The dynamics of carbon and energy intensity in a model of endogenous technical change.

• Author: Bosetti, Valentina

In recent years, a large number of papers have explored different attempts to endogenise technical change in climate models. This recent literature has emphasized that four factors--two inputs and two outputs--should play a major role when modeling 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 energy technical change 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 baseline 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.

1. INTRODUCTION

   Controlling the influence of human activities on climate is not an easy task. The international agreement reached in Kyoto that has so far come into force will have a very small impact on greenhouse gas (GHG) atmospheric concentrations. Stabilizing these concentrations at, for example, twice the preindustrial levels requires per capita global emissions to peak and then decline to (at least) half their 1990 value by the end of the twenty-first century (Cf. Bosetti, Galeotti and Lanza, 2004). This seems to be feasible only through drastic technological change in the energy sector, leading to the substitution of obsolete and dirty technologies with cleaner ones. There are therefore no substitutes for policy in directing innovation efforts toward fostering economic growth and helping the environment at the same time.

   All the above remarks are reflected in climate models, the main quantitative tools designed either to depict long-run energy and pollution scenarios or to assist in climate change policy analysis. Indeed, these models have traditionally accounted for the presence of technical change, albeit usually evolving in an exogenous fashion. More recently, however, models have been proposed where technology changes endogenously and/or its change is induced by deliberate choices of agents and government intervention. Both bottom-up and top-down models--a long standing distinction in energy-economy-environment modeling--have been recently modified in order to accommodate forms of endogenous technical change. As it turns out, the bottom-up approach has mostly experimented with the notion of Learning by Doing, while a few top-down models have entertained the notion of a stock of knowledge which accumulates over time via R&D spending. (1)

   The purpose of this paper is to present and test a new climate model which accounts for various features of technical change. In the new model, dubbed FEEM-RICE v.3, changes in technology affect the economy and climate through modifications of both the energy intensity of production and the carbon emission intensity of energy consumed. The driver of these intensity ratios is a new variable, deemed Energy Technical Change Index (ETCI), which is a convex combination of two stocks, an abatement-based one and an R&D-based one. These stocks are designed to capture the two main modes of endogenous technical change, Learning-by-Doing (LbD) and Learning-by-Researching (LbR).

   Crucial technical change parameters are calibrated in order to obtain a baseline which reproduces the SRES B2 emission scenario (as in Boyer and Nordhaus, 2000) with technical change having both an exogenous and an endogenous component. When stabilization scenarios are simulated, an induced technical change part gets added to those two components. In order to better understand the model structure, we also carry out a number of optimization runs in which key technical change parameters are modified and their impact on energy and carbon intensity are quantified. This sensitivity analysis enables us to test the robustness of the model and to identify the main parameters driving our main results.

   The remainder of the paper is as follows. Section 2 presents the FEEMRICE v.3 model and provides a short technical description of how technical change has been modeled. Section 3 describes the baseline calibration process. Section 4 presents our main results and the conclusions arising from our sensitivity analysis. In section 5, some policy remarks and suggestions for further research close the paper.

2. MODELING INDUCED TECHNICAL CHANGE: THE FEEM-RICE V.3 MODEL

   The FEEM-RICE v.3 model is an extended version of the RICE 99 model by Boyer and Nordhaus (2000). (2) RICE 99 is a Ramsey-Koopmans single sector optimal growth model suitably extended to incorporate the interactions between economic activities and climate. There is one such model for each of the eight macro regions into which the world is divided: USA, Other High Income countries (OHI), OECD Europe (Europe), Russia and Eastern European countries (REE), Middle Income countries (MI), Lower Middle Income countries (LMI), China (CHN), and Low Income countries (LI).

   Within each region a central planner chooses the optimal paths of two control variables, fixed investment and carbon energy input, so as to maximize welfare, defined as the present value of per capita consumption. The value added created via production (net of climate change) according to a constant returns technology is used for investment and consumption, after subtraction of energy spending. The technology is Cobb-Douglas and combines inputs from capital, labour and carbon energy together with the level of technology. In RICE 99, population (taken to be equal to full employment) and technology levels grow over time in an exogenous fashion, whereas capital accumulation is governed by the optimal rate of investment.

   The production function of the original RICE 99 model is (n indexes regions, t time periods):

   [MATHEMATICAL EXPRESSION NOT REPRODUCIBLE IN ASCII] (1)

   where Q is output (gross of climate change effects), A is the exogenously given level of technology and [K.sub.F], CE and L are the inputs from physical capital, carbon energy and labor, respectively, and [p.sub.E] is fossil fuel price. Carbon emissions are proportional to carbon energy, that is:

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