Carbon footprint of cotton production in China: Composition, spatiotemporal changes and driving factors

作者：棉花生物学国家重点实验室 日期：2022-08-30 访问量：

Weibin Huang, Fengqi Wu, Wanrui Han, Qinqin Li, Yingchun Han, Guoping Wang, Lu Feng, Xiaofei Li, Beifang Yang, Yaping Lei, Zhengyi Fan, Shiwu Xiong, Minghua Xin, Yabing Li, Zhanbiao Wang  

Science of The Total Environment

Doi: 10.1016/j.scitotenv.2022.153407

Abstract：

Analyzing the carbon footprint of crop production and proposing low-carbon emission reduction production strategies can help China develop sustainable agriculture under the goal of 'carbon peak and carbon neutrality'. Cotton is an economically important crop in China, but few reports have systematically quantified the carbon footprint of China's cotton production and analyzed its spatiotemporal changes and driving factors. This study used a life cycle approach to analyze the spatiotemporal changes and identify the main components and driving factors of the carbon footprint of cotton production in China between 2004 and 2018 based on statistical data. The results showed that the carbon footprint per unit area of cotton in Northwest China, the Yellow River Basin and the Yangtze River Basin reached 6220.13 kg CO₂eq·ha-1, 3528.14 kg CO₂eq·ha-1 and 2958.56 kg CO₂eq·ha-1, respectively. From 2004 to 2018, the CFa in the Yellow River Basin and Northwest China increased annually, with average increases of 59.87 kg CO₂eq·ha-1 and 260.70 kg CO₂eq·ha-1, respectively, while the CFa in the Yangtze River Basin decreased by an average of 21.53 kg CO₂eq·ha-1 per year. The ridge regression and Logarithmic Mean Divisia Index (LMDI) model showed that fertilizer, irrigation electricity and agricultural film were the main influences on carbon emission growth at the micro level and that the economic factor was the key factor at the macro level. Improving the efficiency of cotton fertilization and electricity use and ensuring the high-quality development of the cotton industry are effective strategies to reduce the carbon footprint of cotton cultivation in the future. This study comprehensively uses statistical data and mathematical modeling to provide theoretical support for accounting and in-depth analysis of cotton carbon emissions. The results are valuable for policy making related to sustainable development and the low-carbon development of the Chinese cotton industry.