On the Effect of Technological Progress on Pollution: a New Distortion in an Endogenous Growth Model (original) (raw)
Related papers
The Dynamics of Carbon and Energy Intensity in a Model of Endogenous Technical Change
The Energy Journal, 2006
In recent years, a large number of papers have explored different attempts to endogenise technical change in climate models. The obvious reason is that technical change is widely considered the main route to achieving a significant reduction in global GHG emissions. This recent literature has emphasized that four factors -two inputs and two outputs -should play a major role when modelling technical change in climate models. The two inputs are R&D investments and Learning by Doing, the two outputs are energy-saving and fuel switching. Indeed, R&D investments and Learning by Doing are the main drivers of a climate-friendly technical change that eventually affect both energy intensity and fuel-mix. In this paper, we present and discuss an extension of the FEEM-RICE model in which these four factors are explicitly accounted for. In our new specification of endogenous technical change, an index of technical progress depends on both Learning by Researching and Learning by Doing. This index enters the equations defining energy intensity (i.e. the amount of carbon energy required to produce one unit of output) and carbon intensity (i.e. the level of carbonization of primarily used fuels). This new specification is embodied in the RICE 99 integrated assessment climate model and then used to generate a business as usual scenario and to analyze the relationship between climate policy and technical change. Sensitivity analysis is performed on different key parameters of the energy module in order to obtain crucial insights into the relative importance of the main channels through which technological changes affects the impact of human activities on climate. In addition, the effectiveness of different possible ways of combining Learning by Researching and Learning by Doing is also investigated.
2008
The issues of technology and uncertainty are very much at the heart of the policy debate of how much to control greenhouse gas emissions. The costs of doing so are in the present and high, while the benefits are very much in the future and, most importantly, they are highly uncertain. Whilst there is broad consensus on the key elements of climate change science and agreement that near-term actions are needed to prevent dangerous anthropogenic interference with the climate system, there is little agreement on the costs and benefits of climate policy. The book looks at different ways of reconciling the needs for sustainability and equity with the costs of action now. Presenting a compendium of methodologies for evaluating the economic impact of technological innovation upon climate-change policy, this book describes a number of mathematical models. The goal is to provide a practitioner's guide for doing the science and economics of climate change. Because the assumptions motivating different problems in the economics of climate change have different complexities, a number of models are presented with varying levels of difficulty: reduced-form and structural, partial-and general-equilibrium, closed-form and computational. A unifying theme of these models is the incorporation of a number of price and quantity instruments and an analysis of their respective efficacies. This book presents models that contain structural uncertainty, i.e. uncertainty that economic agents respond to, via their risk attitudes. The novelty of this book is to relate the effects of risk and risk attitudes to environment-improving technological innovation. This book will be of great interest to students and researchers engaged with climate change, policy-making and technology, as well as to the policy makers caught within the sustainability debate.
The impact of technological change on climate protection and welfare: Insights from the model MIND
Ecological Economics, 2005
Avoiding dangerous climate change is likely to require policies to mitigate CO 2 emissions that are substantially more ambitious than those currently being considered. For such policies, the issue of endogenous technological change becomes important, both to estimate the overall costs and to identify the intertemporally cost-effective combination of mitigation options. In this paper, we first discuss the recent literature that evaluates the potential for endogenous technological change to reduce mitigation costs, and the efforts to incorporate endogenous technological change into pre-existing integrated assessment models. Then we formulate our own integrated assessment model, the Model of INvestment and Technological Development (MIND), which allows analysis of the relationship between specific mitigation options and the costs of ambitious climate protection objectives. Our model reveals two important results. First, the incorporation of technological change in a portfolio of mitigation options can reduce the costs of climate policies substantially. Achieving the ambitious policy goals necessary to avoid dangerous climate change becomes feasible without significant welfare losses. Second, the different mitigation options are of different importance in achieving climate protection goals: improving energy efficiency becomes too costly as a major mitigation option in the long run. In the long run, fossil fuels have to be substituted by renewable energy sources because a backstop technology with the potential of learning-by-doing has the strongest impact on reducing the welfare losses due to climate protection. Furthermore, Carbon Capturing and Sequestration can allow for further reduction in the costs of climate protection and can postpone the need to transform the energy system from a fossil-fuelbased one to a renewables one. D 2005 Published by Elsevier B.V.
Article Economic Growth Assumptions in Climate and Energy Policy
2014
The assumption that the economic growth seen in recent decades will continue has dominated the discussion of future greenhouse gas emissions and the mitigation of and adaptation to climate change. Given that long-term economic growth is uncertain, the impacts of a wide range of growth trajectories should be considered. In particular, slower economic growth would imply that future generations will be relatively less able to invest in emissions controls or adapt to the detrimental impacts of climate change. Taking into consideration the possibility of economic slowdown therefore heightens the urgency of reducing greenhouse gas emissions now by moving to renewable energy sources, even if this incurs short-term economic cost. I quantify this counterintuitive impact of economic growth assumptions on present-day policy decisions in a simple global economy-climate model (Dynamic Integrated model of Climate and the Economy (DICE)). In DICE, slow future growth increases the economically optimal present-day carbon tax rate and the utility of taxing carbon emissions, although the magnitude of the increase is sensitive to model parameters, including the rate of social time preference and the elasticity of the marginal utility of consumption. Future scenario development should specifically include low-growth scenarios, and the possibility of low-growth economic trajectories should be taken into account in climate policy analyses.