dataesr/scientific-paragraphs-categorization huggingface dataset:
Cities in China are on the frontline of low-carbon transition which requires monitoring city-level emissions with low-latency to support timely climate actions. Most existing CO 2 emission inventories lag reality by more than one year and only provide annual totals. To improve the timeliness and temporal resolution of city-level emission inventories, we present Carbon Monitor Cities-China (CMCC), a near-real-time dataset of daily CO 2 emissions from fossil fuel and cement production for 48 major high-emission cities in China. This dataset provides territory-based emission estimates from 2020-01-01 to 2021-12-31 for five sectors: power generation, residential (buildings and services), industry, ground transportation, and aviation. CMCC is developed based on an innovative framework that integrates bottom-up inventory construction and daily emission estimates from sectoral activities and models.
origin of emissions, the in-boundary emissions are typically classified as scope-1, emissions associated with imported electricity are typically classified as scope-2, and scope-3 refers to emissions generated from other trans-boundary activities. In this study, we estimate scope-1 emissions for cities. Many existing CO 2 emission datasets for China are constructed at national or provincial level with a time lag of 2 + years, such as the China Emission Accounts and Datasets (CEADs: https://www. ceads. net/) and the Multi-resolution Emission Inventory (MEIC, http://meicmodel. org/). Furthermore, very few cities disclose their emissions, and the coverage and reliability of inventories based on self-reported data from city governments and similar organizations are difficult to assess  3, 10. China High-Resolution Emission Database (CHRED) and China City Carbon Dioxide Emissions Dataset  11  are the only recent city-level emission inventories for China, but their city-level updates are released every five 5 years with a temporal resolution of one year, which can not meet the soaring demand for near-real-time (NRT) high temporal resolution emission inventories.
City-level emission datasets provide key information for identifying emissions trends across different communities, social classes and functional urban areas, which helps generate a deeper understanding of the relationships between urbanization and emissions, especially for developing countries, which in turn can inform urban climate change mitigation actions. The fifth Intergovernmental Panel on Climate Change (IPCC) report  12  has also highlighted the urgent need for comprehensive city-level CO 2 emission inventories. Given the limited timeliness and quality in existing inventories, most cities have strong demands for low-latency CO 2 emission datasets for more effective and easily-traceable urban climate actions  1, 5, [13] [14] [15] [16]. The future of global decarbonization may rely more and more on the rapidly expanding cities like those in China, where timely emission datasets are lacking.
Monitoring city-level CO 2 emissions at a higher temporal resolution will provide us with critical information to tackle the climate change crisis by facilitating local governments in policymaking and mitigation efficacy assessment  [17] [18] [19] [20] [21] [22]. Recent studies have successfully estimated NRT daily CO 2 emissions for investigating the impacts of COVID-19 on global CO 2 emissions  17, 19, 23. Here we show that an improved framework can be applied at city scale. The 48 major cities in this dataset are selected to represent the landscape of the emission from Chinese cities. Specifically, we select cities based on four criteria: 1. direct-administered municipalities, which are mostly mega-cities (e. g. , Beijing), 2. provincial capitals (e. g. , Guangzhou), 3. high-emission industrial cities (e.
g. , Tangshan), and 4. high-GDP cities (e. g. , Suzhou). all provincial capitals (typically the largest city in a province) are covered to represent cities in each geographic regions, other high-emission and high-GDP cities can reasonably represent the major industrial and commercial activities. We estimated that the total emissions from these 48 cities combined account for about 43. 8% of the total emissions of entire China in 2020 (4. 6 GT over 10. 5 GT CO 2 based on Carbon Monitor  19, 24  , excluding shipping), while only hold 21% of the total population. This also demonstrates that in addition to the overall national mitigation targets, which can be too general or impractical for individual cities, zooming in to key cities with localized actions could be more effective. Here we present the methodology for producing Carbon Monitor Cities-China (CMCC), a near-real-time daily fossil fuel CO 2 emission dataset for 48 major high-emission cities in China.
This dataset provides scope-1 emission estimates from 2020 to 2021 for five sectors: power generation, residential (buildings and services), industry, ground transportation, and aviation. Innovative methods are developed and latest city-specific fossil fuel consumption data are used to integrate bottom-up annual inventories with daily emission estimates from sectoral activities and models. We hope this dataset can provide cities and decision-makers with timely information critical to assessing the efficacy of mitigation policies. This dataset can also support the scientific community for better understanding city-level daily emission dynamics.
Methods Workflow Overview. CMCC is constructed following a three-stage workflow (Fig. 1 ). • The first stage is the construction of annual emission inventories for each city using bottom-up fossil fuel consumption data following IPCC administrative territorial-based accounting methods. • In the second stage, we collect daily activity data for each sector and develop related models to estimate daily variation in emissions for each city. • The final step is to disaggregate annual emissions to daily scale using results from the second stage, so that the annual sectoral total emissions are consistent with results from the first stage. For quality assurance, we collected city-level inventories from other datasets (which only provide annual results) and compared them to our results for validation.
We then performed uncertainty analysis to estimate the uncertainty ranges for each sector accounting for both uncertainties from the annual bottom-up inventories and daily activities. The detailed procedures for these steps are described in later sections. The construction of this dataset follows a different workflow as compared to the global Carbon Monitor Cities dataset we have recently published  20. The global dataset is generated from a top-down spatial downscaling workflow, which does not incorporate energy statistics/inventory data for individual cities. In this work, daily emission estimates are constrained by bottom-up inventories collected from cities, therefore, although the coverage of this dataset is smaller, the accuracy is improved. CMCC covers 48 cities in China and their administrative areas are shown in Fig.
Sectoral consumption of fossil fuels from EBT is used as fossil fuel consumption data to calculate fuel-related emissions. EPCT presents energy input and output during transformation process to complement EBT for cities without city-level EBT. Industry is responsible for a large portion of city CO 2 emissions in China  26  , so we use ISEC to calculate industrial emissions separately based on energy activity data for industrial sub-sectors  27. EBTs, EPCTs and ISECs are acquired from China Energy Statistical Yearbook and city-specific statistical yearbooks. Annual fossil fuel consumption data are acquired from China Energy Statistical Yearbook and city-specific statistical yearbooks, which are the most commonly used data sources for constructing emission inventories for China  28. Since the 2021 China Energy Statistical Yearbook and the 2021 statistical yearbooks for some cities (Shijiazhuang and Handan) have not been released, we compute and use the relative changes in aggregated daily activity data (detailed in later sections) for 2020 versus 2021 and the Carbon Monitor dataset (https://carbonmonitor.
Sector correspondence with CEADs dataset (https://www. ceads. net) is used. Transport sector is divided into three parts, ground transportation, aviation and ships. We allocate them according to the proportion of the transportation sector in the 2020 China City Carbon Dioxide Emission Dataset published by the China City Greenhouse Gas Working Group (https://www. cityghg. com/). The sectoral annual emissions from city emission inventories are then used to constrain the sum of daily emission estimates. Near-real-Time Daily Activities by Sector. CMCC disaggregates the annual emissions into each day using daily activity data and models for each sector (Fig. 1 ). This section describes the NRT daily activity data and models for each sector. Power. For each city, annual emissions from power generation Emis power,y ) can be disaggregated into daily emissions Emis power,d using daily power generation data GElec c,d and the corresponding daily generation-related emission factors GEF power,c,d (Eq.
5): Emis Emis Emis Emis Emis GElec GEF GElec GEF ( ) , (5) = × = × × × , , , , , , , , , ∑ ∑ where subscripts power, y, d, c represent the power sector, year, day and city, respectively. Note that this dataset only covers scope-1 power emissions, meaning that if a city does not have any power generation plants within its territory (solely rely on imported electricity), then no power emissions are accounted for. For cities lacking daily power generation data, city-specific power consumption data (collected from local grid companies) is used to represent the power generation, then the daily emissions from power generation are computed as: ∑ = ×              Emis Emis Emis Emis , ( 6 ) power d power y c d co c d co , , , , , , where the subscript co represents power consumption. Daily emissions from power consumption Emis c,d,co is calculated with daily power consumption data from varying sources for different cities, and the daily power consumption data collected from the local power grid companies (those within a city's administrative area) are the preferred data for use.
We assume that the emissions from total power generation are equal to the emissions from thermal power generation since the thermal power generation contributes over 90% of the emissions from total power generation. For cities with complete daily power consumption data covering the whole accounting period (2020 to 2021 in this dataset), outliers are removed, and missing values are filled with interpolation. For cities with incomplete daily power consumption data, the daily consumption data are simulated based on annual power consumption data and provincial-level daily load curves which are also collected. Therefore, daily power consumption data CElec c,d can be calculated with the daily load curve and the annual power consumption of the city. Normally, the load curve of a city has highly similarity in adjacent years, and the load pattern of the previous year could represent that of the next few years.
We also divide the industrial sector in China subdivided into 10 subsectors in accordance with the Emission Database for Global Atmospheric Research (EDGAR) dataset  34, 35  classification standard and collect the provincial monthly data of 35 types of products relevant to emission (Table  3 ). Note that wood and wood products are not covered due to data availability, which only accounts for 0. 11% of the total emission of the manufacturing industry based on EDGAR 2019. We conduct an assessment of the industrial subsectors for China in EDGAR 2019 and CEADs, and estimate that these 35 industrial products account for about 90% of the manufacturing industries and construction sector. The monthly CO 2 emissions estimated from the industrial production are then disaggregated into a daily scale following the Carbon Monitor's provincial NRT daily industry emissions which is found to be correlated with daily power consumption  29.
Public and other buildings. Each class is presented by a corresponding building model in the China Building Energy Model, and we simulated their daily building energy consumption (BEC) (except for electricity, which is covered in the power sector). The building model simulation is driven by daily air temperature collected at local weather stations for each city as air temperature is the dominant factor governing building energy use (e. g. , heating demand) for all building classes. = + + - + - - - E E E E E E E E E , ( 16 ) We estimate the share of each building class in each city using building stock GIS data and POIs (point of interests). A POI table describes the function of each building and its geographic location, and provides classification for all the buildings, in which 9 classes are closely related to building energy consumption (Table  5 ).
Since we only have four more general building models, the 9 classes are then grouped into 4 classes as listed in Table  5. We consider these 4 major classes can reasonably represent the daily patterns of building energy consumptions from a city. The building boundaries and point-based POIs are acquired from Amap's open data platform (https://lbs. amap. com). The POIs of all buildings in a city are spatially matched to corresponding building boundaries using the Spatial Join function in ArcMap, and then associate the corresponding building surface area to each of the 4 building classes. By aggregating the surface areas for buildings that belong to each class, we calculate the share of different building types in terms of their building surface area for each city. Residential: services. The residential sector also covers some non-building-related fuel consumption in wholesales, retail trade, catering and other small business, which we define as services.
To estimate the daily activities for the services sub-sector, We distribute annual service-related activities into each day using city-level consumer goods retail sales plus the effects of weekends and major holidays. The consumer goods retail sales data is calculated from input-output (IO) tables which we constructed for each city. This approach assumes that a linear relationship exists between daily retail sales and daily energy consumption in the service sector. We first collected monthly statistics of total retail sales of consumer goods from city statistical yearbooks, then we used the monthly consumer price index to eliminate the effect of time on prices, which revised the monthly total retail sales of consumer goods. The monthly total retail sales of consumer goods are then allocated to each day following the moving holiday effect of China, which is presented in the form of holiday impact factors, including weekends and festivals  38, 39.
flightradar24. com). Daily commercial flight data from 47 airports in China during the study period are acquired and attributed to each city (Shanghai and Beijing each has two airports, Binzhou, Suzhou and Dongguan do not have local commercial airports). Flightradar24 data is based on ADS-B signals emitted by aircraft and received by their network of ADS-B receptors. Each flight is characterized by its departure and arrival airports and an aircraft type. We calculate CO 2 emissions from the flight data using the parameterization of Seymour et al. 40  , which takes into account a small deviation from the shortest flight route between the departure and arrival airports. We map the FlightRadar24 aircraft types onto the 133 aircraft types in Seymour et al. 40. We have complemented their database by grouping together known similar aircraft with similar equipment and for aircraft not included in the study, the average coefficients have been computed (depending on their category, commercial planes or business jets).
Limitations. The main purpose of this dataset is to improve the timeliness and temporal resolution of inventories for studying near-real-time fossil-fuel CO 2 emissions and their responses to emergent events and short-term policies. CMCC does not include emissions related to land use, land use change, and forestry, therefore, some emissions caused by long-term urban expansion are not captured. CMCC is constructed based on daily activity data and models that can cover a majority rather than the entire daily emission-related activities due to data availability. Therefore, we acknowledge that a small portion of daily variations on city emissions are not reflected by this dataset. Another limitation is associated with the use of the city similarity matrix to find substitute cities to address the missing data issue, which may introduce additional uncertainties, but we consider our results to represent a meaningful first attempt to capture daily emissions for these cities.
technical Validation The quality of this dataset is assessed by comparing with existing datasets. We perform a comprehensive analysis of uncertainty for total and sectoral CO 2 emissions at both annual and daily scales. Complete results for each stage of the uncertainty analysis is included in the supplementary data file. Validation CMCC against other datasets. Multiple datasets have been constructed to estimate annual CO 2 emissions in China, including CEADs, MEIC, and CHRED. In this study, we validate our dataset CMCC by comparing its annual inventory with the above datasets. CEADs provides the total CO 2 emissions at city level and sub-sector CO 2 emission inventory at provincial level in China in 2019; we compare the total CO 2 emissions for 40 ordinary cities and sub-sector CO 2 emissions for 4 municipalities that are covered by both CEADs and CMCC in 2019.
MEIC provides inventories of national and provincial CO 2 emission data for 2017 and earlier; we compare only 4 municipality cities between MEIC and CMCC. CHRED provides a 5-year interval CO 2 emission accounting inventory for Chinese cities from 2005 to 2020 and we compare CO 2 emissions for the 48 cities in CHRED. Figure  7a  shows the comparison of annual total inventories, which indicates a good agreement among CMCC, CHRED and CEADs. Note that shipping related emissions (typically account for small percentage) are covered in CEADs for some cities but are not reflected in this comparison for CMCC and CHRED to keep the comparing sectors consistent. Figure  7b  shows the comparison of annual sub-sector inventories. The coefficients of determination values (R²) between CHRED and CMCC are 0.
Uncertainty analysis. According to the IPCC guidelines, uncertainties may arise from each step when developing emissions accounting. In this study, we follow the 2006 IPCC Guidelines for National Greenhouse Gas Inventories  25  to conduct an uncertainty analysis of the dataset to quantify the uncertainties of the total and sub-sectoral CO 2 emissions at both annual and daily scales. Error propagation for uncertainty quantification. IPCC recommends two methods to quantify uncertainty: error propagation method and Monte Carlo simulation. The error propagation method follows a more rigorous uncertainty transfer process, allowing for a more accurate range of emissions uncertainty than the Monte Carlo simulation method. The key assumption in error propagation is that the propagation of emissions or uncertainties can be obtained from the uncertainty of activity data, emission factors and other estimated parameters through the error propagation formula.
For the power sector, the uncertainty of daily-scale CO 2 emissions has two sources: the first part of the uncertainty lies in the assumption that the CO 2 emissions from daily power generation can be replaced and represented by the CO 2 emissions from daily power consumption, and the uncertainty of this process is beyond the scope of this paper; the second part of the uncertainty is mainly from the uncertainty of daily power consumption data itself, which is difficult to assess without further investigation. Therefore, based on the completeness of the daily power consumption data, the uncertainty of this part is divided into two categories: For cities with complete daily power consumption data covering the whole accounting period (2020 to 2021 in this dataset), the uncertainty is from the lack of sampling and the aging of equipment, which is relatively small and neglected.
Usage Notes The dataset (CMCC-48-cities-v0629. xlsx) is available from figshare 41 https://doi. org/10. 6084/m9. figshare. 202 64277. v2. Each city has more than 3000 lines of data, which will take a long time to load in Excel. We recommend loading the data with a script that can handle large datasets. Users should also note that the unit of emissions in this dataset is ktCO 2.
Code description. Python code for data generation and visualization is provided (link in the Code Availability section). The code disaggregates annual emissions calculated from energy statistics into daily resolution using daily activity data for each sector. For example, the "correct-save-flightradar24" function uses FlightRadar24 data to estimate daily emissions for each airport. Functions for other sectors follow similar process as detailed in previous sections. The "save-CM-cities" is used for saving data (all sectors) for individual city following a fixed format. The "merge-all-cities-in-folder" function merges all data into one excel sheet with each city as one sub-sheet. Lastly, users can plot the data for each city using the "plot-CM-cities" function.
At this point, comparisons between different machine learning strategies are difficult. Most models were not only developed and validated in one specific context, but their implementations and data-processing routines are dataset-specific. Moreover, general machine learning approaches successful at pathology decoding should be well-suited for brain age modeling too, yet they have never been tested for that purpose  (Gemein et al. 2020; Banville et al. 2020; Engemann et al. 2018). This makes it hard to know whether any strategy is globally optimal and where specific strategies have their preferred niche. As a result, uncertainty is added to comparisons between MEG, EEG and MRI, slowing down efforts of validating M/EEG-based brain age. Finally, to mitigate the impact of selection bias concerning the subjects investigated, it will be crucial to analyze many, socially and culturally diverse M/EEG datasets and find representations that are invariant to confounding effects that can raise issues of fairness and racial bias if remaining unaddressed  (Choy, Baker, and Stavropoulos 2021).
To develop the next generation of M/EEG-derived brain age models, to facilitate processing of larger numbers of diverse M/EEG-data resources and to avoid fragmentation of research efforts, standardized software and reusable benchmarks are needed. In this paper we wish to make a first step in that direction. We provide reusable brain-ageprediction benchmarks for different machine learning strategies validated on multiple M/EEG datasets from different countries. The benchmarks are built on top of highly standardized dataset-agnostic code enabled by the BIDS standard  (Gorgolewski et al. 2016; Niso et al. 2018; Appelhoff et al. 2019). This makes the benchmarks easy to extend in the future for additional datasets. The paper is organized as follows. The method section motivates the choice of the different machine learning benchmarks.
The general data processing approach and software developed for this contribution are presented in the context of the benchmark. The selection of datasets is motivated, and datasets are then described in detail and compared regarding key figures that could provoke differences between benchmarks. Dataset-specific processing steps and peculiarities are highlighted. Then a model validation strategy is developed. The results section presents benchmarks on prediction performance across machine learning models and datasets and different performance metrics. The discussion inspects differences between models, modalities, and datasets, identifying unique niches, safe bets as well as unresolved challenges. The work concludes with practical suggestions on additional benchmarks that can be readily explored using the proposed tools and resources.