Abstract

Xinjiang’s agricultural carbon emissions showed three stages of change, i.e., continued to rise, declined and continued to rise, during 1991–2014. The agriculture belonged to the “low emissions and high efficiency” agriculture category, with a lower agricultural carbon emission intensity. By using the logarithmic mean divisia index decomposition method, agricultural carbon emissions were decomposed into an efficiency factor, a structure factor, an economy factor, and a labour factor. We divided the study period into five stages based on the changes in efficiency factor and economy factor. Xinjiang showed different agricultural carbon emission characteristics at different stages. The degree of impact on agricultural carbon emissions at these stages depended on the combined effect of planting-animal husbandry carbon intensity and agricultural labour productivity. The economy factor was the critical factor to promote the increase in agricultural carbon emissions, while the main inhibiting factor for agricultural carbon emissions was the efficiency factor. The labour factor became more and more obvious in increasing agricultural carbon emissions. Finally, we discuss policy recommendations in terms of the main factors, including the development of agricultural science and technology (S&T), the establishment of three major mechanisms and transfer of rural labour in ethnic areas.

Highlights

  • The logarithmic mean divisia index (LMDI) is a very effective method to study the factors that influence carbon emissions[7,8,9]

  • (2) The economy factor is the critical factor to promote the increase of agricultural carbon emission, while the main inhibiting factor for agricultural carbon emissions was the efficiency factor in Xinjiang (Fig. 6)

  • We analysed the changes in agricultural carbon emissions and identified the main factors that influence agricultural carbon emissions based on different stages in Xinjiang

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Summary

Introduction

The logarithmic mean divisia index (LMDI) is a very effective method to study the factors that influence carbon emissions[7,8,9]. A few studies have applied the decomposition method to analyse agricultural carbon emissions[2,10]. From the perspective of different stages, some researchers paid close attention to agricultural carbon emissions and divided the study period into sub-periods based on the trend of agricultural carbon emissions and the decoupling relationship between agricultural carbon emissions and agricultural economic growth[11,12,13,14]. Through the above overview of the literature, most previous studies divided stages mainly based on the trend of total agricultural carbon emissions and the decoupling relationship between agricultural carbon emissions with the agricultural economic growth without considering different agricultural carbon emission characteristics. The analysis results can provide effective agricultural carbon reduction proposals for policy makers

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