The Dynamics of Carbon and Energy Intensity in a Model of Endogenous Technical Change (original) (raw)
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The Energy Journal, 2009
This paper analyses whether and how a climate policy designed to stabilize greenhouse gases in the atmosphere is likely to change the direction and pace of technical progress. The analysis is performed using an upgraded version of WITCH, a dynamic integrated regional model of the world economy, which features a detailed representation of the energy sector. A non-energy R&D Sector, which enhances the productivity of the capital-labour aggregate, has been added to the energy R&D sector already modelled in WITCH. Then, a global emission trading scheme designed to stabilize CO 2 concentrations at 450 ppm (550 ppm all GHG included) has been introduced. We find that, as a consequence, R&D is re-directed towards energy knowledge and that the ratio of energy to non-energy R&D investment increases significantly, as expected. Nonetheless, total optimal R&D investments decrease, due to a more than proportional contraction of non-energy R&D. Indeed, when non-energy and energy inputs are weakly substitutable, the overall contraction of the economic activity associated with a climate policy may induce the decline in total R&D investments. By contrast, enhanced investments in energy R&D and in the energy sector are found not to "crowd-out" investments in non-energy R&D.
RePEc: Research Papers in Economics, 2010
This paper analyses whether and how a climate policy designed to stabilize greenhouse gases in the atmosphere is likely to change the direction and pace of technical progress. The analysis is performed using an upgraded version of WITCH, a dynamic integrated regional model of the world economy. In this version, a non-energy R&D Sector, which enhances the productivity of the capital-labor aggregate, has been added to the energy R&D sector included in the original WITCH model. We find that, as a consequence of climate policy, R&D is redirected towards energy knowledge. Nonetheless, total R&D investments decrease, due to a more than proportional contraction of non-energy R&D. Indeed, when non-energy and energy inputs are weakly substitutable, the overall contraction of the economic activity associated with a climate policy induces a decline in total R&D investments. However, enhanced investments in energy R&D and in the energy sector are found not to "crowd-out" investments in non-energy R&D.
Modeling Biased Technical Change. Implications for Climate Policy
2010
Climate-economy models aiming at quantifying the costs and effects of climate change impacts and policies have become important tools for climate policy decision-making. Although there are several important dimensions along which models differ, this paper focuses on a key component of climate change economics and policy, namely technical change. This paper tackles the issues of whether technical change is biased towards the energy sectors, the importance of the elasticity of substitution between factors in determining this bias and how mitigation policy is likely to affect it. The analysis is performed using the World Induced Technical Change model, WITCH. Three different versions of the model are proposed. The starting set-up includes endogenous technical change only in the energy sector. A second version introduces endogenous technical change in both the energy and non-energy sectors. A third version of the model embodies different sources of technical change, namely R&D and human capital. Although different formulations of endogenous technical change have only a minor influence on climate policy costs, the macroeconomic effects on knowledge and human capital formation can vary greatly. Abstract Climate-economy models aiming at quantifying the costs and effects of climate change impacts and policies have become important tools for climate policy decision-making. Although there are several important dimensions along which models differ, this paper focuses on a key component of climate change economics and policy, namely technical change. This paper tackles the issues of whether technical change is biased towards the energy sectors, the importance of the elasticity of substitution between factors in determining this bias and how mitigation policy is likely to affect it. The analysis is performed using the World Induced Technical Change model, WITCH. Three different versions of the model are proposed. The starting set-up includes endogenous technical change only in the energy sector. A second version introduces endogenous technical change in both the energy and non-energy sectors. A third version of the model embodies different sources of technical change, namely R&D and human capital. Although different formulations of endogenous technical change have only a minor influence on climate policy costs, the macroeconomic effects on knowledge and human capital formation can vary greatly.
Endogenous Technological Change: Burden or Benefit for Carbon Dioxide Reduction Strategies
We develop an endogenous growth model with capital, labor and energy as production factors and endogenous technological change (ETC) through learning by research and learning by doing. The model constants are calibrated so that the model reproduces the relevant trends over the 1970-2000 period. The model contains a simple climate module, and is used to assess the impact of ITC for a policy that aims at a maximum level of atmospheric CO2 concentration (550 ppmv). ETC increases flexibility and thereby reduces costs, but ETC can also increase costs when lower output levels lead to lower investments in learning. Our numerical simulations show that, for this endogenous growth model, the former effect dominates the latter. ETC leads to lower costs.
Annual Review of Energy and the Environment, 2002
s Abstract Technical change in the energy sector is central for addressing longterm environmental issues, including climate change. Most models of energy, economy, and the environment (E3 models) use exogenous assumptions for this. This is an important weakness. We show that there is strong evidence that technical change in the energy sector is to an important degree induced by market circumstances and expectations and, by implication, by environmental policies such as CO 2 abatement. We classify the main approaches to modeling such induced technical change and review results with particular reference to climate change. Among models with learning by doing, weak responses are only obtained from models that are highly aggregated (lack technological diversity) and/or that equate rates of return to innovation across sectors. Induced technical change broadens the scope of efficient policies toward mitigation, including not just research and development and aggregated market instruments but a range of sectoral-based policies potentially at divergent marginal costs. Furthermore, to the extent that cleaner technologies induced by mitigation diffuse globally, a positive spillover will result that will tend to offset the substitution-based negative spillover usually hypothesized to result from the migration of polluting industries. Initial explorations suggest that this effect could also be very large.
Accounting for Uncertainty Affecting Technical Change in an Economic-Climate Model
SSRN Electronic Journal, 2000
The key role of technological change in the decline of energy and carbon intensities of aggregate economic activities is widely recognized. This has focused attention on the issue of developing endogenous models for the evolution of technological change. With a few exceptions this is done using a deterministic framework, even though technological change is a dynamic process which is uncertain by nature. Indeed, the two main vectors through which technological change may be conceptualized, learning through R&D investments and learning-by-doing, both evolve and cumulate in a stochastic manner. How misleading are climate strategies designed without accounting for such uncertainty? The main idea underlying the present piece of research is to assess and discuss the effect of endogenizing this uncertainty on optimal R&D investment trajectories and carbon emission abatement strategies. In order to do so, we use an implicit stochastic programming version of the FEEM-RICE model, first described in Bosetti, Carraro and Galeotti, (2005). The comparative advantage of taking a stochastic programming approach is estimated using as benchmarks the expected-value approach and the worst-case scenario approach. It appears that, accounting for uncertainty and irreversibility would affect both the optimal level of investment in R&D -which should be higher-and emission reductions -which should be contained in the early periods. Indeed, waiting and investing in R&D appears to be the most cost-effective hedging strategy.
2013
The research reported here gives priority to understanding the inter-temporal resource allocation requirements of a program of technological changes that could halt global warming by completing the transition to a "green" (zero net CO2-emission) production regime within the possibly brief finite interval that remains before Earth's climate is driven beyond a catastrophic tipping point. This paper formulates a multi-phase, just-in-time transition model incorporating carbon-based and carbon-free technical options requiring physical embodiment in durable production facilities, and having performance attributes that are amenable to enhancement by directed R&D expenditures. Transition paths that indicate the best ordering and durations of the phases in which intangible and tangible capital formation is taking place, and capital stocks of different types are being utilized in production, or scrapped when replaced types embodying socially more efficient technologies, are obtained from optimizing solutions for each of a trio of related models that couple the global macro-economy's dynamics with the dynamics of the climate system. They describe the flows of consumption, CO2 emissions and the changing atmospheric concentration of green-house gas (which drives global warming), along with the investment dynamics required for the timely transformation of the production regime. These paths are found as the welfare-optimizing solutions of three different "stacked Hamiltonians", each corresponding to one of our trio of integrated endogenous growth models that have been calibrated comparably to emulate the basic global setting for the "transition planning" framework of dynamic integrated requirements analysis modeling (DIRAM). As the paper's introductory section explains, this framework is proposed in preference to the (IAM) approach that environmental and energy economists have made familiar in integrated assessment models of climate policies that would rely on fiscal and regulatory instruments --but eschew any analysis of the essential technological transformations that would be required for those policies to have the intended effect. Simulation exercises with our models explore the optimized transition paths' sensitivity to parameter variations, including alternative exogenous specifications of the location of a pair of successive climate "tipping points": the first of these initiates higher expected rates of damage to productive capacity by extreme weather events driven by the rising temperature of the Earth's surface; whereas the second, far more serious "climate catastrophe" tipping point occurs at a still higher temperature (corresponding to a higher atmospheric concentration of CO2). In effect, that sets the point before which the transition to a carbon-free global production regime must have been completed in order to secure the possibility of future sustainable development and continued global economic growth.