Total Demand Research Articles

A Comprehensive IoT edge based smart irrigation system for tomato cultivation

Agriculture industry is the primary engine for a country's economic development. Growing crops using minimum irrigation water is a major challenge for farmers. In conventional farming, crops may be affected by various diseases due to inadequate irrigation scheduling. Recent proposals have suggested using Edge-IoT, AI, and distributed computing to accelerate the inference procedure utilized in smart irrigation applications. The use of resource-constrained edge servers and edge devices used to deliver smart agriculture applications can cause latency-sensitive workloads to interfere with one another. To address this issue, we design a long-range (LoRa) edge IoT computing-based sustainable and customized smart irrigation framework to capture the real-time data of tomato plants. This helps in automatic underground drip irrigation scheduling. This also predicts total water demand and usage, and measure plant growth status. The edge-IoT cloud data transmission control and optimization has been enforced using Smart irrigation data optimization and robust transmission (SIDORT) Message Queuing Telemetry Transport (MQTT) system. We develop a hybrid algorithm named Linked least traversal (LLT) for machine-to-machine communication (M2M). Also, a Reinforcement learning (RL) based Optimal Soil Wetness Closeness Policy (OSWCP) for irrigation scheduling has been proposed. The performance of the proposed smart irrigation models has been validated through extensive experiments using real-time data in which OSWCP performance has been measured at a 97.88 % accuracy rate. Additionally, a comparison of our proposed architecture has been accomplished by resolving the existing smart irrigation system challenges.

An evolutionary dynamical analysis of low-carbon technology diffusion among enterprises in the complex network

The diffusion of low-carbon technologies (LCTs) is a critical channel to mitigate carbon emissions. The government plays an important role in guiding the proliferation of LCTs among enterprises, but due to issues such as regulatory costs, unequal resource distribution, and inefficient regulation it is inevitable that there will be inadequate supervision. Therefore, this study incorporates the government’s probabilistic regulation into the research framework and constructs a networked evolutionary game model in which the government, enterprises, and consumers play a role together. Based on the constructed game model, we explore the impact of carbon taxes, subsidies, probabilistic government supervision, market demand, initial fixed investment cost, network size, and firm heterogeneity on the diffusion of LCTs. In addition, we extend the model to consider scenarios involving dynamic market demands and government policies The results demonstrate that factors such as subsidies, probabilistic government supervision, market demand, network size, and firm heterogeneity promote the diffusion of LCTs, while carbon taxes and initial fixed investment cost under a small total demand inhibit the spread of LCTs. Our exploration sheds some light on the diffusion of LCTs from the perspective of demand and supply sides, which provides an effective reference for the formulation of relevant policies.

Development path for China's mega-large urban agglomerations based on the water-energy-food nexus

The Beijing-Tianjin-Hebei (BTH) mega-large urban agglomeration of China (CMUA) has significantly contributed to Chinese socioeconomic development. However, it faces severe shortages in the supply to meet the increasing demand of water, energy, and food resources (WEF). In order to safeguard the resource security of CMUA-BTH and the synergistic development of cities, and to compensate for the lack of dynamic analysis between urban agglomerations and WEF in previous studies. In this study, a coupled CMUA-WEF simulation model is developed based on the system dynamics approach. The model not only studies the three elements of WEF, supply and demand, but also conducts a comparative analysis of the three regions of BTH, and designs a total of four scenarios based on the possibility of future development, so as to facilitate the depiction of the trend of the WEF supply and demand system under different scenarios, and to provide theoretical basis for the government to formulate effective policy. The results show that: (1) Regarding demand, the aggregate demand for energy, food and water in the BTH will increase by about 3–5 times, 3 times, and 2 times, respectively, from 2000 to 2050, among which the total demand for energy will increase most significantly. In contrast, the variation of local resource supply side is small, which leads to the deterioration of resource security. (2) The risk of water shortage is the greatest in resource security, with less than 30% self-sufficiency in water security in all three regions. (3) Scenario analysis for CMUA-BTH indicates that “choking flow” (S3) yields more pronounced effects compared to “open source” (S2), and therefore adhering to the reduction of food waste, promoting energy-saving technologies, and intensifying water-saving measures can effectively reduce the risk of water shortages in the three regions. Optimal resource security in S4 increases Hebei's water pressure by 1.5%, alleviates Beijing's pressure by 7.3%, and the implementation of the Xiongan New Area and the new energy development policy can help realize the synergistic development of BTH and the inter-regional linkage.

Dynamic Cooling – A concept of time-sensitive thermal regulation to cut cooling energy demand in office buildings

This paper focuses on how the existing sealed office typology can adapt to a warming world while minimizing the high energy demand for space cooling, through Dynamic Cooling, by adjusting the cooling temperature profile towards the average human circadian rhythm to mitigate heat stress during times of heightened thermal sensitivity. This concept is explored through a study in which we compared the effects of Dynamic Cooling, constant cooling, and no cooling on the core body temperature of 21 participants as well as a thermal dynamic simulation of 270 office space configurations under the same cooling profiles, comparing the total energy demand, peak loads, and overall hours of discomfort. The results of the study suggest the different conditioning profiles have no direct impact on the overall change in core body temperature throughout the day (difference < 0.04 K) but do influence the overall range throughout (from 0.37 K under constant cooling to 0.53 K under Dynamic Cooling). Additionally, there is no clear correlation between elevated indoor temperatures and CBT during the morning hours, questioning the necessity and effectiveness of pre-lunch cooling. This is underlined by the simulation findings, which reveal a non-linear relationship between savings in cooling demand and a decline in thermal comfort, with a 51.3 % reduction in ideal cooling energy demand and a 25.2 %–36 % increase of Over-Temperature-Kelvin-Hours, from a steady state cooling profile to a profile of Dynamic Cooling. The results of the study and simulation challenge traditional cooling standards and highlight the need for a balance between minimizing thermal discomfort and the necessary cooling energy demand.

Water Reuse via Membrane Technology: A Case Study for Producing Cooling Water from Soft Drink Wastewater

High volumes of water are used in the soft drink industry, where purification can be achieved via membrane technology, allowing it to be reused in operating cooling towers, steam boilers, and closed-loop systems. In this study, ultrafiltration (UF) and nanofiltration (NF) were applied to produce reclaimed water for cooling towers. In experimental studies conducted under a total recycle mode of operation, two UF membranes with molecular weight cut-offs (MWCO) of 10 kDa and 5 kDa, and one NF membrane were tested. The transmembrane pressure was set to 2 bars for UF and 4 bars for NF. Considering the water quality criteria for cooling towers, removal efficiencies were calculated for total suspended solids (TSS), turbidity, and chemical oxygen demand (COD). TSS and turbidity removal rates were 100% for both UF and NF; however, COD removal rates varied. In UF, feed and permeate COD concentrations were 718 - 796 mg/L and 255 - 286 mg/L, corresponding to a removal efficiency of 64-65%. On the other hand, in NF, the feed and permeate COD concentrations were 207 mg/L and 147 mg/L, indicating a 29% removal. Flux decline was not severe; it ranged from 10% to 20%. It was revealed that further treatment is required to meet the COD criteria of 75 mg/L for cooling towers.

A GIS-Based Approach for Urban Building Energy Modeling under Climate Change with High Spatial and Temporal Resolution

The energy demand and associated greenhouse gas (GHG) emissions of buildings are significantly affected by the characteristics of the building and local climate conditions. While energy use datasets with high spatial and temporal resolution are highly needed in the context of climate change, energy use monitoring data are not available for most cities. This study introduces an approach combining building energy simulation, climate change modeling, and GIS spatial analysis techniques to develop an energy demand data inventory enabling assessment of the impacts of climate change on building energy consumption in Shanghai, China. Our results suggest that all types of buildings exhibit a net increase in their annual energy demand under the projected future (2050) climate conditions, with the highest increase in energy demand attributed to Heating, Ventilation, and Cooling (HVAC) systems. Variations in building energy demand are found across building types. Due to the large number of residential buildings, they are the main contributor to the increases in energy demand and associated CO2 emissions. The hourly residential building energy demand on a typical hot summer day (29 July) under the 2050 climate condition at 1 p.m. is found to increase by more than 40%, indicating a risk of energy supply shortage if no actions are taken. The spatial pattern of total annual building energy demand at the individual building level exhibited high spatial heterogeneity with some hotspots. This study provides an alternative method to develop a building energy demand inventory with high temporal resolution at the individual building scale for cities lacking energy use monitoring data, supporting the assessment of building energy and GHG emissions under both current and future climate scenarios at minimal cost.

Considering traffic characteristics: Roadside unit deployment optimization algorithm based on dynamic division of road network subareas

AbstractGiven that the overall coverage deployment method fails to meet information needs in important areas, there are redundancies and deficiencies in the information provided. To enhance communication stability for roadside units (RSUs), improve information coverage at critical intersections and optimize algorithm efficiency. Here, a method for deploying RSUs is proposed that aims to optimize revenue in road network subareas. The road network is divided into several subareas based on critical intersections, node similarity, road segment correlations, and characteristics of RSU information transmission. Then, a roadway accessibility algorithm is developed that accounts for channel fading. Considering the robustness of wire network deployment, an improved traveling salesman problem (TSP) problem is proposed that includes candidate locations and constructs a model for optimal RSU deployment that maximizes consolidated revenue. Finally, using the Sioux Falls network as an example, the RSU deployment strategy is evaluated for the overall network and the road network after being subdivided. The results indicate that subdividing the road network improves the efficiency of the optimization solution, the information coverage of critical intersections increases by 1.8 times. The deployment optimization scheme of RSUs is directly influenced by various parameters such as bandwidth capacity and cost coefficient. When deploying RSUs in road network subareas, variations in total demand have minimal impact on RSU deployment, ensuring a stable deployment scheme.

Agent-based power management in apartment buildings: Tenant preferences and distributed energy resources

Apartment buildings (APBs) consist of multiple residential spaces and common facilities, unlike single-family buildings, and have correspondingly diverse energy demands; however, household preferences and behaviors have received little attention so far. Therefore, bidirectional flows between households and the APB must be considered to manage the APB energy demands by considering dynamic relationships built by households with preferences and behaviors. This study presents a demand-side management system called the unified home energy management system (U-HEMS), which considers distributed energy resources (DERs) for the APB. The U-HEMS was developed by bidirectional flows of management and decision-making processes for households and the APB. In the management process from the household to the APB, the APB power demands were optimized using a two-stage system, whereas in the decision-making process from the APB to the households, the next actions expressed by weighting factors of the household side were determined for the management process based on an agent-based model. The functionality and performance of the U-HEMS were compared with those of conventional approaches for a 100-unit APB, and five case studies were conducted to analyze their power demands considering different aspects: decision-making process, combinations of households’ preferences, reward system, penetration of electric vehicles, and DER capacities. The results reveal that the U-HEMS effectively reduces the overall costs and total peak power demand because a bidirectional model considers the dynamic relationships between households in the APB and DER operations subject to the time-varying pricing policy. Further studies are needed to assess seasonal effects on APB demands.

Modelling the water supply-demand relationship under climate change in the Buffalo River catchment, South Africa

Climate change strains the global water supplies’ capability to meet demands, especially in regions like South Africa, where resources are already scarce. The interconnectedness of water, energy, and food (WEF) exacerbates this challenge, amplifying the impact of climate change on water resource management across these sectors. Thus, in strengthening the long-term resilience and reliability of water resources, a necessity in South Africa, research on climate change and the WEF nexus is needed for water resource planning and development. Employing the WEF nexus approach, we applied the Climate Land-Use Energy and Water Strategies (CLEWS) modelling framework to assess climate change impacts on the water supply-demand relationship, considering the domestic, agriculture (irrigation) and energy generation sectors, and adopting the Buffalo River catchment, KwaZulu-Natal, South Africa, as a case study. A threefold approach was utilized: (1) water supplies and demands and the total unmet demands were quantified; (2) the percentages of water demands covered per sector were derived; and (3) the reliability of the water system to meet each sector’s water demands was computed. The findings projected slight decreases (2%) in the Buffalo River catchment’s total water demands towards the end of the 21st century, mainly due to changes in land suitability for agriculture. While the water system is projected to be reliable for highly populated municipalities (demand coverage index &gt; 70%; reliability index ≥ 20%), it is unreliable for sparsely populated and agriculturally intensive municipalities (demand coverage index ≤ 12%; reliability index = 0%). Such unreliability will strain agricultural production as more than 70% of irrigation water demands come from these municipalities. Nexus-smart water allocation and capacity development plans are recommended to manage these challenges and ensure a just and sustainable water supply-demand relationship in light of climate change.

An economic demand-based framework for prioritization strategies in response to transient amino acid limitations

Cells contain disparate amounts of distinct amino acids, each of which has different metabolic and chemical origins, but the supply cost vs demand requirements of each is unclear. Here, using yeast we quantify the restoration-responses after disrupting amino acid supply, and uncover a hierarchically prioritized restoration strategy for distinct amino acids. We comprehensively calculate individual amino acid biosynthetic supply costs, quantify total demand for an amino acid, and estimate cumulative supply/demand requirements for each amino acid. Through this, we discover that the restoration priority is driven by the gross demand for an amino acid, which is itself coupled to low supply costs for that amino acid. Demand from metabolic requirements dominate the demand-pulls for an amino acid, as exemplified by the largest restoration response upon disrupting arginine supply. Collectively, this demand-driven framework that drives the amino acid economy can identify novel amino acid responses, and help design metabolic engineering applications.

Optimal Early-Bird Discounts and the Challenges of Diversion

Variable pricing and early-bird discounts can shift some demand or the time of purchase from higher-priced periods to lower-priced periods without increasing the total demand. This diversion effect influences optimal price levels, and the present paper presents a model for calculating optimal early-bird discounts when incorporating diversion. The model also reveals the differences in price elasticities and the feasible price interval for positive profit. The paper contributes to the literature on early-bird pricing strategies and offers practical insights for service providers seeking to optimize their pricing strategies. The empirical relevance is documented using survey and real-life experimental data among skiers at a major ski resort in Norway. The main insights of the paper are that the profit-maximizing discounts and the gap between the optimal price with and without diversion increase with the length of the pre-booking period.

Does the Addition of Low-Dose Antibiotics Compromise the Mechanical Properties of Polymethylmethacrylate (PMMA)?

The increasing numbers of total joint replacements and related implant-associated infections demand solutions, which can provide a high-dose local delivery of antibiotics. Antibiotic-loaded bone cement (ALBC) is an accepted treatment method for infected joint arthroplasties. The mechanical properties of low-dose gentamicin-loaded bone cement (BC) in medium- and high-viscosity versions were compared to unloaded BC using a vacuum mixing system. As an additional control group, manual mixed unloaded BC was used. In a uniaxial compression test, ultimate compressive strength, compressive yield strength, and compression modulus of elasticity, as well as ultimate and yield strain, were determined according to ISO 5833-2022 guidelines. All groups exceeded the minimum compressive strength (70 MPa) specified in the ISO 5833 guidelines. Both ALBC groups showed a similar ultimate compressive and yield strength to the unloaded BC. The results showed that vacuum mixing increased the compression strength of BC. ALBC showed similar compressive strength to their non-antibiotic counterparts when vacuum mixing was performed. Added low-dose gentamicin acted as a plasticizer on bone cement. From a biomechanical point of view, the usage of gentamicin-based ALBC formulations is viable.

Assessment of Three Distinct Approaches to Postoperative Pain in Laparoscopic Inguinal Hernia Repair, a Randomized Prospective Study.

Background: Contemporarily, transabdominal preperitoneal repair (TAPP) procedure in inguinal hernia treatment is counted among the routine minimal invasive general surgery practices. Increased patient's comfort, namely less postoperative pain, is considered to be its greatest advantage. However, pain following surgery can still be an important problem. Port site local anesthetic injection (PSLAI), iliohypogastric-/ilioinguinal nerve block (IINB), and preperitoneal local anesthetic spraying (PLAS) are relatively new techniques with sparse data to address this issue. Therefore, we conducted this prospective study to evaluate these three methods in patients who underwent TAPP for inguinal hernia repair. Methods: A total of 99 patients were enrolled and randomized equally into three groups. Every patient received a patient-controlled analgesia (PCA) device. PCA usage, total analgesic demands, and numerical rating scale values were recorded at 2, 6, 12, and 24 hours postoperatively (p.o). Results: Patients' demographic data (age, gender, BMI) did not reveal any significant difference between groups (P > .05). Procedure duration was found to be significantly longer in IINB group compared with others (p < .05). Number of PCA usages, total analgesic demand, additional analgesic requirement did not differ significantly between groups at 24-hour p.o (P > .05). PLAS group was found to have less average NSR score compared with other groups at 24 hours p.o (p < .05). Conclusions: All three procedures show promising outcomes with PLAS technique appearing to be slightly superior in terms of pain management in the immediate postoperative period. However, to reach a conclusion more randomized controlled trials covering various aspects and techniques of minimal invasive approach to inguinal hernia repair should be published.

Assessment of agricultural residue potential for electrification of off-grid communities in the Sawla-Tuna-Kalba District of Ghana

BackgroundThere is a close link between the lack of electricity access and poverty indicators such as illiteracy, high infant mortality, lack of access to health care and malnutrition among others. Most rural farming communities in Ghana lack access to electricity due to the high cost of extending the grid to these communities. This lack of access tends to worsen the gap between urban and rural inhabitants regarding access to education, healthcare and development.MethodsThis study assessed the technical and theoretical potential of agricultural residues in providing electricity to off-grid communities. The study used crop production figures of maize, cassava, millet and groundnut in the Soma and Goyiri farming communities in the Sawla-Tuna-Kalba District to conduct an assessment of the theoretical and technical potential of residues from the crops. The production figures of these crops were obtained from the District Office of the Ministry of Food and Agriculture. Expected electricity demand of households, schools and health centers in the study communities were collected and employed for the projected load demand estimates.ResultsThe study found that 312.23 MWh/day of electricity could be generated from the combined residues of maize, cassava, millet and groundnut from the two communities. This amount of electricity is capable of providing ~ 202 to 263 times the peak electricity demand of the studied communities. Out of the total electricity demand of the two communities, only about 91 kWh/day is needed for use in a school and Community Health Promotion and Services (CHPS) compound, implying that the electricity from crop residues can also help to improve education and health provision in the rural communities.ConclusionIt is concluded that the potential of crop residues in meeting the electricity demand of off-grid communities is enormous. Hence, it must be considered in Ghana’s energy development plans to achieve universal electricity access.

Long-term energy demand modeling and optimal evaluation of solutions to renewable energy deployment barriers in Benin: A LEAP-MCDM approach

Approximately 60 % of Beninese lack access to electricity, relying heavily on traditional energy sources, electricity imports, and low renewable energy integration. This study aims to forecast the energy demand for Benin while reducing greenhouse gas (GHG) emissions and propose alternative solutions to clean energy deployment barriers. The Low Emissions Analysis Platform (LEAP) is used to explore the future energy demand for Benin and associated GHG emissions. Four scenarios have been developed, namely Business as Usual (BAU), High Economic Development (HED), Electrification and Urbanization Growth (EUG), and High Efficient Energy Application (HEEA). In addition, the Fuzzy AHP and TOPSIS techniques have been employed to rank and prioritize alternative solutions to Renewable Energy (RE) deployment barriers in the country. A total of eighteen sub-barriers were identified and classified into five main categories under the four pillars of sustainability (technical, social, economic, and environmental), and nine strategies were identified and proposed to overcome the barriers. The results show that under the BAU scenario, the total energy demand is expected to reach 445 PJ in 2050 from the current demand of around 164 PJ. A total GHG emission of 21 MtCO2e is estimated under the BAU scenario in 2050. However, the HEEA scenario indicates that energy demand would considerably decrease resulting in low GHG emissions from the energy sector. The findings from the Fuzzy AHP ranking show that technical barriers, political and regulatory barriers, and financial and economic barriers, are the top three barriers hindering RE deployment in Benin. Moreover, adequate policy and regulatory framework emerges as the most weighted solution to eliminating barriers to RE deployment in the country. These results encourage the application of effective policy to support sustainable energy transition. The study's approach is applicable to other developing countries outside Benin, offering insights into RE barriers and potential alternative solutions.

Machine learning-based predicting of PCM-integrated building thermal performance: An application under various weather conditions in Morocco

As is now stands, the building sector is among the “big three” significant energy consumer and greenhouse gas contributor in the world. Consequently, there has been a growing movement towards the development and adoption of renewable energy sources, energy efficient measures and energy management strategies. Phase change materials (PCMs) are considered as an alluring option for energy efficiency measures in building. In this paper, a case study building, based on typical residential construction in Morocco, was selected to assess the potential energy savings of adding PCM to the roof for twenty-four cities using the dynamic simulation tool, TRNSYS. Thereafter, thirteen machine learning techniques, including ANN, DT, SVM, ELM, GB, RF, TB, GLRM, GPR, LR, GAM, KRRM and LRR were assessed for predicting the hourly heating, cooling, and total energy consumptions. The models were trained and tested on a dataset that was gathered from the simulations in twenty-four locations in Morocco. The outdoor dry-bulb temperature, the relative humidity, the wind velocity, the wind direction, and the total solar radiation were considered as the key features. The obtained results revealed that using PCM, can effectively lower the total energy demand for all the cities under study, with exception for very cold climate given by Ifrane and Midelt, where the annual total energy consumption shows an increasing trend. Furthermore, comparing the statistical results for each machine learning model, it is found that the SVM model consistently outperforms all models and can be successfully employed to predict the hourly network's outputs of the building.

Paving the way for low-carbon hydrogen supply chain deployment by exploring the potential of renewable energies and multisectoral hydrogen demand: Case study of France

France has set ambitious targets for hydrogen production in its National Roadmap, aiming to install at least 6.5 GW of electrolyzer capacity and produce 700,000 tons of hydrogen annually by 2030. The country is focusing on producing renewable or low-carbon hydrogen primarily through electrolysis. However, it faces significant barriers in rapidly scaling up renewable energy infrastructure and may need to consider import strategies to address potential shortages. Addressing these challenges requires investigating whether the availability of renewable energy for the production of electrolytic hydrogen could become a limiting factor for hydrogen adoption and potentially act as a bottleneck in its market integration. The methodology merges forecasts from the public and private sectors to address both renewable and non-renewable electricity production and the energy needed for rising hydrogen demand. The approach developed involves estimating France’s renewable energy supply up to 2050 and determines how much of this energy can be allocated to hydrogen production to ensure it remains carbon-free and genuinely renewable. Unlike many existing roadmaps that take a more general approach, the innovative part of this study is developing a territorial perspective to conduct a detailed analysis of potential mismatches between hydrogen supply and demand.Three distinct sources of electricity are considered for the electrolyzers, which could be connected to the grid or directly to renewable power plants: low-carbon electricity from the French grid, renewable electricity from re-powered solar and wind farms, and renewable electricity from newly installed power plants. Total electricity demand is projected to rise from 475 TWh/y in 2020 to 754 TWh/y in 2050, with the share of renewable energy increasing from 19% in 2020 to 69% in 2050.The study evaluates the demand for hydrogen in two key sectors, industry, which is heavily dependent on hydrogen, and mobility, which currently has a more modest contribution. Hydrogen demand is expected to increase from nearly 310 ktons per day in 2025 to over 2650 ktons per day by 2050.Given an average specific consumption of 55 kWh of electricity per kg of hydrogen produced, the total electricity demand for electrolytic hydrogen production is projected to grow from 17 TWh/year in 2025 to 146 TWh/year in 2050.It can be concluded that allocating the entire anticipated production from re-powered solar and on-shore wind farms in the coming years will not be sufficient to meet the electricity demand required for electrolytic hydrogen production. To prevent renewable energy from becoming a bottleneck for hydrogen market integration and to avoid the need for hydrogen imports, it is crucial to allocate 5% to 10% of the projected renewable output from newly installed plants to address the increasing hydrogen demand. This result is key to creating an optimal design model for hydrogen supply chains.

Banana Supply Chain in Navi Mumbai, India

This study investigates the banana supply chain in Navi Mumbai, aiming to identify existing supply channels, analyze their efficiency, problems faced by producers and anayse the market potential of banana in the study area. Primary data were collected from respondents to accomplish these objectives. Secondary data were obtained from private and government publications, and review papers. Utilizing a descriptive research design and employing a probability sampling method, the study surveyed 50 farmers, 5 village traders, 5 pre-harvest contractors, 10 wholesalers cum commission agents, and 10 retailers using a semi-structured schedule. Three marketing channels were identified. Among them channel-III (Farmer - Wholesaler cum commission agent - Retailer - Consumer) was the most efficient option, offering the highest producer's share in the consumer's rupee (40.05%) and the highest marketing efficiency (0.67), with a price spread of Rs. 1711.55 per quintal. In the study area, banana producers face challenges such as high input costs, delayed availability of planting materials, and losses due to diseases and pests. Marketing hurdles include lower prices, distant selling unit locations, delayed payments, and inadequate transportation and storage facilities. Village traders, preharvest contractors, wholesalers cum commission agents, and retailers also encounter various obstacles such as price fluctuation, handling and transportation losses, low-quality produce, and labour shortages. As the total market demand for banana in Navi Mumbai was estimated to be ₹62.26 crore, addressing these challenges is vital for enhancing the efficiency and profitability of banana marketing in the study area, necessitating coordinated efforts from policymakers, agricultural stakeholders, and market participants. Establishing FPOs and FPCs can ease access to affordable planting materials and address marketing challenges, benefiting all banana market stakeholders.

The Emission-Reduction Effect of Green Demand Preference in Carbon Market and Macro-Environmental Policy: A DSGE Approach

Along with the new stage of prevention and control of the COVID-19 pandemic and the vision and goals of combatting climate change, the challenges of the transition to a green economy have become more severe. The need for green recovery of the economy, stability and security of energy production and consumption, and the coordination of low-carbon transformation and socio-economic development has become increasingly urgent. This paper proposes a new theoretical framework to study the effect of carbon emission reduction on the mutual application of the carbon market, fiscal policy and monetary policy under the non-homothetic preference of energy product consumption. By constructing an environmental dynamic stochastic general equilibrium (E-DSGE) model with residents’ non-homothetic preferences, this paper finds that coordinating the carbon market and macroeconomic policies can achieve economic and environmental goals. However, the transmission paths for each are different. The carbon market influences producers’ abatement efforts and costs through carbon prices. Monetary policy controls carbon emissions by adjusting interest rates, while fiscal policy controls carbon emissions by adjusting total social demand. Improving non-homothetic preferences will amplify business cycle fluctuations caused by exogenous shocks, thus assuming the role of a “financial accelerator”. Further research shows that non-homothetic preferences influence the heterogeneity of different policy mixes. Finally, this paper discovers that the welfare effects, the relative size and difference of long-term and short-term effects resulting from the different policy mixes, also depend on the level of non-homothetic preferences. The intertemporal substitution mechanism due to the improvement of non-homothetic preferences endows low-carbon production with “option” characteristics. Our study reveals the role of non-homothetic preferences on the effectiveness of policy implementation. It highlights the importance of matching monetary and fiscal policies with the carbon market based on the consumption and production side. It provides ideas for policy practice to achieve the goal of “dual carbon” and promoting coordinated socio-economic development.

Circular Steel for Fast Decarbonization: Thermodynamics, Kinetics, and Microstructure Behind Upcycling Scrap into High-Performance Sheet Steel

Steel production accounts for approximately 8% of all global CO2 emissions, with the primary steelmaking route using iron ores contributing approximately 80% of those emissions, mainly due to the use of fossil-based reductants and fuel. Hydrogen-based reduction of iron oxide is an alternative for primary synthesis. However, to counteract global warming, decarbonization of the steel sector must proceed much faster than the ongoing transition kinetics in primary steelmaking. Insufficient supply of green hydrogen is a particular bottleneck. Realizing a higher fraction of secondary steelmaking is thus gaining momentum as a sustainable alternative to primary production. Steel production from scrap is well established for long products (rails, bars, wire), but there are two main challenges. First, there is not sufficient scrap available to satisfy market needs. Today, only one-third of global steel demand can be met by secondary metallurgy using scrap since many steel products have a lifetime of several decades. However, scrap availability will increase to about two-thirds of total demand by 2050 such that this sector will grow massively in the next decades. Second, scrap is often too contaminated to produce high-performance sheet steels. This is a serious obstacle because advanced products demand explicit low-tolerance specifications for safety-critical and high-strength steels, such as for electric vehicles, energy conversion and grids, high-speed trains, sustainable buildings, and infrastructure. Therefore, we review the metallurgical and microstructural challenges and opportunities for producing high-performance sheet steels via secondary synthesis. Focus is placed on the thermodynamic, kinetic, chemical, and microstructural fundamentals as well as the effects of scrap-related impurities on steel properties.