Time‑Frequency and Causal Dynamics Between Green Logistics, Environmental Factors, and China’s Transportation Sector: A Fourier Toda–Yamamoto and Cross‑Wavelet Approach
Abstract
This study examines causality relationships between the financial performance of China's transport industry and the selected green logistics, environmental, and macroeconomic indicators. Based on the monthly data spanning from January 2010 to December 2020, logarithm of Shanghai Transportation Index (LSZSE) is designated as the explanatory variable, while green logistics performance (LGRL), per capita income (LGDP), CO₂ emissions arising from transport activities (LTCO₂), and the percentage of renewable consumption of energy (LNREC) are employed as explanatory variables. In order to determine the properties of stationarity of the series, the unit root test with the Fourier Augmented Dickey-Fuller (Fourier ADF) that is robust to smooth structural breaks and nonlinear dynamics is employed. Based on the integration results, the Fourier Toda–Yamamoto (FTY) causality test is employed to test for causal relationships among variables of different orders of integration. The empirical test reveals a statistically significant one-way causality running from green logistics (LGRL) to sector performance of transportation (LSZSE) at the 10% level of significance. The cross-wavelet analysis demonstrated that the interactions between the series vary across long- and short-term scales, exhibiting synchronous or lagged structures depending on the period. This may be understood as that advancement in sustainability logistic activities will be able to influence investor sentiment and market worthiness within the industry.
Keywords
Green logistics, SZSE Transportation Index, Environmental Efficiency, Renewable Energy, Fourier Toda–Yamamoto, Wavelet
References
- Adams, S., & Acheampong, A. O. (2019). Reducing carbon emissions: The role of renewable energy and democracy. Journal of Cleaner Production, 240, 118245. https://doi.org/10.1016/j.jclepro.2019.118245.
- Barut, A., Citil, M., Ahmed, Z., Sinha, A., & Abbas, S. (2023). How do economic and financial factors influence green logistics? A comparative analysis of E7 and G7 nations. Environmental Science and Pollution Research, 30(1), 1011–1022. https://doi.org/10.1007/s11356022 222520
- Brown, R. L., Durbin, J., & Evans, J. M. (1975). Techniques for testing the constancy of regression relationships over time. Journal of the Royal Statistical Society: Series B, 37(2), 149–192.
- Chen, W., & Lei, Y. (2017). Path analysis of factors in energy-related CO2 emissions from Beijing’s transportation sector. Transportation Research Part D: Transport and Environment, 50, 473–487.
- Choi, S.-H., & Choi, J.-I. (2017). Analysis of stock price increase and volatility of logistics related companies. Journal of Digital Convergence, 15(2), 135–144. https://doi.org/10.14400/JDC.2017.15.2.135
- Czech, K., Weremczuk, A., & Wielechowski, M. (2022). Transportation industries during the COVID-19 pandemic: Stock market performance of the largest listed companies. Ekonomika i Organizacja Logistyki.
- Dickey, D. A., & Fuller, W. A. (1979). Distribution of the estimators for autoregressive time series. Journal of the American Statistical Association, 74(366), 427–431.
- Dickey, D. A., & Fuller, W. A. (1981). Likelihood ratio statistics for autoregressive time series with a unit root. Econometrica, 49(4), 1057–1072.
- Du, J., Cheng, J., & Ali, K. (2023). Modelling the green logistics and financial innovation on carbon neutrality goal: A fresh insight for BRICST. Geological Journal, 58(7), 2742–2756. https://doi.org/10.1002/gj.4732.
- Enders, W., & Lee, J. (2012). A unit root test using a Fourier series to approximate smooth breaks. Oxford Bulletin of Economics and Statistics, 74(4), 574–599.
