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This essay introduces the political market framework (PMF) and discusses its implications for understanding local sustainability policy. The PMF conceptualizes public policy related to sustainability as the product of exchange between governmental policy suppliers and voter and interest group policy demanders. After presenting a political market model, the role of political institutions is introduced. Institutions structure exchange relationships by determining transaction costs of searching for mutually beneficial agreements, bargaining over outcomes, and monitoring and enforcing decisions. The central implication for research is the need to account for the moderating role that political institutions play in sustainability policy decisions. A research agenda based on the PMF is advanced. The conclusion addresses the limitations of the framework as well as its implications for policy adoptions, program designs, and individual behavior.

Given the increasing prevalence of droughts, unpredictable rainfall patterns, and limited access to dependable water sources in the United States and worldwide, it has become crucial to implement effective irrigation scheduling strategies. Irrigation is triggered when some variables, such as soil moisture or accumulated water deficit, exceed a given threshold in the most common approaches applied in irrigation scheduling. A High-Resolution Land Data Assimilation System (HRLDAS) was used in this study to generate timely and accurate soil moisture and evapotranspiration (ET) data for irrigation management. By integrating HRLDAS products and the crop growth model (AquaCrop), an automated data-driven irrigation scheduling approach was developed and evaluated. For HRLDAS ET and soil moisture, the ET-water balance (ET-WB)-based method and soil-moisture-based method were applied accordingly. The ET-WB-based method showed a 10.6~33.5% water-saving result in dry and set seasons, whereas the soil moisture-based method saved 7.2~37.4% of irrigation water in different weather conditions. Both of these methods demonstrated good results in saving water (with a varying range of 10~40%) without harming crop yield. The optimized thresholds in the two approaches were partially consistent with the default values from the Food and Agriculture Organization and showed a similar trend in the growing season. Furthermore, the forecasted rainfall was integrated into this model to see its water-saving effect. The results showed that an additional 10% of irrigation water, which is 20~50%, can be saved without harming the crop yield. This study automated the data-driven approach for irrigation scheduling by taking advantage of HRLDAS products, which can be generated in a near-real-time manner. The results indicated the great potential of this automated approach for saving water and irrigation decision making.

Diesel-powered, human-driven buses currently dominate public transit options in most U.S. cities, yet they produce health, environmental, and cost concerns. Emerging technologies may improve fleet operations by cost-effectively reducing emissions. This study analyzes both battery-electric buses and self-driving (autonomous) buses from both cost and qualitative perspectives, using the Capital Metropolitan Transportation Authority’s bus fleet in Austin, Texas. The study predicts battery-electric buses, including the required charging infrastructure, will become lifecycle cost-competitive in or before the year 2030 at existing U.S. fuel prices ($2.00/gallon), with the specific year depending on the actual rate of cost decline and the diesel bus purchase prices. Rising diesel prices would result in immediate cost savings before reaching $3.30 per gallon. Self-driving buses will reduce or eliminate the need for human drivers, one of the highest current operating costs of transit agencies. Finally, this study develops adoption schedules for these technologies. Recognizing bus lifespans and driver contracts, and assuming battery-electric bus adoption beginning in year-2020, cumulative break-even (neglecting extrinsic benefits, such as respiratory health) occurs somewhere between 2030 and 2037 depending on the rate of battery cost decline and diesel-bus purchase prices. This range changes to 2028 if self-driving technology is available for simultaneous adoption on new electric bus purchases beginning in 2020. The results inform fleet operators and manufacturers of the budgetary implications of converting a bus fleet to electric power, and what cost parameters allow electric buses to provide budgetary benefits over their diesel counterparts.

The relationship between climate change, water scarcity, and conflict is still debated. Much of the existing work relating resource scarcity to conflict has involved regional-scale analysis linking instances of violent outbreaks to environmental conditions. But how do individual farmers in Africa define conflict? Do they perceive that conflict will change as a function of water scarcity, and, if so, how? Here, we address these questions by surveying farmers in southern Zambia in 2015, where we asked respondents to define conflict, assessed their perceptions of past and future conflict, as well as perceptions of rainfall and water availability. We find that the majority of our respondents (75%) think of conflict as misunderstandings or disagreements between people and that 91% of our sample has experienced past conflict, 70% expect to experience future conflict, and 58% expect to experience future physical violent conflict. When asked about the sources of conflict, respondents mainly mention land grabbing, crop damage by animals, and politics rather than water related issues. However, we find a significant relationship between perceptions of future rainfall decreasing and future physical violent conflict. These results imply that even though respondents do not think water scarcity is a direct source of conflict, the perception of decreased rain in the future is significantly related to the perception that future conflict and future physical violent conflict will occur.

The negative environmental impacts of the current linear system of textile and apparel production are well-documented and require urgent action. The sector lacks an effective recycling system, resulting in massive waste and environmental pollution. This paper presents the results of qualitative research involving textile and apparel industry stakeholders, including representatives from brands and retailers, waste collectors, recyclers, non-profit organizations, academic institutions, and government agencies. Our research focused on stakeholder perceptions of the significance and importance of textile circularity, the challenges that exist for transitioning the textile and apparel industry from a linear system to a circular economy (CE), and resources that exist to support this transition. The results of this study call attention to the following urgent requirements: a consistent definition of CE to promote transparency and accountability and prevent greenwashing; improved systems for materials identification, sorting, and pre-processing of post-consumer textile waste to enable recycling; innovations in mechanical recycling technologies to maintain the value of recycled materials; and new, materials-driven approaches to design and manufacturing that are responsive to feedstock variability and diverse consumer needs. The research findings also suggest the need for flexible, regional CEs that are rooted in community partnerships.

The optimal power flow (OPF) problem, characterized by its inherent complexity and strict constraints, has traditionally been approached using analytical techniques. OPF enhances power system sustainability by minimizing operational costs, reducing emissions, and facilitating the integration of renewable energy sources through optimized resource allocation and environmentally aligned constraints. However, the evolving nature of power grids, including the integration of distributed generation (DG), increasing uncertainties, changes in topology, and load variability, demands more frequent OPF solutions from grid operators. While conventional methods remain effective, their efficiency and accuracy degrade as computational demands increase. To address these limitations, there is growing interest in the use of data-driven surrogate models. This paper presents a critical review of such models, discussing their limitations and the solutions proposed in the literature. It introduces both Analytical Surrogate Models (ASMs) and learned surrogate models (LSMs) for OPF, providing a thorough analysis of how they can be applied to solve both DC and AC OPF problems. The review also evaluates the development of LSMs for OPF, from initial implementations addressing specific aspects of the problem to more advanced approaches capable of handling topology changes and contingencies. End-to-end and hybrid LSMs are compared based on their computational efficiency, generalization capabilities, and accuracy, and detailed insights are provided. This study includes an empirical comparison of two ASMs and LSMs applied to the IEEE standard six-bus system, demonstrating the key distinctions between these models for small-scale grids and discussing the scalability of LSMs for more complex systems. This comprehensive review aims to serve as a critical resource for OPF researchers and academics, facilitating progress in energy efficiency and providing guidance on the future direction of OPF solution methodologies.

Urban landscapes can be transformed by widespread abandonment from population and economic decline. Ecological assembly, sometimes referred to as “greening”, following abandonment can yield valuable ecosystem services, but also can pose a risk to public health. Abandonment can elevate zoonotic vector-borne disease risk by favoring the hyperabundance of commensal pests and pathogen vectors. Though greater biodiversity in abandoned areas can potentially dilute vector-borne pathogen transmission, “greening” can elevate transmission risk by increasing movement of pathogen vectors between fragmented areas and by giving rise to novel human-wildlife interfaces. Idled and derelict infrastructure can further elevate disease risk from vector-borne and water-borne pathogens, which can build up in stagnant and unprotected water that maintenance and routine use of delivery or sanitation systems would otherwise eliminate. Thus, framing “greening” as inherently positive could result in policies and actions that unintentionally exacerbate inequalities by elevating risks rather than delivering benefits. As counter-urbanism is neither a minor pattern of urban development, nor a short-term departure from urban growth, homeowner and municipal management of abandoned areas should account for potential hazards to reduce health risks. Further socioecological assessments of public health risks following abandonment could better ensure the resilience and well-being of communities in shrinking cities.

The Swinomish Indian Tribal Community developed an informal environmental health and sustainability (EHS) curriculum based on Swinomish beliefs and practices. EHS programs developed and implemented by Indigenous communities are extremely scarce. The mainstream view of EHS does not do justice to how many Indigenous peoples define EHS as reciprocal relationships between people, nonhuman beings, homelands, air, and waters. The curriculum provides an alternative informal educational platform for teaching science, technology, engineering, art, and mathematics (STEAM) using identification, harvest, and preparation activities of First Foods and medicines that are important to community members in order to increase awareness and understanding of local EHS issues. The curriculum, called 13 Moons, is founded on a set of guiding principles which may be useful for other Indigenous communities seeking to develop their own curricula.