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A special issue of Sustainability (ISSN 2071-1050). This special issue belongs to the section "Economic and Business Aspects of Sustainability".

Deadline for manuscript submissions: closed (30 September 2023) | Viewed by 29626

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Special Issue Editors

Dr. Feng Liu

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Guest Editor

School of Computer Science and Technology, East China Normal University, Shanghai 200241, China
Interests: computational affection; AI; blockchain
Special Issues, Collections and Topics in MDPI journals

Dr. Peiwan Wang

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Guest Editor

School of Management, University of Science and Technology of China, Hefei 230026, China
Interests: finance

Prof. Dr. Jiayin Qi

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Guest Editor

School of Management, Shanghai University of International Business and Economics, Shanghai 201620, China
Interests: big data; blockchain; artificial intelligence

Special Issue Information

Dear Colleagues,

With the prosperity of the era of artificial intelligence and the wide application of computer science technology led by machine learning in multiple scientific and social fields, research scholars must now focus on how to use AI technology to solve the issues of economy, finance and management in a much more sustainable way. From the perspective of applications, the ongoing innovations in AI technology fill the shortcomings of traditional analysis and management tools, such as deficiency in processing complex information and incapacity of computing big data. From the perspective of research, AI techniques and methods are maintained to keep solving economic, financial and management problems. It is essential to reveal how AI technology impacts management disciplines in enterprises, institutions, industries, and markets, further improve research methods, and promote applications of AI techniques better to serve more efficient operations of the economy and society.

Special issue topics include, but are not limited to：

AI-related econometrics and applied economics;
Blockchain and financial engineering;
Business models;
Business intelligence and knowledge management;
Environment, critical and disaster management;
Financial market management;
Logistics and value chain management;
Management and corporate governance;
Marketing management and brand management;
Information systems management;
Industry economics and industrial organization management;
Innovation and technological change management;
Operations research;
Public policy and management;
Risk management.

Recommend research paradigms and designs for references:

Jiayin Qi, Zhenping Zhang, Seongmin Jeon, Yanquan Zhou, Mining customer requirements from online reviews: A product improvement perspective, Information & Management, 2016, 53(8), 951-963.
Liu et al., "OPO-FCM: A Computational Affection Based OCC-PAD-OCEAN Federation Cognitive Modeling Approach," in IEEE Transactions on Computational Social Systems, 2022. https://doi.org/10.1109/TCSS.2022.3199119.
Peiwan Wang, Lu Zong, Ye Ma. An integrated early warning system for stock market turbulence. Expert Systems with Applications, 2020, 153, 113463.
Jan De Spiegeleer, Dilip B. Madan, Sofie Reyners & Wim Schoutens. Machine learning for quantitative finance: fast derivative pricing, hedging and fitting, Quantitative Finance, 2018, 18(10), 1635-1643,
Liu, F.; Fan, H.-Y.; Qi, J.-Y. Blockchain Technology, Cryptocurrency: Entropy-Based Perspective. Entropy 2022, 24, 557.
Canhoto, I., Clear, F., Artificial intelligence and machine learning as business tools: A framework for diagnosing value destruction potential, Business Horizons, Volume 63, Issue 2, 2020, Pages 183-193, ISSN 0007-6813.

Dr. Feng Liu
Dr. Peiwan Wang
Prof. Dr. Jiayin Qi
Guest Editors

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Keywords

AI methods and applications
technology innovation
machine learning models
economic management
financial management
risk management
policy and governance
optimization system
management system
information system
intelligent business
technical accounting

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Published Papers (10 papers)

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22 pages, 2059 KiB

Open AccessArticle

Analysing the Effects of Scenario-Based Explanations on Automated Vehicle HMIs from Objective and Subjective Perspectives

by Jun Ma and Xuejing Feng

Sustainability 2024, 16(1), 63; https://doi.org/10.3390/su16010063 - 20 Dec 2023

Cited by 2 | Viewed by 1364

Abstract

Automated vehicles (AVs) are recognized as one of the most effective measures to realize sustainable transport. These vehicles can reduce emissions and environmental pollution, enhance accessibility, improve safety, and produce economic benefits through congestion reduction and cost savings. However, the consumer acceptance of and trust in these vehicles are not ideal, which affects the diffusion speed of AVs on the market. Providing transparent explanations of AV behaviour is a method for building confidence and trust in AV technologies. In this study, we investigated the explainability of user interface information in an Automated Valet Parking (AVP) system—one of the first L4 automated driving systems with a large commercial landing. Specifically, we proposed a scenario-based explanation framework based on explainable AI and examined the effects of these explanations on drivers’ objective and subjective performance. The results of Experiment 1 indicated that the scenario-based explanations effectively improved drivers’ situational trust and user experience (UX), thereby enhancing the perception and understanding that drivers had of the system’s intelligence capabilities. These explanations significantly reduced the mental workload and elevated the user performance in objective evaluations. In Experiment 2, we uncovered distinct explainability preferences among new and frequent users. New users sought increased trust and transparency, benefiting from guided explanations. In contrast, frequent users emphasised efficiency and driving safety. The final experimental results confirmed that solutions customised for different segments of the population are significantly more effective, satisfying, and trustworthy than generic solutions. These findings demonstrate that the explanations for individual differences, based on our proposed scenario-based framework, have significant implications for the adoption and sustainability of AVs. Full article

(This article belongs to the Special Issue Artificial Intelligence (AI) for the Sustainable Economics and Business)

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21 pages, 7897 KiB

Open AccessArticle

Hierarchical Model-Predictive-Control-Based Energy Management Strategy for Fuel Cell Hybrid Commercial Vehicles Incorporating Traffic Information

by Yuguo Xu, Enyong Xu, Weiguang Zheng and Qibai Huang

Sustainability 2023, 15(17), 12833; https://doi.org/10.3390/su151712833 - 24 Aug 2023

Cited by 2 | Viewed by 1175

Abstract

With the development of intelligent transportation systems, access to diverse transportation information has become possible. Integrating this information into an energy management strategy will make the energy allocation prospective and thus improve the overall performance of the energy management program. For this reason, this paper proposes a hierarchical model predictive control (MPC) energy management strategy that incorporates traffic information, where the upper layer plans the vehicle’s velocity based on the traffic information and the lower layer optimizes the energy distribution of the vehicle based on the planned velocity. In order to improve the accuracy of the planning speed of the upper strategy, a dung beetle optimization-radial basis function (DBO-RBF) prediction model is constructed, artfully optimizing the RBF neural network using the dung beetle optimization algorithm. The results show that the prediction accuracy is improved by 13.96% at a prediction length of 5 s. Further, when the vehicle passes through a traffic light intersection, the traffic light information is also considered in the upper strategy to plan a more economical speed and improve the traffic efficiency of the vehicle and traffic utilization. Finally, a dynamic programming (DP)-based solver is designed in the lower layer of the strategy, which optimizes the energy distribution of the vehicle according to the velocity planned by the upper layer to improve the economy of the vehicle. The results demonstrate achieving a noteworthy 3.97% improvement in fuel economy compared to the conventional rule-based energy management strategy and allowing drivers to proceed through red light intersections without stopping. This proves a substantial performance enhancement in energy management strategies resulting from the integration of transportation information. Full article

(This article belongs to the Special Issue Artificial Intelligence (AI) for the Sustainable Economics and Business)

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20 pages, 4675 KiB

Open AccessArticle

A Study of the Interaction between User Psychology and Perceived Value of AI Voice Assistants from a Sustainability Perspective

by Shanshan Liu, Jong-Yoon Lee, Yongseok Cheon and Minglu Wang

Sustainability 2023, 15(14), 11396; https://doi.org/10.3390/su151411396 - 22 Jul 2023

Cited by 2 | Viewed by 5369

Abstract

With the development and innovation of artificial intelligence (AI) technology, users can regulate their social lives and personal emotions through continuous interaction with AI voice assistants or chatbots. Based on the value-based adoption model (VAM), this paper examines the differences between different psychological factors with respect to perceived value factors when using AI voice assistants. This study is of great significance to improving AI voice assistant services and provides an important reference point for deeper understanding of user perception and emotional response to AI voice assistants. The aim of this research is to examine whether the usefulness, enjoyment value, perceived emotional value, and functional value of an AI voice assistant vary according to the user’s level of loneliness, resistance to innovation, and infringement of privacy. An online questionnaire created on the Questionnaire Star platform was used in this paper, and a three-way ANOVA was employed using SPSS 21.0 software. The findings suggest that the interaction effects of psychological factors such as loneliness, innovation resistance, and infringement of privacy differ in terms of perceived usefulness and enjoyment when using AI voice assistants, as well as in terms of perceived emotional and functional value. The results of this study provide a theoretical basis for the application and sustainable development of AI voice assistant technology by companies in different countries and regions. At the same time, this paper provides a valuable reference point for promoting urban economic sustainability in the context of digital technology. Full article

(This article belongs to the Special Issue Artificial Intelligence (AI) for the Sustainable Economics and Business)

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19 pages, 997 KiB

Open AccessArticle

Characterisation of Youth Entrepreneurship in Medellín-Colombia Using Machine Learning

by Adelaida Ojeda-Beltrán, Andrés Solano-Barliza, Wilson Arrubla-Hoyos, Danny Daniel Ortega, Dora Cama-Pinto, Juan Antonio Holgado-Terriza, Miguel Damas, Gilberto Toscano-Vanegas and Alejandro Cama-Pinto

Sustainability 2023, 15(13), 10297; https://doi.org/10.3390/su151310297 - 29 Jun 2023

Cited by 1 | Viewed by 1848

Abstract

The aim of this paper is to identify profiles of young Colombian entrepreneurs based on data from the “Youth Entrepreneurship” survey developed by the Colombian Youth Secretariat. Our research results show five profiles of entrepreneurs, mainly differentiated by age and entrepreneurial motives, as well as the identification of relevant skills, capacities, and capabilities for entrepreneurship, such as creativity, learning, and leadership. The sample consists of 633 young people aged between 14 and 28 years in Medellín. The data treatment was approached through cluster analysis using the K-means algorithm to obtain information about the underlying nature and structure of the data. These data analysis techniques provide valuable information that can help to better understand the behaviour of Colombian entrepreneurs. They also reveal hidden information in the data. Therefore, one of the advantages of using statistical and artificial intelligence techniques in this type of study is to extract valuable information that might otherwise go unnoticed. The clusters generated show correlations with profiles that can support the design of policies in Colombia to promote an entrepreneurial ecosystem and the creation and development of new businesses through business regulation. Full article

(This article belongs to the Special Issue Artificial Intelligence (AI) for the Sustainable Economics and Business)

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27 pages, 2009 KiB

Open AccessArticle

Analysis of Countries in Terms of Artificial Intelligence Technologies: PROMETHEE and GAIA Method Approach

by Gokhan Ozkaya and Ayse Demirhan

Sustainability 2023, 15(5), 4604; https://doi.org/10.3390/su15054604 - 4 Mar 2023

Cited by 10 | Viewed by 5153

Abstract

Artificial intelligence development and research leaders in business, industry, and nations gain a major competitive edge. Additionally, it is clear that nations with a well-established national artificial intelligence policy have an edge over others, both technologically and economically. To further their artificial intelligence capability, nations also seek to develop a strategy, vision, structure, and working environment that encourages collaboration between the public sector, private industry, and educational institutions. Artificial intelligence is thought to be a tool that will help bridge the gap between powerful and developing countries growing in the future. Using data from “The Global AI Index” for 2021, this study aims to examine and analyze the present state of artificial intelligence management in 62 nations in terms of talent, infrastructure, business environment, development and research government policy, and commercial efforts. The research used PROMETHEE, which is widely used in multi-criteria decision-making evaluations, and its geometric representation, the GAIA plane. This study also found that the United States of America is the world leader in artificial intelligence (AI) research and development as well as AI investment. The United Kingdom, China, Israel, Canada, the Netherlands, South Korea, and Germany all rank well. China is rapidly catching up to the USA. At the very bottom of the list are nations such as Armenia, Kenya, Egypt, South Africa, and Pakistan. Turkey’s values are more similar to those of nations towards the bottom of the list than of those in the top half. There is a significant gap between the top three countries and the rest of the world in all parameters included in the survey. Except for the ‘State Strategy’ category, Turkey scores quite low compared to the top-performing countries. Decision makers are expected to address the identified global challenges of the study by creating a more comprehensive national AI strategy, both financially and in terms of content. Full article

(This article belongs to the Special Issue Artificial Intelligence (AI) for the Sustainable Economics and Business)

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21 pages, 673 KiB

Open AccessArticle

Role of Smart Farm as a Tool for Sustainable Economic Growth of Korean Agriculture: Using Input–Output Analysis

by Sung-Wook Choi and Yong Jae Shin

Sustainability 2023, 15(4), 3450; https://doi.org/10.3390/su15043450 - 13 Feb 2023

Cited by 8 | Viewed by 3624

Abstract

This study focused on smart farms in Korea to confirm that the fourth industrial revolution technology is a tool that can be used for sustainable economic development in agriculture. This study used the input–output table, which included the production-inducing effects of the demand inducement model, value-added inducing effects, supply shortage effects of the supply inducement model, and the interlinkage effect. These methods were used to compare the impact of smart farm machinery on agriculture and the impact of smart farms on the Korean economy compared to conventional agriculture, thereby confirming the extent of the effect of fourth industrial revolution technology on agriculture. This study was conducted to determine whether this agricultural sector could lead to sustainable economic development. The analysis revealed that the production-inducing effects of smart farms showed a greater ripple effect than those of the agricultural sector. However, both value-added inducing effects and supply shortage effects showed a larger ripple effect in the agricultural sector. Considering only the indirect effect, the smart farms were found to evenly affect secondary and tertiary industries. In the case of value-added inducing and supply shortage effects, agriculture was found to have a greater impact on the secondary industry, whereas smart agri-machines and farms had a greater impact on the tertiary industry. Moreover, according to the interlinkage effect, agriculture was classified as a raw industry with final demand, and smart farms as a manufacturing industry with intermediate demand. These results have several implications. For the fourth industrial revolution technology to lead sustainable economic growth in agriculture, continuous creation of new value by developing various types of business models linked to other industries in consideration of the industrial characteristics of smart farms and agri-machines is necessary. In addition, to fully demonstrate the economic effects of smart farms, continuous management and support are required so that smart technologies can be strategically utilized in the agricultural sector. Full article

(This article belongs to the Special Issue Artificial Intelligence (AI) for the Sustainable Economics and Business)

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28 pages, 1870 KiB

Open AccessArticle

Greenhouse Gases Emissions: Estimating Corporate Non-Reported Emissions Using Interpretable Machine Learning

by Jérémi Assael, Thibaut Heurtebize, Laurent Carlier and François Soupé

Sustainability 2023, 15(4), 3391; https://doi.org/10.3390/su15043391 - 13 Feb 2023

Cited by 5 | Viewed by 3425

Abstract

As of 2022, greenhouse gases (GHG) emissions reporting and auditing are not yet compulsory for all companies, and methodologies of measurement and estimation are not unified. We propose a machine learning-based model to estimate scope 1 and scope 2 GHG emissions of companies not reporting them yet. Our model, designed to be transparent and completely adapted to this use case, is able to estimate emissions for a large universe of companies. It shows good out-of-sample global performances as well as good out-of-sample granular performances when evaluating it by sectors, countries, or revenue buckets. We also compare the model results to those of other providers and find our estimates to be more accurate. Explainability tools based on Shapley values allow the constructed model to be fully interpretable, the user being able to understand which factors split explains the GHG emissions for each particular company. Full article

(This article belongs to the Special Issue Artificial Intelligence (AI) for the Sustainable Economics and Business)

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13 pages, 1625 KiB

Open AccessArticle

Research on Enterprise Digital-Level Classification Based on XGBoost Model

by Qiuxia Ren and Jigan Wang

Sustainability 2023, 15(3), 2699; https://doi.org/10.3390/su15032699 - 2 Feb 2023

Cited by 2 | Viewed by 1795

Abstract

Digital knowledge and information have become significant production variables that have permeated all aspects of life and play a leading and supporting role in the growth of the real economy as the digital economy has developed. Through field research and web research, this study identifies digital-economy-related enterprises as the survey object; summarizes the fundamental information for these enterprises, their level of digitization, and the dilemma and demands of digital-level advancement; and generates survey data for 1936 enterprises. On the basis of these data, this study extracts the elements that influence the improvement of the enterprises’ digital level, applies statistical knowledge and machine learning techniques, and derives an enterprise digitization level index system and associated index score for enterprise digitization level. The experimental results indicate that the region, the time of establishment, the nature of ownership, the number of employees, R&D investment, being a national high-tech enterprise, and the establishment of digital transformation management departments have major effects. The AUC value of the XGBoost model modeled using all feature variables has achieved certain results, and the five assessment indices of the model have been enhanced to varying degrees, with the AUC being 0.9263. Full article

(This article belongs to the Special Issue Artificial Intelligence (AI) for the Sustainable Economics and Business)

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21 pages, 3920 KiB

Open AccessArticle

Neural Network-Augmented Locally Adaptive Linear Regression Model for Tabular Data

by Lkhagvadorj Munkhdalai, Tsendsuren Munkhdalai, Van Huy Pham, Jang-Eui Hong, Keun Ho Ryu and Nipon Theera-Umpon

Sustainability 2022, 14(22), 15273; https://doi.org/10.3390/su142215273 - 17 Nov 2022

Cited by 3 | Viewed by 2302

Abstract

Creating an interpretable model with high predictive performance is crucial in eXplainable AI (XAI) field. We introduce an interpretable neural network-based regression model for tabular data in this study. Our proposed model uses ordinary least squares (OLS) regression as a base-learner, and we re-update the parameters of our base-learner by using neural networks, which is a meta-learner in our proposed model. The meta-learner updates the regression coefficients using the confidence interval formula. We extensively compared our proposed model to other benchmark approaches on public datasets for regression task. The results showed that our proposed neural network-based interpretable model showed outperformed results compared to the benchmark models. We also applied our proposed model to the synthetic data to measure model interpretability, and we showed that our proposed model can explain the correlation between input and output variables by approximating the local linear function for each point. In addition, we trained our model on the economic data to discover the correlation between the central bank policy rate and inflation over time. As a result, it is drawn that the effect of central bank policy rates on inflation tends to strengthen during a recession and weaken during an expansion. We also performed the analysis on CO₂ emission data, and our model discovered some interesting explanations between input and target variables, such as a parabolic relationship between CO₂ emissions and gross national product (GNP). Finally, these experiments showed that our proposed neural network-based interpretable model could be applicable for many real-world applications where data type is tabular and explainable models are required. Full article

(This article belongs to the Special Issue Artificial Intelligence (AI) for the Sustainable Economics and Business)

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9 pages, 270 KiB

Open AccessBrief Report

Benchmarking Biologically-Inspired Automatic Machine Learning for Economic Tasks

by Teddy Lazebnik, Tzach Fleischer and Amit Yaniv-Rosenfeld

Sustainability 2023, 15(14), 11232; https://doi.org/10.3390/su151411232 - 19 Jul 2023

Cited by 7 | Viewed by 1356

Abstract

Data-driven economic tasks have gained significant attention in economics, allowing researchers and policymakers to make better decisions and design efficient policies. Recently, with the advancement of machine learning (ML) and other artificial intelligence (AI) methods, researchers can now solve complex economic tasks with previously unseen performance and ease. However, to use such methods, one is required to have a non-trivial level of expertise in ML or AI, which currently is not standard knowledge in economics. In order to bridge this gap, automatic machine learning (AutoML) models have been developed, allowing non-experts to efficiently use advanced ML models with their data. Nonetheless, not all AutoML models are created equal in general, particularly for the unique properties associated with economic data. In this paper, we present a benchmarking study of biologically inspired and other AutoML techniques for economic tasks. We evaluate four different AutoML models alongside two baseline methods using a set of 50 diverse economic tasks. Our results show that biologically inspired AutoML models (slightly) outperformed non-biological AutoML in economic tasks, while all AutoML models outperformed the traditional methods. Based on our results, we conclude that biologically inspired AutoML has the potential to improve our economic understanding while shifting a large portion of the analysis burden from the economist to a computer. Full article

(This article belongs to the Special Issue Artificial Intelligence (AI) for the Sustainable Economics and Business)

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