Research on collaborative management and optimization of ecological risks in urban agglomeration

城市群 公司治理 集聚经济 业务 传输(电信) 经济地理学 过程(计算) 地理 环境规划 经济 经济增长 计算机科学 电信 财务 操作系统
作者
Wen Zhang,Gengyuan Liu,Francesco Gonella,Linyu Xu,Zhifeng Yang
出处
期刊:Journal of Cleaner Production [Elsevier]
卷期号:372: 133735-133735 被引量:8
标识
DOI:10.1016/j.jclepro.2022.133735
摘要

Collaborative governance is increasingly advocated to address the ecological risk management issues that occur during urban agglomeration developing. However, how to form strong and effective collaboration is still a great challenge among multiple cities in urban agglomeration. By analysing the multiple ecological risk transmission pathways of the case of Pearl River Delta Urban Agglomeration (PRD) in China, this paper aims at deconstructing the complex structure and connection types in urban agglomeration, as well as exploring the inherent mechanism of ecological risk governance to achieve collaboration. Thus, a new Bayesian network model of ecological risk transmission is developed to visualize the key connection notes of risk transmission process. Testing the impacts of (1) number of collaborative cities, (2) spatial distance factor and (3) risk transmission links, we can find the optimal cooperative risk management strategy by reducing the probability of occurrence of key nodes and intervening on the critical path of the risk transmission process. The results show that (1) The current collaborative governance plan in the PRD is mainly formulated by large cities driving small surrounding cities, which is not an optimal strategy. (2) The management effect of ecological risks in urban agglomerations is not necessarily positively correlated with the number of collaborative cities. There are multiple combinations methods under a certain number of collaborative cities and the effects of ecological risk collaborative governance are different. (3) Collaboration governance of urban agglomeration should be based on the overall planning of urban development, and comprehensively consider collaboration number, spatial distance and association between cities.
最长约 10秒,即可获得该文献文件

科研通智能强力驱动
Strongly Powered by AbleSci AI
科研通是完全免费的文献互助平台,具备全网最快的应助速度,最高的求助完成率。 对每一个文献求助,科研通都将尽心尽力,给求助人一个满意的交代。
实时播报
刚刚
努力小周发布了新的文献求助10
1秒前
1秒前
2秒前
121311发布了新的文献求助10
2秒前
彭于晏应助Jiali采纳,获得10
3秒前
棒棒糖完成签到,获得积分10
3秒前
4秒前
4秒前
李莹完成签到,获得积分10
4秒前
只想发财发布了新的文献求助10
4秒前
毛彬完成签到,获得积分10
5秒前
defMain完成签到,获得积分10
5秒前
agentwang完成签到,获得积分10
7秒前
汉堡包应助木川采纳,获得10
7秒前
大川发布了新的文献求助10
8秒前
Vickeliya发布了新的文献求助10
8秒前
充电宝应助安可瓶子采纳,获得30
8秒前
junjunjunjun发布了新的文献求助10
8秒前
可爱的函函应助北冥有鱼采纳,获得10
9秒前
Bruce完成签到,获得积分10
10秒前
11秒前
11秒前
ZOE应助Mon采纳,获得30
13秒前
kkk完成签到,获得积分10
13秒前
香蕉觅云应助文忉嫣采纳,获得10
15秒前
zhangqian完成签到 ,获得积分10
16秒前
互助应助电击小子采纳,获得20
17秒前
kkk发布了新的文献求助10
17秒前
17秒前
18秒前
zzzz完成签到 ,获得积分10
19秒前
热心市民小红花应助Rebekah采纳,获得10
19秒前
传奇3应助星落枝头采纳,获得10
20秒前
20秒前
21秒前
22秒前
121311完成签到,获得积分20
22秒前
22秒前
Iridescent发布了新的文献求助10
23秒前
高分求助中
(应助此贴封号)【重要!!请各用户(尤其是新用户)详细阅读】【科研通的精品贴汇总】 10000
Modern Epidemiology, Fourth Edition 5000
Kinesiophobia : a new view of chronic pain behavior 5000
Molecular Biology of Cancer: Mechanisms, Targets, and Therapeutics 3000
Digital Twins of Advanced Materials Processing 2000
Weaponeering, Fourth Edition – Two Volume SET 2000
Signals, Systems, and Signal Processing 610
热门求助领域 (近24小时)
化学 材料科学 医学 生物 工程类 有机化学 纳米技术 化学工程 生物化学 物理 计算机科学 内科学 复合材料 催化作用 物理化学 光电子学 电极 冶金 细胞生物学 基因
热门帖子
关注 科研通微信公众号,转发送积分 6018248
求助须知:如何正确求助?哪些是违规求助? 7605646
关于积分的说明 16158476
捐赠科研通 5165797
什么是DOI,文献DOI怎么找? 2765030
邀请新用户注册赠送积分活动 1746581
关于科研通互助平台的介绍 1635307