Analysis of coal-related energy consumption in Pakistan: an alternative energy resource to fuel economic development
- PDF / 860,392 Bytes
- 22 Pages / 439.37 x 666.142 pts Page_size
- 88 Downloads / 192 Views
Analysis of coal‑related energy consumption in Pakistan: an alternative energy resource to fuel economic development Muhammad Yousaf Raza1 · Muhammad Tauqir Sultan Shah2 Received: 12 May 2019 / Accepted: 9 September 2019 © Springer Nature B.V. 2019
Abstract During the last 4 decades, the world has changed its focus from imported energy resources to cheap resources either renewable or non-renewable for economic and social development. Currently, coal is the cheapest source of energy in Pakistan that can be used to fulfil the energy demands. This study inspects the causal association among domestic factors such as gross domestic product, coal consumption, rural–urban unemployment, rural– urban population, fiscal deficit and services value added from 1981 to 2017. This paper applies modern techniques to inspect the association between coal consumption and economic development of Pakistan. For this, Ng–Perron unit root test, autoregressive distributed lag models and vector error correction models are employed to examine the causalities between the factors. The research finds a long-run and short-run bidirectional association between economic improvement and coal use. In the short run, the results found a bidirectional causality among gross domestic product (GDP), coal consumption, unemployment, population and overall fiscal deficit. In the long run, GDP and coal use have a bidirectional association and the same is true with the other factors. During the period, cumulative sum (CUSUM) and CUSUM square have proved that structure is good. Moreover, we support the coal consumption in producing cheap energy that clues to financial development and unemployment reduction in Pakistan. The policy suggestions for the consequences are provided below. Keywords Economic growth · Coal consumption · Urban–rural population and unemployment · Ng–Perron test
* Muhammad Yousaf Raza [email protected] Muhammad Tauqir Sultan Shah [email protected] 1
School of Management, Collaborative Innovation Center for Energy Economics and Energy Policy, China Institute for Studies in Energy Policy, China Centre for Energy Economics Research (CCEER), Xiamen University, Xiamen 361005, Fujian, China
2
School of Management, Federal Urdu University of Arts Science and Technology, Islamabad 44000, Pakistan
13
Vol.:(0123456789)
Abbreviations IAT Innovative accounting technique VECM Vector error correction model ARDL Autoregressive distributed lags model J&JT Johansen and Juselius technique HVGC Hsiao’s version of Granger causality LBNL Lawrence Berkeley National Laboratory EC Energy consumption EG Economic growth CC Coal consumption RE Renewable energy L&C Labour and capital FC Fuel consumption GCA Granger causality analysis [ ] Probabilities LCB Lower critical bound UCB Upper critical bound Mt Million tons Mtoe Million tons of oil equivalents GDP Gross domestic product CO2 Carbon dioxide DF Dickey and Fuller PP Phillips and Perron GC Granger causality DV Dependent variable KPK Khyber Pakhtunkhwa VAR Vector
Data Loading...
