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1.
Environ Manage ; 53(1): 4-13, 2014 Jan.
Article in English | MEDLINE | ID: mdl-23793544

ABSTRACT

This article summarizes the primary outcomes of an interdisciplinary workshop in 2010, sponsored by the U.S. National Science Foundation, focused on developing key questions and integrative themes for advancing the science of human-landscape systems. The workshop was a response to a grand challenge identified recently by the U.S. National Research Council (2010a)--"How will Earth's surface evolve in the "Anthropocene?"--suggesting that new theories and methodological approaches are needed to tackle increasingly complex human-landscape interactions in the new era. A new science of human-landscape systems recognizes the interdependence of hydro-geomorphological, ecological, and human processes and functions. Advances within a range of disciplines spanning the physical, biological, and social sciences are therefore needed to contribute toward interdisciplinary research that lies at the heart of the science. Four integrative research themes were identified--thresholds/tipping points, time scales and time lags, spatial scales and boundaries, and feedback loops--serving as potential focal points around which theory can be built for human-landscape systems. Implementing the integrative themes requires that the research communities: (1) establish common metrics to describe and quantify human, biological, and geomorphological systems; (2) develop new ways to integrate diverse data and methods; and (3) focus on synthesis, generalization, and meta-analyses, as individual case studies continue to accumulate. Challenges to meeting these needs center on effective communication and collaboration across diverse disciplines spanning the natural and social scientific divide. Creating venues and mechanisms for sustained focused interdisciplinary collaborations, such as synthesis centers, becomes extraordinarily important for advancing the science.


Subject(s)
Earth, Planet , Ecosystem , Human Activities , Conservation of Natural Resources , Humans
2.
Curr Opin Environ Sustain ; 4(1): 101-105, 2012 Feb.
Article in English | MEDLINE | ID: mdl-25104977

ABSTRACT

DIVERSITAS, the international programme on biodiversity science, is releasing a strategic vision presenting scientific challenges for the next decade of research on biodiversity and ecosystem services: "Biodiversity and Ecosystem Services Science for a Sustainable Planet". This new vision is a response of the biodiversity and ecosystem services scientific community to the accelerating loss of the components of biodiversity, as well as to changes in the biodiversity science-policy landscape (establishment of a Biodiversity Observing Network - GEO BON, of an Intergovernmental science-policy Platform on Biodiversity and Ecosystem Services - IPBES, of the new Future Earth initiative; and release of the Strategic Plan for Biodiversity 2011-2020). This article presents the vision and its core scientific challenges.

4.
Risk Anal ; 29(3): 425-37, 2009 Mar.
Article in English | MEDLINE | ID: mdl-19000075

ABSTRACT

Since the turn of the 21st century, there has been a revival of interest in nuclear power. Two decades ago, the expansion of nuclear power in the United States was halted by widespread public opposition as well as rising costs and less than projected increases in demand for electricity. Can the renewed enthusiasm for nuclear power overcome its history of public resistance that has persisted for decades? We propose that attitudes toward nuclear power are a function of perceived risk, and that both attitudes and risk perceptions are a function of values, beliefs, and trust in the institutions that influence nuclear policy. Applying structural equation models to data from a U.S. national survey, we find that increased trust in the nuclear governance institutions reduces perceived risk of nuclear power and together higher trust and lower risk perceptions predict positive attitudes toward nuclear power. Trust in environmental institutions and perceived risks from global environmental problems do not predict attitudes toward nuclear power. Values do predict attitudes: individuals with traditional values have greater support for, while those with altruistic values have greater opposition to, nuclear power. Nuclear attitudes do not vary by gender, age, education, income, or political orientation, though nonwhites are more supportive than whites. These findings are consistent with, and provide an explanation for, a long series of public opinion polls showing public ambivalence toward nuclear power that persists even in the face of renewed interest for nuclear power in policy circles.


Subject(s)
Nuclear Power Plants , Safety , Humans , Multivariate Analysis , Nuclear Power Plants/standards , Nuclear Power Plants/statistics & numerical data , Reproducibility of Results , Risk Assessment
5.
Nature ; 442(7103): 627, 2006 Aug 10.
Article in English | MEDLINE | ID: mdl-16900177
6.
Science ; 310(5748): 619, 2005 Oct 28.
Article in English | MEDLINE | ID: mdl-16254167
7.
Ambio ; 33(8): 509-12, 2004 Dec.
Article in English | MEDLINE | ID: mdl-15666682

ABSTRACT

Despite the pivotal role human factors (anthropogenic drivers) are presumed to play in global environmental change, substantial uncertainties and contradictory conclusions about them continue. We attempt to further discipline the human factors issue by estimating the effects of two anthropogenic drivers, population and affluence, on a wide variety of global environmental impacts, including greenhouse gas emissions, emissions of ozone depleting substances, and the ecological footprint. Population proportionately increases all types of impacts examined. Affluence typically increases impacts, but the specific effect depends on the type of impact. These findings refocus attention on population and material affluence as principal threats to sustainability and challenge predictions of an ameliorating effect of rising affluence on impacts.


Subject(s)
Ecology/methods , Environment , Environmental Monitoring/methods , Anthropology/methods , Humans , Models, Statistical , Regression Analysis
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