Richard Heeks Uncategorised

The Missing Digital Dimension in Analysis of Climate Adaptation in Mountain Regions

Why is digital tech missing from analyses of climate adaptation in mountain regions?

Digital innovation is and will be central to climate adaptation.  At a macro-level, as shown in figure 1, ICTs can support all aspects of the adaptation strategy lifecycle[i].  At a micro-level, digital tools are supporting households and communities with applications related to weather and pest early warning, water conservation, climate insurance, etc[ii].  A whole series of innovations – AI, robotics, blockchain and more – are further expanding the contribution that digital can make to climate adaptation[iii].

Figure 1: ICTs and Climate Adaptation Strategy

Mountain regions are often cited as being on the front line of climate change[iv] and they are, hence, the locus for widespread climate adaptation initiatives[v].  Those initiatives are analytically reviewed from time to time.  Leading examples of such reviews at both strategic and tactical levels include:

What do all of these multi-initiative analyses have in common?  They make no mention of the role of digital tech within the adaptation strategies or solutions[vi].  There are occasional mentions of components likely underpinned by digital such as scenario modelling or mapping or the role of “scientific information” and data, and there are a few unspecific references to “technology”.  But there is no explicit consideration of digital: snow-making technology is discussed; digital technology is not.

Is this because mountain climate adaptation does not involve digital technology?  No.

The Adaptation at Altitude Solutions Portal provides details of 88 mountain climate adaptation initiatives; available as an online dataset.  Analysis of the dataset plus reference to original project documentation shows that in all but 10 cases – so for 89% of projects – digital technologies were identifiably an important part of the adaptation response[vii].  The most common applications could be related to different stages of response.  Recurrent early-stage uses were for modelling and mapping, e.g. of priority locations for intervention, often involving earth observation data and geographic information systems.  Recurrent late-stage uses were for flood, fire, weather or similar early warning systems; usually mobile phone-based.  The other common application was management information systems, typically to support the operation of community, local government, NGO or central government adaptation centres.  Less common applications included use of drones, and e-commerce and e-learning systems.

Is this analytical absence a problem?  Very likely it is:

  • Failing to identify the contribution of digital technologies to climate adaptation means future initiatives – to the extent they are influenced by past analyses – are less likely themselves to successfully incorporate, and hence to benefit from, digital.
  • Failing to analyse the role of digital in climate adaptation means a failure to learn digital-specific lessons which thus makes future initiatives more likely to fail.  Especially so given that there are digital-specific design, development, implementation and evaluation techniques that cannot simply be transferred from other technologies[viii].
  • Failing to highlight the role of digital in climate adaptation means an absence of consideration of emerging digital innovations such as AI and blockchain that, as noted above, have been demonstrated to have significant potential to accelerate adaptation[ix].

This does not answer the original question of why digital is missing.  Most likely it’s a failure of multi-disciplinarity within research on sustainable mountain development, with an absence of socio-technical digital development expertise – the kind that would immediately recognise the pervasive presence and importance of digital systems.  That’s a reminder of the way in which development studies – with the emergence and growth of its ICT4D sub-discipline including dedicated degree courses and journals – has progressed further than mountain studies.

In terms of implications of all this, many of the sources cited above include an identification of research gaps.  What they have missed, however, is the gap – and future research agenda – around digital and climate adaptation in mountain regions.  That’s a priority topic that should be taken forward.  We welcome thoughts on modalities and collaborations for this.

[i] Adapted from Ospina, A.V. & Heeks, R. (2011) ICTs and Climate Change Adaptation: Enabling Innovative Strategies, Centre for Development Informatics, University of Manchester, UK

[ii] Dittmer, K. M., Wollenberg, E. K., Burns, S., & Shelton, S. W. (2022). Digital Tools for Climate Change Adaptation and Mitigation, TRANSITIONS Policy Brief, IFAD, Rome

[iii] Parra-López, C., Abdallah, S. B., Garcia-Garcia, G., Hassoun, A., Sánchez-Zamora, P., Trollman, H., … & Carmona-Torres, C. (2024). Integrating digital technologies in agriculture for climate change adaptation and mitigation: state of the art and future perspectives. Computers and Electronics in Agriculture, 226, 109412

[iv] McDowell, G., Stephenson, E., & Ford, J. (2014). Adaptation to climate change in glaciated mountain regions. Climatic Change, 126, 77-91; Pepin, N. C., Arnone, E., Gobiet, A., Haslinger, K., Kotlarski, S., Notarnicola, C., … & Adler, C. (2022). Climate changes and their elevational patterns in the mountains of the world. Reviews of Geophysics, 60(1), e2020RG000730.

[v] Muccione, V., Aguilera Rodriguez, J., Scolobig, A., Witton, R., Zwahlen, J., Mackey, A., … & Allen, S. K. (2024). Trends in climate adaptation solutions for mountain regions. Mitigation and Adaptation Strategies for Global Change, 29(7), 74.

[vi] Based on a read-through of each of these sources plus cross-check search for ‘digital’, ‘information’, ‘data’, ‘technol’ and other terms potentially related to digital technology.

[vii] Even where not identifiable, digital technologies will have been used for project management, monitoring and evaluation, and dissemination purposes.

[viii] Pearlson, K. E., Saunders, C. S., & Galletta, D. F. (2024). Managing and Using Information Systems: A Strategic Approach. John Wiley & Sons; Heeks, R.B. (2018) Information and Communication Technologies for Development. Routledge.

[ix] Moncada, N.R. (2023) Blockchain for Climate Innovation, CIFAR Alliance; Leal Filho, W., Wall, T., Mucova, S. A. R., Nagy, G. J., Balogun, A. L., Luetz, J. M., … & Gandhi, O. (2022). Deploying artificial intelligence for climate change adaptation. Technological Forecasting and Social Change, 180, 121662.

Richard Heeks Resilience

Measuring Resilience in Marginalised Urban Communities

How can we best measure the resilience of marginalised urban communities?

These communities have to build their resilience in face of growing environmental shocks and stressors.  A first step will be measuring existing resilience strengths and weaknesses, but past approaches can take a narrow view of resilience or lack quantification.

A new paper reports pilot application of the RABIT (Resilience Benchmarking Assessment and Impact Toolkit) framework, which conceives resilience as nine attributes each with measurable markers.  The framework was used to measure resilience of Masiphumelele: a South African township of formal and informal housing regularly disrupted by flood, fire, storms and violence.

The measurement found resilience strengths in self-organisation and scale of external connections; but weaknesses in robustness and equality.  While the community is relatively good at the coping aspects of resilience such as response and recovery to shocks, it is poor at withstanding shocks and at transforming itself.

This measurement of resilience can then be used as the basis to plan future resilience interventions: feeding results back to key community stakeholders; prioritising resilience weaknesses and resilience-building actions; and then putting those actions into practice.

Richard Heeks Resilience

Rural Resilience Impact of ICTs-in-Agriculture

What impact do ICT-in-agriculture projects have on rural resilience?

To cope with short-term shocks (e.g. conflict, economic crisis) and long-term trends (e.g. climate change), rural areas in developing countries must become more resilient.  Yet we currently know very little about the impact that information and communication technologies (ICTs) can have on resilience-building.

To address this knowledge gap, we undertook a systematic literature review of 45 ICT4Ag cases from Africa and Asia.  We sought to understand both what the resilience impact of ICTs is, and why.

Measuring resilience using the RABIT (Resilience Assessment Benchmarking and Impact Toolkit) framework, current reported evidence suggests ICTs are strengthening rural resilience far more than weakening it.  But the impact is highly uneven.  Household resilience is built far more than community resilience, and there is a strong differential impact across different resilience attributes: equality in particular is reported as being undermined almost as much as enhanced.

In order to explain these outcomes, we developed a new conceptual model (as shown below) of the relationship between ICTs and resilience.  It highlights the importance of individual user motivations, complementary resources required to make ICT4Ag systems support resilience, and the role of wider systemic factors such as institutions and structural relations.

We make a series of recommendations for resilience policy and practice:

  • More equal focus on both household- and community-level resilience.
  • More attention to the resilience-weakening potential of ICTs.
  • Ensuring perceived utility of digital applications among rural users.
  • Encouraging use of more complex ICT4Ag systems.
  • Looking beyond the technology to make parallel, complementary changes in resource provision and development of rural institutions and social structures.

We also draw conclusions about the conceptualisation of resilience: the need for better incorporation of agency and power, and greater clarity on resilience system boundaries and indicators. Overall, for those seeking to strengthen rural resilience through use of ICTs, the paper – “Impact of ICTs-in-Agriculture on Rural Resilience in Developing Countries” – offers new frameworks, new evidence, new practical guidance and a research agenda.

Richard Heeks Resilience

Improving the Measurement of Resilience: Lessons from a RABIT Field Study

How can we measure resilience?

This is a perennial challenge for those working on resilience, and one we have faced in the field in implementing RABIT; the Resilience Assessment Benchmarking and Impact Toolkit.

Precursor challenges are first to define and conceptualise resilience.  With minor variations, definitions are often very similar to that used for RABIT: “the ability of a system to withstand, recover from and adapt to short-term shocks and longer-term change”.  But RABIT’s unique conceptualisation is to understand resilience as a set of foundational (robustness, self-organisation, learning) and enabling (redundancy, rapidity, scale, diversity, flexibility, equality) system attributes.  (For further details, see the journal paper, “Conceptualising the Link Between Information Systems and Resilience: A Developing Country Field Study”.)

To measure resilience, we identified three markers for each of the attributes, derived from prior literature and as shown in Table 1.

 Table 1. Resilience Attributes and Illustrative Markers

We then took this model into the field, applying it in an urban community in Costa Rica’s capital, San Jose.  We used the model to benchmark both the general resilience of the community and also its “e-resilience”; that is, the impact of digital technologies on wider resilience.

Details of findings can, again, be found in the associated journal paper, but the focus here will be what we learned about the markers we had used to measure resilience.  We found a number of problems in practice:

  • There were overlaps: for example, multi-level networks and cross-level interactions under scale, and multi-level governance under robustness might have potential differences but they appeared in practice to be very similar.
  • There were gaps: for example, the markers for rapidity were narrowly conceived around resources and as a result, did not adequately reflect the need for a fast-acting detection-assessment-response sub-system.
  • There were some misallocations: for example, trust belonged with social networks rather than with leadership; and interdependency of system functions related to robustness rather than redundancy.
  • There were over-broad combinations: where rather different characteristics were combined into a single marker; often leading to only one of them being operationalised. For example, “resource access and (intra-/inter-level) partnerships” was only operationalised as “intra-level partnerships”.

Putting all these findings from the field study together, a revised set of markers was developed (see Table 2).  To operationalise them, it will be helpful to develop deductively a set of descriptors and indicators associated with each marker and inductively a set of respondent keywords/phrases associated with each marker.

We encourage others with interests in resilience to make use of this improved basis for measurement, and will be happy to discuss this process.

Table 2. Revised Resilience Markers

 

Richard Heeks Resilience

RABIT: A New Toolkit for Measuring Resilience

As the 21st century proceeds, countries – particularly developing countries – will face a growing series of short-term shocks (economic crises, climate events, violent attacks, health epidemics, etc) and long-term trends (climate change, migration, economic restructuring, new technologies, etc). In abstract terms, we know the solution: countries must become more resilient.

That is because resilience is defined as the ability of vulnerable systems – countries, regions, communities, value chains, organisations – to withstand, recover from, adapt to, and potentially transform amid change and uncertainty. Resilience will therefore play a crucial role in the achievement of development outcomes. It provides a holistic and long-term approach that is rising up the development agenda.

That is the theory. The challenge arises in practice: there are few credible guides that activists and researchers can follow which explain what resilience is, how to apply resilience metrics, and how to use those metrics to shape action. The University of Manchester has therefore developed RABIT: the Resilience Assessment Benchmarking and Impact Toolkit.

To understand resilience, RABIT identifies nine attributes – or sub-properties – of resilience. Three are primary foundations of resilience: robustness, self-organisation, learning. Six are secondary enablers of resilience: redundancy, rapidity, scale, diversity, flexibility, equality. The stronger these are in a community, the more resilient it will be[1].

Each attribute has a series of key markers: indicators that we can use to assess the strength or weakness of each attribute. These can be measured in two main ways:

  • Resilience benchmarking: at the pre-hoc stage of project design, resilience can be benchmarked to establish key areas for resilience-building action during an intervention.
  • Resilience impact assessment: RABIT can be used to assess the impact on resilience of interventions during or after their implementation, to draw lessons learned, and to inform future programming/strategising.

Data can be gathered by document review, focus group, interview, or survey. It is then subject to enumeration that enables a variety of different visualisations, as illustrated in Figure 1. These identify current resilience strengths to build on, and current resilience lacunae that need to be addressed.

rabit-visualisation-examples

Based on the visualisations illustrated in Figure 1 plus further analysis, RABIT then provides the basis for prioritising future interventions which will build resilience. A sample is shown in Table 1, with interventions identified; typically following a discussion of the visualisations with key stakeholders. An indication is provided of which stakeholders – in this case, community-level (C), municipality-level (M) and national-level (N) – will be involved.

RABIT Intervention Priority Table Example.png

Table 1. Sample priority actions to improve resilience

For full details of the Implementation Handbook showing how to use the RABIT toolkit plus case studies of RABIT’s application, see: https://www.niccd.org/resilience.

We are happy to answer questions about application of the framework, and to provide support to those seeking to implement RABIT: niccd.project@gmail.com.

[1] Our illustration will be at the level of individual communities but RABIT is applicable to all and any of the systems described from households to nations.

Richard Heeks Resilience

Urban Resilience: Testing a New Framework on Community Informatics

There are many approaches to understanding urban resilience and an ever-growing literature seeing resilience as catalyst or metaphor, or identifying components or categories or facilitators. But there is surprisingly little work that defines and conceptualises resilience in a systematic way.

Based on a synthesis of past work, we built a new and comprehensive model of resilience: defined as “the ability to withstand and recover from short-term shocks, and to adapt to long-term trends“ and understood as neither a structure nor a function of systems, but as a property of systems.

Our model of resilience sees it consist of three foundational attributes or sub-properties: self-organisation that allows a re-arrangement of functions; robustness to withstand external stressors; and capacity for learning via feedback. Facilitating these are a set of enabling attributes: redundancy, rapidity, scale, diversity, flexibility, and equality.

Resilience Attributes Block Model

An initial application of the model analysed ways in which community informatics – the use of digital technology within urban districts – could strengthen and weaken community resilience.  Analysing attribute by attribute provided a systematic means to assess current evidence: geographic information systems that help planning of physical defences; use of social media to build local organising networks; application of online groups to support Learning and Action Alliances; etc on the plus side. But also creating external dependencies that can undermine local autonomy, and exacerbating inequalities within urban communities.

This current work provides only a general proof-of-concept, showing that this new urban resilience model is viable and applicable to urban development issues. Further work is being undertaken to roll it out in practice as part of RABIT (the Resilience Assessment Benchmarking and Impact Tookit), but we hope the model already offers an integrated and standardised approach to urban resilience.

For more details, the paper “Analysing Urban Community Informatics from a Resilience Perspective” published in the Journal of Community Informatics is available via open access at: http://www.ci-journal.net/index.php/ciej/article/view/1108/1135

Richard Heeks Uncategorized

Overview Model of ICTs, Climate Change and Development

The model shown below indicates the various domains of relation between information and communication technologies (ICTs), climate change and development. These are:
– Mitigation: how ICTs can reduce carbon emissions (but also how they contribute)
– Monitoring: how ICTs can help measure and analyse climate change and its impacts
– Strategy: how ICTs can enable strategic actions on climate change
– Adaptation: how ICTs can help developing countries adapt to climate change in the short- and longer-term

This is still a model under development, so comments and suggestions for improvement are welcome.