Project Planning

Project planning #

Our research cultures won’t change by themselves. We have to make it happen. Planning our research responsibly now, rather than waiting for funders to require environmental statements, means that we are modelling the change we want to see in the world.

Funding Landscape

Currently this section and the next (Data Management Plans) focuses on the UK context. We would like to broaden this. We invite funders and researchers across Europe and globally to contribute their perspectives, to widen the scope of this section (we hope this will be one focus of our first toolkit workshop, at DH Benelux in June 2022).

Within the UK context, in mid 2022, our view is that:

  • The funding landscape is likely to change, and researchers should collaborate proactively with funding bodies to co-produce the next generation of funding allocation mechanisms.

  • In the meanwhile, there is plenty of scope within existing funding application frameworks to propose sustainable research. For example, the Data Management Plan section of UKRI applications can be used to reflect on digital carbon and wider sustainability and justice issues.

  • Funders have signalled that they are open to existing frameworks and guidance being interpreted in this way. The authors of this toolkit welcome this, and invite funders to actively signal the direction of travel and the interim arrangements (especially to peer reviewers).

The funding landscape is at a moment of transition. The UK has declared a climate emergency, as have universities and research organisations across the sector. The major research funding bodies have signalled that decarbonisation, sustainability, and just climate transition are top priorities. Use of Information Communications Technology (ICT) is widely recognised as a major source of carbon emissions.

We need, urgently, to ‘green’ our research. However, most of the major funders do not yet explicitly ask applicants about the sustainability or climate justice dimensions of their projects. The current major UK exception is the Wellcome Trust. Funders are exploring how they can drive sustainability in research project design; however, revisions to application processes take time. This is partly because of justifiable concern not to make application processes unnecessarily onerous (increasing the amount of work that goes into funding applications can have its own implications for equity and justice within the academy).

The Digital Humanities Climate Coalition supports the revision of funding processes to centre sustainability and climate justice. We also argue, however, that existing application frameworks and guidance do implicitly ask for reflection on sustainability and climate justice.

Data Management Plans #

In this section we focus on UKRI Standard Grants (we hope to expand this scope in the future, and would be interested to hear from collaborators, especially across Europe). One area of special interest for this toolkit is the UKRI’s Data Management Plan (DMP). There are also opportunities to include information of this kind in the Ethics, Case for Support, and Justification of Resource sections. A DMP outlines a project’s approach to managing data through a response to a series of prompts on data creation, storage, sharing, and ethics, which are provided in the application guidance. UKRI guidance also points researchers to resources they might find useful in preparing a DMP and encourages applicants to demonstrate knowledge of institutional policies and procedures. Some grant writers find the DMP useful since it tends to focus one on the minute material detail of the research process (exactly what data will be created, by whom, when, etc.). However, the DMP is sometimes handled by specialists who may not be closely involved in the project, and/or via templates or text that is copy-pasted and lightly edited.

Neither the DMP application guidance nor its associated assessment criteria currently explicitly refer to the climate crisis or environmental emergency. However, funders have indicated an openness to researchers proactively interpreting existing DMP guidance for sustainability and climate justice. Reading DMP guidance (e.g. in the AHRC Funding Guide) from a climate justice perspective makes it clear that its prompts are worded with sufficient openness that they enable researchers to respond in ways which align with sectoral and national climate commitments.

Five areas can be immediately considered when writing a DMP:

  1. Energy proportionality. In the Royal Society’s report Digital Technology and the Planet (2020), a key principle of the chapter on Green Computing is ’energy proportionality’. For a researcher writing a climate justice-oriented DMP, the imperative here is not to produce numbers that show their anticipated energy use per month by some measure. Rather it is to demonstrate that the research design seeks to ensure that the resources used (e.g. hardware purchases, compute time, data storage) will be proportional to the results produced (e.g. outputs, anticipated findings, impacts). Minimal Computing approaches may be useful.

  2. Resource proliferation. A key finding of The Shift Project’s report Lean ICT: Towards Digital Sobriety (2019) is that increasing the lifetime of professional laptops from 3 to 5 years could reduce greenhouse gas emissions by up to 37%. Equally, recent and important long form studies like Kate Crawford’s Atlas of AI (2021) have underscored the ecological impacts of device proliferation. For a researcher writing a climate justice-oriented DMP, the imperative here is to justify the environmental costs of new device purchases, to demonstrate alignment with institutional policies on device recycling (e.g. does your institution follow or go beyond Waste from Electrical and Electronic Equipment (WEEE) regulations in their approach), and to consider and explain the benefits of using refurbished devices if appropriate. For example, if external hard drives purchased for a previous project and pooled by your research group are serviceable and within warranty, they may be perfectly usable for a future project that seeks to take a climate justice-oriented approach to data management, and can be described as such in your DMP.

  3. Computationally intensive research. Computer time requires energy use. Lannelongue, Greanley, and Inouye give us tools to calculate this in their 2020 paper ‘Green Algorithms: Quantifying the Carbon Footprint of Computation’. In turn their ‘Green Algorithms’ service gives us an interactive way to understand this energy use relative to driving a car, taking a flight, or planting trees, a way – in short – of describing energy proportionality. For a researcher writing a climate justice-oriented DMP, the imperative here is to describe the decisions taken about the purchase of storage and storage services that relate to and overlap with computationally intensive research activities. For example, that the energy source mix for cloud providers have been investigated, or that data will be structured and stored in ways that reduce compute time and the need for brute force approaches.

  4. Where measurement is challenging, err on the side of caution. Good quantitative data on environmental impacts can be hard to come by. Building capacity in this area is important. However, don’t be shy to make choices that appear more sustainable to you, even if their positive contribution is not quantified.

  5. Identifying relevant standards, frameworks, and guidelines. There are by now many resources that support climate justice across many different aspects of project management. These range from guidelines to minimise the carbon footprint of events, to standards for stakeholder mapping and engagement, to database tools for estimating the carbon intensity of procurement decisions. There are also institution-level standards with project-level relevance (e.g. the Science Based Targets initiative, often referred to as SBTi). These all involve some form of data collection and management, and as such, the DMP is an appropriate place to include them.

These considerations fit within existing guidance. For example, the prompt that asks whether there are ‘any legal and ethical considerations of collecting the data’ (p. 56) raises the possibility of discussing the ethics of over-producing data that need energy and resources to be stored. Similarly, answers to the question ‘How long will the data be stored for and why?’ (p. 56) might reasonably balance long-term preservation of research outputs with the imperative to reduce the proliferation of data on public facing services that are unused for long periods. Finally, the requirement that the applicant and their institution have ‘considered all the risks, and storage will be in line with the institution’s data management policy’ prompts reflection on long-term data storage, the adaptations to climate change required to ensure long-term storage, and the entanglement of institutional data management policies with environmental strategies.

We recognise that not all researchers will have the knowledge and expertise to develop these lines of thinking and to make informed decisions. We are also mindful that researchers may wish to “play it safe” and not risk a bid by including climate justice-oriented practice that may be new to them. However, the scientific advice is clear that there is an imperative to act immediately.

We hope this short guide will enable researchers to be bold in interpreting the DMP guidance, rather than seeing climate justice-oriented actions as another box to tick. Climate justice-oriented data management practices can be threaded through the research programmes of all researchers applying for UKRI funding. There is nothing stopping us apart from developing our knowledge so we can define the appropriate actions to take.

Suggested DMP Citation #

Researchers who have referred to this guidance in developing a DMP may optionally include wording such as, “This DMP is aligned with Digital Humanities Climate Coalition’s 2022 recommendations on data management and climate justice.”

Should you footprint your project? #

“To give just one example, did you know that the very notion of a personal carbon footprint — a concept that’s completely ubiquitous in discussions about personal responsibility — was first popularized by BP as part of a $100 million per year marketing campaign between 2004 and 2006?”

Tracing Big Oil’s PR war to delay action on climate change, Harvard Gazette, Geoffrey Supran, Sept 2021 (Accessed 4 April 2022)

It is not our responsibility, nor our priority, to directly measure the carbon impact of our research. Often we won’t even need to make precise estimates. However, we do all urgently need to develop a degree of carbon literacy, and carrying out footprinting exercises can be one great way to learn. Labos 1point5’s GES1point5 and the Thoughtworks Cloud Carbon Footprint are examples of carbon footprinting tools (for labs and for cloud usage respectively); the GHG Protocol also offers various tools.

The desire for precise data can sometimes be counterproductive. Rough qualitative understandings are often enough to help us make sustainable decisions. Decisions that may initially require research can become fast, easy and intuitive over time. “Is doing it this way more or less carbon intensive than doing it that way? Are they in the same ballpark, or completely different orders of magnitude? Is the intuitive approach OK here, or do we need to assess this more rigorously?”

One useful concept here is Emissions Factors (EF). Put in simple terms, an EF gives a sense of how carbon intensive something usually is. The IPCC maintains a database of EFs. In the UK, the Department for Business, Energy & Industrial Strategy provide spreadsheets which include Emission Factors. By multiplying the values listed there with your own “activity data” (for example, financial spend, or miles travelled in a car, or tonnes of plastic waste disposed, etc.) you can generate an estimate of the carbon impact.

However, definitive EFs are often not available for the type of activities that, as researchers using digital tools, we may want to know about. Thoughtworks’s Cloud Carbon Footprint estimates emissions based on data centre billing data in four usage categories: Compute, Storage, Networking and Memory (RAM). It is partly based on Etsy’s EFs (called the Cloud Jewels).

Travel #

Conferences, workshops, sandpits, seminars, events, meetings, fieldwork. Videoconferencing, teleworking. What should be done in person, and what should be done remotely? What digital tools facilitate sustainable ways of working, and what are their drawbacks or risks? This is a huge topic, and we won’t try to cover everything here. However, here are a few suggested keywords and resources.

Flying #

  • Flying Less in Academia Resource Guide, ed. Ryan Katz-Rosen, is a large curated reading list.
  • Nevins, Joseph. 2014. ‘Academic Jet-Setting in a Time of Climate Destabilization: Ecological Privilege and Professional Geographic Travel’. The Professional Geographer 66, no. 2 (April 3, 2014): 298–310. https://doi.org/10.1080/00330124.2013.784954.
  • Sheller, Mimi. 2018. Mobility Justice: The Politics of Movement in an Age of Extremes. London: Verso.
  • Nevins, Joseph, Stephen Allen and Matt Watson 2022. ‘A path to decolonization? Reducing air travel and resource consumption in higher education’. Travel Behaviour and Society, Volume 26, January 2022, pp.231-239. https://doi.org/10.1016/j.tbs.2021.09.012
  • Glover, Andre, Yolande Strengers, and Tania Lewis. “The Unsustainability of Academic Aeromobility in Australian Universities.” Sustainability: Science, Practice and Policy 13, no. 1 (2017): 1–12.
  • The carbon cost of flying is notorious, so it is worth putting it in perspective: it makes a far smaller contribution than agriculture and forestry, or heating and lighting buildings, or road transport. But it is not insignificant, and it is an area that is comparatively easier to cut down on.
  • There have been some recent breakthroughs in Sustainable Aviation Fuels (SAFs). It is worth noting that these biofuels require large amounts of land which could otherwise be allocated to different mitigation or adaptation functions.

Remote Working and ‘Rebound Effects’ #

  • ‘Rebound effects’ include increased demand for energy caused by remote working which can erode (or even outweigh) savings from less commuting. A good overview is Hook, Andrew, Victor Court, Benjamin K. Sovacool, and Steve Sorrell. 2020. ‘A Systematic Review of the Energy and Climate Impacts of Teleworking’. Environmental Research Letters 15 (9): 093003. https://doi.org/10.1088/1748-9326/ab8a84.
  • A big EU report on teleworking: Samek Lodovic, Manuela et al. 2021. The Impact of Teleworking and Digital Work on Workers and Society. Committee on Employment and Social Affairs, Policy Department for Economic, Scientific and Quality of Life Policies, European Parliament, Luxembourg.

Slow Research, Slow Travel, Slow Scholarship, Slow Food #

  • Conti, Meredith. ‘Slow Academic Travel: An Antidote to “Fly Over” Scholarship in the Age of Climate Crisis’, Theatre Topics 31.1 (2021): https://muse.jhu.edu/article/786251
  • Mountz, Alison, et al. ‘For slow scholarship: A feminist politics of resistance through collective action in the neoliberal university’. ACME: An International Journal for Critical Geographies 14.4 (2015): 1235-1259. https://acme-journal.org/index.php/acme/article/view/1058
  • Günel, Gökçe, Saiba Varma, and Chika Watanabe. “A Manifesto for Patchwork Ethnography.” Fieldsights, June 9, 2020. https://culanth.org/fieldsights/a-manifesto-for-patchwork-ethnography
  • Jungnickel, Katrina. “Getting There… and Back: How Ethnographic Commuting (by Bicycle) Shaped a Study of Australian Backyard Technologists.” Qualitative Research 14, no. 6 (December 1, 2014): 640–55. https://doi.org/10.1177/1468794113481792
  • ‘Should I Attend the Conference? Air Travel Conference / Meeting Justification Tool.’ A quiz, scorecard, and suggestions for how to interpret your scores.
  • A blog post by Filip Vostal criticising slow scholarship: “Research in many disciplines needs to be in synch with the world it investigates; fast and slow again work together in a dialectical interplay here.”
  • Vostal, Filip. 2021. ‘Four Reasons Slow Scholarship Will Not Change Academia’. Impact of Social Sciences (blog). 11 May 2021.

Conferences: Smaller, Online, Hybrid, Unconferences, Alternatives #

Research Voyaging and Hospitality Networks #

  • Cutting down travel is obviously important. So too is making sure that when we do travel, it really counts. We might think more in terms of a researcher tour or researcher voyage.
    • For example, should there be more digital platforms to help universities and other institutions share their resources to enable slower, more sustainable research trips?
  • To offer accommodation at a discount or for free, to think about what you might do along the way (not just at the destination), to notice when researcher voyages intersect (or can be made to intersect) in interesting ways?

Catering events #

Serve Plant-based Food #

  • Dunne, Daisy. 2020. ‘Interactive: What Is the Climate Impact of Eating Meat and Dairy?’ Carbon Brief, 2020.
  • Schiermeier, Quirin. 2019. ‘Eat Less Meat: UN Climate-Change Report Calls for Change to Human Diet’. Nature 572 (7769): 291–92. https://doi.org/10.1038/d41586-019-02409-7.

Choosing research questions #

It is important to adapt our research methods to make them less carbon intensive, and to improve our awareness of our environmental impacts.

But can we also corral our research communities to more directly investigate environmental issues? For example, humans interact with and through Information Communication Technology (ICT) in unpredictable ways; “how ICT changes consumer behavior … seems to be an underexplored, but essential aspect of the causal mechanisms that have to be understood for predicting the environmental impact of digitalization” (Bieser and Hilty 2018). A literature review carried out by Freitag et al. (2021) found no strong consensus about the future of ICT carbon footprints, including “disagreement” whether or not

  • energy efficiencies in ICT are continuing

  • energy efficiencies in ICT are reducing ICT’s carbon footprint

  • ICT’s carbon footprint will stabilize due to saturation in ICT

  • data traffic is independent of ICT emissions

  • ICT will enable emissions savings in other industries

  • renewable energy will decarbonize ICT."

Another literature review on remote working only weakly confirmed the ‘common sense’ view that remote working saves energy; 26 of 39 studies suggest that it does save energy, however “differences in the methodology, scope and assumptions of the different studies make it difficult to estimate ‘average’ energy savings” (Hook et al. 2020).

More broadly, climate change gives rise to a variety of political, ethical, and philosophical issues. Within the arts and humanities, these have long been the concern of the environmental humanities, and fields like the Digital Humanities have plenty to explore in terms of the cultural construction of technologies (everything from email to Bioenergy Carbon Capture and Storage), and the use and interpretation of data and models.

However, climate research funding has heavily favoured the natural sciences (see figure). “Limiting global warming to 1.5°C will require rapid and deep alteration of attitudes, norms, incentives, and politics.

Some of the key climate-change and energy transition puzzles are therefore in the realm of the social sciences” (Overland and Sovacool 2020) — we can also add, the realm of the arts and humanities.

Funding for climate research in the natural and technical sciences versus the social sciences and humanities (USD). The gray areas represent ranges of estimates derived from short and long search strings. Src: Overland, Indra, and Benjamin K. Sovacool. ‘The Misallocation of Climate Research Funding’. Energy Research & Social Science 62 (April 2020): 101349. https://doi.org/10.1016/j.erss.2019.101349.