1. COMSATS University, Islamabad, Lahore Campus, Pakistan; sadia.asif.khan98@gmail.com
2. European University of Lisbon; shifatmista@gmail.com
3. European University of Lisbon; pritamagnihotri@gmail.com

*             Correspondence: sadia.asif.khan98@gmail.com

Keywords: artificial intelligence, sustainability, green human resource management, interrelation

1. INTRODUCTION

Since climate change is affecting many countries, environmental sustainability is becoming more and more crucial to global commercial operations. Sustainability is a standard concept that emphasizes generational fairness and is commonly understood to have three dimensions: the social, economic, and environmental. Environmental degradation and thriving economies are incompatible because attaining environmental sustainability involves risks that may affect company operations and prospects in a highly competitive market [1]. Direct decision-making at all levels is possible with this concept: global, national, and individual consumer. The World Meteorological Organization projects that there is a nearly 50/50 chance that the average world temperature will climb above 1.5 °C in five years, compared to pre-industrial levels [2]. In order to address the issue, the UNFCCC sets minimum requirements for organizing a conference known as COP, taking into account the agenda item of meeting carbon neutrality. In order to meet the global norm for zero carbon, PAS 2060 (internationally recognized specification for carbon neutrality), the UK government committed to implementing the International Norm for Event Sustainability Management Systems [3] and become the first COP to do so. The steps we took to ensure a sustainable summit, avoid and reduce greenhouse gas emissions, and identify takeaways for future COPs and significant events are detailed in the COP26 Sustainability Report [3]. This demonstrates the drive and leadership of nations in the field of sustainable event planning. Because environmental sustainability is so broad and involves so many trade-offs, problems are occasionally rationalized away with excessively straightforward, “sufficient,” and self-serving fixes. Trade has an impact on the environment, both positively and negatively. when we can see, when the economy grows, pollution levels rise and natural resource depletion inevitably affect the environment. In this case, artificial intelligence is essential for solving sustainability-related issues. Artificial intelligence has the potential to significantly impact trade in a number of environmental industries [3-5]. For instance, we can observe the trade of intangible or digital assets in the form of tokens, which can be enhanced by applying blockchain technology to maintain regulatory compliance, speed up transactions, and facilitate the use of digital tokens for quick transactions. Ultimately, this has an impact on the global trade environment [6]. Similarly, there is a trade-off between early investments and long-term environmental benefits, a trade-off between total cost and CO2 emission, and a trade-off between the total cost of production and environmental influence [7–10]. Broadly speaking, organizations are collaborating to bring about trade-offs between different aspects that are critical to development and environmentally sustainable practices. A multi-objective optimization model for understanding emergent trade-offs between different project constraints and environmental feasibility (cost, time, and quality) is proposed by environment trade-off decisions [11]. Artificial intelligence (AI) has been suggested by researchers as a way to increase the environmental sustainability of goods and services [12–13]. By 2030, artificial intelligence might contribute up to $15.7 trillion to the world economy, which is a significant factor in national economic growth [14]. AI can be defined as a collection of several technologies and techniques, which makes it both symbolic and analytical [15]. Artificial intelligence (AI) aims to measure primarily analytical, analytical, intuitive, and empathetic intelligence; it also aims to express human reflective processes [16] or demonstrate features of human intelligence by executing many tasks/decision making [17]. AI continues to face numerous obstacles in a variety of fields. Large-scale entrepreneurship development projects [21], malware detection [22], accurate wind power forecasting and prediction [23], short-term crash risk results could reveal crash hotspots and city planning [21], the relationship between continuous environment states and optimal control decisions [18], crash prediction techniques [19], timely detection of hazardous traffic condition formation [20], and more. AI is only present in a very limited number of environments and operational domains, nevertheless. Cryptocurrencies, for instance, consume as much energy as Finland does [24]. Therefore, we must investigate how AI affects sustainability, as this is a critical factor in resolving the behavioral socioeconomic issues that are related to economies. Future infrastructure energy requirements will rise as a result of the growing demand for AI in sustainability and the resulting growth in data volume. Therefore, methods for data storage, analysis, and visualization are needed in order to integrate AI into a sustainable development research. It is arguable, though, whether artificial intelligence plays a major role in environmental sustainability challenges or not [25]. Researchers have shown how AI affects resource use economically, but there are still open problems about sustainability and how AI affects sustainability. Numerous scholars have conducted a survey of the literature by focusing on a single aspect of sustainability and emphasizing the different difficulties that the industry faces. We attempted to review and emphasize the relationship between sustainability and artificial intelligence in this study.

2. RESEARCH METHODOLOGY

Finding data utilizing the selection criteria for study is the aim of the multi-phase coordinated search technique. Phase 1 entails researching the seven key websites; compile your results. Phase 2: Look through the list of references for every chosen inquiry topic to find any particularly noteworthy articles. Academic e-databases were utilized in the computerized search strategy for this inquiry. These phases result in interconnection of artificial intelligence and sustainability and role in green human resource management. To gather the relevant literature, we used a number of significant electronic databases, such as Science Direct, Sage, and Springer. It is thoroughly examined during scanning measurements, and one of the author’s hypotheses may contain data that is important for doing a literature analysis. Three study assistants separately assessed each component’s quality at that point. Essentially, the aforementioned search methods were used to extract academic publications. It’s possible that significant posts remained after unnecessary documents were eliminated. Only potentially significant studies remained after irrelevant studies were removed by skimming the titles and abstracts. Reports that were left unfinished provide guidance on specific goals or directions for system architecture (and were not generalize). We used the following quality assessment questions to examine each of the investigation’s papers in more detail: Is the field or category correctly described? 2. Is the essay supported by research, or is it just a collection of educated observations compiled by experts? 3. Are the objectives of the research well-defined? 4. Is the environment in which the research was conducted adequately described? 5. Does the research plan effectively accomplish the objectives of the study? 6. Is the last statement clear and succinct? Does the research cover every facet of green human resource management in thoughtful green human resource businesses? 8. Does the research offer a synopsis of the subject? 9. Is an established design used in the study article to aid in the analysis? N (No) = 0 or indeterminate (i.e., no explicit data was provided), P (Partially) = 0.5, and Y (Yes) = 1. This is how the article’s ranking system was created. Only 16 out of the 44 items that underwent thorough analysis in order to facilitate data extraction and aggregation satisfied our inclusion criteria. Right now, the goal is to provide access to paperwork that contains trustworthy details regarding the preliminary investigation. Along with other basic data, the author, year, study techniques, country, and context were all extracted. The investigation’s objectives were taken into consideration when selecting these products. Data were collected, scrutinized, and subsequently extracted. There is more information regarding the results of the merging process in the sections that follow.

3. RESULTS AND ANALYSIS

Parameters from each of the selected publications were to be extracted during the data extraction phase. During the search process for this evaluation, 16 documents were located. The amount of research that we select from internet databases is shown in Figure 1. On the other hand, only three papers came from the MDPI and Springerlink databases, while the rest (n=5) were sourced via the Science Direct database. In addition, Figure 2 shows the chronological history of investigations over time.

Figure 01: Papers in seven selected electronic databases

Figure 02: Chronological order of studies over time

To assess the overall quality of the included research, we used nine quality assessment questions. The maximum score for each question is 1, and the aggregate score was 9. Figure 3 demonstrates the high value of the chosen research, which has a quality score above 5, and more details may be found in Appendix 2.

Figure 03: Quality assessment of all included studies

This study demonstrated that 75% of the selected researchers employed survey research as their primary quantitative strategy. However, just around 25% of all papers using qualitative approach were conceptual papers, empirical investigations, or case studies. The examination of a few selected studies showed that industries have been studied in a variety of settings and countries. Study investigated the small and medium sized industries relevant to RM in eight various scenarios, including two distinct contexts.

4. DISCUSSION AND CONCLUSION

The relevant studies that identified interconnection of artificial intelligence and sustainability and green human resource management were chosen for this evaluation. We looked at five datasets. In addition, abstract analysis, title and keyword analysis of the results, and inclusion and exclusion criteria were used to choose the papers. Our study predicts a large increase in the number of articles reporting studies starting in 2022. Therefore, research on the interconnection of artificial intelligence and sustainability and green human resource management included examples of growing trends in the green human resource management industry. Studies that use interconnection of artificial intelligence and sustainability and green human resource management, however, have received less attention. Sixteen articles from 2015 to 2023 were chosen. The research methods used in the selected articles were then examined. While other approaches were employed less frequently, the survey strategy was employed in the majority of research. We wanted to increase the level of knowledge in this new field through these contributions, as well as our study of the topic and the development of a future research agenda based on a comprehensive evaluation of the literature for the preceding ten years. We inform the research audience about the expanding possibility for further study and contribute to the corpus of knowledge on interconnection of artificial intelligence and sustainability and green human resource management. Even though research on this topic is still in its early stages, it is critically needed to be further explored through rigorous conceptual and empirical work in order to keep up with the rapid changes and unstable nature of the corporate environment. Together with the other papers in this special issue, this review will increase understanding, provide guidance for interconnection of artificial intelligence and sustainability and green human resource management, and encourage creative ideas.

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