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Article | Journal of Political Economy

Transition to Clean Technology

by Daron Acemoglu, Ufuk Akcigit, Douglas Hanley and William R. Kerr

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Abstract

We develop a microeconomic model of endogenous growth where clean and dirty technologies compete in production and innovation, in the sense that research can be directed to either clean or dirty technologies. If dirty technologies are more advanced to start with, the potential transition to clean technology can be difficult both because clean research must climb several rungs to catch up with dirty technology and because this gap discourages research effort directed towards clean technologies. Carbon taxes and research subsidies may nonetheless encourage production and innovation in clean technologies, though the transition will typically be slow. We characterize certain general properties of the transition path from dirty to clean technology. We then estimate the model using a combination of regression analysis on the relationship between R&D and patents, and simulated method of moments using microdata on employment, production, R&D, firm growth, entry and exit from the US energy sector. The model's quantitative implications match a range of moments not targeted in the estimation quite well. We then characterize the optimal policy path implied by the model and our estimates. Optimal policy makes heavy use of research subsidies as well as carbon taxes. We use the model to evaluate the welfare consequences of a range of alternative policies.

Keywords: Technological Innovation; Entrepreneurship; Environmental Sustainability; Green Technology Industry;

Format: Print Find at Harvard Read Now

Citation:

Acemoglu, Daron, Ufuk Akcigit, Douglas Hanley, and William R. Kerr. "Transition to Clean Technology." Special Issue on Climate Change and the Economy. Journal of Political Economy 124, no. 2 (February 2016): 52–104.

About the Author

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William R. Kerr
Dimitri V. D'Arbeloff - MBA Class of 1955 Professor of Business Administration
Entrepreneurial Management

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More from the Author

  • Case | HBS Case Collection | December 2018 (Revised December 2019)

    Modern Automation (A): Artificial Intelligence

    William R. Kerr and James Palano

    This primer is meant to be a field guide to the late 2010s' surge in business use of "Artificial Intelligence" (AI), or enterprise software based in machine learning. First, it provides an overview of the key trends - digitization, connectivity, the continuation of Moore's law, and advancements in machine learning algorithms - which led to the inflection point in adoption of AI. Then, the primer describes the range of use cases of state-of-the-art enterprise AI organized in a taxonomy designed to build generalized understanding of the types of work to which AI can be applied. This knowledge is useful not only to managers looking to best leverage the technology but to those working to manage resulting organizational change and prepare the workforce for transitions into complementary tasks. Finally, key issues and implications of AI adoption are also discussed.

    Keywords: artificial intelligence; digitization; connectivity; Computing; future of work; automation; Technology; Technology Adoption; Employment; Management; Information Technology;

    Citation:

    Kerr, William R., and James Palano. "Modern Automation (A): Artificial Intelligence." Harvard Business School Case 819-084, December 2018. (Revised December 2019.)  View Details
    CiteView DetailsEducators Related
  • Working Paper | HBS Working Paper Series | 2019

    Tech Clusters

    William R. Kerr and Frederic Robert-Nicoud

    Tech clusters like Silicon Valley play a central role for modern innovation, business competitiveness, and economic performance. This paper reviews what constitutes a tech cluster, how they function internally, and the degree to which policy makers can purposefully foster them. We describe the growing influence of advanced technologies for businesses outside of traditional tech fields, the strains and backlash that tech clusters are experiencing, and emerging research questions for theory and empirical work.

    Keywords: clusters; agglomeration; innovation; entrepreneurship; patents;

    Citation:

    Kerr, William R., and Frederic Robert-Nicoud. "Tech Clusters." Harvard Business School Working Paper, No. 20-063, November 2019.  View Details
    CiteView DetailsSSRN Read Now Related
  • Supplement | HBS Case Collection | October 2019

    The Golden Triangle: Back in Business (A)

    William R. Kerr

    Citation:

    Kerr, William R. "The Golden Triangle: Back in Business (A)." Harvard Business School Multimedia/Video Supplement 820-704, October 2019.  View Details
    CiteView DetailsPurchase Related
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