Energy structure dividend, factor allocation efficiency and regional productivity growth-- An empirical examination of energy restructuring in China

Guan, Hongjun; Liye, Dong; Zhao, Aiwu

doi:10.1016/j.enpol.2022.113307

Cited by 9 publications

(3 citation statements)

References 34 publications

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“…In this sense, the structure of consumption of energy is shifting in a cleaner and more sustainable direction. This is also consistent with the conclusions in the literature [6,7]. The aforementioned achievements in energy transition are attributed to the policy-driven transformation practices over the past decade.…”

Section: Energy Investmentsupporting

confidence: 92%

A Study on the Distribution Dynamics, Regional Disparities, and Convergence of China’s Energy Transition

Tian,

Gao,

Hao

2024

Energies

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Energy transition, as a crucial aspect of the country’s high-value-added economic development, involves the construction of an energy transition index system and empirical analysis using methods such as the entropy weighting method, kernel density estimation, Markov chain, Dagum Gini coefficient, σ-convergence, and β-convergence. This study measures the level of energy transition in 280 Chinese cities from 2010 to 2019 and analyzes their evolutionary trends, regional disparities, structural differences, and convergence. The findings reveal that China’s energy transition generally exhibits characteristics of “improvement in development levels and reduction in absolute disparities”. The disparities in energy transition primarily stem from developmental differences among the three major regions, displaying typical σ-convergence and β-convergence characteristics. This analysis contributes to understanding the real level and distribution features of China’s energy transition, providing a basis for identifying focal points for enhancing energy transition in the current and future stages.

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Section: Energy Investmentsupporting

confidence: 92%

A Study on the Distribution Dynamics, Regional Disparities, and Convergence of China’s Energy Transition

Tian,

Gao,

Hao

2024

Energies

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“…The energy structure with too high proportion of coal often has higher carbon emission intensity. Therefore, it is expressed by the ratio of coal consumption to total energy consumption (Guan et al, 2023). Population density (popu).…”

Section: Variables and Data Sourcesmentioning

confidence: 99%

Does digital economy development reduce carbon emission intensity?

2023

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Carbon emissions from human activities are the main cause of climate warming. Under the background of economic and social digital transformation, accurately assessing the carbon emission reduction effect of the development of the digital economy is of great significance for countries to deal with climate warming in the post-COVID-19 era. This paper constructs a dynamic evaluation model of orthogonal projection to measure the level of digital economy development at the provincial level in China from 2007 to 2019. On this basis, the panel fixed effects model and mediation model are used to empirically test the impact of digital economy development on carbon emission intensity and its mechanism. The results indicate that: (1) The development of China’s digital economy is unbalanced among regions, showing a geospatial pattern of decreasing from east to west. (2) China’s carbon emission intensity has a trend of decreasing year by year, and there are geospatial differences of “high in the west and low in the east” and “high in the north and low in the south.” (3) The digital economy development can effectively reduce regional carbon emission intensity through industrial structure optimization effect and resource allocation effect, and the industrial structure optimization effect can suppress carbon emission intensity more obviously. (4) The development of digital economy in different regions has different degrees of reducing carbon emission intensity. The development of digital economy in the eastern region has a stronger inhibitory effect on carbon emission intensity than that in the middle and western regions, and the development of digital economy in economically developed regions can suppress carbon emission intensity more. This paper provides enlightenment for policy makers to deal with climate warming.

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“…The optimal allocation of factors can alleviate the mismatch of factor resources between regions while promoting the flow of factors, thus improving green total factor productivity. Some studies have found that the adjustment of energy structure aggravates the distortion of factor allocation, resulting in an inhibitory effect on productivity which mainly derives from a scale effect (Guan et al 2023 ). As a production factor in the process of urbanization, land resource mismatch will increase resource consumption, aggravate environmental pollution and reduce urban green total factor productivity (Xie et al 2022 ).…”

Section: Theoretical Backgroundmentioning

confidence: 99%

Towards green economic recovery: how to improve green total factor productivity

Wang

2023

Econ Change Restruct

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Achieving green economic recovery is crucial to improving environmental quality and sustainable development. This study examines the influence of new digital infrastructure on green total factor productivity (GTFP) using panel data from 30 regions in China from 2008 to 2019. The results are as follows: (1) New digital infrastructure has a significant improvement effect on GTFP. After a series of robustness tests, the conclusion is still valid. (2) The improvement effect of new digital infrastructure on GTFP shows significant heterogeneity. In regions with high industrial agglomeration, high environmental regulation and strong government environmental preference, the improvement effect of new digital infrastructure on GTFP is more obvious. (3) New digital infrastructure improves GTFP through green technology innovation and factor allocation optimization. The government should strengthen the fiscal incentives for green technology development while increasing R&D investment in fiscal expenditure, thus promoting green economic recovery.

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Energy structure dividend, factor allocation efficiency and regional productivity growth-- An empirical examination of energy restructuring in China

Cited by 9 publications

References 34 publications

A Study on the Distribution Dynamics, Regional Disparities, and Convergence of China’s Energy Transition

A Study on the Distribution Dynamics, Regional Disparities, and Convergence of China’s Energy Transition

Does digital economy development reduce carbon emission intensity?

Towards green economic recovery: how to improve green total factor productivity

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