Drivers of agricultural carbon emissions in Hunan Province, China

Tian, Jianxiang; Yang, Huilin; Xiang, Ping-an; Liu, Dengwang; Lin, Li

doi:10.1007/s12665-015-4777-9

Cited by 27 publications

(22 citation statements)

References 32 publications

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“…The CH 4 emissions are caused by enteric fermentation; CH 4 and N 2 O emissions are triggered by manure emissions. Table 3 shows the carbon emission coefficient associated with major livestock [4,65].…”

Section: Carbon Emissions From Livestock Farmingmentioning

confidence: 99%

Investigating Low-Carbon Agriculture: Case Study of China’s Henan Province

Jiang

2017

Sustainability

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Developing low-carbon agriculture requires investigating the trajectory, decoupling statuses, and driving forces of agricultural carbon emissions. This study explored the evolution of agricultural carbon emissions based on 18 kinds of major carbon emission sources in Henan Province of China, which produces approximately one-tenth of China's total grain output. We then analyzed the relationship between carbon emissions and economic growth using the decoupling elasticity model, and identified the factors driving the decoupling status. This analysis was done with a decoupling elasticity model, using the Logarithmic Mean Divisia Index technique. There were three key results: (1) Agricultural carbon emissions totaled 16.61 million tons in 1999, and increased by 7.99% to 17.93 million tons in 2014, with an average growth rate of approximately 0.65%; (2) The decoupling relationship between agricultural carbon emissions and economic output was dominated by weak decoupling during the study period; (3) Agricultural labor productivity was the leading contributor to changes in agricultural carbon emissions, followed by farming-animal husbandry carbon intensity, labor, and agricultural structure.

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Section: Carbon Emissions From Livestock Farmingmentioning

confidence: 99%

Investigating Low-Carbon Agriculture: Case Study of China’s Henan Province

Jiang

2017

Sustainability

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“…On the contrary, higher levels of economic development are more conducive to improving agricultural labor productivity, increasing farmers' agricultural income, and promoting the transformation of agriculture from extensive development to intensive development. Economic development is related to emissions from environmental pollution and the process of agricultural sustainable development across the globe [71][72][73][74]; thus, per capita GDP was used and noted as PCGDP in this paper to indicate the level of economic development of a country or region.…”

Section: The Level Of Economic Developmentmentioning

confidence: 99%

Evaluation and Influencing Factors of Sustainable Development Capability of Agriculture in Countries along the Belt and Road Route

Wang

Chen

2019

Sustainability

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Agriculture is increasingly facing major challenges such as climate change, scarcity of natural resources, and changing societal demands. To tackle these challenges, there is a pressing need to evolve towards more sustainable agricultural practices. As a result, sustainability stands among the most relevant topics in agricultural research worldwide, and countries along the Belt and Road (B&R) route are no exception. This paper selected 25 indicators from the five subsystems of population, society, economy, environment, and resources in order to build an evaluation index system of agricultural sustainable development capability, and then it used an improved entropy weight method, technique for ordering preference by similarity to an ideal solution (TOPSIS), and coordination degree method to measure the comprehensive capability and coordination of agricultural sustainable development of all countries along the B&R route from 2006 to 2015. First, according to the time dimension, the comprehensive score of sustainable development capability of agriculture along the B&R route: This had an average annual score of 0.3195 which initially decreased, then increased in a fluctuating manner, before finally falling again. Second, according to the spatial dimension, the average comprehensive score of agricultural sustainable development capability showed an evolutionary trend of ‘high–low–high–low–high’ from west to east, which showed an obvious basic spatial pattern of the ‘W’ type. Third, from the perspective of the subsystems of agriculture, although the coordination degree among subsystems in the main grain-producing areas increased continually from 2006 to 2015, the overall level of development needed to be further improved. In order to further clarify the main factors affecting the capability of agricultural sustainable development, this paper selected six explanatory variables: The level of economic development, financial expenditure for agriculture, agricultural foreign direct investment, agricultural labor force, the intensity of agricultural R&D investment, and the level of agricultural informatization. Then, geographically and temporally weighted regression was applied to evaluate the direction and degree of influences of selected factors on sustainability development capability of agriculture. The results showed that the regression coefficients of each variable in 53 countries were positive or negative, which indicated that the influencing factors of agricultural sustainable development capacity had the characteristics of geospatial nonstationarity.

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“…Compared with previous studies, this study went deeper in the following three aspects. First, on the decomposition scale, it was different from the existing decomposition studies on the GHG emissions from AH [39,[41][42][43], which have been done only from the horizontal provincial scales. This study firstly established a vertical linkage of change in AHGI between china and its 31 provinces and clearly revealed the linkage between partial changes and overall changes.…”

Section: Advantages and Limitations Of This Studymentioning

confidence: 99%

“…This is more in line with China's current need to allocate GHG reduction tasks from top to bottom. Second, in terms of driving factors, the existing studies have mostly explained the impact on GHG emissions of AH [39,[41][42][43]. Based on the AHGI calculation formula, we separated the factors affecting the change of China's AHGI into environmental efficiency, productive efficiency, and economic share.…”

Section: Advantages and Limitations Of This Studymentioning

confidence: 99%

“…Luo et al [38] explored the spatial and temporal heterogeneity of CO 2 emission intensity in China's agricultural sector and found that Central China had the highest agricultural CO 2 emission intensity than Western and Eastern China. Tian et al [39,41] studied regional characteristics and driving factor of agricultural carbon emissions in China with LMDI model. The results showed that the traditional agricultural provinces, especially the major crop production areas were the main emission source regions.…”

Section: Introductionmentioning

confidence: 99%

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Study on the Vertical Linkage of Greenhouse Gas Emission Intensity Change of the Animal Husbandry Sector between China and Its Provinces

et al. 2018

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China's carbon intensity (CI) reduction target in 2030 needs to be allocated to each province in order to be achieved. Thus, it is of great significance to study the vertical linkage of CI change between China and its provinces. The existing research on the vertical linkage focuses more on energy-related economic sectors in China; however, attention has not been paid to China's animal husbandry (AH) sector, although the role of the China's AH sector in greenhouse gas (GHG) reduction is increasingly important. This study firstly established a vertical linkage of change in greenhouse gas emission intensity of the animal husbandry sector (AHGI) between China and its 31 provinces based on the logarithmic mean Divisia index (LMDI) decomposing method from the perspective of combining emission reduction with economic development, and quantified the contributions of each province and its three driving factors of environmental efficiency (AHEE), productive efficiency (AHPE), and economic share (AHES) to reducing China's AHGI during the period of 1997-2016. The main results are: (1) The AHGI of China decreased from 5.49 tCO 2 eq/10 4 yuan in 1997 to 2.59 tCO 2 eq/10 4 in 2016, showing a 75.25% reduction. The AHGI in 31 provinces also declined and played a positive role in promoting the reduction of national AHGI, but there were significant inter-provincial differences in the extent of the contribution. Overall, the provinces with higher emission levels contributed the most to the reduction of China's AHGI; (2) The AHPE and AHEE factors in 31 provinces cumulatively contributed to the respective 68.17% and 11.78% reduction of China's AHGI, while the AHES factors of 31 provinces cumulatively inhibited the 4.70% reduction. Overall, the AHPE factor was the main driving factor contributing to the reduction of China's AHGI. In the future, improving the level of AHEE through GHG emissions reduction technology and narrowing the inter-provincial gap of the level of AHPE are two important paths for promoting the reduction of China's AHGI.

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Drivers of agricultural carbon emissions in Hunan Province, China

Cited by 27 publications

References 32 publications

Investigating Low-Carbon Agriculture: Case Study of China’s Henan Province

Investigating Low-Carbon Agriculture: Case Study of China’s Henan Province

Evaluation and Influencing Factors of Sustainable Development Capability of Agriculture in Countries along the Belt and Road Route

Study on the Vertical Linkage of Greenhouse Gas Emission Intensity Change of the Animal Husbandry Sector between China and Its Provinces

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