dataesr/scientific-paragraphs-categorization huggingface dataset:
Various techniques followed by the Data Scientist aid this kind of analysis. Classification is one such technique that draws meaningful information and differentiates the data based on similarities found in its attributes. Artificial Neural Networks (ANN) can also be utilized in many practical scenarios for cause and effect analysis. One such example is the study carried out in the United States that used this technique to identify the factors affected due to differences in hydro-climatology of two different streams. This study successfully inferred how the differences affected the average runoff, flow stability(from baseline) and frequency of floods in the two areas  (Poff, Tokar, & Johnson, 1996). Further improvements in the climate model were also made in 2003  (Knutti, Stocker, Joos, & Plattner, 2003)  to increase its efficiency with larger datasets.
Calculations from this model revealed that almost half the members surpassed the surface heating range that the IPCC estimated earlier. In such challenging scenarios, neural networks can help extensively. It is all used to explore the general trend in climate change and its mechanisms to identify the hidden correlation/attributes in the data that is otherwise not decipherable. The use of Solar energy has become increasingly popular today as it is a renewable source of energy. One of the major drawbacks of this form of energy is that its availability widely depends on meteorological conditions. The efficiency and performance of a solar power plant would depend on these changing conditions. Jang, Bae,  Park, and Sung (2016)  has discussed how we can leverage images captured by the satellite to train a SVM (Support Vector Machine) model and predict the motion of clouds.
This could be a brilliant way to determine when the plant would be most productive and when it would require backup. In this way, data analytics could help manage climate changes for uninterrupted and effective energy production. Climate change also significantly affects coral reefs that are important for the ecosystem as they provide food and shelter for their inhabitants. Thermal stress due to the warming of the ocean could cause infectious diseases, while frequent intense storms could lead to the destruction of the reefs. A rise in sea levels could lead to sedimentation, whose runoff can destroy the corals, while fluctuations in precipitation lead to an increase in runoff that could make the water murky and restrict the light. One such study  (Franco, Hepburn, Smith, Nimrod, & Tucker, 2016)  conducted in 2016 used the Bayesian Network approach to evaluate how climate change disturbances affected the coral reefs.
Reducing digital waste through IS/IT design. As the volumes of data being created, transmitted, and stored have skyrocketed also the volumes of 'digital waste', i. e. unused digital content, have surged. To provide some perspective of this phenomenon, CO2 emissions of You-Tube in 2019 was evaluated to equal a city size of Glasgow. 7  A well-known example of how IS/IT design could reduce CO 2 emissions is not to show video for users who are only listening to audio. This idea of digital waste reduction videos could be taken a step further by default by asking the user if he/she wants to see the video in addition to the audio content. In addition, energy efficiency can be considered in website design. Typically, today's websites are dynamic. While this design approach offers benefits in terms of user experience, it basically requires that the server hosting the website creates the website for the user.
It is currently a vast, largely untapped resource that will be pivotal in transforming the decision making process. At its core, Information Management is about making efficient use of resourcesit's about transforming information about the lifecycle of built assets into actionable intelligence. Effective information management gathers information across the lifecycle of assets from design to creation to operation. At each of these stages, data is collected that informs better decision making, enabling reductions in waste, time and cost, more efficient use of resources, improved design and safety and increased infrastructure resilienceall essential for reducing carbon emissions from our built environment and serving those it was intended for. Although we have the technology to collect large amounts of data it needs to be structured, managed and shared in an effective way to make it valuable.
The Centre for Digital Built Britain (CDBB), through the National Digital Twin programme (NDTp) is creating the Information Management Framework which is a collection of open, technical and non-technical standards, guidance and common resources to enable secure and resilient sharing of data across sector and organizational boundaries. There are already some excellent examples of successful green Information Management in practice. The Energy and Carbon Reporting Framework developed by the CDBB, WMEBoom and the Construction Innovation Hub defines when operational energy and emissions need to be analyzed and reported during the lifecycle of built assets. This tool is providing organizations like NHS Scotland and their supply chain the ability to effectively identify and therefore control the energy and carbon impact of their built assets.
It also supports the Construction Leadership Council's CO2nstructZero programme which sets out how the construction sector can meet the Net Zero challenge. 3. 5. 5. 2. Sharing data to improve climate resilience. The sharing of data is an important part of the process. At the NDTp, the Climate Resilience Demonstrator (CReDo) provides a practical example of how connected data and greater access to the right information across organizational and sectoral boundaries can improve climate adaptation and resilience. Collaborating on CReDo are Anglian Water, BT and UK Power Networks who will use their asset and operations data as well as weather data on a secure, shared basis to inform an increased level of infrastructure resilience. CReDo will integrate data between energy, water and telecoms networks to improve climate resilience decision-making across infrastructure systems.
These sensor-based technologies can continuously collect massive amounts of environmental data to measure emissions and other relevant parameters and adjust based on optimal standards or human preferences and needs. Big data analysis and visualization can be leveraged to unlock insights that will help individuals, organizations and communities track electricity consumption patterns, identify energy leaks, improve energy use efficiency, and find creative ways to conserve energy and adapt to climate change faster and better. • Conducting empirical research to collect first-hand evidence and develop evidence-based guidelines and suggestions to help practitioners design and implement Green IT(green information technology) and Green IS (green information systems) initiatives within their organization.
This generation and future generations need to understand the impacts of climate change on our daily lives and society. IT/IS education could play a critical role in helping individuals and communities in the understanding of the science behind climate change and what steps are needed to alleviate greenhouse gas emissions and global warming caused by climate change and human activity. IS/IT educators could use project-based or problembased learning approaches to engage students to explore specific climate change issues and community-related climate datasets (e. g. , heat, cold, rain, drought, snow, wind, river, lake, ocean, water quality, air quality, and coastal flooding) and to assess the impact of potential solutions by leverage existing software, tools and techniques such as data analysis and visualization, modeling and simulation, augmented reality/virtual reality/mixed reality, and AI techniques.
When students are asked to use technology tools to explore specific climate change issues and datasets of interest or relevance to their local communities, they are more likely to become interested and engage in active learning for systems thinking, complex problem-solving, creativity, and evidence-informed decision-making. In the meantime, they will become familiar with these software, tools, and techniques through learning by using them to analyze data and solve problems in a meaningful way. Students will have a chance to develop interdisciplinary skills to interpret complex data analysis and visualization results into informed decisions and sound policies. The ability and skills to interpret data and communicate results to support the decision-making process are important for IT/IS students to become effective decision makers or problem-solvers in their careers.
IS/IT educators are recommended to come up with concrete learning activities, to develop technology-related case studies, tutorials, projects, and exercises on the topic of climate change, and to encourage their students to work on collaborative or hands-on projects using open-source resources, including various datasets and open-source software and tools available on the Internet, such as CODAP, R, Python, and more. There exists significant levels of climate change, open-source projects on GitHub, which is an excellent way for IT/IS students to learn, practice, and contribute.
IS/IT educators are recommended to come up with concrete learning activities, to develop technology-related case studies, tutorials, projects, and exercises on the topic of climate change, and to encourage their students to work on collaborative or hands-on projects using open-source resources, including various datasets and open-source software and tools available on the Internet, such as CODAP, R, Python, and more. There exists significant levels of climate change, open-source projects on GitHub, which is an excellent way for IT/IS students to learn, practice, and contribute. • Studying the adoption of energy-saving IT in locations like home, businesses and communities, and assessing the impacts of informa-tion technology innovations on human behavior, mitigation of climate change, and relevant policymaking.
Accurate assessment of anthropogenic carbon dioxide (CO 2 ) emissions and their redistribution among the atmosphere, ocean, and terrestrial biosphere in a changing climate is critical to better understand the global carbon cycle, support the development of climate policies, and project future climate change. Here we describe and synthesize datasets and methodology to quantify the five major components of the global carbon budget and their uncertainties. Fossil CO 2 emissions (E FOS ) are based on energy statistics and cement production data, while emissions from land-use change (E LUC ), mainly deforestation, are based on land use and land-use change data and bookkeeping models. Atmospheric CO 2 concentration is measured directly, and its growth rate (G ATM ) is computed from the annual changes in concentration.
5 ± 0. 5 GtC yr -1 (9. 3 ± 0. 5 GtC yr -1 when the cement carbonation sink is included), and E LUC was 0. 9 ± 0. 7 GtC yr -1 , for a total anthropogenic CO 2 emission of 10. 2 ± 0. 8 GtC yr -1 (37. 4 ± 2. 9 GtCO 2 ). Also, for 2020, G ATM was 5. 0 ± 0. 2 GtC yr -1 (2. 4 ± 0. 1 ppm yr -1 ), S OCEAN was 3. 0 ± 0. 4 GtC yr -1 , and S LAND was 2. 9 ± 1 GtC yr -1 , with a B IM of -0. 8 GtC yr -1. The global atmospheric CO 2 concentration averaged over 2020 reached 412. 45 ± 0. 1 ppm. Preliminary data for 2021 suggest a rebound in E FOS relative to 2020 of +4. 8 % (4. 2 % to 5. 4 %) globally. Overall, the mean and trend in the components of the global carbon budget are consistently estimated over the period 1959-2020, but discrepancies of up to 1 GtC yr -1 persist for the representation of annual to semidecadal variability in CO 2 fluxes.
Comparison of estimates from multiple approaches and observations shows (1) a persistent large uncertainty in the estimate of land-use changes emissions, (2) a low agreement between the different methods on the magnitude of the land CO 2 flux in the northern extra-tropics, and (3) a discrepancy between the different methods on the strength of the ocean sink over the last decade. This living data update documents changes in the methods and datasets used in this new global carbon budget and the progress in understanding of the global carbon cycle compared with previous publications of this dataset (Friedlingstein et al.
This choice reflects the difficulty of characterizing the uncertainty in the CO 2 fluxes between the atmosphere and the ocean and land reservoirs individually, particularly on an annual basis, as well as the difficulty of updating the CO 2 emissions from land-use change. A likelihood of 68 % provides an indication of our current capability to quantify each term and its uncertainty given the available information. The uncertainties reported here combine statistical analysis of the underlying data, assessments of uncertainties in the generation of the datasets, and expert judgement of the likelihood of results lying outside this range. The limitations of current information are discussed in the paper and have been examined in detail elsewhere  (Ballantyne et al. , 2015; Zscheischler et al. , 2017).
We also use a qualitative assessment of confidence level to characterize the annual estimates from each term based on the type, amount, quality, and consistency of the evidence as defined by the IPCC  (Stocker et al. , 2013). This paper provides a detailed description of the datasets and methodology used to compute the global carbon budget estimates for the industrial period, from 1750 to 2020, and in more detail for the period since 1959. It also provides decadal averages starting in 1960 including the most recent decade  (2011) (2012) (2013) (2014) (2015) (2016) (2017) (2018) (2019) (2020) , results for the year 2020, and a projection for the year 2021. Finally, it provides cumulative emissions from fossil fuels and land-use change since the year 1750, the pre-industrial period; and since the year 1850, the reference year for historical simulations in IPCC AR6  (Eyring et al.
Methods Multiple organizations and research groups around the world generated the original measurements and data used to complete the global carbon budget. The effort presented here is thus mainly one of synthesis, where results from individual groups are collated, analysed, and evaluated for consistency. We facilitate access to original data with the understanding that primary datasets will be referenced in future work (see Table  2  for how to cite the datasets). Descriptions of the measurements, models, and methodologies follow below, and detailed descriptions of each component are provided elsewhere. This is the 16th version of the global carbon budget and the 10th revised version in the format of a living data update in Earth System Science Data. It builds on the latest published global carbon budget of  Friedlingstein et al.
(2020). The main changes are as follows: the inclusion of (1) data to year 2020 and a projection for the global carbon budget for year 2021, (2) a Kaya analysis to identify the driving factors behind the recent trends in fossil fuel emissions (changes in population, GDP per person, energy use per GDP, and CO 2 emissions per unit energy), (3) an estimate of the ocean sink from models and data products combined, (4) an assessment of the relative contributions of increased atmospheric CO 2 and climate change in driving the land and ocean sinks, and (5) an assessment of the current trends in anthropogenic emissions and implications for the remaining carbon budget for specific climate targets. The main methodological differences between recent annual carbon budgets  (2016) (2017) (2018) (2019) (2020)  are summarized in Table  3  and previous changes since 2006 are provided in Table  A7.
Short-cycle carbon emissionsfor example from combustion of biomass -are not included here but are accounted for in the CO 2 emissions from land use (see Sect. 2. 2). Our estimates of fossil CO 2 emissions are derived using the standard approach of activity data and emission factors, relying on data collection by many other parties. Our goal is to produce the best estimate of this flux, and we therefore use a prioritization framework to combine data from different sources that have used different methods, while being careful to avoid double counting and undercounting of emissions sources. The CDIAC-FF emissions dataset, derived largely from UN energy data, forms the foundation, and we extend emissions to year Y-1 using energy growth rates reported by BP. We then proceed to replace estimates using data from what we consider to be superior sources, for example Annex 1 countries' official submissions to the UNFCCC.
All data points are potentially subject to revision, not just the latest year. For full details see  Andrew and Peters (2021). Other estimates of global fossil CO 2 emissions exist, and these are compared by  Andrew (2020a). The most common reason for differences in estimates of global fossil CO 2 emissions is a difference in which emissions sources are included in the datasets. Datasets such as those published by BP energy company, the US Energy Information Administration, and the International Energy Agency's "CO 2 emissions from fuel combustion" are all generally limited to emissions from combustion of fossil fuels. In contrast, datasets such as PRIMAP-hist, CEDS, EDGAR, and GCP's dataset aim to include all sources of fossil CO 2 emissions. See  Andrew (2020a)  for detailed comparisons and discussion.
Emissions for the rest of the world are derived using projected growth in economic production from the IMF (2022) combined with extrapolated changes in emissions intensity of economic production. More details on the E FOS methodology and its 2021 projection can be found in Appendix C1. The net CO 2 flux from land use, land-use change, and forestry (E LUC , called land-use change emissions in the rest of the text) includes CO 2 fluxes from deforestation, afforestation, logging and forest degradation (including harvest activity), shifting cultivation (cycle of cutting forest for agriculture, then abandoning), and regrowth of forests following wood harvest or abandonment of agriculture. Emissions from peat burning and drainage are added from external datasets. Three bookkeeping approaches (updated estimates of BLUE  (Hansis et al.
In reality, CO 2 variations measured at the stations will not exactly track changes in total atmospheric burden, with offsets in magnitude and phasing due to vertical and horizontal mixing. This effect must be very small on decadal and longer timescales, when the atmosphere can be considered well mixed. Preliminary estimates suggest this effect would increase the annual uncertainty, but a full analysis is not yet available. We therefore maintain an uncertainty around the annual growth rate based on the multiple stations' dataset ranges between 0. 11 and 0. 72 GtC yr -1 , with a mean of 0. 61 GtC yr -1 for 1959-1979 and 0. 17 GtC yr -1 for 1980-2020, when a larger set of stations were available as provided by  Dlugokencky and Tans (2022) , but recognize further exploration of this uncertainty is required.
Ocean CO 2 sink The reported estimate of the global ocean anthropogenic CO 2 sink S OCEAN is derived as the average of two estimates. The first estimate is derived as the mean over an ensemble of eight global ocean biogeochemistry models (GOBMs, Tables 4 and A2). The second estimate is obtained as the mean over an ensemble of seven observation-based data products (Tables  4  and A3 ). An eighth product  (Watson et al. , 2020 ) is shown but is not included in the ensemble average as it differs from the other products by adjusting the flux to a cool, salty ocean surface skin (see Appendix C3. 1 for a discussion of the Watson product). The GOBMs simulate both the natural and anthropogenic CO 2 cycles in the ocean. They constrain the anthropogenic air-sea CO 2 flux (the dominant component of S OCEAN ) by the transport of carbon into the ocean interior, which is also the controlling factor of present-day ocean carbon uptake in the real world.
Anthropogenic perturbations of river carbon and nutrient transport to the ocean are not considered (see Sect. 2. 7). We derive S OCEAN from GOBMs by using a simulation (sim A) with historical forcing of climate and atmospheric The partitioning is based on nearly independent estimates from observations (for G ATM ) and from process model ensembles constrained by data (for S OCEAN and S LAND ) and does not exactly add up to the sum of the emissions, resulting in a budget imbalance (BI M ) which is represented by the difference between the bottom red line (mirroring total emissions) and the sum of carbon fluxes in the ocean, land, and atmosphere reservoirs. All data are in GtC yr -1 (a) and GtC (b). The E FOS estimates are primarily from  Gilfillan and Marland (2021) , with uncertainty of about ±5 % (±1σ ).
Using a non-linear regression approach, i. e. a feed-forward neural network, atmospheric CO 2 , the ONI, and the fossil fuel emissions are used as training data to best match the annual ocean CO 2 sink (i. e. combined S OCEAN estimate from GOBMs and data products) from 1959 through 2020 from this year's carbon budget. Using this relationship, the 2021 S OCEAN can then be estimated from the projected 2021 input data using the non-linear relationship established during the network training. To avoid overfitting, the neural network was trained with Earth Syst. Sci. Data, 14, 1917 Data, 14, -2005 Data, 14, , 2022  https://doi. org/10. 5194/essd-14-1917-2022 P. Friedlingstein et al. : Global Carbon Budget 2021 1929  Table  4. References for the process models, f CO 2 -based ocean data products, and atmospheric inversions.
The DGVM estimate of S LAND does not include the export of carbon to aquatic systems or its historical perturbation, which is discussed in Appendix D3. See Appendix C4 for DGVM evaluation and uncertainty assessment for S LAND , using the International Land Model Benchmarking system (ILAMB;  Collier et al. , 2018). More details on the S LAND methodology can be found in Appendix C4. Like the ocean forecast, the land CO 2 sink (S LAND ) forecast is based on the annual historical and estimated 2021 atmospheric CO 2 concentration  (Dlugokencky and Tans 2021) , historical and estimated 2021 annual global fossil fuel emissions from this year's carbon budget, and the summer (June, July, August) ONI  (NCEP, 2021). All training data are again used to best match S LAND from 1959 through 2020 from this year's carbon budget using a feed-forward neural network.
Details are given below. We note that with these adjustments the inverse results no longer represent the net atmosphere-surface exchange over land-ocean areas as sensed by atmospheric observations. Instead, for land, they become the net uptake of CO 2 by vegetation and soils that is not exported by fluvial systems, similar to the DGVM estimates. For oceans, they become the net uptake of anthropogenic CO 2 , similar to the GOBMs estimates. The inversion systems prescribe global fossil fuel emissions based on the GCP's Gridded Fossil Emissions Dataset version 2021. 2 (GCP-GridFEDv2021. 2; Jones et al. , 2021b) , which is an update to 2019 of the first version of GCP-GridFED presented by  Jones et al. (2021a). GCP-GridFEDv2021. 2 scales gridded estimates of CO 2 emissions from  EDGARv4. 3. 2 (Janssens-Maenhout et al.