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Logarithmic Mean Divisia Index Decomposition Based on Kaya Identity of GHG Emissions from Agricultural Sector in Baltic States

Author

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  • Daiva Makutėnienė

    (Department of Applied Economics, Finance and Accounting, Faculty of Bioeconomy, Agriculture Academy, Vytautas Magnus University, 53361 Kaunas, Lithuania)

  • Dalia Perkumienė

    (Department of Business and Rural Development Management, Faculty of Bioeconomy, Agriculture Academy, Vytautas Magnus University, 53361 Kaunas, Lithuania)

  • Valdemaras Makutėnas

    (Department of Applied Economics, Finance and Accounting, Faculty of Bioeconomy, Agriculture Academy, Vytautas Magnus University, 53361 Kaunas, Lithuania)

Abstract

Greenhouse gas (GHG) emissions from agriculture contribute to climate change. The consequences of unsustainable agricultural activity are polluted water, soil, air, and food. The agricultural sector has become one of the major contributors to global GHG emissions and is the world’s second largest emitter after the energy sector, which includes emissions from power generation and transport. Latvian and Lithuanian agriculture generates about one fifth of GHG emissions, while Estonia generates only about one tenth of the country’s GHG emissions. This paper investigates the GHG trends in agriculture from 1995 to 2019 and the driving forces of changes in GHG emissions from the agricultural sectors in the Baltic States (Lithuania, Latvia, and Estonia), which are helpful for formulating effective carbon reduction policies and strategies. The impact factors have on GHG emissions was analysed by using the Logarithmic Mean Divisia Index (LMDI) method based on Kaya identity. The aim of this study is to assess the dynamics of GHG emissions in agriculture and to identify the factors that have had the greatest impact on emissions. The analysis of the research data showed that in all three Baltic States GHG emissions from agriculture from 1995 to 2001–2002 decreased but later exceeded the level of 1995 (except for Lithuania). The analysis of the research data also revealed that the pollution caused by animal husbandry activities decreased. GHG intensity declined by 2–3% annually, but the structure of agriculture remained relatively stable. The decomposition of GHG emissions in agriculture showed very large temporary changes in the analysed factors and the agriculture of the Baltic States. GHG emissions are mainly increased by pollution due to the growing economy of the sector, and their decrease is mainly influenced by two factors—the decrease in the number of people employed in the agriculture sector and the decreasing intensity of GHGs in agriculture. The dependence of the result on the factors used for the decomposition analysis was investigated by the method of multivariate regression analysis. Regression analysis showed that the highest coefficient of determination (R 2 = 0.93) was obtained for Estonian data and the lowest (R 2 = 0.54) for Lithuanian data. In the case of Estonia, all factors were statistically significant; in the case of Latvia and Lithuania, one of the factors was statistically insignificant. The identified GHG emission factors allowed us to submit our insights for the reduction of emissions in the agriculture of the Baltic States.

Suggested Citation

  • Daiva Makutėnienė & Dalia Perkumienė & Valdemaras Makutėnas, 2022. "Logarithmic Mean Divisia Index Decomposition Based on Kaya Identity of GHG Emissions from Agricultural Sector in Baltic States," Energies, MDPI, vol. 15(3), pages 1-26, February.
  • Handle: RePEc:gam:jeners:v:15:y:2022:i:3:p:1195-:d:743439
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    References listed on IDEAS

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    1. Ang, B. W., 2004. "Decomposition analysis for policymaking in energy:: which is the preferred method?," Energy Policy, Elsevier, vol. 32(9), pages 1131-1139, June.
    2. Yamaji, Kenji & Matsuhashi, Ryuji & Nagata, Yutaka & Kaya, Yoichi, 1993. "A study on economic measures for CO2 reduction in Japan," Energy Policy, Elsevier, vol. 21(2), pages 123-132, February.
    3. Kristiāna Dolge & Dagnija Blumberga, 2021. "Key Factors Influencing the Achievement of Climate Neutrality Targets in the Manufacturing Industry: LMDI Decomposition Analysis," Energies, MDPI, vol. 14(23), pages 1-23, November.
    4. Kerstin Jantke & Martina J. Hartmann & Livia Rasche & Benjamin Blanz & Uwe A. Schneider, 2020. "Agricultural Greenhouse Gas Emissions: Knowledge and Positions of German Farmers," Land, MDPI, vol. 9(5), pages 1-13, April.
    5. Heena Panchasara & Nahidul Hoque Samrat & Nahina Islam, 2021. "Greenhouse Gas Emissions Trends and Mitigation Measures in Australian Agriculture Sector—A Review," Agriculture, MDPI, vol. 11(2), pages 1-16, January.
    6. Piotr Gołasa & Marcin Wysokiński & Wioletta Bieńkowska-Gołasa & Piotr Gradziuk & Magdalena Golonko & Barbara Gradziuk & Agnieszka Siedlecka & Arkadiusz Gromada, 2021. "Sources of Greenhouse Gas Emissions in Agriculture, with Particular Emphasis on Emissions from Energy Used," Energies, MDPI, vol. 14(13), pages 1-20, June.
    7. Athanasios Balafoutis & Bert Beck & Spyros Fountas & Jurgen Vangeyte & Tamme Van der Wal & Iria Soto & Manuel Gómez-Barbero & Andrew Barnes & Vera Eory, 2017. "Precision Agriculture Technologies Positively Contributing to GHG Emissions Mitigation, Farm Productivity and Economics," Sustainability, MDPI, vol. 9(8), pages 1-28, July.
    8. Li, Tianxiang & Baležentis, Tomas & Makutėnienė, Daiva & Streimikiene, Dalia & Kriščiukaitienė, Irena, 2016. "Energy-related CO2 emission in European Union agriculture: Driving forces and possibilities for reduction," Applied Energy, Elsevier, vol. 180(C), pages 682-694.
    9. Ang, B.W. & Goh, Tian, 2019. "Index decomposition analysis for comparing emission scenarios: Applications and challenges," Energy Economics, Elsevier, vol. 83(C), pages 74-87.
    10. Meiting Tu & Ye Li & Lei Bao & Yuao Wei & Olivier Orfila & Wenxiang Li & Dominique Gruyer, 2019. "Logarithmic Mean Divisia Index Decomposition of CO 2 Emissions from Urban Passenger Transport: An Empirical Study of Global Cities from 1960–2001," Sustainability, MDPI, vol. 11(16), pages 1-16, August.
    11. Liu, Lan-Cui & Fan, Ying & Wu, Gang & Wei, Yi-Ming, 2007. "Using LMDI method to analyze the change of China's industrial CO2 emissions from final fuel use: An empirical analysis," Energy Policy, Elsevier, vol. 35(11), pages 5892-5900, November.
    12. Ang, B.W. & Liu, F.L., 2001. "A new energy decomposition method: perfect in decomposition and consistent in aggregation," Energy, Elsevier, vol. 26(6), pages 537-548.
    13. Chuanhe Xiong & Degang Yang & Jinwei Huo, 2016. "Spatial-Temporal Characteristics and LMDI-Based Impact Factor Decomposition of Agricultural Carbon Emissions in Hotan Prefecture, China," Sustainability, MDPI, vol. 8(3), pages 1-14, March.
    14. Ang, B.W. & Zhang, F.Q., 2000. "A survey of index decomposition analysis in energy and environmental studies," Energy, Elsevier, vol. 25(12), pages 1149-1176.
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    2. Yue Yu & Yishuang Xu, 2023. "The Roles of Carbon Trading System and Sustainable Energy Strategies in Reducing Carbon Emissions—An Empirical Study in China with Panel Data," IJERPH, MDPI, vol. 20(8), pages 1-20, April.
    3. Lin Zhang & Jinyan Chen & Faustino Dinis & Sha Wei & Chengzhi Cai, 2022. "Decoupling Effect, Driving Factors and Prediction Analysis of Agricultural Carbon Emission Reduction and Product Supply Guarantee in China," Sustainability, MDPI, vol. 14(24), pages 1-22, December.

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