Measurement and influencing factors of urban traffic ecological resilience in developing countries: A case study of 31 Chinese cities

doi:10.1016/j.regsus.2021.10.001

Abstract

Abstract:

An urban traffic ecosystem is a spatial structure composed of air, population, vehicles, roads, green spaces, and regions. Traffic ecological resilience is a critical issue in high-quality urban development. From the perspective of system optimization, it is important to study the level of urban traffic ecological resilience and analyze its influencing factors. In this study, we evaluated traffic ecological resilience, characterized its spatio-temporal differentiation, and explored its influencing factors by constructing a system of urban traffic ecological resilience and by analyzing the environmental protection and urban construction data in 31 Chinese cities during 2011-2018. By conducting Kernel density analysis, standard deviation ellipse, comprehensive weight determination, panel data regression analysis, and χ² test, we found that traffic ecological resilience was low on the whole and exhibited the temporal trend of “decreasing first and then increasing” and the spatial characteristic of “high in the east, second in the middle, and low in the west”. The cities with high traffic ecological resilience density values were located in Southeast China and tended to move from northwest to southeast. Governance capability, market activity, technological innovation capability, opening degree, and financial resources had significant effects on urban traffic ecological resilience. Finally, we gave some suggestions for improving the urban traffic ecological resilience in Chinese cities as well as other developing countries in the world.

Key words: Urban traffic ecological resilience, Resistant capacity, Absorptive capacity, Restorative capacity, Chinese cities

HU Xiwu, SU Yunqing, REN Kefeng, SONG Fang, XUE Ruixiang. Measurement and influencing factors of urban traffic ecological resilience in developing countries: A case study of 31 Chinese cities[J]. Regional Sustainability, 2021, 2(3): 211-223.

Figures/Tables 11

Fig. 1.

Table 1

Table 2

Evaluation index system of traffic ecological resilience."

Target layer	Criterion layer	Index layer	Index meaning	Comprehensive weight	Type	Index direction
Traffic ecological resilience	Resistant capacity (A)	Population density in the main urban area (A1)	Reflecting the population’s traffic pressure	0.076	Population/Society	-
		Density of civil vehicles in the main urban area (A2)	Reflecting the traffic pressure of cars	0.072	Vehicle/Economy	-
		Passenger traffic (A3)	Reflecting the traffic shock of people flow	0.097	Vehicle/Economy	-
		Freight volume (A4)	Reflecting the traffic shock of logistics	0.038	Vehicle/Economy
		Average traffic volume (A5)	Reflecting the traffic shock of traffic flow	0.079	Vehicle/Economy	-
		Motor vehicle exhaust and particulate matter emissions (A6)	Reflecting the ecological destructive power of motor vehicle emissions	0.046	Air/Ecology	-
		Industrial waste gas and dust emissions (A7)	Reflecting the ecological destructive power of industrial production emissions	0.027	Air/Ecology	-
		Domestic waste gas and dust emissions (A8)	Reflecting the ecological destructive power of residents’ life emissions	0.018	Air/Ecology	-
		Comprehensive air quality index (A9)	Reflecting the degree of air pollution	0.051	Air/Ecology	-
		Road traffic equivalent sound level (A10)	Reflecting the degree of noise pollution	0.011	Environment/Ecology	-
	Absorptive capacity (B)	Per capita road area (B1)	Reflecting the resource allocation power of the road network	0.026	Road/Engineering	+
		Road network density in built-up area (B2)	Reflecting the traffic supply capacity of the road network	0.037	Road/Engineering	+
		Per capita rail transit mileage (B3)	Reflecting the evacuation force of rapid traffic passenger flow	0.031	Road/Engineering	+
		Number of bridges per kilometer of municipal roads (B4)	Reflecting the bridge’s traffic support	0.028	Bridge/Engineering	+
		Per capita sidewalk area (B5)	Reflecting the capacity of slow traffic	0.017	Road/Engineering	+
		Green coverage rate in built-up area (B6)	Reflecting ecological self-purification	0.009	Green space/Ecology	+
		Proportion of days with good air (B7)	Reflecting the ecological tolerance of air	0.017	Air/Ecology	+
		Average annual rainfall (B8)	Reflecting the ecological protection of rainfall	0.025	Climate/Ecology	+
		Average daily wind speed (B9)	Reflecting the air purification power of wind speed	0.012	Climate/Ecology	+
		Proportion of road cleaning and cleaning area (B10)	Reflecting managed service response	0.010	Road/Society	+
	Restorative capacity (C)	Air transport passenger flow growth (C1)	Reflecting the potential conveying capacity of air transportation	0.100	Vehicle/Economy	+
		Increase in passenger traffic by train (C2)	Reflecting the potential transmission capacity of train transportation	0.039	Vehicle/Economy	+
		Increase in bus operating mileage (C3)	Reflecting the potential transport capacity of public transportation	0.017	Vehicle/Economy	+
		Increase in the number of private cars (C4)	Reflecting the potential delivery power of personalized traffic	0.014	Vehicle/Economy	+
		Urban infrastructure investment growth rate (C5)	Reflecting the potential support of supporting facilities	0.023	Supporting/Economy	+
		Increase in investment of waste gas treatment (C6)	Reflecting the potential improvement of air governance	0.020	Air/Engineering	+
		Increase in investment of landscaping (C7)	Reflecting the potential lifting power of air improvement	0.014	Green space /Engineering	+
		Increase in added value of the tertiary industry (C8)	Reflecting the potential lifting power of industrial structure optimization	0.022	Ecology/Economy	+
		Increase in fiscal revenue (C9)	Reflecting the potential improvement of government governance	0.012	Management /Economy	+
		Increase in the number of social organization units (C10)	Reflecting the potential promotion power of social participation	0.012	Management/Society	+

Table 2

Table 3

Urban traffic ecological resilience in 31 Chinese cities from 2011 to 2018."

City	Urban traffic ecological resilience
City	2011	2012	2013	2014	2015	2016	2017	2018	Mean value	Changing rate
Beijing	0.353	0.340	0.376	0.386	0.405	0.449	0.458	0.487	0.407	0.380
Tianjin	0.575	0.582	0.286	0.276	0.263	0.318	0.295	0.256	0.356	-0.555
Shijiazhuang	0.514	0.410	0.194	0.213	0.231	0.263	0.254	0.264	0.293	-0.486
Taiyuan	0.523	0.483	0.241	0.230	0.244	0.327	0.291	0.333	0.334	-0.363
Hohhot	0.531	0.517	0.264	0.281	0.269	0.304	0.258	0.284	0.339	-0.465
Shenyang	0.499	0.477	0.252	0.236	0.265	0.301	0.288	0.323	0.330	-0.353
Changchun	0.567	0.514	0.285	0.276	0.272	0.358	0.335	0.351	0.370	-0.381
Harbin	0.493	0.474	0.238	0.244	0.236	0.293	0.256	0.308	0.318	-0.375
Shanghai	0.489	0.438	0.250	0.270	0.266	0.314	0.297	0.297	0.328	-0.393
Nanjing	0.605	0.572	0.338	0.321	0.353	0.375	0.376	0.371	0.414	-0.387
Hangzhou	0.529	0.517	0.300	0.305	0.308	0.379	0.361	0.369	0.384	-0.302
Hefei	0.644	0.573	0.308	0.309	0.331	0.360	0.351	0.379	0.407	-0.411
Fuzhou	0.579	0.547	0.299	0.309	0.313	0.362	0.359	0.350	0.390	-0.396
Nanchang	0.545	0.549	0.296	0.304	0.313	0.357	0.364	0.353	0.385	-0.352
Jinan	0.545	0.502	0.287	0.288	0.264	0.330	0.316	0.322	0.357	-0.409
Zhengzhou	0.492	0.442	0.250	0.270	0.228	0.292	0.302	0.319	0.324	-0.352
Wuhan	0.563	0.544	0.318	0.296	0.310	0.362	0.338	0.335	0.383	-0.405
Changsha	0.505	0.479	0.271	0.271	0.279	0.347	0.344	0.313	0.351	-0.380
Guangzhou	0.583	0.574	0.297	0.323	0.304	0.374	0.373	0.342	0.396	-0.413
Nanning	0.562	0.518	0.314	0.336	0.295	0.361	0.362	0.324	0.384	-0.423
Haikou	0.623	0.600	0.336	0.353	0.338	0.401	0.367	0.373	0.424	-0.401
Chongqing	0.469	0.449	0.270	0.274	0.257	0.318	0.305	0.306	0.331	-0.348
Chengdu	0.476	0.442	0.259	0.250	0.266	0.322	0.337	0.332	0.336	-0.303
Guiyang	0.492	0.469	0.331	0.289	0.292	0.294	0.316	0.350	0.354	-0.289
Kunming	0.575	0.525	0.299	0.293	0.307	0.370	0.350	0.308	0.378	-0.464
Lhasa	0.605	0.622	0.353	0.350	0.337	0.348	0.373	0.350	0.417	-0.421
Xi’an	0.423	0.365	0.246	0.263	0.248	0.296	0.298	0.284	0.303	-0.329
Lanzhou	0.580	0.497	0.274	0.277	0.260	0.305	0.303	0.309	0.351	-0.467
Xining	0.497	0.418	0.245	0.208	0.225	0.269	0.283	0.247	0.299	-0.503
Yinchuan	0.594	0.508	0.272	0.263	0.269	0.332	0.320	0.297	0.357	-0.500
Urumqi	0.580	0.523	0.261	0.259	0.240	0.263	0.282	0.313	0.340	-0.460
Mean	0.536	0.499	0.284	0.285	0.283	0.334	0.326	0.327	0.359	-0.390

Table 3

Table 4

Table 5

Table 6

Table 7

Table 8

Table 9

Table 10

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Variable	Variable meaning	Variable measurement	Variable type	Data source
TERES_it	Traffic ecological resilience level	Urban traffic ecological resilience value of city i in year t	Dependent	Calculation
Financ_it	Governance capability	Per capita fiscal revenue of city i in year t (10,000 CNY)	Independent	China Statistical Yearbook (National Bureau of Statistics of China, 2011-2019)
Consum_it	Market activity	Total retail sales per capita of city i in year t (10,000 CNY)	Independent	China Statistical Yearbook (National Bureau of Statistics of China, 2011-2019)
Patent_it	Technological innovation capability	Number of patents granted of city i in year t (piece)	Independent	China Statistical Yearbook (National Bureau of Statistics of China, 2011-2019)
Ftrade_it	Opening degree	Per capita total import and export trade of city i in year t (USD)	Independent	China Statistical Yearbook (National Bureau of Statistics of China, 2011-2019)
Deposi_it	Financial resource	Proportion of deposits of financial institutions to GDP in city i in year t (%)	Independent	China Statistical Yearbook (National Bureau of Statistics of China, 2011-2019)

Standard deviation ellipse parameter	2011	2012	2013	2014	2015	2016	2017	2018
Rotation angle θ (°)	77.045	75.959	73.880	73.530	72.906	70.513	73.542	73.394
Standard deviation along the Y-axis (km)	12.759	12.839	12.631	12.550	12.487	12.352	12.422	12.589
Standard deviation along the X-axis (km)	9.232	9.241	9.059	9.117	9.001	8.972	8.999	9.066

Center coordinates	2011	2012	2013	2014	2015	2016	2017	2018
Longitude (°E)	111.792	111.844	111.836	111.917	112.046	112.207	111.964	112.153
Latitude (°N)	33.166	33.065	32.965	32.902	32.950	32.993	32.913	33.182

Variable	LLC test	IPS test	HT test	Fisher test	LM test	Stable state
TERES	-10.073^***	-6.159^***	-3.451^***	222.811^***	10.781^***	Stationary
ln(Financ)	-6.280^***	1.881	-6.908^***	134.154^***	6.659^***	Stationary
Consum	-6.102^***	-5.920^***	-12.749^***	228.902^***	3.870^***	Stationary
ln(Patent)	-2.857^**	-1.339^**	-4.708^***	164.173^***	9.056^***	Stationary
ln(Ftrade)	8.133^***	-3.151^*	-7.165^***	139.855^***	8.133^***	Stationary
ln(Deposi)	-5.237^***	1.242	-4.234^***	143.035^***	9.778^***	Stationary

Variable	Coefficient	Standard error	t value	P value	Upper limit of 95% confidence interval	Lower limit of 95% confidence interval
ln(Financ)	-0.1279	0.0445	-2.8800	0.004	-0.0403	-0.2155
Consum	0.0010	0.0005	2.2200	0.027	0.0019	0.0001
ln(Patent)	-0.0279	0.0162	-1.7200	0.087	0.0041	-0.0599
ln(Ftrade)	-0.1151	0.0521	-2.2100	0.028	-0.0124	-0.2178
ln(Deposi)	-0.1206	0.0476	-2.5300	0.012	-0.0267	-0.2145
Constant	2.6915	0.2508	10.7300	0.000	3.1858	2.1972