Analysis of baseline and alternative air quality scenarios for Pakistan: an integrated approach

Abstract

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 (SO₂, NO_x, and PM_2.5) as well as the associated health impacts of increased emissions are assessed. Baseline emission scenario (BES) shows a growth in emissions of SO₂, NO_x, and PM_2.5 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_2.5) would increase to more than 150 μg/m³ 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.

Appendix

The Pak-IEM model was built around the MARKAL/TIMES modeling environment. A MARKAL/TIMES model requires a core set of data to characterize the energy system that the model represents. Once constructed, the model solves for the least-cost solution that utilizes the available energy carriers (fuels, electricity, heat) and technologies (power plants, refineries, end-use devices) to meet the energy service demands (e.g., space conditioning, industrial process heat, lighting, passenger kilometers traveled), subject to physical limitations (e.g., resource availability, hydro/solar/wind potential), targets (e.g., energy independence and diversity in the terms of imports, renewable share of electricity generation), and policies (e.g., greenhouse gas limits, emission caps, nuclear), and other constraints (e.g., rate of technology adoption) imposed on the system (Pak-IEM 2011). A stylized representation of the underlying energy system depicted in the model is illustrated in Fig. 11.

Fig. 11

MARKAL/TIMES model overview

It is important to distinguish that MARKAL/TIMES, unlike many other models, uses the concept of energy service demand, not final energy demand. This enables the model to evaluate fuel mix and device choices reflecting industry and consumer choices for meeting future service levels, thus emphasizing a deeper penetration into the demand environment. There is a counter opinion, however, that such levels of penetration warrant extremely well-structured data management systems at organizational levels, which may be difficult to visualize for Pakistan (Irfan 2015). However, the MARKAL/TIMES modeling framework, by its very nature, is an evolutionary environment, and with passage of time, modifications will tend to evolve within well-defined and regulated environments and, as such, promote more disciplined data management practices.

These energy service demands can be specified in whatever units are convenient (e.g., vehicle-miles traveled for transportation, lumens for lighting, crop yield per acreage, etc.). In these cases, the end-use devices must be defined in terms that convert final energy carriers into these energy service units. These energy services need to be developed as sub-sector demands, as each serves a unique (non-substitutable) demand and requires a different set of technologies (end-use devices) to provide those services.

Pak-IEM covers the whole of Pakistan’s energy system and can therefore be used for national policy studies. The model has been designed according to the following sectors as summarized in Table 2.

Table 2 Sectoral description of the Pak-IEM model