James McNerney

Role of design complexity in technology improvement

We study a simple model for the evolution of the cost (or more generally the performance) of a technology or production process. The technology can be decomposed into n components, each of which interacts with a cluster of d - 1 other components. Innovation occurs through a series of trial-and-error events, each of which consists of randomly changing the cost of each component in a cluster, and accepting the changes only if the total cost of the cluster is lowered. We show that the relationship between the cost of the whole technology and the number of innovation attempts is asymptotically a power law, matching the functional form often observed for empirical data. The exponent α of the power law depends on the intrinsic difficulty of finding better components, and on what we term the design complexity: the more complex the design, the slower the rate of improvement. Letting d as defined above be the connectivity, in the special case in which the connectivity is constant, the design complexity is simply the connectivity. When the connectivity varies, bottlenecks can arise in which a few components limit progress. In this case the design complexity depends on the details of the design. The number of bottlenecks also determines whether progress is steady, or whether there are periods of stasis punctuated by occasional large changes. Our model connects the engineering properties of a design to historical studies of technology improvement.

Network structure of inter-industry flows

We study the structure of inter-industry relationships using networks of money flows between industries in 45 national economies. We find these networks vary around a typical structure characterized by a Weibull link weight distribution, exponential industry size distribution, and a common community structure. The community structure is hierarchical, with the top level of the hierarchy comprising five industry communities: food industries, chemical industries, manufacturing industries, service industries, and extraction industries.

Historical Costs of Coal-Fired Electricity and Implications for the Future

We study the cost of coal-fired electricity in the United States between 1882 and 2006 by decomposing it in terms of the price of coal, transportation cost, energy density, thermal efficiency, plant construction cost, interest rate, capacity factor, and operations and maintenance cost. The dominant determinants of cost have been the price of coal and plant construction cost. The price of coal appears to fluctuate more or less randomly while the construction cost follows long-term trends, decreasing from 1902 to 1970, increasing from 1970 to 1990, and leveling off since then. Our analysis emphasizes the importance of using long time series and comparing electricity generation technologies using decomposed total costs, rather than costs of single components like capital. By taking this approach we find that the history of coal-fired electricity suggests there is a fluctuating floor to its future costs, which is determined by coal prices. Even if construction costs resumed a decreasing trend, the cost of coal-based electricity would drop for a while but eventually be determined by the price of coal, which fluctuates while showing no long-term trend.

Metal production requirements for rapid photovoltaics deployment

If global photovoltaics (PV) deployment grows rapidly, the required input materials need to be supplied at an increasing rate. In this paper, we quantify the effect of PV deployment levels on the scale of metals production. For example, we find that if cadmium telluride {copper indium gallium diselenide} PV accounts for more than 3% {10%} of electricity generation by 2030, the required growth rates for the production of indium and tellurium would exceed historically-observed production growth rates for a large set of metals. In contrast, even if crystalline silicon PV supplies all electricity in 2030, the required silicon production growth rate would fall within the historical range. More generally, this paper highlights possible constraints to the rate of scaling up metals production for some PV technologies, and outlines an approach to assessing projected metals growth requirements against an ensemble of past growth rates from across the metals production sector. The framework developed in this paper may be useful for evaluating the scalability of a wide range of materials and devices, to inform technology development in the laboratory, as well as public and private research investment.

Growth in metals production for rapid photovoltaics deployment

If global photovoltaics (PV) deployment grows rapidly, the required input materials need to be supplied at an increasing rate. We quantify the effect of PV deployment levels on the scale of annual metals production. If a thin-film PV technology accounts for 25% of electricity generation in 2030, the annual production of thin-film PV metals would need to grow at rates of 15-30% per year. These rates exceed those observed historically for a wide range of metals. In contrast, for the same level of crystalline silicon PV deployment, the required silicon production growth rate falls within the historical range.

Evaluating the Changing Causes of Photovoltaics Cost Reduction

Photovoltaic (PV) module costs have declined rapidly over forty years but the reasons remain elusive. Here we advance a conceptual framework and quantitative method for quantifying the causes of cost changes in a technology, and apply it to PV modules. Our method begins with a cost model that breaks down cost into variables that changed over time. Cost change equations are then derived to quantify each variables contribution. We distinguish between changes observed in variables of the cost model – which we term low-level mechanisms of cost reduction – and research and development, learning-by-doing, and scale economies, which we refer to as high-level mechanisms. We find that increased module efficiency was the leading low-level cause of cost reduction in 1980–2012, contributing almost 25% of the decline. Government-funded and private R&D was the most important high-level mechanism over this period. After 2001, however, scale economies became a more significant cause of cost reduction, approaching R&D in importance. Policies that stimulate market growth have played a key role in enabling PVs cost reduction, through privately-funded R&D and scale economies, and to a lesser extent learning-by-doing. The method presented here can be adapted to retrospectively or prospectively study many technologies, and performance metrics besides cost.

TripEnergy: Estimating Personal Vehicle Energy Consumption Given Limited Travel Survey Data

Estimating personal vehicle energy consumption is important for nationwide climate policy, local and statewide environmental policy, and technology planning. Transportation energy use is complex, depending on vehicle performance and the driving behavior of individuals, as well as on travel patterns of cities and regions. Previous studies combine large samples of travel behavior with fixed estimates of per mile fuel economy or use detailed models of vehicles with limited samples of travel behavior. This paper presents a model for estimating privately operated vehicle energy consumption—TripEnergy—that accurately reconstructs detailed driving behavior across the United States and simulates vehicle performance for different driving conditions. The accuracy of this reconstruction was tested by using out-of-sample predictions, and the vehicle model was tested against microsimulation. TripEnergy consists of a demand model, linking GPS drive cycles to travel survey trips, and a vehicle model, efficiently simulating energy consumption across different types of driving. Because of its ability to link small-scale variation in vehicle technology and driver behavior with large-scale variation in travel patterns, it is expected to be useful for a variety of applications, including technology assessment, cost and energy savings from ecodriving, and the integration of electric vehicle technologies into the grid.