Soft Economic Incentives and Soft Behavioral Interventions on the Public’s Green Purchasing Behaviour - The Evidence from China

Figures & data

Table 1 Soft Policy Mix Stimulus Materials

Policy mix	Contents of Stimulus Materials
(H+Q)	“At present, China’s ecological environment is under enormous pressure, environmental pollution, energy consumption situation is still very serious, rivers dry up, environmental pollution, energy depletion, all this comes from our not green consumption habits, lies in our daily traditional purchasing behavior on the environment inadvertently damage and neglect. The same earth, the same world, limited resources, infinite cycle. Low carbon green consumption, let the earth no longer sigh!”/“ According to statistics, as green logo products are generally 30% more expensive than traditional goods. Therefore, in order to change people’s consumption habits and promote a low-carbon green lifestyle in society, the government has decided to issue green credit vouchers of 4 RMB per bag for consumers who choose to purchase green cleaning laundry detergent with the green logo; and 20 RMB per unit for consumers who purchase energy-saving table lamps”
(H+G)	“In order to better guide green consumption, the government adds labels with environmental information on relevant products, refer to such news and make your choice. Domestic Ali platform survey shows that green consumers have increased 14 times in the past four years, and consumers who have purchased capital-saving and energy-saving products reached 45%. Among them, consumers who have purchased environmentally friendly products are more than 90%. In the population classification, young consumers between 29–35 years old, especially young hot mothers, an average of two people have a green consumer”/ “According to statistics, as green logo products are generally 30% higher than the price of traditional goods. Therefore, in order to change people’s consumption habits and promote a low-carbon green lifestyle in society, the government decided to issue a green credit of 4RMB per bag for consumers who choose to purchase green cleaning laundry detergent with the green logo; and a green credit of 20RMB per unit for consumers who purchase energy-saving table lamps”
(L+Q)	“According to statistics, as green-labeled products are generally 30% more expensive than traditional goods. Therefore, in order to change people’s consumption habits and promote a low-carbon green lifestyle in society, the government has decided to issue green credit vouchers of 2RMB per bag for consumers who choose to purchase green laundry detergent with the green logo; and 10RMB per unit for consumers who purchase energy-saving table lamps”/“At present, China’s The pressure on the ecological environment is huge, environmental pollution, energy consumption is still very serious, rivers dry up, environmental pollution, energy depletion, all this comes from our not green consumption habits, in our daily traditional purchasing behavior on the environment inadvertently damage and neglect. The same earth, the same world, limited resources, infinite cycle. Low carbon green consumption, let the earth no longer sigh!”
(L+G)	“According to statistics, as green-labeled products are generally 30% more expensive than traditional goods. Therefore, in order to change people’s consumption habits and promote a low-carbon green lifestyle in society, the government has decided to issue green credit vouchers of 2RMB per bag for consumers who choose to purchase green laundry detergent with the green logo; and 10RMB per unit for consumers who purchase energy-saving table lamps”/“In order to better In order to better guide green consumption, the government is adding environmental information labels on relevant products, so refer to such news and make your choice. Domestic Ali platform survey shows that green consumers have increased 14 times in the past four years, and 45% of consumers who have purchased energy-saving products. Among them, consumers who have purchased environmentally friendly products are more than 90%. In the population classification, young consumers between 29–35 years old, especially young hot mothers, an average of two people have a green consumer”.

Table 2 Descriptive Statistics of the Single Intervention Policy Sample

Characteristic	Valid N	Valid Percent	Cum. Percent
Gender (valid N = 460)
Male	159	34.6	34.6
Female	301	65.4	100.0
Age (valid N = 460)
25 years old or less	188	40.8	40.8
26–30 years	147	31.9	72.7
31–40 years	100	21.7	94.4
41–50 years	17	3.7	98.0
51 years or above	8	1.9	100.0
Marriage (valid N = 460)
Unmarried	216	46.9	46.9
Married	244	53.1	100.0
Education (valid N = 460)
High school and below	18	3.9	3.9
Specialized colleges	36	7.8	11.7
College	342	74.2	85.9
Postgraduate and above	64	14.1	100.0
City of residence class (valid N = 460)
First-tier cities	94	20.4	20.4
Provincial Capital Cities	223	48.4	68.8
Prefecture-level cities	116	25.2	93.9
County/District Cities	27	6.1	100.0
Employment (valid N = 460)
Students	145	31.5	31.5
Manufacturing\ transportation workers	18	3.9	35.4
Service industry employees	70	15.2	50.5
Government employees, managers	55	11.9	62.5
Education and research personnel	12	2.6	65.1
Small businessmen	160	34.9	100.0
Monthly Income (valid N = 460)
Under 2000 RMB	93	20.2	20.5
Between 2001–3500 RMB	72	15.6	35.8
Between 3501–5000 RMB	50	10.8	46.6
Between 5001–6500 RMB	54	11.7	58.4
Between 6501–8000 RMB	70	15.2	73.5
Above 8000 RMB	121	26.5	100.0

Table 3 Descriptive Statistics for a Sample of Policy Mix

Characteristic	Valid N	Valid Percent	Cum. Percent
Gender (valid N = 556)
Male	292	52.5	52.5
Female	264	47.5	100.0
Age (valid N = 556)
25 years old or less	183	32.9	32.9
26–30 years	192	34.5	67.4
31–40 years	150	27.0	94.4
41–50 years	15	2.7	97.1
51 years or above	16	2.9	100.0
Marriage (valid N = 556)
Unmarried	241	43.3	43.3
Married	315	56.7	100.0
Education (valid N = 556)
High school and below	14	2.5	2.5
Specialized colleges	34	6.1	8.6
College	457	82.2	90.8
Postgraduate and above	51	9.2	100.0
City of residence class (valid N = 556)
First-tier cities	121	21.8	21.8
Provincial Capital Cities	233	41.9	63.7
Prefecture-level cities	147	26.4	90.1
County/District Cities	55	9.9	100.0
Employment (valid N = 556)
Students	214	38.5	38.5
Manufacturing\ transportation workers,	31	5.6	44.1
Service industry employees	93	16.7	60.8
Government employees, managers	48	8.6	69.4
Education and research personnel	12	2.2	71.6
Small businessmen	158	28.4	100.0
Monthly Income (valid N = 556)
Under 2000 RMB	104	18.7	18.7
Between 2001–3500 RMB	53	9.5	28.2
Between 3501–5000 RMB	71	12.8	41.0
Between 5001–6500 RMB	72	12.9	53.9
Between 6501–8000 RMB	103	18.5	72.4
Above 8000 RMB	153	27.6	100.0

Table 4 Balance Test

Variables	PolicyQ	PolicyL	PolicyH	PolicyG
Gender	0.01	0.043	−0.100*	0.026
Age	−0.052	0.018	0.040	0.078
Marriage	−0.05	0.042	0.022	0.040
Education	0.051	0.025	−0.029	0.007
City	−0.032	−0.014	−0.030	0.053
Income	−0.04	−0.02	0.169	0.097
Job	−0.068	0.002	−0.280	0.256

Note: *p < 0.05.

Table 5 Policy Mix Balance Test

Variables	Polic (H+Q)	Policy (H+G)	Policy (L+G)	Policy (L+Q)
Gender	−0.005	0.008	−0.026	0.031
Age	0.073	0.047	0.029	−0.188
Marriage	0.064	−0.003	0.008	−0.111*
Education	0.034	0.008	−0.006	0.04
City	0.025	0.039	−0.031	−0.011
Income	0.061	0.101	−0.075	−0.203
Job	−0.150	−0.096	0.013	0.441*

Note: *p< 0.05.

Table 6 Regression Results of Policy Mix

GC Willingness	Model1	Model2	Model3	Model4
H+Q		0.479**		0.454***
H+G		0.562***		0.371**
L+G		0.426**		0.337**
L+Q		0.452**		0.347**
Gender	0.021	0.023**	−0.131	−0.13
Age	−0.122	−0.115	−0.016	−0.011
Marriage	0.259	0.247	0.182	0.181
Education	−0.026	−0.01	−0.132*	−0.117
Income	0.001	0.001	−0.011	−0.013
Job	−0.034	−0.032	−0.012	−0.011
City	−0.063	−0.057	−0.015	0.011
R-Square	0.019	0.061	0.068	0.067
F	1.239	2.657**	1.939	2.928**

Notes: *p < 0.05; **p < 0.01; ***p < 0.001.

Table 7 Single Policy Regression Results

GC Willingness	Model1	Model2	Model3	Model4
Social Norms		0.247*		0.254*
Green Emotions		0.264*		0.168
Low-intensity subsidies		0.358**		0.259*
High-intensity subsidies		0.312*		0.342*
Gender	−0.220**	−0.224**	−0.148	−0.157
Age	0.097	0.099	0.131*	0.134*
Marriage	−0.112	−0.093	−0.194	−0.177
Education	−0.089	−0.078	−0.043	−0.037
Income	0.180***	0.181***	0.144***	0.146***
Job	0.077**	0.080**	0.029	0.033
City	0.125**	0.125**	0.098*	0.099*
R-Square	0.085	0.084	0.068	0.082
F	7.215***	5.582***	5.693***	4.384***

Notes: *p < 0.05; **p < 0.01; ***p < 0.001.