Building regulations - new non-domestic buildings - modelling of proposed energy improvements: research report

Task 1: Establish Current Baseline

25. Task 1 is to establish the baseline from which to evaluate change. To achieve this, it is first necessary to establish the national build profile for Scotland for the analysis. This requires the derivation of a suitable number of representative building types and sub-types. Building types refer to the function of the building (office, school etc.) which sub-types represent distinct combinations of building type, heating fuel and HVAC strategy. The selected sub-types should be representative of the buildings added to the Scottish building stock over the last few years.

1.4 Establish the National Annual Build Profile for New Buildings

26. To derive the building sub-types an analysis has been undertaken of the EPC database for new non-domestic building over the period from January 2013 to March 2019. This analysis and the resulting sub-types are described below.

27. The EPC database contains information on each of the circa 1,800 EPCs lodged during this period. This includes several parameters such as building type, floor area, EPC rating and some information about the building fabric and services.

28. The client requested that for the purposes of this project, the building types/models should be selected from those used to support changes to building regulations for England and Wales as shown below. These models are readily available and it allows a comparison between national improvements.

England

Office – deep plan, air conditioned

Office – shallow plan, naturally ventilated

Hotel

Hospital

Secondary School (includes sports facilities)

Retail Warehouse

Distribution Warehouse

Wales

Primary School

Office; naturally ventilated

Office, air conditioned

Hotel

Small Warehouse/ Industrial

Medium Warehouse/ Industrial

Large Warehouse/ Industrial

Integrated Health Care Centre

Multi-Residential

A1 Retail (small food)

29. The EPC database does not map directly to these building types but rather uses the building types embedded within SBEM which are based on the UK planning classification system. It was therefore necessary to map these planning classification categories to the building model types available; this mapping is shown in Table 1 and is based on building uses/profiles. In a small number of cases the mapping is a compromise (e.g. universities/colleges have been mapped to primary school). In some cases no logical mapping has been possible, however these cases account for less than 2% of the total floor area. Floor areas stated in Table 1 are the total floor area between January 2013 to March 2019. Given that for the first year the build-rate is significantly underestimated on the database as the requirement for the EPCs had just commenced, it was agreed with the Building Standards Division that the floor area in this table represent a total over 5 years.

Table 1: Mapping of EPC database building types to model types showing total database floor area

EPC Database Building Types	Sub-type for offices	Floor area (m²)	Floor area (%)	Available Model Types
Universities/college		519,230	15.0%	Primary School
General Assembly/Leisure		148,034	4.3%	Retail
Office/Workshop	Office/Workshop; Mixed-mode with Natural Ventilation; Shallow	745	0.0%	Shallow Office NV
	Office/Workshop; Heating and Natural Ventilation; Shallow	132,990	3.8%	Shallow Office NV
	Office/Workshop; Heating and Natural Ventilation; Deep	62,553	1.8%	Shallow Office NV
	Office/Workshop; Air Conditioning; Deep	441,408	12.8%	Deep Office AC
	Office/Workshop; Air Conditioning; Shallow	25,987	0.8%	Deep Office AC
	Office/Workshop; Heating and Mechanical Ventilation; Shallow	38,696	1.1%	Shallow Office NV
	Office/Workshop; Mixed-mode with Natural Ventilation; Deep	2,182	0.1%	Shallow Office NV
	Office/Workshop; Mixed-mode with Mechanical Ventilation; Deep	3,386	0.1%	Shallow Office NV
	Office/Workshop; Heating and Mechanical Ventilation; Deep	5,005	0.1%	Shallow Office NV
Storage/Distribution		127,043	3.7%	Warehouse Distribution
Retail/Financial		296,221	8.6%	Retail
Education		841,403	24.3%	Primary School
Residential space		33,135	1.0%	Hotel
Residential school		59,731	1.7%	Hotel
Restaurant/Cafes/takeaway		33,418	1.0%	Retail
General Industrial		90,428	2.6%	Warehouse Distribution
Hospitals/Care Home		256,331	7.4%	Hospital
Stand alone utility block		687	0.0%
Library/Museum/Gallery		34,458	1.0%
Hotel		166,298	4.8%	Hotel
Emergency service		536	0.0%
Community/Day Centre		47,610	1.4%
Primary Healthcare Building		75,773	2.2%	Deep Office AC
Secure Residential Institution		577	0.0%	Hospital
Misc. 24 hr activity		61	0.0%
Miscellaneous 24 hr activity		9,731	0.3%
Passenger terminal		3,722	0.1%

30. Table 1 also shows how the office building type has been split into sub-types which reflect the two building forms available in the English and Welsh models (deep-plan and shallow-plan). The EPC database does not directly make this deep/shallow-plan distinction, so a definition of deep and shallow plan has been based on the ratio of building floor area to building surface area which is available in the database.

31. On this basis it was found that the EPC database contains 102 sub-type combinations of mapped building type, heating fuel and HVAC strategy. However, for this analysis, it is sufficient to focus on the most common sub-types as these will allow sufficient determination of the impact of any changes to Building Standards – additional sensitivity analyses can be undertaken if necessary on less common, but important, sub-types.

32. Selection of the most prevalent sub-types was undertaken through focussing on the most dominant heating fuels and HVAC strategies. For this purpose, it was necessary to achieve at least 5% of the total floor area represented in the EPC database. Where less than 5% was achieved, the most similar dominant strategy was identified for the analysis (an alternative would be to pro-rata across all heating fuels/HVAC strategies). This mapping is shown in Table 2 and Table 3:

Table 2: Mapping of dominant heating fuel types.

Heating Fuel	Floor Area (%)	Mapped Heating Fuel
Natural Gas	62.4%	Natural Gas
Grid Supplied Electricity	23.0%	Grid Supplied Electricity
Biomass	8.0%	Biomass
District Heating	3.3%	Natural Gas
LPG	1.8%	Natural Gas
Oil	1.2%	Natural Gas
Other	0.1%	Natural Gas
Waste Heat	0.3%	Natural Gas
Biogas	0.0%	Natural Gas
Dual Fuel Appliances (Mineral + Wood)	0.0%	Natural Gas

Table 3: Mapping of dominant HVAC strategies.

HVAC Strategy	Floor area (%)	Mapped HVAC Strategy
Heating and Natural Ventilation	46.3%	Heating and Natural Ventilation
Air Conditioning	31.2%	Air Conditioning
Heating and Mechanical Ventilation	21.9%	Heating and Mechanical Ventilation
Mixed-mode with Mechanical Ventilation	0.2%	Heating and Mechanical Ventilation
Mixed-mode with Natural Ventilation	0.2%	Heating and Natural Ventilation
Unconditioned	0.1%	omit

33. this basis the number of building sub-types is reduced to 44, these are shown in Figure 1. This shows that only four building sub-types account for more than 5% of the total EPC database floor area; these are highlighted in green in Figure 1. A final round of selective mapping was then undertaken to group the remaining sub-types together into groups accounting for more than 5% of the total floor area. This process mapped non-dominant sub-types together into groups accounting for more than 5% of floor area or, in some cases, mapped these to sub-types that already accounted for more than 5% individually. This process results in the twelve sub-types shown in Table 4; these include seven building types, three heating fuels and three HVAC strategies. These twelve sub-types are assumed to comprise the national build mix for this analysis. Table 5 provides a summary of the key dimensions of the seven building types used in this analysis.

Table 4: Twelve building sub-types selected for analysis.

Building Sub-types	Floor Area (m²)	Floor Area (%)
Deep Office AC; Gas; AC	261,444	8%
Deep Office AC; Elec; AC	281,724	8%
Hospital; Gas; NV	256,908	8%
Hotel; Gas; NV	112,741	3%
Hotel; Gas; AC	146,423	4%
Primary School; Biomass; NV	198,659	6%
Primary School; Gas; MV	404,090	12%
Primary School; Gas; NV	757,884	23%
Retail; Gas; AC	280,346	8%
Retail; Elec; AC	197,327	6%
Shallow Office NV; Gas; NV	245,557	7%
Warehouse Distribution; Gas; NV	217,471	6%

Table 5: Sample building type summary of key dimensions

Building Type	Floor area (m²)	Number of Storeys	External Wall Area (m²)	External Glazed Area (m²)
Deep-plan Office	12,100	5	4,000	1,500
Hospital	13,387	5	6,212	1,092
Hotel	1,087	3	903	319
Primary School	2,353	2	1,443	346
Retail	1,250	1	600	60
Shallow-plan Office	2,160	3	1,218	487
Distribution Warehouse	5,262	Warehouse=1 storey Integral office=2 stories	2,463	684

34. In general, the building models will be those used for similar building regulations analysis in England. The one exception is the retail category where there was further investigation as to whether it is best represented by the small retail unit used for analysis in Wales or the larger retail warehouse used for the English analysis. The Welsh retail unit is a small detached retail building with a small office and storage and has a floor area of 1,250m² and the English retail warehouse is a detached building including a large retail floor and back-of-house office and other staff facilities including changing rooms and kitchen with a total floor area of 5,262m². Figure 2 shows the floor area distribution of the retail building category in the EPC database. This shows that there is a small number of retail units with large floor areas (typically one or two units of each size over around 3,000m²) but most retail floor area is in units with floor areas between 1,000m² and 3,000m². On this basis it is recommended that the smaller Welsh retail unit is used for this analysis as its floor area falls within this range.

1.5 Model Building Sub-types

1.5.1 Baseline specs

35. To derive a baseline 2015 compliant specification for the sample buildings identified in the national profile above, as a default the specifications are based on the Section 6 2015 notional building values for the relevant fuel type. These values have been modified to reflect common practice where it is found to differ from the Section 6 2015 notional building values/approach. For example, there have been significant changes in lighting technology, with the significant adoption of LED lighting, over the last five years.

36. The modifications have been informed by a review of the following data source:

• EPC data made available by Scottish Government. There is significant variation in the design specifications used in projects – hence many projects do not simply adopt the values in the notional building. To identify tendencies for a significant difference between actual build and the values used in the notional building a data analysis has been undertaken to identify potential differences. The notional building values have been changed if they are in the lower quartile of energy performance (0-25%) or upper quartile of energy performance (75-100%) of the EPC database distribution i.e. do not tend to be common practice. In such cases, a value around the median has been adopted.

37. We also reviewed information from the consultation responses provided to the Scottish Government’s 2018 Scottish Building Regulations: Review of Energy Standards: ‘Call for Evidence’. This provides evidence on approaches to meeting 2015 standards.^[1] We identified no relevant evidence that differed or added to learning from the EPC database analysis.

38. Any amendments proposed to the baseline specifications from that given in the notional 2015 building(s) have been agreed with Scottish Government and modelled DERs/BERs are kept within 1% of the TER, see Section 1.5.5.

39. The non-domestic EPC database does not contain the same level of detailed information as is present in the domestic EPC database and so fewer conclusions can be drawn from it.

40. Several anomalies were identified in the database, and these cases have been excluded from the analysis described below to derive a robust sample of EPCs from which conclusions can be drawn. The following steps were taken to remove anomalies prior to analysis:

• All EPCs lodged prior to the end of 2016 were removed. The current version of Section 6 came into effect from October 2015. EPCs lodged prior to this will have been for buildings designed to be compliant with an earlier version of Section 6. Given the transitional arrangements and the length of typical design and construction programmes EPCs lodged for several months after this date are also likely to have been compliant with earlier versions of the regulation.
• EPC records which appear to fail to comply with Section 6 (i.e. TER<BER) were removed. Although the database provided was apparently for new-build projects only, it is possible that a significant number of EPCs are lodged incorrectly as being for new-build when they are for previously existing buildings and/or buildings built under previous versions of Section 6 were still being lodged through the sample period.
• Several EPC records were identified which showed the Notional Building average U-value to be zero. The reason for this is not apparent, however these cases have been removed from the analysis.
• Unconditioned buildings have been removed from the analysis.

41. Naturally ventilated buildings have been analysed separately from mechanically ventilated/ cooled buildings to assess the effect of the differences in energy demand balances.

1.5.2 Building Fabric

42. The EPC database does not contain information on individual building elements (walls, windows etc.), rather the database contains the following metrics:

• Air tightness;
• Average (area-weighted) building U-value;
• Average building thermal bridging (alpha-value).

1.5.2.1 Air Tightness

43. Figure 3 and Figure 4 show the distribution of air tightness values for naturally ventilated and mechanically ventilated/cooled buildings respectively. These two distributions are fairly similar showing that the most common air tightness in both cases is between 4 and 5m³/m²/hr @50Pa. This trend is more pronounced for naturally ventilated buildings whilst mechanically ventilated/cooled show a similar but weaker trend.

44. On this basis the air tightness for the compliant solutions is set to 5m³/m²/hr @ 50Pa to match the current notional building.

1.5.2.2 U-values

45. The building average U-value gives only a limited insight into the individual U-values being used for the different fabric elements. Buildings with large amounts of glazing will tend to have higher average U-values than those with less glazing. Similarly those with different built forms will have a different average U-value if a certain envelope element (e.g. wall) takes up a greater or less proportion of the total surface area.

46. Some overall insight is provided by comparing the average U-value of the Actual and Notional buildings. Figure 5 and Figure 6 compare the Actual and Notional average U-values for naturally ventilated and mechanically ventilated/cooled buildings respectively. Both graphs show that there is a tendency for the U-values of the Actual buildings to be less (i.e. better) than those of the Notional buildings, however this tendency appears to be limited.

47. On this basis the U-values for the compliant solutions are set to replicate the current notional building specification.

1.5.2.3 Thermal Bridging

48. The EPC database contains records for EPCs generated through both SBEM and DSM software. SBEM requires that the user inputs thermal bridging values on an individual basis (or uses default values). However in DSM software, the thermal bridges are generally input as a simple percentage adjustment to U-values, the default value being 10%. The EPC records show that the vast majority of DSM EPCs simply use the 10% default value, so the analysis here focusses on SBEM records only.

49. Figure 7 and Figure 8 compare the Actual and Notional thermal bridging values for naturally ventilated and mechanically ventilated/cooled buildings respectively. No clear trend is evident in these two graphs. However, it appears that thermal bridging in Actual buildings tends to be a little worse than Notional buildings. The median values for the Actual and Notional buildings with natural ventilation are 20.3 and 18.5 respectively. For buildings with mechanical ventilation/cooling these values are 20.3 and 18.4. This also suggests that there is not much variation in thermal bridging in relation the building servicing strategy.

50. As the difference between the actual and notional buildings appears relatively small, the thermal bridging for the compliant solutions are set to replicate the current notional building specification.

1.5.3 Building Services

51. The EPC database contains some limited information on individual building service efficiencies. Heating and cooling efficiencies are included in the database, however no other building service parameters can be viewed directly. The EPC database does include data on the modelled energy demand of each end use (heating, cooling, hot water, lighting and fans and pumps), and some conclusions may be drawn from this data.

52. The range of realistic efficiencies for building services is strongly influenced by the type of system installed. Analysis of the different HVAC system types recorded in the EPC database is shown in Figure 9. This shows that three system types dominate:

• Underfloor heating with natural ventilation;
• Radiators with natural ventilation;
• Split or multi-split heating and cooling.

1.5.3.1 Heating

53. The efficiency of a heating system is strongly influenced by the type of heat generator in use. For example, the typical efficiency of a gas boiler is between 85% and 95%, whereas for a heat pump the typical efficiency is much higher (3-6, i.e. 300% to 600%). Figure 10 shows the percentage floor area in the EPC database which is heated by each heat generator type.

54. On this basis the compliant solutions will use either LTHW boilers or ASHPs for heating and hot water, see Table 4. The subsequent graphs analyse the ranges of efficiencies recorded for these two heat generator types.

1.5.3.1.1 LTHW Boiler

The seasonal efficiency of the heat generator in the notional building is dependent on the generator and fuel type. For gas-fired heating, the notional seasonal efficiency is 91%^[2] for side-lit spaces, and for biomass boilers the notional seasonal efficiency is 70% for both side-lit and top-lit spaces. Figure 11 and Figure 12 and Table 6 and Table 7 show that in both cases these notional seasonal boiler efficiencies fall below the 25^th percentile recorded in the EPC database, however Section 1.5.4 shows that the Notional and Actual heating demands are often similar and so the compliant solutions described in Section 1.5.5 have adopted the notional boiler efficiencies.

Table 6: LTHW gas boiler percentiles.

Percentile	Seasonal Efficiency
75th	0.97
50th	0.96
25th	0.92

Table 7: LTHW biomass boiler percentiles.

Percentile	Seasonal Efficiency
75th	0.93
50th	0.92
25th	0.90

1.5.3.1.2 Air Source Heat Pump (ASHP)

55. The data recorded in the EPC database does not explicitly differentiate between ASHP systems drawing heat from outside air to serve water-based systems and those that can move heat within buildings (Variable Refrigerant Flow – VRF) or those that deliver warm air rather than using water as the working fluid (split systems). However, this differentiation can be inferred from the NCM system type. Whilst VRF and split systems are often more efficient, ASHPs serving wet systems are more widely suitable to the new non-domestic building stock than VRF and split systems. For example, VRF and split systems are generally deemed inappropriate for clinical applications due to difficulty in cleaning. They are also more limited by the maximum pipe length they can accommodate (although this is increasing as technology develops). Hence, if ASHPs are to be included in the new notional buildings, it appears sensible to specify them based on characteristics of those ASHPs serving wet systems.

56. Figure 13 and Table 8 show the percentage distribution of ASHP efficiencies for wet heating systems only. When VRF and split systems are included, the recorded efficiencies tend to be higher.

57. The current notional building seasonal efficiency for ASHPs is 1.75. Figure 13 and Table 8 show that this falls well below the 25^th percentile recorded in the EPC database and is therefore not deemed representative of current build.

58. Whilst VRF and split systems are not universally suitable, they are widely used and so it is reasonable to assume that for the two building sub-types in the national profile where ASHP is the heat source (office and retail), these system types might be used. Therefore, the currently compliant solutions for these two building sub-types described in Section 1.5.5 assume a SCoP of 4.0, which is the median value for all ASHP systems in the EPC database (not shown here) and the 75^th percentile for ASHPs serving wet heating systems.

Table 8: ASHP percentiles.

Percentile	Seasonal Efficiency
75th	4.0
50th	3.4
25th	3.2

The EPC database does not contain information on the heating system flow temperatures assumed, and for most heat sources this variable has a minor impact, however ASHP efficiency is strongly influenced by the temperature which it is supplying; this matter is discussed in detail in Section 1.8.

1.5.3.1.3 Radiant Heaters (for top-lit spaces)

59. Although Figure 10 shows that radiant heaters are only used in a small percentage of total floor area recorded in the EPC database, this type of heating is the dominant type for naturally ventilated top-lit spaces such as distribution warehouses. The notional building seasonal efficiency for radiant heaters is 86%. Figure 14 and Table 9 show that this falls slightly below the 25^th percentile recorded in the EPC database. Top-lit naturally-ventilated buildings generally have relatively few parameters which can be improved upon to achieve compliance as there is no cooling, mechanical ventilation and DHW demand is generally low. Therefore, compliance is generally achieved through improvements to heating plant, lighting and fabric. On this basis the compliant solutions described in Section 1.5.5 have used the 75^th percentile (92%) for the naturally ventilated distribution warehouse (i.e. the only sample building in which there is a top-lit space).

Table 9: Radiant heaters percentiles.

Percentile	Seasonal Efficiency
75th	92%
50th	90%
25th	87%

1.5.3.2 Cooling

60. The notional SEER for cooling systems is 4.5. Figure 15 and Table 10 show that this falls between the 25^th percentile and 50th percentile recorded in the EPC database, however Section 1.5.4 shows that the Actual cooling demands are generally much less than the Notional and so the compliant solutions described in Section 1.5.5 have adopted higher cooling efficiencies in cases where cooling is used.

Table 10: Cooling percentiles.

Percentile	Seasonal Efficiency
75th	6.4
50th	5.6
25th	4.0

1.5.4 Routes to compliance

61. Table 11 shows graphs comparing the actual energy demands divided by the notional energy demands for each end use. From this analysis the following trends can be identified:

• Heating demand:
• Naturally ventilated: Most commonly, the actual demand is similar to that of the notional building.
• Mechanically ventilated/cooled: Generally, the actual demand is lower than the notional building.
• Cooling demand:
• Mechanically ventilated/cooled: Tendency for the actual demand to be significantly lower than the notional building.
• Fans and pumps energy demand:
• Naturally ventilated: Tendency for the actual demand to be significantly higher than the notional building.
• Mechanically ventilated/cooled: Tendency for the actual demand to be significantly higher than the notional building.
• Lighting energy demand:
• Naturally ventilated: Tendency for the actual demand to be significantly lower than the notional building.
• Mechanically ventilated/cooled: Tendency for the actual demand to be significantly lower than the notional building.
• Domestic hot water energy demand:
• Naturally ventilated: Tendency for the actual demand to be significantly higher than the notional building.
• Mechanically ventilated/cooled: Tendency for the actual demand to be significantly higher than the notional building.
• Displaced lighting energy demand:
• Naturally ventilated: Tendency for the actual demand to be significantly higher than the notional building.
• Mechanically ventilated/cooled: Tendency for the actual demand to be significantly higher than the notional building.

62. On the basis of this analysis, the following approach has been taken to adjusting buildings to pass within 1% of the TER:

• First alteration: where ASHP is present increase the efficiency up to a maximum SCoP of 4.0 (i.e. 75^th percentile shown in Table 8).
• Second alteration: where cooling is present increase the efficiency of the cooling up to a maximum SEER of 5.6 (i.e. 50^th percentile shown in Table 10).
• Third alteration: where the previous alteration is insufficient, increase the efficiency of general lighting up to a maximum of 95 luminaire lumens per circuit watt.
• Fourth alteration: where the combination of the previous alterations is insufficient, increase the area of PV until compliance is achieved.

63. The current 2015 version of the NCM Modelling Guide specifies that the notional building will have a PV array with the area being the lesser of: (i) 4.5% of the Gross Internal Area (GIA) and (ii) 50% of the roof area. The Guide also specifies that the PV output will be 120kWh/m². An SBEM user cannot specify the output of the actual building PV array in this way so the output per square meter in the actual building is a function of the variables available to the user such as orientation, pitch, ventilation and panel type^[3]. The default nominal efficiency for monocrystalline panels (the most efficient option available to the user) is 8.8%, and the user cannot change this. However, the efficiency required to achieve an annual output of 120kWh/m² is approximately 16% (depending on the variables listed above). Photovoltaic panels are available with efficiencies up to 20% so 16% is a reasonable value for the notional building to have adopted. One effect of this is that if the user inputs a PV array equivalent to 4.5% of the GIA (thus matching the area used by the notional building) then the modelled output in the actual building will be significantly less. If the user inputs the optimal values from the options available in SBEM (south facing, 30° inclination, no or very little shading, monocrystalline, strongly ventilated) then the ratio between the output per square meter for the notional and actual buildings is 1.79 i.e. the output from the actual building is significantly below that of the notional building. This effect is shown in Table 12 below which demonstrates significant increases in PV array size or improved service specifications to achieve compliance compared to the notional building specifications.

1.5.5 Compliant Specifications

64. Adopting the method described above the sample models have been modified away from the notional building specification to achieve a BER within 1% of the TER. The deviations from the notional specifications used to achieve this are shown in Table 12, this is based on the hierarchy described in Paragraph 62:

Table 12: Deviations from notional specification to achieve compliance

Building Sub-types	ASHP SCoP[4] Changed	Cooling SEER Changed	Lighting Efficacy Changed	PV Area Changed: Area Input by User	PV Area Changed: Equivalent Area Based on 120kWh/m²
Deep Office AC; Gas; AC	NA	5.6	80llm/cW	Match notional
Deep Office AC; Elec; AC	4.0	5.6	72llm/cW	Match notional
Hospital; Gas; NV	NA	NA	54llm/cW	0m²	0m²
Hotel; Gas; NV	NA	NA	95llm/cW	Match notional
Hotel; Gas; AC	NA	5.6	95llm/cW	300m²	168m²
Primary School; Biomass; NV	NA	NA	75llm/cW	Match notional
Primary School; Gas; MV	NA	NA	80llm/cW	Match notional
Primary School; Gas; NV	NA	NA	NA	175m²	98m²
Retail; Gas; AC	NA	5.2	NA	Match notional
Retail; Elec; AC	4.0	5.0	NA	Match notional
Shallow Office NV; Gas; NV	NA	NA	70llm/cW	Match notional
Warehouse Distribution; Gas; NV	NA	NA	82llm/cW	Match notional

65. Key results from the modelling are set out in Table 13. The columns show the emissions rates and primary energy values as calculated in SBEMv5.6.a.1, using current carbon emission and primary energy factors.

Table 13: CO₂ emissions rates using Section 6 2015 factors

Model Name	CO2, BER	CO2, TER	CO2, Margin
Deep Office AC; Gas; AC	21.5	21.4	0.5%
Deep Office AC; Elec; AC	22.5	22.4	0.4%
Hospital; Gas; NV	38.4	38.6	-0.5%
Hotel; Gas; NV	74.5	74.2	0.4%
Hotel; Gas; AC	81.1	81.7	-0.7%
Primary School; Biomass; NV	5.5	5.5	0.0%
Primary School; Gas; MV	11.0	11.1	-0.9%
Primary School; Gas; NV	13.1	13.0	0.8%
Retail; Gas; AC	60.9	60.6	0.5%
Retail; Elec; AC	61.5	61.2	0.5%
Shallow Office NV; Gas; NV	14.6	14.7	-0.7%
Warehouse Distribution; Gas; NV	28.7	28.7	0.0%

66. MHCLG has proposed updated factors which it is understood that the Scottish Government will also adopt. Table 14 and Table 15 show the proposed new factors. In contrast, the current Section 6 carbon emission and primary energy factors for all grid-supplied and grid-displaced electricity is 0.519kgCO₂/kWh and 3.07 kWh/kWh respectively. MHCLG proposes that the new electricity factors should vary on a monthly basis rather than using a single figure for the whole year. This approach is intended to reflect the seasonal variations in the UK electricity grid which is increasingly influenced by the seasonal variation in renewable generation and variations in demand, see Table 15.

Table 14: Current and proposed CO₂ and primary energy factors for combustion fuels

Fuel type	Current Section 6 2015 Factors (kgCO2/kWh)	Proposed New Factors (kgCO2/kWh)	Proposed New Factors ( kWh/kWh )
Natural gas	0.216	0.210	1.126
LPG	0.241	0.241	1.141
Biogas	0.098	0.024	1.286
Fuel oil	0.319	0.319	1.180
Coal	0.345	0.375	1.064
Anthracite	0.394	0.395	1.064
Manufactured smokeless fuel (inc. Coke)	0.433	0.366	1.261
Dual fuel (mineral + wood)	0.226	0.087	1.049
Biomass	0.031	0.029	1.037
Waste heat	0.058	0.015	1.063

Table 15: Proposed new CO₂ and primary energy factors for electricity

Month	Grid Supplied Electricity kgCO2/kWh	Grid Supplied Electricity kWh/kWh	PV-Generated Electricity kgCO2/kWh	PV-Generated Electricity kWh/kWh
Jan	0.163	1.602	0.196	1.715
Feb	0.160	1.593	0.190	1.697
Mar	0.153	1.568	0.175	1.645
Apr	0.143	1.530	0.153	1.567
May	0.132	1.487	0.129	1.478
Jun	0.120	1.441	0.106	1.389
Jul	0.111	1.410	0.092	1.330
Aug	0.112	1.413	0.093	1.336
Sep	0.122	1.449	0.110	1.405
Oct	0.136	1.504	0.138	1.513
Nov	0.151	1.558	0.169	1.623
Dec	0.163	1.604	0.197	1.718
Average	0.139	1.513	0.146	1.535
2015 Factor	0.519	3.070	0.519	3.070

67. The compliant modelling results have been post-processed to show the effect of using the proposed new factors in Table 14 and Table 15; these adjusted results are shown in Table 16. The proposed new CO₂ factors for gas, biomass and electricity are all lower than those currently in use, the primary energy factors are mostly lower except biomass, and so the actual and notional building values shown in Table 16 are all lower than those in Table 13. The result is that the compliance margins increase in some cases and reduce in others; the sample buildings range between a 14.2% pass to a 4.5% fail.

68. A change to primary energy as the main target metric would have significant implications for how different specification options perform in relation to each other, compared to when applying the carbon emission metric used in 2015. In particular, it can be seen that whilst the proposed CO₂ for electricity is significantly lower than that of gas (ratio of 0.66), the proposed average annual primary energy factor for electricity is higher than that of gas (ratio of 1.34). This impacts on comparisons between gas and electric options and will mean that electric-heated options will tend to show larger relative reductions in carbon emissions than in primary energy.

69. The proposed change in carbon emission and primary energy factors has a significant impact on the relative attractiveness of different renewable technologies in primary energy terms. The new lower factors for grid-supplied electricity will favour technologies which use electricity (such as heat pumps) and be less favourable for technologies which generate electricity such as CHPs and wind turbines. Conversely renewables which offset fossil fuel use (typically through the generation of heat) will be favoured by these changes, for example solar thermal systems are likely to become more appealing to building design teams. Electricity generated by solar PV arrays is allocated a different set of factors to other electricity; this is intended to reflect the diurnal variation in PV output; PV output occurs during daylight hours when the prevailing grid carbon factor is higher in winter but lower in summer. The net effect of these changes to the factors for solar PV is expected to reduce the attractiveness of this technology.

70. The implications of the change to primary energy as the main target metric will be illustrated and discussed more fully in Section 0, including consideration of the need for a secondary carbon metric (see Section 1.16).

Table 16: CO₂ emissions rates and primary energy values using proposed new factors

Model Name	CO2 BER, kgCO2/m²	CO2 TER, kgCO2/m²	CO2 Margin, kgCO2/m²	Primary Energy BPE, kWh/m²	Primary Energy TPE, kWh/m²	Primary Energy Margin, kWh/m²
Deep Office AC; Gas; AC	7.3	7.0	4.5%	67.8	66.5	1.9%
Deep Office AC; Elec; AC	6.0	6.0	0.6%	65.8	65.3	0.7%
Hospital; Gas; NV	24.4	28.5	-14.2%	157.9	171.7	-8.0%
Hotel; Gas; NV	66.9	67.2	-0.5%	373.2	370.8	0.6%
Hotel; Gas; AC	58.1	57.9	0.4%	354.9	355.6	-0.2%
Primary School; Biomass; NV	2.7	2.6	3.8%	66.3	68.2	-2.8%
Primary School; Gas; MV	6.9	7.1	-2.4%	44.7	45.8	-2.5%
Primary School; Gas; NV	9.3	9.9	-6.1%	57.0	58.8	-3.1%
Retail; Gas; AC	17.9	17.9	-0.1%	182.8	182.1	0.4%
Retail; Elec; AC	16.4	16.2	1.0%	179.2	177.9	0.7%
Shallow Office NV; Gas; NV	9.2	9.2	-0.1%	60.4	60.0	0.6%
Warehouse Distribution; Gas; NV	23.4	23.0	1.6%	135.2	133.7	1.1%

71. Further detail of the results from the compliant solutions is shown in the three tables below, which are broken down by fuel type and by energy end use. Table 17 shows the calculated energy demands; Table 18 shows these converted into primary energy using the proposed new factors and Table 19 shows the CO₂ emissions resulting from the proposed new factors. The totals shown sometimes differ slightly from the sum of the listed fuel demands, which is due to rounding to one decimal place.

Table 17: Calculated energy demand and generation for compliant solutions (kWh/m²)

Model Name	Floor area (m²)	Gas	Biomass	Grid-supplied Electricity	Displaced-electricity	Heating	Cooling	DHW	Auxiliary	Lighting	Total	Total minus Displaced
Deep Office AC; Gas; AC	12,000	10.3	0.0	43.7	5.4	7.2	8.5	3.1	16.7	18.5	54.0	48.6
Deep Office AC; Elec; AC	12,000	0.0	0.0	50.1	5.4	1.6	8.8	2.8	16.8	20.1	50.1	44.7
Hospital; Gas; NV	12,754	92.7	0.0	36.3	0.0	50.8	0.0	41.8	3.0	33.3	128.9	128.9
Hotel; Gas; NV	1,063	307.7	0.0	21.6	5.5	107.9	0.0	199.8	3.9	17.7	329.3	323.8
Hotel; Gas; AC	1,063	238.1	0.0	75.7	16.6	38.3	11.7	199.8	46.3	17.7	313.8	297.2
Primary School; Biomass; NV	2,353	0.0	52.7	13.2	5.3	33.2	0.0	19.5	2.0	11.2	65.9	60.6
Primary School; Gas; MV	2,353	24.9	0.0	16.6	5.4	9.9	0.0	15.0	5.8	10.9	41.6	36.2
Primary School; Gas; NV	2,353	37.1	0.0	14.9	4.7	22.1	0.0	15.0	2.0	12.9	52.0	47.3
Retail; Gas; AC	1,250	10.9	0.0	121.2	5.4	9.0	48.0	1.9	23.5	49.8	132.2	126.8
Retail; Elec; AC	1,250	0.0	0.0	127.0	5.4	2.1	50.1	0.9	23.5	50.4	127.0	121.6
Shallow Office NV; Gas; NV	2,160	35.1	0.0	19.3	5.4	32.1	0.0	3.1	1.8	17.6	54.6	49.2
Warehouse Distribution; Gas; NV	5,262	102.7	0.0	18.4	5.4	85.3	0.0	17.4	0.3	18.2	121.2	115.8

Table 18: Primary energy demand and generation for compliant solutions using proposed new factors ( kWh/m²)

Model Name	Floor area (m²)	Gas	Biomass	Grid-supplied Electricity	Displaced-electricity	Heating	Cooling	DHW	Auxiliary	Lighting	Total	Total minus Displaced
Deep Office AC; Gas; AC	12,000	11.6	0.0	64.3	8.0	8.1	12.4	24.6	27.3	3.4	75.8	67.8
Deep Office AC; Elec; AC	12,000	0.0	0.0	73.8	8.0	2.5	12.7	24.7	29.8	4.1	73.8	65.8
Hospital; Gas; NV	12,754	104.3	0.0	53.7	0.0	57.2	0.0	4.4	49.2	47.1	157.9	157.9
Hotel; Gas; NV	1,063	346.5	0.0	31.9	8.1	121.5	0.0	5.7	26.1	225.0	378.3	370.2
Hotel; Gas; AC	1,063	268.1	0.0	111.3	24.4	43.1	16.8	68.3	26.1	225.0	379.3	354.9
Primary School; Biomass; NV	2,353	0.0	54.6	19.6	7.9	34.4	0.0	3.0	16.6	20.2	74.2	66.3
Primary School; Gas; MV	2,353	28.1	0.0	24.7	8.0	11.2	0.0	8.5	16.1	16.9	52.7	44.7
Primary School; Gas; NV	2,353	41.7	0.0	22.1	6.9	24.9	0.0	3.0	19.1	16.9	63.9	57.0
Retail; Gas; AC	1,250	12.2	0.0	178.5	7.9	10.1	70.2	34.7	73.6	2.1	190.7	182.8
Retail; Elec; AC	1,250	0.0	0.0	187.2	8.0	3.3	73.2	34.7	74.6	1.4	187.2	179.2
Shallow Office NV; Gas; NV	2,160	39.6	0.0	28.7	7.9	36.1	0.0	2.6	26.1	3.5	68.3	60.4
Warehouse Distribution; Gas; NV	5,262	115.7	0.0	27.5	8.0	96.1	0.0	0.4	27.1	19.6	143.2	135.2

Table 19: CO₂ emissions and offsets for compliant solutions using proposed new factors ( kWh/m²)

Model Name	Floor area (m²)	Gas	Biomass	Grid-supplied Electricity	Displaced-electricity	Heating	Cooling	DHW	Auxiliary	Lighting	Total	Total minus Displaced
Deep Office AC; Gas; AC	12,000	2.2	0.0	5.9	0.7	1.5	1.1	2.3	2.5	0.6	8.0	7.3
Deep Office AC; Elec; AC	12,000	0.0	0.0	6.8	0.7	0.2	1.1	2.3	2.7	0.4	6.7	6.0
Hospital; Gas; NV	12,754	19.5	0.0	4.9	0.0	10.7	0.0	0.4	4.5	8.8	24.4	24.4
Hotel; Gas; NV	1,063	64.6	0.0	2.9	0.7	22.7	0.0	0.5	2.4	42.0	67.6	66.9
Hotel; Gas; AC	1,063	50.0	0.0	10.1	2.1	8.0	1.5	6.3	2.4	42.0	60.2	58.1
Primary School; Biomass; NV	2,353	0.0	1.5	1.8	0.7	1.0	0.0	0.3	1.5	0.6	3.4	2.7
Primary School; Gas; MV	2,353	5.2	0.0	2.3	0.7	2.1	0.0	0.8	1.5	3.2	7.6	6.9
Primary School; Gas; NV	2,353	7.8	0.0	2.0	0.6	4.6	0.0	0.3	1.8	3.2	9.9	9.3
Retail; Gas; AC	1,250	2.3	0.0	16.3	0.7	1.9	6.3	3.2	6.8	0.4	18.6	17.9
Retail; Elec; AC	1,250	0.0	0.0	17.1	0.7	0.3	6.6	3.2	6.9	0.1	17.1	16.4
Shallow Office NV; Gas; NV	2,160	7.4	0.0	2.7	0.7	6.7	0.0	0.2	2.4	0.6	9.9	9.2
Warehouse Distribution; Gas; NV	5,262	21.6	0.0	2.6	0.7	17.9	0.0	0.0	2.5	3.7	24.1	23.4