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The atmosphere concentration of CO2 is steadily increasing and causing climate change. To achieve the Paris 1.5 or 2 oC target, negative emissions technologies must be deployed in addition to reducing carbon emissions. The ocean is a large carbon sink but the potential of marine primary producers to contribute to carbon neutrality remains unclear. Here we review the alterations to carbon capture and sequestration of marine primary producers (including traditional ‘blue carbon’ plants, microalgae, and macroalgae) in the Anthropocene, and, for the first time, assess and compare the potential of various marine primary producers to carbon neutrality and climate change mitigation via biogeoengineering approaches. The contributions of marine primary producers to carbon sequestration have been decreasing in the Anthropocene due to the decrease in biomass driven by direct anthropogenic activities and climate change. The potential of blue carbon plants (mangroves, saltmarshes, and seagrasses) is limited by the available areas for their revegetation. Microalgae appear to have a large potential due to their ubiquity but how to enhance their carbon sequestration efficiency is very complex and uncertain. On the other hand, macroalgae can play an essential role in mitigating climate change through extensive offshore cultivation due to higher carbon sequestration capacity and substantial available areas. This approach seems both technically and economically feasible due to the development of offshore aquaculture and a well-established market for macroalgal products. Synthesis and applications: This paper provides new insights and suggests promising directions for utilizing marine primary producers to achieve the Paris temperature target. We propose that macroalgae cultivation can play an essential role in attaining carbon neutrality and climate change mitigation, although its ecological impacts need to be assessed further. To calculate the parameters presented in Table 1, the relevant keywords "mangroves, salt marshes, macroalgae, microalgae, global area, net primary productivity, CO2 sequestration" were searched through the ISI Web of Science and Google Scholar in July 2021. Recent data published after 2010 were collected and used since area and productivity of plants change with decade. For data with limited availability, such as net primary productivity (NPP) of seagrasses and global area and NPP of wild macroalgae, data collection was extended back to 1980. Total NPP and CO2 sequestration for mangroves, salt marshes, seagrasses and wild macroalgae were obtained by the multiplication of area and NPP/CO2 sequestration density and subjected to error propagation analysis. Data were expressed as means ± standard error.

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.

Figure 1-4 data for "Anaconda-shaped Spiral Multi-layered Triboelectric Nanogenerators with Ultra-High Space Efficiency for Wave Energy Harvesting" Figure 1-4 data for "Anaconda-shaped Spiral Multi-layered Triboelectric Nanogenerators with Ultra-High Space Efficiency for Wave Energy Harvesting"

This data set is no longer current – The most current data and all historical data sets can be found at https://data.nrel.gov/submissions/244 This database represents a list of community solar projects identified through various sources as of Dec 2021. The list has been reviewed but errors may exist and the list may not be comprehensive. Errors in the sources e.g. press releases may be duplicated in the list. Blank spaces represent missing information. NREL invites input to improve the database including to - correct erroneous information - add missing projects - fill in missing information - remove inactive projects. Updated information can be submitted to the contact(s) located on the current data set page linked at the top.

Matlab program and data for the paper “An energy consumption rectification method based on Bayesian linear regression and heating degree-days". "simulation model.zip" is the heating house model in Trnsys simulation software. "Example1" and "Example2" is the Matlab program and data in this paper.

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.

Outputs from the Network Innovation Allowance project "Control REACT" (workstream 2), sponsored by National Grid Electricity System Operator (NGESO). This deposit contains underlying data used in this project. The R code (Rmarkdown) and html renders of these workbooks are available in a separate deposit linked below. See description there for further details. In order to run the R scripts, data and code must be arranged in the directory structure given in "Directory Structure.pdf". Wind, solar and net-demand data are derived from raw data made available by Elexon and Solar Sheffield via public APIs. See respective websites for details, our processed (aggregated and cleaned) versions of this data are shared here under a CC-BY license. Weather forecast data are derived from historic operational forecasts from the ECMWF HRES model and are shared under a CC-BY licence. For details on how these were processed please see references. {"references": ["J. Browell and M. Fasiolo, \"Probabilistic Forecasting of regional net-load with conditional extremes and gridded NWP\", IEEE Transactions on Smart Grid, vol. 12, no, 6, pp. 5011-5019, 2021", "C. Gilbert \"Topics in high dimensional energy forecasting\", J. Browell & D. McMillan, degree supervisors; Centre for Doctoral Training in Wind and Marine Energy Systems; Department of Electronic and Electrical Engineering Thesis [PhD] 2021"]}

This is a set of aggregated data tables that underly the key figures in the SERL stats report "Smart Energy Research Lab: Energy use in GB domestic buildings 2021" (Volume 1). The report describes domestic gas and electricity energy use in Great Britain in 2021 based on data from the Smart Energy Research Lab (SERL) Observatory, which consists of smart meter and contextual data from approximately 13,000 homes that are broadly representative of the GB population in terms of region and Index of Multiple Deprivation (IMD) quintile. The report shows how residential energy use in GB varies over time (monthly over the year and half-hourly over the course of the day), with occupant characteristics (number of occupants, tenure), property characteristics (age, size, form, and Energy Performance Certificate (EPC)), by type of heating system, presence of solar panels and of electric vehicles, and by weather, region and IMD quintile.

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