Abstract
The model presented in this paper describes an economy with endogenous technical change and polluting production techniques. The main question we want to address is whether the adoption of “dirty” production processes might lead to a sustainable unique steady state, or guarantee the emergence of multiple equilibria. The application of the original Bogdanov–Takens theorem allows us to characterize the regions of the parametric space where the model exhibits either a global indeterminate equilibrium or a poverty-environment trap.
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Notes
That is to say, two identically endowed economies (with the same initial stock of both physical and natural capital), with different abatement programs, may start at some point to follow completely different equilibrium paths towards the long-run steady state [14]. The rise of multiple equilibria can also generate a vicious poverty-environment trap, and the economy might not be able to escape a low-growth steady state associated to an unsustainable use of natural resources [19]. Local analysis may, thus, be misleading, since it refers to the vicinity of a stationary state, whereas the initial values of the state variables may not belong to such a neighborhood.
The Bogdanov–Takens bifurcation theorem is largely used in mathematics, physics, and biology but has found limited application in economics. To the best of our knowledge, only [13] seems to have applied the methodology in a monetary model in a R 2 ambient space.
Assume ρ is a time preference rate.
This specific production function exhibits constant returns to scale at a disaggregate level because each firm takes z as given. On the contrary, a social planner can internalize this kind of externality, thus obtaining increasing returns.
Negative values of γ are consistent with a slowdown of total factor productivity [25].
For example, only a fraction of the oil burnt in a production process (z) may serve to produce the final output (Y). The remaining is eventually pushed into the atmosphere as a resulting damaging emission load (P).
Although technology is not directly linked to pollution in this model, we assume that each new inventory due to technological advance is cleaner than the previous one. This is consistent with the empirical evidence that developed economic systems also seek a less polluted environment to live in.
Necessary condition for a maximum can be checked by studying the sign of all principal minors of the Hessian matrix for the control variables of the problem, whose determinant is formed by the following signs:
$$\left\vert H\right\vert =\left\vert \begin{array}{ccc} - & 0 & 0 \\ 0 & - & 0 \\ 0 & 0 & - \end{array} \right\vert $$thus obtaining, |H 1| < 0, |H 2| > 0, |H 3| < 0.
Since these bifurcation values are particularly useful for a subsequent numerical simulation of the poverty-environment trap, their complex mathematical solution is not shown here but is available upon request.
The case where s = − 1 is very similar, so we leave it out of this demonstration.
Which is topologically equivalent to the case where γ < 0 in the original model, ℛ.
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Acknowledgments
Financial support from Regione Autonoma della Sardegna (LR 7/10/2007 under the project “ Una rilettura del carattere dualistico dell’economia italiana alla luce dei nuovi schemi interpretativi emersi dal dibattito teorico ed empirico”) and from the Ministero dell’Istruzione, Università e Ricerca Scientifica, PRIN 2006 under the project “Poverty traps and multiple equilibria: a framework to interpret Mezzogiorno’s development”is gratefully acknowledged. Finally, I wish to thank two anonymous referees for their helpful comments, suggestions, and criticisms that have definitely helped to improve the quality of the paper.
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Appendix
Appendix
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1.
The arrow sufficiency theorem holds since the maximized Hamiltonian, evaluated along the optimal control variables, is concave in all the state variables, as we can simply check through the sign of the minors of the Hessian matrix, whose determinant implies the following:
$$\left\vert H\right\vert =\left\vert \begin{array}{ccc} - & 0 & + \\ 0 & - & 0 \\ + & 0 & - \end{array} \right\vert $$and thus, |H 1| < 0, |H 2| > 0, and |H 3| < 0 iff A > 1, that is, the number of designs must necessarily be greater than one.
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2.
Transversality conditions for a free terminal state hold for all shadow prices and are given by
$$\begin{array}{@{}rcl@{}} \lim\limits_{t\rightarrow \infty }\lambda Ke^{-\rho t} &=&\tilde{\lambda}e^{(1-2\sigma )gt}\tilde{K}e^{gt}e^{-\rho t}\\ &=&\tilde{\lambda}\tilde{K}e^{-(2\sigma g+\rho )t}=0 \\ \lim\limits_{t\rightarrow \infty }\mu Ee^{-\rho t} &=&\tilde{\mu}e^{(1-2\sigma )gt}\tilde{E}e^{gt}e^{-\rho t}\\ &=&\tilde{\mu}\tilde{E}e^{-(2\sigma g+\rho )t}=0 \\ \lim\limits_{t\rightarrow \infty }\vartheta Ae^{-\rho t} &=&\tilde{\vartheta}e^{(\rho -\varphi -\gamma )t}\tilde{A}e^{gt}e^{-\rho t}\\ &=&\tilde{\vartheta}\tilde{A}e^{(g-\varphi -\gamma )t}=0 \end{array} $$where \(\tilde{\lambda}, \tilde{\mu}, \tilde{\vartheta}\), and \(\tilde{K}, \tilde{E}, \tilde{A}\), are the shadow prices and the state values on the balanced growth path;
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3.
Moreover, for free time t, we need to show that \(\underset{ t\rightarrow \infty }{\lim }H=0 \), which is always verified due to convergence towards zero of both the discounted utility function, \(\underset{t\rightarrow \infty}{\lim }U(\cdot )e^{-\rho t}=0\), and all the multipliers, as proved above.
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4.
We need first to translate the fixed point associated to ℛ to the origin, by substituting \(\tilde{x}\equiv x-\bar{x}^{\ast }, \tilde{m}\equiv m-\bar{m}^{\ast }, \tilde{q}=q-\bar{q}^{\ast }\), and introduce the new trivial variables \(\mu =\gamma -\bar{\gamma}\) and \(\nu=\rho -\bar{\rho}\), such that
$$\begin{array}{@{}rcl@{}} \overset{\cdot }{\tilde{x}} &=&-(\bar{\rho}+\nu )(\tilde{x}+\bar{x}^{\ast })\\ &&+\frac{(1-\alpha )(\bar{\gamma}+\mu )-\sigma (1+\bar{\gamma}+\mu )}{\sigma (1+\bar{\gamma}+\mu )}\\ &&\times(\tilde{m}+\bar{m}^{\ast })(\tilde{x}+\bar{x}^{\ast })+(\tilde{x}+\bar{x}^{\ast })^{2}\smallskip \\ \overset{\cdot }{\tilde{m}} &=&\frac{\theta +\alpha (\bar{\gamma}+\mu)(\varphi +\bar{\gamma}+\mu )}{(\bar{\gamma}+\mu )(1-\alpha )}(\tilde{m}+\bar{m}^{\ast})\\ &&-\frac{1+\alpha (\bar{\gamma}+\mu )}{1+\bar{\gamma}+\mu}(\tilde{m}+\bar{m}^{\ast })^{2}\\ &&+\frac{1+\bar{\gamma}+\mu }{(\bar{\gamma}+\mu )(1-\alpha )}(\tilde{m}+\bar{ m}^{\ast })(\tilde{q}+\bar{q}^{\ast })^{\omega}\\ \overset{\cdot }{\tilde{q}} &=&-\frac{(\bar{\rho}+\nu )}{\omega }(\tilde{q}+\bar{q}^{\ast })+\frac{(\tilde{m}+\bar{m}^{\ast })(\tilde{q}+\bar{q}^{\ast})^{1+\omega }}{\omega (\tilde{x}+\bar{x}^{\ast })}\\ &&+\frac{(1+\bar{\gamma}+\mu )}{\omega }(\tilde{q}+\bar{q}^{\ast })^{1+\omega} \end{array} $$(8)
A second-order Taylor expansion of the translated vector field (8) with respect to \((\tilde{x},\tilde{m},\tilde{q},\mu ,\nu) \) is therefore made, which leads to
where
We ought to put the translated system (9) in the Jordan normal form characterized by a double-zero eigenvalue and a third eigenvalue given by the trace trJ ∗. This computation is straightforward and needs
where T is a matrix of the eigenvectors
which allows us to put the vector field (10) in the new coordinate change and the following normal form:
where \({\bf{\dot{\tau }}} = {{\rm{T}}^{{{\rm{ - 1}}}}}{J^{ * }}(0){\bf{T\tau }} + {{\bf{T}}^{{{\rm{ - 1}}}}}\tilde{f}_{i}^{{nl}}\left( {{\bf{T\tau }}} \right),{\bar{F}_{i}}\left( {\bf{\tau }} \right) = {{\bf{T}}^{{{\rm{ - 1}}}}}\tilde{f}_{i}^{{nl}}\left( {{\bf{T\tau }}} \right) = {{\bf{T}}^{{{\rm{ - 1}}}}}\tilde{f}_{i}^{{nl}}\left( {{\bf{Tw}}} \right)\) , and B(μ, v) is a matrix that vanishes at μ = 0, and v = 0:
The vector field (11) can be then suspended via a second-order center manifold reduction, which implies \(\dot{\tau}_{3}=0\), (see the mathematical procedure in [21]) and therefore becomes
where \(F_{1}+\mathcal{O}(w_{i}^{3})=\bar{F}_{1}\left( \boldsymbol{\tau }\right)\), and \(F_{2}+\mathcal{O}(w_{i}^{3})=\bar{F}_{2}\left( \boldsymbol{\tau }\right)\), and
with
Moreover, following Freire et al. [20] and Gamero et al. [21], we are able to reduce (13) to a topologically equivalent system:
where ε 1 and ε 2 are the unfolding parameters of the versal deformation matrix, V \((\mu ,\nu )=\left ( \begin {array}{cc} 0 & 1 \\ \varepsilon _{1} & \varepsilon _{2} \end {array} \right ) \), that is
given that D = −v 1 + v 2 z 1 − u 1 v 2 z 3 + u 2 v 1 z 3.
Moreover, the stability of (15), i.e., ε 1 = 0, implies
with
where
Solution to (17) easily gives µ = Φv, where \(\Phi =\frac{\sqrt{\xi _{2}^{2}-4v_{1}\xi _{1}}-\xi _{2}}{2\xi _{1}}.\).
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Bella, G. The Poverty-Environment Trap in a Modified Romer Model with Dirtiness. Environ Model Assess 18, 629–638 (2013). https://doi.org/10.1007/s10666-013-9372-4
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DOI: https://doi.org/10.1007/s10666-013-9372-4