The average energy consumption of a ChatGPT request was estimated at *** watt-hours, nearly ** times that of a regular Google search, which reportedly consumes *** Wh per request. BLOOM had a similar energy consumption, at around **** Wh per request. Meanwhile, incorporating generative AI into every Google search could lead to a power consumption of *** Wh per request, based on server power consumption estimations.

Google’s energy consumption has increased over the last few years, reaching 25.9 terawatt hours in 2023, up from 12.8 terawatt hours in 2019. The company has made efforts to make its data centers more efficient through customized high-performance servers, using smart temperature and lighting, advanced cooling techniques, and machine learning. Datacenters and energy Through its operations, Google pursues a more sustainable impact on the environment by creating efficient data centers that use less energy than the average, transitioning towards renewable energy, creating sustainable workplaces, and providing its users with the technological means towards a cleaner future for the future generations. Through its efficient data centers, Google has also managed to divert waste from its operations away from landfills. Reducing Google’s carbon footprint Google’s clean energy efforts is also related to their efforts to reduce their carbon footprint. Since their commitment to using 100 percent renewable energy, the company has met their targets largely through solar and wind energy power purchase agreements and buying renewable power from utilities. Google is one of the largest corporate purchasers of renewable energy in the world.

Of the leading ten technology companies worldwide based on market capitalization, Samsung is the company consuming the most electricity at nearly ** million megawatt-hours (MWh) based on the company's most recent 2023 figures. Google, Taiwan Semiconductor Manufacturing Company (TSMC), and Microsoft came in second, third, and fourth place in electricity consumption, respectively.

Important: As an research not-for-profit organisation, if you found this dataset useful we would appreciate your time in filling out this short survey.

This dataset contains 3 aggregate datasets from the electricity smart meter data of over 25,000 customers in Great Britain (GB) from March 2021 - March 2022.

For each consumer, we know (via a survey) what low carbon technologies (LCTs) they own. The potential LCT options are: Solar PV, Heat Pump (Air Source, or Ground Source), Electric Vehicle, Battery, Electric Storage Heaters.

For simplicity, this dataset contains only customers with one type of LCT (with the exception of Solar PV, where we include Solar PV + Battery customers as is common in GB). We do not include customers with multiple LCTs (for example home battery + EV)

We include quantiles of usage for each half hour (the "profile") for each type of LCT ownership "archetype", both overall (when season=None) and by season. As is common in the literature, we normalise by the square meterage of the house using open EPC data in GB (https://epc.opendatacommunities.org/) to get the watt hours per square meter. You can also find the raw, unnormalised, kwh values by quantile in this release. These two datasets have the quantiles for each half hour period. In addition, we release the daily quantiles of electricity consumption, in kwh per square meterage, by LCT type.

In summary the data we are releasing, aggregated over 25,000 customers over 1 year of usage from March 2021 - March 2020 is:

• daily_elec_consumption_quantiles_by_lct_ownership.csv - The daily quantiles of usage [kWh/m2] by LCT
• lct_elec_consumption_profiles.csv - The half hourly quantiles of usage [Wh/m2] by LCT by season
• lct_elec_consumption_profiles_kwh.csv - The half hourly quantiles of usage [kWh] by LCT by season

We believe this data will be useful for modelling efforts, as customers with different types of LCTs use energy at different times of the day, and by different amounts daily. By releasing this data openly, we hope forecasting scenarios for the future energy system are more accurate. We have a supporting blog post on our website at https://www.centrefornetzero.org/res/lessons-from-early-adopters-electricity-consumption-profiles/.

The data set records the per capita electricity consumption of 1971-2014 countries along 65 countries along the belt and road. Data sources: IEA,http://www.iea.org/stats/index.asp.Data on electric power production and consumption are collected from national energy agencies by the International Energy Agency (IEA) and adjusted by the IEA to meet international definitions. Data are reported as net consumption as opposed to gross consumption. Net consumption excludes the energy consumed by the generating units. For all countries except the United States, total electric power consumption is equal total net electricity generation plus electricity imports minus electricity exports minus electricity distribution losses.

This repository includes python scripts and input/output data associated with the following publication:

[1] Brown, P.R.; O'Sullivan, F. "Shaping photovoltaic array output to align with changing wholesale electricity price profiles." Applied Energy 2019. https://doi.org/10.1016/j.apenergy.2019.113734

Please cite reference [1] for full documentation if the contents of this repository are used for subsequent work.

Some of the scripts and data are also used in the following working paper:

[2] Brown, P.R.; O'Sullivan, F. "Spatial and temporal variation in the value of solar power across United States electricity markets". Working Paper, MIT Center for Energy and Environmental Policy Research. 2019. http://ceepr.mit.edu/publications/working-papers/705

All code is in python 3 and relies on a number of dependencies that can be installed using pip or conda.

Contents

pvvm.zip : Python module with functions for modeling PV generation, calculating PV revenues and capacity factors, and optimizing PV orientation.

notebooks.zip : Jupyter notebooks, including:

pvvm-pvtos-data.ipynb: Example scripts used to download and clean input LMP data, determine LMP node locations, and reproduce some figures in reference [1]

pvvm-pvtos-analysis.ipynb: Example scripts used to perform the calculations and reproduce some figures in reference [1]

pvvm-pvtos-plots.ipynb: Scripts used to produce additional figures in reference [1]

pvvm-example-generation.ipynb: Example scripts demonstrating the usage of the PV generation model and orientation optimization

html.zip : Static images of the above Jupyter notebooks for viewing without a python kernel

data.zip : Day-ahead and real-time nodal locational marginal prices (LMPs) for CAISO, ERCOT, MISO, NYISO, and ISONE.

At the time of publication of this repository, permission had not been received from PJM to republish their LMP data. If permission is received in the future, a new version of this repository will linked here with the complete dataset.

results.zip : Simulation results associated with reference [1] above, including modeled revenue, capacity factor, and optimized orientations for PV systems at all LMP nodes

Data terms and usage notes

ISO LMP data are used with permission from the different ISOs. Adapting the MIT License (https://opensource.org/licenses/MIT), "The data are provided 'as is', without warranty of any kind, express or implied, including but not limited to the warranties of merchantibility, fitness for a particular purpose and noninfringement. In no event shall the authors or sources be liable for any claim, damages or other liability, whether in an action of contract, tort or otherwise, arising from, out of or in connection with the data or other dealings with the data." Copyright and usage permissions for the LMP data are available on the ISO websites, linked below.

ISO-specific notes:

CAISO data from http://oasis.caiso.com/mrioasis/logon.do are used pursuant to the terms at http://www.caiso.com/Pages/PrivacyPolicy.aspx#TermsOfUse.

ERCOT data are from http://www.ercot.com/mktinfo/prices.

MISO data are from https://www.misoenergy.org/markets-and-operations/real-time--market-data/market-reports/ and https://www.misoenergy.org/markets-and-operations/real-time--market-data/market-reports/market-report-archives/.

PJM data were originally downloaded from https://www.pjm.com/markets-and-operations/energy/day-ahead/lmpda.aspx and https://www.pjm.com/markets-and-operations/energy/real-time/lmp.aspx. At the time of this writing these data are currently hosted at https://dataminer2.pjm.com/feed/da_hrl_lmps and https://dataminer2.pjm.com/feed/rt_hrl_lmps.

NYISO data from http://mis.nyiso.com/public/ are used subject to the disclaimer at https://www.nyiso.com/legal-notice.

ISONE data are from https://www.iso-ne.com/isoexpress/web/reports/pricing/-/tree/lmps-da-hourly and https://www.iso-ne.com/isoexpress/web/reports/pricing/-/tree/lmps-rt-hourly-final. The Material is provided on an "as is" basis. ISO New England Inc., to the fullest extent permitted by law, disclaims all warranties, either express or implied, statutory or otherwise, including but not limited to the implied warranties of merchantability, non-infringement of third parties' rights, and fitness for particular purpose. Without limiting the foregoing, ISO New England Inc. makes no representations or warranties about the accuracy, reliability, completeness, date, or timeliness of the Material. ISO New England Inc. shall have no liability to you, your employer or any other third party based on your use of or reliance on the Material.

Data workup: LMP data were downloaded directly from the ISOs using scripts similar to the pvvm.data.download_lmps() function (see below for caveats), then repackaged into single-node single-year files using the pvvm.data.nodalize() function. These single-node single-year files were then combined into the dataframes included in this repository, using the procedure shown in the pvvm-pvtos-data.ipynb notebook for MISO. We provide these yearly dataframes, rather than the long-form data, to minimize file size and number. These dataframes can be unpacked into the single-node files used in the analysis using the pvvm.data.copylmps() function.

Code license and usage notes

Code (*.py and *.ipynb files) is provided under the MIT License, as specified in the pvvm/LICENSE file.

Updates to the code, if any, will be posted in the non-static repository at https://github.com/patrickbrown4/pvvm_pvtos. The code in the present repository has the following version-specific dependencies:

matplotlib: 3.0.3

numpy: 1.16.2

pandas: 0.24.2

pvlib: 0.6.1

scipy: 1.2.1

tqdm: 4.31.1

To use the NSRDB download functions, modify the "settings.py" file to insert a valid NSRDB API key, which can be requested from https://developer.nrel.gov/signup/. Locations can be specified by passing latitude, longitude floats to pvvm.data.downloadNSRDBfile(), or by passing a string googlemaps query to pvvm.io.queryNSRDBfile(). To use the googlemaps functionality, request a googlemaps API key (https://developers.google.com/maps/documentation/javascript/get-api-key) and insert it in the "settings.py" file.

Note that many of the ISO websites have changed in the time since the functions in the pvvm.data module were written and the LMP data used in the above papers were downloaded. As such, the pvvm.data.download_lmps() function no longer works for all ISOs and years. We provide this function to illustrate the general procedure used, and do not intend to maintain it or keep it up to date with the changing ISO websites. For up-to-date functions for accessing ISO data, the following repository (no connection to the present work) may be helpful: https://github.com/catalyst-cooperative/pudl.

This dataset records statistical data on the total energy consumption and consumption of major energy varieties by industry in Qinghai Province from 2001 to 2022. The data is divided by total consumption, agriculture, forestry, animal husbandry, fishery, industry, mining industry, manufacturing industry, etc. The data is compiled from the Qinghai Provincial Statistical Yearbook released by the Qinghai Provincial Bureau of Statistics. The dataset contains one data table with a total of five fields: Field 1: Industry Field 2: Total Energy Consumption Field 3: Raw coal consumption Field 4: Gasoline Consumption Field 5: Electricity consumption

This dataset contains High Energy Physics related research data and software (re)use indications (formal citations, informal mentions) in scholarly works. All research data and software resources were identified and extracted from CERN Open Data Portal. The (re)use indications were identified by a mix of approaches: use of citation discovery services and multiple search approaches in Google Scholar. All identified research data and software (re)use indications were classified according to their purpose, location, and elements.

The data was collected in 2018 for a PhD thesis on research data and software (re)use indications in scholarly works.

Individuals of large or dark-colored ectothermic species often have a higher reproduction and activity than small or light-colored ones. However, investments into body size or darker colors should negatively affect the fitness of individuals as they increase their growth and maintenance costs. Thus, it is unlikely that morphological traits directly affect species’ distribution and abundance. Yet, this simplification is frequently made in trait-based ecological analyses. Here, we integrated the energy allocation strategies of species into an ecophysiological framework to explore the mechanisms that link species’ morphological traits and population dynamics. We hypothesized that the effects of morphological traits on species’ distribution and abundance are not direct but mediated by components of the energy budget and that species can allocate more energy towards dispersal and reproduction if they compensate their energetic costs by reducing mobility costs or increasing energy uptake. To classify species’ energy allocation strategies, we used easily measured proxies for the mobility costs and energy uptake of butterflies that can be also applied to other taxa. We demonstrated that contrasting effects of morphological traits on distribution and abundance of butterfly species offset each other when species’ energy allocation strategies are not taken into account. Larger and darker butterfly species had wider distributions and were more abundant if they compensated the investment into body size and color darkness (i.e. melanin) by reducing their mobility costs or increasing energy uptake. Adults of darker species were more mobile and foraged less compared to lighter colored ones, if an investment into melanin was indirectly compensated via a size-dependent reduction of mobility costs or increase of energy uptake. Our results indicate that differences in the energy allocations strategies of species account for a considerable part of the variation in species’ distribution and abundance that is left unexplained by morphological traits alone and that ignoring these differences can lead to false mechanistic conclusions. Therefore, our findings highlight the potential of integrating proxies for species’ energy allocation strategies into trait-based models not only for understanding the physiological mechanisms underlying variation in species’ distribution and abundance, but also for improving predictions of the population dynamics of species.

Methods Proxies for mobility costs and energy uptake

As a proxy for the energetic costs of mobility, we measured the wingbeat frequency of 316 individuals of 102 butterfly species using high-speed camera footage taken during the years 2013 to 2017 at different sites in Central Europe (a total of 793,896 frames or 2,646 s). Wingbeat frequencies of individuals in Hz were calculated as wingbeat counts of each scene divided by its length (in s). Subsequently, for each species, we averaged wingbeat frequencies across individuals (median: 3 individuals, min: 1 individual, max: 9 individuals). To integrate across the peak and normal mobility costs of a species, we averaged wingbeat frequencies during in situ and escape flight (i.e. normal/peak flight, DATASET: energy_budget_butterflies-intra_specific_data.csv). When only normal or peak wingbeat frequencies were available for a species (i.e. for 1 and 43 species, respectively), we used values that were predicted based on the relationship between these two variables (mean_flight). Furthermore, while filming, we also recorded the ambient temperature to evaluate whether the wingbeat frequency of species was temperature dependent. However, the correlation between these two variables was not significant (temperature.C).

To obtain a proxy for the energy uptake of adult butterflies, we counted how often individuals were observed collecting nectar on flowers based on the results of a Google Images search (accessed on May 15, 2017). To avoid potential bias of the access point, which could result from Google’s search algorithms, we used the international homepage (i.e. google.com) and searched for the scientific name of a butterfly species. Of the first 100 hits, only images of clearly identifiable and living adult individuals were used for further analyses (DATASET: energy_budget_butterflies-links_google_image_search.xlsx). We assigned each image a value of 1 or 0 depending on whether the individual was observed foraging or not (i.e. whether the proboscis was inside the flower or not), and a value of 0.5 if it sat on a flower but the proboscis was not visible. Hence, to avoid potential observer biases (e.g. the preference of the photographers for taking pictures of butterflies on flowers), butterflies that clearly only sat on flowers were not considered as foraging. Finally, we averaged these values for each species (nectar_foraging_google). A rarefaction analysis showed that standard deviations calculated for an increasing number of randomly sampled images of species remained constant at 0.04 for sample sizes above 32 images. This suggests that our results are not affected by differences among locations and conditions of these observations and, although we used all images sampled for further analyses, it indicates that a relatively small number of images is already sufficient to provide a robust estimate for the propensity of nectar foraging of a species. The reliability of our approach was further confirmed by a positive relationship between image-based estimates and expert classifications of the nectar-foraging propensity of species (P < 0.001, rho = 0.31, n = 436; DATASET: energy_budget_butterflies-expert_nectar_foraging_classification.csv).

Morphological traits

Estimates of the color darkness, body size and wing size of a species were calculated based on scanned dorsal drawings of European butterfly species. In our study, we considered only data for females. Specifically, we used the inverted average RGB (i.e. color lightness) of pixels of the basal third of the wings and the body as an estimate of the color darkness of a species (color_lightness_8bit, DATASET: energy_budget_butterflies-species_level_data.csv). We considered only the basal third of the wings because their distal part is less relevant for thermoregulation in butterflies. As an estimate of the body size of a species, we used the sum of volumes of each pixel row of images of the body surface [π × (½ length of pixel row)2 × pixel edge length in mm; body_volume.mm3]. In addition, we calculated the wing size of images as the number of pixels of the four wings × pixel area in cm2(wing_area.cm2).

Distribution and abundance of species

Regional distributions (i.e. occupancy; OccuEU) were estimated based on gridded distribution data of species across Europe [in a grid of cells with a size of 50 km × 50 km, CGRS]. For each species, regional distributions were calculated by dividing the number of grid cells in which it was present by the total number of grid cells (1,720 grid cells). To calculate the local distribution and abundance of species (i.e. local occupancy and population density; OccuCH, AbundCH), we used survey data for butterfly species assessed as part of the Biodiversity Monitoring Switzerland during the years 2003–2016 (www.biodiversitymonitoring.ch, accessed on October 4, 2017). The monitoring scheme involved the counting of butterflies at 520 regularly placed sites (in a grid of cells with a size of 5 km × 5 km) along transects of 2.5 km length. Transects were visited four to seven times each year during comparable weather conditions. Species abundances were calculated as the average number of individuals per occupied transect and year. Note that this abundance measure is not correlated with the number of generations per year (nbr_generations).

Habitat availability

To account for the potential effect of habitat availability on the distribution and abundance of species, we used gridded distribution information on all 473 larval host plants of butterflies in Switzerland for the years 2003–2016 from the Info Flora Database (accessed on October 18, 2017; a grid of cells with a size of 5 km × 5 km). We considered only larval host plants of the butterfly species because adult butterflies are mainly generalist nectarivores. Based on these data, we then calculated the habitat availability for each butterfly species as the number of grid cells occupied by host plants divided by the total number of grid cells across Switzerland (i.e. occupancy of host plants; OccuCH_hostplants_logit).

Data transformation

To normalize the data, nectar-foraging propensity, habitat availability, local distribution and regional distribution were logit transformed, and wingbeat frequencies, body volume, color darkness, wing area, egg number and local abundance were loge transformed.

This dataset contains High Energy Physics related research data and software (re)use indications (formal citations, informal mentions) in scholarly works. All research data and software resources were identified and extracted from Zenodo. The (re)use indications were identified by a mix of approaches: use of citation discovery services and multiple search approaches in Google Scholar. All identified research data and software (re)use indications were classified according to their purpose, location, and elements.

The data was collected in 2018 for a PhD thesis on research data and software (re)use indications in scholarly works.

The ecological data of Zhangye City from 2001 to 2012 include: the reuse rate of industrial water, the comprehensive utilization rate of industrial solid, the ratio of environmental protection investment to GDP, the per capita water consumption, the share of ecological water, the use intensity of chemical fertilizer, the use intensity of pesticide, the use intensity of agricultural plastic film, and the energy consumption per unit GDP

This dataset contains High Energy Physics related research data and software (re)use indications (formal citations, informal mentions) in scholarly works. All research data and software resources were identified and extracted from INSPIRE-HEP. The (re)use indications were identified by a mix of approaches: use of citation discovery services and multiple search approaches in Google Scholar. All identified research data and software (re)use indications were classified according to their purpose, location, and elements.

The data was collected in 2018 for a PhD thesis on research data and software (re)use indications in scholarly works.

METHODOLOGICAL INFORMATION -------------------------- 1. Description of methods used for collection-generation of data: Two main sources of data have been used for this analysis: TotalEnergies' Universal Registration Documents from 1999 to 2022 and the Global Atlas of Environmental Justice (EJAtlas).8 The EJAtlas provides a dataform to systematically characterise and codify the main dimensions of a conflictive project [21,22]. At the end of 2023, the EJAtlas is reaching 4000 entries. The EJAtlas data collects information using a predetermined case entry sheet that combines both free text descriptions of various aspects of the conflict with a set of categorical variables for the contributor to check when applicable. EJAtlas cases include qualitative and quantitative variables such as a case description, geolocation, the main sources of conflict, project details and actors, data on the conflict stage and on forms of mobilisation, impacts, outcomes, and a reference list. This combination of open text and categorisation allows describing the specificity of the conflicts in their local contexts and at the same time conduct large comparative studies across regional, global or thematic scales. Through a global network of collaborators, the EJAtlas gathers information from local sources, always trying to be as faithful as possible to the local narratives and concerns, and referencing all data and significant claims with secondary data. All data submitted by collaborators is then moderated by a central team following a standard set of rules and format. This allows harnessing the geographic coverage and local specificity of a widespread and diverse network of contributors, while systematising information across the data sample. We found the EJAtlas well suited for this study since the repository already contained a significant amount of information on TotalEnergies environmental conflicts and its methodology facilitated the investigation of global patterns of impacts and resistances to TotalEnergies operations. We also draw on complementary information from conflicts not yet documented in the EJAtlas. 2. Methods for processing the data: All Universal Registration Documents for the company from 1999 to 2022 were screened to extract names of specific projects in seven categories: Coal (8), Oil and Gas exploration and production (368), LNG Terminals (47), Pipelines (64), Refineries (49), Power plants & Renewables (78) and Offsets, Recycling and CCS (15). The project names are reported in Supplementary Table 1. This database of TotalEnergies projects was used to understand the global geographic and operational scope of TotalEnergies, as well as to identify environmental conflicts related to specific projects using specific search strings in Google and Google Scholar (see search strings in Supplementary Methods). The identified conflicts were studied in greater depth and documented in the EJAtlas. We selected all EJAtlas cases involving TotalEnergies, its predecessors Total, Fina and ELF or its subsidiaries during the time of development of a conflict, notably CEPSA until 2011 and Novatek until 2022. At the start of this research, 43 cases involving TotalEnergies were already present in the EJAtlas database. We have subsequently added 7 more conflicts and updated some existing cases to include more detail, and contemporary information on the conflicts: we obtained a final list of 50 cases. We codified the cases by type of operation following the energy flux from extraction to consumption including exploration (11), extraction (17), pipeline (5), LNG (7), oil spills (3), refinery (5) petrochemical (1), and climate litigation (1). A full list of the conflicts is shown in Supplementary Table 2 and an EJAtlas featured map9 allows the reader to browse the different cases.

This data set records the statistical data of average annual domestic energy consumption per capita in Qinghai Province from 1993 to 2002, which is divided by industry, region, affiliation and registration type. The data are collected from the statistical yearbook of Qinghai Province issued by the Bureau of statistics of Qinghai Province. The data set consists of two data tables Average annual energy consumption per capita 1993-2001.xls Average annual energy consumption per capita 1993-2002.xls The data table structure is the same. For example, there are two fields in the data table of average annual energy consumption per capita from 1993 to 2001 Field 1: year Field 2: energy consumption per capita

This dataset records statistical data on the total energy consumption and composition of Qinghai Province from 1980 to 2022, divided by major years. The data is compiled from the Qinghai Provincial Statistical Yearbook released by the Qinghai Provincial Bureau of Statistics. Among them, the energy consumption and related data from 2005 to 2013 were revised based on the results of the second and third national economic censuses. For years 2015 and earlier, electricity was the sum of primary electricity and net electricity inflows. After 2016, electricity was the primary electricity consumption, calculated at equal value (coal consumption for power generation in the current year). Coal includes: raw coal, washed coal, other washed coal, coal products, coke, coke oven gas, coal gangue, blast furnace gas, other coking products, converter gas, other gas, etc. Petroleum includes: crude oil, gasoline, kerosene, diesel, fuel oil, naphtha, lubricating oil, paraffin, solvent oil, petroleum asphalt, petroleum coke, liquefied petroleum gas, refinery dry gas, etc. The dataset contains one data table, and the total energy consumption and composition data tables have a total of six fields: Field 1: Year Field 2: Total Energy Consumption Field 3: Composition of raw coal Field 4: Composition of Crude Oil Field 5: Composition of natural gas Field 6: Water and Electricity Composition

Google’s water withdrawals have increased more than three-fold since 2016, reaching 8.6 billion gallons in 2023. Roughly 90 percent of Google's water withdrawals were for use in its data centers around the world. Water is mainly used for cooling the data centers, and the company's increased water consumption is part of its strategy to reduce energy consumption and emissions, compared to air cooling.

This dataset records the statistical data of energy consumption elasticity coefficient in Qinghai Province from 1990 to 2022, divided by major years. The data is compiled from the Qinghai Provincial Statistical Yearbook released by the Qinghai Provincial Bureau of Statistics. The dataset contains one data table, the energy consumption elasticity coefficient data table, with a total of six fields: Field 1: Year Field 2: Energy consumption growth compared to the previous year Field 3: Electricity consumption growth compared to the previous year Field 4: GDP growth compared to the previous year Field 5: Energy consumption elasticity coefficient Field 6: Elastic coefficient of electricity consumption

The data set records the average energy consumption per 10000 yuan of gross industrial output value in Qinghai Province, and the data is divided according to the energy consumption of gross industrial output value. The data are collected from the statistical yearbook of Qinghai Province issued by the Bureau of statistics of Qinghai Province. The data set consists of three data tables The average energy consumption per 10000 yuan of gross industrial output value is 1997-1998.xls, Average energy consumption per ten thousand yuan of gross industrial output value 1998-1999.xls, The average energy consumption per ten thousand yuan of industrial output value was.xls from 1999 to 2000. The data table structure is the same. For example, there are four fields in the data table of energy consumption per 10000 yuan of gross industrial output value from 1997 to 1998 Field 1: extractive industries Field 2: manufacturing Field 3: Light Industry Field 4: heavy industry

This dataset represents the first comprehensive dataset covering global irrigation water energy consumption and carbon emissions, with a spatial resolution of 0.083° and a temporal span from 2000 to 2010. The dataset includes total energy consumption for irrigation water, total energy-related carbon emissions, energy consumption and carbon emissions implicit in different irrigation water sources (surface water and groundwater), pumping systems (diesel pumps and electric pumps), irrigation systems (drip irrigation, sprinkler irrigation, and surface irrigation), as well as irrigation energy consumption and carbon emissions under future 3°C sustainable irrigation expansion scenarios. This dataset is generated using a 'bottom-up' approach based on physical processes, considering factors such as irrigation water withdrawal, irrigation water sources, groundwater drawdown effects, irrigation systems, pumping systems, regional electricity carbon emission intensity, and electricity trade. Most input parameters have been calibrated and validated using ground observation data. Additionally, the estimation results have been compared and verified against regional statistical reports, ensuring reliability. This dataset fills the gap in energy consumption and carbon emissions in agricultural systems, enabling the identification of regions where future energy supply may constrain irrigation expansion, assessment of the pressure of irrigation on regional energy supply systems, analysis of the impact of carbon tax policies on irrigation agriculture, and assessment of agricultural input-output benefits. Furthermore, it will promote research related to climate-smart agriculture and energy-saving and emission-reduction in agricultural systems.

The data set records the basic situation of energy consumption survey of highway transportation in Qinghai Province, and the data is divided according to the basic situation of energy consumption survey of highway transportation in Qinghai Province. The data are collected from the statistical yearbook of Qinghai Province issued by the Bureau of statistics of Qinghai Province. The data set consists of seven data tables Survey on energy consumption of provincial highway transportation (2010). Xls Survey on energy consumption of provincial highway transportation (2011). Xls Survey on energy consumption of provincial highway transportation (2012). Xls Survey on energy consumption of provincial highway transportation (2013). Xls Survey on energy consumption of provincial highway transportation (2014). Xls Survey on energy consumption of provincial highway transportation (2015). Xls Survey on energy consumption of provincial highway transportation (2016). XLS. The data table structure is the same. For example, there are five fields in the data table of the provincial highway transportation energy consumption survey (2010) Field 1: Indicators Field 2: item Field 3: total number of operating vehicles Field 4: number of vehicles surveyed Field 5: total fuel consumption