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CO2 emissions, renewable energy, and environmental regulations in the EU countries

Environmental Science and Pollution Research volume 27, pages 33615–33635 (2020)Cite this article

Abstract

This paper analyzes the factors explaining the slight decrease of CO2 emissions in the European Union (EU), recorded during the last period. With a focus on 12 EU countries, we apply a panel data analysis over the period 1990 to 2017 and we investigate the impact of renewable energy share in energy production, and the role of EU environmental regulations, in explaining the level of CO2 emissions. Our static and dynamic panel data analysis points to a negative impact of an increased renewable energy share on CO2 emissions, while there is no clear evidence about the role of environmental regulations. It appears that the 2020 climate and energy package contributed to the reduction of pollution level, while the ratification of the Kyoto protocol by the EU countries had no significant influence. At the same time, our findings validate the environmental Kuznets curve (EKC) hypothesis and the pollution halo (PH) hypothesis, showing that foreign companies export eco-friendly technologies. Our results prove to be robust regarding the use of static fixed and random effects models, of two-stage least square models and the use of difference and system generalized method of moments (GMM) frameworks.

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Notes

  1. Figure 1 (Appendix) presents the trend of CO2 emissions in the EU, using World Bank statistics. We can notice a decrease of CO2 emissions starting with the 1980s, while the reduction of CO2 emissions accelerated starting with the 2000s, when the EU legislative packages for climate change enter into force. At the same time, the CO2 intensity (CO2 emissions to GDP ratio) continued to decrease starting with the 1970s.

  2. Tiwari and Albulescu (2016) raised a series of questions about the use of Energy Information Agency (EIA) data for the renewable energy share because the renewable to total energy ratio is incredibly high for a series of countries. Therefore, we prefer to use Enerdata for the renewable share, although these data cover only 12 EU countries.

  3. European Council, Presidency Conclusions—Dublin 25/26 June 1990, Annex II: The Environmental Imperative, Council of the European Union, SN 60/1/90, 1990.

  4. In 2004, the ETS was widened to incorporate the trading certificates of the so-called Kyoto flexible mechanism as compliance tools.

  5. https://www.consilium.europa.eu/uedocs/cms_data/docs/pressdata/en/ec/93135.pdf

  6. According to the EU Commission, the ETS is the EU’s key tool for cutting greenhouse gas emissions from large-scale facilities in the power and industry sectors, as well as the aviation sector. The ETS covers around 45% of the EU’s greenhouse gas emissions. In 2020, the target is for the emissions from these sectors to be 21% lower than in 2005. The sectors not included in the ETS account for 55% of total EU emissions and originate in housing, agriculture, waste, and transport (excluding aviation). The strategy overview of the Commission can be found at: https://ec.europa.eu/clima/policies/strategies/2020_en.

  7. The Regulation lays down obligations on the member states with respect to their minimum contributions for the period from 2021 to 2030 to fulfilling the Union’s target of reducing its greenhouse gas emissions by 30% below 2005 levels in 2030.

  8. Regulation (EU) 2018/841 of the European Parliament and of the Council of 30 May 2018 on the inclusion of greenhouse gas emissions and removals from land use, land use change, and forestry in the 2030 climate and energy framework

  9. Directive (EU) 2018/410 of the European Parliament and of the Council of 14 March 2018 amending Directive 2003/87/EC to enhance cost-effective emission reductions and low-carbon investments, and Decision (EU) 2015/1814, OJ Nr. L 76/3, 2018

  10. https://ec.europa.eu/clima/sites/clima/files/docs/pages/com_2018_733_en.pdf

  11. Enerdata contains statistics for 12 EU countries namely Belgium, Czech Republic, France, Germany, Italy, Netherlands, Poland, Portugal, Romania, Spain, Sweden, and the UK.

  12. https://ec.europa.eu/clima/sites/clima/files/eu_climate_policy_explained_en.pdf

  13. Similar to Chen et al. (2019), we refer here to the renewable energy production and not energy consumption, given data availability of this indicator in Enerdata database. However, renewable electricity production and consumption are closely linked (Twumasi 2017).

  14. Table 13 (Appendix) presents the list of explanatory variables for the two type of estimations addressing the determinants of CO2 emissions (main results) and of CO2 intensity (robustness analysis).

  15. We have also performed a series of panel unit root tests, which provide mixed evidence regarding the stationarity of our variables (these results can be provided by the authors upon request).

  16. An endogeneity bias may also appear in the case of unemployment rate (Lan et al. 2012) or renewable share in total energy production (Chen et al. 2019). In the empirical models addressing the endogeneity issues, we have considered the FDI as an endogenous variable. At the same time, we have performed all the computations considering FDI, unemployment, and renewable share as endogenous. Nevertheless, the results do not change and remain robust (these results can be provided by authors upon request).

  17. The low-order dummy variables of the interaction terms are included in Model 2 only if their coefficients are significant in Model 1.

  18. We do not consider here the FDI structure. Nevertheless, these results are not surprising given the fact that in 2010, for example, the intra-EU inward FDI represented 67% of the EU GDP, while the extra-EU inward FDI represented less than 1% (Faes-Cannito et al. 2012). Furthermore, in 2015, the extra-EU inward FDI was dominated by the USA (41.4%), offshore financial centers (25.8%), and Switzerland (10.8%).

  19. Using GMM errors does not, however, change in a significant way the estimated coefficients.

  20. Arellano-Bond AR (1) test result is ignored in that context given that the first lag of variables is used as instrument.

  21. We have presented in Appendix (Table 14) the results of the instrumental variable analysis. Given the presence of heteroscedasticity signalized by the Pagan-Hall test, as Baum et al. (2003) recall, the GMM estimators perform better compared with the instrumental variable analysis.

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Funding

This work was supported by a grant of the Romanian National Authority for Scientific Research and Innovation, CNCS–UEFISCDI, project number PN-III-P1-1.1-TE-2016-0142.

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  1. Management Department, Politehnica University of Timisoara, 2, P-ta. Victoriei, 300006, Timisoara, Romania

    Claudiu Tiberiu Albulescu, Alin Emanuel Artene, Caius Tudor Luminosu & Matei Tămășilă

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  1. Claudiu Tiberiu Albulescu
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  2. Alin Emanuel Artene
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  3. Caius Tudor Luminosu
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  4. Matei Tămășilă
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Correspondence to Claudiu Tiberiu Albulescu.

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Appendix

Appendix

Fig. 1
figure 1

Dynamic of CO2 emissions and CO2 intensity in the EU (source: World Bank Development Indicators)

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Table 13 Explanatory variables
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Table 14 Determinants of CO2 emissions: instrumental variable analysis (nine EU countries)
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Table 15 Determinants of CO2 emissions: instrumental variable analysis (dummy 2005)
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Table 16 Determinants of CO2 intensity: instrumental variable analysis
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Albulescu, C.T., Artene, A.E., Luminosu, C.T. et al. CO2 emissions, renewable energy, and environmental regulations in the EU countries. Environ Sci Pollut Res 27, 33615–33635 (2020). https://doi.org/10.1007/s11356-019-06155-1

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  • DOI: https://doi.org/10.1007/s11356-019-06155-1

Keywords

  • CO2 emissions
  • Environmental Kuznets curve hypothesis
  • Pollution halo hypothesis
  • Renewable energy production
  • Environmental regulations
  • Panel data analysis

JEL codes

  • Q43
  • Q56
  • F21