Using Horizon Scanning to Build Policy Resilience: Case of Waste Crime

Waste crime is a pressing concern for the waste and resource industry as it is undermining investment, growth and jobs within the industry and threatening the natural environment. However, there is little knowledge of the scale of the problem, the types of criminality and motivations involved, and the precise nature of crime. Environmental regulators are building foresight capabilities to better understand the effect of current and future changes in markets, in technology and in the legislative environment on waste crime and associated behaviours. At the heart of this paper is the question: how can horizon scanning be adopted by environmental regulators to shape decision processes and build resilience to waste crime? We report our efforts to build a toolkit and guidance for conducting horizon scanning, aimed at supporting environmental regulators, investigators and intelligence analysts to build an understanding of—and interpretation of the consequences of—behavioural, market, technological and pollution trends in the waste sector. A review of the academic and grey literature provided insights to organisational approaches and design principles for public sector horizon scanning. Outputs guided discussion at a stakeholder workshop with waste regulators, criminal intelligence and industry professionals to explore institutional challenges and to agree broad design principles for a horizon scanning process. The toolkit supports environmental regulators in applying horizon scanning to policy and wider operational and delivery-focused challenges; learning how to: (1) spot weak signals and emerging trends quickly, (2) examine the evidence around potential threats and opportunities for the future, and (3) take action on strategically important issues to minimise the impact of crime on the environment, society and business. The paper sets out further research needed to integrate horizon scanning with data analytics (e.g., predictive and hotspot analyses) to challenge assumptions about the patterns of change, based largely on historical trends, and to better manage these so there is greater adaptability to current and future trends.