Equity in Renewable Energy Technology Adoption in China: a Review of the Social-Psychological and Demographic Barriers
Tóm tắt
Renewable energy research in China often underestimates the impact of non-technical or human factors on adoption, as well as the impacts of these factors on energy inequality. This review investigates how social-psychological and demographic factors influence the adoption of electric vehicles, photovoltaic panels, solar water heaters, and smart home technology, especially in rural China. Renewable energy technology adoption in China is extensive, but many social-psychological and demographics barriers to greater diffusion have not been addressed in research or policy. Studies suggest that subjective norms, perceived behavioral control, government support, and knowledge about the technology are the most important social-psychological factors affecting adoption intention in China. Demographic factors and concomitant equity issues have received little attention are largely unstudied in China-specific research and constitutes a major research gap. The limited available literature suggests that the significant demographic factors are, income, urban versus rural setting, education, and cultural values weighted towards collectivism and uncertainty avoidance. We conclude with future research opportunities and policy recommendations.
Tài liệu tham khảo
Chen C-f, Wang Y, Adua L, Bai H. Reducing fossil fuel consumption in the household sector by enabling technology and behavior. Energy Res Soc Sci. 2020;60:101402. https://doi.org/10.1016/j.erss.2019.101402.
China’s National Development and Reform Commission (NDRC). 13th FYP development plan for renewable energy. China Energy Portal. 2016. https://chinaenergyportal.org/en/13th-fyp-development-plan-renewable-energy/. Accessed 29 Dec 2020.
Central Compilation & Translation Press. The 13th five-year plan for economic and social development of the People’s Republic of China. Cent. Compil. Transl. Press 2016: 97–9. http://en.ndrc.gov.cn/newsrelease/201612/P020161207645765233498.pdf. Accessed 29 Dec 2020.
Egbue O, Long S. Barriers to widespread adoption of electric vehicles: an analysis of consumer attitudes and perceptions. Energy Policy. 2012;48:717–29. https://doi.org/10.1016/j.enpol.2012.06.009.
•• Du H, Liu D, Sovacool BK, Wang Y, Ma S, Li RYM. Who buys new energy vehicles in China? Assessing social-psychological predictors of purchasing awareness, intention, and policy. Transp Res Part F Traffic Psychol Behav. 2018;58. https://doi.org/10.1016/j.trf.2018.05.008. A thorough evaluation of the social-psychological factors of EV adoption in China.
Sovacool BK, Hirsh RF. Beyond batteries: an examination of the benefits and barriers to plug-in hybrid electric vehicles (PHEVs) and a vehicle-to-grid (V2G) transition. Energy Policy. 2009;37(3):1095–103. https://doi.org/10.1016/j.enpol.2008.10.005.
Adhikari M, Ghimire LP, Kim Y, Aryal P, Khadka SB. Identification and analysis of barriers against electric vehicle use. Sustain. 2020;12(12):1–20. https://doi.org/10.3390/SU12124850.
•• Chen C-f, Shau J, Li J, Nelson H, Wizem A, Cheng J. Linking social-psychological factors with policy expectations: using local voices to understand solar PV poverty alleviation in the Greater Wuhan Area, China. Energy Policy. In press 2020. Utilizes focus groups in villages throughout the Greater Wuhan Area, China to under the influence of social-psychological factors and policy expectations on PV adoption intention.
Lin B, Wu W. Why people want to buy electric vehicle: an empirical study in first-tier cities of China. Energy Policy. 2018;112:233–41. https://doi.org/10.1016/j.enpol.2017.10.026.
Sierzchula W, Bakker S, Maat K, Van Wee B. The influence of financial incentives and other socio-economic factors on electric vehicle adoption. Energy Policy. 2014;68:183–94. https://doi.org/10.1016/j.enpol.2014.01.043.
Zhang X, Wang K, Hao Y, Fan JL, Wei YM. The impact of government policy on preference for NEVs: the evidence from China. Energy Policy. 2013;61:382–93. https://doi.org/10.1016/j.enpol.2013.06.114.
Chen C-f, Xu X, Frey S. Who wants solar water heaters and alternative fuel vehicles? Assessing social-psychological predictors of adoption intention and policy support in China. Energy Res Soc Sci. 2016;15:1–11. https://doi.org/10.1016/j.erss.2016.02.006.
Jin L, He H. Comparison of the electric car market in China and the United States. Int Council Clean Transp. 2019;10:1–13 https://theicct.org/sites/default/files/publications/ICCT_US-China_EV-mkt-%20comp_20190523.pdf. Accessed 7 Jan 2021.
• Huang Y, Qian L. Consumer preferences for electric vehicles in lower tier cities of China: evidences from south Jiangsu region. Transp Res Part D Transp Environ. 2018;63:482–97. https://doi.org/10.1016/j.trd.2018.06.017. A review of consumer preferences for EVs in China.
Qian L, Yin J. Linking Chinese cultural values and the adoption of electric vehicles: the mediating role of ethical evaluation. Transp Res Part D Transp Environ. 2017;56:175–88. https://doi.org/10.1016/j.trd.2017.07.029.
Chen KK. Assessing the effects of customer innovativeness, environmental value and ecological lifestyles on residential solar power systems install intention. Energy Policy. 2014;67:951–61.
Xiang P, Zhang H, Geng L, Zhou K, Wu Y. Individualist-collectivist differences in climate change inaction: the role of perceived intractability. Front Psychol. 2019;10:1–12. https://doi.org/10.3389/fpsyg.2019.00187.
•• Sovacool BK, Abrahamse W, Zhang L, Ren J. Pleasure or profit? Surveying the purchasing intentions of potential electric vehicle adopters in China. Transp Res Part A Policy Pract. 2019;124:69–81. https://doi.org/10.1016/j.tra.2019.03.002. Analyzes Chinese consumers’ motivations for EV adoption intention.
Li X, Li H, Wang X. Farmers’ willingness to convert traditional houses to solar houses in rural area: a survey of 465 households in Chongqing, China. Energy Policy. 2013;63:882–6. https://doi.org/10.1016/j.enpol.2013.09.004.
Wolske KS, Gillingham KT, Schultz PW. Peer influence on household energy behaviours. Nat Energy. 2020;5:202–12. https://doi.org/10.1038/s41560-019-0541-9.
Wang Z, Li J, Liu J, Shuai C. Is the photovoltaic poverty alleviation project the best way for the poor to escape poverty? A DEA and GRA analysis of different projects in rural China. Energy Policy. 2020;137:111105. https://doi.org/10.1016/j.enpol.2019.111105.
Liu X, Sun Y, Kaloustian TS. Cultural factors influencing domestic adoption of solar photovoltaic technology: perspectives from China. China Media Res. 2015;11(4):28–42.
Yuan X, Zuo J, Ma C. Social acceptance of solar energy technologies in China - end users’ perspective. Energy Policy. 2011;39(3):1031–6. https://doi.org/10.1016/j.enpol.2011.01.003.
Karakaya E, Sriwannawit P. Barriers to the adoption of photovoltaic systems: the state of the art. Renew Sust Energ Rev. 2015;49:60–6. https://doi.org/10.1016/j.rser.2015.04.058.
• Li Y, Zhang Q, Wang G, McLellan B, Liu XF, Wang L. a review of photovoltaic poverty alleviation projects in China: Current status, challenge and policy recommendations. Renew Sust Energ Rev. 2018;94:214–33. https://doi.org/10.1016/j.rser.2018.06.012. Identifies challenges in PV poverty alleviation in China and provides decent policy recommendations.
Urban F, Geall S, Wang Y. Solar PV and solar water heaters in China: different pathways to low carbon energy. Renew Sust Energ Rev. 2016;64:531–42. https://doi.org/10.1016/j.rser.2016.06.023.
Geall S, Shen W. Gongbuzeren. Solar energy for poverty alleviation in China: state ambitions, bureaucratic interests, and local realities. Energy Res Soc Sci. 2018;41:238–48. https://doi.org/10.1016/j.erss.2018.04.035.
•• Wang X, Xiong Y, Yang R, Yu P. Social psychological predictors of adoption intention for solar water heaters in rural China. Soc Behav Pers. 2019;47:12. https://doi.org/10.2224/SBP.8549. Utilizes survey data to determine what social-psychological factors influence of SWH adoption intention in rural China.
Yu Z, Gibbs D. Social ties, homophily and heterophily in urban sustainability transitions: user practices and solar water heater diffusion in China. Energy Res Soc Sci. 2018;46:236–44. https://doi.org/10.1016/j.erss.2018.07.029.
Ma B, Song G, Smardon RC, Chen J. Diffusion of solar water heaters in regional China: economic feasibility and policy effectiveness evaluation. Energy Policy. 2014;72:23–34. https://doi.org/10.1016/j.enpol.2014.04.015.
Han J, Mol APJ, Lu Y. Solar water heaters in China: a new day dawning. Energy Policy. 2010;38(1):383–91. https://doi.org/10.1016/j.enpol.2009.09.029.
Ma B, Yu Y, Urban F. Green transition of energy systems in rural China: national survey evidence of households’ discrete choices on water heaters. Energy Policy. 2017;113:559–70. https://doi.org/10.1016/j.enpol.2017.11.046.
Wang X, Guan Z, Wu F. Solar energy adoption in rural China: a sequential decision approach. J Clean Prod. 2017;168:1312–8. https://doi.org/10.1016/j.jclepro.2017.09.094.
•• Ji W, Chan EHW. Critical factors influencing the adoption of smart home energy technology in china: a Guangdong province case study. Energies. 2019;12:21. https://doi.org/10.3390/en12214180. Provides the one of the most thorough analyses of the non-technical factors influencing SHT adoption in China.
Yang F, Xu J. Privacy concerns in China’s smart city campaign: the deficit of China’s Cybersecurity Law. Asia Pac Policy Stud. 2018;5(3):533–43. https://doi.org/10.1002/app5.246.
•• Ji W, Chan EHW. Between users, functions, and evaluations: exploring the social acceptance of smart energy homes in China. Energy Res Soc Sci. 2020;69:101637. https://doi.org/10.1016/j.erss.2020.101637. Ranks social-psychological factors influencing SHT adoption in China by importance and impact on adoption intention.
Rozite V. Intelligent Efficiency: A case study of barriers and solutions - Smart Homes. Connected Devices Alliance 2018.
Dong X, Chang Y, Wang Y, Yan J. Understanding usage of Internet of Things (IOT) systems in China: cognitive experience and affect experience as moderator. Inf Technol People. 2017;30(1):117–38. https://doi.org/10.1108/ITP-11-2015-0272.
Wong JKW, Lueng JKL. Modelling factors influencing the adoption of smart-home technologies. Facilities. 2016;34(13-14):906–23. https://doi.org/10.1108/F-05-2016-0048.
Liu X, Liu X, Luo X, Fu H, Wang M, Li L. Impact of different policy instruments on diffusing energy consumption monitoring technology in public buildings: evidence from Xi’an, China. J Clean Prod. 2020;251:119693. https://doi.org/10.1016/j.jclepro.2019.119693.
Wong K. China: electric cars smash sales record. Energy Intelligence New Energy. 2020. http://www.energyintel.com/pages/eig_article.aspx?DocID=1084240. Accessed 29 Dec 2020.
Qin X, Wu K. A fight against poverty using photovoltaic power. China Business. 2019. http://dianzibao.cb.com.cn/html/2019-03/11/content_71514.htm?div=0. Accessed 7 Jan 2021.
Mauthner F, Weiss W, Spörk-Dür M. Solar Heat Worldwide 2013: solar heat worldwide markets and contribution to the energy supply. IEA Solar Heating & Cooling Programme. 2015. https://www.iea-shc.org/data/sites/1/publications/solar-heat-worldwide-2015.pdf. Accessed Jan. 7, 2021.
Lobaccaro G, Carlucci S, Löfström E. A review of systems and technologies for smart homes and smart grids. Energies. 2016;9(5):1–33. https://doi.org/10.3390/en9050348.
Saul-Rinaldi K, LeBaron R, Caracino J. Making sense of the smart home: applications of smart grid and smart home technologies for home performance industry. National Home Performance Council. 2014. https://www.homeperformance.org/sites/default/files/nhpc_white-paper-making-sense-of-smart-home-final_20140425.pdf. Accessed 7 Jan 2021.
Nacer A, Marhic B, Delahoche L. Smart Home, Smart HEMS, Smart heating: an overview of the latest products and trends. Paper presented at: 2017 6th International Conference on Systems and Control (ICSC); 2017 May 7-9; Batna, Algeria. https://doi.org/10.1109/ICoSC.2017.7958713.
Asare-Bediako B, Riberio PF, Kling WL. Integrated energy optimization with smart home energy management systems,” IEEE PES Innov. Smart Grid Technol Conf Eur 2012:1–8. https://doi.org/10.1109/ISGTEurope.2012.6465696.
Chen C-f, Zarazua de Rubens G, Xu X, Li J. Coronavirus comes home? Energy use, home energy management, and the social-psychological factors of COVID-19. Energy Res Soc Sci. 2020;68:101688. https://doi.org/10.1016/j.erss.2020.101688.
Smart Home: China. Statista 2020. https://www.statista.com/outlook/283/117/smart-home/china. Accessed 29 Dec 2020.
Ajzen I. The theory of planned behavior. Organ Behav Hum Decis Process. 1991;50(2):179–211. https://doi.org/10.1016/0749-5978(91)90020-T.
Schwartz SH. Normative influences on altruism. Adv Exp Soc Psychol. 1977;10:221–79.
Steg L, Perlaviciute G, van der Werff E. Understanding the human dimensions of a sustainable energy transition. Front Psychol. 2015;6:1–17. https://doi.org/10.3389/fpsyg.2015.00805.
Scherbaum CA, Popovich PM, Finlinson S. Exploring individual-level factors related to employee energy-conservation behaviors at work. J Appl Soc Psychol. 2008;38(3):818–35. https://doi.org/10.1111/j.1559-1816.2007.00328.x.
Abrahamse W, Steg L. Factors related to household energy use and intention to reduce it: the role of psychological and socio-demographic variables. Hum Ecol Rev. 2011;18(1):30–40.
Wolske KS, Stern PC, Dietz T. Explaining interest in adopting residential solar photovoltaic systems in the United States: toward an integration of behavioral theories. Energy Res Soc Sci. 2017;25:134–51. https://doi.org/10.1016/j.erss.2016.12.023.
Chen MF. Extending the theory of planned behavior model to explain people’s energy savings and carbon reduction behavioral intentions to mitigate climate change in Taiwan-moral obligation matters. J Clean Prod. 2016;112:1746–53. https://doi.org/10.1016/j.jclepro.2015.07.043.
Davis F. Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Q. 1989;13(3):319–40. https://doi.org/10.1016/S0305-0483(98)00028-0.
Hong S-J, Tam KY. Understanding the adoption of multipurpose information appliances: the case of mobile data services. Inf Syst Res. 2006;17(2):162–79. https://doi.org/10.1287/isre.1060.0088.
Venkatesh V, Thong JYLT, Xu X. Consumer acceptance and use of it: extending the unified theory of acceptance and use of technology. MIS Q. 2012;36(1):157–78.
Kranz, J, Gallenkamp J, Picot A. Exploring the role of control - smart meter acceptance of residential consumers. 16th Am Conf Inf Syst. 2010:4:2639-48. http://www.scopus.com/inward/record.url?eid=2-s2.0-84870312059&partnerID=40&md5=c31a48a3fe8f2df9763ba2a7d1cff552. Accessed 7 Jan 2021.
Park C, Kim H, Kim Y. A study of factors enhancing smart grid consumer engagement. Energy Policy. 2014;72:211–8. https://doi.org/10.1016/j.enpol.2014.03.017.
Huijts NMA, Molin EJE, Steg L. Psychological factors influencing sustainable energy technology acceptance: a review-based comprehensive framework. Renew Sust Energ Rev. 2012;16(1):525–31. https://doi.org/10.1016/j.rser.2011.08.018.
Chen C-f, Xu X, Arpan L. Between the technology acceptance model and sustainable energy technology acceptance model: investigating smart meter acceptance in the United States. Energy Res Soc Sci. 2017;25:93–104. https://doi.org/10.1016/j.erss.2016.12.011.
Helveston JP, Liu Y, Feit EMD, Fuchs E, Klampfl E, Michalek JJ. Will subsidies drive electric vehicle adoption? Measuring consumer preferences in the U.S. and China. Transp Res Part A Policy Pract. 2015;73. https://doi.org/10.1016/j.tra.2015.01.002.
Gallagher KS, Muehlegger E. Giving green to get green? Incentives and consumer adoption of hybrid vehicle technology. J Environ Econ Manag. 2011;61(1):1–15. https://doi.org/10.1016/j.jeem.2010.05.004.
Hofstede G. Culture’s consequences : comparing values, behaviors, institutions, and organizations across nations. California: Sage Publications; 2001.
Gumelar G, Vania A, Maulana H. Do cultural styles predict pro-environment behaviour among slum-area resident of Jakarta? E3S Web Conf. 2019;68:1–5. https://doi.org/10.1051/e3sconf/20186802003.
Sovacool BK, Kester J, Noel L, Zarazua de Rubens G. The demographics of decarbonizing transport: the influence of gender, education, occupation, age, and household size on electric mobility preferences in the Nordic region. Glob Environ Chang. 2019;52:86–100. https://doi.org/10.1016/j.gloenvcha.2018.06.008.
Balta-Ozkan N, Yildirim J, Connor PM, Truckell I, Hart P. Energy transition at local level: analyzing the role of peer effects and socio-economic factors on UK solar photovoltaic deployment. Energy Policy. 2021;148:112004. https://doi.org/10.1016/j.enpol.2020.112004.
Bollinger G, Gillingham K. Peer effects in the diffusion of solar photovoltaic panels. Mark Sci. 2012;31(6):873–1025.
Raafat RM, Chater N, Frith C. Herding in humans. Trends Cogn Sci. 2009;13(10):420–8. https://doi.org/10.1016/j.tics.2009.08.002.
Lai C-N. Sense of community and self-related health: mediating effect of social capital. Sociol Mind. 2013;03:217–22.
Fox WS. Yearning for yesterday: a sociology of nostalgia by Fred Davis. Am J Sociol. 1980;60.
Du F, Zhang J, Li H, Yan J, Galloway S, Lo KL. Modelling the impact of social network on energy savings. Appl Energy. 2016;178:56–65. https://doi.org/10.1016/j.apenergy.2016.06.014.
Kalkbrenner BJ, Roosen J. Citizens’ willingness to participate in local renewable energy projects: the role of community and trust in Germany. Energy Res Soc Sci. 2016;13:60–70. https://doi.org/10.1016/j.erss.2015.12.006.
Walker G, Devine-Wright P. Community renewable energy: what should it mean? Energy Policy. 2008;36(2):497–500. https://doi.org/10.1016/j.enpol.2007.10.019.
• Wu P, Ke S, Gao Y. A review on photovoltaic poverty alleviation projects in China: conjunctures, current status and policy recommendations. E3S Web Conf. 2019;117. https://doi.org/10.1051/e3sconf/201911700012. Reviews the effectiveness of different PV policy instruments on poverty alleviation in China.
The London School of International Communication. The fear of not knowing - managing uncertainty across cultures. 2019. https://www.londonschool.com/lsic/resources/blog/fear-not-knowing-managing-uncertainty-across-cultures/. Accessed 7 Jan 2021.
Lukanov BR, Krieger EM. Distributed solar and environmental justice: exploring the demographic and socio-economic trends of residential PV adoption in California. Energy Policy. 2019;134:110935. https://doi.org/10.1016/j.enpol.2019.110935.
Winther T, Ulsrud K, Saini A. Solar powered electricity access: implications for women’s empowerment in rural Kenya. Energy Res Soc Sci. 2018;44:61–74. https://doi.org/10.1016/j.erss.2018.04.017.
National Bureau of Statistics of China. Residents’ income and consumption expenditure in 2018. 2019. http://www.stats.gov.cn/tjsj/zxfb/201901/t20190121_1645791.html. Accessed 29 Dec 2020.
• Zhang H, Wu K, Qui Y, et al. Solar photovoltaic interventions have reduced rural poverty in China. Nat Commun. 2020;11:1. https://doi.org/10.1038/s41467-020-15826-4. Analyzes the impact of PV poverty alleviation on disposable income and provides policy recommendations to continue success.
Urmee T, Walker E, Bahri PA, Baverstock G, Rezvani S, Saman W. Solar water heaters uptake in Australia – issues and barriers. Sustain Energy Technol Assess. 2017;30:11–23. https://doi.org/10.1016/j.seta.2018.08.006.
Chang KC, Lee TS, Lin WM, Chung KM. Outlook for solar water heaters in Taiwan. Energy Policy. 2008;36(1):66–72. https://doi.org/10.1016/j.enpol.2007.07.030.
Huang J, Tian Z, Fan J. A comprehensive analysis on development and transition of the solar thermal market in China with more than 70% market share worldwide. Energy. 2019;174:611–24. https://doi.org/10.1016/j.energy.2019.02.165.
Bird S, Hernández D. Policy options for the split incentive: increasing energy efficiency for low-income renters. Energy Policy. 2012;48(Supplement C):506–14. https://doi.org/10.1016/j.enpol.2012.05.053.
Chen C-f, Nelson H, Bonilla G, Jones N, Xu X. Beyond technology adoption: examining home energy management systems, energy burdens and climate change perceptions across income groups. Renew Sust Energ Rev. In press 2020.
Sanguinetti A, Karlin B, Ford R. Understanding the path to smart home adoption: segmenting and describing consumers across the innovation-decision process. Energy Res Soc Sci. 2017;46:274–83. https://doi.org/10.1016/j.erss.2018.08.002.
Drehobl A, Ross L, Ayala R. Low-income households, communities of color face high ‘energy burden’ entering recession. ACEEE 2020. https://www.aceee.org/press-release/2020/09/report-low-income-households-communities-color-face-high-energy-burden. Accessed 7 Jan 2021.
Bhati A, Hansen M, Chan CM. Energy conservation through smart homes in a smart city: a lesson for Singapore households. Energy Policy. 2017;104:230–9. https://doi.org/10.1016/j.enpol.2017.01.032.
• Chen C-f, Xu X, Adams J, Brannon J, Li F, Walzem A. When east meets west: understanding residents’ home energy management system adoption intention and willingness to pay in Japan and the United States. Energy Res Soc Sci. 2020;69. https://doi.org/10.1016/j.erss.2020.101616. Provides cross-cultural comparison of the social-psychological factors influencing adoption intention and willingness to pay for HEMs.
Shih TY. Determinates of consumer adoption attitudes: an empirical study of smart home services. Int J E-Adoption. 2013;5(2):40–56. https://doi.org/10.4018/jea.2013040104.
Shin J, Park Y, Lee D. Who will be smart home users? An analysis of adoption and diffusion of smart homes. Technol Forecast Soc Chang. 2018;134:246–53. https://doi.org/10.1016/j.techfore.2018.06.029.
Center on Global Energy Policy. Guide to Chinese climate policy: electric vehicles. Columbia University. No date. https://chineseclimatepolicy.energypolicy.columbia.edu/en/electric-vehicles. Accessed Jan. 1, 2021.
• Nicholls L, Strengers Y, Sadowski J. Social impacts and control in the smart home. Nat Energy. 2020;5(3):180–2. https://doi.org/10.1038/s41560-020-0574-0. Reveals how SHTs can be used for violence against women and contributes to closing the equity gap in research.