
4. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
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Country | Variables | PP (Level) | PP (First Deference) | ADF (Level) | ADF (First Deference) |
---|---|---|---|---|---|
China | CO2e | −0.797 | −3.865 ** | −0.191 | −3.964 ** |
EC | −0.689 | −3.208 ** | 0.326 | −2.983 ** | |
ECPC | −0.861 | −3.326 ** | 0.232 | −3.519 ** | |
EI | −1.769 | −3.478 ** | −1.322 | −3.218 ** | |
IN | 0.259 | −3.438 ** | 1.229 | −3.510 ** | |
PG | −0.861 | −3.518 ** | −0.470 | −3.309 ** | |
India | CO2e | 0.689 | −6.831 * | −0.686 | −6.785 * |
EC | −1.815 | −6.931 * | −1.805 | −6.752 * | |
ECPC | −0.010 | −6.496 * | −0.013 | −6.442 * | |
EI | −1.125 | −5.142 * | 0.328 | −5.318 * | |
IN | 1.249 | −5.358 * | 1.128 | −5.398 * | |
PG | −1.435 | −2.963 ** | 1.365 | −3.451 ** | |
USA | CO2e | 0.187 | −5.017 * | 0.166 | −5.007 * |
EC | 1.594 | −5.440 * | 1.620 | −5.331 * | |
ECPC | −0.962 | −5.256 * | −0.954 | −5.209 * | |
EI | −0.956 | −4.271 * | 6.769 * | −6.241 * | |
IN | −3.655 ** | −4.226 * | −6.711 * | −4.239 * | |
PG | −0.646 | −3.066 * | −1.653 | −4.187 * |
Country | CO2e | EC | ECPC | EI | IN | PG | |
---|---|---|---|---|---|---|---|
China | 2 | 0.183 *** | 0.182 *** | 0.185 *** | 0.132 *** | 0.184 *** | 0.175 *** |
3 | 0.298 *** | 0.295 *** | 0.300 *** | 0.193 *** | 0.298 *** | 0.296 *** | |
4 | 0.367 *** | 0.363 *** | 0.370 *** | 0.209 *** | 0.369 *** | 0.385 *** | |
5 | 0.407 *** | 0.402 *** | 0.411 *** | 0.226 *** | 0.410 *** | 0.448 *** | |
6 | 0.424 *** | 0.414 *** | 0.428 *** | 0.220 *** | 0.426 *** | 0.489 *** | |
India | CO2e | EC | ECPC | EI | IN | PG | |
2 | 0.198 *** | 0.202 *** | 0.195 *** | 0.155 *** | 0.168 *** | 0.175 *** | |
3 | 0.330 *** | 0.338 *** | 0.328 *** | 0.254 *** | 0.269 *** | 0.281 *** | |
4 | 0.419 *** | 0.433 *** | 0.417 *** | 0.304 *** | 0.323 *** | 0.341 *** | |
5 | 0.482 *** | 0.501 *** | 0.483 *** | 0.323 *** | 0.344 *** | 0.373 *** | |
6 | 0.524 *** | 0.551 *** | 0.530 *** | 0.317 *** | 0.334 *** | 0.376 *** | |
USA | CO2e | EC | ECPC | EI | IN | PG | |
2 | 0.149 *** | 0.195 *** | 0.106 *** | 0.192 *** | 0.206 *** | 0.114 *** | |
3 | 0.250 *** | 0.332 *** | 0.150 *** | 0.320 *** | 0.348 *** | 0.177 *** | |
4 | 0.337 *** | 0.428 *** | 0.177 *** | 0.407 *** | 0.448 *** | 0.199 *** | |
5 | 0.388 *** | 0.494 *** | 0.177 *** | 0.465 *** | 0.520 *** | 0.184 *** | |
6 | 0.404 *** | 0.536 *** | 0.147 *** | 0.502 *** | 0.574 *** | 0.176 *** |
Country | F-Stat | Level | 1st Difference | Decision |
---|---|---|---|---|
China | 4.197 *** | 1.99 | 2.94 | Co-integration |
India | 12.19 *** | 1.98 | 2.96 | Co-integration |
USA | 3.207 *** | 1.91 | 2.90 | Co-integration |
Variables | Coefficient | Std. Error | Prob. |
---|---|---|---|
Long-term co-integration results | |||
ECPC+ | 1.655 | 0.062 | 0.000 |
ECPC− | 7.130 | 1.302 | 0.115 |
EC+ | −0.011 | 0.096 | 0.368 |
EC− | −2.597 | 0.241 | 0.059 |
EI+ | −0.631 | 0.029 | 0.029 |
EI− | −0.856 | 0.046 | 0.034 |
IN+ | −0.010 | 0.007 | 0.369 |
IN− | −0.342 | 0.021 | 0.040 |
PG+ | 0.023 | 0.011 | 0.287 |
PG− | 0.357 | 0.018 | 0.033 |
Short-term co-integration results | |||
ECPC+ | 1.927 | 0.060 | 0.020 |
ECPC− | 10.911 | 0.932 | 0.054 |
EC+ | −0.161 | 0.052 | 0.200 |
EC− | −1.583 | 0.072 | 0.029 |
EI+ | −0.509 | 0.029 | 0.036 |
EI− | −1.521 | 0.056 | 0.023 |
IN+ | 0.220 | 0.013 | 0.039 |
IN− | −0.379 | 0.025 | 0.043 |
PG+ | −0.020 | 0.019 | 0.485 |
PG− | 0.297 | 0.014 | 0.031 |
Variables | Coefficient | Std. Error | Prob. |
---|---|---|---|
Long-term co-integration results | |||
ECPC+ | −5.922 | 2.734 | 0.053 |
ECPC− | −15.409 | 12.172 | 0.231 |
EC+ | 0.033 | 0.004 | 0.000 |
EC− | 0.0552 | 0.029 | 0.089 |
EI+ | 21.286 | 5.472 | 0.002 |
EI− | 1.125 | 3.250 | 0.735 |
IN+ | 5.293 | 2.334 | 0.044 |
IN− | 1.144 | 0.644 | 0.103 |
PG+ | 10.996 | 1.172 | 0.000 |
PG− | 2.881 | 2.758 | 0.318 |
Short-term co-integration results | |||
ECPC+ | −0.632 | 0.156 | 0.000 |
ECPC− | −1.646 | 1.340 | 0.245 |
EC+ | 0.016 | 0.005 | 0.017 |
EC− | 3.335 | 3.180 | 0.321 |
EI+ | 3.213 | 0.822 | 0.002 |
EI− | 0.115 | 0.166 | 0.502 |
IN+ | −0.025 | 0.065 | 0.707 |
IN− | 0.770 | 0.134 | 0.000 |
PG+ | 1.174 | 0.377 | 0.009 |
PG− | 0.307 | 0.377 | 0.431 |
Variables | Coefficient | Std. Error | Prob. |
---|---|---|---|
Long-term co-integration results | |||
ECPC+ | 1.289 | 0.587 | 0.064 |
ECPC− | −2.944 | 2.506 | 0.278 |
EC+ | 1.031 | 0.414 | 0.041 |
EC− | 6.276 | 3.862 | 0.148 |
EI+ | −2.106 | 1.442 | 0.187 |
EI− | −0.667 | 0.264 | 0.039 |
IN+ | −0.636 | 0.246 | 0.036 |
IN− | 1.623 | 1.033 | 0.160 |
PG+ | −0.069 | 0.050 | 0.213 |
PG− | −0.122 | 0.147 | 0.434 |
Short-term co-integration results | |||
ECPC+ | 1.7120 | 1.062 | 0.151 |
ECPC− | −0.199 | 1.072 | 0.857 |
EC+ | 1.616 | 0.687 | 0.051 |
EC− | 2.933 | 1.783 | 0.144 |
EI+ | −2.449 | 1.981 | 0.256 |
EI− | −2.254 | 0.567 | 0.005 |
IN+ | −0.996 | 0.359 | 0.027 |
IN− | 1.010 | 0.952 | 0.323 |
PG+ | 0.167 | 0.122 | 0.213 |
PG− | −0.451 | 0.211 | 0.070 |
Emission Type | Country | Statistical Metrics | ANN | SVM | LSTM |
---|---|---|---|---|---|
CO2e | USA | RMSE | 0.020 | 0.021 | 0.021 |
CO2e | USA | MAPE | 1.601 | 1.807 | 1.476 |
CO2e | USA | MBE | −0.011 | −0.009 | −0.012 |
CO2e | CHINA | RMSE | 0.027 | 0.024 | 0.018 |
CO2e | CHINA | MAPE | 2.099 | 1.880 | 1.429 |
CO2e | CHINA | MBE | 0.013 | 0.011 | 0.006 |
CO2e | INDIA | RMSE | 0.035 | 0.032 | 0.019 |
CO2e | INDIA | MAPE | 3.195 | 2.854 | 1.609 |
CO2e | INDIA | MBE | 0.030 | 0.027 | 0.015 |
Country | Year | Actual CO2e | CO2e Predicted by SVM | CO2e Predicted by ANN | CO2e Predicted by LSTM |
---|---|---|---|---|---|
China | 2013 | 9,936,680 | 9,989,303 | 10,007,120 | 9,850,446 |
2014 | 9,894,940 | 9,918,809 | 9,827,684 | 9,742,910 | |
2015 | 9,830,430 | 9,930,304 | 9,971,456 | 10,197,261 | |
2016 | 9,814,310 | 10,519,771 | 9,755,080 | 10,269,797 | |
India | 2013 | 1,966,810 | 1,867,127 | 1,903,673 | 1,869,096 |
2014 | 2,136,870 | 2,000,476 | 2,103,527 | 1,991,179 | |
2015 | 2,150,220 | 2,048,106 | 2,094,522 | 2,058,733 | |
2016 | 2,183,280 | 2,147,102 | 2,115,826 | 2,102,843 | |
USA | 2013 | 5,089,500 | 5,725,395 | 5,093,302 | 5,124,246 |
2014 | 5,102,580 | 5,839,640 | 5,149,849 | 5,158,999 | |
2015 | 4,982,790 | 5,805,609 | 5,022,575 | 5,101,520 | |
2016 | 4,888,640 | 5,802,796 | 4,916,199 | 5,092,697 |
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Ahmed, M.; Huan, W.; Ali, N.; Shafi, A.; Ehsan, M.; Abdelrahman, K.; Khan, A.A.; Abbasi, S.S.; Fnais, M.S. The Effect of Energy Consumption, Income, and Population Growth on CO2 Emissions: Evidence from NARDL and Machine Learning Models. Sustainability 2023, 15, 11956. https://0-doi-org.brum.beds.ac.uk/10.3390/su151511956
Ahmed M, Huan W, Ali N, Shafi A, Ehsan M, Abdelrahman K, Khan AA, Abbasi SS, Fnais MS. The Effect of Energy Consumption, Income, and Population Growth on CO2 Emissions: Evidence from NARDL and Machine Learning Models. Sustainability. 2023; 15(15):11956. https://0-doi-org.brum.beds.ac.uk/10.3390/su151511956
Chicago/Turabian StyleAhmed, Mansoor, Wen Huan, Nafees Ali, Ahsan Shafi, Muhsan Ehsan, Kamal Abdelrahman, Anser Ali Khan, Saiq Shakeel Abbasi, and Mohammed S. Fnais. 2023. "The Effect of Energy Consumption, Income, and Population Growth on CO2 Emissions: Evidence from NARDL and Machine Learning Models" Sustainability 15, no. 15: 11956. https://0-doi-org.brum.beds.ac.uk/10.3390/su151511956