中国资源型城市转型脱碳时空演进及路径选择

doi:10.12118/j.issn.1000-6060.2024.743

摘要/Abstract

摘要：

资源型城市转型脱碳是实现“双碳”目标的关键。测度2006—2021年中国资源型城市转型脱碳水平，采用空间马尔科夫链和模糊集定性比较分析（fsQCA）方法，探究资源型城市转型脱碳的时空演进特征及路径选择。结果表明：（1）资源型城市转型脱碳水平逐年上升，发展格局由以滞后区为核心的集中连片分布向跨越区和先行区为核心的组团式分布进行演变；转型脱碳总体差异明显，区域内差异和成熟型城市分别是导致总体差异变大的主要原因及来源。（2）转型脱碳类型的转移具有稳定性，存在“路径依赖”现象；保持初始状态的概率较大，呈现“俱乐部收敛”特征；并在连续向上转移过程中存在“马太效应”，且空间溢出效应明显。（3）技术、组织、环境因素均不能单独构成实现转型脱碳的必要条件，各因素多重并发，形成资源型城市转型脱碳的3种组态路径，分别为“技术-环境”协同驱动型、“技术-组织”协同驱动型与“技术-组织-环境”联合驱动型，其中绿色技术创新发挥核心作用。（4）不同类型资源型城市倚重不同因素：成长型城市依赖数字技术与环境关注，成熟型城市通过技术创新、产业升级和环境因素等多方面联合驱动，衰退型城市受环境因素影响更大，再生型城市强调技术创新与环境因素协同驱动。研究结果可为中国资源型城市实现转型脱碳提供有益经验和实践启示。

关键词: 资源型城市, 转型脱碳, 空间马尔科夫链, fsQCA, 殊途同归路径

Abstract:

The transformation decarbonization of resource-based cities are essential for advancing China’s carbon peaking and carbon neutrality goals. This study evaluates the transformation decarbonization levels of resource-based cities from 2006 to 2021 and employs a spatial Markov chain model together with fuzzy-set qualitative comparative analysis (fsQCA) to explore their spatiotemporal evolution and transition pathways. The results indicate three key findings: (1) Transformation decarbonization levels have improved steadily over time. Spatial patterns have shifted from a concentrated distribution dominated by lagging areas to a clustered structure characterized by transitional and pioneering zones. Although large overall disparities persist, these differences mainly stem from intra-regional variation and the performance of mature-type cities. (2) Transformation decarbonization types exhibit stable evolutionary trajectories, demonstrating clear “path-dependence”. Cities tend to maintain their initial states, showing prominent “club convergence”. A “Matthew effect” is evident during upward transitions, accompanied by significant spatial spillover effects across neighboring regions. (3) No single factor, technological, organizational, or environmental, constitutes a necessary condition for achieving transformation decarbonization. Instead, these elements interact to form three configurational pathways: A technology-environment synergy pathway, a technology-organization synergy pathway, and a combined technology-organization-environment pathway. Across all pathways, green technology innovation plays a central and catalytic role. (4) Resource-based cities of different developmental stages rely on distinct drivers. Growing cities primarily depend on digital technology and rising environmental awareness. Mature cities are jointly driven by technological innovation, industrial upgrading, and environmental regulation. Declining cities are more strongly influenced by environmental constraints, while regenerating cities rely on the combined momentum of technological innovation and environmental factors. Overall, this study offers empirical evidence and policy-relevant insights to support the transformation decarbonization in resource-based cities of China.

Key words: resource-based cities, transformation decarbonization, spatial Markov chain, fsQCA, different paths lead to the same destination

杨巨星, 孙慧, 周晋楠, 陀才进, 张若威. 中国资源型城市转型脱碳时空演进及路径选择[J]. 干旱区地理, 2026, 49(1): 151-163.

YANG Juxing, SUN Hui, ZHOU Jinnan, TUO Caijin, ZHANG Ruowei. Spatiotemporal evolution and pathway selection of transformation decarbonization in resource-based cities of China[J]. Arid Land Geography, 2026, 49(1): 151-163.

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子系统	维度	指标	测算方法	指标属性	均值	标准差	最小值	最大值
转型	能源转型	煤炭消费占比	煤炭消费量与能源消费总量之比	负向	0.838	0.152	0.124	0.997
	能源转型	能源利用效率	SBM-Malmquist-Luenberger指数法	正向	0.287	0.111	0.098	1.177
	产业转型	产业结构合理化	泰尔指数法	正向	0.296	0.253	0.001	3.430
	产业转型	产业结构高级化	第三产业产值与第二产业产值之比	正向	0.878	0.458	0.094	3.758
	技术转型	绿色技术创新/件	绿色专利申请量	正向	119.000	216.000	0.000	2647.000
	技术转型	数字技术创新/件	数字专利申请量	正向	268.000	544.000	0.000	6106.000
脱碳	碳排放	碳排放总量/10⁴ t	直接排放与间接排放之和	负向	2877.348	1016.082	537.160	4920.000
		碳排放强度/t·(10⁴元)^-1	碳排放总量与GDP之比	负向	4.381	4.323	0.169	31.153
		脱碳率	碳排放强度的变化率	正向	0.030	0.133	-0.592	0.633

空间滞后类型		滞后	起步	跨越	先行
传统无滞后	滞后	0.846	0.140	0.007	0.007
	起步	0.100	0.694	0.199	0.007
	跨越	0.024	0.118	0.698	0.160
	先行	0.003	0.008	0.091	0.899
滞后	滞后	0.914	0.086	0.000	0.000
	起步	0.117	0.777	0.107	0.000
	跨越	0.074	0.222	0.593	0.111
	先行	0.000	0.111	0.111	0.778
起步	滞后	0.849	0.130	0.007	0.014
	起步	0.082	0.738	0.180	0.000
	跨越	0.022	0.089	0.711	0.178
	先行	0.014	0.000	0.069	0.917
跨越	滞后	0.644	0.322	0.034	0.000
	起步	0.123	0.651	0.212	0.014
	跨越	0.027	0.137	0.703	0.132
	先行	0.000	0.008	0.102	0.891
先行	滞后	0.500	0.333	0.000	0.167
	起步	0.020	0.551	0.408	0.020
	跨越	0.008	0.088	0.704	0.200
	先行	0.000	0.005	0.090	0.904

条件变量	方法	精确度/%	上限区域	范围	效应量（d）	P值
绿色技术创新	上限回归分析	90.4	0.122	0.970	0.026	0.000
绿色技术创新	上限包络分析	100.0	0.057	0.970	0.058	0.000
数字技术创新	上限回归分析	88.6	0.154	0.970	0.059	0.000
数字技术创新	上限包络分析	100.0	0.058	0.970	0.060	0.000
能源效率	上限回归分析	99.1	0.008	0.990	0.008	0.647
能源效率	上限包络分析	100.0	0.011	0.990	0.011	0.558
产业结构升级	上限回归分析	92.1	0.042	1.000	0.042	0.160
产业结构升级	上限包络分析	100.0	0.019	1.000	0.019	0.172
环境规制	上限回归分析	93.9	0.055	1.000	0.055	0.057
环境规制	上限包络分析	100.0	0.040	1.000	0.040	0.013
公众环境关注	上限回归分析	95.6	0.052	0.990	0.052	0.049
公众环境关注	上限包络分析	100.0	0.027	0.990	0.027	0.027

条件组态	高转型脱碳水平的解
条件组态	组态1	组态2	组态3	组态4
绿色技术创新	$\bullet$	$\bullet$	$\bullet$	$\bullet$
数字技术创新	$\bullet$	$\bullet$	$\bullet$
能源效率		$\bullet$	$\otimes$	$\bigotimes$
产业结构升级			$\bullet$	$\cdot$
环境规制		$\bullet$	$\bigotimes$	$\bullet$
公众环境关注	$\bullet$			$\bullet$
原始覆盖度	0.634	0.411	0.232	0.224
唯一覆盖度	0.178	0.010	0.020	0.007
一致性	0.842	0.876	0.868	0.918
总体覆盖度	0.677
总体一致性	0.829

条件组态	成长型组态1	成熟型			衰退型组态3	再生型
条件组态	成长型组态1	组态2a	组态2b	组态2c	衰退型组态3	组态4a	组态4b
绿色技术创新	$\bigotimes$	$\bullet$	$\cdot$	$\bullet$	$\cdot$	$\cdot$	$\bullet$
数字技术创新	$\bullet$	$\bullet$	$\bullet$	$\cdot$	$\cdot$	$\bullet$	$\cdot$
能源效率	$\bullet$	$\bullet$		$\bigotimes$		$\cdot$
产业结构升级	$\otimes$		$\bullet$	$\bullet$	$\otimes$		$\otimes$
环境规制	$\bigotimes$		$\bullet$	$\otimes$	$\bullet$		$\cdot$
公众环境关注	$\bullet$	$\bullet$	$\bullet$	$\bigotimes$	$\bullet$	$\cdot$	$\bullet$
原始覆盖度	0.309	0.516	0.328	0.235	0.376	0.749	0.430
唯一覆盖度	0.309	0.236	0.028	0.047	0.376	0.351	0.032
一致性	0.888	0.864	0.914	0.926	0.835	0.927	0.965
总体覆盖度	0.309	0.618			0.376	0.781
总体一致性	0.888	0.880			0.835	0.930