第1回国際シンポ 「共同事実確認の可能性： 政策形成における科学的情報の役割」
Science and Policy: Better Decisions through Joint Fact-Finding and Collaboration
リン・スカーレット（Resources for the Futureシニアフェロー、アメリカ合衆国内務省元副長官）
Good morning and thank you very much, Dr. Morita and Dr. Matsuura, for this invitation. I am delighted to be here. I first want to commend those gathered for their discussions and research on collaboration, science, and public engagement in public decisions.
I want to first offer a context for my observations. I spent eight years at the United States Department of the Interior, including four years as the number two official there, the Deputy Secretary and Chief Operating Officer. To give you a sense of that job, the Department of the Interior manages one in every five acres of the United States, 507 million acres, with a $12 billion budget and 70,000 employees. The lands managed by Interior include national parks, wildlife refuges and lands and waters leased for both offshore and onshore energy and minerals extraction. Interior also manages major dams and irrigation facilities--dams such as Hoover Dam about which many have heard. The Department manages offshore and some land-based permitting, both for renewable energy as well as oil and gas. It is also the chief guardian of endangered species in the United States.
Interior’s multifaceted mission that I have just described lies at the confluence of people, land and water. Its mission puts Interior at the center of complex infrastructure, environmental and resource management issues. For example, should dams be constructed or removed? How and where might the nation access energy, both on land and offshore?
Those questions involve matters of both science and policy, and these arouse, as they do here in Japan, sometimes, conflict. Two decision making questions in particular have gained momentum over the past decade or so in the United States. Specifically, how might we reduce conflict to achieve durable solutions, and second, how might we enhance decision effectiveness in ways that combine environmental, economic and community benefits?
Now, decisions are increasingly unfolding, as you heard Professor Larry Susskind describe, within a framework of collaborative processes. Public participation and collaboration are not new, as they are not new here in Japan, but they are broadening in extent, form and purpose. We see the evolution of these processes, again as you heard Larry Susskind describe, to bring citizens together with scientists and decision makers.
My presentation today links my experience as a practitioner, a former decision maker vested with responsibilities for managing lands, waters, and making decisions about infrastructure, with my observations and engagement as a scholar and analyst of public engagement processes. My remarks are not intended as a how-to for collaborative processes and joint fact-finding that links science and decision making. Instead, I want to look at the context, the challenges and opportunities for thinking about science, collaboration and decision making, and, as I examine the potential of collaborative processes to link scientists, the public and decision makers, to reduce conflict and enhance decision outcomes.
But let me first set the stage for my comments with a little bit more on context. It’s useful to think of the interface of science, collaboration and decision making as involving issues of how problems are defined and priorities developed, how relevant information is identified and generated, how the science and decision making discussion is conducted, how information is used, tested and augmented, and how decisions are then over time adjusted as information evolves. Now these are partly, if you think about it, institutional and procedural questions. They involve questions about how dialogues are initiated, how they are structured and how they are sustained, and they are also questions about how the results of such dialogues can be affirmed as public policy and formal management decisions.
These questions arise from a recognition that many resource management and infrastructure, public health and other decisions involve distributional disputes. Many of these decisions involve debates about the distribution of funds, the distribution of other benefits, the setting of standards--how clean is clean enough, for example--the allocating of liabilities and the siting of facilities. Now, understood in this way, framing of the problem and defining decision boundaries are not, as you heard Larry Susskind state, merely matters for technical and scientific determination.
This observation suggests the relevance of public engagement with technical experts and decision makers. The observation suggests that the metaphor for science and decision making is not one of two separate realms linked only through a handover of information: scientist here, decision maker there, and the body of knowledge handed over. Rather, a more useful metaphor is one in which multiple participants engage in the shared identification of goals, mutual learning and the co-production of relevant knowledge. Within this mutual learning framework, the science and decision making nexus is not only about the information transfer and it’s not simply about the translation of complex scientific and technical information into publicly accessible terms.
I want to focus on several decision frameworks for the co-production of knowledge and its collaborative use, including joint fact-finding. But I first want to mention seven challenges that complicate the intersection of science and decision making.
First are challenges associated with complexity. Understanding ecosystems, understanding public health challenges, climate effects, infrastructure choices and other issues often involve multiple variables and many tradeoffs.
Second are challenges that spring from ever-present change. Natural and built environments are full of dynamic interactions. The backdrop itself is dynamic though the effects of a changing climate, demographic changes, and changes in land uses, for example. But knowledge itself is dynamic. Science itself is a perpetual discovery process.
Now, complexity and change and incomplete knowledge combine to present uncertainties. Resource managers and other public policy and decision makers must make decisions on daily bases and over time, often in the context of incomplete, inconclusive or even ambiguous information. These complexities and uncertainties can confuse the public and give rise to skepticism about the relevance and reliability of that technical and scientific information.
Armed with relevant science and technical knowledge, we also face communication challenges. Communication challenges accompany dialogue across specialization and experiences and they are especially acute between scientists and those who speak the language of politics and personal choice.
But let me add two more challenges to the decision context. Policy challenges do not present themselves in pre-defined problem sets. Defining the scope and scale of the relevant problem set, the compass of a decision, can raise both scientific and social questions. Is the relevant boundary for accumulating and applying information regarding infrastructure siting, for example, a backyard? A stream? A watershed? A continent? Or even a world? Through what processes might we draw appropriate boundaries for a problem set and decision focus? If you think about it, answering these questions demands scientific insights, but these are as much questions of human communities, values and social constructs as they are matters of scientific distinctions and categories.
So I turn now to the final contextual challenge in any interface of science and policy and management decisions. Policy and management decisions are, in essence, about values. Decision makers such as myself ask, “What values do we care about? How clean is clean enough? How do we allocate which resources or situate which infrastructure where?” Scientists ask, “What is reality? How does the world work?” Understanding what is, as you all know, is not the same as exploring and illuminating responses to the question of what do we care about or how should we structure our communities.
Now the knowledge challenges just summarized present a context ripe for conflict, and those conflicts take many forms. Complexity and disagreements about what information is relevant or how to interpret it often lead to what I call “data battles.” Miscommunication is common in the absence of common language. Without community engagement, those assembling relevant information may focus on the wrong question or the wrong priorities or less relevant tradeoffs. The result is often a mismatch of the problem set with community expectations and needs. Mistrust flourishes as interested participants in decisions may conclude, “If I don’t understand you, I don’t believe you.”
That brings me back to our two central questions. How can we enhance public engagement to reduce conflict and how can we enhance results? Traditions of public engagement in the past were largely passive in the United States. The public would comment on plans or options or documents, look at the scientific information that was summarized that was prepared by agencies, experts and others. The relationship of technical and scientific information to public dialogues was also passive: simply that old metaphor of handing over a report or a summary of technical information.
Over the last 15 years, however, limitations of these approaches, some of them described by Larry Susskind, have resulted in an evolution to more active public processes. These processes include contexts for collaborative learning among scientists, decision makers and the public. I want to describe, very briefly, three emerging frameworks: joint fact-finding, about which you heard Larry Susskind speak, something I will call collaborative values assessment, and then collaborative adaptive management. These tools involve discussion processes, not simply the assembly of information. And process can be as important as substance in defining the problem, assembling and communication information, identifying options and settling on a course of action.
Some research, for example, on decisions to site hazardous waste facilities shows that decision sequence, setting and type of public engagement matter. If local authorities first select, for example, a landfill site and then present the public with scientific and engineering information on the suitability of that site, conflict, data battle and stalemate often ensue. If, instead, local authorities first describe a need--say, the need for managing waste--along with desired features of a site, they can then engage interested constituents in evaluating options and relevant science and engineering information often become the focus of deliberation rather than of conflict.
While I was at the Department of the Interior as Deputy Secretary, I was often called on to articulate the case for better integrating science and decision making. And while at Interior, I also set forth collaboration as a central organizing and operating principle. I was engaged in supporting three emerging frameworks for collaborative science, each addressing different kinds of needs. As I noted earlier, these three include joint fact-finding, collaborative values assessment and collaborative adaptive management.
Let me turn first to joint fact-finding. I’m not going to repeat what Larry Susskind already put forth in some detail, but let me just summarize for my purposes. Joint fact-finding involves dialogue and mutual learning among scientists, the public and decision makers. Scientists, decision makers and citizens collaborate in the scoping, conduct and employment of technical and scientific studies to improve decision making.
Now, I don’t intend my remarks to provide a primer on how to conduct joint fact-finding. You’ve heard that from Larry Susskind. But I want to highlight what I view as some of the key elements. Its central purpose is to develop a shared scoping of the problem set, develop a shared understanding of the relevant technical and scientific issues, build a collective understanding of the implications of known information for policy options and actions including, by the way, consideration of experiential knowledge. That is, the knowledge of the fisherman and his fishing practices, the knowledge of the person who actually manages an offshore platform, etc.
Let me give you an example to bring this to life. Let us go to California for a moment to Tomales Bay. What you see here is a place along the coast of California about 40 miles north of San Francisco. The area boasts about 900 plant species and nearly 500 species of birds. But sedimentation of the bay through land management practices has reduced the size of the bay, and the bay is impaired with mercury, nutrients and other contaminants. The area has commercial oyster growing activities, and water quality testing led to health advisories for water contact and fish consumption.
The outbreak of illness from human sewage sparked local action and debates about what was causing this poor water quality. For years, data battles ensued. Some said it was the effluent from wastewater treatment plants. Others said it was dairy farming and those practices. Others said it was other agricultural practices, etc.
The debates lasted for over a decade, but finally the US Geological Survey, requested by some local stakeholders, came in and actually conducted a joint fact-finding process. The process helped to identify what information was lacking and what was needed to better understand the causes of water quality problems. That information then became the basis for a more collaboratively developed watershed plan for the bay. In this case, joint fact-finding was pivotal to collaborative problem solving.
In the case of Tomales Bay the key challenge was one of data battles and conflict over defining the problem and its causes. But other contexts are also ripe for processes of collaborative science and decision making, and I want to just mention two.
I want to first turn to collaborative values assessment. This is the context in which the goals and priorities of citizens themselves are unclear. The second context that I want to mention, collaborative adaptive management, is a context of uncertainty about what management actions or options will provide the actual hoped-for results. I’m going to offer a quick example of each.
With collaborative values assessment, one of the challenges in the United States with offshore oil and gas exploration is the challenge and the prospect or risk of oil spills. But a central challenge is how to focus emergency response in the case of these spills. In such a spill, there is so much to do and so many needs. The selection of priorities and performance measures at the strategic level actually affects the tactics and actions in the moment of the emergency. Several recent efforts to engage stakeholders in collaborative processes with scientists to better assess what citizens care about, what values and public concerns and priorities they have, have been undertaken. That process involves collaborative values assessment that links scientific information about risks and resources with dialogue about public values and concerns in order to set emergency response priorities.
Let me turn to the third example: collaborative adaptive management. I’m going to use the example of the Platte River Recovery Implementation Plan. Now, collaborative adaptive management involves a process to identify goals and select actions to fulfill those goals, evaluate and monitor the results of actions, and then collaboratively revise the actions based on the results of monitoring. Here, I’m going to use the example of the Platte River. The Platte River, as you can see from this map, stretches across three states in the United States: Nebraska, Wyoming and Colorado.
There’s a basin-wide initiative underway, the Platte River Recovery Implementation Plan. It includes federal and state agencies, local landowners, the agricultural community and others. The initiating focus for the program was on protection of four endangered species, but to address the needs of those species involves coordination of groundwater and surface water management and land management, including water use to support agricultural production. A central element of the planning process includes development of a depletion plan to mitigate, offset or prevent reduction in the river’s flows of waters that would affect the species.
There were significant disagreements among participants regarding just what those endangered species actually needed in terms of water flows and water management for their protection. Initially that debate endured and endured as people debated just exactly what the species needed. Ultimately, though a collaborative adaptive management process, folks agreed, “Yes, we want to protect the species. Let us engage in science experimentation. We will try different actions with respect to the species in different locations and see, in fact, how well they affect the livelihood and habitat of the species.”
This put the action to the performance test, the test of whether it is working.
Now, in both cases, decision makers opted to create collaborative science frameworks in the oil spill decision and in the Platte River decision. But I want to end by asking, what are key issues and challenges of these processes of collaborative science and decision making? I would suggest, beyond the collaborative process of dialogue itself, the key question is whether current institutions are sufficient to generate a rich intersection of science and management and policymaking.
Are these institutions capable of embracing and applying decisions that emerge through joint fact-finding and collaborative adaptive management? We have found in the United States, for example, that sometimes collaborative adaptive management processes are undertaken, or joint fact-finding is undertaken, but the laws and regulations don’t allow for their results to actually be applied in decision making.
Do current decision structures facilitate what some have referred to as shared governance that engages all relevant partners and participants in the ultimate decision? In a world of governing silos, are there possible models to point to? I want to mention the Platte River Recovery Implementation Plan as a useful example. In addition to establishing collaborative adaptive management processes for linking stakeholders and scientists, the whole decision process was situated within a broader structure of shared governance. A governing committee was created that would be the deciding body to take the results of that collaborative adaptive management process and turn it into action. The committee was composed not just of agencies, not just of scientists, but of all stakeholders and participants.
We also need regulatory and decision making toolkits to allow for adaptive decision making. If in the collaborative adaptive management, one changes gears, one gets information through those collaborative processes that the actions one has undertaken aren’t working and one needs to change pace and change direction, do the laws allow for that change in the regulatory practice?
I want to just conclude by saying that while I was at Interior, a significant part of our focus to get from the idea of collaborative science to its practice actually involved establishing adaptive management guidelines and collaborative science guidelines. But we also looked at public policy alignment. We created new rules for our national environmental policy act, processes that actually allowed for the results of the collaborative process to be imported into the agency’s decision as its preferred management option. We created new rules for the incorporation of those management options to reflect the results of the collaborative process.
And, of course, there’s the matter of money. We need budget allocations that support these collaborative inditions. This effort is a journey, not a destination. The development and use of these tools is iterative and evolutionary.
I want to end with a word of caution. These efforts are not a panacea for public conflict, but they can contribute to resolving conflicts and achieving durable results. Thank you very much.