IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v13y2020i22p5855-d442416.html
   My bibliography  Save this article

Policy Performance of Green Lighting Industry in China: A DID Analysis from the Perspective of Energy Conservation and Emission Reduction

Author

Listed:
  • Kan Wang

    (School of Management and Economics, Beijing Institute of Technology, Beijing 100081, China
    Center for Energy and Environmental Policy Research, Beijing Institute of Technology, Beijing 100081, China
    Beijing Key Lab of Energy Economics and Environmental Management, Beijing 100081, China
    National Energy Conservation Center, Beijing 100045, China)

  • Li Lei

    (National Energy Conservation Center, Beijing 100045, China)

  • Shuai Qiu

    (China Solid State Lighting Alliance, Beijing 100083, China)

  • Sen Guo

    (School of Economics and Management, North China Electric Power University, Beijing 102206, China)

Abstract

As a component of China’s strategic emerging industries, green lighting is an important industry supporting the high-quality and high-efficiency development of China’s economy, and is also an important way to achieve energy conservation and emission reduction. At present, China has basically established a policy framework to promote the development of green lighting industry, but there is no empirical evidence on the performance of existing policies on energy conservation and emission reduction. Based on the development status of China’s green lighting industry, this paper sorts out the milestones of China’s green lighting industry policy and the current status of the framework of the existing green lighting industry development policies, constructs a policy performance evaluation model for China’s green lighting industry based on the difference-in-difference (DID) model, and evaluates the implementation effects of green lighting industry policies in China from the perspective of energy conservation and emission reduction. The empirical results of China’s 85 cities show that the implementation of green lighting industry policies has significantly promoted regional energy conservation and emission reduction. Finally, this paper puts forward targeted policy recommendations to provide policy support for the transformation of China’s green lighting industry from “large” to “strong”.

Suggested Citation

  • Kan Wang & Li Lei & Shuai Qiu & Sen Guo, 2020. "Policy Performance of Green Lighting Industry in China: A DID Analysis from the Perspective of Energy Conservation and Emission Reduction," Energies, MDPI, vol. 13(22), pages 1-18, November.
    • Handle: RePEc:gam:jeners:v:13:y:2020:i:22:p:5855-:d:442416
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/13/22/5855/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/13/22/5855/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. van de Kaa, Geerten & Greeven, Mark, 2017. "LED standardization in China and South East Asia: Stakeholders, infrastructure and institutional regimes," Renewable and Sustainable Energy Reviews, Elsevier, vol. 72(C), pages 863-870.
    2. Martinot, Eric & Borg, Nils, 1998. "Energy-efficient lighting programs: Experience and lessons from eight countries," Energy Policy, Elsevier, vol. 26(14), pages 1071-1081, December.
    3. Wang, Hung-Jen & Ho, Chia-Wen, 2010. "Estimating fixed-effect panel stochastic frontier models by model transformation," Journal of Econometrics, Elsevier, vol. 157(2), pages 286-296, August.
    4. Ashenfelter, Orley & Card, David, 1985. "Using the Longitudinal Structure of Earnings to Estimate the Effect of Training Programs," The Review of Economics and Statistics, MIT Press, vol. 67(4), pages 648-660, November.
    5. Alemi, Farzad & Rodier, Caroline & Drake, Christiana, 2018. "Cruising and on-street parking pricing: A difference-in-difference analysis of measured parking search time and distance in San Francisco," Transportation Research Part A: Policy and Practice, Elsevier, vol. 111(C), pages 187-198.
    6. Haoran Zhao & Sen Guo & Huiru Zhao, 2018. "Characterizing the Influences of Economic Development, Energy Consumption, Urbanization, Industrialization, and Vehicles Amount on PM 2.5 Concentrations of China," Sustainability, MDPI, vol. 10(7), pages 1-19, July.
    7. Kenneth Houngbedji, 2016. "Abadie’s semiparametric difference-in-differences estimator," Stata Journal, StataCorp LP, vol. 16(2), pages 482-490, June.
    8. Dendir, Seife & Orlov, Alexei G. & Roufagalas, John, 2019. "Do economics courses improve students’ analytical skills? A Difference-in-Difference estimation," Journal of Economic Behavior & Organization, Elsevier, vol. 165(C), pages 1-20.
    9. Sun, Yu & Yan, Karen X., 2019. "Inference on Difference-in-Differences average treatment effects: A fixed-b approach," Journal of Econometrics, Elsevier, vol. 211(2), pages 560-588.
    10. Guo, Fei & Pachauri, Shonali, 2017. "China's Green Lights Program: A review and assessment," Energy Policy, Elsevier, vol. 110(C), pages 31-39.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Lin, Boqiang & Teng, Yuqiang, 2022. "Structural path and decomposition analysis of sectoral carbon emission changes in China," Energy, Elsevier, vol. 261(PB).
    2. Mengyao Liu & Yan Hou & Hongli Jiang, 2023. "The Energy-Saving Effect of E-Commerce Development—A Quasi-Natural Experiment in China," Energies, MDPI, vol. 16(12), pages 1-22, June.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Kuo, Pei-Fen & Shen, Chung-Wei & Chiu, Chui-Sheng, 2021. "The impact of large-scale events: A difference-in-difference model for a Pokémon go safari zone event in Tainan and its effect on bikeshare systems," Transportation Research Part A: Policy and Practice, Elsevier, vol. 149(C), pages 283-299.
    2. Zhang, Yingheng & Li, Haojie & Ren, Gang, 2022. "Quantifying the social impacts of the London Night Tube with a double/debiased machine learning based difference-in-differences approach," Transportation Research Part A: Policy and Practice, Elsevier, vol. 163(C), pages 288-303.
    3. Reynolds, Travis & Kolodinsky, Jane & Murray, Byron, 2012. "Consumer preferences and willingness to pay for compact fluorescent lighting: Policy implications for energy efficiency promotion in Saint Lucia," Energy Policy, Elsevier, vol. 41(C), pages 712-722.
    4. Angrisani, Marco & Atella, Vincenzo & Brunetti, Marianna, 2018. "Public health insurance and household portfolio Choices: Unravelling financial “Side Effects” of Medicare," Journal of Banking & Finance, Elsevier, vol. 93(C), pages 198-212.
    5. Beestermöller, Matthias, 2017. "Striking Evidence? Demand Persistence for Inter-City Buses from German Railway Strikes," Discussion Papers in Economics 31768, University of Munich, Department of Economics.
    6. David Card, 2022. "Design-Based Research in Empirical Microeconomics," American Economic Review, American Economic Association, vol. 112(6), pages 1773-1781, June.
    7. James J. Heckman, 1991. "Randomization and Social Policy Evaluation Revisited," NBER Technical Working Papers 0107, National Bureau of Economic Research, Inc.
    8. Ichimura, Hidehiko & Todd, Petra E., 2007. "Implementing Nonparametric and Semiparametric Estimators," Handbook of Econometrics, in: J.J. Heckman & E.E. Leamer (ed.), Handbook of Econometrics, edition 1, volume 6, chapter 74, Elsevier.
    9. Yanzhao Wang & Jianfei Cao, 2023. "Examining the Effects of Socioeconomic Development on Fine Particulate Matter (PM2.5) in China’s Cities Based on Spatial Autocorrelation Analysis and MGWR Model," IJERPH, MDPI, vol. 20(4), pages 1-23, February.
    10. Jean‐Louis Combes & Xavier Debrun & Alexandru Minea & René Tapsoba, 2018. "Inflation Targeting, Fiscal Rules and the Policy Mix: Cross‐effects and Interactions," Economic Journal, Royal Economic Society, vol. 128(615), pages 2755-2784, November.
    11. Chen, Yi-Yi & Schmidt, Peter & Wang, Hung-Jen, 2014. "Consistent estimation of the fixed effects stochastic frontier model," Journal of Econometrics, Elsevier, vol. 181(2), pages 65-76.
    12. Lina Schollin Ask & Can Liu & Karl Gauffin & Anders Hjern, 2019. "The Effect of Rotavirus Vaccine on Socioeconomic Differentials of Paediatric Care Due to Gastroenteritis in Swedish Infants," IJERPH, MDPI, vol. 16(7), pages 1-10, March.
    13. Irma Perez-Johnson & Jacqueline Kauff & Alan Hershey, "undated". "Giving Noncustodial Parents Options: Employment and Child Support Outcomes of the SHARE Program," Mathematica Policy Research Reports aed55698cd8f49879a98f881c, Mathematica Policy Research.
    14. Subal C. Kumbhakar & Christopher F. Parmeter & Valentin Zelenyuk, 2022. "Stochastic Frontier Analysis: Foundations and Advances I," Springer Books, in: Subhash C. Ray & Robert G. Chambers & Subal C. Kumbhakar (ed.), Handbook of Production Economics, chapter 8, pages 331-370, Springer.
    15. Carman, Hoy F. & Li, Lan & Sexton, Richard J., 2006. "A New Framework for Evaluating Commodity Promotion Programs: What Can We Learn from Disaggregate Data?," 2006 Annual meeting, July 23-26, Long Beach, CA 21229, American Agricultural Economics Association (New Name 2008: Agricultural and Applied Economics Association).
    16. Andrews, Martyn & Bradley, Steve & Upward, Richard, 1999. "Estimating Youth Training Wage Differentials during and after Training," Oxford Economic Papers, Oxford University Press, vol. 51(3), pages 517-544, July.
    17. Tai-Hsin Huang & Yi-Huang Chiu & Chih-Ying Mao, 2021. "Imposing Regularity Conditions to Measure Banks’ Productivity Changes in Taiwan Using a Stochastic Approach," Asia-Pacific Financial Markets, Springer;Japanese Association of Financial Economics and Engineering, vol. 28(2), pages 273-303, June.
    18. Haiyang Lu & Peng Nie & Alfonso Sousa-Poza, 2021. "The Effect of Parental Educational Expectations on Adolescent Subjective Well-Being and the Moderating Role of Perceived Academic Pressure: Longitudinal Evidence for China," Child Indicators Research, Springer;The International Society of Child Indicators (ISCI), vol. 14(1), pages 117-137, February.
    19. Christos Genakos & Mario Pagliero, 2022. "Competition and Pass-Through: Evidence from Isolated Markets," American Economic Journal: Applied Economics, American Economic Association, vol. 14(4), pages 35-57, October.
    20. Harsha Thirumurthy & Joshua Graff Zivin & Markus Goldstein, 2008. "The Economic Impact of AIDS Treatment: Labor Supply in Western Kenya," Journal of Human Resources, University of Wisconsin Press, vol. 43(3), pages 511-552.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jeners:v:13:y:2020:i:22:p:5855-:d:442416. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.