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

Abstract

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

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.

Figures/Tables 10

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References 36

[1]	Geels F, Schot J. The dynamics of transitions: A socio-technical perspective[M]. New York: Routledge. 2010: 11-01.
[2]	Loorbach D, Rotmans J. The practice of transition management: Examples and lessons from four distinct cases[J]. Futures, 2010, 42(3): 237-246. doi: 10.1016/j.futures.2009.11.009
[3]	Zhang H J, Zhao X L, Zhang R D. Synergistic development of heating system decarbonization transition and large-scale renewable energy penetration: A case study of Beijing[J]. Energy Conversion and Management, 2022, 269: 116142, doi: 10.1016/j.enconman.2022.116142.
[4]	Suomalainen K, Wen L, Sheng M Y S, et al. Climate change impact on the cost of decarbonisation in a hydro-based power system[J]. Energy, 2022, 246: 123369, doi: 10.1016/j.energy.2022.123369.
[5]	Nilsson L J, Bauer F, Ahman M, et al. An industrial policy framework for transforming energy and emissions intensive industries towards zero emissions[J]. Climate Policy, 2021, 21(8): 1053-1065. doi: 10.1080/14693062.2021.1957665
[6]	Du K, Li J L. Towards a green world: How do green technology innovations affect total-factor carbon productivity[J]. Energy Policy, 2019, 131: 240-250. doi: 10.1016/j.enpol.2019.04.033
[7]	Ausubel J H. Technical progress and climatic change[J]. Energy Policy, 1995, 23(4): 411-416. doi: 10.1016/0301-4215(95)90166-5
[8]	Bataille C, Waisman H, Colombier M, et al. The need for national deep decarbonization pathways for effective climate policy[J]. Climate Policy, 2016, 16(Suppl. 1): 1-20. doi: 10.1080/14693062.2014.965657
[9]	Holmes K J, Zeitler E, Kerxhalli-kleinfeld M, et al. Scaling deep decarbonization technologies[J]. Earth’s Future, 2021, 9(11): e2021EF002399, doi: 10.1029/2021EF002399.
[10]	吴茜, 陈强强. 甘肃省行业碳排放影响因素及脱钩努力研究[J]. 干旱区地理, 2023, 46(2): 274-283. doi: 10.12118/j.issn.1000-6060.2022.126
	[Wu Xi, Chen Qiangqiang. Influencing factors and decoupling efforts of industry-related carbon emissions in Gansu Province[J]. Arid Land Geography, 2023, 46(2): 274-283.] doi: 10.12118/j.issn.1000-6060.2022.126
[11]	Burandt T, Xiong B, Löffler K, et al. Decarbonizing China’s energy system: Modeling the transformation of the electricity, transportation, heat, and industrial sectors[J]. Applied Energy, 2019, 255: 113820, doi: 10.1016/j.apenergy.2019.113820.
[12]	黄鲁成, 郭鑫, 苗红, 等. 面向碳中和的脱碳成本控制: 优化创新与政策[J]. 科学学研究, 2022, 40(12): 2187-2193.
	[Huang Lucheng, Guo Xin, Miao Hong, et al. Decarbonization cost control for carbon neutrality: Optimizing technological innovation and policies[J]. Studies in Science of Science, 2022, 40(12): 2187-2193.]
[13]	魏一鸣, 韩融, 余碧莹, 等. 全球能源系统转型趋势与低碳转型路径——来自于IPCC第六次评估报告的证据[J]. 北京理工大学学报(社会科学版), 2022, 24(4): 163-188.
	[Wei Yiming, Han Rong, Yu Biying, et al. Global energy systems transition trend and low-carbon transformation pathways: Evidences from the IPCC AR6[J]. Journal of Beijing Institute of Technology (Social Sciences Edition), 2022, 24(4): 163-188.]
[14]	江深哲, 杜浩锋, 徐铭梽. “双碳”目标下能源与产业双重结构转型[J]. 数量经济技术经济研究, 2024, 41(2): 109-130.
	[Jiang Shenzhe, Du Haofeng, Xu Mingzhi. Dual transition of energy and industrial structure under the carbon peaking and neutrality goals[J]. Journal of Quantitative & Technological Economics, 2024, 41(2): 109-130.]
[15]	薛飞, 周民良, 刘家旗. 产业转型升级能否降低碳排放?——来自国家产业转型升级示范区的证据[J]. 产业经济研究, 2023(2): 1-13.
	[Xue Fei, Zhou Minliang, Liu Jiaqi. Can industrial transformation and upgrading reduce carbon emissions? Evidence from national industrial transformation and upgrading demonstration zones[J]. Industrial Economics Research, 2023(2): 1-13.]
[16]	Kirchner M, Schmidt J, Wehrle S. Exploiting synergy of carbon pricing and other policy instruments for deep decarbonization[J]. Joule, 2019, 3(4): 891-893. doi: 10.1016/j.joule.2019.03.006
[17]	徐维祥, 郑金辉, 周建平, 等. 资源型城市转型绩效特征及其碳减排效应[J]. 自然资源学报, 2023, 38(1): 39-57. doi: 10.31497/zrzyxb.20230103
	[Xu Weixiang, Zheng Jinhui, Zhou Jianping, et al. Transformation performance characteristics of resource-based cities and their carbon emission reduction effects[J]. Journal of Natural Resources, 2023, 38(1): 39-57.] doi: 10.31497/zrzyxb.20230103
[18]	张文忠, 余建辉. 中国资源型城市转型发展的政策演变与效果分析[J]. 自然资源学报, 2023, 38(1): 22-38. doi: 10.31497/zrzyxb.20230102
	[Zhang Wenzhong, Yu Jianhui. Policy evolution and transformation effect analysis of sustainable development of resource-based cities in China[J]. Journal of Natural Resources, 2023, 38(1): 22-38.] doi: 10.31497/zrzyxb.20230102
[19]	刘小玲, 唐卓伟, 孙晓华, 等. 要素错配:解开资源型城市转型困境之谜[J]. 中国人口·资源与环境, 2022, 32(10): 88-102.
	[Liu Xiaoling, Tang Zhuowei, Sun Xiaohua, et al. Factors mismatch: Solving the mystery of transformation dilemma faced by resource-based cities[J]. China Population, Resources and Environment, 2022, 32(10): 88-102.]
[20]	席振鑫, 马丽, 金凤君, 等. 黄河流域典型资源型城市工业转型的时空特征、类型与路径[J]. 资源科学, 2023, 45(10): 1977-1991. doi: 10.18402/resci.2023.10.05
	[Xi Zhenxin, Ma Li, Jin Fengjun, et al. Spatiotemporal characteristics, types, and paths of industrial transformation in typical resource-based cities in the Yellow River Basin[J]. Resources Science, 2023, 45(10): 1977-1991.] doi: 10.18402/resci.2023.10.05
[21]	徐英启, 程钰, 王晶晶. 中国资源型城市碳排放效率时空演变与绿色技术创新影响[J]. 地理研究, 2023, 42(3): 878-894. doi: 10.11821/dlyj020220256
	[Xu Yingqi, Cheng Yu, Wang Jingjing. The impact of green technological innovation on the spatiotemporal evolution of carbon emission efficiency of resource-based cities in China[J]. Geographical Research, 2023, 42(3): 878-894.]
[22]	穆佳音, 王金满, 刘彪, 等. 资源型城市碳排放演变及影响因素研究进展[J]. 煤炭学报, 2024, 49(增刊2): 1130-1142.
	[Mu Jiayin, Wang Jinman, Liu Biao, et al. Research progress on the evolution and influencing factors of carbon emissions in resource-based cities[J]. Journal of China Coal Society, 2024, 49(Suppl. 2): 1130-1142.]
[23]	刘晓燕, 孙慧. 资源型产业碳排放驱动因素演化与低碳发展路径选择[J]. 统计与决策, 2019, 35(2): 53-57.
	[Liu Xiaoyan, Sun Hui. Evolution of carbon emission driving factors of resource-based industries and selection of low-carbon development paths[J]. Statistics & Decision, 2019, 35(2): 53-57.]
[24]	张希良, 黄晓丹, 张达, 等. 碳中和目标下的能源经济转型路径与政策研究[J]. 管理世界, 2022, 38(1): 35-66.
	[Zhang Xiliang, Huang Xiaodan, Zhang Da, et al. Research on the pathway and policies for China’s energy and economy transformation toward carbon neutrality[J]. Journal of Management World, 2022, 38(1): 35-66.]
[25]	方文君, 邓峰, 张战仁, 等. 环境目标约束对能源结构低碳转型的影响[J]. 中国人口·资源与环境, 2024, 34(1): 84-96.
	[Fang Wenjun, Dengfeng, Zhang Zhanren, et al. Impact of environmental target constraints on the low-carbon transformation of China’s energy structure[J]. China Population, Resources and Environment, 2024, 34(1): 84-96.]
[26]	高瑜, 李响, 李俊青. 金融科技与技术创新路径——基于绿色转型的视角[J]. 中国工业经济, 2024(2): 80-98.
	[Gao Yu, Li Xiang, Li Junqing. Fintech and the path of technological innovation: From the perspective of green transformation[J]. China Industrial Economics, 2024(2): 80-98.]
[27]	Ai H S, Tan X Q, Zhou S W, et al. The impact of supportive policy for resource-exhausted cities on carbon emission: Evidence from China[J]. Resources Policy, 2023, 85: 103951, doi: 10.1016/j.resourpol.2023.103951.
[28]	史丹, 李少林. 排污权交易制度与能源利用效率——对地级及以上城市的测度与实证[J]. 中国工业经济, 2020(9): 5-23.
	[Shi Dan, Li Shaolin. Emissions trading system and energy use efficiency: Measurements and empirical evidence for cities at and above the prefecture level[J]. China Industrial Economics, 2020(9): 5-23.]
[29]	干春晖, 郑若谷, 余典范. 中国产业结构变迁对经济增长和波动的影响[J]. 经济研究, 2011, 46(5): 4-16, 31.
	[Gan Chunhui, Zheng Ruogu, Yu Dianfan. An empirical study on the effects of industrial structure on economic growth and fluctuations in China[J]. Economic Research Journal, 2011, 46(5): 4-16, 31.]
[30]	丛建辉, 刘学敏, 赵雪如. 城市碳排放核算的边界界定及其测度方法[J]. 中国人口·资源与环境, 2014, 24(4): 19-26.
	[Cong Jianhui, Liu Xuemin, Zhao Xueru. Demarcation problems and the corresponding measurement methods of the urban carbon accounting[J]. China Population, Resources and Environment, 2014, 24(4): 19-26.]
[31]	Liu X L, Vu D, Perera S C, et al. Nexus between water-energy-carbon footprint network: Multiregional input-output and coupling coordination degree analysis[J]. Journal of Cleaner Production, 2023, 430: 139639, doi: 10.1016/j.jclepro.2023.139639.
[32]	周小亮, 吴武林. 中国包容性绿色增长的测度及分析[J]. 数量经济技术经济研究, 2018, 35(8): 3-20.
	[Zhou Xiaoliang, Wu Wulin. The measurement and analysis of the inclusive green growth in China[J]. Journal of Quantitative & Technological Economics, 2018, 35(8): 3-20.]
[33]	Bryan J D, Zuva T. A review on TAM and TOE framework progression and how these models integrate[J]. Advances in Science, Technology and Engineering Systems Journal, 2021, 6(3): 137-145.
[34]	杜运周, 刘秋辰, 陈凯薇, 等. 营商环境生态、全要素生产率与城市高质量发展的多元模式——基于复杂系统观的组态分析[J]. 管理世界, 2022, 38(9): 127-145.
	[Du Yunzhou, Liu Qiuchen, Chen Kaiwei, et al. Ecosystem of doing business, total factor productivity and multiple patterns of high-quality development of Chinese cities: A configuration analysis based on complex systems view[J]. Journal of Management World, 2022, 38(9): 127-145.]
[35]	陶克涛, 张术丹, 赵云辉. 什么决定了政府公共卫生治理绩效?——基于QCA方法的联动效应研究[J]. 管理世界, 2021, 37(5): 128-138, 156, 10.
	[Tao Ketao, Zhang Shudan, Zhao Yunhui. What does determine performance of government public health governance? A study on co-movement effect based on QCA[J]. Journal of Management World, 2021, 37(5): 128-138, 156, 10.]
[36]	夏文浩, 霍瑜, 逯渊, 等. 新疆农业碳排放的时空差异与空间溢出效应分析[J]. 干旱区地理, 2024, 47(6): 1084-1096. doi: 10.12118/j.issn.1000-6060.2023.344
	[Xia Wenhao, Huo Yu, Lu Yuan, et al. Spatialtemporal differences and spatial spillover effects of agricultural carbon emissions in Xinjiang[J]. Arid Land Geography, 2024, 47(6): 1084-1096.] doi: 10.12118/j.issn.1000-6060.2023.344

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

Spatiotemporal evolution and pathway selection of transformation decarbonization in resource-based cities of China

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