Next Article in Journal
How Does A Firm’s Previous Social Network Position Affect Innovation? Evidence from Chinese Listed Companies
Next Article in Special Issue
Analyzing Spatial Congruencies and Mismatches between Supply, Demand and Flow of Ecosystem Services and Sustainable Development
Previous Article in Journal
The Collaborative Management of Sustained Unsustainability: On the Performance of Participatory Forms of Environmental Governance
Previous Article in Special Issue
Assessing Freshwater Provision and Consumption in the Alpine Space Applying the Ecosystem Service Concept
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:

Harnessing Insights from Social-Ecological Systems Research for Monitoring Sustainable Development

Stockholm Resilience Centre, Stockholm University, SE-10691 Stockholm, Sweden
Natural Resources and Environment, Council for Scientific and Industrial Research, Stellenbosch PO Box 320, South Africa
Department of Conservation Ecology and Entomology, Stellenbosch University, Matieland 7602, South Africa
Centre for Complex Systems in Transition, Stellenbosch University, Matieland 7602, South Africa
Humphrey School of Public Affairs, University of Minnesota, Minneapolis, MN 55455, USA
Author to whom correspondence should be addressed.
Sustainability 2019, 11(4), 1190;
Submission received: 28 November 2018 / Revised: 2 February 2019 / Accepted: 19 February 2019 / Published: 23 February 2019


The United Nations’ Agenda 2030 marks significant progress towards sustainable development by making explicit the intention to integrate previously separate social, economic and environmental agendas. Despite this intention, the Sustainable Development Goals (SDGs) which were adopted to implement the agenda, are fragmented in their formulation and largely sectoral. We contend that while the design of the SDG monitoring is based on a systems approach, it still misses most of the dynamics and complexity relevant to sustainability outcomes. We propose that insights from the study of social-ecological systems offer a more integrated approach to the implementation of Agenda 2030, particularly the monitoring of progress towards sustainable development outcomes. Using five key features highlighted by the study of social-ecological systems (SESs) relevant to sustainable development: (1) social-ecological feedbacks, (2) resilience, (3) heterogeneity, (4) nonlinearity, and (5) cross-scale dynamics. We analyze the current set of SDG indicators based on these features to explore current progress in making them operational. Our analysis finds that 59% of the indicators account for heterogeneity, 33% for cross-scale dynamics, 23% for nonlinearities, and 18% and 17%, respectively, for social-ecological feedbacks and resilience. Our findings suggest limited use of complex SES science in the current design of SDG monitoring, but combining our findings with recent studies of methods to operationalize SES features suggests future directions for sustainable development monitoring for the current as well as post 2030 set of indicators.

1. Introduction

The major challenges currently facing the world, including persistent poverty, rising inequalities, biodiversity loss, and climate change, are increasingly recognized as the emergent outcomes of complex social and ecological interactions [1,2,3,4]. Climate change, for example, is recognized as one of the major threats to global health because it affects disease patterns, water and nutrition security, and the severity and frequency of extreme weather events [5,6]. Similarly, movements of resources through international trade and consumption patterns have been shown to affect biodiversity negatively by contributing to habitat destruction [7,8]. These examples are far from unique, as more and more interconnections between social and ecological systems and development challenges are emerging in this more hyper-connected era [1,9].
Importantly, social-ecological connections are not static, but change dynamically over time, and the consequences of changes in these connections are frequently non-linear and uncertain. In some cases, changes can accrue invisibly until a threshold or tipping point is reached [10,11]. In other cases, they are connected in space [12,13], with impacts appearing in seemingly unconnected distant areas, as shown more recently by studies of telecoupling [14,15,16,17,18]. These non-linear dynamics, which connect social and ecological systems across space and time, are key to efforts to achieve sustainable development, and neglecting them could result in recurring or new problem challenges for development.
The increased connectivity between social and ecological systems, and the dynamism associated with these interlinkages, presents two types of challenges to efforts in measuring and tracking progress towards sustainable development goals (SDGs). The first involves the need to more accurately represent the interconnections between social, economic, and ecological systems [19,20,21,22]. Failing to connect these has often resulted in undesirable trade-offs—many current ecological problems result from the neglect of the environment in economic and other measurement efforts [23,24]. The second involves understanding that these connections are not static, but dynamic, requiring the design of monitoring systems to be able to capture these dynamics including spatial, temporal, and cross-sectoral changes [25,26,27].
The first challenge is at the forefront of current efforts to implement Agenda 2030 of the United Nations, which highlights the interlinkages between social, environmental, and economic aspects of sustainable development. Declaration 18 aptly captures this intent: “We are announcing today 17 Sustainable Development Goals with 169 associated targets which are integrated and indivisible…” [28]. In response to this recognition of interlinked systems of nature, society, and economy, several “systems approaches” have been proposed to account for, monitor, and analyze trade-offs between these sub-systems in the implementation of the SDGs [29]. These system approaches highlight the need to capture and relate social, economic, and ecological components in order to assess and monitor progress to sustainable development [29,30,31] and are the main focus of several studies exploring sustainable development trade-offs [32,33,34,35,36].
However, sustainability outcomes are more than the sum of the ecological, economic, and the social “parts” of a system and are in fact also the result of complex interactions, feedbacks, and dynamics within and between systems. The study of complex adaptive systems has highlighted that interactions between individual and diverse components or actors results in emergent behavior or properties at a macro-level that cannot be predicted from micro-level components or properties [37,38,39]. This challenges the assumption underlying many system approaches, that micro-level monitoring of separate social, economic, and ecological variables can then be reconstructed to understand sustainability outcomes including trade-offs or possible future scenarios.
The second challenge can be addressed by broadening from a “systems” to a “complex adaptive systems” approach to monitor and analyze sustainable development, which has the potential to more adequately understand and track sustainability outcomes [22,40]. In an effort to explore what such broadening to a more dynamic and complex approach to sustainable development monitoring might entail, we interrogate the literature on complex social-ecological system (SES) approaches to sustainable development. SES is defined as complex adaptive systems, with strong interdependence and irreducibility between social and ecological systems across multiple scales. Recent reviews of this literature have highlighted key features that constitute complex SES relevant to sustainable development, including the importance of social-ecological interactions and feedbacks, non-linear dynamics, cross-scale (spatial and temporal) dynamics, diversity, and resilience [22,27,41]. Here we explore these key features of SES and evaluate their implications for sustainable development monitoring. We do so by first analyzing the current set of SDG indicators [42] using these features to explore current directions and gauge progress in making the features operational. Using these findings, as well as recent studies on complex SES and sustainable development, we then provide recommendations for sustainable development monitoring for the current set of indicators, as well as future improvements post-2030.

2. Materials and Methods

The core features of complex SES identified in previous reviews [22,27,41,43,44] are: (1) social-ecological feedbacks, (2) resilience, (3) heterogeneity, (4) nonlinearity, and (5) spatial and temporal cross-scale dynamics. We begin by analyzing the current set of SDG indicators in terms of the extent to which they include these core features. We do this using examples from existing SES research on measurable variables, or measurement methods aligned with the core features, and assess the extent to which each proposed SDG indicator uses such variables. We evaluated all 243 indicators from the 17 SDGs (See Appendix A). Below, we clarify the core features and explain how we applied each feature to evaluate the SDG indicators. Through this analysis of current indicators, we present a set of recommendations to harness the potential value that may be added by an SES perspective.

2.1. Social-Ecological Feedbacks

In addition to the components in a system, i.e., social factors (e.g., food security), ecological factors (e.g., forest health), and economic factors (e.g., market prices), interactions between these are also important for understanding and dealing with change. Interactions capture the flow of materials, energy, and waste between components of a system [45], and they create feedbacks when stimuli are fed back to their origin through one or a series of interactions [46,47]. For a feedback to occur, there first has to be an ‘initial’ trigger, represented by a unidirectional interaction from one phenomenon to another. This process can potentially involve a long string of components and interactions. Studies of feedbacks typically include ’process-impact’ relationships, where, for example, agricultural activity results in land degradation, which results in reduced agricultural activity, which in turn may result in further land degradation [48]. There are many similar examples showing different processes and impacts [49,50,51,52,53]. Our analysis of the SDG indicators showed few that account for feedbacks, and thus, we also included any indicators capturing interactions between social and ecological systems, e.g., ecosystem service use or governance of ecosystems [54].

2.2. Resilience

Resilience encompasses three elements: absorptive, adaptive, and transformative capacities [55], which sometimes overlap with one another. Many variables have been suggested to capture these capacities, including diversity of assets [56,57], social and demographic characteristics [58], and also the ability to self-organize, learn, and govern adaptively [59,60,61]. For this purpose, we adopt three of the seven principles of resilience [43], following [62], in which they split the seven principles into those focusing on the resilience of a system itself or its governance. The three principles chosen in this study are about the governance of a system, and the remaining four principles, which are left out, are dealt with in the other features. The three principles are: (1) encourage learning, which is a multifaceted phenomenon, including acquisition of information, skills, and interpreting knowledge differently [63], (2) broaden participation, which refers to the active engagement of relevant stakeholders in the management process [64], and (3) promote polycentric governance systems, which means governance system consisting of multiple governing authorities that interact across different levels of the policy process [65]. In evaluating the indicators, we identified those that mention or imply these three principles in their formulations by looking for mention of the words “learning”, “education”, “governance”, “management”, and “research” in the indicators.

2.3. Heterogeneity

Heterogeneity refers to the differentiation of system components [66]. System differentiation has previously been measured through indices of system diversity [67,68], socioeconomic or geographic characteristics [69,70,71], and indices showing multidimensionality of concepts such as bundles of ecosystem services [72,73,74,75]. Diversity comprises variety, disparity, and balance between system components [76]. This may include biodiversity [77,78], diversity of knowledge systems [79,80,81], diversity of livelihoods [82,83], and diversity or options for response to change [66,84]. Socioeconomic and geographic heterogeneity can be assessed by disaggregation of phenomena according to some criteria. In other studies, social differentiation is depicted through gender, poverty, or income classes [69,71,85,86,87], or through the intersections of such characteristics [88,89]. Finally, multidimensionality has been used to illustrate system components with multiple dimensions not adequately portrayed through single metrics, e.g., poverty, human wellbeing, or ecosystem health [73,90,91]. We identify indicators that capture any of these aspects of heterogeneity by looking for those indicators that are disaggregated by socioeconomic criteria (e.g., gender, sector, etc.), biological diversity (e.g., genetic diversity), or indicators that highlight multiple dimensions (e.g., human wellbeing outcomes). Studies have shown that more diversity is not always good—given that too much diversity could reduce efficiency, and too little could reduce resilience—which emphasizes the need to capture this feature in indicator sets to determine whether or not there is too much, or too little, diversity.

2.4. Nonlinearity

Nonlinearity is defined by the disproportionality between inputs and outputs, unexpected outcomes, or multiple equilibria [92,93,94]. Studies have linked the outcome of feedbacks between variables [95,96] and other factors, including emergence [97,98]. These nonlinear outcomes can result in sharp transitions over thresholds, and ultimately regime shifts (i.e., large, abrupt, and persistent changes in the structure and function of a system) [99,100,101]. Despite this understanding, the underlying assumptions behind most sustainable development indicators are that the phenomena they are measuring are linear.
Measurements or indicators currently used to observe nonlinearity include flickering (i.e., a period of instability before a regime shift occurs [102]), critical slowing down or increasing variance and autocorrelation, and recovery or return rate [103,104,105]. Because these variables generally require complex modelling, long term data, or both, and are therefore not immediately operable, we focused on the presence of stated or known thresholds in proposed indicators. These thresholds included any indicators with a defined limit or thresholds, as well as indicators for which we know existing approaches to define thresholds, e.g., the planetary boundary framework, ecological reserves, and limits [106,107,108]. We also use approaches that define minimum societal or individual requirements for development, such as the safe and just operating space [73,109,110].

2.5. Cross-Scales Dynamics

Recognizing the critical importance of feedbacks between social and ecological systems, SES research also highlights the multi- and cross-scale nature of these intertwined SES connections and feedbacks across time and space [41,111,112]. Studies on these dynamics have highlighted the importance of biophysical teleconnections and social-ecological telecouplings that link transboundary as well as far distant systems to each other [15,113,114]. Similarly, temporal links such as legacy effects, path dependencies, and time lags are the impact of prior actions, processes, natural phenomena, or other circumstances on later conditions and the (often delayed) time it takes for that impact to manifest [41,115]. Determining these cross-scale impacts is a difficult, but essential, task for sustainable development monitoring [12]. Such monitoring requires multi-scalar and multi-decadal data, or models where data are not available [105,116]. As the current indicator set is proposed for national and global reporting (although problematic for capturing cross-scale feedbacks), we focus here on the temporal cross-scale aspects of monitoring and come back to spatial scale in the recommendations. From a review of SDG indicators, little is mentioned regarding temporal dynamics, and so we rather focus on exploring whether indicators are based on time series data, with more than 10 years of data available. While most of the temporal dynamics can be modelled in cases where relationships between variables are known [105,117], time series data can be used to establish relationships, which were previously unknown, by observing how variables vary together.

3. Results

Figure 1 summarizes the proportion of the 243 indicators assessed in our analysis. Appendix A provides the details about how each individual indicator scored across each feature. The feature of heterogeneity was found to be best represented by the SDG indicators assessed (59% of indicators). Indicators included those that track differences in social groups, differences in economic sectors, and diversity of components (e.g., genetic diversity). We found 33% indicators that have the potential to monitor the cross-scale (temporal) feature based on data availability for 10 or more years. The remaining SES features were accounted for in roughly the same percentages of indicators, with 23% of the indicators capturing nonlinearities through some form of thresholds, and 18% and 17% depicting social-ecological interactions and resilience, respectively. Apart from the indicators of social-ecological feedbacks (18%), the majority of the indicators aligning with the remaining four features were social indicators. Overall, more than 70% of the SDG indicators are social indicators. The five different features were not captured equally across goals (Figure 2). Resilience was missing in five goals (1, 3, 7, 8, and 9), and feedbacks were missing in four goals (5, 10, 16, and 17). The rest of the features were represented across almost all the goals, and heterogeneity was captured in all goals. Below we expand on the findings for each of the features.
Social-ecological feedbacks. Indicators of potential social-ecological feedbacks are featured in most goals (Figure 2), and cover the topics of land use, natural disasters, and expenditure on conservation, waste management, and sustainable use of natural resources, among other areas. They included a mix of ecosystem-to-society and society-to-ecosystem flows, such as changes in water use efficiency (Goal 6, indicator 6.4.1) and material footprint per capita (Goal 8, indicators 8.4.1 and 8.4.2). They also included both material and nonmaterial flows, such as collection of waste in cities (Goal 11, indicator 11.6.1) and total expenditure on conservation (Goal 11, indicator 11.4.1). There were also indicators that captured actual feedbacks, e.g., mortality rate attributed to household and ambient air pollution (Goal 3, indicator 3.9.1).
Resilience. Indicators of resilience capacities were in 12 of the 17 goals. All but 2 of the 42 indicators that captured resilience capacities were social indicators. They were mainly concerned with laws and policies to increase the enforcement of national and international laws, to increase public participation in decision making, and to coordinate development assistance. For example, the number of countries reported progress in multi-stakeholder development effectiveness (e.g., Goal 13, indicator 13.3.1), monitoring frameworks that support the achievement of the sustainable development goals (Goal 17, indicator 17.16.1), or the proportion of transboundary basin area with an operational arrangement for water cooperation (Goal 6, indicator 6.5.2). Overall, governance aspects of resilience were spread across all but 5 goals (Figure 2). Most of the governance indicators come from Goal 17, which is about commitment to global partnership and cooperation.
Heterogeneity. Indicators that captured the feature of heterogeneity were related to indicators in 12 goals that differentiated between a phenomenon by type or proportion. The bulk of these (81%, 117 indicators) comprised social indicators (e.g., Goal 1, indicator 1.1.1: Proportion of population below the international poverty line by sex, age, employment status, and geographical location) primarily used by development agencies, where it is important to specify the targeted beneficiaries of different interventions. Of the remaining indicators, 15% were social-ecological indicators (e.g., Goal 11, indicator 11.3.1: Ratio of land consumption rate to population growth rate), and fewer still (4%) were ecological indicators (e.g., Proportion of important sites for terrestrial and freshwater biodiversity that are covered by protected areas, by ecosystem type).
Nonlinearities. Goals 3 and 4 are the only two of the 17 goals which did not have indicators with thresholds in them. These indicators primarily captured social, ecological, and social-ecological thresholds. While there were indicators with clear thresholds, such as the poverty line, other indicators did not explicitly state thresholds, but alluded to those defined elsewhere (e.g., the just space and the planetary boundary frameworks, which are threshold based frameworks) [107,109]. Others still, such as Indicator 12.3.1: Global food loss index, do not have a clearly defined threshold, but clearly alludes to undesired outcomes in the food system and were included.
Cross-scale dynamics (temporal). Of the 80 indicators with more than 10 years of reported data, over half have more than 25 years of reported data. These are mostly social indicators that have been reported for decades, such as the proportion of population below the poverty line. However, there were several ecological and social-ecological indicators too. Long term ecological indicators were primarily linked to protection and protected areas—e.g., Goal 15, Indicator 15.5.1: Red List Index, which is available for 37 years. There were also social-ecological indicators, e.g., Level of water stress: Freshwater withdrawal as a proportion of available freshwater resources (Goal 6, Indicator 6.4.2), which has been reported for the last 56 years. While these indicators did not convey information about temporal feedbacks, they fulfilled the minimum requirements to achieve this goal by existing over long periods, and therefore were available for temporal analyses and reporting.

4. Discussion

Moving from systems to complex adaptive systems approaches to account for the current hyper-connected, complex, and uncertain global context is proving popular across a number of domains, including sustainable development [22,40]. Complex social-ecological system research provides a useful lens for analysis in moving towards more dynamic and systemic monitoring systems. Key features of social-ecological systems such as feedbacks, non-linearities, and resilience are not easy to operationalize [118,119]. However, our findings highlight that there are areas of progress in making these features practical for sustainability monitoring. They also point to areas of sustainable development with relatively few current indicators capturing the key SES features, where future focus in SES theory development and their approaches could assist (Figure 1 and Figure 2). Below we explore areas of progress, as well as areas where progress is not apparent, to provide recommendations for SDG monitoring with a focus on current indicators, as well as future iterations of the SDGs.

4.1. Social-Ecological System (SES) Dynamic Features: Current Progress, Gaps, and Future Directions

4.1.1. Heterogeneity

Our evaluation suggests that some of the features of SES are easier to operationalize for monitoring than others. Heterogeneity, which we analyze using diversity, multidimensionality, and disaggregation measures, was captured by most indicators (59%). Almost all of these indicators particularly captured disaggregation aspects of heterogeneity, which mostly differentiates between age groups, gender, income, type of sector, and other socioeconomic differentiations, and have been highlighted as essential in Agenda 2030 to “leave no one behind” [71,120,121,122]. Progress in methods for disaggregation of data has been rapid and serves a strong foundation for a complex systems approach to monitoring at local to global scales (e.g., [27]). These approaches have been found to highlight power relations, conflicts, and trade-offs, as well as impacts that can potentially reinforce poverty [83,123]. Multidimensionality was not very prevalent, but an increasing focus in research on poverty, equity, and human wellbeing as multidimensional could help to address the lack of such indicators currently proposed in the SDGs [73,91]. However, the paucity of ecological indicators, and especially indicators of ecological diversity in the SDG indicator set, is a limitation. The wide range of research and data on this topic offers potential to rapidly close this gap [67,124,125]. Considering the evidence of the role of enhanced diversity in sustainability and resilience outcomes, a key recommendation would be to focus indicator development on capturing diversity in social, ecological, and social-ecological variables. Research on functional and response diversity highlights this importance and provides examples of potential indicators for use in current SDG monitoring, as well as future indicator development [126,127].

4.1.2. Cross-Scale Dynamics

Many indicators showed potential for capturing temporal dynamics due to the presence of time series data for those indicators (33% have at least 10-year time series). Most SDG indicators have existed for long periods of time, with some already starting in the 1940s following the formalization of international development and the subsequent establishment of institutions such as the United Nations, the World Bank, and the Food and Agriculture Organization. However, the existence of time series data does not mean that temporal dynamics will indeed be captured. This would require interactions between two or more factors to be established (for attribution), after which the relationship between them can be tracked overtime. It is also complicated by questions of what length of time is ‘long enough’ to capture effects [128] and assumptions of how to capture impacts [129]. This raises questions around the fit-for-purpose nature of the time series data available, suggesting the need for new datasets as well as new technologies able to model such data where it is missing. The focus on capturing and avoiding or negotiating trade-offs between SDGs is a major focus of current attention. Complex SES approaches emphasize the need to be able to make such trade-offs clear, not just between SDGs, but also over time and space to reveal hidden trade-offs. The availability of time series data, combined with recent advances in modelling, is increasingly making it possible to reveal temporal dynamics such as legacy effects and time lags [10,105,117,130]. When combined with scenario models and assessments, these indicators also provide an opportunity of exploring future consequences and dynamics and should be a focus of future monitoring efforts. While not explored in this analysis, the challenge of spatial cross-scale dynamics must also be highlighted. Currently, the focus is on national and global indicators, which will not be sufficient to highlight important cross-scale feedbacks and trade-offs within and between SDGs. This is especially important to explore important aspects, including teleconnections, surprise, and cascading effects that tend to dominate in poverty contexts [1,100,131]. New techniques and datasets present opportunities to capture this interplay between ecosystems and societies at multiple scales (see review in [22,132]).

4.1.3. Nonlinearities

Not many current SDG indicators captured nonlinearities, which we assessed by evaluating the presence of thresholds relating to social, ecological, and social-ecological systems within the indicators. Those that did capture thresholds focused primarily on social thresholds as minimum standards, above which people should be lifted [133]. Even within these indicators, and across all SDG indicators, lie assumptions of linear (and infinite) improvements (Figure 1), a major gap in SDG monitoring efforts. Furthermore, while the research and datasets for thresholds and non-linear effects are still relatively new and under development [99,106,107], some potential for improvement lies in several SDG indicators that use data, or variables, in which thresholds are known or predicted [106,107]—for example, use of nitrogen and phosphorus in agricultural systems [52,53]. This presents an opportunity to link these indicators to thresholds in the monitoring and reporting processes. Wilcock et al., 2016 in their review of the non-linear aspects of complex systems also highlight the challenges posed by current global indicators and the lack of early warning systems [134]. They point to hybrid models as a way to generate understanding of system trade-offs associated with non-linearities, an avenue that could be explored to prepare for post-2030 monitoring systems.

4.1.4. Social-Ecological Feedbacks

The paucity of SDG indicators that capture social-ecological feedbacks (18%) is a cause for concern. Advances in capturing them are already available from multiple areas of study, including ecosystem services [135,136,137,138], social-ecological metabolism [45,139,140], and many other human-nature frameworks [141,142], highlighting their importance to many fields of study [47,143,144]. It is a key requirement to capture the effects of ecosystem change on human wellbeing, and the resultant consequences of changes in wellbeing for ecosystems. Feedbacks are at the core of an SES approach to monitoring—both as a cause of features, such as non-linearities, temporal dynamics, and resilience capacities, and also as a consequence of system changes with strong roles in trade-offs, especially important in spatial and temporal cross-scale trade-offs [145]. An SDG monitoring system needs to, therefore, be designed to capture key feedbacks, as well as be able to account for unknown and unpredictable feedbacks as they emerge. New approaches that are able to conceptualize and analyze the system in a more integrated fashion, or at a macro-level scale, offer some promise (e.g., [146,147]).

4.1.5. Resilience

The topic of resilience and resilience indicators are a focus of much research and policy efforts. However, fewer efforts are targeted at the notion of resilience as a system property shaped by, and shaping, the SES features [148]. Our analysis here focused mostly on resilience as it related to system governance and management, while resilience of the system has been shown to be strongly reliant on the other features listed here, e.g., feedbacks and diversity. The low numbers of indicators capturing resilience of governance and management of systems is possibly due to the challenge of quantifying these factors [56,62] and their context-specific nature. However, progress in the monitoring of management effectiveness and governance suggest indicators for use [56]. Research on resilience has especially highlighted the need to foster complex systems approaches and learning [43,63,149] within the governance and management of social-ecological systems. This extends to their use in the development of SDG monitoring systems and could well prove useful as nation states implement and tailor the SDGs and their indicators for use in their contexts [150,151,152]. Furthermore, new research on absorptive, adaptive, and especially transformative capacities [153,154,155] offer useful avenues for indicator development. This research has also highlighted the differences between these capacities, suggesting the need for indicators that track each separately rather than assuming that higher levels of adaptive capacity are also good for transformative change [153,154,155].

4.2. Moving towards Dynamic System Indicators for Sustainable Development

For sustainability to be truly achieved, systems need to be understood not only in terms of the connections among the different components within them, but also the dynamic character of these connections [44](). In addition to biophysical feedbacks and thresholds [106,107], social feedbacks and thresholds [73,156], it is also important to understand thresholds resulting from the interactions between social and ecological systems [157,158]. It is crucial to distil how social and economic targets (such as ending poverty or increasing GDP) primarily affect, and are affected by, ecological targets (such as life on land and life under water). This is akin to observing ‘shadow effects’ where social indicators have ‘ecological shadows’ and vice versa. With limited social-ecological feedbacks and nonlinearities captured in the SDG (Figure 2), there is a possibility that warning of important tipping points will be missed that could have profound consequences. A future focus on the social-ecological nature of systems and systems changes, as well as an acknowledgement of the importance of heterogeneity within and between systems, is key to future efforts to track and adaptively manage sustainable development.
Indicators. Just under 40% of the indicators are conceptually clear, have internationally established methodology and standards, and data regularly produced by countries (UN 2017). The reliance on existing conventional data and indicators will not be sufficient to effectively monitor sustainable development [159]. There are too many individual indicators with no clear indication of what the ‘combined outcome’ of all SDGs will look like [33,36]. This will likely result in uncoordinated and disparate monitoring of indicators, which creates a monitoring burden and often results in silos of implementation [160,161]. To deal with this proliferation problem, it has been suggested to focus on ‘essential’ variables for SDGs that are indispensable and capture the system’s essence, coordination areas, and system transformation [21]. Such forms of indicators focus on the macro level, where sustainability and other system outcomes are realized, rather than the reductionist separation at the micro level, from which such emergence will not be visible. Systems level indicators will be integral to wider monitoring efforts, and in combination with other indicators, should give an indication of the general trajectory of the system as a whole [162].

Author Contributions

Conceptualization: O.S., B.R. and R.B.; methodology: O.S., B.R. and R.B.; validation: M.H., formal analysis: O.S., data curation: O.S.; writing: O.S., writing—review and editing: O.S., B.R., R.B. and M.H.


This research was supported by Sida funded GRAID program at Stockholm Resilience Centre (O.S., B.R. and R.B.). R.B. is also partly supported by the South African Research Chairs Initiative (SARChI) of the Department of Science and Technology and National Research Foundation of South Africa (grant 98766) and a Vetenskapsrådet grant in Sweden (grant 621-2014-5137).

Conflicts of Interest

The authors declare no conflict of interest.

Appendix A

Table A1. Sustainable Development Goal indicators and classification across Social-Ecological System features.
Table A1. Sustainable Development Goal indicators and classification across Social-Ecological System features.
FeedbacksResilienceHeterogeneityNonlinearityCross-Scale Dynamics
GoalIndicatorsSocialEcologicalSocial EcologicalLearningGovernanceParticipationDiversityDisaggregationMulti-DimensionEcological ThresholdsSocial ThresholdsYears
11.1.1. Proportion of population below the international poverty line, by sex, age, employment status, and geographical location (urban/rural)1000000100127
11.2.1. Proportion of population living below the national poverty line, by sex and age1000000100132
11.2.2. Proportion of men, women, and children of all ages living in poverty in all its dimensions according to national definitions100000010010
11.3.1. Proportion of population covered by social protection floors/systems, by sex, distinguishing children, unemployed persons, older persons, persons with disabilities, pregnant women, newborns, work-injury victims, and the poor and the vulnerable100000010010
11.4.1. Proportion of population living in households with access to basic services100000011010
11.4.2. Proportion of total adult population with secure tenure rights to land, with legally recognized documentation and who perceive their rights to land as secure, by sex and by type of tenure001000010010
11.5.1. Number of deaths, missing persons and directly affected persons attributed to disasters per 100,000 population001000000000
11.5.2. Direct economic loss attributed to disasters in relation to global gross domestic product (GDP)001000000000
11.5.3. Number of countries that adopt and implement national disaster risk reduction strategies in line with the Sendai Framework for Disaster Risk Reduction 2015–20301000000000010
11.5.4. Proportion of local governments that adopt and implement local disaster risk reduction strategies in line with national disaster risk reduction strategies100000010000
11.a.1. Proportion of domestically generated resources allocated by the government directly to poverty reduction programs100000010010
11.a.2. Proportion of total government spending on essential services (education, health, and social protection)100000010010
11.a.3. Sum of total grants and non-debt-creating inflows directly allocated to poverty reduction programs as a proportion of GDP100000010010
11.b.1. Proportion of government recurrent and capital spending to sectors that disproportionately benefit women, the poor, and vulnerable groups100000010010
22.1.1. Prevalence of undernourishment1000000000018
22.1.2. Prevalence of moderate or severe food insecurity in the population, based on the Food Insecurity Experience Scale (FIES)100000001013
22.2.1. Prevalence of stunting (height for age <−2 standard deviations from the median of the World Health Organization (WHO) Child Growth Standards) among children under 5 years of age1000000000027
22.2.2. Prevalence of malnutrition (weight for height >+2 or <−2 standard deviation from the median of the WHO Child Growth Standards) among children under 5 years of age, by type (wasting and overweight)1000000100027
22.3.1. Volume of production per labor unit by classes of farming/pastoral/forestry enterprise size001000010000
22.3.2. Average income of small-scale food producers, by sex and indigenous status001000010000
22.4.1. Proportion of agricultural area under productive and sustainable agriculture001000010000
22.5.1. Number of plant and animal genetic resources for food and agriculture secured in either medium- or long-term conservation facilities001100010003
22.5.2. Proportion of local breeds classified as being at risk, not at risk, or at unknown level of risk of extinction0010001100024
22.a.1. The agriculture orientation index for government expenditures100100000000
22.a.2. Total official flows (official development assistance plus other official flows) to the agriculture sector1000000000044
22.b.1. Agricultural export subsidies0010000000022
22.c.1. Indicator of food price anomalies100000000000
33.1.1. Maternal mortality ratio1000000000027
33.1.2. Proportion of births attended by skilled health personnel1000000100027
33.2.1. Under-5 mortality rate1000000000027
33.2.2 Neonatal mortality rate1000000000027
33.3.1 Number of new HIV infections per 1000 uninfected population, by sex, age, and key populations100000010000
33.3.2. Tuberculosis incidence per 100,000 population1000000000017
33.3.3. Malaria incidence per 1000 population0010000000017
33.3.4. Hepatitis B incidence per 100,000 population100000000000
33.3.5. Number of people requiring interventions against neglected tropical diseases1000000000017
33.4.1. Mortality rate attributed to cardiovascular disease, cancer, diabetes, or chronic respiratory disease1000000000017
33.4.2. Suicide mortality rate1000000000017
33.5.1. Coverage of treatment interventions (pharmacological, psychosocial and rehabilitation and aftercare services) for substance use disorders100000000000
33.5.2. Harmful use of alcohol, defined according to the national context as alcohol per capita consumption (aged 15 years and older) within a calendar year in liters of pure alcohol1000000000012
33.6.1. Death rate due to road traffic injuries1000000000017
33.7.1. Proportion of women of reproductive age (aged 15–49 years) who have their need for family planning satisfied with modern methods1000000100017
33.7.2. Adolescent birth rate (aged 10–14 years; aged 15–19 years) per 1000 women in that age group100000010000
33.8.1. Coverage of essential health services (defined as the average coverage of essential services based on tracer interventions that include reproductive, maternal, newborn and child health, infectious diseases, non-communicable diseases and service capacity and access, among the general and the most disadvantaged population)100000001000
33.8.2. Proportion of population with large household expenditures on health as a share of total household expenditure or income100000010000
33.9.1. Mortality rate attributed to household and ambient air pollution0010000000027
33.9.2. Mortality rate attributed to unsafe water, unsafe sanitation and lack of hygiene (exposure to unsafe Water, Sanitation and Hygiene for All (WASH) services)001000000005
33.9.3. Mortality rate attributed to unintentional poisoning100000000005
33.a.1. Age-standardized prevalence of current tobacco use among persons aged 15 years and older1000000000017
33.b.1. Proportion of the target population covered by all vaccines included in their national program100000010000
33.b.2. Total net official development assistance to medical research and basic health sectors1000000000044
33.b.3. Proportion of health facilities that have a core set of relevant essential medicines available and affordable on a sustainable basis100000010000
33.c.1. Health worker density and distribution1000000000017
33.d.1. International Health Regulations (IHR) capacity and health emergency preparedness100000000001
44.1.1. Proportion of children and young people (a) in grades 2/3; (b) at the end of primary; and (c) at the end of lower secondary achieving at least a minimum proficiency level in (i) reading and (ii) mathematics, by sex100000010007
44.2.1. Proportion of children under 5 years of age who are developmentally on track in health, learning and psychosocial well-being, by sex100000010000
44.2.2. Participation rate in organized learning (one year before the official primary entry age), by sex1000000100019
44.3.1 Participation rate of youth and adults in formal and non-formal education and training in the previous 12 months, by sex100000010000
44.4.1. Proportion of youth and adults with information and communications technology (ICT) skills, by type of skill100000011000
44.5.1. Parity indices (female/male, rural/urban, bottom/top wealth quintile and others such as disability status, indigenous peoples and conflict-affected, as data become available) for all education indicators on this list that can be disaggregated100000010000
44.6.1. Proportion of population in a given age group achieving at least a fixed level of proficiency in functional (a) literacy and (b) numeracy skills, by sex100000010000
44.7.1. Extent to which (i) global citizenship education and (ii) education for sustainable development, including gender equality and human rights, are mainstreamed at all levels in (a) national education policies; (b) curricula; (c) teacher education; and (d) student assessment100100010000
44.a.1. Proportion of schools with access to (a) electricity; (b) the Internet for pedagogical purposes; (c) computers for pedagogical purposes; (d) adapted infrastructure and materials for students with disabilities; (e) basic drinking water; (f) single-sex basic sanitation facilities; and (g) basic handwashing facilities (as per the WASH indicator definitions)001000011000
44.b.1. Volume of official development assistance flows for scholarships by sector and type of study100100011007
44.c.1. Proportion of teachers in: (a) pre-primary; (b) primary; (c) lower secondary; and (d) upper secondary education who have received at least the minimum organized teacher training (e.g., pedagogical training), pre-service or in-service, required for teaching at the relevant level in a given country100100010000
55.1.1. Whether or not legal frameworks are in place to promote, enforce, and monitor equality and non-discrimination on the basis of sex100010010010
55.2.1. Proportion of ever-partnered women and girls aged 15 years and older subjected to physical, sexual, or psychological violence by a current or former intimate partner in the previous 12 months, by form of violence and by age100000111010
55.2.2. Proportion of women and girls aged 15 years and older subjected to sexual violence by persons other than an intimate partner in the previous 12 months, by age and place of occurrence100000010010
55.3.1. Proportion of women aged 20–24 years who were married or in a union before age 15 and before age 18100000010010
55.3.2. Proportion of girls and women aged 15–49 years who have undergone female genital mutilation/cutting, by age100000010010
55.4.1. Proportion of time spent on unpaid domestic and care work, by sex, age, and location100000010000
55.5.1. Proportion of seats held by women in (a) national parliaments and (b) local governments100011010010
55.5.2. Proportion of women in managerial positions1000110100112
55.6.1. Proportion of women aged 15–49 years who make their own informed decisions regarding sexual relations, contraceptive use, and reproductive health care100000010010
55.6.2 Number of countries with laws and regulations that guarantee full and equal access to women and men aged 15 years and older to sexual and reproductive health care, information, and education100010010010
55.a.1. (a) Proportion of total agricultural population with ownership or secure rights over agricultural land, by sex; and (b) share of women among owners or rights-bearers of agricultural land, by type of tenure100000010010
55.a.2. Proportion of countries where the legal framework (including customary law) guarantees women’s equal rights to land ownership and/or control100011010010
55.b.1. Proportion of individuals who own a mobile telephone, by sex100000010004
55.c.1. Proportion of countries with systems to track and make public allocations for gender equality and women’s empowerment100010010010
66.1.1. Proportion of population using safely managed drinking water services001000010010
66.2.1. Proportion of population using safely managed sanitation services, including a hand-washing facility with soap and water001000010010
66.3.1. Proportion of wastewater safely treated001000010000
66.3.2. Proportion of bodies of water with good ambient water quality010000010100
66.4.1. Change in water-use efficiency over time001000000100
66.4.2. Level of water stress: freshwater withdrawal as a proportion of available freshwater resources0010000101056
66.5.1. Degree of integrated water resources management implementation (0–100)100010000009
66.5.2. Proportion of transboundary basin area with an operational arrangement for water cooperation100010010000
66.6.1. Change in the extent of water-related ecosystems over time010000000100
66.a.1. Amount of water- and sanitation-related official development assistance that is part of a government-coordinated spending plan100010000009
66.b.1. Proportion of local administrative units with established and operational policies and procedures for participation of local communities in water and sanitation management100011010009
77.1.1. Proportion of population with access to electricity1000000100127
77.1.2. Proportion of population with primary reliance on clean fuels and technology1000000100037
77.2.1. Renewable energy share in the total final energy consumption0010000100027
77.3.1. Energy intensity measured in terms of primary energy and GDP1000000000027
77.a.1. International financial flows to developing countries in support of clean energy research and development and renewable energy production, including in hybrid systems100000000000
77.b.1. Investments in energy efficiency as a proportion of GDP and the amount of foreign direct investment in financial transfer for infrastructure and technology to sustainable development services100000010000
88.1.1. Annual growth rate of real GDP per capita1000000000047
88.10.1. (a) Number of commercial bank branches per 100,000 adults and (b) number of automated teller machines (ATMs) per 100,000 adults1000000000013
88.10.2. Proportion of adults (15 years and older) with an account at a bank or other financial institution or with a mobile-money-service provider100000010006
88.2.1. Annual growth rate of real GDP per employed person1000000000026
88.4.1. Material footprint, material footprint per capita, and material footprint per GDP001000000000
88.4.2. Domestic material consumption, domestic material consumption per capita, and domestic material consumption per GDP0010000000047
88.5.1. Average hourly earnings of female and male employees, by occupation, age, and persons with disabilities100000010000
88.5.2. Unemployment rate, by sex, age, and persons with disabilities100000010008
88.6.1. Proportion of youth (aged 15–24 years) not in education, employment, or training1000000100014
88.7.1. Proportion and number of children aged 5–17 years engaged in child labor, by sex and age100000010010
88.8.1. Frequency rates of fatal and non-fatal occupational injuries, by sex and migrant status100000010000
88.8.2. Level of national compliance with labor rights (freedom of association and collective bargaining) based on International Labor Organization (ILO) textual sources and national legislation, by sex and migrant status100000010000
88.9.1. Tourism direct GDP as a proportion of total GDP and in growth rate001000010000
88.9.2. Proportion of jobs in sustainable tourism industries out of total tourism jobs001000010000
88.a.1. Aid for Trade commitments and disbursements1000000000015
88.b.1. Existence of a developed and operationalized national strategy for youth employment, as a distinct strategy or as part of a national employment strategy100000000000
99.1.1. Proportion of the rural population who live within 2 km of an all-season road100000010000
99.1.2. Passenger and freight volumes, by mode of transport1000000100047
99.2.1. Manufacturing value added as a proportion of GDP and per capita1000000100027
99.2.2. Manufacturing employment as a proportion of total employment1000000100027
99.3.1. Proportion of small-scale industries in total industry value added100000010010
99.3.2. Proportion of small-scale industries with a loan or line of credit100000010010
99.4.1. CO2 emission per unit of value added0010000001027
99.5.1. Research and development expenditure as a proportion of GDP1000000100036
99.5.2. Researchers (in full-time equivalent) per million inhabitants1000000000036
99.a.1. Total official international support (official development assistance plus other official flows) to infrastructure1000000000015
99.b.1. Proportion of medium and high-tech industry value added in total value added1000000100027
99.c.1. Proportion of population covered by a mobile network, by technology1000000100010
1010.1.1. Growth rates of household expenditure or income per capita among the bottom 40 percent of the population and the total population100000010000
1010.2.1. Proportion of people living below 50 percent of median income, by sex, age and persons with disabilities100000010000
1010.3.1. Proportion of population reporting having personally felt discriminated against or harassed in the previous 12 months on the basis of a ground of discrimination prohibited under international human rights law100010000010
1010.4.1. Labor share of GDP, comprising wages and social protection transfers100000010010
1010.5.1. Financial Soundness Indicators100000000000
1010.6.1. Proportion of members and voting rights of developing countries in international organizations100000010000
1010.7.1. Recruitment cost borne by employee as a proportion of yearly income earned in country of destination100000010000
1010.7.2. Number of countries that have implemented well-managed migration policies100000000000
1010.a.1. Proportion of tariff lines applied to imports from least developed countries and developing countries with zero-tariff1000000100012
1010.b.1. Total resource flows for development, by recipient and donor countries and type of flow (e.g., official development assistance, foreign direct investment and other flows)1000000100057
1010.c.1. Remittance costs as a proportion of the amount remitted100000010000
1111.1.1. Proportion of urban population living in slums, informal settlements, or inadequate housing1000000100027
1111.2.1. Proportion of population that has convenient access to public transport, by sex, age, and persons with disabilities100000010000
1111.3.1. Ratio of land consumption rate to population growth rate001000000100
1111.3.2. Proportion of cities with a direct participation structure of civil society in urban planning and management that operate regularly and democratically100011010000
1111.4.1. Total expenditure (public and private) per capita spent on the preservation, protection, and conservation of all cultural and natural heritage, by type of heritage (cultural, natural, mixed and World Heritage Centre designation), level of government (national, regional and local/municipal), type of expenditure (operating expenditure/investment), and type of private funding (donations in kind, private non-profit sector and sponsorship)101010101000
1111.5.1. Number of deaths, missing persons and directly affected persons attributed to disasters per 100,000 population001000000000
1111.5.2. Direct economic loss in relation to global GDP, damage to critical infrastructure and number of disruptions to basic services, attributed to disasters0010000000027
1111.6.1. Proportion of urban solid waste regularly collected and with adequate final discharge out of total urban solid waste generated, by cities001000010000
1111.6.2. Annual mean levels of fine particulate matter (e.g., PM2.5 and PM10) in cities (population weighted)001000000109
1111.7.1. Average share of the built-up area of cities that is open space for public use for all, by sex, age, and persons with disabilities100000010000
1111.7.2. Proportion of persons victim of physical or sexual harassment, by sex, age, disability status, and place of occurrence, in the previous 12 months100000010000
1111.a.1. Proportion of population living in cities that implement urban and regional development plans integrating population projections and resource needs, by size of city001000010000
1111.b.1. Number of countries that adopt and implement national disaster risk reduction strategies in line with the Sendai Framework for Disaster Risk Reduction 2015–20301000100000010
1111.b.2. Proportion of local governments that adopt and implement local disaster risk reduction strategies in line with national disaster risk reduction strategies100010010000
1111.c.1. Proportion of financial support to the least developed countries that is allocated to the construction and retrofitting of sustainable, resilient, and resource-efficient buildings utilizing local materials100000010000
1212.1.1. Number of countries with sustainable consumption and production (SCP) national action plans or SCP mainstreamed as a priority or a target into national policies100010000000
1212.2.1. Material footprint, material footprint per capita, and material footprint per GDP001000000000
1212.2.2. Domestic material consumption, domestic material consumption per capita, and domestic material consumption per GDP0010000000047
1212.3.1. Global food loss index001000000000
1212.4.1. Number of parties to international multilateral environmental agreements on hazardous waste, and other chemicals that meet their commitments and obligations in transmitting information as required by each relevant agreement100010000007
1212.4.2. Hazardous waste generated per capita and proportion of hazardous waste treated, by type of treatment001000010100
1212.5.1. National recycling rate, tons of material recycled001000000000
1212.6.1. Number of companies publishing sustainability reports100000000000
1212.7.1. Number of countries implementing sustainable public procurement policies and action plans100000000000
1212.8.1. Extent to which (i) global citizenship education and (ii) education for sustainable development (including climate change education) are mainstreamed in (a) national education policies; (b) curricula; (c) teacher education; and (d) student assessment100100100000
1212.a.1. Amount of support to developing countries on research and development for sustainable consumption and production and environmentally sound technologies100000000000
1212.b.1. Number of sustainable tourism strategies or policies and implemented action plans with agreed monitoring and evaluation tools100000000000
1212.c.1. Amount of fossil-fuel subsidies per unit of GDP (production and consumption) and as a proportion of total national expenditure on fossil fuels100000010000
1313.1.1. Number of deaths, missing persons and directly affected persons attributed to disasters per 100,000 population001000000000
1313.1.2. Number of countries that adopt and implement national disaster risk reduction strategies in line with the Sendai Framework for Disaster Risk Reduction 2015–20301000000000010
1313.1.3. Proportion of local governments that adopt and implement local disaster risk reduction strategies in line with national disaster risk reduction strategies100010010000
1313.2.1. Number of countries that have communicated the establishment or operationalization of an integrated policy/strategy/plan, which increases their ability to adapt to the adverse impacts of climate change, and foster climate resilience and low greenhouse gas emissions development in a manner that does not threaten food production (including a national adaptation plan, nationally determined contribution, national communication, biennial update report, or other)100000000000
1313.3.1. Number of countries that have integrated mitigation, adaptation, impact reduction and early warning into primary, secondary, and tertiary curricula100010000000
1313.3.2. Number of countries that have communicated the strengthening of institutional, systemic, and individual capacity-building to implement adaptation, mitigation and technology transfer, and development actions100010000000
1313.a.1. Mobilized amount of United States dollars per year between 2020 and 2025 accountable towards the $100 billion commitment100010000010
1313.b.1. Number of least developed countries and small island developing States that are receiving specialized support, and amount of support, including finance, technology, and capacity-building for mechanisms in raising capacities for effective climate change-related planning and management, including focusing on women, youth, and local and marginalized communities100010010010
1414.1.1. Index of coastal eutrophication and floating plastic debris density001000000100
1414.2.1. Proportion of national exclusive economic zones managed using ecosystem-based approaches001000010000
1414.3.1. Average marine acidity (pH) measured at agreed suite of representative sampling stations010000000000
1414.4.1. Proportion of fish stocks within biologically sustainable levels0100000100043
1414.5.1. Coverage of protected areas in relation to marine areas01000000000117
1414.6.1. Progress by countries in the degree of implementation of international instruments aiming to combat illegal, unreported, and unregulated fishing100000000000
1414.7.1. Sustainable fisheries as a proportion of GDP in small island developing States, least developed countries and all countries001000010000
1414.a.1. Proportion of total research budget allocated to research in the field of marine technology100000010000
1414.b.1. Progress by countries in the degree of application of a legal/regulatory/policy/institutional framework, which recognizes and protects access rights for small-scale fisheries100010000010
1414.c.1. Number of countries making progress in ratifying, accepting, and implementing through legal, policy, and institutional frameworks, ocean-related instruments that implement international law, as reflected in the United Nations Convention on the Law of the Sea, for the conservation and sustainable use of the oceans and their resources100010000000
1515.1.1. Forest area as a proportion of total land area0100000100027
1515.1.2. Proportion of important sites for terrestrial and freshwater biodiversity that are covered by protected areas, by ecosystem type01000001000117
1515.2.1. Progress towards sustainable forest management0100000000027
1515.3.1. Proportion of land that is degraded over total land area010000010000
1515.4.1. Coverage by protected areas of important sites for mountain biodiversity01000000000117
1515.4.2 Mountain Green Cover Index0100000000017
1515.5.1. Red List Index0100000000037
1515.6.1. Number of countries that have adopted legislative, administrative, and policy frameworks to ensure fair and equitable sharing of benefits100000000010
1515.7.1. Proportion of traded wildlife that was poached or illicitly trafficked001000010000
1515.8.1. Proportion of countries adopting relevant national legislation and adequately resourcing the prevention or control of invasive alien species100010010000
1515.9.1. Progress towards national targets established in accordance with Aichi Biodiversity Target 2 of the Strategic Plan for Biodiversity 2011–2020001000000100
1515.a.1. Official development assistance and public expenditure on conservation and sustainable use of biodiversity and ecosystems1000000000015
1515.b.1. Official development assistance and public expenditure on conservation and sustainable use of biodiversity and ecosystems1000000000015
1515.c.1. Proportion of traded wildlife that was poached or illicitly trafficked001000010000
1616.1.1. Number of victims of intentional homicide per 100,000 population, by sex and age100000010017
1616.1.2. Conflict-related deaths per 100,000 population, by sex, age, and cause100000010010
1616.1.3. Proportion of population subjected to physical, psychological or sexual violence in the previous 12 months100000010010
1616.1.4. Proportion of population that feel safe walking alone around the area they live100000010010
1616.10.1. Number of verified cases of killing, kidnapping, enforced disappearance, arbitrary detention and torture of journalists, associated media personnel, trade unionists and human rights advocates in the previous 12 months100000000000
1616.10.2. Number of countries that adopt and implement constitutional, statutory, and/or policy guarantees for public access to information100010000000
1616.2.1. Proportion of children aged 1–17 years who experienced any physical punishment and/or psychological aggression by caregivers in the past month100000010000
1616.2.2. Number of victims of human trafficking per 100,000 population, by sex, age, and form of exploitation100000010000
1616.2.3. Proportion of young women and men aged 18–29 years who experienced sexual violence by age 18100000010000
1616.3.1. Proportion of victims of violence in the previous 12 months who reported their victimization to competent authorities or other officially recognized conflict resolution mechanisms100000010000
1616.3.2. Unsentenced detainees as a proportion of overall prison population1000000100014
1616.4.1. Total value of inward and outward illicit financial flows (in current United States dollars)100000000000
1616.4.2. Proportion of seized, found, or surrendered arms whose illicit origin or context has been traced or established by a competent authority in line with international instruments100000010000
1616.5.1. Proportion of persons who had at least one contact with a public official and who paid a bribe to a public official, or were asked for a bribe by those public officials, during the previous 12 months100000010000
1616.5.2. Proportion of businesses that had at least one contact with a public official and that paid a bribe to a public official, or were asked for a bribe by those public officials during the previous 12 months100000010000
1616.6.1 Primary government expenditures as a proportion of original approved budget, by sector (or by budget codes or similar)100000010000
1616.6.2. Proportion of population satisfied with their last experience of public services100000010000
1616.7.1. Proportions of positions (by sex, age, persons with disabilities, and population groups) in public institutions (national and local legislatures, public service, and judiciary) compared to national distributions100000010000
1616.7.2. Proportion of population who believe decision-making is inclusive and responsive, by sex, age, disability, and population group100011010010
1616.8.1. Proportion of members and voting rights of developing countries in international organizations100011010000
1616.9.1. Proportion of children under 5 years of age whose births have been registered with a civil authority, by age100000010007
1616.a.1. Existence of independent national human rights institutions in compliance with the Paris Principles1000000000117
1616.b.1. Proportion of population reporting having personally felt discriminated against or harassed in the previous 12 months on the basis of a ground of discrimination prohibited under international human rights law100000010010
1717.1.1. Total government revenue as a proportion of GDP, by source1000000100026
1717.1.2. Proportion of domestic budget funded by domestic taxes1000000100027
1717.10.1. Worldwide weighted tariff-average1000000000012
1717.11.1. Developing countries’ and least developed countries’ share of global exports1000000100017
1717.12.1. Average tariffs faced by developing countries, least developed countries and small island developing States1000000000012
1717.13.1. Macroeconomic Dashboard100000000000
1717.14.1. Number of countries with mechanisms in place to enhance policy coherence of sustainable development100010000000
1717.15.1. Extent of use of country-owned results frameworks and planning tools by providers of development cooperation100000000000
1717.16.1. Number of countries reporting progress in multi-stakeholder development effectiveness monitoring frameworks that support the achievement of the sustainable development goals100010000000
1717.17.1. Amount of United States dollars committed to public–private and civil society partnerships100000000000
1717.18.1. Proportion of sustainable development indicators produced at the national level with full disaggregation when relevant to the target, in accordance with the Fundamental Principles of Official Statistics100000010000
1717.18.2 Number of countries that have national statistical legislation that complies with the Fundamental Principles of Official Statistics100010000000
1717.18.3. Number of countries with a national statistical plan that is fully funded and under implementation, by source of funding1000100100010
1717.19.1. Dollar value of all resources made available to strengthen statistical capacity in developing countries1000000000111
1717.19.2. Proportion of countries that (a) have conducted at least one population and housing census in the last 10 years; and (b) have achieved 100 per cent birth registration and 80 percent death registration100100010000
1717.2.1. Net official development assistance, total and to least developed countries, as a proportion of the Organization for Economic Cooperation and Development (OECD) Development Assistance Committee donors’ gross national income (GNI)1000100100115
1717.3.1. Foreign direct investment (FDI), official development assistance, and South–South cooperation as a proportion of total domestic budget100010010000
1717.3.2. Volume of remittances (in United States dollars) as a proportion of total GDP1000000100027
1717.4.1. Debt service as a proportion of exports of goods and services1000000100027
1717.5.1. Number of countries that adopt and implement investment promotion regimes for least developed countries100000000010
1717.6.1. Number of science and/or technology cooperation agreements and programs between countries, by type of cooperation100000000010
1717.6.2. Fixed Internet broadband subscriptions per 100 inhabitants, by speed1000000000016
1717.7.1. Total amount of approved funding for developing countries to promote the development, transfer, dissemination, and diffusion of environmentally sound technologies100000000000
1717.8.1. Proportion of individuals using the Internet1000000100022
1717.9.1. Dollar value of financial and technical assistance (including through North–South, South–South, and triangular cooperation) committed to developing countries1000100000115


  1. Liu, J.; Mooney, H.A.; Hull, V.; Davis, S.J.; Gaskell, J.C.; Hertel, T.W.; Lubchenco, J.; Seto, K.C.; Gleick, P.H.; Kremen, C.; et al. Systems integration for global sustainability. Science 2015, 347, 1258832. [Google Scholar] [CrossRef] [PubMed]
  2. Adams, W.M.; Aveling, R.; Brockington, D.; Dickson, B.; Elliott, J.; Hutton, J.; Roe, D.; Vira, B.; Wolmer, W. Biodiversity conservation and the eradication of poverty. Science 2004, 306, 1146–1149. [Google Scholar] [CrossRef] [PubMed]
  3. Motesharrei, S.; Rivas, J.; Kalnay, E.; Asrar, G.R.; Busalacchi, A.J.; Cahalan, R.F.; Cane, M.A.; Colwell, R.R.; Feng, K.; Franklin, R.S.; et al. Modeling sustainability: Population, inequality, consumption, and bidirectional coupling of the Earth and Human Systems. Natl. Sci. Rev. 2016, 3, 470–494. [Google Scholar] [CrossRef]
  4. Hamann, M.; Berry, K.; Chaigneau, T.; Curry, T.; Heilmayr, R.; Henriksson, P.J.G.; Hentati-Sundberg, J.; Jina, A.; Lindkvist, E.; Lopez-Maldonado, Y.; et al. Inequality and the Biosphere. Annu. Rev. Environ. Resour. 2018, 43, 61–83. [Google Scholar] [CrossRef]
  5. Costello, A.; Abbas, M.; Allen, A.; Ball, S.; Bell, S.; Bellamy, R.; Friel, S.; Groce, N.; Johnson, A.; Kett, M.; et al. Managing the health effects of climate change. Lancet 2009, 373, 1693–1733. [Google Scholar] [CrossRef]
  6. Looke, D.F.M.M.; Gottlieb, T.; Jones, C.A. The global challenges of infectious diseases. Med. J. Aust. 2015, 202, 225–226. [Google Scholar] [CrossRef] [PubMed]
  7. Wilting, H.C.; Schipper, A.M.; Bakkenes, M.; Meijer, J.R.; Huijbregts, M.A.J. Quantifying biodiversity losses due to human consumption: A global-scale footprint analysis. Environ. Sci. Technol. 2017, 51, 3298–3306. [Google Scholar] [CrossRef] [PubMed]
  8. Lenzen, M.; Moran, D.; Kanemoto, K.; Foran, B.; Lobefaro, L.; Geschke, A. International trade drives biodiversity threats in developing nations. Nature 2012, 486, 109–112. [Google Scholar] [CrossRef] [PubMed]
  9. Steffen, W.; Broadgate, W.; Deutsch, L.; Gaffney, O.; Ludwig, C. The trajectory of the Anthropocene: The Great Acceleration. Anthr. Rev. 2015, 2, 81–98. [Google Scholar] [CrossRef]
  10. Fremier, A.K.; DeClerck, F.A.J.; Bosque-Pérez, N.A.; Carmona, N.E.; Hill, R.; Joyal, T.; Keesecker, L.; Klos, P.Z.; Martínez-Salinas, A.; Niemeyer, R.; et al. Understanding Spatiotemporal Lags in Ecosystem Services to Improve Incentives. Bioscience 2013, 63, 472–482. [Google Scholar] [CrossRef] [Green Version]
  11. Camill, P.; Clark, J.S. Long-term perspectives on lagged ecosystem responses to climate change: Permafrost in boreal peatlands and the grassland/woodland boundary. Ecosystems 2000, 3, 534–544. [Google Scholar] [CrossRef]
  12. Peters, D.P.C.; Groffman, P.M.; Nadelhoffer, K.J.; Grimm, N.B.; Collins, S.L.; Michener, W.K.; Huston, M.A. Living in an increasingly connected world: A framework for continental-scale environmental science. Front. Ecol. Environ. 2008, 6, 229–237. [Google Scholar] [CrossRef]
  13. Kohlheb, N.; Krausmann, F. Land use change, biomass production and HANPP: The case of Hungary 1961–2005. Ecol. Econ. 2009, 69, 292–300. [Google Scholar] [CrossRef]
  14. Liu, J.; Yang, W.; Li, S. Framing ecosystem services in the telecoupled Anthropocene. Front. Ecol. Environ. 2016, 14, 27–36. [Google Scholar] [CrossRef]
  15. Liu, J.; Dou, Y.; Batistella, M.; Challies, E.; Connor, T.; Friis, C.; Millington, J.D.A.; Parish, E.; Romulo, C.L.; Silva, R.F.B.; et al. Spillover systems in a telecoupled Anthropocene: Typology, methods, and governance for global sustainability. Curr. Opin. Environ. Sustain. 2018, 33, 58–69. [Google Scholar] [CrossRef]
  16. Bruckner, M.; Fischer, G.; Tramberend, S.; Giljum, S. Measuring telecouplings in the global land system: A review and comparative evaluation of land footprint accounting methods. Ecol. Econ. 2015, 114, 11–21. [Google Scholar] [CrossRef] [Green Version]
  17. Stoll, J.S.; Crona, B.I.; Fabinyi, M.; Farr, E.R. Seafood Trade Routes for Lobster Obscure Teleconnected Vulnerabilities. Front. Mar. Sci. 2018, 5, 239. [Google Scholar] [CrossRef]
  18. Eakin, H.; DeFries, R.; Kerr, S.; Lambin, E.F.; Liu, J.; Marcotullio, P.J.; Messerli, P.; Reenberg, A.; Rueda, X.; Swaffield, S.R.; et al. Significance of Telecoupling for Exploration of Land-Use Change. In Rethinking Global Land Use in an Urban Era; Seto, K.C., Reenberg, A., Eds.; MIT Press: Cambridge, MA, USA, 2014; pp. 141–161. ISBN 9780262026901. [Google Scholar]
  19. Nilsson, M.; Griggs, D.; Visbeck, M. Policy: Map the interactions between Sustainable Development Goals. Nature 2016, 534, 320–322. [Google Scholar] [CrossRef] [PubMed]
  20. Griggs, D.; Stafford-Smith, M.; Gaffney, O.; Rockström, J.; Ohman, M.C.; Shyamsundar, P.; Steffen, W.; Glaser, G.; Kanie, N.; Noble, I.; et al. Policy: Sustainable development goals for people and planet. Nature 2013, 495, 305–307. [Google Scholar] [CrossRef] [PubMed]
  21. Reyers, B.; Stafford-Smith, M.; Erb, K.-H.H.; Scholes, R.J.; Selomane, O. Essential Variables help to focus Sustainable Development Goals monitoring. Curr. Opin. Environ. Sustain. 2017, 26–27, 97–105. [Google Scholar] [CrossRef]
  22. Reyers, B.; Folke, C.; Moore, M.-L.; Biggs, R.; Galaz, V. Social-Ecological Systems Insights for Navigating the Dynamics of the Anthropocene. Annu. Rev. Environ. Resour. 2018, 43, 267–289. [Google Scholar] [CrossRef]
  23. Costanza, R.; Kubiszewski, I.; Giovannini, E.; Lovins, H.; McGlade, J.; Pickett, K.E.; Ragnarsdóttir, K.V.; Roberts, D.; De Vogli, R.; Wilkinson, R. Time to leave GDP behind. Nature 2014, 505, 283–285. [Google Scholar] [CrossRef] [PubMed]
  24. Dullinger, S.; Essl, F.; Rabitsch, W.; Erb, K.-H.K.-H.; Gingrich, S.; Haberl, H.; Hulber, K.; Jarosik, V.; Krausmann, F.; Kuhn, I.; et al. Europe’s other debt crisis caused by the long legacy of future extinctions. Proc. Natl. Acad. Sci. USA 2013, 110, 7342–7347. [Google Scholar] [CrossRef] [PubMed]
  25. Folke, C.; Jansson, Å.; Rockström, J.; Olsson, P.; Carpenter, S.R.; Stuart Chapin, F.; Crépin, A.-S.S.; Daily, G.; Danell, K.; Ebbesson, J.; et al. Reconnecting to the biosphere. Ambio 2011, 40, 719–738. [Google Scholar] [CrossRef] [PubMed]
  26. Carpenter, S.R.; Folke, C.; Norström, A.; Olsson, O.; Schultz, L.; Agarwal, B.; Balvanera, P.; Campbell, B.; Castilla, J.C.; Cramer, W.; et al. Program on ecosystem change and society: An international research strategy for integrated social–ecological systems. Curr. Opin. Environ. Sustain. 2012, 4, 134–138. [Google Scholar] [CrossRef]
  27. Reyers, B.; Selomane, O. Social-ecological systems approaches: Revealing and navigating the complex trade-offs of sustainable development. In Ecosystem Services and Poverty Alleviation: Trade-Offs and Governance; Schreckenberg, K., Mace, G., Poudyal, M., Eds.; Routledge: London, UK; New York, NY, USA, 2018; pp. 39–54. [Google Scholar]
  28. UN. Transforming Our World: The 2030 Agenda for Sustainable Development; United Nations: New York, NY, USA, 2015. [Google Scholar]
  29. Barbier, E.; Burgess, J. The Sustainable Development Goals and the systems approach to sustainability. Economics 2017, 11, 1–22. [Google Scholar] [CrossRef]
  30. Young, O.R.; Underdal, A.; Kanie, N.; Andresen, S.; Bernstein, S.; Biermann, F.; Gupta, J.; Haas, P.M.; Iguchi, M.; Kok, M.; et al. Earth System Challenges and a Multi-Layered Approach for the Sustainable Development Goals; POST2015/UNU-IAS Policy Brief #1; United Nations University Institute for the Advanced Study of Sustainability: Tokyo, Japan, 2014. [Google Scholar]
  31. Lim, M.M.L.; Søgaard Jørgensen, P.; Wyborn, C.A. Reframing the sustainable development goals to achieve sustainable development in the anthropocene—A systems approach. Ecol. Soc. 2018, 23, art22. [Google Scholar] [CrossRef]
  32. Lucas, P.; Kok, M.; Nilsson, M.; Alkemade, R. Integrating Biodiversity and Ecosystem Services in the Post-2015 Development Agenda: Goal Structure, Target Areas and Means of Implementation. Sustainability 2013, 6, 193–216. [Google Scholar] [CrossRef] [Green Version]
  33. Griggs, D.; Smith, M.S.; Rockström, J.; Öhman, M.C.; Gaffney, O.; Glaser, G.; Kanie, N. An integrated framework for sustainable development goals. Ecol. Soc. 2014, 19, 49. [Google Scholar] [CrossRef]
  34. Pradhan, P.; Costa, L.; Rybski, D.; Lucht, W.; Kropp, J.P. A Systematic Study of Sustainable Development Goal (SDG) Interactions. Earth’s Future 2017, 5, 1169–1179. [Google Scholar] [CrossRef] [Green Version]
  35. SDSN. Indicators and a Monitoring Framework for Sustainable Development Goals: Launching a Data Revolution for the SDGs; Sustainable Development Solutions Network: New York, NY, USA; Paris, France, 2015. [Google Scholar]
  36. Costanza, R.; Fioramonti, L.; Kubiszewski, I. The UN Sustainable Development Goals and the dynamics of well-being. Front. Ecol. Environ. 2016, 14, 59. [Google Scholar] [CrossRef] [Green Version]
  37. Levin, S.A. Complex Adaptive Systems: Exploring the known, the unknown and the unknowable. Bull. Am. Math. Soc. 2002, 40, 3–19. [Google Scholar] [CrossRef]
  38. Duit, A.; Galaz, V. Governance and Complexity—Emerging Issues for Governance Theory. Governance 2008, 21, 311–335. [Google Scholar] [CrossRef]
  39. Schoon, M.; van der Leeuw, S. Dossier: «À propos des relations natures/sociétés»—The shift toward social-ecological systems perspectives: Insights into the human-nature relationship. Nat. Sci. Soc. 2015, 23, 166–174. [Google Scholar] [CrossRef]
  40. Leach, M.; Reyers, B.; Bai, X.; Brondizio, E.S.; Cook, C.; Díaz, S.; Espindola, G.; Scobie, M.; Stafford-Smith, M.; Subramanian, S.M. Equity and sustainability in the Anthropocene: A social–ecological systems perspective on their intertwined futures. Glob. Sustain. 2018, 1, e13. [Google Scholar] [CrossRef]
  41. Liu, J.; Dietz, T.; Carpenter, S.R.; Alberti, M.; Folke, C.; Moran, E.; Pell, A.N.; Deadman, P.; Kratz, T.; Lubchenco, J.; et al. Complexity of coupled human and natural systems. Science 2007, 317, 1513–1516. [Google Scholar] [CrossRef] [PubMed]
  42. UN. Report of the Interagency and Expert Group on Sustainable Development Goal Indicators; United Nations Economic and Social Council: New York, NY, USA, 2016. [Google Scholar]
  43. Biggs, R.; Schlüter, M.; Schoon, M.L. Principles for Building Resilience, Sustaining Ecosystem Services in Social-Ecological System; Cambridge University Press: Cambridge, UK, 2015; ISBN 978-1-107-08265-6. [Google Scholar]
  44. Preiser, R.; Biggs, R.; De Vos, A.; Folke, C. Social-ecological systems as complex adaptive systems: Organizing principles for advancing research methods and approaches. Ecol. Soc. 2018, 23, art46. [Google Scholar] [CrossRef]
  45. Fischer-Kowalski, M.; Weisz, H. The Archipelago of Social Ecology and the Island of the Vienna School. In Social Ecology. Society–Nature Relations across Time and Space; Springer Int’l Publishing: Cham, Switzerland, 2016; pp. 3–28. ISBN 978-3-319-33324-3. [Google Scholar]
  46. Berryman, A.A. The Conceptual Foundations of Ecological Dynamics. Bull. Ecol. Soc. Am. 1989, 70, 230–236. [Google Scholar]
  47. Hull, V.; Tuanmu, M.N.; Liu, J. Synthesis of human-nature feedbacks. Ecol. Soc. 2015, 20, 17. [Google Scholar] [CrossRef]
  48. Claessens, L.; Schoorl, J.M.; Verburg, P.H.; Geraedts, L.; Veldkamp, A. Modelling interactions and feedback mechanisms between land use change and landscape processes. Agric. Ecosyst. Environ. 2009, 129, 157–170. [Google Scholar] [CrossRef]
  49. Armsworth, P.R.; Daily, G.C.; Kareiva, P.; Sanchirico, J.N. Land market feedbacks can undermine biodiversity conservation. Proc. Natl. Acad. Sci. USA 2006, 103, 5403–5408. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  50. Miller, B.W.; Caplow, S.C.; Leslie, P.W. Feedbacks between Conservation and Social-Ecological Systems. Conserv. Biol. 2012, 26, 218–227. [Google Scholar] [CrossRef] [PubMed]
  51. Sundkvist, Å.; Milestad, R.; Jansson, A. On the importance of tightening feedback loops for sustainable development of food systems. Food Policy 2005, 30, 224–239. [Google Scholar] [CrossRef]
  52. Tilman, D. Global environmental impacts of agricultural expansion: The need for sustainable and efficient practices. Proc. Natl. Acad. Sci. USA 1999, 96, 5995–6000. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  53. Bennett, E.M.; Carpenter, S.R.; Caraco, N.F. Human Impact on Erodable Phosphorus and Eutrophication: A Global Perspective. Bioscience 2001, 51, 227. [Google Scholar] [CrossRef] [Green Version]
  54. Hamann, M.; Biggs, R.; Reyers, B. Mapping social-ecological systems: Identifying “green-loop” and “red-loop” dynamics based on characteristic bundles of ecosystem service use. Glob. Environ. Chang. 2015, 34, 218–226. [Google Scholar] [CrossRef]
  55. Folke, C. Resilience (Republished). Ecol. Soc. 2016, 21, art44. [Google Scholar] [CrossRef]
  56. Carson, M.; Perterson, G. Arctic Council 2016. Arctic Resilience Report; Carson, M., Peterson, G., Eds.; Stockholm Environment Institute & Stockholm Resilience Centre: Stockholm, Sweden, 2016; ISBN 9789186125455. [Google Scholar]
  57. Polasky, S.; Bryant, B.; Hawthorne, P.; Johnson, J.; Keeler, B.; Pennington, D. Inclusive Wealth as a Metric of Sustainable Development. Annu. Rev. Environ. Resour. 2015, 40, 445–466. [Google Scholar] [CrossRef]
  58. Adger, W.N.; Vincent, K. Uncertainty in adaptive capacity. C. R. Geosci. 2005, 337, 399–410. [Google Scholar] [CrossRef]
  59. Engle, N.L.; Lemos, M.C. Unpacking governance: Building adaptive capacity to climate change of river basins in Brazil. Glob. Environ. Chang. 2010, 20, 4–13. [Google Scholar] [CrossRef]
  60. Fazey, I.; Fazey, J.A.; Fischer, J.; Sherren, K.; Warren, J.; Reed, F.; Dovers, S.R.; Noss, R.F. Adaptive leverage capacity for and learning to learn as leverage for social–ecological resilience. Front. Ecol. Environ. 2007, 5, 375–380. [Google Scholar] [CrossRef]
  61. Berkes, F.; Folke, C. Linking Social and Ecological Systems: Management Practices and Social Mechanisms for Building Resilience; Cambridge University Press: Cambridge, UK, 1998; ISBN 0521785626. [Google Scholar]
  62. Quinlan, A.E.; Berbés-Blázquez, M.; Haider, L.J.; Peterson, G.D. Measuring and assessing resilience: Broadening understanding through multiple disciplinary perspectives. J. Appl. Ecol. 2016, 53, 677–687. [Google Scholar] [CrossRef]
  63. Cundill, G.; Leitch, A.; Schultz, L.; Armitage, D.; Peterson, G. Principle 5—Encourage Learning. In Principles for Building Resilience: Sustaining Ecosystem Services in Social-Ecological Systems; Biggs, R., Schluter, M., Schoon, M., Eds.; Cambridge University Press: Cambridge, UK, 2015; pp. 174–192. ISBN 9781107082656. [Google Scholar]
  64. Leitch, A.; Cundill, G.; Schultz, L. 8 Principle 6–Broaden participation. In Principles for Building Resilience: Sustaining Ecosystem Services in Social-Ecological Systems; Biggs, R., Schluter, M., Schoon, M., Eds.; Cambridge University Press: Cambridge, UK, 2015; pp. 201–225. [Google Scholar]
  65. Schoon, M.L.; Robards, M.D.; Meek, C.L.; Galaz, V. Principle 7—Promote polycentric governance systems. In Principles for Building Resilience: Sustaining Ecosystem Services in Social-Ecological Systems; Biggs, R., Schluter, M., Schoon, M.L., Eds.; Cambridge University Press: Cambridge, UK, 2015; pp. 226–250. ISBN 9781316014240 | 9781107082656. [Google Scholar]
  66. Norberg, J.; Cumming, G.S. Complexity Theory for a Sustainable Future; Columbia University Press: New York, NY, USA, 2008; ISBN 0231508867. [Google Scholar]
  67. Scholes, R.J.; Biggs, R. A biodiversity intactness index. Nature 2005, 434, 45–49. [Google Scholar] [CrossRef] [PubMed]
  68. Loh, J.; Green, R.E.; Ricketts, T.; Lamoreux, J.F.; Jenkins, M.; Kapos, V.; Randers, J. The Living Planet Index: Using species population time series to track trends in biodiversity. Philos. Trans. R. Soc. Lond. B. Biol. Sci. 2005, 360, 289–295. [Google Scholar] [CrossRef] [PubMed]
  69. Mudaca, J.D.; Tsuchiya, T.; Yamada, M.; Onwona-Agyeman, S. Household participation in Payments for Ecosystem Services: A case study from Mozambique. For. Policy Econ. 2015, 55, 21–27. [Google Scholar] [CrossRef]
  70. Baker, T.J.; Cullen, B.; Debevec, L.; Abebe, Y. A socio-hydrological approach for incorporating gender into biophysical models and implications for water resources research. Appl. Geogr. 2015, 62, 325–338. [Google Scholar] [CrossRef]
  71. Lau, J.D.; Hicks, C.C.; Gurney, G.G.; Cinner, J.E. Disaggregating ecosystem service values and priorities by wealth, age, and education. Ecosyst. Serv. 2018, 29, 91–98. [Google Scholar] [CrossRef]
  72. Anand, S.; Sen, A.K. Human Development Index: Methodology and Measurement; Human Development Report Office: New York, NY, USA, 1994; 370p. [Google Scholar]
  73. Alkire, S.; Santos, M.E. Measuring Acute Poverty in the Developing World: Robustness and Scope of the Multidimensional Poverty Index. World Dev. 2014, 59, 251–274. [Google Scholar] [CrossRef] [Green Version]
  74. Raudsepp-Hearne, C.; Peterson, G.D.; Bennett, E.M. Ecosystem service bundles for analyzing tradeoffs in diverse landscapes. Proc. Natl. Acad. Sci. USA 2010, 107, 5242–5247. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  75. Hamann, M.; Biggs, R.; Reyers, B. An exploration of human well-being bundles as identifiers of ecosystem service use patterns. PLoS ONE 2016, 11, e0163476. [Google Scholar] [CrossRef] [PubMed]
  76. Kotschy, K.; Biggs, R.; Daw, T.; Folke, C. 3 Principle 1—Maintain diversity and redundancy. In Principles for Building Resilience: Sustaining Ecosystem Services in Social-Ecological Systems; Biggs, R., Schluter, M., Schoon, M., Eds.; Cambridge University Press: Cambridge, UK, 2015; p. 290. ISBN 978-1-107-08265-6. [Google Scholar]
  77. Tilman, D.; Wedin, D.; Knops, J. Productivity and sustainability influenced by biodiversity in grassland ecosystems. Nature 1996, 379, 718–720. [Google Scholar] [CrossRef]
  78. Worm, B.; Barbier, E.B.; Beaumont, N.J.; Duffy, J.E.; Folke, C.; Halpern, B.S.; Jackson, J.B.C.; Lotze, H.K.; Micheli, F.; Palumbi, S.R.; et al. Impacts of biodiversity loss on ocean ecosystem services. Science 2006, 314, 787–790. [Google Scholar] [CrossRef] [PubMed]
  79. Tengö, M.; Hill, R.; Malmer, P.; Raymond, C.M.; Spierenburg, M.; Danielsen, F.; Elmqvist, T.; Folke, C. Weaving knowledge systems in IPBES, CBD and beyond—Lessons learned for sustainability. Curr. Opin. Environ. Sustain. 2017, 26–27, 17–25. [Google Scholar] [CrossRef]
  80. Sterling, E.J.; Filardi, C.; Toomey, A.; Sigouin, A.; Betley, E.; Gazit, N.; Newell, J.; Albert, S.; Alvira, D.; Bergamini, N.; et al. Biocultural approaches to well-being and sustainability indicators across scales. Nat. Ecol. Evol. 2017, 1, 1798–1806. [Google Scholar] [CrossRef] [PubMed]
  81. Díaz, S.; Pascual, U.; Stenseke, M.; Martín-López, B.; Watson, R.; Molnár, Z.; Hill, R.; Chan, K.; Baste, I.; Brauman, K.; et al. An inclusive approach to assess nature’s contributions to people. Science 2018, 359, 270–272. [Google Scholar] [CrossRef] [PubMed]
  82. Xiao, Y.; Drucker, J. Does economic diversity enhance regional disaster resilience? J. Am. Plan. Assoc. 2013, 79, 148–160. [Google Scholar] [CrossRef]
  83. Kent, R.; Dorward, A. Livelihood responses to Lantana camara invasion and biodiversity change in southern India: Application of an asset function framework. Reg. Environ. Chang. 2014, 15, 353–364. [Google Scholar] [CrossRef]
  84. Leslie, P.; McCabe, J.T. Response Diversity and Resilience in Social-Ecological Systems. Curr. Anthropol. 2013, 54, 114–143. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  85. Daw, T.M.; Coulthard, S.; Cheung, W.W.L.; Brown, K.; Abunge, C.; Galafassi, D.; Peterson, G.D.; McClanahan, T.R.; Omukoto, J.O.; Munyi, L. Evaluating taboo trade-offs in ecosystems services and human well-being. Proc. Natl. Acad. Sci. USA 2015, 112, 6949–6954. [Google Scholar] [CrossRef] [PubMed]
  86. Hicks, C.C.; Cinner, J.E. Social, institutional, and knowledge mechanisms mediate diverse ecosystem service benefits from coral reefs. Proc. Natl. Acad. Sci. USA 2014, 111, 17791–17796. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  87. Selomane, O.; Reyers, B.; Biggs, R.; Tallis, H.; Polasky, S. Towards integrated social-ecological sustainability indicators: Exploring the contribution and gaps in existing global data. Ecol. Econ. 2015, 118, 140–146. [Google Scholar] [CrossRef]
  88. Kaijser, A.; Kronsell, A. Climate change through the lens of intersectionality. Env. Politics 2014, 23, 417–433. [Google Scholar] [CrossRef]
  89. Kawarazuka, N.; Locke, C.; McDougall, C.; Kantor, P.; Morgan, M. Bringing analysis of gender and social–ecological resilience together in small-scale fisheries research: Challenges and opportunities. Ambio 2017, 46, 201–213. [Google Scholar] [CrossRef] [PubMed]
  90. Pickett, S.T.A.; Cadenasso, M.L. The Ecosystem as a Multidimensional Concept: Meaning, Model, and Metaphor. Ecosystems 2002, 5, 1–10. [Google Scholar] [CrossRef]
  91. Alkire, S. Dimensions of Human Development. World Dev. 2002, 30, 181–205. [Google Scholar] [CrossRef]
  92. Barbier, E.B.; Koch, E.W.; Silliman, B.R.; Hacker, S.D.; Wolanski, E.; Primavera, J.; Granek, E.F.; Polasky, S.; Aswani, S.; Cramer, L.A.; et al. Coastal ecosystem-based management with nonlinear ecological functions and values. Science 2008, 319, 321–323. [Google Scholar] [CrossRef] [PubMed]
  93. Rial, J.A.; Sr. Pielke, R.A.; Beniston, M.; Claussen, M.; Canadell, J.; Cox, P.; Held, H.; de Noblet-Ducoudré, N.; Prinn, R.; Reynolds, J.F.; et al. Nonlinearities, Feedbacks and Critical Thresholds within the Earth’s Climate System. Clim. Chang. 2004, 65, 11–38. [Google Scholar] [CrossRef]
  94. Drazin, P.G. Nonlinear Systems; Cambridge University Press: Cambridge, UK, 1992; ISBN 9781139172455. [Google Scholar]
  95. Pascual, U.; Palomo, I.; Adams, W.M.; Chan, K.M.A.; Daw, T.M.; Garmendia, E.; Gómez-Baggethun, E.; de Groot, R.S.; Mace, G.M.; Martín-López, B.; et al. Off-stage ecosystem service burdens: A blind spot for global sustainability. Environ. Res. Lett. 2017, 12, 075001. [Google Scholar] [CrossRef] [Green Version]
  96. Scheffer, M.; Carpenter, S.R. Catastrophic regime shifts in ecosystems: Linking theory to observation. Trends Ecol. Evol. 2003, 18, 648–656. [Google Scholar] [CrossRef]
  97. Schlüter, M.; McAllister, R.R.J.; Arlinghaus, R.; Bunnefeld, N.; Eisenack, K.; Hölker, F.; Milner-Gulland, E.J.; Müller, B.; Nicholson, E.; Quaas, M.; et al. New horizons for managing the environment: A review of coupled social-ecological systems modeling. Nat. Resour. Model. 2012, 25, 219–272. [Google Scholar] [CrossRef]
  98. Barabasi, A.-L.; Albert, R. Emergence of scaling in random networks. Science 1999, 286, 509–512. [Google Scholar] [CrossRef] [PubMed]
  99. Biggs, R.; Carpenter, S.R.; Brock, W.A. Turning back from the brink: Detecting an impending regime shift in time to avert it. Proc. Natl. Acad. Sci. USA 2009, 106, 826–831. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  100. Kinzig, A.P.; Ryan, P.; Etienne, M.; Allison, H.; Elmqvist, T.; Walker, B.H. Resilience and regime shifts:assessing cascading effects. Ecol. Soc. 2006, 11, 20. [Google Scholar] [CrossRef]
  101. Scheffer, M.; Carpenter, S.R.S.; Foley, J.A.; Folke, C.; Walker, B. Catastrophic shifts in ecosystems. Nature 2001, 413, 591–596. [Google Scholar] [CrossRef] [PubMed]
  102. Spanbauer, T.L.; Allen, C.R.; Angeler, D.G.; Eason, T.; Fritz, S.C.; Garmestani, A.S.; Nash, K.L.; Stone, J.R. Prolonged instability prior to a regime shift. PLoS ONE 2014, 9, e108936. [Google Scholar] [CrossRef] [PubMed]
  103. Lade, S.J.; Gross, T. Early warning signals for critical transitions: A generalized modeling approach. PLoS Comput. Biol. 2012, 8, e1002360. [Google Scholar] [CrossRef] [PubMed]
  104. Scheffer, M.; Carpenter, S.R.; Lenton, T.M.; Bascompte, J.; Brock, W.; Dakos, V.; Van De Koppel, J.; Van De Leemput, I.A.; Levin, S.A.; Van Nes, E.H.; et al. Anticipating critical transitions. Science 2012, 338, 344–348. [Google Scholar] [CrossRef] [PubMed]
  105. Wang, R.; Dearing, J.A.; Langdon, P.G.; Zhang, E.; Yang, X.; Dakos, V.; Scheffer, M. Flickering gives early warning signals of a critical transition to a eutrophic lake state. Nature 2012, 492, 419–422. [Google Scholar] [CrossRef] [PubMed]
  106. Rockström, J.; Steffen, W.; Noone, K.; Persson, A.; Chapin, F.S.; Lambin, E.F.; Lenton, T.M.; Scheffer, M.; Folke, C.; Schellnhuber, H.J.; et al. A safe operating space for humanity. Nature 2009, 461, 472–475. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  107. Steffen, W.; Richardson, K.; Rockström, J.; Cornell, S.E.; Fetzer, I.; Bennett, E.M.; Biggs, R.; Carpenter, S.R.; de Vries, W.; de Wit, C.A.; et al. Planetary boundaries: Guiding human development on a changing planet. Science 2015, 347, 1259855. [Google Scholar] [CrossRef] [PubMed]
  108. Tharme, R.E. A global perspective on environmental flow assessment: Emerging trends in the development and application of environmental flow methodologies for rivers. River Res. Appl. 2003, 19, 397–441. [Google Scholar] [CrossRef]
  109. Raworth, K. Oxfam Discussion Papers: A Safe and Just Space for Humanity: Can We Live within the Doughnut (Oxfam Discussion Paper); Oxfam: Oxford, UK, 2012; Volume 461. [Google Scholar]
  110. Nussbaum, M.; Sen, A. (Eds.) The Quality of Life; Oxford University Press: Oxford, UK, 1993; ISBN 9780198287971. [Google Scholar]
  111. Adger, W.N.; Brown, K.; Tompkins, E.L. The Political Economy of Cross-Scale Networks in Resource Co-Management. Ecol. Soc. 2005, 10. [Google Scholar] [CrossRef] [Green Version]
  112. Cash, D.W.; Adger, W.N.; Berkes, F.; Garden, P.; Lebel, L.; Olsson, P.; Pritchard, L.; Young, O. Scale and Cross-Scale Dynamics: Governance and Information in a Multilevel World. Ecol. Soc. 2006, 11, art8. [Google Scholar] [CrossRef]
  113. Grimm, N.B.; Foster, D.; Groffman, P.; Grove, J.M.; Hopkinson, C.S.; Nadelhoffer, K.J.; Pataki, D.E.; Peters, D.P. The changing landscape: Ecosystem responses to urbanization and pollution across climatic and societal gradients. Front. Ecol. Environ. 2008, 6, 264–272. [Google Scholar] [CrossRef]
  114. Keys, P.W.; Wang-Erlandsson, L.; Gordon, L.J. Revealing Invisible Water: Moisture Recycling as an Ecosystem Service. PLoS ONE 2016, 11, e0151993. [Google Scholar] [CrossRef] [PubMed]
  115. Barnett, J.; Evans, L.S.; Gross, C.; Kiem, A.S.; Kingsford, R.T.; Palutikof, J.P.; Pickering, C.M.; Smithers, S.G. From barriers to limits to climate change adaptation: Path dependency and the speed of change. Ecol. Soc. 2015, 20, art5. [Google Scholar] [CrossRef]
  116. Dearing, J.A.; Yang, X.; Dong, X.; Zhang, E.; Chen, X.; Langdon, P.G.; Zhang, K.; Zhang, W.; Dawson, T.P. Extending the timescale and range of ecosystem services through paleoenvironmental analyses: The example of the lower Yangtze basin. Proc. Natl. Acad. Sci. USA 2012, 109, E1111–E1120. [Google Scholar] [CrossRef] [PubMed]
  117. Zhang, K.; Dearing, J.A.; Dawson, T.P.; Dong, X.; Yang, X.; Zhang, W. Poverty alleviation strategies in eastern China lead to critical ecological dynamics. Sci. Total Environ. 2015, 506–507, 164–181. [Google Scholar] [CrossRef] [PubMed]
  118. Davidson, J.L.; Jacobson, C.; Lyth, A.; Dedekorkut-Howes, A.; Baldwin, C.L.; Ellison, J.C.; Holbrook, N.J.; Howes, M.J.; Serrao-Neumann, S.; Singh-Peterson, L.; et al. Interrogating resilience: Toward a typology to improve its operationalization. Ecol. Soc. 2016, 21, art27. [Google Scholar] [CrossRef]
  119. Leslie, H.M.; Basurto, X.; Nenadovic, M.; Sievanen, L.; Cavanaugh, K.C.; Cota-Nieto, J.J.; Erisman, B.E.; Finkbeiner, E.; Hinojosa-Arango, G.; Moreno-Báez, M.; et al. Operationalizing the social-ecological systems framework to assess sustainability. Proc. Natl. Acad. Sci. USA 2015, 112, 5979–5984. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  120. Haug, M.R. Measurement in Social Stratification. Annu. Rev. Sociol. 1977, 3, 51–77. [Google Scholar] [CrossRef]
  121. Daw, T.; Brown, K.; Rosendo, S.; Pomeroy, R. Applying the ecosystem services concept to poverty alleviation: The need to disaggregate human well-being. Environ. Conserv. 2011, 38, 370–379. [Google Scholar] [CrossRef]
  122. Unterhalter, E. Poverty, education, gender and the Millennium Development Goals: Reflections on boundaries and intersectionality. Theory Res. Educ. 2012, 10, 253–274. [Google Scholar] [CrossRef]
  123. Dawson, N.; Martin, A. Assessing the contribution of ecosystem services to human wellbeing: A disaggregated study in western Rwanda. Ecol. Econ. 2015, 117, 62–72. [Google Scholar] [CrossRef] [Green Version]
  124. Butchart, S.H.M.; Walpole, M.; Collen, B.; van Strien, A.; Scharlemann, J.P.W.; Almond, R.E.A.; Baillie, J.E.M.; Bomhard, B.; Brown, C.; Bruno, J.; et al. Global biodiversity: Indicators of recent declines. Science 2010, 328, 1164–1168. [Google Scholar] [CrossRef] [PubMed]
  125. WWF. Living Planet Report: Risk and Resilience in a New Era; WWF International: Gland, Switzerland, 2016; ISBN 9783946211068. [Google Scholar]
  126. Boonstra, W.J.; Björkvik, E.; Haider, L.J.; Masterson, V. Human responses to social-ecological traps. Sustain. Sci. 2016, 11, 877–889. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  127. Winter, K.; Lincoln, N.; Berkes, F.; Winter, K.B.; Lincoln, N.K.; Berkes, F. The Social-Ecological Keystone Concept: A Quantifiable Metaphor for Understanding the Structure, Function, and Resilience of a Biocultural System. Sustainability 2018, 10, 3294. [Google Scholar] [CrossRef]
  128. Nelson, G.C. Untangling the Environmentalist’s Paradox: Better Data, Better Accounting, and Better Technology Will Help. Bioscience 2011, 61, 9–10. [Google Scholar] [CrossRef]
  129. Ang, F.; Van Passel, S. Beyond the Environmentalist’s Paradox and the Debate on Weak versus Strong Sustainability. Bioscience 2012, 62, 251–259. [Google Scholar] [CrossRef] [Green Version]
  130. Dallimer, M.; Davies, Z.G.; Diaz-Porras, D.F.; Irvine, K.N.; Maltby, L.; Warren, P.H.; Armsworth, P.R.; Gaston, K.J. Historical influences on the current provision of multiple ecosystem services. Glob. Environ. Chang. 2015, 31, 307–317. [Google Scholar] [CrossRef] [Green Version]
  131. Galaz, V.; Moberg, F.; Olsson, E.K.; Paglia, E.; Parker, C. Institutional and political leadership dimensions of cascading ecological crises. Public Adm. 2011, 89, 361–380. [Google Scholar] [CrossRef]
  132. Galaz, V.; Tallberg, J.; Boin, A.; Ituarte-Lima, C.; Hey, E.; Olsson, P.; Westley, F. Global Governance Dimensions of Globally Networked Risks: The State of the Art in Social Science Research. Risk Hazards Cris. Public Policy 2017, 8, 4–27. [Google Scholar] [CrossRef] [Green Version]
  133. Sachs, J.D.; McArthur, J.W. The Millennium Project: A plan for meeting the Millennium Development Goals. Lancet 2005, 365, 347–353. [Google Scholar] [CrossRef]
  134. Wilcock, S.; Hossain, S.; Poppy, G.M. Managing Complex Systems To Enhance Sustainability. In Stressors in the Marine Environment; Oxford University Press: Oxford, UK, 2016; pp. 301–312. [Google Scholar]
  135. De Groot, R.S. A typology for the classification, description and valuation of ecosystem functions, goods and services. Ecol. Econ. 2002, 41, 393–408. [Google Scholar] [CrossRef] [Green Version]
  136. Müller, F.; Burkhard, B. The indicator side of ecosystem services. Ecosyst. Serv. 2012, 1, 26–30. [Google Scholar] [CrossRef] [Green Version]
  137. Tallis, H.; Mooney, H.A.; Sandy, A.; Balvanera, P.; Cramer, W.; Daniel, K.; Polasky, S.; Reyers, B.; Ricketts, T.; Running, S.; et al. A global system for monitoring ecosystem service change. Bioscience 2012, 62, 977–986. [Google Scholar] [CrossRef]
  138. Reyers, B.; Biggs, R.; Cumming, G.S.; Elmqvist, T.; Hejnowicz, A.P.; Polasky, S. Getting the measure of ecosystem services: A social-ecological approach. Front. Ecol. Environ. 2013, 11, 268–273. [Google Scholar] [CrossRef]
  139. Erb, K.H. How a socio-ecological metabolism approach can help to advance our understanding of changes in land-use intensity. Ecol. Econ. 2012, 76, 8–14. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  140. Kennedy, C.A.; Cuddihy, J.; Engel-Yan, J. The changing metabolism of cities. J. Ind. Ecol. 2007, 11, 43–59. [Google Scholar] [CrossRef]
  141. Binder, C.R.; Hinkel, J.; Bots, P.W.G.; Claudia, P.-W.; Pahl-Wostl, C.; Claudia, P.-W. Comparison of Frameworks for Analyzing Social-ecological Systems. Ecol. Soc. 2013, 18, 26. [Google Scholar] [CrossRef]
  142. Scholz, R.W. Environmental Literacy in Scienceand Society From Knowledge to Decisions; Cambridge University Press: Cambridge, UK, 2011; ISBN 0095-3628. [Google Scholar]
  143. Plahte, E.; Thomas, M.; Omholt, S.W. Feedback loops, stability and multistationarity in dynamical systems. J. Biol. Syst. 1995, 3, 409–413. [Google Scholar] [CrossRef]
  144. Dawson, N.; Grogan, K.; Martin, A.; Mertz, O.; Pasgaard, M.; Vang Rasmussen, L. Environmental justice research shows the importance of social feedbacks in ecosystem service trade-offs. Ecol. Soc. 2017, 22. [Google Scholar] [CrossRef] [Green Version]
  145. Biggs, R.; Schlüter, M.; Biggs, D.; Bohensky, E.L.; BurnSilver, S.; Cundill, G.; Dakos, V.; Daw, T.M.; Evans, L.S.; Kotschy, K.; et al. Toward Principles for Enhancing the Resilience of Ecosystem Services. Annu. Rev. Environ. Resour. 2012, 37, 421–448. [Google Scholar] [CrossRef]
  146. Poppy, G.M.; Chiotha, S.; Eigenbrod, F.; Harvey, C.A.; Honzák, M.; Hudson, M.D.; Jarvis, A.; Madise, N.J.; Schreckenberg, K.; Shackleton, C.M.; et al. Food security in a perfect storm: Using the ecosystem services framework to increase understanding. Philos. Trans. R. Soc. Lond. B. Biol. Sci. 2014, 369, 20120288. [Google Scholar] [CrossRef] [PubMed]
  147. Kafumbata, D.; Jamu, D.; Chiotha, S. Riparian ecosystem resilience and livelihood strategies under test: Lessons from Lake Chilwa in Malawi and other lakes in Africa. Philos. Trans. R. Soc. Lond. B. Biol. Sci. 2014, 369, 20130052. [Google Scholar] [CrossRef] [PubMed]
  148. Folke, C.; Biggs, R.; Norstrom, A.V.; Reyers, B.; Rockstrom, J. Social-ecological resilience and biosphere-based sustainability science. Ecol. Soc. 2016, 21, 41. [Google Scholar] [CrossRef]
  149. Goodwin, H. Measuring and Reporting the Impact of Tourism on Poverty. In Developments in Tourism Research; Elsevier: Amsterdam, The Netherlands, 2011; pp. 1–13. [Google Scholar] [CrossRef]
  150. Cockburn, J.; Palmer, C.; Biggs, H.; Rosenberg, E. Navigating Multiple Tensions for Engaged Praxis in a Complex Social-Ecological System. Land 2018, 7, 129. [Google Scholar] [CrossRef]
  151. Talbot, M.; van den Broeck, D. Shifting from Individual to Collective Action: Living Land’s experience in the Baviaanskloof, South Africa. In Land Restoration: Reclaiming Landscapes for a Sustainable Future; Elsevier: Amsterdam, The Netherlands, 2015; pp. 521–531. ISBN 9780128013533. [Google Scholar]
  152. Swilling, M.; Hajer, M. Governance of urban transitions: Towards sustainable resource efficient urban infrastructures. Environ. Res. Lett. 2017, 12, 125007. [Google Scholar] [CrossRef]
  153. Wilson, S.; Pearson, L.J.; Kashima, Y.; Lusher, D.; Pearson, C. Separating Adaptive Maintenance (Resilience) and Transformative Capacity of Social-Ecological Systems. Ecol. Soc. 2013, 18, art22. [Google Scholar] [CrossRef]
  154. Brown, K.; Westaway, E. Agency, Capacity, and Resilience to Environmental Change: Lessons from Human Development, Well-Being, and Disasters. Annu. Rev. Environ. Resour. 2011, 36, 321–342. [Google Scholar] [CrossRef]
  155. Marshall, N.A.; Park, S.E.; Adger, W.N.; Brown, K.; Howden, S.M. Transformational capacity and the influence of place and identity. Environ. Res. Lett. 2012, 7, 034022. [Google Scholar] [CrossRef] [Green Version]
  156. Raworth, K. Doughnut Economics: Seven Ways to Think Like a 21st Century Economist; Random House Business Books: London, UK, 2017; ISBN 9781473517813. [Google Scholar]
  157. Parlee, B.L.; Geertsema, K.; Willier, A. Social-ecological thresholds in a changing boreal landscape: Insights from cree knowledge of the Lesser Slave Lake region of Alberta, Canada. Ecol. Soc. 2012, 17, art20. [Google Scholar] [CrossRef]
  158. Warner, K. Global environmental change and migration: Governance challenges. Glob. Environ. Chang. 2010, 20, 402–413. [Google Scholar] [CrossRef]
  159. Mace, G.; Baillie, J.E.M. The 2010 Biodiversity Indicators: Challenges for Science and Policy. Conserv. Biol. 2007, 21, 1406–1413. [Google Scholar] [CrossRef] [PubMed]
  160. Stafford-Smith, M.; Griggs, D.; Gaffney, O.; Ullah, F.; Reyers, B.; Kanie, N.; Stigson, B.; Shrivastava, P.; Leach, M.; O’Connell, D. Integration: The key to implementing the Sustainable Development Goals. Sustain. Sci. 2017, 12, 911–919. [Google Scholar] [CrossRef] [PubMed]
  161. Kim, R.E. The emergent network structure of the multilateral environmental agreement system. Glob. Environ. Chang. 2013, 23, 980–991. [Google Scholar] [CrossRef]
  162. Haider, L.J.; Iribarrem, A.; Gardner, T.; Latawiec, A.E.; Alves-Pinto, H.; Strassburg, B. 2 Understanding Indicators And Monitoring For Sustainability in the Context of Complex Social-Ecological Systems. In Sustainability Indicators in Practice; Latawiec, A.E., Agol, D., Eds.; De Gruyter: Boston, MA, USA, 2015. [Google Scholar]
Figure 1. The percentage of SDG indicators analyzed that captured each of the five social-ecological features evaluated in this paper.
Figure 1. The percentage of SDG indicators analyzed that captured each of the five social-ecological features evaluated in this paper.
Sustainability 11 01190 g001
Figure 2. Degree to which indicators under each of the 17 SDGs capture each of the five key features of social-ecological systems dynamics assessed in this paper.
Figure 2. Degree to which indicators under each of the 17 SDGs capture each of the five key features of social-ecological systems dynamics assessed in this paper.
Sustainability 11 01190 g002

Share and Cite

MDPI and ACS Style

Selomane, O.; Reyers, B.; Biggs, R.; Hamann, M. Harnessing Insights from Social-Ecological Systems Research for Monitoring Sustainable Development. Sustainability 2019, 11, 1190.

AMA Style

Selomane O, Reyers B, Biggs R, Hamann M. Harnessing Insights from Social-Ecological Systems Research for Monitoring Sustainable Development. Sustainability. 2019; 11(4):1190.

Chicago/Turabian Style

Selomane, Odirilwe, Belinda Reyers, Reinette Biggs, and Maike Hamann. 2019. "Harnessing Insights from Social-Ecological Systems Research for Monitoring Sustainable Development" Sustainability 11, no. 4: 1190.

Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.

Article Metrics

Back to TopTop