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

全球范围的能源基础设施气候风险预测模型，为弹性能源转型提供信息

• 批准号: 2889320
• 金额: --
• 依托单位: University of Oxford
• 依托单位国家: 英国
• 项目类别: Studentship
• 财政年份: 2023
• 资助国家: 英国
• 起止时间: 2023 至 无数据
• 项目状态: 未结题
• 来源: https://gtr.ukri.org/projects?ref=studentship-2889320
• 关键词: Global; scale; predictive; model; climate

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 investmentsdo 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 notcomprehensively 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 studieson 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 scaletools 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.

气候变化正在推动全球能源基础设施的结构性转变。由于温室气体排放量极高，给经济和社会带来极端的气候危害，所有部门都迫切需要从化石燃料向可再生能源迅速过渡。各国政府、国际组织和私营部门正在共同努力动员和扩大气候融资，以加速能源转型，全球动员的资金数量正在快速增长[5]，[6]。然而，气候变化影响的发生率越来越高，如洪水，飓风和山体滑坡，阻碍了能源脱碳过程和相关的气候投资，并可能威胁现有和新的基础设施[2]-[4]。因此，为了成功脱碳和有效利用气候融资，必须在实施变化时以弹性方式审议物理气候风险对能源基础设施和新开发的事前（或未来）影响[7]，[8]。大多数致力于能源脱碳和气候投资的政府和国际组织并没有在全球范围内科学地评估能源基础设施的事前气候风险[9]，[10]。因此，它们无法系统地将评估的影响纳入其基础设施设计和弹性能源转型背景下的投资。学术界广泛研究了关键基础设施、相互依存网络和能源脱碳过程的气候风险[11]-[17]。然而，他们对能源基础设施气候风险的研究以及对脱碳的研究很少被整合[13]，[15]-[21]。一方面，脱碳研究主要集中在能源规划或过渡成本建模上，没有全面考虑物理气候风险[13]，[15]，[17]，[21]，或者没有进行地理空间分析或定量方法[16]，[19]，[20]。关于气候风险与能源基础设施和脱碳之间的联系，已经有一些定量和地理空间研究，但这些研究仅限于一个国家或少数几个国家的案例研究，而不是全球性的。在这方面，用于分析的全球规模工具很少，但它们尚未演变为有效的决策过程，以系统地指导弹性能源转型[10]，[18]，[22]。上述研究差距主要源于全球范围内的建模复杂性，以及由于耗时和地面数据收集工作而导致的数据不可用和粗粒度数据粒度（低空间分辨率）[23]-[25]。空间数据科学学者正试图部署机器学习和遥感技术，从全球异构源生成更精细的空间分辨率数据集[26]-[28]。然而，这些领域尚未完全融入数据处理，以了解气候风险和脱碳之间的关系。我的研究旨在填补能源基础设施的气候风险如何影响能源系统弹性脱碳的明显研究空白。我建议开发一个全球规模的预测风险模型，使用可量化的证据来衡量能源基础设施的气候风险。该模型旨在为弹性能源转型和有效的气候投资提供信息。它将分两个阶段开发。