Dynamic Differentiated Correlation between Coal and Non-coal Transportation: A VAR Model Analysis of Railway Energy Transportation and Macroeconomy

Wenmi Chai; Rui Zhao; Yang Han; Qian Xiang; Wenbin Wang; Zedong You

doi:10.62177/apemr.v2i4.544

Authors

Wenmi Chai New Energy Technology Research Institute Co., Ltd., CHN ENERGY Investment Group Co., Ltd.
Rui Zhao Chengdu Jinjiang Center for Disease Control and Prevention
Yang Han Shuohuang Railway Development Co., Ltd., CHN ENERGY Investment Group Co., Ltd.
Qian Xiang Laboratory of Intelligent Control, PLA Rocket Force University of Engineering
Wenbin Wang New Energy Technology Research Institute Co., Ltd., CHN ENERGY Investment Group Co., Ltd.
Zedong You New Energy Technology Research Institute Co., Ltd., CHN ENERGY Investment Group Co., Ltd.

DOI:

https://doi.org/10.62177/apemr.v2i4.544

Keywords:

Energy Transportation, Macroeconomy, Vector Autoregression Model, Granger Causality Test, Variance Decomposition

Abstract

As a strategic artery for the development of the national economy, the dynamic correlation between energy transportation and the macroeconomy is particularly important against the backdrop of the restructuring of the global energy supply chain. We take the transportation data of a self-operated railway of an energy enterprise from 2020 to 2025 as a sample, select Gross Domestic Product (GDP), Producer Price Index (PPI), Coal Transportation Plan (CTP), Coal Transportation Volume (CT), Non-coal Transportation Plan (NCTP) and Non-coal Transportation Volume (NCTP) as research objects, construct a Vector Autoregression (VAR) model, and explore the dynamic correlation mechanism between coal and non-coal transportation indicators and the macroeconomy through Granger causality test, impulse response function and variance decomposition. The results show that the coal transportation volume is mainly driven by the planned volume and GDP, with their contribution rates being 35.59% and 20.88% respectively, which reflects the strong planned attribute under the integration mode of production, transportation and marketing; the non-coal transportation volume is significantly affected by GDP and PPI, with the influence degrees being 25.71% and 23.02% respectively, which reflects the market sensitivity under the agency mode. Based on the above-mentioned differentiated correlation characteristics, this study can provide theoretical support and decision-making reference for energy enterprises to formulate differentiated transportation scheduling strategies and improve the response efficiency of the supply chain.

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References

Zhu, L., & Mu, X. W. (2024). Research on the reshaping of the global energy supply chain and trade pattern under the great changes unseen in a century and China’s countermeasures. Prices Monthly, 44(9), 88-94.

Zheng, H. F. (2022). Outlook on the development of macro-economy and energy power. State Grid, (2), 50-51. DOI: 10.3969/j.issn.1673-4726.2022.02.033.

Kulshreshta, M., Nag, B., & Kulshreshta, M. (2008). A multivariate cointegrating vector auto regressive model of freight transport demand: Evidence from Indian Railways. Transportation Research Part A, 35(1), 29-45.

Wijewardana, A., Hong, T., & Charles, M. (2014). An empirical analysis of Australian freight rail demand. Economic Analysis & Policy, 44(1), 21-29.

Zhang, R., & He, Y. L. (2019). Analysis on influencing factors of China’s railway freight volume. Railway Freight Transport, 37(11), 14-21. DOI: 10.16669/j.cnki.issn.1004-2024.2019.11.03.

Xu, H. (2020). Research on the internal relationship between railway freight volume and national economic development. Railway Economics Research, (4), 38-42. DOI: 10.3969/j.issn.1004-9746.2020.04.009.

Sun, B. (2017). Study on the correlation between comprehensive freight transport volume and national economy [Dissertation]. Chang’ an University, Shaanxi.

Jiang, Z. G. (2019). Study on the correlation between comprehensive transport freight volume and national economic development [Dissertation]. Southwest Jiaotong University, Sichuan.

Wan, Y. Y. (2016). Analysis on the correlation between energy consumption and macro-economy in China: Based on time series ARMA model and cointegration test. China High-Tech Enterprises, (11), 1-2.

Liu, J. J., Wang, C., & Zhao, J. Z. (2024). Fed rate hikes, energy price fluctuations and China’s macroeconomic stability. World Economic Papers, (6), 83-100.

Duan, H. H., Han, Y., Kong, X. S., et al. (2023). Research on the scheduling optimization of coal railway heavy vehicles based on time-space network. China Coal, 49(1), 51-57. DOI: 10.19880/j.cnki.ccm.2023.01.006.

National Bureau of Statistics of China. (n.d.). National Data. Retrieved from https://data.stats.gov.cn/easyquery.htm?cn-=A01.

Lu, Y., Peng, W., & Wang, L. Z. (2010). Cointegration analysis of road freight volume, fuel price and national economy. Journal of Transportation Systems Engineering and Information Technology, 10(1), 28-32.

Yu, Q. J., & Liu, Z. P. (2020). Analysis on influencing factors of total freight volume in Anhui Province based on entropy weight method and VAR model. Journal of Suzhou University, 35(6), 36-39. DOI: 10.3969/j.issn.1673-2006.2020.06.009.

Sims, C. A. (1980). Macroeconomics and reality. Econometrica, 48(1), 1-48. DOI: 10.2307/1912017.

Dickey, D. A., & Fuller, W. A. (1979). Distribution of the Estimators for Autoregressive Time Series With a Unit Root. Journal of the American Statistical Association, 74(366), 427–431. https://doi.org/10.2307/2286348.

Akaike, H., 1974. A new look at the statistical model identification. IEEE Transactions on Automatic Control 19, 716–723. https://doi.org/10.1109/tac.1974.1100705.

Schwarz, G. (1978). Estimating the dimension of a model. The Annals of Statistics, 6(2), 461-464. DOI: 10.1214/aos/1176344136.

Akaike, H. (1970). Statistical predictor identification. Annals of the Institute of Statistical Mathematics, 22(2), 203 - 217. DOI: 10.1007/BF02481546.

Hannan, E. J., & Quinn, B. G. (1979). The determination of the order of an autoregression. Journal of the Royal Statistical Society. Series B (Methodological), 41(2), 190 - 195. DOI: 10.1111/j.2517 - 6161. 1979. tb01776. x.

Dickey, D. A., & Fuller, W. A. (1979). Distribution of the estimators for autoregressive time series with a unit root. Journal of the American Statistical Association, 74(366A), 427 - 431. DOI: 10.2307/2286348.

Granger, C. W. J. (1969). Investigating causal relations by econometric models and cross - spectral methods. Econometrica, 37(3), 424 - 438. DOI: 10.2307/1912791.

Hamilton, J. D. (1994). Time Series Analysis. Princeton University Press.

Angrist, J. D., & Pischke, J. -S. (2009). Mostly Harmless Econometrics: An Empiricist’s Companion. Princeton University Press.