Browsing by Subject "Climate change mitigation"

Now showing 1 - 3 of 3
  • Thumbnail Image
    Item
    Assessing a Solar Project and a Virtual Power Purchase Agreement Between the Red Lake Nation and the Minnesota Department of Transportation
    (Minnesota Department of Transportation, 2023-03) Chan, Gabriel; Harrington, Elise; Grimley, Matt
    In this report, we analyze the feasibility of a northern Minnesota solar project and accompanying Virtual Power Purchase Agreement (VPPA) between the Minnesota Department of Transportation (MnDOT) and the Red Lake Nation. We analyze three sets of benefits for MnDOT: government-to-government relations, environmental, and economic. In government-to-government relations, we find great potential for the VPPA to further a first-of-its-kind Tribal energy project and Tribal sovereignty. We also summarize lessons from other governmental entities in pursuing VPPAs. For environmental benefits, we find the array will avoid between 48,000 and 89,000 metric tons of carbon dioxide-equivalent gases, resulting in a monetized environmental value of between $1 million and $9.4 million in avoided emissions-related damages. Finally, for economic benefits while we find the array has a net present value (NPV) between a negative $5.5 million and negative $16.5 million to MnDOT, we identify several project adjustments that could increase the value to more than a positive $3 million in NPV. In conclusion, we recommend MnDOT form a "strike team" to develop the project further, communicate the project clearly, and consult with outside experts on further project opportunities.
  • Thumbnail Image
    Item
    Climate-smart Agriculture: Potentials, Tradeoffs, and Cost-Benefit Analyses in US Corn-Soybean systems
    (2024-07) Yang, Yufeng
    The UN Climate Change Conference 2023, or known as COP 28, highlighted with the first “global stocktake” of the world’s efforts to address climate change under the Paris Agreement, concluded that the current progress was too slow across all areas of climate action. It calls on governments to speed up actions by 2030, from reducing greenhouse gas emissions, to strengthening resilience to a changing climate, and to providing financial and technological support (UNFCCC, 2023). In 2022, the US government announced a five-year investment of billions of dollars in climate-smart agriculture through the Inflation Reduction Act (IRA), which has thrust regenerative agricultural practices, such as the adoption of cover crops (CC) and no-till (NT), into the spotlight. Yet there are significant knowledge gaps on the magnitude, distribution, and economic feasibility of those mitigation opportunities, and where and how to use public subsidies. Climate-smart agricultural practices mitigate climate change via two main paths, the sequestration of soil organic carbon (SOC) and/or the reduction of Nitrous oxide (N2O) emissions. Great efforts in the scientific community have been made towards measuring N2O emissions and SOC sequestrations and understanding their responses to different management practices. However, challenges remain in closing knowledge gap in understanding (1) determinative processes and underlying drivers of spatial-temporal heterogeneity in agricultural climate mitigation potentials, (2) the integrative impacts of conservation practice adoption on GHG emissions and crop production, and (3) how to identify efficient and actionable climate-smart practices in a scalable manner. In this dissertation, I aim to explore the responses of N2O emissions, SOC sequestration, and crop production to various management practices, deliver integrative frameworks of social-environmental evaluations through cost-benefit analysis, and develop efficient and trustable predictive tools for assessing climate-smart practices targeting the corn and soybean cropping systems in the US, which is a globally representative agricultural production area. The climate mitigation potentials associated with cover cropping, no-till, and different fertilizer application strategies will be assessed across field and regional scales. Multiple modeling and inference approaches including process-based model simulations and casual analysis, meta-analysis models, and knowledge-guided machine learning (KGML) models were utilized to tackle unanswered questions regarding the spatial and temporal drivers, potentials and pathways, and quantification methods towards climate change mitigation in the US agroecosystems. In the first study, I simulated and quantified variations of N2O emissions over space, time, and management practices in the Midwest by a process-based model, ecosys, and manifested the impacts of freeze-thaw cycles on N2O emissions in overlooked non-growing seasons using causal inference. In the second study, I built meta-random-forest models and a framework of cost-benefits analysis upon hundreds of paired field observations of N2O emissions, SOC change, and crop yield to visualize the spatial distribution of climate change mitigation potentials of adopting CC and NT. In the third study, I developed KGML models based on advanced machine learning algorithms for predicting crop yield and SOC change under various management scenarios to overcome the hassle and complexity in using process-based models, shedding light on the future of an efficient and trustable SOC simulator. Overall, results and findings presented in this dissertation deliver critical information on the climate change mitigation pathways in agriculture and approaches/frameworks for modeling and evaluating their environmental and economic effectiveness in a timely manner.
  • Thumbnail Image
    Item
    Shifting perspectives: A comparison of travel-time-based and carbon-based accessibility landscapes
    (Journal of Transport and Land Use, 2021) Kinigadner, Julia; Vale, David; Büttner, Benjamin; Wulfhorst, Gebhard
    Undoubtedly, climate change and its mitigation have emerged as main topics in public discourse. While accessibility planning is recognized for supporting sustainable urban and transport development in general, the specific challenge of reducing transport-related greenhouse gas emissions has rarely been directly addressed. Traditionally, accessibility is operationalized in line with the user perception of the transport system. Travel-time-based measures are considered to be closely linked with travel behavior theory, whereas CO2 emissions are not necessarily a major determinant of travel decisions. Given the changed prioritization of objectives, additional emphasis should be placed on the environmental costs of travel rather than solely the user costs. Accessibility analysis could account for this shift in perspectives by using CO2 emissions instead of travel time in the underlying cost function. While losing predictive power in terms of travel behavior compared to other implementations of accessibility, carbon-based accessibility analysis enables a normative understanding of travel behavior as it ought to be. An application in the Munich region visualizes the differences between travel-time-based and carbon-based accessibility by location, transport mode, and specification of the accessibility measure. The emerging accessibility landscapes illustrate the ability of carbon-based accessibility analysis to provide new insights into land use and transport systems from a different perspective. Based on this exercise, several use cases in the context of low-carbon mobility planning are discussed and pathways to further develop and test the method in cooperation with decision-makers are outlined.