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The Dynamics of Carbon and Energy Intensity in a Model of Endogenous Technical Change

Valentina Bosetti, Carlo Carraro and Marzio Galeotti

Year: 2006
Volume: Endogenous Technological Change
Number: Special Issue #1
DOI: 10.5547/ISSN0195-6574-EJ-VolSI2006-NoSI1-9
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Abstract:
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.



A World induced Technical Change Hybrid Model

Valentina Bosetti, Carlo Carraro, Marzio Galeotti, Emanuele Massetti, Massimo Tavoni

Year: 2006
Volume: Hybrid Modeling
Number: Special Issue #2
DOI: 10.5547/ISSN0195-6574-EJ-VolSI2006-NoSI2-2
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Abstract:
The need for a better understanding of future energy scenarios, of their compatibility with the objective of stabilizing greenhouse gas concentrations, and of their links with climate policy, calls for the development of hybrid models. Hybrid because both the technological detail typical of Bottom Up (BU) models and the long run dynamics typical of Top Down (TD) models are crucially necessary. We present WITCH � World Induced Technical Change Hybrid model � a neoclassical optimal growth model (TD) with energy input detail (BU). The model endogenously accounts for technological progress, both through learning curves affecting prices of new vintages of capital and through R&D investments. In addition, the model captures the main economic interrelationships between world regions and is designed to analyze the optimal economic and environment policies in each world region as the outcome of a dynamic game. This paper provides a detailed description of the WITCH model, of its Baseline, and of the model calibration procedure.



Multivariate Convergence toward the SDGs 2, 6 and 7: An Empirical Analysis of World and MENA Region Countries

Carlo Andrea Bollino and Marzio Galeotti

Year: 2024
Volume: Volume 45
Number: Special Issue
DOI:
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Abstract:
This paper provides new evidence on the convergence process toward the achievement of three important SGDs: 2 6 and 7. We collect data on water, energy, and food per capita consumption for 108 countries from 1971 to 2018. We also analyze the group of countries in the MENA region, which is a critical region as far as water and food are concerned. We establish a new notion of multivariate sigma and beta-convergence. For the first notion, we look at the time behavior of the determinant of the covariance matrix of the three variables. For the second notion we use the Arellano-Bond method to jointly estimate the interrelated system of beta convergence equations for water, food, and energy. The results reveal that there is evidence of conditional sigma- convergence and beta-convergence processes for the countries. The multivariate approach reveals that there are spillover effects with complex positive impact of each variable on the others in the analyzed countries. The speed of convergence is computed to assess when the desired levels according to the prescription of the SDG are attained for water, energy, and food per capita consumption by each country. Results have important policy implications for interventions on macro variables. Investment has a positive accelerating effect on water and energy convergence. In addition, openness to foreign trade and inflow of foreign direct investment have a positive accelerating effect on water and food convergence, respectively.





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