Spatial-Temporal Characteristics of the Driving Factors of Agricultural Carbon Emissions: Empirical Evidence from Fujian, China

Abstract

With the development of agricultural modernization, the carbon emissions caused by the agricultural sector have attracted academic and practitioners’ circles’ attention. This research selected the typical agricultural development province in China, Fujian, as the research object. Based on the carbon emission sources of five main aspects in agricultural production, this paper applied the latest carbon emission coefficients released by Intergovernmental Panel on Climate Change of the UN (IPCC) and World Resources Institute (WRI), then used the ordered weighted aggregation (OWA) operator to remeasure agricultural carbon emissions in Fujian from 2008–2017. The results showed that the amount of agricultural carbon emissions in Fujian was 5541.95 × 103 tonnes by 2017, which means the average amount of agricultural carbon emissions in 2017 was 615.78 × 103 tonnes, with a decrease of 13.13% compared with that in 2008. In terms of spatial distribution, agricultural carbon emissions in the eastern coastal areas were less than those in the inland regions. Among them, the highest agricultural carbon emissions were in Zhangzhou, Nanping, and Sanming, while the lowest were in Xiamen, Putian, and Ningde. In addition, this paper selected six influencing variables, the research and development intensity, the proportion of agricultural labor force, the added value of agriculture, the agricultural industrial structure, the per capita disposable income of rural residents, and per capita arable land area, to clarify further the impacts on agricultural carbon emissions. Finally, geographically- and temporally-weighted regression (GTWR) was used to measure the direction and degree of the influences of factors on agricultural carbon emission. The conclusion showed that the regression coefficients of each selected factor in cities were positive or negative, which indicated that the impacts on agricultural carbon emission had the characteristics of geospatial nonstationarity.

Research on Agricultural Carbon Emissions and Regional Carbon Emissions Reduction Strategies in China

“…Additionally, the CH 4 emission of rice in different growth cycles can be affected by climate and hydrothermal conditions. Therefore, based on the existing calculation model of carbon emission coefficient [39], the CH 4 emission factors of rice in this paper was estimated according to the data on climate, soil, and hydrology in different provinces (see Table 2). Change (IPCC) and the greenhouse gas accounting method in China's "Guidelines for the Preparation of Provincial Greenhouse Gas Inventories (Trial)", the N 2 O in the atmosphere mainly comes from agricultural land, animal manure management, and the wastewater treatment processes of the waste treatment sector, among which more than 70% of the nitrogen emissions come from agricultural production.…”

Does Mechanization Improve the Green Total Factor Productivity of China’s Planting Industry?

“…The output variables include gross value of planting output (in thousand CNY) and planting net carbon sink (in thousand tons of CO2-equivalent). The calculation formula, coefficient of agricultural carbon sink and carbon emission are based on the research of Tian et al (2014) [30]and Chen et al (2019) [31]. The main input variables of agricultural production are labor, land, machinery and fertilizer.…”

Carbon Emission Reduction—Carbon Tax, Carbon Trading, and Carbon Offset

“…Based on the carbon emission sources of five main aspects in agricultural production, Chen, Li, Su, and Li [4] applied the latest carbon emission coefficients released by Intergovernmental Panel on Climate Change of the UN (IPCC) and World Resources Institute (WRI), and then used the ordered weighted aggregation (OWA) operator to remeasure agricultural carbon emissions in Fujian from 2008-2017. The research results showed that the regression coefficients of each selected factor in the cities were positive or negative, which indicated that the impacts on agricultural carbon emission had the characteristics of geospatial nonstationarity.…”