The quest for improved air quality may push China to continue its CO2 reduction beyond the Paris Commitment.

China is challenged with the simultaneous goals of improving air quality and mitigating climate change. The "Beautiful China" strategy, launched by the Chinese government in 2020, requires that all cities in China attain 35 µg/m3 or below for annual mean concentration of PM2.5 (particulate matter with aerodynamic diameter less than 2.5 µm) by 2035. Meanwhile, China adopts a portfolio of low-carbon policies to meet its Nationally Determined Contribution (NDC) pledged in the Paris Agreement. Previous studies demonstrated the cobenefits to air pollution reduction from implementing low-carbon energy policies. Pathways for China to achieve dual targets of both air quality and CO2 mitigation, however, have not been comprehensively explored. Here, we couple an integrated assessment model and an air quality model to evaluate air quality in China through 2035 under the NDC scenario and an alternative scenario (Co-Benefit Energy [CBE]) with enhanced low-carbon policies. Results indicate that some Chinese cities cannot meet the PM2.5 target under the NDC scenario by 2035, even with the strictest end-of-pipe controls. Achieving the air quality target would require further reduction in emissions of multiple air pollutants by 6 to 32%, driving additional 22% reduction in CO2 emissions relative to the NDC scenario. Results show that the incremental health benefit from improved air quality of CBE exceeds 8 times the additional costs of CO2 mitigation, attributed particularly to the cost-effective reduction in household PM2.5 exposure. The additional low-carbon energy polices required for China's air quality targets would lay an important foundation for its deep decarbonization aligned with the 2 °C global temperature target.

Role of future scenarios in understanding deep uncertainty in long-term air quality management.

UNLABELLED: The environment and its interactions with human systems, whether economic, social, or political, are complex. Relevant drivers may disrupt system dynamics in unforeseen ways, making it difficult to predict future conditions. This kind of "deep uncertainty" presents a challenge to organizations faced with making decisions about the future, including those involved in air quality management. Scenario Planning is a structured process that involves the development of narratives describing alternative future states of the world, designed to differ with respect to the most critical and uncertain drivers. The resulting scenarios are then used to understand the consequences of those futures and to prepare for them with robust management strategies. We demonstrate a novel air quality management application of Scenario Planning. Through a series of workshops, important air quality drivers were identified. The most critical and uncertain drivers were found to be "technological development" and "change in societal paradigms." These drivers were used as a basis to develop four distinct scenario storylines. The energy and emissions implications of each storyline were then modeled using the MARKAL energy system model. NOx emissions were found to decrease for all scenarios, largely a response to existing air quality regulations, whereas SO2 emissions ranged from 12% greater to 7% lower than 2015 emissions levels. Future-year emissions differed considerably from one scenario to another, however, with key differentiating factors being transition to cleaner fuels and energy demand reductions. IMPLICATIONS: Application of scenarios in air quality management provides a structured means of sifting through and understanding the dynamics of the many complex driving forces affecting future air quality. Further, scenarios provide a means to identify opportunities and challenges for future air quality management, as well as a platform for testing the efficacy and robustness of particular management options across wide-ranging conditions.

Spatially refined aerosol direct radiative forcing efficiencies.

Global aerosol direct radiative forcing (DRF) is an important metric for assessing potential climate impacts of future emissions changes. However, the radiative consequences of emissions perturbations are not readily quantified nor well understood at the level of detail necessary to assess realistic policy options. To address this challenge, here we show how adjoint model sensitivities can be used to provide highly spatially resolved estimates of the DRF from emissions of black carbon (BC), primary organic carbon (OC), sulfur dioxide (SO(2)), and ammonia (NH(3)), using the example of emissions from each sector and country following multiple Representative Concentration Pathway (RCPs). The radiative forcing efficiencies of many individual emissions are found to differ considerably from regional or sectoral averages for NH(3), SO(2) from the power sector, and BC from domestic, industrial, transportation and biomass burning sources. Consequently, the amount of emissions controls required to attain a specific DRF varies at intracontinental scales by up to a factor of 4. These results thus demonstrate both a need and means for incorporating spatially refined aerosol DRF into analysis of future emissions scenario and design of air quality and climate change mitigation policies.