Research in environmental science is often undertaken under the premise that scientific information and knowledge is necessary to inform environmental policy and management. This has led to the widespread development of computerized tools to bridge the divide between scientific analysis of the state of the environment (e.g., water quality, biodiversity, land use change) and environmental policy objectives (e.g., European directives on freshwater quality and terrestrial biodiversity conservation measures). These tools fall approximately into two types, though in practice there is a great deal of overlap. Decision Support Systems (DSS) are typically targeted at supporting policy implementation in a specific context, while Policy Support Systems or (PoSS) have broader aims, including policy formulation and strategy [1
]. In the context of environmental management, we follow [2
], and refer to both of these two types as Environmental Decision Support Systems (EDSS).
In theory, the relationship between science and policy is close and direct [3
]. In practice, this is not always the case, especially when science identifies wicked problems [4
] to which policy makers or environmental managers are unable to respond in conventional normative ways. Consensus is beginning to emerge around the need for integrative, adaptive approaches to environmental management as a means to tackle these kinds of intractable problems [5
]. Adaptive environmental management seeks to integrate project design, management, and monitoring, to provide a framework to systematically test assumptions, promote learning, and supply timely information for management decisions (e.g., [7
]). Involvement of stakeholders is essential to facilitate the processes of generating and sharing different knowledges to improve understanding of the effectiveness of management actions. Policy makers and land managers require better access to the results of scientific analysis, while scientific stakeholders need to better understand other stakeholders’ needs in order to structure and focus their research (e.g., [8
]). Private sector stakeholders, like land-based businesses, are increasingly interested in easy to use web and mobile-based dashboard “business intelligence” tools to better manage their holdings, creating new demands and opportunities to bridge the relationship between policy and management, and the supply and provision of scientific analysis. A need is therefore emerging for new software tools and applications to respond to these demands, bringing new ways of working [9
]. For example, up-to-date information on the state of the physical environment (moisture, erosion, crop growth), and land-based policies and incentives can be accessed directly by land managers through smartphone and tablet applications. This helps them play a more proactive role in environmental management and may reduce the need for intervention by regulators. At the same time, scientists can move from passive provision of information for policy makers, to on-the-ground facilitation of knowledge exchange between all stakeholders. Making applications web-based facilitates access, which might be expected to lead to faster and more widespread adoption by taking advantage of existing internet infrastructure and appealing to users of modern mobile devices.
In this paper, we discuss our recent progress in responding to the challenge of providing web-based digital tools that meet user needs for their adaptive management of natural resources. To this end, we address the following three key research questions:
How can we develop decision-support tools that align better with adaptive management and outcomes-based approaches to environmental management: what are the key requirements of such a system?
How can we make better use of well-established web-based and mobile devices and software for supporting environmental decisions?
How can we involve key stakeholders like scientists, regulators and land managers to better understand these requirements?
The paper is structured as follows. In the next section, we describe some of the most important limitations of EDSS as currently conceived from an adaptive management perspective. Subsequently, we show how we have tried to address these limitations and answer our research questions by engaging policy stakeholders, regulators and resource managers to co-develop an environmental decision-support application for understanding the effectiveness of environmental policy interventions in river catchments in Scotland. We present the results of this process and finish with a general discussion summarizing key points and lessons learnt. Finally we offer some brief recommendations for future development of EDSS.
The application of computerized tools to these kinds of problems has a long history (see e.g., [10
]). The use of computer modelling to facilitate adaptive environmental management was advocated by [13
]. Early DSS (see e.g., [14
]) were conceived as computerized tools to manage operational decisions where either the decision formulation or the solution, or both, were arguable or directly contested [2
]. The literature contains many different types of EDSS, depending on the kinds of decisions they are intended to support, their anticipated or declared end user, and the stage of the environmental policy process to which they are directed. Some, like the SimLucia model ([16
]), developed on behalf of the United Nations Environment Programme (UNEP), are intended to allow high-level policy makers to formulate appropriate long-term responses to environmental change. Others, like QUICKScan ([18
]) are targeted at more local scales and over shorter timeframes e.g., to help multiple stakeholders to negotiate, compare options and understand trade-offs in the implementation of concrete policies. Others, such as the COLLAGE tool, are more specific. COLLAGE is designed to help local stakeholders plan renewable energy installations by balancing generation capacity against local spatial planning concerns ([19
]). While the involvement of end-users is clearly a key aspect of many such systems, this does not necessarily imply the democratization of knowledge and decision-making that lies at the heart of stakeholder-centred approaches to environmental management, i.e., in participatory, collaborative or mediated modelling (e.g., [20
]). An EDSS designed under the conventional paradigm of DSS is a tool to help the competent authorities solve environmental problems, but does not necessarily emphasize sharing and co-construction of knowledge with stakeholders or social learning (sensu
]) as a strategy for managing disagreement resulting from the diverse perspectives and requirements of multiple stakeholders. This means that frequently, such systems are not targeted at stakeholder needs [24
]. From an adaptive management point of view, in which it is often desirable or even essential to share knowledge effectively amongst diverse stakeholder groups, this is problematic. Recently, digital catchment observatories have been suggested as a means to improve knowledge sharing and co-construction with stakeholders [25
At the same time, many EDSS, ostensibly intended for non-scientific stakeholders to pick up and apply to their specific problems or needs, are not used for that purpose [2
]. While the reasons for the lack of uptake by intended end users are diverse [2
] lack of stakeholder involvement at the design stage is clearly a significant factor. Volk et al [24
], in their analysis of four EDSS in landscape and catchment management contexts, concluded that “the appropriate and methodological stakeholder interaction and the definition of ‘what end-users really need and want’ have been documented as general shortcomings of all four examples of DSS.” Understanding and managing the diverse requirements a software application needs to meet—the requirements problem—is recognized as a major challenge in software development generally [29
]. Responding to this challenge by improving the integration of the end user in the development process is a key underlying motivation of the User-Centered Systems Design (UCSD) paradigm [30
]. However, while user-centered approaches have become mainstream in software development circles, they still lack general application in an EDSS context.
Further, since publication of [2
], the role of software in supporting decisions of various kinds in everyday life has grown substantially. Mobile and web-based applications have become ubiquitous in all kinds of contexts, e.g., purchasing or contracting goods and services, banking, navigation, social networking etc. Typically these tools are mobile, web-based, built on Free and Open Source Software (FOSS), and touch user interface-optimized. Yet EDSS for the most part, lag behind the innovation curve. Most EDSS are still stand-alone desktop systems, many require expensive proprietary software and frequently are not optimized or not available for touch-enabled devices. In agriculture, a new generation of decision-support tools has begun to emerge, in the form of Farm Management Information Systems [32
], allowing farm managers to adapt their operations to variables like temperature and precipitation, market prices or policy measures. However, there remains a substantial mismatch between the computationally intensive and conceptually challenging modelling approaches typically used by the scientific community (e.g., [1
]), and the lightweight, web and mobile-based, touch-enabled applications for smart devices that we all use in our daily lives.
Finally, a key limitation of conventional EDSS from our point of view relates to the difficulty of applying them in the context of outcomes-based approaches i.e., the environmental, social or financial improvements that management actions aim to make. The likely result, in terms of environmental improvement or otherwise of a particular measure, should be a determining factor in the choice of measure and where implemented. This requires us to understand not only the potential spatial distribution of particular environmental variables in the landscape e.g., water body or terrestrial habitat status as determined by environmental policies, in order to suggest locations for management measures and interventions, but also the causal logic that connects these measures to the desired outcomes. Conventional EDSS approaches have emphasized the former, while approaches like logic modelling (also known as results chain logic modelling) are frequently applied to the latter [36
]. Logic modelling, which has its origins in program theory [38
] is widely used for planning and evaluation of environmental and agricultural policy measures in the UK and elsewhere [39
]. At present, however, we are unaware of any EDSS software or framework that successfully integrates the aspatial process-based approaches found in logic modelling software like Miradi with the spatial, Geographical Information Systems (GIS)-type approach found in EDSS for rural planning at the level of the management unit (e.g., [10
In the following paper, we present our recent work in this area, and argue that a reappraisal of the process of developing software for EDSS is necessary to take into account adaptive management and outcomes-based approaches to environmental decision-making and the wide range of new smart applications.
Developing software applications to support land managers decisions under outcomes-based approaches to adaptive environmental management requires better stakeholder engagement practices to understand their needs and development requirements, and a stronger focus on new digital tools and open software and data. We have presented an example of a stakeholder-centered process to develop one such tool. By focusing on understanding and managing outcomes of environmental improvement measures, a preliminary review based on initial requirements led to the rejection of a large range of currently available tools and software as unsuitable. Many do not run effectively on the mobile and web-based devices that land managers typically use, and most are proprietary systems that require an upfront financial outlay and do not allow users to modify the source code or customize the application to suit their own needs. These aspects restrict their use to mostly scientific stakeholders and do little to facilitate environmental decision-making at the scale of the land parcel. What stakeholders really need are mobile and web-based devices that are free at the point of use and flexible enough to be easily adapted to meet changing requirements and different user needs.
Under the late nineties and early noughties “out-of-the-box toolkit” paradigm, scientists sought to package their knowledge into marketable products and systems under the assumption that clients would materialize with fully-formed problems requiring solutions. Since then, the world has moved on. Scientists now understand that this kind of hands-off back-office knowledge delivery is ineffective; these kinds of tools are rarely used. Not only do land-managers not want to use a complex computational model, they need software that answers specific questions like “how much will it cost to build a riparian buffer strip along the river that bounds my land, and to what extent will this reduce surface run-off from my arable fields?”, or “what specific interventions would be most appropriate in this part of the catchment, and what incentives and regulations are likely to be relevant?” To provide better answers to these questions, scientific stakeholders need to play strong facilitation roles at all points in the development process, including problem-framing, stakeholder engagement, application development and use.