Economic policy uncertainty (EPU) and firm carbon emissions: Evidence using a China provincial EPU index

Yu, Jiang Tsu; Shi, Xunpeng; Guo, Dongmei; Yang, Longjian

doi:10.1016/j.eneco.2020.105071

Cited by 239 publications

(144 citation statements)

References 63 publications

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“…The samples include Beijing, Tianjin, Hebei, Shanxi, Inner Mongolia, Liaoning, Jilin, Heilongjiang, Shanghai, Jiangsu, Zhejiang, Anhui, Fujian, Jiangxi, Shandong, Henan, Hubei, Hunan, Guangdong, Guangxi, Chongqing, Sichuan, Guizhou, Yunnan, Shaanxi, Gansu, Qinghai, Ningxia, and Xinjiang. The data of EPU are selected from the EPU index proposed by Yu et al (28)…”

Section: Datamentioning

confidence: 99%

“…Since the measures above on EPU are mainly at the national level, the EPU of different regions in China may be different. Hence, we measure the provincial EPU (EPU1) using the EPU index proposed by Yu et al (28). Besides, we use the standardization of China's provincial EPU index (EPU2) to conduct a robustness test.…”

Section: Independent Variable: Epumentioning

confidence: 99%

“…Alternative Measure of EPUIn the above empirical tests, we use the EPU index proposed by Yu et al(28) to measure the EPU. In this subsection, we adopt the standardization of China's provincial EPU index (EPU2) proposed by Yu et al(28) to carry out the robustness test, and the definition of EPU2 is given in Section Variables. The regression results are reported in Columns (4-6) of Table…”

mentioning

confidence: 99%

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Does Economic Policy Uncertainty Matter for Healthcare Expenditure in China? A Spatial Econometric Analysis

Bai

Tang

Zhang

et al. 2021

Front. Public Health

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A growing body of research has documented the determinants of healthcare expenditure, but no known empirical research has focused on investigating the spatial effects between economic policy uncertainty (EPU) and healthcare expenditure. This study aims to explore the spatial effects of EPU on healthcare expenditure using the panel data of 29 regions in China from 2007 to 2017. Our findings show that healthcare expenditure in China has the characteristics of spatial clustering and spatial spillover effects. Our study also shows that EPU has positive spatial spillover effects on healthcare expenditure in China; that is, EPU affects not only local healthcare expenditure but also that in other geographically close or economically connected regions. Our study further indicates that the spatial spillover effects of EPU on healthcare expenditure only exist in the eastern area. The findings of this research provide some key implications for policymakers in emerging markets.

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Section: Datamentioning

confidence: 99%

Section: Independent Variable: Epumentioning

confidence: 99%

mentioning

confidence: 99%

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Does Economic Policy Uncertainty Matter for Healthcare Expenditure in China? A Spatial Econometric Analysis

Bai

Tang

Zhang

et al. 2021

Front. Public Health

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“…A great number of research has proved the adverse impacts of EPU on real economy (Gu et al, 2021;Li et al, 2020), rm investments (Liu et al, 2020;Suh and Yang, 2021;Zhou et al, 2021), innovation activities (Guan et al, 2021;He et al, 2020), nancial markets (Danisman et al, 2020;Nguyen et al, 2020;Phan et al, 2021), and energy market (Xiao and Wang, 2021;Zhang and Yan, 2020). Based on the close link between economic activities and environmental quality, several authors propose the potential outcomes of EPU on environmental quality (Jiang et al, 2019;Yu et al, 2021). According to Jiang et al (2019), EPU may affect carbon dioxide emissions through three channels.…”

Section: Introductionmentioning

confidence: 99%

Environmental quality and the role of economic policy uncertainty, economic complexity, renewable energy, energy intensity: the case of G7 countries

Chu

2021

Preprint

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This study explores the environmental impacts of economic policy uncertainty, economic complexity, renewable energy, and energy intensity for G7 countries. To this end, the study employs Fully Modified Ordinary Least Squares and fixed effect model with Driscoll and Kraay (1998) robust standard errors for a panel dataset from 1997 to 2015. The findings demonstrate a long-term relationship between interested variables and carbon dioxide emissions and ecological footprint. Specifically, high energy intensity propels environmental pollution while economic policy uncertainty and renewable energy prove effective in controlling environmental degradation. The environmental Kuznet curve of economic complexity and environmental quality holds for G7 countries. Moreover, economic policy uncertainty strongly moderates the environmental effect of renewable energy, economic complexity, and energy intensity. While economic policy uncertainty amplifies the beneficial environmental effects of renewable energy and economic complexity, it magnifies the harmful effect of energy intensity on environmental quality. These empirical outcomes allow us to draw useful implications for policymakers to mitigate the environmental degradation.

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“…Beginning in August 2014, crude oil prices experienced a rapid decline, from over US$100 per barrel in August 2014 to just over $30 per barrel by February 2016, which to some extent alleviated the supply of fossil energy and restrained the rapid development of new energy (Albulescu et al, 2020;Ji et al, 2020;Luo et al, 2020;Shi and Shen, 2021). At the same time, in the context of global efforts to reduce climate emissions, increasing the rapid growth of new energy has become an important energy policy for many countries (Yu et al, 2021). However, due to differences in economic development level, energy and resource endowment, primary energy consumption structure and new energy policies, the effect of crude oil price shocks to new energy markets in various countries or regions may vary.…”

Section: Introductionmentioning

confidence: 99%

Dynamic Effect of Structural Oil Price Shocks on New Energy Stock Markets

Zhou

Geng

2021

Front. Environ. Sci.

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This paper decomposes daily crude oil shocks into demand shock, supply shock and risk shock. Then, it employs Diebold and Yilmaz connectedness index approach to explore the differences for the time-varying effect of different types of structural shocks on new energy stock markets in China, Europe and the United States during the period 10 June 2009–30 October 2018. The new findings show that: 1) There are time-varying features of structural shocks to all new energy markets. 2) The crude oil demand shock and risk shock have a large explanatory ability on the returns of all new energy stock markets, while the crude oil supply shock has a small impact. 3) The influences of crude oil demand shocks on the market returns of new energy in China, Europe and the United States are 1.31%, 8.64%, and 4.47%, respectively; however, the affection of crude oil risk shocks to the market returns of new energy in the same markets are 3.17%, 7.91%, and 21.51%, respectively. 4) The crude oil demand shock and supply shock have little impact on any new energy market volatilities, but the effects of crude oil risk shocks to China and the United States’ new energy market volatilities are 2.44% and 3.14%, respectively.

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Economic policy uncertainty (EPU) and firm carbon emissions: Evidence using a China provincial EPU index

Cited by 239 publications

References 63 publications

Does Economic Policy Uncertainty Matter for Healthcare Expenditure in China? A Spatial Econometric Analysis

Does Economic Policy Uncertainty Matter for Healthcare Expenditure in China? A Spatial Econometric Analysis

Environmental quality and the role of economic policy uncertainty, economic complexity, renewable energy, energy intensity: the case of G7 countries

Dynamic Effect of Structural Oil Price Shocks on New Energy Stock Markets

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