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Environmental Data and Impact-Oriented Modeling

Introduction:

TheUnited Nations Sustainable Development Goals(SDGs) include the prevention of environmental degradation that can impacthuman health. Data and publicly facing visualizations provide crucial information to better understand complex, public health problems so that decision makers and the public can understand the problem. This is done through a concept called impact-oriented modeling.

背景:

Most environmental data is collected from multiple methodologies of varying accuracy and precision resulting in poor and difficult data interpretation. Thus, there is a critical need to bring together these different data sources and to create better organization and visualization of the data. One solution is to use impact-oriented modeling to integrate the data and to help users employ and contextualize the data for behavioral modification.

Typically, policymakers value scientific models to help them make decisions that could have wide-ranging impacts. For example, with COVID-19, scientific models continue to be used to help inform policy makers on needed interventions to control the pandemic. Unfortunately, scientific models themselves can be difficult to interpret and understand except for the initiated. Also, scientific models and data need to be coupled with understandable outputs and visualizations to make the greatest impact. Combining these two key elements is the focus of impact-oriented modeling. More and more, policymakers are turning to impact-oriented modeling to help make thoughtful, scientific and research-informed decisions. Similarly, applying impact-oriented modeling to climate change data could help inform both policymakers and the general public about the issues and help inform the current discussions underway. Recently, a paper by Shah et al. (2021) created a 7 step framework for success of impact-oriented modeling that can be applied to climate change data.

Key Considerations for Climate Change Impact-Oriented Modeling:

–Agility:Is the data providing timely information?Environmental data and the implementation of a wide range of global and regional policy commitments on climate change requires responsiveness to new inputs of data and reflect current trends. TheOrganization for Economic Cooperation and Development(OECD), theIntergovernmental Panel on Climate Change(IPCC) and many others provide updated environmental indicators.

–Responsiveness:Does the data respond to new evidence?Models should allow the public and policy-makers to react to the data and should be responsive to the data in an iterative fashion. A feedback loop of action-information-reaction should drive models to continuously evolve along with climate change indicators. Detecting long-term trends of environmental data is affected by numerous factors, including the time span of available data. Moreover, determining which environmental variables are likely to allow for earlier detectable change in the data will allow current and planned monitoring programs to be more efficient in modifying user behaviors. For instance, Kural et al. (2021) developed a new climate-change dataset that comprises different types of adaptation-related activities and may be used to compare adaptability initiatives within and between International Organizations.

–Transparency:Are the data and model’s mechanisms publicly available for validation?The validation of models and data sources is crucial for accuracy verification, iteration and improvement. Many organizations provide environmental data that is fully open-source and make publicly available mechanisms of its models and its assumptions. Backtesting methodologies are crucial to ensure continuous iteration and improvement of data models.

–可用性:Can the data and model be used easily and efficiently?Environmental data and climate change indicators need to be fully accessible and easy to understand by the public. Clearly labeled colors and user friendly fonts are an important strategy for usability.

–Accessibility:Can the data and model be understood and used by a broad audience, irrespective of scientific and other capabilities?Environmental degradation and global warming depends on the cumulative behavior of individuals. As a broader understanding of environmental models and indicators could result in huge success or failures, models must use language and visuals that are simple in order to ensure accurate understanding. For example, the OECD providesvideos and webinarsrelated to environmental topics and targeting global audiences.

–Universality:Do data and the model draw on inputs that are defined and measured consistently?随着处理行业行为的影响ave broader effects, what happens across artificial political boundaries matters across the globe. Therefore, standardization and consistency in measuring environmental indicators allows a universal understanding of global warming. For example, the IPCC provides complete global climate data and scenarios for environmental changes.

–Adaptability:Can the model be modified and adapted?Efforts to provide useful and updated global environmental data demonstrates adaptability of environmental datasets to changes in climate change indicators and environmental policies implemented by policymakers. For example,Mendeley Datasets for Environmental Science and Policyenables researchers to deposit any research data (such as raw and processed data, mechanisms and methods) associated with their research manuscripts.

–Actionability:Does the modeldirect stakeholders to clear next steps and action items?Models that fail to provide action items may contribute to confusion or actions that violate environmental standards. In contrast, environmental models that provide clear actionable implications can contribute to wider positive effects. The environment portal of the OECD helps countries design and implement effective policies to address environmental problems and sustainably manage natural resources.

To Sum Up…

Environmental and climate change models need to fulfill the suggested considerations in order to provide scientific researchers with actionable, behavior-changing, and even life-saving information.

References:

Kural, E., Dellmuth, L. M., & Gustafsson, M. T. (2021). International organizations and climate change adaptation: A new dataset for the social scientific study of adaptation, 1990–2017.Plos one,16(9), e0257101.

Shah, N. R., Lai, D., & Wang, C. J. (2021). An impact-oriented approach to epidemiological modeling.Journal of General Internal Medicine,36(6), 1765-1767.

The Intergovernmental Panel on Climate Change (IPCC):www.ipcc.ch