ArticlePloS one2023
Digital inclusive finance, agricultural green technology innovation and agricultural carbon emissions: Impact mechanism and empirical test.
Article in PloS one, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It has been retracted, and should not be counted. Cited by 4 papers.
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.
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.
Who cites it
4 citing papers in PubMed, 15 citations in OpenAlex.
- Article
- Digital inclusive finance, green technological innovation, and carbon emissions from a spatial perspective.Scientific reports · 2024Article
- Does internationalization improve environmental disclosure willingness and quality? The moderating role of green investors.PloS one · 2024Article
- Deterioration and improvement: The U-shaped impact of industrialization on agricultural carbon productivity in China.Science progressArticle
Corrections and comments
- Retraction · 2025-05-27Cites Retracted Work · Concerns/Issues about Peer Review · Investigation by Journal/Publisher · Unreliable Results and/or Conclusions · · See also: https://pubpeer.com/publications/CC17C56E445F87425CEC366073A89E
- Retracted
Authors and funding
1 author at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The impact of digital financial inclusion (If) and agricultural technology innovation (Gi) on agricultural carbon emissions has attracted wide attention from the academic community, but the inconsistent conclusions of existing studies and the reality that few studies have gathered them into a framework require more evidence to fill this gap, which can contribute more insights to promoting economic development and controlling carbon emissions. Taking the provincial-level relevant data of China's agriculture from 2011 to 2020 as a sample, the GMM method is used to integrally test the relationship between the three factors. The results show that (1) from 2011 to 2020, China's overall agricultural carbon emissions experienced two stages of fluctuating rise (2011-2015) and continuous decline (2015-2020). In 2015, China's agricultural carbon emissions peaked at 1,040 million tons; Overall, Hunan, Hubei, and Henan were the provinces with the largest agricultural carbon emissions; Beijing, Tianjin, and Shanghai are provinces with relatively low agricultural carbon emissions. (2) Although the impact of digital financial inclusion on agricultural carbon emissions is negative, it is not significant. (3) Agricultural technology innovation promoted the reduction of agricultural carbon emissions. If the level of agricultural technology innovation increased by 1 percentage point, agricultural carbon emissions would decrease by 0.09 percentage points. (4) Mechanism analysis showed that agricultural technology innovation could reduce carbon emissions through the efficiency of agricultural resource allocation, and its effect reached 56%. The results can provide a scientific basis for the government to formulate targeted policies, and the methods can be extended to other places.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
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.