Summary:
Achieving China's international commitment to “carbon peak” and “carbon neutrality” at a lower social emission reduction cost is the goal of the carbon emissions allowance (CEA) trading market and a central issue in the field of green finance. In 2023, the Central Financial Work Conference emphasized that one of the key channels for providing high-quality financial services is to dedicate more resources to promoting “green development.” Although the CEA trading market has significantly reduced the carbon emissions of the power industry, investors' weak incentive to participate in trading CEA has hindered the discovery of CEA prices. Little theoretical literature explores how to manage CEA to improve the function of CEA price discovery. However, the answer to this question should form the basis from which the CEA trading market can effectively promote green development. CEA trading originates from the Coase property rights theory, which states that after property rights are clarified, pollution rights trading is the most effective way to solve environmental pollution problems through the market. In terms of the initial allocation of CEA quotas, the Ministry of Ecology and Environment in China has introduced the “2019-2020 National Carbon Emission Trading Quota Setting and Allocation Implementation Plan (Power Industry).” However, this allocation method does not consider uncertainties. Uncertainties affect the demand for CEA quotas, leading to excessively low or high carbon prices, which not only damages corporate value (“transformation risk”) but also squeezes out low-carbon transformation investment. In addition, the lack of risk hedging tools related to CEA assets (“incomplete market”) makes it more difficult to manage CEA price risk. If the CEA trading platform shared the transformation risk by managing CEA quotas, investors would be more willing to participate in CEA trading, and the costs of low-carbon transformation for enterprises would decrease. From a theoretical perspective, three questions remain unanswered. First, how should the trading platform manage CEA in the incomplete market？ Second, what are the mechanisms through which CEA management affects carbon prices and corporate decision-making？ Third, what measures can help enterprises achieve green and low-carbon transformation and control carbon price risk？ To address the above questions, we build a risk management model that integrates firms with heterogeneous emissions intensity, CEA trading, and market incompleteness based on dynamic inventory management theory. First, we use dynamic programming techniques to elucidate the logic behind optimal decision-making for firms and the trading platform. Second, through numerical analysis, we shed light on the risk-sharing of CEA management, carbon price formation mechanisms, and carbon reduction incentive provision. Finally, we explore the impact of advancements in emissions reduction technologies and the establishment of carbon price floors. This analysis helps provide policy recommendations. We identify three main functions of the CEA management mechanism. First, it provides a channel for high-carbon-emissions enterprises to invest in CEA quotas and hedge carbon price risks, which reduces the cost of low-carbon transformation. Second, it allows low-carbon enterprises to sell surplus CEA quotas and thus manage CEA price risks more effectively. Third, the steady-state level of CEA quotas constrains the total carbon emissions of the power industry, and the dynamic evolution of carbon emissions provides a reference for the dynamic path toward achieving carbon peak. We make three key contributions to the literature. First, we combine the Coase property rights theory and dynamic inventory management theory, integrate the opinion equating the essence of inventory management with risk management into the design of the carbon quota management mechanism, and point out how the government (i.e., CEA trading platform) and enterprises can reasonably share risk in an incomplete market through CEA management. Second, three measures can be taken to simultaneously achieve low-carbon production and controllable market risks. The first measure is to increase counter-cyclical fiscal support for the trading platform, which can substantially enhance the risk-taking ability of the platform. Consequently, this measure can not only increase the value of carbon allowance investment but also accelerate the green transformation of enterprises. The second measure involves allocating more financial resources to carbon trading. Financial institutions can provide enterprises with professional services such as carbon trading consulting and custody, which can enhance enterprises' ability to suffer risk. Furthermore, developing CEA forwards and swaps and other derivatives helps enterprises hedge transformation risks. The third measure is government introduction of tax reduction policies to incentivize emissions reduction technology innovation and the implementation of differentiated policies for high-carbon enterprises and low-carbon enterprises, which would help to fully realize the synergistic effect of different enterprises in reducing emissions. Finally, the interactive effect between open market operations and price control plays a crucial role when implementing the CEA adjustment policy. For example, a price limitation policy could encourage low carbon production and reduce the market risk in the short run, but it would break the risk-sharing mechanism, resulting in platform bankruptcy risk.
Blanchard O J. 1990. “Suggestions for A New Set of Fiscal Indicators”, OECD Economics Department Working Paper, No. 79.
[12]
Blanchard, O. 2019. “Public Debt and Low Interest Rates”, American Economic Review, 109(4): 1197～1229.
[13]
Bolton, P., Chen, H. and Wang, N. 2011. “A Unified Theory of Tobin's q, Corporate Investment, Financing, and Risk Management”, Journal of Finance, 66(5): 1545～1578.
[14]
Calvo, G. A., and Reinhart, C. M. 2002. “Fear of Floating”, Quarterly Journal of Economics, 117(2): 379～408.
[15]
DeMarzo, P., He, Z. and Tourre, F. 2023. “Sovereign debt ratchets and welfare destruction”, Journal of Political Economy, 131(10): 2825～2892.
[16]
Dumas, B. 1991. “Super Contact and Related Optimality Conditions”, Journal of Economic Dynamics and Control, 15(4): 675～685.
[17]
Goulder, L. H., Long, X., Lu, J. and Morgenstern, R. D. 2022. “China's Unconventional Nationwide CO2 Emissions Trading System: Cost-Effectiveness and Distributional Impacts”, Journal of Environmental Economics and Management, 111, 102561.
[18]
Goulder, L. H., and Morgenstern, R. D. 2018. “China's Rate-Based Approach to Reducing CO2 Emissions: Attractions, Limitations, and Alternatives”, AEA Papers and Proceedings, 108: 458～462.
[19]
Jiang, W., Sargent, T. J., Wang, N. and Yang, J. 2022. “A p Theory of Government Debt and Taxes,” NBER Working Paper, No. w29931.
[20]
Jorgenson, D. W. 1963. “Capital Theory and Investment Behavior”, American Economic Review, 53(2): 247～259.
[21]
Kollenberg, S., and Taschini, L. 2019. “Dynamic Supply Adjustment and Banking under Uncertainty in An Emission Trading Scheme: The Market Stability Reserve”, European Economic Review, 118: 213～226.
[22]
Newell, R. G., and Pizer, W. A. 2008. “Indexed Regulation”, Journal of Environmental Economics and Management, 56(3): 221～233.
[23]
Pizer, W. A., and Zhang, X. 2018. “China's New National Carbon Market”, AEA Papers and Proceedings,108: 463～467.
[24]
Rebelo, S., Wang, N. and Yang, J. 2022. “Rare disasters, financial development, and sovereign debt”, The Journal of Finance, 77(5): 2719～2764.
[25]
Willner, M., and Perino, G. 2022. “Beyond Control: Policy Incoherence of the EU Emissions Trading System”, Politics and Governance, 10(1): 256～264.
