Global-scale predictive model of climate risks to energy infrastructure toinform resilient energy transition

Climate change is urging global structural shifts in energy infrastructure. With exceedingly high levels of greenhouse gas (GHG) emissions bringing extreme climate hazards to economies and societies, rapid energy transition from fossil fuels to renewable energy is urgently needed in all sectors. Governments, international organisations and the private sector are collectively striving to mobilise and scale up climate finance to accelerate the energy transition, and the amount of globally mobilised finance is fast growing [5], [6].

However, the increasing incidence of climate change impacts, such as flooding, cyclones and landslides, hinder the energy decarbonisation process and associated climate investments and potentially threaten extant and new infrastructure [2]-[4]. Therefore, for successful decarbonisation and effective utilisation of climate finance, ex-ante (or future) impacts of physical climate risks on energy infrastructure and new development must be deliberated in a resilient way as changes are implemented [7], [8].

Most governments and international organisations committing to energy decarbonisation and climate investments

do not scientifically assess ex-ante climate risks to energy infrastructure on a global scale [9], [10]. As a result, they cannot systematically incorporate the assessed impacts into their infrastructure design and investments in the resilient energy transition context. Academia has extensively researched climate risks to critical infrastructure, interdependent networks and energy decarbonisation processes [11]-[17].

However, their studies on climate risks to energy infrastructure and, separately, on decarbonisation have rarely been integrated [13], [15]-[21]. On the one hand, decarbonisation studies were heavily focused on energy planning or transition cost modelling and did not

comprehensively consider physical climate risks [13], [15], [17], [21] or were conducted without geospatial analysis or quantitative approaches [16], [19], [20]. There have been some quantitative and geospatial studies

on the linkages between climate risks to energy infrastructure and decarbonisation, but they were limited to one country or to a few countries in the form of case studies instead of being global. There are very few global scale

tools for analysis in this regard, but they have yet to evolve into effective decision-making processes to guide resilient energy transition systematically [10], [18], [22]. The aforementioned research gaps mainly stem from modelling complexity on a global scale, paired with data unavailability and coarse-grained data granularity (low spatial resolutions) due to the time-consuming and ground-based data collection work [23]-[25].

Spatial data science scholars are trying to deploy machine learning and remote sensing technologies to generate finer spatial resolution datasets from heterogeneous sources globally [26]-[28]. However, these fields have yet to be fully integrated into data processing to understand the relationships between climate risks and decarbonisation. My research aims to fill the glaring research gaps on how climate risks to energy infrastructure influence resilient decarbonisation of energy systems.

I propose developing a global-scale predictive risk model for measuring climate risks to energy infrastructure using quantifiable evidence. The model will be designed to inform resilient energy transition and effective climate investments. It will be developed in two phases.