The driving factors of CO₂ emissions from electricity generation in Spain: A decomposition analysis

ABSTRACT

We apply an index decomposition analysis to investigate the main drivers of CO₂ emissions in the electricity generation sector in Spain over the period 1991–2017. We quantify the impact of five different effects: carbonization, transformation, fossil intensity, electricity intensity, and production effects. Four subperiods are identified. The relevance of the drivers changed over these subperiods. The fossil intensity, electricity intensity, and production effects played an important role in the increase in emissions during the first half of the period, and particularly from 1999 to 2005. In contrast, the carbonization and fossil intensity effects were the dominant drivers of emissions reductions between 2006 and 2010. The research allows an assessment of the impact of different measures on emissions by considering their influence on the different effects, and suggests which sets of measures could be more effective in reducing emissions.

KEYWORDS:

• CO₂ emissions
• decomposition analysis
• electricity generation
• emission drivers
• logarithmic mean Divisia index

Acknowledgments

We are grateful for the constructive comments of the three anonymous reviewers.

Disclosure statement

No potential conflict of interest was reported by the author(s).

Notes

¹ A more recent approach, the production-theoretical decomposition analysis (PDA) based on a production theory framework, examines the changes in the variable from a productive efficiency perspective (see e.g. Wang and Feng 2008, who applied it to the analysis of the CO₂ emissions of China’s construction industry, or Wang, Ang, and Zhou 2018, who analyzed the CO₂ emissions of China, accounting for heterogeneities).

² All greenhouse gas emissions expressed in their CO₂ equivalent have been included in the analysis. However, the magnitude of the other greenhouse gases with respect to the total emissions in electricity generation is insignificant (0.4% in 1990 and 0.9% in 2017), so hereafter we will refer to the total amount of emissions in their CO₂ equivalent as CO₂ emissions.:

³ Some studies refer to this factor as the emission coefficient, particularly when considering a single fuel.

⁴ Another scale variable could be chosen. For example, in a study on the historical evolution of CO₂ emissions in China, Wang, Chen, and Zou (2005), take the population linked to production per capita as such, linking theoretical growth to the conjunction of these two variables (that is, the GDP effect is decomposed into the effects of population and GDP per capita, identifying these as the factors that would determine the “theoretical” variation in emissions).

5 Some studies, such as Malla (2009) and Jiang, Su, and Li (2018), considered the effect of changes in electricity output, instead of economic activity and electricity intensity of GDP, and also found that this was the main contributor to the increase in emissions.

6 Some studies divide this effect into the changes in the share of fossil fuels in thermal power generation and changes in the emission coefficient of each fuel (see e.g., Huda, Okajima, and Suzuki 2017; Li et al. 2018; Zhang et al. 2013).

⁷ This term refers to the shortfall of revenues that arises when the regulated components of retail electricity tariffs are allegedly below the corresponding costs borne by power companies.

8 Rodrigues et al. (2020) found that two factors contributed to the increase in carbon emissions in the EU electricity generation sector in the 2000–2007 period (strong economic growth and a reduction in non-fossil sources in electricity generation due to decreasing shares of nuclear and hydropower) and two factors contributed to their reduction (changes in the fossil fuel mix (substitution of gas for coal) and improvements in the efficiency of electricity use).

Additional information

Funding

This work was supported by the Spanish Ministry of Science, Innovation and Universities and ERDF (grant number: RTI2018-095484-B-I00).