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Number of significant figures to put in a table?

Is there a well founded rule for the number of significant figures to publish?

Here are some specific examples / questions:

Is there any way to relate the number of significant figures to the coefficient of variation? For example, if the estimate is 12.3 and the CV is 50%, does that mean that the information represented by '.3' approaches zero?

If a confidence interval has a range of orders of magnitude, should they still have the same number of significant figures, e.g.:

12.3 (1.2, 123.4) vs 12 (1.2, 120)

Should the number of significant figures in an error estimate be the same or less than the number of significant figures in a mean?

• $\begingroup$ If you can, don't use a table :) A graphic is, IMO, almost always easier to read than a table (he obvious exception being if you don't have many numbers). Journals and their reviewers don't always agree, unfortunately.... $\endgroup$ –  JMS Commented Mar 24, 2011 at 20:26
• 3 $\begingroup$ @JMS Good point, but Tables are useful for summarizing detailed characteristics of statistical units (cross-classified by a factor interest, e.g. clinical diagnosis or whatever), with variables of different types (continuous, nominal, and ordinal), and other results derived from statistical modeling per se (confusion matrix, regression coef. etc.) that won't fit into Figures (or not always if you think of Gelman's approach for showing reg. coef. as dotcharts). We need both; the question is when do we really need a Figure instead of a Table, IMO. $\endgroup$ –  chl Commented Mar 24, 2011 at 22:37
• $\begingroup$ @chi Fair. I did say almost always :). Things like big n-way tables are impossible to (completely) reproduce graphically. It depends on the forum I'd say. Tables have the benefit of being complete, sure, but does your reader actually absorb all that extra information? If there are too many parameters to fit in a graph, I'd contend that a table is often at least difficult to read. However, I do think complete results should be accessible (online, appendix, etc) if for nothing else but reproducibility. In that case I'd also like data & code though! Wandered OT, sorry.. $\endgroup$ –  JMS Commented Mar 25, 2011 at 3:57
• $\begingroup$ Also I think regression coefficients and confusion (correlation, covariance, ...) matrices are usually better suited to a graphical display, dotplots or similar for the former and heatmaps or graphs for the latter. $\endgroup$ –  JMS Commented Mar 25, 2011 at 4:00
• $\begingroup$ @JMS I agree with your point, but in this case there is a figure limit, some other cases there are figure charges. Also, in this case if readers glance over the table and focus on the figures that are presented, then they won't waste time trying to figure out the point of an esoteric figure. But I fully support reproducibility, and while I am at it, I could (if I get around to it) add a visualization of the table to the code that is attached. $\endgroup$ –  David LeBauer Commented Mar 25, 2011 at 5:40

2 Answers 2

I doubt there's a universal rule so I'm not going to make any up. I can share these thoughts and the reasons behind them:

When summaries reflect the data themselves--max, min, order statistics, etc.-- use the same number of significant figures used to record the data in the first place. This provides a consistent representation throughout the document concerning the precision of the data.

When summaries have higher precision than the data, write the values in a way that reflects that extra precision . For instance, a mean of $n$ values has $\sqrt{n}$ times the precision of the individual values: roughly, include one extra significant figure for $3 \le n \le 30$, two for $30 \lt n \le 300$, etc. (This is rounding on a log-10 scale, obviously.)

-Note that the CV does not provide useful information in this regard.

-Some estimates can be obtained with great precision. They don't have to be rounded to match something else. For instance, the mean of 1,000,000 integers might be 10.977 with a standard error of 0.00301. My decision to write the mean to three decimal places (and 4-5 sig figs) was based on the order of magnitude of the SE, which indicates the last digit is partially reliable. The decision to write the SE to three sig figs (five decimal places) is more arbitrary: two sig figs would work; one probably would not; four sig figs would also work and be consistent with the 4-5 sig figs in the mean; more than four sig figs would be overkill. (One could estimate the standard error of the SE itself in terms of the fourth moment of the data, and use that to determine an appropriate amount of rounding, but most of us don't go to such trouble...)

Signal the reader when you are doing substantial rounding . Be especially careful when the report is discussing the statistical test itself . The reason is that people may use your work to check their own calculations. Sometimes even a slight difference can reveal an error. You don't want to cause trouble because you rounded 123 to 120 and someone else, checking the work, obtains 123 and suspects one of you has erred.

Be consistent . You might lose some readers if you list a value as 123 at one point and later reference it as 120.

Don't be ridiculous . (I automatically suspect incompetence when I encounter reports that give statistical results to 15 sig figs when the data have only two sig figs, for instance.)

• 3 $\begingroup$ My very big +1 because it is really a lot of good advices. In the same vein, I like to show to students that it is really pointless to summarize data gathered from surveys (or votes) as % with a lot of decimals without considering sample size (which impacts standard error). $\endgroup$ –  chl Commented Mar 24, 2011 at 22:46

I'd suggest 12 (1.2, 123.4). Omit the .3 since it's nearly meaningless, but many people when they see (1.2, 120) will assume that the last '0' in 120 is significant.

• $\begingroup$ Why do you suggest to omit a decimal for the statistic of interest if you agree to show them in the CIs (i.e., if it's meaningless for 12, why does it make sense for 123.4)? $\endgroup$ –  chl Commented Mar 24, 2011 at 22:50
• $\begingroup$ @chl: it doesn't make much sense, but omitting it might be misleading. If I put in 123.4, someone like you will see the extra digits and just disregard them, no harm done. If I put in 120, many readers will think this is accurate to 3 digits - bad. $\endgroup$ –  AVB Commented Mar 24, 2011 at 23:59
• $\begingroup$ still not clear why you recommend 123.4 instead of 123 (why omit .3 but not .4 in the example?) $\endgroup$ –  David LeBauer Commented Mar 26, 2011 at 23:24

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Significant Figures Rules and Uncertainty 3

Significant figures express the uncertainty of a measurement or number. All measurements have some degree of uncertainty in their value. This is inherent in measuring tools and variations between people taking measurements.

For example, you are in a chemistry lab and need 8 mL of liquid in a beaker. You could just pour water straight into the beaker and quit when you think you hit 8 mL. The error of this measurement is mostly due to your skill. You could use a beaker with markings every 5 mL and get pretty close, give or take a couple mL. You could use a graduated cylinder with markings every tenth of a mL and get measurements between 7.9 and 8.1 mL. Here we see how the uncertainty can be affected by the measuring tool.

Significant Figure Rules

Significant figures express uncertainty or precision. The more significant figures in a measurement, the more precise the measurement. There are six basic rules dealing with significant figures.

• Non-zero digits are always significant.
• All zeros between other significant digits are significant.
• The most significant figure, also called most significant digit, is the leftmost non-zero digit. For example: in the number 0.00321, the most significant figure is the 3.
• The least significant figure, or least significant digit is the rightmost digit. In the number 54.321, the least significant figure is 1. Keep in mind, zero can be the least significant digit. For example, the zero in 4.320 is the least significant figure.
• Any zero digit to the right of the decimal point and at the end of the number is significant. For example 2 has one significant digit, but 2.0 has two significant figures. As another example, in the number 0.002, none of the zeros are significant. Some of them are to the right of the decimal point, but not at the end of the number.
• If no decimal point is present, the rightmost non-zero digit is the least significant figure.
• An exact number has an infinite number of significant digits.

Quick Tip to Calculate Significant Figures Write the number in scientific notation . The numbers ahead of the multiplier are all significant.

Example: How many significant figures are in the following numbers? a) 23,000 b) 0.000504 c) 240.05 d) 4.000

Write each number in scientific notation. a) 2.3 x 10 3 b)5.04 x 10 -4 c) 2.4005 x10 2 d) 4.000 x 10 1

Now count the digits ahead of the multiplier to get the number of significant figures. a) 2 significant figures b) 3 significant figures c) 5 significant figures d) 4 significant figures

Significant Figures and Uncertainty in Calculations

Once you have your measurement, you may use it in a calculation. In a calculation, the uncertainty of the result is determined by the uncertainty of the measurements.

• Addition and Subtraction

In addition and subtraction, the uncertainty is determined by the uncertainty of the least precise measurement, not by the number of significant figures. Example: Add the following three measurements: 24.21 cm, 5.005 cm and 22 cm. If you add them up, you get 51.215 m. The least precise measurement is the 22 cm measurement, so the answer should have the same precision. The value of the calculation would be reported as 51 m.

• Multiplication and Division

In multiplication and division, the number of significant figures in the result with be the same as the number with the smallest number of significant figures. Example: Divide 35.105 grams by 35 mL. If you just divide the two numbers, you get 1.003 g/mL. The value you would report depends on the measurement with the least significant figures. The first measurement has 5 significant and the second has only 2 significant figures. The reported value would then be 1.0 g/mL

• Losing Significant Figures

Significant figures can be ‘lost’ in a calculation. For example, if you have a beaker that weighs 75.206 grams and you add water until the weight is 75.844. The water would weigh the difference between these two values. 75.844 g – 75.206 g = 0.638 g The final result only has 3 significant figures when both measurements had 5 significant figures.

• Exact Numbers

Occasionally, a calculation involves a number with an exact value rather than an approximation. This occurs in calculations using conversion factors, pure numbers or physical constants. The significant figures of these numbers do not affect the end result. For example, if you were to find the average of 10.3 cm, 12.7 cm and 14.5 cm, you would add the three numbers together to get 37.5 cm. You would then divide this by 3 to get the average or 12.5 cm. Even though 3 only has one significant figure, your answer is still 12.5 cm.

The use and rules of significant figures in science and engineering is standard in any field. Measuring is a basic skill in science and everyone needs to work under the same rules. It is best to learn them early and keep them in mind in all your work.

Significant Figures Worksheets

Practice working with significant figures using worksheets:

• Addition and subtraction worksheet [ PDF Worksheet ] [ Answer Key ]
• Multiplication and division worksheet [ PDF Worksheet ] [ Answer Key ]
• Significant figures in decimals and scientific notation [ PDF Worksheet ] [ Answer Key ]

More About Measurements

Learn more about significant figures and measurements:

• What is the Difference Between Accuracy and Precision?
• Why Use 4 Significant Figures for Avogadro’s Number?
• Significant Figures Periodic Table

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• Dissertation
• How to Write a Results Section | Tips & Examples

How to Write a Results Section | Tips & Examples

Published on August 30, 2022 by Tegan George . Revised on July 18, 2023.

A results section is where you report the main findings of the data collection and analysis you conducted for your thesis or dissertation . You should report all relevant results concisely and objectively, in a logical order. Don’t include subjective interpretations of why you found these results or what they mean—any evaluation should be saved for the discussion section .

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Table of contents

How to write a results section, reporting quantitative research results, reporting qualitative research results, results vs. discussion vs. conclusion, checklist: research results, other interesting articles, frequently asked questions about results sections.

When conducting research, it’s important to report the results of your study prior to discussing your interpretations of it. This gives your reader a clear idea of exactly what you found and keeps the data itself separate from your subjective analysis.

Here are a few best practices:

• Your results should always be written in the past tense.
• While the length of this section depends on how much data you collected and analyzed, it should be written as concisely as possible.
• Only include results that are directly relevant to answering your research questions . Avoid speculative or interpretative words like “appears” or “implies.”
• If you have other results you’d like to include, consider adding them to an appendix or footnotes.
• Always start out with your broadest results first, and then flow into your more granular (but still relevant) ones. Think of it like a shoe store: first discuss the shoes as a whole, then the sneakers, boots, sandals, etc.

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If you conducted quantitative research , you’ll likely be working with the results of some sort of statistical analysis .

Your results section should report the results of any statistical tests you used to compare groups or assess relationships between variables . It should also state whether or not each hypothesis was supported.

The most logical way to structure quantitative results is to frame them around your research questions or hypotheses. For each question or hypothesis, share:

• A reminder of the type of analysis you used (e.g., a two-sample t test or simple linear regression ). A more detailed description of your analysis should go in your methodology section.
• A concise summary of each relevant result, both positive and negative. This can include any relevant descriptive statistics (e.g., means and standard deviations ) as well as inferential statistics (e.g., t scores, degrees of freedom , and p values ). Remember, these numbers are often placed in parentheses.
• A brief statement of how each result relates to the question, or whether the hypothesis was supported. You can briefly mention any results that didn’t fit with your expectations and assumptions, but save any speculation on their meaning or consequences for your discussion  and conclusion.

A note on tables and figures

In quantitative research, it’s often helpful to include visual elements such as graphs, charts, and tables , but only if they are directly relevant to your results. Give these elements clear, descriptive titles and labels so that your reader can easily understand what is being shown. If you want to include any other visual elements that are more tangential in nature, consider adding a figure and table list .

As a rule of thumb:

• Tables are used to communicate exact values, giving a concise overview of various results
• Graphs and charts are used to visualize trends and relationships, giving an at-a-glance illustration of key findings

Don’t forget to also mention any tables and figures you used within the text of your results section. Summarize or elaborate on specific aspects you think your reader should know about rather than merely restating the same numbers already shown.

A two-sample t test was used to test the hypothesis that higher social distance from environmental problems would reduce the intent to donate to environmental organizations, with donation intention (recorded as a score from 1 to 10) as the outcome variable and social distance (categorized as either a low or high level of social distance) as the predictor variable.Social distance was found to be positively correlated with donation intention, t (98) = 12.19, p < .001, with the donation intention of the high social distance group 0.28 points higher, on average, than the low social distance group (see figure 1). This contradicts the initial hypothesis that social distance would decrease donation intention, and in fact suggests a small effect in the opposite direction.

Figure 1: Intention to donate to environmental organizations based on social distance from impact of environmental damage.

In qualitative research , your results might not all be directly related to specific hypotheses. In this case, you can structure your results section around key themes or topics that emerged from your analysis of the data.

For each theme, start with general observations about what the data showed. You can mention:

• Recurring points of agreement or disagreement
• Patterns and trends
• Particularly significant snippets from individual responses

Next, clarify and support these points with direct quotations. Be sure to report any relevant demographic information about participants. Further information (such as full transcripts , if appropriate) can be included in an appendix .

When asked about video games as a form of art, the respondents tended to believe that video games themselves are not an art form, but agreed that creativity is involved in their production. The criteria used to identify artistic video games included design, story, music, and creative teams.One respondent (male, 24) noted a difference in creativity between popular video game genres:

“I think that in role-playing games, there’s more attention to character design, to world design, because the whole story is important and more attention is paid to certain game elements […] so that perhaps you do need bigger teams of creative experts than in an average shooter or something.”

Responses suggest that video game consumers consider some types of games to have more artistic potential than others.

Your results section should objectively report your findings, presenting only brief observations in relation to each question, hypothesis, or theme.

It should not  speculate about the meaning of the results or attempt to answer your main research question . Detailed interpretation of your results is more suitable for your discussion section , while synthesis of your results into an overall answer to your main research question is best left for your conclusion .

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I have completed my data collection and analyzed the results.

I have included all results that are relevant to my research questions.

I have concisely and objectively reported each result, including relevant descriptive statistics and inferential statistics .

I have stated whether each hypothesis was supported or refuted.

I have used tables and figures to illustrate my results where appropriate.

All tables and figures are correctly labelled and referred to in the text.

There is no subjective interpretation or speculation on the meaning of the results.

You've finished writing up your results! Use the other checklists to further improve your thesis.

If you want to know more about AI for academic writing, AI tools, or research bias, make sure to check out some of our other articles with explanations and examples or go directly to our tools!

Research bias

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The results chapter of a thesis or dissertation presents your research results concisely and objectively.

In quantitative research , for each question or hypothesis , state:

• The type of analysis used
• Relevant results in the form of descriptive and inferential statistics
• Whether or not the alternative hypothesis was supported

In qualitative research , for each question or theme, describe:

• Recurring patterns
• Significant or representative individual responses
• Relevant quotations from the data

Don’t interpret or speculate in the results chapter.

Results are usually written in the past tense , because they are describing the outcome of completed actions.

The results chapter or section simply and objectively reports what you found, without speculating on why you found these results. The discussion interprets the meaning of the results, puts them in context, and explains why they matter.

In qualitative research , results and discussion are sometimes combined. But in quantitative research , it’s considered important to separate the objective results from your interpretation of them.

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George, T. (2023, July 18). How to Write a Results Section | Tips & Examples. Scribbr. Retrieved August 26, 2024, from https://www.scribbr.com/dissertation/results/

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• Math Article

Significant Figures

Significant figures are used to establish the number which is presented in the form of digits. These digits carry a meaningful representation of numbers. The term significant digits are also used often instead of figures. We can identify the number of significant digits by counting all the values starting from the 1st non-zero digit located on the left. For example, 12.45 has four significant digits.

The significant figures of a given number are those significant or important digits, which convey the meaning according to its accuracy. For example, 6.658 has four significant digits. These substantial figures provide precision to the numbers. They are also termed as significant digits.

Rules for Significant Figures

• All non-zero digits are significant. 198745 contains six significant digits.
• All zeros that occur between any two non zero digits are significant. For example, 108.0097 contains seven significant digits.
• All zeros that are on the right of a decimal point and also to the left of a non-zero digit is never significant. For example, 0.00798 contained three significant digits.
• All zeros that are on the right of a decimal point are significant, only if, a non-zero digit does not follow them. For example, 20.00 contains four significant digits.
• All the zeros that are on the right of the last non-zero digit, after the decimal point, are significant. For example, 0.0079800 contains five significant digits.
• All the zeros that are on the right of the last non-zero digit are significant if they come from a measurement. For example, 1090 m contains four significant digits.

Rounding Significant Figures

A number is rounded off to the required number of significant digits by leaving one or more digits from the right. When the first digit in left is less than 5, the last digit held should remain constant. When the first digit is greater than 5, the last digit is rounded up. When the digit left is exactly 5, the number held is rounded up or down to receive an even number. When more than one digit is left, rounding off should be done as a whole instead of one digit at a time.

There are two rules to round off the significant numbers:

• First, we have to check, up to which digit the rounding off should be performed. If the number after the rounding off digit is less than 5, then we have to exclude all the numbers present on the right side.
• But if the digit next to the rounding off digit is greater than 5, then we have to add 1 to the rounding off digit and exclude the other numbers on the right side.

Significant Figures Examples

Q.1: Identify the number of significant digits/figures in the following given numbers.

45, 0.046, 7.4220, 5002, 3800

Q.2: Write 12.378162 correct to 4 significant digits.

The number 12.378162, rounded to 4 significant digits is 12.38

Hence, 12.38 is the answer.

Practice Questions

• Determine the number of significant digits from the following given numbers.

          84, 0.084, 5.8480, 2005, 8400

• Solve the following 4.76 + 5.62 + 33.21 and find the number of significant digits/figures.
• Estimate the number of significant digits from the following computations. 5.2 x 10 3 x 6.732 x 10 3
• Write 45.378212 correct to 3 significant digits/figures.

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Significant Figures

It is important to be honest when reporting a measurement, so that it does not appear to be more accurate than the equipment used to make the measurement allows. We can achieve this by controlling the number of digits, or significant figures , used to report the measurement.

Determining the Number of Significant Figures

The number of significant figures in a measurement, such as 2.531, is equal to the number of digits that are known with some degree of confidence (2, 5, and 3) plus the last digit (1), which is an estimate or approximation. As we improve the sensitivity of the equipment used to make a measurement, the number of significant figures increases.

Rules for counting significant figures are summarized below.

Zeros within a number are always significant. Both 4308 and 40.05 contain four significant figures.

Zeros that do nothing but set the decimal point are not significant. Thus, 470,000 has two significant figures.

Trailing zeros that aren't needed to hold the decimal point are significant. For example, 4.00 has three significant figures.

If you are not sure whether a digit is significant, assume that it isn't. For example, if the directions for an experiment read: "Add the sample to 400 mL of water," assume the volume of water is known to one significant figure.

Addition and Subtraction with Significant Figures

When combining measurements with different degrees of accuracy and precision, the accuracy of the final answer can be no greater than the least accurate measurement. This principle can be translated into a simple rule for addition and subtraction: When measurements are added or subtracted, the answer can contain no more decimal places than the least accurate measurement.

Multiplication and Division With Significant Figures

The same principle governs the use of significant figures in multiplication and division: the final result can be no more accurate than the least accurate measurement. In this case, however, we count the significant figures in each measurement, not the number of decimal places: When measurements are multiplied or divided, the answer can contain no more significant figures than the least accurate measurement.

Example: To illustrate this rule, let's calculate the cost of the copper in an old penny that is pure copper. Let's assume that the penny has a mass of 2.531 grams, that it is essentially pure copper, and that the price of copper is 67 cents per pound. We can start by from grams to pounds.

We then use the price of a pound of copper to calculate the cost of the copper metal.

There are four significant figures in both the mass of the penny (2.531) and the number of grams in a pound (453.6). But there are only two significant figures in the price of copper, so the final answer can only have two significant figures.

Rounding Off

When the answer to a calculation contains too many significant figures, it must be rounded off.

There are 10 digits that can occur in the last decimal place in a calculation. One way of rounding off involves underestimating the answer for five of these digits (0, 1, 2, 3, and 4) and overestimating the answer for the other five (5, 6, 7, 8, and 9). This approach to rounding off is summarized as follows.

If the digit is smaller than 5, drop this digit and leave the remaining number unchanged. Thus, 1.684 becomes 1.68.

If the digit is 5 or larger, drop this digit and add 1 to the preceding digit. Thus, 1.247 becomes 1.25.

Units | Errors | Significant Figures | Scientific Notation

Back to General Chemistry Topic Review

What it means when scientists say their results are ‘significant’

Post-Doctoral Researcher, South Australian Health & Medical Research Institute

Disclosure statement

Yazad Irani does not work for, consult, own shares in or receive funding from any company or organisation that would benefit from this article, and has disclosed no relevant affiliations beyond their academic appointment.

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This article is part of the series This is research , where we ask academics to share and discuss open access articles that reveal important aspects of science. Today’s piece looks at statistics, and how to interpret them for meaning in the real world.

Let’s face it, scientific papers aren’t exactly page turners. They are written by scientists, for scientists, and often in a language that seems to only vaguely resemble English.

And perhaps one of the most daunting aspects of a scientific paper is the statistics (“stats”) section.

But what do stats really mean in the real world? Here’s an example from leukaemia research to help you break it down.

Read more: Our survey found 'questionable research practices' by ecologists and biologists – here's what that means

Strength of results

Stats are key to good research – they help researchers determine whether the results observed are strong enough to be due to an important scientific phenomenon.

As a research student I would always look for the magic number which indicates statistically significant differences in my experiments: most people agree this number to be 0.05 (you may see this in a paper written as p < 0.05).

When comparing two groups in a scientific study, statistical significance indicated by a p-value of less than 0.05 means that, in the case where there was no real difference between groups, there’s less than a 5% chance of the observed result arising.

But the focus on looking for statistically significant differences can blind us to the bigger picture. As I advanced through my scientific training I learnt to look for biologically significant differences.

Read more: The curious case of the missing workplace teaspoons

Biological significance

Biological significance addresses the question of whether the statistical difference actually means anything in terms of a real outcome, like a disease. Can the result explain how the disease is caused? Does it provide a new avenue to treat the disease? Basically, is it relevant?

A recent paper published in the journal Leukaemia will help explain my point. The paper looked at why some people are able to stop their treatment for chronic myeloid leukaemia without the cancer coming back, while in others the cancer relapsed.

The key finding of this study was that patients who did not relapse had a higher proportion of natural killer cells compared to patients that did relapse.

Natural killer cells are a type of immune cell that controls viral infections and tumours. So, the more cells there are to kill the cancer, the less likely the cancer was to relapse – makes sense!

This finding has the potential to guide doctors in seeing which patients are likely to remain cancer-free after stopping treatment. This is definitely biologically significant.

Read more: My cancer is in remission – does this mean I'm cured?

Not so relevant

Another result from the same paper (Figure 3a if you want to click through to the data) shows a statistically significant difference in a sub-type of natural killer cells (called adaptive natural killer cells). But is this difference biologically relevant?

At this stage there is little evidence of a role for adaptive natural killer cells in the context of leukaemia. Also, the difference between the groups is relatively small, with a large variation within the groups (there are large error bars on the graph).

These factors make it more likely that the differences may be due to the mathematics involved in the statistical test rather than a biological effect. As with any new finding, time and further studies will be vital in working out whether this result actually means anything.

Act like an expert

So how do you pick if the statistical differences have biological value? Being a highly trained expert in the field certainly helps.

Another way to determine if the findings in a paper have biological relevance is to look for other papers that show similar results. If a result is “real” it should be found by other scientists who will build on it and publish more papers.

This means there will be lots of papers for you to read and apply your new-found passion for statistics.

The open access research paper for this analysis is Increased proportion of mature NK cells is associated with successful imatinib discontinuation in chronic myeloid leukemia .

The definition of statistical significance has been edited since this article was first published.

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How To Write a Significance Statement for Your Research

A significance statement is an essential part of a research paper. It explains the importance and relevance of the study to the academic community and the world at large. To write a compelling significance statement, identify the research problem, explain why it is significant, provide evidence of its importance, and highlight its potential impact on future research, policy, or practice. A well-crafted significance statement should effectively communicate the value of the research to readers and help them understand why it matters.

Updated on May 4, 2023

A significance statement is a clearly stated, non-technical paragraph that explains why your research matters. It’s central in making the public aware of and gaining support for your research.

Write it in jargon-free language that a reader from any field can understand. Well-crafted, easily readable significance statements can improve your chances for citation and impact and make it easier for readers outside your field to find and understand your work.

Read on for more details on what a significance statement is, how it can enhance the impact of your research, and, of course, how to write one.

What is a significance statement in research?

A significance statement answers the question: How will your research advance scientific knowledge and impact society at large (as well as specific populations)? 

You might also see it called a “Significance of the study” statement. Some professional organizations in the STEM sciences and social sciences now recommended that journals in their disciplines make such statements a standard feature of each published article. Funding agencies also consider “significance” a key criterion for their awards.

Read some examples of significance statements from the Proceedings of the National Academy of Sciences (PNAS) here .

Depending upon the specific journal or funding agency’s requirements, your statement may be around 100 words and answer these questions:

1. What’s the purpose of this research?

2. What are its key findings?

3. Why do they matter?

4. Who benefits from the research results?

Readers will want to know: “What is interesting or important about this research?” Keep asking yourself that question.

Where to place the significance statement in your manuscript

Most journals ask you to place the significance statement before or after the abstract, so check with each journal’s guide. 

This article is focused on the formal significance statement, even though you’ll naturally highlight your project’s significance elsewhere in your manuscript. (In the introduction, you’ll set out your research aims, and in the conclusion, you’ll explain the potential applications of your research and recommend areas for future research. You’re building an overall case for the value of your work.)

Developing the significance statement

The main steps in planning and developing your statement are to assess the gaps to which your study contributes, and then define your work’s implications and impact.

Identify what gaps your study fills and what it contributes

Your literature review was a big part of how you planned your study. To develop your research aims and objectives, you identified gaps or unanswered questions in the preceding research and designed your study to address them.

Go back to that lit review and look at those gaps again. Review your research proposal to refresh your memory. Ask:

• How have my research findings advanced knowledge or provided notable new insights?
• How has my research helped to prove (or disprove) a hypothesis or answer a research question?
• Why are those results important?

Consider your study’s potential impact at two levels: 

• What contribution does my research make to my field?
• How does it specifically contribute to knowledge; that is, who will benefit the most from it?

Define the implications and potential impact

As you make notes, keep the reasons in mind for why you are writing this statement. Whom will it impact, and why?

The first audience for your significance statement will be journal reviewers when you submit your article for publishing. Many journals require one for manuscript submissions. Study the author’s guide of your desired journal to see its criteria ( here’s an example ). Peer reviewers who can clearly understand the value of your research will be more likely to recommend publication. 

Second, when you apply for funding, your significance statement will help justify why your research deserves a grant from a funding agency . The U.S. National Institutes of Health (NIH), for example, wants to see that a project will “exert a sustained, powerful influence on the research field(s) involved.” Clear, simple language is always valuable because not all reviewers will be specialists in your field.

Third, this concise statement about your study’s importance can affect how potential readers engage with your work. Science journalists and interested readers can promote and spread your work, enhancing your reputation and influence. Help them understand your work.

You’re now ready to express the importance of your research clearly and concisely. Time to start writing.

How to write a significance statement: Key elements 

When drafting your statement, focus on both the content and writing style.

• In terms of content, emphasize the importance, timeliness, and relevance of your research results. 
• Write the statement in plain, clear language rather than scientific or technical jargon. Your audience will include not just your fellow scientists but also non-specialists like journalists, funding reviewers, and members of the public. 

Follow the process we outline below to build a solid, well-crafted, and informative statement. 

Get started

Some suggested opening lines to help you get started might be:

• The implications of this study are… 
• Building upon previous contributions, our study moves the field forward because…
• Our study furthers previous understanding about…

Alternatively, you may start with a statement about the phenomenon you’re studying, leading to the problem statement.

Include these components

Next, draft some sentences that include the following elements. A good example, which we’ll use here, is a significance statement by Rogers et al. (2022) published in the Journal of Climate .

1. Briefly situate your research study in its larger context . Start by introducing the topic, leading to a problem statement. Here’s an example:

‘Heatwaves pose a major threat to human health, ecosystems, and human systems.”

2. State the research problem.

“Simultaneous heatwaves affecting multiple regions can exacerbate such threats. For example, multiple food-producing regions simultaneously undergoing heat-related crop damage could drive global food shortages.”

3. Tell what your study does to address it.

“We assess recent changes in the occurrence of simultaneous large heatwaves.”

4. Provide brief but powerful evidence to support the claims your statement is making , Use quantifiable terms rather than vague ones (e.g., instead of “This phenomenon is happening now more than ever,” see below how Rogers et al. (2022) explained it). This evidence intensifies and illustrates the problem more vividly:

“Such simultaneous heatwaves are 7 times more likely now than 40 years ago. They are also hotter and affect a larger area. Their increasing occurrence is mainly driven by warming baseline temperatures due to global heating, but changes in weather patterns contribute to disproportionate increases over parts of Europe, the eastern United States, and Asia.

5. Relate your study’s impact to the broader context , starting with its general significance to society—then, when possible, move to the particular as you name specific applications of your research findings. (Our example lacks this second level of application.) 

“Better understanding the drivers of weather pattern changes is therefore important for understanding future concurrent heatwave characteristics and their impacts.”

Refine your English

Don’t understate or overstate your findings – just make clear what your study contributes. When you have all the elements in place, review your draft to simplify and polish your language. Even better, get an expert AJE edit . Be sure to use “plain” language rather than academic jargon.

• Avoid acronyms, scientific jargon, and technical terms 
• Use active verbs in your sentence structure rather than passive voice (e.g., instead of “It was found that...”, use “We found...”)
• Make sentence structures short, easy to understand – readable
• Try to address only one idea in each sentence and keep sentences within 25 words (15 words is even better)
• Eliminate nonessential words and phrases (“fluff” and wordiness)

Enhance your significance statement’s impact

Always take time to review your draft multiple times. Make sure that you:

• Keep your language focused
• Provide evidence to support your claims
• Relate the significance to the broader research context in your field

After revising your significance statement, request feedback from a reading mentor about how to make it even clearer. If you’re not a native English speaker, seek help from a native-English-speaking colleague or use an editing service like AJE to make sure your work is at a native level.

Understanding the significance of your study

Your readers may have much less interest than you do in the specific details of your research methods and measures. Many readers will scan your article to learn how your findings might apply to them and their own research. 

Different types of significance

Your findings may have different types of significance, relevant to different populations or fields of study for different reasons. You can emphasize your work’s statistical, clinical, or practical significance. Editors or reviewers in the social sciences might also evaluate your work’s social or political significance.

Statistical significance means that the results are unlikely to have occurred randomly. Instead, it implies a true cause-and-effect relationship.

Clinical significance means that your findings are applicable for treating patients and improving quality of life.

Practical significance is when your research outcomes are meaningful to society at large, in the “real world.” Practical significance is usually measured by the study’s  effect size . Similarly, evaluators may attribute social or political significance to research that addresses “real and immediate” social problems.

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• As Ozempic’s Popularity Soars, Here’s What to Know About Semaglutide and Weight Loss JAMA Medical News & Perspectives May 16, 2023 This Medical News article discusses chronic weight management with semaglutide, sold under the brand names Ozempic and Wegovy. Melissa Suran, PhD, MSJ
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• What to Know About Wegovy’s Rare but Serious Adverse Effects JAMA Medical News & Perspectives December 12, 2023 This Medical News article discusses Wegovy, Ozempic, and other GLP-1 receptor agonists used for weight management and type 2 diabetes. Kate Ruder, MSJ
• GLP-1 Receptor Agonists and Gastrointestinal Adverse Events—Reply JAMA Comment & Response March 12, 2024 Ramin Rezaeianzadeh, BSc; Mohit Sodhi, MSc; Mahyar Etminan, PharmD, MSc
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• GLP-1 Receptor Agonist Use and Risk of Postoperative Complications JAMA Research Letter May 21, 2024 This cohort study evaluates the risk of postoperative respiratory complications among patients with diabetes undergoing surgery who had vs those who had not a prescription fill for glucagon-like peptide 1 receptor agonists. Anjali A. Dixit, MD, MPH; Brian T. Bateman, MD, MS; Mary T. Hawn, MD, MPH; Michelle C. Odden, PhD; Eric C. Sun, MD, PhD
• Glucagon-Like Peptide-1 Receptor Agonist Use and Risk of Gallbladder and Biliary Diseases JAMA Internal Medicine Original Investigation May 1, 2022 This systematic review and meta-analysis of 76 randomized clinical trials examines the effects of glucagon-like peptide-1 receptor agonist use on the risk of gallbladder and biliary diseases. Liyun He, MM; Jialu Wang, MM; Fan Ping, MD; Na Yang, MM; Jingyue Huang, MM; Yuxiu Li, MD; Lingling Xu, MD; Wei Li, MD; Huabing Zhang, MD
• Cholecystitis Associated With the Use of Glucagon-Like Peptide-1 Receptor Agonists JAMA Internal Medicine Research Letter October 1, 2022 This case series identifies cases reported in the US Food and Drug Administration Adverse Event Reporting System of acute cholecystitis associated with use of glucagon-like peptide-1 receptor agonists that did not have gallbladder disease warnings in their labeling. Daniel Woronow, MD; Christine Chamberlain, PharmD; Ali Niak, MD; Mark Avigan, MDCM; Monika Houstoun, PharmD, MPH; Cindy Kortepeter, PharmD

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Sodhi M , Rezaeianzadeh R , Kezouh A , Etminan M. Risk of Gastrointestinal Adverse Events Associated With Glucagon-Like Peptide-1 Receptor Agonists for Weight Loss. JAMA. 2023;330(18):1795–1797. doi:10.1001/jama.2023.19574

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Risk of Gastrointestinal Adverse Events Associated With Glucagon-Like Peptide-1 Receptor Agonists for Weight Loss

• 1 Faculty of Medicine, University of British Columbia, Vancouver, British Columbia, Canada
• 2 StatExpert Ltd, Laval, Quebec, Canada
• 3 Department of Ophthalmology and Visual Sciences and Medicine, University of British Columbia, Vancouver, Canada
• Medical News & Perspectives As Ozempic’s Popularity Soars, Here’s What to Know About Semaglutide and Weight Loss Melissa Suran, PhD, MSJ JAMA
• Special Communication Patents and Regulatory Exclusivities on GLP-1 Receptor Agonists Rasha Alhiary, PharmD; Aaron S. Kesselheim, MD, JD, MPH; Sarah Gabriele, LLM, MBE; Reed F. Beall, PhD; S. Sean Tu, JD, PhD; William B. Feldman, MD, DPhil, MPH JAMA
• Medical News & Perspectives What to Know About Wegovy’s Rare but Serious Adverse Effects Kate Ruder, MSJ JAMA
• Comment & Response GLP-1 Receptor Agonists and Gastrointestinal Adverse Events—Reply Ramin Rezaeianzadeh, BSc; Mohit Sodhi, MSc; Mahyar Etminan, PharmD, MSc JAMA
• Comment & Response GLP-1 Receptor Agonists and Gastrointestinal Adverse Events Karine Suissa, PhD; Sara J. Cromer, MD; Elisabetta Patorno, MD, DrPH JAMA
• Research Letter GLP-1 Receptor Agonist Use and Risk of Postoperative Complications Anjali A. Dixit, MD, MPH; Brian T. Bateman, MD, MS; Mary T. Hawn, MD, MPH; Michelle C. Odden, PhD; Eric C. Sun, MD, PhD JAMA
• Original Investigation Glucagon-Like Peptide-1 Receptor Agonist Use and Risk of Gallbladder and Biliary Diseases Liyun He, MM; Jialu Wang, MM; Fan Ping, MD; Na Yang, MM; Jingyue Huang, MM; Yuxiu Li, MD; Lingling Xu, MD; Wei Li, MD; Huabing Zhang, MD JAMA Internal Medicine
• Research Letter Cholecystitis Associated With the Use of Glucagon-Like Peptide-1 Receptor Agonists Daniel Woronow, MD; Christine Chamberlain, PharmD; Ali Niak, MD; Mark Avigan, MDCM; Monika Houstoun, PharmD, MPH; Cindy Kortepeter, PharmD JAMA Internal Medicine

Glucagon-like peptide 1 (GLP-1) agonists are medications approved for treatment of diabetes that recently have also been used off label for weight loss. 1 Studies have found increased risks of gastrointestinal adverse events (biliary disease, 2 pancreatitis, 3 bowel obstruction, 4 and gastroparesis 5 ) in patients with diabetes. 2 - 5 Because such patients have higher baseline risk for gastrointestinal adverse events, risk in patients taking these drugs for other indications may differ. Randomized trials examining efficacy of GLP-1 agonists for weight loss were not designed to capture these events 2 due to small sample sizes and short follow-up. We examined gastrointestinal adverse events associated with GLP-1 agonists used for weight loss in a clinical setting.

We used a random sample of 16 million patients (2006-2020) from the PharMetrics Plus for Academics database (IQVIA), a large health claims database that captures 93% of all outpatient prescriptions and physician diagnoses in the US through the International Classification of Diseases, Ninth Revision (ICD-9) or ICD-10. In our cohort study, we included new users of semaglutide or liraglutide, 2 main GLP-1 agonists, and the active comparator bupropion-naltrexone, a weight loss agent unrelated to GLP-1 agonists. Because semaglutide was marketed for weight loss after the study period (2021), we ensured all GLP-1 agonist and bupropion-naltrexone users had an obesity code in the 90 days prior or up to 30 days after cohort entry, excluding those with a diabetes or antidiabetic drug code.

Patients were observed from first prescription of a study drug to first mutually exclusive incidence (defined as first ICD-9 or ICD-10 code) of biliary disease (including cholecystitis, cholelithiasis, and choledocholithiasis), pancreatitis (including gallstone pancreatitis), bowel obstruction, or gastroparesis (defined as use of a code or a promotility agent). They were followed up to the end of the study period (June 2020) or censored during a switch. Hazard ratios (HRs) from a Cox model were adjusted for age, sex, alcohol use, smoking, hyperlipidemia, abdominal surgery in the previous 30 days, and geographic location, which were identified as common cause variables or risk factors. 6 Two sensitivity analyses were undertaken, one excluding hyperlipidemia (because more semaglutide users had hyperlipidemia) and another including patients without diabetes regardless of having an obesity code. Due to absence of data on body mass index (BMI), the E-value was used to examine how strong unmeasured confounding would need to be to negate observed results, with E-value HRs of at least 2 indicating BMI is unlikely to change study results. Statistical significance was defined as 2-sided 95% CI that did not cross 1. Analyses were performed using SAS version 9.4. Ethics approval was obtained by the University of British Columbia’s clinical research ethics board with a waiver of informed consent.

Our cohort included 4144 liraglutide, 613 semaglutide, and 654 bupropion-naltrexone users. Incidence rates for the 4 outcomes were elevated among GLP-1 agonists compared with bupropion-naltrexone users ( Table 1 ). For example, incidence of biliary disease (per 1000 person-years) was 11.7 for semaglutide, 18.6 for liraglutide, and 12.6 for bupropion-naltrexone and 4.6, 7.9, and 1.0, respectively, for pancreatitis.

Use of GLP-1 agonists compared with bupropion-naltrexone was associated with increased risk of pancreatitis (adjusted HR, 9.09 [95% CI, 1.25-66.00]), bowel obstruction (HR, 4.22 [95% CI, 1.02-17.40]), and gastroparesis (HR, 3.67 [95% CI, 1.15-11.90) but not biliary disease (HR, 1.50 [95% CI, 0.89-2.53]). Exclusion of hyperlipidemia from the analysis did not change the results ( Table 2 ). Inclusion of GLP-1 agonists regardless of history of obesity reduced HRs and narrowed CIs but did not change the significance of the results ( Table 2 ). E-value HRs did not suggest potential confounding by BMI.

This study found that use of GLP-1 agonists for weight loss compared with use of bupropion-naltrexone was associated with increased risk of pancreatitis, gastroparesis, and bowel obstruction but not biliary disease.

Given the wide use of these drugs, these adverse events, although rare, must be considered by patients who are contemplating using the drugs for weight loss because the risk-benefit calculus for this group might differ from that of those who use them for diabetes. Limitations include that although all GLP-1 agonist users had a record for obesity without diabetes, whether GLP-1 agonists were all used for weight loss is uncertain.

Accepted for Publication: September 11, 2023.

Published Online: October 5, 2023. doi:10.1001/jama.2023.19574

Correction: This article was corrected on December 21, 2023, to update the full name of the database used.

Corresponding Author: Mahyar Etminan, PharmD, MSc, Faculty of Medicine, Departments of Ophthalmology and Visual Sciences and Medicine, The Eye Care Center, University of British Columbia, 2550 Willow St, Room 323, Vancouver, BC V5Z 3N9, Canada ( [email protected] ).

Author Contributions: Dr Etminan had full access to all of the data in the study and takes responsibility for the integrity of the data and the accuracy of the data analysis.

Concept and design: Sodhi, Rezaeianzadeh, Etminan.

Acquisition, analysis, or interpretation of data: All authors.

Drafting of the manuscript: Sodhi, Rezaeianzadeh, Etminan.

Critical review of the manuscript for important intellectual content: All authors.

Statistical analysis: Kezouh.

Obtained funding: Etminan.

Administrative, technical, or material support: Sodhi.

Supervision: Etminan.

Conflict of Interest Disclosures: None reported.

Funding/Support: This study was funded by internal research funds from the Department of Ophthalmology and Visual Sciences, University of British Columbia.

Role of the Funder/Sponsor: The funder had no role in the design and conduct of the study; collection, management, analysis, and interpretation of the data; preparation, review, or approval of the manuscript; and decision to submit the manuscript for publication.

Data Sharing Statement: See Supplement .

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• News & Stories

Q&A: Understanding and Preventing Youth Firearm Violence

Jessika Bottiani discusses her research on the significant disparities in youth firearm violence and how understanding those gaps can help future prevention efforts.

Leslie Booren

August 26, 2024

This summer the United States Surgeon General Dr. Vivek Murthy released a landmark advisory on firearm violence , declaring it a public health crisis. According to the advisory, gun violence reaches across the lifespan and is currently the leading cause of death for children and adolescents in America.

Researchers at Youth-Nex, the UVA Center to Promote Effective Youth Development, have been examining some of the root causes of youth firearm violence disparities to better understand this crisis and how future prevention efforts may work.

Recently, the Society for Research on Adolescence (SRA) recognized Dr. Jessika Bottiani, an associate research professor at the UVA School of Education and Human Development and faculty affiliate at Youth-Nex, and her co-authors with the 2024 Social Policy Publication Award for a paper on the prevention of youth firearm violence disparities . SRA highlighted this review as work that should be read by all policymakers.

We sat down with Bottiani to learn more about this research review.

Q: Your paper examined research on youth firearm violence and firearm risk. What did you find?

A: Our review and synthesis of data demonstrated striking differences in firearm risk across intersectional identities. We separated out different types of firearm violence (e.g., homicide, suicide, injury), which revealed distinctions in risk across different demographic groups–most saliently gun homicide among Black boys and young men in urban settings.

A staggering degree of inequity in firearm fatalities is shouldered by Black boys and young men in this country, where the rate of firearm homicide is more than 20 times higher among Black boys and young men ages 15-24 than for white boys and young men in the same age groups. We also saw higher rates of gun suicide among white and Indigenous American boys and young men in rural areas of the United States.

When we examined rates by geography, we identified intersectional differences in risk that are important for policymakers to understand. For example, we saw that higher rates of firearm homicide among Black boys and young men were most salient in urban areas of the Midwest and south of the United States. Overlaying data onto maps demonstrated how young male suicide by firearm is also clustered geographically, for example, in rural counties in the Midwest and west for Indigenous young males, and in in rural counties in the west for White male youth (who have the second highest rate of suicide by firearm after Indigenous young males).

Q: Why was a review of the research specifically focused on disparities in youth firearm violence needed?

A: A lot of systematic and scoping reviews on firearm violence had come out in the literature around this time, but none of them focused on understanding why Black boys and young men in urban areas were so disproportionately affected, or why we were also seeing gaps affecting rural White boys and young men. This paper presented data that revealed the degree of these disparities and tried to understand the root causes.

We don’t pay enough attention to the role of racist historical policies and regulations that have calcified into today’s racially segregated geographies and poverty. With this paper, we wanted to reveal the way in which youth gun violence is inextricably bound to the history of race, place, and culture in the United States. The paper also delves into cultural norms around guns and masculinity. We feel insights on these aspects of context are vital for understanding how to address youth firearm violence.

Q: What future prevention efforts do you suggest in your paper?

A: We put forth a number of evidence-based solutions for settings ranging from emergency rooms to schools to address firearm violence at the individual level. Yet perhaps more importantly, we also provide suggestions for tackling the structural and sociocultural factors that underlie firearm violence.

At the community level, our recommendations range from violence interrupters to programs and policies that seek to disrupt racial segregation and redress housing inequities. We also note the potential for media campaigns addressing sociocultural norms to be a tool for prevention.

We provided a review of gun restriction and safety policies, and their potential effectiveness in addressing youth firearm violence (while also acknowledging the political climate wherein such policies have been increasingly challenged). We point out that some recent firearm related policies, purportedly race neutral in their language, had harmful impacts specifically on communities and people of color.

Individual level interventions or policies that seek to address only one piece of the puzzle are bound to be ineffective at scale. Rather, what is required are multisector, place-based initiatives that address structural factors related to poverty and the built environment in under-resourced segregated neighborhoods.

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Research shows our bodies go through rapid changes in our 40s and our 60s

For many people, reaching their mid-40s may bring unpleasant signs the body isn’t working as well as it once did. Injuries seem to happen more frequently. Muscles may feel weaker.

A new study, published Wednesday in Nature Aging , shows what may be causing the physical decline. Researchers have found that molecules and microorganisms both inside and outside our bodies are going through dramatic changes, first at about age 44 and then again when we hit 60. Those alterations may be causing significant differences in cardiovascular health and immune function.

The findings come from Stanford scientists who analyzed blood and other biological samples of 108 volunteers ages 25 to 75, who continued to donate samples for several years. 

“While it’s obvious that you’re aging throughout your entire life, there are two big periods where things really shift,” said the study’s senior author, Michael Snyder, a professor of genetics and director of the Center for Genomics and Personalized Medicine at Stanford Medicine. For example, “there’s a big shift in the metabolism of lipids when people are in their 40s and in the metabolism of carbohydrates when people are in their 60s.”

Lipids are fatty substances, including LDL, HDL and triglycerides, that perform a host of functions in the body, but they can be harmful if they build up in the blood.

The scientists tracked many kinds of molecules in the samples, including RNA and proteins, as well as the participants’ microbiomes.

The metabolic changes the researchers discovered indicate not that people in their 40s are burning calories more slowly but rather that the body is breaking food down differently. The scientists aren’t sure exactly what impact those changes have on health.

Previous research showed that resting energy use, or metabolic rate , didn’t change from ages 20 to 60. The new study’s findings don't contradict that.

The changes in metabolism affect how the body reacts to alcohol or caffeine, although the health consequences aren’t yet clear. In the case of caffeine, it may result in higher sensitivity. 

It’s also not known yet whether the shifts could be linked to lifestyle or behavioral factors. For example, the changes in alcohol metabolism might be because people are drinking more in their mid-40s, Snyder said.

For now, Snyder suggests people in their 40s keep a close eye on their lipids, especially LDL cholesterol.

“If they start going up, people might want to think about taking statins if that’s what their doctor recommends,” he said. Moreover, “knowing there’s a shift in the molecules that affect muscles and skin, you might want to warm up more before exercising so you don’t hurt yourself.”

Until we know better what those changes mean, the best way to deal with them would be to eat healthy foods and to exercise regularly, Snyder said.Dr. Josef Coresh, founding director of the Optimal Aging Institute at the NYU Grossman School of Medicine, compared the new findings to the invention of the microscope.

“The beauty of this type of paper is the level of detail we can see in molecular changes,” said Coresh, a professor of medicine at the school. “But it will take time to sort out what individual changes mean and how we can tailor medications to those changes. We do know that the origins of many diseases happen in midlife when people are in their 40s, though the disease may occur decades later.”

The new study “is an important step forward,” said Dr. Lori Zeltser, a professor of pathology and cell biology at the Columbia University Vagelos College of Physicians and Surgeons. While we don’t know what the consequences of those metabolic changes are yet, “right now, we have to acknowledge that we metabolize food differently in our 40s, and that is something really new.”

The shifts the researchers found might help explain numerous age-related health changes, such as muscle loss, because “your body is breaking down food differently,” Zeltser said.

Linda Carroll is a regular health contributor to NBC News. She is coauthor of "The Concussion Crisis: Anatomy of a Silent Epidemic" and "Out of the Clouds: The Unlikely Horseman and the Unwanted Colt Who Conquered the Sport of Kings." 

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• Clin Biochem Rev
• v.29(Suppl 1); 2008 Aug

Significant Figures

Tony badrick.

1 Biochemistry Department, Sullivan Nicolaides Pathology, Taringa, Brisbane, Qld 4068

Peter E Hickman

2 Chemical Pathology, ACT Pathology, Canberra 2606, Australia

• For consistency of reporting the same number of significant figures should be used for results and reference intervals.
• The choice of the reporting interval should be based on analytical imprecision (measurement uncertainty).

Introduction

The results of an analysis can be used for a number of different purposes by a clinician. Comparing a result against a reference interval or a clinical decision point can be used to confirm or reject a diagnosis. But often a result is compared with previous results from a particular patient to monitor treatment, be that result a drug level, a glycated haemoglobin level or a creatinine. In this latter case it is the difference between sequential results that contains the information needed, and this difference is conveyed to a large extent by the number of significant figures, or reporting interval, of an analyte. A further complicating factor for a clinician trying to interpret consecutive results is that they may come from different laboratories, so the clinician may be confronted with a pair of results such as 20 and 20.56!

When reporting a result, we may be faced with the problem “is an apparent change in result real, or simply a reflection of the ‘noise’ due to analytical imprecision and biological variation?” The choice of the reporting interval (incremental value chosen for reporting analyte concentration, e.g. for sodium this is 1 mmol/L, whilst for potassium, it is 0.1 mmol/L) should reflect this background noise.

As the measurement systems used in routine chemistry laboratories produce a continuous signal (spectrophotometric, voltage) what happens when a result is rounded is that a series of results are ‘binned’ together. Thus a result of 20 units actually represents the spread of measured results from 19.5 to 20.4. The ‘rounding’ could hide a significant change in result. For example, the range of results from 18.5 to 20.4 would be rounded to 19 and 20 units, a reported change of 1 unit, but a potential difference of up to 1.9. Often, it would appear that the reporting interval chosen is too small which can give a false impression that a change has occurred.

The reporting interval should be such that any result change is greater than the analytical imprecision. The number of significant figures reported should be dependent on analytical imprecision (standard deviation a , SD a ) and, perhaps, biological variation (intra-individual SD, SD i ), but in practice often is quite arbitrary, and it is apparent that for many assays, analyte concentrations are reported to an excessive number of significant figures. 1

Some Relevant Statistics

It is worth reviewing the relevant statistical basis of differences between two numbers which are the results of an analytical process. We assume that the measurements follow a Gaussian distribution, that is, if we repeatedly measured any sample, those results would follow a Gaussian distribution which is described by its mean and SD. That means that approximately 68% of all those repeated values would lie within the mean ±1 SD, that approximately 96% of those values would lie within the mean ±2 SD and 99.7% of values would lie within the mean ±3 SD. These multipliers of the SD, 1, 2 or 3, are called the standard normal deviates or the z-scores. Any measurement of a parameter has an associated analytical error which is described as the analytical standard deviation (SD a ), so any result will lie within the mean ± 2 SD 96% of the time.

Any biological parameter will also have some biological variation associated with it and this is described by a standard deviation (SD i ), so that a parameter will vary day to day around a mean value with an SD of SD i.

Now we are interested in the question: when do two sequential measurements differ? Each of these measurements is subject to analytical error plus normal biological variation. This total ‘normal’ variation for serial results is equal to the sum of the variation about each measurement, that is total variation = variation in measurement 1 + measurement 2 =

We can simplify this to √2 x z x ((SD a ) 2 + (SD i ) 2 ) 1/2

We are interested if the net result difference is greater than these combined effects or greater than

√2 x z x ((SDa) 2 + (SD i ) 2 ) 1/2 which is called the critical difference or reference change value (RCV).

If we want a 95% confidence that two consecutive results truly differ, then their difference must be greater than

2.77 x ((SD a ) 2 + (SD i ) 2 ) 1/2 , the constant ‘2.77’ being derived from √2 times the z statistic which is 1.96.

Different Approaches to Reporting Intervals

There have been various published approaches to determining an appropriate number of significant figures to report. For example, Hawkins and Johnson suggested that a simple rule of thumb to use for choosing the appropriate number of significant figures was that the SD should be ≤ 0.7 of the unit of reporting. 2

For example, for serum Na, if the SD is ≤ 0.7 mmol/L but > 0.07 mmol/L, report to the nearest 0.1 mmol/L. Data on imprecision should be available at this level of detail to allow mathematical manipulation with rounding only at the final step. In the above example for serum Na a 95% significant change value would be 0.66 x 2.77 = 1.82 mmol/L and rounded up to 2 mmol/L for the user.

One can see the error if the SD is reported as “1 mmol/L” and then multiplied by 2.77 to give a significant change value of 3 mmol/L rather than the correct value of 2.

A second approach is to consider what information is ‘lost’ by the rounding process. As mentioned earlier, the effect of rounding can ‘hide’ an actual difference of 1.9 units. Thus, for a single point the appropriate reporting interval is √2 x z x σ/1.9, where z is the z score at (1-p) confidence interval, and σ is the analytical SD. 1

If we take into account the effect of sequential results, then there have been two different approaches reported in the literature. Badrick et al. calculated reporting intervals based only on the analytical imprecision of the method. 3 Thus reference intervals were determined without the SD i term because we need to be able to report when two results are analytically different. The cause of this difference is significant in the interpretation of those results but biological variation may either increase or decrease in disease.

Another approach is to include biological variation in the calculation. Jones has reported the impact of using this calculation (2.77 x √((SD a ) 2 + (SD i ) 2 ) 1/2 ) 4 and has highlighted the impact of ‘binning’ results. Thus, at low concentrations, using a reference interval which is appropriate for the level of imprecision at high concentrations may lead to a loss of clinically useful information. Note that in the paper of Badrick et al., different concentrations with their respective SD a were used rather than a constant imprecision value across the measurement range.

We have concentrated on the critical difference between two sequential results, but many results are singleton and are interpreted against a reference interval or clinical decision point (either empirical or based on evidence). Under these circumstances, the appropriate reference interval should be related to the imprecision of the measurement alone. Thus, the critical difference becomes 2.77 x SD a , for a 95% confidence interval. 3

This discussion assumes that the laboratory has complete control over the way it reports results and their significant figures. However, often this is not the case as there is an intervening Laboratory Information System which may or may not allow rounding or differential rounding based on a threshold concentration.

In the table we have extracted some data from the papers of Hawkins et al. 1 and Badrick et al. 3 The table gives for a range of commonly measured analytes estimates of reporting interval based on different sources. The justifiable unit magnitude (using p=0.05) is calculated using SD for the 50 th centile rankings of all laboratories enrolled in the General Serum Chemistry program from the Royal College of Pathologists of Australasia Quality Assurance Program Pty. Ltd. (RCPA QAP) 5 This represents current ‘state of the art’. The table also gives the reporting unit intervals that are implicit from a well-known clinical chemistry textbook (Tietz Textbook of Clinical Chemistry 6 ), a learned body (RCPA 7 ), and from the data entry sheet for the General Serum Chemistry program from the RCPA QAP. The final column contains the theoretical reporting interval calculated from method of Badrick et al. 3

Justified reporting unit magnitude (p=0.05) based on performance in RCPA General Serum Chemistry QAP Cycle 68, together with unit size taken from various authorities.

It is apparent from the table that current laboratory reporting interval practice is inappropriate even when only analytical imprecision is considered. A more appropriate approach taking into account laboratory imprecision is seen from the final column.

Even when only analytical imprecision is considered it has been reported that many laboratories use an inappropriate number of significant figures or reporting interval. 1 There is a great need for portability of results and therefore agreement between laboratory information systems in reference intervals and reporting intervals. One of the purposes of the uncertainty of measurement exercise in laboratories should be to critically review the current number of significant figures reported by laboratories and to amend these based on the imprecision of the assay and the biological variation of the analyte. However, we suspect that many laboratories have not taken the opportunity to revise their reporting intervals. When a laboratory does report a result it must be aware that the number of significant figures reported should be carefully considered and be small in comparison to the imprecision and biological variation. We have summarised the current literature and strongly suggest that laboratories ensure that their reporting intervals are fit for the purpose of adding value and not confusion to the differential diagnosis.

Competing Interests: None declared.

Effects of exosomes and inflammatory response on tumor: a bibliometrics study and visualization analysis via CiteSpace and VOSviewer

• Open access
• Published: 30 August 2024
• Volume 150 , article number  405 , ( 2024 )

Cite this article

You have full access to this open access article

• Miao Yu 1 ,
• Yaxuan Jin 1 ,
• Kaize Yuan 1 ,
• Bohao Liu 1 ,
• Ke Zhang 1 ,
• Shuying Li 1 &
• Zhihui Tai 2  

Tumor is a new organism formed by abnormal hyperplasia of local tissue cells under the action of various tumorigenic factors. Inflammation plays a decisive role in inducing tumorigenesis, promoting tumor development, invasion and migration. More and more evidence indicate that exosomes are involved in regulating the formation of tumor microenvironment in the process of proinflammatory carcinogenesis, leading to the stimulation of anti-tumor immune response or systemic immunosuppression, and exosomes play a crucial role in the development of tumor.

The articles on tumor-derived exosomes and inflammatory responses from January 2005 to January 2024 were collected through Web of Science (WOS), and the inclusion criteria were “Article”, “Review Article” and “Early Access”. Articles obtained after excluding “Book Chapters”, “Editorial Material”, “Proceeding Paper”, “Meeting Abstract” and “Retracted Publication”. Bibliometrics and visualization analysis were carried out on the obtained articles using CiteSpace6.2.R6 and VOSviewer1.6.20.

Total of 703 articles were included. The number of published documents showed a fluctuating growth trend year by year. A total of 61 countries have participated in the research on the effects of exosomes and inflammatory responses on tumors, among which China and the United States have the largest influence in this field. The obtained articles have been published in 60 journals around the world, among which PLOS ONE and NAT REV IMMUNOL are the journals with the most published articles and the highest co-citations respectively. The article from French author THERY C was cited the most (202 times). As a major researcher on the basic function of exosomes, THERY C established the gold standard for extraction, separation and identification of exosomes, and found that exosomes promote tumor metastasis through direct regulation of miRNA. Her research has had a huge impact on the field. Keyword co-occurrence analysis indicate that extracellular vesicles, inflammation, cancer, miRNAs, mesenchymal stem cells, drug delivery, gastric cancer and circulating endothelial microparticles are the research hotspot at present stage. The main keywords of the cluster analysis show that extracellular vesicles, human papilloma virus, myeloid cells, tumor macro-environment are the current research hotspots and frontier. The research hotspots have developed over time from the time chart of keywords and clustering, especially after 2016, exosomes have established extensive links with drug delivery, cancer treatment, inflammatory response and other fields. Tumor-derived exosomes stimulate receptor cells to secrete pro-inflammatory cytokines and growth factors, enabling immune and inflammatory cells to perceive the intracellular environment of cancer cells even when cancer cells do not express any tumor-specific antigens. For example, in anoxic environment, cancer cells can secrete exosomes containing pro-inflammatory factors to promote the invasion and metastasis of cancer cells. In the complex tumor microenvironment, both tumor cells and various stromal cells will secrete specific exosomes, and promote the development of tumors through various ways, so that tumor cells have drug resistance, and bring adverse effects on the clinical treatment of tumor patients. MicroRNAs and long noncoding RNA as hot keywords play important roles in regulating and mediating tumor development, and their specificity makes them important biomarkers for cancer prediction and diagnosis. Highlighting word analysis shows that microRNAs secreted by leukemia patients can effectively promote the proliferation of malignant cells and the development of cardiovascular diseases. At the same time, exosomes can induce the secretion of some microRNAs in patients, leading to cardiac repair and regeneration. Therefore, the detection and screening of microRNAs plays a crucial role in predicting the incidence of cardiovascular diseases in patients.

Exosomes have attracted increasing attention due to their significant heterogeneity and ability to regulate the tumor immune microenvironment. However, tumor cell-derived exosomes accelerate tumor progression by enhancing immunosuppression and inflammation, increasing oxidative stress, and promoting angiogenesis, which may lead to poor prognosis.

Graphical abstract

Avoid common mistakes on your manuscript.

Introduction

Exosomes are extracellular vesicles rich in various proteins and nucleic acids (Mathivanan et al. 2010 ; Simons et al. 2009 ). All types of cells can secrete exosomes into various body fluids through polyvesicular bodies (MVBs) with plasma membranes and mediate intercellular communication (Gross et al. 2012 ). Exosomes carry microRNA (miRNA), mRNA, protein and other biology-related substances to complete the transfer and transport between cells (Gross et al. 2012 ), which is an important medium for intercellular communication (Bang and Thum, 2012 ). Its important role in the occurrence and development of diseases has aroused attention in the clinical medical community, including cancer (Qiu et al. 2024 ), neurodegenerative disease (Gao et al. 2021 ), and inflammatory disease (Console et al. 2019 ). The study has shown (Yu et al. 2022 ) that exosomes can be used as a potential biomarker and therapeutic target. Several clinical trials have been conducted to explore the possibility of disease diagnosis, treatment, and monitoring using exosomes, such as the application of exosomes in cardiovascular disease prevention and diagnosis (Zadeh et al. 2020 ), tumor treatment (Fang et al. 2023 ), and early cancer diagnosis (Deng et al. 2022 ).

Inflammation is considered to be a histological response of the body to foreign invasion or damage, which is a basic defense mechanism of the body (Han et al. 2017 ; Tian et al. 2022 ). Recent research on tumor suggested (Bi et al. 2022 ) that inflammation may induce early tumorigenesis, and early tumor can also induce tumor immunity. Proinflammatory carcinogenesis may be the result of environmental changes and multiple cell interactions (Niu et al. 2023 ), such as increased genomic instability (Salmaninejad et al. 2021 ), abnormal cell proliferation (Zhang et al. 2021 ), changes in the stromal environment (Niu et al. 2023 ), and transitions between epithelial and mesenchymal states (Liu et al. 2015 ). Inflammatory factors can activate inflammation-related transcription factors, leading to the activation of pro-tumor signaling pathways, so inflammation can induce tumorigenesis (De Silva et al. 2018 ; Lu et al. 2006 ).

Exosomes also play a driving role in the process of proinflammatory carcinogenesis. Various substances contained in exosomes such as miRNAs (miRs), cytokines, chemokines, and clotting factors (Vlassov et al. 2012 ). Exosomes can cause immune system dysfunction and damage to vascular endothelial cells through their clotting activity and release of cytokines and chemokines (Zadeh et al. 2020 ). At the same time, Due to the protective of exosomes, functional RNAs such as mRNA and miRNA present in exosomes of the blood circulation can avoid rapid degradation and maintain stability compared with free RNAs (Colombo et al. 2014 ; Ge et al. 2014 ), and play the function of long-distance signal transmission and provide conditions for distant tumor metastasis (Tsui et al. 2002 ). Therefore, these exosomes promote the occurrence and development of both malignant tumors and cardiovascular diseases (Zadeh et al. 2020 ; Yue et al. 2020 ). The tumor-derived exosomes can induce changes in immune cell function by stimulating bone marrow cells to produce inflammatory mediators or by direct delivery to target cells via these extracellular vesicles (Rupp et al. 2011 ). The direction of this functional change (stimulation or inhibition) seems to depend on the duration of the interaction between cells and exosomes, i.e. the length of time exposed to inflammatory factors (Altevogt et al. 2014 ). The key factors in this process are the number of exosomes and the presence of soluble immunosuppressive factors in the tumor microenvironment (Peng et al. 2023 ). Meanwhile, tumor, exosomes and inflammation are regulated through complex delivery and signaling pathways, which affect the occurrence and development of tumors.

Citation space software (Cite Space) is an information visualization software based on JAVA language and citation analysis theory jointly developed by Dr. Chaomei Chen of Drexel University and WISE Laboratory of Dalian University of Technology (Chen et al. 2019 ). The structure, rule and distribution of scientific knowledge are presented as a “scientific knowledge map” through visualization. On the one hand, the development of visualization came from Thomas Kuhn's conception of scientific structure, which provides the philosophical basis for Cite Space to find out the rise and fall of paradigms from scientific literature. On the other hand, it came from the conception of structural hole theory, which promotes the birth of various network cooperative maps and the development of citation networks (Shneider et al. 2009 ).

Bibliometric analysis software VOS viewer is a Java-based free software developed by van Eck and Waltman of Science and Technology Research Center of Leiden University in the Netherlands in 2009. It is mainly oriented to literature data, adapted to the analysis of a unidirectional network, and focuses on the visualization of scientific knowledge (van Eck et al. 2010 ).

Both bibliometric analysis and visualization enable co-citations and cluster analysis of authors, journals, institutions, and keywords. However, the data standardization algorithms and visual presentation methods are different, and Cite Space has advantages in revealing the dynamic development patterns of disciplines and discovering the research hotspots based on time series (Börner et al. 2012 ). The VOS viewer software is preferred when there is a large amount of node data or data clarity required (Skupin et al. 2004 ).

Therefore, in this study, visualization and bibliometric analysis were combined to summarize and analyze the global research literature on tumor-derived exosomes and inflammatory response, and to discuss the latest development trend, frontier hot spots and future research trends in this field, providing new ideas for clinical diagnosis and treatment of tumors.

Materials and methods

Inclusion and exclusion criteria of data collection.

The Mesh search term is used to find synonyms of keywords, and the search formula is set as: “TS = (Exosomes OR Exosome*) AND TS = (Inflammations OR Inflammation* OR Innate Inflammatory Response OR Inflammatory Response, Innate OR Innate Inflammatory Responses) AND TS = (Neoplasms OR Neoplasm* OR Tumor OR Neoplasm OR Tumors OR Neoplasia OR Neoplasias OR Cancer OR Cancers OR Malignant Neoplasm OR Malignancy OR Malignancies OR Malignant Neoplasms OR Neoplasm, Malignant OR Neoplasms, Malignant OR Benign Neoplasms OR Benign Neoplasm OR Neoplasms, Benign OR Neoplasm, Benign)”.

Articles published in English from January 1, 2005 to January 31, 2024 were searched, and “Article,” “Review article,” and “Published Online” as article types were selected. Excluding “Book chapters”, “Editorial materials”, “Proceeding paper”, “Meeting abstract” and “Retracted publication”, and total 703 articles were obtained before January 31, 2024 to avoid potential bias from subsequent database updates.

Data collection

English literatures were exported in text and recorded contents with “all recorded and cited references”. The above articles were imported into CiteSpace6.2.R6 and VOSviewer1.6.20 software respectively. The time nodes in the literature analysis were selected from January 2005 to January 31, 2024.

The node type is set to country, institution, author, keyword, reference, and cited author according to the analysis object, and the rest are default options. The excel spreadsheet was used to collect the following data as bibliometric indicators: total number of publications, year, author, country, journal, and most cited publications.

Observation indicators

The visualization map is generated using Cite Space software, and then the number and size of nodes in the formed map, the color of the outer ring of nodes, and the number of connections were compared to identify the importance and degree of correlation of each node. The higher the centrality of a node, the greater the probability that the node co-appears with other nodes in the literature, and the greater its influence in the co-occurrence network. The important information is summarized for qualitative and quantitative analysis according to above principles and data.

There are three visualization methods for the graphs generated by VOS viewer software, which are network visualization, overlay visualization and density visualization. The visualization analysis based on color changes: the color of the project in network visualization depends on the cluster to which the project belongs; The color of the item in the overlay visualization is determined by the score of the item, and the blue, green and yellow are enhanced successively; Each point in the density visualization has a color that indicates the density of the item at that point, ranging from blue to green to yellow.

Annual publications

The number of published articles can indicate the research degree and development profile of the research field to a certain extent. The number of articles published between January 1, 2005 and January 31, 2024 and their corresponding citation trends are shown in Fig.  1 . As can be seen from Fig.  1 , the number of published papers from 2005 to 2015 was relatively small and the growth rate was slow, which was due to the lack of research on exosomes and the unclear related mechanisms. Since 2016, the number of published papers has increased significantly. Thery C published three reviews on exosomes in 2016, which had breakthrough significance in the research on the function and mechanism of exosomes. Since then, authors from more and more countries have participated in the study of exosomes, resulting in a dramatic increase in the number of published papers, and in 2021, the number of published papers reached the peak of the past 20 years.

2005–2024 Trend chart of publication

In general, the number of papers related to tumor-derived exosomes and inflammatory response showed a trend of fluctuating growth and rapid growth, indicating a good development trend in this field. Based on the fitting curve of R2 = 0.9246 (y = 1.4169e0.335x), it is predicted that publication output will show potential growth in 2024, and will also show sustained levels of growth in the future.

Collaboration of authors from different countries

Based on the articles collected, authors from 61 countries studied tumor-derived exosomes and inflammatory responses, most of which were concentrated in the Northern Hemisphere. In addition, author collaboration links between countries and regions are also mainly located in the Northern Hemisphere. For the southern Hemisphere, Australian scholars have more prominent research results and contributions in this field, and maintain a high frequency of contact with researchers in other countries.

The top ten countries or regions with the largest number of published papers in this field are shown in Table  1 . A total of seven countries have centrality greater than 0.1, namely China, the United States, Italy, Germany, Iran, the United Kingdom and the Netherlands. The country with the highest number of publications is China (285, 40.54%), and the second place is the United States (171, 24.32%), China and the United States as the two countries with the most publications, together accounting for more than half of the total, which indicates that they have a significant influence in this field.

The United States has formed cooperation networks or cooperation belts with dozens of countries in this field. China has a prominent advantage in the number of published papers, but the degree of cooperation and node centrality with other countries are relatively less (see Fig.  2 ).

Country cooperation chart

The agency of author affiliation

The 703 articles on tumor exosomes associated with inflammatory responses came from 305 institutions. The top 11 institutions were ranked according to the number of published articles (Table  2 ), among which the institution with the largest number of published articles was Shanghai Jiao Tong University, with 19 articles, followed by PCSHE, with 16 articles.

The cooperation among institutions is shown in Fig.  3 . In all partnerships, the denser the lines of the cooperation network, the more extensive the cooperation between institutions. Due to geographical location and other factors, the more opportunities for institutional cooperation in the same country, the closer the contact, showing that institutional cooperation has a significant regional. Shanghai Jiao Tong University and Fudan University have gradually formed a larger cooperative network center, and PCSHE has worked closely with the State University System of Florida. Among them, the number of papers published by Chinese institutions is high, and the cooperation network of American institutions is close, which has promoted the development of this field.

The collaborative network of the authors' institutions

Published journal analysis

Between January 2005 and January 2024, studies on tumor-derived exosomes and inflammatory responses were published in 60 journals worldwide, and Table  3 lists the top 10 journals in number of articles published. Front immunol was the most published journal (n = 5.83%) with an impact factor of 7.3. Among the top 10 journals are five Q1 journals and five Q2 journals. Among the top 10 most cited journals, Q1 accounted for 6 and Q2 accounted for 4, indicating that these journals have a high influence and evaluation in this field, indirectly reflecting that the research results in this field have a greater contribution, the overall research level is high. The influence of a journal is largely determined by the number of citations it receives, as the number of citations reflects the extent to which its articles are cited and used by scholars and researchers in the field. Of the 10 most-cited journals, PLOS ONE was cited the most frequently (1158 times).

The cluster analysis results of journals and co-cited journals are shown in Figs.  4 and 5 . VOS viewer software can be used to directly observe the collaboration network among journals and its detailed cooperation status. Journals are divided into four categories according to their co-citation frequency, and articles in the same type of journals may have similar research directions and internal logic.

Clustering analysis of periodicals

Cluster analysis of co-cited journals

The double mapping superposition diagram of the journal study is shown in Fig.  6 . The prominent yellow in the diagram is the citation path. The left side represents the type of journal cited, the right side represents the type of journal published in the article, the English on the figure is the research field represented by these journals, and the left side represent articles published in journals related to molecular, biological, immunological research that are cited in journal articles related to molecular, biological, genetic research. Relevant studies have laid a foundation for the study of the relationship between tumor-derived exosomes and inflammatory response.

Double mapping overlay of journal research

Author analysise

There were 448 authors in the selected articles. The top 11 authors ranked according to the number of published articles and frequency of citations, which are shown in Table  4 . The results show that the teams of Zhang, Hoong-ge, Grizzle, William, and the teams of Kwon, Yoojung, Kim, Youngmi have produced more articles and made outstanding research contributions.

As shown in Fig.  7 , a total of 14 author cooperation networks have been formed in this field, and the 11 authors with the highest number of publications are all in the above two teams.

Author cooperation network diagram

The article citation frequency is sorted. The 10 authors with the most citation frequency is shown in Table  5 . Among them, the article by THERY C from France has been cited 202 times and has the highest citation frequency. As the main researcher on the basic function of exosomes, THERY C established the gold standard for extraction, separation and identification of exosomes-the ultra-fast centrifuge method (Tkach et al. 2017 ). It was confirmed that miRNA can be transported into target cells by entering small EVs, and play a role in directly regulating miRNA targets and helping virus spread, which indicates that exosomes promote tumor spread and metastasis. It was found that all EVs could activate T cells, but small vesicles and medium vesicles were induced into Th1 type, while large vesicles were induced into Th2 type. This difference was due to the surface of large vesicles being rich in CD40, while the surface of small vesicles and medium vesicles being rich in CD80 (Tkach et al. 2017 ; Tkach et al. 2016 ). On this basis, the comparison between tumor-derived exosomes and immune-cell-derived exosomes showed that 100 k precipitate products in tumor cells were very different from 100 k precipitate products in DC cells, and the effect of malignant tumor cell-derived EV on immune cell secretion of inflammatory factors was also different, the stimulating effect of exosomes is the most obvious (Fan et al. 2018 ). This is because the contents of tumor exosomes and immune exosomes are different and specific, and the tumor-derived exosomes have a prominent stimulating effect on the secretion of inflammatory factors. These differences and characteristics are inevitably related to the physical basis of exosomes such as size and contents. THERY C’s research is closely related to the study of tumor-derived exosomes and inflammatory responses, which has promoted the development of this field.

The cited frequency of the selected articles

The cited frequency of the 703 included papers was ranked, among which the top 11 cited articles were listed in Table  6 . Among the 11 articles, 9 were related to the basic function of exosomes, 1 was about exosomal immunosuppression, and 1 was about tumor-derived exosomes. The title “Minimal information for studies of extracellular vesicles 2018 (MISEV2018): a position statement of the International Society for Extracellular Vesicles and update of the MISEV2014 guidelines” was cited 84 times, which is the highest cited frequency. Under the leadership of Thery C, this paper systematically summarizes the exosome research in the past 10 years, including naming principles, isolation and extraction techniques, evolution characterization of EVs, and biological activities, which provide convenience and guidance for subsequent researchers. Also cited as a high-frequency article “The biology, function, and biomedical applications of exosomes” introduced the biogenetic mechanism of exosomes in detail: double invagination of plasma membrane and formation of intracellular polyvesicles (MVB) containing intracellular vesicles (ILV). Exosome heterogeneity: The heterogeneity of exosomes reflects their size, content, functional effects on recipient cells, and cell origin. Intercellular communication: the proteome in exosomes mirrors the proteome of the original cell, exosome proteins from cancer cells, which can selectively induce specific signals in recipient cells to regulate processes such as development, immune response, and disease when exosome proteins from cancer cells undergo carcinogenic changes and secreted into exosomes again.

Figure  8 shows the cited article with outburst. As the citation with the strongest outburst (intensity = 21.47) in the relevant studies, and the above mentioned articles has the outbreak duration since 2020, which reflects the significance of this article in the study on the correlation between tumour-derived exosomes and inflammatory response.

Top 25 references with the strongest citation bursts

Keywords co-occurrence

Keywords represent the central theme of a paper, and keyword co-occurrence analysis can quickly capture the research hotspots in a certain field. The keywords were summarized using VOSviewer as shown in Fig.  9 . Keywords are displayed in a larger font, and the larger the nodes in the graph, the more frequently they appear; The darker the color, the higher the frequency. According to the node size and color depth, it can be intuitively seen that extracellular vesicles (378), inflammation (218), cancer (142), expression (137), mesenchymal stem cells (133), Micrornas (99) and etc. are highly researched heat in this field.

Keyword density map

The average publication year of each keyword is analyzed in Fig.  10 . The changes from blue to yellow in the figure represent the passage of time, which also indicates that the use of keywords develops with the passage of time. Immunity is the earliest keyword, which is closely related to the development history of exosomes, followed by extracellular vesicles and inflammation. In recent years, the development of obesity, expression, mesenchymal stem cells, microRNAs and etc. high-frequency keywords are basically concentrated in 2019–2020.

Keyword overlay diagram

The above results indicate that the direction of research in this field has changed with the passage of time. From basic research to integration with inflammation and tumor. This process promotes the development of related fields and points the way for future development from basic research to integration with inflammation and cancer.

Keywords cluster

Cluster analysis is a statistical analysis technique that divides research objects into relatively homogeneous groups, which can intuitively understand the general research direction of the analyzed field or discipline.

A total of 9 clusters are generated by the keyword cluster analysis, as shown in Fig.  11 , which include #0 drug delivery, #1 progression, #2 gastric cancer, #3 circulating endothelial microparticles, #4 T cells, #5 long noncoding RNA, #6 adipose tissue, #7 extracellular vesicles, #8 tumor microenvironment. These clusters cover three fields respectively. Cluster #0, #3, #5, and #7 are related to exosomes, cluster #1, #2, and #8 are related to tumors, and cluster #4 is related to immunity. In fact, the identification of these keywords and clusters helps to reveal current research hotspots and frontiers, and provide references for future research in this field.

Keyword clustering diagram

Each cluster keyword reflects the research progress and trend in this field according to the change of time, which are shown in Fig.  12 . It can be seen from the figure that there were few studies on exosomes, and more exploration on extracellular vesicles and progression from January 2005 to December 2015.

Keyword clustering time diagram

Since January 2016, the research on exosomes has been widely distributed in various fields. In particular, the research on exosomes and drug delivery, cancer treatment, and immunity have pushed to a new high after THERY C revealed the intercellular communication of extracellular vesicles in October 2016, and the exploration of exosomes from various fields and angles has laid a foundation for the study of clinical related diseases.

Emergent word analysis

Emergent word analysis can detect the words with high frequency change rate from a large number of subject words in a certain period of time, and can be used to highlight the most active research areas in a specific field. It indicates that the research related to the keyword may be at the forefront of research if a keyword is still in the research explosion period in recent years. Figure  13 shows the top 22 keywords with the highest burst intensity. Among them, the prominent word “MicroRNAs” has been hot for 5 years since 2012, and its popularity continues to soar due to its dual role in the diagnosis and treatment of cardiovascular diseases. On the one hand, microRNAs secreted by leukemia patients can effectively promote the proliferation of malignant cells and the development of cardiovascular diseases (Chinese Society of Hematology et al. 2019); On the other hand, exosomes can induce the secretion of some microRNAs in patients, leading to cardiac repair and regeneration, and their repair and regeneration functions are widely used in ischemia–reperfusion injury (Hematology Oncology Committee et al 2021 ). For example, scientists have found that human umbilical cord mesenchymal stem cell-derived exosomes promote cardiac repair after ischemic injury by protecting cardiomyocytes from apoptosis and promoting cell proliferation and angiogenesis, but human umbilical cord mesenchymal stem cells without exosomes can hardly improve cardiac function (Zhao et al. 2015 ; Yu et al. 2015 ). The reason for this dual nature is that there are many different types of microRNAs, each of them performs similar or very different functions. Therefore, detection and screening of microRNAs plays a crucial role in predicting treatment resistance and cardiovascular disease incidence in patients (Zadeh et al. 2020 ).

The top 22 keywords with the highest burst intensity

The emergent word “obesity”, as the latest emergent word in recent years, is closely related to inflammation, tumor and exosomes. Obesity is a chronic inflammatory disease (Ouchi et al. 2011 ); It’s also a risk factor for breast cancer (Fan et al. 2014 ). Its mechanism is closely related to miR-140 encapsulated by exosomes derived from adipocytes (Gernapudi et al. 2015 ). It increases breast cancer cell migration and promotes cancer progression while affecting hypoxia-inducing factor α1 activity and enhance the aggressiveness of breast cancer cells in vitro and in vivo. The clarification of the relationship among exosomes, inflammation and tumor will play an important role in guiding clinical drug use and treatment (La Camera et al. 2021 ).

In this study, 703 literatures from the Web of Science database were visualized by using Citespace6.6.R6 and VOSviewer1.6.20 software to analyze the overall situation and research hotspots related to tumor-derived exosomes and immune response in the past two decades.

Two developmental stages of exosome-related research

The number of papers on the relationship between tumor exosomes and inflammatory response is increasing year by year. The 703 articles on exosomes were published from January 2005 to January 2024. The growth in the number of publications related to research is divided into two phases according to the rate of research development and research progress reflected in the number and trend of papers published each year, and the research concentration. The first period was from January 1, 2005 to December 31, 2015, and the number of published articles in this period grew at a slower rate, although the mechanism of intercellular communication for extracellular vesicles was basically known (van Niel et al. 2022 ), however, the study of exosomes is still in the stage of exploring the mechanism. The second phase is from January 1, 2016 to January 31, 2024, and this period is growing rapidly, and the research on exosomes is more in-depth and extensive.

Tumor cells and their microenvironment typically produce a large number of immunomodulatory molecules that have a negative (suppressor) or positive (activator) effect on the function of immune cells. Tumor microenvironment (TME) can shift immune response from tumor destructive mode to tumor promoting mode based on its composition (Maia et al. 2018 ).

The components such as immune cells, soluble mediators (cytokines, chemokines, angiogenesis factors, lymphangiogenesis factors, and growth factors) and cell receptors in TME play key roles in the immune response (Bejarano et al. 2021 ). The discovery of the communication mechanism of exosomes provides a new idea for the occurrence of tumor immune microenvironment, which is achieved by inhibiting the function of the immune system and preventing uncontrolled inflammation (Othman et al. 2019 ). For example, exosomes can induce immunosuppression by initiating apoptosis of immune cells (Barros et al. 2018 ; Keryer-Bibens et al. 2006 ). The high concentrations of galectin-9 protein are contained in released exosomes of EBV-infected nasopharyngeal cells (Keryer-Bibens et al. 2006 ; Klibi et al. 2009 ), which can induce apoptosis of mature Th1 lymphocytes. Another example, colorectal cancer or melanoma cell-derived exosomes help tumors escape from the immune system by triggering the ability to activate FAS-dependent apoptosis of CD8 T cells (Andreola et al. 2002 ; Ma et al. 2020 ). A large number of experimental results have gradually confirmed that exosomes are mysterious objects for “cancer immunoediting”.

Geographical distribution of authors for selected articles

The authors from China and the United States published the most articles in terms of authors' countries and regions, these publications accounted for more than half of all collected articles. It shows that scholars from these two countries have done more research on this field and played a leading role in the development of this field. The authors of the articles are basically based on the institution, and all the issuing institutions and countries are independent. Therefore, it is necessary to strengthen the cooperation and exchange between research institutions and researchers in various countries, which is conducive to the flow of information, the innovation of research methods, and the rapid development of the field.

Hotspots and frontiers

Keywords are the research theme and core content of the literature, and the use of keyword co-occurrence analysis can help understand the distribution and growth of various research hotspots on a specific topic. The relationship between tumour-derived exosomes and inflammatory response was revealed, and the research hotspots and frontier development status in this field were further determined through using Citespace to conduct co-occurrence map analysis, cluster analysis, outburst word analysis and time zone map analysis for keywords. At the same time, MCA analysis and visual analysis are carried out by using keywords, and the research direction in this field is highlighted by judging the similarity of different keywords.

MiRNA plays an important role in regulating, mediating and predicting tumor

MiRNAs have attracted much attention as a research hotspot and frontier in this field according to keyword co-occurrence, clustering, keyword emergence and age analysis. microRNA is a type of non-coding RNA (ncRNA) rich in exosomes (Cheng et al. 2014 ). It is also an important carrier and component of exosomes as intercellular material exchange and information exchange (Théry et al. 2011 ). Its presence reflects the tumor progression, indicates the communication between cells in the tumor microenvironment, and its regulation of tumor cell growth (Chiodoni et al. 2019 ). The process of gastric cancer can be regarded as a significant case: Tsai and colleagues found that microRNAs participate in the process of gastric cancer induced by Helicobacter pylori infection, revealing that exosomal microRNAs play an important role in the occurrence, development, metastasis, angiogenesis and chemotherapy resistance of gastric cancer (Tsai et al. 2020 ). Shimoda et al. reported that during the infection of gastric epithelial cells by H. pylori, the expression of mesenchymal epithelial transformation factor (MET) protein activated by exosomes in macrophages was enhanced, which promoted the occurrence and progression of gastric cancer (Shimoda et al. 2016 ). Another example, miR-140 encapsulated by adipocyte derived exosomes can increase the migration of breast cancer cells and promote cancer progression (Gernapudi et al. 2015 ). It also induced the activity of hypoxia-inducing factor α1 and enhanced the invasiveness of breast cancer cells in vitro and in vivo (La Camera et al. 2021 ). Breast cancer cells can secrete miRNA-144 or miRNA-126, which leads to differentiation and remodeling metabolism in beige fat cells, and remodeling fat cells induce tumor proliferation in breast cancer (Wu et al. 2019 ; Dos Santos et al. 2023 ). Thus, the interaction between breast cancer and cancer-associated fat cells forms a mutually reinforcing cycle in cancer metastasis.

Limitations

In this study, the articles in the Web of Science database of English core journals were searched using CiteSpace and VOSviewer software, and the 703 literatures were collected, and the research progress of tumor-derived exosomes and inflammatory responses were analyzed in recent years. While these results provide some valuable insights, the study does have some limitations: First, the study lacks other databases or articles published in other languages, so future studies must use databases such as Pubmend or Scopus to expand their coverage; Second, keyword and reference analysis cannot provide enough information to reveal deeper research motivations and specific research processes, and older articles tend to have higher citation rates, while newly published high-quality literature is cited less frequently. Finally, bibliometrics is more suitable for the analysis of macro trends than for the identification of subtle process mechanisms.

Conclusions

This study shows the hot spots and frontiers in the research field related to tumor-derived exosomes and inflammation. The close relationship among tumor, exosome and inflammation are found through articles analysis. Exosomes act as important mediators between tumor and inflammation, which may accelerate tumor progression by enhancing immunosuppression and inflammation, increasing oxidative stress, inhibiting anti-tumor immune response, or promoting angiogenesis. Due to the heterogeneity of exosomes, it provides a new method for clinical diagnosis and treatment. The exploration of extracellular communication mechanisms and pathways of exosomes from different sources will make outstanding contributions to clinical and multi-field research.

Data availability

No datasets were generated or analysed during the current study.

Abbreviations

Polyvesicular bodies

Impact factor

Journal citation reports

Altevogt P et al (2014) Novel insights into exosome-induced, tumor-associated inflammation and immunomodulation. Semin Cancer Biol 28:51–57. https://doi.org/10.1016/j.semcancer.2014.04.008

Article   PubMed   Google Scholar  

Andreola G et al (2002) Induction of lymphocyte apoptosis by tumor cell secretion of FasL-bearing microvesicles. J Exp Med 195:1303–1316. https://doi.org/10.1084/jem.20011624

Article   PubMed   PubMed Central   Google Scholar  

Bang C, Thum T et al (2012) Exosomes: new players in cell-cell communication. Int J Biochem Cell Biol 44:2060–2064. https://doi.org/10.1016/j.biocel.2012.08.007

Barros FM et al (2018) Exosomes and immune response in cancer: Friends or Foes? Front Immunol 9:730. https://doi.org/10.3389/fimmu.2018.00730

Bejarano L, Jordāo MJC, Joyce JA (2021) Therapeutic targeting of the tumor microenvironment. Cancer Discov 11:933–959. https://doi.org/10.1158/2159-8290.CD-20-1808

Bi Q, Wu JY et al (2022) Tumor-associated inflammation: the tumor-promoting immunity in the early stages of tumorigenesis. J Immunol Res 2022:3128933. https://doi.org/10.1155/2022/3128933

Börner K et al (2012) Design and update of a classification system: the UCSD map of science. PLoS ONE 7:e39464. https://doi.org/10.1371/journal.pone.0039464

Chen C, Song M (2019) Visualizing a field of research: a methodology of systematic scientometric reviews. PLoS ONE 14:e0223994. https://doi.org/10.1371/journal.pone.0223994

Cheng L et al (2014) Exosomes provide a protective and enriched source of miRNA for biomarker profiling compared to intracellular and cell-free blood. J Extracell Ves. https://doi.org/10.3402/jev.v3.23743

Article   Google Scholar  

Chiodoni C et al (2019) Cell communication and signaling: how to turn bad language into positive one. J Exp Clin Cancer Res CR 38:128. https://doi.org/10.1186/s13046-019-1122-2

Chinese Society of Hematology, Chinese Medical Association (2019) Zhonghua xue ye xue za zhi = Zhonghua xueyexue zazhi, 40:89–97. https://doi.org/10.3760/cma.j.issn.0253-2727.2019.02.001

Colombo M et al (2014) Biogenesis, secretion, and intercellular interactions of exosomes and other extracellular vesicles. Ann Rev Cell Dev Biol 30:255–289. https://doi.org/10.1146/annurev-cellbio-101512-122326

Console L, Scalise M, Indiveri C (2019) Exosomes in inflammation and role as biomarkers. Clin Chim Acta Int J Clin Chem 488:165–171. https://doi.org/10.1016/j.cca.2018.11.009

Deng Y, Sun Z et al (2022) Biosensor-based assay of exosome biomarker for early diagnosis of cancer. Front Med 16(2):157–175. https://doi.org/10.1007/s11684-021-0884-z

Dos Santos Z et al (2023) The impact of lipid metabolism on breast cancer: a review about its role in tumorigenesis and immune escape. Cell Commun Signal 21:161. https://doi.org/10.1186/s12964-023-01178-1

Fan Q et al (2018) The emerging role of exosome-derived non-coding RNAs in cancer biology. Cancer Lett 414:107–115. https://doi.org/10.1016/j.canlet.2017.10.040

Fan L, Strasser-Weippl K, Li JJ, St Louis J, Finkelstein DM, Yu KD, Chen WQ, Shao ZM, Goss PE (2014) Breast cancer in China. Lancet Oncol 15:e279–e289. https://doi.org/10.1016/S1470-2045(13)70567-9

Fang X, Lan H et al (2023) Pancreatic cancer and exosomes: role in progression, diagnosis, monitoring, and treatment. Front Oncol 13:1149551. https://doi.org/10.3389/fonc.2023.1149551

Gao P, Li X et al (2021) Diagnostic and therapeutic potential of exosomes in neurodegenerative diseases. Front Aging Neurosci 13:790863. https://doi.org/10.3389/fnagi.2021.790863

Ge Q, Zhou Y, Lu J et al (2014) miRNA in plasma exosome is stable under different storage conditions. Molecules (Basel, Switzerland) 19:1568–1575. https://doi.org/10.3390/molecules19021568

Gernapudi R et al (2015) Targeting exosomes from preadipocytes inhibits preadipocyte to cancer stem cell signaling in early-stage breast cancer. Breast Cancer Res Treat 150:685–695. https://doi.org/10.1007/s10549-015-3326-2

Gross JC et al (2012) Active Wnt proteins are secreted on exosomes. Nat Cell Biol 14:1036–1045. https://doi.org/10.1038/ncb2574

Han L, Xu J, Xu Q et al (2017) Extracellular vesicles in the tumor microenvironment: therapeutic resistance, clinical biomarkers, and targeting strategies. Med Res Rev 37:1318–1349. https://doi.org/10.1002/med.21453

Hematology Oncology Committee, Chinese Anti-Cancer Association et al (2021) Zhonghua xue ye xue za zhi = Zhonghua xueyexue zazhi, 42:705–716. https://doi.org/10.3760/cma.j.issn.0253-2727.2021.09.001

Keryer-Bibens C et al (2006) Exosomes released by EBV-infected nasopharyngeal carcinoma cells convey the viral latent membrane protein 1 and the immunomodulatory protein galectin 9. BMC Cancer 6:283. https://doi.org/10.1186/1471-2407-6-283

Klibi J et al (2009) Blood diffusion and Th1-suppressive effects of galectin-9-containing exosomes released by Epstein-Barr virus-infected nasopharyngeal carcinoma cells. Blood 113:1957–1966. https://doi.org/10.1182/blood-2008-02-142596

La Camera G et al (2021) Adipocyte-derived extracellular vesicles promote breast cancer cell malignancy through HIF-1α activity. Cancer Lett 521:155–168. https://doi.org/10.1016/j.canlet.2021.08.021 . ( Advance online publication )

Liu ZX, Wei EQ et al (2015) Zhejiang da xue xue bao. Yi xue ban = Journal of Zhejiang University. Med Sci 44:211–216. https://doi.org/10.3785/j.issn.1008-9292.2015.03.015

Lu H, Ouyang W, Huang C (2006) Inflammation, a key event in cancer development. Molecul Cancer Res MCR 4:221–233. https://doi.org/10.1158/1541-7786.MCR-05-0261

Ma J, Zhang H, Tang K, Huang B (2020) Tumor-derived microparticles in tumor immunology and immunotherapy. Eur J Immunol 50:1653–1662. https://doi.org/10.1002/eji.202048548

Maia J et al (2018) Exosome-based cell-cell communication in the tumor microenvironment. Front Cell Dev Biol 6:18. https://doi.org/10.3389/fcell.2018.00018

Mathivanan S, Ji H, Simpson RJ (2010) Exosomes: extracellular organelles important in intercellular communication. J Prot 73:1907–1920. https://doi.org/10.1016/j.jprot.2010.06.006

Niu T, Zhou F (2023) Inflammation and tumor microenvironment. Zhong nan da xue xue. bao Yi xue ban = journal of central south university. Med Sci 48:1899–1913. https://doi.org/10.11817/j.issn.1672-7347.2023.230231

Othman N, Jamal R, Abu N (2019) Cancer-derived exosomes as effectors of key inflammation-related players. Front Immunol. https://doi.org/10.3389/fimmu.2019.02103

Ouchi N, Parker JL, Lugus JJ, Walsh K (2011) Adipokines in inflammation and metabolic disease. Nat Rev Immunol 11:85–97. https://doi.org/10.1038/nri2921

Peng Y M, Luo X M et al (2023) Sichuan da xue xue bao. Yi xue ban = Journal of Sichuan University. Medical science edition, 54:505–509. https://doi.org/10.12182/20230560502 .

Qiu H, Lian J et al (2024) Application of exosomes in tumor immunity: recent progresses. Front Cell Dev Biol 12:1372847. https://doi.org/10.3389/fcell.2024.1372847

Rupp AK, Rupp C, Keller S, Brase JC, Ehehalt R, Fogel M, Moldenhauer G, Marmé F, Sültmann H, Altevogt P (2011) Loss of EpCAM expression in breast cancer derived serum exosomes: role of proteolytic cleavage. Gynecol Oncol 122:437–446. https://doi.org/10.1016/j.ygyno.2011.04.035

Salmaninejad A, Ilkhani K et al (2021) Genomic instability in cancer: molecular mechanisms and therapeutic potentials. Curr Pharm Des 27:3161–3169. https://doi.org/10.2174/1381612827666210426100206

Shimoda A et al (2016) Exosomes as nanocarriers for systemic delivery of the Helicobacter pylori virulence factor CagA. Sci Rep 6:18346. https://doi.org/10.1038/srep18346

Shneider AM (2009) Four stages of a scientific discipline; four types of scientist. Trends Biochem Sci 34:217–223. https://doi.org/10.1016/j.tibs.2009.02.002

Silva De et al (2018) Effects of exosomes from LPS-activated macrophages on adipocyte gene expression, differentiation, and insulin-dependent glucose uptake. J Physiol Biochem 74:559–568. https://doi.org/10.1007/s13105-018-0622-4

Simons M, Raposo G (2009) Exosomes–vesicular carriers for intercellular communication. Curr Opin Cell Biol 21:575–581. https://doi.org/10.1016/j.ceb.2009.03.007

Skupin A et al (2004) (2004) The world of geography: visualizing a knowledge domain with cartographic means. Proc Natl Acad Sci USA 101:5274–5278. https://doi.org/10.1073/pnas.0307654100

Théry C (2011) Exosomes: secreted vesicles and intercellular communications. F1000 Biol Rep 3:15. https://doi.org/10.3410/B3-15

Tian Y et al (2022) The role of exosomes in inflammatory diseases and tumor-related. Inflamm Cells 11:1005. https://doi.org/10.3390/cells11061005

Tkach M et al (2017) Qualitative differences in T-cell activation by dendritic cell-derived extracellular vesicle subtypes. EMBO J 36:3012–3028. https://doi.org/10.15252/embj.201696003

Tkach M, Théry C (2016) Communication by extracellular vesicles: where we are and where we need to go. Cell 164:1226–1232. https://doi.org/10.1016/j.cell.2016.01.043

Tsai CC et al (2020) NF-κB/miR-18a-3p and miR-4286/BZRAP1 axis may mediate carcinogenesis in Helicobacter pylori-associated gastric cancer. Biomed Pharm Biomed Pharma 132:110869. https://doi.org/10.1016/j.biopha.2020.110869

Tsui NB, Ng E et al (2002) Stability of endogenous and added RNA in blood specimens, serum, and plasma. Clin Chem 48:1647–1653. https://doi.org/10.1093/clinchem/48.10.1647

Van Eck NJ, Waltman L (2010) Software survey: VOSviewer, a computer program for bibliometric mapping. Scientometrics 84:523–538. https://doi.org/10.1007/s11192-009-0146-3

Van Niel G et al (2022) Challenges and directions in studying cell-cell communication by extracellular vesicles. Nature Rev Mol Cell Biol 23:369–382. https://doi.org/10.1038/s41580-022-00460-3

Vlassov AV et al (2012) Exosomes: current knowledge of their composition, biological functions, and diagnostic and therapeutic potentials. Biochem Biophys Acta 1820:940–948. https://doi.org/10.1016/j.bbagen.2012.03.017

Wu Q et al (2019) Exosomes from the tumour-adipocyte interplay stimulate beige/brown differentiation and reprogram metabolism in stromal adipocytes to promote tumour progression. J Exp Clin Cancer Res CR 38(1):223. https://doi.org/10.1186/s13046-019-12

Yu D, Li Y et al (2022) Exosomes as a new frontier of cancer liquid biopsy. Mol Cancer 21:56. https://doi.org/10.1186/s12943-022-01509-9

Yu B, Kim HW, Gong M et al (2015) Exosomes Secreted from GATA-4 overexpressing mesenchymal stem cells serve as a reservoir of anti-apoptotic microRNAs for cardioprotection. Int J Cardiol 182:349–360. https://doi.org/10.1016/j.ijcard.2014.12.043

Yue Y et al (2020) Interleukin-10 deficiency alters endothelial progenitor cell-derived exosome reparative effect on myocardial repair via integrin-linked kinase enrichment. Circ Res 126:315–329. https://doi.org/10.1161/CIRCRESAHA.119.315829

Zadeh FJ, Ghasemi Y et al (2020) Do exosomes play role in cardiovascular disease development in hematological malignancy? Mol Biol Rep 47:5487–5493. https://doi.org/10.1007/s11033-020-05453-z

Zhang F, Qiao S (2021) Research progress on the relationship between inflammation and colorectal cancer. Ann Gastroenterol Surg 6:204–211. https://doi.org/10.1002/ags3.12517

Zhao Y, Sun X, Cao W et al (2015) Exosomes derived from human umbilical cord mesenchymal stem cells relieve acute myocardial ischemic injury. Stem Cells Int 2015:761643. https://doi.org/10.1155/2015/761643

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This study is supported by the project of the Natural Science Foundation of Hebei Province (No: H2022209048).

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Miao Yu, Yaxuan Jin, Kaize Yuan, Bohao Liu, Na Zhu, Ke Zhang & Shuying Li

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Study conception and design: Miao Yu, Yaxuan Jin, Kaize Yuan and Ke Zhang, Shuying Li. Main data analysis and manuscript draft: Miao Yu, Yaxuan Jin, Kaize Yuan, Bohao Liu, Na Zhu, Ke Zhang, Shuying Li, Zhihui Tai. All authors contributed to the article and approved the submitted version.

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Yu, M., Jin, Y., Yuan, K. et al. Effects of exosomes and inflammatory response on tumor: a bibliometrics study and visualization analysis via CiteSpace and VOSviewer. J Cancer Res Clin Oncol 150 , 405 (2024). https://doi.org/10.1007/s00432-024-05915-y

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Can green credit policies improve the digital transformation of heavily polluting enterprises: A quasi-natural experiment based on difference-in-differences

Roles Conceptualization, Writing – review & editing

Affiliation School of Economics and Management, North University of China, Taiyuan, Shanxi, China

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Affiliation School of Economics and Management, North University of China, Guiyang, Guizhou, China

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Affiliation School of Economics and Management, North University of China, Xingtai, Hebei Province, China

• Xuan Zhou, 
• Dejia Yuan, 
• Zhengwei Geng

• Published: August 29, 2024
• https://doi.org/10.1371/journal.pone.0307722
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The digital transformation of the manufacturing industry is closely linked to green credit policies, which jointly promote the development of the manufacturing industry towards a more environmentally friendly, efficient and sustainable development. Based on the research sample of China’s manufacturing A-share listed companies from 2008 to 2022, this paper uses the difference-in- differences (DID) method to analyze the impact of green credit policies on the digital transformation of heavily polluting enterprises. The results show that green credit policies significantly inhibit the digital transformation of heavily polluting enterprises. In terms of the adjustment mechanism, the R&D investment of enterprises and the financial background of senior executives have weakened the inhibitory effect of green credit policies on the digital transformation of heavily polluting enterprises. When the R&D investment is low, the inhibitory effect of the policy is more significant, but with the increase of R&D investment, the inhibitory effect of the policy gradually weakens, indicating that there is a substitution relationship between the two. Enterprises with senior financial expertise have a deeper understanding of financial feasibility and benefit analysis, and are more receptive to the high-risk investment of digital transformation, while their financial network resources can help broaden financing channels, reduce financing constraints, and further reduce the financial difficulty of digital transformation. In addition, the green credit policy has a stronger inhibitory effect on the digital transformation of non-state-owned enterprises and enterprises that do not hold bank shares. The conclusions of this paper are expected to provide some policy implications for the subsequent green credit policies in promoting the digital transformation of the manufacturing industry.

Citation: Zhou X, Yuan D, Geng Z (2024) Can green credit policies improve the digital transformation of heavily polluting enterprises: A quasi-natural experiment based on difference-in-differences. PLoS ONE 19(8): e0307722. https://doi.org/10.1371/journal.pone.0307722

Editor: Juan E. Trinidad-Segovia, University of Almeria: Universidad de Almeria, SPAIN

Received: March 29, 2024; Accepted: July 10, 2024; Published: August 29, 2024

Copyright: © 2024 Zhou et al. 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 within the manuscript and its Supporting Information files.

Funding: The author(s) received no specific funding for this work.

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

Introduction

Today, as the world experiences rapid digital technology and rising environmental issues, the challenges facing businesses are more complex and urgent. The frontier of digital technology has not only changed the business landscape, but also redefined the position of enterprises in global competition. At the same time, global environmental problems, such as climate change and resource depletion, are threatening the sustainable development of enterprises. As a result, digital transformation and environmental protection, as the two major themes that will lead the future development, are gradually becoming the core elements shaping the corporate strategy. On the one hand, driven by the current wave of digitization, the manufacturing industry is undergoing a profound change, and digital transformation has become a strategic choice for enterprises in meeting future challenges and seizing opportunities, as a strategic initiative integrating advanced technology and innovative thinking, which is leading the manufacturing industry into a new era. The digitalization of the manufacturing industry is a product of internal and external environmental factors [ 1 ], which has a significant impact on the production process and management process of enterprises, which not only changes the traditional production methods, but also leads to a major transformation of enterprise management, marketing, product innovation and other levels. On the other hand, environmental protection is of great importance in today’s global economy. Manufacturing companies must comply with increasingly stringent environmental regulations and standards, which is not only a social responsibility for enterprises, but also an important way to achieve sustainable development. Environmental requirements are driving companies to innovate in technology and business models, and to explore new development opportunities. Through R&D and application of environmental protection technologies, enterprises can develop new products and services, and open up new markets, which can not only enable the manufacturing industry to meet the regulatory requirements of the green environment and social expectations, but also improve resource utilization efficiency, reduce operational risks, enhance market competitiveness, and explore innovation and development opportunities. Driven by digitalization and environmental protection, manufacturing enterprises should integrate environmental protection into their strategic planning, promote green transformation, and achieve a win-win situation of economic and environmental benefits.

As a financial instrument to encourage environmental initiatives, green credit policies provide a new source of funding for companies, and by rewarding environmental measures, they may play a key role in driving companies to participate more actively in the process of digital transformation. From the perspective of capital, the digital transformation of the manufacturing industry requires huge financial support, and this policy may provide enterprises with a way of sustainable financing, which is expected to alleviate the huge financial pressure they may face in the digital transformation. From the perspective of incentive mechanism, green credit policies may also become a driving force for enterprises to take the initiative to move towards digital transformation. With the emphasis of the government and society on environmental responsibility, enterprises are expected to obtain more favorable green credit terms by adopting digital technologies to improve the efficiency of production processes, reduce resource waste, and reduce environmental emissions.

However, while green credit policies may encourage firms to invest more in environmentally friendly technologies in the short term, their specific impact on long-term technological innovation by firms, especially digital transformation, which requires a significant investment of capital and time to bear fruit, remains an area of challenge and unanswered questions. Firstly, the complexity of digital transformation is reflected in the fact that it is not just a technological update, but a comprehensive organizational change. It involves adjustments to company culture, employee training, and the integration of new technologies on a number of levels, all of which take time and effort to change. While short-term green credit policies incentives may push companies to make initial investments in environmentally friendly technologies, to achieve digital transformation in the true sense of the word, companies need longer-term plans and commitments. Second, investments in digital transformation are not permanent and require a continuous injection of capital at different stages. The incentives provided by green credit policies in the short term may not be able to meet the funding needs for the entire transformation cycle. Enterprises may receive some financial support in the initial stage, but the scale and frequency of financial investment may gradually increase as the project deepens and expands. In summary, enterprises must explore the relationship between green finance and digital transformation more actively while pursuing sustainable development. Especially for those heavily polluting enterprises, digital transformation is not only a need to enhance their competitiveness, but also an urgent requirement to fulfill their social responsibility. In this context, whether green credit policy can become a catalyst to promote the accelerated digital transformation of heavily polluting enterprises is a question that deserves in-depth exploration.

Currently, academics have conducted a lot of research on manufacturing digital transformation and green credit policies respectively. On the one hand, studies have shown that digital transformation helps alleviate the information asymmetry between investors and enterprises, and between enterprises and product supply and demand markets, enabling investors to more accurately assess the value and potential of enterprises [ 2 ]. At the same time, the information asymmetry between enterprises and product supply and demand markets has also been alleviated to a certain extent, which leads to more efficient operation of the market. Enterprise digital transformation through digital technology, enterprises can more easily access to financing channels and financing information, improve the flexibility and efficiency of financing, can ease the enterprise financing constraints and reduce the cost of financing, which provides a wider range of financial support for the development and expansion of enterprises, and helps to promote the innovation and upgrading of the manufacturing industry [ 3 ]. In addition, digital transformation can significantly improve the innovation efficiency of enterprises, especially green innovation [ 4 ]. Digital knowledge management (KM) has a significant positive impact on technological innovation, mainly through absorptive capacity, adaptive capacity and innovative capacity [ 5 ]. Meanwhile, the digital transformation of high-tech industries has a positive effect on both technological innovation and achievement transformation [ 6 ]. On the other hand, in terms of green credit policies, the introduction of the Green Credit Guidelines in 2012 marked the official implementation of green credit policies, which is the core of China’s green credit policies system and an important perspective for many scholars to study [ 7 ]. However, most current studies show that the implementation effect of green credit policies is not satisfactory [ 8 ]. On the one hand, green credit policies will inhibit bank loans and long-term financing of heavy polluting enterprises through financing constraint theory and financing cost theory [ 9 ], and significantly reduce long-term bank loans of heavy polluting enterprises [ 10 ]. On the other hand, the green credit policy significantly inhibits the level of technological innovation of heavy polluters [ 11 ]. Maybe the policy will improve the sustainable development of enterprises in the short term, but it has no long-term effect [ 12 ] and promotes poorly managed zombie enterprises [ 13 ].

In summary, digital transformation and green credit policies are key factors in the process of high-quality development of the manufacturing industry in terms of technological innovation, transformation and upgrading. At present, there is a large number of literatures on the digital transformation of the manufacturing industry and green credit policies, but few studies combine the two to explore the relationship between green credit policies and the digital transformation of the manufacturing industry. Therefore, the marginal contributions of this paper may be: Firstly, the uniqueness of the research: This paper may be the first time to deeply explore the relationship between digital transformation in the manufacturing industry and green credit policies, combining these two key areas for research. This research is unique in that it connects the two key themes of digital transformation and environmental policies, filling a gap in the existing literature and providing a new research perspective for the academic community. Secondly, the importance of research to academia and practice: This paper fills the gap in the academic understanding of the relationship between digital transformation and green development in the manufacturing industry, and provides new ideas and methods for solving problems in this field. At the same time, the research results of this paper are of great significance for practice, which can provide useful reference suggestions for China’s green credit policies formulation and digital transformation of the manufacturing industry, promote the sustainable development of the manufacturing industry, and promote the development of China’s economy in a greener and more innovative direction. Thirdly, the theoretical and empirical contributions of the research: By exploring the impact mechanism of green credit policies on the digital transformation of the manufacturing industry, this paper expands the existing theoretical framework and provides new ideas and perspectives for theoretical research. Besides, this paper provides new empirical evidence based on empirical data, deepens the understanding of the mechanism of green credit policies in the process of digital transformation, and provides strong support for practice in related fields. Fourthly, the potential impact of the research: The research results of this paper are expected to have a profound impact on policy-making and practice. By proposing more effective green credit policies to promote the sustainable development of the manufacturing industry, this paper will help guide the government and enterprises to better formulate policies and strategies, promote the development of China’s manufacturing industry in a more digital, green and sustainable direction, and contribute to the realization of high-quality economic development.

Materials and methods

Theoretical analysis and research hypothesis.

Digital transformation typically requires large-scale capital investments to meet the costs of building information technology infrastructure, procuring innovative technologies and training employees. Such investment is necessary to drive enterprises to achieve business process optimization, improve productivity, expand market share, and enhance innovation. However, the introduction of “the Green Credit Guidelines” tends to exacerbate the financing constraints of heavy polluters [ 14 ], which in turn may hinder their active participation in digital transformation. Firstly, from a financial perspective, the financial requirements for digital transformation are usually large, including but not limited to the updating of IT infrastructure, the construction of big data analytics platforms, the introduction of artificial intelligence technologies and related training costs. Heavily polluting enterprises usually face higher environmental risks, and from the "principal-agent cost theory" and "modern contract theory", it can be seen that the principal-agent cost between the bank, as a creditor, and the enterprise will increase with the increase in project risks, including the costs of identification, monitoring, management and auditing. The cost of identification, monitoring, management and auditing, etc. will lead banks to adopt a more conservative strategy when considering costs and benefits. Meanwhile, according to the "risk compensation theory", in order to compensate for the potential environmental risks and possible default risks in the future, banks and financial institutions may require heavy polluting enterprises to pay higher financing costs or put forward more stringent lending conditions [ 15 ], such as higher interest rates or additional collateral, in order to obtain the price of risk-bearing compensation. This will lead to higher financing costs for heavy polluters [ 16 ]. This means a tighter financial situation for heavy polluting enterprises who are already under pressure to make environmental improvements, reducing their ability to invest in digital transformation.

Secondly, from the perspective of environmental protection and governance costs, the environmental regulatory effect brought about by “the Green Credit Guidelines” will increase the rectification efforts of heavy polluting enterprises to reduce pollution and emissions, which will to some extent reduce the priority and capital investment in digital transformation projects, thus slowing down the process of digital transformation. On the one hand, heavy polluting enterprises may need to reallocate resources in order to comply with the requirements of “the Green Credit Guidelines”, which means that enterprises may need to invest more R&D funds and human resources into the end-of-pollution treatment [ 17 ], reducing the allocation of funds and resources in digital transformation. This not only makes digital transformation projects significantly less economically attractive within enterprises, but also further inhibits the pace of transformation in the digital field for heavily polluting enterprises. On the other hand, the process of environmental protection management may involve changes such as re-planning of production lines, optimization of production processes, and upgrading of environmental protection facilities. This not only requires the investment of a large amount of resources, but also may lead to disruptions and uncertainties in the production process, bringing additional disturbances to the normal operation of the enterprise. Accordingly, the author proposes the following hypothesis:

H1: “The Green Credit Guidelines” significantly inhibit the digital transformation of heavy polluters.

The amount and quality of an enterprise’s R&D investment is directly related to its innovative capacity and future development potential. In today’s competitive market, firms that are able to increase their R&D investment on a sustained basis are usually more likely to be able to adapt to market changes and meet future challenges. High levels of R&D investment may play a key role in the digital transformation of heavily polluting firms in weakening the disincentive effect of green credit policies. Firstly, increased R&D investment can make firms more technologically innovative [ 18 ], accelerate their digital transformation process, and promote the adoption of more advanced digital technologies. This not only improves productivity and product quality, but also helps to reduce environmental emissions, thus meeting the expectations of green credit policies on environmental requirements. Technological innovation makes enterprises more flexible in digital transformation and allows them to better respond to the environmental standards of the policy, thus weakening the inhibiting effect of the policy on digital transformation. Secondly, a high level of R&D investment helps to improve the productivity of enterprises, and through the application of digital technology, enterprises are able to manage and utilize resources more effectively. Initiatives such as optimizing the supply chain and implementing smart manufacturing can reduce the waste of energy and raw materials and lessen the burden on the environment. This efficient use of resources makes it easier for firms to adapt to the environmental requirements of the policy, diminishing the constraints of green credit policies on digital transformation. Once again, increased investment in R&D demonstrates a firm’s commitment to innovation and sustainability. This strategic shift makes firms more inclined to adopt digital technologies to improve productivity and reduce environmental impacts. For heavily polluting firms, digital transformation is not only a technological upgrade, but also a necessary tool to comply with the SDGs. Investments in research and development lead companies towards a digitalization path that is consistent with green credit policies, slowing down the disincentive effect of the policies.

At the same time, investment in R&D is not only about technical aspects, but also includes investment in training and culture. By improving employees’ understanding and ability to apply digital technologies, companies can better adapt to the level of technology required for digital transformation and more easily comply with green credit policies. Building green awareness and a culture of sustainability can help firms better integrate digital technologies and mitigate the disincentive effect of policies on digital transformation. In addition, the relationship between R&D investment intensity and enterprise survivability shows a "U" non-linear relationship, i.e., R&D investment intensity can greatly improve the survivability of enterprises after reaching a certain level [ 19 ]. This implies that a moderate increase in R&D investment by enterprises in the process of digital transformation can improve their competitive position in the market while increasing their innovation ability, and mitigate the potential inhibitory effect of green credit policy on their digital transformation. Overall, corporate R&D investment may affect corporate digital transformation on multiple levels by driving technological innovation, improving productivity, promoting sustainable development, and fostering corporate culture. Efforts in all these areas can help weaken the inhibitory effect of green credit policies on the digital transformation of heavy polluting enterprises and enable them to carry out their digital transformation more smoothly. Accordingly, the author proposes the following research hypothesis:

H2: Firms’ R&D investment weakens the dampening effect of “the Green Credit Guidelines” on the digital transformation of heavily polluting firms.

The digital transformation of an enterprise is inherently a high-risk business investment, as it involves huge capital investment in new technologies, systems, training and human resources, and such high-cost, resource-intensive investment poses a greater financial challenge to the enterprise. Importantly, digital transformation is usually characterized by greater uncertainty, with technology risk being a key consideration. The introduction of new technologies may lead to technology integration issues and additional costs, and the results and rewards of digital transformation usually take longer to become apparent. In addition, digital transformation requires a cultural shift within the organization, including employee training and adaptation to new ways of working, and this cultural change can be a complex and time-consuming process. Top echelon theory suggests that executives with a financial background typically have a greater tolerance for risk. This trait may have a significant impact in the project decision-making process, making executives more willing to take risks and thus increasing the likelihood that firms will choose riskier projects [ 20 ]. Because executives with a financial background typically have a deeper understanding of national policies, market volatility, and financial instruments, they may be more responsive to financial incentives in green credit policies. Compared to their counterparts with non-financial backgrounds, they may be able to utilize green credit resources more effectively in digital transformation and reduce the cost of corporate finance, which in turn will make them more confident in dealing with potential risks, and thus more willing to choose higher-risk investments in corporate projects, leading to a smooth digital technology transition.

At the same time, as executives with a financial background usually have profound financial knowledge and risk management skills, they have a deeper understanding of financial feasibility and benefit analysis. Therefore, they pay more attention to the financial feasibility of enterprise digital transformation in the decision-making process, which helps to establish a more efficient financial review and decision-making process [ 21 ], and can more accurately assess the positive impact of green credit policies on the enterprise’s financial position compared to others. This financial sensitivity makes them more capable of reducing potential uncertainties through rational financial strategies, and more able to increase enterprises’ acceptance of digital transformation, thus more actively promoting enterprises to follow the path of green transformation. Additionally, executives with financial background can use their own financial network resources to establish bank-enterprise contacts, broaden financing channels, reduce the information asymmetry between the enterprise and the bank, so that the enterprise can obtain more funds to alleviate the degree of enterprise financing constraints [ 22 ], and further reduce the financial difficulty of digital transformation. Based on the above analysis, the author puts forward the following research hypotheses:

H3: Executive financial background weakens the dampening effect of “The Green Credit Guidelines” on digital transformation of heavily polluting firms.

Research design

Model building..

“The Green Credit Guidelines” issued in 2012 provide a good quasi-natural experiment to study the impact of green credit policies on the digital transformation of manufacturing industries. According to the characteristics of this policy, heavily polluting firms should be affected firstly because they face higher environmental risks. Therefore, this paper includes heavily polluting enterprises in the experimental group and non-heavily polluting enterprises in the control group.

Data sources.

This paper takes listed companies in China’s manufacturing industry from 2008 to 2022 as the initial sample, and in order to improve the data quality and ensure the validity of the empirical analysis, the initial sample [ 23 ] is screened in accordance with the following criteria: (1) exclude companies with financial anomalies during the sample period, such as ST,* ST, and PT; (2) exclude companies that change their industries between heavy polluting enterprises and non- heavy polluting enterprises during the sample period; (3) exclude key data companies with serious missing data; (4) to avoid extreme values interfering with the findings of this paper, all continuous variables are subjected to the upper and lower 1% shrinkage. Through the above screening, the final sample includes 660 companies with a total of 9,345 observations, of which heavy polluting enterprises contain 220 companies and non- heavy polluting enterprises contain 440 companies; the data used in the study come from the CSMAR database, the iFind database, the Wind database, the National Bureau of Statistics, and MarkData.com , among others.

Variable selection.

Explained variable . The explained variable in this paper is the level of digital transformation of the enterprise, referring to the research results of Chen et al. (2021) [ 24 ]: Based on the statistics of 99 digital-related word frequencies in four dimensions: digital technology application, Internet business model, intelligent manufacturing, and modern information system, the digital transformation index of manufacturing enterprises was constructed by using text analysis method and expert scoring method. First, use text analytics to construct Digit_text variables. The first step is to collect the annual reports of listed companies in the manufacturing industry from 2008 to 2022 and convert them into text format, and then extract the text of the business analysis part through Python. The second step is to extract a certain number of samples of enterprises that have been successful in digital transformation through manual judgment. In the third step, the selected samples were processed by word segmentation and word frequency statistics to screen out high-frequency words related to digital transformation, which can be divided into four dimensions: digital technology application, Internet business model, intelligent manufacturing and modern information system, which suggests that we can construct the digital transformation index of enterprises from four dimensions (see Table 1 ). In the fourth step, based on the words formed in the third step, the text before and after is extracted from the total sample of listed companies, and the text combinations with high frequency are found. The fifth step is to supplement the keywords on the basis of the existing literature to form the final word segmentation dictionary. In the sixth step, based on the self-built word segmentation dictionary, the Jieba function is used to segment all samples, and the number of keyword disclosures is counted from four aspects: digital technology application, Internet business model, intelligent manufacturing and modern information system, so as to reflect the degree of transformation of the enterprise in all aspects. On this basis, the word frequency data was standardized, and the entropy method was used to determine the weight of each index, and finally the Digit text index was obtained.

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Secondly, according to the description of the above keywords in the annual report, the number of disclosures, and the production and operation of the enterprise, the expert scoring method is used to judge the degree of digital transformation of each company. Specifically, if "digitalization" is the main investment direction of the enterprise in the year, or "digitalization" has been integrated into the main business of the enterprise (including production, operation, R&D, sales and management, etc.), the Digit_score variable is scored with 3 points; If the enterprise’s relevant investment involves "digitalization", but "digitalization" is not the main investment direction at this stage, or the company’s main business has not yet achieved deep integration with "digitalization", 2 points will be scored for the Digit_score variable; If the company only touches on a small aspect of "digitalization", or only mentions it in its development strategy and business plan, the Digit_score is set at 1; If there is no mention of "digitalization" in the company’s annual report, or if the annual report reflects that the company has not implemented digital transformation, the Digit_score score is 0.

Finally, on the basis of the obtained Digit_text and Digit_score, the final total index Digit is synthesized according to the weight of 50% each, so as to fully reflect the degree of digital transformation of manufacturing enterprises.

Explanatory variable . Based on the principle of DID model, the explanatory variable is the interaction “Post*Treat” (DID) of the policy dummy variable (Post) and the industry dummy variable (Treat). Since “The Green Credit Guidelines” came into effect on 24 February 2012, 2012 is used as a time dummy variable in this article, and for 2012 and subsequent years, Post is equal to 1, otherwise it is equal to 0. Referring to previous studies [ 25 ], this paper selects the Catalogue of Classified Management Industries for Environmental Protection Verification of Listed Companies issued by the Ministry of Environmental Protection in 2008 to identify heavy polluting enterprises, and if they belong to the heavy polluting industries mentioned in the 2008 Ministry of Environmental Protection Notice, they are defined as heavy polluting enterprises. Treat is a grouping dummy variable, with 1 for heavily polluting enterprises and 0 for non-heavily polluting enterprises.

Control variables . In order to avoid the estimation bias caused by omitted variables, this paper refers to the results of previous research [ 26 ], and selects the following variables as the control variables in the empirical process: (1) Size, (2) Lev, (3) ROE, (4) Tobin Q, (5) Liquid, (6) Cashflow, (7) Loss, (8) Dual.

In summary, the specific definitions of the variables are shown in Table 2 .

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Descriptive statistics and analysis.

After the data in this paper were analyzed by descriptive statistics, the results are shown in Table 3 . It can be seen that the level of digital transformation (Digit) of China’s heavy polluting enterprises has a maximum value of 757, a minimum value of 0, and a standard deviation of 42.2630, indicating that there is a large difference in the degree of digital transformation among enterprises. The current ratio (Liquid) has a maximum value of 204.7421, a minimum value of 0.1065, and a standard deviation of 4.4500, indicating that there are also large differences in current ratios among firms. A higher liquidity ratio may indicate a more flexible operation and liquidity, while a lower liquidity ratio may indicate that a company is facing a shortage of funds or assets that cannot be liquidated quickly. Taken together, the descriptive statistics of both the level of digital transformation and the current ratio reveal that there are large differences in the operational management of China’s heavy polluters, and that these differences may have an important impact on the competitiveness and long-term development of the enterprises.

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Results and discussion

Benchmark regression.

Table 4 shows the empirical results of the impact of green credit policies on the digital transformation of heavy polluting enterprises, columns (1) and (2) are the cases of regression alone and adding control variables and fixing the year and individual, respectively. It can be concluded that the DID coefficients are all significantly negative, and the implementation of green credit policies significantly inhibits the digital transformation of heavily polluting enterprises, and hypothesis 1 is verified. The possible explanation is that at present, bank credit is the main financing method for most enterprises in China, and the introduction of the “The Green Credit Guidelines” will make banks more inclined to provide financial support to environmental protection enterprises, while heavy polluting enterprises are difficult to obtain financial support from banks due to serious environmental risks, which will eventually lead to a lack of funds for heavy polluting enterprises, thereby inhibiting a series of technological research and development activities such as digital transformation.

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Robustness check

Parallel trend test..

To ensure that the results of this paper are not affected by other policies and events, referring to the study of Zhang and Hu (2022) [ 27 ], the event study method is used to introduce multiple time dummy variables to construct early and lagged policy variables, and regressions are added while keeping the control variables unchanged. The results of the four coefficients before the promulgation of the policy and the coefficients in the last nine periods are shown in Table 5 , and the parallel trend test chart is shown in Fig 1 , the DID coefficients in the first four periods of the policy are not significant, while the coefficients in the nine periods after the promulgation of the policy are significantly negative. Therefore, the experimental group and the control group are comparable before the implementation of the policy in 2012, and the difference-in-difference regression model in this paper conforms to the parallel trend hypothesis, indicating that the original regression results are robust.

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Placebo testing.

In order to ensure that the impact of “the Green Credit Guidelines” on the digital transformation of heavy polluting enterprises can truly reflect the effect of the policy without being influenced by other factors, drawing on the research results of Guo and Yin (2023) [ 17 ], an experimental group is randomly generated to simulate a situation that is not affected by the green credit policy, in order to compare the differences between the experimental group and the control group before the implementation of the policy. This is done by randomly, year-by-year and no-putback sampling 2008–2022 enterprises as the experimental group and the rest of the enterprises as the control group, and substituting them into model (1) for regression respectively. The probability density distribution of the coefficient estimates in the placebo test was obtained after 500 random draws and regression tests (see Fig 2 ). As can be seen from Fig 2 , the coefficient estimates from the placebo test are mainly distributed around zero, indicating that the original regression results are robust.

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In order to eliminate the endogeneity problem caused by potential selection bias, ensure the robustness of the research results, and improve the comparability of the experimental and control groups in terms of digital transformation, the propensity score matching method was used to conduct the robustness test, drawing on the study of Li (2023) [ 28 ]. All control variables in model (1) are selected as matching indicators in the propensity score matching model, and a Logit model is selected to estimate the propensity score, and then nearest-neighbor matching is used to re-match the experimental and control groups to ensure that there is no difference in other factors between the matched experimental and control groups except for the policy differences, and then subsequently re-estimate the model (1). Fig 3 shows that there is a significant difference between the experimental and control groups before matching, and Fig 4 shows the same trend after matching; the DID coefficient is still significantly negative at the 1% level from column (1) of Table 6 , which further validates the robustness of the findings of this paper.

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Replacement of core explanatory variables.

This paper replaces the explanatory variables with reference to the research results of Wu et al. (2021) [ 29 ], which are statistically derived from a total of 76 digitization-related word frequencies in five dimensions, namely, artificial intelligence technology, big data technology, cloud computing technology, blockchain technology, and the use of digital technology. The regression results are shown in column (2) of Table 6 , and the coefficient of DID is still significantly negative, which again verifies the robustness of the findings of this paper.

Treatment of endogenous problems.

Lagging the core explanatory variables by one period helps to alleviate the endogeneity problem and improves the model’s ability to explain time correlation and long-term causality. In this paper, by regressing the core explanatory variable DID with one period lag, the results are shown in column (3) of Table 6 , and the DID coefficient of is still significantly negative, which indicates that the findings of this paper are still robust after taking into account the time lag effect.

Mechanism of action tests

Moderating effects of r&d investment..

The results of the moderating effect test for R&D inputs reported in column (1) of Table 7 show a significantly negative coefficient for R&D inputs and a significantly positive coefficient for the interaction term between R&D inputs and green credit policies. This reflects that overall, enterprise R&D investment weakens the inhibitory effect of the Guidelines on the digital transformation of heavily polluting enterprises, and the inhibitory effect exerted by the policy is more obvious when R&D investment is low, but the inhibitory effect brought about by the policy gradually decreases with the increase of enterprise R&D investment, which suggests that there is a significant substitution relationship between R&D investment and the “the Green Credit Guidelines” in influencing the digital transformation of heavily polluting enterprises, and Hypothesis 2 can be verified. Firstly, the reason why R&D investment can attenuate the inhibitory effect of the green credit policy on the digital transformation of heavy polluting enterprises may be that by strengthening R&D investment, enterprises are more likely to improve their technological level, adopt more environmentally friendly technologies and production methods, and receive more support under the green credit policy, thus alleviating the policy’s restriction on the funds required for digital transformation. At the same time, it may indicate that policymakers recognize and encourage firms that meet their environmental goals through independent R&D, as these firms are more likely to succeed in digital transformation; second, the disincentive effect of the policy is relatively more pronounced when R&D inputs are low, which may be due to the fact that the policy puts more emphasis on promoting the digital transformation of firms through financial support, whereas, in the case of low R&D inputs, firms may be more rely on the green credit support provided by the government; finally, the inhibitory effect brought by the policy gradually decreases as the R&D investment of enterprises increases, which suggests that there is an obvious substitution relationship between the R&D investment and the green credit policy in influencing the digital transformation of heavily polluting enterprises, and the possible explanation is that enterprises may prefer to choose to meet the environmental protection requirements through independent R&D, instead of overly relying on the government’s green credit policies.

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Moderating effects of executive financial background.

The test results of the moderating effect of executive financial background reported in column (2) of Table 7 show that the coefficient of executive financial background is negative but insignificant and the coefficient of its interaction term with green credit policies is significantly positive, which suggests that executive financial background weakens the inhibitory effect of “the Green Credit Guidelines” on the digital transformation of heavily polluting firms, and Hypothesis 3 is verified. The possible reasons for this are as follows, the advantage of executive financial background lies in its greater tolerance to the high-risk nature of digital transformation. This is mainly reflected in the fact that financial expertise makes them more sensitive to the financial incentives of green credit policies and more effective in utilizing green credit resources, thus reducing the cost of corporate financing and increasing the acceptance of digital transformation as a high-risk investment. At the same time, gold executives with financial backgrounds have a deeper understanding of financial feasibility and benefit analysis, which reduces uncertainty through rational financial strategies and pushes enterprises to follow the green transformation path more actively. In addition, their financial contacts help broaden financing channels and reduce financing constraints, further easing the financial difficulty of digital transformation.

Heterogeneity analysis

Whether the enterprise is a state-owned enterprise..

In this paper, state-owned enterprises (SOEs) and non-state-owned enterprises (NSOEs) are regressed separately, and the results, as shown in columns (1) and (2) of Table 8 , indicate that the inhibitory effect of green credit policy on digital transformation is significantly higher for NSOEs than for SOEs. The possible explanations are as follows: firstly, SOEs and non-SOEs play different roles in China’s economic environment, with SOEs usually having easier access to government support and financing, while non-SOEs may be more dependent on indirect financing such as bank loans. Green credit policies may lead banks to be more prudent in approving loans and may place greater constraints on the financing needs of non-SOEs, thus inhibiting their digital transformation process; secondly, green credit policies usually require companies to take more steps in environmental compliance to qualify for loans. Non-state-owned enterprises may need more time and resources to meet these requirements, and thus may face greater resistance in the digital transformation process; finally, state-owned enterprises may enjoy market monopolies or more government support in some cases, which may make them more able to bear the costs of digital transformation. In contrast, non-State-owned enterprises may operate in more competitive market environments and be more vulnerable to green credit policies, as digital transformation requires greater capital investment.

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Whether the enterprise holds shares in the bank.

In this paper, firms holding bank shares and firms not holding bank shares are regressed separately. The results show that the inhibition effect of green credit policies on the digital transformation of non-state-owned enterprises is significantly higher than that of state-owned enterprises. The possible explanations are as follows: firstly, that the green credit policy may impose stricter environmental requirements on heavily polluting firms that do not hold bank shares by strengthening loan approval criteria, thereby limiting their access to funds for digital transformation. In contrast, firms that hold bank shares may be more likely to fulfill the conditions of green credit policies due to closer relationships with financial institutions such as banks. Secondly, firms with different shareholding structures may adopt different strategies in responding to green credit policies. Firms that do not hold bank shares may be more inclined to adopt a strategy of directly confronting environmental requirements by adapting their production and management practices to reduce environmental impacts, while relatively slowing down the pace of digital transformation. In contrast, firms with bank holdings may be more likely to obtain funding through green credits and thus invest more aggressively in digital transformation in order to adapt to environmental trends.

Conclusions and policy recommendations

Based on “the Green Credit Guidelines” issued in 2012, this paper selects China’s manufacturing A-share listed companies from 2008 to 2022 as the research sample. Based on the existing research, this paper uses the DID method to investigate and evaluate the impact of green credit policies on the digital transformation of heavily polluting enterprises. The research results show that: Firstly, the green credit policy, represented by “the Green Credit Guidelines”, has a significant inhibitory effect on the digital transformation of heavily polluting enterprises. Secondly, from the perspective of the adjustment mechanism, the R&D investment and the financial background of senior executives weaken the inhibition effect of “the Green Credit Guidelines” on the digital transformation of heavily polluting enterprises, and when the R&D investment is low, the inhibitory effect of the policy is more obvious, but with the increase of enterprise R&D investment, the inhibitory effect of the policy gradually decreases, that is, the R&D investment of enterprises and the Guidelines have an obvious substitution relationship in affecting the digital transformation of heavily polluting enterprises. Thirdly, “the Green Credit Guidelines” has a significantly stronger inhibitory effect on the digital transformation of non-SOE heavy polluting enterprises than that of SOEs; it has a significant inhibitory effect on the digital transformation of heavy polluting enterprises that do not hold shares in a bank, while the effect on heavy polluting enterprises that hold shares in a bank is insignificant.

Based on the above conclusions, this paper puts forward the following policy recommendations from the perspectives of government and enterprises.

On the one hand, the government should launch a special digital transformation loan program to provide heavily polluting enterprises with preferential conditions such as low interest rates and extended repayment periods, so as to ensure that they receive adequate financial support in the process of digital transformation. At the same time, the government should encourage enterprises to increase R&D investment, such as through tax incentives and scientific research funding support, to encourage enterprises to increase R&D investment in the field of digitalization. Flexibly adjust the green credit conditions according to the level of enterprise R&D investment, and provide more flexible credit support for enterprises with low R&D investment. In addition, the government should implement differentiated green credit policies. Formulate differentiated policies according to the nature and shareholding of enterprises, and promote close cooperation between non-state-owned enterprises and non-bank shares and financial institutions to ensure that these enterprises can obtain favorable financial support. On the other hand, enterprises should actively apply for the government’s digital transformation loan program to take advantage of low interest rates and flexible repayment terms to reduce financing pressure and ensure the funds needed for digital upgrading. At the same time, enterprises should increase R&D investment and increase digital technology R&D and innovation activities to improve their competitiveness. In addition, enterprises should pay attention to financial literacy training such as digital literacy of senior executives, and encourage enterprises to participate in training programs to enhance their understanding and support for digital transformation. Finally, companies should optimize their financing structures and strengthen financial cooperation. Specifically, non-state-owned enterprises should explore flexible financing methods and establish close cooperation with financial institutions to obtain favorable financial support. Companies with bank stakes should optimize their financing structures and leverage their banking relationships to obtain better financing conditions to support digital transformation.

Supporting information

https://doi.org/10.1371/journal.pone.0307722.s001

Acknowledgments

We would like to express my sincere thanks to the editors and reviewers of the magazine. Thank you for your meticulous review of my manuscript and your valuable comments during your busy schedule. Your professional insights and constructive suggestions have greatly improved the quality and scientific of this paper, and provided important guidance for the refinement and improvement of this study. We have benefited greatly from your hard work and patience in the course of my research. We know that your valuable time and energy play an important role in advancing academic research and knowledge. Therefore, we would like to express my heartfelt respect and gratitude to you for your selfless dedication.

Thank you again for your attention and support to my manuscript, and look forward to your continued guidance and help in the future.

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Solar photovoltaic module end-of-life waste management regulations: international practices and implications for the kingdom of saudi arabia.

1. Introduction

1.1. literature survey, 1.2. research methodology, 1.2.1. research design, 1.2.2. data collection, 1.2.3. analytical framework, 1.2.4. criteria for analysis.

• Comprehensiveness: the scope and depth of regulations covering the life cycle of solar PV modules.
• Stakeholder Engagement: the involvement of manufacturers, consumers, and recyclers in the EOL process.
• Enforcement and Compliance: mechanisms in place to enforce regulations and ensure compliance.

1.2.5. Link to Objectives and Research Questions

• What are the best practices in solar PV EOL waste management among leading countries?
• How can these practices be adapted to fit the Saudi context under Vision 2030?
• What are the potential benefits of implementing these international practices in Saudi Arabia?

1.2.6. Inclusion and Exclusion Criteria

• The policy document must address solar photovoltaic (PV) end-of-life waste management, with a focus on recycling, reuse, and disposal of PV components.
• The policy document must be accessible to the public and written in English.
• The policy document must have been published by 2024.
• The policy document does not pertain to solar PV end-of-life waste management.
• The policy document is not publicly accessible or is written in a language other than English.
• Legal Framework : evaluation of the legal and regulatory structures governing solar PV end-of-life waste management in each country.
• Policy Goals : analysis of the objectives outlined in the policies concerning solar PV end-of-life waste management.
• Policy Tools : review of the instruments utilized to achieve the goals related to solar PV end-of-life waste management.
• Implementation : examination of the effectiveness of policy implementation regarding solar PV end-of-life waste management.
• Policy Effectiveness : assessment of how successful the policies have been in managing solar PV end-of-life waste.
• Stakeholder Engagement : Analysis of the involvement of key stakeholders, including industry and civil society, in the development and execution of solar PV end-of-life waste management policies.

2. Solar PV Module Waste Composition

Composition of solar pv waste, 3. countries generating higher solar pv end-of-life waste volumes, 4.1. national solid waste law.

• The new Solid Waste Law stipulates that “solid waste” refers to items and substances that are in a solid, semi-solid, or gaseous state, contained in containers, and are generated from various activities such as production, daily life, and other activities. These items and substances have lost their original usefulness, are discarded or abandoned, despite potentially still having value.
• In the new Solid Waste Law, “solid waste” refers to objects and substances that are subject to management under laws and administrative regulations, except for waste that has undergone treatment to reduce its volume and hazardousness, meets national product quality standards, and does not pose a risk to public health or the environment. Additionally, any items that do not meet the standards and procedures for solid waste identification are not classified as solid waste.
• Producers’ Responsibility : the law requires producers of products to establish a sound EOL management system and to bear the primary responsibility for the collection, transportation, and disposal of EOL waste generated by their products.
• Collection and Disposal : Producers of products are required to set up collection points for EOL waste generated by their products and to ensure the proper disposal of such waste. In addition, producers are required to publish information about the EOL waste management system on their websites and in product manuals.
• Environmental Protection : Producers are responsible for ensuring that the disposal of EOL and it does not cause harm to the environment or human health. This includes proper handling and disposal of hazardous materials, such as lead, cadmium, and other toxic substances, which may be present in the panels.
• Reporting and Record-Keeping : producers of products are required to submit annual reports on the EOL waste management activities and to maintain records of the EOL waste collected and disposed of.
• Public Information : the law requires producers of products to provide information to the public about their EOL waste management activities and to promote public awareness of the importance of proper EOL waste management.
• Penalties : Companies that violate the provisions of the National Solid Waste Law, including those related to the management of EOL solid waste, may face fines and other penalties. The amount of the fine will depend on the severity of the violation and the extent of any environmental damage caused.

4.2. Specifications for Recycling and Reusing Thin-Film Solar Panels in Construction Applications (GB/T 38785-2020)

• Strategies for the collection, transit, and processing of discarded panels, extraction, and refinement of valuable components, alongside secure management and elimination of toxic substances;
• Establishes benchmarks for assessing the environmental repercussions of recycling and repurposing thin-film solar panels, alongside recommendations for the architectural and manufacturing phases to enhance recyclability and reusability;
• Seeks to advance the photovoltaic sector’s ecofriendly growth by advocating for the conscientious disposal of waste panels and effective resource utilization.

4.3. Regulations for the Control of Pollution from Storage and Landfill of Nonhazardous Industrial Solid Waste (GB 18599-2020)

• Comprehensive guidelines for the design, construction, operation, and closure of nonhazardous industrial solid waste storage and landfill sites;
• Criteria for site selection, groundwork, sludge management, gasses emission control, and overall site supervision to avoid pollution;
• Directions on acceptable waste categories for these sites, detailing procedures for waste reception, processing, and transportation;
• Requirements for continuous monitoring, documentation, and emergency response plans to efficiently identify and address environmental risks;
• Emphasizes community engagement in waste management processes and outlines methods for effective communication with local communities and stakeholders.

4.4. Technical Guidelines for the Recycling of Electrical and Electronic Equipment Waste (GB/T 23685-2009)

• Detailed procedures for the gathering, storing, transport, and dealing of WEEE, including the design and operational standards for collection facilities and safe handling practices for hazardous components;
• Guidelines for the extraction and purification of valued materials from WEEE, like metals and plastics. It contains techniques for material separation, reprocessing processes, and the potential reutilization of components;
• Standards for assessing the environmental effect of WEEE recycling activities, considering energy consumption and greenhouse gas emissions, to promote sustainability in recycling practices;
• The importance of community awareness and education regarding the proper management of WEEE, including recommendations for educational campaigns and community engagement efforts;
• Encouragement of the advancement of a healthy market for recycled materials to support a circular economy and decrease the waste generation.

4.5. Definitions Related to the Recuperation of Waste Products (GB/T 20861-2007)

• Offers a detailed compilation of definitions and terms relevant to the recovery of waste materials, encompassing aspects like sorting, collection, transportation, and processing of waste;
• Covers a wide range of terms related to the recovery and recycling operations for various waste materials, including plastics, metals, paper, and glass;
• Introduces vocabulary related to the environmental and economic advantages of waste recovery, emphasizing efficient resource use, principles for carbon footprint assessment, and circular economy;
• Objectives for standard harmonization to facilitate waste recovery global collaboration and communication;
• Highlights the reputation of exact and uniform terminology to enhance mutual cooperation and understanding between shareholders in waste recycling and recovery industries.

4.6. Observations

5. the united states of america (usa), 5.1. national legislation: resource conservation and recovery act (rcra).

• Solid Waste Definition Clarification: A new guideline was established to delineate when discarded PV panels are considered as solid waste, exempting them from RCRA mandates under certain conditions. This clarification aids the solar sector in navigating EOL waste management with reduced regulatory ambiguity;
• Hazardous Waste Regulation Adjustments: a conclusive regulation now omits specific PV panel types from being treated under hazardous waste guidelines, alleviating the solar industry’s regulatory obligations and fostering the recycling and repurposing of PV panels;
• PV Panels Conditional Exclusion: a conditional exclusion has been formulated for PV panels managed under specific criteria, enabling their recycling in a manner that is both environmentally responsible and exempt from the RCRA’s stringent regulations;
• Recycling Standards for PV Panels: the EPA has set forth standards for the recycling processes of PV panels, ensuring their environmentally sound management and recycling practices;
• National Recycling Capacity Assessment for PV Panels: An analysis to gauge the United States’ recycling industry’s capacity to handle PV panel waste was conducted. This assessment is instrumental in shaping future policy directions for PV panel waste management.

5.2. Overview of State-Level Regulations for Managing End-of-Life Waste from Solar PV Systems in the U.S.

5.3. observation, 6.1. waste management and public cleansing law (1970), 6.2. the resource recycling act (2013), 6.3. promotion of recycling of small waste electrical and electronic equipment (small appliance recycling act) (2013), 6.4. japan photovoltaic energy association (jpea) recycling guidelines (2014).

• Some of the key recommendations in the guidelines include the following:
• Manufacturers and importers of PV panels should establish a system for the collection and disposal of their products at the end of their useful life;
• Recycling companies should be certified by the government and follow appropriate safety and environmental regulations;
• PV panels should be dismantled and recycled to the extent possible, with materials such as glass, aluminum, and copper separated and sent for recycling;
• Hazardous materials contained within PV panels, such as lead and cadmium, should be managed and disposed of properly.

6.5. Ministry of the Environment’s Guidelines for the Sound Material-Cycle Society (2018)

6.6. observations, 7.1. the national programme on solar pv waste management provides a framework for managing eol solar pv waste (2020).

• Developing a comprehensive regulatory framework for the managing of PV EOL;
• Establishing a mechanism for the collection, transportation, and storage of PV EOL waste;
• Creating a system for the environmentally sound disposal of EOL solar PV waste;
• Promoting research and development in the area of solar PV waste management;
• Building capacity for the management of EOL solar PV waste;
• Creating awareness among stakeholders about the importance of sustainable solar PV waste management.

7.2. The E-Waste (Management) Rules (2016)

7.3. cpcb guidelines on the environmentally sustainable management of eol solar pv waste (2018), 7.4. observations, 8.1. the electrical and electronic equipment act (elektrog) (2005), 8.2. the waste electrical and electronic equipment directive (weee) (2012), 8.3. the german solar association (bsw), 8.4. observations, 9. global concern about solar pv end-of-life waste recycling and management, 9.1. challenges in recycling solar pv waste, 9.1.1. technological and economic barriers, 9.1.2. regulatory and logistical issues, 9.2. potential environmental impacts, 10. the kingdom of saudi arabia, 10.1. saudi arabia waste management law, 10.2. observations, 11. lesson learned for the ksa.

• Extended Producer Responsibility (EPR) : mandating that manufacturers take responsibility for the entire life cycle of their products, including the design, take-back programs, and covering recycling costs.
• Public Awareness and Education Campaigns : launching initiatives to educate the public about the importance of recycling and proper waste management practices.
• Public–Private Partnerships (PPPs) : fostering collaborations between the government and private sector to develop and operate recycling facilities and waste management programs.
• Extended Producer Responsibility (EPR) : implement policies that require manufacturers to manage the life cycle of their products, ensuring they are recyclable and facilitating take-back programs.
• Public Awareness and Education Campaigns : develop and execute campaigns to raise public awareness about EOL waste and encourage community participation in recycling efforts.
• Public–Private Partnerships (PPPs) : encourage partnerships to invest in and manage recycling infrastructure and waste management programs effectively.
• Development of Recycling Infrastructure : invest in specialized facilities and technologies to handle the anticipated increase in solar PV waste.

12. Conclusions and Policy Implications

• Detailed policy analysis to support the development of robust EOL waste management regulations.
• Exploration of advanced technological solutions for recycling and disposal.
• Strategies for effective stakeholder engagement and public awareness.
• Economic assessment of EOL waste management practices to ensure sustainability.

Author Contributions

Data availability statement, acknowledgments, conflicts of interest.

• Van Opstal, W.; Smeets, A. Circular economy strategies as enablers for solar PV adoption in organizational market segments. Sustain. Prod. Consum. 2023 , 35 , 40–54. [ Google Scholar ] [ CrossRef ]
• International Renewable Energy Agency. Global Renewables Outlook. 2020. Available online: https://www.irena.org/ (accessed on 4 March 2024).
• Ali, A.; Malik, S.A.; Shafiullah, M.; Malik, M.Z.; Zahir, M.H. Policies and regulatisons for solar photovoltaic end-of-life waste management: Insights from China and the USA. Chemosphere 2023 , 340 , 139840. [ Google Scholar ] [ CrossRef ]
• Zhang, H.; Yu, Z.; Zhu, C.; Yang, R.; Yan, B.; Jiang, G. Green or not? Environmental challenges from photovoltaic technology. Environ. Pollut. 2023 , 320 , 121066. [ Google Scholar ] [ CrossRef ] [ PubMed ]
• International Renewable Energy Agency (IRENA). End-of-Life Management: Solar Photovoltaic Panels. 2016. Available online: https://www.irena.org/ (accessed on 4 March 2024).
• International Renewable Energy Agency. Renewable Energy Target Setting. 2015. Available online: https://www.irena.org/-/media/Files/IRENA/Agency/Publication/2015/IRENA_RE_Target_Setting_2015.pdf (accessed on 4 March 2024).
• Chowdhury, M.S.; Rahman, K.S.; Chowdhury, T.; Nuthammachot, N.; Techato, K.; Akhtaruzzaman, M.; Tiong, S.K.; Sopian, K.; Amin, N. An overview of solar photovoltaic panels’ end-of-life material recycling. Energy Strategy Rev. 2020 , 27 , 100431. [ Google Scholar ] [ CrossRef ]
• Sikarwar, V.S.; Peela, N.R.; Vuppaladadiyam, A.K.; Ferreira, N.L.; Mašláni, A.; Tomar, R.; Pohořelý, M.; Meers, E.; Jeremiáš, M. Thermal plasma gasification of organic waste stream coupled with CO 2 -sorption enhanced reforming employing different sorbents for enhanced hydrogen production. RSC Adv. 2022 , 12 , 6122–6132. [ Google Scholar ] [ CrossRef ] [ PubMed ]
• Oteng, D.; Zuo, J.; Sharifi, E. An expert-based evaluation on end-of-life solar photovoltaic management: An application of Fuzzy Delphi Technique. Sustain. Horiz. 2022 , 4 , 100036. [ Google Scholar ] [ CrossRef ]
• Wang, X.; Tian, X.; Chen, X.; Ren, L.; Geng, C. A review of end-of-life crystalline silicon solar photovoltaic panel recycling technology. Sol. Energy Mater. Sol. Cells 2022 , 248 , 111976. [ Google Scholar ] [ CrossRef ]
• Majewski, P.; Al-shammari, W.; Dudley, M.; Jit, J.; Lee, S.-H.; Myoung-Kug, K.; Sung-Jim, K. Recycling of solar PV panels- product stewardship and regulatory approaches. Energy Policy 2021 , 149 , 112062. [ Google Scholar ] [ CrossRef ]
• Chiang, P.F.; Han, S.; Claire, M.J.; Maurice, N.J.; Vakili, M.; Giwa, A.S. Sustainable Treatment of Spent Photovoltaic Solar Panels Using Plasma Pyrolysis Technology and Its Economic Significance. Clean Technol. 2024 , 6 , 432–452. [ Google Scholar ] [ CrossRef ]
• Ndalloka, Z.N.; Nair, H.V.; Alpert, S.; Schmid, C. Solar photovoltaic recycling strategies. Sol. Energy 2024 , 270 , 112379. [ Google Scholar ] [ CrossRef ]
• Jain, S.; Sharma, T.; Gupta, A.K. End-of-life management of solar PV waste in India: Situation analysis and proposed policy framework. Renew. Sustain. Energy Rev. 2022 , 153 , 111774. [ Google Scholar ] [ CrossRef ]
• D’Adamo, I.; Mazzanti, M.; Morone, P. Rosa, Assessing the relation between waste management policies and circular economy goals. Waste Manag. 2022 , 154 , 27–35. [ Google Scholar ] [ CrossRef ] [ PubMed ]
• Yu, H.F.; Hasanuzzaman; Rahim, N.A.; Amin, N.; Adzman, N.N. Global Challenges and Prospects of Photovoltaic Materials Disposal and Recycling: A Comprehensive Review. Sustainability 2022 , 14 , 8567. [ Google Scholar ] [ CrossRef ]
• Nain, P.; Kumar, A. A state-of-art review on end-of-life solar photovoltaics. J. Clean. Prod. 2022 , 343 , 130978. [ Google Scholar ] [ CrossRef ]
• Umoh, J.M.; Choube, P.R.; George, E.E. Challenges of Electronic Waste in Nigeria: Implications for Policy Planning. Int. J. Innov. Eng. Res. Technol. 2021 , 8 , 280–288. [ Google Scholar ] [ CrossRef ]
• Ozoegwu, C.G.; Akpan, P.U. Solar energy policy directions for safer and cleaner development in Nigeria. Energy Policy 2021 , 150 , 112141. [ Google Scholar ] [ CrossRef ]
• Oteng, D.; Zuo, J.; Sharifi, E. A scientometric review of trends in solar photovoltaic waste management research. Sol. Energy 2021 , 224 , 545–562. [ Google Scholar ] [ CrossRef ]
• Li, Y.; Wang, G.; Shen, B.; Zhang, Q.; Liu, B.; Xu, R. Conception and policy implications of photovoltaic modules end-of-life management in China. WIREs Energy Environ. 2021 , 10 , e387. [ Google Scholar ] [ CrossRef ]
• Sharma, A.; Pandey, S.; Kolhe, M. Global review of policies & guidelines for recycling of solar PV modules. Int. J. Smart Grid Clean Energy 2019 , 8 , 597–610. [ Google Scholar ] [ CrossRef ]
• Lameirinhas, R.A.M.; Torres, J.P.N.; de Melo Cunha, J.P. A Photovoltaic Technology Review: History, Fundamentals and Applications. Energies 2022 , 15 , 1823. [ Google Scholar ] [ CrossRef ]
• Nain, P.; Kumar, A. Initial metal contents and leaching rate constants of metals leached from end-of-life solar photovoltaic waste: An integrative literature review and analysis. Renew. Sustain. Energy Rev. 2020 , 119 , 109592. [ Google Scholar ] [ CrossRef ]
• Mulazzani, A.; Eleftheriadis, P.; Leva, S. Recycling c-Si PV Modules: A Review, a Proposed Energy Model and a Manufacturing Comparison. Energies 2022 , 15 , 8419. [ Google Scholar ] [ CrossRef ]
• Del Pero, F.; Delogu, M.; Berzi, L.; Escamilla, M. Innovative device for mechanical treatment of End of Life photovoltaic panels: Technical and environmental analysis. Waste Manag. 2019 , 95 , 535–548. [ Google Scholar ] [ CrossRef ]
• Paiano, A. Photovoltaic waste assessment in Italy. Renew. Sustain. Energy Rev. 2015 , 41 , 99–112. [ Google Scholar ] [ CrossRef ]
• Fiandra, V.; Sannino, L.; Andreozzi, C. Photovoltaic waste as source of valuable materials: A new recovery mechanical approach. J. Clean. Prod. 2023 , 385 , 135702. [ Google Scholar ] [ CrossRef ]
• Yadav, D.; Saraf, A.K.; Rathee, N.S. Assessment of PV waste generation in India. Mater. Today Proc. 2023, in press . [ CrossRef ]
• Divya, A.; Adish, T.; Kaustubh, P.; Zade, P.S. Review on recycling of solar modules/panels. Sol. Energy Mater. Sol. Cells 2023 , 253 , 112151. [ Google Scholar ] [ CrossRef ]
• Sah, D.; Chitra; Kumar, S. Investigation and recovery of copper from waste silicon solar module. Mater. Chem. Phys. 2023 , 296 , 127205. [ Google Scholar ] [ CrossRef ]
• D’Adamo, I.; Ferella, F.; Gastaldi, M.; Ippolito, N.M. Rosa, Circular solar: Evaluating the profitability of a photovoltaic panel recycling plant. Waste Manag. Res. 2023 , 41 , 1144–1154. [ Google Scholar ] [ CrossRef ]
• Oteng, D.; Zuo, J.; Sharifi, E. An evaluation of the impact framework for product stewardship on end-of-life solar photovoltaic modules: An environmental lifecycle assessment. J. Clean. Prod. 2023 , 411 , 137357. [ Google Scholar ] [ CrossRef ]
• Faircloth, C.C.; Wagner, K.H.; Woodward, K.E.; Rakkwamsuk, P.; Gheewala, S.H. The environmental and economic impacts of photovoltaic waste management in Thailand. Resour. Conserv. Recycl. 2019 , 143 , 260–272. [ Google Scholar ] [ CrossRef ]
• Ziemińska-Stolarska, A.; Pietrzak, M.; Zbiciński, I. Effect of Recycling on the Environmental Impact of a High-Efficiency Photovoltaic Module Combining Space-Grade Solar Cells and Optical Micro-Tracking. Energies 2023 , 16 , 3302. [ Google Scholar ] [ CrossRef ]
• Su, L.C.; Ruan, H.D.; Ballantine, D.J.; Lee, C.H.; Cai, Z.W. Release of metal pollutants from corroded and degraded thin-film solar panels extracted by acids and buried in soils. Appl. Geochem. 2019 , 108 , 104381. [ Google Scholar ] [ CrossRef ]
• GB/T 38785-2020 ; General Technical Requirements of Thin-Film Photovoltaic Module Recycling and Reusing for Use in Building. Standardization Administration of China: Beijing, China, 2020.
• GB 18599-2020 ; Standard for Pollution Control on the Non-Hazardous Industrial Solid Waste Storage and Landfill. Standardization Administration of China: Beijing China, 2009.
• GB/T 23685-2009 ; General Technical Specifications of Recovering for Waste Electrical and Electronic Equipment. Standardization Administration of China: Beijing, China, 2021.
• GB/T 20861-2007 ; Terminology of Waste Product Recovery. Standardization Administration of China: Beijing, China, 2007.
• Curtis, T.L.; Buchanan, H.; Heath, G.; Smith, L.; Shaw, S. Solar Photovoltaic Module Recycling: A Survey of U.S. Policies and Initiatives ; National Renewable Energy Lab. (NREL): Golden, CO, USA, 2021.
• Filipović, P.; Dović, D.; Ranilović, B.; Horvat, I. Numerical and experimental approach for evaluation of thermal performances of a polymer solar collector. Renew. Sustain. Energy Rev. 2019 , 112 , 127–139. [ Google Scholar ] [ CrossRef ]
• Popescu, G.H.; Mieila, M.; Nica, E.; Andrei, J.V. The emergence of the effects and determinants of the energy paradigm changes on European Union economy. Renew. Sustain. Energy Rev. 2018 , 81 , 768–774. [ Google Scholar ] [ CrossRef ]
• de C. Neves, V.T.; Sales, E.A.; Perelo, L.W. Influence of lipid extraction methods as pre-treatment of microalgal biomass for biogas production. Renew. Sustain. Energy Rev. 2016 , 59 , 160–165. [ Google Scholar ] [ CrossRef ]
• United States Environmental Protection Agency (EPA). Resource Conservation and Recovery Act (RCRA) Laws and Regulations. Available online: https://www.epa.gov/rcra (accessed on 3 April 2024).
• Domínguez, A.; Geyer, R. Photovoltaic waste assessment of major photovoltaic installations in the United States of America. Renew. Energy 2019 , 133 , 1188–1200. [ Google Scholar ] [ CrossRef ]
• California’s Department of Resources Recycling and Recovery (CalRecycle). CalRecycle-2021. Available online: https://www.calrecycle.ca.gov/ (accessed on 4 April 2024).
• Department of Toxic Control Substances California. DTSC Regulations. 2019. Available online: https://dtsc.ca.gov/who-we-are/ (accessed on 5 April 2024).
• California Department of Public Health. Title 22 (2015) Regulation. Available online: https://www.cdph.ca.gov/ (accessed on 8 April 2024).
• Brokaw, P. SB 489 California Solar PV EOL Waste Management Bill. 2015. Available online: http://www.leginfo.ca.gov/pub/15-16/bill/sen/sb_0451-0500/sb_489_cfa_20150821_163820_asm_floor.html (accessed on 8 April 2024).
• Washington State Department of Ecology. Solar Panel Recycling Program. Available online: https://ecology.wa.gov/Waste-Toxics/Reducing-recycling-waste/Our-recycling-programs/Solar-panels (accessed on 9 April 2024).
• Sahle-Demessie, E. End-of-Life Management of Photovoltaic Solar Panels in the United States ; EPA/600/R-23/186; U.S. Environmental Protection Agency: Washington, DC, USA, 2023.
• Washington State Legislature. Electronic Waste Recycling Act. 2006. Available online: https://apps.leg.wa.gov/rcw/dispo.aspx?cite=70.95N (accessed on 10 April 2024).
• Washington State Department of Ecology. Dangerous Waste Regulations (WAC). Available online: https://ecology.wa.gov/regulations-permits/guidance-technical-assistance/dangerous-waste-guidance (accessed on 9 April 2024).
• New York State Department of Environmental Conservation. Solid Waste Management. Available online: https://www.dec.ny.gov/regulations/regulations.html (accessed on 10 April 2024).
• New York State Energy Research and Development Authority (NYSERDA). Guidebook for Local Governments. 2022. Available online: https://www.nyserda.ny.gov/-/media/Project/Nyserda/Files/Programs/NY-Sun/solar-guidebook.pdf (accessed on 10 April 2024).
• The New York State Senate. Electronic Equipment Recycling and Reuse Act. 2022. Available online: https://www.nysenate.gov/legislation/laws/ENV/27-2601 (accessed on 11 April 2024).
• New York State Department of Environmental Conservation (DEC). Hazardous Waste Program. 2022. Available online: https://www.dec.ny.gov/chemical/8483.html (accessed on 11 April 2024).
• Minnesota Pollution Control Agency. Electronic Waste Program. Available online: https://www.pca.state.mn.us/search?search=Electronic+Waste+Program (accessed on 12 April 2024).
• Legislature Minnesota. Minnesota Statutes, Section 115A.1310. 2022. Available online: https://www.revisor.mn.gov/index/statute/S10868478?year=2022 (accessed on 12 April 2024).
• Minnesota Legislature. Hazardous Waste Generator Rules. 2022. Available online: https://www.revisor.mn.gov/rules/7045/ (accessed on 12 April 2024).
• Oregon Department of Environmental Quality. E-Cycles Program. Available online: https://www.oregon.gov/Pages/search-results.aspx (accessed on 12 April 2024).
• Department of Environmental Quality. Administrative Rules (OAR) 340-104. Available online: https://secure.sos.state.or.us/oard/displayChapterRules.action?selectedChapter=80 (accessed on 13 April 2024).
• S. USA. Hazardous Waste Reporting System. Available online: https://www.oregon.gov/deq/hazards-and-cleanup/hw/pages/hw-reporting.aspx (accessed on 13 April 2024).
• Vermont Department of Environmental Conservation. E-Cycles Program. Available online: https://dec.vermont.gov/search/node?keys=E-Cycles+Program (accessed on 13 April 2024).
• Legislature Vermont. Vermont Statutes, Title 10, Chapter 159. 2022. Available online: https://legislature.vermont.gov/statutes/chapter/10/159 (accessed on 14 April 2024).
• Department of Environmental Conservation. Hazardous Waste Management Regulations. Available online: https://dec.vermont.gov/search/node?keys=Hazardous+Waste+Management+Regulations+ (accessed on 14 April 2024).
• Colorado General Assembly. Electronic Recycling Jobs Act. Available online: https://leg.colorado.gov/sitewide-search?search_api_views_fulltext=Electronic+Recycling+Jobs+Act+ (accessed on 15 April 2024).
• Colorado Department of Public Health and Environment. Universal Waste Regulations. Available online: https://cdphe.colorado.gov/search?search=Universal+Waste+Regulations+ (accessed on 15 April 2024).
• Colorado Department of Public Health and Environment. Hazardous Waste Regulations. 2023. Available online: https://cdphe.colorado.gov/all-regulations/hazardous-waste-regulations (accessed on 15 April 2024).
• Connecticut Secretary of the State. Hazardous Waste Management Regulations. Available online: https://eregulations.ct.gov/eRegsPortal/Search/RCSAResults (accessed on 15 April 2024).
• Connecticut and Department of Energy & Environmental Protection. E-Waste Recycling Program. 2022. Available online: https://portal.ct.gov/DEEP/Search-Results?SearchKeyword=E-Waste Recycling Program (accessed on 16 April 2024).
• Connecticut and Department of Energy & Environmental Protection. Universal Waste Regulations. Available online: https://portal.ct.gov/DEEP/Waste-Management-and-Disposal/Hazardous-Waste/Universal-Waste (accessed on 16 April 2024).
• State of Rhode Island-Department of Environmental Management. E-Waste Recycling Program. Available online: https://dem.ri.gov/search?search_api_fulltext=E-Waste Recycling Program (accessed on 16 April 2024).
• State of Rhode Island-Department of Environmental Management. Universal Waste Regulations. Available online: https://dem.ri.gov/search?search_api_fulltext=Waste Regulations (accessed on 16 April 2024).
• State of Rhode Island-Department of Environmental Management. Hazardous Waste Management Regulations. Available online: https://dem.ri.gov/search?search_api_fulltext=Waste Regulations (accessed on 17 April 2024).
• Feldman, S.; Beidle; Benson; Carter; Elfreth; Ellis; Ferguson; Griffith; Guzzone; Hester; et al. Clean Energy Jobs Act; Maryland General Assembly. 2019. Available online: https://mgaleg.maryland.gov/2019RS/bills/sb/sb0516f.pdf (accessed on 17 April 2024).
• Maryland Department of the Environment. Hazardous Waste Regulations. Available online: https://mde.maryland.gov/programs/land/hazardouswaste/pages/index.aspx (accessed on 17 April 2024).
• Maryland and Maryland Department of the Environment. Electronic Waste Recycling Program. Available online: https://mde.maryland.gov/programs/Land/RecyclingandOperationsprogram/Pages/ecycling.aspx (accessed on 17 April 2024).
• Murakami, S.; Yamamoto, H.; Toyota, T. Potential Impact of Consumer Intention on Generation of Waste Photovoltaic Panels: A Case Study for Tokyo. Sustainability 2021 , 13 , 10507. [ Google Scholar ] [ CrossRef ]
• Order, C. Waste Management and Public Cleansing Law. 2002. Available online: https://www.env.go.jp/en/laws/recycle/02.pdf (accessed on 18 April 2024).
• Sakai, S. Municipal solid waste management in Japan. Waste Manag. 1996 , 16 , 395–405. [ Google Scholar ] [ CrossRef ]
• Mansouri, A.; Kacha, L. Waste Management System in Japan. 2017. Available online: https://www.researchgate.net/publication/321214329_Waste_Management_System_in_Japan (accessed on 18 April 2024).
• Amemiya, T. Current State and Trend of Waste and Recycling in Japan. Int. J. Earth Environ. Sci. 2018 , 3 . [ Google Scholar ] [ CrossRef ]
• Tasaki, T. Experiences of Japanese Container and Packaging Recycling Act. 2008. Available online: https://www.researchgate.net/publication/303912284_Experiences_of_Japanese_Container_and_Packaging_Recycling_Act (accessed on 18 April 2024).
• Uwasu, M.; Naito, T.; Yabar, H.; Hara, K. Assessment of Japanese recycling policies for home electric appliance: Cost-effectiveness analysis and socioeconomic and technological implications. Environ. Dev. 2013 , 6 , 21–33. [ Google Scholar ] [ CrossRef ]
• Yun, J.; Park, I.-s. Act for Resource Recycling of Electrical and Electronic Equipment and Vehicles. 2007. Available online: https://www.env.go.jp/en/recycle/asian_net/Country_Information/Law_N_Regulation/Korea/Korea_RoHS_ELV_April_2007_EcoFrontier.pdf (accessed on 18 April 2024).
• Japanese Law Translation. Promotion of Recycling of Small Waste Electrical and Electronic Equipment. Available online: https://www.japaneselawtranslation.go.jp/en/laws/view/3209/en (accessed on 18 April 2024).
• Liu, X.; Yu, J.; Okubo, K. Analysis of the Efficiency of Various Waste Electrical and Electronic Equipment’s Collection Routes: A Case Study Focusing on Collection Route for Waste Mobile Phones in the Tohoku Area of Japan. Recycling 2021 , 6 , 13. [ Google Scholar ] [ CrossRef ]
• Kenning, T. Japan Issues Guidelines on ‘Proper Disposal’ of Used Solar Modules. PVTech 2017. Available online: https://www.pv-tech.org/japan-issues-guidelines-on-proper-disposal-of-used-solar-modules/ (accessed on 18 April 2024).
• Ministry of the Environment Government of Japan. Guidelines for the Sound Material-Cycle Society. 2018. Available online: https://www.env.go.jp/en/index.html (accessed on 19 April 2024).
• Hotta, Y. Recycling Policy: The Sound Material Cycle Society and 3R Concepts from Japan to Developing Asia ; The Royal Society of Chemistry: London, UK, 2013; pp. 162–186. [ Google Scholar ] [ CrossRef ]
• Sheoran, M.; Kumar, P.; Sharma, S.; Bukya, M. Current situation analysis of solar PV waste management in India. Mater. Today Proc. 2022 , 58 , 773–782. [ Google Scholar ] [ CrossRef ]
• Rathore, N.; Panwar, N.L. Strategic overview of management of future solar photovoltaic panel waste generation in the Indian context. Waste Manag. Res. 2021 , 40 , 504–518. [ Google Scholar ] [ CrossRef ]
• EU-India TCP. PV Waste Management in India. 2014. Available online: https://www.eeas.europa.eu/delegations/india/eu-and-india-launch-%E2%80%9Cpv-waste-management-india%E2%80%9D-report_und_en (accessed on 19 April 2024).
• Gautam, A.; Shankar, R. Vrat, Managing end-of-life solar photovoltaic e-waste in India: A circular economy approach. J. Bus. Res. 2022 , 142 , 287–300. [ Google Scholar ] [ CrossRef ]
• Shetty, S. Solar PV Panels Bought under E-waste Rules in India. SOLARQUATER 2022. Available online: https://solarquarter.com/2022/05/28/solar-pv-panels-bought-under-e-waste-rules-in-india/ (accessed on 19 April 2024).
• Central Pollution Control Board (CPCB). Environmentally Sustainable Management of EOL Solar PV Waste. Available online: https://cpcb.nic.in/technical-guidelines/ (accessed on 20 April 2024).
• El-Khawad, L.; Bartkowiak, D.; Kümmerer, K. Improving the end-of-life management of solar panels in Germany. Renew. Sustain. Energy Rev. 2022 , 168 , 112678. [ Google Scholar ] [ CrossRef ]
• Federal Ministry for the Environment, Nature Conservation, Nuclear Safety. The “Elektrogesetz. 2018. Available online: https://elektrogesetz.com/ (accessed on 20 April 2024).
• Frey, O. Electronic waste take back in accordance with the German Electrical and Electronic Equipment Act (ElektroG). Wasser Abfall 2008 , 10 , 34–36. [ Google Scholar ]
• Walther, G.; Steinborn, J.; Spengler, T.; Luger, T.; Herrmann, C. Implementation of the WEEE-directive—Economic effects and improvement potentials for reuse and recycling in Germany. Int. J. Adv. Manuf. Technol. 2010 , 47 , 461–474. [ Google Scholar ] [ CrossRef ]
• Dias, P.; Javimczik, S.; Benevit, M.; Veit, H. Recycling WEEE: Polymer characterization and pyrolysis study for waste of crystalline silicon photovoltaic modules. Waste Manag. 2017 , 60 , 716–722. [ Google Scholar ] [ CrossRef ]
• Mihai, F.; Gnoni, M.; Meidiana, C.; Ezeah, C.; Elia, V. Waste Electrical and Electronic Equipment (WEEE): Flows, Quantities, and Management—A Global Scenario. In Electronic Waste Management and Treatment Technology ; Heinemann: Butterworth, Malaysia, 2019; pp. 1–34. [ Google Scholar ] [ CrossRef ]
• Larsen, K. End-of-life PV: Then what? Renew. Energy Focus 2009 , 10 , 48–53. [ Google Scholar ] [ CrossRef ]
• C. [Bundesverband S. (BSW) e. V. Koernig Berlin (Germany)]. German Solar Industry Association. We open the solar market; Bundesverband Solarwirtschaft (BSW) e.V. Wir oeffnen den Solarmarkt. Germany. 2006. Available online: https://www.solarwirtschaft.de/en/association/ (accessed on 20 April 2024).
• Kubier, A.; Wilkin, R.T.; Pichler, T. Cadmium in Soils and Groundwater: A Review. Appl. Geochem. 2019 , 108 , 104388. [ Google Scholar ] [ CrossRef ]
• Ahmad, W.; Alharthy, R.D.; Zubair, M.; Ahmed, M.; Hameed, A.; Rafique, S. Toxic and heavy metals contamination assessment in soil and water to evaluate human health risk. Sci. Rep. 2021 , 11 , 17006. [ Google Scholar ] [ CrossRef ] [ PubMed ]
• Sverdrup, H.U.; van Allen, O.; Haraldsson, H.V. Assessing Aspects of Cadmium Supply, Recycling and Environmental Pollution with Respect to Future Photovoltaic Technology Demands and Envionmental Policy Goals. Water Air Soil Pollut. 2024 , 235 , 247. [ Google Scholar ] [ CrossRef ]
• Plant, J.A.; Korre, A.; Reeder, S.; Smith, B.; Voulvoulis, N. Chemicals in the environment: Implications for global sustainability. Appl. Earth Sci. 2005 , 114 , 65–97. [ Google Scholar ] [ CrossRef ]
• Wu, J.; Cao, Y.; Pan, W.; Pan, W. Mercury and Its Effects on Environment and Human’s Health. In Coal Fired Flue Gas Mercury Emission Controls ; Wu, J., Cao, Y., Pan, W., Pan, W., Eds.; Springer: Berlin/Heidelberg, Germany, 2015; pp. 1–17. [ Google Scholar ] [ CrossRef ]
• Hassan, A.I.; Saleh, H.M. Chapter 7—Toxicity and hazardous waste regulations. In Hazardous Waste Management ; Elsevier: Amsterdam, The Netherlands, 2022; pp. 165–182. [ Google Scholar ] [ CrossRef ]
• Mitra, S.; Maiti, D.K. Environmental problems and management aspects of waste electrical and electronic equipment and use of clean energy for sustainable development. In Environmental Management of Waste Electrical and Electronic Equipment ; Elsevier: Amsterdam, The Netherlands, 2021; pp. 3–21. [ Google Scholar ] [ CrossRef ]
• Ali, A. Transforming Saudi Arabia’s Energy Landscape towards a Sustainable Future: Progress of Solar Photovoltaic Energy Deployment. Sustainability 2023 , 15 , 8420. [ Google Scholar ] [ CrossRef ]
• Aldhubaib, H.A. Electrical energy future of Saudi Arabia: Challenges and opportunities. Front. Energy Res. 2022 , 10 , 1005081. [ Google Scholar ] [ CrossRef ]
• Alnatheer, O. The potential contribution of renewable energy to electricity supply in Saudi Arabia. Energy Policy 2005 , 33 , 2298–2312. [ Google Scholar ] [ CrossRef ]
• Ali, A.; Malik, S. Solar Photovoltaic End-of-Life Disposal Policy Assessment for the Kingdom of Saudi Arabia. In International Association for Energy Economics (IAEE) ; IAEE: Riyadh, Saudi Arabia, 2023. [ Google Scholar ]
• Xu, Y.; Li, J.; Tan, Q.; Peters, A.L.; Yang, C. Global status of recycling waste solar panels: A review. Waste Manag. 2018 , 75 , 450–458. [ Google Scholar ] [ CrossRef ]

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Ali, A.; Islam, M.T.; Rehman, S.; Qadir, S.A.; Shahid, M.; Khan, M.W.; Zahir, M.H.; Islam, A.; Khalid, M. Solar Photovoltaic Module End-of-Life Waste Management Regulations: International Practices and Implications for the Kingdom of Saudi Arabia. Sustainability 2024 , 16 , 7215. https://doi.org/10.3390/su16167215

Ali A, Islam MT, Rehman S, Qadir SA, Shahid M, Khan MW, Zahir MH, Islam A, Khalid M. Solar Photovoltaic Module End-of-Life Waste Management Regulations: International Practices and Implications for the Kingdom of Saudi Arabia. Sustainability . 2024; 16(16):7215. https://doi.org/10.3390/su16167215

Ali, Amjad, Md Tasbirul Islam, Shafiqur Rehman, Sikandar Abdul Qadir, Muhammad Shahid, Muhammad Waseem Khan, Md. Hasan Zahir, Asif Islam, and Muhammad Khalid. 2024. "Solar Photovoltaic Module End-of-Life Waste Management Regulations: International Practices and Implications for the Kingdom of Saudi Arabia" Sustainability 16, no. 16: 7215. https://doi.org/10.3390/su16167215

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