粤港澳大湾区地表水体经济社会驱动要素

杨振华; 赵铜铁钢; 田雨; 杨芳; 郑炎辉; 陈晓宏

PDF(2837 KB)

中国环境科学 ›› 2023, Vol. 43 ›› Issue (12) : 6778-6787.

环境影响评价与管理

粤港澳大湾区地表水体经济社会驱动要素

杨振华¹, 赵铜铁钢², 田雨³, 杨芳⁴, 郑炎辉⁵, 陈晓宏²

作者信息 +

Socio-economic drivers of surface water bodies in the Guangdong-Hong Kong-Macao Greater Bay Area

YANG Zhen-hua¹, ZHAO Tong-tie-gang², TIAN Yu³, YANG Fang⁴, ZHENG Yan-hui⁵, CHEN Xiao-hong²

Author information +

文章历史 +

摘要

采用水体解译—环境库兹涅茨曲线识别—驱动关系分析的研究思路,基于Landsat影像提取粤港澳大湾区长序列地表水体动态,将环境库兹涅茨曲线应用于土地利用/覆被变化分析,辨析地表水体长期均衡与短期波动的经济社会驱动要素.结果表明:近30a大湾区城市地表水体总体上呈先升后降的非线性特征,且取决于水体与耕地、不透水面之间的转化特征,1990~2000年地表水体面积增长了1323.14km²,2000年以后呈波动下降;粤港澳大湾区地表水面率与单位面积GDP的EKC总体呈快速上升—快速下降—平缓回升的N型特征,体现出人为覆被对水体挤占与生态修复/水库扩容工程对水体的扩张;DARDL-UECM模型明确了模拟结果的准确性(R²>0.7, P<0.000)和各因子的贡献率,无论短期波动阶段还是长期均衡阶段,不透水面、林地和耕地面积比重均为城市地表水体的主要驱动要素,三者累计贡献率均值为0.96和0.93.

Abstract

Focusing on the Great Bay Area of Guangdong-Hong Kong-Macao (GBA), the Environmental Kuznets Curve (EKC) of surface water and its socio-economic drivers were identified in the paper.Specifically, the Landsat images were utilized to extract the long-term sequence of surface water dynamics, the EKC was obtained through land use/cover change analysis and the driving factors of long-term equilibrium and short-term fluctuation were illustrated. The results shown that the urban surface water bodies in the GBA generally showed a nonlinear characteristic of first rise and then fall, and depended on the transformation characteristics between water bodies and cropland and impervious surfaces in the past 30a, with the surface water body area increasing by 1323.14km² from 1990 to 2000, and then fluctuating and decreasing from 2000 onwards; the EKC of the surface water rate and GDP per unit area generally showed a N-type characteristics of rapid decline and gentle rebound reflect the loss/gain of water bodies by anthropogenic cover and ecological restoration/reservoir expansion projects; the DARDL-UECM model clarifies the accuracy of the simulation results (R²>0.7, P<0.000) and the contribution of factors. Regardless of the short-term fluctuation stage or long-term equilibrium stage, ratios of impervious surface, forested land and cropland area share are the main driving factors for urban surface water bodies, and the mean values of the cumulative contributions of the three factors are 0.96 and 0.93.

导出引用

杨振华, 赵铜铁钢, 田雨, 杨芳, 郑炎辉, 陈晓宏. 粤港澳大湾区地表水体经济社会驱动要素[J]. 中国环境科学. 2023, 43(12): 6778-6787

YANG Zhen-hua, ZHAO Tong-tie-gang, TIAN Yu, YANG Fang, ZHENG Yan-hui, CHEN Xiao-hong. Socio-economic drivers of surface water bodies in the Guangdong-Hong Kong-Macao Greater Bay Area[J]. China Environmental Science. 2023, 43(12): 6778-6787

中图分类号： X196

参考文献

[1] 刘年磊,蒋洪强,吴文俊.基于不确定性的水资源优化配置模型及其实证研究[J]. 中国环境科学, 2014,34(6):1607-1613. Liu N L, Jiang H Q, Wu W J. Empirical research of optimal allocation model of water resources under uncertainties[J]. China Environmental Science, 2014,34(6):1607-1613.
[2] 李崇巍,王志慧,汤秋鸿,等.1986~2019年黄河流域地表水体动态变化及其影响因素[J]. 地理学报, 2022,77(5):1153-1168. Li C W, Wang Z H, Tang Q H, et al. Dynamics of surface water area in the Yellow River Basin and its influencing mechanism during 1986~ 2019 based on Google Earth Engine[J]. Acta Geographica Sinica, 2022,77(5):1153-1168.
[3] 付颖,徐新良,通拉嘎,等.近百年来北京市地表水体时空变化特征及驱动力分析[J]. 资源科学, 2014,36(1):75-83. Fu Y, Xu X L, Tong L G, et al. Analysis on spatiotemporal variation characteristics and driving forces of surface water bodies in Beijing in recent 100years[J]. Resource Science, 2014,36(1):75-83.
[4] 王海云,匡耀求,文薪荐,等.粤港澳大湾区生态网络构建及廊道优化[J]. 中国环境科学, 2022,42(5):2289-2298. Wang H Y, Kuang Y Q, Wen X J, et al. Ecological network construction and corridor optimization in the Greater Bay Area of Guangdong, Hong Kong and Macao[J]. China Environmental Science, 2022,42(5):2289-2298.
[5] 任南琪,张建云,王秀蘅.全域推进海绵城市建设,消除城市内涝,打造宜居环境[J]. 环境科学学报, 2020,40(10):3481-3483. Ren N Q, Zhang J Y, Wang X H. Promoting the sponge city construction widely to eliminate urban waterlogging and create livable environment[J] Journal of Environmental Science, 2020,40(10):3481-3483.
[6] 冯琳,张婉婷,张钧珂,等.三峡库区面源污染的时空特征及EKC分析[J]. 中国环境科学, 2022,42(7):3325-3333. Feng L, Zhang W T, Zhang J K, et al. Spatial and temporal characteristics and EKC analysis of non-point source pollution in the Three Gorges Reservoir Area[J]. China Environmental Science, 2022, 42(7):3325-3333.
[7] Pata U K, Aydin M. Testing the EKC hypothesis for the top six hydropower energy-consuming countries: Evidence from Fourier Bootstrap ARDL procedure[J]. Journal of Cleaner Production, 2020, 264:121699.
[8] 何媛婷,王石英,袁再健,等.珠江三角洲土地利用变化及其对城市化发展的响应[J]. 生态环境学报, 2020,29(2):303-310. He Y T, Wang S Y, Yuan Z J, et al. Land use change and its response to urbanization in the Pearl River Delta[J]. Journal of Ecological Environment, 2020,29(2):303-310.
[9] 程子浩,刘先锋,林港特,等.近40年粤港澳大湾区桑基鱼塘演变监测和分析[J]. 湿地科学与管理, 2021,17(3):29-35. Cheng Z H, Liu X F, Lin G T, et al. Changes in fish ponds in the Guangdong-Hong Kong-Macao Greater Bay Area over the past 40 years[J]. Wetland science and management, 2021,17(3):29-35.
[10] Huang W J, Duan W L, Nover D, et al. An integrated assessment of surface water dynamics in the Irtysh River Basin during 1990~2019 and exploratory factor analyses[J]. Journal of Hydrology, 2021,593.
[11] Grossman G M, Krueger A B. Economic growth and the environment[J]. The quarterly journal of economics, 1995,110(2):353-377.
[12] 孙博文,程志强.市场一体化的工业污染排放机制:长江经济带例证[J]. 中国环境科学, 2019,39(2):868-878. Sun B W, Cheng Z Q. Research on industrial pollution discharge mechanism of market integration: Taking the Yangtze River Economic Belt as an example[J]. China Environmental Science, 2019,39(2):868-878.
[13] 袁凯华,甘臣林,杨慧琳,等.建设用地扩张与碳排放增长的EKC验证及特征分解研究——以武汉市为例[J]. 中国土地科学, 2019,33(1):56-64. Yuan K H, Gan C L, Yang H L, et al. Validation of the EKC and characteristics decomposition between construction land expansion and carbon emission: A case study of Wuhan City[J]. China Land Science, 2019,33(1):56-64.
[14] 陈操操,张妍,刘春兰,等.北京市能源消费与经济增长关系的协整检验分析[J]. 环境科学, 2012,33(6):2139-2144. Chen C C, Zhang Y, Liu C L, et al. Energy consumption and GDP growth in Beijing: Cointegration and causality analysis[J] Environmental Science, 2012,33(6):2139-2144.
[15] Sarkodie S A. How to apply the novel dynamic ARDL simulations (dynardl) and Kernel-based regularized least squares (krls)[J]. method X, 2020:11.
[16] 粤港澳大湾区城市群年鉴编纂委员会.粤港澳大湾区城市年鉴[M]. 北京:方志出版社, 2020. Compilation Committee of the Yearbook of Guangdong-Hong Kong- Macao Greater Bay urban agglomeration. Yearbook of Guangdong- Hong Kong-Macao Greater Bay[M]. Beijing: Local chronicles press, 2019.
[17] 冯凯东,毛德华,王宗明,等.基于GEE和遥感大数据的1986—2015年全球城镇用地扩张占用水体时空特征[J]. 地理科学, 2022,42(1): 143-151. Feng K D, Mao D H, Wang Z M, et al. Spatial and temporal characteristics of water bodies occupied by global urban land expansion from 1986 to 2015 based on Gee and remote sensing big data[J]. Science GeographicaSinica, 2022,42(1):143-151.
[18] Yang J, Huang X. 30m annual land cover and its dynamics in China from 1990 to 2019[J]. Earth Syst. Sci. Data Discuss., 2021[2022-02-08].
[19] 鲁晓东,许罗丹,熊莹.水资源环境与经济增长:EKC假说在中国八大流域的表现[J]. 经济管理, 2016,38(1):20-29. Lu X D, Xu L D, Xiong Y. Water resources environment and economic growth: Performance of EKC hypothesis in China's eight major basins[J] Economic Management, 2016,38(1):20-29.
[20] 郭嘉铭,金良,董锁成.呼和浩特市环境库兹涅茨曲线与环境影响因素分析[J]. 干旱区资源与环境, 2015,29(4):143-148. Guo J M, Jin L, Dong S C. The environmental Kuznets curve and the environmental influencing factors in Hohhot[J]. Resources and Environment in Arid Areas, 2015,29(4):143-148.
[21] Zhou C, Wang S, Wang J. Examining the influences of urbanization on carbon dioxide emissions in the Yangtze River Delta, China: Kuznets curve relationship[J]. Science of The Total Environment, 2019,675: 472-482.
[22] Mcfeeters. S. K. The use of the normalized difference water index (NDWI) in the delineation of open water features[J]. International Journal of Remote Sensing, 1996,17(7):1425-1432.
[23] 徐涵秋.利用改进的归一化差异水体指数(MNDWI)提取水体信息的研究[J]. 遥感学报, 2005,9(5):79-85. Xu H Q. Research on extracting water information using improved normalized difference water index (MNDWI)[J]. Journal of remote sensing, 2005,9(5):79-85.
[24] Steve F, Pat L. S, Song G, et al. Cloud detection algorithm comparison and validation for operational Landsat data products[J]. Remote sensing of environment, 2017,194:379-390.
[25] Wang X, Xiao X, Zou Z, et al. Gainers and losers of surface and terrestrial water resources in China during 1989-2016[J]. Nat Commun, 2020,11(1):3471.
[26] Kaika D, Zervas E. The Environmental Kuznets Curve (EKC) theory—Part A: Concept, causes and the CO₂ emissions case[J]. Energy Policy, 2013, 62: 1392-1402.
[27] 樊胜岳,麻亮亮.中国大陆足迹家族的环境库兹涅茨曲线分析[J]. 自然资源学报, 2016,31(9):1452-1462. Fan S Y, Ma L H. Environmental Kuznets curve analysis of footprint family in Chinese Mainland[J]. Journal of Natural Resources, 2016, 31(9):1452-1462.
[28] 王芳,曹一鸣,陈硕.反思环境库兹涅茨曲线假说[J]. 经济学(季刊), 2020,19(1):81-100. Wang F, Cao Y M, Chen S. Reflection on the environmental kuznets curve hypothesis[J] Economics (Quarterly), 2020,19(1):81-100.
[29] 徐建伟,傅泽强,谢园园,等.基于EKC假说的经济增长与资源和环境协调性分析——以铁岭市为例[J]. 环境工程技术学报, 2016,6(3): 290-294. XU J W, FU Z Q, XIE Y Y, et al. Coordination analysis on economic growth and resources and environment based on EKC: a case study on Tieling City[J]. Journal of Environmental Engineering Technology, 2016,6(3):290-294.
[30] 张茹倩,李鹏辉,徐丽萍.城镇化对新疆土地利用碳排放的影响及其耦合关系[J]. 生态学报, 2022,42(13):5226-5242. Zhang R Q, Li P H, Xu L P. Effects of urbanization on carbon emission from land use in Xinjiang and their coupling relationship. Acta Ecologica Sinica, 2022,42(13):5226-5242.