Abstract
The UN's Sustainable Development Goals for 2030 aim on one hand at inclusive growth and eradicating poverty, and on the other at preserving environments. The relation between development and the environment has been studied extensively since the 1990s, documenting inverted U-shaped relations between per capita income and indicators of environmental degradation. This paper revisits the issue with machine learning techniques and novel disaggregate data to model these relationships heterogeneously across economic indicators. Results suggest that development gradually improves the efficiency of consuming the earth's nonrenewable resources, but increased efficiency alone is not sufficient to offset growth in scale. Development shifts reliance on one nonrenewable source to another, and on average we find successive inverted U-shapes in deforestation, air pollution and carbon intensities, followed by a J-shape in per capita carbon output. Local economic circumstances further determine the shape, amplitude, and location of tipping points in environmental output. The general implications of the estimated dynamics are explored by extrapolating environmental output to 2030 under simplistic scenario's. The results are a reminder that immediate, and sustained global efforts are required to preserve our environment.
| Original language | English |
|---|---|
| Article number | 109221 |
| Journal | Renewable and Sustainable Energy Reviews |
| Volume | 114 |
| DOIs | |
| Publication status | Published - 1 Oct 2019 |
UN SDGs
This output contributes to the following UN Sustainable Development Goals (SDGs)
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SDG 1 No Poverty
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SDG 8 Decent Work and Economic Growth
Keywords
- Air pollution
- Carbon emission
- Deforestation
- Economic development
- Environmental Kuznets curves
- Machine learning
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