Co-production of knowledge via engagement and collaboration between stakeholder communities and scientific communities can improve the usefulness of scientific results by decision makers (high confidence).
Integrating data on human drivers of the carbon cycle into Earth system and ecosystem models improves representation of carbon-climate feedbacks and increases the usefulness of model output to decision makers (high confidence).
Attribution, accounting, and projections of carbon cycle fluxes increase the usefulness of carbon cycle science for decision-making purposes (very high confidence).
Developing stronger linkages among research disciplines for Earth system processes, carbon management, and carbon prediction, with a focus on consistent and scalable datasets as model inputs, will improve joint representation of natural and managed systems needed for decision making (high confidence).
Co-production of knowledge via engagement and collaboration between stakeholder communities and scientific communities can improve the usefulness of scientific results by decision makers (high confidence).
Integrating data on human drivers of the carbon cycle into Earth system and ecosystem models improves representation of carbon-climate feedbacks and increases the usefulness of model output to decision makers (high confidence).
Attribution, accounting, and projections of carbon cycle fluxes increase the usefulness of carbon cycle science for decision-making purposes (very high confidence).
Developing stronger linkages among research disciplines for Earth system processes, carbon management, and carbon prediction, with a focus on consistent and scalable datasets as model inputs, will improve joint representation of natural and managed systems needed for decision making (high confidence).
|Very High||Likely||As Likely As Not||Unlikely||Very Unlikely|
|≥ 9 in 10||≥ 2 in 3||≈ 1 in 2||≤ 1 in 3||≤ 1 in 10|
Note: Confidence levels are provided as appropriate for quantitative, but not qualitative, Key Findings and statements. See Guide to this Report for more on uncertainty of numerical estimates.
West, T. O., N. P. Gurwick, M. E. Brown, R. Duren, S. Mooney, K. Paustian, E. McGlynn, E. L. Malone, A. Rosenblatt, N. Hultman, and I. B. Ocko, 2018: Chapter 18: Carbon cycle science in support of decision making. In Second State of the Carbon Cycle Report (SOCCR2): A Sustained Assessment Report [Cavallaro, N., G. Shrestha, R. Birdsey, M. A. Mayes, R. G. Najjar, S. C. Reed, P. Romero-Lankao, and Z. Zhu (eds.)]. U.S. Global Change Research Program, Washington, DC, USA, pp. 728-759, https://doi.org/10.7930/SOCCR2.2018.Ch18.
Recent decades have seen continually increased interest in how best to reduce net carbon emissions, including maintaining or augmenting natural and managed carbon stocks (Griscom et al., 2017) and decreasing anthropogenic carbon emissions. Decisions about carbon management extend from future energy production and technology planning to designs for urban infrastructure and refurbishment; transportation; and agriculture, forest, and natural resource management. Over this same time period, scientists have conducted extensive basic and applied research on biogeochemical cycles, land-cover change, watershed to Earth System Modeling, climate change, and energy efficiency, all of which inform the understanding of the efficacy of various carbon management options (CCSP 2007). However, the information needs of decision makers differ from the objectives that drive basic science to understand natural carbon cycling. Explicitly identifying the information that various decision makers will use, including the form in which they need it, is critical for taking carbon cycle science from laboratory to management action. While much progress has been made in understanding individual components of both fundamental and applied science contributing to decision-making frameworks (see Figure 18.1), additional work is needed to connect these components to address existing research and policy questions.
Methods for connecting and integrating basic and applied carbon cycle research take a number of forms. For example, researchers can 1) simplify complex models to provide mean estimates for given activities (e.g., a complex nitrogen cycle model providing mean and uncertainty estimates for nitrous oxide [N2O] emissions); 2) interpret biogeochemical model results to estimate net carbon flux associated with particular activities (e.g., natural disturbance contributions to global carbon fluxes versus net emissions associated with the management of natural disturbances); or 3) aggregate and analyze scientific data in a different manner to address specific questions (e.g., national emissions estimates versus attribution of net emissions associated with particular activities). These approaches to connect basic science and decision making have most often been employed post hoc, harvesting results from foundational research that already has been conducted to inform decisions, rather than designing and organizing large research programs around user-defined information needs (Lemos and Morehouse 2005). Post hoc methods often are used to synthesize, and sometimes simplify, fundamental research findings for common applications and decision making, including in the Intergovernmental Panel on Climate Change’s (IPCC) 2006 Guidelines for National Greenhouse Gas Inventories (IPCC 2006) and the U.S. Department of Agriculture’s (USDA) Methods for Entity-Scale Inventory (USDA 2014).
While organizations make decisions with whatever information they have available, multiple, competing interests are generally at play in setting priorities, so the quality and credibility of information can influence decisions about carbon management. Some decisions about carbon cycle management require only coarse-level estimates or discipline-specific knowledge, while others benefit from more nuanced analysis or multidisciplinary research. Multidisciplinary research is particularly needed to inform economy-wide carbon management targets (e.g., to maintain atmospheric carbon dioxide [CO2] within a safe operating space for humanity; Rockstrom et al., 2009) and to understand links among sectors (e.g., soil carbon in the land sector associated with biofuel production in the energy sector). Collaborations between scientists and practitioners increase the chances that information intended to inform decisions is actually needed and delivered in a highly useful manner. For decisions affecting multiple sectors, collaborations among scientists of many disciplines tend to produce knowledge that is more credible and practical in the eyes of multiple stakeholders compared to knowledge produced in more siloed environments (Weaver et al., 2014). Although collaborations have increased (Mooney et al., 2013), there remain lost opportunities for effective carbon cycle management that could be captured via more integration.
Federal, state, and local policymakers; company executives; energy managers; urban designers; natural resource managers; families; and individuals make short- and long-term decisions that can influence the carbon cycle. These entities require adequate information from science-based analyses to inform their choices and to understand how management, technologies, or behavioral decisions can affect net carbon emissions or carbon stock changes. Meanwhile, scientists are developing more sophisticated monitoring, data interpretation, and modeling methods that could be relevant to these decision makers, providing more refined understanding. An important but challenging part of carbon cycle science is ensuring that scientists have sufficient understanding of decision makers’ needs to produce information that actually is usable by decision makers and that funding organizations place sufficient priority on actionable science. To facilitate strategic, effective use of carbon cycle science in carbon management, as well as to provide insights about the opportunities and constraints that shape the availability of user-driven carbon cycle science now and in the future, this chapter provides information on national and international needs for carbon cycle information, current status of research to inform carbon cycle and greenhouse gas (GHG) management, and future needs. It also focuses on the sectors of agriculture, forestry, and other land uses (AFOLU) and discusses energy and associated carbon sources in the context of integrated carbon cycle systems.
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:
How much can emissions be reduced from transportation versus power generation versus building sectors, and at what costs?
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)?
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)?
How can carbon be managed from procurement through production and inventory management (Benjaafar et al., 2013)?
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)?
How will the consequences of different sets of agricultural and forest management practices on a single tract of land add up?
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.
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.
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 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.
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).
Carbon cycle science supports decisions in a number of national and international contexts. For example, decisions about managing ecosystems such as national or state forests require integrating stakeholder perspectives with scientific input on the consequences or alternative policy approaches for ecosystems, emissions, and climate (BLM 2016). At the international level, as countries establish goals to stabilize carbon and GHG concentrations in the atmosphere, the scientific community should play an important role in assessing carbon budgets and developing the technologies, methods, and practices for reducing net GHG emissions and managing carbon stocks. Global efforts to slow deforestation, improve human health, and decrease global GHG emissions will be aided by substantial input from the international scientific community and respective national agencies. In all of these examples, and many others, improvements in the quality and process of scientific input can help inform sound decision making. Recent research on CH4 emissions provides a notable example of fundamental carbon cycle science used in decision making. Reducing anthropogenic CH4 emissions has become a high priority for policymakers, given the potential for near-term climate benefits and the relative tractability1 of monitoring and mitigating emissions from many sectors. Concerted effort to develop relationships among scientists and decision makers has enabled progress in identifying information needs, developing technology to provide needed information, and establishing science questions that evaluate existing knowledge. With respect to policy drivers, new laws and rules have been enacted to mitigate and measure CH4 emissions in California and other key regions and sectors in the United States (Federal Register 2016a, 2016b). Atmospheric or “top-down” scientific methods for detecting, quantifying, and attributing CH4 fluxes have dramatically improved. For example, satellite observations have enabled scientists to identify concentrated regions of CH4 emissions, information relevant to policy and management that previously had not been well known or understood (Kort et al., 2014). Recent field studies have revealed evidence of a long-tail statistical distribution of emissions sources in the U.S. natural gas supply chain, where a relatively small number of superemitters dominate key regions and sectors (Brandt et al., 2014; Zavala-Araiza et al., 2015; Zimmerle et al., 2015). Some stakeholders (e.g., California Air Resources Board) already have applied the atmospheric and field research findings to make corrections to CH4 inventory estimates. Additionally, recent advances in remote sensing of CH4 point sources (Frankenberg et al., 2016; Thompson et al., 2016) demonstrate the potential to efficiently detect leaks from point sources.
Because the demand for tailored knowledge is often urgent, specific, and only weakly aligned with incentives that drive fundamental research, consulting firms and non-governmental organizations (NGOs) have often met this demand. These institutions have generated a great deal of user-driven science over the decades. For example, the World Wildlife Fund (WWF) and the Carbon Disclosure Project (CDP) partnered with multiple, large, U.S.-based corporations to produce The 3% Solution, an analysis of the business case for businesses to achieve net savings of up to $190 billion by 2020 through measures to reduce carbon emissions (WWF and CDP 2013). Woods Hole Research Center, in collaboration with the U.S. Agency for International Development (USAID), produced a map of aboveground carbon stocks in Mexico. The map built on information already assembled by Mexico’s government for its National Forest Inventory and met a clear need to advance the estimates of Mexico’s forest carbon stocks at both national and municipal levels (Cartus et al., 2014; WHRC 2014). As these examples illustrate, contract-driven science is sometimes made publicly available, such as when governmental agencies provide funding to support projects in the public interest or when private-sector entities and NGOs partner to develop analyses of common interest. However, the private contract model has limitations. Many products of contract research remain outside the public domain, and users without the resources to purchase these goods cannot easily access tailored information for their decision-making needs. User institutions that lack these resources are typically smaller and also have less influence than their larger counterparts in a variety of forums. This imbalance in access to information has profound implications because, as many chapters in this report demonstrate, carbon management has consequences for all of society, not only the entity making a particular decision. Because user-driven science that does not enter the public domain is difficult to access, further characterization of its contributions or extent are not included in this chapter. In spite of this, significant effort should be placed on accessing relevant science that is outside the public domain in order to determine whether this science has sufficient value to impact the decision-making process.
The carbon research community performed a great deal of work in the past decade with the aim of improving decision making in agriculture, energy production and consumption, building infrastructure design and maintenance, transportation, and many other sectors that consume fossil fuels or generate land-based emissions. This research filled knowledge gaps that helped decision makers understand multiple impacts of land-management decisions. Research foci included, for example, ecosystem disturbance (e.g., fire and pest outbreaks), human health and risk, indirect land-use change, efficient production throughout commodity supply chains, full life cycle energy and emissions impacts of ecosystems and production systems, and how these analyses change under alternative land-management scenarios. Federal guidance to U.S. agencies documents how full GHG accounting has been incorporated into environmental impact analyses under current and alternative scenarios (Federal Register 2016b). Briefly illustrated here is the potential impact of scientific input on land management through examples of land-use policy and of terrestrial management on the carbon cycle.
The use of carbon cycle science for decisions on carbon emissions reductions in agriculture is relevant for a wide suite of societal and policy questions relating to the direct impacts of land-use decisions on energy, emissions, health, and ecosystems (see Ch. 5: Agriculture). For example, carbon cycle science from multiple disciplines informs dialogue and decisions about the role biofuels can play in the energy economy. Biofuels can include dedicated energy crops, agricultural wastes and residues, and CH4 from agricultural wastes. The use of biofuels can decrease GHG emissions, depending on net changes in biomass growth stocks across the landscape (e.g., harvest rates, deforestation, and indirect land-use change) and on the net efficiency of converting biomass to energy (see Ch. 3: Energy Systems). Biofuel policy options have complex and highly variable implications for carbon emissions that are a function of energy expended in production, processing, and use of biofuels; indirect land-use change; and ecological and economic costs and benefits of biofuels (Paustian et al., 2001). In seeking solutions to energy, environmental, and food challenges, biofuels can either contribute positively or negatively to existing societal issues (Tilman et al., 2009). Full carbon cycle analysis and modeling are key to ensuring that policies and resulting actions actually lower carbon emissions instead of raising them. Such analyses continue to be used to ascertain the benefit of biomass to reduce net emissions, including biomass burning (Cherubini et al., 2011; Johnson 2009; Khanna and Crago 2012; Miner et al., 2014; Mitchell et al., 2012; Tian et al., 2018) and forest thinning to reduce wildfire risks (Campbell et al., 2012; Mitchell et al., 2009). Analyses at different spatial scales (e.g., plot, national, and global) and temporal scales (e.g., years, decades, and centuries) can yield different conclusions for land-related carbon issues, indicating the need to synthesize or integrate approaches used across scales (i.e., plant growth models, land-use change models, integrated assessment models (IAMs), and natural resource supply models).
While some carbon management strategies are still being debated within the science community, a number of strategies have been well documented and quantified. Some of them are summarized from results in preceding chapters of this report (see Table 18.1). Many land-based strategies are associated with changes in management. Humans have a long history of altering the landscape and associated carbon stocks around the world since initial settlement and population expansion (Sanderman et al., 2017; Köhl et al., 2015). People have changed forests to agricultural areas and vice versa; changed management of soils, forests, grasslands, and other ecosystems; and developed urban and suburban areas. There is a robust literature of observations and carbon stock comparisons under different land uses and management regimes that provides guidance for managing natural resources, fossil resources, and renewables with regard to carbon. Potential sequestration rates have been estimated by aggregating data from hundreds of paired plots, and the data have been used for national scale estimates (U.S. EPA 2016) and global default values for numerous management practices across land, energy, and transportation sectors (IPCC 2006). Research has moved beyond estimating the influence of management changes within a sector, to evaluating how change in one land or energy sector causes changes in other land or energy sectors.
|Activity||Impact on GHGs||Potential Reductionb||Co-Effects|
|Afforestation and improved forest management (Ch. 9, 12)c||Increase in net removals from the atmosphere.|
Reduction in emissions by avoiding the conversion of forests and grasslands to other cover types.
Increase in carbon removals from the atmosphere by promoting the conversion of other land covers to forests or grasslands.
|30 to 330 teragrams of carbon (Tg C) per year (U.S. only)||Potential impacts on food production, biodiversity, net forest resources, and counter harvesting elsewhere (i.e., leakage), resulting from increased forestland area.|
|Managing grasslands (Ch. 10)c||Increase in net removals from the atmosphere and in biomass and soil carbon storage by improving grazing practices and grasslands management.||Tens of Tg C per year (U.S. only)||Shifts in species composition.|
|Reducing methane (CH4) emissions from livestock (Ch. 5)c||Reduction in net agriculture emissions by controlling livestock CH4 emissions.||13 to 19 Tg C per year||Potential co-benefits such as improved feed efficiency or productivity in livestock.|
|Cropland management practices (Ch. 5, 12)c||Increase in organic residue inputs and soil carbon stocks by reducing tillage and summer fallow, implementing cover cropping, or managing nutrients to increase plant production.|
Reduction in CH4 and nitrous oxide (N2O) emissions by optimizing nitrogen fertilization and water management.
|Soil carbon stock increases of up to 3 megagrams of carbon per hectare; up to 80% reduction in CH4 (especially rice) and N2O, depending on crop, environment, and combination of practices.||Potential co-benefits such as improved soil productivity and lower costs for nitrogen fertilizers.|
Increased organic carbon for improved buffering capacity, water holding capacity, soil fertility, and tilth.
Reduced water use (especially rice).
|Reducing wetland and coastal ecosystem loss (Ch. 13, 15)c||Reduction in emissions by avoiding the loss of wetlands and coastal estuaries.|
Increase in carbon sequestration by restoring drained wetlands, though possibly increasing CH4 emissions.
|Based on the amount of wetlands converted to other land uses in Canada and the United States, restoring all wetland acreage, leading to a gross but highly unrealistic estimate of 43 Tg C per year.||Potential impacts on coastal zone development.|
Increased protection of property from storms.
Reduced export of nutrients to the ocean.
Restored wetlands via improved flood abatement and water quality, but with only about 21% functional compared to functionality of undisturbed sites.
|Urban mitigation (Ch. 4)c||Reduction in city carbon emissions by implementing or improving urban development pathways, building codes, transportation planning, electricity supply, or biotic planning (e.g., tree planting). |
Reduction in CH4 leakage, for example, by upgrading infrastructure.
|Data unavailable for a comprehensive assessment of mitigation potential.||Implications for air quality, urban heat island, and human health, among the many co-effects and priorities for consideration.|
|Increasing bioenergy (Ch. 3)c||Possible reduction or increase in net GHG emissions by substituting biofuel for fossil fuel. Impacts dependent on fuel source and effects on production and consumption cycles.||Estimates of mitigation potential based on life cycle analysis unavailable, though biofuel supply is potentially large.||Increased agricultural commodity prices and land-use changes in other regions, dependent on extent of land supplying the biofuel.|
Increased forest harvesting in response to higher demands for forest biomass, possibly followed by forest area expansion.
a Table includes GHG emissions reductions, carbon stock increases, and avoidance of carbon losses.
b Potential reductions are in addition to baseline.
c Chapter titles—3: Energy Systems; 4: Understanding Urban Carbon Fluxes; 5: Agriculture; 9: Forests; 10: Grasslands; 12: Soils; 13: Terrestrial Wetlands; 15: Tidal Wetlands and Estuaries.
The many land-management options available to reduce net GHG emissions or increase removal of GHGs from the atmosphere (see Table 18.1), taken together, could reduce net emissions by 100 to 500 teragrams of carbon (Tg C) per year, with co-effects becoming highly significant in the high end of this range. Therefore, decisions about land-management policies must take into account the co-effects, which may be positive or negative, along with the potential benefits in terms of reducing GHGs. One of the most significant negative impacts of altering land management to increase carbon storage is a potential reduction in land area devoted to food production if the amount of additional land required exceeds the area of “marginal” (i.e., not productive for crops) land available. On the other hand, positive co-effects may result from management practices that increase soil fertility along with carbon storage, or those that increase protection of water quality or damage from storms and floods.
Although traditionally considered the province of biophysical science, the demand for actionable results has increasingly drawn attention to the need for research from sociology, psychology, and human behavior to inform carbon management. Research in these fields has identified obstacles to effective carbon management, and the approaches to overcome them, at individual to institutional scales (Ross et al., 2016). In researching the interests and understandings held by different actors in Mexico’s program for monitoring, reporting, and verifying (MRV) REDD+, Deschamps Ramírez and Larson (2017) found tension arising from poor understanding of international reporting requirements and the roles and responsibilities of subnational institutions. Weaknesses in understanding and social relations among key institutions limit the effectiveness of carbon management even when decision makers possess and understand strong biophysical analyses (Deschamps Ramírez and Larson 2017). Individuals respond strongly to default options and associated social norms, as demonstrated in comparisons of decisions about whether or not to participate in organ donor programs among different countries. Default settings on furnaces and other appliances to conserve energy, with the option for owners or users to change that setting, could produce widespread behavior shifts and associated changes in carbon emissions (Ross et al., 2016). Efforts to support the capacity of businesses to manage carbon involves research but can fall outside traditional academic frameworks. For example, the Sustainable Purchasing Leadership Council (SPLC) evaluated third-party tools for estimating supplier sustainability across an entire supply base (SPLC 2018). Although these tools focus more broadly than carbon, SPLC’s work summarizing and evaluating them demonstrates the type of collaboration that spurs user-driven science and produces actionable recommendations.
Assuming adequate organization, communication, and funding is in place, there are a number of scientific and technical challenges associated with better connecting basic and applied science for decision-making purposes. This section discusses current capabilities and needs for data, modeling, accounting, and broad system approaches for carbon management.
Data for basic carbon research and decision making are often similar, although they typically are used independently instead of informing one another. For example, global climate models rely on national and global datasets on human activities and land management. Conversely, models of natural resource ecosystems and economics that inform land management require input on global changes in total land resources, commodity markets, and climate. A revised assessment of existing data, across disciplines, could help basic and use-inspired research on carbon and also address interrelated climate and carbon research issues.
Inventory data on fossil fuel emissions and land emissions and sinks are estimated nationally (e.g., U.S. EPA 2016) and reported internationally under the United Nations Framework Convention on Climate Change (UNFCCC). Advances in carbon cycle science are reflected in carbon modeling and accounting used to produce the inventory data. For example, field experiments that collect data on fertilizer application methods and timing, livestock and manure management, soil management, and other activities can be incorporated into models that estimate GHG emissions, thereby refining the national carbon budget.
Inventory data provide information on emissions sources and sinks and how net emissions change with land management or fuel supplies. To be most useful for local and regional planning, these data often require spatial distribution (West et al., 2014) or additional information on land-cover, land-use, and ecosystem characteristics that may be provided by satellite remote-sensing or economic survey data. Integrating inventory and remote-sensing data can provide new data products to understand local and regional carbon dynamics (Huang et al., 2015) and to inform land-management and policy decisions. Using integrated data on land use and management in climate modeling activities may become increasingly important (Hurtt et al., 2011) to facilitate consideration of climate feedbacks in local and regional decision making.
Although inventory data often serve as the basis for understanding human-induced impacts on the carbon cycle and subsequent decision making on carbon mitigation strategies, other datasets can provide additional or complementary estimates. For example, fossil fuel emissions can be estimated by the production of fossil fuels (U.S. EPA 2016) or by the consumption of fossil fuels (Patarasuk et al., 2016). The same is true for land-based emissions, which can be estimated using ground-level survey data from the Forest Inventory Analysis or the National Agricultural Statistics Service (West et al., 2011) or using atmospheric concentration data and modeled with atmospheric transport and inversion models (Schuh et al., 2013). The survey or inventory data represent “bottom-up” estimates while the atmospheric data represent a “top-down” approach. Reconciling data and approaches benefits both basic and applied science. Earth System Models (ESMs) require accurate base-level data and also need multiple ways to evaluate results. Similarly, inventory data used in models for decision making could benefit from alternative estimation approaches that evaluate existing inventory estimates (Jacob et al., 2016). Also needed are continued development and reconciling of data collection and modeling approaches to estimate carbon stocks and fluxes, requiring coordination among researchers, decision makers, and funding sources (see Box 18.1, Key Data Needs for Decision Making on Terrestrial Carbon).
Research models and decision support tools that can forecast future changes, as well as integrate and analyze current and past conditions, can provide solutions to challenges presented by climate change. At the broadest level, capabilities include assessment and decision-making tools that analyze feedbacks between human activities and the global carbon cycle. These capabilities can enable decision makers to 1) assess how changes in the carbon cycle will affect human activities and the ecosystems on which they depend and 2) evaluate how human activities—past, present, and future—impact the carbon cycle.
For national-scale planning and in international agreements and negotiations, national GHG inventories have consistently been recognized as essential parts of the model-data system. Policy developments of the past few years have reinforced the global recognition of the need for high-quality and regularly reported GHG inventories. Increasing numbers of developing (i.e., UNFCCC non-Annex 1) countries produce annual GHG inventories and submit them to the UNFCCC using an extensive set of guidelines for national GHG reporting based on IPCC GHG inventory reporting guidelines (IPCC 1996, 2003, 2006). Deforestation and forest degradation constitute a major source of carbon emissions in many developing countries; the Global Forest Observations Initiative (GFOI) has developed guidance for using remotely sensed and ground-based data for forest monitoring and reporting of reduced emissions from deforestation, forest degradation, and associated activities produced in cooperation with UN-REDD and Forest Carbon Partnership Facility (FCPF) initiatives (http://www.gfoi.org/methods-guidance).
Most GHG inventories rest on estimates of the emissions associated with a particular activity (e.g., amount of CO2 emitted per amount of fuel combusted). The factors that relate activities to emissions are called emissions factors. For sectors dominated by fossil fuels (e.g., power generation, transportation, and manufacturing), emissions factors are well constrained (IPCC 2006). Therefore, the major limitation to estimating emissions accurately is the ability to collect, organize, and verify the activity data (e.g., numbers of transformers upgraded, hectares of perennial plants established for bioenergy, and number of cattle raised on forage known to reduce CH4 production). For biogenic-driven GHG emissions, such as those associated with agriculture and forestry, there is much greater variability in the emissions rate per unit of activity (e.g., N2O emissions per unit of fertilizer added) because of heterogeneity in climate and soil conditions and in management practices. Dynamic process-based models offer an alternative approach that can account for this heterogeneity (Del Grosso et al., 2002; Li 2007), but using these models requires sufficient capacity (e.g., trained staff, functioning institutions).
GHG inventories that use activity data and emissions factors (or activity-specific process modeling) are referred to as bottom-up approaches (see Section 18.4.1). All national GHG inventories use this approach, which, by definition, attributes emissions sources and sinks to identifiable entities and activities and lends itself to policy applications to reduce emissions and incentivize sinks. Examples of spatially explicit, high-resolution model-data systems for major source categories include fossil fuel emissions (Gurney et al., 2012; Gurney et al., 2009), forest dynamics (USDA 2015), biofuels (Frank et al., 2011), and land-use change (Sleeter et al., 2012; Woodall et al., 2015). These data combine knowledge of biophysical processes with data on human activities and economics that can help municipalities or geopolitical regions understand and quantify carbon emissions and sinks, thereby informing decision making. Challenges to these bottom-up approaches, aside from improving data quality on both activities and emissions factors to reduce uncertainties, include ensuring completeness and avoiding double-counting of sources.
In addition to inventories, the carbon cycle science community develops projections that scale from local mitigation options to global impacts and, conversely, from global economic forces to local strategies. Many countries incorporate land-use emissions into their overall climate targets in some way, and these projections inform national and international strategies to address CO2 emissions, carbon management options, and other sustainability goals. These estimates of future land-use sources and sinks are useful for decision making because they stem from a reliable, scientifically sound, and transparent process (U.S. Department of State 2016). Because this work reflects the development and use of new approaches in carbon cycle science, further work is widely acknowledged as being helpful to increasing the usefulness of land-use emissions projections.
Models and decision tools have also been designed to help industry, business, or other entities (e.g., universities, land-management agencies, farmers, and ranchers) assess their emissions and develop mitigation strategies. In a regulatory environment where emissions are in some way limited by law, models and decision tools are essential for planning, forecasting, and monitoring emissions reductions. These tools also are widely used in voluntary carbon accounting and reporting to generate and sell carbon credits from a variety of activities (CARB 2018).
Models and decision support tools for inventory and forecasting in the AFOLU sector at the scale of the farm, woodlot, or business have been developed and are increasingly deployed as tools to guide implementation of government-sponsored conservation programs. These tools can help inform decisions to reduce the GHG footprint of agricultural commodities through supply-chain management by agricultural industries and to support agricultural offsets in carbon cap-and-trade systems (see examples below).
COMET-Farm (cometfarm.nrel.colostate.edu; Paustian et al., 2018)—Helps farmers and other landowners estimate carbon benefits associated with implementing practices supported by conservation programs of the USDA Natural Resources Conservation Service (Eve et al., 2014).
Cool-Farm Tool (CFT; www.coolfarmtool.org/CoolFarmTool; Hillier et al., 2011)—A product of the Cool Farm Alliance, CFT is designed for use by farmers and is intended to support the Alliance’s global mission of enabling millions of growers to make more informed on-farm decisions that reduce their environmental impact.
DNDC (Denitrification-Decomposition) process-based biogeochemical model (Li 2007)—Used by institutions like the California Air Resources Board to support CH4 reductions from rice farming as an agricultural GHG offset in California’s GHG emissions reduction program (Haya et al., 2016).
ExACT (Ex-Ante Carbon balance Tool; www.fao.org/tc/exact/ex-act-home/en)—Estimates CO2 equivalent (CO2e)2 emissions based on a project’s implementation as compared to a “business-as-usual” scenario. Project designers can use ExACT as a planning tool to help prioritize mitigation-activity terms.
ALU ( Agriculture and Land Use) national GHG inventory software—Assists countries in completing their national inventories. This tool was developed to meet a U.S. governmental priority of increasing the number of countries developing robust GHG inventories to create transparent, evidence-based understanding of global GHG emissions.
Climate Change, Agriculture, and Food Security–Mitigation Options Tool ( CCAFS–MOT)—Identifies practices in Africa, Asia, and Latin America that can reduce emissions and sequester carbon on agricultural lands. MOT prioritizes effective mitigation options for many different crops according to mitigation potential, considering current management practices, climate, and soil characteristics.
National Oceanic and Atmospheric Administration (NOAA) Annual Greenhouse Gas Index—Compares the total combined warming effects of GHGs (including CO2, CH4, N2O, and chlorofluorocarbons) to their 1990 baseline levels.
Bioenergy Atlas—Includes maps enabling the comparison of biomass feedstocks, biopower, and biofuels data from the U.S. Department of Energy (DOE), U.S. Environmental Protection Agency (EPA), and USDA. (Software hosted by DOE’s National Renewable Energy Laboratory.)
Global Carbon Atlas—Aggregates global carbon data to explore, visualize, and interpret global and regional carbon information and changes from both human activities and natural processes. (Supported by the Global Carbon Project, www.globalcarbonproject.org; and BNP Paribas.)
Comparable decision support tools for carbon management have been developed for other sectors. For example, USAID’s Clean Energy Emissions Reduction (CLEER) tool, based on internationally accepted methodologies, enables users to calculate changes in GHG emissions resulting from adoption of geothermal; wind; hydroelectric and solar energy generation; upgrades of transmission and distribution systems; increases in building energy efficiency; heating, ventilation, and air conditioning system efficiency improvements; fuel switching; capture of stranded natural gas by flaring; use of biomass for energy; and use of anaerobic digesters to capture CH4 from livestock manure (USAID 2018).
Integrated Assessment Models merit particular attention because they constitute a distinct field of research and serve a unique role in decision support. Among decision support tools for carbon management, IAMs are unique in estimating economy-wide responses, including GHG emissions, to different management and policy options. The objective of these models is to capture the primary interactions and interdependencies between natural and human systems (e.g., economic sectors) through a series of scenarios that represent plausible policy interventions (Weyent 2017). These models can help understand feedbacks among carbon sources and sinks at national and global scales (see Figure 18.3), given specified emissions targets or implementation of carbon strategies (Grassi et al., 2017; Iyer et al., 2015). Integrative modeling frameworks that include land sector, energy sector, transportation, and other interconnected carbon sources and sinks have continued to develop more detailed model structures and higher-resolution data input (Kyle et al., 2011; Wise et al., 2014).
IAMs, designed to answer questions about carbon management, include 1) social and economic factors that drive GHG emissions as well as a representation of biogeochemical cycles that determine the fate of those emissions and 2) the effects on climate and human welfare. The dynamic interactions among sectors in these models mean that they can reveal nonintuitive outcomes. Actions in one sector or geography can influence those in another, and a common goal of carbon management policy is to limit the accumulation of CO2 in the atmosphere. Therefore, understanding the economy-wide influences of policy choices is critical both to assess the actual consequences of a single policy on carbon accumulation in the atmosphere and to have a realistic idea of the level of atmospheric CO2 that could be achieved with multiple countries and multiple policies.
Continued efforts to integrate IAMs, ESMs, carbon accounting, and national-scale resource modeling will help develop consistency in data input across these modeling platforms. The combination of global IAMs, national and subnational natural resource economic models, carbon accounting methods, land-use change models, energy technology, and market analyses are all needed to estimate carbon management strategies in a comprehensive manner from the local to global scale (see Box 18.2, Carbon Modeling Needs for Decision Making). As one example, a process using IAMs, global and national natural resource (i.e., timber) models, and inventory data (i.e., field surveys) was conducted in the development of the United States Mid-Century Strategy for Deep Decarbonization (White House 2016).
Data and models that estimate changes in carbon flux often were not initially developed for estimating direct and indirect net carbon changes associated with given activities. This is true for country-level inventory data reported by sector (U.S. EPA 2016), biogeochemical cycle models (Del Grosso et al., 2002), and integrated climate models (Wise et al., 2009). In many cases, incorporating the influence of particular activities on upstream or downstream energy, land use, and associated GHG emissions significantly changes estimates of the realized carbon savings. Full GHG accounting of all emissions related to a given activity can significantly augment or reduce reported emissions compared to partial or incomplete accounting.
Accounting of carbon fluxes and stock changes in ecosystems or industrial systems dates back to early work on energy input and output models and systems modeling (Odum 1994) and has evolved rapidly since then. A systems analysis can be developed to understand and quantify net carbon exchange associated with specific management activities (Schlamadinger and Marland 1996). Such analyses, for example, consider disturbance (e.g., widespread tree mortality and erosion from hurricanes or ice storms), forest regrowth over time, landscape area boundary, and forest growth trends over time in the absence of disturbance (Lippke et al., 2011; Lippke et al., 2012). Fossil fuel offsets associated with harvested wood and wood products are also included in these system-scale carbon budgets. These types of analyses often are conducted to illustrate the methods and provide an averaged national answer. To be useful for decision making, full carbon accounting would need to be conducted for regions that have obvious differences in ecosystem attributes, climate regimes, and social and economic drivers (see Box 18.3, Carbon Accounting Needs for Informing Decision Making).
Past development of carbon accounting methods suggests a number of basic carbon accounting guidelines. Properly defining time and space boundaries of the system or activity of interest is an essential first step, and highlighted below are additional guidelines.
Stock Changes Are Less Prone to Error than Adding up All Biological Fluxes and Uptakes. This finding is currently guiding analyses by EPA’s Science Advisory Board Panel on Biogenic Emissions from Stationary Sources on net carbon emissions from the use of biomass for energy production (U.S. EPA 2014). The stock change approach also has been the chosen method for estimating net emissions from forests and agricultural soils (U.S. EPA 2016). Trying to simulate all fluxes in and out of a system is useful for understanding ecosystem processes and climate feedbacks, but the increased complexity may introduce additional error and uncertainty. In contrast, changes in carbon stocks inherently combine the net result of multiple fluxes into and out of a given stock entity. Differences in complex models and stock change methods are exemplified in an analysis by Hayes et al. (2012).
Accounting for Energy and Emissions One-Level Upstream and Downstream Is Often Sufficient to Capture Adequately the Total Flux Associated with an Activity of Interest. When estimating emissions associated with changes in fertilizer application rates, for example, the fuels used to process the fertilizer (e.g., natural gas) should be considered (i.e., Level 1 upstream), but the energy used to mine the fuel (e.g., natural gas; Level 2 upstream) is often statistically insignificant (West and Marland 2002). Although exceptions should always be considered, accounting for emissions of both Level 1 upstream and downstream (e.g., transporting the fuel) of the activity of interest remains a good general rule.
Establishing the Proper Reference Point (System that Exists Prior to Changes in Management) Is Essential. The reference point is the current system, prior to a change in activity (see Figure 18.4). The reference point should not be chosen at a time prior to the current activity (e.g., based on historical trends), nor should it be arbitrarily chosen before or after activities associated with the new or alternative management. This issue is currently debated in regard to some forest management techniques (Campbell et al., 2012; Hurteau and North 2009).
A Baseline Trajectory May Be Conceptually More Comprehensive Than a Reference Point But May Have More Uncertainty. Models that project changes in land use, fossil fuel combustion, or other GHG emissions can be particularly useful for understanding future scenarios. However, the trend line for the future trajectory can be uncertain, and using baselines to compare new or alternative systems should only be done with caution (Buchholz et al., 2014). The use of a reference point or baseline should be decided based on the certainty associated with baseline projections (see Figure 18.4). For example, a baseline of forest growth (e.g., increased growth until forest maturation) is well established in forest growth curves, whereas future changes in land use based on commodity markets is less certain. There may also be policy considerations that influence whether baselines or reference points are more appropriate for a given context.
Combining several of the aforementioned capabilities (e.g., data collection, modeling, and accounting) can help facilitate the use of research products for both decision making and the next generation of new relevant scientific analyses (West et al., 2013). Data assimilation systems have been under development to bring together inventory-based datasets, atmospheric modeling, global land models, and accounting procedures. Integrating these research areas using data assimilation, where appropriate, can help researchers explore data similarities and differences, reconcile data differences, and potentially integrate datasets to attain enhanced data products or model results with reduced bias, reduced uncertainty, and improved agreement with observations. Past efforts include 1) a project in the midwestern United States (Ogle et al., 2006), 2) a North American continental analysis (Hayes et al., 2012; Huntzinger et al., 2012), and 3) similar analyses in Europe (Le Quéré et al., 2015). Of these analyses, those for the midwestern United States and Europe resulted in little to no statistical difference between bottom-up and top-down emissions estimates, indicating promising capability in using one method to constrain another and in integrating methods for a more comprehensive and potentially more accurate estimate. There also is an indication that atmospheric inversion model estimates (i.e., top-down estimates) can be useful in smaller regions, but they are potentially less informative or accurate at continental or global scales (Lauvaux et al., 2012). Accounting issues also were identified and resolved between atmospheric estimates and terrestrial-based estimates so that the two methods could be compared and contrasted, contributing to a new lexicon that helped define land-based fluxes in a manner consistent with fluxes observed from atmospheric measurements (Chapin et al., 2006; Hayes and Turner 2012).
Although reconciling bottom-up and top-down estimates can help build confidence in existing estimates, thereby forming a stronger foundation for decision making, other existing modeling systems could be combined to improve national and global decision making about carbon. Largely independent efforts continue for climate modeling, land-use modeling, global and regional economic modeling, and energy modeling. Coordinating these modeling activities so that, at a minimum, output from one model can be used as input for other models would help in coordinating decisions that inherently affect or are affected by climate, land use, and energy production and consumption (see Figure 18.1). This effort would require high-level coordination among research organizations that support modeling in different research fields covering fundamental, applied, and social sciences (see Box 18.4, Research Needs for Integrative Observation and Monitoring Systems).
Carbon cycle science to date has made significant advancements in understanding carbon dynamics and feedbacks between global carbon and climate. For these advances to be more useful in decision making, increased understanding and quantification are needed regarding how individual activities affect carbon sinks and emissions, both directly and indirectly. This information would aid accounting of energy consumption, fossil fuel combustion, as well as land-related emissions and sinks (see Table 18.2). Science-based estimates of net emissions associated with activities, complete with statistical uncertainty, may then be scaled up using relatively high resolution data on environmental conditions and human activities. This information then can be used to better understand how decisions under consideration by public and private entities may impact carbon sources and sinks.
|Decision-Making Goal||Information Gap||Research Activity Need|
|Prioritize activities and geographic regions for soil carbon sequestration and net greenhouse gas (GHG) emissions reductions.||Predict changes in soil carbon based on regional changes in land-management practices.||Calibrate existing soil models with field data and develop multivariate meta- analyses of field data.|
|Consider carbon stock changes in private and public forest management plans.||Understand net carbon stock changes associated with land-management strategies.||Assess forest carbon stocks and net changes in stocks at the regional and landscape levels associated with fire, regrowth, harvesting, thinning, and wildfire management.|
|Consider carbon stock changes in land-use planning and in legislation and policies that affect national and global land use.||Understand the connections between direct and indirect land-use change and national and global changes in population, diet, affluence, technology, energy, and water use.||Integrate science-based carbon stock and flux estimates, including uncertainty estimates, with global and regional socioeconomic models.|
|Increase the use of bioenergy, bioproducts, and renewable energy.||Compare net emissions of alternative technologies to existing technologies and capture regional differences, if warranted.||Conduct life cycle analyses (LCAs) for all proposed bioenergy, bioproducts, and renewable technologies and compare these analyses with LCAs for fossil fuel technologies.|
|Incentivize sustainable bioenergy.||Develop accurate bioenergy emissions accounting at individual facilities.||Calibrate existing forestry models to accurately reflect forest owner planting responses to market signals.|
|Protect vulnerable high-carbon landscapes.||Identify land areas at high risk of settlement conversion.||Project trends in urban development and land-management choices.|
|Maximize carbon mitigation on lands at risk of natural disturbance.||Project natural disturbances and their carbon impacts.||Develop region-specific carbon accounting protocols and management guidance.|
|Optimize national gross domestic production (GDP), its factors, and GHG emissions.||Understand factors of GDP and emissions and how those factors can be used to decrease emissions while positively affecting GDP.||Include GHG emissions in analyses of GDP and national economic growth.|
|Optimize energy production and consumption for reduced carbon emissions.||Understand fuel mixes, substitutes, combustion efficiencies, energy intensity, and carbon intensity associated with energy production and use.||Develop and integrate models that investigate carbon intensity of fuel use at local to national scales, with feedbacks to other related sectors (e.g., land resources and bioenergy).|
Many land-management decisions at the U.S. Federal and state level (i.e., conservation programs) over the past decade could not have been made without the previous generation of work on carbon cycle science and efforts that supported basic research, fostered co-production of knowledge, and linked scientific inputs with the needs for inventories, assessments, projections, and decision making. Yet, with the evolving interests of communities and policymakers, as well as new policy requirements for implementing and setting national goals, new needs have emerged that emphasize input from the scientific community at the international, national, and subnational levels. Establishing strong partnerships among scientists, stakeholders, and funding sources may be essential for making effective use of carbon-related research over the coming years.
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Mitigation of methane (CH4) emissions—particularly point sources from the energy, waste, and some agricultural sectors—has strong near-term tractability because it involves detecting and repairing local fugitive emissions rather than economy-wide shifts in energy and transportation infrastructure associated with fossil fuel carbon dioxide (CO2) mitigation. Monitoring anthropogenic CH4 fluxes is generally more tractable (with existing technology) than monitoring CO2 fluxes, since the latter includes large, confounding fluxes from the biosphere. However, area sources of CH4 such as wetlands and some agricultural fluxes (e.g., rice and enteric livestock emissions) continue to present a challenge.↩
Carbon dioxide equivalent (CO2e): Amount of CO2 that would produce the same effect on the radiative balance of Earth’s climate system as another greenhouse gas, such as methane (CH4) or nitrous oxide (N2O), on a 100-year timescale. For comparison to units of carbon, each kg CO2e is equivalent to 0.273 kg C (0.273 = 1/3.67). See Box P.2, p. 12, in the Preface for details.↩