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SIMPLE-G: A multiscale framework for integration of economic and biophysical determinants of sustainability
We introduce SIMPLE-G, a Simplified International Model of agricultural Prices, Land use, and the Environment- Gridded version, which is a novel tool for evaluating sustainability policies in a global context while factoring in local heterogeneity in land and water resources and natural ecosystem services. This multi-scale model can provide boundary conditions for local decision makers, as well as capturing feedback from local policies to national and global scales. Additionally, to illustrate its value in environmental analysis, we provide two applications of the model. First, we quantify the local stresses on land and water resources due to global changes in population, income, and productivity. Second, we quantify the global impacts of local policy responses and adaptations to water scarcity.
Stochastic DC optimal power flow with reserve saturation
We propose an optimization framework for stochastic optimal power flow with uncertain loads and renewable generator capacity. Our model follows previous work in assuming that generator outputs respond to load imbalances according to an affine control policy, but introduces a model of saturation of generator reserves by assuming that when a generator’s target level hits its limit, it abandons the affine policy and produces at that limit. This is a particularly interesting feature in models where wind power plants, which have uncertain upper generation limits, are scheduled to provide reserves to balance load fluctuations. Here, the resulting model is a nonsmooth nonconvex two-stage stochastic program, and we use a stochastic approximation method to find stationary solutions to a smooth approximation. Computational results on 6-bus and 118-bus test instances demonstrate that by considering the effects of saturation, our model can yield solutions with lower expected generation costs (at the same target line violation probability level) than those obtained from a model that enforces the affine policy to stay within generator limits with high probability.
Community vulnerability is the key determinant of diverse energy burdens in the United States
Low-income households generally experience a high energy burden; however, the factors influencing energy burdens are beyond socio-economics. This study explores the relationships between the multidimensionality of community vulnerability factors and energy burden across multiple geospatial levels in the United States. Our study found the distribution of energy burden in 2020 showed a great deal of variety, ranging from a minimum of 2.93 % to a maximum of 30.45 % across 3142 counties. The results of non-spatial and spatial regressions showed that the vulnerability ranks of socioeconomic, household composition and disability, minority and language, household type and transportation, and COVID mortality rate are significant predictors of energy burdens at the national level. However, at the regional level, only socioeconomic, minority and language significantly influence energy burdens. Minority and language negatively impact energy burdens except for the South East-Central region. Additionally, our analyses highlight the need to consider community vulnerability indicators' spatial homogeneity and heterogeneity. At the national level, only the epidemiological factors index is a spatially homogeneous predictor; on the regional and state level, the spatially homogeneous predictors such as socioeconomic status, household composition and disability, and household type and transportation vary by region. Such a region-sensitive relationship between energy burden and the predictors indicates spatial heterogeneity. Here this study suggests policy recommendations through the lens of the multidimensionality of community vulnerability factors. Implementing flexible national energy policies while making particular energy assistance policies for the vulnerable population at the regional or state levels is essential.
A tri-level optimization model for interdependent infrastructure network resilience against compound hazard events
Resilient operation of interdependent infrastructures against compound hazard events is essential for maintaining societal well-being. To address consequence assessment challenges in this problem space, we propose a novel policy-guided tri-level optimization model applied to a proof-of-concept case study with fuel distribution and transportation networks – encompassing one realistic network; one fictitious, yet realistic network; as well as networks drawn from three synthetic distributions. Mathematically, our approach takes the form of a defender-attacker-defender (DAD) model—a multi-agent tri-level optimization, comprised of a defender, attacker, and an operator acting in sequence. Here, in this study, our notional operator may choose proxy actions to operate an interdependent system comprised of fuel terminals and gas stations (functioning as supplies) and a transportation network with traffic flow (functioning as demand) to minimize unmet demand at gas stations. A notional attacker aims to hypothetically disrupt normal operations by reducing supply at the supply terminals, and the notional defender aims to identify best proxy defense policy options which include hardening supply terminals or allowing alternative distribution methods such as trucking reserve supplies. We solve our DAD formulation at a metropolitan scale and present practical defense policy insights against hypothetical compound hazards. We demonstrate the generalizability of our framework by presenting results for a realistic network; a fictitious, yet realistic network; as well as for three networks drawn from synthetic distributions. Additionally, we demonstrate the scalability of the framework by investigating runtime performance as a function of the network size. Steps for future research are also discussed.
Economic evaluation on CO₂-EOR of onshore oil fields in China
Carbon dioxide enhanced oil recovery (CO₂-EOR) and sequestration in depleted oil reservoirs is a plausible option for utilizing anthropogenic CO₂ to increase oil production while storing CO₂ underground. Evaluation of the storage resources and cost of potential CO₂-EOR projects is an essential step before the commencement of large-scale deployment of such activities. In this paper, a hybrid techno-economic evaluation method, including a performance model and cost model for onshore CO₂-EOR projects, has been developed based on previous studies. Total 296 onshore oil fields, accounting for about 70% of total mature onshore oil fields in China, were evaluated by the techno-economic method. The key findings of this study are summarized as follows: (1) deterministic analysis shows there are approximately 1.1 billion tons (7.7 billion barrels) of incremental crude oil and 2.2 billion tons CO₂ storage resource for onshore CO₂-EOR at net positive revenue within the Chinese oil fields reviewed under the given operating strategy and economic assumptions. (2) Sensitivity study highlights that the cumulative oil production and cumulative CO₂ storage resource are very sensitive to crude oil price, CO₂ cost, project lifetime, discount rate and tax policy. High oil price, short project lifetime, low discount rate, low CO₂ cost, and low tax policy can greatly increase the net income of the oil enterprise, incremental oil recovery and CO₂ storage resource. (3) From this techno-economic evaluation, the major barriers to large-scale deployment of CO₂-EOR include complex geological conditions, low API of crude oil, high tax policy, and lack of incentives for the CO₂-EOR project.
Improving the prediction of daily reservoir releases over the CONUS using conditioned LSTM
Reservoirs play a vital role in regulating streamflow timing and variability for hydroelectricity, flood control, water supply, irrigation, and recreation. Despite their importance, many reservoirs lack comprehensive operational guidelines, making their management complex due to conflicting operational objectives. Hence traditional policy-based reservoir models often fail to capture real-world conditions accurately and they depend on perfect streamflow predictions, which are not always available. In contrast, data-driven models like Long Short-Term Memory (LSTM) networks offer a robust alternative. This study introduces an approach that integrates reservoir characteristics—such as main use, climate, and maximum capacity—into the LSTM model to enhance reservoir release predictions. Using data from nearly 200 reservoirs in the contiguous United States (CONUS), our conditioned LSTM model (LSTM_cond) was compared with both the vanila LSTM and a traditional policy-based approach. Furthermore, our results show that while both LSTM_cond and LSTM perfoms better than the policy-based approach, LSTM_cond consistently outperforms LSTM for hydroelectric, water supply, irrigation, and recreation reservoirs. The KGE median values for LSTM_cond for out-sample reservoirs are 0.764, 0.565, 0.821, and 0.779, respectively, for the aforementioned reservoir types, which are consistently higher that the corresponding KGE values of 0.737, 0.413, 0.775, and 0.713 of LSTM, demonstrating its advantages in improving generalizability.
Validation of gamma-ray detection techniques for safeguards monitoring at natural uranium conversion facilities
Recent IAEA circulars and policy papers have sought to implement safeguards when any purified aqueous uranium solution or uranium oxides suitable for isotopic enrichment or fuel fabrication exists. Under the revised policy, IAEA Policy Paper 18, the starting point for nuclear material under safeguards was reinterpreted, suggesting that purified uranium compounds should be subject to safeguards procedures no later than the first point in the conversion process. In response to this technical need, a combination of simulation models and experimental measurements were employed to develop and validate concepts of nondestructive assay monitoring systems in a natural uranium conversion plant (NUCP). In particular, uranyl nitrate (UO 2 (NO 3 ) 2 ) solution exiting solvent extraction was identified as a key measurement point (KMP), where gamma-ray spectroscopy was selected as the process monitoring tool. The Uranyl Nitrate Calibration Loop Equipment (UNCLE) facility at Oak Ridge National Laboratory was employed to simulate the full-scale operating conditions of a purified uranium-bearing aqueous stream exiting the solvent extraction process in an NUCP. Nondestructive assay techniques using gamma-ray spectroscopy were evaluated to determine their viability as a technical means for drawing safeguards conclusions at NUCPs, and if the IAEA detection requirements of 1 significant quantity (SQ) can be met in a timely way. This work investigated gamma-ray signatures of uranyl nitrate circulating in the UNCLE facility and evaluated various gamma-ray detector sensitivities to uranyl nitrate. These detector validation activities include assessing detector responses to the uranyl nitrate gamma-ray signatures for spectrometers based on sodium iodide, lanthanum bromide, and high-purity germanium detectors. The results of measurements under static and dynamic operating conditions at concentrations ranging from 10–90 g U/L of natural uranyl nitrate are presented. A range of gamma-ray lines is examined, including attenuation for transmission measurement of density and concentration. It was determined that transmission-corrected gamma-ray spectra provide a reliable way to monitor the 235 U concentration of uranyl nitrate solution in transfer pipes in NUCPs. Furthermore, existing predictive and analysis methods are adequate to design and realize practical designs. The 137 Cs transmission source employed in this work is viable but not optimal for 235 U densitometry determination. Validated simulations assessed the viability of 133 Ba and 57Co as alternative densitometry sources. All three gamma-ray detectors are viable for monitoring natural uranium feed; although high-purity germanium is easiest to interpret, it is, however, the least attractive as an installation instrument. Overall, for monitoring throughput in a facility such as UNCLE, emulating the uranium concentration and pump speeds of the Springfields conversion facility in the United Kingdom, an uncertainty of less than 0.17% is required in order to detect the diversion of 1 SQ of uranyl nitrate through changes in uranium concentration over an accountancy period of one year with a detection probability of 50%. As a result, calibrated gamma-ray detection systems are capable of determining the concentration of uranium content in NUCPs, it is only in combination with verifiable operator declarations and supporting data, such as flow rate and enrichment, that safeguards conclusions can be drawn.
Sensitivity analysis of high resolution gamma-ray detection for safeguards monitoring at natural uranium conversion facilities
Under the policies proposed by recent International Atomic Energy Agency (IAEA) circulars and policy papers, implementation of safeguards exists when any purified aqueous uranium solution or uranium oxides suitable for isotopic enrichment or fuel fabrication exists. Under IAEA Policy Paper 18, the starting point for nuclear material under safeguards was reinterpreted, suggesting that purified uranium compounds should be subject to safeguards procedures no later than the first point in the conversion process. In response to this technical need, a combination of simulation models and experimental measurements were employed in previous work to develop and validate gamma-ray nondestructive assay monitoring systems in a natural uranium conversion plant (NUCP). In particular, uranyl nitrate (UO 2 (NO 3 ) 2 ) solution exiting solvent extraction was identified as a key measurement point (KMP). Passive nondestructive assay techniques using high resolution gamma-ray spectroscopy were evaluated to determine their viability as a technical means for drawing safeguards conclusions at NUCPs, and if the IAEA detection requirements of 1 significant quantity (SQ) can be met in a timely manner. Building upon the aforementioned previous validation work on detector sensitivity to varying concentrations of uranyl nitrate via a series of dilution measurements, this work investigates detector response parameter sensitivities to gamma-ray signatures of uranyl nitrate. The full energy peak efficiency of a detection system is dependent upon the sample, geometry, absorption, and intrinsic efficiency parameters. Perturbation of these parameters translates into corresponding variations of the 185.7 keV peak area of the 235 U in uranyl nitrate. Such perturbations in the assayed signature impact the quality or versatility of the safeguards conclusions drawn. Given the potentially high throughput of uranyl nitrate in NUCPs, the ability to assay 1 SQ of material requires uncertainty «1%. Accounting for material self-shielding properties, pipe thickness, and source-detector orientation is instrumental in determining the robustness of gamma-ray detection in the process monitoring of uranyl nitrate in NUCPs. Monte Carlo models and ray-tracing models were employed to determine the sensitivity of the detected 185.7 keV photon to self-shielding properties, pipe thickness, and source-detector geometry. Considering the implementation of the detection of 1 SQ, diversion of 1 SQ becomes essentially undetectable given the systematic uncertainty, in addition to considerations such as propagating uncertainties due to pipe offset/position, as well as minor variations in pipe thickness. Consequently, pipe thickness was the most sensitive variable in affecting full energy efficiency of the 185.7 keV signature peak with up to 8% variation in efficiency for ±0.5 mm changes in Schedule 40 304L stainless steel piping. Furthermore, computation of the attenuation correction factor of the uranyl nitrate solution [CF(AT) (i.e. εsample)] using Parker's method using with the approximation for the geometrical factor κ≈π/4 was validated through experimental, Monte Carlo and ray-tracing calculations for a uranyl nitrate filled transfer pipe segment. Furthermore, quantifying sensitivity in detector position, as well as voiding effects due to bubbly flow or laminar flow with an air gap in the uranyl nitrate becomes increasingly important as considerations from (static) design-scale measurements translate into (dynamic) field operations tests.
An agent-based modeling tool supporting bioenergy and bio-product community communication regarding cellulosic bioeconomy development
The emergence of the cellulosic bioeconomy requires not only adequate technological, economic, and policy advances, but also effective communication and coordination among the multiple stakeholders in the bioeconomy community. Aiming to facilitate stakeholder communication and collaboration for cellulosic bioeconomy growth, an agent-based model is developed as a community communication tool in this study. The tool simulates the behaviors and interactions of key stakeholders, as well as the responses and interactions between the stakeholders and biophysical, socioeconomic, and environmental conditions. The agent behavior rules are developed and validated based on behavior and economic theories and information attained from a stakeholder focus group study and a land use survey. A pilot study in a Central Illinois watershed shows stakeholder synergies, i.e., positive feedback loops across multiple stakeholders. The modeling tool's role in facilitating community communication is demonstrated via the focus group study. A comparison of policy scenarios suggests that the cellulosic bioeconomy development is fastest under a portfolio of policies combining the views of the multiple stakeholders. Critically, subsidy for small-scale bio-facilities is essential as it rapidly initializes an early market for cellulosic feedstocks and the adoption of Miscanthus. As a result, the communication tool is applied to bringing stakeholders and researchers together to develop shared visions, assess barriers, and explore solution portfolios to initialize and speed up the regional cellulosic bioeconomy.
Climate effects of future aerosol reductions for achieving carbon neutrality in China
To limit the global warming to 1.5 °C above pre-industrial levels, beyond which the most dangerous impacts of climate change will occur, achieving carbon neutrality by the mid-21st century is essential. As the largest developing country and a significant contributor to carbon dioxide (CO 2 ) emissions, China has announced its ambitious climate commitment to pass carbon peak before 2030 and to achieve carbon neutrality by 2060. Both climate policies and regional clean air actions have been implemented for reductions in fossil fuel emissions, including the emissions of short-lived aerosols and precursors. Furthermore, in the context of pursuing carbon neutrality in China, aerosol reductions due to clean air actions and pollution control policies are very likely to have a great impact on climate. In this study, climate effects of aerosol reductions due to China's clean air actions under localized future emission scenarios are investigated using the Community Earth System Model version 1 (CESM1). Fully coupled and atmosphere-only experiments in years of carbon peak (2030) and carbon neutrality (2060) are performed with anthropogenic emissions of aerosols and precursors under “Current-goals” (Current) and “Carbon-Neutral” (Neutral) scenarios from the Dynamic Projection for Emissions in China (DPEC) model that consider socioeconomic development, climate policy, and pollution control actions (Figs. S1–S4 online). In addition, a present-day emissions simulation (PD) is conducted as the reference case (Supplementary materials online). Another sensitivity simulation is also conducted, with black carbon (BC) emissions set to follow the Neutral experiment but other emissions kept at the present-day levels (Neutral_BC), to quantify the relative roles of reducing strongly absorbing BC and other aerosols on future climate towards carbon neutrality. Greenhouse gases concentrations are kept at the 2015 levels in all simulations (Text S1 online).
Next-Generation Reverse Logistics Networks of Photovoltaic Recycling: Perspectives and Challenges
With the growing adoption of solar energy as a key component of the global energy transition and its new industrial policy (the Inflation Reduction Act and others), the United States is witnessing a significant increase in solar investments. This surge in installations and domestic and reshored manufacturing of solar photovoltaic (PV) panels brings with it a pressing issue: the proper management of end-of-life (EoL) PV panels. As these panels are decommissioned, either due to reaching the end of their lifespans or due to breakage across the various stages of the forward supply chain, it becomes crucial to establish efficient reverse supply chain logistics systems to address the challenges associated with their disposal, while also unlocking the value of the inherent materials that are of critical value for other forward supply chains. This perspective article examines the challenges regarding EoL PV panels and relevant reverse supply chain and logistics networks, and proposes future research directions based on the gaps observed among academic research, industry, and policy-making challenges. We identify the main bottlenecks and hurdles including, among others, the lack of supportive regulations and absence of structured, optimized recycling infrastructure. To this end, it is proposed that the key to achieving a sustainable reverse supply chain network for solar PV panels lies in relentless end-to-end supply chain cost optimization efforts supported by enabling policies. Moreover, it is proposed that designing systematic decision-making modeling frameworks is vital for examining different possibilities and scenarios for state, region or nation-wide optimization of solar PV reverse supply chain networks. Indicative to this effect, we discuss the development of a Resource-Task-Network (RTN)-based model and demonstrate its application and benefits through a case study. We wrap up with conclusions and future research directions.
Locked into Copenhagen pledges - Implications of short-term emission targets for the cost and feasibility of long-term climate goals
This paper provides an overview of the AMPERE intermodeling comparison with focus on the implications of near-term policies for the costs and attainability of long-term climate objectives. Ten modeling teams participated in the project to explore the consequences of global emissions following the proposed policy stringency of the national pledges from the Copenhagen Accord and Cancún Agreements to 2030. Specific features compared to earlier assessments are the explicit consideration of near-term 2030 emissions targets as well as the systematic sensitivity analysis for the availability and potential of mitigation technologies. Our estimates show that a 2030 mitigation effort comparable to the pledges would result in a further "lock-in" of the energy system into fossil fuels and thus impede the required energy transformation to reach low greenhouse-gas stabilization levels (450ppm CO2e). Major implications include significant increases in mitigation costs, increased risk that low stabilization targets become unattainable, and reduced chances of staying below the proposed temperature change target of 2C. With respect to technologies, we find that following the pledge pathways to 2030 would narrow policy choices, and increases the risks that some currently optional technologies, such as nuclear or carbon capture and storage (CCS), will become "a must" by 2030.
What does the future hold for utility electricity efficiency programs?
Here, this study develops projections of future spending and savings from electricity efficiency programs funded by electric utility customers in the United States through 2030 based on three scenarios. Our analysis relies on detailed bottom-up modeling of current state energy efficiency policies, demand-side management and integrated resource plans, and regulatory decisions. The three scenarios represent a range of potential outcomes given the policy environment at the time of the study and uncertainties in the broader economic and state policy environment in each state. We project spending to increase to $8.6 billion in 2030 in the medium scenario, about a 45 percent increase relative to 2016 spending. In the high case, annual spending increases to $11.1 billion in 2030 and remains relatively flat in the low case ($6.8 billion in 2030). Our analysis suggests that electricity efficiency programs funded by utility customers will continue to impact load growth significantly at least through 2030, as savings as a percent of retail sales are forecast at 0.7 percent in the medium scenario and 0.98 percent in the high scenario.
Prioritizing material recovery for end-of-life printed circuit boards
Highlights: Black-Right-Pointing-Pointer Material recovery driven by composition, choice of ranking, and weighting. Black-Right-Pointing-Pointer Economic potential for new recycling technologies quantified for several metrics. Black-Right-Pointing-Pointer Indicators developed for materials incurring high eco-toxicity costs. Black-Right-Pointing-Pointer Methodology useful for a variety of stakeholders, particularly policy-makers. - Abstract: The increasing growth in generation of electronic waste (e-waste) motivates a variety of waste reduction research. Printed circuit boards (PCBs) are an important sub-set of the overall e-waste stream due to the high value of the materials contained within them and potential toxicity. This work explores several environmental and economic metrics for prioritizing the recovery of materials from end-of-life PCBs. A weighted sum model is used to investigate the trade-offs among economic value, energy saving potentials, and eco-toxicity. Results show that given equal weights for these three sustainability criteria gold has the highest recovery priority, followed by copper, palladium, aluminum, tin, lead, platinum, nickel, zinc, and silver. However, recovery priority will change significantly due to variation in the composition of PCBs, choice of ranking metrics, and weighting factors when scoring multiple metrics. These results can be used by waste management decision-makers to quantify the value and environmental savings potential for recycling technology development and infrastructure. They can also be extended by policy-makers to inform possible penalties for land-filling PCBs or exporting to the informal recycling sector. The importance of weighting factors when examining recovery trade-offs, particularly for policies regarding PCB collection and recycling are explored further.
Analysis of potential for reducing emissions of greenhouse gases in municipal solid waste in Brazil, in the state and city of Rio de Janeiro
Highlights: ► We constructed future scenarios of emissions of greenhouse gases in waste. ► Was used the IPCC methodology for calculating emission inventories. ► We calculated the costs of abatement for emissions reduction in landfill waste. ► The results were compared to Brazil, state and city of Rio de Janeiro. ► The higher the environmental passive, the greater the possibility of use of biogas. - Abstract: This paper examines potential changes in solid waste policies for the reduction in GHG for the country of Brazil and one of its major states and cities, Rio de Janeiro, from 2005 to 2030. To examine these policy options, trends in solid waste quantities and associated GHG emissions are derived. Three alternative policy scenarios are evaluated in terms of effectiveness, technology, and economics and conclusions posited regarding optimal strategies for Brazil to implement. These scenarios are been building on the guidelines for national inventories of GHG emissions (IPCC, 2006) and adapted to Brazilian states and municipalities’ boundaries. Based on the results, it is possible to say that the potential revenue from products of solid waste management is more than sufficient to transform the current scenario in this country into one of financial and environmental gains, where the negative impacts of climate change have created a huge opportunity to expand infrastructure for waste management.
Incentivizing secondary raw material markets for sustainable waste management
Highlights: • An economic model of secondary raw material markets is presented. •Policies to enhance sustainable waste management at landfills are compared. • Correcting potential misperceptions of secondary raw materials seems quite promising. • Our theoretical model supports Circular Economy and landfilling mining initiatives. - Abstract: Notwithstanding several policy initiatives in many countries over a number of years, there remains a general sense that too much municipal solid waste is generated and that too much of the waste that is generated is landfilled. There is an emerging consensus that a sustainable approach to waste management requires further development of secondary raw material markets. The purpose of this paper is to propose a theoretical economic model that focuses upon this stage of a sustainable waste management program and explores policy options that could motivate efficiency in secondary raw material markets. In particular, we show how firm profit and social welfare optimizing objectives can be reconciled in a two-product market of waste management processes: landfilling and material reclamation. Our results provide theoretical support for building out recent Circular Economy initiatives as well as for the relatively recent emergence of landfill mining as a means for procuring secondary raw materials.
Towards integral utilization of grape pomace from winemaking process: A review
Highlights: • Wine industry is one of the food industries that most generate residues around the world. • Environmental benefits can be achieved with a waste management policy. • Grape pomace can be a source of healthy and technological compounds. • Seedless grape pomace is generally rich in phenolic compounds and fibres. • Grape seeds are mainly explored for their oil and phenolic compounds. - Abstract: Grape is the main fruit crop in several countries. Although many grape-based food products can be found in the market, studies have shown that around 75% of the world grape production is destined for the wine industry. Grape pomace is an abundant by-product from the wine industry, which consists of the remaining skin, seeds and stalks and represents around 25% of total grape weight used in the winemaking process. In countries such as Italy, France and Spain, where wine production is more relevant, the annual grape pomace generation can reach nearly 1200 tonnes per year. In order to reach a sustainable winemaking process there is a need of a waste reduction policy. Several studies explore this subject using grape pomace as a source of healthy and technological compounds that could be applied in animal feed, pharmaceutical, cosmetic or food industry to improve stability and nutritional characteristics, and in cosmetic industry, where grape seeds oil is widely used. This review aims to approach the recent winemaking scenario and the benefits achieved when a waste management policy is implemented, as well as to compare available extractive technologies and a wide alternative of uses for grape pomace.