How do we measure social performance and why is it important when developing Sustainable Plus Energy Neighbourhoods (SPENs)?
Amongst the many advantages that SPEN can provide, the most essential ones are economies of scale and cost-effectiveness for building owners, district regeneration, unlocking investment opportunities and many services and techniques that may not be available at building scale, such as wastewater recycling, rainwater harvesting or district heating and cooling. But most importantly, SPENs have the potential to turn deprived neighbourhoods into liveable and valued ones through better collaboration, community involvement, job creation, boosting of the local economy, reduced energy poverty, improved public spaces and better air quality.
Having said that, the social dimension of sustainability can often be perceived as a rather volatile, intangible and even contested concept. In this troubled road, having bespoke indicators to measure social performance in hand can really make the journey easier.
Addressing neighbourhood social performance in the Horizon 2020 project, syn.ikia has brought to light some of shortcomings of indicator-based monitoring of SPENs, providing a great learning opportunity on both monitoring and neighbourhoods in general.
These are the six lessons learned from syn.ikia’s process of developing social sustainability indicators to evaluate the performance SPEN.
Lesson 1: the socio-technical dimension
It is widely understood that positive-energy neighbourhoods can only be achieved through integrated, socio-technical innovation given that the human dimension is deeply entangled with the technical. For example, energy efficiency could drive down the cost of living to increase housing affordability. Residents’ behaviour influences energy use. Environmental quality influences health. And environmental degradation disproportionately hits the poorest harder. In syn.ikia, we mapped these possible synergies, trade-offs and conditional relationships between the technological aspects of the energy transition and various social outcomes to help gauge the multiple impacts of SPEN performance. We delivered this mapping in the form of impact networks as part of our multidimensionality framework.
Lesson 2: peculiarities of the neighbourhood scale
Depending on the spatial scale, different aspects of social performance gain importance. For example, demographic variables are more interesting at national level, employment and social status at city level, social interactions at hyperlocal levels. It may seem that SPEN should just focus on the latter, but it is not so straightforward. Indicators on democratic legitimacy, justice, participation, focusing on the process of decision-making are also important for SPEN and these indicators usually appear on the city scale. Furthermore, indicators on human outcomes, like health, are scale independent. In syn.ikia, we took a step back from the neighbourhood level and adopted an multiscale approach to consider a wider range of metrics.
Lesson 3: managing the contested nature of social sustainability
One of the challenges when framing urban sustainability indicators is the great variety of metrics available. This becomes even more obvious when looking at the social sustainability indicators which often means a great array of often inconsistent references, making the review unfeasible. This can be explained by the larger role political agendas, and case-specific practices play in the social sustainability discourse. In syn.ikia, a data saturation index was developed to signal when to stop reviewing. For each new reference parsed, the number of new, unique indicators was registered. Over time, this number decreased, reaching saturation at 10 references, pooling 338 indicators, preselected into 56 unique indicators.
Lesson 4: interpretation and diagnosis instead of benchmarking
Questions like, “what should be the optimal number of grocery stores in a 500-metre radius” are common in monitoring. It is comfortable to see standard values for each indicator to measure progress, but such answers may be harder to get than expected. In some cases, results are not comparable, metrics may not be usable for the full spectrum of evaluation, or maybe there is an optimal value lying between too much and too little. Take internal social cohesion, which could signal both social isolation and a gated community. Other indicators are outright descriptive, such as the heterogeneity of the community. In syn.ikia, these considerations translated into steering away from explicit targets and benchmarks. Many indicators included normative and descriptive components, with interpretation guidelines for the auditor assessing social sustainability in a particular SPEN.
Lesson 5: managing subjective and objective assessments of performance.
Good scoring on indicators may not always translate into satisfaction with the indicated performance. Affordability of housing may be high, and people might still perceive difficulties in the access to housing. The design choice to handle this in syn.ikia has been to include perception metrics for the indicators that are sensitive to subjectivity. For instance, the social cohesion indicator includes a survey testing for the negative effects of social isolation, and a list of enablers that usually contribute to higher cohesion, such as inviting public spaces for non-planned activities.
Lesson 6: monitoring the distributional aspects of performance.
The evaluation results may reveal disparities among social groups. An important pillar of social sustainability relates to justice, providing equitable access to opportunities, local services, and elimination of social, procedural, and environmental discrimination. It would be difficult to create a single index, or indicator for equity, as all areas of performance could have latent disparities, and examining them all would bloat an equity indicator. Instead, in syn.ikia, a separate data collection and audit process is proposed for vulnerable groups (such as elderly or ethnic minorities), or groups that are more sensitive to a certain area of performance (such as families, who rely on local childcare facilities more). This separation allows the statistical analysis of whether being in a target group influences the outcome of the indicator or not, for example through an analysis of variance.
More than any dimension of sustainability, social performance brings to the forefront the complexity of neighbourhood scale monitoring. These lessons are not unique to measuring social performance, hopefully the design choices made during the syn.ikia project can inspire other frameworks to become better decision support systems. SPEN monitoring is more than just developing a list of indicators, it is an essential resource for neighbourhood communities in their sustainable transition journey.
This article is based on Bukovszki, V.; Balázs, R.; Mafé, C.; Reith, A. Six Lessons Learned by Considering Social Sustainability in Plus-Energy Neighbourhoods. In Proceedings of the In the neighbourhood. Vienna Congress on Sustainable Building; Waltjen, T., Ed.; IBO Verlag: Vienna, 2021; pp. 26–28.
Check the full syn.ikia Methodology Framework for Plus Energy Buildings and Neighbourhoods.