Is Price Commitment a Better Solution to Control Carbon Emissions and Promote Technology Investment? - Fan, Xiaoshuai; Chen, Kanglin; Chen, Ying-Ju (Management Science, 2023)
Recent years have seen considerable debate about the practicability of a global quantity/price commitment to control carbon emissions and tackle environmental issues. In this paper, we study the impact of the cap-and-trade policy (quantity commitment) and the carbon tax policy (price commitment) on a firm’s technology investment and production decisions. The main feature captured in our model is that there exist correlated uncertainties between the sales market (demand uncertainty) and the permit trading market (permit price volatility) under the cap-and-trade policy. The correlation relationship stands on the following intuition. The demands for final products affect firms’ production output, which generates the needs of emission permits and influences the permit price. We show that under the cap-and-trade policy, with the uncertainty of the future emission price, the firm could flexibly adjust its production quantity to enhance its profit, resulting in low incentives to invest in clean technology. However, as the (positive) correlation between the sales market and the permit trading market increases, the production flexibility is constrained so that the firm has to increase its technology investment to hedge against the future risk of a high emission price. Making a comparison between the cap-and-trade and carbon tax policies, we find that when the correlation coefficient is moderate, the carbon tax policy generates a multiwin situation (i.e., more technology investment, higher expected profit and consumer surplus, and fewer carbon emissions). Case studies are provided to illustrate the implications and model variants are examined to check the robustness of the main results. Overall, our analysis sheds light on recent debate over carbon pricing and identifies the important role of correlated uncertainties in carbon policy design. This paper was accepted by Charles Corbett, operations management. Funding: This work was supported by the stable support plan program of Shenzhen Natural Science Fund [Program Contract 20200925160533002] and the National Natural Science Foundation of China [Grant 72101105]. Supplemental Material: The data files and online appendix are available at https://doi.org/10.1287/mnsc.2022.4365 .