The CleanEnergyBot is a Telegram bot providing real-time electricity usage, CO2 forecasts, and energy-saving tips in Ireland, using data from EirGrid and GPT-3 analysis. It helps users make eco-friendly energy choices by comparing emissions data with EU standards.

Food Waste Management web application with stats of monthly Food Wastage, Costs, Quantity Purchased, CO2 Emissions and monthly Food Donations. Donations made seamless using Map API. Personal Inventory to keep track of food items. Also the platform provides options for Recipe search and Nutrition Analysis of ingredients.

We have a dataset which contains various features of the car based on which we predict the Carbon dioxide emission.

Data Analysis Project

🏭 A project for analyzing global CO₂ emissions using R, leveraging OWID data to create 12 visualizations that explore historical trends, economic relationships, and top emitters. Includes a linear regression of per-capita emissions on GDP per capita and supports reproducible workflows via Makefile, RMarkdown, and Docker.

This project showcases a comprehensive analysis of CO2 emissions in a fictitious cheese manufacturing supply chain using both graph databases and relational databases.

Performs an SDA analysis in an EE-MRIO framework using WIOD world input output tables and CO2 emissions data (2000-2014). Includes code on extraction of data.

CO2 emission and runtime analysis of different scheduling strategies

Data scraped from the Australian energy market to focus on the analysis of the dataset time features behaviour. The evaluation is complemented by an initial attempt of a forecast of CO2 emissions from electricity production.

Time serie analysis of CO2 emission at Mauna Loa

This project analyzes the factors influencing CO2 emissions in France through **machine learning models**. Using both **economic** and **environmental data,to provide valuable insights for policy-making and environmental management.

Project using linear regression and data science to predict and analysis of global warming using co2 emission over years

R program and respective libraries used for a project on the analysis of CO2 emissions under various factors using statistical tools such as linear regression and descriptive statistics

A Python web scraper using Selenium to extract global energy data from the IEA website. Collects data such as energy mix, electricity mix, CO2 emissions, and yearly statistics for countries. Saves results in a CSV file for analysis. Ideal for researchers and data enthusiasts interested in energy trends.

Internship Week 1 Submission - CO2 Emission Analysis

The project involves analyzing a dataset of vehicles to gain insights into their CO2 emissions. The dataset was sourced from an open data repository and the analysis was conducted using Python programming language.

Analyzing CO2 emissions trends using Python, exploring population and GDP impacts. Implemented ARIMA, LSTM, RandomForestRegressor, and Prophet models for forecasting.

Analysis for CO2 emissions from the MRV system (for ships)

No description available

Comprehensive analysis and forecasting of CO2 emissions across multiple countries. Utilizes machine learning and time-series models to predict future emissions based on historical data from 1990-2020.

Predictive analysis of vehicle CO2 emissions using RandomForest with a deep dive into feature influence via SHAP values. This project leverages machine learning to understand and predict emissions based on extensive vehicle data.

A repository containing the code and data used for my Bachelor Project "CO2 and The Internet, a quantitative study on the environmental impact of the top Web sites in the world". It is a comprehensive analysis of the internet's emissions and its effects on the environment.

This model is deployment-ready, designed to predict ship fuel consumption and CO2 emissions with high accuracy (R² = 0.9321 for fuel and R² = 0.9932 for emissions). It integrates machine learning, time series analysis, enhance fuel efficiency, and reduce emissions, enabling real-time decision-making for sustainable shipping operations.

Regression analysis with machine learning techniques is the focus of this study. By utilizing regression analysis, one or more independent factors, such as engine size, cylinder count, and fuel consumption metrics, may be utilized to predict a continuous dependent variable, in this instance CO2 emissions.

Carbon Dioxide is a major component of Green House Gas (GHG) emissions, accounting for 81% of the total emissions. Released naturally (respiration, ocean drive, and decomposition) or as a consequence of human activity (cement production, burning of fossil fuels such as coal, oil and natural gas, etc.), carbon dioxide has played a significant role in Global Warming. It is a phenomenon whose existence can be found in the melting of polar ice caps or progressive rise in the annual global temperatures. India, an emerging economy is the fourth largest producer of CO2 emissions, behind China, USA and the European Union. A nation, with the drive to become a leading superpower in the coming years, India’s booming economy and development poses a serious challenge to the levels of carbon dioxide emissions. Overtaking Russia, to become the third largest producer of electricity, India still relies on coal as the biggest source of electricity, whose burning yields CO2 into the atmosphere. Awaken by the adversities of CO2 emissions, India has signed the Paris Agreement and pledged to reduce the CO2 levels to 30-35% of the level in the year 2005. This agreement is set to be starting in 2020. To have a better understanding of what the challenge is going to be in the year 2020, prior to the implementation of the Paris agreement, this paper aims at forecasting the levels of CO2 emissions and its constituents (solid, liquid and gaseous fuels) in India. The technique used in the forecasting is Exponential Smoothing. Starting from 1960, first two years data has been used for initialization and the value of model parameters (alpha and beta) has been optimized with data from 1962-2017 using Solver. The forecast is estimated for the years 2018-2020. Keywords: Predictive Analytics, CO2 emissions, Time Series Analysis, R, Double Exponential Smoothing, Paris Agreement

A short R script that attempts to visualize carbon emissions by country in three different ways and the same visualizations after they were exported to Adobe Illustrator so additional features could be added to the visualizations. This data set came from the Carbon Dioxide Information Analysis Center, http://cdiac.ornl.gov/, via Gapminder’s site.(http://www.gapminder.org/data) This data shows the CO2 emissions per capita of 201 countries in metric tons. I chose to look at the top 50 emitters in 5 year increments between 1970 and 2010, which seemed long enough to see significant trends, but not so far back as to be overwhelming or suffer from large amounts of missing data. To determine who the top 50 emitters were I summed each row (from 1970 – 2010), sorted from largest to smallest and took the countries with the 50 largest amounts. The categorical variables being compared are Country and Year.

Predictive Analysis of State CO2 Emissions

Big Data Analysis and Green Computing: Profiling HPC Power and Tracking CO2 Emissions

CO2 Emissions By Sector Analysis

Influence of the nature and well being of a Country in its CO2 emissions