Environment and Development : Penalized Non-Parametric Inference of Global Trends in Deforestation, Pollution and Carbon
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 p...
| Main Authors: | , , , |
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| Language: | English |
| Published: |
World Bank, Washington, DC
2019
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| Subjects: | |
| Online Access: | http://documents.worldbank.org/curated/en/263441551118664212/Environment-and-Development-Penalized-Non-Parametric-Inference-of-Global-Trends-in-Deforestation-Pollution-and-Carbon http://hdl.handle.net/10986/31330 |
| Summary: | 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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