Evidence map›Paper›PMID 36767766›Full record

ArticleInternational journal of environmental research and public health2023

Analysis of the Spatial Differentiation and Promotion Potential for Agricultural Eco-Efficiency-Evidence of Pollution's Strong Disposability.

Dongmei Shi, Lili Ren, Hongyu Li, Haizhen Zhang, Rufei Zhang

Abstract read
In one paragraph

Article in International journal of environmental research and public health, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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0cells of the map it votes in
0citing papers in PubMed
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1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

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

The trial behind it

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Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

5 authors.

Dongmei ShiCollege of Economics, Hebei GEO University, Shijiazhuang 050030, China.
Lili RenBusiness School, Hebei Normal University, Shijiazhuang 050024, China.
Hongyu LiThe Graduate School, Woosuk University, Wanju-gun 55338, Republic of Korea.
Haizhen ZhangCollege of Economics, Hebei GEO University, Shijiazhuang 050030, China.ORCID 0000-0002-0807-4843
Rufei ZhangCollege of Economics, Hebei GEO University, Shijiazhuang 050030, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Agricultural eco-efficiency is an important indicator used to measure agriculture's high-quality and sustainable development. Therefore, this paper uses the EBM-Super-ML method with strong disposability of undesired output to calculate Chinese agricultural eco-efficiency and uses a geographical detector to measure the driving force of the factor. The research conclusions are mainly reflected in three aspects. Firstly, from the perspective of eco-efficiency changes, the overall mean value of agricultural eco-efficiency increased by 3.5%, and the regional heterogeneity is significant, with the fastest growth in the eastern region. Secondly, the results of driving force analysis show that the main driving factors of agricultural eco-efficiency divergence are capital inputs, total carbon emissions, labor inputs, agricultural film residues, fertilizer use, and pesticide residues, with driving forces of 0.43, 0.37, 0.34, 0.31, 0.28, and 0.20, respectively. Finally, from the perspective of eco-efficiency improvement potential, the mean value of output improvement potential is 5%, and the input factor is 7%. Among the non-desired outputs, the reduction rate of agricultural films can reach 40%. Among the input factors, labor input has the highest potential for intensive use, while agricultural machinery has a negative effect. Therefore, strengthening the development of the agricultural service industry is of great significance to improve the utilization rate of mechanical equipment and reduce the undesired output of agriculture.

Indexed as

AgriculturePesticide ResiduesChinaEconomic DevelopmentEfficiencySustainable DevelopmentPesticide Residuesagricultural carbon emissionsagricultural ecological efficiencyfactor driver forcepromotion potentialstrong disposability

Identifiers

PMID36767766
PMCPMC9916263

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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.