Deep Green


Cities are the biggest CO2 emitters globally, and therefore it is necessary to address the redesign of their infrastructural apparatus towards carbon neutrality, and material and energetic circularity. In other words, it is critical for the cities to learn how to convert what they expel as waste, or pollution, into a raw material that can be used for new production processes.

This entails innovative strategies of waste management, water conservation and recycling, renewable energy production and carbon trading. It also involves implementing technologies for the filtration and re-metabolisation of air pollution.

Cities as a refuge
However, the cities are also our most effective refuge from the potentially devastating effects of climate change. This is particularly true for those regions of the world that are most vulnerable to drought, flooding and famine.

We can design resilient cities that use their size and collective energy to create refuge for both humans and displaced wildlife, that promote the emergence of positive microclimate, replenish depleted water sources and restore degraded terrains, thus pushing back on processes such as desertification, land erosion and contamination.

This entails innovative strategies for urban re-greening and re-wilding, as well as for urban agriculture. The problems affecting our cities are also the same problems that face humankind as a whole. After all, we all inhabit the collective Urbansphere, which is why a global model for a green city planning is required.

Mobilise collective agency
A critical need for urban planning today is to mobilize a collective agency and system intelligence to face the challenges ahead. In this way, local solutions can be evolved in response to the given challenge.

In this recent collaboration with UNDP, the ecoLogicStudio together with academic partners The Synthetic Landscape Lab at Innsbruck University and the Urban Morphogenesis Lab at the Bartlett UCL, has been testing a potential of Artificial Intelligence to develop a new green planning interface.

At its core, this application uses algorithms to analyse hi-resolution urban landscape and infrastructure data to produce simulated scenarios of sustainable urban development. The data is open sourced GIS information, available even for rather remote and underdeveloped regions of the world.

The Case of Guatemala City
This issue is most evident in the case of Guatemala City. Guatemala City is situated on a complex and highly unstable terrain surrounded by mountains and volcanoes, some of which are still active. Its ecosystems, originally very rich in biodiversity, are now made fragile by unchecked urbanisation and, given its climatic zone, the effects of climate change.

In Guatemala City this scenario is exacerbated by a serious lack of waste management.

The Guatemala City garbage dump is the biggest landfill in Central America containing over a third of the total garbage in the country.

99% of Guatemala's 2,240 garbage sites have no environmental systems and are classified as "illegal."

Only a new design methodology powered by big data gathering and the production of algorithmic design scenarios that enable randomness within their mathematical model can deal with such high degree of complexity and level of informality.

Our approach creates an interface between bottom-up processes of self-organisation, such as the many local waste recycling activities that are emerging out of necessity in the areas closer to the dumping sites; and the strategic decision making that occurs at municipal, national and international level.

The aim is to find new synergies and direct investments into where and when there is a biggest potential to engender a positive change.

Deep Green VR room:

Project by: ecoLogicStudio with the Synthetic Landscape Lab at Innsbruck University, the Urban Morphogenesis Lab at the Bartlett UCL
Commissioned by: UNDP (United Nation Development Program) with partner cities Guatemala City, Mogadishu, Vranje.
Design team: Claudia Pasquero, Marco Poletto with Eirini Tsomokou, Lixi Zhou, Xiaomeng Kong, Michael Brewster, Thole Althoff, Stephan Sigl.

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