Our research strategy
We’re developing a research strategy to guide our work at the Animal Welfare Evidence Centre over the next decade. This will guide our work to fill the evidence gaps and prioritise impactful research, to increase our understanding of why people cause harm to animals.

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Key research aims
The Centre’s research will be guided by four key pillars:
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1
Understanding the causes of animal harm.
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2
Find what works to prevent animal harm.
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3
Deepening understanding of public awareness, attitudes and cultural norms concerning animals.
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Applying a systemic approach to animal welfare, recognising how challenges facing animals, people and the planet are interlinked.
Our research will focus on the drivers and causes of human harm towards animals. We will work collaboratively with partners within and without the animal welfare field to develop the evidence that can drive long-term change.

Proposed priority areas
Our research will initially focus on six key areas:
Have your say
We're inviting feedback on these from stakeholders in the animal welfare, academic, and public sectors through our consultation.
Socio-economic circumstances and animal harm
Evidence suggests socio-economic circumstances, such as the cost of veterinary treatment; access to support or adequate housing, can lead to intentional or unintentional harm to animals. The relationship between harm and socio-economic factors is complex, and we lack clear data on the scale, structural and individual factors, and mechanisms of this relationship in the UK. Better research is needed to understand how both structural socio-economic factors and inequalities, alongside specific individual situations, can affect animals. This will enable the Centre to work with partners to design evidence‑based interventions. The Centre will produce research to fill these evidence gaps.
Domestic abuse and animal harm
Research consistently finds that animals are at risk of harm in households where domestic abuse occurs, and that threats to pets can be a significant barrier to victims leaving. Yet animal welfare remains largely absent from mainstream domestic abuse policy, risk assessment practice, and legal protections.
This is also an area of active policy development, with a live parliamentary bill (the Domestic Abuse (Pets) Bill), a Defra-funded research programme, and the first formal government recognition of pets in the Violence Against Women and Girls strategy. There is a unique window of opportunity for evidence to shape policy in real time.
Demographics, geography and “who” harms animals
Understanding who is most likely to harm animals, and where harm is concentrated, is necessary for designing effective prevention initiatives, but we lack evidence of this in the UK.
Most research treats perpetration and attitudes as uniform across the population, potentially obscuring significant variations by age. Existing evidence suggests there are real and consistent differences across several dimensions. The Evidence Centre will conduct research and collect data to fill these evidence gaps and develop a deeper understanding of the demographic and geographic distribution of harm to animals.
What works to prevent animal harm
The Centre will help the sector evaluate and draw lessons from any programmes, projects and initiatives that address harm to animals, to better understand what works. We will distil existing evidence and collaborate with a range of stakeholders to ensure that prevention initiatives are robustly designed and evaluated, with key learnings used to shape the work of the sector and partners.
Public attitudes, cognitive dissonance and the “nation of animal lovers”
The RSPCA's Animal Kindness Index consistently finds that just over 70% of UK adults describe themselves as animal lovers. Yet a significant proportion of those same people buy intensively farmed meat, purchase pets with extreme conformations or participate in other behaviours that can cause harm to animals. Understanding why this gap between attitudes and actions persists, and whether and how it can be closed, is a key question to prevent animal harm at scale. It is also a question where evidence is still developing and where insights from adjacent fields (psychology, behavioural science and sociology) have not yet been systematically applied.
AI and animal welfare
Artificial Intelligence (AI) systems are rapidly being deployed across UK animal sectors; from smart farming sensors and automated slaughterhouse monitoring to AI triage apps for pets and camera systems in zoos and wildlife parks. These tools can enable earlier detection of disease, and potentially reduce reliance on some invasive practices, which could meaningfully improve welfare outcomes if carefully implemented.
However, AI can also entrench harmful systems by making intensive production more efficient, increasing the surveillance and instrumentalisation of animals, and reducing direct human contact, which may weaken the human–animal bond. The Centre will investigate the impact of AI on the welfare of animals.
Help shape our strategy
As we move from initial scoping toward the launch of our first three-year research strategy, we’re inviting stakeholders from the animal welfare, academic, and public sectors to input into an online consultation to help refine our strategic research priorities.
Our goal is to build a more coherent evidence ecosystem that aligns research with the most pressing real-world challenges; from the systemic drivers of neglect to the implications of emerging technologies like AI. By contributing your expertise, you’ll help us identify critical gaps, reduce duplication across the sector, and ensure our research provides the robust data needed to inform effective policy and practice.
We’re seeking broad engagement to ensure our framework reflects the diverse perspectives of those working on the frontline of animal protection and social justice.
Please share your expertise through our online feedback process. Your input will ensure our independent, peer-reviewed research drives effective, long-term systemic change for animal welfare.



