中国资源型城市碳排放的时空演变与影响因素——基于能源消费视角

doi:10.3969/j.issn.1004-9479.2026.06.20250481

摘要/Abstract

摘要：

研究基于地级市层面的能源、环境、社会及经济数据，核算出118个资源型城市2006—2021年的工业碳排放，并构建能源消费结构的绿色质量指标（GREENQ），结合双变量空间自相关分析等方法和门槛-STIRPAT模型等工具，系统揭示了时序上能源绿色转型与碳排放之间的空间交互和耦合特征，并深入探究能源绿色转型对资源型城市碳排放的影响机制。研究发现：①中国资源型城市工业碳排放整体上呈现出“北高南低、东部高中西部增长”的空间格局，随时间推移，工业碳排放总量呈现出显著的空间集聚特征。绿色质量指标的空间分异与碳排放关联特征明显，呈现多极化集聚态势。②能源绿色转型作为门槛变量，对碳排放呈现出明显的单一门槛效应，门槛值为0.426 7，且具有阶段性影响特征，绿色质量指标与碳排放呈显著负相关。③在不同生命周期的资源型城市中，绿色质量指标在不同水平阶段、时间演变维度、经济分区以及工业基础类别等多个维度呈现显著异质性，工业碳排放的影响机制随之呈现明显异质性特征。最后从优化调整能源消费结构、加强创新要素集聚、提高能源效率等方面提出对策建议。

关键词: 碳排放, 能源绿色转型, 时空演变, 门槛-STIRPAT模型, 资源型城市

Abstract:

This study uses prefecture-level data on energy, environment, society, and the economy to calculate industrial carbon emissions in 118 resource cities from 2006 to 2021. It constructs a green quality indicator (GREENQ) for the energy consumption structure. The study combines bivariate spatial autocorrelation analysis and the STIRPAT-threshold effect model to reveal the spatial interaction and coupling characteristics between energy green transition and carbon emissions in a time series, and to explore the influence mechanism of energy green transition on carbon emissions in resource cities. The study found that:①The overall industrial carbon emissions in China's resource-based cities exhibit a spatial pattern of "higher in the north and lower in the south, and higher in the east, while the central and western regions show a growing trend. Over time, the total industrial carbon emissions show a significant spatial clustering characteristic. The spatial differentiation of green quality indicators is closely related to carbon emissions, presenting a multi-polarized clustering feature.②As a threshold variable (GREENQ), the energy green transition shows a significant single threshold effect on carbon emissions, with a threshold value of 0.426 7. The phased impact is significant, and the green quality indicators are significantly negatively correlated with carbon emissions.③Different resource-based cities at different life cycles show significant heterogeneity in multiple dimensions, such as different levels of green quality indicators, time evolution dimensions, economic divisions, and industrial base categories. The impact mechanism of industrial carbon emissions also presents obvious heterogeneity characteristics. Finally, countermeasures and suggestions are proposed from various aspects, including adjusting the energy consumption structure, strengthening the concentration of innovative elements, and enhancing energy efficiency.

Key words: carbon_emissions, energy green transition, spatial-temporal evolution, STIRPAT-threshold effect model, resource-based cities

段冶, 刘阳, 李迪, 韩增林. 中国资源型城市碳排放的时空演变与影响因素——基于能源消费视角[J]. 世界地理研究, 2026, 35(6): 103-121.

Ye DUAN, Yang LIU, Di LI, Zenglin HAN. The spatio-temporal evolution and influencing factors of carbon emissions in China's resource-based cities: A perspective of energy consumption[J]. World Regional Studies, 2026, 35(6): 103-121.

图/表 14

图1 资源型城市研究概况可视化注：该图基于自然资源部标准地图服务网站下载的审图号为GS（2023）2767号的标准地图制作，底图无修改。以下地图同。

Fig. 1 Resource-based city research overview visualization

图2 工业碳排放空间演变格局

Fig. 2 Spatial evolution pattern of industrial carbon emissions

图3 能源消费结构的绿色质量空间演变格局

Fig. 3 Spatial evolution pattern of green quality of energy consumption structure

图4 不同类型资源型城市 GREENQ 时间演变趋势

Fig. 4 GREENQ time evolution trend of different resource-based cities

图5 资源型城市 GREENQ 与工业碳排放的空间关联特征

Fig. 5 Spatial correlation between GREENQ and industrial carbon emissions in resource-based cities

表1 描述性统计

Tab. 1 Descriptive statistics

变量	观测值	平均数	标准差	最小值	中位数	最大值
lnCE	1 872	-0.853	1.337	-5.741	-0.845	2.916
lnGREENQ	1 872	-0.492	0.341	-1.232	-0.632	2.772
lnUR	1 872	-0.753	0.341	-2.074	-0.713	-0.036
lnRGDP	1 872	10.376	0.710	4.595	10.404	12.456
lnRD	1 872	-4.900	0.818	-7.947	-4.883	-1.576
lnEI	1 872	-2.297	0.809	-4.868	-2.231	1.821
lnDOO	1 872	-3.231	1.349	-12.815	-3.139	0.377
lnPOP	1 872	1.662	0.203	-0.381	1.688	1.945

表2 方差膨胀系数

Tab. 2 Variance inflation factor

系数	lnGREENQ	lnUR	lnRGDP	lnRD	lnEI	lnDOO	lnPOP	整体
VIF	1.06	2.38	2.31	1.49	1.34	1.21	1.20	1.57

表3 门槛效应自抽样检验结果

Tab.3 Bootstrap sampling test results of threshold effect

临界值

10% 5% 1%

GREENQ

表4 门槛估计值及置信区间

Tab. 4 Threshold estimates and confidence intervals

门槛变量/核心解释变量	门槛顺序	门槛值	95%置信区间	BS次数
能源消费结构的绿色质量（GREENQ）	单一门槛	-0.849 3	（-0.874 2,-0.845 5）	300
能源消费结构的绿色质量（GREENQ）	双重门槛	-0.967 5	(-0.976 0，-0.956 4)	300

表5 单一门槛回归结果

Tab. 5 Results of single threshold regression

变量	门槛模型
lnEI	0.079 7^***(3.67)
lnUR	0.351 2^***(4.28)
lnRD	0.079 2^***(4.07)
lnRGDP	0.489 2^***(14.28)
lnER	-1.887 3^***(-5.09)
lnDOO	0.030 2(1.77)
lnPOP	-0.288 1(-1.55)
lnGREENQ(GREENQ $≤$ 0.426 7)	-0.534 1^***(-5.57)
lnGREENQ(GREENQ $>$ 0.426 7)	-0.258 0^***(-3.95)
Cons	-4.663 7^***(-8.61)
N	1 872
adj. R²	0.291 6

表5 单一门槛回归结果

Tab. 5 Results of single threshold regression

变量	门槛模型
lnEI	0.079 7^***(3.67)
lnUR	0.351 2^***(4.28)
lnRD	0.079 2^***(4.07)
lnRGDP	0.489 2^***(14.28)
lnER	-1.887 3^***(-5.09)
lnDOO	0.030 2(1.77)
lnPOP	-0.288 1(-1.55)
lnGREENQ(GREENQ $≤$ 0.426 7)	-0.534 1^***(-5.57)
lnGREENQ(GREENQ $>$ 0.426 7)	-0.258 0^***(-3.95)
Cons	-4.663 7^***(-8.61)
N	1 872
adj. R²	0.291 6

表6 不同水平区间资源型城市异质性

Tab. 6 Heterogeneity of resource-based cities at different levels

变量	0<GREENQ≤0.426 7	0.426 7<GREENQ≤0.610 8	0.610 8<GREENQ
lnGREENQ	-1.987 0^**(-3.04)	-0.706 9^**(-3.11)	0.140 1(1.95)
控制变量	Yes	Yes	Yes
Cons	-10.489 2^***(-5.99)	-4.253 3^***(-5.15)	-7.960 8^***(-5.84)
N	397	824	651
adj.R²	0.357 2	0.284 4	0.245 1

表7 不同时间资源型城市异质性

Tab. 7 Heterogeneity of resource-based cities at different times

变量	2006—2010年	2011—2015年	2016—2021年
lnGREENQ	-0.439 4^**(-2.68)	-0.305 1^***(-4.27)	-0.207 9^*(-2.25)
控制变量	Yes	Yes	Yes
Cons	1.346 6 (0.25)	-5.452 1 (-1.15)	-1.843 7^*(-1.90)
N	585	585	702
adj.R²	0.104 8	-0.186 3	-0.003 6

表8 中国四大区域资源型城市异质性

Tab. 8 Heterogeneity of resource-based cities in China's four major regions

变量	西部城市	中部城市	东部城市	东北城市
lnGREENQ	-0.367 3^***(-4.28)	-0.342 9^**(-2.93)	0.863 7^***(6.37)	0.119 4(0.99)
控制变量	Yes	Yes	Yes	Yes
Cons	-4.551 5^***(-5.58)	3.223 7(0.93)	-11.429 6(-1.11)	2.784 7(0.64)
N	656	592	304	320
adj.R²	0.378 9	0.316 6	0.487 0	0.187 0

表9 工业与非工业城市异质性

Tab. 9 Heterogeneity of industrial and non-industrial cities

变量	非老工业城市	老工业城市
lnGREENQ	-0.284 5^***(-3.66)	-0.053 9(-0.71)
控制变量	Yes	Yes
Cons	-3.633 4^***(-4.76)	0.054 0(0.05)
N	960	912
adj. R²	0.408 7	0.202 2

参考文献 55

[1]	秦大河.气候变化科学与人类可持续发展.地理科学进展,2014,33(7):874-883.
	QIN D. Climate change science and human sustainable development .Progress in Geography, 2014, 33(7): 874-883.
[2]	赵贤,刘金程.人工智能对城市能源转型的影响及作用机制.资源科学,2026,48(3):568-583.
	ZHAO X, LIU J. Impact of artificial intelligence on urban energy transition and its mechanisms. Resources Science, 2026, 48(3): 568-583.
[3]	卢奇秀.中国能源研究会理事长史玉波:新能源产业步入新发展阶段.中国能源报,2025-09-29,002.
	LU Q. SHI Y, The chairman of the China Energy Research Society: The new energy industry has entered a new stage of development. China Energy News,2025-09-29,002.
[4]	田旭,李照令,耿涌,等.碳中和目标下中国钢铁行业碳减排的资源环境影响.资源科学,2024,46(4):700-716.
	TIAN X, LI Z, GENG Y, et al. Resource and environmental impacts of carbon emission reduction in China's steel industry under the carbon neutrality goal. Resources Science, 2024, 46(4): 700-716.
[5]	杨巨星,孙慧,周晋楠,等.中国资源型城市转型脱碳时空演进及路径选择.干旱区地理,2024,743:1-14.
	YANG J, SUN H, ZHOU J, et al. Spatial-temporal evolution and path selection of carbon dioxide emission reduction in China's resource-based cities during transformation. Arid Zone Geography, 2024, 743: 1-14.
[6]	吴茜,陈强强.甘肃省行业碳排放影响因素及脱钩努力研究.干旱区地理,2023,46(2): 274-283.
	WU Q, CHEN Q. Research on the influencing factors of industry carbon emissions and decoupling efforts in Gansu province. Arid Zone Geography, 2023, 46(2): 274-283.
[7]	李晨潇,刘凯莉,马思睿.绿色金融发展对省域能源消费结构转型影响的空间效应.经济地理,2024,44(8):148-157.
	LI C, LIU K, MA S. Spatial effects of green finance development on the transformation of provincial energy consumption structure. Economic Geography, 2024, 44(8): 148-157.
[8]	贺灿飞,毛熙彦.中国环境经济地理的研究主题展望.地理科学,2021,41(9):1497-1504.
	HE C, MAO X. Prospects for research topics in environmental economic geography of China. Geographical Science, 2021, 41(9): 1497-1504.
[9]	胡晓辉,董柯.可持续性转型研究动态与经济地理学展望.地理研究,2022,41(1):18-33.
	HU X, DONG K.Research trends on sustainable transformation and prospects of economic geography.Geographical Research,2022,41(1): 18-33.
[10]	胡晓辉,董柯,杨宇.战略耦合演化视角下的区域经济韧性分析框架.地理研究,2021,40(12):3272-3286.
	HU X, DONG K, YANG Y. Analysis framework for regional economic resilience from the perspective of strategic coupling evolution. Geographical Research, 2021, 40(12): 3272-3286.
[11]	余振,龚惠文,胡晓辉.可持续性转型地理研究综述与展望.地理科学进展,2021,40(3):498-510.
	YU Z, GONG H, HU X. Review and prospect of geographical research on sustainable transformation. Progress in Geography, 2021, 40(3): 498-510.
[12]	王文棋,刘兆德,赵虎.中国资源型城市转型发展全景知识图谱:演进脉络、热点追踪及未来展望.地理科学,2024,44(5):785-795.
	WANG W, LIU Z, ZHAO H.Panoramic knowledge map of transformation and development of China's resource-based cities-evolutionary context,hotspot tracking and future prospects.Geographical Science,2024,44(5): 785-795.
[13]	胡晓辉,朱晟君, HASSINK R.超越"演化":老工业区重构研究进展与范式反思.地理研究,2020,39(5):1028-1044.
	HU X, ZHU S, HASSINK R. Beyond "evolution": Research progress and paradigm reflection on the reconstruction of old industrial areas. Geographical Research, 2020, 39(5):1028-1044.
[14]	陈妍,梅林.东北地区资源型城市经济转型发展波动特征与影响因素——基于面板数据模型的分析.地理科学,2017,37(7):1080-1086.
	CHEN Y, MEI L. Fluctuation characteristics and influencing factors of economic transformation and development in resource-based cities in Northeast China: An analysis based on panel data model. Geographical Science, 2017, 37(7):1080-1086.
[15]	陈妍,梅林.东北地区资源型城市转型过程中社会-经济-环境协调演化特征.地理研究,2018,37(2):307-318.
	CHEN Y, MEI L. Characteristics of social-economic-environmental co-evolution in the transformation process of resource-based cities in Northeast China. Geographical Research, 2018, 37(2):307-318.
[16]	陈妍,王士君,梅林.东北地区非资源型城市与资源型城市产业转型的对比研究.地理研究,2021,40(3):808-820.
	CHEN Y, WANG S, MEI L. Comparative study on industrial transformation of non-resource-based cities and resource-based cities in Northeast China. Geographical Research, 2021, 40(3):808-820.
[17]	王晓楠,孙威.黄河流域资源型城市转型效率及其影响因素.地理科学进展,2020,39(10):1643-1655.
	WANG X, SUN W. Transformation efficiency and influencing factors of resource-based cities in the Yellow River Basin. Progress in Geography, 2020, 39(10):1643-1655.
[18]	焦华富,许吉黎.社会空间视角下成熟型煤炭资源城市地域功能结构研究:以安徽省淮南市为例.地理科学,2016,36(11):1670-1678.
	JIAO H, XU J.Research on the regional functional structure of mature coal resources cities from the perspective of social space:A case study of Huainan City,Anhui Province.Geographical Science,2016,36(11):1670-1678.
[19]	殷洁,罗小龙,程叶青,等.基于企业家型城市理论的工矿资源型城市转型——以马鞍山市为例.地理科学,2010,30(3):329-335.
	YIN J, LUO X, CHENG Y, et al. Transformation of industrial and mining resource-based cities based on the theory of entrepreneurial cities: A case study of Ma'anshan City. Geographical Science, 2010,30(3):329-335.
[20]	卢硕,张文忠,余建辉,等.资源型城市演化阶段识别及其发展特征.地理学报,2020,75(10):2180-2191.
	LU S, ZHANG W, YU J, et al. Identification of evolution stages of resource-based cities and their development characteristics. Acta Geographica Sinica, 2020, 75(10):2180-2191.
[21]	李汝资,宋玉祥,李雨停,等.吉林省资源型城市转型阶段识别及其特征成因分析.地理科学,2016,36(1):90-98.
	LI R, SONG Y, LI Y, et al. Identification of the transition stage of resource-based cities in Jilin Province and analysis of the causes of their characteristics. Geographical Science, 2016, 36(1):90-98.
[22]	仇方道,张春丽,郭梦梦,等.中国再生性资源型城市创新能力与工业转型耦合协调分析.地理科学,2020,40(7):1092-1103.
	QIU F, ZHANG C, GUO M, et al. Coupling and coordination analysis of innovation capacity and industrial transformation in China's regenerative resource-based cities. Geographical Science, 2020, 40(7):1092-1103.
[23]	文琦,侯凯元,郑殿元,等.成长型资源城市产业转型能力评价与优化路径——以榆林市为例.地理科学,2022,42(4):682-691.
	WEN Q, HOU K, ZHENG D, et al. Evaluation and optimization path of industrial transformation capability of growth-oriented resource cities: A case study of Yulin City. Geographical Science, 2022, 42(4):682-691.
[24]	郭艺,曹贤忠,曾刚.高铁建设对特殊类型地区产业转型升级的影响:来自资源型城市的证据.地理研究,2023,42(5):1326-1342.
	GUO Y, CAO X, ZENG G.The impact of high-speed railway construction on industrial transformation and upgrading in special types of regions:Evidence from resource-based cities.Geographical Research,2023,42(5):1326-1342.
[25]	PORTER M. The competitive advantage of nations. Harvard Business Review,1990,68(2):73-93.
[26]	GYLFASON T, HERBERTSSON T, ZOEGA G. A mixed blessing-Natural resources and economic growth. Macroeconomic Dynamics, 1999,3(2):204-225.
[27]	BARNES T, BRITTON J, COFFEY W, et al. Canadian economic geography at the millennium. Canadian Geographies-Geographies Canadiennes,2000,44(1):4-24.
[28]	GYLFASON T. Natural resources, education, and economic development. European Economic Review, 2001,45(4):847-859.
[29]	谭玲玲,肖双.基于全要素生产率视角资源型城市低碳转型效果评价模型.中国矿业, 2018,27(2):58-64.
	TAN L, XIAO S. An evaluation model for the low-carbon transformation effect of resource-based cities from the perspective of total factor productivity. China Mining, 2018,27(2):58-64.
[30]	王学军,侯睿,王玲.基于TOPSIS法的资源型城市低碳转型评价体系研究:以焦作市为例.生态经济,2015,31(11):42-46.
	WANG X, HOU R, WANG L. Research on the evaluation system for low-carbon transformation of resource-based cities based on TOPSIS Method - Taking Jiaozuo City as an example. Ecological Economy,2015,31(11): 42-46.
[31]	肖双.资源型城市低碳转型效果测度及影响因素实证研究.烟台:山东工商学院,2018.
	XIAO S. Measurement of the effect of low-carbon transformation in resource-based cities and empirical analysis of its influencing factors. Yantai:Shandong Institute of Business and Technology,2018.
[32]	徐君,高厚宾,王育红.生态文明视域下资源型城市低碳转型战略框架及路径设计.管理世界,2014(6):178-179.
	XU J, GAO H, WANG Y. The strategic framework and path design for low-carbon transformation of resource-based cities from the perspective of ecological civilization. Management World,2014(6):178-179.
[33]	孙秀梅,闫肃.TOE框架下资源型城市低碳转型驱动路径——基于我国108个资源型城市的fsQCA研究.科技进步与对策,2023,40(23):72-81.
	SUN X, YAN S. The driving path for low-carbon transformation of resource-based cities under the TOE framework: An fsQCA study based on 108 resource-based cities in China. Progress in Science and Technology and Policy,2023,40(23):72-81.
[34]	谭伟,赵晗雪."碳达峰、碳中和"目标下金融支持资源型城市低碳转型路径探索:以鸡西市为例.黑龙江金融,2023(12):28-31.
	TAN W, ZHAO H."Exploring the path of financial support for the low-carbon transformation of resource-based cities under the 'carbon peak and carbon neutrality' goals-A case study of Jixi City.Heilongjiang Finance,2023(12):28-31.
[35]	YU R Q, LUO Z Y. Research on the influence mechanism of factor misallocation on the transformation efficiency of resource-based cities based on the optimization direction function calculation method.Sustainability,2023,15(12):9800.
[36]	朱丽,曹梦莹,刘瑞杰."双碳"目标下煤炭资源型城市能源转型评价与障碍因子.环境科学,2024,45(12):6858-6869.
	ZHU L, CAO M, LIU R. Evaluation and obstacle factors of energy transformation in coal-rich cities under the "carbon neutrality" goal. Environmental Science, 2024, 45(12):6858-6869.
[37]	李相远,高中显,吴文华,等.山东省资源型城市能源转型能力评价与影响因素研究.中国矿业,2022,31(6):69-78.
	LI X, GAO Z, WU W, et al. Evaluation of energy transformation capacity and influencing factors of resource-based cities in Shandong province. China Mining, 2022,31(6):69-78.
[38]	吴青龙,王建明,郭丕斌.开放STIRPAT模型的区域碳排放峰值研究—以能源生产区域山西省为例.资源科学,2018,40(5):1051-1062.
	WU Q, WANG J, GUO P. Research on regional carbon emission peak of the Open STIRPAT Model: A case study of the energy production region of Shanxi province. Resources Science, 2018, 40(5): 1051-1062.
[39]	张江艳.基于扩展STIRPAT模型LMDI分解的碳排放脱钩因素.环境科学,2024,45(4):1888-1897.
	ZHANG J. Factors leading to carbon emission decoupling based on LMDI Decomposition of the Extended STIRPAT Model. Environmental Science,2024,45(4):1888-1897.
[40]	田华征,马丽.中国工业碳排放强度变化的结构因素解析.自然资源学报,2020,35(3):639-653.
	TIAN H, MA L. Analysis of structural factors for changes in China's industrial carbon emission intensity. Journal of Natural Resources, 2020, 35(3):639-653.
[41]	韦彦汀,李思佳,张华.成渝城市群碳排放时空特征及其影响因素.中国环境科学,2022,42(10):4807-4816.
	WEI Y, LI S, ZHANG H. Spatial and temporal characteristics of carbon emissions in the Chengdu-Chongqing urban agglomeration and their influencing factors. China Environmental Science,2022,42(10):4807-4816.
[42]	任晓松,孙莎.数字经济对中国城市工业碳生产率的赋能效应.资源科学,2022,44(12):2399-2414.
	REN X, SUN S. The empowering effect of digital economy on the carbon production efficiency of urban industries in China. Resources Science, 2022, 44(12):2399-2414.
[43]	梁树宽,张芃芃,臧文嘉.产业结构、能源消费结构和节能减排效果的动态演进与门槛效应研究.生态经济,2023,39(6):54-60.
	LIANG S, ZHANG P, ZANG W. Dynamic evolution and threshold effect analysis of industrial structure, energy consumption structure and energy conservation and emission reduction effects. Ecological Economy,2023,39(6):54-60.
[44]	李辉,庞博,朱法华,等.碳减排背景下我国与世界主要能源消费国能源消费结构与模式对比.环境科学,2022,43(11):5294-5304.
	LI H, PANG B, ZHU F, et al. Comparison of energy consumption structures and patterns between China and major energy-consuming countries in the world under the context of carbon emission reduction. Environmental Science, 2022, 43(11):5294-5304.
[45]	苗阳,刑文杰,鲍健强.城市能源结构低碳化指标体系及实现路径研究.生态经济,2016,32(5):53-57.
	MIAO Y, XING W, BAO J. Research on the index system and realization path of low-carbonization of urban energy structure. Ecological Economy, 2016, 32(5):53-57.
[46]	柳亚琴,孙薇,朱治双.碳市场对能源结构低碳转型的影响及作用路径.中国环境科学,2022,42(9):4369-4379.
	LIU Y, SUN W, ZHU Z. The impact and mechanism of the carbon market on the low-carbon transformation of energy structure. China Environmental Science, 2022, 42(9):4369-4379.
[47]	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.自然资源学报,2023,38(1):22-38.
	ZHANG W, YU J. Policy evolution and effect analysis of transformation and development in China's resource-based cities. Journal of Natural Resources, 2023, 38(1):22-38.
[48]	段冶,刘阳.资源型城市工业能源碳排放效应及影响因素研究.辽宁师范大学学报(自然科学版),2025,48(3):308-318.
	DUAN Y, LIU Y. Research on the carbon emission effects and influencing factors of industrial energy in resource-based cities. Journal of Liaoning Normal University (Natural Science Edition), 2025, 48(3):308-318.
[49]	CHI K, GIRAY G, et al. Introducing a new measure of energy transition: Green quality of energy mix and its impact on CO₂ emissions. Energy Economics,2023(122):106702.
[50]	赵慧亭.天津市能源消费碳排放变化的因素分解分析.天津:天津财经大学,2021.
	ZHAO H. Analysis of the factor decomposition of carbon emission changes in energy consumption in Tianjin. Tianjin: Tianjin University of Finance and Economics,2021.
[51]	辛奕霖,刘艳军,柳力玮.中国老工业城市人口增长与收缩对碳排放强度的影响效应.地理研究,2024,43(3):558-576.
	XIN Y, LIU Y, LIU L. The impact of population growth and shrinkage in China's old industrial cities on carbon emission intensity. Geographical Research, 2024, 43(3):558-576.
[52]	徐英启,程钰,王晶晶,等.中国低碳试点城市碳排放效率时空演变与影响因素.自然资源学报,2022,37(5):1261-1276.
	XU Y, CHENG Y, WANG J, et al. Spatiotemporal evolution and influencing factors of carbon emission efficiency in China's low-carbon pilot cities. Journal of Natural Resources, 2022, 37(5): 1261-1276.
[53]	武志勇,王则仁,马永红.研发投入、国际化程度与制造企业价值的门槛效应分析.科技进步与对策,2020,37(14):94-101.
	WU Z, WANG Z, MA Y. Analysis of threshold effects of R&D investment, internationalization degree and enterprise value in manufacturing industries. Progress in Science and Technology and Policy, 2020, 37(14): 94-101.
[54]	闫志俊,徐志龙,张继彤.数字时代的"韧性密码":虚拟集聚与城市经济韧性.财经研究,2025,51(7): 49-63.
	YAN Z, XU Z, ZHANG J. "Resilience Code" in the digital age: Virtual agglomeration and urban economic resilience. Journal of Finance and Economics, 2025, 51(7):49-63.
[55]	王家明,余志林.资源型城市低碳技术进步驱动碳减排的多重异质性.中国人口·资源与环境,2022,32(11):156-170.
	WANG J, YU Z. The multiple heterogeneities of low-carbon technological progress driving carbon emission reduction in resource-based cities. China Population, Resources and Environment, 2022, 32(11):156-170.