In this research, an integrated framework on the big Earth data analysis has been developed in the context of the geomorphology of Jordan. The research explores the correlation between several thematic datasets, including machine learning and multidisciplinary geospatial data. GIS mapping is...

To reach the global net-zero goal, the level of carbon emissions has to fall substantially at speed rarely seen in history, highlighting the need to identify structural breaks in carbon emission patterns and understand forces that could bring about such breaks. In this paper, we identify and...

As of 2022, greenhouse gases (GHG) emissions reporting and auditing are not yet compulsory for all companies, and methodologies of measurement and estimation are not unified. We propose a machine learning-based model to estimate scope 1 and scope 2 GHG emissions of companies not reporting them...

The rise of prosumers - individuals who both produce and consume energy - presents a significant opportunity to reshape energy markets and achieve carbon neutrality. However, current energy trading models struggle to effectively track emissions and incentivize sustainable consumption behaviors....

Transformative technologies like generative artificial intelligence promise to accelerate productivity growth across many sectors, but they also present new risks from potential misuse. We develop a multi-sector technology adoption model to study the optimal regulation of transformative...

We consider an environment in which there is substantial uncertainty about the potential negative external effects of AI algorithms. We find that subjecting algorithm implementation to regulatory approval or mandating testing is insufficient to implement the social optimum. When testing costs...