Drought is a natural hazard which has slow onset and assessment of its vulnerability is important for water resources management and sustainable development particularly in the most vulnerable areas to climate change and its worse impacts. Pakistan is facing acute water scarcity and regionalization of drought is a pre‐planning strategy for coping the severe condition of water management in the country. This study is conducted to identify homogenous climatic regions (HCRs) with respect to drought condition based on Reconnaissance Drought Index using climate data of 55 metrological stations across Pakistan. For this purpose two statistical methods, cluster analysis and L‐moments technique were used. First, cluster analysis was performed using site characteristics of the stations which identified five homogenous groups. Second, two statistical measures that is, discordancy measures and heterogeneity measure were implied after some regional adjustments, to satisfy the homogeneity of the homogenous groups developed in step one based on at‐site statistics of the stations using L‐moments technique. These statistical measures established validity of five homogeneous climatic regions for Pakistan. Third, to assess these HCRs several results using site characteristics and climate data of the stations were calculated which show that elevation has key role in the rainfall pattern and aridity of the regions. According to the results, there is high variability among these regions with respect to water recourses. First two regions consists most elevated areas with enough rainfall and glacier. Regions (3) and (4) include most vulnerable and drought‐prone areas of Sindh and Baluchistan provinces while Region (5) has enough rainfall in monsoon season. This study findings will help in adaptation, mitigation and future's planning regarding climate, water resources management, monitoring droughts and its projections etc. in different areas and fill the gaps in existing literature in Pakistan.

In this study, an ensemble of statistically downscaled 14 multi‐global climate models for RCP4.5 and RCP8.5 emission scenarios was employed to implement a comprehensive assessment of climate change impacts over Pakistan in order to identify the future hotspots cities in terms of changes in temperature and precipitation. The analyses focused on the minimum, maximum and average temperature and precipitation in three time‐slices: 2006–2035, 2041–2070, and 2071–2,100. Average temperature is projected to increase by 2.6°C under RCP4.5 while 5.1°C under RCP8.5 by the end of this century with the north side of Pakistan (mainly over North Pakistan—NP, Monsoon Region—MR and Khyber Pakhtunkhwa—KP) presenting the highest changes in the temperatures. Wetter conditions are expected in the future over Pakistan, mainly over the MR. In general, air temperature and precipitation showed linear positive correlation over Pakistan in both RCP scenarios. Hotspot cities where extreme climate, that is, the hottest, dryer and wetter, exists were also identified. Hyderabad will likely become the hottest city of Pakistan by end century with the highest average temperature reaching 29.9°C under RCP4.5 and 32.0°C under RCP8.5 followed by Jacobabad, Bahawalnagar, and Bahawalpur. Most of the hottest cities are detected in areas on the southern side of Pakistan. On the other hand, the wettest cities, Murree, Balakot and Muzaffarabad, are located in the MR. Dry conditions are likely to be prevalent in Dalbandin followed by Khanpur and Jacobabad under both RCPs. The uncertainties of the projections were also evaluated. For precipitation, for example, there are a large number of outliers indicating the high variability/uncertainties of the projections. These uncertainties are clearer when the probability density functions are analysed for individual sub‐domains in Pakistan.

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.