Valuation of New Carbon Asset CCER

Unofficial AI-generated summary based on the public title and abstract. Not an official translation.

🔬研究者:Provides a comparative analysis of four valuation models (income-based, GBM, LSTM) for carbon offsets, with implications for pricing methodologies.

🏢実務担当者:Offers scenario-specific guidance for selecting carbon credit valuation methods based on data availability and market conditions.

🏛政策担当者:Highlights the need for robust valuation frameworks in carbon offset markets, relevant for market design and integrity.

As a critical carbon offset mechanism, China’s Certified Emission Reduction (CCER) plays a pivotal role in achieving the “dual carbon” targets. With the relaunch of its trading market, refining the CCER valuation framework has become imperative. This study develops a multidimensional CCER valuation methodology based on both the income and market approaches. Under the income approach, two probabilistic models—discrete and continuous emission distribution frameworks—are proposed to quantify CCER value. Under the market approach, a Geometric Brownian Motion (GBM) model and a Long Short-Term Memory (LSTM) neural network model are constructed to capture nonlinear temporal dynamics in CCER pricing. Through a systematic comparative analysis of the outputs and methodologies of these models, this study identifies optimal pricing strategies to enhance CCER valuation. Results reveal significant disparities among models in predictive accuracy, computational efficiency, and adaptability to market dynamics. Each model exhibits distinct strengths and limitations, necessitating scenario-specific selection based on data availability, application context, and timeliness requirements to strike a balance between precision and efficiency. These findings offer both theoretical and practical insights to support the development of the CCER market.

• semanticscholar https://doi.org/10.3390/su18020940first seen 2026-05-15 17:05:59