Decoupling and Decomposition Analysis of Agricultural Carbon Emissions: Evidence from Heilongjiang Province, China

Abstract

Ensuring food security and curbing agricultural carbon emissions are both global policy goals. The evaluation of the relationship between grain production and agricultural carbon emissions is important for carbon emission reduction policymaking. This paper took Heilongjiang province, the largest grain-producing province in China, as a case study, estimated its grain production-induced carbon emissions, and examined the nexus between grain production and agricultural carbon emissions from 2000 to 2018, using decoupling and decomposition analyses. The results of decoupling analysis showed that weak decoupling occurred for half of the study period; however, the decoupling state and coupling state occurred alternately, and there was no definite evolving path from coupling to decoupling. Using the log mean Divisia index (LMDI) method, we decomposed the changes in agricultural carbon emissions into four factors: agricultural economy, agricultural carbon emission intensity, agricultural structure, and agricultural labor force effects. The results showed that the agricultural economic effect was the most significant driving factor for increasing agricultural carbon emissions, while the agricultural carbon emission intensity effect played a key inhibiting role. Further integrating decoupling analysis with decomposition analysis, we found that a low-carbon grain production mode began to take shape in Heilongjiang province after 2008, and the existing environmental policies had strong timeliness and weak persistence, probably due to the lack of long-term incentives for farmers. Finally, we suggested that formulating environmental policy should encourage farmers to adopt environmentally friendly production modes and technologies through taxation, subsidies, and other economic means to achieve low-carbon agricultural goals in China.

Analysis of Agricultural Carbon Emissions and Carbon Sinks in the Yellow River Basin Based on LMDI and Tapio Decoupling Models

“…In addition to being an important source of carbon emissions, crops themselves have the function of being carbon sinks, fixing carbon in the soil, which is of great significance for carbon emission reduction. However, previous studies calculating [31][32][33] carbon emissions have tended to ignore the importance of carbon sequestration in the process of crop production, resulting in a large deviation between the results of the accounting and the actual situation, and giving policy recommendations that cannot address the root causes of carbon emissions. The accurate assessment of carbon emissions is an important prerequisite for formulating effective carbon reduction policies and ensuring their implementation, thus adding carbon sinks into the carbon emission system is of great significance.…”

Prediction model and demonstration of regional agricultural carbon emissions based on PCA-GS-KNN: a case study of Zhejiang province, China

“…④ Factors influencing agricultural carbon emissions. For instance, decoupling and decomposition analysis methods are used to study the relationship between grain production and agricultural carbon emissions in Heilongjiang Province, a large grain province in China, to obtain the relationship between grain production and agricultural carbon emissions for 2000-2018 (Huang and Zhang 2021). The industrial structure and carbon emissions situation in Zhejiang province is analyzed and the impact of industrial structure on carbon emissions intensity is examined Zhu and Zhang (2021).…”

Study on the Decoupling and Interaction Effect between Industrial Structure Upgrading and Carbon Emissions under Dual Carbon Targets

“…(2) Research on carbon emission measurement and influencing factors: Cui Heng [8] and Chen et al [9] used energy consumption and energy carbon emissions coefficient to calculate carbon emissions and determined that the influencing factors of carbon emissions include economic growth, industrial structure, energy structure, etc. Zhang et al [10] used the carbon emissions coefficient method to measure the agricultural carbon emissions, structure and carbon emission efficiency in Guizhou Province and analyzed the sensitive relationship between agricultural carbon emissions and economic development through Tapio elasticity index. Yang et al [11] measured the total carbon emissions by energy consumption and conversion coefficient of energy to standard coal and carbon emissions coefficient.…”