JP

To meet the long-term goals of the Paris Agreement on climate change—keeping global warming well below 2°C and ideally capping it at 1.5°C—humanity will ultimately need to achieve net zero emissions of greenhouse gases (GHGs) into the atmosphere. To date emissions reduction efforts have largely focused on decarbonizing the two economic sectors responsible for the most emissions, electric power and transportation. Other approaches aim to remove carbon from the atmosphere and store it through carbon capture technology, biofuel cultivation and massive tree planting.  

To meet the world’s growing demand for energy amid efforts to stabilize the global climate will require the deployment of low‑carbon energy sources on a massive scale. But mobilizing the financial resources, technological advances, public opinion and political resolve needed to move toward net zero emissions will not be easy. Moreover, the economic and environmental risks posed by such a fundamental energy transition are considerable.

Summary: To meet the long-term goals of the Paris Agreement on climate change—keeping global warming well below 2°C and ideally capping it at 1.5°C—humanity will ultimately need to achieve net zero emissions of greenhouse gases (GHGs) into the atmosphere. To date emissions reduction efforts have largely focused on decarbonizing the two economic sectors responsible for the most emissions, electric power and transportation. Other approaches aim to remove carbon from the atmosphere and store it through carbon capture technology, biofuel cultivation and massive tree planting.  

As it turns out, planting trees is not the only way forestry can help in climate mitigation; how we use wood harvested from trees may also make a difference. Recent studies have shown that engineered wood products—composed of wood and various types of adhesive to enhance physical strength—involve far fewer carbon dioxide emissions than mineral-based building materials, and at lower cost. Now new research explores the potential environmental and economic impact in the U.S. of substituting lumber for energy-intensive building products such as cement and steel, which account for nearly 10 percent of human-made GHG emissions and are among the hardest to reduce.

Comparing the economic and emissions impacts of replacing CO2-intensive building materials (e.g. steel and concrete) with lumber products in the U.S. under an economy-wide cap-and-trade policy consistent with the nation’s Paris Agreement GHG emissions reduction pledge, the study found that the CO2-intensity (tons of CO2 emissions per dollar of output) of lumber production is about 20 percent less than that of fabricated metal products, under 50 percent that of iron and steel, and under 25 percent that of cement. In addition, shifting construction toward lumber products lowers the GDP cost of meeting the emissions cap by approximately $500 million and reduces the carbon price.

Abstract: Probabilistic estimates of climate system properties often rely on the comparison of model simulations to observed temperature records and an estimate of the internal climate variability. In this study, we investigate the sensitivity of probability distributions for climate system properties in the Massachusetts Institute of Technology Earth System Model to the internal variability estimate. In particular, we derive probability distributions using the internal variability extracted from 25 different Coupled Model Intercomparison Project Phase 5 models. We further test the sensitivity by pooling variability estimates from models with similar characteristics. We find the distributions to be highly sensitive when estimating the internal variability from a single model. When merging the variability estimates across multiple models, the distributions tend to converge to a wider distribution for all properties. This suggests that using a single model to approximate the internal climate variability produces distributions that are too narrow and do not fully represent the uncertainty in the climate system property estimates.

Summary: In a previous paper focused on four major rain-fed breadbasket crops—maize, rice, soybean and wheat—the author developed a set of crop yield statistical emulators and showed that they could produce results comparable to those generated by an ensemble of global gridded crop model (GGCM) simulations upon which they were trained. This new study provides statistical emulators of GGCMs to estimate irrigated crop yields and associated irrigation water withdrawals for maize, rice, soybean and wheat. Those emulators are estimated using data from an ensemble of simulations from five GGCMs from the Inter-Sectoral Impact Model Intercomparison Project Fast Track project. Crop-specific response functions for each GGCM are estimated at the grid-cell level over the globe. Validation exercises confirm that the statistical emulators are able to replicate the crop models’ spatial patterns of irrigated crop yields and irrigation water withdrawals reasonably well, both in terms of levels and changes over time, although accuracy varies by model and by region. This study therefore provides a reliable and computationally efficient alternative to global gridded crop models.

 

 

Pages

Subscribe to JP