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作 者:卫小华 张立杰[1,2] WEI Xiaohua;ZHANG Lijie(College of Economics and Management,Xinjiang University,Urumqi Xinjiang 830046,China;College of Textiles and Clothing,Xinjiang University,Urumqi Xinjiang 830046,China)
机构地区:[1]新疆大学经济与管理学院,新疆乌鲁木齐830046 [2]新疆大学纺织与服装学院,新疆乌鲁木齐830046
出 处:《生态经济》2019年第7期129-134,173,共7页Ecological Economy
基 金:国家自然科学基金项目“棉花国家储备最优规模及吸储抛储优化研究”(71363052);新疆维吾尔自治区软科学项目“新疆纺织服装产业可持续发展研究”(2018D07018);新疆大学经济与管理学院丝路基金项目“资源环境约束条件下棉花生产规模优化研究”(JGSL17053)
摘 要:低碳农业是当前我国农业经济转型发展的必然选择。文章以碳排放系数法测算新疆棉花种植碳排放量,采用灰色线性规划模型对不同目标导向下的碳排放体系进行比较。结果表明:(1)2001—2016年,新疆棉花碳排放量演化趋势可分为三个阶段:高速增长、平稳增长及波动增长阶段;化肥、农膜的大量投入是新疆棉花产业碳排放的主要来源;(2)资源环境约束下,农膜使用产生的碳排放量取代化肥成为最主要的碳源,而化肥、农地翻耕及灌溉依然是主要的“碳排部门”;(3)资源环境约束具有显著的碳减排效果,资源环境约束下经济效益导向优化方案的碳排放量相比2016年下降3.82%;而生态效益导向优化方案的碳排放量相比2016年下降29.87%。Low-carbon agriculture is an inevitable choice for the current transformation and development of China's agricultural economy. In this paper, the carbon emission coefficient method is used to calculate the carbon emission of cotton planting in Xinjiang, and the gray linear programming model is used to compare the carbon emission systems under different target orientations. The results show that:(1) The evolution trend of Xinjiang's cotton carbon emissions can be divided into three stages from 2001 to 2016: high-speed growth, steady growth and volatility growth stage. The large investment in fertilizer and agricultural film is the main source of carbon emissions in Xinjiang's cotton industry.(2) Under the constraints of resources and environment, the carbon emissions generated by the use of agricultural film instead of fertilizers have become the main carbon source, while fertilizer, agricultural land conversion and irrigation are still the main carbon emission departments.(3) The resource and environment constraints have significant carbon emission reduction effects. The carbon emissions of the economic benefit-oriented optimization scheme under the resource and environment constraints have decreased by 3.82% compared with 2016, while the eco-effectiveness-oriented optimization scheme have lower 29.87% than that of 2016.
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