Hostname: page-component-cd9895bd7-dk4vv Total loading time: 0 Render date: 2024-12-25T07:01:47.073Z Has data issue: false hasContentIssue false
  • English
  • Français

Demystifying the environmental Kuznets curve: turning a black box into a policy tool

Published online by Cambridge University Press:  30 March 2001

THEODORE PANAYOTOU
THEODORE PANAYOTOU
Affiliation:
Department of Economics and Harvard Institute for International Development, Harvard University, One Eliot Street, Cambridge, MA 02138, USA
Get access Rights & Permissions [Opens in a new window]

Abstract

The reduced-form approach to the income–environment relationship has been a useful first step towards answering the question of how economic growth affects the environment. However, without an explicit consideration of the underlying determinants of environmental quality, the scope for policy intervention is unduly circumscribed. In this paper a modest attempt is made to incorporate explicit policy considerations into the income–environment relationship and to explore its determinants as a step towards a better understanding of this relationship and its potential as a policy tool. The role of the rate of economic growth and population density is also explored. A main finding is that at least in the case of ambient SO2 levels, policies and institutions can significantly reduce environmental degradation at low income levels and speed up improvements at higher income levels, thereby flattening the EKC and reducing the environmental price of economic growth.

Type
Research Article
Information
Environment and Development Economics , Volume 2 , Issue 4 , November 1997 , pp. 465 - 484
Copyright
© 1997 Cambridge University Press

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

Footnotes

The author is grateful to Jeffrey Vincent for insightful discussions, to Xiang Yu for expert assistance with data processing and statistical analysis, and to Jennifer Watts for uncommon patience and efficiency.