Skip to main content

Advertisement

SpringerLink
Log in

Evaluation of a consumer incentive program for an energy-efficient product in South Korea

  • Original Article
  • Published:
Energy Efficiency Aims and scope Submit manuscript

Abstract

Because green products are expected to reduce environmental pollution, save natural resources, and protect public health, they are regarded as a useful instrument of sustainable economic growth. Although many policies exist to promote the purchase of green products, their successful adoption requires the voluntary participation of individual consumers and the implementation of an appropriate incentive system. This study presents an ex ante evaluation of consumer preferences when faced with an incentive program that aims to promote the purchase of green products, specifically an energy-efficient LCD television. By using a conjoint analysis with a mixed logit model, we explore the effects of the incentive program on electric power consumption and the consequential reduction in greenhouse gas emissions in South Korea. Our simulation results suggest that when a consumer receives both 5 % of the purchase price of the green product in the form of “incentive points” and a one-million Korean won income tax deduction, the electric power consumption of LCD televisions nationwide will reduce by 50 GWh, thus reducing overall CO2 emissions by 21,200 t.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2

Similar content being viewed by others

Notes

  1. The Intergovernmental Panel on Climate Change (2014) report estimates that global economic losses would be 0.2–2.0 % of income given a global mean temperature rise of up to 2.5 °C.

  2. Throughout this paper, an energy-efficient LCD TV is defined as an LCD TV that consumes less power compared with other LCD TVs.

  3. Examples include applications of DCMs in the context of a new electronic payment system for credit cards (Kim et al. 2007a), an electric vehicle (Ewing and Sarigollu 2000; Hong et al. 2012; Kim et al. 2007b; McFadden and Train 2000), and a new telecommunications service (Ahn et al. 2006; Lee et al. 2006).

  4. For the detailed estimation procedures, see Koop et al. (2007) and Rossi et al. (2005).

  5. Gender: male (50.1 %), female (49.4 %)

    Age: 20s (21.5 %), 30s (26.3 %), 40s (28.2 %), 50s (24.0 %)

  6. Other potential attributes not included in this survey (such as brand and resolution) are assumed to be identical across options. Moreover, this assumption was explained in detail to all respondents.

  7. In South Korea, electric charges are imposed based on a progressive scale; the rate was 258.7 KRW/kWh for the 300–400-kWh range in July 2011.

  8. Although a consumer can use these accumulated points in many ways, the cash-back option was assumed to be the only viable one because of the complexity of the survey. This means that consumers are able to use saved points at affiliated stores instead of exchanging them for cash.

  9. According to the tax laws of South Korea, if a person earns 50 million KRW annually and receives an additional benefit of a 2-million KRW tax deduction, he/she may save approximately 0.2 million KRW per year in tax.

  10. Similarly, tax deductions are also given for energy saving buildings in Japan, high-efficiency household items in the USA, and purchasing public transport tickets in Canada.

  11. The estimation model was described in “Model specifications.”

  12. In general, the parameters in a mixed logit model are assumed to have normal distributions, with the exception of some attributes that have specific signs. Those are usually assumed to be log-normal distributions to ensure that the parameters have either positive or negative values (Train 2009). The attributes “point-saving rate,” “additional tax deduction,” and “TV price” are assumed to be log-normal distributions; the other attributes are assumed to be normally distributed.

  13. A total of 100 datasets were collected from a commercial transaction website in South Korea (http://www.danawa.com/).

  14. The statistical significance of D SONY _ r has a minor effect on the overall research stream because the important consideration for a market simulation is the relationship between the price of a TV and its energy efficiency level, not the brand variable.

  15. The power consumption of each grade is calculated from the energy thresholds of the Ministry of the Knowledge Economy (2011) by assuming that all TVs are identical in terms of screen size (40 in.) and 3D functionality.

  16. The price of TVs in each grade is calculated by using Eq. 10. The average price (1.65 million KRW) is fixed as the reference price for third-grade TVs, and the prices for the other four grades are subsequently calculated by using the estimation result (−3545 KRW/1 W).

  17. Poorly conceived incentive programs might lead to adverse effects. For example, Jacob and Levitt (2003) analyze the relationship between incentives for teachers and their cheating behavior. Levitt and Dubner (2005) also suggest the case of fine imposition to show how poorly designed incentive systems may lead to failure. However, it is difficult to quantify the adverse and/or negative effect of an incentive program because it is not easy to grasp the adverse behavior of individual actors, not to mention collect such behavior as usable data.

References

  • Ahn, J., Lee, J., Lee, J.-D., & Kim, T.-Y. (2006). An analysis of consumer preference among wireless LAN and mobile internet services. ETRI Journal, 28(2), 205–215.

    Article  MathSciNet  Google Scholar 

  • Baumann, H., Boons, F., & Bragd, A. (2002). Mapping the green product development field: engineering, policy and business perspectives. Journal of Cleaner Production, 10, 409–425.

    Article  Google Scholar 

  • Bopp, M., Kaczynski, A. T., & Wittman, P. (2011). The relationship of eco-friendly attitudes with walking and biking to work. Journal of Public Health Management and Practice, 17(5), E9–E17.

    Article  Google Scholar 

  • Brecard, D., Hlaimi, B., Lucas, S., Perraudeau, Y., & Salladarre, F. (2009). Determinants of demand for green products: an application to eco-label demand for fish in Europe. Ecological Economics, 69, 115–125.

    Article  Google Scholar 

  • Chen, C. (2001). Design for the environment: a quality-based model for green product development. Management Science, 47(2), 250–263.

    Article  Google Scholar 

  • Dahm, M. J., Samonte, A. V., & Shows, A. R. (2009). Organic foods: do eco-friendly attitudes predict eco-friendly behaviors? Journal of American College Health, 58(3), 195–202.

    Article  Google Scholar 

  • Dangelico, R. M., & Pontrandolfo, P. (2010). From green product definitions and classifications to the green option matrix. Journal of Cleaner Production, 18, 1608–1628.

    Article  Google Scholar 

  • Dangelico, R. M., & Pujari, D. (2010). Mainstreaming green product innovation: why and how companies integrate environmental sustainability. Journal of Business Ethics, 95, 471–486.

    Article  Google Scholar 

  • Ewing, G., & Sarigollu, E. (2000). Assessing consumer preferences for clean-fuel vehicles: a discrete choice experiment. Journal of Public Policy and Marketing, 19(1), 106–118.

    Article  Google Scholar 

  • Han, H., Hsu, L.-T. J., Lee, J.-S., & Sheu, C. (2011). Are lodging customers ready to go green? An examination of attitudes, demographics, and eco-friendly intentions. International Journal of Hospitality Management, 30(2), 345–355.

    Article  Google Scholar 

  • Hong, J., Koo, Y., Jeong, G., & Lee, J. (2012). Ex-ante evaluation of profitability and government’s subsidy policy on vehicle-to-grid system. Energy Policy, 42, 95–104.

    Article  Google Scholar 

  • Intergovernmental Panel on Climate Change (2014). Climate change 2014: impacts, adaptation, and vulnerability. Available online at: http://www.ipcc.ch/report/ar5/wg2/ (accessed Sep 14, 2014).

  • Jacob, B. A., & Levitt, S. D. (2003). Rotten apples: an investigation of the prevalence and predictors of teacher cheating. Quarterly Journal of Economics, 118(3), 843–877.

    Article  Google Scholar 

  • Kim, T.-Y., Choi, J. Y., & Lee, J. (2007a). Research on the consumer’s behavior and valuation of the payment instruments using Bayesian mixed logit model. International Telecommunications Policy Review, 14(1), 1–18.

    MathSciNet  Google Scholar 

  • Kim, Y., Jeong, G., Ahn, J., & Lee, J.-D. (2007b). Consumer preferences for alternative fuel vehicles in South Korea. International Journal of Automotive Technology and Management, 7, 327–342.

    Article  Google Scholar 

  • Koo, Y., Kim, C. S., Hong, J., Choi, I.-J., & Lee, J. (2012). Consumer preferences for automobile energy-efficiency grades. Energy Economics, 34, 446–451.

    Article  Google Scholar 

  • Koop, G., Poirier, D. J., & Tobias, J. L. (2007). Bayesian econometric methods. New York: Cambridge University Press.

    Book  Google Scholar 

  • Korea Energy Economics Institute (2005). Research on a strategy establishment for Unite Nations framework convention on climate change [in Korean].

  • Korean Government (2014). A roadmap for achieving national target of reducing greenhouse gas emission. The Government of Korea [in Korean]

  • Lee, S. (2010). Study on a retrospective estimation of household energy consumption. Korea Energy Economics Institute. Report on the basic research 10–05 [in Korean]

  • Lee, K.-H., & Kim, J.-W. (2011). Integrating suppliers into green product innovation development: an empirical case study in the semiconductor industry. Business Strategy and the Environment, 20, 527–538.

    Article  Google Scholar 

  • Lee, J., Kim, Y., Lee, J.-D., & Park, Y. (2006). Estimating the extent of potential competition in the Korean mobile telecommunications market: switching costs and number portability. International Journal of Industrial Organization, 24, 107–124.

    Article  Google Scholar 

  • Levitt, S. D., & Dubner, S. J. (2005). Freakonomics: a rogue economist explores the hidden side of everything. New York: William Morrow.

    Google Scholar 

  • McFadden, D. (1974). Conditional logit analysis of qualitative choice behavior. In P. Zarembka (Ed.), Frontiers in econometrics (pp. 105–142). New York: Academic.

    Google Scholar 

  • McFadden, D., & Train, K. (2000). Mixed MNL models for discrete response. Journal of Applied Econometrics, 15, 447–470.

    Article  Google Scholar 

  • Mills, B., & Schleich, J. (2010). What’s driving energy efficient appliance label awareness and purchase propensity? Energy Policy, 38, 814–825.

    Article  Google Scholar 

  • Ministry of the Environment (2011). The second basic scheme for promoting green purchasing. The Government of Korea [in Korean]

  • Ministry of the Knowledge Economy (2011). Starting new energy-efficiency grades for TV. The Government of Korea [in Korean]

  • Murray, A., & Mills, B. (2011). Read the label! Energy Star appliance label awareness and uptake among U.S. consumers. Energy Economics, 33, 1103–1110.

    Article  Google Scholar 

  • Ottman, J. A., Stafford, E. R., & Hartman, C. L. (2006). Avoiding green marketing myopia: ways to improve consumer appeal for environmentally preferable products. Environment, 48(5), 22–36.

    Article  Google Scholar 

  • Raghavarao, D., Wiley, J. B., & Chitturi, P. (2011). Choice-based conjoint analysis: models and designs. Florida: CRC Press.

    Google Scholar 

  • Rokka, J., & Uusitalo, L. (2008). Preference for green packaging in consumer product choices: do consumers care? International Journal of Consumer Studies, 32, 516–525.

    Article  Google Scholar 

  • Rossi, P. E., Allenby, G. M., & McCulloch, R. (2005). Bayesian statistics and marketing. West Sussex: Wiley.

    Book  Google Scholar 

  • Royne, M. B., Levy, M., & Martinez, J. (2011). The public health implications of consumers’ environmental concern and their willingness to pay for an eco-friendly product. The Journal of Consumer Affairs, 45(2), 329–343.

    Article  Google Scholar 

  • Saphores, J.-D. M., Nixon, H., Ogunseitan, O. A., & Shapiro, A. A. (2007). California households’ willingness to pay for ‘green’ electronics. Journal of Environmental Planning and Management, 50(1), 113–133.

    Article  Google Scholar 

  • Simon, F. L. (1992). Marketing green products in the triad. The Columbia Journal of World Business, 27, 268–285.

    Google Scholar 

  • Statistics Korea (2011). http://kostat.go.kr/portal/korea/index.action (assessed on 9 December 2011)

  • Train, K. E. (2009). Discrete choice methods with simulation (2nd ed.). New York: Cambridge University Press.

    Book  Google Scholar 

  • Young, W., Hwang, K., McDonald, S., & Oates, C. J. (2010). Sustainable consumption: green consumer behaviour when purchasing products. Sustainable Development, 18, 20–31.

    Google Scholar 

Download references

Acknowledgments

This research was funded by the Korea Environment Institute under Grant GP 2011-06.

Author information

Authors and Affiliations

  1. Department of Information & Industrial Engineering, College of Engineering, Yonsei University, 50 Yonsei-Ro, Seodaemun-gu, Seoul, 120-749, South Korea

    Youngsang Cho

  2. Korea Environment Institute, 215 Jinheungno, Eunpyeong-gu, Seoul, 122-706, South Korea

    Yoonmo Koo & Misuk Lee

  3. Technology Management Economics and Policy Program, Seoul National University, 1 Gwanak-ro, Gwanak-gu, Seoul, 151-742, South Korea

    Sung-Yoon Huh

Authors
  1. Youngsang Cho
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Yoonmo Koo
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Sung-Yoon Huh
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Misuk Lee
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Misuk Lee.

Appendix

Appendix

Table 10 shows the estimation results of the conditional logit model with the same dataset. If IIA exists, the choice probability of two alternatives should not be changed when irrelevant alternatives vary. In order to test the IIA assumption, we conduct the same estimation without the highest level of energy consumption (250 W) alternative and compare it with the results in Table 10. The Hausman test results suggest that the IIA assumption should be rejected at a significance level of 1 %. Thus, it is reasonable to use the mixed logit model, which relaxes the IIA assumption.

Although the IIA assumption is violated, the conditional logit model might be useful because it can analyze the effect of socioeconomic variables by inserting interaction terms between attributes and sociodemographic variables. However, we can find only a few meaningful interaction terms when we use gender and age as socioeconomic variables. Therefore, it is reasonable to use the mixed logit model in this paper.

Table 10 Estimation results of the conditional logit model
Full size table

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Cho, Y., Koo, Y., Huh, SY. et al. Evaluation of a consumer incentive program for an energy-efficient product in South Korea. Energy Efficiency 8, 745–757 (2015). https://doi.org/10.1007/s12053-014-9319-x

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s12053-014-9319-x

Keywords

Search

Navigation