Investigation of Factors Affecting the Acceptance of Hydroelectric Power Plants: Van Yüzüncü Yıl University Ercis Vocational School Students Sample

Year 2023, Volume: 27 Issue: 3, 447 - 456, 25.12.2023

Onur Özdemir , Özer Özaydın , Veysel Yılmaz

Abstract

In this study, the factors affecting the acceptance of hydroelectric power
plants are examined with a structural model. In the model, perceived risk,
perceived benefit, perceived trust and acceptance intention were considered as
internal variables, while the knowledge score regarding hydroelectric power
plants was evaluated as an external variable. The research model was designed by
considering that students' knowledge of hydroelectric power plants will directly
affect their perceptions of risk, benefit and trust towards power plants, and that
these variables may have an impact on the acceptance of hydroelectric power
plants. In addition, it was also investigated whether environmental sensitivity
levels of individuals play a mediator role in risk→acceptance, trust→acceptance
and benefit→acceptance relations. According to the results of the analysis, it was
determined that there was a positive relationship between the knowledge score
and risk perception regarding hydroelectric power plants, and a negative
relationship between the knowledge score and the perception of trust and benefit.
One point increase in total knowledge score; It has been determined that it will
cause an increase of 0.347 points in risk perception and a decrease of 0.116 and
0.087 points in trust and benefit perceptions, respectively. As a result, it has been
revealed that perceived trust is the most important factor affecting the acceptance
of hydroelectric power plants.

Keywords

Hydroelectric power plants, Perceived risk, Perceived benefit, Acceptance of power plants, Statistics

Hidroelektrik Santrallerin Kabulüne Etki Eden Faktörlerin Araştırılması: Van Yüzüncü Yıl Üniversitesi Erciş Meslek Yüksekokulu Öğrencileri Örneklemi

Abstract

Bu çalışmada, hidroelektrik santrallerin kabulüne etki eden faktörler bir
yapısal model ile incelenmiştir. Modelde algılanan risk, algılanan yarar, algılanan
güven ve kabul niyeti içsel değişken olarak ele alınırken, hidroelektrik santrallere
ilişkin bilgi puanı dışsal değişken olarak değerlendirilmiştir. Araştırma modeli,
öğrencilerin hidroelektrik santralleri hakkında bilgilerinin, santrallere yönelik risk,
yarar ve güven algılarını doğrudan etkileyeceği, bu değişkenlerin de hidroelektrik
santralleri kabulüne etkileri olabileceği değerlendirilerek kurgulanmıştır. Ayrıca
kişilerin çevresel duyarlılık düzeylerinin, risk → kabul, güven → kabul ve
yarar→kabul ilişkilerinde düzenleyici bir rol oynayıp oynamadığı da araştırılmıştır.
Analiz sonuçlarına göre hidroelektrik santrallere ilişkin bilgi puanı ile risk algısı
arasında pozitif, bilgi puanı ile güven ve yarar algısı arasında negatif bir yönde
ilişki olduğu belirlenmiştir. Toplam bilgi puanındaki bir puanlık artış; risk
algısında 0.347 puanlık bir artışa, güven ve yarar algılarında ise sırasıyla 0.106 ve
0.087 puanlık azalışa neden olacağı tespit edilmiştir. Sonuç olarak algılanan
güvenin hidroelektrik santrallerin kabulünü etkileyen en önemli faktör olduğu
ortaya çıkmıştır.

Keywords

Hidroelektrik Santraller, Algılanan Risk, Algılanan Yarar, Santrallerin Kabulü, İstatistik

References

• [1] Erdem, K.O.Ç., Kadir, K. 2015. Enerji KaynaklarıYenilenebilir Enerji Durumu. Mühendis ve Makine, 56(668), 36-47.
• [2] https://enerji.gov.tr/bilgi-merkezi-enerjihidrolik (Erişim Tarihi: 03.04.2023)
• [3] Üçüncü, O., Demirel, Ö. 2020. HES projelerinin olumsuz çevresel etkileri üzerinde alınacak önlemler ve koruma eylemleri; Kılıçlı regülatörü ve HES projesi örneği. Türkiye Peyzaj Araştırmaları Dergisi, 3(1), 31-39.
• [4] Mayeda, A. M., Boyd, A. D. 2020. Factors influencing public perceptions of hydpower projects: A systematic literatüre rewiew. Renewable and Sustainable Energy Reviews. 121, 109713.
• [5] Jang, Y., Park, E. 2020. Social acceptance of nuclear power plants in Korea: The role of public perceptions following the Fukushima accident. Renewable and Sustainable Energy Reviews, 128, 109894.
• [6] Bronfman, N.C., Jimenez, R.B., Arevalo, P.C., Cifuentes, L.A. 2012. Understanding social acceptance of electricity generation sources. Energy policy, 46, 246-252.
• [7] Xiao, O., Liu, H., Feldman, M.W. 2017. How does trust affect acceptance of a nuclear power plant (NPP): A survey among people living with Qinshan NPP in China. PloS One, 12(11), e0187941.
• [8] Alzahrani, S.M., Alwafi, A.M., Alshehri, S.M. 2023. A framework of examining the factors affecting public acceptance of nuclear power plant: Case study in Saudi Arabia. Nuclear Engineering and Technology. 55(3), 908-918.
• [9] Wang, Y., Gu, J., Wu, J. 2020. Explaining local residents acceptance of rebuilding nuclear power plants: The roles of perceived general benefit and perceived local benefit. Energy Policy, 140, 111410.
• [10] Zhu, W., Lu, S., Huang, Z., Zeng, J., Wei, J. 2020. Study on public acceptance of nuclear power plants: Evidence from China. Human and Ecological Risk Assessment: An International Journal, 26(4), 873-889.
• [11] Liu, F., Lyu, T., Pan, L., Wang, F. 2017. Influencing factors of public support for modern coal-fired power plant projects: An empiricial study from China. Energy Policy, 105, 398-406.
• [12] Mah, D.N.Y., Hills, P., Tao, J. 2014. Risk perceptions, trust and public engagement in nuclear decision-making in Hong Kong. Energy Policy. 73, 368-390.
• [13] Tanaka, Y. 2004. Major psychological factors determining public acceptance of the siting of the nuclear facilities. Journal of Applied Social Psychology. 34(6), 1147-1165.
• [14] Yıldız, A., Arı, E. 2019. An Investigation on The Social Acceptance of Nuclear Energy: A Case Study on University Students. İzmir Journal of Economics. 34(2), 191-211.
• [15] Kimura, H., Suzuki, A. 2003. Exploring research of factors affecting public acceptance of nuclear Energy. Result of a survey at Suginami ward, Tokyo. Nippon Genshiryoku Gakkai Wabun Ronbunshi, 2(1), 68-75.
• [16] Wold, H. 1974. Causal flows with latent variables: partings of the ways in the light of NIPALS modelling. European economic review, 5(1), 67-86.
• [17] Wold, H. 1980. Model construction and evaluation when theoretical knowledge is scarce: Theory and application of partial least squares. In Evaluation of econometric models, Academic Press. 47-74.
• [18] Wold, H. (1982). “Soft modelling: the basic design and some extensions”, in Joreskog, K.G. and Wold, H. (Eds), Systems Under Indirect Observations: Part II, North-Holland, Amsterdam. 36-37.
• [19] Fornell, C., Bookstein, F.L. 1982. Two structural equation models: LISREL and PLS applied to consumer exit-voice theory. Journal of Marketing research, 19(4), 440-452.
• [20] Schneeweiss, H. 1991. Models with latens variables: LISREL versus PLS. Statistica Neerlandica, 45(2), 145-157.
• [21] Vinzi, V.E., Trinchera, L., Amato, S. 2010. PLS path modelling: from foundations to recent developments and open issues for model assesment and improvement. Handbook of partial least squares; Concepts, methods and applications, 47-82.
• [22] Wang, Y., Li, J. 2016. A causal model explaining Chinese university students’ acceptance of nuclear power. Progress in Nuclear Energy, 88, 165-174.
• [23] Lopez-Navarro, M.A., Llorens-Monzonis, J., Tortosa-Edo, V. 2013. The effect of Social trust on citizens’ health risk perception in the context of a petrochemical industrial complex. International Journal of Environmental Research and Public Health, 10(1), 399-416.
• [24] Visschers, V.H., Keller, C., Siegrist, M. 2011. Climate change benefits and Energy supply benefits as determinants of acceptance of nuclear power stations: Investigating an explanatory model. Energy policy. 39(6), 3621- 3629.
• [25] Bronfman, N.C., Vazquez, E.L., Dorantes, G. 2009. An empiricial study for the direct and indirect links between trust in regulatory institutions and acceptability of hazards. Safety Science. 47(5), 686-692.
• [26] Siegrist, M. 1999. A causal model explaining the perception and acceptance of gene technology. Journal of Applied Social Psychology, 29, 2093- 2106.
• [27] Siegrist, M. 2000. The influence of trust and perceptions of risks and benefits on the acceptance of gene technology. Risk Analysis, 20, 195-203.
• [28] Siegrist, M., Cousin, M.E., Kastenholz, H., Wiek, A. 2007. Public acceptance of nanotechnology foods and food packaginf: the influence of effect and trust. Appetite, 49, 459-466.
• [29] Champion, V.L., Skinner, C.S. 2008. The health belief model. Health behavior and health education: theory, research and practice, (4), 189-193.
• [30] Finucane, M.L., Alhakami, A., Slovic, P., Johnson, S.M. 2000. The affect heuristic in judgments of risks and benefits. Journal of Behavioral Decision Making, 13, 1-17.
• [31] Frewer, L.J., Howard, C., Shepherd, R. 1998. Understanding public attitudes to technology. Journal of Risk Research, 1, 221-235.
• [32] Wallquist, L., Visschers, V.H.M., Siegrist, M. 2010. Impact of knowledge and misconceptions on benefit and risk perception of CSS. Environmental Science & Technology, 44, 6557- 6562.
• [33] Hammami, S.M., Triki, A. 2016. Identifying the determinants of community acceptance of renewable energy technologies: The case study of a wind Energy Project from Tunisia. Renewable and Sustainable Reviews, 54, 151- 160.
• [34] Ricci, M., Bellaby, P., Flynn, R. 2008. What do we know about public perceptions and acceptance of hydrogen? A critical Review and new case study evidence. International Journal of Hydrogen Energy, 33(21), 5868-5880.
• [35] Tarigan, A.K., Bayer, S.B., Langhelle, O., Thesen, G. 2012. Estimating determinants of public acceptance of hydrogen vehicles and refuelling stations in greater Stavanger. International Journal of Hydrogen Energy, 37(7), 6063-6073.
• [36] Rijnsoever, F.J., Farla, J.C. 2014. Identifying and explaining public preferences for the attributes of energy technologies. Renewable and Sustainable Energy reviews, 31, 71-82.
• [37] Sherry-Brennan, F., Devine-Wright, H., DevineWright, P. 2010. Public understanding of hydrogen energy: a theoretical approach. Energy Policy, 38(10), 5311-5319.
• [38] Fornell, C., Larcker, D.F. 1981. Evaluating structural equation models with unobservable variables and measurement error. Journal of Marketing Research, 18(1), 39-50.
• [39] Hair, J.F., Tatham, R.L., Anderson, R.E., Black, W. 1998. Multivariate data analysis with readings, and 5th Edn. Englewood Cliffs, NJ: Prentice Hall.
• [40] Henseler, J., Ringle, C.M., Sarstedt, M. 2015. A new criterion for assessing discriminant validity in varience-based structural equation modelling. Journal of the Academy of Marketing Science, 43, 115-135.