Technology Cost Reduction Research Articles

Assessing the Potential of Smart Windows for Energy Efficiency in Tropical Buildings: A Review of Current Research and Future Directions

Abstract Smart windows have energy-saving potential in buildings in tropical climates. Characterized by high solar radiation, humidity, and temperature, tropical climates demand innovative solutions for energy-efficient building design. Smart windows, which can regulate the transmission of light and heat through different thermochromic, photochromic, or electrochromic technologies, are promising to reduce energy consumption in such buildings. Several emerging window technologies, such as gasochromic, hydrochromic, polymer-dispersed liquid crystal, and suspended particle device technologies, also have promising energy-saving potential. However, their high initial costs, durability, and reliability of these technologies limit their applicability. Prospects for smart windows in buildings in tropical climates include advancements in materials science, cost reduction, and integration of smart window technology with other building systems, such as lighting and heating, ventilation, and air conditioning systems. The potential benefits of smart windows for energy-saving s in buildings in tropical climates are substantial, up to 37%. Thus, further research and development in this area would lead to significant advancements in sustainable building design for a better future.

Digital technology and domestic value-added ratio in export: Evidence from China's pilot zones for integrating informatization and industrialization

Accelerating the penetration of digital technology into industrial transformation is an important concern for developing economies that dedicate to achieve high-quality development of foreign trade. Regarding China's pilot zones for integrating informatization and industrialization (PZII) as an exogenous shock of digital technology application, this study sheds light on effects of digital technology on domestic value-added ratio in export (DVAR) based on fine-grained firm-level data covering 2002–2013. Our finding reveals that digital technology improves firms’ DVAR, and the results remain consistent after conducting a series of robustness checks and addressing potential endogeneity. The heterogeneity analysis suggests that digital technology exerts significantly positive effects on DVAR of processing trade rather than general trade firms, foreign rather than domestic firms, and eastern rather than middle and western firms. The mechanism test discloses that digital technology positively affects DVAR through cost reduction effects and technology progress effects. Further investigation shows that market competition plays a positive role in moderating effects of digital technology on firms’ DVAR. Lastly, this study puts forward corresponding policy implications based on the conclusions.

Decarbonizing the US Energy System

Recent rapid and unexpected cost reductions in decarbonization technologies have accelerated the cost-effective decarbonization of the US economy, with greenhouse gas (GHG) emissions falling by 20% from 2005 to 2020. The literature on US economy-wide decarbonization focuses on maximizing long-term GHG emissions reduction strategies that rely mostly on renewable energy expansion, electrification, and efficiency improvements to achieve net-zero GHG emissions by 2050. While these studies provide a valuable foundation, further research is needed to properly support decarbonization policy development and implementation. In this review, we identify key decarbonization analysis gaps and opportunities, including issues related to cross-sectoral linkages, spatial and temporal granularity, consumer behavior, emerging technologies, equity and environmental justice, and political economy. We conclude by discussing the implications of these analysis gaps for US decarbonization pathways and how they relate to challenges facing major global emitters.

Integration between Value Engineering Technology and Activity-based Costing and their Impact on Reducing Costs: Applied Research in Baghdad Soft Drinks Company/Private Shareholding

The research mainly aims to integrate activity-based costing and value engineering and consider their role in reducing costs for industrial facilities in the face of the modern business environment characterized by many developments and rapid changes. The research problem is that traditional cost accounting methods are insufficient to provide organizational units with useful information in many administrative decisions, especially those related to product quality, to calculate costs and meet requirements correctly and can be replaced by modern cost management methods. Moreover, to find compatibility between them in a competitive environment in the public and private sectors, The study is based on the following hypotheses: There is a significant relationship between the integration of value engineering technology and activity-based costing and cost reduction, and the integration of value engineering technology and activity-based costing has a significant effect on cost reduction.

Predicting the economic feasibility of solar-based net-zero emission buildings (NZEBs) in the United States non-residential sector

Buildings have significantly contributed to high energy demand (30.3%) and GHG emissions (26.1%) worldwide. Consequently, many developed countries have set carbon-neutral targets for 2050, mandating that all new constructions thereafter be designed as net-zero emission buildings (NZEBs). To achieve this goal, one of the most practical sources of renewable energy, solar power, is employed in this study to estimate the economic feasibility of implementing NZEBs in the non-residential sector of the United States. While past studies have typically estimated the economic payback period of solar power technology by assuming fixed values for “PV energy conversion rates (%)” and “investment costs ($USD)”, this paper carefully investigated the economic feasibility of “solar-based NZEBs” by precisely tracking these dynamic future changes in PV panels. These techno-economic variables were carefully predicted using a statistical technique known as the five-parameter logistic (5 PL) function. The results show that Site 4 is the only solar region suitable for implementing “PV-integrated NZEBs”, reaching a payback period of 8.44 years in 2040 (Scenario 1). Furthermore, with small technological improvements in PV energy efficiency (%), buildings located in Site 3 and Site 4 can meet the net-zero emission target with payback periods of less than 10 years in 2034 (Scenario 2). Finally, starting in 2044, the “PV-integrated system” will have a payback period of approximately 7 years in most study locations, even without federal support for the solar investment tax credit (SITC). Accordingly, federal support for solar power generation will be more effective if the SITC rate is gradually reduced by 2.5% annually starting in 2033. In conclusion, this paper suggests the economic feasibility of implementing solar-based NZEBs in the United States non-residential sector by considering the synergetic effect of technological improvement and cost reduction in PV-integrated systems.

Clinical diagnostic value of targeted next‑generation sequencing for infectious diseases (Review).

As sequencing technology transitions from research to clinical settings, due to technological maturity and cost reductions, metagenomic next‑generation sequencing (mNGS) is increasingly used. This shift underscores the growing need for more cost‑effective and universally accessible sequencing assays to improve patient care and public health. Therefore, targeted NGS (tNGS) is gaining prominence. tNGS involves enrichment of target pathogens in patient samples based on multiplex PCR amplification or probe capture with excellent sensitivity. It is increasingly used in clinical diagnostics due to its practicality and efficiency. The present review compares the principles of different enrichment methods. The high positivity rate of tNGS in the detection of pathogens was found in respiratory samples with specific instances. tNGS maintains high sensitivity (70.8‑95.0%) in samples with low pathogen loads, including blood and cerebrospinal fluid. Furthermore, tNGS is effective in detecting drug‑resistant strains of Mycobacterium tuberculosis, allowing identification of resistance genes and guiding clinical treatment decisions, which is difficult to achieve with mNGS. In the present review, the application of tNGS in clinical settings and its current limitations are assessed. The continued development of tNGS has the potential to refine diagnostic accuracy and treatment efficacy and improving infectious disease management. However, further research to overcome technical challenges such as workflow time and cost is required.

Study on Design and Fabrication of 4 Wheel Steering Mechanism

This study focuses on the design and fabrication of a four-wheeled steering (4WS) mechanism, a technology that enables all four wheels of a vehicle to turn, thus enhancing control and manoeuvrability. Four-wheel steering allows for significant improvement in a vehicle's turning circle at low speeds by enabling the rear wheels to turn in the opposite direction to the front wheels. At high speeds, this system contributes to increased stability and a reduced risk of over steer. Despite these advantages, 4WS systems are complex and expensive, which has limited their prevalence in the automotive market. The Honda Prelude, introduced in 1987, was the first car equipped with 4WS. However, the technology has not been widely adopted, largely due to driver unfamiliarity with the control dynamics at low speeds. This research aims to explore the potential benefits of 4WS and assess its future adoption prospects as technological advancements and cost reductions are realized. Through detailed analysis and experimental fabrication, this study seeks to contribute to the understanding and potential application of 4WS in modern vehicles.

Green hydrogen prospects in Peninsular Malaysia: a techno-economic analysis via Monte Carlo simulations

According to Malaysia's National Energy Transition Roadmap, hydrogen is a critical component of the country's energy transition. However, there is a scarcity of hydrogen studies for Peninsular Malaysian states, which limits discussions on green hydrogen production. This study employs a Monte Carlo model to assess the economic and technical factors influencing the success of green hydrogen in Peninsular Malaysia. The study focuses on three target years: 2023, 2030, and 2050, representing various stages of technological development and market adoption. The levelized cost of hydrogen (LCOH) of a 1-MW Proton Exchange Membrane (PEM) electrolyzer system ranges from $5.39 to $10.97 per kg in 2023, highlighting early-stage challenges and uncertainties. A 6-MW PEM electrolyzer system could achieve an LCOH of $3.50 to $4.72 per kg by 2030, indicating better prospects. Because of technological advancements and cost reductions, a 20-MW PEM electrolyzer system could achieve an LCOH of $3.12 to $3.64 per kg in 2050. The findings indicate that the northern regions of Peninsular Malaysia have consistently low LCOH values due to favorable geographical conditions. Due to minor variations in solar capacity factors, uncertainty distributions in LCOH remain stable across different regions. Some states may face increased uncertainty, emphasizing the need for additional policy support mechanisms to mitigate risks associated with green hydrogen investments. The sensitivity analysis shows that key cost drivers are shifting, with early-stage electrolyzer investments dominating in 2023 and electricity prices becoming more important in 2030 and 2050. Future research could focus on optimizing green hydrogen systems for areas with underdeveloped green hydrogen industries. This study contributes to informed discussions about green hydrogen production by emphasizing the importance of tailored strategies that consider local conditions and highlighting the role of Peninsular Malaysia in the energy transition.

Economic effects of sustainable energy technology progress under carbon reduction targets: An analysis based on a dynamic multi-regional CGE model

Advancing sustainable energy technologies (SET) is crucial for achieving carbon neutrality and effectively addressing climate change. Unlike most studies that focus on individual sustainable energy policies, this research constructs a recursive computable general equilibrium (CGE) model with comprehensive sustainable energy modules to analyze the macroeconomic effects of multiple policies related to SET advancement. Covering 8 regions, 13 energy sectors, and 37 intermediate input sectors, the study evaluates the economic and ecological impacts of various SET advancement scenarios from a global economic perspective, aiming for consistent carbon reduction. The findings reveal that : (1) SET implementation reduces electricity costs for residents, optimizes industrial structure, decreases fossil fuel usage, enhances energy use structure, and lowers carbon taxes; (2) the collective impact of renewable energies surpasses that of individual sources, with newer renewables like wind and solar power outperforming traditional ones such as hydropower and nuclear power; and (3) advancements in alternative energy technology have had diverse effects on the global energy environment and economic development. Based on these conclusions, we recommend that: (1) prioritizing the development of solar and wind power in the short term; (2) optimizing SET advancement mechanisms to support progress, including enhancing the market-based price formation for sustainable energy generation and promoting technological innovation and cost reduction; (3) encouraging a synergistic transformation of renewable energy with coal, gas power, and other sources to maximize policy synergies.

Techno-economic analysis of the effect of a novel price-based control system on the hydrogen production for an offshore 1.5 GW wind-hydrogen system

The cost of green hydrogen production is very dependent on the price of electricity. A control system that can schedule hydrogen production based on forecast wind speed and electricity price should therefore be advantageous for large-scale wind-hydrogen systems. This work presents a novel price-based control system integrated in a techno-economic analysis of hydrogen production from offshore wind. A polynomial regression model that predicts wind power production from wind speed input was developed and tested with real-world datasets from a 2.3 MW floating offshore wind turbine. This was combined with a mathematical model of a PEM electrolyzer and used to simulate hydrogen production. A novel price-based control system was developed to decide when the system should produce hydrogen and when it should sell electricity to the grid. The model and control system can be used in real-world wind-hydrogen systems and require only the forecast wind speed, electricity price and selling price of hydrogen as inputs. 11 test scenarios based on 10 years of real-world wind speed and electricity price data are proposed and used to evaluate the effect the price-based control system has on the levelized cost of hydrogen (LCOH). Both current and future (2050) costs and technologies are used, and the results show that the novel control system lowered the LCOH in all scenarios by 10–46%. The lowest LCOH achieved with current technology and costs was 6.04 $/kg H2. Using the most optimistic forecasts for technology improvements and cost reductions in 2050, the model estimated a LCOH of 0.96 $/kg H2 for a grid-connected offshore wind farm and onshore hydrogen production, 0.82 $/kg H2 using grid electricity (onshore) and 4.96 $/kg H2 with an off-grid offshore wind-hydrogen system. When the electricity price from the period 2013–2022 was used on the 2050 scenarios, the resulting LCOH was approximately twice as high.

Distributed control of energy storages for multi-time-step peak load shaving in a microgrid

Distributed energy storages (ESs) will be widely used in the future smart microgrids due to their continuous technology improvement and cost reduction. Meanwhile, the distributed control method also gains increasing attention for controlling distributed devices to protect user privacy and reduce computation burden compared with the centralized scheme. In this paper, a distributed control method of ESs is proposed for multi-time-step peak load shaving in a microgrid. Considering the ES efficiency is related to its power, an optimization is constructed to minimize the power loss during ES operations when performing peak load shaving function. By analyzing the net load curve and the ES available capacity, the multi-time-step peak load shaving problem is transformed into single-time-step ES operations at each time step. The Combine-Then-Adapt diffusion strategy is united with the consensus + innovation strategy to realize the peak load shaving in a fully distributed way. Case studies verify the efficiency of the proposed method.

Pneumonia detection based on RSNA dataset and anchor-free deep learning detector.

Pneumonia is a highly lethal disease, and research on its treatment and early screening tools has received extensive attention from researchers. Due to the maturity and cost reduction of chest X-ray technology, and with the development of artificial intelligence technology, pneumonia identification based on deep learning and chest X-ray has attracted attention from all over the world. Although the feature extraction capability of deep learning is strong, existing deep learning object detection frameworks are based on pre-defined anchors, which require a lot of tuning and experience to guarantee their excellent results in the face of new applications or data. To avoid the influence of anchor settings in pneumonia detection, this paper proposes an anchor-free object detection framework and RSNA dataset based on pneumonia detection. First, a data enhancement scheme is used to preprocess the chest X-ray images; second, an anchor-free object detection framework is used for pneumonia detection, which contains a feature pyramid, two-branch detection head, and focal loss. The average precision of 51.5 obtained by Intersection over Union (IoU) calculation shows that the pneumonia detection results obtained in this paper can surpass the existing classical object detection framework, providing an idea for future research and exploration.

Higher-speed PONs based on data center technology and optics [Invited

Data center interconnects are currently the fastest growing market for optical interconnect technology and innovations. Data centers must upgrade their infrastructure regularly to keep up with increasing bandwidth demand from digital services. Successive Ethernet generations based on intensity-modulated direct-detection progressed to 50 GBd per lane and will reach 100 GBd per lane before the end of this decade. Also, the continued reduction in complexity, power consumption, and cost of coherent technologies will eventually lead to adoption of it in short-reach coherent in the intra-data center, which will further drive volumes of low-cost coherent technologies. Passive optical networks (PONs) depend mostly on the availability of high-volume, low-cost components. In this paper we will review the benefits as well as challenges to overcome in adoption of the Ethernet data center ecosystem as a low-power, cost-effective technology for next-generation time-division-multiplexed PONs.

Investigating the Impact of New Energy Policy on the Market for New Energy Vehicles

As global concerns about environmental sustainability continue to rise, electric vehicles (EVs) are gradually becoming a significant component of the global automotive market as a clean and low-carbon transportation option. This study, through a review of relevant literature and an analysis of market data, examines the influence of new energy policies on the international market for electric vehicles. The research findings indicate that the market share of electric vehicles has steadily increased over the past few years. Government environmental policies and incentive measures have played a crucial role in promoting electric vehicles. Furthermore, ongoing technological advancements and cost reductions have also contributed to the widespread adoption of electric vehicles. However, electric vehicles face certain challenges, such as inadequate charging infrastructure and limitations in battery technology. In the future, with further technological developments and policy support, electric vehicles are poised to play a more significant role in the international market and contribute to global sustainability efforts.

Identifying Key Drivers for a National Transition to Low Carbon Energy using Agent-based Supply Chain Models

Understanding robust pathways to achieve affordable, reliable, and equitable energy transitions to a decarbonized society has received growing attention. While existing energy transition literature emphasizes the role of government policy and technological innovation, little has been written to integrate “top-down” approaches encompassing government policy and “bottom-up” individual enterprise-level decisions. Therefore, here we develop a hybrid top-down, bottom-up production consumption model towards intra-national energy transition planning using a decentralized agent-based approach, including a detailed geographic coverage utilizing GIS to map individual energy units. This enables considering SC-level dynamics of end-users pursuing economic objectives subject to regulatory constraints. Two different case studies are reported. The first study reveals that green hydrogen consumption in the Indian state of Tamil Nadu may grow only up to 3% by 2042 from the current nil usage, even with aggressive technological cost reductions and a fixed carbon price of $20/ton CO2, but demand could grow more rapidly if stringent criteria pollutant emissions limits are imposed. In the second case study, which considers natural gas usage across India, the power and city gas sectors are found to be the consistent demand drivers, with their share rising from ∼40% of the total in 2022 to ∼60% by 2032. This is consistent with IEA and IRENA projections. The proposed agent-based model thus provides multiple benefits: It enables the identification of key sectors driving the energy transition; it helps analyze critical factors like resource availability, supply chain infrastructure, and economics; and it can be used to develop decision support tools to investigate clean energy policies.

Assessment of Floating Photovoltaic (FPV) Systems as an Alternative Electricity Generation Source: A Case Study from Sudan

The deployment of large-scale photovoltaic (PV) systems has gained global attention due to technological advancements and cost reductions. Floating PV (FPV) is becoming a preferred alternative, offering efficiency and environmental benefits. It can be installed on dams lakes and reservoirs, generating clean energy without competing for land use. Despite the promising prospects of floating PV, there has been a lack of analysis on its implementation in Sudan. To fill this gap, the present study delves into the feasibility and potentiality of FPV at Merowe Dam and Khashm El Girba Dam in Sudan, providing crucial insights for the first time. The developed systems were evaluated using PVsyst. The results show that investing in FPV systems on dam reservoirs significantly improves power system reliability while generating additional clean energy, meeting the rising demand, and reducing carbon emissions. This highlights the potential of FPV systems as a promising solution for sustainable energy growth, mitigating environmental concerns, and ensuring a greener future.

Projections of the costs of medium- and heavy-duty battery-electric and fuel cell vehicles (2020-2040) and related economic issues

This study investigates the economic viability of various medium- and heavy-duty battery-electric and hydrogen fuel cell vehicles compared to their diesel equivalents from 2020 to 2040. Using the ADVISOR simulation program, performance and energy consumption were evaluated under unique road, load, and design parameters, adjusted for projected technological advancements. The initial cost, total cost of ownership (TCO), and payback periods were calculated for each vehicle class over 5-year and 15-year periods. Notably, due to technology cost reductions, both battery-electric and fuel cell vehicles witness significant cost decreases between 2020 and 2030 and a slower reduction from 2030 to 2040. Given stable electricity prices, declining hydrogen prices, and slightly rising diesel prices, the TCO of electrified trucks typically undercuts diesel trucks before their purchase costs align. By 2025, most battery-electric trucks achieve TCO competitiveness, with payback periods reducing to <4 years by 2030. However, fuel cell vehicles face longer payback periods due to high hydrogen fuel costs. By 2030, if hydrogen cost falls below $7/kg, fuel cell trucks can achieve 3–5-year payback periods, and fuel cell buses can recoup costs within a year. By 2030, both battery-electric and hydrogen fuel cell trucks are expected to be competitively priced against diesel vehicles, assuming a continued decrease in battery and fuel cell costs coupled with moderate electricity and hydrogen prices. Sensitivity analysis confirmed the robustness of these conclusions despite reasonable fluctuations in major cost variables.

The future of road freight transport and alternative technologies: A case study for Italy

The truck sector produces one-fourth of the greenhouse gas emissions in Italy. Therefore, it calls for an urgent emission reduction to achieve the national mid-century net-zero pledges. In this work, a methodology is developed, based on a model for estimating the long-term energy transitions, the ModUlar energy system Simulation Environment (MUSE), to project the long-term freight heavy-duty sector evolution in Italy. The methodology applies a bottom-up description of the sector, where technologies are characterised in terms of costs, efficiencies, and emissions. Results show that efficiency improvements and technological cost reduction due to learning can only reduce the sector emissions by a limited amount. In fact, although, lowering costs of alternative powertrains and fuels will have a significant effect on reducing tailpipe emissions, this should occur alongside a ban of internal combustion engines. Additional renewable generation should need to grow between 30 and 64 TW/y to substantially reduce emissions and leading to a zero-tailpipe truck sector by 2050. The replacement of conventional engines by alternative powertrains remains dependent on distribution and fuelling infrastructure expansion, which should go hand in hand with the promotion of electric and hydrogen-fuelled vehicles.

Offshore wind power in the Asia-Pacific: Expert elicitation on costs and policies

Offshore wind power is an important technology option for decarbonising the electricity sector. An emerging region for the deployment of offshore wind is the Asia-Pacific. We conduct an expert elicitation of future cost expectations for offshore wind in the Asia-Pacific region, covering fixed-bottom and floating offshore wind technologies. We also examine views on policies that support the more rapid cost reductions for fixed bottom and floating offshore wind. We find expectations of decreases in the average levelized cost of electricity (LCOE) to USD72/MWh in 2040 (fixed-bottom) and USD81/MWh in 2050 (floating). The largest factors contributing to falling technology costs are identified as installation costs and capital costs for turbines and foundations, along with increased capacity factors. The creation of policy targets along with improved regulatory streamlining are identified as important policy measures that governments could implement to support technology cost reductions. We also find that preferred policy mixes depend on the level of technological maturity. Competitive tendering processes are identified as important for both technologies, but floating offshore wind auctions should be introduced after technology costs have fallen. In the near term, other policies are identified as more important for floating offshore technologies.

New models for feasibility assessment and electrolyser optimal sizing of hydrogen production from dedicated wind farms and solar photovoltaic farms, and case studies for Scotland and Vietnam

Although green hydrogen produced from dedicated wind and PV plants is considered an alternative route to achieve net zero targets, its assessment models are only being developed in recent years. Even though electrolyser is known to account for a large portion of the investment costs, there is still a lack of sizing models for this equipment, which can negatively affect the feasibility of plants. Moreover, the impacts of future technology maturity and cost reduction on the feasibility of hydrogen production plants are rarely analysed and reported. Realising the research gaps from previous studies, this paper proposes a comprehensive feasibility assessment model for green hydrogen production from dedicated offshore wind and PV plants, utilising net present value, discounted payback time, and levelised cost of energy and of hydrogen (LCOH) as economic evaluation criteria. Furthermore, a unique sizing model is presented, allowing the optimal electrolyser size with the lowest LCOH to be found. Two case studies investigating a hypothetical offshore wind farm in Scotland and a hypothetical PV farm in Vietnam to produce green hydrogen have been carried out, in which the impacts of technology maturity and cost reduction on the LCOH are analysed. It is projected that the LCOH in Scotland and Vietnam case studies, with specific conditions and assumptions of the two cases, would reduce from 3.10 and 3.05 $/kg in 2030 to the lowest values of 2.16 and 1.59 $/kg in 2050, respectively. Additionally, the optimal electrolyser capacity is found to equal 89.3% of the offshore wind plant capacity, and 57.7% of the PV plant capacity. Finally, a sensitivity analysis is conducted, which revealed the capital cost of renewable energy plants has the most significant effect on the final LCOH.