Growth pattern changes in China’s energy consumption

Guang, Fengtao; Wen, Le

doi:10.1007/s11356-020-09206-0

Cited by 14 publications

(6 citation statements)

References 44 publications

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“…Letters from the English alphabet are corresponding to each one sector and are used in the next tables. The fundamental equations that constitute the pylons for an I-O model and the equations for the computation of BLs' and the t.I-Ms' and their synthetic components are the follows (Augusztinovics, 1970;Bekhet, 2011Bekhet, , 2012Cardenete et al, 2017;Chuenchum et al, 2018;Cuello et al, 1992;de Mesnard, 2002de Mesnard, , 2004de Mesnard, , 2007ade Mesnard, , 2007bDietzenbacher, 2005;Dietzenbacher and Lahr, 2004;Freytag and Fricke, 2017;Guang and Wen, 2020;Hirschman, 1958;Ivanova, 2014;Jensen et al, 1979, pp. 20-22;Kelly, 2015;Kelly et al, 2016;Kolokontes et al, 2018Kolokontes et al, , 2019Lahiri, 2000;Lenzen, 2003;Leontief, 1936Leontief, , 1951Lopes and Neder, 2017;Meng et al, 2019;Milana, 1985;Miller and Blair, 2009, pp.…”

Section: Methodsmentioning

confidence: 99%

“…I-O multipliers are used from the policy-makers for the management of developmental programming and constitute a common tool for the investigation of economic perspectives (Chisari et al, 2019;Freytag and Fricke, 2017;Guang and Wen, 2020;Guerra and Sancho, 2012;Haslop et al, 2017;Humavindu and Stage, 2013;Ivanova, 2014;Kakderi and Tasapoulou, 2017;Kelly et al, 2016;Lopes and Neder, 2017;Mariolis and Soklis, 2020;Mastronardi et al, 2012;Meng et al, 2019;Muller-Hansen et al, 2017;Nguyen et al, 2018;Nguyen et al, 2019;Romero et al, 2019;Sancho, 2013;Thomassin, 2018). Dietzenbacher (2005) had underlined that all the I-O multipliers are not the same suitable for all the occasions, their interpretations are differentiated and their usefulness are depended on the problem that one addresses.…”

Section: Introductionmentioning

confidence: 99%

“…Dietzenbacher (2005) had underlined that all the I-O multipliers are not the same suitable for all the occasions, their interpretations are differentiated and their usefulness are depended on the problem that one addresses. There is a copiousness of indicators (Kolokontes et al, 2019) that have been constructed to estimate multiplicative effects on output, income, employment, emissions and so forth (Dietzenbacher, 2005;Sancho, 2013;Guang and Wen, 2020), or just to order the sectors in rankings in comparison with an average mental sector (Rasmussen, 1956, pp. 133-138;Hazari, 1970).…”

Section: Introductionmentioning

confidence: 99%

“…Five issues, at least, are remained cardinal as for the derived results from the multipliers and their choice from the policy-makers for the growing planning. The first topic is the interpretation of results (de Mesnard, 2002(de Mesnard, , 2004(de Mesnard, , 2007a(de Mesnard, , 2007bDietzenbacher, 2005;Guang and Wen, 2020;Kolokontes et al, 2019): What is exactly measured by each one index? The second issue regards the precision of indices and its margins (Kolokontes et al, 2019;Milana, 1985;Romero et al, 2019).…”

Section: Introductionmentioning

confidence: 99%

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Decomposition Analysis for the Comparison and the Comprehension of Conventional Input-Output Impacts’ Indicators: An Empirical Paradigm

Kolokontes

Kontogeorgos

Loizou

et al. 2020

SAEB

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Sometimes, the priorities in the growing patterns create dubiousness, surprises and are proved unsuitable to re-form and redress the distortions of economy, magnifying them or even emerging news. When the external or the individual interferences are remained out of the planning, then the knowledge of why the various indices yield conflicting sectoral rankings can help the policy-makers to plan ameliorated strategies. In this paper a decomposition analysis for the components of conventional backward linkages’ (BLs’) indices and the corresponding type I multipliers (t.I-Ms’) has been taken place, in order to support the comparison and the comprehension of conflictions that are recorded on their derived sectoral rankings. For the empirical paradigm, data from the Greek economy have been used. The indicators’ appropriateness for the developmental planning has been scrutinized giving an emphasis on the model’s causality, the initial exogenous stimuli and the “intrasectoral initial trends for impacts’ generation”. The analysis provides a proof that the BL’s indices are strongly tendentious and the t.I-Ms’ are preferable for the medium-to-long run growing planning.

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Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Decomposition Analysis for the Comparison and the Comprehension of Conventional Input-Output Impacts’ Indicators: An Empirical Paradigm

Kolokontes

Kontogeorgos

Loizou

et al. 2020

SAEB

View full text Add to dashboard Cite

show abstract

“…Yin (2019) used China 's interregional input-output (CMRIO) model and structural decomposition analysis (SDA) method to estimate the embodied carbon emission intensity of China 's export trade, and analyzed the trend of China 's export trade carbon emission intensity and its driving factors. Guang and Wen (2020) combined environmental input-output method and structural decomposition analysis to study the factors affecting the change of energy consumption in China.…”

Section: Literature Reviewmentioning

confidence: 99%

Driving Factors of Carbon Emissions in G20 Countries: A Weighted Average Structural Decomposition Approach

Xia

2023

Preprint

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As a major part of global development governance, G20 countries ' carbon emissions account for 80 % of global carbon emissions. In order to achieve the "carbon neutrality" goal proposed by the United Nations, it is important to compare and analyze the driving factors of carbon emissions in G20 countries and provide suggestions for their carbon elimination. Based on the carbon emission input-output data of seventeen countries out of G20 in the EORA database, this paper compares the driving factors of carbon emissions between the countries from 1990 to 2016 using the weighted average structural decomposition and K-mean model. This paper focus on four driving factors including carbon emission intensity, final demand structure, export structure and production structure. The carbon emission intensity and final demand structure are the main factors of carbon emission reduction and the other two factors have little effect. In G20 countries, Italy was in the first category because it does best on the final demand structure factor, however Australia is in the last category because it does not make full use of four factors. Therefore, the development of energy-saving technologies and adjustment of demand structure has become an important means for countries to transform and achieve carbon neutrality.

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A land-use-constrained, generation–transmission model for electricity generation through solar photovoltaic technology: a case study of south India

Kumar

Jayanti

2021

Clean Techn Environ Policy

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Growth pattern changes in China’s energy consumption

Cited by 14 publications

References 44 publications

Decomposition Analysis for the Comparison and the Comprehension of Conventional Input-Output Impacts’ Indicators: An Empirical Paradigm

Decomposition Analysis for the Comparison and the Comprehension of Conventional Input-Output Impacts’ Indicators: An Empirical Paradigm

Driving Factors of Carbon Emissions in G20 Countries: A Weighted Average Structural Decomposition Approach

A land-use-constrained, generation–transmission model for electricity generation through solar photovoltaic technology: a case study of south India

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