Environmental cost of natural resources utilization and economic growth: Can China shift some burden through globalization for sustainable development?

Abstract

This study aims to investigate the relationship between CO 2 emissions in China and its prospective determinants, namely economic growth, globalization, financial development, and natural resources during the period from 1980 to 2017. We present more detailed analyses across multiple econometric approaches within a multivariate system, e.g., the Bayer-Hanck combined cointegration approach, the ARDL bounds test approach, ARDL estimates in short run and long run, robustness check by cointegration regressions (i.e., FMOLS, DOLS, CCR), and Granger causality approach in the frequency domain. Our results show that economic growth and natural resources have a positive impact on CO 2 emissions in China, although globalization contributes to improving its environmental quality. Meanwhile, there is no statistical evidence that CO 2 emissions in China could be affected by financial development. The causality analysis reveals that globalization, economic growth and natural resources all lead to CO 2 emissions in the long run, while financial development only causes the short-run CO 2 emissions, which further demonstrate our findings. Such findings are meaningful for policymakers to achieve the sustainable development in China.

Does fiscal decentralization and eco‐innovation promote sustainable environment? A case study of selected fiscally decentralized countries

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Does the stringency of government interventions for COVID19 reduce the negative impact on market growth? Evidence from Pacific and South Asia

“…Given the widespread impact of Covid-19, this unfortunate outbreak has become akin to an economic crisis (Sharif et al, 2020). A natural recourse for investments during market instability is treasury securities (Sadiq et al, 2018;Umar et al, 2020;. However, COVID-19 0 s pressure on public finances has also resulted in a decline in yields on treasury instruments.…”

Does the COVID-19 pandemic affect the tourism industry in China? Evidence from extreme quantiles approach

“…Before applying any econometric technique to the data, such as regression analysis, it is necessary to determine the unit root or trend and coherence of data which may cause issues in the statistical inference of time series data (Su, Cai, et al, 2021;Su, Qin, Tao, Shao, et al, 2020;Umar, Su, Rizvi, et al, 2021). This study uses the Augmented Dickey-Fuller test (ADF) for unit root and the Zivot-Andrew (ZA) test, which is famous for scrutinizing the data stationarity of the time series with a structural break (Umar, Ji, Kirikkaleli, Shahbaz, et al, 2020;Umar, Ji, Kirikkaleli, et al, 2021). Table 3 exhibits the unit root test outcomes, which suggest that all the variables such as TOR, COVID-19, ER, and OIL have unit roots at a level.…”