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The sheer scale and diversity of transportation make it a formidable sector to decarbonize. Here, we consider an emerging opportunity to reduce carbon emissions: the growing adoption of semi-autonomous vehicles, which can be programmed to mitigate stop-and-go traffic through intelligent speed commands and, thus, reduce emissions. But would such dynamic eco-driving move the needle on climate change? A comprehensive impact analysis has been out of reach due to the vast array of traffic scenarios and the complexity of vehicle emissions. We address this challenge with large-scale scenario modeling efforts and by using multi-task deep reinforcement learning with a carefully designed network decomposition strategy. We perform an in-depth prospective impact assessment of dynamic eco-driving at 6,011 signalized intersections across three major US metropolitan cities, simulating a million traffic scenarios. Overall, we find that vehicle trajectories optimized for emissions can cut city-wide intersection carbon emissions by 11-22%, without harming throughput or safety, and with reasonable assumptions, equivalent to the national emissions of Israel and Nigeria, respectively. We find that 10% eco-driving adoption yields 25%-50% of the total reduction, and nearly 70% of the benefits come from 20% of intersections, suggesting near-term implementation pathways. However, the composition of this high-impact subset of intersections varies considerably across different adoption levels, with minimal overlap, calling for careful strategic planning for eco-driving deployments. Moreover, the impact of eco-driving, when considered jointly with projections of vehicle electrification and hybrid vehicle adoption remains significant. More broadly, this work paves the way for large-scale analysis of traffic externalities, such as time, safety, and air quality, and the potential impact of solution strategies., Comment: In review

For fast-tracking climate change response, green hydrogen is key for achieving greenhouse gas neutral energy systems. Especially Sub-Saharan Africa can benefit from it enabling an increased access to clean energy through utilizing its beneficial conditions for renewable energies. However, developing green hydrogen strategies for Sub-Saharan Africa requires highly detailed and consistent information ranging from technical, environmental, economic, and social dimensions, which is currently lacking in literature. Therefore, this paper provides a comprehensive novel approach embedding the required range of disciplines to analyze green hydrogen cost-potentials in Sub-Saharan Africa. This approach stretches from a dedicated land eligibility based on local preferences, a location specific renewable energy simulation, locally derived sustainable groundwater limitations under climate change, an optimization of local hydrogen energy systems, and a socio-economic indicator-based impact analysis. The capability of the approach is shown for case study regions in Sub-Saharan Africa highlighting the need for a unified, interdisciplinary approach.

We present a new cosmologically motivated mock redshift survey of the Dusty Star-Forming Galaxy population. Our mock survey is based on the Bolshoi-Planck dark-matter halo simulation and covers an area of 5.3 sq. degree. Using a semi-empirical approach, we generate a light cone and populate the dark-matter haloes with galaxies. Infrared properties are assigned to the galaxies based on theoretical and empirical relations from the literature. Additionally, background galaxies are gravitationally lensed by dark-matter haloes along the line-of-sight assuming a point-mass model approximation. We characterize the mock survey by measuring the star formation rate density, integrated number counts, redshift distribution, and infrared luminosity function. When compared with single-dish and interferometric observations, the predictions from our mock survey closely follow the compiled results from the literature. We have also directed this study towards characterizing one of the extragalactic legacy surveys to be observed with the TolTEC camera at the Large Millimeter Telescope: the 0.8 sq. degree Ultra Deep Survey, with expected depths of 0.025, 0.018 and 0.012 mJy beam$^{-1}$ at 1.1, 1.4 and 2.0 mm. Exploiting the clustering information in our mock survey, we investigate its impact on the effect of flux boosting by the fainter population of dusty galaxies, finding that clustering can increase the median boosting by 0.5 per cent at 1.1 mm, 0.8 per cent at 1.4 mm and, 2.0 per cent at 2.0 mm, and with higher dispersion., Comment: 17 pages, 12 figures

Identifying stop-and-go events (SAGs) in traffic flow presents an important avenue for advancing data-driven research for climate change mitigation and sustainability, owing to their substantial impact on carbon emissions, travel time, fuel consumption, and roadway safety. In fact, SAGs are estimated to account for 33-50% of highway driving externalities. However, insufficient attention has been paid to precisely quantifying where, when, and how much these SAGs take place -necessary for downstream decision making, such as intervention design and policy analysis. A key challenge is that the data available to researchers and governments are typically sparse and aggregated to a granularity that obscures SAGs. To overcome such data limitations, this study thus explores the use of traffic reconstruction techniques for SAG identification. In particular, we introduce a kernel-based method for identifying spatio-temporal features in traffic and leverage bootstrapping to quantify the uncertainty of the reconstruction process. Experimental results on California highway data demonstrate the promise of the method for capturing SAGs. This work contributes to a foundation for data-driven decision making to advance sustainability of traffic systems., Comment: Presented at NeurIPS 2023 workshops: Tackling Climate Change with Machine Learning & Computational Sustainability