Carbon Emissions Learning Lab (CELL)

CELL is a learning tool for navigating net zero - a simulation, powered by real-world data, to support hospital leaders to become climate leaders through informed, sustainability-oriented decision-making.

The CELL resources page is here to help you learn more about the initiatives explored on CELL through guides, case studies and good practices.

Please note that we will continue to update this page as we continue to develop our resources.

Leadership and governance

Explore ways in which senior management can identify initiatives that can set your hospital towards lowering carbon emissions

Clinical Practices

The resources in this section contain several initiatives that change clinical practices, with the aim of reducing different types of greenhouse gas emissions.

Operations

The hospital's operations have a major impact on its emissions. The resources in this section contain initiatives that can help to reduce emissions in different operational areas

Supply chain and procurement

The hospital's wider supply chain includes waste generation, the purchasing of food, clinical supplies and other items. This section provides resources to address scope 3 emissions

CELL data model

Interested to learn more about the model behind CELL? Read on...

  • Development of the simulation model

    An important part of the learning experience of the simulation is to analyse the information that is provided and to conclude whether a particular initiative contributes to the hospital’s emission reduction efforts in a significant way (relative to other initiatives that are available). The impact of individual initiatives is highlighted in the Debrief slides and detailed in the “Details of initiatives” section of this manual. 

    The development of the simulation model is based on: 

    1. Baseline emissions definition for hospital (location: London).
    2. Adaptation of baseline emissions to other locations in the simulation.
    3. Definition of baseline operating costs.
    4. Definition of impact of initiatives, differentiated by location when appropriate.
  • Steps taken to arrive at baseline estimate for hospitals in the CELL simulation

    The following steps have been taken to arrive at baseline estimates for the hospitals in the CELL simulation. 

    Step 1:

    For London, the starting point for the baseline definition has been the article “Health care’s response to climate change: a carbon footprint assessment of the NHS in England” published in The Lancet (Tennison et al, 2021). In particular, the most recent data (2019) from the appendix of the article (“Supplementary data appendix 2”) has been used. This table contains CO2e data for the entire English NHS and includes Scope 1, 2, and 3 emissions. 

    • Each of the baseline data items for the NHS have been prorated to arrive at a first estimate of emissions for a 500-bed hospital. In other words, each quantity of emissions has been divided by the number of beds in NHS England (95,805) and multiplied by 500. The NHS bed data is the closest available in terms of date to the NHS England emissions data. 
    • Subsequently, an account had to be made of the fact that a substantial part of NHS emissions is driven by activity outside the hospitals (GPs, clinics, etc.). To take account of this, only the emissions data related to ‘Ambulances’ and ‘Acute’ from the table in the Lancet article were included in the hospital baseline emissions data.
    • The resulting emissions data still showed several anomalies compared to other data sources. As a result, the following specific adjustments were made: 
      • Emissions from water were arrived at by multiplying the emission factor from water supply and water treatment (Source: UK Government emissions factor data 2019 -3) by the quantity of water consumption by the NHS (Source: BRE Group), prorated to include only water use by Ambulances and Acute. 
      • Since the emissions data for Ambulances and Acute were not broken up between anesthetic gases and metered dose inhalers (MDI), an estimate was made that only 10% of MDI emissions occur in hospitals. 

    The resulting emissions baseline provides an estimate of the greenhouse gas emissions of a typical 500-bed hospital in the United Kingdom. 

    Step 2:

    To adjust the baseline emissions data to other locations available in the simulation, the following adjustments were necessary to take account of the most significant differences between locations: Adjust emission factor (or ‘carbon footprint’) for electricity and water, based on the latest publicly available information. For electricity, water and petrol, the cost data has also been made specific to each location (see Step 3). 

    • Adjust the efficiency of solar panels for each location, based on data on average hours of sunshine per day per location.
    • The effectiveness of the initiative related to upgrading the air conditioning has been made dependent on the location.

     

    Step 3:

    The baseline operating costs are set on the basis of the largest operating cost items of a typical hospital (excluding rent) and are organized in a manner that makes it possible to analyse the cost impact of selected initiatives: Electricity, water, and petrol costs are all reported separately and are based on the baseline consumption data and the most recently available price data for each location. 

    • An estimate for annual food costs is based on the number of meals served and an estimated cost per meal of $5.50.
    • Costs for staff, medicines, depreciation and other are best estimates based on benchmark data from hospitals in different locations.
    • Training costs are entirely determined by the user’s decisions. It is therefore implicit that training costs that are not related to the hospital’s sustainability efforts are included in the cost item “Other”.

    Each of the cost items can be impacted by the initiatives available to the users of the simulation. 

    Step 4:

    The definition of the impact of initiatives on emissions, costs and other performance metrics is based on available research and data and has been validated with climate scientists and healthcare managers. For some initiatives (e.g., definition of solar panels), quantification can be relatively precise. For many initiatives, there is inevitably some judgement involved in estimating their impacts. Even the impact of apparently straightforward initiatives like the implementation of LED lighting will depend on the type of lighting used initially, the characteristics of the new LED lights, the speed of implementation, etc. Hence, the objective is not to present a scenario that covers all 500-bed hospitals in a particular location, but rather a typical hospital on the basis of plausible and realistic assumptions. 

    The simulation model is based on the following baseline emission and cost data: 

      Chicago  Dubai  London  Nairobi  Rio de Janeiro  Singapore 
    Water EF 

    (CO2-e KG/’000L) 

    		 0.84     2.00  0.42  0.42  0.42  1.30 
    Electricity EF 

    (CO2-e KG/kWh) 

    		 0.39      0.47  0.22  0.25  0.13  0.42 
    Petrol EF 

    (CO2-e KG/L) 

    		 2.35  2.35  2.35  2.35  2.35  2.35 
                 
    Electricity Price 

    ($/kWh) 

    		 0.15     0.07    0.38  0.18  0.16  0.24 
    Petrol Price 

    ($/L) 

    		 1.17      0.86  2.43  1.54  1.05  1.67 
    Water Price 

    ($/‘000) (inc. Sewage) 

    		 1.14    2.72  1.88    0.54  0.93  1.05