Cross-city insights on sustainable consumption: consistency and disparities in willingness to buy and pay for green air conditioners

Abstract

The intensification of climate change action has made air conditioners a key target for emission reductions. This study examines the factors influencing residents’ willingness to buy (WTB) and willingness to pay (WTP) for green air conditioners across six cities in the Pearl River Delta (PRD) region, aiming to understand consumer behavior and inform targeted market strategies. Using a novel Contingent Valuation Method (CVM), this study surveyed 1732 residents through online and face-to-face interviews. Binary logistic and ordered logistic regression analyses identified key factors affecting WTB and WTP, including gender, income, education, knowledge of green air conditioners, and confidence in their emission reduction potential. However, the study reveals significant regional disparities in WTP and payment amounts through the Kruskal–Wallis H and Mann–Whitney U tests. The results also highlight Shenzhen has significant difference and highest payment value than other cities. These findings provide valuable insights into regional disparities and common factors in green consumption, offering guidance for market strategies and policy development aimed at promoting green air conditioners.

Graphical abstract

Keywords

Contingent valuation method / Green air conditioners / WTB and WTP / Influence factors / Consistency and difference

Highlight

	● It first investigates residents’ WTB and WTP for green air conditioners.
	● Income, education, knowledge level, and confidence level are key factors.
	● Male, high-income, and high-education residents are more willing to pay.
	● WTB is consistent across cities, while WTP and its payment value vary widely.
	● Jiangmen, Zhuhai have higher WTP, while Shenzhen has the highest payment.

References

[1] Amirudin A, Inoue C, Grause G. (2023). Assessment of factors influencing Indonesian residents’ intention to use a deposit–refund scheme for PET bottle waste. Circular Economy, 2(4): 100061

[2] Cai K, Xie Y, Song Q, Sheng N, Wen Z. (2021). Identifying the status and differences between urban and rural residents’ behaviors and attitudes toward express packaging waste management in Guangdong Province, China. Science of the Total Environment, 797: 148996

[3] Carson R T, Hanemann W M. (2005). Valuing environmental changes. Handbook of Environmental Economics, 2: 821–936

[4] Duan H, He B, Song J, Li W, Liu Z. (2023). Preference of consumers for higher-grade energy-saving appliances in hierarchical Chinese cities. Journal of Environmental Management, 345: 118806

[5] Fraas A G, Lutter R W, Wietelman D C. (2019). The energy paradox in seemingly competitive industries: the use of energy-efficient equipment on heavy-duty tractor trailers. Energy Policy, 129: 467–480

[6] He R, Jin J, Qiu X, Zhang C, Yan J. (2023). Rural residents’ climate change perceptions, personal experiences, and purchase intention–behavior gap in energy-saving refrigeration appliances in Southwest China. Environmental Impact Assessment Review, 98: 106967

[7] IPCC (2021). Climate Change 2021: the Physical Science Basis. Cambridge, United Kingdom and New York, USA: Cambridge University Press

[8] IPCC (2021). Climate Change 2022: Mitigation of Climate Change. Cambridge, United Kingdom and New York, USA: Cambridge University Press

[9] Ke J, Cai K, Yuan W, Li J, Song Q. (2022). Promoting solid waste management and disposal through contingent valuation method: a review. Journal of Cleaner Production, 379: 134696

[10] Ke J, Sheng N, Song Q, Yuan W, Li J. (2024). Empirical evidence on the characteristics and influencing factors of carbon emissions from household appliances operation in the Pearl River Delta region, China. Applied Energy, 376: 124191

[11] Knetsch J L, Riyanto Y E, Zong J. (2012). Gain and loss domains and the choice of welfare measure of positive and negative changes. Journal of Benefit-Cost Analysis, 3(4): 1–18

[12] LeedyPOrmrod J (2020). Practical Research: Planning and Design. 12th edition. New York: Pearson

[13] Li F, Guo Y, Liu B. (2024a). Impact of government subsidies and carbon inclusion mechanism on carbon emission reduction and consumption willingness in low-carbon supply chain. Journal of Cleaner Production, 449: 141783

[14] Li L, Yuan X. (2024). The influence of energy-saving information in online reviews on green home appliance purchase behavior based on machine learning. Energy and Building, 314: 114296

[15] Li X, Liu P, Feng M, Jordaan S M, Cheng L, Ming B, Chen J, Xie K, Liu W. (2024b). Energy transition paradox: solar and wind growth can hinder decarbonization. Renewable & Sustainable Energy Reviews, 192: 114220

[16] Liddle B, Loi T S A, Owen A D, Tao J. (2020). Evaluating consumption and cost savings from new air-conditioner purchases: the case of Singapore. Energy Policy, 145: 111722

[17] Pohlmann J T. (2004). Use and interpretation of factor analysis in the journal of educational research: 1992–2002. Journal of Educational Research, 98(1): 14–23

[18] Shen H, Lin H, Han W, Wu H. (2023). ESG in China: a review of practice and research, and future research avenues. China Journal of Accounting Research, 16(4): 100325

[19] Shen J, Saijo T. (2009). Does an energy efficiency label alter consumers’ purchasing decisions? A latent class approach based on a stated choice experiment in Shanghai. Journal of Environmental Management, 90(11): 3561–3573

[20] Song Q, Li J. (2015). Greenhouse gas emissions from the usage of typical e-products by households: a case study of China. Climatic Change, 132(4): 615–629

[21] Sun Y. (2024). The real effect of innovation in environmental, social, and governance (ESG) disclosures on ESG performance: An integrated reporting perspective. Journal of Cleaner Production, 460: 142592

[22] Tong X, Wang T, Li J, Wang X. (2024). Extended producer responsibility to reconstruct the circular value chain. Circular Economy, 3(1): 100076

[23] Valera E H, Cremades R, Van Leeuwen E, Van Timmeren A. (2023). Additive manufacturing in cities: closing circular resource loops. Circular Economy, 2(3): 100049

[24] WangB, Deng N, LiuX, SunQ, WangZ (2021). Effect of energy efficiency labels on household appliance choice in China: sustainable consumption or irrational intertemporal choice? Resources, Conservation and Recycling, 169: 105458

[25] Wang X E, Li W, Song J, Duan H, Fang K, Diao W. (2020). Urban consumers’ willingness to pay for higher-level energy-saving appliances: focusing on a less developed region. Resources, Conservation and Recycling, 157: 104760

[26] WangZ, Sun Q, WangB, ZhangB (2019). Purchasing intentions of Chinese consumers on energy-efficient appliances: Is the energy efficiency label effective? Journal of Cleaner Production, 238: 117896

[27] WangZ, Wang X, GuoD (2017). Policy implications of the purchasing intentions towards energy-efficient appliances among China’s urban residents: Do subsidies work? Energy Policy, 102: 430–439

[28] Ward D O, Clark C D, Jensen K L, Yen S T. (2011). Consumer willingness to pay for appliances produced by Green Power Partners. Energy Economics, 33(6): 1095–1102

[29] Wei J, Chen H, Long R, Zhao F. (2019). Application of the capability maturity model to evaluating the carbon capability maturity of urban residents in 10 Eastern provinces of China. Resources, Conservation and Recycling, 148: 11–22

[30] Yadav R, Pathak G S. (2016). Young consumers’ intention towards buying green products in a developing nation: extending the theory of planned behavior. Journal of Cleaner Production, 135: 732–739

[31] Zha D, Yang G, Wang W, Wang Q, Zhou D. (2020). Appliance energy labels and consumer heterogeneity: a latent class approach based on a discrete choice experiment in China. Energy Economics, 90: 104839

[32] Zhang Y, Song B. (2023). Does energy-efficiency label affect appliance price? Empirical analysis of the new national standard air conditioners in China. Energy, 269: 126847

[33] Zhang Y, Wen Z, Hu Y, Zhang T. (2022). Waste flow of wet wipes and decision-making mechanism for consumers’ discarding behaviors. Journal of Cleaner Production, 364: 132684

[34] Zhang Y, Xiao C, Zhou G. (2020a). Willingness to pay a price premium for energy-saving appliances: role of perceived value and energy efficiency labeling. Journal of Cleaner Production, 242: 118555

[35] ZhangZ, Sheng N, ZhaoD, CaiK, YangG, SongQ (2023). Are residents more willing to buy and pay for electric vehicles under the “carbon neutrality”? Energy Reports, 9: 510–521

[36] Zhang Z, Zhang P, Zhao Y, Chen X, Zong Z, Wu K, Zhou J. (2020b). Survey-based air-conditioning demand response for critical peak reduction considering residential consumption behaviors. Energy Reports, 6: 3303–3315

[37] Zhang Z, Zhu W. (2023). Residential mobility and household energy-saving appliances purchasing behavior in urban areas: evidence from China. Energy Reports, 9: 3642

[38] Zhu L, Song Q, Sheng N, Zhou X. (2019). Exploring the determinants of consumers’ WTB and WTP for electric motorcycles using CVM method in Macao. Energy Policy, 127: 64–72

[39] Zhu Y, Rao H. (2024). Does low carbon city pilot promote urban carbon unlocking? A heterogeneity analysis based on machine learning. Cities, 147: 104815

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Higher Education Press 2025