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What is the Value of Scientific Knowledge? An Application to Global Warming Using the PRICE Model

William D. Nordhaus and David Popp

Year: 1997
Volume: Volume18
Number: Number 1
DOI: 10.5547/ISSN0195-6574-EJ-Vol18-No1-1
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Abstract:
Governments must cope with the enormous uncertainties about both future climate change as well as the costs and benefits of slowing climate change. This study analyses the value of improved information about a variety of geophysical and economic processes. The value of information is estimated using the "PRICE model" which is a probabilistic extension of earlier models of the economics of global warming. The study uses five different approaches to estimating the value of information about all uncertain parameters and about individual parameters. It is estimated that the value of early information is between $1 and $2 billion for each year that resolution of uncertainty is moved toward the present. We estimate that the most important uncertain variables are the damages of climate change and the costs of reducing greenhouse gas emissions. Resolving the uncertainties about these two parameters would contribute 75 percent of the value of improved knowledge.



The Transition to Endogenous Technical Change in Climate-Economy Models: A Technical Overview to the Innovation Modeling Comparison Project

Jonathan Kohler, Michael Grubb, David Popp and Ottmar Edenhofer 

Year: 2006
Volume: Endogenous Technological Change
Number: Special Issue #1
DOI: 10.5547/ISSN0195-6574-EJ-VolSI2006-NoSI1-2
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Abstract:
This paper assesses endogenous technical change (ETC) in climate-economy models, using the models in the Innovation Modeling Comparison Project (IMCP) as a representative cross-section. ETC is now a feature of most leading models. Following the new endogenous growth literature and the application of learning curves to the energy sector, there are two main concepts employed: knowledge capital and learning curves. The common insight is that technical change is driven by the development of knowledge capital and its characteristics of being partly non-rival and partly non-excludable. There are various different implementations of ETC. Recursive CGE models face particular difficulties in incorporating ETC and increasing returns. The main limitations of current models are: the lack of uncertainty analysis; the limited representation of the diffusion of technology; and the homogeneous nature of agents in the models including the lack of representation of institutional structures in the innovation process.



Comparison of Climate Policies in the ENTICE-BR Model

David Popp

Year: 2006
Volume: Endogenous Technological Change
Number: Special Issue #1
DOI: 10.5547/ISSN0195-6574-EJ-VolSI2006-NoSI1-7
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Abstract:
This paper uses the ENTICE-BR model to study the effects of various climate stabilization policies. Because the ENTICE-BR model includes benefits from reduced climate damages, it is possible to calculate the net economic impact of each policy. In general, only the least restrictive concentration limit is welfare enhancing. While the policies are welfare enhancing in simulations using optimistic assumptions about the potential of the backstop energy technology, such assumptions mean that the backstop is also used in the no-policy base case, so that climate change itself is less of a problem. Finally, assumptions about the nature of R&D markets are important. Removing the assumption of partial crowding out from energy R&D nearly doubles the gains from policy-induced energy R&D.



Inter-temporal R&D and capital investment portfolios for the electricity industry’s low carbon future

Nidhi R. Santen, Mort D. Webster, David Popp, and Ignacio Pérez-Arriaga

Year: 2017
Volume: Volume 38
Number: Number 6
DOI: https://doi.org/10.5547/01956574.38.6.nsan
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Abstract:
A pressing question facing policy makers today in developing a long-term strategy to manage carbon emissions from the electric power sector is how to appropriately balance investment in R&D for driving innovation in emerging low-and zero-carbon technologies with investment in commercially available technologies for meeting existing energy needs. Likewise, policy makers need to determine how to allocate limited funding across multiple technologies. Unfortunately, existing modeling tools to study these questions lack a realistic representation of electric power system operations, the innovation process, or both. In this paper, we present a new modeling framework for long-term R&D and electricity generation capacity planning that combines an economic representation of endogenous non-linear technical change with a detailed representation of the power system. The model captures the complementary nature of technologies in the power sector; physical integration constraints of the system; and the opportunity to build new knowledge capital as a non-linear function of R&D and accumulated knowledge, reflective of the diminishing marginal returns to research inherent in the energy innovation process. Through a series of numerical experiments and sensitivity analyses - with and without carbon policy - we show how using frameworks that do not incorporate these features can over-or under-estimate the value of different emerging technologies, and potentially misrepresent the cost-effectiveness of R&D opportunities.





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