Electricity consumption and economic growth nexus in China: an autoregressive distributed lag approach

  • Xiaojian Zhong
  • Hongyi Jiang
  • Chen Zhang
  • Rui ShiEmail author
Research Article


This study attempts to investigate the relationship among electricity consumption, economic growth, and employment in China. Distinct from most of the previous studies, our empirical research identifies a long-run equilibrium cointegration relationship among the three covariates during the period of 1971–2009 with the recently developed autoregressive distributed lag (ARDL) bounds testing approach. The parameters are estimated through a long-run static solution of the estimated ARDL model and short-run dynamic solutions of the error correction model. The estimated models successfully pass diagnostic tests and both the long-run and short-run elasticities are found to be statistically significant. The study also indicates the existence of short-run and long-run causalities from electricity consumption and employment to economic growth. Results of this study show that electricity serves as an important driver of economic growth. Based on these results, several policy prescriptions on energy use and economic development are suggested for China.


Electricity consumption Economic growth China Granger causality ARDL model Cointegration 



This work was funded by the Fundamental Research Funds for Fujian Province Public-interest Scientific Institution, Grant 2017R1101003-7, and the National Natural Science Foundation of China (NSFC), Grant 40701063. We also sincerely thank Prof. Yusheng Yang of Fujian Normal University for your kind support in this work.


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Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2019

Authors and Affiliations

  1. 1.Institute of GeographyFujian Normal UniversityFuzhouChina
  2. 2.State Key Laboratory of Subtropical Mountain Ecology (Funded by Ministry of Science and Technology and Fujian Province), School of Geographical SciencesFujian Normal UniversityFuzhouChina
  3. 3.Department of Applied Mathematics and StatisticsJohns Hopkins UniversityBaltimoreUSA
  4. 4.Department of Environmental Health and EngineeringJohns Hopkins UniversityBaltimoreUSA

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