In this study, an attempt has been made to develop inventory of greenhouse gas (GHG) emissions for Pakistan at the national and sectoral level. The emission profile includes carbon dioxide (CO), methane (CH), and nitrous oxide (NO). In 2012, GHG emissions from different sectors of economy are estimated at 367 Tg COeq. Out of this, CO emissions were 179 Tg; CH emissions were 107 Tg COeq; and NO emissions were 81 Tg COeq. Energy and agriculture sectors contribute approximately 89% of national GHG emissions. Industrial processes, waste, and land use change and forestry (LUCF) sectors contribute the remaining 11% GHG emissions. A comparison with the 1994 GHG emission inventory of Pakistan shows that GHG emissions in Pakistan from 1994 to 2012 have increased at an annual growth rate of 4.1% and yet anticipated to increase further for meeting the national developmental goals; however, the per capita emissions in Pakistan will remain low when compared with the global average.

This study aims to assess the current and future air pollution and associated health impacts in Pakistan. In this study, the Pakistan Integrated Energy Model (Pak-IEM) is used to assess current and future energy consumption in Pakistan. To assess air pollution levels and associated health impacts, we used the Greenhouse gas and Air pollution INteractions and Synergies (GAINS) model. A linkage has been established between both the models to feed the energy outputs from Pak-IEM into GAINS for exploring different scenarios. Mainly, the emissions of three air pollutants (SO, NO, and PM) as well as the associated health impacts of increased emissions are assessed. Baseline emission scenario (BES) shows a growth in emissions of SO, NO, and PM by a factor of 2.4, 2.2, and 2.5 between 2007 and 2030. In terms of health impacts, by 2030, annual mean concentrations of fine particles (PM) would increase to more than 150 μg/m in some parts of Punjab region of Pakistan, for which loss in statistical life expectancy is calculated to increase from 30 to 60 months in 2007 up to 60-100 months in 2030 on average.

No abstract

Agriculture contributes around a quarter to Pakistan's economy and is closely linked with the variability of monsoon rainfall. The prediction of monsoon rains with sufficient lead time has immense importance for the planning and management of water resources and agriculture. In this study, Multiple Linear Regression (MLR) and Principal Component Regression (PCR) methods are employed to predict monsoon rainfall, and their performances are compared for June–September (JJAS) for the period 1961–2014 over the monsoon region of Pakistan. Rainfall data of Meteorological stations are used as the predictand. In the MLR method, predictors are carried out from sea level pressure (SLP) and sea surface temperature (SST) of the National Centers for Environmental Prediction (NCEP) reanalysis datasets. The PCR method first calculates principal components (PCs) from SLP and SST data, and these PCs are then combined with the regression technique and used as predictors. The performance of both models is tested using statistical measures such as root mean square error (RMSE), mean absolute error (MAE), bias and the correlation coefficient to evaluate the skill of the forecast. The agreement between actual and predicted rainfall data provides evidence for reasonably accurate predictions from both methods. The MLR and PCR models explained 84.6 and 92.2% of the variation of data, and the multiple correlation coefficients are 0.92 and 0.96 respectively. The correlation coefficient for the verification period (2005–2014) is 0.73 for MLR and 0.89 for PCR. The values of mean bias, MAE and RMSE are −5.5, 20.0 and 25.1mm for MLR, and −0.42, 16.2 and 16.6mm for PCR, respectively. The results indicate that the PCR model forecast is slightly better than that of the MLR model.