Energy consumption flow and regional economic development: evidence from 25 economies

Tang, Bao-Jun; Gong, Pi-qin; Xiao, Yu-chong; Wang, Huaiyu

doi:10.1108/jm2-09-2014-0074

Cited by 11 publications

(5 citation statements)

References 30 publications

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“…The work depicts that, in rich nations, the boost in prosperity over time associates with an augment in e/E ratio. Tang et al, (2017) inspected that there is an approximately downbeat affiliation amid EEII and gross household product (GDP) per capita, though there are a few omissions, like Russia and Taiwan. Kalu et al, (2019) inspected that there is tough proof of the survival of a long-run affiliation amid power usage and pointers of fiscal expansion.…”

Section: Literature Reviewmentioning

confidence: 99%

Energy Consumption and Agricultural Economic Growth Nexus: Evidence From India

Inumula

Singh²,

Solanki

2020

IJEEP

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This study aims to empirically test the relationship between agriculture economic growth and energy consumption in India covering the annual time series data for the period 1985-2017 on four economic indicators namely agricultural value added (constant 2010 US$) as an alternate favoring fiscal development of agriculture, energy spending represented by agricultural electricity consumption (GWh), agricultural gas consumption (mmcft) and agricultural oil consumption (tons) in India. The study variables are assessed for stationary using the ADF tests and after confirming the same order of integration, the Johansen's Co-integration Test is exercised to find the extended association amid agriculture growth and energy consumption. Both the Trace and Lmax tests found that there exists one co-integrating equation in the system. The co-integration test confirms the long run equilibrium relation between energy consumption and agricultural economic growth in India. The short run relationships are tested by using the VECM methodology and finally the impulse responses are studied for the forecast horizon of ten years period to assess the performance of agricultural growth Vis a Vis energy consumption by imposing one standard deviation shock to the independent variables.

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Section: Literature Reviewmentioning

confidence: 99%

Energy Consumption and Agricultural Economic Growth Nexus: Evidence From India

Inumula

Singh²,

Solanki

2020

IJEEP

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“…Moreover investigated the quality of connection between ERASMUS and European policy. Tang et al (2017) aims to study correlation between energy consumption flow from embodied energy inventory index (EEII) and regional economic growth. The results show negative relationship between EEII and GDP per capita.…”

Section: Introductionmentioning

confidence: 99%

A gene expression programming model for economy growth using knowledge-based economy indicators

Ahmadi

Taghizadeh

2019

JM2

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Purpose The purpose of this paper is to focus on modeling economy growth with indicators of knowledge-based economy (KBE) introduced by World Bank for a case study in Iran during 1993-2013. Design/methodology/approach First, for grouping and reducing the number of variables, Tukey method and the principal component analysis are used. Also for modeling, 67 per cent of data is used for training in the two approaches of ARDL bounds testing and gene expression programming (GEP) and 33 per cent of them for testing the models. Then, the result models are compared with fitness function and Akaike information criteria (AIC). Findings The GEP model with fitness 945.7461 for training data and 954.8403 for testing data from 1000 is better than ARDL bounds testing model with fitness 335.5479 from 1000. In addition, according to model comparison tools (AIC), the GEP model has an extremely larger weight in comparison with ARDL bounds model. Therefore, the GEP model is introduced for future use in academia. Practical implications Knowledge and information is one of the most basic sources of wealth in economists’ sight. Thus, using KBE indicators appears essential in economic growth regarding daily progress in knowledge processes and its different theories. It is also extremely important to determine an appropriate model for KBE indicators which play a highly important role in the allocation of the economic resources of the country in an optimal manner. Originality/value This paper introduced a novel expression for economy growth using KBE indicators. All the data and the indicators are extracted from Word Bank service between 1993 and 2013.

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“…Several previous studies were used as references in this research. The economic sector was conducted by Al-Khouri (2007), Benos et al (2011), Brauer and Marlin (2010), Euske and Malina (2014), Gohmann et al (2013), Kouneva-Loewenthal and Vojvodic (2012) and Wolfram and Mohr (2010); the regional economies by Degelsegger-Márquez et al (2017), Dutta (2009), Libecap (2011), England (2006), Tang et al (2017) and Yuen (2008); and the gross regional income by Benni et al (2012), Caruana-Galizia (2013), Chan and Lam (2000), Guerreiro (2014), Jesuit et al (2003), Jordá et al (2014), Janssen (2003) and Yao et al (2005). No previous study has comprehensively studied the mediating effects of tourist value perception on the determination of economic sector, especially in Kalimantan, Indonesia.…”

Section: Introductionmentioning

confidence: 99%

Determination of economic sector in East Kalimantan, Indonesia

Zaini

2018

JCEFTS

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Purpose This paper aims to analyze the superior economic sector by looking at its contribution to the gross regional domestic product (GRDP) of East Kalimantan Province, the economic base, the multiplier effect and the strength of inter-sectoral linkages. Design/methodology/approach This study was designed through two research approaches, namely, quantitative and qualitative method. This is intended to complement the results of the phenomenon under study and to strengthen the analysis. Secondary data were analyzed by the level of contribution of the economic sectors to the GRDP, and the base sector was determined through the location quotient approach. The two methods of calculation helped to reveal the dominant economic sectors in East Kalimantan Province. The Input Output (IO) Table in 2016 was made up dated from the 2009 IO Table to be used as a basis for building Social Accounting Matrix data or known as the East Kalimantan Regional Socio-Economic Balance System (SEBS) (a matrix of 49 × 49 sectors) in 2017 by using the RAS method. To be consistent, these SEBS data are then aggregated so all commodities are combined into economic sectors used to determine the leading sector on the East Kalimantan Province SEBS in 2016 (a matrix of 41 × 41 sectors). Findings Based on the assessment by scoring of the criteria for determining the leading economic sectors in East Kalimantan, i.e. the contribution of the economic sector to GRDP, the economic base, the multiplier effect (income, production factor, and output) and the linkages between sectors, both backward and forward linkage, shows the ten leading sectors as follows: the trade; paper and printed goods; financial institutions and other financial services; fertilizer; chemical and other rubber products; hotel and restaurant; general government; fisheries; excavation; and mining without oil and gas. Originality/value Similar research has never been done before in East Kalimantan; this is one of the originalities of this present study. No previous study has comprehensively studied the mediating effects of tourist value perception on the determination of economic sector, especially in Kalimantan, Indonesia.

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Energy consumption flow and regional economic development: evidence from 25 economies

Cited by 11 publications

References 30 publications

Energy Consumption and Agricultural Economic Growth Nexus: Evidence From India

Energy Consumption and Agricultural Economic Growth Nexus: Evidence From India

A gene expression programming model for economy growth using knowledge-based economy indicators

Determination of economic sector in East Kalimantan, Indonesia

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