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Publication - Report

Climate change: evidence review of mitigation options in the Built Environment sector

Published: 19 Jan 2017
Part of:
Environment and climate change
ISBN:
9781786527547

Evidence review of potential climate change mitigation measures in the Built Environment sector.

53 page PDF

645.9kB

53 page PDF

645.9kB

Contents
Climate change: evidence review of mitigation options in the Built Environment sector
4 Avoid: reduce energy demand

53 page PDF

645.9kB

4 Avoid: reduce energy demand

Mitigation options such as insulation and consumer behaviour change can help reduce energy consumption. These options and their potential co-benefits are considered below.

4.1 Insulation and other building fabric improvements

Qualitative evidence - health and comfort benefits

Building fabric improvements, including insulation and draught-proofing, form a key focus of the literature associated with climate change mitigation in the built environment. Insulation can provide health benefits by maintaining more comfortable indoor temperatures that are warmer in winter and avoid over-heating in summer (Liddell and Morris, 2010). In the Scottish context, under-heating is currently more of a problem than over-heating. There are mortality impacts associated with cold temperatures in the UK - more deaths occur in winter (National Records of Scotland, 2015) and cold indoor temperatures are closely linked to this, with the risks being greatest for residents in homes which are not well insulated (Wilkinson et al., 2009). The World Health Organisation recommends a minimum temperature of 21 oC in living rooms, and 18 oC in all other rooms (for a period of 9 hours in every 24, or 16 hours in 24 over the weekend). For elderly and infirm households, the recommendation is 23 oC in the living room and 18°C in other rooms, to be achieved for 16 hours in every 24.

Warmth and energy efficiency improvements can result in improved general, respiratory and mental health as well as reductions in absence from work. These improvements are most significant when action is targeted at individuals experiencing chronic respiratory disease or inadequate heating (Thomson et al., 2013). Improvements in insulation can result in direct effects on winter mortality and potentially morbidity as well indirect effects e.g. through reductions in mould growth (Wilkinson, 2009), which in turn can reduce Disability Adjusted Life Years ( DALYs), with a DALY considered one lost year of "healthy" life ( WHO, 2016).

Table 4‑1 Health effects of the UK Stock Scenarios

Fabric improvements Improved ventilation control
Premature death -7 -6
DALYs -62 -48

Data are change per million population compared with baseline (2010). Negative values show reductions in disease burdens.

Source Wilkinson et al. (2009)

Caution is required, however, as there is an increasing evidence base on the possible adverse impacts of air tightness on indoor air pollution from radon and other pollutants, and the need for better (and correctly used) ventilation to address this ( e.g. Wilkinson et al. 2009; Gupta and Kapsali 2015; Shrubsole et al., 2015, Shrubsole et al., 2012).

Furthermore, over-heating, must not be dismissed as an issue. Research examining the potential for overheating in existing building stock ( e.g. AECOM, 2012 and references within) which suggests that type of building ( e.g. small, top floor, purpose built flats) and age of building (with the 1960s being identified) can be linked to the potential for overheating. There is currently limited research on overheating (in existing building stock) specific to Scotland; research on overheating risk in buildings housing vulnerable people is planned by ClimateXChange (2016). There is emerging research on new build low energy buildings in Scotland, with a focus on the Passivhaus approach of lightweight, airtight construction and mechanical ventilation and heat recovery ( MVHR). This suggests that a combination of poor design, effective heat retention and occupant behaviour has created health concerns due to overheating, especially among vulnerable groups (Morgan, et al., 2015). Morgan et al. (2015) suggests that there has been a lack of awareness of overheating in Scotland and its links to occupant behaviour and energy efficiency measures. Aspects relating to an individual's personal level of comfort and discomfort also need to be taken into consideration ( e.g. Lamond, 2011). Further research is, therefore, necessary on how best to adapt the Passivhaus approach to the Scottish climate and culture, and on the potential benefits of the approach for carbon savings and occupant health (Citizens Advice Scotland, 2016). In terms of potential adverse side effects, over-reliance on MVHR to draw in air from the outside can potentially lead to increased levels of Volatile Organic Compounds ( VOCs) and pathogens (Roaf et al., 2009). Others suggest that naturally ventilated buildings designed not to require MVHR are more beneficial both for thermal comfort and the general quality of indoor environments (Aynsley, 1999). Finally, there are also some concerns over the ability of householders to understand and use MVHR systems to optimise their benefits (Macintosh and Steemers, 2005). Lack of user understanding can be linked to poor guidance and inadequate training (Gupta and Kapsali, 2015). While inadequate commissioning of the systems can also cause insufficient air supply and corresponding poor indoor air quality (Gupta and Kapsali, 2015).

Mental health benefits complement the above physical health benefits. Inefficiently warmed homes cause thermal discomfort (Gilbertson, Grimsley and Green, 2012), and the associated fuel poverty can result in a range of stress responses including financial concerns over high energy bills (Anderson et al., 2000) and debt (Tod et al., 2012). Mental health impacts have been quantified in an innovative study relating to the Kirklees Warm Front programme. An overall cost benefit ratio of 0.2:1 was calculated for the whole project indicating that 20% of all costs were recouped due to health benefits. Of this 50% were attributed to mental health benefits (Liddell, Morris and Langdon, 2011).

The introduction of energy efficiency measures results in statistically significant reductions in stress ( IEA, 2014). The extent to which these benefits can be captured and used in the assessment of the impacts of energy efficiency policy is an area of current academic discussion ( IEA, 2014).

Heating only part of a home can be deployed as a measure to reduce fuel costs ( e.g. Chard and Walker, 2016). There is therefore the potential for an increase in usable space if energy efficiency measures result in more rooms being heated, thus improving an individual's relationship with their home and offering increased privacy and opportunities for studying and entertaining (Thomson, et al., 2013). Further research to understand these relationships in more detail is required in order to identify where and how policy measures can be targeted to maximise co-benefits to those most in need. There are also evidence gaps regarding the impact that housing improvements may have on reducing health inequalities (Thomson et al., 2013).

Qualitative evidence - fuel cost savings

Fuel cost savings arising from increased efficiency are also a key feature of research studies, with the potential to address fuel poverty discussed in the next section. There is the potential for substantial financial benefits through investment in energy efficiency. Sovacool (2015) indicates that £1 invested in UK Warm Front programme produced as much as £1 to £36.30 in benefits over a 20-year period. These benefits include monetised energy savings and resulting reductions in household costs but not health benefits. How these savings are spent by households determines the size of the net energy saving and any related co-benefits. Actual energy savings are often lower than anticipated, due to the 'rebound effect'. This can be because households choose to spend some or all of the money saved on fuel or on other goods and services (with associated GHG emissions), and because they tend to take some of the energy efficiency gains in the form of increased thermal comfort rather than reduced energy use. The results of various studies, including emerging evidence on Scottish households from a project undertaken by the Centre for Energy Policy at Strathclyde University ( CEP, forthcoming), show that the rebound effect is generally larger than is often assumed and that it applies asymmetrically across the population, with households on lower incomes taking back greater amounts of the savings from energy efficiency interventions as heat (and light).

The 'prebound' effect is a related but much less understood phenomenon that arises from under-consumption of energy by householders, leading to models over-predicting actual consumption and the savings from energy efficiency measures. This effect may result from householders choosing or adapting to lower internal temperatures, but it also results from behaviours to limit energy use, and so is a strong indicator of fuel poverty (Galvin and Sunikka-Blank, 2016). More research is needed on the prevalence, causes and impacts of self-limiting energy behaviours as the evidence for this, whilst consistent, remains largely anecdotal. New research indicating self-limiting behaviours with a group of households on the Isle of Skye contributes to this evidence base (Baker et al., 2016; Citizens Advice Scotland, 2016).

The magnitude of the fuel and GHG emissions savings from fabric improvements thus depends on how households choose to spend the money they save from lower energy bills. If the house was previously underheated, they may choose to turn the thermostat up, thus taking some or all of the savings as increased thermal comfort. There could also be a rebound effect if the money saved is spent on other high-carbon goods or services such as foreign travel or electrical appliances. Comfort savings or increased expenditure could be considered as co-benefits if this helps to increase quality of life and reduce social inequality, though the climate mitigation benefit may be lower than expected ( IEA, 2014). Energy efficiency and associated financial savings can, therefore, help address fuel poverty and have social equity benefits.

An increased understanding of these effects would enable better targeting of energy efficiency and fuel poverty schemes at households and communities where they can leverage the greatest co-benefits to poverty and health. Such an understanding would also enable a better understanding of geographic and socio-economic differences in householders' responses to energy efficiency interventions, as well as better modelling and forecasting of energy demand and emissions from the Scottish housing stock. Finally, it would enable the identification of, and support for, more effective and individualised behavioural and lifestyle change measures.

Qualitative evidence - fuel poverty

Fuel poverty can be defined in different ways - 1) As greater than ten percent of income being spent on staying warm (Boardman, 2010) and 2) Where the required fuel costs are above the median level and if the household were to spend that amount their residual income would be below the official poverty line (Hills, 2012). Fuel poverty is due to a mixture of factors including high energy costs, poor housing quality and lower incomes ( IEA, 2014). Poorly constructed and poorly maintained houses are more likely to be inhabited by people with low incomes and this has indirect impacts on equity and health outcomes (Liddell and Morris, 2010).

The energy efficiency measures identified in this report offer opportunities to reduce fuel poverty, reflecting upfront fuel cost savings to consumers. Energy efficiency measures can be a longer term solution rather than for example, fuel payments, because they address a key cause of fuel poverty ( IEA, 2014). The potential rebound effect can be considered to be positive if it helps reduce poverty or achieve broader well-being ( IEA, 2014). With regard to equity impacts there is an emerging literature that considers which socioeconomic groups could benefit most from grant based schemes. This can make a substantial contribution from an equalities perspective reflecting that the health and well-being benefits are greatest amongst vulnerable groups. This includes the elderly, children in low income households [3] and those with existing illnesses ( e.g. respiratory problems). These benefits can have broader implications for example the link between healthy homes and reductions in absenteeism from school and work. Reduced school absences have been identified in Howden-Chapman et al., 2008, 2009, 2012, and Preval et al., 2010 and reduced work absences in Howden-Chapman et al, 2007; Laing and Baker, 2006.

Health, and mental health, are generally thought to be both influences on and outcomes of energy behaviours and vulnerability to fuel poverty (Annesi-Maesano et al., 2014; Bernstein et al., 2008; Bornehag et al., 2001; CASD, 2010; Citizens advice Scotland, 2016; Fisk et al., 2007; Grant et al., 1989; Howden-Chapman et al., 2012; Jones, 1999; Kim et al., 2013; Liddell and Morris, 2010; Mendell et al., 2011; Nandasena et al., 2012; Thomson et al., 2003). Research is needed to understand these relationships in more detail including where and how policy measures can be targeted to maximise co-benefits to those most in need.

Qualitative evidence - economic benefits

The literature also indicates that there could be broader economic benefits from energy efficiency in the built environment, including supporting local jobs, with investment potentially supporting 9.2 to 17.1 jobs per million EUR invested (Copenhagen Economics, 2012; European Commission, 2011; Lehr, et al., 2012 in IEA, 2014). Retrofitting insulation has the possibility of providing local 'green' jobs (see e.g. Cecere and Mazzanti, 2015; Consoli et al., 2015; Lehr et al., 2012; Ryan and Campbell, 2012; UNEP, 2008; WHO, 2014).

A UK based study (Verco and Cambridge Econometrics, 2014) suggests that energy efficiency programs can deliver substantial positive economic impacts, with energy efficiency measures identified including draft proofing and loft insulation. The study used English Household survey data and found that energy efficiency programs could deliver £3.20 through increased Gross Domestic Product ( GDP) for every pound invested by the government, and the equivalent of a 0.6% GDP increase by 2030 compared to a baseline scenario. The investment assumed in the modelling totals £127.5 billion over a 20-year period, this covers direct government funding to low income homes (£26.9bn) and £73.2 bn from able-to-pay homes (which is supported by £27.4 bn of government loan schemes). The model also estimated a net growth of 108,000 jobs per annum over the period 2020 - 2030. Research (Lecca, et al., 2014) also suggests that if householders were to, because of cost savings, spend more money on goods and services then this would have positive impacts on GDP.

Energy efficiency in the non-domestic sector can also bring a number of benefits including reducing costs, increasing value and risk reduction ( IEA, 2014). Reductions in costs include less money spent on energy but also on maintenance. Risk reduction and the helping of companies maintain a competitive advantage while increasing value relates to Corporate Social Responsibility opportunities. The value of industrial productivity and operational benefits can be up to 2.5 times greater than the value of energy savings ( IEA, 2014). Improvements in productivity include reduced operating costs, reduced energy use and a resulting increased profit. It is possible that the increased competitiveness achieved through energy efficiency can filter down to the individual level, increasing job satisfaction and improving working conditions (Ryan and Campbell, 2012).

Quantitative models

Two key existing sets of models were identified which were of relevance to the Scottish context. These models are:

  • Health Impact of Domestic Energy Efficiency Measures ( HIDEEM)
  • Energy and Environmental Prediction model

Health Impact of Domestic Energy Efficiency Measures

The HIDEEM model enables the quantification of co-benefits such as economic energy savings, comfort benefits, air quality benefits, and the quantified impacts of improved life expectancy. HIDEEM was developed for DECC by the UCL Energy Institute and the Complex Built Environment Systems Group, in collaboration with the London School of Hygiene and Tropical Medicine (Hamilton et al., 2015; Payne et al., 2015; UCL, 2016).

The model provides a bottom-up household-level estimate of indoor environmental exposure in the housing stock of Great Britain. It captures changes in exposures following the application of energy efficiency measures, with resulting changes in health. This includes impacts on heart and circulatory disease, strokes, cancers, as well as respiratory illness and mental health disorders (Payne et al., 2015).

The aim of HIDEEM is to quantify indoor environmental conditions and monetise health impacts associated with energy efficiency changes in houses, in line with DECC's policy measures and interventions. The model's two main components are a physics-based model of the indoor environment in UK houses, and models quantifying associated health impacts of exposure changes using life table methods. Health impacts are monetised through Quality Adjusted Life Years ( QALYs). For example, HIDEEM estimates the value of health benefits arising from cavity wall insulation for a typical home as approximately £1,000, assuming an investment lifetime of 42 years. This is double the up-front investment cost for an easy-to-treat cavity wall, estimated as approximately £500 (Payne et al., 2015).

Data requirements

Example: HIDEEM

Hamilton et al., (2015) use the HIDEEM model to assess potential public health impacts arising from changes to indoor air quality and temperature if energy efficiency retrofits are undertaken in English dwellings to meet the 2030 carbon reduction targets. The study incorporates three retrofit scenarios: Scenario 1 assumes that fabric and ventilation retrofits are installed to meet building regulations. Scenario 2 utilises the same assumptions as Scenario 1, but with additional ventilation for houses at risk of poor ventilation and subsequent air quality. Finally, Scenario 3 utilises the same assumptions as Scenario 1, but with no additional ventilation.

The study identified changes in the QALYs over 50 years. QALYs are quality-adjusted life years and are a measure of length and quality of life in relation to health ( NICE, 2016). The study found a positive impact of Scenario 1 due to improved temperatures and a reduced exposure to air pollutants, while Scenarios 2 and 3 led to an overall negative health impact due to an overall increase in indoor pollutant exposure.

The study thus found that energy efficiency retrofits in housing can deliver improved health if properly combined with ventilation measures to reduce exposure to air pollutants.

Scenario Net mortality ( QALYs) per 10 000 individuals age >50 95% credible intervals
Scenario 1 2,241 2,085 to 2,397
Scenario 2 -728 -864 to - 592
Scenario 3 -539 -678 to - 399

HIDEEM uses survey data from the English Housing Survey ( EHS), including data on the archetype dwelling forms, to inform assumptions on English dwelling stock ( e.g. in relation to property type - detached, semi-detached, terraces and flats, floor area and notional permeability). EHS data incorporates information on age, sex and specific exposure changes. Life Tables are set up using age-specific population and mortality data.

For the Scottish context, data from the Scottish Household survey would need to be applied. The Scottish Household survey collects data relating to property and information on age and sex of occupants, which suggests that this could be used as a base for adaptation of the HIDEEM model to the Scottish context.

As with the use of all models, limits need to be acknowledged and model outcomes caveated accordingly. For example, Baker (2016) identifies two key weaknesses, 1) the uncertainties introduced by insufficient consideration of occupant behaviour, and 2) those introduced through insufficient consideration of toxicity. The former is a problem inherent in all housing models and so in this respect HIDEEM is the best currently available option however, the latter is potentially highly problematic as the range of VOCs covered is very limited and evidence from toxicological studies shows that mixtures of VOCs behave differently to individual VOCs in isolation ( e.g. Groten, Feron, & Suhnel, 2001). This is unlikely to be a significant problem for applying HIDEEM to existing unimproved stock. However, it will become more significant when the approach is applied to the use of more complex manufactured materials and PassivHaus-type buildings. It is important therefore to take these impacts into consideration when the model is used. It is also worth noting the uncertainties in modelling economic savings for the health benefits of energy efficiency interventions, with a recent evidence review (Citizens Advice Scotland, 2016) finding that energy efficiency interventions generally only act to delay the first reporting of symptoms. In this respect, there are no doubt some savings to be had from reduced numbers of GP appointments and associated support, but given the dominance of lifestyle factors in determining the severity and outcomes of health problems any further estimates of savings should be treated with a high degree of caution.

The Energy and Environment Prediction ( EEP) Model

Addressing aggregate emissions of larger urban areas will be crucial to reduce emissions, as opposed to a more ad-hoc house-by-house methodology. However, there are only a limited number of modelling tools attempting to quantify the impact of energy efficiency measures on a city scale. One such model is the Energy and Environment Prediction ( EEP) tool developed in collaboration with local authorities in Wales to estimate impacts on energy use in the existing urban built environment (Jones et al., 2007). The model consists of a user interface linked to a GIS database which is in turn linked to a series of sub-models on housing energy, non-domestic energy and health.

Data from the EEP tool ( e.g. on housing stock) was used in a study by Jones et al. (2007) to identify whether people's physical and mental health was affected by the quality of their housing (for the former) and the characteristics of the surrounding neighbourhood (for the latter). Physical health aspects examined home injuries alongside population data. While mental health aspects used two tools - one to assess neighbourhood quality and one to assess the mental health of the people. Findings show that mental health was overwhelmingly associated with economic factors such as jobs and income, and significantly associated with area deprivation and social capital (Jones et al., 2007). However, the research could not verify that older buildings had more negative effects on physical health (relating to hazards) than new buildings.

Data requirements

The EEP model and associated analysis suffers from several potentials barriers to use in a Scottish context. These include the substantial amount of data processing required to collect and process the data (Jones et al., 2007). The EEP housing sub-model for example, requires information on the size, shape and age of properties, with the method employed using a desktop survey, historical data to help estimate buildings age and a drive by survey of 55,000 dwellings (Jones et al., 2007). While the domestic energy sub-model is based on the UK government Standard Assessment procedure and data including information on glazing, fabric, space heating and fuel costs is used.

4.2 Behaviour change

Qualitative evidence

Addressing behaviour change is complex; behaviours are shaped by a great many factors which are not only individual but are also contextual (Lopes et al., 2012). Studies of behaviour change require an interdisciplinary approach including both qualitative and quantitative evidence and can, therefore, be complex and resource intensive. Behaviours are not constant, but change over time depending on accumulated experiences (Lopes et al., 2012).

A range of benefits might stem from behaviour change in the built environment sector, and unlike infrastructure development with timescales ranging from years to decades, savings stemming from behaviour change can often be achieved in the much shorter term (Cabinet Office, 2011).

Improved energy use behaviours through optimal heating and ventilation behaviour can also lead to improved health and increased wellbeing through improved indoor temperature, reduced air pollution from pollutants such as such as radon, reduced mould and more comfortable variations of indoor temperatures ( e.g. Citizens advice Scotland, 2016; Johnson et al., 2009). Improved indoor heating and ventilation can also hold social benefits as insufficient warmth, comfort and 'social acceptability' of a home might limit opportunities and willingness to build social networks by inviting friends and peers home ( DECC, 2014b). Recent Scottish research (Ellsworth-Krebs, 2016) explores meanings and concepts of thermal and home comfort in the context of energy demand highlighting the importance of acknowledging homemaking ( e.g. the potential preference for heritage features) when providing energy advice.

Furthermore, behaviour change including proportionately larger energy use during off-peak periods might provide indirect benefits to users through macroeconomic stability, reduced network losses and better energy services achieved at a lower cost (European Environment Agency, 2013; Ryan and Campbell, 2012). Energy efficiency improvements, and thus a reduced expenditure on energy bills, can drive increased consumer spending and, together with increased spending on energy efficiency products and reduced energy prices, can have the indirect effect of improving national competitiveness and supporting employment (Ryan and Campbell, 2012).

One of the challenges in achieving behaviour change is that people cannot fully understand the costs and benefits to themselves due to incomplete or uncertain information ( DECC, 2014a). Inadequate information and a lack of understanding or access to information on energy efficiency measures lead to ill-informed choices that may not be optimal for individuals and the environment ( DECC, 2014a). For example, a study conducted in Scotland looked at the effectiveness of installing new heating systems in houses. It revealed that many of the selected home owners did not fully understand how to operate their new heating systems, therefore, they were not being used in the most sustainable manner (Foster et al., 2016). Similarly, experiences of cold weather and houses that are expensive to heat created a perception that any heat is good. However, this was leading to inefficient systems, for example, hot water pipework that released heat due to insufficient insulation (Foster et al., 2016). A UK study found that the daily and seasonal operation of heating and ventilation systems was poorly understood due to a lack of easy-to-follow user guides and insufficient training during handover to users (Gupta and Kapsali, 2016). It can, therefore, be seen that both suppliers and occupiers need access to information to make choices that lead to efficient, sustainable homes and lifestyles. Research of relevance in the Scottish context is ESRC funded work on Smarter Homes which looks at how domestic low carbon technology influences demand with the work being undertaken (between 2013 and 2017), in part at the University of St Andrews.

A further challenge is that of scalability and this is important in understanding the quantification of potential benefits. Many policy actions that target behaviour change are focused on small changes at the individual level, the justification being that they are a precursor to larger scale behaviour change or that they initiate increased involvement in the political process of change (Creamer, 2015). However, the effects of these small, individual level behaviour changes are often hard to predict. It is uncertain to what extent they contribute to the overall, high level sustainability agenda. For example, some evidence suggests that individuals who change one part of their lifestyle to be more 'pro-environmental' may view this as off-setting another behaviour that is detrimental to the environment, thus provoking a negative rebound effect rather than an overall uptake of a sustainable lifestyle (Creamer, 2015).

Perhaps the most apparent and quantifiable co-benefits associated with behaviour change are both short and longer term financial impacts. However, the cost savings could be used to purchase more goods and services with an associated carbon impact (the previously discussed 'rebound effect'). However, modelling by Druckman et al. (2011) estimates that the rebound effect for lowering the household thermostat is relatively low, with just 7% of the energy savings being lost if households spend their energy cost savings following their usual consumption patterns.

Unlike investments such as insulation and new appliances, behaviour changes can often be achieved at no upfront cost (Cabinet Office, 2011). Savings from smart-metering, altered consumption patterns and optimised heating and ventilation behaviour can thus free up capital for other requirements, which is particularly relevant for households in economically disadvantaged communities (European Environment Agency, 2013; Kobus et al., 2015). Although potential rebound effects pose challenges to measures targeting energy efficiency and reduced energy use, households can be encouraged to recycle the financial benefits reaped through initial behaviour changes to finance more extensive infrastructure improvements such as insulation, which can address fuel poverty in the longer term.

Whilst campaigns to encourage energy-saving behaviour need to be simple, policymakers need to be wary of the 'blanket' approach as they are more successful if they are tailored to the community or to the level of innovation (McMichael and Shipworth, 2013). For example, in rural Scotland, it was found that the role of community-led organisations in encouraging behaviour change was crucial (Creamer, 2015). These organisations are embedded in the community and are therefore best placed to use local knowledge and establish networks to promote more sustainable lifestyles. They also have the necessary connections to build community capacity and increase individuals' willingness to change (Creamer, 2015). Research currently being led by the University of Edinburgh aims to analyse how the patterns of energy efficiency and heating demand differ between societies. This work, due to finish in June 2018, could be key in creating campaigns that are tailored to communities and society groups.

In the UK, 18% of the country's carbon footprint is made up of carbon dioxide emissions from non-domestic buildings, in particular, workplaces (Lockton et al., 2011). Promoting behaviour change in the commercial setting presents a challenge, as the individuals who are required to make the change do not pay the energy bills. This means that the financial incentive for change is significantly reduced (Christina et al., 2015). Business tasks often take priority and energy management can be seen as an additional task. In some cases, energy management can be seen as conflicting with business operations due to competition for individual workers' time (Christina et al., 2015). Research at the Edinburgh Napier University is seeking to investigate the energy consumption patterns in small to medium enterprises, assessing the possibility for behavioural change to reduce energy consumption. This work started in October 2014 and is due to be completed at the end October 2016.

The reduction in financial incentives for behaviour change in terms of investment in energy efficiency measures can also be seen in rental houses. The cost of energy efficiency improvements are borne by landlords whilst the benefits (lower energy bills) are received by the tenant and therefore there is less incentive for landlords to make energy savings.

Quantitative evidence

There is limited modelling of these benefits and challenges within a co-benefits framework and there is recognition (Smith et al., 2016) that further research on behaviour change from a social and behavioural science perspective is required to more fully understand how savings can be achieved in the longer term. Quantitative modelling outcomes of relevance e.g. regarding the rebound effect ( e.g. Druckman et al., 2011) have been detailed in the previous section, with the rebound effect identified as a key future research area (discussed in Chapter 8).

4.3 Building design

A number of building design criteria exist which can help reduce energy demands. These include the orientation of buildings; shading of buildings; passive cooling and passive heating ( e.g. Pacheco et al. 2012) as well as optimisation for natural light. The benefits of associated energy demand reduction include future energy security and air quality benefits and are detailed in Chapter 7. The optimisation for natural light can result in increased worker productivity (Figueiro et al., 2002).


Contact

Email: Debbie Sagar