2020
DOI: 10.1371/journal.pone.0240800
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The influencing factors and spillover effects of interprovincial agricultural carbon emissions in China

Abstract: Agricultural carbon emissions have become the constraints of agricultural low-carbon and circular economy development in China. China’s agricultural production faces severe pressures and challenges in agricultural carbon reduction. In this paper, we take observation for the 31 provinces in china from 1997 to 2017, to explore the influencing factors and spatial spillover effects of agricultural by estimating spatial panel data models. The results show that China’s agricultural carbon emissions will continue to … Show more

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Cited by 17 publications
(9 citation statements)
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References 22 publications
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“…Spatial autocorrelation analyses were conducted to establish if the chosen specimens have spatial autocorrelation [ 54 ]. In this section, Global Moran’s I was used to study the space association of the regional coordination degree in regard to farmland transfer and CLGUE.…”
Section: Methodsmentioning
confidence: 99%
“…Spatial autocorrelation analyses were conducted to establish if the chosen specimens have spatial autocorrelation [ 54 ]. In this section, Global Moran’s I was used to study the space association of the regional coordination degree in regard to farmland transfer and CLGUE.…”
Section: Methodsmentioning
confidence: 99%
“…On the one hand, under certain output conditions, the factor quality improvement will lead to the continuous reduction of the number of factors invested in the agricultural sector. When the number of factor inputs is reduced, the carbon emissions of the agricultural sector will also be reduced (Tian et al, 2016;Chen et al, 2020). On the other hand, with the improvement of agricultural land quality, the government's investment in the agricultural sector will continue to increase, thereby improving the factor efficiency.…”
Section: Benchmark Regressionmentioning
confidence: 99%
“…Agriculture, as the primary industry, is the second largest source and driver of carbon emissions in the world, and its carbon emissions in agronomy and animal husbandry cannot be ignored [ 16 , 25 , 26 , 27 , 28 , 29 , 31 , 32 , 33 , 34 , 35 , 36 , 37 , 38 , 39 , 40 , 41 , 42 ]. In calculating the level of agricultural carbon emissions in Indonesia, scholars had proved that there were an inverted “U”-shaped assumption of the environmental Kuznets curve, carrying out rice cultivation and increasing the ratio of renewable energy use could reduce the impact of carbon emission [ 32 ].When Leitão and Balogh (2020) measured the relationship between Portuguese agriculture and energy consumption, they found that energy consumption played a role in promoting agricultural carbon emissions [ 33 ].…”
Section: Introductionmentioning
confidence: 99%
“…The overall difference of China’s agricultural carbon emission showed a “U”-shaped correlation with economic development, and the carbon emission of each province showed a downward trend. However, the downward trend was different in the differentiated provincial regions, showing a strong spatial agglomeration distribution pattern [ 34 , 35 , 36 ]. At the same time, China’s agricultural carbon emissions will continue to increase in the future.…”
Section: Introductionmentioning
confidence: 99%
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