陕西省城市绿色增长水平时空演变特征及影响因素解析

doi:10.31497/zrzyxb.20220114

摘要/Abstract

摘要：

绿色增长是实现人地协调的一种可持续发展模式,对区域绿色发展和生态文明建设的推进具有重要作用。研究以陕西省为例,在系统梳理绿色增长内涵及特征的基础上,构建了绿色增长系统作用机制框架及评价指标体系,运用TOPSIS-灰色关联分析、泰尔指数等方法分析了2008—2017年城市绿色增长水平的时空演变特征及影响因素。结果表明：（1）陕西省城市绿色增长水平总体呈现“上升—下降—上升”的“N”型波动上升变化过程。（2）空间分布上,城市绿色增长水平存在明显的区域差异,且有进一步扩大的趋势;空间演化上,地区内差异略呈下降趋势,而地区间差异上升趋势明显。（3）区域生态环境（X₃）、生活质量环境（X₄）和政策支持环境（X₅）是制约陕西省绿色增长水平的主要障碍因素,且不同准则层在城市间的障碍作用存在明显的空间分异。（4）全省层面上,对城市绿色增长水平阻碍作用最大的5个因子依次是废气治理设施处理能力X₄₃、单位GDP能耗下降率X₃₁、科研和技术服务从业人员数X₅₄、建成区绿化覆盖率X₄₉和一般工业固体废物综合利用率X₄₂;在城市层面上,X₅₄的阻碍作用最强,其次是X₄₃、X₃₁、每万人拥有公交车和出租车辆数X₄₈以及人均GDP X₂₁。（5）外部环境驱动方面,产业结构、科技创新能力两个要素对陕西绿色增长的贡献度最大,其次是贸易水平、市场化水平、城镇化水平和人口密度。据此,从构建多元化能源结构体系、完善绿色增长相关政策制度、优化外部环境条件等五方面提出对策建议。

关键词: 绿色增长水平, 时空演变, TOPSIS-灰色关联分析, 影响因素, 陕西省

Abstract:

Green growth is a sustainable development mode to realize the coordination between human and land, which plays an important role in promoting regional green development and ecological civilization construction. Taking Shaanxi province as an example, based on the systematic analysis of the connotation and characteristics of green growth, this paper constructs the mechanism framework and evaluation index system of green growth system, and analyzes the spatiotemporal evolution characteristics and influencing factors of urban green growth level from 2008 to 2017 by using TOPSIS grey correlation analysis and Theil index. The results show that: (1) The level of urban green growth in Shaanxi province presents a "rise - drop - rise" fluctuation process. (2) In terms of spatial distribution, there are obvious regional differences in the level of urban green growth, and there is a trend of further expansion; in terms of spatial evolution, the intra-regional differences show a slight downward trend, while the inter-regional differences have an obvious upward trend. (3) Regional ecological environment (X₃), quality of life environment (X₄) and policy support environment (X₅) are the main obstacles to the green growth level of Shaanxi, and there are obvious spatial differences in the barrier effect of different standard layers among cities. (4) On the provincial level, the five factors that hinder the level of urban green growth are the treatment capacity of waste gas treatment facilities (X₄₃), the decline rate of energy consumption per unit GDP (X₃₁), the number of employees engaged in scientific research and technical services (X₅₄), the green coverage rate of built-up areas (X₄₉), and the comprehensive utilization rate of general industrial solid waste (X₄₂); on the urban level, X₅₄ has the strongest blocking effect, followed by X₄₃, X₃₁, the number of buses and taxis per 10000 people (X₄₈) and the per capita GDP (X₂₁). (5) In the aspect of external environment driving, industrial structure, scientific and technological innovation ability contribute the most to Shaanxi's green growth, followed by trade level, marketization level, urbanization level and population density. Finally, the paper puts forward countermeasures and suggestions from five aspects, such as building a diversified energy structure system, improving green growth related policies and systems, and optimizing external environmental conditions.

Key words: green growth level, spatio-temporal evolution, TOPSIS and grey relational analysis, influencing factor, Shaanxi province

刘宇峰, 原志华, 郭玲霞, 封建民, 孔伟, 党晨萌. 陕西省城市绿色增长水平时空演变特征及影响因素解析[J]. 自然资源学报, 2022, 37(1): 200-220.

LIU Yu-feng, YUAN Zhi-hua, GUO Ling-xia, FENG Jian-min, KONG Wei, DANG Chen-meng. Spatio-temporal characteristics of urban green growth level and its influencing factors in Shaanxi province[J]. JOURNAL OF NATURAL RESOURCES, 2022, 37(1): 200-220.

图/表 14

表1

图1

表2

绿色增长水平评价指标体系及权重

序号	准则层/权重	要素层	单位	指标性质	主要参考文献	权重
1	自然基础环境X₁/0.0789	X₁₁：人均水资源供应量^*	m³	正向指标	[1,41,42]	0.0326
2		X₁₂：人均耕地面积^*	hm²/人	正向指标	[1,25,41]	0.0198
3		X₁₃：造林面积^**	hm²	正向指标	[11,41]	0.0266
4	社会经济环境 X₂/0.1583	X₂₁：人均GDP^*	元/人	正向指标	[1,29]	0.0415
5		X₂₂：人口自然增长率^*	‰	适中指标	[9,25,29]	0.0065
6		X₂₃：第三产业占GDP的比例^*	%	适中指标	[1,29,41]	0.0069
7		X₂₄：城镇居民人均可支配收入^*	元	正向指标	[25,29,41]	0.0377
8		X₂₅：农村居民人均纯收入^**	元	正向指标	[41,43,44]	0.0283
9		X₂₆：全社会固定资产投资^**	10⁸元	正向指标	[19,29,45]	0.0374
10	区域生态环境 X₃/0.2043	X₃₁：单位GDP能耗下降率^*	%	正向指标	[1,25,29]	0.0761
11		X₃₂：二氧化硫排放量^**	t	负向指标	[9,25,29]	0.0132
12		X₃₃：氮氧化物排放量^**	t	负向指标	[25,46]	0.0166
13		X₃₄：烟(粉)尘排放量^**	t	负向指标	[9,19]	0.0221
14		X₃₅：化学需氧量排放量^**	t	负向指标	[11,25,39]	0.0227
15		X₃₆：工业固体废弃物产生量^**	10⁴t	负向指标	[25,44]	0.0111
16		X₃₇：工业废水排放总量^**	10⁴t	负向指标	[25,29,44]	0.0298
17		X₃₈：规模以上工业企业能源消费量^**	10⁴tce	负向指标	[47]	0.0126
18	生活质量环境 X₄/0.3510	X₄₁：生活垃圾无害化处理率^*	%	正向指标	[1,25,29]	0.0405
19		X₄₂：一般工业固体废物综合利用率^*	%	正向指标	[19,25,41]	0.0171
20		X₄₃：废气治理设施处理能力^**	10⁴m³/h	正向指标	[47,48]	0.0626
21		X₄₄：废水治理设施处理能力^**	10⁴t/d	正向指标	[47,48]	0.0292
22		X₄₅：用水普及率^**	%	正向指标	[9,25]	0.0267
23		X₄₆：燃气普及率^**	%	正向指标	[9]	0.0352
24		X₄₇：每万人拥有医疗机构床位数^**	张	正向指标	[9,25,43]	0.0203
25		X₄₈：每万人拥有公交车和出租车辆数^**	标台	正向指标	[1,9,25]	0.0445
26		X₄₉：建成区绿化覆盖率^*	%	正向指标	[1,25,29]	0.0380
27		X_4,10：城市人均公园绿地面积^**	m²/人	正向指标	[1,25,29]	0.0368
28	政策支持环境 X₅/0.2075	X₅₁：节能环保支出占GDP的比例^**	%	正向指标	[25,44]	0.0303
29		X₅₂：科学技术支出占GDP的比例^**	%	正向指标	[9,25,29]	0.0287
30		X₅₃：水利建设投资完成额^**	10⁴元	正向指标	[48]	0.0181
31		X₅₄：教育经费支出占GDP的比例^**	%	正向指标	[25,43,44]	0.0220
32		X₅₅：科研、技术服务从业人员数^**	10⁴人	正向指标	[25,41]	0.1084

表2

图2

图3

图4

图5

表3

图6

表4

图7

图8

图9

图10

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机构/学者	内涵/侧重点	出处
联合国亚洲及太平洋经济社会委员会（UNESCAP）	绿色增长是为了实现低碳和全社会成员的发展而推行的环境可持续的经济发展过程	UNESCAP环境与发展部长会议^[25,26]
经济合作与发展组织（OECD）	绿色增长是指在防止代价昂贵的环境破坏、气候变化、生物多样化丧失和以不可持续的方式使用自然资源的同时,追求经济增长和发展;绿色增长是在确保自然资产能继续提供人类福祉所需的资源和环境服务的同时,促进经济增长和发展	《绿色增长宣言》《迈向绿色增长》^[27,28,29]
韩国	绿色增长指通过清洁能源、绿色技术开发以及绿色革新,最小化使用能源、资源,减少气候变化和环境污染,确保增长动力,创造工作岗位,实现经济环境和谐相融的增长方式	《低碳绿色增长基本法》^[30]
国外代表性学者	Meyer等^[31]认为绿色增长的核心是将经济增长与资源消耗解耦;Reilly等^[32]从经济、环境、社会三方面阐述了绿色增长的内涵;Jacobs等^[33]指出绿色增长的本质是追求显著环境保护的经济增长	见参考文献[31,32,33]
国内代表性学者	张江雪等^[34]指出科技进步与创新是绿色增长的重要支撑,只有拓宽技术创新的目标,加大科技与环境保护力度,才能实现经济、资源与环境的协调统一;韩晶等^[35]认为绿色增长的核心是提升资源环境绩效,激励产业发展向集约、低碳、绿色转型,而实现绿色增长的关键在于绿色创新能力的提升;武春友等^[36]对绿色增长给出的定义是：通过绿色技术创新驱动与制度安排,用最小的资源消耗和环境代价,创造出资源效率、环境友好、社会包容与和谐的经济增长与最大发展效益	见参考文献[34,35,36]

年份	分级标准
年份	低水平区 (0, A-S]	中等水平区 (A-S, A]	较高水平区 (A, A+S]	高水平区 (A+S, 1)
2008	(0, 0.1889]	(0.1889, 0.3257]	(0.3257, 0.4625]	(0.4625, 1)
2009	(0, 0.1899]	(0.1899, 0.3055]	(0.3055, 0.4212]	(0.4212, 1)
2010	(0, 0.2153]	(0.2153, 0.3251]	(0.3251, 0.4349]	(0.4349, 1)
2011	(0, 0.2005]	(0.2005, 0.3338]	(0.3338, 0.4670]	(0.4670, 1)
2012	(0, 0.2084]	(0.2084, 0.3295]	(0.3295, 0.4506]	(0.4506, 1)
2013	(0, 0.1954]	(0.1954, 0.3092]	(0.3092, 0.4230]	(0.4230, 1)
2014	(0, 0.1847]	(0.1847, 0.3249]	(0.3249, 0.4650]	(0.4650, 1)
2015	(0, 0.2168]	(0.2168, 0.3280]	(0.3280, 0.4393]	(0.4393, 1)
2016	(0, 0.1990]	(0.1990, 0.3306]	(0.3306, 0.4623]	(0.4623, 1)
2017	(0, 0.1920]	(0.1920, 0.3233]	(0.3233, 0.4546]	(0.4546, 1)

年份	总体差异	数值/贡献率(%)
年份	总体差异	地区内差异	地区间差异	陕北地区	关中地区	陕南地区
2008	0.0719	0.0594/82.5	0.0126/17.5	0.0035/3.2	0.0976/87.7	0.0102/9.1
2009	0.0602	0.0546/90.6	0.0057/9.4	0.0236/20.3	0.0911/78.4	0.0016/1.3
2010	0.0486	0.0372/76.4	0.0115/23.6	0.0010/1.5	0.0650/92.9	0.0039/5.6
2011	0.0651	0.0476/73.0	0.0176/27.0	0.0171/17.4	0.0720/73.4	0.0090/9.2
2012	0.0562	0.0437/77.6	0.0126/22.4	0.0074/8.5	0.0694/79.6	0.0104/11.9
2013	0.0586	0.0384/65.6	0.0202/34.4	0.0119/15.2	0.0586/75.3	0.0073/9.5
2014	0.0760	0.0578/76.1	0.0181/23.9	0.0090/7.8	0.0891/77.4	0.0170/14.8
2015	0.0505	0.0380/75.2	0.0125/24.8	0.0036/4.4	0.0565/69.0	0.0218/26.6
2016	0.0660	0.0544/82.4	0.0116/17.6	0.0072/6.3	0.0804/69.7	0.0277/24.0
2017	0.0693	0.0461/66.6	0.0231/33.4	0.0163/17.0	0.0686/71.6	0.0109/11.4