The climate, health, and economic outcomes across different carbon pricing policies to achieve China's climate goals

The benefits and costs of carbon pricing policies (CPPs) are not equally distributed across regions, and the specific CPP options would inevitably affect their ancillary benefits and costs and how they are distributed across different regions. This brings up challenging and ongoing questions within CPPs—how policymakers weigh the overall efficiency across policies with the potential equity trade-offs at hand. Here, we bridge an economy-energy-enviro-health assessment model and combine it with 5000 Monte Carlo simulations to comprehensively evaluate the economic, climate, and health outcomes of China's CPPs, including regional emission trading scheme (ETS), national ETS, and carbon tax (CT). We find that switching regional to national ETS—much like China's policy progress over the past decade—can increase the net benefit by 143 (135–151, 90% confidence intervals) billion RMB, yet these ancillary net benefits distribute unevenly across regions. Fossil fuels supplying provinces (e.g., Shanxi), for instance, are particularly burdensome under national ETS. Allocating more generous CO2 caps to economically developing provinces may improve regional equity under the national ETS but come at the expense of reduced policy efficiency. Moreover, we find that carbon tax can generate the largest net benefits among our focused CPPs, up to 303 (294–311) and 159 (159–160) billion RMB higher than regional and national ETSs in 2030. Differentiating from ETS, economically disadvantaged regions can gain more net benefits under the CT policy, thus decreasing the Gini coefficient over regional net benefits from 0.26 (national ETS) and 0.23 (regional ETS) to 0.20. These spatially explicit analyses uncover differential aggregated and distributional outcomes by different CPPs, which are highly policy-relevant, such as how and where to introduce compensation schemes or which carbon pricing options are more economically optimal.