How does environmental concern influence public acceptability of congestion charging? Evidence from Beijing

Figures & data

Table 1. Attributes and levels of the policy alternatives

	Attributes
Policy Alternatives	Toll area	Toll period	Charging level (CNY)	Revenue use
1	Within the 3^rd ring	Rush hours on weekdays	10	Unspecified use
2	Within the 4^th ring	Daytime of weekdays	20	Unspecified use
3	Within the 3^rd ring	Daytime of weekdays	40	Unspecified use
4	Within the 3^rd ring	Daytime of weekdays	30	To improve the public transit
5	Within the 4^th ring	Rush hours on weekdays	30	Unspecified use
6	Within the 4^th ring	Daytime of weekdays	10	To improve the public transit
7	Within the 4^th ring	Rush hours on weekdays	40	To improve the public transit
8	Within the 3^rd ring	Rush hours on weekdays	20	To improve the public transit

Table 2. Socio-economic characteristics of respondents under each policy alternative

		Mean (S.D.)
Policy Alternatives	Frequency	Gender (1 = male, 0 = female)	Age (AGE)	Income (Monthly household income, 1000CNY)	Education (School years)
1	112	0.64 (0.48)	38.97 (11.90)	15.52 (10.33)	16.1 (2.5)
2	117	0.56 (0.50)	38.84 (13.27)	13.56 (9.59)	15.6 (2.9)
3	111	0.56 (0.50)	37.45 (11.84)	14.25 (9.25)	15.8 (2.7)
4	102	0.56 (0.50)	37.90 (11.62)	15.19 (10.00)	15.8 (2.4)
5	117	0.62 (0.49)	37.43 (11.36)	13.60 (8.74)	16.0 (2.4)
6	93	0.54 (0.50)	38.16 (11.96)	13.96 (10.00)	15.9 (2.6)
7	102	0.64 (0.48)	39.13 (13.16)	12.63 (8.95)	15.4 (2.6)
8	106	0.58 (0.50)	36.72 (10.96)	13.03 (8.48)	16.0 (2.5)
Total	860	0.59 (0.49)	38.06 (12.01)	13.97 (9.44)	15.8 (2.6)
One-way ANOVA: F value (Sig.)		0.702 (0.671)	0.553 (0.794)	1.187 (0.307)	0.849 (0.547)
Beijing census		0.52^a	35.70^b	15.67^c	-

^aPopulation census of Beijing in 2010 (State Statistics Bureau, 2011)；^b median age of inhabitants in Beijing (Beijing Municipal Bureau of Statistics, 2013)； ^c calculated based on the average wage of workers in urban units in Beijing (Beijing Municipal Bureau of Statistics, 2013).

Table 3. Respondents’ expected effects of congestion charge, general and traffic-related environmental concerns in cities in related studies

		Expected policy effects (Mean, 1–7 graded)		General environmental concerns (“interest in environment” or “Considerably more resources should be used to protect the environment”)		Traffic-related environmental concerns (“Motor traffic is among the largest threats to the environment”)
Studies	Location	Congestion reduction	Environment improvement	Mean (1–5 graded)	Agreed rate (%)	Mean (1–5 graded)	Agreed rate (%)
This study	Beijing	3.8	3.7	4.2 ^b	81% ^b	3.3 ^c	48% ^c
Schuitema, Steg, and Rothengatter (2010)	The Netherlands	2.8	2.5
Zheng et al. (2014)	Brisbane, Melbourne	5.0 ^a	4.9 ^a
Börjesson, Eliasson, and Hamilton (2016)	Gothenburg			3.8 ^a		3.2 ^a
Eliasson and Jonsson (2011)	Stockholm			3.8 ^a
Hamilton et al. (2014)	Stockholm				70%		55%
	Helsinki				70%		49%
	Lyon				88%		66%

^acalculated based on the given data of the related studies; ^bcalculated based on the survey data of “The air pollution in Beijing is serious” and “Global warming is an important issue;” ^ccalculated based on the survey data of “Automobile exhaust is the main cause of air pollution in Beijing” and “Automobile exhaust is an important cause of global warming.”

Table 4. Correlations among environmental concerns ^a.

Environmental concerns	(1)	(2)	(3)	(4)	(5)	(6)	(7)
(1) The air pollution in Beijing is serious	1.00
(2) Global warming is an important issue	0.116**	1.00
(3) Regional industrial pollution is the main cause of air pollution in Beijing	0.234**	0.120**	1.00
(4) Industrial emissions are an important cause of global warming	0.168**	0.337**	0.313**	1.00
(5) Automobile exhaust is the main cause of air pollution in Beijing	0.141**	0.137**	−0.008	0.042	1.00
(6) Automobile exhaust is an important cause of global warming	0.082**	0.224**	0.063*	0.180**	0.455**	1.00
(7) Residents can reduce their environmental impact by taking public transit	0.088**	0.212**	0.032	0.156**	0.303**	0.267**	1.00

^aKendall’s correlations; **P < 0.01, *P < 0.05

Table 5. Bivariate correlations between environmental concerns and selected demographic variables ^a.

	Demographic variables
Environmental concern items	age	gender	education	income
(1) The air pollution in Beijing is serious	0.043	−0.087*	0.086*	0.071*
(2) Global warming is an important issue	−0.012	−0.123**	−0.097**	−0.072*
(3) Regional industrial pollution is the main cause of air pollution in Beijing	0.017	−0.072*	0.088**	0.103**
(4) Industrial emissions are an important cause of global warming	0.019	−0.066	0.076*	0.052
(5) Automobile exhaust is the main cause of air pollution in Beijing	0.103**	−0.106**	−0.153**	−0.143**
(6) Automobile exhaust is an important cause of global warming	−0.007	−0.159**	−0.122**	−0.181**
(7) Residents can reduce their environmental impact by taking public transit	0.112**	−0.049	−0.125**	−0.110**

^aSpearman’s correlations; **P < 0.01, *P < 0.05

Table 6. Rotated factor loadings of environmental concern variables

	Factor ^a
Environmental concerns	Industrial Pollution	Traffic Pollution
The air pollution in Beijing is serious	0.594
Global warming is an important issue	0.538
Regional industrial pollution is the main cause of air pollution in Beijing	0.761
Industrial emissions are an important cause of global warming	0.725
Automobile exhaust is the main cause of air pollution in Beijing		0.823
Automobile exhaust is an important cause of global warming		0.790
Residents can reduce their environmental impact by taking public transit		0.646

^aUsing the four square maximum orthogonal rotation method.

Table 7. Description of variables

Variable name	Description	Value	Mean (S.D.)
Attitude	To what extent the respondent would support the congestion charge policy alternative	3-graded scale: 1 for not support, 3 for support ^a	1.89 (0.873)
PersonLf	To what extent the respondent expects the policy impact on their personal life	5-graded scale: 1 for very unfavorable, 5 for very favorable	2.94 (0.807)
TraffImprov	To what extent the respondent agrees with the statement: Implementation of congestion charge will effectively reduce traffic congestion in Beijing	5-graded scale: 1 for strongly disagree, 5 for strongly agree	2.79 (0.948)
EnvImprov	To what extent the respondent agrees with the statement: Implementation of congestion charge will effectively improve the air quality of Beijing	Same as above	2.77 (1.000)
Charge	Amount of congestion charge	10, 20, 30, 40 CNY	25.12 (11.060)
Period	The period that been charged for driving	Rush hours = 1, daytime = 0	0.51 (0.500)
Area	The congested areas that been charged	4^th ring = 1, 3^rd ring = 0	0.50 (0.500)
RevUse	The use of revenue raised from implementing a congestion charge	To improve public transit = 1, unspecified = 0	0.47 (0.499)
AirPollut	The air pollution in Beijing is serious	1 = agree, 0 = disagree ^b	0.91 (0.289)
GlobWarm	Global warming is an important issue	Same as above	0.71 (0.455)
RegIndus_BJ	Regional industrial pollution is the major cause of air pollution in Beijing	Same as above	0.76 (0.426)
Indus_Glob	Industrial emissions are an important cause of global warming	Same as above	0.78 (0.416)
Exh_BJ	Automobile exhaust emission is the major cause of air pollution in Beijing	Same as above	0.51 (0.500)
Exh_Glob	Automobile exhaust emission is an important cause of global warming	Same as above	0.44 (0.497)
Tran_Env	Residents can reduce their environmental impact by taking public transit	Same as above	0.72 (0.448)

^ato simplify the ordinal regression models, the dependent variable, “policy attitude,” is combined from 7 levels to 3 levels, which are “not support, neutral, and support;” ^b “disagree” and “agree” are merged from “strongly disagree, disagree, neutral” and “strongly agree, agree,” respectively.

Table 8. Influencing factors of support for the congestion charge (N = 860)

	Coefficient (S.E.)
Variable [Reference level]	Model 1	Model 2	Model 3
PersonLf [very favorable]
Very unfavorable	−3.322**(0.856)	−3.443**(0.846)	−3.228**(0.849)
Unfavorable	−3.152**(0.764)	−3.241**(0.751)	−3.067**(0.755)
Neutral	−1.995**(0.718)	−2.089**(0.705)	−1.913**(0.707)
Favorable	−0.562 (0.740)	−0.680 (0.726)	−0.548 (0.730)
TraffImprov [strongly agree]
Strongly disagree	−2.212**(0.786)	−2.249**(0.780)	−2.108**(0.791)
Disagree	−1.207 (0.715)	−1.297 (0.705)	−1.137 (0.719)
Neutral	0.003 (0.681)	−0.077 (0.670)	0.145 (0.687)
Agree	1.055 (0.703)	0.989 (0.692)	1.182 (0.708)
EnvImprov [strongly agree]
Strongly disagree	−1.021 (0.695)	−0.794 (0.680)	−1.167 (0.729)
Disagree	−0.860 (0.680)	−0.683 (0.663)	−1.065 (0.714)
Neutral	−0.346 (0.645)	−0.158 (0.627)	−0.526 (0.683)
Agree	0.739 (0.663)	0.922 (0.646)	0.643 (0.694)
AirPollut [agree]: disagree	−0.454 (0.274)
GlobWarm [agree]: disagree	0.294 (0.185)
RegIndus_BJ [agree]: disagree	−0.145 (0.191)
Indus_Glob [agree]: disagree	0.020 (0.206)
Exh_BJ [agree]: disagree	−0.406*(0.171)
Exh_Glob [agree]: disagree	−0.313 (0.179)
Tran_Env [agree]: disagree	−0.308 (0.183)
INDUS factor		0.103 (0.077)	0.082 (0.079)
TRANS factor		0.263**(0.088)	0.271**(0.090)
Gender [male]: female	−0.382*(0.154)	−0.362*(0.158)	−0.369*(0.159)
Age			0.003 (0.007)
Income			0.003 (0.009)
Education			0.032 (0.034)
Period [rush hours]: daytime	−0.370*(0.159)	−0.371*(0.153)	−0.361*(0.154)
Area [4^th ring]: 3^rd ring			0.049 (0.155)
RevUse [public transit]: unspecified			−0.175 (0.154)
Charge			−0.003 (0.007)
Intercept [Support]
Not support	−3.809**(0.687)	−3.332**(0.686)	−2.814**(0.970)
Neutral	−2.288**(0.678)	−1.835**(0.680)	−1.305 (0.966)
−2 log likelihood	1032.82	1310.53	1312.77
Likelihood-ratio chi-square	507.63**	492.37**	497.69**
Pseudo R^2	0.509	0.497	0.503
Parallel line test	4.203	19.149	4.195

**P < 0.01, *P < 0.05

Figure 1. Respondents’ perception of the main causes of air pollution in Beijing

Figure 2. The industrial and traffic related environmental concerns of respondents in Beijing

Table

	1 Strongly disagree	2 Disagree	3 Neutral	4 Agree	5 Strongly agree
(1) The air pollution in Beijing is serious.
(2) Global warming is an important issue.
(3) Regional industrial pollution is the main cause of air pollution in Beijing.
(4) Industrial emissions are an important cause of global warming.
(5) Automobile exhaust is the main cause of air pollution in Beijing
(6) Automobile exhaust is an important cause of global warming
(7) Residents can reduce their environmental impact by taking public transit

Table

Introduction of the Congestion Charge Policy Scheme Assume that the toll area of the Beijing congestion charge is set within the 4^th (3^rd) ring. The charge time is from 7:00 to 20:00 (7:00 ~ 9:00, and 17:00 ~ 20:00) on weekdays, and residents will be charged 10 (20/30/40) CNY each time they drive into the toll area during this time. What the policy expects to achieve: (1) The decrease of traffic volume, the decrease of traffic congestion time and the increase of vehicle speed; (2) The improvement of air quality. Improvement of public transit: The revenue will be used to form a special fund, all of which will be used to improve Beijing’s public transit situation, including bus capacity expansion and bus priority measures such as adding bus and subway lines, increasing departure frequency, prolonging operation time, improving arrival accuracy, setting bus lanes, etc. The fund will be managed by special account, and publish the balance of payments regularly. (Potential adverse effects: public transit may be more crowded.) Now please consider the impact of congestion charges on family budgets and travel patterns. When answering, please assume that this policy has been implemented and you are facing options such as paying congestion charges, changing transportation modes or changing travel time.

Table

1	2	3	4	5	6	7
Strongly oppose	Oppose	Somewhat oppose	Neutral	Somewhat favor	Favor	Strongly favor

Table

1	2	3	4	5	6	7
Very unfavorable	Unfavorable	Somewhat unfavorable	Neutral	Somewhat favorable	Favorable	Very favorable

Table

1	2	3	4	5	6	7
Strongly disagree	Disagree	Somewhat disagree	Neutral	Somewhat agree	Agree	Strongly agree

Table

1	2	3	4	5	6	7
Strongly disagree	Disagree	Somewhat disagree	Neutral	Somewhat agree	Agree	Strongly agree

Schuitema, G., L. Steg, and J. A. Rothengatter. 2010. “The Acceptability, Personal Outcome Expectations, and Expected Effects of Transport Pricing Policies.” Journal of Environmental Psychology 30 (4): 587–593.

 Web of Science ®Google Scholar

Zheng, Z., Z. Liu, C. Liu, and N. Shiwakoti. 2014. “Understanding Public Response to A Congestion Charge: A Random-effects Ordered Logit Approach.” Transportation Research Part A: Policy and Practice 70: 117–134.

 Web of Science ®Google Scholar

Börjesson, M., J. Eliasson, and C. Hamilton. 2016. “Why Experience Changes Attitudes to Congestion Pricing: The Case of Gothenburg.” Transportation Research Part A: Policy and Practice 85: 1–16.

 Web of Science ®Google Scholar

Eliasson, J., and L. Jonsson. 2011. “The Unexpected “Yes”: Explanatory Factors behind the Positive Attitudes to Congestion Charges in Stockholm.” Transport Policy 18: 636–647. doi:10.1016/j.tranpol.2011.03.006.

 Web of Science ®Google Scholar

Hamilton, C. J., J. Eliasson, K. Brundell-Freij, C. Raux, S. Souche, K. Kiiskilää, and J. Tervonen, 2014. “Determinants of Congestion Pricing Acceptability.” CTS Working Paper No. 2014. 11. SE-100 44 Stockholm, Centre for Transport Studies, KTH Royal Institute of Technology.

 Google Scholar