5 Land Use and Forestry
Communities can reduce their carbon footprint and address the impacts of climate change through a variety of green infrastructure approaches. Approaches such as promoting compact land use patterns, incorporating natural resources into development patterns, reforesting urban areas, and promoting sustainable agriculture can enhance the resilience of the community, reduce emissions, and improve quality of life. These measures help communities prepare for the increasing frequency of extreme weather events and other climate impacts, safeguarding their resources and competitiveness.
5.1 Framework
The impact of land use impacts on carbon emissions or sequestration (negative emissions) can be modeled through satellite imagery and analysis of remote sensing data of urban land use changes planned by cities. Cities should aim to reduce their outward expansion through compact development and increase urban carbon storage through reforestation, as described in the guide on Urban Reforestation. Other strategies like soil conservation on agricultural land within the Metropolitan Council region can also contribute, although they were found to be relatively minor in the scenario planning model.
To evaluate the impact of various land use and green infrastructure strategies, understand that each land cover type within our region has a distinct multiplier for carbon sequestration and carbon stocks. Using satellite imagery for information on the region’s land cover, we can make informed predictions regarding the potential for changes in land use and cover to influence greenhouse gas emissions.
5.1.1 Land Cover Baseline and Forecast
A high-resolution land cover map for the Metropolitan Council region for the year 2016 was generated using support vector machine image classification on Planet RapidEye 5m Imagery. This classification was merged with the 2019 edition of the National Land Cover Database to create a 12-class land cover map. For more information on the image classification methodology, see (Milnar and Ramaswami 2020).
Each land cover type has a unique parameter for carbon sequestration rates (additional carbon stored on an area basis, per year) and existing carbon stock (amount of carbon already stored in that land cover).
| Land cover | 2016 [Baseline] | 2040 [Busines-as-Usual] | Sequestration (mg C/ha/year) | Stock (C) |
|---|---|---|---|---|
| Agriculture | 40.66 | 84.39 | 0.19 | 41 |
| Barren | 157.13 | 147.86 | 0.01 | 0 |
| Forest | 205.90 | 343.63 | 0.62 | 115 |
| Grass | 1,012.95 | 1,035.19 | 0.42 | 77 |
| Grassland | 82.66 | 258.32 | 0.42 | 77 |
| Impervious | 7,995.26 | 7,719.22 | 0.00 | 33 |
| Parking Lot | 978.74 | 842.09 | 0.00 | 33 |
| Shrub | 0.94 | 1.38 | 0.29 | 77 |
| Trees | 3,471.62 | 3,485.43 | 1.30 | 115 |
| Water | 0.00 | 0.00 | 0.00 | 0 |
| Wetland | 12.55 | 41.48 | 1.50 | 297 |
| Woody Wetland | 19.79 | 19.21 | 0.62 | 117 |
5.1.2 Land Use Baseline and Forecast
The 2016 land use baseline data has been derived from the Metropolitan Council Generalized Land Use1 geospatial layer for all cities and townships. This data has been divided into three categories based on land use type: Urban Infill, Exurban Development, and Urban Expansion.
Exurban Development encompasses the following land use types: Undeveloped, Park/Recreation/Preserve, Agriculture, or Farmstead. All other land use types were categorized as Urban Infill in the baseline data.
| Land use | 2018 [Baseline] | 2040 [Busines-as-Usual] |
|---|---|---|
| Agricultural | 9.20 | 8.93 |
| Airport | 2.96 | 2.87 |
| Extractive | 0.00 | 0.00 |
| Farmstead | 0.00 | 0.00 |
| Golf Course | 213.49 | 207.13 |
| Industrial and Utility | 1,075.95 | 1,109.74 |
| Institutional | 1,153.58 | 1,040.25 |
| Major Highway | 540.96 | 524.83 |
| Major Railway | 256.50 | 248.85 |
| Manufactured Housing Park | 0.00 | 0.00 |
| Mixed Use Commercial | 79.23 | 91.52 |
| Mixed Use Industrial | 113.81 | 117.38 |
| Mixed Use Residential | 130.29 | 131.25 |
| Multifamily | 780.74 | 786.49 |
| Office | 202.83 | 234.29 |
| Open Water | 0.00 | 0.00 |
| Park, Recreational, or Preserve | 1,423.53 | 1,381.11 |
| Railway | 0.00 | 725.74 |
| Retail and Other Commercial | 628.29 | 0.00 |
| Seasonal Vacation | 0.00 | 0.00 |
| Single Family Attached | 1,059.27 | 1,067.07 |
| Single Family Detached | 5,990.57 | 5,993.18 |
| Undeveloped | 317.01 | 307.56 |
5.1.3 Urban Form
In the coming decades, population growth will bring about changes in land use and land cover, which can impact existing carbon stocks and storage potential.
What are Biogenic Emissions?
Biogenic emissions arise from natural processes, predominantly the decomposition of organic matter. Vegetation (plants) utilize carbon dioxide for growth and sequester carbon as they grow. Plant-based carbon can be stored for long periods of time in soils or in woody biomass (e.g., tree trunks). However, when plants tissues die, microbes decompose plant tissues and release carbon back into the atmosphere. The rate of microbial decomposition and the return of carbon into the atmosphere depend on mulitple factors.
To assess the impact of urban vegetation on carbon stock changes and sequestration, the method outlined in (Milnar and Ramaswami 2020) was used to calculate these values for the study period of 2018-2040.
About this strategy Our assumption is that cities can control (or at least influence) the urban expansion of their city into forested or green areas to minimize loss of carbon stocsk and future rates of carbon sequestration. This reference scenario shows the expected carbon stock and carbon sequestration values under the business-as-usual scenario versus a more compact development scenario.
GHG Impact Calculation
\[CO_2sqlc = (\text{Sequestration} + \text{Land Cover Change}) \times 44/12\] Where:
\(\text{Sequestration} = \sum{(Ai \times SRi)}\)
\(\text{Land Cover Change} = \sum{(Ait-1 × CDi ) - (Ait \times CDi )]}\)
And:
- \(\text{Sequestration} = \text{annual net sequestration from vegetation for a given city}\)
- \(\text{Land Cover Change = annual organic carbon stock change from land conversion for a given city, averaged over the study period}\)
- \(44/12 = \text{factor to convert Mg C to Mg } CO_2e\)
- \(Ai = \text{Area of vegetation of cover type i (ha)}\)
- \(SRi = \text{Annual net sequestration rate for cover type i. (Mg C ha-1 yr-1)}\)
- \(CDi = \text{Carbon stock density factor for vegetation in cover type i (Mg C ha-1)}\)
- \(i = 1 ,2, 3, ...n\) \(\text{land cover types}\)
- \(t = \text{year for which biogenic emissions were calculated and compared to Scope 1+2 emissions for each city}\)
| Variables | 2018 Baseline | 2040 BAU | 2040 Scenario |
|---|---|---|---|
| NA | NA | -5,371.26 | -5,380.98 |
| NA | NA | -5,371.26 | -10,763.57 |
| NA | NA | -10,605.56 | -5,380.98 |
| NA | NA | -10,605.56 | -10,763.57 |
For the City of Minneapolis, there does not appear to be a significant difference in carbon sequestration and stocks between the two scenarios presented.
5.1.4 Conservation Tillage
Conventional agricultural practices, such as annual plowing, loosen the soil in preparation for planting. However, this can result in soil particles being exposed to wind and erosion, causing a loss of soil nutrients and organic matter over time. It is estimated that cultivated soils have lost 50-70% of their soil carbon stock due to traditional tilling methods. Adoption of conservation tillage practices, such as no-till or reduced-till agriculture, can minimize topsoil disturbance and reduce the loss of nutrients and organic matter in the soil.
Conservation tillage has two main effects on greenhouse gas emissions:
- By minimizing soil disturbance, existing soil carbon can be stored longer-term. Frequent plowing exposes soil carbon to microbial decomposition.
- By reducing plowing, there is a lower consumption of fossil fuels from farm equipment.
In Minnesota, conservation tillage initiatives have been established by a variety of stakeholders, including the state and federal government, private sector companies, environmental advocacy groups, and academia. As a result, nearly half of all Minnesota counties growing corn and soy have at least 25% of planted acres using reduced tillage practices, while about a quarter of the counties have at least 25% of planted acres using no-till practices, according to the Crop Residue Management Survey.
About this strategy Our assumption is that cities can control (or at least influence) the amount of conservation tillage that happens in their community. The strategy shows the maximum conservation tillage increase in carbon stock.
GHG Impact Calculation
\[ \text{Conservation Tillage Avoided Emissions} = (C+T) \times A \] Where:
\(C\): Increased Biogenic Carbon Stock (Mg CO2/ha). Baseline soil organic carbon (SOC) stock is 41 Mg C/ha. It has been estimated that SOC content can increase from 1% up to 1.54% under conservation tillage2, thus carbon content can be increased from an average of 41 Mg C/ha to 63 Mg C/ha. This increase was assumed to increase over a period of 20 years. Whether the sequestration is maintained is highly uncertain.
\(T\): Avoided emissions from tractor fossil fuel use (Mg CO2/ha). The Natural Resources Conservation Service estimates no-till agriculture can reduce tractor fuel consumption by 3.2 gallons/acre6. Emissions from reduced fuel use were computed using a wells-to-wheels emissions factor7
\(A\): Area in Conservation Tillage (ha): The baseline area of no-till agriculture as reported in the 2017 USDA Census of Agriculture and this amount is changed for each scenario.
| scenario | bau | bau | scenario | scenario |
| var | sequestration_tonnes_co2e_per_year | stock_tonnes_co2e_per_year (land conversion emissions) | sequestration_tonnes_co2e_per_year | stock_tonnes_co2e_per_year (land conversion emissions) |
| value | -5518 | -9751 | -5518 | -9784 |
5.1.5 Urban Tree Planting
Trees in urban areas have a high concentration of carbon stored in their canopies and are often the largest source of biogenic carbon in an urban ecosystem. By increasing the tree canopy in cities, it is possible to offset some carbon emissions and also provide other benefits, such as reducing energy costs for cooling and improving subjective well-being.
A mature urban forest can hold up to 115 Mg of carbon per hectare. Additionally, a tree that grows for one year can sequester carbon annually at a rate of 1.27 Mg of carbon per hectare per year. Both of these processes are included in the scenario planning model, which shows that urban reforestation can contribute significantly to a carbon sink, provided there is enough land for reforestation. This is likely to be more feasible in suburban communities than in central cities. However, it is important to note that urban trees grow quickly but may also die young. As a result, to maximize the potential carbon mitigation from urban reforestation, careful tree management and an understanding of the life cycle of urban forestry is necessary.
Many initiatives have been launched globally, including the Trillion Trees Initiative, which aims to restore forests in order to slow down climate change. Some cities, such as Los Angeles, have also launched projects to plant large numbers of trees, with the Los Angeles Million Trees Plan estimating that up to 2.5 million trees could potentially be planted in the city. Recently, the City of Tucson announced a plan to plant one million trees by 2030.
In addition to mitigating the effects of climate change through carbon storage in woody biomass, urban trees offer a wide range of benefits, such as reducing the energy consumption of buildings, reducing urban heat, and improving subjective well-being. However, there are few empirical models to quantify the effect on building energy use, and existing data from St. Paul suggests that the effect may be small compared to the carbon sink effect. Additionally, several studies have shown socio-economic inequality in tree canopy coverage, making equity an essential consideration in the spatial planning of urban reforestation.
GROWING SHADE
The Metropolitan Council, in partnership with the Nature Conservancy and the Tree Trust, has developed an online application called Growing Shade to help communities track and prioritize tree planting in areas with thin tree canopies. The app uses data from multiple sources, such as demographic information about neighborhoods, locations of businesses, schools, and parks, to create a detailed picture of a city at the neighborhood level. Nonprofits, cities, and communities can use the data to identify the best locations to plant trees, which can help to address issues such as high asthma rates, extreme temperatures, and the legacy of racial injustices.
GHG Impact Calculation
\[ \text{Sequestered Carbon (in Mg CO_2 e)} = CD × A \times 11/3 \] Where:
- \(CD\) = Carbon stock density of urban trees in Mg/ha, a factor of above ground biomass, (44.1 Mg C/ha, Nowak et al 2013), and belowground biomass to one meter (71 Mg C/ha, Pouyat et al. 20065) for a total stock density of 115.1 Mg C /ha.
- \(A\) = Increased area of tree canopy under tree planting scenarios, in hectares.
- \(11/3\) = Factor to convert Mg C to Mg CO2
References
The Metropolitan Council has been regularly creating generalized land use plans for the Twin Cities region since 1984 to fulfill its legal obligations and aid in long-term planning for the area. The Council utilizes this land use information to track growth and assess shifting patterns in land usage for various urban purposes. To plan for future needs and financing of Metropolitan services, such as Transit and Wastewater Services, the Council combines the land use trend data with its projections of households and jobs. In collaboration with local government units, the land use and forecast data are also used to assess expansions of the Metropolitan Urban Service Area (MUSA).↩︎