中国农业碳排放经验分解与峰值预测——基于动态政策情景视角
投稿时间:2020-01-03    点此下载全文
引用本文:褚力其,姜志德,任天驰.中国农业碳排放经验分解与峰值预测——基于动态政策情景视角[J].中国农业大学学报,2020,25(10):187-201
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作者单位E-mail
褚力其 西北农林科技大学 经济管理学院, 陕西 杨凌 712100 cnclq@foxmail.com 
姜志德 西北农林科技大学 经济管理学院, 陕西 杨凌 712100 zhidej@sina.com 
任天驰 中国农业大学 经济管理学院, 北京 100101  
基金项目:国家自然科学基金面上项目(71573212);国家重点研发计划(2016YFC0503703)
中文摘要:运用广义迪式指数分解(GDIM)考察了1985—2017年中国农业碳排放驱动因素,并根据因素贡献差异设置动态政策情景对2018—2030年的全国农业碳排放量进行模拟与预测。研究发现:财政支出规模是引致碳排放增长的首要因素,而财政支出强度与产出强度为促进碳排放减少的关键因素;从时间段来看,1985—2010年我国碳排放增长表现为“产值规模带动”到“生产支出带动”,2010年后呈现“政策规制效应”下的年际间交替增减变化态势;在动态情景预测中,政策规制情景和绿色低碳情景分别在2025和2020年达到峰值,高速发展情景和绿色低碳情景在2030年的碳排放总量相差近10亿t。由此可见,出台“奖补”与“规制”并行的政策手段、借助市场作用优化农业生产投入结构、提高农资消耗品使用率是促进低碳生产的长久之计。
中文关键词:农业碳排放  农业政策  广义迪式指数分解  动态政策情景  峰值预测
 
Empirical decomposition and peak prediction of agricultural carbon emissions in China: From the perspective of dynamic policy scenarios
Abstract:The generalized Dig index exponential decomposition(GDIM)was used to examine the driving factors of agricultural carbon emissions in China from 1985 to 2017, and a dynamic policy scenario was set up to simulate and predict the national agricultural carbon emissions from 2018 to 2030 based on factor contribution differences. The study found that: The scale of fiscal expenditure was the primary factor leading to the growth of carbon emissions, the intensity of fiscal expenditure and output strength were the key factors contributing to the decline; In terms of time period, the growth of China's carbon emissions from 1985 to 2010 was expressed as “Driven by the scale of output value” to “Produced by production expenditure”. After 2010, an alternate inter-annual increase and decrease under the “Policy regulation effect”; In the dynamic scenario forecast, the policy regulation scenario and the green low-carbon scenario will reach peaks on 2025 and 2020, respectively. At the peak, the total carbon emissions in the high-speed development scenario and the green low-carbon scenario in 2030 will be close to 1 billion tons. Thus it can be seen that promulgating the policy measures of “reward and compensation” and “regulation”, optimizing the structure of agricultural production input with the help of the market, and increasing the utilization rate of agricultural consumables are long-term measures to promote low-carbon production.
keywords:agricultural carbon emissions  agricultural fiscal expenditure  generalized dimorphic exponential decomposition  dynamic scenario simulation  peak prediction
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