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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

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.

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.

QTDIAN - Quantification of Technological DIffusion and sociAl constraiNts - is a toolbox of qualitative and quantitative descriptions of socio-technical and political aspects of the energy transition that influence the overall potential, the rate of energy-related technology and service diffusion and the design of the future energy system. The output of QTIDIAN is empirically founded datasets of social and political drivers and barriers of the transition, both in the form of raw data describing past and current developments and manipulated to constitute consistent quantifications of the storylines. Here you can download the data for six QTDIAN themes: Socially feasible scaling of energy technologies Policy preferences & dynamics Barriers to infrastructural development (wind energy, grid development) Citizen energy Private energy demand Further information on the QTDIAN modelling toolbox and the data can be found in the SENTINEL Deliverable 2.3 and Deliverable 2.4: S��sser, D., al Rakouki, H., & Lilliestam, J.(2021). The QTDIAN modelling toolbox���Quantification of social drivers and constraints of the diffusion of energy technologies. Deliverable 2.3. Sustainable Energy Transitions Laboratory (SENTINEL) project. Potsdam: Institute for Advanced Sustainability Studies (IASS). S��sser, D., Pickering, B., Chatterjee, S., Oreggioni, G., Stavrakas, V., & Lilliestam, J.(2021). Integration of socio-technological transition constraints into energy demand and systems models. Deliverable 2.5. Sustainable Energy Transitions Laboratory (SENTINEL) project. Potsdam: Institute for Advanced Sustainability Studies (IASS).

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

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.

AbstractShipbuilding is an energy-intensive industrial sector that produces a significant amount of waste, pollution and air emissions. However, the International Maritime Organization concentrates only on reducing emissions during the operational phase. In order to completely phase out emissions from the shipping industry, a life-cycle approach must be taken. The study implemented the proposed transdisciplinary energy management framework in a Bangladeshi shipyard. The framework aims to support shipyard decision makers in making rational and optimized decisions to make shipyards sustainable, while maintaining good product quality and reducing relative cost. This is achieved by applying the Fuzzy Analytical Hierarchy Process and Fuzzy Order of Preference by Similarity to Ideal Solution methods to identify optimal solutions. In addition to making shipyards more sustainable, the framework can enhance both the business and socio-economic prospects of the shipyard and promote the reputation of the shipyard and improve its competitiveness and, in line with this, lead to the promotion of nationally determined contributions under the Paris Agreement for States. The implementation of the framework shows that the political and legal discipline, the social criteria and the implementation of ISO 14001 and cyber security were the most important criteria and options for the yard's decision makers.

# Data from: Blue Carbon Benefits from Global Saltmarsh Restoration [https://doi.org/10.5061/dryad.pc866t1vp](https://doi.org/10.5061/dryad.pc866t1vp) This README file was generated on 12th September 2023 by Victoria Mason. **Title of Dataset:** Blue carbon benefits from global saltmarsh restoration. **Author information:** * Victoria G. Mason, Bangor University/Royal Netherlands Institute for Sea Research (NIOZ), victoria.mason@nioz.nl (*Corresponding author*) * Annette Burden, UK Centre for Ecology & Hydrology * Graham Epstein, University of Exeter/University of Victoria * Lucy L. Jupe, Wildfowl & Wetlands Trust * Kevin A. Wood, Wildfowl & Wetlands Trust * Martin W. Skov, Bangor University **Summary of dataset:** These data include all data which were extracted or derived from relevant studies on global saltmarsh carbon storage and greenhouse gas flux. Data were obtained following screening of 29,182 peer reviewed published studies for relevant data, which were then extracted from 431 studies via text, tables and figures. We then used a meta-analysis to assess drivers of variation in global saltmarsh and greenhouse gas flux. * Date of literature search: 21st January 2022. * Date of data extraction: February - March 2022 * Literature search conducted via: Scopus + Web of Science ## Description of the data and file structure The contents of these data include: * **Full dataset (Aug2023\_GlobalCarbonReview\_FullDataset.xls):** All data extracted from 431 relevant studies and used in analysis. This includes a title page, metadata (with descriptions of column headers) and the full dataset. Response variables included: * Carbon stock * Percentage organic carbon * Bulk density * Sediment accretion rate * Carbon accumulation rate * Carbon dioxide flux * Methane flux * Nitrous oxide flux **\- Data on each included study \(Aug2023\_GlobalCarbonReview\_IncludedStudies\.xls\):** List of each study included in the final analysis, and its metadata. This includes a title page, metadata (with descriptions of column headers) and the dataset. All data include standard deviation (SD) and n (number of replicates) where provided by the original study, which were used to calculate Hedge's *g* effect sizes reported in the subsequent study. | Frequently used abbreviations: | | | ------------------------------ | --- | | C | carbon | | OC | organic carbon | | GHG | greenhouse gas | | bd | bulk density (g cm-3 dry sediment) | | Y/N | yes/no | | ref | reference | | lat | latitude | | long | longitude | | rest | restoration | | prec | precipitation | | sal | salinity | | acc | accretion | | resp | respiration | | SR | soil respiration (appears for CO2 flux) | | ER | ecosystem respiration (appears for CO2 flux) | | n | number of samples included in mean/standard deviation | | sd | standard deviation | All abbreviations used are outlined in the ‘Metadata’ worksheet of .xls files. **Data specific information for Aug2023\_GlobalCarbonReview\_FullDataset.xls:** Number of variables: 88 Number of cases/rows: 2055 Variables included: See 'Metadata' sheet **Data specific information for** **Aug2023\_GlobalCarbonReview\_IncludedStudies.xls:** Number of variables: 47 Number of cases/rows: 431 Variables included: See 'Metadata' sheet **Empty cells:** Cells are empty where data on that variable were not provided by the original study from which they were extracted. For example, where a study provided data on carbon stock variables, but not greenhouse gas flux. For further details, see the 'Metadata' sheets of each file. ## Sharing/Access information These data are available via Dryad, and described in ‘Blue Carbon Benefits from Global Saltmarsh Restoration’, in Global Change Biology. **DOI:** 10.1111/gcb.16943 Data were extracted from 431 published peer reviewed articles, the details of which can be found in the attached datasheets. Coastal saltmarshes are found globally, yet are 25–50% reduced compared to their historical cover. Restoration is incentivised by the promise that marshes are efficient storers of ‘blue’ carbon, although the claim lacks substantiation across global contexts. We synthesised data from 431 studies to quantify the benefits of saltmarsh restoration to carbon accumulation and greenhouse gas uptake. The results showed global marshes store approximately 1.41–2.44 Pg carbon. Restored marshes had very low greenhouse gas (GHG) fluxes and rapid carbon accumulation, resulting in a mean net accumulation rate of 64.70 t CO2e ha-1 y-1. Using this estimate and potential restoration rates, we find saltmarsh regeneration could result in 12.93–207.03 Mt CO2e accumulation per year, offsetting the equivalent of up to 0.51% global-energy-related CO2 emissions – a substantial amount, considering marshes represent <1% of Earth’s surface. Carbon accumulation rates and GHG fluxes varied contextually with temperature, rainfall and dominant vegetation, with the eastern costs of the USA and Australia being particular hotspots for carbon storage. Whilst the study reveals paucity of data for some variables and continents, suggesting a need for further research, the potential for saltmarsh restoration to offset carbon emissions is clear. The ability to facilitate natural carbon accumulation by saltmarshes now rests principally on the action of the management-policy community and on financial opportunities for supporting restoration.