The contemporary global environment is characterised by the convergence of interactive and multifaceted challenges that collectively form a highly complex system. To give an example of this complexity, consider how the impacts of climate change extend beyond rising temperatures and erratic weather patterns, and synergise with biodiversity loss, as affected ecosystems struggle to keep up with the pace of change.
In turn, biodiversity loss, itself a critical concern, also links tightly with deforestation, whereby the clearing of forests not only diminishes species richness but also exacerbates climate change through reduced carbon sequestration and changes in albedo. Deforestation is mostly carried out in the name of the increasing food demand of growing human populations, and wherever the demand is not met by expansions in cultivated land, it is met by the intensification of food production through increased usage of polluting fertilisers and pesticides. This pollution further amplifies the strain on ecosystems and biodiversity, closing a negative feedback look that affects the initial goal of increasing food production itself, all the while having also aggravated climate change via greenhouse gas emission and reduced carbon sequestration by the now depleted agricultural soils.
Accounting for this complex network of feedback loops is central in navigating the complexities of environmental governance. However, traditional decision-making, once effective under the simpler environmental paradigms of a past with fewer humans, falters when applied to contemporary challenges. Traditionally, state administration was carried out by mostly isolated sectors with only a limited capacity of accounting for each other’s needs. This arguably outdated administration architecture struggles to handle the complexity of current issues, resulting in prolonged debates, politicisation, and heightened emotional discourse among sector representatives.
The advent of data science, information technology and artificial intelligence presents a promising avenue for addressing the complexity of current environmental systems. These tools provide the capability to model intricate dynamics and analyse vast datasets, offering a potential solution to the challenges posed by the tightly linked network of global environmental issues. However, the translation of these advancements to effective use within regional, national and international administrations remains a work in progress, with significant room for improvement.
In addressing these challenges, my research fits within the gap between data science and territorial planning. With a particular focus on food production, energy production, biodiversity conservation, and land-use conflicts, I typically use a diverse array of spatially explicit, computational methods to enhance the effectiveness of environmental governance, aiming at offering pragmatic solutions that would resonate with the intricate dynamics of the Earth’s ecosystems.