格网尺度下滨州市土地利用碳排放核算及驱动因素分析

On Analysis of Land Use Carbon Emissions Accounting and Its Driving Factors at Grid-scale in Binzhou City

本研究基于ODIAC公布的全球碳排放栅格数据和土地利用数据,运用碳排放系数法和空间叠加技术以网格为单元估算滨州市2010年、2015年、2020年土地利用碳排放,并借助地理加权回归分析(GWR)模型探究影响土地利用碳排放的驱动机理,为推动土地利用方式向低碳化转变及制订精细化管理措施提供科学依据。结果表明:(1)从时间上看,2010—2020年滨州市土地利用碳排放整体呈增长趋势,其中增长幅度较大用地类型为耕地和建设用地;(2)从空间上看,滨州市土地利用碳排放呈现明显的全局正相关和稳定的局部自相关特征,集聚效应明显;碳排放高值区整体由各区县主城区向四周蔓延,其中滨城区最为明显,而无棣县和沾化区北部呈现轻微的碳汇量增加趋势。(3)基于GWR模型运行结果发现,各影响因素在不同时间、不同地区对碳排放的影响程度均不相同。其中,2010年、2015年、2020年人口数量、地区生产总值(GDP)、工业密度与土地利用碳排放始终呈正相关,其中工业密度影响最显著;而土地利用综合指数由负相关转变为明显的正相关,耕地破碎化指数由正相关转为明显的负相关,且具有明显的空间依赖效应。基于上述研究结果,滨州市应兼顾经济发展,并制订差别化的碳减排政策及目标,城市化方面应制订低碳城市建设计划,促进能源转型;农业方面利用现代农业的先进技术,发展新型绿色低碳新农业。

Based on the global carbon emissions grid data and land use data published by ODIAC,this study used the carbon emission co-efficient method and spatial superposition technology to estimate the carbon emissions of land use in Binzhou City in 2010,2015 and 2020.By using the grid as a unit,the author explored the driving mechanism of land use carbon emissions with the help of the geographically weighted regression analysis(GWR) model,which provided a scientific basis for promoting the lowcarbon transformation of land use and formulating refined management measures.The results showed that:(1) From the perspective of time,the carbon emissions of land use in Binzhou city showed an overall growth trend from 2010 to 2020,and the types of land with a large growth rate were cultivated land and construction land;(2) From the perspective of space,the carbon emissions of land use in Binzhou city were characterized by obviously global positive correlation and stable local autocorrelation,and the agglomeration effect was obvious.The high-value areas of carbon emissions spread from the main urban areas of each district and county to their surrounding areas,among which Bincheng District is the most obvious,while Wudi County and the northern part of Zhanhua District showed a slight increase in carbon sink;(3) Based on the operating results of GWR model,it was found that the influence degree of each factor on carbon emissions was different in different time and different regions.Among them,the population,GDP and industrial density in 2010,2015 and 2020 were always positively correlated with land use carbon emissions,with industrial density exerting the most significant impact.What's more the land use comprehensive index changed from negative correlation to obviously positive correlation,and the cultivated land fragmentation index changed from positive correlation to obviously negative correlation,and had an obvious spatial dependence effect.Based on the above research results,Binzhou city should take economic development into account and formulate differentiated policies and objectives for carbon emissions reduction.In terms of urbanization,a low-carbon urban construction plan should be formulated to promote energy transformation;in agriculture,the advanced technology of modern agriculture should be used to develop new green low-carbon new agricultural production methods.