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Purpose: This research project was initiated when the Vuntut Gwitchin Government invited researchers to Old Crow, Yukon to develop research questions based on the concerns of the community. This resulted in an IPY research project that seeks to integrate traditional and scientific approaches, generate new environmental knowledge of local, national and international significance, establish a legacy of community-based monitoring, create a strategic action plan to manage responses to future climate-driven effects, and serve as a useful model to other northern communities experiencing and adapting to the impacts of climate change. The goals of the project include: documenting the history of environmental change from the last interglacial to the present in Old Crow Flats; assessing the distribution and abundance of vegetation and wildlife in relation to the changing physical environment; evaluating the impact of changes in the physical and biological environment on traditional food sources of the Vuntut Gwitchin First Nation; and developing a long-term monitoring program for the Old Crow Flats to be continued by the Vuntut Gwitchin First Nation. Summary: The Old Crow Flats in northern Yukon is the homeland of the Vuntut Gwitchin First Nation and is a wetland ecosystem of international significance. Local observations indicate that this area is undergoing pronounced and unprecedented changes. To understand these changes, this project uses both traditional knowledge and scientific methods to investigate the hydrology, permafrost, plants and animals of the Old Crow Flats, as well as food security issues in surrounding communities. The use of paleo-environmental research techniques is also allowing researchers to assess conditions in the area during previous interglacial periods. Together, this information will be used to establish a legacy of community-based monitoring, create a strategic action plan to manage this important area, and serve as a model to other northern communities experiencing and adapting to the impacts of climate change. This multidisciplinary project is comprised of eight components focusing on: geology and Paleoenvironments (Froese); hydrology and biology (Wolfe); permafrost (Burn); dendrochronology (Pisaric); vegetation (Lantz); food security (Chan); muskrat (Humphries); and moose (Cooley).

This data publication offers updated data about low-carbon energy technology (LCET) patents and citations links to the scientific literature. Compared to a [previous version](https://doi.org/10.4119/unibi/2941555), it also contains data on biofuels and fuels from waste technologies. The updated version also contains the code (R-scripts) that have been used to (1) compile the data and (2) to reproduce the statistical analysis including figures and tables presented in the final paper Hötte, Pichler, Lafond (2021): "The rise of science in low-carbon energy technologies", RSER. DOI: [10.1016/j.rser.2020.110654](10.1016/j.rser.2020.110654). This data publication contains different data sets (in .RData and (long-term archivable) .tsv format). Further information about each data set is provided in more detail below. - "all_papers.RData" : Data on scientific papers from Microsoft Academic Graph (MAG), 3 columns: Paper ID, Paper year, cited (binary 0-1, indicates whether the paper is cited by a patent). - "all_patents.RData" : Data on USPTO utility patents, 6 columns: Patent number, Patent year (grant year), CPC class, Patent date, Patent title, citing_to_science (binary 0-1, indicates whether the patent is citing to science). - "LCET_patents.RData" : Subset of LCET patents, 6 columns: Patent number, Patent year (grant year), Technology type, CPC class, Patent date, Patent title. - "LCET_patent_citations.RData" : Citations from LCET patents to other patents, 2 columns: citing, cited (Patent numbers). - "LCET_subset_with_metainfo_final.RData" : Citations from LCET patents to scientific papers from MAG, complemented by meta-information on patents and papers, 18 columns: Patent number, Paper ID, Patent year, Paper year, Technology type, WoS field, Patent title, Paper title, DOI, Confidence Score, Citation type, Reference type, Journal/ Conf. name, Journal ID, Conference ID, CPC class, Patent date, US patent. - "patent:citations.RData": Patent citations among all patents (not only LCET), 2 columns: citing, cited (Patent numbers). Moreover, this data publication contains a folder "code" with 2 subfolders: - "R_code_create_data" contains the R-scripts used to create the data sample. - "R_code_plots_and_figures" contains all R-scripts used to make the statistical analyses presented in the text (including figures and tables). Please check the read-me documents in the code folder for further detail. ### License and terms of use ### This data is licensed under the CC BY 4.0 license. See: https://creativecommons.org/licenses/by/4.0/legalcode Please find the full license text below. If you want to use the data, do not forget to give appropriate credit by citing this article: Kerstin Hötte, Anton Pichler, François Lafond, The rise of science in low-carbon energy technologies, Renewable and Sustainable Energy Reviews, Volume 139, 2021. https://doi.org/10.1016/j.rser.2020.110654 ### LCET definition and concepts ### LCET are defined by Cooperative Patent Classification (CPC) codes. CPC offers "tags" that are assigned to patents that are useful for the adaptation and mitigation of climate chagen. LCET are identified by YO2E codes, i.e. that are assigned to technologies that contribute to the "REDUCTION OF GREENHOUSE GAS [GHG] EMISSIONS, RELATED TO ENERGY GENERATION, TRANSMISSION OR DISTRIBUTION". Only the subset of Y02E01 ("Energy generation through renewable energy sources"), Y02E03 ("Energy generation of nuclear origin") and Y02E5 ("Technologies for the production of fuel of non-fossil origin") technologies are used. 10 different LCET are distinguished: Solar PV, Wind, Solar thermal, Ocean power, Hydroelectric, Geothermal, Biofuels, Fuels from waste, Nuclear fission and Nuclear fusion. More information about the Y02-tags can be found in: Veefkind, Victor, et al. "A new EPO classification scheme for climate change mitigation technologies." World Patent Information 34.2 (2012): 106-111. DOI: [https://doi.org/10.1016/j.wpi.2011.12.004](https://doi.org/10.1016/j.wpi.2011.12.004) ### Data sources and compilation ### The data was generated by the merge of different data sets. 1.) Patent data from USPTO was downloaded here: https://bulkdata.uspto.gov/ 2.) Complementary data on grant year and patent title was taken from: https://cloud.google.com/blog/products/gcp/google-patents-public-datasets-connecting-public-paid-and-private-patent-data 3.) Citations to science come from the Reliance on Science (RoS) data set https://zenodo.org/record/3685972 (v23, Feb. 24, 2020) DOI: 10.5281/zenodo.3685972 The directory ("code") offers the R-scripts that were used to process MAG data and to link it to patent data. The header of the R-scripts offer additional technical information about the subsetting procedures and data retrieval. For more information about the patent data, see: Pichler, A., Lafond, F. & J, F. D. (2020), Technological interdependencies predict innovation dynamics, Working paper pp. 1–33. URL: [https://arxiv.org/abs/2003.00580](https://arxiv.org/abs/2003.00580) For more information about MAG data, see: Marx, Matt, and Aaron Fuegi. "Reliance on science: Worldwide front‐page patent citations to scientific articles." Strategic Management Journal 41.9 (2020): 1572-1594. DOI: [https://doi.org/10.1002/smj.3145](https://doi.org/10.1002/smj.3145) Marx, Matt and Fuegi, Aaron, Reliance on Science: Worldwide Front-Page Patent Citations to Scientific Articles. Boston University Questrom School of Business Research Paper No. 3331686. DOI: [http://dx.doi.org/10.2139/ssrn.3331686 ](http://dx.doi.org/10.2139/ssrn.3331686 ) ### Detailed information about the data ### - "all_papers.RData" : Data on scientific papers from Microsoft Academic Graph (MAG), 3 columns: Paper ID: Unique paper-identifier used by MAG Paper year: Year of publication cited: binary 0-1, indicates whether the paper is cited by a patent, citation links are made in the text body and front-page of the patent, and added by examiners and applicants. - "all_patents.RData" : Data on USPTO utility patents, 6 columns: Patent number: Number given by USPTO. Can be used for manual patent search in http://patft.uspto.gov/netahtml/PTO/srchnum.htm (numeric) Patent year: Year when the patent was granted (numeric) CPC class: Detailed 8-digit CPC code (numeric) Patent date: Exact date of patent granting (numeric) Patent title: Short title (character) citing_to_science: binary 0-1, indicates whether the patent is citing to science as identified by citation links in RoS. (numeric) - "LCET_patents.RData" : Subset of LCET patents, 6 columns: Patent number: (numeric) Patent year: (numeric) Technology type: Short code used to tag 10 different types of LCET (pv, (nuclear) fission, (solar) thermal, (nuclear) fusion, wind, geo(termal), sea (ocean power), hydro, biofuels, (fuels from) waste) (character) CPC class: Detailed 8-digit CPC code (character) Patent date: (numeric) Patent title: (numeric) - "LCET_patent_citations.RData" : Citations from LCET patents to other patents, 2 columns: citing: Number of citing patent (numeric) cited: Number of cited patent (numeric) - "LCET_subset_with_metainfo_final.RData" : Citations from LCET patents to scientific papers from MAG, complemented by meta-information on patents and papers, 18 columns: Patent number: see above (numeric) Paper ID: see above (numeric) Patent year: see above (numeric) Paper year: see above (numeric) Technology type: see above (character) WoS field: Web of Science field of research, WoS fields were probabilistically assigned to papers and are used as given by RoS (character) Patent title: see above (character) Paper title: Title of scientific article (character) DOI: Paper DOI if available (character) Confidence Score: Reliability score of citation link (numeric). Links were probabilistically assigned. See Marx and Fuegi 2019 for further detail. Citation type: Indicates whether citation made in text body of patent document or its front page (character) Reference type: Examiner or applicant added citation link (or unknown). (character) Journal/ Conf. name: Name of journal or conference proceeding where the cited paper was published (character) Journal ID: Journal identifier in MAG (numeric) Conference ID: Conference identifier in MAG (numeric) CPC class: see above (character) Patent date: see above (numeric) US patent: binary US-patent indicator as provided by RoS (numeric) - "patent:citations.RData": Patent citations among all patents (not only LCET), 2 columns: citing: Number of citing patent (numeric) cited: Number of cited patent (numeric) **Note:** The citation links were probabilistically retrieved. During the analysis, we identified manually some false-positives are removed them from the "LCET_subset_with_metainfo_final.RData" data set. The list is available, too: "list_of_false_positives.tsv" We do not claim to have a perfect coverage, but expect a precision of >98% as described by Marx and Fuegi 2019. ### Statistics about the data ### Full data set: - #papers in MAG: 179,083,029 - #all patents: 10,160,667 - #citing patents: 2,058,233 - #cited papers: 4,404,088 - #citation links from patents to papers: 34,959,193 LCET subset: - #LCET patents: 65,305 - #citing LCET patents: 22,017 - #cited papers: 103,645 - #citation links from LCET patents to papers: 396,504 Meta-information: Papers: - Publication year, 251 Web-of-Science (WoS) categories, Journal/ conference proceedings name, DOI, Paper title Patents: - Grant year, >240,000 hierarchical CPC classes, 10 LCET types Citation links: - Reference type, citation type, reliability score If you have further questions about the data or suggestions, please contact: **kerstin.hotte@oxfordmartin.ox.ac.uk** ### Acknowledgements ### The authors want to thank the Center for Research Data Management of Bielefeld University and in particular Cord Wiljes for excellent support. ### License issues ### Terms of use of the source data: - Reliance on Science data [https://zenodo.org/record/3685972](https://zenodo.org/record/3685972), Open Data Commons Attribution License (ODC-By) v1.0, https://opendatacommons.org/licenses/by/1.0/ - "Google Patents Public Data” by IFI CLAIMS Patent Services and Google (https://cloud.google.com/blog/products/gcp/google-patents-public-datasets-connecting-public-paid-and-private-patent-data), Creative Commons Attribution 4.0 International License (CC BY 4.0), https://console.cloud.google.com/marketplace/details/google_patents_public_datasets/google-patents-public-data - USPTO patent data (https://bulkdata.uspto.gov/), see: https://bulkdata.uspto.gov/data/2020TermsConditions.docx

An extensive compilation of field data on net primary productivity (NPP) of natural and agricultural ecosystems worldwide was synthesized in the 1970s and early 1980s by Prof. H. Lieth, Dr. G. Esser and others. Much of this work was carried out at the University of Osnabrueck, Germany. More than 700 single point estimates of NPP or biomass were extracted from the scientific literature, each with a geographical reference (latitude/longitude). The literature cited dates from 1869 to 1982, with the majority of references from the 1960s and 1970s. Although this data set has not been updated since the 1980s, it represents a wealth of information for use in model development and validation. In the early 1970s, a subset of these NPP data was used by Lieth, Esser and co-workers to develop and test a series of statistical-correlative models of NPP as a function of mean annual temperature and precipitation. The later versions of these models included modifications for soil, seasonality, agriculture, and other human influences ("Osnabrück Biosphere Mode,""High Resolution Biosphere Model," etc.). Most of the 720 unique NPP records (632, or 88 percent) have been matched to a bibliography of 356 references from the primary literature. The original form of this bibliography contained many more references than records, including multiple sources for the same author and study, as well as additional references to data on standing biomass, soils, and so forth. Since this is a useful resource in its own right, an edited and corrected compilation of these 858 references is available here with the cross-references to the NPP records highlighted. Of the 720 unique NPP records, about two-thirds have above-ground NPP estimates that range between 1 and 8530 g/m2/year (dry matter) -- or 2923 g/m2/year, excluding doubtful values, wetlands, and crops/pastures and other likely managed systems. Total NPP, for which more than half of the sites have estimates, ranges from 3 to 9320 g/m2/year (dry matter) -- or 3580 g/m2/year, excluding doubtful values, wetlands, and crops/pastures and other likely managed systems. Each record includes a site identifier, latitude, longitude, author, country, NPP estimates, vegetation type, and other variables. The vegetation-type field begins with a generalized biome type (including tundra, forest, Mediterranean, savanna, grassland, desert, wetland, and a number of managed vegetation types) and is followed by more specific vegetation terminology derived from the original data. Caution is advised in using these biome/vegetation types because they were not defined consistently within the original data set and nearly 200 sites lack any vegetation designation. To achieve completeness in a single synthesis file, a single NPP value (NPP_C) is included for each site that represents the sum of above-ground (ANPP) and below-ground (BNPP) components, expressed in grams of carbon per square meter per year (g C/m2/year). Where BNPP was not reported, it was assumed to be equal to ANPP. A ratio of 0.475 was used to convert dry biomass weight to carbon content. Total NPP was estimated as TNPP (where available), or as the sum of ANPP and BNPP (or from ANPP x 2, if BNPP was not estimated), and then converted to g C/m2/year.

Dieser Band trägt zu einer reflexiven Perspektive auf die Wirkung von Wissenschaft und Forschung für Nachhaltigkeit und entsprechenden Dilemmata bei. Die Beiträge entwickeln reflexive Perspektiven, was als ein Nachhaltigkeitsdilemma gelten kann, auf welche weiteren Formen von Widersprüchen Nachhaltigkeitsforschung trifft und wie mit ihnen umgegangen werden kann. Empirische Fallstudien aus Themenfeldern wie der Stadtplanung, dem Recht, der Bioökonomie, der Medizin, gehen konkreten Konflikten, Widersprüchen und Spannungsfeldern nach, die Dilemmapotenziale bergen. Schließlich werden Herausforderungen für Wissenschaft und Forschung diskutiert, wie sie sich in Theoriearbeit, Forschungspraxis und Forschungsförderung stellen. This volume contributes to a reflective perspective on dilemmas in research on and the implementation of sustainable development. Its contributions develop reflective perspectives on what can be considered dilemmas in relation to sustainability, what other forms of contradictions sustainability research encounters and how they can be dealt with. Empirical case studies cover subject areas such as urban planning, law, bioeconomy and medicine, investigating specific conflicts, contradictions and tensions that harbour the potential for dilemmas. Finally, the book discusses any challenges for academia and research in this field arising in theory, research practice and research funding.

Leaching dataset of dissolved organic carbon (DOC) and nitrogen (DON), nitrate (NO3+) and ammonium (NH4+) were collected from 6 cropping treatments (corn, switchgrass, miscanthus, native grass mix, restored prairie and poplar) established in the Bioenergy Cropping System Experiment (BCSE) which is a part of Great Lakes Bioenergy Research Center (www.glbrc.org) and Long Termn Ecological Research (LTER) program (www.lter.kbs.msu.edu). The site is located at the W.K. Kellogg Biological Station (42.3956° N, 85.3749° W and 288 m above sea level), 25 km from Kalamazoo in southwestern Michigan, USA. Prenart soil water samplers made of Teflon and silica (http://www.prenart.dk/soil-water-samplers/) were installed in blocks 1 and 2 of the BCSE (Fig. S1), and Eijkelkamp soil water samplers made of ceramic (http://www.eijkelkamp.com) were installed in blocks 3 and 4 (there were no soil water samplers in block 5). All samplers were installed at 1.2 m depth at a 45° angle from the soil surface, approximately 20 cm into the unconsolidated sand of the 2Bt2 and 2E/Bt horizons. Beginning in 2009, soil water was sampled at weekly to biweekly intervals during non-frozen periods (April to November) by applying 50 kPa of vacuum for 24 hours, during which water was collected in glass bottles. During the 2009 and 2010 sampling periods we obtained fewer soil water samples from blocks 1 and 2 where Prenart lysimeters were installed. We observed no consistent differences between the two sampler types in concentrations of the analytes reported here. Depending on the volume of leachate collected, water samples were filtered using either 0.45 µm pore size, 33-mm-dia. cellulose acetate membrane filters when volumes were <50 ml, or 0.45 µm, 47-mm-dia. Supor 450 membrane filters for larger volumes. Samples were analyzed for NO3-, NH4+, total dissolved nitrogen (TDN), and DOC. The NO3- concentration was determined using a Dionex ICS1000 ion chromatograph system with membrane suppression and conductivity detection; the detection limit of the system was 0.006 mg NO3--N L-1. The NH4+ concentration in the samples was determined using a Thermo Scientific (formerly Dionex) ICS1100 ion chromatograph system with membrane suppression and conductivity detection; the detection limit of the system was similar. The DOC and TDN concentrations were determined using a Shimadzu TOC-Vcph carbon analyzer with a total nitrogen module (TNM-1); the detection limit of the system was ~0.08 mg C L-1 and ~0.04 mg N L-1. DON concentrations were estimated as the difference between TDN and dissolved inorganic N (NO3- + NH4+) concentrations. The NH4+ concentrations were only measured in the 2013-2015 crop-years, but they were always small relative to NO3- and thus their inclusion or lack of it was inconsequential to the DON estimation. Leaching rates were estimated on a crop-year basis, defined as the period from planting or emergence of the crop in the year indicated through the ensuing year until the next year’s planting or emergence. For each sampling point, the concentration was linearly interpolated between sampling dates during non-freezing periods (April through November). The concentrations in the unsampled winter period (December through March) were also linearly interpolated based on the preceding November and subsequent April samples. Solute leaching (kg ha-1) was calculated by multiplying the daily solute concentration in pore-water (mg L -1) by the modeled daily drainage rates (m3 ha-1) from the overlying soil. The drainage rates were obtained using the SALUS (Systems Approach for Land Use Sustainability) model (Basso and Ritchie, 2015). SALUS simulates yield and environmental outcomes in response to weather, soil, management (planting dates, plant population, irrigation, nitrogen fertilizer application, tillage), and crop genetics. The SALUS water balance sub-model simulates surface run-off, saturated and unsaturated water flow, drainage, root water uptake, and evapotranspiration during growing and non-growing seasons (Basso and Ritchie, 2015). Drainage amounts and rates simulated by SALUS have been validated with measurements using large monolith lysimeters at a nearby site at KBS (Basso and Ritchie, 2005). On days when SALUS predicted no drainage, the leaching was assumed to be zero. The volume-weighted mean concentration for an entire crop-year was calculated as the sum of daily leaching (kg ha-1) divided by the sum of daily drainage rates (m3 ha-1). Weather data for the model were collected at the nearby KBS LTER meteorological station (lter.kbs.msu.edu). Leaching losses of dissolved organic carbon (DOC) and nitrogen (DON) from agricultural systems are important to water quality and carbon and nutrient balances but are rarely reported; the few available studies suggest linkages to litter production (DOC) and nitrogen fertilization (DON). In this study we examine the leaching of DOC, DON, NO3-, and NH4+ from no-till corn (maize) and perennial bioenergy crops (switchgrass, miscanthus, native grasses, restored prairie, and poplar) grown between 2009 and 2016 in a replicated field experiment in the upper Midwest U.S. Leaching was estimated from concentrations in soil water and modeled drainage (percolation) rates. DOC leaching rates (kg ha-1 yr-1) and volume-weighted mean concentrations (mg L-1) among cropping systems averaged 15.4 and 4.6, respectively; N fertilization had no effect and poplar lost the most DOC (21.8 and 6.9, respectively). DON leaching rates (kg ha-1 yr-1) and volume-weighted mean concentrations (mg L-1) under corn (the most heavily N-fertilized crop) averaged 4.5 and 1.0, respectively, which was higher than perennial grasses (mean: 1.5 and 0.5, respectively) and poplar (1.6 and 0.5, respectively). NO3- comprised the majority of total N leaching in all systems (59-92%). Average NO3- leaching (kg N ha-1 yr-1) under corn (35.3) was higher than perennial grasses (5.9) and poplar (7.2). NH4+ concentrations in soil water from all cropping systems were relatively low (<0.07 mg N L-1). Perennial crops leached more NO3- in the first few years after planting, and markedly less after. Among the fertilized crops, the leached N represented 14-38% of the added N over the study period; poplar lost the greatest proportion (38%) and corn was intermediate (23%). Requiring only one third or less of the N fertilization compared to corn, perennial bioenergy crops can substantially reduce N leaching and consequent movement into aquifers and surface waters. readme files are given that describe the data table

This dataset contains the data used in the manuscript "Physiological adaptation of the diatom Pseudo-nitzschia delicatissima under copper starvation" accepted for publication in April 2023 in Marine Environmental Research. In the open ocean and particularly in iron (Fe)-limited environment, copper (Cu) deficiency might limit the growth of phytoplankton species. Cu is an essential trace metal used in electron-transfer reactions, such as respiration and photosynthesis, when bound to specific enzymes. Some phytoplankton species, such as the diatom Pseudo-nitzschia spp. can cope with Cu starvation through adaptative strategies. This dataset contains the data collected during the experimental starvation of a strain of the diatom P. delicatissima under laboratory controlled conditions.

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.

This Excel model encodes the analytical capability required to undertake an application of the adapted National Infrastructure Systems Modelling (NISMOD) capability, which has been developed by the Infrastructure Transitions Research Consortium (ITRC) - a UK based research consortium, led by the University of Oxford. Through a partnership between the United Nations Office for Project Service (UNOPS) and the ITRC, an initial national infrastructure assessment for Curaçao, known as Evidence-Based Infrastrastucture Assessment, has been performed. The focus of this model lies on one priority sector for the SIDS Curaçao: 'Waste Management'.