Although urban greening is universally recognized as an essential part of sustainable and climate-responsive cities, a growing literature on green gentrification argues that new green infrastructure, and greenspace in particular, can contribute to gentrification, thus creating social and racial inequalities in access to the benefits of greenspace and further environmental and climate injustice. In response to limited quantitative evidence documenting the temporal relationship between new greenspaces and gentrification across entire cities, let alone across various international contexts, we employ a spatially weighted Bayesian model to test the green gentrification hypothesis across 28 cities in 9 countries in North America and Europe. Here we show a strong positive and relevant relationship for at least one decade between greening in the 1990s–2000s and gentrification that occurred between 2000–2016 in 17 of the 28 cities. Our results also determine whether greening plays a “lead”, “integrated”, or “subsidiary” role in explaining gentrification.
Transitioning to more sustainable energy technologies
is a vital
step in the move toward reducing global greenhouse gas emissions.
However, several physical constraints could hinder the implementation
of these technologies, and many of the raw materials required to produce
new infrastructure are scarce, nonrenewable, and nonsubstitutable.
Various factors relating to material extraction and processing activities
may also affect the security and sociopolitical aspects of future
supply lines. Here, we introduce methods for quantifying three key
indicators relating to raw material supplies for specific production
processes: (1) overall supply risk, (2) environmental impacts from
sourcing raw materials, and (3) environmental justice threats at sourcing
locations. The use of the proposed methods is demonstrated via an
exploratory case study examining projected electricity production
scenarios within the European Union. Results suggest that renewable
sources of electricityparticularly wind, solar, and geothermal
technologiesare more likely to exacerbate supply risks and
environmental issues than other technologies. Furthermore, projected
expansions of wind and solar technologies mean that all three indicators
appear likely to rise significantly systemwide by 2050. Ultimately,
the methods represent a much-needed first attempt at providing practitioners
with simple and robust approaches for integrating factors relating
specifically to raw material supply into energy modeling and other
applications.
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