Evidence map›Paper›PMID 38318057›Full record

ArticleHeliyon2024

Re-measurement and influencing factors of agricultural eco-efficiency under the 'dual carbon' target in China.

Li Yang, Zhenyu Guan, Shiying Chen, Zhenhua He

Abstract read
In one paragraph

Article in Heliyon, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
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0citing papers in PubMed
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1 · What the graph read from it

What it found

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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

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3 · Its place in the literature

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0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

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5 · Who and what money

Authors and funding

4 authors.

Li YangSchool of Economics and Management, Ningxia University, Yinchuan, 750021, China.
Zhenyu GuanSchool of Information, Renmin University of China, Beijing, 100872, China.
Shiying ChenSchool of Economics and Management, Zhejiang Ocean University, Zhoushan, 316022, China.
Zhenhua HeAcademic Affairs Office, Xinhua College of Ningxia University, Yinchuan, 750021, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Given that agriculture is both a carbon source and sink, the sustainability goals of carbon peaking and neutrality place high demands on the green and low-carbon agricultural development in China, and the exploration of a realistic path for a sustainable agricultural development is urgently needed. Under the above 'dual carbon' target, this study focused on the key issue of how to improve China's agricultural eco-efficiency (AEE) and constructed an innovative AEE indicator system that can reflect carbon constraint and coordinated agricultural economic development, resource use and ecological environment. The super-efficient slack-based measured Data Envelopment Analysis (SBM-DEA) method, which considers undesirable outputs, was applied to re-measure the AEE of 30 provinces and cities in China from 2001 to 2020, and its spatial and temporal evolution was analysed in conjunction with kernel density estimation. The Tobit regression model was used to explore various influencing factors by region. The results show that the AEE re-measurements, which take into account the 'dual carbon' requirement, are significantly better than the traditional AEE. From 2001 to 2020, China had an overall V-shaped fluctuation curve AEE, with a small decline and several inter-annual fluctuations, and exhibited a large potential to rise. China's AEE showed a spatially uneven regional development at different stages of distribution and evident multi-polar differentiation. Inter-provincial differences were observed in China's AEE, and the vicious circle of low-level green and low-carbon agricultural development was difficult to break. Urbanisation had a significant positive effect on national and eastern AEE but a significant negative effect on central AEE. The agricultural carbon offset rate had a significant effect on AEE nationally and in the three regions. Thus, the introduction of 'dual carbon' target effectively drove the development of AEE. Agricultural industry structure inhibited the improvement of AEE nationally and in the western region. Agricultural economic development hindered the national AEE improvement but promoted that of the central region, where China showed an environment Kuznets curve. Hopefully, this study can provide data support and theoretical reference for the green and low-carbon agricultural development and help achieve the 'dual carbon' target.

Indexed as

Agricultural carbon emissionsAgricultural eco-efficiencyCarbon neutralityCarbon peakingSustainable development

Identifiers

PMID38318057
PMCPMC10839593

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.