This research aims to find the best model for predicting the Standardized Precipitation Index (SPI) and the Standardized Precipitation and Evapotranspiration Index (SPEI) in the future. The study estimates SPI and SPEI at different time scales, ranging from 1 to 48 months. To predict drought, Random Forest (RF) models are used based on lag times of 1–12 months for the estimated drought indices (SPI and SPEI). Accuracy and error metrics like Nash–Sutcliffe efficiency (NSE), root‐mean‐square error (RMSE), producer accuracy (PA), user accuracy (UA), and Choen’s kappa are used to assess the models. The NSE values for the SPI at varying time scales (1, 3, 6, 9, 12, and 48 months) indicate that Bahawalpur, Rawalpindi, Murree, and Sargodha stations have the highest NSE values of 0.1148, 0.5868, 0.8302, 0.9196, 0.9516, 0.9801, and 0.9845, respectively. Similarly, the RMSE values for SPI at these time scales show that Bahawalpur, Rawalpindi, Murree, and Sargodha stations have the lowest RMSE values of 0.6187, 0.6094, 0.4091, 0.2865, 0.2275, 0.1594, and 0.1106, respectively. The NSE and variance explained for SPI and SPEI at a 1‐month time scale were found to be poor, but they improved as the time scale increased. On the other hand, the RMSE values for SPI and SPEI at a 1‐month time scale were found to be high but decreased with longer time scales. The stations that exhibit the highest values of the NSE for the SPEI at various time scales (1, 3, 6, 9, 12, and 48 months) are Rawalpindi, Jhelum, Murree, Mianwali, Rawalpindi, and Sargodha, respectively. These stations have NSE values of 0.0784, 0.6074, 0.8353, 0.9225, 0.9542, 0.9760, and 0.9896, respectively. Similarly, the stations with the lowest RMSE values for SPEI at these time scales are Sargodha, Murree, Murree, Murree, Murree, and Sargodha, with RMSE values of 1.002, 0.5909, 0.3993, 0.2626, 0.2132, 0.1546, and 0.0941, respectively. The analysis reveals a distinct pattern indicating that stations situated at higher elevations exhibit a more pronounced correlation between the SPI and SPEI indices in comparison to stations at lower elevations. Notably, Murree, Jhelum, Sialkot, and Rawalpindi demonstrate a statistically significant and strong correlation between the SPI and SPEI. Overall, the results show that SPEI is a better drought index for classifying and monitoring meteorological drought in stations with lower elevations. However, in stations with higher elevations, the selected indices provide similar information, but with some differences.

The emission of carbon dioxide (CO₂) is considered one of the main factors responsible for one of the greatest challenges faced by the world today: climate change. On the other hand, with the increase in energy demand due to the increase in population and industrialization, the emission of CO₂ has increased rapidly in the past few decades. However, the world’s leaders, including the United Nations, are now taking serious action on how to minimize the emission of CO₂ into the atmosphere. Towards this end, accurate modeling and monitoring of historical CO₂ can help in the development of rational policies. This study aims to analyze the carbon emitted by the Group Twenty (G20) countries for the period 1971–2021. The datasets include CO₂ emissions, nonrenewable energy (NREN), renewable energy (REN), Gross Domestic Product (GDP), and Urbanization (URB). Various regression-based models, including multiple linear regression models, quantile regression models, and panel data models with different variants, were used to quantify the influence of independent variables on the response variable. In this study, CO₂ is a response variable, and the other variables are covariates. The ultimate objective was to choose the best model among the competing models. It is noted that the USA, Canada, and Australia produced the highest amount of CO₂ consistently for the entire duration; however, in the last decade (2011–2021) it has decreased to 12.63–17.95 metric tons per capita as compared to the duration of 1971–1980 (14.33–22.16 metric tons per capita). In contrast, CO₂ emissions have increased in Saudi Arabia and China recently. For modeling purposes, the duration of the data has been divided into two independent, equal parts: 1971–1995 and 1996–2021. The panel fixed effect model (PFEM) and panel mixed effect model (PMEM) outperformed the other competing models using model selection and model prediction criteria. Different models provide different insights into the relationship between CO₂ emissions and independent variables. In the later duration, all models show that REN has negative impacts on CO₂ emissions, except the quantile regression model with tau = 0.25. In contrast, NREN has strong positive impacts on CO₂ emissions. URB has significantly negative impacts on CO₂ emissions globally. The findings of this study hold the potential to provide valuable information to policymakers on carbon emissions and monitoring globally. In addition, results can help in addressing some of the sustainable development goals of the United Nation Development Programme.

This study mainly focuses on spatiotemporal and inter-seasonal meteorological drought characteristics. Random Effect Logistic Regression Model (RELRM) and Conditional Fixed Effect Logistic Regression Model (CFELRM) are used to identify the spatiotemporal and inter-seasonal characteristics of meteorological drought in selected stations. The log-likelihood Ratio Chi-Square (LRCST) and Wald chi-square tests (WCTs) are used to assess the significance of RELRM and CFELRM. The Hausman test (HT) is applied to select the appropriate model between RELRM and CFELRM. For instance, HT suggests the CFELRM as an appropriate model in spring-to-summer spatiotemporal drought modelling. The significant coefficient from CFELRM indicates that an increment in moisture conditions of the spring season will decrease the probability of drought in the summer. The odds ratio of 0.1942 means that 19.42% chance of being in a higher category. Similarly, in summer-to-autumn using RELRM the computed odds ratio of 0.0673 shows that 6.73% chance of being in a higher category.

Sustainable watershed development focuses on building resilience to drought through better water resource management, ecosystem protection, and adaptation strategies. In this study, the spatiotemporal dynamics and inter-seasonal characteristics of meteorological drought across Ankara Province, Turkey, were investigated and compared using a conditional fixed effect logistic regression model (CFELogRM) and a random effect logistic regression model (RELogRM). To assess the statistical validity and effectiveness of these models, we conducted significance tests, including the log-likelihood ratio chi-square, and Wald chi-square tests. The obtained p-values associated with both the RELogRM and CFELogRM models for the selected seasons demonstrate their statistical significance. Additionally, we conducted the Hausman test (HT) to compare the efficiency of the RELogRM and CFELogRM models. Remarkably, the results of the HT suggest that RELogRM is the optimal model for modeling fall-to-winter season drought dynamics across the study area. Notably, the significant coefficient derived from RELogRM indicates a statistically significant negative correlation between spring moisture conditions and the probability of summer droughts. Specifically, the odds ratio of 0.2416 reflects a 24.16% reduction in the likelihood of transitioning to a higher drought category, emphasizing the crucial role of antecedent moisture conditions in influencing drought propensity.

The Standardized Precipitation Index (SPI) and Standardized Precipitation Evapotranspiration Index (SPEI) are applied in this study to examine drought features at different Punjab Province stations. We examine drought occurrence through drought frequency, concentrating on spatial and temporal scales and considering different geomorphologies. A modified Mann-Kendall test and multiple linear regression are used to examine monthly data from 12 weather stations collected between 1980 and 2020. Stations at high elevations have very variable precipitation, which can lead to periodic droughts. Surprisingly, SPEI shows more drought sensitivity than SPI. The severity of the drought has decreased recently, especially after the 1998–2002 peak period. Correlation analysis demonstrates a substantial link between SPI and SPEI at high elevation stations, with Murree displaying the strongest correlation (0.9334) on a one-month time frame, and Bahawalpur exhibiting a comparably lower correlation (0.6824). Positive trends in SPI and SPEI values are revealed by Mann-Kendall trend tests, which are most prominent at higher elevations and on the northern side of Punjab. On a one-month time scale, the Mianwali station has the strongest trend in SPEI (0.1497), whereas Bahawalnagar has negligible trends in both SPI (0.0214) and SPEI (0.0459). At high elevation sites, the frequency of drought is constantly high, and SPEI routinely detects greater drought at low elevations. Similar drought frequencies are shown by both indexes for the northern region of Punjab. The results of a multiple linear regression model demonstrate the significant impact of meteorological variables on monthly mean precipitation, which advances our knowledge of Punjab's spatiotemporal drought dynamics and provides important information for planning for resilience and water resource management.