再生型资源型城市产业的结构性增长研究——以唐山市为例

doi:10.11821/dlyj201704009

摘要/Abstract

摘要：

资源型城市产业转型是学术界普遍关注的一个热点问题。基于2002年和2012年唐山市投入产出数据,采用增长态归因矩阵模型和Probit模型,研究了唐山市分行业总体的结构性增长和相关影响因素。结果表明：2002-2012年唐山市煤炭采选业和金属冶炼及压延业总体上都没有呈现结构性增长,基本摆脱了对煤炭和钢铁的高度依赖;具有一定技术含量的制造业如专用设备制造业、仪器仪表及文化办公用机械制造业、信息传输软件和信息技术服务业总体呈现结构性增长,呈现出产业转型的趋势;产业总体呈现结构性增长的影响因素中,影响力系数呈现负向影响,感应度系数、产业增加值占GDP比重和产业直接增加值率则呈现正向影响。因此,资源型城市产业转型需要提高直接增加值率,更加关注提升与下游产业的关联效应。

关键词: 资源型城市, 结构性增长, 产业, 影响因素, 唐山市

Abstract:

Industrial transformation is a hot topic for resource-based cities. Based on the input-output table of Tangshan in 2002 and 2012, the whole structural increase of industries in Tangshan and their impact factors are analyzed by the increasing causative matrix and probit model. The results show that, there was not a structural increase in the sector of coal mining and processing and the sector of metal smelting and pressing from 2002 to 2012. Tangshan has been out of the development mode with the high dependency on coal and steel. Some sectors have experienced a structural increase characterized by the high technology, such as special equipment, instrumentation and cultural office machinery manufacturing, as well as information transmission software and information technology services. All of the indications illustrate that Tangshan has shown a trend of industrial transformation. As for the impact factors of industrial structural increase, it is found that the industrial influence coefficient has a negative effect, while the industrial sensitivity coefficient, GDP proportion of industrial value added, and industrial direct value added ratio have positive effects. Finally, it is helpful to improve the ratio of direct value added and linkages with downstream industries for the industrial transformation of the resource-based cities.

Key words: resource-based cities, structural increase, industry, influential factor, Tangshan city

李江苏, 唐志鹏. 再生型资源型城市产业的结构性增长研究——以唐山市为例[J]. 地理研究, 2017, 36(4): 707-718.

Jiangsu LI, Zhipeng TANG. An analysis of industrial structure increase of regenerative resource-based cities: A case of Tangshan city[J]. GEOGRAPHICAL RESEARCH, 2017, 36(4): 707-718.

图/表 6

图1

表1

表2

2002-2012年唐山市产业总体的结构性增长计算结果"

部门编号	$∑ k d ik$	是否总体结构性增长	引致i部门结构性增长最大的k部门
1	-0.1572	否	纺织业
2	-4.1114	否	电力热力的生产和供应业
3	7.3970	是	燃气的生产和供应业
4	2.6600	是	金属矿采选业
5	-0.4122	否	非金属矿及其他矿采选业
6	-0.5144	否	住宿餐饮业
7	-0.6098	否	批发零售业
8	0.2282	是	纺织服装鞋帽皮革羽绒及其制品业
9	-0.5538	否	其他制造业
10	-0.0981	否	化学工业
11	-0.3184	否	石油加工炼焦及核燃料加工业
12	-1.1163	否	通信设备计算机及其他电子设备制造业
13	-0.3850	否	木材加工及家具制造业
14	-1.9092	否	电力热力的生产和供应业
15	-0.4869	否	金属制品业
16	-0.3284	否	仪器仪表及文化办公用机械制造业
17	0.2463	是	专用设备制造业
18	-1.1855	否	其他设备制造业
19	-0.9099	否	仪器仪表及文化办公用机械制造业
20	-0.4104	否	信息传输软件和信息技术服务业
21	0.0578	是	仪器仪表及文化办公用机械制造业
22	0.2953	是	燃气的生产和供应业
23	-2.1595	否	造纸印刷及文教体育用品制造业
24	0.7343	是	燃气的生产和供应业
25	-0.3163	否	水的生产和供应业
26	-0.3802	否	建筑业
27	3.1121	是	批发零售业
28	1.1444	是	燃气的生产和供应业
29	-0.4338	否	金融保险业
30	1.2607	是	信息传输软件和信息技术服务业
31	-0.7560	否	水的生产和供应业
32	1.4380	是	房地产业
33	-1.0216	否	纺织服装鞋帽皮革羽绒及其制品业

表2

表3

表4

表5

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部门编号	影响力系数变化值	感应度系数变化值	产业增加值占GDP比例变化值	产业直接增加值率变化值
1	-0.1095	-0.0950	-0.0583	0.0818
2	0.4253	0.8638	-0.0154	-0.3486
3	0.1103	0.3374	0.0030	-0.1455
4	-0.3911	1.6190	0.1326	0.2629
5	0.2638	0.1811	-0.0209	-0.2789
6	0.0798	0.0151	-0.0128	-0.1105
7	-0.1092	-0.4972	-0.0037	-0.0185
8	-0.2068	-0.0481	-0.0084	0.0587
9	0.1007	-0.2432	-0.0148	-0.1360
10	0.1301	-0.1038	-0.0153	0.0174
11	0.1115	0.2099	0.0222	0.0217
12	0.1378	-0.7708	-0.0276	-0.0535
13	0.1476	-0.1601	-0.0343	-0.0686
14	0.0063	0.5891	0.0624	-0.0074
15	-0.0493	-0.5181	0.0342	0.0393
16	-0.0731	-0.0446	0.0049	0.0211
17	-0.0967	0.0692	-0.0048	0.0504
18	0.0435	-0.4995	-0.0136	-0.0470
19	0.4270	-0.1267	-0.0001	-0.3965
20	0.2076	-0.2526	-0.0051	-0.1498
21	-0.1863	-0.2385	-0.0018	0.1513
22	0.6024	0.1246	-0.0513	-0.5753
23	0.3342	0.0041	-0.0030	-0.1623
24	-0.2647	-0.3858	0.0001	0.0385
25	-0.0943	-0.2176	0.0008	0.0555
26	0.0208	-0.2805	0.0049	0.0083
27	-0.6876	0.2637	0.0277	0.4811
28	0.0972	0.9812	0.1195	-0.1019
29	0.1703	-0.0856	-0.1115	-0.2001
30	-0.4689	-0.1055	-0.0038	0.3598
31	0.0619	-0.0483	-0.0096	-0.1444
32	-0.5547	-0.0230	0.0085	0.4771
33	-0.1861	-0.5136	-0.0046	0.1398

	x₁	x₂	x₃	x₄
x₁	1.00	-0.014	-0.334	-0.958^***
x₂	-0.014	1.00	0.608^***	-0.002
x₃	-0.334	0.608^***	1.00	0.346^**
x₄	-0.958^***	-0.002	0.346^**	1.00

	变量	估计参数	常数项	对数似然值	LR统计量	R²	样本数
模型1分别单独估计各影响因素	x₁	-2.368^**	-0.466^*	-17.265	7.479	0.178	33
	x₂	1.108^*	-0.459^*	-18.813	4.384	0.104	33
	x₃	11.537^*	-0.454^*	-19.094	3.823	0.091	33
	x₄	2.191^**	-0.406^*	-18.779	4.453	0.106	33
模型2同时估计影响因素	x₁	-2.854^***	-0.458^*	-14.406	13.199	0.314	33
模型2同时估计影响因素	x₂	1.619^**	-0.458^*	-14.406	13.199	0.314	33
模型3同时估计影响因素	x₂	1.379^**	-0.404	-16.221	9.568	0.227	33
模型3同时估计影响因素	x₄	2.508^**	-0.404	-16.221	9.568	0.227	33