Global datasets of geospatial-AI-resolved energy consumers including climate-driven energy demands, geographical and socioeconomic realities for a transition reset.

IF 5.8 2区 综合性期刊 Q1 MULTIDISCIPLINARY SCIENCES
Diego Moya, Dennis Copara, Sara Giarola, Adam Hawkes
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引用次数: 0

Abstract

Traditional models deliberately simplify millions of consumers into a single, homogeneous, representative agent with perfect market knowledge and rational expectations, limiting their capacity to capture real-world complexities. To address this limitation in mainstream models, this article provides global datasets to parametrise energy consumers within climate-energy-economy models considering climate-driven energy demand, socioeconomic and demographic factors. The datasets emerge from applying geospatial artificial intelligence, machine learning and big data analytics on a range of geospatial parameters at 1 km2 resolution. Twenty distinctive energy consumers are defined using three heterogeneous geospatial features, eight diverse and two evolving parameters. This parametrisation of consumers strengthens the applicability of climate-energy-economy models to guide effective, equitable and just climate policy design. This comprehensive analysis of complex interactions between climate, socioeconomic and demographic factors supports more realistic decision-making for a sustainable transition reset. This research emphasises the geospatial distribution of energy consumers to enhance technoeconomic assessment, understanding consumer dynamics for consumer-led resource allocation and informed policy implementation. These datasets can be used in climate-energy-economy models to parametrise consumers beyond traditional approaches.

地理空间人工智能解决的全球能源消费者数据集,包括气候驱动的能源需求、转型重置的地理和社会经济现实。
传统模型故意将数百万消费者简化为具有完美市场知识和理性预期的单一、同质、代表性代理,从而限制了它们捕捉现实世界复杂性的能力。为了解决主流模型中的这一局限性,本文提供了全球数据集,在考虑气候驱动的能源需求、社会经济和人口因素的气候-能源-经济模型中对能源消费者进行参数化。这些数据集来自于将地理空间人工智能、机器学习和大数据分析应用于1平方公里分辨率的一系列地理空间参数。使用三个不同的地理空间特征,八个不同的和两个不断变化的参数定义了二十个不同的能源消费者。这种消费者参数化增强了气候-能源-经济模型的适用性,以指导有效、公平和公正的气候政策设计。这项对气候、社会经济和人口因素之间复杂相互作用的综合分析,为可持续转型重置提供了更现实的决策支持。本研究强调能源消费者的地理空间分布,以加强技术经济评估,了解消费者动态,以实现消费者主导的资源配置和知情的政策实施。这些数据集可用于气候-能源-经济模型,超越传统方法对消费者进行参数化。
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来源期刊
Scientific Data
Scientific Data Social Sciences-Education
CiteScore
11.20
自引率
4.10%
发文量
689
审稿时长
16 weeks
期刊介绍: Scientific Data is an open-access journal focused on data, publishing descriptions of research datasets and articles on data sharing across natural sciences, medicine, engineering, and social sciences. Its goal is to enhance the sharing and reuse of scientific data, encourage broader data sharing, and acknowledge those who share their data. The journal primarily publishes Data Descriptors, which offer detailed descriptions of research datasets, including data collection methods and technical analyses validating data quality. These descriptors aim to facilitate data reuse rather than testing hypotheses or presenting new interpretations, methods, or in-depth analyses.
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