The dynamic relationship between energy consumption and economic growth in China

Rathnayaka, R. M. K. T.; Seneviratna, D.M.K.N.; Wei, Long

doi:10.1080/15567249.2015.1084402

Cited by 45 publications

(13 citation statements)

References 8 publications

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“…Christensen et al ( 2018 ) showed the possibility of greater climate changes indicating substantial higher uncertainty. Rathnayaka et al ( 2018 ) find bidirectional causality between GDP and energy consumption in China, which has a significant contribution to policymakers. Hao et al ( 2018 ) evaluated rural energy consumption and GDP in China using a vector error correction model.…”

Section: Literature Reviewmentioning

confidence: 99%

How COVID-19 shock will drive the economy and climate? A data-driven approach to model and forecast

Khurshid

Khan

2020

Environ Sci Pollut Res

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The pandemic shock puts the world on quarantine and paused economic operations that affected energy consumption and economic output. This study analyzed the impact of the COVID-19 shock on GDP, energy consumption, and climate change then forecasted the situation until 2032 using the system dynamic modeling approach. The outcomes reveal that the pandemic shock will decrease the growth by 1.3% in 2020 and 1.32% in 2021. The current shortfall, low energy consumption, and delay in completion of energy-related projects can reduce the GDP by 5.2% in 2020. The effect will penetrate the system and will cause further losses in the upcoming years. The energy consumption and quarantine situation will improve the climate situation and drop the average temperature by 0.049 and 0.021 o C in 2020 and 2021. The aggregate demand and supply side measures such as national spending, lowering the lending rate, and cuts in income taxes can help in diffusing the situation. The government should start operations on ongoing energy projects, give relaxation to SME's with tight SOPs to secure jobs, and prevent possible GDP losses. The decline in oil prices provides an opportunity to cut fossil fuel subsidies and implement a carbon pricing mechanism.

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Section: Literature Reviewmentioning

confidence: 99%

How COVID-19 shock will drive the economy and climate? A data-driven approach to model and forecast

Khurshid

Khan

2020

Environ Sci Pollut Res

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“…If a series is non-stationary, it may lead to erroneous results before using them for further analysis [10]. For this purpose, this study employs three statistical methods to test the stationary and non-stationary conditions.…”

Section: Panel Unit Root Testsmentioning

confidence: 99%

The impact of urbanization and financial development on electricity intensity in China: Evidence from panel VAR approach

Duan

2020

E3S Web Conf.

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This paper aims at exploring the impact of urbanization and financial development on electricity intensity in China during the period 2004-2018. By employing a panel vector autoregressive (VAR) approach, the study finds that the electricity intensity response to one standard deviation shock on urbanization shows a negative impact, and a positive shock to financial development initially increases electricity intensity and eventually decreases electricity intensity. Our analysis is important for policy makers for improving electricity efficiency planning and sustainable economic development policies.

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“…Using provincial panel data from 1995 to 2008, Zhang and Xu [3] identified a causal relationship between China's economic growth and increased energy consumption at both the national and regional levels. Using data from 1980 to 2014 on China's GDP per capita and total energy consumption, Rathnayaka et al [4] found a bidirectional causal relationship between energy consumption and economic growth in the long term. Zhang and Cheng [5] applied a multivariate model of economic growth, energy use, carbon emissions, and capital and urban population and suggested a unidirectional Granger causality from GDP to energy consumption and a unidirectional Granger causality running from energy consumption to carbon emissions in the long run.…”

Section: Literature Reviewmentioning

confidence: 99%

Economic Transition, Technology Change, and Energy Consumption in China: A Provincial-Level Analysis

Liu

Lin

et al. 2019

Energies

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This paper conducts panel analysis to evaluate the effects of a structural economic shift from the industrial to the tertiary sector, a reduction in industrial overcapacity, and improvements in energy efficiency on energy consumption using data for 30 Chinese provinces from 1995 to 2015. We find that, at the national level, the structural shift to the tertiary sector, the reduction in cement and steel production, and the increase in energy efficiency in the industrial sector all have statistically significantly negative effects. We also divide the sample into three geographic and economic regions to evaluate regional differences. We find that the gross domestic product (GDP) share of the tertiary sector shows its greatest impact on reducing energy consumption in the eastern region, a decline in heavy industry production would reduce energy demand more in the central region, and improvement in industrial electricity efficiency would also help reduce energy consumption the most in eastern China. We also forecast energy consumption in China will reach 4.8–4.9 billion tonnes of coal equivalent (tce) in 2020 and further grow to 5.0–5.4 billion tce in 2030.

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The dynamic relationship between energy consumption and economic growth in China

Cited by 45 publications

References 8 publications

How COVID-19 shock will drive the economy and climate? A data-driven approach to model and forecast

How COVID-19 shock will drive the economy and climate? A data-driven approach to model and forecast

The impact of urbanization and financial development on electricity intensity in China: Evidence from panel VAR approach

Economic Transition, Technology Change, and Energy Consumption in China: A Provincial-Level Analysis

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