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Green technology adoption: An empirical study of the Southern California garment cleaning industry

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  • Bryan Bollinger

Abstract

Green technology adoption may be limited by a variety of factors including a lack of sufficient private incentives, long equipment replacement cycles, or lack of information regarding the green alternatives. In California, multiple policy tools including financial incentives, command-and-control regulation, and information and training were used to overcome these obstacles in order to reduce the use of the polluting technology used in traditional dry cleaning. Exploiting the changing regulatory environment, I evaluate the effectiveness of the different policy tools by estimating a dynamic, durable goods replacement model with entry and exit. Because the strict regulations affect future years only, identifying the discount rate is crucial, which I estimate to be 0.94. Using counterfactual simulations under alternative policy regimes, I find that the provision of information and training offered through demonstrations increased adoption of that technology (wet cleaning) by over 200 %. Price incentives (via fees and grants) would have been ineffective at achieving widespread adoption of green technologies but were effective at accelerating adoption when combined with a future ban on polluting technologies. Using this combination of policy instruments led to a net welfare gain of $71 million (NPV) in 2002 when the policies were implemented. Copyright Springer Science+Business Media New York 2015

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  • Bryan Bollinger, 2015. "Green technology adoption: An empirical study of the Southern California garment cleaning industry," Quantitative Marketing and Economics (QME), Springer, vol. 13(4), pages 319-358, December.
  • Handle: RePEc:kap:qmktec:v:13:y:2015:i:4:p:319-358
    DOI: 10.1007/s11129-015-9163-0
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    4. Christopher J. Blackburn & Mallory E. Flowers & Daniel C. Matisoff & Juan Moreno‐Cruz, 2020. "Do Pilot and Demonstration Projects Work? Evidence from a Green Building Program," Journal of Policy Analysis and Management, John Wiley & Sons, Ltd., vol. 39(4), pages 1100-1132, September.
    5. Christopher J. Blackburn & Mallory E. Flowers & Daniel C. Matisoff & Juan Moreno-Cruz, 2018. "Do Pilot and Demonstration Projects Work?," CESifo Working Paper Series 7252, CESifo.
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    10. Olivier De Groote & Frank Verboven, 2016. "Subsidies and myopia in technology adoption: evidence from solar photovoltaic systems," Working Papers of Department of Economics, Leuven 547933, KU Leuven, Faculty of Economics and Business (FEB), Department of Economics, Leuven.
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    14. De Xia & Wenhua Chen & Qinglu Gao & Rui Zhang & Yundong Zhang, 2021. "Research on Enterprises’ Intention to Adopt Green Technology Imposed by Environmental Regulations with Perspective of State Ownership," Sustainability, MDPI, vol. 13(3), pages 1-19, January.
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    More about this item

    Keywords

    Dynamic programming; Discrete choice; Technology adoption; Information provision; Importance sampling; Regulated markets; Environmental policy; Green technology; D21; L16; L20; L51; 033; C61;
    All these keywords.

    JEL classification:

    • D21 - Microeconomics - - Production and Organizations - - - Firm Behavior: Theory
    • L16 - Industrial Organization - - Market Structure, Firm Strategy, and Market Performance - - - Industrial Organization and Macroeconomics; Macroeconomic Industrial Structure
    • L20 - Industrial Organization - - Firm Objectives, Organization, and Behavior - - - General
    • L51 - Industrial Organization - - Regulation and Industrial Policy - - - Economics of Regulation
    • C61 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Optimization Techniques; Programming Models; Dynamic Analysis

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