Powered by OpenAIRE graph
Found an issue? Give us feedback
image/svg+xml Jakob Voss, based on art designer at PLoS, modified by Wikipedia users Nina and Beao Closed Access logo, derived from PLoS Open Access logo. This version with transparent background. http://commons.wikimedia.org/wiki/File:Closed_Access_logo_transparent.svg Jakob Voss, based on art designer at PLoS, modified by Wikipedia users Nina and Beao Journal of Environme...arrow_drop_down
image/svg+xml Jakob Voss, based on art designer at PLoS, modified by Wikipedia users Nina and Beao Closed Access logo, derived from PLoS Open Access logo. This version with transparent background. http://commons.wikimedia.org/wiki/File:Closed_Access_logo_transparent.svg Jakob Voss, based on art designer at PLoS, modified by Wikipedia users Nina and Beao
Journal of Environmental Management
Article . 2023 . Peer-reviewed
License: Elsevier TDM
Data sources: Crossref
https://doi.org/10.2139/ssrn.4...
Article . 2023 . Peer-reviewed
Data sources: Crossref
versions View all 3 versions
addClaim

Livestock Greenhouse Gas Emission and Mitigation Potential in China

Authors: Dawei He; Xiangzheng Deng; Xinsheng Wang; Fan Zhang;

Livestock Greenhouse Gas Emission and Mitigation Potential in China

Abstract

Livestock is an important source of greenhouse gas emissions (GHGE) in China. Understanding the greenhouse gas (GHG) emission trends and reduction strategies in livestock is crucial for promoting low-carbon transformation of the livestock sector (LS) and achieving the goal of "carbon peak and carbon neutralization". First, based on the life cycle assessment and IPCC coefficient methods, we calculated the GHGE of the LS in 31 provinces of China from 2000 to 2020 and identified the temporal and spatial evolution of GHG emission intensity. The LMDI method was then used to analyze the influence of efficiency, structure, economy, and population size on GHGE. Finally, the STIRPAT model was used to simulate the future evolution trend of the LS emissions under the SSPs scenario. The results revealed that the GHGE in the life cycle of livestock production decreased from 535.47 Mt carbon dioxide equivalent (CO2e) in 2000 to 532.18 Mt CO2e in 2020, and the main source was CH4 emissions from enteric fermentation of livestock. Economic and efficiency factors markedly influenced the changes in GHGE from the LS in China. Further, economic factors contributed >40% to the increase in GHGE in most provinces. Under the SSP1, SSP2, and SSP4 scenarios, livestock production can achieve the carbon peak target in 2030. Under the baseline scenario (SSP2), the GHGE of China's LS in 2030 and 2060 are expected to be 491.48 Mt CO2e and 352.11 Mt CO2e, respectively. The focus of mitigation measures for livestock production in the future is to optimize the production structure of the LS, promote the low-carbon transformation of the energy structure of livestock feeding, and establish an efficient and intensive management model. In addition, we focus on emission reduction in key areas, such as Northeast and Northwest China, while optimizing diet and reducing food waste from the consumer side.

Related Organizations
Keywords

Greenhouse Effect, Greenhouse Gases, China, Livestock, Food, Animals, Refuse Disposal

  • BIP!
    Impact byBIP!
    selected citations
    These citations are derived from selected sources.
    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).
    57
    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 1%
    influence
    This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
    Top 10%
    impulse
    This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
    Top 1%
Powered by OpenAIRE graph
Found an issue? Give us feedback
selected citations
These citations are derived from selected sources.
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!
57
Top 1%
Top 10%
Top 1%
Upload OA version
Are you the author of this publication? Upload your Open Access version to Zenodo!
It’s fast and easy, just two clicks!