Evidence map›Paper›PMID 31782096›Full record

ArticleEnvironmental science and pollution research international2020

Dynamic causality among urban agglomeration, electricity consumption, construction industry, and economic performance: generalized method of moments approach.

Munir Ahmad, Gul Jabeen

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Article in Environmental science and pollution research international, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.

0numbers the graph read from it
0cells of the map it votes in
8citing papers in PubMed
–field-weighted citation impact
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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3 · Its place in the literature

Who cites it

8 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

2 authors.

Munir AhmadSchool of Economics, Zhejiang University, Hangzhou, 310027, China. munirahmad@zju.edu.cn.ORCID http://orcid.org/0000-0002-4376-8410
Gul JabeenSchool of Economics and Management, North China Electric Power University, Beijing, 102206, China. guljabeen@ncepu.edu.cn.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

This paper is a maiden empirical attempt to analyze the dynamic causal linkages among urban agglomeration, electricity consumption, construction industry, and economic performance, making use of simultaneous structural equations. A national panel of 30 provinces and three sub-national panels of China, for time span 2000 to 2016, have been estimated employing system and difference generalized method of moments (GMM) estimator. A construction industry-augmented model of economic growth has been proposed, incorporating construction industry and urban agglomeration as exogenous shocks to the aggregate production and electricity consumption as the input of production function. The core empirical results are first, the urban agglomeration and construction industry positively cause electricity consumption but are not caused by the same. It revealed the critical role played by urban agglomeration along with construction industry in boosting electricity consumption. Second, the economic performance positively causes electricity consumption, urban agglomeration, and construction industry and is also caused by the same. Third, urban agglomeration causes the construction industry and is caused by the same. It exposed the mutual role of urban agglomeration and urban industry in reinforcing each other in the times of high economic performance. Finally, among the three regions, the eastern zone is found to be strongest in terms of linkages among urban agglomeration, construction industry, electricity consumption, and economic performance. The intermediate zone is moderately strong, while the western zone is found to have the least strong linkages as compared with the two regions. These results are in line with the sub-national level of economic development of China. These findings, in terms of statistical significance, are highly robust across all the panels. Furthermore, depending upon empirical results, the related strategies are proposed.

Indexed as

Construction IndustryChinaEconomic DevelopmentElectricityEmploymentChinaConstruction industryConstruction industry-augmented growth modelEconomic performanceElectricity consumptionUrban agglomeration

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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.