Linking Historical Roots and Current Methodologies of Engineering Systems

This paper reviews the historical context and present impact of two sets of literature: the work of Joseph Schumpeter and the field of Strategy Development. Schumpeter’s theories about the impact of technology or innovation on the economy are an important input into modern Engineering Systems (ES) thinking. Meanwhile, Strategy Development is an active contemporary methodology that is relevant to Engineering Systems. Both Schumpeter and the scholars in Strategy Development are concerned with how firms perform, but Schumpeter's approach is descriptive while Strategy Development is prescriptive. The approach in this paper is as follows. It first introduces the theories of Schumpeter on innovation and the major ideas within Strategy Development. Next, two historical reviews are presented. One review looks forward to find the impact that Schumpeter has had on modern fields; the second review looks backward to understand the roots of Strategy Development. These historical reviews are initially done independently. The final section asks whether there are direct historical links between Schumpeter and the scholars or ideas of Strategy Development. The major result of this investigation is that Schumpeter’s influence is widespread as are the roots of Strategy Development. The results also show that the writing of Schumpeter is related to Strategy literature because many of Schumpeter’s ideas have become foundational realities for Strategy Development. Meanwhile, this connection is just one of many for each field, and the link between Schumpeter and Strategy Development does not appear to be the most important

Technology Variation vs. R&D Uncertainty: What Matters Most for Energy Patent Success?

R&D is an uncertain activity with highly skewed outcomes. Nonetheless, most recent empirical studies and modeling estimates of the potential of technological change focus on the average returns to research and development (R&D) for a composite technology and contain little or no information about the distribution of returns to R&D—which could be important for capturing the range of costs associated with climate change mitigation policies—by individual technologies. Through an empirical study of patent citation data, this paper adds to the literature on returns to energy R&D by focusing on the behavior of the most successful innovations for six energy technologies, allowing us to determine whether uncertainty or differences in technologies matter most for success. We highlight two key results. First, we compare the results from an aggregate analysis of six energy technologies to technology-by-technology results. Our results show that existing work that assumes diminishing returns but assumes one generic technology is too simplistic and misses important differences between more successful and less successful technologies. Second, we use quantile regression techniques to learn more about patents that have a high positive error term in our regressions – that is, patents that receive many more citations than predicted based on observable characteristics. We find that differences across technologies, rather than differences across quantiles within technologies, are more important. The value of successful technologies persists longer than those of less successful technologies, providing evidence that success is the culmination of several advances building upon one another, rather than resulting from one single breakthrough. Diminishing returns to research efforts appear most problematic during rapid increases of research investment, such as experienced by solar energy in the 1970s.

Balancing solar PV deployment and RD&D: A comprehensive framework for managing innovation uncertainty in electricity technology investment planning

We present a new framework for studying the socially optimal level of generating capacity and public RD&D investments for the electric power sector under decision-dependent technical change uncertainty. We construct a bottom-up stochastic electricity generation capacity expansion model with uncertain endogenous RD&D-based technical change, focusing on solar PV RD&D investment planning for its current prominent role in sustainable energy and climate policy deliberations. We characterize the decision-dependent process of technical change uncertainty as unknown outcomes of RD&D investments that increase the likelihood of success with increasing amounts of RD&D, and calibrate to a novel expert elicitation dataset that accounts for this decision-dependence. The problem is framed as a multi-stage decision under uncertainty, where the decision maker learns and adapts to new information between decision periods. Specifically, our application considers four decision stages, with the decision-maker choosing investment levels for new capacity and solar PV RD&D, while learning about RD&D outcomes that can reduce solar PV costs between each stage. The problem is thus formulated to match the manner in which real-world decisions about RD&D investments in renewable energy are made, and avoids common assumptions of perfect foresight, or uncertainty but no learning, that are often used in practice. Numerical results show that when uncertainty and learning features are both included, the optimal solar PV RD&D investment strategy changes from solutions using other methods. Considering uncertainty and learning results in solar RD&D investment differences as high as 20 percent lower in the first-stage, and 300 percent higher in later stages. We also show that when uncertainty is considered without learning, the fraction of new solar PV capacity investments can be depressed. Overall, this paper shows that it is possible to unify several realistic features of the deployment and development problem for the electricity sector to meet sustainability goals into one framework

An approximate dynamic programming framework for modeling global climate policy under decision-dependent uncertainty

Analyses of global climate policy as a sequential decision under uncertainty have been severely restricted by dimensionality and computational burdens. Therefore, they have limited the number of decision stages, discrete actions, or number and type of uncertainties considered. In particular, other formulations have difficulty modeling endogenous or decision-dependent uncertainties, in which the shock at time t+1 depends on the decision made at time t. In this paper, we present a stochastic dynamic programming formulation of the Dynamic Integrated Model of Climate and the Economy (DICE), and the application of approximate dynamic programming techniques to numerically solve for the optimal policy under uncertain and decision-dependent technological change. We compare numerical results using two alternative value function approximation approaches, one parametric and one non-parametric. Using the framework of dynamic programming, we show that an additional benefit to near-term emissions reductions comes from a probabilistic lowering of the costs of emissions reductions in future stages, which increases the optimal level of near-term actions

Technology variation vs. R&D uncertainty: What matters most for energy patent success?

R&D is an uncertain activity with highly skewed outcomes. Nonetheless, most recent empirical studies and modeling estimates of the potential of technological change focus on the average returns to research and development (R&D) for a composite technology and contain little or no information about the distribution of returns to R&D – which could be important for capturing the range of costs associated with climate change mitigation policies – by individual technologies. Through an empirical study of patent citation data, this paper adds to the literature on the outcomes of energy R&D by focusing on the behavior of the most successful innovations for six energy technologies, allowing us to determine whether uncertainty or differences in technologies matter most for success. We highlight two key results. First, we compare the results from an aggregate analysis of six energy technologies to technology-by-technology results. Our results show that existing work that assumes diminishing returns but assumes one generic technology is too simplistic and misses important differences between more successful and less successful technologies. Second, we use quantile regression techniques to learn more about patents that have a high positive error term in our regressions – that is, patents that receive many more citations than predicted based on observable characteristics. We find that differences across technologies, rather than differences across quantiles within technologies, are more important. The value of successful technologies persists longer than those of less successful technologies, providing evidence that success is the culmination of several advances building upon one another, rather than resulting from one single breakthrough. Diminishing returns to research activities appear most problematic during rapid increases of research investment, such as experienced by solar energy in the 1970s.National Science Foundation (U.S.) (Grant 0825915

New modeling framework for the electric power sector

Thesis: Ph. D., Massachusetts Institute of Technology, Engineering Systems Division, February 2013.Cataloged from PDF version of thesis.Includes bibliographical references (pages 303-315).Effectively balancing existing technology adoption and new technology development is critical for successfully managing carbon dioxide (CO2) emissions from the fossil-dominated electric power generation sector. The long infrastructure lifetimes of power plant investments mean that deployment decisions made today will influence carbon dioxide emissions long into the future. New technology development and R&D decisions can help reduce the overall costs of reducing emissions, but there are multiple technology investments to choose from, and returns to R&D are inherently uncertain. These features of the technology "deployment versus development" question create unique challenges for decision makers charged with managing cumulative carbon dioxide emissions from the electricity sector. Unfortunately, current quantitative decision support tools ultimately lack one or more of three overarching features jointly necessary to provide useful insights about an optimal balance between R&D program and power plant investments. They lack (1) resolution of the critical structure of the electricity sector, (2) an explicit endogenous representation of the effects of learning-by-searching technological change, and/or (3) an efficient decision-analytic framework to explore multiple technology investment options under uncertainty in the returns to R&D. This dissertation presents a new quantitative decision support framework that allows for the study of socially optimal R&D and capital investment decisions for the power generation sector. Through a novel integration of classical electricity generation investment planning methods, economic modeling of endogenous R&D-driven technological change, and emerging numerical stochastic optimization techniques, the new framework (1) explicitly accounts for the complementary roles that generating technologies play within the electric power system, (2) considers the characteristics of the uncertainty in the technology innovation process, and (3) identifies flexible, adaptive R&D investment strategies for multiple technologies for decision makers to consider. A series of numerical experiments with the new model reveal that (1) the optimal near-term R&D investment strategy under technological change uncertainty and adapting between decisions can be different than the optimal strategy assuming perfect foresight, and may be higher or lower; (2) the timing that a technology should be deployed to meet a specific carbon target dictates the direction and magnitude of the difference in these decisions; (3) increasing the level of uncertainty tends to increase near-term R&D investments; and (4) increasing right-skewness of the uncertainty (i.e., decreasing the likelihood of higher than average returns), reduces R&D spending throughout the planning horizon.by Nidhi Rana Santen.Ph. D

An Approximate Dynamic Programming Framework for Modeling Global Climate Policy under Decision-Dependent Uncertainty

for modeling global climate policy under decision-dependent uncertaint

An Approximate Dynamic Programming Framework for Modeling Global Climate Policy under Decision-Dependent Uncertainty

Analyses of global climate policy as a sequential decision under uncertainty have been severely restricted by dimensionality and computational burdens. Therefore, they have limited the number of decision stages, discrete actions, or number and type of uncertainties considered. In particular, other formulations have difficulty modeling endogenous or decision-dependent uncertainties, in which the shock at time t+1 depends on the decision made at time t. In this paper, we present a stochastic dynamic programming formulation of the Dynamic Integrated Model of Climate and the Economy (DICE), and the application of approximate dynamic programming techniques to numerically solve for the optimal policy under uncertain and decision-dependent technological change. We compare numerical results using two alternative value function approximation approaches, one parametric and one non-parametric. Using the framework of dynamic programming, we show that an additional benefit to near-term emissions reductions comes from a probabilistic lowering of the costs of emissions reductions in future stages, which increases the optimal level of near-term actions.

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

This paper explores cost-effective low-carbon R&D and capital investment portfolios for the electricity generation sector through 2060. We present a novel method for long-term planning by combining an economic model of endogenous non-linear technical change and a generation capacity planning model with key features of the electricity system. The model captures the complementary nature of technologies in the power sector; physical integration constraints; and the opportunity to build new knowledge capital as a non-linear function of R&D and accumulated knowledge, which reflects the diminishing marginal returns to research characteristic of the energy innovation process. We show portfolios for future scenarios with and without carbon emission limits, and demonstrate the importance of including various features by comparing results from a reference version of the model to results from alternative versions that omit these features. Our results caution that using economic frameworks that do not incorporate critical electricity and innovation system features may over- or under-estimate the value of emerging technologies, and therefore the cost-effectiveness of R&D opportunities