浙江大湾区生态安全预警及障碍因子分析

doi:10.16258/j.cnki.1674-5906.2023.10.015

生态环境学报 ›› 2023, Vol. 32 ›› Issue (10): 1861-1872.DOI: 10.16258/j.cnki.1674-5906.2023.10.015

浙江大湾区生态安全预警及障碍因子分析

李瑞发(), 崔旺来, 司凌涵, 鲍声望, 杨帆

浙江海洋大学经济与管理学院，浙江舟山 316022

收稿日期:2023-07-29 出版日期:2023-10-18 发布日期:2024-01-16
通讯作者: *杨帆。E-mail: yang-fan@zjou.edu.cn
作者简介:李瑞发（2000年生），男，硕士研究生，研究方向为城市生态安全评估等。E-mail: 3061976130@qq.com
基金资助:
国家社会科学基金重大项目(22&ZD152);国家社会科学基金项目(20BZZ063)

Ecological Security Early Warning and Obstacle Factor Analysis in the Zhejiang Greater Bay Area

LI Ruifa(), CUI Wanglai, SI Linghan, BAO Shengwang, YANG Fan

School of Economics and Management, Zhejiang Ocean University, Zhoushan 316022, P. R. China

Received:2023-07-29 Online:2023-10-18 Published:2024-01-16

摘要/Abstract

摘要：

湾区是区域建设的发展热点和突出亮点，生态安全是湾区可持续发展的必要前提，生态安全预警研究在生态安全研究领域具有典型性和代表性。以浙江大湾区为例，基于DPSIR模型构建浙江大湾区生态安全评价指标体系，对其2000、2005、2010、2015和2020年生态安全预警指数进行综合评价，采用灰色模型从县域层面模拟预测浙江大湾区2025、2030、2035年的生态安全状况，同时利用障碍度模型诊断制约浙江大湾区生态安全的障碍性因素。结果表明，1）2000－2020年浙江大湾区的生态安全状况在逐步改善，平均生态安全预警指数从0.496提升至0.518。但区域间存在差异，中西部地区生态安全状况较好，而东北部和南部地区生态状况不佳，应加强生态安全管理和监管。2）用灰色模型预测湾区2025－2035年生态状况将不断提升，到2035年，平均生态安全预警指数将从0.523增加到0.545，将有47.7%的县（区、市）达到轻度预警及无预警状态。但生态安全形势依然严峻，部分地区的生态安全预警指数值呈波动和下降趋势，表明其生态环境存在不稳定甚至恶化的情况，必须加大生态保护力度，防止这些区域的生态状况进一步恶化。3）准则层障碍度方面障碍度差距在逐渐缩小且整体上有所降低，指标层障碍度方面包括环保投资、城镇化率、生态系统服务价值、政府科技投入、国民生产总值、人口密度是制约浙江大湾区生态安全的主要障碍因子，应针对上述障碍因子采取针对性措施予以解决。研究结果可为浙江大湾区未来生态安全的调控和管理提供决策参考，并为其他地区生态安全研究提供借鉴。

关键词: 生态安全预警, 灰色模型, 障碍因子, 障碍度模型, 生态安全评价, 浙江大湾区

Abstract:

The Bay Area is a prominent highlight and development hotspot, with construction and ecological security being essential for sustainable development. Research on ecological security early warning is representative in this field. This study takes the Zhejiang Greater Bay Area as an example and constructs an evaluation index system based on the DPSIR model to assess ecological security. The ecological security warning index is evaluated for 2000, 2005, 2010, 2015, and 2020. The grey model is then used to simulate and predict the ecological security status of the Zhejiang Greater Bay Area at the county level for 2025, 2030, 2035, and 2050. Additionally, the obstacle degree model is used to diagnose factors that restrict ecological security. The results indicate that 1) from 2000 to 2020, the ecological security situation in the Zhejiang Greater Bay Area has gradually improved, with the average ecological security early warning index increasing from 0.496 to 0.518. However, there are regional differences, with better ecological security in the central and western regions, and poor ecological conditions in the northeastern and southern regions. Therefore, it is necessary to strengthen ecological security management and supervision in these areas. 2) Grey model predictions show that the ecological conditions in the bay area will continue to improve from 2025 to 2035, with the average ecological security early warning index projected to increase from 0.523 to 0.545. By 2035, 47.7% of counties, districts, and cities are expected to reach a mild warning or no warning state. However, the ecological security situation remains severe in some areas, with fluctuations and a downward trend in the ecological security early warning index, indicating unstable or even deteriorating ecological conditions. It is necessary to intensify efforts in ecological protection to prevent further deterioration of the ecological situation in these regions. 3) The obstacle degree in the criterion layer is gradually decreasing and overall narrowing, with the main factors restricting ecological security of the Zhejiang Greater Bay Area including environmental protection investment, urbanization rate, ecosystem service value, government technology investment, gross domestic product, and population density. Targeted measures should be implemented to address these obstacles. The research results can inform decision-making in regulating and managing future ecological security in the Greater Bay Area of Zhejiang, and provide insights for ecological security research in other regions.

Key words: ecological security warning, grey model, obstacle factor, obstacle model, ecological security evaluation, Zhejiang Greater Bay Area

中图分类号:

李瑞发, 崔旺来, 司凌涵, 鲍声望, 杨帆. 浙江大湾区生态安全预警及障碍因子分析[J]. 生态环境学报, 2023, 32(10): 1861-1872.

LI Ruifa, CUI Wanglai, SI Linghan, BAO Shengwang, YANG Fan. Ecological Security Early Warning and Obstacle Factor Analysis in the Zhejiang Greater Bay Area[J]. Ecology and Environment, 2023, 32(10): 1861-1872.

图/表 10

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准则层	指标层	数据来源	性质	权重
驱动 (D)	D1: 国民生产总值	统计数据	+	0.049
	D2: 人均可支配收入	统计数据	+	0.013
	D3: 第三产业产值占GDP比重	统计数据	+	0.03
	D4: 城镇化率	统计数据	+	0.089
压力 (P)	P1: 人口密度	统计数据	−	0.162
	P2: 人口自然增长率	统计数据	−	0.063
	P3: 工业产值占GDP比重	统计数据	−	0.022
	P4: 工业废水排放量	统计数据	−	0.087
状态 (S)	S1: 年平均温度	统计数据	−	0.02
	S2: 年降水量	统计数据	+	0.044
	S3: 坡度	DEM数据	−	0.043
	S4: 生态系统服务价值	模型计算	+	0.092
	S5: 生态系统弹性	模型计算	+	0.012
影响 (I)	I1: 植被归一化指数	MODIS产品	+	0.018
	I2: 最大斑块指数	Fragstats计算	+	0.02
	I3: 香浓均度指数	Fragstats计算	+	0.018
	I4: 蔓延度指数	Fragstats计算	+	0.019
响应 (R)	R1: 政府环保投资	统计数据	+	0.082
	R2: 城市污水处理率	统计数据	+	0.031
	R3: 建成区绿化覆盖率	统计数据	+	0.03
	R4: 政府科技投入	统计数据	+	0.057

准则层	指标层	数据来源	性质	权重
驱动 (D)	D1: 国民生产总值	统计数据	+	0.049
	D2: 人均可支配收入	统计数据	+	0.013
	D3: 第三产业产值占GDP比重	统计数据	+	0.03
	D4: 城镇化率	统计数据	+	0.089
压力 (P)	P1: 人口密度	统计数据	−	0.162
	P2: 人口自然增长率	统计数据	−	0.063
	P3: 工业产值占GDP比重	统计数据	−	0.022
	P4: 工业废水排放量	统计数据	−	0.087
状态 (S)	S1: 年平均温度	统计数据	−	0.02
	S2: 年降水量	统计数据	+	0.044
	S3: 坡度	DEM数据	−	0.043
	S4: 生态系统服务价值	模型计算	+	0.092
	S5: 生态系统弹性	模型计算	+	0.012
影响 (I)	I1: 植被归一化指数	MODIS产品	+	0.018
	I2: 最大斑块指数	Fragstats计算	+	0.02
	I3: 香浓均度指数	Fragstats计算	+	0.018
	I4: 蔓延度指数	Fragstats计算	+	0.019
响应 (R)	R1: 政府环保投资	统计数据	+	0.082
	R2: 城市污水处理率	统计数据	+	0.031
	R3: 建成区绿化覆盖率	统计数据	+	0.03
	R4: 政府科技投入	统计数据	+	0.057

预警等级	预警情况	ESEI	生态安全状况
I	严重预警	≤0.482	很差
II	中度预警	(0.482, 0.53]	差
III	轻度预警	(0.53, 0.593]	一般
IV	无预警	>0.593	好

预警等级	预警情况	ESEI	生态安全状况
I	严重预警	≤0.482	很差
II	中度预警	(0.482, 0.53]	差
III	轻度预警	(0.53, 0.593]	一般
IV	无预警	>0.593	好

误差状况	相对误差间隔百分比					平均相对误差
评价结果的相对误差	0‒1%	1‒2%	2‒3%	3‒4%	>4%	平均相对误差
误差结果占比	33.85%	27.69%	18.46%	10.77%	9.23%	1.91%

浙江大湾区生态安全预警及障碍因子分析

Ecological Security Early Warning and Obstacle Factor Analysis in the Zhejiang Greater Bay Area

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因子障碍度排名	年份
因子障碍度排名	2000	2005	2010	2015	2020
1	R1 (9.163)	S4 (10.101)	S4 (10.213)	S4 (10.621)	S4 (10.626)
2	D4 (8.863)	R1 (8.956)	R1 (9.464)	R1 (9.624)	P1 (5.874)
3	S4 (8.323)	D4 (8.338)	D4 (8.086)	D4 (6.496)	D4 (5.843)
4	R4 (6.063)	R4 (6.158)	R4 (6.369)	R4 (5.923)	R1 (5.244)
5	D1 (4.472)	D1 (4.768)	D1 (4.893)	D1 (4.974)	D1 (4.544)

年份	障碍度排名	滨江区		海曙区		江北区		南湖区		嘉善县		海盐县
年份	障碍度排名	障碍因子	障碍度	障碍因子	障碍度	障碍因子	障碍度	障碍因子	障碍度	障碍因子	障碍度	障碍因子	障碍度
2000	1	P1	21.85	S4	19.43	S4	18.85	S4	18.78	S4	15.76	S4	14.36
	2	S4	17.07	R4	14.44	P1	18.72	P1	18.62	R1	14.50	D4	12.66
	3	R1	12.97	D3	8.05	R4	11.10	R4	10.08	D4	13.38	R1	9.76
	4	D4	6.72	R1	7.85	D1	9.50	S2	8.54	P1	10.31	P1	8.97
	5	S2	6.54	P1	7.32	D4	7.07	D4	7.36	R4	7.61	R4	8.91
2005	1	P1	23.63	S4	19.96	S4	19.77	S4	18.28	S4	15.57	S4	15.31
	2	S4	16.18	R4	12.59	P1	16.55	P1	15.08	R1	14.31	R1	13.98
	3	R1	13.56	P2	9.30	R4	9.68	R4	10.09	D4	12.06	D4	12.68
	4	P4	6.90	S2	8.31	D1	8.34	S2	8.76	P1	8.43	R4	9.44
	5	D1	6.60	D3	7.91	D4	8.10	D1	7.65	S2	8.10	P1	7.79
2010	1	P1	24.87	S4	18.18	S4	17.78	S4	20.76	S4	17.05	S4	16.48
	2	S4	16.55	R4	12.63	P1	13.68	P1	15.57	R1	13.01	R1	13.68
	3	R1	14.64	R1	11.35	R4	9.85	R4	11.40	R4	10.55	D4	12.15
	4	P2	9.28	S2	8.23	R1	9.11	S2	9.07	D4	10.51	R4	9.91
	5	D1	5.16	S3	6.41	D1	7.64	D4	7.21	P1	8.14	P1	7.53
2015	1	P1	28.07	S4	21.19	S4	20.33	S4	21.50	S4	16.82	S4	16.93
	2	S4	15.84	R4	13.75	P1	13.66	P1	14.31	R1	13.55	R1	13.51
	3	R1	13.47	R3	7.65	R4	10.53	R4	10.45	D4	11.72	D4	10.65
	4	P2	10.88	D1	7.56	D1	8.48	S2	7.50	R4	8.90	R4	9.06
	5	D3	3.64	S3	7.49	R3	6.03	D1	7.50	S2	8.63	D1	6.91
2020	1	P1	26.85	S4	17.24	S4	19.47	S4	21.30	S4	20.80	S4	17.21
	2	S4	16.14	R1	17.02	R1	15.00	R4	11.51	R4	11.34	D4	11.07
	3	R1	12.62	R4	12.55	R4	11.05	D1	7.46	D4	10.13	R4	10.39
	4	P2	11.08	S3	6.10	D1	7.57	S2	6.84	D1	8.38	R1	9.84
	5	R3	5.21	S2	5.97	D4	5.81	D4	6.22	S2	7.36	D1	7.67