what are the 5 components of an academic essay

Gordon Harvey’s Elements of the Academic Essay

The “Elements of the Academic Essay” is a taxonomy of academic writing by Gordon Harvey. It identifies the key components of academic writing across the disciplines and has been widely influential. Below is a complete list (with descriptions).

Elements of an Essay

“Your main insight or idea about a text or topic, and the main proposition that your essay demonstrates. It should be true but arguable (not obviously or patently true, but one alternative among several), be limited enough in scope to be argued in a short composition and with available evidence, and get to the heart of the text or topic being analyzed (not be peripheral). It should be stated early in some form and at some point recast sharply (not just be implied), and it should govern the whole essay (not disappear in places).”  — Gordon Harvey, “Elements of the Academic Essay”

• See this fuller discussion of some of the scholarly debates about the thesis statement:  The Thesis Statement
• See this piece on the pros and cons of having a thesis statement:  Pros and Cons of Thesis Statements
• See this piece on working with students without a thesis:  What To Do When There’s No Thesis
• And see this piece for working with students with varied levels of thesis development:  A Pseudo-Thesis

“The intellectual context that you establish for your topic and thesis at the start of your essay, in order to suggest why someone, besides your instructor, might want to read an essay on this topic or need to hear your particular thesis argued—why your thesis isn’t just obvious to all, why other people might hold other theses (that you think are wrong). Your motive should be aimed at your audience: it won’t necessarily be the reason you first got interested in the topic (which could be private and idiosyncratic) or the personal motivation behind your engagement with the topic. Indeed it’s where you suggest that your argument isn’t idiosyncratic, but rather is generally interesting. The motive you set up should be genuine: a misapprehension or puzzle that an intelligent reader (not a straw dummy) would really have, a point that such a reader would really overlook. Defining motive should be the main business of your introductory paragraphs, where it is usually introduced by a form of the complicating word ‘But.'”  — Gordon Harvey, “The Elements of the Academic Essay”

“The data—facts, examples, or details—that you refer to, quote, or summarize to support your thesis. There needs to be enough evidence to be persuasive; it needs to be the right kind of evidence to support the thesis (with no obvious pieces of evidence overlooked); it needs to be sufficiently concrete for the reader to trust it (e.g. in textual analysis, it often helps to find one or two key or representative passages to quote and focus on); and if summarized, it needs to be summarized accurately and fairly.”  –Gordon Harvey, “The Elements of the Academic Essay

“The work of breaking down, interpreting, and commenting upon the data, of saying what can be inferred from the data such that it supports a thesis (is evidence for something). Analysis is what you do with data when you go beyond observing or summarizing it: you show how its parts contribute to a whole or how causes contribute to an effect; you draw out the significance or implication not apparent to a superficial view. Analysis is what makes the writer feel present, as a reasoning individual; so your essay should do more analyzing than summarizing or quoting.”  — Gordon Harvey, “The Elements of the Academic Essay”

“The recurring terms or basic oppositions that an argument rests upon, usually literal but sometimes a ruling metaphor. These terms usually imply certain assumptions—unstated beliefs about life, history, literature, reasoning, etc. that the essayist doesn’t argue for but simply assumes to be true. An essay’s keyterms should be clear in their meaning and appear throughout (not be abandoned half-way); they should be appropriate for the subject at hand (not unfair or too simple—a false or constraining opposition); and they should not be inert clichés or abstractions (e.g. “the evils of society”). The attendant assumptions should bear logical inspection, and if arguable they should be explicitly acknowledged.”  — Gordon Harvey, “The Elements of the Academic Essay”

One of the most common issues we address in the writing center is the issue of structure. Many students never consciously address structure in the way that they consciously formulate a thesis. This is ironic because the two are inseparable – that is, the way you formulate an argument (structure) is essential to the argument itself (thesis). Thus, when emphasizing the importance of structure to students, it is important to remind them that structure cannot be developed in the absence of a strong thesis: you have to know what you’re arguing before you decide how to argue it.

As a writing tutor, your first task in addressing issues of structure will be to try and gauge if the student writer has an idea of what good structure looks like. Some students understand good structure, even if it’s just at an intuitive level, while others do not. If comprehension seems lacking, it may be useful to actually stop and explain what good structure looks like.

Some Ways of Thinking about Structure:

The structure of the paper should be progressive; the paper should “build” throughout. That is, there should be a logical order to the paper; each successive paragraph should build on the ideas presented in the last. In the writing center we are familiar with the scattershot essay in which the student throws out ten arguments to see what sticks. Such essays are characterized by weak or nonexistent transitions such as “My next point…” or “Another example of this…”.

Some students will understand structure better with the help of a metaphor. One particularly nice metaphor (courtesy of Dara) is to view the structure of an academic paper as a set of stairs. The paper begins with a small step; the first paragraph gives the most simple assumption or support for the argument. The paper then builds, slowly and gradually towards the top of the staircase. When the paper reaches its conclusion, it has brought the reader up to the top of the staircase to a point of new insight. From the balcony the reader can gaze out upon the original statement or question from higher ground.

How Gordon Harvey describes structure in his “Elements of the Academic Essay”:

“The sections should follow a logical order, and the links in that order should be apparent to the reader (see “stitching”). But it should also be a progressive order—there should have a direction of development or complication, not be simply a list or a series of restatements of the thesis (“Macbeth is ambitious: he’s ambitious here; and he’s ambitious here; and he’s ambitions here, too; thus, Macbeth is ambitious”). And the order should be supple enough to allow the writer to explore the topic, not just hammer home a thesis.”

“Words that tie together the parts of an argument, most commonly (a) by using transition (linking or turning) words as signposts to indicate how a new section, paragraph, or sentence follows from the one immediately previous; but also (b) by recollection of an earlier idea or part of the essay, referring back to it either by explicit statement or by echoing key words or resonant phrases quoted or stated earlier. The repeating of key or thesis concepts is especially helpful at points of transition from one section to another, to show how the new section fits in.”  — Gordon Harvey, “The Elements of the Academic Essay”

Persons or documents, referred to, summarized, or quoted, that help a writer demonstrate the truth of his or her argument. They are typically sources of (a) factual information or data, (b) opinions or interpretation on your topic, (c) comparable versions of the thing you are discussing, or (d) applicable general concepts. Your sources need to be efficiently integrated and fairly acknowledged by citation.”  — Gordon Harvey, “The Elements of the Academic Essay”

When you pause in your demonstration to reflect on it, to raise or answer a question about it—as when you (1) consider a counter-argument—a possible objection, alternative, or problem that a skeptical or resistant reader might raise; (2) define your terms or assumptions (what do I mean by this term? or, what am I assuming here?); (3) handle a newly emergent concern (but if this is so, then how can X be?); (4) draw out an implication (so what? what might be the wider significance of the argument I have made? what might it lead to if I’m right? or, what does my argument about a single aspect of this suggest about the whole thing? or about the way people live and think?), and (5) consider a possible explanation for the phenomenon that has been demonstrated (why might this be so? what might cause or have caused it?); (6) offer a qualification or limitation to the case you have made (what you’re not saying). The first of these reflections can come anywhere in an essay; the second usually comes early; the last four often come late (they’re common moves of conclusion).”  — Gordon Harvey, “The Elements of the Academic Essay”

“Bits of information, explanation, and summary that orient the reader who isn’t expert in the subject, enabling such a reader to follow the argument. The orienting question is, what does my reader need here? The answer can take many forms: necessary information about the text, author, or event (e.g. given in your introduction); a summary of a text or passage about to be analyzed; pieces of information given along the way about passages, people, or events mentioned (including announcing or “set-up” phrases for quotations and sources). The trick is to orient briefly and gracefully.”  — Gordon Harvey, “The Elements of the Academic Essay”

“The implied relationship of you, the writer, to your readers and subject: how and where you implicitly position yourself as an analyst. Stance is defined by such features as style and tone (e.g. familiar or formal); the presence or absence of specialized language and knowledge; the amount of time spent orienting a general, non-expert reader; the use of scholarly conventions of form and style. Your stance should be established within the first few paragraphs of your essay, and it should remain consistent.”  — Gordon Harvey, “The Elements of the Academic Essay”

“The choices you make of words and sentence structure. Your style should be exact and clear (should bring out main idea and action of each sentence, not bury it) and plain without being flat (should be graceful and a little interesting, not stuffy).”  — Gordon Harvey, “The Elements of the Academic Essay”

“It should both interest and inform. To inform—i.e. inform a general reader who might be browsing in an essay collection or bibliography—your title should give the subject and focus of the essay. To interest, your title might include a linguistic twist, paradox, sound pattern, or striking phrase taken from one of your sources (the aptness of which phrase the reader comes gradually to see). You can combine the interesting and informing functions in a single title or split them into title and subtitle. The interesting element shouldn’t be too cute; the informing element shouldn’t go so far as to state a thesis. Don’t underline your own title, except where it contains the title of another text.”  — Gordon Harvey, “The Elements of the Academic Essay”

A student’s argument serves as the backbone to a piece of writing. Often expressed in the form of a one-sentence thesis statement, an argument forms the basis for a paper, defines the writer’s feelings toward a particular topic, and engages the reader in a discussion about a particular topic. Because an argument bears so much weight on the success of a paper, students may spend hours searching for that one, arguable claim that will carry them through to the assigned page limit. Formulating a decent argument about a text is tricky, especially when a professor does not distribute essay prompts—prompting students to come to the Writing Center asking that eternal question: “ What  am I going to write about?!”

Formulating the Idea of an Argument (Pre-Writing Stage)

Before a student can begin drafting a paper, he or she must have a solid argument. Begin this process by looking at the writing assignment rubric and/or prompt assigned by the professor. If no particular prompt was assigned, ask the student what interests him or her in the class? Was there a reading assignment that was particularly compelling and/or interesting? Engage the student in a conversation about the class or the paper assignment with a pen and paper in their hand. When an interesting idea is conveyed, ask them to jot it down on a paper. Look for similarities or connections in their written list of ideas.

If a student is still lost, it’s helpful to remind them to remember to have a  motive  for writing. Besides working to pass a class or getting a good grade, what could inspire a student to write an eight page paper and enjoy the process? Relating the assigned class readings to incidents in a student’s own life often helps create a sense of urgency and need to write an argument. In an essay entitled “The Great Conversation (of the Dining Hall): One Student’s Experience of College-Level Writing,” student Kimberly Nelson remembers her passion for Tolkien fueled her to write a lengthy research paper and engage her friends in discussions concerning her topic (290).

Additional ideas for consultations during the pre-writing stage .

Formulating the Argument

The pre-writing stage is essential because arguments must “be limited enough in scope to be argued in a short composition” according to Harvey’s  Elements of the Academic Essay . Narrow down the range of ideas so the student may write a more succinct paper with efficient language. When composing an argument (and later, a thesis), avoid definitive statements—arguments are  arguable , and a great paper builds on a successive chain of ideas grounded in evidence to support an argument. It is of paramount importance to remind your student that the argument will govern the entire paper and not “disappear in places” (Harvey). When composing an actual paper, it’s helpful to Post-It note a summary of your argument on your computer screen to serve as a constant reminder of  why  you are writing.

Difficulties with Arguments and International Students

When international students arrive at Pomona College, they are often unsure of what the standard academic writing expectations are. If a student submits a draft to you devoid of any argument, it’s important to remember that the conventions of their home country may not match up to the standards we expect to see here. Some countries place more of an emphasis on a summary of ideas of others rather than generating entirely new arguments. If this is the case for your student, (gently) remind him or her that most Pomona College professors expect to see new arguments generated from the students and that “summary” papers are frowned upon. Don’t disparage their previous work—use the ideas present in their paragraphs as a launching point for crafting a new, creative argument.

“Students, like all writers, must fictionalize their audience.”

– Fred Pfister and Joanne Petrik, “A Heuristic Model for Creating a Writer’s Audience” (1980)

The main purpose of imagining or fictionalizing an audience is to allow the student to position his/her paper within the discourse and in conversation with other academics. By helping the student acknowledge the fact that both the writer (the student) and the reader (the audience) play a role in the writing process, the student will be better able to clarify and strengthen his/her argument.

Moreover, the practice of fictionalizing the audience should eventually help the student learn how to become his/her own reader. By adopting the role of both the writer and the reader, the student will be able to further develop his ability to locate his/her text in a discourse community.

During a consultation, you may notice that a student’s argument does not actually engage in a conversation with the members of its respective discourse community. If his/her paper does not refer to other texts or ask questions that are relevant to this particular discourse, you may need to ask the student to imagine who his/her audience is as well as what the audience’s reaction to the paper may look like.

Although the student’s immediate answer will most likely be his/her professor, you should advise the student to attempt imagining an audience beyond his/her class—an audience composed of people who are invested in this discourse or this specific topic.

If your student cannot imagine or fictionalize such an audience, it may be because the student may not believe that he/she know enough about the topic to address such a knowledgeable audience. In this case, you should advise the student to pretend that he/she is an expert on the topic or that the student’s paper will be published and read by other members of the discourse community.

The student, however, should not pander to the audience and “undervalue the responsibility that [he/she] has to [the] subject” (Ede and Lunsford, 1984). Advise him/her to avoid re-shaping the paper so that it merely caters to or appeases the audience.

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• Parts of an Academic Essay
• The Writing Process
• Rhetorical Modes as Types of Essays
• Stylistic Considerations
• Literary Analysis Essay - Close Reading
• Unity and Coherence in Essays
• Proving the Thesis/Critical Thinking
• Appropriate Language

Test Yourself

• Essay Organization Quiz
• Sample Essay - Fairies
• Sample Essay - Modern Technology

In a way, these academic essays are like a court trial.  The attorney, whether prosecuting the case or defending it, begins with an opening statement explaining the background and telling the jury what he or she intends to prove (the thesis statement).  Then, the attorney presents witnesses for proof (the body of the paragraphs).  Lastly, the attorney presents the closing argument (concluding paragraph).

The Introduction and Thesis

There are a variety of approaches regarding the content of the introduction paragraph such as a brief outline of the proof, an anecdote, explaining key ideas, and asking a question.  In addition, some textbooks say that an introduction can be more than one paragraph.  The placement of the thesis statement is another variable depending on the instructor and/or text.  The approach used in this lesson is that an introduction paragraph gives background information leading into the thesis which is the main idea of the paper, which is stated at the end.

The background in the introductory paragraph consists of information about the circumstances of the thesis. This background information often starts in the introductory paragraph with a general statement which is then refined to the most specific sentence of the essay, the thesis. Background sentences include information about the topic and the controversy. It is important to note that in this approach, the proof for the thesis is not found in the introduction except, possibly, as part of a thesis statement which includes the key elements of the proof. Proof is presented and expanded on in the body.

Some instructors may prefer other types of content in the introduction in addition to the thesis.  It is best to check with an instructor as to whether he or she has a preference for content. Generally, the thesis must be stated in the introduction.

The thesis is the position statement. It must contain a subject and a verb and express a complete thought. It must also be defensible. This means it should be an arguable point with which people could reasonably disagree. The more focused and narrow the thesis statement, the better a paper will generally be.

If you are given a question in the instructions for your paper, the thesis statement is a one-sentence answer taking a position on the question.

If you are given a topic instead of a question, then in order to create a thesis statement, you must narrow your analysis of the topic to a specific controversial issue about the topic to take a stand. If it is not a research paper, some brainstorming (jotting down what comes to mind on the issue) should help determine a specific question.

If it is a research paper, the process begins with exploratory research which should show the various issues and controversies which should lead to the specific question.  Then, the research becomes focused on the question which in turn should lead to taking a position on the question.

These methods of determining a thesis are still answering a question. It’s just that you pose a question to answer for the thesis.  Here is an example.

Suppose, one of the topics you are given to write about is America’s National Parks. Books have been written about this subject. In fact, books have been written just about a single park. As you are thinking about it, you may realize how there is an issue about balancing between preserving the wilderness and allowing visitors. The question would then be Should visitors to America’s National Parks be regulated in order to preserve the wilderness?

One thesis might be There is no need for regulations for visiting America’s National Parks to preserve the wilderness.

 Another might be There should be reasonable regulations for visiting America’s National Parks in order to preserve the wilderness.

Finally, avoid using expressions that announce, “Now I will prove…” or “This essay is about …” Instead of telling the reader what the paper is about, a good paper simply proves the thesis in the body. Generally, you shouldn’t refer to your paper in your paper.

Here is an example of a good introduction with the thesis in red:

Not too long ago, everyday life was filled with burdensome, time-consuming chores that left little time for much more than completing these tasks.  People generally worked from their homes or within walking distance to their homes and rarely traveled far from them.  People were limited to whatever their physical capacities were.  All this changed dramatically as new technologies developed.  Modern technology has most improved our lives through convenience, efficiency, and accessibility.

Note how the background is general and leads up to the thesis.   No proof is given in the background sentences about how technology has improved lives.

Moreover, notice that the thesis in red is the last sentence of the introduction. It is a defensible statement.

A reasonable person could argue the opposite position:  Although modern technology has provided easier ways of completing some tasks, it has diminished the quality of life since people have to work too many hours to acquire these gadgets, have developed health problems as a result of excess use, and have lost focus on what is really valuable in life.

Quick Tips:

The introduction opens the essay and gives background information about the thesis.

Do not introduce your supporting points  (proof) in the introduction unless they are part of the thesis; save these for the body.

The thesis is placed at the end of the introductory paragraph.

Don’t use expressions like “this paper will be about” or “I intend to show…”

For more information on body paragraphs and supporting evidence, see Proving a Thesis – Evidence and Proving a Thesis – Logic, and Logical Fallacies and Appeals in Related Pages on the right sidebar.

Body paragraphs give proof for the thesis.  They should have one proof point per paragraph expressed in a topic sentence. The topic sentence is usually found at the beginning of each body paragraph and, like a thesis, must be a complete sentence. Each topic sentence must be directly related to and support the argument made by the thesis.

After the topic sentence, the rest of the paragraph should go on to support this one proof with examples and explanation. It is the details that support the topic sentences in the body paragraphs that make the arguments strong.

If the thesis statement stated that technology improved the quality of life, each body paragraph should begin with a reason why it has improved the quality of life.  This reason is called a  topic sentence .  Following are three examples of body paragraphs that provide support for the thesis that modern technology has improved our lives through convenience, efficiency, and accessibility:

     Almost every aspect of our lives has been improved through convenience provided by modern technology.  From the sound of music from an alarm clock in the morning to the end of the day being entertained in the convenience of our living room, our lives are improved.  The automatic coffee maker has the coffee ready at a certain time.  Cars or public transportation bring people to work where computers operate at the push of a button.  At home, there’s the convenience of washing machines and dryers, dishwashers, air conditioners, and power lawn mowers.  Modern technology has made life better with many conveniences.

     Not only has technology improved our lives through convenience, it has improved our lives through efficiency. The time saved by machines doing most of the work leaves more time for people to develop their personal goals or to just relax.  Years ago, when doing laundry could take all day, there wasn’t time left over to read or go to school or even just to take a leisurely walk.  Nowadays, people have more time and energy than ever to simply enjoy their lives and pursue their goals thanks to the efficiency of modern technology.

     Accessibility to a wide range of options has been expanded through modern technology.  Never before could people cross a continent or an ocean in an afternoon.  Travel is not the only way technology has created accessibility.  Software which types from voice commands has made using computers more accessible for school or work.  People with special needs have many new options thanks to modern technology such as special chairs or text readers.  Actually, those people who need hearing aids as a result of normal aging have access to continued communication and enjoyment of entertainment they did not previously have.  There are many ways technology has improved lives through increased accessibility.

Notice how these proof paragraphs stick to one proof point introduced in the topic sentences in red. These three paragraphs, not only support the original thesis, but go on to give details and explanations which explain the proof point in the topic sentence.

Quick Tips on Body Paragraphs

The body of your essay is where you give your main support for the thesis.

Each body paragraph should start with a Topic Sentence that is directly related to and supports the thesis statement.

Each body paragraph should also give details and explanations that further support the poof point for that paragraph.

Don’t use enumeration such as first, second, and third. The reader will know by the topic sentence that it is a new proof point.

See Proving the Thesis in Related Pages on the right sidebar for more information on proof.

The Conclusion

Instructors vary of what they expect in the conclusion; however, there is general agreement that conclusions should not introduce any new proof points, should include a restatement of the thesis, and should not contain any words such as “In conclusion.”

Some instructors want only a summary of the proof and a restatement of the thesis. Some instructors ask for a general prediction or implication of the information presented without a restatement of thesis. Still others may want to include a restatement along with a general prediction or implication of the information presents. Be sure to review assignment instructions or check with instructor.  If your assignment instructions don’t specify, just sum up the proof and restate the thesis.

Example which sums up proof and restates thesis :

Modern technology has created many conveniences in everyday from waking up to music to having coffee ready to getting to work and doing a day’s work.  The efficiency provided by technology gives people more time to enjoy life and pursue personal development, and the accessibility has broadened options for travel, school, and work.  Modern technology has improved our lives through convenience, efficiency, and accessibility.

See how the thesis statement was restated in red. The two major arguments about the possible locations proven to be incorrect were also included to remind the reader of the major proof points made in the paper.

Example which makes a general prediction or implication of the information presented:

Modern technology has created many conveniences in everyday life from waking up to music to having coffee ready to getting to work and doing a day’s work.  The efficiency provided by technology gives people more time to enjoy life and pursue personal development, and the accessibility has broadened options for travel, school, and work.  Without it, everyday life would be filled with burdensome tasks and be limited to our neighborhood and our physical capacity. Here’s an example of a conclusion with a general prediction or implication statement with a restatement of thesis.

Modern technology has created many conveniences in everyday life from waking up to music to having coffee ready to getting to work and doing a day’s work.  The efficiency provided by technology gives people more time to enjoy life and pursue personal development, and the accessibility has broadened options for travel, school, and work.  Without it, everyday life would be filled with burdensome tasks and be limited to our neighborhood and our physical capacity. Modern technology has improved our lives through convenience, efficiency, and accessibility.

Quick Tips for Conclusions

• The conclusion brings the essay to an end and is typically the shortest paragraph.
• It is important to not introduce new ideas or information here.
• Unless otherwise specified in your assignment, just sum up the proof and restate the conclusion.
• Some instructors may want the concluding paragraph to contain a general prediction or observation implied from the information presented.
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Almost every course you will encounter in college will include writing assignments. One of the most common writing assignments is known as an essay. While the content and style of  essay projects will vary across the disciplines, there are a number of key components that all good essays include. This section of the guide walks you through some of the basic components of the essay genre.  Here are some general thoughts before you get started.  

• A good essay is well-organized and structured. Good essays have a clear introduction, thesis, and conclusion. Body paragraphs in the essay connect back to the thesis. 
• In college, we are no longer tied to a five-paragraph essay (unless an instructor specifically asks for this). Our essays in college can range in length. Some projects may be more than 10 pages, so it would be impossible to use only 5 paragraphs for an essay of this length. 
• Because we are no longer tied to a 5-paragraph essay, we do not have to include "three points" in our thesis statement as we may have done in other courses. 
• Essays should be cohesive and have a good flow. We can create this flow by using transition words and phrases to connect one point to the next. 
• Remember to the review the directions before you start. One can produce a wonderfully-written essay, but if it does not meet the project's parameters, it will not usually receive a passing grade.
• Schedule a meeting with your instructor or tutor before you begin. Visit  http://baker.mywconline.com/  to schedule a meeting with a professional tutor. 

• Parts of an Essay This handout breaks down an essay into it core parts. This short video will provide you with essay structure help.
• Creating a Strong Thesis Statement Here are some brief tips about how to write a strong thesis statement for your college writing project.
• How to Write an Excellent Introduction This handout leads you through a number of successful strategies to garner reader interest and transition into your thesis statement.
• Creating Body Paragraphs This resource walks you through paragraph creation including how to implement good topic sentences, proper organization, and excellent development.
• Crafting a Strong Conclusion We often focus on creating a strong introduction, but crafting a well-written conclusion is just as important.
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• November 30, 2022
• Academic Advice

How To Write an Academic Essay: A Beginner’s Guide

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During college, you must participate in many writing assessments, one of the most important being academic essays. Unfortunately, only a few are well-informed about the process of academic writing. 

If you’re reading this, you probably want to learn how to write an academic essay. Follow our guide! Here we’ll introduce the concept of the academic essay, the five components of an academic essay, the format of an academic essay, and more.

Ready to master your academic essay writing? Let’s go!

What Is an Academic Essay?

An academic essay is writing created to initiate debate, defend an idea, or present a new point of view by supporting it with evidence. 

One of the most important components that differentiate it from typical essays you have written in high school is supporting ideas with evidence. If you claim that, for example, “divorces have a negative impact on young children,” you need to find sources that support your argument to make it more convincing.

Interpreting facts is another essential element of a successful academic piece of writing. Academic essays should be written in a formal tone, with a set structure, and have a critical, based, and objective viewpoint. You should be able to understand and transmit different points of view to your readers in a simple but formal manner. 

Are you still trying to figure out what steps you should take to start writing? Keep scrolling!  

How To Write an Academic Essay

Writing an academic essay can initially seem intimidating, especially if you are unfamiliar with the rules and requirements. 

The time and effort spent on the writing task might differ depending on the topic, word limit, deadline, and other factors. However, the key steps, including preparing for the writing, creating a thesis statement, introduction, conclusion, and editing process, must be included in every academic writing style. 

By following the detailed list of actions below, you can start and finish your essay in no time.

Prepare to write your essay

Before going into the technical part of the writing process, one piece of advice you should keep in mind is planning. Planning is as important as the writing process. If you plan correctly, you will have sufficient time to perform every step successfully. Failure to plan will lead to a messy essay and, worst-case scenario, an unfinished writing piece. 

Understand your assignment

First and foremost, before you take any action regarding writing your essay, you must ensure you have clearly understood every tiny detail that your instructor has provided you—this step will determine your academic essay’s effectiveness. But why is that? Understanding the assignment in detail will leave no space for any irrelevant information that would lead to wasted time and, ultimately, a lower grade. 

Develop your essay topic

If your instructor doesn’t give you a specific topic, you should spend some time finding a topic that fits the requirements. Finding a topic sounds easy, but finding the right one requires more than just a simple google search.

So, ensure you develop an original topic, as it adds more value to your academic writing. However, ensure that there is enough evidence from other sources to help you back up your arguments. You can do this by researching similar topics from trusted sources.

Do your research and take notes

Once you determine the topic, go on and do some research. This part takes a lot of effort since there are countless sources online, and obviously, you have to choose some of the best. 

Depending on your topic, there might be cases where online sources are not available, and you’ll also have to visit local libraries. Whatever the case, you need to take notes and highlight the components you want to include in your essay. 

A quick tip: Go back to your topic often to avoid getting swayed or influenced by other less relevant ideas. 

Come up with a clear thesis statement

An excellent academic essay contains a strong thesis statement. A strong thesis statement successfully narrows your topic into a specific area of investigation. It should also intrigue your readers and initiate debate. 

A good thesis statement is:

• NOT a question  
• NOT a personal opinion

Create a structure

After gathering all the necessary information, you can begin outlining your main ideas. The primary academic essay structure is classified into the following 

• Introduction

Failure to maintain these three components in your academic essay will result in a poorly written assignment. Luckily, you can easily avoid that by following our guide.

Writing the introduction

Your essay will be divided into paragraphs of equal importance, but the introductory part should always stand out. You must make your introduction as presentable as possible and get the reader’s attention. Work on it as if you were to get graded only by the evaluation of that first paragraph. 

The purpose of the introduction is to demonstrate that your thoughts and ideas are logical and coherent. Also, depending on the word limit, you can use more than one paragraph.  

Hook the reader

All forms of writing benefit from an attractive hook. If you have no idea of how to hook the reader, you can go the safe way and choose a recent fact or statistic. Statistics will give your essay credibility, surprise the readers, and make them want to keep reading.  

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Give background on the topic

Now that you have the reader’s attention, you should strive to expand the essay’s key points but to a limit since the introduction is only one part of the whole essay. You should generally explain what gaps from previous sources you will cover and what others have covered so far. 

Present your thesis statement

When introducing your thesis statement, you can present it as a statement of fact or controversial. If you decide to give a statement fact, it will be challenging to keep your audience engaged since facts can be easily proven. But presenting it more controversially will keep your audience awake and can even result in a better grade overall.

Writing the body of your essay

The body part of your essay is where you’ll expand all of your ideas presented in the introduction. It’s essential to stay consistent and not include irrelevant information. Since it is the longest part of your essay, you can easily get lost, and to prevent that, it would be best to map an internal outline specific to each paragraph. This way, you know what to include and where. 

Paragraph structure

Each paragraph should follow a specific structure. It should begin with an introductory sentence that tells the reader the main ideas you will discuss in the paragraph. It’s advisable to point back to your thesis statement to identify the relationship between it and the existing idea. Also, ensure that each paragraph demonstrates new ideas.  

Length of the body paragraphs

Depending on the topic and the arguments you’ve gathered, it’s advisable not to exceed 200 words per paragraph in academic writing. If your paragraphs are too long and contain unnecessary wording, it will become difficult for the reader to follow your point. So keep them clear and concise.

Writing the conclusion

Congratulation, now you’ve made it to the last paragraph of your essay. The conclusion’s primary purpose is to summarize the ideas presented throughout your essay. Writing a good conclusion should take little time since you know what the essay contains. However, be aware of what points you should or shouldn’t include.

What to include in your conclusion 

A strong conclusion needs to have an introductory sentence. In some cases, if your instructor approves, it can include other areas that need to be investigated in the future. But at its core, it should only remind the reader about the main arguments discussed.

What not to include in your conclusion 

You should at all times refrain from including new ideas. Since the essay ends with the conclusion, don’t go into details or support new points. Doing that will confuse the reader and result in a poor grade. 

Editing your essay

Without a doubt, editing is just as important as writing. No matter how careful you are during writing, there’s a high possibility that there will be some slip-ups. These can range from spelling mistakes to grammar, punctuation, and so on. We suggest you spend time doing other things and return to the essay again. This will help you notice errors that you otherwise wouldn’t. 

Tips for Writing a Great College Essay

Now that you have a clear idea of the process of writing an academic essay, we have a few more tips: 

• Always cite your sources
• Gather enough sources to support your thesis statement
• Keep your sentences short and comprehensive
• Start the research as early as possible 
• Do not skip revising 

The Bottom Line

Writing an academic essay is a complex task. But with the right tools, guidance, and willingness to learn from your mistakes, you will master academic writing in no time. Make sure to follow each of the abovementioned steps and practice as much as possible. And don’t forget to edit!

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Chapter 5: The Essay

Learning Objectives

• Combine your knowledge of paragraphs and summary in a longer format.
• Write the parts of an essay: the introduction, the body, and the conclusion.
• Practise writing either a descriptive, narrative, expository, or persuasive essay.
• Practise five ways to hook the reader with your first sentence.
• Back up your claim with relevant evidence.
• Differentiate evidence from experience and evidence from a source.
• Signal your point of view in your first sentence so it is clear to the reader.

Now that you have practised writing different types of paragraphs—including descriptive, narrative, expository, and persuasive—as well as learning how to summarize, you’re ready to put your skills to work in a longer piece of writing: the essay.

Essays require you to use many of the skills you learned, such as argument, exposition, summary, “hooking” the reader, and so forth, in a more extended format. Ideally, they capture the reader’s attention and keep it throughout by expressing what you want to say in a lively and forthright manner, as well as including evidence for your claim. You also explain the relevance of your evidence and clearly indicate where it comes from.

Building Blocks of Academic Writing Copyright © 2020 by Carellin Brooks is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License , except where otherwise noted.

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Writing Help

• Writing Process

Starting an Essay

Essay structure, writing a thesis statement, introduction paragraphs, body paragraphs, conclusions.

• Paragraph Structure
• Paraphrase, Summarize, and Synthesize
• Writing Genres
• Sentence Structure
• Punctuation and Grammar
• Using Bias-Free Language
• Revision, Editing, and Proofreading
• Mastering the Literature Review This link opens in a new window
• Annotated Bibliography
• Help This link opens in a new window

Note: This guide was used/adapted with the permission of Baker College. For more information please visit the Baker College Writing Guide . 

Almost every course you will encounter in college will include writing assignments. One of the most common writing assignments is known as an essay. While the content and style of essay projects will vary across the disciplines, there are several key components that all good essays include. This section of the guide walks you through some of the basic components of the essay genre.  Here are some general thoughts before you get started.  

• A good essay is well-organized and structured. Good essays have a clear introduction, thesis, and conclusion. Body paragraphs in the essay connect back to the thesis. 
• Essays should be cohesive and have a good flow. We can create this flow by using transition words and phrases to connect one point to the next. 
• Remember to review the directions before you start. One can produce a wonderfully written essay, but if it does not meet the project's parameters, it will not usually receive a passing grade.
• Tips for Writing Your Thesis Drafting a thesis statement can be intimidating, but there are a variety of resources to help.
• Strong Introduction Paragraphs Review tips on starting your paper strong.
• Creating Body Paragraphs This resource walks you through paragraph creation including how to implement good topic sentences, proper organization, and excellent development.
• Crafting a Strong Conclusion We often focus on creating a strong introduction, but crafting a well-written conclusion is just as important.
• << Previous: Writing Process
• Next: Paragraph Structure >>
• Last Updated: Nov 30, 2023 1:00 PM
• URL: https://bethelu.libguides.com/writinghelp

• If you are writing in a new discipline, you should always make sure to ask about conventions and expectations for introductions, just as you would for any other aspect of the essay. For example, while it may be acceptable to write a two-paragraph (or longer) introduction for your papers in some courses, instructors in other disciplines, such as those in some Government courses, may expect a shorter introduction that includes a preview of the argument that will follow.  
• In some disciplines (Government, Economics, and others), it’s common to offer an overview in the introduction of what points you will make in your essay. In other disciplines, you will not be expected to provide this overview in your introduction.  
• Avoid writing a very general opening sentence. While it may be true that “Since the dawn of time, people have been telling love stories,” it won’t help you explain what’s interesting about your topic.  
• Avoid writing a “funnel” introduction in which you begin with a very broad statement about a topic and move to a narrow statement about that topic. Broad generalizations about a topic will not add to your readers’ understanding of your specific essay topic.  
• Avoid beginning with a dictionary definition of a term or concept you will be writing about. If the concept is complicated or unfamiliar to your readers, you will need to define it in detail later in your essay. If it’s not complicated, you can assume your readers already know the definition.  
• Avoid offering too much detail in your introduction that a reader could better understand later in the paper.
• picture_as_pdf Introductions

Academics Hub

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What Are The 5 Parts of An Academic Essay? Improve Your Writing Skills

Academic essay writing is a piece of writing meant to persuade someone of something or merely to inform the reader on a topic. The essay must include numerous crucial components for the reader to be convinced or fully informed, and it must flow logically. The introduction, body, and conclusion are the three main components of an essay. Five paragraphs in a standard short essay can supply the reader with enough information in a short amount of time. Academic essay writing is a part of a student’s academic life. They have to submit their academic tasks during their educational career. Without the submission of their assignments, students can’t score high grades in exams.

But, students, due to many reasons, struggle with their homework. They wonder and ask: who can do my homework? Can I get assistance with an academic essay? Or, Can I seek help from a professional  essay writer ? Well! You can ask for help from online academic writing service providers. In this blog, I have discussed the five parts of an academic essay and how you can improve your essay writing skills.

Parts of Academic Essay

Essay writing is a type of academic assignment that assists students to improve their writing, critical thinking, introspection, and analytical skills. Your professor may provide you with different types of essays such as narrative, expository, persuasive, and comparative. Teachers provide students with details such as which essay to write and what topic to write it on.  When writing a 5-paragraph essay, students must ensure that they capture content in five parts that address different but related topics.

Introduction

In academic writing, it is standard for students to begin each work with an introduction. The introduction, which follows a five-part format, sets the tone for the author’s claims in the main text. Students must ensure three things in this section: that they have the readers’ attention, that they have contextualized their message, and that the text’s objective is apparent. In short, the introduction consists of a hook, background information about the issue, and a thesis statement.

Hook: Students should begin the opening section with a hook to increase readers’ interest. This should ideally be a strong, eye-catching remark that attracts the reader’s attention and entices them to continue reading the essay. If a researcher knows how to create a hook, they know who they’re writing for. Furthermore, the purpose of the hook is to ensure that the audience is fully engaged in the essay right from start.

Background Information: The greatest technique to make the audience interested in what the writers say in their work is to provide background information on the topic. To put it another way, commencing this section of a five-part essay with assertions and arguments without providing readers with material that familiarises them with issues important to the topic is like leading blind followers. Background information informs the readers about the topic’s context.

Thesis Statement: It is the declaration that authors make at the end of their essay’s introduction section. Essentially, the purpose of this remark is to demonstrate the writer’s major point throughout the text. Furthermore, all of the body paragraphs should provide content that supports this assertion.

Arguments in Academic Essay

The body is the second part that makes up an essay. Students go into the topic in depth by analyzing evidence that supports their position. Writers use the thesis statement as a guide to offer readers appropriate details about the topic. The first part of the body of an essay emphasizes the writer’s arguments.

Writer’s Claims: The fundamental content of an argumentative essay’s body paragraphs is claimed. Students make claims in support of the thesis statement in the first part of this section. These claims, in general, differ from the writer’s personal opinion since they are supported by evidence from scholarly texts. Furthermore, authors can make as many assertions as they choose. However, there should not be too many statements throughout the article if the author’s voice is to be heard. Then, for each assertion made by students, there must be evidence to back it up as well as the author’s perspective.

Evidence: In an academic essay, facts and figures discovered by students while researching the topic are known as evidence. Authors should rely on scholarly sources to find evidence to support their statements, according to academic writing guidelines. The most popular criteria for recognizing scholarly sources are:

• Sources must have an author with prominent academic credentials
• Sources must be published, such as journal articles and books
• And, sources must be written in a formal language without jargon or slang.

Counter Argument

Counter arguments are addressed in the third part of an essay, which is the second portion of the main body. In an ideal world, a writer would not explain arguments in support of a topic without acknowledging opposing viewpoints. The counter-argument section is where the authors mention critical and scholarly discussions that refute the thesis statement’s claim. Writers should back up these counter-arguments with information from academic sources to ensure that the overall document is scholarly.

After mentioning counter-arguments to the thesis’ major argument, students should reject these statements. The rebuttal is the fourth portion of the paragraph of the academic essay, which is also the third half of the main text. In this situation, writers confront opposing arguments to persuade readers of the truth of the thesis statement’s proposition. Then, while writing refutations, the ideal strategy is to point out any significant faults in the counter-argument.  In short, the purpose of this section is to persuade readers that, despite opposing evidence, the writer’s thesis is sound.

Essentials of compelling paragraphs: Students must ensure that what they write in these sections is compelling while organizing three body sections from 5 parts of an academic essay. A topic phrase should be used to begin each of the three body paragraphs of a five-paragraph essay — the argument, counter-argument, and rebuttal sections. Scholars who understand how to create a topic sentence, for example, recognize that it establishes the writer’s attention in that paragraph. All topic phrases, however, should be related to the thesis statement to have a correct logical flow.

As a result, each paragraph should end with a concluding sentence. This statement, in turn, connects the topic phrase to the thesis statement and establishes a link to the next section. Furthermore, evidence, analysis, and interpretation make up the substance between the theme and the ending words. 

The conclusion is the final part of an academic essay. It is, in essence, a place where authors may unwind. Basically, the risks of readers losing interest in the author’s purpose are always high near the end of an essay. As a result, in the conclusion section, authors should restate the thesis statement to remind the audience of the paper’s original purpose. A description of the claims, including the writer’s arguments, counter-arguments, and refutations, should follow. Furthermore, despite conflicting evidence, researchers should be clear that their arguments are valid. The final remark, on the other hand, should represent the writer’s final thought.

How to Improve Academic Essay Writing Skills?

Many people undervalue the significance of excellent writing skills. Continue reading to find out how you can help people improve their written communication abilities, which is perhaps one of the most important skills to have. Academic writing is entirely based on statistics and facts. Creating an academic paper is not a one-day task. A good academic paper contains diligent research through credible scholarly sources, data collation, content assimilation, and final paper drafting. All of these tasks necessitate effective management abilities.

• You can improve your academic writing skills by planning out your day and dedicating some time to writing activities.
• Reading well-written and formatted work can help you gain a better understanding of scientific writing style.
• Most importantly, reading books and papers on similar or closely related topics will assist you in discovering your analytical abilities.
• Reading the works of different authors also helps you learn different aspects of writing to communicate with a larger audience.
• Don’t use unnecessary vocabulary in academic essay writing. 
• It is critical to understand the words you use and how they relate to the context. Avoid using jargon in your academic essay so that it is accessible and understandable to all readers.
• Improve your grammar by focusing on the fundamentals.
• Read aloud because errors that pass through the eyes may not pass through the ears.
• If you are aware of the most common errors, make a list of them and search for each type of error separately.

Academic essay writing is a crucial part of a student’s academic life. An academic essay requires research, time, effort, and effective writing skills. Students often find it tedious to compose compelling essays. Academic essay writing consists of five parts. Everybody can write an effective paper after following all the above-mentioned parts of essay writing. Also, in this blog, I have discussed a few points of how you can improve your writing skills. If you still don’t want to craft your paper, get online assistance from essay writers.

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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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Climate damage projections beyond annual temperature

Investment incentive reduced by climate damages can be restored by optimal policy

Climate economics support for the UN climate targets

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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All authors contributed to the design of the analysis. M.K. conducted the analysis and produced the figures. All authors contributed to the interpretation and presentation of the results. M.K. and L.W. wrote the manuscript.

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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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Received : 25 January 2023

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DOI : https://doi.org/10.1038/s41586-024-07219-0

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