Journal of Economy and Technology最新文献

This paper proposes a novel index, ECI (Electricity Climate-Compatibility Index), to measure global fossil fuel electricity generation alignment with climate targets. Net anthropogenic carbon dioxide emissions (CO₂) must approach zero by mid-century to stabilise the mean temperature to well below 2°C. Pursuing carbon neutrality will require immediate action if we are to avoid the economic risks associated with a delayed, more abrupt energy transition. This paper reviewed the existing literature and found insufficient indicators for country-level climate targets in electricity generation. Therefore, the paper proposes a novel electricity climate-compatibility index (ECI) to address the gap. The index computes climate compatibility or incompatibility as the difference between fossil fuel electricity generation permitted in the Integrated Assessment Model (IAM) country-level climate scenarios and that which is generated from operational, under-construction and planned power generation assets. The ECI correlates positively in specific instances with relatable metrics such as Energy Transition Index (ETI) and Climate Change Performance Index (CCPI) but provides a better understanding of the climate-incompatible generation of fossil fuel plants for a decarbonised power sector.

The spectacular concept of integrating OCR with the Internet of Things (IoT) and Wireless Sensor Networks (WSN), presented in this study, intends to increase the autonomy and social inclusion of people with visual impairments and deaf-blindness. This application converts text from photographs into real-time Braille representations by utilizing the capabilities of Optical Character Recognition (OCR) technology, Braille displays, and IoT. Braille is a tactile form of writing which assists the visually impaired by representing letters and numbers with raised dots. Text recognition and conversion through OCR helps people with vision issues by converting printed text into accessible formats like audio or Braille, assisting people in with recognizing things around them. Individuals with visual impairments are given autonomous access to critical information by smoothly integrating modern technologies, boosting their self-confidence and promoting active social involvement. This OCR-Braille approach's primary objective is to minimize the knowledge gap and promote social equality. Users' ease of access to labels, educational resources, and digital information about their environment fosters a sense of empowerment and independence. This ground-breaking study not only eliminates the accessibility gap but also provides the groundwork for a society that is better linked and accessible. Individuals have access to the tools they need for independently navigating their surroundings through simple IoT connectivity and WSN. This research lays the path for a future for assistive technology to catalyze equality and independence, independent of one's visual ability, by fusing assistive technology and IoT connectivity.

Accurately predicting Customer Lifetime Value (CLV) is paramount in optimizing customer relationship management. This study introduces a novel deep learning approach, employing a neural network model to forecast CLV for a leading international e-commerce retailer. This investigation delves into the interplay of key performance indicators—Net Promoter Score (NPS), Average Transaction Value (ATV), and Customer Effort Score (CES)—and their collective impact on CLV. The research utilized a dataset of 15,000 customer profiles and crafted a sequential neural network with dense layers optimized through hyperparameter tuning and regularization to thwart overfitting. The model's efficacy was assessed on a 10 % test set, revealing its adeptness at capturing intricate, nonlinear predictor-customer lifetime value (CLV) relationships. Consistency in training and test performance metrics underscored the model's generalizability, while high R-squared and explained variance scores confirmed the predictive strength of the chosen factors. This research seeks to provide a solution for building an artificial intelligence model with an artificial neural network algorithm to predict customer lifetime value. By incorporating this principle, the study aims to leverage the power of neural networks to forecast CLV, enabling retailers to make informed decisions and optimize customer relationships accurately. This study finds that Net Promoter Score (NPS) and Customer Effort Score (CES) have a powerful impact on the neural network model's ability to predict Customer Lifetime Value (CLV) accurately. On the other hand, while Average Transaction Value (ATV) exhibits the least impact, it still significantly contributes to the accuracy of Customer Lifetime Value (CLV) predictions. These results underscore the importance of incorporating customer feedback metrics like NPS and CES when building predictive models for CLV estimation. Integrating critical customer metrics into neural networks gives retailers enhanced insights, enabling precise customer segmentation, resource allocation, and strategic growth. The study paves the way for future research to refine further CLV prediction, including dataset expansion, customer profile enrichment, and prescriptive marketing optimization.

A research gap exists regarding how research and development (R&D) expenditures contribute to the generation of innovative spillovers through knowledge-intensive business services (KIBS), particularly within the manufacturing and business services subsectors. These spillovers have a networking effect on innovation products in the manufacturing and services subsectors, as captured by the knowledge production function (KPF). The study proposes a two-stage model for analyzing the elasticities of production and economies of scale that arise from estimating singular knowledge production functions within a regional trade system. This model is tested for the case of the trade agreement of the United States, Mexico, and Canada (USMCA). The main results express that R&D expenditures embodied in KIBS spillovers have a significant effect on the generation of innovation outputs, in each of the USMCA countries and, reveal increasing returns to scale. Also, KPF elasticities, for the capital and the labor variables, proved to be inelastic and exhibited constant returns to scale. Additionally, the different estimates in each one of the USMCA countries expose diverse technologies of scale among the three countries.

In many countries of the Global South, transmission network expansion faces unique challenges due to the diversity of available resources and specific geographic conditions. These challenges are particularly evident in regions with complex geographies, such as the Andean region, where elevation differences and irregular topography pose significant obstacles to the development of electricity infrastructure. This study addresses these challenges by applying non-conventional strategies for transmission grid expansion, with a particular emphasis on adapting to the unique geographic features of the Andean region. To illustrate this, a case study called EC-45 bus system is developed using Ecuador as a country representative of the geographic diversity of the Andean region. Furthermore, this work presents a Mixed Integer Linear Programming (MILP) model that prioritizes the repowering and reconfiguration of existing lines over the construction of new lines. The methodology employed evaluates the investment required for the construction of new lines, considering construction costs for three site types (<1000 meters above sea level (m.a.s.l.), ≥1000 m.a.s.l., and combined). Quantitative results show that using non-conventional solution methods can generate savings of up to 30.60% in investment costs compared to the solution provided by the Linear Disjunctive model. This approach offers a way towards more efficient management of resources, especially in countries with complex geographies such as the Andean one.