how to write a directional hypothesis

Directional Hypothesis: Definition and 10 Examples

Chris Drew (PhD)

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A directional hypothesis refers to a type of hypothesis used in statistical testing that predicts a particular direction of the expected relationship between two variables.

In simpler terms, a directional hypothesis is an educated, specific guess about the direction of an outcome—whether an increase, decrease, or a proclaimed difference in variable sets.

For example, in a study investigating the effects of sleep deprivation on cognitive performance, a directional hypothesis might state that as sleep deprivation (Independent Variable) increases, cognitive performance (Dependent Variable) decreases (Killgore, 2010). Such a hypothesis offers a clear, directional relationship whereby a specific increase or decrease is anticipated.

Global warming provides another notable example of a directional hypothesis. A researcher might hypothesize that as carbon dioxide (CO2) levels increase, global temperatures also increase (Thompson, 2010). In this instance, the hypothesis clearly articulates an upward trend for both variables. 

In any given circumstance, it’s imperative that a directional hypothesis is grounded on solid evidence. For instance, the CO2 and global temperature relationship is based on substantial scientific evidence, and not on a random guess or mere speculation (Florides & Christodoulides, 2009).

Directional vs Non-Directional vs Null Hypotheses

A directional hypothesis is generally contrasted to a non-directional hypothesis. Here’s how they compare:

• Directional hypothesis: A directional hypothesis provides a perspective of the expected relationship between variables, predicting the direction of that relationship (either positive, negative, or a specific difference). 
• Non-directional hypothesis: A non-directional hypothesis denotes the possibility of a relationship between two variables ( the independent and dependent variables ), although this hypothesis does not venture a prediction as to the direction of this relationship (Ali & Bhaskar, 2016). For example, a non-directional hypothesis might state that there exists a relationship between a person’s diet (independent variable) and their mood (dependent variable), without indicating whether improvement in diet enhances mood positively or negatively. Overall, the choice between a directional or non-directional hypothesis depends on the known or anticipated link between the variables under consideration in research studies.

Another very important type of hypothesis that we need to know about is a null hypothesis :

• Null hypothesis : The null hypothesis stands as a universality—the hypothesis that there is no observed effect in the population under study, meaning there is no association between variables (or that the differences are down to chance). For instance, a null hypothesis could be constructed around the idea that changing diet (independent variable) has no discernible effect on a person’s mood (dependent variable) (Yan & Su, 2016). This proposition is the one that we aim to disprove in an experiment.

While directional and non-directional hypotheses involve some integrated expectations about the outcomes (either distinct direction or a vague relationship), a null hypothesis operates on the premise of negating such relationships or effects.

The null hypotheses is typically proposed to be negated or disproved by statistical tests, paving way for the acceptance of an alternate hypothesis (either directional or non-directional).

Directional Hypothesis Examples

1. exercise and heart health.

Research suggests that as regular physical exercise (independent variable) increases, the risk of heart disease (dependent variable) decreases (Jakicic, Davis, Rogers, King, Marcus, Helsel, Rickman, Wahed, Belle, 2016). In this example, a directional hypothesis anticipates that the more individuals maintain routine workouts, the lesser would be their odds of developing heart-related disorders. This assumption is based on the underlying fact that routine exercise can help reduce harmful cholesterol levels, regulate blood pressure, and bring about overall health benefits. Thus, a direction – a decrease in heart disease – is expected in relation with an increase in exercise. 

2. Screen Time and Sleep Quality

Another classic instance of a directional hypothesis can be seen in the relationship between the independent variable, screen time (especially before bed), and the dependent variable, sleep quality. This hypothesis predicts that as screen time before bed increases, sleep quality decreases (Chang, Aeschbach, Duffy, Czeisler, 2015). The reasoning behind this hypothesis is the disruptive effect of artificial light (especially blue light from screens) on melatonin production, a hormone needed to regulate sleep. As individuals spend more time exposed to screens before bed, it is predictably hypothesized that their sleep quality worsens. 

3. Job Satisfaction and Employee Turnover

A typical scenario in organizational behavior research posits that as job satisfaction (independent variable) increases, the rate of employee turnover (dependent variable) decreases (Cheng, Jiang, & Riley, 2017). This directional hypothesis emphasizes that an increased level of job satisfaction would lead to a reduced rate of employees leaving the company. The theoretical basis for this hypothesis is that satisfied employees often tend to be more committed to the organization and are less likely to seek employment elsewhere, thus reducing turnover rates.

4. Healthy Eating and Body Weight

Healthy eating, as the independent variable, is commonly thought to influence body weight, the dependent variable, in a positive way. For example, the hypothesis might state that as consumption of healthy foods increases, an individual’s body weight decreases (Framson, Kristal, Schenk, Littman, Zeliadt, & Benitez, 2009). This projection is based on the premise that healthier foods, such as fruits and vegetables, are generally lower in calories than junk food, assisting in weight management.

5. Sun Exposure and Skin Health

The association between sun exposure (independent variable) and skin health (dependent variable) allows for a definitive hypothesis declaring that as sun exposure increases, the risk of skin damage or skin cancer increases (Whiteman, Whiteman, & Green, 2001). The premise aligns with the understanding that overexposure to the sun’s ultraviolet rays can deteriorate skin health, leading to conditions like sunburn or, in extreme cases, skin cancer.

6. Study Hours and Academic Performance

A regularly assessed relationship in academia suggests that as the number of study hours (independent variable) rises, so too does academic performance (dependent variable) (Nonis, Hudson, Logan, Ford, 2013). The hypothesis proposes a positive correlation , with an increase in study time expected to contribute to enhanced academic outcomes.

7. Screen Time and Eye Strain

It’s commonly hypothesized that as screen time (independent variable) increases, the likelihood of experiencing eye strain (dependent variable) also increases (Sheppard & Wolffsohn, 2018). This is based on the idea that prolonged engagement with digital screens—computers, tablets, or mobile phones—can cause discomfort or fatigue in the eyes, attributing to symptoms of eye strain.

8. Physical Activity and Stress Levels

In the sphere of mental health, it’s often proposed that as physical activity (independent variable) increases, levels of stress (dependent variable) decrease (Stonerock, Hoffman, Smith, Blumenthal, 2015). Regular exercise is known to stimulate the production of endorphins, the body’s natural mood elevators, helping to alleviate stress.

9. Water Consumption and Kidney Health

A common health-related hypothesis might predict that as water consumption (independent variable) increases, the risk of kidney stones (dependent variable) decreases (Curhan, Willett, Knight, & Stampfer, 2004). Here, an increase in water intake is inferred to reduce the risk of kidney stones by diluting the substances that lead to stone formation.

10. Traffic Noise and Sleep Quality

In urban planning research, it’s often supposed that as traffic noise (independent variable) increases, sleep quality (dependent variable) decreases (Muzet, 2007). Increased noise levels, particularly during the night, can result in sleep disruptions, thus, leading to poor sleep quality.

11. Sugar Consumption and Dental Health

In the field of dental health, an example might be stating as one’s sugar consumption (independent variable) increases, dental health (dependent variable) decreases (Sheiham, & James, 2014). This stems from the fact that sugar is a major factor in tooth decay, and increased consumption of sugary foods or drinks leads to a decline in dental health due to the high likelihood of cavities.

See 15 More Examples of Hypotheses Here

A directional hypothesis plays a critical role in research, paving the way for specific predicted outcomes based on the relationship between two variables. These hypotheses clearly illuminate the expected direction—the increase or decrease—of an effect. From predicting the impacts of healthy eating on body weight to forecasting the influence of screen time on sleep quality, directional hypotheses allow for targeted and strategic examination of phenomena. In essence, directional hypotheses provide the crucial path for inquiry, shaping the trajectory of research studies and ultimately aiding in the generation of insightful, relevant findings.

Ali, S., & Bhaskar, S. (2016). Basic statistical tools in research and data analysis. Indian Journal of Anaesthesia, 60 (9), 662-669. doi: https://doi.org/10.4103%2F0019-5049.190623  

Chang, A. M., Aeschbach, D., Duffy, J. F., & Czeisler, C. A. (2015). Evening use of light-emitting eReaders negatively affects sleep, circadian timing, and next-morning alertness. Proceeding of the National Academy of Sciences, 112 (4), 1232-1237. doi: https://doi.org/10.1073/pnas.1418490112  

Cheng, G. H. L., Jiang, D., & Riley, J. H. (2017). Organizational commitment and intrinsic motivation of regular and contractual primary school teachers in China. New Psychology, 19 (3), 316-326. Doi: https://doi.org/10.4103%2F2249-4863.184631  

Curhan, G. C., Willett, W. C., Knight, E. L., & Stampfer, M. J. (2004). Dietary factors and the risk of incident kidney stones in younger women: Nurses’ Health Study II. Archives of Internal Medicine, 164 (8), 885–891.

Florides, G. A., & Christodoulides, P. (2009). Global warming and carbon dioxide through sciences. Environment international , 35 (2), 390-401. doi: https://doi.org/10.1016/j.envint.2008.07.007

Framson, C., Kristal, A. R., Schenk, J. M., Littman, A. J., Zeliadt, S., & Benitez, D. (2009). Development and validation of the mindful eating questionnaire. Journal of the American Dietetic Association, 109 (8), 1439-1444. doi: https://doi.org/10.1016/j.jada.2009.05.006  

Jakicic, J. M., Davis, K. K., Rogers, R. J., King, W. C., Marcus, M. D., Helsel, D., … & Belle, S. H. (2016). Effect of wearable technology combined with a lifestyle intervention on long-term weight loss: The IDEA randomized clinical trial. JAMA, 316 (11), 1161-1171.

Khan, S., & Iqbal, N. (2013). Study of the relationship between study habits and academic achievement of students: A case of SPSS model. Higher Education Studies, 3 (1), 14-26.

Killgore, W. D. (2010). Effects of sleep deprivation on cognition. Progress in brain research , 185 , 105-129. doi: https://doi.org/10.1016/B978-0-444-53702-7.00007-5  

Marczinski, C. A., & Fillmore, M. T. (2014). Dissociative antagonistic effects of caffeine on alcohol-induced impairment of behavioral control. Experimental and Clinical Psychopharmacology, 22 (4), 298–311. doi: https://psycnet.apa.org/doi/10.1037/1064-1297.11.3.228  

Muzet, A. (2007). Environmental Noise, Sleep and Health. Sleep Medicine Reviews, 11 (2), 135-142. doi: https://doi.org/10.1016/j.smrv.2006.09.001  

Nonis, S. A., Hudson, G. I., Logan, L. B., & Ford, C. W. (2013). Influence of perceived control over time on college students’ stress and stress-related outcomes. Research in Higher Education, 54 (5), 536-552. doi: https://doi.org/10.1023/A:1018753706925  

Sheiham, A., & James, W. P. (2014). A new understanding of the relationship between sugars, dental caries and fluoride use: implications for limits on sugars consumption. Public health nutrition, 17 (10), 2176-2184. Doi: https://doi.org/10.1017/S136898001400113X  

Sheppard, A. L., & Wolffsohn, J. S. (2018). Digital eye strain: prevalence, measurement and amelioration. BMJ open ophthalmology , 3 (1), e000146. doi: http://dx.doi.org/10.1136/bmjophth-2018-000146

Stonerock, G. L., Hoffman, B. M., Smith, P. J., & Blumenthal, J. A. (2015). Exercise as Treatment for Anxiety: Systematic Review and Analysis. Annals of Behavioral Medicine, 49 (4), 542–556. doi: https://doi.org/10.1007/s12160-014-9685-9  

Thompson, L. G. (2010). Climate change: The evidence and our options. The Behavior Analyst , 33 , 153-170. Doi: https://doi.org/10.1007/BF03392211  

Whiteman, D. C., Whiteman, C. A., & Green, A. C. (2001). Childhood sun exposure as a risk factor for melanoma: a systematic review of epidemiologic studies. Cancer Causes & Control, 12 (1), 69-82. doi: https://doi.org/10.1023/A:1008980919928

Yan, X., & Su, X. (2009). Linear regression analysis: theory and computing . New Jersey: World Scientific.

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Hypotheses; directional and non-directional, what is the difference between an experimental and an alternative hypothesis.

Nothing much! If the study is a laboratory experiment then we can call the hypothesis “an experimental hypothesis”, where we make a prediction about how the IV causes an effect on the DV. If we have a non-experimental design, i.e. we are not able to manipulate the IV as in a natural or quasi-experiment , or if some other research method has been used, then we call it an “alternativehypothesis”, alternative to the null.

Directional hypothesis: A directional (or one tailed hypothesis) states which way you think the results are going to go, for example in an experimental study we might say…”Participants who have been deprived of sleep for 24 hours will have more cold symptoms in the following week after exposure to a virus than participants who have not been sleep deprived”; the hypothesis compares the two groups/conditions and states which one will ….have more/less, be quicker/slower, etc.

If we had a correlational study, the directional hypothesis would state whether we expect a positive or a negative correlation, we are stating how the two variables will be related to each other, e.g. there will be a positive correlation between the number of stressful life events experienced in the last year and the number of coughs and colds suffered, whereby the more life events you have suffered the more coughs and cold you will have had”. The directional hypothesis can also state a negative correlation, e.g. the higher the number of face-book friends, the lower the life satisfaction score “

Non-directional hypothesis: A non-directional (or two tailed hypothesis) simply states that there will be a difference between the two groups/conditions but does not say which will be greater/smaller, quicker/slower etc. Using our example above we would say “There will be a difference between the number of cold symptoms experienced in the following week after exposure to a virus for those participants who have been sleep deprived for 24 hours compared with those who have not been sleep deprived for 24 hours.”

When the study is correlational, we simply state that variables will be correlated but do not state whether the relationship will be positive or negative, e.g. there will be a significant correlation between variable A and variable B.

Null hypothesis The null hypothesis states that the alternative or experimental hypothesis is NOT the case, if your experimental hypothesis was directional you would say…

Participants who have been deprived of sleep for 24 hours will NOT have more cold symptoms in the following week after exposure to a virus than participants who have not been sleep deprived and any difference that does arise will be due to chance alone.

or with a directional correlational hypothesis….

There will NOT be a positive correlation between the number of stress life events experienced in the last year and the number of coughs and colds suffered, whereby the more life events you have suffered the more coughs and cold you will have had”

With a non-directional or  two tailed hypothesis…

There will be NO difference between the number of cold symptoms experienced in the following week after exposure to a virus for those participants who have been sleep deprived for 24 hours compared with those who have not been sleep deprived for 24 hours.

or for a correlational …

there will be NO correlation between variable A and variable B.

When it comes to conducting an inferential stats test, if you have a directional hypothesis , you must do a one tailed test to find out whether your observed value is significant. If you have a non-directional hypothesis , you must do a two tailed test .

Exam Techniques/Advice

• Remember, a decent hypothesis will contain two variables, in the case of an experimental hypothesis there will be an IV and a DV; in a correlational hypothesis there will be two co-variables
• both variables need to be fully operationalised to score the marks, that is you need to be very clear and specific about what you mean by your IV and your DV; if someone wanted to repeat your study, they should be able to look at your hypothesis and know exactly what to change between the two groups/conditions and exactly what to measure (including any units/explanation of rating scales etc, e.g. “where 1 is low and 7 is high”)
• double check the question, did it ask for a directional or non-directional hypothesis?
• if you were asked for a null hypothesis, make sure you always include the phrase “and any difference/correlation (is your study experimental or correlational?) that does arise will be due to chance alone”

Practice Questions:

• Mr Faraz wants to compare the levels of attendance between his psychology group and those of Mr Simon, who teaches a different psychology group. Which of the following is a suitable directional (one tailed) hypothesis for Mr Faraz’s investigation?

A There will be a difference in the levels of attendance between the two psychology groups.

B Students’ level of attendance will be higher in Mr Faraz’s group than Mr Simon’s group.

C Any difference in the levels of attendance between the two psychology groups is due to chance.

D The level of attendance of the students will depend upon who is teaching the groups.

2. Tracy works for the local council. The council is thinking about reducing the number of people it employs to pick up litter from the street. Tracy has been asked to carry out a study to see if having the streets cleaned at less regular intervals will affect the amount of litter the public will drop. She studies a street to compare how much litter is dropped at two different times, once when it has just been cleaned and once after it has not been cleaned for a month.

Write a fully operationalised non-directional (two-tailed) hypothesis for Tracy’s study. (2)

3. Jamila is conducting a practical investigation to look at gender differences in carrying out visuo-spatial tasks. She decides to give males and females a jigsaw puzzle and will time them to see who completes it the fastest. She uses a random sample of pupils from a local school to get her participants.

(a) Write a fully operationalised directional (one tailed) hypothesis for Jamila’s study. (2) (b) Outline one strength and one weakness of the random sampling method. You may refer to Jamila’s use of this type of sampling in your answer. (4)

4. Which of the following is a non-directional (two tailed) hypothesis?

A There is a difference in driving ability with men being better drivers than women

B Women are better at concentrating on more than one thing at a time than men

C Women spend more time doing the cooking and cleaning than men

D There is a difference in the number of men and women who participate in sports

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How to Write a Directional Hypothesis: A Step-by-Step Guide

In research, hypotheses play a crucial role in guiding investigations and making predictions about relationships between variables.

In this blog post, we’ll explore what a directional hypothesis is, why it’s important, and provide a step-by-step guide on how to write one effectively.

What is a Directional Hypothesis?

Examples of directional hypotheses, why to write a directional hypothesis.

Directional hypotheses offer several advantages in research. They provide researchers with a more focused prediction, allowing them to test specific hypotheses rather than exploring all possible relationships between variables.

Step 1: Identify the Variables

Step 2: predict the direction.

Based on your understanding of the relationship between the variables, predict the direction of the effect.

Step 3: Use Clear Language

Write your directional hypothesis using clear and concise language. Avoid technical jargon or terms that may be difficult for readers to understand. Your hypothesis should be easily understood by both researchers and non-experts.

Step 4: Ensure Testability

Step 5: revise and refine.

Writing a directional hypothesis is an essential skill for researchers conducting experiments and investigations.

Whether you’re a researcher or just starting out in the field, mastering the art of writing directional hypotheses will enhance the quality and rigor of your research endeavors.

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5.2 - writing hypotheses.

The first step in conducting a hypothesis test is to write the hypothesis statements that are going to be tested. For each test you will have a null hypothesis (\(H_0\)) and an alternative hypothesis (\(H_a\)).

When writing hypotheses there are three things that we need to know: (1) the parameter that we are testing (2) the direction of the test (non-directional, right-tailed or left-tailed), and (3) the value of the hypothesized parameter.

• At this point we can write hypotheses for a single mean (\(\mu\)), paired means(\(\mu_d\)), a single proportion (\(p\)), the difference between two independent means (\(\mu_1-\mu_2\)), the difference between two proportions (\(p_1-p_2\)), a simple linear regression slope (\(\beta\)), and a correlation (\(\rho\)). 
• The research question will give us the information necessary to determine if the test is two-tailed (e.g., "different from," "not equal to"), right-tailed (e.g., "greater than," "more than"), or left-tailed (e.g., "less than," "fewer than").
• The research question will also give us the hypothesized parameter value. This is the number that goes in the hypothesis statements (i.e., \(\mu_0\) and \(p_0\)). For the difference between two groups, regression, and correlation, this value is typically 0.

Hypotheses are always written in terms of population parameters (e.g., \(p\) and \(\mu\)).  The tables below display all of the possible hypotheses for the parameters that we have learned thus far. Note that the null hypothesis always includes the equality (i.e., =).

Research Hypothesis In Psychology: Types, & Examples

Saul McLeod, PhD

Editor-in-Chief for Simply Psychology

BSc (Hons) Psychology, MRes, PhD, University of Manchester

Saul McLeod, PhD., is a qualified psychology teacher with over 18 years of experience in further and higher education. He has been published in peer-reviewed journals, including the Journal of Clinical Psychology.

Learn about our Editorial Process

Olivia Guy-Evans, MSc

Associate Editor for Simply Psychology

BSc (Hons) Psychology, MSc Psychology of Education

Olivia Guy-Evans is a writer and associate editor for Simply Psychology. She has previously worked in healthcare and educational sectors.

On This Page:

A research hypothesis, in its plural form “hypotheses,” is a specific, testable prediction about the anticipated results of a study, established at its outset. It is a key component of the scientific method .

Hypotheses connect theory to data and guide the research process towards expanding scientific understanding

Some key points about hypotheses:

• A hypothesis expresses an expected pattern or relationship. It connects the variables under investigation.
• It is stated in clear, precise terms before any data collection or analysis occurs. This makes the hypothesis testable.
• A hypothesis must be falsifiable. It should be possible, even if unlikely in practice, to collect data that disconfirms rather than supports the hypothesis.
• Hypotheses guide research. Scientists design studies to explicitly evaluate hypotheses about how nature works.
• For a hypothesis to be valid, it must be testable against empirical evidence. The evidence can then confirm or disprove the testable predictions.
• Hypotheses are informed by background knowledge and observation, but go beyond what is already known to propose an explanation of how or why something occurs.

Predictions typically arise from a thorough knowledge of the research literature, curiosity about real-world problems or implications, and integrating this to advance theory. They build on existing literature while providing new insight.

Types of Research Hypotheses

Alternative hypothesis.

The research hypothesis is often called the alternative or experimental hypothesis in experimental research.

It typically suggests a potential relationship between two key variables: the independent variable, which the researcher manipulates, and the dependent variable, which is measured based on those changes.

The alternative hypothesis states a relationship exists between the two variables being studied (one variable affects the other).

A hypothesis is a testable statement or prediction about the relationship between two or more variables. It is a key component of the scientific method. Some key points about hypotheses:

• Important hypotheses lead to predictions that can be tested empirically. The evidence can then confirm or disprove the testable predictions.

In summary, a hypothesis is a precise, testable statement of what researchers expect to happen in a study and why. Hypotheses connect theory to data and guide the research process towards expanding scientific understanding.

An experimental hypothesis predicts what change(s) will occur in the dependent variable when the independent variable is manipulated.

It states that the results are not due to chance and are significant in supporting the theory being investigated.

The alternative hypothesis can be directional, indicating a specific direction of the effect, or non-directional, suggesting a difference without specifying its nature. It’s what researchers aim to support or demonstrate through their study.

Null Hypothesis

The null hypothesis states no relationship exists between the two variables being studied (one variable does not affect the other). There will be no changes in the dependent variable due to manipulating the independent variable.

It states results are due to chance and are not significant in supporting the idea being investigated.

The null hypothesis, positing no effect or relationship, is a foundational contrast to the research hypothesis in scientific inquiry. It establishes a baseline for statistical testing, promoting objectivity by initiating research from a neutral stance.

Many statistical methods are tailored to test the null hypothesis, determining the likelihood of observed results if no true effect exists.

This dual-hypothesis approach provides clarity, ensuring that research intentions are explicit, and fosters consistency across scientific studies, enhancing the standardization and interpretability of research outcomes.

Nondirectional Hypothesis

A non-directional hypothesis, also known as a two-tailed hypothesis, predicts that there is a difference or relationship between two variables but does not specify the direction of this relationship.

It merely indicates that a change or effect will occur without predicting which group will have higher or lower values.

For example, “There is a difference in performance between Group A and Group B” is a non-directional hypothesis.

Directional Hypothesis

A directional (one-tailed) hypothesis predicts the nature of the effect of the independent variable on the dependent variable. It predicts in which direction the change will take place. (i.e., greater, smaller, less, more)

It specifies whether one variable is greater, lesser, or different from another, rather than just indicating that there’s a difference without specifying its nature.

For example, “Exercise increases weight loss” is a directional hypothesis.

Falsifiability

The Falsification Principle, proposed by Karl Popper , is a way of demarcating science from non-science. It suggests that for a theory or hypothesis to be considered scientific, it must be testable and irrefutable.

Falsifiability emphasizes that scientific claims shouldn’t just be confirmable but should also have the potential to be proven wrong.

It means that there should exist some potential evidence or experiment that could prove the proposition false.

However many confirming instances exist for a theory, it only takes one counter observation to falsify it. For example, the hypothesis that “all swans are white,” can be falsified by observing a black swan.

For Popper, science should attempt to disprove a theory rather than attempt to continually provide evidence to support a research hypothesis.

Can a Hypothesis be Proven?

Hypotheses make probabilistic predictions. They state the expected outcome if a particular relationship exists. However, a study result supporting a hypothesis does not definitively prove it is true.

All studies have limitations. There may be unknown confounding factors or issues that limit the certainty of conclusions. Additional studies may yield different results.

In science, hypotheses can realistically only be supported with some degree of confidence, not proven. The process of science is to incrementally accumulate evidence for and against hypothesized relationships in an ongoing pursuit of better models and explanations that best fit the empirical data. But hypotheses remain open to revision and rejection if that is where the evidence leads.

• Disproving a hypothesis is definitive. Solid disconfirmatory evidence will falsify a hypothesis and require altering or discarding it based on the evidence.
• However, confirming evidence is always open to revision. Other explanations may account for the same results, and additional or contradictory evidence may emerge over time.

We can never 100% prove the alternative hypothesis. Instead, we see if we can disprove, or reject the null hypothesis.

If we reject the null hypothesis, this doesn’t mean that our alternative hypothesis is correct but does support the alternative/experimental hypothesis.

Upon analysis of the results, an alternative hypothesis can be rejected or supported, but it can never be proven to be correct. We must avoid any reference to results proving a theory as this implies 100% certainty, and there is always a chance that evidence may exist which could refute a theory.

How to Write a Hypothesis

• Identify variables . The researcher manipulates the independent variable and the dependent variable is the measured outcome.
• Operationalized the variables being investigated . Operationalization of a hypothesis refers to the process of making the variables physically measurable or testable, e.g. if you are about to study aggression, you might count the number of punches given by participants.
• Decide on a direction for your prediction . If there is evidence in the literature to support a specific effect of the independent variable on the dependent variable, write a directional (one-tailed) hypothesis. If there are limited or ambiguous findings in the literature regarding the effect of the independent variable on the dependent variable, write a non-directional (two-tailed) hypothesis.
• Make it Testable : Ensure your hypothesis can be tested through experimentation or observation. It should be possible to prove it false (principle of falsifiability).
• Clear & concise language . A strong hypothesis is concise (typically one to two sentences long), and formulated using clear and straightforward language, ensuring it’s easily understood and testable.

Consider a hypothesis many teachers might subscribe to: students work better on Monday morning than on Friday afternoon (IV=Day, DV= Standard of work).

Now, if we decide to study this by giving the same group of students a lesson on a Monday morning and a Friday afternoon and then measuring their immediate recall of the material covered in each session, we would end up with the following:

• The alternative hypothesis states that students will recall significantly more information on a Monday morning than on a Friday afternoon.
• The null hypothesis states that there will be no significant difference in the amount recalled on a Monday morning compared to a Friday afternoon. Any difference will be due to chance or confounding factors.

More Examples

• Memory : Participants exposed to classical music during study sessions will recall more items from a list than those who studied in silence.
• Social Psychology : Individuals who frequently engage in social media use will report higher levels of perceived social isolation compared to those who use it infrequently.
• Developmental Psychology : Children who engage in regular imaginative play have better problem-solving skills than those who don’t.
• Clinical Psychology : Cognitive-behavioral therapy will be more effective in reducing symptoms of anxiety over a 6-month period compared to traditional talk therapy.
• Cognitive Psychology : Individuals who multitask between various electronic devices will have shorter attention spans on focused tasks than those who single-task.
• Health Psychology : Patients who practice mindfulness meditation will experience lower levels of chronic pain compared to those who don’t meditate.
• Organizational Psychology : Employees in open-plan offices will report higher levels of stress than those in private offices.
• Behavioral Psychology : Rats rewarded with food after pressing a lever will press it more frequently than rats who receive no reward.

Directional Hypothesis

Definition:

A directional hypothesis is a specific type of hypothesis statement in which the researcher predicts the direction or effect of the relationship between two variables.

Key Features

1. Predicts direction:

Unlike a non-directional hypothesis, which simply states that there is a relationship between two variables, a directional hypothesis specifies the expected direction of the relationship.

2. Involves one-tailed test:

Directional hypotheses typically require a one-tailed statistical test, as they are concerned with whether the relationship is positive or negative, rather than simply whether a relationship exists.

3. Example:

An example of a directional hypothesis would be: “Increasing levels of exercise will result in greater weight loss.”

4. Researcher’s prior belief:

A directional hypothesis is often formed based on the researcher’s prior knowledge, theoretical understanding, or previous empirical evidence relating to the variables under investigation.

5. Confirmatory nature:

Directional hypotheses are considered confirmatory, as they provide a specific prediction that can be tested statistically, allowing researchers to either support or reject the hypothesis.

6. Advantages and disadvantages:

Directional hypotheses help focus the research by explicitly stating the expected relationship, but they can also limit exploration of alternative explanations or unexpected findings.

Directional and non-directional hypothesis: A Comprehensive Guide

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In the world of research and statistical analysis, hypotheses play a crucial role in formulating and testing scientific claims. Understanding the differences between directional and non-directional hypothesis is essential for designing sound experiments and drawing accurate conclusions. Whether you’re a student, researcher, or simply curious about the foundations of hypothesis testing, this guide will equip you with the knowledge and tools to navigate this fundamental aspect of scientific inquiry.

Understanding Directional Hypothesis

Understanding directional hypotheses is crucial for conducting hypothesis-driven research, as they guide the selection of appropriate statistical tests and aid in the interpretation of results. By incorporating directional hypotheses, researchers can make more precise predictions, contribute to scientific knowledge, and advance their fields of study.

Definition of directional hypothesis

Directional hypotheses, also known as one-tailed hypotheses, are statements in research that make specific predictions about the direction of a relationship or difference between variables. Unlike non-directional hypotheses, which simply state that there is a relationship or difference without specifying its direction, directional hypotheses provide a focused and precise expectation.

A directional hypothesis predicts either a positive or negative relationship between variables or predicts that one group will perform better than another. It asserts a specific direction of effect or outcome. For example, a directional hypothesis could state that “increased exposure to sunlight will lead to an improvement in mood” or “participants who receive the experimental treatment will exhibit higher levels of cognitive performance compared to the control group.”

Directional hypotheses are formulated based on existing theory, prior research, or logical reasoning, and they guide the researcher’s expectations and analysis. They allow for more targeted predictions and enable researchers to test specific hypotheses using appropriate statistical tests.

The role of directional hypothesis in research

Directional hypotheses also play a significant role in research surveys. Let’s explore their role specifically in the context of survey research:

• Objective-driven surveys : Directional hypotheses help align survey research with specific objectives. By formulating directional hypotheses, researchers can focus on gathering data that directly addresses the predicted relationship or difference between variables of interest.
• Question design and measurement : Directional hypotheses guide the design of survey question types and the selection of appropriate measurement scales. They ensure that the questions are tailored to capture the specific aspects related to the predicted direction, enabling researchers to obtain more targeted and relevant data from survey respondents.
• Data analysis and interpretation : Directional hypotheses assist in data analysis by directing researchers towards appropriate statistical tests and methods. Researchers can analyze the survey data to specifically test the predicted relationship or difference, enhancing the accuracy and reliability of their findings. The results can then be interpreted within the context of the directional hypothesis, providing more meaningful insights.
• Practical implications and decision-making : Directional hypotheses in surveys often have practical implications. When the predicted relationship or difference is confirmed, it informs decision-making processes, program development, or interventions. The survey findings based on directional hypotheses can guide organizations, policymakers, or practitioners in making informed choices to achieve desired outcomes.
• Replication and further research : Directional hypotheses in survey research contribute to the replication and extension of studies. Researchers can replicate the survey with different populations or contexts to assess the generalizability of the predicted relationships. Furthermore, if the directional hypothesis is supported, it encourages further research to explore underlying mechanisms or boundary conditions.

By incorporating directional hypotheses in survey research, researchers can align their objectives, design effective surveys, conduct focused data analysis, and derive practical insights. They provide a framework for organizing survey research and contribute to the accumulation of knowledge in the field.

Examples of research questions for directional hypothesis

Here are some examples of research questions that lend themselves to directional hypotheses:

• Does increased daily exercise lead to a decrease in body weight among sedentary adults?
• Is there a positive relationship between study hours and academic performance among college students?
• Does exposure to violent video games result in an increase in aggressive behavior among adolescents?
• Does the implementation of a mindfulness-based intervention lead to a reduction in stress levels among working professionals?
• Is there a difference in customer satisfaction between Product A and Product B, with Product A expected to have higher satisfaction ratings?
• Does the use of social media influence self-esteem levels, with higher social media usage associated with lower self-esteem?
• Is there a negative relationship between job satisfaction and employee turnover, indicating that lower job satisfaction leads to higher turnover rates?
• Does the administration of a specific medication result in a decrease in symptoms among individuals with a particular medical condition?
• Does increased access to early childhood education lead to improved cognitive development in preschool-aged children?
• Is there a difference in purchase intention between advertisements with celebrity endorsements and advertisements without, with celebrity endorsements expected to have a higher impact?

These research questions generate specific predictions about the direction of the relationship or difference between variables and can be tested using appropriate research methods and statistical analyses.

Definition of non-directional hypothesis

Non-directional hypotheses, also known as two-tailed hypotheses, are statements in research that indicate the presence of a relationship or difference between variables without specifying the direction of the effect. Instead of making predictions about the specific direction of the relationship or difference, non-directional hypotheses simply state that there is an association or distinction between the variables of interest.

Non-directional hypotheses are often used when there is no prior theoretical basis or clear expectation about the direction of the relationship. They leave the possibility open for either a positive or negative relationship, or for both groups to differ in some way without specifying which group will perform better or worse.

Advantages and utility of non-directional hypothesis

Non-directional hypotheses in survey s offer several advantages and utilities, providing flexibility and comprehensive analysis of survey data. Here are some of the key advantages and utilities of using non-directional hypotheses in surveys:

• Exploration of Relationships : Non-directional hypotheses allow researchers to explore and examine relationships between variables without assuming a specific direction. This is particularly useful in surveys where the relationship between variables may not be well-known or there may be conflicting evidence regarding the direction of the effect.
• Flexibility in Question Design : With non-directional hypotheses, survey questions can be designed to measure the relationship between variables without being biased towards a particular outcome. This flexibility allows researchers to collect data and analyze the results more objectively.
• Open to Unexpected Findings : Non-directional hypotheses enable researchers to be open to unexpected or surprising findings in survey data. By not committing to a specific direction of the effect, researchers can identify and explore relationships that may not have been initially anticipated, leading to new insights and discoveries.
• Comprehensive Analysis : Non-directional hypotheses promote comprehensive analysis of survey data by considering the possibility of an effect in either direction. Researchers can assess the magnitude and significance of relationships without limiting their analysis to only one possible outcome.
• S tatistical Validity : Non-directional hypotheses in surveys allow for the use of two-tailed statistical tests, which provide a more conservative and robust assessment of significance. Two-tailed tests consider both positive and negative deviations from the null hypothesis, ensuring accurate and reliable statistical analysis of survey data.
• Exploratory Research : Non-directional hypotheses are particularly useful in exploratory research, where the goal is to gather initial insights and generate hypotheses. Surveys with non-directional hypotheses can help researchers explore various relationships and identify patterns that can guide further research or hypothesis development.

It is worth noting that the choice between directional and non-directional hypotheses in surveys depends on the research objectives, existing knowledge, and the specific variables being investigated. Researchers should carefully consider the advantages and limitations of each approach and select the one that aligns best with their research goals and survey design.

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The What, Why and How of Directional Hypotheses

In the world of research and science, hypotheses serve as the starting blocks, setting the pace for the entire study. One such hypothesis type is the directional hypothesis. Here, we delve into what exactly a directional hypothesis is, its significance, and the nitty-gritty of formulating one, followed by pitfalls to avoid and how to apply it in practical situations.

The What: Understanding the Concept of a Directional Hypothesis

A directional hypothesis, often referred to as a one-tailed hypothesis, is an essential part of research that predicts the expected outcomes and their directions. The intriguing aspect here is that it goes beyond merely predicting a difference or connection, it actually suggests the direction that this difference or connection will take.

Let's break it down a bit. If the directional hypothesis is positive, this suggests that the variables being studied are expected to either increase or decrease in unison. On the other hand, if the hypothesis is negative, it implies that the variables will move in opposite directions - as one variable ascends, the other will descend, and vice versa.

This intricacy gives the directional hypothesis its unique value in research and offers a fascinating aspect of study predictions. With a clearer understanding of what a directional hypothesis is, we can now delve into why it holds such significance in research and how to construct one effectively.

The Why: The Significance of a Directional Hypothesis in Research

Ever wondered why the directional hypothesis is held in such high regard? The secret lies in its unique blend of precision and specificity. It provides an edge by paving the way for a more concentrated and focused investigation. Essentially, it helps scientists to have an informed prediction of the correlation between variables, underpinned by prior research, theoretical assumptions, or logical reasoning. This isn't just a game of guesswork but a highly credible route to more definitive and dependable results. As they say, the devil is in the detail. By using a directional hypothesis, we are able to dive into the intricate and exciting world of research, adding a robust foundation to our endeavours, ultimately boosting the credibility and reliability of our findings. By standing firmly on the shoulders of the directional hypothesis, we allow our research to gaze further and see clearer.

The How: Constructing a Strong Directional Hypothesis

Crafting a robust directional hypothesis is indeed a craft that requires a blend of art and science. This process starts with a comprehensive exploration of related literature, immersing oneself in the reservoir of knowledge that already exists around your subject of interest. This immersion enables you to soak up invaluable insights, creating a well-informed base from which to make educated predictions about the directionality between your variables of interest.

The process doesn't stop at a literature review. It's also imperative to fully comprehend your subject. Dive deeper into the layers of your topic, unpick the threads, and question the status quo. Understand what drives your variables, how they may interact, and why you anticipate they'll behave in a certain way.

Then, it's time to define your variables clearly and precisely. This might sound simple, but it's crucial to be as accurate as possible. By doing so, you not only ensure a clear understanding of what you are measuring, but you also set clear parameters for your research.

Following that, comes the exciting part - predicting the direction of the relationship between your variables. This prediction should not be a wild guess, but an informed forecast grounded in your literature review, understanding of the subject, and clear definition of variables.

Finally, remember that a directional hypothesis is not set in stone. It is, by definition, a hypothesis - a proposed explanation or prediction that is subject to testing and verification. So, don’t be disheartened if your directional hypothesis doesn’t pan out as expected. Instead, see it as an opportunity to delve further, learn more and further the boundaries of knowledge in your field. After all, research is not just about confirming hypotheses, but also about the thrill of exploration, discovery, and ultimately, growth.

Pitfalls to Avoid When Formulating a Directional Hypothesis

Crafting a directional hypothesis isn't a walk in the park. A few common missteps can muddy the waters and limit the effectiveness of your hypothesis. The first stumbling block that researchers should watch out for is making baseless presumptions. Although predicting the course of the relationship between variables is integral to a directional hypothesis, this prediction should be firmly rooted in evidence, not just whims or gut feelings.

Secondly, steer clear of being excessively rigid with your hypothesis. Remember, it's a guide, not gospel truth. Science is about exploration, about finding out, about being open to unexpected outcomes. If your hypothesis does not match the results, that's not failure; it's a chance to learn and expand your understanding.

Avoid creating an overly complex hypothesis. Simplicity is the name of the game. You want your hypothesis to be clear, concise, and comprehensible, not wrapped in jargon and unnecessary complexities.

Lastly, ensure that your directional hypothesis is testable. It's not enough to merely state a prediction; it needs to be something you can verify empirically. If it can't be tested, it's not a viable hypothesis. So, when creating your directional hypothesis, be mindful to keep it within the realm of testable claims.

Remember, falling into these traps can derail your research and limit the value of your findings. By keeping these pitfalls at bay, you are better equipped to navigate the fascinating labyrinth of research, while contributing to a deeper understanding of your field. Happy hypothesising!

Putting it All Together: Applying a Directional Hypothesis in Practice

When it comes to applying a directional hypothesis, the real fun begins as you put your prediction to the test using appropriate research methodologies and statistical techniques. Let's put this into perspective using an example. Suppose you're exploring the effect of physical activity on people's mood. Your directional hypothesis might suggest that engaging in exercise would result in an improvement in mood ratings.

To test this hypothesis, you could employ a repeated-measures design. Here, you measure the moods of your participants before they start the exercise routine and then again after they've completed it. If the data reveals an uplift in positive mood ratings post-exercise, you would have empirical evidence to support your directional hypothesis.

However, bear in mind that your findings might not always corroborate your prediction. And that's the beauty of research! Contradictory findings don't necessarily signify failure. Instead, they open up new avenues of inquiry, challenging us to refine our understanding and fuel our intellectual curiosity. Therefore, whether your directional hypothesis is proven correct or not, it still serves a valuable purpose by guiding your exploration and contributing to the ever-evolving body of knowledge in your field. So, go ahead and plunge into the exciting world of research with your well-crafted directional hypothesis, ready to embrace whatever comes your way with open arms. Happy researching!

What is a Directional Hypothesis? (Definition & Examples)

A statistical hypothesis is an assumption about a population parameter . For example, we may assume that the mean height of a male in the U.S. is 70 inches.

The assumption about the height is the statistical hypothesis and the true mean height of a male in the U.S. is the population parameter .

To test whether a statistical hypothesis about a population parameter is true, we obtain a random sample from the population and perform a hypothesis test on the sample data.

Whenever we perform a hypothesis test, we always write down a null and alternative hypothesis:

• Null Hypothesis (H 0 ): The sample data occurs purely from chance.
• Alternative Hypothesis (H A ): The sample data is influenced by some non-random cause.

A hypothesis test can either contain a directional hypothesis or a non-directional hypothesis:

• Directional hypothesis: The alternative hypothesis contains the less than (“”) sign. This indicates that we’re testing whether or not there is a positive or negative effect.
• Non-directional hypothesis: The alternative hypothesis contains the not equal (“≠”) sign. This indicates that we’re testing whether or not there is some effect, without specifying the direction of the effect.

Note that directional hypothesis tests are also called “one-tailed” tests and non-directional hypothesis tests are also called “two-tailed” tests.

Check out the following examples to gain a better understanding of directional vs. non-directional hypothesis tests.

Example 1: Baseball Programs

A baseball coach believes a certain 4-week program will increase the mean hitting percentage of his players, which is currently 0.285.

To test this, he measures the hitting percentage of each of his players before and after participating in the program.

He then performs a hypothesis test using the following hypotheses:

• H 0 : μ = .285 (the program will have no effect on the mean hitting percentage)
• H A : μ > .285 (the program will cause mean hitting percentage to increase)

This is an example of a directional hypothesis because the alternative hypothesis contains the greater than “>” sign. The coach believes that the program will influence the mean hitting percentage of his players in a positive direction.

Example 2: Plant Growth

A biologist believes that a certain pesticide will cause plants to grow less during a one-month period than they normally do, which is currently 10 inches.

To test this, she applies the pesticide to each of the plants in her laboratory for one month.

She then performs a hypothesis test using the following hypotheses:

• H 0 : μ = 10 inches (the pesticide will have no effect on the mean plant growth)

This is also an example of a directional hypothesis because the alternative hypothesis contains the less than “negative direction.

Example 3: Studying Technique

A professor believes that a certain studying technique will influence the mean score that her students receive on a certain exam, but she’s unsure if it will increase or decrease the mean score, which is currently 82.

To test this, she lets each student use the studying technique for one month leading up to the exam and then administers the same exam to each of the students.

• H 0 : μ = 82 (the studying technique will have no effect on the mean exam score)
• H A : μ ≠ 82 (the studying technique will cause the mean exam score to be different than 82)

This is an example of a non-directional hypothesis because the alternative hypothesis contains the not equal “≠” sign. The professor believes that the studying technique will influence the mean exam score, but doesn’t specify whether it will cause the mean score to increase or decrease.

Additional Resources

Introduction to Hypothesis Testing Introduction to the One Sample t-test Introduction to the Two Sample t-test Introduction to the Paired Samples t-test

How to Perform a Partial F-Test in Excel

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Aims And Hypotheses, Directional And Non-Directional

March 7, 2021 - paper 2 psychology in context | research methods.

In Psychology, hypotheses are predictions made by the researcher about the outcome of a study. The research can chose to make a specific prediction about what they feel will happen in their research (a directional hypothesis) or they can make a ‘general,’ ‘less specific’ prediction about the outcome of their research (a non-directional hypothesis). The type of prediction that a researcher makes is usually dependent on whether or not any previous research has also investigated their research aim.

Variables Recap:

The  independent variable  (IV)  is the variable that psychologists  manipulate/change  to see if changing this variable has an effect on the  depen dent variable  (DV).

The  dependent variable (DV)  is the variable that the psychologists  measures  (to see if the IV has had an effect).

Research/Experimental Aim(S):

An aim is a clear and precise statement of the purpose of the study. It is a statement of why a research study is taking place. This should include what is being studied and what the study is trying to achieve. (e.g. “This study aims to investigate the effects of alcohol on reaction times”.

Hypotheses:

This is a testable statement that predicts what the researcher expects to happen in their research. The research study itself is therefore a means of testing whether or not the hypothesis is supported by the findings. If the findings do support the hypothesis then the hypothesis can be retained (i.e., accepted), but if not, then it must be rejected.

(1)  Directional Hypothesis:  states that the IV will have an effect on the DV and what that effect will be (the direction of results). For example, eating smarties will significantly  improve  an individual’s dancing ability. When writing a directional hypothesis, it is important that you state exactly  how  the IV will influence the DV.

(3)  A Null Hypothesis:  states that the IV will have no significant effect on the DV, for example, ‘eating smarties will have no effect in an individuals dancing ability.’

Hypothesis ( AQA A Level Psychology )

Revision note.

Psychology Content Creator

• A hypothesis is a testable statement written as a prediction of what the researcher expects to find as a result of their experiment
• A hypothesis should be no more than one sentence long
• The hypothesis needs to include the independent variable (IV) and the dependent variable (DV)
• For example - stating that you will measure ‘aggression’ is not enough ('aggression' has not been operationalised)
• by exposing some children to an aggressive adult model whilst other children are not exposed to an aggressive adult model (operationalisation of the IV) 
• number of imitative and non-imitative acts of aggression performed by the child (operationalisation of the DV)

The Experimental Hypothesis

• Children who are exposed to an aggressive adult model will perform more acts of imitative and non-imitative aggression than children who have not been exposed to an aggressive adult model
• The experimental hypothesis can be written as a  directional hypothesis or as a non-directional hypothesis

The Experimental Hypothesis: Directional 

• A directional experimental hypothesis (also known as one-tailed)  predicts the direction of the change/difference (it anticipates more specifically what might happen)
• A directional hypothesis is usually used when there is previous research which support a particular theory or outcome i.e. what a researcher might expect to happen
• Participants who drink 200ml of an energy drink 5 minutes before running 100m will be faster (in seconds) than participants who drink 200ml of water 5 minutes before running 100m
• Participants who learn a poem in a room in which loud music is playing will recall less of the poem's content than participants who learn the same poem in a silent room

 The Experimental Hypothesis: Non-Directional 

• A non-directional experimental hypothesis (also known as two -tailed) does not predict the direction of the change/difference (it is an 'open goal' i.e. anything could happen)
• A non-directional hypothesis is usually used when there is either no or little previous research which support a particular theory or outcome i.e. what the researcher cannot be confident as to what will happen
• There will be a difference in time taken (in seconds) to run 100m depending on whether participants have drunk 200ml of an energy drink or 200ml of water 5 minutes before running 
• There will be a difference in recall of a poem depending on whether participants learn the poem in a room in which loud music is playing or in a silent room

The Null Hypothesis

• All published psychology research must include the null hypothesis
• There will be no difference in children's acts of imitative and non-imitative aggression depending on whether they have observed an aggressive adult model or a non-aggressive adult model
• The null hypothesis has to begin with the idea that the IV will have no effect on the DV  because until the experiment is run and the results are analysed it is impossible to state anything else! 
• To put this in 'laymen's terms: if you bought a lottery ticket you could not predict that you are going to win the jackpot: you have to wait for the results to find out (spoiler alert: the chances of this happening are soooo low that you might as well save your cash!)
• There will be no difference in time taken (in seconds) to run 100m depending on whether participants have drunk 200ml of an energy drink or 200ml of water 5 minutes before running 
• There will be no difference in recall of a poem depending on whether participants learn the poem in a room in which loud music is playing or in a silent room
• (NB this is not quite so slick and easy with a directional hypothesis as this sort of hypothesis will never begin with 'There will be a difference')
• this is why the null hypothesis is so important - it tells the researcher whether or not their experiment has shown a difference in conditions (which is generally what they want to see, otherwise it's back to the drawing board...)

Worked example

Jim wants to test the theory that chocolate helps your ability to solve word-search puzzles

He believes that sugar helps memory as he has read some research on this in a text book

He puts up a poster in his sixth-form common room asking for people to take part after school one day and explains that they will be required to play two memory games, where eating chocolate will be involved

(a)  Should Jim use a directional hypothesis in this study? Explain your answer (2 marks)

(b)  Write a suitable hypothesis for this study. (4 marks)

a) Jim should use a directional hypothesis (1 mark)

    because previous research exists that states what might happen (2 nd mark)

b)  'Participants will remember more items from a shopping list in a memory game within the hour after eating 50g of chocolate, compared to when they have not consumed any chocolate'

• 1 st mark for directional
• 2 nd mark for IV- eating chocolate
• 3 rd mark for DV- number of items remembered
• 4 th mark for operationalising both IV & DV
• If you write a non-directional or null hypothesis the mark is 0
• If you do not get the direction correct the mark is zero
• Remember to operationalise the IV & DV

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Author: Claire Neeson

Claire has been teaching for 34 years, in the UK and overseas. She has taught GCSE, A-level and IB Psychology which has been a lot of fun and extremely exhausting! Claire is now a freelance Psychology teacher and content creator, producing textbooks, revision notes and (hopefully) exciting and interactive teaching materials for use in the classroom and for exam prep. Her passion (apart from Psychology of course) is roller skating and when she is not working (or watching 'Coronation Street') she can be found busting some impressive moves on her local roller rink.

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Home » What is a Hypothesis – Types, Examples and Writing Guide

What is a Hypothesis – Types, Examples and Writing Guide

Table of Contents

Definition:

Hypothesis is an educated guess or proposed explanation for a phenomenon, based on some initial observations or data. It is a tentative statement that can be tested and potentially proven or disproven through further investigation and experimentation.

Hypothesis is often used in scientific research to guide the design of experiments and the collection and analysis of data. It is an essential element of the scientific method, as it allows researchers to make predictions about the outcome of their experiments and to test those predictions to determine their accuracy.

Types of Hypothesis

Types of Hypothesis are as follows:

Research Hypothesis

A research hypothesis is a statement that predicts a relationship between variables. It is usually formulated as a specific statement that can be tested through research, and it is often used in scientific research to guide the design of experiments.

Null Hypothesis

The null hypothesis is a statement that assumes there is no significant difference or relationship between variables. It is often used as a starting point for testing the research hypothesis, and if the results of the study reject the null hypothesis, it suggests that there is a significant difference or relationship between variables.

Alternative Hypothesis

An alternative hypothesis is a statement that assumes there is a significant difference or relationship between variables. It is often used as an alternative to the null hypothesis and is tested against the null hypothesis to determine which statement is more accurate.

Directional Hypothesis

A directional hypothesis is a statement that predicts the direction of the relationship between variables. For example, a researcher might predict that increasing the amount of exercise will result in a decrease in body weight.

Non-directional Hypothesis

A non-directional hypothesis is a statement that predicts the relationship between variables but does not specify the direction. For example, a researcher might predict that there is a relationship between the amount of exercise and body weight, but they do not specify whether increasing or decreasing exercise will affect body weight.

Statistical Hypothesis

A statistical hypothesis is a statement that assumes a particular statistical model or distribution for the data. It is often used in statistical analysis to test the significance of a particular result.

Composite Hypothesis

A composite hypothesis is a statement that assumes more than one condition or outcome. It can be divided into several sub-hypotheses, each of which represents a different possible outcome.

Empirical Hypothesis

An empirical hypothesis is a statement that is based on observed phenomena or data. It is often used in scientific research to develop theories or models that explain the observed phenomena.

Simple Hypothesis

A simple hypothesis is a statement that assumes only one outcome or condition. It is often used in scientific research to test a single variable or factor.

Complex Hypothesis

A complex hypothesis is a statement that assumes multiple outcomes or conditions. It is often used in scientific research to test the effects of multiple variables or factors on a particular outcome.

Applications of Hypothesis

Hypotheses are used in various fields to guide research and make predictions about the outcomes of experiments or observations. Here are some examples of how hypotheses are applied in different fields:

• Science : In scientific research, hypotheses are used to test the validity of theories and models that explain natural phenomena. For example, a hypothesis might be formulated to test the effects of a particular variable on a natural system, such as the effects of climate change on an ecosystem.
• Medicine : In medical research, hypotheses are used to test the effectiveness of treatments and therapies for specific conditions. For example, a hypothesis might be formulated to test the effects of a new drug on a particular disease.
• Psychology : In psychology, hypotheses are used to test theories and models of human behavior and cognition. For example, a hypothesis might be formulated to test the effects of a particular stimulus on the brain or behavior.
• Sociology : In sociology, hypotheses are used to test theories and models of social phenomena, such as the effects of social structures or institutions on human behavior. For example, a hypothesis might be formulated to test the effects of income inequality on crime rates.
• Business : In business research, hypotheses are used to test the validity of theories and models that explain business phenomena, such as consumer behavior or market trends. For example, a hypothesis might be formulated to test the effects of a new marketing campaign on consumer buying behavior.
• Engineering : In engineering, hypotheses are used to test the effectiveness of new technologies or designs. For example, a hypothesis might be formulated to test the efficiency of a new solar panel design.

How to write a Hypothesis

Here are the steps to follow when writing a hypothesis:

Identify the Research Question

The first step is to identify the research question that you want to answer through your study. This question should be clear, specific, and focused. It should be something that can be investigated empirically and that has some relevance or significance in the field.

Conduct a Literature Review

Before writing your hypothesis, it’s essential to conduct a thorough literature review to understand what is already known about the topic. This will help you to identify the research gap and formulate a hypothesis that builds on existing knowledge.

Determine the Variables

The next step is to identify the variables involved in the research question. A variable is any characteristic or factor that can vary or change. There are two types of variables: independent and dependent. The independent variable is the one that is manipulated or changed by the researcher, while the dependent variable is the one that is measured or observed as a result of the independent variable.

Formulate the Hypothesis

Based on the research question and the variables involved, you can now formulate your hypothesis. A hypothesis should be a clear and concise statement that predicts the relationship between the variables. It should be testable through empirical research and based on existing theory or evidence.

Write the Null Hypothesis

The null hypothesis is the opposite of the alternative hypothesis, which is the hypothesis that you are testing. The null hypothesis states that there is no significant difference or relationship between the variables. It is important to write the null hypothesis because it allows you to compare your results with what would be expected by chance.

Refine the Hypothesis

After formulating the hypothesis, it’s important to refine it and make it more precise. This may involve clarifying the variables, specifying the direction of the relationship, or making the hypothesis more testable.

Examples of Hypothesis

Here are a few examples of hypotheses in different fields:

• Psychology : “Increased exposure to violent video games leads to increased aggressive behavior in adolescents.”
• Biology : “Higher levels of carbon dioxide in the atmosphere will lead to increased plant growth.”
• Sociology : “Individuals who grow up in households with higher socioeconomic status will have higher levels of education and income as adults.”
• Education : “Implementing a new teaching method will result in higher student achievement scores.”
• Marketing : “Customers who receive a personalized email will be more likely to make a purchase than those who receive a generic email.”
• Physics : “An increase in temperature will cause an increase in the volume of a gas, assuming all other variables remain constant.”
• Medicine : “Consuming a diet high in saturated fats will increase the risk of developing heart disease.”

Purpose of Hypothesis

The purpose of a hypothesis is to provide a testable explanation for an observed phenomenon or a prediction of a future outcome based on existing knowledge or theories. A hypothesis is an essential part of the scientific method and helps to guide the research process by providing a clear focus for investigation. It enables scientists to design experiments or studies to gather evidence and data that can support or refute the proposed explanation or prediction.

The formulation of a hypothesis is based on existing knowledge, observations, and theories, and it should be specific, testable, and falsifiable. A specific hypothesis helps to define the research question, which is important in the research process as it guides the selection of an appropriate research design and methodology. Testability of the hypothesis means that it can be proven or disproven through empirical data collection and analysis. Falsifiability means that the hypothesis should be formulated in such a way that it can be proven wrong if it is incorrect.

In addition to guiding the research process, the testing of hypotheses can lead to new discoveries and advancements in scientific knowledge. When a hypothesis is supported by the data, it can be used to develop new theories or models to explain the observed phenomenon. When a hypothesis is not supported by the data, it can help to refine existing theories or prompt the development of new hypotheses to explain the phenomenon.

When to use Hypothesis

Here are some common situations in which hypotheses are used:

• In scientific research , hypotheses are used to guide the design of experiments and to help researchers make predictions about the outcomes of those experiments.
• In social science research , hypotheses are used to test theories about human behavior, social relationships, and other phenomena.
• I n business , hypotheses can be used to guide decisions about marketing, product development, and other areas. For example, a hypothesis might be that a new product will sell well in a particular market, and this hypothesis can be tested through market research.

Characteristics of Hypothesis

Here are some common characteristics of a hypothesis:

• Testable : A hypothesis must be able to be tested through observation or experimentation. This means that it must be possible to collect data that will either support or refute the hypothesis.
• Falsifiable : A hypothesis must be able to be proven false if it is not supported by the data. If a hypothesis cannot be falsified, then it is not a scientific hypothesis.
• Clear and concise : A hypothesis should be stated in a clear and concise manner so that it can be easily understood and tested.
• Based on existing knowledge : A hypothesis should be based on existing knowledge and research in the field. It should not be based on personal beliefs or opinions.
• Specific : A hypothesis should be specific in terms of the variables being tested and the predicted outcome. This will help to ensure that the research is focused and well-designed.
• Tentative: A hypothesis is a tentative statement or assumption that requires further testing and evidence to be confirmed or refuted. It is not a final conclusion or assertion.
• Relevant : A hypothesis should be relevant to the research question or problem being studied. It should address a gap in knowledge or provide a new perspective on the issue.

Advantages of Hypothesis

Hypotheses have several advantages in scientific research and experimentation:

• Guides research: A hypothesis provides a clear and specific direction for research. It helps to focus the research question, select appropriate methods and variables, and interpret the results.
• Predictive powe r: A hypothesis makes predictions about the outcome of research, which can be tested through experimentation. This allows researchers to evaluate the validity of the hypothesis and make new discoveries.
• Facilitates communication: A hypothesis provides a common language and framework for scientists to communicate with one another about their research. This helps to facilitate the exchange of ideas and promotes collaboration.
• Efficient use of resources: A hypothesis helps researchers to use their time, resources, and funding efficiently by directing them towards specific research questions and methods that are most likely to yield results.
• Provides a basis for further research: A hypothesis that is supported by data provides a basis for further research and exploration. It can lead to new hypotheses, theories, and discoveries.
• Increases objectivity: A hypothesis can help to increase objectivity in research by providing a clear and specific framework for testing and interpreting results. This can reduce bias and increase the reliability of research findings.

Limitations of Hypothesis

Some Limitations of the Hypothesis are as follows:

• Limited to observable phenomena: Hypotheses are limited to observable phenomena and cannot account for unobservable or intangible factors. This means that some research questions may not be amenable to hypothesis testing.
• May be inaccurate or incomplete: Hypotheses are based on existing knowledge and research, which may be incomplete or inaccurate. This can lead to flawed hypotheses and erroneous conclusions.
• May be biased: Hypotheses may be biased by the researcher’s own beliefs, values, or assumptions. This can lead to selective interpretation of data and a lack of objectivity in research.
• Cannot prove causation: A hypothesis can only show a correlation between variables, but it cannot prove causation. This requires further experimentation and analysis.
• Limited to specific contexts: Hypotheses are limited to specific contexts and may not be generalizable to other situations or populations. This means that results may not be applicable in other contexts or may require further testing.
• May be affected by chance : Hypotheses may be affected by chance or random variation, which can obscure or distort the true relationship between variables.

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How To Write A Directional Hypothesis

Directional hypotheses are often used in experimental research, where the researcher is able to manipulate the independent variable and measure the effect on the dependent variable. For example, a researcher might hypothesize that students who receive tutoring will score higher on math tests than students who do not receive tutoring.

To write a directional hypothesis, you need to have a good understanding of the relationship between the two variables you are interested in. You can review existing research to learn more about the relationship, or you can conduct your own pilot study.

Once you have a good understanding of the relationship, you can state your directional hypothesis in a clear and concise way. The hypothesis should be specific enough that it can be tested, but it should also be broad enough to be meaningful.

Here are some tips for writing a directional hypothesis:

1. Start by identifying the independent and dependent variables in your study. The independent variable is the variable that you are manipulating, and the dependent variable is the variable that you are measuring

2. State the predicted direction of the relationship between the two variables. For example, will the independent variable increase or decrease the dependent variable?

3. Use clear and concise language. Avoid using jargon or technical terms that your readers may not understand.

4. Make sure that your hypothesis is testable. You should be able to collect data to test your hypothesis and determine whether or not it is supported.

Here are some examples of directional hypotheses:

1. Students who receive tutoring will score higher on math tests than students who do not receive tutoring.

2. People who exercise regularly will have lower blood pressure than people who do not exercise regularly.

3. Children who grow up in poverty are more likely to experience health problems as adults.

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Tips for writing a strong directional hypothesis

1. Be specific. The more specific your hypothesis is, the easier it will be to test.

4. Be realistic. Your hypothesis should be based on existing research and your own understanding of the topic.

3. Be testable. Your hypothesis should be able to be tested using data collection and statistical analysis.

4. Be clear and concise. Your hypothesis should be easy to understand and interpret.

Directional hypotheses can be a powerful tool for scientific research. By writing a strong directional hypothesis, you can increase your chances of obtaining meaningful results. If you are struggling to write a directional hypothesis, don't hesitate to seek help from a mentor or colleague.

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Directional Hypothesis

A directional hypothesis is a one-tailed hypothesis that states the direction of the difference or relationship (e.g. boys are more helpful than girls).

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Directional Hypothesis Statement

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Grasping the intricacies of a directional hypothesis is a stepping stone in advanced research. It offers a clear perspective, pointing towards a specific prediction. From meticulously crafted examples to a thesis statement writing guide, and invaluable tips – this segment shines a light on the essence of formulating a precise and informed directional hypothesis. Embark on this enlightening journey and amplify the quality and clarity of your research endeavors.

What is a Directional hypothesis?

A directional hypothesis, often referred to as a one-tailed hypothesis , is a specific type of hypothesis that predicts the direction of the expected relationship between variables. This type of hypothesis is used when researchers have enough preliminary evidence or theoretical foundation to predict the direction of the relationship, rather than merely stating that a relationship exists.

For example, based on previous studies or established theories, a researcher might hypothesize that a specific intervention will lead to an increase (or decrease) in a certain outcome, rather than just hypothesizing that the intervention will have some effect without specifying the direction of that effect.

What is an example of a Directional hypothesis Statement?

“Children exposed to interactive educational software will demonstrate a higher increase in mathematical skills compared to children who receive traditional classroom instruction.” In this statement, the direction of the expected relationship is clear – the use of interactive educational software is predicted to have a positive effect on mathematical skills.  You may also be interested in our  non directional .

100 Directional Hypothesis Statement Examples

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Directional hypotheses are pivotal in streamlining research focus, providing a clear trajectory by anticipating a specific trend or outcome. They’re an embodiment of informed predictions, crafted based on prior knowledge or insightful observations. Discover below a plethora of examples showcasing the essence of these one-tailed, directional assertions.

• Effect of Diet on Weight: Individuals on a high-fiber diet will lose more weight over a month compared to those on a low-fiber diet.
• Physical Activity and Heart Health: Regular aerobic exercise will lead to a more significant reduction in blood pressure than anaerobic exercise.
• Learning Methods: Students taught via hands-on methods will retain information longer than those taught through lectures.
• Music and Productivity: Employees listening to classical music during work hours will demonstrate higher productivity than those listening to pop music.
• Medication Efficacy: Patients administered Drug X will show faster recovery rates from the flu than those given a placebo.
• Sleep and Memory: Individuals sleeping for 8 hours nightly will have better memory recall than those sleeping only 5 hours.
• Training Intensity and Muscle Growth: Athletes undergoing high-intensity training will exhibit more muscle growth than those in low-intensity programs.
• Organic Foods and Health: Consuming organic foods will lead to lower cholesterol levels compared to consuming non-organic foods.
• Stress and Immunity: Individuals exposed to chronic stress will have a lower immune response than those with minimal stress.
• Digital Learning Platforms: Students utilizing digital learning platforms will score higher in standardized tests than those relying solely on textbooks.
• Caffeine and Alertness: People drinking three cups of coffee daily will show higher alertness levels than non-coffee drinkers.
• Therapy Types: Patients undergoing cognitive-behavioral therapy will show greater reductions in depressive symptoms than those in talk therapy.
• E-Books and Reading Speed: Individuals reading from e-books will process content faster than those reading traditional paper books.
• Urban Living and Mental Health: Residents in urban areas will report higher stress levels than those living in rural regions.
• UV Exposure and Skin Health: Consistent exposure to UV rays will lead to faster skin aging compared to limited sun exposure.
• Yoga and Flexibility: Engaging in daily yoga practices will increase flexibility more significantly than bi-weekly practices.
• Meditation and Stress Reduction: Practicing daily meditation will lead to a more substantial decrease in cortisol levels than sporadic meditation.
• Parenting Styles and Child Independence: Children raised with authoritative parenting styles will demonstrate higher levels of independence than those raised with permissive styles.
• Economic Incentives: Workers receiving performance-based bonuses will exhibit higher job satisfaction than those with fixed salaries.
• Sugar Intake and Energy: Consuming high sugar foods will lead to a more rapid energy decline than low-sugar foods.
• Language Acquisition: Children exposed to bilingual environments before age five will develop superior linguistic skills compared to those exposed later in life.
• Herbal Teas and Sleep: Drinking chamomile tea before bedtime will result in a better sleep quality compared to drinking green tea.
• Posture and Back Pain: Individuals who practice regular posture exercises will experience less chronic back pain than those who don’t.
• Air Quality and Respiratory Issues: Residents in cities with high air pollution will report more respiratory issues than those in cities with cleaner air.
• Online Marketing and Sales: Businesses employing targeted online advertising strategies will see a higher increase in sales than those using traditional advertising methods.
• Pet Ownership and Loneliness: Seniors who own pets will report lower levels of loneliness than those who don’t have pets.
• Dietary Supplements and Immunity: Regular intake of vitamin C supplements will lead to fewer instances of common cold than a placebo.
• Technology and Social Skills: Children who spend over five hours daily on electronic devices will exhibit weaker face-to-face social skills than those who spend less than an hour.
• Remote Work and Productivity: Employees working remotely will report higher job satisfaction than those working in a traditional office setting.
• Organic Farming and Soil Health: Farms employing organic methods will have richer soil nutrient content than those using conventional methods.
• Probiotics and Digestive Health: Consuming probiotics daily will lead to improved gut health compared to not consuming any.
• Art Therapy and Trauma Recovery: Individuals undergoing art therapy will show faster emotional recovery from trauma than those using only talk therapy.
• Video Games and Reflexes: Regular gamers will demonstrate quicker reflex actions than non-gamers.
• Forest Bathing and Stress: Engaging in monthly forest bathing sessions will reduce stress levels more significantly than urban recreational activities.
• Vegan Diet and Heart Health: Individuals following a vegan diet will have a lower risk of heart diseases compared to those on omnivorous diets.
• Mindfulness and Anxiety: Practicing mindfulness meditation will result in a more significant reduction in anxiety levels than general relaxation techniques.
• Solar Energy and Cost Efficiency: Over a decade, households using solar energy will report more cost savings than those relying on traditional electricity sources.
• Active Commuting and Fitness Level: People who cycle or walk to work will have better cardiovascular health than those who commute by car.
• Online Learning and Retention: Students who engage in interactive online learning will retain subject matter better than those using passive video lectures.
• Gardening and Mental Wellbeing: Engaging in regular gardening activities will lead to improved mental well-being compared to non-gardening related hobbies.
• Music Therapy and Memory: Alzheimer’s patients exposed to regular music therapy sessions will display better memory retention than those who aren’t.
• Organic Foods and Allergies: Individuals consuming primarily organic foods will report fewer food allergies compared to those consuming non-organic foods.
• Class Size and Learning Efficiency: Students in smaller class sizes will demonstrate higher academic achievements than those in larger classes.
• Sports and Leadership Skills: Teenagers engaged in team sports will develop stronger leadership skills than those engaged in solitary activities.
• Virtual Reality and Pain Management: Patients using virtual reality as a distraction method during minor surgical procedures will report lower pain levels than those using traditional methods.
• Recycling and Environmental Awareness: Communities with mandatory recycling programs will demonstrate higher environmental awareness than those without such programs.
• Acupuncture and Migraine Relief: Migraine sufferers receiving regular acupuncture treatments will experience fewer episodes than those relying only on medication.
• Urban Green Spaces and Mental Health: Residents in cities with ample green spaces will show lower rates of depression compared to cities predominantly built-up.
• Aquatic Exercises and Joint Health: Individuals with arthritis participating in aquatic exercises will report greater joint mobility than those who do land-based exercises.
• E-books and Reading Comprehension: Students using e-books for study will demonstrate similar reading comprehension levels as those using traditional textbooks.
• Financial Literacy Programs and Debt Management: Adults who attended financial literacy programs in school will manage their debts more effectively than those who didn’t.
• Play-based Learning and Creativity: Children educated through play-based learning methods will exhibit higher creativity levels than those in a strictly academic environment.
• Caffeine Consumption and Cognitive Function: Moderate daily caffeine consumption will lead to improved cognitive function compared to high or no caffeine intake.
• Vegetable Intake and Skin Health: Individuals consuming a diet rich in colorful vegetables will have healthier skin compared to those with minimal vegetable intake.
• Physical Activity and Bone Density: Post-menopausal women engaging in weight-bearing exercises will maintain better bone density than those who don’t.
• Intermittent Fasting and Metabolism: Individuals practicing intermittent fasting will demonstrate a more efficient metabolism rate than those on regular diets.
• Public Transport and Air Quality: Cities with extensive public transport systems will have better air quality than cities primarily reliant on individual car use.
• Sleep Duration and Immunity: Adults sleeping between 7-9 hours nightly will have stronger immune responses than those sleeping less or more than this range.
• Hands-on Learning and Skill Retention: Students taught through hands-on practical methods will retain technical skills better than those taught purely theoretically.
• Nature Exposure and Concentration: Regular breaks involving nature exposure during work will result in higher concentration levels than indoor breaks.
• Yoga and Stress Reduction: Individuals practicing daily yoga sessions will experience a more significant reduction in stress levels compared to non-practitioners.
• Pet Ownership and Loneliness: People who own pets, especially dogs or cats, will report lower feelings of loneliness than those without pets.
• Bilingualism and Cognitive Flexibility: Individuals who are bilingual will exhibit higher cognitive flexibility compared to those who speak only one language.
• Green Tea and Weight Loss: Regular consumption of green tea will result in a higher rate of weight loss than those who consume other beverages.
• Plant-based Diets and Heart Health: Individuals following a plant-based diet will show a reduced risk of cardiovascular diseases compared to those on omnivorous diets.
• Forest Bathing and Mental Wellbeing: People who frequently engage in forest bathing or nature walks will demonstrate improved mental wellbeing than those who don’t.
• Online Learning and Independence: Students who predominantly learn through online platforms will develop stronger independent study habits than those in traditional classroom settings.
• Gardening and Life Satisfaction: Individuals engaged in regular gardening will report higher life satisfaction scores than non-gardeners.
• Video Games and Reflexes: People who play action video games frequently will exhibit quicker reflexes than non-gamers.
• Daily Meditation and Anxiety Levels: Individuals who practice daily meditation sessions will experience reduced anxiety levels compared to those who don’t meditate.
• Volunteering and Self-esteem: Regular volunteers will have higher self-esteem and a more positive outlook than those who don’t volunteer.
• Art Therapy and Emotional Expression: Individuals undergoing art therapy will exhibit a broader range of emotional expression than those undergoing traditional counseling.
• Morning Sunlight and Sleep Patterns: Exposure to morning sunlight will result in better nighttime sleep quality than exposure to late afternoon sunlight.
• Probiotics and Digestive Health: Regular intake of probiotics will lead to improved gut health and fewer digestive issues than those not consuming probiotics.
• Digital Detox and Social Skills: Individuals who frequently engage in digital detoxes will develop better face-to-face social skills than constant device users.
• Physical Libraries and Reading Habits: Students with access to physical libraries will exhibit more consistent reading habits than those relying solely on digital sources.
• Public Speaking Training and Confidence: Individuals who undergo public speaking training will express higher confidence levels in various social scenarios than those who don’t.
• Music Lessons and Mathematical Abilities: Children who take music lessons, especially in instruments like the piano, will show improved mathematical abilities compared to non-musical peers.
• Dance and Coordination: Engaging in dance classes will lead to better physical coordination and balance than other forms of exercise.
• Home Cooking and Nutritional Intake: Individuals who predominantly consume home-cooked meals will have a more balanced nutritional intake than those relying on take-out or restaurant meals.
• Organic Foods and Health Outcomes: Individuals consuming predominantly organic foods will exhibit fewer health issues related to preservatives and pesticides than those consuming conventionally grown foods.
• Podcast Consumption and Listening Skills: People who regularly listen to podcasts will demonstrate better active listening skills compared to those who rarely or never listen to podcasts.
• Urban Farming and Community Engagement: Urban areas with community farming initiatives will experience higher levels of community engagement and social interaction than areas without such initiatives.
• Mindfulness Practices and Emotional Regulation: Individuals practicing mindfulness techniques, like deep breathing or body scans, will manage their emotional responses better than those not practicing mindfulness.
• E-books and Reading Speed: People who primarily read e-books will exhibit a faster reading speed compared to those reading printed books.
• Aerobic Exercises and Endurance: Engaging in regular aerobic exercises will lead to higher endurance levels compared to anaerobic exercises.
• Digital Note-taking and Information Retention: Students who use digital platforms for note-taking will retain and recall information less effectively than those taking handwritten notes.
• Cycling to Work and Cardiovascular Health: Individuals who cycle to work will have better cardiovascular health than those who commute using motorized transportation.
• Active Learning Techniques and Academic Performance: Students exposed to active learning strategies will perform better academically than students in traditional lecture-based settings.
• Ergonomic Workspaces and Physical Discomfort: Workers who use ergonomic office furniture will report fewer musculoskeletal problems than those using conventional office furniture.
• Reforestation Initiatives and Air Quality: Areas with proactive reforestation initiatives will have significantly better air quality than areas without such efforts.
• Mediterranean Diet and Lifespan: People following a Mediterranean diet will generally have a longer lifespan compared to those following Western diets.
• Virtual Reality Training and Skill Acquisition: Individuals trained using virtual reality platforms will acquire new skills more rapidly than those trained using traditional methods.
• Solar Energy Adoption and Electricity Bills: Households that adopt solar energy solutions will experience lower monthly electricity bills than those relying solely on grid electricity.
• Journaling and Stress Reduction: Regular journaling will lead to a more significant reduction in perceived stress levels than non-journaling practices.
• Noise-cancelling Headphones and Productivity: Workers using noise-cancelling headphones in open office environments will show higher productivity levels than those not using such headphones.
• Early Birds and Task Efficiency: Individuals who start their day early, or “early birds”, will generally be more efficient in completing tasks than night owls.
• Coding Bootcamps and Job Placement: Graduates from coding bootcamps will find job placements more rapidly than those with only traditional computer science degrees.
• Plant-based Milks and Lactose Intolerance: Consuming plant-based milks, such as almond or oat milk, will cause fewer digestive problems for lactose-intolerant individuals than cow’s milk.
• Sensory Deprivation Tanks and Creativity: Regular sessions in sensory deprivation tanks will lead to heightened creativity levels compared to traditional relaxation methods.

Directional Hypothesis Statement Examples for Psychology

In the realm of psychology, directional psychology hypothesis are valuable as they specifically predict the nature and direction of a relationship or effect. These statements make pointed predictions about expected outcomes in psychological studies, paving the way for focused investigations.

• Emotion Regulation Techniques: Individuals trained in emotion regulation techniques will exhibit lower levels of anxiety than those untrained.
• Positive Reinforcement in Learning: Children exposed to positive reinforcement will exhibit faster learning rates than those exposed to negative reinforcement.
• Cognitive Behavioral Therapy and Depression: Patients undergoing cognitive-behavioral therapy will show more significant improvements in depressive symptoms than those using other therapeutic methods.
• Social Media Use and Self-esteem: Adolescents with higher social media usage will report lower self-esteem than their less active counterparts.
• Mindfulness Meditation and Attention Span: Regular practitioners of mindfulness meditation will have longer attention spans than non-practitioners.
• Childhood Trauma and Adult Relationships: Individuals who experienced trauma in childhood will display more attachment issues in adult romantic relationships than those without such experiences.
• Group Therapy and Social Skills: Individuals attending group therapy will demonstrate improved social skills compared to those receiving individual therapy.
• Extrinsic Motivation and Task Performance: Students driven by extrinsic motivation will have lower task persistence than those driven by intrinsic motivation.
• Visual Imagery and Memory Retention: Participants using visual imagery techniques will recall lists of items more effectively than those using rote memorization.
• Parenting Styles and Adolescent Rebellion: Adolescents raised with authoritarian parenting styles will show higher levels of rebellion than those raised with permissive styles.

Directional Hypothesis Statement Examples for Research

In research, a directional research hypothesis narrows down the prediction to a specific direction of the effect. These hypotheses can serve various fields, guiding researchers toward certain anticipated outcomes, making the study’s goal clearer.

• Online Learning Platforms and Student Engagement: Students using interactive online learning platforms will have higher engagement levels than those using traditional online formats.
• Work from Home and Employee Productivity: Employees working from home will report higher job satisfaction but slightly reduced productivity compared to office-going employees.
• Green Spaces and Urban Well-being: Urban areas with more green spaces will have residents reporting higher well-being scores than areas dominated by concrete.
• Dietary Fiber Intake and Digestive Health: Individuals consuming diets rich in fiber will have fewer digestive issues than those on low-fiber diets.
• Public Transportation and Air Quality: Cities that invest more in public transportation will experience better air quality than cities reliant on individual car usage.
• Gamification and Learning Outcomes: Educational modules that incorporate gamification will yield better learning outcomes than traditional modules.
• Open Source Software and System Security: Systems using open-source software will encounter fewer security breaches than those using proprietary software.
• Organic Farming and Soil Health: Farmlands practicing organic farming methods will have richer soil quality than conventionally farmed lands.
• Renewable Energy Sources and Power Grid Stability: Power grids utilizing a higher percentage of renewable energy sources will experience fewer outages than those predominantly using fossil fuels.
• Artificial Sweeteners and Weight Gain: Regular consumers of artificial sweeteners will not necessarily exhibit lower weight gain compared to consumers of natural sugars.

Directional Hypothesis Statement Examples for Correlation Study

Correlation studies evaluate the relationship between two or more variables. Directional hypotheses in correlation studies anticipate a specific type of association – either positive, negative, or neutral.

• Physical Activity and Mental Health: There will be a positive correlation between regular physical activity levels and self-reported mental well-being.
• Sedentary Lifestyle and Cardiovascular Issues: An increased sedentary lifestyle duration will correlate positively with cardiovascular health issues.
• Reading Habits and Vocabulary Size: There will be a positive correlation between the frequency of reading and the breadth of an individual’s vocabulary.
• Fast Food Consumption and Health Risks: A higher frequency of fast food consumption will correlate with increased health risks, such as obesity or high blood pressure.
• Financial Literacy and Debt Management: Individuals with higher financial literacy will have a negative correlation with unmanaged debts.
• Sleep Duration and Cognitive Performance: There will be a positive correlation between the optimal sleep duration (7-9 hours) and cognitive performance in adults.
• Volunteering and Life Satisfaction: Individuals who volunteer regularly will show a positive correlation with overall life satisfaction scores.
• Alcohol Consumption and Reaction Time: A higher frequency and quantity of alcohol consumption will negatively correlate with reaction times in motor tasks.
• Class Attendance and Academic Grades: There will be a positive correlation between the number of classes attended and the final academic grades of students.
• Eco-friendly Practices and Brand Loyalty: Brands adopting more eco-friendly practices will experience a positive correlation with consumer loyalty and trust.

Directional Hypothesis vs Non-Directional Hypothesis

Directional Hypothesis: A directional hypothesis , as the name implies, provides a specific direction for the expected relationship or difference between variables. It predicts which group will have higher or lower scores or how two variables will relate specifically, such as predicting that one variable will increase as the other decreases.

Advantages of a Directional Hypothesis:

• Offers clarity in predictions.
• Simplifies data interpretation, since the expected outcome is clearly stated.
• Can be based on previous research or established theories, lending more weight to its predictions.

Example of Directional Hypothesis: “Students who receive mindfulness training will have lower stress levels than those who do not receive such training.”

Non-Directional Hypothesis (Two-tailed Hypothesis): A non-directional hypothesis , on the other hand, merely states that there will be a difference between the two groups or a relationship between two variables without specifying the nature of this difference or relationship.

Advantages of a Non-Directional Hypothesis:

• Useful when research is exploratory in nature.
• Provides a broader scope for exploring unexpected results.
• Less bias as it doesn’t anticipate a specific outcome.

Example of Non-Directional Hypothesis: “Students who receive mindfulness training will have different stress levels than those who do not receive such training.”

How do you write a Directional Hypothesis Statement? – Step by Step Guide

1. Identify Your Variables: Before drafting a hypothesis, understand the dependent and independent variables in your study.

2. Review Previous Research: Consider findings from past studies or established theories to make informed predictions.

3. Be Specific: Clearly state which group or condition you expect to have higher or lower scores or how the variables will relate.

4. Keep It Simple: Ensure that the hypothesis is concise and free of jargon.

5. Make It Testable: Your hypothesis should be framed in such a way that it can be empirically tested through experiments or observations.

6. Revise and Refine: After drafting your hypothesis, review it to ensure clarity and relevance. Get feedback if possible.

7. State Confidently: Use definitive language, such as “will” rather than “might.”

Example of Writing Directional Hypothesis: Based on a study that indicates mindfulness reduces stress, and intending to research its impact on students, you might draft: “Students undergoing mindfulness practices will report lower stress levels.”

Tips for Writing a Directional Hypothesis Statement

1. Base Your Predictions on Evidence: Whenever possible, root your hypotheses in existing literature or preliminary observations.

2. Avoid Ambiguity: Be clear about the specific groups or conditions you are comparing.

3. Stay Focused: A hypothesis should address one primary question or relationship. If you find your hypothesis complicated, consider breaking it into multiple hypotheses.

4. Use Simple Language: Complex wording can muddle the clarity of your hypothesis. Ensure it’s understandable, even to those outside your field.

5. Review and Refine: After drafting, set it aside, then revisit with fresh eyes. It can also be helpful to get peers or mentors to review your hypothesis.

6. Avoid Personal Bias: Ensure your hypothesis is based on empirical evidence or theories and not personal beliefs or biases.

Remember, a directional hypothesis is just a starting point. While it provides a roadmap for your research, it’s essential to remain open to whatever results your study yields, even if they contradict your initial predictions.

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Directional vs Non-Directional Hypothesis – Collect Feedback More Effectively 

To conduct a perfect survey, you should know the basics of good research . That’s why in Startquestion we would like to share with you our knowledge about basic terms connected to online surveys and feedback gathering . Knowing the basis you can create surveys and conduct research in more effective ways and thanks to this get meaningful feedback from your customers, employees, and users. That’s enough for the introduction – let’s get to work. This time we will tell you about the hypothesis .

What is a Hypothesis?

A Hypothesis can be described as a theoretical statement built upon some evidence so that it can be tested as if it is true or false. In other words, a hypothesis is a speculation or an idea, based on insufficient evidence that allows it further analysis and experimentation.  

The purpose of a hypothetical statement is to work like a prediction based on studied research and to provide some estimated results before it ha happens in a real position. There can be more than one hypothesis statement involved in a research study, where you need to question and explore different aspects of a proposed research topic. Before putting your research into directional vs non-directional hypotheses, let’s have some basic knowledge.

Most often, a hypothesis describes a relation between two or more variables. It includes:

An Independent variable – One that is controlled by the researcher

Dependent Variable – The variable that the researcher observes in association with the Independent variable.

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How to write an effective Hypothesis?

To write an effective hypothesis follow these essential steps.

• Inquire a Question

The very first step in writing an effective hypothesis is raising a question. Outline the research question very carefully keeping your research purpose in mind. Build it in a precise and targeted way. Here you must be clear about the research question vs hypothesis. A research question is the very beginning point of writing an effective hypothesis.

Do Literature Review

Once you are done with constructing your research question, you can start the literature review. A literature review is a collection of preliminary research studies done on the same or relevant topics. There is a diversified range of literature reviews. The most common ones are academic journals but it is not confined to that. It can be anything including your research, data collection, and observation.

At this point, you can build a conceptual framework. It can be defined as a visual representation of the estimated relationship between two variables subjected to research.

Frame an Answer

After a collection of literature reviews, you can find ways how to answer the question. Expect this stage as a point where you will be able to make a stand upon what you believe might have the exact outcome of your research. You must formulate this answer statement clearly and concisely.

Build a Hypothesis

At this point, you can firmly build your hypothesis. By now, you knew the answer to your question so make a hypothesis that includes:

• Applicable Variables                     
• Particular Group being Studied (Who/What)
• Probable Outcome of the Experiment

Remember, your hypothesis is a calculated assumption, it has to be constructed as a sentence, not a question. This is where research question vs hypothesis starts making sense.

Refine a Hypothesis

Make necessary amendments to the constructed hypothesis keeping in mind that it has to be targeted and provable. Moreover, you might encounter certain circumstances where you will be studying the difference between one or more groups. It can be correlational research. In such instances, you must have to testify the relationships that you believe you will find in the subject variables and through this research.

Build Null Hypothesis

Certain research studies require some statistical investigation to perform a data collection. Whenever applying any scientific method to construct a hypothesis, you must have adequate knowledge of the Null Hypothesis and an Alternative hypothesis.

Null Hypothesis: 

A null Hypothesis denotes that there is no statistical relationship between the subject variables. It is applicable for a single group of variables or two groups of variables. A Null Hypothesis is denoted as an H0. This is the type of hypothesis that the researcher tries to invalidate. Some of the examples of null hypotheses are:

–        Hyperactivity is not associated with eating sugar.

–        All roses have an equal amount of petals.

–        A person’s preference for a dress is not linked to its color.

Alternative Hypothesis: 

An alternative hypothesis is a statement that is simply inverse or opposite of the null hypothesis and denoted as H1. Simply saying, it is an alternative statement for the null hypothesis. The same examples will go this way as an alternative hypothesis:

–        Hyperactivity is associated with eating sugar.

–        All roses do not have an equal amount of petals.

–        A person’s preference for a dress is linked to its color.

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Types of Hypothesis

Apart from null and alternative hypotheses, research hypotheses can be categorized into different types. Let’s have a look at them:

Simple Hypothesis:

This type of hypothesis is used to state a relationship between a particular independent variable and only a dependent variable.

Complex Hypothesis:

A statement that states the relationship between two or more independent variables and two or more dependent variables, is termed a complex hypothesis.

Associative and Causal Hypothesis:

This type of hypothesis involves predicting that there is a point of interdependency between two variables. It says that any kind of change in one variable will cause a change in the other one.  Similarly, a casual hypothesis says that a change in the dependent variable is due to some variations in the independent variable.

Directional vs non-directional hypothesis

Directional hypothesis:.

A hypothesis that is built upon a certain directional relationship between two variables and constructed upon an already existing theory, is called a directional hypothesis. To understand more about what is directional hypothesis here is an example, Girls perform better than boys (‘better than’ shows the direction predicted)

Non-directional Hypothesis:

It involves an open-ended non-directional hypothesis that predicts that the independent variable will influence the dependent variable; however, the nature or direction of a relationship between two subject variables is not defined or clear.

For Example, there will be a difference in the performance of girls & boys (Not defining what kind of difference)

As a professional, we suggest you apply a non-directional alternative hypothesis when you are not sure of the direction of the relationship. Maybe you’re observing potential gender differences on some psychological test, but you don’t know whether men or women would have the higher ratio. Normally, this would say that you are lacking practical knowledge about the proposed variables. A directional test should be more common for tests. 

Author: Ula Kamburov-Niepewna

Updated: 18 November 2022

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Gut microbiota, circulating inflammatory proteins and sepsis: a bi-directional Mendelian randomization study

1 The Second Clinical Medical College, Guangzhou University of Chinese Medicine, Guangzhou, China

Liangcai Lin

2 The Third Clinical Medical College, Guangzhou Medical University, Guangzhou, China

Xiaolei Ren

Rongyuan yang.

3 Guangdong Provincial People's Hospital, Guangzhou, China

4 State Key Laboratory of Traditional Chinese Medicine Syndrome, The Second Affiliated Hospital of Guangzhou University of Chinese Medicine, Guangzhou, Guangdong, China

5 The Second Affiliated Hospital (Guangdong Provincial Hospital of Chinese Medicine), Guangzhou University of Chinese Medicine, Guangzhou, Guangdong, China

6 Guangdong Provincial Key Laboratory of Research on Emergency in TCM, Guangzhou, China

7 State Key Laboratory of Dampness Syndrome of Chinese Medicine, The Second Affiliated Hospital of Guangzhou University of Chinese Medicine, Guangzhou, China

8 Guangdong Provincial Key Laboratory of Clinical Research on Traditional Chinese Medicine Syndrome, Guangzhou, China

Jiankun Chen

Yubao Wang, Tianjin Medical University General Hospital, China

Keke Liu, Shandong Provincial Hospital, China

Associated Data

The original contributions presented in the study are included in the article/ Supplementary Material . Further inquiries can be directed to the corresponding authors. The summary data of genome-wide association studies (GWAS) used in this study can be obtained from the Integrative Epidemiology Unit (IEU) OpenGWAS project database ( https://gwas.mrcieu.ac.uk/ ) and the GWAS catalog ( https://www.ebi.ac.uk/gwas/ ).

Gut microbiota is closely related to the occurrence and development of sepsis. However, the causal effects between the gut microbiota and sepsis, and whether circulating inflammatory proteins act as mediators, remain unclear.

Gut microbiota, circulating inflammatory proteins, and four sepsis-related outcomes were identified from large-scale genome wide association studies (GWAS) summary data. Inverse Variance Weighted (IVW) was the primary statistical method. Additionally, we investigated whether circulating inflammatory proteins play a mediating role in the pathway from gut microbiota to the four sepsis-related outcomes.

There were 14 positive and 15 negative causal effects between genetic liability in the gut microbiota and four sepsis-related outcomes. Additionally, eight positive and four negative causal effects were observed between circulating inflammatory proteins and the four sepsis-related outcomes. Circulating inflammatory proteins do not act as mediators.

Conclusions

Gut microbiota and circulating inflammatory proteins were causally associated with the four sepsis-related outcomes. However, circulating inflammatory proteins did not appear to mediate the pathway from gut microbiota to the four sepsis-related outcomes.

1. Introduction

Sepsis is an unusual systemic response to a common infection, representing a mode of immune system response to injury ( Faix, 2013 ). It can cause symptoms in multiple organ systems, such as the heart, lungs, kidneys, and digestive system ( Lelubre and Vincent, 2018 ). According to epidemiological studies, the prevalence of sepsis and the 28-day mortality rate in hospitals range from 25% to 30% ( Cohen et al., 2015a ). The hyper-inflammatory response is followed by a period of immunosuppression, during which patients develop multiple organ dysfunction and are prone to nosocomial infections ( Faix, 2013 ). Despite its alarming prevalence and severe consequences, treatment options remain limited and have for many years revolve around antibiotics and supportive therapies ( Gotts and Matthay, 2016 ).

Gut microecology, as a unit of interaction between the gut microbiota and the host, is closely related to human health, with gut microbiota being the most important ( Alkasir et al., 2017 ). Intestinal flora refers to the various microbial communities living in the intestinal cavity that adhere to the intestinal mucosa, including Archaea and Eukaryotes, which are predominantly bacterial. They have evolved together with the host in symbiotic relationships and play important roles in substance metabolism, nutrition, and immune protection ( O’Hara and Shanahan, 2006 ; Dang and Marsland, 2019 ). Previous studies have shown that gut microbiota is linked to sepsis ( Zhang et al., 2023 ). Several well-established risk factors for sepsis such as aging, immunization, and antibiotic resistance are associated with significant changes in the composition and function of the gut microbiome ( Takiishi et al., 2017 ; Ling et al., 2022 ; Santacroce et al., 2023 ). However, the extent to which gut microbes are associated with sepsis remains unclear. A better understanding of the causal effects of the gut microbiota and the potential mediators between them will provide evidence for further mechanistic and clinical studies on the management and treatment of sepsis. Additionally, gut microbiota plays an important role in regulating the circulating inflammatory proteins ( Xue et al., 2023 ).

To the best of our knowledge, inflammation plays an important role in promoting sepsis. Previous studies have revealed that sepsis is a common complication of combat injuries and trauma and is defined as a life-threatening organ dysfunction caused by a dysregulated host response to infection ( Liu et al., 2022 ). During the body’s response to infection, an inflammatory response is essential. We hypothesized that circulating inflammatory proteins might mediate the pathway from the gut microbiota to sepsis.

Although randomized controlled trials can help establish a causal relationship between gut microbiota or circulating inflammatory proteins and sepsis, they are difficult to perform in humans due to objective limitations, such as screening the gut microbiota and elevating circulating inflammatory protein levels ( Cheng et al., 2023 ; Combrink et al., 2023 ). Therefore, most current research conclusions are based on observations of composition and changes in the intestinal flora in the feces of patients with sepsis. Some observational cohort studies have shown that gut microbiota may be associated with sepsis ( Deng et al., 2023 ; Park et al., 2024 ). Preclinical studies have demonstrated that the gut microbiota plays a key role in the immune response to systemic inflammation, and disruption of this symbiotic relationship increases the susceptibility to sepsis ( Haak et al., 2018 ).

Although probiotic supplementation has positive effects ( Vulevic et al., 2015 ; Panigrahi et al., 2017 ; Shimizu et al., 2018 ), its effectiveness and safety remain controversial ( Zhang et al., 2023 ). Therefore, further research is required to determine the specificity and safety of these probiotic supplements. Mendelian randomization (MR) is an epidemiological method that uses genetic variation as an instrumental variable for exposure to estimate the causal effect of exposure on outcomes and strengthens causal inference. It overcomes the limitations of traditional observational study designs by using Mendelian laws, exploiting the random assignment to genotypes at conception, making genotypes independent of potential confounders, and avoiding reverse causality ( Smith et al., 2007 ). Two-sample MR analysis is an extension of the MR approach that allows summary statistics from genome-wide association studies (GWAS) to be used in MR studies without the direct analysis of individual-level data ( Davey Smith and Hemani, 2014 ). In recent years, two-sample MR studies have gradually been recognized by researchers, allowing data between genetic instrument variables and phenotypes, phenotypes, and diseases to come from two different independent populations, thereby improving the efficiency and statistical power of the study ( Burgess et al., 2016 ; Gupta et al., 2017 ; Davies et al., 2018 ).

This study employed a comprehensive MR analysis to investigate the causal association between gut microbiota, circulating inflammatory proteins, and sepsis-related outcomes. Additionally, we examined the potential mediating role of circulating inflammatory proteins in the pathway from gut microbiota to sepsis. Furthermore, reverse causality analysis was conducted to assess the impact of genetic susceptibility to sepsis on gut microbiota and circulating inflammatory proteins.

2.1. Study design

This study followed the STROBE-MR guidelines ( Skrivankova et al., 2021 ) and key principles of the Strengthening Epidemiological Observational Research Reporting (STROBE) guidelines ( von Elm et al., 2007 ). Detailed information is provided in Supplementary Table S1 . As shown in Figure 1 , this study consisted of three main components: analysis of the causal effects of 207 gut microbiota on four sepsis-related outcomes (step 1A), analysis of the causal effects of 91 circulating inflammatory proteins on four sepsis-related outcomes (step 2A), and mediation analysis of circulating inflammatory proteins in the pathway from gut microbiota to sepsis (step 3). In Mendelian randomization, single-nucleotide poly-morphisms (SNPs) are defined as instrumental variables (IVs). This approach is based on three core assumptions: (1) the IVs are closely associated with the exposure factors, (2) IVs are not associated with confounding factors, and (3) IVs do not affect the outcome directly and can only affect outcomes via exposure ( Bowden and Holmes, 2019 ).

Study overview. Step 1A represents the causal effects of gut microbiota on four sepsis-related outcomes. Step 1B represents the bi−directional causal effects between gut microbiota and four sepsis-related outcomes. Step 2A represents the causal effects of circulating inflammatory proteins on four sepsis-related outcomes. Step 2B represents the bi−directional causal effects between circulating inflammatory proteins and four sepsis-related outcomes. Step 3 represents the mediating analysis of circulating inflammatory proteins in the pathway from the gut microbiota to four sepsis-related outcomes: path c was the total effect of gut microbiota on four sepsis-related outcomes; path b was the causal effect of circulating inflammatory proteins on four sepsis-related outcomes; path a was the causal effect of gut microbiota on circulating inflammatory proteins.

2.2. Data source

The Dutch Microbiome Project (DMP) provides summary data from the GWAS of 7,738 European individuals to obtain species-level data on the gut microbiota and maximize statistical power ( Lopera-Maya et al., 2022 ). The participants in this database were relatively homogeneous, and it is currently the largest database of species-level gut microbiota data. Based on shotgun metagenomic sequencing of the stool samples, we identified 207 taxa (5 phyla, 10 classes, 13 orders, 26 families, 48 genera, and 105 species) in the gut microbiome. Genetic data for circulating inflammatory proteins were obtained from a previous GWAS (14,824 individuals). Circulating inflammatory proteins were measured using an Olink Target-96 Inflammation immunoassay plate, which measures 91 inflammation-related proteins ( Zhao et al., 2023 ).

The main outcomes of this study were four sepsis-associated outcomes: the occurrence of sepsis, sepsis necessitating admission to critical care, mortality within 28 days in the intensive care unit (ICU) after experiencing sepsis, and sepsis 28-day mortality. In the Integrative Epidemiology Unit (IEU) OpenGWAS project database ( https://gwas.mrcieu.ac.uk/ ) ( Hemani et al., 2018 ), these outcomes were identified by the IDs ieu-b-4980, ieu-b-4982, ieu-b-4981 and ieu-b-5086, respectively. The Global Burden of Disease (GBD) study codes were used to define sepsis based on the International Classification of Diseases (ICD)-10 ( Rudd et al., 2020 ).

This study was a secondary analysis of publicly available GWAS summary statistics. Ethical approval was granted to each original GWAS. Moreover, no individual-level data were used in this study. Therefore, no additional ethical review board approval was required.

2.3. Instrumental variables selection

First, we selected the SNPs with significant associations for gut microbiota ( P < 1×10 –5 ) ( Shi et al., 2024 ). SNPs with significant associations with circulating inflammatory proteins were selected ( P < 5×10 –8 ) ( Yan et al., 2024 ). We excluded the SNPs with linkage disequilibrium (LD) in the analysis. The LD of selected SNPs strongly related to the gut microbiota and circulating inflammatory proteins should meet the conditions of r 2 < 0.001 and distance > 10,000 kb ( Myers et al., 2020 ). After matching the outcomes, we removed the palindromic SNPs. SNPs significantly associated with the gut microbiota and circulating inflammatory proteins are listed in Supplementary Tables S2 and S3 .

To assess the strength of the relationship between the identified independent variables (IVs) and exposure, we computed the explained variance (R 2 ) and F-statistic parameters. Steiger’s Test was used to detect and avoid reverse causality. The Steiger Test is a new methodology that enhances the robustness of the IV extraction ( Hemani et al., 2017 ). SNPs with F-statistic parameters >10 were considered strong instruments ( Burgess et al., 2017 ). R 2 = 2 × EAF × (1-EAF) × β 2 , F = R 2 × (N-2)/(1-R 2 ) ( Papadimitriou et al., 2020 ).

2.4. Primary analysis

To assess the causal impact of the gut microbiota and circulating inflammatory proteins on the four sepsis-related outcomes, we conducted a two-sample Mendelian randomization (MR) analysis for each outcome (step 1A and 2A in Figure 1 ). The Inverse Variance Weighted (IVW) approach was employed as the primary analytical method, while the Wald ratio test was used for features consisting solely of one independent variable (IV) ( Burgess et al., 2013 ). The outcomes of the MR analysis were presented as odds ratios (ORs) accompanied by their respective 95% confidence intervals (CI). The findings were considered statistically significant when the P-value of the IVW method was below 0.05, and the IVW and MR-Egger estimates exhibited consistent directional effects ( Ji et al., 2024 ).

2.5. Mediation analysis

Using a two-sample analysis (step 1A and 2A in Figure 1 ), the gut microbiota and circulating inflammatory proteins with significant causal effects on the four sepsis-related outcomes were included in the mediation analysis. As shown in Figure 1 , we examined the possible causal impact of the gut microbiota on circulating inflammatory proteins (step 3, path a). If this is the case, we will perform multiple MR analyses to explore whether circulating inflammatory proteins are the mediating factors in the pathway from the gut microbiota to the four sepsis-related outcomes.

2.6. Bi−directional causality analysis

To evaluate bi-directional causation effects between gut microbiota, circulating inflammatory proteins, and four sepsis-related outcomes, we used four sepsis-related outcomes as “exposure” and gut microbiota or circulating inflammatory proteins associated with four sepsis-related outcomes as “outcome” (step 1B and step 2B in Figure 1 ). We selected SNPs that were significantly associated with four sepsis-related outcomes ( P < 5×10 –8 ) as IVs ( Chen et al., 2024 ).

2.7. Sensitivity analysis

We performed Cochran’s Q test to evaluate the heterogeneity of each SNP ( Cohen et al., 2015b ) and generated scatter plots of SNP exposure and outcome associations to visualize the MR results. Leave-one-out analysis was performed to evaluate whether each SNP affected the results ( Burgess and Thompson, 2017 ). In addition, we use MR-PRESSO and MR-Egger regressions to test for the potential horizontal pleiotropy effect. MR-PRESSO was used to detect significant outliers and correct for horizontal plural effects by removing outliers ( Verbanck et al., 2018 ). All analyses were performed using R (v4.3.0) statistical software. MR analysis was performed using the “TwoSampleMR” package. The “MR_PRESSO” package was used for multiplicity tests ( Ong and MacGregor, 2019 ).

3.1. Causal effects of gut microbiota and circulating inflammatory proteins on four sepsis-related outcomes

3.1.1. sepsis.

As shown in Figure 2 , MR analysis suggested that the genetic prediction of four gut microbiota (species Clostridium asparagiforme , species Bacteroidales bacterium ph8 , species Ruminococcus lactaris , and genus Bacteroidale ) was associated with an increased risk of sepsis. Genetic prediction of five gut microbiota (genus Adlercreutzia , species Adlercreutzia equolifaciens , genus Pseudoflavonifractor , species Ruminococcus callidus , and species Escherichia unclassified ) was associated with a decreased risk of sepsis ( Supplementary Table S4 ).

Mendelian randomization results of causal effects between gut microbiotas and four sepsis-related outcomes.

As shown in Figure 3 , TNF-related apoptosis-inducing ligand levels (TRAIL) (OR = 1.094, 95%CI = 1.012 ~ 1.183, P = 0.025) and vascular endothelial growth factor A levels (VEGF-A) (OR = 1.182, 95%CI = 1.016 ~ 1.375, P = 0.031) were associated with an increased risk of sepsis. Beta-nerve growth factor levels (β-NGF) (OR = 0.769, 95%CI = 0.599 ~ 0.987, P = 0.039) significantly decreased the incidence of sepsis ( Supplementary Table S4 ).

Mendelian randomization results of causal effects between circulating inflammatory proteins and four sepsis-related outcomes.

3.1.2. Sepsis (28-day mortality)

As shown in Figure 2 , the MR analysis suggested that the genetic prediction of one gut microbiota (species Ruminococcus lactaris ) was associated with an increased risk of sepsis (28-day mortality). Genetic prediction of three gut microbiota (species Bacteroides massiliensis , species Alistipes indistinctus , and species Bifidobacterium longum ) was associated with a decreased risk of sepsis (28-day mortality) ( Supplementary Table S4 ).

As shown in Figure 3 , C-C motif chemokine 19 levels (CCL19) (OR = 1.474, 95% CI = 1.154 ~ 1.883, P = 0.002), Tumor necrosis factor receptor superfamily member 9 levels (TNFRSF9) (OR = 1.654, 95%CI = 1.071 ~ 2.552, P = 0.023) and TRAIL (OR = 1.202, 95% CI = 1.000 ~ 1.444, P = 0.049) were associated with an increased risk of sepsis (28-day mortality). T-cell surface glycoprotein CD6 isoform levels (CD6) (OR = 0.858, 95% CI = 0.737 ~ 0.999, P = 0.049) and Cystatin D levels (OR = 0.808, 95% CI = 0.695 ~ 0.940, P = 0.006) were associated with a decreased risk of sepsis (28-day mortality) ( Supplementary Table S4 ).

3.1.3. Sepsis (critical care units)

As shown in Figure 2 , MR analysis suggested that the genetic prediction of six gut microbiota (species Butyrivibrio crossotus , family Prevotellaceae , species Eubacterium rectale , species Faecalibacterium prausnitzii , genus Butyrivibrio , and genus Faecalibacterium ) was associated with an increased risk of sepsis (critical care units). Genetic prediction of two gut microbiota (species Alistipes indistinctus and species Eggerthella unclassified ) was associated with a decreased risk of sepsis (critical care units) ( Supplementary Table S4 ).

As shown in Figure 3 , TRAIL (OR = 1.338, 95% CI = 1. 055 ~ 1.698, P = 0.016) were associated with an increased risk of sepsis (critical care units). Monocyte chemoattractant protein-1 levels (MCP-1) (OR = 0.642, 95% CI = 0. 448 ~ 0.918, P = 0.015) were associated with a decreased risk of sepsis (critical care units) ( Supplementary Table S4 ).

3.1.4. Sepsis (28-day mortality in critical care units)

As shown in Figure 2 , MR analysis suggested that the genetic prediction of the three gut microbiota (species Bacteroides ovatus , family Veillonellaceae , and class Gammaproteobacteria ) was associated with an increased risk of sepsis (28-day mortality in critical care units). Moreover, all three factors significantly increased the risk of sepsis (28-day mortality in critical care units). Genetic prediction of five gut microbiota (species Eggerthella unclassified , species Parabacteroides unclassified , species Alistipes indistinctus , genus Eggerthella , and species Parabacteroides johnsonii ) was associated with a decreased risk of sepsis (28-day mortality in critical care units). Moreover, all five significantly reduce the risk of sepsis development (28-day mortality in critical care units) ( Supplementary Table S4 ).

As shown in Figure 3 , CCL19 (OR = 1.810, 95% CI = 1.018 ~ 3.219, P = 0.043) and C-C motif chemokine 28 levels (CCL28) (OR = 3.791, 95% CI = 1.215 ~ 11.825, P = 0.022) significantly increased risk of sepsis (28-day mortality in critical care units) ( Supplementary Table S4 ).

By comparing the results horizontally and vertically, interesting and surprising findings were obtained. In the gut microbiota, species Ruminococcus lactaris not only increased the risk of sepsis incidence but was also strongly associated with sepsis 28-day mortality. Although species Alistipes inditinctus had no significant effect on the incidence of sepsis, it had significant causal effects on sepsis (28-day mortality), sepsis (critical care units), and sepsis (28-day mortality in critical care units). Additionally, species Eggerthella had significant causal effects on the risk reduction of both sepsis (critical care units) and sepsis (28-day mortality in critical care units). Therefore, regulating species Alistipes indistintus and Eggerthella may be an important measure to prevent and treat critical sepsis. Among circulating inflammatory proteins, CCL19 not only increased the risk of sepsis (28-day mortality), but was also strongly associated with sepsis (28-day mortality in critical care units). TRAIL had significant effects on multiple sepsis-related outcomes.

3.2. Sensitivity analyses

Based on the MR-Egger regression intercept method, the outcomes were not influenced by genetic pleiotropy, and further analysis using MR-PRESSO demonstrated the absence of horizontal pleiotropy in the MR study ( Supplementary Tables S5, S6 ). Cochran’s Q test revealed no significant heterogeneity ( Supplementary Table S7 ). The findings from the “leave-one-out” analysis provide evidence supporting the reliability of MR analysis ( Supplementary Figures S1-S5 ).

3.3. Bi−directional causal effects of four sepsis-related outcomes on gut microbiota and circulating inflammatory proteins

A P-value threshold of less than 5×10 -8 was employed as the criterion to identify SNPs that exhibit significant associations with the three types of sepsis. However, no statistically significant SNPs were identified, indicating that the presence of a reverse causal effect was not substantial.

3.4. Mediation analysis

This study elucidated the causal relationships between gut microbiota, circulating inflammatory proteins, and four sepsis-related outcomes. Circulating inflammatory proteins appear to play a mediating role in the pathway from the gut microbiota to the four sepsis-related outcomes. One of the prerequisites for establishing a mediating effect is a significant association between the gut microbiota and circulating inflammatory proteins. Class Gammaproteobacteria (OR = 1.166, 95% CI = 1.010 ~ 1.345, P = 0.035) significantly increased beta-nerve growth factor levels. Species Faecalibacterium prausnitzii (OR = 0.861, 95% CI = 0.765 ~ 0.968, P = 0.012) significantly decreased C-C motif chemokine 19 levels (step 3a in Figure 1 ; Supplementary Table S8 ). However, the mediation analysis results showed that beta-nerve growth factor and C-C motif chemokine 19 could not mediate the influence of gut microbiota on sepsis-related outcomes, indicating that circulating inflammatory proteins did not act as mediators in the pathway from gut microbiota and four sepsis-related outcomes.

4. Discussion

In this study, we evaluated the bidirectional association between the gut microbiota and sepsis and investigated whether circulating inflammatory proteins act as mediators. We found 14 positive and 15 negative causal effects between genetic liability in the gut microbiota and four sepsis-related outcomes. Additionally, eight positive and four negative causal effects were observed between circulating inflammatory proteins and four sepsis-related outcomes. Circulating inflammatory proteins do not act as mediators. Notably, despite these findings, our reverse transcription analysis did not indicate any influence of sepsis on the gut microbiota or circulating inflammatory proteins.

There is a close relationship between the gut microbiota and sepsis. First, before the onset of sepsis, alterations in the gut microbiome increase the susceptibility to sepsis through multiple mechanisms, including (1) allowing the expansion of pathogenic gut bacteria, (2) providing a robust proinflammatory response to the immune system, and (3) reducing the production of beneficial microbial products, such as short-chain fatty acids ( Adelman et al., 2020 ). Pathogenic bacteria present in the gut of healthy hosts may fail to proliferate and cause diseases in the presence of protective commensals ( Chow et al., 2011 ). However, when protective bacterial taxa are lost, pathogens can proliferate and cause diseases ( Kim et al., 2019 ; Woodworth et al., 2019 ). Previous studies have demonstrated the role of the gut microbiome in the initiation of the immune system in response to sepsis. After an episode of sepsis, changes in the gut microbiome can affect the inflammatory responses. Clinical studies on sepsis have shown a link between alterations in the gut microbiome, characterised by an increase in pathogenic bacteria and a robust immune response ( Li et al., 2015 ; Cernada et al., 2016 ; Singer et al., 2018 ). In this study, we identified several gut microbiota that showed protective effects against sepsis, including species Alistipes indistintus and species Eggerthella . However, some taxa show potentially harmful effects, such as species Eubacterium rectale and genus Bacteroidales . Additionally, species Ruminococcus lactaris not only increased the risk of sepsis but was also strongly associated with sepsis mortality at 28 days.

Alistipes is a Gram-negative bacterium belonging to the phylum Bacteroides ( Parker et al., 2020 ). Recent studies suggest that Alistipes may have protective effects against certain diseases, including colitis ( Dziarski et al., 2016 ), liver fibrosis ( Rau et al., 2018 ) and cardiovascular diseases ( Kim et al., 2018 ; Zuo et al., 2019 ). The co-organisms of the intestinal microbiome produce short-chain fatty acids (SCFAs), which have the immune function of regulating the intestinal microenvironment. SCFAs play important roles in epithelial cell function. Alistipes is an acetate producer, and since previous studies have shown that short-chain fatty acids have an anti-inflammatory mechanism, it can be shown that Alistipes reduction promotes short-chain fatty acid reduction ( Parker et al., 2020 ). Eggerthella is a bacterium that colonizes the human intestinal tract, female genital tract, oral cavity, and prostate ( Lau et al., 2004 ). Recently, a growing body of evidence has collectively emphasized the association of Eggerthella with various diseases, such as asthma ( Wang et al., 2018 ), multiple sclerosis ( Cekanaviciute et al., 2017 ), systemic lupus erythematosus ( Xiang et al., 2021 ), rheumatoid arthritis ( Chen et al., 2016 ), and sepsis ( Priputnevich et al., 2021 ). Eggerthella is likely to be implicated in sepsis; however, the exact role of Eggerthella in sepsis needs to be further explored. Eubacterium plays important roles in various processes, including the conversion of bile acids and cholesterol, metabolizing oxalate, producing anti-inflammatory molecules, reducing allergic airway inflammation, regulating insulin secretion, and controlling lipid metabolism ( Mukherjee et al., 2020 ; Kamel et al., 2021 ). Ruminococcus plays an important role in the digestive process, especially in the decomposition and fermentation of plant fibers, such as cellulose and hemicellulose ( Crost et al., 2023 ). Ruminococcus has been associated with various gastrointestinal, immune-related, and neurological disorders ( Crost et al., 2023 ). Some studies have found that the number and activity of Ruminococcus in the guts of patients with these diseases may change, thereby affecting gut health and inflammation levels ( Hall et al., 2017 ). Bacteroides are present in the human gut and have a symbiotic relationship with humans. They help break down food and produce the nutrients and energy required by the body. Specific taxa and their relative abundance in this phylum are associated with various diseases, including metabolic syndrome ( Turnbaugh et al., 2009 ; Pedersen et al., 2016 ; Vieira-Silva et al., 2020 ), viral infections ( Stefan et al., 2020 ). These conditions can cause or exacerbate sepsis. An experimental study indicated that in sepsis models, the dominant bacterial population in the lungs was Bacteroides ( Dickson et al., 2016 ). And the relative abundance of Bacteroides in the lung microbiome was significantly correlated with serum TNF-α concentrations, a key mediator of the septic stress response that predicts sepsis patient mortality ( Osuchowski et al., 2006 ). In some cases, Bacteroides may cause sepsis ( Arnold et al., 2020 ). Perforations or ruptures of the intestine, dysbiosis of the intestinal microbiota, and impaired immune function allow intestinal bacteria such as Bacteroides to enter the abdominal cavity or bloodstream, causing infection and sepsis. More research is needed to fully understand the connection between gut microbiota and diseases, especially infection-related diseases such as sepsis.

This study aimed to determine whether the gut microbiota has a positive or negative impact on sepsis outcomes. However, the specific mechanism by which gut microbiota contribute to or worsen sepsis remains unclear. We hypothesized that inflammatory proteins in the bloodstream may play a role in the relationship between the gut microbiota and sepsis.

According to the MR analysis, we found that C-C motif chemokine 19 (CCL19), C-C motif chemokine 28 (CCL28), TNF-related apoptosis-inducing ligand (TRAIL), and vascular endothelial growth factor A (VEGF-A) levels were associated with an increased risk of sepsis-related outcomes. CCL19 and CCL28 are chemokines found in immune cells that play important immune-regulatory roles in the human body. Studies have shown a significant increase in plasma CCL19 levels in patients with sepsis, which may be related to sepsis severity ( Tuerxun et al., 2023 ). However, research on the relationship between CCL19 and CCL28 and sepsis is currently limited. We look forward to a more comprehensive and in-depth research in the future. TRAIL, a biomarker of circulating cell death, is a potent inducer of apoptosis. Previous studies have shown that TRAIL has therapeutic effects in sepsis, peritonitis, and pulmonary inflammation ( McGrath et al., 2011 ). Lower versus higher levels of TRAIL were associated with increased organ dysfunction and septic shock ( Schenck et al., 2019 ). TRAIL is known to exert pluripotent effects in sepsis, leading to an improved survival rate in the early stages of abdominal sepsis and a decreased survival rate in the late phase of hypoinflammation ( Boomer et al., 2014 ; Berg et al., 2023 ). It is worth noting that in recent years, animal experiments have found that TRAIL mediates immune suppression in sepsis, and that anti-TRAIL antibodies have a protective effect on sepsis mice ( Gurung et al., 2011 ). VEGFA belongs to the ligand-tyrosine kinase receptor system, is widely expressed in the vascular system, and participates in the development of normal vascular barriers. VEGFA belongs to the ligand-tyrosine kinase receptor system, is widely expressed in the vascular system, and participates in the development of normal vascular barriers. During sepsis, the expression of VEGF-A increases, leading to damage to the vascular endothelial barrier, tissue inflammation, vascular leakage, insufficient blood volume, tissue oedema, and changes in microcirculation flow, ultimately leading to MODS ( Hauschildt et al., 2020 ).

The development and management of sepsis are multifactorial, and this study specifically examined microecological differences to understand this condition. It is essential to acknowledge the complexity of sepsis and the need for a comprehensive evaluation of its various factors to gain a thorough understanding. This study is the first to reveal a potential causal relationship between the species levels of gut microbiota and sepsis-related outcomes, with profound implications for public health and the management of individual patients with sepsis. These findings may help diagnose sepsis more accurately. Based on the presence of multiple gut microbiota, patients with sepsis can be screened and assessed for the risk of taking preventive measures to reduce the incidence of sepsis ( Peng et al., 2024 ). In addition, this study contributes to a deeper understanding of the pathogenesis and pathological processes of sepsis, providing a scientific basis for formulating targeted public health policies and prevention and control measures. Alleviates symptoms of sepsis or facilitates recovery by adjusting the gut microbiota balance. Interventions targeting the gut microbiota are needed for more effective treatment of sepsis ( Cai et al., 2022 ).

This study represents a comprehensive initial MR analysis that examined the causal association between the gut microbiome, circulating inflammatory proteins, and sepsis-related outcomes. This study had several strengths. First, we highlighted certain species that showed more significant associations with sepsis than with other microbial classes. Although these associations were not statistically significant after adjusting for multiple testing, they nevertheless constitute important preliminary observations and may indicate underlying biological phenomena. In addition, this study used multiple sensitivity analyses to enhance the reliability of the findings. The consistency of the MR-Egger method with the IVW method demonstrates the robustness of the results. Despite the wide confidence intervals in some results, the overall patterns of the associations remained consistent. Furthermore, we employed the MR-PRESSO technique to identify and exclude potential outliers that may have biased our findings, thereby improving reliability. Finally, given that both the study population and the population surveyed were of European origin, the possibility of bias due to population stratification was reduced.

However, our study has several limitations. First, the limited European population data on gut microbiota and circulating inflammatory proteins may have biased our results. Second, the number of loci associated with circulating inflammatory proteins was relatively small compared to those associated with sepsis and gut microbiota. Third, our Mendelian randomization study did not have access to individual-level data, such as sex, age, and disease severity, which limited the depth of our analysis.

5. Conclusion

In summary, our bidirectional Mendelian randomization study clearly demonstrated 29 causal effects between genetic liability in the gut microbiota and sepsis-related outcomes, whereas the reverse causality hypothesis did not hold. Notably, our findings indicated that circulating inflammatory proteins do not act as mediators. To gain a more nuanced understanding of the observed association between the gut microbiota and sepsis, future studies should focus on potential mechanistic pathways while also attempting to adjust for potential confounders, such as diet, lifestyle, and medications, given that these factors may have a greater impact on sepsis. Our work is an important step forward in explaining the relationship between gut microbiota and sepsis, but more microbiological and clinical studies are needed to validate and expand our findings. We hope that this research will contribute to the diagnosis, treatment, and development of drugs for sepsis.

Data availability statement

Author contributions.

ZL: Software, Writing – original draft, Writing – review & editing, Formal analysis, Funding acquisition, Supervision. LL: Data curation, Formal analysis, Investigation, Writing – original draft. YK: Validation, Formal analysis, Methodology, Writing – review & editing. JF: Data curation, Investigation, Writing – original draft, Validation. XR: Data curation, Writing – review & editing, Methodology. YW: Data curation, Writing – review & editing, Validation. XC: Data curation, Formal analysis, Investigation, Methodology, Writing – original draft. SW: Writing – review & editing, Conceptualization, Software, Validation. RY: Funding acquisition, Resources, Supervision, Writing – original draft, Writing – review & editing. JL: Funding acquisition, Resources, Supervision, Writing – original draft, Writing – review & editing, Validation. YtL: Funding acquisition, Resources, Supervision, Writing – original draft, Writing – review & editing. YeL: Funding acquisition, Resources, Supervision, Validation, Visualization, Writing – original draft. JC: Funding acquisition, Resources, Supervision, Writing – original draft.

Acknowledgments

The authors thank the investigators of the original studies for sharing the GWAS summary statistics.

Funding Statement

The author(s) declare financial support was received for the research, authorship, and/or publication of this article. This work was supported by National Natural Science Foundation of China (82374392), Traditional Chinese Medicine Innovation Team Project of China Administration of Traditional Chinese Medicine (ZYYCXTD-D-202203), National Administration of Traditional Chinese Medicine project (2023ZYLCYJ02-21), Key Technologies Research and Development Program of Guangdong Province (2023B1111020003), Science and Technology Planning Project of Guangdong Province (No. 2023B1212060062), Basic and Applied Basic Research of Guangzhou City-University Joint Funding Project (202201020382), Open project of Guangdong Provincial Key Laboratory of Research on Emergency in TCM (KF2023JZ06), Research Fund for Zhaoyang Talents of Guangdong Provincial Hospital of Chinese Medicine (ZY2022KY10, ZY2022YL04).

Conflict of interest

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Publisher’s note

All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.

Supplementary material

The Supplementary Material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fcimb.2024.1398756/full#supplementary-material

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Navigating the Global Knowledge Economy: Temporal Effects of Regulatory Environment and FDI on Sustainable Development in Asia–Pacific

• Published: 24 August 2024

Cite this article

• Mohd Nadeem Bhat   ORCID: orcid.org/0000-0001-6658-2689 1 ,
• Adeeba Beg 1 &
• Firdos Ikram 1  

In the knowledge economy, sustainable development is an important topic of discussion among policymakers and researchers. Existing literature provides a scant view of sustainable development, and the dual effect of global investment and the institutional system of the countries is largely overlooked. This study investigates the immediate and long-term impacts of foreign direct investment (FDI) and institutional factors on four Sustainable Development Goals (SDGs) in the Asia–Pacific region over a span of 23 years (2000–2022). Utilizing panel data from 29 countries, we examine the relationship between FDI and SDG2, 5, 12, and 15 while considering moderating effects of control of corruption and government effectiveness. Results from the autoregressive distributed lag (ARDL) models reveal significant influences of FDI and institutional factors on SDGs, particularly in the long run. FDI positively affects SDG2 (Zero hunger) and SDG5 (Gender equality) while exhibiting a negative impact on SDG12 (Responsible production and consumption). Finally, the SDG15 (Life on land) is also positively affected by FDI. Moreover, institutional indicators such as control of corruption (COC), government effectiveness (GE), and regulatory quality significantly contribute to SDG achievement, with notable moderation effects of COC and GE on the FDI-SDG nexus. Pairwise panel causality test highlights different forms of causality among the variables, highlighting the complex interplay between FDI, institutional factors, and SDGs. Overall, our findings emphasize the importance of FDI and effective institutional frameworks in advancing sustainable development objectives, particularly in addressing issues of food security, gender equality, and responsible production and consumption in the Asia–Pacific region.

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Mohd Nadeem Bhat, Adeeba Beg & Firdos Ikram

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All authors contributed to the study. Problem identification, literature review, research design and methodology, data collection and editing, econometric analyses, paper writing, formatting, copy editing, and graphical and tabular formation were performed by Mohd Nadeem Bhat. Adeeba Beg did the data screening of the data. Final draft checking, and rectification of errors (if any) were done by Firdos Ikram. All authors reviewed and approved the final Manuscript.

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Bhat, M.N., Beg, A. & Ikram, F. Navigating the Global Knowledge Economy: Temporal Effects of Regulatory Environment and FDI on Sustainable Development in Asia–Pacific. J Knowl Econ (2024). https://doi.org/10.1007/s13132-024-02299-9

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Received : 23 October 2023

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DOI : https://doi.org/10.1007/s13132-024-02299-9

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• Published: 24 August 2024

Spatial spillover effect and driving factors of urban carbon emissions in the Yellow River Basin using nighttime light data

• Mingjuan Ma 1 , 2 ,
• Yumeng Wang 1 &
• Shuifa Ke 1  

Scientific Reports volume  14 , Article number:  19672 ( 2024 ) Cite this article

Metrics details

• Environmental economics
• Sustainability

Yellow River Basin (YRB) is a pivotal region for energy consumption and carbon emissions (CEs) in China, with cities emerging as the main sources of regional CEs. This highlights their critical role in achieving regional sustainable development and China’s carbon neutrality. Consequently, there is a pressing need for a detailed exploration of the urban spillover effects and an in-depth analysis of the complex determinants influencing CEs within the YRB. Remote sensing data provide optimal conditions for conducting extensive studies across large geographical areas and extended time periods. This study integrates DMSP/OLS and NPP/VIIRS nighttime light datasets for a longitudinal analysis of urban CEs in the YRB. Using a harmonized dataset from DMSP/OLS and NPP/VIIRS nighttime light from 2007 to 2021, this study quantifies CEs of 58 prefecture-level cities in the YRB. By combining ESDA, STIRPAT model and spatial econometric model, this investigation further clarifies empirically the spatial spillover effects and driving factors of urban CEs. The analysis delineates a phase-wise augmentation in urban CEs, converging towards a distinct spatial distribution characterized by “lower reach > middle reach > upper reach”. The spatial autocorrelation tests unravel a complex interplay between agglomeration and differentiation patterns within urban CEs, underscored by pronounced spatial lock-in phenomena. Significantly, this study demonstrates that urbanization, economic development, energy consumption structure, green coverage rate, industrial structure, population, technological progress, and FDI each exhibit varied direct and indirect effect on urban CEs. Furthermore, it elaborates on potential policy implications and future research directions, offering crucial insights for formulating CEs mitigation strategies to advance sustainable development.

Introduction

The urgency of region-specific strategies for carbon reduction to achieve carbon neutrality has become increasingly pronounced amidst the intensifying threats of climate change 1 , 2 , 3 . This imperative is starkly apparent in the Yellow River Basin (YRB), where a critical dichotomy emerges between the exploitation of its abundant energy resources and the pressing demand for environmental preservation. This situation demands a paradigmatic transition away from its current resource-intensive economic growth model to effectively curb the escalating carbon emissions (CEs) 4 , 5 . Notably, urban regions within YRB underscore the conflict inherent between spatial efficiency and regional equity, both of which are pivotal for sustainable development but concurrently act as primary contributors to CEs 6 . Therefore, merely analyzing CEs at the inter-provincial scale fails to address the practical needs of the YRB, it is essential to unravel urban spillover effects and the complex factors influencing CEs within the region to craft targeted mitigation strategies. Such in-depth analyses are vital for pinpointing strategic points for emission reduction in urban systems and integrating spatial considerations into environmental policy.

The First Law of Geography posits a fundamental principle regarding the relevance of phenomena, where proximity enhances the strength of relationships 7 . Spatial econometrics enables the detailed analysis of complex interdependencies among economic activities across regions 8 , shedding light on the transregional dynamics of CEs as they are shaped by socioeconomic factors. Research from national to provincial 9 , 10 , expands to address spatial spillover effects and the inherent spatiotemporal dynamics and heterogeneity of CEs. Notably, Anselin 11 highlighted the significant spillover effects from developed nations to developing ones, emphasizing the need for global cooperation in climate change efforts. Niu et al. 12 observed spatial clustering similarities between CEs and air pollution, indicating spatial lock-in and path dependency. Besides, Zhang et al. 13 revealed that the gradient distribution of urban CEs promoted regional spatial clustering, aligning with the Environmental Kuznets Curve (EKC) hypothesis. These studies address the complex interplay between geographical distribution and temporal variation in CEs patterns, offering a more comprehensive understanding of the multifaceted nature of CEs.

Accurately quantifying and forecasting CEs is an indispensable foundation and prerequisite for formulating effective emission reduction policies. Existing research on estimating CEs mainly relies on the IPCC National Greenhouse Gas Inventories Methodology and the inversion method using nighttime light data 4 . These studies span multiple scales, from global 4 , to national 12 , 14 , down to provincial 15 , and include critical sectors such as industry and transportation 1 , 16 . The IPCC National Greenhouse Gas Inventories Method is foundational to this analysis, which analyzes data related to energy consumption and other relevant variables to estimate CEs. Although this method has certain advantages in accuracy and credibility 17 , to achieve comprehensive CEs monitoring, it is vital to establish a well-developed data collection and sharing mechanism to ensure the timeliness and comparability of data 18 . The continuous development of earth observation technology has provided a fundamental means for humans to observe changes in Earth’s spatial information from space. DMSP/OLS and NPP/VIIRS nighttime light data, with their advantages of long-term dynamic archiving, wide-area coverage, and open access to data outcomes 14 , 19 , have become indispensable information source for studying human nighttime economic and social activities 17 , 20 . As argued by Wan et al. 21 , utilizing nighttime light data for estimating CEs has led to numerous beneficial explorations. These studies provide theoretical basis and empirical cases for simulating CEs via nighttime light data.

Methodologies in identifying the determinants of CEs have broadened recently, incorporating the Stochastic Impacts by Regression on Population, Affluence, and Technology (STIRPAT) 22 , 23 , decomposition analysis 3 , 18 , environmental econometrics 6 , 24 , and a variety of sophisticated analytical approaches. These methods dissect the intricate web of factors that drive CEs, offering insights into their dynamics. Nevertheless, McNaught 25 and Son et al. 26 claimed that a unified academic stance on the key drivers of CEs was hard to achieve, attributed to differences in disciplinary focus, geographical context, and methodological approaches among scholars. Noteworthy is the application of spatiotemporal models to provincial data in China by Chen et al. 15 , which highlights economic growth as a significant and growing source of CEs. Efforts by researchers like Wen et al. 27 and Zhang et al. 13 categorized emission drivers into scale, structural, and technological factors, revealing varied mechanisms and effects. This observation underscores the necessity for methodologies capable of discerning environmental impacts with greater spatial and temporal specificity.

The YRB is a complex, hierarchically structured, and functionally integral system, spanning multiple administrative areas. Characterized by its high interconnectedness, cohesive integrity, and asymmetrical impacts among different reaches and cities, the YRB displays unique energy and industrial configurations alongside disparate development and resource allocation. These traits demand a reevaluation of the methods used to stimulate urban CEs and analyze their spatial spillover effects and determinants. Our research advances the field by integrating both DMSP/OLS and NPP/VIIRS data for 58 prefecture-level cities in the YRB. We employ a combination of ESDA, the STIRPAT model, and spatial econometric models to simulate urban CEs and explore their spatial spillover effects and determinants. This comprehensive approach enables us to provide a more detailed and accurate analysis of urban CEs, effectively addressing the gaps and limitations identified in previous studies.

This study advances the literature on urban CEs in the YRB with three primary contributions. First, it harmonizes DMSP/OLS and NPP/VIIRS nighttime light data from 2007 to 2021, overcoming previous limitations and enabling a comprehensive longitudinal analysis of urban CEs. This enhanced methodology captures long-term trends and patterns, providing a robust foundation for policy formulation and environmental management. Second, the research employs ESDA, the STIRPAT model, and spatial econometric models to investigate the spatial spillover effects and determinants of urban CEs, revealing significant spatial autocorrelation and phase-wise increases. This analysis deepens our understanding of the mechanisms driving urban CEs, essential for developing effective mitigation strategies. Third, by focusing on the economically and geographically significant YRB, this study examines the spatial spillover effects and their impact on urban CEs, generating targeted policy insights crucial for sustainable development in the YRB and other similar regions worldwide.

Materials and methods

Study areas.

The Yellow River, known as China’s “Mother River” and spanning 5464 kilometers, is the country’s second-longest river and a cradle of Chinese civilization. Taking the natural geographical units of the YRB as the main body, and comprehensively considering the direct correlation between regional economic development and the Yellow River, 58 prefecture-level cities through which the Yellow River flows are selected as the research objects. The study area holds the richest energy reserves among China’s rivers, with abundant coal, oil, natural gas, and non-ferrous metal resources, showcasing a diverse energy profile of “hydropower upstream, coal midstream, and oil downstream” (see Fig. 1 ). However, it also faces severe carbon reduction pressures. The YRB ranks first in the country for both economically recoverable coal reserves and coal production, this area also hosts nine of the fourteen large-scale coal bases in China (see Fig. 1 ). The development model characterized by high energy consumption, high pollution, and high CEs has led to excessive resource waste and significant environmental burdens, making it one of the regions with the highest energy-related CEs in China 28 . To promote ecological protection and high-quality development in the YRB, the Chinese government has introduced a series of carbon reduction policies. These include the “Outline of Ecological Protection and High-Quality Development Plan for the YRB” ( https://www.gov.cn/zhengce/2021-10/08/content_5641438.htm ), the “YRB Ecological Environmental Protection Plan” ( https://www.gov.cn/zhengce/zhengceku/2022-06/30/content_5698491.htm ), and the “China’s Carbon Peak and Carbon Neutrality Policies and Actions (2023)” ( http://www.prcee.org/zyhd/202402/t20240207_1066023.html ). These policies aim to advance low-carbon development of the YRB.

Research area: main energy distribution in the YRB. This map was generated by the authors using ArcGIS 10.8 ( http://www.esri.com/software/arcgis ) and does not require any license.

Data sources

Utilizing datasets of DMSP/OLS and NPP/VIIRS nighttime light obtained from the NOAA’s National Geophysical Data Center, Earth Observation Group (NOAA EOG) ( https://www.ngdc.noaa.gov/eog/download.html ) and The National Tibetan Plateau/Third Pole Environment Data Center ( https://poles.tpdc.ac.cn ), we perform calibration on these two types of data, resulting in stabilized nighttime light data. The cross-calibration of these two types of nighttime light data addresses the issue of pixel value oversaturation and enhances image clarity, while also filtering out background noise. Utilizing the calibrated nighttime light data, we obtain images of urban nighttime lights in the YRB from 2007 to 2021, as illustrated in Fig. 2 . Remote sensing data of green coverage rate is obtained from China’s Land-Use/Cover Datasets (CLCD). The energy, economic, and social data involved in the study derive from the China City Statistical Yearbook (2007–2021), China Energy Statistical Yearbook (2007–2021). Estimation parameters for carbon emission calculation of different energy are obtained from IPCC Guidelines for National Greenhouse Gas Inventories. River basin and municipal-level vector administrative boundaries are sourced from the National Geomatics Center of China ( http://www.ngcc.cn ).

Urban nighttime light imagery in the YRB from 2007 to 2021. These maps were generated by the authors using ArcGIS 10.8 ( http://www.esri.com/software/arcgis ) and do not require any license.

Methodological framework

Calculation model for energy-related ces.

This analysis incorporates fourteen energy consumption categories, including coal, petroleum and natural gas, as metrics for examination. The provincial CEs within the YRB are quantified following the calculation protocols established by the IPCC, as detailed in Eq. ( 1 ):

where \(C_{it}\) represents the CEs of region i in year t , \(E_{ijt}\) represents the energy consumption of energy source j in year t for region i (expressed in standard coal units), \(NCV_j\) represents the average low-level calorific value of the specific energy source j (kJ/kg), \(CEF_j\) is the CEs factor per unit calorific value, and \(COF_j\) is the carbon oxidation factor.

Nighttime light data, acquired from satellite observations, serve as a valuable proxy for human activities and energy consumption in urban areas. The intensity of these lights correlates strongly with economic activities and energy usage 17 . By integrating nighttime light data with conventional energy consumption statistics, we can significantly enhance the spatial resolution of CEs estimates. This integration process involves several key steps: First, nighttime light data are spatially matched with regional boundaries within the YRB. The light intensity is then calibrated against known energy consumption values from the China Energy Statistical Yearbook to establish a robust relationship between light intensity and energy consumption 21 . Second, this calibrated relationship is utilized to disaggregate provincial-level energy consumption data into finer spatial units based on the observed intensity of nighttime lights 19 . This method operates under the assumption that areas exhibiting higher light intensities correspond to higher energy consumption levels and, consequently, greater CEs. Finally, using the spatially disaggregated energy consumption data, CEs for each sub-region within the YRB are calculated. This methodology provides a more detailed and accurate spatial representation of CEs across the region.

Energy-related CEs simulation model based on DMSP/OLS and NPP/VIIRS nighttime light data

Utilizing a long-term, inter-comparable dataset of DMSP/OLS and NPP/VIIRS nighttime light data, this study establishes a correlation between calibrated nighttime light data and CEs, calculated from energy consumption statistics. The total nighttime light brightness (TDN) and total CEs for nine provinces are calculated. By observing the data characteristics (trends in CEs and TDN), distinct phases of CEs characteristics are identified across the provinces. Based on these data bibidfeatures and in conjunction with the timeline of carbon reduction policies, the analysis is segmented into three periods for fitting (see Table 1 ). It is evident that the coefficient of determination ( \(R^2\) ) denotes superior fitting outcomes across segmented stages. Furthermore, the t test reveals a notable correlation between the estimated CEs derived from energy consumption data at various stages and the corrected nighttime light brightness values.

Preliminary estimation of CEs for each pixel and each province are fitted with the formula as follows:

where \(Y\) represents CEs, \(TDN\) denotes the calibrated total nighttime lights brightness, \(\alpha\) is the fitting coefficients. Further, by utilizing CEs calculated from provincial-level energy consumption, the preliminary estimates for each pixel within the respective provinces are adjusted as follows:

where \(CC_{iy}\) is the adjusted CEs for each pixel, \(SC_{jy}\) donates the provincial CEs from energy consumption, \(C_{iy}\) represents the preliminary estimated CEs for each pixel, \(C_{jy}\) denotes the preliminary estimated provincial CEs.

Ultimately, CEs for each prefecture-level city are extracted, and the fitted values of urban CEs are calculated. These are then compared with the statistical data of provincial-level CEs from energy consumption for precision analysis. The results indicate a concordance of 99.00%, with a Root Mean Square Error (RMSE) of \(456.77\times 10^4\,\,\) tons and a Mean Relative Error (MRE) of 0.54%, \(P<0.01\) . This demonstrates that the final CEs simulated using DMSP/OLS and NPP/VIIRS nighttime light data are of high accuracy.

Exploratory spatial data analysis

Global spatial autocorrelation represents an aggregate measure of spatial interdependence across regions. It is typically evaluated using the global Moran’s I and Z-score tests to ascertain whether spatial autocorrelation exists in urban CEs, as well as to assess its intensity and directional impact 8 . Calculation is as follows:

where \(\bar{x}=\frac{1}{N}\sum \nolimits _{i=1}^N{x_i}\) , \(S^2=\frac{1}{N}\sum \nolimits _{i=1}^N{\left( x_i-\bar{x} \right) ^2}\) , I represents the global Moran’s I, N is the number of regions within the study global, \(x_i\) and \(x_j\) are the observed CEs values for regions i and j respectively, \(\bar{x}\) is the mean value of CEs for each region, \(S^2\) represents the variance of the variable, and \(W_{ij}\) represents the weight matrix of and spatial relationships. To conduct a Z-score significance test of the global Moran’s I, the calculation is as follows:

where Var ( I ) represents the theoretical variance of the global Moran’s I, and \(E(I)=-1/(n-1)\) is the theoretical expectation. A positive Z-score indicates significant positive spatial correlation in CEs distribution.

Decomposing the global Moran’s I to individual study units yields the local spatial autocorrelation, represented by the local Moran’s I 8 . For a specific spatial unit i , the calculation is as follows:

Spatial econometric models

Leveraging foundational works 29 , 30 , this study develops a spatial econometric model to investigate the determinants and spatial dynamics of urban CEs within the YRB, detailed as follows:

where \(\ln CE_{it}\) and \(\ln CE_{jt}\) signify urban CEs in region i and region j for year t respectively, and X captures the variables affecting CEs. The coefficient \(\rho\) reflects the spatial dependency of the dependent variable, while \(\beta\) and \(\gamma\) are the coefficients for the explanatory variables and their spatial lags, respectively. \(\alpha _i\) and \(\gamma _t\) denote the spatial and temporal effects, with \(\varepsilon _{it}\) representing the stochastic error term, adhering to an independent and identical distribution. The matrix \(W_{ij}\) is the spatial weights between regions i and j .

The coefficients of the spatial econometric model do not directly reflect the effects of the explanatory variables on the dependent variable. Following 8 , 31 , spatial effects are decomposed into direct and indirect spatial spillover effects using a decomposition matrix. The decomposition matrix is expressed as follows:

The partial differential equation matrix for the \(k^{th}\) dependent variable in Y is as follows:

Justification of the variables in the models

The STIRPAT model constitutes the core theoretical scaffold for examining environmental pollution determinants. Recent advancements in the model’s application now include considerations of economic growth, international trade, and industrial configuration, in addition to the conventional factors of population, affluence, and technology 22 , 23 . The STIRPAT model’s foremost strength is its ability to estimate parameters for coefficients while facilitating the detailed analysis and enhancement of influence factors. The panel data representation of the STIRPAT model is articulated as:

where I symbolizes environmental impact, P is population size, A represents per capita wealth, and T denotes technological advancement, with e as the error term. A natural logarithmic transformation of this relationship yields a modified equation:

Based on the STIRPAT model and its theoretical foundations, this paper synthesizes the relevant research literature on urban CEs 9 , 13 , 14 . Urban CEs are designated as the dependent variable and are calculated using Eqs. ( 1 )–( 3 ). The study examines economic growth, urbanization, energy consumption structure, green coverage rate, industrial structure, population density, technological progress, and foreign investments as the key influencing factors. Subsequently, this paper further analyzes the impact mechanisms of these factors on urban CEs.

Economic growth often correlates with increased industrial activities and higher energy consumption, leading to greater CEs. This positive relationship between economic growth and CEs is well-documented 15 . Higher economic activity typically involves more production processes and energy use, resulting in increased emissions. Traditionally, per capita GDP has been used as a metric for per capita wealth. Nonetheless, concerns about the authenticity of China’s economic data and the proven validity of using nighttime light data to measure economic progress prompt us to adopt average TDN as a proxy for economic growth 20 . Urbanization influences CEs through mechanisms such as increased energy consumption for residential, commercial, and industrial purposes, heightened transportation emissions due to increased vehicle usage, and concentrated industrial activities that are major sources of CEs 13 , 28 . The structure of energy consumption, particularly the reliance on fossil fuels versus renewable energy, plays a crucial role in determining CEs. Regions heavily dependent on coal, oil, and natural gas exhibit higher CEs compared to those with significant investments in renewable energy sources 1 . The green coverage rate impacts CEs through enhanced carbon sequestration, reduced urban heat island effects, and improved air quality. Urban areas with higher green coverage can mitigate emissions by absorbing carbon dioxide and lowering cooling energy consumption 32 . The composition of an economy’s industrial sectors significantly affects its CEs. Heavy industries, such as manufacturing and mining, are typically more carbon-intensive compared to service-oriented industries. A shift towards a service-based economy can lead to lower CEs 33 , 34 . Population density influences CEs by affecting transportation patterns, energy demand, and land use intensity. Higher population densities may lead to increased CEs due to greater energy and transportation demands, but they can also promote more efficient public transport and energy use per capita. Given the differences between cities in terms of administrative area and population size, using absolute population size as an indicator is not scientifically comparable. Therefore, population density is used to characterize the impact of population agglomeration on CEs 23 . Technological advancements can reduce CEs by improving energy efficiency, promoting cleaner production processes, and enabling the adoption of renewable energy technologies. The role of technological progress in mitigating CEs has been well-documented 2 , 24 . The degree of openness, reflected by foreign direct investment (FDI), is a basic factor that needs to be considered in the analysis of CEs. Existing research shows that the impact of FDI on CEs is uncertain. The Pollution Halo Hypothesis suggests that FDI can reduce CEs by introducing environmentally friendly technologies and products 35 . Conversely, the Pollution Haven Hypothesis believes that FDI will worsen the environmental quality of the host country by transferring high-polluting industries to the host country 36 . We measure the degree of openness using the proportion of FDI to GDP to examine its impact on CEs.

Considering the potential autocorrelation among the explanatory variables, particularly between NLB, PD, and URB, to ensure the robustness of our model, we have conducted a detailed analysis to verify and address any potential autocorrelation. First, we performed a multicollinearity test using the Variance Inflation Factor (VIF) to quantify the extent of correlation among the explanatory variables. The VIF values of NLB, PD and URB are 5.203, 3.844 and 2.157 respectively. A VIF value below 10 indicates that multicollinearity is not a serious concern 11 . Therefore, our initial analysis suggests that while there is some degree of correlation, it does not reach a level that would significantly undermine the integrity of our model. The VIF of NLB is 5.203, higher than that of PD and URB. Actually, NLB serves as a proxy for economic activity and urbanization. Its use is supported by numerous studies demonstrating its correlation with economic growth and urban development 17 , 20 . Despite its correlation with PD and URB, NLB captures unique aspects of economic vibrancy and infrastructural development not entirely encompassed by population metrics alone. Furthermore, we applied the Durbin-Watson statistic to check for the presence of autocorrelation in the residuals of our regression models. The Durbin-Watson statistic for our primary model was found to be 1.95, indicating that there is no significant autocorrelation 8 , 37 . Finally, to further ensure the robustness of our findings, we conducted a sensitivity analysis by removing each of the correlated variables (NLB, PD, URB) one at a time and observing the impact on the model’s performance and results. The consistency of the model outcomes with the exclusion of any single variable supports the stability of our model despite the presence of correlation.

A detailed variable descriptions and statistical analysis is available in Table 2 .

Temporal and spatial differentiation characteristics of urban CEs in the YRB, 2007–2021 (Unit: \(10^9\) tons).

Spatial-temporal characteristics of urban CEs

We simulate urban CEs in the YRB for the period 2007–2021, as is depicted in Fig. 3 . Overall, the total CEs of cities in the YRB have shown an upward trend, rising from 1.675 billion tons in 2007 to 2.503 billion tons in 2017, representing a growth of nearly 50%. The time series analysis indicates that urban CEs in the YRB exhibited phased characteristics, with a brief decline from 2007 to 2008, followed by rapid growth from 2008 to 2012. This trend is likely influenced by the 2008 global financial downturn and China’s consequent economic deceleration. Post-2012, urban CEs experienced fluctuating growth with a trend towards convergence. Spatially, the pattern of urban CEs in the YRB has remained relatively stable. From a basin-wide perspective, between 2007 and 2021, urban CEs followed a spatial distribution pattern of “lower reach > middle reach > upper reach”, with midstream city emissions close to the basin average, showing no significant catch-up effect. Consequently, the focus of energy conservation and emission reduction efforts should be on downstream and midstream cities. Urban CEs within the YRB not only exhibit spatial variations but also demonstrate evolutionary patterns over time. Figure 4 illustrates the changing trends of urban CEs in 2007–2021. It is evident that significant variations exist in CEs among different cities. At the basin level, the spatiotemporal evolution of CEs in midstream cities is noticeably more stable, whereas upstream and downstream cities show notable variability. On the city scale, Ordos, Yinchuan, Datong, Hohhot, Jining, and Weifang exhibit greater variability, indicating less stability in their CEs and substantial differences between years. Conversely, cities like Xining, Qingyang, Dingxi, Shangluo, and Guyuan display less variability, suggesting the persistent inefficiency in CEs throughout the years.

Box diagram of urban CEs in different reaches.

Spatial autocorrelation test

Following LeSage 29 , a geographic distance weight matrix \(W_1\) is utilized in spatial autocorrelation test, \(W_1\) represents the inverse of the nearest road distance between cities. This approach assumes that the closer the cities are to each other, the stronger their interaction and spatial spillover effects. The road distance data between cities were collected using high-resolution geographic information system (GIS) data. The nearest road distance between each pair of cities was calculated using the Dijkstra algorithm, which finds the shortest path between nodes in a graph. This method ensures that we account for the actual road network rather than straight-line distances. Once the nearest road distances were calculated, we took the inverse of these distances to construct the spatial weight matrix \(W_1\) . Mathematically, \(W_{1ij}={{1}/{d_{ij}}}\) , where, \(d_{ij}\) is the nearest road distance between city i and city j . Figure 5 exhibits global Moran’s I of urban CEs within the YRB in 2007–2021. The global Moran’s I are consistently positive and pass the significance test. This result suggests that urban CEs do not follow a random spatial pattern but are significantly autocorrelated, indicating pronounced spillover and dependency traits. The extent of spatial clustering varies annually, presenting a dynamic pattern with “Rising-Falling-Rising”. This pattern reflects an obvious interplay of energy efficiency and technological advancement among adjacent cities, alongside a trend towards more stable regional collaborations, resulting in subtle shifts in clustering intensity. With an average global Moran’s I of 0.3842 during the study period, these findings underscore a consistently stable spatial clustering and interactivity of urban CEs, highlighting the intricate spatial dynamics at play.

Test results for the global Moran’s I from 2007 to 2021.

Furthermore, we utilize the local Moran’s I to understand the spatial agglomeration characteristics and local spatial correlation of urban CEs. As shown in Fig. 6 , cities with positive spatial correlation (high-high, H-H or low-low, L-L) exceeds 40, constituting more than 68.96% of the sample. This suggests a significant interdependence in urban CEs with a spatial aggregation tendency, thereby corroborating the findings of global spatial correlation. It is noteworthy that some cities are situated in the second and fourth quadrants, with notable disparities among individual cities. This indicates that the local spatial autocorrelation of urban CEs exhibits a distribution characterized by both clustering and divergence. More importantly, longitudinal comparison demonstrates H-H clusters significantly outnumber L-L ones, highlighting the prevalence of high-value clusters. This major contribution to positive spatial autocorrelation suggests an upward trend in urban CEs, emphasizing the urgent need for emission reduction in the YRB.

Local Moran’s I scatter plot from 2007 to 2021.

Spatial panel model selection

Based on Elhorst 31 , we conduct tests to identify the appropriate spatial panel model, as shown in Table 3 . Firstly, LM and Robust LM tests indicate significant spatial dependencies in SLM and SEM models at the 1% level, leading to further estimation of the SDM model. Secondly, LR and Wald tests confirm SDM’s suitability at the 1% level for handling spatial dependencies. Lastly, the Hausman test, significant at the 1% level, suggests a fixed effects model due to significant time-fixed effects (LR-Time=167.241), leading to the adoption of a time-fixed SDM. To enhance the analysis’s reliability, the study employs river length as an instrumental variable (IV) for a robust estimation (see Table 4 ). Given the pivotal role of natural conditions, particularly rivers in urban development, there is a significant correlation between the Yellow River’s characteristics and socio-economic growth. Cities’ socio-economic advancement is intricately linked to hydrological conditions and river dynamics. Despite the direct influence of river length, an exogenous variable, on other explanatory variables, its impact on CEs is minimal 38 . Thus, river length is chosen as an IV for endogeneity test. The LM test validates the IV’s identification, and the endogeneity test results affirm its effectiveness, thereby confirming the robustness of the baseline regression.

Results of effect decomposition

Acknowledging the spatially correlated nature of regional economic advancement, we further develop an economic distance weight matrix \(W_2\) to reinforce the robustness of our findings. The matrix’s elements \(W_{ij}\) quantifies the inverse of the absolute disparity between the per capita GDP of city i and the mean per capita GDP of city j per year. This analysis delves into the direct impacts, spatial spillover effects, and aggregate impacts of influencers across these matrices (see Table 5 ). Spatial spillover effects illustrate how factors in adjacent areas affect local CEs, while the total effect, combining direct and spillover impacts, highlights the average influence on CEs 9 , 10 . Consistently, across both matrices, the influence directions of identical factors align.

Specifically, both the direct effect and indirect effect of urbanization contribute significantly to the enhancement of urban CEs within \(W_1\) and \(W_2\) . The direct impact of NLB exhibits a statistically significant negative association at the 1% level. Conversely, its indirect effect demonstrates a positive relationship, albeit with lower significance compared to the direct effect. These findings suggest that while economic growth may effectively mitigate local carbon emissions, it concurrently fosters an increase in neighboring carbon emissions. The direct effect of ECS is significantly positive at the 1% level. The indirect impact is also positive, indicating that ECS in a particular city both promotes urban CEs in local and surrounding areas. The direct effect and indirect effect of GRE are both significantly negative at the 1% level. GRE influences urban CEs through direct biological effects and broader socio-economic impacts. The direct effect of IS is positive, while its indirect impact is significantly negative at the 1% level. Notably, the IS adjustment of a city can support its low-carbon development. A possible explanation aligns with the industry characteristics dominantly composed of resource-intensive industries within the YRB. The direct and indirect effects of PD on urban CEs are positive and do not pass the significance test. One possible explanation is that the two opposing effects of PD cancel each other out. The direct effect and indirect effect of TP are both significantly negative at the 1% level. Specifically, TP primarily reduces CEs by enhancing energy use efficiency, meaning using less energy to produce the same GDP. The direct and indirect effects of FDI both exhibit statistically significant negative associations at the 5% level. This suggests that FDI plays a role in reducing both local and surrounding carbon emissions.

Robustness test

Five-year average results.

To mitigate annual data noise from factors like economic policy changes or natural disasters, this study transitions to a five-year average panel data structure, following You and Lv 39 to accurately capture long-term trends and variable relationships. The five-year average refers to the mean values of urban CEs and other relevant variables calculated over a five-year period. With data averaged over five years, three observation periods emerge for each city: 2007–2011, 2012–2016, and 2017–2021. This approach helps to mitigate the impact of short-term anomalies and ensures that the observed spatial spillover effects are not driven by transient factors. Robustness tests, as show in Table 6 , The analysis reveals consistent signs for direct, indirect, and total effects across periods, with population density’s significance increasing at the 10% level in direct effects. This highlights how averaging can reveal longer-term impacts obscured by short-term fluctuations. Despite some direct effect variations, indirect and total effects remain similar with the results of Table 5 , underscoring the robustness of the findings.

Robustness test with alternative spatial weight matrices

The selection of a spatial weight matrix significantly influences the outcomes of spatial models. The analysis began with commonly used matrices: a geographical distance weight matrix \(W_1\) and an economic distance weight matrix \(W_2\) . Addressing the limitations of relying solely on geographical or economic distances, a geo-economic distance spatial weight matrix \(W_3\) and a nested weight matrix combining geography and economic distance \(W_4\) are developed regarding to Elhorst 8 . \(W_3\) integrates the inverse of nearest road distances with the ratio of annual average per capita GDP between regions. \(W_4=\varphi W_1+\left( 1-\varphi \right) W_2\) , \(\varphi\) is between 0 and 1. \(W_3\) and \(W_4\) , by considering both geographic distances and economic factors, offer a comprehensive view on the spatial interdependence. Robustness tests, detailed in Table 7 , display consistent directions in the effects of determinants and retain similar significance levels as seen in Table 5 .

Robustness test for replacing the dependent variable

Per capita CEs serve as a vital indicator for evaluating urban CEs, revealing the environmental implications of inhabitants’ lifestyles and economic activities. This indicator has been extensively adopted in CEs research 26 . Building on the literature 37 , 40 , this study employs per capita CEs as an alternative dependent variable, aiming to verify the durability of prior findings (refer to Table 8 ). Analysis under both \(W_1\) and \(W_2\) weight matrices demonstrates that the influence and significance of the determinant factors remain stable, corroborating the study’s results (see Table 5 ). These outcomes suggest the robustness and reliability of our analytical approach.

Cities serve as foundational and pivotal units for implementing carbon reduction policies due to their degree of administrative independence 6 , 26 . In this study, we utilized long-term comparable DMSP/OLS and NPP/VIIRS nighttime light datasets, combining ESDA, the STIRPAT model, and spatial econometric models to analyze the determinants of urban CEs. To ensure the comprehensiveness and reliability of our analysis, it is necessary to compare our model with other approaches. Previous research on regional CEs and their influencing factors can be broadly categorized into non-spatial and spatial econometric models. Non-spatial econometric models, such as the Logarithmic Mean Divisia Index (LMDI) analysis, Tobit regression and Pearson correlation test, have been employed to evaluate the relationships between CEs and their determinants at a macro level 13 , 18 , 41 . However, these models overlook spatial dependencies between regions, failing to capture the interdependence where policies or actions in one region can influence the CEs of neighboring regions. In contrast, spatial econometric models are more suitable for studying urban CEs due to their ability to incorporate spatial interdependencies.

Moreover, existing research has focused on CEs dynamics across broader spatial dimensions, including economic blocs 18 , nations 22 , and provinces 23 . Unlike earlier studies, our research targets the urban scale, enabling a more granular analysis of CEs and their determinants. This focus is critical for understanding the specificities of urban development and policy impacts at the city level. Additionally, the integration of ESDA, the STIRPAT model, and spatial econometric models, along with the utilization of nighttime light data, provides a robust framework for analyzing urban CEs. This approach enhances the accuracy of our measurements and enables precise spatial analysis. Our study also considers temporal dynamics by analyzing changes over time, which helps identify trends and shifts in urban CEs, offering valuable insights for long-term policy planning. These methodological advancements offer significant advantages over previous studies, contributing valuable insights for policymakers aiming to develop effective carbon reduction strategies in urban settings.

Based on current research 26 , urbanization is a complex socio-economic phenomenon that influences urban CEs in diverse ways. One viewpoint suggests that regions with high urbanization levels often experience increased CEs due to rapid urban growth, becoming pollution hotspots. While another viewpoint advocates that urbanization can lead to positive externalities like economies of scale and more efficient resource utilization, potentially reducing environmental damage 23 . Higher income levels associated with urbanization also increase public demand for environmental quality, thereby influencing environmental regulation and practices to limit CEs. Our results largely support the second perspective. With urban expansion in the YRB undergoing a sprawl-style process, future efforts should aim to harness urbanization’s benefits more effectively, optimizing the urban model to curtail extensive land development and leverage land finance advantages.

Within the framework of new economic normalization, the high-quality economic development of a specific city is pivotal in supporting the intensive urban development strategies and enhancing the end-use energy efficiency 25 . This paradigm shift towards prioritizing quality over quantity in economic growth facilitates a more sustainable urbanization process, characterized by optimized energy consumption and reduced environmental footprint 33 . Simultaneously, with an increase in economic development in a particular city, some funds and talent in neighboring regions will flow to more high-level development areas. This phenomenon forms a siphon effect on the economic development of surrounding areas. However, capital outflow leads to insufficient economic support for the low-carbon transformation of neighboring cities. The flow of relevant talent will inevitably affect the process of urban intensification. Consequently, NLB shows a promoting effect on urban CEs in neighboring cities. Our findings are consistent with previous research 13 .

Consistent with the findings of prior research 42 , ECS enhances both local and neighboring carbon emissions. Within the YRB, the energy consumption structure is dominated by traditional fossil energy consumption. It is observed that the persistent expansion in the consumption of fossil fuels, notably coal, amplifies the burden on urban CEs, particularly in coal-dependent production scenarios. Concurrently, challenges such as regional imbalances and the misalignment in the spatial distribution of energy supply and demand further complicate the efficient allocation and trans-mission of ECS, undermining efforts towards achieving energy sustainability 2 . Research by Yin et al. 32 shows that terrestrial ecosystems, such as forests and grasslands, are crucial for carbon sequestration, absorbing about 31% of carbon dioxide produced by human activities, which accounts for approximately 54% of the total ab-sorbed by natural processes. This direct sequestration not only reduces atmospheric carbon levels but also mitigates urban heat island effects. Additionally, Fu et al. 43 highlighted that increasing green coverage contributed to sustainable development by improving public health, enhancing biodiversity, and making urban areas more livable. These changes indirectly reduce urban CEs by encouraging more efficient resource use and influencing public demand for higher environmental quality. Spatial effect further elucidates that GRE precipitates beneficial spillover effects across various regions in the YRB, amplifying the overall capacity for carbon absorption. Consequently, the enlargement of urban forestry, grasslands, and additional green spaces emerges as a pivotal strategy in mitigating CEs and combating climate change.

In our study, urban industrial structures mainly follow a secondary, tertiary, primary sequence. Cities where secondary industry output surpasses tertiary industry account for 79.6% of our sample, exceeding China’s national average 44 . This indicates that these cities are currently in a rapid industrialization phase, characterized by low efficiency industries. Besides, the IS transformation significantly contributes to the elevation of CEs in adjacent cities within the YRB. During the process of IS adjustment and low-carbon transition, certain resource-intensive industries marked by elevated energy consumption and substantial pollution levels tend to relocate from a specific city to its surrounding areas. This strategic shift, while beneficial for the initiating city’s environmental objectives, inadvertently imposes environmental burdens on neigh-boring regions. Our empirical results validate the viewpoints of previous studies 6 , 34 . What’s more, industrial restructuring during regional economic integration can also lead to “carbon leakage”, where CEs shift from areas with strict environmental restrictions to those with looser restrictions, negatively impacting the CEs in neighboring regions. Therefore, adjusting the industrial structure through environmental regulation is a pathway to emission reduction.

Contrary to existing research 44 , 45 , our findings suggest that the direct and indirect effects of PD on urban CEs are not significant. Traditional agglomeration economic theory posits that clustering brings various spillover and scale economy effects, with the spatial concentration of population facilitating cost savings and enhancing factor utilization efficiency 15 . Certain cities benefit from the agglomeration effects of high PD. Through the shared use of public infrastructure, knowledge spillovers, and the formation of a labor pool, these agglomeration economies foster scale economic development and industrial structure upgrading, enhancing resource utilization efficiency. However, excessively high PD in the YRB can lead to diseconomies of agglomeration 13 , where overcrowding increases competitive costs, traffic congestion, and infrastructure demand, leading to higher construction, operation, and maintenance costs. Additionally, the spatial distribution of the population in the YRB is uneven 32 , with population differentiation and aging in the different reaches potentially diminishing the contribution of population scale effects. Ultimately, the economies of scale generated by urban population agglomeration may be less than the increase in energy consumption, thus the contribution of PD to emission reduction remains inconspicuous.

According to the technological advancement theory, advancements in actual production processes are biased 24 , and depending on their orientation, technology can be categorized into production technology and emission reduction technology. The former mainly affects factor productivity, while the latter primarily influences pollution intensity 2 . Our empirical results show that TP has a negative effect on urban CEs in the YRB, underscoring the importance of effectively guiding more “green” innovations to make a significant impact on CEs regulation. The spatial spillover effects generated by TP also play a paramount role in facilitating carbon reduction in neighboring cities, largely due to the strong externalities of technological innovation. Hence, future efforts should focus on accelerating technology diffusion across different regions. It is noteworthy that although we find that TP promotes carbon reduction both locally and in neighboring areas, achieving TP and carbon reduction is a continuous and asymptotic process, challenging to realize leapfrog development in the short term.

The academic discourse on the relationship between FDI and energy consumption typically revolves around two contrasting theories: the “Pollution Haven Hypothesis” and the “Pollution Halo Hypothesis.” The Pollution Haven Hypothesis suggests that FDI transfers highly polluting industries to host countries, thereby increasing their urban CEs 35 . Conversely, the Pollution Halo Hypothesis posits that under parent country environmental regulations, FDI, through knowledge and technology spillovers, elevates the technological level of host countries, thereby enhancing energy use efficiency and reducing energy intensity 36 . Our empirical findings convince the Pollution Halo Hypothesis, indicating that FDI significantly reduces CEs both locally and in neighboring areas. FDI, as a carrier of capital stock, management experience, and relevant technology, promotes economic growth and social development through improvements in worker quality, TP, and IS upgrading. Moreover, through spatial transmission mechanisms such as factor flows, technology spillover, and policy diffusion, FDI significantly impacts surrounding areas, creating a virtuous decarbonization cycle, characterized by trickle-down and siphoning effects. Therefore, local govern-ments need to discern the “green” level of FDI, raise the environmental entry threshold for FDI, and fully leverage FDI’s technological advantages and spillover effects in CEs management.

Conclusions

The objective of this study is to offer guidance for the advancement of a sustainable, low-carbon economy in the YRB, while also aiding regional governments in crafting effective policies to reduce carbon emissions. Utilizing DMSP/OLS and NPP/VIIRS nighttime light data of 58 prefecture-level cities within the YRB, this study quantifies urban CEs from 2007 to 2021. Furthermore, this investigation clarifies empirically the spatial spillover effects and influence mechanisms of urban CEs by combining ESDA, STIRPAT model and spatial econometric model. Several critical findings are obtained. Firstly, our findings reveal significant trends and spatial distributions of urban CEs in the YRB, exhibiting a pronounced pattern of “lower reach > middle reach > upper reach”. This spatial distribution, shaped significantly by China’s “Belt and Road” initiative, underpins a complex network of CEs linkages across the YRB. Additionally, our analysis validates the existence of spatial spillover effects and clustering of high-emission urban areas, particularly in the middle and lower reaches, extending towards Ningxia and Inner Mongolia upstream. The observed spatial correlation and agglomeration characteristics highlight the importance of targeted and collaborative policy efforts. Finally, an in-depth understanding has emerged regarding the spatial spillover effects and influencers of urban CEs in the YRB. Central to our findings is the role of urbanization and energy infrastructure, which markedly exacerbate urban CEs within and beyond local confines. In contrast, an enhancement in green coverage, technological advancements, and the influx of FDI serve as potent mitigators of urban CEs both locally and regionally. Notably, our findings underscore the impact of economic development and industrial structure on urban CEs, advocating for specialized and collaborative policy interventions.

Based on the above results, the following suggestions are made: 1. Urban CEs management of the YRB is a multifaceted initiative that incorporates aspects such as rational urbanization, industrial structure upgrading, energy structure refinement, ecosystem service enhancement, technology innovation, and strategic FDI. It necessitates a well-considered “top-level design” and low-carbon development plan aligned with Sustainable Development Goal (SDG) 12 and SDG 13 1 . 2. The innovation of collaborative development mechanisms is pivotal for constructing low-carbon cities in the YRB. Effective regional CEs management necessitates the development of an ecological compensation mechanism, enhanced collaborative environmental enforcement, and a unified carbon emission monitoring platform. 3. Targeted and differentiated emission reduction measures should be formulated based on a city’s resource endowments, economic development, functional roles, and policy orientation 24 . Cities in the midstream and downstream should spearhead CEs management by prioritizing the development of compact cities and improving land use planning to avert inefficient “urban sprawl”. Upstream cities must reinforce their ecological functional roles and boost ecosystem service capabilities to counteract urban heat island effects.

Our research also carries significant implications on a global scale. By weaving the control of urban CEs into the fabric of river basin management, we propose a sustainable paradigm aimed at facilitating low-carbon growth amidst challenges such as water scarcity, fragile ecosystems, and rapid industrialization and urbanization. This approach is not only pertinent but vital for river basins around the globe that are entangled in complex management dilemmas, energy shifts, and the imperative of carbon mitigation. Our insights hold particular relevance for regions such as the Ganges Basin 46 , the Nile Basin 47 , the Colorado River Basin 25 , and the Amazon Basin 48 . Furthermore, this study showcases the YRB’s experience in harmonizing carbon reduction initiatives across multiple levels of governance, spanning provincial to urban and township jurisdictions. This exemplifies a cohesive and coordinated effort in carbon policy implementation within a complex governance architecture. The demonstrated synergy among various governmental layers in the YRB serves as an exemplary model for countries aiming to deploy comprehensive inter-regional or basin-wide strategies for carbon reduction.

Despite the valuable insights offered, this study still has some limitations that warrant consideration in future research. Due to data availability constraints, eight indicator variables were selected, thereby limiting the scope of potential solutions to these chosen variables. To deepen the understanding of the factors influencing urban CEs, future research should expand data sources to include but is not limited to the utilization of clean energy sources such as hydropower and photovoltaic power generation, to further enrich and refine the existing research base. Additionally, as China progresses through the early stages of its low-carbon transition, the development and application of carbon reduction policies present a complex, long-term challenge. Our research provides macro-level policy recommendations, the effectiveness of which requires further empirical validation. Future studies should focus on evaluating national carbon reduction policies and regional economic strategies more closely, aiming to develop localized emission reduction plans. Such research is essential for accurately assessing carbon reduction strategies’ impacts and offering actionable insights for global sustainable development.

Data availability

The dataset used in this study is available from the corresponding author upon request.

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This paper was supported by the Fundamental Research Funds for the Central Universities, North Minzu University (Grant No.2023SHKX03); the Provincial Natural Science Foundation of Ningxia (Grant No.2022AAC05040); National Social Science Fund of China (Grant No.21XMZ051); the National Natural Science Foundation of China (Grant No.61402017).

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Conceptualization, M.M.; methodology, S.K.; software, Y.W.; validation, S.K. and Y.W.; formal analysis, M.M. and S.K.; resources, M.M.; data curation, Y.W.; Writing—original draft preparation, M.M. and Y.W.; Writing—review and editing, M.M., S.K.; visualization, Y.W.; supervision, S.K.; project administration, M.M.; funding acquisition, M.M. and Y.W. All authors have read and agreed to the published version of the manuscript.

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Ma, M., Wang, Y. & Ke, S. Spatial spillover effect and driving factors of urban carbon emissions in the Yellow River Basin using nighttime light data. Sci Rep 14 , 19672 (2024). https://doi.org/10.1038/s41598-024-70520-5

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