Powered by OpenAIRE graph
Found an issue? Give us feedback
image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/ Russian Journal of E...arrow_drop_down
image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
Russian Journal of Economics
Article . 2022 . Peer-reviewed
License: CC BY NC ND
Data sources: Crossref
image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
Russian Journal of Economics
Article . 2022
Data sources: DOAJ
image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
ZENODO
Article . 2022
License: CC BY
Data sources: ZENODO
image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
ZENODO
Article . 2022
License: CC BY
Data sources: ZENODO
versions View all 5 versions
addClaim

This Research product is the result of merged Research products in OpenAIRE.

You have already added 0 works in your ORCID record related to the merged Research product.

Environmental tradeoffs of agricultural growth in Russian regions and possible sustainable pathways for 2030

Authors: Anton S. Strokov; Vladimir Y. Potashnikov;

Environmental tradeoffs of agricultural growth in Russian regions and possible sustainable pathways for 2030

Abstract

The paper analyses the current ecological consequences of agricultural growth in Russia’s main regions (oblast level) during 2011–2019. Our main hypothesis was that local environ­mental risks, like waste concentration, would be closely related to global climate risks such as greenhouse gas (GHG) emissions from the production of crops, meat, milk, eggs, and from land use change (LUC) activities leading to a larger carbon footprint. We first analyze official data for agricultural waste and find that 30% of it is concentrated in just two regions (Belgorod and Kursk), while they produce only 10% of agricultural value of Russia. Next, we find that manure nutrients have a high concentration in regions where the livestock production is not balanced with appropriate nutrient use on croplands (Dagestan, Astrakhan, Leningrad, and Pskov regions) which might lead to the pollution of soils and local waters. Next, we test the GLOBIOM partial equilibrium model to evaluate proper agricultural protein production quantities in Russian regions and respective GHG emissions from crop, livestock and land use change activities. We find that 21% of the GHG emission in 2019 came from the conversion of former abandoned agricultural land into cropland (starting from 2011). While some regions such as Krasnodar, Rostov, and Stavropol increase productivity with low carbon footprint, others, like Amur and Bryansk, increase production by cropland expansion without respective productivity growth which leads to higher carbon footprint. Our results for livestock operations show that the main hypothesis did not hold up because regions which increase meat production, like Belgorod, Kursk, Pskov, and Leningrad, have a lower carbon footprint due to the production of pork meat and poultry which have lower GHG emissions due to specific digestion. On the other hand, these regions experience a higher environmental footprint due to the large concentration of waste which could be harmful for local eco­systems. Finally, we use the model to project possible future development up to 2030. Our results show the possible growth of crop and livestock products in most of the regions driven by external demand for food. The extensive scenario shows additional GHG emissions from cropland expansion, while the intensive scenario reveals a larger growth rate accompanied by productivity growth and lower carbon footprint, which is essential in harmonizing the current agricultural and climate policy of Russia.

Keywords

agricultural concentration, carbon footprint, greenhouse gas emissions, externalities, Economics as a science, environmental policy, partial equilibrium modelling, HB71-74

  • BIP!
    Impact byBIP!
    citations
    This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
    2
    popularity
    This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
    Top 10%
    influence
    This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
    Average
    impulse
    This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
    Average
    OpenAIRE UsageCounts
    Usage byUsageCounts
    visibility views 6
    download downloads 6
  • 6
    views
    6
    downloads
    Data sourceViewsDownloads
    ZENODO66
    Powered byOpenAIRE UsageCounts
Powered by OpenAIRE graph
Found an issue? Give us feedback
visibility
download
citations
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
views
OpenAIRE UsageCountsViews provided by UsageCounts
downloads
OpenAIRE UsageCountsDownloads provided by UsageCounts
2
Top 10%
Average
Average
6
6
Green
gold