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Digital inclusive finance, agricultural green technology innovation and agricultural carbon emissions: Impact mechanism and empirical test
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Digital inclusive finance, agricultural green technology innovation and agricultural carbon emissions: Impact mechanism and empirical test
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Journal Article
Digital inclusive finance, agricultural green technology innovation and agricultural carbon emissions: Impact mechanism and empirical test
2023
Overview
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.
