江苏省农业碳排放动态变化影响因素分析及趋势预测
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摘要
[目的]在分析2000~2015年江苏省农业碳排放的基础上,描述其影响因素,并预测2016~2030年江苏省的农业碳排放量。[方法]运用农业碳排放估算方式计算2000~2015年江苏省农业碳排放量,并运用多元回归模型分解碳排放的影响因素,最后利用灰色预测模型预测2020~2030年江苏省的农业碳排放量。[结果]2000~2015年江苏省农业碳排放量主要呈现三段式变化。2000~2003年,表现为小幅度的先增长后下降趋势;2004~2010年,农业碳排放量呈现快速增长趋势;2011~2015年,农业碳排放总量开始缓步减少。对影响因素的分析结果表明,江苏省农业碳排放的主要影响因素来源于经济发展方面,如单位农业能源消耗、农业人口人均GDP等表征指标。农业科研人员数量对碳排放量减少起到一定的作用。预计2016~2030年江苏省农业碳排放量会持续缓步下降趋势。[结论]2000~2015年江苏省农业碳排放量主要呈现三段式变化。2000~2003年,表现为小幅度的先增长后下降趋势;2004~2010年,农业碳排放量呈现快速增长趋势;2011~2015年,农业碳排放总量开始缓步减少。预测2016~2030年间将呈持续缓步下降趋势。
Based on the analysis of agricultural carbon emissions in Jiangsu in 2000 ~ 2015,the influencing factors were described and the agricultural carbon emissions in Jiangsu province in 2020 ~ 2030 were predicted. Agricultural carbon emission in Jiangsu province was calculated by using agricultural carbon emission estimation method,and the influence factors of carbon emission were decomposed by multiple regression model. Finally,the agricultural carbon emissions of Jiangsu province in 2020 ~ 2030 were predicted by the grey forecasting model in 2000 ~2015. Agricultural carbon emissions in Jiangsu province mainly showed two stages of change in 2000 ~ 2015. It showed a slight increase first and then decrease in 2000 ~ 2004; There has been a substantial increase in 2005 ~2015. It showed a slow decline trend until 2011. The analysis of the influencing factors showed that the main influencing factors of agricultural carbon emissions in Jiangsu come from economic development,such as unit agricultural energy consumption,agricultural population per capita GDP and other indicators. The number of agricultural researchers plays a role in reducing carbon emissions. It is estimated that the agricultural carbon emissions in Jiangsu will continue to decline slowly in 2020 ~ 2030. As a major agricultural province in China,the research and prediction of carbon emissions in Jiangsu province has a certain reference for the study of agricultural carbon emissions in other regions of China. By increasing the number of agricultural researchers and innovative agricultural production,we can achieve the goal of agricultural emission reduction. At the same time,it plays a certain role in accelerating the development of China's agriculture into the low carbon development model.
Based on the analysis of agricultural carbon emissions in Jiangsu in 2000 ~ 2015,the influencing factors were described and the agricultural carbon emissions in Jiangsu province in 2020 ~ 2030 were predicted. Agricultural carbon emission in Jiangsu province was calculated by using agricultural carbon emission estimation method,and the influence factors of carbon emission were decomposed by multiple regression model. Finally,the agricultural carbon emissions of Jiangsu province in 2020 ~ 2030 were predicted by the grey forecasting model in 2000 ~2015. Agricultural carbon emissions in Jiangsu province mainly showed two stages of change in 2000 ~ 2015. It showed a slight increase first and then decrease in 2000 ~ 2004; There has been a substantial increase in 2005 ~2015. It showed a slow decline trend until 2011. The analysis of the influencing factors showed that the main influencing factors of agricultural carbon emissions in Jiangsu come from economic development,such as unit agricultural energy consumption,agricultural population per capita GDP and other indicators. The number of agricultural researchers plays a role in reducing carbon emissions. It is estimated that the agricultural carbon emissions in Jiangsu will continue to decline slowly in 2020 ~ 2030. As a major agricultural province in China,the research and prediction of carbon emissions in Jiangsu province has a certain reference for the study of agricultural carbon emissions in other regions of China. By increasing the number of agricultural researchers and innovative agricultural production,we can achieve the goal of agricultural emission reduction. At the same time,it plays a certain role in accelerating the development of China's agriculture into the low carbon development model.
引文
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[2]孙赫,梁红梅,常学礼,等.中国土地利用碳排放及其空间关联.经济地理,2015(3):154~162
[3]李秋萍,李长建,肖小勇,等.中国农业碳排放的空间效应研究.干旱区资源与环境,2015,29(4):30~35
[4]庞丽.我国农业碳排放的区域差异与影响因素分析.干旱区资源与环境,2014,28(12):1~7
[5]吴贤荣,张俊飚,田云,等.中国省域农业碳排放:测算、效率变动及影响因素研究---基于DEA-Malmquist指数分解方法与Tobit模型运用.资源科学,2014,36(1):129~138
[6]刘立平.河南省农业碳排放时空特征及影响因素研究.水土保持研究,2014,21(4):179~183,189
[7]张小平,王龙飞.甘肃省农业碳排放变化及影响因素分析.干旱区地理,2014,37(5):1029~1035
[8]李立,周灿,李二玲,等.基于投入视角的黄淮海平原农业碳排放与经济发展脱钩研究.生态与农村环境学报,2013,29(5):551~558
[9]郭忻怡,闫庆武,谭晓悦,等.基于DMSP/OLS与NDVI的江苏省碳排放空间分布模拟.世界地理研究,2016,25(4):102~110
[10]陆尤尤.基于“压力-状态-响应”机制的江苏省农业面源污染研究.南京:南京农业大学,2012
[11]吴梅.江苏省碳排放驱动因素的动态特征研究.南京:南京信息工程大学,2015
[12]廖顺宝,姬广兴,侯鹏敏,等.面向粮食产量空间化的多元回归分析模型中的两个关键问题探讨.自然资源学报,2015,30(11):1922~1932
[13]梁友嘉,徐中民.基于GIS的降水多元回归模型在黑河干流山区的应用.草业科学,2011,28(9):1581~1588
[14]卢阳.基于灰色线性回归组合模型的金融预测方法.统计与决策,2017(10):91~93
[15]顾景枝,余国新,陈文博.新疆林果业生产风险度量与评估---基于GM(1,1)模型与信息扩散理论的实证分析.中国农业资源与区划,2015,36(6):82~88
[16]张贵军,朱永明,张蓬涛,等.石家庄市耕地资源人口承载力评价.中国农业资源与区划,2013,34(6):120~126