using i and we in research papers

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Can You Use I or We in a Research Paper?

4-minute read

• 11th July 2023

Writing in the first person, or using I and we pronouns, has traditionally been frowned upon in academic writing . But despite this long-standing norm, writing in the first person isn’t actually prohibited. In fact, it’s becoming more acceptable – even in research papers.

 If you’re wondering whether you can use I (or we ) in your research paper, you should check with your institution first and foremost. Many schools have rules regarding first-person use. If it’s up to you, though, we still recommend some guidelines. Check out our tips below!

When Is It Most Acceptable to Write in the First Person?

Certain sections of your paper are more conducive to writing in the first person. Typically, the first person makes sense in the abstract, introduction, discussion, and conclusion sections. You should still limit your use of I and we , though, or your essay may start to sound like a personal narrative .

 Using first-person pronouns is most useful and acceptable in the following circumstances.

When doing so removes the passive voice and adds flow

Sometimes, writers have to bend over backward just to avoid using the first person, often producing clunky sentences and a lot of passive voice constructions. The first person can remedy this. For example: 

Both sentences are fine, but the second one flows better and is easier to read.

When doing so differentiates between your research and other literature

When discussing literature from other researchers and authors, you might be comparing it with your own findings or hypotheses . Using the first person can help clarify that you are engaging in such a comparison. For example: 

 In the first sentence, using “the author” to avoid the first person creates ambiguity. The second sentence prevents misinterpretation.

When doing so allows you to express your interest in the subject

In some instances, you may need to provide background for why you’re researching your topic. This information may include your personal interest in or experience with the subject, both of which are easier to express using first-person pronouns. For example:

Expressing personal experiences and viewpoints isn’t always a good idea in research papers. When it’s appropriate to do so, though, just make sure you don’t overuse the first person.

When to Avoid Writing in the First Person

It’s usually a good idea to stick to the third person in the methods and results sections of your research paper. Additionally, be careful not to use the first person when:

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●  It makes your findings seem like personal observations rather than factual results.

●  It removes objectivity and implies that the writing may be biased .

●  It appears in phrases such as I think or I believe , which can weaken your writing.

Keeping Your Writing Formal and Objective

Using the first person while maintaining a formal tone can be tricky, but keeping a few tips in mind can help you strike a balance. The important thing is to make sure the tone isn’t too conversational.

 To achieve this, avoid referring to the readers, such as with the second-person you . Use we and us only when referring to yourself and the other authors/researchers involved in the paper, not the audience.

It’s becoming more acceptable in the academic world to use first-person pronouns such as we and I in research papers. But make sure you check with your instructor or institution first because they may have strict rules regarding this practice.

 If you do decide to use the first person, make sure you do so effectively by following the tips we’ve laid out in this guide. And once you’ve written a draft, send us a copy! Our expert proofreaders and editors will be happy to check your grammar, spelling, word choice, references, tone, and more. Submit a 500-word sample today!

Is it ever acceptable to use I or we in a research paper?

In some instances, using first-person pronouns can help you to establish credibility, add clarity, and make the writing easier to read.

How can I avoid using I in my writing?

Writing in the passive voice can help you to avoid using the first person.

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Can You Use First-Person Pronouns (I/we) in a Research Paper?

Research writers frequently wonder whether the first person can be used in academic and scientific writing. In truth, for generations, we’ve been discouraged from using “I” and “we” in academic writing simply due to old habits. That’s right—there’s no reason why you can’t use these words! In fact, the academic community used first-person pronouns until the 1920s, when the third person and passive-voice constructions (that is, “boring” writing) were adopted–prominently expressed, for example, in Strunk and White’s classic writing manual “Elements of Style” first published in 1918, that advised writers to place themselves “in the background” and not draw attention to themselves.

In recent decades, however, changing attitudes about the first person in academic writing has led to a paradigm shift, and we have, however, we’ve shifted back to producing active and engaging prose that incorporates the first person.

Can You Use “I” in a Research Paper?

However, “I” and “we” still have some generally accepted pronoun rules writers should follow. For example, the first person is more likely used in the abstract , Introduction section , Discussion section , and Conclusion section of an academic paper while the third person and passive constructions are found in the Methods section and Results section .

In this article, we discuss when you should avoid personal pronouns and when they may enhance your writing.

It’s Okay to Use First-Person Pronouns to:

• clarify meaning by eliminating passive voice constructions;
• establish authority and credibility (e.g., assert ethos, the Aristotelian rhetorical term referring to the personal character);
• express interest in a subject matter (typically found in rapid correspondence);
• establish personal connections with readers, particularly regarding anecdotal or hypothetical situations (common in philosophy, religion, and similar fields, particularly to explore how certain concepts might impact personal life. Additionally, artistic disciplines may also encourage personal perspectives more than other subjects);
• to emphasize or distinguish your perspective while discussing existing literature; and
• to create a conversational tone (rare in academic writing).

The First Person Should Be Avoided When:

• doing so would remove objectivity and give the impression that results or observations are unique to your perspective;
• you wish to maintain an objective tone that would suggest your study minimized biases as best as possible; and
• expressing your thoughts generally (phrases like “I think” are unnecessary because any statement that isn’t cited should be yours).

Usage Examples

The following examples compare the impact of using and avoiding first-person pronouns.

Example 1 (First Person Preferred):

To understand the effects of global warming on coastal regions,  changes in sea levels, storm surge occurrences and precipitation amounts  were examined .

[Note: When a long phrase acts as the subject of a passive-voice construction, the sentence becomes difficult to digest. Additionally, since the author(s) conducted the research, it would be clearer to specifically mention them when discussing the focus of a project.]

We examined  changes in sea levels, storm surge occurrences, and precipitation amounts to understand how global warming impacts coastal regions.

[Note: When describing the focus of a research project, authors often replace “we” with phrases such as “this study” or “this paper.” “We,” however, is acceptable in this context, including for scientific disciplines. In fact, papers published the vast majority of scientific journals these days use “we” to establish an active voice.   Be careful when using “this study” or “this paper” with verbs that clearly couldn’t have performed the action.   For example, “we attempt to demonstrate” works, but “the study attempts to demonstrate” does not; the study is not a person.]

Example 2 (First Person Discouraged):

From the various data points  we have received ,  we observed  that higher frequencies of runoffs from heavy rainfall have occurred in coastal regions where temperatures have increased by at least 0.9°C.

[Note: Introducing personal pronouns when discussing results raises questions regarding the reproducibility of a study. However, mathematics fields generally tolerate phrases such as “in X example, we see…”]

Coastal regions  with temperature increases averaging more than 0.9°C  experienced  higher frequencies of runoffs from heavy rainfall.

[Note: We removed the passive voice and maintained objectivity and assertiveness by specifically identifying the cause-and-effect elements as the actor and recipient of the main action verb. Additionally, in this version, the results appear independent of any person’s perspective.] 

Example 3 (First Person Preferred):

In contrast to the study by Jones et al. (2001), which suggests that milk consumption is safe for adults, the Miller study (2005) revealed the potential hazards of ingesting milk.  The authors confirm  this latter finding.

[Note: “Authors” in the last sentence above is unclear. Does the term refer to Jones et al., Miller, or the authors of the current paper?]

In contrast to the study by Jones et al. (2001), which suggests that milk consumption is safe for adults, the Miller study (2005) revealed the potential hazards of ingesting milk.  We confirm  this latter finding.

[Note: By using “we,” this sentence clarifies the actor and emphasizes the significance of the recent findings reported in this paper. Indeed, “I” and “we” are acceptable in most scientific fields to compare an author’s works with other researchers’ publications. The APA encourages using personal pronouns for this context. The social sciences broaden this scope to allow discussion of personal perspectives, irrespective of comparisons to other literature.]

Other Tips about Using Personal Pronouns

• Avoid starting a sentence with personal pronouns. The beginning of a sentence is a noticeable position that draws readers’ attention. Thus, using personal pronouns as the first one or two words of a sentence will draw unnecessary attention to them (unless, of course, that was your intent).
• Be careful how you define “we.” It should only refer to the authors and never the audience unless your intention is to write a conversational piece rather than a scholarly document! After all, the readers were not involved in analyzing or formulating the conclusions presented in your paper (although, we note that the point of your paper is to persuade readers to reach the same conclusions you did). While this is not a hard-and-fast rule, if you do want to use “we” to refer to a larger class of people, clearly define the term “we” in the sentence. For example, “As researchers, we frequently question…”
• First-person writing is becoming more acceptable under Modern English usage standards; however, the second-person pronoun “you” is still generally unacceptable because it is too casual for academic writing.
• Take all of the above notes with a grain of salt. That is,  double-check your institution or target journal’s author guidelines .  Some organizations may prohibit the use of personal pronouns.
• As an extra tip, before submission, you should always read through the most recent issues of a journal to get a better sense of the editors’ preferred writing styles and conventions.

Wordvice Resources

For more general advice on how to use active and passive voice in research papers, on how to paraphrase , or for a list of useful phrases for academic writing , head over to the Wordvice Academic Resources pages . And for more professional proofreading services , visit our Academic Editing and P aper Editing Services pages.

Should I Use “I”?

What this handout is about.

This handout is about determining when to use first person pronouns (“I”, “we,” “me,” “us,” “my,” and “our”) and personal experience in academic writing. “First person” and “personal experience” might sound like two ways of saying the same thing, but first person and personal experience can work in very different ways in your writing. You might choose to use “I” but not make any reference to your individual experiences in a particular paper. Or you might include a brief description of an experience that could help illustrate a point you’re making without ever using the word “I.” So whether or not you should use first person and personal experience are really two separate questions, both of which this handout addresses. It also offers some alternatives if you decide that either “I” or personal experience isn’t appropriate for your project. If you’ve decided that you do want to use one of them, this handout offers some ideas about how to do so effectively, because in many cases using one or the other might strengthen your writing.

Expectations about academic writing

Students often arrive at college with strict lists of writing rules in mind. Often these are rather strict lists of absolutes, including rules both stated and unstated:

• Each essay should have exactly five paragraphs.
• Don’t begin a sentence with “and” or “because.”
• Never include personal opinion.
• Never use “I” in essays.

We get these ideas primarily from teachers and other students. Often these ideas are derived from good advice but have been turned into unnecessarily strict rules in our minds. The problem is that overly strict rules about writing can prevent us, as writers, from being flexible enough to learn to adapt to the writing styles of different fields, ranging from the sciences to the humanities, and different kinds of writing projects, ranging from reviews to research.

So when it suits your purpose as a scholar, you will probably need to break some of the old rules, particularly the rules that prohibit first person pronouns and personal experience. Although there are certainly some instructors who think that these rules should be followed (so it is a good idea to ask directly), many instructors in all kinds of fields are finding reason to depart from these rules. Avoiding “I” can lead to awkwardness and vagueness, whereas using it in your writing can improve style and clarity. Using personal experience, when relevant, can add concreteness and even authority to writing that might otherwise be vague and impersonal. Because college writing situations vary widely in terms of stylistic conventions, tone, audience, and purpose, the trick is deciphering the conventions of your writing context and determining how your purpose and audience affect the way you write. The rest of this handout is devoted to strategies for figuring out when to use “I” and personal experience.

Effective uses of “I”:

In many cases, using the first person pronoun can improve your writing, by offering the following benefits:

• Assertiveness: In some cases you might wish to emphasize agency (who is doing what), as for instance if you need to point out how valuable your particular project is to an academic discipline or to claim your unique perspective or argument.
• Clarity: Because trying to avoid the first person can lead to awkward constructions and vagueness, using the first person can improve your writing style.
• Positioning yourself in the essay: In some projects, you need to explain how your research or ideas build on or depart from the work of others, in which case you’ll need to say “I,” “we,” “my,” or “our”; if you wish to claim some kind of authority on the topic, first person may help you do so.

Deciding whether “I” will help your style

Here is an example of how using the first person can make the writing clearer and more assertive:

Original example:

In studying American popular culture of the 1980s, the question of to what degree materialism was a major characteristic of the cultural milieu was explored.

Better example using first person:

In our study of American popular culture of the 1980s, we explored the degree to which materialism characterized the cultural milieu.

The original example sounds less emphatic and direct than the revised version; using “I” allows the writers to avoid the convoluted construction of the original and clarifies who did what.

Here is an example in which alternatives to the first person would be more appropriate:

As I observed the communication styles of first-year Carolina women, I noticed frequent use of non-verbal cues.

Better example:

A study of the communication styles of first-year Carolina women revealed frequent use of non-verbal cues.

In the original example, using the first person grounds the experience heavily in the writer’s subjective, individual perspective, but the writer’s purpose is to describe a phenomenon that is in fact objective or independent of that perspective. Avoiding the first person here creates the desired impression of an observed phenomenon that could be reproduced and also creates a stronger, clearer statement.

Here’s another example in which an alternative to first person works better:

As I was reading this study of medieval village life, I noticed that social class tended to be clearly defined.

This study of medieval village life reveals that social class tended to be clearly defined.

Although you may run across instructors who find the casual style of the original example refreshing, they are probably rare. The revised version sounds more academic and renders the statement more assertive and direct.

Here’s a final example:

I think that Aristotle’s ethical arguments are logical and readily applicable to contemporary cases, or at least it seems that way to me.

Better example

Aristotle’s ethical arguments are logical and readily applicable to contemporary cases.

In this example, there is no real need to announce that that statement about Aristotle is your thought; this is your paper, so readers will assume that the ideas in it are yours.

Determining whether to use “I” according to the conventions of the academic field

Which fields allow “I”?

The rules for this are changing, so it’s always best to ask your instructor if you’re not sure about using first person. But here are some general guidelines.

Sciences: In the past, scientific writers avoided the use of “I” because scientists often view the first person as interfering with the impression of objectivity and impersonality they are seeking to create. But conventions seem to be changing in some cases—for instance, when a scientific writer is describing a project she is working on or positioning that project within the existing research on the topic. Check with your science instructor to find out whether it’s o.k. to use “I” in their class.

Social Sciences: Some social scientists try to avoid “I” for the same reasons that other scientists do. But first person is becoming more commonly accepted, especially when the writer is describing their project or perspective.

Humanities: Ask your instructor whether you should use “I.” The purpose of writing in the humanities is generally to offer your own analysis of language, ideas, or a work of art. Writers in these fields tend to value assertiveness and to emphasize agency (who’s doing what), so the first person is often—but not always—appropriate. Sometimes writers use the first person in a less effective way, preceding an assertion with “I think,” “I feel,” or “I believe” as if such a phrase could replace a real defense of an argument. While your audience is generally interested in your perspective in the humanities fields, readers do expect you to fully argue, support, and illustrate your assertions. Personal belief or opinion is generally not sufficient in itself; you will need evidence of some kind to convince your reader.

Other writing situations: If you’re writing a speech, use of the first and even the second person (“you”) is generally encouraged because these personal pronouns can create a desirable sense of connection between speaker and listener and can contribute to the sense that the speaker is sincere and involved in the issue. If you’re writing a resume, though, avoid the first person; describe your experience, education, and skills without using a personal pronoun (for example, under “Experience” you might write “Volunteered as a peer counselor”).

A note on the second person “you”:

In situations where your intention is to sound conversational and friendly because it suits your purpose, as it does in this handout intended to offer helpful advice, or in a letter or speech, “you” might help to create just the sense of familiarity you’re after. But in most academic writing situations, “you” sounds overly conversational, as for instance in a claim like “when you read the poem ‘The Wasteland,’ you feel a sense of emptiness.” In this case, the “you” sounds overly conversational. The statement would read better as “The poem ‘The Wasteland’ creates a sense of emptiness.” Academic writers almost always use alternatives to the second person pronoun, such as “one,” “the reader,” or “people.”

Personal experience in academic writing

The question of whether personal experience has a place in academic writing depends on context and purpose. In papers that seek to analyze an objective principle or data as in science papers, or in papers for a field that explicitly tries to minimize the effect of the researcher’s presence such as anthropology, personal experience would probably distract from your purpose. But sometimes you might need to explicitly situate your position as researcher in relation to your subject of study. Or if your purpose is to present your individual response to a work of art, to offer examples of how an idea or theory might apply to life, or to use experience as evidence or a demonstration of an abstract principle, personal experience might have a legitimate role to play in your academic writing. Using personal experience effectively usually means keeping it in the service of your argument, as opposed to letting it become an end in itself or take over the paper.

It’s also usually best to keep your real or hypothetical stories brief, but they can strengthen arguments in need of concrete illustrations or even just a little more vitality.

Here are some examples of effective ways to incorporate personal experience in academic writing:

• Anecdotes: In some cases, brief examples of experiences you’ve had or witnessed may serve as useful illustrations of a point you’re arguing or a theory you’re evaluating. For instance, in philosophical arguments, writers often use a real or hypothetical situation to illustrate abstract ideas and principles.
• References to your own experience can explain your interest in an issue or even help to establish your authority on a topic.
• Some specific writing situations, such as application essays, explicitly call for discussion of personal experience.

Here are some suggestions about including personal experience in writing for specific fields:

Philosophy: In philosophical writing, your purpose is generally to reconstruct or evaluate an existing argument, and/or to generate your own. Sometimes, doing this effectively may involve offering a hypothetical example or an illustration. In these cases, you might find that inventing or recounting a scenario that you’ve experienced or witnessed could help demonstrate your point. Personal experience can play a very useful role in your philosophy papers, as long as you always explain to the reader how the experience is related to your argument. (See our handout on writing in philosophy for more information.)

Religion: Religion courses might seem like a place where personal experience would be welcomed. But most religion courses take a cultural, historical, or textual approach, and these generally require objectivity and impersonality. So although you probably have very strong beliefs or powerful experiences in this area that might motivate your interest in the field, they shouldn’t supplant scholarly analysis. But ask your instructor, as it is possible that they are interested in your personal experiences with religion, especially in less formal assignments such as response papers. (See our handout on writing in religious studies for more information.)

Literature, Music, Fine Arts, and Film: Writing projects in these fields can sometimes benefit from the inclusion of personal experience, as long as it isn’t tangential. For instance, your annoyance over your roommate’s habits might not add much to an analysis of “Citizen Kane.” However, if you’re writing about Ridley Scott’s treatment of relationships between women in the movie “Thelma and Louise,” some reference your own observations about these relationships might be relevant if it adds to your analysis of the film. Personal experience can be especially appropriate in a response paper, or in any kind of assignment that asks about your experience of the work as a reader or viewer. Some film and literature scholars are interested in how a film or literary text is received by different audiences, so a discussion of how a particular viewer or reader experiences or identifies with the piece would probably be appropriate. (See our handouts on writing about fiction , art history , and drama for more information.)

Women’s Studies: Women’s Studies classes tend to be taught from a feminist perspective, a perspective which is generally interested in the ways in which individuals experience gender roles. So personal experience can often serve as evidence for your analytical and argumentative papers in this field. This field is also one in which you might be asked to keep a journal, a kind of writing that requires you to apply theoretical concepts to your experiences.

History: If you’re analyzing a historical period or issue, personal experience is less likely to advance your purpose of objectivity. However, some kinds of historical scholarship do involve the exploration of personal histories. So although you might not be referencing your own experience, you might very well be discussing other people’s experiences as illustrations of their historical contexts. (See our handout on writing in history for more information.)

Sciences: Because the primary purpose is to study data and fixed principles in an objective way, personal experience is less likely to have a place in this kind of writing. Often, as in a lab report, your goal is to describe observations in such a way that a reader could duplicate the experiment, so the less extra information, the better. Of course, if you’re working in the social sciences, case studies—accounts of the personal experiences of other people—are a crucial part of your scholarship. (See our handout on  writing in the sciences for more information.)

You may reproduce it for non-commercial use if you use the entire handout and attribute the source: The Writing Center, University of North Carolina at Chapel Hill

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“I” & “We” in Academic Writing: Examples from 9,830 Studies

I analyzed a random sample of 9,830 full-text research papers, uploaded to PubMed Central between the years 2016 and 2021, in order to explore whether first-person pronouns are used in the scientific literature, and how?

I used the BioC API to download the data (see the References section below).

Popularity of first-person pronouns in the scientific literature

In our sample of 9,830 articles, 93.8% used the first-person pronouns “I” or “We”. The use of the pronoun “We” was a lot more prevalent than “I” (93.1% versus 13.9%, respectively).

In fact, even articles written by single authors were more likely to use “We” instead of “I”. Out of 9,830 articles, 39 were written by single authors: 8 of them used “I” and 19 used “We”.

The following table describes the use of first-person pronouns in each section of the research article:

Use of the pronoun “I”

The pronoun “I” was mostly used in the Methods section (present in 7.29% of all methods sections in our sample).

For example:

“In general, I assumed a steady-state and a closed-population.” Link to the article on PubMed

“I” was also prevalent in the Results section (6.28%). But here, all of its uses were to quote participants’ answers, such as:

The respondents stated, “ I am scared to get infected and infect my family when I go home” Link to the article on PubMed

“I” was scarcely used in the Discussion section, for example:

“Based on this observation, I suggest that future research on this population seek to increase the participation of Indigenous communities.” Link to the article on PubMed

And only 1 article of 9,830 used the pronoun “I” in the abstract:

“By using the largest publicly available cancer incidence statistics (20 million cases), I show that incidence of 20 most prevalent cancer types in relation to patients’ age closely follows the Erlang probability distribution (R 2  = 0.9734-0.9999).” Link to the article on PubMed

Use of the pronoun “We”

The pronoun “We” was primarily used in the Discussion section (in 85.65% of the cases). For example:

“Although we cannot rule out this potential bias, we expect that missing data in our analysis did not depend on our dependent variable.” Link to the article on PubMed

Followed by the Methods section (68.29%). For example:

“Depending on the severity and chronicity of disease, we applied three different time frames” Link to the article on PubMed

Then followed by the Introduction (64.31%). For example:

“Instead of using time series analysis, we conducted a manipulative field experiment.” Link to the article on PubMed

“We” is used to a lesser extent in the Results section (52.36%). For example:

“In contrast, we did not find differences in survival when mutants where challenged with acute oxidative stress (Figure S5)” Link to the article on PubMed

And a lot less in the Abstract. For example:

“In this study, we analyzed and summarized seven RCTs and four meta-analyses.” Link to the article on PubMed

Article quality and the use first-person pronouns

The following table compares articles that used first-person pronouns with those that did not use first-person pronouns regarding:

• The number of citations per year received by these articles
• The impact factor of the journals in which these articles were published

The data show that higher-quality articles (those that bring more citations and those published in high-impact journals) tend to use first-person pronouns.

In other words, high article quality is correlated with the use of first-person pronouns.

• Comeau DC, Wei CH, Islamaj Doğan R, and Lu Z. PMC text mining subset in BioC: about 3 million full text articles and growing,  Bioinformatics , btz070, 2019.

Further reading

• Paragraph Length: Data from 9,830 Research Papers
• Can a Research Title Be a Question? Real-World Examples
• How Long Should a Research Paper Be? Data from 61,519 Examples
• How Many References to Cite? Based on 96,685 Research Papers
• How Old Should References Be? Based on 3,823,919 Examples

Using “I” in Academic Writing

Traditionally, some fields have frowned on the use of the first-person singular in an academic essay and others have encouraged that use, and both the frowning and the encouraging persist today—and there are good reasons for both positions (see “Should I”).

I recommend that you not look on the question of using “I” in an academic paper as a matter of a rule to follow, as part of a political agenda (see webb), or even as the need to create a strategy to avoid falling into Scylla-or-Charybdis error. Let the first-person singular be, instead, a tool that you take out when you think it’s needed and that you leave in the toolbox when you think it’s not.

Examples of When “I” May Be Needed

• You are narrating how you made a discovery, and the process of your discovering is important or at the very least entertaining.
• You are describing how you teach something and how your students have responded or respond.
• You disagree with another scholar and want to stress that you are not waving the banner of absolute truth.
• You need “I” for rhetorical effect, to be clear, simple, or direct.

Examples of When “I” Should Be Given a Rest

• It’s off-putting to readers, generally, when “I” appears too often. You may not feel one bit modest, but remember the advice of Benjamin Franklin, still excellent, on the wisdom of preserving the semblance of modesty when your purpose is to convince others.
• You are the author of your paper, so if an opinion is expressed in it, it is usually clear that this opinion is yours. You don’t have to add a phrase like, “I believe” or “it seems to me.”

Works Cited

Franklin, Benjamin. The Autobiography of Benjamin Franklin . Project Gutenberg , 28 Dec. 2006, www.gutenberg.org/app/uploads/sites/3/20203/20203-h/20203-h.htm#I.

“Should I Use “I”?” The Writing Center at UNC—Chapel Hill , writingcenter.unc.edu/handouts/should-i-use-i/.

webb, Christine. “The Use of the First Person in Academic Writing: Objectivity, Language, and Gatekeeping.” ResearchGate , July 1992, doi: 10.1111/j.1365-2648.1992.tb01974.x.

J.S.Beniwal 05 August 2017 AT 09:08 AM

I have borrowed MLA only yesterday, did my MAEnglish in May 2017.MLA is of immense help for scholars.An overview of the book really enlightened​ me.I should have read it at bachelor's degree level.

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Dr. Raymond Harter 25 September 2017 AT 02:09 PM

I discourage the use of "I" in essays for undergraduates to reinforce a conversational tone and to "self-recognize" the writer as an authority or at least a thorough researcher. Writing a play is different than an essay with a purpose.

Osayimwense Osa 22 March 2023 AT 05:03 PM

When a student or writer is strongly and passionately interested in his or her stance and argument to persuade his or her audience, the use of personal pronoun srenghtens his or her passion for the subject. This passion should be clear in his/her expression. However, I encourage the use of the first-person, I, sparingly -- only when and where absolutely necessary.

Eleanor 25 March 2023 AT 04:03 PM

I once had a student use the word "eye" when writing about how to use pronouns. Her peers did not catch it. I made comments, but I think she never understood what eye was saying!

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• Are first-person pronouns acceptable in scientific writing?

February 23, 2011 Filed under Blog , Featured , Popular , Writing  

Interestingly, this rule seems to have originated with Francis Bacon to give scientific writing more objectivity.

In Eloquent Science (pp. 76-77), I advocate that first-person pronouns are acceptable in limited contexts. Avoid their use in rote descriptions of your methodology (“We performed the assay…”). Instead, use them to communicate that an action or a decision that you performed affects the outcome of the research.

NO FIRST-PERSON PRONOUN: Given option A and option B, the authors chose option B to more accurately depict the location of the front. FIRST-PERSON PRONOUN: Given option A and option B, we chose option B to more accurately depict the location of the front.

So, what do other authors think? I have over 30 books on scientific writing and have read numerous articles on this point. Here are some quotes from those who expressed their opinion on this matter and I was able to find from the index of the book or through a quick scan of the book.

“Because of this [avoiding first-person pronouns], the scientist commonly uses verbose (and imprecise) statements such as “It was found that” in preference to the short, unambiguous “I found.” Young scientists should renounce the false modesty of their predecessors. Do not be afraid to name the agent of the action in a sentence, even when it is “I” or “we.”” — How to Write and Publish a Scientific Paper by Day and Gastel, pp. 193-194 “Who is the universal ‘it’, the one who hides so bashfully, but does much thinking and assuming? “ It is thought that … is a meaningless phrase and unnecessary exercise in modesty. The reader wants to know who did the thinking or assuming, the author, or some other expert.” — The Science Editor’s Soapbox by Lipton, p. 43 “I pulled 40 journals at random from one of my university’s technical library’s shelves…. To my surprise, in 32 out of the 40 journals, the authors indeed made liberal use of “I” and “we.” — Style for Students by Joe Schall, p. 63 “Einstein occasionally used the first person. He was not only a great scientist, but a great scientific writer. Feynman also used the first person on occasion, as did Curie, Darwin, Lyell, and Freud. As long as the emphasis remains on your work and not you, there is nothing wrong with judicious use of the first person.” — The Craft of Scientific Writing by Michael Alley, p. 107 “One of the most epochal papers in all of 20th-century science, Watson and Crick’s article defies nearly every major rule you are likely to find in manuals on scientific writing…. There is the frequent use of “we”…. This provides an immediate human presence, allowing for constant use of active voice. It also gives the impression that the authors are telling us their actual thought processes.” — The Chicago Guide to Communicating Science by Scott L. Montgomery, p. 18 “We believe in the value of a long tradition (which some deplore) arguing that it is inappropriate for the author of a scientific document to refer to himself or herself directly, in the first person…. There is no place for the subjectivity implicit in personal intrusion on the part of the one who conducted the research—especially since the section is explicitly labeled “Results”…. If first-person pronouns are appropriate anywhere in a dissertation, it would be in the Discussion section…because different people might indeed draw different inferences from a given set of facts.” — The Art of Scientific Writing by Ebel et al., p. 79. [After arguing for two pages on clearly explaining why the first person should not be used…] “The first person singular is appropriate when the personal element is strong, for example, when taking a position in a controversy. But this tends to weaken the writer’s credibility. The writer usually wants to make clear that anyone considering the same evidence would take the same position. Using the third person helps to express the logical impersonal character and generality of an author’s position, whereas the first person makes it seem more like personal opinion.” — The Scientist’s Handbook for Writing Papers and Dissertations by Antoinette Wilkinson, p. 76.

So, I can find only one source on my bookshelf advocating against use of the first-person pronouns in all situations (Wilkinson). Even the Ebel et al. quote I largely agree with.

Thus, first-person pronouns in scientific writing are acceptable if used in a limited fashion and to enhance clarity.

Isn’t it telling that Ebel et al begin their argument against usage of the first person with the phrase ““We believe …”?

That is a reall good point, Kirk. Thanks for pointing that out!

This argument is approximately correct, but in my opinion off point. The use of first person should always be minimized in scientific writing, but not because it is unacceptable or even uncommon. It should be minimized because it is ineffective, and it is usually badly so. Specifically, the purpose of scientific writing is to create a convincing argument based on data collected during the evaluation of a hypothesis. This is basic scientific method. The strength of this argument depends on the data, not on the person who collected it. Using first person deemphasises the data, which weakens the argument and opens the door for subjective criticism to be used to rebut what should be objective data. For example, suppose I hypothesized that the sun always rises in the east, and I make daily observations over the course of a year to support that hypothesis. I could say, “I have shown that the sun always rises in the east”. A critic might respond by simply saying that I am crazy, and that I got it wrong. In other words, it can easily become an argument about “me”. However, if I said “Daily observations over the course of a year showed that the sun always rises in the east”, then any subsequent argument must rebut the data and not rebut “me”. Actually, I would never say this using either of those formulations. I would say, “Daily observations over the course of a year were consistent with the hypothesis that the sun always rises in the east.” This is basic scientific expository writing.

Finally, if one of my students EVER wrote “it was found that …”, I would hit him or her over the head with a very large stick. That is just as bad as “I found that …”, and importantly, those are NOT the only two options. The correct way to say this in scientific writing is, “the data showed that …”.

In general, I agree with you. We should omit ourselves from our science to emphasize what the data demonstrate.

My only qualification is that, as scientists, the collection, observation, and interpretation of data is difficult to disconnect from its human aspects. Being a human endeavor, science is necessarily affected by the humans themselves who do the work.

Thanks for your comment!

i could not understand why 1st person I is used with plural verb

Not sure that I completely understand your question, but grammatically “I” should only be used with a singular verb. If you use “I” in scientific writing, only do so with single-authored papers.

Does that answer your question?

why do we use ‘have’ with ‘i’ pronoun?

I wouldn’t view it as “I” goes with the plural verb “have”, but that “have” can be used with a number of different persons, regardless of whether it is singular or plural.

First person singular: “I have” Second person singular: “You have” Third person singular: “He/She/It has”

First person plural: “We have” Second person plural: “All of you have” Third person plural: “They have”

I know it perhaps doesn’t make sense, but that is the way English works.

I hope that helps.

I think there are a few cases where personal pronouns would be acceptable. If you are introducing a new section in a thesis or even an article, you might want to say “we begin with a description of the data in section 2” etc, rather than the cumbersome “this paper will begin with …”. Also in discussions of future work, it would make sense to say “we intend to explore X, Y and Z”.

I loved reading this, my Prof. and I were debating about this. He wants me to say “I analyzed” and I want to say “problem notification database analysed revealed that…”

I’m writing a paper for a conference. I wonder if I can defy a Professor in Korea:)

I disagree that writing in the 3rd person makes writing more objective. I also disagree that it “opens the door for subjective criticism to be used to rebut what should be objective data”. In fact, using the 3rd person obstructs reality. There are people behind the research who both make mistakes and do great things. It is no less true for science than it is for other subjects that 3rd person obstructs the author of an action and makes the idea being conveyed less clear. I find it odd that scientific writing guides instruct authors to BOTH use active voice AND use only the 3rd person. It is impossible to do both. Active voice means that there is a subject, a strong verb (not a version of the verb “to be”) and an object. When I say “The solution was mixed”, it is BOTH 3rd person and passive voice. The only way to construct that sentence without passive voice is to say “We mixed the solution”. Honestly, after spending most of the first part of my life in English classes and then transitioning to science, I find most scientific writing an abomonination.

Hi Kathleen,

I think it is great that you have had your feet in both English and science. For many of us who have struggled as writers, those people are great role models to aspire to.

An anecdote: my wife’s research student turned in a brief report on his work to date. She was showing me how well written his work was, really pretty advanced for an undergrad physics student. Later, she found out that he was trying to decide between majoring in physics and majoring in English.

Hi David. Thanks very much for your tips. Very interesting article. Did you just tweet that you should keep “I” and “We” out of the abstract? I am translating a psychology article from Spanish into English, and I’ve come up against an unwieldly sentence (the very last one in the abstract) that basically wants to say “We propose a number of strategies for improving the impact of the psychological treatments[…]” Would you say it’s a no-no? I tend to avoid personal pronouns in academic articles as much as poss, but it just sounds like the most natural option in this case. Perhaps I could put, “This article proposes a number of treatments…”? Strictly speaking it’s not the article that’s doing the proposing, obviously. I’d be very grateful to have your opinion. Thanks a lot. Best regards. Louisa

Yes, it’s difficult. How about going passive? “A number of treatments are proposed….”?

The comments against using first person, which are rampant in science education, are silly. Go read Nature or Science. I believe Kathleen makes a fantastic point.

Just happened across this blog while searching for something else, and procrastination rules, ok?

My pet hate is lecturers who uncritically criticise students for using the third person. Close behind is institutional guidance/insistence on third person ‘scientific writing’. Both are hugely ironic, the first because it is typically uncritical and purely traditional (we are employed to teach others to be critical and challenge tradition), the second because there is so little empirical evidence to suggest that the scientific method is third person.

I very much appreciated Bill Lott’s response because a) it was critical and b) it discussed the issue of good and bad writing as opposed to first and third person. However I would still suggest that the way he would report his exemplar data is all but first person:

“Daily observations over the course of a year were consistent with the hypothesis that the sun always rises in the east.”

Who did the observations if not the first person? All that is missing is My or Our at the beginning of the sentence and hey presto

Another facet of writing is that it disappears if not frequently watered and tended to.

Even though this is an old article, I’d like to add my 2c to the thread.

I think the use of the 3rd person is pompous, verbose and obtuse – it uses many words to say the same thing in a flowery way.

“It is the opinion of the author that” as opposed to “I think that”

Anybody reading the article knows that it’s written by a person / persons who did the research on the topic, who are either presenting their findings or opinion. The whole 3rd person thing seems to be a game, and I for one, HATE writing about myself in the 3rd person.

That being said, it seems to be the convention that the 3rd person is used, and I probably will write my paper in the 3rd person anyway, just to not rock the boat.

But I wish that the pomposity would stop and we would get more advocates for writing in plain English.

Hi, i was wondering… can “We” be said in a scientific school report?

Depends on the context, I guess. I would follow the same advice as above.

Thanks for all the tips. Don’t forget that in the future historians are going to want to know who did what and when. Scientists may not think it important, but historians will (especially if it is a significant contribution). Furthermore, by not revealing particulars regarding individual contributions opens the door for many scientists to falsify the historical record in their favor (I have experienced this first hand in a recent publication).

i think it is soo weird to use first person in reports…….third persons will be more effective when used and that will give a clear explanations to the audience

Even Nature journals are encouraging “we” in the manuscript.

“Nature journals prefer authors to write in the active voice (“we performed the experiment…”) as experience has shown that readers find concepts and results to be conveyed more clearly if written directly.”

https://www.nature.com/authors/author_resources/how_write.html

[…] Is trouwens iets dat blijkbaar al lang voor discussies zorgt, als je deze links bekijkt: Are first-person pronouns acceptable in scientific writing? : eloquentscience.com Use of the word "I" in scientific papers Zelfs wikipedia heeft er een artikel over: […]

[…] There was some discussion on Twitter about whether or not to write in the 1st person. The Lab & Field pointed out that Francis Bacon may have been responsible for the movement to avoid it in scientific writing…  […]

[…] ¿Son aceptables los pronombres en primera persona en publicaciones científicas? [ENG] […]

[…] do discuss this among themselves. For example, see Yateendra Joshi and Professor David M. Schultz. Professor Schultz notes that the use of the first person in science appears to be as common among […]

[…] http://eloquentscience.com/2011/02/are-first-person-pronouns-acceptable-in-scientific-writing/ […]

[…] There’s no rule about the passive voice in science. People seem to think that it’s “scientific” writing, but it isn’t. It’s just bad writing. There’s actually no rule against first person pronouns either! Read this for more on the use of the first-person in scientific writing. […]

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David M. Schultz is a Professor of Synoptic Meteorology at the Centre for Atmospheric Science, Department of Earth and Environmental Sciences, and the Centre for Crisis Studies and Mitigation, The University of Manchester. He served as Chief Editor for Monthly Weather Review from 2008 to 2022. In 2014 and 2017, he received the University of Manchester Teaching Excellence Award, the only academic to have twice done so. He has published over 190 peer-reviewed journal articles. [Read more]

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Pronoun Usage in Academic Writing: ‘I,’ ‘We,’ and ‘They’

• By Zain Ul Abadin
• No Comments

Introduction:

Welcome to a comprehensive exploration of pronoun usage in academic writing. This article serves as your indispensable guide to understanding the judicious use of ‘I,’ ‘We,’ and ‘They’ in research papers. Whether you are a novice researcher seeking guidance or an experienced scholar aiming to refine your skills, this guide empowers you to articulate your ideas with scholarly finesse. By the time you’ve journeyed through these pages, you will have the tools to strike a harmonious balance between academic rigor and engaging prose.

1. The Significance of Pronouns in Academic Writing

Pronouns are the unsung heroes of academic writing. They link concepts, reinforce clarity, and create a cohesive narrative in your research. The strategic deployment of pronouns plays a pivotal role in shaping your text, influencing the reader’s understanding, and maintaining the language of research. As we delve deeper, you’ll discover the profound impact pronouns have on readability, coherence, and the overall quality of your academic work.

2. Pronoun Usage in Academic Writing:

Here we arrive at the heart of the matter: understanding when and how to utilize ‘I,’ ‘We,’ and ‘They’ within the context of research papers. Each of these pronouns has its unique strengths, and a thorough understanding of their application is key to scholarly success.

3. Language of Research: Formality and Precision:

In the world of academic writing, precision and formality are non-negotiable. The language of research demands utmost clarity, and even the smallest nuances in expression matter. In this section, we explore the significance of maintaining a formal tone, ensuring that your academic work radiates professionalism.

4. When to Use ‘I’ in Research Papers

Expressing personal experience:.

The ‘I’ pronoun can be a powerful tool for infusing your research paper with your own experiences, insights, and observations. Personalizing your work not only connects you with your readers but also adds authenticity to your writing. We encourage you to provide specific examples from your journey, as well as your interpretations of research findings. When used effectively, ‘I’ offers a human touch to the language of research.

Presenting Research Findings:

When presenting your research findings, ‘I’ allows you to assert your authorship and authority. By choosing ‘I,’ you emphasize that you are the architect of your research, the analyst of your data, and the interpreter of your findings. This section helps you balance the use of ‘I’ to maintain a professional tone in presenting your research.

5. ‘We’ in Research Papers: Dos and Don’ts

Collaborative research:.

Collaborative research projects often call for the use of ‘We’ to denote shared responsibility and contributions. Clearly defining roles, acknowledging teamwork, and underlining collective efforts can help you leverage the power of ‘We’ to enhance credibility and trustworthiness in your academic work.

Overuse and Its Consequences:

However, overusing ‘We’ can detract from your paper’s formality and professionalism. To avoid this, you can balance ‘We’ with passive voice or opt for a rephrasing when it feels excessive. Evaluating the necessity of ‘We’ in specific contexts is also key to maintaining a formal tone.

6. The Inclusive ‘They’

In the modern academic landscape, inclusivity is of utmost importance. ‘They’ serves as a gender-neutral pronoun, promoting diversity and accommodating those who identify beyond the gender binary. This section unpacks the significance of ‘They’ and offers strategies for using it effectively to create an inclusive academic environment .

7. Can You Use ‘We’ in a Research Paper?

Is it Okay to Use ‘We’?

This section addresses the age-old question of whether it is permissible to use ‘We’ in research papers. We explore the nuances of its acceptability, shedding light on when and how it can be appropriately incorporated.

Collaborative Research and ‘We’:

For collaborative research, ‘We’ is often indispensable. We delve into the dynamics of collaborative work and provide guidance on using ‘We’ effectively in such contexts.

8. Can You Use ‘I’ in Research Papers?

Personal perspective in research:.

Unleash the power of ‘I’ to express your personal viewpoints, experiences, and insights in a scholarly and professional manner. This section offers a balanced approach to using ‘I’ effectively without veering into informality.

Striking a Balance:

Striking a balance is the key to maintaining a professional tone. This section helps you navigate the fine line between personal engagement and scholarly authority.

9. Is It Okay to Use ‘They’ in Research Papers?

Embracing gender neutrality:.

Explore the significance of using ‘They’ as a gender-neutral pronoun to foster inclusivity and create a scholarly environment that respects diverse identities.

Practical Application:

This section provides practical guidance on how to incorporate ‘They’ effectively while avoiding potential pitfalls and misconceptions in your research.

10. Language of Research: A Summary

A concise summary of the key takeaways on maintaining a formal, precise, and professional language in academic writing.

11. Conclusion

In this comprehensive guide, we have journeyed through the labyrinth of pronoun usage in academic writing, whether it’s ‘I,’ ‘We,’ or ‘They.’ Armed with the knowledge and strategies presented here, you are equipped to navigate this intricate terrain with confidence and precision. Precision and professionalism remain the watchwords of the language of research, and your mastery of pronouns is a vital step toward achieving excellence in academic writing.

12. Frequently Asked Questions (FAQs)

Q: can ‘i’ be used in a research paper .

A: Yes, ‘I’ can be used, but its usage should be measured and context-appropriate.

Q: Is it acceptable to use ‘We’ in a research paper?

A: Yes, ‘We’ can be used, particularly in collaborative research, but transparency about author contributions is crucial.

Q: When should ‘They’ be used in research papers?

A: ‘They’ is apt for referring to individuals or groups in a general sense or when gender-neutral language is required.

Q: how can common pronoun usage mistakes in academic writing be avoided.

A: Avoiding mistakes involves clear contextual use, maintaining objectivity, and providing transparency about authorship.

Q: What role do pronouns play in research papers?

A: Pronouns are linguistic tools that enhance the clarity, cohesiveness, and engagement of academic writing by replacing nouns, thus avoiding repetition.

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The art of crafting a concise research paper, crafting a wining research proposal: 10 key compon, how to use ai research paper generator.

• Tips & Guides

How To Avoid Using “We,” “You,” And “I” in an Essay

• Posted on October 27, 2022 October 27, 2022

Maintaining a formal voice while writing academic essays and papers is essential to sound objective. 

One of the main rules of academic or formal writing is to avoid first-person pronouns like “we,” “you,” and “I.” These words pull focus away from the topic and shift it to the speaker – the opposite of your goal.

While it may seem difficult at first, some tricks can help you avoid personal language and keep a professional tone.

Let’s learn how to avoid using “we” in an essay.

What Is a Personal Pronoun?

Pronouns are words used to refer to a noun indirectly. Examples include “he,” “his,” “her,” and “hers.” Any time you refer to a noun – whether a person, object, or animal – without using its name, you use a pronoun.

Personal pronouns are a type of pronoun. A personal pronoun is a pronoun you use whenever you directly refer to the subject of the sentence. 

Take the following short paragraph as an example:

“Mr. Smith told the class yesterday to work on our essays. Mr. Smith also said that Mr. Smith lost Mr. Smith’s laptop in the lunchroom.”

The above sentence contains no pronouns at all. There are three places where you would insert a pronoun, but only two where you would put a personal pronoun. See the revised sentence below:

“Mr. Smith told the class yesterday to work on our essays. He also said that he lost his laptop in the lunchroom.”

“He” is a personal pronoun because we are talking directly about Mr. Smith. “His” is not a personal pronoun (it’s a possessive pronoun) because we are not speaking directly about Mr. Smith. Rather, we are talking about Mr. Smith’s laptop.

If later on you talk about Mr. Smith’s laptop, you may say:

“Mr. Smith found it in his car, not the lunchroom!” 

In this case, “it” is a personal pronoun because in this point of view we are making a reference to the laptop directly and not as something owned by Mr. Smith.

Why Avoid Personal Pronouns in Essay Writing

We’re teaching you how to avoid using “I” in writing, but why is this necessary? Academic writing aims to focus on a clear topic, sound objective, and paint the writer as a source of authority. Word choice can significantly impact your success in achieving these goals.

Writing that uses personal pronouns can unintentionally shift the reader’s focus onto the writer, pulling their focus away from the topic at hand.

Personal pronouns may also make your work seem less objective. 

One of the most challenging parts of essay writing is learning which words to avoid and how to avoid them. Fortunately, following a few simple tricks, you can master the English Language and write like a pro in no time.

Alternatives To Using Personal Pronouns

How to not use “I” in a paper? What are the alternatives? There are many ways to avoid the use of personal pronouns in academic writing. By shifting your word choice and sentence structure, you can keep the overall meaning of your sentences while re-shaping your tone.

Utilize Passive Voice

In conventional writing, students are taught to avoid the passive voice as much as possible, but it can be an excellent way to avoid first-person pronouns in academic writing.

You can use the passive voice to avoid using pronouns. Take this sentence, for example:

“ We used 150 ml of HCl for the experiment.”

Instead of using “we” and the active voice, you can use a passive voice without a pronoun. The sentence above becomes:

“150 ml of HCl were used for the experiment.” 

Using the passive voice removes your team from the experiment and makes your work sound more objective.

Take a Third-Person Perspective

Another answer to “how to avoid using ‘we’ in an essay?” is the use of a third-person perspective. Changing the perspective is a good way to take first-person pronouns out of a sentence. A third-person point of view will not use any first-person pronouns because the information is not given from the speaker’s perspective.

A third-person sentence is spoken entirely about the subject where the speaker is outside of the sentence.

Take a look at the sentence below:

“In this article you will learn about formal writing.”

The perspective in that sentence is second person, and it uses the personal pronoun “you.” You can change this sentence to sound more objective by using third-person pronouns:

“In this article the reader will learn about formal writing.”

The use of a third-person point of view makes the second sentence sound more academic and confident. Second-person pronouns, like those used in the first sentence, sound less formal and objective.

Be Specific With Word Choice

You can avoid first-personal pronouns by choosing your words carefully. Often, you may find that you are inserting unnecessary nouns into your work. 

Take the following sentence as an example:

“ My research shows the students did poorly on the test.”

In this case, the first-person pronoun ‘my’ can be entirely cut out from the sentence. It then becomes:

“Research shows the students did poorly on the test.”

The second sentence is more succinct and sounds more authoritative without changing the sentence structure.

You should also make sure to watch out for the improper use of adverbs and nouns. Being careful with your word choice regarding nouns, adverbs, verbs, and adjectives can help mitigate your use of personal pronouns. 

“They bravely started the French revolution in 1789.” 

While this sentence might be fine in a story about the revolution, an essay or academic piece should only focus on the facts. The world ‘bravely’ is a good indicator that you are inserting unnecessary personal pronouns into your work.

We can revise this sentence into:

“The French revolution started in 1789.” 

Avoid adverbs (adjectives that describe verbs), and you will find that you avoid personal pronouns by default.

Closing Thoughts

In academic writing, It is crucial to sound objective and focus on the topic. Using personal pronouns pulls the focus away from the subject and makes writing sound subjective.

Hopefully, this article has helped you learn how to avoid using “we” in an essay.

When working on any formal writing assignment, avoid personal pronouns and informal language as much as possible.

While getting the hang of academic writing, you will likely make some mistakes, so revising is vital. Always double-check for personal pronouns, plagiarism , spelling mistakes, and correctly cited pieces. 

 You can prevent and correct mistakes using a plagiarism checker at any time, completely for free.

Quetext is a platform that helps you with all those tasks. Check out all resources that are available to you today.

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We Vs. They: Using the First & Third Person in Research Papers

Writing in the first , second , or third person is referred to as the author’s point of view . When we write, our tendency is to personalize the text by writing in the first person . That is, we use pronouns such as “I” and “we”. This is acceptable when writing personal information, a journal, or a book. However, it is not common in academic writing.

Some writers find the use of first , second , or third person point of view a bit confusing while writing research papers. Since second person is avoided while writing in academic or scientific papers, the main confusion remains within first or third person.

In the following sections, we will discuss the usage and examples of the first , second , and third person point of view.

First Person Pronouns

The first person point of view simply means that we use the pronouns that refer to ourselves in the text. These are as follows:

Can we use I or We In the Scientific Paper?

Using these, we present the information based on what “we” found. In science and mathematics, this point of view is rarely used. It is often considered to be somewhat self-serving and arrogant . It is important to remember that when writing your research results, the focus of the communication is the research and not the persons who conducted the research. When you want to persuade the reader, it is best to avoid personal pronouns in academic writing even when it is personal opinion from the authors of the study. In addition to sounding somewhat arrogant, the strength of your findings might be underestimated.

For example:

Based on my results, I concluded that A and B did not equal to C.

In this example, the entire meaning of the research could be misconstrued. The results discussed are not those of the author ; they are generated from the experiment. To refer to the results in this context is incorrect and should be avoided. To make it more appropriate, the above sentence can be revised as follows:

Based on the results of the assay, A and B did not equal to C.

Second Person Pronouns

The second person point of view uses pronouns that refer to the reader. These are as follows:

This point of view is usually used in the context of providing instructions or advice , such as in “how to” manuals or recipe books. The reason behind using the second person is to engage the reader.

You will want to buy a turkey that is large enough to feed your extended family. Before cooking it, you must wash it first thoroughly with cold water.

Although this is a good technique for giving instructions, it is not appropriate in academic or scientific writing.

Third Person Pronouns

The third person point of view uses both proper nouns, such as a person’s name, and pronouns that refer to individuals or groups (e.g., doctors, researchers) but not directly to the reader. The ones that refer to individuals are as follows:

• Hers (possessive form)
• His (possessive form)
• Its (possessive form)
• One’s (possessive form)

The third person point of view that refers to groups include the following:

• Their (possessive form)
• Theirs (plural possessive form)

Everyone at the convention was interested in what Dr. Johnson presented. The instructors decided that the students should help pay for lab supplies. The researchers determined that there was not enough sample material to conduct the assay.

The third person point of view is generally used in scientific papers but, at times, the format can be difficult. We use indefinite pronouns to refer back to the subject but must avoid using masculine or feminine terminology. For example:

A researcher must ensure that he has enough material for his experiment. The nurse must ensure that she has a large enough blood sample for her assay.

Many authors attempt to resolve this issue by using “he or she” or “him or her,” but this gets cumbersome and too many of these can distract the reader. For example:

A researcher must ensure that he or she has enough material for his or her experiment. The nurse must ensure that he or she has a large enough blood sample for his or her assay.

These issues can easily be resolved by making the subjects plural as follows:

Researchers must ensure that they have enough material for their experiment. Nurses must ensure that they have large enough blood samples for their assay.

Exceptions to the Rules

As mentioned earlier, the third person is generally used in scientific writing, but the rules are not quite as stringent anymore. It is now acceptable to use both the first and third person pronouns  in some contexts, but this is still under controversy.  

In a February 2011 blog on Eloquent Science , Professor David M. Schultz presented several opinions on whether the author viewpoints differed. However, there appeared to be no consensus. Some believed that the old rules should stand to avoid subjectivity, while others believed that if the facts were valid, it didn’t matter which point of view was used.

First or Third Person: What Do The Journals Say

In general, it is acceptable in to use the first person point of view in abstracts, introductions, discussions, and conclusions, in some journals. Even then, avoid using “I” in these sections. Instead, use “we” to refer to the group of researchers that were part of the study. The third person point of view is used for writing methods and results sections. Consistency is the key and switching from one point of view to another within sections of a manuscript can be distracting and is discouraged. It is best to always check your author guidelines for that particular journal. Once that is done, make sure your manuscript is free from the above-mentioned or any other grammatical error.

You are the only researcher involved in your thesis project. You want to avoid using the first person point of view throughout, but there are no other researchers on the project so the pronoun “we” would not be appropriate. What do you do and why? Please let us know your thoughts in the comments section below.

I am writing the history of an engineering company for which I worked. How do I relate a significant incident that involved me?

Hi Roger, Thank you for your question. If you are narrating the history for the company that you worked at, you would have to refer to it from an employee’s perspective (third person). If you are writing the history as an account of your experiences with the company (including the significant incident), you could refer to yourself as ”I” or ”My.” (first person) You could go through other articles related to language and grammar on Enago Academy’s website https://enago.com/academy/ to help you with your document drafting. Did you get a chance to install our free Mobile App? https://www.enago.com/academy/mobile-app/ . Make sure you subscribe to our weekly newsletter: https://www.enago.com/academy/subscribe-now/ .

Good day , i am writing a research paper and m y setting is a company . is it ethical to put the name of the company in the research paper . i the management has allowed me to conduct my research in thir company .

thanks docarlene diaz

Generally authors do not mention the names of the organization separately within the research paper. The name of the educational institution the researcher or the PhD student is working in needs to be mentioned along with the name in the list of authors. However, if the research has been carried out in a company, it might not be mandatory to mention the name after the name in the list of authors. You can check with the author guidelines of your target journal and if needed confirm with the editor of the journal. Also check with the mangement of the company whether they want the name of the company to be mentioned in the research paper.

Finishing up my dissertation the information is clear and concise.

How to write the right first person pronoun if there is a single researcher? Thanks

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Can you use I in a research paper

In years past, the standard practice in pedagogy was a rejection of the use of I and other first-person pronouns in English language research papers and other academic writing. This position was based on the impression that writers will write with more clarity and objectivity if they avoid self-referencing via the use of I and other first-person words. A good example is the 1918 classic manual by Strunk and White titled “Elements of Style” which had the following advice for students:

“place yourself in the background,” writing “in a way that draws the reader’s attention to the sense and substance of the writing, rather than to the mood and temper of the author.” (70)

According to this traditional view, the ideal rhetorical stance for an academic writer that is undertaking any form of “scientific writing” is to sound dispassionate, impersonal, and (supposedly) unbiased. This doctrine was specifically true for scientific papers where the academic community had in a sense agreed upon that only a passive voice should be used and that the use of personal pronouns should be limited in general, where one avoids using both first person and second person pronouns.

Example of passive voice vs active voice 

 A: Active voice 

– We completed all of the experiments during the second quarter of 2022.

B: Passive voice 

– All of the experiments were completed during the second quarter of 2022.

However, in recent times, though some still hold on to the old doctrine of avoiding first-person pronouns, there has been a significant paradigm shift from this rigid position where the strict rules have to some degree been disregarded, and the use of I in research papers has become more widely accepted and practiced all over the world. For the proponents of the use of I and other first-person pronouns in research papers, the old objectivity argument is an illusion that does not exist.

Here is an aggregation of a few expert opinions about whether you can use I in a research paper.

The APA has a long-standing tradition of allowing the use of the first-person pronoun I in its research papers. More specifically, this policy dates as far back as the second edition of the APA Style Manual which was released in 1974 and has persisted to the manual’s seventh edition [section 4.16] introduced in 2019. Information on this policy can also be found in the seventh edition of the “Concise Guide to the APA Style” published in 2020 as well as on the APA website. According to the APA website:

“Many writers believe the ‘no first-person’ myth, which is that writers cannot use first-person pronouns such as “I” or “we” in an APA Style paper. This myth implies that writers must instead refer to themselves in the third person (e.g., as ‘the author’ or ‘the authors’). However, APA Style has no such rule against using first-person pronouns and actually encourages their use to avoid ambiguity in attribution!”

The association goes even further to provide some clarity by stating that:

“When writing an APA Style paper by yourself, use the first-person pronoun “I” to refer to yourself. And use the pronoun “we” when writing an APA Style paper with others.”

The examples below offer even more clarity as to how to use I in an APA research paper.

“I think……..”

“I believe………”

“I interviewed the participants………”

“I analyzed the data……….”

“My analysis of the data revealed……….”

“We concluded……..”

“Our results showed……..”

In summary, rather than say “The author [third person] interviewed the participants,” the APA allows the use of “I [first person] interviewed the participants.”

The “Advice from the editors” series of the MLA website leaves the use of I in a research paper entirely to the discretion of the writer. The editor in question – Michael Kandel recommends that:

“you [should] not look on the question of using “I” in an academic paper as a matter of a rule to follow, as part of a political agenda (see Webb), or even as the need to create a strategy to avoid falling into Scylla-or-Charybdis error. Let the first-person singular be, instead, a tool that you take out when you think it’s needed and that you leave in the toolbox when you think it’s not.”

Kandel then provides the following examples on when to use and when not to use I in a research paper:

Examples of when I may be necessary

• You are narrating how you made a discovery, and the process of your discovering is important or at the very least entertaining.
• You are describing how you teach something and how your students have responded or respond.
• You disagree with another scholar and want to stress that you are not waving the banner of absolute truth.
• You need I for rhetorical effect, to be clear, simple, or direct.

Examples of when I should not be considered

• It’s off-putting to readers, generally, when I appears too often. You may not feel one bit modest, but remember the advice of Benjamin Franklin, still excellent, on the wisdom of preserving the semblance of modesty when your purpose is to convince others.
• You are the author of your paper, so if an opinion is expressed in it, it is usually clear that this opinion is yours. You don’t have to add a phrase like, “I believe” or “it seems to me.”

Duke University

“Whether working within scientific disciplines, the social sciences, or the humanities, writers often struggle with how to infuse academic material with a unique, personal “voice.” Many writers have been told by teachers not to use the first-person perspective (indicated by words such as I, we, my, and our) when writing academic papers. However, in certain rhetorical situations, self-references can strengthen our argument and clarify our perspective. Depending on the genre and discipline of the academic paper, there may be some common conventions for use of the first person that the writer should observe.” “In addition to observing conventions for first-person references, writers should ask themselves, “What is my personal investment in this piece of work?” The question of whether or not to mention oneself—to I, or not to I—should be considered within this larger context. Although they are not always necessary or advisable, writers should be aware that self-references and use of a personal voice can potentially strengthen an academic argument, when used sparingly and selectively.”

University of British Columbia

“Academic writing is formal in tone and meant to be objective, using cited sources to support an argument or position. This assumes the focus is not the author, but rather the writing. The first-person point of view is considered informal, and is not encouraged in academic writing. First-person can appear to weaken the credibility of the writer in research and argument, as it reads as the writer’s personal opinion. The third-person point of view is often used as an alternative to [the] first-person as the “voice” in academic writing.

Examples of using effective alternatives to the first-person:

• wrong example: I was reading a study about the rise of feudalism in medieval Europe, and I noticed that social class structure seemed to be clearly determined. (1st person)
• correct example: This study about the rise of feudalism in medieval Europe reveals that social class structure was clearly determined. (3rd person)

In the wrong example, the focus is on the reader or author of the study while the correct example focuses directly on the study and its findings.

Some general examples for changing first person to third person:

University of Arizona

“ Personal writing, such as for a reflective essay, or a “personal response” discussion posting, can be written in the first person (using “I” and “me”) and may use personal opinions and anecdotes as evidence for the point you are trying to make. Most academic papers (Exposition, Persuasion, and Research Papers) should generally be written in [the] third-person, referring to other authors and researchers from credible and academic sources to support your argument rather than stating your own personal experiences.”

First-person example (only suitable for personal writing):

• I think Shakespeare’s play  Hamlet is about the relationships between family members. I really liked the play, and in some ways, the characters reminded me of my own family.

Third-person correction (suitable for all other academic writing):

• Shakespeare’s play  Hamlet  deals with the relationships between family members. In Examining Hamlet, Arnold Latimer describes these relationships as “conflicted” (2005, p. 327).

The pronouns I, me and my have been removed in the second example and instead replaced by academic sources as evidence.

The few sources cited above seem to indicate that even with the paradigm shift from avoidance to acceptance of the use of I in a research paper, opinion is still somewhat divided. However, if I were to take sides, I’ll adopt the advice from MLA and Duke University, both of which imply moderate discretionary use of I when it is most appropriate in a research paper. But as a student, it is very important to follow the instructions from your faculty, department, and/or course instructor. So, consider the following advice from APA:

            “As always, defer to your instructors’ guidelines when writing student papers. For example, your instructor may ask students to avoid using first-person language. If so, follow that guideline for work in your class.”

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Using “I”: Is First Person Appropriate in Research Papers?

This article seeks to explore the question of whether using first-person language is an appropriate technique when writing a research paper. The aim is to investigate how and why this style might be used, as well as what potential advantages or disadvantages it may have on the overall quality of academic work. Additionally, we will consider if certain circumstances may warrant its utilization despite prevailing opinion against it. Finally, the implications that such usage has for readers’ understanding and interpretation of scholarship are explored. By looking at previous studies on the use of “I” in scholarly communication as well as conducting an analysis through survey responses from academics across disciplines, this article offers insight into current practice regarding personal pronoun usage within higher education contexts.

I. Introduction to the Use of First Person in Research Papers

Ii. definition of “i” and its place in academic writing, iii. benefits of using the first person perspective in research papers, iv. pitfalls associated with incorrect usage of “i” in research papers, v. suggestions for appropriate contexts where it is acceptable to utilize the term ‘i’, vi. summative evaluation regarding when it is permissible to use ‘i’ within an academic setting, vii. conclusion: final thoughts on adopting a balanced approach towards making judicious decisions about when to include “i.

As a researcher, you may find yourself in situations where it is appropriate to use the first person point of view. In academic research papers, however, this practice can be considered controversial by some instructors and readers. This section will provide an overview of when using the first person “I” or “we” can be beneficial for your writing.

• Can Research Papers Be in First Person?

The short answer is yes! Depending on the type of paper you are writing—particularly persuasive arguments or literature reviews—using “I” statements can help draw attention to important points being made in your paper while also providing personal insights into how those claims were arrived at. However, it should only ever be used judiciously; if overused throughout a paper then it could appear overly biased and unscholarly. It’s important that your work maintains a level of objectivity as well as demonstrating authority through citing relevant sources throughout the piece wherever possible.

In academic writing, the pronoun “I” is often a point of contention. Many scholars debate whether or not it should be used when discussing research and findings in essays and papers. Generally speaking, many professors frown upon its use due to its subjective nature; however, there are some contexts where “I” can be employed effectively without compromising the objective quality of an assignment.

• Personal narratives: In certain forms of literary expression such as creative nonfiction essays or autobiographies, first-person narration from the author’s perspective allows for effective storytelling.
• Scientific experiments: When describing methods used during experimentation conducted by oneself (or other team members), using personal pronouns gives readers clarity regarding who carried out specific tasks and measurements.
• “Critical I”: A trend gaining traction within academia involves incorporating opinions into research papers with careful consideration given to how they fit within current discourse surrounding topics. Here authors may selectively utilize “I” depending on what points need conveying most clearly while maintaining objectivity.

One of the major benefits of using the first person perspective in research papers is that it allows writers to make a personal connection with their readers. It creates an intimate relationship between writer and reader, allowing for more engaging writing and closer analysis. Additionally, this approach can be effective in drawing out subtle nuances from otherwise complex topics.

In addition to these advantages, utilizing the first person point-of-view when crafting a research paper has several other benefits worth noting. For instance, by employing “I” statements within one’s work can add credibility to arguments made as they demonstrate a degree of ownership over ideas presented; this is especially true if those ideas are challenging or counterintuitive. Furthermore, making use of first person pronouns also helps bring humanity into an often sterile academic discourse – injecting emotion into what may have been dry material.

Can Research Papers Be In First Person? Yes! As evidenced above there are many beneficial uses for incorporating the first person perspective within research papers. Not only does doing so lend authenticity and credibility to written works but it also provides authors with additional tools for conveying meaning effectively.

• Enables writers to create connections with their readers
• Allows greater engagement
• Adds credibility through ownership

“By embracing the ‘I’ voice throughout our work we bring warmth and authenticity which increases its impact” (Hallett & Kocovsky 2020) .

One of the pitfalls associated with incorrectly using “I” in research papers is that it can detract from the intended objectivity. Research should be conducted and presented objectively, leaving out opinions or personal perspectives which might bias the paper. When “I” is used inappropriately, such as when making a statement about results of an experiment for example, this lack of objectivity could come into question.

Another potential issue with incorrect usage of “I” relates to questions about whether research papers can be written in first person at all – after all, isn’t that what “we” are trying to avoid? Generally speaking, there are some occasions where its permissible and even encouraged – for example during discussion sections. However care must always taken not to get carried away; otherwise any attempts at presenting an impartial perspective may go awry.

In What Situations is ‘I’ Acceptable? The term ‘I’ can be used in many contexts where it would be appropriate. Firstly, when writing an opinion piece or essay such as a blog post or letter to the editor. In these cases, using ‘I’ is acceptable and even encouraged for personalizing your message. Additionally, informal communication like emails may also benefit from first person usage of ‘I’ – this applies to both business correspondence as well as everyday communication between friends and family members.

Secondly, academic papers are often written with first-person language as well; however, there are certain guidelines that must be followed here. For instance: research papers should use third person pronouns instead of the pronoun “I.” The only exception is when discussing one’s own work experience or citing oneself directly in the paper – then it becomes necessary to utilize ‘I’. Overall though if you adhere to these rules then incorporating ‘I’ into scholarly pieces should not lead to any issues of unprofessionalism within academia!

Using “I” in Academic Writing In academic writing, use of the pronoun “I” is typically limited. The exception to this rule comes when an author is expressing a personal opinion or reflection on their own experiences within the field. At times, authors may also choose to refer to themselves as ‘the researcher’ and explain that they conducted research for example purposes. When deciding whether using ‘I’ in an academic setting is permissible there are several factors at play.

The first factor which should be taken into consideration is the type of paper being written; research papers require adherence to objectivity and should therefore abstain from any direct references to oneself unless it can serve some kind of purpose for furthering one’s argumentation or exploration within the subject matter. On the other hand, essays allow more freedom with regards usage of personal pronouns including but not limited too ‘me’, ‘mine’, and ‘I’. However regardless if one chooses to include such language it must still fit alongside scholarly works related topics ensuring that all necessary criteria has been met before submission.

• Can research papers be written in first person? No – doing so would remove any sense of objectivity.

Therefore while occasional cases permit authorship expression via pronoun inclusion these instances are few and far between when compared against standard scholarship practices wherein maintaining impartiality remain paramount .

In conclusion, the most important factor to consider when deciding whether or not to include “I” in English writing is context. Ultimately, one should adopt a balanced approach that takes into account both formal and informal contexts while also being mindful of their audience. Acknowledge when using “I” can add personal depth and tone without straying too far from accepted conventions for each scenario.

Finally, it’s worth noting that including “I” is often encouraged in certain types of academic writing such as research papers. It provides insight into how you arrived at your conclusions through reflection on data-driven evidence or argumentation – giving readers further insight into the process behind your work.

• However , its use must be thoughtful and appropriate; avoiding overly flowery language or self-serving tangents.

In conclusion, it is clear that using “I” in research papers is a complex issue. While there may be some instances when first-person perspective can be used effectively, generally this should not be employed without careful consideration of the implications and potential criticisms from reviewers or readers. A thorough understanding of established conventions related to academic writing must also inform any decisions about whether or not to use “I” in a research paper; above all else, the aim should always be clarity and professionalism within one’s work.

Is it acceptable to use “we” in scientific papers?

Some of us were taught in school that the use of first-person personal pronouns makes scientific writing subjective. But it’s not true. Using we or I in a research paper does not always shift the spotlight away from the research. And writing in the third person or using passive voice does not make a piece of research writing objective. So, if a reviewer or thesis advisor tells you to remove all first-person references from your manuscript, know that it is not incorrect to use I or we in a paper, despite what many people believe.

So, the short answer to the question in the title is yes. It is acceptable to use we in your paper to refer to you and your co-authors. Whether you use first person pronouns or not is a writing style choice.

Of course, if your publisher’s guidelines for authors say “don’t use I or we in your manuscript”, avoid using I or we when there are valid alternatives. When the publication of your paper is at stake, don’t argue with the journal editor on matters of writing style. It’s not worth the candle. The good news is that most peer-reviewed journals allow the use of first-person pronouns.

The authorial we (or I ) in scientific papers is not only acceptable but also effective in some cases—for example, when passive voice may introduce ambiguity . For example, compare these two sentences:

Three analyses were conducted by the researchers.

We conducted three analyses.

In the first sentence, it is not clear who the researchers are. Are they the authors of the study or other researchers? However, there is no ambiguity in the second sentence.

Also, it’s natural to write in the first person about a research you and your co-authors personally conducted. Compare

We found an old manuscript

The authors of this paper found an old manuscript

an old manuscript was found .

Finally, writing in the first person is more persuasive than writing impersonal prose, as Helen Sword says in Stylish Academic Writing :

“When we muzzle the personal voice, we risk subverting our whole purpose as researchers, which is to foster change by communicating new knowledge to our intended audience in the most effective and persuasive way possible.”

If you’re not sure whether you should use we in scientific writing, write in a way you’re comfortable with. But avoid awkward expressions such as to the best knowledge of the authors of this paper or the analysis conducted by the authors of this study . Sometimes there is no better option than using first-person pronouns in academic writing. Finally, if you still have doubts, get other people’s opinion.

Do you need a freelance editor for a scientific paper? Send me a message at [email protected].

About Cristina N.

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Solving an Important Issue in Academic Writing: Can You Use I in a Research Paper?

What will a professor answer if you ask: Can you say I in a research paper? Most professors will answer with a strict no to that question. But is this a one-dimensional issue? Isn’t there more depth to the problem?

You’re also wondering: why can’t I say I in a research paper, when I am the one writing it? There’s an interesting discussion around this issue. Most students would prefer more liberty in academic writing, so they can add uniqueness to their papers and express themselves in any way they want. The academic format is too strict and doesn’t allow for such flexibility.

When you’re working on projects that involve creative writing, using I is not a problem. A research paper, however, is more of an analytic and critical thinking paper, so the guidelines are different. In essence, you’re advised against using I, we, or you in this type of writing.

THE ISSUE OF USING WE IN A RESEARCH PAPER: WHEN IS THIS LANGUAGE ACCEPTABLE?

When you’re providing your own point of view, using I is the natural form of expression that comes to mind. Let’s take an example: we’ll assume you’re writing a research paper from social studies, focused on children living with alcoholic parents. In the introduction, you’ll be required to explain what this research paper is about.

In this research paper, I explored the negative influence that alcoholic parents have on the development on their children.

This seems like the simplest way to describe what your research is focused on. It is an acceptable form of academic writing, but it’s not the style that most academics recommend. This is what the recommended formulation would sound like:

Research has explored the negative influence that alcoholic parents have on the development on their children.

Yes; it sounds weird. No; it’s not how you usually talk when communicating with people around you. Yes; it involves some passive language. Still, it’s the recommended form of academic expression.

There are professors who insist that passive language must be avoided as much as possible, so the sentences will be clearer and more readable. Others, however, will insist on avoiding the use of first-person language. There’s a conflict of opinions here, so the best way to figure out how to write your research paper is by asking direct questions to your professor. When you need more detailed instructions, there’s no shame in asking for them.

THE FINAL ANSWER: CAN YOU USE I IN RESEARCH PAPER?

• If your professor or mentor says you should write in the most natural way, then it’s okay to use I in your research paper.
• If you’re referring to the reader and yourself, or you were working on the research paper as part of a team, then it’s okay to use we, too.
• It’s not OK to use we when you’re only referring to yourself.
• If your professor tells you that using I is not appropriate in research paper writing, then you should definitely avoid that form of expression. This means you’ll have to rely on passive language, so you’ll avoid first-person writing.

What if you don’t get precise a precise guide for the style of your research paper? Maybe you cannot reach the professor or your email message gets no answer.

In that case, it’s best to stick to the traditional format of research paper writing. What does that mean? – Avoid using I and we!

WHAT’S THE CORRECT WAY TO WRITE A RESEARCH PAPER?

When someone tells you that you should avoid using first person in academic writing, you probably need more information. The instruction is not enough to convince you that avoidance of I is the right way to write a research paper.

There are several factors that go in favor of this point of view:

• In science and academics, the use of I is considered rather arrogant and self-serving. The most important thing to remember is that you’re not focused on yourself as a writer, but on the research as something that serves the reader and the academic community.
• It’s best to avoid personal pronouns when engaged in persuasive writing. Saying I believe is not persuasive enough. Here’s another example: Based on my findings, I concluded that alcoholic parents have a negative influence over the emotional development of their children. The more convincing way to formulate that statement would be this one: Based on the research findings, it may be concluded that alcoholic parents have a negative influence over the emotional development of their children. You see? It’s important to focus on the research; not on yourself.
• It’s also important to avoid the use of you when writing a research paper, since that form of expression is usually implemented when providing instructions or addressing the reader directly. In a research paper, you’re not doing that.

DO YOU NEED HELP TO FIND YOUR ACADEMIC WRITING STYLE?

All these guidelines seem rather simple, don’t they? You’ll just avoid first and second person, and you’ll write your research paper in a format that’s acceptable for the academic community, right? Wrong!

The third person, as a generally used style in academic writing, can impose some difficulties. You cannot use he or she in a research paper, since you’re not writing about particular persons. Instead, you’ll use indefinite pronouns to refer to the subject, while avoiding feminine or masculine terminology.

Finally, there are always some exceptions from the rules, and that makes it even harder for you to find the right style. Who said that college or university education was easy?

Fortunately, there is a solution. You may always buy research paper online. You’ll find the perfect research paper writing service and you’ll collaborate with a professional PhD writer. The writer will take your requirements into consideration, and they will write the perfect research paper that meets all academic writing standards. The good news is that you can hire a professional service for a really affordable price!

AI Index Report

Welcome to the seventh edition of the AI Index report. The 2024 Index is our most comprehensive to date and arrives at an important moment when AI’s influence on society has never been more pronounced. This year, we have broadened our scope to more extensively cover essential trends such as technical advancements in AI, public perceptions of the technology, and the geopolitical dynamics surrounding its development. Featuring more original data than ever before, this edition introduces new estimates on AI training costs, detailed analyses of the responsible AI landscape, and an entirely new chapter dedicated to AI’s impact on science and medicine.

Read the 2024 AI Index Report

The AI Index report tracks, collates, distills, and visualizes data related to artificial intelligence (AI). Our mission is to provide unbiased, rigorously vetted, broadly sourced data in order for policymakers, researchers, executives, journalists, and the general public to develop a more thorough and nuanced understanding of the complex field of AI.

The AI Index is recognized globally as one of the most credible and authoritative sources for data and insights on artificial intelligence. Previous editions have been cited in major newspapers, including the The New York Times, Bloomberg, and The Guardian, have amassed hundreds of academic citations, and been referenced by high-level policymakers in the United States, the United Kingdom, and the European Union, among other places. This year’s edition surpasses all previous ones in size, scale, and scope, reflecting the growing significance that AI is coming to hold in all of our lives.

Steering Committee Co-Directors

Ray Perrault

Steering committee members.

Erik Brynjolfsson

John Etchemendy

Katrina Ligett

Terah Lyons

James Manyika

Juan Carlos Niebles

Vanessa Parli

Yoav Shoham

Russell Wald

Staff members.

Loredana Fattorini

Nestor Maslej

Letter from the co-directors.

A decade ago, the best AI systems in the world were unable to classify objects in images at a human level. AI struggled with language comprehension and could not solve math problems. Today, AI systems routinely exceed human performance on standard benchmarks.

Progress accelerated in 2023. New state-of-the-art systems like GPT-4, Gemini, and Claude 3 are impressively multimodal: They can generate fluent text in dozens of languages, process audio, and even explain memes. As AI has improved, it has increasingly forced its way into our lives. Companies are racing to build AI-based products, and AI is increasingly being used by the general public. But current AI technology still has significant problems. It cannot reliably deal with facts, perform complex reasoning, or explain its conclusions.

AI faces two interrelated futures. First, technology continues to improve and is increasingly used, having major consequences for productivity and employment. It can be put to both good and bad uses. In the second future, the adoption of AI is constrained by the limitations of the technology. Regardless of which future unfolds, governments are increasingly concerned. They are stepping in to encourage the upside, such as funding university R&D and incentivizing private investment. Governments are also aiming to manage the potential downsides, such as impacts on employment, privacy concerns, misinformation, and intellectual property rights.

As AI rapidly evolves, the AI Index aims to help the AI community, policymakers, business leaders, journalists, and the general public navigate this complex landscape. It provides ongoing, objective snapshots tracking several key areas: technical progress in AI capabilities, the community and investments driving AI development and deployment, public opinion on current and potential future impacts, and policy measures taken to stimulate AI innovation while managing its risks and challenges. By comprehensively monitoring the AI ecosystem, the Index serves as an important resource for understanding this transformative technological force.

On the technical front, this year’s AI Index reports that the number of new large language models released worldwide in 2023 doubled over the previous year. Two-thirds were open-source, but the highest-performing models came from industry players with closed systems. Gemini Ultra became the first LLM to reach human-level performance on the Massive Multitask Language Understanding (MMLU) benchmark; performance on the benchmark has improved by 15 percentage points since last year. Additionally, GPT-4 achieved an impressive 0.97 mean win rate score on the comprehensive Holistic Evaluation of Language Models (HELM) benchmark, which includes MMLU among other evaluations.

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This paper is in the following e-collection/theme issue:

Published on 22.4.2024 in Vol 26 (2024)

Patient and Staff Experience of Remote Patient Monitoring—What to Measure and How: Systematic Review

Authors of this article:

• Valeria Pannunzio 1 , PhD   ; 
• Hosana Cristina Morales Ornelas 2 , MSc   ; 
• Pema Gurung 3 , MSc   ; 
• Robert van Kooten 4 , MD, PhD   ; 
• Dirk Snelders 1 , PhD   ; 
• Hendrikus van Os 5 , MD, PhD   ; 
• Michel Wouters 6 , MD, PhD   ; 
• Rob Tollenaar 4 , MD, PhD   ; 
• Douwe Atsma 7 , MD, PhD   ; 
• Maaike Kleinsmann 1 , PhD  

1 Department of Design, Organisation and Strategy, Faculty of Industrial Design Engineering, Delft University of Technology, Delft, Netherlands

2 Department of Sustainable Design Engineering, Faculty of Industrial Design Engineering, Delft University of Technology, Delft, Netherlands

3 Walaeus Library, Leiden University Medical Center, Leiden, Netherlands

4 Department of Surgery, Leiden University Medical Center, Leiden, Netherlands

5 National eHealth Living Lab, Department of Public Health & Primary Care, Leiden University Medical Center, Leiden, Netherlands

6 Department of Surgery, Netherlands Cancer Institute – Antoni van Leeuwenhoek, Amsterdam, Netherlands

7 Department of Cardiology, Leiden University Medical Center, Leiden, Netherlands

Corresponding Author:

Valeria Pannunzio, PhD

Department of Design, Organisation and Strategy

Faculty of Industrial Design Engineering

Delft University of Technology

Landbergstraat 15

Delft, 2628 CE

Netherlands

Phone: 31 15 27 81460

Email: [email protected]

Background: Patient and staff experience is a vital factor to consider in the evaluation of remote patient monitoring (RPM) interventions. However, no comprehensive overview of available RPM patient and staff experience–measuring methods and tools exists.

Objective: This review aimed at obtaining a comprehensive set of experience constructs and corresponding measuring instruments used in contemporary RPM research and at proposing an initial set of guidelines for improving methodological standardization in this domain.

Methods: Full-text papers reporting on instances of patient or staff experience measuring in RPM interventions, written in English, and published after January 1, 2011, were considered for eligibility. By “RPM interventions,” we referred to interventions including sensor-based patient monitoring used for clinical decision-making; papers reporting on other kinds of interventions were therefore excluded. Papers describing primary care interventions, involving participants under 18 years of age, or focusing on attitudes or technologies rather than specific interventions were also excluded. We searched 2 electronic databases, Medline (PubMed) and EMBASE, on February 12, 2021.We explored and structured the obtained corpus of data through correspondence analysis, a multivariate statistical technique.

Results: In total, 158 papers were included, covering RPM interventions in a variety of domains. From these studies, we reported 546 experience-measuring instances in RPM, covering the use of 160 unique experience-measuring instruments to measure 120 unique experience constructs. We found that the research landscape has seen a sizeable growth in the past decade, that it is affected by a relative lack of focus on the experience of staff, and that the overall corpus of collected experience measures can be organized in 4 main categories (service system related, care related, usage and adherence related, and health outcome related). In the light of the collected findings, we provided a set of 6 actionable recommendations to RPM patient and staff experience evaluators, in terms of both what to measure and how to measure it. Overall, we suggested that RPM researchers and practitioners include experience measuring as part of integrated, interdisciplinary data strategies for continuous RPM evaluation.

Conclusions: At present, there is a lack of consensus and standardization in the methods used to measure patient and staff experience in RPM, leading to a critical knowledge gap in our understanding of the impact of RPM interventions. This review offers targeted support for RPM experience evaluators by providing a structured, comprehensive overview of contemporary patient and staff experience measures and a set of practical guidelines for improving research quality and standardization in this domain.

Introduction

Background and aim.

This is a scenario from the daily life of a patient:

A beeping sound, and a message appears on the smartphone screen: “Reminder: check glucose before bedtime.” Time to go to sleep, indeed, you think while putting down your book and reaching for the glucometer. As you wipe the drop of blood away, you make sure that both Bluetooth and Wi-Fi are on in your phone. Then, the reading is sent: you notice it seems to be rather far from your baseline. While you think of what you might have done differently, a slight agitation emerges: Is this why you feel so tired? The phone beeps again: “Your last glucose reading seems atypical. Could you please try again? Remember to follow these steps.” Groaning, you unwrap another alcohol wipe, rub your finger with it, and test again: this time, the results are normal.

Some patients will recognize certain aspects of this scenario, particularly the ones using a form of remote patient monitoring (RPM), sometimes referred to as remote patient management. RPM is a subset of digital health interventions that aim to improve patient care through digitally transmitted, health-related patient data [ 1 ]. Typically, RPM interventions include the use of 1 or more sensors (including monitoring devices, wearables, or implants), which collect patient data in or out of the hospital to be used for remote clinical decision-making. Partly due to a rapid expansion during the COVID-19 pandemic [ 2 - 5 ], the RPM domain has by now expanded to reach a broad range of medical specialties, sensing technologies, and clinical contexts [ 1 , 6 , 7 ].

RPM is presented as a strategy for enabling health care systems worldwide to face the pressing challenges posed by aging populations [ 8 - 10 ], including the dwindling availability of health care workers [ 11 ] and rising health care costs [ 12 ]. This is because deploying effective RPM solutions across health systems holds the potential to reduce health care resources use, while maintaining or improving care quality. However, evidence regarding RPM effectiveness at scale is mixed [ 13 ]. Few large-scale trials demonstrating a meaningful clinical impact of RPM have been conducted so far, and more research is urgently needed to clarify and address determinants of RPM effectiveness [ 7 ].

Among these determinants, we find the experience of patients and staff using RPM interventions. As noticeable in the introductory scenario, RPM introduces radical experiential changes compared to in-person care; patients might be asked to download and install software; pair, charge, and wear monitoring devices; submit personal data; or attend alerts or calls, all in the midst of everyday life contexts and activities. Similarly, clinical and especially nursing staff might be asked to carry out data analysis and administrative work and maintain remote contact with patients, often without a clear definition of roles and responsibilities and in addition to usual tasks [ 14 ].

Because of these changes, patient and staff experience constitutes a crucial aspect to consider when evaluating RPM interventions. Next to qualitative methods of experience evaluation, mixed and quantitative methods are fundamental, especially to capture information from large pools of users. However, the current RPM experience-measuring landscape suffers from a lack of methodological standardization, reflected in both what is measured and how it is measured. Regarding what is measured, it has been observed that a large number of constructs are used in the literature, often without a clear specification of their significance. This can be noticed even regarding popular constructs, such as satisfaction: Mair and Whitten [ 15 ], for instance, observe how the meaning of the satisfaction construct is seldom defined in patient surveys, leaving readers “unable to discern whether the participants said they were satisfied because telemedicine didn't kill them, or that it was ‘OK,’ or that it was a wonderful experience.” Previous work also registers a broad diversity in the instruments used to measure a specific construct. For instance, in their review of RPM interventions for heart failure, Kraai et al [ 16 ] report that none of the papers they examined used the same survey to measure patient satisfaction, and only 1 was assessed on validity and reliability.

In this proliferation of constructs and instruments, no comprehensive overview exists of their application to measuring patient and staff experience in the RPM domain. The lack of such an overview negatively affects research in this domain in at least 2 ways. At the level of primary research, RPM practitioners and researchers have little guidance on how to include experience measuring in their study designs. At the level of secondary research, the lack of consistently used measures makes it hard to compare results between different studies and RPM solutions. Altogether, the lack of standardization in experience measuring constitutes a research gap that needs to be bridged in order for RPM to fully deliver on its promises.

In this review, this gap is addressed through an effort to provide a structured overview of patient and staff experience constructs and instruments used in RPM evaluation. First, we position the role of RPM-related patient and staff experience within the broader system of care using the Quadruple Aim framework. Next, we describe the systematic review we performed of patient and staff experience–relevant constructs and instruments used in contemporary research aimed at evaluating RPM interventions. After presenting and discussing the results of this review, we propose a set of guidelines for RPM experience evaluators and indicate directions for further research.

The Role of Patient and Staff Experience in RPM

Many characterizations of patient and staff experience exist [ 17 - 19 ], some of which distinguish between determinants of experience and experience manifestations [ 20 ]. For our review, we maintained this distinction, as we aimed to focus on the broad spectrum of factors affecting and affected by patient and staff experience. To do so, we adopted the general conceptualization of patient and staff experience as characterized in the Quadruple Aim, a widely used framework for health system optimization centered around 4 overarching goals: improving the individual experience of care, improving the experience of providing care, improving the health of populations, and reducing the per capita cost of care [ 21 ]. Adopting a Quadruple Aim perspective allows health system researchers and innovators to recognize not only the importance of patient and staff experience in their own rights but also the inextricable relations of these 2 goals to the other dimensions of health system performance [ 22 ]. To clarify the nature of these relations in the RPM domain, we provide a schematic overview in Figure 1 .

Next, we refer to the numbers in Figure 1 to touch upon prominent relationships between patient and staff experience in RPM within the Quadruple Aim framework and provide examples of experience constructs relevant to each relationship:

• Numbers 1 and 2: The characteristics of specific RPM interventions directly affect the patient and staff experience. Examples of experience constructs related to this mechanism are expressed in terms of usability or wearability , which are attributes of systems or products contributing to the care experience of patients and the work experience of staff.
• Numbers 3 and 4: Patient and staff experiences relate to each other through care delivery. Human connections, especially in the form of carer-patient relationships, represent a major factor in both patient and staff experience. An example of experience constructs related to this mechanism is expressed in terms of the quality of the relationship .
• Numbers 5 and 6: A major determinant of patient experience is represented by the health outcomes achieved as a result of the received care. An example of a measure of quality related to this mechanism is expressed in terms of the quality of life , which is an attribute of patient experience directly affected by a patient’s health status. In contrast, patient experience itself is a determinant of the clinical effectiveness of RPM interventions. For example, the patient experience afforded by a given intervention is a determinant of both adoption of and adherence to that intervention, ultimately affecting its clinical impact. In a recent review, for instance, low patient adherence was identified as the main factor associated with ineffective RPM services [ 23 ].
• Number 7: Similarly, staff experience can be a determinant of clinical effectiveness. Experience-related issues, such as alarm fatigue , contribute to medical errors and lower the quality of care delivery [ 24 ].
• Number 8: Staff experience can also impact the cost of care. For example, the time effort required for the use of a given intervention can constitute a source of extra costs. More indirectly, low staff satisfaction and excessive workload can increase health care staff turnover, resulting in additional expenses at the level of the health system.

Overall, the overview in Figure 1 can help us grasp the nuances of the role of patient and staff experience on the overall impact of RPM interventions, as well as the importance of measuring experience factors, not only in isolation, but also in relation to other dimensions of care quality. In this review, we therefore covered a broad range of experience-relevant factors, including both experiential determinants (eg, usability) and manifestations (eg, adherence). Overall, this study aimed to obtain a comprehensive set of experience constructs and corresponding measurement instruments used in contemporary RPM research and to propose an initial set of guidelines for improving methodological standardization in this domain.

Protocol Registration and PRISMA Guidelines

The study protocol was registered in the PROSPERO (International Prospective Register of Systematic Reviews) database (CRD42021250707). This systematic review adhered to the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines. The PRISMA checklist is provided in Multimedia Appendix 1 [ 25 ].

Criteria for Study Eligibility

Our study population consisted of adult (≥18 years old) patients and staff members involved as participants in reported RPM evaluations. Full-text papers reporting instances of patient and staff experience measuring in RPM interventions, written in English, and published after January 1, 2011, were considered for eligibility.

For the scope of our review, we considered as RPM any intervention possessing the following characteristics:

• Sensor-based patient monitoring, intended as the use of at least 1 sensor to collect patient information at a distance. Therefore, we excluded interventions that were purely based on the collection of “sensor-less” self-reported measures from patients. This is because we believe the use of sensors constitutes a key element of RPM and one that strongly contributes to experiential aspects in this domain. However, we adopted a broad definition of “sensor,” considering as such, for instance, smartphone cameras (eg, postoperative wound-monitoring apps) and analog scales or thermometers (eg, interventions relying on patients submitting manually entered weights or temperatures). By “at a distance,” we meant not only cases in which data were transferred from nonclinical environments, such as home monitoring, but also cases such as tele–intensive care units (tele-ICUs), in which data were transferred from one clinical environment to another. Furthermore, we included interventions relying on both continuous and intermittent monitoring.
• Clinical decision-making as an intended use of remotely collected data. Therefore, we excluded interventions in which the collected data were meant to be used exclusively for research purposes and not as a stage of development of an RPM intervention to be adopted in patient care. For instance, we excluded cases in which the remotely collected patient data were only used to test research hypotheses unrelated to the objective of implementing RPM interventions (eg, for drug development purposes). This is because in this review we were interested in RPM as a tool for the provision of remote patient care, rather than as an instrument for research. We also excluded interventions in which patients themselves were the only recipients of the collected data and no health care professional was involved in the data analysis and use.

Furthermore, we excluded:

• Evaluations of attitudes, not interventions: contributions in which only general attitudes toward RPM in abstract were investigated, rather than 1 or more specific RPM interventions.
• Not reporting any evaluation: contributions not focusing on the evaluation of 1 or more specific RPM interventions, for instance, papers providing theoretical perspectives on the field (eg, research frameworks or theoretical models).
• Evaluation of technology, not interventions: contributions only focused on evaluating RPM-related technology, for instance, papers focused on testing sensors, software, or other service components in isolation rather than as a part of any specific RPM intervention.
• Not just RPM: contributions not specifically focused on RPM but including RPM interventions in their scope of research, for instance, papers reporting on surveys obtained from broad cohorts of patients (including RPM recipients) in a noncontrolled way. An example of such contributions would be represented by studies focusing on patient experience with mobile health apps in general, covering both interventions involving RPM and interventions not including any kind of patient monitoring, without a clear way to distinguish between the 2 kinds of interventions in the contribution results. This was chosen in order to maintain the review focus on RPM interventions. Instead, papers including both RPM and other forms of care provisions within the same intervention were included, as well as papers comparing RPM to non-RPM interventions in a controlled way.
• Primary care intervention only: interventions only involving general practitioners (GPs) and other primary care practitioners as health care providers of the RPM intervention. This is because we expected marked differences between the implementation of RPM in primary care and at other levels of care, due to deep dissimilarities in settings, workflows, and routines. Examples of RPM interventions only involving primary care providers included kiosk systems (for which a common measuring point was provided to many patients) or pharmacy-managed medication-monitoring programs. RPM interventions involving primary care providers and providers from higher levels of care, however, were included in the review.
• Staff-to-staff intervention: contributions reporting on interventions exclusively directed at staff, for instance, papers reporting on RPM methods aimed at monitoring stress levels of health care workers.
• Target group other than patient or staff: contributions aimed at collecting experience measures in target groups other than patients or staff, for instance, papers investigating the experience in RPM for informal caregivers.

Search Method

To identify relevant publications, the following electronic databases were searched: (1) Medline (PubMed) and (2) EMBASE. Search terms included controlled terms from Medical Subject Headings (MeSH) in PubMed and Emtree in EMBASE, as well as free-text terms. Query term selection and structuring were performed collaboratively by authors VP, HCMO, and PG (who is a clinical librarian at the Leiden University medical library). The full search strategies are reported in Multimedia Appendix 2 . Because the aim of the review was to paint a contemporary picture of experience measures used in RPM, only studies published starting from January 1, 2011, were included.

Study Selection

Study selection was performed by VP and HCMO, who used Rayyan, an online research tool for managing review studies [ 26 ], to independently screen both titles and abstracts in the initial screening and full texts in the final screening. Discrepancies were solved by discussion. A flowchart of study selection is depicted in Figure 2 .

Quality Appraisal

The objective of this review was to provide a comprehensive overview of the relevant literature, rather than a synthesis of specific intervention outcomes. Therefore, no papers were excluded based on the quality appraisal, in alignment with similar studies [ 27 ].

Data Extraction and Management

Data extraction was performed independently by VP and HCMO. The extraction was performed in a predefined Microsoft Excel sheet designed by VP and HCMO. The sheet was first piloted in 15 included studies and iterated upon to optimize the data extraction process. The full text of all included studies was retrieved and uploaded in the Rayyan environment. Next, the full text of each included study was examined and relevant data were manually inputted in the predefined Excel sheet. Discrepancies were resolved by discussion. The following data types were extracted: (1) general study information (authors, title, year of publication, type of study, country or countries); (2) target disease(s), intervention, or clinical specialty; (3) used patient or staff experience evaluation instrument and measured experience construct; (4) evidence base, if indicated; and (5) number of involved staff or patient participants. By “construct,” we referred to the “abstract idea, underlying theme, or subject matter that one wishes to measure using survey questions” [ 28 ]. To identify the measured experience construct, we used the definition provided in the source contribution, whenever available.

Data Analysis

First, we analyzed the collected data through building general overviews depicting the kind of target participants (patients or staff) of the collected experience measures and their use over time. To organize the diverse set of results collected through the systematic review, we then performed a correspondence analysis (CA) [ 29 ], a multivariate statistical technique used for exploring and displaying relationships between categorical data. CA transforms a 2-way table of frequencies between a row and a column variable into a visual representation of relatedness between the variables. This relatedness is expressed in terms of inertia, which represents “a measure of deviation from independence” [ 30 ] between the row and column variables. Any deviations from the frequencies expected if the row and column variables were completely independent from each other contribute to the total inertia of the model. CA breaks down the inertia of the model by identifying mutually independent (orthogonal) dimensions on which the model inertia can be represented. Each successive dimension explains less and less of the total inertia of the model. On each dimension, relatedness is expressed in terms of the relative closeness of rows to each other, as well as the relative closeness of columns to each other. CA has been previously used to find patterns in systematic review data in the health care domain [ 31 ].

In our case, a 2-way table of frequencies was built based on how often any given instrument (eg, System Usability Scale [SUS]) was used to measure any given construct (eg, usability) in the included literature. Therefore, in our case, the total inertia of the model represented the amassed evidence base for relatedness between the collected experience constructs and measures, based on how they were used in the included literature.

To build the table of frequencies, the data extracted from the systematic review underwent a round of cleaning, in which the formulation of similar constructs was made more homogeneous: for instance, “time to review,” “time to response,” and “time for task” were merged under 1 label, “time effort.” An overview of the merged construct formulations is provided in Multimedia Appendix 3 . The result of the CA was a model where 2 dimensions contributed to more than 80% of the model’s inertia (explaining 44.8% and 35.7%, respectively) and where none of the remaining 59 dimensions contributed more than 7.3% to the remaining inertia. This gap suggests the first 2 dimensions to express meaningful relationships that are not purely based on random variation. A 2D solution was thus chosen.

General Observations

A total of 158 studies reporting at least 1 instance of patient or staff experience measuring in RPM were included in the review. The included studies covered a broad range of RPM interventions, most prominently diabetes care (n=30, 19%), implanted devices (n=12, 7.6%), and chronic obstructive pulmonary disease (COPD; n=10, 6.3%). From these studies, we reported 546 experience-measuring instances in RPM, covering 160 unique experience-measuring instruments used to measure 120 unique experience constructs.

Our results included 4 kinds of versatile (intended as nonspecific) experience-measuring instruments: the custom survey, log file analysis, protocol database analysis, and task analysis. All of them can be used for measuring disparate kinds of constructs:

• By “custom survey,” we refer to survey instruments created to evaluate patient or staff experience in connection to 1 specific RPM study and only for that study.
• By “log file analysis,” we refer to the set of experience assessment methods based on the automatic collection of data through the RPM digital infrastructures themselves [ 32 ]; examples are clicks, uploads, views, or other forms of interactions between users and the RPM digital system. This set of methods is typically used to estimate experience-relevant constructs, such as adherence and compliance.
• By “protocol database analysis,” we refer to the set of experience assessment methods based on the manual collection of data performed by RPM researchers within a specific research protocol; an example of a construct measured with these instruments is the willingness to enroll.
• By “task analysis,” we refer to the set of experience assessment methods based on the real-life observation of users interacting with the RPM system [ 33 ].

In addition to these 4 instruments, our results included a large number of specific instruments, such as standard indexes, surveys, and questionnaires. Overall, the most frequently reported instrument was, by far, the custom survey (reported in 155/546, 28.39%, instances), while the most frequently reported experience construct was satisfaction (85/546, 15.57%), closely followed by quality of life (71/546, 13%).

Target Participants and Timeline

We found large differences in the number of RPM-relevant experience constructs and instruments used for patients and for staff (see Figure 3 ). We also found instruments used for both patients and staff. Either these were broadly used instruments (eg, the SUS) that were administered to both patients and staff within the same study, or they were measures of interactions between patients and staff (eg, log file analysis instruments recording the number of remote contacts between patients and nursing assistants).

RPM research appears to focus much more on patient experience than on staff experience, which was investigated in only 20 (12.7%) of the 158 included papers. Although it is possible that our exclusion criteria contributed to the paucity of staff experience measures, only 2 (0.1%) of 2092 studies were excluded for reporting on interventions directed exclusively at staff. Of the 41 (2%) studies we excluded for reporting on primary care interventions, we found 6 (15%) studies reporting on staff experience, a rate comparable to the one in the included sample. Furthermore, although our choice to exclude papers reporting on the RPM experience of informal caregivers might have contributed to a reduction in the number of collected constructs and measures, only 2 (0.1%) of 2092 studies were excluded for this reason, and the constructs used in these contributions were not dissimilar from the ones found in the included literature.

Among the included contributions that did investigate staff experience, we noticed that the number of participant staff members involved in the reported studies was only reported in a minority of cases (9/20, 45%).

Furthermore, a time-based overview of the collected results ( Figure 4 ) provided us with an impression of the expansion of the field in the time frame of interest for both patient and staff experience measures.

Correspondence Analysis

The plotted results of the CA of experience constructs are shown in Figure 5 . Here, we discuss the outlook and interpretation of each dimension.

The first dimension explained more than 44% of the model’s inertia. The contributions of this dimension showed which constructs had the most impact in determining its orientation: satisfaction (36%) and to a lesser extent adherence (26%) and quality of life (17%). On the negative (left) side of this dimension, we found constructs such as satisfaction, perceptions, and acceptability, which are associated with subjective measures of patient and staff experience and relate to how people feel or think in relation to RPM interventions. On the positive (right) side of this dimension, we found constructs such as adherence, compliance, and quality of life, which are associated with objectivized measures of patient and staff experience. By “objectivized measures,” we referred to measures that are meant to capture phenomena in a factual manner, ideally independently from personal biases and subjective opinions. Adherence and compliance, particularly, are often measured through passive collection of system data (eg, log file analysis) that reflect objective measures of the way patients or staff interact with RPM propositions. Even in the case of (health-related) quality of life, which can include subjective connotations and components, measures usually aim at capturing an estimation of the factual impact of health status on a person’s overall life quality.

In this sense, we attributed a distinction between how people feel versus what happens experience constructs to this first dimension. We noted that a similar distinction (between subjective vs objective measures of engagement in remote measurement studies) was previously proposed as a meaningful differentiation to structure “a field impeded by incoherent measures” [ 27 ].

The second dimension explained 35% of the model’s inertia. The contributions of this dimension showed which constructs had the most impact in determining its orientation: quality of life (62%) and adherence (24%). On the negative (bottom) side of this dimension, we found constructs such as quality of life, depression, and anxiety, which are often used as experiential descriptors of health outcomes. On the positive (top) side of this dimension, we found adherence, compliance, and frequency, which are often used as descriptions of the interactions of patients or staff with a specific (RPM) system. Thus, we attributed a distinction between health-relevant versus system-relevant experience constructs to this second dimension.

Based on the results of CA, we proposed a categorization of patient and staff experience–related constructs into 4 partly overlapping clusters. Coherent with the offered explanation of the 2 dimensions and in consideration of the constructs found in each area, we labeled these as service system–related experience measures, care-related experience measures, usage- and adherence-related experience measures, and health outcome–related experience measures. In Figure 6 , we display the results of the CA labeled through this categorization. In this second visualization, we presented the results on a logarithmic scale to improve the visibility of constructs close to the center of the axes. Overall, this categorization of patient and staff experience constructs used in the RPM literature paints a landscape of the contemporary research in this field, which shows a mix of influences from clinical disciplines, health psychology, human factors engineering, service design, user research, systems engineering, and computer science.

A visualization of the reported patient experience constructs and some of the related measuring instruments, organized by the categories identified in the CA, is available in Figure 7 . A complete version of this visual can be found in Multimedia Appendix 4 , and an interactive version can be found in [ 34 ]. In this figure, we can note the limited crossovers between constructs belonging to different categories, with the exception of versatile instruments, such as custom survey and log file analysis.

Recommendations

In the light of the collected findings, here we provide a set of recommendations to RPM patient and staff experience evaluators, in terms of both what to measure and how to measure it ( Figure 8 ). Although these recommendations are functional to strengthen the quality of individual research protocols, they are also meant to stimulate increased standardization in the field as a whole.

Regarding what to measure, we provide 4 main recommendations. The first is to conduct structured evaluations of staff experience next to patient experience. Failing to evaluate staff experience leads to risks, such as undetected staff nonadherence, misuse, and overworking. Although new competencies need to be developed in order for staff to unlock the untapped potential of RPM [ 35 ], seamless integration with existing clinical workflows should always be pursued and monitored.

The second recommendation is to consider experience constructs in all 4 clusters indicated in Figure 6 , as these represent complementary facets of an overall experiential ensemble. Failing to do so exposes RPM evaluators to the risk of obtaining partial information (eg, only shedding light on how people feel but not on what happens in terms of patient and staff experience in RPM).

The third recommendation is to explicitly define and report a clear rationale regarding which aspects of patient and staff experience to prioritize in evaluations, depending on the goals and specificities of the RPM intervention. This rationale should ideally be informed by preliminary qualitative research and by a collaborative mapping of the expected relationships between patient and staff experience and other components of the Quadruple Aim framework for the RPM intervention at hand. Failing to follow this recommendation exposes RPM evaluators to the risk of obtaining results that are logically detached from each other and as such cannot inform organic improvement efforts. Virtuous examples of reporting a clear rationale were provided by Alonso-Solís et al [ 36 ] and den Bakker et al [ 37 ], who offered detailed accounts of the considerations used to guide the selection of included experience measures. Several existing frameworks and methods can be used to map such considerations, including the nonadoption, abandonment, scale-up, spread, and sustainability (NASSS) framework [ 38 ] and the logical framework [ 39 ]. A relatively lightweight method to achieve such an overview can also be represented by the use of Figure 1 as a checklist to inventory possible Quadruple Aim relationships for a specific RPM intervention.

The fourth recommendation is to routinely reassess the chosen set of experience measures after each iteration of the RPM intervention design. Initial assumptions regarding relationships between experience factors and other dimensions of intervention quality should be verified once the relevant data are available, and new ones should be formulated, if necessary. If the RPM intervention transitions from research stages to implementation as the standard of care, it is recommended to keep on collecting at least some basic experience measures for system quality monitoring and continuous improvement. Failing to update the set of collected measures as the RPM intervention progresses through successive development stages exposes RPM evaluators to the risk of collecting outdated information, hindering iterative improvement processes.

Regarding how to measure RPM patient and staff experience, we provide 2 main recommendations. The first is to work with existing, validated and widely used instruments as much as possible, only creating new instruments after a convincing critique against current ones. Figure 7 can be used to find existing instruments measuring a broad range of experience-relevant constructs so as to reduce the need to create new ones.

For instance, researchers interested in evaluating certain experience constructs, ideally informed by preliminary qualitative research, might consult the full version of Figure 7 (available in Multimedia Appendix 4 or as an interactive map in Ref. [ 34 ]) to find their construct of interest on the left side of the graph, follow the connecting lines to the existing relevant measures on the right, and identify the most frequently used ones. They can also use the visual to consider other possibly relevant constructs.

Alternatively, researchers can use the open access database of this review [ 40 ] and especially the “extracted data” Excel file to search for the construct of interest and find details of papers in the RPM domain in which the construct was previously measured.

Failing to follow this recommendation exposes RPM researchers to the risk of obtaining results that cannot be compared to meaningful benchmarks, compared to other RPM interventions, or be included in meta-analyses.

The second recommendation is to consider adopting automatic, “passive” methods of experience data collection, such as the ones we referred to in this review as log file analysis, so as to obtain actionable estimates of user behavior with a reduced need for patients and staff to fill tedious surveys [ 41 ] or otherwise provide active input. Failing to consider automatically collected log file data on patient and staff experience constitutes a missed opportunity in terms of both the quality and cost of evaluation data. We recognize such nascent data innovations as promising [ 42 ] but also in need of methodological definition, particularly in terms of an ethical evaluation of data privacy and access [ 43 , 44 ] in order to avoid exploitative forms of prosumption [ 45 ].

Principal Findings

This study resulted in a structured overview of patient and staff experience measures used in contemporary RPM research. Through this effort, we found that the research landscape has seen a sizeable growth in the past 10 years, that it is affected by a relative lack of focus on staff experience, and that the overall corpus of collected measures can be organized in 4 main categories (service system–related, care-related, usage- and adherence-related, and health outcome–related experience measures). Little to no consensus or standardization was found in the adopted methods. Based on these findings, a set of 6 actionable recommendations for RPM experience evaluators was provided, with the aim of improving the quality and standardization of experience-related RPM research. The results of this review align with and expand on recent contributions in the field, with particular regard to the work of White et al [ 27 ].

Directions for Further Research

Fruitful future research opportunities have been recognized in various areas of RPM experience measuring. Among them, we stress the need for comparative studies investigating patient and staff experience factors across different RPM interventions; for studies clarifying the use, potential, and limitations of log file analysis in this domain; and (most importantly) for studies examining the complex relationships between experience factors, health outcomes, and cost-effectiveness in RPM.

Ultimately, we recognize the need for integrated data strategies for RPM, intended as processes and rules that define how to manage, analyze, and act upon RPM data, including continuously collected experience data, as well as clinical, technical, and administrative data. Data strategies can represent a way to operationalize a systems approach to health care innovation, described by Komashie et al [ 46 ] as “a way of addressing health delivery challenges that recognizes the multiplicity of elements interacting to impact an outcome of interest and implements processes or tools in a holistic way.” As complex, adaptive, and partly automated systems, RPM interventions require sophisticated data strategies in order to function and improve [ 47 ]; continuous loops of system feedback need to be established and analyzed in order to monitor the impact of RPM systems and optimize their performance over time, while respecting patients’ and staff’s privacy. This is especially true in the case of RPM systems including artificial intelligence (AI) components, which require continuous monitoring and updating of algorithms [ 48 - 50 ]. We characterize the development of integrated, interdisciplinary data strategies as a paramount challenge in contemporary RPM research, which will require closer collaboration between digital health designers and health care professionals [ 51 - 53 ]. We hope to have provided a small contribution to this overall goal through our effort to structure the current landscape of RPM patient and staff experience evaluation.

Strengths and Limitations

We acknowledge both strengths and limitations of the chosen methodologies. The main strength of this review is its extensive focus, covering a large number of experience measures and RPM interventions. However, a limitation introduced by such a broad scope is the lack of differentiation by targeted condition, clinical specialty, RPM intervention characteristics, geographical area, or other relevant distinctions. Furthermore, limitations were introduced by choices, such as focusing exclusively on contributions in English and on nonprimary care and nonpediatric RPM interventions.

Contemporary patient and staff experience measuring in RPM is affected by a lack of consensus and standardization, affecting the quality of both primary and secondary research in this domain. This issue determines a critical knowledge gap in our understanding of the effectiveness of RPM interventions, which are known to bring about radical changes to the care experience of both patients and staff. Bridging this knowledge gap appears to be critical in a global context of urgent need for increased resource effectiveness across health care systems, including through the increased adoption of safe and effective RPM. In this context, this review offers support for RPM experience evaluators by providing a structured overview of contemporary patient and staff experience measures and a set of practical guidelines for improving research quality and standardization in this domain.

Acknowledgments

We gratefully acknowledge Jeroen Raijmakers, Francesca Marino, Lorena Hurtado Alvarez, Alexis Derumigny, and Laurens Schuurkamp for the help and advice provided in the context of this research.

Neither ChatGPT nor other generative language models were used in this research or in the manuscript preparation or review.

Data Availability

The data sets generated and analyzed during this review are available as open access in Ref. [ 40 ].

Authors' Contributions

VP conceived the study, performed the systematic review and data analysis, and was mainly responsible for the writing of the manuscript. HCMO collaborated on study design, performed independent screening of contributions, and collaborated on data analysis. RvK provided input to the study design and execution. PG supported query term selection and structuring. MK provided input on manuscript framing and positioning. DS provided input on the design, execution, and reporting of the correspondence analysis. All authors revised and made substantial contributions to the manuscript.

Conflicts of Interest

None declared.

PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) checklist.

Full search strategies.

Overview of the merged construct formulations .

Reported patient experience constructs and associated measuring instruments (complete visual).

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Abbreviations

Edited by T de Azevedo Cardoso; submitted 25.04.23; peer-reviewed by M Tai-Seale, C Nöthiger, M Gasmi ; comments to author 29.07.23; revised version received 25.08.23; accepted 20.02.24; published 22.04.24.

©Valeria Pannunzio, Hosana Cristina Morales Ornelas, Pema Gurung, Robert van Kooten, Dirk Snelders, Hendrikus van Os, Michel Wouters, Rob Tollenaar, Douwe Atsma, Maaike Kleinsmann. Originally published in the Journal of Medical Internet Research (https://www.jmir.org), 22.04.2024.

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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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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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Key facts about the abortion debate in America

The U.S. Supreme Court’s June 2022 ruling to overturn Roe v. Wade – the decision that had guaranteed a constitutional right to an abortion for nearly 50 years – has shifted the legal battle over abortion to the states, with some prohibiting the procedure and others moving to safeguard it.

As the nation’s post-Roe chapter begins, here are key facts about Americans’ views on abortion, based on two Pew Research Center polls: one conducted from June 25-July 4 , just after this year’s high court ruling, and one conducted in March , before an earlier leaked draft of the opinion became public.

This analysis primarily draws from two Pew Research Center surveys, one surveying 10,441 U.S. adults conducted March 7-13, 2022, and another surveying 6,174 U.S. adults conducted June 27-July 4, 2022. Here are the questions used for the March survey , along with responses, and the questions used for the survey from June and July , along with responses.

Everyone who took part in these surveys is a member of the Center’s American Trends Panel (ATP), an online survey panel that is recruited through national, random sampling of residential addresses. This way nearly all U.S. adults have a chance of selection. The survey is weighted to be representative of the U.S. adult population by gender, race, ethnicity, partisan affiliation, education and other categories.  Read more about the ATP’s methodology .

A majority of the U.S. public disapproves of the Supreme Court’s decision to overturn Roe. About six-in-ten adults (57%) disapprove of the court’s decision that the U.S. Constitution does not guarantee a right to abortion and that abortion laws can be set by states, including 43% who strongly disapprove, according to the summer survey. About four-in-ten (41%) approve, including 25% who strongly approve.

About eight-in-ten Democrats and Democratic-leaning independents (82%) disapprove of the court’s decision, including nearly two-thirds (66%) who strongly disapprove. Most Republicans and GOP leaners (70%) approve , including 48% who strongly approve.

Most women (62%) disapprove of the decision to end the federal right to an abortion. More than twice as many women strongly disapprove of the court’s decision (47%) as strongly approve of it (21%). Opinion among men is more divided: 52% disapprove (37% strongly), while 47% approve (28% strongly).

About six-in-ten Americans (62%) say abortion should be legal in all or most cases, according to the summer survey – little changed since the March survey conducted just before the ruling. That includes 29% of Americans who say it should be legal in all cases and 33% who say it should be legal in most cases. About a third of U.S. adults (36%) say abortion should be illegal in all (8%) or most (28%) cases.

Generally, Americans’ views of whether abortion should be legal remained relatively unchanged in the past few years , though support fluctuated somewhat in previous decades.

Relatively few Americans take an absolutist view on the legality of abortion – either supporting or opposing it at all times, regardless of circumstances. The March survey found that support or opposition to abortion varies substantially depending on such circumstances as when an abortion takes place during a pregnancy, whether the pregnancy is life-threatening or whether a baby would have severe health problems.

While Republicans’ and Democrats’ views on the legality of abortion have long differed, the 46 percentage point partisan gap today is considerably larger than it was in the recent past, according to the survey conducted after the court’s ruling. The wider gap has been largely driven by Democrats: Today, 84% of Democrats say abortion should be legal in all or most cases, up from 72% in 2016 and 63% in 2007. Republicans’ views have shown far less change over time: Currently, 38% of Republicans say abortion should be legal in all or most cases, nearly identical to the 39% who said this in 2007.

However, the partisan divisions over whether abortion should generally be legal tell only part of the story. According to the March survey, sizable shares of Democrats favor restrictions on abortion under certain circumstances, while majorities of Republicans favor abortion being legal in some situations , such as in cases of rape or when the pregnancy is life-threatening.

There are wide religious divides in views of whether abortion should be legal , the summer survey found. An overwhelming share of religiously unaffiliated adults (83%) say abortion should be legal in all or most cases, as do six-in-ten Catholics. Protestants are divided in their views: 48% say it should be legal in all or most cases, while 50% say it should be illegal in all or most cases. Majorities of Black Protestants (71%) and White non-evangelical Protestants (61%) take the position that abortion should be legal in all or most cases, while about three-quarters of White evangelicals (73%) say it should be illegal in all (20%) or most cases (53%).

In the March survey, 72% of White evangelicals said that the statement “human life begins at conception, so a fetus is a person with rights” reflected their views extremely or very well . That’s much greater than the share of White non-evangelical Protestants (32%), Black Protestants (38%) and Catholics (44%) who said the same. Overall, 38% of Americans said that statement matched their views extremely or very well.

Catholics, meanwhile, are divided along religious and political lines in their attitudes about abortion, according to the same survey. Catholics who attend Mass regularly are among the country’s strongest opponents of abortion being legal, and they are also more likely than those who attend less frequently to believe that life begins at conception and that a fetus has rights. Catholic Republicans, meanwhile, are far more conservative on a range of abortion questions than are Catholic Democrats.

Women (66%) are more likely than men (57%) to say abortion should be legal in most or all cases, according to the survey conducted after the court’s ruling.

More than half of U.S. adults – including 60% of women and 51% of men – said in March that women should have a greater say than men in setting abortion policy . Just 3% of U.S. adults said men should have more influence over abortion policy than women, with the remainder (39%) saying women and men should have equal say.

The March survey also found that by some measures, women report being closer to the abortion issue than men . For example, women were more likely than men to say they had given “a lot” of thought to issues around abortion prior to taking the survey (40% vs. 30%). They were also considerably more likely than men to say they personally knew someone (such as a close friend, family member or themselves) who had had an abortion (66% vs. 51%) – a gender gap that was evident across age groups, political parties and religious groups.

Relatively few Americans view the morality of abortion in stark terms , the March survey found. Overall, just 7% of all U.S. adults say having an abortion is morally acceptable in all cases, and 13% say it is morally wrong in all cases. A third say that having an abortion is morally wrong in most cases, while about a quarter (24%) say it is morally acceptable in most cases. An additional 21% do not consider having an abortion a moral issue.

Among Republicans, most (68%) say that having an abortion is morally wrong either in most (48%) or all cases (20%). Only about three-in-ten Democrats (29%) hold a similar view. Instead, about four-in-ten Democrats say having an abortion is morally  acceptable  in most (32%) or all (11%) cases, while an additional 28% say it is not a moral issue. 

White evangelical Protestants overwhelmingly say having an abortion is morally wrong in most (51%) or all cases (30%). A slim majority of Catholics (53%) also view having an abortion as morally wrong, but many also say it is morally acceptable in most (24%) or all cases (4%), or that it is not a moral issue (17%). Among religiously unaffiliated Americans, about three-quarters see having an abortion as morally acceptable (45%) or not a moral issue (32%).

• Religion & Abortion

What the data says about abortion in the U.S.

Support for legal abortion is widespread in many countries, especially in europe, nearly a year after roe’s demise, americans’ views of abortion access increasingly vary by where they live, by more than two-to-one, americans say medication abortion should be legal in their state, most latinos say democrats care about them and work hard for their vote, far fewer say so of gop, most popular.

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