Lead Author:
Tristram O. West, DOE Office of Science
Contributing Authors:
Noel P. Gurwick, U.S. Agency for International Development
Molly E. Brown, University of Maryland
Riley Duren, NASA Jet Propulsion Laboratory
Siân Mooney, Arizona State University
Keith Paustian, Colorado State University
Emily McGlynn, University of California, Davis
Elizabeth L. Malone, Independent Researcher
Adam Rosenblatt, University of North Florida
Nathan Hultman, University of Maryland
Ilissa B. Ocko, Environmental Defense Fund
Science Lead:
Paty Romero-Lankao, National Center for Atmospheric Research (currently at National Renewable Energy Laboratory)
Review Editor:
Emily J. Pindilli, U.S. Geological Survey
Federal Liaisons:
Nancy Cavallaro, USDA National Institute of Food and Agriculture
Gyami Shrestha, U.S. Carbon Cycle Science Program and University Corporation for Atmospheric Research

Carbon Cycle Science in Support of Decision Making

Diverse institutions demand information about the carbon cycle that enables them to meet their particular objectives and interests. For example, stakeholders wishing to prioritize actions for reducing emissions need to know the distribution among sectors (e.g., transportation, infrastructure, buildings, power generation, and land management), as well as the technical, economic, and behavioral potential for reducing these emissions in different sectors and locations. Illustrative questions that stakeholders including decision makers ask include:

  1. How much can emissions be reduced from transportation versus power generation versus building sectors, and at what costs?

  2. What actions are consumers likely to take, and which kinds of technologies (e.g., smart meters) and campaigns (e.g., foot-in-the-door models) are likely to result in behavioral change (Scott 1977; Mogles et al., 2017)?

  3. How much methane (CH4) leaks into the atmosphere from natural gas wells and pipelines, and how does that leakage influence the attractiveness of natural gas as a “bridge” fuel (Miller et al., 2013)?

  4. How can carbon be managed from procurement through production and inventory management (Benjaafar et al., 2013)?

  5. How fast will different agricultural practices build soil carbon or reduce CH4 emissions from cattle, and how will these rates vary geographically (Olander et al., 2014)?

  6. How will the consequences of different sets of agricultural and forest management practices on a single tract of land add up?

18.2.1 Variety in Types of Users and Their Needs

Users of carbon cycle science to reduce emissions include 1) carbon registries and protocol developers (Gonzalez 2014; Climate Action Reserve 2018), 2) businesses that have made voluntary commitments to reducing GHG emissions from their supply chains (Christopher 2011; Tseng and Hung 2014; CISCO 2017; Walmart 2017), 3) utilities developing strategies for reducing their GHG footprints (Consolidated Edison 2016), 4) state and municipal governments committed to reducing GHG emissions in their public and private sectors (Carbon Neutral Cities Alliance 2018; Elizondo et al., 2017), and 5) non-governmental organizations and research institutes producing roadmaps to achieve different atmospheric CO2 targets (UCS 2009). In addition, national governments and international organizations rely on carbon cycle science combined with policy and management practices to identify the primary socioeconomic drivers of carbon emissions (e.g., Fricko et al., 2017; Rogelj et al., 2018) and to understand how well science-based recommendations for carbon budgets align with global commitments for carbon management (Fricko et al., 2017; Burke et al., 2018; Rogelj et al., 2018). These users vary in the types of decisions they make about carbon cycle management; their capacity to support research or engage with research institutions; their maturity in defining their information needs; and their potential to impact regional, national, or global carbon pools. Mapping these capacities with an eye toward producing information in formats that align with standard business practices would be a valuable contribution for social science research.

18.2.2 Institutional Arrangements for Meeting User Demand

Despite having identified numerous users of carbon cycle science and the deep knowledgebase summarized within this report, tailoring and synthesizing carbon cycle science to make it truly useful to specific institutions continue to present a challenge. In carbon management, as in numerous other realms of decision making that benefit from technical input, the traditional science supply paradigm for producing usable or socially robust knowledge (i.e., provide the research results, and somebody will eventually use them) remains problematic and usually ineffective. The disconnect between knowledge production and consumption is particularly apparent when applying cross-disciplinary research to societies (Dilling 2007). In contrast, various initiatives have demonstrated that beginning research by identifying user information demands, subsequently working intensively with users to understand those needs in detail, ultimately leads to science products that are actually used (Zell et al., 2012). User-driven science, however, thrives when institutions shift their priorities to meet user needs and set reward structures accordingly.

Co-Production of Knowledge

The hybrid approach that has enabled user demand to take advantage of carbon cycle science within the confines of existing institutional structures has been referred to as the co-production of knowledge by scientists and the user community (Cash et al., 2006; Dilling and Lemos 2011). This coordination entails establishing a shared vision that a decision- making process requires, and ensuring that the decision makers receive information in a usable format and at an appropriate time (Brown and Escobar 2013). In addition to engaging stakeholders, co-production of knowledge also emphasizes collaboration across scientific disciplines. Although cross-disciplinary research has received considerable discussion over the past few decades, institutional cultures within a number of large organizations that have especially robust research capacity continue to impede collaborations in the absence of strong direction and leadership to do otherwise (Mooney et al., 2013; Weaver et al., 2014). Overcoming barriers between the sciences (see McGreavy et al., 2015) remains a challenge to producing information that effectively influences decision making. Examples of ­co-production and user-driven research in which carbon cycle science has informed management action include development of the Southeast Florida Regional Climate Change Compact (Georgetown Climate Center 2017), the Maryland Carbon Monitoring System (University of Maryland 2016), and methods for reducing emissions from deforestation and forest degradation plus (REDD+; see Section 18.3.2) accounting in Mexico (Birdsey et al., 2013).

Boundary Organizations

Boundary organizations facilitate interactions between science producers and users by helping to structure the flow of information from basic and applied research to decision making, enabling improved engagement and stronger relationships across disciplines (Kirchhoff et al., 2013; see Figure 18.2). They focus on activities that engage all carbon cycle science disciplines and promote opportunities to foster interdisciplinary and intramural collaboration (Clark et al., 2016). Diverse non-governmental organizations have played a strong role engaging with carbon cycle research activities to understand and apply the science. A primary objective of these organizations is to support and present science in ways that enable local and individual action that links science to decision making at a variety of scales.


Figure 18.2: Evolution in the Complexity of Knowledge Production and User Participation

Figure 18.2: On the vertical axis, the complexity of knowledge production increases from low (where production is predominately focused on increasing fundamental knowledge) to high (where production aims to help solve societal problems). On the horizontal axis, the complexity of user participation changes from low to high as users become increasingly active in the knowledge-creation process. Mode 1 represents the concept that societal benefits accrue because of the separation of science from society, where science is separated from society to maintain objectivity and credibility. Mode 2 organizes science production at increasing levels of interaction and integration across disciplines (from multidisciplinary to transdisciplinary) and across the science-society divide. In postnormal science, scientific knowledge alone is not enough to solve societal problems; therefore, interaction between producers and users of science across the sciencesociety interface entails specific involvement of stakeholders throughout the process. [Figure source: Redrawn from Kirchhoff et al., 2013, copyright Annual Reviews (www.annualreviews.org), used with permission.]


The North American Carbon Program (NACP) is an example of a boundary program that supports scientists’ efforts to engage in social, economic, and policy-relevant research to improve how carbon cycle science is conducted and ensure ­policy-relevant findings (NACP; Michalak et al., 2011). A co-authorship network analysis using data from publications of core NACP members indicates that the structure and collaborative pathways within the NACP community created an effective boundary organization (Brown et al., 2016). Results illustrate that the NACP community expanded its research on human and social impacts on the carbon cycle, contributing to a better understanding of how human and physical processes interact with one another. NACP has formed a tightly connected community with many social pathways through which knowledge may flow, and it has expanded its network of institutions involved in carbon cycle research over the past several years (Brown et al., 2016). Further coordination of research in social science, economics, business management, and carbon cycle science should enable decision makers to understand the motivations for people’s actions that either directly or indirectly affect the carbon cycle (see Ch. 6: Social Science Perspectives on Carbon) and the situations in which refined understanding of the biophysical carbon cycle can influence business decisions such as supplier selection for creating low-carbon supply chains (Hsu et al., 2013).

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