Horizon Scanning: A Process for Identifying Emerging Signals of Change Shaping the Future of Natural Resources Management

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Horizon Scanning: A Process for Identifying
Emerging Signals of Change Shaping the Future of
Natural Resources Management
David N. Bengston, Tuomas Mauno & Teppo Hujala
To cite this article: David N. Bengston, Tuomas Mauno & Teppo Hujala (02 Apr 2024): Horizon
Scanning: A Process for Identifying Emerging Signals of Change Shaping the Future of Natural
Resources Management, Society & Natural Resources, DOI: 10.1080/08941920.2024.2335392
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RESEARCH NOTE
SOCIETY & NATURAL RESOURCES
Horizon Scanning: A Process for Identifying Emerging
Signals of Change Shaping the Future of Natural
Resources Management
David N. Bengstona , Tuomas Maunob, and Teppo Hujalab
aNorthern Research Station, USDA Forest Service, St. Paul, MN, USA; bFaculty of Science, Forestry and
Technology, School of Forest Sciences, University of Eastern Finland, Joensuu, Finland
ABSTRACT
Horizon scanning is a process to generate foresight – insight into how
the future could unfold. Horizon scanning involves systematically
searching a wide range of information sources for early signals of
change, collecting those signals in a database, and exploring their
possible implications for the future. The focus is often on external
change in the broad contextual environment of a field or organiza-
tion, i.e., the social, economic, technological, and governance domains.
External change could be highly disruptive and is unlikely to be on
the radar of decision makers. Early awareness of changes and moni-
toring their development over time can help natural resource plan-
ners, managers, and policymakers prepare for emerging challenges
and opportunities. This paper introduces horizon scanning, character-
izes different approaches and techniques, outlines the basic process,
and briefly describes artificial intelligence in horizon scanning.
Introduction
More than 100 years ago, conservation champion Theodore Roosevelt declared: “In
utilizing and conserving the natural resources of the Nation, the one characteristic
more essential than any other is foresight” (Roosevelt 1919, 548). Foresight is essential
but also elusive, due to the prevalence of surprise and fundamental uncertainties about
the future (Makridakis et al. 2010). Generating foresight is also problematic in a time
of accelerating change (Azhar 2021).
Horizon scanning can help overcome these challenges. Since there are no data about
the future, horizon scanning generates foresight – insight into how the future could
unfold – by identifying emerging signals of change. This output of horizon scanning
is a vital and often missing input into long-range planning in natural resources, as
well as an input into other foresight methods, such as scenario planning and the
futures wheel. Horizon scanning is a core tool in Futures Studies (Bell 1997) and has
been applied in many fields, from business (Mühlroth and Grottke 2018) to outdoor
recreation (Westphal 2022). Increasing use of horizon scanning in natural resources
This work was authored as part of the Contributor’s official duties as an Employee of the United States Government and is therefore a work
of the United States Government. In accordance with 17 U.S.C. 105, no copyright protection is available for such works under U.S. Law.
CONTACT David N. Bengston david.bengston@usda.gov USDA Forest Service, Northern Research Station, 1992
Folwell Avenue, St. Paul, MN 55108 USA
https://doi.org/10.1080/08941920.2024.2335392
ARTICLE HISTORY
Received 6 October 2023
Accepted 26 February
2024
KEYWORDS
Articial intelligence;
emerging issues;
environmental scanning;
foresight; horizon
scanning; signals of
change

2 D. N. BENGSTON ETAL.
and the environment in recent years has been stimulated in part by the long-running
annual scans of emerging issues in biological conservation carried out by Sutherland
et al. (2024).
The essence of horizon scanning is systematically searching diverse information
sources for early signals of change that could affect a given field or organization,
collecting those signals in a database, and exploring their possible implications. The
focus is often on external change in the broad contextual environment of a field, topic
of interest, or organization, i.e., the social, economic, technological, and governance
contexts. External change could be highly disruptive and is unlikely to be on the radar
of decision makers. Early awareness of nascent changes and monitoring their devel-
opment over time can help natural resource planners, managers, and policymakers
prepare for emerging challenges and opportunities.
This paper introduces horizon scanning as a process to generate foresight by iden-
tifying signals of change that could affect natural resource-related fields and organi-
zations. The next section characterizes different approaches to horizon scanning,
followed by an outline of the basic process. The increasing role for artificial intelligence
in horizon scanning is then briefly described and a conclusion asserts that value of
this tool is likely to grow given the accelerating pace of change and the need to pre-
pare for an uncertain future.
Key Characteristics of Horizon Scanning
Horizon scanning encompasses a diversity of approaches and techniques. Definitions
vary because of the wide range of approaches, but common to all definitions is the
core objective of identifying emerging signals of change: “Horizon scanning aims to
identify emerging issues and events which may present themselves as threats or oppor-
tunities for society and policy” (Amanatidou etal. 2012, 209). Beyond this basic goal,
many characteristics describe the various approaches to horizon scanning. For example,
horizon scanning may be:
• Exploratory or issue-centered. e goal of exploratory horizon scanning is to iden-
tify the full spectrum of signals of change that could aect a given eld or orga-
nization, while issue-centered scanning focuses on monitoring a previously
identied issue, driver of change, or trend, and searching for signals that conrm
or disconrm its continued emergence (Wintle, Kennicutt II, and Sutherland
2020). Both approaches may be used in a large-scale scanning project, i.e., an
initial phase of broad exploratory scanning, followed by an in-depth phase of
issue-centered scanning.
• Ongoing, periodic, or one-time. Ongoing horizon scanning eorts gather signals
of change continuously over multiple years (e.g., Hines, Bengston, et al. 2019), and
periodic scanning occurs at regular intervals, like the annual biological conserva-
tion scans of Sutherland etal. (2024). Many horizon scanning projects are one-time
eorts (e.g., Spaniol and Rowland 2022).
• Automated or manual. Automated horizon scanning uses AI or text-mining to
identify potential emerging signals of change (e.g., Ishigaki et al. 2022). Manual

SOCIETY & NATURAL RESOURCES 3
searching relies on individuals to search for signals. Most automated scanning
employs a hybrid or semi-automated approach, using automated scanning to iden-
tify signals and analysts to examine and prioritize the signals.
• Expert-based, team-based, crowd-sourced, or individual. Manual horizon scanning
can be carried out by a group of experts in a Delphi-like process (e.g., Oldekop
et al. 2020), a diverse and trained scanning team (e.g., Hines, Bengston, et al.
2019), a group of participants crowd-sourced from within an organization (e.g.,
Hiltunen 2011) or crowd-sourced through a widely distributed open call (e.g.,
Esmail et al. 2023), or a by single person (e.g., Coote 2011).
• In-house or purchased. Some organizations carry out their own horizon scanning
eorts internally (e.g., Hines, Bengston, et al. 2019), while others purchase
custom-designed scanning services from a foresight consultancy (e.g., Shaping
Tomorrow 2024).
• Short- to long-term. e time horizon for scanning can vary depending on the
timeframe for decision making and objectives of the scanning exercise (Cuhls,
der Giessen, and Toivanen 2015). Corporate horizon scanning eorts tend to have
short time horizons, oen as short as few years (e.g., Mühlroth and Grottke
2018).
• Regional to global. e geographic scale of horizon scanning projects can range
from regional (Spaniol and Rowland 2022) to national (e.g., van Rij 2010), to
global (e.g., Sutherland et al. 2024), depending on the objectives of the project.
• Pre-dened or open. Within the eld of interest for a scanning project, the specic
topics scanned may be specied in advance in a “domain map” (Hines et al. 2018),
or the topics and tags may be allowed to emerge inductively from the scanning
process, i.e., a grounded theory approach.
Each of these characteristics represents a decision that needs to be made in setting
up a horizon scanning project. Additional considerations include whether horizon
scanning is requested by policymakers or other stakeholders to fulfill a specific need
for foresight or is unsolicited (Wintle, Kennicutt II, and Sutherland 2020). Finally,
horizon scanning may be a stand-alone activity (e.g., Coote 2011) or embedded as an
early stage of a full strategic foresight project (e.g., Taylor et al. 2019).
Outline of the Horizon Scanning Process
The scanning process has been outlined in many ways, with different steps and
levels of detail highlighted (e.g., Gordon and Glenn 2009, Day and Schoemaker
2006, Amanatidou etal. 2012). But four main steps are common to most scanning
processes: (1) Scoping and Framing, (2) Scanning and Collecting, (3) Analyzing
and Synthesizing, and (4) Communicating and Using Results (Figure 1). Scoping
and Framing involves defining the nature of a horizon scanning project based on
the characteristics described in the preceding section, i.e., should it be exploratory
or centered on previously identified issues? Should it be ongoing, periodic, or
one-time? A key aspect of scoping a horizon scanning project is to specify the
guiding question that clearly states the overall objective (Wintle, Kennicutt II, and
Sutherland 2020).

4 D. N. BENGSTON ETAL.
Scanning and Collecting is the core step of the horizon scanning process and consists
of searching for signals of change, tagging them with descriptive terms, and collecting
the signals in a searchable database. It is important to search diverse information
sources, including fringe sources, because change often develops on the periphery, far
from mainstream thinking (Schwartz 1996). Many types of signals that could affect
the future of the area of focus are gathered, such as emerging issues and developments,
trends, countertrends, broad drivers of change, “weak signals” or earliest signs of a
change (Miles and Saritas 2012; Coote 2012; Cuhls, der Giessen, and Toivanen 2015)
and potential wild cards (Bengston 2023). Information about each change signal is
collected in a template or form (e.g., Bishop 2009). Simple templates are preferable;
detailed and complex templates may impede the scanning process by placing an exces-
sive burden on scanners (Géring, Király, and Tamássy 2021). Links to individual signals
and information about them are collected and stored, usually in an online cloud-based
bookmarking tool, or in a spreadsheet on a cloud-based file-sharing system.
Analyzing and Synthesizing involves various “sense-making” activities aimed at pri-
oritizing and understanding the signals of change collected (Konnola etal. 2012). This
step may involve a variety of techniques and approaches, including grouping individual
signals into broader categories, ranking their importance using multiple criteria (e.g.,
credibility, likelihood, impact), developing hypotheses about the meaning of signals,
and exploring possible direct and indirect implications using the Futures Wheel
(Bengston 2015).
Finally, Communicating and Using Results is a vital step. To be useful, the outputs
of scanning must be interpreted and presented in appropriate formats for various user
groups (Hines 2019). Typical scanning outputs include periodic newsletters or articles
summarizing significant emerging issues (e.g., Coote 2011), blog posts, in-depth articles,
technical reports (e.g., Westphal 2022), and presentations to planners, managers, and
Figure 1. Horizon scanning process.

SOCIETY & NATURAL RESOURCES 5
policymakers. Communicating horizon scanning also involves interaction with and
feedback from users so that the scanning process can be adjusted to produce more
appropriate and useful information.
Articial Intelligence and the Future of Horizon Scanning
Most horizon scanning projects still use labor-intensive manual or semiautomated
scanning (Mühlroth and Grottke 2022). But with recent advances in artificial intelli-
gence (AI), horizon scanning is likely to shift toward much greater use of fully auto-
mated approaches (Hines, Bengston, et al. 2019). AI has the potential to carry out
some aspects of horizon scanning with greater efficiency, thoroughness, and speed.
An important strength of AI is its ability to examine vast amounts of data rapidly
and accurately – volumes of data that would not be feasible to analyze manually
(Mühlroth and Grottke 2022). AI algorithms can continuously scan the internet,
searching everything from the latest scientific journals to speculative blog and social
media posts for emerging signals of change. AI can classify and cluster these signals
and uncover patterns in this extensive array of information that would be indiscernible
to a human analyst (van Belkom 2020). In addition to searching the internet, AI-based
systems could also be used to query experts for their ideas about emerging signals of
change, similar to the Delphi-like manual horizon scanning process used by Sutherland
et al. (2024).
At its current state of development, AI also has important limitations in horizon
scanning, such as dependence on vast quantities of historical data (van Belkom 2020).
Another significant limitation is a lack of creativity, which is essential for identifying
potential weak signals of change.
A recent application of AI in horizon scanning is Mühlroth and Grottke (2022)
data mining model designed to help firms identify technological innovations and
trends. The model includes components covering all aspects of the horizon scanning
process and requires minimal input from human analysts while setting it up. In a
retrospective analysis to test the system, the model was applied to three case studies
of emerging technologies and it was able to identify these technologies early on.
Concluding Comment
The practical benefits of horizon scanning are indicated by its widespread use in
public, private, and nonprofit organizations in many fields. A key benefit is to alert
planners and decisionmakers to emerging risks and opportunities early enough to
prepare for them (van Rij 2010). Other benefits include creating a longer-term per-
spective, decreasing reaction time to rapid change, and encouraging a broader view
of the future. In 2009, Sutherland and Woodruff argued that horizon scanning is
underutilized in environmental and natural resource practice and should become a
standard tool. The increasing use of this practice in natural resource applications in
recent years suggests growing recognition of its usefulness. The value and use of
horizon scanning is likely to continue to grow given the accelerating pace of change
and the need to prepare for an uncertain future.

6 D. N. BENGSTON ETAL.
Acknowledgements
The authors thank Fulbright Finland Foundation and World Learning for enabling the first
author’s Fulbright Specialist mission from the United States to Finland, during which the manu-
script was prepared.
Funding
is work was nancially supported by the Saastamoinen Foundation and e Finnish Cultural
Foundation. e research was completed in aliation with the Research Council of Finland Flagship
UNITE [decision no. 357906] and with ForTran project funded by the Strategic Research Council
established within the Research Council of Finland [decision numbers 358473 and 358523].
ORCID
David N. Bengston http://orcid.org/0000-0002-7358-1059
Teppo Hujala http://orcid.org/0000-0002-7905-7602
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