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

Greenhouse gas (GHG) emissions from agriculture contribute to climate change. The consequences of unsustainable agricultural activity are polluted water, soil, air, and food. sector has become one the major contributors global GHG is world’s second largest emitter after energy sector, which includes power generation transport. Latvian Lithuanian generates about fifth emissions, while Estonia only tenth country’s emissions. This paper investigates trends in 1995 2019 driving forces changes sectors Baltic States (Lithuania, Latvia, Estonia), helpful for formulating effective carbon reduction policies strategies. impact factors have on was analysed by using Logarithmic Mean Divisia Index (LMDI) method based Kaya identity. aim this study assess dynamics identify that had greatest analysis research data showed all three 2001–2002 decreased but later exceeded level (except Lithuania). also revealed pollution caused animal husbandry activities decreased. intensity declined 2–3% annually, structure remained relatively stable. decomposition very large temporary States. mainly increased due growing economy their decrease influenced two factors—the number people employed decreasing GHGs agriculture. dependence result used investigated multivariate regression analysis. Regression highest coefficient determination (R2 = 0.93) obtained Estonian lowest 0.54) data. In case Estonia, were statistically significant; Latvia Lithuania, insignificant. identified emission allowed us submit our insights