地球信息科学学报 ›› 2021, Vol. 23 ›› Issue (3): 405-418.doi: 10.12082/dqxxkx.2021.200097
蔡一乐1(), 曹诗颂1,*(
), 杜明义1, 李善飞2, 陈姗姗3
收稿日期:
2020-03-02
修回日期:
2020-06-12
出版日期:
2021-03-25
发布日期:
2021-05-25
通讯作者:
曹诗颂
E-mail:onejoycc@163.com;caoshisong@bucea.edu.cn
作者简介:
蔡一乐(1997- ),男,江苏张家港人,硕士生,主要从事人为热排放格局、过程及机理研究。E-mail: onejoycc@163.com
基金资助:
CAI Yile1(), CAO Shisong1,*(
), DU Mingyi1, LI Shanfei2, CHEN Shanshan3
Received:
2020-03-02
Revised:
2020-06-12
Online:
2021-03-25
Published:
2021-05-25
Contact:
CAO Shisong
E-mail:onejoycc@163.com;caoshisong@bucea.edu.cn
Supported by:
摘要:
人为热一定程度上影响着城市的局地环境和微气候。以2016年中国地级市为研究对象,首先采用了能源消耗清单法结合Suomi-NPP(National Polar-orbiting Partnership)VIIRS(Visible Infrared Imaging Radiometer Suite)夜间灯光数据的方法估算了格网尺度的人为热通量;其次,分别使用最小二乘法和地理加权回归法模型在全局和局部尺度上研究不同因素对人为热总量的影响;进一步使用自然断点法划分出其中的主导因素。得出以下结论:① 各地级市的人为热总量具有显著的空间差异,京津冀、长江三角洲、珠江三角洲城市群所在的中国东南地区,人为热总量相对较高;② 能源消耗、民用汽车数量、人均生产总值是全局尺度上人为热总量的主要驱动因素;人口密度、第二产业占比、道路密度和建成区面积对人为热总量的影响呈现出较强的空间异质性;外商直接投资额则在全局尺度对人为热总量的影响较低。③ 主导因素分析表明无主导因素的地级市主要位于中国的西南部,以能源消耗、民用汽车数量、人均生产总值为单一主导因素的地级市主要聚集于中国的东南部、中部及东北部、西北部,并在其周边交叉地区形成了一些数量较少的双重主导因素地级市。本文的研究为政府相关部门对于人为热调控政策的制定提供了依据。
蔡一乐, 曹诗颂, 杜明义, 李善飞, 陈姗姗. 中国地级市人为热总量的估算及驱动因素分析[J]. 地球信息科学学报, 2021, 23(3): 405-418.DOI:10.12082/dqxxkx.2021.200097
CAI Yile, CAO Shisong, DU Mingyi, LI Shanfei, CHEN Shanshan. Estimation and Analysis of Driving Factors of Total AHF in Prefecture-Level of China[J]. Journal of Geo-information Science, 2021, 23(3): 405-418.DOI:10.12082/dqxxkx.2021.200097
表1
《中国统计年鉴2017》[23]中290个地级市的数据统计"
数据名 | 平均值 | 中位数 | 标准差 | 最小值 | 最大值 |
---|---|---|---|---|---|
AHF总量/W | 42 054.04 | 23 349.00 | 51 499.27 | 2528.00 | 378 730.00 |
人均生产总值/元 | 68 949.56 | 60 912.50 | 34 483.69 | 17 890.00 | 200 022.00 |
人口密度/(人/km2) | 480.80 | 344.52 | 569.18 | 2.71 | 6273.56 |
第二产业占比/% | 43.67 | 44.60 | 10.22 | 13.57 | 71.34 |
能源消耗/万千瓦时 | 199 5058.48 | 1 232 921.50 | 2 171 124.84 | 197 798.00 | 15 267 700.00 |
外商直接投资额/万美元 | 94 437.14 | 24 617.50 | 226 183.58 | 3.00 | 2 432 909.00 |
道路密度/% | 109.33 | 106.91 | 54.77 | 1.64 | 230.74 |
民用汽车数量/辆 | 762 404.27 | 513 544.00 | 789 434.97 | 13 200.00 | 5 138 074.00 |
建成区面积/km2 | 162.08 | 91.00 | 204.34 | 14.00 | 1563.00 |
表3
2016年中国地级市人为热总量与其驱动因素的全局回归分析(OLS模型)"
变量名 | 系数值 | 标准差 | T统计量 | 概率健壮度 | 方差膨胀因子 |
---|---|---|---|---|---|
截距 | -3.547 | 0.895 | -3.963 | 0.000 | |
人均生产总值 | 0.249 | 0.078 | 3.177 | 0.005 | 1.835 |
人口密度 | -0.125 | 0.047 | -2.683 | 0.012 | 2.920 |
第二产业占比 | -0.314 | 0.124 | -2.532 | 0.018 | 1.273 |
能源消耗 | 0.627 | 0.059 | 10.706 | 0.000 | 3.008 |
外商直接投资额 | -0.052 | 0.020 | -2.624 | 0.038 | 2.176 |
道路密度 | -0.054 | 0.051 | -1.066 | 0.237 | 2.053 |
民用汽车数量 | 0.247 | 0.054 | 4.581 | 0.000 | 3.187 |
建成区面积 | 0.320 | 0.061 | 5.274 | 0.000 | 3.146 |
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