Next Article in Journal
Sustainability of Palm Oil: Drivers of Consumers’ Preferences
Next Article in Special Issue
A Quantitative Analysis of Socio-Economic Determinants Influencing Crop Drought Vulnerability in Sub-Saharan Africa
Previous Article in Journal
The Rich Picture Method: A Simple Tool for Reflective Teaching and Learning about Sustainable Food Systems
Previous Article in Special Issue
Sustainable Production of Sweet Sorghum as a Bioenergy Crop Using Biosolids Taking into Account Greenhouse Gas Emissions
Open AccessArticle

Net Greenhouse Gas Emissions from Agriculture in China: Estimation, Spatial Correlation and Convergence

1
College of Management, Sichuan Agricultural University, Chengdu 611130, China
2
College of Forestry, Northwest A&F University, Xianyang 712100, China
*
Authors to whom correspondence should be addressed.
Sustainability 2019, 11(18), 4817; https://doi.org/10.3390/su11184817
Received: 15 June 2019 / Revised: 7 August 2019 / Accepted: 30 August 2019 / Published: 4 September 2019
(This article belongs to the Special Issue Global Warming, Environmental Governance and Sustainability Issues)
The agricultural ecosystem has dual attributes of greenhouse gas (GHG) emission and absorption, which both influence the net amount of GHG. To have a clearer understanding of the net GHG effect, we linked up the emission and absorption of the agricultural ecosystem, estimated the net emissions of 30 provinces in China from 2007 to 2016, then explored the spatial correlation from global and local perspectives by Moran’s I, and finally tested the convergence of the net emissions by α convergence test, conditional β convergence test and spatial econometric methods. The results were: (1) The average of provincial agricultural net GHG emissions was around 4999.916 × 104 t, showing a fluctuating trend in the 10 years. Meanwhile, the gaps among provinces were gradually widening, as the provinces with high emissions were mainly agglomerated in the middle reaches of the Yangtze River, while those with less emissions mainly sat in the northwest. (2) The net emissions correlated spatially in close provinces. The agglomeration centers were located in the middle reaches of the Yangtze River and the northern coastal region, showing “high–high” and “low–low” agglomeration, respectively. (3) The net emissions did not achieve α convergence or conditional β convergence in the whole country, but the growth rate had a significant positive spillover effect among adjacent provinces, and two factors, the quantity of the labor force and the level of agricultural economy, had a negative impact on the rate. It is suggested that all provinces could strengthen regional cooperation to reduce agricultural net GHG emissions. View Full-Text
Keywords: net greenhouse gas emissions; agriculture; spatial correlation; Moran’s I; α convergence; conditional β convergence net greenhouse gas emissions; agriculture; spatial correlation; Moran’s I; α convergence; conditional β convergence
Show Figures

Figure 1

MDPI and ACS Style

Wu, H.; Huang, H.; Tang, J.; Chen, W.; He, Y. Net Greenhouse Gas Emissions from Agriculture in China: Estimation, Spatial Correlation and Convergence. Sustainability 2019, 11, 4817.

AMA Style

Wu H, Huang H, Tang J, Chen W, He Y. Net Greenhouse Gas Emissions from Agriculture in China: Estimation, Spatial Correlation and Convergence. Sustainability. 2019; 11(18):4817.

Chicago/Turabian Style

Wu, Haoyue; Huang, Hanjiao; Tang, Jin; Chen, Wenkuan; He, Yanqiu. 2019. "Net Greenhouse Gas Emissions from Agriculture in China: Estimation, Spatial Correlation and Convergence" Sustainability 11, no. 18: 4817.

Find Other Styles
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map by Country/Region

1
Search more from Scilit
 
Search
Back to TopTop