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Transparency in the Paris Agreement

Establishing a credible and effective transparency system will be both crucial and challenging for the climate regime based on the pledge and review process established in the Paris Agreement. The Agreement provides for review of achievements under national pledges (Nationally Determined Contributions, or NDCs), but much of this information will become available only well after key steps in the launch of this latest attempt to control human influence on the climate. Still, in these early years, information and understanding of individual and collective performance, and of relative national burdens under the NDCs, will play an important role in the success or failure of the Agreement. However, because of the phasing of various steps in the 5-year cycles under the Agreement and the unavoidable delays of two or more years to produce and review government reports, the Climate Convention and other intergovernmental institutions are ill-suited to carry out timely analyses of progress. Consequently, in advance of formal procedures, academic and other non-governmental groups are going to provide analyses based on available data and their own methodologies. We explore this transparency challenge, using the MIT Economic Projection and Policy Analysis (EPPA) model, to construct sample analyses, and consider ways that efforts outside official channels can make an effective contribution to the success of the Agreement.

To estimate the long-term impact of climate change on crop yields, scientists usually use one of two methods. The first, process-based crop models, simulate the combined mechanistic effects of weather, soil conditions, pest damage and other factors on crop growth and yields. The second, statistical techniques, make observation-based estimates to simulate the effect of weather on crop yields. Both approaches have drawbacks.

A simpler, faster way to assess climate change impact on crop yields
Statistical emulators of maize, rice, soybean and wheat yields from global gridded crop models

This study provides statistical emulators of crop yields based on global gridded crop model simulations from the Inter-Sectoral Impact Model Intercomparison Project Fast Track project. The ensemble of simulations is used to build a panel of annual crop yields from five crop models and corresponding monthly summer weather variables for over a century at the grid cell level globally. This dataset is then used to estimate, for each crop and gridded crop model, the statistical relationship between yields, temperature, precipitation and carbon dioxide. This study considers a new functional form to better capture the non-linear response of yields to weather, especially for extreme temperature and precipitation events, and now accounts for the effect of soil type. In- and out-of-sample validations show that the statistical emulators are able to replicate spatial patterns of yields crop levels and changes overtime projected by crop models reasonably well, although the accuracy of the emulators varies by model and by region. This study therefore provides a reliable and accessible alternative to global gridded crop yield models. By emulating crop yields for several models using parsimonious equations, the tools provide a computationally efficient method to account for uncertainty in climate change impact assessments.

When hearing the words “greenhouse gas,” most people think immediately of carbon dioxide. This is indeed the greenhouse gas that is currently producing the greatest impact on the Earth’s rapidly changing climate. But it is far from the only one making its mark, and for mitigating climate change it’s important to be able to compare the effects of the various gases that contribute to warming the planet.

But that’s not easy to do.

Explained: Greenhouse gases
Economic Projection with Non-homothetic Preferences: The Performance and Application of a CDE Demand System

In computable general equilibrium modeling, whether the simulation results are consistent to a set of valid own-price and income demand elasticities that are observed empirically remains a key challenge in many modeling exercises. To address this issue, the Constant Difference of Elasticities (CDE) demand system has been adopted by some models since the 1990s. However, perhaps due to complexities of the system, the applications of CDE systems in other models are less common. Furthermore, how well the system can represent the given elasticities is rarely discussed or examined in existing literature. The study aims at bridging these gaps by revisiting calibration details of the system, exploring conditions where the calibrated elasticities of the system can better match a set of valid target elasticities, and presenting strategies to incorporate the system into GTAP8inGAMS—a global computable general equilibrium model written in GAMS and MPSGE modeling languages. It finds that the calibrated elasticities can be matched to the target ones more precisely if the corresponding sectorial expenditure shares are lower, target own-price demand elasticities are lower, and target income demand elasticities are higher. It also verifies that for the GTAP8inGAMS with a CDE system, the model responses can successfully replicate the calibrated elasticities under various price and income shocks.

 

The skies above Southeast Asia are often dimmed by a persistent haze, due largely to high concentrations of aerosols emitted from fires set intentionally to clear forests for oil palm plantations, burn agricultural waste or serve some other human need. The forest-clearing fires are of particular concern, as most occur on peatland, which burns longer and generates more smoke than other biomass sources.

A persistent haze
Human Health and Economic Impacts of Ozone Reductions by Income Group

Low-income households may be disproportionately affected by ozone pollution and ozone policy. We quantify how three factors affect the relative benefits of ozone policies with household income: (1) unequal ozone reductions; (2) policy delay; and (3) economic valuation methods. We model ozone concentrations under baseline and policy conditions across the full continental U.S. to estimate the distribution of ozone-related health impacts across nine income groups. We enhance an economic model to include these impacts across household income categories, and present its first application to evaluate the benefits of ozone reductions for low income households. We find that mortality incidence rates decrease with increasing income. Modeled ozone levels yield a median of 11 deaths/100,000 people in 2005. Proposed policy reduces these rates by 13%. Ozone reductions are highest among low income households, which increases their relative welfare gains by up to 4%, and decreases them for the rich by up to 8%. The median value of reductions in 2015 is either $30 billion (in 2006 U.S. dollars) or $1 billion if reduced mortality risks are valued with willingness-to-pay or as income from increased life expectancy. Ozone reductions were relatively twice as beneficial for the lowest compared to the highest income households. The valuation approach affected benefits more than a policy delay or differential ozone reductions with income.

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