The number of internet users in the United States was forecast to continuously increase between 2024 and 2029 by in total 13.5 million users (+4.16 percent). After the ninth consecutive increasing year, the number of users is estimated to reach 337.67 million users and therefore a new peak in 2029. Notably, the number of internet users of was continuously increasing over the past years.Depicted is the estimated number of individuals in the country or region at hand, that use the internet. As the datasource clarifies, connection quality and usage frequency are distinct aspects, not taken into account here.The shown data are an excerpt of Statista's Key Market Indicators (KMI). The KMI are a collection of primary and secondary indicators on the macro-economic, demographic and technological environment in up to 150 countries and regions worldwide. All indicators are sourced from international and national statistical offices, trade associations and the trade press and they are processed to generate comparable data sets (see supplementary notes under details for more information).

As of January 2024, there were approximately 331 million internet users in the United States. Meanwhile, around 239 million were social media users. As of the third quarter of 2022, over 90 percent of U.S. internet users accessed the internet via any mobile phone.

Country-level AI adoption indicators and sector breakdown for the United States in 2025, combining public datasets and modeled estimates.

As of October 2025, 6.04 billion individuals worldwide were internet users, which amounted to 73.2 percent of the global population. Of this total, 5.66 billion, or 68.7 percent of the world's population, were social media users. Global internet usage Connecting billions of people worldwide, the internet is a core pillar of the modern information society. Northern Europe ranked first among worldwide regions by the share of the population using the internet in 2025. In the Netherlands, Norway, and Saudi Arabia, 99 percent of the population used the internet as of February 2025. North Korea was at the opposite end of the spectrum, with virtually no internet usage penetration among the general population, ranking last worldwide. Eastern Asia was home to the largest number of online users worldwide—over 1.34 billion at the latest count. Southern Asia ranked second, with around 1.2 billion internet users. China, India, and the United States rank ahead of other countries worldwide by the number of internet users. Worldwide internet user demographics As of 2024, the share of female internet users worldwide was 65 percent, five percent less than that of men. Gender disparity in internet usage was bigger in African countries, with around a 10-percent difference. Worldwide regions, like the Commonwealth of Independent States and Europe, showed a smaller usage gap between these two genders. As of 2024, global internet usage was higher among individuals between 15 and 24 years old across all regions, with young people in Europe representing the most considerable usage penetration, 98 percent. In comparison, the worldwide average for the age group of 15 to 24 years was 79 percent. The income level of the countries was also an essential factor for internet access, as 93 percent of the population of the countries with high income reportedly used the internet, as opposed to only 27 percent of the low-income markets.

This layer shows Computers and Internet Use. This is shown by state and county boundaries. This service contains the 2017-2021 release of data from the American Community Survey (ACS) 5-year data, and contains estimates and margins of error. There are also additional calculated attributes related to this topic, which can be mapped or used within analysis. This layer is symbolized to show Percentage of Households with a Broadband Internet Subscription. To see the full list of attributes available in this service, go to the "Data" tab, and choose "Fields" at the top right. Current Vintage: 2018-2022ACS Table(s): DP02, S2801Data downloaded from: Census Bureau's API for American Community Survey Date of API call: January 18, 2022National Figures: data.census.govThe United States Census Bureau's American Community Survey (ACS):About the SurveyGeography & ACSTechnical DocumentationNews & UpdatesThis ready-to-use layer can be used within ArcGIS Pro, ArcGIS Online, its configurable apps, dashboards, Story Maps, custom apps, and mobile apps. Data can also be exported for offline workflows. Please cite the Census and ACS when using this data.Data Note from the Census:Data are based on a sample and are subject to sampling variability. The degree of uncertainty for an estimate arising from sampling variability is represented through the use of a margin of error. The value shown here is the 90 percent margin of error. The margin of error can be interpreted as providing a 90 percent probability that the interval defined by the estimate minus the margin of error and the estimate plus the margin of error (the lower and upper confidence bounds) contains the true value. In addition to sampling variability, the ACS estimates are subject to nonsampling error (for a discussion of nonsampling variability, see Accuracy of the Data). The effect of nonsampling error is not represented in these tables.Data Processing Notes:Boundaries come from the Cartographic Boundaries via US Census TIGER geodatabases. Boundaries are updated at the same time as the data updates, and the boundary vintage appropriately matches the data vintage as specified by the Census. These are Census boundaries with water and/or coastlines clipped for cartographic purposes. For state and county boundaries, the water and coastlines are derived from the coastlines of the 500k TIGER Cartographic Boundary Shapefiles. The original AWATER and ALAND fields are still available as attributes within the data table (units are square meters). The States layer contains 52 records - all US states, Washington D.C., and Puerto Rico. The Counties (and equivalent) layer contains 3221 records - all counties and equivalent, Washington D.C., and Puerto Rico municipios. See Areas Published. Percentages and derived counts, and associated margins of error, are calculated values (that can be identified by the "_calc_" stub in the field name), and abide by the specifications defined by the American Community Survey.Field alias names were created based on the Table Shells.Margin of error (MOE) values of -555555555 in the API (or "*****" (five asterisks) on data.census.gov) are displayed as 0 in this dataset. The estimates associated with these MOEs have been controlled to independent counts in the ACS weighting and have zero sampling error. So, the MOEs are effectively zeroes, and are treated as zeroes in MOE calculations. Other negative values on the API, such as -222222222, -666666666, -888888888, and -999999999, all represent estimates or MOEs that can't be calculated or can't be published, usually due to small sample sizes. All of these are rendered in this dataset as null (blank) values.

United States Trade Balance: Advanced Technology Products data was reported at -10.321 USD bn in May 2018. This records a decrease from the previous number of -9.518 USD bn for Apr 2018. United States Trade Balance: Advanced Technology Products data is updated monthly, averaging -1.877 USD bn from Jan 1989 (Median) to May 2018, with 353 observations. The data reached an all-time high of 5.221 USD bn in Dec 1998 and a record low of -15.841 USD bn in Nov 2017. United States Trade Balance: Advanced Technology Products data remains active status in CEIC and is reported by US Census Bureau. The data is categorized under Global Database’s USA – Table US.JA003: Trade Statistics: Census Basis.

This statistic represents the use of technology devices among U.S. adults with or without disability, as of 2016. As of November 2016, disabled Americans were less likely to possess tech devices or have home broadband, in comparison to Americans without disabilities.

As of June 2024, 99 percent of adults in the United States between 18 and 49 years were internet users, making it the age group with the highest level of internet penetration in the country. A further share of 97 percent of adults using the internet were between 18 and 29 years old. Mobile internet usage Mobile internet usage continues to surge in the United States, with 96.2 percent of internet users accessing the web via phones as of the third quarter of 2023. In April 2024, YouTube's mobile app led with a 74 percent audience reach, while TikTok topped weekly engagement among social apps. Mobile apps and privacy Mobile apps became an essential part of mobile users, this high usage raised new concerns about data privacy. By June 2023, three in four internet users supported data localization to protect their information. Additionally, As of September 2024, 13.5 percent of paid iOS apps stated that they collected user data, with 88 percent of this data used to enhance app functionality.

Graph and download economic data for Future Technology Spending; Percent Expecting Decreases for New York (DISCONTINUED) (TSFDNA156MNFRBNY) from Jul 2001 to May 2024 about information technology, NY, expenditures, percent, and manufacturing.

United States SBP: Business Practices: Adopted/Expanded Use of Digital Technology data was reported at 27.700 % in 10 Jan 2022. This records an increase from the previous number of 27.100 % for 03 Jan 2022. United States SBP: Business Practices: Adopted/Expanded Use of Digital Technology data is updated weekly, averaging 26.400 % from Nov 2021 (Median) to 10 Jan 2022, with 9 observations. The data reached an all-time high of 27.700 % in 10 Jan 2022 and a record low of 26.100 % in 22 Nov 2021. United States SBP: Business Practices: Adopted/Expanded Use of Digital Technology data remains active status in CEIC and is reported by U.S. Census Bureau. The data is categorized under Global Database’s United States – Table US.S043: Small Business Pulse Survey: Weekly, Beg Monday (Discontinued).

LearnPlatform is a technology platform in the kindergarten-12th grade (K-12) market providing a broadly interoperable platform to the breadth of educational technology (edtech) solutions in the United States K-12 field. A key component of edtech effectiveness is integrated reporting on tool usage and, where applicable, evidence of efficacy. With COVID closures, LearnPlatform is a resource to measure whether students are accessing digital resources within distance learning constraints. This platform provides a source of data to understand if students are accessing digital resources, and where resources have disparate usage and impact. This study includes educational technology usage across over 8,000 tools used in the education field in 2020.

United States Exports: FAS: Advanced Technology Products data was reported at 31.395 USD bn in Sep 2018. This records an increase from the previous number of 29.797 USD bn for Aug 2018. United States Exports: FAS: Advanced Technology Products data is updated monthly, averaging 17.785 USD bn from Jan 1989 (Median) to Sep 2018, with 357 observations. The data reached an all-time high of 34.853 USD bn in Mar 2018 and a record low of 5.762 USD bn in Jan 1989. United States Exports: FAS: Advanced Technology Products data remains active status in CEIC and is reported by US Census Bureau. The data is categorized under Global Database’s USA – Table US.JA003: Trade Statistics: Census Basis.

Actual value and historical data chart for United States High Technology Exports Percent Of Manufactured Exports

United States Imports: Customs: Advanced Technology Products data was reported at 44.050 USD bn in Sep 2018. This records an increase from the previous number of 42.222 USD bn for Aug 2018. United States Imports: Customs: Advanced Technology Products data is updated monthly, averaging 19.456 USD bn from Jan 1989 (Median) to Sep 2018, with 357 observations. The data reached an all-time high of 46.141 USD bn in Nov 2017 and a record low of 3.704 USD bn in Jan 1989. United States Imports: Customs: Advanced Technology Products data remains active status in CEIC and is reported by US Census Bureau. The data is categorized under Global Database’s United States – Table US.JA003: Trade Statistics: Census Basis.

Scenario data from the Electrification Futures Study Scenarios of Electric Technology Adoption and Power Consumption for the United States report. Annual projections from 2017 to 2050 of electric technology adoption and energy consumption for five scenarios reference electrification medium electrification high electrification electrification potential and low electricity growth. Each scenario assumes moderate technology advancement as described by Jadun et al. 2017 https//www.nrel.gov/docs/fy18osti/70485.pdf.

Financial overview and grant giving statistics of State Science and Technology Institute

US Renewable Energy Programs

Generation, States, Programs, Contacts, and More

By [source]

About this dataset

The Renewable Energy Program Database in the United States is a comprehensive source of quantitative information on renewable energy incentive and regulatory policies, program parameters, and generation data. It features a holistic overview of the production and utilization of renewable energy across all fifty states in the United States making it a terrific asset for anyone looking to understand sustainable energy practices and analyze how they can apply their knowledge to help create more robust policies. With this dataset, analysts can not only assess current usage and trends but also uncover potential development opportunities that would benefit from additional investment incentives or other regulatory solutions. Moreover, it also provides insight into how we could better use existing technologies to build a greener future for our planet!

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How to use the dataset

This dataset enables you to explore and analyze the use of renewable energy programs in the US. It is organized into two main files: program_type.csv, which provides information on the types of renewable energy programs available in the United States; and parameter_set_sector.csv, which provides detailed quantitative information about renewable energy incentive and regulatory policies, program parameters and generation data across all fifty states. The dataset can be used to track trends in renewable energy policy across states, compare different types of renewable energy programs, explore financial incentives for green initiatives or identify high-performing practices at state levels.

To get started with this dataset, choose a file from either program_type or parameter set sectors and open it up in Kaggle Notebooks or some other data analysis platform of your choice. From there you will have access to all available parameters including name (name of the programme), primary technologies (the dominant technology used within that programme) as well as other variables related to those programmes like sectors (catergories based on geographical scope) total funding(total funding provided) etcetera.. Once you are familiar with that particular dataset look for relationships between different variables by creating visualizations such as scatterplots or bar graphs. In addition you can also view your analysis through geospatial maps by linking a map visualization application such as leaflet map box etcetera..

Research Ideas

• Analyzing the effectiveness of renewable energy incentives and policies by state, to inform decisions about enacting new policies or reforming current programs.
• Creating predictive models based on the dataset to estimate potential future trends in renewable energy use and investment in specific sectors or markets within each state.
• Comparing across states in terms of their relative success with renewable energy programs, providing insight into best practices that other states could adopt or replicate

Acknowledgements

If you use this dataset in your research, please credit the original authors. Data Source

License

License: CC0 1.0 Universal (CC0 1.0) - Public Domain Dedication No Copyright - You can copy, modify, distribute and perform the work, even for commercial purposes, all without asking permission. See Other Information.

Columns

File: program_type.csv | Column name | Description | |:--------------|:---------------------------------------------------| | name | The name of the renewable energy program. (String) |

File: parameter_set_sector.csv

Acknowledgements

If you use this dataset in your research, please credit the original authors. If you use this dataset in your research, please credit .

Case Studies in Public Interest Technology Today, there are multiple ways where digital technologies adversely impacts the public interest, whether that’s the spread of misinformation online, the loss of privacy, the threat of algorithmic discrimination, and more. Public interest technology is an emerging field that seeks to use cross-disciplinary techniques to research and address these issues in order to advance the public interest. For this dissertation, I present three different case studies of public interest tech research projects, each of which focuses on a different technology and relevant public interest. In Chapter 2, I research how Facebook’s advertising algorithms can discriminate by race and ethnicity. In Chapter 3, I test how the predictability of Social Security Number (SSN) assignment based on easily accessible data about Americans presents a risk of identity theft. In Chapter 4, I demonstrate how TraceFi, a Wi-Fi based collocation detection technology, can be deployed for COVID-19 contact tracing. I propose how we can adapt Lawrence Lessig’s pathetic dot model as the “Three Forces Model of Public Interest Tech” to understand the current dysfunctional state of relationships between technology, society, and the public interest, where the public interest is often affected as an output of technology but not fully considered as an input. The three forces of the law, norms, and market can affect a given technology or vice versa which in turn affects the public interest. For different combinations of technologies and public interests, the amount of force exerted by the law, norms, or market could also differ and so could the degree of feedback between the technology and each of the forces. Since the normative goal of public interest tech as a field is to ultimately advance the public interest, the goal state of the Three Forces Model demonstrates how the public interest can be an input for the law, norms, and market in how they affect a technology’s design and usage, which would in turn affect the public interest. Stakeholders relevant to each of the forces can consider the public interest as a priority in how they interact with a technology and its designer. In Chapter 5, I present how we can apply the Three Forces Model for Public Interest Tech to each case study to describe the current state and the ideal goal state. In order to effectively respond to the multiple ways of how digital technologies have adversely impacted the public interest, we need a “whole-society” strategy that coordinates our laws, norms, and markets in how they interact with our technologies to prioritize the public interest. As public interest technologists, we need to work across disciplines to advance the public interest. Let’s get started.

Financial overview and grant giving statistics of Tri-State Education and Technology Foundation

Financial overview and grant giving statistics of United States Technology Education Foundation