Decomposition analysis of electricity use in the Jordanian industrial sector

Al-Ghandoor, A.; Al‐Hinti, I.; Mukattash, Adnan; Al-Abdallat, Yousef

doi:10.1080/14786461003782724

Cited by 15 publications

(6 citation statements)

References 35 publications

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“…Different industrial sectors also have different impacts on carbon emissions. Some empirical studies prove that almost all industrial scale growth will make a positive contribution to carbon emissions (Al-Ghandoor, 2010). However, the contribution of the tertiary industry is significantly smaller than that of the secondary industry (Liu and Lung, 2010).…”

Section: Literature Reviewmentioning

confidence: 99%

Industrial structure optimization, population agglomeration, and carbon emissions—Empirical evidence from 30 provinces in China

Liang

Huang

2023

Front. Environ. Sci.

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The sustainability of the ecological environment has been greatly threatened. Based on carbon emissions and combined with the panel data of 30 provinces in China from 2003 to 2020, this paper studied the various mechanisms of industrial structure optimization and population agglomeration on carbon emissions. The fixed effect model, panel threshold model and spatial spillover model are used to study the direct and indirect effects of industrial structure optimization and population agglomeration on carbon emissions, and the robustness of the results is tested in various ways. In terms of direct effects, the industrial structure optimization has a significant negative effect on carbon emissions, and the significance level is 1%. Population agglomeration has a significant positive effect on carbon emissions, with a significance level of 1%. In terms of indirect effects, 1) by adding the cross term of industrial structure optimization and population agglomeration, it is proved that population agglomeration can promote the carbon emission reduction effect of industrial structure optimization. 2) Population agglomeration was used as the threshold variable to verify the interval effect of industrial structure optimization on carbon emission reduction. The results show that the industrial structure optimization has a double threshold effect of population agglomeration on carbon emissions, and the threshold values are 2.1137 and 5.9263, respectively. And the larger the population agglomeration interval, the weaker the inhibition effect of industrial structure optimization on carbon emissions. 3) The industrial structure optimization, population agglomeration and carbon emissions have significant spatial spillover effects. The industrial structure optimization in neighboring areas has a significant inhibitory effect on carbon emissions, and the population agglomeration in neighboring areas has a significant promoting effect on carbon emissions.

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Section: Literature Reviewmentioning

confidence: 99%

Industrial structure optimization, population agglomeration, and carbon emissions—Empirical evidence from 30 provinces in China

Liang

Huang

2023

Front. Environ. Sci.

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“…Secondly, with their characteristics of openness, collaboration sharing,and connectivity,the Internet and Internet of Things are progressively becoming crucial tools for production and application that integrate with traditional resources to drive industrial structural upgrades [29] . Thirdly,in conjunction with technologies like big data analytics,could computing,the Industrial Internet,and Internet of Things, pollution control strategies can be optimized across multiple dimensions,enabling real-time dynamic collection of various factors related to enterprise emission activities.This facilitates communication across different networks even among enterprise departmentsand production equipment,resulting in enhanced energy management e ciency [30] . Considering that the main implication of the spatial functional division in urban agglomeration is that the Central City develops industries based on productive service industries and the Peripheral City develops industries based on manufacturing industries, and that optimizing the pattern of environmental governance, promoting the upgrading of the industrial structure and improving the e ciency of the energy structure are all conducive to carbon emissions reduction in both the productive service industry and the manufacturing industry, this paper puts forwards Hypothesis 3:…”

Section: Moderating Effect Of the Digital Economy On Spatial Function...mentioning

confidence: 99%

Impact of Spatial Functional Division in Urban Agglomeration on Urban Carbon Emissions: An Empirical Study of 19 Urban Agglomerations in China

Chen,

Wang,

Zhang

2024

Preprint

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This paper empirically examines the impact of spatial functional division in urban agglomeration on carbon emissions in Central and Peripheral City using data from 19 urban agglomerations in China spanning from 2010 to 2019. The findings indicate that: (1) The spatial functional division in urban agglomeration contributes to a reduction in carbon emissions in Central City but exacerbates them in Peripheral City. (2) Technological progress partially mediates the effect of spatial functional division in urban agglomeration on carbon emission reduction in Central City, while both technological progress and manufacturing agglomeration have partial mediating effects on the increase of carbon emissions due to spatial functional division in urban agglomeration in Peripheral City. (3) Industrial collaborative agglomeration and digital economy enhance the carbon emission reduction effect of spatial functional division in urban agglomerations for Central City; however, industrial collaborative agglomeration mitigates the exacerbating role of spatial functional division in urban agglomeration on carbon emissions for Peripheral City. (4) The impact of spatial function division in urban agglomeration on carbon emissions exhibits regional heterogeneity within urban agglomerations. The findings hold certain reference significance for relevant countries when formulating public policies that strike a balance between economic development and carbon emissions amidst deepening of spatial functional division in urban agglomeration.

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“…In this process, a large number of highly polluting industries were replaced by service industries, and the industrial structure was gradually advanced. The structure dominated by fossil energy has also been greatly improved (Al-Ghandoor, 2010). Through the demonstration effect and industrial correlation effect, the regional carbon-emission and intensity were reduced.…”

Section: Theoretical Analysis and Research Hypothesismentioning

confidence: 99%

Heterogeneous impact of artificial intelligence on carbon emission intensity: Empirical test based on provincial panel data in China

Zhang

Dong

et al. 2023

Front. Ecol. Evol.

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IntroductionEnergy conservation and emission reduction, as a major policy of China for a long time, has been put on the key strategic position. Based on the panel data of 30 provinces, cities and districts in China from 2006 to 2019.MethodsThis paper uses fixed effect model and spatial Durbin model to explore the effect and mechanism of artificial intelligence (AI) on regional carbon emission intensity (CEI).ResultsThe results show that: (1) there is a significant inverted U-shaped between AI and CEI, that is, with the deepening of the development of AI, CEI first increases and then decreases. (2) There is a significant spatial correlation between the development of AI and CEI in China. (3) AI has a significant spatial spillover effect on CEI of adjacent regions, and it shows an inverted U-shaped track-from promoting to restraining.DiscussionThe conclusion provides policy implications for the formulation of AI development strategy and so on during the specific period.

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Decomposition analysis of electricity use in the Jordanian industrial sector

Cited by 15 publications

References 35 publications

Industrial structure optimization, population agglomeration, and carbon emissions—Empirical evidence from 30 provinces in China

Industrial structure optimization, population agglomeration, and carbon emissions—Empirical evidence from 30 provinces in China

Impact of Spatial Functional Division in Urban Agglomeration on Urban Carbon Emissions: An Empirical Study of 19 Urban Agglomerations in China

Heterogeneous impact of artificial intelligence on carbon emission intensity: Empirical test based on provincial panel data in China

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