2000—2017年江苏省县域尺度碳平衡时空变化特征分析

彭瑞, 吴丹, 高洁, 呼和涛力

长江科学院院报 ›› 2023, Vol. 40 ›› Issue (12) : 65-72.

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长江科学院院报 ›› 2023, Vol. 40 ›› Issue (12) : 65-72. DOI: 10.11988/ckyyb.20221203
水土保持与生态修复

2000—2017年江苏省县域尺度碳平衡时空变化特征分析

  • 彭瑞1, 吴丹2, 高洁1, 呼和涛力2
作者信息 +

Temporal and Spatial Characteristics of Carbon Balance at County Scale in Jiangsu Province from 2000 to 2017

  • PENG Rui1, WU Dan2, GAO Jie1, HUHE Tao-li2
Author information +
文章历史 +

摘要

县域碳平衡时空变化特征研究在理论上不仅有助于构建县域尺度碳平衡评价体系,在现实意义上也有助于县域空间规划和低碳发展策略制定。以县域碳排放和固碳量数据为基础,通过构建碳平衡系数对江苏省2000—2017年县域碳平衡进行时空特征分析。结果表明:江苏省碳排放量在2001—2011年增长速度较快,而后呈现小幅度浮动。空间上呈现“南高北低”的分布格局,高碳排放区主要集中在经济发达的苏南地区。植被固碳量总体增长起伏变化不大。空间上呈现“四周高中心低”的分布格局,高碳汇区主要分布在自然资源丰富和植被覆盖率高的区县。碳失衡县域个数由2000年的53个增长到2017年的93个,可见碳失衡严重区域增幅明显。碳失衡严重的区县与高碳排放量空间分布格局相似,主要分布在南京市中心、苏州姑苏区、扬州广陵区、徐州泉山区。建议从减碳和增汇两方面推进“双碳”目标实现进程。

Abstract

The research on temporal and spatial characteristics of carbon balance at county scale is valuable not only for the theoretical construction of a county-scale carbon balance evaluation system but also for practical applications such as spatial planning and the formulation of low-carbon development strategies. By constructing the carbon balance coefficient, we analyzed the temporal and spatial characteristics of carbon balance in Jiangsu Province from 2000 to 2017 based on data of carbon emission and carbon sequestration at county level. The study reveals that, first, carbon emissions in Jiangsu Province experienced rapid growth from 2001 to 2011, followed by a small fluctuation. In terms of spatial pattern, higher carbon emissions were observed in the economically developed southern region of the province, while the northern region exhibited lower emissions. Second, vegetation carbon sequestration displayed a consistent overall growth with minimal fluctuations. The spatial pattern of carbon sequestration revealed a characteristic distribution of higher values around the periphery and lower values towards the center. Counties with abundant natural resources and high vegetation coverage accounted for the majority of high carbon sink areas. Finally, the number of counties with carbon imbalance increased from 53 in 2000 to 93 in 2017, indicating a significant rise in areas experiencing severe carbon imbalance. Districts and counties with severe carbon imbalance exhibit a similar spatial distribution pattern to those with high carbon emissions, primarily located in the central regions of Nanjing, Gusu District of Suzhou, Guangling District of Yangzhou, and Quanshan District of Xuzhou. Based on these findings, we recommended to promote carbon reduction and increased carbon sink capabilities to achieve the goal of “double carbon” (simultaneous reduction of carbon emissions and enhancement of carbon sequestration).

关键词

碳排放 / 固碳量 / 碳平衡 / 碳失衡 / 县域尺度

Key words

carbon emission / carbon sequestration / carbon balance / carbon imbalance / county scale

引用本文

导出引用
彭瑞, 吴丹, 高洁, 呼和涛力. 2000—2017年江苏省县域尺度碳平衡时空变化特征分析[J]. 长江科学院院报. 2023, 40(12): 65-72 https://doi.org/10.11988/ckyyb.20221203
PENG Rui, WU Dan, GAO Jie, HUHE Tao-li. Temporal and Spatial Characteristics of Carbon Balance at County Scale in Jiangsu Province from 2000 to 2017[J]. Journal of Changjiang River Scientific Research Institute. 2023, 40(12): 65-72 https://doi.org/10.11988/ckyyb.20221203
中图分类号: X24   

参考文献

[1] 冯长春, 赵燕菁, 王富海, 等. 面向碳中和的规划响应[J]. 城市规划, 2022, 46(2): 25-31.
[2] 程如华. 江苏省“双碳”目标面临的挑战、机遇和实现路径研究[J]. 江苏科技信息, 2022, 39(6):62-65.
[3] 胡雪瑶, 张子龙, 陈兴鹏, 等. 县域经济发展时空差异和影响因素的地理探测: 以甘肃省为例[J]. 地理研究, 2019, 38(4): 772-783.
[4] 卢 露. 碳中和背景下完善我国碳排放核算体系的思考[J]. 西南金融, 2021, 42(12): 15-27.
[5] GUAN D, LIU Z, GENG Y, et al. The Gigatonne Gap in China’s Carbon Dioxide Inventories[J]. Nature Climate Change, 2012, 2(9): 672-675.
[6] 赖碧海. 江西省近二十年碳源碳汇变化研究[D]. 南昌: 南昌大学, 2021.
[7] LONG Z, ZHANG Z, LIANG S, et al. Spatially Explicit Carbon Emissions at the County Scale[J]. Resources, Conservation & Recycling, 2021, 173(2): 105706.
[8] 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[J]. 世界地理研究, 2016, 25(4): 102-110.
[9] 陈晓杰, 张长城, 张金亭, 等. 基于CASA模型的植被净初级生产力时空演变格局及其影响因素: 以湖北省为例[J]. 水土保持研究, 2022, 29(3): 253-261.
[10]胡 欢, 章锦河, 熊 杰, 等. 河北省碳源碳汇测算及碳减排压力分析[J]. 地理与地理信息科学, 2016, 32(3): 61-67.
[11]FANG G, GAO Z, TIAN L, et al. What Drives Urban Carbon Emission Efficiency? - Spatial Analysis Based on Nighttime Light Data[J]. Applied Energy, 2022, 312: 118772.
[12]ZHAO Y, CHEN R, ZANG P, et al. Spatiotemporal Patterns of Global Carbon Intensities and Their Driving Forces[J]. Science of the Total Environment, 2022, 818: 151690.
[13]赵荣钦, 刘 英, 马 林, 等. 基于碳收支核算的河南省县域空间横向碳补偿研究[J]. 自然资源学报, 2016, 31(10): 1675-1687.
[14]赵荣钦, 张 帅, 黄贤金, 等. 中原经济区县域碳收支空间分异及碳平衡分区[J]. 地理学报, 2014, 69(10): 1425-1437.
[15]许萍萍, 赵言文, 陈颢明, 等. 江苏省农田生态系统碳源/汇、碳足迹动态变化[J]. 水土保持通报, 2018, 38(5): 238-243.
[16]CHEN J D, XU C, WANG Y Z, et al. Carbon Neutrality Based on Vegetation Carbon Sequestration for China’s Cities and Counties: Trend, Inequality and Driver[J]. Resources Policy, 2021, 74: 102403.
[16]CHEN J, XU C, WANG Y, et al. Carbon Neutrality Based on Vegetation Carbon Sequestration for China’s Cities and Counties: Trend, Inequality and Driver[J]. Resources Policy, 2021, 74: 102403.
[17]徐国泉, 蔡 珠, 封士伟. 基于二阶段LMDI模型的碳排放时空差异及影响因素研究: 以江苏省为例[J]. 软科学, 2021, 35(10): 107-113.
[18]高 标, 房 骄, 李玉波. 基于STIRPAT模型的区域农业碳排放影响因素分析[J]. 环境科学与技术, 2016, 39(10): 190-197.
[19]WANG C, WANG F, ZHANG X, et al. Influencing Mechanism of Energy-Related Carbon Emissions in Xinjiang Based on the Input-Output and Structural Decomposition Analysis[J]. Journal of Geographical Sciences, 2017, 27(3): 365-384.
[20]杨元合, 石 岳, 孙文娟, 等. 中国及全球陆地生态系统碳源汇特征及其对碳中和的贡献[J]. 中国科学: 生命科学, 2022, 52(4): 534-574.
[21]谭显春, 赖海萍, 顾佰和, 等. 主体功能区视角下的碳排放核算: 以广东省为例[J]. 生态学报, 2018, 38(17): 6292-6301.
[22]卢俊宇, 黄贤金, 戴 靓, 等. 基于时空尺度的中国省级区域能源消费碳排放公平性分析[J]. 自然资源学报, 2012, 27(12): 2006-2017.
[23]张 赫, 彭千芮, 王 睿, 等. 中国县域碳汇时空格局及影响因素[J]. 生态学报, 2020, 40(24): 8988-8998.
[24]赵荣钦, 黄贤金, 彭补拙. 南京城市系统碳循环与碳平衡分析[J]. 地理学报, 2012, 67(6): 758-770.
[25]王少剑, 谢紫寒, 王泽宏. 中国县域碳排放的时空演变及影响因素[J]. 地理学报, 2021, 76(12): 3103-3118.
[26]CHEN J, GAO M, CHENG S, et al. County-Level CO2 Emissions and Sequestration in China during 1997-2017[J]. Scientific Data, 2020, 7(1): 391.
[27]CHEN J, FAN W, LI D, et al. Driving Factors of Global Carbon Footprint Pressure: Based on Vegetation Carbon Sequestration[J]. Applied Energy, 2020, 267: 114914.
[28]李甜甜. 江苏省农田碳源、碳汇分布特征及影响因素分析[D]. 南昌: 江西财经大学, 2017.
[29]孙小祥, 张华兵, 于英鹏. 江苏沿海地区农田生态系统碳源/汇时空变化及公平性研究[J]. 中国农业资源与区划, 2021, 42(10): 56-64.
[30]FANG J, YU G, LIU L, et al. Climate Change, Human Impacts, and Carbon Sequestration in China[J]. Proceedings of the National Academy of Sciences of the United States of America, 2018, 115(16): 4015-4020.
[31]TANG X, ZHAO X, BAI Y, et al. Carbon Pools in China’s Terrestrial Ecosystems: New Estimates Based on an Intensive Field Survey[J]. Proceedings of the National Academy of Sciences of the United States of America, 2018, 115(16): 4021-4026.
[32]彭俊铭, 吴仁海. 基于LMDI的珠三角能源碳足迹因素分解[J]. 中国人口·资源与环境, 2012, 22(2): 69-74.
[33]沈 杨, 汪聪聪, 高 超, 等. 基于城市化的浙江省湾区经济带碳排放时空分布特征及影响因素分析[J]. 自然资源学报, 2020, 35(2): 329-342.
[34]庞国伟, 山琳昕, 杨勤科, 等. 陕西省不同地貌类型区植被覆盖度时空变化特征及其影响因素[J]. 长江科学院院报, 2021, 38(3): 51-58, 76.
[35]余文梦, 张婷婷, 沈大军. 基于随机森林模型的我国县域碳排放强度格局与影响因素演进分析[J]. 中国环境科学, 2022, 42(6): 2788-2798.

基金

中国工程院战略研究与咨询项目(2022-XZ-33-04);常州大学科技项目(ZMF20020441)

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