Study on the Vertical Linkage of Greenhouse Gas Emission Intensity Change of the Animal Husbandry Sector between China and Its Provinces

Abstract

China's carbon intensity (CI) reduction target in 2030 needs to be allocated to each province in order to be achieved. Thus, it is of great significance to study the vertical linkage of CI change between China and its provinces. The existing research on the vertical linkage focuses more on energy-related economic sectors in China; however, attention has not been paid to China's animal husbandry (AH) sector, although the role of the China's AH sector in greenhouse gas (GHG) reduction is increasingly important. This study firstly established a vertical linkage of change in greenhouse gas emission intensity of the animal husbandry sector (AHGI) between China and its 31 provinces based on the logarithmic mean Divisia index (LMDI) decomposing method from the perspective of combining emission reduction with economic development, and quantified the contributions of each province and its three driving factors of environmental efficiency (AHEE), productive efficiency (AHPE), and economic share (AHES) to reducing China's AHGI during the period of 1997-2016. The main results are: (1) The AHGI of China decreased from 5.49 tCO 2 eq/10 4 yuan in 1997 to 2.59 tCO 2 eq/10 4 in 2016, showing a 75.25% reduction. The AHGI in 31 provinces also declined and played a positive role in promoting the reduction of national AHGI, but there were significant inter-provincial differences in the extent of the contribution. Overall, the provinces with higher emission levels contributed the most to the reduction of China's AHGI; (2) The AHPE and AHEE factors in 31 provinces cumulatively contributed to the respective 68.17% and 11.78% reduction of China's AHGI, while the AHES factors of 31 provinces cumulatively inhibited the 4.70% reduction. Overall, the AHPE factor was the main driving factor contributing to the reduction of China's AHGI. In the future, improving the level of AHEE through GHG emissions reduction technology and narrowing the inter-provincial gap of the level of AHPE are two important paths for promoting the reduction of China's AHGI.

Achieving Environmentally Sustainable Livestock Production

“…In their paper, Cai et al [9] carry out a national level study on a reduction of the emission intensities (per unit of the monetary value of output) of non-CO 2 greenhouse gases arising from the livestock sector in China, through an analysis of the contribution of each province to the overall national emissions. They especially concentrate on the role of three "driving factors," i.e., environmental efficiency, productive efficiency and economic share, in determining the national greenhouse gas emission intensity.…”

Study on the Relationship between Economic Growth of Animal Husbandry and Carbon Emission Based on Logarithmic Average Index Method and Decoupling Model: A Case Study of Heilongjiang Province

“…In his proposal, Cai suggested that in order to achieve China's carbon intensity reduction target for 2030, it should be allocated to each province. He also explored the vertical linkage relationship between China and its 31 provinces' livestock carbon emission intensity changes using the Logarithmic Mean Divisia Index model (LMDI) [15]. While there are studies suggesting the importance of integrating the unique aspects of animal husbandry development in various provinces and examining the factors and methods of carbon emissions from animal husbandry based on local conditions, there is currently a lack of literature on this topic.…”

Analysis of spatial and temporal characteristics of carbon emission efficiency of pig farming and the influencing factors in China

“…Livestock production accounts for a significant share of the global greenhouse gas (GHG) balance, including carbon dioxide (CO 2 ), methane (CH 4 ), ammonia (NH 3 ), and nitrous oxide (N 2 O) released into the atmosphere (7). There are many calculation and assessment methods to study carbon emission efficiencies, such as the stochastic frontier model, regression model, data envelopment analysis (DEA) method, and unexpected output (Slack-Based Measure) model (8)(9)(10)(11). Most studies have adopted the unexpected output (Slack-Based Measure) model to calculate carbon emission efficiency.…”