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  • image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
    Authors: Pahwa, Anmol; Jaller, Miguel;

    This work models a last-mile network design problem for an e-retailer with a capacitated two-echelon distribution structure - typical in e-retail last-mile distribution, catering to a market with a stochastic and dynamic daily customer demand requesting delivery within time-windows. Considering the distribution evnironment, this work formulates last-mile network design problem for this e-retailer as a dynamic-stochastic two capacitated location routing problem with time-windows. In doing so, this work splits the last-mile network design problem into its constituent strategic, tactical, and operational decisions. Here, the strategic decisions undertake long-term planning to develop a distribution structure with appropriate distribution facilities and a suitable delivery fleet to service the expected customer demand in the planning horizon. The tactical decisions pertain to medium-term day-to-day planning of last-mile delivery operations to establish efficient goods flow in this distribution structure to service the daily stochastic customer demand. And finally, operational decisions involve immediate short-term planning to fine-tune this last-mile delivery to service the requests arriving dynamically through the day. Note, the last-mile network design problem formulated as a location routing problem constitutes three subproblems encompassing facility location problem, customer allocation problem, and vehicle routing problem, each of which are NP-hard combinatorial optimization problems. To this end, this work develops an adaptive large neighborhood search meta-heuristic algorithm that searches through the neighborhood by destroying and consequently repairing the solution thereby reconfiguring large portions of the solution with specific operators that are chosen adaptively in each iteration of the algorithm, hence the name adaptive large neighborhood search. Further, considering the stochastic and dynamic nature of the delivery environment, this work develops a Monte-Carlo framework simulating each day in the planning horizon, with each day divided into 1-hr timeslots, and with each time-slot accepting customer requests for service by the end of the day. In particular, the framework assumes the e-retailer will delay route commitments until the last-feasible time-slot to accumulate customer requests and consequently assign them to an uncommitted delivery route. Note, a delivery route is committed once the e-retailer starts loading packages assigned to this delivery route onto the delivery vehicle assigned for this delivery route. At the end of every time-slot then, this framework assumes the e-retailer integrates the new customer requests by inserting these customer nodes into such uncommitted delivery routes in a manner that results in the least increase in distribution cost keeping the customer-distribution facility allocation fixed. Thus, the framework iterates through the time-slots with the e-retailer processing route commitments, accumulating customer requests, and subsequently integrating them into the delivery operations for the day. E-commerce has the potential to make urban goods flow economically viable, environmentally efficient, and socially equitable. However, as e-retailers compete with increasingly consumer-focused services, urban freight witnesses a significant increase in associated distribution costs and negative externalities particularly affecting those living close to logistics clusters. Hence, to remain competitive, e-retailers deploy alternate last-mile distribution strategies. These alternate strategies, such as those that include use of electric delivery trucks for last-mile operations, a fleet of crowdsourced drivers for last-mile delivery, consolidation facilities coupled with light-duty delivery vehicles for a multi-echelon distribution, or collection points for customer pickup, can restore sustainable urban goods flow. Thus, in this study, the authors investigate the opportunities and challenges associated with such alternate last-mile distribution strategies for an e-retailer offering expedited service with rush delivery within strict timeframes. To this end, the authors formulate a last-mile network design (LMND) problem as a dynamic-stochastic two-echelon capacitated location routing problem with time-windows (DS-2E-C-LRP-TW) addressed with an adaptive large neighborhood search (ALNS) metaheuristic.

    image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/ ZENODOarrow_drop_down
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    ZENODO
    Dataset . 2023
    License: CC 0
    Data sources: ZENODO
    DRYAD
    Dataset . 2023
    License: CC 0
    Data sources: Datacite
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      ZENODO
      Dataset . 2023
      License: CC 0
      Data sources: ZENODO
      DRYAD
      Dataset . 2023
      License: CC 0
      Data sources: Datacite
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    Authors: Minx, Jan C.; Lamb, William F.; Andrew, Robbie M.; Canadell, Josep G.; +13 Authors

    Comprehensive and reliable information on anthropogenic sources of greenhouse gas emissions is required to track progress towards keeping warming well below 2°C as agreed upon in the Paris Agreement. Here we provide a dataset on anthropogenic GHG emissions 1970-2019 with a broad country and sector coverage. We build the dataset from recent releases from the “Emissions Database for Global Atmospheric Research” (EDGAR) for CO2 emissions from fossil fuel combustion and industry (FFI), CH4 emissions, N2O emissions, and fluorinated gases and use a well-established fast-track method to extend this dataset from 2018 to 2019. We complement this with information on net CO2 emissions from land use, land-use change and forestry (LULUCF) from three available bookkeeping models.

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    ZENODO
    Dataset . 2021
    License: CC BY
    Data sources: Datacite
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    ZENODO
    Dataset . 2021
    License: CC BY
    Data sources: Datacite
    image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
    ZENODO
    Dataset . 2021
    License: CC BY
    Data sources: Datacite
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      ZENODO
      Dataset . 2021
      License: CC BY
      Data sources: Datacite
      image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
      ZENODO
      Dataset . 2021
      License: CC BY
      Data sources: Datacite
      image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
      ZENODO
      Dataset . 2021
      License: CC BY
      Data sources: Datacite
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    Authors: Valenti, Wagner Cotroni; Moraes-Valenti, Patricia; Fonseca, Tamara; Dioniso S. Sampaio; +6 Authors

    Indicators of economic sustainability obtained for the 8 systems of LTS studied. Monoc. = monoculture; sub-trop. = subtropical; IMTA = integrated multi trophic aquaculture; “-“ = no data.

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    ZENODO
    Dataset . 2023
    License: CC BY
    Data sources: ZENODO
    image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
    ZENODO
    Dataset . 2023
    License: CC BY
    Data sources: Datacite
    image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
    ZENODO
    Dataset . 2023
    License: CC BY
    Data sources: ZENODO
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    ZENODO
    Dataset . 2023
    License: CC BY
    Data sources: Datacite
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      ZENODO
      Dataset . 2023
      License: CC BY
      Data sources: ZENODO
      image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
      ZENODO
      Dataset . 2023
      License: CC BY
      Data sources: Datacite
      image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
      ZENODO
      Dataset . 2023
      License: CC BY
      Data sources: ZENODO
      image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
      ZENODO
      Dataset . 2023
      License: CC BY
      Data sources: Datacite
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    Authors: Dunn, Jessica; Slattery, Margaret; Kendall, Alissa; Ambrose, Hanjiro; +1 Authors

    Batteries have the potential to significantly reduce greenhouse gas emissions from on-road transportation. However, environmental and social impacts of producing lithium-ion batteries, particularly cathode materials, and concerns over material criticality are frequently highlighted as barriers to widespread electric vehicle adoption. Circular economy strategies, like reuse and recycling, can reduce impacts and secure regional supplies. To understand the potential for circularity, we undertake a dynamic global material flow analysis of pack-level materials that includes scenario analysis for changing battery cathode chemistries and electric vehicle demand. Results are produced regionwise and through the year 2040 to estimate the potential global and regional circularity of lithium, cobalt, nickel, manganese, iron, aluminum, copper, and graphite, although the analysis is focused on the cathode materials. Under idealized conditions, retired batteries could supply 60% of cobalt, 53% of lithium, 57% of manganese, and 53% of nickel globally in 2040. If the current mix of cathode chemistries evolves to a market dominated by NMC 811, a low cobalt chemistry, there is potential for 85% global circularity of cobalt in 2040. If the market steers away from cathodes containing cobalt, to an LFP-dominated market, cobalt, manganese, and nickel become less relevant and reach circularity before 2040. For each market to benefit from the recovery of secondary materials, recycling and manufacturing infrastructure must be developed in each region. This data was collected through various sources, including from EV Volumes, International Energy Agency, Argonne National Lab, and published articles. A model was created with R to process the data.  R is required to open the models.

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    ZENODO
    Dataset . 2023
    License: CC 0
    Data sources: ZENODO
    image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
    Smithsonian figshare
    Dataset . 2021
    License: CC BY NC
    DRYAD
    Dataset . 2023
    License: CC 0
    Data sources: Datacite
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      ZENODO
      Dataset . 2023
      License: CC 0
      Data sources: ZENODO
      image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
      Smithsonian figshare
      Dataset . 2021
      License: CC BY NC
      DRYAD
      Dataset . 2023
      License: CC 0
      Data sources: Datacite
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    Authors: Brady, Hannah; Barsotti, Gabrielle; Davis, Jordan; Norris, Carly; +1 Authors

    In recent years, there has been increased attention and focus from the public on the environmental impact of professional sports organizations. Significant opportunities exist for Major League Baseball (MLB) teams to both reduce their own environmental footprint, and that of their fans, through sustainability initiatives. Despite stadiums using upwards of ten million gallons of water per year and having the same energy needs as a small city, no MLB team has completed a public-facing quantification of their total environmental footprint. This project calculated the carbon footprint and water consumption of the Tampa Bay Rays for the 2019 regular season. We analyzed Scope 1, 2, and 3 GHG emissions to identify hotspots within the Rays’ operations, supply chains, and transportation. Fan transportation was found to be the largest source of GHGs, followed by food production for concessions. The cooling tower and restrooms were identified as the largest sources of onsite water usage. We created a repository of best practices as a resource for stadium managers that includes strategies to reduce GHGs and water use coupled with scenario analyses estimating potential reductions. The following recommendations are highlighted as the largest reduction opportunities: (1) prioritizing fan engagement to switch to more sustainable modes of transportation, and (2) offering and highlighting more vegetarian options at concessions. To further reduce emissions and water usage, MLB teams should prioritize sub-metering electricity and water lines and installing more efficient equipment. Data was provided by the Tampa Bay Rays and collected at Tropicana Field (where the Rays play) by Jordan Davis during the summer of 2021.

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    ZENODO
    Dataset . 2022
    License: CC 0
    Data sources: ZENODO
    DRYAD
    Dataset . 2022
    License: CC 0
    Data sources: Datacite
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      image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/ ZENODOarrow_drop_down
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      ZENODO
      Dataset . 2022
      License: CC 0
      Data sources: ZENODO
      DRYAD
      Dataset . 2022
      License: CC 0
      Data sources: Datacite
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    Authors: Mehta, Piyush; Siebert, Stefan; Kummu, Matti; Deng, Qinyu; +4 Authors

    The expansion of irrigated agriculture has increased global crop production but resulted in widespread stress to freshwater resources. Ensuring that increases in irrigated production only occur in places where water is relatively abundant is a key objective of sustainable agriculture, and knowledge of how irrigated land has evolved is important for measuring progress towards water sustainability. Yet a spatially detailed understanding of the evolution of global area equipped for irrigation (AEI) is missing. Here we utilize the latest sub-national irrigation statistics (covering 17298 administrative units) from various official sources to develop a gridded (5 arc-min resolution) global product of AEI for the years 2000, 2005, 2010, and 2015. We find that AEI increased by 11% from 2000 (297 Mha) to 2015 (330 Mha) with locations of both substantial expansion (e.g., northwest India, northeast China) and decline (e.g., Russia). Combining these outputs with information on green (i.e., rainfall) and blue (i.e., surface and ground) water stress, we also examine to what extent irrigation has expanded unsustainably (i.e., in places already experiencing water stress). We find that more than half (52%) of irrigation expansion has taken place in regions that were already water stressed, with India alone accounting for 36% of global unsustainable expansion. These findings provide new insights into the evolving patterns of global irrigation with important implications for global water sustainability and food security. Recommended citation: Mehta, P., Siebert, S., Kummu, M. et al. Half of twenty-first century global irrigation expansion has been in water-stressed regions. Nat Water (2024). https://doi.org/10.1038/s44221-024-00206-9 Open-access peer reviewed publication available at https://www.nature.com/articles/s44221-024-00206-9 Files G_AEI_*.ASC were produced using the GMIA dataset[https://data.apps.fao.org/catalog/iso/f79213a0-88fd-11da-a88f-000d939bc5d8]. Files MEIER_G_AEI_*.ASC were produced using Meier et al. (2018) dataset [https://doi.pangaea.de/10.1594/PANGAEA.884744].

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    ZENODO
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    ZENODO
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    ZENODO
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    ZENODO
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    ZENODO
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      ZENODO
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      ZENODO
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      ZENODO
      Dataset . 2023
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    {"references": ["Liu, Z., Ciais, P., Deng, Z., Lei, R., Davis, S. J., Feng, S., Zheng, B., Cui, D., Dou, X., Zhu, B., Guo, R., Ke, P., Sun, T., Lu, C., He, P., Wang, Y., Yue, X., Wang, Y., Lei, Y., Zhou, H., Cai, Z., Wu, Y., Guo, R., Han, T., Xue, J., Boucher, O., Boucher, E., Chevallier, F., Tanaka, K., Wei, Y., Zhong, H., Kang, C., Zhang, N., Chen, B., Xi, F., Liu, M., Br\u00e9on, F.-M., Lu, Y., Zhang, Q., Guan, D., Gong, P., Kammen, D. M., He, K. & Schellnhuber, H. J. (2020). Near-real-time monitoring of global CO2 emissions reveals the effects of the COVID-19 pandemic. Nature Communications 11, 5172 (2020). https://doi.org/10.1038/s41467-020-18922-7", "Meinshausen, M., Smith, S. J., Calvin, K., Daniel, J. S., Kainuma, M. L. T., Lamarque, J. F., Matsumoto, K., Montzka, S. A., Raper, S. C. B., Riahi, K., Thomson, A., Velders, G. J. M., & van Vuuren, D. P. (2011). The RCP greenhouse gas concentrations and their extensions from 1765 to 2300. Climatic Change, 109(1\u20132), 213\u2013241. https://doi.org/10.1007/s10584-011-0156-z", "Moss, R. H., Edmonds, J. A., Hibbard, K. A., Manning, M. R., Rose, S. K., van Vuuren, D. P., Carter, T. R., Emori, S., Kainuma, M., Kram, T., Meehl, G. A., Mitchell, J. F. B., Nakicenovic, N., Riahi, K., Smith, S. J., Stouffer, R. J., Thomson, A. M., Weyant, J. P. & Wilbanks, T. J. (2010). The next generation of scenarios for climate change research and assessment. Nature, 463(7282), 747\u2013756. https://doi.org/10.1038/nature08823", "Myhre, G., Highwood, E. J., Shine, K. P., & Stordal, F. (1998). New estimates of radiative forcing due to well mixed greenhouse gases. Geophysical Research Letters, 25(14), 2715\u20132718. https://doi.org/10.1029/98gl01908", "Strassmann, K. M. and Joos, F. (2018). The Bern Simple Climate Model (BernSCM) v1.0: an extensible and fully documented open-source re-implementation of the Bern reduced-form model for global carbon cycle\u2013climate simulations, Geosci. Model Dev., 11, 1887\u20131908, https://doi.org/10.5194/gmd-11-1887-2018", "Thomas, M. A., and Lin, T. (2018). A dual model for emulation of thermosteric and dynamic sea-level change. Climatic Change, 148(1\u20132), 311\u2013324. https://doi.org/10.1007/s10584-018-2198-y"]} Supplementary materials for Gonzalez, A. R., & Lin, T. (2022). Translated Emission Pathways (TEPs): Long-Term Simulations of COVID-19 CO2 Emissions and Thermosteric Sea Level Rise Projections. Earth's Future. In Press. Summary: This study introduces climate science to a broader audience by presenting an accessible research framework and environmental data related to the ongoing COVID-19 pandemic. A series of translated emission pathways (TEPs) were constructed based on the CO2 emission patterns from the various phases of COVID-19 response. In addition to resembling the forcing scenarios used within climate research, a thermosteric sea level rise analysis was incorporated to further emphasize the environmental benefits that can be obtained from long-term sustainability. As a promising start for including the general public in climate change discussion, this research promotes collective environmental action that mirrors the recommendations of the scientific community. We acknowledge the Carbon Monitor initiative (Liu et al., 2020) for providing the COVID-19 CO2 sectoral emission data used to construct the proposed TEPs. In addition, we acknowledge the developers of the BernSCM (Strassmann and Joos, 2018) that was utilized in this study to relate TEP CO2 emissions to their respective CO2 atmospheric concentrations. Furthermore, we thank the Texas Tech University McNair Scholars Program and the Multi-Hazard Sustainability (HazSus) research group for guidance and support throughout the course of this study. Analyses presented herein were performed using the RedRaider computing cluster at Texas Tech University. We thank the team at the High Performance Computing Center (HPCC) for their generous support. In addition, the equipment support from the Vice President for Research & Innovation for T.L.'s HazSus Research Group is gratefully acknowledged.

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    ZENODO
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    ZENODO
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    ZENODO
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      ZENODO
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      ZENODO
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    Authors: Heracles Polatidis; Andrew Barney; Dias Haralambopoulos; Gobind Pillai; +2 Authors

    Abstract This paper presents REACT-DECARB, an energy planning decarbonisation platform employing renewable energy sources coupled with storage for islands. The paper implements the energy scenario creation and economic evaluation steps of the platform on eight geographic islands in seven countries within the EU. Twenty-one technologically feasible energy scenarios, applicable to the specific conditions of each island, are specified and their economic assessment via a levelized cost of energy (LCOE) calculation is then performed. The main aim of this application is to verify the noted steps of the platform as well as to test its flexibility across geographically, socially and dimensionally disparate islands with various scenario generation methods. The results of the economic analysis show a wide variation of LCOE depending primarily on whether full island autonomy is assumed. In some cases the islands’ scenarios’ costs approach current market prices but are never below them; some scenarios are, however, below the current price of the island’s thermal generation. The sensitivity and uncertainty of the economic performance results’ and the variables used to calculate them are evaluated and discussed for two of the islands. The overall analysis and application has shown that the REACT-DECARB platform is suitable for different islands, regardless of location and size and can be useful for island energy planners.

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    Sustainable Energy Technologies and Assessments
    Article . 2021 . Peer-reviewed
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      Sustainable Energy Technologies and Assessments
      Article . 2021 . Peer-reviewed
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    Authors: Youngflesh, Casey; Montgomery, Graham; Saracco, James; Miller, David; +5 Authors

    Bird capture data were collected as part of the Monitoring Avian Productivity and Survivorship (MAPS) program, a collaborative long-term bird-banding project operating across North America. Data were obtained from 179 banding stations. Each banding station consisted of 6–20 mist nets operated approximately every ten days beginning as early as May 1 (start date varying slightly by location) through August 8 (ordinal dates 121–220 in a non-leap year), which span the breeding season for most birds in North America. Only species/locations/years with at least 15 total captures, at least 5 of those being juveniles, species/locations with at least 5 years of data, and species with at least 15 locations/years of data were considered. Bird breeding phenology was calculated using the capture dates of juvenile birds at MAPS stations. This measure of breeding phenology is indicative of the time of year at which young birds are fledging. For each species, at each location, in each year, our metric of breeding phenology was the mean date of first capture across all juveniles captured at that station that year. Following Saracco et al. 2019, we exclude subsequent captures of the same individual after its first capture. For each station, effort hours was calculated as the proportion of net-hours (total area of mist nets multiplied by the number of hours that these nets were deployed) during the period where juveniles were captured, excluding the first 2.5% of juvenile captures to remove outliers, following the procedure used by Saracco et al. 2019. Climate data was downloaded from climatena.ca. Works cited J. F. Saracco, R. B. Siegel, L. Helton, S. L. Stock, D. F. DeSante, Phenology and productivity in a montane bird assemblage: Trends and responses to elevation and climate variation. Glob. Change Biol. 25, 985–996 (2019). Changes in phenology in response to ongoing climate change have been observed in numerous taxa around the world. Differing rates of phenological shifts across trophic levels have led to concerns that ecological interactions may become increasingly decoupled in time, with potential negative consequences for populations. Despite widespread evidence of phenological change and a broad body of supporting theory, large-scale multi-taxa evidence for demographic consequences of phenological asynchrony remains elusive. Using data from a continental-scale bird banding program, we assess the impact of phenological dynamics on avian breeding productivity in 41 species of migratory and resident North American birds breeding in and around forested areas. We find strong evidence for a phenological optimum where breeding productivity decreases in years with both particularly early or late phenology and when breeding occurs early or late relative to local vegetation phenology. Moreover, we demonstrate that landbird breeding phenology did not keep pace with shifts in the timing of vegetation green-up over a recent 18-year period, even though avian breeding phenology has tracked green-up with greater sensitivity than arrival for migratory species. Species whose breeding phenology more closely tracked green-up tend to migrate shorter distances (or are resident over the entire year) and breed earlier in the season. These results showcase the broadest-scale evidence yet of the demographic impacts of phenological change. Future climate change-associated phenological shifts will likely result in a decrease in breeding productivity for most species, given that bird breeding phenology is failing to keep pace with climate change. Data from the Monitoring Avian Productivity and Survivorship (MAPS) program are curated and managed by The Institute for Bird Populations and were queried from the MAPS database on 2019-10-16.

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      ZENODO
      Dataset . 2023
      License: CC 0
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      DRYAD
      Dataset . 2023
      License: CC 0
      Data sources: Datacite
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  • image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/

    Chapter 1 : Principles of Renewable Energy & Solar Energy 01 - 50 Chapter 2 : Wind Energy 01– 29 Chapter 3 : Ocean Thermal Energy 01 - 27 Chapter 4 : Storage of Energy 01 - 52 Chapter 5 : Geothermal Energy Sources 01 - 25

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    ZENODO
    Book . 2023
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    ZENODO
    Book . 2023
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    ZENODO
    Book . 2023
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    ZENODO
    Book . 2023
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    ZENODO
    Book . 2023
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    Book . 2023
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    ZENODO
    Book . 2023
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      ZENODO
      Book . 2023
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      ZENODO
      Book . 2023
      License: CC BY
      Data sources: Datacite
      image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
      ZENODO
      Book . 2023
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      Data sources: Datacite
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      ZENODO
      Book . 2023
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      ZENODO
      Book . 2023
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      ZENODO
      Book . 2023
      License: CC BY
      Data sources: ZENODO
      image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
      ZENODO
      Book . 2023
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  • image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
    Authors: Pahwa, Anmol; Jaller, Miguel;

    This work models a last-mile network design problem for an e-retailer with a capacitated two-echelon distribution structure - typical in e-retail last-mile distribution, catering to a market with a stochastic and dynamic daily customer demand requesting delivery within time-windows. Considering the distribution evnironment, this work formulates last-mile network design problem for this e-retailer as a dynamic-stochastic two capacitated location routing problem with time-windows. In doing so, this work splits the last-mile network design problem into its constituent strategic, tactical, and operational decisions. Here, the strategic decisions undertake long-term planning to develop a distribution structure with appropriate distribution facilities and a suitable delivery fleet to service the expected customer demand in the planning horizon. The tactical decisions pertain to medium-term day-to-day planning of last-mile delivery operations to establish efficient goods flow in this distribution structure to service the daily stochastic customer demand. And finally, operational decisions involve immediate short-term planning to fine-tune this last-mile delivery to service the requests arriving dynamically through the day. Note, the last-mile network design problem formulated as a location routing problem constitutes three subproblems encompassing facility location problem, customer allocation problem, and vehicle routing problem, each of which are NP-hard combinatorial optimization problems. To this end, this work develops an adaptive large neighborhood search meta-heuristic algorithm that searches through the neighborhood by destroying and consequently repairing the solution thereby reconfiguring large portions of the solution with specific operators that are chosen adaptively in each iteration of the algorithm, hence the name adaptive large neighborhood search. Further, considering the stochastic and dynamic nature of the delivery environment, this work develops a Monte-Carlo framework simulating each day in the planning horizon, with each day divided into 1-hr timeslots, and with each time-slot accepting customer requests for service by the end of the day. In particular, the framework assumes the e-retailer will delay route commitments until the last-feasible time-slot to accumulate customer requests and consequently assign them to an uncommitted delivery route. Note, a delivery route is committed once the e-retailer starts loading packages assigned to this delivery route onto the delivery vehicle assigned for this delivery route. At the end of every time-slot then, this framework assumes the e-retailer integrates the new customer requests by inserting these customer nodes into such uncommitted delivery routes in a manner that results in the least increase in distribution cost keeping the customer-distribution facility allocation fixed. Thus, the framework iterates through the time-slots with the e-retailer processing route commitments, accumulating customer requests, and subsequently integrating them into the delivery operations for the day. E-commerce has the potential to make urban goods flow economically viable, environmentally efficient, and socially equitable. However, as e-retailers compete with increasingly consumer-focused services, urban freight witnesses a significant increase in associated distribution costs and negative externalities particularly affecting those living close to logistics clusters. Hence, to remain competitive, e-retailers deploy alternate last-mile distribution strategies. These alternate strategies, such as those that include use of electric delivery trucks for last-mile operations, a fleet of crowdsourced drivers for last-mile delivery, consolidation facilities coupled with light-duty delivery vehicles for a multi-echelon distribution, or collection points for customer pickup, can restore sustainable urban goods flow. Thus, in this study, the authors investigate the opportunities and challenges associated with such alternate last-mile distribution strategies for an e-retailer offering expedited service with rush delivery within strict timeframes. To this end, the authors formulate a last-mile network design (LMND) problem as a dynamic-stochastic two-echelon capacitated location routing problem with time-windows (DS-2E-C-LRP-TW) addressed with an adaptive large neighborhood search (ALNS) metaheuristic.

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    ZENODO
    Dataset . 2023
    License: CC 0
    Data sources: ZENODO
    DRYAD
    Dataset . 2023
    License: CC 0
    Data sources: Datacite
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      ZENODO
      Dataset . 2023
      License: CC 0
      Data sources: ZENODO
      DRYAD
      Dataset . 2023
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      Data sources: Datacite
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  • image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
    Authors: Minx, Jan C.; Lamb, William F.; Andrew, Robbie M.; Canadell, Josep G.; +13 Authors

    Comprehensive and reliable information on anthropogenic sources of greenhouse gas emissions is required to track progress towards keeping warming well below 2°C as agreed upon in the Paris Agreement. Here we provide a dataset on anthropogenic GHG emissions 1970-2019 with a broad country and sector coverage. We build the dataset from recent releases from the “Emissions Database for Global Atmospheric Research” (EDGAR) for CO2 emissions from fossil fuel combustion and industry (FFI), CH4 emissions, N2O emissions, and fluorinated gases and use a well-established fast-track method to extend this dataset from 2018 to 2019. We complement this with information on net CO2 emissions from land use, land-use change and forestry (LULUCF) from three available bookkeeping models.

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    ZENODO
    Dataset . 2021
    License: CC BY
    Data sources: Datacite
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    ZENODO
    Dataset . 2021
    License: CC BY
    Data sources: Datacite
    image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
    ZENODO
    Dataset . 2021
    License: CC BY
    Data sources: Datacite
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      ZENODO
      Dataset . 2021
      License: CC BY
      Data sources: Datacite
      image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
      ZENODO
      Dataset . 2021
      License: CC BY
      Data sources: Datacite
      image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
      ZENODO
      Dataset . 2021
      License: CC BY
      Data sources: Datacite
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  • image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
    Authors: Valenti, Wagner Cotroni; Moraes-Valenti, Patricia; Fonseca, Tamara; Dioniso S. Sampaio; +6 Authors

    Indicators of economic sustainability obtained for the 8 systems of LTS studied. Monoc. = monoculture; sub-trop. = subtropical; IMTA = integrated multi trophic aquaculture; “-“ = no data.

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    ZENODO
    Dataset . 2023
    License: CC BY
    Data sources: ZENODO
    image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
    ZENODO
    Dataset . 2023
    License: CC BY
    Data sources: Datacite
    image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
    ZENODO
    Dataset . 2023
    License: CC BY
    Data sources: ZENODO
    image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
    ZENODO
    Dataset . 2023
    License: CC BY
    Data sources: Datacite
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      ZENODO
      Dataset . 2023
      License: CC BY
      Data sources: ZENODO
      image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
      ZENODO
      Dataset . 2023
      License: CC BY
      Data sources: Datacite
      image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
      ZENODO
      Dataset . 2023
      License: CC BY
      Data sources: ZENODO
      image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
      ZENODO
      Dataset . 2023
      License: CC BY
      Data sources: Datacite
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    Authors: Dunn, Jessica; Slattery, Margaret; Kendall, Alissa; Ambrose, Hanjiro; +1 Authors

    Batteries have the potential to significantly reduce greenhouse gas emissions from on-road transportation. However, environmental and social impacts of producing lithium-ion batteries, particularly cathode materials, and concerns over material criticality are frequently highlighted as barriers to widespread electric vehicle adoption. Circular economy strategies, like reuse and recycling, can reduce impacts and secure regional supplies. To understand the potential for circularity, we undertake a dynamic global material flow analysis of pack-level materials that includes scenario analysis for changing battery cathode chemistries and electric vehicle demand. Results are produced regionwise and through the year 2040 to estimate the potential global and regional circularity of lithium, cobalt, nickel, manganese, iron, aluminum, copper, and graphite, although the analysis is focused on the cathode materials. Under idealized conditions, retired batteries could supply 60% of cobalt, 53% of lithium, 57% of manganese, and 53% of nickel globally in 2040. If the current mix of cathode chemistries evolves to a market dominated by NMC 811, a low cobalt chemistry, there is potential for 85% global circularity of cobalt in 2040. If the market steers away from cathodes containing cobalt, to an LFP-dominated market, cobalt, manganese, and nickel become less relevant and reach circularity before 2040. For each market to benefit from the recovery of secondary materials, recycling and manufacturing infrastructure must be developed in each region. This data was collected through various sources, including from EV Volumes, International Energy Agency, Argonne National Lab, and published articles. A model was created with R to process the data.  R is required to open the models.

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    ZENODO
    Dataset . 2023
    License: CC 0
    Data sources: ZENODO
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    Smithsonian figshare
    Dataset . 2021
    License: CC BY NC
    DRYAD
    Dataset . 2023
    License: CC 0
    Data sources: Datacite
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      ZENODO
      Dataset . 2023
      License: CC 0
      Data sources: ZENODO
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      Smithsonian figshare
      Dataset . 2021
      License: CC BY NC
      DRYAD
      Dataset . 2023
      License: CC 0
      Data sources: Datacite
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    Authors: Brady, Hannah; Barsotti, Gabrielle; Davis, Jordan; Norris, Carly; +1 Authors

    In recent years, there has been increased attention and focus from the public on the environmental impact of professional sports organizations. Significant opportunities exist for Major League Baseball (MLB) teams to both reduce their own environmental footprint, and that of their fans, through sustainability initiatives. Despite stadiums using upwards of ten million gallons of water per year and having the same energy needs as a small city, no MLB team has completed a public-facing quantification of their total environmental footprint. This project calculated the carbon footprint and water consumption of the Tampa Bay Rays for the 2019 regular season. We analyzed Scope 1, 2, and 3 GHG emissions to identify hotspots within the Rays’ operations, supply chains, and transportation. Fan transportation was found to be the largest source of GHGs, followed by food production for concessions. The cooling tower and restrooms were identified as the largest sources of onsite water usage. We created a repository of best practices as a resource for stadium managers that includes strategies to reduce GHGs and water use coupled with scenario analyses estimating potential reductions. The following recommendations are highlighted as the largest reduction opportunities: (1) prioritizing fan engagement to switch to more sustainable modes of transportation, and (2) offering and highlighting more vegetarian options at concessions. To further reduce emissions and water usage, MLB teams should prioritize sub-metering electricity and water lines and installing more efficient equipment. Data was provided by the Tampa Bay Rays and collected at Tropicana Field (where the Rays play) by Jordan Davis during the summer of 2021.

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    ZENODO
    Dataset . 2022
    License: CC 0
    Data sources: ZENODO
    DRYAD
    Dataset . 2022
    License: CC 0
    Data sources: Datacite
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      ZENODO
      Dataset . 2022
      License: CC 0
      Data sources: ZENODO
      DRYAD
      Dataset . 2022
      License: CC 0
      Data sources: Datacite
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    Authors: Mehta, Piyush; Siebert, Stefan; Kummu, Matti; Deng, Qinyu; +4 Authors

    The expansion of irrigated agriculture has increased global crop production but resulted in widespread stress to freshwater resources. Ensuring that increases in irrigated production only occur in places where water is relatively abundant is a key objective of sustainable agriculture, and knowledge of how irrigated land has evolved is important for measuring progress towards water sustainability. Yet a spatially detailed understanding of the evolution of global area equipped for irrigation (AEI) is missing. Here we utilize the latest sub-national irrigation statistics (covering 17298 administrative units) from various official sources to develop a gridded (5 arc-min resolution) global product of AEI for the years 2000, 2005, 2010, and 2015. We find that AEI increased by 11% from 2000 (297 Mha) to 2015 (330 Mha) with locations of both substantial expansion (e.g., northwest India, northeast China) and decline (e.g., Russia). Combining these outputs with information on green (i.e., rainfall) and blue (i.e., surface and ground) water stress, we also examine to what extent irrigation has expanded unsustainably (i.e., in places already experiencing water stress). We find that more than half (52%) of irrigation expansion has taken place in regions that were already water stressed, with India alone accounting for 36% of global unsustainable expansion. These findings provide new insights into the evolving patterns of global irrigation with important implications for global water sustainability and food security. Recommended citation: Mehta, P., Siebert, S., Kummu, M. et al. Half of twenty-first century global irrigation expansion has been in water-stressed regions. Nat Water (2024). https://doi.org/10.1038/s44221-024-00206-9 Open-access peer reviewed publication available at https://www.nature.com/articles/s44221-024-00206-9 Files G_AEI_*.ASC were produced using the GMIA dataset[https://data.apps.fao.org/catalog/iso/f79213a0-88fd-11da-a88f-000d939bc5d8]. Files MEIER_G_AEI_*.ASC were produced using Meier et al. (2018) dataset [https://doi.pangaea.de/10.1594/PANGAEA.884744].

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    ZENODO
    Dataset . 2023
    License: CC BY
    Data sources: Datacite
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    ZENODO
    Dataset . 2022
    License: CC BY
    Data sources: Datacite
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    ZENODO
    Dataset . 2023
    License: CC BY
    Data sources: Datacite
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    ZENODO
    Dataset . 2023
    License: CC BY
    Data sources: ZENODO
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    ZENODO
    Dataset . 2023
    License: CC BY
    Data sources: ZENODO
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      ZENODO
      Dataset . 2023
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      Data sources: Datacite
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      ZENODO
      Dataset . 2022
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      Data sources: Datacite
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      ZENODO
      Dataset . 2023
      License: CC BY
      Data sources: Datacite
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      ZENODO
      Dataset . 2023
      License: CC BY
      Data sources: ZENODO
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      ZENODO
      Dataset . 2023
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    {"references": ["Liu, Z., Ciais, P., Deng, Z., Lei, R., Davis, S. J., Feng, S., Zheng, B., Cui, D., Dou, X., Zhu, B., Guo, R., Ke, P., Sun, T., Lu, C., He, P., Wang, Y., Yue, X., Wang, Y., Lei, Y., Zhou, H., Cai, Z., Wu, Y., Guo, R., Han, T., Xue, J., Boucher, O., Boucher, E., Chevallier, F., Tanaka, K., Wei, Y., Zhong, H., Kang, C., Zhang, N., Chen, B., Xi, F., Liu, M., Br\u00e9on, F.-M., Lu, Y., Zhang, Q., Guan, D., Gong, P., Kammen, D. M., He, K. & Schellnhuber, H. J. (2020). Near-real-time monitoring of global CO2 emissions reveals the effects of the COVID-19 pandemic. Nature Communications 11, 5172 (2020). https://doi.org/10.1038/s41467-020-18922-7", "Meinshausen, M., Smith, S. J., Calvin, K., Daniel, J. S., Kainuma, M. L. T., Lamarque, J. F., Matsumoto, K., Montzka, S. A., Raper, S. C. B., Riahi, K., Thomson, A., Velders, G. J. M., & van Vuuren, D. P. (2011). The RCP greenhouse gas concentrations and their extensions from 1765 to 2300. Climatic Change, 109(1\u20132), 213\u2013241. https://doi.org/10.1007/s10584-011-0156-z", "Moss, R. H., Edmonds, J. A., Hibbard, K. A., Manning, M. R., Rose, S. K., van Vuuren, D. P., Carter, T. R., Emori, S., Kainuma, M., Kram, T., Meehl, G. A., Mitchell, J. F. B., Nakicenovic, N., Riahi, K., Smith, S. J., Stouffer, R. J., Thomson, A. M., Weyant, J. P. & Wilbanks, T. J. (2010). The next generation of scenarios for climate change research and assessment. Nature, 463(7282), 747\u2013756. https://doi.org/10.1038/nature08823", "Myhre, G., Highwood, E. J., Shine, K. P., & Stordal, F. (1998). New estimates of radiative forcing due to well mixed greenhouse gases. Geophysical Research Letters, 25(14), 2715\u20132718. https://doi.org/10.1029/98gl01908", "Strassmann, K. M. and Joos, F. (2018). The Bern Simple Climate Model (BernSCM) v1.0: an extensible and fully documented open-source re-implementation of the Bern reduced-form model for global carbon cycle\u2013climate simulations, Geosci. Model Dev., 11, 1887\u20131908, https://doi.org/10.5194/gmd-11-1887-2018", "Thomas, M. A., and Lin, T. (2018). A dual model for emulation of thermosteric and dynamic sea-level change. Climatic Change, 148(1\u20132), 311\u2013324. https://doi.org/10.1007/s10584-018-2198-y"]} Supplementary materials for Gonzalez, A. R., & Lin, T. (2022). Translated Emission Pathways (TEPs): Long-Term Simulations of COVID-19 CO2 Emissions and Thermosteric Sea Level Rise Projections. Earth's Future. In Press. Summary: This study introduces climate science to a broader audience by presenting an accessible research framework and environmental data related to the ongoing COVID-19 pandemic. A series of translated emission pathways (TEPs) were constructed based on the CO2 emission patterns from the various phases of COVID-19 response. In addition to resembling the forcing scenarios used within climate research, a thermosteric sea level rise analysis was incorporated to further emphasize the environmental benefits that can be obtained from long-term sustainability. As a promising start for including the general public in climate change discussion, this research promotes collective environmental action that mirrors the recommendations of the scientific community. We acknowledge the Carbon Monitor initiative (Liu et al., 2020) for providing the COVID-19 CO2 sectoral emission data used to construct the proposed TEPs. In addition, we acknowledge the developers of the BernSCM (Strassmann and Joos, 2018) that was utilized in this study to relate TEP CO2 emissions to their respective CO2 atmospheric concentrations. Furthermore, we thank the Texas Tech University McNair Scholars Program and the Multi-Hazard Sustainability (HazSus) research group for guidance and support throughout the course of this study. Analyses presented herein were performed using the RedRaider computing cluster at Texas Tech University. We thank the team at the High Performance Computing Center (HPCC) for their generous support. In addition, the equipment support from the Vice President for Research & Innovation for T.L.'s HazSus Research Group is gratefully acknowledged.

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    ZENODO
    Dataset . 2022
    License: CC BY
    Data sources: Datacite
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    ZENODO
    Dataset . 2022
    License: CC BY
    Data sources: ZENODO
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    ZENODO
    Dataset . 2022
    License: CC BY
    Data sources: Datacite
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      ZENODO
      Dataset . 2022
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      Data sources: Datacite
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      ZENODO
      Dataset . 2022
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      Data sources: ZENODO
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      ZENODO
      Dataset . 2022
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      Data sources: Datacite
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    Authors: Heracles Polatidis; Andrew Barney; Dias Haralambopoulos; Gobind Pillai; +2 Authors

    Abstract This paper presents REACT-DECARB, an energy planning decarbonisation platform employing renewable energy sources coupled with storage for islands. The paper implements the energy scenario creation and economic evaluation steps of the platform on eight geographic islands in seven countries within the EU. Twenty-one technologically feasible energy scenarios, applicable to the specific conditions of each island, are specified and their economic assessment via a levelized cost of energy (LCOE) calculation is then performed. The main aim of this application is to verify the noted steps of the platform as well as to test its flexibility across geographically, socially and dimensionally disparate islands with various scenario generation methods. The results of the economic analysis show a wide variation of LCOE depending primarily on whether full island autonomy is assumed. In some cases the islands’ scenarios’ costs approach current market prices but are never below them; some scenarios are, however, below the current price of the island’s thermal generation. The sensitivity and uncertainty of the economic performance results’ and the variables used to calculate them are evaluated and discussed for two of the islands. The overall analysis and application has shown that the REACT-DECARB platform is suitable for different islands, regardless of location and size and can be useful for island energy planners.

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    Sustainable Energy Technologies and Assessments
    Article . 2021 . Peer-reviewed
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      Sustainable Energy Technologies and Assessments
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    Authors: Youngflesh, Casey; Montgomery, Graham; Saracco, James; Miller, David; +5 Authors

    Bird capture data were collected as part of the Monitoring Avian Productivity and Survivorship (MAPS) program, a collaborative long-term bird-banding project operating across North America. Data were obtained from 179 banding stations. Each banding station consisted of 6–20 mist nets operated approximately every ten days beginning as early as May 1 (start date varying slightly by location) through August 8 (ordinal dates 121–220 in a non-leap year), which span the breeding season for most birds in North America. Only species/locations/years with at least 15 total captures, at least 5 of those being juveniles, species/locations with at least 5 years of data, and species with at least 15 locations/years of data were considered. Bird breeding phenology was calculated using the capture dates of juvenile birds at MAPS stations. This measure of breeding phenology is indicative of the time of year at which young birds are fledging. For each species, at each location, in each year, our metric of breeding phenology was the mean date of first capture across all juveniles captured at that station that year. Following Saracco et al. 2019, we exclude subsequent captures of the same individual after its first capture. For each station, effort hours was calculated as the proportion of net-hours (total area of mist nets multiplied by the number of hours that these nets were deployed) during the period where juveniles were captured, excluding the first 2.5% of juvenile captures to remove outliers, following the procedure used by Saracco et al. 2019. Climate data was downloaded from climatena.ca. Works cited J. F. Saracco, R. B. Siegel, L. Helton, S. L. Stock, D. F. DeSante, Phenology and productivity in a montane bird assemblage: Trends and responses to elevation and climate variation. Glob. Change Biol. 25, 985–996 (2019). Changes in phenology in response to ongoing climate change have been observed in numerous taxa around the world. Differing rates of phenological shifts across trophic levels have led to concerns that ecological interactions may become increasingly decoupled in time, with potential negative consequences for populations. Despite widespread evidence of phenological change and a broad body of supporting theory, large-scale multi-taxa evidence for demographic consequences of phenological asynchrony remains elusive. Using data from a continental-scale bird banding program, we assess the impact of phenological dynamics on avian breeding productivity in 41 species of migratory and resident North American birds breeding in and around forested areas. We find strong evidence for a phenological optimum where breeding productivity decreases in years with both particularly early or late phenology and when breeding occurs early or late relative to local vegetation phenology. Moreover, we demonstrate that landbird breeding phenology did not keep pace with shifts in the timing of vegetation green-up over a recent 18-year period, even though avian breeding phenology has tracked green-up with greater sensitivity than arrival for migratory species. Species whose breeding phenology more closely tracked green-up tend to migrate shorter distances (or are resident over the entire year) and breed earlier in the season. These results showcase the broadest-scale evidence yet of the demographic impacts of phenological change. Future climate change-associated phenological shifts will likely result in a decrease in breeding productivity for most species, given that bird breeding phenology is failing to keep pace with climate change. Data from the Monitoring Avian Productivity and Survivorship (MAPS) program are curated and managed by The Institute for Bird Populations and were queried from the MAPS database on 2019-10-16.

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    Chapter 1 : Principles of Renewable Energy & Solar Energy 01 - 50 Chapter 2 : Wind Energy 01– 29 Chapter 3 : Ocean Thermal Energy 01 - 27 Chapter 4 : Storage of Energy 01 - 52 Chapter 5 : Geothermal Energy Sources 01 - 25

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