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Production-based pollution versus deforestation: optimal policy with state-independent and-dependent environmental absorption efficiency restoration process

Research output: Contribution to journal › Article › peer-review

Abstract

An important yet largely unexamined issue is how the interaction between deforestation and pollution affects economic and environmental sustainability.This article seeks to bridge the gap by introducing a dynamic model of pollution accumulation where polluting emissions can be mitigated and the absorption efficiency of pollution sinks can be restored. We assume that emissions are due to a production activity, and we include deforestation both as an additional source of emissions and as a cause of the exhaustion of environmental absorption efficiency. To account for the fact that the switching of natural sinks to a pollution source can be either possible, and in such a case even reversible, or impossible, we consider that restoration efforts can be either independent from or dependent on environmental absorption efficiency, i.e., state-independent versus state-dependent restoration efforts. We determine (i) whether production or deforestation is the most detrimental from environmental and social welfare perspectives, and (ii) how state-dependent restoration process affects pollution accumulation and deforestation policies and the related environmental and social welfare consequences.

Original languageEnglish
Pages (from-to)1-26
Number of pages26
JournalAnnals of Operations Research
Volume292
Issue number1
DOIs
StatePublished - 1 Sep 2020

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

  1. SDG 1 - No Poverty
    SDG 1 No Poverty
  2. SDG 15 - Life on Land
    SDG 15 Life on Land
  3. SDG 17 - Partnerships for the Goals
    SDG 17 Partnerships for the Goals

Keywords

  • Deforestation
  • Environmental absorption efficiency
  • History dependence
  • Optimal pollution
  • Restoration process

ASJC Scopus subject areas

  • General Decision Sciences
  • Management Science and Operations Research

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