Markov chain based modelling and prediction of natural gas allocation structure in Pakistan

Abstract

Purpose

The purpose of this paper is to analyze the historical gas allocation pattern for seeking appropriate arrangement and utilization of potentially insufficient natural gas supply available in Pakistan up to 2030.

Design/methodology/approach

This study presents Markov chain-based modeling of historical gas allocation data followed by its validation through error evaluation. Structural prediction using classical Chapman–Kolmogorov method and varying-order polynomial regression in the historical transition matrices are presented.

Findings

Markov chain model reproduces the terminal state vector with 99.8 per cent accuracy, thus demonstrating its validity for capturing the history. Lower order polynomial regression results in better structural prediction compared with higher order ones in terms of closeness with Markov approach-based prediction.

Research limitations/implications

The data belongs to a certain geographic region with specific gas demand and supply profile. The proposition may be tested further by researchers to check the validity for other comparable structural predictions/analyses.

Practical implications

This study can facilitate policy-making in the field of natural gas allocation and management in Pakistan specifically and other comparable countries generally.

Originality/value

Two major literature gaps filled through this study are: first, Markov chain model becomes stationary when projected using Chapman–Kolmogorov relation in terms of a fixed, average transition matrix resulting in an equilibrium state after a finite number of future steps. Second, most of the previous studies analyze various gas consumption sectors individually, thus lacking integrated gas allocation policy.

Citation

Ahmad, H. and Hayat, N. (2020), "Markov chain based modelling and prediction of natural gas allocation structure in Pakistan", International Journal of Energy Sector Management, Vol. 14 No. 5, pp. 911-933. https://doi.org/10.1108/IJESM-12-2019-0002