Suchergebnisse

"In many countries, unreliable inputs, particularly those lacking storage, can significantly limit a firm's productivity. In the case of an increasing frequency of blackouts, a firm may change factor shares in a number of ways. It may decide to self generate electricity, to purchase intermediate goods that it used to produce directly, or to improve its technical efficiency. We examine how industrial firms responded to China's severe power shortages in the early 2000s. Fast-growing demand coupled with regulated electricity prices led to blackouts that varied in degree over location and time. Our data consist of annual observations from 1999 to 2004 for approximately 32,000 energy-intensive, enterprises from all industries. We estimate the losses in productivity due to factor-neutral and factor-biased effects of electricity scarcity. Our results suggest that enterprises re-optimize among factors in response to electricity scarcity by shifting from energy (both electric and non-electric sources) into materials---a shift from "make" to "buy." These effects are strongest for firms in textiles, timber, chemicals, and metals. Contrary to the literature, we do not find evidence of an increase in self generation. Finally, we find that these productivity changes, while costly to firms, led to small reductions in carbon emissions"--National Bureau of Economic Research web site

Global climate change and other environmental challenges require the development of new energy technologies with lower emissions. In the near-term, R&D investments, either by government or the private sector, can bring down the costs of these lower emission technologies. However, the results of R&D are uncertain, and there are many potential technologies that may turn out to play an effective role in the future energy mix. In this paper, we address the problem of allocating R&D across technologies under uncertainty. Specifically, given two technologies, one with lower costs at present, but the other with greater uncertainty in the returns to R&D, how should one allocate the R&D budget? We develop a multi-stage stochastic dynamic programming version of an integrated assessment model of climate and economy that represents endogenous technological change through R&D decisions for two substitutable non-carbon backstop technologies. Using the model, we demonstrate that near-term R&D into the higher cost technology is justified, and that the amount of R&D into the high cost technology increases with both the variance in the uncertainty in returns to R&D and with the skewness of the uncertainty. We also present an illustrative case study of wind and solar photovoltaic technologies, and show that poor R&D results in early periods do not necessarily mean that investment should not continue.