Evidence map›Paper›PMID 35103939›Full record

ArticleEnvironmental science and pollution research international2022

Linking electricity demand and economic growth in China: evidence from wavelet analysis.

Hamid Mahmood, Jun Wen, Muhammad Zakaria, Samia Khalid

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Article in Environmental science and pollution research international, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
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1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

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Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

2 citing papers in PubMed.

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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

4 authors.

Hamid MahmoodSchool of Economics and Finance, Xi'an Jiaotong University, Xi'an, Shaanxi, People's Republic of China. hamid.xjtu@gmail.com.
Jun WenSchool of Economics and Finance, Xi'an Jiaotong University, Xi'an, Shaanxi, People's Republic of China.
Muhammad ZakariaDepartment of Economics, COMSATS University Islamabad, Islamabad Campus, Pakistan.
Samia KhalidSchool of Economics and Finance, Xi'an Jiaotong University, Xi'an, Shaanxi, People's Republic of China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The study empirically examines the association between electricity demand and economic growth in China in a time-frequency framework. Wavelet coherence analysis and phase difference methods are applied to find the co-movement and causality between variables using monthly data for 1999 to 2017 time period. The results of the wavelet power spectrum show that both series have high fluctuations at high frequencies. The findings of wavelet coherence reveal co-movements between electricity demand and economic growth at different frequency levels. However, this association is stronger at low-frequency levels. Evidence from the phase difference indicates that electricity is causing economic growth with a positive sign. The results of wavelet-based correlation also show a high correlation between these two variables. For robustness analysis, linear and nonlinear causality tests are applied to find causality between variables over time. Both linear and nonlinear causality tests reveal bidirectional causality between variables. It corroborates the result of wavelet causality that both variables cause each other at different frequency levels.

Indexed as

Economic DevelopmentWavelet AnalysisCarbon DioxideChinaElectricityCarbon DioxideEconomic growthElectricity consumptionNonlinear causalityWavelet

Identifiers

PMID35103939

What OpenQuestion holds

Textmetadata
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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.