Restricted accessResearch articleFirst published online 2026
How to encourage the public to actively practice green and low-carbon consumption? The interaction between individual psychology and urban situational factors
Most studies explaining green and low-carbon consumption behavior mainly focus on a single influence at the individual psychological level, while ignoring the multilevel interaction between macro situational factors and individual factors in nested cities. Therefore, based on large-scale survey data and national statistical data from 35 cities in China, this article integrates individual-level psychological factors with urban-level background factors, employs a multilevel structural equation model (MSEM) to investigate the key influencing factors and mechanisms of individual green and low-carbon consumption behavior. It examines consumption behavior with four different characteristics: green food consumption, green low-carbon travel, green energy consumption, and green living. The results indicate that at the individual level, consumer perceived value and perceived behavioral control actively promote four types of green and low-carbon consumption practices, with conditional value having the most extensive promotion effect. At the urban level, environmental pollution, economic development, and social development can all encourage low-carbon consumption behavior. Moreover, the level of social development positively moderates the relationship between environmental pollution perception and green consumption intention. The level of environmental pollution positively moderates the relationship between environmental attitudes and green low-carbon travel. However, the level of economic development suppresses the promotion effect of environmental pollution perception on environmental attitudes. Therefore, in the formulation of environmental policies, full consideration should be given to the development status of each region. To strengthen regional infrastructure and improve the incentive system for green and low-carbon markets, it is essential to utilize information dissemination channels flexibly.
VollebregtMMuggeRThuerridlC, et al.Reducing without losing: reduced consumption and its implications for well-being. Sustain Prod Consum2024; 45: 91–103.
2.
LiCWangXMWangLP. Forecasting carbon emissions peak in Chinese household consumption and selecting low-carbon development strategies: a study based on the extended SPIRPAT model. Environ Prog Sustain Energy2024a; 44(1): e14537.
3.
GuoXGWangLFGuoY, et al.The impact of sustainable consumption behaviour on natural resource conservation in China: a cross-sectional analysis. Resourc Policy2024; 89: 104610.
4.
WangLPChenLJJinSC, et al.Forecasting the green behaviour level of Chinese enterprises: a conjoined application of the autoregressive integrated moving average (ARIMA) model and multi-scenario simulation. Technol Soc2025a; 81: 102825.
5.
GaziMAIMasudAAYusofMF, et al.The green mindset: how consumers’ attitudes, intentions, and concerns shape their purchase decisions. Environ Res Commun2024; 6: 25009.
6.
ChwialkowskaABhattiWABujacA, et al.An interplay of the consumption values and green behavior in developed markets: a sustainable development viewpoint. Sustain Dev2024; 32(4): 3771–3785.
7.
WangLPChenLJLiC. Research on strategies for improving green product consumption sentiment from the perspective of big data. J Retail Consum Serv2024a; 79: 103802.
8.
MeneboMMKvaleHEHBajracharyaM, et al.Social exclusion and green consumption: the multi-motive theory approach. Sustain Dev2023; 31: 3857–3868.
9.
YangMHChenHLongRY, et al.How does government regulation shape residents’ green consumption behavior? A multi-agent simulation considering environmental values and social interaction. J Environ Manag2023; 331: 117231.
10.
WeiHFLiZChudheryMAZ, et al.How does consumers’ face consciousness influence green self-efficacy and consumption behavior, and how does electronic and social media persuasion moderate these relationships?Comput Human Behav2024; 153: 108091.
11.
WangLPGaoPPLiC. Public response to heterogeneous environmental policies: scenario-based experiments from interest appeal, implementation costs, and commitment mechanism. Energy Sourc B Econ Plann Policy2024b; 19(1): 2304905.
12.
ChenLJZhangJLYouY. Air pollution, environmental perceptions, and citizen satisfaction: a mediation analysis. Environ Res2020; 184: 109287.
13.
LiCWangXMWangLP. Interplay of virtual and physical channels in propagating green behavior: a study integrating motivation–opportunity–ability and theory of planned behavior. Environ Dev2024b; 50: 100997.
14.
GolobUPodnarKWederF. Reimagining the sustainable consumer: why social representations of sustainable consumption matter. Bus Ethics Environ Responsibil2024; 33(4): 847–859.
15.
RozenkowskaK. Theory of planned behavior in consumer behavior research: a systematic literature review. Int J Consum Stud2023; 47: 2670–2700.
16.
WangLZhangQWongPPW. Purchase intention for green cars among Chinese millennials: merging the value–attitude–behavior theory and theory of planned behavior. Front Psychol2022; 13: 786292.
17.
AjzenI. The theory of planned behavior. Organ Behav Hum Decis Process1991; 50: 179–211.
18.
ShenJLiangHYZafarAU, et al.Influence by osmosis: social media green communities and pro-environmental behavior. Comput Human Behav2023; 143: 107706.
19.
LiCWangXMWangLP. Examining the crucial societal incentive approaches and their impact mechanisms to enhance the purchase intention of carbon-labeled products: a conjoined application of PLS-SEM and NCA methodologies. Energy Environ2024c; 37(2): 877–924.
20.
ZhangYXTaoWW. Will energy efficiency affect appliance price? An empirical analysis of refrigerators in China based on hedonic price model. Energy Policy2020; 147: 11818.
21.
ShethJNNewmanBIGrossBL. Why we buy what we buy: a theory of consumption values. J Bus Res1991; 22: 159–170.
22.
SongMLXieQJWangSH, et al.Could environmental regulation and R&D tax incentives affect green product innovation?J Cleaner Prod2020; 258: 120849.
23.
RahmanINanuLSozenE. The adoption of environmental practices in craft breweries: the role of owner-managers’ consumption values, motivation, and perceived business challenges. J Cleaner Prod2023; 416: 137948.
24.
LiCWangYLWangLP. Guided by the goal of “double carbon”, what is the carbon emission reduction effect of the promotion and application of green technology in China?Environ Res2024d; 245: 117974.
25.
ChwialkowskaA. Money and status or clear conscience and clean air: should we vary the marketing interventions depending on tourist's cultural background?J Travel Tourism Market2021; 38: 75–92.
26.
BennettRVijaygopalR. Consumer attitudes towards electric vehicles: effects of product user stereotypes and self-image congruence. Eur J Mark2018; 52: 499–527.
27.
JoshiYUniyalDPSangroyaD. Investigating consumers’ green purchase intention: examining the role of economic value, emotional value and perceived marketplace influence. J Cleaner Prod2021; 328: 129638.
28.
GarrouchKGhaliZ. On linking the perceived values of mobile shopping apps, customer well-being, and customer citizenship behavior: moderating role of customer intimacy. J Retail Consum Serv2023; 74: 103396.
29.
KhanSNMohsinM. The power of emotional value: exploring the effects of values on green product consumer choice behavior. J Cleaner Prod2017; 150: 65–74.
30.
NgPMLCheungCTYLitKK, et al.Green consumption and sustainable development: the effects of perceived values and motivation types on green purchase intention. Bus Strategy Environ2024; 33: 1024–1039.
31.
KashifUHongCNaseemS, et al.Assessment of millennial organic food consumption and moderating role of food neophobia in Pakistan. Curr Psychol2023; 42: 1504–1515.
32.
GarletTBde MedeirosJFRibeiroJLD, et al.Understanding ethical products: definitions and attributes to consider throughout the product lifecycle. Sustain Prod Consum2024; 45: 228–243.
33.
JiaYJChengSJShiR. Decision-making behavior of rural residents’ domestic waste classification in northwestern of China: analysis based on environmental responsibility and pollution perception. J Cleaner Prod2021; 326: 129374.
34.
TanXRHanLJZhangXY, et al.A review of current air quality indexes and improvements under the multi-contaminant air pollution exposure. J Environ Manag2021; 279: 111681.
35.
MajerJMHenscherHAReuberP, et al.The effects of visual sustainability labels on consumer perception and behavior: a systematic review of the empirical literature. Sustain Prod Consum2022; 33: 1–14.
36.
WongEYCChanFFYSoS. Consumer perceptions on product carbon footprints and carbon labels of beverage merchandise in Hong Kong. J Cleaner Prod2020; 242: 118404.
37.
GrymshiDCrespo-CebadaEElghannamA, et al.Understanding consumer attitudes towards ecolabeled food products: a latent class analysis regarding their purchasing motivations. Agribusiness2021; 38: 93–107.
38.
LiSWangJW. Pilot policies for low-carbon cities, low-carbon literacy of residents, and green technology innovation in enterprises. Chin Popul Resour Environ2023; 33: 93–103. in Chinese.
39.
ZhangJFQinYCZhangLJ, et al.The impact of built environment on residents’ green consumption intention: an empirical study from Zhengzhou. Geograph Res2021; 40: 2914–2929.
40.
WangLPChenLJWangZJ, et al.Exploring strategies for promoting green technology innovation in enterprises from the perspective of inter-enterprise interaction and enterprise behavioral preferences. Renew Energy2025b; 250: 123287.
41.
WuHXueYHaoY, et al.How does Internet development affect energy-saving and emission reduction? Evidence from China. Energy Econ2021; 103: 105577.
42.
LongRYWangJQChenH, et al.Applying multilevel structural equation modeling to energy-saving behavior: the interaction of individual- and city-level factors. Energy Policy2023; 174: 113423.
43.
QiaoSYaoTWangN, et al. “Externally observing” and “internally reflecting”: a study on green consumption intention caused by environmental threats: a dual path model based on moral emotions. Nankai Bus Rev2024; 27: 137–149. in Chinese.
44.
SunYHLiuNNZhaoMZ. Factors and mechanisms affecting green consumption in China: a multilevel analysis. J Cleaner Prod2019; 209: 481–493.
45.
ZhouZFLiuJHZengHX, et al.How does soil pollution risk perception affect farmers’ pro-environmental behavior? The role of income level. J Environ Manag2020; 270: 110806.
46.
TonderEVFullertonSBeerLTD. Cognitive and emotional factors contributing to green customer citizenship behaviours: a moderated mediation model. J Consum Market2020; 37(6): 639–650.
47.
ParkHJLinLM. Exploring attitude–behavior gap in sustainable consumption: comparison of recycled and upcycled fashion products. J Bus Res2020; 117: 623–628.
48.
RuXJWangSYYanS. Exploring the effects of normative factors and perceived behavioral control on individual’ s energy-saving intention: an empirical study in eastern China. Resour Conserv Recycl2018; 134: 91–99.
49.
RauschTMKopplinCS. Bridge the gap: consumers’ purchase intention and behavior regarding sustainable clothing. J Cleaner Prod2021; 278: 123882.
50.
NekmahmudMFeketefarkasM. Why not green marketing? Determinates of consumers intention to green purchase decision in a new developing nation. Sustainability2020; 12: 7880.
51.
AkhtarRSultanaSMasudMM, et al.Consumers’ environmental ethics, willingness, and green consumerism between lower and higher income groups. Resour Conserv Recycl2021; 168: 105274.
52.
LinHMWuJYLiangJC, et al.A review of using multilevel modeling in e-learning research. Comput Educ2023; 198: 104762.
53.
PreacherKJZhangZZyphurMJ. Multilevel structural equation models for assessing moderation within and across levels of analysis. Psychol Methods2016; 21: 189–205.
54.
FangJWenZLWuY. Multi-layer regulation effect based on structural equation modeling. Adv Psychol Sci2018; 26: 781–788.
55.
XingYFLiMQLiaoYH. Trust, identity, and public-sphere pro-environmental behavior in China: An extended attitude–behavior–context theory. Front Psychol2022; 13: 919578.
56.
HenselerJRingleCMSarstedtM. A new criterion for assessing discriminant validity in variance-based structural equation modeling. J Acad Mark Sci2015; 43(1): 115–135.
57.
VaithilingamSOngCSMoisescuOI, et al.Robustness checks in PLS-SEM: a review of recent practices and recommendations for future applications in business research. J Bus Res2024; 173: 114465.
58.
ZhangGXShenLSuB. Temperature change and daily urban–rural residential electricity consumption in northwestern China: responsiveness and inequality. Energy Econ2023; 126: 106973.
59.
RuokamoEMerilainenTKarhinenS, et al.The effect of information nudges on energy saving: observations from a randomized field experiment in Finland. Energy Policy2022; 161: 112731.
60.
SaariUADambergSFroemblingL, et al.Sustainable consumption behavior of Europeans: the influence of environmental knowledge and risk perception on environmental concern and behavioral intention. Ecol Econ2021; 189: 107155.
61.
HairJFSarstedtMRingleCM. Rethinking some of the rethinking of partial least squares. Eur J Mark2019; 53: 566–584.
62.
GuentherPGuentherMRingleCM, et al.Improving PLS-SEM use for business marketing research. Ind Mark Manag2023; 111: 127–142.
