Ambition Levers

General descriptions of ambition levels 1-4

Levels are based on increasing abatement efforts

Level 1: minimum abatement effort

This level is based on projections of historical trends. It includes the forecast decarbonisation for the electrical grid, government policies including the projected phase-out of gas boilers by 2035, and the University’s future estate plans.

Level 2: ambitious but achievable

This intermediate scenario is more ambitious than the projection of historical trends but does not fully capitalise on all available solutions.

Level 3: very ambitious but achievable

Implements current technology improvements and best practices observed across different countries.

Level 4: extraordinarily ambitious and extreme

This level consists of significant changes beyond the norm. It requires extensive efforts such as major societal/behavioural shifts, substantial technological advances and rapid adoption of advanced technologies, as well as large-scale infrastructure development.

Travel

Aviation sector emissions intensity

This lever controls the emission factors associated with aviation, such as when sustainable aviation fuels (SAF) get adopted, or technological advances in aircraft design and engine technologies are implemented. Increasing the ambition level for this lever results in lower greenhouse gas emissions per kilometre travelled via air.

Population

Space efficiency

This lever controls the space occupancy for the university population, and the period over which the change in space usage is projected.

Context

Greater space efficiency means less space is needed per person. This means less heat and power is needed, providing that we consolidate into less space and dispose of unneeded buildings or can lock floors out of use and not heat them. It also means that less construction is needed as population grows.

Things to consider

This lever operates by adjusting the space demand in metres squared. This is calculated based on the projected population growth and the ambition level set for space efficiency. The space efficiency ambition level indicates how ambitiously the university wants to maintain or reduce the space occupancy to estimate the total floor area required.

Home Students

This lever controls the population growth for home students and the period over which the growth is projected. It includes undergraduate and postgraduate (taught and research) home students, both full-time and part-time.

Context

In the academic year 2023/2024, 39% of Imperial students were UK nationals. While there are no direct emissions resulting from home students, this lever influences other factors, like energy use and buildings occupancy, which contribute to GHG emissions.

The UK national student population influences scope 3 emissions, which include student commuting, student business travel, and student domestic travel. Additionally, this lever impacts the overall energy consumption, hence affecting scope 1 and 2 emissions.

Things to consider

Home and Overseas students are differentiated by fee categories. Data for this lever is sourced from the annual published University Statistics guide. This data provides a university-wide population breakdown based on fee status, as well as the total population for each faculty.

The growth rate of the UK student population is applied evenly across all faculties, based on their current proportions. As a result, faculties do not have individual growth rates but instead follow the overall growth of the university population. 

This lever is included in the calculator to show the sensitivity of carbon emissions to growth in population.

Overseas Students

This lever controls the growth rate of international students and the period over which the growth is projected. It includes undergraduate and postgraduate (taught and research) overseas students, both part-time and full-time.

Context

In the academic year 2023/2024, 60.9% of the total students at Imperial were international students. While there are no direct emissions resulting from overseas students, this lever affects other factors, like energy use and building occupancy, thereby influences the university’s overall carbon footprint.

The population of overseas students has a considerable effect on scope 3 emissions, including student commuting, student business travel, and most significantly student travel to home countries. Since this travel often involves aviation, it results in high greenhouse gas emissions. For instance, the model indicates that at least 30% of the total cumulative emissions between 2018 and 2050 are attributed to travel activities of international students to their home countries. Additionally, this lever influences the overall energy consumption, contributing to scopes 1 and 2 emissions.

Things to consider

Home and Overseas students are differentiated by fee categories. Data for this lever is sourced from the annual published University Statistics guide. This data provides a university-wide population breakdown based on fee status, as well as the total population for each faculty.

The growth rate of the international student population is applied evenly across all faculties, based on their current proportions. As a result, faculties do not have individual growth rates but instead follow the overall growth of the student population. 

This lever is included in the calculator to show the sensitivity of carbon emissions to different population growth rates. Additionally, it helps explore how different approaches might affect Imperial’s decarbonisation.

Staff

This lever controls the population growth of academic, research and support staff at Imperial, and the period over which the growth is projected.

Context

Staff population does not generate direct emissions but is linked to other levers that do. The number of staff at the university impacts scopes 1 and 2 emissions through energy use and building occupancy. Similarly, staff commuting and business travel contribute to scope 3 emissions.

Things to consider

Data for this lever is sourced from the annual published University Statistics guide, which provides a university-wide population breakdown.

The growth rate of the staff population is applied evenly across all faculties and university operational units including Campus Services, Registry, and HR, based on their current proportions. As a result, faculties and operational units do not have individual growth rates but instead follow the overall growth of the university population. 

This lever is included in the calculator as a potential alternative the university could take to reduce its total greenhouse gas emissions. Additionally, it helps explore how different approaches might affect the university’s decarbonisation.

Staff Business Travel

Staff long-haul demand

This lever controls the passenger-kilometre demand for long-haul business trips taken by staff members. It also sets the start year and the duration over which the travel demand will change.

Context

Staff members are expected to undertake business trips as part of their work and research activities. These trips often involve research visits and collaborations with other institutions, attending international conferences, and conducting fieldwork in various locations across the world. Such travel is essential for advancing research and fostering academic partnerships.

However, business travel contributes significantly to Imperial’s carbon footprint. In 2019, greenhouse gas emissions resulting from business travel activities reached 19,000 tCO₂e, with 94% of these emissions produced from aviation (Samra et al., 2023a).

Things to consider

This lever includes all staff business trips that involve travel distances exceeding 500 km. It accounts for all staff categories, including academic, research and support staff.

Data for air travel is collected from Egencia, which is the travel management company Imperial uses. Egencia’s data does not distinguish between travel undertaken by staff and that undertaken by students. To address this limitation, it is assumed that 80% of the travel data corresponds to staff travel. Egencia’s database does not capture all business-related travel as some of it is booked separately and claimed via expenses. Consequently, a 30% leakage factor is applied to the aviation data. This leakage factor accounts for the proportion of travel activities that are not included in the Egencia data to ensure a more accurate estimate of total business-related air travel emissions. The lever operates by adjusting the passenger-kilometre demand for staff long-haul travel. This demand is calculated based on the selected population trajectory and the ambition level set for staff long-haul travel demand. The population trajectory refers to the expected growth or number of staff over time, while the ambition level for staff long-haul demand indicates how ambitiously the university aims to maintain or reduce demand for business air travel.

Staff short-haul demand

This lever controls the passenger-kilometre demand for short-haul business trips taken by staff members. It also sets the start year and the duration over which the travel demand will change.

Context

Staff members are expected to undertake business trips as part of their work and research activities. These trips often involve research visits and collaborations with other institutions, attending international conferences, and conducting fieldwork in various locations across the world. Such travel is essential for advancing research and fostering academic partnerships.

However, business travel contributes significantly to Imperial’s carbon footprint. In 2019, GHG emissions resulting from business travel activities reached 19,000 tCO₂e, with 94% of these emissions produced from aviation (Samra et al., 2023b).

Things to consider

This lever includes all staff business trips that involve travel distances within 500 km. It accounts for all staff categories, including academic, research and support staff.

Data for air and rail travel is collected from Egencia, which is the travel management company that Imperial uses for most of our travel bookings. Egencia’s data does not distinguish between travel undertaken by staff and that undertaken by students. To address this limitation, it is assumed that 80% of the travel data corresponds to staff travel.

Egencia’s database does not capture all business-related travel. Consequently, a 30% leakage factor is applied to the air travel data, and 80% to the rail travel data. This leakage factor accounts for the proportion of travel activities that are not included in the Egencia data to ensure a more accurate estimate of total business-related travel emissions.

The lever operates by adjusting the passenger-kilometre demand for staff short-haul travel. This demand is calculated based on the selected population trajectory and the ambition level set for staff short-haul travel demand. The population trajectory refers to the expected growth in number of staff over time, while the ambition level for staff travel demand indicates how ambitiously the university aims to maintain or reduce demand for short-haul business travel.

Staff long-haul travel classes

This lever controls the share of demand by travel class from the total travel demand, for long-haul aviation. There are 4 different classes assumed for aviation: first class, business class, premium economy and economy classes.

Things to consider

All staff categories are considered in this lever.

Staff short-haul travel classes

This lever controls the share of demand by travel class from the total travel demand, for short-haul aviation. There are 4 different classes assumed for short-haul aviation: first class, business class, premium economy and economy classes.

Things to consider

All staff categories are considered in this lever.

Staff short-haul travel modes

This lever controls the share of demand by travel mode from the total travel demand, for short-haul travel. There are 4 different travel modes considered in the model: Air travel, rail travel, bus and car.

Things to consider

This lever works with the total emissions resulting from all business travel activities. This is calculated based on the adjusted travel demand for each engine type and travel mode, and their emission factors.

Emission factors are sourced from the government’s Department for Environment, Farming and Rural Affairs (DEFRA) which publishes emission factors for a range of activities in the UK.

A 30% leakage factor is assumed for aviation and 80% for trains, as Egencia data does not contain all business-related travel.

As Egencia does not provide any accurate distance measurements for cars and buses, bus demand is assumed to be equivalent to 2% of train demand and car demand equivalent to 5%.

Student Business Travel

Student long-haul demand

This lever controls the passenger-kilometre demand for long-haul business trips taken by students. It also sets the duration over which travel demand changes and the start year.

Context

Business travel contributes significantly to Imperial’s carbon footprint. In the academic year 2019-2020, student flights resulted in 19,400 tCO₂e (Samra et al., 2023b).

Things to consider

This lever includes all student business trips that involve travel distances exceeding 500 km. Only research postgraduate students (part-time and full-time) are considered for student business travel.

Data for air travel is collected from Egencia, which is the travel management company that Imperial uses. Egencia’s data does not distinguish between travel undertaken by staff and that undertaken by students. To address this limitation, it is assumed that 20% of the travel data corresponds to student travel.

Egencia’s database does not capture all business-related travel. Consequently, a 30% leakage factor is applied to the aviation data. This leakage factor accounts for the proportion of travel activities that are not included in the Egencia data to ensure a more accurate estimate of total business-related air travel emissions.

The lever operates by adjusting the passenger-kilometre demand for student long-haul travel. This demand is calculated based on the selected population trajectory and the ambition level set for student long-haul travel demand. The population trajectory refers to the expected growth in number of students over time, while the ambition level for travel demand indicates how ambitiously the university aims to maintain or reduce demand for student long-haul business air travel.

Student short-haul demand

This lever controls the passenger-kilometre demand for short-haul business trips taken by students. It also sets the duration over which travel demand changes and the start year.

Things to consider

This lever relates to all student business trips that involve travel distances within 500 km. Only research postgraduate students (part-time and full-time) are considered for student business travel.

Data for air travel is collected from Egencia, which is a travel management company that provides global travel data. Egencia’s data does not distinguish between travel undertaken by staff and that undertaken by students. To address this limitation, it is assumed that 20% of the travel data corresponds to student travel.

Egencia’s database does not capture all business-related travel. Consequently, a 30% leakage factor is applied to the air travel data, and 80% to the rail travel data. These leakage factors account for the proportion of travel activities that are not included in the Egencia data to ensure a more accurate estimate of total business-related travel emissions.

The lever operates by adjusting the passenger-kilometre demand for student short-haul travel. This demand is calculated based on the selected population trajectory and the ambition level set for student short-haul travel demand. The population trajectory refers to the expected growth in number of students over time, while the ambition level for travel demand indicates how ambitiously the university aims to maintain or reduce demand for short-haul business travel.

Student long-haul travel classes

This lever controls the % share of demand by travel class from the total travel demand for students, for long-haul aviation. There are 4 different classes assumed for aviation: first class, business class, premium economy and economy classes.

Things to consider

This lever relates to all student business trips that involve travel distances exceeding 500 km. Only research postgraduate students (part-time and full-time) are considered for student business travel.

Data for air travel is collected from Egencia, which is a travel management company that provides global travel data. Egencia’s data does not distinguish between travel undertaken by staff and that undertaken by students. To address this limitation, it is assumed that 20% of the travel data corresponds to student travel.

Egencia’s database does not capture all business-related travel. Consequently, a 30% leakage factor is applied to the air travel data, and 80% to the rail travel data. These leakage factors account for the proportion of travel activities that are not included in the Egencia data to ensure a more accurate estimate of total business-related travel emissions.

Student short-haul travel classes

This lever controls the share of demand by travel class, for short-haul aviation. There are 4 different classes assumed for short-haul aviation: first class, business class, premium economy and economy classes.

Things to consider

This lever relates to all student business trips that involve travel distances within 500 km. Only research postgraduate students (part-time and full-time) are considered for student business travel.

Data for air travel is collected from Egencia, which is a travel management company that provides global travel data. Egencia’s data does not distinguish between travel undertaken by staff and that undertaken by students. To address this limitation, it is assumed that 20% of the travel data corresponds to student travel.

Egencia’s database does not capture all business-related travel. Consequently, a 30% leakage factor is applied to the air travel data, and 80% to the rail travel data. These leakage factors account for the proportion of travel activities that are not included in the Egencia data to ensure a more accurate estimate of total business-related travel emissions.

Student short-haul travel modes

This lever controls the share of demand by travel mode, for short-haul travel. There are 4 different travel modes considered in the model: Air travel, rail travel, bus and car.

Things to consider

This lever works with the total emissions resulting from all student business travel activities. This is calculated based on the adjusted travel demand for each travel mode and engine type, and its emission factor.

Emission factors are sourced from the government’s Department for Environment, Farming and Rural Affairs (DEFRA) which publishes emission factors for a range of activities in the UK.

This lever relates to all student business trips that involve travel distances exceeding 500 km. Only research postgraduate students (part-time and full-time) are considered for student business travel.

Data for air travel is collected from Egencia, which is a travel management company that provides global travel data. Egencia’s data does not distinguish between travel undertaken by staff and that undertaken by students. To address this limitation, it is assumed that 20% of the travel data corresponds to student travel.

Egencia’s database does not capture all business-related travel. Consequently, a 30% leakage factor is applied to the air travel data, and 80% to the rail travel data. These leakage factors account for the proportion of travel activities that are not included in the Egencia data to ensure a more accurate estimate of total business-related travel emissions.

As Egencia does not provide any accurate distance measurements for cars and buses, bus demand is assumed to be equivalent to 2% of train demand and car demand equivalent to 5%.

Student Commuting

This module controls the demand level and commute modes for student weekly commuting between their place of residence in London and the campus.

Student commuting demand 

This lever sets the ambition level for the weekly commuting demand by students for every faculty.

Things to consider

This lever works with the demand of passenger kilometre for weekly student commuting (commuting demand trajectory). This is calculated based on the population trajectory and the adjusted commuting demand ambition level.

Undergraduate students do not commute to Imperial over the summer, Christmas and Easter holidays. These holidays sum up to a total of 21 weeks.

Taught postgraduates have a 44-week work and eight weeks of Christmas and Easter holidays.

Research postgraduates have a 47-week work year and are allowed five weeks of annual leave, with no official breaks.

Student commute modes 

This lever controls the share of each travel mode by students. The commute modes considered for students are walking, cycling, underground, bus, train (overground), car, taxi, and motorcycles.

Things to consider

This lever works with the emissions resulting from student commuting activities. This is calculated based on the adjusted commuting demand trajectory for each engine share and the corresponding emission factors.

The emission factors for this lever are sourced from the government’s Department for Environment, Farming and Rural Affairs (DEFRA) which publishes emission factors for a range of activities in the UK.

Staff Commuting

Staff commuting demand 

This lever sets the ambition level for the weekly commuting demand by staff for every faculty.

Things to consider

This lever works with the demand of passenger kilometre for weekly staff commuting (commuting demand trajectory). This is calculated based on the population trajectory and the adjusted commuting demand ambition level.

Staff have a 44-week work year and are entitled to five weeks of annual leave. The university is closed over Christmas and Easter holidays for a total of three weeks.

Staff commute modes

This lever controls the share of each travel mode by university staff. The commute modes considered for staff are walking, cycling, underground, bus, train (overground), car, taxi, motorcycles, coach and DLR.

Things to consider

This lever works with the emissions resulting from staff commuting activities (commuting demand trajectory). This is calculated based on the adjusted staff commuting demand trajectory for each engine share and the corresponding emission factors.

Emission factors are sourced from the government’s Department for Environment, Farming and Rural Affairs (DEFRA) which publishes emission factors for a range of activities in the UK.

Student travel home

Number of long-haul trips (overseas)

This lever controls the total number of long-haul trips taken per overseas student in one year.

Things to consider

This lever is related to the demand trajectory for long-haul overseas travel, calculated based on the overseas population trajectory and the adjusted long-haul demand travel ambition level.

Number of short-haul trips (overseas)

This lever controls the total number of short-haul trips taken by each overseas student in one year

Things to consider

This lever gives the demand trajectory for short-haul overseas travel. The lever works with the overseas population trajectory and the adjusted short-haul demand ambition level.

Short-haul transport modes (overseas)

This lever controls the share of transport mode for short-haul overseas travel destinations. The modes considered are air and rail.

Things to consider

This lever is related to the “home to London” short-haul overseas travel emissions. The lever works with the share of engine type and the corresponding emission factor to calculate the resulting emissions.

Emission factors are sourced from the government’s Department for Environment, Farming and Rural Affairs (DEFRA) which publishes emission factors for a range of activities in the UK.

Number of domestic trips

This lever controls the total number of trips taken per each national student per year for domestic travel destinations

Things to consider

As data for destinations of home students is not available, the distance from London to Snaresbrook, the UK population centre, is used.

This lever is related to the demand trajectory for domestic travel, calculated based on the national student population trajectory and the adjusted domestic travel demand ambition level.

Domestic travel transport modes

This lever controls the share of transport mode for short-haul domestic travel destinations from the total travel demand. The modes considered are air and rail

Things to consider

This lever is related to the “home to London” domestic travel emissions, calculated based on the share of engine type and the corresponding emission factor.

Emission factors are sourced from the government’s Department for Environment, Farming and Rural Affairs (DEFRA) which publishes emission factors for a range of activities in the UK.

Fleet lever

Demand for fleet vehicles

This lever controls the ambition level for the demand for fleet vehicles in km/person.

Things to consider

Data from fuel cards was used to track the distance covered by each vehicle and to determine its fuel efficiency based on the most common vehicle model. Since there is no data available on the travel distance and electricity usage of our electric vehicles, their usage was estimated to be equivalent to 100% of the distance travelled by diesel vehicles.

This lever adjusts the total distance demand for fleet vehicles, measured in kilometres. The demand is calculated using the projected population trajectory and the shift in demand determined by the ambition level.

Share of engine types 

This lever controls the share of engine types in the university fleet. This includes diesel, petrol, plug-in hybrid electric vehicles (PHEVs), and fully electric vehicles (EVs)

Things to consider

Fleet vehicle data and fuel type were collected from the university’s insurance records.

This lever adjusts the distance demand for each engine type, measured in kilometres. The demand is calculated using the adjusted total demand for fleet vehicles and the share of engine type according to the set ambition level.

Number of vehicles in fleet

This lever controls the total number of vehicles in Imperial’s fleet.

Things to consider

Fleet vehicle data was collected from the university’s insurance records.

Procurement

Capital expenditure

This lever controls how much capital is spent on major equipment and projects at Imperial, as well as the emissions associated with each unit of capex spent.

Furniture

This lever controls the demand of furniture per person and the associated emissions per unit of furniture

Medical supplies

This lever controls the demand of medical supplies per person and the associated emissions per unit of medical supplies provided

Laboratory services

This lever controls the demand for materials, equipment and services required for laboratories and research activities at Imperial. It also controls the emissions resulting from these procurement activities.

Business services

This lever controls the demand of and emissions from business services. This relates to library business services, technical services, insurance and legal services, financial services, education services, and miscellaneous services.

Food and catering

This lever controls the demand for food and beverages, and equipment and services at Imperial. The lever also controls the associated emissions with food and catering services.

Paper products

This lever controls the demand of paper products per person and the associated emissions per unit of paper products purchased.

Estates

This lever controls the demand of services by the university estates. This includes utilities, health, safety and security services, and estates operations.

Other products

This lever controls the demand per person of all other products that were not listed separately under the procurement module and the associated emissions per unit of each product.

Building construction and refurbishment

This lever relates to mass timber building construction, low carbon material use, and refurbishment and interventions. This module is related to the embodied emissions of new buildings construction and buildings refurbishment activities such as glazing, insulation and timber use.

Mass timber building construction

This lever controls the share of mass timber used in the structural frame of the new building area.

Context

Steel and cement are carbon-intensive construction materials to produce. If we continue to build structural frames of new buildings with steel and cement at the current average of global floor area per person, the associated emissions between 2020 and 2050 would reach 16 Gt CO₂e globally (Churkina et al., 2020). Mass timber buildings have 43% lower embodied GHG emissions on average than their reinforced concrete alternatives (Duan et. al., 2022).

Things to consider

This lever operates by adjusting how much of the structural frame of the new building area is to be constructed with mass timber.

The amount of mass timber used depends on how much future building area needs, and the average material intensity of timber for commercial and residential buildings. The additional construction area required is determined by the population trajectory selected and the settings of the space occupancy lever, which influence the amount of space needed per person. Material intensity represents the quantity of material used per square meter of construction area.

Low carbon material use

This lever controls the emission factors associated with the production of timber, concrete and steel. This is expected to decrease with higher levels of ambition.

Context

Emission factors are representative numbers that tell us how much of a pollutant is released to the atmosphere from an associated activity. In this context, the activity is the production of cement, steel and timber, which are the main construction materials used for new buildings. These are emission factors per tonne of produced product (e.g., 0.14 t CO₂-eq. per tonne of concrete, and 0.42 t CO₂-eq. per tonne of engineered timber).

Emission factors may decrease through manufacturing the same products using lower-carbon methods of production.

Things to consider

This lever functions by reducing the emission factors of concrete, steel, and timber dynamically based on projected future improvements in the associated production facilities.

Given concerns over the sustainability of the production of construction materials, many actions are being studied to reduce greenhouse gas emissions such as the use of more efficient machinery and lower emission fuels. Especially several improvements in cement production (e.g., clinker substitution, alternative fuels and improved kiln efficiency) together with carbon capture and storage can reduce CO₂-eq. emissions from cement production up to 88% by 2050 relative to 2020 (Georgiades et. al., 2023).

Refurbishment and interventions

This lever controls the share of non-refurbished buildings constructed before 2000 that will be refurbished between 2025 and 2030. The refurbishment activities considered are glazing and insulation, and both are controlled by this lever.

Context

The university buildings are divided into three main categories. The first two categories are buildings that are constructed before 2000 and refurbished, and those constructed after 2000. The third category where refurbishment activities will occur is buildings constructed before 2000 that are non-refurbished.

Things to consider

This lever functions by adjusting the emissions associated with installing insulation and glazing in non-refurbished building spaces. These emissions are calculated based on three key factors: the non-refurbished area of buildings, the material intensity of glazing or insulation, and the emissions factor for each of these materials.

Buildings energy supply

This module is related to the main energy sources that do – or could – satisfy the university’s demand for energy and their emissions. These are solar PV, grid electricity, heat pumps and Combined Heat and Power (CHP) plants

Zero emissions grid electricity

This lever controls the share of demand met by zero-carbon electricity. This electricity is procured through contracts known as Power Purchase Agreements (PPA) from renewable energy sources, contributing to financing additional grid decarbonisation.

Things to consider

Remaining electricity demand is met by Renewable Energy Guarantees of Origin (REGO) electricity, which has an emissions ratio of 30 gCO₂e per kWh. Average UK grid electricity is not considered.

Solar panels

This lever controls the capacity (in kWp) of rooftop solar PV installed in Imperial. The available capacity is based on ARUP’s work to look at what is possible on our campuses.

Air source heat pumps

This lever controls the share of heat demand that is supplied from air source heat pumps

Water source heat pumps (South Kensington only)

This lever controls the share of heat demand that is supplied from water source heat pumps

Combined Heat & Power plant (South Kensington only)

This lever specifies the retirement year and the capacity factor of the existing CHP plant in South Kensington campus. The CHP is set to operate at full capacity up until the retirement year. The capacity factor determines how much energy the CHP is producing.

Buildings energy demand

This model controls the energy demand of the university based on population and user behaviour. This includes heating hours and set temperature, lighting hours and potential energy savings for different efficiency levels.

Heating hours per day

This lever controls the duration of space heating per day.

Indoor temperature 

This lever sets the internal space temperature.

Things to consider

This lever is used to determine the total space heat demand in the university. The lever adjusts the indoor-outdoor temperature difference, which impacts the energy demand per square metre, hence the space heat demand.

Lighting efficiency

This lever controls the duration of lighting hours per day and the share of LED lighting at Imperial

Things to consider

This lever works with the required lumens per metre squared and the adjusted lighting hours and LED share to estimate the electricity demand for lighting.

Lab energy efficiency measures

This lever controls the ambition level of potential energy savings that could be achieved at Imperial laboratories.

Things to consider

This lever works with the potential lab energy saving measures, such as defrosting freezers and replacing old equipment with new energy efficient ones, leading to a reduction in lab energy demand.

Carbon offsetting (scope 3 emissions)

This module controls the annual offset demand for scope 3 greenhouse gas emissions and is not yet displayed on the webtool.

Things to consider

The actual volume of offset emissions is calculated based on the total cumulative emissions (MtCO₂e) in the year 2040 and the annual offset demand selected by the user. The offset demand is ramped up linearly between 2025 and 2040.

Churkina, G. et al. (2020) ‘Buildings as a global carbon sink’, Nature Sustainability 2020 3:4, 3(4), pp. 269–276. Available at: https://doi.org/10.1038/s41893-019-0462-4.

Samra, R. et al. (2023a) Sustainable Travel Policy. London. Available at: https://www.imperial.ac.uk/media/imperial-college/about/sustainability/Imperial-Sustainable-Travel-Policy-2024.pdf (Accessed: 23 November 2024).

Samra, R. et al. (2023b) Sustainable travel policy. London.