Driving Factors of Energy Consumption in the Developed Regions of Developing Countries: A Case of Zhejiang Province, China

Qing, Ganghua; Luo, Yifan; Huang, Weiwei; Wang, Wanjue; Yue, Zijing; Wang, Jie; Li, Qingyi; Jiang, Shuhan; Sun, Shijun

doi:10.3390/atmos12091196

Cited by 11 publications

(6 citation statements)

References 39 publications

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“…In this paper, based on the basic principle of Kaya identity [34], the extended LMDI method is used to decompose electricity consumption by production sector and household sector [35]. The decomposition model on electricity consumption for production sector is constructed as follows:

P C_{1} = \sum_{i} ((), \frac{P C_{i}}{E C_{i}} \times \frac{E C_{i}}{G D P_{i}} \times \frac{G D P_{i}}{G D P} \times \frac{G D P}{P_{e}} \times P_{e}) = \sum_{i} ((), f_{i} \times d_{i} \times s_{i} \times g \times p_{1})

where,

f_{i} = \frac{P C_{i}}{E C_{i}} ， d_{i} = \frac{E C_{i}}{G D P_{i}} ， s_{i} = \frac{G D P_{i}}{G D P} ， g = \frac{G D P}{P_{e}} ， p_{1} = P_{e}

…”

Section: Methods and Data Sourcesmentioning

confidence: 99%

P{C}_{1}=\sum\limits _{i}\left(\frac{P{C}_{i}}{E{C}_{i}}\times \frac{E{C}_{i}}{GD{P}_{i}}\times \frac{GD{P}_{i}}{GDP}\times \frac{GDP}{{P}_{e}}\times {P}_{e}\right)=\sum\limits _{i}\left({f}_{i}\times {d}_{i}\times {s}_{i}\times g\times {p}_{1}\right)

where,

{f}_{i}=\frac{P{C}_{i}}{E{C}_{i}},{d}_{i}=\frac{E{C}_{i}}{GD{P}_{i}},{s}_{i}=\frac{GD{P}_{i}}{GDP},g=\frac{GDP}{{P}_{e}},{p}_{1}={P}_{e}

…”

Section: Methods and Data Sourcesmentioning

confidence: 99%

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Decomposition analysis on factors affecting electricity substitution in Guangdong province, China

Chen

Liu

Kuang

et al. 2023

IET Cyber-Phy Sys Theory & Ap

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Electricity substitution is an effective measure for reducing pollutant emissions and promoting the consumption of renewable energy. The current research lacks a quantitative analysis method for the factors affecting regional electricity substitution. This article proposes a decomposition model of the factors affecting electricity substitution based on Logarithmic Mean Divisia Index method to expand the depth and breadth of electricity substitution. Results show that: (1) Macro‐level electricity substitution in Guangdong province develops steadily, with 1694.93 × 108 kWh of total electricity substitution quantity during the period 2006–2020. The electricity substitution quantity in production sector accounts for 83.92% of the total, which is much larger than that in household sector. (2) Sustained improvement of labour productivity gives the largest contribution (3592.32 × 108 kWh) to the increase of electricity substitution in production sector during the period 2006–2020. Living standard effect has the largest contribution (452.21 × 108 kWh) to the increase of electricity substitution in household sector during the period 2006–2020. Electrification level effect and population effect have significant impact on electricity substitution development, while industrial structure has little impact on electricity substitution. (3) Energy intensity effect and energy consumption growth effect negatively influence electricity substitution in Guangdong province. The decline of energy intensity in the production sector has driven the decrease of electricity substitution quantity, contributing −2540.36 × 108 kWh to electricity substitution during the period 2006–2020. Policy implications like continuously promoting breakthroughs in electricity substitution technologies, improvement of electrification rate in the sectors with potential for substitution, and improvement the price mechanism for fossil fuels and electricity can promote the deepening development of electricity substitution and the realisation of the carbon neutrality goal.

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P C_{1} = \sum_{i} ((), \frac{P C_{i}}{E C_{i}} \times \frac{E C_{i}}{G D P_{i}} \times \frac{G D P_{i}}{G D P} \times \frac{G D P}{P_{e}} \times P_{e}) = \sum_{i} ((), f_{i} \times d_{i} \times s_{i} \times g \times p_{1})

where,

f_{i} = \frac{P C_{i}}{E C_{i}} ， d_{i} = \frac{E C_{i}}{G D P_{i}} ， s_{i} = \frac{G D P_{i}}{G D P} ， g = \frac{G D P}{P_{e}} ， p_{1} = P_{e}

…”

Section: Methods and Data Sourcesmentioning

confidence: 99%

P{C}_{1}=\sum\limits _{i}\left(\frac{P{C}_{i}}{E{C}_{i}}\times \frac{E{C}_{i}}{GD{P}_{i}}\times \frac{GD{P}_{i}}{GDP}\times \frac{GDP}{{P}_{e}}\times {P}_{e}\right)=\sum\limits _{i}\left({f}_{i}\times {d}_{i}\times {s}_{i}\times g\times {p}_{1}\right)

where,

{f}_{i}=\frac{P{C}_{i}}{E{C}_{i}},{d}_{i}=\frac{E{C}_{i}}{GD{P}_{i}},{s}_{i}=\frac{GD{P}_{i}}{GDP},g=\frac{GDP}{{P}_{e}},{p}_{1}={P}_{e}

…”

Section: Methods and Data Sourcesmentioning

confidence: 99%

Decomposition analysis on factors affecting electricity substitution in Guangdong province, China

Chen

Liu

Kuang

et al. 2023

IET Cyber-Phy Sys Theory & Ap

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“…(2,15) This means that 1.2 billion tons of GHG are emitted annually to produce and discard clothing. (2,10,15,16) Most clothing is discarded into a landfill as there exists no clearly established recycling method and furthermore, unsold clothing is often incinerated to maintain brand value. (17,18) Fortunately, a new market has emerged where clothing can be resold or upcycled as done by such companies providing resources as ThredUp and Earth911.…”

Section: Introductionmentioning

confidence: 99%

“…2,15 This means that 1.2 billion tons of GHG are emitted annually to produce and discard clothing. 2,10,15,16 Most clothing is discarded into a landfill as there exists no clearly established recycling method and furthermore, unsold clothing is often incinerated to maintain brand value. 17,18…”

Section: Introductionmentioning

confidence: 99%

From waste to wearable: an alternative waste stream for unusable textiles turned into piezoelectric textiles

et al. 2023

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“…The latter include subsidence of the daylight surface in the absence of backfilling the mined-out area [1]- [3], accumula-tion of waste rock dumps and tailings over large areas, as well as atmosphere pollution with coalmine methane and products of blasting operations, pollution of surface water bodies with highly mineralized mine waters [4]- [6]. The coal mining industry is still an important component of the economy in a number of countries, such as Poland, Russia, Ukraine, USA, China, India, where coal is a raw material for energy and metallurgy [7]- [9].…”

Section: Introductionmentioning

confidence: 99%

Granulometric composition research of mine rocks as a material for backfilling the mined-out area in coal mines

Petlovanyi¹,

Malashkevych²,

Sai³

et al. 2021

Min. miner. depos.

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Purpose. Determination of the granulometric characteristics and loosening coefficient of mine rocks formed during stope operations from undercutting the bottom rocks as a potential backfill material based on a set of mine and laboratory research. Methods. To do research, a complex methodology is used, which includes a photographic surveying the mass of destroyed rocks from undercutting the bottom rocks in mine conditions, digital processing and determining the granulometric composition in the software package, sampling the mine rocks at the surface complex, and reconstructing the granulometric composition, similar to the full-scale mine conditions in the laboratory. Sieve analysis, laboratory balance and laboratory container are used to determine the granulometric composition and loosening coefficient. Findings. The dependence between the granulometric composition and loosening coefficient of rocks, which increases by 33% within fractions of 0-50 mm and by 8% within fractions of 50-140 mm, has been revealed. It has been determined that the destroyed rocks in the face within fractions of 0-140 mm have a bulk density of 1.28 g/cm3, loosening coefficient of 1.7, the voidness of and the maximum reserve for the backfill mass compaction with mine rocks is 41.9%. An analytical assessment of the volumes of the formed cavities of the mined-out area in the longwall face and gobed mine workings, as well as the prospects and completeness of their filling with mine rocks have been performed. Originality. It has been determined that the loosening coefficient of mine rocks destroyed in the stope face by KA-200 shearer changes according to the logarithmic dependence on their granulometric composition. This makes it possible to control the granulometric characteristics of the backfill material to achieve the maximum density of the backfill mass. Practical implications. Further research can serve as a basis for determining the rational parameters for the formation and placement of backfill masses during complex-mechanized selective mining of coal seams using various methods of backfilling operations.

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Driving Factors of Energy Consumption in the Developed Regions of Developing Countries: A Case of Zhejiang Province, China

Cited by 11 publications

References 39 publications

Decomposition analysis on factors affecting electricity substitution in Guangdong province, China

Decomposition analysis on factors affecting electricity substitution in Guangdong province, China

From waste to wearable: an alternative waste stream for unusable textiles turned into piezoelectric textiles

Granulometric composition research of mine rocks as a material for backfilling the mined-out area in coal mines

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