Introduction

The GDyn Data Base, used with the GDyn model, is based on the GTAP Data Base, which describes the global economy in a given year (2001 for the GTAP 6 Data Base; Dimaranan 2006). The GTAP Data Base is augmented with additional data required for GDyn, the dynamic version of the GTAP model. This chapter covers the preparation of these additional elements of the GDyn Data Base1 and the manipulation and aggregation of the GDyn Data Base with various utility programs. The focus is on the additional data requirements, with minimal discussion of the dynamic parameters (see Chapter 3). Readers interested in the underlying GTAP Data Base are referred to Dimaranan (2006).

The GTAP Data Base (Dimaranan, 2006) is changed in the following three ways to obtain a database for GDyn:

1. Five new arrays, listed in Table 4.1, are added to the standard GTAP data file. The values of the regional savings array also change from those in the standard GTAP Data Base, although the dimensions of the array do not change. These changes are discussed in Section 2.

2. A new parameters file contains the seven new parameters used in the dynamic theory (Table 4.2). The methodology for estimating econometrically, and in some cases calibrating these parameters, was examined in detail in Chapter 3. In this chapter the procedure for assigning values to these parameters in the standard GDyn Data Base is outlined. These changes are discussed in Section 3.

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Introduction

The dynamic theory in Chapter 2 describes various new parameters governing international capital mobility. This chapter examines what we can learn from country-panel data about the magnitude of these additional parameters, corresponding calibration procedures, and the manipulation of the parameters with an aggregation program. The new parameter file containing parameters used in the dynamic theory is a GEMPACK header array file; its contents are listed in Table 3.1.

The first new parameter listed in Table 3.1 is INC. This parameter is the initial income level across simulations and is used to calculate welfare measures in multiperiod simulations. It is represented in the unit US$ millions. The rest of the parameters in Table 3.1 can be grouped according to their role in the model: lagged adjustment parameters, elasticity of rate of return to capital with respect to capital stock, and parameters determining the allocation of regional wealth and composition of regional capital. These three parameter types are discussed in turn in this chapter.

Parameters Determining Lagged Adjustments

The investment theory presented in Chapter 2 is expressed in terms of gross rather than net rates of return, and it allows only zero or positive gross rates of return. However, net of depreciation, rates of return may be negative, and they may decline to the negative of the depreciation rate.

Introduction

The GDyn model presented in Chapter 2 inherits from the standard GTAP model its specification of the regional household demand system and, in particular, the treatment of saving. As in the standard GTAP model, regional households in GDyn spend their income according to a Cobb-Douglas per capita utility function specified over three sources of utility: private consumption, government consumption, and real saving. Because the model is not forward-looking but is recursively dynamic, the utility function is static – it represents utility from present but not future consumption. The practice of including saving in the static utility function derives from Howe (1975) and allows regional households to value saving in a temporal settings. Because of the Cobb-Douglas functional form, the average propensity to save is fixed, and saving is a fixed proportion of income in each region.

There are several unwelcome implications of this assumption. Because propensities to save are fixed and incomes are rising over time, countries in which saving substantially exceeds investment, like Japan, accumulate unrealistically large stocks of foreign assets. If like China they also exhibit high rates of growth in income, at the end of long-run GDyn simulations that span many decades, such countries may end up owning a large part of the wealth of the whole world. Although such outcomes cannot altogether be ruled out a priori, they are very strong predictions resting on a very weak empirical basis.

Introduction and Motivation

Land-use change – in particular deforestation – has contributed substantially to current greenhouse gas concentrations in the atmosphere. In addition, agriculture and forestry have a potentially very significant role to play in stabilizing greenhouse gas concentrations. According to results from the EMF-21 (Energy Modeling Forum) study models, land-based mitigation strategies are a significant part of the mitigation portfolio required for a climate stabilization policy, accounting for anywhere from 18 to 72% of total abatement by 2050 and 15 to 44% of total abatement by 2100 (Rose et al. 2007). The efficiency of any strategy, however, depends critically on the baseline land-use changes. In turn, those changes depend on many factors, the most important of which are population and per capita income growth, as well as the degree of integration in the global economy.

In this chapter, we modify and extend the GDyn model to investigate the role of population and per capita income growth and of global economic integration in determining long-run patterns of land-use change, where that change is decomposed by agro-ecological zone (AEZ).We are able to isolate the impact of each of these three factors on the development of land use in the long-run through a series of carefully designed simulations. Although the impact of population and income growth on the derived demand for land is relatively predictable, once supply-side constraints are brought to bear, the picture becomes more complex.