Climate Policy

Impact of climate variability and change on the ozone response to NOx emissions reductions

Abstract: Climate variability may introduce a range of air quality and health outcomes for a given emission control policy. While the influence of meteorology on air quality is well established, shifting climate baselines may affect the predicted response of air pollution to emission changes. When using air quality models for decision-making, it is important to quantify how various sources of uncertainty and variability affect the likelihood that a prospective emission control strategy will achieve a given air quality target. Here, we leverage an ensemble approach to assess how climate variability and change impact the sensitivity of ozone to nitrogen oxides (NOx) emissions reductions. We consider three sources of variability: (1) inter-annual variability caused by natural forcings such as year-to-year variations in solar radiation, (2) unforced natural climate variability due to inherent stochasticity in the climate system, (3) external climate forcing uncertainty due to a range of possible future greenhouse gas emission trajectories. We use climate fields from a five-member initial condition ensemble of the Massachusetts Institute of Technology Integrated Global System Model linked to the Community Atmosphere Model (MIT IGSM-CAM) to drive offline simulations of the high-performance GEOS-Chem chemical transport model (GCHP). Analysis of the simulated ozone response to a 10% reduction in anthropogenic NOx emissions reveals the largest spread in polluted urban areas. In these areas, the ensemble standard deviation and the inter-annual standard deviation of the ozone response can be on the order of 10-30% of the ensemble and multi-year means. In most areas, future climate variability does not induce a change in ozone photochemical regime. However, currently planned ozone control strategies in urban areas that experience a transitional or mixed photochemical regime are less robust to climate uncertainty.

Implications of Policy Design and Socioeconomic Uncertainties for Energy Transitions and Sectoral Responses

Abstract: Studies exploring energy transitions typically focus on a single or small set of scenarios, often with idealized policy assumptions (e.g. with global carbon pricing and significant negative emissions). However, there are countless possible ways the future could unfold, with different implications for energy transitions. In this work, we develop a probabilistic multi-sector coupled human-natural system model and explore both deep uncertainty about climate policy design and parametric uncertainty about socioeconomic assumptions, and the implications of those uncertainties for energy transitions and sectoral responses. To reflect policy design uncertainty, we utilize a set of increasingly stringent global emissions pathways comprised of increasingly stringent regional GHG constraints, and consider both “Optimistic” and “Pessimistic” design conditions that represent deep uncertainties for climate strategy, including whether or not there is international emissions trading, coverage of land use emissions and availability of carbon dioxide removal technologies. For each of these scenarios, we then run large ensembles of our model, sampling from probability distributions for uncertain socioeconomic parameters (e.g. productivity growth, population, technology costs, fossil resources). Using this approach, we can quantify uncertainty in the future energy mix and sectoral responses (e.g. emissions, output and energy use) and how that uncertainty shifts for different policy design assumptions.

Results suggest many possible energy mixes are consistent with a given global emissions pathway, and the policy design has significant implications for future energy mixes. In particular, whether or not international emissions trading is allowed results in vastly different amounts of BECCS and afforestation pursued globally, which in turn affects how much fossil energy can continue to be used and decarbonization strategies employed in different regions and sectors. This approach demonstrates the importance of considering uncertainty when planning for energy transitions and that planning for a single future is risky.

Impacts of future climate change on global food production: perspectives from model simulations of multiple emission scenarios

Abstract: Climate change significantly impacts crop growth and production, which may undermine the resilience of global food systems. Predicting long-term dynamics of future food production under different emissions scenarios is critical for ensuing global food security. However, large variations exist in current food production projections due to uncertainties in future climate projections, their coarse resolutions, and the missing of several important processes affecting crop yield in current crop models. Here, we predicted future changes in the yields of major crops until the end of the 21st century under four emission scenarios (including Reference, Paris Forever, Paris 2°C and Paris 1.5°C), using the Dynamic Land Ecosystem Model (DLEM) as driven by the newly developed ensembles of high-resolution future climate projections (with a spatial resolution of 0.5°x 0.5°). The DLEM includes mechanistic representations of dynamic crop growth processes and agricultural management practices as affected by multiple environmental stresses, and its performance in simulating the yields of major crops has been validated at multiple scales from site to global. The developed ensembles of future climate projections were constructed with the combination of large ensembles of the MIT Integrated Global system Modeling (IGSM) zonal climate projections and multiple climate models participated in Coupled Model Intercomparison Project Phases 6 (CMIP6). Our results indicate that the projected food production varies largely both among different future scenarios and among different climate projections of the same scenario. Our study assesses the impacts of future climate change on global food systems from a risk-based perspective and quantifies the uncertainties, which have important implications for future agricultural climate change adaptation measures.

When and How to Use Economy-Wide Models for Environmental Policy Analysis

Abstract: We describe the factors researchers should consider in deciding when and how to use computational general equilibrium (CGE) models for environmental policy analysis instead of partial equilibrium or engineering models. Special attention is given to modeling the social costs and benefits of regulations and the role played by labor markets. CGE models excel at quantifying interactions across different sectors of the economy, factor-market outcomes, and the distributional consequences of policy, all using a comprehensive set of the resource constraints faced by agents. The ceteris paribus nature of these experiments allows a skilled modeler to develop a systematic understanding of the connection between model assumptions and policy outcomes. Using CGE models to address environmental policy questions involves challenges, including the representation of narrow and technology-specific regulatory designs, data and aggregation issues, and the development of methods to improve model transparency and validity.

To achieve a stable climate will require rapid, dramatic reductions in greenhouse gas emissions resulting from human activities. This can be done by transitioning energy generation from fossil fuels to clean energy sources, and by removing those gases—primarily carbon dioxide (CO2)— from the atmosphere. The latter approach relies on the development of technologies that capture the gas from the air and store it underground, and the cultivation of “nature-based solutions” that increase ground-level absorption of airborne CO2.

Tapping the land for climate solutions
A Tool for Air Pollution Scenarios (TAPS v1.0) to enable global, long-term, and flexible study of climate and air quality policies

Authors' Summary: Understanding policy effects on human-caused air pollutant emissions is key for assessing related health impacts. We develop a flexible scenario tool that combines updated emissions data sets, long-term economic modeling, and comprehensive technology pathways to clarify the impacts of climate and air quality policies. Results show the importance of both policy levers in the future to prevent long-term emission increases from offsetting near-term air quality improvements from existing policies.

Abstract: Air pollution is a major sustainability challenge – and future anthropogenic precursor and greenhouse gas (GHG) emissions will greatly affect human well-being. While mitigating climate change can reduce air pollution both directly and indirectly, distinct policy levers can affect these two interconnected sustainability issues across a wide range of scenarios.

We help to assess such issues by presenting a public Tool for Air Pollution Scenarios (TAPS) that can flexibly assess pollutant emissions from a variety of climate and air quality actions, through the tool’s coupling with socioeconomic modeling of climate change mitigation. In this study, we develop and implement TAPS with three components: recent global and fuel-specific anthropogenic emissions inventories, scenarios of emitting activities to 2100 from the MIT Economic Projection and Policy Analysis (EPPA) model, and emissions intensity trends based on recent scenario data from the Greenhouse Gas–Air Pollution Interactions and Synergies (GAINS) model.

An initial application shows that in scenarios with less climate and pollution policy ambition, near-term air quality improvements from existing policies are eclipsed by long-term emissions increases – particularly from industrial processes that combine sharp production growth with less stringent pollution controls in developing regions. Additional climate actions would substantially reduce air pollutant emissions related to fossil fuel (such as sulfur and nitrogen oxides), while further pollution controls would lead to larger reductions for ammonia and organic carbon (OC).

Future applications of TAPS could explore diverse regional and global policies that affect these emissions, using pollutant emissions results to drive global atmospheric chemical transport models to study the scenarios’ health impacts.

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