Drivers Of Efficiency Research Articles

Study on the spatio-temporal coupling and drivers of agricultural carbon emission efficiency and food security

In light of global climate change and environmental challenges, reducing agricultural carbon emissions while maintaining food security has become a critical concern for sustainable agricultural development. This study examines 13 provinces in primary grain-producing regions, utilising data from 1999 to 2022 to analyse the spatio-temporal heterogeneity and driving factors of the coupled and coordinated development of agricultural carbon emission efficiency and food security. The findings indicate that: 1) The proportion of carbon emissions from agricultural inputs in primary grain-producing regions exceeds 80%, whereas the contribution of carbon sinks from rice, maise, and wheat surpasses 95%. Both agricultural carbon emissions and carbon sinks in the Yellow River Basin are significantly elevated; 2) The general trend of agricultural carbon emission efficiency and food security is increasing, spatially characterised by a pattern of high levels in the north and south, and low levels in the south and high levels in the north, respectively. The integration of the two systems is progressing positively, and the trailing form of food security has emerged as a developmental trend, with the degree of food security limiting the coordinated advancement of both. 3) The overall spatial disparity exhibits a declining tendency, with hypervariable density being the primary contributor to this spatial difference. The overall polarisation of the primary grain-producing regions has diminished. The level of heterogeneity in the Songhua River basin progressively intensifies; 4) Rural human capital and financial support for agriculture, urbanisation rate and soil erosion control, agricultural machinery power and soil erosion control are the main interaction factors. The economic status and rural human capital will facilitate the integrated and harmonious development of the two systems, while the agricultural disaster rate will impede this integrated and harmonious development. Ultimately, policy solutions are proposed to optimise agricultural inputs and raise their utilisation efficiency, adopt diversified regional development strategies, promote regional connection and coordinated development, and reinforce the management of drivers and policy support.

Finding Critical Factors for Developing Dynamic Models to Optimize Greenhouse Solar Dryer’s Environmental Conditions

This paper is focused on the formulation of a mathematical model for greenhouse solar dryer systems in the context of determining the principal drivers of efficiency of the solar dryer. The model was built by evaluating the correlation coefficients of each term and only those terms which had high levels of significance were retained. To test this model, experiments were replicated with measurements being taken in the different sections of the greenhouse to provide a large spectrum of the environmental conditions involved. The values of the technical coefficients are predicated on internal air temperature, humidity ratio and weight of the product with fixed scales, avoiding sophisticated complex models prone to overfitting. By using terms with high correlation, the model is less likely to be affected by outliers and total correlation or cross correlation is low, thereby making the model practical. The outcomes of this research, therefore, offer a scientifically viable and a practical approach to enhancing drying processes in greenhouse solar dryer systems.

Balancing Water Yield and Water Use Efficiency Between Planted and Natural Forests: A Global Analysis.

Climate warming is projected to affect hydrological cycle in forest ecosystems and makes the forest-water relationship more controversial. Currently, planted forests are gaining more public attention due to their role in carbon sequestration and wood production relative to natural forests. However, little is known about how the global patterns and drivers of water yield and water-use efficiency (WUE) differ between planted and natural forests. Here, we conduct a global analysis to compare water yield and WUE in planted and natural forests using 946 observations from 112 published studies. The results showed that global average water yield coefficient was 0.29 for planted forests and 0.34 for natural forests. Planted forests exhibited lower water yield coefficient (p < 0.05) in three climatic regions (arid, dry subhumid, and humid regions), but higher (p < 0.01) WUE only in arid region, compared with natural forests. Both water yield coefficient and WUE in planted forests were significantly lower (p < 0.05) than that in natural forests for stand characteristic groups (stand density, average tree height, leaf area index [LAI], and basal area). Additionally, stand density within the ranging between 1000 to 2000 stem ha-1 can maximize the water yield and WUE in planted and natural forests. Water yield coefficient in planted forests was primarily controlled by the factors related to tree growth (i.e., tree height, DBH), while that of natural forest mainly affected by stand structure (i.e., LAI, stand density, DBH). WUE in planted forest was more sensitive to climate than in natural forests. This work highlights the critical role of natural forests in water supply and the importance of tree species selection and stand management (e.g., stand density adjustment) in plantations in future forest restoration policies and climate change mitigation.

OPTIMIZING ENERGY EFFICIENCY: A COMPREHENSIVE ANALYSIS OF BUILDING DESIGN PARAMETERS

This study explores the critical role of financial incentives, energy performance certifications, government policies, and advanced technologies in driving the adoption of energy-efficient building practices. Financial incentives, such as tax credits, rebates, and grants, are shown to significantly reduce the upfront costs associated with energy-efficient technologies, making these innovations more accessible to developers and building owners. The research highlights the effectiveness of energy performance certifications, like LEED and Energy Star, in enhancing the market value and energy performance of buildings by providing a structured framework for evaluating sustainability. Government policies and building codes, including the International Energy Conservation Code (IECC) and ASHRAE standards, are identified as key drivers of energy efficiency, compelling developers to meet stringent performance standards. Additionally, the integration of renewable energy technologies, such as solar panels and geothermal heat pumps, alongside smart building automation systems, plays a vital role in reducing energy consumption and improving operational efficiency. However, regional disparities in the availability of financial incentives and enforcement of policies present challenges to widespread adoption. The study concludes that expanding financial support, strengthening policy enforcement, and increasing public and industry awareness of the long-term financial and environmental benefits of energy-efficient buildings are essential for accelerating the transition to sustainable building practices. By addressing these challenges, the construction industry can make significant strides toward reducing energy consumption, lowering greenhouse gas emissions, and achieving global sustainability goals.

Exploring the Impact of Innovation practices, Technologies on the Operational Efficiency of Commercial Banks in Nigeria

In the rapidly evolving financial landscape, innovation has become a critical driver of operational efficiency for commercial banks. This study explores the impact of technological and process innovations on the operational efficiency of commercial banks in Nigeria. It aims to understand how innovative practices and technologies influence cost reduction, service delivery, customer satisfaction, and overall financial performance. Using a straightforward random sampling technique and a descriptive survey design, the study selected 388 participants at random from the 97,026 commercial bank employees in Calabar Metropolis, Cross River State, who make up the entire population. The research employs a mixed-methods approach, combining quantitative data analysis from financial reports of major Nigerian banks with qualitative insights from interviews with banking professionals. The findings indicate that the adoption of digital banking platforms, automated teller machines (ATMs), mobile banking applications, and advanced data analytics significantly enhance operational efficiency. These innovations reduce transaction costs, minimize human error, and improve service speed and reliability. Furthermore, process innovations such as streamlined loan processing and automated compliance checks contribute to more efficient banking operations. The study highlights the high cost of technology implementation, cybersecurity risks, and the need for continuous staff training as the challenges that may hinder innovation to strive. Despite these challenges, the positive impact of innovation on operational efficiency is evident, suggesting that continued investment in innovative technologies and practices is crucial for the competitive advantage and sustainability of commercial banks in Nigeria. The study further recommends that Nigerian commercial banks prioritize technology adoption and process improvements to enhance their operational capabilities and meet the dynamic needs of their customers.

Unlocking the Synergy: Increasing productivity through Human-AI collaboration in the industry 5.0 Era

The prevailing trajectory of technological evolution emphasizes the sustainable development of human-AI collaboration. In this study, we employ the coupling coordination degree model to evaluate the dynamics of human-AI collaboration in China and match it with listed companies. Through panel models, the study not only quantifies the contribution of such collaboration to enhancing company input–output efficiency but also explores how it serves as a catalyst for technological catch-up. Our findings indicate that the integration of human capital with AI emerges as a potent driver of company efficiency, with the extent of the impact also tied to organizational characteristics. Furthermore, the scale of investment and organizational size play a crucial role in the effectiveness of HIC, underscoring the adaptability of human-AI collaboration strategies to various organizational contexts and the importance of tailored implementation. Our research highlights the inherent collaborative potential of AI within the Industry 5.0 framework, advocating for the fusion of human creativity with AI precision to foster a development paradigm that is resource-efficient, cost-effective, and human-centric.

The Effect of Work-Life Balance, Product Branding, and Use of Fintech Technology on Operational Efficiency of Start-ups in Yogyakarta

This study examines the effects of work-life balance, product branding, and fintech technology on the operational efficiency of start-up companies in Yogyakarta. A quantitative approach was employed, with data collected from 80 respondents using a structured questionnaire and analyzed with SPSS version 26. The results indicate that all three variables have significant positive effects on operational efficiency. Product branding had the strongest impact, followed by fintech technology and work-life balance. The findings suggest that start-up managers can enhance operational performance by prioritizing employee well-being, investing in effective branding strategies, and adopting fintech solutions. These insights contribute to the understanding of key drivers of operational efficiency in the start-up ecosystem and offer practical implications for improving start-up sustainability.

Economic sanctions and energy efficiency: Evidence from Iranian industrial sub-sectors

Improving energy efficiency is vital for reducing energy consumption and can have substantial impacts on reducing carbon emissions. This study investigates the impact of sanctions on Iran's energy efficiency across different industrial sub-sectors from 2015 to 2019. We compute a sanction intensity index for each industrial sub-sector using principal component analysis. This index measures how much each sub-sector has been affected by sanctions. Additionally, energy efficiency is measured using the directional distance function method, considering the emission impacts as undesirable outputs. We use fixed effects regression analysis, adjusting for various manifestations of cross-sectional (spatial) and temporal dependence and controlling for other sectoral drivers of energy efficiency. We find a significant negative effect of sanction intensity on energy efficiency at the industrial sub-sector level in Iran. The negative effect of sanctions on energy efficiency is significantly larger for industrial units with higher import dependency.

Beyond skyline and borders: Unraveling the evolution and drivers of tourism green development efficiency in the Beijing–Tianjin–Hebei urban agglomeration, China

Beyond skyline and borders: Unraveling the evolution and drivers of tourism green development efficiency in the Beijing–Tianjin–Hebei urban agglomeration, China

Efficiency analysis of the innovation-driven sustainable logistics industry

The logistics industry is at the heart of socio-economic development. Disruption in the logistics and supply chain industry grounded many sectors to a halt at the height of COVID-19 with economic and environmental implications. This study examines the efficiency and interconnections among innovation, energy, infrastructure, and logistics performance as the logistics industry strives to attain a green supply chain while maintaining economic growth and environmental sustainability. To capture these relationships, a network data envelopment analysis model is designed for efficiency analysis. 36 % of the evaluated countries had below-average efficiency. Results also indicate innovation and energy sustainability as drivers of efficiency and sustainability in the logistics industry. This implies the need for innovation in logistics energy infrastructure by decarbonizing the logistics industry. Regional and national partnerships in the technological development of green energy to power the logistics industry are recommended.

Optimizing logistics and supply chain management through advanced analytics: Insights from industries

This review paper explores the transformative role of advanced analytics in optimizing logistics and supply chain management, offering insights into industry applications, best practices, and future trends. As global supply chains become increasingly complex, integrating advanced analytics—encompassing data mining, predictive analytics, machine learning, and big data—has emerged as a critical driver of efficiency, cost reduction, and enhanced decision-making. The paper discusses how various industries, including manufacturing, retail, healthcare, and transportation, leverage advanced analytics to address specific challenges and improve overall supply chain performance. Additionally, it highlights the best practices for successful implementation, such as aligning analytics initiatives with business objectives, investing in the right technology infrastructure, and fostering a culture of data-driven decision-making. The paper also addresses the challenges and barriers to implementation, including technological, organizational, and regulatory hurdles. Finally, it examines emerging technologies like AI, IoT, and blockchain, poised to revolutionize supply chain management further. It identifies key opportunities for growth in areas such as sustainability, risk management, and customization. The paper concludes by emphasizing the importance of advanced analytics in shaping the future of logistics and supply chain strategies, offering recommendations for industry stakeholders to harness these technologies effectively.

Random forest machine learning for maize yield and agronomic efficiency prediction in Ghana

Maize (Zea mays) is an important staple crop for food security in Sub-Saharan Africa. However, there is need to increase production to feed a growing population. In Ghana, this is mainly done by increasing acreage with adverse environmental consequences, rather than yield increment per unit area. Accurate prediction of maize yields and nutrient use efficiency in production is critical to making informed decisions toward economic and ecological sustainability. We trained the random forest machine learning algorithm to predict maize yield and agronomic efficiency in Ghana using soil, climate, environment, and management factors, including fertilizer application. We calibrated and evaluated the performance of the random forest machine learning algorithm using a 5 × 10-fold nested cross-validation approach. Data from 482 maize field trials consisting of 3136 georeferenced treatment plots conducted in Ghana from 1991 to 2020 were used to train the algorithm, identify important predictor variables, and quantify the uncertainties associated with the random forest predictions. The mean error, root mean squared error, model efficiency coefficient and 90 % prediction interval coverage probability were calculated. The results obtained on test data demonstrate good prediction performance for yield (MEC = 0.81) and moderate performance for agronomic efficiency (MEC = 0.63, 0.55 and 0.54 for AE-N, AE-P and AE-K, respectively). We found that climatic variables were less important predictors than soil variables for yield prediction, but temperature was of key importance to yield prediction and rainfall to agronomic efficiency. The developed random forest models provided a better understanding of the drivers of maize yield and agronomic efficiency in a tropical climate and an insight towards improving fertilizer recommendations for sustainable maize production and food security in Sub-Saharan Africa.

Technological catch-up, nonmonotonicity, and convergence: Parametric evidence from the BRICS and European banking systems

IT-driven productivity growth offers banks an intriguing opportunity to differentiate their offerings in a monopolistic market, introduce attractive products, enhance customer service, streamline back-office processes, and ultimately achieve the twin goals of cost minimization and profit maximization. However, there has been no convincing explanation for the observed divergence in the IT-productivity literature, raising doubts about whether IT can significantly improve performance in contemporary banking markets. The article examines the role of IT-led productivity growth in governing the direction and magnitude of intra-industry and inter-country convergence by investigating the potential nonmonotonicity of IT in influencing frontier-based efficiency of the BRICS and European banking markets. Findings reveal U-shaped associations between IT and cost (profit) efficiency, suggesting that excessive investment in IT may explain the productivity conundrum for BRICS nations. IT capital is not a significant driver of cost efficiency for European nations. Nevertheless, R&D spending significantly influences frontier efficiency, reinforcing that European banks can achieve frontier-level performance by investing in innovative solutions. Inter-regional comparisons reveal that BRICS banks are converging with their European counterparts by leveraging IT solutions, while diminishing marginal benefits for the latter reinforces the presence of a catch-up effect. Intra-industry comparisons reveal that size, age, and R&D intensity drive technological catch-up and convergence.

Spatial differentiation and driving factors of scientific and technological innovation efficiency under the background of the digital economy

Scientific and technological innovation (STI) activities are facilitated under the influence of the digital economy and depend on digital elements to support innovation activities. This study measures digital element inputs and outputs using data related to the digital economy and estimates STI efficiency in 29 provincial regions in mainland China from 2010 to 2021 employing the global super-efficiency slacks-based measure (SBM) model. Then, spatial characteristics and driving forces of STI efficiency are quantified using spatial autocorrelation and geographic detectors. The results reveal that STI efficiency exhibits a fluctuating, W-shaped pattern, and the spatial correlation effect shows a gradually declining trend from 2010 to 2021. The findings reveal that the core drivers of STI efficiency are digital financial development, e-commerce development, digital access level, and related factors. Therefore, strengthening inter-regional innovation links, promoting the construction of 5 G mobile internet and accelerating the integration of digital industries into traditional industries are recommended.

Expand or not? Trade-offs of the remanufacturer on recycling channels

The integration of online trading has emerged as a crucial driver of operational efficiency in the reverse supply chain. This study expands the recycling model to include three distinct scenarios: basic-offline recycling (BR), single-platform recycling (SR), and dual-platform recycling (DR). The research delves into the implications of varying platform numbers, profit-sharing contracts, consumer recycling preferences, and recycling price adjustment speeds on supply chain stability within a multi-period decision-making framework. Findings reveal that the SR model is not always yield higher profits for remanufacturers through lower revenue allocation proportions to platforms, in stark contrast to the DR model. Furthermore, rapid decision adjustments do not consistently enhance the stability of the recycling system across multiple decision cycles. Remanufacturers' expansion strategies are significantly impacted by the costs associated with acquiring e-waste from individual recyclers. Importantly, an excess of platforms does not always result in favorable outcomes. Conversely, the SR model proves superior to the DR model in practical contexts that may arise. These insights offer valuable guidance to remanufacturers seeking to develop viable expansion strategies that foster mutually beneficial relationships among individual recyclers, remanufacturers, and platforms.

Artificial intelligence as a driver of efficiency in air passenger transport: A systematic literature review and future research avenues

Despite the claim that artificial intelligence (AI) has the potential to increase efficiency in and for airlines, current literature is limited concerning models and frameworks to assess AI applications and their implications for airline efficiency. In response, we a) conceptualize and propose an AI-Airline-Efficiency-Model (AAEM) that allows for a more structured management approach for a systematic review and analysis of existing literature, and b) present a framework explicating the identified areas of AI application for airline efficiency based on a the AAEM model. In particular, using the four AI elements Machine Learning, Deep Learning, Reinforcement Learning and Natural Language Processing and their applications within six identified airline departments, we systematically review and analyze key attributes and characteristics of both AI and airline efficiencies to critically assess current research efforts. We found that AI applications are built around four overarching improvement areas predictive analytics, resource optimization, safety & autonomous processes and passenger experience, but lack a cross-department and inter-organizational focus and are often theoretical in nature. This study provides insight into most prevalent AI applications and the less popular applications applied in and for passenger transport, thereby presenting the dominating AI techniques that are covered by existing literature as well as highlighting a wide range of emerging AI techniques with the potential to become more influential for future studies. We discuss theoretical and managerial implications and offer avenues for future research.

AI as a Driver of Efficiency in Waste Management and Resource Recovery

AI as a Driver of Efficiency in Waste Management and Resource Recovery

Process optimization and economic evaluation of continuous multi-column capture for monoclonal antibody – A case study

In recent years, due to significant improvements achieved in upstream processing of monoclonal antibody (mAb) manufacturing, the focus on productivity and cost-effectiveness has shifted towards downstream processing. Protein A affinity chromatography has been widely employed as the capture step for mAb purification and serves as a key driver of economic efficiency on downstream operations. To enhance the purification process and alleviate manufacturing constraints while reducing costs, continuous processing, such as multi-column capture (MCC), has gained attention. This study aimed to establish and optimize the protein A MCC process and to evaluate it with traditional single-column capture mode. Through a series of verification runs, it was determined that there was no significant difference in yield and quality attributes across different loops. Furthermore, annual cost of goods (COGs) was evaluated with the conventional single-column batch process. The results showed that MCC process reduced operation cost by 18 % and almost doubled productivity in the specific scenario.

Digital Transformation and Environmental, Social, and Governance Performance from a Human Capital Perspective

The strategic adoption of digital technologies has increasingly been recognized as a crucial driver of cost reduction and operational efficiency in enterprises. It optimizes production processes and promotes sustainable growth. In this context, understanding the specific impact of digital transformation on enterprises’ environmental, social, and governance (ESG) performance holds significant practical value for promoting sustainable development in China’s economy and society. This study focused on Chinese A-share listed enterprises from 2010 to 2022, specifically exploring the role of digital transformation in enhancing ESG performance from the perspective of human capital. Our findings reveal that digital transformation significantly augments their ESG performance. Notably, the improvements are more pronounced in non-state-owned enterprises compared to state-owned ones. Specifically, digital transformation initiatives contribute to ESG performance enhancement by increasing the extent of high-quality labor and elevating the skill levels of the existing workforce. Furthermore, environmental regulation moderates the positive impact of corporate digital transformation on the quantity and skill level of labor, thus influencing firm-level ESG performance. The study sheds light on the transformative role of digital transformation and its implications for ESG performance improvement by elucidating the mechanisms through which digital transformation affects human capital and interacts with regulatory environments.

Vegetable cultivation in Eastern Nepal: resource use efficiency and socio-economic drivers of adoption

Vegetable cultivation in Eastern Nepal: resource use efficiency and socio-economic drivers of adoption