Calculation, elasticity and regional differences of agricultural greenhouse gas shadow prices

Global warming is one of the major threats to human survival and social development. Agriculture, as an important source of greenhouse gas (GHG) emissions, cannot be ignored. China is the worlds largest carbon emitter, and if it does not actively participate, other countries in the world will not be able to achieve the 1.5 degree temperature control target. Hence, the issue of Chinas agricultural emissions reduction is worthy of attention. As part of this study a framework for estimating agricultural GHG emissions was constructed. A directional distance function was then used to estimate the cost of emission reduction from the perspective of economic output. Furthermore, through the economic elasticity of shadow prices, agricultural economic development and emission reduction were included in the same framework to study the regional gap of agricultural emission reduction models. Finally, reducing agricultural emission reduction costs was discussed from the perspective of economy, technology, and policy. We found that (1) Agricultural emission reduction costs have phased characteristics and regional differences, and differentiated emission reduction cost improvement measures can help with efficient emission reduction. (2) The emission reduction cost in developed regions is more likely to be affected by technological progress and the strength of environmental governance by government. The emission reduction cost in regions dominated by planting is affected by the industrial structure and energy consumption structure. The emission reduction cost in underdeveloped regions is affected by the economic level. (3) We must give full play to the leading role of benchmarking regions in reducing emissions. (c) 2021 Elsevier B.V. All rights reserved.

Automated summary

The study established a framework for estimating agricultural GHG emissions, and efficient emission reduction was achieved through differentiated emission reduction cost improvement measures, while regional emission reduction costs were influenced by different factors.