Spain׳s fulfillment of its Kyoto commitments and its fundamental greenhouse gas (GHG) emission reduction drivers

Mancisidor, Itziar Martínez de Alegría; Basañez, Aitor; Basurto, Pablo Díaz de; Fernández–Sainz, Ana

doi:10.1016/j.rser.2015.12.208

Cited by 27 publications

(10 citation statements)

References 13 publications

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“…The authors in [24] analyzed the impact on GHG emissions of GDP per capita, population, renewable energy, energy intensity and the economic crisis in Spain for years 2000-2011, finding that GDP per capita and the penetration of renewable energy sources are the main drivers of GHG emissions growth. The coefficient of determination (R2) obtained in [24] ranged from 0.96 to 0.99, depending on the specification of the model. However, the variables used could be non-stationary, as a unit root test of the sample was not reported.…”

Section: Extended Literature Review and Hypothesis Developmentmentioning

confidence: 99%

“…Increased population leads to higher levels of consumption and production, which would produce higher GHG emissions. (+) [21,23,24,29,36];…”

Section: Population [Pop]mentioning

confidence: 99%

“…(+) [1,18,20,24,33,36,38] Fuel prices [FP] Higher prices lead to lower energy consumption levels. The variable used is estimated as an weighted average of different fuel prices (see below a more detailed explanation of the variable used) (−) [1,23,36];…”

Section: Primary and Final Energy Intensity [Pei] And [Fei]mentioning

confidence: 99%

“…This variable consists in the share of RES in total gross inland consumption. (−) [18,19,23,24] Structure of the economy [SE]…”

Section: Carbon Intensity [Ci]mentioning

confidence: 99%

“…Other deficiencies found in the research of determinants of GHG emissions are the non-inclusion of variables with high explanatory power in stochastic models (such as fuel mix, the structure of the economy or the fleet of vehicles), the use of own estimations and heterogeneous data sources (as [21]); multicollinearity among variables (as in [19,20,22]) and non-stationary data series (as [23][24][25]).…”

Section: Introductionmentioning

confidence: 99%

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Greenhouse Gas Emissions Growth in Europe: A Comparative Analysis of Determinants

Sánchez

Martín-Ortega²

2020

Sustainability

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Understanding the underlying reasons for greenhouse gas (GHG) emissions trends in different countries is fundamental for climate change mitigation. This paper identifies the main determinants that affect GHG emissions growth and assesses their impact and differences among countries in Europe. Previous studies have produced inconclusive results and presented several limitations, such as the lack of quality of the data used, the reduced identification of determinants and the use of methods that did not enable hypothesis testing. Conversely, this research identifies an extended list of determinants of GHG emissions, performs an in-depth statistical analysis and contrasts the significance of determinants using panel data and multiple linear regression models for the period 1990–2017 for the main Eurozone countries. The study found that GDP and final energy intensity are the main drivers for the reduction of GHG emissions in Europe. Furthermore, energy prices are not significant and heterogeneous results are found for the renewable energy, fuel mix and carbon intensity determinants, pointing to a different behavior at the country level. The uneven impact of the main determinants of GHG emission growth suggest that a differentiated application of European policies at country level will enhance the efficiency of mitigation efforts in Europe.

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Section: Extended Literature Review and Hypothesis Developmentmentioning

confidence: 99%

“…Increased population leads to higher levels of consumption and production, which would produce higher GHG emissions. (+) [21,23,24,29,36];…”

Section: Population [Pop]mentioning

confidence: 99%