Global Environmental Issues

Chapter 2

From Science to Policy

Frances Harris

2.1 Introduction

More than 400 years ago Francis Bacon argued that scientists should play a major role in government (Bacon, 1625). Science underpins so much of what we take for granted on a daily basis, and emerging scientific research will identify the pathways and opportunities available to us in the future, as well as the conflicts and potential disasters we need to avoid. However, while scientists continue to develop new information relevant to global environmental issues, their role in government is limited by the distance that still exists between scientists and policy-making (Figure 2.1). Ideally, every decision concerning our environment is based on sound academic research, which is converted into sensible policy for protecting the environment. However, there are many factors which affect the conversion of up-to-date information about our environment into environmental, political, economic and social policies. The transfer of knowledge from practising scientists and researchers to decision-makers is a fraught process: there are many players in the process itself, and it impacts on the many stakeholders who wish to have their views taken into consideration. Along the way, the media presents its view of events, and ultimately the public may raise its concerns via pressure groups, public consultations or even demonstrations. Global environmental issues are not just scientific issues: they are also issues of governance, and this chapter seeks to illustrate some of the concerns surrounding the way environmental science influences, or fails to influence, policy-making. It will show that there are multiple layers of negotiation as science becomes policy.

Figure 2.1 Factors affecting the interpretation of scientific research to develop environmental, political, economic and social policies.

2.2 Research

Academic research on the environment comes in many forms. An array of academic disciplines contributes to the environmental science underpinning the issues in this book, from basic biology and chemistry through to planning, agricultural sciences, engineering, and oceanography. There is pure research, that focuses on seeking to improve knowledge for knowledge's sake, and there is applied research, which can see an end use to the knowledge. There are arguments over how resources, for example public sector funds, should be shared between these two approaches, although applied research is often based on the knowledge gained from pure research. Systems research considers the interactions between many components of a more complex ecosystem (including agro-ecosystems, see Box 2.1). Often research on global environmental issues requires an interdisciplinary focus, integrating the expertise from many fields of knowledge. Solving global environmental issues may require research which brings academics together with practitioners, people who will use the science to develop practicable solutions for use in society.

Science has traditionally taken a reductionist approach: breaking problems into smaller and smaller segments, and examining each segment before putting the pieces of research together to create a whole. While this allows precise research on specific interactions, it can also result in increasing numbers of component research topics which need to be addressed before the fragments can be put together. This can be a time-consuming process, which is problematic for projects with tight time schedules. The reductionist approach is often carried out as laboratory experiments undertaken within controlled conditions, with only one variable changing at a time. The strength of a reductionist scientific method is that it relates to concepts, practices and technologies that are based on tried and tested theories leading to universally applicable results, or to results related to a specific set of conditions. Knowledge is generated through rigorous procedures that attempt to control variables in order to get quantitative results with a high degree of precision for statistical analysis. Robust results may be used to convince others, make confident recommendations and may also be extrapolated to different contexts. The results are tested to ensure potential general applicability but may not be ideally suited to particular situations.

An alternative is to take a more holistic, or ‘systems’, approach. Sometimes this is developed from combining components of the reductionist approach, while at other times the properties of the system as a whole are studied without considering more detailed components of the individual parts. This raises the question of where the boundaries of a particular research study lie. While reductionist and detailed studies have clear research boundaries and controlled conditions, holistic systems set wider boundaries to define the system. The systems approach can be attractive in that it is better able to deal with issues on a bigger scale, and with multiple interactions. Proponents of the systems approach feel that this is more realistic, considering the interactions and complexity within the real world. However, the concern is that with so many variables changing at once, and the resulting complexity, it is hard to be sure of the implications of making changes to the system without understanding the underlying relationships between all the components of the system. Systems are often dynamic, with the potential for both positive and negative feedback mechanisms. Thus systems research is sometimes considered messy, as it studies multiple interactions, non-linear relationships and juggles many parameters.

One method of counteracting the apparent dichotomy of reductionist versus holistic research is to have a series of research studies at different scales, where the results of one study are nested within higher-scale studies. The results of very specific research studies, often carried out at a small scale under controlled conditions, then need to be scaled up. Larger-scale testing can be expensive, so that often researchers find it more practicable to work on a more manageable small scale (Lyon et al., 2005).

Box 2.1 Agro-ecosystems research

Research involving biodiversity can combine small- and large-scale research methods. For example, research on ecosystems necessarily requires a wider, landscape scale approach. Typically researchers collect data on the frequency of species within habitats, and then scale up measurements depending on the frequency of those habitats in the wider landscape. Projects focusing on biodiversity and measuring species richness carry out research at different scales, tailoring methods to the species being studied. For example, species such as plants and insects can be looked at over a small transect or in a quadrat whereas birds operate over a larger area. Another example of researchers merging results from different scales and at different locations globally is the assessment of the global nitrogen balance, and the implications of agricultural developments on this (Norse and Tschirley, 2000).

New challenges are presented when research moves from pure ecology to agro-ecosystems. Agricultural research is generally conducted through reductionist, factorial experiments at scales which differ from those at which farmers operate. For example, plot trials are used to make recommendation for farmers’ practices at the field scale. Agro-ecosystems research requires the analysis of all interactions in a farming system, which usually entails working at a larger scale, both spatially and temporally (Drinkwater, 2002). Additionally, more than one scale may be studied to gain further insight into a particular aspect of an agro-ecosystem by, for example, using subplots within a larger experiment to test a specific hypothesis. Research on GM crops has highlighted many of these issues (see section 2.7).

An attempt to value organic versus conventional farming linked up data from many scales and in many forms: ecological monitoring data as well as economic valuations of agro-ecosystem outputs. This involved collecting data on biodiversity and nutrient leaching and allocating a cost to the benefit or disadvantage of these things, and then combining this with farm financial data. The outcome was an assessment of the economic and environmental impacts of organic and conventional farming methods in terms of pounds per hectare (Cobb et al., 1999). While such an analysis is complicated, it may be more relevant to the farmers, who must operate holistically and therefore require recommendations that take into consideration the multiple objectives they have to meet.

A systems perspective to research offers many advantages (Nissani, 1997), and there have been many papers arguing in favour of integrating social and biological research (e.g. Berkes and Folke, 1998). Advantages include improved relevance of results to beneficiaries and a greater uptake of results by end users. Such approaches also present challenges: finding a common language and a common methodological approach; integrating the results of social and biological research; effectively integrating the results of research carried out at different scales, so that scientific advances at the level of the genome, the plant, the field and the ecosystem are integrated together; maintaining the greatest relevance to the farming system and the wider food production system. Furthermore, a systems perspective (focusing on whole agro-ecosystem or farming system) requires research to consider the needs of all actors, including farmers, agriculture-related businesses, scientists and social scientists, and also the relationships between all actors.

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