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How to Write and Publish a Research Paper for a Peer-Reviewed Journal

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• Published: 30 April 2020
• Volume 36 , pages 909–913, ( 2021 )

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• Clara Busse   ORCID: orcid.org/0000-0002-0178-1000 1 &
• Ella August   ORCID: orcid.org/0000-0001-5151-1036 1 , 2  

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Communicating research findings is an essential step in the research process. Often, peer-reviewed journals are the forum for such communication, yet many researchers are never taught how to write a publishable scientific paper. In this article, we explain the basic structure of a scientific paper and describe the information that should be included in each section. We also identify common pitfalls for each section and recommend strategies to avoid them. Further, we give advice about target journal selection and authorship. In the online resource 1 , we provide an example of a high-quality scientific paper, with annotations identifying the elements we describe in this article.

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Introduction

Writing a scientific paper is an important component of the research process, yet researchers often receive little formal training in scientific writing. This is especially true in low-resource settings. In this article, we explain why choosing a target journal is important, give advice about authorship, provide a basic structure for writing each section of a scientific paper, and describe common pitfalls and recommendations for each section. In the online resource 1 , we also include an annotated journal article that identifies the key elements and writing approaches that we detail here. Before you begin your research, make sure you have ethical clearance from all relevant ethical review boards.

Select a Target Journal Early in the Writing Process

We recommend that you select a “target journal” early in the writing process; a “target journal” is the journal to which you plan to submit your paper. Each journal has a set of core readers and you should tailor your writing to this readership. For example, if you plan to submit a manuscript about vaping during pregnancy to a pregnancy-focused journal, you will need to explain what vaping is because readers of this journal may not have a background in this topic. However, if you were to submit that same article to a tobacco journal, you would not need to provide as much background information about vaping.

Information about a journal’s core readership can be found on its website, usually in a section called “About this journal” or something similar. For example, the Journal of Cancer Education presents such information on the “Aims and Scope” page of its website, which can be found here: https://www.springer.com/journal/13187/aims-and-scope .

Peer reviewer guidelines from your target journal are an additional resource that can help you tailor your writing to the journal and provide additional advice about crafting an effective article [ 1 ]. These are not always available, but it is worth a quick web search to find out.

Identify Author Roles Early in the Process

Early in the writing process, identify authors, determine the order of authors, and discuss the responsibilities of each author. Standard author responsibilities have been identified by The International Committee of Medical Journal Editors (ICMJE) [ 2 ]. To set clear expectations about each team member’s responsibilities and prevent errors in communication, we also suggest outlining more detailed roles, such as who will draft each section of the manuscript, write the abstract, submit the paper electronically, serve as corresponding author, and write the cover letter. It is best to formalize this agreement in writing after discussing it, circulating the document to the author team for approval. We suggest creating a title page on which all authors are listed in the agreed-upon order. It may be necessary to adjust authorship roles and order during the development of the paper. If a new author order is agreed upon, be sure to update the title page in the manuscript draft.

In the case where multiple papers will result from a single study, authors should discuss who will author each paper. Additionally, authors should agree on a deadline for each paper and the lead author should take responsibility for producing an initial draft by this deadline.

Structure of the Introduction Section

The introduction section should be approximately three to five paragraphs in length. Look at examples from your target journal to decide the appropriate length. This section should include the elements shown in Fig.  1 . Begin with a general context, narrowing to the specific focus of the paper. Include five main elements: why your research is important, what is already known about the topic, the “gap” or what is not yet known about the topic, why it is important to learn the new information that your research adds, and the specific research aim(s) that your paper addresses. Your research aim should address the gap you identified. Be sure to add enough background information to enable readers to understand your study. Table 1 provides common introduction section pitfalls and recommendations for addressing them.

The main elements of the introduction section of an original research article. Often, the elements overlap

Methods Section

The purpose of the methods section is twofold: to explain how the study was done in enough detail to enable its replication and to provide enough contextual detail to enable readers to understand and interpret the results. In general, the essential elements of a methods section are the following: a description of the setting and participants, the study design and timing, the recruitment and sampling, the data collection process, the dataset, the dependent and independent variables, the covariates, the analytic approach for each research objective, and the ethical approval. The hallmark of an exemplary methods section is the justification of why each method was used. Table 2 provides common methods section pitfalls and recommendations for addressing them.

Results Section

The focus of the results section should be associations, or lack thereof, rather than statistical tests. Two considerations should guide your writing here. First, the results should present answers to each part of the research aim. Second, return to the methods section to ensure that the analysis and variables for each result have been explained.

Begin the results section by describing the number of participants in the final sample and details such as the number who were approached to participate, the proportion who were eligible and who enrolled, and the number of participants who dropped out. The next part of the results should describe the participant characteristics. After that, you may organize your results by the aim or by putting the most exciting results first. Do not forget to report your non-significant associations. These are still findings.

Tables and figures capture the reader’s attention and efficiently communicate your main findings [ 3 ]. Each table and figure should have a clear message and should complement, rather than repeat, the text. Tables and figures should communicate all salient details necessary for a reader to understand the findings without consulting the text. Include information on comparisons and tests, as well as information about the sample and timing of the study in the title, legend, or in a footnote. Note that figures are often more visually interesting than tables, so if it is feasible to make a figure, make a figure. To avoid confusing the reader, either avoid abbreviations in tables and figures, or define them in a footnote. Note that there should not be citations in the results section and you should not interpret results here. Table 3 provides common results section pitfalls and recommendations for addressing them.

Discussion Section

Opposite the introduction section, the discussion should take the form of a right-side-up triangle beginning with interpretation of your results and moving to general implications (Fig.  2 ). This section typically begins with a restatement of the main findings, which can usually be accomplished with a few carefully-crafted sentences.

Major elements of the discussion section of an original research article. Often, the elements overlap

Next, interpret the meaning or explain the significance of your results, lifting the reader’s gaze from the study’s specific findings to more general applications. Then, compare these study findings with other research. Are these findings in agreement or disagreement with those from other studies? Does this study impart additional nuance to well-accepted theories? Situate your findings within the broader context of scientific literature, then explain the pathways or mechanisms that might give rise to, or explain, the results.

Journals vary in their approach to strengths and limitations sections: some are embedded paragraphs within the discussion section, while some mandate separate section headings. Keep in mind that every study has strengths and limitations. Candidly reporting yours helps readers to correctly interpret your research findings.

The next element of the discussion is a summary of the potential impacts and applications of the research. Should these results be used to optimally design an intervention? Does the work have implications for clinical protocols or public policy? These considerations will help the reader to further grasp the possible impacts of the presented work.

Finally, the discussion should conclude with specific suggestions for future work. Here, you have an opportunity to illuminate specific gaps in the literature that compel further study. Avoid the phrase “future research is necessary” because the recommendation is too general to be helpful to readers. Instead, provide substantive and specific recommendations for future studies. Table 4 provides common discussion section pitfalls and recommendations for addressing them.

Follow the Journal’s Author Guidelines

After you select a target journal, identify the journal’s author guidelines to guide the formatting of your manuscript and references. Author guidelines will often (but not always) include instructions for titles, cover letters, and other components of a manuscript submission. Read the guidelines carefully. If you do not follow the guidelines, your article will be sent back to you.

Finally, do not submit your paper to more than one journal at a time. Even if this is not explicitly stated in the author guidelines of your target journal, it is considered inappropriate and unprofessional.

Your title should invite readers to continue reading beyond the first page [ 4 , 5 ]. It should be informative and interesting. Consider describing the independent and dependent variables, the population and setting, the study design, the timing, and even the main result in your title. Because the focus of the paper can change as you write and revise, we recommend you wait until you have finished writing your paper before composing the title.

Be sure that the title is useful for potential readers searching for your topic. The keywords you select should complement those in your title to maximize the likelihood that a researcher will find your paper through a database search. Avoid using abbreviations in your title unless they are very well known, such as SNP, because it is more likely that someone will use a complete word rather than an abbreviation as a search term to help readers find your paper.

After you have written a complete draft, use the checklist (Fig. 3 ) below to guide your revisions and editing. Additional resources are available on writing the abstract and citing references [ 5 ]. When you feel that your work is ready, ask a trusted colleague or two to read the work and provide informal feedback. The box below provides a checklist that summarizes the key points offered in this article.

Checklist for manuscript quality

Data Availability

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Acknowledgments

Ella August is grateful to the Sustainable Sciences Institute for mentoring her in training researchers on writing and publishing their research.

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Busse, C., August, E. How to Write and Publish a Research Paper for a Peer-Reviewed Journal. J Canc Educ 36 , 909–913 (2021). https://doi.org/10.1007/s13187-020-01751-z

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• 1 Department of Maternal and Child Health, University of North Carolina Gillings School of Global Public Health, 135 Dauer Dr, 27599, Chapel Hill, NC, USA.
• 2 Department of Maternal and Child Health, University of North Carolina Gillings School of Global Public Health, 135 Dauer Dr, 27599, Chapel Hill, NC, USA. [email protected].
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• DOI: 10.1007/s13187-020-01751-z

Communicating research findings is an essential step in the research process. Often, peer-reviewed journals are the forum for such communication, yet many researchers are never taught how to write a publishable scientific paper. In this article, we explain the basic structure of a scientific paper and describe the information that should be included in each section. We also identify common pitfalls for each section and recommend strategies to avoid them. Further, we give advice about target journal selection and authorship. In the online resource 1, we provide an example of a high-quality scientific paper, with annotations identifying the elements we describe in this article.

Keywords: Manuscripts; Publishing; Scientific writing.

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Home » How to Publish a Research Paper – Step by Step Guide

How to Publish a Research Paper – Step by Step Guide

Table of Contents

Publishing a research paper is an important step for researchers to disseminate their findings to a wider audience and contribute to the advancement of knowledge in their field. Whether you are a graduate student, a postdoctoral fellow, or an established researcher, publishing a paper requires careful planning, rigorous research, and clear writing. In this process, you will need to identify a research question , conduct a thorough literature review , design a methodology, analyze data, and draw conclusions. Additionally, you will need to consider the appropriate journals or conferences to submit your work to and adhere to their guidelines for formatting and submission. In this article, we will discuss some ways to publish your Research Paper.

How to Publish a Research Paper

To Publish a Research Paper follow the guide below:

• Conduct original research : Conduct thorough research on a specific topic or problem. Collect data, analyze it, and draw conclusions based on your findings.
• Write the paper : Write a detailed paper describing your research. It should include an abstract, introduction, literature review, methodology, results, discussion, and conclusion.
• Choose a suitable journal or conference : Look for a journal or conference that specializes in your research area. You can check their submission guidelines to ensure your paper meets their requirements.
• Prepare your submission: Follow the guidelines and prepare your submission, including the paper, abstract, cover letter, and any other required documents.
• Submit the paper: Submit your paper online through the journal or conference website. Make sure you meet the submission deadline.
• Peer-review process : Your paper will be reviewed by experts in the field who will provide feedback on the quality of your research, methodology, and conclusions.
• Revisions : Based on the feedback you receive, revise your paper and resubmit it.
• Acceptance : Once your paper is accepted, you will receive a notification from the journal or conference. You may need to make final revisions before the paper is published.
• Publication : Your paper will be published online or in print. You can also promote your work through social media or other channels to increase its visibility.

How to Choose Journal for Research Paper Publication

Here are some steps to follow to help you select an appropriate journal:

• Identify your research topic and audience : Your research topic and intended audience should guide your choice of journal. Identify the key journals in your field of research and read the scope and aim of the journal to determine if your paper is a good fit.
• Analyze the journal’s impact and reputation : Check the impact factor and ranking of the journal, as well as its acceptance rate and citation frequency. A high-impact journal can give your paper more visibility and credibility.
• Consider the journal’s publication policies : Look for the journal’s publication policies such as the word count limit, formatting requirements, open access options, and submission fees. Make sure that you can comply with the requirements and that the journal is in line with your publication goals.
• Look at recent publications : Review recent issues of the journal to evaluate whether your paper would fit in with the journal’s current content and style.
• Seek advice from colleagues and mentors: Ask for recommendations and suggestions from your colleagues and mentors in your field, especially those who have experience publishing in the same or similar journals.
• Be prepared to make changes : Be prepared to revise your paper according to the requirements and guidelines of the chosen journal. It is also important to be open to feedback from the editor and reviewers.

List of Journals for Research Paper Publications

There are thousands of academic journals covering various fields of research. Here are some of the most popular ones, categorized by field:

General/Multidisciplinary

• Nature: https://www.nature.com/
• Science: https://www.sciencemag.org/
• PLOS ONE: https://journals.plos.org/plosone/
• Proceedings of the National Academy of Sciences (PNAS): https://www.pnas.org/
• The Lancet: https://www.thelancet.com/
• JAMA (Journal of the American Medical Association): https://jamanetwork.com/journals/jama

Social Sciences/Humanities

• Journal of Personality and Social Psychology: https://www.apa.org/pubs/journals/psp
• Journal of Consumer Research: https://www.journals.uchicago.edu/journals/jcr
• Journal of Educational Psychology: https://www.apa.org/pubs/journals/edu
• Journal of Applied Psychology: https://www.apa.org/pubs/journals/apl
• Journal of Communication: https://academic.oup.com/joc
• American Journal of Political Science: https://ajps.org/
• Journal of International Business Studies: https://www.jibs.net/
• Journal of Marketing Research: https://www.ama.org/journal-of-marketing-research/

Natural Sciences

• Journal of Biological Chemistry: https://www.jbc.org/
• Cell: https://www.cell.com/
• Science Advances: https://advances.sciencemag.org/
• Chemical Reviews: https://pubs.acs.org/journal/chreay
• Angewandte Chemie: https://onlinelibrary.wiley.com/journal/15213765
• Physical Review Letters: https://journals.aps.org/prl/
• Journal of Geophysical Research: https://agupubs.onlinelibrary.wiley.com/journal/2156531X
• Journal of High Energy Physics: https://link.springer.com/journal/13130

Engineering/Technology

• IEEE Transactions on Neural Networks and Learning Systems: https://ieeexplore.ieee.org/xpl/RecentIssue.jsp?punumber=5962385
• IEEE Transactions on Power Systems: https://ieeexplore.ieee.org/xpl/RecentIssue.jsp?punumber=59
• IEEE Transactions on Medical Imaging: https://ieeexplore.ieee.org/xpl/RecentIssue.jsp?punumber=42
• IEEE Transactions on Control Systems Technology: https://ieeexplore.ieee.org/xpl/RecentIssue.jsp?punumber=87
• Journal of Engineering Mechanics: https://ascelibrary.org/journal/jenmdt
• Journal of Materials Science: https://www.springer.com/journal/10853
• Journal of Chemical Engineering of Japan: https://www.jstage.jst.go.jp/browse/jcej
• Journal of Mechanical Design: https://asmedigitalcollection.asme.org/mechanicaldesign

Medical/Health Sciences

• New England Journal of Medicine: https://www.nejm.org/
• The BMJ (formerly British Medical Journal): https://www.bmj.com/
• Journal of the American Medical Association (JAMA): https://jamanetwork.com/journals/jama
• Annals of Internal Medicine: https://www.acpjournals.org/journal/aim
• American Journal of Epidemiology: https://academic.oup.com/aje
• Journal of Clinical Oncology: https://ascopubs.org/journal/jco
• Journal of Infectious Diseases: https://academic.oup.com/jid

List of Conferences for Research Paper Publications

There are many conferences that accept research papers for publication. The specific conferences you should consider will depend on your field of research. Here are some suggestions for conferences in a few different fields:

Computer Science and Information Technology:

• IEEE International Conference on Computer Communications (INFOCOM): https://www.ieee-infocom.org/
• ACM SIGCOMM Conference on Data Communication: https://conferences.sigcomm.org/sigcomm/
• IEEE Symposium on Security and Privacy (SP): https://www.ieee-security.org/TC/SP/
• ACM Conference on Computer and Communications Security (CCS): https://www.sigsac.org/ccs/
• ACM Conference on Human-Computer Interaction (CHI): https://chi2022.acm.org/

Engineering:

• IEEE International Conference on Robotics and Automation (ICRA): https://www.ieee-icra.org/
• International Conference on Mechanical and Aerospace Engineering (ICMAE): http://www.icmae.org/
• International Conference on Civil and Environmental Engineering (ICCEE): http://www.iccee.org/
• International Conference on Materials Science and Engineering (ICMSE): http://www.icmse.org/
• International Conference on Energy and Power Engineering (ICEPE): http://www.icepe.org/

Natural Sciences:

• American Chemical Society National Meeting & Exposition: https://www.acs.org/content/acs/en/meetings/national-meeting.html
• American Physical Society March Meeting: https://www.aps.org/meetings/march/
• International Conference on Environmental Science and Technology (ICEST): http://www.icest.org/
• International Conference on Natural Science and Environment (ICNSE): http://www.icnse.org/
• International Conference on Life Science and Biological Engineering (LSBE): http://www.lsbe.org/

Social Sciences:

• Annual Meeting of the American Sociological Association (ASA): https://www.asanet.org/annual-meeting-2022
• International Conference on Social Science and Humanities (ICSSH): http://www.icssh.org/
• International Conference on Psychology and Behavioral Sciences (ICPBS): http://www.icpbs.org/
• International Conference on Education and Social Science (ICESS): http://www.icess.org/
• International Conference on Management and Information Science (ICMIS): http://www.icmis.org/

How to Publish a Research Paper in Journal

Publishing a research paper in a journal is a crucial step in disseminating scientific knowledge and contributing to the field. Here are the general steps to follow:

• Choose a research topic : Select a topic of your interest and identify a research question or problem that you want to investigate. Conduct a literature review to identify the gaps in the existing knowledge that your research will address.
• Conduct research : Develop a research plan and methodology to collect data and conduct experiments. Collect and analyze data to draw conclusions that address the research question.
• Write a paper: Organize your findings into a well-structured paper with clear and concise language. Your paper should include an introduction, literature review, methodology, results, discussion, and conclusion. Use academic language and provide references for your sources.
• Choose a journal: Choose a journal that is relevant to your research topic and audience. Consider factors such as impact factor, acceptance rate, and the reputation of the journal.
• Follow journal guidelines : Review the submission guidelines and formatting requirements of the journal. Follow the guidelines carefully to ensure that your paper meets the journal’s requirements.
• Submit your paper : Submit your paper to the journal through the online submission system or by email. Include a cover letter that briefly explains the significance of your research and why it is suitable for the journal.
• Wait for reviews: Your paper will be reviewed by experts in the field. Be prepared to address their comments and make revisions to your paper.
• Revise and resubmit: Make revisions to your paper based on the reviewers’ comments and resubmit it to the journal. If your paper is accepted, congratulations! If not, consider revising and submitting it to another journal.
• Address reviewer comments : Reviewers may provide comments and suggestions for revisions to your paper. Address these comments carefully and thoughtfully to improve the quality of your paper.
• Submit the final version: Once your revisions are complete, submit the final version of your paper to the journal. Be sure to follow any additional formatting guidelines and requirements provided by the journal.
• Publication : If your paper is accepted, it will be published in the journal. Some journals provide online publication while others may publish a print version. Be sure to cite your published paper in future research and communicate your findings to the scientific community.

How to Publish a Research Paper for Students

Here are some steps you can follow to publish a research paper as an Under Graduate or a High School Student:

• Select a topic: Choose a topic that is relevant and interesting to you, and that you have a good understanding of.
• Conduct research : Gather information and data on your chosen topic through research, experiments, surveys, or other means.
• Write the paper : Start with an outline, then write the introduction, methods, results, discussion, and conclusion sections of the paper. Be sure to follow any guidelines provided by your instructor or the journal you plan to submit to.
• Edit and revise: Review your paper for errors in spelling, grammar, and punctuation. Ask a peer or mentor to review your paper and provide feedback for improvement.
• Choose a journal : Look for journals that publish papers in your field of study and that are appropriate for your level of research. Some popular journals for students include PLOS ONE, Nature, and Science.
• Submit the paper: Follow the submission guidelines for the journal you choose, which typically include a cover letter, abstract, and formatting requirements. Be prepared to wait several weeks to months for a response.
• Address feedback : If your paper is accepted with revisions, address the feedback from the reviewers and resubmit your paper. If your paper is rejected, review the feedback and consider revising and resubmitting to a different journal.

How to Publish a Research Paper for Free

Publishing a research paper for free can be challenging, but it is possible. Here are some steps you can take to publish your research paper for free:

• Choose a suitable open-access journal: Look for open-access journals that are relevant to your research area. Open-access journals allow readers to access your paper without charge, so your work will be more widely available.
• Check the journal’s reputation : Before submitting your paper, ensure that the journal is reputable by checking its impact factor, publication history, and editorial board.
• Follow the submission guidelines : Every journal has specific guidelines for submitting papers. Make sure to follow these guidelines carefully to increase the chances of acceptance.
• Submit your paper : Once you have completed your research paper, submit it to the journal following their submission guidelines.
• Wait for the review process: Your paper will undergo a peer-review process, where experts in your field will evaluate your work. Be patient during this process, as it can take several weeks or even months.
• Revise your paper : If your paper is rejected, don’t be discouraged. Revise your paper based on the feedback you receive from the reviewers and submit it to another open-access journal.
• Promote your research: Once your paper is published, promote it on social media and other online platforms. This will increase the visibility of your work and help it reach a wider audience.

Journals and Conferences for Free Research Paper publications

Here are the websites of the open-access journals and conferences mentioned:

Open-Access Journals:

• PLOS ONE – https://journals.plos.org/plosone/
• BMC Research Notes – https://bmcresnotes.biomedcentral.com/
• Frontiers in… – https://www.frontiersin.org/
• Journal of Open Research Software – https://openresearchsoftware.metajnl.com/
• PeerJ – https://peerj.com/

Conferences:

• IEEE Global Communications Conference (GLOBECOM) – https://globecom2022.ieee-globecom.org/
• IEEE International Conference on Computer Communications (INFOCOM) – https://infocom2022.ieee-infocom.org/
• IEEE International Conference on Data Mining (ICDM) – https://www.ieee-icdm.org/
• ACM SIGCOMM Conference on Data Communication (SIGCOMM) – https://conferences.sigcomm.org/sigcomm/
• ACM Conference on Computer and Communications Security (CCS) – https://www.sigsac.org/ccs/CCS2022/

Importance of Research Paper Publication

Research paper publication is important for several reasons, both for individual researchers and for the scientific community as a whole. Here are some reasons why:

• Advancing scientific knowledge : Research papers provide a platform for researchers to present their findings and contribute to the body of knowledge in their field. These papers often contain novel ideas, experimental data, and analyses that can help to advance scientific understanding.
• Building a research career : Publishing research papers is an essential component of building a successful research career. Researchers are often evaluated based on the number and quality of their publications, and having a strong publication record can increase one’s chances of securing funding, tenure, or a promotion.
• Peer review and quality control: Publication in a peer-reviewed journal means that the research has been scrutinized by other experts in the field. This peer review process helps to ensure the quality and validity of the research findings.
• Recognition and visibility : Publishing a research paper can bring recognition and visibility to the researchers and their work. It can lead to invitations to speak at conferences, collaborations with other researchers, and media coverage.
• Impact on society : Research papers can have a significant impact on society by informing policy decisions, guiding clinical practice, and advancing technological innovation.

Advantages of Research Paper Publication

There are several advantages to publishing a research paper, including:

• Recognition: Publishing a research paper allows researchers to gain recognition for their work, both within their field and in the academic community as a whole. This can lead to new collaborations, invitations to conferences, and other opportunities to share their research with a wider audience.
• Career advancement : A strong publication record can be an important factor in career advancement, particularly in academia. Publishing research papers can help researchers secure funding, grants, and promotions.
• Dissemination of knowledge : Research papers are an important way to share new findings and ideas with the broader scientific community. By publishing their research, scientists can contribute to the collective body of knowledge in their field and help advance scientific understanding.
• Feedback and peer review : Publishing a research paper allows other experts in the field to provide feedback on the research, which can help improve the quality of the work and identify potential flaws or limitations. Peer review also helps ensure that research is accurate and reliable.
• Citation and impact : Published research papers can be cited by other researchers, which can help increase the impact and visibility of the research. High citation rates can also help establish a researcher’s reputation and credibility within their field.

About the author

Muhammad Hassan

Researcher, Academic Writer, Web developer

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Published a research paper? What next??

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In our earlier editorials, we have already discussed the importance of conducting good-quality medical research, composing an original research paper, and getting the paper published successfully.[ 1 , 2 , 3 ] We have also given a roadmap for reviewing an original research paper.[ 4 ] The current editorial deals with some important post-publication issues that every author should be acquainted with.

Replying to Letters to Editor Received on the Published Manuscript

After the publication of the research paper, the editors may receive one or more “letters to editors,” supporting or criticizing or commenting on the published research paper. If the contents of such letters arouse genuine concerns/issues, the editor will ask for a rebuttal/reply to the same, from the authors of the original paper and publish both of these in a subsequent issue of the journal. It is the responsibility of the corresponding author of the original paper to contact the co-authors and provide a reply that has been drafted and approved by all the authors.[ 5 ] Such correspondence indicates that the paper has aroused sufficient interest in the readers. Replying to such letters gives the authors an opportunity to explain their research findings anew and also address issues that may not have been addressed in their research paper (published earlier).

Editorial Commentaries

The editor may invite an editorial commentary on the accepted research paper, which is usually published in the same issue as the original paper. The commentary is usually written by an expert in the concerned field (who would probably also have reviewed the article and recommended its publication). The purpose of the commentary is to provide a balanced view for interpreting the results of the study and give insights into the clinical applicability/relevance of the study findings. Such commentaries also reflect the experience and the opinion of the expert who is writing the commentary.

MEDLINE and Other indexation

The prestige of a publication rests in its representation in an “indexed journal” of a highly rated database such as MEDLINE, PubMed, Scopus, Embase, and Web of Science. Such indexation not only increases the prestige of a journal but also provides wider access to its content. MEDLINE, of the US National Library of Medicine (NLM), is one of the most widely used biomedical journal citation databases, containing more than 26 million articles (from 1946 to the present) published in more than 5,200 journals. It is available through PubMed free of charge and by subscription via database vendors (Ovid and EBSCO).[ 6 , 7 ] Publishers submit journals to the National Institutes of Health (NIH)-chartered advisory committee, the Literature Selection Technical Review Committee (LSTRC), which reviews and recommends journals for indexation in MEDLINE based on the scientific policy and scientific quality.[ 6 ] PubMed ( www.pubmed.gov ), developed by the National Center for Biotechnology Information (NCBI) at the US NLM, is a free search engine for retrieval of the literature, having more than 30 million citations and abstracts on biomedical and life sciences across several NLM literature resources.[ 6 , 7 ] It provides access to all of MEDLINE, journals/manuscripts deposited in PubMed Central (PMC), and NCBI Bookshelf. The update occurs daily with reference data supplied directly by publishers, often before a journal issue is released. To be indexed in PubMed, a journal should be selected as a MEDLINE journal or be deposited to the PMC.[ 6 ]

Medical Subject Headings (MeSH) terms—a controlled and hierarchically organized vocabulary thesaurus—are used by the NLM to index and search biomedical literature. They provide an overview of an article's content through a set of terms pertaining to main headings (descriptors) and subheadings (qualifiers), with yearly updates. Indexers (generally librarians trained to read MEDLINE published articles) assign relevant MeSH indexing terms based on the content/concept of an article, using words from an official MeSH list.[ 8 ] The manual assignment of MeSH terms is laborious, subjective, time-consuming, and expensive, which has led to the development of the Medical Text Indexer (MTI), a MeSH prediction tool that assists NLM indexers by providing recommendations for MeSH terms.[ 9 ]

Scopus ( www.scopus.com ) is another subscription-based citation database produced by Elsevier Co. and maintained by independent subject matter experts. It indexes about 4,600 health science titles including 100% of MEDLINE and Embase coverage.[ 10 ]

Embase (Excerpta Medica Database) ( www.embase.com ) is a biomedical as well as a pharmacological bibliographic database. This subscription-based Elsevier database (32 million records of over 8,500 currently published journals since 1947) assists information managers and pharmacovigilance in licensed drugs. Emtree is the Embase thesaurus, and all journals listed in MEDLINE are also registered in Embase, with additional 2,900 journals unique to it.[ 11 ]

The Web of Science (Thomson Reuters) is an interdisciplinary subscription-based database with records (from 1900 onward) of multiple bibliographic databases. It includes Science Citation Index Expanded (SCI-EXPANDED), a medical database, and helps in article recovery and citation search.[ 7 ]

Publons ( http://publons.com/ ), a free commercial website, combines publications, citation metrics, peer-reviews, and journal editorial work, all in one place. It serves as a platform for publishers to seek and connect to peer-reviewers, reports global peer-review activity, and provides peer-review training for early-career researchers.[ 12 ]

Google Scholar ( http://scholar.google.com/ ), a mainstream free academic crawler-based search engine, has content across academic disciplines, countries, and languages, and over 380 million records. Indexing in Google Scholar enhances accessibility, sharing, and online citation worldwide, particularly for open-access (OA) journals. Google Scholar offers free alerts (via email) of citations to the authors of their publications indexed with it, once the authors register for such alerts. A Google Scholar search also focuses on individual articles and not journals, improves article retrieval (including unpublished conference material), shows more frequently cited works higher in search, and lists the papers citing original papers (via “Cited by”).[ 13 ]

Posting of the Pdf of the Final Published Article on Websites/Servers

Posting of the complete paper or the portable document format (“pdf”) of the final published paper on websites/servers (self-archiving) may be done only if the paper has been published in an OA journal and if the journal policy allows such a posting (this precaution is to be taken to prevent copyright infringement). Some journals allow the revised accepted (pre-print) version of the manuscript to be shared but not the final printed pdf version. These details of the necessary permissions required are usually given on the website of the journal/publisher. It is the responsibility of the author who is posting such a pre-print version to check whether the journal policy allows him/her to do so. Usually, the journal editors/publishers send the final, published pdf copy to the corresponding author. The corresponding author may then share it with the co-authors of the paper. However, it should be remembered that the copyright of the paper is with the publisher (usually) and the publisher provides the pdf to the corresponding author with a rider that mentions that “the pdf is for personal and educational purposes only and should not be distributed or printed commercially or distributed systematically.” This is especially true for the non-OA journals.

Getting the Research Noticed by the Medical Fraternity

It is necessary that a research paper gets read by the medical fraternity all over the world. For this, there are various avenues such as sharing the title and abstract or their links with professional colleagues on social media (such as Telegram, Whatsapp, Facebook, Twitter, and LinkedIn) or via emails on groups/listserv, displaying the title and abstract on the website of the journal (in the table of contents of the journal) or the website of the institution where the work was carried out, posting the title and abstract on websites such as the Researchgate ( www.researchgate.net ) or Mendeley ( www.mendeley.com ), discussion of the paper at journal clubs, and inclusion of the results in subsequent presentations at conferences/seminars by the authors. There may be some limitations on sharing the full text of the paper or the pdf version of the published paper as discussed earlier in this editorial. The journal editor/publisher may also send details to the corresponding author on how to increase the visibility of their paper. The increased visibility is most likely to translate into better citations and research impact for the paper. The visibility of the paper can be enhanced by (after checking the journal's OA policy)

• The journal's website and its bibliographic linking.
• Institutional open archive repository, where one may post the pdf in the archive with a link to the article on the journal's website.
• By depositing the article in a subject-based OAI-PMH (Open Archives Initiative Protocol for Metadata Harvesting) compliant repository (subject-wise list of repositories is available from http://opcit.eprints.org/explorearchives.shtml#disciplinary ).
• Linking the paper from as many websites as possible using citation and social bookmarking tools such as GetCited ( http://www.getcited.org/add/ ), CiteULike ( http://www.citeulike.org/register ), Connotea ( http://www.connotea.org/register ), Zotero ( http://www.zotero.org/ ), and Stumbleupon ( http://www.stumbleupon.com/sign_up.php?pre2 = hp_join ).
• Linking the article from an appropriate topic in Wikipedia.
• Depositing the paper with the NLM's PubMed Central, if the authors have received an NIH grant ( http://www.nihms.nih.gov/db/sub.cgi ) as NIH insists that publicly funded research should be available to everyone without having to pay.
• Linking the paper from the author's personal/institutional web pages.

The Journal of Postgraduate Medicine approves self-archiving of articles (final accepted version) on OAI (Open Archives Initiative)—compliant institutional/subject-based repository.[ 14 ] Self-archiving enables maximum visibility, impact, access, and usage and can be done by the author himself or via digital archivers in the author's institution/library.[ 15 ] Self-archiving can be expedited by the installation of OAI-compliant Eprint Archives in university/research institutions, and self-archiving pre-peer-review preprints (without the embargo period) and post-peer-review post-prints (or corrigenda file) (after the embargo period) on the author's personal website, company/institutional repository or archive, not-for-profit subject-based preprint servers or repositories.[ 15 , 16 ] “Embargo period” refers to the time post-publication (commonly ranging from 12 to 24 months), after which a subscribed article is made freely available/openly accessible to users.

Open Access

Traditionally, publishing an article would incur no charges toward author submission, peer-review, and publication. However, users are often charged a subscription fee for full-article access, which limits free access to the literature. The novel concept of OA has emerged in the last two decades through pioneers such as BioMed Central and Public Library of Science (PLoS) with online-only journals.[ 17 ] Fully-OA journals make all their articles freely and immediately accessible online (without embargo period) under a Creative Commons (CC) or equivalent open copyright license permitting anyone to “read, download, copy, distribute, print, search, or link to the full-texts of articles, crawl them for indexing, pass them as data to software, or use them for any other lawful purpose” through two established routes—“gold” and “green”.[ 18 , 19 ] In the “gold” route, the authors pay a fee—an article processing charge (APC)–to facilitate free and immediate access to their published articles. These charges are journal-specific and may range from 500 to 5000 US dollars, which are often prohibitive and unaffordable by Indian authors. If financial support for OA research is provided by the author's institution or organizations, such as the Indian Council of Medical Research (ICMR), and INCLEN Trust (International Clinical Epidemiology Network- INCLEN), then Indian authors may be able to afford these publication charges. In the “green” route, the author publishes the research article in any journal and then archives it in an institutional repository (University, a central repository [e.g. PubMed Central] or an OA website) based on the journal self-archiving policies.[ 17 , 19 ] This balances the researcher's freedom to publish and share work, and the publisher's control on quality. Publishing in a reputable OA journal provides versatility and visibility, and in return the academic researcher receives a higher research impact through citation counts.[ 20 ]

Many OA journals use CC licenses, an easy alternative to standard copyrights, permitting authors to determine the use of their work broadly, minus the need to look into individual permission requests. A CC license allows copying and redistribution of material in any medium/format (sharing) and remixing, transforming, and building upon the material (adapting). Restrictive elements such as “Attribution” require users to cite the creator of the work, “Non-Commercial” prohibits users from making commercial use of the work, “No Derivates” prohibit users from making modifications to the work, and “Share Alike” require users to apply the same licenses to a new work they create with the original work. These clauses limit the re-use, but provide useful protection to scholars, research subjects, and the OA nature of the publication. The elements can be combined, as is the Attribution-Noncommercial-Share Alike license, or CC-BY-NC-SA, or the Attribution-Non-Commercial-No Derivatives or CC-BY-NC-ND.[ 21 ]

The increase in the number of OA journals has often led to questions about their quality. This and the increasing pressure on researchers to “publish” or “perish” has fostered an increasing growth in medical journals, hoping to attract eager young academic researchers to publish their work in their journals.[ 5 ] The Directory of Open Access Journals (DOAJ) is a community-curated database that provides comprehensive access and quality control over the content of OA scientific and scholarly journals. DOAJ aims to increase the visibility and ease of use of OA journals, thereby increasing their usage and impact.[ 22 ]

The Journal Impact Factor and Personal Research Impact Factors

Several journal-/author-/article-level metric tools are available via indexing databases (e.g., Scopus and Web of Science) that enable users to track the scholarly impact of a journal, author, or article. One of the most popular ways of assessing a journal's importance is via its journal impact factor (JIF). The 2-year JIF (in any given year) is the ratio between the number of citations received in that year for publications in that journal in the two preceding years and the total number of “citable items” published in that journal during the two previous years. An impact factor could also consider shorter or longer periods of citations and sources.[ 23 ]

Author impact factor (AIF) similarly evaluates the impact of an individual author; however, because the number is generally not large, other citation metrics are used.[ 24 ] One such prominent influence measure is “h-index” (or Hirsch index), which effectively combines papers (indicating quantity/productivity) with citations (indicating quality/impact), thus evaluating an individual author's publication career. It is a count of the largest number of papers (h) from an author that has at least (h) number of citations. It enables a comparison of researchers from the same field with equally long careers, predicts future scientific achievements, and helps in decisions pertaining to tenure positions/grants.[ 25 ] The “i10-index” (introduced by Google), in contrast, is a simple tally of a researcher's publications with at least 10 citations. The “i10-index” is straightforward, easy to calculate (using “My Citations” on Google Scholar), and helps to identify important/influential papers out of an author's publications (those that are cited at least 10 times). However, it is restricted to Google Scholar and does not account for the total number of publications and total citations of an author, thus not giving a clear impression of an author's research productivity.[ 26 ]

Citation analysis involves measuring the number of citations that a particular work has received, indicating the overall quality of that work, whereas citation count is the total number of an individual's citations. Citation counts measure the impact and performance of individual researchers as well as departments, research institutions, universities, books, journals, and nations.[ 27 ]

Citation-based metrics may take years to accumulate and are not always the best indicator of practical impact in fields, such as clinical medicine. Article-level metrics (ALMs) measure the impact/uptake of an individual journal article on the scientific community post-publication and include usage, citations, social bookmarking and dissemination activity, media and blog coverage, discussion activity and ratings.[ 28 ] They thus measure the dissemination and reach of published research articles in practical fields. PLOS uses the category labels of Viewed, Cited, Saved, Discussed, and Recommended.[ 28 ] ALMs are valuable to researchers (track and share the impact of published work), research institutions, funders, and publishers. The PLOS Application Programming Interface (API) for ALMs is freely and publicly available from https://web.archive.org/web/20140408224328/http://api.plos.org/alm/using-the-alm-api/ and allows users with programming skills to extract data for various research purposes.[ 29 ]

The ORCID (Open Researcher and Contributor ID) [ https://orcid.org/register ]

ORCID, via its unique 16-digit author identifier, provides a digital name—or an iD—that uniquely and persistently identifies researchers and other contributors to their research effort. By connecting iDs to different research activities (grant proposal submissions, manuscripts to journal publishers, and datasets to data repositories) and affiliations across multiple research information platforms, ORCID enables recognition and reduces the reporting burden for researchers. As a researcher and author, it is important to be recognized and receive full credit for their contributions and research in work. The ORCID provides a unique identifier for the research that is linked to names rather than institutions, thus researchers can maintain the same iD throughout their career, even when their institutional affiliation changes.[ 30 ] It allows researchers to receive full credit for their contributions and eliminates mistaken identity, especially when there are multiple authors with the same name. It also makes the submission process easier by allowing users to sign in to multiple journal submission sites with one username and password. ORCID can be applied to research outputs to identify, validate, and confirm authorship as well as track research output. Some journals now print the ORCID number of an author in the publication. A single click on the displayed ORCID numbers gives the reader the entire list of publications by the author. It also easily integrates with other databases such as Crossref, ResearchID, and SCOPUS.[ 30 , 31 ]

Researcher ID (developed by Thomas Reuters and used in Web of Science) and Scopus Author ID (developed by Elsevier and used in Scopus) are similar identifiers provided by subscription-based proprietary systems. Researcher ID (obtained by creating a Researcher ID account) allows researchers to manage their publication lists, track citations and h-index, and identify potential collaborators. A Scopus Author ID is automatically assigned to an author with a Scopus-indexed publication and enables tracking publications indexed in the Scopus citation database and building metric reports.[ 32 ]

Concluding Remarks

In the current scenario, increasing importance is being given to research and publications—as a measure of individual/institutional progress as well as a benchmark to determine recruitment, promotions, and funding. Thus, it has become mandatory for an individual to keep working on quality research, followed by writing and publishing it successfully.[ 33 ] However, publication is not the end of an author's work but the beginning of another important process. The value of a publication lies in its wide accessibility and impact. Bibliographic databases, such as MEDLINE, Embase, and Scopus, compile data from a selection of journals (such documented journals are “indexed” in that database), thus improving the visibility and access on a literature search. The use of database-specific thesaurus/controlled vocabulary such as MeSH (for MEDLINE) and Emtree (for EMBASE) enables precise search outcomes.[ 7 ] An increase in the number of OA journals and processes such as “self-archiving” has opened avenues for the wide dissemination of published information.[ 15 ] Many articles are freely available online immediately after publication (gold OA), whereas many permit the authors to archive in an institutional repository, subject to journal policies.[ 17 ] The use of the CC license and its restrictive elements allows the authors to choose how their work can be used.[ 21 ] Bibliometrics help to measure academic/scholarly activity and scientific impact, but should not be obsessed over.[ 23 ] Their shortcomings in the true measurement of the impact of journals, articles, and authors have necessitated a suitable replacement, one with a more effective and meaningful evaluation of the true influence of research. The availability of free resources such as ORCID has linked an individual's research over various platforms and provided consolidated data of one's research activities. It is important that authors are aware of these post-publication resources and utilize them proactively to disseminate one's research and ensure a meaningful research impact.

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Home → Get Published → How to Publish a Research Paper: A Step-by-Step Guide

How to Publish a Research Paper: A Step-by-Step Guide

Jordan Kruszynski

• January 4, 2024

You’re in academia.

You’re going steady.

Your research is going well and you begin to wonder: ‘ How exactly do I get a research paper published?’

If this is the question on your lips, then this step-by-step guide is the one for you. We’ll be walking you through the whole process of how to publish a research paper.

Publishing a research paper is a significant milestone for researchers and academics, as it allows you to share your findings, contribute to your field of study, and start to gain serious recognition within the wider academic community. So, want to know how to publish a research paper? By following our guide, you’ll get a firm grasp of the steps involved in this process, giving you the best chance of successfully navigating the publishing process and getting your work out there.

Understanding the Publishing Process

To begin, it’s crucial to understand that getting a research paper published is a multi-step process. From beginning to end, it could take as little as 2 months before you see your paper nestled in the pages of your chosen journal. On the other hand, it could take as long as a year .

Below, we set out the steps before going into more detail on each one. Getting a feel for these steps will help you to visualise what lies ahead, and prepare yourself for each of them in turn. It’s important to remember that you won’t actually have control over every step – in fact, some of them will be decided by people you’ll probably never meet. However, knowing which parts of the process are yours to decide will allow you to adjust your approach and attitude accordingly.

Each of the following stages will play a vital role in the eventual publication of your paper:

• Preparing Your Research Paper
• Finding the Right Journal
• Crafting a Strong Manuscript
• Navigating the Peer-Review Process
• Submitting Your Paper
• Dealing with Rejections and Revising Your Paper

Step 1: Preparing Your Research Paper

It all starts here. The quality and content of your research paper is of fundamental importance if you want to get it published. This step will be different for every researcher depending on the nature of your research, but if you haven’t yet settled on a topic, then consider the following advice:

• Choose an interesting and relevant topic that aligns with current trends in your field. If your research touches on the passions and concerns of your academic peers or wider society, it may be more likely to capture attention and get published successfully.
• Conduct a comprehensive literature review (link to lit. review article once it’s published) to identify the state of existing research and any knowledge gaps within it. Aiming to fill a clear gap in the knowledge of your field is a great way to increase the practicality of your research and improve its chances of getting published.
• Structure your paper in a clear and organised manner, including all the necessary sections such as title, abstract, introduction (link to the ‘how to write a research paper intro’ article once it’s published) , methodology, results, discussion, and conclusion.
• Adhere to the formatting guidelines provided by your target journal to ensure that your paper is accepted as viable for publishing. More on this in the next section…

Step 2: Finding the Right Journal

Understanding how to publish a research paper involves selecting the appropriate journal for your work. This step is critical for successful publication, and you should take several factors into account when deciding which journal to apply for:

• Conduct thorough research to identify journals that specialise in your field of study and have published similar research. Naturally, if you submit a piece of research in molecular genetics to a journal that specialises in geology, you won’t be likely to get very far.
• Consider factors such as the journal’s scope, impact factor, and target audience. Today there is a wide array of journals to choose from, including traditional and respected print journals, as well as numerous online, open-access endeavours. Some, like Nature , even straddle both worlds.
• Review the submission guidelines provided by the journal and ensure your paper meets all the formatting requirements and word limits. This step is key. Nature, for example, offers a highly informative series of pages that tells you everything you need to know in order to satisfy their formatting guidelines (plus more on the whole submission process).
• Note that these guidelines can differ dramatically from journal to journal, and details really do matter. You might submit an outstanding piece of research, but if it includes, for example, images in the wrong size or format, this could mean a lengthy delay to getting it published. If you get everything right first time, you’ll save yourself a lot of time and trouble, as well as strengthen your publishing chances in the first place.

Step 3: Crafting a Strong Manuscript

Crafting a strong manuscript is crucial to impress journal editors and reviewers. Look at your paper as a complete package, and ensure that all the sections tie together to deliver your findings with clarity and precision.

• Begin by creating a clear and concise title that accurately reflects the content of your paper.
• Compose an informative abstract that summarises the purpose, methodology, results, and significance of your study.
• Craft an engaging introduction (link to the research paper introduction article) that draws your reader in.
• Develop a well-structured methodology section, presenting your results effectively using tables and figures.
• Write a compelling discussion and conclusion that emphasise the significance of your findings.

Step 4: Navigating the Peer-Review Process

Once you submit your research paper to a journal, it undergoes a rigorous peer-review process to ensure its quality and validity. In peer-review, experts in your field assess your research and provide feedback and suggestions for improvement, ultimately determining whether your paper is eligible for publishing or not. You are likely to encounter several models of peer-review, based on which party – author, reviewer, or both – remains anonymous throughout the process.

When your paper undergoes the peer-review process, be prepared for constructive criticism and address the comments you receive from your reviewer thoughtfully, providing clear and concise responses to their concerns or suggestions. These could make all the difference when it comes to making your next submission.

The peer-review process can seem like a closed book at times. Check out our discussion of the issue with philosopher and academic Amna Whiston in The Research Beat podcast!

Step 5: Submitting Your Paper

As we’ve already pointed out, one of the key elements in how to publish a research paper is ensuring that you meticulously follow the journal’s submission guidelines. Strive to comply with all formatting requirements, including citation styles, font, margins, and reference structure.

Before the final submission, thoroughly proofread your paper for errors, including grammar, spelling, and any inconsistencies in your data or analysis. At this stage, consider seeking feedback from colleagues or mentors to further improve the quality of your paper.

Step 6: Dealing with Rejections and Revising Your Paper

Rejection is a common part of the publishing process, but it shouldn’t discourage you. Analyse reviewer comments objectively and focus on the constructive feedback provided. Make necessary revisions and improvements to your paper to address the concerns raised by reviewers. If needed, consider submitting your paper to a different journal that is a better fit for your research.

For more tips on how to publish your paper out there, check out this thread by Dr. Asad Naveed ( @dr_asadnaveed ) – and if you need a refresher on the basics of how to publish under the Open Access model, watch this 5-minute video from Audemic Academy !

Final Thoughts

Successfully understanding how to publish a research paper requires dedication, attention to detail, and a systematic approach. By following the advice in our guide, you can increase your chances of navigating the publishing process effectively and achieving your goal of publication.

Remember, the journey may involve revisions, peer feedback, and potential rejections, but each step is an opportunity for growth and improvement. Stay persistent, maintain a positive mindset, and continue to refine your research paper until it reaches the standards of your target journal. Your contribution to your wider discipline through published research will not only advance your career, but also add to the growing body of collective knowledge in your field. Embrace the challenges and rewards that come with the publication process, and may your research paper make a significant impact in your area of study!

Looking for inspiration for your next big paper? Head to Audemic , where you can organise and listen to all the best and latest research in your field!

Keep striving, researchers! ✨

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How to Write and Publish Your Research in a Journal

Last Updated: February 26, 2024 Fact Checked

Choosing a Journal

Writing the research paper, editing & revising your paper, submitting your paper, navigating the peer review process, research paper help.

This article was co-authored by Matthew Snipp, PhD and by wikiHow staff writer, Cheyenne Main . C. Matthew Snipp is the Burnet C. and Mildred Finley Wohlford Professor of Humanities and Sciences in the Department of Sociology at Stanford University. He is also the Director for the Institute for Research in the Social Science’s Secure Data Center. He has been a Research Fellow at the U.S. Bureau of the Census and a Fellow at the Center for Advanced Study in the Behavioral Sciences. He has published 3 books and over 70 articles and book chapters on demography, economic development, poverty and unemployment. He is also currently serving on the National Institute of Child Health and Development’s Population Science Subcommittee. He holds a Ph.D. in Sociology from the University of Wisconsin—Madison. There are 13 references cited in this article, which can be found at the bottom of the page. This article has been fact-checked, ensuring the accuracy of any cited facts and confirming the authority of its sources. This article has been viewed 697,553 times.

Publishing a research paper in a peer-reviewed journal allows you to network with other scholars, get your name and work into circulation, and further refine your ideas and research. Before submitting your paper, make sure it reflects all the work you’ve done and have several people read over it and make comments. Keep reading to learn how you can choose a journal, prepare your work for publication, submit it, and revise it after you get a response back.

Things You Should Know

• Create a list of journals you’d like to publish your work in and choose one that best aligns with your topic and your desired audience.
• Prepare your manuscript using the journal’s requirements and ask at least 2 professors or supervisors to review your paper.
• Write a cover letter that “sells” your manuscript, says how your research adds to your field and explains why you chose the specific journal you’re submitting to.

• Ask your professors or supervisors for well-respected journals that they’ve had good experiences publishing with and that they read regularly.
• Many journals also only accept specific formats, so by choosing a journal before you start, you can write your article to their specifications and increase your chances of being accepted.
• If you’ve already written a paper you’d like to publish, consider whether your research directly relates to a hot topic or area of research in the journals you’re looking into.

• Review the journal’s peer review policies and submission process to see if you’re comfortable creating or adjusting your work according to their standards.
• Open-access journals can increase your readership because anyone can access them.

• Scientific research papers: Instead of a “thesis,” you might write a “research objective” instead. This is where you state the purpose of your research.
• “This paper explores how George Washington’s experiences as a young officer may have shaped his views during difficult circumstances as a commanding officer.”
• “This paper contends that George Washington’s experiences as a young officer on the 1750s Pennsylvania frontier directly impacted his relationship with his Continental Army troops during the harsh winter at Valley Forge.”

• Scientific research papers: Include a “materials and methods” section with the step-by-step process you followed and the materials you used. [5] X Research source
• Read other research papers in your field to see how they’re written. Their format, writing style, subject matter, and vocabulary can help guide your own paper. [6] X Research source

• If you’re writing about George Washington’s experiences as a young officer, you might emphasize how this research changes our perspective of the first president of the U.S.
• Link this section to your thesis or research objective.
• If you’re writing a paper about ADHD, you might discuss other applications for your research.

• Scientific research papers: You might include your research and/or analytical methods, your main findings or results, and the significance or implications of your research.
• Try to get as many people as you can to read over your abstract and provide feedback before you submit your paper to a journal.

• They might also provide templates to help you structure your manuscript according to their specific guidelines. [11] X Research source

• Not all journal reviewers will be experts on your specific topic, so a non-expert “outsider’s perspective” can be valuable.

• If you have a paper on the purification of wastewater with fungi, you might use both the words “fungi” and “mushrooms.”
• Use software like iThenticate, Turnitin, or PlagScan to check for similarities between the submitted article and published material available online. [15] X Research source

• Header: Address the editor who will be reviewing your manuscript by their name, include the date of submission, and the journal you are submitting to.
• First paragraph: Include the title of your manuscript, the type of paper it is (like review, research, or case study), and the research question you wanted to answer and why.
• Second paragraph: Explain what was done in your research, your main findings, and why they are significant to your field.
• Third paragraph: Explain why the journal’s readers would be interested in your work and why your results are important to your field.
• Conclusion: State the author(s) and any journal requirements that your work complies with (like ethical standards”).
• “We confirm that this manuscript has not been published elsewhere and is not under consideration by another journal.”
• “All authors have approved the manuscript and agree with its submission to [insert the name of the target journal].”

• Submit your article to only one journal at a time.
• When submitting online, use your university email account. This connects you with a scholarly institution, which can add credibility to your work.

• Accept: Only minor adjustments are needed, based on the provided feedback by the reviewers. A first submission will rarely be accepted without any changes needed.
• Revise and Resubmit: Changes are needed before publication can be considered, but the journal is still very interested in your work.
• Reject and Resubmit: Extensive revisions are needed. Your work may not be acceptable for this journal, but they might also accept it if significant changes are made.
• Reject: The paper isn’t and won’t be suitable for this publication, but that doesn’t mean it might not work for another journal.

• Try organizing the reviewer comments by how easy it is to address them. That way, you can break your revisions down into more manageable parts.
• If you disagree with a comment made by a reviewer, try to provide an evidence-based explanation when you resubmit your paper.

• If you’re resubmitting your paper to the same journal, include a point-by-point response paper that talks about how you addressed all of the reviewers’ comments in your revision. [22] X Research source
• If you’re not sure which journal to submit to next, you might be able to ask the journal editor which publications they recommend.

Expert Q&A

You might also like.

• If reviewers suspect that your submitted manuscript plagiarizes another work, they may refer to a Committee on Publication Ethics (COPE) flowchart to see how to move forward. [23] X Research source Thanks Helpful 0 Not Helpful 0

• ↑ https://www.wiley.com/en-us/network/publishing/research-publishing/choosing-a-journal/6-steps-to-choosing-the-right-journal-for-your-research-infographic
• ↑ https://link.springer.com/article/10.1007/s13187-020-01751-z
• ↑ https://libguides.unomaha.edu/c.php?g=100510&p=651627
• ↑ http://www.canberra.edu.au/library/start-your-research/research_help/publishing-research
• ↑ https://writingcenter.fas.harvard.edu/conclusions
• ↑ https://writing.wisc.edu/handbook/assignments/writing-an-abstract-for-your-research-paper/
• ↑ https://www.springer.com/gp/authors-editors/book-authors-editors/your-publication-journey/manuscript-preparation
• ↑ https://apus.libanswers.com/writing/faq/2391
• ↑ https://academicguides.waldenu.edu/library/keyword/search-strategy
• ↑ https://ifis.libguides.com/journal-publishing-guide/submitting-your-paper
• ↑ https://www.springer.com/kr/authors-editors/authorandreviewertutorials/submitting-to-a-journal-and-peer-review/cover-letters/10285574
• ↑ http://www.apa.org/monitor/sep02/publish.aspx
• ↑ Matthew Snipp, PhD. Research Fellow, U.S. Bureau of the Census. Expert Interview. 26 March 2020.

About This Article

To publish a research paper, ask a colleague or professor to review your paper and give you feedback. Once you've revised your work, familiarize yourself with different academic journals so that you can choose the publication that best suits your paper. Make sure to look at the "Author's Guide" so you can format your paper according to the guidelines for that publication. Then, submit your paper and don't get discouraged if it is not accepted right away. You may need to revise your paper and try again. To learn about the different responses you might get from journals, see our reviewer's explanation below. Did this summary help you? Yes No

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Acute Cardiac Events in Hospitalized Older Adults With Respiratory Syncytial Virus Infection

• 1 Division for Heart Disease and Stroke Prevention, National Center for Chronic Disease Prevention and Health Promotion, Centers for Disease Control and Prevention, Atlanta, Georgia
• 2 Coronavirus and Other Respiratory Viruses Division, National Center for Immunization and Respiratory Diseases, Centers for Disease Control and Prevention, Atlanta, Georgia
• 3 US Public Health Service Commissioned Corps, Rockville, Maryland
• 4 California Emerging Infections Program, Oakland
• 5 Colorado Department of Public Health and Environment, Denver
• 6 Connecticut Emerging Infections Program, Yale School of Public Health, New Haven
• 7 Division of Infectious Diseases, Emory University School of Medicine, Atlanta, Georgia
• 8 Georgia Emerging Infections Program, Georgia Department of Public Health, Atlanta
• 9 Research, Atlanta Veterans Affairs Medical Center, Decatur, Georgia
• 10 Emerging Infections Program, Maryland Department of Health, Baltimore
• 11 Michigan Department of Health and Human Services, Lansing
• 12 Health Protection Bureau, Minnesota Department of Health, St. Paul
• 13 New Mexico Emerging Infections Program, University of New Mexico, Albuquerque
• 14 Division of Epidemiology, New York State Department of Health, Albany
• 15 School of Medicine and Dentistry, University of Rochester, Rochester, New York
• 16 Public Health Division, Oregon Health Authority, Portland
• 17 Division of Infectious Diseases, Vanderbilt University Medical Center, Nashville, Tennessee
• 18 Epidemiology Bureau, Salt Lake County Health Department, Salt Lake City, Utah
• 19 Division of Global Health Protection, Global Health Center, Centers for Disease Control and Prevention, Atlanta, Georgia
• Editor's Note RSV Vaccination—The Juice Is Worth the Squeeze Tracy Y. Wang, MD, MHS, MSc JAMA Internal Medicine

Question   What are the frequency and severity of acute cardiac events among hospitalized adults aged 50 years or older with laboratory-confirmed respiratory syncytial virus (RSV) infection?

Findings   In this cross-sectional study of 6248 hospitalized adults with RSV infection, 22% of patients experienced an acute cardiac event, most often acute heart failure (16%). Acute cardiac events occurred more often among those with (33%) vs without (9%) underlying cardiovascular disease and were associated with nearly twice the risk of severe outcomes.

Meaning   Findings of this study suggest acute cardiac events are common among hospitalized older adults with RSV infection and are associated with severe clinical outcomes.

Importance   Respiratory syncytial virus (RSV) infection can cause severe respiratory illness in older adults. Less is known about the cardiac complications of RSV disease compared with those of influenza and SARS-CoV-2 infection.

Objective   To describe the prevalence and severity of acute cardiac events during hospitalizations among adults aged 50 years or older with RSV infection.

Design, Setting, and Participants   This cross-sectional study analyzed surveillance data from the RSV Hospitalization Surveillance Network, which conducts detailed medical record abstraction among hospitalized patients with RSV infection detected through clinician-directed laboratory testing. Cases of RSV infection in adults aged 50 years or older within 12 states over 5 RSV seasons (annually from 2014-2015 through 2017-2018 and 2022-2023) were examined to estimate the weighted period prevalence and 95% CIs of acute cardiac events.

Exposures   Acute cardiac events, identified by International Classification of Diseases, 9th Revision, Clinical Modification or International Statistical Classification of Diseases, Tenth Revision, Clinical Modification discharge codes, and discharge summary review.

Main Outcomes and Measures   Severe disease outcomes, including intensive care unit (ICU) admission, receipt of invasive mechanical ventilation, or in-hospital death. Adjusted risk ratios (ARR) were calculated to compare severe outcomes among patients with and without acute cardiac events.

Results   The study included 6248 hospitalized adults (median [IQR] age, 72.7 [63.0-82.3] years; 59.6% female; 56.4% with underlying cardiovascular disease) with laboratory-confirmed RSV infection. The weighted estimated prevalence of experiencing a cardiac event was 22.4% (95% CI, 21.0%-23.7%). The weighted estimated prevalence was 15.8% (95% CI, 14.6%-17.0%) for acute heart failure, 7.5% (95% CI, 6.8%-8.3%) for acute ischemic heart disease, 1.3% (95% CI, 1.0%-1.7%) for hypertensive crisis, 1.1% (95% CI, 0.8%-1.4%) for ventricular tachycardia, and 0.6% (95% CI, 0.4%-0.8%) for cardiogenic shock. Adults with underlying cardiovascular disease had a greater risk of experiencing an acute cardiac event relative to those who did not (33.0% vs 8.5%; ARR, 3.51; 95% CI, 2.85-4.32). Among all hospitalized adults with RSV infection, 18.6% required ICU admission and 4.9% died during hospitalization. Compared with patients without an acute cardiac event, those who experienced an acute cardiac event had a greater risk of ICU admission (25.8% vs 16.5%; ARR, 1.54; 95% CI, 1.23-1.93) and in-hospital death (8.1% vs 4.0%; ARR, 1.77; 95% CI, 1.36-2.31).

Conclusions and Relevance   In this cross-sectional study over 5 RSV seasons, nearly one-quarter of hospitalized adults aged 50 years or older with RSV infection experienced an acute cardiac event (most frequently acute heart failure), including 1 in 12 adults (8.5%) with no documented underlying cardiovascular disease. The risk of severe outcomes was nearly twice as high in patients with acute cardiac events compared with patients who did not experience an acute cardiac event. These findings clarify the baseline epidemiology of potential cardiac complications of RSV infection prior to RSV vaccine availability.

• Editor's Note RSV Vaccination—The Juice Is Worth the Squeeze JAMA Internal Medicine

Read More About

Woodruff RC , Melgar M , Pham H, et al. Acute Cardiac Events in Hospitalized Older Adults With Respiratory Syncytial Virus Infection. JAMA Intern Med. Published online April 15, 2024. doi:10.1001/jamainternmed.2024.0212

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AI Index: State of AI in 13 Charts

In the new report, foundation models dominate, benchmarks fall, prices skyrocket, and on the global stage, the U.S. overshadows.

This year’s AI Index — a 500-page report tracking 2023’s worldwide trends in AI — is out.

The index is an independent initiative at the Stanford Institute for Human-Centered Artificial Intelligence (HAI), led by the AI Index Steering Committee, an interdisciplinary group of experts from across academia and industry. This year’s report covers the rise of multimodal foundation models, major cash investments into generative AI, new performance benchmarks, shifting global opinions, and new major regulations.

Don’t have an afternoon to pore through the findings? Check out the high level here.

A Move Toward Open-Sourced

This past year, organizations released 149 foundation models, more than double the number released in 2022. Of these newly released models, 65.7% were open-source (meaning they can be freely used and modified by anyone), compared with only 44.4% in 2022 and 33.3% in 2021.

But At a Cost of Performance?

Closed-source models still outperform their open-sourced counterparts. On 10 selected benchmarks, closed models achieved a median performance advantage of 24.2%, with differences ranging from as little as 4.0% on mathematical tasks like GSM8K to as much as 317.7% on agentic tasks like AgentBench.

Biggest Players

Industry dominates AI, especially in building and releasing foundation models. This past year Google edged out other industry players in releasing the most models, including Gemini and RT-2. In fact, since 2019, Google has led in releasing the most foundation models, with a total of 40, followed by OpenAI with 20. Academia trails industry: This past year, UC Berkeley released three models and Stanford two.

Industry Dwarfs All

If you needed more striking evidence that corporate AI is the only player in the room right now, this should do it. In 2023, industry accounted for 72% of all new foundation models.

Prices Skyrocket

One of the reasons academia and government have been edged out of the AI race: the exponential increase in cost of training these giant models. Google’s Gemini Ultra cost an estimated $191 million worth of compute to train, while OpenAI’s GPT-4 cost an estimated $78 million. In comparison, in 2017, the original Transformer model, which introduced the architecture that underpins virtually every modern LLM, cost around $900.

What AI Race?

At least in terms of notable machine learning models, the United States vastly outpaced other countries in 2023, developing a total of 61 models in 2023. Since 2019, the U.S. has consistently led in originating the majority of notable models, followed by China and the UK.

Move Over, Human

As of 2023, AI has hit human-level performance on many significant AI benchmarks, from those testing reading comprehension to visual reasoning. Still, it falls just short on some benchmarks like competition-level math. Because AI has been blasting past so many standard benchmarks, AI scholars have had to create new and more difficult challenges. This year’s index also tracked several of these new benchmarks, including those for tasks in coding, advanced reasoning, and agentic behavior.

Private Investment Drops (But We See You, GenAI)

While AI private investment has steadily dropped since 2021, generative AI is gaining steam. In 2023, the sector attracted $25.2 billion, nearly ninefold the investment of 2022 and about 30 times the amount from 2019 (call it the ChatGPT effect). Generative AI accounted for over a quarter of all AI-related private investments in 2023.

U.S. Wins $$ Race

And again, in 2023 the United States dominates in AI private investment. In 2023, the $67.2 billion invested in the U.S. was roughly 8.7 times greater than the amount invested in the next highest country, China, and 17.8 times the amount invested in the United Kingdom. That lineup looks the same when zooming out: Cumulatively since 2013, the United States leads investments at $335.2 billion, followed by China with $103.7 billion, and the United Kingdom at $22.3 billion.

Where is Corporate Adoption?

More companies are implementing AI in some part of their business: In surveys, 55% of organizations said they were using AI in 2023, up from 50% in 2022 and 20% in 2017. Businesses report using AI to automate contact centers, personalize content, and acquire new customers. 

Younger and Wealthier People Worry About Jobs

Globally, most people expect AI to change their jobs, and more than a third expect AI to replace them. Younger generations — Gen Z and millennials — anticipate more substantial effects from AI compared with older generations like Gen X and baby boomers. Specifically, 66% of Gen Z compared with 46% of boomer respondents believe AI will significantly affect their current jobs. Meanwhile, individuals with higher incomes, more education, and decision-making roles foresee AI having a great impact on their employment.

While the Commonwealth Worries About AI Products

When asked in a survey about whether AI products and services make you nervous, 69% of Aussies and 65% of Brits said yes. Japan is the least worried about their AI products at 23%.  

Regulation Rallies

More American regulatory agencies are passing regulations to protect citizens and govern the use of AI tools and data. For example, the Copyright Office and the Library of Congress passed copyright registration guidance concerning works that contained material generated by AI, while the Securities and Exchange Commission developed a cybersecurity risk management strategy, governance, and incident disclosure plan. The agencies to pass the most regulation were the Executive Office of the President and the Commerce Department. 

The AI Index was first created to track AI development. The index collaborates with such organizations as LinkedIn, Quid, McKinsey, Studyportals, the Schwartz Reisman Institute, and the International Federation of Robotics to gather the most current research and feature important insights on the AI ecosystem. 

More News Topics

Evidence Review of the Adverse Effects of COVID-19 Vaccination and Intramuscular Vaccine Administration

Vaccines are a public health success story, as they have prevented or lessened the effects of many infectious diseases. To address concerns around potential vaccine injuries, the Health Resources and Services Administration (HRSA) administers the Vaccine Injury Compensation Program (VICP) and the Countermeasures Injury Compensation Program (CICP), which provide compensation to those who assert that they were injured by routine vaccines or medical countermeasures, respectively. The National Academies of Sciences, Engineering, and Medicine have contributed to the scientific basis for VICP compensation decisions for decades.

HRSA asked the National Academies to convene an expert committee to review the epidemiological, clinical, and biological evidence about the relationship between COVID-19 vaccines and specific adverse events, as well as intramuscular administration of vaccines and shoulder injuries. This report outlines the committee findings and conclusions.

Read Full Description

• Digital Resource: Evidence Review of the Adverse Effects of COVID-19 Vaccination
• Digital Resource: Evidence Review of Shoulder Injuries from Intramuscular Administration of Vaccines
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• Published: 17 April 2024

The economic commitment of climate change

• Maximilian Kotz   ORCID: orcid.org/0000-0003-2564-5043 1 , 2 ,
• Anders Levermann   ORCID: orcid.org/0000-0003-4432-4704 1 , 2 &
• Leonie Wenz   ORCID: orcid.org/0000-0002-8500-1568 1 , 3  

Nature volume  628 ,  pages 551–557 ( 2024 ) Cite this article

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• Environmental economics
• Environmental health
• Interdisciplinary studies
• Projection and prediction

Global projections of macroeconomic climate-change damages typically consider impacts from average annual and national temperatures over long time horizons 1 , 2 , 3 , 4 , 5 , 6 . Here we use recent empirical findings from more than 1,600 regions worldwide over the past 40 years to project sub-national damages from temperature and precipitation, including daily variability and extremes 7 , 8 . Using an empirical approach that provides a robust lower bound on the persistence of impacts on economic growth, we find that the world economy is committed to an income reduction of 19% within the next 26 years independent of future emission choices (relative to a baseline without climate impacts, likely range of 11–29% accounting for physical climate and empirical uncertainty). These damages already outweigh the mitigation costs required to limit global warming to 2 °C by sixfold over this near-term time frame and thereafter diverge strongly dependent on emission choices. Committed damages arise predominantly through changes in average temperature, but accounting for further climatic components raises estimates by approximately 50% and leads to stronger regional heterogeneity. Committed losses are projected for all regions except those at very high latitudes, at which reductions in temperature variability bring benefits. The largest losses are committed at lower latitudes in regions with lower cumulative historical emissions and lower present-day income.

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Paul Waidelich, Fulden Batibeniz, … Sonia I. Seneviratne

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Sven N. Willner, Nicole Glanemann & Anders Levermann

Climate economics support for the UN climate targets

Martin C. Hänsel, Moritz A. Drupp, … Thomas Sterner

Projections of the macroeconomic damage caused by future climate change are crucial to informing public and policy debates about adaptation, mitigation and climate justice. On the one hand, adaptation against climate impacts must be justified and planned on the basis of an understanding of their future magnitude and spatial distribution 9 . This is also of importance in the context of climate justice 10 , as well as to key societal actors, including governments, central banks and private businesses, which increasingly require the inclusion of climate risks in their macroeconomic forecasts to aid adaptive decision-making 11 , 12 . On the other hand, climate mitigation policy such as the Paris Climate Agreement is often evaluated by balancing the costs of its implementation against the benefits of avoiding projected physical damages. This evaluation occurs both formally through cost–benefit analyses 1 , 4 , 5 , 6 , as well as informally through public perception of mitigation and damage costs 13 .

Projections of future damages meet challenges when informing these debates, in particular the human biases relating to uncertainty and remoteness that are raised by long-term perspectives 14 . Here we aim to overcome such challenges by assessing the extent of economic damages from climate change to which the world is already committed by historical emissions and socio-economic inertia (the range of future emission scenarios that are considered socio-economically plausible 15 ). Such a focus on the near term limits the large uncertainties about diverging future emission trajectories, the resulting long-term climate response and the validity of applying historically observed climate–economic relations over long timescales during which socio-technical conditions may change considerably. As such, this focus aims to simplify the communication and maximize the credibility of projected economic damages from future climate change.

In projecting the future economic damages from climate change, we make use of recent advances in climate econometrics that provide evidence for impacts on sub-national economic growth from numerous components of the distribution of daily temperature and precipitation 3 , 7 , 8 . Using fixed-effects panel regression models to control for potential confounders, these studies exploit within-region variation in local temperature and precipitation in a panel of more than 1,600 regions worldwide, comprising climate and income data over the past 40 years, to identify the plausibly causal effects of changes in several climate variables on economic productivity 16 , 17 . Specifically, macroeconomic impacts have been identified from changing daily temperature variability, total annual precipitation, the annual number of wet days and extreme daily rainfall that occur in addition to those already identified from changing average temperature 2 , 3 , 18 . Moreover, regional heterogeneity in these effects based on the prevailing local climatic conditions has been found using interactions terms. The selection of these climate variables follows micro-level evidence for mechanisms related to the impacts of average temperatures on labour and agricultural productivity 2 , of temperature variability on agricultural productivity and health 7 , as well as of precipitation on agricultural productivity, labour outcomes and flood damages 8 (see Extended Data Table 1 for an overview, including more detailed references). References  7 , 8 contain a more detailed motivation for the use of these particular climate variables and provide extensive empirical tests about the robustness and nature of their effects on economic output, which are summarized in Methods . By accounting for these extra climatic variables at the sub-national level, we aim for a more comprehensive description of climate impacts with greater detail across both time and space.

Constraining the persistence of impacts

A key determinant and source of discrepancy in estimates of the magnitude of future climate damages is the extent to which the impact of a climate variable on economic growth rates persists. The two extreme cases in which these impacts persist indefinitely or only instantaneously are commonly referred to as growth or level effects 19 , 20 (see Methods section ‘Empirical model specification: fixed-effects distributed lag models’ for mathematical definitions). Recent work shows that future damages from climate change depend strongly on whether growth or level effects are assumed 20 . Following refs.  2 , 18 , we provide constraints on this persistence by using distributed lag models to test the significance of delayed effects separately for each climate variable. Notably, and in contrast to refs.  2 , 18 , we use climate variables in their first-differenced form following ref.  3 , implying a dependence of the growth rate on a change in climate variables. This choice means that a baseline specification without any lags constitutes a model prior of purely level effects, in which a permanent change in the climate has only an instantaneous effect on the growth rate 3 , 19 , 21 . By including lags, one can then test whether any effects may persist further. This is in contrast to the specification used by refs.  2 , 18 , in which climate variables are used without taking the first difference, implying a dependence of the growth rate on the level of climate variables. In this alternative case, the baseline specification without any lags constitutes a model prior of pure growth effects, in which a change in climate has an infinitely persistent effect on the growth rate. Consequently, including further lags in this alternative case tests whether the initial growth impact is recovered 18 , 19 , 21 . Both of these specifications suffer from the limiting possibility that, if too few lags are included, one might falsely accept the model prior. The limitations of including a very large number of lags, including loss of data and increasing statistical uncertainty with an increasing number of parameters, mean that such a possibility is likely. By choosing a specification in which the model prior is one of level effects, our approach is therefore conservative by design, avoiding assumptions of infinite persistence of climate impacts on growth and instead providing a lower bound on this persistence based on what is observable empirically (see Methods section ‘Empirical model specification: fixed-effects distributed lag models’ for further exposition of this framework). The conservative nature of such a choice is probably the reason that ref.  19 finds much greater consistency between the impacts projected by models that use the first difference of climate variables, as opposed to their levels.

We begin our empirical analysis of the persistence of climate impacts on growth using ten lags of the first-differenced climate variables in fixed-effects distributed lag models. We detect substantial effects on economic growth at time lags of up to approximately 8–10 years for the temperature terms and up to approximately 4 years for the precipitation terms (Extended Data Fig. 1 and Extended Data Table 2 ). Furthermore, evaluation by means of information criteria indicates that the inclusion of all five climate variables and the use of these numbers of lags provide a preferable trade-off between best-fitting the data and including further terms that could cause overfitting, in comparison with model specifications excluding climate variables or including more or fewer lags (Extended Data Fig. 3 , Supplementary Methods Section  1 and Supplementary Table 1 ). We therefore remove statistically insignificant terms at later lags (Supplementary Figs. 1 – 3 and Supplementary Tables 2 – 4 ). Further tests using Monte Carlo simulations demonstrate that the empirical models are robust to autocorrelation in the lagged climate variables (Supplementary Methods Section  2 and Supplementary Figs. 4 and 5 ), that information criteria provide an effective indicator for lag selection (Supplementary Methods Section  2 and Supplementary Fig. 6 ), that the results are robust to concerns of imperfect multicollinearity between climate variables and that including several climate variables is actually necessary to isolate their separate effects (Supplementary Methods Section  3 and Supplementary Fig. 7 ). We provide a further robustness check using a restricted distributed lag model to limit oscillations in the lagged parameter estimates that may result from autocorrelation, finding that it provides similar estimates of cumulative marginal effects to the unrestricted model (Supplementary Methods Section 4 and Supplementary Figs. 8 and 9 ). Finally, to explicitly account for any outstanding uncertainty arising from the precise choice of the number of lags, we include empirical models with marginally different numbers of lags in the error-sampling procedure of our projection of future damages. On the basis of the lag-selection procedure (the significance of lagged terms in Extended Data Fig. 1 and Extended Data Table 2 , as well as information criteria in Extended Data Fig. 3 ), we sample from models with eight to ten lags for temperature and four for precipitation (models shown in Supplementary Figs. 1 – 3 and Supplementary Tables 2 – 4 ). In summary, this empirical approach to constrain the persistence of climate impacts on economic growth rates is conservative by design in avoiding assumptions of infinite persistence, but nevertheless provides a lower bound on the extent of impact persistence that is robust to the numerous tests outlined above.

Committed damages until mid-century

We combine these empirical economic response functions (Supplementary Figs. 1 – 3 and Supplementary Tables 2 – 4 ) with an ensemble of 21 climate models (see Supplementary Table 5 ) from the Coupled Model Intercomparison Project Phase 6 (CMIP-6) 22 to project the macroeconomic damages from these components of physical climate change (see Methods for further details). Bias-adjusted climate models that provide a highly accurate reproduction of observed climatological patterns with limited uncertainty (Supplementary Table 6 ) are used to avoid introducing biases in the projections. Following a well-developed literature 2 , 3 , 19 , these projections do not aim to provide a prediction of future economic growth. Instead, they are a projection of the exogenous impact of future climate conditions on the economy relative to the baselines specified by socio-economic projections, based on the plausibly causal relationships inferred by the empirical models and assuming ceteris paribus. Other exogenous factors relevant for the prediction of economic output are purposefully assumed constant.

A Monte Carlo procedure that samples from climate model projections, empirical models with different numbers of lags and model parameter estimates (obtained by 1,000 block-bootstrap resamples of each of the regressions in Supplementary Figs. 1 – 3 and Supplementary Tables 2 – 4 ) is used to estimate the combined uncertainty from these sources. Given these uncertainty distributions, we find that projected global damages are statistically indistinguishable across the two most extreme emission scenarios until 2049 (at the 5% significance level; Fig. 1 ). As such, the climate damages occurring before this time constitute those to which the world is already committed owing to the combination of past emissions and the range of future emission scenarios that are considered socio-economically plausible 15 . These committed damages comprise a permanent income reduction of 19% on average globally (population-weighted average) in comparison with a baseline without climate-change impacts (with a likely range of 11–29%, following the likelihood classification adopted by the Intergovernmental Panel on Climate Change (IPCC); see caption of Fig. 1 ). Even though levels of income per capita generally still increase relative to those of today, this constitutes a permanent income reduction for most regions, including North America and Europe (each with median income reductions of approximately 11%) and with South Asia and Africa being the most strongly affected (each with median income reductions of approximately 22%; Fig. 1 ). Under a middle-of-the road scenario of future income development (SSP2, in which SSP stands for Shared Socio-economic Pathway), this corresponds to global annual damages in 2049 of 38 trillion in 2005 international dollars (likely range of 19–59 trillion 2005 international dollars). Compared with empirical specifications that assume pure growth or pure level effects, our preferred specification that provides a robust lower bound on the extent of climate impact persistence produces damages between these two extreme assumptions (Extended Data Fig. 3 ).

Estimates of the projected reduction in income per capita from changes in all climate variables based on empirical models of climate impacts on economic output with a robust lower bound on their persistence (Extended Data Fig. 1 ) under a low-emission scenario compatible with the 2 °C warming target and a high-emission scenario (SSP2-RCP2.6 and SSP5-RCP8.5, respectively) are shown in purple and orange, respectively. Shading represents the 34% and 10% confidence intervals reflecting the likely and very likely ranges, respectively (following the likelihood classification adopted by the IPCC), having estimated uncertainty from a Monte Carlo procedure, which samples the uncertainty from the choice of physical climate models, empirical models with different numbers of lags and bootstrapped estimates of the regression parameters shown in Supplementary Figs. 1 – 3 . Vertical dashed lines show the time at which the climate damages of the two emission scenarios diverge at the 5% and 1% significance levels based on the distribution of differences between emission scenarios arising from the uncertainty sampling discussed above. Note that uncertainty in the difference of the two scenarios is smaller than the combined uncertainty of the two respective scenarios because samples of the uncertainty (climate model and empirical model choice, as well as model parameter bootstrap) are consistent across the two emission scenarios, hence the divergence of damages occurs while the uncertainty bounds of the two separate damage scenarios still overlap. Estimates of global mitigation costs from the three IAMs that provide results for the SSP2 baseline and SSP2-RCP2.6 scenario are shown in light green in the top panel, with the median of these estimates shown in bold.

Damages already outweigh mitigation costs

We compare the damages to which the world is committed over the next 25 years to estimates of the mitigation costs required to achieve the Paris Climate Agreement. Taking estimates of mitigation costs from the three integrated assessment models (IAMs) in the IPCC AR6 database 23 that provide results under comparable scenarios (SSP2 baseline and SSP2-RCP2.6, in which RCP stands for Representative Concentration Pathway), we find that the median committed climate damages are larger than the median mitigation costs in 2050 (six trillion in 2005 international dollars) by a factor of approximately six (note that estimates of mitigation costs are only provided every 10 years by the IAMs and so a comparison in 2049 is not possible). This comparison simply aims to compare the magnitude of future damages against mitigation costs, rather than to conduct a formal cost–benefit analysis of transitioning from one emission path to another. Formal cost–benefit analyses typically find that the net benefits of mitigation only emerge after 2050 (ref.  5 ), which may lead some to conclude that physical damages from climate change are simply not large enough to outweigh mitigation costs until the second half of the century. Our simple comparison of their magnitudes makes clear that damages are actually already considerably larger than mitigation costs and the delayed emergence of net mitigation benefits results primarily from the fact that damages across different emission paths are indistinguishable until mid-century (Fig. 1 ).

Although these near-term damages constitute those to which the world is already committed, we note that damage estimates diverge strongly across emission scenarios after 2049, conveying the clear benefits of mitigation from a purely economic point of view that have been emphasized in previous studies 4 , 24 . As well as the uncertainties assessed in Fig. 1 , these conclusions are robust to structural choices, such as the timescale with which changes in the moderating variables of the empirical models are estimated (Supplementary Figs. 10 and 11 ), as well as the order in which one accounts for the intertemporal and international components of currency comparison (Supplementary Fig. 12 ; see Methods for further details).

Damages from variability and extremes

Committed damages primarily arise through changes in average temperature (Fig. 2 ). This reflects the fact that projected changes in average temperature are larger than those in other climate variables when expressed as a function of their historical interannual variability (Extended Data Fig. 4 ). Because the historical variability is that on which the empirical models are estimated, larger projected changes in comparison with this variability probably lead to larger future impacts in a purely statistical sense. From a mechanistic perspective, one may plausibly interpret this result as implying that future changes in average temperature are the most unprecedented from the perspective of the historical fluctuations to which the economy is accustomed and therefore will cause the most damage. This insight may prove useful in terms of guiding adaptation measures to the sources of greatest damage.

Estimates of the median projected reduction in sub-national income per capita across emission scenarios (SSP2-RCP2.6 and SSP2-RCP8.5) as well as climate model, empirical model and model parameter uncertainty in the year in which climate damages diverge at the 5% level (2049, as identified in Fig. 1 ). a , Impacts arising from all climate variables. b – f , Impacts arising separately from changes in annual mean temperature ( b ), daily temperature variability ( c ), total annual precipitation ( d ), the annual number of wet days (>1 mm) ( e ) and extreme daily rainfall ( f ) (see Methods for further definitions). Data on national administrative boundaries are obtained from the GADM database version 3.6 and are freely available for academic use ( https://gadm.org/ ).

Nevertheless, future damages based on empirical models that consider changes in annual average temperature only and exclude the other climate variables constitute income reductions of only 13% in 2049 (Extended Data Fig. 5a , likely range 5–21%). This suggests that accounting for the other components of the distribution of temperature and precipitation raises net damages by nearly 50%. This increase arises through the further damages that these climatic components cause, but also because their inclusion reveals a stronger negative economic response to average temperatures (Extended Data Fig. 5b ). The latter finding is consistent with our Monte Carlo simulations, which suggest that the magnitude of the effect of average temperature on economic growth is underestimated unless accounting for the impacts of other correlated climate variables (Supplementary Fig. 7 ).

In terms of the relative contributions of the different climatic components to overall damages, we find that accounting for daily temperature variability causes the largest increase in overall damages relative to empirical frameworks that only consider changes in annual average temperature (4.9 percentage points, likely range 2.4–8.7 percentage points, equivalent to approximately 10 trillion international dollars). Accounting for precipitation causes smaller increases in overall damages, which are—nevertheless—equivalent to approximately 1.2 trillion international dollars: 0.01 percentage points (−0.37–0.33 percentage points), 0.34 percentage points (0.07–0.90 percentage points) and 0.36 percentage points (0.13–0.65 percentage points) from total annual precipitation, the number of wet days and extreme daily precipitation, respectively. Moreover, climate models seem to underestimate future changes in temperature variability 25 and extreme precipitation 26 , 27 in response to anthropogenic forcing as compared with that observed historically, suggesting that the true impacts from these variables may be larger.

The distribution of committed damages

The spatial distribution of committed damages (Fig. 2a ) reflects a complex interplay between the patterns of future change in several climatic components and those of historical economic vulnerability to changes in those variables. Damages resulting from increasing annual mean temperature (Fig. 2b ) are negative almost everywhere globally, and larger at lower latitudes in regions in which temperatures are already higher and economic vulnerability to temperature increases is greatest (see the response heterogeneity to mean temperature embodied in Extended Data Fig. 1a ). This occurs despite the amplified warming projected at higher latitudes 28 , suggesting that regional heterogeneity in economic vulnerability to temperature changes outweighs heterogeneity in the magnitude of future warming (Supplementary Fig. 13a ). Economic damages owing to daily temperature variability (Fig. 2c ) exhibit a strong latitudinal polarisation, primarily reflecting the physical response of daily variability to greenhouse forcing in which increases in variability across lower latitudes (and Europe) contrast decreases at high latitudes 25 (Supplementary Fig. 13b ). These two temperature terms are the dominant determinants of the pattern of overall damages (Fig. 2a ), which exhibits a strong polarity with damages across most of the globe except at the highest northern latitudes. Future changes in total annual precipitation mainly bring economic benefits except in regions of drying, such as the Mediterranean and central South America (Fig. 2d and Supplementary Fig. 13c ), but these benefits are opposed by changes in the number of wet days, which produce damages with a similar pattern of opposite sign (Fig. 2e and Supplementary Fig. 13d ). By contrast, changes in extreme daily rainfall produce damages in all regions, reflecting the intensification of daily rainfall extremes over global land areas 29 , 30 (Fig. 2f and Supplementary Fig. 13e ).

The spatial distribution of committed damages implies considerable injustice along two dimensions: culpability for the historical emissions that have caused climate change and pre-existing levels of socio-economic welfare. Spearman’s rank correlations indicate that committed damages are significantly larger in countries with smaller historical cumulative emissions, as well as in regions with lower current income per capita (Fig. 3 ). This implies that those countries that will suffer the most from the damages already committed are those that are least responsible for climate change and which also have the least resources to adapt to it.

Estimates of the median projected change in national income per capita across emission scenarios (RCP2.6 and RCP8.5) as well as climate model, empirical model and model parameter uncertainty in the year in which climate damages diverge at the 5% level (2049, as identified in Fig. 1 ) are plotted against cumulative national emissions per capita in 2020 (from the Global Carbon Project) and coloured by national income per capita in 2020 (from the World Bank) in a and vice versa in b . In each panel, the size of each scatter point is weighted by the national population in 2020 (from the World Bank). Inset numbers indicate the Spearman’s rank correlation ρ and P -values for a hypothesis test whose null hypothesis is of no correlation, as well as the Spearman’s rank correlation weighted by national population.

To further quantify this heterogeneity, we assess the difference in committed damages between the upper and lower quartiles of regions when ranked by present income levels and historical cumulative emissions (using a population weighting to both define the quartiles and estimate the group averages). On average, the quartile of countries with lower income are committed to an income loss that is 8.9 percentage points (or 61%) greater than the upper quartile (Extended Data Fig. 6 ), with a likely range of 3.8–14.7 percentage points across the uncertainty sampling of our damage projections (following the likelihood classification adopted by the IPCC). Similarly, the quartile of countries with lower historical cumulative emissions are committed to an income loss that is 6.9 percentage points (or 40%) greater than the upper quartile, with a likely range of 0.27–12 percentage points. These patterns reemphasize the prevalence of injustice in climate impacts 31 , 32 , 33 in the context of the damages to which the world is already committed by historical emissions and socio-economic inertia.

Contextualizing the magnitude of damages

The magnitude of projected economic damages exceeds previous literature estimates 2 , 3 , arising from several developments made on previous approaches. Our estimates are larger than those of ref.  2 (see first row of Extended Data Table 3 ), primarily because of the facts that sub-national estimates typically show a steeper temperature response (see also refs.  3 , 34 ) and that accounting for other climatic components raises damage estimates (Extended Data Fig. 5 ). However, we note that our empirical approach using first-differenced climate variables is conservative compared with that of ref.  2 in regard to the persistence of climate impacts on growth (see introduction and Methods section ‘Empirical model specification: fixed-effects distributed lag models’), an important determinant of the magnitude of long-term damages 19 , 21 . Using a similar empirical specification to ref.  2 , which assumes infinite persistence while maintaining the rest of our approach (sub-national data and further climate variables), produces considerably larger damages (purple curve of Extended Data Fig. 3 ). Compared with studies that do take the first difference of climate variables 3 , 35 , our estimates are also larger (see second and third rows of Extended Data Table 3 ). The inclusion of further climate variables (Extended Data Fig. 5 ) and a sufficient number of lags to more adequately capture the extent of impact persistence (Extended Data Figs. 1 and 2 ) are the main sources of this difference, as is the use of specifications that capture nonlinearities in the temperature response when compared with ref.  35 . In summary, our estimates develop on previous studies by incorporating the latest data and empirical insights 7 , 8 , as well as in providing a robust empirical lower bound on the persistence of impacts on economic growth, which constitutes a middle ground between the extremes of the growth-versus-levels debate 19 , 21 (Extended Data Fig. 3 ).

Compared with the fraction of variance explained by the empirical models historically (<5%), the projection of reductions in income of 19% may seem large. This arises owing to the fact that projected changes in climatic conditions are much larger than those that were experienced historically, particularly for changes in average temperature (Extended Data Fig. 4 ). As such, any assessment of future climate-change impacts necessarily requires an extrapolation outside the range of the historical data on which the empirical impact models were evaluated. Nevertheless, these models constitute the most state-of-the-art methods for inference of plausibly causal climate impacts based on observed data. Moreover, we take explicit steps to limit out-of-sample extrapolation by capping the moderating variables of the interaction terms at the 95th percentile of the historical distribution (see Methods ). This avoids extrapolating the marginal effects outside what was observed historically. Given the nonlinear response of economic output to annual mean temperature (Extended Data Fig. 1 and Extended Data Table 2 ), this is a conservative choice that limits the magnitude of damages that we project. Furthermore, back-of-the-envelope calculations indicate that the projected damages are consistent with the magnitude and patterns of historical economic development (see Supplementary Discussion Section  5 ).

Missing impacts and spatial spillovers

Despite assessing several climatic components from which economic impacts have recently been identified 3 , 7 , 8 , this assessment of aggregate climate damages should not be considered comprehensive. Important channels such as impacts from heatwaves 31 , sea-level rise 36 , tropical cyclones 37 and tipping points 38 , 39 , as well as non-market damages such as those to ecosystems 40 and human health 41 , are not considered in these estimates. Sea-level rise is unlikely to be feasibly incorporated into empirical assessments such as this because historical sea-level variability is mostly small. Non-market damages are inherently intractable within our estimates of impacts on aggregate monetary output and estimates of these impacts could arguably be considered as extra to those identified here. Recent empirical work suggests that accounting for these channels would probably raise estimates of these committed damages, with larger damages continuing to arise in the global south 31 , 36 , 37 , 38 , 39 , 40 , 41 , 42 .

Moreover, our main empirical analysis does not explicitly evaluate the potential for impacts in local regions to produce effects that ‘spill over’ into other regions. Such effects may further mitigate or amplify the impacts we estimate, for example, if companies relocate production from one affected region to another or if impacts propagate along supply chains. The current literature indicates that trade plays a substantial role in propagating spillover effects 43 , 44 , making their assessment at the sub-national level challenging without available data on sub-national trade dependencies. Studies accounting for only spatially adjacent neighbours indicate that negative impacts in one region induce further negative impacts in neighbouring regions 45 , 46 , 47 , 48 , suggesting that our projected damages are probably conservative by excluding these effects. In Supplementary Fig. 14 , we assess spillovers from neighbouring regions using a spatial-lag model. For simplicity, this analysis excludes temporal lags, focusing only on contemporaneous effects. The results show that accounting for spatial spillovers can amplify the overall magnitude, and also the heterogeneity, of impacts. Consistent with previous literature, this indicates that the overall magnitude (Fig. 1 ) and heterogeneity (Fig. 3 ) of damages that we project in our main specification may be conservative without explicitly accounting for spillovers. We note that further analysis that addresses both spatially and trade-connected spillovers, while also accounting for delayed impacts using temporal lags, would be necessary to adequately address this question fully. These approaches offer fruitful avenues for further research but are beyond the scope of this manuscript, which primarily aims to explore the impacts of different climate conditions and their persistence.

Policy implications

We find that the economic damages resulting from climate change until 2049 are those to which the world economy is already committed and that these greatly outweigh the costs required to mitigate emissions in line with the 2 °C target of the Paris Climate Agreement (Fig. 1 ). This assessment is complementary to formal analyses of the net costs and benefits associated with moving from one emission path to another, which typically find that net benefits of mitigation only emerge in the second half of the century 5 . Our simple comparison of the magnitude of damages and mitigation costs makes clear that this is primarily because damages are indistinguishable across emissions scenarios—that is, committed—until mid-century (Fig. 1 ) and that they are actually already much larger than mitigation costs. For simplicity, and owing to the availability of data, we compare damages to mitigation costs at the global level. Regional estimates of mitigation costs may shed further light on the national incentives for mitigation to which our results already hint, of relevance for international climate policy. Although these damages are committed from a mitigation perspective, adaptation may provide an opportunity to reduce them. Moreover, the strong divergence of damages after mid-century reemphasizes the clear benefits of mitigation from a purely economic perspective, as highlighted in previous studies 1 , 4 , 6 , 24 .

Historical climate data

Historical daily 2-m temperature and precipitation totals (in mm) are obtained for the period 1979–2019 from the W5E5 database. The W5E5 dataset comes from ERA-5, a state-of-the-art reanalysis of historical observations, but has been bias-adjusted by applying version 2.0 of the WATCH Forcing Data to ERA-5 reanalysis data and precipitation data from version 2.3 of the Global Precipitation Climatology Project to better reflect ground-based measurements 49 , 50 , 51 . We obtain these data on a 0.5° × 0.5° grid from the Inter-Sectoral Impact Model Intercomparison Project (ISIMIP) database. Notably, these historical data have been used to bias-adjust future climate projections from CMIP-6 (see the following section), ensuring consistency between the distribution of historical daily weather on which our empirical models were estimated and the climate projections used to estimate future damages. These data are publicly available from the ISIMIP database. See refs.  7 , 8 for robustness tests of the empirical models to the choice of climate data reanalysis products.

Future climate data

Daily 2-m temperature and precipitation totals (in mm) are taken from 21 climate models participating in CMIP-6 under a high (RCP8.5) and a low (RCP2.6) greenhouse gas emission scenario from 2015 to 2100. The data have been bias-adjusted and statistically downscaled to a common half-degree grid to reflect the historical distribution of daily temperature and precipitation of the W5E5 dataset using the trend-preserving method developed by the ISIMIP 50 , 52 . As such, the climate model data reproduce observed climatological patterns exceptionally well (Supplementary Table 5 ). Gridded data are publicly available from the ISIMIP database.

Historical economic data

Historical economic data come from the DOSE database of sub-national economic output 53 . We use a recent revision to the DOSE dataset that provides data across 83 countries, 1,660 sub-national regions with varying temporal coverage from 1960 to 2019. Sub-national units constitute the first administrative division below national, for example, states for the USA and provinces for China. Data come from measures of gross regional product per capita (GRPpc) or income per capita in local currencies, reflecting the values reported in national statistical agencies, yearbooks and, in some cases, academic literature. We follow previous literature 3 , 7 , 8 , 54 and assess real sub-national output per capita by first converting values from local currencies to US dollars to account for diverging national inflationary tendencies and then account for US inflation using a US deflator. Alternatively, one might first account for national inflation and then convert between currencies. Supplementary Fig. 12 demonstrates that our conclusions are consistent when accounting for price changes in the reversed order, although the magnitude of estimated damages varies. See the documentation of the DOSE dataset for further discussion of these choices. Conversions between currencies are conducted using exchange rates from the FRED database of the Federal Reserve Bank of St. Louis 55 and the national deflators from the World Bank 56 .

Future socio-economic data

Baseline gridded gross domestic product (GDP) and population data for the period 2015–2100 are taken from the middle-of-the-road scenario SSP2 (ref.  15 ). Population data have been downscaled to a half-degree grid by the ISIMIP following the methodologies of refs.  57 , 58 , which we then aggregate to the sub-national level of our economic data using the spatial aggregation procedure described below. Because current methodologies for downscaling the GDP of the SSPs use downscaled population to do so, per-capita estimates of GDP with a realistic distribution at the sub-national level are not readily available for the SSPs. We therefore use national-level GDP per capita (GDPpc) projections for all sub-national regions of a given country, assuming homogeneity within countries in terms of baseline GDPpc. Here we use projections that have been updated to account for the impact of the COVID-19 pandemic on the trajectory of future income, while remaining consistent with the long-term development of the SSPs 59 . The choice of baseline SSP alters the magnitude of projected climate damages in monetary terms, but when assessed in terms of percentage change from the baseline, the choice of socio-economic scenario is inconsequential. Gridded SSP population data and national-level GDPpc data are publicly available from the ISIMIP database. Sub-national estimates as used in this study are available in the code and data replication files.

Climate variables

Following recent literature 3 , 7 , 8 , we calculate an array of climate variables for which substantial impacts on macroeconomic output have been identified empirically, supported by further evidence at the micro level for plausible underlying mechanisms. See refs.  7 , 8 for an extensive motivation for the use of these particular climate variables and for detailed empirical tests on the nature and robustness of their effects on economic output. To summarize, these studies have found evidence for independent impacts on economic growth rates from annual average temperature, daily temperature variability, total annual precipitation, the annual number of wet days and extreme daily rainfall. Assessments of daily temperature variability were motivated by evidence of impacts on agricultural output and human health, as well as macroeconomic literature on the impacts of volatility on growth when manifest in different dimensions, such as government spending, exchange rates and even output itself 7 . Assessments of precipitation impacts were motivated by evidence of impacts on agricultural productivity, metropolitan labour outcomes and conflict, as well as damages caused by flash flooding 8 . See Extended Data Table 1 for detailed references to empirical studies of these physical mechanisms. Marked impacts of daily temperature variability, total annual precipitation, the number of wet days and extreme daily rainfall on macroeconomic output were identified robustly across different climate datasets, spatial aggregation schemes, specifications of regional time trends and error-clustering approaches. They were also found to be robust to the consideration of temperature extremes 7 , 8 . Furthermore, these climate variables were identified as having independent effects on economic output 7 , 8 , which we further explain here using Monte Carlo simulations to demonstrate the robustness of the results to concerns of imperfect multicollinearity between climate variables (Supplementary Methods Section  2 ), as well as by using information criteria (Supplementary Table 1 ) to demonstrate that including several lagged climate variables provides a preferable trade-off between optimally describing the data and limiting the possibility of overfitting.

We calculate these variables from the distribution of daily, d , temperature, T x , d , and precipitation, P x , d , at the grid-cell, x , level for both the historical and future climate data. As well as annual mean temperature, \({\bar{T}}_{x,y}\) , and annual total precipitation, P x , y , we calculate annual, y , measures of daily temperature variability, \({\widetilde{T}}_{x,y}\) :

the number of wet days, Pwd x , y :

and extreme daily rainfall:

in which T x , d , m , y is the grid-cell-specific daily temperature in month m and year y , \({\bar{T}}_{x,m,{y}}\) is the year and grid-cell-specific monthly, m , mean temperature, D m and D y the number of days in a given month m or year y , respectively, H the Heaviside step function, 1 mm the threshold used to define wet days and P 99.9 x is the 99.9th percentile of historical (1979–2019) daily precipitation at the grid-cell level. Units of the climate measures are degrees Celsius for annual mean temperature and daily temperature variability, millimetres for total annual precipitation and extreme daily precipitation, and simply the number of days for the annual number of wet days.

We also calculated weighted standard deviations of monthly rainfall totals as also used in ref.  8 but do not include them in our projections as we find that, when accounting for delayed effects, their effect becomes statistically indistinct and is better captured by changes in total annual rainfall.

Spatial aggregation

We aggregate grid-cell-level historical and future climate measures, as well as grid-cell-level future GDPpc and population, to the level of the first administrative unit below national level of the GADM database, using an area-weighting algorithm that estimates the portion of each grid cell falling within an administrative boundary. We use this as our baseline specification following previous findings that the effect of area or population weighting at the sub-national level is negligible 7 , 8 .

Empirical model specification: fixed-effects distributed lag models

Following a wide range of climate econometric literature 16 , 60 , we use panel regression models with a selection of fixed effects and time trends to isolate plausibly exogenous variation with which to maximize confidence in a causal interpretation of the effects of climate on economic growth rates. The use of region fixed effects, μ r , accounts for unobserved time-invariant differences between regions, such as prevailing climatic norms and growth rates owing to historical and geopolitical factors. The use of yearly fixed effects, η y , accounts for regionally invariant annual shocks to the global climate or economy such as the El Niño–Southern Oscillation or global recessions. In our baseline specification, we also include region-specific linear time trends, k r y , to exclude the possibility of spurious correlations resulting from common slow-moving trends in climate and growth.

The persistence of climate impacts on economic growth rates is a key determinant of the long-term magnitude of damages. Methods for inferring the extent of persistence in impacts on growth rates have typically used lagged climate variables to evaluate the presence of delayed effects or catch-up dynamics 2 , 18 . For example, consider starting from a model in which a climate condition, C r , y , (for example, annual mean temperature) affects the growth rate, Δlgrp r , y (the first difference of the logarithm of gross regional product) of region r in year y :

which we refer to as a ‘pure growth effects’ model in the main text. Typically, further lags are included,

and the cumulative effect of all lagged terms is evaluated to assess the extent to which climate impacts on growth rates persist. Following ref.  18 , in the case that,

the implication is that impacts on the growth rate persist up to NL years after the initial shock (possibly to a weaker or a stronger extent), whereas if

then the initial impact on the growth rate is recovered after NL years and the effect is only one on the level of output. However, we note that such approaches are limited by the fact that, when including an insufficient number of lags to detect a recovery of the growth rates, one may find equation ( 6 ) to be satisfied and incorrectly assume that a change in climatic conditions affects the growth rate indefinitely. In practice, given a limited record of historical data, including too few lags to confidently conclude in an infinitely persistent impact on the growth rate is likely, particularly over the long timescales over which future climate damages are often projected 2 , 24 . To avoid this issue, we instead begin our analysis with a model for which the level of output, lgrp r , y , depends on the level of a climate variable, C r , y :

Given the non-stationarity of the level of output, we follow the literature 19 and estimate such an equation in first-differenced form as,

which we refer to as a model of ‘pure level effects’ in the main text. This model constitutes a baseline specification in which a permanent change in the climate variable produces an instantaneous impact on the growth rate and a permanent effect only on the level of output. By including lagged variables in this specification,

we are able to test whether the impacts on the growth rate persist any further than instantaneously by evaluating whether α L  > 0 are statistically significantly different from zero. Even though this framework is also limited by the possibility of including too few lags, the choice of a baseline model specification in which impacts on the growth rate do not persist means that, in the case of including too few lags, the framework reverts to the baseline specification of level effects. As such, this framework is conservative with respect to the persistence of impacts and the magnitude of future damages. It naturally avoids assumptions of infinite persistence and we are able to interpret any persistence that we identify with equation ( 9 ) as a lower bound on the extent of climate impact persistence on growth rates. See the main text for further discussion of this specification choice, in particular about its conservative nature compared with previous literature estimates, such as refs.  2 , 18 .

We allow the response to climatic changes to vary across regions, using interactions of the climate variables with historical average (1979–2019) climatic conditions reflecting heterogenous effects identified in previous work 7 , 8 . Following this previous work, the moderating variables of these interaction terms constitute the historical average of either the variable itself or of the seasonal temperature difference, \({\hat{T}}_{r}\) , or annual mean temperature, \({\bar{T}}_{r}\) , in the case of daily temperature variability 7 and extreme daily rainfall, respectively 8 .

The resulting regression equation with N and M lagged variables, respectively, reads:

in which Δlgrp r , y is the annual, regional GRPpc growth rate, measured as the first difference of the logarithm of real GRPpc, following previous work 2 , 3 , 7 , 8 , 18 , 19 . Fixed-effects regressions were run using the fixest package in R (ref.  61 ).

Estimates of the coefficients of interest α i , L are shown in Extended Data Fig. 1 for N  =  M  = 10 lags and for our preferred choice of the number of lags in Supplementary Figs. 1 – 3 . In Extended Data Fig. 1 , errors are shown clustered at the regional level, but for the construction of damage projections, we block-bootstrap the regressions by region 1,000 times to provide a range of parameter estimates with which to sample the projection uncertainty (following refs.  2 , 31 ).

Spatial-lag model

In Supplementary Fig. 14 , we present the results from a spatial-lag model that explores the potential for climate impacts to ‘spill over’ into spatially neighbouring regions. We measure the distance between centroids of each pair of sub-national regions and construct spatial lags that take the average of the first-differenced climate variables and their interaction terms over neighbouring regions that are at distances of 0–500, 500–1,000, 1,000–1,500 and 1,500–2000 km (spatial lags, ‘SL’, 1 to 4). For simplicity, we then assess a spatial-lag model without temporal lags to assess spatial spillovers of contemporaneous climate impacts. This model takes the form:

in which SL indicates the spatial lag of each climate variable and interaction term. In Supplementary Fig. 14 , we plot the cumulative marginal effect of each climate variable at different baseline climate conditions by summing the coefficients for each climate variable and interaction term, for example, for average temperature impacts as:

These cumulative marginal effects can be regarded as the overall spatially dependent impact to an individual region given a one-unit shock to a climate variable in that region and all neighbouring regions at a given value of the moderating variable of the interaction term.

Constructing projections of economic damage from future climate change

We construct projections of future climate damages by applying the coefficients estimated in equation ( 10 ) and shown in Supplementary Tables 2 – 4 (when including only lags with statistically significant effects in specifications that limit overfitting; see Supplementary Methods Section  1 ) to projections of future climate change from the CMIP-6 models. Year-on-year changes in each primary climate variable of interest are calculated to reflect the year-to-year variations used in the empirical models. 30-year moving averages of the moderating variables of the interaction terms are calculated to reflect the long-term average of climatic conditions that were used for the moderating variables in the empirical models. By using moving averages in the projections, we account for the changing vulnerability to climate shocks based on the evolving long-term conditions (Supplementary Figs. 10 and 11 show that the results are robust to the precise choice of the window of this moving average). Although these climate variables are not differenced, the fact that the bias-adjusted climate models reproduce observed climatological patterns across regions for these moderating variables very accurately (Supplementary Table 6 ) with limited spread across models (<3%) precludes the possibility that any considerable bias or uncertainty is introduced by this methodological choice. However, we impose caps on these moderating variables at the 95th percentile at which they were observed in the historical data to prevent extrapolation of the marginal effects outside the range in which the regressions were estimated. This is a conservative choice that limits the magnitude of our damage projections.

Time series of primary climate variables and moderating climate variables are then combined with estimates of the empirical model parameters to evaluate the regression coefficients in equation ( 10 ), producing a time series of annual GRPpc growth-rate reductions for a given emission scenario, climate model and set of empirical model parameters. The resulting time series of growth-rate impacts reflects those occurring owing to future climate change. By contrast, a future scenario with no climate change would be one in which climate variables do not change (other than with random year-to-year fluctuations) and hence the time-averaged evaluation of equation ( 10 ) would be zero. Our approach therefore implicitly compares the future climate-change scenario to this no-climate-change baseline scenario.

The time series of growth-rate impacts owing to future climate change in region r and year y , δ r , y , are then added to the future baseline growth rates, π r , y (in log-diff form), obtained from the SSP2 scenario to yield trajectories of damaged GRPpc growth rates, ρ r , y . These trajectories are aggregated over time to estimate the future trajectory of GRPpc with future climate impacts:

in which GRPpc r , y =2020 is the initial log level of GRPpc. We begin damage estimates in 2020 to reflect the damages occurring since the end of the period for which we estimate the empirical models (1979–2019) and to match the timing of mitigation-cost estimates from most IAMs (see below).

For each emission scenario, this procedure is repeated 1,000 times while randomly sampling from the selection of climate models, the selection of empirical models with different numbers of lags (shown in Supplementary Figs. 1 – 3 and Supplementary Tables 2 – 4 ) and bootstrapped estimates of the regression parameters. The result is an ensemble of future GRPpc trajectories that reflect uncertainty from both physical climate change and the structural and sampling uncertainty of the empirical models.

Estimates of mitigation costs

We obtain IPCC estimates of the aggregate costs of emission mitigation from the AR6 Scenario Explorer and Database hosted by IIASA 23 . Specifically, we search the AR6 Scenarios Database World v1.1 for IAMs that provided estimates of global GDP and population under both a SSP2 baseline and a SSP2-RCP2.6 scenario to maintain consistency with the socio-economic and emission scenarios of the climate damage projections. We find five IAMs that provide data for these scenarios, namely, MESSAGE-GLOBIOM 1.0, REMIND-MAgPIE 1.5, AIM/GCE 2.0, GCAM 4.2 and WITCH-GLOBIOM 3.1. Of these five IAMs, we use the results only from the first three that passed the IPCC vetting procedure for reproducing historical emission and climate trajectories. We then estimate global mitigation costs as the percentage difference in global per capita GDP between the SSP2 baseline and the SSP2-RCP2.6 emission scenario. In the case of one of these IAMs, estimates of mitigation costs begin in 2020, whereas in the case of two others, mitigation costs begin in 2010. The mitigation cost estimates before 2020 in these two IAMs are mostly negligible, and our choice to begin comparison with damage estimates in 2020 is conservative with respect to the relative weight of climate damages compared with mitigation costs for these two IAMs.

Data availability

Data on economic production and ERA-5 climate data are publicly available at https://doi.org/10.5281/zenodo.4681306 (ref. 62 ) and https://www.ecmwf.int/en/forecasts/datasets/reanalysis-datasets/era5 , respectively. Data on mitigation costs are publicly available at https://data.ene.iiasa.ac.at/ar6/#/downloads . Processed climate and economic data, as well as all other necessary data for reproduction of the results, are available at the public repository https://doi.org/10.5281/zenodo.10562951  (ref. 63 ).

Code availability

All code necessary for reproduction of the results is available at the public repository https://doi.org/10.5281/zenodo.10562951  (ref. 63 ).

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Acknowledgements

We gratefully acknowledge financing from the Volkswagen Foundation and the Deutsche Gesellschaft für Internationale Zusammenarbeit (GIZ) GmbH on behalf of the Government of the Federal Republic of Germany and Federal Ministry for Economic Cooperation and Development (BMZ).

Open access funding provided by Potsdam-Institut für Klimafolgenforschung (PIK) e.V.

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Extended data figures and tables

Extended data fig. 1 constraining the persistence of historical climate impacts on economic growth rates..

The results of a panel-based fixed-effects distributed lag model for the effects of annual mean temperature ( a ), daily temperature variability ( b ), total annual precipitation ( c ), the number of wet days ( d ) and extreme daily precipitation ( e ) on sub-national economic growth rates. Point estimates show the effects of a 1 °C or one standard deviation increase (for temperature and precipitation variables, respectively) at the lower quartile, median and upper quartile of the relevant moderating variable (green, orange and purple, respectively) at different lagged periods after the initial shock (note that these are not cumulative effects). Climate variables are used in their first-differenced form (see main text for discussion) and the moderating climate variables are the annual mean temperature, seasonal temperature difference, total annual precipitation, number of wet days and annual mean temperature, respectively, in panels a – e (see Methods for further discussion). Error bars show the 95% confidence intervals having clustered standard errors by region. The within-region R 2 , Bayesian and Akaike information criteria for the model are shown at the top of the figure. This figure shows results with ten lags for each variable to demonstrate the observed levels of persistence, but our preferred specifications remove later lags based on the statistical significance of terms shown above and the information criteria shown in Extended Data Fig. 2 . The resulting models without later lags are shown in Supplementary Figs. 1 – 3 .

Extended Data Fig. 2 Incremental lag-selection procedure using information criteria and within-region R 2 .

Starting from a panel-based fixed-effects distributed lag model estimating the effects of climate on economic growth using the real historical data (as in equation ( 4 )) with ten lags for all climate variables (as shown in Extended Data Fig. 1 ), lags are incrementally removed for one climate variable at a time. The resulting Bayesian and Akaike information criteria are shown in a – e and f – j , respectively, and the within-region R 2 and number of observations in k – o and p – t , respectively. Different rows show the results when removing lags from different climate variables, ordered from top to bottom as annual mean temperature, daily temperature variability, total annual precipitation, the number of wet days and extreme annual precipitation. Information criteria show minima at approximately four lags for precipitation variables and ten to eight for temperature variables, indicating that including these numbers of lags does not lead to overfitting. See Supplementary Table 1 for an assessment using information criteria to determine whether including further climate variables causes overfitting.

Extended Data Fig. 3 Damages in our preferred specification that provides a robust lower bound on the persistence of climate impacts on economic growth versus damages in specifications of pure growth or pure level effects.

Estimates of future damages as shown in Fig. 1 but under the emission scenario RCP8.5 for three separate empirical specifications: in orange our preferred specification, which provides an empirical lower bound on the persistence of climate impacts on economic growth rates while avoiding assumptions of infinite persistence (see main text for further discussion); in purple a specification of ‘pure growth effects’ in which the first difference of climate variables is not taken and no lagged climate variables are included (the baseline specification of ref.  2 ); and in pink a specification of ‘pure level effects’ in which the first difference of climate variables is taken but no lagged terms are included.

Extended Data Fig. 4 Climate changes in different variables as a function of historical interannual variability.

Changes in each climate variable of interest from 1979–2019 to 2035–2065 under the high-emission scenario SSP5-RCP8.5, expressed as a percentage of the historical variability of each measure. Historical variability is estimated as the standard deviation of each detrended climate variable over the period 1979–2019 during which the empirical models were identified (detrending is appropriate because of the inclusion of region-specific linear time trends in the empirical models). See Supplementary Fig. 13 for changes expressed in standard units. Data on national administrative boundaries are obtained from the GADM database version 3.6 and are freely available for academic use ( https://gadm.org/ ).

Extended Data Fig. 5 Contribution of different climate variables to overall committed damages.

a , Climate damages in 2049 when using empirical models that account for all climate variables, changes in annual mean temperature only or changes in both annual mean temperature and one other climate variable (daily temperature variability, total annual precipitation, the number of wet days and extreme daily precipitation, respectively). b , The cumulative marginal effects of an increase in annual mean temperature of 1 °C, at different baseline temperatures, estimated from empirical models including all climate variables or annual mean temperature only. Estimates and uncertainty bars represent the median and 95% confidence intervals obtained from 1,000 block-bootstrap resamples from each of three different empirical models using eight, nine or ten lags of temperature terms.

Extended Data Fig. 6 The difference in committed damages between the upper and lower quartiles of countries when ranked by GDP and cumulative historical emissions.

Quartiles are defined using a population weighting, as are the average committed damages across each quartile group. The violin plots indicate the distribution of differences between quartiles across the two extreme emission scenarios (RCP2.6 and RCP8.5) and the uncertainty sampling procedure outlined in Methods , which accounts for uncertainty arising from the choice of lags in the empirical models, uncertainty in the empirical model parameter estimates, as well as the climate model projections. Bars indicate the median, as well as the 10th and 90th percentiles and upper and lower sixths of the distribution reflecting the very likely and likely ranges following the likelihood classification adopted by the IPCC.

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Kotz, M., Levermann, A. & Wenz, L. The economic commitment of climate change. Nature 628 , 551–557 (2024). https://doi.org/10.1038/s41586-024-07219-0

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• Repairs to the iconic Tyrannosaurus Rex at the Saint Louis Science Center
• Profiles of SLU Research Institute Fellows providing insight and leadership in areas such as public health , workers’ safety and compensation , female entrepreneurship , and more.
• Profiles of student researchers working on public opinion polling across Missouri , new wearable touch-based technologies , and more.

This edition also includes:   

•  Letters from University President Fred P. Pestello, Ph.D., and SLU Research Institute Director Ken Olliff
• Statistics on research growth from across the University
• Highlights from the first year of the Taylor Geospatial Institute (TGI), including a conversation with inaugural Executive Director Nadine Alameh, Ph.D.  
• A look at the latest cohort of SLU Research Institute Fellows
• A selection of books published by SLU authors in the past year
• Announcements from across the University’s research community

The cover of the Impact Report features a printed circuit board designed by Nicolas Prudencio, a graduate student in the Collaborative Haptics, Robotics, and Mechatronics (CHROME) Lab, under the supervision of Jenna Gorlewicz, Ph.D., associate professor of mechanical engineering, SLU Research Institute Fellow, and director of the CHROME Lab.

The circuit board was designed as part of a collaboration with Gallaudet University and the DeafBlind community to develop wearable touch-based technologies. More information can be found in the story “ A Touch of Ingenuity. ”

Stories from the Impact Report have been featured in the Monday edition of Newslink since the start of the spring 2024 semester. They will continue to be featured through the summer.

A submission process for the next edition will be announced to the campus community in summer 2024.

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Teens and social media: Key findings from Pew Research Center surveys

For the latest survey data on social media and tech use among teens, see “ Teens, Social Media, and Technology 2023 .” 

Today’s teens are navigating a digital landscape unlike the one experienced by their predecessors, particularly when it comes to the pervasive presence of social media. In 2022, Pew Research Center fielded an in-depth survey asking American teens – and their parents – about their experiences with and views toward social media . Here are key findings from the survey:

Pew Research Center conducted this study to better understand American teens’ experiences with social media and their parents’ perception of these experiences. For this analysis, we surveyed 1,316 U.S. teens ages 13 to 17, along with one parent from each teen’s household. The survey was conducted online by Ipsos from April 14 to May 4, 2022.

This research was reviewed and approved by an external institutional review board (IRB), Advarra, which is an independent committee of experts that specializes in helping to protect the rights of research participants.

Ipsos invited panelists who were a parent of at least one teen ages 13 to 17 from its KnowledgePanel , a probability-based web panel recruited primarily through national, random sampling of residential addresses, to take this survey. For some of these questions, parents were asked to think about one teen in their household. (If they had multiple teenage children ages 13 to 17 in the household, one was randomly chosen.) This teen was then asked to answer questions as well. The parent portion of the survey is weighted to be representative of U.S. parents of teens ages 13 to 17 by age, gender, race, ethnicity, household income and other categories. The teen portion of the survey is weighted to be representative of U.S. teens ages 13 to 17 who live with parents by age, gender, race, ethnicity, household income and other categories.

Here are the questions used  for this report, along with responses, and its  methodology .

Majorities of teens report ever using YouTube, TikTok, Instagram and Snapchat. YouTube is the platform most commonly used by teens, with 95% of those ages 13 to 17 saying they have ever used it, according to a Center survey conducted April 14-May 4, 2022, that asked about 10 online platforms. Two-thirds of teens report using TikTok, followed by roughly six-in-ten who say they use Instagram (62%) and Snapchat (59%). Much smaller shares of teens say they have ever used Twitter (23%), Twitch (20%), WhatsApp (17%), Reddit (14%) and Tumblr (5%).

Facebook use among teens dropped from 71% in 2014-15 to 32% in 2022. Twitter and Tumblr also experienced declines in teen users during that span, but Instagram and Snapchat saw notable increases.

TikTok use is more common among Black teens and among teen girls. For example, roughly eight-in-ten Black teens (81%) say they use TikTok, compared with 71% of Hispanic teens and 62% of White teens. And Hispanic teens (29%) are more likely than Black (19%) or White teens (10%) to report using WhatsApp. (There were not enough Asian teens in the sample to analyze separately.)

Teens’ use of certain social media platforms also varies by gender. Teen girls are more likely than teen boys to report using TikTok (73% vs. 60%), Instagram (69% vs. 55%) and Snapchat (64% vs. 54%). Boys are more likely than girls to report using YouTube (97% vs. 92%), Twitch (26% vs. 13%) and Reddit (20% vs. 8%).

Majorities of teens use YouTube and TikTok every day, and some report using these sites almost constantly. About three-quarters of teens (77%) say they use YouTube daily, while a smaller majority of teens (58%) say the same about TikTok. About half of teens use Instagram (50%) or Snapchat (51%) at least once a day, while 19% report daily use of Facebook.

Some teens report using these platforms almost constantly. For example, 19% say they use YouTube almost constantly, while 16% and 15% say the same about TikTok and Snapchat, respectively.

More than half of teens say it would be difficult for them to give up social media. About a third of teens (36%) say they spend too much time on social media, while 55% say they spend about the right amount of time there and just 8% say they spend too little time. Girls are more likely than boys to say they spend too much time on social media (41% vs. 31%).

Teens are relatively divided over whether it would be hard or easy for them to give up social media. Some 54% say it would be very or somewhat hard, while 46% say it would be very or somewhat easy.

Girls are more likely than boys to say it would be difficult for them to give up social media (58% vs. 49%). Older teens are also more likely than younger teens to say this: 58% of those ages 15 to 17 say it would be very or somewhat hard to give up social media, compared with 48% of those ages 13 to 14.

Teens are more likely to say social media has had a negative effect on others than on themselves. Some 32% say social media has had a mostly negative effect on people their age, while 9% say this about social media’s effect on themselves.

Conversely, teens are more likely to say these platforms have had a mostly positive impact on their own life than on those of their peers. About a third of teens (32%) say social media has had a mostly positive effect on them personally, while roughly a quarter (24%) say it has been positive for other people their age.

Still, the largest shares of teens say social media has had neither a positive nor negative effect on themselves (59%) or on other teens (45%). These patterns are consistent across demographic groups.

Teens are more likely to report positive than negative experiences in their social media use. Majorities of teens report experiencing each of the four positive experiences asked about: feeling more connected to what is going on in their friends’ lives (80%), like they have a place where they can show their creative side (71%), like they have people who can support them through tough times (67%), and that they are more accepted (58%).

When it comes to negative experiences, 38% of teens say that what they see on social media makes them feel overwhelmed because of all the drama. Roughly three-in-ten say it makes them feel like their friends are leaving them out of things (31%) or feel pressure to post content that will get lots of comments or likes (29%). And 23% say that what they see on social media makes them feel worse about their own life.

There are several gender differences in the experiences teens report having while on social media. Teen girls are more likely than teen boys to say that what they see on social media makes them feel a lot like they have a place to express their creativity or like they have people who can support them. However, girls also report encountering some of the pressures at higher rates than boys. Some 45% of girls say they feel overwhelmed because of all the drama on social media, compared with 32% of boys. Girls are also more likely than boys to say social media has made them feel like their friends are leaving them out of things (37% vs. 24%) or feel worse about their own life (28% vs. 18%).

When it comes to abuse on social media platforms, many teens think criminal charges or permanent bans would help a lot. Half of teens think criminal charges or permanent bans for users who bully or harass others on social media would help a lot to reduce harassment and bullying on these platforms. 

About four-in-ten teens say it would help a lot if social media companies proactively deleted abusive posts or required social media users to use their real names and pictures. Three-in-ten teens say it would help a lot if school districts monitored students’ social media activity for bullying or harassment.

Some teens – especially older girls – avoid posting certain things on social media because of fear of embarrassment or other reasons. Roughly four-in-ten teens say they often or sometimes decide not to post something on social media because they worry people might use it to embarrass them (40%) or because it does not align with how they like to represent themselves on these platforms (38%). A third of teens say they avoid posting certain things out of concern for offending others by what they say, while 27% say they avoid posting things because it could hurt their chances when applying for schools or jobs.

These concerns are more prevalent among older teen girls. For example, roughly half of girls ages 15 to 17 say they often or sometimes decide not to post something on social media because they worry people might use it to embarrass them (50%) or because it doesn’t fit with how they’d like to represent themselves on these sites (51%), compared with smaller shares among younger girls and among boys overall.

Many teens do not feel like they are in the driver’s seat when it comes to controlling what information social media companies collect about them. Six-in-ten teens say they think they have little (40%) or no control (20%) over the personal information that social media companies collect about them. Another 26% aren’t sure how much control they have. Just 14% of teens think they have a lot of control.

Despite many feeling a lack of control, teens are largely unconcerned about companies collecting their information. Only 8% are extremely concerned about the amount of personal information that social media companies might have and 13% are very concerned. Still, 44% of teens say they have little or no concern about how much these companies might know about them.

Only around one-in-five teens think their parents are highly worried about their use of social media. Some 22% of teens think their parents are extremely or very worried about them using social media. But a larger share of teens (41%) think their parents are either not at all (16%) or a little worried (25%) about them using social media. About a quarter of teens (27%) fall more in the middle, saying they think their parents are somewhat worried.

Many teens also believe there is a disconnect between parental perceptions of social media and teens’ lived realities. Some 39% of teens say their experiences on social media are better than parents think, and 27% say their experiences are worse. A third of teens say parents’ views are about right.

Nearly half of parents with teens (46%) are highly worried that their child could be exposed to explicit content on social media. Parents of teens are more likely to be extremely or very concerned about this than about social media causing mental health issues like anxiety, depression or lower self-esteem. Some parents also fret about time management problems for their teen stemming from social media use, such as wasting time on these sites (42%) and being distracted from completing homework (38%).

Note: Here are the questions used  for this report, along with responses, and its  methodology .

CORRECTION (May 17, 2023): In a previous version of this post, the percentages of teens using Instagram and Snapchat daily were transposed in the text. The original chart was correct. This change does not substantively affect the analysis.

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