Impact of county agglomeration economic differentiation on carbon emissions: A case study of 1384 counties in China

doi:10.3969/j.issn.1004-9479.2025.09.20240169

Abstract

Abstract:

The agglomeration economy not only plays an extremely important role in the development process of regional industrialization, but also makes the hierarchical differentiation of the scale of the agglomeration economy between regional economic units increasingly prominent. As the regional agglomeration economies continue to diverge, the world's environmental problems are becoming increasingly severe, which is posing a serious challenge to the achievement of carbon reduction targets in all regions. Given that, by taking China's 1384 counties as an example, this paper explores the effect of county agglomeration economic differentiation on carbon emissions through empirical tests. The results indicate that: ① Overall, agglomeration economic differentiation significantly increases the level of carbon emissions in Chinese counties. ②Labor mobility and local investment incentives are important mechanisms for agglomeration economic differentiation to increase the level of carbon emissions in China's counties.③ Although agglomeration economic differentiation reduces the level of county carbon emissions in central and eastern China, it increases the level of county carbon emissions in western China. In addition, as the agglomeration economic differentiation level rises, the carbon-increasing effect caused by county agglomeration economic differentiation shows an "inverted U-shape" trend. Based on the above conclusions, this paper makes the following policy recommendations:①Local governments should promote the process of taking on technology-intensive or service-oriented low-carbon emission industries in the county, thereby reducing the carbon-increasing effect of the divergence of agglomeration economies. ② In order to enhance or realize the carbon-reducing effect of agglomeration economic differentiation, local governments should formulate complementary industrial development policies in accordance with the characteristics of different regions at the current stage, so as to ultimately reduce the carbon emission level of the region.

Key words: agglomeration economies, labor mobility, local investment incentives, low carbon development

摘要：

随着区域集聚经济分化的不断加剧，世界环境问题也日益严峻，给各个区域碳减排目标的实现带来了挑战。在此背景下，本文以中国1 384个区县为例，通过实证检验探讨了县域集聚经济分化对碳排放的影响效应。研究发现：①整体而言，集聚经济分化显著提高了中国县域的碳排放水平；②劳动力流动与本地投资激励是集聚经济分化提高中国县域碳排放水平的重要机制；③虽然集聚经济分化提高了中国西部地区的碳排放水平，但却降低了中部与东部地区的碳排放水平。基于上述结论，本文提出以下政策建议：⁵加快本地产业结构转型升级，推动本地集聚经济分化对碳排放的影响由增排转向减排；②根据不同区域的产业发展阶段制定差异化的区域碳减排战略，以增强或实现集聚经济分化的碳减排作用，最终降低区域的碳排放水平。

关键词: 集聚经济, 劳动力流动, 本地投资激励, 低碳发展

Yang LU, Xiao LIU, Huan CHEN, Zhangqi ZHONG. Impact of county agglomeration economic differentiation on carbon emissions: A case study of 1384 counties in China[J]. World Regional Studies, 2025, 34(9): 116-129.

陆扬, 刘晓, 陈欢, 钟章奇. 县域集聚经济分化对碳排放的影响——以中国1 384个区县为例[J]. 世界地理研究, 2025, 34(9): 116-129.

Figures/Tables 6

Tab.1 Descriptive statistics for the main variables

变量	均值	方差	最大值	最小值	中位数	样本量
$C O 2$	0.213	1.585	3.447	-18.421	0.354	29 083
$G a p_A E$	3.046	14.725	13.448	-6.437	4.087	29 083
$G a p_A E 2$	1.040	1.096	10.133	0.000	0.776	29 083
$P o p$	3.598	0.656	5.630	0.010	3.664	28 557
$I n v e s t$	12.421	2.338	17.735	6.720	12.528	23 813
$D e g r e e$	1.046	1.309	5.974	0.000	0.699	29 027
$I E_O u t p u t$	12.454	4.588	18.244	0.693	12.609	28 641
$I E_Q u a n t i t y$	3.637	1.794	8.115	0.010	3.638	29 083
$C o m$	11.815	1.898	16.294	6.118	11.839	29 083
$F E$	0.213	0.041	3.064	0.009	0.152	29 083
$F D$	12.314	1.872	18.313	1.386	12.282	29 083
$O p e n$	0.211	0.065	1.721	0.008	0.118	29 083
$E R$	0.004	0.000 1	0.031	0.000 1	0.004	29 083

Tab.1 Descriptive statistics for the main variables

变量	均值	方差	最大值	最小值	中位数	样本量
$C O 2$	0.213	1.585	3.447	-18.421	0.354	29 083
$G a p_A E$	3.046	14.725	13.448	-6.437	4.087	29 083
$G a p_A E 2$	1.040	1.096	10.133	0.000	0.776	29 083
$P o p$	3.598	0.656	5.630	0.010	3.664	28 557
$I n v e s t$	12.421	2.338	17.735	6.720	12.528	23 813
$D e g r e e$	1.046	1.309	5.974	0.000	0.699	29 027
$I E_O u t p u t$	12.454	4.588	18.244	0.693	12.609	28 641
$I E_Q u a n t i t y$	3.637	1.794	8.115	0.010	3.638	29 083
$C o m$	11.815	1.898	16.294	6.118	11.839	29 083
$F E$	0.213	0.041	3.064	0.009	0.152	29 083
$F D$	12.314	1.872	18.313	1.386	12.282	29 083
$O p e n$	0.211	0.065	1.721	0.008	0.118	29 083
$E R$	0.004	0.000 1	0.031	0.000 1	0.004	29 083

Fig.1 Degree of economic differentiation of county agglomeration and carbon emission level

Tab.2 Benchmark regression and robustness test results

变量	（1）CO₂	（2）CO₂	（3）CO₂
$G a p_A E$	0.033^***	0.054^***	0.031^***
	(0.001 89)	(0.017 5)	(0.001 89)
$F D$	0.149^***	0.147^***	0.154^***
	(0.016 6)	(0.016 6)	(0.017 1)
$I E_O u t p u t$	0.101^***	0.098^***	0.129^***
	(0.008 98)	(0.009 13)	(0.010 5)
$C o m$	0.035	0.034	0.048^**
	(0.020 1)	(0.020 0)	(0.020 3)
$F E$	0.079^***	0.080^***	0.074^***
	(0.067 2)	(0.067 3)	(0.063 7)
$U I S$	－0.001^***	－0.001^***	－0.001^***
	(6.33E-07)	(6.04E-07)	(9.63E-07)
$I E_Q u a n t i t y$			－0.058^***
			(0.013 0)
$O p e n$	0.006	0.007	0.008
	(0.034 8)	(0.034 6)	(0.034 5)
$E R$	0.039^***	0.039^***	0.039^***
	(1.165)	(1.166)	(1.180)
时间效应	$Y E S$	$Y E S$	$Y E S$
个体效应	$Y E S$	$Y E S$	$Y E S$
$R 2$	0.763	0.763	0.765
$N$	29 083	29 083	28 641

Tab.2 Benchmark regression and robustness test results

变量	（1）CO₂	（2）CO₂	（3）CO₂
$G a p_A E$	0.033^***	0.054^***	0.031^***
	(0.001 89)	(0.017 5)	(0.001 89)
$F D$	0.149^***	0.147^***	0.154^***
	(0.016 6)	(0.016 6)	(0.017 1)
$I E_O u t p u t$	0.101^***	0.098^***	0.129^***
	(0.008 98)	(0.009 13)	(0.010 5)
$C o m$	0.035	0.034	0.048^**
	(0.020 1)	(0.020 0)	(0.020 3)
$F E$	0.079^***	0.080^***	0.074^***
	(0.067 2)	(0.067 3)	(0.063 7)
$U I S$	－0.001^***	－0.001^***	－0.001^***
	(6.33E-07)	(6.04E-07)	(9.63E-07)
$I E_Q u a n t i t y$			－0.058^***
			(0.013 0)
$O p e n$	0.006	0.007	0.008
	(0.034 8)	(0.034 6)	(0.034 5)
$E R$	0.039^***	0.039^***	0.039^***
	(1.165)	(1.166)	(1.180)
时间效应	$Y E S$	$Y E S$	$Y E S$
个体效应	$Y E S$	$Y E S$	$Y E S$
$R 2$	0.763	0.763	0.765
$N$	29 083	29 083	28 641

Tab.3 Regression results of instrumental variables

变量	（1）CO₂	（2）CO₂
$G a p_A E$	4.314^***	2.155^***
	(0.277)	(0.147)
控制变量	$Y E S$	$Y E S$
时间效应	$N O$	$Y E S$
个体效应	$Y E S$	$Y E S$
$U n d e r i d e n t i f i c a t i o n t e s t$	38.041^***	47.508^***
$W e a k i d e n t i f i c a t i o n t e s t$	9.708	11.963^*
$H a n s e n J s t a t i s t i c$	2.217	5.562
$N$	29 019	29 019

Tab.3 Regression results of instrumental variables

变量	（1）CO₂	（2）CO₂
$G a p_A E$	4.314^***	2.155^***
	(0.277)	(0.147)
控制变量	$Y E S$	$Y E S$
时间效应	$N O$	$Y E S$
个体效应	$Y E S$	$Y E S$
$U n d e r i d e n t i f i c a t i o n t e s t$	38.041^***	47.508^***
$W e a k i d e n t i f i c a t i o n t e s t$	9.708	11.963^*
$H a n s e n J s t a t i s t i c$	2.217	5.562
$N$	29 019	29 019

Tab.4 Mechanism test of county agglomeration economic differentiation affecting carbon emission level

变量	（1） $P o p$	（2）CO₂	（3） $I n v e s t$	（4）CO₂
$G a p_A E$	0.004^*		0.039^***
	(0.000 5)		(0.003 5)
$P o p$		0.182^***
		(0.057 9)
$I n v e s t$				0.061^***
				(0.007 31)
控制变量	$Y E S$	$Y E S$	$Y E S$	$Y E S$
时间效应	$Y E S$	$Y E S$	$Y E S$	$Y E S$
个体效应	$Y E S$	$Y E S$	$Y E S$	$Y E S$
$R 2$	0.161	0.763	0.898	0.730
$N$	28 557	28 946	23 813	24 230

Tab.4 Mechanism test of county agglomeration economic differentiation affecting carbon emission level

变量	（1） $P o p$	（2）CO₂	（3） $I n v e s t$	（4）CO₂
$G a p_A E$	0.004^*		0.039^***
	(0.000 5)		(0.003 5)
$P o p$		0.182^***
		(0.057 9)
$I n v e s t$				0.061^***
				(0.007 31)
控制变量	$Y E S$	$Y E S$	$Y E S$	$Y E S$
时间效应	$Y E S$	$Y E S$	$Y E S$	$Y E S$
个体效应	$Y E S$	$Y E S$	$Y E S$	$Y E S$
$R 2$	0.161	0.763	0.898	0.730
$N$	28 557	28 946	23 813	24 230

Tab.5 The test of regional heterogeneity of agglomeration economic differentiation affecting carbon emission

变量	（1）CO₂	（2） $p o p$	（3）CO₂	（4） $I n v e s t$	（5）CO₂
$G a p_A E × D 1$	－0.014^***	0.008^***		－0.026^**
	(0.002 1)	(0.000 6)		(0.006 6)
$G a p_A E × D 2$	－0.011^**	－0.003		0.057^***
	(0.002 4)	(0.000 9)		(0.006 7)
$G a p_A E × D 3$	0.094^***	0.009^***		0.026^***
	(0.005 4)	(0.000 9)		(0.005 2)
$P o p × D 1$			－0.744^***
			(0.150)
$P o p × D 2$			－0.290^***
			(0.072 6)
$P o p × D 3$			0.889^***
			(0.107)
$I n v e s t × D 1$					0.124^***
					(0.007 8)
$I n v e s t × D 2$					0.133^***
					(0.005 4)
$I n v e s t × D 3$					0.577^***
					(0.009 0)
控制变量	$Y E S$	$Y E S$	$Y E S$	$Y E S$	$Y E S$
时间效应	$Y E S$	$Y E S$	$Y E S$	$Y E S$	$Y E S$
个体效应	$Y E S$	$Y E S$	$Y E S$	$Y E S$	$Y E S$
$R 2$	0.769	0.163	0.767	0.898	0.743
$N$	29 083	28 557	28 946	23 813	24 230

Tab.5 The test of regional heterogeneity of agglomeration economic differentiation affecting carbon emission

变量	（1）CO₂	（2） $p o p$	（3）CO₂	（4） $I n v e s t$	（5）CO₂
$G a p_A E × D 1$	－0.014^***	0.008^***		－0.026^**
	(0.002 1)	(0.000 6)		(0.006 6)
$G a p_A E × D 2$	－0.011^**	－0.003		0.057^***
	(0.002 4)	(0.000 9)		(0.006 7)
$G a p_A E × D 3$	0.094^***	0.009^***		0.026^***
	(0.005 4)	(0.000 9)		(0.005 2)
$P o p × D 1$			－0.744^***
			(0.150)
$P o p × D 2$			－0.290^***
			(0.072 6)
$P o p × D 3$			0.889^***
			(0.107)
$I n v e s t × D 1$					0.124^***
					(0.007 8)
$I n v e s t × D 2$					0.133^***
					(0.005 4)
$I n v e s t × D 3$					0.577^***
					(0.009 0)
控制变量	$Y E S$	$Y E S$	$Y E S$	$Y E S$	$Y E S$
时间效应	$Y E S$	$Y E S$	$Y E S$	$Y E S$	$Y E S$
个体效应	$Y E S$	$Y E S$	$Y E S$	$Y E S$	$Y E S$
$R 2$	0.769	0.163	0.767	0.898	0.743
$N$	29 083	28 557	28 946	23 813	24 230

References 46

[1]	OTTAVIANO G, THISSE J. Agglomeration and Economic Geography. Handbook of Regional and Urban Economics. 2004.
[2]	JAMIL K, LIU D, GUL R, et al. Do remittance and renewable energy affect CO₂ emissions? An empirical evidence from selected G-20 countries. Energy & Environment, 2022, 33(5):916-932.
[3]	LIU Z, GUAN D, WEI W, et al. Reduced carbon emission estimates from fossil fuel combustion and cement production in China. Nature, 2015,524(7565):335-338.
[4]	LUO F, GUO Y, YAO M, et al. Carbon emissions and driving forces of China's power sector: Input-output model based on the disaggregated power sector. Journal of Cleaner Production, 2020 (268): 121925.
[5]	WANG C, GUO Y, SHAO S, et al. Regional carbon imbalance within China: An application of the Kaya-Zenga index. Journal of Environmental Management, 2020 (262):110378.
[6]	姜宛贝,刘卫东.中国经济空间格局演变及其CO₂排放效应.资源科学, 2021(4):722-732.
	JIANG W, LIU W. Effect of change of the spatial pattern of economic activities on CO₂ emissions in China. Resources Science, 2021(4):722-732.
[7]	CHEN X, SHUAI C, WU Y, et al. Analysis on the carbon emission peaks of China's industrial, building, transport, and agricultural sectors. Science of the Total Environment, 2020 (709):135768.
[8]	王少剑,谢紫寒,王泽宏.中国县域碳排放的时空演变及影响因素.地理学报, 2021(12):3103-3118.
	WANG S, XIE Z, WANG Z. The spatiotemporal pattern evolution and influencing factors of CO₂ emissions at the county level of China. Acta Geographica Sinica, 2021 (12):3103-3118.
[9]	张卓群,张涛,冯冬发.中国碳排放强度的区域差异、动态演进及收敛性研究.数量经济技术经济研究, 2022 (4):67-87.
	ZHANG Z, ZHANG T, FENG D. Study on regional differences, dynamic evolution and convergence of carbon emission intensity in China. Journal of Quantitative & Technological Economics, 2022 (4):67-87.
[10]	SAIDI K, OMRI A. The impact of renewable energy on carbon emissions and economic growth in 15 major renewable energy-consuming countries. Environmental Research, 2020(186): 109567.
[11]	LIANG X Y, FAN M, XIAO Y T, et al. Temporal-spatial characteristics of energy-based carbon dioxide emissions and driving factors during 2004-2019, China. Energy, 2022 (261): 124965.
[12]	YU S, ZHANG Q, HAO J L, et al. Development of an extended STIRPAT model to assess the driving factors of household carbon dioxide emissions in China. Journal of Environmental Management, 2023 (325): 116502.
[13]	YI M, LIU Y, SHENG M, et al. Effects of digital economy on carbon emission reduction: New evidence from China. Energy Policy, 2022 (171): 113271.
[14]	孙鹏博,葛力铭.通向低碳之路:高铁开通对工业碳排放的影响.世界经济, 2021(10):201-224.
	SUN P, GE L. The way to low carbon emission: Impact of high-speed railway on industrial carbon emissions. The Journal of World Economy, 2021 (10):201-224.
[15]	LI Z, LI R, MALIK M, et al. Determinants of carbon emission in China: How good is green investment? Sustainable Production and Consumption, 2021 27: 392-401.
[16]	毛熙彦,贺灿飞,王佩玉,等.中国环境产品进出口贸易对碳排放的影响.自然资源学报, 2022 (5):1321-1337.
	MAO X, HE C, WANG P, et al. Effects of China's trade on environmental goods on carbon emissions. Journal of Natural Resources, 2022(5):1321-1337.
[17]	李新安,李慧.外资引入、技术进步偏向影响了制造业的碳排放吗——来自我国27个制造行业面板数据模型的实证检验.中国软科学, 2022(1):159-170.
	LI X, LI H. Do foreign investment and technological progress bias affect the carbon emission of China's manufacturing industry?Based on the empirical research of manufacturing sector segmentation data. China Soft Science, 2022(1):159-170.
[18]	YU Y, ZHANG N. Low-carbon city pilot and carbon emission efficiency: Quasi-experimental evidence from China. Energy Economics, 2021 96: 105125.
[19]	付华,李国平,朱婷.中国制造业行业碳排放:行业差异与驱动因素分解.改革, 2021(5):38-52.
	FU H, LI G, ZH T. Carbon emission of China's manufacturing industry: Industry differences and decomposition of driving factors. Reform, 2021(5):38-52.
[20]	FANG K, LI C, TANG Y, et al. China's pathways to peak carbon emissions: New insights from various industrial sectors. Applied Energy, 2022( 306): 118039.
[21]	YAN B, WANG F, DONG M, et al. How do financial spatial structure and economic agglomeration affect carbon emission intensity? Theory extension and evidence from China. Economic Modelling, 2022 (108): 105745.
[22]	WANG Y, YIN S, FANG X, et al. Interaction of economic agglomeration, energy conservation and emission reduction: Evidence from three major urban agglomerations in China. Energy, 2022 (241): 122519.
[23]	HUANG C, WANG J, WANG C, et al. Does tourism industry agglomeration reduce carbon emissions? Environmental Science and Pollution Research, 2021 (28): 30278-30293.
[24]	何文举,张华峰,陈雄超,等.中国省域人口密度、产业集聚与碳排放的实证研究——基于集聚经济、拥挤效应及空间效应的视角.南开经济研究, 2019(2):207-225.
	HE W, ZHANG H, CHEN X, et al. An empirical study about population density, economic agglomeration and carbon emission state of Chinese provinces: Based on the perspective of agglomeration economy effects, Congestion Effects and Spatial Effects. Nankai Economic Studies, 2019(2):207-225.
[25]	邵帅,张可,豆建民.经济集聚的节能减排效应:理论与中国经验.管理世界, 2019(1):36-60.
	SHAO S, ZHANG K, DOU J. Effects of economic agglomeration on energy saving and emission reduction: Theory and empirical evidence from China. Journal of Management World, 2019(1):36-60.
[26]	谢地,荣莹,叶子祺.城市高质量发展与城市群协调发展:马克思级差地租的视角.经济研究, 2022 (10):156-172.
	XIE D, RONG Y, YE Z. Urban high-quality development and coordinated development of urban agglomerations: The perspective of Marx's differential land rent theory. Economic Research Journal, 2022(10): 156-172.
[27]	WANG Z, LI Y, CAI H, et al. Regional difference and drivers in China's carbon emissions embodied in internal trade. Energy Economics, 2019 (83): 217-228.
[28]	DONG F, WANG Y, SU B, et al. The process of peak CO₂ emissions in developed economies: A perspective of industrialization and urbanization. Resources, Conservation and Recycling, 2019 (141): 61-75.
[29]	YU Q, LI M, LI Q, et al. Economic agglomeration and emissions reduction: Does high agglomeration in China's urban clusters lead to higher carbon intensity? Urban Climate, 2022 (43): 101174.
[30]	WANG B, SUN Y, WANG Z. Agglomeration effect of CO₂emissions and emissions reduction effect of technology: A spatial econometric perspective based on China's province-level data. Journal of Cleaner Production, 2018 (204): 96-106.
[31]	MI K, ZHUANG R. Producer services agglomeration and carbon emission reduction:An empirical test based on panel data from China. Sustainability, 2022,14(6): 3618.
[32]	WANG J, DONG X, DONG K. How does ICT agglomeration affect carbon emissions? The case of Yangtze River Delta urban agglomeration in China. Energy Economics, 2022, 111: 106107.
[33]	KRUGMAN P. Increasing returns and economic geography. Journal of Political Economy, 1991,99(3): 483-499.
[34]	BALDWIN R. Agglomeration and endogenous capital. European Economic Review, 1999,43(2): 253-280.
[35]	MARTIN P, OTTAVIANO G. Growing locations: Industry location in a model of endogenous growth. European Economic Review, 1999,43(2): 281-302.
[36]	钟粤俊,陆铭,奚锡灿.集聚与服务业发展——基于人口空间分布的视角.管理世界, 2020(11):35-49.
	ZHONG Y, LU M, XI X. Agglomeration and service industry development: Based on the perspective of population spatial distribution. Journal of Management World, 2020(11):35-49.
[37]	王硕,殷凤.集聚效应对服务业FDI区位选择的影响:基于产业维度的再分解与测度.世界经济研究, 2021(12):103-115.
	WANG S, YIN F. The influence of agglomeration effect on fdi location choice in services: Re-decomposition and measurement based on industrial dimension. World Economy Studies, 2021(12):103-115.
[38]	李振,沈言言.经济集聚与中国私人部门PPP投资的区位选择.经济经纬, 2022(2):24-34.
	LI Z, SHEN Y. Economic agglomeration and location choice of China's private sector PPP investment. Economic Survey, 2022(2):24-34.
[39]	田成诗,陈雨.人口虹吸、集聚与城市能源效率——以沪苏浙皖地区为例.统计研究, 2022(5):93-106.
	TIAN C, CHEN Y. Population siphoning, agglomeration and urban energy efficiency: Take the Shanghai-Jiangsu-Zhejiang-Anhui as an example. Statistical Research, 2022(5):93-106.
[40]	APERGIS N, ELEFTHERIOU S, PAYNE J. The relationship between international financial reporting standards, carbon emissions, and R&D expenditures: Evidence from European manufacturing firms. Ecological Economics, 2013 (88): 57-66.
[41]	KWAKWA P. The effect of industrialization, militarization, and government expenditure on carbon dioxide emissions in Ghana. Environmental Science and Pollution Research, 2022 (29): 1-14.
[42]	WU L, SUN L, QI P, et al. Energy endowment, industrial structure upgrading, and CO2 emissions in China: Revisiting resource curse in the context of carbon emissions. Resources Policy, 2021 (74): 102329.
[43]	HE A, XUE Q, ZHAO R, et al. Renewable energy technological innovation, market forces, and carbon emission efficiency. Science of the Total Environment, 2021 (796): 148908.
[44]	林伯强,谭睿鹏.中国经济集聚与绿色经济效率.经济研究, 2019(2):119-132.
	LIN B, TAN R. Economic agglomeration and green economy efficiency in China. Economic Research Journal, 2019(2):119-132.
[45]	ANGRIST J, KRUEGER A. Does compulsory school attendance affect schooling and earnings? The Quarterly Journal of Economics, 1991,106(4):979-1014.
[46]	张建武,高聪,赵菁.中国人口、经济、产业重心空间分布演变轨迹——基于1978~2019年省级数据的分析.中国人口科学, 2021(1):64-78.
	ZHANG J, GAO C, ZHAO J. Research on the changing spatial gravity of China's population, economy, and indutry center: Based on the provincial data from 1978 to 2019. Chinese Journal of Population Science, 2021(1):64-78.