Environmental, Social, and Governance (ESG)-Based Stocks and Bitcoin: A Sustainable Investment Perspective – Evidence from Indonesia

In this rapidly evolving digital era, crypto investment has become an unstoppable global phenomenon. In recent days, investors have increasingly viewed cryptocurrency as an alternative digital safe haven. Its supporters, mostly the younger generations who are equipped with strong financial literacy, often refer to crypto as digital gold, believing that this asset function similarly to precious metals during periods of economic uncertainty. Global crypto transactions have reached USD 6.34 billion in 2025 and are projected to grow to USD 18.26 billion by 2033, growing of 14.5% from 2026 to 2033. Bitcoin, the largest cryptocurrency asset, has increasingly appeared in the portfolios of both retail and institutional investors [1]. Moreover, Bitcoin is often considered a diversification alternative due to its relatively low correlation with conventional capital markets [2].

On the other hand, Bitcoin exhibits extremely high annual price volatility, reaching 75.8% during the 2014–2024 period, which reflects its high-risk nature. Such volatility indicates the potential for sharp price fluctuations over a short period, making Bitcoin more suitable for investors with high risk tolerance. Bitcoin’s high price volatility not only affects investors’ financially and behaviorally but also raises environmental concerns due to the substantial energy consumption associated with the proof-of-work mechanism used in the Bitcoin network. Data from the Cambridge Centre for Alternative Finance [3] indicate a significant energy consumption, highlighting its environmental implications. It is estimated that global Bitcoin mining consumes between 110 and 173 terawatt-hours of electricity annually, comparable to the electricity consumption of mid-sized countries such as Malaysia, Sweden, and Pakistan. A significant portion of this electricity is generated from fossil energy sources, including coal and natural gas, which emit large amounts of carbon dioxide and contribute to global warming and air pollution [4]. Additionally, Bitcoin mining has resulted in substantial amounts of electronic waste (e-waste) due to the short lifespan of mining hardware, particularly application-specific integrated circuit (ASIC) miners, which require frequent replacement. Consequently, e-waste is estimated to reach tens of thousands of tons annually, comparable to the total electronic waste produced by a small European country.

When Bitcoin prices surge, creating strong economic incentives, mining activities increase significantly, leading to higher energy consumption. Conversely, when prices decline sharply, many miners suspend their operations, resulting in unstable and inefficient energy consumption patterns. In other words, Bitcoin’s volatile prices are significant contributing factor to understand fluctuations in energy consumption and carbon emissions from mining activities [5], suggesting that environmental concerns should not be overlooked in sustainable investment discussions.

However, despite the controversy surrounding Bitcoin for its perceived conflict with green economy principles, the blockchain technology underlying it demonstrates potential to enhance transparency and efficiency in ESG-related practices, particularly in areas such as supply chain tracking or environmental data verification. The tension between the environmental costs of Bitcoin mining and the potential benefits of blockchain technology highlights the importance of examining the Bitcoin-ESG relationship in the context of sustainable investment. Moreover, with rising awareness of sustainability and corporate social responsibility, along with growing concerns about corporate social responsibility and climate change, ESG investment has gained prominence and become a central consideration in global investment decisions. Corporations have increasingly incorporated ESG-related practices into their business strategies. Furthermore, studies suggest that the disclosure of ESG attributes can enhance corporate financial performance while also promoting a low-carbon economy [6, 7].

Despite these ongoing dynamics, there is growing potential to integrate Bitcoin and ESG principles within an investment strategy. Analyses of such strategies are essential for supporting global net-zero emission targets. Sustainable investment strategies may indirectly function as carbon offset in crypto investment while simultaneously providing diversification benefits that reduce portfolio risks. Incorporating crypto assets and ESG equities into investment portfolios has emerged as an attractive approach, particularly for investors concerned with promoting green economy.

Research on sustainable investment strategies, particularly those examining the relationship between Bitcoin investment and ESG equities, remains limited in developing markets, especially in Asia. Less than 5% of literature on ESG investment focuses on this region [8] despite Asia accounting for one of the largest shares of cryptocurrency transaction volumes [9]. Indonesia, for instance, was among the top ten countries worldwide in terms of cryptocurrency investment in 2025. Nevertheless, studies examining Bitcoin-ESG investment strategies remain scarce. A study conducted by Kakinuma [10] in Japan, Hong Kong, South Korea, Thailand, and Indonesia—suggests that a combination of ESG equities and Bitcoin reduces risks. However, Arif et al. [11] found that portfolios combining both green and conventional assets exhibited higher return connectedness during the Covid-19 crisis, thereby offering limited diversification benefits in reducing portfolio risk during the same period. This difference may arise because Kakinuma [10] did not distinguish between the normal economic conditions (2014-2019) and the pandemic crisis period (2020-2022), which could influence the relationship between ESG equities and Bitcoin. Indonesian ESG equities were among the assets examined in Kakinuma’s [10] study. A recent study conducted by Asih et al. [12] indicated that pairing ESG-based assets with Bitcoin may provide diversification benefits.

The limited literature examining Bitcoin-ESG equities integration in Indonesia highlights the importance of this study. While Asih et al. [12] used data covering up to one year of the post-Covid-19 recovery period, no subsequent study has examined sustainable investment strategies in Indonesia when markets returned to normal after the pandemic. Indonesia is a developing market with high cryptocurrency growth, but there is minimal research related to this. This study aims to investigate the role of ESG-based assets in providing diversification benefits for Bitcoin investments. In contrast to Asih et al. [12], this study adopts a dynamic approach that captures return spillovers between the two asset markets and identifies which market serves as the net transmitter of shocks. Capital markets play a critical role in developing sustainable and responsible investment products, which further underscores the relevance of this study. In supporting the net-zero emission program, sustainable investment strategies demand further investigation, as they may offset carbon footprint associated with cryptocurrency investments while also reducing investment risk through diversification.

This study investigates the potential of ESG-based assets in diversifying Bitcoin portfolios using the Generalized Vector Autoregression (G-VAR) framework to capture the dynamic relationships between Bitcoin returns and ESG index returns. The analysis focuses on estimating return spillovers across the assets simultaneously. High spillovers between Bitcoin and ESG indicate strong connectedness, which may limit potential for portfolio diversification. In contrast, low spillovers indicate assets’ independence, thereby increasing chances for diversification and risk reduction. Analyzing spillovers using the G-VAR framework is therefore essential for evaluating the effectiveness of incorporating Bitcoin and ESG equities into portfolios for risk management.

The G-VAR model is selected for its ability to capture temporal interdependence among financial variables as well as its flexibility in analyzing bidirectional relationships without exogeneity assumptions. Unlike the Standard-VAR model, the G-VAR framework is insensitive to variable ordering.

This study employs daily closing data for Bitcoin and the FTSE Indonesia ESG Index covering a period from January 6, 2023, to December 30, 2024, yielding 515 daily return observations after computing log returns. Bitcoin price data (BTC/USD) was obtained from capitalmarket.com, a platform providing comprehensive and accurate financial data. ESG performance was measured using the FTSE Indonesia ESG Index compiled by FTSE Russell. This index was selected because it is the most established sustainability-screened benchmark for the Indonesian equity market, providing a direct, investable proxy for ESG-based equities in a major emerging market.

Because the claim that 2023–2024 represents a normal post-COVID regime should not be merely asserted, we verify it with an objective, data-driven regime test rather than a narrative judgment. For each asset we test for a structural break in volatility (squared returns) using the Bai-Perron supF test, and we require that the most recent estimated break precede the sample start (6 January 2023). We further compare the pre-window and in-window periods using the Fligner-Killeen test of equal variances and the Wilcoxon rank-sum test of distributional equality to confirm that volatility shifts to a lower regime. The results of these tests are reported in Table 1; because the analysis window begins after the last estimated volatility break for both assets, the sample lies entirely within a single, lower-volatility post-break regime.

Connectedness between the two returns series is measured using the Diebold-Yilmaz [25, 26] framework built on a generalized forecast error variance decomposition (G-FEVD) of the estimated VAR. The object of analysis is return connectedness, all variables and tables are based on daily returns. The total connectedness index (TCI) and the directional FROM/TO and net measures are computed from the G-FEVD at a forecast horizon of H = 10 days, the conventional choice in this literature. Because GFEVD-based connectedness can be sensitive to the choice of horizon, we assess robustness by tracing the connectedness measures dynamically through a 100-day rolling window rather than relying on a single static horizon.

Table 1. Volatility regime/structural break tests (squared returns)

Generalized Forecast Error Variance Decomposition (G-FEVD) is employed to measure the contribution shock from each asset to the other assets within the VAR system. FEVD provides insights into the extent to which exogeneous shocks originating from one asset influence others, thereby revealing the dynamic interdependence between assets over time. In this study, the variance decomposition was computed using the G-FEVD method proposed by Lanne and Nyberg [27], which ensures decomposition results sum to one without requiring orthogonalization of shocks. The resulting G-FEVD estimates are then used to conduct spillover analysis within the Generalized VAR (G-VAR) framework developed by Diebold and Yilmaz [25]. G-FEVD forms the basis for constructing spillover matrix, which captures the degree to which shocks from one asset affects other assets in the system. The use of generalized approach allows decomposition to be independent of variable ordering, making the results more robust. The total return spillover index is then used to quantify the contribution of one asset’s return spillover to the total forecast error variance, as measured by the following equation:

$S^g(H)=\frac{\sum_{\substack{i, j=1 \\ i \neq j}}^N \tilde{\theta}_{i j}^g(H)}{\sum_{i, j=1}^N \tilde{\theta}_{i j}^g(H)} \cdot 100=\frac{\sum_{\substack{i, j=1 \\ i \neq j}}^N \tilde{\theta}_{i j}^g(H)}{N} \cdot 100$

$\tilde{\theta}_{i j}^g(H)$ denotes the H-step-ahead forecast error variance decomposition, where H = 1, 2, …

In the subsequent stage, the rolling-window sample technique is employed over the observation period. This method allows the identification of the periods in which the total return spillovers become extreme. A higher level of total return spillovers suggests lower diversification potential between assets. Moreover, the total return spillover index provides insights into the extent of spillover across assets. The generalized VAR method can further identify the direction of spillovers, known as directional return spillovers. Directional return spillovers received by asset $i$ and originating from all other assets $j$ are defined as follows:

$S_{i .}^g(H)=\frac{\sum_{\substack{j=1 \\ j \neq i}}^N \tilde{\theta}_{i j}^g(H)}{\sum_{i, j=1}^N \tilde{\theta}_{i j}^g(H)} \cdot 100=\frac{\sum_{\substack{j=1 \\ j \neq i}}^N \tilde{\theta}_{i j}^g(H)}{N} \cdot 100$

Meanwhile, directional return spillovers transmitted from asset $i$ to all other assets $j$ are formulated as follows:

$S^g{ }_{. i}(H)=\frac{\sum_{\substack{j=1 \\ j \neq i}}^N \tilde{\theta}_{j i}^g(H)}{\sum_{i, j=1}^N \tilde{\theta}_{j i}^g(H)} \cdot 100=\frac{\sum_{\substack{j=1 \\ j \neq i}}^N \tilde{\theta}_{j i}^g(H)}{N} \cdot 100$

Based on the directional spillovers above, net spillover transmitted from asset $i$ to all other assets $j$ can be calculated using the following equation:

$S^g{ }_i(H)=S^g{ }_{. i}(H)-S^g{ }_{i \cdot}(H)$

Net spillover measures the difference between volatility transmitted to other assets and volatility received from them.