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Abstract Local food systems can have economic and social benefits by providing income for producers and improving community connections. Ongoing global climate change and the acute COVID-19 pandemic crisis have shown the importance of building equity and resilience in local food systems. We interviewed ten stakeholders from organizations and institutions in a U.S. midwestern city exploring views on past, current, and future conditions to address the following two objectives: 1) Assess how local food system equity and resilience were impacted by the COVID-19 pandemic, and 2) Examine how policy and behavior changes could support greater equity and resilience within urban local food systems. We used the Community Capitals Framework to organize interviewees’ responses for qualitative analyses of equity and resilience. Four types of community capital were emphasized by stakeholders: cultural and social, natural, and political capital. Participants stated that the local food system in this city is small; more weaknesses in food access, land access, and governance were described than were strengths in both pre- and post-pandemic conditions. Stakeholder responses also reflected lack of equity and resilience in the local food system, which was most pronounced for cultural and social, natural and political capitals. However, local producers’ resilience during the pandemic, which we categorized as human capital, was a notable strength. An improved future food system could incorporate changes in infrastructure (e.g., food processing), markets (e.g., values-based markets) and cultural values (e.g., valuing local food through connections between local producers and consumers). These insights could inform policy and enhance community initiatives and behavior changes to build more equitable and resilient local food systems in urban areas throughout the U.S. Midwest. 

Abstract The majority of food in the US is distributed through global/national supply chains that exclude locally-produced goods. This situation offers opportunities to increase local food production and consumption and is influenced by constraints that limit the scale of these activities. We conducted a study to assess perspectives of producers and consumers engaged in food systems of a major Midwestern city. We examined producers’ willingness to include/increase cultivation of local foods and consumers’ interest in purchasing/increasing local foods. We used focus groups of producers (two groups of conventional farmers, four local food producers) and consumers (three conventional market participants, two locavores) to pose questions about production/consumption of local foods. We transcribed discussions verbatim and examined text to identify themes, using separate affinity diagrams for producers and consumers. We found producers and consumers are influenced by thestatus quoand real and perceived barriers to local foods. We also learned participants believed increasing production and consumption of local foods would benefit their community and creating better infrastructure could enhance efforts to scale up local food systems. Focus group participants also indicated support from external champions/programs could support expansion of local foods. We learned that diversifying local food production was viewed as a way to support local community, increase access to healthy foods and reduce environmental impacts of conventional production. Our research indicates that encouraging producers and consumers in local food systems will be more successful when support for the local community is emphasized. 

Abstract Most farmland in the US Corn Belt is used to grow row crops at large scales (e.g., corn, soybean) that are highly processed before entering the human food stream rather than specialty crops grown in smaller areas and meant for direct human consumption (table food). Bolstering local table food production close to urban populations in this region through peri-urban agriculture (PUA) could enhance sustainability and resilience. Understanding factors influencing PUA producers' preferences and willingness to produce table food would enable supportive planning and policy efforts. This study combined land use visualization and survey data to examine the potential for increased local table food production for the US Corn Belt. We developed a spatial visualization of current agricultural land use and a future scenario with increased table food production designed to meet 50% of dietary requirements for a metropolitan population in 2050. A survey was administered to row crop (1360) and specialty crop (55) producers near Des Moines, Iowa, US to understand current and intended agricultural land use and factors influencing production. Responses from 316 row crop and 25 specialty crop producers were eligible for this analysis. A future scenario with increased table food production would require less than 3% of available agricultural land and some additional producers (approximately 130, primarily for grain production). Survey responses indicated PUA producers planned small increases in table food production in the next three to five years. Producer plans, including land rental for table food production, could provide approximately 25% of residents' fruit, vegetables, and grains, an increase from the baseline of 2%. Row crop producers ranked food safety regulations, and specialty producers ranked labor concerns as strong influences on their decision-making. Both groups indicated that crop insurance and processing facilities were also important. Increasing table food production by clustering mid-scale operations to increase economies of scale and strengthening supply chains and production infrastructure could provide new profitable opportunities for farmers and more resilient food systems for growing urban regions in the US Corn Belt. Continuing to address producer factors and landscape-scale environmental impacts will be critical in considering food system sustainability challenges holistically. 

Typical Meteorological Year (TMY) datasets, widely used in building energy modeling, overlook Urban Heat Island (UHI) effects and future climate trends by relying on long-term data from rural stations such as airports. This study addresses this limitation by integrating Urban Weather Generator (UWG) simulations with CCWorldWeatherGen projections to produce microclimate-adjusted and future weather scenarios. These datasets were then incorporated into an Urban Building Energy Modeling (UBEM) framework using Urban Modeling Interface (UMI) to evaluate energy performance across a low-income residential neighborhood in Des Moines, Iowa. Results show that UHI intensity will rise from an annual average of 0.55 °C under current conditions to 0.60 °C by 2050 and 0.63 °C by 2080, with peak intensities in summer. The UHI elevates cooling Energy Use Intensity (EUI) by 7% today, with projections indicating a sharp increase—91% by 2050 and 154% by 2080. The UHI will further amplify cooling demand by 2.3% and 6.2% in 2050 and 2080, respectively. Conversely, heating EUI will decline by 20.0% by 2050 and 40.1% by 2080, with the UHI slightly reducing heating demand. Insulation mitigates cooling loads but becomes less effective for heating demand over time. These findings highlight the need for climate-adaptive policies, building retrofits, and UHI mitigation to manage future cooling demand. 

Typical Meteorological Year (TMY) datasets, widely used in building energy modeling, overlook Urban Heat Island (UHI) effects and future climate trends by relying on long-term data from rural stations such as airports. This study addresses this limitation by integrating Urban Weather Generator (UWG) simulations with CCWorldWeatherGen projections to produce microclimate-adjusted and future weather scenarios. These datasets were then incorporated into an Urban Building Energy Modeling (UBEM) framework using Urban Modeling Interface (UMI) to evaluate energy performance across a lowincome residential neighborhood in Des Moines, Iowa. Results show that UHI intensity will rise from an annual average of 0.55 °C under current conditions to 0.60 °C by 2050 and 0.63 °C by 2080, with peak intensities in summer. The UHI elevates cooling Energy Use Intensity (EUI) by 7% today, with projections indicating a sharp increase—91% by 2050 and 154% by 2080. The UHI will further amplify cooling demand by 2.3% and 6.2% in 2050 and 2080, respectively. Conversely, heating EUI will decline by 20.0% by 2050 and 40.1% by 2080, with the UHI slightly reducing heating demand. Insulation mitigates cooling loads but becomes less effective for heating demand over time. These findings highlight the need for climate-adaptive policies, building retrofits, and UHI mitigation to manage future cooling demand. 

Land surface temperature (LST) derived from satellite observations and weather modeling has been widely used for investigating Earth surface-atmosphere energy exchange and radiation budget. However, satellite-derived LST has a trade-off between spatial and temporal resolutions and missing observations caused by clouds, while there are limitations such as potential bias and expensive computation in model calibration and simulation for weather modeling. To mitigate those limitations, we proposed a WRFM framework to estimate LST at a spatial resolution of 1 km and temporal resolution of an hour by integrating the Weather Research and Forecasting (WRF) model and MODIS satellite data using the morphing technique. We tested the framework in eight counties, Iowa, USA, including urban and rural areas, to generate hourly LSTs from June 1st to August 31st, 2019, at a 1 km resolution. Upon evaluation with in-situ LST measurements, our WRFM framework has demonstrated its ability to capture hourly LSTs under both clear and cloudy conditions, with a root mean square error (RMSE) of 2.63 K and 3.75 K, respectively. Additionally, the assessment with satellite LST observations has shown that the WRFM framework can effectively reduce the bias magnitude in LST from the WRF simulation, resulting in a reduction of the average RMSE over the study area from 4.34 K (daytime) and 4.12 K (nighttime) to 2.89 K (daytime) and 2.75 K (nighttime), respectively, while still capturing the hourly patterns of LST. Overall, the WRFM is effective in integrating the complementary advantages of satellite observations and weather modeling and can generate LSTs with high spatiotemporal resolutions in areas with complex landscapes (e.g., urban). 

Urban areas often experience higher air temperatures than their surrounding rural counterparts, a phenomenon known as the urban heat island (UHI) effect. This significant human-induced alteration of urban microclimates has notable consequences, especially on urban energy consumption and resulting economic implications. This study presents an in-depth analysis of the UHI effect on urban building energy consumption in a US Midwest neighbourhood. Utilizing a three-phase methodology, the research first simulated UHI intensities with current and future Typical Meteorological Year (TMY) data, integrated with the Local Climate Zone (LCZ) classification system and the Urban Weather Generator (UWG) model. The second phase employed the urban modelling interface (umi) for building energy simulation, capturing the UHI impact on both residential and commercial buildings. The third phase demonstrates that UHI effects lead to reduced heating demand but increased cooling requirements in the future, with residential areas being more affected. The study's findings reveal critical challenges for urban planners and policymakers, emphasizing the need for sustainable designs to address fluctuating heating and cooling demands in changing climates. 

The U.S. Department of Energy (DOE) offers building reference prototypes for energy use modeling in commercial and residential buildings. However, these reference prototypes have traditionally been treated in isolation, neglecting the impact of neighboring objects on local microclimate. In urban energy models, where the intricate interaction of urban elements significantly shapes environmental conditions, it becomes more important to reconsider the conventional treatment of building reference prototypes. In this paper we aim to discern potential disparities in energy consumption estimations using DOE prototypes at an urban scale. The Urban Modeling Interface (UMI) was chosen as the simulation platform to incorporate the shadow effect from neighboring objects on building energy use across six scenarios with different shadow coverage by neighboring objects. We found that trees as neighboring structures can decrease cooling load by up to 29%. These results highlight the importance of considering the urban context in energy use estimation of buildings. 

Local food systems, in which consumers source food from nearby farmers, offer a sustainable alternative to the modern industrial food supply system. However, scaling up local food production to meet consumer demand will require farmers to allocate more land to this purpose. This paper describes an agent-based model that represents commodity-producing Iowa farmers and their decisions about converting some of their acreage to specialty crop production for local consumption. Farmer agents’ land-use decisions are informed by messages passed to them via their social connections with other farmers in their communities and messages from agricultural extension agents. Preliminary experimentation revealed that leveraging extension agents to increase the frequency and strength of messages to farmers in support of local food production has a modest positive impact on adoption. By itself, however, this intervention is unlikely to yield significant improvements to food system sustainability. 

Successful urban systems-related climate-action-support tools enable urban stakeholders to communicate and collaborate across and beyond their respective disciplines to identify innovative, transformative solutions to increase urban infrastructure resilience and sustainability. The actions of humans within buildings and the relationship of buildings to their near-building environments (aka microclimate) constitute one understudied urban system with significant impact on urban energy use strongly impacted by a warming urban climate. This interdisciplinary research team lead by an architect at a large research university collaborates with local community partners to identify evidence-based approaches for the integration of human behavior data, building energy use characteristics, future climate scenarios, and near-building microclimate data. The team has built a prototypical model, which integrates urban trees into urban energy models based on a large-scale inventory and probabilistic occupancy data based on a neighborhood wide energy use survey. To ensure that these urban energy models are equitable, however, the needs of marginalized populations must be included- especially those most vulnerable to the consequences of a changing climate. The paper reports on two intertwined research strands. First of all, the team’s best practices for gathering data from individuals facing marginalization as well as the application of this residential occupancy data into neighborhood energy models. The second strand addresses trees in urban landscapes and their capacity to modify temperatures in the near-building environment, which is important for reducing summer heat loads on building surfaces. Preliminary results for an urban neighborhood strategies are reported.