Although research shows that acceptance, trust,and risk perception are often related, little is known about the underlying patterns of causality among the three constructs. In the context of a waterborne disease outbreak, we explored via zeroorder/ partial correlation analysis whether acceptance predicts both trust and risk perception (associationist model), or whether trust influences risk perception and acceptance (causal chain model). The results supported the causal chain model suggesting a causal role for trust.A subsequent path analysis confirmed that the effect of trust on acceptance is fully mediated by risk perception. It also revealed that trust is positively predicted by prior institutional trust and communication with the public. Implications of the findings for response strategies to contamination events are discussed.
In low-income regions, drinking water is often derived from groundwater sources, which might spread diarrheal disease if they are microbiologically polluted. This study aimed to investigate the occurrence of fecal contamination in 147 improved groundwater sources in Juba, South Sudan and to assess potential contributing risk factors, based on bivariate statistical analysis. Thermotolerant coliforms (TTCs) were detected in 66% of the investigated sources, including 95 boreholes, breaching the health-based recommendations for drinking water. A significant association (p<. 0.05) was determined between the presence of TTCs and the depth of cumulative, long-term prior precipitation (both within the previous five days and within the past month). No such link was found to short-term rainfall, the presence of latrines or damages in the borehole apron. However, the risk factor analysis further suggested, to a lesser degree, that the local topography and on-site hygiene were additionally significant. In summary, the analysis indicated that an important contamination mechanism was fecal pollution of the contributing groundwater, which was unlikely due to the presence of latrines; instead, infiltration from contaminated surface water was more probable. The reduction in fecal sources in the environment in Juba is thus recommended, for example, through constructing latrines or designating protection areas near water sources. The study results contribute to the understanding of microbiological contamination of groundwater sources in areas with low incomes and high population densities, tropical climates and weathered basement complex environments, which are common in urban sub-Saharan Africa.
This study developed methodology for statistically assessing groundwater contamination mechanisms. It focused on microbial water pollution in low-income regions. Risk factors for faecal contamination of groundwater-fed drinking-water sources were evaluated in a case study in Juba, South Sudan. The study was based on counts of thermotolerant coliforms in water samples from 129 sources, collected by the humanitarian aid organisation Médecins Sans Frontières in 2010. The factors included hydrogeological settings, land use and socio-economic characteristics. The results showed that the residuals of a conventional probit regression model had a significant positive spatial autocorrelation (Moran’s I = 3.05, I-stat = 9.28); therefore, a spatial model was developed that had better goodness-of-fit to the observations. The most significant factor in this model (p-value 0.005) was the distance from a water source to the nearest Tukul area, an area with informal settlements that lack sanitation services. It is thus recommended that future remediation and monitoring efforts in the city be concentrated in such low-income regions. The spatial model differed from the conventional approach: in contrast with the latter case, lowland topography was not significant at the 5% level, as the p-value was 0.074 in the spatial model and 0.040 in the traditional model. This study showed that statistical risk-factor assessments of groundwater contamination need to consider spatial interactions when the water sources are located close to each other. Future studies might further investigate the cut-off distance that reflects spatial autocorrelation. Particularly, these results advise research on urban groundwater quality. © 2016, The Author(s).
Residents’ perceived safety is key to improving livelihoods and reducing disparities between neighborhoods in Sweden. Neighborhood interventions may be more cost-effective than individual-level interventions in addressing major societal issues such as unequal levels of safety between neighborhoods. However, most studies investigating the impact of neighborhood characteristics on perceived safety suffer from either poor data quality, too few respondents per statistical unit, large units of analysis, or a lack of longitudinally collected data. This study aims to fill this gap by combining property-specific longitudinal sociodemographic data with customer satisfaction survey data (N = 147,965) collected between 2013–2014 and 2016–2021 in Gothenburg. Using two multilevel models, we examined the relationship between perceived safety and both property-level and area-level structural characteristics, testing three hypotheses. Consistent with prior research, we find that sociodemographic and urban environmental characteristics influenced perceptions of safety. The multilevel analyses reveal that proximity to the square is associated with lower levels of perceived safety, particularly among residents living within 0–100 m of the square in socioeconomically disadvantaged neighborhoods. Moreover, the results show that living in taller buildings of 10–16 floors is associated with lower levels of safety.
As global and local visions for sustainable living environments are increasingly supported by policies and concrete practices in construction, the building and housing sector is seeking to mitigate its environmental impact as well as assume a greater social responsibility. The overarching policy objectives set to concretize what a sustainable housing development entails, however, tend to rely on equivocal terminology, allowing a varied interpretation by key industry practitioners. Though in line with an ecological modernization paradigm in policy, the promotion of a market-driven environmentalism in housing faces multiple challenges as varying interests and perspectives collide. Supported by empirical findings of a semi-structured interview study conducted with housing developers in a new =green' urban district in Göteborg, Sweden, theoretical frameworks surrounding the paradoxical path towards a sustainable housing development are presented. Inconsistencies between outspoken ambitions; social dimensions; and the framing of efficiency in new housing are discussed. Possibilities for the housing sector are given in the recognition of new forms of development, where a systemic perspective is required in the alignment between how industry, policy and the market perceives housing development and what is actually sustainable.
Quantifying differences in resource use and waste generation between individual households and exploring the reasons for the variations observed implies the need for disaggregated data on household activities and related physical flows. The collection of disaggregated data for water use, gas use, electricity use, and mobility has been reported in the literature and is normally achieved through sensors and computational algorithms. This study focuses on collecting disaggregated data for goods consumption and related waste generation at the level of individual households. To this end, two data collection approaches were devised and evaluated: (1) triangulating shopping receipt analysis and waste component analysis and (2) tracking goods consumption and waste generation using a smartphone. A case study on two households demonstrated that it is possible to collect quantitative data on goods consumption and related waste generation on a per unit basis for individual households. The study suggested that the type of data collected can be relevant in a number of different research contexts: eco-feedback; user-centered research; living-lab research; and life cycle impacts of household consumption. The approaches presented in this study are most applicable in the context of user-centered or living-lab research. For the other contexts, alternative data sources (e.g., retailers and producers) may be better suited to data collection on larger samples, though at a lesser level of detail, compared with the two data collection approaches devised and evaluated in this study.
Many studies have highlighted the importance of retrofitting to mitigate the energy use of building stocks. An important step in the development of renovation strategy and energy conservation advising is to gather information of the energy performance of the existing buildings. However, renovation strategies must also consider the socio-economic challenges associated with the cost of energy retrofitting. This paper describes the development of an energy atlas of the multifamily building stock in Sweden for visualizing and analyzing energy use and renovation needs. The atlas has been developed using Extract Transform and Load technology (ETL) to aggregate information on the energy performance, building ownership, renovation status, and socio-economic status of inhabitants from various data sources. The atlas can visualize the energy use and renovation status of multifamily buildings in 2D maps and 3D models, displaying data for either individual buildings or aggregated data on spatial scales ranging from 250 × 250 m squares through district and municipality to county areas. A demonstration of its use on national and city scales indicates that energy retrofits of multifamily buildings reaching a service life of 50 years can reduce the energy use of the existing building stock by up to 50% relative to 1990. However, costs associated with renovation and energy retrofits of multifamily buildings can be problematic, especially in economically weak suburbs. A good understanding of past and future renovation needs and socio-economic consequences is important in the development of a sustainable national renovation strategy. © 2017
In Sweden and many European countries, the building stock increased at a rapid pace during the period of 1950-1975. This aging building stock is in need of renovation which is a challenge and an opportunity in reaching the energy performance targets as well as decreasing societal inequities. The contribution of this thesis is to demonstrate how economic, social and resource usage challenges of renovating the building stock can be analyzed and described using comprehensive building-specific data. In this thesis, building ownership types, area socio-economic characteristics, building energy performance, and investments in renovation, are analyzed for the Gothenburg multi-family dwelling stock. Measured energy usage from the Swedish Energy Performance Certificate was used in the analysis. The data quality of the Energy Performance Certificate was assessed for the purpose of analyzing the building stock. The Energy Performance Certificates were matched with official building information from the Swedish Land Survey and area socio-economic information from Statistics Sweden. Using this dataset, rent increases due to renovations are estimated and compared. To broaden the analysis, a case study in a renovation project of multi-family dwellings in an economically disadvantaged area is also presented. In the case study renovation, the implemented energy usage reducing measure of volumetric billing of water was found to increase rents. The case study is used to exemplify how energy usage reducing renovation projects, with little consideration for social city development targets, can aggravate economic segregation. There is positive progress towards the 2020 targets of greenhouse gas emission reduction and improved energy efficiency in the Swedish housing sector; while there is little progress toward the Gothenburg city target to reduce segregation. The Swedish housing sector is rather part of the economic and geographic segregation process in Gothenburg. Renovation will be needed to reduce differences in living standards. The upcoming renovation need is a challenge, for mainly municipally owned companies, in reaching greenhouse gas and energy usage reduction without raising rent and increasing economic segregation.
Det är under all kritik att så många doktorander mår dåligt och inte färdigställer sina avhandlingar. Vi mer seniora akademiker har ett medmänskligt ansvar att etablera strukturer som tar hand om våra kollegor. Omtanke om våra doktoranders utveckling behövs då vi designar strukturer för doktorerande så väl som handledande. I denna artikel argumenterar jag för att omtanken om doktorandens utveckling bör vara en utgångspunkt i handledningsarbetet. Med hjälp av omtanke blir det enklare att: hitta rätt nivå för krav och mål, erkänna brister i handledarskapet, prata problem i satta maktrelationer, hantera utmaningar från den våldsamma akademin, med mera. Att bry sig om doktorandens utveckling innebär många olika saker under resans gång. Även om det i artikeln konkretiseras vad omtanke om doktorandens utveckling kan innebära, så är poängen snarare att det blir enklare att inse vad god handledning är, ifall omtanken om doktorandens väl är en utgångpunkt i handledningsarbetet.
Renovations of the housing rental stock have become a political concern since they have been claimed to drive gentrification and affect tenants’ everyday lives as well as long-term housing conditions. Furthermore, new actors have entered the market, partly as a result of high supply on the international capital markets creating a flow of capital into market segments. This has led to a critique of private equity in the housing sector, and raised the question of the extent to which ownership of the rental stock matters for housing affordability. Yet there seems to be little systematic research on this topic. This study uses a unique dataset covering the entire rental housing stock in Sweden to address whether there are differences in renovation investments between different ownership groups. The purpose of this article is to increase understanding of how ownership affects renovation processes, and specifically to analyse to what extent, and how, private and public actors differ in renovation and rent setting decisions. Our results demonstrate that public housing companies raised rents less and renovated more, particularly in the lower-income segments of the multi-family building stock between 2014 and 2020. © 2023 The Author(s).
The financing of energy efficiency measures and renovations is key to reaching energy efficiency targets for the housing sector. The purpose of this article is to add the Swedish case of how capital market funds have become accessible and used by public and private housing companies, in particular for energy efficiency measures. The core of this article are interviews with representatives of Swedish housing companies made during the spring of 2021 with the purpose of mapping how public and larger private housing companies finance renovation and energy efficiency measures, and to what extent funds from the capital market are used for these purposes. In this article, we have found that capital market funds are commonly used by the Swedish public and the largest private housing companies. Bonds are less costly compared to bank loans, and green bonds are 0.02–0.03 percentage points less costly than conventional bonds. Furthermore, control systems that investigate the values of building portfolios as security for bonds are poor. A conclusion is that governmental control systems over the capital market issuing bonds for the housing market could be needed to avert future housing bubbles. © 2022, The Author(s).
Byggsektorn står inför ett stort renoveringsbehov. Byggnader från miljonprogramstiden har nått en ålder då många olika byggnadsdelar når sin förväntade livslängd. Detta renoveringsbehov är inte jämnt fördelat bland Sveriges fastighetsägare. Det stora flertalet av de ännu ej renoverade byggnaderna från miljonprogramstiden ägs av fastighetsbolag knutna till SABO, men det finns även många privata fastighetsägare och bostadsrättsföreningar som har ett eftersläpande renoveringsbehov eller som har renoverat men fortfarande har en hög energianvändning och alltså troligtvis inte genomfört nödvändiga energieffektiviseringsåtgärder. Det finns också olika förutsättningar för att göra de investeringar som krävs, vilket beror på företagens soliditet, fastigheternas värde och möjligheten att få lån, utrymme för hyresökning etc. Detta gör att renoveringstakten inte kommer att täcka renoveringsbehovet och att vissa delar av fastighetsbeståndet kommer att vara mer eftersatt.
Byggsektorn är inne i en period av mycket hög aktivitet. Över 700 000 nya bostäder efterfrågas, samtidigt som miljonprogrammets byggnader börjar nå sin förväntade livslängd. Detta är en enorm utmaning, men det är också en möjlighet, där vi kan nå energi- och klimatmål och skapagoda boendemiljöer till rimlig kostnad.
We define the water sensitive home by assigning attributes to relevant aspects of water in the home. The intention of this study is to look at more physical attributes and leaving the social dimensions to later, more applied studies. Fig. 1 below illustrates the attributes. Table 1 gives the unit and indicator used for assessing each attribute. We build upon the work done by Hellström et al. (2004), Lundin and Morrison (2002) when we define attributes. The attributes are grouped depending on their mathematical nature, environmental loads are negative per definition, risks cannot be completely avoided, resource consumption should be limited but is needed and thus have an optimal level, likewise is accessibility regulated by authorities. In different contexts the optimal levels are different, the "sides" of the green building in fig. 1, depending on regulations. Furthermore a minimal, or human right, level has been identified based on WHO (2011).
We define the water sensitive home by assigning attributes to relevant aspects of water in the home. The intention of this study is to look at more physical attributes and leaving the social dimensions to later, more applied studies. Fig. 1 below illustrates the attributes. Table 1 gives the unit and indicator used for assessing each attribute. We build upon the work done by Hellström et al. (2004), Lundin and Morrison (2002) when we define attributes. The attributes are grouped depending on their mathematical nature, environmental loads are negative per definition, risks cannot be completely avoided, resource consumption should be limited but is needed and thus have an optimal level, likewise is accessibility regulated by authorities. In different contexts the optimal levels are different, the "sides" of the green building in fig. 1, depending on regulations. Furthermore a minimal, or human right, level has been identified based on WHO (2011).
Domestic water payment schemes are often a product of their time, place and what is perceived to be customary. Aspects that payment schemes can take into account include resource conservation, equity, maintainability, and profitability. In contemporary Sweden profitable environmentally sustainable solutions are promoted, such as the introduction of volumetric billing of water in rental apartments. This paper describes the detailed consequences of this change in the payment structure for domestic water in terms of reduced resource consumption, direct impact on household economies and perceptions of the system's change process. By combining high-resolution quantitative data on water usage and socio-economic household characteristics with qualitative data from semistandardized interviews with residents, it is possible to identify the different impacts of the system's change and how the process was experienced. It was shown that while water usage decreased by 30%, 63% of the households had increased monthly costs, and unemployed residents were further disadvantaged and closer to social exclusion. Focusing on making environmental sustainability profitable, as posited in ecological modernization theory, may shadow negative impacts on social sustainability.
Energy usage in the Swedish building stock has changed significantly the last 20 years. The target of reducing the Green Houses Gas (GHG) emissions by 40% till 2020 seems reachable through improved energy efficiency and a switch to less GHG emitting heat sources. The goal of GHG neutrality by 2050 will however require further initiatives. This paper is primarily a review of the contemporary Swedish building stock state and research. Different research bodies, such as government research institutes, universities, and companies, have different financing and foci. They present their analyses the built environment differently. This paper creates a structure amongst these actors by dividing them in groups and by identifying topics where there are different positions held by researchers and practitioners in the fields related to building stock. Data from Energy Performance Certificates (EPC) is used to display how energy usage varies depending on building age. This gives an indication on how the building stock is developing and what possibilities there are to reach environmental targets.
Abstract The EU directive to create Energy Performance Certificates (EPC) for all buildings was implemented in Sweden as a tool to advise building owners on possible improvements and to give energy efficiency visible market value. The Swedish EPCs include measured energy usage. Currently 82% of the buildings have EPCs; this database makes it possible to create overview and to validate models of the building stock in an unprecedented high detail. However, the process of issuing Swedish EPCs has received criticism from real estate agents, real estate owners, Energy Experts, and Boverket, the national agency responsible for EPC data collection. In order to use the EPC data for describing the building stock it is necessary to assess and remediate the data quality. This has been done by merging the EPC data with databases of the Housing and Urban Development office and one of the larger real estate companies in Sweden, Riksbyggen. The Swedish EPC specific area measurement, Atemp, is found to vary according to methods of derivation. The method of estimating Atemp is improved using a stepwise regression model (R2 = 0.979). This method can be applied to subsets of EPCs depending on the intended way of describing the building stock.
The European building stock was renewed at a rapid pace during the period 1950-1975. In many European countries the building stock from this time needs to be renovated. There is an opportunity to introduce energy efficiency measures in the renovation process, but in this process social aspects should also be taken into account. The purpose of this article is to provide an estimate of the economic and societal challenge of renovating and energy retrofitting the aging building stock. Building specific data on energy usage and previous renovation investments made in the multi-family dwellings in Gothenburg (N = 5 098) is aligned with data on tenure type and average income. Based on conducted energy retrofitting projects, costs are estimated for renovating and energy retrofitting multi-family dwellings that will reach the service life of 50 years before 2026. It is found that the pace of renovation needs to increase and that there is risk of increasing societal inequity due to rent increases in renovated buildings. © 2016 The Authors.
The European building stock was renewed at a rapid pace during the period 1950–1975.In many European countries, the building stock from this time needs to be renovated, and thereare opportunities to introduce energy efficiency measures in the renovation process. Informationavailability and increasingly available analysis tools make it possible to assess the impact of policyand regulation. This article describes methods developed for analyzing investments in renovationand energy performance based on building ownership and inhabitant socio-economic informationdeveloped for Swedish authorities, to be used for the Swedish national renovations strategy in2019. This was done by analyzing measured energy usage and renovation investments made duringthe last 30 years, coupled with building specific official information of buildings and resident areacharacteristics, for multi-family dwellings in Gothenburg (N = 6319). The statistical analyses showthat more costly renovations lead to decreasing energy usage for heating, but buildings that havebeen renovated during the last decades have a higher energy usage when accounting for currentheating system, ownership, and resident socio-economic background. It is appropriate to includean affordability aspect in larger renovation projects since economically disadvantaged groups areover-represented in buildings with poorer energy performance.
This study investigates previous refurbishment activities linked to spatial distribution of individual buildings. The work identifies where larger changes has been made (or not), to what extent and in relation to type of ownership. A specific aim is to test selected parameters from the Swedish Property register to analyze refurbishments using the multi-family residen-tial building stock of the city of Gothenburg, 7172 buildings, as an example. The stock has been divided into six age-classes: before 1931, 1931-1945, 1946-1960, 1961-1975, 1976-1990, and 1991-2005. Extensive refurbish-ment activities were carried out in the stock constructed before 1946. Almost half of the stock has not been refurbished to a larger extent. There is no clear relation between type of ownership and larger refurbishments for the total stock, however the tenant owned stock is less refurbished for the periods covering 1931-1975. The property register is a valuable source for studying major refurbishment activities in lager stocks. Further studies are needed to obtain a more nuanced picture of the refurbishment activities.
While the energy transition of the EU housing stock is now being intensified with the launch of the Renovation Wave, economic inequalities are increasing in many OECD countries, which has effects on housing-related inequalities and the demand of affordable housing. The Renovation Wave is thus an opportunity to improve housing quality for low-income households, but also entails risks for increased rents. In Sweden, the standard of housing is relatively high and energy poverty in multifamily housing is rare, meaning that there are limited social benefits to be achieved from extensive energy retrofitting; moreover, Sweden lacks a social housing sector, which limits protection of the worst-off residents. This paper thus explores whether the limited social benefits of the Renovation Wave weigh up against the risks that it entails for the worst-off in the Swedish context. This is done within a normative framework for just energy transitioning that is developed within the context of the Renovation Wave and increasing economic inequalities, consisting of four ordered principles: (1) The equal treatment principle; (2) The priority principle; (3) The efficiency principle; and (4) The principle of procedural fairness. Analysis showed that to be considered just according to our framework, the Swedish energy transition of housing should, in contradistinction to what is suggested in the Renovation Wave, limit the imposition of extensive energy retrofitting in low-income areas. Finally, having identified a mismatch between the most effective approaches in terms of energy savings and the most acceptable approaches in terms of social justice, we offer policy recommendations on how to bridge this mismatch in a Swedish context. © 2021 by the authors.
Energy Performance Certificates are currently one of the most extensive data sources about the energy performance of the EUs building stock and consequently provide support for researchers and policy makers in energy regulation. As Energy Performance Certificates are being renewed, there are new possibilities to study energy performance development over time and to evaluate the building-specific effect of energy policies and measures. This paper aims to explore this possibility. In Sweden, owners of multifamily buildings had to obtain their first Energy Performance Certificate no later than the end of 2008, and with a period of validity of 10 years many owners have now obtained a second Energy Performance Certificate for their building(s). This enables unprecedented quantitative, building-specific evaluations of the change in energy performance over time. However, comparability between old and new Energy Performance Certificates must be assured. This study develops a novel three-step method to attain comparability between old and renewed Energy Performance Certificates. Results show that while many pairs of Energy Performance Certificates were considered comparable, procedural changes in methods for determining heated floor area in Swedish Energy Performance Certificates caused an overestimation of energy performance improvement of approximately 7 kWh/m2 per building which had to be corrected for. The results of this paper indicate that old and renewed Energy Performance Certificates can be utilised to successfully map development of energy performance and enable evaluation of the impact on energy performance from policies and measures that have been carried out between the two points of audit. © 2019 The Authors
Although renovation costs can lead to rent increases in energy retrofitting, it is often assumed that reductions in energy costs will counterbalance the rent increase. In Swedish multifamily housing, energy costs for heating are however generally included as a fixed component in the monthly rent, meaning that the rent increase after energy retrofitting corresponds to the net change in rent level as well as energy costs for heating. This makes Sweden a methodologically advantageous setting for studying tenants' cost burden of energy retrofitting. The aim of this study was thus to investigate how energy performance improvement has affected rent increases in Swedish renovation projects between 2013 and 2019. Utilising a national database of multifamily housing, it was found that energy retrofitting entailed a cost relief for tenants in renovation projects with smaller investments. However, in renovation projects with larger investments, energy retrofitting entailed a cost burden for tenants. Moreover, public housing companies had conducted a high share of the extensive energy retrofits, leading to low-income tenant groups being disproportionately subjected to cost burdens of energy retrofitting. On the contrary, light energy retrofits with a cost relief for energy efficiency had been rather evenly distributed across income groups. These results indicate ongoing conflicts with the ability-to-pay principle in the energy transition of Swedish multifamily housing, and suggest that if low-investment energy retrofits are not sufficient for upcoming objectives and requirements, subsidies could be needed to compensate low-income tenants for the cost burden of extensive energy retrofitting. © 2022 The Author(s)
Improving energy performance of the housing stock continues to be an important undertaking in the energy transition of many EU member states. However, tendencies of low-income households generally living in buildings with low energy performance pose a challenge for this transition, and cases of ‘renoviction’ and ‘green gentrification’ are becoming more and more noticed in the scientific community. More so, questions regarding the distributive justice of costs and burdens in the energy transition of the housing stock have been raised. In this paper, we approach this problem from a perspective of energy performance metrics. Although energy performance (kWh/m2, year) is generally lower in buildings inhabited by low-income households, residential density—and thus building utilisation—tends to be higher. By measuring per capita energy use instead of area-normalised energy use, we investigate if a high residential density can offset a low energy performance and change the perception of which buildings are considered energy inefficient and which are not. Results showed that by measuring per capita energy use instead of area-normalised energy use, energy inefficient buildings were found in high-income city centres instead of in low-income suburbs of Swedish cities. Moreover, there has been an unjust distribution of the imposition of the energy transition over the past decade where the residents with the initially lowest per capita energy use have carried a disproportionately high share of the energy savings. This suggests that a change of energy performance metrics could offer an approach for a more socially just and sustainable energy transition of the housing stock. © 2020 The Author(s)
Improved energy efficiency in the housing stock is an important undertaking in the energy transition but is associated with both opportunities and risks. While there are possibilities to reduce energy inequalities in the housing stock, inequalities also risk being aggravated as actions for energy efficiency usually aim at the least energy efficient - and thus sometimes the least privileged - parts of the housing stock. In this paper, we use two different energy performance metrics (kWh/m2 and kWh/capita) to investigate the energy inequality in the Swedish multifamily building stock and explore the effects of these inequalities in the energy transition. More specifically, we investigate the implementation of individual metering and billing of energy for heating, which was recently implemented in the least energy efficient part of the housing stock. It was found that low-income households were overrepresented in the affected buildings. The consequence of this implementation is thus that the strongest protection against energy poverty in Sweden (collective billing for heating) is removed in a part of the housing stock where two of the predictors for energy poverty - low income and low energy performance - are overrepresented. It was concluded that acknowledging inequalities is crucial to avoid risks associated with the energy transition.
To ensure building construction with low heating demand, efficient use of sustainable energy carriers, and neutrality between heating technologies, Sweden recently introduced weighting factors (WFs) for different energy carriers which are now used in Energy Performance Certificates (EPCs). As EPC ratings are gaining increased influence in Swedish energy policy and regulation, with recent examples of buildings' EPC rating acting as base for imperative regulatory requirements, the introduction of WFs is likely to have significant effects on how policy and regulations are distributed in the multifamily building stock. As residents often are directly or indirectly affected by policy that either impose or trigger measures to be undertaken in their building, the aim of this paper is to analyse how WFs affect the assessed energy performance of buildings in different resident income groups. The results show that overall, reduced energy performance from WFs was more common in high-income areas than in low-income areas. However, although the total number of buildings in the lowest EPC ratings was reduced after introducing WFs, the resulting income distribution among worst-performing buildings was more skewed towards low-income households than before introducing WFs. As imperative regulatory requirements previously have targeted worst-performing buildings, these results indicate that energy-related inequalities in the housing stock have become more prominent and should be considered as to not disproportionately burden low-income residents in the energy transition of the housing stock.
Building databases are important assets when estimating and planning for national energy savings from energy retrofitting. However, databases often lack information on building characteristics needed to determine the feasibility of specific energy conservation measures. In this paper, machine learning methods are used to enrich the Swedish database of Energy Performance Certificates with building characteristics relevant for a chosen set of energy retrofitting packages. The study is limited to the Swedish multifamily building stock constructed between 1945 and 1975, as these buildings are facing refurbishment needs that advantageously can be combined with energy retrofitting. In total, 514 ocular observations were conducted in Google Street View of two building characteristics that were needed to determine the feasibility of the chosen energy retrofitting packages: (i) building type and (ii) suitability for additional façade insulation. Results showed that these building characteristics could be predicted with an accuracy of 88.9% and 72.5% respectively. It could be concluded that machine learning methods show promising potential to enrich building databases with building characteristics relevant for energy retrofitting, which in turn can improve estimations of national energy savings potential.
The undesirable legacy of radioactive concrete (blue concrete) in post-war dwellings contributes to increased indoor radon levels and health threats to occupants. Despite continuous decontamination efforts, blue concrete still remains in the Swedish building stock due to low traceability as the consequence of lacking systematic documentation in technical descriptions and drawings and resource-demanding large-scaled radiation screening. The paper aims to explore the predictive inference potential of learning Bayesian networks for evaluating the presence probability of blue concrete. By integrating blue concrete records from indoor radon measurements, pre-demolition audit inventories, and building registers, it is possible to estimate buildings with high probabilities of containing blue concrete and encode the dependent relationships between variables. The findings show that blue concrete is estimated to be present in more than 30% of existing buildings, more than the current expert assumptions of 18–20%. The probability of detecting blue concrete depends on the distance to historical blue concrete manufacturing plants, building class, and construction year, but it is independent of floor area and basements. Multifamily houses and buildings built between 1960 and 1968 or nearby manufacturing plants are more likely to contain blue concrete. Despite heuristic, the data-driven approach offers an overview of the extent and the probability distribution of blue concrete-prone buildings in the regional building stock. The paper contributes to method development for pattern identification for hazardous building materials, i.e., blue concrete, and the trained models can be used for risk-based inspection planning before renovation and selective demolition. © 2023 The Author(s)
Indoor radon represents a health hazard for occupants. However, the indoor radon measurement rate is low in Sweden because of no mandatory requirements. Measuring indoor radon on an urban scale is complicated, machine learning exploiting existing data for pattern identification provides a cost-efficient approach to estimate indoor radon exposure in the building stock. Extreme gradient boosting (XGBoost) models and deep neural network (DNN) models were developed based on indoor radon measurement records, property registers, and geogenic information. The XGBoost models showed promising results in predicting indoor radon intervals for different types of buildings with macro-F1 between 0.93 and 0.96, whereas the DNN models attained macro-F1 between 0.64 and 0.74. After that, the XGBoost models trained on the national indoor radon dataset were transferred to fit building registers in metropolitan regions to estimate the indoor radon intervals in non-measured and measured buildings by regions and building classes. By comparing the prediction results and the statistical summary of indoor radon intervals in measured buildings, the model uncertainty and validity were determined. The study ascertains the prediction performance of machine learning models in classifying indoor radon intervals and discusses the benefits and limitations of the data-driven approach. The research outcomes can assist preliminary large-scale indoor radon distribution estimation for relevant authorities and guide onsite measurements for prioritized building stock prone to indoor radon exposure.
The presence of hazardous materials inhibits material circularity. The existing residential buildings are exposed to the risk of the unforeseen presence of asbestos-containing materials during the demolition or renovation process. Estimating the potential occurrence of contaminated building components can therefore facilitate semi-selective demolition and decontamination planning. The study aims to investigate the prediction possibility of seven frequently detected asbestos-containing materials by using artificial neural networks based on a hazardous material dataset from pre-demolition audit inventories and national building registers. Through iterative model evaluation and careful hyperparameter tuning, the prediction performance for each asbestos-containing material was benchmarked. A high level of accuracy was obtained for asbestos pipe insulation and ventilation channel, yet barely any patterns were found for asbestos floor mats. Artificial neural networks show potential for classifying specific asbestos components and can enhance the knowledge of their detection patterns. However, more quality data are needed to bring the models into practice for risk assessment for not yet inventoried residential buildings. The proposed screening approach for in situ asbestos-containing materials has high applicability for the quality assurance of recycled materials in circular value chains.
Hazardous materials encountered during building renovation or demolition processes not only result in uncertainty in cost estimation and the lead time but also hampers material recyclability and reuse. Therefore, the paper discusses the possibility of predicting the extent of the hazardous materials, including asbestos, PCB, mercury, and CFC, through data mining techniques based on registered records. Pre-demolition audits contain observation data that can be used as a sample for statistical prediction through careful processing. By developing an innovative approach of merging data from environmental inventories with building registers, the positive ratio of remaining hazardous materials in the Gothenburg building stock can be estimated. The study highlights the challenges of creating a training dataset by completing information from the existing environmental inventory, providing new insight into digital protocol development for enhancing material circularity.
The presence of hazardous materials hinders the circular economy in construction and demolition waste management. However, traditional environmental investigations are costly and time-consuming, and thus lead to limited adoption. To deal with these challenges, the study investigated the possibility of employing registered records as input data to achieve in situ hazardous building materials management at a large scale. Through characterizing the eligible building groups in question, the risk of unexpected cost and delay due to acute abatement could be mitigated. Merging the national building registers and the environmental inventory from renovated and demolished buildings in the City of Gothenburg, a training dataset was created for data validation and statistical operations. Four types of inventories were evaluated to identify the building groups with adequate data size and data quality. The observations’ representativeness was described by plotting the distribution of building features between the Gothenburg dataset and the training dataset. Evaluating the missing data and the positive detection rates affirmed that reports and protocols could locate hazardous materials in the building stock. The asbestos and polychlorinated biphenyl (PCB)-containing materials with high positive detection rates were highlighted and discussed. Moreover, the potential inventory types and building groups for future machine learning prediction were delineated through the cross-validation matrix. The novel study contributes to the method development for assessing the risk of residual hazardous materials in buildings.
Assessment of the presence of hazardous materials in buildings is essential for improving material recyclability, increasing working safety, and lowering the risk of unforeseen cost and delay in demolition. In light of these aspects, machine learning has been viewed as a promising approach to complement environmental investigations and quantify the risk of finding hazardous materials in buildings. In view of the increasing number of related studies, this article aims to review the research status of hazardous material management and identify the potential applications of machine learning. Our exploratory study consists of a two-fold approach: science mapping and critical literature review. By evaluating the references acquired from a literature search and complementary materials, we have been able to pinpoint and discuss the research gaps and opportunities. While pilot research has been conducted in the identification of hazardous materials, source separation and collection, extensive adoption of the available machine learning methods was not found in this field. Our findings show that (1) quantification of asbestos-cement roofing is possible from the combination of remote sensing and machine learning algorithms, (2) characterization of buildings with asbestos-containing materials is progressive by using statistical methods, and (3) separation and collection of asbestos-containing wastes can be addressed with a hybrid of image processing and machine learning algorithms. Analysis from this study demonstrates the method applicability and provides an orientation to the future implementation of the European Union Construction and Demolition Waste Management Protocol. Furthermore, establishing a comprehensive environmental inventory database is a key to facilitating a transition toward hazard-free circular construction
Hazardous materials in buildings cause project uncertainty concerning schedule and cost estimation, and hinder material recovery in renovation and demolition. The study aims to identify patterns and extent of polychlorinated biphenyls (PCBs) and asbestos materials in the Swedish building stock to assess their potential presence in pre-demolition audits. Statistics and machine learning pipelines were generated for four PCB and twelve asbestos components based on environmental inventories. The models succeeded in predicting most hazardous materials in residential buildings with a minimum average performance of 0.79, and 0.78 for some hazardous components in non-residential buildings. By employing the leader models to regional building registers, the probability of hazardous materials was estimated for non-inspected building stocks. The geospatial distribution of buildings prone to contamination was further predicted for Stockholm public housing to demonstrate the models’ application. The research outcomes contribute to a cost-efficient data-driven approach to evaluating comprehensive hazardous materials in existing buildings.
Identifying in situ hazardous materials can improve demolition waste recyclability and reduce project uncertainties concerning cost overrun and delay. With the attempt to characterize their detection patterns in buildings, the study investigates the prediction potential of machine learning techniques with hazardous waste inventories and building registers as input data. By matching, validating, and assuring the quality of empirical data, a hazardous material dataset for training, testing, and validation was created. The objectives of the explorative study are to highlight the challenges in machine learning pipeline development and verify two prediction hypotheses. Our findings show an average of 74% and 83% accuracy rates in predicting asbestos pipe insulation in multifamily houses and PCB joints or sealants in school buildings in two major Swedish cities Gothenburg and Stockholm. Similarly, 78% and 83% of recall rates were obtained for imbalanced classification. By correlating the training sample size and cross-validation accuracy, the bias and variance issues were assessed in learning curves. In general, the models perform well on the limited dataset, yet collecting more training data can improve the model's generalizability to other building stocks, meanwhile decreasing the chance of overfitting. Furthermore, the average impact on the model output magnitude of each feature was illustrated. The proposed applied machine learning approach is promising for in situ hazardous material management and could support decision-making regarding risk evaluation in selective demolition work. © 2022 The Author(s)
Several building-stock modelling techniques have been employed to investigate the impact of energy efficiency measures (EEM), where the description of the building-stock generally consists of an age-type classification to specify building characteristics for groups of buildings. Such descriptions lack the appropriate level of detail to differentiate the potential for EEM within age groups. This paper proposes a methodology for building-stock description using building-specific data and measured energy use to augment an age-type building-stock classification. By integrating building characteristics from energy performance certificates, measured energy use and envelope areas from a 2.5D GIS model, the building-stock description reflects the heterogeneity of the building-stock. The proposed method is validated using a local building portfolio (N = 433) in the city of Gothenburg, where modelled results for space heating and domestic hot water are compared to data from measurements, both on an individual building level and for the entire portfolio. Calculated energy use based on the building-stock description of the portfolio differ less than 3% from measured values, with 42% of the individual buildings being within a 20% margin of measured energy use indicating further work is needed to reduce or quantify the uncertainty on a building level.