Toward a carbon neutral campus
A scalable approach to estimate carbon storage and biosequestration, an example from University of Michigan
Bibliographic Data
| ID | 21720951 |
|---|---|
| Authors | Rebecca Tonietto, Rebecca K Tonietto (0009-0004-9667-5850, University of Michigan–Flint), Lara O’Brien (University of Michigan), Cyrus Van Haitsma (University of Michigan), Chenyang Su (University of Michigan), Nicole Blankertz (University of Michigan–Flint), Hannah Grace Shaheen Mosiniak, Hannah Mosiniak (University of Michigan), Caleb Short (University of Michigan–Flint), Heather A Dawson (0000-0001-5182-514X, University of Michigan–Flint) |
| Year | 2021 |
| Volume | 22 |
| Issue | 5 |
| Pages | 1108-1124 |
| Publication date | 2021-08-16 |
| Open Access | Yes |
| Type | ARTICLE |
| Venue | International Journal of Sustainability in Higher Education (JOURNAL) |
| Journal identifiers | ISSN: 1467-6370 • E-ISSN: 1758-6739 |
| Publisher | Emerald (PUBLISHER) |
| DOI | 10.1108/ijshe-05-2020-0188 |
| OpenAlex | W3173062229 |
| Language | EN |
| Citations received | 1 |
| References cited | 39 |
Purpose The University of Michigan (U-M) is planning its course toward carbon neutrality. A key component in U-M carbon accounting is the calculation of carbon sinks via estimation of carbon storage and biosequestration on U-M landholdings. Here, this paper aims to compare multiple remote sensing methods across U-M natural lands and urban campuses to determine the accurate and efficient protocol for land assessment and ecosystem service valuation that other institutions may scale as relevant. Design/methodology/approach This paper tested three remote sensing methods to determine land use and land cover (LULC), namely, unsupervised classification, supervised classification and supervised classification incorporating delineated wetlands. Using confusion matrices, this paper tested remote sensing approaches to ground-truthed data, the paper obtained via field-based vegetation surveys across a subset of U-M landholdings. Findings In natural areas, supervised classification incorporating delineated wetlands was the most accurate and efficient approach. In urban settings, maps incorporating institutional knowledge and campus tree surveys better estimated LULC. Using LULC and literature-based carbon data, this paper estimated that U-M lands store 1.37–3.68 million metric tons of carbon and sequester 45,000–86,000 Mt CO 2 e/yr, valued at $2.2m–$4.3m annually ($50/metric ton, social cost of carbon). Originality/value This paper compared methods to identify an efficient and accurate remote sensing methodology to identify LULC and estimate carbon storage, biosequestration rates and economic values of ecosystem services provided
Carbon accounting · Ecosystem · Ecosystem services · Environmental resource management · Geography · Greenhouse gas · Land cover · Land use · Remote sensing · Wetland · Computer Science · Conservation, Biodiversity, and Resource Management · Economic and Environmental Valuation · Environmental Science · Land Use and Ecosystem Services · Ecology
Effects of national ecological restoration projects on carbon sequestration in China from 2001 to 2010
Carbon storage and sequestration by trees in urban and community areas of the United States
Global estimates of the value of ecosystems and their services in monetary units
Quest for a sustainable university
The Environmental Justice Dimensions of Climate Change
Grasslands may be more reliable carbon sinks than forests in California
Forest cover, carbon sequestration, and wildlife habitat
Changes in the global value of ecosystem services
| Citations per year | 0.33 |
|---|---|
| Citation span | 2023 - 2023 (1) |
| Highly cited | No |