## 4 Results and Discussion
### Long-Run and Short-Run Evidence
\begin{table}
\begin{tabular}{c c c c c} \hline \multicolumn{5}{c}{**Dependent variable is InCO2e: ARDL(1, 1, 0, 1, 1, 0, 0, 1) selected based on AIC**} \\ \hline \multicolumn{5}{c}{**Panel A: long-run Analysis**} \\ \hline
**Variable** & **Coefficient** & **Std. Error** & **T- Ratio** & \(P\)**-value** \\ \hline LNCA & 0.072391 & 0.026865 & 2.694625 & 0.0148 \\ \hline LNEN & 0.826146 & 0.241722 & 3.417754 & 0.0031 \\ \hline LNFO & 0.607328 & 0.136929 & 4.435357 & 0.0003 \\ \hline LNGDPPC & 0.211959 & 0.054088 & 3.918779 & 0.0010 \\ \hline LNISD & \(-\)0.042691 & 0.041699 & \(-\)1.023784 & 0.3195 \\ \hline LNTF & \(-\)0.031709 & 0.158234 & \(-\)0.200393 & 0.8434 \\ \hline LNWA & 0.706882 & 0.245003 & 2.885204 & 0.0099 \\ \hline C & \(-\)2.742992 & 1.074045 & \(-\)2.553890 & 0.0199 \\ \hline \multicolumn{5}{c}{**Panel B: Short-run Analysis**} \\ \hline \(\Delta\) LNCA & 0.031187 & 0.015035 & 2.074271 & 0.0527 \\ \hline \(\Delta\) LNEN & 0.615764 & 0.203512 & 3.025696 & 0.0073 \\ \hline \(\Delta\) INFO & 0.312632 & 0.088522 & 3.531681 & 0.0024 \\ \hline \(\Delta\) LNGDPPC & \(-\)0.046796 & 0.078172 & \(-\)0.598633 & 0.5569 \\ \hline \(\Delta\) LNISD & \(-\)0.031819 & 0.030053 & \(-\)1.058762 & 0.3037 \\ \hline \(\Delta\) LNTF & \(-\)0.023634 & 0.116811 & \(-\)0.202329 & 0.8419 \\ \hline \(\Delta\) LNWA & 0.206519 & 0.209902 & 0.983881 & 0.3382 \\ \hline ECM (\(-\)1) & \(-\)0.745345 & 0.136764 & \(-\)5.449872 & 0.0000 \\ \hline \multicolumn{5}{c}{**Panel C. Residual Diagnostic Test**} \\ \hline  & \multicolumn{2}{c}{R-squared} & 0.997706 & \\ \hline  & \multicolumn{2}{c}{Adjusted R-squared} & 0.996086 & \\ \hline  & \multicolumn{2}{c}{Durbin-Watson stat} & 2.698081 & \\ \hline  & \multicolumn{2}{c}{F-statistic} & 16.0557*** & \\ \hline \(\chi\)2 SERIAL & 1.7655 (0.202) & & \\ \hline \(\chi\)2 NORMAL & 0.0724 (0.964) & & \\ \hline \(\chi\)2 ARCH & 0.1557 (0.696) & & \\ \hline \(\chi\)2 RESET & 0.7142 (0.48) & & \\ \hline \end{tabular} Note. – ** and *** signify the probability and the significance level at 5% and 10%. \(\chi^{2}\) SERIAL show the serial correlation, \(\chi^{2}\) NORMAL indicates the normality test, \(\chi^{2}\)ARCH indicates the autoregressive conditional heteroskedasticity test and \(\chi^{2}\) RESET represents Ramsey Reset test with their p-values.**  
\end{table} 
Table 7: Long-run and short-run evidence results. influences, including temperature variations, pests, health problems, droughs and other changes the country is likely to endure in the future. The linkage between CO2 emission and energy consumption enhances the maximum energy consumption of about 640 kg per capita of oil equivalent. Recently, the economic maneuver is lower than its level, but it is expected that CO2 emissions will endure and rise progressively over a period of time until the level of threshold is extended [52]. The change in the climate in Pakistan is fundamentally caused by the emission of greenhouse gasses. The key source of greenhouse gas emissions is the activities of humans, such as deforestation, urbanization, industrialization, transportation, agriculture, waste, energy use and livestock [53]. Some studies emphasize the linkage of CO2 emission with agriculture policies, agriculture land expansion and deforestation, greenhouse gas emission mitigation in agriculture, climatic variations and congestion influence on productivity [54; 55; 56; 57; 58]. However, our study demonstrated the linkage of CO2 emissions to agricultural productivity, including cropped area, energy use, fertilizer offtake, gross domestic product per capita, improved seed distribution, total food grains and water availability in Pakistan. Long-run evidence results showed a positive influence regarding cropped area, energy use, fertilizer offtake, GDP per capita and water availability, but improved seed distribution and total food grains had a negative linkage with CO2 emissions in Pakistan.