Regional difference decomposition and its spatiotemporal dynamic evolution of Chinese agricultural carbon emission: considering carbon sink effect

Cui, Yu; Khan, Sufyan Ullah; Deng, Yue; Zhao, Minjuan

doi:10.1007/s11356-021-13442-3

Cited by 58 publications

(20 citation statements)

References 62 publications

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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%

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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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Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

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

Zhong

2022

IJERPH

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“…The spatial dynamic analysis model (SDDM) was used to study the impact of different technological progress factors on carbon emissions [8]. Cui et al (2021) explored spatiotemporal dynamic evolution of carbon emission intensity and per capita carbon emissions from planting industry in 31 provinces in China across 20 years. The spatial inequality is measured by Theil index and its contribution rate [9].…”

Section: Literature Review 21 Spatiotemporal Analysis Of Carbon Emissionmentioning

confidence: 99%

“…Cui et al (2021) explored spatiotemporal dynamic evolution of carbon emission intensity and per capita carbon emissions from planting industry in 31 provinces in China across 20 years. The spatial inequality is measured by Theil index and its contribution rate [9]. Wang et al (2020) employed the standard deviation ellipse method and tapio decoupling method to reveal the spatiotemporal characteristics of the relationship between carbon emissions from transportation industry and economic growth [10].…”

Section: Literature Review 21 Spatiotemporal Analysis Of Carbon Emissionmentioning

confidence: 99%

Spatiotemporal Analysis of Influencing Factors of Carbon Emission in Public Buildings in China

Liu

Zhang

2022

Buildings

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The rapid development of public buildings has greatly increased the country’s energy consumption and carbon emissions. Excessive carbon emissions contribute to global warming. This paper aims to measure the carbon emissions in the operation of public buildings, and to identify the multiple influencing factors of carbon emissions in operational public buildings. First, the spatial and temporal variation characteristics of carbon emissions from public buildings in 30 provinces of China from 2008–2019 are analyzed. Second, a green building index is constructed, and the STIRPAT (Stochastic Impacts by Regression on Population, Affluence, and Technology) model is utilized to explore the relationship between each influencing factor and carbon emissions, using spatial and temporal geographically weighted regression analysis. The results show that the effects of population, urbanization rate, GDP per capita, green building index, and industrial structure on carbon emissions from public buildings all show spatial correlation and differences. There are east-west differences in the operational carbon emissions of public buildings in China’s provinces. Cluster evolution shows a spatially increasing trend from west to east. To some extent, policymakers can develop appropriate policies for different provinces through the findings.

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“…Liu et al ( 2021a , b , c ) summed the relationship between agricultural carbon emissions and economic development. Cui et al ( 2021 ) estimated the impact of carbon sink on agricultural carbon emission by introducing the effect of carbon sink on carbon emission. It is not difficult to see that in many studies, there are more studies on the differences of carbon emissions in China’s industries and fewer studies to explore the evolution and influencing factors of regional carbon emission differences, which is not conducive to the unification of China’s national carbon market.…”

Section: Introductionmentioning

confidence: 99%

Research on carbon emission differences decomposition and spatial heterogeneity pattern of China’s eight economic regions

Zhang

2022

Environ Sci Pollut Res

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To explore the sources of regional carbon emission differences and the evolution characteristics of spatial heterogeneity pattern, this paper first calculates the corresponding carbon emissions according to the relevant statistical data of eight economic regions in China from 2005 to 2019. It analyzes the overall differences and temporal and spatial evolution characteristics of regional carbon emissions combined with the visualization method of GIS. Then, the total carbon emission difference is decomposed by the Theil index to find out the primary sources affecting the regional carbon emission difference. Finally, the driving factors affecting the spatial heterogeneity pattern of regional carbon emissions are studied with the help of the Geodetector method. The results show that (1) significant differences in carbon emissions among China’s eight economic regions. The contribution rate of inter-regional and intra-regional differences of carbon emissions in different regions to the overall carbon emission difference is diverse. (2) Regional carbon emissions are affected by single driving factors and the interaction of two driving factors. The interaction has an increasing impact on the determinant of regional carbon emission spatial differentiation. (3) The factor detection results and interaction detection results, respectively, show that the level of energy consumption, industrialization, and technological development has always been the main driving factors affecting the spatial heterogeneity pattern of regional carbon emissions, and the critical interaction factors have multiple spatial superposition interaction effects. Therefore, regional carbon emission reduction should consider the national strategic objectives and own regional characteristics and implement differentiated emission reduction schemes.

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Regional difference decomposition and its spatiotemporal dynamic evolution of Chinese agricultural carbon emission: considering carbon sink effect

Cited by 58 publications

References 62 publications

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

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

Spatiotemporal Analysis of Influencing Factors of Carbon Emission in Public Buildings in China

Research on carbon emission differences decomposition and spatial heterogeneity pattern of China’s eight economic regions

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