Daily CO 2 emission for China ’ s provinces in 2019 and 2020

20 Tracking China’s national and regional CO2 emission trends is becoming ever more crucial. The country recently pledged to achieve ambitious emissions reduction targets, however, high-resolution datasets for provincial level CO2 emissions in China are still lacking. This study provides daily CO2 emission datasets for China’s 31 provinces, including for the first 24 time, the province of Tibet. The inventory covers the emissions from three industrial sectors (power, industry and ground transport) during 2019 to 2020, with its temporal resolution at a daily level. In addition, the variations in CO2 emissions for seasonal, weekly and holiday periods have been uncovered at a provincial level for the first time. This new data was added 28 to further analyze the impact that weekends and holidays have on China’s CO2 emissions. Over weekend periods, carbon emissions are shown to reduce by around 3%. Spring Festival meanwhile, has the greatest impact on the reduction of China’s CO2 emissions. This detailed and time-related inventory will facilitate a more local and adaptive management of China’s 32 CO2 emissions during both the COVID-19 pandemic’s recovery and the ongoing energy transition. The data are archived at https://doi.org/10.5281/zenodo.4730175 (Cui et al., 2021).


Introduction
China has the largest CO2 emission worldwide, accounting for about 28% of global CO2 emissions in 2019 (Friedlingstein et al., 2020). China is also the fastest growing country in between 2018 and 2019 (Friedlingstein et al., 2019;Friedlingstein et al., 2020). In contrast, the growth rate of global carbon emissions is only 0.1%. China, therefore, faces considerable pressure from the international community to reduce emissions. The estimates of China's CO2 emissions carry significant uncertainties, and the differences in estimates of China's carbon 48 emissions between inventories from EDGAR, CDIAC, and CEADs approach 15% (Liu et al., 2015). Having timely and accurate CO2 emission estimates based on fossil fuel combustion and cement production is therefore fundamental prerequisite to designing evidenced-based policies for reducing China's CO2 emissions.

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The achievement of China's national CO2 mitigation target will rely on the implementations of certain actions and policies at provincial levels. Previous studies have compiled the provincial emission inventories, which generally include the annual carbon emissions from energy-and industry-related sectors in 30 provinces (except for Tibet) in mainland China 56 (Shan et al., 2018). However, these studies are based on provincial energy statistics and often have a time lag of one or more years.
The timely updating of CO2 emissions data is a critical step for China's provinces to achieve the carbon neutrality plans. Moreover, the outbreak of the COVID-19 pandemic has resulted 60 in extra uncertainties regarding China's future CO2 emissions trajectory (Liu et al., 2020a;Liu et al., 2020b). During the outbreak of the COVID-19 pandemic (January-June 2020) in China, the national CO2 emissions were reduced by 3.7% (-187.2 Mt CO2) compared to the same period in 2019. Therefore, there is an urgent need to obtain a timely CO2 emission dataset at 64 provincial level (Liu et al., 2020c) so that the post-COVID emission dynamics can be tracked accordingly.
In a recent study, Liu et al. (2020a;2020b) described the Carbon Monitor of Fossil Fuel CO2 68 Emissions dataset, which provides daily CO2 emissions data up till December 31st, 2020, for 6 sectors and 12 largest emitting countries, plus the rest of the world as an aggregate. The global fossil fuel CO2 emissions was separated into sectors of power generation (~40% globally), industrial production (~30%), transportation (~20%, categorized as ground, air and 72 shipping) and residential consumption (~10%). This product is evaluated against preliminary national energy usage data for all or part of the year 2020, thereby providing a full picture of all the CO2 emission drivers, including the pandemic (seasonality, working days and holidays, weather and the economy). By acknowledging the uncertainties more than just the inventories, 76 such a dataset can provide more up-to-date information than official inventories (UNFCCC, 2020a, b, c) and international CO2 emissions datasets (BP, 2020;Crippa et al., 2020;Friedlingstein et al., 2020 ;Gilfillan et al., 2020;IEA, 2020), which have a time lag of 6 to 16 months after the last month of reported emissions. In this study, based on China's daily emissions at the national scale, which are taken from the Carbon Monitor and provincial sectoral weight factors related to CO2 emissions, we estimate mainland China's daily CO2 emissions from electricity, industry and ground transport sectors in all 31 provinces. The full names of these 31 provinces and their corresponding 84 abbreviations are shown in Table 1. CO2 emissions from Tibet are included in this dataset for the first time. This detailed and timely inventory will facilitate a more local and adaptive management of CO2 emissions in the process of cutting carbon emissions and achieving carbon neutrality.

Materials and Methods
This dataset accounts for the daily changes in provincial CO2 emissions in mainland China for the years 2019 and 2020. Daily provincial CO2 emissions are estimated from three sectors: 96 power, industry, and ground transport. It considers CO2 emissions estimates based on administrative territories, while the emissions from international aviation and shipping are excluded (Shan et al., 2017). The estimates of national CO2 emissions from 2019 to 2020 is derived from the Carbon Monitor dataset (data available at https://carbonmonitor.org).

National daily CO2 emissions in 2019 and 2020
China's daily CO2 emissions estimates are based on a near-real-time daily dataset of the 104 global CO2 emissions from fossil fuel and cement production since January 1, 2019, as published by the Carbon Monitor (data available at https://carbonmonitor.org/) (Liu, Ciais et al. 2020). Emission estimates from the Carbon Monitor are calculated on a national basis and by sectors, thereby gaining from past experiences in constructing annual inventories and 108 newly compiled activity data (Liu, Ciais et al. 2020).
The Carbon Monitor has calculated China's daily CO2 emissions since January 1, 2019. It is separated into several key emission sectors: power sector, industrial production, residential 112 consumption, ground transport, air transport and ship transport. For the first time then, China's daily emission estimates are produced for these sectors based on regularly updated dynamic activity data.

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China's near real-time activity data includes daily data for electricity generation, and monthly production data for cement, steel and other energy-intensive industrial sectors. In addition, it includes hourly traffic congestion data for 22 cities, daily maritime and aircraft transportation activity data, and previous-year fuel usage data for both residential and commercial buildings 120 that has been corrected for air temperature.

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Daily provincial CO2 emissions are estimated by daily national CO2 emissions multiplied by the provincial weight factor. The sectoral provincial daily CO2 emission can be obtained as follows:

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(1) where Epro represents daily provincial emissions, Ei,china refers to daily national CO2 emissions, and Ri,p denotes the weight factor of province p from i sector.

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Due to the lack of either daily or monthly provincial energy consumption data, we used alternative indicators that reflect the provincial activity data of the corresponding sector in place of the provincial activity data.

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The weight factors for each province equal the amount of provincial alternative indicators divided by the national amount of alternative indicators. The equation is as follows: (2) where Pi, p and Pi,n represent the provincial and national amount of alternative indicators for 140 sector i, respectively. For the power, industry and ground transport sectors, the alternative indicators are thermal power generation, cement production and vehicle ownership. The data on provincial thermal power generation, cement production and vehicle ownership were obtained from China's National Bureau of Statistics of China (NBSC).

Provincial weight factor estimation
The provincial dataset constructed in this study includes daily CO2 emission data from the 148 three main polluting sectors (power, industry and ground transport) in China, which together account for more than 90% of the total emissions. CO2 emissions from residential consumption and aviation are not considered in the provincial dataset for three reasons. Firstly, residential and aviation totally accounted for less than 9% of China's average daily 152 CO2 emissions (Table 2). Secondly, currently there is no suitable statistical indicator that can be used as a provincial weight factor to divide residential sector emissions from the national level into the provincial level. Thirdly, it is very difficult to count aviation emissions within provincial territories.

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For the power sector, we collected monthly data on thermal power generation from 31 provinces in the Chinese mainland from January 2019 to December 2020. Due to the limitations on daily thermal power generation, we assumed that the change in ratio from 160 provincial thermal power generation to national thermal power generation is negligible when looked at on a monthly scale. We therefore used this ratio of provincial thermal generation to national generation on a monthly scale as the provincial weight factor for the daily scale of  Table 3.
For the industry sector, CO2 emissions from steel, cement, chemicals and other industries are calculated on a national scale according to the data provided by the Carbon Monitor. However, 168 at a provincial scale, only the data on cement production was available, with other indicators from the steel, chemical and other industries were missing. Considering the intermediate processes, industrial cement processes have the highest proportion of emissions in the industrial sector. The ratio of the provincial production of industrial cement to the national 172 production was taken as the provincial weight factor for this industry sector (Table 4).

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For the ground transport sector, we used the ratio of provincial vehicle ownership to national vehicle ownership for the year 2018 as the provincial weight factor for the ground transport sector (Table 5). Due to a lack of monthly and daily data on provincial vehicle ownership after 2018, however, we considered the fact that there have been a change of less than 2.3% in 180 two years in the provincial share of vehicle ownership in the country, and thus assumed the change in the ratio of provincial vehicle ownership to national vehicle ownership to be negligible.

Provincial emissions over the weekdays, weekends and holidays
In this dataset, we add two attributions -week and holiday -to provincial daily emissions. where Erowe represents reduced emissions over the weekend, Ead_Mo2Frr and Ead_Sa2Su refer to the 208 average daily emissions from Monday to Friday, and average daily emissions from Saturday to Sunday, respectively.

Trends in total emissions
216 For most provinces, there is a valley shape in average daily emission in February (Table 7).
For the year 2019, BJ province has its minimum monthly average emissions in April. The minimum monthly average emissions of 5 provinces (SH, FJ, CQ, GX and SC) occur between 220 May to July. YN province has its minimum monthly average emission in September. The minimum monthly average emissions for TJ, HEN and HUB provinces meanwhile, occur in October. Tibet province has its minimum average monthly emission in December. In 2020 however, the minimum monthly average emissions for all provinces was in February, except 224 for BJ (April), SH (October) and HUB (March). The highest average daily emissions are mainly found in summer (June to August) or/and winter (November to January), and these are called summer peak and winter peak, respectively (Fig. 1). The provinces that follow this pattern for the summer peak emissions  (Table 7).

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Referring to minimum daily emissions (Table 8), this day fell on the 5 th February for 25 provinces (except for BJ, SH, HEN, CQ, SC and Tibet), which was the first day of the seven-day holiday for the Spring Festival in 2019. From those that did not follow this trend, BJ and SH province emitted their minimum value of CO2 on the 1 st May, which is the first July; and HLJ, FJ and QH were all on the 9 th September. In 2020, maximum daily emissions occurred on a day between November and January in all provinces except for IM, JL, FJ, 252 HEN, Tibet, HLJ and QH provinces. IM, JL, FJ, HEN and Tibet provinces emitted their maximum CO2 on a day between June and August, and HLJ and QH emitted their maximum CO2 on the 22 nd October. 256

Trends in emissions from the power sector
Provincial daily CO2 emissions from mainland China's power sector show two trends during 260 an annual period: the "W" shape trend and the "U" shape trend. In most Chinese provinces (25 provinces, except for the six provinces of Tibet, QH, SAX, GS, SC and GZ), daily CO2 emissions show two peaks and two valleys during an annual period, which we called the "W" trend ( Fig. 2, a-aa). The two peaks occur in July-August and December-January, and we refer 264 to them as the summer peak and the winter peak, respectively. The summer peak in the power sector is due to the widespread use of air conditioning, whereas the winter peak is due to heating. The valleys occur during the Spring Festival period and the National Day holiday, which are the two most important seven-day holidays in China.

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The remaining six provinces (Tibet, QH, SAX, GS, SC and GZ), which are all western provinces, daily CO2 emissions show the pattern of one peak and one valley during an annual period, which we call the "U" trend ( Fig. 2, ab-ae). These western provinces have lower 272 average summer temperatures and therefore have no summer peak of emissions. However, the winter peaks can be seen as daily CO2 emissions from the power sector in these provinces.

Trends in emissions from industry sector
Provincial daily CO2 emissions from the industry sector in mainland China show two trends during an annual period: the "inverted-U" shape trend and the "line" shape trend.

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In 16 northern provinces of China, including LN, HLJ, JL, HEN, IM, GS, SX, SD, HEB, SAX, NX, XJ, QH, Tibet, TJ, and BJ, daily CO2 emissions remain high from June to November, and then drop down from December to January, which we called the "inverted-U" shape trend (Fig. 3).

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In 15 southern provinces of China, including YN, JX, FJ, GX, JS, HUB, GZ, GD, HAN, SH, CQ, AH, ZJ, SC, and HUN, daily CO2 emissions reach their lowest point during the Spring Festival holiday, then slowly rise and reach the peak at the end of the year, which we call the "line" shape trend (Fig. 4). 288

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The impact that holidays have on daily CO2 emissions trends is more pronounced in the ground transportation sector (Fig. 6). For the year 2020 however, COVID-19 has had a more In GS and QH provinces, the power sector accounts for more than 70% of the total daily 312 emissions in winter and less than 30% of the total daily emissions in summer, while the industry sector accounts for more than 60% of the total daily emissions in winter. In the provinces of HLJ, JL, LN, SAX and HEB, the power sector contributes around 60% of the total daily emissions in winter, while in summer, the contribution from the industry sector 316 reaches over 40%, which is close to or exceeds the contribution of daily emissions from the power sector. In SH and BJ, the contribution to total daily emissions from the power sector reaches its maximum in winter, and its minimum in the periods of April-May and September-October. The ground transportation sector is the sector that contributes the second 320 largest volume of emissions in the period of April-May and September-October. This is especially true in BJ, where the transportation sector accounts for about 40% of total emissions in April-May and September-October, which is close to the emissions from the power sector during that period. Meanwhile, CO2 emissions in Tibet are mostly contributed 324 by the industry sector. In summer, the industry sector contributes over 90% of the daily CO2 emissions, while in winter, it decreases to around 80% with the rest of the daily emissions mainly coming from ground transport. 328

The effect of the weekend on CO2 emissions
In a week, the lowest average daily emissions are observed on Saturday and Sunday (Fig. 7). Emission reductions over the weekend were seen to exceed 50 thousand tons of CO2 per day 332 in the provinces of SD, GD and JS (Fig. 8a). Among 28 of China's 31 provinces, a substantial decrease in CO2   Out of the 31 provinces, BJ has the most prominent reduction of emissions on weekends, 340 accounting for 4.03% of the average daily emissions on weekdays. GZ meanwhile, has the lowest reduction of emissions on weekends, accounting for only 2.35% of average daily CO2 emissions on weekdays. In the remaining provinces, the reduction of emissions over the weekend is equivalent to around 3% of average daily CO2 emissions on weekdays (Fig. 8b).

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The decreased emissions from the power, industry and ground transport sectors on weekends vary across provinces. However, for the provinces of XJ, SX, NX, SD, JS, AH, TJ, GS, SAX, and IM, we see that the main reduction in emissions comes from the power sector (Table 9), 348 because the power sector is also the main contributor to those emissions generally (Fig. 6).
For the provinces of Tibet, HUB, JX, HUN, YN, GZ, FJ, GX, HAN, CQ and SC, the industrial sector is also both the main driver of CO2 emissions and the sector with the highest reductions on weekends. For China's northeast region, GD, ZJ, HEN, HEB, as well as for BJ 352 and SH, the reduction of CO2 emissions on weekends mainly comes down to ground contribution sector, which, especially in BJ and SH, is responsible for over 70% and nearly 50% of the emissions reduction on weekends, respectively.  Table 6. During 364 the period of 2019-2020, the average daily emissions on holidays (dark blue lines in Fig. 9) can be seen to be less than average daily emissions on normal days (light blue lines in Fig. 9), which means that CO2 emissions are reduced on holidays.

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In 2019, compared to other holidays, the largest reduction in average daily CO2 emissions was recorded during Spring Festival (Fig. 10a). The maximum reduction of average daily CO2 emissions in that period reached 375.08 thousand tons of CO2 in JS province, while the minimum reduction of average daily CO2 emissions in that period reached 5.5 thousand tons 372 of CO2 in Tibet. Considering the durations of China's national holidays (Table 6), the cumulative reduction of CO2 emissions on Spring Festival are the largest compared to other holidays (Fig. 10c). In 2020, the joint Mid-Autumn Festival and National Day holidays led to an 8-day holiday whose impacts on CO2 emissions exceeded that of the Spring Festival (Fig. 10b) Table 6), the cumulative reduction of CO2 emissions at a national level reached 44447.45 thousand tons of CO2, which is close to 0.5% of the national emissions for 404 2020. The total reduction of CO2 emissions over the 7 holidays was 91350.96 thousand tons of CO2, which is equivalent to 1.01% of national annual emissions. The provinces of SD, JS and GD contribute the highest reduction of emissions during holiday periods, accounting for 8.90%, 7.43% and 7.53% respectively, of China's total reduction of emissions on holidays. 408

Comparison with CEADs dataset
Although the time range of the CEADs (Carbon Emission Accounts & Datasets, 412 https://www.CEADs.net.cn/ ) dataset and this study do not match up, the proportion of provincial emissions changed little in those two years. The most recent available data in the CEADs inventory is for the year 2018, and thus this was used for comparison with the sectoral provincial data in this study for the year 2019. In addition, our inventory estimates 416 CO2 emissions in Tibet, while the CEADs dataset does not include them. In order to carry out a comparison with the CEADs's provincial contributions, this inventory only considers the provincial contributions from 30 provinces (which excludes Tibet). Fig. 11 shows the provincial contribution differences between the inventories from the CEADs and those from 420 this study for the power, industry and ground transportation sectors.  Figs.11b and 11e), a large difference in the emissions contribution rate can be seen for HEB 428 province between the CEADs dataset and this study. The CEADs inventory states that HEB province accounts for nearly 15% of the national CO2 emissions coming from the power sector, while our inventory considers it to be only 4.34%. HEB province is the largest emissions emitter in the CEADs dataset, while our inventory ranks it as the 13 th . This 432 difference may be due to the fact that emissions from cement production processes are not the main emissions driver in HEB's industry sector. Regarding ground transportation, according to the CEADs dataset, SH contributes around 7%, while this inventory states that it only contributes 1.70%. However, the contribution ratio for the provinces of SD, JS, ZJ, HEN and 436 HEB, were higher in this inventory than in CEADs's estimates.
The likeliest cause for the discrepancies of sectoral provincial contributions between the CEADs dataset and this study, is the method used in each inventory. The CEADs calculates 440 provincial emissions based on energy consumption during sectoral processes. However, it is difficult to estimate daily emissions based on energy-related methods due to a lack of daily energy consumption data. To calculate the provincial weight factor, estimate the daily provincial weight factors and improve the time precision of the emission inventory to a daily 444 level, this inventory uses high temporal resolution alternative indicators, such as thermal power generation for the power sector, cement production for the industry sector and vehicle ownership for ground transportation. However, some uncertainties will still be introduced due to the poor consistencies between alternative indicators and actual emission processes in some 448 provinces.

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The uncertainties in this inventory come from two sources: the uncertainties of near-real national CO2 emissions from the Carbon Monitor, and the uncertainties from the estimates of each provincial weight factor. The uncertainties of near-real national CO2 emissions from the Carbon Monitor have been discussed in detail in (Liu et al, 2020a, b). The uncertainties from 456 the emissions from the power and industry sectors come from monthly statistical data of thermal power generation and cement production. Uncertainties from the monthly statistics were derived from 10,000 Monte Carlo simulations carried out to estimate a 68% confidence interval (1 sigma) for provincial thermal power generation and cement production in China.

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The uncertainties of the ground transportation sector mainly come from the inter-annual variability of provincial vehicle ownership, which are based on the estimates in the annual data of vehicle ownership from the China Bureau of Statistics within (±2.3%).

Conclusions
Estimating China's provincial CO2 emissions is fraught with problems, such as data 468 availability and the time lag of one or more years in the data itself. In the context of a sustained COVID-19 pandemic and China's commitment to achieve its peak carbon emissions before 2030 and then drop to carbon neutrality before 2060, annual provincial emission inventories have become untenable. The provincial daily CO2 emissions dataset 472 presented here increases the temporal resolution of the emissions inventory and estimates the daily CO2 emissions from the power, industry and ground transportation sectors in 31 provinces of mainland China. This study also notably includes the CO2 emission estimates for Tibet for the first time; something which was not done previously due to the lack of available 476 energy-related statistic data. This dataset adds the two attributes of the "week" and "holiday" to provincial daily emission, which can be used to analyse the impact of weekends and holidays on China's CO2 emissions. The provincial emissions based on the estimates in this inventory are in good agreement with 480 those in the CEADs dataset. However, this inventory improves the temporal resolution to a daily level compared to only annual emissions estimates provided in the CEADs dataset. This dataset will be near-real time updated (may be one month behind the actual time). However, more work is still required in order to improve the provincial daily CO2 emission estimates 484 from the lower emitting sectors, such as the residential, aviation and shipping sectors.

Competing interests
The author declares that they have no conflict of interest.   Table S1.    abbreviations of the names of these 15 provinces in southern China. The full names corresponding to these abbreviations are shown in Table 1.  Table S1.

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The full names corresponding to these abbreviations are shown in Table 1.