Special issue: Towards an eco-social pedagogy
Introduction
As our understanding of the climate crisis deepens, many government jurisdictions and world scientists have declared the world is in a state of climate emergency (Ripple et al., 2020). Our capacity to manage complex systems-based challenges such as climate change, energy transitions, human health and well-being, ecosystem services and economic stability requires new systemic ways of working across different sectors, actors and contexts (Gudde et al., 2021; Scoones, 2016).
At the most recent COP 27 (UNCCC, 2024), the collective global community agreed to triple finance to developing countries, from the previous goal of US$100 billion annually, to US$300 billion annually by 2035, and secure efforts of all actors to work together to scale up finance to developing countries, from public and private sources, to the amount of US$1.3 trillion per year by 2035. It is estimated that the total energy investment worldwide is expected to exceed US$3.3 trillion in 2025, with nearly three-quarters of that budget set to go towards clean technologies (International Energy Agency, 2025). However positive these shifts in investment are, current greenhouse gas emission targets are not in line with the 1.5 °C Paris Agreement temperature goals (OECD, 2024).
Community resilience at the local level integrates adaptation and mitigation to advance local and regional sustainable development. This is enabled by increased local cooperation facilitated by access to adequate financial resources, particularly for vulnerable regions, sectors and groups, as well as inclusive governance and coordinated policies (IPCC, 2023).
In the impending years of increasing climate dysfunction, there is a need to ensure that funding targeted at supporting community-based adaptive capacity translates into desired improvements. This requires the ability to understand how interventions create change and an understanding of what a resilient community looks like within its own context. While recognising that every community is unique in its regional, economic and cultural elements, it would be impractical to develop a funding methodology on a case-by-case basis. Therefore, there is a need to develop universal principles and processes that are consistently actionable across multiple applications that effectively target social benefit.
Community resilience
Community resilience has gained momentum in the climate adaptation academic, policy, international development, donor and practitioner circles (Béné et al., 2014; Brown and Westaway, 2011). Despite the growing focus on community resilience in both academic and practising spheres, there is no agreed understanding on what exactly resilience is (Béné et al., 2014; Graveline and Germain, 2022; Walker et al., 2004).
There are two strands of literature on the definition of community resilience. The first school of thought is derived from psychology of development and mental health (Norris et al., 2008). This theory encompasses contemporary understandings of stress, adaptation, wellness and resource dynamics in the face of adverse disturbance or adversity.
The second strand of literature originates in ecological science and builds on Holling’s (1973) definition of resilience that is based on a complex adaptive systems (CAS) approach (Berkes and Ross, 2016), later evolving into resilience as a multidimensional emergent property of a complex adaptive system (Béné et al., 2014). This encompasses a more holistic approach that would embrace a wider temporal boundary of underlying effects and drivers. This second strand is the primary lens that we apply to community resilience as it is a primary focus of community-led local development (CLLD).
Time is an important construct in our understanding of resilience (Kulig et al., 2013). In some situations, change may emerge slowly with no clear time delineation between the initial and the transformed conditions. Therefore, different types of decision making may be appropriate to facilitate resilience-based responses, requiring specific adaptation learning and engagement processes. This may differ from responding to a natural disaster with a sudden change. The former may require a more general resilience while the latter may require a more disaster-specific resilience (Carpenter et al., 2012). Community resilience includes a community’s well-being and the potential for broader transformative actions (Béné et al., 2014; Berkes and Ross, 2016; Engbersen et al., 2024; Whitney et al., 2017), emphasising the importance of stability and of community thriving outside disaster circumstances. This more holistic interpretation that includes the provision of services that create and nurture well-being and sustainability, and the community’s capacity to adapt to both short- and long-term change, is the direction of intent for this research.
Conditions that enable social resilience
It is important to recognise some of the fundamental conditions required for communities to most efficiently manage increasing uncertainty and flourish within a landscape of social flux. Figure 1 illustrates some of the aspects required for communities to create self-governing frameworks that support social development. This is not considered to be a definitive list of social resilience conditions; rather, it is a descriptive model from which to explore how development investment can support and reinforce fundamental enablers of community resilience.
Conditions that support community resilience
No doubt other perspectives on resilience (for example, emergency planning, disaster relief, social capital, human health, ecosystem/biosphere) could create alternate lenses for conceptualising core resilience components; however, our CLLD perspective prioritises the following attributes, some of which are likely transferable to other contexts as well:
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Shared values: explicit or implicit fundamental beliefs, concepts and principles underlying community culture. This would include the recognition of shared circumstances and the development of a common social identity, be that by place, culture or beliefs.
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Agency: the capacity of individuals or groups to make choices independently and to act unobstructedly in pursuit of their goals. It involves sharing of power and the ability to influence one’s environment and circumstances.
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Connectivity: related to comprehensive, varied and multiplied (redundant) relationships among individuals, institutions and resources within a community. This facilitates information and resource sharing, resource generation and mobilisation, and coherent resilience-enhancing collective action.
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Adaptive and transformative capacity: described as the stability of social relations, high levels of social capital and strong, varied options to meet the community’s needs. This includes the ability to adjust to change and the ability to take advantage of opportunities.
The necessity for building transformative capacity beyond adaptation
Adaptive and transformational approaches both address the process of change, but they can differ in scale, depth and ambition. For example, the IPPC defines climate adaptation as the process of adjustment to actual or expected climate and its effects, to moderate harm or exploit beneficial opportunities (IPCC, 2023). Transformative action, however, engenders entirely new practices that involve deep structural reform, a complete change in mindset, major shifts in perceptions or values and changing institutional or behavioural norms (Berrang-Ford et al., 2011). Resilience theorists argue that enhancing a system’s adaptive capacity offers a credible pathway for maintaining system functioning (Folke et al., 2002). Current political arenas are also more likely to favour adaptation relative to transformative change because it works to maintain the established order and address near-term problems. Citizens and their elected officials are more comfortable with adaptation because it appears less radical than transformation, which involves uncertain outcomes and the associated costs of system restructuring. While both are necessary, only a transformational mindset will address the deeply entrenched political, social and economic normative behaviours that underpin and entrench the current global polycrisis.
The real-world challenge is how do we develop practical tools and methodologies that enable and support innovative behaviours that lead to transformational social change while meeting the need for robust financial governance. Increasing pressure on public services combined with the reduction in funding available to deliver those services leads to the imperative for funding providers to evidence the impact, and to commissioning-encouraged competition among service providers. However, in the real world change often happens through a collective of organisations working in partnership with each other and with the people who use their services.
Measuring social impact
Understanding the impact of funded programmes of work remains critical to ensuring value for money, improving governance, learning lessons from each project and incorporating the learnings to enable progress going forward. For outcomes assessment to support both early-stage assessment and post-project outcomes evaluation, the metric design and measurement reliability need to meet these criteria:
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relevance to understanding real-world conditions
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transparency for recipients to understand the metrics and complete assessments
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results that have the capacity to support decision making.
Figure 2 sets out a simplified overview of the parameters of social, environmental, well-being, economic and carbon impact, and a summary of some of the most used tools for understanding these parameters.
Outcomes mapping tools and summary of useful metrics criteria
Summary of social, economic and environmental impact evaluation tools
While it is widely recognised that it is hard to measure social and environmental value, attempts to quantify the complexity of social change run the risk of oversimplifying important benefits that become reduced to a simplified indicator that can claim greater rigour in terms of data quality than is warranted (Arvidson et al., 2010). There are any number of established and emerging evaluation methodologies; this article will briefly compare the most commonly used tools.
Table 1 sets out a summary of the commonly used assessment methodologies for understanding how projects qualify and measure outcomes. While each of these has specific strengths and weaknesses, their capacity to confidently satisfy those three primary conditions set out earlier – that is, relevance to understanding real-world conditions, transparency for recipients to understand and complete, and that results have the capacity to support decision making – is less than ideal.
To propose a path towards resolving these practical challenges, we are including into this assessment tools comparison the Human Learning Systems (HLS) approach. HLS offers a relational and learning-focused approach to public service, with particular attention paid to the interconnectedness of individuals and their environments. This approach aims to create public services that are more responsive, adaptive and ultimately support human freedom and flourishing (Lowe et al., 2020). One of the key factors influencing the development of HLS is an ongoing conversation among engaged practitioners and a wider participative process. This involves a range of public service and non-profit leaders managing complexity in their work (Lowe et al., 2020). HLS focuses organisations on the development of preconditions that generate outcomes; however, the manifestation of improvement itself is often a non-linear process, requiring a complexity-informed approach to evaluation, including a realistic assessment of assigning cause and effect relationships (Lowe et al., 2020).
Summary of project outcomes assessment tools and applicability to social funding
| Assessment methodology | Objective | Strengths | Weaknesses | Relevance to understanding real-world conditions | Transparency for recipients to understand and complete | Results have the capacity to support decision making |
|---|---|---|---|---|---|---|
| Social return on investment (SROI) | Measures social, environmental and economic outcomes using monetary values | Useful tool for exploring ripple effects of interventions through a community | Prone to user bias and overestimating outcomes. Unsuitable for comparative analysis | Baseline studies are not specific to the application. Oversimplified numerical metric | Assigning deadweight, attribution and displacement is complex | Useful for exploring potential ripple effects, takes depth of understanding to use as a predictive tool |
| Local economic multiplier effect (LM3) | Maps local economic value impacts of funding | Simple metric to complete if using generic data | Cascading effect based on generic data unless locally researched | Can be a useful proxy for local predictive fiscal impacts | Intuitive with training, requires specialist to do bespoke assessment | Simple proxy model is useful for exploring procurement options |
| Cost benefit analysis (CBA) | Evaluates a project’s or decision’s financial benefits and costs | Aids in simplifying complex decisions by translating them into quantifiable terms | Precision of forecasts and assumptions, can result in faulty conclusions | Unsuitable for community projects, more suited to nature based or capex assessment | May be unsuitable for smaller projects as it requires capital and resources | Social benefit is difficult to monetise, presenting challenges for decision making |
| Whole-life carbon (life cycle) analysis | Measures the greenhouse gas impacts of a product or service across its entire life cycle | Comprehensive understanding of carbon through supply chain | Is highly data dependent, likely to underestimate costs due to truncation error | Provides transparency through the supply chain, if data is available | Requires specialist support. Not suitable for intervention-based projects | Can be useful for pre- and post-intervention assessment |
| Well-being mapping | Supports starting and sustaining new behaviours that benefit health and well-being | Useful for reflecting on social networks, valuing contacts and motivating change | Potential for oversimplification, difficult to measure subjective experiences | Can be prone to user bias and subjective interpretation of well-being | Usually needs support to deliver, but intuitive process | Helps to understand complexity and see the problem in a different way |
| HLS | Embraces the complexity of the real world and enables effective working in complexity | Focus on individual needs, continuous learning, a holistic approach to complex systems | Constant iterative learning cycles can bring challenges for change management | Is more likely to reflect real-world conditions if used correctly. Can lead to over complexing | Early adopters may struggle with application of concepts in the absence of support | Learning cycles naturally create adaptive decision making |
The conclusion from this assessment of practicality is that none of the tools outlined perform particularly well as a reliable indicator of real-world conditions, pre- or post-project.
There is a need, therefore, to think about how we move beyond the current preoccupation with attempting to apply surface-level numerical or monetary metrics, towards developing funding methodologies that reward flexibility and innovation, create trust-based relationships within the funding hierarchy landscape, and recognise and celebrate capacity building and co-delivery as key outcomes of success. The following sections explore an adaption of the HLS-based approach that also incorporates spatial, temporal and ethical dimensions.
An HLS approach
The HLS approach, pioneered by researchers at Newcastle University, set out to explore new approaches to funding, commissioning and leading in complexity. Published in 2017, the Exploring the New World report (Lowe et al., 2017) defined and developed the HLS approach to bring to life what effective practice in complexity looks like.
An important part of moving away from outcomes-based metrics for community funding involved reassuring a range of stakeholders that participants would behave responsibly and that any perceived risks associated with non-compliance would be mitigated (Lowe et al., 2020). These relationships are multidimensional, in that they involve both upstream and downstream partners with complex agendas and data needs.
As discussed earlier, a community’s resilience requires its members to have agency, as adaptation of the system is made possible by the increase of the range of possible adaptive behaviours within the community (Roundy et al., 2018). By integrating a participative model with meaningful community engagement in project co-design, we are mirroring the HLS creators’ reframing of public service as action research to support multi-level learning in public management that has its roots in understanding the creation of desirable outcomes (Hesselgreaves et al., 2024).
In the context of HLS, learning as an outcome means that the desired result is not just a specific achievement, but a continuous process of adaptation and improvement within a complex system (Lowe et al., 2020). Learning is a key driver of improvement in complex environments and is necessary in responding to the limitations brought about by uncertainty in a world of complexity (French and Lowe, 2018).
Learning in a complexity-informed system is inspired by approaches to action learning, where action is embedded into the learning process. Action learning is broadly defined as learning from with each other through concrete experience and critical reflection (Zuber-Skerritt, 2002) and can take many, evolving forms, for example appreciative inquiry, reflective practice, learning communities, learning partnerships and rapid learning circles (Lowe et al., 2020).
Wilson (2023) emphasises the importance of creating safe environments for talking authentically about uncertainties and mistakes with the purpose of collective improvement, which is consistent with similar methods of community co-creation. This is critically important when funding community adaptation where many projects are innovating and experimenting within a complex environment.
A systems-based approach to community development addresses the community as a complex network of interconnected parts, including individuals, organisations, policies and environmental factors. Systems thinking within the context of community development considers how changes in one area can impact on others across the whole system, rather than focusing on isolated problems; it emphasises collaboration among diverse stakeholders to achieve sustainable change.
A systems-based approach also recognises that interventions within a system will have multiple consequences – some intended, some not. Hence, a wider range of indicators and system outcomes must be assessed to monitor collateral benefits as well as unintended consequences (Lorenc and Oliver, 2014). This inevitably leads to a need for tools that are responsive to how the system is changing and facilitate the ability to assign cause and effect relationships.
HLS and project programme delivery
In response to the Scottish government’s call for Test of Change options for creating funding models to deliver CLLD, an HLS-based approach was explored and developed. Through HLS we attempted to define how we might understand and measure funded project outcomes in a way that supported innovation; built trust-based relationships between the funding body, delivery agencies and their audiences; and attempted to encourage and support a more systems-based approach to delivering social change.
While we recognised the strengths and achievements of previous funding programmes, we also recognised that the funding process needed to evolve and adapt to a new environment and the challenges of shifting towards a post-Covid, post-Brexit, low-carbon economy. Experience showed that the traditional evidence-based approach to CLLD struggled to deliver systemic change, primarily because of the interrelated complexity of social systems, combined with a funding system that by its nature creates competition and disincentivises innovation and risk-taking.
The project sought to address inherent inefficiencies seen in many funding programmes and brought about by creating a competitive, market-based approach to delivering third-sector services. This approach inherently stifles cooperation and knowledge exchange, creating isolated if not competing agents for social change. This builds mistrust within the change-maker ecosystem and inhibits flexibility, innovation and the capacity to adapt plans to changing user needs.
A core challenge of setting metrics-based outcomes is the gap between predicting how a programme will evolve and the actual experience of its delivery. This creates a misalignment between what has been committed to and what is happening on the ground. Programmes that have no flexibility to realign programme goals and outcomes inevitably lead to a gaming of the system where projects are reporting what the funder is expecting to hear, rather than the typical rough and tumble of social development.
Early-stage development of ideas is usually based on predictive understanding of future conditions and their response to some form of intervention, based on prior learning. Forecasting outcomes is often based on underlying assumptions using a steady-state model that sees the future as a linear continuation of the past and present (Salonen et al., 2023). However, many of the more challenging system intervention projects are not based on prior experience and are hence more experimental in nature. In Kolb and Kolb’s (1984) learning cycle based on the notion of ‘experience’, learning is the process whereby knowledge is created through the transformation of experience. For Kolb and Kolb, experience is not knowledge, but only a foundation for the creation of knowledge (Elkjaer, 2018).
Hence, for the early phase of project design, experience is best gained by undertaking a series of iterative steps, where ideas are formed, structured and tested within the stakeholder network. In practice, this is often done hastily in fear of missing deadlines, and the lack of time and resources result in these important early learning cycles not being fully optimised. However, it is critical that ideas are tested and refined before drafting long and complex business plans or funding applications. This early-stage delivery planning is critical to developing a coherent strategy, engaging with stakeholders and reflecting on the potential for the intervention to achieve its outcomes. While this early new venture often begins with the intentions, entrepreneurial alertness and creativity of a single individual, the founding of a business is a relational activity that requires a multilayered network – or ecosystem – of individual and organisational actors, including mentors, funders, suppliers, support service professionals, incubators and customers (Jack and Anderson, 2022).
In this case, learning from a business entrepreneurial framework like the business model canvas (BMC) can be useful. The BMC, developed by Osterwalder (2013), is a visual tool with elements describing an organisation’s value proposition, infrastructure, customers and finances. Business model innovation involves rethinking how an organisation creates and captures social value. The example below (Figure 3) is a customised model for social/community-based entrepreneurship using an HLS framework, exploring the whole system on a single page, facilitating an overview of the project strategy and delivery.
Example of a blank systems model canvas
The adapted HLS BMC was an attempt to encourage project planners to take a whole-system approach and accelerate early-stage learning cycles.
In an HLS approach, learning cycles are embedded into the programme delivery model and create opportunities for both experiential and reflective learning, leading to adaptation. A more critical, systems-based approach opens up a wider metacognitive perspective on the project, which encourages learners to examine and, if necessary, change beliefs, values and inherent assumptions, such as seeing the forest as a whole and having an idea of alternative woods (Salonen et al., 2023). This, therefore, accounts for the challenges that arise when adaptive learning leads to changes in programme delivery plans, requiring staff and management teams to adopt flexible work practices.
Developing a universal, pre-specified outcomes methodology that is fit for purpose within any given community context will be at best problematic, if not impossible. Instead of attempting to apply measures that are not just impractical but fundamentally inapplicable to complex adaptive systems, we must explore alternative methodologies that have the capacity to support and inform specific, deliverable and practical decision making. As a near-term alternative, and a long-term complement, to tracking community resilience changes resulting from the funded projects, we propose the following approach.
HLS lays out a process that recognises that the agencies closest to the community delivering the work are likely to have the best perspective on how to discover and understand what is happening within their community. By refocusing on improving the complex adaptive system navigation capabilities within the organisations, we will inherently move towards improving outcomes as the organisations develop and improve the processes and mechanisms by which they operate, without ossifying them – as fixed procedures are incongruous with operating in complex adaptive system environments.
This also recognises that organisations start from many different stages in their development: some are embryonic, while others may have quite well-developed learning, engagement and system-wide approaches integrated into their daily activities. The question is, what kind of organisational culture is required for complex adaptive system environments, and how can funders design programmes to structurally boost those behaviours in change-making agencies?
The template in Figure 4 below sets out a theoretical timeline of the progress of a typical funding programme that many project managers are likely to be familiar with. The stages of developing an idea, undertaking consultation, drafting and submitting a proposal, delivering the programme and a post-project review and report would be recognised as a typical pathway from idea to completion. To manage funder governance and risk, projects are required to predict outcomes sometimes many months in advance. This could be described as requiring the collation and analysis of data based on different types of knowledge.
The figure outlines a typical implementation cycle and introduces the linearity of time and space within adaptive learning cycles. While it is convenient to map learning cycles as circular, they are generally, in practice, more progressive. Early project design is often based on prior experience applied to a current condition, producing a desired future outcome. Early iterative design stages are based on hypothesised risk/management learning cycles. Delivery stages however are often attention-consuming activities punctuated by progress reports benchmarked against pre-agreed milestones. Typically, learning cycles are experiential, based on action/impact observations in real time. Inevitably potential reporting challenges arise when predicted vs actual outcomes are not aligned.
In Stage 3, post-project analysis and learning is reflective, and is undervalued in many community-led funded projects. Outcomes in the community should be the core focus of an evaluation despite the inherent challenges, as community members are unlikely to be a coherent group easily surveyed and evaluated. They will generally have varied and subjective perspectives on reality, various needs/interests and contrasting views of what effective change means. In addition, community improvement outcomes can take time and are the result of a combination of activities. It can be difficult to attribute outcomes to particular initiatives and to separate out what caused these changes to occur (Haliday and Marwick, 2009). However, to reach and recognise the end results of a community development project, a clear understanding and application of community work is necessary both in the interface with community members and to communicate up the line to those funding the projects.
The work undertaken on behalf of the 2021 CLLD programme in the Scottish Borders included an attempt to develop a set of indicators that would seek to benchmark the maturity of an organisation’s cultural management systems. Table 2 sets out a set of key indicators for each category and examples of some descriptive text. The template follows an HLS-based format and sets out nine indicators that describe best practice organisational culture and behaviour. The template was developed using a simple interactive Excel tool that asked a series of questions based on a 1–5 ranking, where 1 would indicate either no or not well-developed mechanisms and 5 would indicate operating at an optimum level. Articulating what constituted best practice was very much a work in progress, and further work to align indicators within differing contexts would benefit specific audiences. However, the interactive flexibility of the Excel software would make the preloading by sector (the subsequent questions asked) relatively simple.
Adaptive HLS project delivery framework through the stages of project design, development, delivery and review culture framework benchmarking tool
HLS culture benchmark indicators
| Category | Key indicator | Description |
|---|---|---|
| Human (co-design) |
Co-design | How does your organisation engage with the groups that you work with? How do you work to build trust within your organisation? |
| Building trust | ||
| Post-project feedback | ||
| Learning (as an outcome) |
Shared values | How do you align activities and collaborations with core objectives? Is learning a key part of the way you deliver projects? How do you measure the learning of your users and clients? |
| Learning as an outcome | ||
| Shared learning | ||
| Systems (co-delivery) |
Partnerships | How well do you work in partnership with other organisations to deliver better outcomes for your clients/communities? |
| Carbon awareness | ||
| Well-being |
An example (Figure 5) of the 1–5 framework (for the human/co-design) is outlined below. The organisation is encouraged to ‘find itself’ within the framework in an honest and transparent way as an exercise in creating a dialogue between funder and delivery agency.
The tool has several features that potentially add value to delivery organisations and funding agencies. It is applicable across a multitude of sectors and consolidates and promotes a level of self-awareness. It can be used at the start of the programme as a benchmarking tool and again at the end, with the emphasis being on the development and self-improvement of the organisation as a key outcome of the programme. The 1–5 scale creates a set of actions for improvement within each of the sectors, hence creating a temporal indicator of change over time. Learning as an outcome is a relatively new concept, and one that might be a challenge for quality within the context of a small community organisation. The iterative HLS learning cycle was a useful template for exploring how learning works at different levels, but it is still a challenge to adapt it into a simple-to-understand set of metrics for managing community-based funding.
One way of thinking about how good outcomes are delivered is to focus on the processes that create outcomes instead of only the outcomes themselves. For example, if an organisation has improved its organisational capacity and can be trusted to deliver the best outcomes for the users of its service, then we could potentially evidence this as a learning-based outcome. To explore this, we developed a simple benchmarking tool (Figure 5) that profiled the organisation’s capacity to capture learning and create internal improvement cycles. This tool set out a series of statements that would reflect the maturity of an organisation’s delivery capacity and operational mechanisms. While this is not evidence of outcomes, it would, when used honestly, reflect and support a culture of integrity and set out a pathway for continuous improvement not unlike many management frameworks.
A simple radar graphic (Figure 6) summarised the nine indicators. The important note was that the tool was not designed to be a pass/fail criterion but rather a benchmark from which to progress towards optimising programme delivery and governance systems. There was also no minimum bar from which to compare or exclude developed/undeveloped results. It was important for this exercise to be undertaken from the perspective of trust that was to be a catalyst for improvement, rather than a pass/fail or comparative exercise.
One of the key challenges with any model for understanding change in an adaptive complex system is creating a learning environment that articulates and embraces spatial, temporal and ethical dimensions (Salonen et al., 2023). There are several key aspects of the planetary social pedagogical (PSP) approach that, when seen as a complementary methodology, bring a number of additional dimensions to an HLS framework.
Example of the interactive culture benchmarking tool
Example of pre- and post-project culture benchmarking exercise
HLS and planetary social pedagogy (HLS +)
In this section, we set out a framework that explores the relationship between the HLS and PSP approaches as a model for building a new approach to developing a universal funding philosophy.
Salonen et al. (2023) recognise the importance of location to the human evolutionary experience; however, society is becoming increasingly digitised and globally connected. For example, individuals are increasingly distanced from the once visceral experience of obtaining food towards an increasingly complex and vulnerable global supply chain. As the impacts of climate change become more critical, local solutions will increasingly depend on community cohesion and local adaptation.
The conceptualisation of a temporal dimension to social development is critical to understanding cause and effect relationships and the historical pathways to present conditions. Shifting the pathways, however, does not take place in a vacuum as transformation draws on resilience from a diverse range of scales and sources. It requires the re-combination of new and existing skills and experience, learning and change to create windows of opportunity that are aligned with biosphere resilience (Folke et al., 2016).
A planetary responsibility approach recognises the role of intergenerational resilience and futures literacy as important tools in articulating and imagining a desired future. Coupled with the understanding that socio-ecological systems are complex adaptive systems that interact dynamically at both slow and fast time scales, this suggests that learning cycles also need to be adaptive over time to account for and reflect these dynamic cause and effect mechanisms (Gunderson and Holling, 2002).
Without careful consideration of whose and which views are included within adaptation planning and evaluation processes, there is a risk of privileging certain ways of seeing the world that can further create and reinforce structural inequality, vulnerability and marginalisation within adaptation processes (Olazabal et al., 2024). Salonen et al. (2023) saw this ethical dimension as being an essential part of the worldview. This included two primary principles of ensuring (a) the ecological conditions for diverse life forms on the planet and (b) that human rights are ‘natural, universal and equal’. PSP argues that planetary well-being combines both anthropogenic and ecocentric needs. As such they propose that decision making needs to be based on an ethical sensitivity that takes account of individual, collective and planetary responsibilities.
This simplified descriptive matrix (Table 3) might be a starting point for discussion for how we might articulate an HLS (+) approach that could embody a broader set of indicators for intergenerational resilience.
HLS (+) descriptive matrix
| Spatial | Temporal | Ethical | |
|---|---|---|---|
| Human | Outcome-dependent relationships recognised and inclusive within a shared context | Understanding the needs of both present and future communities | Values based on rights that are natural, universal and equal (human and ecosystems) |
| Learning | Understanding the learning needs and offerings of contributing stakeholders | Using dynamic cyclic learning theory (cognitive, metacognitive and epistemic) | Valuing ‘stories of change’ that expand our moral circle to include all humans and the non-human world |
| Systems | Recognising that community resilience systems are highly integrated spatially | Understanding of why current systems have evolved, how they currently operate and how they need to change | Taking a planetary responsibility approach to well-being |
Current (as well as many of the emerging) pedagogies are carrying over the taken-for-granted root metaphors that shape our analysis and guide our approach to life, such as perpetuating a culture that is overshooting the sustaining capacity of the natural systems and marginalising non-commoditised relationships and skills (Bowers, 2002). Therefore, any approach to increasing community resilience that does not include transforming the social conditioning is likely to have partial and fleeting effects.
Learning opportunities
The design and delivery of the Scottish Borders CLLD HLS Test of Change exercise was not without its challenges and subsequent learning opportunities. This could be primarily explored through three perspectives of the agents involved: (1) the HLS design team; (2) the local authority and local action group members; and (3) the community-based organisations who participated in the funding round.
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For early adopters, the HLS philosophy was sometimes difficult to conceptualise and was initially seen as adding complication to an already burdensome exercise. However, after a short training session there was universal encouragement for a more simplified, holistic approach and the terms and concepts rapidly became normalised.
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It was clear that the previous competitive-based funding mindset would take time to re-evolve. Furthermore, there needed to be mechanisms within the funding programme that facilitated multi-partnership working. While sometimes challenging, there is a need to recognise that multi-sector (systems-based) programmes require significantly longer lead-in times, and a suggestion that early-stage funding could be available to provide resources to develop more complex systems-based solutions. This early work might require a more structured and interventionalist approach by the funding agency to shift the culture from a competitive-based to a more collaborative partnership-based approach.
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The relationship between the voluntary community action group (LAG) and the administrative body overseeing the delivery of the fund was critical in both designing the funding process and aligning a traditionally risk-averse mindset to a trust-based mechanism. Key to success was having an influential intermediary within the organisation that understood the internal culture as well as embracing the need for systemic change. However, without embedding the recognition of the need for change laterally and vertically through the organisation, the initiative is left vulnerable and its longer-term viability is compromised.
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Moving away from outcomes-based metrics and subsequently qualifying learning-based and systems-based outcomes was a challenge. For the statutory authority with legal responsibility for good governance in delivering public sector funding, abandoning traditional outcomes-based metrics for learning-based metrics was a significant early barrier. Subsequently, this proved a ‘bridge too far’ for the organisation and was not universally embraced. This led to a hybrid approach of both learning- and outcomes-based metrics that arguably overcomplicated the process.
Future iterations of a more progressive approach to developing methodologies and language in CLLD funding will evolve over time only if there is a willingness to adopt a learning-focused culture across the delivery spectrum, from statutory agents to community groups. A learning-based culture can only work in an environment where trust-based relationships are prioritised over oversight and risk-minimising governance, and one where experimentation and adaptive learning is celebrated. But perhaps as important is to challenge the belief systems that are to date failing to address the underlying dysfunctionality of social development funding that often perpetuates the underlying causes of social inequality.
Discussion and conclusion
Traditional approaches have been deeply concentrated on data that overlooks critical components of the human experience, such as beliefs, social norms and value judgements. This is why we must evolve how we approach strategy and design of funding programmes. Our current assessments are missing critical insights that may alter key conclusions, adversely influencing our strategy and thus our outcomes. We can mistakenly design programmes that exacerbate the challenges due to the contradictory movements of large-scale forces like governments and community-level self-organising (Adger, 2003).
The HLS (+) approach is an attempt to blend two emerging philosophies that embody adaptive learning and whole-systems thinking into a coherent funding model that supports and encourages innovative community-led solutions. HLS has a pedagogical relevance as it has social applicability to those who need an educational or adaptive learning approach to solving complex social issues. PSP blends anthropogenic and ecocentric perspectives that value both the human and non-human world (Salonen and Ahlberg, 2012). However, the global increase in individualism, the disconnecting of individuals from society and global consumerism are driving some of the biggest cultural changes in the past few decades (Hofstede, 2011). Funding programmes of work that have the spatial, temporal and ethical capital to build planetary resilience must be prioritised.
Funding programme designs that support and encourage a deeper questioning of wider resilience ensures an increase in intentionality, agency, power and experimentation. This in turn grows community resilience and supports learning, which is key to more effective investments into climate resilience. We would like to reiterate that this is not a miracle approach that bypasses the political, economic and social systems that contribute to the tangible reality of our global, national and local communities. The multilayer, spatial, temporal and ethical analysis facilitated with an HLS (+) approach allows funders and grantees to consider system-scale elements such as local economic drivers and political conditions, along with the community-led insights of the local community in determining the strategy, metrics and practices for resilience building initiatives.
Addressing community resilience in polycrisis requires a fundamental shift in funding methodologies, moving beyond rigid, preselected metric-driven approaches to more adaptive, learning-based models. The challenges often associated with operating in a constantly adapting system highlight the limitations of overly rigid, inflexible and formal planning (Roundy et al., 2018). The HLS framework provides a viable alternative by placing trust, agency and systemic understanding at the centre of funding decisions. This approach acknowledges the complex, non-linear nature of community change, emphasising relationships, shared learning and long-term impact rather than short-term, predefined outcomes.
Normative dimensions need to involve value judgements about priorities: whose resilience, and which social-ecological system (Berkes and Ross, 2016). This gets to the heart of the profoundly philosophical question that we believe sets the foundation of every social change movement: how do we understand value? Funding is an explicit practice of assigning value to some of the most critical aspects of our lives. The decisions that shape our everyday experience are informed by the fundamental value judgements that we make about the world.
Ultimately, embracing and boosting complex adaptive system-informed behaviours and trust-based funding models has the potential to support the empowerment of communities to self-organise, adapt and thrive amid ongoing global challenges.
Declarations and conflicts of interest
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Conflicts of interest statement
The authors declare no conflicts of interest with this work. All efforts to sufficiently anonymise the authors during peer review of this article have been made. The authors declare no further conflicts with this article.
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