Dataset on CO₂ Emissions, Economic Growth, and RE Stock Trading in India
Description
Dataset Description This dataset contains annual time series data for India from 2002 to 2022, compiled to study the relationship between economic growth, environmental quality, and the development of the renewable energy (RE) sector in capital markets. Variables Included The dataset includes the following variables: CO₂ Emissions (tons per capita) Gross Domestic Product (GDP) – measured in Real GDP per capita (in constant 2015 USD) GDP Squared (GDP²) – to capture potential non-linear effects Traded Value of Renewable Energy Equities – annual traded value of RE firms' equities, calculated as the product of the traded share quantities and their respective market prices. Then, those values are normalized by dividing with constant real GDP of India of that year. Greenhouse Gas (GHG) Emissions – included as an alternative measure of environmental quality Data Sources Data on CO₂ emissions is sourced from the International Energy Agency (IEA, 2025), whereas GHG emissions data is obtained from the website of Our World in Data. GDP and GDP²: Obtained from the World Development Indicators (WDI) database Traded Value of RE Equities: Collected from Centre for Monitoring Indian Economy (CMIE) Prowess IQ database for renewable energy companies listed at NSE India Ltd., in India. Values were aggregated annually and deflated to constant prices. How to Interpret the Data All variables are organized as annual time series. Log transformation is applied to continuous variables (e.g., CO₂, GDP, RE traded value) to ensure scale consistency and facilitate regression analysis. GDP² is computed from the log-transformed GDP to allow analysis of potential non-linear relationships with emissions. Each row represents a single year, and each column corresponds to one of the variables listed above. This dataset can be used for: Time series econometric modeling Policy analysis on green finance, emissions, and sustainable growth Replication studies or extended analyses in environmental economics or financial development research It is provided in Excel format and ready for use in statistical software such as R, Stata, EViews, or Python.