Ecological economic modeling in a pluralistic, participatory society

Maxwell, J. A.; Randall, Alan

doi:10.1016/0921-8009(89)90007-4

Cited by 13 publications

(3 citation statements)

References 8 publications

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“…Modelling of agricultural production and land use systems (at crop, farm and regional levels, and incorporating both ecological and economic perspectives) has become more complex, sophisticated and accessible in the last 15 years since the microcomputer revolution. Simulation modelling is increasingly seen as essential for professional research in ecological economics (Maxwell & Randall, 1989;Costanza et al, 1993). Several reviews over that period (Monteith, 1981;Bennett & Macpherson, 1985;Seligman, 1990;Philip, 1991), however, have questioned the value of such sophisticated crop and soil process models as a means of bringing about change in producers' behaviour.…”

Section: The Ambiguous Record Of Modelling Randdmentioning

confidence: 99%

Some issues in the design of agricultural decision support systems

Cox

1996

Agricultural Systems

166

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Section: The Ambiguous Record Of Modelling Randdmentioning

confidence: 99%

Some issues in the design of agricultural decision support systems

Cox

1996

Agricultural Systems

166

View full text Add to dashboard Cite

“…In response, policymakers and the public are now finding it difficult to incorporate scientific understanding of uncertainty into their decision-making. This is clearly not a new problem for those familiar with science policy and policy science (see Maxwell and Randall (1989) on the pluralistic policy-formulation process; Sarewitz and Pielke (2007) on the mismatch between the supply-side delivery of climate science and the largely unmet demand for knowledge to achieve specific societal outcomes; and Parry (2009) who recognised that in the Fourth Assessment in 2007 the IPCC was unable to address successfully the single most important policy question about confronting climate change: What combinations of emission reduction and adaptation can best reduce the impacts of climate change?). So what can be done to bridge the gap between the pluralistic goals of policy and science so that the end users of knowledge can develop better applications of knowledge that implement policy at their local scale?…”

Section: Science Meets Policy In Changing Urban Environmentsmentioning

confidence: 98%

Climate adaptation engineering: a new direction for environmental science, engineering and technology in urban environments

Kearns

2011

International Journal of Sustainable Development & World Ec

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Over the last few decades, environmental scientists have played a critical role in tracking and describing the flows of urban resources such as energy, water, materials, wastes, greenhouse gases and chemical contaminants through cities, their regions and the global commons. There have been major technological advances in environmental chemistry, analytical instrumentation and numerical modelling of the transport of contaminants through air, water, soil and biota. There is now a much greater appreciation of the complexity of biogeochemical, hydrological, social and economic interactions that affect the behaviour of chemical contaminants in urban, industrial, agricultural and natural ecosystems.Arguably, this past emphasis has been on the descriptive and predictive approaches to urban environmental research with a strong emphasis on the biophysical, chemical, biogeochemical, hydrological and atmospheric sciences. The emerging challenge is how environmental scientists can more effectively bring their knowledge to those decision-makers who are responsible for changing urban and industrial processes that cause environmental impacts in the first place. As the global urban population grows, there are a range of new and complex urban climate adaptation challenges and opportunities in the built environment. These challenges include housing the vulnerable, improving indoor air quality, lowering health impacts in cities and adaptations to heat stress. The complexity and scale of these challenges will require environmental scientists to work collaboratively with other urban professionals, including engineers, planners, designers, economists, policymakers, managers and lawyers, and communities to bring about climate adaptation in urban environments.

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“…In practice, existing power structures may limit the degree of democratic stakeholder control that can be realised in any particular project (maxwell & Randall 1989;Cox 1996;Hare et al 2003). also, real conflicts of interest sometimes exist that cannot be handled by democratic participation alone.…”

Section: (4) Making Sound Comparisons Between Alternative Farm Managementioning

confidence: 99%

Better simulation modelling to support farming systems innovation: Review and synthesis

Woodward

Romera

Beskow

et al. 2008

New Zealand Journal of Agricultural Research

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Simulation modelling is a methodology that appears highly suitable for use in farming systems innovation. However, if the aim is to improve farming systems by supporting farmer behaviour change, model-based approaches seem to have delivered surprisingly little observable benefit to date. This paper identifies the problems underlying this apparent lack of impact, and proposes better approaches to improve on-farm benefits from farming systems modelling. A key principle that has been neglected in farm simulation modelling is intimate involvement of clients (defined as "the individuals or groups whose approval is needed for change to be implemented") throughout the innovation process. We argue that client participation is essential in the problem definition, model design and testing and policy design and evaluation phases of model-based research projects. The role of a simulation model within the innovation process, then, is to be a jointlycreated "virtual world" wherein experiments may be conducted to facilitate learning about the relevant system. We argue that whole farm simulation models that use decision rules to specify alternative farm management strategies are the best available form of virtual world models of farming systems. Besides appropriate client input, high quality models require excellent software development practices and strenuous attention to building user confidence. The latter should include analysing the model to assess its stability and sensitivity properties, before using it to simulate experiments that compare several management alternatives under a range of environmental and local conditions. This approach allows estimation of the variations in farm system performance that are likely to result from interactions between initial farm state, farm management policy and future weather and markets. On the other hand, using simulation models to discover "optimal" farm systems would usually be inappropriate due to the complexity arising from multiple-stakeholder views, multiple-criteria, and the dynamic nature of farming systems problems. An improved systems modelling methodology is proposed that should be better able to provide benefits into farming practice.

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Ecological economic modeling in a pluralistic, participatory society

Cited by 13 publications

References 8 publications

Some issues in the design of agricultural decision support systems

Some issues in the design of agricultural decision support systems

Climate adaptation engineering: a new direction for environmental science, engineering and technology in urban environments

Better simulation modelling to support farming systems innovation: Review and synthesis

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