In order to realize the goal of optimal use of energy sources and cleaner environment at a
minimal cost, researchers; field professionals; and industrialists have identified the …

Corporations have come under pressure from investors and other stakeholders to disclose
and reduce their greenhouse gas emissions (GHG). Corporate GHG footprints, proxying for …

Adequate CO2 is essential for vegetation, but industrial chimneys and land, space and
oceanic vehicles exert tons of excessive CO2 and are mostly responsible for the …

In this study, three novel machine learning algorithms of additive regression-random forest
(AR-RF), Iterative Classifier Optimizer (ICO-AR-RF), and multi-scheme (MS-RF) were …

By analysing biochemical composition, assessing soil quality, projecting yields, predicting
productivity, identifying illnesses, and predicting productivity, artificial intelligence (AI) has …

Paying attention to human activities in terms of land grazing infrastructure, crops, forest
products, and carbon impact, the so-called ecological impact (EF) is one of the most …

The paper provides insight of CO 2 emission prediction model using machine learning.
Traditionally researchers have used statistical techniques such as regression, t-test …

The impact of global warming on the environment is a significant concern, and finding
effective ways to address climate change is a priority. This paper investigates how Artificial …

The purpose of this research is to build an estimated model for the ecological footprint (EF)
in the G20 countries in the period of 1999–2018, the last two decades. These countries have …

China is one of the world's largest producers and consumers of cement, making carbon
emissions in the cement industry a focal point of current research and practice. This study …