环境约束下中国工业化与城镇化的关系演变——效率分析视角

doi:10.13249/j.cnki.sgs.2017.01.011

摘要/Abstract

摘要：

将工业化视为投入,城镇化视为产出,基于中国各省份2001~2012年的工业化、城镇化和污染物排放数据,测算了传统效率值、环境效率值及效率指数,据此分析各省份工业化与城镇化的关系及其演变。研究发现：全国总体层面的城镇化传统效率值和环境效率值均在下降。分地区来看,东部地区通过工业化来推进城镇化的实施空间有限,中部地区需要调整发展方式,西部地区尚有一定余地。在城镇化过程中,工业化存在前期“负向规律机制”和后期“正向反馈机制”两种作用。为实现城镇化低碳发展,需要转变工业和经济发展方式,改善对外贸易结构,同时提高环境污染治理投资效果。

关键词: 工业化, 城镇化, 效率分析, 环境约束

Abstract:

Industrialization is the main driving force of China's urbanization. With the development of China's economy, its role needs to be reexamined. In this article, we investigate the dynamic relationship between industrialization and urbanization at provincial level in China with DEA framework. Using data on Chinese provincial industrialization, urbanization and pollution in 2001-2012, based on the index models of Malmquist and Malquist-Luenberger, we calculate conventional and environmental efficiency valuesof industrialization and relevant efficiency change indexes. Further, we analysis the effects of major influencing factorson these indexes, including economic growth, industrial development, economic openness and environmental protection. We find that: firstly, from the whole point of view, conventional and environmental urbanization efficiency is decreasing with the development of economy and the deepening of industrialization. So, under the current situation, it is not advisable to promote urbanization by encouraging industrialization on a national level. Secondly, the urbanization policy depends on regional circumstances. Eastern region has limited capacity to promote urbanization by industrialization. It should seek driving forces for urbanization other than manufacturing industry, such as service industry. Central region should adjust their developing strategy. It should alter the extensive mode of development to the intensive, and reduce resource consumption in industrial production. Western region still has space to accommodate urbanization growth with the help of industrialization. It is still feasible to elevate urbanization level through the development of industrialization. Thirdly, the analysis of tobit panel data model indicates that there exist two mechanisms in the urbanization process. The first one may be called “negative effect mechanism” and the second one may be called “positive feedback mechanism”. In the earlier stage of industrialization, the negative effect mechanism plays the leading role and makes the urbanization efficiency decrease. In the later stage of industrialization, the positive feedback mechanism takes part in and makes the urbanization efficiency increase through stimulating the development of service industry. Since the function of negative effect mechanism is inevitable, it needs to play role of externalities produced by industrialization to maximize the urbanization outputlevel. Fourthly, to realize the low-carbon developmentof urbanization, provincial governmentshould shift the development way of industrialization and economy, change the composition of foreign trade, and improve the performance of investment on pollution regulation.

Key words: industrialization, urbanization, efficiency analysis, environmental constraints

中图分类号:

F291.1

王庆喜, 钱遂, 庞尧. 环境约束下中国工业化与城镇化的关系演变——效率分析视角[J]. 地理科学, 2017, 37(1): 92-101.

Qingxi Wang, Sui Qian, Yao Pang. Evolutional Relationship Between Industrialization and Urbanization Under Environmental Constraints in China: DEA Analysis Perspective[J]. SCIENTIA GEOGRAPHICA SINICA, 2017, 37(1): 92-101.

图/表 4

图1

表1

传统效率值和环境效率值的Moran’s I指数"

年份	2001	2002	2003	2004	2005	2006	2007	2008	2009	2010	2011	2012
MI1	0.229	0.257	0.279	0.234	0.282	0.288	0.265	0.232	0.229	0.197	0.186	0.167
$p$ 值	0.016	0.009	0.005	0.014	0.005	0.004	0.007	0.015	0.016	0.03	0.037	0.051
MI2	0.38	0.367	0.308	0.265	0.259	0.272	0.226	0.158	0.161	-0.071	-0.084	-0.113
$p$ 值	0.000	0.001	0.003	0.007	0.009	0.007	0.017	0.061	0.058	0.616	0.655	0.738

表1

表2

Malmquist和Malmquist-Luenberger指数及其分解"

Malmquist指数								Malmquist-Luenberger指数
省份	TFP	EFF	TECH	省份	TFP	EFF	TECH	省份	TFP	EFF	TECH	省份	TFP	EFF	TECH
北京	1.065	1.000	1.065	湖南	0.891	0.937	0.951	北京	1.004	1.000	1.004	湖南	0.937	0.983	0.954
天津	1.075	1.019	1.055	广东	0.995	0.987	1.008	天津	0.984	0.995	0.989	广东	1.002	1.001	1.002
河北	0.945	0.978	0.966	广西	0.866	0.961	0.901	河北	0.967	0.997	0.970	广西	0.965	1.000	0.965
山西	0.965	0.985	0.979	海南	0.936	1.000	0.936	山西	0.996	0.993	1.003	海南	0.995	1.000	0.995
内蒙	0.961	0.990	0.971	重庆	0.945	0.961	0.983	内蒙	0.986	1.000	0.986	重庆	1.001	1.006	0.996
辽宁	1.021	0.999	1.022	四川	0.905	0.929	0.974	辽宁	0.994	0.989	1.005	四川	0.949	1.000	0.949
吉林	0.995	0.988	1.007	贵州	0.887	0.989	0.897	吉林	0.969	0.989	0.980	贵州	0.997	1.000	0.997
黑龙	0.965	1.035	0.932	云南	0.950	0.993	0.957	黑龙	0.960	0.993	0.968	云南	1.010	1.004	1.006
上海	1.083	1.015	1.067	陕西	0.973	0.993	0.980	上海	1.008	1.007	1.000	陕西	0.996	0.994	1.002
江苏	1.006	0.976	1.032	甘肃	0.969	1.027	0.943	江苏	1.014	0.991	1.023	甘肃	0.986	0.988	0.998
浙江	1.019	0.999	1.021	青海	0.922	0.956	0.964	浙江	1.018	0.994	1.024	青海	0.970	1.001	0.968
安徽	0.890	0.936	0.951	宁夏	0.947	0.975	0.972	安徽	0.992	0.986	1.006	宁夏	1.000	1.047	0.955
福建	0.981	0.973	1.009	新疆	0.997	1.000	0.997	福建	0.986	0.982	1.004	新疆	0.981	0.994	0.986
江西	0.878	0.954	0.921	平均	0.965	0.983	0.981	江西	0.936	0.979	0.956	平均	0.986	0.997	0.990
山东	0.999	0.992	1.008	东部	0.999	0.991	1.007	山东	1.000	1.000	1.000	东部	0.995	0.996	0.998
河南	0.941	0.976	0.963	中部	0.940	0.976	0.964	河南	0.996	1.000	0.996	中部	0.973	0.989	0.983
湖北	0.979	0.981	0.999	西部	0.944	0.980	0.963	湖北	0.984	0.982	1.002	西部	0.988	1.004	0.984

表2

表3

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解释变量	被解释变量：传统效率值					被解释变量：环境效率值
解释变量	系数		标准误差	p值	系数	标准误差	p值
Lnpcgdp	-0.4010^**	0.1983	0.0430		0.6156^***	0.1634	0.0000
Lnpcgdp2	0.0113	0.0099	0.2550		-0.0343^***	0.0082	0.0000
Industrializaton	3.9890^***	0.6329	0.0000		1.5419^***	0.4804	0.0010
Thirdindustry	0.1689^*	0.0931	0.0700		0.2610^***	0.0807	0.0010
Indagglomeration	-2.0746^***	0.1909	0.0000		-0.6991^***	0.1445	0.0000
Indprofit	-0.0615	0.1183	0.6030		0.1549^*	0.0851	0.0690
Foreigntrade	-0.0144^***	0.0451	0.0010		-0.0890^***	0.0327	0.0060
Foreigninvestment	0.0005	0.0029	0.8720		0.0051^*	0.0028	0.0730
Coalconsumption	0.0013	0.0270	0.9630		-0.0361^**	0.0174	0.0380
Pollutioncontrol	0.0279^***	0.0093	0.0030		0.0329^***	0.0069	0.0000
Easternregion	0.0646	0.1007	0.5210		-0.0644	0.0453	0.1550
Centralregion	-0.0547	0.0955	0.5670		0.0176	0.0364	0.6280
_Constant	3.5161	0.9759	0.0000		-2.1115	0.8416	0.0120
test of sigma_u=0	chibar2(01)= 289.33; Prob≥chibar2 = 0.000			chibar2(01)= 141.30; Prob≥chibar2 = 0.000