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Research Paper Analysis: How to Analyze a Research Article + Example

Why might you need to analyze research? First of all, when you analyze a research article, you begin to understand your assigned reading better. It is also the first step toward learning how to write your own research articles and literature reviews. However, if you have never written a research paper before, it may be difficult for you to analyze one. After all, you may not know what criteria to use to evaluate it. But don’t panic! We will help you figure it out!

In this article, our team has explained how to analyze research papers quickly and effectively. At the end, you will also find a research analysis paper example to see how everything works in practice.

• 🔤 Research Analysis Definition

📊 How to Analyze a Research Article

✍️ how to write a research analysis.

• 📝 Analysis Example
• 🔎 More Examples

🔗 References

🔤 research paper analysis: what is it.

A research paper analysis is an academic writing assignment in which you analyze a scholarly article’s methodology, data, and findings. In essence, “to analyze” means to break something down into components and assess each of them individually and in relation to each other. The goal of an analysis is to gain a deeper understanding of a subject. So, when you analyze a research article, you dissect it into elements like data sources , research methods, and results and evaluate how they contribute to the study’s strengths and weaknesses.

📋 Research Analysis Format

A research analysis paper has a pretty straightforward structure. Check it out below!

Research articles usually include the following sections: introduction, methods, results, and discussion. In the following paragraphs, we will discuss how to analyze a scientific article with a focus on each of its parts.

How to Analyze a Research Paper: Purpose

The purpose of the study is usually outlined in the introductory section of the article. Analyzing the research paper’s objectives is critical to establish the context for the rest of your analysis.

When analyzing the research aim, you should evaluate whether it was justified for the researchers to conduct the study. In other words, you should assess whether their research question was significant and whether it arose from existing literature on the topic.

Here are some questions that may help you analyze a research paper’s purpose:

• Why was the research carried out?
• What gaps does it try to fill, or what controversies to settle?
• How does the study contribute to its field?
• Do you agree with the author’s justification for approaching this particular question in this way?

How to Analyze a Paper: Methods

When analyzing the methodology section , you should indicate the study’s research design (qualitative, quantitative, or mixed) and methods used (for example, experiment, case study, correlational research, survey, etc.). After that, you should assess whether these methods suit the research purpose. In other words, do the chosen methods allow scholars to answer their research questions within the scope of their study?

For example, if scholars wanted to study US students’ average satisfaction with their higher education experience, they could conduct a quantitative survey . However, if they wanted to gain an in-depth understanding of the factors influencing US students’ satisfaction with higher education, qualitative interviews would be more appropriate.

When analyzing methods, you should also look at the research sample . Did the scholars use randomization to select study participants? Was the sample big enough for the results to be generalizable to a larger population?

You can also answer the following questions in your methodology analysis:

• Is the methodology valid? In other words, did the researchers use methods that accurately measure the variables of interest?
• Is the research methodology reliable? A research method is reliable if it can produce stable and consistent results under the same circumstances.
• Is the study biased in any way?
• What are the limitations of the chosen methodology?

How to Analyze Research Articles’ Results

You should start the analysis of the article results by carefully reading the tables, figures, and text. Check whether the findings correspond to the initial research purpose. See whether the results answered the author’s research questions or supported the hypotheses stated in the introduction.

To analyze the results section effectively, answer the following questions:

• What are the major findings of the study?
• Did the author present the results clearly and unambiguously?
• Are the findings statistically significant ?
• Does the author provide sufficient information on the validity and reliability of the results?
• Have you noticed any trends or patterns in the data that the author did not mention?

How to Analyze Research: Discussion

Finally, you should analyze the authors’ interpretation of results and its connection with research objectives. Examine what conclusions the authors drew from their study and whether these conclusions answer the original question.

You should also pay attention to how the authors used findings to support their conclusions. For example, you can reflect on why their findings support that particular inference and not another one. Moreover, more than one conclusion can sometimes be made based on the same set of results. If that’s the case with your article, you should analyze whether the authors addressed other interpretations of their findings .

Here are some useful questions you can use to analyze the discussion section:

• What findings did the authors use to support their conclusions?
• How do the researchers’ conclusions compare to other studies’ findings?
• How does this study contribute to its field?
• What future research directions do the authors suggest?
• What additional insights can you share regarding this article? For example, do you agree with the results? What other questions could the researchers have answered?

Now, you know how to analyze an article that presents research findings. However, it’s just a part of the work you have to do to complete your paper. So, it’s time to learn how to write research analysis! Check out the steps below!

1. Introduce the Article

As with most academic assignments, you should start your research article analysis with an introduction. Here’s what it should include:

• The article’s publication details . Specify the title of the scholarly work you are analyzing, its authors, and publication date. Remember to enclose the article’s title in quotation marks and write it in title case .
• The article’s main point . State what the paper is about. What did the authors study, and what was their major finding?
• Your thesis statement . End your introduction with a strong claim summarizing your evaluation of the article. Consider briefly outlining the research paper’s strengths, weaknesses, and significance in your thesis.

Keep your introduction brief. Save the word count for the “meat” of your paper — that is, for the analysis.

2. Summarize the Article

Now, you should write a brief and focused summary of the scientific article. It should be shorter than your analysis section and contain all the relevant details about the research paper.

Here’s what you should include in your summary:

• The research purpose . Briefly explain why the research was done. Identify the authors’ purpose and research questions or hypotheses .
• Methods and results . Summarize what happened in the study. State only facts, without the authors’ interpretations of them. Avoid using too many numbers and details; instead, include only the information that will help readers understand what happened.
• The authors’ conclusions . Outline what conclusions the researchers made from their study. In other words, describe how the authors explained the meaning of their findings.

If you need help summarizing an article, you can use our free summary generator .

3. Write Your Research Analysis

The analysis of the study is the most crucial part of this assignment type. Its key goal is to evaluate the article critically and demonstrate your understanding of it.

We’ve already covered how to analyze a research article in the section above. Here’s a quick recap:

• Analyze whether the study’s purpose is significant and relevant.
• Examine whether the chosen methodology allows for answering the research questions.
• Evaluate how the authors presented the results.
• Assess whether the authors’ conclusions are grounded in findings and answer the original research questions.

Although you should analyze the article critically, it doesn’t mean you only should criticize it. If the authors did a good job designing and conducting their study, be sure to explain why you think their work is well done. Also, it is a great idea to provide examples from the article to support your analysis.

4. Conclude Your Analysis of Research Paper

A conclusion is your chance to reflect on the study’s relevance and importance. Explain how the analyzed paper can contribute to the existing knowledge or lead to future research. Also, you need to summarize your thoughts on the article as a whole. Avoid making value judgments — saying that the paper is “good” or “bad.” Instead, use more descriptive words and phrases such as “This paper effectively showed…”

Need help writing a compelling conclusion? Try our free essay conclusion generator !

5. Revise and Proofread

Last but not least, you should carefully proofread your paper to find any punctuation, grammar, and spelling mistakes. Start by reading your work out loud to ensure that your sentences fit together and sound cohesive. Also, it can be helpful to ask your professor or peer to read your work and highlight possible weaknesses or typos.

📝 Research Paper Analysis Example

We have prepared an analysis of a research paper example to show how everything works in practice.

No Homework Policy: Research Article Analysis Example

This paper aims to analyze the research article entitled “No Assignment: A Boon or a Bane?” by Cordova, Pagtulon-an, and Tan (2019). This study examined the effects of having and not having assignments on weekends on high school students’ performance and transmuted mean scores. This article effectively shows the value of homework for students, but larger studies are needed to support its findings.

Cordova et al. (2019) conducted a descriptive quantitative study using a sample of 115 Grade 11 students of the Central Mindanao University Laboratory High School in the Philippines. The sample was divided into two groups: the first received homework on weekends, while the second didn’t. The researchers compared students’ performance records made by teachers and found that students who received assignments performed better than their counterparts without homework.

The purpose of this study is highly relevant and justified as this research was conducted in response to the debates about the “No Homework Policy” in the Philippines. Although the descriptive research design used by the authors allows to answer the research question, the study could benefit from an experimental design. This way, the authors would have firm control over variables. Additionally, the study’s sample size was not large enough for the findings to be generalized to a larger population.

The study results are presented clearly, logically, and comprehensively and correspond to the research objectives. The researchers found that students’ mean grades decreased in the group without homework and increased in the group with homework. Based on these findings, the authors concluded that homework positively affected students’ performance. This conclusion is logical and grounded in data.

This research effectively showed the importance of homework for students’ performance. Yet, since the sample size was relatively small, larger studies are needed to ensure the authors’ conclusions can be generalized to a larger population.

🔎 More Research Analysis Paper Examples

Do you want another research analysis example? Check out the best analysis research paper samples below:

• Gracious Leadership Principles for Nurses: Article Analysis
• Effective Mental Health Interventions: Analysis of an Article
• Nursing Turnover: Article Analysis
• Nursing Practice Issue: Qualitative Research Article Analysis
• Quantitative Article Critique in Nursing
• LIVE Program: Quantitative Article Critique
• Evidence-Based Practice Beliefs and Implementation: Article Critique
• “Differential Effectiveness of Placebo Treatments”: Research Paper Analysis
• “Family-Based Childhood Obesity Prevention Interventions”: Analysis Research Paper Example
• “Childhood Obesity Risk in Overweight Mothers”: Article Analysis
• “Fostering Early Breast Cancer Detection” Article Analysis
• Lesson Planning for Diversity: Analysis of an Article
• Journal Article Review: Correlates of Physical Violence at School
• Space and the Atom: Article Analysis
• “Democracy and Collective Identity in the EU and the USA”: Article Analysis
• China’s Hegemonic Prospects: Article Review
• Article Analysis: Fear of Missing Out
• Article Analysis: “Perceptions of ADHD Among Diagnosed Children and Their Parents”
• Codependence, Narcissism, and Childhood Trauma: Analysis of the Article
• Relationship Between Work Intensity, Workaholism, Burnout, and MSC: Article Review

We hope that our article on research paper analysis has been helpful. If you liked it, please share this article with your friends!

• Analyzing Research Articles: A Guide for Readers and Writers | Sam Mathews
• Summary and Analysis of Scientific Research Articles | San José State University Writing Center
• Analyzing Scholarly Articles | Texas A&M University
• Article Analysis Assignment | University of Wisconsin-Madison
• How to Summarize a Research Article | University of Connecticut
• Critique/Review of Research Articles | University of Calgary
• Art of Reading a Journal Article: Methodically and Effectively | PubMed Central
• Write a Critical Review of a Scientific Journal Article | McLaughlin Library
• How to Read and Understand a Scientific Paper: A Guide for Non-scientists | LSE
• How to Analyze Journal Articles | Classroom

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Research Article

RLetters: A Web-Based Application for Text Analysis of Journal Articles

* E-mail: [email protected]

Affiliation Department of Philosophy and Religious Studies, Louisiana State University, Baton Rouge, Louisiana, United States of America

• Charles H. Pence

• Published: January 5, 2016
• https://doi.org/10.1371/journal.pone.0146004
• Reader Comments

While textual analysis of the journal literature is a burgeoning field, there is still a profound lack of user-friendly software for accomplishing this task. RLetters is a free, open-source web application which provides researchers with an environment in which they can select sets of journal articles and analyze them with cutting-edge textual analysis tools. RLetters allows users without prior expertise in textual analysis to analyze word frequency, collocations, cooccurrences, term networks, and more. It is implemented in Ruby and scripts are provided to automate deployment.

Citation: Pence CH (2016) RLetters: A Web-Based Application for Text Analysis of Journal Articles. PLoS ONE 11(1): e0146004. https://doi.org/10.1371/journal.pone.0146004

Editor: Jean Peccoud, Virginia Tech, UNITED STATES

Received: January 26, 2015; Accepted: December 11, 2015; Published: January 5, 2016

Copyright: © 2016 Charles H. Pence. This is an open access article distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited

Data Availability: All relevant data are available via Figshare ( https://dx.doi.org/10.6084/m9.figshare.2010117 ).

Funding: Work on RLetters was supported by the National Science Foundation, NSF 215 #SES-1456573, and the National Evolutionary Synthesis Center (NESCent), NSF 216 #EF-0905606.

Competing interests: The authors have declared that no competing interests exist.

Introduction

The journal literature is massive, and grows at an astounding rate. One estimate, for example, puts the size of the Google Scholar index at around 160 million documents [ 1 ]. Depending on the database and measurement method utilized, estimates for its annual growth rate vary, but one research team offers values ranging from 2.2% to 9% [ 2 ]. If the higher numbers are right, then more than ten million articles are published every year, and the entire literature will double in just under a decade. This fact has a variety of implications. First and foremost, it is and will remain exceptionally difficult for individual researchers to keep pace with the rate of publication, even within their own specialties [ 3 ]. Equally troubling, it is exceptionally difficult to perform meta-level studies of the journal literature. If we wish to consult a statistically significant, broad, longitudinal cross-section of publications, it is more and more evident that we simply cannot read enough articles.

With the digitization of the journal literature, however, a new way of answering such questions is beginning to emerge. A substantial fraction of the journal literature has now been digitized, numbering in the hundreds of millions of pages. This opens up opportunities to use automated tools to examine at a broad scale how research happens. Unfortunately, however, there is a lack of tools for studying this corpus of texts in a rigorous yet user-friendly way.

In order to address the lack of tools for automated analysis of the journal literature, we have created RLetters, a general-purpose, web-based tool optimized for the analysis of journal articles in plain text. RLetters may be deployed by any user desiring to host their own searchable and analyzable archive of journal articles, and requires only a web server running Ruby on Rails and an Apache Solr server, each of which is relatively simple to deploy. RLetters comes with scripts which can automate the process of deployment to a new server or virtual machine. A JSON-based API for search is already available, and further interoperability is planned for future versions.

When compared to extant tools for textual analysis, we believe that RLetters offers advantages over most of the solutions now available. Several, including TAPoR Tools [ 4 ] and MONK [ 5 ], require that the user upload texts into the system, making it prohibitive to study a large corpus. Some, such as MONK, require for full capability that the texts be marked up manually in a format like TEI [ 6 ], which, again, is impossible for analysis of a large corpus. Journal article analyses must be performed using unadorned plain text, as this is often the only format available from publishers. Other tools, such as Google’s N-grams viewer [ 7 ] and JSTOR’s Data for Research [ 8 ], have fixed corpora of text against which they are deployed, and thus cannot be readily applied to a user’s preferred area of research. No general-purpose tool presently available is optimized for journal articles—the challenges presented by the analysis of millions of small texts (rather than a much smaller number of much larger texts) are unique and significant. Finally, some current programs (such as SEASR [ 9 ] or TAPoR Tools) require the user to chain together many smaller analysis steps to perform common data analyses, presenting a usability challenge. RLetters resolves each of these concerns.

Text Analysis Methods

A wide variety of analysis methods are implemented in RLetters. The more common analyses are described quickly, and two of RLetters’s more powerful algorithms are discussed in more detail.

It is advised, as with all digital humanities tools, that users interested in working with these analysis methods for publishable work not treat RLetters as a black box. Each of the analysis methods available has its limitations and its advantages, and some varieties of data for which it is suitable and some for which it is not. Users should locate papers or books describing each method (some of which are provided as citations in the following) and investigate these to determine whether or not their analyses provide the sort of insights they require.

Common Analysis Methods

Compute term frequency information..

Users can compute term frequency tables for a given dataset, for either single words or multiple-word phrases (n-grams). These are the most common inputs for other kinds of textual analysis algorithms, meaning that users can easily extract term frequencies and use them to run their own analyses locally if desired. The options and features in the word frequency generator are modeled after those found in KWIC Concordance [ 10 ], modified and expanded to accommodate the needs of some of the other analysis algorithms.

Compute Term Network.

Users can visualize the network of words occurring in the immediate vicinity of a given focal word of interest, an analysis useful for determining which words often “travel together” in the literature [ 11 ].

Extract Proper Names.

All proper names (of persons, locations, organizations, and so forth) found in journal articles can be extracted. This analysis can be useful to detect locations of field research, organizational networks, etc. This analysis uses the Stanford Natural Language Processing toolkit [ 12 ].

Graph by Publication Date.

Users can graph the publication dates of a dataset, which is particularly useful if the dataset contains only those articles which match a complex search.

Export Citations.

Lastly, a dataset can be exported in a variety of citation formats to a user’s citation manager, including EndNote and BibTeX.

Craig Zeta: Compare Word Usage in Two Datasets

Users can request an analysis of words which are likely to mark out an article’s belonging to one dataset as opposed to another. The algorithm implemented here is the Zeta algorithm, as described originally by Burrows [ 13 ] and extended by Craig and Kinney [ 14 ]. This algorithm is aimed at the determination of difference . Consider some corpus, divided into two groups, A and B. The Zeta algorithm tells us which words within A mark a text out as (probably) belonging to A rather than B (and vice versa). Further, the Zeta algorithm has the advantage of providing us with words that are far less likely to appear overall, as opposed to words singled out by traditional T-tests. For example, while a T-test might pick out the more frequent use of a common word like ‘upon’ as a marker of membership in one group rather than another, the Zeta algorithm is more likely to give us much rarer, and hence more meaningful words.

The Zeta algorithm is relatively simple to describe. We take our two groups of texts (A and B) and divide them into chunks of 500 words. For each word w in the corpus, we then simply add the fraction of A chunks in which w appears and the fraction of B chunks in which w does not appear. The maximal Zeta score for a word is thus 2—for words that appear in every chunk of A and in no chunk of B. The minimal score would be 0—for words that appear in no chunk of A and in every chunk of B. Clearly, high-scoring words are a very good indicator of a text’s belonging to the A group, and low-scoring words are a very good indicator of membership in the B group.

The output of the algorithm, therefore, is two collections of “marker words”—words that, if found in a given random sample of text, mark that text out as, respectively, much more likely to be a member of dataset A or a member of dataset B. For an example of how this algorithm can be used in practice, see below.

Cooccurrence and Collocation Analysis

Users can use RLetters to locate both statistically significant immediate pairs of words (known as collocations), or to detect significant correlations between words at the sentence, paragraph, or article level (known as cooccurrences) [ 15 ].

Collocation analysis is commonly used to track technical terms or immediate patterns of word usage. To refer to a common example from linguistics, collocation analysis can detect that “strong tea” is a common locution in English, and that “powerful computers” is as well, but that both “powerful tea” and “strong computers” would be highly unusual, as these two pairs are very rarely found in a standard English corpus. Collocation analysis can also detect technical term usage, such as the strong association of word pairs like “genetic drift” or “statistical mechanics.”

Cooccurrence analysis extends collocation analysis to look for significant correlations within sections of a document of a given size. (Common sizes include 25 words, roughly corresponding to sentence-level cooccurrences, 100 words [paragraph-level], 500 words [section-level], and entire articles.) The significance of cooccurrences can be measured using a variety of traditional statistical measures, including mutual information, T-tests, or log-likelihood. Cooccurrences provide a different sort of information about texts than collocations. Sentence- and paragraph-level cooccurrence provides interesting information about patterns of speech—for instance, that researchers working on human decision-making very often speak of “recognition” and “inference” in the same sentence, but only very rarely connect “recognition” and “aversion” (drawn from an unpublished study in progress).

Implementation

The backend of RLetters consists of an Apache Solr server [ 16 ], used both as a search engine and, effectively, as a single-table database. This server contains document records consisting of metadata about each document (citation data, license information, etc.), and a detailed index of the full text of each article.

The frontend is implemented as a web application in Ruby on Rails. The web application first proceeds by allowing the user to select a research question in which he or she is interested. The next step is to provide data for the analysis—a set of articles of interest, saved as a permanent object called a ‘dataset’. Datasets are constructed by performing complicated searches using the search interface, which allows users to search on a variety of different metadata fields (including author, title, journal, licensing information, etc.) and filter the results by author, journal, and publication date. Once the desired set of articles has been located, it is named and saved for future use in analyses. See Fig 1 for a screenshot of RLetters’ search interface.

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In this screenshot of RLetters’ test database, containing the entirety of PLoS Neglected Tropical Diseases , we see an example page of search results, showing links to filter the data and retrieve more information about each article.

https://doi.org/10.1371/journal.pone.0146004.g001

The dataset is then analyzed using the algorithm appropriate to answering the intended question. Data analyses in RLetters are not interactive, as some are quite computationally expensive. Analysis is thus executed in the background and, upon completion, users are e-mailed and return to the site to retrieve their results. While it is difficult to provide accurate timing for how long a given job will take, users are shown a list of estimated completion times for datasets of various sizes, and can use these as a rough idea of how long their analysis might take to complete. All results produced in RLetters may be viewed online as well as exported, both in open formats (such as CSV spreadsheets) and, in many cases, as visualizations (such as PDF files).

The source code of RLetters can be freely downloaded from its website, http://www.rletters.net , or at its GitHub page, http://github.com/rletters/rletters . It is released as open-source software under the MIT License, and is under active development. Further, it comes with a set of scripts which automate the deployment of an RLetters instance to any RHEL 7 or CentOS 7 server.

A demonstration instance of RLetters may be found at https://demo.rletters.net , containing a variety of full-text articles from various PLoS journals. The demonstration database contains 13,555 articles and the full suite of RLetters analysis tools.

Input Data and Copyright

As with most textual analysis endeavors, the hardest problem with performing large-scale analysis of the journal literature is obtaining rights to the full text of journal articles. RLetters takes as its input a set of XML files containing the full text of articles as well as metadata about them, including authors, title, journal, DOI, and licensing information. With Open Access articles, such as those published in the PLoS journals or those available as part of the PubMed Central Open Access Subset [ 17 ], simple transformations from the freely available NLM XML/JATS format [ 18 ] allow for rapid data input into RLetters (see the collection of scripts at http://github.com/cpence/evotext ).

For closed access content, RLetters has the ability to store only the metadata for an article and a link to its full text as stored on an external server. When an analysis of a closed-access article is requested that requires the full plain text of an article, it is fetched, analyzed, and destroyed when the analysis completes. Future versions of RLetters will include support for interfacing with the text-mining APIs of journal publishers, such as that provided by Elsevier [ 19 ].

To demonstrate the sort of analysis that RLetters can perform, an example analysis was run using the RLetters Demo, available publicly at https://demo.rletters.net . As mentioned above, this installation contains some thirteen thousand articles from PLoS journals, taken from their freely available NLM XML-format full text. These data are available under the CC-BY license. The server was run using the latest version of RLetters (v2.0.0).

Our example considers the following question. Imagine a researcher with a moderately-computational article, attempting to decide whether to submit his or her article to PLoS Biology or PLoS Computational Biology . The publication guidelines for PLoS Computational Biology state that it seeks manuscripts which “further our understanding of living systems at all scales—from molecules and cells, to patient populations and ecosystems –through the application of computational methods.” But how might a researcher be more certain that his or her article is “computational enough” to qualify? Here, the Zeta algorithm can help. Applying Zeta to the corpora of PLoS Computational Biology and PLoS Biology can give us lists of marker words—and if a sufficient number of the Computational Biology marker words appear in our researcher’s paper, he or she can make a much more educated guess about the appropriate home for the manuscript in question.

To get these sets of marker words, two datasets were constructed—one containing all articles from PLoS Biology and one containing all articles from PLoS Computational Biology . The marker lists were generated, and RLetters also created word clouds for each set of marker words, included as Fig 2A and 2B . If a random article contains words from the right-hand word cloud, that is, it is likely to be within the scope of PLoS Computational Biology , while if it contains words in the left-hand cloud, it is likely to belong better at PLoS Biology .

The fifty words (scaled according to strength of inference) that let us infer that a randomly selected manuscript likely belongs in PLoS Biology rather than in PLoS Computational Biology (A) and vice versa (B). Word clouds generated by RLetters.

https://doi.org/10.1371/journal.pone.0146004.g002

The marker words show a few expected trends, and a few surprises. On the unsurprising side, Computational Biology papers are more likely to discuss “models,” “simulations,” “parameters,” “value(s),” “networks,” and “dynamics.” Biology papers, on the other hand, are more likely to discuss “animals,” “mice,” “genes,” and the details of experimental protocol, such as “staining,” “antibodies,” “PCR,” “GFP,” and tissue sections measured in “ μm .”

More interestingly, however, we see changing patterns of usage in the way that experiments themselves and their results are discussed. In Biology articles, for example, “experiments” are discussed, while in Computational Biology articles, “experimental” is the common usage. A follow-up collocation analysis indicates that some of the most frequent pairs including “experimental” are “experimental data,” “results,” “observations,” “evidence,” and “studies.” Further, Biology articles are more tentative about their results—the words “suggesting,” “indicated,” and “whether” are all markers for PLoS Biology .

This example, therefore, points to more than a simple difference in focus between PLoS Biology and PLoS Computational Biology . The very language used to discuss the experimental data that these articles invoke is profoundly different. Consulting these lists of marker words should, in fact, make it very clear which disciplinary idiom a given article uses.

There is a demonstrated need for usable applications in text analysis of the journal literature, and RLetters helps to fill this need. We provide here a full-featured web application that can be deployed by anyone wishing to analyze a corpus of journal articles. Our example demonstrates that even in relatively straightforward applications, this level of textual analysis can offer interesting and useful insights into the journal literature that provide tangible benefits to researchers.

Acknowledgments

Many thanks to Grant Ramsey, my collaborator on the evoText project, which uses RLetters as its backend. Thanks as well to David L. Hoover, who introduced me to many of these algorithms at the Digital Humanities Summer Institute (DHSI) 2012, and to the editor and two anonymous referees at this journal for helpful comments. Work on RLetters was supported by the National Science Foundation, NSF #SES-1456573, and the National Evolutionary Synthesis Center (NESCent), NSF #EF-0905606.

Author Contributions

Conceived and designed the experiments: CHP. Performed the experiments: CHP. Analyzed the data: CHP. Wrote the paper: CHP. Wrote the software: CHP.

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• 6. Ide N, Véronis J, editors (1995) Text encoding initiative: background and context. Dordrecht: Kluwer.
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• 9. Ashton AT (2011) Semantically rich tools for text exploration: TEI and SEASR. In: Digital Humanities 2011. Stanford, CA, pp. 270–271.
• 10. Tsukamoto S (2002) KWIC Concordance for Windows: easy access to corpora. In: Saito T, Nakamura J, Yamazaki S, editors, Language and computers, English corpus linguistics in Japan, Amsterdam: Rodopi. pp. 327–340.
• 12. Manning CD, Surdeanu M, Bauer J, Finkel J, Bethard SJ, et al. (2014) The Stanford CoreNLP Natural Language Processing Toolkit. In: Proceedings of 52nd Annual Meeting of the Association for Computational Linguistics: System Demonstrations. Baltimore, MD: Association for Computational Linguistics, pp. 55–60.
• 14. Craig H, Kinney AF (2009) Shakespare, computers, and the mystery of authorship. Cambridge: Cambridge University Press.
• 15. Manning CD, Schütze H (1999) Foundations of statistical natural language processing. Cambridge, MA: The MIT Press.
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36 Online Research Tools for Students

For many students, the most challenging part about writing a research paper is the research. Even the best students often don’t know how to conduct research or even where to start.

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This article by Custom-writing.org experts provides a list of great research tools that will be useful at every stage of the process. The collection includes everything necessary to write a great paper, from online public libraries to dissertation databases. There are also some data analysis and data visualization research tools, as well as organizers for scholars. The list includes brief descriptions for each of the tools. All you need is to continue reading, choose the tools you like most, and get a fantastic result!

• 👣 Research Steps
• 💡 Topic Generating Tools
• Research Databases
• Digital Libraries
• Discipline-Oriented Libraries
• Dissertation Databases
• 🔬 Data Analysis Tools
• 📈 Data Visualization Tools
• 📑 Organizers for Scholars

1. 👣 Doing Research: Key Steps

Regardless of the subject field, all research has a similar structure. Such an approach facilitates the mutual understanding of scientists from neighboring or even distant domains. As a rule, scientific texts are challenging to write and read. That is why you need to observe the following procedure.

• Topic selection. Surprisingly, this is the most creative part of a research project. The subject area shall be topical and relevant, and the title must be concise and informative.
• Literature review and concept development . To write something new, you need to know what has already been written by other scientists. Study the available literature on your subject and define what statement or concept you will defend in your research.
• Empirical part: data collection and analysis. Accumulate the evidence to support your thesis statement .
• Conclusions and recommendations. Any research finishes with generalizations of the findings. You can as well give general suggestions for your successors in research. At this step, online summarizer would be a highly effective tool.

The following sections provide you with tools and techniques to facilitate each of the four stages. There is also a list of tools helping to organize the entire research procedure.

2. 💡 Topic Generating Tools

In science and academia, nobody receives a ready-made topic to work on. As a rule, you are given a direction in which you should look for an unexplored field of knowledge. With this direction in mind, you can brainstorm a compelling topic that would be engaging. There are multiple tools to make the task an easy one.

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3. 📚 Research Tools for Making a Literature Review

3.1. research databases.

So, you have created a word document and noted the title. What next? You should look for the most authoritative works in the required sphere. How do you know which ones are the most influential? There are online research tools that create lists of the most cited scientific articles.

3.2. Digital Libraries

Once you have found enough references, you need to study them. Visiting conventional libraries is often a waste of time since many contemporary research documents are accessible on the web. Digital libraries are usually paid web research tools, but many universities and colleges purchase a subscription for their students.

3.3. Discipline-Oriented Libraries

If you are working in a narrow scientific field, multidisciplinary libraries may not meet your expectations and needs. Besides, if various disciplines discuss your research question, the search for references becomes a daunting task. Then you should explore discipline-oriented libraries. They function just like any other digital library but provide access to works in only one area of knowledge.

3.4. Dissertation Databases

Ph.D. theses are usually written by young scientists. They are interested in being cited as much as possible, as it raises their researcher’s status. For this reason, top universities allow free access to Masters’ and Ph.D. papers written by their students. You can use these databases in your research.

4. 🔬 Research Tools for Data Analysis

Data analysis is an essential part of any empirical research. It requires discipline-specific skills and knowledge of research instruments. Below you can find just a small share of data analysis tools available online or downloadable for most operating systems.

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5. 📈 Data Visualization Tools

When your research findings are ready, the worst thing you can do is pour all the statistical data on your future readers. Visualization of all those percentages, ratios, and correlations makes your paper engaging and easy to follow. Respect your reader’s time and try not to turn your research paper into a quiz.

6. 📑 Tools to Organize Your Research Process

Good organization is something needed on every research step. Below is a list of the most useful organizational tools for scholars.

Most people work with the software they are used to, ignoring the new and more functional alternatives. It is often rewarding to invest your time into exploring a new tool than to research and write your thesis in the same old way. Share your opinion about the described instruments in the comments and suggest your favorite ones!

🔗 References

• Tools for Researchers: Augusta University
• Research Tools: Smithsonian Libraries
• Useful Research Tools: Oregon tech Libraries
• Tools for Researchers: University at Albany
• Conducting Research: WUSTL Libraries
• Organizing a Research Project: Duke University
• 15 Steps to Good Research: Georgetown University Library
• WRITING A RESEARCH PAPER: UW-MAdison Writing Center
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DOI: 10.14569/IJACSA.2015.060930 PDF

Online Paper Review Analysis

Author 1: Doaa Mohey El-Din Author 2: Hoda M.O. Mokhtar Author 3: Osama Ismael

International Journal of Advanced Computer Science and Applications(IJACSA), Volume 6 Issue 9, 2015.

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Abstract: Sentiment analysis or opinion mining is used to automate the detection of subjective information such as opinions, attitudes, emotions, and feelings. Hundreds of thousands care about scientific research and take a long time to select suitable papers for their research. Online reviews on papers are the essential source to help them. The reviews save reading time and save papers cost. This paper proposes a new technique to analyze online reviews. It is called sentiment analysis of online papers (SAOOP). SAOOP is a new technique used for enhancing bag-of-words model, improving the accuracy and performance. SAOOP is useful in increasing the understanding rate of review's sentences through higher language coverage cases. SAOOP introduces solutions for some sentiment analysis challenges and uses them to achieve higher accuracy. This paper also presents a measure of topic domain attributes, which provides a ranking of total judging on each text review for assessing and comparing results across different sentiment techniques for a given text review. Finally, showing the efficiency of the proposed approach by comparing the proposed technique with two sentiment analysis techniques. The comparison terms are based on measuring accuracy, performance and understanding rate of sentences.

Doaa Mohey El-Din, Hoda M.O. Mokhtar and Osama Ismael, “Online Paper Review Analysis” International Journal of Advanced Computer Science and Applications(IJACSA), 6(9), 2015. http://dx.doi.org/10.14569/IJACSA.2015.060930

@article{El-Din2015, title = {Online Paper Review Analysis}, journal = {International Journal of Advanced Computer Science and Applications}, doi = {10.14569/IJACSA.2015.060930}, url = {http://dx.doi.org/10.14569/IJACSA.2015.060930}, year = {2015}, publisher = {The Science and Information Organization}, volume = {6}, number = {9}, author = {Doaa Mohey El-Din and Hoda M.O. Mokhtar and Osama Ismael} }

Copyright Statement: This is an open access article licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, even commercially as long as the original work is properly cited.

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Impact of industrial robots on environmental pollution: evidence from China

• Yanfang Liu 1  

Scientific Reports volume  13 , Article number:  20769 ( 2023 ) Cite this article

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• Environmental sciences
• Environmental social sciences

The application of industrial robots is considered a significant factor affecting environmental pollution. Selecting industrial wastewater discharge, industrial SO 2 emissions and industrial soot emissions as the evaluation indicators of environmental pollution, this paper uses the panel data model and mediation effect model to empirically examine the impact of industrial robots on environmental pollution and its mechanisms. The conclusions are as follows: (1) Industrial robots can significantly reduce environmental pollution. (2) Industrial robots can reduce environmental pollution by improving the level of green technology innovation and optimizing the structure of employment skills. (3) With the increase in emissions of industrial wastewater, industrial SO 2 , and industrial dust, the impacts generated by industrial robots are exhibiting trends of a “W” shape, gradual intensification, and progressive weakening. (4) Regarding regional heterogeneity, industrial robots in the eastern region have the greatest negative impact on environmental pollution, followed by the central region, and the western region has the least negative impact on environmental pollution. Regarding time heterogeneity, the emission reduction effect of industrial robots after 2013 is greater than that before 2013. Based on the above conclusions, this paper suggests that the Chinese government and enterprises should increase investment in the robot industry. Using industrial robots to drive innovation in green technology and optimize employment skill structures, reducing environmental pollution.

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Introduction

Since the reform and opening up, China’s rapid economic growth has created a world-renowned “economic growth miracle” 1 . With the rapid economic growth, China’s environmental pollution problem is becoming more and more serious 2 . According to the “ Global Environmental Performance Index Report ” released by Yale University in the United States in 2022, China’s environmental performance index scores 28.4 points, ranking 160th out of 180 participating countries. The aggravation of environmental pollution not only affects residents’ health 3 , but also affects the efficiency of economic operation 4 . According to calculation of the General Administration of Environmental Protection, the World Bank and the Chinese Academy of Sciences, China’s annual losses caused by environmental pollution account for about 10% of GDP. Exploring the factors that affect environmental pollution and seeking ways to reduce environmental pollution are conducive to the development of economy within the scope of environment.

Industrial robots are machines that can be automatically controlled, repeatedly programmed, and multi-purpose 5 . They replace the low-skilled labor force engaged in procedural work 6 , reducing the raw materials required for manual operation. Industrial robots improve the clean technology level and energy efficiency of coal combustion, reducing pollutant emissions in front-end production. Industrial robots also monitor the energy consumption and sewage discharge in the production process in real time. The excessive discharge behavior of enterprises in the production process is regulated, reducing the emission of pollutants in the end treatment. Based on the selection and coding of literature (Appendix A ), this paper uses the meta-analysis method to compare the impacts of multiple factors such as economics, population, technology, and policy on environmental pollution. As shown in Table 1 , compared to other factors, industrial robots demonstrate greater advantages in reducing environmental pollution. There is a lack of research on the relationship between industrial robots and environmental pollution in China. With the advent of artificial intelligence era, China’s industrial robot industry has developed rapidly. According to data released by the International Federation of Robotics (IFR), from 1999 to 2019, China’s industrial robot ownership and installation shows an increasing trend year by year (Fig.  1 ). In 2013 and 2016, China’s industrial robot installation (36,560) and ownership (349,470) exceeds Japan for the first time, becoming the world’s largest country in terms of installation and ownership of industrial robots. Whether the application of industrial robots in China contributes to the reduction of environmental pollution? What is the mechanism of the impact of China’s industrial robots on environmental pollution? Researching this issue is crucial for filling the gaps in existing research and providing a reference for other countries to achieve emission reduction driven by robots.

Industrial robot installations in the world’s top five industrial robot markets from 1999 to 2019.

Based on the above analysis, this paper innovatively incorporates industrial robots and environmental pollution into a unified framework. Based on the panel data of 30 provinces in China from 2006 to 2019, this paper uses the ordinary panel model and mediating effect model to empirically test the impact of industrial robots on China’s environmental pollution and its transmission channels. The panel quantile model is used to empirically analyze the heterogeneous impact of industrial robots on environmental pollution under different environmental pollution levels.

Literature review

A large number of scholars have begun to study the problem of environmental pollution. Its research content mainly includes two aspects: The measurement of environmental pollution and its influencing factors. Regarding the measurement, some scholars have used SO 2 emissions 7 , industrial soot emissions 8 and PM2.5 concentration 9 and other single indicators to measure the degree of environmental pollution. The single indicator cannot fully and scientifically reflect the degree of environmental pollution. To make up for this defect, some scholars have included industrial SO 2 emissions, industrial wastewater discharge and industrial soot emissions into the environmental pollution evaluation system, and used the entropy method to measure environmental pollution level 10 . This method ignores the different characteristics and temporal and spatial trends of different pollutants, which makes the analysis one-sided. Regarding the influencing factors, economic factors such as economic development level 11 , foreign direct investment 12 and income 13 , population factors such as population size 14 and urbanization level 15 , energy consumption 16 all have an impact on environmental pollution. Specifically, economic development and technological innovation can effectively reduce environmental pollution 17 . The expansion of population size can aggravate environmental pollution. Income inequality can reduce environmental pollution, but higher income inequality may aggravate environmental pollution 18 . There are “pollution heaven hypothesis” and “pollution halo hypothesis” between foreign direct investment and environmental pollution 19 . Technological factors also have a non-negligible impact on environmental pollution 20 .

With continuous deepening of research, scholars have begun to focus on the impact of automation technology, especially industrial robot technology, on the environment. Ghobakhloo et al. 21 theoretically analyzed the impact of industrial robots on energy sustainability, contending that the application of industrial robots could foster sustainable development of energy. Using data from multiple countries, a few scholars have empirically analyzed the effect of industrial robots on environmental pollution (Table 2 ). Luan et al. 22 used panel data from 73 countries between 1993 and 2019 to empirically analyze the impact of industrial robots on air pollution, finding that the use of industrial robots intensifies environmental pollution. Using panel data from 66 countries from 1993 to 2018, Wang et al. 23 analyzed the impact of industrial robots on carbon intensity and found that industrial robots can reduce carbon intensity. On the basis of analyzing the overall impact of industrial robots on environmental pollution, some scholars conducted in-depth exploration of its mechanism. Based on data from 72 countries between 1993 and 2019, Chen et al. 5 explored the impact of industrial robots on the ecological footprint, discovering that industrial robots can reduce the ecological footprint through time saving effect, green employment effect and energy upgrading effect. Using panel data from 35 countries between 1993 and 2017, Li et al. 24 empirically examined the carbon emission reduction effect of industrial robots, finding that industrial robots can effectively reduce carbon emissions by increasing green total factor productivity and reducing energy intensity. Although the above studies have successfully estimated the overall impact of industrial robots on environmental pollution and its mechanisms, they have not fully considered the role of technological progress, labor structure and other factors in the relationship between the two. These studies all chose data from multiple countries as research samples and lack research on the relationship between industrial robots and environmental pollution in China, an emerging country.

The above literature provides inspiration for this study, but there are still shortcomings in the following aspects: Firstly, there is a lack of research on the relationship between industrial robots and environmental pollution in emerging countries. There are significant differences between emerging and developed countries in terms of institutional background and the degree of environmental pollution. As a representative emerging country, research on the relationship between industrial robots and environmental pollution in China can provide reliable references for other emerging countries. Secondly, theoretically, the study of the impact of industrial robots on environmental pollution is still in its initial stage. There are few studies that deeply explore its impact mechanism, and there is a lack of analysis of the role of technological progress and labor structure in the relationship between the two.

The innovations of this paper are as follows: (1) In terms of sample selection, this paper selects panel data from 30 provinces in China from 2006 to 2019 as research samples to explore the relationship between industrial robots and environmental pollution in China, providing references for other emerging countries to improve environmental quality using industrial robots. (2) In terms of theory, this paper is not limited to revealing the superficial relationship between industrial robots and environmental pollution. it starts from a new perspective and provides an in-depth analysis of how industrial robots affect environmental pollution through employment skill structure and green technology innovation. This not only enriches research in the fields of industrial robots and the environment, but is also of great significance in guiding the direction of industrial policy and technology research and development.

Theoretical analysis and hypothesis

Industrial robots and environmental pollution.

As shown in Fig.  2 , the impact of industrial robots on environmental pollution is mainly reflected in two aspects: Front-end production and end treatment. In front-end production, industrial robots enable artificial substitution effects 25 . Manual operation is replaced by machine operation, reducing the raw materials needed for manual operation. Through the specific program setting of industrial robots, clean energy is applied to industrial production 26 . The use of traditional fuels such as coal and oil is reduced. In terms of end treatment, the traditional pollutant concentration tester only measures a single type of pollutant. Its data cannot be obtained in time. It is easy to cause pollution incidents. Industrial robots can measure a variety of pollutants, and have the function of remote unmanned operation and warning. It reflects the pollution situation in time, reducing the probability of pollution incidents. The use of robots can upgrade sewage treatment equipment and improve the accuracy of pollution treatment, reducing pollutant emissions. Based on the above analysis, this paper proposes hypothesis 1.

The impact of industrial robots on environmental pollution.

Hypothesis 1

The use of industrial robots can reduce environmental pollution.

Mediating effect of green technology innovation

Industrial robots can affect environmental pollution by promoting green technology innovation. The transmission path of “industrial robots-green technology innovation-environmental pollution” is formed. Industrial robots are the materialization of technological progress in the field of enterprise R&D. Its impact on green technology innovation is mainly manifested in the following two aspects: Firstly, industrial robots classify known knowledge, which helps enterprises to integrate internal and external knowledge 27 . The development of green technology innovation activities of enterprises is promoted. Secondly, enterprises can simulate existing green technologies through industrial robots. The shortcomings of green technology in each link are found. Based on this, enterprises can improve and perfect green technology in a targeted manner. Industrial robots can collect and organize data, which enables enterprises to predict production costs and raw material consumption. Excessive procurement by enterprises can occupy working capital. Inventory backlog leads to warehousing, logistics and other expenses, increasing storage costs 28 . Forecasting the consumption of raw materials allows enterprises to purchase precisely, preventing over-procurement and inventory backlog, thereby reducing the use of working capital and storage costs 29 . The production cost of enterprises is reduced. Enterprises have more funds for green technology research and development.

The continuous innovation of green technology is helpful to solve the problem of environmental pollution. Firstly, green technology innovation helps use resources better 30 , lowers dependence on old energy, and reduces environmental damage. Secondly, green technology innovation promotes the greening of enterprises in manufacturing, sales and after-sales 31 . The emission of pollutants in production process is reduced. Finally, green technology innovation improves the advantages of enterprises in market competition 32 . The production possibility curve expands outward, which encourages enterprises to carry out intensive production. Based on the above analysis, this paper proposes hypothesis 2.

Hypothesis 2

Industrial robots can reduce environmental pollution through green technology innovation.

Mediating effect of employment skill structure

Industrial robots can affect environmental pollution through employment skill structure. The transmission path of “industrial robots-employment skill structure-environmental pollution” is formed. Industrial robots have substitution effect and creation effect on the labor force, improving the employment skill structure. Regarding the substitution effect, enterprises use industrial robots to complete simple and repetitive tasks to improve production efficiency, which crowds out low-skilled labor 6 . Regarding the creation effect, industrial robots create a demand for new job roles that matches automation, such as robot engineers, data analysts, machine repairers, which increases the number of highly skilled labor 33 . The reduction of low-skilled labor and increase of high-skilled labor improve employment skill structure.

High-skilled labor is reflected in the level of education 34 . Its essence is to have a higher level of skills and environmental awareness, which is the key to reducing environmental pollution. Compared with low-skilled labor, high-skilled labor has stronger ability to acquire knowledge and understand skills, which improves the efficiency of cleaning equipment and promotes emission reduction. The interaction and communication between highly skilled labor is also crucial for emission reduction. The excessive wage gap between employees brings high communication costs, which hinders the exchange of knowledge and technology between different employees. The increase in the proportion of high-skilled labor can solve this problem and improve the production efficiency of enterprises 35 . The improvement of production efficiency enables more investment in emission reduction research, decreasing pollutant emissions. Based on the above analysis, this paper proposes hypothesis 3.

Hypothesis 3

Industrial robots can reduce environmental pollution by optimizing employment skills structure.

Model construction and variable selection

Model construction, panel data model.

The panel data model is a significant statistical method, first introduced by Mundlak 36 . Subsequently, numerous scholars have used this model to examine the baseline relationships between core explanatory variables and explained variables 37 . To test the impact of industrial robots on environmental pollution, this paper sets the following panel data model:

In formula ( 1 ), Y it is the explained variable, indicating the degree of environmental pollution in region i in year t . IR it is the core explanatory variable, indicating the installation density of industrial robots in region i in year t . X it is a series of control variables, including economic development level (GDP), urbanization level (URB), industrial structure (EC), government intervention (GOV) and environmental regulation (ER). \(\lambda i\) is the regional factor. \(\varphi t\) is the time factor. \(\varepsilon it\) is the disturbance term.

Mediating effect model

To test the transmission mechanism of industrial robots affecting environmental pollution, this paper sets the following mediating effect model:

In formula ( 2 ), M is the mediating variable, which mainly includes green technology innovation and employment skill structure. Formula ( 2 ) measures the impact of industrial robots on mediating variables. Formula ( 3 ) measures the impact of intermediary variables on environmental pollution. According to the principle of mediating effect 38 , the direct effect \(\theta 1\) , mediating effect \(\beta 1 \times \theta 2\) and total effect \(\alpha 1\) satisfy \(\alpha 1 = \theta 1 + \beta 1 \times \theta 2\) .

Panel quantile model

The panel quantile model was first proposed by Koenke and Bassett 39 . It is mainly used to analyze the impact of core explanatory variables on the explained variables under different quantiles 40 . To empirically test the heterogeneous impact of industrial robots on environmental pollution under different levels of environmental pollution, this paper sets the following panel quantile model:

In formula ( 4 ), \(\tau\) represents the quantile value. \(\gamma 1\) reflects the difference in the impact of industrial robots on environmental pollution at different quantiles. \(\gamma 2\) indicates the different effects of control variables at different quantiles.

Variable selection

Explained variable.

The explained variable is environmental pollution. Considering the timeliness and availability of data, this paper selects industrial wastewater discharge, industrial SO 2 emissions and industrial soot emissions as indicators of environmental pollution.

Explanatory variable

According to production theory, industrial robots can enhance production efficiency 41 . Efficient production implies reduced energy wastage, which in turn decreases the emission of pollutants. Industrial robots can upgrade pollution control equipment, heightening the precision in pollution treatment and reducing pollutant discharge. Referring to Acemoglu and Restrepo 25 , this paper selects the installation density of industrial robots as a measure. The specific formula is as follows:

In formula ( 5 ), Labor ji is the number of labor force in industry j in region i . IR jt is the stock of industrial robot use in industry j in the year t .

Mediating variable

Green technology innovation. Industrial robots can increase the demand for highly-skilled labor 42 , subsequently influencing green technology innovation. Compared to ordinary labor, highly-skilled labor possesses a richer knowledge base and technological learning capability, improving the level of green technology innovation. Green technology innovation can improve energy efficiency 43 , reducing pollution generated by energy consumption. The measurement methods of green technology innovation mainly include three kinds: The first method is to use simple technology invention patents as measurement indicators. Some of technical invention patents are not applied to the production process of enterprise, they cannot fully reflect the level of technological innovation. The second method is to use green product innovation and green process innovation as measurement indicators. The third method is to use the number of green patent applications or authorizations as a measure 44 . This paper selects the number of green patent applications as a measure of green technology innovation.

Employment skill structure. The use of industrial robots reduces the demand for labor performing simple repetitive tasks and increases the need for engineers, technicians, and other specialized skilled personnel, improving the employment skill structure 45 . Compared to ordinary workers, highly-skilled laborers typically have a stronger environmental awareness 46 . Such environmental consciousness may influence corporate decisions, prompting companies to adopt eco-friendly production methods, thus reducing environmental pollution. There are two main methods to measure the structure of employment skills: One is to use the proportion of employees with college degree or above in the total number of employees as a measure. The other is to use the proportion of researchers as a measure. The educational level can better reflect the skill differences of workers. This paper uses the first method to measure the employment skill structure.

Control variable

Economic development level. According to the EKC hypothesis 47 , in the initial stage of economic development, economic development mainly depends on input of production factors, which aggravates environmental pollution. With the continuous development of economy, people begin to put forward higher requirements for environmental quality. The government also begins to adopt more stringent policies to control environmental pollution, which can reduce the level of environmental pollution. According to Liu and Lin 48 , This paper uses per capita GDP to measure economic development level.

Urbanization level. The improvement of urbanization level has both positive and negative effects on pollution. Urbanization can improve the agglomeration effect of cities. The improvement of agglomeration effect can not only promote the sharing of public resources such as infrastructure, health care, but also facilitate the centralized treatment of pollution. The efficiency of environmental governance is improved 49 . The acceleration of urbanization can increase the demand for housing, home appliances and private cars, which increases pollutant emissions 50 . This paper uses the proportion of urban population to total population to measure the level of urbanization.

Industrial structure. Industrial structure is one of the key factors that determine the quality of a country’s environmental conditions 51 . The increase in the proportion of capital and technology-intensive industries can effectively improve resource utilization efficiency and improve resource waste 52 . This paper selects the ratio of the added value of the tertiary industry to the secondary industry to measure industrial structure.

Government intervention. Government intervention mainly affects environmental pollution from the following two aspects: Firstly, the government can give high-tech, energy-saving and consumption-reducing enterprises relevant preferential policies, which promotes the development of emission reduction technologies for these enterprises 53 . Secondly, the government strengthens environmental regulation by increasing investment in environmental law enforcement funds, thus forcing enterprises to save energy and reduce emissions 54 . This paper selects the proportion of government expenditure in GDP to measure government intervention.

Environmental regulation. The investment in environmental pollution control is conducive to the development of clean and environmental protection technology, optimizing the process flow and improving the green production efficiency of enterprises 55 . Pollutant emissions are reduced. This paper selects the proportion of investment in pollution control to GDP to measure environmental regulation.

Data sources and descriptive statistics

This paper selects the panel data of 30 provinces in China from 2006 to 2019 as the research sample. Among them, the installation data of industrial robots are derived from International Federation of Robotics (IFR). The data of labor force and employees with college degree or above are from China Labor Statistics Yearbook . Other data are from the China Statistical Yearbook . The descriptive statistics of variables are shown in Table 3 . Considering the breadth of application and the reliability of analysis capabilities, this paper uses Stata 16 for regression analysis.

Results analysis

Spatial and temporal characteristics of environmental pollution and industrial robots in china, environmental pollution.

Figure  3 a shows the overall trend of average industrial wastewater discharge in China from 2006 to 2019. From 2006 to 2019, the discharge of industrial wastewater shows a fluctuating downward trend, mainly due to the improvement of wastewater treatment facilities and the improvement of treatment capacity. Figure  3 b shows the changing trend of average industrial wastewater discharge in 30 provinces of China from 2006 to 2019. Industrial wastewater discharge in most provinces has declined. There are also some provinces such as Fujian, Guizhou and Qinghai, which have increased industrial wastewater discharge. Their emission reduction task is very arduous.

Industrial wastewater discharge from 2006 to 2019.

Figure  4 a shows the overall trend of average industrial SO 2 emissions in China from 2006 to 2019. From 2006 to 2019, industrial SO 2 emissions shows a fluctuating downward trend, indicating that air pollution control and supervision are effective. Figure  4 b shows the trend of average industrial SO 2 emissions in 30 provinces of China from 2006 to 2019. Similar to industrial wastewater, industrial SO 2 emissions decrease in most provinces.

Industrial SO 2 emissions from 2006 to 2019.

Figure  5 a shows the overall trend of average industrial soot emissions in China from 2006 to 2019. Different from industrial wastewater and industrial SO 2 , the emission of industrial soot is increasing year by year. From the perspective of governance investment structure, compared with industrial wastewater and industrial SO 2 , the investment proportion of industrial soot is low. From the perspective of source, industrial soot mainly comes from urban operation, industrial manufacturing and so on. The acceleration of urbanization and the expansion of manufacturing scale have led to an increase in industrial soot emissions. Figure  5 b shows the trend of industrial soot emissions in 30 provinces in China from 2006 to 2019. The industrial soot emissions in most provinces have increased.

Industrial soot emissions from 2006 to 2019.

Figure  6 shows the spatial distribution characteristics of industrial wastewater, industrial SO 2 and industrial soot emissions. The three types of pollutant emissions in the central region are the largest, followed by the eastern region, and the three types of pollutant emissions in the western region are the smallest. Due to resource conditions and geographical location, the central region is mainly dominated by heavy industry. The extensive development model of high input and consumption makes its pollutant emissions higher than the eastern and western regions. The eastern region is mainly capital-intensive and technology-intensive industries, which makes its pollutant emissions lower than the central region. Although the leading industry in the western region is heavy industry, its factory production and transportation scale are not large, which produces less pollutants.

Spatial distribution characteristics of industrial wastewater, industrial SO 2 and industrial soot.

Industrial robots

Figure  7 a shows the overall trend of installation density of industrial robots in China from 2006 to 2019. From 2006 to 2019, the installation density of industrial robots in China shows an increasing trend year by year. The increase of labor cost and the decrease of industrial robot cost make enterprises use more industrial robots, which has a substitution effect on labor force. The installation density of industrial robots is increased. Figure  7 b shows the trend of installation density of industrial robots in 30 provinces of China from 2006 to 2019. The installation density of industrial robots in most provinces has increased. Among them, the installation density of industrial robots in Guangdong Province has the largest growth rate. The installation density of industrial robots in Heilongjiang Province has the smallest growth rate.

Installation density of industrial robots from 2006 to 2019.

Figure  8 shows the spatial distribution characteristics of installation density of industrial robots. The installation density of industrial robots in the eastern region is the largest, followed by the central region, and the installation density of industrial robots in the western region is the smallest. The eastern region is economically developed and attracts lots of talents to gather here, which provides talent support for the development of industrial robots. Advanced technology also leads to the rapid development of industrial robots in the eastern region. The economy of western region is backward, which inhibits the development of industrial robots.

Spatial distribution characteristics of industrial robots.

Benchmark regression results

Table 4 reports the estimation results of the ordinary panel model. Among them, the F test and LM test show that the mixed OLS model should not be used. The Hausman test shows that the fixed effect model should be selected in the fixed effect model and random effect model. This paper selects the estimation results of the fixed effect model to explain.

Regarding the core explanatory variable, industrial robots have a significant negative impact on the emissions of industrial wastewater, industrial SO 2 and industrial soot. Specifically, industrial robots have the greatest negative impact on industrial soot emissions, with a coefficient of -0.277 and passing the 1% significance level. The negative impact of industrial robots on industrial wastewater discharge is second, with an estimated coefficient of -0.242, which also passes the 1% significance level. The negative impact of industrial robots on industrial SO 2 emissions is the smallest, with an estimated coefficient of -0.0875 and passing the 10% significant level. Compared with industrial wastewater and SO 2 , industrial robots have some unique advantages in reducing industrial soot emissions. Firstly, in terms of emission sources, industrial soot emissions mainly come from physical processes such as cutting. These processes can be significantly improved through precise control of industrial robots. Industrial SO 2 comes from the combustion process. Industrial wastewater originates from various industrial processes. It is difficult for industrial robots to directly control these processes. Secondly, in terms of source control and terminal treatment, industrial robots can reduce excessive processing and waste of raw materials, thereby controlling industrial soot emissions at the source. For industrial SO 2 and industrial wastewater, industrial robots mainly play a role in terminal treatment. Since the terminal treatment of industrial SO 2 and industrial wastewater often involves complex chemical treatment processes, it is difficult for industrial robot technology to fully participate in these processes. This makes the impact of industrial robots in the field of industrial SO 2 and industrial wastewater more limited than that in the field of industrial soot.

Regarding the control variables, the level of economic development has a significant inhibitory effect on industrial SO 2 emissions. The higher the level of economic development, the stronger the residents’ awareness of environmental protection, which constrains the pollution behavior of enterprises. The government also adopts strict policies to control pollutant emissions. The impact of urbanization level on the discharge of industrial wastewater, industrial SO 2 and industrial soot is significantly negative. The improvement of urbanization level can improve the efficiency of resource sharing and the centralized treatment of pollutants, reducing environmental pollution. The industrial structure significantly reduces industrial SO 2 and industrial soot emissions. The upgrading of industrial structure not only reduces the demand for energy, but also improves the efficiency of resource utilization. The degree of government intervention only significantly reduces the discharge of industrial wastewater. The possible reason is that to promote economic development, the government invests more money in high-yield areas, which crowds out investment in the environmental field. Similar to the degree of government intervention, environmental regulation has a negative impact on industrial wastewater discharge. The government’s environmental governance investment has not given some support to the enterprise’s clean technology research, which makes the pollution control investment not produce good emission reduction effect.

Mediation effect regression results

Green technology innovation.

Table 5 reports the results of intermediary effect model when green technology innovation is used as an intermediary variable. Industrial robots can have a positive impact on green technology innovation. For every 1% increase in the installation density of industrial robots, the level of green technology innovation increases by 0.722%. After adding the green technology innovation, the estimated coefficient of industrial robots has decreased, which shows that the intermediary variable is effective.

In the impact of industrial robots on industrial wastewater discharge, the mediating effect of green technology innovation accounts for 8.17% of the total effect. In the impact of industrial robots on industrial SO 2 emissions, the mediating effect of green technology innovation accounts for 11.8% of the total effect. In the impact of industrial robots on industrial soot emissions, the mediating effect of green technology innovation accounts for 3.72% of the total effect.

Employment skill structure

Table 6 reports the results of intermediary effect model when the employment skill structure is used as an intermediary variable. Industrial robots have a positive impact on the employment skill structure. For every 1% increase in the installation density of industrial robots, the employment skill structure is improved by 0.0837%. Similar to green technology innovation, the intermediary variable of employment skill structure is also effective.

In the impact of industrial robots on industrial wastewater discharge, the mediating effect of employment skill structure accounts for 6.67% of the total effect. In the impact of industrial robots on industrial SO 2 emissions, the mediating effect of employment skill structure accounts for 20.66% of the total effect. In the impact of industrial robots on industrial soot emissions, the mediating effect of employment skill structure accounts for 15.53% of the total effect.

Robustness test and endogeneity problem

Robustness test.

To ensure the robustness of the regression results, this paper tests the robustness by replacing core explanatory variables, shrinking tail and replacing sample. Regarding the replacement of core explanatory variables, in the benchmark regression, the installation density of industrial robots is measured by the stock of industrial robots. Replacing the industrial robot stock with the industrial robot installation quantity, this paper re-measures the industrial robot installation density. Regarding the tail reduction processing, this paper reduces the extreme outliers of all variables in the upper and lower 1% to eliminate the influence of extreme outliers. Regarding the replacement of samples, this paper removes the four municipalities from the sample. The estimation results are shown in Table 7 . Industrial robots still have a significant negative impact on environmental pollution, which confirms the robustness of benchmark regression results.

Endogeneity problem

Logically speaking, although the use of industrial robots can reduce environmental pollution, there may be reverse causality. Enterprises may increase the use of industrial robots to meet emission reduction standards, which increases the use of industrial robots in a region. Due to the existence of reverse causality, there is an endogenous problem that cannot be ignored between industrial robots and environmental pollution.

To solve the impact of endogenous problems on the estimation results, this paper uses the instrumental variable method to estimate. According to the selection criteria of instrumental variables, this paper selects the installation density of industrial robots in the United States as the instrumental variable. The trend of the installation density of industrial robots in the United States during the sample period is similar to that of China, which is consistent with the correlation characteristics of instrumental variables. The application of industrial robots in the United States is rarely affected by China’s economic and social factors, and cannot affect China’s environmental pollution, which is in line with the exogenous characteristics of instrumental variables.

Table 8 reports the estimation results of instrumental variable method. Among them, the column (1) is listed as the first stage regression result. The estimated coefficient of instrumental variable is significantly positive, which is consistent with the correlation. Column (2), column (3) and column (4) of Table 8 are the second stage regression results of industrial wastewater, industrial SO 2 and industrial soot emissions as explanatory variables. The estimated coefficients of industrial robots are significantly negative, which again verifies the hypothesis that industrial robots can reduce environmental pollution. Compared with Table 4 , the absolute value of estimated coefficient of industrial robots is reduced, which indicates that the endogenous problems caused by industrial robots overestimate the emission reduction effect of industrial robots. The test results prove the validity of the instrumental variables.

Panel quantile regression results

Traditional panel data models might obscure the differential impacts of industrial robots at specific pollution levels. To address this issue, this paper uses a panel quantile regression model to empirically analyze the effects of industrial robots across different environmental pollution levels.

Table 9 shows that industrial robots have a negative impact on industrial wastewater discharge. With the increase of the quantile of industrial wastewater discharge, the regression coefficient of industrial robots shows a W-shaped change. Specifically, when the industrial wastewater discharge is in the 0.1 quantile, the regression coefficient of industrial robot is − 0.229, and it passes the 1% significant level. When the industrial wastewater discharge is in the 0.25 quantile, the impact of industrial robots on industrial wastewater discharge is gradually enhanced. Its regression coefficient decreases from − 0.229 to − 0.256. When the industrial wastewater discharge is in the 0.5 quantile, the regression coefficient of industrial robot increases from − 0.256 to − 0.152. When the industrial wastewater discharge is at the 0.75 quantile, the regression coefficient of industrial robot decreases from − 0.152 to − 0.211. When the industrial wastewater discharge is in the 0.9 quantile, the regression coefficient of industrial robot increases from − 0.211 to − 0.188. For every 1% increase in the installation density of industrial robots, the discharge of industrial wastewater is reduced by 0.188%.

When industrial wastewater discharge is at a low percentile, the use of industrial robots can replace traditional production methods, reducing energy waste and wastewater discharge. As industrial wastewater discharge increases, the production process becomes more complex. Industrial robots may be involved in high-pollution, high-emission productions, diminishing the robots’ emission-reducing effects. When industrial wastewater discharge reaches high levels, pressured enterprises seek environmentally friendly production methods and use eco-friendly industrial robots to reduce wastewater discharge. As wastewater discharge continues to rise, enterprises tend to prioritize production efficiency over emission control, weakening the negative impact of industrial robots on wastewater discharge. When wastewater discharge is at a high percentile, enterprises should balance production efficiency and environmental protection needs, by introducing eco-friendly industrial robots to reduce wastewater discharge.

Table 10 shows that with the increase of industrial SO 2 emission quantile level, the negative impact of industrial robots on industrial SO 2 emissions gradually increases. Specifically, when industrial SO 2 emissions are below 0.5 quantile, the impact of industrial robots on industrial SO 2 emissions is not significant. When the industrial SO 2 emissions are above 0.5 quantile, the negative impact of industrial robots on industrial SO 2 emissions gradually appears.

When industrial SO 2 emissions are at a low percentile, the application of industrial robots primarily aims to enhance production efficiency, not to reduce SO 2 emissions. Enterprises should invest in the development of eco-friendly industrial robots, ensuring they are readily available for deployment when a reduction in industrial SO 2 emissions is necessary. As industrial SO 2 emissions continue to rise, both the government and the public pay increasing attention to the issue of SO 2 emissions. To meet stringent environmental standards, enterprises begin to use industrial robots to optimize the production process, reduce reliance on sulfur fuels, and consequently decrease SO 2 emissions. Enterprises should regularly evaluate the emission reduction effectiveness of industrial robots, using the assessment data to upgrade and modify the robots’ emission reduction technologies.

Table 11 shows that with the increase of industrial soot emissions quantile level, the negative impact of industrial robots on industrial soot emissions gradually weakens. Specifically, when industrial soot emissions are below 0.75 quantile, industrial robots have a significant negative impact on industrial soot emissions. This negative effect decreases with the increase of industrial soot emissions. When the industrial soot emissions are above 0.75 quantile, the negative impact of industrial robots on industrial soot emissions gradually disappears.

When industrial soot emissions are at a low percentile, they come from a few sources easily managed by industrial robots. As industrial soot emissions increase, the sources become more diverse and complex, making it harder for industrial robots to control. Even with growing environmental awareness, it may take time to effectively use robots in high-emission production processes and control industrial soot emissions. Enterprises should focus on researching how to better integrate industrial robot technology with production processes that have high soot emission levels. The government should provide financial and technical support to enterprises, assisting them in using industrial robots more effectively for emission reduction.

Figure  9 intuitively reflects the trend of the regression coefficient of industrial robots with the changes of industrial wastewater, industrial SO 2 and industrial soot emissions. Figure  9 a shows that with the increase of industrial wastewater discharge, the regression coefficient of industrial robots shows a W-shaped trend. Figure  9 b shows that with the increase of industrial SO 2 emissions, the regression coefficient of industrial robots gradually decreases. The negative impact of industrial robots on industrial SO 2 emissions is gradually increasing. Figure  9 c shows that with the increase of industrial soot emissions, the regression coefficient of industrial robots shows a gradual increasing trend. The negative impact of industrial robots on industrial soot emissions has gradually weakened. Figure  9 a, b and c confirm the estimation results of Tables 9 , 10 and 11 .

Change of quantile regression coefficient.

Heterogeneity analysis

Regional heterogeneity.

This paper divides China into three regions: Eastern, central and western regions according to geographical location. The estimated results are shown in Table 12 . The industrial robots in eastern region have the greatest negative impact on three pollutants, followed by central region, and the industrial robots in western region have the least negative impact on three pollutants. The use of industrial robots in eastern region far exceeds that in central and western regions. The eastern region is far more than central and western regions in terms of human capital, technological innovation and financial support. Compared with central and western regions, the artificial substitution effect, upgrading of sewage treatment equipment and improvement of energy utilization efficiency brought by industrial robots in eastern region are more obvious.

Time heterogeneity

The development of industrial robots is closely related to policy support 56 . In 2013, the Ministry of Industry and Information Technology issued the “ Guiding Opinions on Promoting the Development of Industrial Robot Industry ”. This document proposes: By 2020, 3 to 5 internationally competitive leading enterprises and 8 to 10 supporting industrial clusters are cultivated. In terms of high-end robots, domestic robots account for about 45% of the market share, which provides policy support for the development of industrial robots. Based on this, this paper divides the total sample into two periods: 2006–2012 and 2013–2019, and analyzes the heterogeneous impact of industrial robots on environmental pollution in different periods. The estimation results are shown in Table 13 . Compared with 2006–2012, the emission reduction effect of industrial robots during 2013–2019 is greater.

The use of industrial robots can effectively reduce environmental pollution, which is consistent with hypothesis 1. This is contrary to the findings of Luan et al. 22 , who believed that the use of industrial robots would exacerbate air pollution. The inconsistency in research conclusions may be due to differences in research focus, sample size, and maturity of industrial robot technology. In terms of research focus, this paper mainly focuses on the role of industrial robots in reducing pollutant emissions during industrial production processes. Their research focuses more on the energy consumption caused by the production and use of industrial robots, which could aggravate environmental pollution. In terms of sample size, the sample size of this paper is 30 provinces in China from 2006 to 2019. These regions share consistency in economic development, industrial policies and environmental regulations. Their sample size is 74 countries from 1993 to 2019. These countries cover different geographical, economic and industrial development stages, affecting the combined effect of robots on environmental pollution. In terms of the maturity of industrial robots, the maturity of industrial robot technology has undergone tremendous changes from 1993 to 2019. In the early stages, industrial robot technology was immature, which might cause environmental pollution. In recent years, industrial robot technology has gradually matured, and its operating characteristics have become environmentally friendly. Their impact on environmental pollution has gradually improved. This paper mainly conducts research on the mature stage of industrial robot technology. Their research covers the transition period from immature to mature industrial robot technology. The primary reason that the use of industrial robots can reduce environmental pollution is: The use of industrial robots has a substitution effect on labor force, which reduces the raw materials needed for manual operation. For example, in the industrial spraying of manufacturing industry, the spraying robot can improve the spraying quality and material utilization rate, thereby reducing the waste of raw materials by manual operation. Zhang et al. 57 argued that energy consumption has been the primary source of environmental pollution. Coal is the main energy in China, and the proportion of clean energy is low 58 . In 2022, clean energy such as natural gas, hydropower, wind power and solar power in China accounts for only 25.9% of the total energy consumption, which can cause serious environmental pollution problems. Industrial robots can promote the use of clean energy in industrial production and the upgrading of energy structure 24 . The reduction of raw materials and the upgrading of energy structure can control pollutant emissions in front-end production. On September 1, 2021, the World Economic Forum (WEF) released the report “ Using Artificial Intelligence to Accelerate Energy Transformation ”. The report points out that industrial robots can upgrade pollution monitoring equipment and sewage equipment, which reduces pollutant emissions in end-of-pipe treatment. Ye et al. 59 also share the same viewpoint.

The use of industrial robots can reduce environmental pollution through green technology innovation, which is consistent with hypothesis 2. Industrial robots promote the integration of knowledge, which helps enterprises to carry out green technology innovation activities. Meanwhile, Jung et al. 60 suggested that industrial robots can lower production costs for companies, allowing them to invest in green technology research. The level of green technology innovation is improved. Green technology innovation reduces environmental pollution through the following three aspects: Firstly, the improvement of energy utilization efficiency. China’s utilization efficiency of traditional energy sources such as coal is not high. The report of “ 2013-Global Energy Industry Efficiency Research ” points out that China’s energy utilization rate is only ranked 74th in the world in 2013. Low energy efficiency brings serious environmental pollution problems 61 . Du et al. 62 found that the innovation of green technologies, such as clean coal, can enhance energy efficiency and decrease environmental pollution. Secondly, the production of green products. Green technology innovation accelerates the green and recyclable process of production, thereby reducing the pollutants generated in production process. Thirdly, the improvement of enterprise competitive advantage. Green technology innovation can enable enterprises to gain greater competitive advantage in green development 63 . The supply of environmentally friendly products increases, which not only meets the green consumption needs of consumers, but also reduces the emission of pollutants.

Industrial robots can reduce environmental pollution by optimizing the structure of employment skills, which is consistent with hypothesis 3. Autor et al. 64 contended that industrial robots would replace conventional manual labor positions, reducing the demand for low-skilled labor. Industrial robots represent the development of numerical intelligence. With the continuous development of digital intelligence, the demand for high-skilled labor in enterprises has increased. Koch et al. 65 demonstrated that the use of industrial robots in Spanish manufacturing firms leads to an increase in the number of skilled workers. In February 2020, the Ministry of Human Resources and Social Security, the State Administration of Market Supervision and the National Bureau of Statistics jointly issues 16 new professions such as intelligent manufacturing engineering and technical personnel, industrial Internet engineering and technical personnel, and virtual reality engineering and technical personnel to the society. These new occupations increase the demand for highly skilled labor. The reduction of low-skilled labor and increase of high-skilled labor optimize the structure of employment skills. The optimization of employment skill structure narrows the wage gap between employees, reducing the communication cost of employees. Employees learn and exchange technology with each other, which not only improves the absorption capacity of clean technology. It also improves the production efficiency of enterprises and increases corporate profits, so that enterprises can use more funds for clean technology research and development, thereby reducing environmental pollution.

Conclusions and policy recommendations

Based on the panel data of 30 provinces in China from 2006 to 2019, this paper uses the panel data model and mediating effect model to empirically test the impact of industrial robots on environmental pollution and its transmission mechanism. This paper uses panel quantile model, regional samples and time samples to further analyze the heterogeneous impact of industrial robots on environmental pollution. The conclusions are as follows: (1) Industrial robots can significantly reduce environmental pollution. For every 1% increase in industrial robots, the emissions of industrial wastewater, industrial SO 2 , and industrial dust and smoke decrease by − 0.242%, − 0.0875%, and − 0.277%. This finding is contrary to that of Luan et al. 22 , who argued that the use of industrial robots exacerbates air pollution. The results of this paper provide a contrasting perspective, highlighting the potential value of industrial robots in mitigating environmental pollution. (2) Industrial robots can reduce environmental pollution by improving green technology innovation level and optimizing employment skills structure. In the impact of industrial robots on industrial wastewater discharge, the mediating effect of green technology innovation accounts for 8.17% of total effect. The mediating effect of employment skill structure accounts for 6.67% of total effect. In the impact of industrial robots on industrial SO 2 emissions, the mediating effect of green technology innovation accounts for 11.8% of total effect. The mediating effect of employment skill structure accounts for 20.66% of total effect. In the impact of industrial robots on industrial soot emissions, the mediating effect of green technology innovation accounts for 3.72% of total effect. The mediating effect of employment skill structure accounts for 15.53% of total effect. While Obobisa et al. 66 and Zhang et al. 67 highlighted the role of green technological innovation in addressing environmental pollution. Chiacchio et al. 68 and Dekle 69 focused on the effects of industrial robots on employment. The mediating impact of technology and employment in the context of robots affecting pollution hasn’t been addressed. Our research provides the first in-depth exploration of this crucial intersection. (3) Under different environmental pollution levels, the impact of industrial robots on environmental pollution is different. Among them, with the increase of industrial wastewater discharge, the impact of industrial robots on industrial wastewater discharge shows a “W-shaped” change. With the increase of industrial SO 2 emissions, the negative impact of industrial robots on industrial SO 2 emissions is gradually increasing. On the contrary, with the increase of industrial soot emissions, the negative impact of industrial robots on industrial soot emissions gradually weakens. (4) Industrial robots in different regions and different periods have heterogeneous effects on environmental pollution. Regarding regional heterogeneity, industrial robots in eastern region have the greatest negative impact on environmental pollution, followed by central region, and western region has the least negative impact on environmental pollution. Regarding time heterogeneity, the negative impact of industrial robots on environmental pollution in 2013–2019 is greater than that in 2006–2012. Chen et al. 5 and Li et al. 24 both examined the overarching impact of industrial robots on environmental pollution. They did not consider the varying effects of robots on pollution across different regions and time periods. Breaking away from the limitations of previous holistic approaches, our study offers scholars a deeper understanding of the diverse environmental effects of industrial robots.

According to the above research conclusions, this paper believes that the government and enterprises can promote emission reduction through industrial robots from the following aspects.

Increase the scale of investment in robot industry and promote the development of robot industry. China’s industrial robot ownership ranks first in the world. Its industrial robot installation density is lower than that of developed countries such as the United States, Japan and South Korea. The Chinese government should give some financial support to robot industry and promote the development of robot industry, so as to effectively reduce environmental pollution. The R&D investment of industrial robots should be increased so that they can play a full role in reducing raw material consumption, improving energy efficiency and sewage treatment capacity.

Give full play to the role of industrial robots in promoting green technology innovation. Industrial robots can reduce environmental pollution through green technology innovation. The role of industrial robots in innovation should be highly valued. The advantages of knowledge integration and data processing of industrial robots should be fully utilized. Meanwhile, the government should support high-polluting enterprises that do not have industrial robots from the aspects of capital, talents and technology, so as to open up the channels for these enterprises to develop and improve clean technology by using industrial robots.

Give full play to the role of industrial robots in optimizing employment skills structure. The use of industrial robots can create jobs with higher skill requirements and increase the demand for highly skilled talents. China is relatively short of talents in the field of emerging technologies. The education department should actively build disciplines related to industrial robots to provide talent support for high-skilled positions. Enterprises can also improve the skill level of the existing labor force through on-the-job training and job competition.

Data availability

The datasets used or analyzed during the current study are available from Yanfang Liu on reasonable request.

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Liu, Y. Impact of industrial robots on environmental pollution: evidence from China. Sci Rep 13 , 20769 (2023). https://doi.org/10.1038/s41598-023-47380-6

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In the U.S. and around the world, inflation is high and getting higher

Two years ago, with millions of people out of work and central bankers and politicians striving to lift the U.S. economy out of a pandemic-induced recession , inflation seemed like an afterthought. A year later, with unemployment falling and the inflation rate rising, many of those same policymakers insisted that the price hikes were “transitory” – a consequence of snarled supply chains, labor shortages and other issues that would right themselves sooner rather than later.

Now, with the inflation rate higher than it’s been since the early 1980s, Biden administration officials acknowledge that they  missed their call . According to the latest report from the Bureau of Labor Statistics, the annual inflation rate in May was 8.6%, its highest level since 1981, as measured by the consumer price index . Other  inflation metrics  also have shown significant increases over the past year or so, though not quite to the same extent as the CPI.

With inflation in the United States running at its highest levels in some four decades, Pew Research Center decided to compare the U.S. experience with those of other countries, especially its peers in the developed world. An earlier version of this post was published in November 2021.

The Center relied primarily on data from the Organization for Economic Cooperation and Development (OECD), most of whose 38 member states are highly developed democracies. The OECD collects a  wide range of data  about its members, facilitating cross-national comparisons. We chose to use quarterly inflation measures, both because they’re less volatile than monthly figures and because they were available for all but one OECD country (Costa Rica, which joined the OECD in May 2021). Quarterly inflation data also were available for seven non-OECD countries with sizable national economies, so we included them in the analysis as well.

For each country, we calculated year-over-year inflation rates going back to the first quarter of 2010 and ending in the first quarter of this year. We also calculated how much those rates had risen or fallen since the start of the COVID-19 pandemic in the first quarter of 2020.

To get a sense of longer-term inflation trends in the U.S., we analyzed two measures besides the commonly cited consumer price index: The  Consumer Price Index Retroactive Series  (R-CPI-U-RS) from the Bureau of Labor Statistics, and the  Personal Consumption Expenditures Price Index  from the Bureau of Economic Analysis.

Inflation in the United States was relatively low for so long that, for entire generations of Americans, rapid price hikes may have seemed like a relic of the distant past. Between the start of 1991 and the end of 2019, year-over-year inflation averaged about 2.3% a month, and exceeded 5.0% only four times. Today, Americans rate inflation as the  nation’s top problem , and President Joe Biden has said addressing the problem is his top domestic priority .

But the U.S. is  hardly the only place  where people are experiencing inflationary whiplash. A Pew Research Center analysis of data from 44 advanced economies finds that, in nearly all of them, consumer prices have risen substantially since pre-pandemic times.

In 37 of these 44 nations, the average annual inflation rate in the first quarter of this year was at least twice what it was in the first quarter of 2020, as COVID-19 was beginning its deadly spread. In 16 countries, first-quarter inflation was more than four times the level of two years prior. (For this analysis, we used data from the Organization for Economic Cooperation and Development, a group of mostly highly developed, democratic countries. The data covers 37 of the 38 OECD member nations, plus seven other economically significant countries.)

Among the countries studied, Turkey had by far the highest inflation rate in the first quarter of 2022: an eye-opening 54.8%. Turkey has experienced high inflation for years, but it shot up in late 2021 as the government pursued  unorthodox economic policies , such as cutting interest rates rather than raising them.

The country where inflation has grown  fastest  over the past two years is Israel. The annual inflation rate in Israel had been below 2.0% (and not infrequently negative) every quarter from the start of 2012 through mid-2021; in the first quarter of 2020, the rate was 0.13%. But after a relatively mild recession , Israel’s consumer price index began rising quickly: It averaged 3.36% in the first quarter of this year, more than 25 times the inflation rate in the same period in 2020.

Besides Israel, other countries with very large increases in inflation between 2020 and 2022 include Italy, which saw a nearly twentyfold increase in the first quarter of 2022 compared with two years earlier (from 0.29% to 5.67%); Switzerland, which went from ‑0.13% in the first quarter of 2020 to 2.06% in the same period of this year; and Greece, a country that knows something about economic turbulence . Following the Greek economy’s near-meltdown in the mid-2010s, the country experienced several years of low inflation – including more than one bout of deflation, the last starting during the first spring and summer of the pandemic. Since then, however, prices have rocketed upward: The annual inflation rate in Greece reached 7.44% in this year’s first quarter – nearly 21 times what it was two years earlier (0.36%).

Annual U.S. inflation in the first quarter of this year averaged just below 8.0% – the 13th-highest rate among the 44 countries examined. The first-quarter inflation rate in the U.S. was almost four times its level in 2020’s first quarter.

Regardless of the absolute level of inflation in each country, most show variations on the same basic pattern: relatively low levels before the  COVID-19 pandemic  struck in the first quarter of 2020; flat or falling rates for the rest of that year and into 2021, as many governments sharply curtailed most economic activity; and rising rates starting in mid- to late 2021, as the world struggled to get back to something approaching normal.

But there are exceptions to that general dip-and-surge pattern. In Russia, for instance, inflation rates rose steadily throughout the pandemic period before surging in the wake of its invasion of Ukraine . In Indonesia, inflation fell early in the pandemic and has remained at low levels. Japan has continued its years-long struggle with inflation rates that are too  low . And in Saudi Arabia, the pattern was reversed: The inflation rate surged  during  the pandemic but then fell sharply in late 2021; it’s risen a bit since, but still is just 1.6%.

Inflation doesn’t appear to be done with the developed world just yet. An  interim report  from the OECD found that April’s inflation rate ran ahead of March’s figure in 32 of the group’s 38 member countries.

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ABOUT PEW RESEARCH CENTER  Pew Research Center is a nonpartisan fact tank that informs the public about the issues, attitudes and trends shaping the world. It conducts public opinion polling, demographic research, media content analysis and other empirical social science research. Pew Research Center does not take policy positions. It is a subsidiary of  The Pew Charitable Trusts .

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