A spatiotemporal framework for the analytical study of optimal growth under transboundary pollution

We construct a spatiotemporal frame for the study of optimal growth under transboundary pollution. Space is continuous and polluting emissions originate in the intensity of use of the production input. Pollution flows across locations following a diffusion process. The objective functional of the economy is to set the optimal production policy over time and space to maximize welfare from consumption, taking into account a negative local pollution externality and the diffusive nature of pollution. Our framework allows for space and time dependent preferences and productivity, and does not restrict diffusion speed to be space-independent. This provides a comprehensive setting to analyze pollution diffusion with a close account of geographic heterogeneity. The involved optimization problem is infinite-dimensional. We propose an alternative method for an analytical characterization of the optimal paths and the asymptotic spatial distributions. The method builds on a deep economic concept of pollution spatiotemporal welfare effect, which makes it definitely useful for economic analysis.

[1]  Anastasios Xepapadeas,et al.  Optimal Control in Space and Time and the Management of Environmental Resources , 2014 .

[2]  A. Lunardi Analytic Semigroups and Optimal Regularity in Parabolic Problems , 2003 .

[3]  Raouf Boucekkine,et al.  Geographic environmental Kuznets curves: the optimal growth linear-quadratic case , 2019, Mathematical Modelling of Natural Phenomena.

[4]  Raouf Boucekkine,et al.  Growth and agglomeration in the heterogeneous space: a generalized AK approach , 2018, Journal of Economic Geography.

[5]  Raouf Boucekkine,et al.  Environmental quality versus economic performance: A dynamic game approach , 2011 .

[6]  R. Nagel,et al.  One-parameter semigroups for linear evolution equations , 1999 .

[7]  Javier de Frutos,et al.  Spatial vs. non-spatial transboundary pollution control in a class of cooperative and non-cooperative dynamic games , 2018, Eur. J. Oper. Res..

[8]  Emilio Barucci,et al.  Technology adoption and accumulation in a vintage-capital model , 2001 .

[9]  Giorgio Fabbri,et al.  Geographical structure and convergence: A note on geometry in spatial growth models , 2016, J. Econ. Theory.

[10]  E. Dockner,et al.  International Pollution Control: Cooperative versus Noncooperative Strategies , 1993 .

[11]  Paulo Brito,et al.  The dynamics of growth and distribution in a spatially heterogeneous world , 2022, Portuguese Economic Journal.

[12]  Alain Bensoussan,et al.  Representation and Control of Infinite Dimensional Systems, 2nd Edition , 2007, Systems and control.

[13]  Carmen Camacho,et al.  Land use dynamics and the environment , 2014 .

[14]  R. Barro,et al.  Economic Growth, 2nd Edition , 2003 .

[15]  Vladimir M. Veliov,et al.  Distributed optimal control models in environmental economics: a review , 2019, Mathematical Modelling of Natural Phenomena.

[16]  H. Brezis Functional Analysis, Sobolev Spaces and Partial Differential Equations , 2010 .

[17]  Luca Vincenzo Ballestra,et al.  The spatial AK model and the Pontryagin maximum principle , 2016 .

[18]  Emilio Barucci,et al.  Investment in a vintage capital model , 1998 .

[19]  Guiomar Martín-Herrán,et al.  Spatial effects and strategic behavior in a multiregional transboundary pollution dynamic game , 2016, Journal of Environmental Economics and Management.

[20]  E. Coddington,et al.  Theory of Ordinary Differential Equations , 1955 .

[21]  P. Lions,et al.  User’s guide to viscosity solutions of second order partial differential equations , 1992, math/9207212.