Can art hedge against economic policy uncertainty?: New insights through the NARDL model

Sun, Qinglin; Zhang, Zhiyuan

doi:10.1016/j.frl.2023.103706

Cited by 2 publications

(2 citation statements)

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“…The results under the PP test reveal that EDU and Internet series are I (0) stationary, while DCO 2 , HT, and FD series are I (1) stationary. The results of unit root tests fulfill the prerequisite of the ARDL and NARDL approaches [53][54][55][56][57][58][59][60][61][62][63][64][65][66][67], which states that the variable series must meet the modeling requirements of the first order and below without unit roots (stationary or first-order stationary). Table 4 lists the results of the Brock-Dechert-Scheinkman (BDS) test.…”

Section: Resultsmentioning

confidence: 73%

“…As previously stated, Equation (2) assumes that the effects of variable changes on CO 2 emissions are symmetric; thus, this paper uses the non-linear autoregressive distributed lag (NARDL) model developed by Shin and Yu [33]. The NARDL model is an advanced method based on an ARDL approach, which allows the nonlinear asymmetry and cointegration relationship of small samples to be discussed in a single equation, in order to identify the effect of decomposition of explanatory variables into positive and negative changes on the explained variables [54][55][56][57][58]. In the extant literature, the NARDL model is widely used in the analysis of environmental problems [59][60][61][62][63][64][65][66][67].…”

Section: The Non-linear Autoregressive Distributed Lag (Nardl) Modelmentioning

confidence: 99%

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Green Household Technology and Its Impacts on Environmental Sustainability in China

Meng,

Zhang,

Wang

et al. 2023

Sustainability

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China has made a commitment to achieve carbon neutrality by 2060, and promoting a green lifestyle is an essential means to this end. The primary aim of this study is to investigate the asymmetric impact of green household technology on environmental sustainability in China. To that end, we have employed linear and non-linear auto-regressive distributed lag models to identify this complicated effect. The empirical results suggest that green household technology’s positive change exerts significant and negative effect on carbon emission in the short and long terms. And the impacts of green household technology’s negative change on carbon emission are significantly negative but smaller than its positive change in the long run, while insignificant in the short term. The estimates endorse the asymmetric impact of green household technology on carbon emissions both in the short and long term. This finding suggests that the improvement of green household technology can reduce carbon emissions, while a decline in it causes carbon emissions to rise, and technological retrogression plays a less influential role than its development. This research is a groundbreaking point in discussing the way towards environmental sustainability from a green household technology perspective, which considers the asymmetric effect and provides meaningful insights for China to achieve sustainable development.

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