This paper presents a comprehensive study leveraging Support Vector Machine (SVM) regression and Principal Component Regression (PCR) to analyze carbon dioxide emissions in a global dataset of 62 countries and their dependence on idiosyncratic, country-specific parameters. The objective is to understand the factors contributing to carbon dioxide emissions and identify the most predictive elements. The analysis provides country-specific emission estimates, highlighting diverse national trajectories and pinpointing areas for targeted interventions in climate change mitigation, sustainable development, and the growing carbon credit markets and green finance sector. The study aims to support policymaking with accurate representations of carbon dioxide emissions, offering nuanced information for formulating effective strategies to address climate change while informing initiatives related to carbon trading and environmentally sustainable investments.

This story originally appeared in Grist and is part of the Climate Desk collaboration. In 2023, the fast-fashion giant Shein was everywhere. Influencers' "#sheinhaul" videos advertised the company's trendy styles on social media, garnering billions of views. At every step, data was created, collected, and analyzed. To manage all this information, the fast fashion industry has begun embracing emerging AI technologies.

A new Carbon Majors Database report, which examines carbon dioxide emissions, found that just 57 companies were responsible for 80 percent of the global carbon dioxide emissions between 2016 and 2022. ExxonMobil, which topped the list of United States companies, contributed 1.4 percent of all global carbon dioxide emissions. It has net zero emissions targets. Nearly 200 parties adopted the 2015 Paris Agreement, committing to reduce greenhouse gas emissions. However, 58 of the 100 state- and investor-owned companies in the Carbon Majors Database have since increased their production.

The rapid ascent in carbon dioxide emissions is a major cause of global warming and climate change, which pose a huge threat to human survival and impose far-reaching influence on the global ecosystem. Therefore, it is very necessary to effectively control carbon dioxide emissions by accurately predicting and analyzing the change trend timely, so as to provide a reference for carbon dioxide emissions mitigation measures. This paper is aiming to select a suitable model to predict the near-real-time daily emissions based on univariate daily time-series data from January 1st, 2020 to September 30st, 2022 of all sectors (Power, Industry, Ground Transport, Residential, Domestic Aviation, International Aviation) in China. We proposed six prediction models, which including three statistical models: Grey prediction (GM(1,1)), autoregressive integrated moving average (ARIMA) and seasonal autoregressive integrated moving average with exogenous factors (SARIMAX); three machine learning models: artificial neural network (ANN), random forest (RF) and long short term memory (LSTM). To evaluate the performance of these models, five criteria: Mean Squared Error (MSE), Root Mean Squared Error (RMSE), Mean Absolute Error (MAE), Mean Absolute Percentage Error (MAPE) and Coefficient of Determination () are imported and discussed in detail. In the results, three machine learning models perform better than that three statistical models, in which LSTM model performs the best on five criteria values for daily emissions prediction with the 3.5179e-04 MSE value, 0.0187 RMSE value, 0.0140 MAE value, 14.8291% MAPE value and 0.9844 value.

Data and cloud are not virtual technology. They need costly infrastructure and electricity. Researchers forecast that in the future their emissions could be much more than expected. Who does not make us sleep the night? A few weeks ago the UK break the temperature record, for the first time the temperature rose over 40 C. The summer nights are warm and humid and it is hard to sleep on similar days.

No one likes sitting at a red light. But signalized intersections aren't just a minor nuisance for drivers; vehicles consume fuel and emit greenhouse gases while waiting for the light to change. What if motorists could time their trips so they arrive at the intersection when the light is green? While that might be just a lucky break for a human driver, it could be achieved more consistently by an autonomous vehicle that uses artificial intelligence to control its speed. In a new study, MIT researchers demonstrate a machine-learning approach that can learn to control a fleet of autonomous vehicles as they approach and travel through a signalized intersection in a way that keeps traffic flowing smoothly.

This article is part of a VB special issue. Read the full series here: The metaverse - How close are we? Some companies believe that the metaverse -- a yet-to-be-realized, internet-like series of connected worlds -- has enormous potential in the enterprise. For example, it could be used to improve work productivity by allowing employees to train or collaborate in workplace-like virtual environments. Or it could host home and office tours, a boon for a real estate market contending with pandemic travel restrictions.

The estimates for the economic costs of inventory mismanagement in the non-grocery retail sector in the US in 2018 amounted to USD 300Bn and the retail fashion sector have been destroying surplus inventory! This is both a financial and environmental wastage that application of Machine Learning for better forecasting product demand, recommendation algorithms to better target and match supply with demand and supply chain optimisation may assist with. A report undertaken by PWC and commissioned by Microsoft set out that applying AI to four key sectors of the economy (Energy, Transportation, Agriculture and Water) alone would result in material reductions of Green House Gas Emissions, whilst also driving economic growth and substantial increase in jobs. More specifically, the report set out that by 2030 AI applied to the four sectors could enable the creation of 38 million jobs, $5.2 Trillion of GDP growth and 2.4 Gigatons of Carbon Dioxide emissions (or a 4% reduction). These are vast numbers and align the benefits of economic growth with climate goals. Accenture Strategy forecast that standalone 5G network technology may create 3 million jobs across the US and $500Bn of GDP growth. More recently a BCG Study forecast that 5G may drive the addition of approximately 4.5 Million Jobs and an increase of About $1.5 Trillion in US GDP Over this decade in the US alone! Standalone 5G networks alongside AI will enable the AIoT across the Edge of the network and a whole new era of innovation ranging from 5G enabled smart glasses for the Metaverse to other next generation wearables and dynamically responsive intelligent agents across our homes and workplaces. Intelligence here is not defined as AGI at the level of the human brain and rather ranging from Narrow AI (ANI) and increasingly Broad AI (Artificial Broad Intelligence, ABI) that may multitask but not quite match the capabilities of the human brain.

How often do we link terms like data science, artificial intelligence (AI), and machine learning with futuristic advancement, such as highly sophisticated robots and space ships as public transport? Why do we not associate them with a greener area, cleaner air, or flourishing biodiversity? Fourth Industrial Revolution technologies such as AI are enabling humanity to harness information and data to revolutionise education, energy, healthcare, agriculture, transportation, and many other service areas. AI helps us makes the world a better place, from traffic management in urban mobility to enhancing the efficiency of renewable energies to predict crop needs and other innovative solutions in smart agriculture. AI is becoming a key tool for facilitating a circular economy and building smart cities that use their resources efficiently.

"Thanks to AI, the airline saved 480,000 gallons of fuel in six months." When Greta Thunberg boarded a transatlantic zero-emissions yacht she garnered the attention of citizens of the world on the fact that aviation is a polluter of the environment that we continuously ignore. The giant industry is responsible for producing 915 million tonnes of carbon dioxide emissions along with other dangerous gases that cause environmental changes like cirrus clouds. These emissions constitute two percent of the world's greenhouse emissions. From the electrification of jets to biofuel many ideas have been suggested to make flying more eco friendly.