Environment and Development: Penalized Non-Parametric Inference of Global Trends in Deforestation, Pollution and Carbon

Bo Pieter Johannes Andree, Harun Dogo, Andres Chamorro, Phoebe Spencer

Research output: Contribution to JournalArticleAcademicpeer-review

Abstract

This paper revisits the issue of environment and development raised in the 1992 World Development Report, with new analysis tools and data. The paper discusses inference and interpretation in a machine learning framework. The results suggest that production gradually favors conserving the earth's resources as gross domestic product increases, but increased efficiency alone is not sufficient to offset the effects of growth in scale. Instead, structural change in the economy shapes environmental outcomes across GDP. The analysis finds that average development is associated with an inverted $U$-shape in deforestation, pollution, and carbon intensities. Per capita emissions follow a $J$-curve. Specifically, poverty reduction occurs alongside degrading local environments and higher income growth poses a global burden through carbon. Local economic structure further determines the shape, amplitude, and location of tipping points of the Environmental Kuznets Curve. The models are used to extrapolate environmental output to 2030. The daunting implications of continued development are a reminder that immediate and sustained global efforts are required to mitigate forest loss, improve air quality, and shift the global economy to a 2°pathway.
Original languageEnglish
JournalWorld Bank Policy Research Working Paper
Publication statusPublished - 25 Feb 2019

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Pollution
Carbon
Inference
Deforestation
Environmental Kuznets curve
Income growth
Structural change
Economic structure
Inverted-U
J-curve
Gross domestic product
Machine learning
Air quality
Resources
Poverty reduction
Burden
Global economy

Keywords

  • Environment
  • Development
  • Penalized Inference
  • Non-parametric models
  • Kernel Regularized Least Squares

Cite this

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Environment and Development: Penalized Non-Parametric Inference of Global Trends in Deforestation, Pollution and Carbon. / Andree, Bo Pieter Johannes; Dogo, Harun; Chamorro, Andres; Spencer, Phoebe.

In: World Bank Policy Research Working Paper, 25.02.2019.

Research output: Contribution to JournalArticleAcademicpeer-review

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AB - This paper revisits the issue of environment and development raised in the 1992 World Development Report, with new analysis tools and data. The paper discusses inference and interpretation in a machine learning framework. The results suggest that production gradually favors conserving the earth's resources as gross domestic product increases, but increased efficiency alone is not sufficient to offset the effects of growth in scale. Instead, structural change in the economy shapes environmental outcomes across GDP. The analysis finds that average development is associated with an inverted $U$-shape in deforestation, pollution, and carbon intensities. Per capita emissions follow a $J$-curve. Specifically, poverty reduction occurs alongside degrading local environments and higher income growth poses a global burden through carbon. Local economic structure further determines the shape, amplitude, and location of tipping points of the Environmental Kuznets Curve. The models are used to extrapolate environmental output to 2030. The daunting implications of continued development are a reminder that immediate and sustained global efforts are required to mitigate forest loss, improve air quality, and shift the global economy to a 2°pathway.

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