The COVID-19 pandemic is the single largest event in contemporary
history in terms of the global restriction of mobility, with the
majority of the world population experiencing various forms of
“lockdown”. This phenomenon incurred increased
amounts of teleworking and time spent at home, fewer trips to
shops, closure of retail outlets selling non-essential goods,
and the near disappearance of leisure and recreational
activities. This paper presents a novel method for an
economy-wide estimate of the emissions reductions caused by the
restriction of movement. Using a global multiregional
macro-economic model complemented by Google Community Mobility
Reports (CMRs) and national transport data, we cover 129
individual countries and quantify direct and indirect global
emissions reductions of greenhouse gases (GHG; 1173 Mt),
PM
2.5
(0.23 Mt), SO
2
(1.57 Mt), and
NO
x
(3.69 Mt). A
statistically significant correlation is observed between
cross-country emission reductions and the stringency of mobility
restriction policies. Due to the aggregated nature of the CMRs,
we develop different scenarios linked to consumption, work, and
lifestyle aspects. Global reductions are on the order of
1–3% (GHG), 1–2% (PM
2.5
),
0.5–2.8% (SO
2
), and 3–4%
(NO
x
). Our results can
help support crucial decision making in the post-COVID world,
with quantified information about how direct and indirect
consequences of mobility changes benefit the environment.
The construction and operation of buildings account for significant environmental impacts, including greenhouse gas (GHG) emissions, energy demand, resource consumption and waste generation. While the operation of buildings is fairly well regulated and globally considered in the pathways to net-zero mid-century targets, a different picture emerges when looking at the other life cycle stages, which incur the so-called embodied impacts. These cover raw material extraction and product manufacturing through to construction and end of life activities. Only a handful of examples exist where such embodied carbon (EC) emissions are enshrined in law with most of the ongoing debate still around estimating and understanding where such emissions occur and how to mitigate them. Building structures account for a significant share of a building’s embodied emissions and they also are the building element with the longest service life, thus presenting potential lock-in challenges for choices made today. To support the ongoing global effort to mitigate embodied carbon and equip engineers and designers worldwide with easy-to-use and robust calculation tools, we describe a real-time decision-support tool to aid building design that leverages machine learning (ML) methods from computer science to speed-up the computationally expensive process of finite element analysis (FEA) traditionally exploited in structural engineering. We demonstrate that replacing FEA calculations with a model learnt using ML from a large dataset offers real time decision support while guaranteeing the same level of confidence and accuracy that a traditional FEA-based method would offer at the design stage. The tool has been developed both as a standalone version and as a plugin for Trimble SketchUp to maximise its usability and diffusion. It offers results correlated with uncertainty analysis in the form of probability density functions to account for the inherent variability of input data that characterises early stages in the design process. This research contributes to the ongoing global efforts to decarbonising the built environment and offers an immediately implementable method and tool for doing so.
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