Meta’s electricity use has increased in recent years, as newer data centers have come online. In 2022, the company's electricity usage surpassed 11.5 terawatt-hours, a 22-percent year-over-year increase. Before 2021 the company was known as Facebook.

Meta’s renewable energy commitments

Over the past few years, Facebook/Meta has been moving towards using more renewable energy, with the goal of achieving 100 percent renewable energy to support facilities reached in 2020. This establishment of renewables also allows other nearby businesses to have the option to use renewable energy. Meta is one of the largest corporate purchasers of renewable energy worldwide.

Emissions and water consumption

Meta has set goals to reduce its carbon footprint by 50 percent in 2030. In recent years, the company was able to separate growth in the business from increased emissions, annually reducing their operational greenhouse gas emissions. The company is also aware of its water consumption and has committed to a circular system that allows for the reuse of water consumed.

According to a study conducted in October 2021, the video-sharing app TikTok was the mobile social media app with the highest energy consumption among the most popular apps in its category. TikTok reportedly had a measured 15.81 milliamperes per hour (mAh) energy consumption. The Facebook mobile app followed, with an energy consumption corresponding to 12.36 mAh. The YouTube mobile app reported the smallest amount of energy consumed, at 8.58 mAh.

Meta's electricty consumption increased 33 percent year-on-year in 2023, surpassing 15 terawatt-hours. In comparison to 2019, the company's power consumption nearly tripled. A key factor for Microsoft's power consumption rising has been the expansion of its global data centers. Meta's electricity intensity (consumption normalized by revenue generated) also grew by some 30 percent year-over-year in 2023.

Facebook's use of renewable energy has increased significantly in recent years. In 2020, the company reached its goal of 100 percent renewable energy usage, up from a renewable-share of 35 percent in 2015.

The Utility Energy Registry (UER) is a database platform that provides streamlined public access to aggregated community-scale utility-reported energy data. The UER is intended to promote and facilitate community-based energy planning and energy use awareness and engagement. On April 19, 2018, the New York State Public Service Commission (PSC) issued the Order Adopting the Utility Energy Registry under regulatory CASE 17-M-0315. The order requires utilities under its regulation to develop and report community energy use data to the UER. This dataset includes electricity and natural gas usage data reported at the ZIP Code level collected under a data protocol in effect between 2016 and 2021. Other UER datasets include energy use data reported at the city, town, village, and county level. Data collected after 2021 were collected according to a modified protocol. Those data may be found at https://data.ny.gov/Energy-Environment/Utility-Energy-Registry-Monthly-ZIP-Code-Energy-Us/g2x3-izm4. Data in the UER can be used for several important purposes such as planning community energy programs, developing community greenhouse gas emissions inventories, and relating how certain energy projects and policies may affect a particular community. It is important to note that the data are subject to privacy screening and fields that fail the privacy screen are withheld. The New York State Energy Research and Development Authority (NYSERDA) offers objective information and analysis, innovative programs, technical expertise, and support to help New Yorkers increase energy efficiency, save money, use renewable energy, and accelerate economic growth. reduce reliance on fossil fuels. To learn more about NYSERDA’s programs, visit nyserda.ny.gov or follow us on X, Facebook, YouTube, or Instagram.

The Utility Energy Registry (UER) is a database platform that provides streamlined public access to aggregated community-scale energy data. The UER is intended to promote and facilitate community-based energy planning and energy use awareness and engagement. On April 19, 2018, the New York State Public Service Commission (PSC) issued the Order Adopting the Utility Energy Registry under regulatory CASE 17-M-0315. The order requires utilities and CCA administrators under its regulation to develop and report community energy use data to the UER.

This dataset includes electricity and natural gas usage data reported by utilities at the county level. Other UER datasets include energy use data reported at the city, town, and village, and ZIP code level.

Data in the UER can be used for several important purposes such as planning community energy programs, developing community greenhouse gas emissions inventories, and relating how certain energy projects and policies may affect a particular community. It is important to note that the data are subject to privacy screening and fields that fail the privacy screen are withheld.

The New York State Energy Research and Development Authority (NYSERDA) offers objective information and analysis, innovative programs, technical expertise, and support to help New Yorkers increase energy efficiency, save money, use renewable energy, and reduce reliance on fossil fuels. To learn more about NYSERDA’s programs, visit nyserda.ny.gov or follow us on X, Facebook, YouTube, or Instagram.

The global mega data center market is experiencing robust growth, driven by the exponential increase in data generation from various sources, including cloud computing, the Internet of Things (IoT), and big data analytics. The market's Compound Annual Growth Rate (CAGR) is expected to remain strong throughout the forecast period (2025-2033), fueled by increasing digitalization across industries and the rising demand for high-performance computing capabilities. Key drivers include the need for enhanced data security, scalability, and energy efficiency, leading organizations to invest in larger, more sophisticated mega data center facilities. The market is segmented by type (e.g., hyperscale, colocation) and application (e.g., cloud computing, enterprise IT), with hyperscale data centers dominating the market share due to their ability to handle massive volumes of data. Leading companies like Apple, Facebook, Google, Microsoft, and others are making significant investments in expanding their global mega data center footprint to meet the growing demand, leading to a highly competitive landscape. Geographic expansion, particularly in regions with favorable regulatory environments and access to renewable energy sources, presents significant growth opportunities. While high capital expenditure and operational costs pose challenges, the long-term benefits of improved efficiency and scalability outweigh these restraints, ensuring continued market expansion. Despite these positive trends, the market faces certain constraints. These include the increasing complexity of managing mega data centers, the need for specialized expertise, and the growing concerns regarding energy consumption and environmental impact. Addressing these challenges through technological advancements in areas such as energy-efficient cooling systems and sustainable infrastructure will be critical for the sustained growth of the mega data center market. Furthermore, stringent regulations related to data privacy and security will continue to shape the market landscape, compelling companies to invest heavily in cybersecurity measures. Overall, the forecast suggests continued strong growth for the global mega data center market, driven by technological innovation, increasing data volumes, and the need for robust, scalable data infrastructure across diverse industries. However, navigating regulatory landscapes and addressing environmental concerns will play a crucial role in determining the long-term trajectory of this rapidly evolving sector.

In terms of carbon dioxide emissions from social media apps in 2021, Youtube (0.46 grams of carbon dioxide equivalent per minute) was first in the ranking, closely followed by Facebook (0.79 gEqCO2) and LinkedIn (0.71 gEqCO2), all apps with low carbon emissions. The social network with the most impactful news feed in terms of carbon dioxide emissions was Tik Tok. This is because this social network is based exclusively on watching videos and preloads videos from the newsfeed.

Of the leading ten technology companies worldwide based on market capitalization, Samsung is the company consuming the most electricity at nearly 30 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.

It includes the questionnaires involved in the data research and the corresponding answers collected. In addition, it also includes some charts obtained from the analysis. Variables involved in linear regression analysis are also included in the file.

Meta's total operational greenhouse gas emissions amounted to 50,610 metric tons of CO₂ equivalent in 2023. Meta cut its operational emissions by 94 percent in 2020 from a 2017 baseline. This was primarily achived by matching 100 percent of the electricity use of its data centers and offices with renewable energy and addressing residual emissions with carbon removal projects. Since then, Meta's operations have produced net zero emissions.

The German Internet Panel (GIP) is an infrastructure project. The GIP serves to collect data on individual attitudes and preferences that are relevant for political and economic decision-making processes.

The questionnaire contains numerous experimental variations in the survey instruments. For further information, please refer to the study documentation.

Topics: Environmental attitudes and behaviour: acceptance of higher prices for environmentally friendly products; acceptance of energy savings against climate change; frequency of purchasing environmentally friendly products; frequency of energy savings; assessment of the probability of contributing to environmental protection by purchasing environmentally friendly products; assessment of the probability of reducing climate change by limiting personal energy consumption; recall of the given answers to the above questions; certainty of one´s own assessments.

Political attitudes: Agreement on political positions (experiment to test the effects of differently verbalised response scales): the state should take measures to reduce income disparities; workers need strong trade unions to protect their working conditions and wages; large income disparities are justified in order to adequately reward different talents and achievements; for a society to be just, differences in people´s living standards should be small; social benefits lead to more equality in society.

EU: Self-classification for European unification; classification of the views of parties to European unification (CDU/CSU, SPD, AfD, FDP, Bündnis 90/ Die Grünen, Die Linke; left-right classification of the European Parliament; intention to vote in the next European elections; advocacy of abolishing the EU if most people do not agree with EU decisions; satisfaction with EU services; agreement with various statements: bad for the German economy or cultural life in Germany is undermined when people come from other countries to live here (nativism); good understanding and assessment of important EU policy issues or politicians in the EU care about what people think (internal/external effectiveness).

Possession of mobile phone, computer or laptop and tablet computer; mobile phone is smartphone; smartphone type; use of mobile phone, computer or laptop and tablet computer to access the internet; use of another device (e.g. e-book reader) to access the internet; use of social networks (Facebook, Instagram, LinkedIn, Twitter, Xing) on the internet or as mobile app.

Conjoint Experiment on preferred pension level for a fictitious person with selected attributes (name, income, contribution years, children, partner´s co-supply)

Conjoint experiment with three decisions on the preferred level of unemployment benefit (ALG I, ALG II, sanctions in the case of ALG II) for a fictitious person with selected attributes (name, age, reason for unemployment, motivation, children, date).

Experiment on the preferred pension reform from different reform alternatives with different attributes (retirement age, pension level, contribution rate and on the retirement without deductions before reaching retirement age).

Experiment on the responsibility of the state for (free) child care in day-care centres with different question text (for children of all parents, for children of working parents, for children of single parents, for children of migrants).

Debt brake: Preferred timing for the debt brake of the federal and state governments; evaluation of the debt brake; probability of compliance with the debt brake from 2020 by the state of the main residence; opinion on compliance with the debt brake if other states do not comply with it; state of the main residence in the state fiscal equalization system recipient state or donor state; tax evasion: estimated extent of tax evasion in Germany in percent; opinion on the justifiability of tax evasion.

Demography: sex; age (year of birth, categorized); highest educational degree; highest professional qualification; marital status; household size; employment status; German citizenship; frequency of private Internet usage; federal state; migration background.

Additionally coded: Respondent ID; household ID, GIP; person ID (within the household); year of recruitment (2012, 2014, 2018); interview date; current online status; assignment to experimental groups.

Questionnaire evaluation (interesting, varied, relevant, long, difficult, too personal); assessment of the survey as a whole; respondent made further comments on the questionnaire.

This statistic shows the distribution of electricity suppliers in Great Britain (UK) in 2014, by social media usage. 85 percent of respondents who used Facebook used a 'Big Six' supplier, as did 82 percent of respondents who used Twitter.