Regional Analysis

Contribution of double-cropped maize ethanol in Brazil to sustainable development

Abstract: Sustainable energy and food production can include double-cropping where two crops are produced sequentially on land required for one crop to maximize resource use. In Brazil, this system involves maize being planted as a second crop following soybean to generate ethanol, thus allowing for combined food–energy production. However, the impacts of such production systems on several sustainable development goals (SDG) and associated indirect land-use changes have not yet fully been explored.

We evaluate the fast-expanding food–energy system of double-cropped maize ethanol in the Central-West region of Brazil with respect to SDG impacts, combining life-cycle environmental and computable general equilibrium socio-economic models.

We find that this system provides renewable and affordable energy (5 billion litres of ethanol, 600 MWh of electrical power) and feed (4 million tons of distillers dried grains), reduces greenhouse gas emissions (9.3 million to 13.2 million tCO2e), saves land (160,000 ha), boosts regional income and consumption, improves food security and benefits ecosystems and human health. Underlying drivers associated with this were the integration of feedstock supply into existing practices and the use of eucalyptus chips to provide process energy. The sustainability of this production system is improved further by carbon capture and storage.

Gaussian process regression-based Bayesian optimisation (G-BO) of model parameters – a WRF model case study of southeast Australia heat extremes

Key Points

• Our study optimises WRF model parameters for Southeast Australia heat extremes, enhancing the accuracy of the model simulation. 

• G-BO method finds optimal parameter ranges, substantially improving the simulation of temperature and humidity. 

• Results suggest updating WRF model's default settings for better extreme heat event simulations.

Abstract 

In Numerical Weather Prediction (NWP) models, such as the Weather Research and Forecasting (WRF) model, parameter uncertainty in physics parameterization schemes significantly impacts model output. Our study adopts a Bayesian probabilistic approach, building on prior research that identified temperature (T) and relative humidity (Rh) as sensitive to three key WRF parameters during southeast Australia’s extreme heat events. Using Gaussian process regression-based Bayesian Optimisation (G-BO), we accurately estimated the optimal distributions for these parameters. Results show that the default values are outside their corresponding optimal distribution bounds for two of the three parameters, suggesting the need to reconsider these default values. Additionally, the robustness of the optimal parameter distributions is validated by their application to an independent extreme heat event, not included in the optimisation process. In this test, the optimised parameters substantially improved the simulation of T and Rh, highlighting their effectiveness in enhancing simulation accuracy during extreme heat conditions. 

Plain Language Summary 

This study aims to enhance the accuracy of a numerical weather model called the Weather Research and Forecasting (WRF) model, especially for simulating extreme heat events in Southeast Australia. Typically, the accuracy of such models depends on specific settings, which are often set to default values. Our research used a method known as Gaussian process regression-based Bayesian Optimisation (G-BO) to determine the best range of values for these settings. We found that the default settings were not optimal. By applying G-BO, we identified more effective values that substantially improved the model’s simulations of temperature and humidity during heat extremes. This improvement was consistent even when tested on an independent extreme heat event. These advancements are vital for more accurate weather forecasting, which is essential for emergency services, electricity management, and agriculture planning during extreme heat conditions.

Zero hunger. Affordable and clean energy. Reduced inequalities. These are among the  sustainable development goals that the United Nations has established in pursuit of the long-term well-being of the Earth and its inhabitants. But achieving goals like these—whether by the UN’s 2030 deadline or beyond—requires a detailed understanding of the many complex, interconnected, co-evolving natural, social and technological systems upon which all life depends.

Upgrading computational tools for sustainable development
Role of hydrogen in low-carbon energy transition

Abstract: Dr Sergey Paltsev discussed the role of hydrogen as a low carbon solution in reducing emissions across various sectors of the economy including transportation, industrial uses, and for energy storage. He acknowledged hydrogen’s potential benefits, but also noted substantial challenges, such as the high costs and infrastructural demands associated with its production and use. Dr Paltsev recognised the unique context of New Zealand, noting that with our light vehicle fleet, hydrogen may not be the ideal solution for transportation decarbonisation, suggesting instead that New Zealand’s air and maritime traffic sectors might benefit from alternative strategies. Considering factors like New Zealand’s energy security and high electricity costs, Dr Paltsev highlighted the need for significant financial backing and thoughtful consideration before focusing on hydrogen in the energy transition.

Substantial increase in perfluorocarbons CF4 (PFC-14) and C2F6 (PFC-116) emissions in China

Abstract: The perfluorocarbons tetrafluoromethane (CF4, PFC-14) and hexafluoroethane (C2F6, PFC-116) are potent greenhouse gases with near-permanent atmospheric lifetimes relative to human timescales, and global warming potentials thousands of times that of  CO2.

Using long-term atmospheric observations from a Chinese network and an inverse modelling approach (top-down method), we determined that CF4 emissions in China increased from 4.7 (4.2-5.0, 68% uncertainty interval) Gg yr-1 in 2012 to 8.3 (7.7-8.9) Gg yr-1 in 2021, and C2F6 emissions in China increased from 0.74 (0.66-0.80) Gg yr-1 in 2011 to 1.32 (1.24-1.40) Gg yr-1 in 2021, both increasing by approximately 78%. Combined emissions of CF4 and C2F6 in China reached 78 Mt CO2-eq in 2021. The absolute increase in emissions of each substance in China between 2011-2012 and 2017-2020 was similar to (for CF4), or greater than (for C2F6), the respective absolute increase in global emissions over the same period.  Substantial CF4 and C2F6 emissions were identified in the less populated western regions of China, probably due to emissions from the expanding aluminum industry in these resource-intensive regions. It is likely that the aluminum industry dominates CF4 emissions in China, while the aluminum and semiconductor industries both contribute to C2F6 emissions.

Based on atmospheric observations, this study validates the emission magnitudes reported in national bottom-up inventories and provides insights into detailed spatial distributions and emission sources beyond what is reported in national bottom-up inventories.

Significance: We investigate the emissions of two potent greenhouse gases, tetrafluoromethane (CF4, PFC-14) and hexafluoroethane (C2F6, PFC-116), in China.

Based on atmospheric observations within China, we report substantial increases in CF4 and C2F6 emissions in China over the last decade. These increases in national emissions are sufficient to explain the entire increases in global emissions over the same period. We suggest that substantial CF4 and C2F6 emissions could be due to by-product emissions from the aluminum industry in the less populated and less economically developed western regions in China.

The findings highlight the importance of mitigating CF4 and C2F6 emissions in China and provide guidance for directing mitigation strategies towards specific regions and/or industries.

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