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Carrots and sticks for new technology: Abating greenhouse gas emissions in a heterogeneous and uncertain world

Author

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  • Robalino, David
  • Lempert, Robert

Abstract

Many governments use technology incentives as an important component of their greenhouse gas abatement strategies. These “carrots” are intended to encourage the initial diffusion of new, greenhouse-gas-emissions-reducing technologies, in contrast to carbon taxes and emissions trading which provide a “stick” designed to reduce emissions by increasing the price of high-emitting technologies for all users. Technology incentives appear attractive, but their record in practice is mixed and economic theory suggests that in the absence of market failures, they are inefficient compared to taxes and trading. This study uses an agent-based model of technology diffusion and exploratory modeling, a new technique for decision-making under conditions of extreme uncertainty, to examine the conditions under which technology incentives should be a key building block of robust climate change policies. We find that a combined strategy of carbon taxes and technology incentives, as opposed to carbon taxes alone, is the best approach to greenhouse gas emissions reductions if the social benefits of early adoption sufficiently exceed the private benefits. Such social benefits can occur when economic actors have a wide variety of cost/performance preferences for new technologies and either new technologies have increasing returns to scale or potential adopters can reduce their uncertainty about the performance of new technologies by querying the experience of other adopters. We find that if decision-makers hold even modest expectations that such social benefits are significant or that the impacts of climate change will turn out to be serious then technology incentive programs may be a promising hedge against the threat of climate change.

Suggested Citation

  • Robalino, David & Lempert, Robert, 2000. "Carrots and sticks for new technology: Abating greenhouse gas emissions in a heterogeneous and uncertain world," MPRA Paper 12002, University Library of Munich, Germany.
  • Handle: RePEc:pra:mprapa:12002
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    File URL: https://mpra.ub.uni-muenchen.de/12002/1/MPRA_paper_12002.pdf
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    References listed on IDEAS

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    Citations

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    Cited by:

    1. Giorgio Fagiolo & Andrea Roventini, 2017. "Macroeconomic Policy in DSGE and Agent-Based Models Redux: New Developments and Challenges Ahead," Journal of Artificial Societies and Social Simulation, Journal of Artificial Societies and Social Simulation, vol. 20(1), pages 1-1.
    2. Beck, Jessica & Kempener, Ruud & Cohen, Brett & Petrie, Jim, 2008. "A complex systems approach to planning, optimization and decision making for energy networks," Energy Policy, Elsevier, vol. 36(8), pages 2803-2813, August.
    3. Balint, T. & Lamperti, F. & Mandel, A. & Napoletano, M. & Roventini, A. & Sapio, A., 2017. "Complexity and the Economics of Climate Change: A Survey and a Look Forward," Ecological Economics, Elsevier, vol. 138(C), pages 252-265.
    4. Li, Francis G.N. & Trutnevyte, Evelina & Strachan, Neil, 2015. "A review of socio-technical energy transition (STET) models," Technological Forecasting and Social Change, Elsevier, vol. 100(C), pages 290-305.
    5. Robalino, David A. & Jenkins, Carol & El Maroufi, Karim, 2002. "Risks and macroeconomic impacts of HIV/AIDS in the Middle East and North Africa : why waiting to intervene can be costly," Policy Research Working Paper Series 2874, The World Bank.
    6. Tomas Balint & Francesco Lamperti & Antoine Mandel & Mauro Napoletano & Andrea Roventini & Sandro Sapio, 2017. "Complexity and the economics of climate change : a survey and a look foreward," Sciences Po publications info:hdl:2441/1nlv566svi8, Sciences Po.
    7. Gritsevskyi, Andrii & Nakicenovi, Nebojsa, 2000. "Modeling uncertainty of induced technological change," Energy Policy, Elsevier, vol. 28(13), pages 907-921, November.

    More about this item

    Keywords

    climate change; technology policy; uncertainty; agent-based modeling; exploratory modeling; social interactions;

    JEL classification:

    • H21 - Public Economics - - Taxation, Subsidies, and Revenue - - - Efficiency; Optimal Taxation
    • C63 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Computational Techniques
    • D61 - Microeconomics - - Welfare Economics - - - Allocative Efficiency; Cost-Benefit Analysis
    • D80 - Microeconomics - - Information, Knowledge, and Uncertainty - - - General
    • Q54 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Environmental Economics - - - Climate; Natural Disasters and their Management; Global Warming

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