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• Published: 12 November 2022

Measuring rurality in health services research: a scoping review

• Robin Danek 1 ,
• Justin Blackburn 2 ,
• Marion Greene 2 ,
• Olena Mazurenko 2 &
• Nir Menachemi 2 , 3  

BMC Health Services Research volume  22 , Article number:  1340 ( 2022 ) Cite this article

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This study is a scoping review of the different methods used to measure rurality in the health services research (HSR) literature.

We identified peer-reviewed empirical studies from 2010–2020 from seven leading HSR journals, including the Journal of Rural Health, that used any definition to measure rurality as a part of their analysis. From each study, we identified the geographic unit (e.g., county, zip code) and definition (e.g., Rural Urban Continuum Codes, Rural Urban Commuting Areas) used to classify categories of rurality. We analyzed whether geographic units and definitions used to classify rurality differed by focus area of studies, including costs, quality, and access to care. Lastly, we examined the number of rural categories used by authors to assess rural areas.

In 103 included studies, five different geographic units and 11 definitions were used to measure rurality. The most common geographic units used to measure rurality were county ( n  = 59, 57%), which was used most frequently in studies examining cost ( n  = 12, 75%) and access ( n  = 33, 57.9%). Rural Urban Commuting Area codes were the most common definition used to measure rurality for studies examining access ( n  = 13, 22.8%) and quality ( n  = 10, 44%). The majority of included studies made rural versus urban comparisons ( n  = 82, 80%) as opposed to focusing on rural populations only ( n  = 21, 20%). Among studies that compared rural and urban populations, most studies used only one category to identify rural locations ( n  = 49 of 82 studies, 60%).

Geographic units and definitions to determine rurality were used inconsistently within and across studies with an HSR focus. This finding may affect how health disparities by rural location are determined and thus how resources and federal funds are allocated. Future research should focus on developing a standardized system to determine under what circumstances researchers should use different geographic units and methods to determine rurality by HSR focus area.

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Introduction

According to some federal estimates, nearly 1 in 5 Americans live in rural areas [ 1 ]. In general, compared to their urban counterparts, rural Americans are older [ 2 ], more likely to be disabled or a veteran of the US military [ 3 , 4 ], receive Medicaid or be uninsured [ 5 ], and have lower median incomes [ 6 ]. Rural Americans also have higher rates of obesity [ 7 ], cardiovascular diseases [ 8 ], and substance use disorders [ 9 ]. However, as policymakers become increasingly interested in addressing health disparities between urban and rural populations, it is important to assess and evaluate the different methods used to define rurality so that true disparities can be accurately captured and addressed.

There is no standard way to measure ‘rurality’ or what qualifies as a ‘rural’ area. Even within the US government, multiple definitions of rurality exist which contributes to variability in federal estimates of the size of the rural population. For example, the US Department of Agriculture estimates there are 46 million rural Americans (14%), while the Census Bureau estimates there are nearly 60 million rural residents (18%) resulting in an almost 30% relative difference [ 10 , 11 ]. Likewise, health services researchers use multiple geographic units and definitions to measure rurality, such as county [ 12 , 13 , 14 ], zip code or rural–urban commuting patterns [ 13 , 14 , 15 , 16 , 17 ]. Importantly, some key outcome measures, such as access to care estimates and the incidence of breast cancer were shown to be sensitive to the rural measurement method used [ 18 ]. Inconsistent usage of these definitions can influence policy decisions, as demonstrated by Kozhimannil et al., (2018) who documented loss of obstetric services in rural areas. In their study, researchers were unable to use appropriate measurements of rurality because the dataset restricted them to using county only, which masked differences in loss of services within counties that varied in their degrees of rurality [ 19 ]. Likewise, other research has indicated significant variability in how rurality is measured across social and health sciences [ 20 ]. Over time, several calls have been made to better understand how rurality is measured and to move towards standardization in measures of rurality in health services research (HSR) [ 12 , 15 , 20 , 21 , 22 , 23 , 24 , 25 ]. Although the selection of a rural definition might be a function of data availability, this creates challenges in generalizability and comparability across studies, making policy development difficult [ 24 ].

The purpose of the current study is to identify and describe the different definitions used to measure rurality in health services literature and to determine the frequency in which each definition is used. In addition, we stratify studies based on their focus area to determine whether the type of rural definition used is consistent within similar topics across studies. Each definition and measurement approach may have benefits and drawbacks that are not fully understood in the process of policy development. Ultimately, because the definition used to measure rurality can affect how conclusions are drawn, our study will be useful to policymakers, researchers, and other stakeholders interested in addressing health disparities in rural areas.

Our approach follows the general guidelines of the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) [ 25 ]. Although our study is not technically a systematic review, we apply similar methods to identify, screen and include articles for analysis. Specifically, we included peer-reviewed empirical studies from the HSR literature that used any method to measure rurality as a part of their analysis. Thus, we included studies that analyzed and described differences between rural and urban populations and studies that focused solely on rural populations.

For the current study, we focused on the health services research, including the delivery of health care services to rural populations, which previous research has shown to be problematic in rural areas and different for rural populations than for urban [ 26 , 27 ]. Our inclusion criteria included empirical publications where the primary dependent variable is consistent with studies that meet the criteria for HSR, as it is defined by the Agency for Healthcare Research and Quality (AHRQ), the leading federal funding agency of studies aimed at improving the performance of health care. According to AHRQ, HSR includes a “multidisciplinary field of scientific investigation that studies how social factors, financing systems, organizational structures and processes, health technologies and personal behaviors affect access to health care, the quality and cost of health care, and ultimately, our health and well-being [ 28 ]”. Since the focus was on empirical studies, we excluded letters to the editors, commentaries, case studies, executive summaries and non-peer-reviewed governmental reports.

For our analysis, we identified studies published from 2010 to 2020 that met the inclusion criteria from seven leading HSR journals as identified by prior research, including the Journal of Rural Health [ 29 ]. The Journal of Rural Health is a peer-reviewed journal focused on research examining rural health policy, health care delivery, and population health. The other included journals were Health Affairs, Medical Care, Health Services Research, the American Journal of Public Health, Medical Care Research and Review, and the Journal of Healthcare Management. In these journals, we searched for the word “rural” and “rurality” in titles and abstracts to identify studies for further assessment. Because the Journal of Rural Health exclusively publishes studies conducted in rural populations and related topics, we excluded the words “rural” from our search, and instead used HSR terms of “cost,” “quality,” and “access” in our search strategy.

Once our initial sample was identified, titles were screened by a single researcher (RD) to determine their eligibility for inclusion in the study (see Fig.  1 : Flow Diagram of Included Studies). Studies that were commentaries or editorials, conducted outside of the United States, and did not use an HSR outcome (e.g., quality, cost, access) as a primary focus were eliminated. Journals that cater to international health services research are not likely to use US definitions of rurality and were therefore excluded. An additional author (NM) screened a random subsample of final abstracts and fully agreed on the extracted variables, thus minimizing concerns of intercoder reliability.

Flow Diagram of Included Studies

For each article that met all the inclusion criteria, we identified the specific geographic unit and definition used to measure rurality. In order to be included in the final sample, each study had to specify at least the geographic unit used to measure rurality (e.g., county, zip code, etc.) or a definition used to determine rurality (e.g., Rural Urban Continuum Codes, Urban Influence Codes). Once the geographic unit and definitions were determined, each study was categorized into 4 broad categories including costs, quality of care, access to care, or ‘other’ topics. Studies coded as ‘other’ included those that focused on the organizational structure of rural hospitals, rural public health delivery systems, the nursing workforce, or workforce issues in Critical Access Hospitals but did not fit into the main HSR categories. Additionally, we extracted the type of data analyzed (primary or secondary), whether the study compared rural and urban populations or focused exclusively on rural populations.

To analyze the data, we calculated the frequency in which each measurement of rurality appears among included studies and within each HSR category. Likewise, we also determined whether study characteristics, as described above, are associated with specific definitions used to measure rurality. We conducted Chi-square analyses to explore how key article characteristics are related to measurements of rurality used by authors. The Institutional Review Board at Indiana University determined this study was exempt from human subject’s oversight. All analyses were performed in Stata version 16.

Our search strategy resulted in a sample of 296 studies, of which 103 met our inclusion criteria. The majority of included studies made rural versus urban comparisons ( n  = 82, 80%) as opposed to focusing on rural populations only ( n  = 21, 20%) (see Table 1 ). Among studies that compared rural and urban populations, most studies used only one category to identify rural locations ( n  = 49 of 82 studies, 60%). More than half of the studies were categorized as focusing on access ( n  = 57, 55%), followed by quality ( n  = 23, 22%), and costs ( n  = 16, 16%). Almost all of the included studies (90%) used secondary data sources.

The most common geographic units used to measure rurality were county (57%) or zip code (35%). The use of population density (4%) and other geographic units of measurement (2%) were uncommon among included studies. With respect to the definition used to determine rurality, the most common approaches utilized were Rural Urban Commuting Area codes (29%) and Metropolitan Statistical Areas (15%). Less common approaches included Rural Urban Continuum Codes (12%), Urban Influence Codes (11%), or the use of a state or federal county designation (11%) to determine rural locations. Overall, 11 different methods to determine rurality were identified among included studies (see Table 1 ).

We present bivariate crosstabulations between HSR focus area (e.g., cost, quality, access) and geographic unit in Table 2 , and between HSR focus area and definition used in Table 3 . As shown in Table 2 , the geographic unit of county was most commonly used in studies that focused on cost (75%), access (57.9%), and other HSR outcomes (71.4%). The most common geographic unit used among studies focused on quality was zip code (47.8%). In contrast, as seen in Table 3 , there was no clear pattern in the use of definition to determine rurality across studies with a different HSR focus.

Among the 82 studies that compared rural and urban areas, 91% reported a difference in primary outcome by geographic location. In Table 4 , we examined whether studies that reported a significant difference between rural and urban areas differed with respect to geographic unit, the definition of rurality, and number of categories used to categorize rural locations. No significant differences were identified.

After assessing HSR studies focused on rural health, we found that five different geographic units and eleven definitions were used to measure rurality. Among the geographic units used, county was the most common representing more than half of all studies. Among the definitions used to determine rurality, RUCA was the most common, but was used in less than one-third of studies, highlighting the variability in definitions used by researchers. Prior research has described the different ways that rurality has been measured globally—and how these measurements have evolved over time [ 20 ]. Our US-based study, focuses on measurements of rurality by HSR foci and highlights inconsistences in definitions utilized by the HSR focus of included studies. Among studies focused on costs, county was the most common geographic unit, whereas for studies that focused on access, zip codes were the most frequently used geographic unit. Likewise, there were different patterns in the use of methodological approaches by articles with different HSR foci.

Relying heavily on the use of county as the geographic unit to determine rurality can be problematic given that county-level measurement can undercount rural locations [ 24 ]. In particular, this criticism is commonly noted of the US Office Management and Budget (OMB) who created Metropolitan and Micropolitan Statistical Areas (MSAs), a commonly used measure of rurality, to classify counties as either urban or rural respectively. Metropolitan areas are defined as an area with a large population nucleus surrounded by adjacent communities, whereas a Micropolitan area is defined as an area with a smaller nucleus [ 30 ]. As the OMB cautions, this definition excludes rural areas that reside within large counties that possess urbanized areas elsewhere. In fact, the OMB advises explicitly against using their approach to determine program funding, lest rural programs be overlooked using their classification system to determine rurality in a metro area. This is an important point for researchers to consider, as inappropriate usage of geographic measurements and methods can lead to spurious conclusions about outcomes related to HSR studies of rural areas.

In the current review, we found that RUCA codes were the most commonly used definition to determine rurality, although this definition was used in less than one-third of included studies. Unlike other definitions, RUCA codes are based on census tracts but have been converted to ZIP codes. RUCA codes employ a far more nuanced classification system in which zip codes are used to make up 10 primary codes and 33 secondary codes that in turn are categorized into 6 different classification systems, A through F [ 30 ], based on how many different levels of rurality are needed. The majority of included studies used only one measure of rurality which fails to make use of the multiple levels of rurality afforded by the use of RUCA codes. Similarly, Urban Influence Codes divide nonmetro areas into twelve different codes based on adjacency to metro and nonmetro areas in their classification scheme, although this method was used far less frequently than RUCA codes overall—and were frequently used with binary measures of rural location as well. In many cases, either of these two methods may be particularly beneficial to rural HSR researchers, as they provide a currently missed opportunity to examine the effect of HSR outcomes at several different levels of rurality, rather than just by one single category, as commonly observed in our sample.

We found that studies with different HSR foci were inconsistent in their usage of geographic units and definitions to determine rurality. Studies whose HSR focus was access used county most frequently as its geographic unit of measurement, an arguably less precise measure than zip code, as counties may cover a larger expanse of land and obscure difficulties accessing care in larger, urban centers for rural populations. This is an important finding as this may indicate that HSR studies focused on cost or quality may be better suited to use county as their unit of measurement than access studies where adjacency to urban centers that offer more health care services is particularly important. When studies focused on access utilize county as their geographic unit, it is incumbent on the researcher to justify their method because county fails to measure adjacency reliability. The use of zip codes may provide a more granular, and thus a more precise way to measure access to care in rural areas.

In almost all included studies, the authors did not provide an explanation for why they chose a particular method of rurality. Their decision to use a specific approach may be a function of data availability, given that most included studies used secondary data sets. More research is needed to determine how many approaches to measure rurality are possible with commonly used datasets used by authors conducting rural research. Another potential solution proposed by previous research is enabling researchers from institutions outside of federal agencies to access more granular geographic data than commonly available in large, nationally representative datasets (Zhand et al., 2019) [ 31 ]. In their study of rural cancer disparities, Zahnd et al. (2019) note that enabling researchers to access more granular geographic data may lead them to use more appropriate definitions of rurality than is currently available in the limited publicly available data sets. Given that authors are rarely given a choice in definitions, they should be encouraged to explicitly state why their chosen methods to measure rurality were used, including if it was the only available option. Moreover, others have suggested that researchers consider the region when appropriate, when determining what method of rurality to use for their analyses [ 32 ]. In cases where the dataset used offers multiple options for determining rurality, researchers should justify their primary approach and conduct a sensitivity analysis to state whether their conclusions would change if any alternative approaches are used.

The majority of included studies used only one category to measure rural locations and therefore may be missing important differences in health outcomes when comparing degrees of ruralness and rural and urban populations. Furthermore, we found no consistent or discernable pattern when the number of categories of rurality varied in included studies. Likewise, some authors used unusual descriptors such as ‘highly rural,’ [ 33 , 34 ] ‘isolated rural’, or super rural, which makes it further challenging to synthesize findings across studies that use different categorizations and terminology as opposed to using currently existing [ 35 , 36 , 37 , 38 ]. In order to address this issue, researchers should consider weighting data sets in order to create urban and rural populations that are representative of the populations being studied, and thus making it easier to compare different populations in analyses. Additionally, when faced with using multiple categories of rurality with small cell sizes, researchers should consider collapsing rural categories into more than one category, avoiding creating two dichotomous urban–rural categories frequently used in research involving rural populations. Researchers may also consider conducting additional analyses using urban/rural categories and then comparing results to when multiple, more granular categories of rurality are used.

We note that most definitions of rurality are defined in terms of not being metropolitan, as opposed to being defined by a set of criteria completely separate from urbanicity. In fact, when defining rural–urban measurements, the metropolitan groupings are usually defined first, with rural areas making up ‘everything else’ that isn’t considered metropolitan or micropolitan. This is problematic because what is considered rural can vary drastically depending on the definition of “metropolitan,” as evident by vast differences between definitions of rurality. This can lead to problems evaluating rural research even within HSR because inconsistent measurements of rurality can lead to heterogeneity of results due to arbitrary measurements.

Our findings suggest that the tools used to measure rurality are used inconsistently, potentially leading to spurious results and conclusions. This is particularly concerning as the measurements used to determine rurality and what qualifies as rural can affect the allocation of federal funds for rural areas. This is especially important as it relates to access to care and funding to improve health care access or program planning for rural areas [ 25 ]. Likewise, findings from this study may encourage local and state policymakers to use more granular definitions of rurality, such as zip code, to design programs that target areas in high need.

There are several limitations to our study worth noting. First, our search strategy may have limited the number of studies included. We recognize that many rural HSR studies exist that are not published in the journals we focused upon, including journals that were not identified as among the top HSR journals or were published in clinical or medical journals. Second, our search yielded only 103 included studies, which made conducting more sophisticated analyses to examine associations between health outcomes and geographic units of measurement and methods to determine rurality not possible. Lastly, we recognize the potential for publication bias to have affected our conclusions. Publication bias occurs when journals favor the publication of studies which report statistically significant results. Thus, it is possible that studies with null findings—especially if it is a function of rural measurement– are underrepresented in the literature and therefore excluded from our analysis.

In conclusion, we found that the geographic units and definitions to determine rurality were used inconsistently within and across studies with an HSR focus area. The use of effective measures of rurality have implications to both rural health policy and additional HSR research that builds upon a presumably known relationship between measurements of rurality and health outcomes in the literature. Tools used to measure rurality can affect how conclusions about health disparities are determined and, in turn, how funds are allocated to programs in rural areas. Future research should focus on developing a standardized system to determine under what circumstances HSR researchers should use different geographic units and methods to determine rurality by HSR focus area.

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Managing Logistics and Supply Chain in Rural Areas: A Systematic Analysis of the Literature and Future Directions

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Despite half of the world population lives not in metropolitan areas but in sparsely populated zones, the rural contexts have been at the periphery of logistics and supply chain management research. The main aim of this paper is to explore the state-of-the-art of the literature on rural supply chain management using a systematic approach. A sample of 51 papers from different disciplines with a management focus have been retrieved and analysed in details. A literature classification framework based on three different topic areas and related sub-topic areas was proposed. Seven research gaps have been identified that may inspire future research in this area. Interestingly, none of the papers identified has an explicit focus on the link between rural supply chain management and local development. To the best of the authors’ knowledge, this study is the first attempting to collect, analyse and classify scientific papers related to rural supply chain management.

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Due to space limitation, the references in square brackets concerning background or methodological papers have been incorporated in the text. To ensure the transparency of method used, the bibliographical references cited in Sect.  4 have been reported in the text indicating the author/s and the year of publications. However, the full list of the 51 papers included in the final sample is available on request.

Gebresenbet, G., Ljungberg, D.: Coordination and route optimization of agricultural goods transport to attenuate environmental impact. J. Agric. Eng. Res. 80 (4), 329–342 (2001). https://doi.org/10.1006/jaer.2001.0746

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Salemink, K., Strijker, D., Bosworth, G.: Rural development in the digital age: a systematic literature review on unequal ICT availability, adoption, and use in rural areas. J. Rural Stud. 54 , 360–371 (2017). https://doi.org/10.1016/j.jrurstud.2015.09.001

Lagorio, A., Pinto, R., Golini, R.: Research in urban logistics: a systematic literature review. Int. J. Phys. Distrib. Logist. Manag. 46 (10), 908–931 (2016). https://doi.org/10.1108/IJPDLM-01-2016-0008

Khan, K.S., Kunz, R., Kleijnen, J., Antes, G.: Five steps to conducting a systematic review. J. R. Soc. Med. 96 (3), 118–121 (2003). https://doi.org/10.1258/jrsm.96.3.118

Tranfield, D., Denyer, D., Smart, P.: Towards a methodology for developing evidence-informed management knowledge by means of systematic review. Br. J. Manag. 14 , 207–222 (2003). https://doi.org/10.1111/1467-8551.00375

Seuring, S., Müller, M.: From a literature review to a conceptual framework for sustainable supply chain management. J. Clean. Prod. 16 , 1699–1710 (2008). https://doi.org/10.1016/j.jclepro.2008.04.020

Petticrew, M., Roberts, H.: Systematic Reviews in the Social Sciences: A Practical Guide. Blackwell Publishing, Malden (2006). https://doi.org/10.1080/14733140600986250

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Tu, W., Sui, D.Z.: A state transformed by information: Texas regional economy in the 1990s. Reg. Stud. 45 (4), 525–543 (2011). https://doi.org/10.1080/00343400903241568

Van Gaasbeck, K.A.: A rising tide: measuring the economic effects of broadband use across California. Soc. Sci. J. 45 (4), 691–699 (2008). https://doi.org/10.1016/j.soscij.2008.09.017

EPRS: Short food supply chains and local food systems in the EU (2016). http://www.fao.org/family-farming/detail/en/c/427183/ . Accessed 15 Feb 2020

EPRS: Local agriculture and short food supply chains (2013). https://epthinktank.eu/2013/10/14/local-agriculture-and-short-food-supply-chains/ . Accessed 15 Feb 2020

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Evangelista, P., Williger, B., Gebresenbet, G., Micheletti, S. (2021). Managing Logistics and Supply Chain in Rural Areas: A Systematic Analysis of the Literature and Future Directions. In: Bevilacqua, C., Calabrò, F., Della Spina, L. (eds) New Metropolitan Perspectives. NMP 2020. Smart Innovation, Systems and Technologies, vol 178. Springer, Cham. https://doi.org/10.1007/978-3-030-48279-4_15

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A Closer Look at Rural Populations: Multistate Research Monitors Changes and Issues Affecting Rural Areas

Rural areas make up 72% of the nation’s land area, house 46 million people and are essential to agriculture, natural resources, recreation and environmental sustainability.

These areas are constantly changing, and many face complex challenges such as limited access to health care, education and jobs. Events like the Great Recession and the COVID-19 pandemic have highlighted how such challenges can lead to major disruptions to the environmental, economic, social and physical wellbeing of rural communities.

Understanding the dynamics of rural population change is key to effectively addressing current and future challenges. Hatch Multistate Research Project W4001: Social, Economic and Environmental Causes and Consequences of Demographic Change in Rural America  has pioneered the use of integrated, data-intensive, scientific methods to identify solutions for rural community resilience.

Building on the groundwork laid by previous iterations of the project, this group’s work has helped policymakers, businesses, utility companies, school administrators, law enforcement, health care providers and others make decisions that meet the needs of rural areas now and for generations to come.

Over the last three decades, the W4001 project and its predecessors have built a sustained multidisciplinary, multistate approach to rural population research. Project members have a wide range of skills and are familiar with diverse rural settings across the nation. This framework has allowed the project to assess rural populations in a comprehensive way while also drilling down into specific issues.

Population Trends

After discovering that rural populations are shrinking due to young adult outmigration, fewer births and increased mortality, project members created a database that details county-level, age-specific net migration trends. Hundreds of thousands of regional planners, insurance companies, school districts, senior housing developers, public health agencies and other stakeholders have used the database to understand rural needs and market demand and to inform infrastructure development and resource allocation.

These findings were also included in the President’s Agricultural and Rural Prosperity Task Force 2017 report to guide policy and programs that reflect current and projected trends. Research and outreach also helped numerous state governments prepare for and facilitate the 2020 Census. Research has also informed census data users about the limitations in using Census 2020 data to understand population change.

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In recent years, the project’s research has helped address multiple major national health crises. For example, the project provided essential information about the effects of the COVID-19 pandemic on rural communities, guiding states’ social distancing policies, resource allocation, testing and reopening strategies. Project members also met with President Biden’s COVID-19 Health Equity Task force to discuss issues related to rural population health and community development.

This research-backed information has helped save lives. In particular, researchers developed a user-friendly model of disease transmission and briefs on rural vulnerabilities related to age and chronic disease prevalence, disparities in testing and case and death counts, the spread of misinformation and economic impacts.

Additionally, this project was the first to identify rising rural opioid overdose rates and explanations for those trends. This information shaped national legislation, influenced the design of an interactive data visualization tool that helps communities assess and respond to the overdose crisis, and led to rapid resource allocation.

Natural Resources

Many rural communities and economies have been highly dependent on natural resources. This project is monitoring transitions in natural resource dependency, providing critical information for the resilience of rural areas.

In Utah, research and outreach raised awareness of changes in the natural resource dependency of rural communities and the effects these changes can have on well-being, including mental health. Research also helped the Governor of Michigan and state public policy makers understand the social justice implications of transitioning to renewable energy systems.

Natural Disasters

Many rural areas are highly susceptible to natural disasters. Recent research showed that there is a higher risk of flooding in rural tracts and tracts with larger proportions of older adults and socioeconomically vulnerable groups. These findings could help improve flood estimates and flood resource allocation. Researchers are also looking at the risk of wildfires to populations living in the wildland-urban interface.

Diversity, Equity, Inclusion and Access

Project members are looking at a variety of social justice issues in rural areas. The project’s research has shown that diversity and tolerance can create social capital and economic prosperity in rural areas, providing evidence for polices that help marginalized groups, such as disabled, ethnic minority and LGBTQ+ residents.

In particular, research on racial residential integration in rural areas raised awareness about place-based attributes that might promote racial equity. Studies also illuminated the health care needs of immigrant agricultural workers in rural areas and showed that there are more barriers to health care access for Hispanics in rural communities with historically small, but rapidly growing, Hispanic populations.

Other recent studies have addressed subprime lending and foreclosure issues in rural areas. The W4001 team also continues to refine the work of previous committees, which led to changes in the official measurements of poverty and underemployment and the distribution of safety net resources.

Sharing the Science

Working closely with other public and private institutions, federal agencies and professional societies has further enhanced the group’s analysis, outreach and impact. To share their findings and help build capacity to access, use and analyze demographic data for program development, decision-making, resource allocation and service provision, project members have:

• Consulted at the highest levels of federal policy for committees within the Office of Management and Budget, U.S. Census Bureau, and U.S. Office of National Drug Control Policy
• Met numerous national and international NGOs, including the National Academy of Sciences, National Institutes of Health, United Nations and charitable foundations
• Conducted briefings, workshops and consultations with state policymakers, Extension agents, community organizations, and other stakeholder groups
• Delivered presentations to professional association and research conferences and published hundreds of peer-reviewed papers
• Broadcast findings to the public through over 50 interviews with media outlets, including in the New York Times, Wall Street Journal, Washington Post, USA Today, NPR, AP News, Bloomberg News, Salon, The Hill, SLATE, The Guardian, Buzzfeed News, Vice, and more
• Taught hundreds of undergraduate students and supervised numerous research projects and dissertations

Project Funding and Participation

W4001: Social, Economic and Environmental Causes and Consequences of Demographic Change in Rural America (2017-2022) was funded in part by the Hatch Multistate Research Fund, administered by USDA-NIFA, and by additional grants to project members. This project’s Hatch Multistate funding has been extended through 2027, and members continue to leverage external funding to extend and build on research to meet project objectives. See what the project hopes to accomplish next.

The group currently comprises 39 members from over 25 institutions. Members have expertise in sociology, geography, economics, natural resource management, Extension and other disciplines, and have won numerous teaching and research awards from their institutions and professional associations.

Participating Land-grant Universities include: Auburn University, University of Connecticut, Cornell University, University of Illinois, Iowa State University, Kansas State University, Louisiana State University, University of Minnesota, University of Missouri, Montana State University, University of Nevada-Reno, University of New Hampshire, Ohio State University, Oregon State University, Pennsylvania State University, Utah State University, Washington State University and University of Wisconsin.

Other participating institutions include: Brigham Young University, Macalester College, McGill University (Canada), Michigan Technological University, Middlebury College, University of Mississippi, University of Montana, Syracuse University, University of Texas at San Antonio, and USDA’s Economic Research Service.

Photo: Downtown Durham, New Hampshire. Image courtesy of University of New Hampshire. 

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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

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

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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.

Reviewed by:

Copyright © 2024 Zhang, Guo, Liu, Ji and Yu. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

*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

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Water Supply Challenges in Rural Areas: A Case Study from Central Kazakhstan

Alua omarova.

1 Department of Public Health, Karaganda Medical University, Gogol Street 40, Karaganda 100008, Kazakhstan; ur.liam@2191_aula (A.O.); zk.umgk@vehsilak (M.K.); zk.umgk@avotebmagamsod (R.D.)

Kamshat Tussupova

2 Division of Water Resources Engineering, Lund University, Box 118, SE-221 00 Lund, Sweden; [email protected]

3 Center for Middle Eastern Studies, Lund University, Box 221, SE-221 00 Lund, Sweden

Peder Hjorth

Marat kalishev, raushan dosmagambetova.

Rural water supplies have traditionally been overshadowed by urban ones. That must now change, as the Sustainable Development Goals calls for water for all. The objective of the paper is to assess the current access to and the perceived water quality in villages with various types of water supply. The survey was carried out during July–December 2017 in four villages in central Kazakhstan. Overall, 1369 randomly selected households were interviewed. The results revealed that even though villagers were provided with tap water, significant numbers used alternative sources. There were three reasons for this situation: residents’ doubts regarding the tap water quality; use of other sources out of habit; and availability of cheaper or free sources. Another problem concerned the volume of water consumption, which dropped sharply with decreased quality or inconvenience of sources used by households. Moreover, people gave a poor estimate to the quality and reliability of water from wells, open sources and tankered water. The paper suggests that as well decentralization of water management as monitoring of both water supply and water use are essential measures. There must be a tailor-made approach to each village for achieving the Sustainable Development Goal of providing rural Kazakhstan with safe water.

1. Introduction

The target task of the Millennium Development Goal (MDG) 7.C was to halve the number of the population with no access to safe drinking water and basic sanitary facilities by the year 2015 [ 1 , 2 , 3 ]. Through implementing this target, the proportion of people who have access to a basic drinking water service grew from 81% to 89% from 2000 to 2015 [ 4 , 5 ]. However, a weakness of the MDGs monitoring was an insufficient attention to water safety [ 1 , 6 ], which became a key element of the target task for water supply and sanitation upon design of the Sustainable Development Goals (SDG 6).

According to the United Nations Resolution 64/292: “The human right to water entitles everyone to sufficient, safe, acceptable, physically accessible and affordable water for personal and domestic uses” [ 2 , 7 ]. Therefore, SDG 6.1 call for full coverage of safely managed drinking water by 2030. The “Safely managed drinking water” indicator includes the three following conditions: accessible on premises, available when needed and free from contamination [ 8 , 9 ].

This goal is a huge challenge for all countries, not only for low- and middle-income ones [ 10 ]. The commitment to “leave no one behind” requires a focus on rural areas, which is typically neglected [ 4 , 11 , 12 , 13 ]. About 844 million people on Earth do still not have access to basic water supplies and 79% of them are rural residents [ 14 ]. At the same time, 2.1 billion people have no safely managed drinking water supply system service. This means that 14.9% of the urban- and 45.2% of the rural population need improved services [ 9 ].

A person needs 50 to 100 litres of water per day to meet physiological and hygienic needs [ 15 , 16 , 17 ]. People facing a limit of 20 litres per capita per day will therefore be exposed to a high level of health concerns. Rural residents usually live in worse economic conditions than urban ones and this affects the volume of water use [ 18 , 19 ].

Kazakhstan is one of the countries on the Eurasian continent that experiences the most severe water shortages. Water shortage and its poor quality have been determined as vital issues threatening the future prosperity of the country [ 20 , 21 , 22 ]. Furthermore, in Kazakhstan the coverage of water supply in the urban and rural areas differ significantly. Approximately 90% of urban people have access to safely managed drinking water, whereas in the rural areas this rate is only 28% [ 5 , 23 ]. Therefore, rural areas constitute the greatest challenge in the efforts to provide safe water for all.

The objective of the paper is to assess the current access to and the perceived water quality in the villages with various types of water supply. Although official statistics on water access per person in each village are available, that do not reflect the complex realities of the current situation. Therefore, a questionnaire survey was carried out in villages in the central part of Kazakhstan to illustrate this complexity and the obtained data was compared with the official one. The factors affecting the volume of water consumption and preferences to use alternative sources among centralized water supply users were identified. In addition, people’s satisfaction with the quality of drinking water and the reliability of different services were evaluated.

2. Materials and Methods

2.1. source description.

Drinking water is domestic water used for both drinking and hygiene purposes [ 24 ]. It can be supplied from different sources. Figure 1 shows the six available sources for such water in Kazakhstan. Centralized water provision is distributed through taps and standpipes, with water supplied from either surface or groundwater and this water is usually treated. Standpipes are provided along the pipelines at specified intervals. However, tap water inside a house is available only at the expense of a house owner. The government provides the centralized water supply, therefore the local administrative authority shall regularly check it for the presence of contaminants. Decentralized water supplies from boreholes and wells do not have any delivery services to houses and can be used public or individual. A permit for drilling new boreholes and wells is provided by the local administrative authority based on prior investigation of the field. They are also intended to do regular water quality tests throughout the operation period. However, the population sometimes use unregistered boreholes and wells, which means no control by the local administrative authority. Other sources of drinking water, such as tankered water and water from open sources, are not considered safe. However, due to the absence of water supply alternatives tankered water is included in official statistics and is regarded as a makeshift measure for drinking water supply provision for the population. Water is delivered to villages in a tanker, usually once a week and people pay for each litre on site. A company selected by the local administrative authority is responsible for a timely delivery and for the quality of water. Finally, an open source can be a spring, river or lake. They are completely absent in the official statistics and are utilized by individuals [ 25 ].

Sources of drinking water.

Rural people have to use multi-sources due to the lack of a stable water supply system in the villages. Households usually classify them based on their purpose for using water [ 26 ]. For instance, tap water for drinking, wells for hygiene, rainwater and thawed water for garden irrigation, etc.

2.2. Area Description

The study was carried out in the Bukhar-Zhyrau district (49°57.21′ N–73°43.01′ E, 500–700 m elevation, 14,576 km 2 ), located in the central part of Kazakhstan. The climate is continental with an average temperature of +19 to +21 °C in July and −15 to −17 °C in January, in addition to an average annual precipitation of 300–350 mm. The topography is flat and most of the territory of the district is covered by the Kazakh Uplands. A population of 64,683 (in 2017) live in 67 villages scattered throughout the region [ 27 ].

Groundwater is the main water resource and the population is provided with various types of water supply. Centralized piped water supply is used by 51,752 people, including 6083 standpipe users and 45,669 in-house water conduit users. Decentralized water supply is used by 12,431 people, including 9001 borehole users and 3430 well users. Finally, tankered water is used by 500 people [ 27 ]. To make a complete pattern of basic advantages and disadvantages of water supply in the region under study four villages, each with the largest percentage of users of one of the three types of water supply, were selected for further investigation ( Table 1 ): Botakara with mixed (both centralized and decentralized), Dubovka and Karazhar—centralized, and Asyl—tankered water supply.

Number of population in investigated villages by type of water supply according to the official data and the sample size.

2.3. Questionnaire Development

The questionnaire was developed based on the findings of a pilot study conducted by Tussupova et al. [ 28 , 29 ] in the Kazakh and Russian language, since both Kazakh and Russian speakers reside in the region under study. An ethical approval was obtained from the Bioethics Committee (Karaganda State Medical University, Karaganda, Kazakhstan, Protocol #110 of 17.10.2016) and the questionnaire was accepted during a session of the Scientific Evaluation Committee (Karaganda State Medical University, Karaganda, Kazakhstan, Protocol #6 of 14.06.2017). This study was approved and verified by the local administrative authority of Bukhar-Zhyrau district. The respondents were aware that participation therein was voluntary and that they could renounce providing any information at any time without reasons. All the persons polled signed an informed data collection consent statement.

The aim of the questionnaire was to assess what sources were used by the rural population and their satisfaction with the quality and quantity of the drinking water supply. The questionnaire covered the following topics: type of source mostly used for drinking purposes, reasons for searching for other water sources despite having a tap at home, volume of water consumption, time spent on water collection, additional purchase of bottled water, household water treatment methods, perceived quality and reliability of water supply systems.

2.4. Sample Collection

2.4.1. calculation of sample size.

The survey was carried out during July-December 2017. First, the official data provided by the local administrative authority for information about water supply systems available in the given region was studied. Then 1369 randomly selected households in four villages were interviewed. Finally, the obtained data was analysed aided with STATISTICA 13.3 (StatSoft, Tulsa, OK, USA) software. The sample size was calculated using the following formula [ 30 ]:

where n is the required sample size; p and q is a part and its inverse value in each class of the general totality ( p = 0.5; q = 0.5); Z α is a constant (set by convention according to the accepted α error and whether it is a one-sided or two-sided effect) as shown on Table 2 :

Critical values of Z for standardized normal distribution.

N is general totality amount ( N 1 = 6252; N 2 = 4114; N 3 = 1035; N 4 = 294); ∆—the difference in effect of two interventions which is required (estimated effect size) (∆ = 5% ):

Provided inevitable loss amongst the participants in the course of the study (for various reasons), the calculated sample size was increased by 20%:

In the course of questionnaire survey 25 persons resigned from the investigation: four from Botakara; three from Dubovka; seven from Karazhar, and 11 from Asyl. Thus, the total number of the respondents was 1369 instead of 1394.

2.4.2. Calculation of Water Consumption

Those households that use the tap pay for each m3 of water according to the meter readings. The respondents indicated the volume of water consumption ( x ) according to the payment receipts for the last month. When analyzing, water consumption per person per day (L) was calculated by the following formula:

Households that use sources without any delivery services collect and store water in tanks. During the interview, the respondents indicated the volume of tanks ( x ) and how often they had to fetch water. According to the findings, water consumption per person per day (L) was calculated as follows:

2.5. Description of Respondents

The questionnaire included the answers of one family member over 18 years who was responsible for water use from each household. The overall burden of collecting and using water in population is usually much higher in women than in men [ 31 ]. Our results have also confirmed this fact, since 63% of respondents were women and the remaining 37% were men. The respondents were between 19–70 years old. On average, 80% of them had lived in the studied villages from birth and each household included one to nine persons. Since the selection of the households was randomized, the level of education within the communities surveyed was not specifically studied.

3.1. Villages with Access to Tap Water

Comparing the official data from Table 1 and the collected data from Table 3 , it was found that the residents often used alternative water sources, even though they were provided with tap water supply. According to official data, 42.55% of the population of Botakara village had a water pipe in a house and 7% of them used standpipes outdoors, but only 25.35% of the respondents indicated taps as a source of drinking water and 51.44%—standpipes. In addition, 34.49% of the villagers had registered boreholes, and 15.96% had registered wells in their yards. Nevertheless, our data showed that only 16.51% and 6.74% used this kind of sources.

Percentage of respondents by the drinking water sources according to the collected data.

The situation was different in Dubovka village. There, 100% of the population was provided with centralized water supply and 98.06% of them had water taps inside their houses ( Table 1 ). However, nearly half of the respondents indicated alternative water points as a source of drinking water due to the time limited water service ( Table 3 ). Private unregistered boreholes and wells were used by 23.52% and 17.34% of the respondents, respectively. Moreover, 15.44% of villagers preferred to use water from natural open sources.

A similar situation was observed in Karazhar village. According to the data in Table 1 , 100% of the population was provided with centralized water supply. Nevertheless, as many as about 78% of the respondents indicated other water sources: 28.57% had unregistered boreholes, 31.31% unregistered wells and 18.24% independently brought water from natural open sources ( Table 3 ). The central water supply in the village was served all year round on a scheduled basis, four hours in the morning and three in evening. According to the respondents’ description, tap water was muddy. Therefore, people had to let water run for a long time, as well as to settle and boil it before each use.

The amount of used water depended on a source of water supply used by households and the time required to transport water from a source to a house. The linear regression between the volume of water consumption, a water supply source and the time spent on water collection was moderately downhill (R = −0.633; p = 0.01) ( Figure 2 ). This relationship showed that in 99% of cases with increasing time of water transporting, its consumption decreased. A type of water source and the time of water transportation to a house explained 40% of the variation in water consumption among the respondents, the remaining 60% of the variation was caused by influence of other unaccounted factors.

Water consumption in terms of a water supply source used by households and the time spent on water collection.

As shown in Figure 3 , 27.21% of the respondents in Botakara, 27.55% in Dubovka and 17.63% in Karazhar bought bottled water. However, Karazhar village differed from the other two in the frequency and quantity of buying bottled water. In Botakara and Dubovka 50% of people who bought bottled water did this irregularly, while in Karazhar villagers had to purchase it two or three times a week. In the first two villages, residents bought average 4.18 and 4.71 litres at a time respectively. In Karazhar this number was 6.2 litres.

Additional purchase of bottled water.

Some households treated drinking water at household level ( Figure 4 ). In Karazhar 49.54% of the respondents used some methods of household treatment, while this number in Botakara and Dubovka was 26.28% and 25.42% respectively. For this treatment, 76.07% of the respondents who purified water in Karazhar said that they used a factory filter. More than half of them changed a filter once a month and spent an average of 885 tenge (US $2.48 as on August 31, 2018) on each piece.

Use of household water treatment methods in the villages.

Multiple p-level comparisons by the Kruskal-Wallis test showed that water from taps in houses, outdoor standpipes and boreholes was no different in satisfaction with the quality of drinking water and reliability of sources according to the respondents ( Table 4 ). Quality and reliability are not independent factors. System breakdown impacts both quantity and quality, as the water is frequently of poor quality after such an event. Thus, reliability was essentially a measure of how often there was a problem concerning the delivery of water of an acceptable quality. In Dubovka and Karazhar villages, there were statistically significant differences in the quality indicators of water taken from wells and open sources, in contrast to water from the sources mentioned above. The villagers in Dubovka who used wells and open sources were not satisfied with its quality and reliability, as they rated them as “poor” (81% and 71.73% respectively) and “unreliable” (94.06% and 86.7% respectively). Almost the same situation was observed in Karazhar: 66.87% of villagers were not satisfied with the quality of water from wells and 74.77% from open sources. Also, 76.6% and 85.11% of the respondents considered the use of wells and open sources respectively to be unreliable.

Level of satisfaction with the quality of used drinking water and reliability of sources according to the respondents’ assessment.

1 Significant at p < 0.05.

Figure 5 shows the subjective assessment of the price and quality of drinking water given by the respondents depending on a used water source on a scale from one to ten. They stated the quality of drinking water in points in accordance with their impression, where one point was low and ten points was good quality. The price was converted into points based on the impression of the cost of drinking water, where one point was acceptable and ten points was expensive. The ratio of quality and price was calculated as follows:

Subjective assessment of quality-price ratio on drinking water by the respondents.

The residents of Botakara gave a high estimate in comparison with the other two villages; the estimates in Karazhar were very low (not above 5.7 points for taps and standpipes). The assessment given by the villagers fell depending on a used water source in the following sequence: tap > standpipe > borehole > well > open source. In most cases, people believed that the costs of an agreement with a third party for drilling a well as well as independent water transportation from open sources did not conform to water quality. This number for water from wells in Botakara was estimated at 4.14 points, in Dubovka at 2 points and in Karazhar at 1.7 points. The residents of the last two villages also used open sources and rated them at 1.97 and 1.35 points respectively.

3.2. Villages with Tankered Water

In Kazakhstan, a number of villages have an acute water shortage due to the lack of sources in their territory. It is estimated that the economic condition of the villages is poor. The population is provided with limited volumes of tankered water, the quality of which is doubtful. At the time of the study, in the Bukhar-Zhyrau district, there were four similar villages. One of them was Asyl, where 294 people lived. All people there used tankered water. The distance of water delivery was 17 km from a water source.

In Asyl village, the collected data coincided with the official ones, but the reason was the absence of alternative source of drinking water supply in the territory. There was only one tanker for the whole settlement, which brought water once a week according to the schedule (every Friday at midday local time). Therefore, when the transport broke down, the population had no drinking water for two–four weeks. Water tankers must be cleaned and disinfected before use at least once every three months [ 32 ]. According to the interview with the driver, this requirement was not always met.

The average water consumption in the village was 41.67 litres per person per day. Some residents stated that they spent an average of 103 minutes (for round trip) for self-delivery of water from alternative sources to a house. The data showed that 68.78% of the respondents bought bottled water as needed for drinking and cooking only. In case of water shortage or lack of delivery, most villagers used rainwater and thawed water for hygiene purposes.

In the village 44.44% of residents indicated that they regularly treated drinking water at home, 24.87% of them boiled water before consumption, and 67.72% used a factory filter. However, the issue was that the population did not know how to operate it properly. This was evident from the fact that 50.26% of those who used filters at home had not changed them it from the moment of purchase.

In Asyl village, the level of satisfaction with the quality of water and reliability of the source was very low ( Table 5 ). Since 77.78% of residents believed that, its quality was “poor”, and 98.94% estimated the reliability of tankered water supply as “unreliable”. Furthermore, villagers considered that the price of tankered water was not in line with its quality. They rated it at only 2 points.

Level of satisfaction with the quality and reliability of tankered water supply according to the respondents’ assessment.

4. Discussion

Tap water installed in villages by the government was not able to fully satisfy the populations’ drinking water demands. There had been some constant interruptions in the systems due to technical problems, which in turn worsened the quality of the supplied water. The quality was further reduced, because the population had underused the system’s capabilities [ 33 , 34 , 35 ]. Even though villagers were provided with tap water by the government, significant numbers used water from alternative sources of an unknown quality. When analyzing the reasons that led to this situation, it turned out that respondents most often indicated in the questionnaire the following: doubts regarding the quality of tap water; use of other sources by habit, as they were accustomed to it during water scarcity; and availability of cheaper or free water sources. The villagers also explained that scheduled water supply was the reason for searching for other water sources despite having a tap at home. This was especially the case during summer time, when water consumption increased due to garden irrigation.

Another problem concerned the quality of water supply for the residents from unregistered boreholes and wells in the villages. These boreholes and wells were not tested for compliance with the sanitary standards before and during the operation. Due to acute water supply shortage, the population also had to use water from open sources; brackish water from underground sources recommended only for domestic purposes as well as rain and thawed water. This situation was regarded as highly unsatisfactory.

A study of the water use characteristics was greatly significant for a sustainable development of rural regions, especially in countries with a deficiency of water resources. The more time people spent on water transportation from a source to a house, the less water they consumed to the detriment of their physiological and hygienic needs. Moreover, the amount of water used dropped sharply with decreased quality or inconvenience related to a source of water supply used by households.

Water consumption among taps, standpipes and boreholes users was found to be 50 to 200 litres per person per day, while this number among open sources and tankered water users did not reach 50 litres per day. Other factors affecting the amount of water consumption included religious obligations, water price, family income and climate condition, as well as relations and intentions in regard to preservation of water resources [ 36 , 37 , 38 , 39 ].

The population considered additional purchase of bottled water and treating water at home to be desperate measures. Bottled water was needed in periods of acute water shortage, when percentage of purchase was especially high in the village with tankered water. Water was treated at home in villages where residents doubted the water quality and took responsibility for its additional treatment. People who were the most satisfied with the quality of used drinking water and reliability of sources lived in Botakara, because they did not use water from open sources, and it was in this area where the majority of boreholes and wells had been registered. The less satisfied people lived in Dubovka and Karazhar due to low quality of water from wells and open sources, and in Asyl because of tankered water. People gave a poor estimate to reliability of these sources, although they still consumed the water from them.

In spite of the fact that the government tries to provide rural regions with tap water supply, the study has revealed various challenges in this endeavour. It is necessary to find a balance between the quantity and quality of water. In villages where there is a need to prioritize access to sufficient water quantity, the water consumption can be increased by means of timely repair and maintenance of the system, which is in turn a guarantee of uninterrupted supply of drinking water. In villages where the water quality is the dominant factor, priorities should be directed to appropriate drinking water treatment methods and training to encourage the population to choose the right water source. To this end, there should be an emphasis on making the healthy benefits of tap water associated with its high microbiological quality widely known. Moreover, to reduce the stress on limited water resources, there is a need for a more effective management and implementation of water preservation measures as well as improvement of the technical conditions of water supply lines, and sewage facilities. There is also a need for efficient and hygienic water use training for the population.

The villagers thought that the costs of an agreement with a third party for drilling a well and independent water transportation from open sources as well as the price of tankered water were not in line with its quality. Even while there was one source of water for taps and standpipes in each village, satisfaction with its quality and reliability varied due to technical problems in water supply plants. Upon their assessment of the price and quality of drinking water subject to the water source used, the respondents gave more points to tap water than to standpipe in all villages under investigation. This was because in this case they estimated the quality of the water as well as the convenience service. Obviously, water from the centralized system cannot be considered to be safe as long as users occasionally prefer other, uncontolled sources.

5. Conclusions

Decentralization of water management, monitoring of both water supply and water use and a tailor-made approach to each village are necessary to achieve the Sustainable Development Goals objective of providing rural people with safely managed drinking water. Providing safe water supply to rural Kazakhstan will be a tremendous challenge that the government needs to tackle as soon as possible.

It is only in cooperation with the local community, government bodies can identify systemic sustainability problems, and develop and implement policies for water access in premises; water that is available as needed and free from contamination. This cooperation will also ensure sustainable public health and bring economic benefits to villages. Consequently, this analysis of consumer demand on the existing water supply systems in the villages and people’s preferences in choosing the source of drinking water can contribute to more effective water supply planning and, thereby, support a sustainable development of rural regions.

Author Contributions

A.O. and K.T. planned the structure of the study. A.O. studied the official data, and performed the questionnaire survey. A.O. carried out the analysis of collected data with supervision from K.T. A.O. wrote the first version of the paper; K.T., P.H. and M.K. contributed in an equal manner to the paper by adding comments and writing parts of the final paper, R.D. assisted in replying to the reviewer comments and making the final corrections of the paper.

This research received no external funding.

Conflicts of Interest

The authors declare no conflict of interest.