Evidence map›Paper›PMID 39516246›Full record

ArticleScientific reports2024

Research on the impact and mechanism of digital economy on China's food production capacity.

Jue Wang, Yanyan Dong, Heng Wang

Abstract read
In one paragraph

Article in Scientific reports, 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
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
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

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

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

3 authors.

Jue WangSchool of Economics, Yunnan Minzu University, Kunming, 650504, Yunnan, China.
Yanyan DongSchool of Economics, Yunnan Minzu University, Kunming, 650504, Yunnan, China.
Heng WangSchool of Economics and Management, Xianyang Normal University, Xianyang, 712000, Shaanxi, China. wangheng@snnu.edu.cn.

Funding

Key Research Base of Humanities and Social Sciences under the Ministry of Education 22JJD790052National Social Science Fund General Project 22BGL195
6 · The paper itself

Abstract

Enhancing and strengthening food production capacity has always been a top priority in agricultural research, serving as a cornerstone for ensuring national food security and stable economic development. This study, based on panel data spanning from 2011 to 2021 across 30 provinces in China, delves into the mechanism through which the digital economy impacts food production capacity. Employing a double fixed effect model, a mediation effect model, and a panel threshold model, we uncover several key findings: The digital economy significantly boosts food production capacity, with robustness tests affirming the reliability of our results. Mechanism analysis reveals that the digital economy enhances food production capacity by elevating total factor productivity and bolstering agricultural resilience. The threshold effect underscores that urbanization levels exhibit a single-threshold impact, wherein the influence of the digital economy on food production capacity intensifies upon crossing this threshold. Heterogeneity analysis reveals that the digital economy significantly boosts food production capacity in central and primary grain-producing regions, while its impact is comparatively weaker in the eastern and western regions, as well as in non-primary grain-producing areas. In summary, this research sheds light on the pivotal role of the digital economy in augmenting food production capacity, offering valuable insights into regional variations and thresholds in its impact across China.

Indexed as

AgricultureEconomic DevelopmentFood SupplyChinaHumansUrbanizationDigital economyFood production capacityRegional heterogeneityThreshold effect

Identifiers

PMID39516246
PMCPMC11549298

What OpenQuestion holds

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Registered trials

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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.