Spatial pattern of China’s agricultural carbon emission performance

Liu, Moucheng; Yang, Limei

doi:10.1016/j.ecolind.2021.108345

Cited by 107 publications

(49 citation statements)

References 28 publications

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“…In particular, this paper focuses on the carbon emission efficiency of the consumption of resources (such as fertilizers, pesticides, diesel, plastic film, plowing, and electricity) and the impact mechanism in grain production, rather than life cycle carbon footprint measurement (excluding the biological carbon sequestration of different food varieties). When calculating the carbon emissions of different grain varieties (wheat, rice, and maize), this paper draws on the research approaches of Zhang et al ( 2019 ) and Liu and Yang ( 2021 ) and mainly examines the carbon emissions caused by the consumption of resources, such as fertilizers, pesticides, diesel, plastic film, plowing, and electricity, in grain production. It is assumed that these resources have a fixed carbon emission coefficient when they are provided.…”

Section: Methodology and Datamentioning

confidence: 99%

The influence mechanism and spatial effect of carbon emission intensity in the agricultural sustainable supply: evidence from china's grain production

2022

Environ Sci Pollut Res

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Agricultural carbon mitigation is critical for China to encourage the sustainable development of agriculture and achieve the carbon peak by 2030 and carbon neutrality by 2060. By exploring the impact mechanism of the carbon emission intensity (CEI) of grain production, we can effectively promote the low-carbon transformation of agricultural production and ensure the sustainable development of the food supply. This article analyzes the temporal and spatial evolution of the total carbon emission (TCE) and CEI of staple crops and adopts a dynamic spatial model to explore the influence mechanism and spatial spillover effects of the CEI of grain production based on evidence from China’s major grain-producing provinces from 2002 to 2018. The results indicate that the TCEs of rice, wheat, and maize fluctuate upward and that the CEI in most producing areas decreases with low-low agglomeration (or high-high agglomeration). Among the influencing factors, technology is the main factor reducing CEI. Technical efficiency, urbanization, industrial structure, agricultural agglomeration, and agricultural trade openness can be transmitted to neighboring areas through spatial spillover mechanisms. The spatial spillover mechanisms are resource flow, technology spillover, and policy learning, producing the demonstration effect and siphon effect. Based on our findings, agricultural technology innovation and popularization, urbanization, optimization of the agricultural structure, financial payments, and factor flow among regions should be improved to encourage the low carbon transformation of grain production.

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Section: Methodology and Datamentioning

confidence: 99%

The influence mechanism and spatial effect of carbon emission intensity in the agricultural sustainable supply: evidence from china's grain production

2022

Environ Sci Pollut Res

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“…This paper takes agriculture as the research object in a narrow sense (planting industry). According to previous research, the carbon sources of the planting industry mainly include fertilizer, pesticide, agricultural film, irrigation, ploughing, machinery, and diesel oil [ 22 , 36 , 37 , 38 ]. Therefore, the agricultural carbon emissions measured in this paper include these seven aspects.…”

Section: Methodsmentioning

confidence: 99%

“…Therefore, the agricultural carbon emissions measured in this paper include these seven aspects. Drawing on the method of Huang, Xu, Wang, Zhang, Gao and Chen [ 36 , 37 , 38 ], the following formula for calculating agricultural carbon emissions is constructed:

where n is the number of agricultural input elements (carbon sources); E is the total amount of agricultural carbon emissions, which is equal to the sum of carbon emissions from various carbon sources; E i is the carbon emissions of the i-th agricultural input element, including fertilizers, pesticide, agricultural film, irrigation, farming, machinery, diesel oil; C i is the amount of the i-th agricultural input element; and δ i is the carbon emission coefficient of the i-th agricultural input element ( Table 1 ).…”

Section: Methodsmentioning

confidence: 99%

Digital Economy, Agricultural Technological Progress, and Agricultural Carbon Intensity: Evidence from China

Zhong

2022

IJERPH

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China is the largest carbon emitter in the world, with agricultural carbon emissions accounting for 17% of China’s total carbon emissions. Agricultural carbon emission reduction has become the key to achieving the “Double Carbon” goal. At the same time, the role of the digital economy in achieving the “dual carbon” goal cannot be ignored as an important engine to boost the high-quality development of China’s economy. Therefore, this paper uses the panel data of 30 provinces in mainland China from 2011 to 2019 to construct a spatial Durbin model and a mediation effect model to explore the impact of the digital economy on agricultural carbon intensity and the mediating role of agricultural technological progress. The research results show that: (1) China’s agricultural carbon intensity fluctuated and declined during the study period, but the current agricultural carbon intensity is still at a high level; (2) The inhibitory effect of the digital economy on agricultural carbon intensity is achieved by promoting agricultural technological progress, and the intermediary role of agricultural technological progress has been verified; (3) The digital economy can significantly reduce the carbon intensity of agriculture, and this inhibition has a positive spatial spillover effect. According to the research conclusions, the government should speed up the development of internet technology and digital inclusive finance, support agricultural technology research and improve farmers’ human capital, and strengthen regional cooperation to release the contribution of digital economy space.

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“…According to Tian et al (2014), China's agricultural carbon emission intensity is highest in the western region of the country, followed by the central region, and lowest in the eastern region. By analyzing data from 30 provinces, Liu and Yang (2021) concluded that China's agricultural carbon emission performance would probably improve in spatial scope and temporal trends. Cui et al (2021) analyzed regional differences in carbon emission intensity and carbon emission per capita from the plantation industry, and their findings indicate "intra-regional convergence and inter-regional divergence" in emission intensity agglomeration and carbon emission per capita.…”

Section: Characteristics Of Agricultural Carbon Emissionsmentioning

confidence: 99%

Spatiotemporal Characteristics of Agricultural Carbon Emissions: Empirical Evidence from 31 Chinese Provinces

Tan

Zheng

2022

Preprint

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A crucial part of China’s overall carbon emission reduction plan is the reduction of agricultural carbon emissions. Thus, it is important to identify the factors influencing agricultural carbon emissions in each province and explore the key to effectively reducing these emissions. Based on data from 31 provinces in China from 2007 to 2020, we analyzed the spatial and temporal trends in the evolution of agricultural carbon emissions in each province and determined the spatial autocorrelation of provincial carbon emissions. We further used the geographically and temporally weighted regression model to analyze the spatiotemporal evolution of the driving factors of provincial agricultural carbon emissions. The results showed that agricultural carbon emissions were spatially autocorrelated among provinces. The final results indicated that different provinces could achieve agricultural carbon emission reduction by adopting different strategies most suitable to their situations, such as by using green fertilizers, and adopting sustainability-oriented agricultural technology.

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Spatial pattern of China’s agricultural carbon emission performance

Cited by 107 publications

References 28 publications

The influence mechanism and spatial effect of carbon emission intensity in the agricultural sustainable supply: evidence from china's grain production

The influence mechanism and spatial effect of carbon emission intensity in the agricultural sustainable supply: evidence from china's grain production

Digital Economy, Agricultural Technological Progress, and Agricultural Carbon Intensity: Evidence from China

Spatiotemporal Characteristics of Agricultural Carbon Emissions: Empirical Evidence from 31 Chinese Provinces

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