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Understanding the Differences Between Scientist I, Scientist II, and Senior Scientist Jobs

In the life science staffing industry, it is common to find variations in how different companies define Scientist I, Scientist II, and Senior Scientist roles, even though these titles are widely used across the industry. This blog post aims to provide a general overview of each role and highlight the key differences between them, in order to assist job seekers and employers in understanding how the titles may be perceived differently by various audiences.

The Scientist I, Scientist II, and Senior Scientist roles are all important positions within life science companies. Each role has unique responsibilities and qualifications, and it is important to understand the differences between them to make informed placement decisions.

Scientist I Jobs

A Scientist I is an entry-level position that typically requires a Bachelor’s or Master’s degree in a life science field. They are responsible for conducting experiments and analyzing data to support research projects. A Scientist I may also assist with the development of new products and technologies. Advantages of hiring a Scientist I include their eagerness to learn and grow within the company, and their lower salary compared to more experienced scientists. However, they may require more supervision and guidance compared to more experienced scientists.

A Scientist II is a mid-level position that typically requires a PhD in a life science field or several years of experience as a Scientist I. They are responsible for designing and executing experiments, analyzing data, and interpreting results. A Scientist II may also be responsible for managing projects and supervising junior scientists. Advantages of hiring a Scientist II include their advanced scientific knowledge and experience, and their ability to work independently. However, they may require a higher salary compared to a Scientist I.

Senior Scientist Jobs

A Senior Scientist is a senior-level position that typically requires a PhD in a life science field and several years of experience as a Scientist II. They are responsible for leading research projects, developing new products and technologies, and managing other scientists. Advantages of hiring a Senior Scientist include their ability to provide scientific leadership and strategic guidance, and their extensive experience in the field. However, they may require the highest salary compared to a Scientist I or Scientist II.

Factors to Consider When Hiring for Each Role

When hiring for any of these roles, employers should consider factors such as the candidate’s education and experience, job responsibilities, and leadership abilities. Job seekers should also consider their own qualifications and experience to determine which role may be appropriate for them.

The Scientist I, Scientist II, and Senior Scientist roles all play important roles in life science companies. It is important to understand the differences between these roles to make informed hiring decisions and for job seekers to identify the appropriate role for their qualifications and experience. At our staffing company, we can help both job seekers and employers navigate these roles and find the best fit for their needs.

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This question is about what a principal scientist does and principal scientist .

What is the hierarchy of job titles for scientists?

The hierarchy of job titles for scientists includes two to three levels of scientist roles followed by senior and principal roles. An entry-level position as a scientist is known as a Scientist I position.

After about one to three years of experience as a Scientist I, you should be promoted to a Scientist II position. Some companies may have a Scientist III position; it depends on the company's size and the industry.

You are eligible for a senior scientist spot after five or more years of experience in a Scientist II position. A senior scientist helps manage Scientists I and II work on the study and works relatively independently of the principal scientist.

The last level of the hierarchy is a principal scientist. The principal scientist is accountable for the entire lab and all of the research it produces. A principal scientist takes the lead in research and development, and everyone, including the senior scientist, must report to the principal scientist.

The hierarchy of scientific job titles often extends beyond the principal scientist role, particularly in larger organizations or those engaged in extensive research and development activities. Following the role of principal scientist, there are further levels of seniority that scientists can aspire to.

The role of a director of science or research director is typically the next step after the principal scientist. This position involves not just overseeing the research but also playing a significant part in determining the strategic direction of the organization's scientific endeavors. A director of science is often responsible for managing multiple principal scientists and ensuring that their collective efforts are aligned with the organization's overall goals.

Scientist I

Scientist II

Senior Scientist

Principal Scientist

Director of Science / Research Director

In some organizations, there may be a further level known as vice president (VP) of research or chief scientific officer (CSO). This position is usually part of the executive leadership team and plays a critical role in shaping the company's scientific policies. The VP of research or CSO ensures that all research and development activities support the company's mission, strategic objectives, and growth plans.

Vice President of Research / Chief Scientific Officer

This hierarchy provides a clear progression path for scientists starting at an entry-level position to potentially reach executive management levels. However, it's important to note that climbing this ladder requires not just technical expertise and experience but also a broader understanding of business and strategic planning.

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Principal Scientist Jobs

Learn more about principal scientist jobs.

Is a principal scientist higher than a senior scientist?

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Policy Library

3.2.1 research faculty: hiring and promotion guidelines.

Research Faculty members are not eligible for tenure. While they are subject to many of the general hiring and promotion criteria for tenure-track Faculty, there are significant differences. The following sections detail established positions in the Research Faculty and their promotion criteria. 

Research Faculty titles include: 

Research Scientist 

Research Engineer 

Research Technologist 

Research Associate 

Extension Professional 

A person is normally hired into a Scientist, Engineer, Technologist, Associate, or Extension Professional position, where appropriate, on the basis of the field of their most recent educational degree or their experience. Standards of evaluation will generally be based on the standards of that field. There are levels of I, II, Senior, and Principal for each of these titles. 

Research Associate Titles 

The title of Research Associate is held by research personnel who meet all normal requirements, but for whom the title of Engineer, Scientist, or Technologist is not appropriate. The title is intended for professionals for whom a specific need exists, but because of the different nature of their education or experience, should not be classified (at least initially) in the Research Engineer/Scientist/Technologist structures. In determining when it will be suitable to use the Research Associate title structure, reliance will be placed on comparison with the established criteria for Research Engineer/Scientist/Technologist. That is, the qualifications for Research Associate should have an equivalency to Research Engineer/Scientist/Technologist but will differ in some particular aspect. In general, it will offer more flexibility in considering the candidate's total qualifications and suitability for employment at Georgia Tech. The title is intended to be broad enough in scope to include any professional categories appropriate to the Institute's needs. Examples include medical doctors, health and safety professionals, social scientists, architects, and management experts. 

Extension Professional Titles 

The title of Extension Professional is held by research personnel that fulfill the extension and service mission of Georgia Tech to the State of Georgia and beyond. This mission includes, but is not limited to, technology-based economic development, technology commercialization and deployment, entrepreneurship, start-up company incubation, and business and industry outreach. Extension Professionals also provide educational programs for business and industry in support of these missions and facilitate and foster increased industrial engagement and sponsorship of applied research activities with Georgia Tech. 

Extension Professional appointments are made on the basis of merit and the special qualifications of the individual and follow the same general ranking, hiring, and promotion principles as the other professional research faculty ranks. Extension Professional ranks include the same levels as for the other titles above. Promotion criteria, including education and time in rank, shall follow the research titles as outlined in the following section; however, equivalent extension impacts and accomplishments versus research accomplishments will be considered by the promotion review boards. 

Promotion to a Higher Rank 

Following are normal requirements for consideration for promotion to a higher rank. These experience and performance criteria may also be used for determining the initial rank when hiring professional research personnel. Credit for previous academic or research professional experience should be explicitly stated in writing at the time of employment. In addition to these criteria, to be considered for promotion will normally require a number of years in rank, as follows: 

Research Scientist II – Three (3) years as Research Scientist I 

Senior Research Scientist – Four (4) years as Research Scientist II 

For candidates holding the Doctoral degree, employment prior to employment at Georgia Tech will be considered if adequately documented, and the four-year time in rank requirement reduced to two (2) years for candidates so qualified. Employment prior to Georgia Tech plus employment at Georgia Tech must be four years or more with a minimum of two (2) years in rank at Georgia Tech. 

Principal Research Scientist - Five (5) years as Senior Research Scientist 

As used in this Handbook, "years of experience," "years in rank," and "years at Georgia Tech" are to be calculated as of July 1st of the year in which the promotion would take effect. Note: In the above and following sections, the term "Research Scientist" is used to indicate any one of the following: Research Scientist, Research Engineer, Research Technologist, Research Associate, or Extension Professional.  

The following sections describe the credentials, competency, and performance expected of the identified ranks. Requirements for professional registration and other legal or professional certification are not identified in these guidelines as prerequisites for promotion. Instead, this formal evidence of competency is expected to be provided by persons assigned to duties that require them. 

Research Scientist I 

This is the initial rank held by research personnel who have at least a bachelor's degree and who will be performing on a professional level. 

Research Scientist II 

In addition to the years-in-rank requirement, this rank requires one (1) of the following: 

A Master’s degree and three (3) years of relevant full-time experience after completion of that degree, 

A Master’s degree and five (5) years of relevant full-time experience after completion of a Bachelor’s degree, or 

A Doctoral degree. 

Qualified candidates who are recommended by the normal administrative process will not be reviewed by a Presidential committee. Professional recognition in one's research field will be expected. 

In addition to the candidate’s education and experience, the promotion recommendation shall include substantive evidence of the candidate's progress toward developing the capabilities for performing at the level expected of research professionals in the same field holding senior Research Faculty ranks at Georgia Tech. Such evidence might consist of papers published or contributed to, significant managerial efforts on sponsored projects, products developed and delivered to the sponsor community such as software or hardware and documented impacts of these products, or equivalent teaching responsibilities performed in an instructional unit. 

Senior Research Scientist 

A Master’s degree and seven (7) years of relevant full-time experience after completion of that degree, 

A Master’s degree and nine (9) years of relevant full-time experience after completion of a Bachelor’s degree, or 

A Doctoral degree and four (4) years of relevant full-time experience after completion of a Bachelor's degree. 

The rank of Senior Research Scientist is reserved for those professionals who have demonstrated a level of scholarly achievement and technical, managerial, and entrepreneurial productivity commensurate with the highest standards of Georgia Tech. Achievements should include recognized contributions to their specific technical disciplines; supervision of other research professionals through review and approval of proposals, technical reports and other communications; and representation of Georgia Tech to external organizations for the purpose of obtaining, managing, and performing high-quality sponsored research programs. Preference will be shown for qualified personnel holding a Doctoral degree in their specified discipline. 

In addition to the basic requirements, above, demonstrated superior performance of professional duties is required as follows: 

Peer recognition of mastery of a complex and difficult field of specialization as demonstrated through authorship of refereed papers and/or products developed and delivered to the sponsor community such as software or hardware, and documented impacts of these products. The latter may come in the form of sponsor satisfaction testimonials. While emphasis will be given to authorship of journal and symposium papers which have been refereed, recognition will also be given to contributions to other journals, organizational publications, widely distributed reports which effect an education and technology information transfer; and at least two (2) of the following B through E. 

Important technical contributions and innovation as documented in formal reports of several projects over a minimum time of four (4) years prior to recommendation for promotion. 

Supervision of others' work by virtue of being a program manager, project director/principal investigator, co-project director/principal investigator, or task leader on sponsored research of such magnitude as to require guidance and supervision of other professionals. 

Substantial documented contributions in sponsored program development. 

Superior ability in representing the School/Center/Laboratory/Georgia Tech in service to and dealings with outside organizations. 

Principal Research Scientist 

In addition to the years-in-rank requirement, this rank requires either: 

A Master's degree and eleven (11) years' relevant full-time experience; or 

A Doctoral degree and seven (7) years' relevant full-time experience. 

At least the most recent three (3) years of relevant experience shall have been at a responsible technical or managerial level. Preference will be shown for qualified personnel holding a Doctoral degree in their specific discipline. 

In addition to the basic requirements above, the candidate for the rank of Principal Research Scientist must be outstanding in item A below and have demonstrated outstanding capabilities in at least two (2) of the research or service activities B through D: 

Clear evidence of consistent performance in the making of original and innovative contributions that are nationally recognized for their excellence as documented by external peer review. At least three (3) letters of evaluation must be obtained by the Institute from highly qualified persons in the candidate's professional field who are not employed by the Institute. 

Leadership in developing and managing a technical thrust involving related projects. Special consideration will be given to programs involving a broad participation by research and instructional Faculty and Students. 

Substantial contributions to Georgia Tech by service to the Institute, the State, the Nation, or to the candidate's profession. 

Broad recognition of technical stature as evidenced by invited papers or seminars, session chairperson at national symposia, memberships on national committees, offices in professional societies, or other appropriate honors. 

Joint Appointments in Instructional Units 

Instances may arise where it is appropriate for a Research Faculty member not in an Instructional Unit to receive a joint appointment to such a Unit. See Section 3.3.1 concerning Joint Appointments. 

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How to Become a Research Scientist

Industry Advice Science & Mathematics

Professionals with a background in biotechnology can choose to pursue many lucrative careers . One of the most common choices is to become a research scientist. These individuals work in drug and process development, consistently conducting research and performing experiments to help move the biotechnology industry forward. 

“At the highest level, a research scientist is somebody who can design and execute experiments to prove or disprove a hypothesis,” says Jared Auclair , director of the biotechnology and bioinformatics programs at Northeastern. “Within the world of biotechnology, that can mean a number of different things, from creating new drugs to improving the process of how we make a drug.”

Professionals in this industry are often drawn to the wide array of applications of this work, as well as the consistently positive career outlook. The average salary of a biotechnology research scientist is $85,907 per year, with plenty of opportunities for increased salary potential depending on specializations, location, and years of experience. 

These factors—alongside the growing demand for advancement in biotechnology over the last few decades—have led many aspiring biotechnologists to consider a career in research science. Below we offer five steps professionals can take to kick-start a career in this field.

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5 Steps to Become a Research Scientist

1. acquire the necessary technical skills..

According to Auclair, there are four main applications of research science within the biotechnology field:

• Molecular Biology
• Process Science
• Biochemistry
• Analytical Biotechnology

Professionals hoping to pursue a career in research science must begin by deciding which of these four areas is the best fit for their interests and backgrounds. They must then acquire the specific skill sets they need to excel in that area. 

Below, Auclair breaks down some of the key skills and knowledge required within each of these specializations:

• Molecular biologists should focus on developing a complex understanding of DNA and learn how to do a Polymerase Chain Reaction alongside other DNA-related experiments. 
• Process scientists must understand cell biology and how to work with living mammalian cells, as well as how to perform analytical experiments using mass spectrometry and other analytical tools.
• Biochemists should focus on obtaining the skills necessary to make a protein drug, including the expression and purification of proteins.
• Analytical biotechnicians must become comfortable with techniques like mass spectrometry—a process that uncovers what drug products are at a molecular level.

One efficient way aspiring research scientists can obtain these specific skill sets is to pursue a master’s degree in biotechnology at a top university like Northeastern. 

“The biotech program is designed in collaboration with industry so that we’re meeting their needs,” Auclair says. “This includes training students with the skills they need to be a successful research scientist.”

The curriculum of Northeastern’s program explores the core competencies required to excel in the general biotechnology field and provides students with the unique subsets of skills they need to specialize in a specific area of research science. Students can even declare one of 10 industry-aligned concentrations, including options that directly relate with these common research science roles.

“Especially in industry, most people who are doing research science—who are actually doing the experiments and helping think about experiments with some of the senior leaders in the company—are people with a master’s degree,” Auclair says.

2. Become a critical thinker.

Alongside honing technical skills, Auclair says that critical thinking abilities are key for aspiring research scientists. 

“It’s important to become a critical thinker and a problem solver, and to challenge yourself wherever you can to step outside of your comfort zone,” Auclair says. 

Though critical thinking is a common requirement among most professional career paths, it is especially important for research scientists, who are constantly tasked with innovating and thinking creatively to solve problems.

Northeastern’s master’s in biotechnology program is designed to help students grow in this regard. “Everything we do within the program is geared [toward] making you a critical thinker and a problem solver,” Auclair says. “We try to define classes and assessments to make you think, [and] we also hire most of the faculty in our program directly from the industry, so they bring with them real-world experience that they can talk about with the students.”

These real-world case studies are a core component of Northeastern’s approach to learning, and they help prepare students to think critically about their work. By bringing this exposure into the classroom, students also graduate better prepared to tackle current industry challenges and adapt to evolving trends .

3. Hone your “power skills.”

It’s no longer enough for research scientists in biotechnology to have obtained the technical skills needed to complete their work. Today, many employers require an array of industry-specific “power skills”—previously known as “soft skills”—among candidates for research science roles.

Below we explore the top three “power skills” for biotechnology research scientists:

• Communication: As a research scientist, “you must be able to communicate scientific information to both technical and non-technical people,” Auclair says. For this reason, professionals should work to hone their verbal and written communication styles, focusing specifically on the variances in each depending on which audience they’re interacting with.
• Presentation Ability: Research scientists must be able to present their findings clearly and concisely to a variety of different audiences, ranging from fellow scientists to investors to C-suite executives. Research scientists must be comfortable in front of a group and know how to speak about their experiments and conclusions in an engaging and informative way.
• Teamwork: Although one might think a research scientist’s work is very siloed, today’s professionals must be very comfortable working with others in a lab environment. They must become comfortable sharing ideas, providing feedback to others in their cohort, and tweaking their experiments based on contributed findings.

Northeastern offers students the chance to explore each of these core “power skills” during their time within the master’s in biotechnology program. For example, the university offers countless opportunities for students to collaborate with and present to classmates, instructors, and even industry-leading organizations through Northeastern’s experiential learning opportunities, giving them the chance to apply these skills in both classroom and real-world situations early on.

Learn More: How to Become a Biotechnologist: Build Your Soft Skills

4. Obtain hands-on experience.

One of the most effective ways an aspiring research scientist can prepare for a career in this field is to obtain experiences working in a real lab. While finding these kinds of opportunities can be difficult for those just breaking into the field, programs like Northeastern’s MS in biotechnology bake hands-on learning directly into the curriculum. 

“Students do essentially four to six months [working in the] industry, and put what they learn in the classroom…into practice,” Auclair says.

These opportunities, known as co-ops , provide students with the chance to work within top organizations in the industry and explore the real-world challenges of the field from inside a functioning lab.

Did You Know: Northeastern’s program provides students with exposure to the tools and equipment used within labs in the industry. This access to cutting-edge technology reduces the learning curve and allows students to dive into their work as soon as they graduate.

Another unique way Northeastern provides hands-on experience is through Experiential Network (XN) Projects . Students who participate in these projects are typically paired with a sponsor from an active biotech company that has a real-world problem they need to solve. Then, “under the guidance of a faculty member, students spend the semester trying to come up with solutions to that problem,” Auclair says. “It’s all student-driven.”

Hands-on learning opportunities like these give students a competitive advantage when it comes to applying for jobs. “The experiential learning piece [of our program] is what has our students actually stand out above others in the field,” Auclair says, because employers like to see that their candidates are capable of applying their skills in a real-world environment. 

5. Grow your network.

Research shows that 85 percent of all jobs today are filled through networking, making it more important than ever for professionals across industries to invest time and energy into building these vital relationships.

Professionals hoping to establish a career as a research scientist are no exception. These individuals should aim to develop connections with organizations and individuals within the greater biotech industry early on in their careers, and use those relationships to help carve their path forward.

Northeastern’s master’s in biotechnology program has strategically created many great opportunities for students to network throughout their time in the program. They are encouraged to build relationships with their classmates, guest speakers, faculty, and even the industry leaders they meet through co-ops and XN projects. As a result, they establish various impactful connections with individuals at different stages in their careers, all before they graduate.

Learn More: Networking Tips for Scientists

Another way Northeastern’s program supports networking is through opportunities for student/faculty collaboration. “We encourage our students to interact with our own faculty who are research scientists as much as possible, whether that’s volunteering in their lab or finding a half an hour to talk to them about what they’re doing,” Auclair says. “We want our students to be exposed to as many research scientists as possible while they’re in the program.”

Take the Next Step

Pursuing a master’s degree in biotechnology from a top university like Northeastern is a great way for aspiring research scientists to break into the field. Students in these programs can hone related skill sets, grow their professional networks, and experience hands-on learning, all while pursuing graduate-level education. 

Learn more about how a master’s in biotechnology can set you up for success as a research scientist on our program page , then get in touch with our enrollment coaches who can help you take the first step.

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Explanation of Staff Scientist Position and Titles to Academic Colleagues

FROM : Nina F. Schor, M.D., Ph.D., Deputy Director for Intramural Research, NIH SUBJECT : NIH Staff Scientists Titles of Associate Scientist and Senior Associate Scientist – Academic Equivalents

A Staff Scientist at the NIH is an NIH employee usually appointed to a non-tenure track, renewable position. Staff Scientists hold doctoral degrees and are selected by an Institute or Center (IC) to support the long-term research of a specific Senior Investigator or to run a specific core facility. Those who work for a specific Senior Investigator generally do not receive independent resources, but they work independently and have sophisticated skills and knowledge essential to the work of the laboratory; independently design experiments; and guide the day-to-day work of students and trainees in the laboratory. Those who run core facilities do manage the budget of that facility and often have up to 25% of their time to conduct or participate in research related to the core facility’s area of science. Staff Scientists are analogous to research track faculty in most U.S. medical school departments.

The Intramural Research Program has established the titles of Associate Scientist and Senior Associate Scientist to signify and acknowledge Staff Scientists who have achieved successively higher levels of expertise, skill, and independence. As the specific roles and duties of Staff Scientists vary from IC to IC (see the  Catalogue of the Wide Variety of Functions for Staff Scientists and the Levels of Proficiency ), the conferral of these titles is decided at the IC level. However, the process and criteria for such conferral have been determined and specified at the NIH-wide level (see the  Fair Review Principles for Nominations of Staff Scientists for Associate Scientist and Senior Associate Scientist Titles ). The designations of Associate Scientist and Senior Associate Scientist are the equivalent of Research Associate Professor and Research Professor, respectively, in most U.S. medical school departments.

First Established: Sunday, June 25, 2023

This page was last updated on Tuesday, June 27, 2023

1.1.2.2 Research scientist vs. research engineer

There’s much confusion about the role of a research engineer. This is a rare role, often seen at major research labs in the industry. Loosely speaking, if the role of a research scientist is to come up with original ideas, the role of a research engineer is to use their engineering skills to set up and run experiments for these ideas. The research scientist role typically requires a Ph.D. and/or first author papers at top-tier conferences. The research engineer role doesn’t, though publishing papers always helps.

For some teams, there’s no difference between a research scientist and a research engineer. Research scientists should, first and foremost, be engineers. Both research scientists and engineers come up with ideas and implement those ideas. A researcher might also act as an advisor guiding research engineers in their own research. It’s not uncommon to see research scientists and research engineers be equal contributors to papers 7 . The different job titles are mainly a product of bureaucracy -- research scientists are supposed to have bigger academic clout and are often better paid than research engineers.

Startups, to attract talents, might be more generous with the job titles. A candidate told me he chose a startup over a FAAAM company because the startup gave him the title of a research scientist, while that big company gave him the title of a research engineer.

Akihiro Matsukawa gave an interesting perspective on the difference between the research scientist and the research engineer with his post: Research Engineering FAQs .

7 : Notable examples include “ Attention Is All You Need ” from Google and “ Language Models are Unsupervised Multitask Learners ” from OpenAI.

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Scientist or a Researcher, which one are you?

Is there a difference between scientists and researchers? Is one an occupation and the other a mission or lifestyle? Does it really matter in the end?

Everyone starts to explore and research at a very early age. If not in the early childhood, then most certainly during high school projects.

Do these early activities define us as researchers, or are we simply learning valuable skills that we will need in life and in the future career? Which might just happen to be a research career.

In my opinion there are many more researchers than scientists in the world, but both work in science.

What’s the difference? Let’s take basic science for example. We all agree that it exists, but there is no such thing as basic research, so there is a difference.

What is a scientist?

Scientists live, breathe and dream science. They never really stop. To a certain extent science to them is very personal. It is a way of life. The creative chaos. Creativity before order.

They find challenges and thrill in the traditional scientific approach that everyone learns about in science or biology classes at school (hypothesis, facts, experimentation and final confirmation or disapproval of a hypothesis). They combine it with a lot of questions, thoughts, mistakes and dead ends with the aim to generate data. Data that they believe will eventually lead to asking the final question that yields the final answer. Answer they have been looking for or never even expected to happen.

All this combined forms my idea of science and the scientists. Perhaps this description is a bit too romantic and old-fashioned, but it’s the way I see it.

What is a researcher?

Research is more focused and structured . For example, a PhD student who gets a position in an established research group will follow guidelines, methods and a path that has already been laid out or at the very least outlined. This PhD student is a researcher. Research for me means less freedom and creativity (although these are of course still required) and more focus and much clearer goals, e.g. the number of publications in the next couple of years. Not that I underestimate research, but I still see a difference.

Even so, being the head of R&D in a company, I view myself as a researcher.

While research is more likely to be defined as an occupation, with clearly defined focus and goals, the people doing it still have to be scientists in their core. And perhaps the best description of a scientist is a person who still maintains the ability to connect facts, in the way only kids can, while playing with sophisticated instruments.

Scientist or a Researcher, it is good to save your work in an electronic lab notebook.

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Research Engineer vs. Research Scientist: What Are the Differences?

Learn about the two careers and review some of the similarities and differences between them.

A career in research can be both exciting and rewarding. If you’re interested in this field, you may be wondering if you should become a research engineer or research scientist. Both of these positions conduct research and develop new products or processes, but there are some key differences between them. In this article, we compare the job titles research engineer and research scientist, and we provide information on what you can expect from each role.

What is a Research Engineer?

Research Engineers are responsible for designing and conducting scientific experiments to test the feasibility of new engineering designs. They work in a variety of industries, such as the automotive, aerospace and medical fields. Research Engineers often specialize in a particular area, such as materials science or electrical engineering. They use their knowledge of physics, mathematics and chemistry to develop new products or improve existing ones. They also work on teams with other engineers and scientists to find solutions to complex problems. Research Engineers typically have a bachelor’s degree in engineering.

What is a Research Scientist?

Research Scientists conduct experiments and analyze data to increase scientific knowledge. They develop new methods to collect data and improve upon existing techniques. Research Scientists typically specialize in a particular field, such as biology, chemistry, physics, geology or engineering. They use their expertise to develop new products or solve problems for their company or client. Research Scientists typically work in laboratories or offices, and they may work with other Scientists or technicians. They typically present their findings in reports or scientific papers.

Research Engineer vs. Research Scientist

Here are the main differences between a research engineer and a research scientist.

Research engineers typically perform more complex tasks than research scientists. They often supervise teams of researchers and study participants, manage research projects and develop experimental designs and procedures. Research engineers also conduct research themselves and use the data they collect to evaluate and improve existing engineering processes.

Research scientists usually have a narrower focus in their work than research engineers. They commonly perform three main duties: conducting scientific experiments, analyzing research data and writing scientific papers. These activities help research scientists contribute to their field of study and advance scientific knowledge.

Job Requirements

Research engineers and research scientists often need a bachelor’s degree in engineering or science to enter the field. However, many employers prefer candidates with a master’s degree or higher. Additionally, research engineers and research scientists might need specific licenses or certifications to work with certain materials or in certain environments. For example, someone who wants to work with hazardous materials might need to obtain a license from the Occupational Safety and Health Administration (OSHA).

Work Environment

Research engineers work in a variety of environments, depending on the type of engineering they specialize in. For example, mechanical engineers often work in manufacturing facilities and construction sites to ensure that machines are safe for use. Civil engineers may work outdoors or in office buildings to design roads and bridges. Electrical engineers typically work in laboratories where they test electrical systems.

Research scientists usually work in an office environment, but some may travel to visit research locations. They also spend time in laboratories conducting experiments and analyzing data.

Both research engineers and research scientists need to have excellent problem-solving skills. This is because a large part of their job is finding solutions to problems that people or businesses are facing. They also both need to be able to effectively communicate their findings to other people, whether it is in writing through reports or papers or verbally through presentations.

Both research engineers and research scientists need to have strong analytical skills. This enables them to take data they have collected and use it to identify trends and make predictions. They also need to be able to think critically about the information they are working with and come up with creative solutions to problems.

Research engineers typically need to have more technical skills than research scientists. This is because they often work with designing and developing products or systems that address the needs that their research has identified. They may also need to have programming skills to create prototypes or models of their designs. Research scientists usually do not need to have as many technical skills, but they may benefit from having computer skills to help them analyze data.

Research engineers earn an average salary of $89,682 per year, while research scientists earn an average salary of $93,368 per year. Both of these salaries may vary depending on the type of research you’re doing, the size of the company you work for and the level of experience you have.

Cytotechnologist vs. Pathologist: What Are the Differences?

Communications manager vs. marketing manager: what are the differences, you may also be interested in..., principal vs. senior analyst: what are the differences, general manager vs. hr manager: what are the differences, care coordinator vs. case manager: what are the differences, chief revenue officer vs. ceo: what are the differences.

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• CAREER FEATURE
• 01 April 2024

How scientists are making the most of Reddit

• Hannah Docter-Loeb 0

Hannah Docter-Loeb is a freelance writer in Washington DC.

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Reddit’s many ‘subreddit’ communities offer channels for discussing science and are of interest to social-media scholars. Credit: Amy Lombard/New York Times/Redux/eyevine

It has been almost 18 months since Elon Musk purchased Twitter, now known as X. Since the tech mogul took ownership, in October 2022, the number of daily active users of the platform’s mobile app has fallen by around 15%, and in April 2023 the company cut its workforce by 80%. Thousands of scientists are reducing the time they spend on the platform ( Nature 613 , 19–21; 2023 ). Some have gravitated towards newer social-media alternatives, such as Mastodon and Bluesky. But others are finding a home on a system that pre-dates Twitter: Reddit.

The site was founded in 2005, originally as one all-encompassing forum where users (known as redditors) could post content such as links, texts, images and videos. Anonymous user upvote (or downvote) and comment on each other’s content, deciding on what performs well enough to reach others’ feeds.

Social media for scientists

Today, Reddit is divided into communities, called subreddits, each with volunteer moderators who review content. These subreddits have names that begin with ‘r/’ and are devoted to all sorts of subjects, such as literature, solo travel and Washington DC. Reddit is regularly irreverent: r/trees is for people to share content about marijuana, whereas r/marijuanaenthusiasts is the place to look at trees. It is sometimes dangerous — some communities have amplified conspiracy theories. And there are subreddits devoted to science, ranging from the broad r/science to more specific ones, such as r/bacteriophages.

As of December 2023, according to Reddit’s own statistics, the site had 73 million daily active users, more than 100,000 active communities and had amassed over 16 billion posts and comments. In February 2024, it was the eighth most visited website in the world, ahead of both Amazon and TikTok (see go.nature.com/3tugxbq ). And on 20 March, the company floated on the New York Stock Exchange, where it was initially valued at US$6.4 billion. With most researchers now needing to pay to download useful amounts of data on X, Reddit is another option to survey the Internet hivemind. Although changes made last year threaten researchers’ ability to pull data as easily as they once did, Reddit says access to its data continues to be free for non-commercial researchers and academics.

“As the social-media landscape started changing, we really started thinking about the other spaces besides Twitter that people are using,” says Nicholas Proferes, a social media researcher at Arizona State University’s School of Social and Behavioral Sciences in Phoenix, who co-authored reviews on the use of Reddit for research 1 , 2 . Here, Nature reports on how Reddit is providing scientists with continued avenues for connecting with other researchers, gathering data and engaging with the public.

Networking and collaboration

Yvette Cendes’s journey on Reddit began in 2014. Cendes, who is currently a postdoctoral scholar at the Harvard–Smithsonian Center for Astrophysics in Cambridge, Massachusetts, found herself with some downtime during her PhD studies in astronomy, and started poking around on the platform. She came across a thread in which users were panicking over how imminent γ-ray bursts from supernovae were going to wreak havoc and kill people — something that she knew to be untrue. She resolved to jump into the comments and clear things up, and this was the start of her science-communication career.

Since then, Cendes has made a name for herself on Reddit and even created her own subreddit, with nearly 17,000 members. “It’s a very good way to get good knowledge out there,” she says.

Scientists also use Reddit to get tips and tricks from other scientists. The r/biotech subreddit features news about biotechnology innovations and career advice; r/datascience is a community specifically for data-science professionals. There’s even a subreddit devoted to electron microscopy, from which users can seek guidance on the technology.

Yvette Cendes discusses astronomy as a science and a career on Reddit. Credit: Floris Looijesteijn

Not everyone is as forthcoming with their names and credentials on Reddit, which can make networking a bit more challenging than on other sites, says Cendes. But the pseudoanonymity can also be beneficial. Groups such as r/labrats offer safe spaces for scientists to discuss their research or dilemmas with others of similar backgrounds (and these groups are sometimes used by science journalists looking for article ideas). The anonymity provides some protection for people to post without fear of retaliation, and to seek counsel. In one discussion, for instance, a user laments how their principal investigator published a paper based on their research without giving credit, and considers hiring legal support.

Reddit can also be a great jumping-off point for early-career scientists or those trying to pivot between specialties. Kevin Ortiz Ceballos, a graduate student at Harvard University’s Department of Astronomy, happened upon one of Cendes’ posts about how to become an astronomer back when he was in secondary school. He credits it with helping him to switch from literature to physics and eventually astrophysics. Engaging in conversations about professional astronomy before entering the field himself was a huge asset.

“The fact that Yvette made it so accessible gave me the tools I needed to take the necessary steps to study and prepare what I needed to get into astronomy grad school,” he says. The two have since connected in person, and even collaborated on a project that was recently submitted for publication.

With all of its subspaces, Reddit can be overwhelming at first. Cendes encourages potential users to take it slowly, find the communities they are most interested in and go from there — putting keywords in the search function and perusing the different subreddits that come up.

Research and analysis

The information embedded in posts and comments from Reddit’s millions of users can also be a treasure trove for researchers studying online behaviours. In 2022, NASA collaborated with master’s students at the University of British Columbia in Vancouver, Canada, to use Reddit data to locate landslides (see go.nature.com/3tlum6t ). The team scraped the site for mentions of ‘landslide’, before analysing and validating relevant mentions to add to the NASA landslides database. According to the team, this verification was needed because a Reddit post about the song ‘Landslide’ by the rock band Fleetwood Mac might “give us insight about the changes and challenges of life, but it doesn’t do much for global disaster detection”.

TikTok for physics: influencers aim to spark interest in science

A 2021 review 2 in Social Media + Society , co-authored by Proferes, chronicled 727 manuscripts published between 2010 and 2020, that made use of Reddit data. These studies spanned all sorts of disciplines — from computer science to medicine to social science.

One reason that Reddit is ripe for research is that there are few bureaucratic hurdles to clear compared with what’s required for other studies involving human beings. “It is a publicly accessible web forum in the US and so is not considered to be human-subjects research,” says Proferes. Institutional review boards view Reddit research as “exempt from ethical review”, he says.

However, Proferes and his co-authors emphasize the need for intentionality and sensitivity when collecting data from the site. Consider a subreddit such as r/opiates. Data on substance use are often difficult to procure from in-person interviews or other social science methods, but because of Reddit’s anonymity, people are more open to sharing such information on the platform. However, using the subreddit for research could be seen as invasive by a community that considers itself a semi-private anonymous support network. Certain communities on Reddit are also wary of scientific researchers.

The 2024 review co-authored by Proferes 1 lists some of these considerations and suggests steps such as obfuscating usernames in published work and collaborating with moderators.

“Academia and data populations have a very sore history of, frankly, academics coming in and just taking,” says Proferes. The online community “is not getting any benefit whatsoever. It is very exploitative. There’s some real historical reasons, too, why folks may be highly suspicious or dubious about researchers coming in, even in these digital spaces.”

Research findings derived from Reddit posts should be shared with users, says Sarah Gilbert. Credit: Steven Shea

“It’s really easy when you’re working with these large data sets to just think of the data points in them as literal data,” says Sarah Gilbert, research director of the Citizens and Technology Lab at Cornell University in Ithaca, New York, and a co-author of the review. “Spending time in the community and learning the norms and actually reading it, it turns that data into people. It gives a better sense of who is going to be included, more like human-subject research.”

Gilbert also recommends sharing whatever published research comes out of trawling through Reddit data with those who provided the information. “Hopefully what you learnt is beneficial to the community so they can see data is used for something,” she says.

Connecting with non-scientists

Reddit can be a way for scientists to use their expertise to answer any questions the general public might have, says Cendes. She is a regular on r/space, educating users about topics such as the James Webb Space Telescope.

Kelly Zimmerman, a PhD candidate in ecology at Montclair State University in New Jersey, has connected with and educated other users on Reddit. When she started on the platform about 12 years ago, she mostly used it to find journal articles of interest on r/ecology and r/biology. But, like Cendes, she noticed how curious users were about scientific topics that were in her area of expertise, and she now often engages in discussions on subreddits such as r/whatisthisbug.

Thousands of scientists are cutting back on Twitter, seeding angst and uncertainty

Although she previously used X, Zimmerman thinks that Reddit provides a more engaging experience. “I felt like I was just talking into a void — there wasn’t a lot of response on Twitter,” she says.

One way for scientists to try their hand at science communication on Reddit is through ‘ask me anything’ (AMA) sessions, in which researchers answer users’ questions in their own time. Moderators pull in verified researchers to provide responses — even renowned theoretical physicist Stephen Hawking participated. (To schedule an AMA with r/askscience, you can e-mail the moderators.)

With both AMAs and general discussion forums, there is an art to making sure that information is communicated effectively and succinctly. “We’re trying to keep it as scientific as possible, but in layman’s terms, so that non-scientists can understand cutting-edge science that’s coming out right now,” says Zimmerman, who also moderates some science subreddits.

Nathan Allen, a synthetic chemist based in Milwaukee, Wisconsin, and a former moderator at r/science, likens it to writing a persuasive e-mail. “On Reddit, you have got to convince the general public that this has some general interest to them, and you’ve got to develop it and build the message and make sure people stay on point,” he says. “You get a lot of practice writing concise explanations of complicated things that people who aren’t necessarily scientists are able to digest and understand.”

When using Reddit in any capacity, Zimmerman encourages scientists to make sure to read the rules before making a post or comment, and to mind their manners, just as they would on any other social-media platform. “Be polite,” she says. “Just because you’re an anonymous username doesn’t mean you should be rude to other people.”

Jennifer Cole, a biologist and anthropologist at Royal Holloway University of London, notes that using Reddit for scientific communication is not without its problems. Moderators do a lot of work behind the scenes and often face a torrent of abuse for trying to maintain standards, says Cole. And although using people’s real names can help with credibility, it can also make academics and experts targets for harassment and abuse. Although the site does not provide support for users who experience abuse, a spokesperson for Reddit noted that the platform has policies to prohibit both harassment and the sharing of personal or confidential information, and that these policies are enforced by the internal safety teams.

It can also be used to spread falsehoods. R/conspiracy has repeatedly posted misinformation about COVID-19 and vaccines. Climate deniers are also present on the platform, although a decade ago the science forum specifically banned climate change deniers. Asked about misinformation, the Reddit spokesperson said that because Reddit is governed by upvotes and downvotes, quality and accurate information tend to rise to the top.

Interviewees agree that Reddit is at its core a social media platform, and social media has the potential to be toxic. But when scientists engage, there’s also a lot of great scientific communication and debunking of misinformation. “Don’t be afraid to talk to the people,” Zimmerman says. Those “who are not scientists are just as curious as we are. There’s nothing special about being a scientist. We are like everybody else, and sometimes folks forget that.”

Nature 628 , 221-223 (2024)

doi: https://doi.org/10.1038/d41586-024-00906-y

Fiesler, C., Zimmer, M., Proferes, N., Gilbert, S. & Jones, N. Proc. ACM Hum. Comp. Interact. 8 , 5 (2024).

Article   Google Scholar  

Proferes, N., Jones, N., Gilbert, S., Fiesler, C. & Zimmer, M. Soc. Media Soc . https://doi.org/10.1177/20563051211019004 (2021).

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Watch CBS News

Why is looking at a solar eclipse dangerous without special glasses? Eye doctors explain.

By Sara Moniuszko

Edited By Allison Elyse Gualtieri

Updated on: April 8, 2024 / 8:54 AM EDT / CBS News

The solar eclipse will be visible for millions of Americans on April 8, 2024, making many excited to see it — but how you watch it matters, since it can be dangerous for your eyes. 

A  solar eclipse occurs when the moon passes between the sun and Earth, blocking the sun's light . When the moon blocks some of the sun, it's a partial solar eclipse, but when moon lines up with the sun, blocking all of its light, a total solar eclipse occurs,  NASA explains . Either way, you need eye protection when viewing.

"The solar eclipse will be beautiful, so I hope that everyone experiences it — but they need to experience it in the right way," said Dr. Jason P. Brinton, an ophthalmologist and medical director at Brinton Vision in St. Louis.

Here's what to know to stay safe.

Why is looking at a solar eclipse dangerous?

Looking at the sun — even when it's partially covered like during an eclipse — can cause eye damage.

There is no safe dose of solar ultraviolet rays or infrared radiation, said  Dr. Yehia Hashad , an ophthalmologist, retinal specialist and the chief medical officer at eye health company Bausch + Lomb.

"A very small dose could cause harm to some people," he said. "That's why we say the partial eclipse could also be damaging. And that's why we protect our eyes with the partial as well as with the full sun."

Some say that during a total eclipse, it's safe to view the brief period time when the moon completely blocks the sun without eye protection. But experts warn against it. 

"Totality of the eclipse lasts only about 1 to 3 minutes based on geographic location, and bright sunlight suddenly can appear as the moon continues to move," notes an eclipse viewing guide published in JAMA , adding, "even a few seconds of viewing the sun during an eclipse" can temporarily or permanently damage your vision. 

Do I need special glasses for eclipse viewing?

Yes.  Eclipse glasses are needed to protect your eyes if you want to look at the eclipse.

Regular sunglasses aren't protective enough for eclipse viewing — even if you stack more than one. 

"There's no amount of sunglasses that people can put on that will make up for the filtering that the ISO standard filters and the eclipse glasses provide," Brinton said.

You also shouldn't look at the eclipse through a camera lens, phone, binoculars or telescope, according to NASA, even while wearing eclipse glasses. The solar rays can burn through the lens and cause serious eye injury.

Eclipse glasses must comply with the  ISO 12312-2 international safety standard , according to NASA, and should have an "ISO" label printed on them to show they comply. The American Astronomical Society  has a list  of approved solar viewers.

Can't find these, or they're sold out near you? You can also  make homemade viewers ,   which allow you to observe the eclipse indirectly — just don't accidentally look at the sun while using one.

How to keep kids safe during the solar eclipse

Since this eclipse is expected to occur around the time of dismissal for many schools across the country, it may be tempting for students to view it without the proper safety precautions while getting to and from their buses. That's why some school districts are  canceling classes early so kids can enjoy the event safely with their families.

Dr. Avnish Deobhakta, vitreoretinal surgeon at New York Eye and Ear Infirmary at Mount Sinai, said parents should also be careful because it can be difficult for children to listen or keep solar eclipse glasses on. 

"You want to actually, in my opinion, kind of avoid them even looking at the eclipse, if possible," he said. "Never look directly at the sun, always wear the right eclipse sunglasses if you are going to look at the sun and make sure that those are coming from a reliable source."

Brinton recommends everyone starts their eclipse "viewing" early, by looking at professional photos and videos of an eclipse online or visiting a local planetarium. 

That way, you "have an idea of what to expect," he said. 

He also recommends the foundation  Prevent Blindness , which has resources for families about eclipse safety.

What happens if you look at a solar eclipse without eclipse glasses?

While your eyes likely won't hurt in the moment if you look at the eclipse without protection, due to lowered brightness and where damage occurs in the eye, beware: The rays can still cause damage .

The harm may not be apparent immediately. Sometimes trouble starts to appear one to a few days following the event. It could affect just one or both eyes.

And while some will regain normal visual function, sometimes the damage is permanent. 

"Often there will be some recovery of the vision in the first few months after it, but sometimes there is no recovery and sometimes there's a degree to which it is permanent," Brinton said. 

How long do you have to look at the eclipse to damage your eyes?

Any amount of time looking at the eclipse without protection is too long, experts say. 

"If someone briefly looks at the eclipse, if it's extremely brief, in some cases there won't be damage. But damage can happen even within a fraction of a second in some cases," Brinton said. He said he's had patients who have suffered from solar retinopathy, the official name for the condition.

Deobhakta treated a patient who watched the 2017 solar eclipse for 20 seconds without proper eye protection. She now has permanent damage in the shape of a crescent that interferes with her vision. 

"The crescent that is burned into the retina, the patient sees as black in her visual field," he said. "The visual deficit that she has will never go away."

How to know if you've damaged your eyes from looking at the eclipse

Signs and symptoms of eye damage following an eclipse viewing include headaches, blurred vision, dark spots, changes to how you see color, lines and shapes. 

Unfortunately, there isn't a treatment for solar retinopathy.

"Seeing an eye care professional to solidify the diagnosis and for education I think is reasonable," Brinton said, but added, "right now there is nothing that we do for this. Just wait and give it time and the body does tend to heal up a measure of it."

Sara Moniuszko is a health and lifestyle reporter at CBSNews.com. Previously, she wrote for USA Today, where she was selected to help launch the newspaper's wellness vertical. She now covers breaking and trending news for CBS News' HealthWatch.

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Original research article, how has the rural digital economy influenced agricultural carbon emissions agricultural green technology change as a mediated variable.

• 1 Guiyang Institute of Humanities and Technology, Guiyang, China
• 2 Binary University of Management and Entrepreneurship, Selangor, Malaysia
• 3 Business School, Nanjing Normal University, Nanjing, China
• 4 College of Economics and Management, Nanjing University of Aeronautics and Astronautics, Nanjing, China
• 5 School of Politics and Economic Administration, Guizhou Minzu University, Guiyang, China

Digital economy is being closely integrated with agricultural development and tapping into its unique potential to alleviate agriculture’s carbon emissions To explore the mechanism of how digital economy reduce the agricultural carbon emissions, this paper constructs a systematic evaluation method with extend STIRPAT model and panel data drawn from 29 provinces (or municipalities and autonomous regions) in the Chinese mainland from 2013–2020. The results show that the development of the rural digital economy has a significant negative influence on agricultural CEs, and this result is still valid given robustness tests. Second, the alleviation of CEs based on the rural digital economy is more significant in the higher technological investment zones than that in the lower technological investment zones, and the central and eastern regions also have more significant CEs reduction effect. Third, the influence mechanism analysis shows that agricultural green technology change is an effective means to promote the rural digital economy’s CEs reduction effect. This paper not only provide new empirical evidence for understanding nexus between digital economy and agricultural carbon reduction, but also give constructive policy implication to improve agricultural green development.

1 Introduction

Alleviating carbon emission is receiving more and more attention globally ( Ma S. et al., 2022 ). To maintain harmonious coexistence between humans and natures and realize the United Nations’ Sustainable Development Goals, Chinese central government pledged the global stakeholders that the Chinese people will try their best to have CEs peak before 2030 and achieve carbon neutrality before 2060, which demonstrates a strong determination to solve the problem of climate change. Activities of agricultural sector not only release CO 2 but also hold carbon sequestration function, and the CEs and sequestration function make agricultural production activities have function of maintaining the carbon balance in the atmospheric. However, agricultural CEs have obvious spatial heterogeneity ( Charkovska et al., 2019 ). Faced with issues such as global economic instability, rising energy demand, frequent adverse weather conditions, and expanding food demand ( Fahad et al., 2022 ), the Chinese government should attach importance to cutting agricultural CEs. China is a large and longstanding agricultural country with widespread and extensive agricultural production activities. In the traditional agricultural production mode, the overuse of pesticides and chemical fertilizers, land ploughing and irrigation, as well as the problems of low production efficiency and unreasonable resource allocation in the agricultural production process, will directly or indirectly lead to more agricultural CEs and their higher intensity, thereby seriously restricting the development of low-carbon and high-quality agriculture. The 14th Five-Year Plan for National Agricultural Green Development emphasizes building an agricultural industry system with characteristic of green, low-carbon, and circular, while the 2023 Government Work Report further emphasizes the need to continuously improve the ecological environment and achieve low-carbon and sustainable development.

The digital economy plays an important role in promoting the full and balanced development between urban and rural areas, and its development has driven the economic development of agricultural and rural areas ( Zhao et al., 2023 ). In China, the digital transformation of agriculture sector has shown initial results. According to the Information Center of the Ministry of Agriculture and Rural Affairs, the informatization level of national agricultural production in 2020 was 22.5% and the national level of agricultural product quality and safety traceability informatization was 22.1%. In 2021, the online retail sales agricultural production nationwide has reached 2.05 trillion Yuan, with growth rate of 11.3% compared to the level of the previous year. The construction of digital rural areas has been promoted extensively, with 117 digital rural pilot projects established nationwide, nine agricultural IoT demonstration provinces delineated, and 100 digital agriculture pilot projects established. Alongside these tremendous achievements, the digital economy has a positive impact on carbon emissions from agricultural production ( Zhao et al., 2023 ). Thus, the problem is how to realize the coordinated relationship between them. Would the rural digital economy development bring fresh momentum to reducing agricultural CEs? Meanwhile, how can the rural digital economy empower the reduction of agricultural CEs? Exploring these issues has important practical value for the development of the rural digital economy and improving the reduction of agricultural CEs while also contributing to policy enlightenment in terms of achieving the great mission of China’s “Carbon Peak and Carbon Neutrality”.

The main contribution of this paper comparing to the existing literature are as following. First, we use the extend STIRPAT model to explore the influence mechanism of agricultural digital economy on the agricultural carbon emission. Second, the agricultural green production efficiency is used as a proxy for agricultural green technology change, which not only considering the quantity of the agricultural green development, but also capture the quality of agricultural green development. Third, this paper use three dimensions to measure the agricultural digital economy. Digital infrastructure in rural areas, digitalization of agriculture, and rural digital finance).

The rest of this paper is organized as follows. Section 2 is the literature review. The theory base and research hypnosis are showed in Section 3 . Section 4 describes the models and data used in this paper. Section 5 analysis the estimation results. Section 6 gave the conclusion and presents the policy implications.

2 Literature review

So far, the relevant studies relating to CEs focus on the challenges faced by China in realizing its CEs reduction strategy and corresponding countermeasures. Hu (2021) , OuYang (2021) and others have analyzed the severe challenges faced by China in realizing the goals of dual carbon strategy in terms of international and domestic perspectives, respectively. Liu et al. (2021) and others have analyzed the problems that exist in China in the context of carbon neutrality from on the viewpoint of energy structure, and have put forward countermeasures such as energy conservation and efficiency improvement, while accelerating the transformation and further promotion of energy structures. Adopting another approach, some scholars have conducted empirical analysis on the CEs reduction effect of the carbon trading pilot policy implemented by the Chinese government through the synthetic control method ( Li et al., 2021 ; Yang et al., 2021 ), and have argued that China’s carbon trading pilot policy has played a significant role in the reduction of CEs, but there are problems such as insufficient market driving force for low-carbon innovation, poor pilot policy incentives, and regional heterogeneity. At the same time, Chen et al. (2016) have emphasized that increasing CEs reduces green total factor productivity (GTFP) based on studying the relationship between CEs and GTFP and economic development, and Wang et al. (2019) have also reached the same conclusion in relation to GTFP in agriculture economy development.

In addition, many researchers have devoted attention to agricultural CEs and carried out relevant research on the characteristics and calculation of agricultural CEs, agricultural CEs reduction policies, and influencing factors. Jin and other authors (2021) have explored the structural characteristics of China’s agricultural CEs, and drawn the conclusion that agricultural CEs in China have a phased upward trend alongside regional and provincial heterogeneity. In terms of policy research, Zhang et al. (2001) compared different environmental and economic instruments and argued that the environmental tax system has been more advantageous; Zheng et al. (2011) elaborated on a number of low-carbon special plans and proposed relevant recommendations, such as the establishment of a Chinese low-carbon agricultural model. Based on evolutionary game theory, Fan et al. (2011) suggested that government support and intervention can guide agricultural source farmers to choose CEs reduction strategies. In terms of influencing factors, the empirical studies of Xu et al. (2022a) and Xu et al. (2022b) have suggested that agricultural mechanization and the rural finance service have significant preventative effects on agricultural CEs. Furthermore, He et al. (2020) have discussed the status and role of green production efficiency in agriculture in various provinces.

The digital economy, a new engine of high-quality economic growth, has also attracted extensive attention and discussion in the academic community in recent years. On the one hand, there is research on the definition of the digital economy. Li et al. (2021a) characterize the digital economy on macro, meso- and micro-levels, asserting it includes four levels, namely, broad, middle, narrow and narrowest, and explored the mechanism and evolution process involved in data becoming a production factor ( Li et al., 2021b ). On the other hand, researches about digital economy are mainly about the comprehensive effect of digital economy, and they have put forward the argument that the digital economy can reduce environmental pollution ( Deng, 2022 ), while driving high-quality urban development and promoting a specific economic pattern, which aim to coordinate development between regions ( Zhao et al., 2020 ).

Especially since the strategy “Carbon Peak and carbon neutrality” was put forward, the relationship between the digital economy and carbon emission has become an important topic, and academia has also carried out extensive research ( Yu et al., 2022 ). While researchers hold different conclusion on the nexus between digital economy and carbon emissions. Most studies show that the digital economy has improved the environmental situation, and provided impetus for emission reduction, Wang (2022) point out the digital economy is helpful to reducing the carbon emissions. Zhang (2022a) find that the digital economy plays a significant role in carbon emission reduction. They all conduct their research based on China’s urban data. However, some studies hold that the digital economy has a heterogeneous influence on CEs. Some scholars ( Salahuddin et al., 2015 ; Avom et al., 2020 ) believe that, as the core foundation of the digital economy, the development of digital technology will lead to a large amount of power consumption and energy consumption, thereby increasing carbon emissions.

Furthermore, there are many researches focusing on the development of the digital economy in rural areas. According to theoretical analysis, the existing literature mainly pays attention to the mechanisms or development paths of the rural digital economy. Wang et al. (2021) , Yin and others (2020) and others have explored the significance, practice mode and mechanism of the digital economy development in agriculture production and rural regions, and believe that it should be promoted by, respectively, accelerating the construction of rural digital infrastructure, promoting agricultural digitalization, and developing rural e-commerce. Some researches on digital inclusive finance (DIF) have argued that DIF can push the regional convergence of green economic growth while less developed regions experience a more significant convergence effect ( Wang et al., 2022 ).

Many studies have also been carried out focusing on the influence of digital economy on CEs, mainly adopting the empirical analysis method with panel data based on province- or city-level contexts in China, and have found that digital economy growth can significantly alleviate the intensity of CEs ( Xu et al., 2022 ; Guo et al., 2023 ), however, there exist certain regional differences ( Miao et al., 2022 ; Xie, 2022 ).

A few researches have focused on the correlation between digital economy growth and agricultural CEs in China or foreign countries, and these literature mainly concentrate on the introduction of information and communications technology (ICT) into the field of smart agriculture, the promotion of sustainable agriculture, and the reduction of chemical use on the basis of embedding artificial intelligence ( Patrício and Rieder, 2018 ), sensors ( Basnet and Bang, 2018 ), robotics, and remote sensing technologies ( Huang et al., 2018 ) into agricultural modernization processes. ICT, as a main focus of advanced technology trends, can promote comprehensive productivity efficiency, total factor efficiency (TFP) and agricultural sustainability ( Dlodlo and Kalezhi, 2015 ). The prevalence of ICT not only promotes agricultural productivity and TFP, but also improves the progress of sustainable agricultural development. Ma S. Z. et al. (2022) focus on the nexus between the development of the agricultural digital economy and agricultural CEs; their conclusions emphasize that digital economy development inhibits agricultural CEs. In addition, advances in agricultural technologies, the optimization of agricultural industrial structure, and improvements in rural education all significantly inhibit the agricultural CEs in the research area. Adding to the influence factors outlined above, Zhang J. et al. (2022) emphasize that the development of DIF has significantly reduced agricultural CEs. Unlike other countries or regions, China’s agricultural digital economy mostly stresses the digital transformation of rural industrial models ( Wu, 2021 ), agricultural industries ( Zhao MJ. et al., 2022 ; Zhao YL. et al., 2022 ) and the effectiveness of the digital economy ( Xie, 2020 ). These studies all pay attention to the innovative developments in digital agriculture ( Wang et al., 2020 ). Through the systematic review of the literature outlined above, three main shortcomings can be found in the existing research: First, although many researchers have devoted attention to the correlation between the digital economy and CEs, more of them have studied this on urban level, and rarely extended this correlation to the rural development context, hence there is a lack of research that directly and empirically tests the correlations between the rural digital economy and agricultural CEs. Second, when analyzing heterogeneity, most existing studies only conduct sub-sample studies by region, and consider to a lesser extent the role of R&D in leading the high-quality development of the digital economy. Third, the path or mechanisms of the digital economy in rural areas in relation to the reduction of agricultural CEs is unclear, hence this requires further research. Considering the three points mentioned above, this article measures the intensity and amount of agricultural CEs, the progress in agricultural green technology and the development level of rural digital economy at a provincial level in China and tests empirically the nexus between rural digital economy and agricultural CEs. Meanwhile, this study not only examines the regional heterogeneity of the rural digital economy on agricultural CEs, it also analyzes the heterogeneity of this in relation to the science and technology investment level.

3 The mechanism and research hypotheses

The digital economy is an advanced economic mode with data as the important production factor and its development depends on the ability to obtain data. The establishment of a digital infrastructure not only realizes the utilization and transmission of data information, but also improves the efficiency of data circulation, thereby accelerating the process of digital infrastructure construction, the latter having become an indispensable foundation for the promotion of the growth of the digital economy. China has ascribed importance to the construction of digital infrastructure, and since 2018, the Politburo of the Central Committee has repeatedly stressed the need to accelerate the roll out and promotion of new digital infrastructure and its construction. At the same time, the construction of digital infrastructure is an important prerequisite for the integration of the digital and rural economies; whether it is agricultural informatization, agricultural product trading e-commerce, or the rural digital finance development, the prerequisite is it must be a complete rural digital infrastructure construction.

The reports of the China Academy of Information and Communications Technology believe that the definition of the digital economy can be divided into industrial digitization and digital industrialization, whereby industrial digitalization means the output and efficiency improvement brought about by the introduction of ICT into traditional industries. With the empowerment of digital technology, an environmental monitoring system for agricultural pre-production and production can be established, while new formats such as rural e-commerce goods can be formed after production, thereby realizing the transformation of traditional agriculture into a scientifically based and efficient modern model.

The integration of the digital and rural economies has improved the practice model of digital financial services in China’s “San Nong” field. The development of the digital economy has spawned updated financial models while the innovative development of digital finance has continuously added new momentum to the digital economy. The integration of ICT and traditional finance provides the possibility of opening up the farmers’ “last mile”. Furthermore, digital finance enables rural areas to address difficulties in accessing affordable financing at a low cost, fully leveraging the inclusive and the sharing advantages of digital finance, thereby contributing to the rural revitalization strategy while promoting the in-depth and comprehensive growth of the digital economy in today’s China.

Based on these insights, this article mainly explores the effect and mechanism of the rural digital economy growth level (explained from three aspects: rural digital infrastructure construction, agricultural digitalization, and development of the rural digital finance development) on agricultural CEs while also examining the intermediary effect of green technologies progress, which was measured by the agricultural green technological efficiency (see Figure 1 ).

Figure 1 . Model of the impact of the rural digital economy on agricultural CEs.

3.1 Digital infrastructure in rural areas

Digital infrastructure as a foundation for the development of the digital economy plays an important role in realizing agricultural digitalization and rural digital finance. It contributes to promoting the deep development of the digital economy while limiting the digital economy’s CEs. The agricultural CEs reduction effect of rural digital infrastructure construction is mainly manifested in the following two aspects: First, rural digital infrastructure construction can guide residents in rural areas to form green environmental protection concepts. The development of ICT enables rural residents to accelerate their access to the online environment, understand news and public opinion related to environmental pollution, and develop green and environmental protection concepts, thereby promoting the formation of informal environmental regulations on the Internet ( Xu, 2014 ) while helping to alleviate agricultural CEs and reshaping patterns of rural environmental governance. Second, the establishment of perfect rural digital infrastructures can reduce the limitations of geographical space, promote information interconnections and sharing, and help achieve a rational allocation of resources, thereby reducing the energy consumption caused by spatial and time factors in production and life, improving energy efficiency while unleashing CEs reduction effects.

3.2 Digitalization of agriculture

In terms of agricultural production management, the technology of big data analysis can promote the establishment of large-scale and standardized agricultural production bases, realize scientific analysis and reasonable predictions of crop sowing, output and demand, while reducing the imbalance between supply and demand and the waste of resources caused by insufficient and asymmetric information. In addition, through modern information processing technologies such as remote sensing satellites, real-time data collection, monitoring and analysis of agricultural production can be realized, and a scientific environmental monitoring system can be established so as to improve the allocation efficiency of production factor, grasp changes in the ecological environment, accurately measure CEs and trace them in time, thereby promoting effective governance and green development.

Digital technology can also continuously enrich the marketing methods of agricultural products, forming new sales models, i.e., rural e-commerce and live streaming. The continuous popularization of the rural Internet has connected farmers to online consumption cyberspace, realized “point-to-point” transactions, and reduced resource waste and CEs caused by the problems of information asymmetry and high transaction costs in traditional agricultural sales models. In terms of logistics and distribution, low-carbon logistics has become an important future development direction. The Vision 2035 Plan points out that green and low-carbon development should be promoted in the transportation industry while low-carbon freight logistics should also be realized. Aim to achieve development of the low-carbon logistics, relying on digital technology, the logistics and distribution industry is gradually replacing traditional fuel vehicles with clean energy electric vehicles, and accelerating the application of drones in rural areas for logistics distribution to reduce CEs. Regarding the latter, Jingdong drones have been used in some rural areas of Suqian City, Jiangsu Province, and this has already achieved normalized delivery ( Lin et al., 2020 ). Relying on artificial intelligence technology can also promote the intelligence of agricultural product logistics systems, while the establishment of rural smart logistics information platform can optimize distribution routes, achieve resource intensification, continuously save costs, improve efficiency, and deepen the digital economy’s Carbon reduction effect.

3.3 Rural digital finance

The development of rural digital finance has promoted the establishment of rural environmental protection service platforms. Participation in environmental governance and other activities has effectively increased farmers’ enthusiasm for engaging in environmental protection and has helped to improve their sense of social responsibility ( Meng et al., 2022 ; Dong et al., 2023 ). Taking the “Ant Forest” in Alipay’s personal carbon account platform as an example, users collect online energy and plant virtual trees to achieve real afforestation projects in reality, which attracts lots of subscribers to participate in environmental protection actions. In addition, it not only provides a sense of gain for the masses, but also promotes agricultural green development and reduces CEs. Furthermore, the rural environmental protection service platform built by relying on the digital finance development can also analyze the information of platform users through big data technology while rationally allocating resources, thereby reducing agricultural CEs. For example, Alipay’s “garbage sorting and recycling platform” is specially set up for problems such as the low recycling rate of domestic waste, supporting door-to-door collection of waste items so that the resource recycling rate is improved. Digital finance promotes green growth and green technological significantly ( Wu et al., 2022 ; Razzaq and Yang, 2023 ). Mobile payment and online financial services can continuously reduce farmers’ dependence on financial institutions, not only reducing the transaction costs of paper money but also promoting the rational layout of financial business outlets, lowering resource consumption, while uniting both economic and environmental benefits.

In addition, digital finance can effectively compensate for the neglect of traditional finance in rural areas. In the traditional financial environment, farmers have difficulty in financing and own single source of funds, which is not conducive to introduce new agricultural technologies and form the extensive production methods, resulting in more agricultural CEs, hence more serious agricultural pollution problems. The promotion and application of digital finance has broadened the channels of farmers’ capital sources, assisted them to introduce efficient and low-carbon new agricultural technologies, and formed a green agricultural business model, thereby continuously reducing agricultural CEs’ intensity and promoting green agricultural development. Besides, digital finance can also alleviate the misallocation of financial resources and provide more career options for rural residents.

3.4 The progress of agricultural green technology

Generally speaking, a valuable way to achieve high-quality agricultural development is via green agricultural technological change ( Deng et al., 2022 ).

In the existing agricultural economics research, more studies focus on green technological change or environmental technological change using different methods to assess agricultural green technology’s efficiency or that of environmentally friendly technology’s efficiency. According to the existing study on agricultural green technology change (AGTC) of China, the improvement of China’s agricultural productivity is overestimated due to ignoring the influence of environmental factors. Considering the regional heterogeneity of environmental conditions, agricultural technological change in rural China shows an increase trend, while there is a descending trend in the eastern, western, and central regions respectively. The northeast region has experienced an obvious decline in levels of technological change, while technological change without environmental constraints has exhibited a descending trend from eastern to western China ( Jiang et al., 2022 ). He et al. (2021) have identified some important factors affecting agricultural green innovation efficiency, such as the level of agricultural technologies’ diffusion, absorption, implementation, and informatization, the amounts of agricultural extension workers, the average schooling of households, and levels of agricultural mechanization.

To estimate the green efficiency of agricultural production, Korhonen and Luptacik (2004) developed and extended the DEA considering environmental aspects. Existing literature usually through two ways to calculate the green efficiency, one is choosing the environmental factors as the inputs, the other is taking the environmental factors, especially the bad environmental results as bad outputs. The SBM-DEA taking account undesirable outputs is a widely used model to deal with economic and ecological issues ( Liu et al., 2022 ). In this paper, we also chose the SBM-DEA model to estimate the agricultural green production efficiency, taking the carbon emission as the bad output in the DEA model.

In view of the above analysis regarding how the rural digital economy influences agricultural CEs, this article puts forward two research hypothesizes.

Hypothesis 1:. The rural digital economy may reduce the level and intensity of agricultural CEs significantly.

Hypothesis 2:. The rural digital economy may reduce CEs through green technological innovation efficiencies.

4 Research design

4.1 constructing the modelling.

The STIRPAT (Stochastic Impacts by Regression on Population, Affluence, and Technology) model initially proposed by Dietz and Rosa (1994) explores the factors influencing atmospheric emissions, such as socioeconomic, demographic, and technological issues. In the existing literature, the STIRPAT model mainly has been introduced to explore the causes of CEs in different industries, countries or cross-government economic organizations. These researches have concluded that certain factors such as rising population and affluence levels, the growth of urbanization, the structure of economic development and energy consumption as well as the energy mix and related technological issues are all responsible for increasing emissions. The STIRPAT model is in introduced in our study and is extended from a base IPAT model, which was initially proposed by Ehrlich and Holdren (1971) . The advantage of this is that it allows for appropriate decomposition of population, technology, and wealth, while also adding other issues when analyzing environmental impact factors. The expression is:

where I i is the influence in observational unit i from population P , affluence A and technology T . μ i is the random error term, α、η、κ and φ are the parameters.

The fixed-effects model can be used to control regional invisible differentiation, so the endogeneity issue generated by invisible or unchanging is addressed ( Liu et al., 2024 ). Because of the advantages of fixed-effects, here we choose the fixed-effects model.

To effectively avoid the heteroscedasticity of the model, this article converts the terms in Equation 1 into their logarithms as follows:

where i indicates province; t indicates time; λ i indicates provincial fixed effects; and ε i t represents random error terms. β is the coefficient that this article focuses on, and it is expected to be negative.

A E i t stands for the agricultural CEs intensity of the i th province (city) in the t year; A D I G i t represents the comprehensive level of rural digital economy growth in the t year of i th province (city), which is the core explanatory variable of this paper. In York et al. (2003) , the STIRPAT model was introduced to interpret the technology term, which can be composed of more than one variable considering the needs of a given study. In the STIRPAT model, the estimated coefficients of core explanatory variables can be clarified as environmental effect elasticities, which means the percentage change of CEs for one percentage change in digital economy growth.

Thus in our paper we choose certain control variables, including urbanization rate ( U R B A N i t ), level of agricultural mechanization ( M E C H i t ), planting structure ( S T R U i t ), agrochemical input intensity ( C H E M i t ), traffic ( T R A N i t ), rural electricity use ( E L E C i t ) to represent the population, affluence and technology of a given rural area.

Digital agriculture is conducive to the green transformation of agricultural industry, meanwhile, the progress of green technologies can decrease the CEs level of agricultural production. Thus, the influence path of digital agricultural economy on CEs can be expressed as the following models, as shown in (3) to (5) .

Here, Eq. 5 is the total effect model, Eq. 4 is the estimated model of the agricultural digital economy on agricultural green production efficiency, and Eq. 3 is the estimated model that considers both the agricultural digital economy and the mediating mechanism. Where, the mediator variable is the variable GTFP, the green agricultural production efficiency. The coefficient ω 1 in the formula (5) reflects the overall effect of the digital economy on the agricultural CEs, the coefficient λ 2 represents the direct effect of digital economy on the agricultural CEs, and the magnitude of the mediating effect can be determined by ω 1 − λ 2 . If the coefficient ω 1 , λ 2 and ζ 1 are all significant, and λ 2 < ω 1 or the significance of λ 2 is lower than ω 1 , it can be inferred that the mediating effect exists.

4.2 Variable selection

1. Variable to be explained: Agricultural carbon intensity (AE). In this study, agricultural CEs intensity is chosen to measure the level of agricultural CEs in provinces. Agricultural CEs intensity is expressed by the ratio of total agricultural CEs to agricultural added value. The total amounts of agricultural CEs of each province were calculated from six dimensions: agricultural fertilizer, pesticide, farm PE film, agricultural diesel, tilling and irrigation ( Li et al., 2011 ).

The CEs estimation formula is:

where variable E is the total CEs generated by agriculture production. E i stands for the CEs amount of various carbon sources, T i is the amount of i th carbon source, and δ i is the CEs coefficient of i th carbon source. The CEs coefficients of different carbon sources are listed as follows: 0.896 kg kg -1 for agricultural fertilizers, 4.934 kg kg -1 for pesticides, 5.180 kg kg -1 for agricultural film, 0.593 kg kg -1 for agricultural diesel, and 312.600 kg km -2 for ploughing. Agricultural irrigation is 25 kg hm -2 ( Dubey and Lal, 2009 ). After calculating the total agricultural CEs of each province, divide by the agricultural added value of each province to get the agricultural CEs intensity of each province (kg/10,000 yuan). The average values of total agricultural CEs and agricultural CEs’ intensity from 2013–2020 in each province (municipality) are shown in Figure 2 . The top five average agricultural CEs are Henan, Shandong, Heilongjiang, Hebei and Anhui, mainly in the major agricultural provinces. Nearly half of whole country have agricultural carbon emissions exceeding five million tons. From the viewpoint of agricultural CEs’ intensity, the top five areas are Gansu, Jilin, Inner Mongolia, Shanxi and Xinjiang, which produce large volumes of CEs per 10,000 yuan of agricultural added value, all exceeding 180kg, on the one hand because they may be dominated by extensive agricultural production methods, while on the other hand it is also related to the less development level of the agricultural digital economy.

2. Core explanatory variable: Rural Digital Economy Development Index (ADIG). Based on the existing research, this paper selects 10 indicators such as rural Internet penetration rate and agricultural meteorological observation stations from the three aspects of rural digital economy infrastructure construction, agricultural digitalization, and rural digital services, and constructs an evaluation index system for the growth level of the digital economy in rural areas, as shown in Table 1 . The Internet penetration rate in rural areas is assessed using the proportion of rural Internet broadband access users to the rural population in an area, while the number of Taobao villages is taken from the Ali Research Institute’s China Taobao Village Research Report , 1 the DIF coverage breadth index is obtained from the digital inclusive financial index data of Peking University ( Guo et al., 2020 ) measured by account coverage status, including the number of Alipay accounts per 10,000 people, the ratio of Alipay card users, and the average amounts of bank cards bound to an Alipay account. Other metric data is available directly. Among these, the average population served by postal outlets is a negative indicator while the others are positive indicators. In this research, the entropy method is introduced to measure 10 indicators of rural digital economy growth at three dimensions in order to get the rural digital economy development index of each province (city).

Figure 2 . Average level of total agricultural CEs amounts and intensity in each province (city), 2013–2020.

Table 1 . Evaluation index system of rural digital economy development.

The growth level of the rural digital economy in every province (city) in 2013 and 2020 are shown in Figure 3 . It is found that there is significant heterogeneity in the growth level of the rural digital economy between different regions and different years.

3. Mediated variables: Green efficiency agricultural development (GE). In the existing literature, the total factor productivity (TFP) calculated by DEA-Malmquist index is always used to measure the technological change, while using the Malmquist index will sacrifice time information. Thus, this paper uses agricultural green technological efficiency with environmental constraints. In the DEA model of this paper, agricultural added value was defined as the good output, agricultural CEs constitute the bad output, meanwhile the sown area of crops, fixed capital investment and the agricultural workers were set as the input variables.

Figure 3 . Comparison of comprehensive scores of rural digital economy development in 29 provinces (municipalities and districts) in China, 2013–2020.

From Figure 4 , it is obvious that the green agricultural technological efficiency of less than half province is more than 1, which means that more than half of provinces have less efficient green agricultural technologies. Thus, for China, there is still more space to improve the green technologies. In this paper, we use GE to stand for green technological efficiency.

4. Control variables. Due to the complexity of factors influencing the agricultural carbon emission, considering only the impact of the agricultural digital economy on agricultural CEs might lead to bias, and even serious endogeneity issues. Therefore, the following variables are selected to ensure the comprehensiveness and accuracy of empirical analysis. Is complexity and variables: 1) Urbanization rate (URBAN), measured by the proportion of urban population in a region to total population in the same area; 2) The level of agricultural mechanization (MECH), expressed as the total power of agricultural machinery; 3) Planting structure (STRU), expressed as the ratio of the grain sown area to the crop sown area; 4) Agricultural chemical input intensity (CHEM), expressed as the ratio of fertilizer use to the crop sown area; 5) Traffic conditions (TRAN), expressed as the sum of railway operating mileage and highway mileage; 6) Rural electricity consumption (ELEC), expressed in terms of agricultural power generation. The above variables are logarithmic.

Figure 4 . Average green agricultural technological efficiency of 29 provinces, 2013–2020.

Considering the availability of data, the Institute uses all data for 29 provinces (cities) in China from 2013–2020 (excluding Shanghai, Tibet, Taiwan, Hong Kong and Macao), which are derived from the China Statistical Yearbook (2014–2021) 2 and China Rural Statistical Yearbook (2014–2021), the EPS data platform, the Ali Research Institute Report, and the Peking University Digital Inclusive Finance Index (2011–2020). The descriptive results for all variables chosen are shown in Table 2 .

Table 2 . Description of main variables and descriptive statistical analysis.

As shown in Table 2 , except for lnELEC , all other variables have very small fluctuation trends, namely, less than 1.

5 Empirical results and analysis

5.1 estimates of basic regression model.

Firstly, only the core explanatory variable, namely, rural digital economy development composite score (ADIG) is considered, while the mixed-, fixed- and random-effects model is selected, and the F-test is 25.04 and the p -value is 0.0000, and the fixed-effect model should be selected. The Hausmann test shows that χ 2 is 4.77 and the p -value is 0.029, choosing a fixed-effect model. The other control variables were then added, and mixed-, fixed-, and random-effects models were selected, and the F-test was 42.79 and the p -value was 0.0000, and the fixed-effect model should be selected. The Hausmann test showed that χ 2 was 17.29 and the p -value was 0.0156, choosing a fixed-effect model.

Table 3 reports the baseline estimation of the influence effect of the rural digital economy development on the intensity of agricultural CEs. 1) considers only the core explanatory variable, and finds that the rural digital economy growth significantly reduces agricultural CEs intensity at the 1% level. Adding control variables to column 2), it is found that for every 1 unit increase in the growth level of rural digital economy, agricultural CEs intensity decreases by 40.01%, and this negative impact is still significant at the 1% level, thus validating the research hypothesis. For one thing, the development of the rural digital economy accelerates rural residents’ access to the network environment, not only promoting information interconnection and sharing while realizing the rational allocation of resources, but also helps rural residents establish the concept of green consumption and to develop informal network environment regulations, thereby reducing agricultural CEs intensity. And for another, the close combination of digital technology and agriculture helps farmers to, respectively, grasp agricultural production data accurately, improve production efficiency, and effectively reduce agricultural pollution caused by waste of resources. In addition, in an environment marked by the continuous development of rural digital finance, rural residents can broaden financing channels, introduce efficient and low-carbon new agricultural technologies, form a green business model, and promote the transformation of traditional extensive agricultural production methods to intensive ones, thereby realizing the agricultural CEs reduction effect of the rural digital economy.

Table 3 . Baseline regression results.

5.2 Endogeneity test

To alleviate the impact of endogeneity on empirical results, this article also verifies the relationship between agricultural digital economy with a lag of one period and agricultural CEs, the results are in the column 3) in Table 3 . The results of Table 3 have verified the negative impact of agricultural digital economy on agricultural carbon emissions. If the digital economy is an endogenous variable, then the estimation results in this paper are biased. This paper will test the core explanatory variable and each control variable with a lag of one period to overcome the possible reverse causal relationship between contemporaneous variables. The corresponding empirical results are shown in column 4) of Table 3 . The regression results show that the coefficient of the core explanatory variable is −0.4564, with a p -value of 0.047, excluding the possibility that agricultural digital economy is an endogenous variable.

5.3 Robustness test

1. Replace the explanatory variable. In the baseline regression, the logarithmic form of agricultural CEs intensity was used as the explanatory variable. In order to further enhance the robustness of the conclusion, the dependent variable was replaced with the total amounts of agricultural CEs (logarithmic value) for robustness testing, and the results are shown in columns 1) and 2), Table 4 . With the variables to be replaced, the growth of the rural digital economy still has a significant negative impact on agricultural CEs.

2. Exclude part of sampling. Considering substantial heterogeneity in the levels digital economy growth among Chinese provinces, in order to further strength the robustness of the conclusions, the data of two provinces with a digital economy scale of more than 15 trillion yuan and 12 provinces (cities) with a digital economy scale of more than one trillion yuan of 2020 are excluded. The results in column 3) and column 4) of Table 4 show that the development of rural digital economy still has a significant negative impact on agricultural CEs, and this negative impact has become stronger, which may be due to the fact that the digital economy in these provinces is on the rise, with accelerated development speed and greater development potential, so it is easier to reduce agricultural CEs intensity.

Table 4 . Robustness test results.

5.4 Heterogeneity analysis

1. Regional heterogeneity. This study categorizes the samples into four parts: eastern, central, western and northeastern regions for sub-sample regression, and discusses the regional heterogeneous impact of rural digital economy development on agricultural CEs intensity in the four parts. The estimations of regional heterogeneity analysis are shown in Table 5 ; for the eastern and central China, the development of rural digital economy still has a significant negative impact on agricultural CEs intensity and the central China have greater influence than their eastern counterparts while the western China is not significant. Possible explanations are: the eastern region has a good economic development foundation; the digital economy came early; it has a relatively complete rural digital economy infrastructure; and the integration and development of digital technology and agriculture is higher. Meanwhile, the central region is China’s most important agricultural production zone, the central government places greater focus on agricultural input, especially its green agricultural policy and finance support, which may lead to a larger and more significant negative impact on the intensity of agricultural CEs. The development and application of digital technology in the western region started late, that is might the reason why the impact is not significant. But it is not rational to deny its rapid upward phase and the low-carbon development potential of agriculture. The results also show that the coefficient of the rural digital economy development in the northeast region is positive, indicating that the development of the rural digital economy may increase the intensity of agricultural CEs. The development of the digital economy in northeast China is relatively backward, its digital infrastructure is not yet perfect, the coverage of rural digital finance is small, the proportion of secondary industry is large, while the integration of digital technology and agriculture is not complete.

2. Heterogeneity of scientific investment. As the primary productive and innovative force, the increased science and technology investment plays an important supporting role in the reduction of CEs and the growth of the digital economy. On the one hand, advances in science and technology have a direct impact on CEs’ reduction. At present, technological progress is an important driving force for the reduction of CEs and green development, while investment in science and technology helps to promote green technology innovations ( Yang et al., 2019 ; Gu et al., 2022 ), saving production costs, promoting the professional division of labor in various fields, and improving productivity, thereby directly reducing CEs. On the other hand, the progress of science and technology will also promote the progress of digital technologies such as AI and big data, accelerating the development process of industrial digitalization and digital industrialization, thereby promoting the high-quality development of the digital economy, thus further reducing CEs.

Table 5 . Results of regional heterogeneity analysis.

To examine the impact of rural digital economy development on agricultural CEs’ intensity against the background of different scientific and technological inputs, this paper divides 29 provinces (municipalities) into high and low sample groups for heterogeneity analysis based on the average science and technology expenditures in each province (municipality) over 2013–2020, and the results are shown in Table 6 . For the high-tech input group, the development of the rural digital economy still had a significant negative impact on the intensity of agricultural CEs, while the low-tech input group was not significantly negative. This shows that high scientific and technological investment can help promote the green development of agriculture while reducing the intensity of agricultural CEs. The development of the rural digital economy is premised on the completion and improvement of rural digital infrastructure as well as the production, transportation, sales of agricultural products, as well as the supervision, measurement, and traceability of CEs in the whole process of agricultural digitalization, which depends on sound digital infrastructure. High levels of investment in science and technology is conducive to promoting scientific and technological innovation and building a higher quality digital economy infrastructure, thereby providing the realization method and technical guarantee required for the close integration of digital technology and agriculture while promoting the reduction of agricultural CEs. At the same time, the continuous inflow of high-tech labor as a result of government investment in science and technology in the form of subsidies can enhance the level of local innovation, thereby promoting the sustainable and high-quality development of the digital economy and realizing the digital economy’s capacity to reduce CEs. Therefore, local governments should vigorously promote innovation-driven development strategies, increase financial support for science and technology, establish a sound incentive system, and encourage applied research and technological innovation in key fields. In addition, local governments can also increase the weight and proportion of indicators such as scientific and technological investment and their application in the government assessment index system, design a sound talent introduction system, and pay attention to cultivating high-quality talent ( Bian et al., 2020 ), so as to achieve high-quality development and deepen the digital economy’s CEs reduction effects.

Table 6 . Analysis results of scientific and technological inputs’ heterogeneity.

5.5 Mediated effect analysis

From above analysis, it is obvious that the digital economy development has ability to decrease the agriculture CEs intensity and amounts. Further to explore the influence mechanism of the digital economy development on the agriculture CEs, the model 3) and model 4) mentioned in Section 4.1 is run using Stata software. To directly and conveniently compare the mediating effects with the estimates of the basic model of digital economy influence on agricultural CEs’ intensity, the baseline regression results in Table 3 were listed again in column 1), Table 7 . The dependent variable in column 2) is the mediator variable agriculture green efficiency (GE), while the explanatory variable focused on in this paper, agricultural digital economy (ADIG), is significantly positive, consistent with expectations. The dependent variable in column 3) is the agricultural CEs intensity (lnAE). After adding the mediating variable GE, the explanatory variable agricultural digital economy (ADIG) remained significantly negative at the 1% level, while the mediating variable agricultural green efficiency (GE) was significantly negative.

Table 7 . Analysis results of mediating effect.

Comparing the results of Table 3 and Table 7 , the coefficient β = 0.4001 with 1% significance, the coefficient λ 2 = -0.3375 is significant at 1% level, besides the coefficient ζ 1 = 0.9143 is significant at 5% level, the mediating effect is β − λ 2 = -0.0626, and the mediating effect of green agricultural technology exists through the empirically analysis. The coefficient −0.4001 show the total effect, and means when the agricultural digital economy increases one unit, the agricultural CEs will decrease 40.01%. The coefficient −0.3375 is the direct effect of agricultural digital economy with one unit increase on the agricultural CEs reduction is 33.75%. The gap between the total and direct effect is the mediating effect.

6 Discussion

6.1 the construction of agricultural digital economy indicators.

Based on the existing researches, this paper mainly focuses on the three aspects of rural digital economy infrastructure, digitalization of agriculture and rural digital services to construct the indicator of agricultural digital economy. This indicator not only consider the hardware and software agricultural digital economy level, but also digital service level. In Zhao et al. (2023) study, the indicators of digitalization level mainly focus on two aspects of digital economy infrastructure and digital economy service level, while they choose the digitization levels to substitute the rural digitalization index. In our study, we use the agricultural digital economy, which is closely related to the development agriculture and rural areas, and can better reflect the digitization level of agriculture.

6.2 The main effect of agricultural digital economy on agricultural carbon emission

In the existing studies, the level of digitalization can significantly reduce the agricultural carbon emission ( Zhao et al., 2023 ), although their research chose the carbon emission intensity of different agricultural sector, cropping and livestock sector respectively. Even in the city level or other sector of China, most studies also hold the same conclusion as our study, such as Wang et al. (2022) , Zhang W. et al. (2022) . And our study also support the carbon emission reduction effect of digital economy.

6.3 The mediating effect of agricultural digital economy on agricultural carbon emission intensity

Through the mediating effect analysis, it is obvious that the agricultural green production technology is an important mechanism for the development of the digital economy’s capacity to alleviate agricultural CEs. The same results are also evident in the research of Rong et al. (2023) . They emphasize that green technology can effectively suppress agricultural CEs directly, which has significantly negative spatial spillover effects on agricultural CEs in both the short and long term. Except for the influence mechanism, Guo et al. (2023) underline that the role of agricultural green technology in reducing agricultural CEs is particularly dominant in the main grain-producing areas. Zhao et al. (2023) emphasis digitalization can reduce China’s carbon intensity by promoting the agricultural technological input. This can support our influence mechanism of agricultural digital economy on the agricultural carbon emission. Except for the agricultural technology inputs, Zhao et al. (2023) also emphasis the role of human capital level and urbanization rate. In our research we use the agricultural green production efficiency as the mediating variable, which both considering the input and output of agricultural technology, and considering the agricultural green transformation.

6.4 Discussion of heterogeneity in the impact of agricultural digital economy on the agricultural carbon emissions

In Zhao et al. (2023) study, the carbon reduction effect is slightly greater in the central and western regions than that in the eastern regions, which is slightly different with our results, one reason is the different research period, the former chose the 2006–2018, while we chose the 2013–2020, considering the fact China’s digital economy has entered a mature period since the year 2013, thus we choose the 2013 is more rational for agricultural digital economy. Other reasons such as the region and province chosen difference also would lead to the less reduction effect of west region.

7 Conclusion and policy implications

This study uses the data of 29 provinces (cities) in China from 2013–2020 in order to measure the intensity of agricultural CEs as well as the development level of rural digital economy in each province. On this basis, the influence of the development of the rural digital economy on agricultural CEs is empirically estimated. The results show that: 1) the development of the rural digital economy could significantly reduce the intensity of agricultural CEs, a conclusion which is still valid after robustness test such as replacing the explanatory variables and removing some samples. The overall environmental effect is 40.01%, which means the agricultural CEs would decrease 40.01% when the agricultural digital economy increase one unit, the direct effect of digital economy on the agricultural CEs reduction is 33.75%; 2) The alleviation of CEs based on the rural digital economy is more significant in the higher technological investment zones than that in the lower technological investment zones, and the central and eastern regions also have more significant CEs reduction effect. 3) The influence mechanism analysis shows that agricultural green technology change is an effective means to promote the rural digital economy’s CEs reduction effect, and the mediating effect is −6.26%, which means the agricultural CEs would decrease 6.26% for one unit agricultural digital economy increase, through mediating effect of the agricultural green technology. Based on the above conclusions, this article puts forward the policy recommendations as follows.

Firstly, continuously improve the level of agricultural digital economy. Including build a complete rural digital economy infrastructure, strength the agricultural digitalization and promote the agricultural finance service. Further promote the full coverage of rural Internet, accelerate the construction of rural 5G networks, realize the in-depth application of agricultural Internet, and establish a smart agricultural technology system. Accelerate information interconnection and sharing, build a unified Big Data platform for agricultural and rural development, and provide solid information infrastructure support for the rural digital economy and agricultural digitalization, so as to accelerate the agricultural CEs reduction effect of the rural digital economy. Besides, increase the accessibility and coverage of agricultural finance is crucial for the green transformation of agricultural industry. The agricultural green development balances the agricultural industry growth and the sustainability of the rural environment.

Secondly, focus on achieving the balanced the rural digital economy development in various regions and better effect of agricultural CEs reduction. On the one hand, it is necessary to strengthen the interconnection and information sharing of various regions while deepening cooperation to promote the establishment of data sharing platforms. On the other hand, it is necessary to raise financial investment in the central, western and northeast regions, implement coordinated and sustainable digital economy development policies in accordance with local conditions, strive to eliminate the digital divide between regions, and bring into play the CEs reduction effect of digital economy. Meanwhile, the central China and western China can also take the initiative to expand foreign cooperation, such as introducing information technology to empower agriculture through free trade zone cooperation, thereby giving full scope to local comparative advantages, hence accelerating the digitization transformation of agriculture ( Guo, 2021 ) while realizing the coordinated the digital economy development between regions.

Thirdly, the government should pay attention to agricultural green development, because the agricultural carbon reduction effect of digital economy needs to be achieved through the mediating variable of agricultural green technology change. Considering the peculiarity of agricultural development, there is a need to increase financial support and incentives for science and technology, set up special funds to encourage agricultural green technology R&D and innovation levels, continuously strengthen the scientific and technological research and technology research capacity of low-carbon technologies, while promoting agriculture’s turn to low-carbon and green development.

8 Limitations

This paper has some shortcomings and can be further analyzed. The assessment of agricultural digital economy has consistently constituted an important issue and challenge in related research. Although this paper assesses the agricultural digital economy by establishing a novel evaluation framework, because of the availability and measurability of data, some regions and some indicators cannot be included in the evaluation system in this paper. Thus, there is still space to further improve the evaluation methodology in the future, to enhance the comprehensiveness and scientific rigor of the research. Furthermore, since the agricultural digitalization and CEs are highly influence by the grassroots government, the role of township-level government played in the agricultural green development and agricultural digital economy is very direct and important. While the related data on the grassroots government is relatively incomplete, which would not provide sufficient evidence for our study. If we would get enough data of township level government, we would conduct more comprehensive research in this area.

Data availability statement

The raw data supporting the conclusions of this article will be made available by the authors, without undue reservation.

Author contributions

HZ: Writing–original draft, Conceptualization, Funding acquisition, Investigation, Resources. KG: Conceptualization, Data curation, Formal Analysis, Methodology, Writing–original draft, Resources. ZL: Conceptualization, Funding acquisition, Investigation, Writing–original draft, Data curation, Formal Analysis, Methodology, Validation. ZJ: Data curation, Formal Analysis, Methodology, Project administration, Resources, Visualization, Writing–original draft. JY: Data curation, Formal Analysis, Software, Writing–review and editing.

The author(s) declare that financial support was received for the research, authorship, and/or publication of this article. This work was supported by Guizhou Planning Office of Philosophy and Social Science grant numbers 22GZQN28.

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

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Keywords: agricultural carbon emissions, agricultural green technology efficiency, rural digital economy, rural digital finance, digitalization of agriculture

Citation: Zhang H, Guo K, Liu Z, Ji Z and Yu J (2024) How has the rural digital economy influenced agricultural carbon emissions? Agricultural green technology change as a mediated variable. Front. Environ. Sci. 12:1372500. doi: 10.3389/fenvs.2024.1372500

Received: 18 January 2024; Accepted: 20 March 2024; Published: 08 April 2024.

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*Correspondence: Jinna Yu, [email protected]

This article is part of the Research Topic

Low-Carbon Economy and Sustainable Development: Driving Force, Synergistic Mechanism, and Implementation Path