About the Project KAPSARC is developing the KAPSARC Toolkit for Behavioral Analysis (KTAB), an open source software platform, to support modeling and analysis of collective decisionmaking processes (CDMPs). KTAB is intended to be the standard platform for analyzing bargaining problems, generalized voting models and policy decision-making. It is our intent to use KTAB to assemble the building blocks for a broad class of CDMPs. Typical models in KTAB will draw on the insights of subject matter experts regarding decision makers and influencers in a methodical, consistent manner; and then assist researchers to identify feasible outcomes that are the result of CDMPs. Key Points China, the world’s largest emitter of carbon dioxide, is taking steps to combat the effects of climate change on its environment. The path it takes to mitigate the effects of pollution will have a significant impact on the global carbon reduction agenda. In this study, we focus on the political feasibility of implementing a carbon tax in China within the next five years. We do this using the KAPSARC Toolkit for Behavioral Analysis (KTAB) platform, a model of collective decision-making processes (CDMPs) developed at KAPSARC to assess the expected support in China for, and reactions to, this potential policy choice. Our key findings are as follows: The majority of Chinese actors are expected to view as positive the application of a weak carbon tax. A tough, clearly articulated and enforceable carbon tax, however, does not appear to be politically feasible in the current Chinese context. Large energy companies, particularly heavy carbon emitters, may oppose the principle of a carbon tax, but recognize the need to engage in the policy discussion to help shape any potential outcome. Momentum behind a carbon tax in China is slow but steady. Even if energy companies have substantial political clout and are able to disrupt progress on a carbon tax, this would not be sufficient to derail the weak but growing consensus that favors some form of tax on carbon.