Related papers: Estimation of Corporate Greenhouse Gas Emissions v…

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…

Corporate Greenhouse Gas (GHG) emission targets are important metrics in sustainable investing [12, 16]. To provide a comprehensive view of company emission objectives, we propose an approach to source these metrics from company public…

The most recent concern of all people on Earth is the increase in the concentration of greenhouse gas in the atmosphere. The concentration of these gases has risen rapidly over the last century and if the trend continues it can cause many…

As organizations face increasing pressure to understand their corporate and products' carbon footprints, artificial intelligence (AI)-assisted calculation systems for footprinting are proliferating, but with widely varying levels of rigor…

The construction industry is one of the main producers of greenhouse gasses (GHG). Quantifying the amount of air pollutants including GHG emissions during a construction project has become an additional project objective to traditional…

Despite the new Corporate Sustainability Reporting Directive from the European Union, which presses large enterprises to be more transparent about their GHG emissions, and though large technology- or advisory firms might peddle otherwise,…

Corporate carbon emissions data is disclosed by approximately 65% of large and mid-sized companies globally, despite being a key indicator of corporate climate performance. With investors increasingly looking to integrate climate risk into…

Climate change is profoundly affecting nearly all aspects of life on earth, including human societies, economies and health. Various human activities are responsible for significant greenhouse gas emissions, including data centres and other…

Climate change is one of the greatest challenges facing humanity, and we, as machine learning experts, may wonder how we can help. Here we describe how machine learning can be a powerful tool in reducing greenhouse gas emissions and helping…

The Paris Agreement aims to reach net zero greenhouse gas (GHG) emissions in the second half of the 21st century, and the Oil & Gas sector plays a key role in achieving this transition. Understanding progress in emission reductions in the…

The EU targets 10 Mt of green hydrogen production by 2030, but has not committed to targets for 2040. Green hydrogen competes with carbon capture and storage, biomass and imports in reaching emissions reductions; earlier studies have…

The continuous rise in CO2 emission into the environment is one of the most crucial issues facing the whole world. Many countries are making crucial decisions to control their carbon footprints to escape some of their catastrophic outcomes.…

Human-produced emissions are growing at an alarming rate, causing already observable changes in the climate and environment in general. Each year global carbon dioxide emissions hit a new record, and it is reported that 0.5% of total US…

Machine learning (ML) requires using energy to carry out computations during the model training process. The generation of this energy comes with an environmental cost in terms of greenhouse gas emissions, depending on quantity used and the…

Machine Learning (ML) workloads have rapidly grown in importance, but raised concerns about their carbon footprint. Four best practices can reduce ML training energy by up to 100x and CO2 emissions up to 1000x. By following best practices,…

Air pollution is a major driver of climate change. Anthropogenic emissions from the burning of fossil fuels for transportation and power generation emit large amounts of problematic air pollutants, including Greenhouse Gases (GHGs). Despite…

It is important for policymakers to understand which financial policies are effective in increasing climate risk disclosure in corporate reporting. We use machine learning to automatically identify disclosures of five different types of…

From an environmental standpoint, there are a few crucial aspects of training a neural network that have a major impact on the quantity of carbon that it emits. These factors include: the location of the server used for training and the…

This paper addresses the environmental impacts linked to hazardous emissions from gas turbines, with a specific focus on employing various machine learning (ML) models to predict the emissions of Carbon Monoxide (CO) and Nitrogen Oxides…

Greenhouse gas (GHG) metrics, that is, conversion factors to evaluate the emissions of non-CO2 climate forcers on a common scale with CO2, serve crucial functions upon the implementation of the Paris Agreement. While different metrics have…