Asia's Carbon Markets Strategic Imperatives for Corporations 2025

Page 29 of 54 · WEF_Asia's_Carbon_Markets_Strategic_Imperatives_for_Corporations_2025.pdf

Roadmap step #4: Decarbonization economic analysis The marginal abatement cost curve (MACC) framework can be used for rigorous economic analysis to identify cost-effective decarbonization levers by assessing abatement potential against costs – and its practical significance will grow as carbon prices converge (see Figure 12). Innovative financing – including green bonds, sustainability-linked loans and carbon asset management – unlocks capital for decarbonization while enhancing liquidity through monetized emissions reductions. China National Building Material Group (CNBM) provides an example of using the carbon price to inform decarbonization investment and leveraging future carbon credit revenue to finance current projects (see Case Study 1). MACC1 analysis for key decarbonization levers (illustrative) FIGURE 12 Notes: 1. MACC = marginal abatement cost curve; marginal cost = cost to abate one additional unit (of carbon). 2. No-regret investments include, for example, high-efficiency motors, LED lighting to improve energy efficiency, utilization of waste water biogas to manage waste and resources. 3. Market-ready technologies assume a carbon price of $100. Source: Bain & Company analysis. The marginal abatement cost curve framework can be used to identify cost-effective decarbonization levers by assessing abatement potential against costs.Cost ($/ tCO2e)200 100 -100 -2000Negative marginal cost = cost/carbon savingsNo-regret investments2 Commer cialized but not yet cost competitiveMarket-ready technologies3 Not yet available for commer cial investmentEmerging technologies Each box represents a CO2 abatement technology Carbon abatement potential of decarbonization lever (Mt CO2e) From this technology , implementation cost is already higher than cost of purchasing carbon allowance Carbon price Asia’s Carbon Markets: Strategic Imperatives for Corporations 29
Ask AI what this page says about a topic: