Energy-related GHG emission in agriculture of the European countries: An application of the Generalized Divisia Index

Qin, Yan; Yin, Jieting; Baležentis, Tomas; Makutėnienė, Daiva; Štreimikienė, Dalia

doi:10.1016/j.jclepro.2017.07.010

Cited by 72 publications

(32 citation statements)

References 28 publications

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“…Nevertheless, the results of Eco-(in)efficiency and energy use analyses which showed that inefficiency and energy use excesses are also observed in countries with higher income levels, add a question mark for whether EU convergence is only a necessary or/and sufficient conditionfor rendering the EU agricultural sectors more sustainable. The findings of the paper, extend the debate of previous papers [12,16] on the necessity of developing more advanced methods on agricultural sustainability assessments, especially in economies where technological, institutional and scale factors are rather complex. …”

Section: Ekc Testing Resultssupporting

confidence: 77%

“…As far as agricultural studies are concerned, the latest relevant literature has shown a remarkable progress in developing synthetic methods towards sustainability assessment, lying far from a simple measurement of output production or input use [12,13,16]. More precisely, until recently, most of the attention was given to the technical efficiency of agricultural sectors.…”

Section: Dea Applications On Agricultural Sectormentioning

confidence: 99%

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Testing for Environmental Kuznets Curve in the EU Agricultural Sector through an Eco-(in)Efficiency Index

2017

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Studies on agricultural production practices advance within international literature and new methods are proposed in order to assess the agricultural sustainability, either at farm or macro level. The present paper builds on these advancements and develops a synthetic Eco-(in)efficiency index by employing a directional distance function-data envelopment analysis (DEA) model. This index is used in order to assess the sustainability of the EU agricultural sector for the period 1999-2012 on a country level. Furthermore, Eco-(in)efficiency, together with the energy use and greenhouse gas (GHG) emissions are regressed on the gross domestic product (GDP) of EU countries, in order to check for any environmental Kuznets curve relationship existence. Results signify that efficiency improvements are possible, both towards output development and GHG emissions reduction. In addition, the potential of each country in adopting more sustainable production practices is not totally connected with its economic development, as Eco-(in)efficiency and GDP levels of EU countries seem to be linked with an N-Shaped curve.

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Section: Ekc Testing Resultssupporting

confidence: 77%

Section: Dea Applications On Agricultural Sectormentioning

confidence: 99%

Testing for Environmental Kuznets Curve in the EU Agricultural Sector through an Eco-(in)Efficiency Index

2017

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“…According to existing studies [66,78,79], the sectoral structure of the economy and the composition of energy consumption (solid fuels, oil, gas, and renewable energy), as well as the economic complexity [27] influence the level of pollutant emissions. The economic complexity was chosen as the primary explanatory variable because it is considered an accurate predictor of economic growth and expression of a skill-based, knowledge-based and sophisticated production [22], which was not included before in a panel analysis of environmental impact.…”

Section: Model and Datamentioning

confidence: 99%

“…The economic complexity was chosen as the primary explanatory variable because it is considered an accurate predictor of economic growth and expression of a skill-based, knowledge-based and sophisticated production [22], which was not included before in a panel analysis of environmental impact. The reason for including energy consumption structure as the second explanatory variable is related to the fact that the increase of fossil share in energy consumption influences the carbon emissions and environment deterioration [66,78,79] and improvement of the energy consumption structure has a great impact on greening the economy and transitioning to a lower carbon-stage.…”

Section: Model and Datamentioning

confidence: 99%

“…Energy consumption structure, economic structure and energy efficiency exert a significant impact on pollutant emissions and can be considered as essential factors of low-carbon development [64][65][66]. Several methods have been developed in order to provide appropriate tools to analyse sectoral emission trends [67], simulate energy consumption and GHG emissions under different conditions [68], select economically feasible technologies [69] and models to optimize energy consumption structure [70] or improve energy structure in the industry [71].…”

Section: Introductionmentioning

confidence: 99%

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The Relationship between Economic Complexity, Energy Consumption Structure and Greenhouse Gas Emission: Heterogeneous Panel Evidence from the EU Countries

2019

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The aim of the paper is to examine the long-term relationship between economic complexity, energy consumption structure, and greenhouse gas emission, within a panel of European Union countries and two subpanels: (i) European economies with higher economic complexity and (ii) European economies with a lower level of economic complexity. Taking into consideration the heterogeneity among European countries, the heterogeneous panel technique is used, including panel estimation through fully modified least squares (FMOLS) and dynamic ordinary least squares (DOLS). The empirical findings indicate a long-term equilibrium relationship between economic complexity, energy consumption structure and greenhouse gas emission within all three panels. Economic complexity and energy consumption structure have a statistically significant impact on greenhouse gas emission within all panels, but the influence is higher within the subpanel of countries with a lower level of economic complexity, suggesting a higher risk of pollution as the economic complexity grows and as the energy balance inclines in favor of non-renewable energy consumption. Our paper suggests that the economic complexity is a variable that must be taken into consideration when national economic and energy policies are shaped. Finally, policy implications for each panel of countries are discussed.

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The carbon emissions calculation, decomposition and peak scenario simulation of meat, egg, and milk in China: from a production and consumption perspective

Zhou,

Chen,

et al. 2024

Clean Techn Environ Policy

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Characterizing and quantifying livestock carbon emissions helps mitigate temperature rise and potential climate risks. Here, the spatial and temporal characteristics of China's carbon emissions of meat, egg, and milk are analyzed from the perspective of production and consumption. A driving factor decomposition model of carbon emissions of meat, egg, and milk production and consumption is established using the logarithmic mean Divisia index (LMDI). Using a static scenario analysis and dynamic Monte Carlo simulations (MCS), the evolution trajectory of carbon peak in meat, egg, and milk production and consumption in China during 2021-2030 is analyzed. China's carbon emissions from meat, egg, and milk production and consumption have risen in 2020, reaching 794.1 and 465.88 Mt, respectively. Moreover, western China has the highest carbon emissions in production (33.84%). For consumption, the carbon emissions are mainly concentrated in the eastern region (42.96%). The key drivers of carbon emissions for this production and consumption are the farmland value and residential income effects, respectively. Conversely, reverse urbanization, agricultural industry structure, and food money purchasing power effects play significant suppressive roles. Under the business-as-usual scenario (BAU), China's carbon emissions from meat, egg, and milk production and consumption in 2030 will increase by 40.1% and 4.86% from 2020, respectively. The middle low-carbon scenario (Mid-LC) mitigates carbon emission growth by 24.51% and −5.7%, respectively, for the production and consumption of meat, egg, and milk in 2030 compared to 2020. In the highly low-carbon scenario (Hig-LC), production and consumption will achieve a carbon peak by 2030. Ultimately, this study highlights the importance of reducing carbon emissions from livestock on production and consumption, providing a new perspective for green development in China and other emerging economies.

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Energy-related GHG emission in agriculture of the European countries: An application of the Generalized Divisia Index

Cited by 72 publications

References 28 publications

Testing for Environmental Kuznets Curve in the EU Agricultural Sector through an Eco-(in)Efficiency Index

Testing for Environmental Kuznets Curve in the EU Agricultural Sector through an Eco-(in)Efficiency Index

The Relationship between Economic Complexity, Energy Consumption Structure and Greenhouse Gas Emission: Heterogeneous Panel Evidence from the EU Countries

The carbon emissions calculation, decomposition and peak scenario simulation of meat, egg, and milk in China: from a production and consumption perspective

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