Abstract
This paper develops a long-run consumer optimization model with endogenous pollution and endogenous population. The positive check increases mortality if pollution increases. The optimal path is sustainable if it provides non-decreasing consumption for a non-decreasing population. As usually, optimality and sustainability may conflict; with population endogenous to pollution, this conflict may ultimately lead the human species toward self-imposed extinction. Not even technical progress can warrant sustainability.
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- 1.
In spite of my emphasis on pollutants, the model can be generalized to natural resources since resource depletion can be seen as pollution in the extended sense (Keeler et al. 1971).
- 2.
Air pollution consists of several components, of which particulate matter (PM) and ozone are the most dangerous (WHO 2004). The term particulate matter (PM) refers to solid airborne particles of varying size, chemical composition and origin. For example, the particles in PM 10 have a diameter of less than 10 μm and are mainly combustion-derived, either from traffic or from energy production, often from long-distance sources. Existing evidence suggests that the smaller the particles are, the more deeply into the lung they penetrate (WHO 2004). Air pollution increases mortality mainly through an increase in respiratory and cardiovascular diseases and lung cancer (Samet et al. 2000), but an increase in skin cancer is also reported (Brunekreef and Holgate 2002). All age groups are affected, but unborn and young children as well as the elderly are the most vulnerable (Pope and Dockery 2006).
- 3.
Some studies suggest, however, that fertility may respond to environmental degradation both because it is causing poverty and because toxins etc. cause miscarriage (Lutz et al. 2005). Because the emphasis of this paper is on the positive check, the fertility effects are excluded, for simplicity.
- 4.
For a review, see Tahvonen and Salo (1996).
- 5.
- 6.
The slope of the entire time path for γ C/L depends on lim S→0 γ C/L . This and the cases n(S ∗)>0 and n(S ∗)=0 are not considered for shortness.
- 7.
The critical obstacle, preventing a full calibration on real data is that, on order to focus on population and pollution, no production function is specified in the model. The main simplification is that the stock of capital (another state variable) is left away, which makes the optimization procedure much simpler and the phase portrait much more intuitive.
- 8.
Calculations are performed by Mathematica 7.0. Time-elimination method is used to derive the saddle paths (Mulligan and Sala-i-Martin 1991).
- 9.
Note, that in the presence of technical progress, however, the case with rising per capita consumption and rising population in the steady state is possible, see Sect. 4.
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Appendix: Local Stability of the Steady States
Appendix: Local Stability of the Steady States
Lets write \(\dot{S}=\varphi ( S,E ) \) and \(\dot{E}=\phi ( S,E ) \). In a steady state it hods \(\dot{S}=\dot{E}=0\) implying
The Jacobian of the model is
As evaluated around a steady state, its elements become
in which the last row is derived by using (12) and (11b). Because ϕ E contains the undefined second derivative n(S), we write
The expression in the square brackets is the difference in the slopes of the phase lines \(\dot{S}=0\) and \(\dot{E}=0\) and \(( -\varphi_{E} ) \cdot\phi_{E}=-\frac{n^{\prime}E}{ ( \rho-n ) ( \theta -1 ) }\) is positive for all E>0. In steady states 1 and 3 the slope of the \(\dot{E}=0\)-line is steeper than that of the \(\dot{S}=0\)-line (see Fig. 2) making the square brackets negative. Thus, \(\operatorname{DET} J<0\) and these steady states are saddles. In steady state 2 the slope of the \(\dot {E}=0\)-line is smaller (possibly negative) than the slope of the \(\dot{S}=0\)-line and the value of the square brackets is positive. The trace of the Jacobian is
Therefore, this steady state is an unstable node or focus.
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Lehmijoki, U. (2013). Optimal Pollution, Optimal Population, and Sustainability. In: Crespo Cuaresma, J., Palokangas, T., Tarasyev, A. (eds) Green Growth and Sustainable Development. Dynamic Modeling and Econometrics in Economics and Finance, vol 14. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-34354-4_2
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