Tools

In this section, we highlight 12 tools, used to estimate carbon stocks and flux, that are still being used by urban developers and the scientific community. This means that tools that are no longer being updated or are considered a previous version of a more current tool are excluded from this section. Furthermore, these tool descriptions and names are not an exhaustive list. That is, a certain tool’s functionality and example based from one country or region may share qualities of similar tools from another country or region under a different name or title. The goal here is not to provide an exhaustive list of urban carbon management tools. Instead, the goal is to provide examples of what kinds of tools are readily available. Qualitative synthesis of the ensuing information in this section can be found in Table 2.

Table 2. Basic characteristics of urban carbon management tools.

Note: This table was modelled after USDA (2019); where ‘L’ denotes low difficulty (e.g., a two horn' or less learning curve), ‘M’ denotes medium difficulty (e.g., a one week or less leammg curve), and ‘H’ denotes high difficulty (i.e., a major time investment that may require collaboration with a qualified research scientist).

Building life cycle cost programs

The National Institute of Standards and Technology developed a computational analysis tool for equipment and buildings called the building life cycle cost (BLCC). The U.S. Department of Energy has lauded it as a useful life cycle assessment (LCA) or costing (LCC) tool (US DOE, 2019). By incorporating energy escalation rates and other LCA factors, the BLCC enables the comparison of building components or building designs. This tool is relatively easy to use, but may require some initial training.

It is important to note that the BLCC is only one LCA or LCC tool of many. These kinds of tools are vast and range in capabilities. Listing and describing them all exceeds the scope of this chapter and section. Nonetheless, it is important to note that the BLCC, and other tools like it, can be used to assist urban planners and scientists to assess the viability and economical benefit of various urban agriculture and energy efficiency strategies. As with any data computational tool, it is vital to input accurate and comprehensive information. For instance, ignoring energy costs over time and focusing mostly on capital costs in an LCC analysis could result in purchasing more expensive system components.

Carbon calculation tool

The Carbon Calculation Tool (CCT) reads data from the U.S. Forest Sen-ice’s Forest Inventory and Analysis (FLA) Program in order to generate state-level estimates of carbon stocks (Smith et al., 2007). CCT also has the ability to produce national carbon stocks and flux data. This tool’s functionality is dependent on FIA file types, and it requires that data be imported. It is estimated that CCT requires minimal tune investment to leam and use (USDA, 2019).

Carbon footprint calculators

Carbon footprint calculators have been popular for some time. They generally take self-reported information about a person’s or group’s behavior and calculate the amount of GHGs emitted. These tools can calculate individual to country level carbon footprints (Mulrow et al.. 2019). There are many available online which have been developed by small and large organizations, including nonprofit organizations and the U.S. federal government.

These tools can be used to help urban planners and scientists encourage behavior that reduces urban carbon emissions. For instance, an individual could use the calculator developed by the Environmental Protection Agency (EPA) to assess their household carbon footprint (US EPA, 2016). Such information is designed to encourage activities that reduce carbon emissions. However, being aware of one’s household or individual carbon footprint does not necessarily lead to behavior change. This is an important point for urban practitioners and scientists who are trying to manage or reduce carbon emissions.^[1]

Carbon monitoring system

The National Aeronautics and Space Administration’s (NASA’s) Carbon Monitoring System (CMS) takes data from various sources to estimate carbon net flux for the 48 continental states of the U.S. This tool and dataset go further in that they help in predicting the evolution of global carbon sinks (NASA CMS, 2019). In other words, CMS has a global focus but also provides granularity to regional and county level carbon estimates by using NASA’s satellite observations and modelling capabilities (Hagen et al., 2016). Carbon sequestration dynamics are assessed while also providing map displays at 100-meter resolution. This tool is stated to require minimal tune investment to use, however, the user must incorporate then- own data (USDA, 2019).

Carnegie antes Stanford approach

The Carnegie Ames Stanford Approach (CASA) project uses NASA satellites to collect carbon stock and exchange data. It takes multi-year global data and runs it through simulation models at a local to global scale. CASA is capable of more than just carbon estimation, it can also analyze dynamics of the nitrogen cycle as driven by climate change and laud use (US NASA, 2019). It is considered to require minimal tune investment to use successfiilly (USDA. 2019).

Forest vegetation simulator

The Forest Vegetation Simulator (FVS) is a useful carbon calculation tool for ecosystem and wood products. It serves as a growth simulation tool that can model forest vegetation at an individual-tree and forest stand level in the U.S. (US FS, 2019b). Some of the specific benefits are that it recognizes all major tree species, can facilitate environmental disturbances within simulation, and yields useful outputs for tree canopy cover (US FS, 2019a). It is important to note that FVS tends to require a moderate level of tune investment to leam and use successfully (USDA, 2019).

Geographic information system

The geographic information system (GIS) is a framework that incorporates many types of geogr aphical data. It has the ability to layer spatial information at small and large scales, and can even project two- dimensional and three-dimensional imagery. GIS also has robust synergistic capabilities, i.e., it can be incorporated into many software platforms and can link to other platforms to assess historical data and to project outcomes. However, the system and its capabilities are vast. It, therefore, can take a considerable amount of time to leam how to use if prior experience is lacking. Nonetheless, it is a powerful tool that could be used by scientists and urban planners, for example, to locate zoning space for urban infrastructure or to identify urban locations for afforestation.

i-Tree

i-Tree is an urban forest management tool that is peer reviewed from the USDA Forest Sendee. It uses tree inventory data to quantify environmental factors, such as energy conservation, air quality, carbon emission reductions, and stormwater control (USDAFS, 2019b). USDAFS (2019b) lauds i-Tree's reach, stating that it has been used around the world to report on the benefits trees provide at the individual tree level up to the state level (Eudreny et al., 2017; Nowak et ah, 2018). It is free to use and has supplemental tools for analyses and utility programming. The supplemental analysis tools include Landscape, which facilitates the exploration of geospatial data, Hydro, which can simulate the effects stream flow and water quality have on trees and laud cover. Canopy, which uses Google Maps to produce statistically valid estimates of tree and laud cover, and Species, which helps urban planners assess tree species, given environmental and geographic factors, and more. The supplemental utility programming includes the Pest Detection Module, which is a protocol for assessing trees for insect or disease problems, and Storm, which gives communities the ability to assess storm damage following an actual severe storm event. This tool is considered easy to use and tends to take only a few hours of tune investment to leam (USDA, 2019).

LANDIS

LANDIS is a landscape tool designed to simulate forest growth and disturbances, such as climate change effects at the national forest and landscape level. It displays landscapes as a grid of cells and does not allow for analysis at the individual tree level (USDA FS, 2019a). LANDIS has variants available, each of which specializes in different simulation tasks. For example, LANDIS-II is a decadal to multi-century forecasting tool that is highly customizable.

Many have used LANDIS in analyses, assessments, and published works spanning from at least the late 1990s (USDA FS, 2019a). However, this tool tends to require a major time investment to leam and use successfiilly. It is suggested to partner with a qualified research scientist (USDA, 2019).

Light detection and ranging

Light Detection and Ranging (LIDAR) is a remote sensing method that uses pulsed laser light to create profiles of natural and manmade environments (NOAA, 2018). LIDAR allows for the examination of these environments at a topogr aphical and bathymetric level. In other words, the LIDAR system can yield images and data for surface level environments (i.e., aboveground) as well as for seabed and riverbed environments (i.e., underwater or below sea level). This system could be used in carbon management strategies when needed. For example, urban planners could use LIDAR information to identify natural or manmade bodies of water when trying to locate water sources that could supply an urban farm or cooling fluids for a CHOP. Many of the datasets and display interfaces are readily available for download and use online.

NED

NED is a decision-support software that leverages the strengths of several USDA Forest Sen-ice software products. It aids in developing strategic and management plans. NED is ideal for use at the landscape level, meaning that it is not ideal at the forest stand or individual tree level. Rather, it helps to assess the complex ecosystem dynamics (e.g., aesthetics, wildlife, water dynamics, and wood production) that emerge at the landscape level for meeting objectives and goals. It is important to note that this system utilizes very traditional growth and yield modeling, and requires a moderate amount of time investment to use successfully (USDA, 2019).

Scenario analysis

Scenario analysis allows for the structured assessment of several contingencies in qualitative or quantitative forms. The scale of assessment spans the spectrum of urban planning, i.e., it can be used to analyze a single building or component to national or global scales. There is an abundance of scenario analysis techniques and tools, some of which have already been described. A few other popular scenario analysis techniques and tools are computational in nature and include machine learning and agent-based modelling. Not all scenario analysis approaches are driven by computer software packages. For instance, in-person surveys could be used to generate responses of what actions organizations or people could take given a scenario.

Generally, these are used to forecast future outcomes. In some cases, historical data is used to help forecast future events. For the purposes of urban carbon management, scenario analysis could help assess which individual or hybrid strategies mitigate carbon emissions the greatest. Scenario analysis is largely dependent on the data that is used in its modelling. For this reason, among others, this technique often requires a relatively large amount of time to master. It may be prudent to work with an expert (e.g., an experienced computer coder and/or scientist) when employing scenario analysis to ensure critical factors are incorporated into the model(s).

Unique challenges in managing individual carbon emissions

One way to approach the problem of carbon management is from the bottom up (see Figure 5). Taking this perspective, human society’s contribution to carbon flux is simply an aggregation of individual decisions. The carbon effects of deforestation, for example, can be linked to the demand for agricultural products, which require cleared land and sun exposure. This demand for land, in turn, arises from individual tastes for ingredients, such as beef, palm oil and soy beans (Hosouuma et al., 2012), that will be grown there. No matter the scale or the industry, carbon emissions can be traced back to an individual need or desire.

The primary tool for taking a bottom-up approach to carbon management is the individual carbon footprint calculator. Such calculators have proliferated in the form of mobile phone applications and online tools (Mulrow et al., 2019). The typical calculator instructs the user to measure a range of personal activities that ultimately generate, or in rare cases, sequester carbon emissions. The simplest calculators derive carbon emissions based on energy-related activities alone, while more detailed calculators consider lifestyle and consumption choices, such as food and travel (Padgett et al., 2008). Some even include the individual’s share of collective emissions from government activities (depending on citizenship) in the final carbon footprint tally (West et al., 2015).

There is a famous business and policy maxim that says “What gets measured gets managed”, but whether the measurement of personal carbon footprints will prompt individuals to better manage then

Figure 5. Different ways of approaching and calculating carbon footprints: (A) Top-down footprint: Divides economic activity and the resultant carbon emissions of a society among the population, yielding an individual-scale carbon footprint; (B) Bottom-ip footprint: The carbon intensity and activity levels of an individual are surveyed and summed, yielding a unique individual carbon footprint (used in individual carbon footprint calculators); and (C) Behavioral influence: Individuals can leam from their carbon footprint and be empowered to choose activities that will lower their footprint.

emissions (i.e., reduce them) is in doubt. In a recent survey on carbon footprint calculator usage, Mulrow et al. (2019) found that of the individuals who had previously used a calculator, less than 16% could recall the actual results they were given. Furthermore, 67% of respondents who had used a calculator noted that the exercise did not motivate them to make any changes in their energy consumption activities. Finally, when asked “Do you know what 1 kg of CO, emissions represents in terms of daily activity?”, only 9% of respondents felt confident that they could provide an activity level (e.g., miles driven or hours of computer use) equivalent to this level of carbon emissions (see Figure 6).

This survey, together with a comprehensive review of 31 existing online calculators, revealed that there is much room for improvement in carbon calculator design and implementation (Mulrow et al.,

2019). Further footprinting efforts should aim to create memorable and action-inducing outcomes from the calculator experience. It is worth asking whether widespread carbon footprint knowledge is even necessary for reducing emissions at a societal scale.

It is unclear whether better and more precise carbon measurement at the individual scale could deliver robust emissions reduction. Furthermore, there are powerful proxies for carbon emissions that are already widely measured and managed. These are energy demand and economic activity. A study of environmental beliefs, behaviors and carbon footprints revealed that income levels were the best predictor of carbon footprint, ranking above stated environmental beliefs and dietaiy habits (Boucher, 2016). Rather than attempt to measure and manage carbon emissions directly, a more effective strategy may be to reduce energy demand or to facilitate cost increases for carbon-intensive activities through policy. Nevertheless, individual carbon footprint calculators should continue to improve and be researched in order to identify tool effectiveness and strategic importance.

Figure 6. Survey responses, regarding behavior/knowledge nnpact of using carbon footprint calculators.

• [1] Challenges involving individual behavior change and the use of carbon footprint calculators are further explained later inthis chapter, in the 3.3 Unique Challenges in Managing Individual Carbon Emissions section.