Affecting factors of double parking violations on urban trips

Kadkhodaei, Masoud; Shad, Rouzbeh; Ziaee, Seyed Ali

doi:10.1016/j.tranpol.2022.02.015

Cited by 7 publications

(1 citation statement)

References 37 publications

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“…Some analytical methods are needed in the study of the influencing factors. For example, the bootstrap autoregressive distributed lag bound test (Abid et al, 2022), the logarithmic mean divisia index method (Dong et al, 2021), the Mann-Whitney U and the Kruskal-Wallis statistical tests (Kadkhodaei et al, 2022), the SOR unit root test (Caglar et al, 2021b), the wellknown IPAT identity and the STIRPAT model (York et al, 2003). Among them, STIRPAT model is a multi-variable non-linear model, which can be extended to include more indicators to study the influencing factors, so it is widely used in studies of environmental impact factors (Zhao et al, 2009;Bargaoui et al, 2014;Jin et al, 2016).…”

Section: Literature Reviewmentioning

confidence: 99%

Role of green energy technology on ecological footprint in China: Evidence from Beijing-Tianjin-Hebei region

Hui-qing

Hao

et al. 2022

Front. Environ. Sci.

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In order to investigate the impact of green energy technology on the environmental sustainability of China, take the Beijing-Tianjin-Hebei region as an example, this paper first calculates the per capita ecological footprint (ef), ecological carrying capacity (ec) and ecological deficit (ed) of China and Beijing-Tianjin-Hebei region from 1990 to 2019 by using the ecological footprint (EF) model, and then uses an expanded STIRPAT model and Partial Least Squares (PLS) regression to explore the impact and importance of green energy technology on EF in China and Beijing-Tianjin-Hebei region. It is found that the ec of China and Beijing-Tianjin-Hebei region is much lower than that of the ef from 1990 to 2019. It is always in the state of ecological deficit, and the sustainable development is faced with severe challenges. Progress in green energy technology can significantly reduce the EF of China and Beijing-Tianjin-Hebei region. The importance of each factor on the EF of China and Beijing-Tianjin-Hebei region is different. The degree of dependence on foreign trade and urbanization rate are important influencing factors of Beijing’s EF. Urbanization rate, per capita GDP, population size, energy consumption per unit GDP and built-up area are the important influencing factors of EF in Tianjin and Hebei. Therefore, to reduce the EF of Beijing, Tianjin and Hebei, it is necessary to accelerate the progress of green energy technology, develop compact ecological city and change people’s consumption patterns.

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