Carbon Credit Dataset & Financial Modelling – India & Global

Carbon Credit Dataset & Methodology Report This dataset is part of broader open access initiative to advance climate finance, carbon market transparency, and data-driven research. A structured framework for emission accounting, financial modelling, and carbon market research This dataset provides a structured and transparent framework for analysing the carbon credit economy using empirical data, greenhouse gas (GHG) accounting, and financial modelling. It combines sector-wise Excel models, revenue forecasts, and reproducible emission calculations based on IPCC (2006/AR6), India’s Carbon Credit Trading Scheme (CCTS, 2023), UNFCCC CDM methodologies, Verra, and Gold Standard frameworks. Dataset Components Include: Emission Modelling: Baseline and project emissions for solar, forestry, agriculture, energy, and improved cookstove projects. Financial Modelling: NPV, IRR, payback period, with and without carbon revenue. 10-Year Forecasts: Revenue projections for solar irrigation, ICS, afforestation, and multi-project portfolios. Documentation: Assumptions, formulas, emission factors, and data sources (IPCC, CEA, FAOSTAT, UNFCCC, IEA, RBI). Full Report & Metadata: Methodology report (PDF), README, provenance documentation, glossary, codebook, and changelog. File Structure: Carbon-Credit-Dataset-Manoj-Rawat-2025.pdf – Full documentation including methodology, formulas, references, and charts. Excel Models: Carbon, Solar, ICS, Afforestation (AR), and Portfolio Supporting Files: README.txt, DATA_PROVENANCE.txt, METHODS_SUMMARY.txt, VARIABLES_CODEBOOK.txt, LICENSE.txt (CC BY 4.0), CITATION.cff, RIS.ris Purpose and Users: This dataset is designed for researchers, policymakers, MRV platforms, analysts, renewable energy developers, and financial institutions. It supports emission baselining, carbon credit estimation (tCO₂e), climate finance research, policy analysis, and academic teaching. Standards and Licensing: Compliant with IPCC 2006 Guidelines, IPCC AR6, UNFCCC CDM, and CCTS (India, 2023) Aligned with FAIR Data Principles (Findable, Accessible, Interoperable, Reusable) Licensed under Creative Commons Attribution 4.0 (CC BY 4.0) No personal, confidential, or proprietary data included