Driving factors and decoupling trend analysis between agricultural CO₂ emissions and economic development in China based on LMDI and Tapio decoupling

Abstract

<abstract> <p>Based on mathematical models, in-depth analysis about the interrelationship between agricultural CO₂ emission and economic development has increasingly become a hotly debated topic. By applying two mathematical models including logarithmic mean divisia index (LMDI) and Tapio decoupling, this work aims to study the driving factor and decoupling trend for Chinese agricultural CO₂ emission from 1996 to 2020. Firstly, the intergovernmental panel on climate change (IPCC) method is selected to estimate the agricultural CO₂ emission from 1996 to 2020, and the LMDI model is adopted to decompose the driving factors of agricultural CO₂ emission into four agricultural factors including economic development, carbon emission intensity, structure, and labor effect. Then, the Tapio decoupling model is applied to analyze the decoupling state and development trend between the development of agricultural economy and CO₂ emission. Finally, this paper puts forward some policies to formulate a feasible agricultural CO₂ emission reduction strategy. The main research conclusions are summarized as follows: 1) During the period from 1996 to 2020, China's agricultural CO₂ emission showed two stages, a rapid growth stage (1996–2015) and a rapid decline stage (2016–2020). 2) Agricultural economic development is the first driving factor for the increase of agricultural CO₂ emission, while agricultural labor factor and agricultural production efficiency factor play two key inhibitory roles. 3) From 1996 to 2020, on the whole, China's agricultural sector CO₂ emission and economic development showed a weak decoupling (WD) state. The decoupling states corresponding to each time period are strong negative decoupling (SND) (1996–2000), expansive negative decoupling (END) (2001–2005), WD (2006–2015) and strong decoupling (SD) (2016–2020), respectively.</p> </abstract>