Deep dive into global CO2 emissions & temperature changes. Quantifies trends, correlations, and explores lagged effects & causality. Utilizes clustering to identify climate patterns and builds a predictive model for "what-if" scenarios. Delivers critical insights into climate change impact for informed decision-making.

🌍 AI-powered, gamified carbon footprint tracker. Features real-time emission monitoring, daily quests, global leaderboards, and image-based CO2 estimation. Built with Next.js 15, AI features for personalize insight and as AI Assistant, and MongoDB.

🏭 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 is about energy efficiency and renewable energy topic. Developed multivariate time series model to forecast global warming. Analyzed various causes of global warming including energy consumption, emissions; examined correlation and causality of temperature, CO2 concentration, population time series. Discovered the logical connections, contributory factors, etc.

Global CO2 Emissions from fossil-fuels annually since 1751 till 2014.

Data display and processing code for better use of Carbon Monitor Cities (global city-level daily CO2 emissions dataset)

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Aim: Need To Predict Primary Fuel And Capacity_mw For Global Power Plant Dataset. Problem Statment: An affordable, reliable, and environmentally sustainable power sector is central to modern society. Governments, utilities, and companies make decisions that both affect and depend on the power sector. For example, if governments apply a carbon price to electricity generation, it changes how plants run and which plants are built over time. On the other hand, each new plant affects the electricity generation mix, the reliability of the system, and system emissions. Plants also have significant impact on climate change, through carbon dioxide (CO2) emissions; on water stress, through water withdrawal and consumption; and on air quality, through sulfur oxides (SOx), nitrogen oxides (NOx), and particulate matter (PM) emissions. The Global Power Plant Database is an open-source open-access dataset of grid-scale (1 MW and greater) electricity generating facilities operating across the world. The actual Database currently contains nearly 35000 power plants in 167 countries, representing about 72% of the world's capacity. Entries are at the facility level only, generally defined as a single transmission grid connection point. Generation unit-level information is not currently available. But in our study we will be working on the dataset only for INDIA. The data set contains only 908 rows and 25 columns. The data set provides information of all the power plant situated at diffrent loactions in india. Features of dataset: country: symbolic country Name country_long: Full country Name name : Name of the Power Plant gppd_idnr : 10-12 character type ID of the power plant capacity_mw : Electricity generating capacity in megawatts latitude : Geo location of plant in decimal degerees longitude : Geo location of plant in decimal degerees primary_fuel : Primary fuel used for electricity genrration. other_fuel1 : Energy source used in electricity generation or export other_fuel2 : Energy source used in electricity generation or export other_fuel3 : Energy source used in electricity generation or export commissioning_year: year of opertaion of power plant or when the power plant start. owner : Majority shareholder of the power plant source: Entity reporting the data url : Web document corresponding to the sourcefield geolocation_source :Attribution for geolocation information wepp_id : A reference to a unique plant identifier in the widely-used PLATTS-WEPP database. year_of_capacity_data: year the capacity information was reported generation_gwh_2013 : electricity generation in gigawatt-hours reported for the year 2013 generation_gwh_2014 : electricity generation in gigawatt-hours reported for the year 2014 generation_gwh_2015 : electricity generation in gigawatt-hours reported for the year 2015 generation_gwh_2016 : electricity generation in gigawatt-hours reported for the year 2016 generation_gwh_2017 : electricity generation in gigawatt-hours reported for the year 2017 generation_data_source : electricity generation in gigawatt-hours reported for the year 2014 estimated_generation_gwh : attribution for the reported generation information

Carbon-dioxide is the most commonly occurring greenhouse gas in the atmosphere. Due to the increase in anthropogenic activities, there is an imbalance in nature and human activities. Carbon-dioxide emissions have become the primary source for this imbalance and a shift in global climate cycle. Countries all around the globe are taking initiatives to become carbon-negative. Technologies are being developed for carbon capture and sequestration. Thus, it has become all the more important to monitor this activity now than ever. This study involves creating a database for CO2 emissions for every state in the United States of America over the span of 3 decades from 1980 to 2017. The database helps in analysing the carbon intensity, energy related emissions and carbon intensity change with the change in economy.

In-depth look into the global CO2 emissions at the country-level

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

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.

Visualizes 'Co2 Emissions' and 'Global Temperature' as a graph.

Comparing Hurricane/Typhoon data from 1851-2014 with global CO2 emissions to evaluate a possible correlation

Analyze current climate change data to identify trends in global temperature, CO2 emissions, and their environmental impact.

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

In recent year, there are increasing concerns over the greenhouse gases in that many environmental issues are associated with the emission of the greenhouse gases. Methane (CH4) and carbon dioxide (CO2) are two major greenhouse gases. Many techniques have been applied to mitigate these greenhouse gases in the environment, among which the dry reforming of methane [1] is particularly used for the production of syngas, an intermediate for producing fuels. 〖CH〗_4 + 〖CO〗_2 ⇌ 2H_2 + 2CO This method provides a possible solution for both global warming and energy shortages. Therefore, many studies have focused on the evaluation of the catalysts in order to promote this reaction as much as possible. This project aims to predict and optimise the catalytic dry reforming of methane using artificial neural network (ANN) algorithms. Based on the experimental data from previous studies, an ANN model will be constructed and developed to investigate the efficiency of different catalyst compositions, which will assist in analysing experimental data and reduce experimental costs in further studies. This report intends to give a general introduction of this project, and the objectives and expected contributions of the project will be introduced in the following parts. A preliminary introduction to the artificial neural network (ANN) algorithm used in the experiment will also be given in this report.

This project explores the relationships between CO2 emissions, renewable energy adoption, and life expectancy across countries and over time. Using three datasets from [Our World in Data](https://ourworldindata.org/), the group will investigate how environmental policies and energy transitions might influence public health outcomes.

This entails the analysis of a dataset containing global fossil fuel emissions data from 1750 to 2021.

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Data Analytics on Global CO2 Emission

This repository contains global CO2 emissions analysis using Tableau.

A Tableau dashboard showcasing 250 years of global CO2 emissions data (1750-2021).

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Little data analysis project with mongoDB and C++

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Analysis of global CO2 emissions data using Python as part of the CareerFoundry Data Analytics program.

Unearthing the Environmental Impact of Human Activity: A Global CO2 Emission Analysis

Analysis of CO2 emissions Global using a ML