CS2: Carbon Markets and Climate Finance

Global emissions from deforestation continue to exceed the carbon removals achieved through afforestation and reforestation, underscoring the urgent need to halt tropical forest loss and strengthen mechanisms that incentivize forest conservation and restoration. Forest-based carbon credits, namely "reducing emissions from deforestation and forest degradation in developing countries" or "REDD+" plays a critical role in this context by offering performance-based economic incentives that contribute to climate change mitigation while providing rainforest-rich developing countries with access to untied climate finance. 


However, the underlying institutional concept of forest-based carbon credits of "REDD+", is not entirely a just concept when evaluated from Rawls' Theory of Justice, while complementary measures can make it closer to justice. This paper also questions the assumption of carbon markets themselves as inherently unjust by applying Nozick's libertarian approach as a critical lens. Ultimately, this research argues that while the institutional concept of forest carbon credits does not constitute perfect justice, it can approximate Rawlsian justice through appropriate complementary measures. 


To ground these theoretical arguments empirically, this paper employs a normative testing. On a theoretical level, complementary measures include making carbon credits a regressive trading system for affordability, enhancing financial management and greenhouse gas (GHG) inventory training via capacity building for governance improvement, and low-carbon technology transfer for future emission reductions. On a practical level, this research proposes ways to strengthen the integrity and demand for REDD+ credits through jurisdictional REDD+ programs, integration into a compliance market, and the use of pricing instruments that stabilize revenue expectations.

Global carbon pricing mechanisms are intensifying as emissions allowance futures markets mature. This structural evolution in carbon finance has elevated derivatives trading, particularly those indexed to futures contracts, to central mechanisms for risk allocation. However, pricing carbon options remains challenging due to the limitations of traditional models in capturing policy-sensitive and technology-driven market dynamics. This study addresses four defining characteristics of allowance trading: (a) the no-arbitrage constraint, (b) the binary terminal price convergence property, (c) the instantaneous triggering of abatement measures below allowance prices, and (d) multi–compliance period Markets. We develop a risk-neutral pricing framework by constructing a stochastic differential equation (SDE) for allowance prices and deriving the governing partial differential equation (PDE) for carbon options, which is subsequently solved using physics-informed neural networks (PINNs). Our research progresses through three phases: First, we formalize the dynamic pricing process by rigorously embedding these characteristics into mathematical models. Second, we establish single-period and multi-period pricing architectures to quantify how iterative allocation rules and policy cycles propagate through option price formation. Finally, the PINN-based solutions enable a systematic analysis of how multi-period frameworks transmit uncertainty and how abatement costs influence option valuations. Crucially, carbon options exhibit significantly lower time value compared to conventional commodity options, attributable to the risk of price collapse. Furthermore, the analysis reveals a counterintuitive suppression effect, whereby rising future allowance prices reduce multi-period option premiums. This effect directly stems from how banking reallocates compliance risk across periods. This framework advances carbon derivatives pricing by integrating micro-mechanistic rigor with macro-policy responsiveness.

Of the many carbon markets, few, including India and China, have intensity-based targets. Building on the extant literature exploring the intensity-based carbon markets, we present a Marginal Abatement Cost Curves (MACCs) based approach to model the Indian carbon credit trading scheme. We use aggregate sectoral MACCs to derive facility-level MACCS. Using these facility-level MACCS, the aggregate supply and demand curves are then evaluated to determine the equilibrium price, facility-level outcomes, and sectoral and aggregate outcomes in terms of trading positions, costs, and emissions. For each facility, the net carbon position accounts for mitigation and carbon-trading costs, including output changes, which are modeled through supply price elasticity. We use the facility-level baseline and targets to illustrate the model's operation and to show how the modelling framework can inform carbon market design questions, including carbon price, price floor, and ceiling. We will conclude by sharing our perspectives on how to improve the representation of renewable energy and electrification options within them. 

Türkiye is in the process of establishing a national Emissions Trading System (ETS), with a pilot phase expected to precede full implementation later in the decade. The effectiveness of this ETS will be particularly critical for hard-to-abate industrial sectors such as cement, iron–steel, and aluminium, which together represent a major share of Türkiye's industrial greenhouse gas emissions. While energy efficiency, fuel switching, and renewable electricity procurement are already economically viable in many cases, deep decarbonization options-most notably carbon capture and storage (CCS) and hydrogen-based production routes-remain high-cost and largely uncompetitive under current market conditions.
This study investigates the carbon price levels that would be required under Türkiye's ETS to make such high-cost abatement options economically feasible. Using a marginal abatement cost (MAC) framework, the analysis synthesizes cost estimates from international literature and applies them to representative deep abatement pathways in each sector. These include CCS in cement production, CCS-enabled and hydrogen-based direct reduced iron routes in iron–steel, and electricity- and hydrogen-driven decarbonization pathways in aluminium production.
The results show substantial variation across sectors. CCS in cement becomes feasible at carbon prices of roughly 100–180 €/tCO₂, while transitional NG-DRI-CCS options in iron–steel may be viable at 90–120 €/tCO₂. In contrast, hydrogen-based steelmaking and near-zero aluminium production require much higher prices, often exceeding 150–250 €/tCO₂ and, in some cases, reaching 400 €/tCO₂ or more. These findings suggest that relying solely on a uniform ETS price signal to trigger deep industrial decarbonization would require very high carbon prices, raising competitiveness and carbon leakage concerns. Consequently, Türkiye's ETS is likely to be most effective when complemented by targeted support instruments such as carbon contracts for difference, investment subsidies, and infrastructure development for CO₂ transport, storage, and hydrogen