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Abstract Environmental merits are a common motivation for many urban agriculture (UA) projects. One powerful way of quantifying environmental impacts is with life cycle assessment (LCA): a method that estimates the environmental impacts of producing, using, and disposing of a good. LCAs of UA have proliferated in recent years, evaluating a diverse range of UA systems and generating mixed conclusions about their environmental performance. To clarify the varied literature, we performed a systematic review of LCAs of UA to answer the following questions: What is the scope of available LCAs of UA (geographic, crop choice, system type)? What is the environmental performance and resource intensity of diverse forms of UA? How have these LCAs been done, and does the quality and consistency allow the evidence to support decision making? We searched for original, peer-reviewed LCAs of agricultural production at UA systems, and selected and evaluated 47 papers fitting our analysis criteria, covering 88 different farms and 259 production systems. Focusing on yield, water consumption, greenhouse gas emissions, and cumulative energy demand, using functional units based on mass of crops grown and land occupied, we found a wide range of results. We summarized baseline ranges, identified trends across UA profiles, andmore »highlighted the most impactful parts of different systems. There were examples of all types of systems—across physical set up, crop type, and socio-economic orientation—achieving low and high impacts and yields, and performing better or worse than conventional agriculture. However, issues with the quality and consistency of the LCAs, the use of conventional agriculture data in UA settings, and the high variability in their results prevented us from drawing definitive conclusions about the environmental impacts and resource use of UA. We provided guidelines for improving LCAs of UA, and make a strong case that more research on this topic is necessary to improve our understanding of the environmental impacts and benefits of UA.« less

Under the risk of drought, unreliable water supplies, and growing water demand, there is a growing need worldwide to explore alternative water sources to meet the demand for irrigation in agriculture and other outdoor activities. This paper estimates stocks, production capacities, economic costs, energy implications, and greenhouse gas (GHG) emissions associated with recycled water, desalinated brackish and seawater, and stormwater in California, the largest US state and the most significant fresh and processed food producer. The combined recycled water and stormwater supply could increase the share of alternative water use in urban land irrigation (parks and golf courses) from the current rate of 4.6% to 48% and in agriculture from 0.82% to 5.4% while increasing annual water costs by $900 million (1.8% of California’s annual agricultural revenue) and energy use by 710 GWh (0.28% of California’s annual electricity consumption). The annual supply of alternative water greatly exceeds the amount of water currently used in the food processing industry. In case studies of high-value agricultural produce, conventional water use was found to contribute approximately 17%, 12%, 4.1%, and 1.7% to the total GHG emissions of avocados, lemons, celery, and strawberries, respectively. However, materials (mostly packaging) contribute 46%, 26%, 47%, andmore »66%, and diesel use on farms 18%, 28%, and 14% for lemons, celery, and strawberries, respectively (data for avocados were not available). Switching to recycled water or stormwater would increase the total GHG emissions of one serving size of packaged strawberries, celery, lemons, and avocados by 3.0%, 7.8%, 11%, and 27%, respectively, desalinated brackish water by 23%, 58%, 150%, and 210%, and desalinated seawater by 35%, 88%, 230%, and 320%. Though switching to alternative water will increase costs, energy demand, and GHG emissions, they could be offset by turning to less environmentally damaging materials in agricultural production and sales (especially packaging).

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Electricity consumption and greenhouse gas (GHG) emissions associated with wastewater flows from residential and commercial water use in three major cities of the United States are analyzed and compared for the period 2010–2018. Contributions of unit wastewater treatment processes and electricity sources to the overall emissions are considered. Tucson (Arizona), Denver (Colorado), and Washington, DC were chosen for their distinct locations, climatic conditions, raw water sources, wastewater treatment technologies, and electric power mixes. Denver experienced a 20% reduction in treated wastewater volumes per person despite a 16% increase in population. In Washington, DC, the reduction was 19%, corresponding to a 16% increase in population, and in Tucson 14% despite a population growth of 3%. The electricity intensity per volume of treated wastewater was higher in Tucson (1 kWh m⁻³) than in Washington, DC (0.7 kWh m⁻³) or Denver (0.5 kWh m⁻³). Tucson’s GHG emissions per person were about six times higher compared to Denver and four times higher compared to Washington, DC. Wastewater treatment facilities in Denver and Washington, DC generated a quarter to third of their electricity needs from onsite biogas and lowered their GHG emissions by offsetting purchases from the grid, including coal-generated electricity. The higher GHGmore »emission intensity in Tucson is a reflection of coal majority in the electricity mix in the period, gradually replaced with natural gas, solar, and biogas. In 2018, the GHG reduction was 20% when the share of solar electricity increased to 14% from zero in 2016. In the analysis period, reduced wastewater volumes relative to the 2010 baseline saved Denver 44 000 MWh, Washington, DC 11 000 MWh and Tucson 7000 MWh of electricity. As a result, Washington, DC managed to forgo 21 000 metric tons of CO_2-eqand Denver 34 000 metric tons, while Tucson’s cumulative emissions increased by 22 000 metric tons of CO_2-eq. This study highlights the variability observed in water systems and the opportunities that exist with water savings to allow for wastewater generation reduction, recovering energy from onsite biogas, and using energy-efficient wastewater treatment technologies.

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Integrated management of food–energy–water systems (FEWS) requires a unified, flexible and reproducible approach to incorporate the interdependence between sectors, and include the risk of non-stationary environmental variations due to climate change. Most of the recently developed methods in the literature fall short of one or more aspects in such integration. In this article, we propose a novel approach based upon fundamentals of decision theory and reinforcement learning that (1) quantifies and propagates uncertainty, (2) incorporates resource interdependence, (3) includes the impact of uncontrolled variables such as climate variations, and (4) adaptively optimizes management decisions to minimize the costs and environmental impacts of crop production. Moreover, the proposed method is robust to problem-specific complexities and is easily reproducible. We illustrate the framework on a real-world case study in Ventura County, California.