Among the contributions expected from forest sectors in policies of climate mitigation, one consists in increasing forest carbon stocks by changing management practices. This activity, generally referred to as Improved Forest Management (IFM), is of major importance in the Congo Basin forests, where 20 millions of hectares are now managed for timber production. The carbon benefit generated by IFM activities is often obtained by a reduction of harvesting pressures on forest resources. In the case of Extension of Rotation Age/Cutting Cycle (ERA) projects, the reduction of emissions comes from the increase of Minimum Cutting Diameters (MCD) and/or the extension of Felling Cycle Duration (FCD). However, such activities have negative consequences for the profitability of timber companies. Climate instruments such as the mechanism of Reducing emissions from deforestation and forest degradation and the role of conservation, sustainable management of forests and enhancement of forest carbon stocks in developing countries (REDD+) promote a compensatory approach to cover these income losses by the valuation of avoided carbon emissions. To determine the feasibility of such a carbon-based compensation, it is necessary to predict over the long term both the dynamics of forest carbon and the time schedule of timber incomes. The two are closely interrelated. Selective logging can alter the structure, the floristic composition, and thus, the carbon stocks of tropical forests. Modelling these forest-logging relationships is challenging. Selective logging implies to deploy a species level representation of timber harvesting but the high diversity of tropical forests, in pair with the scarcity of data, hinders the correct fitting of species-specific models. We developed a bioeconomic approach coupling a mixture of inhomogeneous matrix models for forest dynamics and an object-oriented model for forest logging companies' operations. For forest dynamics, our methodology addresses the cha