Bitcoin Research in Business and Economics: A Bibliometric and Topic Modeling Review

Abstract

This study conducts a bibliometric review of Bitcoin research in the Business and Economics domains, using VOSviewer to visualize network structures and Bidirectional Encoder Representations from Transformers Topic (BERTopic) to derive semantically coherent topic clusters. The analysis identifies five major research themes: (1) Diversification, hedging, and safe-haven properties; (2) Market dynamics, efficiency, and investor behavior; (3) Bitcoin price and volatility prediction attempts; (4) Environmental impact of Bitcoin; and (5) Financial impact of Central Bank Digital Currency (CBDC). Based on these themes, the study recommends further investigation into the influence of Exchange-Traded Fund (ETF) approvals, regulatory frameworks, and institutional investor participation on Bitcoin’s safe-haven potential; the role of market dynamics and regulatory interventions; early detection of herding behavior and price bubbles; the integration of machine learning and deep-learning models for price prediction; the environmental costs associated with mining; and the evolving regulatory and implementation challenges of CBDCs. Overall, this review synthesizes existing scholarship and outlines future research directions for the rapidly evolving cryptocurrency ecosystem.

1. Introduction

Bitcoin emerged in response to the 2008 financial crisis, representing a significant innovation in the field of digital currencies [1,2]. As a peer-to-peer system operating on blockchain technology, it enables decentralized value transfer without reliance on traditional financial intermediaries [3,4]. Bitcoin is introduced into circulation through mining, in which computational resources are used to validate transactions and secure the network [5,6].

A defining feature of Bitcoin is its fixed maximum supply of 21 million coins, which introduces programmed scarcity similar to gold [7]. Mining rewards decrease over time through periodic halving events, increasing mining difficulty and contributing to Bitcoin’s deflationary design [8,9,10].

Since its introduction in 2008, Bitcoin has experienced a tumultuous history (Figure 1). Its decentralized architecture, predictable issuance schedule, and recent regulatory developments, such as the approval of Exchange-Traded Funds (ETFs), have strengthened its position as both a speculative asset and a potential store of value. These characteristics have driven notable academic interest, particularly in the Business and Economics domain. However, research on Bitcoin in this field remains diverse and fragmented, and a structured mapping of research themes is still needed.

The purpose of this study is to map and analyze the intellectual structure of Bitcoin research within the Business and Economics domain. Rather than synthesizing individual empirical findings, this study identifies publication trends, thematic clusters, and topic evolution. By clarifying areas of concentration and fragmentation, it provides a structured foundation for future scholarly inquiry.

Previous studies have primarily relied on narrative or thematic reviews [11,12,13,14], which, while informative, offer limited insight into the field’s underlying research structure. To address this limitation, we employ a bibliometric and topic modeling approach. Using Web of Science data, we first conduct bibliometric analysis with Python-based visualization tools and VOSviewer [15]. We then restrict the dataset to research classified under Business and Economics and apply Bidirectional Encoder Representations from Transformers Topic (BERTopic) to extract thematic clusters and examine topic evolution.

This study identifies five major research themes in the Business and Economics literature: (1) Diversification, hedging, and safe-haven properties; (2) Market dynamics, efficiency, and investor behavior; (3) Bitcoin price and volatility prediction attempts; (4) Environmental impact of Bitcoin; and (5) Financial impact of Central Bank Digital Currency (CBDC). These themes are used to interpret the current research landscape and outline opportunities for future investigation.

The remainder of this paper is organized as follows. The Materials and Methods section detail the data collection procedures and analytical workflow. The Bibliometric Analysis section presents publication trends and network visualizations. The Literature Findings section interprets thematic clusters and discusses research directions. The Discussion and Conclusions section summarizes key insights and implications for future research.

2. Materials and Methods

This study employs a bibliometric and topic modeling framework to map the intellectual structure of Bitcoin-related academic research. Rather than synthesizing individual empirical findings, the goal is to identify publication trends, research clusters, and thematic evolution within the Business and Economics domain. This approach provides a structured understanding of how Bitcoin has been positioned and discussed in academic literature and highlights areas requiring further investigation. To ensure methodological rigor and transparency, this study adhered to the guidelines outlined in the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) statement [16,17,18].

2.1. Research Questions

To guide the research design and ensure clarity in data collection and analysis, the following research questions were defined:

• RQ1. What are the global academic publication trends on Bitcoin?

This question examines how Bitcoin-related research has evolved over time across disciplines. It aims to identify major shifts in scholarly attention and highlight the areas where academic engagement has been most active.

• RQ2. Which academic domain publishes the most Bitcoin-related research, and what are the primary topics within that domain?

This question seeks to determine which discipline contributes the largest share of Bitcoin scholarship and to characterize the key topics emphasized within that field. Understanding domain-specific perspectives clarifies how Bitcoin is conceptualized across academic communities.

• RQ3. What are the major research themes within the Business and Economics domain, and what research opportunities arise from these themes?

This question focuses on identifying dominant thematic clusters within Business and Economics and assessing the theoretical and empirical gaps that present opportunities for future research.

2.2. Research Methodology

2.2.1. Data Collection and Screening

Data were retrieved on 17 June 2024, from the Web of Science Core Collection using the keyword “Bitcoin,” restricted to the document type “article.” Web of Science was selected due to its standardized bibliographic records, curated indexing policies, and its widespread use in citation-based analysis [19,20,21]. Although this may exclude studies indexed in other databases, the objective of analyzing peer-reviewed research in high-impact journals justifies this selection [22]. The systematic selection process is detailed in the PRISMA flow diagram shown in Figure 2.

The amount of raw data collected for the target analysis was 5244. Afterwards, we screened the metadata of the articles to remove (1) articles missing abstracts, (2) articles not written in English, (3) patents and books, and (4) duplicate data. During this process, a total of 370 data were removed. The initial dataset contained 5244 records. Metadata screening removed articles with missing abstracts, non-English articles, patents, books, and duplicates, excluding 370 records. Title and abstract screening then removed articles irrelevant to Bitcoin. To capture broad publication trends, no discipline-level inclusion criteria were applied during preliminary bibliometric analysis. A total of 4828 articles were retained for this stage. For the main topic modeling analysis, only articles categorized under the primary research area Business and Economics were included, yielding 2306 articles.

2.2.2. Bibliometric Analysis Procedure

For preliminary bibliometric analysis, publication and citation trends were visualized using the Plotly 5.22.0 in Python [23]. Specifically, we utilized bar graphs and line graphs to easily visualize annual publication counts and citation numbers. Subsequently, visualization of global publications by country were conducted using the Geopandas 0.14.4 in Python [24]. Subsequently, we employed VOSviewer 1.6.20 for bibliographic analysis following the guidelines [25,26,27]. We selected a bibliometric analysis method where the relationships between items are determined based on the number of times they cite each other [27,28]. Additionally, we opted for the average normalized citations score. This option normalizes each article’s citation count based on the average citation count within its field, facilitating a more equitable comparison of the article’s influence [27,28,29]. Consequently, a cluster related to the overall research on Bitcoin is formed through VOSviewer bibliographic coupling. Thereafter, Complementary visualizations (e.g., pie charts) were generated using Plotly.

2.2.3. Topic Modeling with BERTopic

To identify thematic structures within the Business and Economics domain, BERTopic 0.14.1 was applied to the full abstract corpus [30]. Following standard preprocessing practices, missing abstracts were removed, text was lowercased, high-frequency stopwords were filtered, domain-specific lexical variants were normalized (e.g., “cryptocurrencies” → “cryptocurrency”), and whitespace was standardized.

Sentence-level embeddings were generated using a Sentence-BERT model (multi-qa-miniLM-L6-cos-v1). Subsequently, Uniform Manifold Approximation and Projection (UMAP) was used for nonlinear dimensionality reduction, and Hierarchical Density-Based Spatial Clustering of Applications with Noise (HDBSCAN) was applied to identify dense semantic regions. Finally, Class-based Term Frequency-Inverse Document Frequency (c-TF-IDF) was employed to derive interpretable topic representations.

This pipeline follows the standard BERTopic methodology, ensuring reproducibility while capturing coherent latent topic structures and thematic clusters [30]. Through this pipeline, BERTopic enabled the extraction of latent topic structures, coherent thematic clusters, and cross-year topic evolution patterns, thereby identifying major research trajectories and emerging areas in the Bitcoin the literature.

3. Bibliometric Analysis

3.1. Overall Publication Trends on Bitcoin

3.1.1. Overall Publication Trends

The number of published articles and citations per year are shown in Figure 3 and

Table 1. Number of citations by publication year.

Publication Year	Number of Citations	Number of Publications
2012	21	1
2013	723	3
2014	521	7
2015	3971	22
2016	5487	34
2017	7428	58
2018	15,409	158
2019	24,789	523
2020	23,763	695
2021	16,776	862
2022	10,682	996
2023	4506	1010
2024	420	459

. The highest number of Bitcoin-related articles was published in 2023 (1010 articles). Likely due to the time required for the publishing process including peer review, the price surges in 2021 and 2022 appear to have influenced the number of publications in 2022 and 2023. Overall, there has been a consistent upward trend since the first article in 2012, indicating growing scholarly attention toward Bitcoin.

Citations peaked for articles published in 2019, a period associated with a sharp expansion in scholarly output. The substantial rise in publications from 2018 to 2019 corresponds with accelerated academic engagement, leading to higher cumulative citation levels for articles published during that period.

Beyond these quantitative patterns, shifts in academic output broadly coincide with major developments in the cryptocurrency ecosystem. The rapid growth between 2017 and 2021 aligns with significant market events, including institutional entry, derivatives expansion, and evolving regulatory frameworks. A moderation in citation growth after 2022 parallels post-pandemic market normalization and reduced volatility.

3.1.2. Overall Publication by Country

Figure 4 visualizes countries based on the number of Bitcoin-related publications. Countries with higher publication counts are represented in darker shades, while countries with fewer publications are shown in lighter shades.

In terms of publication volume, China (735), the United States (555), and the United Kingdom (302) account for the largest shares (

Table 2. Top 10 countries based on the number of published articles.

Rank	Country	Publications	Citations	Citations per Publication
1	China	735	13,057	17.76
2	United States	555	14,720	26.52
3	United Kingdom	302	13,362	44.25
4	India	219	3432	15.67
5	South Korea	191	4325	22.64
6	Australia	161	4896	30.41
7	Italy	158	2976	18.84
8	France	147	6801	46.27
9	Turkey	144	2202	15.29
10	Spain	128	3214	25.11

). However, when considering citations per article as an indicator of influence, France records the highest average citations per article (46.27), followed by the United Kingdom (44.25) and Australia (30.41). This indicates that publication volume does not directly correspond to scholarly impact.

Figure 5 depicts a citation graph among countries. The size of the circles represents the number of publications, while the color gradient of the dots indicates average normalized citations. Average normalized citations score is calculated in VOSviewer, which normalizes each article’s citation count based on the average citation count within its field, facilitating a more equitable comparison of the article’s influence [28]. Since older published papers can accumulate many citations over time, the average normalized citations metric considers this temporal factor, allowing for a fair evaluation when comparing with more recently published papers [29]. A value closer to 2.0 (yellow) indicates stronger influence of a country’s published articles, while a value closer to 0.5 (blue) indicates weaker influence. Since average normalized citations score relies on field-level averages and can be affected by subfield citation variations and publication age, it is used as a supplementary indicator rather than a definitive measure of impact.

Overall, Figure 5 indicates that China, the United States, and the United Kingdom constitute the core of the global Bitcoin research network, as reflected by their large node sizes and dense citation links. In contrast, countries such as France and Australia exhibit higher average normalized citation scores despite publishing fewer articles, suggesting greater per-paper scholarly influence. Several emerging-market countries appear at the periphery with lower normalized citation scores, indicating more limited global impact.

These patterns highlight structural differences across research ecosystems. High-output countries benefit from extensive institutional funding and established collaborative networks, while countries with smaller publication volumes often show concentrated influence driven by a few highly cited studies. This asymmetry contributes to the observed clustering in the global research structure.

3.1.3. Top Cited Article Analysis

Table 3. Top 10 cited articles.

Rank	Article	Citations	Research Areas
1	Tschorsch and Scheuermann [31]	846	Computer Science
2	Dyhrberg [32]	782	Business and Economics
3	Corbet et al. [33]	739	Business and Economics
4	Urquhart [34]	712	Business and Economics
5	Böhme et al. [4]	711	Business and Economics
6	Bouri et al. [35]	700	Business and Economics
7	Cheah and Fry [36]	689	Business and Economics
8	Baur et al. [37]	622	Business and Economics
9	Katsiampa [38]	561	Business and Economics
10	Dyhrberg [39]	523	Business and Economics

presents the ten most cited articles. All were published between 2015 and 2018, reflecting an early period of intensified academic interest in Bitcoin. With the exception of the top-ranked Computer Science study, all highly cited papers fall within the Business and Economics domain. This concentration indicates that the most influential contributions to Bitcoin research have largely been shaped by economic and financial perspectives, reinforcing the importance of examining developments within this domain.

3.1.4. Top Cited Domain Analysis

Table 4. Main research areas assigned to articles.

Research Areas	Count	Proportion (%)
Business and Economics	2306	47.76
Computer Science	1240	25.68
Mathematics	211	4.37
Science and Technology—Other Topics	202	4.18
Engineering	139	2.88
Physics	136	2.82
Environmental Sciences and Ecology	91	1.88
Government and Law	87	1.8
Social Sciences—Other Topics	56	1.16
Operations Research and Management Science	33	0.68

and Figure 6 summarize the distribution of research areas. Business and Economics represents the largest share of Bitcoin-related publications (47.76%), followed by Computer Science (25.68%). Together, these two fields account for more than 70% of the total literature, indicating that academic inquiry into Bitcoin is primarily concentrated in economic/financial and computational perspectives.

Subsequently, we identified the journals that have published the most articles (

Table 5. Top 10 published journals.

Journal	Count	Citations
Finance Research Letters	307	15,180
IEEE Access	219	7908
Research in International Business and Finance	123	5174
International Review of Financial Analysis	109	5147
Physica a-statistical mechanics and its applications	81	3513
Journal of Risk and Financial Management	77	3068
Financial Innovation	76	2780
Economics Letters	76	2472
North American Journal of Economics and Finance	68	2087
Mathematics	56	1732

). Consistent with the domain analysis, most articles were published in the field of Business and Economics, with the journal Finance Research Letters being the most prolific. Thereafter, the journal IEEE Access in the field of Computer Science came in second place.

Figure 7 visualizes the citation network among journals. Finance-oriented journals such as Finance Research Letters and Physica A occupy central positions, reflecting their strong cross-citation connectivity and their role as core venues for Bitcoin research. In contrast, journals such as IEEE Access publish a high volume of papers but show lower average normalized citation scores, suggesting more diffuse influence. Additionally, several specialized journals appear at the periphery with limited cross-citation links, indicating more isolated streams of research within the broader Bitcoin literature.

3.2. Research Trends in Business and Economics Domain

In the preceding section, the study examined the general publication trends in academia and identified that the Business and Economics domain contains the highest research activity. This section narrows down the scope to this domain to analyze research trends in greater depth.

3.2.1. Publication Trends in Business and Economics

Figure 8 summarizes annual publication and citation trends. Bitcoin-related publications in Business and Economics increased steadily from the first article in 2014, reaching a peak in 2023 (507 articles).

Similarly to the findings from the broader bibliometric analysis, citations were highest for articles published in 2019 (

Table 6. Number of citations in the targeted domain.

Publication Year	Number of Citations
2014	3
2015	2866
2016	3312
2017	4188
2018	9268
2019	13,259
2020	13,122
2021	9926
2022	6645
2023	2773
2024	304

). The strong citation performance of 2019–2020 reflects a substantial expansion of scholarly output during this period, which amplified cumulative citation levels.

Beyond these quantitative patterns, publication growth closely aligns with major developments in global financial markets. The surge between 2017 and 2021 corresponds with rising institutional participation, the introduction of regulated derivatives and intensified regulatory scrutiny in major economies. Elevated academic activity during 2019–2020 further coincides with rapid expansion of Bitcoin-related financial products, fintech-driven retail entry, and heightened macroeconomic uncertainty during the pre- and post-pandemic period [40,41]. In contrast, the slowdown in citation growth after 2022 parallels the normalization of cryptocurrency trading volumes and stabilization of macro-financial conditions. Taken together, these trends indicate that research activity in the Business and Economics domain is closely influenced by external market cycles and structural shifts in Bitcoin’s financial infrastructure.

3.2.2. Publication by Country in Business and Economics

Figure 9 displays country-level publication activity within the Business and Economics domain. Countries with higher publication counts are represented in darker shades of purple, while those with fewer publications are depicted in lighter shades of purple.

The United States (275), China (227), and the United Kingdom (177) produce the largest number of articles (

Table 7. Top 10 countries based on the publication volume in the targeted domain.

Rank	Country	Publications	Citations	Citations per Publication
1	United States	275	7838	28.5
2	China	227	4955	21.83
3	United Kingdom	177	10,015	56.58
4	France	112	5980	53.39
5	Turkey	91	1384	15.21
6	India	87	1637	18.82
7	Australia	85	3509	41.28
8	Spain	68	1945	28.6
9	South Korea	67	1268	18.93
10	Germany	63	1654	26.25

). However, citation influence differs substantially: the United Kingdom records the highest citations per article (56.58), followed by France (53.39) and Australia (41.28). This indicates that publication volume does not directly correspond to per-paper scholarly impact.

Figure 10 illustrates normalized citation patterns across countries. Although the United States has the highest publication count, its normalized citation score is lower than that of the United Kingdom and France. In addition, several countries with relatively small publication volumes, such as Ireland, Vietnam, Lebanon, Pakistan, and Russia, exhibit comparatively high normalized citation scores. This pattern occurs because even a small number of highly cited articles can elevate a country’s normalized impact, whereas countries producing a large number of papers tend to display lower normalized averages due to dilution effects. These results indicate that productivity and normalized scholarly influence should be interpreted independently when evaluating cross-country research patterns.

3.2.3. Top Cited Article Analysis in Business and Economics

Table 8. Top 10 cited articles in the targeted domain.

Rank	Title	Authors	Citations
1	Bitcoin, gold and the dollar—A GARCH volatility analysis	Dyhrberg [32]	782
2	Exploring the dynamic relationships between cryptocurrencies and other financial assets	Corbet et al. [33]	739
3	The inefficiency of Bitcoin	Urquhart [34]	712
4	Bitcoin: Economics, Technology, and Governance	Böhme et al. [4]	711
5	On the hedge and safe haven properties of Bitcoin: Is it really more than a diversifier?	Bouri et al. [35]	700
6	Speculative bubbles in Bitcoin markets? An empirical investigation into the fundamental value of Bitcoin	Cheah and Fry [36]	689
7	Bitcoin: Medium of exchange or speculative assets?	Baur et al. [37]	622
8	Volatility estimation for Bitcoin: A comparison of GARCH models	Katsiampa [38]	561
9	Hedging capabilities of bitcoin. Is it the virtual gold?	Dyhrberg [39]	523
10	The economics of Bitcoin price formation	Ciaian et al. [42]	510

summarizes the ten most cited articles in the Business and Economics domain. These influential studies reflect several major themes: (1) Bitcoin volatility and price prediction, (2) cross-asset relationships, (3) market efficiency, (4) market dynamics, (5) safe-haven and hedging properties, (6) speculative bubbles, and (7) determinants of Bitcoin price. Collectively, these themes highlight the central focus of high-impact research on the financial and economic characteristics of Bitcoin.

3.2.4. Top Cited Journal Analysis

Table 9. Main journals by publication volume in the targeted domain.

Rank	Journal	Count	Proportion (%)	Citations	Citations per Publication
1	Finance Research Letters	307	13.31	15,180	49.45
2	Research in International Business and Finance	123	5.33	3513	28.56
3	International Review of Financial Analysis	109	4.73	5147	47.22
4	Journal of Risk and Financial Management	77	3.34	835	10.84
5	Economics Letters	76	3.3	7908	104.05
	Financial Innovation	76	3.3	923	12.14
7	North American Journal of Economics and Finance	68	2.95	1279	18.81
8	International Review of Economics and Finance	54	2.34	762	14.11
9	Journal of International Financial Markets Institutions and Money	51	2.21	2472	48.47
10	Technological Forecasting and Social Change	48	2.08	2087	43.48

and Figure 11 identify the journals with the highest publication volumes. Finance Research Letters is the most prolific outlet, followed by Research in International Business and Finance and International Review of Financial Analysis. In terms of citations per article, Economics Letters shows the highest influence (104.05), despite a relatively smaller publication count.

Figure 12 presents the journal-level citation network. Finance-oriented journals such as Finance Research Letters and Physica A occupy central positions, reflecting strong cross-citation connectivity and their role as primary channels in Bitcoin-related finance research. Journals with smaller publication volumes such as International Review of Financial Analysis and Technological Forecasting and Social Change exhibit high normalized citation scores, indicating concentrated impact from a limited number of highly cited studies. In contrast, journals positioned at the periphery publish Bitcoin research within more specialized sub-fields, resulting in weaker inter-journal connectivity and limited integration into the broader citation network. These patterns indicate that journal influence in the Business and Economics domain is shaped by differences in publication scope, cross-citation density, and thematic specialization, which together drive the cluster structures observed in the journal network.

4. Literature Findings in Business and Economics Domain

This study conducts a bibliometric and topic modeling analysis of Bitcoin research within the Business and Economics domain. Broad thematic patterns were first identified through highly cited articles and BERTopic clustering, after which subtopics and conceptual linkages were examined based on the research questions. The number of topics and representative keywords extracted by BERTopic are summarized in

Table 10. Main clusters identified through BERTopic.

Topic	Keywords
1	‘cryptocurrency’, ‘market’, ‘bubble’, ‘asset’, ‘risk’, ‘portfolio’, ‘investors’, ‘crypto’, ‘herding’, ‘financial’, ‘users’, ‘results’, ‘returns’, ‘price’
2	‘safe’, ‘haven’, ‘stablecoins’, ‘asset’, ‘gold’, ‘nft’, ‘market’, ‘hedge’, ‘hedging’, ‘against’, ‘cryptocurrency’, ‘properties’, ‘pandemic’, ‘stock’
3	‘blockchain’, ‘technology’, ‘fee’, ‘transaction’, ‘business’, ‘users’, ‘network’, ‘mining’, ‘transactions’, ‘how’, ‘accounting’, ‘based’, ‘transition’, ‘innovation’
4	‘model’, ‘garch’, ‘volatility’, ‘using’, ‘conditional’, ‘returns’, ‘results’, ‘realized’, ‘sample’, ‘forecasting’, ‘risk’, ‘best’, ‘market’, ‘forecast’
5	‘futures’, ‘spot’, ‘discovery’, ‘price’, ‘trading’, ‘market’, ‘contracts’, ‘cme’, ‘information’, ‘introduction’, ‘fluctuation’, ‘find’, ‘prices’, ‘results’
6	‘model’, ‘learning’, ‘price’, ‘machine’, ‘prediction’, ‘neural’, ‘deep’, ‘lstm’, ‘data’, ‘error’, ‘forecasting’, ‘prices’, ‘forecast’, ‘network’
7	‘cbdc’, ‘digital’, ‘currency’, ‘money’, ‘currencies’, ‘central’, ‘bank’, ‘monetary’, ‘payment’, ‘use’, ‘system’, ‘value’, ‘cryptocurrency’, ‘business’
8	‘efficiency’, ‘market’, ‘multifractal’, ‘inefficiency’, ‘time’, ‘multifractality’, ‘hypothesis’, ‘efficient’, ‘cryptocurrency’, ‘returns’, ‘asymmetric’, ‘adaptive’, ‘inefficient’, ‘long’

.

The main themes of each topic were assigned based on dominant keywords and the underlying corpus associated with each cluster, consistent with prior topic modeling studies [40,43,44]. The clusters primarily reflect the following research domains: (1) financial characteristics of the Bitcoin market, (2) relationships between cryptocurrencies and other assets, and the potential of Bitcoin as a safe-haven, (3) volatility prediction using statistical models such as Generalized AutoRegressive Conditional Heteroskedasticity (GARCH), (4) Price prediction using machine learning, (5) CBDCs and the financial system, (6) Determinants of Bitcoin price variation.

These themes reflect structural developments in cryptocurrency markets, investor behavior, and global regulatory dynamics. Research on diversification, hedging, and safe-haven properties expanded during periods of macroeconomic stress and institutional adoption, driven by the regime-dependent correlation between Bitcoin and traditional assets [45,46,47]. Market efficiency and behavioral factors form another major stream due to speculative trading, persistent inefficiencies, and herding tendencies observed in decentralized markets [36,48]. Volatility- and forecasting-oriented studies remain central because of Bitcoin’s extreme price swings, which continue to motivate the use of statistical and machine learning models [38,49]. Environmental impact research has grown following global concerns regarding the carbon footprint of Proof-of-Work (PoW) mining [50]. Finally, the rapid expansion of CBDC initiatives has triggered academic interest in their interaction with cryptocurrencies and monetary policy [51].

Integrating bibliometric mapping with topic-model interpretation, the major research streams were consolidated into the following areas: (1) Diversification, hedging, and safe-haven properties; (2) Market dynamics, efficiency, and investor behavior; (3) Bitcoin price and volatility prediction; (4) Environmental impact of Bitcoin; and (5) Financial Implications of CBDC.

To ensure that the thematic discussions reflect influential and academically validated work, representative studies within each cluster were selected using consistent and transparent criteria, including citation impact and alignment with the dominant topic keywords identified by BERTopic. These criteria reduce potential subjectivity by grounding the selection process in measurable indicators of academic influence rather than arbitrary choices. Highly cited papers typically signal stronger methodological rigor and greater recognition within the field, thereby providing a reliable foundation for synthesizing the conceptual development of each research stream.

From Section 4.1 onward, these representative papers structure the thematic analysis, and Section 4.6 synthesizes cross-cutting explanations for inconsistent or contradictory findings across the literature.

4.1. Diversification, Hedging, and Safe-Haven Properties

Since this review focuses exclusively on studies in the fields of Business and Economics, significant attention has been given to Bitcoin’s market efficiency and its incorporation into investment portfolios [52,53,54,55]. Especially, many studies have investigated Bitcoin’s potential for diversification, its correlations with other assets, and the safe-haven properties [56,57,58,59]. This section provides an in-depth review of these findings.

4.1.1. Cryptocurrency’s Role in Portfolio Diversification and Hedging

The relationship between Bitcoin and traditional assets is complex and multifaceted. Studies reveal varying correlation patterns under different market conditions, highlighting Bitcoin’s potential in portfolio diversification and hedging strategies [60,61]. Zeng et al. [62] studied the dynamic interdependence between the returns of Bitcoin, stocks, oil, and gold. The results showed that the connectedness between Bitcoin and traditional assets is weak. Additionally, the authors identified the presence of asymmetric spillover effects between Bitcoin and traditional assets. According to Aslanidis et al. [63], the correlations among cryptocurrencies changed over time but are mostly positively correlated, while the correlation with traditional assets is negligible. Furthermore, Mensi et al. [64] examined the correlations between Bitcoin and the Dow Jones Global Index, as well as Islamic equity returns. The authors found that the co-movement between Bitcoin and Islamic equity returns varies by country. For instance, Canada shows weak co-movement, whereas the United States and Japan exhibit relatively strong coherence. In addition, Terraza et al. [65] compared the Bitcoin, gold market, and stock indices including S&P 500 before and during the Coronavirus Disease 2019 (COVID-19) pandemic. The results showed significant dynamic conditional correlations between Bitcoin, gold, and the stock markets. Notably, during the COVID-19 period, Bitcoin provided better diversification opportunities by reducing the risk of major stock markets. Additionally, Aliu et al. [66] demonstrated that fluctuations in gold prices can influence changes in Bitcoin prices. However, such high coherence was primarily observed in the short-term and diminished over the long-term. The impact of Bitcoin on stock market and United States dollar remained ambiguous.

Due to Bitcoin’s unique characteristic of fluctuating correlations with other assets, research actively explored its inclusion in portfolios for hedging purposes and to ascertain diversification benefits. For instance, Briere et al. [67] analyzed Bitcoin investments from the perspective of the Unites States investors with diverse portfolios that include both traditional assets and alternative investments. According to the research findings, Bitcoin exhibited significantly low correlations with other assets, and even a small allocation to Bitcoin demonstrated significant improvements in the risk-return tradeoff of well-diversified portfolios. According to Alfieri et al. [68], the low correlation with existing asset classes suggested Bitcoin’s potential to offer diversification opportunities within portfolios. Additionally, Kumaran [69] confirmed that cryptocurrencies can function independently from market indices, serving as a diversification option that enhances risk-adjusted returns and supports optimal portfolio construction. These diverse academic discussions highlight Bitcoin’s unique properties that can clearly provide benefits in portfolio diversification and hedging strategies.

4.1.2. Comparison Between Bitcoin and Traditional Safe-Haven Asset

Bitcoin is gaining attention as a potential safe-haven asset during periods of high economic uncertainty [35,70,71]. Safe-haven assets refer to investments that maintain or increase their value during periods of market volatility or economic downturns [72]. Its decentralized nature, operating independently of central banks or political influence, theoretically shields it from inflationary pressures or political instability [73,74]. There is ongoing academic debate about whether Bitcoin can function as an actual safe-haven asset, what characteristics make Bitcoin suitable or unsuitable for this role, and the potential implications of its performance during periods of financial instability compared to assets like gold.

There is a clear distinction between traditional safe-haven assets and Bitcoin [75]. Traditional safe-haven assets include gold, United States Treasury bonds, and the Swiss franc. These assets are known for their stability, liquidity, and performance during economic crises, widely trusted as secure stores of value during turbulent times [76,77]. For instance, Baur and McDermott [78] highlighted that gold has been a consistent safe-haven during periods of extreme market stress. Yarovaya et al. [79] also confirmed that United States Treasuries experienced relatively modest depreciation in response to COVID-19, in addition to their high sustainability. In contrast, Bitcoin has shown extreme price volatility over its short history and exhibits high market volatility. According to Cheah and Fry [36], Bitcoin’s price fluctuations are driven more by speculation than intrinsic value, leading to significant volatility.

In addition, traditional safe-haven assets are well-regulated. For instance, gold is subjected to strict international trading standards and regulations, ensuring its reliability as a safe-haven [80]. However, Bitcoin has witnessed recent developments such as ETF launches and regulatory frameworks, yet it operates within diverse regulatory landscapes worldwide, contributing to its unpredictability [81]. Alexander and Heck [82] underscored persistent challenges arising from regulatory inconsistencies within the Bitcoin spot and derivative markets, such as inadequate price stability and susceptibility to manipulative trading.

Lastly, traditional safe-haven assets are widely trusted in global markets, with many investors recognizing their function as safe stores of value [83]. In contrast, while Bitcoin has attained gradual recognition in recent years, it still earns relatively limited trust compared to traditional safe-haven assets. Auer and Claessens [84] argued that despite Bitcoin’s increasing recognition, its acceptance as a safe-haven asset remains limited due to its volatility and lack of regulatory oversight.

4.1.3. Bitcoin’s Performance During Market Crises

Despite its distinct contrast with traditional assets, Bitcoin’s behavior during recent crises such as the COVID-19 pandemic or geopolitical turmoil demonstrates its potential as a safe-haven asset [85,86,87,88]. Existing research indicates that Bitcoin’s performance is highly sensitive to geopolitical and macroeconomic stress [35,73]. While it can exhibit safe-haven characteristics by preserving value during certain crises, this behavior is not consistent and varies across periods and market conditions.

Marobhe [89] reported that although cryptocurrencies experienced a sharp decline during the onset of the COVID-19 pandemic, they rebounded quickly by April, displaying notable resilience relative to traditional financial markets. This stands in contrast to stock market indices, which remained vulnerable until June 2021. Syuhada et al. [77] further highlighted the inconsistent nature of Bitcoin’s safe-haven behavior, noting that gold portfolios experienced substantial downside-risk reduction during the pandemic, whereas Bitcoin’s risk-mitigating performance was unstable.

Other studies also produced mixed conclusions. Kumar [90] found that both gold and Bitcoin exhibited safe-haven properties amid pandemic-induced stock market stress. Similarly, Stensås et al. [91] identified safe-haven behavior for Bitcoin during several major geopolitical events, including the 2016 Unites States election, the 2016 Brexit referendum, and the 2015 Chinese market bubble collapse. In contrast, Bampinas and Panagiotidis [92] showed that Bitcoin’s safe-haven role during the COVID-19 pandemic and the Russia–Ukraine war was limited.

Additional evidence points to time-varying safe-haven characteristics. Yousfi et al. [93] demonstrated that gold and Bitcoin can function as long-term safe-haven assets, although their protective properties fluctuate in the short and medium run, while oil exhibits relatively weak safe-haven effectiveness. Będowska-Sójka et al. [94] further emphasized that cryptocurrency prices remain largely disconnected from underlying economic risks, thereby casting doubt on their reliability as safe-haven assets.

4.1.4. The Evolution and Prospects of Bitcoin as a Safe-Haven Asset

The evidence presented in Section 4.1.1, Section 4.1.2 and Section 4.1.3 shows that Bitcoin’s safe-haven characteristics remain incomplete and highly dependent on the nature of shocks and market regimes. Although Bitcoin has occasionally demonstrated defensive behavior, such responses lack the persistence and cross-crisis consistency observed in traditional safe-haven assets.

Nevertheless, Bitcoin’s safe-haven potential may evolve as the broader cryptocurrency ecosystem matures. Developments such as the introduction of regulated investment vehicles and increased institutional participation can enhance market depth and reduce sensitivity to asset-specific shocks, potentially strengthening Bitcoin’s defensive role during periods of stress. Clearer regulatory frameworks may also contribute to greater predictability in how Bitcoin behaves under macroeconomic uncertainty, addressing factors that currently limit its ability to function as a stable store of value during crises.

Despite these structural developments, Bitcoin still lacks historical reliability, liquidity resilience, and long-term behavioral stability associated with established safe-haven assets. Existing research therefore describes Bitcoin as a diversifying instrument with emerging safe-haven characteristics. Whether Bitcoin can develop into a more consistent protective asset remains an open empirical question that requires further evidence across future crisis episodes.

4.2. Market Dynamics, Efficiency and Investor Behavior

This section explores a broad range of studies on the various factors influencing Bitcoin’s market dynamics and efficiency. It reviews research focusing on the risks associated with Bitcoin’s market behavior and covers topics such as the regulatory environment, investor behavior, price bubbles, volatility, liquidity, and macroeconomic factors. Through this comprehensive exploration, the section aims to enhance the understanding of Bitcoin’s price dynamics, provide insights for policymaking, and offer a thorough assessment of the challenges and opportunities related to using cryptocurrency as an investment tool.

4.2.1. Liquidity, Volatility, Contagion Phenomena and Spillover Effect

Liquidity refers to the ease with which assets can be traded without generating substantial price movements, and it is a central component of market efficiency [95,96]. Markets with higher liquidity tend to exhibit narrower spreads, lower transaction costs, and more effective price discovery. Prior studies indicate that liquidity conditions in cryptocurrency markets are shaped by trading volume, the number of active participants, overall market attention, and regulatory developments [97,98,99].

Empirical evidence highlights that the liquidity of Bitcoin and other cryptocurrencies is highly dynamic and often inconsistent across periods, reflecting the multifaceted structure of cryptocurrency markets [100,101,102,103,104]. Liquidity and volatility are tightly interconnected: lower liquidity typically amplifies price volatility, whereas greater liquidity contributes to short-term stability [105]. Due to the absence of long-standing valuation benchmarks and its limited market history, Bitcoin is particularly prone to abrupt volatility spikes, which can propagate to smaller cryptocurrencies through contagion channels and momentum effects.

The existing literature also showed that liquidity conditions influence portfolio decisions and market efficiency. Borri [106] noted that cryptocurrencies may enhance portfolio returns and offer hedging benefits, but liquidity constraints implied that such allocations should remain modest. Sensoy [107] further emphasized that Bitcoin’s liquidity critically affects its informational efficiency. Al-Yahyaee et al. [108] found that although high liquidity improves efficiency across quantiles, elevated volatility undermines these gains, indicating that market efficiency is maximized when liquidity is high and volatility remains contained.

Intraday dynamics provide additional insight into the microstructure of Bitcoin markets. Eross et al. [109] showed that volatility peaks during major global equity market openings, with liquidity displaying similar intensification. Takaishi and Adachi [110] documented that although liquidity increased substantially after 2013, it remained limited during several later periods. Additionally, Su et al. [111] found that trading volume, volatility, and liquidity of Bitcoin increase significantly during overlapping trading hours between London and New York (LNY). Conversely, the authors confirmed that trading against JPY showed peak activity during both Asian and LNY time zones. Finally, Zhang and Li [112] suggested that unique volatility is positively related to expected returns in cryptocurrencies, robust to various factors such as size, liquidity, trading volume, and price.

4.2.2. Impact of News and Regulatory Signals

Bitcoin’s regulatory environment is highly dynamic, reflecting the asset’s short history, rapid global adoption, and persistent volatility [113,114]. As a result, news concerning regulatory changes and macroeconomic conditions has substantial influence on investor sentiment and, consequently, on market dynamics.

Lyócsa et al. [115] showed that Bitcoin volatility reacts strongly to news related to regulatory actions, investor sentiment, and security breaches at cryptocurrency exchanges. Notably, Bitcoin was relatively insensitive to many scheduled macroeconomic announcements such as Unites States inflation reports or monetary policy statements, yet became more volatile in response to forward-looking indicators, including consumer confidence surveys. Zhou [116] similarly highlighted that news intensity and regulatory messaging significantly shape short-term market sentiment, reinforcing the sensitivity of Bitcoin’s volatility to information shocks.

Beyond discrete announcements, researchers have developed quantitative measures of regulatory attitudes. For instance, Bonaparte and Bernile [117] constructed an index capturing investor sentiment toward cryptocurrency regulation. The authors found that although the index does not meaningfully affect long-term price levels due to persistent optimism among investors, it significantly influences short-term volatility and trading volume. Complementing this perspective, Biktimirov E. N. and Biktimirov L. E. [118] demonstrated that emotional responses to news vary by topic. Specifically, investment-related emotions positively correlated with Bitcoin returns, while regulation and other topics did not show significant relationships.

4.2.3. Herding Behavior, Price Bubbles and Speculative Trading

Existing research shows that the cryptocurrency market exhibits a strong tendency toward herding behavior, where investors disregard their own information and follow the actions of others. Such herd-driven trading contributes to market inefficiencies and heightened volatility. Drivers of herding include information scarcity, fear of missing out (FOMO), and responsibility aversion, particularly under conditions of elevated uncertainty [114,119,120,121,122].

Empirical studies generally confirm that herding intensifies during periods of market stress. Bouri et al. [123] suggested that herding becomes more pronounced when uncertainty rises, while Tomás et al. [48] reported strong herding during bearish conditions. Gurdgiev and O’Loughlin [124] highlighted the role of investor sentiment in predicting cryptocurrency price movements, reinforcing the influence of collective biases. Ballis and Drakos [125] further showed that investors frequently imitate others’ actions without relying on their own fundamental assessments. Consistent with these findings, Kallinterakis and Wang [126] argued that the lack of fundamental anchors and sentiment-driven trading amplify herding dynamics. Koch and Dimpfl [127] also illustrated how surges in attention captured through Google search trends or social media activity synchronize movements across major cryptocurrencies.

However, specific studies indicate that herding phenomena manifest only in certain periods or under particular circumstances. According to De Almeida Junior [128], investor herding behavior occurs in specific years and responds to significant events such as El Salvador’s adoption of Bitcoin. Scharnowski and Shi [129] suggested that herding behavior is stronger when market returns are positive and diminish during bearish phases. Blasco and Corredor [130] reported varying herding behavior depending on the exchange size, with smaller exchanges showing strong herding behavior while larger exchanges lead the price-setting process based on their own information and beliefs. Ali [131] clarified that the nature and magnitude of short-term herding behavior differ significantly from medium to long-term behavior in the cryptocurrency market, indicating that while short-term herding exists, this tendency diminishes over the medium to long term as cryptocurrencies adjust their prices independently based on their intrinsic value. Moreover, the author discovered that herding behavior is more pronounced during periods of high uncertainty. Ha and Lee [132] additionally argued that Bitcoin price movements cannot be attributed solely to herding, given the interplay between fundamental strategies, volatility, and social interactions.

Herding behavior is closely linked to speculative dynamics and bubble formation. Prior research showed that collective momentum trading can create abnormal price run-ups that resemble bubbles [33,36,133,134]. These surges attract speculative investors seeking short-term gains, further amplifying volatility [135,136,137,138,139]. Persistent price acceleration can subsequently undermine confidence, destabilize markets, and prompt regulatory intervention.

4.2.4. Research Directions for Enhancing Bitcoin Market Stability and Efficiency

Existing studies highlight that Bitcoin’s market dynamics are shaped by liquidity conditions, volatility regimes, regulatory signals, information flows, and investor behavior. These elements interact with producing heterogeneous pricing outcomes, emphasizing the need for more integrated analytical frameworks.

Future research should examine the joint effects of liquidity and volatility across different market regimes, as these interactions are central to understanding the formation of contagion channels and spillovers in cryptocurrency markets. In addition, assessing the long-term influence of regulatory changes and major news events can support the development of predictive models that more accurately capture market responses.

Further work is also needed on the mechanisms underlying herding, speculative trading, and bubble formation. Modeling how these behaviors emerge and dissipate under varying conditions may help identify early-warning indicators for market instability. Research on institutional participation, cross-market interdependence, and global transmission mechanisms can contribute to improving market resilience. Finally, studies incorporating investor psychology and behavioral biases may deepen understanding of cryptocurrency market reactions and assist policymakers in designing effective regulatory and educational approaches.

4.3. Bitcoin Price and Volatility Prediction Attempts

Predicting Bitcoin’s price and volatility has been a central challenge in cryptocurrency research, given its direct implications for investment strategies, market stability, and risk management. Prior studies have employed a wide range of econometric and machine learning models to forecast short-term and long-term dynamics, offering insights into the drivers of Bitcoin’s fluctuations [38,133,140,141,142,143]. This section reviews these approaches and synthesizes key findings regarding predictive performance and model behavior.

4.3.1. Modeling Price and Volatility Using Statistical Methodologies

Modeling Bitcoin’s price and volatility using traditional statistical tools is challenging, given the asset’s nonlinear structure and rapidly evolving market environment [144,145]. Nevertheless, classical econometric approaches including Autoregressive Integrated Moving Average (ARIMA), GARCH, and stochastic volatility frameworks remain widely applied because they offer interpretability, statistical tractability, and well-established inference procedures [146,147,148,149].

By applying these statistical tools to the historical price data of Bitcoin, researchers aimed to uncover patterns in market volatility and gain predictive insights into future price movements. For example, Munim et al. [150] compared and utilized ARIMA and neural network autoregressive models to predict Bitcoin prices, demonstrating that the ARIMA model still performs strongly in forecasting the highly volatile Bitcoin prices. Dyhrberg [32] demonstrated that asymmetric GARCH models can be useful for risk management when negative shocks are anticipated in the Bitcoin market, making them potentially ideal for risk-averse investors. Carporale et al. [151] conducted hypothesis testing on Bitcoin volatility using four statistical methods including ARIMA and confirmed that there is no evidence of seasonal patterns.

Overall, these findings indicate that traditional econometric methods continue to offer meaningful insight into Bitcoin’s volatility structure, although they may face inherent limitations when modeling strongly nonlinear or regime-dependent market dynamics.

4.3.2. Modeling Price and Volatility Using Machine Learning and Deep Learning Methodologies

Machine learning (ML) and deep learning (DL) techniques have become increasingly prominent in Bitcoin price and volatility forecasting, largely because they can capture nonlinear patterns and high-dimensional dependencies that traditional econometric models may overlook. These data-driven approaches ranging from neural networks and Recurrent Neural Networks (RNNs) to Long Short-Term Memory (LSTM) and Convolutional Neural Networks (CNNs) utilize historical and auxiliary information to model complex temporal structures in cryptocurrency markets.

Empirical evidence shows that ML and DL models often outperform classical statistical approaches, particularly when incorporating heterogeneous data sources. Sun et al. [152] employed Light Gradient Boosting Machine using daily observations from 42 major cryptocurrencies combined with economic indicators, reporting enhanced trend prediction accuracy. Sebastião and Godinho [153] examined the top three cryptocurrencies and demonstrated that an ensemble of five ML models yields profitable trading strategies, even in periods of adverse market conditions. Dutta et al. [145] compared LSTM and GRU architectures for daily Bitcoin forecasting and found that GRU provides superior predictive performance. Liu et al. [154] introduced 40 variables capturing market conditions, public interest, and macroeconomic indicators, applying stacked denoising autoencoders to achieve improved directional and level forecasting.

Overall, ML and DL methodologies meaningfully advance Bitcoin forecasting by modeling nonlinearities, capturing latent structures, and integrating diverse data sources. However, their performance depends strongly on model design, feature engineering, and data frequency, highlighting the need for careful benchmarking against traditional econometric baselines.

4.3.3. The Future of Bitcoin Forecasting: Integrating Real-Time Data and Hybrid Methodologies

Recent research on Bitcoin forecasting reflects a shift toward more dynamic and data-rich modeling frameworks that combine traditional econometric insights with the flexibility of ML and DL architectures. While classical time-series models such as ARIMA and GARCH continue to serve as important benchmarks, advances in real-time data availability, computational capacity, and hybrid modeling techniques have expanded the analytical toolkit for forecasting Bitcoin’s price and volatility.

A growing body of work highlights the benefits of integrating statistical models with ML or DL approaches to improve predictive accuracy and robustness. Hybrid frameworks can capture long-term temporal structure while simultaneously modeling nonlinear dependencies and interactions across market, behavioral, and macroeconomic variables. In parallel, the increasing use of high-frequency data, blockchain-level activity metrics, and real-time sentiment signals from social media and news sources provides richer information for forecasting models, particularly during periods of heightened uncertainty.

Future research should therefore focus on several key directions. First, refining hybrid econometric–ML models that leverage the strengths of both methodologies may enhance stability and interpretability, especially in regime-shifting environments. Second, more systematic incorporation of real-time and high-frequency data can improve responsiveness to sudden market shocks and contribute to early warning indicators for volatility spikes. Third, evaluating the sensitivity of forecasting models to regulatory changes, macroeconomic announcements, and sentiment-driven information flows remains critical for understanding price formation mechanisms. Finally, greater emphasis on model transparency, comparative benchmarking, and out-of-sample validation will support the development of forecasting systems that are both empirically reliable and practically applicable.

Collectively, these directions underscore the need for forecasting frameworks that are adaptable, data-intensive, and theoretically grounded, reflecting the evolving structure of cryptocurrency markets.

4.4. Environmental Impact of Bitcoin

The environmental implications of Bitcoin mining have become a central theme in the recent literature, driven by concerns regarding energy consumption, carbon emissions, and electronic waste [155,156,157,158]. These concerns stem from the computational intensity of PoW mining, its evolving geographic footprint, and the rapid turnover of specialized mining hardware [159,160,161]. This section synthesizes empirical evidence on these environmental challenges and outlines key methodological debates within the literature.

4.4.1. Energy Consumption of Cryptocurrency Mining

Bitcoin’s PoW mechanism requires the continuous execution of energy-intensive computations, making electricity consumption one of the most prominent environmental concerns associated with cryptocurrency mining [162,163,164,165]. Prior estimates indicate that the annual electricity usage of Bitcoin mining exceeds that of several medium-sized countries, reflecting the industrial scale of mining operations and their concentration in locations with low-cost electricity [166,167,168,169]. Such geographic clustering often amplifies environmental pressures by linking mining intensity to carbon-intensive regional energy grids.

Empirical studies have examined both the magnitude and the financial implications of mining-related energy use. Wang et al. [170] showed that rising environmental concerns can influence cryptocurrency prices and broader market sentiment, highlighting that energy consumption is not merely an ecological issue but one with financial-market relevance. Complementing this perspective, Sarkodie et al. [171] demonstrated that increases in Bitcoin trading volume significantly expand long-term energy footprints, with dynamic shocks magnifying these effects. These findings suggest a bidirectional interaction between mining activity and market dynamics, where energy use both influences and responds to trading behavior and investor expectations.

4.4.2. Carbon Footprint

The carbon footprint of cryptocurrency mining is substantial due to its heavy reliance on fossil fuels [172,173,174]. Studies have shown that a significant portion of Bitcoin mining occurs in regions with high coal usage, such as certain provinces in China [175,176]. This results in considerable carbon emissions contributing to global climate change [177]. Even as some mining operations shift towards renewable energy sources, the overall impact remains significant due to the sheer scale of energy required.

Recent empirical research has examined the determinants and dynamics of these emissions. Polemis and Tsionas [157] analyzed daily panel data from 50 countries and identified the principal drivers of Bitcoin’s carbon footprint, emphasizing the need for sustainable energy strategies in regions with rapidly growing mining activity. Qin et al. [178] investigated the dynamic interactions among Bitcoin prices, carbon emissions, and energy consumption, showing that rising Bitcoin prices historically induced additional emissions through intensified mining activity, although this relationship weakened over time as broader macroeconomic and energy-market factors moderated the linkage. Complementing these findings, Zribi et al. [177] showed that heightened environmental concerns around cryptocurrencies can exert downward pressure on both carbon and energy footprints by influencing mining incentives and investor behavior.

Taken together, these studies indicate that Bitcoin’s carbon emissions are shaped by a combination of regional energy mixes, market-driven mining incentives, and broader economic conditions, underscoring the difficulty of reducing emissions without structural shifts in both energy sourcing and mining practices.

4.4.3. Electronic Waste and Resource Depletion

Cryptocurrency mining also generates substantial electronic waste, primarily due to the short operational lifespan of specialized hardware such as Application-Specific Integrated Circuits (ASICs) [179,180]. As mining difficulty increases and more efficient devices enter the market, older hardware rapidly becomes obsolete, leading to large volumes of discarded equipment [181]. The disposal and recycling of these components present significant environmental concerns, including resource depletion and improper waste handling [182,183,184].

Building on these concerns, recent empirical studies have examined the scale and broader implications of this accelerated hardware turnover. De Vries and Stoll [179] identified a growing reliance on short-lived ASIC devices and noted that insufficient recycling practices can release toxic chemicals and heavy metals into surrounding ecosystems. The rapid replacement cycle further strains global semiconductor supply chains, as mining-driven demand competes with industrial and consumer applications. Jana et al. [180] projected future trajectories of Bitcoin-related energy use and electronic waste, suggesting that protocol-level adjustments such as modifying block size or altering transaction fee structures could help mitigate hardware churn and reduce the associated environmental burden.

Overall, existing evidence shows that the environmental impact of electronic waste arises not only from the volume of discarded hardware but also from structural incentives that promote continual device replacement. Addressing these challenges will require coordinated technological and policy-oriented interventions that extend beyond incremental improvements in hardware efficiency.

4.4.4. Mitigation Efforts and Sustainable Practices

Efforts to mitigate the environmental impacts of cryptocurrency mining have expanded in response to growing regulatory attention and sustainability concerns [163,182,185]. A major direction involves increasing the use of renewable energy sources for mining operations. Rather than focusing solely on cleaner energy inputs, recent studies note a broader shift in mining geography, as facilities increasingly relocate to regions with abundant hydroelectric, wind, or geothermal resources, thereby reducing carbon intensity [168,186,187]. In parallel, discussions around transitioning from energy-intensive PoW mechanisms to more efficient consensus protocols such as Proof-of-Stake (PoS) continue to gain traction, and several cryptocurrencies have already implemented such changes [168,188].

Beyond technological adjustments, researchers emphasize the importance of addressing the broader social and economic implications of mining. Mining clusters can generate employment opportunities, support infrastructure investment, and stimulate economic activity in host regions. At the same time, they may also impose burdens such as increased electricity demand, noise pollution, and land-use conflicts [50,189]. As a result, cooperative governance involving mining companies, local communities, and regional authorities is considered essential to ensuring that mining activities contribute to sustainable development rather than exacerbating local inequalities [175].

Collectively, these initiatives illustrate that mitigation efforts span energy sourcing, protocol design, and community-level governance. While meaningful progress has been made, particularly through renewable energy integration and the emergence of more efficient consensus mechanisms, the environmental sustainability of cryptocurrency mining ultimately depends on coordinated technological, regulatory, and regional planning approaches.

4.4.5. Navigating the Environmental Challenges of Bitcoin Mining

Taken together, the reviewed literature demonstrates that Bitcoin’s environmental impact is multifaceted, arising from high energy demand, carbon-intensive mining geographies, and substantial electronic waste generation driven by rapid hardware turnover. While mitigation efforts including renewable-energy integration, efficiency-oriented hardware design, and evolving governance frameworks have produced incremental improvements, the underlying economic incentives shaping mining behavior remain largely unchanged.

An additional persistent obstacle in this domain is the methodological fragmentation across studies. Divergent assumptions regarding hardware lifespan, energy-mix composition, carbon-intensity coefficients, and post-2021 mining relocation patterns have produced findings that are often difficult to reconcile. This limits the ability to construct a consistent environmental baseline and underscores the need for standardized reporting frameworks and more temporally granular data. Such consistency is particularly critical for scholars seeking to evaluate mining sustainability in a rapidly evolving technological and regulatory landscape.

Ultimately, these environmental challenges reveal several important research opportunities. First, the economic trade-offs between mining profitability, energy sourcing, and external environmental costs remain insufficiently quantified. Second, the relocation of mining infrastructure opens avenues to study regional competitiveness, energy-market dynamics, and local economic spillovers. Third, the growing tension between environmental regulation and mining incentives offers a fertile setting for examining policy effectiveness, compliance behavior, and market adaptation. Advancing research within these areas will help clarify how environmental constraints shape Bitcoin’s long-term viability.

4.5. Financial Impact of CBDCs

Although Bitcoin and CBDCs are often discussed together as digital currencies, the two represent fundamentally different approaches to monetary design, governance, and market functionality [190,191,192]. Recent literature frequently examines CBDCs alongside cryptocurrencies to assess their comparative implications for financial stability, market efficiency, and the broader payments ecosystem [193,194,195]. This section synthesizes key themes regarding the financial impact of CBDCs.

4.5.1. Conceptual Differences Between Bitcoin and CBDCs

Bitcoin operates as a decentralized, permissionless network designed to function independently of state control, appealing to users seeking autonomy, censorship resistance, and alternative value storage [196,197]. In contrast, CBDCs are issued and regulated by central banks and are intended to integrate seamlessly with existing monetary frameworks [194,198]. These divergent governance structures lead to fundamentally different economic roles: while Bitcoin functions primarily as a speculative or investment-oriented asset, CBDCs aim to enhance monetary-policy transmission, support payment-system modernization, and strengthen financial stability.

4.5.2. Enhancing Payment Systems and Economic Efficiency with CBDC

CBDC can improve payment systems and financial inclusion by providing a secure and efficient digital payment option. This has the potential to enhance the speed and transparency of payment systems, and increase the stability, efficiency, and accessibility of the financial system [195]. Therefore, understanding the impact of CBDC adoption, which offers these new possibilities, is crucial [192,199]. Moreover, CBDC could strengthen the execution and monitoring of central bank policies and enhance transaction efficiency in financial markets [170,200].

Research articles related to CBDC evaluated these potential impacts and explored the implications of CBDC on financial markets and the broader economy. For instance, Wang et al. [201] found that financial markets react more sensitively to uncertainty related to CBDC developments, indicating the significant influence of CBDC on financial stability and monetary policy. Ding et al. [202] argued that CBDCs can facilitate electronic payments and offer benefits for supply chain management, thereby enhancing operational efficiency across economic sectors. Overall, these studies highlight CBDCs as a tool for modernizing payment infrastructures and potentially improving systemic efficiency.

4.5.3. Market Reactions and Investor Perceptions

Although CBDCs differ significantly from cryptocurrencies, investors often interpret CBDC developments as signals relevant to the broader digital asset ecosystem [203,204,205].

For instance, Scharnowski [203] explored how cryptocurrency investors perceive CBDCs by analyzing market reactions to central bank speeches. The study found that cryptocurrency investors did not view CBDC issuance as a threat to cryptocurrencies. Instead, the positive stance of central banks towards CBDCs was generally interpreted as a positive signal for the cryptocurrency market. Additionally, Mzoughi et al. [204] reported similar findings in examining market reactions to CBDC launches in the Bahamas and Nigeria, suggesting that CBDC implementation interacts with cryptocurrency markets in nuanced and context-dependent ways. These studies indicate that CBDCs influence market sentiment, even if investors do not equate them with decentralized cryptocurrencies.

4.5.4. Implications of CBDC for the Banking Sector

The introduction of CBDCs raises important questions regarding commercial banks’ roles in intermediation, liquidity provision, and competitive positioning. Bhaskar et al. [205] argued that CBDCs could significantly reshape the traditional banking sector by altering the structure of deposit funding and challenging existing business models. Nabilou [193] further highlighted concerns that CBDC issuance may affect financial stability by reducing banks’ access to retail deposits and complicating the transmission of monetary policy. These challenges are compounded by the complex legal questions surrounding CBDC issuance, which necessitate careful regulatory adjustments to ensure coherence with existing financial laws and supervisory mandates.

As CBDC development accelerates globally, commercial banks must strategically assess how changes in payment infrastructures and customer behavior may influence their operations [206]. Researchers emphasized the need to evaluate how CBDCs affect banks’ traditional functions, including liquidity management, credit creation, and relationship-based banking [194]. Understanding the implications for asset management, compliance obligations, and competition dynamics is essential for navigating this structural transition.

In conclusion, while CBDCs present opportunities for financial innovation, they also pose significant risks and operational challenges for commercial banks. Denecker et al. [207] argued that successful adaptation will require proactive engagement by banks, including updating regulatory frameworks, implementing new technological infrastructure, and ensuring seamless integration with emerging CBDC ecosystems.

4.5.5. Technological and Regulatory Considerations in CBDC Implementation

As highlighted, implementing CBDCs involves resolving technological challenges to successfully integrate into the financial system [208,209]. Implementing CBDCs requires addressing significant technological considerations such as security, scalability, and personal data protection. These requirements reflect the fact that CBDCs must operate at the scale of national payment systems, where high transaction throughput and uninterrupted service availability are essential. Consequently, the development of architectures that are not only secure but also computationally and energy efficient becomes a core design priority.

To support these operational requirements, central banks increasingly rely on advanced modeling, simulation, and optimization tools to anticipate uncertainties associated with early CBDC deployments such as fluctuations in transaction volume, network stress conditions, and operational risk exposures [200]. These analytical approaches enable policymakers to evaluate design trade-offs, identify potential vulnerabilities in advance, and ensure system robustness before large-scale implementation.

From a regulatory perspective, CBDCs introduce several fundamental challenges. Nabilou [193] noted that CBDC issuance could affect the central bank’s monopoly over base money, alter monetary policy transmission, and potentially introduce new risks to payment-system stability and the balance sheets of credit institutions. Furthermore, CBDCs raise complex compliance obligations concerning privacy protection, data governance, anti-money laundering (AML) regulation, and counter-terrorism financing [210,211,212]. Balancing enhanced regulatory oversight with the preservation of user trust, confidentiality, and civil liberties remains one of the most delicate design tensions for policymakers.

Taken together, these considerations highlight the importance of establishing a coherent regulatory and technological framework that integrates security, privacy, legal compliance, and supervisory mechanisms. Such a framework is essential to ensure that CBDCs can be deployed safely, operate efficiently, and interoperate seamlessly with existing financial systems.

4.5.6. Synthesizing the Economic and Regulatory Dimensions of CBDC Implementation

The evidence reviewed across Section 4.5.1, Section 4.5.2, Section 4.5.3, Section 4.5.4 and Section 4.5.5 shows that CBDCs occupy a unique position within the digital-currency landscape, combining the technological attributes of distributed payment systems with the institutional responsibilities of sovereign monetary authorities. As a result, their implementation must be assessed not only as a technical innovation but as a structural change within national financial systems. While CBDCs promise improvements in transaction efficiency, financial inclusion, and monetary-policy transmission, these benefits coexist with concerns regarding financial stability, the reconfiguration of banking intermediation, and potential shifts in market behavior.

Commercial banks, in particular, may face meaningful adjustments to their liquidity management, business models, and client relationships depending on how CBDC architecture is designed. The emerging findings suggest that the scale and direction of these impacts vary across jurisdictions, reflecting differences in regulatory environments, institutional structures, and central-bank communication strategies. This underscores the need for ongoing coordination between central banks and financial institutions to ensure that CBDC adoption reinforces, rather than disrupts, existing financial-market functions.

At the regulatory level, the literature consistently highlights challenges related to privacy protection, data governance, AML compliance, operational security, and the legal status of sovereign digital money. Designing CBDCs that provide adequate oversight while maintaining user trust and safeguarding civil liberties remains one of the central policy tensions. The technical requirements for scalability, cybersecurity, and system resilience further complicate these regulatory considerations, revealing the necessity of integrated technological and legal frameworks.

Future research should deepen analysis of the long-term economic effects of CBDC adoption, evaluate the robustness and privacy implications of alternative technological architectures, and clarify how regulatory harmonization across jurisdictions can support cross-border interoperability. These avenues of inquiry will be essential for establishing a coherent foundation for CBDCs as they transition from conceptual proposals and pilot programs toward real-world implementation.

4.6. Sources of Inconsistent Findings in Prior Studies

Despite the extensive growth of Bitcoin research within the Business and Economics domain, prior studies frequently reach conflicting conclusions. These inconsistencies arise from substantial heterogeneity in empirical design, measurement, market context, and methodological choices. A systematic synthesis of these sources of divergence is essential to contextualize the findings reviewed and to provide a coherent bridge to the broader implications discussed in Section 5.

First, inconsistent sample periods represent a major source of divergence. Bitcoin markets have experienced pronounced structural transitions, from early retail-dominated trading to the emergence of derivatives markets, intensifying regulatory interventions, and various geopolitical disruptions. These temporal discontinuities lead to conflicting findings on correlations, volatility regimes, hedging and safe-haven properties, contagion patterns, and market efficiency.

Second, methodological heterogeneity amplifies discrepancies in empirical conclusions. Linear time-series frameworks, GARCH-family models, quantile-based approaches, wavelet methodologies, and ML or DL techniques each capture distinct statistical properties of Bitcoin markets. Consequently, studies frequently reach contrasting inferences regarding volatility persistence, return predictability, safe-haven behavior, and cross-market spillovers. ML and DL models, in particular, tend to detect nonlinear patterns and latent structures absent in classical econometric specifications, contributing to diverse interpretations of Bitcoin’s risk dynamics.

Third, differences in variable construction and operational definitions introduce further inconsistency. Key constructs such as returns, volatility, liquidity, sentiment, or safe-haven status are defined and measured unevenly across studies. Safe-haven behavior, for instance, is defined narrowly as crisis-period uncorrelation in some research, whereas others rely on extreme-quantile dependence or tail-risk responses. Sentiment measures range from social-media-based indices to lexicon-derived scores and LLM-generated classifiers. Such definitional variability can produce materially different findings even under similar research questions.

Fourth, data source heterogeneity contributes to conflicting results. Exchange-level differences in trading volume, liquidity, data quality, and market microstructure can meaningfully affect estimates of volatility, spillover intensity, and efficiency. Studies analyzing Bitcoin in isolation often report different conclusions from those incorporating gold, equities, altcoins, or macroeconomic indicators. These discrepancies are especially pronounced in research on diversification benefits, cross-asset connectedness, and safe-haven characteristics.

Finally, structural shocks and regime-dependent effects complicate generalization across studies. Major regulatory announcements, exchange hacks, the introduction of Bitcoin futures, the COVID-19 pandemic, geopolitical tensions, and the emergence of CBDCs all alter market behavior in time-varying ways. Environmental impact studies similarly diverge depending on whether they incorporate shifts in mining geography, relocation patterns, changes in regional energy mixes, or varying assumptions about hardware efficiency and carbon intensity. Different treatments of such events frequently yield incompatible conclusions.

Taken together, these sources of inconsistency show that contradictions in the literature arise not from isolated analytical disagreements but from foundational differences in temporal scope, methodological design, variable definitions, and market context. This underscores the need for greater standardization in empirical practices, transparent reporting of modeling assumptions, and robustness checks that explicitly account for structural breaks and regime shifts.

5. Discussion

This study provides a comprehensive examination of Bitcoin research using an integrated framework employing bibliometric and topic modeling, offering a structured overview of how academic attention has evolved within the Business and Economics domain. The analysis addressed the three research questions by (1) identifying publication patterns and global research distribution, (2) determining the disciplinary concentration of Bitcoin scholarship and the thematic structures within the Business and Economics domain, and (3) synthesizing the major research themes and gaps that persist across empirical studies.

Regarding RQ1, publication trends revealed a sustained expansion of Bitcoin-related research since 2012, with notable concentration in China, the United States, and the United Kingdom. However, citation patterns exhibited meaningful cross-country asymmetries, indicating that productivity and scholarly influence do not always align. Furthermore, the top-cited articles were largely concentrated between 2015 and 2018, primarily within the Business and Economics domain, which accounted for 47.76% of the total dataset. The journals most frequently publishing Bitcoin research highlight the interdisciplinary emphasis across economics, finance, and computer science.

In response to the first part of RQ2, the Business and Economics domain emerged as the most active academic field discussing Bitcoin. Accordingly, further bibliometric analysis was performed on this domain specifically, followed by thematic classification informed by topic modeling. Five major research areas were identified: (1) diversification, hedging, and safe-haven properties, (2) market dynamics, efficiency, and investor behavior, (3) price and volatility prediction methodologies, (4) environmental implications of Bitcoin mining, and (5) financial and systemic considerations of CBDCs. Together, these themes demonstrate that Bitcoin scholarship has broadened its analytical focus from asset-specific characteristics to questions involving financial stability, environmental sustainability, and monetary policy transmission.

Addressing RQ3, this review identified several critical gaps in the existing literature. While substantial research has examined Bitcoin’s investment properties and price behavior, its macro-financial role remains underexplored, particularly in relation to institutional adoption, regulatory evolution, and cross-market transmission mechanisms. Environmental research remains fragmented, with inconsistent assumptions regarding energy-mix composition, mining relocation, and hardware efficiency. Similarly, CBDC-related studies are conceptually rich but empirically thin, lacking robust data-driven analysis of monetary-policy interactions, financial stability, and interoperability with decentralized cryptocurrencies. These gaps highlight the need for more longitudinal, cross-disciplinary, and methodologically transparent research approaches.

Beyond documenting empirical patterns, this review also highlights a deeper structural characteristic of Bitcoin scholarship. As shown in Section 4.6, inconsistencies in sample periods, modeling approaches, variable definitions, and data sources contribute to divergent or conflicting findings. Recognizing these structural sources of inconsistency is essential for interpreting literature and designing future research capable of integrating results across time, markets, and methodological traditions.

The findings of this research have several implications for regulators, investors, and researchers. For policymakers, the identified themes can guide the prioritization of risk-monitoring areas such as volatility transmission, liquidity fragility, and the environmental externalities of mining. For institutional investors, evidence on asset co-movements, diversification potential, and regime-dependent hedging performance can inform risk management and portfolio allocation strategies. For researchers, the thematic synthesis offers a structured agenda for expanding empirical coverage, particularly in areas such as market microstructure under institutional trading, dynamic regulatory effects, the economics of mining relocation, and the interaction between CBDCs and decentralized assets.

Despite the strengths of combining bibliometric mapping with BERTopic-based semantic clustering, several methodological limitations should be acknowledged. First, the exclusive reliance on the Web of Science Core Collection may omit relevant studies indexed in Scopus or discipline-specific databases, potentially leading to selection bias. Second, citation-based indicators such as bibliographic coupling and normalized citations are sensitive to disciplinary citation norms and publication age and should be interpreted as relative rather than absolute measures of influence. Finally, topic modeling results depend on preprocessing choices, embedding models, and clustering hyperparameters, while standardized BERTopic settings were used to enhance reproducibility. These limitations do not undermine the findings but outline considerations for future research aiming to deepen or extend this review. While methodological constraints inherent to bibliometric and topic modeling approaches cannot be fully eliminated, future research may consider these factors when extending or refining the present analysis.

6. Conclusions

This research provides a comprehensive overview of Bitcoin studies by combining bibliometric analysis and topic modeling to map research trends and identify dominant and emerging themes. The findings confirm that Bitcoin research has expanded significantly over the past decade, particularly within the Business and Economics domain. The five identified thematic clusters illustrate the breadth of academic inquiry spanning financial, behavioral, technological, and environmental dimensions. This study contributes by offering one of the first integrated bibliometric–topic modeling syntheses of Bitcoin research specifically within the Business and Economics domain.

The review identifies several coherent directions for future research. First, research on Bitcoin’s portfolio role, including its safe-haven, hedging, and diversification properties, would benefit from integrating institutional trading activity, macroeconomic uncertainty, and evolving regulatory conditions. Second, studies of market efficiency, volatility transmission, and contagion should incorporate liquidity regimes, investor behavior, and cross-asset linkages to capture regime-dependent dynamics more accurately. Third, hybrid modeling approaches that integrate machine learning, deep learning, and traditional econometric methods hold promise for improving predictive accuracy in cryptocurrency price modeling.

Environmental sustainability remains a central research priority, particularly regarding hardware turnover, mining relocation, and long-term adjustments in the global energy mix following regulatory interventions. Finally, the rapid global development of CBDCs highlights the need for empirical analysis of monetary policy transmission, banking sector impacts, and the interoperability between sovereign digital currencies and decentralized cryptocurrencies.

While this review provides a consolidated assessment of the Bitcoin literature, the results should be interpreted with awareness that bibliometric and topic modeling techniques may not fully capture very small or highly specialized research streams. Future research can build on these findings by applying complementary qualitative or domain-specific approaches to further refine thematic structures.

Overall, the findings chart the developmental trajectory of Bitcoin scholarship over the past decade and provide a structured foundation for future research at the intersection of finance, technology, and public policy. As the cryptocurrency ecosystem continues to evolve, the field will increasingly require integrative, cross-disciplinary, and methodologically transparent research to address the economic, environmental, and systemic challenges associated with digital assets.