Net Zero Report 1

Department of Chemistry, University of York

1 Introduction

The current climate emergency has sparked legislation change to make the UK reach net zero carbon emissions by 2050. Carbon emissions are universally broken down into three different scopes depending on their source. These are detailed below as the following:

• Scope 1: Direct emission by organisation property

• Scope 2: Emissions associated with purchased electricity

• Scope 3: Other (indirect) emissions

The University of York has committed to net zero emissions from Scope 1 and 2 with a 30% reduction in Scope 3 (from 2017-2019 baseline) by 2030, and net zero emissions in all three scopes by 2050.

As one of the largest departments in the university, the University of York Chemistry Department formed the Net Zero Working Group to assess their current state of emissions and identify potential pathways to achieve these targets. This report focuses on the first objective and describes the methodology used to quantify and breakdown the full carbon footprint of the department. for the 2021/22 academic year.

1.1 Categorising the emissions sources

1.1.1 Scope 1

Directly emitted emissions by the university are solely made up of the footprint of the fuel used in the combined heat and power (CHP) plant on Campus West. At present, it is estimated that approximately 60% of the electricity supplied to the university is from CHP, however this is likely to increase in future years with the ongoing installation of a second CHP plant on Campus East (see Figure 1). Scope 1 emissions for the chemistry department are therefore made up of the footprint of chemistry’s share of the electricity usage in the university multiplied by 60%.

Figure 1: Photograph of the new Heslington East CHP plant

1.1.2 Scope 2

Scope 2 emissions are made up from the footprint of the remaining 40% of the electricity usage which is supplied by the national grid.

1.1.3 Scope 3

Scope 3 covers a wider range of emissions. This includes:

• Purchased goods
• Staff and student commuting
• Well to tank emissions for the scope 1 and 2 emissions (associated with the extraction, refining and transport of the raw fuels)
• Transmission and distribution of the scope 2 emissions (associated with energy losses from the power plant to the organisation)
• Water usage (and treatment)
• Homeworking (based on office equipment and house heating)
• Managed vehicles
• Construction (associated with building both past and present, where the impact is shared over the lifetime of the building)

All of this information is highlighted in Figure 2.

Figure 2: Schematic highlighting the University of York carbon source breakdown by Scope

2 Methodology

2.1 Scope 1 & 2 electricity usage

The university contains utility meters for electricity, heat, water and gas usage located across campus in all the different buildings. The data for all the chemistry meters and the university total usage is available from Business Energy Intelligence (BEI) and can be downloaded up to a resolution of 15 minutes via static reports.

Electricity data in kWh was converted into kg of carbon dioxide equivalents (kgCO₂e) using conversion factors from the government for the different sources as shown in (Equation 1 and Equation 2). Since the conversion factors are for specific years, and an academic year spans multiple years, the electricity data is grouped by year and summed to give total emissions.

\[ C_{\ CHP} = \sum_y 0.6\times E_y \times f_{\ CHP,\ y} \tag{1}\]

\[ C_{\ grid} = \sum_y 0.4\times E_{\ y} \times f_{\ grid,\ y} \tag{2}\]

Where:

• C_CHP is the academic year carbon emissions from combined heat and power

• C_grid is the academic year carbon emissions from grid electricity

• E_y is the electricity usage for calendar year, y, in kWh

• f_{CHP, y} is the carbon factor for CHP electricity for calendar year, y

• f_{grid, y} is the carbon factor for grid electricity for calendar year, y

Numerous conversion factors for generating carbon emissions from specific variables are used in this report. All non-purchasing conversion factors (f) have been collated and displayed in Table 1.

Table 1: Carbon factors (f) for different sources and their corresponding calendar years

Source	Units	f (2021)	f (2022)
Natural Gas (for CHP)	kgCO2e kWh-1	0.1707	0.19338
Grid Electricity	kgCO2e kWh-1	0.2123	0.19338
WTT Natural Gas	kgCO2e kWh-1	0.03135	0.0311
WTT Grid Electricity	kgCO2e kWh-1	0.05529	0.04625
WTT of T&D of Grid Electricity	kgCO2e kWh-1	0.004890	0.00423
T&D Grid Electricity	kgCO2e kWh-1	0.01879	0.01769
Water Supply	kgCO2e m-3	0.1490	0.149
Water Treatment	kgCO2e m-3	0.2720	0.272
Homeworking	kgCO2e FTE hours -1	0.3408	0.3408

2.2 Scope 3

2.2.1 Purchasing

The carbon footprint of purchasing is more difficult to accurately calculate. Since these are indirect emissions of a wide range of sources there are no conversion factors available. Instead carbon emissions are estimated in Equation 3 through categorisation of the supplier based on what they sell and application of DEFRA carbon factors for these categories which convert the monetary cost of an item into a carbon number. This is the methodology used by the university and there are clearly certain flaws with this approach. However, at present it is regarded as the best estimate, and the assessment of the accuracy of the methodology are not part of the scope of this report.

\[ C_{\ p} = \sum_i m_{\ i} \times f_{\ s,\ i} \tag{3}\]

Where:

• C_p is the carbon emissions from purchased goods

• m_i is the monetary cost of purchased item, i, in £

• f_{s, i} is the carbon factor for the supplier, s, of purchased item i

The chemistry department has a database of everything purchased in the department for each academic year. The database contains a breadth of information on each purchase including:

• workorder number and name
• account number and name
• cat5 number name
• monetary cost
• supplier name
• description

One particular issue with the chemistry department’s purchasing database is the number of suppliers that are not categorised with a carbon factor. Approximately 50% of the 21/22 total purchasing spend does not have an applicable carbon factor with the current methodology. Much of this is large, one-off purchases of specialist equipment from manufacturers not on the university carbon factor list. This is thought to be due to the purchasing system removing suppliers not regularly used.

To combat this, carbon factors were estimated using the ‘account name’ sub-category in the purchasing spreadsheet. Mean average carbon factors were determined for each ‘account name’; which is effectively a description of what is being bought; as similar purchases are likely to have similar carbon emissions. These carbon factors were then joined to the account names of previously uncategorised purchases.

One final issue was the use of a credit card during purchasing. This leads to item being given the incorrect supplier of Barclaycard which has an incredibly low carbon factor. As such, the Barclaycard transactions were reclassified manually (but reproducibly for different academic years) using certain words in the product descriptions.

2.2.2 Commuting

In March 2022, the university in partnership with TPS Consulting and York council conducted a survey of staff and students to determine the distribution of commuting modes and then subsequently calculate the associated carbon emissions. The survey was not broken down by academic department, however it is not thought that inter-department variability would lead to significant inaccuracies. As such, the overall university carbon footprint was scaled down to the size of the chemistry department using the proportion of staff and students in the chemistry department relative to the university as a whole, as is shown in Equation 4.

\[ C_{\ com, \ chem} = \sum_{t,\ o} C_{\ com,\ uni,\ t,\ o} \times \frac{n_{\ chem,\ o}}{n_{\ uni,\ o}} \tag{4}\]

Where:

• C_{com, chem} is the carbon emissions from commuting in the chemistry department

• C_{com, uni, o, t} is the carbon emissions from commuting in the university for each mode of transport, t, and occupation, o

• n_{chem, o} is the number of chemistry persons of occupation, o

• n_{uni, o} is the number of university persons of occupation, o

2.2.3 Well to tank of generation, transmission and distribution

A Well-to-Tank emissions factor, also known as upstream or indirect emissions, is an average of all the GHG emissions released into the atmosphere from the production, processing and delivery of a fuel or energy vector. Conversion factors for the well to tank emission associated with the Scope 1 and Scope 2 items described in this report are provided by the government with the WTT emissions calculated using the department electricity usage in Equation 5.

\[ C_{\ WTT,\ s} = \sum_{s,\ y} E_{s, \ y} \times f_{\ WTT,\ s, \ y} \tag{5}\]

Where:

• C_WTT is the well to tank carbon emissions resulting from the generation, transmission and distribution of electricity from source, s

• E_{s, y} is the electricity usage for source, s, during calendar year, y, in kWh

• f_{WTT, s, y} is the carbon factor for well to tank emissions for electricity source, s, and calendar year, y

2.2.4 Water consumption and treatment

Water usage is available for the department and its associated carbon emissions are calculated in Equation 6. However, the location of water meters is sparse and thus only data for D block and CHyM are available. The carbon footprint for this section is therefore an underestimate.

\[ C_w = w \times(f_{water\ supply} + f_{water\ treatment}) \tag{6}\]

Where:

• C_w is the carbon emissions from water usage

• w is the volume of water used in the academic year in m³

• f_{water supply} is the carbon factor for water supply

• f_{water treatment} is the carbon factor for water treatment

2.2.5 Homeworking

The number of homeworking hours was determined by taking the days of commuting for each member of staff and subtracting from 5, from the commuting survey. This was then converted into hours by multiplying by 8 hours / day. The gov.uk conversion factor spreadsheet (0.34075 kgCO2e / FTE working hour) provided a conversion factor based on office equipment and house heating while staff or students are working from home, which was multiplied with the number of homeworking hours. As with commuting, the data was university wide and was scaled down to chemistry. The same assumption is made that the chemistry department is representative of the university as a whole in terms of homeworking.

\[ C_{hw} = \frac{n_{chem,\ staff}}{n_{survey}}\sum_i (5-D_{c, i})\times 8 \times f_{hw} \tag{7}\]

Where:

• C_hw is the carbon emissions associated with home working

• n_{chem, staff} is the number of staff members in the chemistry department

• n_survey is the number of staff members who answered the survey

• D_{c, i} is the number of days spent commuting of staff member i

• f_hw is the carbon factor for homeworking

3 Results

3.1 Electricity

3.1.1 Chemistry department electricity breakdown

For the 2021/22 academic year, the chemistry department used 3.7 GWh of electricity. Figure 3 shows that there is a reasonably even distribution across the department but with E and F block using the most electricity. The majority of the electricity usage is baseline (Figure 4), with non-working hours (effect also seen at the weekend) only 20% lower than during the working day.

Figure 3: Chemistry Department electricity breakdown by block

Figure 4: Mean average diurnal profiles of the department’s electricity usage facetted by day of week

3.1.2 Breakdown by scope

At present, carbon emissions are similar per unit of electricity generated for CHP and grid sources (Figure 5). As such, the scope distribution of the electricity carbon footprint follows the source contribution split of 60:40 for CHP:grid. However, grid electricity is predicted to decrease substantially over the next 2-3 decades as the government transitions to cleaner fuels and renewable energy. With a current trajectory towards increasing CHP usage, concerns are raised about how the University of York will achieve net zero since this will not decrease emissions as would occur with the grid electricity source.

Figure 5: Electricity carbon emissions broken down by scope

3.1.3 Comparing the department to whole university

In comparison to the university as a whole (Figure 6), the chemistry department uses ~ 11% of the university’s total electricity. Although one of the larger departments, chemistry only represents approximately 4% of the total staff and students in the university. This gives us what I would expect to be, one of, if not the largest, electricity carbon footprints per person in the university. This is perhaps not surprising given the energy intensive nature of the research carried out.

Figure 6: Pie chart highlighting the proportion of the Universities 36 MWh of electricity usage that the Chemistry Department is responsible for

3.2 Purchasing

Purchasing represents a massive proportion (86%) of the department’s carbon footprint which is around 12x the carbon emitted from electricity usage. This can be broken down into different subsections with some example breakdowns in Figure 7 and Figure 8.

‘Equipment - research high value’ and ‘other external payments’ are the two largest categories for purchasing in 2021/22. Within ‘Equipment - research high value’ there are large purchases from Rigaku and TofWerk. Unsure what Rigaku purchase was, however, TofWerk was the purchase of a iodide CIMS instrument. Large purchases in ‘Other external payments’ are mostly from the ‘Champion Distribution’ work order from companies such as WAGENINGEN FOOD AND BIOBASED RESEARCH, OWS RESEARCH FOUNDATION, PROCESS DESIGN CENTER BV and SQ CONSULT. Does anyone have any information on what these are?

Some of the subcategories can be further broken down. The example shown in a breakdown of chemical usage. This is only somewhat helpful, since the majority are labelled as “Laboratory Chemicals (Hazard level 3)” and the labeling is determined by the person filling out the categories of their purchase order. This is included just to highlight that the more detail given when generating a purchase order, the better understanding we can get of our purchasing carbon footprint.

Until the suppliers and carbon numbers are fixed, a comparison of the breakdown of purchasing between the chemistry department and the university as a whole will not be undertaken.

Figure 7: Calculated carbon emissions for purchasing broken down by the account name

Figure 8: Calculated carbon emissions for the chemicals account name broken down further by the cat5 name

3.3 Commuting

The overall commuting footprint for students was 129000 kgCO2e and staff was 107000 kgCO2e with the full breakdown presented in Figure 9. Despite staff only making up 21 % of the total number of people in the department, they contribute 45 % of the commuting footprint.

Figure 9: Carbon emissions associated with commuting broken down by mode of transport and by occupation in the department

3.4 Total footprint summary

Figure 10 summarises all the information above into a single pie chart. By hovering over each pie segment, you view the magnitude and proportion of the department’s total footprint of that particular sector. Total carbon emissions are calculated as 9.4 ktCO₂e and are dominated by Scope 3 sources.

Figure 10: Full breakdown of the Chemistry Department’s carbon footprint. nb. hover over each segment to bring up the legend for the source, manitude and % of the whole footprint

3.4.1 Breakdown by block: teaching labs

It is possible to generate a breakdown by block, or even research group provided all workorder numbers are collated for the area of interest. An example is given below in Figure 11 for teaching laboratories, in which every purchase is under a single work order. *note only purchasing and electricity usage included here, things such as commuting cannot be broken down further than the department as a whole.

Figure 11: Breakdown of carbon emissions from teaching laboratory purchases and electrciity usage.

3.4.2 Total footprint vs University

Department of Chemistry represents approximately 10% of the University of York’s carbon footprint despite only representing 4% of University students and staff (Figure 12).

Figure 12: Pie chart highlighting the proportion of the Universities 118 kTCO2e of electricity usage that the Chemistry Department is responsible for.

3.5 Concluding remarks

The carbon footprint of the department has been calculated and resulted in some interesting findings, namely the domination of Scope 3 emissions. Whilst this is consistent with the Universities overall footprint, both use the same methodology for the purchasing footprint calculation which has not been thoroughly evaluated for accuracy. However, this is at present our ‘best guess’ for annual carbon emissions and can be used as a baseline for future net zero work. Future work will focus on using this collated data to identify potential pathways to net zero and will be covered in a second report.