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.

Q 2001 OPA (Omam Publihm M a t i o n ) N.V. Published by b Undn the Gordon and B d Science Fnblilhcn imprint.Public sector decision-making typically involves complex problems that are often not completely understood. In these problems, there are invariably unmodeled issues that can greatly impact the acceptability of solutions. Modeling to Generate Alternatives (MGA) is an approach for addressing unmodeled issues in an optimization context. MGA techniques are used to generate a small number of good, yet very different, solutions to optimization problems. Because these solutions are different in decision space, they may differ considerably in performance when unmodeled objectives an considered. Many problems are sufiiciently complex that traditional optimization solution procedures, and therefore traditional MGA techniques, are not readily applicable. Two techniques for performing MGA using genetic algorithms (GAS) arc investigated and compared. One of these techniques, which uses specialized MGA operators. is shown to produce solutions that an both better in quality and more different. This technique is also demonstrated for a realistic air quality management problem.

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/m3or 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 CO2mitigation, 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 PM2.5target 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 CO2emissions 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 CO2mitigation, attributed particularly to the cost-effective reduction in household PM2.5exposure. 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.

Integrated Assessment Models (IAMs) characterize the interactions among human and earth systems. IAMs typically have been applied to investigate future energy, land use, and emission pathways at global to continental scales. Recent directions in IAM development include enhanced technological detail, greater spatial and temporal resolution, and the inclusion of air pollutant emissions. These developments expand the potential applications of IAMs to include support for air quality management and for coordinated environmental, climate, and energy planning. Furthermore, these IAMs could help decision makers more fully understand tradeoffs and synergies among policy goals, identify important cross-sector interactions, and, via scenarios, consider uncertainties in factors such as population and economic growth, technology development, human behavior, and climate change. A version of the Global Change Assessment Model with U.S. state-level resolution (GCAM-USA) is presented that incorporates U.S.-specific emission factors, pollutant controls, and air quality and energy regulations. Resulting air pollutant emission outputs are compared to U.S. Environmental Protection Agency 2011 and projected inventories. A Quality Metric is used to quantify GCAM-USA performance for several pollutants at the sectoral and state levels. This information provides insights into the types of applications for which GCAM-USA is currently well suited and highlights where additional refinement may be warranted. While this analysis is specific to the U.S., the results indicate more generally the importance of enhanced spatial resolution and of considering national and sub-national regulatory constraints within IAMs.

Exposure to fine particulate matter (PM 2.5 ) from fuel combustion significantly contributes to global and US mortality. Traditional control strategies typically reduce emissions for specific air pollutants and sectors to maintain pollutant concentrations below standards. Here we directly set national PM 2.5 mortality cost reduction targets within a global human-earth system model with US state-level energy systems, in scenarios to 2050, to identify endogenously the control actions, sectors, and locations that most cost-effectively reduce PM 2.5 mortality. We show that substantial health benefits can be cost-effectively achieved by electrifying sources with high primary PM 2.5 emission intensities, including industrial coal, building biomass, and industrial liquids. More stringent PM 2.5 reduction targets expedite the phaseout of high emission intensity sources, leading to larger declines in major pollutant emissions, but very limited co-benefits in reducing CO 2 emissions. Control strategies limiting health damages achieve the greatest emission reductions in the East North Central and Middle Atlantic states.

Abstract. This article presents a methodology for creating anthropogenic emission inventories that can be used to simulate future regional air quality. The Emission Scenario Projection (ESP) methodology focuses on energy production and use, the principal sources of many air pollutants. Emission growth factors for energy system categories are calculated using the MARKAL energy system model. Growth factors for non-energy sectors are based on economic and population projections. These factors are used to grow a 2005 emissions inventory through 2050. The approach is demonstrated for two emission scenarios for the United States. Scenario 1 extends current air regulations through 2050, while Scenario 2 adds a hypothetical CO 2 mitigation policy. Although both scenarios show significant reductions in air pollutant emissions through time, these reductions are more pronounced in Scenario 2, where the CO 2 policy results in the adoption of technologies with lower emissions of both CO 2 and traditional air pollutants. The methodology is expected to play an important role within an integrated modeling framework that supports the US EPA's investigations of linkages among emission drivers, climate and air quality.

There are many technological pathways that can lead to reduced carbon dioxide emissions. However, these pathways can have substantially different impacts on other environmental endpoints, such as air quality and energy-related water demand. This study uses an integrated assessment model with state-level resolution of the energy system to compare environmental impacts of alternative low-carbon pathways for the United States. One set of pathways emphasizes nuclear energy and carbon capture and storage, while another set emphasizes renewable energy, including wind, solar, geothermal power, and bioenergy. These are compared with pathways in which all technologies are available. Air pollutant emissions, mortality costs attributable to particulate matter smaller than 2.5 μm in diameter, and energy-related water demands are evaluated for 50% and 80% carbon dioxide reduction targets in 2050. The renewable low-carbon pathways require less water withdrawal and consumption than the nuclear and carbon capture pathways. However, the renewable low-carbon pathways modeled in this study produce higher particulate matter-related mortality costs due to greater use of biomass in residential heating. Environmental co-benefits differ among states because of factors such as existing technology stock, resource availability, and environmental and energy policies.

Over the coming decades, new energy production technologies and the policies that oversee them will affect human health, the vitality of our ecosystems, and the stability of the global climate. The GLIMPSE decision model framework provides insights about the implications of technology and policy decisions on these outcomes. Using GLIMPSE, decision makers can identify alternative techno-policy futures, examining their air quality, health, and short- and long-term climate impacts. Ultimately, GLIMPSE will support the identification of cost-effective strategies for simultaneously achieving performance goals for these metrics. Here, we demonstrate the utility of GLIMPSE by analyzing several future energy scenarios under existing air quality regulations and potential CO2 emission reduction policies. We find opportunities for substantial cobenefits in setting both climate change mitigation and health-benefit based air quality improvement targets. Though current policies which prioritize public health protection increase near-term warming, establishing policies that also reduce greenhouse gas emissions may offset warming in the near-term and lead to significant reductions in long-term warming.