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A methodological guide for applying the social-ecological system (SES) framework: a review of quantitative approaches

• Stefan Partelow Stefan Partelow Leibniz Centre for Tropical Marine Research (ZMT), Bremen, Germany; Jacobs University, Bremen, Germany

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The following is the established format for referencing this article:

https://doi.org/10.5751/ES-13493-270439

• Introduction
• Framework and Review Methodology
• Responses to This Article
• Acknowledgments
• Data Availability
• Literature Cited

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Copyright and permissions, introduction.

The social-ecological systems framework (SESF) remains one of the most highly cited and empirically applied conceptual frameworks for diagnosing social-ecological systems (Ostrom, 2007, 2009, McGinnis and Ostrom 2014). Notably, the SESF does not have a methodological guide or a standardized set of procedures to empirically apply it. This is to some extent by design, to allow flexibility in how methods are adapted to diverse contexts (McGinnis and Ostrom 2014). However, this has led to highly heterogeneous applications and challenges in designing a coherent set of data collection and analysis methods across cases.

A main challenge is that methodology is a general term, which actually refers to a set of stepwise specific procedures which can include study design, conceptualization of variables and indictors for data collection, empirical or secondary data collection, data processing and cleaning, data analysis, as well as data visualization, communication, and sharing. Although the SESF provides a uniform set of variables, it does not indicate any of the other necessary steps for a robust scientific study. Applying the SESF is not a method itself, but it is arguably a theory-derived conceptual guide for focusing the methods a researcher does choose on a set of variables that have previous empirical support in shaping commons, institutional development and change, and/or collective action outcome. Thus, scholars are forced to either mirror previous studies or develop their own procedures, leaving heterogeneous applications that enable contextually tailored approaches but hinder comparability across studies.

The focus of this study is to explicitly synthesize the methods applied in SESF studies by systematically reviewing published quantitative applications of the SESF and to develop a methodological guide for the framework’s continued application while highlighting the challenges in current literature. A guide is useful so that scholars can map their methodological choices more transparently, sparking reflections for their own study designs and better enabling the systematic communication of study methodological decisions to others. To apply the SES framework, a series of methodological steps are needed. These steps have been referred to by Partelow (2018) as methodological gaps, because if they are not explicitly defined by authors, they can lead to a lack of transparency for future comparability and interpretability by other scholars. The methodological gaps include: the (1) variable definition gap, (2) variable to indicator gap, (3) the measurement gap, (4) the data transformation gap.

Focusing on methodologies is important for two reasons. First, synthesis research to build theoretical insights across SES applications has been a challenge because the full spectrum of methodological designs and concept definitions are often not fully published or are simply too heterogeneous for making contextually meaningful comparisons (Thiel et al. 2015, Partelow 2018, Cumming et al. 2020, Cox et al. 2021). For example, Villamayor-Tomas et al. (2020) found that the majority of reviewed models from 30 SESF studies were lacking detail regarding what methods or approaches were used to identify the relationships between variables that the authors were presenting. Second, the SESF itself does not provide any explanation of the factors or causal relationships that are shaping the observed SES problem or phenomena. The framework only provides a common vocabulary and a diagnostic conceptual organization of 1st-tier component interactions, not a procedure regarding how or which methods should be applied with the SESF to investigate these factors.

The methodological guide proposed from this review is applicable, in our view, to all future applications of the framework, both quantitative and qualitative. Nonetheless, quantitative methods were used as the basis for the review because they typically follow systematic procedures for data collection and analysis through the discipline of statistics, which in the data collection phase, translates empirical observations into comparable sets of numbers that can be analyzed with standardized analytical techniques. Specifically defined indicators and variables are needed for quantification along with specific steps to appropriately transform and analyze data, in contrast to qualitative studies, in which reproducibility and generalizable measurement may not be possible or is not the goal of the research. Reproducible criteria for how variables are measured in qualitative studies is by nature more difficult because a primary objective in many qualitative contexts is the rich analysis of data, contexts, and processes not easily reduced to individual variables (Queirós et al. 2017) and often focused on broader knowledge transferability than specific data comparability (Guba 1981).

Previous studies have outlined sets of questions or procedures for applying the framework more specifically, such as for conceptualizing and defining the case SES and action situation (Hinkel et al. 2014, 2015, Partelow 2016). However, there is no systematic or procedural guide with a focus on outlining different methodological strategies and choices. As such, this review aims to make two major contributions. First, to review current applications of the SESF to compile a multi-step guide of methodological steps for applying the SESF framework. Second, to use these results as a base for constructively analyzing current trends, inconsistencies, and challenges in applying the framework to date and to highlight needed methodological advancements and paths forward in SESF research. Through a systematic review of SESF methodologies, we explored the methodological heterogeneity and gaps across the literature and discuss how this heterogeneity can lead to ambiguity for synthesis work. Combined with feedback from a survey regarding ongoing SESF challenges from 22 co-authors of publications included in this review, we identified methodological strategies at each step of study design, data collection, and analysis and then we provide a synthetic methodological guide to inform future applications, while also positing critical reflections on the limitations of current approaches.

FRAMEWORK AND REVIEW METHODOLOGY

Social-ecological systems framework.

The SESF was developed to conduct institutional analyses on natural resource systems and diagnose related collective action challenges. The core of the framework provides a decomposable list of variables situated around an “ action situation ” in which actors make decisions and actions based on the available information within their positions, which enables researchers to structure diagnostic inquiry and compare findings. Although most empirical applications of the SESF have established some theoretical ties to the study of the commons and collective action (Partelow 2018), the SESF was conceived and gained traction as a useful tool for the broader characterization and analysis of SES sustainability (Ostrom 2009) and as a “ theory-neutral ” framework that can be used with other theories or to build new theories (McGinnis and Ostrom 2014, Cox et al. 2016). For a more complete history of the SESF and its connection to the institutional analysis and design (IAD) framework, see its foundational publications (Ostrom, 2007, 2009, McGinnis and Ostrom 2014) as well as previous syntheses and reviews (Thiel et al. 2015, Partelow 2018, 2019).

The SESF is divided into several 1st-tier components representing social and ecological as well as external factors and system interactions and outcomes, each divided into multiple 2nd-tier variables (Ostrom 2009; Table 1). By breaking down an SES into a set of decomposable, nested, and generalizable concepts, the SESF aims to achieve a dual purpose, (1) facilitating an understanding of the specific and contextual factors influencing SES outcomes at a fine local scale and (2) also sharing a common general vocabulary of variables to facilitate the identification of commonalities across cases to build policy recommendations and theory at varying levels of generalizability (Basurto and Ostrom 2009, Ostrom and Cox 2010).

Although the existing literature suggests that the SESF is being successfully applied as a contextually adaptable tool for local SES case analysis, synthetic analysis remains a critical challenge, and the goal of comparability across studies has arguably not been fully realized (Partelow 2018). Scholars applying the SESF have been innovative and exploratory in how their data are collected, analyzed, and reused, leading to methodological pluralism, heterogeneity, and often ambiguity in how the SESF is or should be applied, such as the lack of clarity in how case-relevant variables should be selected and measured (Partelow 2018), as well as difficulties with ambiguous or abstract variable definitions (Hinkel et al. 2014, Thiel et al. 2015). Existing SES and commons database synthesis efforts exist but are made more difficult by the broad range of methodological approaches and inconsistencies with how the framework is applied and variables measured (Cox et al. 2020, 2021). Recent synthesis work of the SESF has noted challenges including both the lack and heterogeneity of information on variable relationships and causal inferences across publications, limiting analysis to only the co-occurrence of variables across SESF studies (Villamayor-Tomas et al. 2020). Social-ecological systems framework applications are taking different approaches to selecting, justifying, measuring, and analyzing SESF variables and lack precision in concepts and measurements (Cumming et al. 2020). We therefore identify methodological inconsistencies in applying the SESF as one major ongoing hurdle to comparable and synthetic SES research, and thus the primary focus of our review.

This study applied systematic review methods to peer-reviewed literature collected from SCOPUS, Web of Science Core Collection, and Google Scholar between August to September 2020 (with a follow-up search in January 2021) to identify any literature applying the SESF with some degree of quantitative data analysis (Appendix 1, Fig. A1.1). The initial SCOPUS and Web of Science title/abstract search used search terms (TITLE-ABSTRACT ("social-ecological system* framework" OR “ social ecological system* framework ” ) OR "SES framework") OR TITLE-ABSTRACT ("social-ecological system*" AND "framework" AND Ostrom")) OR TITLE-ABSTRACT ("social-ecological system*" AND "SESF")) and a follow-up search with Google Scholar to identify any additional publications, which after removing duplicates resulted in an initial set of 330 peer-reviewed publications. Because a key focus of this review is on the heterogeneity of explicit methodological procedures and variable measurements affecting generalizability, comparability, and reproducibility of results, we chose to focus on completely or mixed-methods quantitative applications of the SESF, which are more likely to face limitations in these regards. These criteria included all publications that applied the SESF and analyzed any amount of quantitative raw or transformed data. Publications with any ambiguities with regard to these criteria were discussed between co-authors to reach consensus on inclusion in the review. A title/abstract scan removed all publications not applying the SESF, followed by a full-text review to identify those applying a quantitative analysis, which identified 46 publications. A follow-up search in January 2021 identified 4 additional publications and 1 additional publication was identified during peer-review, resulting in a total of 51 publications for final review. Each article was evaluated using a standardized coding form that was pre-tested by the authors for consistency. The review followed two guiding questions: (1) How is the SESF being applied with quantitative/mixed-methods quantitative approaches (sectors, research aims, and analytical methods)? (2) How are the 2nd-tier SESF variables being applied (variable selection criteria, data collection, measurable indicator selection criteria)?

To answer these questions, we coded the following data from each publication: purpose for applying the SESF, focal SES analyzed, data analysis methods, challenges in applying the SESF, 2nd-tier variable selection and inclusion criteria, measurable indicator selection, data collection methods, and data type. We make an important distinction between “ variables, ” or the generally defined 2nd-tier concepts of the SESF, and “ indicators ” referring to how the variables are actually measured. Any ambiguities during the coding and evaluation process were flagged and discussed between co-authors to reach consensus. Initial coding was completed in February 2021. To gather more explicit reflections from researchers regarding SESF methodological challenges, critiques, and reflections, a researcher survey was also conducted. The survey questionnaire was distributed to all corresponding authors of the reviewed publications starting in February 2021 and consisted of Likert scale and full-text response questions about their experiences with the SESF. The full list of reviewed publications can be found in Appendix 2, 2nd-tier SESF variable indicators from reviewed publications in Appendix 3, and the evaluation forms, procedure, and author survey questionnaire in Appendix 4. The guide steps were developed based on gaps and trends in the SESF literature, in particular the previously noted methodological gaps in the SESF (Partelow 2018) and were further iterated based on the results of the review, researcher survey, experiences in planning our own research with the SESF, and on-going discussions between novice and experienced SESF researchers in our working group.

A multi-step methodological guide for applying the social-ecological systems (SES) framework

Our findings indicate that researchers applying the SESF make a series of methodological choices that can be organized into a multi-step guide that includes all the aggregated choice options across studies at each step. We present this as a 10-step methodological guide and decision tree (Fig. 1). The steps are arranged in what we identified as a generally logical order, but the specific order of operations is likely to vary based on specific research aims. The branches within the decision tree for each numbered step are not all-encompassing, but instead represent, for each step, the categories that were identified and coded in the reviewed SESF publications, with a handful of potential additional categories identified by the authors. A total of 22 complete responses to the SESF researcher survey were received from co-authors of the 51 reviewed publications. Likert-scale survey responses are presented in Figure 2, and Appendix 1 (Table A1.1) summarizes categories of responses to the short answer survey questions.

(1) What is the primary purpose for applying the SESF?

The SESF is generally positioned as a tool to guide diagnostic SES inquiry, but how it is actually applied varies substantially. One application may develop theoretically derived hypotheses on how 2nd-tier variables are linked to collective action and self-organization in a case (e.g., Klümper and Theesfeld 2017, Su et al. 2020). Others might take an inductive approach, using the SESF to code and compare local perceptions of the SES (e.g., Ziegler et al. 2019, Partelow et al. 2021), or use the SESF basis to develop a model of individual actor behavior in an SES (e.g., Cenek and Franklin 2017, Lindkvist et al. 2017).

Most respondents to the researcher survey stated that it was clear how to apply the SESF to their research, how to use the SESF to support theory building and testing, and how to identify relevant variables for a given case. The SESF was typically chosen by respondents because of its clear and coherent organizational structure and comprehensive coverage of a wide range of social and ecological dimensions, however, nearly a third (n = 7) of respondents chose the SESF at least in part due to its origins in the study of the commons and collective action theory. In our synthetic review, we broadly categorized the purpose for applying the framework as extracted from introduction and methods sections of reviewed publications. Although most studies incorporate multiple objectives, the majority of reviewed publications applied the framework with the primary aim of predicting explanatory social-ecological drivers of (typically a small number of) measured dependent variables representing SES outcomes (e.g., Fujitani et al. 2020, Okumu and Muchapondwa 2020; n = 31). The remaining publications were divided between characterization of SESs through descriptive or diagnostic measurements of the important variables (e.g., Leslie et al. 2015, Rocha et al. 2020; n = 10), testing or projecting potential future SES scenarios through simulations or models of system behavior (e.g., Baur and Binder 2015, Cenek and Franklin 2017; n = 5), or social learning aimed at understanding or better integrating local SES user knowledge and perspectives (e.g., Delgado-Serrano et al. 2015, Oviedo and Bursztyn 2016; n = 5). This broader purpose or goal in applying the SESF informs a wide heterogeneity of methodological decisions and considerations leading to the final study outcome.

(2) Is inter- or transdisciplinary research needed to appropriately conduct the study?

Research with the SESF often requires the integration of concepts and data from a wide array of disciplines. Researchers must consider whether adequately analyzing, describing, or diagnosing an SES may require the integration of diverse knowledge types and formats. This integration can take place across multiple dimensions, levels, and scales (Guerrero et al. 2018). Common criticisms of the SESF, for instance, note that the framework itself developed from disciplinary roots in the social sciences, and it is lacking an equivalent depth of consideration of ecological processes and theories (Epstein et al. 2013, Vogt et al. 2015). Our review found that ecological variables are underrepresented compared to social variables in SESF studies (Table 2), and SESF researchers are also more likely to rely on secondary data for ecological variables than for social variables (Fig. 3).

Integration of different scientific disciplinary expertise (interdisciplinary; Hicks et al. 2010, Bennett et al. 2016) or of scientific and non-scientific expertise (transdisciplinary; Caniglia et al. 2021, Lam et al. 2021) can influence how and to what extent all social and biophysical components and dynamics of the SES are investigated, as well as for whom the study outcomes are relevant and meaningful (Guerrero et al. 2018). Many reviewed publications included stakeholders in the research through household surveys or interviews, but only 12 studies were identified that actually integrated stakeholders into the study co-design process, either by influencing the research questions or objectives, or by playing a direct role in the selection and evaluation of relevant SESF variables. Including relevant non-scientific stakeholders at multiple stages in the research can increase knowledge exchange and research influence (Reyers et al. 2015) and the SESF has been demonstrated as a tool to enhance communication between actors in SES governance (Gurney et al. 2019, Partelow et al. 2019). Reflecting on the appropriate type and level of integration should be an important early methodological consideration in SESF research design.

(3) What is the focal SES(s) of analysis and factors determining its boundaries?

Defining the SES and its boundaries is essential for determining how the individual variables are analyzed in relation to what the internal and external influences on those variables are. The focal sector will also determine the degree to which the analysis could be compared to another study or the practical implications of the findings. Most studies are still applying the SESF to classic common pool resource problems (van Laerhoven et al. 2020) in sectors such as forestry and fisheries (Appendix 1, Table A1.2), providing a larger library of sector-specific comparable studies and variables for authors studying these SESs to reference in designing their own research. The SESF is place-based in design, and researchers should also consider what is within the study system and what is external to its context, and this justification should be established based on the research objectives. For example, SESs often have fuzzy social and ecological boundaries that are not easily delineated and often do not align with each other, and how a researcher bounds the system in their study can have implications for the study findings. The focal SESs in the reviewed literature were described or analyzed with boundaries based on social (n = 29), ecological (n = 8), or mixed or fuzzy factors (n = 12; Appendix 1, Table A1.2). A study might have increased clarity or relevance to policymakers by bounding their analysis by administrative borders but fail to adequately capture important ecological processes not conforming to these social boundaries. We have included defining scope and SES boundary clarification as a key step in our guide because of its methodological implications for the rest of the study, but direct researchers to an existing detailed procedure for conceptualizing and defining the focal SES and institutional action situation of analysis (Hinkel et al. 2015).

(4) What are the primary unit(s) of analysis, number of units, and scales of analysis?

Who or what does the study hope to specifically inform? What is the best spatial fit for the SES phenomena being studied? Although most SESF studies are situated within the case context of one or more SESs, actual units of analysis might range from individual aquaculture ponds (Partelow et al. 2018) to residential neighborhoods (Schmitt-Harsh and Mincey 2020) to administrative provinces (Dressel et al. 2018). The selection of unit of analysis, including number of units compared and spatial and temporal levels of analysis, all impact the granularity and types of generalizations that can be made by the study findings and may also reflect certain practical considerations in terms of data collection. We coded units of analysis at the individual (e.g., individual survey respondent), local (e.g., community), or regional (e.g., geographic region or administrative level encompassing multiple communities or governance units) spatial level. Local and individual units were the most common, followed by regional units ranging from political districts (Dressel et al. 2018, Rocha et al. 2020) to large social-ecological regions (Leslie et al. 2015; Table 3). We categorized studies comparing 30 or more units as large-N, following the central limit theorem (with some studies comparing multiple units of analysis). Large-N comparisons of individual or local units were the most common in the reviewed literature, with only two large-N studies comparing regional units. Additionally, although we identified eight publications analyzing cases across multiple countries, only three cross-national studies collected empirical data (including two studies from the same project: Aaron MacNeil and Cinner 2013; Cinner et al. 2012), with the rest reliant entirely on existing secondary data sources. Although our review focused primarily on coding the number and spatial level of units of analysis, we also emphasize the importance of a wide range of critical scales or dimensions for SES analysis. See Glaser and Glaeser 2014 for further reflections on these dimensions.

(5) Which 2nd-tier SESF variables are being examined and what are the inclusion or exclusion criteria?

No empirical studies examine all of the 2nd-tier variables in the framework. Clearly communicating which 2nd-tier variables were selected, and why or why not, improves understandability and comparability. Ambiguities regarding interpreting, selecting, and defining relevant 2nd-tier variables for a given case were the most frequently reported negative aspect of applying the SESF in our survey. Respondents noted the subjectivity in how variables can be defined, allowing for great flexibility but diminishing comparability. Challenges also exist with interpreting whether high or low “ states ” of a variable may lead to favorable or unfavorable outcomes (e.g., variable hypotheses). Of the 51 reviewed publications, 26 provided clear documentation of all 2nd-tier variables being examined (Fig. 4). The remaining 25 publications were excluded from 2nd-tier variable and indicator analysis because they were either opting not to apply the 2nd-tier variables or lacked clarity regarding which (if any) 2nd-tier variables were being examined. For example, some studies were merging parts of the SESF with other conceptual frameworks, and others provided only a list of indicators categorized by the 1st-tier components, without conclusive indication of which (if any) 2nd-tier variables they aligned with. In some studies, there was a purposive decision to not to apply the 2nd-tier variables by study authors, such as in modeling approaches focused on individual unit behavior within the SES rather than broader SES components. However, in many studies the reasoning was unclear. Some of the 25 excluded publications included alternative 2nd-tier variable definitions or numbering schemes without specifying if these alterations were intended to be interpreted as unmodified, modified, or entirely new 2nd-tier variables (Roquetti et al. 2017, Okumu and Muchapondwa 2020). Modifications to the framework, including adding variables, should be justified while noting the theoretical inclusion criteria that the included variables were based on (Frey and Cox 2015, Partelow 2018). Because journal word counts are often a limiting factor, authors might consider including a clearly formatted 2nd-tier variable appendix as supplementary material (Leslie et al. 2015, Foster and Hope 2016, Dressel et al. 2018, Osuka et al. 2020).

Each study selects this subset of variables based on criteria such as expected relevance to the study. Was a variable excluded because it was not empirically meaningful for the case, because it was potentially relevant but not easily empirically measurable, or because it was not in the authors’ interest to examine it? Was a variable included because the authors have formulated a clear hypothesis for its case relevance or because an abundance of secondary data are readily available to measure it? In the reviewed publications, existing literature and theory was the most common reported criteria, followed by local SES actor expert knowledge, as well as data availability and scarcity influencing variable selection (Table 2). Most studies reported only a general list of inclusion/exclusion criteria (e.g., “ our variables were selected based on literature review and expert knowledge ” ), rather than specific criteria for every included variable in either the main text or supplementary material. Additionally, in five studies we could find no basis for why the selected variables were chosen. Clearly formulated hypotheses for why each included variable was relevant to a case were only identified in eight studies (Leslie et al. 2015, Foster and Hope 2016, Dressel et al. 2018, Partelow et al. 2018, Haider et al. 2019, Rana and Miller 2019a, Osuka et al. 2020, Rocha et al. 2020). Inclusion and exclusion criteria are not always clear-cut and might be based on multiple theoretical, methodological, or logistical aspects. Particularly for quantitative approaches, 2nd-tier variable inclusion and exclusion is likely to also be influenced by statistical factors. In many cases, adding additional variables may need to be weighed against the potential loss of statistical power that this may entail. Similarly, some otherwise relevant variables might be omitted from a study because preliminary data exploration shows high multi-collinearity in their measurements (e.g., Gurney et al. 2016). Documenting not only inclusion criteria but also exclusion criteria should be strongly considered by authors, particularly when 2nd-tier variables may have been omitted for reasons beyond solely a lack of case relevance.

(6) How are selected 2nd-tier variables being measured?

Can the variable be directly measured empirically, given the study design and data collection method? Most of the 2nd-tier variables are concepts and are not directly measurable (at least quantitatively) without specifying one or multiple indicators to represent the concept empirically or to specify its empirical meaning, thus these indicators often form the true unit of comparison in many SESF studies. Even if studies examine the same 2nd-tier variable, they likely select different indicators to specify and measure them. In such cases, what indicators are selected, how many, and why should be considered. Almost half (n = 10) of survey respondents disagreed that it was clear how to identify relevant measurable indicators, and respondents also noted subjectivity and inconsistencies regarding where a given indicator might be coded into the SESF. Our findings suggest heterogeneous and context-dependent indicator selection decisions, with most publications collecting indicators from a wide range of sources and data types. Examples of this indicator diversity for variables RS5 and A2 are shown in Table 4. Study-specific interpretations of 2nd-tier variables and related choice of measurable indicators were highly varied, and reviewed publications were inconsistent in documenting which measurable indicators were applied. Because existing SESF case studies are likely to be an important resource and reference point when identifying appropriate measurable indicators, specificity in documentation of this step when publishing SESF research is critical to improve interpretability and comparability of findings. A selection of all 2nd-tier variable indicators that could be clearly identified in our synthesis can be found in Appendix 3.

(7) What data collection methods are used for the selected indicators?

Social-ecological systems framework studies are likely to rely on a range of different data collection methods and both primary and secondary sources in collecting data for a heterogeneous range of variables in often data-scarce contexts, and researchers should carefully consider the implications for their study design and analysis. Primary data collection ensures complete researcher control over how variables and indicators are measured but is often not feasible across a wide and mixed range of variables. Secondary data collection is often more feasible but may have issues of ambiguity regarding the data quality and clarity of data collection and measurement. Almost all primary data are being collected via social science methods such as questionnaires, interviews, and focus groups (Table 5). Across the 26 studies with clearly articulated 2nd-tier variable selections, primary ecological or biophysical survey data were collected to measure only 9 indicators. Overall, primary data collection is more common than reliance on secondary data. Comparing data collection methods by 1st-tier SESF components suggests that researchers using the SESF are collecting a higher proportion of their social variable data from primary sources compared to their ecological variable data (Fig. 3). However, this trend is highly heterogeneous at the 2nd-tier level (Fig. 4). Thirteen studies relied only on primary data, 20 studies on only secondary data, and 15 studies collected data from a mixture of primary and secondary sources. Our findings indicate that data collection methods across the reviewed literature are wide-ranging with most individual studies applying multiple data collection methods and mixed data types.

(8) What type of data is measured for the selected indicators?

Heterogeneity in data sources and collection methods in SESF studies is likely to result in a range of data types or formats. Schmitt-Harsh and Mincey 2020, for example, combined continuous quantitative indicators calculated from GIS data with ordinal indicators from a multiple-choice survey and binary presence/absence classifications of residential properties. Measuring indicators with a range of mixed data types (e.g., continuous, ordinal, categorical) might facilitate the inclusion of more SESF variables but limits the types of statistical analyses available or requires extensive data processing and transformation. Documentation regarding which indicators were data transformed for analysis was not consistent enough across publications to evaluate in full, however min-max normalization was the most frequent transformation identified. The type or format of the collected data can also add a further layer of abstraction to interpreting or comparing SESF variables in a given study and should be made transparent. For example, two studies seemingly defining the same indicator, e.g. "Kilograms of fish catch," may measure it in different ways, such as from a numeric value (e.g., 37 kg) to a qualitative ordinal scale (e.g., below average, average, above average). These differences in measurement may lead to notable differences in interpretation.

(9) What data analysis methods are being applied?

Data analysis methods broadly encompass the techniques for collection and analysis of data to draw insights. Because the SESF is to an extent only a selection of potentially relevant variables, it can be applied to any number of analysis methods that are determined by the research objectives. The choice of analysis method influences (or is influenced by) overall study design, sample sizes, variable selection, data collection, as well as the inferences that can be made regarding the SESF variables being evaluated and external validity of the study findings. In some regard then, the choice of analysis method encompasses all the previous steps in this methodological guide. We coded the data analysis methods used in the reviewed literature into 11 general categories, provided in Table 6, including potential advantages and disadvantages that researchers might have to weigh with each approach, as well as example studies that exemplify each category.

Studies generally applied multiple analysis methods, but the most frequently coded approach included explanatory/dependent variable analyses (n = 31). Fourteen studies focused on characterizing one or multiple SESs through descriptive or comparative assessments of SESF variables rather than explicitly analyzing causal mechanisms or dependent variables. We further differentiated these SES characterization studies into “ descriptive ” characterization studies (n = 7), which assess and compare variable measures without a normative value judgement, and “ evaluative ” characterization studies (n = 7), which provide a normative score (such as from 0-1), alongside supporting theory or literature, for how high or low measures for each variable relate to the evaluative criteria, e.g., potential for sustainability or collective action. Twelve studies utilized modeling and simulation-based analyses (n = 12) to investigate SES structure and behavior, including agent-based and system dynamics models. Seven studies used participatory modeling and evaluation techniques, exploring local expert knowledge and perceptions of the SES as a key source of scientific insight in what are often otherwise data-scarce SES contexts. An additional seven publications applied meta-analyses of the published literature or other existing aggregated case databases. Notably, only one of these studies specifically synthesized empirical SESF literature (Villamayor-Tomas et al. 2020), while the rest used the SESF as a coding tool for existing aggregated cross-case data. We labeled another category as mixed-conceptual (n = 6), representing studies that drew from other conceptual or theoretical frameworks, typically adapting only certain components, or heavily modified versions, of the SESF. Although the results of such studies may be less directly comparable to other SESF applications, they represent one way in which the SESF is being adapted to explore new theoretical insights and lines of inquiry beyond its original design.

(10) Is study SES data publicly available?

Data transparency, including data sharing as well as other contextual information such as how the data were generated or limitations regarding the data, is a critical component of creating more comparable SES knowledge. Eight of the reviewed publications identified an available data source, evaluated by the criteria of whether the publication, journal page, or linked supplementary material explicitly identified a publicly available source for the study data. Although the majority of survey respondents agreed that using the SESF made it more likely that their empirical data can be compared with other SESF studies, this question also had the largest number of neutral responses (7) of all of the questions. Response comments noted the diversity of SES case contexts and uniqueness of each case as challenges. Supplementary publication materials, synthetic databases, and open-source repositories are examples of useful strategies for increasing comparability across heterogeneous SES studies. Several databases have been developed in an attempt to facilitate data synthesis and comparison across SES cases, such as the Dartmouth SESMAD project (Cox et al. 2020; https://sesmad.dartmouth.edu/ ), SES Library ( https://seslibrary.asu.edu/ ), and more context specific databases such as the International Forestry Resources and Institutions (IFRI; http://ifri.forgov.org ) and Nepal Irrigation Institutions and Systems (NIIS; https://ulrichfrey.eu/en/niis/ ). How well a given case dataset “ fits ” to the content structure of these databases may vary depending on how the SESF was applied for a given study. Open-source data repositories provide more flexibility for authors regarding how or in what format they share their SES case data but may be less immediately comparable to other cases.

The SESF partly aims to provide a common language of variables to coordinate and compare findings, while simultaneously allowing for adaptability by not specifying which variables or methods should be applied to case-specific contexts (McGinnis and Ostrom 2014). It has become increasingly clear that there is a tension between these two goals (Thiel et al. 2015, Partelow 2018). The contextual adaptability of the SESF has been empirically demonstrated (Partelow 2018) and is arguably its core strength, but so far there has been little progress in building synthetic and cumulative SES knowledge from across empirical SESF cases (Schlager and Cox 2018, Villamayor-Tomas et al. 2020). Social-ecological systems frameworks’ study comparability has been challenged by inconsistent applications, interpretations, definitions, and measures (Cumming et al. 2020), which may be exacerbated by the lack of clear procedures or guidance for how to actually apply the SESF (Partelow 2018). Our methodological guide attempts to address this by providing a set of steps or decisions that encourage researchers to critically reflect upon and provide transparency regarding these methodological decisions, which can improve both contextualized study designs while enabling cross-study comparability without limiting flexibility. In the following sections, we discuss the above trends and gaps in the reviewed literature and reflect on how they have influenced our presentation of the guide, which emphasizes transparency over rigid procedure. Transparency emerged as the key issue during the review and coding process when we noted inconsistencies in documenting what we viewed as key methodological decisions in applying the SESF.

Methods used in the SESF literature are highly heterogeneous

Quantitative applications of the SESF are highly heterogeneous. Two non-mutually exclusive perspectives can be considered. The SESF applications generally require interdisciplinary knowledge to operationalize the many variables, i.e., variable selection, data collection, data transformation, analysis, etc. The framework is also applied to understand different contextual problems. Thus, researchers will choose different methodological strategies because there is no current guide or template. More applications may be needed until a reasonable saturation point of studies applying similar methods can be meaningfully compared within contexts.

Using quantitative data is typically employed to facilitate hypothesis testing, prediction, and forecasting. The majority of reviewed publications relied heavily on explanatory/outcome variable analysis methods such as linear and logistic regression techniques. However, several publications in this review noted the limitation of these methods in narrowing analyses of SESs to a series of linear pairwise relationships that often involve investigating the explanatory power of a wide range of social-ecological indicators on only a single or small number of dependent variables representing overall outcomes. Development of more experimental methods and large time-scale studies are needed to advance research into SES causal mechanisms (Table 6; Cumming et al. 2020). Methodological transparency is critically important when making theoretical jumps to generalizability, necessitating clarity and transparency regarding the causal inferences and variable relationships being reported (Villamayor-Tomas et al. 2020).

Social-ecological systems research and the SESF itself draw heavily from complex systems theory, conceptualizing SESs as components with a high degree of interaction or connections, forming a network with often nonlinear, dynamic, and emergent properties (Berkes et al. 2003, Ostrom 2009, Preiser et al. 2018). Despite this, previous critical reflections have identified a lack of SES research that empirically applies these concepts of complexity, such as modeling approaches that explore the connections, dynamics, and feedback effects within SESs rather than simply analyses of pairwise relations between variables (Pulver et al. 2018, Cumming et al. 2020, Gomez-Santiz et al. 2021). To be certain, the often data-scarce and open nature of many SES contexts can obscure attempts to explore the interdependent and interactive effects in more detail, and the SESF’s focus on variables rather than connections adds further ambiguity as to how researchers should conceptualize an SES (Pulver et al. 2018). Still, if we accept that complex systems have emergent properties, then it is clear that our SES methodological toolkit needs to explore ways to expand beyond sums of variable-outcome interactions and into methods that focus on capturing, rather than reducing, complexity. Several publications in our review explore promising analytical techniques in these directions, including agent-based modeling to test the emergent properties of individual actor and resource unit behavior on SES outcomes (Cenek and Franklin 2017, Lindkvist et al. 2017), supervised and unsupervised machine learning to analyze policy impacts on SESs (Rana and Miller 2019b) and assess spatial SES archetypes (Rocha et al. 2020), and system dynamics modeling to simulate SES dynamics under various scenarios (Baur and Binder 2015).

Integrative participatory methods, those which involve local actors in knowledge co-production and study design, are some of the most promising and feasible approaches for improving our understanding of SES complexity in information-scarce contexts. They can further lead to better forecasting and scenario building that inform policy and actionable change because of the embedded nature of knowledge creation and learning with those actors directly involved in social-ecological change processes (Eelderink et al. 2020, Caniglia et al. 2021). Notable approaches from our review include participatory fuzzy cognitive mapping to create SES dynamics models based on stakeholder knowledge (Ziegler et al. 2019) and prospective structural analysis to support SES scenario building (Delgado-Serrano et al. 2015). Such strategically designed integration may come at the cost of time and resources and may require a shared learning process to integrate differing knowledge systems and epistemologies (e.g., transdisciplinarity; Tengö et al. 2014, Norström et al. 2020). Nonetheless, it can promote stakeholder ownership and local study relevance while providing scientists with improved knowledge of important social and ecological components and processes within the SES (Reed et al. 2014, Fischer et al. 2015, Guerrero et al. 2018).

In calling for more transdisciplinary SES research, it is pertinent to consider the tension between case specificity and the need for comparability. This is because transdisciplinary and other knowledge co-production methods have been more often associated with case-specific research than that designed to allow generalizability across multiple cases. However, recent literature demonstrates that knowledge co-production approaches are increasingly being applied with decision makers working across multiple regions or even countries (Gurney et al. 2019). We do not view the need for broadly comparable SES research as being diametrically opposed to case-focused and problem-driven or action-oriented research. Although empirical applications are growing, published SESF research is still relatively scarce, and the sample becomes smaller still when subdivided into more granular categories such as methodological approach or sector (Appendix 1, Table A1.2; Partelow 2018). Although recent literature rightfully pushes for SES research to move beyond the exploration and into theory development (Cumming et al. 2020, Cox et al. 2021), we particularly emphasize the need for more (and more diverse) empirical SESF applications to identify patterns of both more broadly comparable, as well as more context specific, SES variables and interactions across cases. In their post-Ostrom agenda, Cumming et al. 2020 charted a path forward for theory-oriented SES research via “ middle-range ” theory development in which building explanations of highly complex SES phenomena might entail building partial theories with a bounded or contextual applicability rather than one all-encompassing SES theory. More highly detailed case-specific SES studies play an important building block in developing new hypotheses and theories to test (Guerrero et al. 2018), and “ filling out ” the SESF literature with more wide-ranging cases is needed for these bounded explanations to emerge. This will likely lead to not only bounded theories but also more bounded SES frameworks covering a more specific and comparable range of contexts, such as SES frameworks for specific resource sectors (Partelow 2018), governance arrangements, or geographic or social-cultural contexts.

The SES literature has made note of a number of gaps that limit the accumulation of knowledge from individual case studies to broader theoretical generalizations (Cox et al. 2021). Both syntheses of diverse case studies and large-scale comparative research projects are key for enabling empirically robust theory building, but current SESF literature struggles to do both (Partelow 2018). Additionally, although we identified 21 large-N comparative studies, most units of analysis were at the individual or local level (rather than, e.g., comparisons of multiple SES cases) and sampled within a limited spatial context (e.g., within one district), likely reducing the external validity beyond that context (Poteete et al. 2010). Only two reviewed studies applied large-N analyses to regional units of analysis, which has been identified as a critical and under-represented focal level of SES analysis (Rounsevell et al. 2012, Glaser and Glaeser 2014), suggesting that researchers are facing a challenge in creating broadly comparative SES research at larger spatial levels. To some extent this may reflect a collective action problem in scientific research itself, in which the collective goal of large-scale SES research may be offset by costs of coordination and collaboration, incentivizing smaller projects at the individual level (Cox et al. 2021). However, it also reflects trade-offs in study design between comparability and case-specificity, in which comparing a wider and more diverse range of SES contexts may necessitate measuring a more general list of broadly relevant variables, risking overgeneralization or missing key variables that are highly relevant but not to all cases (Gurney et al. 2019). Because the SESF itself is decomposable into multiple levels of generalization, one approach for large-N SES analyses is to compare a range of broad, universally relevant 2nd-tier variables across all SES cases, while also including more bounded and decomposed (e.g., 3rd-tier variables), which might be highly influential but only within a subset of cases (Gurney et al. 2019). Still, these approaches are likely to have high resource and coordination costs, suggesting the need for continued synthetic analysis of case-specific SESF research. Several reviewed studies synthesized secondary case databases to assess patterns across multiple SESs, however only one specifically synthesized patterns across existing empirical SESF studies, and this meta-analysis noted challenges regarding methodological transparency that limited the level of detail for case comparison (Villamayor-Tomas et al. 2020). It is evident from these patterns in the literature that further attention to methodological transparency and documentation in SESF studies is needed.

Methodological transparency issues: two main challenges

We identified continued ambiguity regarding 2nd-tier variable and measurable indicator selection as perhaps one of the most critical methodological challenges facing between-study SESF comparability and middle-range theory development. Methodological transparency is a broader academic challenge but should not necessarily be attributed to carelessness or negligence. A variety of reasons exist, ranging from scientific publishing standards regarding short and concise methods, journal word counts and formatting requirements, and procedural doubt or the “ fear ” of showing too much. Or, publications may simply have enough documentation to support the findings being presented, only lacking in certain explicit details at the meta-analytical level. Furthermore, many SESF publications are interdisciplinary, and methodological assumptions regarded as common knowledge in one field or discipline may need to be explained to scholars in another field in interdisciplinary journals. Regardless, we encourage SESF researchers to be as transparent as possible regarding the methodological steps we have outlined, such as making full use of supplementary materials to share these extra layers of methodological procedure (i.e., the choices at each step of the guide). Below we reflect on two specific transparency challenges identified in this review:

Transparency challenge 1: which 2nd-tier variables are being applied and why?

The SESF 2nd-tier variables lack clarity in how to conceptualize and measure them for a given case, and many researchers are finding it difficult and subjective to link their case SES data to the generalized concepts, which are the SESF 2nd-tier variables. Although the majority of surveyed authors stated that they understood how to identify relevant variables for a case, both publications and survey respondents noted recurring challenges regarding how to conceptualize or define the 2nd-tier variables within their specific case context, or how to categorize existing empirical and secondary data to specific variables. Importantly, the variable selection criteria in many studies is often unclear, which hinders learning in the research community, interpretability, and cross-case comparisons. One critical building block to SESF research is identifying which 2nd-tier variables are relevant or generalizable across specific SES contexts (McGinnis and Ostrom 2014). However, it is often unclear if the inclusion or exclusion of variables is deductive and theory driven (e.g., hypothesis-based), inductive (e.g., participatory evaluation), or because available secondary data aligns with particular variables. It could also be that certain variables are relevant across a larger number of cases, or that they are less abstract and easier to conceptualize and measure than others. Criteria for variable modifications including the inclusion of new variables are also often unclear and lacking justification (Partelow 2018). We argue that although there is no specifically right or wrong approach to applying the SESF variables, it is clear from our review that the lack of consistency and transparency limits both the ability to compare and contrast study findings with others.

Transparency challenge 2: how are 2nd-tier variables being measured?

To quantitatively measure abstract concepts, such as many of the 2nd-tier SESF variables, one or more empirically measurable indicators are required. Nearly all the variables could have many different possible indicators, such as RS5 - System productivity, in which indicators range from coastal chlorophyll levels, to kilograms of production of a resource unit, to average park visitation (Table 4). The context of those indicators presumably matters in each case, and the role that each plays in the case when abstracted to the broader concept of “ system productivity ” , may not mean the same thing outside of those contexts. Even indicators that appear similar on the surface may be representing different conceptual phenomena in the SES, such as A1, i.e., number of actors; different studies measure the number of relevant actors in terms of a raw population value, or as population density in a given spatial unit, or as a ratio of another population. Each measure informs us about the same concept in ways that might confer different insights or highlight different phenomena. Most surveyed researchers found it unclear how to select appropriate measurable indicators for the variables in their research (Fig. 2) and documentation of indicator selection was inconsistent in the reviewed literature. Indeed, indicator selection is an often messy process driven by data availability and feasibility. Numerous publications noted challenges in data scarcity (Budiharta et al. 2016, Lindkvist et al. 2017, Filbee-Dexter et al. 2018, Rana and Miller 2019b, Rocha et al. 2020), and studies are often relying on a wide range of primary and secondary sources to collect indicator data (Table 5), which may vary in structure, comprehensiveness, feasibility, and quality (Neumann and Graeff 2015). As such, research with the SESF is often by practical necessity relying on incomplete or low-quality data sources or using certain available data as proxies for other indicators. Transparency regarding how these decisions were made will help future researchers learn how to deal with those issues and enhance the interpretability of study findings.

Standardizing SES indicators is not a feasible or arguably desirable approach given the range of case contexts and research objectives across individual SESF studies. We rather encourage continued empirical applications so that patterns of context specific indicator measures may emerge, even when generalizability is not the core objective. Increased transparency regarding SESF variable and empirical indicator selection can aid in this cumulative accumulation of knowledge. As existing SESF studies are one of the most important references for researchers operationalizing the SESF variables in their work, we further suggest the development of a more comprehensive and accessible database of SESF variables and measurable indicators, such as the wiki-type format proposed by Cox et al. 2021 as an important path forward.

Applying the multi-step methodological guide to the SESF

This review builds on the methodological gaps identified by Partelow 2018, by providing a full methodological guide to the SESF. We see this guide as being supplemental to existing SESF guides in the literature, including guides for conceptualizing a case SES and related institutional and collective action challenges (Hinkel et al. 2015), for characterizing an SES at the local level (Delgado-Serrano and Ramos 2015), and for coevolving SESF research with sustainability science (Partelow 2016).

Our guide should be considered a multi-step, rather than step-by-step, procedure. We recognize that different research goals and researcher interests will align with different methodological trajectories. For example, a theory-driven researcher might first select the 2nd-tier variables and the hypotheses they expect to be important for collective action in their case SES, after which they might identify a set of measurable indicators, whereas another researcher applying a more inductive approach might apply participatory modeling methods to identify important SES factors and only in the analysis stage code these to the SESF variables. We see this flexibility as a strength of the framework, and although we present our methodological steps in what we interpret as a broadly logical order, we encourage researchers using this guide to answer these questions in the order that makes sense for their own research. The steps of this guide may best be interpreted as key “ decision points ” and questions that a researcher should be able to answer and clearly document with the long-term goal of building and improving comparable research with the SESF.

Although this guide was specifically developed around a review of quantitative applications of the SESF, we believe it is applicable to all future applications of the framework including qualitative approaches, and it may be able to inform SES studies beyond the SESF. Both quantitative and qualitative studies are critical for progressing the field. For example, descriptive SESF analyses have been found to often include case descriptions of a large range of variables that are then ignored in explanations of case outcomes, leading to confusion about which variables are actually relevant (Villamayor-Tomas et al. 2020). This also warrants some reflection by researchers on the anticipated level of generalizability of the research, where, in many cases, a more in-depth case study may simply be less focused on generalizability in lieu of a richer descriptive analysis of a specific context. Still, clear and formal narrative summaries answering the questions in this guide (even simple visual diagrams of the variable relationships identified, as suggested by Villamayor-Tomas et al. 2020) could improve generalizability and accessibility of SES findings for synthetic analysis even in cases where creating generalizable findings is not a priority, without compromising the depth of the overall analysis. Our guide was developed with an understanding of this current state of the SESF literature, and we expect more context-specific and potentially more standardized procedures to eventually develop based out of these more specialized versions of the SESF, similar to existing SESF modifications for marine aquaculture (Johnson et al. 2019), lobster and benthic small-scale fisheries (Basurto et al. 2013, Partelow and Boda 2015), urban stormwater management (Flynn and Davidson 2016) and food systems research (Marshall 2015).

Our review analyzed the step-by-step decisions scholars have made when applying the SESF with quantitative methods. With this review data, we have developed a multi-step methodological guide for new applications of the SESF, while also examining current trends and discussing challenges. Our guide and discussion aim to promote methodological transparency as the basis for enhancing comparability across publications and making diagnostic place-based research more meaningfully tailored to context. Still, our review found that researchers are finding it unclear how to apply the SESF to create comparable research, particularly in the areas of variable and indicator selection, and the methodological decisions being made within studies are often ambiguous. Although we noted a high degree of methodological heterogeneity in quantitative SESF applications, analyses are still skewed toward certain methods and case sectors. We call for more empirical applications of the SESF and encourage both methodological plurality and case diversity, alongside enhanced methodological transparency. In doing so, comparability and synthesis can emerge across varying methodological, theoretical, sector-specific, and other dimensions. We argue that this can move our understanding of SESs as complex adaptive systems forward and help resolve tensions between the need for contextual adaptability and the need for comparison.

RESPONSES TO THIS ARTICLE

ACKNOWLEDGMENTS

This project was made possible through funding by the German Ministry of Research and Education (BMBF) under the project COMPASS: Comparing Aquaculture System Sustainability (grant number 031B0785). We are thankful to the editors and anonymous reviewers for their detailed and insightful comments.

DATA AVAILABILITY

The data that support the findings of this study are publicly available at https://figshare.com/s/e81b2ff83543c5bb0aac . The 51 publications evaluated for this review are listed in Appendix 2. Code sharing is not applicable to this article because results are descriptive summaries.

LITERATURE CITED

Aaron MacNeil, M., and J. E. Cinner. 2013. Hierarchical livelihood outcomes among co-managed fisheries. Global Environmental Change 23(6):1393-1401. https://doi.org/10.1016/j.gloenvcha.2013.04.003

Aswani, S., G. G. Gurney, S. Mulville, J. Matera, and M. Gurven. 2013. Insights from experimental economics on local cooperation in a small-scale fishery management system. Global Environmental Change 23(6):1402–1409. https://doi.org/10.1016/j.gloenvcha.2013.08.003

Basurto, X., S. Gelcich, and E. Ostrom. 2013. The social-ecological system framework as a knowledge classificatory system for benthic small-scale fisheries. Global Environmental Change 23(6):1366-1380. https://doi.org/10.1016/j.gloenvcha.2013.08.001

Basurto, X., and E. Ostrom. 2009. The core challenges of moving beyond Garrett Hardin. Journal of Natural Resources Policy Research 1(3):255-259. https://doi.org/10.1080/19390450903040447

Baur, I., and C. R. Binder. 2015. Modeling and assessing scenarios of common property pastures management in Switzerland. Ecological Economics 119:292-305. https://doi.org/10.1016/j.ecolecon.2015.09.019

Bennett, N. J., R. Roth, S. C. Klain, K. M. A. Chan, D. A. Clark, G. Cullman, G. Epstein, M. P. Nelson, R. Stedman, T. L. Teel, R. E. W. Thomas, C. Wyborn, D. Curran, A. Greenberg, J. Sandlos, and D. Veríssimo. 2016. Mainstreaming the social sciences in conservation. Conservation Biology 31(1):56-66. https://doi.org/10.1111/cobi.12788

Berkes, F., J. Colding, and C. Folke. 2003. Navigating social-ecological systems: building resilience for complexity and change. Cambridge University Press, Cambridge, UK. https://doi.org/10.1017/CBO9780511541957

Budiharta, S., E. Meijaard, J. A. Wells, N. K. Abram, and K. A. Wilson. 2016. Enhancing feasibility: incorporating a socio-ecological systems framework into restoration planning. Environmental Science and Policy 64:83-92. https://doi.org/10.1016/j.envsci.2016.06.014

Caniglia, G., C. Luederitz, T. von Wirth, I. Fazey, B. Martín-López, K. Hondrila, A. König, H. von Wehrden, N. A. Schäpke, M. D. Laubichler, and D. J. Lang. 2021. A pluralistic and integrated approach to action-oriented knowledge for sustainability. Nature Sustainability 4(2):93-100. https://doi.org/10.1038/s41893-020-00616-z

Cenek, M., and M. Franklin. 2017. An adaptable agent-based model for guiding multi-species Pacific salmon fisheries management within a SES framework. Ecological Modelling 360:132-149. https://doi.org/10.1016/j.ecolmodel.2017.06.024

Christou, M., V. Sgardeli, A. C. Tsikliras, G. Tserpes, and K. I. Stergiou. 2020. A probabilistic model that determines the social ecological system (SES) attributes that lead to successful discard management. Reviews in Fish Biology and Fisheries 30(1):109–119. https://doi.org/10.1007/s11160-020-09593-0

Cinner, J. E., T. R. McClanahan, M. Aaron MacNeil, N. A. J. Graham, T. M. Daw, A. Mukminin, D. A. Feary, A. L. Rabearisoa, A. Wamukota, N. Jiddawi, S. J. Campbell, A. H. Baird, F. A. Januchowski-Hartley, S. Hamed, R. Lahari, T. Morove, and J. Kuange. 2012. Comanagement of coral reef social-ecological systems. Proceedings of the National Academy of Sciences 109(14):5219-5222. https://doi.org/10.1073/pnas.1121215109

Cox, M., G. G. Gurney, J. M. Anderies, E. Coleman, E. Darling, G. Epstein, U. J. Frey, M. Nenadovic, E. Schlager, and S. Villamayor-Tomas. 2021. Lessons learned from synthetic research projects based on the Ostrom Workshop frameworks. Ecology and Society 26(1):17. https://doi.org/10.5751/ES-12092-260117

Cox, M., S. Villamayor-Tomas, N. C. Ban, G. Epstein, L. Evans, F. Fleischman, M. Nenadovic, G. A. Garcia-Lopez, F. van Laerhoven, C. Meek, I. P. Ibarra, and M. Schoon. 2020. From concepts to comparisons: a resource for diagnosis and measurement in social-ecological systems. Environmental Science and Policy 107:211-216. https://doi.org/10.1016/j.envsci.2020.02.009

Cox, M., S. Villamayor-Tomas, G. Epstein, L. Evans, N. C. Ban, F. Fleischman, M. Nenadovic, and G. Garcia-Lopez. 2016. Synthesizing theories of natural resource management and governance. Global Environmental Change 39:45-56. https://doi.org/10.1016/j.gloenvcha.2016.04.011

Cumming, G. S., G. Epstein, J. M. Anderies, C. I. Apetrei, J. Baggio, Ö. Bodin, S. Chawla, H. S. Clements, M. Cox, L. Egli, G. G. Gurney, M. Lubell, N. Magliocca, T. H. Morrison, B. Müller, R. Seppelt, M. Schlüter, H. Unnikrishnan, S. Villamayor-Tomas, and C. M. Weible. 2020. Advancing understanding of natural resource governance: a post-Ostrom research agenda. Current Opinion in Environmental Sustainability 44:26-34. https://doi.org/10.1016/j.cosust.2020.02.005

Dancette, R., and L. Sebastien. 2019. The actor in 4 dimensions: a relevant methodology to analyze local environmental governance and inform Ostrom’s social-ecological systems framework. MethodsX 6:1798–1811. https://doi.org/10.1016/j.mex.2019.07.025

Delgado-Serrano, M. del M., E. Oteros-Rozas, P. Vanwildemeersch, C. Ortíz-Guerrero, S. London, and R. Escalante. 2015. Local perceptions on social-ecological dynamics in Latin America in three community-based natural resource management systems. Ecology and Society 20(4):24. https://doi.org/10.5751/ES-07965-200424

Delgado-Serrano, M. del M., and P. Ramos. 2015. Making Ostrom’s framework applicable to characterise social ecological systems at the local level. International Journal of the Commons 9(2):808-830. https://doi.org/10.18352/ijc.567

Dressel, S., G. Ericsson, and C. Sandström. 2018. Mapping social-ecological systems to understand the challenges underlying wildlife management. Environmental Science and Policy 84:105-112. https://doi.org/10.1016/j.envsci.2018.03.007

Eelderink, M., J. M. Vervoort, and F. van Laerhoven. 2020. Using participatory action research to operationalize critical systems thinking in social-ecological systems. Ecology and Society 25(1):16. https://doi.org/10.5751/ES-11369-250116

Epstein, G., J. M. Vogt, S. K. Mincey, M. Cox, and B. Fischer. 2013. Missing ecology: integrating ecological perspectives with the social-ecological system framework. International Journal of the Commons 7(2):432-453. https://doi.org/10.18352/ijc.371

Filbee-Dexter, K., C. C. Symons, K. Jones, H. A. Haig, J. Pittman, S. M. Alexander, and M. J. Burke. 2018. Quantifying ecological and social drivers of ecological surprise. Journal of Applied Ecology 55(5):2135-2146. https://doi.org/10.1111/1365-2664.13171

Fischer, J., T. A. Gardner, E. M. Bennett, P. Balvanera, R. Biggs, S. Carpenter, T. Daw, C. Folke, R. Hill, T. P. Hughes, T. Luthe, M. Maass, M. Meacham, A. V. Norström, G. Peterson, C. Queiroz, R. Seppelt, M. Spierenburg, and J. Tenhunen. 2015. Advancing sustainability through mainstreaming a social-ecological systems perspective. Current Opinion in Environmental Sustainability 14:144-149. https://doi.org/10.1016/j.cosust.2015.06.002

Flynn, C. D., and C. I. Davidson. 2016. Adapting the social-ecological system framework for urban stormwater management: the case of green infrastructure adoption. Ecology and Society 21(4):19. https://doi.org/10.5751/ES-08756-210419

Foster, T., and R. Hope. 2016. A multi-decadal and social-ecological systems analysis of community waterpoint payment behaviours in rural Kenya. Journal of Rural Studies 47(A):85-96. https://doi.org/10.1016/j.jrurstud.2016.07.026

Frey, U. J., and M. Cox. 2015. Building a diagnostic ontology of social-ecological systems. International Journal of the Commons 9(2):595-618. https://doi.org/10.18352/ijc.505

Fujitani, M. L., C. Riepe, T. Pagel, M. Buoro, F. Santoul, R. Lassus, J. Cucherousset, and R. Arlinghaus. 2020. Ecological and social constraints are key for voluntary investments into renewable natural resources. Global Environmental Change 63:102125. https://doi.org/10.1016/j.gloenvcha.2020.102125

Glaser, M., and B. Glaeser. 2014. Towards a framework for cross-scale and multi-level analysis of coastal and marine social-ecological systems dynamics. Regional Environmental Change 14(6):2039-2052. https://doi.org/10.1007/s10113-014-0637-5

Gomez-Santiz, F., M. Perevochtchikova, and D. Ezzine-de-Blas. 2021. Behind the scenes: scientific networks driving the operationalization of the social-ecological system framework. Science of the Total Environment 787:147473. https://doi.org/10.1016/j.scitotenv.2021.147473

Guba, E. G. 1981. Criteria for assessing the trustworthiness of naturalistic inquiries. ECTJ 29(2):75-91. https://doi.org/10.1007/BF02766777

Guerrero, A. M., N. J. Bennett, K. A. Wilson, N. Carter, D. Gill, M. Mills, C. D. Ives, M. J. Selinske, C. Larrosa, S. Bekessy, F. A. Januchowski-Hartley, H. Travers, C. A. Wyborn, and A. Nuno. 2018. Achieving the promise of integration in social-ecological research: a review and prospectus. Ecology and Society 23(3):38. https://doi.org/10.5751/ES-10232-230338

Gurney, G. G., J. E. Cinner, J. Sartin, R. L. Pressey, N. C. Ban, N. A. Marshall, and D. Prabuning. 2016. Participation in devolved commons management: multiscale socioeconomic factors related to individuals’ participation in community-based management of marine protected areas in Indonesia. Environmental Science and Policy 61:212-220. https://doi.org/10.1016/j.envsci.2016.04.015

Gurney, G. G., E. S. Darling, S. D. Jupiter, S. Mangubhai, T. R. McClanahan, P. Lestari, S. Pardede, S. J. Campbell, M. Fox, W. Naisilisili, N. A. Muthiga, S. D’agata, K. E. Holmes, and N. A. Rossi. 2019. Implementing a social-ecological systems framework for conservation monitoring: lessons from a multi-country coral reef program. Biological Conservation 240:108298. https://doi.org/10.1016/j.biocon.2019.108298

Haider, L. J., B. Neusel, G. D. Peterson, and M. Schlüter. 2019. Past management affects success of current joint forestry management institutions in Tajikistan. Environment, Development and Sustainability 21(5):2183-2224. https://doi.org/10.1007/s10668-018-0132-0

Hicks, C. C., C. Fitzsimmons, and N. V. C. Polunin. 2010. Interdisciplinarity in the environmental sciences: barriers and frontiers. Environmental Conservation 37(4):464-477. https://doi.org/10.1017/S0376892910000822

Hinkel, J., P. W. G. Bots, and M. Schlüter. 2014. Enhancing the Ostrom social-ecological system framework through formalization. Ecology and Society 19(3):51. https://doi.org/10.5751/ES-06475-190351

Hinkel, J., M. E. Cox, M. Schlüter, C. R. Binder, and T. Falk. 2015. A diagnostic procedure for applying the social-ecological systems framework in diverse cases. Ecology and Society 20(1):32. https://doi.org/10.5751/ES-07023-200132

Hoque, S. F., R. Hope, S. T. Arif, T. Akhter, M. Naz, and M. Salehin. 2019. A social-ecological analysis of drinking water risks in coastal Bangladesh. Science of the Total Environment 679:23–34. https://doi.org/10.1016/j.scitotenv.2019.04.359

Johnson, T. R., K. Beard, D. C. Brady, C. J. Byron, C. Cleaver, K. Duffy, N. Keeney, M. Kimble, M. Miller, S. Moeykens, M. Teisl, G. P. van Walsum, and J. Yuan. 2019. A social-ecological system framework for marine aquaculture research. ustainability 11(9):2522. https://doi.org/10.3390/su11092522

Kelly, R. P., A. L. Erickson, L. A. Mease, W. Battista, J. N. Kittinger, and R. Fujita. 2015. Embracing thresholds for better environmental management. Philosophical Transactions of the Royal Society B: Biological Sciences 370(1659):20130276. https://doi.org/10.1098/rstb.2013.0276

Klümper, F., and I. Theesfeld. 2017. The land-water-food nexus: expanding the social-ecological system framework to link land and water governance. Resources 6(3):28. https://doi.org/10.3390/resources6030028

Lam, D. P. M., M. E. Freund, J. Kny, O. Marg, M. Mbah, L. Theiler, M. Bergmann, B. Brohmann, D. J. Lang, and M. Schäfer. 2021. Transdisciplinary research: towards an integrative perspective. GAIA - Ecological Perspectives for Science and Society 30(4):243-249. https://doi.org/10.14512/gaia.30.4.7

Leslie, H. M., X. Basurto, M. Nenadovic, L. Sievanen, K. C. Cavanaugh, J. J. Cota-Nieto, B. E. Erisman, E. Finkbeiner, G. Hinojosa-Arango, M. Moreno-Báez, S. Nagavarapu, S. M. W. Reddy, A. Sánchez-Rodríguez, K. Siegel, J. J. Ulibarria-Valenzuela, A. H. Weaver, and O. Aburto-Oropeza. 2015. Operationalizing the social-ecological systems framework to assess sustainability. Proceedings of the National Academy of Sciences 112(19):5979-5984. https://doi.org/10.1073/pnas.1414640112

Lindkvist, E., X. Basurto, and M. Schlüter. 2017. Micro-level explanations for emergent patterns of self-governance arrangements in small-scale fisheries—A modeling approach. PLOS ONE 12(4):e0179439. https://doi.org/10.1371/journal.pone.0175532

Marshall, G. R. 2015. A social-ecological systems framework for food systems research: accommodating transformation systems and their products. International Journal of the Commons 9(2):881. https://doi.org/10.18352/ijc.587

McGinnis, M. D., and E. Ostrom. 2014. Social-ecological system framework: initial changes and continuing challenges. Ecology and Society 19(2):30. https://doi.org/10.5751/ES-06387-190230

Naiga, R., and M. Penker. 2014. Determinants of users’ willingness to contribute to safe water provision in rural Uganda. Lex localis - Journal of Local Self-Government 12(3):695–714. https://doi.org/10.4335/12.3.695-714(2014)

Neumann, R., and P. Graeff. 2015. Quantitative approaches to comparative analyses: data properties and their implications for theory, measurement and modelling. European Political Science 14(4):385-393. https://doi.org/10.1057/eps.2015.59

Norström, A. V., C. Cvitanovic, M. F. Löf, S. West, C. Wyborn, P. Balvanera, A. T. Bednarek, E. M. Bennett, R. Biggs, A. de Bremond, B. M. Campbell, J. G. Canadell, S. R. Carpenter, C. Folke, E. A. Fulton, O. Gaffney, S. Gelcich, J.-B. Jouffray, M. Leach, M. Le Tissier, B. Martín-López, E. Louder, M.-F. Loutre, A. M. Meadow, H. Nagendra, D. Payne, G. D. Peterson, B. Reyers, R. Scholes, C. I. Speranza, M. Spierenburg, M. Stafford-Smith, M. Tengö, S. van der Hel, I. van Putten, and H. Österblom. 2020. Principles for knowledge co-production in sustainability research. Nature Sustainability 3(3):182-190. https://doi.org/10.1038/s41893-019-0448-2

Okumu, B., and E. Muchapondwa. 2020. Determinants of successful collective management of forest resources: evidence from Kenyan community forest associations. Forest Policy and Economics 113:102122. https://doi.org/10.1016/j.forpol.2020.102122

Ostrom, E. 2007. A diagnostic approach for going beyond panaceas. Proceedings of the National Academy of Sciences 104(39):15181-15187. https://doi.org/10.1073/pnas.0702288104

Ostrom, E. 2009. A general framework for analyzing sustainability of social-ecological systems. Science 325(5939):419-422. https://doi.org/10.1126/science.1172133

Ostrom, E., and M. Cox. 2010. Moving beyond panaceas: a multi-tiered diagnostic approach for social-ecological analysis. Environmental Conservation 37(4):451-463. https://doi.org/10.1017/S0376892910000834

Osuka, K., S. Rosendo, M. Riddell, J. Huet, M. Daide, E. Chauque, and M. Samoilys. 2020. Applying a social-ecological systems approach to understanding local marine management trajectories in Northern Mozambique. Sustainability 12(9):3904. https://doi.org/10.3390/su12093904

Oviedo, A. F. P., and M. Bursztyn. 2016. The fortune of the commons: participatory evaluation of small-scale fisheries in the Brazilian Amazon. Environmental Management 57(5):1009-1023. https://doi.org/10.1007/s00267-016-0660-z

Partelow, S. 2016. Coevolving Ostrom’s social-ecological systems (SES) framework and sustainability science: four key co-benefits. Sustainability Science 11(3):399-410. https://doi.org/10.1007/s11625-015-0351-3

Partelow, S. 2018. A review of the social-ecological systems framework: applications, methods, modifications, and challenges. Ecology and Society 23(4):36. https://doi.org/10.5751/ES-10594-230436

Partelow, S. 2019. Analyzing natural resource governance with the social-ecological systems framework. Pages 65-93 in F. Nunan, editor. Governing renewable natural resources: theories and frameworks. Routledge, London, UK. https://doi.org/10.4324/9780429053009-4

Partelow, S., and C. Boda. 2015. A modified diagnostic social-ecological system framework for lobster fisheries: case implementation and sustainability assessment in Southern California. Ocean and Coastal Management 114:204-217. https://doi.org/10.1016/j.ocecoaman.2015.06.022

Partelow, S., M. Fujitani, V. Soundararajan, and A. Schlüter. 2019. Transforming the social-ecological systems framework into a knowledge exchange and deliberation tool for comanagement. Ecology and Society 24(1):15. https://doi.org/10.5751/ES-10724-240115

Partelow, S., A. Jäger, and A. Schlüter. 2021. Linking fisher perceptions to social-ecological context: mixed method application of the SES framework in Costa Rica. Human Ecology 49(2):187-203. https://doi.org/10.1007/s10745-021-00228-x

Partelow, S., P. Senff, N. Buhari, and A. Schlüter. 2018. Operationalizing the social-ecological systems framework in pond aquaculture. International Journal of the Commons 12(1):485-518. https://doi.org/10.18352/ijc.834

Poteete, A. R., M. A. Janssen, and E. Ostrom. 2010. Broadly comparative field-based research. Pages 64-88 in A. R. Poteete, M. A. Janssen, and E. Ostrom, editors. Working together: collective action, the commons and multiple methods in practice. Princeton University Press, Princeton, New Jersey, USA. https://doi.org/10.1515/9781400835157.64

Preiser, R., R. Biggs, A. De Vos, and C. Folke. 2018. Social-ecological systems as complex adaptive systems: organizing principles for advancing research methods and approaches. Ecology and Society 23(4):46. https://doi.org/10.5751/ES-10558-230446

Pulver, S., N. Ulibarri, K. L. Sobocinski, S. M. Alexander, M. L. Johnson, P. F. McCord, and J. Dell’Angelo. 2018. Frontiers in socio-environmental research: components, connections, scale, and context. Ecology and Society 23(3):23. https://doi.org/10.5751/ES-10280-230323

Queirós, A., D. Faria, and F. Almeida. 2017. Strengths and limitations of qualitative and quantitative research methods. European Journal of Education Studies 3(9):369-387.

Rana, P., and D. C. Miller. 2019a. Explaining long-term outcome trajectories in social-ecological systems. PLoS ONE 14(4):e0215230. https://doi.org/10.1371/journal.pone.0215230

Rana, P., and D. C. Miller. 2019b. Machine learning to analyze the social-ecological impacts of natural resource policy: insights from community forest management in the Indian Himalaya. Environmental Research Letters 14(2):024008. https://doi.org/10.1088/1748-9326/aafa8f

Reed, M. S., L. C. Stringer, I. Fazey, A. C. Evely, and J. H. J. Kruijsen. 2014. Five principles for the practice of knowledge exchange in environmental management. Journal of Environmental Management 146:337-345. https://doi.org/10.1016/j.jenvman.2014.07.021

Reyers, B., J. L. Nel, P. J. O’Farrell, N. Sitas, and D. C. Nel. 2015. Navigating complexity through knowledge coproduction: mainstreaming ecosystem services into disaster risk reduction. Proceedings of the National Academy of Sciences 112(24):7362-7368. https://doi.org/10.1073/pnas.1414374112

Rocha, J., K. Malmborg, L. Gordon, K. Brauman, and F. DeClerck. 2020. Mapping social-ecological systems archetypes. Environmental Research Letters 15(3):034017. https://doi.org/10.1088/1748-9326/ab666e

Roquetti, D. R., E. M. Moretto, and S. M. P. Pulice. 2017. Dam-forced displacement and social-ecological resilience: The Barra Grande hydropower plant in southern Brazil. Ambiente y Sociedade 20(3):115-134. https://doi.org/10.1590/1809-4422asoc153r2v2032017

Rounsevell, M. D. A., B. Pedroli, K.-H. Erb, M. Gramberger, A. G. Busck, H. Haberl, S. Kristensen, T. Kuemmerle, S. Lavorel, M. Lindner, H. Lotze-Campen, M. J. Metzger, D. Murray-Rust, A. Popp, M. Pérez-Soba, A. Reenberg, A. Vadineanu, P. H. Verburg, and B. Wolfslehner. 2012. Challenges for land system science. Land Use Policy 29(4):899-910. https://doi.org/10.1016/j.landusepol.2012.01.007

Schlager, E., and M. Cox. 2018. The IAD framework and the SES framework: an introduction and assessment of the Ostrom workshop frameworks. Pages in C. M. Weible and P. A. Sabatier, editors. Theories of the policy process. Routledge, New York, New York, USA. https://doi.org/10.4324/9780429494284-7

Schmitt-Harsh, M. L., and S. K. Mincey. 2020. Operationalizing the social-ecological system framework to assess residential forest structure: a case study in Bloomington, Indiana. Ecology and Society 25(2):14. https://doi.org/10.5751/ES-11564-250214

Sharma, D., I. Holmes, G. Vergara-Asenjo, W. N. Miller, M. Cunampio, R. B. Cunampio, M. B. Cunampio, and C. Potvin. 2016. A comparison of influences on the landscape of two social-ecological systems. Land Use Policy 57:499–513. https://doi.org/10.1016/j.landusepol.2016.06.018

Su, Y., E. Araral, and Y. Wang. 2020. The effects of farmland use rights trading and labor outmigration on the governance of the irrigation commons: evidence from China. Land Use Policy 91:104378. https://doi.org/10.1016/j.landusepol.2019.104378

Tengö, M., E. S. Brondizio, T. Elmqvist, P. Malmer, and M. Spierenburg. 2014. Connecting diverse knowledge systems for enhanced ecosystem governance: the multiple evidence base approach. Ambio 43(5):579-591. https://doi.org/10.1007/s13280-014-0501-3

Thiel, A., M. E. Adamseged, and C. Baake. 2015. Evaluating an instrument for institutional crafting: how Ostrom’s social-ecological systems framework is applied. Environmental Science and Policy 53:152-164. https://doi.org/10.1016/j.envsci.2015.04.020

van Laerhoven, F., M. Schoon, and S. Villamayor-Tomas. 2020. Celebrating the 30th Anniversary of Ostrom’s Governing the Commons : traditions and trends in the study of the commons, revisited. International Journal of the Commons 14(1):208-224. https://doi.org/10.5334/ijc.1030

Villamayor-Tomas, S., C. Oberlack, G. Epstein, S. Partelow, M. Roggero, E. Kellner, M. Tschopp, and M. Cox. 2020. Using case study data to understand SES interactions: a model-centered meta-analysis of SES framework applications. Current Opinion in Environmental Sustainability 44:48-57. https://doi.org/10.1016/j.cosust.2020.05.002

Vogt, J. M., G. B. Epstein, S. K. Mincey, B. C. Fischer, and P. McCord. 2015. Putting the “ E ” in SES: unpacking the ecology in the Ostrom social-ecological system framework. Ecology and Society 20(1):55. https://doi.org/10.5751/ES-07239-200155

Yandle, T., D. S. Noonan, and B. Gazley. 2016. Philanthropic support of aational parks: analysis using the social-ecological systems framework. Nonprofit and Voluntary Sector Quarterly 45(4). https://doi.org/10.1177/0899764016643612

Ziegler, J. P., S. E. Jones, and C. T. Solomon. 2019. Local stakeholders understand recreational fisheries as social-ecological systems but do not view governance systems as influential for system dynamics. International Journal of the Commons 13(2):1035-1048. https://doi.org/10.5334/ijc.945

Figures, Tables, & Appendices ×

Fig. 1. A methodological guide for applying the SESF. All decision tree branches for each step represent “and/or” considerations. Categories were coded based on the reviewed publications. † denotes categories which were not coded from the reviewed publications, but which we identify as additional potential considerations for that step.

Fig. 2. Summary of Likert-scale responses to social-ecological systems framework (SESF) researcher survey. n = 23 responses.

Fig. 3. Sankey flow diagrams summarizing how coded SESF variable indicators (categorized by the four most frequently applied SESF 1st-tier components) in the reviewed literature are associated with data collection type (left) and data type (right).

Fig. 4. 2nd-tier variable frequency and indicator data source (n = 26 publications which clearly documented which 2nd-tier variables were examined).

Table 1. 1st- and 2nd-tier variables of the SESF. Adapted from McGinnis and Ostrom (2014).

Table 2. 2nd-tier variable frequency by 1st-tier component category (n = 26 publications), and general variable selection criteria (n = 51 publications). Note: SESF = social-ecological systems framework, SES = social-ecological system.

Table 3. Spatial level of units of analysis vs. number of units being compared. Some studies contain multiple units of analysis (e.g., households and communities).

Table 4. Indicators for two of the most frequently applied 2nd-tier variables, RS5 and A2, extracted from reviewed publications. Multiple indicators separated by commas.

Table 5. Data collection methods and data measurement type for social-ecological systems framework (SESF) 2nd-tier variable indicators. Derived from n = 26 publications in which the examined 2nd-tier variables could be clearly identified.

Table 6. Study design and quantitative data analysis methods. Because many studies apply multiple analytical methods, the sum of number of publications across categories is greater than 51. Note: SES = social-ecological systems, SESF = social-ecological systems framework.

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Bronfenbrenner’s Ecological Systems Theory

Olivia Guy-Evans, MSc

Associate Editor for Simply Psychology

BSc (Hons) Psychology, MSc Psychology of Education

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

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Saul McLeod, PhD

Editor-in-Chief for Simply Psychology

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

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

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Bronfenbrenner’s ecological systems theory posits that an individual’s development is influenced by a series of interconnected environmental systems, ranging from the immediate surroundings (e.g., family) to broad societal structures (e.g., culture).

These systems include the microsystem, mesosystem, exosystem, macrosystem, and chronosystem, each representing different levels of environmental influences on an individual’s growth and behavior.

Key Takeaways

• The theory views child development as a complex system of relationships affected by multiple levels of the surrounding environment.
• Bronfenbrenner divided the environment into five systems: microsystem, mesosystem, exosystem, macrosystem, and chronosystem.
• The microsystem is the most influential level, encompassing the child’s immediate environment such as family and school.
• The theory has significant implications for educational practice and understanding diverse developmental contexts.

The Five Ecological Systems

Bronfenbrenner (1977) suggested that the child’s environment is a nested arrangement of structures, each contained within the next. He organized them in order of how much of an impact they have on a child.

He named these structures the microsystem, mesosystem, exosystem, macrosystem and the chronosystem.

Because the five systems are interrelated, the influence of one system on a child’s development depends on its relationship with the others.

1. The Microsystem

The microsystem is the first level of Bronfenbrenner’s theory and is the things that have direct contact with the child in their immediate environment.

It includes the child’s most immediate relationships and environments. For example, a child’s parents, siblings, classmates, teachers, and neighbors would be part of their microsystem.

Relationships in a microsystem are bi-directional, meaning other people can influence the child in their environment and change other people’s beliefs and actions. The interactions the child has with these people and environments directly impact development.

The child is not just a passive recipient but an active contributor in these bidirectional interactions.

Example: Supportive parents who read to their child and provide educational activities may positively influence cognitive and language skills. Or, children with friends who bully them at school might develop self-esteem issues. 

2. The Mesosystem

The mesosystem is where a person’s individual microsystems do not function independently but are interconnected and assert influence upon one another.

The mesosystem involves interactions between different microsystems in the child’s life. These interactions can have significant impacts on the child’s development.

Example: A child whose parents are actively involved in their school life, such as attending parent-teacher conferences and volunteering for school events, may perform better academically.

This is because the interaction between the family microsystem and the school microsystem (forming the mesosystem) creates a supportive environment for learning.

Another example could be the interaction between a child’s peer group and family. If a child’s friends value academic achievement, this attitude might influence the child’s behavior at home, leading to more time spent on homework and studying.

3. The Exosystem

The exosystem is a component of the ecological systems theory developed by Urie Bronfenbrenner in the 1970s.

It incorporates other formal and informal social structures such as local governments, friends of the family, and mass media.

While not directly interacting with the child, the exosystem still influences the microsystems. 

Example: A parent’s workplace policies can significantly affect a child’s development. If a company offers flexible working hours or work-from-home options, parents might have more time to spend with their children, positively impacting the child’s emotional development and family relationships.

Another example could be local government decisions. If a city council decides to close down a community center or library due to budget cuts, this could limit a child’s access to educational resources and after-school activities, potentially affecting their academic and social development.

4. The Macrosystem

The macrosystem focuses on how cultural elements affect a child’s development, consisting of cultural ideologies, attitudes, and social conditions that children are immersed in.

Beliefs about gender roles, individualism, family structures, and social issues establish norms and values that permeate a child’s microsystems. 

The macrosystem differs from the previous ecosystems as it does not refer to the specific environments of one developing child but the already established society and culture in which the child is developing.

Example: In a society that highly values individual achievement, children might be encouraged to be more competitive and self-reliant.

This could influence parenting styles in the microsystem, with parents focusing more on personal accomplishments and independence.

Conversely, in a culture that emphasizes collective harmony, children might be raised to prioritize group needs over individual desires.

This could manifest in the microsystem as parents encouraging more cooperative play and shared decision-making among siblings.

5. The Chronosystem

The fifth and final level of Bronfenbrenner’s ecological systems theory is known as the chronosystem .

The chronosystem relates to shifts and transitions over the child’s lifetime. These environmental changes can be predicted, like starting school, or unpredicted, like parental divorce or changing schools when parents relocate for work, which may cause stress.

Aging itself interacts with shifting social expectations over the lifespan within the chronosystem.

How children respond to expected and unexpected life transitions depends on the support of their ecological systems.

Example: The introduction of widespread internet access and social media represents a significant chronosystem change for many children.

This technological shift has altered how children interact with peers, access information, and spend their leisure time, potentially affecting their social skills, cognitive development, and even sleep patterns.

Another example could be a major historical event like a global pandemic.

Children growing up during such a time might experience disruptions in their education (shift to online learning), changes in family dynamics (parents working from home), and altered social interactions (social distancing), all of which can have long-lasting effects on their development.

The Bioecological Model

It is important to note that Bronfenbrenner (1994) later revised his theory and instead named it the ‘Bioecological model’.

Bronfenbrenner became more concerned with the proximal development processes, meaning the enduring and persistent forms of interaction in the immediate environment.

His focus shifted from environmental influences to developmental processes individuals experience over time.

‘…development takes place through the process of progressively more complex reciprocal interactions between an active, evolving biopsychological human organism and the persons, objects, and symbols in its immediate external environment.’ ( Bronfenbrenner, 1995 ).

Bronfenbrenner also suggested that to understand the effect of these proximal processes on development, we have to focus on the person, context, and developmental outcome, as these processes vary and affect people differently.

While his original ecological systems theory emphasized the role of environmental systems, his later bioecological model focused more closely on micro-level interactions.

The bioecological shift highlighted reciprocal processes between the actively evolving individual and their immediate settings. This represented an evolution in Bronfenbrenner’s thinking toward a more dynamic developmental process view.

However, the bioecological model still acknowledged the broader environmental systems from his original theory as an important contextual influence on proximal processes.

The bioecological focus on evolving person-environment interactions built upon the foundation of his ecological systems theory while bringing developmental processes to the forefront.

Classroom Application

The Ecological Systems Theory has been used to link psychological and educational theory to early educational curriculums and practice. The developing child is at the center of the theory, and all that occurs within and between the five ecological systems is done to benefit the child in the classroom.

• According to the theory, teachers and parents should maintain good communication with each other and work together to benefit the child and strengthen the development of the ecological systems in educational practice.
• Teachers should also understand the situations their students’ families may be experiencing, including social and economic factors that are part of the various systems.
• According to the theory, if parents and teachers have a good relationship, this should positively shape the child’s development.
• Likewise, the child must be active in their learning, both academically and socially. They must collaborate with their peers and participate in meaningful learning experiences to enable positive development.

There are lots of studies that have investigated the effects of the school environment on students. Below are some examples:

Lippard  et al. (2017) conducted a study to test Bronfenbrenner’s theory. They investigated the teacher-child relationships through teacher reports and classroom observations.

They found that these relationships were significantly related to children’s academic achievement and classroom behavior, suggesting that these relationships are important for children’s development and supports the Ecological Systems Theory.

Wilson et al. (2002) found that creating a positive school environment through a school ethos valuing diversity has a positive effect on students’ relationships within the school. Incorporating this kind of school ethos influences those within the developing child’s ecological systems.

Langford et al. (2014) found that whole-school approaches to the health curriculum can positively improve educational achievement and student well-being. Thus, the development of the students is being affected by the microsystems.

Critical Evaluation

Bronfenbrenner’s model quickly became very appealing and accepted as a useful framework for psychologists, sociologists, and teachers studying child development.

The ecological systems theory is thought to provide a holistic approach that includes all the systems children and their families are involved in, reflecting the dynamic nature of actual family relationships.

Paat (2013) considers how Bronfenbrenner’s theory is useful when it comes to the development of immigrant children. They suggest that immigrant children’s experiences in the various ecological systems are likely to be shaped by their cultural differences.

Understanding these children’s ecology can aid in strengthening social work service delivery for these children.

Limitations

A limitation of the Ecological Systems Theory is that there is limited research examining the mesosystems, mainly the interactions between neighborhoods and the family of the child. Therefore, the extent to which these systems can shape child development is unclear.

Another limitation of Bronfenbrenner’s theory is that it is difficult to empirically test the theory. The studies investigating the ecological systems may establish an effect, but they cannot establish whether the systems directly cause such effects.

Furthermore, this theory can lead to assumptions that those who do not have strong and positive ecological systems lack in development.

Whilst this may be true in some cases, many people can still develop into well-rounded individuals without positive influences from their ecological systems.

For instance, it is not true to say that all people who grow up in poverty-stricken areas of the world will develop negatively. Similarly, if a child’s teachers and parents do not get along, some children may not experience any negative effects if it does not concern them.

As a result, people should try to avoid making broad assumptions about individuals using this theory.

Evolution and Relevance of Bronfenbrenner’s Theory in the 21st Century

Bronfenbrenner’s theory of human development has undergone significant evolution since its inception in the 1970s, raising questions about its current relevance and application.

Initially conceptualized as an ecological model focused primarily on contextual influences, it matured into a more sophisticated bioecological model emphasizing the critical role of proximal processes in development.

The mature version of the theory, often referred to as the bioecological model, places proximal processes at its core.

These processes are defined as “enduring forms of interaction in the immediate environment” and are considered the primary engines of development.

Central to the mature theory is the Process-Person-Context-Time (PPCT) model . This model emphasizes the interplay between four key elements:

• Process: The core proximal processes driving development
• Person: Individual characteristics that influence these processes
• Context: The environmental systems in which development occurs
• Time: The temporal aspect of development, including both individual life course and historical time

Despite these advancements, the theory’s relevance in the 21st century has been a subject of debate. Kelly and Coughlan (2019) found significant links between Bronfenbrenner’s ecological systems theory and contemporary frameworks for youth mental health recovery.

Their research suggests that the components of mental health recovery are embedded in an “ecological context of influential relationships,” aligning with Bronfenbrenner’s emphasis on the importance of interconnected environmental systems.

However, the rapid technological advancements of the 21st century have raised questions about how well Bronfenbrenner’s theory accommodates these changes.

The theory’s relevance is further challenged by common misapplications in contemporary research.

Many scholars continue to apply outdated versions or misinterpret key concepts when claiming to use Bronfenbrenner’s theory, as pointed out by other scholars .

These misapplications often involve focusing solely on contextual influences without considering proximal processes, or failing to account for the time dimension in research designs.

Despite these challenges, Bronfenbrenner’s theory remains a valuable framework for understanding human development in the 21st century.

Its comprehensive nature allows for the examination of development in various contexts and across different life stages.

The theory’s emphasis on the interplay between individual characteristics, environmental influences, and temporal factors provides a nuanced approach to understanding the complexities of modern human development.

To maintain its relevance, researchers and practitioners must understand the theory’s evolution and apply it correctly.

This involves recognizing the centrality of proximal processes, considering the role of technology in developmental contexts, and designing studies that capture the dynamic nature of development over time.

By adapting the theory to include modern contexts while maintaining its core principles, Bronfenbrenner’s bioecological model can continue to provide valuable insights into human development in the 21st century and beyond.

Neo-ecological theory

Navarro & Tudge (2022) proposed the neo-ecological theory, an adaptation of the bioecological theory. Below are their main ideas for updating Bronfenbrenner’s theory to the technological age:

• Virtual microsystems should be added as a new type of microsystem to account for online interactions and activities. Virtual microsystems have unique features compared to physical microsystems, like availability, publicness, and asychnronicity.
• The macrosystem (cultural beliefs, values) is an important influence, as digital technology has enabled youth to participate more in creating youth culture and norms.
• Proximal processes, the engines of development, can now happen through complex interactions with both people and objects/symbols online. So, proximal processes in virtual microsystems need to be considered.

Background On Urie Bronfenbrenner

Urie Bronfenbrenner was born in Moscow, Russia, in 1917 and experienced turmoil in his home country as a child before immigrating to the United States at age 6.

Witnessing the difficulties faced by children during the unrest and rapid social change in Russia shaped his ideas about how environmental factors can influence child development.

Bronfenbrenner went on to earn a Ph.D. in developmental psychology from the University of Michigan in 1942.

At the time, most child psychology research involved lab experiments with children briefly interacting with strangers.

Bronfenbrenner criticized this approach as lacking ecological validity compared to real-world settings where children live and grow. For example, he cited Mary Ainsworth’s 1970 “Strange Situation” study , which observed infants with caregivers in a laboratory.

Bronfenbrenner argued that these unilateral lab studies failed to account for reciprocal influence between variables or the impact of broader environmental forces.

His work challenged the prevailing views by proposing that multiple aspects of a child’s life interact to influence development.

In the 1970s, drawing on foundations from theories by Vygotsky, Bandura, and others acknowledging environmental impact, Bronfenbrenner articulated his groundbreaking Ecological Systems Theory.

This framework mapped children’s development across layered environmental systems ranging from immediate settings like family to broad cultural values and historical context.

Bronfenbrenner’s ecological perspective represented a major shift in developmental psychology by emphasizing the role of environmental systems and broader social structures in human development.

The theory sparked enduring influence across many fields, including psychology, education, and social policy.

Frequently Asked Questions

What is the main contribution of bronfenbrenner’s theory.

The Ecological Systems Theory has contributed to our understanding that multiple levels influence an individual’s development rather than just individual traits or characteristics.

Bronfenbrenner contributed to the understanding that parent-child relationships do not occur in a vacuum but are embedded in larger structures.

Ultimately, this theory has contributed to a more holistic understanding of human development, and has influenced fields such as psychology, sociology, and education.

What could happen if a child’s microsystem breaks down?

If a child experiences conflict or neglect within their family, or bullying or rejection by their peers, their microsystem may break down. This can lead to a range of negative outcomes, such as decreased academic achievement, social isolation, and mental health issues.

Additionally, if the microsystem is not providing the necessary support and resources for the child’s development, it can hinder their ability to thrive and reach their full potential.

How can the Ecological System’s Theory explain peer pressure?

The ecological systems theory explains peer pressure as a result of the microsystem (immediate environment) and mesosystem (connections between environments) levels.

Peers provide a sense of belonging and validation in the microsystem, and when they engage in certain behaviors or hold certain beliefs, they may exert pressure on the child to conform. The mesosystem can also influence peer pressure, as conflicting messages and expectations from different environments can create pressure to conform.

Bronfenbrenner, U. (1974). Developmental research, public policy, and the ecology of childhood . Child development, 45 (1), 1-5.

Bronfenbrenner, U. (1977). Toward an experimental ecology of human development . American psychologist, 32 (7), 513.

Bronfenbrenner, U. (1979). The ecology of human development: Experiments by nature and design. Harvard University Press.

Bronfenbrenner, U. (1995). Developmental ecology through space and time: A future perspective .

Bronfenbrenner, U., & Evans, G. W. (2000). Developmental science in the 21st century: Emerging questions, theoretical models, research designs and empirical findings . Social development, 9 (1), 115-125.

Bronfenbrenner, U., & Ceci, S. J. (1994). Nature-nurture reconceptualised: A bio-ecological model . Psychological Review, 10 (4), 568–586.

Bronfenbrenner, U., & Morris, P. A. (1998). The ecology of developmental processes. In W. Damon & R. M. Lerner (Eds.),  Handbook of child psychology: Theoretical models of human development  (5th ed., pp. 993–1028). John Wiley & Sons, Inc..

Hayes, N., O’Toole, L., & Halpenny, A. M. (2017). Introducing Bronfenbrenner: A guide for practitioners and students in early years education . Taylor & Francis.

Kelly, M., & Coughlan, B. (2019). A theory of youth mental health recovery from a parental perspective . Child and Adolescent Mental Health, 24 (2), 161-169.

Langford, R., Bonell, C. P., Jones, H. E., Pouliou, T., Murphy, S. M., Waters, E., Komro, A. A., Gibbs, L. F., Magnus, D. & Campbell, R. (2014). The WHO Health Promoting School framework for improving the health and well‐being of students and their academic achievement . Cochrane database of systematic reviews, (4) .

Leventhal, T., & Brooks-Gunn, J. (2000). The neighborhoods they live in: the effects of neighborhood residence on child and adolescent outcomes . Psychological Bulletin, 126 (2), 309.

Lippard, C. N., La Paro, K. M., Rouse, H. L., & Crosby, D. A. (2018, February). A closer look at teacher–child relationships and classroom emotional context in preschool . In Child & Youth Care Forum 47 (1), 1-21.

Navarro, J. L., & Tudge, J. R. (2022). Technologizing Bronfenbrenner: neo-ecological theory.  Current Psychology , 1-17.

Paat, Y. F. (2013). Working with immigrant children and their families: An application of Bronfenbrenner’s ecological systems theory . Journal of Human Behavior in the Social Environment, 23 (8), 954-966.

Rosa, E. M., & Tudge, J. (2013). Urie Bronfenbrenner’s theory of human development: Its evolution from ecology to bioecology.  Journal of family theory & review ,  5 (4), 243-258.

Rhodes, S. (2013).  Bronfenbrenner’s Ecological Theory  [PDF]. Retrieved from http://uoit.blackboard.com

Tudge, J. R., Mokrova, I., Hatfield, B. E., & Karnik, R. B. (2009). Uses and misuses of Bronfenbrenner’s bioecological theory of human development.  Journal of family theory & review ,  1 (4), 198-210.

Wilson, P., Atkinson, M., Hornby, G., Thompson, M., Cooper, M., Hooper, C. M., & Southall, A. (2002). Young minds in our schools-a guide for teachers and others working in schools . Year: YoungMinds (Jan 2004).

Further Information

Bronfenbrenner, U. (1974). Developmental research, public policy, and the ecology of childhood. Child Development, 45.

• A-Z Publications

Annual Review of Ecology, Evolution, and Systematics

Volume 40, 2009, review article, species distribution models: ecological explanation and prediction across space and time.

• Jane Elith 1 , and John R. Leathwick 2
• View Affiliations Hide Affiliations Affiliations: 1 School of Botany, The University of Melbourne, Victoria 3010, Australia; email: [email protected] 2 National Institute of Water and Atmospheric Research, Hamilton, New Zealand; email: [email protected]
• Vol. 40:677-697 (Volume publication date December 2009) https://doi.org/10.1146/annurev.ecolsys.110308.120159
• First published as a Review in Advance on September 23, 2009
• © Annual Reviews

Species distribution models (SDMs) are numerical tools that combine observations of species occurrence or abundance with environmental estimates. They are used to gain ecological and evolutionary insights and to predict distributions across landscapes, sometimes requiring extrapolation in space and time. SDMs are now widely used across terrestrial, freshwater, and marine realms. Differences in methods between disciplines reflect both differences in species mobility and in “established use.” Model realism and robustness is influenced by selection of relevant predictors and modeling method, consideration of scale, how the interplay between environmental and geographic factors is handled, and the extent of extrapolation. Current linkages between SDM practice and ecological theory are often weak, hindering progress. Remaining challenges include: improvement of methods for modeling presence-only data and for model selection and evaluation; accounting for biotic interactions; and assessing model uncertainty.

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Literature Cited

• Addicott JF , Aho JM , Antolin MF , Padilla DK , Richardson JS . et al. 1987 . Ecological neighborhoods: scaling environmental patterns. Oikos 49 : 340– 46 [Google Scholar]
• Allen TFH , Starr TB . 1982 . Hierarchy: Perspectives for Ecological Complexity Chicago: Univ. Chicago Press [Google Scholar]
• Araújo MB , Guisan A . 2006 . Five (or so) challenges for species distribution modelling. J. Biogeogr. 33 : 1677– 88 [Google Scholar]
• Araújo MB , New M . 2007 . Ensemble forecasting of species distributions. Trends Ecol. Evol. 22 : 42– 47 [Google Scholar]
• Araújo MB , Pearson RG , Thuiller W , Erhard M . 2005 . Validation of species-climate impact models under climate change. Global Change Biol. 11 : 1504– 13 [Google Scholar]
• Austin MP . 1985 . Continuum concept, ordination methods and niche theory. Annu. Rev. Ecol. Syst. 16 : 39– 61 [Google Scholar]
• Austin MP . 2002 . Spatial prediction of species distribution: an interface between ecological theory and statistical modelling. Ecol. Model. 157 : 101– 18 [Google Scholar]
• Austin MP . 2007 . Species distribution models and ecological theory: a critical assessment and some possible new approaches. Ecol. Model. 200 : 1– 19 [Google Scholar]
• Austin MP , Cunningham RB . 1981 . Observational analysis of environmental gradients. Proc. Ecol. Soc. Aust. 11 : 109– 19 [Google Scholar]
• Austin MP , Nicholls AO , Margules CR . 1990 . Measurement of the realized qualitative niche: environmental niches of five eucalypt species. Ecol. Monogr. 60 : 161– 77 [Google Scholar]
• Barry SC , Elith J . 2006 . Error and uncertainty in habitat models. J. Appl. Ecol. 43 : 413– 23 [Google Scholar]
• Beever EA , Swihart RK , Bestelmeyer BT . 2006 . Linking the concept of scale to studies of biological diversity: evolving approaches and tools. Divers. Distrib. 12 : 229– 35 [Google Scholar]
• Berteaux D , Humphries MM , Krebs CJ , Lima M , McAdam AG . et al. 2006 . Constraints to projecting the effects of climate change on mammals. Clim. Res. 32 : 151– 58 [Google Scholar]
• Booth TH , Nix HA , Hutchinson MF , Jovanovic T . 1988 . Niche analysis and tree species introduction. For. Ecol. Manag. 23 : 47– 59 [Google Scholar]
• Box EO . 1981 . Predicting physiognomic vegetation types with climate variables. Vegetatio 45 : 127– 39 [Google Scholar]
• Burgman MA , Fox JC . 2003 . Bias in species range estimates from minimum convex polygons: implications for conservation and options for improved planning. Anim. Conserv. 6 : 19– 28 [Google Scholar]
• Burgman MA , Lindenmayer DB , Elith J . 2005 . Managing landscapes for conservation under uncertainty. Ecology 86 : 2007– 17 [Google Scholar]
• Burnham KP , Anderson DR . 2002 . Model Selection and Inference: A Practical Information-Theoretic Approach. New York: Springer-Verlag 488 , 2nd. [Google Scholar]
• Cade BS , Noon BR , Flather CH . 2005 . Quantile regression reveals hidden bias and uncertainty in habitat models. Ecology 86 : 786– 800 [Google Scholar]
• Capen DE . 1981 . The use of multivariate statistics in studies of wildlife habitat. Gen.Tech. Rep. RM-87 Rocky Mt. For. Range Exp. Stn., USDA For. Serv [Google Scholar]
• Caruana R , Niculescu-Mizil A . 2006 . An empirical comparison of supervised learning algorithms Presented at Proc. Int. Conf. Machine Learn., 23rd, Pittsburgh, PA [Google Scholar]
• Cushman SA , McGarigal K . 2002 . Hierarchical, multi-scale decomposition of species-environment relationships. Landscape Ecol. 17 : 637– 46 [Google Scholar]
• De Marco P , Diniz-Filho JAF , Bini LM . 2008 . Spatial analysis improves species distribution modelling during range expansion. Biol. Lett. 4 : 577– 80 [Google Scholar]
• Dormann CF . 2007 . Promising the future? Global change projections of species distributions. Basic Appl. Ecol. 8 : 387– 97 [Google Scholar]
• Dormann CF , McPherson JM , Araujo MB , Bivand R , Bolliger J . et al. 2007 . Methods to account for spatial autocorrelation in the analysis of species distributional data: a review. Ecography 30 : 609– 28 [Google Scholar]
• Drake JM , Randin C , Guisan A . 2006 . Modelling ecological niches with support vector machines. J. Appl. Ecol. 43 : 424 [Google Scholar]
• Drielsma M , Ferrier S . 2009 . Rapid evaluation of metapopulation persistence in highly variegated landscapes. Biol. Conserv. 142 : 529– 40 [Google Scholar]
• Dungan JL , Perry JN , Dale MRT , Legendre P , Citron-Pousty S . et al. 2002 . A balanced view of scale in spatial statistical analysis. Ecography 25 : 626– 40 [Google Scholar]
• Elith J , Graham C . 2009 . Do they? How do they? WHY do they differ? … on finding reasons for differing performances of species distribution models. Ecography 32 : 66– 77 [Google Scholar]
• Elith J , Graham CH , Anderson RP , Dudík M , Ferrier S . et al. 2006 . Novel methods improve prediction of species’ distributions from occurrence data. Ecography 29 : 129– 51 [Google Scholar]
• Elith J , Leathwick JR . 2009 . Conservation prioritization using species distribution models. Spatial Conservation Prioritization: Quantitative Methods and Computational Tools A Moilanen, KA Wilson, HP Possingham Oxford: Oxford Univ. Press 70– 93 [Google Scholar]
• Elith J , Leathwick JR , Hastie T . 2008 . A working guide to boosted regression trees. J. Anim. Ecol. 77 : 802– 13 [Google Scholar]
• Ferrier S , Guisan A . 2006 . Spatial modelling of biodiversity at the community level. J. Appl. Ecol. 43 : 393– 404 [Google Scholar]
• Ferrier S , Watson G . 1997 . An evaluation of the effectiveness of environmental surrogates and modelling techniques in predicting the distribution of biological diversity. Consult. Rep. NSW Natl. Parks Wildl. Serv. Dep. Environ., Sport Territ., Environ. Aust., Canberra. http://www.deh.gov.au/biodiversity/publications/technical/surrogates/ [Google Scholar]
• Ferrier S , Watson G , Pearce J , Drielsma M . 2002 . Extended statistical approaches to modelling spatial pattern in biodiversity: the north-east New South Wales experience. I. Species-level modelling. Biodivers. Conserv. 11 : 2275– 307 [Google Scholar]
• Fitzpatrick MC , Weltzin JF , Sanders NJ , Dunn RR . 2007 . The biogeography of prediction error: Why does the introduced range of the fire ant overpredict its native range?. Global Ecol. Biogeog. 16 : 24– 33 [Google Scholar]
• Fleishman E , MacNally R , Fay JP , Murphy DD . 2001 . Modeling and predicting species occurrence using broad-scale environmental variables: an example with butterflies of the Great Basin. Conserv. Biol. 15 : 1674– 85 [Google Scholar]
• Foody GM . 2008 . GIS: biodiversity applications. Prog. Phys. Geog. 32 : 223– 35 [Google Scholar]
• Franklin J . 2009 . Mapping Species Distributions: Spatial Inference and Prediction Cambridge, UK: Cambridge Univ. Press In press [Google Scholar]
• Gaston KJ , Chown SL , Evans KL . 2008 . Ecogeographical rules: elements of a synthesis. J. Biogeogr. 35 : 483– 500 [Google Scholar]
• Graham CH , Ferrier S , Huettman F , Moritz C , Peterson AT . 2004a . New developments in museum-based informatics and applications in biodiversity analysis. Trends Ecol. Evol. 19 : 497– 503 [Google Scholar]
• Graham CH , Ron SR , Santos JC , Schneider CJ , Moritz C . 2004b . Integrating phylogenetics and environmental niche models to explore speciation mechanisms in dendrobatid frogs. Evolution 58 : 1781– 93 [Google Scholar]
• Grinnell J . 1904 . The origin and distribution of the chestnut-backed chickadee. Auk 21 : 364– 65 [Google Scholar]
• Guisan A , Thuiller W . 2005 . Predicting species distribution: offering more than simple habitat models. Ecol. Lett. 8 : 993– 1009 [Google Scholar]
• Guisan A , Zimmermann NE . 2000 . Predictive habitat distribution models in ecology. Ecol. Model. 135 : 147– 86 [Google Scholar]
• Hamazaki T . 2002 . Spatiotemporal prediction models of cetacean habitats in the mid-western North Atlantic ocean (From Cape Hatteras, North Carolina, USA to Nova Scotia, Canada). Mar. Mamm. Sci. 18 : 920– 39 [Google Scholar]
• Hastie T , Tibshirani R , Friedman JH . 2009 . The Elements of Statistical Learning: Data Mining, Inference, and Prediction. New York: Springer-Verlag 744 , 2nd. [Google Scholar]
• Heikkinen R , Luoto M , Araújo MB , Virkkala R , Thuiller W , Sykes MT . 2006 . Methods and uncertainties in bioclimatic envelope modelling under climate change. Prog. Phys. Geog. 30 : 751– 77 [Google Scholar]
• Hoffmann A , Kellermann V . 2006 . Revisiting heritable variation and limits to species distribution: recent developments. Isr. J. Ecol. Evol. 52 : 247– 61 [Google Scholar]
• Hooten MB , Wikle CK , Dorazio RM , Royle JA . 2007 . Hierarchical spatiotemporal matrix models for characterizing invasions. Biometrics 63 : 558– 67 [Google Scholar]
• Hortal J , Jiménez-Valverde A , Gómez JF , Lobo JM , Baselga A . 2008 . Historical bias in biodiversity inventories affects the observed environmental niche of the species. Oikos 117 : 847– 58 [Google Scholar]
• Huston MA . 2002 . Critical issues for improving predictions. Predicting Species Occurrences: Issues of Accuracy and Scale JM Scott, PJ Heglund, ML Morrison, MG Raphael, WA Wall et al. 7– 24 Covelo, CA: Island Press [Google Scholar]
• Iverson LR , Prasad AM , Bossenbroek J , Sydnor D , Schwartz MW . 2009 . Modeling potential movements of an ash threat: the emerald ash borer. Advances in Threat Assessment and Their Application to Forest and Rangeland Management . http://www.threats.forestencyclopedia.net J Pye, M Raucher [Google Scholar]
• Jiménez-Valverde A , Lobo JM , Hortal J . 2008 . Not as good as they seem: the importance of concepts in species distribution modelling. Divers. Distrib. 14 : 885– 90 [Google Scholar]
• Jones J . 2001 . Habitat selection studies in avian ecology: a critical review. Auk 118 : 557– 62 [Google Scholar]
• Kearney M , Phillips BL , Tracy CR , Christian KA , Betts G . et al. 2008 . Modelling species distributions without using species distributions: the cane toad in Australia under current and future climates. Ecography 31 : 423– 34 [Google Scholar]
• Kearney M , Porter WP . 2009 . Mechanistic niche modelling: combining physiological and spatial data to predict species’ ranges. Ecol. Lett. 12 : 334– 50 [Google Scholar]
• Keith DA , Akçakaya HR , Thuiller W , Midgley GF , Pearson RG . et al. 2008 . Predicting extinction risks under climate change: coupling stochastic population models with dynamic bioclimatic habitat models. Biol. Lett. 4 : 560– 63 [Google Scholar]
• Kitchener AC , Dugmore AJ . 2000 . Biogeographical change in the tiger, Panthera tigris . Anim. Conserv. 3 : 113– 24 [Google Scholar]
• Kozak KH , Graham CH , Wiens JJ . 2008 . Integrating GIS-based environmental data into evolutionary biology. Trends Ecol. Evol. 23 : 141– 48 [Google Scholar]
• Latimer AM , Wu SS , Gelfand AE , Silander JA . 2006 . Building statistical models to analyze species distributions. Ecol. Appl. 16 : 33– 50 [Google Scholar]
• Leathwick JR , Austin MP . 2001 . Competitive interactions between tree species in New Zealand's old-growth indigenous forests. Ecology 82 : 2560– 73 [Google Scholar]
• Leathwick JR , Elith J , Chadderton L , Rowe D , Hastie T . 2008 . Dispersal, disturbance, and the contrasting biogeographies of New Zealand's diadromous and nondiadromous fish species. J. Biogeogr. 35 : 1481– 97 [Google Scholar]
• Leathwick JR , Whitehead D . 2001 . Soil and atmospheric water deficits and the distribution of New Zealand's indigenous tree species. Funct. Ecol. 15 : 233– 42 [Google Scholar]
• Legendre P. 1993 . Spatial autocorrelation: trouble or new paradigm?. Ecology 74 : 1659– 73 [Google Scholar]
• Levin SA . 1992 . The problem of pattern and scale in ecology. Ecology 73 : 1943– 67 [Google Scholar]
• Mac Nally R . 2000 . Regression and model-building in conservation biology, biogeography and ecology: the distinction between—and reconciliation of—‘predictive’ and ‘explanatory’ models. Biodivers. Conserv. 9 : 655– 71 [Google Scholar]
• MacArthur RH . 1958 . Population ecology of some warblers of northeastern coniferous forests. Ecology 39 : 599– 619 [Google Scholar]
• MacKenzie DI , Nichols JD , Lachman GB , Droege S , Royle JA . et al. 2002 . Estimating site occupancy rates when detection probabilities are less than one. Ecology 83 : 2248– 55 [Google Scholar]
• Mackey BG , Lindenmayer DB . 2001 . Towards a hierachical framework for modelling the spatial distribution of animals. J. Biogeogr. 28 : 1147– 66 [Google Scholar]
• Manly BFJ , McDonald LL , Thomas DL , McDonald TL , Erickson WP . 2002 . Resource Selection by Animals: Statistical Design and Analysis for Field Studies. Dordrecht: Kluwer 221 , 2nd. [Google Scholar]
• Maravelias CD , Reid DG . 1997 . Identifying the effects of oceanographic features and zooplankton on prespawning herring abundance using generalised additive models. Mar. Ecol. Prog. Ser. 147 : 1– 9 [Google Scholar]
• McCune B . 2006 . Non-parametric models with automatic interactions. J. Veg. Sci. 17 : 819– 30 [Google Scholar]
• Midgley GF , Hughes GO , Thuiller W , Rebelo AG . 2006 . Migration rate limitations on climate change-induced range shifts in Cape Proteaceae. Divers. Distrib. 12 : 555 [Google Scholar]
• Miller J , Franklin J , Aspinall R . 2007 . Incorporating spatial dependence in predictive vegetation models. Ecol. Model. 202 : 225– 42 [Google Scholar]
• Miller JR , Turner MG , Smithwick EAH , Dent CL , Stanley EH . 2004 . Spatial extrapolation: the science of predicting ecological patterns and processes. BioScience 54 : 310– 20 [Google Scholar]
• Moisen GG , Frescino TS . 2002 . Comparing five modeling techniques for predicting forest characteristics. Ecol. Model. 157 : 209– 25 [Google Scholar]
• Murray A . 1866 . The Geographical Distribution of Mammals London: Day & Son [Google Scholar]
• Nix H . 1986 . A biogeographic analysis of Australian elapid snakes. Atlas of Elapid Snakes of Australia R Longmore 4– 15 Canberra: Aust. Gov. Publ. Serv. [Google Scholar]
• Olden JD , Lawler JJ , Poff NL . 2008 . Machine learning methods without tears: a primer for ecologists. Q. Rev. Biol. 83 : 171– 93 [Google Scholar]
• Pearce J , Ferrier S . 2000 . Evaluating the predictive performance of habitat models developed using logistic regression. Ecol. Model. 133 : 225– 45 [Google Scholar]
• Pearce JL , Boyce MS . 2006 . Modelling distribution and abundance with presence-only data. J. Appl. Ecol. 43 : 405– 12 [Google Scholar]
• Pearson RG . 2007 . Species’ Distribution Modeling for Conservation Educators and Practitioners. Synthesis. New York: Am. Mus. Natl. Hist. http://ncep.amnh.org [Google Scholar]
• Pearson RG , Dawson TP . 2003 . Predicting the impacts of climate change on the distribution of species: Are bioclimate envelope models useful?. Global Ecol. Biogeog. 12 : 361– 71 [Google Scholar]
• Pearson RG , Thuiller W , Araújo MB , Martinez-Meyer E , Brotons L . et al. 2006 . Model-based uncertainty in species range prediction. J. Biogeogr. 33 : 1704– 11 [Google Scholar]
• Peterson AT . 2006 . Uses and requirements of ecological niche models and related distributional models. Bioinformatics 3 : 59– 72 [Google Scholar]
• Phillips SJ , Anderson RP , Schapire RE . 2006 . Maximum entropy modeling of species geographic distributions. Ecol. Model. 190 : 231– 59 [Google Scholar]
• Phillips SJ , Dudik M , Elith J , Graham C , Lehmann A . et al. 2009 . Sample selection bias and presence-only models of species distributions. Ecol. Appl. 19 : 181– 97 [Google Scholar]
• Planque B , Buffaz L . 2008 . Quantile regression models for fish recruitment environment relationships: four case studies. Mar. Ecol. Prog. Ser. 357 : 213– 23 [Google Scholar]
• Poff NL . 1997 . Landscape filters and species traits: towards mechanistic understanding and prediction in stream ecology. J. North Am. Benthol. Soc. 16 : 391– 409 [Google Scholar]
• Prasad AM , Iverson LR , Liaw A . 2006 . Newer classification and regression tree techniques: bagging and random forests for ecological prediction. Ecosystems 9 : 181– 99 [Google Scholar]
• Rangel TFLVB , Diniz-Filho JAF , Bini LM . 2006 . Towards an integrated computational tool for spatial analysis in macroecology and biogeography. Global Ecol. Biogeog. 15 : 321– 27 [Google Scholar]
• Reineking B , Schröder B . 2006 . Constrain to perform: regularization of habitat models. Ecol. Model. 193 : 675– 90 [Google Scholar]
• Richards CL , Carstens BC , Lacey Knowles L . 2007 . Distribution modelling and statistical phylogeography: an integrative framework for generating and testing alternative biogeographical hypotheses. J. Biogeogr. 34 : 1833– 45 [Google Scholar]
• Rouget M , Richardson DM . 2003 . Inferring process from pattern in plant invasions: a semimechanistic model incorporating propagule pressure and environmental factors. Am. Nat. 162 : 713– 24 [Google Scholar]
• Royle JA , Dawson DK , Bates S . 2004 . Modeling abundance effects in distance sampling. Ecology 85 : 1591– 97 [Google Scholar]
• Ruegg KC , Hijmans RJ , Moritz C . 2006 . Climate change and the origin of migratory pathways in the Swainson's thrush, Catharus ustulatus . J. Biogeogr. 33 : 1172– 82 [Google Scholar]
• Sagarin RD , Gaines SD , Gaylord B . 2006 . Moving beyond assumptions to understand abundance distributions across the ranges of species. Trends Ecol. Evol. 21 : 524– 30 [Google Scholar]
• Saunders SC , Chen J , Drummer TD , Gustafson EJ , Brosofske KD . 2005 . Identifying scales of pattern in ecological data: a comparison of lacunarity, spectral and wavelet analyses. Ecol. Complex. 2 : 87– 105 [Google Scholar]
• Schimper AFW . 1903 . Plant-Geography upon a Physiological Basis Transl. WR Fisher Oxford: Clarendon Press (From German) [Google Scholar]
• Schröder B . 2008 . Challenges of species distribution modeling belowground. J. Plant Nutr. Soil Sci. 171 : 325– 37 [Google Scholar]
• Schröder B , Seppelt R . 2006 . Analysis of pattern–process interactions based on landscape models—overview, general concepts, and methodological issues. Ecol. Model. 199 : 505– 16 [Google Scholar]
• Schurr FM , Midgley GF , Rebelo AG , Reeves G , Poschlod P . et al. 2007 . Colonization and persistence ability explain the extent to which plant species fill their potential range. Global Ecol. Biogeog. 16 : 449– 59 [Google Scholar]
• Soberon J . 2007 . Grinnellian and Eltonian niches and geographic distributions of species. Ecol. Lett. 10 : 1115– 23 [Google Scholar]
• St-Louis V , Pidgeon AM , Clayton MK , Locke BA , Bash D . et al. 2009 . Satellite image texture and a vegetation index predict avian biodiversity in the Chihuahuan Desert of New Mexico. Ecography 32 : 468– 80 [Google Scholar]
• Stauffer DE . 2002 . Linking populations and habitats: Where have we been? Where are we going?. Predicting Species Occurrences: Issues of Accuracy and Scale JM Scott, PJ Heglund, ML Morrison, MG Raphael, WA Wall et al. 53– 61 Covelo, CA: Island Press [Google Scholar]
• Steyerberg EW , Eijkemans MJC , Habbema JDF . 1999 . Stepwise selection in small data sets: a simulation study of bias in logistic regression analysis. J. Clin. Epidemiol. 52 : 935– 42 [Google Scholar]
• Stockwell D , Peters D . 1999 . The GARP modelling system: problems and solutions to automated spatial prediction. Int. J. Geogr. Inf. Sci. 13 : 143– 58 [Google Scholar]
• Swenson NG . 2008 . The past and future influence of geographic information systems on hybrid zone, phylogeographic and speciation research. J. Evol. Biol. 21 : 421– 34 [Google Scholar]
• Thuiller W , Richardson DM , Pysek P , Midgley GF , Hughes GO . et al. 2005 . Niche-based modelling as a tool for predicting the risk of alien plant invasions at a global scale. Global Change Biol. 11 : 2234– 50 [Google Scholar]
• Tobalske C . 2002 . Effects of spatial scale on the predictive ability of habitat models for the Green Woodpecker in Switzerland. Predicting Species Occurrences: Issues of Accuracy and Scale JM Scott, PJ Heglund, ML Morrison, MG Raphael, WA Wall et al. 197– 204 Covelo, CA: Island Press [Google Scholar]
• Venables WN , Dichmont CM . 2004 . GLMs, GAMs and GLMMs: an overview of theory for applications in fisheries research. Fish. Res. 70 : 319– 37 [Google Scholar]
• Vierling KT , Vierling LA , Gould WA , Martinuzzi S , Clawges RM . 2008 . Lidar: shedding new light on habitat characterization and modeling. Front. Ecol. Environ. 6 : 90– 98 [Google Scholar]
• Wharton TN , Kriticos DJ . 2004 . The fundamental and realized niche of the Monterey Pine aphid, Essigella californica (Essig) (Hemiptera : Aphididae): implications for managing softwood plantations in Australia. Divers. Distrib. 10 : 253– 62 [Google Scholar]
• Whittaker RJ . 1956 . Vegetation of the Great Smoky Mountains. Ecol. Monogr. 26 : 1– 80 [Google Scholar]
• Whittingham MJ , Stephens PA , Bradbury RB , Freckleton RP . 2006 . Why do we still use stepwise modelling in ecology and behavior?. J. Anim. Ecol. 75 : 1182– 89 [Google Scholar]
• Williams JW , Jackson ST , Kutzbac JE . 2007 . Projected distributions of novel and disappearing climates by 2100 AD. Proc. Natl. Acad. Sci. USA 104 : 5738– 42 [Google Scholar]
• Wright JW , Davies KF , Lau JA , McCall AC , McKay JK . 2006 . Experimental verification of ecological niche modeling in a heterogeneous environment. Ecology 87 : 2433– 39 [Google Scholar]

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Promoting the health of refugee women: a scoping literature review incorporating the social ecological model

Affiliations.

• 1 Joseph J. Zilber School of Public Health, University of Wisconsin - Milwaukee, Milwaukee, USA. [email protected].
• 2 Joseph J. Zilber School of Public Health, University of Wisconsin - Milwaukee, Milwaukee, USA.
• 3 Department of Communication Studies, Towson University, Towson, USA.
• 4 College of Nursing, University of Wisconsin-Milwaukee, Milwaukee, USA.
• 5 Center for Advancing Population Science, Medical College of Wisconsin, Milwaukee, USA.
• 6 Master of Sustainable Peacebuilding, College of Nursing, University of Wisconsin-Milwaukee, Milwaukee, USA.
• 7 Jackson School of International Studies, Western Washington University, Bellingham, USA.
• PMID: 33485342
• PMCID: PMC7825239
• DOI: 10.1186/s12939-021-01387-5

The health of refugee women after settlement in a new country, can be adversely or positively affected by individual, interpersonal, community, and organizational factors. While much of the previous literature highlights these factors individually, there is a lack of comprehensive synthesis regarding how the factors interact to influence the health of refugee women. We conducted a thematic analysis in our literature review to elucidate how providers can work with refugee women to prevent adverse health outcomes and intervene at multiple levels to improve their health outcomes after resettlement. We reviewed peer-reviewed literature from 2009 to 2019 from Google Scholar, JSTOR, Global Health, PubMed, CINAHL, Sociological Abstracts, and Social Service Abstracts, and also used citation chaining, to identify relevant information pertaining to refugee women's health. The key terms used for our literature review were, health care, violence, social support, and mental health. In total, we included 52 articles, 3 books, and 8 other sources. We found that refugee women are vulnerable to violence during migration and typically have high rates of post-traumatic stress disorder. There were also concerns of secondary victimization by providers after resettlement. We also found that social support is an important factor for reducing isolation, and improving access to health care, as well as improving mental health outcomes. However, social support was often difficult to maintain, and was moderated by factors such as English language fluency. Health care was influenced by health literacy, cultural difference, communication concerns, and access issues. The findings suggest that at the individual and interpersonal levels there is a need to address language barriers, improve provider-patient communication, and provide appropriate medical and mental health screenings. At the organizational level, inter-organizational communication and awareness are vital. At the community level, providers can work with community leaders, to educate, create dialogue and collaboration, to help facilitate understanding and bolster community social support. Improved communication and knowledge about the unique needs and concerns of refugee women through an integrated, multi-system approach is necessary to improve their health outcomes.

Keywords: Health equity; Literature review; Refugee women’s health; Social ecological model.

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Conflict of interest statement

MH, AC, OO, JW, and LMV have non-financial competing interests. These authors work or have worked directly with refugees and refugee serving organizations.

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The Social Ecological Model: A Framework for Understanding COVID-19 Vaccine Uptake among Healthcare Workers—A Scoping Review

Damian naidoo.

1 Discipline of Psychology, School of Applied Human Sciences, Howard College, University of KwaZulu-Natal, Durban 4041, South Africa

2 Health Promotion Unit, KwaZulu-Natal Department of Health, Pietermaritzburg, Private Bag X9051, Pietermaritzburg 3200, South Africa

Anna Meyer-Weitz

Kaymarlin govender.

3 HEARD, College of Law and Management Studies, University of Kwazulu-Natal, Durban 4041, South Africa

Associated Data

Not applicable.

Vaccination plays a crucial role in combating the global COVID-19 pandemic. Immunizing all healthcare workers (HCWs) is essential for increasing vaccine confidence and acceptance within the general population. Understanding the factors that hinder or facilitate vaccine uptake among HCWs is of utmost importance, considering they are among the first to be vaccinated. This review follows Arksey and O’Malley’s five-stage methodological framework. We searched PubMed, Web of Science, ProQuest, WorldCat Discovery, and Google Scholar for peer-reviewed articles published from 2020 to 2023. A descriptive analysis and narrative synthesis approach were employed to collect and synthesize data. Using the social-ecological model as a framework, the literature was categorized into themes at the intrapersonal, interpersonal, organizational, community, and policy levels. We reviewed a total of fifty-three published academic articles, with the majority of studies conducted in Ethiopia and Nigeria. The intention for vaccine uptake resulted in an unsatisfactory (52%) overall uptake rate among HCWs. Individual-level determinants associated with vaccine uptake included being male, middle-aged, being a physician, having a higher level of education, and having a chronic illness. This review identified significant barriers at each level, such as safety concerns, perceived scientific uncertainty, vaccine ineffectiveness, lack of trust in stakeholders, and religious beliefs. Additionally, we identified facilitators at each level, with the most common factors promoting intention to uptake being the desire to protect oneself and others and a high perceived susceptibility to contracting COVID-19. This review highlights the existence of significant barriers to vaccine uptake on the African continent. Given that HCWs play a crucial role in guiding the public’s vaccination decisions, it is imperative to prioritize education and training efforts about the safety and effectiveness of COVID-19 vaccines.

1. Introduction

The World Health Organization (WHO) approved several vaccines against COVID-19 for global distribution in various regions [ 1 , 2 ]. Vaccines manufactured by Pfizer, Oxford/AstraZeneca, Moderna, Janssen, Sputnik V, Sinovac, and Sinopharm, among others, were authorized and made available in Africa [ 2 , 3 ]. In the first quarter of 2021, mass vaccination programs commenced in several African countries [ 2 , 3 , 4 ]. These campaigns were planned in 31 African countries until 2022 [ 5 ]. Egypt was the first African country to begin vaccination on 24 January 2021, followed by South Africa on 17 February 2021, and Zimbabwe on 18 February 2021 [ 4 ]. During the distribution of the COVID-19 vaccination, there have been substantial problems with vaccine nationalism and access equity [ 6 ] Hence, Africa and other low-and middle-income countries (LMICs) have low COVID-19 vaccine coverage [ 7 ]. As a result, the COVAX global initiative was established to ensure equitable and timely access to vaccines worldwide [ 8 ]. The continent received more than 892 million vaccine doses, with the COVAX facility accounting for 64% of the total vaccinations received [ 9 ]. Much progress has been made in increasing vaccine shipments to countries [ 10 , 11 ]. Despite greater access to COVID-19 vaccinations, the COVID-19 pandemic has exposed numerous flaws in African healthcare systems, particularly in the aftermath of the Delta and Omicron variants [ 10 , 12 ]. As of 16 October 2022, only 24% of the African continent’s population had been vaccinated, compared to a global coverage of 64% [ 13 ]. According to the WHO, Africa is on track to reach the global vaccination coverage target of 70% by April 2025 [ 13 ]. As vaccine supply has increased worldwide, it has become clear that COVID-19 vaccine hesitancy (VH) challenges vaccine uptake [ 14 , 15 ] in Africa [ 8 , 16 ], particularly in Western and Central Africa [ 17 ]. The WHO ranked VH as one of the top ten threats to global health [ 14 , 16 ] and defines it as “a delay in acceptance or refusal of vaccines despite availability of vaccination services” [ 18 ] (p. 899). This broad definition highlights variability by stating that VH varies between vaccine types, contexts, geographical regions, and over time. This phenomenon has been exacerbated by the current COVID-19 pandemic [ 15 , 19 ].

Due to a global shortage of COVID-19 vaccines, governments have prioritized high-risk groups for vaccination [ 11 , 20 , 21 ]. Despite African countries prioritizing healthcare workers (HCWs), vaccine coverage remains low due to VH and a lack of vaccination services and fear of its side effects, especially in rural areas, leaving the vast majority of front-line workers unprotected [ 4 , 11 , 22 ]. Studies showed that not all HCWs are prepared to receive the COVID-19 vaccine when it becomes available in their country [ 8 , 22 , 23 ]. Concerns have been raised about VH among HCWs throughout Africa [ 11 , 22 ]. Vaccine acceptance (VA) and hesitancy have been a global problem, particularly in African settings [ 16 , 24 , 25 ]. Historical, structural, and other systemic dynamics contribute to VH in the African continent [ 7 , 8 ], and are a remaining threat to Africa’s vaccination programmes [ 17 ]. The increased polio outbreaks in Nigeria have been argued to stem from misinformation and public distrust in vaccination between 2002 and 2006 and subsequent polio outbreaks on three continents [ 8 , 26 ]. Furthermore, mass deworming programmes in Ghana were rejected due to community misconceptions [ 8 ]. Furthermore, trust in current vaccines has been eroded by a history of colonial medical and vaccine research abuse in Africa [ 7 ]. African populations were frequently subjected to unethical testing in the name of scientific advancement [ 7 , 27 ]. At the beginning of 2021, Tanzania’s health minister announced that the country would forgo COVID-19 vaccination due to concerns about vaccine safety and would instead depend on traditional and household herbs and medicines for prevention and cure [ 28 , 29 ].

There are numerous barriers and drivers that influence vaccination intention (VI) and uptake, ranging from individual psychological, socio-cultural, and environmental factors that influence HCW’s willingness to be vaccinated [ 30 , 31 , 32 , 33 ]. The Social Ecological Model (SEM) was initially developed by Urie Bronfenbrenner [ 34 ] and later adapted by McLeroy and colleagues [ 35 ]. This framework, widely used in public health and social sciences, aims to comprehend the various factors influencing human behaviour and health outcomes [ 34 , 35 ]. It acknowledges that individuals exist within different social systems and that multiple levels of influence interact to shape their behaviours [ 35 ]. These levels are as follows: Intrapersonal Level: this level focuses on the characteristics and attributes of individuals, including factors such as knowledge, attitudes, beliefs, skills, and biological factors. Interpersonal Level: The interpersonal level involves the impact of relationships and social networks on an individual. It includes family, friends, peers, co-workers, and other social connections. Organizational Level: The organizational level pertains to formal and informal rules, policies, and practices. It can encompass schools, workplaces, community organizations, and religious institutions. Organizational factors can affect access to resources, opportunities, and social norms. Community Level: The community level encompasses the physical and social environment in which individuals reside. It includes the characteristics of the community, such as its infrastructure, social capital, and cultural norms. Community factors can influence social norms, social networks, and the availability of resources and services. Policy Level: The policy level represents the broader social, economic, and political context in which individuals and communities are situated. It encompasses public policies, laws, social inequality, and cultural values.

In light of continuous COVID-19 infections and the likelihood of future pandemics, HCW’s hesitation in vaccination uptake remains an area of concern. Given that HCWs are among the first to be vaccinated, it is critical to understand factors that pose barriers or facilitate vaccine uptake. In light of this, we used the five-level SEM to segment the levels of influence (intrapersonal, interpersonal, organizational, community, and policy level) to provide a more comprehensive and nuanced understanding of how these factors shape vaccine-related behaviours. The identified factors were organized into barriers and facilitators to clarify their influence on VA and VH. While a review had been conducted on VA on the African continent among HCWs [ 36 ], this review focused on factors and barriers influencing COVID-19 vaccine acceptance, intention for uptake, and hesitancy among HCWs on the African continent in lieu of informing intervention approaches to address likely barriers in future immunization programmes.

This scoping review was conducted using Arksey and O’Malley’s methodological framework [ 37 ]. The following five-stage framework proposed was as follows: “(1) Identifying the research questions, (2) Searching for relevant studies, (3) Selecting studies, (4) Charting the data, and (5) Collating, summarising, and reporting the results” [ 37 ] (p. 22). This review includes the Preferred Reporting Items for Systematic Review and Meta-Analysis extension for Scoping Reviews (PRISMA-ScR) checklist ( Supplementary Materials S1 ) [ 38 ]. A review protocol was submitted to the University of KwaZulu-Natal (UKZN) Humanities and Social Sciences Research Ethics Committee (HSSREC)—Application number: 00013262.

2.1. Identifying the Research Questions

• What is the rate of uptake of COVID-19 vaccinations among HCWs?
• What socio-demographic factors are associated with VA or VH among HCWs?
• What factors act as barriers or facilitators for vaccine uptake among HCWs?

2.2. Searching for Relevant Studies

A comprehensive literature search was conducted in five databases: Web of Science, WorldCat Discovery, PubMed, Google Scholar, and ProQuest to retrieve studies related to the above research questions, and the search period for the review spanned from 2020 to 2023. The final search was completed in May 2023. The COVID-19 pandemic was the motivating factor behind this timeline. The following search terms were applied, using a variation of MEsH terms and keywords for each database: “COVID-19 vaccines”, “COVID-19”, “SARS-CoV-2 vaccines”, “associated factors”, “intention”, “barriers”, “drivers”, “acceptance”, “hesitancy”, “Africa”, “Healthcare workers”, “vaccine uptake”, “vaccine refusal”, “HCWs”, “COVID-19 vaccination uptake”, “COVID-19 vaccination intention”, “COVID-19 vaccine willingness”. The final search strategies for WorldCat Discovery and PubMed are in Appendix A , Table A1 , Table A2 and Table A3 .

2.3. Study Selection

After thoroughly screening the titles and abstracts, inclusion and exclusion criteria were established initially and studies were considered using the Population–Concept–Context (PCC) framework to determine their eligibility for this review. Full-text eligible studies met the following inclusion criteria: (1) literature type: academic/published journals (peer-reviewed journals); (2) language: studies that were published in the English language; (3) timeline: studies that were published between 2021 and 2023, (4) location: studies conducted in Africa; (5) vaccines: COVID-19 vaccines; (6) populations: HCWs—using the WHO definition of HCWs [ 39 ] (7) study designs: quantitative, qualitative, or mix-methods studies; (8) studies that specifically address the research questions. The following were excluded: grey literature (unpublished journals, reports and documents, conference papers, memoranda, theses, letters, and protocols) and reviews (scoping and systematic).

2.4. Charting Data

Data extraction from the included peer-reviewed studies was conducted using a standardized Microsoft Excel data collection sheet. A reviewer (D.N) extracted data from included reviews, which was then independently verified by a second reviewer (A.M-W). The following data fields were extracted from each study: author, year of publication, country, data collection period, methodology and study design, population characteristics, sample size, and measurement scales. The VI, VH, and VA levels among HCWs were analysed, summarised, and compared using simple descriptive statistics (percentages). A narrative synthesis approach [ 40 ] was utilized to acquire, synthesize, and map the literature utilizing the SEM to group facilitators and barriers to the uptake of the COVID-19 vaccine. All data were reported using thematic narratives [ 41 ].

2.5. Collating, Summarising, and Reporting the Results

The results have been compiled and summarized. Following a description of the study’s characteristics, the relevant influencing factors are presented using the SEM. Barriers and facilitators impacting the uptake of COVID-19 vaccines were categorized into various levels, including socio-demographic characteristics, individual factors, social factors, institutional factors, community factors, and policy factors.

A total of 180 records were identified from the five database searches: Web of Science ( n = 20), WorldCat Discovery ( n = 16), PubMed ( n = 41), Google Scholar ( n = 55) and ProQuest ( n = 48). After removing duplicates using EndNote (V.X9), 145 records remained for a title and abstract screening. We excluded 69 articles that did not meet the selection criteria, leaving 76 for a review of the full-text articles. The full-text screening was conducted to assess eligibility before further data extraction. Following the inclusion and exclusion assessment criteria, studies were further excluded because they did not address research questions ( n = 8), focused solely on vaccine uptake ( n = 5), and were non-peer-reviewed ( n = 10), resulting in 53 articles included in the final review. The PRISMA flow diagram below illustrates the selection process in Figure 1 .

PRISMA flow diagram: selection of included studies. Adapted from [ 42 ].

3.1. Descriptive Analysis of Articles

The majority of the articles included in this review were conducted in Ethiopia (23%), followed by Nigeria (17%), Egypt (13%), South Africa (8%), and Ghana (8%). The remaining articles were conducted in Cameroon, Uganda, Somalia, Tanzania, Namibia, Malawi, Zambia, The Democratic Republic of Congo (DRC), Guinea, Sudan, Sierra Leone, and Tunisia. Two articles focused on multiple African countries, including Nigeria, Cameroon, Sierra Leone, DRC, and Uganda. Please refer to Table 1 for the number of countries reviewed and Appendix B , Table A4 for the included study characteristics.

Illustrates the number of countries reviewed.

The majority of the studies used a quantitative cross-sectional design (88%), while six studies employed a mixed-method design (8%), and one used a qualitative design (4%). This review specifically focused on HCWs, with the exception of a study conducted by Toure and colleagues [ 43 ], which also surveyed the general adult population. Since this review had specific exclusion criteria, only the sampled population of HCWs was considered. The sample size of the included studies varied from 15 to 7763 participants. Among the sampled HCWs, the majority were physicians (83%), followed by nurses (73%), pharmacists (49%), medical laboratory technicians (47%), and midwives (42%).

3.2. Survey Instruments/Measurement Scales

There are various types of measurement scales or survey instruments used in research. The articles reviewed in this study employed two types of measurement scales, dichotomous scales and Likert scales, to assess VH or VA. A dichotomous question presents only two possible answer options [ 44 ]. This type of question is considered closed-ended because the options are predetermined by the investigator. Dichotomous questions are used when there are only two possible values for the subject being examined [ 44 ]. On the other hand, a Likert scale is a rating scale used to evaluate opinions, attitudes, or behaviours. It consists of a statement or question followed by a set of answer statements, typically five, seven, or nine in number [ 45 ].

In this review, 12 studies utilized Likert scales, while 36 studies utilized dichotomous scales to measure vaccine uptake. Upon screening the articles, variations in measurement approaches were identified. For example, authors assessed VH or VA using a Likert scale in the following ways. El-Sokkary and colleagues [ 46 ] measured vaccination intention by asking participants to indicate their intention to undergo COVID-19 vaccination on a three-point scale: “agree”, “neutral”, or “disagree”. Fares and colleagues [ 47 ] measured the decision to receive the COVID-19 vaccine with three options: “yes”, “no”, or “undecided”. In their study, the term “hesitant” was used for the undecided group. Wiysonge and colleagues [ 48 ] assessed vaccine acceptance by using the statement, “I will take the COVID-19 vaccine when one becomes available”. This statement had seven response options ranging from “strongly disagree” to “strongly agree”. The responses were later transformed into a binary variable, with responses 1 to 4 categorized as “vaccine hesitancy” and responses 5 to 7 categorized as “vaccine acceptance”.

In terms of dichotomous scales, VH and VA were assessed as follows, Adejumo and colleagues [ 49 ] evaluated participants’ willingness to receive the COVID-19 vaccine using single-item questions with “yes” or “no” responses. Yilma and colleagues [ 50 ] assessed vaccine acceptability by asking, “If a COVID-19 vaccine is proven safe and effective and is available, will you get vaccinated?” Participants who responded with “definitely not” or “probably not” were categorized as having vaccine non-acceptance, while those who responded with “probably” or “definitely” were categorized as willing to accept the COVID-19 vaccination.

3.3. The Uptake Rate of the COVID-19 Vaccines among HCWs

Table 2 presents the characteristics and COVID-19 vaccine uptake rates among HCWs represented in studies contained in this review.

COVID-19 vaccine uptake rates by author and country.

Fifty-two studies reported on COVID-19 vaccination acceptability, intention, and hesitancy. In this review, most of these studies reported HCWs’ hesitation to accept the COVID-19 vaccines on the African continent. A qualitative study conducted by Ashipala and colleagues [ 63 ] did not provide information on nurses’ uptake of COVID-19 vaccines.

Twenty-seven studies reported on the intention to accept the COVID-19 vaccine. Intention to accept the vaccine varied dramatically from 21% to 90.1%. Notably, Fares and colleagues [ 47 ] found that Egypt (21%) had the lowest intention rate, while Adeniyi and colleagues [ 52 ] reported that South Africa (90.1%) had the highest intention rate. Based on the included studies in this review, the intention rate to uptake the COVID-19 vaccine among HCWs was below average [ 23 , 25 , 46 , 47 , 54 , 59 , 76 , 77 , 80 , 83 , 88 ]. Conversely, fourteen studies reported an above-average intention rate [ 48 , 49 , 51 , 52 , 55 , 60 , 69 , 71 , 79 , 82 , 88 , 91 , 92 ]. The overall average intention rate for HCWs to uptake the COVID-19 vaccines across all included studies was approximately 52%, indicating a suboptimal level of uptake among this population.

Medical students expressed a lack of willingness to accept the COVID-19 vaccine, with an acceptance rate ranging from 34.7% to 45.4%. A study conducted by Saied and colleagues [ 83 ] in Egypt found that only 34.7% of medical students were willing to accept the vaccine, which was disappointing. Most (45.7%) medical students hesitated to accept the vaccine. In addition, 71% intended to take the vaccine but would postpone doing so to wait and observe its effects on those who received it before making a decision themselves.

Twenty-nine studies examined HCWs’ hesitancy towards receiving the COVID-19 vaccine. The degree of hesitancy varied across these studies, ranging from 13.3% to 79%. Fares and colleagues [ 47 ] reported the highest VH rate (79%) in Egypt.

Subsequent studies reported HCWs’ acceptance towards the COVID-19 vaccines [ 43 , 57 , 65 , 68 , 74 , 75 , 78 , 86 , 87 , 89 , 90 , 93 , 95 ]. Among these ten studies, over half of the participants were vaccinated with at least one dose (see Figure 2 ). A study by Watermeyer and colleagues [ 90 ] reported the highest vaccination rate (90%) in South Africa. Additionally, a study conducted in Ethiopia by Zewude and Belachew [ 95 ] further depicted the intention to accept the second dose. Approximately 28.3% of HCWs were VH to accept the second dose.

An illustration of COVID-19 vaccine uptake rates among the included studies in Africa [ 23 , 47 , 50 , 51 , 52 , 55 , 57 , 59 , 61 , 63 , 65 , 67 , 69 , 71 , 73 , 75 , 77 , 79 , 81 , 82 , 84 , 86 , 88 , 89 , 91 , 92 , 95 ].

3.4. Socio-Demographic Determinants Associated with VA or VH

Table 3 reports various socio-demographic (individual level) factors influencing vaccine uptake. These factors varied across HCWs on the African continent. Twelve socio-demographic factors were associated with vaccine uptake in this review. Seven socio-demographic factors were prominent in influencing vaccine uptake. These included gender, age, level of education, marital status, presence of chronic illness, living area, and cadre. These factors were further divided into two categories, which include COVID-19 vaccine uptake associated with hesitancy and associated with acceptance. Factors associated with COVID-19 vaccine uptake included being male, middle-aged (older than 40), being a physician, and having a tertiary-level education. In contrast, factors associated with hesitancy towards the COVID-19 vaccine were females younger than 40 and having a tertiary education. Interestingly, a tertiary-level education was a significant factor associated with VA and VH among HCWs.

Socio-demographic determinants associated with vaccine uptake.

The following factors associated with VA were gender [ 23 , 46 , 56 , 65 , 67 , 72 , 74 , 76 , 77 , 79 , 80 , 87 ], age [ 43 , 46 , 48 , 54 , 56 , 57 , 65 , 74 , 87 , 94 ], education level [ 43 , 46 , 50 , 52 , 67 , 75 , 78 ], belonging to religion [ 48 , 74 ], marital status [ 43 , 72 , 76 , 77 , 78 ], being a parent [ 95 ], absence of pregnancy [ 43 ], presence of chronic illness [ 43 , 56 , 59 , 77 ], living area [ 65 , 67 , 77 , 79 ], cadre [ 23 , 43 , 48 , 49 , 51 , 53 , 57 , 59 , 61 , 65 , 73 , 79 , 80 , 87 ], and income level [ 43 , 46 ].

In contrast, the following factors were associated with VH, gender [ 50 , 55 , 85 , 86 , 89 , 94 ], age [ 50 , 53 , 58 , 64 , 73 , 86 , 94 ], ethnicity [ 64 ], education level [ 50 , 55 , 70 , 85 ], religion [ 71 ], marital status [ 58 ], presence of chronic illness [ 62 ], cadre [ 50 , 58 , 64 , 71 , 84 , 93 ], and income level [ 58 ].

3.5. Barriers and Facilitators Affecting Vaccine Uptake among HCWs

At the intrapersonal level, three themes emerged: vaccine-related factors, COVID-19, and psychosocial factors. Within the theme of COVID-19 vaccines, ten sub-themes were identified, all acting as barriers to vaccine uptake. The most prominent sub-theme was safety concerns, which was reported as the primary barrier [ 23 , 25 , 43 , 47 , 50 , 51 , 55 , 56 , 57 , 60 , 61 , 65 , 66 , 67 , 68 , 69 , 70 , 72 , 74 , 75 , 76 , 77 , 78 , 81 , 82 , 83 , 84 , 85 , 86 , 88 , 90 , 91 , 92 , 95 ]. However, only three studies mentioned confidence in the COVID-19 vaccines, facilitating uptake [ 47 , 52 , 88 ]. Numerous studies [ 23 , 47 , 55 , 56 , 61 , 66 , 68 , 69 , 70 , 74 , 75 , 77 , 81 , 82 , 85 , 90 , 91 ] highlighted the prevalent mistrust in science among HCWs, often rooted in the belief that the COVID-19 vaccine has not undergone sufficient clinical trials. Concerns about the vaccine’s effectiveness were reported in 16 studies [ 23 , 25 , 65 , 67 , 69 , 70 , 76 , 77 , 78 , 82 , 84 , 85 , 86 , 88 , 92 , 95 ], with some expressing doubts about its ability to protect against COVID-19, particularly in Africa. In contrast, only one study reported that the vaccine was effective against COVID-19 [ 74 ]. Three studies mentioned that HCWs preferred alternative treatments to the COVID-19 vaccine, such as hydroxychloroquine, azithromycin, and ivermectin [ 61 , 81 , 94 ]. The subsequent studies reported on other COVID-19 vaccine-related barriers, which included poor vaccine knowledge [ 66 ], negative perceptions toward the vaccine [ 43 ], preference for waiting for another type of vaccine [ 70 ], and not considering the vaccine a priority [ 70 ]. Vaccine safety, mistrust in science, and efficacy were major concerns among HCWs within this theme. The following study [ 95 ] reported barriers to the uptake of the second vaccine dose, such as discomfort during the first dose and the belief that sufficient immunity had already been acquired.

The second theme in this level was COVID-19, with four sub-themes identified. The perception of susceptibility to contracting COVID-19 among HCWs was mentioned as both a barrier and a facilitator for vaccine uptake. HCWs who perceived themselves to be at a higher risk of contracting COVID-19 [ 25 , 47 , 59 , 63 , 88 , 92 ] were more willing to get vaccinated compared to those who perceived themselves to have a low risk [ 23 , 66 , 67 , 78 , 91 ]. HCWs who believed they needed the vaccine for protection were more likely to get vaccinated than those who relied on their immune system to prevent infection [ 65 , 68 , 76 , 77 , 95 ]. A prior diagnosis of COVID-19 was mentioned as a barrier to vaccine uptake as some HCWs believed that they had gained natural immunity and did not need the vaccine [ 23 , 67 , 91 , 92 ]. Side effects of COVID-19, such as loss of smell and taste, were mentioned as facilitators for vaccine uptake [ 56 ].

The final sub-theme at this level was psychosocial factors, which are individual factors that affect vaccine uptake. In separate studies, HCWs with pre-existing health conditions were mentioned as barriers and facilitators [ 56 , 59 ]. Female HCWs planning to conceive were less likely to get vaccinated [ 67 , 70 , 91 ]. Religious beliefs also played a role as a barrier, with Christian HCWs expressing concerns about the vaccine containing the mark of the beast [ 55 , 56 , 61 , 66 , 70 , 81 , 95 ]. Other barriers to uptake at this level included prior adverse reactions to vaccines [ 23 , 61 ], fear of needles and injections [ 70 ], and opposition to vaccinations in general [ 91 ].

At the interpersonal level, a significant factor relating to influences was discovered. HCWs reported that their relationships with colleagues played a role in encouraging vaccine uptake [ 63 ]. HCWs mentioned that their colleagues influenced their decision to get vaccinated. The connection between HCWs and their families also emerged as a crucial sub-theme. The desire to protect their loved ones motivated HCWs to receive the COVID-19 vaccine, as mentioned in eight studies [ 25 , 60 , 72 , 78 , 84 , 88 , 91 , 92 ].

Moreover, one study found that HCWs who had experienced the loss of a loved one due to COVID-19 were more likely to get vaccinated [ 55 ]. Within this theme, two barriers were identified. In one study, HCWs expressed the need for permission from their families before getting the COVID-19 vaccine [ 70 ]. In another study, HCWs reported facing disapproval from their families regarding the COVID-19 vaccine [ 66 ]. The last sub-theme explored religious leaders’ influences on HCWs, indicating that discouragement from religious leaders also acted as a barrier [ 66 ].

At the institutional level, there are significant challenges in the environmental structures. One identified barrier is the lack of trust in stakeholders, such as government and pharmaceutical companies [ 25 , 43 , 56 , 57 , 68 , 81 , 90 ]. Furthermore, a study [ 66 ] found that some HCWs would refuse the vaccine because government officials themselves did not accept it. The accessibility of the vaccine was mentioned as a barrier in four studies [ 63 , 65 , 70 , 75 ]. In contrast, one study suggested that the easy availability of the COVID-19 vaccine could be a reason for its uptake [ 63 ]. The workplace environment of HCWs also influences vaccine uptake. Lack of support from employers was identified as a barrier, leading HCWs to reject the vaccine [ 66 ]. Conversely, another study revealed that some HCWs felt compelled to accept the COVID-19 vaccine to continue working, per their company’s policy [ 91 ].

At the community level, a prevailing theme was centred around shared norms and myths. Within this overarching theme, three sub-themes were identified. Multiple studies [ 52 , 78 , 91 , 92 ] emphasized that HCWs viewed the uptake of the COVID-19 vaccine as a crucial public health responsibility for ending the pandemic. However, specific barriers to vaccine uptake were also identified. Several studies [ 23 , 25 , 57 , 61 , 63 , 67 , 70 , 78 ] observed that limited access to reliable information hindered the willingness of HCWs to receive the vaccine. Social media emerged as a significant influencer, with seven studies [ 57 , 60 , 63 , 68 , 70 , 72 , 90 ] reporting that HCWs subscribed to misinformation or conspiracy theories. These theories included beliefs that the vaccine was intentionally designed to cause harm to people in Africa, sterilize the African population, or even cause COVID-19.

At the policy level, an important theme that emerged was the implementation of COVID-19 policies. Within this theme, two specific sub-themes were identified. The first sub-theme focused on strategies to encourage HCWs to get vaccinated. It was supported by three studies, which highlighted that HCWs would be required to receive the vaccine to travel in the future [ 47 , 60 , 63 ]. Additionally, two studies indicated that HCWs are willing to accept the COVID-19 vaccine because it is free of charge [ 74 , 88 ]. However, it is worth noting that there is also a barrier at this level. This barrier stems from mandatory vaccination policies, which make HCWs feel coerced into accepting the vaccines [ 82 , 89 ]. HCWs believe they lack control over their health-related behaviours and refuse to be controlled by others, resulting in their rejection of the COVID-19 vaccine. Table 4 summarizes the factors influencing vaccine uptake.

Factors influencing vaccine uptake.

4. Discussion

VH and refusal continue to jeopardize COVID-19 vaccination coverage in LMICs [ 23 ]. The fight against COVID-19 requires widespread vaccination uptake and acceptance [ 96 ]. In this review, 53 articles were selected and analysed, focusing on the intention, socio-demographical determinants, and factors influencing vaccine uptake. In this review, most studies were conducted in Ethiopia and Nigeria. The intention to take the COVID-19 vaccine is a challenge globally. We found that the proportion of HCWs who intend to take the COVID-19 vaccine was unsatisfactory (52%), with the intention rate ranging from 21% to 90.1%. This finding aligns with a global review by Li and colleagues [ 97 ] and Ghare and colleagues [ 98 ], who found similar acceptance rates among HCWs ranging from 27.7% to 77.3% and 30% to 98.9% (respectively). HCWs in Africa, particularly in countries such as Egypt, Uganda, and the DRC, seem hesitant about the uptake of the COVID-19 vaccination.

The results pertaining to VH in the studies are likely to be influenced to some extent by the timing of various Information, Education, and Communication (IEC) interventions within the different African countries and vaccine availability at the time of the respective studies. It should also be considered that despite the timing of the studies and vaccine availability in the respective African countries, research findings on vaccine side effects are likely to have played and continue to play a role in VH in particular African countries [ 99 ]. Furthermore, as outlined earlier, the previous negative experiences of many African countries with vaccines impact views about the desirability and safety of vaccines [ 100 ].

A better understanding of the factors influencing the uptake of COVID-19 vaccines is required to improve vaccine acceptance. Accordingly, this review was conducted using the SEM, which identified several factors that influence the uptake of COVID-19 vaccines. These factors were classified into five levels: intrapersonal, interpersonal, organizational, community, and policy. We found that socio-demographic determinants (intrapersonal level factors) were associated with COVID-19 vaccination. Li and colleagues’ [ 97 ] systematic review and Ghare and colleagues’ review [ 98 ] aligns with the findings of this scoping review. Socio-demographic determinants associated with COVID-19 vaccine uptake included being male, older age, physician, level of education, and presence of chronic illness. Studies have identified gender differences as a significant cause of VH in low-income countries [ 56 , 101 ]. VA was found to be significantly associated with gender, and specifically the male gender. Naidoo and colleagues’ [ 102 ] review reported that men were more accepting of the COVID-19 vaccines among the general African population. This finding is highly noteworthy in African society, where men make most family decisions, regardless of profession or social status [ 56 ]. In this review, we found that women were more likely than men to reject the COVID-19 vaccine. While Saied and colleagues [ 84 ] noticed that HCWs’ age could explain the difference in uptake; older HCWs appear more accepting due to the prevalence of co-morbidities and a high perceived susceptibility to contracting COVID [ 99 ].

Using the SEM, we have identified significant barriers within the five levels. Prominent individual-level barriers include vaccine safety and efficacy concerns and HCWs’ mistrust of science. Contrary to common assumptions that HCWs would have a positive attitude toward COVID-19 vaccines because of their expertise, Verger and colleagues [ 103 ] and El-Sokkary and colleagues [ 46 ] point out that HCWs are not a homogeneous group and that the vast majority are not immunization experts. Various information sources shape the general public’s vaccine knowledge, influencing vaccination attitudes, perceptions, and uptake [ 104 ]. Many studies have shown that individuals who lack adequate knowledge about vaccines or vaccine-preventable diseases (VPDs) are more prone to harbour a negative attitude towards vaccination [ 105 , 106 ]. The development of COVID-19 vaccines exposed a lack of knowledge in immunology among HCWs [ 46 ]. Two studies [ 25 , 81 ] cited that HCWs preferred using alternative treatments over accepting the COVID-19 vaccine. According to Oriji and colleagues [ 81 ], some (17%) respondents have already taken Hydroxychloroquine and Azithromycin as prophylaxis treatment for COVID-19. Allagoa and colleagues [ 56 ] and Oriji and colleagues [ 81 ] reported that most respondents who received the COVID-19 vaccine preferred a single-dose vaccine. The number of vaccine doses may have a negative impact on vaccination uptake. Religious beliefs were among the factors associated with vaccine refusal. Studies reviewed [ 55 , 56 , 81 ] discovered that those of Christian faith were more risk-averse regarding the uptake of the COVID-19 vaccines. However, fatalistic ideas combined with religious beliefs have been found to facilitate questioning about the efficacy of COVID-19 vaccines and that religious fatalism negatively impacts the acceptance of the SARS-CoV-2 vaccine [ 107 ].

Misinformation, primarily spread through social media, has fostered distrust in government officials, regulatory agencies, and pharmaceutical companies [ 102 ]. The media, particularly social media, has been a significant source of speculation and misinformation about the pandemic and COVID-19 vaccines [ 108 ]. According to some HCWs, the media has exaggerated the severity of the side effects of the vaccines [ 108 ]. HCWs are a trustworthy source of health information. Their acceptance or rejection of COVID-19 vaccines may impact the broader population’s acceptance and uptake of COVID-19 vaccines [ 23 ]. The low intention rate is due to the rapid development of COVID-19 vaccines, concerns about the vaccines’ safety and effectiveness, and cultural and social norms.

On a positive note, our review also identified facilitators at each level. At the intrapersonal level, HCWs’ high perceived susceptibility to COVID-19 and the desire to protect themselves were prominent factors. The African concept of ubuntu, which emphasizes interconnectedness and collective responsibility, influenced COVID-19 vaccine uptake at the interpersonal and community levels. HCWs were eager to receive the vaccine to protect their loved ones and saw it as a public responsibility to end the pandemic.

Governments, public health agencies, and private healthcare systems should collaborate in making educational resources available to inform HCWs about the vaccine’s safety, importance, and the negative consequences of refusing or delaying vaccination [ 69 ]. Most studies emphasized how crucial it is for stakeholders to inform and increase HCW awareness of COVID-19 vaccines. It is now up to various stakeholders and policymakers to take effective action to spread as much knowledge as possible among HCWs to increase vaccine acceptance and, thereby, address the pandemic’s detrimental effects on healthcare systems and socio-economic conditions. When tailored education campaigns are targeted to specific attitudes, beliefs, and experiences, they are beneficial [ 100 ]. The findings from this review will assist in the roll-out of other vaccination programmes.

Strengths and Limitations

The majority of articles reviewed adopted a quantitative approach. The present review investigates factors influencing HCWs’ intention and uptake of COVID-19 vaccines. Limitations are inherent in a scoping review approach. Some limitations should be considered in this review. This review did not undertake a quality or risk assessment bias of the included studies. Only studies published in English were considered. There is a bias in the body of literature towards VH. Due to the heterogeneity in the definition and assessment of VH in different studies, not all studies reported VH rates among HCWs. In some studies, the measurement scales used to assess the intention to uptake and VH rates for COVID-19 vaccines were either dichotomous or Likert. The varied sample size would be attributed to selection bias in studies focusing on HCWs. Social desirability on self-reported VH among the HCWs can also not be ruled out. At the time of data collection, some studies did not receive the COVID-19 vaccine. Therefore, intentions and VH may have influenced participants’ responses. The trends in acceptance might have changed after the vaccination programmes were implemented.

5. Conclusions

Preventive measures are essential to the global effort to mitigate the pandemic’s consequences. As a result, enormous resources have been dedicated to developing effective and safe COVID-19 vaccines. Using the SEM, this review explored various factors affecting the uptake, allowing for a more comprehensive understanding of vaccine uptake and the development of effective interventions. VI and VH rates vary greatly across countries or regions within the same country. Furthermore, the VI and VH rate is influenced by various factors. Most studies reviewed found significant barriers that affected vaccine uptake on the African continent among HCWs, resulting in a subpar intention to use COVID-19 vaccines. The low level of trust in COVID-19 vaccines and the concerns about the long-term efficacy of the vaccines, as well as the possible long-term side effects associated with the vaccine uptake, play a role in decision-making regarding vaccination. HCWs are influential in informing the general public about vaccines. Therefore, it is crucial to prioritize engagement with key stakeholders to address HCWs’ negative perceptions about vaccines and where they exist in efforts to increase vaccine uptake.

To improve vaccine uptake using the SEM, interventions should target multiple levels simultaneously. At an individual level, understand their concerns and reasons for hesitancy. Provide accurate information to address myths and misconceptions by implementing strategies addressing knowledge gaps and building trust among HCWs. At an organizational level, healthcare facilities should prioritize vaccination by educating staff, offering paid time off for vaccination and side effects, improving access by getting vaccinated as quickly and conveniently as possible, and incentivizing vaccination. They set the culture—if the leadership gets vaccinated, others will follow and leverage social networks and community influencers can have a synergistic effect on increasing vaccine acceptance and uptake. By considering the various levels of influence, the SEM provides a comprehensive framework for understanding and addressing VH and holistically promoting vaccine uptake.

Supplementary Materials

The following supporting information can be downloaded at: https://www.mdpi.com/article/10.3390/vaccines11091491/s1 , Supplementary Materials S1: PRISMA-ScR-Fillable-Checklist—HCWs.

Appendix A. Search Strategy

WorldCat Discovery search strategy.

PubMed search strategy.

ProQuest search strategy.

Included Study Characteristics.

Funding Statement

This research received no external funding.

Author Contributions

D.N., the first author, was responsible for the conceptualization and design of this research paper. He gathered data for the study, conducted data analysis, and authored the article. Supervised by Professor A.M.-W., who also gathered data for the study, conducted data analysis, and reviewed and provided constructive feedback. K.G. reviewed various drafts of the paper and provided feedback to the senior author. All authors have read and agreed to the published version of the manuscript.

Institutional Review Board Statement

Informed consent statement, data availability statement, conflicts of interest.

The authors declare no conflict of interest.

Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content.

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Impact of the circular economy on ecological footprint: evidence from Germany

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• Published: 10 September 2024

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• Kazi Musa 1 ,
• Saira Tufail 2 ,
• Naila Erum   ORCID: orcid.org/0000-0003-0806-2882 1 ,
• Jamaliah Said 1 &
• Abd Hadi Mustaffa 3  

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The circular economy practices contribute to sustainable development by maximising efficiency, utilising renewable resources, extending product lifespans, and implementing waste reduction strategies. This study investigates the individual impacts of four sources of the circular economy on the ecological footprint in Germany, a country that is among the pioneers in establishing a comprehensive roadmap for the circular economy. The four sources examined are renewable energy consumption (REC), recycling, reuse, and repair of materials. Using time series data from 1990 to 2021, the study employed the dynamic autoregressive distributed lag (ARDL) simulation technique and also applied kernel-based linear regression (KRLS) to test the robustness of the results. The findings revealed that reuse practices significantly reduce the ecological footprint in both the short and long run. REC and repair also substantially decrease the ecological footprint, as shown by the simulation analysis. Conversely, while recycling is generally considered crucial for minimising environmental impact, in this study, it was found to contribute to environmental degradation. This paradox may be attributed to the nascent state of the recycling industry and data limitations. The results from KRLS confirm the findings of the dynamic ARDL. It is recommended that policymakers develop measures that are appropriate, efficient, and targeted to enhance the role of each source of the circular economy in reducing the ecological footprint in Germany. The major limitation of the study is its reliance on the indirect measures of circular economy attributed to the non-availability of data on direct measures.

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Source: Dynamic ARDL post-estimation simulations. The black dots specify the predicted value, and dark blue to light blue lines denote 75%, 90%, and 95% confidence intervals, respectively

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Abokersh MH, Norouzi M, Boer D, Cabeza LF, Casa G, Prieto C et al (2021) A framework for sustainable evaluation of thermal energy storage in circular economy. Renew Energy 175:686–701

Article   Google Scholar  

Acheampong RA, Cugurullo F (2019) Capturing the behavioural determinants behind the adoption of autonomous vehicles: conceptual frameworks and measurement models to predict public transport, sharing and ownership trends of self-driving cars. Transp Res f: Traffic Psychol Behav 62:349–375

Adedoyin FF, Erum N, Taşkin D, Chebab D (2023) Energy policy simulation in times of crisis: revisiting the impact of renewable and non-renewable energy production on environmental quality in Germany. Energy Rep 9:4749–4762

Ahmad M, Jiang P, Majeed A, Umar M, Khan Z, Muhammad S (2020) The dynamic impact of natural resources, technological innovations and economic growth on ecological footprint: an advanced panel data estimation. Resour Policy 69:101817

Ahmed Z, Hasan MK, Islam MJ (2023) Urban waste management and circular economy in Bangladesh: a systematic review. J Int Environ Appl Sci 18(2):55–63

Google Scholar  

Ali M, Kennedy CM, Kiesecker J, Geng Y (2018) Integrating biodiversity offsets within circular economy policy in China. J Clean Prod 185:32–43

All-Party Parliamentary Sustainable Resource Group (2014) Remanufacturing: towards a resource efficient economy. Professional Engineering Pub, pp 263

Alnajem M, Mostafa MM, ElMelegy AR (2021) Mapping the first decade of circular economy research: a bibliometric network analysis. J Ind Prod Eng 38(1):29–50

Androniceanu A, Kinnunen J, Georgescu I (2021) Circular economy as a strategic option to promote sustainable economic growth and effective human development. J Int Stud 14(1):60–73

Ansari MA (2022) Re-visiting the environmental Kuznets curve for ASEAN: a comparison between ecological footprint and carbon dioxide emissions. Renew Sustain Energy Rev 168:112867

Article   CAS   Google Scholar  

Baars J, Domenech T, Bleischwitz R, Melin HE, Heidrich O (2021) Circular economy strategies for electric vehicle batteries reduce reliance on raw materials. Nat Sustain 4(1):71–79

Belmonte-Ureña LJ, Plaza-Úbeda JA, Vazquez-Brust D, Yakovleva N (2021) Circular economy, degrowth and green growth as pathways for research on sustainable development goals: a global analysis and future agenda. Ecol Econ 185:107050

Ben Mbarek M, Saidi K, Rahman MM (2018) Renewable and non-renewable energy consumption, environmental degradation and economic growth in Tunisia. Qual Quant 52:1105–1119

Bradley K, Persson O (2022) Community repair in the circular economy–fixing more than stuff. Local Environ 27(10–11):1321–1337

Brando P, Macedo M, Silvério D, Rattis L, Paolucci L, Alencar A et al (2020) Amazon wildfires: scenes from a foreseeable disaster. Flora 268:151609

Breunig C, Busemeyer MR (2012) Fiscal austerity and the trade-off between public investment and social spending. J Eur Publ Policy 19(6):921–938

Chauhan A, Jakhar SK, Chauhan C (2021) The interplay of circular economy with Industry 4.0 enabled smart city drivers of healthcare waste disposal. J Clean Prod 279:123854

Chang Q, Goplerud M (2024) Generalized kernel regularized least squares. Polit Anal 32(2):157–171

Chui KT, Lytras MD, Visvizi A (2018) Energy sustainability in smart cities: artificial intelligence, smart monitoring, and optimization of energy consumption. Energies 11(11):2869

Corona B, Shen L, Reike D, Carreón JR, Worrell E (2019) Towards sustainable development through the circular economy—a review and critical assessment on current circularity metrics. Resour Conserv Recycl 151:104498

Corvellec H, Stowell AF, Johansson N (2022) Critiques of the circular economy. J Ind Ecol 26(2):421–432

Destek MA, Sinha A (2020) Renewable, non-renewable energy consumption, economic growth, trade openness and ecological footprint: evidence from organisation for economic co-operation and development countries. J Clean Prod 242:118537

Fatimah YA, Govindan K, Murniningsih R, Setiawan A (2020) Industry 4.0 based sustainable circular economy approach for smart waste management system to achieve sustainable development goals: a case study of Indonesia. J Clean Prod 269:122263

GFN (2017) Global footprint network, advancing the science of sustainability, Geneva. Accessed from www.footprintnetwork.org/annual-report-2017

Hasanah M, Putra M (2021) Improvement of ecological intelligence through educational comics as a of learning resources . Prosiding Seminar Nasional Lingkungan Lahan Basah

Hassan Q, Sameen AZ, Salman HM, Jaszczur M, Al-Jiboory AK (2023) Hydrogen energy future: advancements in storage technologies and implications for sustainability. J Energy Storage 72:108404

Hickel J (2020) The sustainable development index: measuring the ecological efficiency of human development in the anthropocene. Ecol Econ 167:106331

Horbach J, Rammer C (2020) Circular economy innovations, growth and employment at the firm level: empirical evidence from Germany. J Ind Ecol 24(3):615–625

Iakovenko V, Pavlov R, Pavlova T, Levkovich O (2022) Transformational opportunities for business entities in the circular economy. In: Koval V, Kazancoglu Y, Lakatos E-S (eds) international conference on sustainable, circular management and environmental engineering. Springer Nature Switzerland, Cham, pp 95–103

Isern J, Barranco F, Deniz D, Lesonen J, Hannuksela J, Carrillo RR (2020) Reconfigurable cyber-physical system for critical infrastructure protection in smart cities via smart video-surveillance. Pattern Recogn Lett 140:303–309

Islam KN, Sarker T, Taghizadeh-Hesary F, Atri AC, Alam MS (2021) Renewable energy generation from livestock waste for a sustainable circular economy in Bangladesh. Renew Sustain Energy Rev 139:110695

Jang YC, Lee G, Kwon Y, Lim JH, Jeong JH (2020) Recycling and management practices of plastic packaging waste towards a circular economy in South Korea. Resour Conserv Recycl 158:104798

Jeon H (2022) Renewable versus non-renewable: the role of electricity generation to economic growth. Electr J 35(6):107140

Jordan S, Philips AQ (2018a) Cointegration testing and dynamic simulations of autoregressive distributed lag models. Stata J 18(4):902–923

Jordan S, Philips AQ (2018b) DYNARDL: Stata module to dynamically simulate autoregressive distributed lag (ARDL) models. Econ papers

Keller F, Voss RL, Lee RP, Meyer B (2022) Life cycle assessment of global warming potential of feedstock recycling technologies: case study of waste gasification and pyrolysis in an integrated inventory model for waste treatment and chemical production in Germany. Resour Conserv Recycl 179:106106

Khan MI, Al-Ghamdi SG (2023) Hydrogen economy for sustainable development in GCC countries: a SWOT analysis considering current situation, challenges, and prospects. Int J Hydrogen Energy 48(28):10315–10344

Khurshid A, Khan K, Cifuentes-Faura J (2023) 2030 agenda of sustainable transport: can current progress lead towards carbon neutrality? Transp Res Part D: Transp Environ 122:103869

Kirchherr J, Reike D, Hekkert M (2017) Conceptualizing the circular economy: an analysis of 114 definitions. Resour Conserv Recycl 127:221–232

Knäble D, de Quevedo Puente E, Pérez-Cornejo C, Baumgärtler T (2022) The impact of the circular economy on sustainable development: a European panel data approach. Sustain Prod Consum 34:233–243

Kongbuamai N, Bui Q, Yousaf HMAU, Liu Y (2020) The impact of tourism and natural resources on the ecological footprint: a case study of ASEAN countries. Environ Sci Pollut Res 27:19251–19264

Korhonen J, Honkasalo A, Seppälä J (2018a) Circular economy: the concept and its limitations. Ecol Econ 143:37–46

Korhonen J, Nuur C, Feldmann A, Birkie SE (2018b) Circular economy as an essentially contested concept. J Clean Prod 175:544–552

Kristoffersen E, Blomsma F, Mikalef P, Li J (2020) The smart circular economy: a digital-enabled circular strategies framework for manufacturing companies. J Bus Res 120:241–261

Lah O (2016) Circular economy policies and strategies of Germany. In: Anbumozhi V, Kim J (eds) Towards a circular economy: corporate management and policy pathways. ERIA Research Project Report 2014-44. ERIA, Jakarta, pp 59–74

Lederer J, Gassner A, Kleemann F, Fellner J (2020) Potentials for a circular economy of mineral construction materials and demolition waste in urban areas: a case study from Vienna. Resour Conserv Recycl 161:104942

Llorente-González LJ, Vence X (2020) How labour-intensive is the circular economy? A policy-orientated structural analysis of the repair, reuse and recycling activities in the European Union. Resour Conserv Recycl 162:105033

Majeed MT, Mazhar M (2019) Financial development and ecological footprint: a global panel data analysis. Pakistan Journal of Commerce and Social Sciences (PJCSS) 13(2):487–514

Mavropoulos A, Nilsen AW (2020) Industry 4.0 and circular economy: towards a wasteless future or a wasteful planet?. John Wiley & Sons

Mies A, Gold S (2021) Mapping the social dimension of the circular economy. J Clean Prod 321:128960

Mihăiţă AS, Dupont L, Chery O, Camargo M, Cai C (2019) Evaluating air quality by combining stationary, smart mobile pollution monitoring and data-driven modelling. J Clean Prod 221:398–418

Moraga G, Huysveld S, Mathieux F, Blengini GA, Alaerts L, Van Acker K, ... Dewulf J (2019) Circular economy indicators: what do they measure?. Resour Conserv Recycl 146:452–461

Moalem RM, Mosgaard MA (2021) A critical review of the role of repair cafés in a sustainable circular transition. Sustainability 13(22):12351

Murray A, Skene K, Haynes K (2017) The circular economy: an interdisciplinary exploration of the concept and application in a global context. J Bus Ethics 140:369–380

Neumann J, Petranikova M, Meeus M, Gamarra JD, Younesi R, Winter M, Nowak S (2022) Recycling of lithium-ion batteries—current state of the art, circular economy, and next generation recycling. Adv Energy Mater 12(17):2102917

Ogunmakinde OE, Egbelakin T, Sher W (2022) Contributions of the circular economy to the UN sustainable development goals through sustainable construction. Resour Conserv Recycl 178:106023

Philips AQ, Rutherford A, Whitten GD (2016) Dynamic pie: a strategy for modeling trade-offs in compositional variables over time. Am J Polit Sci 60(1):268–283

Phillips S, Agarwal A, Jordan P (2018) The sound produced by a dripping tap is driven by resonant oscillations of an entrapped air bubble. Sci Rep 8(1):9515

Repp L, Hekkert M, Kirchherr J (2021) Circular economy-induced global employment shifts in apparel value chains: job reduction in apparel production activities, job growth in reuse and recycling activities. Resour Conserv Recycl 171:105621

Sambamurthy S, Raghuvanshi S, Sangwan KS (2021) Environmental impact of recycling spent lithium-ion batteries. Procedia CIRP 98:631–636

Sarkodie SA, Strezov V (2019) Effect of foreign direct investments, economic development and energy consumption on greenhouse gas emissions in developing countries. Sci Total Environ 646:862–871

Schulze G (2016) Growth within: a circular economy vision for a competitive Europe. Ellen MacArthur Foundation and the McKinsey Center for Business and Environment 1–22

Shamsuyeva M, Endres HJ (2021) Plastics in the context of the circular economy and sustainable plastics recycling: comprehensive review on research development, standardization and market. Compos Part c: Open Access 6:100168

CAS   Google Scholar  

Shanmugam V, Das O, Neisiany RE, Babu K, Singh S, Hedenqvist MS, ... Ramakrishna S (2020) Polymer recycling in additive manufacturing: an opportunity for the circular economy. Mater Circ Econ 2:1–11

Shishegar S, Duchesne S, Pelletier G, Ghorbani R (2021) A smart predictive framework for system-level storm water management optimization. J Environ Manag 278:111505

Simoens MC, Leipold S (2021) Trading radical for incremental change: the politics of a circular economy transition in the German packaging sector. J Environ Plan Policy Manag 23(6):822–836

Sobierajewicz P, Adamczyk J, Dylewski R (2023) Ecological and economic assessment of the reuse of steel halls in terms of LCA. Appl Sci 13(3):1597

Strielkowski W, Streimikiene D, Fomina A, Semenova E (2019) Internet of energy (IoE) and high-renewables electricity system market design. Energies 12(24):4790

Sušnik J, Masia S, Kravčík M, Pokorný J, Hesslerová P (2022) Costs and benefits of landscape-based water retention measures as nature-based solutions to mitigating climate impacts in eastern Germany, Czech Republic, and Slovakia. Land Degrad Dev 33(16):3074–3087

Sverko Grdic Z, Krstinic Nizic M, Rudan E (2020) Circular economy concept in the context of economic development in EU countries. Sustainability 12(7):3060

Ul-Durar S, Awan U, Varma A, Memon S, Mention AL (2023) Integrating knowledge management and orientation dynamics for organization transition from eco-innovation to circular economy. J Knowl Manag 27(8):2217–2248

Ünal M, Ünal F (2022) Ecological footprint reduction behaviors of individuals in Turkey in the context of ecological sustainability. Sustainability 15(1):63

Velasco-Muñoz JF, Mendoza JMF, Aznar-Sánchez JA, Gallego-Schmid A (2021) Circular economy implementation in the agricultural sector: definition, strategies and indicators. Resour Conserv Recycl 170:105618

Wang X, Li Y, Liu N, Zhang Y (2020) An urban material flow analysis framework and measurement method from the perspective of urban metabolism. J Clean Prod 257:120564

Wei H, Fan W, Wang X, Lu N, Dong X, Zhao Y, ... Zhao Y (2017) Integrating supply and social demand in ecosystem services assessment: a review. Ecosyst Serv 25:15–27

Winans K, Kendall A, Deng H (2017) The history and current applications of the circular economy concept. Renew Sustain Energy Rev 68:825–833

Wu C, Chen B, Huang X, Wei YD (2020) Effect of land-use change and optimization on the ecosystem service values of Jiangsu province, China. Ecol Indic 117:106507

Yang Y, Pilanci M, Wainwright MJ (2017) Randomized sketches for kernels: fast and optimal nonparametric regression. Ann Stat 45(3):991–1023

Zero Waste Scotland (2015) Circular economy evidence building programme

Zink T, Geyer R (2017) Circular economy rebound. J Ind Ecol 21(3):593–602

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Acknowledgements

The authors are grateful to the Accounting Research Institute (ARI-HICoE), Universiti Teknologi MARA, Shah Alam, Malaysia, and the Ministry of Higher Education for providing research funding.

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Accounting Research Institute (HICoE), University Teknology MARA (UiTM), Shah Alam, Selangor, Malaysia

Kazi Musa, Naila Erum & Jamaliah Said

Department of Economics, Fatima Jinnah Women University, Rawalpindi, Punjab, Pakistan

Saira Tufail

Faculty of Business Management & Professional Studies, Management & Science University (MSU), Shah Alam, Selangor, Malaysia

Abd Hadi Mustaffa

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ST has performed empirical analysis. AHM wrote the literature review. KM worked on the introduction and conclusion. NE worked on results and discussions. JS reviewed the manuscript and provided fruitful comments.

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Musa, K., Tufail, S., Erum, N. et al. Impact of the circular economy on ecological footprint: evidence from Germany. Environ Sci Pollut Res (2024). https://doi.org/10.1007/s11356-024-34857-8

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Received : 07 March 2024

Accepted : 26 August 2024

Published : 10 September 2024

DOI : https://doi.org/10.1007/s11356-024-34857-8

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