Supply Mismatch Research Articles

Estimating the impact range of circular economy actions for buildings on basic material demand in the European Union: A combination of qualitative and quantitative approaches

A circular economy in buildings can contribute to reduce the demand for emission-intensive basic materials and thus, to achieve the climate targets of the European Union. However, quantifying the impact on material demand is challenged by the unpredictability of the future. We combined qualitative and quantitative approaches to improve the understanding of such future developments. In detail, expert interviews were conducted to identify influencing factors and to collect estimates for modelling prospective material demand. The results indicate that the adaption is driven by existing and new policies, climate change as well as demographics. This is contrasted by barriers, such as the economic non-viability, the limited availability of substitutes, and the mismatch of supply and demand as well as building regulations. Moreover, the type of influencing factor reflects the maturity of individual strategies related to a circular economy. The estimated impact of circular economy actions on annual material demand for buildings varies between −42 % and +25 % in 2050 compared to a reference development. However, there are not only differences between but also within individual actions. In brief, the maturity of the circular economy actions is reflected by the range of impact on material demand. The novel combination of the qualitative influencing factors and the quantitative modelling results reveals an interrelationship between the drivers and barriers and the impact on material demand. This underlines that the prospective exploitation of these potentials can be actively influenced by adapting the policy framework conditions. This is particularly relevant for less renowned circular economy strategies and actions that are characterized by high potential and high unpredictability. The presented results lay the foundation for a more systematic consideration of material reduction potentials related to a circular economy in prospective scenarios of buildings in the European Union.

Pv-load matching based on combination of different consumers: A case study in Swedish contexts

The transition towards sustainability necessitates robust growth of renewable energy, especially solar power. However, increasing the shares of solar power may lead to mismatch of electricity supply and demand, thus worse solar power performance could be received due to lower self-consumption and self-sufficiency. This issue has attracted more attention recently as it also requires additional costs for controlling and battery integration. This study combines several load profiles from consumers having different behaviors with different proportions to find out the optimal shares with higher level of PV penetration and self-sufficiency. A visualizing matching layout is proposed to present the PV-load matching level of all the possibilities at different seasons based on a Swedish context. It is found an 8% improvement of self-sufficiency reaching 70% in summer without additional equipment and scarification of self-consumption. The results could assist in the design and planning of the future power sector. Regarding specific targets, various optimal solutions could be found based on the layout.

Silica-based aerogels encapsulate organic/inorganic composite phase change materials for building thermal management

Phase change energy storage materials are widely used in thermal management because they reduce energy consumption and effectively address issues such as energy supply and demand mismatches. However, challenges such as leakage in solid-liquid phase change materials (PCMs), flammability in organic solid-liquid PCMs, and supercooling and phase separation in inorganic solid-liquid PCMs still need to be resolved for their practical applications. In this study, we used methyl palmitate, dodecahydrate disodium hydrogen phosphate, and decahydrate sodium carbonate as the main PCMs, sodium carboxymethyl cellulose as an emulsifier and thickener, and silica aerogel as the encapsulating material to successfully prepare a novel shape-stabilized organic/inorganic composite PCM (CPCM). The prepared CPCM exhibits excellent shape stability and flame retardancy without supercooling or phase separation issues. In addition, the prepared CPCM has a high latent heat value (174.1 J/g) and a suitable phase transition temperature (22.9 °C), and its thermal performance remains basically unchanged after 100 heating/cooling cycles. Furthermore, this CPCM also demonstrates exceptional performance in building thermal management. This study provides a new solution to the problems of flammability, supercooling, phase separation and easy leakage of CPCM currently used in the field of building thermal management.

Pretransplantation Cardiac Workup of the Kidney Transplant Recipient for Coronary Heart Disease

Abstract As the prevalence of chronic kidney disease (CKD) increases globally, a greater number of CKD patients are surviving to become end-stage kidney disease (ESKD) and avail kidney transplantation as means of therapy. Furthermore, with the global mismatch of demand and supply of organs, a large number of ESKD patients remain on the waitlist of deceased donor renal transplantation for longer times. The risk of cardiovascular diseases (CVD) rises exponentially with advancing stages of CKD. The elevated risk in these patients also accounts for CVD as the leading cause of mortality in these patients as compared to the underlying kidney disease. Wherein, there is ample justification for pretransplantation screening of a potential transplantation recipient, the benefits of such screening remain unproven in literature. In this narrative review, we discuss the uncertainties associated with pretransplantation cardiac screening including the appropriate tests, the interpretation of their results, and the complications of the same and deal with the cardiac screening pertaining predominantly to coronary heart disease.

Structure-activity relationship between pore morphology and thermochemical energy storage properties based on MgCO3/MgO

Thermochemical energy storage (TCES) is a pivotal technology for addressing the space-time mismatches in energy supply and demand. MgCO3/MgO carrier offers the advantages of high energy density, seasonal storage capability, and abundant nontoxic reserves. However, it is encumbered by poor exothermic activity and kinetic irreversibility. In this study, innovative preparation approaches for MgCO3/MgO have been developed, involving the regulation of physical morphology, so as to reveal the structure-activity relationship between pore morphology and TCES properties. The results indicate that SD-cotton-MgO (MgO prepared by structure-directing agent, cotton) could carbonate up to 76.98 %, facilitated by its mesopores that aid in the transport of reactants. SGSC-MgO (MgO prepared by sol-gel self-combustion method) exhibits the fastest reaction rate at the initial stage, with a rate of 593.1 μmol/min/g adsorbent because distributed macropores expose the other nanopores. As for SD-CPM-MgO (MgO prepared by structure-directing agent, carboniferous polysaccharide microsphere), owning to long pore distance of micropores hindering CO2 diffusion, its kinetics gradually decreases as the CPM size decreases. Porous media with mixed aperture could exhibit strong kinetic performance. Furthermore, the heat storage density and maximum power of SD-cotton-MgO increase as the endothermic temperature rises, achieving a peak energy density of 1385.7 kJ/kg MgCO3 at 400 °C. Still, its heat release density increases and then decreases with exothermic temperature, reaching 1964.3 kJ/kg MgO at 320 °C. Finally, the cycle stability is contingent on the activation of the chemical modification for the first heat release and antidegradation of the physical structure for subsequent cycles. SD-cotton-MgO doped with Na0.5K0.5NO3 maintains at 50 % after 30 cycles, exhibiting no collapse or agglomeration due to its robust porous structure with strong thermal stability and permeability.

Exploring the financing gap for entrepreneurial firms in a developing economy: a case of mismatched expectations?

PurposeThis paper investigates the financing preferences and practices of Senegalese entrepreneurial firms, with a particular focus on understanding the gaps between the two and how they may contribute to financing constraints in developing economies. By juxtaposing the preferences of different financing options against their degree of usage, this study attempts to reveal the mismatch in demand and supply of entrepreneurial firms financing in Senegal.Design/methodology/approachA structured questionnaire was used to survey 524 entrepreneurial firms, and data was analyzed using various statistical methods.FindingsThe results indicate that the most preferred sources of financing for Senegalese entrepreneurial firms are self-financing and short-term bank loans. Short-term funding horizons are also much more preferred than their long-term counterparts. However, there is a mismatch between financing preferences and practices, particularly with regards to equity sources, which were found to be more preferred than used. The study argues that a combination of preferences, firm, and owner characteristics can explain the choice and frequency of usage of financing sources.Originality/valueThis study contributes to the literature by contrasting preferences and practices, revealing gaps between theory and practice, and providing better insight into the real financing needs of entrepreneurial firms in developing economies. To the authors’ knowledge, this is the first study to examine the financing preferences of Senegalese entrepreneurial firms, making it an important contribution to the literature on entrepreneurial firms financing in developing economies.

Impact of dataset sampling period on building thermal models used for flexibility activation

Buildings could help solve the supply and demand mismatch of electricity by providing energy flexibility to the grid. Thermal models of a dwelling are required to properly exploit their flexibility. The first step in developing such models is gathering on-site measurements. The quality of these measurements impacts the performance of the resulting models and, in turn, the harnessed flexibility. This research aims to evaluate the effect of the measurement sampling period on the achieved flexibility in a dwelling. In this paper, flexibility is realized through passive heat storage in the building by using model predictive control. The effect of the dataset sampling period on modeling accuracy and flexibility is evaluated. To assess the impact of the sampling period, four types of modeling techniques are trained by datasets with different sampling periods. The performance of these models for flexibility activation is assessed in two case studies. These case studies have similar structures with the main difference being their heating system. One building is equipped with electric heaters while the other uses underfloor heating. The findings show that decreasing the sampling period below 1 h does not lead to noticeable improvements in energy flexibility. On the other hand, deploying a sampling period larger than 1-h leads to a controller that underperforms. Using a sampling period of 2 h could sabotage the harnessed flexibility up to 51 %.

Assessing the impact of the dataset’s size and quality on building thermal models for energy flexibility

The demand side is required to increase its energy flexibility to tackle the demand–supply mismatch caused by integration of renewable energy sources into the energy mix. Model Predictive Control (MPC) is one of the promising measures for achieving this goal. MPC requires operational data from the building to train its predictive model. The quality of this data affects the performance of MPC. This paper aims to analyze the impact of a dataset’s size and excitation method on flexibility harnessed by MPC. Five datasets have been generated that differ in their size and excitation method. They are used to train three modeling algorithms on two buildings. Next, generated predictive models are integrated into an MPC framework, to assess the impact of datasets on the performance of MPCs for activating the energy flexibility of a building. The case studies are two similar dwellings where one is equipped with Electric Heaters (EH), and the other uses an UnderFloor Heating (UFH) system. Results show that the flexibility of the EH case is more sensitive to the size of the dataset. Furthermore, it is shown that the suitability of the excitation signal for flexibility activation depends on the modeling algorithm. The results show that choosing an ill-suited dataset might cost the MPC 35 % and 20 % of the potential flexibility for EH and UFH cases, respectively.

From bottlenecks to inflation: Impact of global supply-chain disruptions on inflation in select Asian economies

Supply-chain bottlenecks emerged alongside unprecedented inflationary surges amid the resulting mismatches in supply and demand for goods and services throughout the COVID-19 pandemic. This study employs a local projection approach to investigate the inflationary impact of global supply-chain pressures in the Philippines and select East Asian neighbors. We find that supply-chain disruptions are followed by sizable and statistically significant increases in headline inflation in the Philippines, Rep. of Korea, Thailand, and Singapore. Supply-chain shocks also have a considerable impact on the tradable segment of core inflation. Meanwhile, non-tradable inflation has a generally smaller, short-lived, and insignificant response to global supply-chain pressures reflecting the limited presence of second-round, indirect effects.

Pricing and Capacity Allocation in Opaque Selling

To solve the supply and demand mismatch problem, intermediaries have emerged as an opaque channel for service providers to dispose of leftover capacity. This paper focuses on pricing and capacity allocation in opaque selling. We develop a Stackelberg game model, where two capacity-constrained service providers first decide the capacity allocated to an intermediary who then determines the opaque price based on its capacity. We examine the conditions under which the service providers should cooperate with the intermediary, and find that the opaque price and capacity allocation decisions are affected by product type, service providers’ capacity, and commission rate. Moreover, we find that the service providers may fall into the prisoner's dilemma when distributing through the intermediary. It indicates that although opaque selling can bring service providers more profit, it may not help them achieve the optimal profit. In view of this, we propose an alliance policy to eliminate the prisoners’ dilemma and examine when the service providers should ally. Results show that this policy can improve the profits of the service providers and the whole supply chain if the commission rate is adequately low. Our findings suggest that opaque selling can be less beneficial than previously shown and demonstrate the importance of an alliance policy.

Revealing the supply-demand relationship of urban cultural ecosystem services: The combination of open-source spatial model and topic model

Cultural ecosystem services (CES) are of significant interest due to their effectiveness in assessing the impact of ecosystem degradation on human well-being. The evaluation of spatial coordination between the supply and demand of urban CES is crucial for guiding the sustainable development of urban areas. However, current research and survey methods are time-consuming and laborious, lacking in full integration of beneficiary opinions, and insufficient in examining multiple CES in an integrated manner. In this study, we have developed a novel framework that integrates an investigation-driven approach with a natural language processing topic model to assess the supply and demand of CES and their spatial correspondence. Using the main urban area of Xi'an as a case study, we analyzed the spatial coherence of supply and demand including aesthetic, cultural, educational, recreational, and spiritual services. Our findings demonstrate that the fusion of multiple models can effectively evaluate the CES supply from a natural perspective of nature and the demand for CES from a beneficiary perspective. High-supply areas for CES are concentrated in the center region of the study area and in proximity to roads and rivers. Aesthetic service supply was higher than that of other service types, followed by recreational and spiritual services supply. Demand is notably high in the city's hinterland, with substantial interest in recreational and aesthetic services. We observed mismatches between supply and demand across service categories ranging from 33.58% to 67.64%, with supply exceeding demand in most regions. Therefore, tailored measures should be implemented to address the supply and demand mismatch of CES based on local conditions. Our research introduces a new technical tool for analyzing the supply-demand matching relationship of CES and establishes a theoretical foundation for sustainable urban management.

Thermal performance of packed-bed latent heat storage tank integrated with flat-plate collectors under intermittent loads of building heating

Packed-bed latent heat storage (PBLHS) system has been widely studied in the solar energy application area due to its properties of solving the time-space mismatch of energy supply and demand. The paper delves into the cyclic thermal performance of PBLHS tanks, seamlessly integrated with flat-plate collectors, to manage building heat loads effectively. A 1D transient mathematic model of PBLHS integrated with the solar collector for a building is established using MATLAB which is validated with the experimental results from the literature. Based on this model, the effect of different sizes, flow rates, inlet temperature thresholds and different heating months are discussed. The results show that: 1). Building heat load can have a significant influence on the thermal performance of the PBLHS system. 2). The optimal size of the tank for a 96 m2 house is 1.0*1.0 m and the optimal flow rate is 0.05 kg/s 3). The weather data can significantly affect the thermal performance of PBLHS, the storage tank heating durations for one week in different heating months are 108.4h, 73.6h,70.8h and 81.7h. These insights are pivotal for the optimal design of PBLHS tanks, paving the way for more efficient building heating solutions.

Forest Disturbance Thresholds and Cumulative Hydrological Impacts

AbstractForest disturbance threshold is defined as a critical disturbance level (e.g., % of forest cover change) in forested landscapes above which significant hydrological impacts are detected. Determining disturbance thresholds is critically important for supporting forest management to ensure the sustaining of ecological and hydrological functions. However, there are very few quantitative evaluations of forest disturbance thresholds globally. In this study, we applied a well‐tested methodology (the modified double mass curve) to derive the long‐term, continuous hydrological response curves and then to quantify forest disturbance thresholds on annual streamflow in 42 forested watersheds in British Columbia, Canada. The results show that forest disturbance thresholds for significant and cumulative hydrological impacts vary from 7% to 52% of cumulative equivalent clear‐cut area with an average of 17% or from 8% to 52% of disturbed area with an average of 19%. Climate (inter‐annual and intra‐annual) and watershed properties exert critical controls on forest disturbance thresholds. Watersheds with greater snowfall proportions (more annual precipitation falling as snow), more desynchronizations (temporal mismatching) of energy demand and water supply at the intra‐annual scale, less diverse ecosystems, and larger watershed sizes have lower forest disturbance thresholds. Given the present forest disturbance levels in the central interior of British Columbia, about half (53%) of the forested watersheds have already crossed the average disturbance threshold. These results highlight that watershed planning and management using forest disturbance thresholds must carefully consider local climate and watershed properties. The methodology can be effectively and robustly extended elsewhere around the globe.

Matching supply and demand of cooling service provided by urban green and blue space

Urban blue-green space (UBGS) has been extensively investigated for its climate regulation capability in the context of climate change. However, current efforts are limited toward the cooling effects mismatch in supply and demand of UBGS. This study adapts the framework of ecosystem supply and demand to quantify the supply-demand matching of the cooling effects of UBGS. Two indices were proposed to evaluate cooling provision and demand of UBGS: Cooling Supply Index (CSI), accounting for cooling contribution and spatial effect, and Cooling Demand Index (CDI), considering physical and social dimensions of cooling demand. Four-quadrant analysis and bivariate local indicators of spatial autocorrelation (BiLISA) analysis were employed to distinguish the supply-demand matching status: high supply with high demand, low supply with high demand, low supply with low demand, and high supply with low demand. The results show that 1) CSI and CDI yield great performance in quantifying cooling supply and demand, respectively; 2) the four-quadrant analysis and bivariate LISA analysis are complementary to each other, and can target those areas in different matching statuses for climate-sensitive planning and design; and 3) particular attention should be given to areas characterized by low supply with high demand. This study marks a crucial step toward integrating social aspects into the analysis of cooling effects provided by UBGS. It enriches the analysis framework for urban ecosystem services and provides valuable guidance for landscape planning to mitigate heat stress.

Research on day-ahead optimal dispatching of virtual power plants considering the coordinated operation of diverse flexible loads and new energy

Large-scale new energy access to the power grid poses significant challenges to its stable operation. Differentiated user-side power consumption patterns further widen peak-valley differences in power demand. This paper focuses on operation scheduling problems of virtual power plants with coordinated optimization of diverse flexible loads and new energy, through efficient aggregation and optimization control of new energy and demand-side resources, improving power supply and demand mismatch. Firstly, Long Short-Term Memory and Latin Hypercube Sampling were employed for predicting the day-ahead output of wind and photovoltaic power. Secondly, wind and photovoltaic power, batteries and a pumped storage plant were aggregated into a virtual power plant, and the day-ahead optimization scheduling model was constructed considering system operation costs, energy curtailment costs and demand response costs. Finally, a simulation analysis was conducted. The results show that when large-scale new energy accesses to the power grid, traditional “Generation varies with Load” regulation modes will cause massive energy waste, while the “Generation-Load Interaction” regulation mode can achieve the linkage optimization between the generation side and the demand side, enhancing the system acceptance of new energy. Demand response can optimize users’ power consumption behaviors, reducing the charging costs by 52.13 % and heating costs by 0.84 %.

Automated tariff design for energy supply–demand matching based on Bayesian optimization: Technical framework and policy implications

With the emergence of renewable energy sources, designing tariffs becomes crucial to match unstable energy supply with varying energy demand. However, traditional tariff design frameworks depend on a certain type of tariff and the demand response of customers, which makes designing tariffs rigid and hard to automate. To overcome the challenges, in this paper, we propose an automated tariff design framework for energy supply–demand matching based on Bayesian optimization. The proposed framework allows us to design an optimized tariff for any given objective of energy matching automatically. This automation can provide flexibility in designing tariffs for energy management. Moreover, we propose a novel matching evaluation metric focusing on the mismatch of actual energy supply–demand patterns related to the stability and reliability of energy management systems. Adopting it along with traditional economic utility, actual energy pattern matching and economic values can be deliberately balanced in the automated tariff design. Through simulations with real datasets, we demonstrate that the proposed framework can effectively design tariffs in the real world such as time-of-use and demand charge tariffs, considering energy patterns and economic costs separately. Finally, we provide potential policy implications resulting from the automated tariff design.

Approach to Liver Transplantation: Is There a Difference between East and West?

Liver transplantation (LT) remains the only curative treatment for end-stage liver disease as well as acute liver failure. With the exponential increase in organ demand due to the increasing incidence and prevalence of liver diseases, the need to overcome the supply and demand mismatch has arisen. In this review, we discuss the current universal status of LT, emphasizing various LT practices worldwide.

The analysis of performance due to impact of bullwhip effect in Covid: select Indian sector perspective

Purpose The purpose of the study is to analyze and measure the impact of disruption in demand which causes the bullwhip effect. The bullwhip effect impacts the performance of firm. Just like everything else, covid has had an impact on the disruption of supply chain too leading to the need of measuring the bullwhip effect of select Indian sectors. The comparison on bullwhip effect is drawn in pre- and during covid era in major sectors. The study helps to understand, analyze and measure the impact of covid and its challenges to supply chain. Design/methodology/approach The empirical study is carried out on five major select Indian sectors which have the largest market capitalization in Indian economy, namely, FMCG (fast-moving consumer goods), automobile, utility, consumer durable and IT (information technology). The disruption in the supply chain is measured in terms of bullwhip effect. The novel metric ratio of bullwhip effect is computed which is based on demand–supply mismatch and analyzed based on 10 years of observations. The data is analyzed twice, first from 2011 to 2019 (pre-covid era) and second from 2019 to 2021 (during covid era). Each time, Bombay Stock Exchange (BSE) sectoral indices are used to compute the bullwhip ratio, and empirical data is collected using Prowess. The firms listed in BSE represent most of the sector. Such panel data helps us to analyze inter- and intraindustry bullwhip effect. The changes in the bullwhip effect for various BSE listed firms are analyzed pre- and during covid era. These changes are specifically studied at the manufacturer end of the supply chain. Later regression analysis is performed to study the changes required in production based on the demand. The various strategies that cause or mitigate the impact of covid in intraindustry can be derived from the study. The disruption in production is analyzed based on the disruption in demand and profit before interest and tax (PBIT). Findings In pre-covid era, the percentage of demand disruption was low in select sectors but not exactly zero. Covid caused the disruptions in supply chain across the globe which resulted in bullwhip effect in Indian sectors too. Yet some of the sectors were able to cope better with the situation as compared to others. In the present study, same is analyzed statistically, and results are derived for practical significance. Research limitations/implications The empirical data is having the observations of past 10 years to analyze the pattern of demand disruption in the firms and hence the sectors. The impact of covid is studied on performance, which is analyzed in terms of PBIT. The impact of other factors (political, social, marketing policies, etc.) that may cause disruption in the supply chain of a firm is not considered in the study. Originality/value Study is unique, as it measures disruption and provides a peerless way to study the inter- and intrasectors. To analyze the impact of bullwhip effect on sector performance, it is very much required to first measure the bullwhip; this measure of bullwhip as a ratio of the slopes of demand and supply is a novel approach. The study emphasizes that the impact of covid is not the same among the firms, and hence among the sectors. Also, it is found that the impact of such adversities can be mitigated, and performance of firm can remain intact in turbulent times too.

Multiscale Ecological Zoning Management with Coupled Ecosystem Service Bundles and Supply–Demand Balance, the Case of Hangzhou, China

Grasping the interrelationship between the supply and demand of ecosystem services (ESs) and spatial scale characteristics is the foundation for effective ecological zoning management, which helps to realize a win–win situation for both ecological protection and economic development. This paper focuses on the following three real problems: mismatch in ES supply and demand evaluation, mechanical and subjective delineation of ecological zoning, and rough management strategies, and constructs a multi-scale ecological zoning management framework for the “comprehensive evaluation of supply and demand, ecological zoning, and enhancement of human well-being”. This study integrates the InVEST model, SOM, Z-score quadrant matching, and coordination degree method, and applies them to the ecological management zoning of Hangzhou. The results show that (1) the spatial differentiation of ESs in Hangzhou is significant. The spatial pattern of the five types of ES supply varies at the county scale and the grid scale on which ES demand is concentrated and is consistent at different scales. (2) ES supply–demand matching in Hangzhou is at the basic coordination and can be divided into four modes including HH, LH, LL, and HL at both the county and grid scales. On the small scale, the proportion of mismatches declines slightly, but the severity rises. (3) ES supply is divided into four categories as follows: the food production bundle, the carbon storage bundle, the ESs balancing bundle, and the ESs depleting bundle, and clarifies the priority of ES management. (4) Construct an ecological management practice path, delineates 6 ecological management zones at the county scale and 19 secondary management zones at the grid scale. Targeted measures are proposed in terms of supply–demand adjustment strategies, ecological management strategies, and key implementation areas. This study helps to incorporate the interaction between the supply and demand of ESs into the planning framework and provides decision-making support for refined ecological management.

Supply–demand effectiveness: capturing the effects of supply and demand mismatches in operational performance measurement

Supply–demand effectiveness: capturing the effects of supply and demand mismatches in operational performance measurement