Main Themes
The main themes raised in the workshop are summarized in this section; the rest of this report provides more detail on the presentations and discussions.
A major theme at the workshop was the increased recognition, particularly among natural scientists working on the U.S. Carbon Cycle Science Plan (CCSP) in the field, of the importance of human activities to the carbon cycle. Comments at the workshop indicated that the CCSP is increasingly interested in human activities for at least three reasons:
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The role of human activities in shaping the terrestrial carbon sink is greater than previously believed. Christopher Field, who heads the CCSP Scientific Steering Committee, stated that the CCSP’s research is addressing only about a third of the carbon sink in the United States and that the rest must be understood by examining human activities. These activities include land cover transformation, the suppression of fires, a shift from open dumping of waste to landfilling, and changes in agricultural management. Understanding of such activities is critical for estimating the future size of the sink.
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Carbon cycle projections are more sensitive to uncertainties about carbon emissions than to uncertainties about the natural science of the carbon cycle.
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An increased interest in developing models that lead to atmospheric concentrations that might be set as policy targets (sometimes including
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sequestration targets) has raised questions about the human implications and feasibility of reaching these targets.
Discussions at the workshop generated a series of research suggestions (see Box 1) in response to the interests of participants associated with the CCSP. Among the recurring themes in these suggestions were three substantive research needs linking human activities and the carbon cycle:
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Need to analyze, test, and improve social assumptions in emissions models and scenarios. Existing models and scenarios are built on unrealistic social assumptions and are not well supported by relevant theory or data. They do not include intelligent agents or represent feedbacks among model elements (e.g., response of human fertility to changing economic conditions and age distributions; response of consumption and income distribution to changes in trade). Analyses of the models could rule out some scenarios as socially impossible or at least allow for estimates of differential likelihood among scenarios. They could also lead to future scenarios based on more realistic assumptions about social processes. Regional-level studies and models can help strengthen understanding of human dimensions of the carbon cycle.
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Need for better process understanding of how social and economic forces drive the carbon cycle. Topics mentioned included the driving forces of energy use in developing countries, the sources of “endogenous” technological change, the intended and unintended effects of past policies, and the causes of rapid changes in human activity and lifestyles (e.g., recent worldwide fertility decline; patterns of increasingly consumptive living). Understanding of these processes would be facilitated by good historical records. It may also require developing new indicators—for example, indicators of energy services, distinct from energy consumption, that can facilitate analysis of development paths that decrease the carbon intensity of economic development.
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Need for better analyses working backward from policy objectives. These analyses identify the policy and behavioral changes required to achieve a given environmental outcome rather than identifying the environmental outcomes likely to arise as a result of given policy and behavioral changes. They can be used to assess the feasibility of policy targets, including sequestration policies.
BOX 1 Some Suggestions for Future Research, with Examples Substantive Research Needs Analyze, test, and improve social assumptions underlying emissions models and scenarios
Improve process understanding of how social and economic forces drive the carbon cycle
Develop better analyses working backward from policy objectives Cross-Cutting Activities Build a long and continuing historical record of human activities shaping the carbon cycle
Develop emissions scenarios independently of the intergovernmental process Use regional analyses to:
Give more attention to uncertainties in data and scenarios
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In addition, four major themes cutting across substantive research areas also repeatedly arose in workshop discussions:
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Need for a long and continuing historical record of human activities shaping the carbon cycle. A long observational record of key activities (e.g., land use/land cover transformations, fossil fuel use, environmental treaties and policies, and agricultural land management practices) could be built from historical sources and from archaeological data, supplemented by remotely sensed data for recent times. Such data are necessary to quantify the trajectory of carbon sources and sinks in terms of social as well as biophysical drivers, to account for their current state, and to project future effects of human activities on the carbon cycle. A good historical record would provide the observational base needed for research on the substantive themes just noted, as well as for other substantive research on human interactions with the carbon cycle. Some of the necessary data collection is being done in two major international research programs, on Land Use/Land Cover Change (LUCC) and Past Global Changes (PAGES). However, because these programs have their own independent research priorities, this work is often not explicitly linked to the carbon cycle.
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Need to develop emissions scenarios independently of the intergovernmental process so that plausible but politically unpalatable scenarios can be given due consideration.
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Value of regional analyses for integrating the social sciences and natural sciences. Regional analyses, possibly including focused studies of selected regions, can provide venues for better interdisciplinary integration. Regional work should include efforts to scale up to the region (e.g., by using household-level data or agent-based models), analyses across regions (e.g., by comparing multiple case studies), and investigation of interactions across scales.
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Need for more attention to uncertainties in scenarios and data. More attention is needed to the quality of data used as input to models, especially when data were estimated by “backfilling” methods. Models could be used to identify and elaborate uncertainties, and there could be stronger efforts to estimate the likelihoods of scenarios.