Abstract
This paper estimates the eco-efficiency, shadow price and margina abatement costsof dairy farms in Lithuania based on Farm Accounting Data Network (FADN) data from 2015, 2017, and 2019. The Slacks-Based Measure-Data Envelopment Analysis (SBM-DEA) model is applied for approximating the environmental production technology. Herd size, labour, feed costs, agricultural land, capital are considered as inputs, whereas milk production and greenhouse gas (GHG) emission are used as desirable output and undesirable output respectively. To reflect the heterogeneity of farms in terms of GHG emissions, farm-specific emission parameters are calculated. The results show that the mean eco-efficiency remained relatively stable, ranging from 0.55 to 0.59, and that the GHGs emission reduction potential ranged between 20% and 25% over the period considered. The shadow price was not stable and increased from 41.54 EUR/t CO2-eq in 2015 to more than 55.89 EUR/t CO2-eq in 2019. The potential GHGs emission reductions of 1% in 2015, 2017 and 2019 resulted in marginal abatement costs of EUR 41.58, EUR 54.74, and EUR 31.68 respectively. Results across farm size classes imply that farms with the highest GHG emission have the lowest marginal abatement costs, which means that policy actions should target the most polluting farms. The findings have also revealed that farms are more inefficient in terms of land use, capital, and feed as opposed to GHG emission. Our results suggest that the large dairy farms require more attention in regards to their environmental productivity improvements, whereas small farms are underperforming in feed use and capital utilisation. Thus, policy interventions are needed for improvement of farm productivity, yet they should also address the environmental dimension of sustainabilityas well.
Baležentis, T.;Dabkienė, V.;Štreimikienė, D. 2022. Eco-efficiency and shadow price of greenhouse gas emissions in Lithuanian dairy farms: An application of the slacks-based measure.Journal of cleaner production: Elsevier B.V. ISSN 0959-6526. 356, 1318572, p. 1-14. DOI:10.1016/j.jclepro.2022.131857. [Social Sciences Citation Index (Web of Science); Scopus; GEOBASE].